celinelee/thestack_py312_fstrings · Datasets at Hugging Face

import warnings import torch from torch.nn import GroupNorm, LayerNorm from torch.nn.modules.batchnorm import _BatchNorm from torch.nn.modules.instancenorm import _InstanceNorm from ..utils import build_from_cfg from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS @OPTIMIZER_BUILDERS.register_module class DefaultOptimizerConstructor: """Default constructor for optimizers. By default each parameter share the same optimizer settings, and we provide an argument ``paramwise_cfg`` to specify parameter-wise settings. It is a dict and may contain the following fields: - ``custom_keys`` (dict): Specified parameters-wise settings by keys. If one of the keys in ``custom_keys`` is a substring of the name of one parameter, then the setting of the parameter will be specified by ``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will be ignored. It should be noted that the aforementioned ``key`` is the longest key that is a substring of the name of the parameter. If there are multiple matched keys with the same length, then the key with lower alphabet order will be chosen. ``custom_keys[key]`` should be a dict and may contain fields ``lr_mult`` and ``decay_mult``. See Example 2 below. - ``bias_lr_mult`` (float): It will be multiplied to the learning rate for all bias parameters (except for those in normalization layers and offset layers of DCN). - ``bias_decay_mult`` (float): It will be multiplied to the weight decay for all bias parameters (except for those in normalization layers, depthwise conv layers, offset layers of DCN). - ``norm_decay_mult`` (float): It will be multiplied to the weight decay for all weight and bias parameters of normalization layers. - ``dwconv_decay_mult`` (float): It will be multiplied to the weight decay for all weight and bias parameters of depthwise conv layers. - ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning rate for parameters of offset layer in the deformable convs of a model. - ``bypass_duplicate`` (bool): If true, the duplicate parameters would not be added into optimizer. Default: False. Note: 1. If the option ``dcn_offset_lr_mult`` is used, the constructor will override the effect of ``bias_lr_mult`` in the bias of offset layer. So be careful when using both ``bias_lr_mult`` and ``dcn_offset_lr_mult``. If you wish to apply both of them to the offset layer in deformable convs, set ``dcn_offset_lr_mult`` to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``. 2. If the option ``dcn_offset_lr_mult`` is used, the constructor will apply it to all the DCN layers in the model. So be careful when the model contains multiple DCN layers in places other than backbone. Args: model (:obj:`nn.Module`): The model with parameters to be optimized. optimizer_cfg (dict): The config dict of the optimizer. Positional fields are - `type`: class name of the optimizer. Optional fields are - any arguments of the corresponding optimizer type, e.g., lr, weight_decay, momentum, etc. paramwise_cfg (dict, optional): Parameter-wise options. Example 1: >>> model = torch.nn.modules.Conv1d(1, 1, 1) >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9, >>> weight_decay=0.0001) >>> paramwise_cfg = dict(norm_decay_mult=0.) >>> optim_builder = DefaultOptimizerConstructor( >>> optimizer_cfg, paramwise_cfg) >>> optimizer = optim_builder(model) Example 2: >>> # assume model have attribute model.backbone and model.cls_head >>> optimizer_cfg = dict(type='SGD', lr=0.01, weight_decay=0.95) >>> paramwise_cfg = dict(custom_keys={ '.backbone': dict(lr_mult=0.1, decay_mult=0.9)}) >>> optim_builder = DefaultOptimizerConstructor( >>> optimizer_cfg, paramwise_cfg) >>> optimizer = optim_builder(model) >>> # Then the `lr` and `weight_decay` for model.backbone is >>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for >>> # model.cls_head is (0.01, 0.95). """ def __init__(self, optimizer_cfg, paramwise_cfg=None): if not isinstance(optimizer_cfg, dict): raise TypeError( "optimizer_cfg should be a dict", f"but got {type(optimizer_cfg)}" ) self.optimizer_cfg = optimizer_cfg self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg self.base_lr = optimizer_cfg.get("lr", None) self.base_wd = optimizer_cfg.get("weight_decay", None) self._validate_cfg() def _validate_cfg(self): if not isinstance(self.paramwise_cfg, dict): raise TypeError( "paramwise_cfg should be None or a dict, " f"but got {type(self.paramwise_cfg)}" ) if "custom_keys" in self.paramwise_cfg: if not isinstance(self.paramwise_cfg["custom_keys"], dict): raise TypeError( "If specified, custom_keys must be a dict, " f'but got {type(self.paramwise_cfg['custom_keys'])}' ) if self.base_wd is None: for key in self.paramwise_cfg["custom_keys"]: if "decay_mult" in self.paramwise_cfg["custom_keys"][key]: raise ValueError("base_wd should not be None") # get base lr and weight decay # weight_decay must be explicitly specified if mult is specified if ( "bias_decay_mult" in self.paramwise_cfg or "norm_decay_mult" in self.paramwise_cfg or "dwconv_decay_mult" in self.paramwise_cfg ): if self.base_wd is None: raise ValueError("base_wd should not be None") def _is_in(self, param_group, param_group_list): # assert is_list_of(param_group_list, dict) param = set(param_group["params"]) param_set = set() for group in param_group_list: param_set.update(set(group["params"])) return not param.isdisjoint(param_set) def add_params(self, params, module, prefix="", is_dcn_module=None): """Add all parameters of module to the params list. The parameters of the given module will be added to the list of param groups, with specific rules defined by paramwise_cfg. Args: params (list[dict]): A list of param groups, it will be modified in place. module (nn.Module): The module to be added. prefix (str): The prefix of the module is_dcn_module (int|float|None): If the current module is a submodule of DCN, `is_dcn_module` will be passed to control conv_offset layer's learning rate. Defaults to None. """ # get param-wise options custom_keys = self.paramwise_cfg.get("custom_keys", {}) # first sort with alphabet order and then sort with reversed len of str sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True) bias_lr_mult = self.paramwise_cfg.get("bias_lr_mult", 1.0) bias_decay_mult = self.paramwise_cfg.get("bias_decay_mult", 1.0) norm_decay_mult = self.paramwise_cfg.get("norm_decay_mult", 1.0) dwconv_decay_mult = self.paramwise_cfg.get("dwconv_decay_mult", 1.0) bypass_duplicate = self.paramwise_cfg.get("bypass_duplicate", False) dcn_offset_lr_mult = self.paramwise_cfg.get("dcn_offset_lr_mult", 1.0) # special rules for norm layers and depth-wise conv layers is_norm = isinstance(module, (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)) is_dwconv = ( isinstance(module, torch.nn.Conv2d) and module.in_channels == module.groups ) for name, param in module.named_parameters(recurse=False): param_group = {"params": [param]} if not param.requires_grad: params.append(param_group) continue if bypass_duplicate and self._is_in(param_group, params): warnings.warn( f"{prefix} is duplicate. It is skipped since " f"bypass_duplicate={bypass_duplicate}" ) continue # if the parameter match one of the custom keys, ignore other rules is_custom = False for key in sorted_keys: if key in f"{prefix}.{name}": is_custom = True lr_mult = custom_keys[key].get("lr_mult", 1.0) param_group["lr"] = self.base_lr * lr_mult if self.base_wd is not None: decay_mult = custom_keys[key].get("decay_mult", 1.0) param_group["weight_decay"] = self.base_wd * decay_mult break if not is_custom: # bias_lr_mult affects all bias parameters # except for norm.bias dcn.conv_offset.bias if name == "bias" and not (is_norm or is_dcn_module): param_group["lr"] = self.base_lr * bias_lr_mult if ( prefix.find("conv_offset") != -1 and is_dcn_module and isinstance(module, torch.nn.Conv2d) ): # deal with both dcn_offset's bias & weight param_group["lr"] = self.base_lr * dcn_offset_lr_mult # apply weight decay policies if self.base_wd is not None: # norm decay if is_norm: param_group["weight_decay"] = self.base_wd * norm_decay_mult # depth-wise conv elif is_dwconv: param_group["weight_decay"] = self.base_wd * dwconv_decay_mult # bias lr and decay elif name == "bias" and not is_dcn_module: # TODO: current bias_decay_mult will have affect on DCN param_group["weight_decay"] = self.base_wd * bias_decay_mult params.append(param_group) is_dcn_module = False for child_name, child_mod in module.named_children(): child_prefix = f"{prefix}.{child_name}" if prefix else child_name self.add_params( params, child_mod, prefix=child_prefix, is_dcn_module=is_dcn_module ) def __call__(self, model): if hasattr(model, "module"): model = model.module optimizer_cfg = self.optimizer_cfg.copy() # if no paramwise option is specified, just use the global setting if not self.paramwise_cfg: optimizer_cfg["params"] = model.parameters() return build_from_cfg(optimizer_cfg, OPTIMIZERS) # set param-wise lr and weight decay recursively params = [] self.add_params(params, model) optimizer_cfg["params"] = params return build_from_cfg(optimizer_cfg, OPTIMIZERS)

import warnings import torch from torch.nn import GroupNorm, LayerNorm from torch.nn.modules.batchnorm import _BatchNorm from torch.nn.modules.instancenorm import _InstanceNorm from ..utils import build_from_cfg from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS @OPTIMIZER_BUILDERS.register_module class DefaultOptimizerConstructor: """Default constructor for optimizers. By default each parameter share the same optimizer settings, and we provide an argument ``paramwise_cfg`` to specify parameter-wise settings. It is a dict and may contain the following fields: - ``custom_keys`` (dict): Specified parameters-wise settings by keys. If one of the keys in ``custom_keys`` is a substring of the name of one parameter, then the setting of the parameter will be specified by ``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will be ignored. It should be noted that the aforementioned ``key`` is the longest key that is a substring of the name of the parameter. If there are multiple matched keys with the same length, then the key with lower alphabet order will be chosen. ``custom_keys[key]`` should be a dict and may contain fields ``lr_mult`` and ``decay_mult``. See Example 2 below. - ``bias_lr_mult`` (float): It will be multiplied to the learning rate for all bias parameters (except for those in normalization layers and offset layers of DCN). - ``bias_decay_mult`` (float): It will be multiplied to the weight decay for all bias parameters (except for those in normalization layers, depthwise conv layers, offset layers of DCN). - ``norm_decay_mult`` (float): It will be multiplied to the weight decay for all weight and bias parameters of normalization layers. - ``dwconv_decay_mult`` (float): It will be multiplied to the weight decay for all weight and bias parameters of depthwise conv layers. - ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning rate for parameters of offset layer in the deformable convs of a model. - ``bypass_duplicate`` (bool): If true, the duplicate parameters would not be added into optimizer. Default: False. Note: 1. If the option ``dcn_offset_lr_mult`` is used, the constructor will override the effect of ``bias_lr_mult`` in the bias of offset layer. So be careful when using both ``bias_lr_mult`` and ``dcn_offset_lr_mult``. If you wish to apply both of them to the offset layer in deformable convs, set ``dcn_offset_lr_mult`` to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``. 2. If the option ``dcn_offset_lr_mult`` is used, the constructor will apply it to all the DCN layers in the model. So be careful when the model contains multiple DCN layers in places other than backbone. Args: model (:obj:`nn.Module`): The model with parameters to be optimized. optimizer_cfg (dict): The config dict of the optimizer. Positional fields are - `type`: class name of the optimizer. Optional fields are - any arguments of the corresponding optimizer type, e.g., lr, weight_decay, momentum, etc. paramwise_cfg (dict, optional): Parameter-wise options. Example 1: >>> model = torch.nn.modules.Conv1d(1, 1, 1) >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9, >>> weight_decay=0.0001) >>> paramwise_cfg = dict(norm_decay_mult=0.) >>> optim_builder = DefaultOptimizerConstructor( >>> optimizer_cfg, paramwise_cfg) >>> optimizer = optim_builder(model) Example 2: >>> # assume model have attribute model.backbone and model.cls_head >>> optimizer_cfg = dict(type='SGD', lr=0.01, weight_decay=0.95) >>> paramwise_cfg = dict(custom_keys={ '.backbone': dict(lr_mult=0.1, decay_mult=0.9)}) >>> optim_builder = DefaultOptimizerConstructor( >>> optimizer_cfg, paramwise_cfg) >>> optimizer = optim_builder(model) >>> # Then the `lr` and `weight_decay` for model.backbone is >>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for >>> # model.cls_head is (0.01, 0.95). """ def __init__(self, optimizer_cfg, paramwise_cfg=None): if not isinstance(optimizer_cfg, dict): raise TypeError( "optimizer_cfg should be a dict", f"but got {type(optimizer_cfg)}" ) self.optimizer_cfg = optimizer_cfg self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg self.base_lr = optimizer_cfg.get("lr", None) self.base_wd = optimizer_cfg.get("weight_decay", None) self._validate_cfg() def _validate_cfg(self): if not isinstance(self.paramwise_cfg, dict): raise TypeError( "paramwise_cfg should be None or a dict, " f"but got {type(self.paramwise_cfg)}" ) if "custom_keys" in self.paramwise_cfg: if not isinstance(self.paramwise_cfg["custom_keys"], dict): raise TypeError( "If specified, custom_keys must be a dict, " f'but got {type(self.paramwise_cfg["custom_keys"])}' ) if self.base_wd is None: for key in self.paramwise_cfg["custom_keys"]: if "decay_mult" in self.paramwise_cfg["custom_keys"][key]: raise ValueError("base_wd should not be None") # get base lr and weight decay # weight_decay must be explicitly specified if mult is specified if ( "bias_decay_mult" in self.paramwise_cfg or "norm_decay_mult" in self.paramwise_cfg or "dwconv_decay_mult" in self.paramwise_cfg ): if self.base_wd is None: raise ValueError("base_wd should not be None") def _is_in(self, param_group, param_group_list): # assert is_list_of(param_group_list, dict) param = set(param_group["params"]) param_set = set() for group in param_group_list: param_set.update(set(group["params"])) return not param.isdisjoint(param_set) def add_params(self, params, module, prefix="", is_dcn_module=None): """Add all parameters of module to the params list. The parameters of the given module will be added to the list of param groups, with specific rules defined by paramwise_cfg. Args: params (list[dict]): A list of param groups, it will be modified in place. module (nn.Module): The module to be added. prefix (str): The prefix of the module is_dcn_module (int|float|None): If the current module is a submodule of DCN, `is_dcn_module` will be passed to control conv_offset layer's learning rate. Defaults to None. """ # get param-wise options custom_keys = self.paramwise_cfg.get("custom_keys", {}) # first sort with alphabet order and then sort with reversed len of str sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True) bias_lr_mult = self.paramwise_cfg.get("bias_lr_mult", 1.0) bias_decay_mult = self.paramwise_cfg.get("bias_decay_mult", 1.0) norm_decay_mult = self.paramwise_cfg.get("norm_decay_mult", 1.0) dwconv_decay_mult = self.paramwise_cfg.get("dwconv_decay_mult", 1.0) bypass_duplicate = self.paramwise_cfg.get("bypass_duplicate", False) dcn_offset_lr_mult = self.paramwise_cfg.get("dcn_offset_lr_mult", 1.0) # special rules for norm layers and depth-wise conv layers is_norm = isinstance(module, (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)) is_dwconv = ( isinstance(module, torch.nn.Conv2d) and module.in_channels == module.groups ) for name, param in module.named_parameters(recurse=False): param_group = {"params": [param]} if not param.requires_grad: params.append(param_group) continue if bypass_duplicate and self._is_in(param_group, params): warnings.warn( f"{prefix} is duplicate. It is skipped since " f"bypass_duplicate={bypass_duplicate}" ) continue # if the parameter match one of the custom keys, ignore other rules is_custom = False for key in sorted_keys: if key in f"{prefix}.{name}": is_custom = True lr_mult = custom_keys[key].get("lr_mult", 1.0) param_group["lr"] = self.base_lr * lr_mult if self.base_wd is not None: decay_mult = custom_keys[key].get("decay_mult", 1.0) param_group["weight_decay"] = self.base_wd * decay_mult break if not is_custom: # bias_lr_mult affects all bias parameters # except for norm.bias dcn.conv_offset.bias if name == "bias" and not (is_norm or is_dcn_module): param_group["lr"] = self.base_lr * bias_lr_mult if ( prefix.find("conv_offset") != -1 and is_dcn_module and isinstance(module, torch.nn.Conv2d) ): # deal with both dcn_offset's bias & weight param_group["lr"] = self.base_lr * dcn_offset_lr_mult # apply weight decay policies if self.base_wd is not None: # norm decay if is_norm: param_group["weight_decay"] = self.base_wd * norm_decay_mult # depth-wise conv elif is_dwconv: param_group["weight_decay"] = self.base_wd * dwconv_decay_mult # bias lr and decay elif name == "bias" and not is_dcn_module: # TODO: current bias_decay_mult will have affect on DCN param_group["weight_decay"] = self.base_wd * bias_decay_mult params.append(param_group) is_dcn_module = False for child_name, child_mod in module.named_children(): child_prefix = f"{prefix}.{child_name}" if prefix else child_name self.add_params( params, child_mod, prefix=child_prefix, is_dcn_module=is_dcn_module ) def __call__(self, model): if hasattr(model, "module"): model = model.module optimizer_cfg = self.optimizer_cfg.copy() # if no paramwise option is specified, just use the global setting if not self.paramwise_cfg: optimizer_cfg["params"] = model.parameters() return build_from_cfg(optimizer_cfg, OPTIMIZERS) # set param-wise lr and weight decay recursively params = [] self.add_params(params, model) optimizer_cfg["params"] = params return build_from_cfg(optimizer_cfg, OPTIMIZERS)

from pathlib import Path import shutil import os import sys def convert_repo_to_fork(path2repo): path_fork = path2repo.parent / "yolov5-icevision" # path of our fork if path_fork.is_dir(): shutil.rmtree(path_fork) path_fork.mkdir(exist_ok=True) _ = shutil.copytree(path2repo, path_fork, dirs_exist_ok=True) (path_fork / "yolov5").mkdir(exist_ok=True) sources = [ path_fork / d for d in next(os.walk(path_fork))[1] if "git" not in d and "yolo" not in d ] dests = [(path_fork / "yolov5") / source.stem for source in sources] for source, dest in zip(sources, dests): _ = shutil.move(source, dest) init_content = """ __version__ = "4.0.0" import imp _, path, _ = imp.find_module('yolov5') """ f = open(path_fork / "yolov5" / "__init__.py", "w+") f.write(init_content) f.close() manifest_content = """ # Include the README include *.md # Include the license file include LICENSE # Include setup.py include setup.py # Include the data files recursive-include yolov5/data * recursive-include yolov5/models * """ f = open(path_fork / "MANIFEST.in", "w+") f.write(manifest_content) f.close() pypi_release_content = """ name: PyPi Release on: release: types: [created] workflow_dispatch: jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.x' - name: Setup dependencies run: python -m pip install --user --upgrade setuptools wheel - name: Build package run: python setup.py sdist bdist_wheel - name: Publish package to TestPyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.test_pypi_password }} repository_url: https://test.pypi.org/legacy/ verbose: true - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.pypi_password }} verbose: true """ f = open(path_fork / ".github/workflows/pypi-release.yml", "w+") f.write(pypi_release_content) f.close() setup_cfg_content = """ # reference: https://setuptools.readthedocs.io/en/latest/userguide/declarative_config.html [metadata] name = yolov5-icevision version = 4.0.0 author = ultralytics author_email = glenn.jocher@ultralytics.com description = YOLOv5 long_description = file: README.md long_description_content_type = text/markdown keywords = object detection, machine learning license = GNU General Public License v3.0 classifiers = Development Status :: 4 - Beta Intended Audience :: Developers Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Topic :: Scientific/Engineering :: Artificial Intelligence Topic :: Scientific/Engineering :: Image Recognition [options] python_requires = >=3.6,<4 zip_safe = False include_package_data = True packages = find: install_requires = Cython matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.2 Pillow PyYAML>=5.3.1 scipy>=1.4.1 tensorboard>=2.2 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 seaborn>=0.11.0 [options.extras_require] all = pandas """ f = open(path_fork / "setup.cfg", "w+") f.write(setup_cfg_content) f.close() setup_py_content = """ from setuptools import setup if __name__ == "__main__": setup() """ f = open(path_fork / "setup.py", "w+") f.write(setup_py_content) f.close() def replace_imports(path): bads = ["from utils", "from models", "import utils", "import models"] goods = [ "from yolov5.utils", "from yolov5.models", "import yolov5.utils", "import yolov5.models", ] with open(path, "r") as file: filedata = file.read() if any(bad in filedata for bad in bads): for bad, good in zip(bads, goods): filedata = filedata.replace(bad, good) with open(path, "w") as file: file.write(filedata) for root, _, files in os.walk(path_fork): for f in files: if f.endswith(".py"): replace_imports(os.path.join(root, f)) with open(path_fork / ".gitignore", "r+") as f: d = f.readlines() f.seek(0) for i in d: if i.strip() != "*.cfg": f.write(i) f.truncate() if __name__ == "__main__": path2repo = Path(sys.argv[1]) convert_repo_to_fork(path2repo) print( f"Conversion completed successfully. Please find the fork here: {str(path2repo.parent / "yolov5-icevision")}" )

from pathlib import Path import shutil import os import sys def convert_repo_to_fork(path2repo): path_fork = path2repo.parent / "yolov5-icevision" # path of our fork if path_fork.is_dir(): shutil.rmtree(path_fork) path_fork.mkdir(exist_ok=True) _ = shutil.copytree(path2repo, path_fork, dirs_exist_ok=True) (path_fork / "yolov5").mkdir(exist_ok=True) sources = [ path_fork / d for d in next(os.walk(path_fork))[1] if "git" not in d and "yolo" not in d ] dests = [(path_fork / "yolov5") / source.stem for source in sources] for source, dest in zip(sources, dests): _ = shutil.move(source, dest) init_content = """ __version__ = "4.0.0" import imp _, path, _ = imp.find_module('yolov5') """ f = open(path_fork / "yolov5" / "__init__.py", "w+") f.write(init_content) f.close() manifest_content = """ # Include the README include *.md # Include the license file include LICENSE # Include setup.py include setup.py # Include the data files recursive-include yolov5/data * recursive-include yolov5/models * """ f = open(path_fork / "MANIFEST.in", "w+") f.write(manifest_content) f.close() pypi_release_content = """ name: PyPi Release on: release: types: [created] workflow_dispatch: jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.x' - name: Setup dependencies run: python -m pip install --user --upgrade setuptools wheel - name: Build package run: python setup.py sdist bdist_wheel - name: Publish package to TestPyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.test_pypi_password }} repository_url: https://test.pypi.org/legacy/ verbose: true - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@master with: user: __token__ password: ${{ secrets.pypi_password }} verbose: true """ f = open(path_fork / ".github/workflows/pypi-release.yml", "w+") f.write(pypi_release_content) f.close() setup_cfg_content = """ # reference: https://setuptools.readthedocs.io/en/latest/userguide/declarative_config.html [metadata] name = yolov5-icevision version = 4.0.0 author = ultralytics author_email = glenn.jocher@ultralytics.com description = YOLOv5 long_description = file: README.md long_description_content_type = text/markdown keywords = object detection, machine learning license = GNU General Public License v3.0 classifiers = Development Status :: 4 - Beta Intended Audience :: Developers Programming Language :: Python :: 3.6 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Topic :: Scientific/Engineering :: Artificial Intelligence Topic :: Scientific/Engineering :: Image Recognition [options] python_requires = >=3.6,<4 zip_safe = False include_package_data = True packages = find: install_requires = Cython matplotlib>=3.2.2 numpy>=1.18.5 opencv-python>=4.1.2 Pillow PyYAML>=5.3.1 scipy>=1.4.1 tensorboard>=2.2 torch>=1.7.0 torchvision>=0.8.1 tqdm>=4.41.0 seaborn>=0.11.0 [options.extras_require] all = pandas """ f = open(path_fork / "setup.cfg", "w+") f.write(setup_cfg_content) f.close() setup_py_content = """ from setuptools import setup if __name__ == "__main__": setup() """ f = open(path_fork / "setup.py", "w+") f.write(setup_py_content) f.close() def replace_imports(path): bads = ["from utils", "from models", "import utils", "import models"] goods = [ "from yolov5.utils", "from yolov5.models", "import yolov5.utils", "import yolov5.models", ] with open(path, "r") as file: filedata = file.read() if any(bad in filedata for bad in bads): for bad, good in zip(bads, goods): filedata = filedata.replace(bad, good) with open(path, "w") as file: file.write(filedata) for root, _, files in os.walk(path_fork): for f in files: if f.endswith(".py"): replace_imports(os.path.join(root, f)) with open(path_fork / ".gitignore", "r+") as f: d = f.readlines() f.seek(0) for i in d: if i.strip() != "*.cfg": f.write(i) f.truncate() if __name__ == "__main__": path2repo = Path(sys.argv[1]) convert_repo_to_fork(path2repo) print( f"Conversion completed successfully. Please find the fork here: {str(path2repo.parent / 'yolov5-icevision')}" )

from pandac.PandaModules import Point3, VBase3, Vec4, Vec3 objectStruct = {'Objects': {'1149705583.09Shochet': {'Type': 'Island','Name': 'VegasIsland','File': '','Environment': 'Interior','Minimap': False,'Objects': {'1149705605.5Shochet': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1149705605.5Shochet0': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1149705607.02Shochet': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1149705607.63Shochet': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1149705619.08Shochet': {'Type': 'Cell Portal Area','Name': 'cell_pier','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1149705619.08Shochet0': {'Type': 'Parlor Game','Category': 'Blackjack','BetMultiplier': '1','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(421.053, -131.608, 5.287),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/Cardtable_HalfCircle'}},'1149705632.05Shochet': {'Type': 'Parlor Game','Category': 'Holdem','BetMultiplier': '1','Hpr': VBase3(45.0, 0.0, 0.0),'Pos': Point3(443.184, -123.256, 5.295),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/Cardtable_Pill'}},'1169451658.54Shochet': {'Type': 'Searchable Container','Aggro Radius': 5.0,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(508.36, -134.581, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/desk_gov'},'searchTime': '6.0','type': 'Desk'},'1169451790.01Shochet': {'Type': 'Searchable Container','Aggro Radius': 5.0,'Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(480.699, -161.909, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/wellA'},'searchTime': '6.0','type': 'WellA'},'1171348677.72Shochet': {'Type': 'Interactive Prop','Hpr': VBase3(96.302, 0.0, 0.0),'Pos': Point3(305.078, -115.012, 4.769),'Scale': VBase3(1.0, 1.0, 1.0),'Visual': {'Model': 'models/props/dummy_zero'},'interactAble': 'player','interactType': 'hit'},'1186785500.34Shochet': {'Type': 'Player Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Index': 1,'Pos': Point3(485.258, -86.884, 5.213),'Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'All','Visual': {'Color': (0.5, 0.5, 0.5, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1149706548.67Shochet': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1149706548.67Shochet0': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1149706577.28Shochet': {'Type': 'Cell Portal Area','Name': 'cell_spanish_town','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1149706632.97Shochet': {'Type': 'Spawn Node','Aggro Radius': '12.0000','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': '12.0000','Pause Chance': '100','Pause Duration': '30','Pos': Point3(500.886, 153.538, 45.292),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Scorpion','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','Visual': {'Model': 'models/misc/smiley'}},'1157596044.44jasyeung': {'Type': 'Townsperson','Category': 'MedicineMan','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(-173.418, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(454.118, 103.165, 41.541),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169192829.53Shochet': {'Type': 'Townsperson','Category': 'Commoner','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(128.625, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(572.566, 129.349, 42.792),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169616338.61Shochet': {'Type': 'Townsperson','Category': 'Shipwright','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(95.14, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(594.082, 77.364, 42.876),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Player','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0},'1169616428.63Shochet': {'Type': 'Object Spawn Node','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(470.936, 243.777, 49.657),'Priority': '1','Scale': VBase3(1.0, 1.0, 1.0),'SpawnDelay': '10','Spawnables': 'Buried Treasure','Visual': {'Color': (0.8, 0.2, 0.65, 1),'Model': 'models/misc/smiley'},'startingDepth': '5'},'1171691663.17Shochet': {'Type': 'Townsperson','Category': 'Gypsy','AnimSet': 'default','AuraFX': 'None','CustomModel': 'None','GhostColor': 'None','GhostFX': 0,'Greeting Animation': '','Hpr': VBase3(-161.55, 0.0, 0.0),'Instanced World': 'None','Level': '37','Notice Animation 1': '','Notice Animation 2': '','Patrol Radius': 12,'Pos': Point3(446.386, 327.483, 54.071),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Requires Quest Interest': False,'Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Start State': 'Idle','StartFrame': '0','Team': 'Villager','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','spawnTimeBegin': 0.0,'spawnTimeEnd': 0.0}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1154059325.91Shochet': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1154059325.91Shochet0': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1154059341.09Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(223.47, -25.232, 5.178),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Pig','Start State': 'Walk','StartFrame': '0'},'1154059344.67Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(230.53, -15.957, 5.073),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Chicken','Start State': 'Walk','StartFrame': '0'},'1154059351.97Shochet': {'Type': 'Animal','Hpr': Point3(0.0, 0.0, 0.0),'Patrol Radius': 12,'Pos': Point3(239.433, -6.269, 4.768),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Rooster','Start State': 'Walk','StartFrame': '0'},'1154059362.19Shochet': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(-93.119, -296.023, 1.391),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'Crab','Start State': 'Idle','StartFrame': '0'},'1154059366.69Shochet': {'Type': 'Creature','Boss': False,'Boss Name': 'Anonymous','Hpr': Point3(0.0, 0.0, 0.0),'Level': '37','Patrol Radius': 12,'Pos': Point3(267.997, 4.319, 7.507),'PoseAnim': '','PoseFrame': '','Respawns': True,'Scale': VBase3(1.0, 1.0, 1.0),'Species': 'FlyTrap','Start State': 'Idle','StartFrame': '0'},'1157596022.35jasyeung': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1157596022.35jasyeung0': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1165018948.05sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_1','Hpr': VBase3(-18.331, 0.0, 0.0),'Pos': Point3(-219.917, -319.235, 0.595),'Scale': VBase3(1.0, 1.0, 1.0)},'1165018950.47sdnaik': {'Type': 'Locator Node','Name': 'portal_exterior_2','Hpr': VBase3(68.97, 0.0, 0.0),'Pos': Point3(-285.103, -58.817, 44.049),'Scale': VBase3(1.0, 1.0, 1.0)},'1169192695.17Shochet': {'Type': 'Port Collision Sphere','Name': 'VegasPort','Hpr': Point3(0.0, 0.0, 0.0),'Pos': Point3(147.216, -168.582, 0.0),'Scale': VBase3(470.212, 470.212, 470.212),'VisSize': '','Visual': {'Color': (0.5, 0.5, 1.0, 0.2),'Model': 'models/misc/smiley'}},'1169192874.58Shochet': {'Type': 'Cell Portal Area','Name': 'cell_green_area','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1165019061.13sdnaik': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': '12.0000','Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(119.094, -74.897, 4.167),'PoseAnim': '','PoseFrame': '','PropFXLeft': 'None','PropFXRight': 'None','PropLeft': 'None','PropRight': 'None','Scale': VBase3(1.0, 1.0, 1.0),'Spawnables': 'Bat','Start State': 'Ambush','StartFrame': '0','Team': '1','TrailFX': 'None','TrailLeft': 'None','TrailRight': 'None','VisSize': '','Visual': {'Color': (0, 0, 0.65, 1),'Model': 'models/misc/smiley'}}},'Pos': Point3(0.0, 0.0, 0.0),'Scale': VBase3(1.0, 1.0, 1.0)},'1169192882.95Shochet': {'Type': 'Cell Portal Area','Name': 'cell_shanty_town','Hpr': Point3(0.0, 0.0, 0.0),'Objects': {'1165019080.61sdnaik': {'Type': 'Spawn Node','AnimSet': 'default','AuraFX': 'None','Hpr': Point3(0.0, 0.0, 0.0),'Min Population': '2','Patrol Radius': 12,'Pause Chance': 100,'Pause Duration': 30,'Pos': Point3(180.048, 221.152, 66.347),'PoseAnim': 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""" Various ugly utility functions for twill. Apart from various simple utility functions, twill's robust parsing code is implemented in the ConfigurableParsingFactory class. """ import os import re from collections import namedtuple from lxml import html try: import tidylib except (ImportError, OSError): # ImportError can be raised when PyTidyLib package is not installed # OSError can be raised when the HTML Tidy shared library is not installed tidylib = None from . import log, twill_ext from .errors import TwillException Link = namedtuple('Link', 'text, url') class Singleton: """A mixin class to create singleton objects.""" def __new__(cls, *args, **kwargs): it = cls.__dict__.get('__it__') if it is not None: return it cls.__it__ = it = object.__new__(cls) return it @classmethod def reset(cls): cls.__it__ = None class ResultWrapper: """Deal with request results, and present them in a unified form. These objects are returned by browser._journey()-wrapped functions. """ def __init__(self, response): self.response = response self.encoding = response.encoding try: self.tree = html.fromstring(self.text) except ValueError: # may happen when there is an XML encoding declaration self.tree = html.fromstring(self.content) self.xpath = self.tree.xpath self._fix_forms() @property def url(self): """"Get the url of the result page.""" return self.response.url @property def http_code(self): """Get the http status code of the result page.""" return self.response.status_code @property def text(self): """Get the text of the result page.""" return self.response.text @property def content(self): """Get the binary content of the result page.""" return self.response.content @property def headers(self): """Get the headers of the result page.""" return self.response.headers @property def title(self): """Get the title of the result page.""" try: return self.xpath('//title[1]/text()')[0] except IndexError: return None @property def links(self): """Get all links in the result page.""" return [Link(a.text_content(), a.get('href')) for a in self.xpath('//a[@href]')] def find_link(self, pattern): """Find a link with a given pattern on the result page.""" regex = re.compile(pattern) for link in self.links: if regex.search(link.text) or regex.search(link.url): return link return None def form(self, formname=1): """Get the form with the given name on the result page""" forms = self.forms if isinstance(formname, str): # first, try ID for form in forms: form_id = form.get('id') if form_id and form_id == formname: return form # next, try regex with name regex = re.compile(formname) for form in forms: name = form.get('name') if name and regex.search(name): return form # last, try number try: formnum = int(formname) - 1 if not 0 <= formnum < len(forms): raise IndexError except (ValueError, IndexError): return None else: return forms[formnum] def _fix_forms(self): """Fix forms on the page for use with twill.""" # put all stray fields into a form orphans = self.xpath('//input[not(ancestor::form)]') if orphans: form = [b'<form>'] for orphan in orphans: form.append(html.tostring(orphan)) form.append(b'</form>') form = b''.join(form) self.forms = html.fromstring(form).forms self.forms.extend(self.tree.forms) else: self.forms = self.tree.forms # convert all submit button elements to input elements, since # otherwise lxml will not recognize them as form input fields for form in self.forms: for button in form.xpath("//button[@type='submit']"): button.tag = 'input' def trunc(s, length): """Truncate a string to a given length. The string is truncated by cutting off the last (length-4) characters and replacing them with ' ...' """ if s and len(s) > length: return s[:length - 4] + ' ...' return s or '' def print_form(form, n): """Pretty-print the given form, with the assigned number.""" info = log.info name = form.get('name') if name: info('\nForm name=%s (#%d)', name, n + 1) else: info('\nForm #%d', n + 1) if form.inputs is not None: info('## __Name__________________' ' __Type___ __ID________ __Value__________________') for n, field in enumerate(form.inputs, 1): value = field.value if hasattr(field, 'value_options'): items = ', '.join( f"'{getattr(opt, "name", opt)}'" for opt in field.value_options) value_displayed = f'{value} of {items}' else: value_displayed = f'{value}' field_name = field.name field_type = getattr(field, 'type', 'select') field_id = field.get('id') strings = ( f'{n:2}', f'{trunc(field_name, 24):24}', f'{trunc(field_type, 9):9}', f'{trunc(field_id, 12):12}', trunc(value_displayed, 40)) info(' '.join(strings)) info('') def make_boolean(value): """Convert the input value into a boolean.""" value = str(value).lower().strip() # true/false if value in ('true', 'false'): return value == 'true' # 0/nonzero try: ival = int(value) except ValueError: pass else: return bool(ival) # +/- if value in ('+', '-'): return value == '+' # on/off if value in ('on', 'off'): return value == 'on' raise TwillException(f"unable to convert '{value}' into true/false") def make_int(value): """Convert the input value into an int.""" try: ival = int(value) except Exception: pass else: return ival raise TwillException(f"unable to convert '{value}' into an int") def set_form_control_value(control, value): """Set the given control to the given value The controls can be checkboxes, select elements etc. """ if isinstance(control, html.InputElement): if control.checkable: try: value = make_boolean(value) except TwillException: # if there's more than one checkbox, # it should be a html.CheckboxGroup, see below. pass else: control.checked = value elif control.type not in ('submit', 'image'): control.value = value elif isinstance(control, (html.TextareaElement, html.RadioGroup)): control.value = value elif isinstance(control, html.CheckboxGroup): if value.startswith('-'): value = value[1:] try: control.value.remove(value) except KeyError: pass else: if value.startswith('+'): value = value[1:] control.value.add(value) elif isinstance(control, html.SelectElement): # for ListControls we need to find the right *value*, # and figure out if we want to *select* or *deselect* if value.startswith('-'): add = False value = value[1:] else: add = True if value.startswith('+'): value = value[1:] # now, select the value. options = [opt.strip() for opt in control.value_options] option_names = [(c.text or '').strip() for c in control.getchildren()] full_options = dict(zip(option_names, options)) for name, opt in full_options.items(): if value not in (name, opt): continue if isinstance(control.value, html.MultipleSelectOptions): if add: control.value.add(opt) elif opt in control.value: control.value.remove(opt) else: if add: control.value = opt else: control.value = None break else: raise TwillException('Attempt to set an invalid value') else: raise TwillException('Attempt to set value on invalid control') def _all_the_same_submit(matches): """Check if a list of controls all belong to the same control. For use with checkboxes, hidden, and submit buttons. """ name = value = None for match in matches: if not isinstance(match, html.InputElement): return False if match.type not in ('submit', 'hidden'): return False if name is None: name = match.name value = match.value elif match.name != name or match.value != value: return False return True def _all_the_same_checkbox(matches): """Check if a list of controls all belong to the same checkbox. Hidden controls can combine with checkboxes, to allow form processors to ensure a False value is returned even if user does not check the checkbox. Without the hidden control, no value would be returned. """ name = None for match in matches: if not isinstance(match, html.InputElement): return False if match.type not in ('checkbox', 'hidden'): return False if name is None: name = match.name else: if match.name != name: return False return True def unique_match(matches): """Check whether a match is unique""" return (len(matches) == 1 or _all_the_same_checkbox(matches) or _all_the_same_submit(matches)) def run_tidy(html): """Run HTML Tidy on the given HTML string. Return a 2-tuple (output, errors). (None, None) will be returned if PyTidyLib (or the required shared library for tidy) isn't installed. """ from .commands import options require_tidy = options.get('require_tidy') if not tidylib: if require_tidy: raise TwillException( 'Option require_tidy is set, but PyTidyLib is not installed') return None, None opts = {key[5:].replace('_', '-'): value for key, value in options.items() if key.startswith('tidy_')} clean_html, errors = tidylib.tidy_document(html, opts) return clean_html, errors def _equiv_refresh_interval(): """Get smallest interval for which the browser should follow redirects. Redirection happens if the given interval is smaller than this. """ from .commands import options return options.get('equiv_refresh_interval') def is_hidden_filename(filename): """Check if this is a hidden file (starting with a dot).""" return filename not in ( '.', '..') and os.path.basename(filename).startswith('.') def is_twill_filename(filename): """Check if the given filename has the twill file extension.""" return filename.endswith(twill_ext) and not is_hidden_filename(filename) def make_twill_filename(name): """Add the twill extension to the name of a script if necessary.""" if name not in ('.', '..'): twillname, ext = os.path.splitext(name) if not ext: twillname += twill_ext if os.path.exists(twillname): name = twillname return name def gather_filenames(arglist): """Collect script files from within directories.""" names = [] for arg in arglist: name = make_twill_filename(arg) if os.path.isdir(name): for dirpath, dirnames, filenames in os.walk(arg): dirnames[:] = [ d for d in dirnames if not is_hidden_filename(d)] for filename in filenames: if not is_twill_filename(filename): continue filename = os.path.join(dirpath, filename) names.append(filename) else: names.append(name) return names

""" Various ugly utility functions for twill. Apart from various simple utility functions, twill's robust parsing code is implemented in the ConfigurableParsingFactory class. """ import os import re from collections import namedtuple from lxml import html try: import tidylib except (ImportError, OSError): # ImportError can be raised when PyTidyLib package is not installed # OSError can be raised when the HTML Tidy shared library is not installed tidylib = None from . import log, twill_ext from .errors import TwillException Link = namedtuple('Link', 'text, url') class Singleton: """A mixin class to create singleton objects.""" def __new__(cls, *args, **kwargs): it = cls.__dict__.get('__it__') if it is not None: return it cls.__it__ = it = object.__new__(cls) return it @classmethod def reset(cls): cls.__it__ = None class ResultWrapper: """Deal with request results, and present them in a unified form. These objects are returned by browser._journey()-wrapped functions. """ def __init__(self, response): self.response = response self.encoding = response.encoding try: self.tree = html.fromstring(self.text) except ValueError: # may happen when there is an XML encoding declaration self.tree = html.fromstring(self.content) self.xpath = self.tree.xpath self._fix_forms() @property def url(self): """"Get the url of the result page.""" return self.response.url @property def http_code(self): """Get the http status code of the result page.""" return self.response.status_code @property def text(self): """Get the text of the result page.""" return self.response.text @property def content(self): """Get the binary content of the result page.""" return self.response.content @property def headers(self): """Get the headers of the result page.""" return self.response.headers @property def title(self): """Get the title of the result page.""" try: return self.xpath('//title[1]/text()')[0] except IndexError: return None @property def links(self): """Get all links in the result page.""" return [Link(a.text_content(), a.get('href')) for a in self.xpath('//a[@href]')] def find_link(self, pattern): """Find a link with a given pattern on the result page.""" regex = re.compile(pattern) for link in self.links: if regex.search(link.text) or regex.search(link.url): return link return None def form(self, formname=1): """Get the form with the given name on the result page""" forms = self.forms if isinstance(formname, str): # first, try ID for form in forms: form_id = form.get('id') if form_id and form_id == formname: return form # next, try regex with name regex = re.compile(formname) for form in forms: name = form.get('name') if name and regex.search(name): return form # last, try number try: formnum = int(formname) - 1 if not 0 <= formnum < len(forms): raise IndexError except (ValueError, IndexError): return None else: return forms[formnum] def _fix_forms(self): """Fix forms on the page for use with twill.""" # put all stray fields into a form orphans = self.xpath('//input[not(ancestor::form)]') if orphans: form = [b'<form>'] for orphan in orphans: form.append(html.tostring(orphan)) form.append(b'</form>') form = b''.join(form) self.forms = html.fromstring(form).forms self.forms.extend(self.tree.forms) else: self.forms = self.tree.forms # convert all submit button elements to input elements, since # otherwise lxml will not recognize them as form input fields for form in self.forms: for button in form.xpath("//button[@type='submit']"): button.tag = 'input' def trunc(s, length): """Truncate a string to a given length. The string is truncated by cutting off the last (length-4) characters and replacing them with ' ...' """ if s and len(s) > length: return s[:length - 4] + ' ...' return s or '' def print_form(form, n): """Pretty-print the given form, with the assigned number.""" info = log.info name = form.get('name') if name: info('\nForm name=%s (#%d)', name, n + 1) else: info('\nForm #%d', n + 1) if form.inputs is not None: info('## __Name__________________' ' __Type___ __ID________ __Value__________________') for n, field in enumerate(form.inputs, 1): value = field.value if hasattr(field, 'value_options'): items = ', '.join( f"'{getattr(opt, 'name', opt)}'" for opt in field.value_options) value_displayed = f'{value} of {items}' else: value_displayed = f'{value}' field_name = field.name field_type = getattr(field, 'type', 'select') field_id = field.get('id') strings = ( f'{n:2}', f'{trunc(field_name, 24):24}', f'{trunc(field_type, 9):9}', f'{trunc(field_id, 12):12}', trunc(value_displayed, 40)) info(' '.join(strings)) info('') def make_boolean(value): """Convert the input value into a boolean.""" value = str(value).lower().strip() # true/false if value in ('true', 'false'): return value == 'true' # 0/nonzero try: ival = int(value) except ValueError: pass else: return bool(ival) # +/- if value in ('+', '-'): return value == '+' # on/off if value in ('on', 'off'): return value == 'on' raise TwillException(f"unable to convert '{value}' into true/false") def make_int(value): """Convert the input value into an int.""" try: ival = int(value) except Exception: pass else: return ival raise TwillException(f"unable to convert '{value}' into an int") def set_form_control_value(control, value): """Set the given control to the given value The controls can be checkboxes, select elements etc. """ if isinstance(control, html.InputElement): if control.checkable: try: value = make_boolean(value) except TwillException: # if there's more than one checkbox, # it should be a html.CheckboxGroup, see below. pass else: control.checked = value elif control.type not in ('submit', 'image'): control.value = value elif isinstance(control, (html.TextareaElement, html.RadioGroup)): control.value = value elif isinstance(control, html.CheckboxGroup): if value.startswith('-'): value = value[1:] try: control.value.remove(value) except KeyError: pass else: if value.startswith('+'): value = value[1:] control.value.add(value) elif isinstance(control, html.SelectElement): # for ListControls we need to find the right *value*, # and figure out if we want to *select* or *deselect* if value.startswith('-'): add = False value = value[1:] else: add = True if value.startswith('+'): value = value[1:] # now, select the value. options = [opt.strip() for opt in control.value_options] option_names = [(c.text or '').strip() for c in control.getchildren()] full_options = dict(zip(option_names, options)) for name, opt in full_options.items(): if value not in (name, opt): continue if isinstance(control.value, html.MultipleSelectOptions): if add: control.value.add(opt) elif opt in control.value: control.value.remove(opt) else: if add: control.value = opt else: control.value = None break else: raise TwillException('Attempt to set an invalid value') else: raise TwillException('Attempt to set value on invalid control') def _all_the_same_submit(matches): """Check if a list of controls all belong to the same control. For use with checkboxes, hidden, and submit buttons. """ name = value = None for match in matches: if not isinstance(match, html.InputElement): return False if match.type not in ('submit', 'hidden'): return False if name is None: name = match.name value = match.value elif match.name != name or match.value != value: return False return True def _all_the_same_checkbox(matches): """Check if a list of controls all belong to the same checkbox. Hidden controls can combine with checkboxes, to allow form processors to ensure a False value is returned even if user does not check the checkbox. Without the hidden control, no value would be returned. """ name = None for match in matches: if not isinstance(match, html.InputElement): return False if match.type not in ('checkbox', 'hidden'): return False if name is None: name = match.name else: if match.name != name: return False return True def unique_match(matches): """Check whether a match is unique""" return (len(matches) == 1 or _all_the_same_checkbox(matches) or _all_the_same_submit(matches)) def run_tidy(html): """Run HTML Tidy on the given HTML string. Return a 2-tuple (output, errors). (None, None) will be returned if PyTidyLib (or the required shared library for tidy) isn't installed. """ from .commands import options require_tidy = options.get('require_tidy') if not tidylib: if require_tidy: raise TwillException( 'Option require_tidy is set, but PyTidyLib is not installed') return None, None opts = {key[5:].replace('_', '-'): value for key, value in options.items() if key.startswith('tidy_')} clean_html, errors = tidylib.tidy_document(html, opts) return clean_html, errors def _equiv_refresh_interval(): """Get smallest interval for which the browser should follow redirects. Redirection happens if the given interval is smaller than this. """ from .commands import options return options.get('equiv_refresh_interval') def is_hidden_filename(filename): """Check if this is a hidden file (starting with a dot).""" return filename not in ( '.', '..') and os.path.basename(filename).startswith('.') def is_twill_filename(filename): """Check if the given filename has the twill file extension.""" return filename.endswith(twill_ext) and not is_hidden_filename(filename) def make_twill_filename(name): """Add the twill extension to the name of a script if necessary.""" if name not in ('.', '..'): twillname, ext = os.path.splitext(name) if not ext: twillname += twill_ext if os.path.exists(twillname): name = twillname return name def gather_filenames(arglist): """Collect script files from within directories.""" names = [] for arg in arglist: name = make_twill_filename(arg) if os.path.isdir(name): for dirpath, dirnames, filenames in os.walk(arg): dirnames[:] = [ d for d in dirnames if not is_hidden_filename(d)] for filename in filenames: if not is_twill_filename(filename): continue filename = os.path.join(dirpath, filename) names.append(filename) else: names.append(name) return names

# Copyright 2020 Ram Rachum and collaborators. # This program is distributed under the MIT license. from __future__ import annotations import math import inspect import re import abc import random import itertools import collections.abc import statistics import concurrent.futures import enum import functools import numbers from typing import (Iterable, Union, Optional, Tuple, Any, Iterator, Type, Sequence, Callable, Hashable, Mapping, TypeVar) import dataclasses import more_itertools import numpy as np from .utils import ImmutableDict from . import utils from . import exceptions @dataclasses.dataclass(order=True, frozen=True) class ActionObservation(utils.NiceDataclass): action: Optional[Action] observation: Observation class _ActionType(abc.ABCMeta):# collections.abc.Sequence): __iter__ = lambda cls: iter(cls.all_actions) __len__ = lambda cls: len(cls.all_actions) def __getitem__(cls, i: int): if i >= len(cls): raise IndexError for j, item in enumerate(cls): if j == i: return cls raise RuntimeError @property def n_neurons(cls) -> int: try: return cls._n_neurons except AttributeError: cls._n_neurons = len(cls) return cls._n_neurons _action_regex_head = re.compile(r'[A-Za-z0-9.]') _action_regex_tail = re.compile(r'[A-Za-z0-9_.\-/>]*') _action_regex = re.compile(f'^{_action_regex_head.pattern}' f'{_action_regex_tail.pattern}$') @functools.total_ordering class Action(metaclass=_ActionType): all_actions: Sequence[Action] n_neurons: int def __lt__(self, other): return self.all_actions.index(self) < self.all_actions.index(other) def slugify(self) -> str: raw = str(self) first_letter = raw[0] prefix = '' if _action_regex_head.fullmatch(first_letter) else '0' characters = ((c if _action_regex_tail.fullmatch(c) else '-') for c in raw) result = f'{prefix}{''.join(characters)}' assert _action_regex.fullmatch(result) return result def to_neurons(self) -> np.ndarray: # Implementation for simple discrete actions. Can override. try: return self._to_neurons except AttributeError: self._to_neurons = np.array([int(self == action) for action in type(self)], dtype=np.float64) return self._to_neurons @classmethod def from_neurons(cls, neurons: Iterable) -> Action: # Implementation for simple discrete actions. Can override. return cls[tuple(neurons).index(1)] class Observation(abc.ABC): state: State legal_actions: Tuple[Action, ...] is_end: bool reward: numbers.Real n_neurons: int @abc.abstractmethod def to_neurons(self) -> np.ndarray: '''Represent the observation as an array of numbers for a neural network.''' raise NotImplementedError PlayerId = TypeVar('PlayerId', bound=Hashable) class State(abc.ABC): Observation: Type[Observation] Action: Type[Action] is_end: bool player_id_to_observation: ImmutableDict[PlayerId, Observation] @abc.abstractmethod def get_next_state_from_actions(self, player_id_to_action: Mapping[PlayerId, Action]) -> State: raise NotImplementedError @staticmethod @abc.abstractmethod def make_initial() -> State: '''Create an initial world state that we can start playing with.''' raise NotImplementedError class _SinglePlayerStateType(abc.ABCMeta): @property def Observation(cls) -> _SinglePlayerStateType: return cls class SinglePlayerState(State, Observation, metaclass=_SinglePlayerStateType): player_id_to_observation = property(lambda self: ImmutableDict({None: self})) @abc.abstractmethod def get_next_state_from_action(self, action: Action) -> SinglePlayerState: raise NotImplementedError def get_next_state_from_actions(self, player_id_to_action: Mapping[PlayerId, Action]) \ -> SinglePlayerState: return self.get_next_state_from_action(more_itertools.one(player_id_to_action.values())) from . import strategizing

# Copyright 2020 Ram Rachum and collaborators. # This program is distributed under the MIT license. from __future__ import annotations import math import inspect import re import abc import random import itertools import collections.abc import statistics import concurrent.futures import enum import functools import numbers from typing import (Iterable, Union, Optional, Tuple, Any, Iterator, Type, Sequence, Callable, Hashable, Mapping, TypeVar) import dataclasses import more_itertools import numpy as np from .utils import ImmutableDict from . import utils from . import exceptions @dataclasses.dataclass(order=True, frozen=True) class ActionObservation(utils.NiceDataclass): action: Optional[Action] observation: Observation class _ActionType(abc.ABCMeta):# collections.abc.Sequence): __iter__ = lambda cls: iter(cls.all_actions) __len__ = lambda cls: len(cls.all_actions) def __getitem__(cls, i: int): if i >= len(cls): raise IndexError for j, item in enumerate(cls): if j == i: return cls raise RuntimeError @property def n_neurons(cls) -> int: try: return cls._n_neurons except AttributeError: cls._n_neurons = len(cls) return cls._n_neurons _action_regex_head = re.compile(r'[A-Za-z0-9.]') _action_regex_tail = re.compile(r'[A-Za-z0-9_.\-/>]*') _action_regex = re.compile(f'^{_action_regex_head.pattern}' f'{_action_regex_tail.pattern}$') @functools.total_ordering class Action(metaclass=_ActionType): all_actions: Sequence[Action] n_neurons: int def __lt__(self, other): return self.all_actions.index(self) < self.all_actions.index(other) def slugify(self) -> str: raw = str(self) first_letter = raw[0] prefix = '' if _action_regex_head.fullmatch(first_letter) else '0' characters = ((c if _action_regex_tail.fullmatch(c) else '-') for c in raw) result = f'{prefix}{"".join(characters)}' assert _action_regex.fullmatch(result) return result def to_neurons(self) -> np.ndarray: # Implementation for simple discrete actions. Can override. try: return self._to_neurons except AttributeError: self._to_neurons = np.array([int(self == action) for action in type(self)], dtype=np.float64) return self._to_neurons @classmethod def from_neurons(cls, neurons: Iterable) -> Action: # Implementation for simple discrete actions. Can override. return cls[tuple(neurons).index(1)] class Observation(abc.ABC): state: State legal_actions: Tuple[Action, ...] is_end: bool reward: numbers.Real n_neurons: int @abc.abstractmethod def to_neurons(self) -> np.ndarray: '''Represent the observation as an array of numbers for a neural network.''' raise NotImplementedError PlayerId = TypeVar('PlayerId', bound=Hashable) class State(abc.ABC): Observation: Type[Observation] Action: Type[Action] is_end: bool player_id_to_observation: ImmutableDict[PlayerId, Observation] @abc.abstractmethod def get_next_state_from_actions(self, player_id_to_action: Mapping[PlayerId, Action]) -> State: raise NotImplementedError @staticmethod @abc.abstractmethod def make_initial() -> State: '''Create an initial world state that we can start playing with.''' raise NotImplementedError class _SinglePlayerStateType(abc.ABCMeta): @property def Observation(cls) -> _SinglePlayerStateType: return cls class SinglePlayerState(State, Observation, metaclass=_SinglePlayerStateType): player_id_to_observation = property(lambda self: ImmutableDict({None: self})) @abc.abstractmethod def get_next_state_from_action(self, action: Action) -> SinglePlayerState: raise NotImplementedError def get_next_state_from_actions(self, player_id_to_action: Mapping[PlayerId, Action]) \ -> SinglePlayerState: return self.get_next_state_from_action(more_itertools.one(player_id_to_action.values())) from . import strategizing

from typing import Dict, TextIO from utils.file import write_enum from utils.string import to_pascal_case def read_template() -> str: template_file: TextIO = open("./file_templates/csharp-enum.cs", "r") return template_file.read() def as_enum_row(key: object, json: object) -> str: enum_name = to_pascal_case(key) hex_val = f"0x{json[key]["unicode"]}" return f" {enum_name} = {hex_val},\n" def as_csharp_enum(icon_json: Dict): enum_template = read_template() enum_rows = "" for key in icon_json: enum_rows += as_enum_row(key, icon_json) updated_enum = enum_template.replace("<<Contents>>", enum_rows) write_enum(updated_enum, "FontAwesomeCodes.cs")

from typing import Dict, TextIO from utils.file import write_enum from utils.string import to_pascal_case def read_template() -> str: template_file: TextIO = open("./file_templates/csharp-enum.cs", "r") return template_file.read() def as_enum_row(key: object, json: object) -> str: enum_name = to_pascal_case(key) hex_val = f"0x{json[key]['unicode']}" return f" {enum_name} = {hex_val},\n" def as_csharp_enum(icon_json: Dict): enum_template = read_template() enum_rows = "" for key in icon_json: enum_rows += as_enum_row(key, icon_json) updated_enum = enum_template.replace("<<Contents>>", enum_rows) write_enum(updated_enum, "FontAwesomeCodes.cs")

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- from typing import Tuple, Dict, List, Any import itertools import configparser as CP from datetime import timedelta, datetime import dateutil.parser from traitlets import TraitType from traitlets.config.configurable import Configurable from ._debug_utils import debug_print from .dependencies import Dependencies from .constants import Constants, Schema from .my_utils import split_lex, adjust_path, is_env_var, get_env_var, is_collection, strip_if_quoted from .engine import Engine class Parser(object): default_options:Dict[str,Any] = {} traits_dict:Dict[str,TraitType] = {} @classmethod def initialize(cls, config:Configurable): cls.traits_dict = config.traits() config.observe(Parser.observe_config_changes) cls.init_default_options(config) @staticmethod def observe_config_changes(change:Dict[str,str]): if change.get('type') == 'change': name = change.get('name') obj = Parser._OPTIONS_TABLE.get(name.lower().replace("-", "").replace("_", "")) if "init" not in obj: Parser.default_options[change.get('name')] = change.get('new') @classmethod def init_default_options(cls, config:Configurable): for obj in cls._OPTIONS_TABLE.values(): if "abbreviation" not in obj: name = obj.get("flag") if "init" in obj: cls.default_options[name] = obj.get("init") elif name in cls.traits_dict: cls.default_options[name] = getattr(config, name) elif "abbreviation" not in obj: raise Exception(f"missing init in _OPTIONS_TABLE for option: {name}") @classmethod def parse(cls, _line:str, _cell:str, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->List[Dict[str,Any]]: is_cell = _cell is not None cell = f"{_line}\n{_cell or ""}" cell = cell.strip() code = cell # # split string to queries # suppress_all_results = False # tuple: sections:List[Dict[str,str]] = [] if is_cell: magic_section_name = Constants.MAGIC_NAME section_lines:List[str] = [] previous_line = " " # should be init to space for the below to work # note: splitlines don't remove the \n suffix, each line endswith \n for line in code.splitlines(True): lstripped_line = line.lstrip() if (lstripped_line.startswith(Constants.IPYKERNEL_CELL_MAGIC_PREFIX) and (previous_line.isspace() or (len(sections) > 0 and sections[-1].get("type") == "line_magic"))): if len(section_lines) > 0: sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) magic_word = lstripped_line.split(None, 1)[0] magic_section_name = magic_word[len(Constants.IPYKERNEL_CELL_MAGIC_PREFIX):] lstripped_line = lstripped_line[len(magic_word):].lstrip() if magic_section_name == Constants.MAGIC_NAME: section_lines = [] if lstripped_line.isspace() else [lstripped_line] else: section_lines = [lstripped_line] elif magic_section_name != Constants.MAGIC_NAME: section_lines.append(line) elif (lstripped_line.startswith(Constants.IPYKERNEL_LINE_MAGIC_PREFIX) and (previous_line.isspace() or (len(sections) > 0 and sections[-1].get("type") == "line_magic"))): magic_word = lstripped_line.split(None, 1)[0] magic_name = magic_word[len(Constants.IPYKERNEL_LINE_MAGIC_PREFIX):] lstripped_line = lstripped_line[len(magic_word):].lstrip() if magic_name == Constants.MAGIC_NAME: if not lstripped_line.isspace(): sections.append({"type": "line_magic", "name": magic_name, "body": lstripped_line}) else: sections.append({"type": "line_magic", "name": magic_name, "body": lstripped_line}) elif line.isspace(): if len(section_lines) > 0: not_commented_lines = [1 for seg_line in section_lines if not seg_line.lstrip().startswith("//")] if (len(not_commented_lines) > 0): sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) section_lines = [] else: section_lines.append(line) previous_line = line if len(section_lines) > 0: if magic_section_name == Constants.MAGIC_NAME: not_commented_lines = [1 for seg_line in section_lines if not seg_line.lstrip().startswith("//")] if (len(not_commented_lines) > 0): sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) else: sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) if len(sections) > 0: last_query = sections[-1].get("body").strip() if last_query == ";": suppress_all_results = True sections = sections[:-1] if len(sections) == 0: sections.append({"type": "cell_magic", "name": Constants.MAGIC_NAME, "body": ""}) else: sections.append({"type": "line_magic", "name": Constants.MAGIC_NAME, "body": code.strip()}) # # parse code to kql and options # parsed_sections = [] last_connection_string = "" for section in sections: parsed_section = cls._parse_one_section(section, is_cell, config, engines, user_ns) connection_string = parsed_section.get("connection_string") if connection_string: last_connection_string = connection_string elif len(parsed_section.get("command")) == 0: parsed_section["connection_string"] = last_connection_string if suppress_all_results: parsed_section.get("options")["suppress_results"] = True parsed_sections.append(parsed_section) if len(parsed_sections) > 0: parsed_sections[-1]["last_query"] = True return parsed_sections @classmethod def _parse_one_section(cls, section:Dict[str,str], is_cell:bool, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->Dict[str,Any]: """Separate input into (connection info, KQL statements, options)""" cell, command = cls._parse_kql_command(section, user_ns) command_name = command.get("command") if command_name is not None and command_name != "submit": cell_rest, options = cls._parse_kql_options(cell.strip(), is_cell, config, user_ns) cell_rest = cls._update_kql_command_params(command, cell_rest, user_ns) cls._validate_kql_command_params(command) if cell_rest: raise ValueError(f"command --{command_name} has too many parameters") parsed_query = {"connection_string": "", "query": "", "options": options, "command": command} return parsed_query # split to max 2 parts. First part, parts[0], is the first string. # parts = [part.strip() for part in cell.split(None, 1)] parts = split_lex(cell) # print(parts) if not parts: kql, options = cls._parse_kql_options("", is_cell, config, user_ns) parsed_query = {"connection_string": "", "query": kql, "options": options, "command": {}} return parsed_query # # replace substring of the form $name or ${name}, in windows also %name% if found in env variabes # connection_string = None conn_str = parts[0].strip() if not conn_str.startswith(('-', '+')): _was_quoted, conn_str = strip_if_quoted(conn_str) # # connection taken from a section in dsn file (file name have to be define in config.dsn_filename or specified as a parameter) # if is_collection(conn_str, "["): section = conn_str[1:-1].strip() # parse to get flag, for the case that the file nema is specified in the options code = cell[len(parts[0]):] kql, options = cls._parse_kql_options(code, is_cell, config, user_ns) parser = CP.ConfigParser() dsn_filename = adjust_path(options.get("dsn_filename", config.dsn_filename)) parser.read(dsn_filename) cfg_dict = dict(parser.items(section)) cfg_dict_lower = {k.lower().replace("_", "").replace("-", ""): v for (k, v) in cfg_dict.items()} for e in engines: if e.get_mandatory_key() in cfg_dict_lower.keys(): all_keys = set(itertools.chain(*e.get_valid_keys_combinations())) connection_kv = [f"{k}='{v}'" for k, v in cfg_dict_lower.items() if v and k in all_keys] connection_string = f"{e.get_uri_schema_name()}://{";".join(connection_kv)}" break # # connection specified starting with one of the supported prefixes # elif "://" in conn_str: sub_parts = conn_str.strip().split("://", 1) if (len(sub_parts) == 2 and sub_parts[0].lower().replace("_", "").replace("-", "") in list(itertools.chain(*[e._ALT_URI_SCHEMA_NAMES for e in engines]))): connection_string = conn_str # # connection specified as database@cluster # elif "@" in conn_str and "|" not in conn_str and "'" not in conn_str and '"' not in conn_str: connection_string = conn_str # # connection not specified, override default # if connection_string is None: connection_string = "" code = cell else: code = cell[len(parts[0]):] # # parse code to kql and options # kql, options = cls._parse_kql_options(code.strip(), is_cell, config, user_ns) kql = options.pop("query", None) or kql connection_string = options.pop("conn", None) or connection_string parsed_query = {"connection_string": connection_string.strip(), "query": kql, "options": options, "command": {}} return parsed_query @classmethod def parse_old(cls, line:str, cell:str, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->List[Dict[str,Any]]: """Separate input into (connection info, KQL statements, options)""" is_cell = cell is not None cell = f"{line}\n{cell or ""}" cell = cell.strip() parsed_queries = [] cell, command = cls._parse_kql_command(cell, user_ns) command_name = command.get("command") if command_name is not None and command_name != "submit": cell_rest, options = cls._parse_kql_options(cell.strip(), is_cell, config, user_ns) cell_rest = cls._update_kql_command_params(command, cell_rest, user_ns) cls._validate_kql_command_params(command) if cell_rest: raise ValueError(f"command --{command_name} has too many parameters") parsed_queries.append({"connection_string": "", "query": "", "options": options, "command": command}) return parsed_queries # split to max 2 parts. First part, parts[0], is the first string. # parts = [part.strip() for part in cell.split(None, 1)] parts = split_lex(cell) # print(parts) if not parts: kql, options = cls._parse_kql_options("", is_cell, config, user_ns) parsed_queries.append({"connection_string": "", "query": kql, "options": options, "command": {}}) return parsed_queries # # replace substring of the form $name or ${name}, in windows also %name% if found in env variabes # connection_string = None conn_str = parts[0].strip() if not conn_str.startswith(('-', '+')): _was_quoted, conn_str = strip_if_quoted(conn_str) # # connection taken from a section in dsn file (file name have to be define in config.dsn_filename or specified as a parameter) # if is_collection(conn_str, "["): section = conn_str[1:-1].strip() # parse to get flag, for the case that the file nema is specified in the options code = cell[len(parts[0]):] kql, options = cls._parse_kql_options(code, is_cell, config, user_ns) parser = CP.ConfigParser() dsn_filename = adjust_path(options.get("dsn_filename", config.dsn_filename)) parser.read(dsn_filename) cfg_dict = dict(parser.items(section)) cfg_dict_lower = {k.lower().replace("_", "").replace("-", ""): v for (k, v) in cfg_dict.items()} for e in engines: if e.get_mandatory_key() in cfg_dict_lower.keys(): all_keys = set(itertools.chain(*e.get_valid_keys_combinations())) connection_kv = [f"{k}='{v}'" for k, v in cfg_dict_lower.items() if v and k in all_keys] connection_string = f"{e.get_uri_schema_name()}://{";".join(connection_kv)}" break # # connection specified starting with one of the supported prefixes # elif "://" in conn_str: sub_parts = conn_str.strip().split("://", 1) if (len(sub_parts) == 2 and sub_parts[0].lower().replace("_", "").replace("-", "") in list(itertools.chain(*[e._ALT_URI_SCHEMA_NAMES for e in engines]))): connection_string = conn_str # # connection specified as database@cluster # elif "@" in conn_str and "|" not in conn_str and "'" not in conn_str and '"' not in conn_str: connection_string = conn_str # # connection not specified, override default # if connection_string is None: connection_string = "" code = cell else: code = cell[len(parts[0]):] # # split string to queries # suppress_all_results = False queries:List[str] = [] if is_cell: queryLines:List[str] = [] last_line:str = None for last_line in code.splitlines(True): # note: splitlines don't remove the \n suffix, each line endswith \n if last_line.isspace(): if len(queryLines) > 0: queries.append("".join(queryLines)) queryLines = [] else: queryLines.append(last_line) if len(queryLines) > 0: queries.append("".join(queryLines)) if len(queries) > 0: last_query = queries[-1].strip() if last_query == ";": suppress_all_results = True queries = queries[:-1] if len(queries) == 0: queries.append("") else: queries.append(code.strip()) # # parse code to kql and options # for query in queries: kql, options = cls._parse_kql_options(query.strip(), is_cell, config, user_ns) kql = options.pop("query", None) or kql connection_string = options.pop("conn", None) or connection_string if suppress_all_results: options["suppress_results"] = True parsed_queries.append({"connection_string": connection_string.strip(), "query": kql, "options": options, "command": {}}) return parsed_queries # Note: None as default is valid # commands that have no parameters, typekey should not exit or set to None # default max_params is 1 if type is not None otherwise 0 # default min_params is 1 if type is not None otherwise 0 _COMMANDS_TABLE = { "version": {"flag": "version", "type": None}, "banner": {"flag": "banner", "type": None}, "usage": {"flag": "usage", "type": None}, "submit": {"flag": "submit", "type": None}, # default "help": {"flag": "help", "type": "str", "default": "help"}, "faq": {"flag": "faq", "type": None}, "palette": {"flag": "palette", "type": None}, "palettes": {"flag": "palettes", "type": None}, # "config": {"flag": "config", "type": "str", "default": None}, "config": {"flag": "config", "type": "not_quoted_str", "allow_none": True, "max_params": 1, "min_params": 0}, "bugreport": {"flag": "bug_report", "type": None}, "conn": {"flag": "conn", "type": "str", "default": None}, # should be per connection "cache": {"flag": "cache", "type": "str", "allow_none": True}, "cachecreate": {"flag": "cache_create", "type": "str", "allow_none": True}, "cacheappend": {"flag": "cache_append", "type": "str", "allow_none": True}, "cachecreateorappend": {"flag": "cache_create_or_append", "type": "str", "allow_none": True}, "cacheremove": {"flag": "cache_remove", "type": "str", "allow_none": True}, "cachelist": {"flag": "cache_list", "type": None}, "cachestop": {"flag": "cache_stop", "type": None}, "usecache": {"flag": "use_cache", "type": "str", "allow_none": True}, "usecachestop": {"flag": "use_cache_stop", "type": "str", "allow_none": True}, "schema": {"flag": "schema", "type": "str", "default": None}, "clearssodb": {"flag": "clear_sso_db", "type": None}, "py": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "pyro": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "pyrw": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "activatekernel": {"flag": "activate_kernel", "type": None}, "deactivatekernel": {"flag": "deactivate_kernel", "type": None}, "linemagic": {"flag": "line_magic", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "cellmagic": {"flag": "cell_magic", "type": "not_quoted_str", "allow_none": True, "max_params": 3, "min_params": 3}, } @classmethod def _parse_kql_command(cls, section:Dict[str,str], user_ns:Dict[str,Any])->Tuple[str,Dict[str,Any]]: code = section.get("body") if section.get("name") != Constants.MAGIC_NAME: name = section.get("name").replace("_", "").replace("-", "") if name in ["py", "pyro", "pyrw"]: obj = cls._COMMANDS_TABLE.get(name) return ("", {"command": obj.get("flag"), "obj": obj, "params": [section.get("body"), name]}) else: # line/cell magic name = section.get("type") obj = cls._COMMANDS_TABLE.get(name.replace("_", "")) command_name = obj.get("flag") params = [] body = section.get("body") if command_name == "cell_magic": first_line = body.split("\n",1)[0] params.append(first_line.strip()) body = body[len(first_line) + 1:] params.append(body) params.append(section.get("name")) return ("", {"command": command_name, "obj": obj, "params": params}) # kql section lookup_key = None trimmed_code = code skip_words_count = 0 obj = None params_type = None command_name = None params = [] words = code.split() more_words_count = len(words) for word in words: more_words_count -= 1 if params_type == "not_quoted_str" and not word.startswith("-"): # break pass if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_code) if skip_words_count > 0: skip_words_count -= 1 trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] continue # command elif command_name is None: if not word.strip().startswith("--"): break word = word[2:] if word.startswith("-"): raise ValueError(f"unknown command --{word}, commands' prefix should be a double hyphen-minus, not a triple hyphen-minus") lookup_key = word.lower().replace("_", "").replace("-", "") obj = cls._COMMANDS_TABLE.get(lookup_key) if obj is None: raise ValueError(f"unknown command --{word}") command_name = obj.get("flag") trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] params_type = obj.get("type") if params_type is None: break # option elif word.startswith("-"): break # command's parameters else: command = {"command": command_name, "obj": obj, "params": params} EXIT_ON_OPTION = True trimmed_code, params = cls._parse_kql_command_params(command, trimmed_code, EXIT_ON_OPTION, user_ns) break if command_name is None: return (code.strip(), {}) if command_name == "python": params = params or [""] params.append(lookup_key) # type of python "py", "pyro", "pyrw" elif command_name in ["line_magic", "cell_magic"]: body = params[0] if params else "" magic_word = body.split(None, 1)[0] body = body.lstrip()[len(magic_word):] params = [] if command_name == "cell_magic": first_line = body.split("\n", 1)[0] params.append(first_line.strip()) body = body[len(first_line) + 1:] else: body = body.lstrip() params.append(body) ipykernel_prefix_len = 0 if command_name == "cell_magic" and magic_word.startswith(Constants.IPYKERNEL_CELL_MAGIC_PREFIX): ipykernel_prefix_len = len(Constants.IPYKERNEL_CELL_MAGIC_PREFIX) elif command_name == "line_magic" and magic_word.startswith(Constants.IPYKERNEL_LINE_MAGIC_PREFIX): ipykernel_prefix_len = len(Constants.IPYKERNEL_LINE_MAGIC_PREFIX) magic_name = magic_word[ipykernel_prefix_len:] params.append(magic_name) # the magic name if magic_name in ["py", "pyro", "pyrw"]: obj = cls._COMMANDS_TABLE.get(magic_name) body = params[0] if command_name == "line_magic" else f"{params[0]}\n{params[1]}" return ("", {"command": obj.get("flag"), "obj": obj, "params": [body, magic_name]}) return (trimmed_code.strip(), {"command": command_name, "obj": obj, "params": params}) @classmethod def _parse_kql_command_params(cls, command:dict, code:str, exit_on_options:bool, user_ns:Dict[str,Any])->Tuple[str,List[str]]: trimmed_code = code.strip() params = command.get("params") obj = command.get("obj") params_type = obj.get("type") if params_type == "not_quoted_str": if exit_on_options and trimmed_code.startswith("-"): # DO NOTHING, EXIT pass else: params.append(trimmed_code) trimmed_code = "" elif params_type is not None: skip_words_count = 0 command_name = command.get("command") words = code.split() for word in words: if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_code) if skip_words_count > 0: skip_words_count -= 1 trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] continue # option if exit_on_options and word.startswith("-"): break # command's parameters if params_type == "str" and word[0] in ["'",'"']: quoted_string, skip_words_count = cls.parse_quote(trimmed_code) param = quoted_string skip_words_count -= 1 else: param = word params.append(cls._parse_value("command", obj, command_name, param, user_ns)) trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] return trimmed_code.strip(), params @classmethod def _update_kql_command_params(cls, command:dict, cell_rest:str, user_ns:Dict[str,Any])->str: params = command.get("params") if len(params) == 0: obj = command.get("obj") if obj.get("type") == "not_quoted_str": params.append(cell_rest) cell_rest = "" elif len(cell_rest) > 0 and obj.get("type") is not None: DONT_EXIT_ON_OPTION = False cell_rest, params = cls._parse_kql_command_params(command, cell_rest, DONT_EXIT_ON_OPTION, user_ns) cell_rest = "" elif "default" in obj: params.append(obj.get("default")) command["params"] = params return cell_rest @classmethod def _validate_kql_command_params(cls, command:dict): params = command.get("params") command_name = command.get("command") obj = command.get("obj") _type = obj.get("type") max_params = obj.get("max_params") or (1 if _type is not None else 0) if len(params) > max_params: raise ValueError(f"command --{command_name} has too many parameters") min_params = obj.get("min_params") or (1 if _type is not None else 0) if len(params) < min_params: raise ValueError(f"command --{command_name} is missing parameter") @classmethod def validate_query_properties(cls, schema:str, properties:Dict[str,Any])->None: if type(properties) == dict: usupported_properties = [] for p in properties: prop = cls._QUERY_PROPERTIES_TABLE[p] prop_schema_list = prop.get("schema") if type(prop_schema_list) == list and schema not in prop_schema_list and len(prop_schema_list) > 0 and schema is not None: usupported_properties.append(p) if len(usupported_properties) > 0: raise ValueError(f"query properties {usupported_properties} are not supported by current connection") _QUERY_PROPERTIES_TABLE = { # NOT DOCUMENTED - (OptionBlockSplittingEnabled): Enables splitting of sequence blocks after aggregation operator. [Boolean] "block_splitting_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionDatabasePattern): Database pattern overrides database name and picks the 1st database that matches the pattern. # '*' means any database that user has access to. [String] "database_pattern": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - If set, don't fuse projection into ExternalData operator. [bool] "debug_query_externaldata_projection_fusion_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - The percentage of threads to fanout execution to for external data nodes. [int] "debug_query_fanout_threads_percent_external_data": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionDeferPartialQueryFailures): If true, disables reporting partial query failures as part of the result set. [Boolean] "deferpartialqueryfailures": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionMaterializedViewShuffleQuery): A hint to use shuffle strategy for materialized views that are referenced in the query. The property is an array of materialized views names and the shuffle keys to use. examples: 'dynamic([ { "Name": "V1", "Keys" : [ "K1", "K2" ] } ])' (shuffle view V1 by K1, K2) or 'dynamic([ { "Name": "V1" } ])' (shuffle view V1 by all keys) [dynamic] "materialized_view_shuffle": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "dict"}, # (OptionMaxMemoryConsumptionPerQueryPerNode): Overrides the default maximum amount of memory a whole query may allocate per node. [UInt64] "max_memory_consumption_per_query_per_node": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionMaxMemoryConsumptionPerIterator): Overrides the default maximum amount of memory a query operator may allocate. [UInt64] "maxmemoryconsumptionperiterator": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionMaxOutputColumns): Overrides the default maximum number of columns a query is allowed to produce. [Long] "maxoutputcolumns": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionNoRequestTimeout): Enables setting the request timeout to its maximum value. [Boolean] "norequesttimeout": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionNoTruncation): Enables suppressing truncation of the query results returned to the caller. [Boolean] "notruncation": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionPushSelectionThroughAggregation): If true, push simple selection through aggregation [Boolean] "push_selection_through_aggregation": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionAdminSuperSlackerMode): If true, delegate execution of the query to another node [Boolean] "query_admin_super_slacker_mode": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (QueryBinAutoAt): When evaluating the bin_auto() function, the start value to use. [LiteralExpression] "query_bin_auto_at": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (QueryBinAutoSize): When evaluating the bin_auto() function, the bin size value to use. [LiteralExpression] "query_bin_auto_size": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorAfterDefault): The default parameter value of the cursor_after() function when called without parameters. [string] "query_cursor_after_default": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - (OptionQueryCursorAllowReferencingStreamingIngestionTables): Enable usage of cursor functions over databases which have streaming ingestion enabled. [boolean] "query_cursor_allow_referencing_streaming_ingestion_tables": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryCursorBeforeOrAtDefault): The default parameter value of the cursor_before_or_at() function when called without parameters. [string] "query_cursor_before_or_at_default": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorCurrent): Overrides the cursor value returned by the cursor_current() or current_cursor() functions. [string] "query_cursor_current": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorDisabled): Disables usage of cursor functions in the context of the query. [boolean] "query_cursor_disabled ": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryCursorScopedTables): List of table names that should be scoped to cursor_after_default .. # cursor_before_or_at_default (upper bound is optional). [dynamic] "query_cursor_scoped_tables": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "dict"}, # (OptionQueryDataScope): Controls the query's datascope -- whether the query applies to all data or just part of it. ['default', 'all', or 'hotcache'] "query_datascope": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['default', 'all', 'hotcache']}, # (OptionQueryDateTimeScopeColumn): Controls the column name for the query's datetime scope (query_datetimescope_to / query_datetimescope_from). [String] "query_datetimescope_column": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDateTimeScopeFrom): Controls the query's datetime scope (earliest) # used as auto-applied filter on query_datetimescope_column only (if defined). [DateTime] "query_datetimescope_from": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDateTimeScopeTo): Controls the query's datetime scope (latest) # used as auto-applied filter on query_datetimescope_column only (if defined). [DateTime] "query_datetimescope_to": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDistributionNodesSpanSize): If set, controls the way sub-query merge behaves: # the executing node will introduce an additional level in the query hierarchy for each sub-group of nodes; the size of the sub-group is set by this option. [Int] "query_distribution_nodes_span": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "int"}, # (OptionQueryFanoutNodesPercent): The percentage of nodes to fanour execution to. [Int] "query_fanout_nodes_percent": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryFanoutThreadsPercent): The percentage of threads to fanout execution to. [Int] "query_fanout_threads_percent": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryForceRowLevelSecurity): If specified, forces Row Level Security rules, even if row_level_security policy is disabled [Boolean] "query_force_row_level_security": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryLanguage): Controls how the query text is to be interpreted. ['csl','kql' or 'sql'] "query_language": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['csl', 'kql', 'sql']}, # NOT DOCUMENTED - (RemoteMaterializeOperatorInCrossCluster): Enables remoting materialize operator in cross cluster query. "query_materialize_remote_subquery": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionMaxEntitiesToUnion): Overrides the default maximum number of columns a query is allowed to produce. [Long] "query_max_entities_in_union": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryNow): Overrides the datetime value returned by the now(0s) function. [DateTime] # note: cannot be relative to now() "query_now": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionDebugPython): If set, generate python debug query for the enumerated python node (default first). [Boolean or Int] "query_python_debug": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryResultsApplyGetSchema): If set, retrieves the schema of each tabular data in the results of the query instead of the data itself. [Boolean] "query_results_apply_getschema": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryResultsCacheMaxAge): If positive, controls the maximum age of the cached query results that Kusto is allowed to return [TimeSpan] "query_results_cache_max_age": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - (CostBasedOptimizerBroadcastJoinBuildMax): Max Rows count for build in broadcast join. "query_optimization_broadcast_build_maxSize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (CostBasedOptimizerBroadcastJoinProbeMin): Min Rows count for probe in broadcast join. "query_optimization_broadcast_probe_minSize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (CostBasedOptimizer): Enables automatic optimizations. "query_optimization_costbased_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionOptimizeInOperator): Optimizes in operands serialization. "query_optimization_in_operator": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (CostBasedOptimizerShufflingCardinalityThreshold): Shuffling Cardinality Threshold. "query_optimization_shuffling_cardinality": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (OptionQueryRemoteEntitiesDisabled): If set, queries cannot access remote databases / clusters. [Boolean] "query_remote_entities_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (RemoteInOperandsInQuery): Enables remoting in operands. "query_remote_in_operands": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionProgressiveQueryMinRowCountPerUpdate): Hint for Kusto as to how many records to send in each update # (Takes effect only if OptionProgressiveQueryIsProgressive is set) "query_results_progressive_row_count": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionProgressiveProgressReportPeriod): Hint for Kusto as to how often to send progress frames (Takes effect only if OptionProgressiveQueryIsProgressive is set) "query_results_progressive_update_period": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionTakeMaxRecords): Enables limiting query results to this number of records. [Long] "query_take_max_records": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryConsistency): Controls query consistency. ['strongconsistency' or 'normalconsistency' or 'weakconsistency'] "queryconsistency": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['strongconsistency', 'normalconsistency', 'weakconsistency']}, # (OptionRequestBlockRowLevelSecurity): If specified, blocks access to tables for which row_level_security policy is enabled [Boolean] "request_block_row_level_security": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestCalloutDisabled): If set, callouts to external services are blocked. [Boolean] "request_callout_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestDescription): Arbitrary text that the author of the request wants to include as the request description. [String] "request_description": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionRequestExternalTableDisabled): If specified, indicates that the request cannot invoke code in the ExternalTable. [bool] "request_external_table_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionDoNotImpersonate): If specified, indicates that the service shouldn't impersonate the caller's identity. [bool] "request_impersonation_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestReadOnly): If specified, indicates that the request must not be able to write anything. [Boolean] "request_readonly": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestEntitiesDisabled): If specified, indicates that the request cannot access remote databases and clusters. [bool] "request_remote_entities_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestSandboxedExecutionDisabled): If specified, indicates that the request cannot invoke code in the sandbox. [bool] "request_sandboxed_execution_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionResponseDynamicSerialization): Controls the serialization of 'dynamic' values in result sets. ['string', 'json'] "response_dynamic_serialization": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['string', 'json']}, # NOT DOCUMENTED - (OptionResponseDynamicSerialization_2): Controls the serialization of 'dynamic' string and null values in result sets. ['legacy', 'current'] "response_dynamic_serialization_2": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['legacy', 'current']}, # (OptionResultsProgressiveEnabled): If set, enables the progressive query stream "results_progressive_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionSandboxedExecutionDisabled): If set, using sandboxes as part of query execution is disabled. [Boolean] "sandboxed_execution_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionServerTimeout): Overrides the default request timeout. [TimeSpan] # is capped by 1hour "servertimeout": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionTruncationMaxRecords): Overrides the default maximum number of records a query is allowed to return to the caller (truncation). [Long] "truncationmaxrecords": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionTruncationMaxSize): Overrides the dfefault maximum data size a query is allowed to return to the caller (truncation). [Long] "truncationmaxsize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionValidatePermissions): Validates user's permissions to perform the query and doesn't run the query itself. [Boolean] "validate_permissions": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # For either implicit or explicit cross-application queries, specify resources you will be accessing # see https://dev.loganalytics.io/documentation/Using-the-API/Cross-Resource-Queries "workspaces": {"schema": [Schema.LOG_ANALYTICS], "type": "list"}, # For either implicit or explicit cross-application queries, specify resources you will be accessing # see: https://dev.applicationinsights.io/documentation/Using-the-API/Cross-Resource-Queries "applications": {"schema": [Schema.APPLICATION_INSIGHTS, Schema.AIMON], "type": "list"}, # The timespan over which to query data. This is an ISO8601 time period value. This timespan is applied in addition to any that are specified in the query expression. # see: https://docs.microsoft.com/en-us/rest/api/application-insights/query/get "timespan": {"schema": [Schema.APPLICATION_INSIGHTS, Schema.AIMON, Schema.LOG_ANALYTICS], "type": "iso8601_duration"}, } # all lookup keys in table, must be without spaces, underscores and hypthen-minus, because parser ignores them _OPTIONS_TABLE:Dict[str,Dict[str,Any]] = { "ad": {"abbreviation": "autodataframe"}, "autodataframe": {"flag": "auto_dataframe", "type": "bool"}, "se": {"abbreviation": "shorterrors"}, "shorterrors": {"flag": "short_errors", "type": "bool"}, "f": {"abbreviation": "feedback"}, "feedback": {"flag": "feedback", "type": "bool"}, "sci": {"abbreviation": "showconninfo"}, "showconninfo": {"flag": "show_conn_info", "type": "str", "allow_none": True}, "c2lv": {"abbreviation": "columnstolocalvars"}, "columnstolocalvars": {"flag": "columns_to_local_vars", "type": "bool"}, "sqt": {"abbreviation": "showquerytime"}, "showquerytime": {"flag": "show_query_time", "type": "bool"}, "sq": {"abbreviation": "showquery"}, "showquery": {"flag": "show_query", "type": "bool"}, "sql": {"abbreviation": "showquerylink"}, "showquerylink": {"flag": "show_query_link", "type": "bool"}, "qld": {"abbreviation": "querylinkdestination"}, "querylinkdestination": {"flag": "query_link_destination", "type": "str"}, "esr": {"abbreviation": "enablesuppressresult"}, "enablesuppressresult": {"flag": "enable_suppress_result", "type": "bool"}, "pfi": {"abbreviation": "plotlyfsincludejs"}, "plotlyfsincludejs": {"flag": "plotly_fs_includejs", "type": "bool"}, "pw": {"abbreviation": "popupwindow"}, "popupwindow": {"flag": "popup_window", "type": "bool", "init": False}, "al": {"abbreviation": "autolimit"}, "autolimit": {"flag": "auto_limit", "type": "int", "allow_none": True}, "dl": {"abbreviation": "displaylimit"}, "displaylimit": {"flag": "display_limit", "type": "int", "allow_none": True}, "wait": {"abbreviation": "timeout"}, "to": {"abbreviation": "timeout"}, "timeout": {"flag": "timeout", "type": "int", "allow_none": True}, "ptst": {"abbreviation": "prettytablestyle"}, "prettytablestyle": {"flag": "prettytable_style", "type": "str"}, "var": {"abbreviation": "lastrawresultvar"}, "lastrawresultvar": {"flag": "last_raw_result_var", "type": "str"}, "tp": {"abbreviation": "tablepackage"}, "tablepackage": {"flag": "table_package", "type": "str"}, "pp": {"abbreviation": "plotpackage"}, "plotpackage": {"flag": "plot_package", "type": "str"}, "df": {"abbreviation": "dsnfilename"}, "dsnfilename": {"flag": "dsn_filename", "type": "str", "allow_none": True}, "vc": {"abbreviation": "validateconnectionstring"}, "validateconnectionstring": {"flag": "validate_connection_string", "type": "bool"}, "aps": {"abbreviation": "autopopupschema"}, "autopopupschema": {"flag": "auto_popup_schema", "type": "bool"}, "jd": {"abbreviation": "jsondisplay"}, "jsondisplay": {"flag": "json_display", "type": "str"}, "sjd": {"abbreviation": "schemajsondisplay"}, "schemajsondisplay": {"flag": "schema_json_display", "type": "str"}, "pd": {"abbreviation": "palettedesaturation"}, "palettedesaturation": {"flag": "palette_desaturation", "type": "float"}, "pn": {"abbreviation": "palettename"}, "paramsdict": {"flag": "params_dict", "type": "dict", "init": None}, "palettename": {"flag": "palette_name", "type": "str"}, "cache": {"flag": "cache", "type": "str", "allow_none": True}, "usecache": {"flag": "use_cache", "type": "str", "allow_none": True}, "tempfoldername": {"flag": "temp_folder_name", "type": "str"}, "cachefoldername": {"flag": "cache_folder_name", "type": "str"}, "exportfoldername": {"flag": "export_folder_name", "type": "str"}, "addkqlreftohelp": {"flag": "add_kql_ref_to_help", "type": "bool"}, "addschematohelp": {"flag": "add_schema_to_help", "type": "bool"}, "notebookapp": {"flag": "notebook_app", "type": "str"}, "debug": {"flag": "debug", "type": "bool"}, "checkmagicversion": {"flag": "check_magic_version", "type": "bool"}, "showwhatnew": {"flag": "show_what_new", "type": "bool"}, "showinitbanner": {"flag": "show_init_banner", "type": "bool"}, "warnmissingdependencies": {"flag": "warn_missing_dependencies", "type": "bool"}, "warnmissingenvvariables": {"flag": "warn_missing_env_variables", "type": "bool"}, "allowsinglelinecell": {"flag": "allow_single_line_cell", "type": "bool"}, "allowpycommentsbeforecell": {"flag": "allow_py_comments_before_cell", "type": "bool"}, "kqlmagickernel": {"flag": "kqlmagic_kernel", "type": "bool"}, "extrasrequire": {"flag": "extras_require", "type": "str"}, "testnotebookapp": {"flag": "test_notebook_app", "type": "str"}, "cloud": {"flag": "cloud", "type": "str"}, "enablesso": {"flag": "enable_sso", "type": "bool"}, "ssodbgcinterval": {"flag": "sso_db_gc_interval", "type": "int"}, "authusehttpclient": {"flag": "auth_use_http_client", "type": "bool"}, "tryazclilogin": {"flag": "try_azcli_login", "type": "bool"}, "tryazcliloginbyprofile": {"flag": "try_azcli_login_by_profile", "type": "bool"}, "tryvscodelogin": {"flag": "try_vscode_login", "type": "bool"}, "tryazcliloginsubscription": {"flag": "try_azcli_login_subscription", "type": "str", "allow_none": True}, "trytoken": {"flag": "try_token", "type": "dict", "allow_none": True}, "trymsi": {"flag": "try_msi", "type": "dict", "allow_none": True}, "idtag": {"abbreviation": "requestidtag"}, "requestidtag": {"flag": "request_id_tag", "type": "str", "allow_none": True}, "apptag": {"abbreviation": "requestapptag"}, "requestapptag": {"flag": "request_app_tag", "type": "str", "allow_none": True}, "usertag": {"abbreviation": "requestusertag"}, "requestusertag": {"flag": "request_user_tag", "type": "str", "allow_none": True}, "maxage": {"abbreviation": "requestcachemaxage"}, "requestcachemaxage": {"flag": "request_cache_max_age", "type": "int", "allow_none": True}, "dcln": {"abbreviation": "devicecodeloginnotification"}, "devicecodeloginnotification": {"flag": "device_code_login_notification", "type": "str"}, "dcne": {"abbreviation": "devicecodenotificationemail"}, "devicecodenotificationemail": {"flag": "device_code_notification_email", "type": "str"}, "saveas": {"flag": "save_as", "type": "str", "init": None}, "saveto": {"flag": "save_to", "type": "str", "init": None}, "query": {"flag": "query", "type": "str", "init": None}, "conn": {"flag": "conn", "type": "str", "init": None}, "queryproperties": {"flag": "query_properties", "type": "dict", "init": None}, "pc": {"abbreviation": "palettecolors"}, "palettecolors": {"flag": "palette_colors", "type": "int"}, "pr": {"abbreviation": "palettereverse"}, "palettereverse": {"flag": "palette_reverse", "type": "bool", "init": False}, "ps": {"abbreviation": "popupschema"}, "popupschema": {"flag": "popup_schema", "type": "bool", "init": False}, "did": {"abbreviation": "displayid"}, "displayid": {"flag": "display_id", "type": "bool", "init": False}, "displayhandlers": {"flag": "display_handlers", "type": "dict", "init": {}}, "pi": {"abbreviation": "popupinteraction"}, "popupinteraction": {"flag": "popup_interaction", "type": "str"}, "tempfilesserver": {"flag": "temp_files_server", "type": "str"}, "tempfilesserveraddress": {"flag": "temp_files_server_address", "type": "str", "allow_none": True}, "kernellocation": {"flag": "kernel_location", "type": "str"}, "kernelid": {"flag": "kernel_id", "type": "str", "allow_none": True}, "notebookserviceaddress": {"flag": "notebook_service_address", "type": "str", "allow_none": True}, "dtd": {"abbreviation": "dynamictodataframe"}, "dynamictodataframe": {"flag": "dynamic_to_dataframe", "type": "str"}, "tempfolderlocation": {"flag": "temp_folder_location", "type": "str"}, "pl": {"abbreviation": "plotlylayout"}, "plotlylayout": {"flag": "plotly_layout", "type": "dict", "allow_none": True}, "atw": {"abbreviation": "authtokenwarnings"}, "authtokenwarnings": {"flag": "auth_token_warnings", "type": "bool"}, "ecbp": {"abbreviation": "enablecurlybracketsparams"}, "enablecurlybracketsparams": {"flag": "enable_curly_brackets_params", "type": "bool"}, "nop": {"flag": "nop", "type": "bool", "init": False}, # does nothing, useful to indicate option part when no options are required "av": {"abbreviation": "assignvar"}, "assignvar": {"flag": "assign_var", "type": "str", "allow_none": True}, "cv": {"abbreviation": "cursorvar"}, "cursorvar": {"flag": "cursor_var", "type": "str", "allow_none": True}, "ismagic": {"flag": "is_magic", "type": "bool"}, "caim": {"abbreviation": "codeauthinteractivemode"}, "codeauthinteractivemode": {"flag": "code_auth_interactive_mode", "type": "str", "allow_none": True}, } @classmethod def validate_override(cls, name:str, config:Configurable, **override_options)->Dict[str,Any]: """validate the provided option are valid""" options = {} for key, value in override_options.items(): obj = cls._get_obj(key, allow_abbr=True) if obj.get("flag") in config.read_only_trait_names: raise ValueError(f"option '{key}' in {name} is readony, cannot be set") cls._convert(name, obj, key, value) cls._validate_config_trait(name, obj, key, value, config) options[obj.get("flag")] = value return options @classmethod def parse_option(cls, dict_name:str, key:str, value:str, config:Configurable=None, lookup:Dict[str,Dict[str,Any]]=None, user_ns:Dict[str,Any]=None, allow_abbr:bool=None, force:bool=False): """validate the provided option are valid return normalized key and value""" obj = cls._get_obj(key, lookup=lookup, allow_abbr=allow_abbr) value = cls._parse_value(dict_name, obj, key, value, user_ns=user_ns) cls._validate_config_trait(dict_name, obj, key, value, config) key_name = obj.get("flag", key) if config.is_read_only(key_name) and value != getattr(config, key_name): # done to raise the proer error setattr(config, key_name, value) return key_name, value @classmethod def _parse_value(cls, dict_name:str, obj:Dict[str,Any], key:str, string:str, user_ns:Dict[str,Any])->Any: _type = obj.get("type") if string == "" and _type == "str": return string # if we allow to bring value from python, we also allow from env variables # when we parse env vironment with option we fon't use user_ns if string.startswith('$') and user_ns: env_var_name = string[1:] if not is_env_var(env_var_name): raise ValueError(f"failed to parse referred value, due environment variable {env_var_name} not set") string = get_env_var(env_var_name) _was_quoted, value = strip_if_quoted(string) else: try: value = eval(string, None, user_ns) except: # if no user_ns it means parse is for environment var, and it just may be an unquoted object if user_ns: raise value = string # check value is of the right type try: return cls._convert(dict_name, obj, key, value) except: raise @classmethod def parse_config_key(cls, key:str, config:Configurable, allow_abbr:bool=None)->Tuple[str,str,Any]: """validate the provided option key is valid return normalized key""" obj = cls._get_obj(key, allow_abbr=allow_abbr) name = obj.get("flag") if "init" in obj: value = obj.get("init") elif name in cls.traits_dict: value = getattr(config, name) else: raise f"internal error '{key}' has no init value and not defined as Kqlmagic traitlet" return name, value @classmethod def _get_obj(cls, key:str, lookup:Dict[str,Dict[str,Any]]=None, allow_abbr:bool=None)->Dict[str,Any]: lookup_key = key.lower().replace("-", "").replace("_", "") lookup_table = lookup or cls._OPTIONS_TABLE obj = lookup_table.get(lookup_key) if obj is None: raise ValueError(f"unknown option '{key}'") if obj.get("abbreviation"): obj = lookup_table.get(obj.get("abbreviation")) if not allow_abbr is not True: raise ValueError(f"unknown option '{key}". (Found option abbreviation "{key}' for {obj.get("flag")})") return obj @classmethod def _parse_kql_options(cls, code:str, is_cell:bool, config:Configurable, user_ns:Dict[str,Any])->Tuple[str,Dict[str,Any]]: trimmed_kql = code trimmed_kql = trimmed_kql.strip() suppress_results = False if trimmed_kql.endswith(";"): suppress_results = not is_cell if is_cell: lines = trimmed_kql.splitlines(True) if lines[-1].strip() == ";": suppress_results = True if suppress_results: trimmed_kql = trimmed_kql[:-1].strip() words = trimmed_kql.split() properties = {} table = options = cls.default_options.copy() if not words: return ("", options) num_words = len(words) first_word = 0 if num_words - first_word >= 2 and words[first_word + 1] == "<<": options["result_var"] = words[first_word] trimmed_kql = trimmed_kql[trimmed_kql.find("<<") + 2:] first_word += 2 obj = None key = None opt_key = None key_state = True option_type = None is_option = True is_property = False skip_words_count = 0 for word in words[first_word:]: if key_state: if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_kql) if skip_words_count > 0: skip_words_count -= 1 trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] continue is_option = word.startswith("-") is_property = word.startswith("+") option_type = "option" if is_option else "query property" if not is_option and not is_property: break # validate it is not a command if is_option and word.startswith("--"): raise ValueError(f"invalid {option_type} '{word}', cannot start with a bouble hyphen-minus") trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] word = word[1:] bool_value = True if word[0].startswith("!"): bool_value = False word = word[1:] if "=" in word: parts = word.split("=", 1) key = parts[0] value = parts[1] else: key = word value = None if is_option: lookup_key = key.lower().replace("-", "").replace("_", "") obj = cls._OPTIONS_TABLE.get(lookup_key) table = options else: lookup_key = key.lower() obj = cls._QUERY_PROPERTIES_TABLE.get(lookup_key) table = properties if obj is not None: if obj.get("abbreviation") is not None: obj = cls._OPTIONS_TABLE.get(obj.get("abbreviation")) if obj.get("flag") in config.read_only_trait_names: raise ValueError(f"{option_type} {key} is readony, cannot be set") _type = obj.get("type") opt_key = obj.get("flag") or lookup_key if _type == "bool" and value is None: table[opt_key] = bool_value else: if not bool_value: raise ValueError(f"{option_type} {key} cannot be negated") if value is not None: table[opt_key] = cls._parse_value("options" if is_option else "query properties", obj, key, value, user_ns) else: key_state = False else: raise ValueError(f"unknown {option_type} '{key}'") else: trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] table[opt_key] = cls._parse_value("options", obj, key, word, user_ns) key_state = True first_word += 1 # validate using config traits if key_state and is_option: cls._validate_config_trait("options", obj, key, options.get(opt_key), config) if not key_state: raise ValueError(f"{option_type} '{opt_key}' must have a value") if options.get("query_properties"): properties.update(options["query_properties"]) options["query_properties"] = properties if suppress_results: options["suppress_results"] = True return (trimmed_kql.strip(), options) @classmethod def _parse_comment(cls, word:str, _str:str)->Tuple[str,int]: comment = None skip_words_count = 0 if word.startswith("//"): idx_start = _str.find(word) idx_end = _str[idx_start:].find("\n") if idx_end > 0: idx_end = idx_start + idx_end comment = _str[idx_start:idx_end] else: comment = _str[idx_start:] comment_words = comment.split() skip_words_count = len(comment_words) return comment, skip_words_count @classmethod def parse_and_get_kv_string(cls, conn_str:str, user_ns:Dict[str,Any], keep_original_key:bool=None)->Dict[str,Any]: rest = conn_str rest = rest.strip() _was_quoted, rest = strip_if_quoted(rest) matched_kv = {} delimiter_required = False lp_idx = rest.find("(") eq_idx = rest.find("=") sc_idx = rest.find(";") l_char = "(" if eq_idx < 0 and sc_idx < 0 else "=" if lp_idx < 0 else "(" if lp_idx < eq_idx and lp_idx < sc_idx else "=" r_char = ")" if l_char == "(" else ";" extra_delimiter = None if r_char == ";" else "." while len(rest) > 0: l_idx = rest.find(l_char) r_idx = rest.find(r_char) if l_idx < 0: if l_char == "(": # string ends with delimiter if extra_delimiter is not None and extra_delimiter == rest: break else: raise ValueError("invalid key/value string, missing left parethesis.") # key only at end of string elif r_idx < 0: key = rest val = "" rest = "" # key only else: key = rest[:r_idx].strip() val = "" rest = rest[r_idx + 1:].strip() # key only elif r_idx >= 0 and r_idx < l_idx: if l_char == "(": raise ValueError("invalid key/value string, missing left parethesis.") else: key = rest[:r_idx].strip() val = "" rest = rest[r_idx + 1:].strip() # key and value else: key = rest[:l_idx].strip() rest = rest[l_idx + 1:].strip() r_idx = rest.find(r_char) if r_idx < 0: if l_char == "(": raise ValueError("invalid key/value string, missing right parethesis.") else: val = rest rest = "" else: val = rest[:r_idx].strip() rest = rest[r_idx + 1:].strip() if extra_delimiter is not None: if key.startswith(extra_delimiter): key = key[1:].strip() elif delimiter_required: raise ValueError("invalid key/value string, missing delimiter.") delimiter_required = True # key exist if len(key) > 0: if keep_original_key is True: lookup_key = key else: val = cls._parse_value("key/value", {"type": "str"}, key, val, user_ns) lookup_key = key.lower().replace("-", "").replace("_", "") matched_kv[lookup_key] = val # no key but value exist elif len(val) > 0: raise ValueError("invalid key/value string, missing key.") # no key, no value in parenthesis mode elif l_char == "(": raise ValueError("invalid key/value string, missing key.") return matched_kv @classmethod def parse_quote(cls, string:str): string = string.strip() delimiter = string[0] delimiter_len = 1 triple_quote = len(string) > 2 and string[1] == delimiter and string[2] == delimiter if triple_quote: delimiter_len = 3 delimiter = delimiter * 3 quoted_string_len = string[3:].find(delimiter) else: escape = False quoted_string_len = -1 count = 0 for c in string[1:]: if c == "\\": escape = not escape elif escape: pass elif c == delimiter: quoted_string_len = count break count += 1 if quoted_string_len >= 0: trimmed_string = string[quoted_string_len + 2 * delimiter_len:] if len(trimmed_string) > 0 and not trimmed_string[0].isspace(): raise SyntaxError("invalid syntax after quoted string, should be followed by whitespace only") quoted_string = string[delimiter_len:quoted_string_len + delimiter_len] quoted_words = len(quoted_string.split()) if len(quoted_string)> 0: if quoted_string[-1].isspace(): quoted_words += 1 if quoted_string[0].isspace(): quoted_words += 1 else: quoted_words = 1 return delimiter + quoted_string + delimiter, quoted_words else: raise SyntaxError("EOL while scanning quoted string") @classmethod def _convert(cls, name:str, obj:Dict[str,Any], key:str, value:Any)->Any: if value is None: if obj.get("allow_none"): return None else: raise ValueError(f"option '{key}' doesn't allow None value.") _type = None try: _type = obj.get("type") if _type == "int": if float(value) != int(value): raise ValueError return int(value) elif _type == "uint": if float(value) != int(value) or int(value) < 0: raise ValueError return int(value) elif _type == "float": return float(value) elif _type == "bool": if type(value) == str: if value.lower() == 'true': return True elif value.lower() == 'false': return False else: raise ValueError elif bool(value) != int(value): raise ValueError return bool(value) elif _type == "dict": return dict(value) elif _type == "list": if type(value) == str: value = [value] return list(value) elif _type == "enum": enum_values = obj.get("values", []) if enum_values.index(value) >= 0: return value else: raise ValueError elif _type == "iso8601_duration": # There are four ways to express a time interval: # Start and end, such as "2007-03-01T13:00:00Z/2008-05-11T15:30:00Z" # Start and duration, such as "2007-03-01T13:00:00Z/P1Y2M10DT2H30M" # Duration and end, such as "P1Y2M10DT2H30M/2008-05-11T15:30:00Z" # Duration only, such as "P1Y2M10DT2H30M", with additional context information value_list = [value] if type(value) != list else list(value)[:2] if len(value_list) == 0: raise ValueError elif len(value_list) == 1: value = value_list[0] if isinstance(value, timedelta): isodate = Dependencies.get_module("isodate", message="timedelta convertion to iso8601 duration format is not supported without isodate module, use instead a datetime range format, or already converted string") # will throw if does not exist value = isodate.duration_isoformat(value) elif type(value) != str: raise ValueError return value else: start_value = value_list[0] end_value = value_list[1] if isinstance(start_value, timedelta): isodate = Dependencies.get_module("isodate", dont_throw=True) if isodate: start_value = isodate.duration_isoformat(start_value) else: end_datetime = end_value if isinstance(end_value, datetime) else dateutil.parser.isoparse(end_value) start_value = end_datetime - start_value elif isinstance(end_value, timedelta): isodate = Dependencies.get_module("isodate", dont_throw=True) if isodate: end_value = isodate.duration_isoformat(end_value) else: start_datetime = end_value if isinstance(start_value, datetime) else dateutil.parser.isoparse(start_value) end_value = end_value + start_datetime value_list = [v.strftime('%Y-%m-%dT%H:%M:%S%ZZ') if isinstance(v, datetime) else str(v) for v in [start_value, end_value]] return "/".join(value_list) else: return str(value) except Exception as e: option_type = "property" if name == "query properties" else "option" due_message = f"{e}" or f"invalid '{_type}' of value '{value}'" raise ValueError(f"failed to set {option_type} '{key}' in {name}, due to {due_message}") @classmethod def _validate_config_trait(cls, dict_name:str, obj:Dict[str,Any], key:str, value:Any, config:Configurable)->None: # validate using config traits name = obj.get("flag") if isinstance(config, Configurable) and name in cls.traits_dict: # # save current value # try: new_value = cls._convert(dict_name, obj, key, value) trait:TraitType = cls.traits_dict.get(name) if hasattr(trait, "_validate"): validated_value = trait._validate(config, new_value) return except Exception as error: raise ValueError(f"failed to set option '{key}' in {dict_name}, due to invalid value '{value}'. Exception: {error}")

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- from typing import Tuple, Dict, List, Any import itertools import configparser as CP from datetime import timedelta, datetime import dateutil.parser from traitlets import TraitType from traitlets.config.configurable import Configurable from ._debug_utils import debug_print from .dependencies import Dependencies from .constants import Constants, Schema from .my_utils import split_lex, adjust_path, is_env_var, get_env_var, is_collection, strip_if_quoted from .engine import Engine class Parser(object): default_options:Dict[str,Any] = {} traits_dict:Dict[str,TraitType] = {} @classmethod def initialize(cls, config:Configurable): cls.traits_dict = config.traits() config.observe(Parser.observe_config_changes) cls.init_default_options(config) @staticmethod def observe_config_changes(change:Dict[str,str]): if change.get('type') == 'change': name = change.get('name') obj = Parser._OPTIONS_TABLE.get(name.lower().replace("-", "").replace("_", "")) if "init" not in obj: Parser.default_options[change.get('name')] = change.get('new') @classmethod def init_default_options(cls, config:Configurable): for obj in cls._OPTIONS_TABLE.values(): if "abbreviation" not in obj: name = obj.get("flag") if "init" in obj: cls.default_options[name] = obj.get("init") elif name in cls.traits_dict: cls.default_options[name] = getattr(config, name) elif "abbreviation" not in obj: raise Exception(f"missing init in _OPTIONS_TABLE for option: {name}") @classmethod def parse(cls, _line:str, _cell:str, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->List[Dict[str,Any]]: is_cell = _cell is not None cell = f"{_line}\n{_cell or ''}" cell = cell.strip() code = cell # # split string to queries # suppress_all_results = False # tuple: sections:List[Dict[str,str]] = [] if is_cell: magic_section_name = Constants.MAGIC_NAME section_lines:List[str] = [] previous_line = " " # should be init to space for the below to work # note: splitlines don't remove the \n suffix, each line endswith \n for line in code.splitlines(True): lstripped_line = line.lstrip() if (lstripped_line.startswith(Constants.IPYKERNEL_CELL_MAGIC_PREFIX) and (previous_line.isspace() or (len(sections) > 0 and sections[-1].get("type") == "line_magic"))): if len(section_lines) > 0: sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) magic_word = lstripped_line.split(None, 1)[0] magic_section_name = magic_word[len(Constants.IPYKERNEL_CELL_MAGIC_PREFIX):] lstripped_line = lstripped_line[len(magic_word):].lstrip() if magic_section_name == Constants.MAGIC_NAME: section_lines = [] if lstripped_line.isspace() else [lstripped_line] else: section_lines = [lstripped_line] elif magic_section_name != Constants.MAGIC_NAME: section_lines.append(line) elif (lstripped_line.startswith(Constants.IPYKERNEL_LINE_MAGIC_PREFIX) and (previous_line.isspace() or (len(sections) > 0 and sections[-1].get("type") == "line_magic"))): magic_word = lstripped_line.split(None, 1)[0] magic_name = magic_word[len(Constants.IPYKERNEL_LINE_MAGIC_PREFIX):] lstripped_line = lstripped_line[len(magic_word):].lstrip() if magic_name == Constants.MAGIC_NAME: if not lstripped_line.isspace(): sections.append({"type": "line_magic", "name": magic_name, "body": lstripped_line}) else: sections.append({"type": "line_magic", "name": magic_name, "body": lstripped_line}) elif line.isspace(): if len(section_lines) > 0: not_commented_lines = [1 for seg_line in section_lines if not seg_line.lstrip().startswith("//")] if (len(not_commented_lines) > 0): sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) section_lines = [] else: section_lines.append(line) previous_line = line if len(section_lines) > 0: if magic_section_name == Constants.MAGIC_NAME: not_commented_lines = [1 for seg_line in section_lines if not seg_line.lstrip().startswith("//")] if (len(not_commented_lines) > 0): sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) else: sections.append({"type": "cell_magic", "name": magic_section_name, "body": "".join(section_lines)}) if len(sections) > 0: last_query = sections[-1].get("body").strip() if last_query == ";": suppress_all_results = True sections = sections[:-1] if len(sections) == 0: sections.append({"type": "cell_magic", "name": Constants.MAGIC_NAME, "body": ""}) else: sections.append({"type": "line_magic", "name": Constants.MAGIC_NAME, "body": code.strip()}) # # parse code to kql and options # parsed_sections = [] last_connection_string = "" for section in sections: parsed_section = cls._parse_one_section(section, is_cell, config, engines, user_ns) connection_string = parsed_section.get("connection_string") if connection_string: last_connection_string = connection_string elif len(parsed_section.get("command")) == 0: parsed_section["connection_string"] = last_connection_string if suppress_all_results: parsed_section.get("options")["suppress_results"] = True parsed_sections.append(parsed_section) if len(parsed_sections) > 0: parsed_sections[-1]["last_query"] = True return parsed_sections @classmethod def _parse_one_section(cls, section:Dict[str,str], is_cell:bool, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->Dict[str,Any]: """Separate input into (connection info, KQL statements, options)""" cell, command = cls._parse_kql_command(section, user_ns) command_name = command.get("command") if command_name is not None and command_name != "submit": cell_rest, options = cls._parse_kql_options(cell.strip(), is_cell, config, user_ns) cell_rest = cls._update_kql_command_params(command, cell_rest, user_ns) cls._validate_kql_command_params(command) if cell_rest: raise ValueError(f"command --{command_name} has too many parameters") parsed_query = {"connection_string": "", "query": "", "options": options, "command": command} return parsed_query # split to max 2 parts. First part, parts[0], is the first string. # parts = [part.strip() for part in cell.split(None, 1)] parts = split_lex(cell) # print(parts) if not parts: kql, options = cls._parse_kql_options("", is_cell, config, user_ns) parsed_query = {"connection_string": "", "query": kql, "options": options, "command": {}} return parsed_query # # replace substring of the form $name or ${name}, in windows also %name% if found in env variabes # connection_string = None conn_str = parts[0].strip() if not conn_str.startswith(('-', '+')): _was_quoted, conn_str = strip_if_quoted(conn_str) # # connection taken from a section in dsn file (file name have to be define in config.dsn_filename or specified as a parameter) # if is_collection(conn_str, "["): section = conn_str[1:-1].strip() # parse to get flag, for the case that the file nema is specified in the options code = cell[len(parts[0]):] kql, options = cls._parse_kql_options(code, is_cell, config, user_ns) parser = CP.ConfigParser() dsn_filename = adjust_path(options.get("dsn_filename", config.dsn_filename)) parser.read(dsn_filename) cfg_dict = dict(parser.items(section)) cfg_dict_lower = {k.lower().replace("_", "").replace("-", ""): v for (k, v) in cfg_dict.items()} for e in engines: if e.get_mandatory_key() in cfg_dict_lower.keys(): all_keys = set(itertools.chain(*e.get_valid_keys_combinations())) connection_kv = [f"{k}='{v}'" for k, v in cfg_dict_lower.items() if v and k in all_keys] connection_string = f"{e.get_uri_schema_name()}://{';'.join(connection_kv)}" break # # connection specified starting with one of the supported prefixes # elif "://" in conn_str: sub_parts = conn_str.strip().split("://", 1) if (len(sub_parts) == 2 and sub_parts[0].lower().replace("_", "").replace("-", "") in list(itertools.chain(*[e._ALT_URI_SCHEMA_NAMES for e in engines]))): connection_string = conn_str # # connection specified as database@cluster # elif "@" in conn_str and "|" not in conn_str and "'" not in conn_str and '"' not in conn_str: connection_string = conn_str # # connection not specified, override default # if connection_string is None: connection_string = "" code = cell else: code = cell[len(parts[0]):] # # parse code to kql and options # kql, options = cls._parse_kql_options(code.strip(), is_cell, config, user_ns) kql = options.pop("query", None) or kql connection_string = options.pop("conn", None) or connection_string parsed_query = {"connection_string": connection_string.strip(), "query": kql, "options": options, "command": {}} return parsed_query @classmethod def parse_old(cls, line:str, cell:str, config:Configurable, engines:List[Engine], user_ns:Dict[str,Any])->List[Dict[str,Any]]: """Separate input into (connection info, KQL statements, options)""" is_cell = cell is not None cell = f"{line}\n{cell or ''}" cell = cell.strip() parsed_queries = [] cell, command = cls._parse_kql_command(cell, user_ns) command_name = command.get("command") if command_name is not None and command_name != "submit": cell_rest, options = cls._parse_kql_options(cell.strip(), is_cell, config, user_ns) cell_rest = cls._update_kql_command_params(command, cell_rest, user_ns) cls._validate_kql_command_params(command) if cell_rest: raise ValueError(f"command --{command_name} has too many parameters") parsed_queries.append({"connection_string": "", "query": "", "options": options, "command": command}) return parsed_queries # split to max 2 parts. First part, parts[0], is the first string. # parts = [part.strip() for part in cell.split(None, 1)] parts = split_lex(cell) # print(parts) if not parts: kql, options = cls._parse_kql_options("", is_cell, config, user_ns) parsed_queries.append({"connection_string": "", "query": kql, "options": options, "command": {}}) return parsed_queries # # replace substring of the form $name or ${name}, in windows also %name% if found in env variabes # connection_string = None conn_str = parts[0].strip() if not conn_str.startswith(('-', '+')): _was_quoted, conn_str = strip_if_quoted(conn_str) # # connection taken from a section in dsn file (file name have to be define in config.dsn_filename or specified as a parameter) # if is_collection(conn_str, "["): section = conn_str[1:-1].strip() # parse to get flag, for the case that the file nema is specified in the options code = cell[len(parts[0]):] kql, options = cls._parse_kql_options(code, is_cell, config, user_ns) parser = CP.ConfigParser() dsn_filename = adjust_path(options.get("dsn_filename", config.dsn_filename)) parser.read(dsn_filename) cfg_dict = dict(parser.items(section)) cfg_dict_lower = {k.lower().replace("_", "").replace("-", ""): v for (k, v) in cfg_dict.items()} for e in engines: if e.get_mandatory_key() in cfg_dict_lower.keys(): all_keys = set(itertools.chain(*e.get_valid_keys_combinations())) connection_kv = [f"{k}='{v}'" for k, v in cfg_dict_lower.items() if v and k in all_keys] connection_string = f"{e.get_uri_schema_name()}://{';'.join(connection_kv)}" break # # connection specified starting with one of the supported prefixes # elif "://" in conn_str: sub_parts = conn_str.strip().split("://", 1) if (len(sub_parts) == 2 and sub_parts[0].lower().replace("_", "").replace("-", "") in list(itertools.chain(*[e._ALT_URI_SCHEMA_NAMES for e in engines]))): connection_string = conn_str # # connection specified as database@cluster # elif "@" in conn_str and "|" not in conn_str and "'" not in conn_str and '"' not in conn_str: connection_string = conn_str # # connection not specified, override default # if connection_string is None: connection_string = "" code = cell else: code = cell[len(parts[0]):] # # split string to queries # suppress_all_results = False queries:List[str] = [] if is_cell: queryLines:List[str] = [] last_line:str = None for last_line in code.splitlines(True): # note: splitlines don't remove the \n suffix, each line endswith \n if last_line.isspace(): if len(queryLines) > 0: queries.append("".join(queryLines)) queryLines = [] else: queryLines.append(last_line) if len(queryLines) > 0: queries.append("".join(queryLines)) if len(queries) > 0: last_query = queries[-1].strip() if last_query == ";": suppress_all_results = True queries = queries[:-1] if len(queries) == 0: queries.append("") else: queries.append(code.strip()) # # parse code to kql and options # for query in queries: kql, options = cls._parse_kql_options(query.strip(), is_cell, config, user_ns) kql = options.pop("query", None) or kql connection_string = options.pop("conn", None) or connection_string if suppress_all_results: options["suppress_results"] = True parsed_queries.append({"connection_string": connection_string.strip(), "query": kql, "options": options, "command": {}}) return parsed_queries # Note: None as default is valid # commands that have no parameters, typekey should not exit or set to None # default max_params is 1 if type is not None otherwise 0 # default min_params is 1 if type is not None otherwise 0 _COMMANDS_TABLE = { "version": {"flag": "version", "type": None}, "banner": {"flag": "banner", "type": None}, "usage": {"flag": "usage", "type": None}, "submit": {"flag": "submit", "type": None}, # default "help": {"flag": "help", "type": "str", "default": "help"}, "faq": {"flag": "faq", "type": None}, "palette": {"flag": "palette", "type": None}, "palettes": {"flag": "palettes", "type": None}, # "config": {"flag": "config", "type": "str", "default": None}, "config": {"flag": "config", "type": "not_quoted_str", "allow_none": True, "max_params": 1, "min_params": 0}, "bugreport": {"flag": "bug_report", "type": None}, "conn": {"flag": "conn", "type": "str", "default": None}, # should be per connection "cache": {"flag": "cache", "type": "str", "allow_none": True}, "cachecreate": {"flag": "cache_create", "type": "str", "allow_none": True}, "cacheappend": {"flag": "cache_append", "type": "str", "allow_none": True}, "cachecreateorappend": {"flag": "cache_create_or_append", "type": "str", "allow_none": True}, "cacheremove": {"flag": "cache_remove", "type": "str", "allow_none": True}, "cachelist": {"flag": "cache_list", "type": None}, "cachestop": {"flag": "cache_stop", "type": None}, "usecache": {"flag": "use_cache", "type": "str", "allow_none": True}, "usecachestop": {"flag": "use_cache_stop", "type": "str", "allow_none": True}, "schema": {"flag": "schema", "type": "str", "default": None}, "clearssodb": {"flag": "clear_sso_db", "type": None}, "py": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "pyro": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "pyrw": {"flag": "python", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "activatekernel": {"flag": "activate_kernel", "type": None}, "deactivatekernel": {"flag": "deactivate_kernel", "type": None}, "linemagic": {"flag": "line_magic", "type": "not_quoted_str", "allow_none": True, "max_params": 2, "min_params": 2}, "cellmagic": {"flag": "cell_magic", "type": "not_quoted_str", "allow_none": True, "max_params": 3, "min_params": 3}, } @classmethod def _parse_kql_command(cls, section:Dict[str,str], user_ns:Dict[str,Any])->Tuple[str,Dict[str,Any]]: code = section.get("body") if section.get("name") != Constants.MAGIC_NAME: name = section.get("name").replace("_", "").replace("-", "") if name in ["py", "pyro", "pyrw"]: obj = cls._COMMANDS_TABLE.get(name) return ("", {"command": obj.get("flag"), "obj": obj, "params": [section.get("body"), name]}) else: # line/cell magic name = section.get("type") obj = cls._COMMANDS_TABLE.get(name.replace("_", "")) command_name = obj.get("flag") params = [] body = section.get("body") if command_name == "cell_magic": first_line = body.split("\n",1)[0] params.append(first_line.strip()) body = body[len(first_line) + 1:] params.append(body) params.append(section.get("name")) return ("", {"command": command_name, "obj": obj, "params": params}) # kql section lookup_key = None trimmed_code = code skip_words_count = 0 obj = None params_type = None command_name = None params = [] words = code.split() more_words_count = len(words) for word in words: more_words_count -= 1 if params_type == "not_quoted_str" and not word.startswith("-"): # break pass if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_code) if skip_words_count > 0: skip_words_count -= 1 trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] continue # command elif command_name is None: if not word.strip().startswith("--"): break word = word[2:] if word.startswith("-"): raise ValueError(f"unknown command --{word}, commands' prefix should be a double hyphen-minus, not a triple hyphen-minus") lookup_key = word.lower().replace("_", "").replace("-", "") obj = cls._COMMANDS_TABLE.get(lookup_key) if obj is None: raise ValueError(f"unknown command --{word}") command_name = obj.get("flag") trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] params_type = obj.get("type") if params_type is None: break # option elif word.startswith("-"): break # command's parameters else: command = {"command": command_name, "obj": obj, "params": params} EXIT_ON_OPTION = True trimmed_code, params = cls._parse_kql_command_params(command, trimmed_code, EXIT_ON_OPTION, user_ns) break if command_name is None: return (code.strip(), {}) if command_name == "python": params = params or [""] params.append(lookup_key) # type of python "py", "pyro", "pyrw" elif command_name in ["line_magic", "cell_magic"]: body = params[0] if params else "" magic_word = body.split(None, 1)[0] body = body.lstrip()[len(magic_word):] params = [] if command_name == "cell_magic": first_line = body.split("\n", 1)[0] params.append(first_line.strip()) body = body[len(first_line) + 1:] else: body = body.lstrip() params.append(body) ipykernel_prefix_len = 0 if command_name == "cell_magic" and magic_word.startswith(Constants.IPYKERNEL_CELL_MAGIC_PREFIX): ipykernel_prefix_len = len(Constants.IPYKERNEL_CELL_MAGIC_PREFIX) elif command_name == "line_magic" and magic_word.startswith(Constants.IPYKERNEL_LINE_MAGIC_PREFIX): ipykernel_prefix_len = len(Constants.IPYKERNEL_LINE_MAGIC_PREFIX) magic_name = magic_word[ipykernel_prefix_len:] params.append(magic_name) # the magic name if magic_name in ["py", "pyro", "pyrw"]: obj = cls._COMMANDS_TABLE.get(magic_name) body = params[0] if command_name == "line_magic" else f"{params[0]}\n{params[1]}" return ("", {"command": obj.get("flag"), "obj": obj, "params": [body, magic_name]}) return (trimmed_code.strip(), {"command": command_name, "obj": obj, "params": params}) @classmethod def _parse_kql_command_params(cls, command:dict, code:str, exit_on_options:bool, user_ns:Dict[str,Any])->Tuple[str,List[str]]: trimmed_code = code.strip() params = command.get("params") obj = command.get("obj") params_type = obj.get("type") if params_type == "not_quoted_str": if exit_on_options and trimmed_code.startswith("-"): # DO NOTHING, EXIT pass else: params.append(trimmed_code) trimmed_code = "" elif params_type is not None: skip_words_count = 0 command_name = command.get("command") words = code.split() for word in words: if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_code) if skip_words_count > 0: skip_words_count -= 1 trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] continue # option if exit_on_options and word.startswith("-"): break # command's parameters if params_type == "str" and word[0] in ["'",'"']: quoted_string, skip_words_count = cls.parse_quote(trimmed_code) param = quoted_string skip_words_count -= 1 else: param = word params.append(cls._parse_value("command", obj, command_name, param, user_ns)) trimmed_code = trimmed_code[trimmed_code.find(word) + len(word):] return trimmed_code.strip(), params @classmethod def _update_kql_command_params(cls, command:dict, cell_rest:str, user_ns:Dict[str,Any])->str: params = command.get("params") if len(params) == 0: obj = command.get("obj") if obj.get("type") == "not_quoted_str": params.append(cell_rest) cell_rest = "" elif len(cell_rest) > 0 and obj.get("type") is not None: DONT_EXIT_ON_OPTION = False cell_rest, params = cls._parse_kql_command_params(command, cell_rest, DONT_EXIT_ON_OPTION, user_ns) cell_rest = "" elif "default" in obj: params.append(obj.get("default")) command["params"] = params return cell_rest @classmethod def _validate_kql_command_params(cls, command:dict): params = command.get("params") command_name = command.get("command") obj = command.get("obj") _type = obj.get("type") max_params = obj.get("max_params") or (1 if _type is not None else 0) if len(params) > max_params: raise ValueError(f"command --{command_name} has too many parameters") min_params = obj.get("min_params") or (1 if _type is not None else 0) if len(params) < min_params: raise ValueError(f"command --{command_name} is missing parameter") @classmethod def validate_query_properties(cls, schema:str, properties:Dict[str,Any])->None: if type(properties) == dict: usupported_properties = [] for p in properties: prop = cls._QUERY_PROPERTIES_TABLE[p] prop_schema_list = prop.get("schema") if type(prop_schema_list) == list and schema not in prop_schema_list and len(prop_schema_list) > 0 and schema is not None: usupported_properties.append(p) if len(usupported_properties) > 0: raise ValueError(f"query properties {usupported_properties} are not supported by current connection") _QUERY_PROPERTIES_TABLE = { # NOT DOCUMENTED - (OptionBlockSplittingEnabled): Enables splitting of sequence blocks after aggregation operator. [Boolean] "block_splitting_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionDatabasePattern): Database pattern overrides database name and picks the 1st database that matches the pattern. # '*' means any database that user has access to. [String] "database_pattern": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - If set, don't fuse projection into ExternalData operator. [bool] "debug_query_externaldata_projection_fusion_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - The percentage of threads to fanout execution to for external data nodes. [int] "debug_query_fanout_threads_percent_external_data": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionDeferPartialQueryFailures): If true, disables reporting partial query failures as part of the result set. [Boolean] "deferpartialqueryfailures": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionMaterializedViewShuffleQuery): A hint to use shuffle strategy for materialized views that are referenced in the query. The property is an array of materialized views names and the shuffle keys to use. examples: 'dynamic([ { "Name": "V1", "Keys" : [ "K1", "K2" ] } ])' (shuffle view V1 by K1, K2) or 'dynamic([ { "Name": "V1" } ])' (shuffle view V1 by all keys) [dynamic] "materialized_view_shuffle": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "dict"}, # (OptionMaxMemoryConsumptionPerQueryPerNode): Overrides the default maximum amount of memory a whole query may allocate per node. [UInt64] "max_memory_consumption_per_query_per_node": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionMaxMemoryConsumptionPerIterator): Overrides the default maximum amount of memory a query operator may allocate. [UInt64] "maxmemoryconsumptionperiterator": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionMaxOutputColumns): Overrides the default maximum number of columns a query is allowed to produce. [Long] "maxoutputcolumns": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionNoRequestTimeout): Enables setting the request timeout to its maximum value. [Boolean] "norequesttimeout": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionNoTruncation): Enables suppressing truncation of the query results returned to the caller. [Boolean] "notruncation": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionPushSelectionThroughAggregation): If true, push simple selection through aggregation [Boolean] "push_selection_through_aggregation": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionAdminSuperSlackerMode): If true, delegate execution of the query to another node [Boolean] "query_admin_super_slacker_mode": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (QueryBinAutoAt): When evaluating the bin_auto() function, the start value to use. [LiteralExpression] "query_bin_auto_at": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (QueryBinAutoSize): When evaluating the bin_auto() function, the bin size value to use. [LiteralExpression] "query_bin_auto_size": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorAfterDefault): The default parameter value of the cursor_after() function when called without parameters. [string] "query_cursor_after_default": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - (OptionQueryCursorAllowReferencingStreamingIngestionTables): Enable usage of cursor functions over databases which have streaming ingestion enabled. [boolean] "query_cursor_allow_referencing_streaming_ingestion_tables": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryCursorBeforeOrAtDefault): The default parameter value of the cursor_before_or_at() function when called without parameters. [string] "query_cursor_before_or_at_default": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorCurrent): Overrides the cursor value returned by the cursor_current() or current_cursor() functions. [string] "query_cursor_current": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryCursorDisabled): Disables usage of cursor functions in the context of the query. [boolean] "query_cursor_disabled ": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryCursorScopedTables): List of table names that should be scoped to cursor_after_default .. # cursor_before_or_at_default (upper bound is optional). [dynamic] "query_cursor_scoped_tables": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "dict"}, # (OptionQueryDataScope): Controls the query's datascope -- whether the query applies to all data or just part of it. ['default', 'all', or 'hotcache'] "query_datascope": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['default', 'all', 'hotcache']}, # (OptionQueryDateTimeScopeColumn): Controls the column name for the query's datetime scope (query_datetimescope_to / query_datetimescope_from). [String] "query_datetimescope_column": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDateTimeScopeFrom): Controls the query's datetime scope (earliest) # used as auto-applied filter on query_datetimescope_column only (if defined). [DateTime] "query_datetimescope_from": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDateTimeScopeTo): Controls the query's datetime scope (latest) # used as auto-applied filter on query_datetimescope_column only (if defined). [DateTime] "query_datetimescope_to": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionQueryDistributionNodesSpanSize): If set, controls the way sub-query merge behaves: # the executing node will introduce an additional level in the query hierarchy for each sub-group of nodes; the size of the sub-group is set by this option. [Int] "query_distribution_nodes_span": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "int"}, # (OptionQueryFanoutNodesPercent): The percentage of nodes to fanour execution to. [Int] "query_fanout_nodes_percent": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryFanoutThreadsPercent): The percentage of threads to fanout execution to. [Int] "query_fanout_threads_percent": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryForceRowLevelSecurity): If specified, forces Row Level Security rules, even if row_level_security policy is disabled [Boolean] "query_force_row_level_security": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryLanguage): Controls how the query text is to be interpreted. ['csl','kql' or 'sql'] "query_language": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['csl', 'kql', 'sql']}, # NOT DOCUMENTED - (RemoteMaterializeOperatorInCrossCluster): Enables remoting materialize operator in cross cluster query. "query_materialize_remote_subquery": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionMaxEntitiesToUnion): Overrides the default maximum number of columns a query is allowed to produce. [Long] "query_max_entities_in_union": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryNow): Overrides the datetime value returned by the now(0s) function. [DateTime] # note: cannot be relative to now() "query_now": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionDebugPython): If set, generate python debug query for the enumerated python node (default first). [Boolean or Int] "query_python_debug": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryResultsApplyGetSchema): If set, retrieves the schema of each tabular data in the results of the query instead of the data itself. [Boolean] "query_results_apply_getschema": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionQueryResultsCacheMaxAge): If positive, controls the maximum age of the cached query results that Kusto is allowed to return [TimeSpan] "query_results_cache_max_age": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # NOT DOCUMENTED - (CostBasedOptimizerBroadcastJoinBuildMax): Max Rows count for build in broadcast join. "query_optimization_broadcast_build_maxSize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (CostBasedOptimizerBroadcastJoinProbeMin): Min Rows count for probe in broadcast join. "query_optimization_broadcast_probe_minSize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (CostBasedOptimizer): Enables automatic optimizations. "query_optimization_costbased_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionOptimizeInOperator): Optimizes in operands serialization. "query_optimization_in_operator": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (CostBasedOptimizerShufflingCardinalityThreshold): Shuffling Cardinality Threshold. "query_optimization_shuffling_cardinality": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # NOT DOCUMENTED - (OptionQueryRemoteEntitiesDisabled): If set, queries cannot access remote databases / clusters. [Boolean] "query_remote_entities_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (RemoteInOperandsInQuery): Enables remoting in operands. "query_remote_in_operands": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionProgressiveQueryMinRowCountPerUpdate): Hint for Kusto as to how many records to send in each update # (Takes effect only if OptionProgressiveQueryIsProgressive is set) "query_results_progressive_row_count": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionProgressiveProgressReportPeriod): Hint for Kusto as to how often to send progress frames (Takes effect only if OptionProgressiveQueryIsProgressive is set) "query_results_progressive_update_period": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionTakeMaxRecords): Enables limiting query results to this number of records. [Long] "query_take_max_records": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionQueryConsistency): Controls query consistency. ['strongconsistency' or 'normalconsistency' or 'weakconsistency'] "queryconsistency": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['strongconsistency', 'normalconsistency', 'weakconsistency']}, # (OptionRequestBlockRowLevelSecurity): If specified, blocks access to tables for which row_level_security policy is enabled [Boolean] "request_block_row_level_security": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestCalloutDisabled): If set, callouts to external services are blocked. [Boolean] "request_callout_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestDescription): Arbitrary text that the author of the request wants to include as the request description. [String] "request_description": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionRequestExternalTableDisabled): If specified, indicates that the request cannot invoke code in the ExternalTable. [bool] "request_external_table_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionDoNotImpersonate): If specified, indicates that the service shouldn't impersonate the caller's identity. [bool] "request_impersonation_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestReadOnly): If specified, indicates that the request must not be able to write anything. [Boolean] "request_readonly": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestEntitiesDisabled): If specified, indicates that the request cannot access remote databases and clusters. [bool] "request_remote_entities_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionRequestSandboxedExecutionDisabled): If specified, indicates that the request cannot invoke code in the sandbox. [bool] "request_sandboxed_execution_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionResponseDynamicSerialization): Controls the serialization of 'dynamic' values in result sets. ['string', 'json'] "response_dynamic_serialization": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['string', 'json']}, # NOT DOCUMENTED - (OptionResponseDynamicSerialization_2): Controls the serialization of 'dynamic' string and null values in result sets. ['legacy', 'current'] "response_dynamic_serialization_2": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "enum", "values": ['legacy', 'current']}, # (OptionResultsProgressiveEnabled): If set, enables the progressive query stream "results_progressive_enabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # NOT DOCUMENTED - (OptionSandboxedExecutionDisabled): If set, using sandboxes as part of query execution is disabled. [Boolean] "sandboxed_execution_disabled": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # (OptionServerTimeout): Overrides the default request timeout. [TimeSpan] # is capped by 1hour "servertimeout": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "str"}, # (OptionTruncationMaxRecords): Overrides the default maximum number of records a query is allowed to return to the caller (truncation). [Long] "truncationmaxrecords": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionTruncationMaxSize): Overrides the dfefault maximum data size a query is allowed to return to the caller (truncation). [Long] "truncationmaxsize": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "uint"}, # (OptionValidatePermissions): Validates user's permissions to perform the query and doesn't run the query itself. [Boolean] "validate_permissions": {"schema": [Schema.AZURE_DATA_EXPLORER], "type": "bool"}, # For either implicit or explicit cross-application queries, specify resources you will be accessing # see https://dev.loganalytics.io/documentation/Using-the-API/Cross-Resource-Queries "workspaces": {"schema": [Schema.LOG_ANALYTICS], "type": "list"}, # For either implicit or explicit cross-application queries, specify resources you will be accessing # see: https://dev.applicationinsights.io/documentation/Using-the-API/Cross-Resource-Queries "applications": {"schema": [Schema.APPLICATION_INSIGHTS, Schema.AIMON], "type": "list"}, # The timespan over which to query data. This is an ISO8601 time period value. This timespan is applied in addition to any that are specified in the query expression. # see: https://docs.microsoft.com/en-us/rest/api/application-insights/query/get "timespan": {"schema": [Schema.APPLICATION_INSIGHTS, Schema.AIMON, Schema.LOG_ANALYTICS], "type": "iso8601_duration"}, } # all lookup keys in table, must be without spaces, underscores and hypthen-minus, because parser ignores them _OPTIONS_TABLE:Dict[str,Dict[str,Any]] = { "ad": {"abbreviation": "autodataframe"}, "autodataframe": {"flag": "auto_dataframe", "type": "bool"}, "se": {"abbreviation": "shorterrors"}, "shorterrors": {"flag": "short_errors", "type": "bool"}, "f": {"abbreviation": "feedback"}, "feedback": {"flag": "feedback", "type": "bool"}, "sci": {"abbreviation": "showconninfo"}, "showconninfo": {"flag": "show_conn_info", "type": "str", "allow_none": True}, "c2lv": {"abbreviation": "columnstolocalvars"}, "columnstolocalvars": {"flag": "columns_to_local_vars", "type": "bool"}, "sqt": {"abbreviation": "showquerytime"}, "showquerytime": {"flag": "show_query_time", "type": "bool"}, "sq": {"abbreviation": "showquery"}, "showquery": {"flag": "show_query", "type": "bool"}, "sql": {"abbreviation": "showquerylink"}, "showquerylink": {"flag": "show_query_link", "type": "bool"}, "qld": {"abbreviation": "querylinkdestination"}, "querylinkdestination": {"flag": "query_link_destination", "type": "str"}, "esr": {"abbreviation": "enablesuppressresult"}, "enablesuppressresult": {"flag": "enable_suppress_result", "type": "bool"}, "pfi": {"abbreviation": "plotlyfsincludejs"}, "plotlyfsincludejs": {"flag": "plotly_fs_includejs", "type": "bool"}, "pw": {"abbreviation": "popupwindow"}, "popupwindow": {"flag": "popup_window", "type": "bool", "init": False}, "al": {"abbreviation": "autolimit"}, "autolimit": {"flag": "auto_limit", "type": "int", "allow_none": True}, "dl": {"abbreviation": "displaylimit"}, "displaylimit": {"flag": "display_limit", "type": "int", "allow_none": True}, "wait": {"abbreviation": "timeout"}, "to": {"abbreviation": "timeout"}, "timeout": {"flag": "timeout", "type": "int", "allow_none": True}, "ptst": {"abbreviation": "prettytablestyle"}, "prettytablestyle": {"flag": "prettytable_style", "type": "str"}, "var": {"abbreviation": "lastrawresultvar"}, "lastrawresultvar": {"flag": "last_raw_result_var", "type": "str"}, "tp": {"abbreviation": "tablepackage"}, "tablepackage": {"flag": "table_package", "type": "str"}, "pp": {"abbreviation": "plotpackage"}, "plotpackage": {"flag": "plot_package", "type": "str"}, "df": {"abbreviation": "dsnfilename"}, "dsnfilename": {"flag": "dsn_filename", "type": "str", "allow_none": True}, "vc": {"abbreviation": "validateconnectionstring"}, "validateconnectionstring": {"flag": "validate_connection_string", "type": "bool"}, "aps": {"abbreviation": "autopopupschema"}, "autopopupschema": {"flag": "auto_popup_schema", "type": "bool"}, "jd": {"abbreviation": "jsondisplay"}, "jsondisplay": {"flag": "json_display", "type": "str"}, "sjd": {"abbreviation": "schemajsondisplay"}, "schemajsondisplay": {"flag": "schema_json_display", "type": "str"}, "pd": {"abbreviation": "palettedesaturation"}, "palettedesaturation": {"flag": "palette_desaturation", "type": "float"}, "pn": {"abbreviation": "palettename"}, "paramsdict": {"flag": "params_dict", "type": "dict", "init": None}, "palettename": {"flag": "palette_name", "type": "str"}, "cache": {"flag": "cache", "type": "str", "allow_none": True}, "usecache": {"flag": "use_cache", "type": "str", "allow_none": True}, "tempfoldername": {"flag": "temp_folder_name", "type": "str"}, "cachefoldername": {"flag": "cache_folder_name", "type": "str"}, "exportfoldername": {"flag": "export_folder_name", "type": "str"}, "addkqlreftohelp": {"flag": "add_kql_ref_to_help", "type": "bool"}, "addschematohelp": {"flag": "add_schema_to_help", "type": "bool"}, "notebookapp": {"flag": "notebook_app", "type": "str"}, "debug": {"flag": "debug", "type": "bool"}, "checkmagicversion": {"flag": "check_magic_version", "type": "bool"}, "showwhatnew": {"flag": "show_what_new", "type": "bool"}, "showinitbanner": {"flag": "show_init_banner", "type": "bool"}, "warnmissingdependencies": {"flag": "warn_missing_dependencies", "type": "bool"}, "warnmissingenvvariables": {"flag": "warn_missing_env_variables", "type": "bool"}, "allowsinglelinecell": {"flag": "allow_single_line_cell", "type": "bool"}, "allowpycommentsbeforecell": {"flag": "allow_py_comments_before_cell", "type": "bool"}, "kqlmagickernel": {"flag": "kqlmagic_kernel", "type": "bool"}, "extrasrequire": {"flag": "extras_require", "type": "str"}, "testnotebookapp": {"flag": "test_notebook_app", "type": "str"}, "cloud": {"flag": "cloud", "type": "str"}, "enablesso": {"flag": "enable_sso", "type": "bool"}, "ssodbgcinterval": {"flag": "sso_db_gc_interval", "type": "int"}, "authusehttpclient": {"flag": "auth_use_http_client", "type": "bool"}, "tryazclilogin": {"flag": "try_azcli_login", "type": "bool"}, "tryazcliloginbyprofile": {"flag": "try_azcli_login_by_profile", "type": "bool"}, "tryvscodelogin": {"flag": "try_vscode_login", "type": "bool"}, "tryazcliloginsubscription": {"flag": "try_azcli_login_subscription", "type": "str", "allow_none": True}, "trytoken": {"flag": "try_token", "type": "dict", "allow_none": True}, "trymsi": {"flag": "try_msi", "type": "dict", "allow_none": True}, "idtag": {"abbreviation": "requestidtag"}, "requestidtag": {"flag": "request_id_tag", "type": "str", "allow_none": True}, "apptag": {"abbreviation": "requestapptag"}, "requestapptag": {"flag": "request_app_tag", "type": "str", "allow_none": True}, "usertag": {"abbreviation": "requestusertag"}, "requestusertag": {"flag": "request_user_tag", "type": "str", "allow_none": True}, "maxage": {"abbreviation": "requestcachemaxage"}, "requestcachemaxage": {"flag": "request_cache_max_age", "type": "int", "allow_none": True}, "dcln": {"abbreviation": "devicecodeloginnotification"}, "devicecodeloginnotification": {"flag": "device_code_login_notification", "type": "str"}, "dcne": {"abbreviation": "devicecodenotificationemail"}, "devicecodenotificationemail": {"flag": "device_code_notification_email", "type": "str"}, "saveas": {"flag": "save_as", "type": "str", "init": None}, "saveto": {"flag": "save_to", "type": "str", "init": None}, "query": {"flag": "query", "type": "str", "init": None}, "conn": {"flag": "conn", "type": "str", "init": None}, "queryproperties": {"flag": "query_properties", "type": "dict", "init": None}, "pc": {"abbreviation": "palettecolors"}, "palettecolors": {"flag": "palette_colors", "type": "int"}, "pr": {"abbreviation": "palettereverse"}, "palettereverse": {"flag": "palette_reverse", "type": "bool", "init": False}, "ps": {"abbreviation": "popupschema"}, "popupschema": {"flag": "popup_schema", "type": "bool", "init": False}, "did": {"abbreviation": "displayid"}, "displayid": {"flag": "display_id", "type": "bool", "init": False}, "displayhandlers": {"flag": "display_handlers", "type": "dict", "init": {}}, "pi": {"abbreviation": "popupinteraction"}, "popupinteraction": {"flag": "popup_interaction", "type": "str"}, "tempfilesserver": {"flag": "temp_files_server", "type": "str"}, "tempfilesserveraddress": {"flag": "temp_files_server_address", "type": "str", "allow_none": True}, "kernellocation": {"flag": "kernel_location", "type": "str"}, "kernelid": {"flag": "kernel_id", "type": "str", "allow_none": True}, "notebookserviceaddress": {"flag": "notebook_service_address", "type": "str", "allow_none": True}, "dtd": {"abbreviation": "dynamictodataframe"}, "dynamictodataframe": {"flag": "dynamic_to_dataframe", "type": "str"}, "tempfolderlocation": {"flag": "temp_folder_location", "type": "str"}, "pl": {"abbreviation": "plotlylayout"}, "plotlylayout": {"flag": "plotly_layout", "type": "dict", "allow_none": True}, "atw": {"abbreviation": "authtokenwarnings"}, "authtokenwarnings": {"flag": "auth_token_warnings", "type": "bool"}, "ecbp": {"abbreviation": "enablecurlybracketsparams"}, "enablecurlybracketsparams": {"flag": "enable_curly_brackets_params", "type": "bool"}, "nop": {"flag": "nop", "type": "bool", "init": False}, # does nothing, useful to indicate option part when no options are required "av": {"abbreviation": "assignvar"}, "assignvar": {"flag": "assign_var", "type": "str", "allow_none": True}, "cv": {"abbreviation": "cursorvar"}, "cursorvar": {"flag": "cursor_var", "type": "str", "allow_none": True}, "ismagic": {"flag": "is_magic", "type": "bool"}, "caim": {"abbreviation": "codeauthinteractivemode"}, "codeauthinteractivemode": {"flag": "code_auth_interactive_mode", "type": "str", "allow_none": True}, } @classmethod def validate_override(cls, name:str, config:Configurable, **override_options)->Dict[str,Any]: """validate the provided option are valid""" options = {} for key, value in override_options.items(): obj = cls._get_obj(key, allow_abbr=True) if obj.get("flag") in config.read_only_trait_names: raise ValueError(f"option '{key}' in {name} is readony, cannot be set") cls._convert(name, obj, key, value) cls._validate_config_trait(name, obj, key, value, config) options[obj.get("flag")] = value return options @classmethod def parse_option(cls, dict_name:str, key:str, value:str, config:Configurable=None, lookup:Dict[str,Dict[str,Any]]=None, user_ns:Dict[str,Any]=None, allow_abbr:bool=None, force:bool=False): """validate the provided option are valid return normalized key and value""" obj = cls._get_obj(key, lookup=lookup, allow_abbr=allow_abbr) value = cls._parse_value(dict_name, obj, key, value, user_ns=user_ns) cls._validate_config_trait(dict_name, obj, key, value, config) key_name = obj.get("flag", key) if config.is_read_only(key_name) and value != getattr(config, key_name): # done to raise the proer error setattr(config, key_name, value) return key_name, value @classmethod def _parse_value(cls, dict_name:str, obj:Dict[str,Any], key:str, string:str, user_ns:Dict[str,Any])->Any: _type = obj.get("type") if string == "" and _type == "str": return string # if we allow to bring value from python, we also allow from env variables # when we parse env vironment with option we fon't use user_ns if string.startswith('$') and user_ns: env_var_name = string[1:] if not is_env_var(env_var_name): raise ValueError(f"failed to parse referred value, due environment variable {env_var_name} not set") string = get_env_var(env_var_name) _was_quoted, value = strip_if_quoted(string) else: try: value = eval(string, None, user_ns) except: # if no user_ns it means parse is for environment var, and it just may be an unquoted object if user_ns: raise value = string # check value is of the right type try: return cls._convert(dict_name, obj, key, value) except: raise @classmethod def parse_config_key(cls, key:str, config:Configurable, allow_abbr:bool=None)->Tuple[str,str,Any]: """validate the provided option key is valid return normalized key""" obj = cls._get_obj(key, allow_abbr=allow_abbr) name = obj.get("flag") if "init" in obj: value = obj.get("init") elif name in cls.traits_dict: value = getattr(config, name) else: raise f"internal error '{key}' has no init value and not defined as Kqlmagic traitlet" return name, value @classmethod def _get_obj(cls, key:str, lookup:Dict[str,Dict[str,Any]]=None, allow_abbr:bool=None)->Dict[str,Any]: lookup_key = key.lower().replace("-", "").replace("_", "") lookup_table = lookup or cls._OPTIONS_TABLE obj = lookup_table.get(lookup_key) if obj is None: raise ValueError(f"unknown option '{key}'") if obj.get("abbreviation"): obj = lookup_table.get(obj.get("abbreviation")) if not allow_abbr is not True: raise ValueError(f"unknown option '{key}'. (Found option abbreviation '{key}' for {obj.get('flag')})") return obj @classmethod def _parse_kql_options(cls, code:str, is_cell:bool, config:Configurable, user_ns:Dict[str,Any])->Tuple[str,Dict[str,Any]]: trimmed_kql = code trimmed_kql = trimmed_kql.strip() suppress_results = False if trimmed_kql.endswith(";"): suppress_results = not is_cell if is_cell: lines = trimmed_kql.splitlines(True) if lines[-1].strip() == ";": suppress_results = True if suppress_results: trimmed_kql = trimmed_kql[:-1].strip() words = trimmed_kql.split() properties = {} table = options = cls.default_options.copy() if not words: return ("", options) num_words = len(words) first_word = 0 if num_words - first_word >= 2 and words[first_word + 1] == "<<": options["result_var"] = words[first_word] trimmed_kql = trimmed_kql[trimmed_kql.find("<<") + 2:] first_word += 2 obj = None key = None opt_key = None key_state = True option_type = None is_option = True is_property = False skip_words_count = 0 for word in words[first_word:]: if key_state: if skip_words_count == 0: _comment, skip_words_count = cls._parse_comment(word, trimmed_kql) if skip_words_count > 0: skip_words_count -= 1 trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] continue is_option = word.startswith("-") is_property = word.startswith("+") option_type = "option" if is_option else "query property" if not is_option and not is_property: break # validate it is not a command if is_option and word.startswith("--"): raise ValueError(f"invalid {option_type} '{word}', cannot start with a bouble hyphen-minus") trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] word = word[1:] bool_value = True if word[0].startswith("!"): bool_value = False word = word[1:] if "=" in word: parts = word.split("=", 1) key = parts[0] value = parts[1] else: key = word value = None if is_option: lookup_key = key.lower().replace("-", "").replace("_", "") obj = cls._OPTIONS_TABLE.get(lookup_key) table = options else: lookup_key = key.lower() obj = cls._QUERY_PROPERTIES_TABLE.get(lookup_key) table = properties if obj is not None: if obj.get("abbreviation") is not None: obj = cls._OPTIONS_TABLE.get(obj.get("abbreviation")) if obj.get("flag") in config.read_only_trait_names: raise ValueError(f"{option_type} {key} is readony, cannot be set") _type = obj.get("type") opt_key = obj.get("flag") or lookup_key if _type == "bool" and value is None: table[opt_key] = bool_value else: if not bool_value: raise ValueError(f"{option_type} {key} cannot be negated") if value is not None: table[opt_key] = cls._parse_value("options" if is_option else "query properties", obj, key, value, user_ns) else: key_state = False else: raise ValueError(f"unknown {option_type} '{key}'") else: trimmed_kql = trimmed_kql[trimmed_kql.find(word) + len(word):] table[opt_key] = cls._parse_value("options", obj, key, word, user_ns) key_state = True first_word += 1 # validate using config traits if key_state and is_option: cls._validate_config_trait("options", obj, key, options.get(opt_key), config) if not key_state: raise ValueError(f"{option_type} '{opt_key}' must have a value") if options.get("query_properties"): properties.update(options["query_properties"]) options["query_properties"] = properties if suppress_results: options["suppress_results"] = True return (trimmed_kql.strip(), options) @classmethod def _parse_comment(cls, word:str, _str:str)->Tuple[str,int]: comment = None skip_words_count = 0 if word.startswith("//"): idx_start = _str.find(word) idx_end = _str[idx_start:].find("\n") if idx_end > 0: idx_end = idx_start + idx_end comment = _str[idx_start:idx_end] else: comment = _str[idx_start:] comment_words = comment.split() skip_words_count = len(comment_words) return comment, skip_words_count @classmethod def parse_and_get_kv_string(cls, conn_str:str, user_ns:Dict[str,Any], keep_original_key:bool=None)->Dict[str,Any]: rest = conn_str rest = rest.strip() _was_quoted, rest = strip_if_quoted(rest) matched_kv = {} delimiter_required = False lp_idx = rest.find("(") eq_idx = rest.find("=") sc_idx = rest.find(";") l_char = "(" if eq_idx < 0 and sc_idx < 0 else "=" if lp_idx < 0 else "(" if lp_idx < eq_idx and lp_idx < sc_idx else "=" r_char = ")" if l_char == "(" else ";" extra_delimiter = None if r_char == ";" else "." while len(rest) > 0: l_idx = rest.find(l_char) r_idx = rest.find(r_char) if l_idx < 0: if l_char == "(": # string ends with delimiter if extra_delimiter is not None and extra_delimiter == rest: break else: raise ValueError("invalid key/value string, missing left parethesis.") # key only at end of string elif r_idx < 0: key = rest val = "" rest = "" # key only else: key = rest[:r_idx].strip() val = "" rest = rest[r_idx + 1:].strip() # key only elif r_idx >= 0 and r_idx < l_idx: if l_char == "(": raise ValueError("invalid key/value string, missing left parethesis.") else: key = rest[:r_idx].strip() val = "" rest = rest[r_idx + 1:].strip() # key and value else: key = rest[:l_idx].strip() rest = rest[l_idx + 1:].strip() r_idx = rest.find(r_char) if r_idx < 0: if l_char == "(": raise ValueError("invalid key/value string, missing right parethesis.") else: val = rest rest = "" else: val = rest[:r_idx].strip() rest = rest[r_idx + 1:].strip() if extra_delimiter is not None: if key.startswith(extra_delimiter): key = key[1:].strip() elif delimiter_required: raise ValueError("invalid key/value string, missing delimiter.") delimiter_required = True # key exist if len(key) > 0: if keep_original_key is True: lookup_key = key else: val = cls._parse_value("key/value", {"type": "str"}, key, val, user_ns) lookup_key = key.lower().replace("-", "").replace("_", "") matched_kv[lookup_key] = val # no key but value exist elif len(val) > 0: raise ValueError("invalid key/value string, missing key.") # no key, no value in parenthesis mode elif l_char == "(": raise ValueError("invalid key/value string, missing key.") return matched_kv @classmethod def parse_quote(cls, string:str): string = string.strip() delimiter = string[0] delimiter_len = 1 triple_quote = len(string) > 2 and string[1] == delimiter and string[2] == delimiter if triple_quote: delimiter_len = 3 delimiter = delimiter * 3 quoted_string_len = string[3:].find(delimiter) else: escape = False quoted_string_len = -1 count = 0 for c in string[1:]: if c == "\\": escape = not escape elif escape: pass elif c == delimiter: quoted_string_len = count break count += 1 if quoted_string_len >= 0: trimmed_string = string[quoted_string_len + 2 * delimiter_len:] if len(trimmed_string) > 0 and not trimmed_string[0].isspace(): raise SyntaxError("invalid syntax after quoted string, should be followed by whitespace only") quoted_string = string[delimiter_len:quoted_string_len + delimiter_len] quoted_words = len(quoted_string.split()) if len(quoted_string)> 0: if quoted_string[-1].isspace(): quoted_words += 1 if quoted_string[0].isspace(): quoted_words += 1 else: quoted_words = 1 return delimiter + quoted_string + delimiter, quoted_words else: raise SyntaxError("EOL while scanning quoted string") @classmethod def _convert(cls, name:str, obj:Dict[str,Any], key:str, value:Any)->Any: if value is None: if obj.get("allow_none"): return None else: raise ValueError(f"option '{key}' doesn't allow None value.") _type = None try: _type = obj.get("type") if _type == "int": if float(value) != int(value): raise ValueError return int(value) elif _type == "uint": if float(value) != int(value) or int(value) < 0: raise ValueError return int(value) elif _type == "float": return float(value) elif _type == "bool": if type(value) == str: if value.lower() == 'true': return True elif value.lower() == 'false': return False else: raise ValueError elif bool(value) != int(value): raise ValueError return bool(value) elif _type == "dict": return dict(value) elif _type == "list": if type(value) == str: value = [value] return list(value) elif _type == "enum": enum_values = obj.get("values", []) if enum_values.index(value) >= 0: return value else: raise ValueError elif _type == "iso8601_duration": # There are four ways to express a time interval: # Start and end, such as "2007-03-01T13:00:00Z/2008-05-11T15:30:00Z" # Start and duration, such as "2007-03-01T13:00:00Z/P1Y2M10DT2H30M" # Duration and end, such as "P1Y2M10DT2H30M/2008-05-11T15:30:00Z" # Duration only, such as "P1Y2M10DT2H30M", with additional context information value_list = [value] if type(value) != list else list(value)[:2] if len(value_list) == 0: raise ValueError elif len(value_list) == 1: value = value_list[0] if isinstance(value, timedelta): isodate = Dependencies.get_module("isodate", message="timedelta convertion to iso8601 duration format is not supported without isodate module, use instead a datetime range format, or already converted string") # will throw if does not exist value = isodate.duration_isoformat(value) elif type(value) != str: raise ValueError return value else: start_value = value_list[0] end_value = value_list[1] if isinstance(start_value, timedelta): isodate = Dependencies.get_module("isodate", dont_throw=True) if isodate: start_value = isodate.duration_isoformat(start_value) else: end_datetime = end_value if isinstance(end_value, datetime) else dateutil.parser.isoparse(end_value) start_value = end_datetime - start_value elif isinstance(end_value, timedelta): isodate = Dependencies.get_module("isodate", dont_throw=True) if isodate: end_value = isodate.duration_isoformat(end_value) else: start_datetime = end_value if isinstance(start_value, datetime) else dateutil.parser.isoparse(start_value) end_value = end_value + start_datetime value_list = [v.strftime('%Y-%m-%dT%H:%M:%S%ZZ') if isinstance(v, datetime) else str(v) for v in [start_value, end_value]] return "/".join(value_list) else: return str(value) except Exception as e: option_type = "property" if name == "query properties" else "option" due_message = f"{e}" or f"invalid '{_type}' of value '{value}'" raise ValueError(f"failed to set {option_type} '{key}' in {name}, due to {due_message}") @classmethod def _validate_config_trait(cls, dict_name:str, obj:Dict[str,Any], key:str, value:Any, config:Configurable)->None: # validate using config traits name = obj.get("flag") if isinstance(config, Configurable) and name in cls.traits_dict: # # save current value # try: new_value = cls._convert(dict_name, obj, key, value) trait:TraitType = cls.traits_dict.get(name) if hasattr(trait, "_validate"): validated_value = trait._validate(config, new_value) return except Exception as error: raise ValueError(f"failed to set option '{key}' in {dict_name}, due to invalid value '{value}'. Exception: {error}")

import sys import click import os import datetime from unittest import TestCase, main from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames, get_frame_shape from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label from frigate.motion import MotionDetector from frigate.edgetpu import LocalObjectDetector from frigate.objects import ObjectTracker import multiprocessing as mp import numpy as np import cv2 from frigate.object_processing import COLOR_MAP, CameraState class ProcessClip(): def __init__(self, clip_path, frame_shape, config): self.clip_path = clip_path self.frame_shape = frame_shape self.camera_name = 'camera' self.frame_manager = DictFrameManager() self.frame_queue = mp.Queue() self.detected_objects_queue = mp.Queue() self.camera_state = CameraState(self.camera_name, config, self.frame_manager) def load_frames(self): fps = EventsPerSecond() skipped_fps = EventsPerSecond() stop_event = mp.Event() detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000) current_frame = mp.Value('d', 0.0) ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel panic -i {self.clip_path} -f rawvideo -pix_fmt rgb24 pipe:".split(" ") ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.frame_shape[0]*self.frame_shape[1]*self.frame_shape[2]) capture_frames(ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue, 1, fps, skipped_fps, stop_event, detection_frame, current_frame) ffmpeg_process.wait() ffmpeg_process.communicate() def process_frames(self, objects_to_track=['person'], object_filters={}): mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8) mask[:] = 255 motion_detector = MotionDetector(self.frame_shape, mask) object_detector = LocalObjectDetector(labels='/labelmap.txt') object_tracker = ObjectTracker(10) process_fps = mp.Value('d', 0.0) detection_fps = mp.Value('d', 0.0) current_frame = mp.Value('d', 0.0) stop_event = mp.Event() process_frames(self.camera_name, self.frame_queue, self.frame_shape, self.frame_manager, motion_detector, object_detector, object_tracker, self.detected_objects_queue, process_fps, detection_fps, current_frame, objects_to_track, object_filters, mask, stop_event, exit_on_empty=True) def objects_found(self, debug_path=None): obj_detected = False top_computed_score = 0.0 def handle_event(name, obj): nonlocal obj_detected nonlocal top_computed_score if obj['computed_score'] > top_computed_score: top_computed_score = obj['computed_score'] if not obj['false_positive']: obj_detected = True self.camera_state.on('new', handle_event) self.camera_state.on('update', handle_event) while(not self.detected_objects_queue.empty()): camera_name, frame_time, current_tracked_objects = self.detected_objects_queue.get() if not debug_path is None: self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values()) self.camera_state.update(frame_time, current_tracked_objects) for obj in self.camera_state.tracked_objects.values(): print(f"{frame_time}: {obj["id"]} - {obj["computed_score"]} - {obj["score_history"]}") return { 'object_detected': obj_detected, 'top_score': top_computed_score } def save_debug_frame(self, debug_path, frame_time, tracked_objects): current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}") # draw the bounding boxes on the frame for obj in tracked_objects: thickness = 2 color = (0,0,175) if obj['frame_time'] != frame_time: thickness = 1 color = (255,0,0) else: color = (255,255,0) # draw the bounding boxes on the frame box = obj['box'] draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj["score"]*100)}% {int(obj["area"])}", thickness=thickness, color=color) # draw the regions on the frame region = obj['region'] draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0)) cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR)) @click.command() @click.option("-p", "--path", required=True, help="Path to clip or directory to test.") @click.option("-l", "--label", default='person', help="Label name to detect.") @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.") @click.option("--debug-path", default=None, help="Path to output frames for debugging.") def process(path, label, threshold, debug_path): clips = [] if os.path.isdir(path): files = os.listdir(path) files.sort() clips = [os.path.join(path, file) for file in files] elif os.path.isfile(path): clips.append(path) config = { 'snapshots': { 'show_timestamp': False, 'draw_zones': False }, 'zones': {}, 'objects': { 'track': [label], 'filters': { 'person': { 'threshold': threshold } } } } results = [] for c in clips: frame_shape = get_frame_shape(c) process_clip = ProcessClip(c, frame_shape, config) process_clip.load_frames() process_clip.process_frames(objects_to_track=config['objects']['track']) results.append((c, process_clip.objects_found(debug_path))) for result in results: print(f"{result[0]}: {result[1]}") positive_count = sum(1 for result in results if result[1]['object_detected']) print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).") if __name__ == '__main__': process()

import sys import click import os import datetime from unittest import TestCase, main from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames, get_frame_shape from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label from frigate.motion import MotionDetector from frigate.edgetpu import LocalObjectDetector from frigate.objects import ObjectTracker import multiprocessing as mp import numpy as np import cv2 from frigate.object_processing import COLOR_MAP, CameraState class ProcessClip(): def __init__(self, clip_path, frame_shape, config): self.clip_path = clip_path self.frame_shape = frame_shape self.camera_name = 'camera' self.frame_manager = DictFrameManager() self.frame_queue = mp.Queue() self.detected_objects_queue = mp.Queue() self.camera_state = CameraState(self.camera_name, config, self.frame_manager) def load_frames(self): fps = EventsPerSecond() skipped_fps = EventsPerSecond() stop_event = mp.Event() detection_frame = mp.Value('d', datetime.datetime.now().timestamp()+100000) current_frame = mp.Value('d', 0.0) ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel panic -i {self.clip_path} -f rawvideo -pix_fmt rgb24 pipe:".split(" ") ffmpeg_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.frame_shape[0]*self.frame_shape[1]*self.frame_shape[2]) capture_frames(ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue, 1, fps, skipped_fps, stop_event, detection_frame, current_frame) ffmpeg_process.wait() ffmpeg_process.communicate() def process_frames(self, objects_to_track=['person'], object_filters={}): mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8) mask[:] = 255 motion_detector = MotionDetector(self.frame_shape, mask) object_detector = LocalObjectDetector(labels='/labelmap.txt') object_tracker = ObjectTracker(10) process_fps = mp.Value('d', 0.0) detection_fps = mp.Value('d', 0.0) current_frame = mp.Value('d', 0.0) stop_event = mp.Event() process_frames(self.camera_name, self.frame_queue, self.frame_shape, self.frame_manager, motion_detector, object_detector, object_tracker, self.detected_objects_queue, process_fps, detection_fps, current_frame, objects_to_track, object_filters, mask, stop_event, exit_on_empty=True) def objects_found(self, debug_path=None): obj_detected = False top_computed_score = 0.0 def handle_event(name, obj): nonlocal obj_detected nonlocal top_computed_score if obj['computed_score'] > top_computed_score: top_computed_score = obj['computed_score'] if not obj['false_positive']: obj_detected = True self.camera_state.on('new', handle_event) self.camera_state.on('update', handle_event) while(not self.detected_objects_queue.empty()): camera_name, frame_time, current_tracked_objects = self.detected_objects_queue.get() if not debug_path is None: self.save_debug_frame(debug_path, frame_time, current_tracked_objects.values()) self.camera_state.update(frame_time, current_tracked_objects) for obj in self.camera_state.tracked_objects.values(): print(f"{frame_time}: {obj['id']} - {obj['computed_score']} - {obj['score_history']}") return { 'object_detected': obj_detected, 'top_score': top_computed_score } def save_debug_frame(self, debug_path, frame_time, tracked_objects): current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}") # draw the bounding boxes on the frame for obj in tracked_objects: thickness = 2 color = (0,0,175) if obj['frame_time'] != frame_time: thickness = 1 color = (255,0,0) else: color = (255,255,0) # draw the bounding boxes on the frame box = obj['box'] draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color) # draw the regions on the frame region = obj['region'] draw_box_with_label(current_frame, region[0], region[1], region[2], region[3], 'region', "", thickness=1, color=(0,255,0)) cv2.imwrite(f"{os.path.join(debug_path, os.path.basename(self.clip_path))}.{int(frame_time*1000000)}.jpg", cv2.cvtColor(current_frame, cv2.COLOR_RGB2BGR)) @click.command() @click.option("-p", "--path", required=True, help="Path to clip or directory to test.") @click.option("-l", "--label", default='person', help="Label name to detect.") @click.option("-t", "--threshold", default=0.85, help="Threshold value for objects.") @click.option("--debug-path", default=None, help="Path to output frames for debugging.") def process(path, label, threshold, debug_path): clips = [] if os.path.isdir(path): files = os.listdir(path) files.sort() clips = [os.path.join(path, file) for file in files] elif os.path.isfile(path): clips.append(path) config = { 'snapshots': { 'show_timestamp': False, 'draw_zones': False }, 'zones': {}, 'objects': { 'track': [label], 'filters': { 'person': { 'threshold': threshold } } } } results = [] for c in clips: frame_shape = get_frame_shape(c) process_clip = ProcessClip(c, frame_shape, config) process_clip.load_frames() process_clip.process_frames(objects_to_track=config['objects']['track']) results.append((c, process_clip.objects_found(debug_path))) for result in results: print(f"{result[0]}: {result[1]}") positive_count = sum(1 for result in results if result[1]['object_detected']) print(f"Objects were detected in {positive_count}/{len(results)}({positive_count/len(results)*100:.2f}%) clip(s).") if __name__ == '__main__': process()

# Copyright (c) OpenMMLab. All rights reserved. import copy import warnings from abc import ABCMeta from collections import defaultdict from logging import FileHandler import torch.nn as nn from mmcv.runner.dist_utils import master_only from mmcv.utils.logging import get_logger, logger_initialized, print_log class BaseModule(nn.Module, metaclass=ABCMeta): """Base module for all modules in openmmlab. ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional functionality of parameter initialization. Compared with ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes. - ``init_cfg``: the config to control the initialization. - ``init_weights``: The function of parameter initialization and recording initialization information. - ``_params_init_info``: Used to track the parameter initialization information. This attribute only exists during executing the ``init_weights``. Args: init_cfg (dict, optional): Initialization config dict. """ def __init__(self, init_cfg=None): """Initialize BaseModule, inherited from `torch.nn.Module`""" # NOTE init_cfg can be defined in different levels, but init_cfg # in low levels has a higher priority. super(BaseModule, self).__init__() # define default value of init_cfg instead of hard code # in init_weights() function self._is_init = False self.init_cfg = copy.deepcopy(init_cfg) # Backward compatibility in derived classes # if pretrained is not None: # warnings.warn('DeprecationWarning: pretrained is a deprecated \ # key, please consider using init_cfg') # self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) @property def is_init(self): return self._is_init def init_weights(self): """Initialize the weights.""" is_top_level_module = False # check if it is top-level module if not hasattr(self, '_params_init_info'): # The `_params_init_info` is used to record the initialization # information of the parameters # the key should be the obj:`nn.Parameter` of model and the value # should be a dict containing # - init_info (str): The string that describes the initialization. # - tmp_mean_value (FloatTensor): The mean of the parameter, # which indicates whether the parameter has been modified. # this attribute would be deleted after all parameters # is initialized. self._params_init_info = defaultdict(dict) is_top_level_module = True # Initialize the `_params_init_info`, # When detecting the `tmp_mean_value` of # the corresponding parameter is changed, update related # initialization information for name, param in self.named_parameters(): self._params_init_info[param][ 'init_info'] = f'The value is the same before and ' \ f'after calling `init_weights` ' \ f'of {self.__class__.__name__} ' self._params_init_info[param][ 'tmp_mean_value'] = param.data.mean() # pass `params_init_info` to all submodules # All submodules share the same `params_init_info`, # so it will be updated when parameters are # modified at any level of the model. for sub_module in self.modules(): sub_module._params_init_info = self._params_init_info # Get the initialized logger, if not exist, # create a logger named `mmcv` logger_names = list(logger_initialized.keys()) logger_name = logger_names[0] if logger_names else 'mmcv' from ..cnn import initialize from ..cnn.utils.weight_init import update_init_info module_name = self.__class__.__name__ if not self._is_init: if self.init_cfg: print_log( f'initialize {module_name} with init_cfg {self.init_cfg}', logger=logger_name) initialize(self, self.init_cfg) if isinstance(self.init_cfg, dict): # prevent the parameters of # the pre-trained model # from being overwritten by # the `init_weights` if self.init_cfg['type'] == 'Pretrained': return for m in self.children(): if hasattr(m, 'init_weights'): m.init_weights() # users may overload the `init_weights` update_init_info( m, init_info=f'Initialized by ' f'user-defined `init_weights`' f' in {m.__class__.__name__} ') self._is_init = True else: warnings.warn(f'init_weights of {self.__class__.__name__} has ' f'been called more than once.') if is_top_level_module: self._dump_init_info(logger_name) for sub_module in self.modules(): del sub_module._params_init_info @master_only def _dump_init_info(self, logger_name): """Dump the initialization information to a file named `initialization.log.json` in workdir. Args: logger_name (str): The name of logger. """ logger = get_logger(logger_name) with_file_handler = False # dump the information to the logger file if there is a `FileHandler` for handler in logger.handlers: if isinstance(handler, FileHandler): handler.stream.write( 'Name of parameter - Initialization information\n') for name, param in self.named_parameters(): handler.stream.write( f'\n{name} - {param.shape}: ' f"\n{self._params_init_info[param]["init_info"]} \n") handler.stream.flush() with_file_handler = True if not with_file_handler: for name, param in self.named_parameters(): print_log( f'\n{name} - {param.shape}: ' f"\n{self._params_init_info[param]["init_info"]} \n ", logger=logger_name) def __repr__(self): s = super().__repr__() if self.init_cfg: s += f'\ninit_cfg={self.init_cfg}' return s class Sequential(BaseModule, nn.Sequential): """Sequential module in openmmlab. Args: init_cfg (dict, optional): Initialization config dict. """ def __init__(self, *args, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.Sequential.__init__(self, *args) class ModuleList(BaseModule, nn.ModuleList): """ModuleList in openmmlab. Args: modules (iterable, optional): an iterable of modules to add. init_cfg (dict, optional): Initialization config dict. """ def __init__(self, modules=None, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.ModuleList.__init__(self, modules) class ModuleDict(BaseModule, nn.ModuleDict): """ModuleDict in openmmlab. Args: modules (dict, optional): a mapping (dictionary) of (string: module) or an iterable of key-value pairs of type (string, module). init_cfg (dict, optional): Initialization config dict. """ def __init__(self, modules=None, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.ModuleDict.__init__(self, modules)

# Copyright (c) OpenMMLab. All rights reserved. import copy import warnings from abc import ABCMeta from collections import defaultdict from logging import FileHandler import torch.nn as nn from mmcv.runner.dist_utils import master_only from mmcv.utils.logging import get_logger, logger_initialized, print_log class BaseModule(nn.Module, metaclass=ABCMeta): """Base module for all modules in openmmlab. ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional functionality of parameter initialization. Compared with ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes. - ``init_cfg``: the config to control the initialization. - ``init_weights``: The function of parameter initialization and recording initialization information. - ``_params_init_info``: Used to track the parameter initialization information. This attribute only exists during executing the ``init_weights``. Args: init_cfg (dict, optional): Initialization config dict. """ def __init__(self, init_cfg=None): """Initialize BaseModule, inherited from `torch.nn.Module`""" # NOTE init_cfg can be defined in different levels, but init_cfg # in low levels has a higher priority. super(BaseModule, self).__init__() # define default value of init_cfg instead of hard code # in init_weights() function self._is_init = False self.init_cfg = copy.deepcopy(init_cfg) # Backward compatibility in derived classes # if pretrained is not None: # warnings.warn('DeprecationWarning: pretrained is a deprecated \ # key, please consider using init_cfg') # self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) @property def is_init(self): return self._is_init def init_weights(self): """Initialize the weights.""" is_top_level_module = False # check if it is top-level module if not hasattr(self, '_params_init_info'): # The `_params_init_info` is used to record the initialization # information of the parameters # the key should be the obj:`nn.Parameter` of model and the value # should be a dict containing # - init_info (str): The string that describes the initialization. # - tmp_mean_value (FloatTensor): The mean of the parameter, # which indicates whether the parameter has been modified. # this attribute would be deleted after all parameters # is initialized. self._params_init_info = defaultdict(dict) is_top_level_module = True # Initialize the `_params_init_info`, # When detecting the `tmp_mean_value` of # the corresponding parameter is changed, update related # initialization information for name, param in self.named_parameters(): self._params_init_info[param][ 'init_info'] = f'The value is the same before and ' \ f'after calling `init_weights` ' \ f'of {self.__class__.__name__} ' self._params_init_info[param][ 'tmp_mean_value'] = param.data.mean() # pass `params_init_info` to all submodules # All submodules share the same `params_init_info`, # so it will be updated when parameters are # modified at any level of the model. for sub_module in self.modules(): sub_module._params_init_info = self._params_init_info # Get the initialized logger, if not exist, # create a logger named `mmcv` logger_names = list(logger_initialized.keys()) logger_name = logger_names[0] if logger_names else 'mmcv' from ..cnn import initialize from ..cnn.utils.weight_init import update_init_info module_name = self.__class__.__name__ if not self._is_init: if self.init_cfg: print_log( f'initialize {module_name} with init_cfg {self.init_cfg}', logger=logger_name) initialize(self, self.init_cfg) if isinstance(self.init_cfg, dict): # prevent the parameters of # the pre-trained model # from being overwritten by # the `init_weights` if self.init_cfg['type'] == 'Pretrained': return for m in self.children(): if hasattr(m, 'init_weights'): m.init_weights() # users may overload the `init_weights` update_init_info( m, init_info=f'Initialized by ' f'user-defined `init_weights`' f' in {m.__class__.__name__} ') self._is_init = True else: warnings.warn(f'init_weights of {self.__class__.__name__} has ' f'been called more than once.') if is_top_level_module: self._dump_init_info(logger_name) for sub_module in self.modules(): del sub_module._params_init_info @master_only def _dump_init_info(self, logger_name): """Dump the initialization information to a file named `initialization.log.json` in workdir. Args: logger_name (str): The name of logger. """ logger = get_logger(logger_name) with_file_handler = False # dump the information to the logger file if there is a `FileHandler` for handler in logger.handlers: if isinstance(handler, FileHandler): handler.stream.write( 'Name of parameter - Initialization information\n') for name, param in self.named_parameters(): handler.stream.write( f'\n{name} - {param.shape}: ' f"\n{self._params_init_info[param]['init_info']} \n") handler.stream.flush() with_file_handler = True if not with_file_handler: for name, param in self.named_parameters(): print_log( f'\n{name} - {param.shape}: ' f"\n{self._params_init_info[param]['init_info']} \n ", logger=logger_name) def __repr__(self): s = super().__repr__() if self.init_cfg: s += f'\ninit_cfg={self.init_cfg}' return s class Sequential(BaseModule, nn.Sequential): """Sequential module in openmmlab. Args: init_cfg (dict, optional): Initialization config dict. """ def __init__(self, *args, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.Sequential.__init__(self, *args) class ModuleList(BaseModule, nn.ModuleList): """ModuleList in openmmlab. Args: modules (iterable, optional): an iterable of modules to add. init_cfg (dict, optional): Initialization config dict. """ def __init__(self, modules=None, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.ModuleList.__init__(self, modules) class ModuleDict(BaseModule, nn.ModuleDict): """ModuleDict in openmmlab. Args: modules (dict, optional): a mapping (dictionary) of (string: module) or an iterable of key-value pairs of type (string, module). init_cfg (dict, optional): Initialization config dict. """ def __init__(self, modules=None, init_cfg=None): BaseModule.__init__(self, init_cfg) nn.ModuleDict.__init__(self, modules)

"""create DOEs and execute design workflow Caution: This module requires fa_pytuils and delismm! Please contatct the developers for these additional packages. """ import os from collections import OrderedDict import datetime import numpy as np import matplotlib.pyplot as plt from delismm.model.doe import LatinizedCentroidalVoronoiTesselation, DOEfromFile from delismm.model.samplecalculator import getY from delismm.model.customsystemfunction import BoundsHandler, AbstractTargetFunction from fa_pyutils.service.systemutils import getRunDir from tankoh2.control_sf import createWindingDesign from tankoh2 import programDir, log, pychain from tankoh2.service import indent dome = 'circle' # isotensoid circle safetyFactor = 1 # 2.25 lb = OrderedDict([('r', 500.), ('lzylByR', 0.01), ('dp', 0.13 * safetyFactor)]) # [mm, - , MPa] ub = OrderedDict([('r', 1600.), ('lzylByR', 12.), ('dp', 0.5 * safetyFactor)]) useFibreFailure = False numberOfSamples = 201 class TankWinder(AbstractTargetFunction): """""" name = 'tank winder' def __init__(self, lb, ub, runDir): """""" resultNames = ['frpMass', 'volume', 'area', 'lzylinder', 'numberOfLayers', 'angles', 'hoopLayerShifts'] AbstractTargetFunction.__init__(self, lb, ub, resultNames=resultNames) self.doParallelization = [] self.runDir = runDir self.allowFailedSample = True def _call(self, parameters): """call function for the model""" runDir = getRunDir(basePath=os.path.join(self.runDir), useMilliSeconds=True) r, lzyl, burstPressure = parameters result = createWindingDesign(dzyl=r * 2, lzylByR=lzyl, burstPressure=burstPressure, minPolarOpening=r / 10, runDir=runDir, domeType=pychain.winding.DOME_TYPES.ISOTENSOID if dome == 'isotensoid' else pychain.winding.DOME_TYPES.CIRCLE, useFibreFailure = useFibreFailure) return result volumeFunc = lambda r, lzylByR: (4 / 3 * np.pi * r ** 3 + r * lzylByR * np.pi * r ** 2) """[m**3]""" def plotGeometryRange(radii, lzylByRs, plotDir='', show=False, samples=None): """ :param radii: tuple with min and max radius [mm] :param lzylByRs: tuple with min and max lzylByR [-] :return: None """ radii = np.array(radii) / 1e3 # convert to m if samples is not None: samplesR, samplesLzylByR = samples[:2, :] samplesR = samplesR / 1e3 fig = plt.figure(figsize=(15,6)) axes = [fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2)] axes[1].set_yscale("log") for ax in axes: ax.set_title("Parameter bounds") ax.set_xlabel('Radius [m]') ax.set_ylabel('Volume [m^3]') color = 'tab:blue' for lzylByR in lzylByRs: x = np.linspace(*radii,11) volumes = [volumeFunc(r, lzylByR) for r in x] ax.plot(x, volumes, color=color, label=f'lzylByR={lzylByR}') color = 'tab:orange' ax.legend() if samples is not None: volumes = volumeFunc(samplesR, samplesLzylByR) ax.scatter(samplesR, volumes, label=f'samples') if plotDir: plt.savefig(plotDir+'/geometryRange.png') if show: plt.show() def main(): sampleFile = '' + 'C:/PycharmProjects/tankoh2/tmp/doe_circle_20210520_135237_cvt/sampleX.txt' startTime = datetime.datetime.now() names = list(lb.keys()) runDir = getRunDir(f'doe_{dome}_{'puckff' if useFibreFailure else 'puckiff'}', basePath=os.path.join(programDir, 'tmp')) winder = TankWinder(lb, ub, runDir) if sampleFile: lcvt = DOEfromFile(sampleFile) else: lcvt = LatinizedCentroidalVoronoiTesselation(numberOfSamples, len(names)) sampleX = BoundsHandler.scaleToBoundsStatic(lcvt.sampleXNormalized, list(lb.values()), list(ub.values())) plotGeometryRange([lb['r'], ub['r']],[lb['lzylByR'], ub['lzylByR']], plotDir=runDir, samples=sampleX) lcvt.xToFile(os.path.join(runDir, 'sampleX.txt')) lcvt.xToFileStatic(os.path.join(runDir, 'sampleX_bounds.txt'), sampleX) sampleY = getY(sampleX, winder, verbose=True, runDir=runDir) # store samples lcvt.yToFile(os.path.join(runDir, 'sampleY.txt'), winder, sampleY) # lcvt.xyToFile(os.path.join(runDir, 'full_doe2.txt'), winder, sampleY, True) allSamples = [names + winder.resultNames] for inputSample, outputSample in zip(sampleX.T, sampleY): if hasattr(outputSample, '__iter__'): allSamples.append(list(inputSample) + list(outputSample)) else: allSamples.append(list(inputSample) + list([outputSample])) with open(os.path.join(runDir, 'full_doe.txt'), 'w') as f: f.write(indent(allSamples, hasHeader=True)) duration = datetime.datetime.now() - startTime log.info(f'runtime {duration.seconds} seconds') if __name__ == '__main__': if 1: main() else: plotGeometryRange([lb['r'], ub['r']],[lb['lzylByR'], ub['lzylByR']], show=True)

"""create DOEs and execute design workflow Caution: This module requires fa_pytuils and delismm! Please contatct the developers for these additional packages. """ import os from collections import OrderedDict import datetime import numpy as np import matplotlib.pyplot as plt from delismm.model.doe import LatinizedCentroidalVoronoiTesselation, DOEfromFile from delismm.model.samplecalculator import getY from delismm.model.customsystemfunction import BoundsHandler, AbstractTargetFunction from fa_pyutils.service.systemutils import getRunDir from tankoh2.control_sf import createWindingDesign from tankoh2 import programDir, log, pychain from tankoh2.service import indent dome = 'circle' # isotensoid circle safetyFactor = 1 # 2.25 lb = OrderedDict([('r', 500.), ('lzylByR', 0.01), ('dp', 0.13 * safetyFactor)]) # [mm, - , MPa] ub = OrderedDict([('r', 1600.), ('lzylByR', 12.), ('dp', 0.5 * safetyFactor)]) useFibreFailure = False numberOfSamples = 201 class TankWinder(AbstractTargetFunction): """""" name = 'tank winder' def __init__(self, lb, ub, runDir): """""" resultNames = ['frpMass', 'volume', 'area', 'lzylinder', 'numberOfLayers', 'angles', 'hoopLayerShifts'] AbstractTargetFunction.__init__(self, lb, ub, resultNames=resultNames) self.doParallelization = [] self.runDir = runDir self.allowFailedSample = True def _call(self, parameters): """call function for the model""" runDir = getRunDir(basePath=os.path.join(self.runDir), useMilliSeconds=True) r, lzyl, burstPressure = parameters result = createWindingDesign(dzyl=r * 2, lzylByR=lzyl, burstPressure=burstPressure, minPolarOpening=r / 10, runDir=runDir, domeType=pychain.winding.DOME_TYPES.ISOTENSOID if dome == 'isotensoid' else pychain.winding.DOME_TYPES.CIRCLE, useFibreFailure = useFibreFailure) return result volumeFunc = lambda r, lzylByR: (4 / 3 * np.pi * r ** 3 + r * lzylByR * np.pi * r ** 2) """[m**3]""" def plotGeometryRange(radii, lzylByRs, plotDir='', show=False, samples=None): """ :param radii: tuple with min and max radius [mm] :param lzylByRs: tuple with min and max lzylByR [-] :return: None """ radii = np.array(radii) / 1e3 # convert to m if samples is not None: samplesR, samplesLzylByR = samples[:2, :] samplesR = samplesR / 1e3 fig = plt.figure(figsize=(15,6)) axes = [fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2)] axes[1].set_yscale("log") for ax in axes: ax.set_title("Parameter bounds") ax.set_xlabel('Radius [m]') ax.set_ylabel('Volume [m^3]') color = 'tab:blue' for lzylByR in lzylByRs: x = np.linspace(*radii,11) volumes = [volumeFunc(r, lzylByR) for r in x] ax.plot(x, volumes, color=color, label=f'lzylByR={lzylByR}') color = 'tab:orange' ax.legend() if samples is not None: volumes = volumeFunc(samplesR, samplesLzylByR) ax.scatter(samplesR, volumes, label=f'samples') if plotDir: plt.savefig(plotDir+'/geometryRange.png') if show: plt.show() def main(): sampleFile = '' + 'C:/PycharmProjects/tankoh2/tmp/doe_circle_20210520_135237_cvt/sampleX.txt' startTime = datetime.datetime.now() names = list(lb.keys()) runDir = getRunDir(f'doe_{dome}_{"puckff" if useFibreFailure else "puckiff"}', basePath=os.path.join(programDir, 'tmp')) winder = TankWinder(lb, ub, runDir) if sampleFile: lcvt = DOEfromFile(sampleFile) else: lcvt = LatinizedCentroidalVoronoiTesselation(numberOfSamples, len(names)) sampleX = BoundsHandler.scaleToBoundsStatic(lcvt.sampleXNormalized, list(lb.values()), list(ub.values())) plotGeometryRange([lb['r'], ub['r']],[lb['lzylByR'], ub['lzylByR']], plotDir=runDir, samples=sampleX) lcvt.xToFile(os.path.join(runDir, 'sampleX.txt')) lcvt.xToFileStatic(os.path.join(runDir, 'sampleX_bounds.txt'), sampleX) sampleY = getY(sampleX, winder, verbose=True, runDir=runDir) # store samples lcvt.yToFile(os.path.join(runDir, 'sampleY.txt'), winder, sampleY) # lcvt.xyToFile(os.path.join(runDir, 'full_doe2.txt'), winder, sampleY, True) allSamples = [names + winder.resultNames] for inputSample, outputSample in zip(sampleX.T, sampleY): if hasattr(outputSample, '__iter__'): allSamples.append(list(inputSample) + list(outputSample)) else: allSamples.append(list(inputSample) + list([outputSample])) with open(os.path.join(runDir, 'full_doe.txt'), 'w') as f: f.write(indent(allSamples, hasHeader=True)) duration = datetime.datetime.now() - startTime log.info(f'runtime {duration.seconds} seconds') if __name__ == '__main__': if 1: main() else: plotGeometryRange([lb['r'], ub['r']],[lb['lzylByR'], ub['lzylByR']], show=True)

""" This module controls defines celery tasks and their applicable schedules. The celery beat server and workers will start when invoked. Please add internal-only celery tasks to the celery_tasks plugin. When ran in development mode (CONFIG_LOCATION=<location of development.yaml configuration file. To run both the celery beat scheduler and a worker simultaneously, and to have jobs kick off starting at the next minute, run the following command: celery -A consoleme.celery_tasks.celery_tasks worker --loglevel=info -l DEBUG -B """ from __future__ import absolute_import import json # We use a separate SetEncoder here so we cannot use ujson import os import sys import time from datetime import datetime, timedelta from typing import Any, Dict, List, Tuple, Union import celery import sentry_sdk import ujson from asgiref.sync import async_to_sync from billiard.exceptions import SoftTimeLimitExceeded from botocore.exceptions import ClientError from celery import group from celery.app.task import Context from celery.concurrency import asynpool from celery.schedules import crontab from celery.signals import ( task_failure, task_prerun, task_received, task_rejected, task_retry, task_revoked, task_success, task_unknown, ) from cloudaux import sts_conn from cloudaux.aws.iam import get_all_managed_policies from cloudaux.aws.s3 import list_buckets from cloudaux.aws.sns import list_topics from cloudaux.aws.sts import boto3_cached_conn from retrying import retry from sentry_sdk.integrations.aiohttp import AioHttpIntegration from sentry_sdk.integrations.celery import CeleryIntegration from sentry_sdk.integrations.redis import RedisIntegration from sentry_sdk.integrations.tornado import TornadoIntegration from consoleme.config import config from consoleme.lib.account_indexers import ( cache_cloud_accounts, get_account_id_to_name_mapping, ) from consoleme.lib.aws import ( cache_all_scps, cache_org_structure, get_enabled_regions_for_account, ) from consoleme.lib.aws_config import aws_config from consoleme.lib.cache import ( retrieve_json_data_from_redis_or_s3, store_json_results_in_redis_and_s3, ) from consoleme.lib.cloud_credential_authorization_mapping import ( generate_and_store_credential_authorization_mapping, ) from consoleme.lib.dynamo import IAMRoleDynamoHandler, UserDynamoHandler from consoleme.lib.git import store_iam_resources_in_git from consoleme.lib.plugins import get_plugin_by_name from consoleme.lib.policies import get_aws_config_history_url_for_resource from consoleme.lib.redis import RedisHandler from consoleme.lib.requests import cache_all_policy_requests from consoleme.lib.self_service.typeahead import cache_self_service_typeahead from consoleme.lib.templated_resources import cache_resource_templates from consoleme.lib.timeout import Timeout asynpool.PROC_ALIVE_TIMEOUT = config.get("celery.asynpool_proc_alive_timeout", 60.0) default_retry_kwargs = { "autoretry_for": (Exception,), "retry_backoff": True, "retry_kwargs": {"max_retries": config.get("celery.default_max_retries", 5)}, } class Celery(celery.Celery): def on_configure(self) -> None: sentry_dsn = config.get("sentry.dsn") if sentry_dsn: sentry_sdk.init( sentry_dsn, integrations=[ TornadoIntegration(), CeleryIntegration(), AioHttpIntegration(), RedisIntegration(), ], ) app = Celery( "tasks", broker=config.get( f"celery.broker.{config.region}", config.get("celery.broker.global", "redis://127.0.0.1:6379/1"), ), backend=config.get( f"celery.backend.{config.region}", config.get("celery.broker.global", "redis://127.0.0.1:6379/2"), ), ) if config.get("redis.use_redislite"): import tempfile import redislite redislite_db_path = os.path.join( config.get("redis.redislite.db_path", tempfile.NamedTemporaryFile().name) ) redislite_client = redislite.Redis(redislite_db_path) redislite_socket_path = f"redis+socket://{redislite_client.socket_file}" app = Celery( "tasks", broker=config.get(f"{redislite_socket_path}/1"), backend=config.get(f"{redislite_socket_path}/2"), ) app.conf.result_expires = config.get("celery.result_expires", 60) app.conf.worker_prefetch_multiplier = config.get("celery.worker_prefetch_multiplier", 4) app.conf.task_acks_late = config.get("celery.task_acks_late", True) if config.get("celery.purge") and not config.get("redis.use_redislite"): # Useful to clear celery queue in development with Timeout(seconds=5, error_message="Timeout: Are you sure Redis is running?"): app.control.purge() log = config.get_logger() red = RedisHandler().redis_sync() aws = get_plugin_by_name(config.get("plugins.aws", "default_aws")) auth = get_plugin_by_name(config.get("plugins.auth", "default_auth"))() group_mapping = get_plugin_by_name( config.get("plugins.group_mapping", "default_group_mapping") )() internal_celery_tasks = get_plugin_by_name( config.get("plugins.internal_celery_tasks", "default_celery_tasks") ) stats = get_plugin_by_name(config.get("plugins.metrics", "default_metrics"))() internal_policies = get_plugin_by_name( config.get("plugins.internal_policies", "default_policies") )() REDIS_IAM_COUNT = 1000 @app.task(soft_time_limit=20) def report_celery_last_success_metrics() -> bool: """ For each celery task, this will determine the number of seconds since it has last been successful. Celery tasks should be emitting redis stats with a deterministic key (In our case, `f"{task}.last_success"`. report_celery_last_success_metrics should be ran periodically to emit metrics on when a task was last successful. We can then alert when tasks are not ran when intended. We should also alert when no metrics are emitted from this function. """ function = f"{__name__}.{sys._getframe().f_code.co_name}" log_data = {"function": function} current_time = int(time.time()) global schedule for _, t in schedule.items(): task = t.get("task") last_success = int(red.get(f"{task}.last_success") or 0) if last_success == 0: log_data["message"] = "Last Success Value is 0" log_data["task_last_success_key"] = f"{task}.last_success" log.error(log_data) stats.gauge(f"{task}.time_since_last_success", current_time - last_success) red.set(f"{task}.time_since_last_success", current_time - last_success) red.set( f"{function}.last_success", int(time.time()) ) # Alert if this metric is not seen stats.count(f"{function}.success") stats.timer("worker.healthy") return True def get_celery_request_tags(**kwargs): request = kwargs.get("request") sender_hostname = "unknown" sender = kwargs.get("sender") if sender: try: sender_hostname = sender.hostname except AttributeError: sender_hostname = vars(sender.request).get("origin", "unknown") if request and not isinstance( request, Context ): # unlike others, task_revoked sends a Context for `request` task_name = request.name task_id = request.id receiver_hostname = request.hostname else: try: task_name = sender.name except AttributeError: task_name = kwargs.pop("name", "") try: task_id = sender.request.id except AttributeError: task_id = kwargs.pop("id", "") try: receiver_hostname = sender.request.hostname except AttributeError: receiver_hostname = "" tags = { "task_name": task_name, "task_id": task_id, "sender_hostname": sender_hostname, "receiver_hostname": receiver_hostname, } tags["expired"] = kwargs.get("expired", False) exception = kwargs.get("exception") if not exception: exception = kwargs.get("exc") if exception: tags["error"] = repr(exception) if isinstance(exception, SoftTimeLimitExceeded): tags["timed_out"] = True return tags @task_prerun.connect def refresh_dynamic_config_in_worker(**kwargs): tags = get_celery_request_tags(**kwargs) log_data = {"function": f"{__name__}.{sys._getframe().f_code.co_name}"} dynamic_config = red.get("DYNAMIC_CONFIG_CACHE") if not dynamic_config: log.error({**log_data, "error": "Unable to retrieve Dynamic Config from Redis"}) return dynamic_config_j = json.loads(dynamic_config) if config.CONFIG.config.get("dynamic_config", {}) != dynamic_config_j: log.debug( { **log_data, **tags, "message": "Refreshing dynamic configuration on Celery Worker", } ) config.CONFIG.config["dynamic_config"] = dynamic_config_j @task_received.connect def report_number_pending_tasks(**kwargs): """ Report the number of pending tasks to our metrics broker every time a task is published. This metric can be used for autoscaling workers. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-received :param sender: :param headers: :param body: :param kwargs: :return: """ tags = get_celery_request_tags(**kwargs) tags.pop("task_id", None) stats.timer("celery.new_pending_task", tags=tags) @task_success.connect def report_successful_task(**kwargs): """ Report a generic success metric as tasks to our metrics broker every time a task finished correctly. This metric can be used for autoscaling workers. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-success :param sender: :param headers: :param body: :param kwargs: :return: """ tags = get_celery_request_tags(**kwargs) red.set(f"{tags["task_name"]}.last_success", int(time.time())) tags.pop("error", None) tags.pop("task_id", None) stats.timer("celery.successful_task", tags=tags) @task_retry.connect def report_task_retry(**kwargs): """ Report a generic retry metric as tasks to our metrics broker every time a task is retroed. This metric can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-retry :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Retry", } # Add traceback if exception info is in the kwargs einfo = kwargs.get("einfo") if einfo: log_data["traceback"] = einfo.traceback error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.retried_task", tags=error_tags) @task_failure.connect def report_failed_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task fails. This is also called when a task has hit a SoftTimeLimit. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-failure :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Failure", } # Add traceback if exception info is in the kwargs einfo = kwargs.get("einfo") if einfo: log_data["traceback"] = einfo.traceback error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.failed_task", tags=error_tags) @task_unknown.connect def report_unknown_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a worker receives an unknown task. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-unknown :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Unknown", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.unknown_task", tags=error_tags) @task_rejected.connect def report_rejected_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task is rejected. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-rejected :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Rejected", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.rejected_task", tags=error_tags) @task_revoked.connect def report_revoked_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task is revoked. This metric can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-revoked :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Revoked", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.revoked_task", tags=error_tags) @retry( stop_max_attempt_number=4, wait_exponential_multiplier=1000, wait_exponential_max=1000, ) def _add_role_to_redis(redis_key: str, role_entry: Dict) -> None: """ This function will add IAM role data to redis so that policy details can be quickly retrieved by the policies endpoint. IAM role data is stored in the `redis_key` redis key by the role's ARN. Parameters ---------- redis_key : str The redis key (hash) role_entry : Dict The role entry Example: {'name': 'nameOfRole', 'accountId': '123456789012', 'arn': 'arn:aws:iam::123456789012:role/nameOfRole', 'templated': None, 'ttl': 1562510908, 'policy': '<json_formatted_policy>'} """ try: red.hset(redis_key, role_entry["arn"], json.dumps(role_entry)) except Exception as e: # noqa stats.count( "cache_roles_for_account.error", tags={"redis_key": redis_key, "error": str(e), "role_entry": role_entry}, ) log_data = { "message": "Error syncing Account's IAM roles to Redis", "account_id": role_entry["account_id"], "arn": role_entry["arn"], "role_entry": role_entry, } log.error(log_data, exc_info=True) raise @app.task(soft_time_limit=3600) def cache_cloudtrail_errors_by_arn() -> Dict: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" log_data: Dict = {"function": function} cloudtrail_errors: Dict = internal_policies.error_count_by_arn() if not cloudtrail_errors: cloudtrail_errors = {} red.setex( config.get( "celery.cache_cloudtrail_errors_by_arn.redis_key", "CLOUDTRAIL_ERRORS_BY_ARN", ), 86400, json.dumps(cloudtrail_errors), ) log_data["number_of_roles_with_errors"]: len(cloudtrail_errors.keys()) log.debug(log_data) return log_data @app.task(soft_time_limit=1800) def cache_policies_table_details() -> bool: iam_role_redis_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") all_iam_roles = red.hgetall(iam_role_redis_key) items = [] accounts_d = async_to_sync(get_account_id_to_name_mapping)() cloudtrail_errors = {} cloudtrail_errors_j = red.get( config.get( "celery.cache_cloudtrail_errors_by_arn.redis_key", "CLOUDTRAIL_ERRORS_BY_ARN", ) ) if cloudtrail_errors_j: cloudtrail_errors = json.loads(cloudtrail_errors_j) s3_error_topic = config.get("redis.s3_errors", "S3_ERRORS") all_s3_errors = red.get(s3_error_topic) s3_errors = {} if all_s3_errors: s3_errors = json.loads(all_s3_errors) for arn, role_details_j in all_iam_roles.items(): role_details = ujson.loads(role_details_j) error_count = cloudtrail_errors.get(arn, 0) s3_errors_for_arn = s3_errors.get(arn, []) for error in s3_errors_for_arn: error_count += int(error.get("count")) account_id = arn.split(":")[4] account_name = accounts_d.get(str(account_id), "Unknown") resource_id = role_details.get("resourceId") items.append( { "account_id": account_id, "account_name": account_name, "arn": arn, "technology": "iam", "templated": red.hget( config.get("templated_roles.redis_key", "TEMPLATED_ROLES_v2"), arn.lower(), ), "errors": error_count, "config_history_url": async_to_sync( get_aws_config_history_url_for_resource )(account_id, resource_id, arn, "AWS::IAM::Role"), } ) s3_bucket_key: str = config.get("redis.s3_bucket_key", "S3_BUCKETS") s3_accounts = red.hkeys(s3_bucket_key) if s3_accounts: for account in s3_accounts: account_name = accounts_d.get(str(account), "Unknown") buckets = json.loads(red.hget(s3_bucket_key, account)) for bucket in buckets: bucket_arn = f"arn:aws:s3:::{bucket}" s3_errors_for_arn = s3_errors.get(bucket_arn, []) error_count = 0 for error in s3_errors_for_arn: error_count += int(error.get("count")) items.append( { "account_id": account, "account_name": account_name, "arn": f"arn:aws:s3:::{bucket}", "technology": "s3", "templated": None, "errors": error_count, } ) sns_topic_key: str = config.get("redis.sns_topics_key", "SNS_TOPICS") sns_accounts = red.hkeys(sns_topic_key) if sns_accounts: for account in sns_accounts: account_name = accounts_d.get(str(account), "Unknown") topics = json.loads(red.hget(sns_topic_key, account)) for topic in topics: error_count = 0 items.append( { "account_id": account, "account_name": account_name, "arn": topic, "technology": "sns", "templated": None, "errors": error_count, } ) sqs_queue_key: str = config.get("redis.sqs_queues_key", "SQS_QUEUES") sqs_accounts = red.hkeys(sqs_queue_key) if sqs_accounts: for account in sqs_accounts: account_name = accounts_d.get(str(account), "Unknown") queues = json.loads(red.hget(sqs_queue_key, account)) for queue in queues: error_count = 0 items.append( { "account_id": account, "account_name": account_name, "arn": queue, "technology": "sqs", "templated": None, "errors": error_count, } ) resources_from_aws_config_redis_key: str = config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ) resources_from_aws_config = red.hgetall(resources_from_aws_config_redis_key) if resources_from_aws_config: for arn, value in resources_from_aws_config.items(): resource = json.loads(value) technology = resource["resourceType"] # Skip technologies that we retrieve directly if technology in [ "AWS::IAM::Role", "AWS::SQS::Queue", "AWS::SNS::Topic", "AWS::S3::Bucket", ]: continue account_id = arn.split(":")[4] account_name = accounts_d.get(account_id, "Unknown") items.append( { "account_id": account_id, "account_name": account_name, "arn": arn, "technology": technology, "templated": None, "errors": 0, } ) s3_bucket = None s3_key = None if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("cache_policies_table_details.s3.bucket") s3_key = config.get( "cache_policies_table_details.s3.file", "policies_table/cache_policies_table_details_v1.json.gz", ) async_to_sync(store_json_results_in_redis_and_s3)( items, redis_key=config.get("policies.redis_policies_key", "ALL_POLICIES"), s3_bucket=s3_bucket, s3_key=s3_key, ) stats.count( "cache_policies_table_details.success", tags={"num_roles": len(all_iam_roles.keys())}, ) return True @app.task(soft_time_limit=2700, **default_retry_kwargs) def cache_roles_for_account(account_id: str) -> bool: # Get the DynamoDB handler: dynamo = IAMRoleDynamoHandler() cache_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") # Only query IAM and put data in Dynamo if we're in the active region if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: client = boto3_cached_conn( "iam", account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) paginator = client.get_paginator("get_account_authorization_details") response_iterator = paginator.paginate() all_iam_resources = {} for response in response_iterator: if not all_iam_resources: all_iam_resources = response else: all_iam_resources["UserDetailList"].extend(response["UserDetailList"]) all_iam_resources["GroupDetailList"].extend(response["GroupDetailList"]) all_iam_resources["RoleDetailList"].extend(response["RoleDetailList"]) all_iam_resources["Policies"].extend(response["Policies"]) for k in response.keys(): if k not in [ "UserDetailList", "GroupDetailList", "RoleDetailList", "Policies", "ResponseMetadata", "Marker", "IsTruncated", ]: # Fail hard if we find something unexpected raise RuntimeError("Unexpected key {0} in response".format(k)) # Store entire response in S3 async_to_sync(store_json_results_in_redis_and_s3)( all_iam_resources, s3_bucket=config.get("cache_iam_resources_for_account.s3.bucket"), s3_key=config.get( "cache_iam_resources_for_account.s3.file", "get_account_authorization_details/get_account_authorization_details_{account_id}_v1.json.gz", ).format(account_id=account_id), ) iam_roles = all_iam_resources["RoleDetailList"] async_to_sync(store_json_results_in_redis_and_s3)( iam_roles, s3_bucket=config.get("cache_roles_for_account.s3.bucket"), s3_key=config.get( "cache_roles_for_account.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="iam_roles", account_id=account_id), ) ttl: int = int((datetime.utcnow() + timedelta(hours=36)).timestamp()) # Save them: for role in iam_roles: role_entry = { "arn": role.get("Arn"), "name": role.get("RoleName"), "resourceId": role.get("RoleId"), "accountId": account_id, "ttl": ttl, "policy": dynamo.convert_role_to_json(role), "templated": red.hget( config.get("templated_roles.redis_key", "TEMPLATED_ROLES_v2"), role.get("Arn").lower(), ), } # DynamoDB: dynamo.sync_iam_role_for_account(role_entry) # Redis: _add_role_to_redis(cache_key, role_entry) # Run internal function on role. This can be used to inspect roles, add managed policies, or other actions aws().handle_detected_role(role) # Maybe store all resources in git if config.get("cache_iam_resources_for_account.store_in_git.enabled"): store_iam_resources_in_git(all_iam_resources, account_id) stats.count("cache_roles_for_account.success", tags={"account_id": account_id}) return True @app.task(soft_time_limit=3600) def cache_roles_across_accounts() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" cache_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") log_data = {"function": function, "cache_key": cache_key} accounts_d = async_to_sync(get_account_id_to_name_mapping)() tasks = [] if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev"]: # First, get list of accounts # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") in ["prod", "dev"]: tasks.append(cache_roles_for_account.s(account_id)) else: if account_id in config.get("celery.test_account_ids", []): tasks.append(cache_roles_for_account.s(account_id)) results = group(*tasks).apply_async() # results.join() forces function to wait until all tasks are complete results.join() else: dynamo = IAMRoleDynamoHandler() # In non-active regions, we just want to sync DDB data to Redis roles = dynamo.fetch_all_roles() for role_entry in roles: _add_role_to_redis(cache_key, role_entry) # Delete roles in Redis cache with expired TTL all_roles = red.hgetall(cache_key) roles_to_delete_from_cache = [] for arn, role_entry_j in all_roles.items(): role_entry = json.loads(role_entry_j) if datetime.fromtimestamp(role_entry["ttl"]) < datetime.utcnow(): roles_to_delete_from_cache.append(arn) if roles_to_delete_from_cache: red.hdel(cache_key, *roles_to_delete_from_cache) for arn in roles_to_delete_from_cache: all_roles.pop(arn, None) log_data["num_roles"] = len(all_roles) # Store full list of roles in a single place. This list will be ~30 minutes out of date. async_to_sync(store_json_results_in_redis_and_s3)( all_roles, redis_key=cache_key, redis_data_type="hash", s3_bucket=config.get( "cache_roles_across_accounts.all_roles_combined.s3.bucket" ), s3_key=config.get( "cache_roles_across_accounts.all_roles_combined.s3.file", "account_resource_cache/cache_all_roles_v1.json.gz", ), ) stats.count(f"{function}.success") log_data["num_accounts"] = len(accounts_d) log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_managed_policies_for_account(account_id: str) -> Dict[str, Union[str, int]]: managed_policies: List[Dict] = get_all_managed_policies( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, ) all_policies: List = [] for policy in managed_policies: all_policies.append(policy.get("Arn")) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_managed_policies": len(all_policies), } log.debug(log_data) stats.count( "cache_managed_policies_for_account", tags={"account_id": account_id, "num_managed_policies": len(all_policies)}, ) policy_key = config.get("redis.iam_managed_policies_key", "IAM_MANAGED_POLICIES") red.hset(policy_key, account_id, json.dumps(all_policies)) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="managed_policies", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_policies, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=120) def cache_managed_policies_across_accounts() -> bool: function = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_managed_policies_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_managed_policies_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_s3_buckets_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_s3_buckets_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_s3_buckets_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_sqs_queues_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_sqs_queues_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_sqs_queues_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_sns_topics_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_sns_topics_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_sns_topics_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_sqs_queues_for_account(account_id: str) -> Dict[str, Union[str, int]]: all_queues: set = set() enabled_regions = async_to_sync(get_enabled_regions_for_account)(account_id) for region in enabled_regions: client = boto3_cached_conn( "sqs", account_number=account_id, assume_role=config.get("policies.role_name"), region=region, read_only=True, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) paginator = client.get_paginator("list_queues") response_iterator = paginator.paginate(PaginationConfig={"PageSize": 1000}) for res in response_iterator: for queue in res.get("QueueUrls", []): arn = f"arn:aws:sqs:{region}:{account_id}:{queue.split("/")[4]}" all_queues.add(arn) sqs_queue_key: str = config.get("redis.sqs_queues_key", "SQS_QUEUES") red.hset(sqs_queue_key, account_id, json.dumps(list(all_queues))) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_sqs_queues": len(all_queues), } log.debug(log_data) stats.count( "cache_sqs_queues_for_account", tags={"account_id": account_id, "number_sqs_queues": len(all_queues)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="sqs_queues", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_queues, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_sns_topics_for_account(account_id: str) -> Dict[str, Union[str, int]]: # Make sure it is regional all_topics: set = set() enabled_regions = async_to_sync(get_enabled_regions_for_account)(account_id) for region in enabled_regions: topics = list_topics( account_number=account_id, assume_role=config.get("policies.role_name"), region=region, read_only=True, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) for topic in topics: all_topics.add(topic["TopicArn"]) sns_topic_key: str = config.get("redis.sns_topics_key", "SNS_TOPICS") red.hset(sns_topic_key, account_id, json.dumps(list(all_topics))) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_sns_topics": len(all_topics), } log.debug(log_data) stats.count( "cache_sns_topics_for_account", tags={"account_id": account_id, "number_sns_topics": len(all_topics)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="sns_topics", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_topics, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_s3_buckets_for_account(account_id: str) -> Dict[str, Union[str, int]]: s3_buckets: List = list_buckets( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, read_only=True, ) buckets: List = [] for bucket in s3_buckets["Buckets"]: buckets.append(bucket["Name"]) s3_bucket_key: str = config.get("redis.s3_buckets_key", "S3_BUCKETS") red.hset(s3_bucket_key, account_id, json.dumps(buckets)) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_s3_buckets": len(buckets), } log.debug(log_data) stats.count( "cache_s3_buckets_for_account", tags={"account_id": account_id, "number_s3_buckets": len(buckets)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="s3_buckets", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( buckets, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @retry( stop_max_attempt_number=4, wait_exponential_multiplier=1000, wait_exponential_max=1000, ) def _scan_redis_iam_cache( cache_key: str, index: int, count: int ) -> Tuple[int, Dict[str, str]]: return red.hscan(cache_key, index, count=count) @app.task(soft_time_limit=1800) def clear_old_redis_iam_cache() -> bool: function = f"{__name__}.{sys._getframe().f_code.co_name}" # Do not run if this is not in the active region: if config.region != config.get("celery.active_region", config.region): return False # Need to loop over all items in the set: cache_key: str = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") index: int = 0 expire_ttl: int = int((datetime.utcnow() - timedelta(hours=6)).timestamp()) roles_to_expire = [] # We will loop over REDIS_IAM_COUNT items at a time: try: while True: results = _scan_redis_iam_cache(cache_key, index, REDIS_IAM_COUNT) index = results[0] # Verify if the role is too old: for arn, role in results[1].items(): role = json.loads(role) if role["ttl"] <= expire_ttl: roles_to_expire.append(arn) # We will be complete if the next index is 0: if not index: break except: # noqa log_data = { "function": function, "message": "Error retrieving roles from Redis for cache cleanup.", } log.error(log_data, exc_info=True) raise # Delete all the roles that we need to delete: try: if roles_to_expire: red.hdel(cache_key, *roles_to_expire) except: # noqa log_data = { "function": function, "message": "Error deleting roles from Redis for cache cleanup.", } log.error(log_data, exc_info=True) raise stats.count(f"{function}.success", tags={"expired_roles": len(roles_to_expire)}) return True @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_resources_from_aws_config_for_account(account_id) -> dict: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" s3_bucket = config.get("aws_config_cache.s3.bucket") s3_key = config.get( "aws_config_cache.s3.file", "aws_config_cache/cache_{account_id}_v1.json.gz" ).format(account_id=account_id) dynamo = UserDynamoHandler() # Only query in active region, otherwise get data from DDB if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: results = aws_config.query( config.get( "cache_all_resources_from_aws_config.aws_config.all_resources_query", "select * where accountId = '{account_id}'", ).format(account_id=account_id), use_aggregator=False, account_id=account_id, ) ttl: int = int((datetime.utcnow() + timedelta(hours=36)).timestamp()) redis_result_set = {} for result in results: result["ttl"] = ttl if result.get("arn"): if redis_result_set.get(result["arn"]): continue redis_result_set[result["arn"]] = json.dumps(result) if redis_result_set: async_to_sync(store_json_results_in_redis_and_s3)( redis_result_set, redis_key=config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ), redis_data_type="hash", s3_bucket=s3_bucket, s3_key=s3_key, ) dynamo.write_resource_cache_data(results) else: redis_result_set = async_to_sync(retrieve_json_data_from_redis_or_s3)( s3_bucket=s3_bucket, s3_key=s3_key ) async_to_sync(store_json_results_in_redis_and_s3)( redis_result_set, redis_key=config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ), redis_data_type="hash", ) log_data = { "function": function, "account_id": account_id, "number_resources_synced": len(redis_result_set), } log.debug(log_data) return log_data @app.task(soft_time_limit=3600) def cache_resources_from_aws_config_across_accounts() -> Dict[ str, Union[Union[str, int], Any] ]: function = f"{__name__}.{sys._getframe().f_code.co_name}" resource_redis_cache_key = config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ) log_data = { "function": function, "resource_redis_cache_key": resource_redis_cache_key, } tasks = [] # First, get list of accounts accounts_d = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") in ["prod", "dev"]: tasks.append(cache_resources_from_aws_config_for_account.s(account_id)) else: if account_id in config.get("celery.test_account_ids", []): tasks.append(cache_resources_from_aws_config_for_account.s(account_id)) if tasks: results = group(*tasks).apply_async() results.join() # Delete roles in Redis cache with expired TTL all_resources = red.hgetall(resource_redis_cache_key) if all_resources: expired_arns = [] for arn, resource_entry_j in all_resources.items(): resource_entry = ujson.loads(resource_entry_j) if datetime.fromtimestamp(resource_entry["ttl"]) < datetime.utcnow(): expired_arns.append(arn) if expired_arns: for expired_arn in expired_arns: all_resources.pop(expired_arn, None) red.hdel(resource_redis_cache_key, *expired_arns) log_data["number_of_resources"] = len(all_resources) # Cache all resource ARNs into a single file. Note: This runs synchronously with this task. This task triggers # resource collection on all accounts to happen asynchronously. That means when we store or delete data within # this task, we're always going to be caching the results from the previous task. if config.region == config.get( "celery.active_region", config.region ) or config.get("environment") in ["dev"]: # Refresh all resources after deletion of expired entries all_resources = red.hgetall(resource_redis_cache_key) s3_bucket = config.get("aws_config_cache_combined.s3.bucket") s3_key = config.get( "aws_config_cache_combined.s3.file", "aws_config_cache_combined/aws_config_resource_cache_combined_v1.json.gz", ) async_to_sync(store_json_results_in_redis_and_s3)( all_resources, s3_bucket=s3_bucket, s3_key=s3_key ) stats.count(f"{function}.success") return log_data @app.task(soft_time_limit=1800) def get_iam_role_limit() -> dict: """ This function will gather the number of existing IAM Roles and IAM Role quota in all owned AWS accounts. """ function: str = f"{__name__}.{sys._getframe().f_code.co_name}" num_accounts = 0 num_roles = 0 if not config.get("celery.get_iam_role_limit.enabled"): return {} success_message = "Not running - Inactive region" if config.region == config.get( "celery.active_region", config.region ) and config.get("environment") in ["prod", "dev"]: @sts_conn("iam") def _get_delivery_channels(**kwargs) -> list: """Gets the delivery channels in the account/region -- calls are wrapped with CloudAux""" return kwargs.pop("client").get_account_summary(**kwargs) success_message = "Task successfully completed" # First, get list of accounts accounts_d: Dict = async_to_sync(get_account_id_to_name_mapping)() num_accounts = len(accounts_d.keys()) for account_id, account_name in accounts_d.items(): try: iam_summary = _get_delivery_channels( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, ) num_iam_roles = iam_summary["SummaryMap"]["Roles"] iam_role_quota = iam_summary["SummaryMap"]["RolesQuota"] iam_role_quota_ratio = num_iam_roles / iam_role_quota num_roles += num_iam_roles log_data = { "function": function, "message": "IAM role quota for account", "num_iam_roles": num_iam_roles, "iam_role_quota": iam_role_quota, "iam_role_quota_ratio": iam_role_quota_ratio, "account_id": account_id, "account_name": account_name, } stats.gauge( f"{function}.quota_ratio_gauge", iam_role_quota_ratio, tags={ "num_iam_roles": num_iam_roles, "iam_role_quota": iam_role_quota, "account_id": account_id, "account_name": account_name, }, ) log.debug(log_data) except ClientError as e: log_data = { "function": function, "message": "Error retrieving IAM quota", "account_id": account_id, "account_name": account_name, "error": e, } stats.count(f"{function}.error", tags={"account_id": account_id}) log.error(log_data, exc_info=True) sentry_sdk.capture_exception() raise log_data = { "function": function, "num_accounts": num_accounts, "num_roles": num_roles, "message": success_message, } log.debug(log_data) return log_data @app.task(soft_time_limit=300) def cache_policy_requests() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" requests = async_to_sync(cache_all_policy_requests)() log_data = { "function": function, "num_requests": len(requests), "message": "Successfully cached requests", } return log_data @app.task(soft_time_limit=300) def cache_cloud_account_mapping() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" account_mapping = async_to_sync(cache_cloud_accounts)() log_data = { "function": function, "num_accounts": len(account_mapping.accounts), "message": "Successfully cached cloud account mapping", } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_credential_authorization_mapping() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" authorization_mapping = async_to_sync( generate_and_store_credential_authorization_mapping )() log_data = { "function": function, "message": "Successfully cached cloud credential authorization mapping", "num_group_authorizations": len(authorization_mapping), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_scps_across_organizations() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" scps = async_to_sync(cache_all_scps)() log_data = { "function": function, "message": "Successfully cached service control policies", "num_organizations": len(scps), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_organization_structure() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" org_structure = async_to_sync(cache_org_structure)() log_data = { "function": function, "message": "Successfully cached organization structure", "num_organizations": len(org_structure), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_resource_templates_task() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" templated_file_array = async_to_sync(cache_resource_templates)() log_data = { "function": function, "message": "Successfully cached resource templates", "num_templated_files": len(templated_file_array.templated_resources), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_self_service_typeahead_task() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" self_service_typeahead = async_to_sync(cache_self_service_typeahead)() log_data = { "function": function, "message": "Successfully cached roles and templates for self service typeahead", "num_templated_files": len(self_service_typeahead.typeahead_entries), } log.debug(log_data) return log_data schedule_30_minute = timedelta(seconds=1800) schedule_45_minute = timedelta(seconds=2700) schedule_6_hours = timedelta(hours=6) schedule_minute = timedelta(minutes=1) schedule_5_minutes = timedelta(minutes=5) schedule_24_hours = timedelta(hours=24) schedule_1_hour = timedelta(hours=1) if config.get("development", False): # If debug mode, we will set up the schedule to run the next minute after the job starts time_to_start = datetime.utcnow() + timedelta(minutes=1) dev_schedule = crontab(hour=time_to_start.hour, minute=time_to_start.minute) schedule_30_minute = dev_schedule schedule_45_minute = dev_schedule schedule_1_hour = dev_schedule schedule_6_hours = dev_schedule schedule_5_minutes = dev_schedule schedule = { "cache_roles_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_roles_across_accounts", "options": {"expires": 1000}, "schedule": schedule_45_minute, }, "clear_old_redis_iam_cache": { "task": "consoleme.celery_tasks.celery_tasks.clear_old_redis_iam_cache", "options": {"expires": 180}, "schedule": schedule_6_hours, }, "cache_policies_table_details": { "task": "consoleme.celery_tasks.celery_tasks.cache_policies_table_details", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, "report_celery_last_success_metrics": { "task": "consoleme.celery_tasks.celery_tasks.report_celery_last_success_metrics", "options": {"expires": 60}, "schedule": schedule_minute, }, "cache_managed_policies_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_managed_policies_across_accounts", "options": {"expires": 1000}, "schedule": schedule_45_minute, }, "cache_s3_buckets_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_s3_buckets_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "cache_sqs_queues_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_sqs_queues_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "cache_sns_topics_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_sns_topics_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "get_iam_role_limit": { "task": "consoleme.celery_tasks.celery_tasks.get_iam_role_limit", "options": {"expires": 300}, "schedule": schedule_24_hours, }, "cache_cloudtrail_errors_by_arn": { "task": "consoleme.celery_tasks.celery_tasks.cache_cloudtrail_errors_by_arn", "options": {"expires": 300}, "schedule": schedule_1_hour, }, "cache_resources_from_aws_config_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_resources_from_aws_config_across_accounts", "options": {"expires": 300}, "schedule": schedule_1_hour, }, "cache_policy_requests": { "task": "consoleme.celery_tasks.celery_tasks.cache_policy_requests", "options": {"expires": 1000}, "schedule": schedule_5_minutes, }, "cache_cloud_account_mapping": { "task": "consoleme.celery_tasks.celery_tasks.cache_cloud_account_mapping", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_credential_authorization_mapping": { "task": "consoleme.celery_tasks.celery_tasks.cache_credential_authorization_mapping", "options": {"expires": 1000}, "schedule": schedule_5_minutes, }, "cache_scps_across_organizations": { "task": "consoleme.celery_tasks.celery_tasks.cache_scps_across_organizations", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_organization_structure": { "task": "consoleme.celery_tasks.celery_tasks.cache_organization_structure", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_resource_templates_task": { "task": "consoleme.celery_tasks.celery_tasks.cache_resource_templates_task", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, "cache_self_service_typeahead_task": { "task": "consoleme.celery_tasks.celery_tasks.cache_self_service_typeahead_task", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, } if internal_celery_tasks and isinstance(internal_celery_tasks, dict): schedule = {**schedule, **internal_celery_tasks} if config.get("celery.clear_tasks_for_development", False): schedule = {} app.conf.beat_schedule = schedule app.conf.timezone = "UTC"

""" This module controls defines celery tasks and their applicable schedules. The celery beat server and workers will start when invoked. Please add internal-only celery tasks to the celery_tasks plugin. When ran in development mode (CONFIG_LOCATION=<location of development.yaml configuration file. To run both the celery beat scheduler and a worker simultaneously, and to have jobs kick off starting at the next minute, run the following command: celery -A consoleme.celery_tasks.celery_tasks worker --loglevel=info -l DEBUG -B """ from __future__ import absolute_import import json # We use a separate SetEncoder here so we cannot use ujson import os import sys import time from datetime import datetime, timedelta from typing import Any, Dict, List, Tuple, Union import celery import sentry_sdk import ujson from asgiref.sync import async_to_sync from billiard.exceptions import SoftTimeLimitExceeded from botocore.exceptions import ClientError from celery import group from celery.app.task import Context from celery.concurrency import asynpool from celery.schedules import crontab from celery.signals import ( task_failure, task_prerun, task_received, task_rejected, task_retry, task_revoked, task_success, task_unknown, ) from cloudaux import sts_conn from cloudaux.aws.iam import get_all_managed_policies from cloudaux.aws.s3 import list_buckets from cloudaux.aws.sns import list_topics from cloudaux.aws.sts import boto3_cached_conn from retrying import retry from sentry_sdk.integrations.aiohttp import AioHttpIntegration from sentry_sdk.integrations.celery import CeleryIntegration from sentry_sdk.integrations.redis import RedisIntegration from sentry_sdk.integrations.tornado import TornadoIntegration from consoleme.config import config from consoleme.lib.account_indexers import ( cache_cloud_accounts, get_account_id_to_name_mapping, ) from consoleme.lib.aws import ( cache_all_scps, cache_org_structure, get_enabled_regions_for_account, ) from consoleme.lib.aws_config import aws_config from consoleme.lib.cache import ( retrieve_json_data_from_redis_or_s3, store_json_results_in_redis_and_s3, ) from consoleme.lib.cloud_credential_authorization_mapping import ( generate_and_store_credential_authorization_mapping, ) from consoleme.lib.dynamo import IAMRoleDynamoHandler, UserDynamoHandler from consoleme.lib.git import store_iam_resources_in_git from consoleme.lib.plugins import get_plugin_by_name from consoleme.lib.policies import get_aws_config_history_url_for_resource from consoleme.lib.redis import RedisHandler from consoleme.lib.requests import cache_all_policy_requests from consoleme.lib.self_service.typeahead import cache_self_service_typeahead from consoleme.lib.templated_resources import cache_resource_templates from consoleme.lib.timeout import Timeout asynpool.PROC_ALIVE_TIMEOUT = config.get("celery.asynpool_proc_alive_timeout", 60.0) default_retry_kwargs = { "autoretry_for": (Exception,), "retry_backoff": True, "retry_kwargs": {"max_retries": config.get("celery.default_max_retries", 5)}, } class Celery(celery.Celery): def on_configure(self) -> None: sentry_dsn = config.get("sentry.dsn") if sentry_dsn: sentry_sdk.init( sentry_dsn, integrations=[ TornadoIntegration(), CeleryIntegration(), AioHttpIntegration(), RedisIntegration(), ], ) app = Celery( "tasks", broker=config.get( f"celery.broker.{config.region}", config.get("celery.broker.global", "redis://127.0.0.1:6379/1"), ), backend=config.get( f"celery.backend.{config.region}", config.get("celery.broker.global", "redis://127.0.0.1:6379/2"), ), ) if config.get("redis.use_redislite"): import tempfile import redislite redislite_db_path = os.path.join( config.get("redis.redislite.db_path", tempfile.NamedTemporaryFile().name) ) redislite_client = redislite.Redis(redislite_db_path) redislite_socket_path = f"redis+socket://{redislite_client.socket_file}" app = Celery( "tasks", broker=config.get(f"{redislite_socket_path}/1"), backend=config.get(f"{redislite_socket_path}/2"), ) app.conf.result_expires = config.get("celery.result_expires", 60) app.conf.worker_prefetch_multiplier = config.get("celery.worker_prefetch_multiplier", 4) app.conf.task_acks_late = config.get("celery.task_acks_late", True) if config.get("celery.purge") and not config.get("redis.use_redislite"): # Useful to clear celery queue in development with Timeout(seconds=5, error_message="Timeout: Are you sure Redis is running?"): app.control.purge() log = config.get_logger() red = RedisHandler().redis_sync() aws = get_plugin_by_name(config.get("plugins.aws", "default_aws")) auth = get_plugin_by_name(config.get("plugins.auth", "default_auth"))() group_mapping = get_plugin_by_name( config.get("plugins.group_mapping", "default_group_mapping") )() internal_celery_tasks = get_plugin_by_name( config.get("plugins.internal_celery_tasks", "default_celery_tasks") ) stats = get_plugin_by_name(config.get("plugins.metrics", "default_metrics"))() internal_policies = get_plugin_by_name( config.get("plugins.internal_policies", "default_policies") )() REDIS_IAM_COUNT = 1000 @app.task(soft_time_limit=20) def report_celery_last_success_metrics() -> bool: """ For each celery task, this will determine the number of seconds since it has last been successful. Celery tasks should be emitting redis stats with a deterministic key (In our case, `f"{task}.last_success"`. report_celery_last_success_metrics should be ran periodically to emit metrics on when a task was last successful. We can then alert when tasks are not ran when intended. We should also alert when no metrics are emitted from this function. """ function = f"{__name__}.{sys._getframe().f_code.co_name}" log_data = {"function": function} current_time = int(time.time()) global schedule for _, t in schedule.items(): task = t.get("task") last_success = int(red.get(f"{task}.last_success") or 0) if last_success == 0: log_data["message"] = "Last Success Value is 0" log_data["task_last_success_key"] = f"{task}.last_success" log.error(log_data) stats.gauge(f"{task}.time_since_last_success", current_time - last_success) red.set(f"{task}.time_since_last_success", current_time - last_success) red.set( f"{function}.last_success", int(time.time()) ) # Alert if this metric is not seen stats.count(f"{function}.success") stats.timer("worker.healthy") return True def get_celery_request_tags(**kwargs): request = kwargs.get("request") sender_hostname = "unknown" sender = kwargs.get("sender") if sender: try: sender_hostname = sender.hostname except AttributeError: sender_hostname = vars(sender.request).get("origin", "unknown") if request and not isinstance( request, Context ): # unlike others, task_revoked sends a Context for `request` task_name = request.name task_id = request.id receiver_hostname = request.hostname else: try: task_name = sender.name except AttributeError: task_name = kwargs.pop("name", "") try: task_id = sender.request.id except AttributeError: task_id = kwargs.pop("id", "") try: receiver_hostname = sender.request.hostname except AttributeError: receiver_hostname = "" tags = { "task_name": task_name, "task_id": task_id, "sender_hostname": sender_hostname, "receiver_hostname": receiver_hostname, } tags["expired"] = kwargs.get("expired", False) exception = kwargs.get("exception") if not exception: exception = kwargs.get("exc") if exception: tags["error"] = repr(exception) if isinstance(exception, SoftTimeLimitExceeded): tags["timed_out"] = True return tags @task_prerun.connect def refresh_dynamic_config_in_worker(**kwargs): tags = get_celery_request_tags(**kwargs) log_data = {"function": f"{__name__}.{sys._getframe().f_code.co_name}"} dynamic_config = red.get("DYNAMIC_CONFIG_CACHE") if not dynamic_config: log.error({**log_data, "error": "Unable to retrieve Dynamic Config from Redis"}) return dynamic_config_j = json.loads(dynamic_config) if config.CONFIG.config.get("dynamic_config", {}) != dynamic_config_j: log.debug( { **log_data, **tags, "message": "Refreshing dynamic configuration on Celery Worker", } ) config.CONFIG.config["dynamic_config"] = dynamic_config_j @task_received.connect def report_number_pending_tasks(**kwargs): """ Report the number of pending tasks to our metrics broker every time a task is published. This metric can be used for autoscaling workers. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-received :param sender: :param headers: :param body: :param kwargs: :return: """ tags = get_celery_request_tags(**kwargs) tags.pop("task_id", None) stats.timer("celery.new_pending_task", tags=tags) @task_success.connect def report_successful_task(**kwargs): """ Report a generic success metric as tasks to our metrics broker every time a task finished correctly. This metric can be used for autoscaling workers. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-success :param sender: :param headers: :param body: :param kwargs: :return: """ tags = get_celery_request_tags(**kwargs) red.set(f"{tags['task_name']}.last_success", int(time.time())) tags.pop("error", None) tags.pop("task_id", None) stats.timer("celery.successful_task", tags=tags) @task_retry.connect def report_task_retry(**kwargs): """ Report a generic retry metric as tasks to our metrics broker every time a task is retroed. This metric can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-retry :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Retry", } # Add traceback if exception info is in the kwargs einfo = kwargs.get("einfo") if einfo: log_data["traceback"] = einfo.traceback error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.retried_task", tags=error_tags) @task_failure.connect def report_failed_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task fails. This is also called when a task has hit a SoftTimeLimit. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-failure :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Failure", } # Add traceback if exception info is in the kwargs einfo = kwargs.get("einfo") if einfo: log_data["traceback"] = einfo.traceback error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.failed_task", tags=error_tags) @task_unknown.connect def report_unknown_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a worker receives an unknown task. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-unknown :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Unknown", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.unknown_task", tags=error_tags) @task_rejected.connect def report_rejected_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task is rejected. The metric emited by this function can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-rejected :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Rejected", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.rejected_task", tags=error_tags) @task_revoked.connect def report_revoked_task(**kwargs): """ Report a generic failure metric as tasks to our metrics broker every time a task is revoked. This metric can be used for alerting. https://docs.celeryproject.org/en/latest/userguide/signals.html#task-revoked :param sender: :param headers: :param body: :param kwargs: :return: """ log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "message": "Celery Task Revoked", } error_tags = get_celery_request_tags(**kwargs) log_data.update(error_tags) log.error(log_data) error_tags.pop("error", None) error_tags.pop("task_id", None) stats.timer("celery.revoked_task", tags=error_tags) @retry( stop_max_attempt_number=4, wait_exponential_multiplier=1000, wait_exponential_max=1000, ) def _add_role_to_redis(redis_key: str, role_entry: Dict) -> None: """ This function will add IAM role data to redis so that policy details can be quickly retrieved by the policies endpoint. IAM role data is stored in the `redis_key` redis key by the role's ARN. Parameters ---------- redis_key : str The redis key (hash) role_entry : Dict The role entry Example: {'name': 'nameOfRole', 'accountId': '123456789012', 'arn': 'arn:aws:iam::123456789012:role/nameOfRole', 'templated': None, 'ttl': 1562510908, 'policy': '<json_formatted_policy>'} """ try: red.hset(redis_key, role_entry["arn"], json.dumps(role_entry)) except Exception as e: # noqa stats.count( "cache_roles_for_account.error", tags={"redis_key": redis_key, "error": str(e), "role_entry": role_entry}, ) log_data = { "message": "Error syncing Account's IAM roles to Redis", "account_id": role_entry["account_id"], "arn": role_entry["arn"], "role_entry": role_entry, } log.error(log_data, exc_info=True) raise @app.task(soft_time_limit=3600) def cache_cloudtrail_errors_by_arn() -> Dict: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" log_data: Dict = {"function": function} cloudtrail_errors: Dict = internal_policies.error_count_by_arn() if not cloudtrail_errors: cloudtrail_errors = {} red.setex( config.get( "celery.cache_cloudtrail_errors_by_arn.redis_key", "CLOUDTRAIL_ERRORS_BY_ARN", ), 86400, json.dumps(cloudtrail_errors), ) log_data["number_of_roles_with_errors"]: len(cloudtrail_errors.keys()) log.debug(log_data) return log_data @app.task(soft_time_limit=1800) def cache_policies_table_details() -> bool: iam_role_redis_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") all_iam_roles = red.hgetall(iam_role_redis_key) items = [] accounts_d = async_to_sync(get_account_id_to_name_mapping)() cloudtrail_errors = {} cloudtrail_errors_j = red.get( config.get( "celery.cache_cloudtrail_errors_by_arn.redis_key", "CLOUDTRAIL_ERRORS_BY_ARN", ) ) if cloudtrail_errors_j: cloudtrail_errors = json.loads(cloudtrail_errors_j) s3_error_topic = config.get("redis.s3_errors", "S3_ERRORS") all_s3_errors = red.get(s3_error_topic) s3_errors = {} if all_s3_errors: s3_errors = json.loads(all_s3_errors) for arn, role_details_j in all_iam_roles.items(): role_details = ujson.loads(role_details_j) error_count = cloudtrail_errors.get(arn, 0) s3_errors_for_arn = s3_errors.get(arn, []) for error in s3_errors_for_arn: error_count += int(error.get("count")) account_id = arn.split(":")[4] account_name = accounts_d.get(str(account_id), "Unknown") resource_id = role_details.get("resourceId") items.append( { "account_id": account_id, "account_name": account_name, "arn": arn, "technology": "iam", "templated": red.hget( config.get("templated_roles.redis_key", "TEMPLATED_ROLES_v2"), arn.lower(), ), "errors": error_count, "config_history_url": async_to_sync( get_aws_config_history_url_for_resource )(account_id, resource_id, arn, "AWS::IAM::Role"), } ) s3_bucket_key: str = config.get("redis.s3_bucket_key", "S3_BUCKETS") s3_accounts = red.hkeys(s3_bucket_key) if s3_accounts: for account in s3_accounts: account_name = accounts_d.get(str(account), "Unknown") buckets = json.loads(red.hget(s3_bucket_key, account)) for bucket in buckets: bucket_arn = f"arn:aws:s3:::{bucket}" s3_errors_for_arn = s3_errors.get(bucket_arn, []) error_count = 0 for error in s3_errors_for_arn: error_count += int(error.get("count")) items.append( { "account_id": account, "account_name": account_name, "arn": f"arn:aws:s3:::{bucket}", "technology": "s3", "templated": None, "errors": error_count, } ) sns_topic_key: str = config.get("redis.sns_topics_key", "SNS_TOPICS") sns_accounts = red.hkeys(sns_topic_key) if sns_accounts: for account in sns_accounts: account_name = accounts_d.get(str(account), "Unknown") topics = json.loads(red.hget(sns_topic_key, account)) for topic in topics: error_count = 0 items.append( { "account_id": account, "account_name": account_name, "arn": topic, "technology": "sns", "templated": None, "errors": error_count, } ) sqs_queue_key: str = config.get("redis.sqs_queues_key", "SQS_QUEUES") sqs_accounts = red.hkeys(sqs_queue_key) if sqs_accounts: for account in sqs_accounts: account_name = accounts_d.get(str(account), "Unknown") queues = json.loads(red.hget(sqs_queue_key, account)) for queue in queues: error_count = 0 items.append( { "account_id": account, "account_name": account_name, "arn": queue, "technology": "sqs", "templated": None, "errors": error_count, } ) resources_from_aws_config_redis_key: str = config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ) resources_from_aws_config = red.hgetall(resources_from_aws_config_redis_key) if resources_from_aws_config: for arn, value in resources_from_aws_config.items(): resource = json.loads(value) technology = resource["resourceType"] # Skip technologies that we retrieve directly if technology in [ "AWS::IAM::Role", "AWS::SQS::Queue", "AWS::SNS::Topic", "AWS::S3::Bucket", ]: continue account_id = arn.split(":")[4] account_name = accounts_d.get(account_id, "Unknown") items.append( { "account_id": account_id, "account_name": account_name, "arn": arn, "technology": technology, "templated": None, "errors": 0, } ) s3_bucket = None s3_key = None if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("cache_policies_table_details.s3.bucket") s3_key = config.get( "cache_policies_table_details.s3.file", "policies_table/cache_policies_table_details_v1.json.gz", ) async_to_sync(store_json_results_in_redis_and_s3)( items, redis_key=config.get("policies.redis_policies_key", "ALL_POLICIES"), s3_bucket=s3_bucket, s3_key=s3_key, ) stats.count( "cache_policies_table_details.success", tags={"num_roles": len(all_iam_roles.keys())}, ) return True @app.task(soft_time_limit=2700, **default_retry_kwargs) def cache_roles_for_account(account_id: str) -> bool: # Get the DynamoDB handler: dynamo = IAMRoleDynamoHandler() cache_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") # Only query IAM and put data in Dynamo if we're in the active region if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: client = boto3_cached_conn( "iam", account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) paginator = client.get_paginator("get_account_authorization_details") response_iterator = paginator.paginate() all_iam_resources = {} for response in response_iterator: if not all_iam_resources: all_iam_resources = response else: all_iam_resources["UserDetailList"].extend(response["UserDetailList"]) all_iam_resources["GroupDetailList"].extend(response["GroupDetailList"]) all_iam_resources["RoleDetailList"].extend(response["RoleDetailList"]) all_iam_resources["Policies"].extend(response["Policies"]) for k in response.keys(): if k not in [ "UserDetailList", "GroupDetailList", "RoleDetailList", "Policies", "ResponseMetadata", "Marker", "IsTruncated", ]: # Fail hard if we find something unexpected raise RuntimeError("Unexpected key {0} in response".format(k)) # Store entire response in S3 async_to_sync(store_json_results_in_redis_and_s3)( all_iam_resources, s3_bucket=config.get("cache_iam_resources_for_account.s3.bucket"), s3_key=config.get( "cache_iam_resources_for_account.s3.file", "get_account_authorization_details/get_account_authorization_details_{account_id}_v1.json.gz", ).format(account_id=account_id), ) iam_roles = all_iam_resources["RoleDetailList"] async_to_sync(store_json_results_in_redis_and_s3)( iam_roles, s3_bucket=config.get("cache_roles_for_account.s3.bucket"), s3_key=config.get( "cache_roles_for_account.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="iam_roles", account_id=account_id), ) ttl: int = int((datetime.utcnow() + timedelta(hours=36)).timestamp()) # Save them: for role in iam_roles: role_entry = { "arn": role.get("Arn"), "name": role.get("RoleName"), "resourceId": role.get("RoleId"), "accountId": account_id, "ttl": ttl, "policy": dynamo.convert_role_to_json(role), "templated": red.hget( config.get("templated_roles.redis_key", "TEMPLATED_ROLES_v2"), role.get("Arn").lower(), ), } # DynamoDB: dynamo.sync_iam_role_for_account(role_entry) # Redis: _add_role_to_redis(cache_key, role_entry) # Run internal function on role. This can be used to inspect roles, add managed policies, or other actions aws().handle_detected_role(role) # Maybe store all resources in git if config.get("cache_iam_resources_for_account.store_in_git.enabled"): store_iam_resources_in_git(all_iam_resources, account_id) stats.count("cache_roles_for_account.success", tags={"account_id": account_id}) return True @app.task(soft_time_limit=3600) def cache_roles_across_accounts() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" cache_key = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") log_data = {"function": function, "cache_key": cache_key} accounts_d = async_to_sync(get_account_id_to_name_mapping)() tasks = [] if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev"]: # First, get list of accounts # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") in ["prod", "dev"]: tasks.append(cache_roles_for_account.s(account_id)) else: if account_id in config.get("celery.test_account_ids", []): tasks.append(cache_roles_for_account.s(account_id)) results = group(*tasks).apply_async() # results.join() forces function to wait until all tasks are complete results.join() else: dynamo = IAMRoleDynamoHandler() # In non-active regions, we just want to sync DDB data to Redis roles = dynamo.fetch_all_roles() for role_entry in roles: _add_role_to_redis(cache_key, role_entry) # Delete roles in Redis cache with expired TTL all_roles = red.hgetall(cache_key) roles_to_delete_from_cache = [] for arn, role_entry_j in all_roles.items(): role_entry = json.loads(role_entry_j) if datetime.fromtimestamp(role_entry["ttl"]) < datetime.utcnow(): roles_to_delete_from_cache.append(arn) if roles_to_delete_from_cache: red.hdel(cache_key, *roles_to_delete_from_cache) for arn in roles_to_delete_from_cache: all_roles.pop(arn, None) log_data["num_roles"] = len(all_roles) # Store full list of roles in a single place. This list will be ~30 minutes out of date. async_to_sync(store_json_results_in_redis_and_s3)( all_roles, redis_key=cache_key, redis_data_type="hash", s3_bucket=config.get( "cache_roles_across_accounts.all_roles_combined.s3.bucket" ), s3_key=config.get( "cache_roles_across_accounts.all_roles_combined.s3.file", "account_resource_cache/cache_all_roles_v1.json.gz", ), ) stats.count(f"{function}.success") log_data["num_accounts"] = len(accounts_d) log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_managed_policies_for_account(account_id: str) -> Dict[str, Union[str, int]]: managed_policies: List[Dict] = get_all_managed_policies( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, ) all_policies: List = [] for policy in managed_policies: all_policies.append(policy.get("Arn")) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_managed_policies": len(all_policies), } log.debug(log_data) stats.count( "cache_managed_policies_for_account", tags={"account_id": account_id, "num_managed_policies": len(all_policies)}, ) policy_key = config.get("redis.iam_managed_policies_key", "IAM_MANAGED_POLICIES") red.hset(policy_key, account_id, json.dumps(all_policies)) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="managed_policies", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_policies, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=120) def cache_managed_policies_across_accounts() -> bool: function = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_managed_policies_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_managed_policies_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_s3_buckets_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_s3_buckets_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_s3_buckets_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_sqs_queues_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_sqs_queues_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_sqs_queues_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=120) def cache_sns_topics_across_accounts() -> bool: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" # First, get list of accounts accounts_d: List = async_to_sync(get_account_id_to_name_mapping)() for account_id in accounts_d.keys(): if config.get("environment") == "prod": cache_sns_topics_for_account.delay(account_id) else: if account_id in config.get("celery.test_account_ids", []): cache_sns_topics_for_account.delay(account_id) stats.count(f"{function}.success") return True @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_sqs_queues_for_account(account_id: str) -> Dict[str, Union[str, int]]: all_queues: set = set() enabled_regions = async_to_sync(get_enabled_regions_for_account)(account_id) for region in enabled_regions: client = boto3_cached_conn( "sqs", account_number=account_id, assume_role=config.get("policies.role_name"), region=region, read_only=True, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) paginator = client.get_paginator("list_queues") response_iterator = paginator.paginate(PaginationConfig={"PageSize": 1000}) for res in response_iterator: for queue in res.get("QueueUrls", []): arn = f"arn:aws:sqs:{region}:{account_id}:{queue.split('/')[4]}" all_queues.add(arn) sqs_queue_key: str = config.get("redis.sqs_queues_key", "SQS_QUEUES") red.hset(sqs_queue_key, account_id, json.dumps(list(all_queues))) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_sqs_queues": len(all_queues), } log.debug(log_data) stats.count( "cache_sqs_queues_for_account", tags={"account_id": account_id, "number_sqs_queues": len(all_queues)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="sqs_queues", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_queues, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_sns_topics_for_account(account_id: str) -> Dict[str, Union[str, int]]: # Make sure it is regional all_topics: set = set() enabled_regions = async_to_sync(get_enabled_regions_for_account)(account_id) for region in enabled_regions: topics = list_topics( account_number=account_id, assume_role=config.get("policies.role_name"), region=region, read_only=True, sts_client_kwargs=dict( region_name=config.region, endpoint_url=f"https://sts.{config.region}.amazonaws.com", ), ) for topic in topics: all_topics.add(topic["TopicArn"]) sns_topic_key: str = config.get("redis.sns_topics_key", "SNS_TOPICS") red.hset(sns_topic_key, account_id, json.dumps(list(all_topics))) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_sns_topics": len(all_topics), } log.debug(log_data) stats.count( "cache_sns_topics_for_account", tags={"account_id": account_id, "number_sns_topics": len(all_topics)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="sns_topics", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( all_topics, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_s3_buckets_for_account(account_id: str) -> Dict[str, Union[str, int]]: s3_buckets: List = list_buckets( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, read_only=True, ) buckets: List = [] for bucket in s3_buckets["Buckets"]: buckets.append(bucket["Name"]) s3_bucket_key: str = config.get("redis.s3_buckets_key", "S3_BUCKETS") red.hset(s3_bucket_key, account_id, json.dumps(buckets)) log_data = { "function": f"{__name__}.{sys._getframe().f_code.co_name}", "account_id": account_id, "number_s3_buckets": len(buckets), } log.debug(log_data) stats.count( "cache_s3_buckets_for_account", tags={"account_id": account_id, "number_s3_buckets": len(buckets)}, ) if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: s3_bucket = config.get("account_resource_cache.s3.bucket") s3_key = config.get( "account_resource_cache.s3.file", "account_resource_cache/cache_{resource_type}_{account_id}_v1.json.gz", ).format(resource_type="s3_buckets", account_id=account_id) async_to_sync(store_json_results_in_redis_and_s3)( buckets, s3_bucket=s3_bucket, s3_key=s3_key ) return log_data @retry( stop_max_attempt_number=4, wait_exponential_multiplier=1000, wait_exponential_max=1000, ) def _scan_redis_iam_cache( cache_key: str, index: int, count: int ) -> Tuple[int, Dict[str, str]]: return red.hscan(cache_key, index, count=count) @app.task(soft_time_limit=1800) def clear_old_redis_iam_cache() -> bool: function = f"{__name__}.{sys._getframe().f_code.co_name}" # Do not run if this is not in the active region: if config.region != config.get("celery.active_region", config.region): return False # Need to loop over all items in the set: cache_key: str = config.get("aws.iamroles_redis_key", "IAM_ROLE_CACHE") index: int = 0 expire_ttl: int = int((datetime.utcnow() - timedelta(hours=6)).timestamp()) roles_to_expire = [] # We will loop over REDIS_IAM_COUNT items at a time: try: while True: results = _scan_redis_iam_cache(cache_key, index, REDIS_IAM_COUNT) index = results[0] # Verify if the role is too old: for arn, role in results[1].items(): role = json.loads(role) if role["ttl"] <= expire_ttl: roles_to_expire.append(arn) # We will be complete if the next index is 0: if not index: break except: # noqa log_data = { "function": function, "message": "Error retrieving roles from Redis for cache cleanup.", } log.error(log_data, exc_info=True) raise # Delete all the roles that we need to delete: try: if roles_to_expire: red.hdel(cache_key, *roles_to_expire) except: # noqa log_data = { "function": function, "message": "Error deleting roles from Redis for cache cleanup.", } log.error(log_data, exc_info=True) raise stats.count(f"{function}.success", tags={"expired_roles": len(roles_to_expire)}) return True @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_resources_from_aws_config_for_account(account_id) -> dict: function: str = f"{__name__}.{sys._getframe().f_code.co_name}" s3_bucket = config.get("aws_config_cache.s3.bucket") s3_key = config.get( "aws_config_cache.s3.file", "aws_config_cache/cache_{account_id}_v1.json.gz" ).format(account_id=account_id) dynamo = UserDynamoHandler() # Only query in active region, otherwise get data from DDB if config.region == config.get("celery.active_region", config.region) or config.get( "environment" ) in ["dev", "test"]: results = aws_config.query( config.get( "cache_all_resources_from_aws_config.aws_config.all_resources_query", "select * where accountId = '{account_id}'", ).format(account_id=account_id), use_aggregator=False, account_id=account_id, ) ttl: int = int((datetime.utcnow() + timedelta(hours=36)).timestamp()) redis_result_set = {} for result in results: result["ttl"] = ttl if result.get("arn"): if redis_result_set.get(result["arn"]): continue redis_result_set[result["arn"]] = json.dumps(result) if redis_result_set: async_to_sync(store_json_results_in_redis_and_s3)( redis_result_set, redis_key=config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ), redis_data_type="hash", s3_bucket=s3_bucket, s3_key=s3_key, ) dynamo.write_resource_cache_data(results) else: redis_result_set = async_to_sync(retrieve_json_data_from_redis_or_s3)( s3_bucket=s3_bucket, s3_key=s3_key ) async_to_sync(store_json_results_in_redis_and_s3)( redis_result_set, redis_key=config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ), redis_data_type="hash", ) log_data = { "function": function, "account_id": account_id, "number_resources_synced": len(redis_result_set), } log.debug(log_data) return log_data @app.task(soft_time_limit=3600) def cache_resources_from_aws_config_across_accounts() -> Dict[ str, Union[Union[str, int], Any] ]: function = f"{__name__}.{sys._getframe().f_code.co_name}" resource_redis_cache_key = config.get( "aws_config_cache.redis_key", "AWSCONFIG_RESOURCE_CACHE" ) log_data = { "function": function, "resource_redis_cache_key": resource_redis_cache_key, } tasks = [] # First, get list of accounts accounts_d = async_to_sync(get_account_id_to_name_mapping)() # Second, call tasks to enumerate all the roles across all accounts for account_id in accounts_d.keys(): if config.get("environment") in ["prod", "dev"]: tasks.append(cache_resources_from_aws_config_for_account.s(account_id)) else: if account_id in config.get("celery.test_account_ids", []): tasks.append(cache_resources_from_aws_config_for_account.s(account_id)) if tasks: results = group(*tasks).apply_async() results.join() # Delete roles in Redis cache with expired TTL all_resources = red.hgetall(resource_redis_cache_key) if all_resources: expired_arns = [] for arn, resource_entry_j in all_resources.items(): resource_entry = ujson.loads(resource_entry_j) if datetime.fromtimestamp(resource_entry["ttl"]) < datetime.utcnow(): expired_arns.append(arn) if expired_arns: for expired_arn in expired_arns: all_resources.pop(expired_arn, None) red.hdel(resource_redis_cache_key, *expired_arns) log_data["number_of_resources"] = len(all_resources) # Cache all resource ARNs into a single file. Note: This runs synchronously with this task. This task triggers # resource collection on all accounts to happen asynchronously. That means when we store or delete data within # this task, we're always going to be caching the results from the previous task. if config.region == config.get( "celery.active_region", config.region ) or config.get("environment") in ["dev"]: # Refresh all resources after deletion of expired entries all_resources = red.hgetall(resource_redis_cache_key) s3_bucket = config.get("aws_config_cache_combined.s3.bucket") s3_key = config.get( "aws_config_cache_combined.s3.file", "aws_config_cache_combined/aws_config_resource_cache_combined_v1.json.gz", ) async_to_sync(store_json_results_in_redis_and_s3)( all_resources, s3_bucket=s3_bucket, s3_key=s3_key ) stats.count(f"{function}.success") return log_data @app.task(soft_time_limit=1800) def get_iam_role_limit() -> dict: """ This function will gather the number of existing IAM Roles and IAM Role quota in all owned AWS accounts. """ function: str = f"{__name__}.{sys._getframe().f_code.co_name}" num_accounts = 0 num_roles = 0 if not config.get("celery.get_iam_role_limit.enabled"): return {} success_message = "Not running - Inactive region" if config.region == config.get( "celery.active_region", config.region ) and config.get("environment") in ["prod", "dev"]: @sts_conn("iam") def _get_delivery_channels(**kwargs) -> list: """Gets the delivery channels in the account/region -- calls are wrapped with CloudAux""" return kwargs.pop("client").get_account_summary(**kwargs) success_message = "Task successfully completed" # First, get list of accounts accounts_d: Dict = async_to_sync(get_account_id_to_name_mapping)() num_accounts = len(accounts_d.keys()) for account_id, account_name in accounts_d.items(): try: iam_summary = _get_delivery_channels( account_number=account_id, assume_role=config.get("policies.role_name"), region=config.region, ) num_iam_roles = iam_summary["SummaryMap"]["Roles"] iam_role_quota = iam_summary["SummaryMap"]["RolesQuota"] iam_role_quota_ratio = num_iam_roles / iam_role_quota num_roles += num_iam_roles log_data = { "function": function, "message": "IAM role quota for account", "num_iam_roles": num_iam_roles, "iam_role_quota": iam_role_quota, "iam_role_quota_ratio": iam_role_quota_ratio, "account_id": account_id, "account_name": account_name, } stats.gauge( f"{function}.quota_ratio_gauge", iam_role_quota_ratio, tags={ "num_iam_roles": num_iam_roles, "iam_role_quota": iam_role_quota, "account_id": account_id, "account_name": account_name, }, ) log.debug(log_data) except ClientError as e: log_data = { "function": function, "message": "Error retrieving IAM quota", "account_id": account_id, "account_name": account_name, "error": e, } stats.count(f"{function}.error", tags={"account_id": account_id}) log.error(log_data, exc_info=True) sentry_sdk.capture_exception() raise log_data = { "function": function, "num_accounts": num_accounts, "num_roles": num_roles, "message": success_message, } log.debug(log_data) return log_data @app.task(soft_time_limit=300) def cache_policy_requests() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" requests = async_to_sync(cache_all_policy_requests)() log_data = { "function": function, "num_requests": len(requests), "message": "Successfully cached requests", } return log_data @app.task(soft_time_limit=300) def cache_cloud_account_mapping() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" account_mapping = async_to_sync(cache_cloud_accounts)() log_data = { "function": function, "num_accounts": len(account_mapping.accounts), "message": "Successfully cached cloud account mapping", } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_credential_authorization_mapping() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" authorization_mapping = async_to_sync( generate_and_store_credential_authorization_mapping )() log_data = { "function": function, "message": "Successfully cached cloud credential authorization mapping", "num_group_authorizations": len(authorization_mapping), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_scps_across_organizations() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" scps = async_to_sync(cache_all_scps)() log_data = { "function": function, "message": "Successfully cached service control policies", "num_organizations": len(scps), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_organization_structure() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" org_structure = async_to_sync(cache_org_structure)() log_data = { "function": function, "message": "Successfully cached organization structure", "num_organizations": len(org_structure), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_resource_templates_task() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" templated_file_array = async_to_sync(cache_resource_templates)() log_data = { "function": function, "message": "Successfully cached resource templates", "num_templated_files": len(templated_file_array.templated_resources), } log.debug(log_data) return log_data @app.task(soft_time_limit=1800, **default_retry_kwargs) def cache_self_service_typeahead_task() -> Dict: function = f"{__name__}.{sys._getframe().f_code.co_name}" self_service_typeahead = async_to_sync(cache_self_service_typeahead)() log_data = { "function": function, "message": "Successfully cached roles and templates for self service typeahead", "num_templated_files": len(self_service_typeahead.typeahead_entries), } log.debug(log_data) return log_data schedule_30_minute = timedelta(seconds=1800) schedule_45_minute = timedelta(seconds=2700) schedule_6_hours = timedelta(hours=6) schedule_minute = timedelta(minutes=1) schedule_5_minutes = timedelta(minutes=5) schedule_24_hours = timedelta(hours=24) schedule_1_hour = timedelta(hours=1) if config.get("development", False): # If debug mode, we will set up the schedule to run the next minute after the job starts time_to_start = datetime.utcnow() + timedelta(minutes=1) dev_schedule = crontab(hour=time_to_start.hour, minute=time_to_start.minute) schedule_30_minute = dev_schedule schedule_45_minute = dev_schedule schedule_1_hour = dev_schedule schedule_6_hours = dev_schedule schedule_5_minutes = dev_schedule schedule = { "cache_roles_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_roles_across_accounts", "options": {"expires": 1000}, "schedule": schedule_45_minute, }, "clear_old_redis_iam_cache": { "task": "consoleme.celery_tasks.celery_tasks.clear_old_redis_iam_cache", "options": {"expires": 180}, "schedule": schedule_6_hours, }, "cache_policies_table_details": { "task": "consoleme.celery_tasks.celery_tasks.cache_policies_table_details", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, "report_celery_last_success_metrics": { "task": "consoleme.celery_tasks.celery_tasks.report_celery_last_success_metrics", "options": {"expires": 60}, "schedule": schedule_minute, }, "cache_managed_policies_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_managed_policies_across_accounts", "options": {"expires": 1000}, "schedule": schedule_45_minute, }, "cache_s3_buckets_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_s3_buckets_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "cache_sqs_queues_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_sqs_queues_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "cache_sns_topics_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_sns_topics_across_accounts", "options": {"expires": 300}, "schedule": schedule_45_minute, }, "get_iam_role_limit": { "task": "consoleme.celery_tasks.celery_tasks.get_iam_role_limit", "options": {"expires": 300}, "schedule": schedule_24_hours, }, "cache_cloudtrail_errors_by_arn": { "task": "consoleme.celery_tasks.celery_tasks.cache_cloudtrail_errors_by_arn", "options": {"expires": 300}, "schedule": schedule_1_hour, }, "cache_resources_from_aws_config_across_accounts": { "task": "consoleme.celery_tasks.celery_tasks.cache_resources_from_aws_config_across_accounts", "options": {"expires": 300}, "schedule": schedule_1_hour, }, "cache_policy_requests": { "task": "consoleme.celery_tasks.celery_tasks.cache_policy_requests", "options": {"expires": 1000}, "schedule": schedule_5_minutes, }, "cache_cloud_account_mapping": { "task": "consoleme.celery_tasks.celery_tasks.cache_cloud_account_mapping", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_credential_authorization_mapping": { "task": "consoleme.celery_tasks.celery_tasks.cache_credential_authorization_mapping", "options": {"expires": 1000}, "schedule": schedule_5_minutes, }, "cache_scps_across_organizations": { "task": "consoleme.celery_tasks.celery_tasks.cache_scps_across_organizations", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_organization_structure": { "task": "consoleme.celery_tasks.celery_tasks.cache_organization_structure", "options": {"expires": 1000}, "schedule": schedule_1_hour, }, "cache_resource_templates_task": { "task": "consoleme.celery_tasks.celery_tasks.cache_resource_templates_task", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, "cache_self_service_typeahead_task": { "task": "consoleme.celery_tasks.celery_tasks.cache_self_service_typeahead_task", "options": {"expires": 1000}, "schedule": schedule_30_minute, }, } if internal_celery_tasks and isinstance(internal_celery_tasks, dict): schedule = {**schedule, **internal_celery_tasks} if config.get("celery.clear_tasks_for_development", False): schedule = {} app.conf.beat_schedule = schedule app.conf.timezone = "UTC"

import pathlib import os from typing import Optional import vswhere def find_cmake() -> Optional[pathlib.Path]: # search in PATH for p in os.getenv('PATH').split(';'): cmake = pathlib.Path(p) / 'cmake.exe' if cmake.exists(): return cmake # default path cmake = pathlib.Path("C:/Program Files/CMake/bin/cmake.exe") if cmake.exists(): return cmake # visual studio path = vswhere.get_latest_path() if path: vspath = pathlib.Path(path) cmake = vspath / 'Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/cmake.exe' if cmake.exists(): # add path to MSBuild msbuild_path = vspath / 'MSBuild\\Current\\Bin' os.environ['PATH'] = f'{msbuild_path};{os.environ['PATH']}' return cmake return None

import pathlib import os from typing import Optional import vswhere def find_cmake() -> Optional[pathlib.Path]: # search in PATH for p in os.getenv('PATH').split(';'): cmake = pathlib.Path(p) / 'cmake.exe' if cmake.exists(): return cmake # default path cmake = pathlib.Path("C:/Program Files/CMake/bin/cmake.exe") if cmake.exists(): return cmake # visual studio path = vswhere.get_latest_path() if path: vspath = pathlib.Path(path) cmake = vspath / 'Common7/IDE/CommonExtensions/Microsoft/CMake/CMake/bin/cmake.exe' if cmake.exists(): # add path to MSBuild msbuild_path = vspath / 'MSBuild\\Current\\Bin' os.environ['PATH'] = f'{msbuild_path};{os.environ["PATH"]}' return cmake return None

"""Script to check the configuration file.""" import argparse import asyncio from collections import OrderedDict from collections.abc import Mapping, Sequence from glob import glob import logging import os from typing import Any, Callable, Dict, List, Tuple from unittest.mock import patch from homeassistant import bootstrap, core from homeassistant.config import get_default_config_dir from homeassistant.exceptions import HomeAssistantError from homeassistant.helpers.check_config import async_check_ha_config_file import homeassistant.util.yaml.loader as yaml_loader # mypy: allow-untyped-calls, allow-untyped-defs REQUIREMENTS = ("colorlog==4.7.2",) _LOGGER = logging.getLogger(__name__) # pylint: disable=protected-access MOCKS: Dict[str, Tuple[str, Callable]] = { "load": ("homeassistant.util.yaml.loader.load_yaml", yaml_loader.load_yaml), "load*": ("homeassistant.config.load_yaml", yaml_loader.load_yaml), "secrets": ("homeassistant.util.yaml.loader.secret_yaml", yaml_loader.secret_yaml), } SILENCE = ("homeassistant.scripts.check_config.yaml_loader.clear_secret_cache",) PATCHES: Dict[str, Any] = {} C_HEAD = "bold" ERROR_STR = "General Errors" def color(the_color, *args, reset=None): """Color helper.""" # pylint: disable=import-outside-toplevel from colorlog.escape_codes import escape_codes, parse_colors try: if not args: assert reset is None, "You cannot reset if nothing being printed" return parse_colors(the_color) return parse_colors(the_color) + " ".join(args) + escape_codes[reset or "reset"] except KeyError as k: raise ValueError(f"Invalid color {k!s} in {the_color}") from k def run(script_args: List) -> int: """Handle check config commandline script.""" parser = argparse.ArgumentParser(description="Check Home Assistant configuration.") parser.add_argument("--script", choices=["check_config"]) parser.add_argument( "-c", "--config", default=get_default_config_dir(), help="Directory that contains the Home Assistant configuration", ) parser.add_argument( "-i", "--info", nargs="?", default=None, const="all", help="Show a portion of the config", ) parser.add_argument( "-f", "--files", action="store_true", help="Show used configuration files" ) parser.add_argument( "-s", "--secrets", action="store_true", help="Show secret information" ) args, unknown = parser.parse_known_args() if unknown: print(color("red", "Unknown arguments:", ", ".join(unknown))) config_dir = os.path.join(os.getcwd(), args.config) print(color("bold", "Testing configuration at", config_dir)) res = check(config_dir, args.secrets) domain_info: List[str] = [] if args.info: domain_info = args.info.split(",") if args.files: print(color(C_HEAD, "yaml files"), "(used /", color("red", "not used") + ")") deps = os.path.join(config_dir, "deps") yaml_files = [ f for f in glob(os.path.join(config_dir, "**/*.yaml"), recursive=True) if not f.startswith(deps) ] for yfn in sorted(yaml_files): the_color = "" if yfn in res["yaml_files"] else "red" print(color(the_color, "-", yfn)) if res["except"]: print(color("bold_white", "Failed config")) for domain, config in res["except"].items(): domain_info.append(domain) print(" ", color("bold_red", domain + ":"), color("red", "", reset="red")) dump_dict(config, reset="red") print(color("reset")) if domain_info: if "all" in domain_info: print(color("bold_white", "Successful config (all)")) for domain, config in res["components"].items(): print(" ", color(C_HEAD, domain + ":")) dump_dict(config) else: print(color("bold_white", "Successful config (partial)")) for domain in domain_info: if domain == ERROR_STR: continue print(" ", color(C_HEAD, domain + ":")) dump_dict(res["components"].get(domain)) if args.secrets: flatsecret: Dict[str, str] = {} for sfn, sdict in res["secret_cache"].items(): sss = [] for skey in sdict: if skey in flatsecret: _LOGGER.error( "Duplicated secrets in files %s and %s", flatsecret[skey], sfn ) flatsecret[skey] = sfn sss.append(color("green", skey) if skey in res["secrets"] else skey) print(color(C_HEAD, "Secrets from", sfn + ":"), ", ".join(sss)) print(color(C_HEAD, "Used Secrets:")) for skey, sval in res["secrets"].items(): if sval is None: print(" -", skey + ":", color("red", "not found")) continue print(" -", skey + ":", sval) return len(res["except"]) def check(config_dir, secrets=False): """Perform a check by mocking hass load functions.""" logging.getLogger("homeassistant.loader").setLevel(logging.CRITICAL) res: Dict[str, Any] = { "yaml_files": OrderedDict(), # yaml_files loaded "secrets": OrderedDict(), # secret cache and secrets loaded "except": OrderedDict(), # exceptions raised (with config) #'components' is a HomeAssistantConfig # noqa: E265 "secret_cache": None, } # pylint: disable=possibly-unused-variable def mock_load(filename): """Mock hass.util.load_yaml to save config file names.""" res["yaml_files"][filename] = True return MOCKS["load"][1](filename) # pylint: disable=possibly-unused-variable def mock_secrets(ldr, node): """Mock _get_secrets.""" try: val = MOCKS["secrets"][1](ldr, node) except HomeAssistantError: val = None res["secrets"][node.value] = val return val # Patches to skip functions for sil in SILENCE: PATCHES[sil] = patch(sil) # Patches with local mock functions for key, val in MOCKS.items(): if not secrets and key == "secrets": continue # The * in the key is removed to find the mock_function (side_effect) # This allows us to use one side_effect to patch multiple locations mock_function = locals()[f"mock_{key.replace("*", "")}"] PATCHES[key] = patch(val[0], side_effect=mock_function) # Start all patches for pat in PATCHES.values(): pat.start() if secrets: # Ensure !secrets point to the patched function yaml_loader.yaml.SafeLoader.add_constructor("!secret", yaml_loader.secret_yaml) try: res["components"] = asyncio.run(async_check_config(config_dir)) res["secret_cache"] = OrderedDict(yaml_loader.__SECRET_CACHE) for err in res["components"].errors: domain = err.domain or ERROR_STR res["except"].setdefault(domain, []).append(err.message) if err.config: res["except"].setdefault(domain, []).append(err.config) except Exception as err: # pylint: disable=broad-except print(color("red", "Fatal error while loading config:"), str(err)) res["except"].setdefault(ERROR_STR, []).append(str(err)) finally: # Stop all patches for pat in PATCHES.values(): pat.stop() if secrets: # Ensure !secrets point to the original function yaml_loader.yaml.SafeLoader.add_constructor( "!secret", yaml_loader.secret_yaml ) bootstrap.clear_secret_cache() return res async def async_check_config(config_dir): """Check the HA config.""" hass = core.HomeAssistant() hass.config.config_dir = config_dir components = await async_check_ha_config_file(hass) await hass.async_stop(force=True) return components def line_info(obj, **kwargs): """Display line config source.""" if hasattr(obj, "__config_file__"): return color( "cyan", f"[source {obj.__config_file__}:{obj.__line__ or "?"}]", **kwargs ) return "?" def dump_dict(layer, indent_count=3, listi=False, **kwargs): """Display a dict. A friendly version of print yaml_loader.yaml.dump(config). """ def sort_dict_key(val): """Return the dict key for sorting.""" key = str(val[0]).lower() return "0" if key == "platform" else key indent_str = indent_count * " " if listi or isinstance(layer, list): indent_str = indent_str[:-1] + "-" if isinstance(layer, Mapping): for key, value in sorted(layer.items(), key=sort_dict_key): if isinstance(value, (dict, list)): print(indent_str, str(key) + ":", line_info(value, **kwargs)) dump_dict(value, indent_count + 2) else: print(indent_str, str(key) + ":", value) indent_str = indent_count * " " if isinstance(layer, Sequence): for i in layer: if isinstance(i, dict): dump_dict(i, indent_count + 2, True) else: print(" ", indent_str, i)

"""Script to check the configuration file.""" import argparse import asyncio from collections import OrderedDict from collections.abc import Mapping, Sequence from glob import glob import logging import os from typing import Any, Callable, Dict, List, Tuple from unittest.mock import patch from homeassistant import bootstrap, core from homeassistant.config import get_default_config_dir from homeassistant.exceptions import HomeAssistantError from homeassistant.helpers.check_config import async_check_ha_config_file import homeassistant.util.yaml.loader as yaml_loader # mypy: allow-untyped-calls, allow-untyped-defs REQUIREMENTS = ("colorlog==4.7.2",) _LOGGER = logging.getLogger(__name__) # pylint: disable=protected-access MOCKS: Dict[str, Tuple[str, Callable]] = { "load": ("homeassistant.util.yaml.loader.load_yaml", yaml_loader.load_yaml), "load*": ("homeassistant.config.load_yaml", yaml_loader.load_yaml), "secrets": ("homeassistant.util.yaml.loader.secret_yaml", yaml_loader.secret_yaml), } SILENCE = ("homeassistant.scripts.check_config.yaml_loader.clear_secret_cache",) PATCHES: Dict[str, Any] = {} C_HEAD = "bold" ERROR_STR = "General Errors" def color(the_color, *args, reset=None): """Color helper.""" # pylint: disable=import-outside-toplevel from colorlog.escape_codes import escape_codes, parse_colors try: if not args: assert reset is None, "You cannot reset if nothing being printed" return parse_colors(the_color) return parse_colors(the_color) + " ".join(args) + escape_codes[reset or "reset"] except KeyError as k: raise ValueError(f"Invalid color {k!s} in {the_color}") from k def run(script_args: List) -> int: """Handle check config commandline script.""" parser = argparse.ArgumentParser(description="Check Home Assistant configuration.") parser.add_argument("--script", choices=["check_config"]) parser.add_argument( "-c", "--config", default=get_default_config_dir(), help="Directory that contains the Home Assistant configuration", ) parser.add_argument( "-i", "--info", nargs="?", default=None, const="all", help="Show a portion of the config", ) parser.add_argument( "-f", "--files", action="store_true", help="Show used configuration files" ) parser.add_argument( "-s", "--secrets", action="store_true", help="Show secret information" ) args, unknown = parser.parse_known_args() if unknown: print(color("red", "Unknown arguments:", ", ".join(unknown))) config_dir = os.path.join(os.getcwd(), args.config) print(color("bold", "Testing configuration at", config_dir)) res = check(config_dir, args.secrets) domain_info: List[str] = [] if args.info: domain_info = args.info.split(",") if args.files: print(color(C_HEAD, "yaml files"), "(used /", color("red", "not used") + ")") deps = os.path.join(config_dir, "deps") yaml_files = [ f for f in glob(os.path.join(config_dir, "**/*.yaml"), recursive=True) if not f.startswith(deps) ] for yfn in sorted(yaml_files): the_color = "" if yfn in res["yaml_files"] else "red" print(color(the_color, "-", yfn)) if res["except"]: print(color("bold_white", "Failed config")) for domain, config in res["except"].items(): domain_info.append(domain) print(" ", color("bold_red", domain + ":"), color("red", "", reset="red")) dump_dict(config, reset="red") print(color("reset")) if domain_info: if "all" in domain_info: print(color("bold_white", "Successful config (all)")) for domain, config in res["components"].items(): print(" ", color(C_HEAD, domain + ":")) dump_dict(config) else: print(color("bold_white", "Successful config (partial)")) for domain in domain_info: if domain == ERROR_STR: continue print(" ", color(C_HEAD, domain + ":")) dump_dict(res["components"].get(domain)) if args.secrets: flatsecret: Dict[str, str] = {} for sfn, sdict in res["secret_cache"].items(): sss = [] for skey in sdict: if skey in flatsecret: _LOGGER.error( "Duplicated secrets in files %s and %s", flatsecret[skey], sfn ) flatsecret[skey] = sfn sss.append(color("green", skey) if skey in res["secrets"] else skey) print(color(C_HEAD, "Secrets from", sfn + ":"), ", ".join(sss)) print(color(C_HEAD, "Used Secrets:")) for skey, sval in res["secrets"].items(): if sval is None: print(" -", skey + ":", color("red", "not found")) continue print(" -", skey + ":", sval) return len(res["except"]) def check(config_dir, secrets=False): """Perform a check by mocking hass load functions.""" logging.getLogger("homeassistant.loader").setLevel(logging.CRITICAL) res: Dict[str, Any] = { "yaml_files": OrderedDict(), # yaml_files loaded "secrets": OrderedDict(), # secret cache and secrets loaded "except": OrderedDict(), # exceptions raised (with config) #'components' is a HomeAssistantConfig # noqa: E265 "secret_cache": None, } # pylint: disable=possibly-unused-variable def mock_load(filename): """Mock hass.util.load_yaml to save config file names.""" res["yaml_files"][filename] = True return MOCKS["load"][1](filename) # pylint: disable=possibly-unused-variable def mock_secrets(ldr, node): """Mock _get_secrets.""" try: val = MOCKS["secrets"][1](ldr, node) except HomeAssistantError: val = None res["secrets"][node.value] = val return val # Patches to skip functions for sil in SILENCE: PATCHES[sil] = patch(sil) # Patches with local mock functions for key, val in MOCKS.items(): if not secrets and key == "secrets": continue # The * in the key is removed to find the mock_function (side_effect) # This allows us to use one side_effect to patch multiple locations mock_function = locals()[f"mock_{key.replace('*', '')}"] PATCHES[key] = patch(val[0], side_effect=mock_function) # Start all patches for pat in PATCHES.values(): pat.start() if secrets: # Ensure !secrets point to the patched function yaml_loader.yaml.SafeLoader.add_constructor("!secret", yaml_loader.secret_yaml) try: res["components"] = asyncio.run(async_check_config(config_dir)) res["secret_cache"] = OrderedDict(yaml_loader.__SECRET_CACHE) for err in res["components"].errors: domain = err.domain or ERROR_STR res["except"].setdefault(domain, []).append(err.message) if err.config: res["except"].setdefault(domain, []).append(err.config) except Exception as err: # pylint: disable=broad-except print(color("red", "Fatal error while loading config:"), str(err)) res["except"].setdefault(ERROR_STR, []).append(str(err)) finally: # Stop all patches for pat in PATCHES.values(): pat.stop() if secrets: # Ensure !secrets point to the original function yaml_loader.yaml.SafeLoader.add_constructor( "!secret", yaml_loader.secret_yaml ) bootstrap.clear_secret_cache() return res async def async_check_config(config_dir): """Check the HA config.""" hass = core.HomeAssistant() hass.config.config_dir = config_dir components = await async_check_ha_config_file(hass) await hass.async_stop(force=True) return components def line_info(obj, **kwargs): """Display line config source.""" if hasattr(obj, "__config_file__"): return color( "cyan", f"[source {obj.__config_file__}:{obj.__line__ or '?'}]", **kwargs ) return "?" def dump_dict(layer, indent_count=3, listi=False, **kwargs): """Display a dict. A friendly version of print yaml_loader.yaml.dump(config). """ def sort_dict_key(val): """Return the dict key for sorting.""" key = str(val[0]).lower() return "0" if key == "platform" else key indent_str = indent_count * " " if listi or isinstance(layer, list): indent_str = indent_str[:-1] + "-" if isinstance(layer, Mapping): for key, value in sorted(layer.items(), key=sort_dict_key): if isinstance(value, (dict, list)): print(indent_str, str(key) + ":", line_info(value, **kwargs)) dump_dict(value, indent_count + 2) else: print(indent_str, str(key) + ":", value) indent_str = indent_count * " " if isinstance(layer, Sequence): for i in layer: if isinstance(i, dict): dump_dict(i, indent_count + 2, True) else: print(" ", indent_str, i)

""" Common solar physics coordinate systems. This submodule implements various solar physics coordinate frames for use with the `astropy.coordinates` module. """ from contextlib import contextmanager import numpy as np import astropy.units as u from astropy.coordinates import ConvertError, QuantityAttribute from astropy.coordinates.baseframe import BaseCoordinateFrame, RepresentationMapping from astropy.coordinates.representation import ( CartesianDifferential, CartesianRepresentation, CylindricalRepresentation, SphericalDifferential, SphericalRepresentation, UnitSphericalRepresentation, ) from astropy.time import Time from sunpy.sun.constants import radius as _RSUN from sunpy.time.time import _variables_for_parse_time_docstring from sunpy.util.decorators import add_common_docstring from sunpy.util.exceptions import SunpyUserWarning from .frameattributes import ObserverCoordinateAttribute, TimeFrameAttributeSunPy _J2000 = Time('J2000.0', scale='tt') __all__ = ['SunPyBaseCoordinateFrame', 'BaseHeliographic', 'HeliographicStonyhurst', 'HeliographicCarrington', 'Heliocentric', 'Helioprojective', 'HeliocentricEarthEcliptic', 'GeocentricSolarEcliptic', 'HeliocentricInertial', 'GeocentricEarthEquatorial'] def _frame_parameters(): """ Returns formatting dictionary to use with add_common_docstring to populate frame docstrings """ ret = {} # Each text block is missing the first indent because it already exists in the frame docstring ret['data'] = ("data : `~astropy.coordinates.BaseRepresentation` or ``None``\n" " A representation object or ``None`` to have no data\n" " (or use the coordinate component arguments, see below).") ret['common'] = (f"obstime : {_variables_for_parse_time_docstring()["parse_time_types"]}\n" " The time of the observation. This is used to determine the\n" " position of solar-system bodies (e.g., the Sun and the Earth) as\n" " needed to define the origin and orientation of the frame.\n" " representation_type : `~astropy.coordinates.BaseRepresentation`, str, optional\n" " A representation class or string name of a representation class.\n" " This may change the valid coordinate component arguments from the\n" " defaults (see above). For example, passing\n" " ``representation_type='cartesian'`` will make the frame expect\n" " Cartesian coordinate component arguments (typically, ``x``, ``y``,\n" " and ``z``).\n" " copy : bool, optional\n" " If `True` (default), make copies of the input coordinate arrays.") ret['lonlat'] = ("lon : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`, optional\n" " The longitude coordinate for this object (``lat`` must also be\n" " given and ``data`` must be ``None``).\n" " Not needed if ``data`` is given.\n" " lat : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`, optional\n" " The latitude coordinate for this object (``lon`` must also be\n" " given and ``data`` must be ``None``).\n" " Not needed if ``data`` is given.") ret['radius'] = ("radius : `~astropy.units.Quantity`, optional\n" " The radial distance coordinate from Sun center for this object.\n" " Defaults to the radius of the Sun. Not needed if ``data`` is given.") ret['distance_sun'] = ("distance : `~astropy.units.Quantity`, optional\n" " The distance coordinate from Sun center for this object.\n" " Not needed if ``data`` is given.") ret['distance_earth'] = ("distance : `~astropy.units.Quantity`, optional\n" " The distance coordinate from Earth center for this object.\n" " Not needed if ``data`` is given.") ret['xyz'] = ("x : `~astropy.units.Quantity`, optional\n" " X-axis coordinate for this object. Not needed if ``data`` is given.\n" " y : `~astropy.units.Quantity`, optional\n" " Y-axis coordinate for this object. Not needed if ``data`` is given.\n" " z : `~astropy.units.Quantity`, optional\n" " Z-axis coordinate for this object. Not needed if ``data`` is given.") ret['observer'] = ("observer : `~sunpy.coordinates.frames.HeliographicStonyhurst`, str\n" " The location of the observer. If a string is provided,\n" " it must be a solar system body that can be parsed by\n" " `~sunpy.coordinates.ephemeris.get_body_heliographic_stonyhurst`\n" " at the time ``obstime``. Defaults to Earth center.") ret['rsun'] = ("rsun : `~astropy.units.Quantity`\n" " The radius of the Sun in length units. Used to convert a 2D\n" " coordinate (i.e., no ``radius`` component) to a 3D coordinate by\n" " assuming that the coordinate is on the surface of the Sun. Defaults\n" " to the photospheric radius as defined in `sunpy.sun.constants`.") ret['equinox'] = (f"equinox : {_variables_for_parse_time_docstring()["parse_time_types"]}\n" " The date for the mean vernal equinox.\n" " Defaults to the J2000.0 equinox.") return ret class SunPyBaseCoordinateFrame(BaseCoordinateFrame): """ Base class for sunpy coordinate frames. This class is not intended to be used directly and has no transformations defined. * Defines the frame attribute ``obstime`` for observation time. * Defines a default wrap angle of 180 degrees for longitude in spherical coordinates, which can be overridden via the class variable ``_wrap_angle``. * Inject a nice way of representing the object which the coordinate represents. """ obstime = TimeFrameAttributeSunPy() default_representation = SphericalRepresentation default_differential = SphericalDifferential frame_specific_representation_info = { SphericalDifferential: [RepresentationMapping('d_lon', 'd_lon', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_lat', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_distance', u.km/u.s)], } _wrap_angle = 180*u.deg # for longitude in spherical coordinates def __init__(self, *args, **kwargs): self.object_name = None # If wrap_longitude=False is passed in, do not impose a specific wrap angle for the frame if not kwargs.pop('wrap_longitude', True): self._wrap_angle = None super().__init__(*args, **kwargs) # If obstime is specified, treat the default observer (None) as explicitly set if self.obstime is not None and self.is_frame_attr_default('observer'): self._attr_names_with_defaults.remove('observer') return def represent_as(self, base, s='base', in_frame_units=False): data = super().represent_as(base, s, in_frame_units=in_frame_units) # If a frame wrap angle is set, use that wrap angle for any spherical representations. if self._wrap_angle is not None and \ isinstance(data, (UnitSphericalRepresentation, SphericalRepresentation)): data.lon.wrap_angle = self._wrap_angle return data def __str__(self): # We override this here so that when you print a SkyCoord it shows the # observer as the string and not the whole massive coordinate. if getattr(self, "object_name", None): return f"<{self.__class__.__name__} Coordinate for '{self.object_name}'>" else: return super().__str__() @property def _is_2d(self): return (self._data is not None and self._data.norm().unit is u.one and u.allclose(self._data.norm(), 1*u.one)) def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) # TODO: Remove this after the minimum Astropy dependency includes astropy/astropy#12005 cls._fix_property_docstrings() @classmethod def _fix_property_docstrings(cls): # This class method adds docstrings to properties dynamically created by # BaseCoordinateFrame.__init_subclass__(). Accordingly, this method needs to itself be # called from SunPyBaseCoordinateFrame.__init_subclass__() to work for our subclasses. property_docstrings = { 'default_representation': "Default representation for position data", 'default_differential': "Default representation for differential data", 'frame_specific_representation_info': "Mapping for frame-specific component names", } for prop, docstring in property_docstrings.items(): if getattr(cls, prop).__doc__ is None: setattr(getattr(cls, prop), '__doc__', docstring) # TODO: Remove this after the minimum Astropy dependency includes astropy/astropy#12005 SunPyBaseCoordinateFrame._fix_property_docstrings() class BaseHeliographic(SunPyBaseCoordinateFrame): """ Base class for HeliographicCarrington (HGC) and HeliographicStonyhurst (HGS) frames. This class is not intended to be used directly and has no transformations defined. """ frame_specific_representation_info = { SphericalRepresentation: [RepresentationMapping('lon', 'lon', u.deg), RepresentationMapping('lat', 'lat', u.deg), RepresentationMapping('distance', 'radius', None)], SphericalDifferential: [RepresentationMapping('d_lon', 'd_lon', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_lat', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_radius', u.km/u.s)], } rsun = QuantityAttribute(default=_RSUN, unit=u.km) def make_3d(self): """ Returns a fully 3D coordinate based on this coordinate. If this coordinate is only 2D (i.e., no ``radius`` component) or is a unit vector (i.e., the norm of the coordinate is unity), a new coordinate is created that corresponds to the surface of the Sun. That is, the 3D coordinate will retain the ``lon`` and ``lat``, and ``radius`` will be set to the frame's ``rsun`` frame attribute. If this coordinate is already fully 3D, it is directly returned, even if it does not lie on the surface of the Sun. Returns ------- frame : `~sunpy.coordinates.frames.BaseHeliographic` The fully 3D coordinate """ if self._is_2d: return self.realize_frame(self._data * self.rsun) # The coordinate is already 3D return self @add_common_docstring(**_frame_parameters()) class HeliographicStonyhurst(BaseHeliographic): """ A coordinate or frame in the Stonyhurst Heliographic (HGS) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the projection of the Sun-Earth line onto the Sun's equatorial plane. This system is also know as the Heliocentric Earth Equatorial (HEEQ) system when represented using Cartesian components. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``representation_type`` must be keyword arguments):: HeliographicStonyhurst(lon, lat, obstime=obstime) HeliographicStonyhurst(lon, lat, radius, obstime=obstime) HeliographicStonyhurst(x, y, z, representation_type='cartesian', obstime=obstime) Parameters ---------- {data} {lonlat} {radius} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(1*u.deg, 1*u.deg, 2*u.km, ... frame="heliographic_stonyhurst", ... obstime="2010/01/01T00:00:45") >>> sc <SkyCoord (HeliographicStonyhurst: obstime=2010-01-01T00:00:45.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (1., 1., 2.)> >>> sc.frame <HeliographicStonyhurst Coordinate (obstime=2010-01-01T00:00:45.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (1., 1., 2.)> >>> sc = SkyCoord(HeliographicStonyhurst(-10*u.deg, 2*u.deg)) >>> sc <SkyCoord (HeliographicStonyhurst: obstime=None, rsun=695700.0 km): (lon, lat) in deg (-10., 2.)> >>> sc = SkyCoord(CartesianRepresentation(0*u.km, 45*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", ... frame="heliographic_stonyhurst") >>> sc <SkyCoord (HeliographicStonyhurst: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (90., 2.54480438, 45.04442252)> """ name = "heliographic_stonyhurst" @add_common_docstring(**_frame_parameters()) class HeliographicCarrington(BaseHeliographic): """ A coordinate or frame in the Carrington Heliographic (HGC) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis and Y-axis rotate with a period of 25.38 days. This system differs from Stonyhurst Heliographic (HGS) in its definition of longitude. This longitude is an "apparent" longitude because it takes into account the time it takes for light to travel from the Sun's surface to the observer. Thus, the observer needs to be specified to be able to transform to any other coordinate frame. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``observer`` must be a keyword argument):: HeliographicCarrington(lon, lat, obstime=obstime, observer=observer) HeliographicCarrington(lon, lat, radius, obstime=obstime, observer=observer) If you want to define the location in HGC such that the observer for the coordinate frame is the same as that location (e.g., the location of an observatory in its corresponding HGC frame), use ``observer='self'``:: HeliographicCarrington(lon, lat, radius, obstime=obstime, observer='self') Parameters ---------- {data} {lonlat} {radius} {observer} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(1*u.deg, 2*u.deg, 3*u.km, ... frame="heliographic_carrington", ... observer="earth", ... obstime="2010/01/01T00:00:30") >>> sc <SkyCoord (HeliographicCarrington: obstime=2010-01-01T00:00:30.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (lon, lat, radius) in (deg, deg, km) (1., 2., 3.)> >>> sc = SkyCoord([1,2,3]*u.deg, [4,5,6]*u.deg, [5,6,7]*u.km, ... obstime="2010/01/01T00:00:45", ... observer="self", ... frame="heliographic_carrington") >>> sc <SkyCoord (HeliographicCarrington: obstime=2010-01-01T00:00:45.000, rsun=695700.0 km, observer=self): (lon, lat, radius) in (deg, deg, km) [(1., 4., 5.), (2., 5., 6.), (3., 6., 7.)]> >>> sc = SkyCoord(CartesianRepresentation(0*u.km, 45*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", ... frame="heliographic_carrington") >>> sc <SkyCoord (HeliographicCarrington: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km, observer=None): (lon, lat, radius) in (deg, deg, km) (90., 2.54480438, 45.04442252)> """ name = "heliographic_carrington" _wrap_angle = 360*u.deg observer = ObserverCoordinateAttribute(HeliographicStonyhurst) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if not isinstance(self.observer, BaseCoordinateFrame) and self.observer == 'self' and self._is_2d: raise ValueError("Full 3D coordinate (including radius) must be specified " "when observer='self'.") @add_common_docstring(**_frame_parameters()) class Heliocentric(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric system, which is observer-based. - The origin is the center of the Sun. - The Z-axis is aligned with the Sun-observer line. - The Y-axis is aligned with the component of the vector to the Sun's north pole that is perpendicular to the Z-axis. This frame defaults to a Cartesian component representation, which is known as Heliocentric Cartesian (HCC). This frame can also be represented using cylindrical components, where where ``rho`` is the impact parameter and ``psi`` is the position angle. ``psi`` is measured relative to the west limb, rather than solar north, so is shifted by 90 degrees compared to the convention of the Heliocentric Radial (HCR) system. A new instance can be created using the following signatures (note that if supplied, ``obstime``, ``observer``, and ``representation_type`` must be keyword arguments):: Heliocentric(x, y, z, obstime=obstime, observer=observer) Heliocentric(rho, psi, z, representation_type='cylindrical', obstime=obstime, observer=observer) Parameters ---------- {data} {xyz} {observer} {common} Examples -------- >>> from astropy.coordinates import SkyCoord, CartesianRepresentation >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(CartesianRepresentation(10*u.km, 1*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-05T00:00:50.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in km (10., 1., 2.)> >>> sc = SkyCoord([1,2]*u.km, [3,4]*u.m, [5,6]*u.cm, ... obstime="2011/01/01T00:00:54", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-01T00:00:54.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in (km, m, cm) [(1., 3., 5.), (2., 4., 6.)]> >>> sc = SkyCoord(CylindricalRepresentation(10*u.km, 60*u.deg, 10*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-05T00:00:50.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in km (5., 8.66025404, 10.)> """ default_representation = CartesianRepresentation default_differential = CartesianDifferential frame_specific_representation_info = { CylindricalRepresentation: [RepresentationMapping('phi', 'psi', u.deg)] } observer = ObserverCoordinateAttribute(HeliographicStonyhurst) def represent_as(self, base, s='base', in_frame_units=False): data = super().represent_as(base, s, in_frame_units=in_frame_units) # For cylindrical representations, wrap the `psi` component (natively `phi`) at 360 deg if isinstance(data, CylindricalRepresentation): data.phi.wrap_at(360*u.deg, inplace=True) return data @add_common_docstring(**_frame_parameters()) class Helioprojective(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Helioprojective Cartesian (HPC) system, which is observer-based. - The origin is the location of the observer. - ``Tx`` (aka "theta_x") is the angle relative to the plane containing the Sun-observer line and the Sun's rotation axis, with positive values in the direction of the Sun's west limb. - ``Ty`` (aka "theta_y") is the angle relative to the Sun's equatorial plane, with positive values in the direction of the Sun's north pole. - ``distance`` is the Sun-observer distance. This system is frequently used in a projective form without ``distance`` specified. For observations looking very close to the center of the Sun, where the small-angle approximation is appropriate, ``Tx`` and ``Ty`` can be approximated as Cartesian components. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``observer`` must be keyword arguments):: Helioprojective(Tx, Ty, obstime=obstime, observer=observer) Helioprojective(Tx, Ty, distance, obstime=obstime, observer=observer) Parameters ---------- {data} Tx : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` The theta_x coordinate for this object. Not needed if ``data`` is given. Ty : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` The theta_y coordinate for this object. Not needed if ``data`` is given. distance : `~astropy.units.Quantity` The distance coordinate from the observer for this object. Not needed if ``data`` is given. {observer} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(0*u.deg, 0*u.deg, 5*u.km, ... obstime="2010/01/01T00:00:00", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2010-01-01T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, km) (0., 0., 5.)> >>> sc = SkyCoord(0*u.deg, 0*u.deg, ... obstime="2010/01/01T00:00:00", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2010-01-01T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty) in arcsec (0., 0.)> >>> sc = SkyCoord(CartesianRepresentation(1*u.AU, 1e5*u.km, -2e5*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) (137.87948623, -275.75878762, 1.00000112)> """ frame_specific_representation_info = { SphericalRepresentation: [RepresentationMapping('lon', 'Tx', u.arcsec), RepresentationMapping('lat', 'Ty', u.arcsec), RepresentationMapping('distance', 'distance', None)], SphericalDifferential: [RepresentationMapping('d_lon', 'd_Tx', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_Ty', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_distance', u.km/u.s)], UnitSphericalRepresentation: [RepresentationMapping('lon', 'Tx', u.arcsec), RepresentationMapping('lat', 'Ty', u.arcsec)], } rsun = QuantityAttribute(default=_RSUN, unit=u.km) observer = ObserverCoordinateAttribute(HeliographicStonyhurst) @property def angular_radius(self): """ Angular radius of the Sun as seen by the observer. The ``rsun`` frame attribute is the radius of the Sun in length units. The tangent vector from the observer to the edge of the Sun forms a right-angle triangle with the radius of the Sun as the far side and the Sun-observer distance as the hypotenuse. Thus, the sine of the angular radius of the Sun is ratio of these two distances. """ from sunpy.coordinates.sun import _angular_radius # avoiding a circular import if not isinstance(self.observer, HeliographicStonyhurst): if self.observer is None: raise ValueError("The observer must be defined, not `None`.") raise ValueError("The observer must be fully defined by specifying `obstime`.") return _angular_radius(self.rsun, self.observer.radius) def make_3d(self): """ This method calculates the third coordinate of the Helioprojective frame. It assumes that the coordinate point is on the surface of the Sun. If a point in the frame is off limb then NaN will be returned. Returns ------- new_frame : `~sunpy.coordinates.frames.Helioprojective` A new frame instance with all the attributes of the original but now with a third coordinate. """ # Skip if we already are 3D if not self._is_2d: return self if not isinstance(self.observer, BaseCoordinateFrame): raise ConvertError("Cannot calculate distance to the Sun " f"for observer '{self.observer}' " "without `obstime` being specified.") rep = self.represent_as(UnitSphericalRepresentation) lat, lon = rep.lat, rep.lon # Check for the use of floats with lower precision than the native Python float if not set([lon.dtype.type, lat.dtype.type]).issubset([float, np.float64, np.longdouble]): raise SunpyUserWarning("The Helioprojective component values appear to be lower " "precision than the native Python float: " f"Tx is {lon.dtype.name}, and Ty is {lat.dtype.name}. " "To minimize precision loss, you may want to cast the values to " "`float` or `numpy.float64` via the NumPy method `.astype()`.") # Calculate the distance to the surface of the Sun using the law of cosines cos_alpha = np.cos(lat) * np.cos(lon) c = self.observer.radius**2 - self.rsun**2 b = -2 * self.observer.radius * cos_alpha # Ignore sqrt of NaNs with np.errstate(invalid='ignore'): d = ((-1*b) - np.sqrt(b**2 - 4*c)) / 2 # use the "near" solution if self._spherical_screen: sphere_center = self._spherical_screen['center'].transform_to(self).cartesian c = sphere_center.norm()**2 - self._spherical_screen['radius']**2 b = -2 * sphere_center.dot(rep) # Ignore sqrt of NaNs with np.errstate(invalid='ignore'): dd = ((-1*b) + np.sqrt(b**2 - 4*c)) / 2 # use the "far" solution d = np.fmin(d, dd) if self._spherical_screen['only_off_disk'] else dd return self.realize_frame(SphericalRepresentation(lon=lon, lat=lat, distance=d)) _spherical_screen = None @classmethod @contextmanager def assume_spherical_screen(cls, center, only_off_disk=False): """ Context manager to interpret 2D coordinates as being on the inside of a spherical screen. The radius of the screen is the distance between the specified ``center`` and Sun center. This ``center`` does not have to be the same as the observer location for the coordinate frame. If they are the same, then this context manager is equivalent to assuming that the helioprojective "zeta" component is zero. This replaces the default assumption where 2D coordinates are mapped onto the surface of the Sun. Parameters ---------- center : `~astropy.coordinates.SkyCoord` The center of the spherical screen only_off_disk : `bool`, optional If `True`, apply this assumption only to off-disk coordinates, with on-disk coordinates still mapped onto the surface of the Sun. Defaults to `False`. Examples -------- .. minigallery:: sunpy.coordinates.Helioprojective.assume_spherical_screen >>> import astropy.units as u >>> from sunpy.coordinates import Helioprojective >>> h = Helioprojective(range(7)*u.arcsec*319, [0]*7*u.arcsec, ... observer='earth', obstime='2020-04-08') >>> print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 0.99660825), ( 319., 0., 0.99687244), ( 638., 0., 0.99778472), ( 957., 0., 1.00103285), (1276., 0., nan), (1595., 0., nan), (1914., 0., nan)]> >>> with Helioprojective.assume_spherical_screen(h.observer): ... print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 1.00125872), ( 319., 0., 1.00125872), ( 638., 0., 1.00125872), ( 957., 0., 1.00125872), (1276., 0., 1.00125872), (1595., 0., 1.00125872), (1914., 0., 1.00125872)]> >>> with Helioprojective.assume_spherical_screen(h.observer, only_off_disk=True): ... print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 0.99660825), ( 319., 0., 0.99687244), ( 638., 0., 0.99778472), ( 957., 0., 1.00103285), (1276., 0., 1.00125872), (1595., 0., 1.00125872), (1914., 0., 1.00125872)]> """ try: old_spherical_screen = cls._spherical_screen # nominally None center_hgs = center.transform_to(HeliographicStonyhurst(obstime=center.obstime)) cls._spherical_screen = { 'center': center, 'radius': center_hgs.radius, 'only_off_disk': only_off_disk } yield finally: cls._spherical_screen = old_spherical_screen @add_common_docstring(**_frame_parameters()) class HeliocentricEarthEcliptic(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric Earth Ecliptic (HEE) system. - The origin is the center of the Sun. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the Sun-Earth line. - The Z-axis (+90 degrees latitude) is aligned with the component perpendicular to the X-axis of the mean ecliptic pole at the observation time. Parameters ---------- {data} {lonlat} {distance_sun} {common} """ @add_common_docstring(**_frame_parameters()) class GeocentricSolarEcliptic(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Geocentric Solar Ecliptic (GSE) system. - The origin is the center of the Earth. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the Earth-Sun line. - The Z-axis (+90 degrees latitude) is aligned with the component perpendicular to the X-axis of the mean ecliptic pole at the observation time. Parameters ---------- {data} {lonlat} {distance_earth} {common} Notes ----- Aberration due to Earth motion is not included. """ @add_common_docstring(**_frame_parameters()) class HeliocentricInertial(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric Inertial (HCI) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the solar ascending node on the ecliptic (mean J2000.0). Parameters ---------- {data} {lonlat} {distance_sun} {common} Notes ----- The solar ascending node on the ecliptic lies on the intersection of the solar equatorial plane with the ecliptic plane, not on the intersection of the celestial equatorial plane with the ecliptic plane. """ @add_common_docstring(**_frame_parameters()) class GeocentricEarthEquatorial(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Geocentric Earth Equatorial (GEI) system. - The origin is the center of the Earth. - The Z-axis (+90 degrees latitude) is aligned with the Earth's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the mean (not true) vernal equinox. Parameters ---------- {data} {lonlat} {distance_earth} {equinox} {common} Notes ----- Aberration due to Earth motion is not included. """ equinox = TimeFrameAttributeSunPy(default=_J2000)

""" Common solar physics coordinate systems. This submodule implements various solar physics coordinate frames for use with the `astropy.coordinates` module. """ from contextlib import contextmanager import numpy as np import astropy.units as u from astropy.coordinates import ConvertError, QuantityAttribute from astropy.coordinates.baseframe import BaseCoordinateFrame, RepresentationMapping from astropy.coordinates.representation import ( CartesianDifferential, CartesianRepresentation, CylindricalRepresentation, SphericalDifferential, SphericalRepresentation, UnitSphericalRepresentation, ) from astropy.time import Time from sunpy.sun.constants import radius as _RSUN from sunpy.time.time import _variables_for_parse_time_docstring from sunpy.util.decorators import add_common_docstring from sunpy.util.exceptions import SunpyUserWarning from .frameattributes import ObserverCoordinateAttribute, TimeFrameAttributeSunPy _J2000 = Time('J2000.0', scale='tt') __all__ = ['SunPyBaseCoordinateFrame', 'BaseHeliographic', 'HeliographicStonyhurst', 'HeliographicCarrington', 'Heliocentric', 'Helioprojective', 'HeliocentricEarthEcliptic', 'GeocentricSolarEcliptic', 'HeliocentricInertial', 'GeocentricEarthEquatorial'] def _frame_parameters(): """ Returns formatting dictionary to use with add_common_docstring to populate frame docstrings """ ret = {} # Each text block is missing the first indent because it already exists in the frame docstring ret['data'] = ("data : `~astropy.coordinates.BaseRepresentation` or ``None``\n" " A representation object or ``None`` to have no data\n" " (or use the coordinate component arguments, see below).") ret['common'] = (f"obstime : {_variables_for_parse_time_docstring()['parse_time_types']}\n" " The time of the observation. This is used to determine the\n" " position of solar-system bodies (e.g., the Sun and the Earth) as\n" " needed to define the origin and orientation of the frame.\n" " representation_type : `~astropy.coordinates.BaseRepresentation`, str, optional\n" " A representation class or string name of a representation class.\n" " This may change the valid coordinate component arguments from the\n" " defaults (see above). For example, passing\n" " ``representation_type='cartesian'`` will make the frame expect\n" " Cartesian coordinate component arguments (typically, ``x``, ``y``,\n" " and ``z``).\n" " copy : bool, optional\n" " If `True` (default), make copies of the input coordinate arrays.") ret['lonlat'] = ("lon : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`, optional\n" " The longitude coordinate for this object (``lat`` must also be\n" " given and ``data`` must be ``None``).\n" " Not needed if ``data`` is given.\n" " lat : `~astropy.coordinates.Angle` or `~astropy.units.Quantity`, optional\n" " The latitude coordinate for this object (``lon`` must also be\n" " given and ``data`` must be ``None``).\n" " Not needed if ``data`` is given.") ret['radius'] = ("radius : `~astropy.units.Quantity`, optional\n" " The radial distance coordinate from Sun center for this object.\n" " Defaults to the radius of the Sun. Not needed if ``data`` is given.") ret['distance_sun'] = ("distance : `~astropy.units.Quantity`, optional\n" " The distance coordinate from Sun center for this object.\n" " Not needed if ``data`` is given.") ret['distance_earth'] = ("distance : `~astropy.units.Quantity`, optional\n" " The distance coordinate from Earth center for this object.\n" " Not needed if ``data`` is given.") ret['xyz'] = ("x : `~astropy.units.Quantity`, optional\n" " X-axis coordinate for this object. Not needed if ``data`` is given.\n" " y : `~astropy.units.Quantity`, optional\n" " Y-axis coordinate for this object. Not needed if ``data`` is given.\n" " z : `~astropy.units.Quantity`, optional\n" " Z-axis coordinate for this object. Not needed if ``data`` is given.") ret['observer'] = ("observer : `~sunpy.coordinates.frames.HeliographicStonyhurst`, str\n" " The location of the observer. If a string is provided,\n" " it must be a solar system body that can be parsed by\n" " `~sunpy.coordinates.ephemeris.get_body_heliographic_stonyhurst`\n" " at the time ``obstime``. Defaults to Earth center.") ret['rsun'] = ("rsun : `~astropy.units.Quantity`\n" " The radius of the Sun in length units. Used to convert a 2D\n" " coordinate (i.e., no ``radius`` component) to a 3D coordinate by\n" " assuming that the coordinate is on the surface of the Sun. Defaults\n" " to the photospheric radius as defined in `sunpy.sun.constants`.") ret['equinox'] = (f"equinox : {_variables_for_parse_time_docstring()['parse_time_types']}\n" " The date for the mean vernal equinox.\n" " Defaults to the J2000.0 equinox.") return ret class SunPyBaseCoordinateFrame(BaseCoordinateFrame): """ Base class for sunpy coordinate frames. This class is not intended to be used directly and has no transformations defined. * Defines the frame attribute ``obstime`` for observation time. * Defines a default wrap angle of 180 degrees for longitude in spherical coordinates, which can be overridden via the class variable ``_wrap_angle``. * Inject a nice way of representing the object which the coordinate represents. """ obstime = TimeFrameAttributeSunPy() default_representation = SphericalRepresentation default_differential = SphericalDifferential frame_specific_representation_info = { SphericalDifferential: [RepresentationMapping('d_lon', 'd_lon', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_lat', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_distance', u.km/u.s)], } _wrap_angle = 180*u.deg # for longitude in spherical coordinates def __init__(self, *args, **kwargs): self.object_name = None # If wrap_longitude=False is passed in, do not impose a specific wrap angle for the frame if not kwargs.pop('wrap_longitude', True): self._wrap_angle = None super().__init__(*args, **kwargs) # If obstime is specified, treat the default observer (None) as explicitly set if self.obstime is not None and self.is_frame_attr_default('observer'): self._attr_names_with_defaults.remove('observer') return def represent_as(self, base, s='base', in_frame_units=False): data = super().represent_as(base, s, in_frame_units=in_frame_units) # If a frame wrap angle is set, use that wrap angle for any spherical representations. if self._wrap_angle is not None and \ isinstance(data, (UnitSphericalRepresentation, SphericalRepresentation)): data.lon.wrap_angle = self._wrap_angle return data def __str__(self): # We override this here so that when you print a SkyCoord it shows the # observer as the string and not the whole massive coordinate. if getattr(self, "object_name", None): return f"<{self.__class__.__name__} Coordinate for '{self.object_name}'>" else: return super().__str__() @property def _is_2d(self): return (self._data is not None and self._data.norm().unit is u.one and u.allclose(self._data.norm(), 1*u.one)) def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) # TODO: Remove this after the minimum Astropy dependency includes astropy/astropy#12005 cls._fix_property_docstrings() @classmethod def _fix_property_docstrings(cls): # This class method adds docstrings to properties dynamically created by # BaseCoordinateFrame.__init_subclass__(). Accordingly, this method needs to itself be # called from SunPyBaseCoordinateFrame.__init_subclass__() to work for our subclasses. property_docstrings = { 'default_representation': "Default representation for position data", 'default_differential': "Default representation for differential data", 'frame_specific_representation_info': "Mapping for frame-specific component names", } for prop, docstring in property_docstrings.items(): if getattr(cls, prop).__doc__ is None: setattr(getattr(cls, prop), '__doc__', docstring) # TODO: Remove this after the minimum Astropy dependency includes astropy/astropy#12005 SunPyBaseCoordinateFrame._fix_property_docstrings() class BaseHeliographic(SunPyBaseCoordinateFrame): """ Base class for HeliographicCarrington (HGC) and HeliographicStonyhurst (HGS) frames. This class is not intended to be used directly and has no transformations defined. """ frame_specific_representation_info = { SphericalRepresentation: [RepresentationMapping('lon', 'lon', u.deg), RepresentationMapping('lat', 'lat', u.deg), RepresentationMapping('distance', 'radius', None)], SphericalDifferential: [RepresentationMapping('d_lon', 'd_lon', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_lat', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_radius', u.km/u.s)], } rsun = QuantityAttribute(default=_RSUN, unit=u.km) def make_3d(self): """ Returns a fully 3D coordinate based on this coordinate. If this coordinate is only 2D (i.e., no ``radius`` component) or is a unit vector (i.e., the norm of the coordinate is unity), a new coordinate is created that corresponds to the surface of the Sun. That is, the 3D coordinate will retain the ``lon`` and ``lat``, and ``radius`` will be set to the frame's ``rsun`` frame attribute. If this coordinate is already fully 3D, it is directly returned, even if it does not lie on the surface of the Sun. Returns ------- frame : `~sunpy.coordinates.frames.BaseHeliographic` The fully 3D coordinate """ if self._is_2d: return self.realize_frame(self._data * self.rsun) # The coordinate is already 3D return self @add_common_docstring(**_frame_parameters()) class HeliographicStonyhurst(BaseHeliographic): """ A coordinate or frame in the Stonyhurst Heliographic (HGS) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the projection of the Sun-Earth line onto the Sun's equatorial plane. This system is also know as the Heliocentric Earth Equatorial (HEEQ) system when represented using Cartesian components. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``representation_type`` must be keyword arguments):: HeliographicStonyhurst(lon, lat, obstime=obstime) HeliographicStonyhurst(lon, lat, radius, obstime=obstime) HeliographicStonyhurst(x, y, z, representation_type='cartesian', obstime=obstime) Parameters ---------- {data} {lonlat} {radius} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(1*u.deg, 1*u.deg, 2*u.km, ... frame="heliographic_stonyhurst", ... obstime="2010/01/01T00:00:45") >>> sc <SkyCoord (HeliographicStonyhurst: obstime=2010-01-01T00:00:45.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (1., 1., 2.)> >>> sc.frame <HeliographicStonyhurst Coordinate (obstime=2010-01-01T00:00:45.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (1., 1., 2.)> >>> sc = SkyCoord(HeliographicStonyhurst(-10*u.deg, 2*u.deg)) >>> sc <SkyCoord (HeliographicStonyhurst: obstime=None, rsun=695700.0 km): (lon, lat) in deg (-10., 2.)> >>> sc = SkyCoord(CartesianRepresentation(0*u.km, 45*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", ... frame="heliographic_stonyhurst") >>> sc <SkyCoord (HeliographicStonyhurst: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km): (lon, lat, radius) in (deg, deg, km) (90., 2.54480438, 45.04442252)> """ name = "heliographic_stonyhurst" @add_common_docstring(**_frame_parameters()) class HeliographicCarrington(BaseHeliographic): """ A coordinate or frame in the Carrington Heliographic (HGC) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis and Y-axis rotate with a period of 25.38 days. This system differs from Stonyhurst Heliographic (HGS) in its definition of longitude. This longitude is an "apparent" longitude because it takes into account the time it takes for light to travel from the Sun's surface to the observer. Thus, the observer needs to be specified to be able to transform to any other coordinate frame. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``observer`` must be a keyword argument):: HeliographicCarrington(lon, lat, obstime=obstime, observer=observer) HeliographicCarrington(lon, lat, radius, obstime=obstime, observer=observer) If you want to define the location in HGC such that the observer for the coordinate frame is the same as that location (e.g., the location of an observatory in its corresponding HGC frame), use ``observer='self'``:: HeliographicCarrington(lon, lat, radius, obstime=obstime, observer='self') Parameters ---------- {data} {lonlat} {radius} {observer} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(1*u.deg, 2*u.deg, 3*u.km, ... frame="heliographic_carrington", ... observer="earth", ... obstime="2010/01/01T00:00:30") >>> sc <SkyCoord (HeliographicCarrington: obstime=2010-01-01T00:00:30.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (lon, lat, radius) in (deg, deg, km) (1., 2., 3.)> >>> sc = SkyCoord([1,2,3]*u.deg, [4,5,6]*u.deg, [5,6,7]*u.km, ... obstime="2010/01/01T00:00:45", ... observer="self", ... frame="heliographic_carrington") >>> sc <SkyCoord (HeliographicCarrington: obstime=2010-01-01T00:00:45.000, rsun=695700.0 km, observer=self): (lon, lat, radius) in (deg, deg, km) [(1., 4., 5.), (2., 5., 6.), (3., 6., 7.)]> >>> sc = SkyCoord(CartesianRepresentation(0*u.km, 45*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", ... frame="heliographic_carrington") >>> sc <SkyCoord (HeliographicCarrington: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km, observer=None): (lon, lat, radius) in (deg, deg, km) (90., 2.54480438, 45.04442252)> """ name = "heliographic_carrington" _wrap_angle = 360*u.deg observer = ObserverCoordinateAttribute(HeliographicStonyhurst) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if not isinstance(self.observer, BaseCoordinateFrame) and self.observer == 'self' and self._is_2d: raise ValueError("Full 3D coordinate (including radius) must be specified " "when observer='self'.") @add_common_docstring(**_frame_parameters()) class Heliocentric(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric system, which is observer-based. - The origin is the center of the Sun. - The Z-axis is aligned with the Sun-observer line. - The Y-axis is aligned with the component of the vector to the Sun's north pole that is perpendicular to the Z-axis. This frame defaults to a Cartesian component representation, which is known as Heliocentric Cartesian (HCC). This frame can also be represented using cylindrical components, where where ``rho`` is the impact parameter and ``psi`` is the position angle. ``psi`` is measured relative to the west limb, rather than solar north, so is shifted by 90 degrees compared to the convention of the Heliocentric Radial (HCR) system. A new instance can be created using the following signatures (note that if supplied, ``obstime``, ``observer``, and ``representation_type`` must be keyword arguments):: Heliocentric(x, y, z, obstime=obstime, observer=observer) Heliocentric(rho, psi, z, representation_type='cylindrical', obstime=obstime, observer=observer) Parameters ---------- {data} {xyz} {observer} {common} Examples -------- >>> from astropy.coordinates import SkyCoord, CartesianRepresentation >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(CartesianRepresentation(10*u.km, 1*u.km, 2*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-05T00:00:50.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in km (10., 1., 2.)> >>> sc = SkyCoord([1,2]*u.km, [3,4]*u.m, [5,6]*u.cm, ... obstime="2011/01/01T00:00:54", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-01T00:00:54.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in (km, m, cm) [(1., 3., 5.), (2., 4., 6.)]> >>> sc = SkyCoord(CylindricalRepresentation(10*u.km, 60*u.deg, 10*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="heliocentric") >>> sc <SkyCoord (Heliocentric: obstime=2011-01-05T00:00:50.000, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (x, y, z) in km (5., 8.66025404, 10.)> """ default_representation = CartesianRepresentation default_differential = CartesianDifferential frame_specific_representation_info = { CylindricalRepresentation: [RepresentationMapping('phi', 'psi', u.deg)] } observer = ObserverCoordinateAttribute(HeliographicStonyhurst) def represent_as(self, base, s='base', in_frame_units=False): data = super().represent_as(base, s, in_frame_units=in_frame_units) # For cylindrical representations, wrap the `psi` component (natively `phi`) at 360 deg if isinstance(data, CylindricalRepresentation): data.phi.wrap_at(360*u.deg, inplace=True) return data @add_common_docstring(**_frame_parameters()) class Helioprojective(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Helioprojective Cartesian (HPC) system, which is observer-based. - The origin is the location of the observer. - ``Tx`` (aka "theta_x") is the angle relative to the plane containing the Sun-observer line and the Sun's rotation axis, with positive values in the direction of the Sun's west limb. - ``Ty`` (aka "theta_y") is the angle relative to the Sun's equatorial plane, with positive values in the direction of the Sun's north pole. - ``distance`` is the Sun-observer distance. This system is frequently used in a projective form without ``distance`` specified. For observations looking very close to the center of the Sun, where the small-angle approximation is appropriate, ``Tx`` and ``Ty`` can be approximated as Cartesian components. A new instance can be created using the following signatures (note that if supplied, ``obstime`` and ``observer`` must be keyword arguments):: Helioprojective(Tx, Ty, obstime=obstime, observer=observer) Helioprojective(Tx, Ty, distance, obstime=obstime, observer=observer) Parameters ---------- {data} Tx : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` The theta_x coordinate for this object. Not needed if ``data`` is given. Ty : `~astropy.coordinates.Angle` or `~astropy.units.Quantity` The theta_y coordinate for this object. Not needed if ``data`` is given. distance : `~astropy.units.Quantity` The distance coordinate from the observer for this object. Not needed if ``data`` is given. {observer} {rsun} {common} Examples -------- >>> from astropy.coordinates import SkyCoord >>> import sunpy.coordinates >>> import astropy.units as u >>> sc = SkyCoord(0*u.deg, 0*u.deg, 5*u.km, ... obstime="2010/01/01T00:00:00", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2010-01-01T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, km) (0., 0., 5.)> >>> sc = SkyCoord(0*u.deg, 0*u.deg, ... obstime="2010/01/01T00:00:00", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2010-01-01T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty) in arcsec (0., 0.)> >>> sc = SkyCoord(CartesianRepresentation(1*u.AU, 1e5*u.km, -2e5*u.km), ... obstime="2011/01/05T00:00:50", observer="earth", frame="helioprojective") >>> sc <SkyCoord (Helioprojective: obstime=2011-01-05T00:00:50.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) (137.87948623, -275.75878762, 1.00000112)> """ frame_specific_representation_info = { SphericalRepresentation: [RepresentationMapping('lon', 'Tx', u.arcsec), RepresentationMapping('lat', 'Ty', u.arcsec), RepresentationMapping('distance', 'distance', None)], SphericalDifferential: [RepresentationMapping('d_lon', 'd_Tx', u.arcsec/u.s), RepresentationMapping('d_lat', 'd_Ty', u.arcsec/u.s), RepresentationMapping('d_distance', 'd_distance', u.km/u.s)], UnitSphericalRepresentation: [RepresentationMapping('lon', 'Tx', u.arcsec), RepresentationMapping('lat', 'Ty', u.arcsec)], } rsun = QuantityAttribute(default=_RSUN, unit=u.km) observer = ObserverCoordinateAttribute(HeliographicStonyhurst) @property def angular_radius(self): """ Angular radius of the Sun as seen by the observer. The ``rsun`` frame attribute is the radius of the Sun in length units. The tangent vector from the observer to the edge of the Sun forms a right-angle triangle with the radius of the Sun as the far side and the Sun-observer distance as the hypotenuse. Thus, the sine of the angular radius of the Sun is ratio of these two distances. """ from sunpy.coordinates.sun import _angular_radius # avoiding a circular import if not isinstance(self.observer, HeliographicStonyhurst): if self.observer is None: raise ValueError("The observer must be defined, not `None`.") raise ValueError("The observer must be fully defined by specifying `obstime`.") return _angular_radius(self.rsun, self.observer.radius) def make_3d(self): """ This method calculates the third coordinate of the Helioprojective frame. It assumes that the coordinate point is on the surface of the Sun. If a point in the frame is off limb then NaN will be returned. Returns ------- new_frame : `~sunpy.coordinates.frames.Helioprojective` A new frame instance with all the attributes of the original but now with a third coordinate. """ # Skip if we already are 3D if not self._is_2d: return self if not isinstance(self.observer, BaseCoordinateFrame): raise ConvertError("Cannot calculate distance to the Sun " f"for observer '{self.observer}' " "without `obstime` being specified.") rep = self.represent_as(UnitSphericalRepresentation) lat, lon = rep.lat, rep.lon # Check for the use of floats with lower precision than the native Python float if not set([lon.dtype.type, lat.dtype.type]).issubset([float, np.float64, np.longdouble]): raise SunpyUserWarning("The Helioprojective component values appear to be lower " "precision than the native Python float: " f"Tx is {lon.dtype.name}, and Ty is {lat.dtype.name}. " "To minimize precision loss, you may want to cast the values to " "`float` or `numpy.float64` via the NumPy method `.astype()`.") # Calculate the distance to the surface of the Sun using the law of cosines cos_alpha = np.cos(lat) * np.cos(lon) c = self.observer.radius**2 - self.rsun**2 b = -2 * self.observer.radius * cos_alpha # Ignore sqrt of NaNs with np.errstate(invalid='ignore'): d = ((-1*b) - np.sqrt(b**2 - 4*c)) / 2 # use the "near" solution if self._spherical_screen: sphere_center = self._spherical_screen['center'].transform_to(self).cartesian c = sphere_center.norm()**2 - self._spherical_screen['radius']**2 b = -2 * sphere_center.dot(rep) # Ignore sqrt of NaNs with np.errstate(invalid='ignore'): dd = ((-1*b) + np.sqrt(b**2 - 4*c)) / 2 # use the "far" solution d = np.fmin(d, dd) if self._spherical_screen['only_off_disk'] else dd return self.realize_frame(SphericalRepresentation(lon=lon, lat=lat, distance=d)) _spherical_screen = None @classmethod @contextmanager def assume_spherical_screen(cls, center, only_off_disk=False): """ Context manager to interpret 2D coordinates as being on the inside of a spherical screen. The radius of the screen is the distance between the specified ``center`` and Sun center. This ``center`` does not have to be the same as the observer location for the coordinate frame. If they are the same, then this context manager is equivalent to assuming that the helioprojective "zeta" component is zero. This replaces the default assumption where 2D coordinates are mapped onto the surface of the Sun. Parameters ---------- center : `~astropy.coordinates.SkyCoord` The center of the spherical screen only_off_disk : `bool`, optional If `True`, apply this assumption only to off-disk coordinates, with on-disk coordinates still mapped onto the surface of the Sun. Defaults to `False`. Examples -------- .. minigallery:: sunpy.coordinates.Helioprojective.assume_spherical_screen >>> import astropy.units as u >>> from sunpy.coordinates import Helioprojective >>> h = Helioprojective(range(7)*u.arcsec*319, [0]*7*u.arcsec, ... observer='earth', obstime='2020-04-08') >>> print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 0.99660825), ( 319., 0., 0.99687244), ( 638., 0., 0.99778472), ( 957., 0., 1.00103285), (1276., 0., nan), (1595., 0., nan), (1914., 0., nan)]> >>> with Helioprojective.assume_spherical_screen(h.observer): ... print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 1.00125872), ( 319., 0., 1.00125872), ( 638., 0., 1.00125872), ( 957., 0., 1.00125872), (1276., 0., 1.00125872), (1595., 0., 1.00125872), (1914., 0., 1.00125872)]> >>> with Helioprojective.assume_spherical_screen(h.observer, only_off_disk=True): ... print(h.make_3d()) <Helioprojective Coordinate (obstime=2020-04-08T00:00:00.000, rsun=695700.0 km, observer=<HeliographicStonyhurst Coordinate for 'earth'>): (Tx, Ty, distance) in (arcsec, arcsec, AU) [( 0., 0., 0.99660825), ( 319., 0., 0.99687244), ( 638., 0., 0.99778472), ( 957., 0., 1.00103285), (1276., 0., 1.00125872), (1595., 0., 1.00125872), (1914., 0., 1.00125872)]> """ try: old_spherical_screen = cls._spherical_screen # nominally None center_hgs = center.transform_to(HeliographicStonyhurst(obstime=center.obstime)) cls._spherical_screen = { 'center': center, 'radius': center_hgs.radius, 'only_off_disk': only_off_disk } yield finally: cls._spherical_screen = old_spherical_screen @add_common_docstring(**_frame_parameters()) class HeliocentricEarthEcliptic(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric Earth Ecliptic (HEE) system. - The origin is the center of the Sun. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the Sun-Earth line. - The Z-axis (+90 degrees latitude) is aligned with the component perpendicular to the X-axis of the mean ecliptic pole at the observation time. Parameters ---------- {data} {lonlat} {distance_sun} {common} """ @add_common_docstring(**_frame_parameters()) class GeocentricSolarEcliptic(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Geocentric Solar Ecliptic (GSE) system. - The origin is the center of the Earth. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the Earth-Sun line. - The Z-axis (+90 degrees latitude) is aligned with the component perpendicular to the X-axis of the mean ecliptic pole at the observation time. Parameters ---------- {data} {lonlat} {distance_earth} {common} Notes ----- Aberration due to Earth motion is not included. """ @add_common_docstring(**_frame_parameters()) class HeliocentricInertial(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Heliocentric Inertial (HCI) system. - The origin is the center of the Sun. - The Z-axis (+90 degrees latitude) is aligned with the Sun's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the solar ascending node on the ecliptic (mean J2000.0). Parameters ---------- {data} {lonlat} {distance_sun} {common} Notes ----- The solar ascending node on the ecliptic lies on the intersection of the solar equatorial plane with the ecliptic plane, not on the intersection of the celestial equatorial plane with the ecliptic plane. """ @add_common_docstring(**_frame_parameters()) class GeocentricEarthEquatorial(SunPyBaseCoordinateFrame): """ A coordinate or frame in the Geocentric Earth Equatorial (GEI) system. - The origin is the center of the Earth. - The Z-axis (+90 degrees latitude) is aligned with the Earth's north pole. - The X-axis (0 degrees longitude and 0 degrees latitude) is aligned with the mean (not true) vernal equinox. Parameters ---------- {data} {lonlat} {distance_earth} {equinox} {common} Notes ----- Aberration due to Earth motion is not included. """ equinox = TimeFrameAttributeSunPy(default=_J2000)

import os.path as osp from copy import deepcopy from datetime import datetime import ignite.distributed as idist import mmcv from functools import partial from ignite.contrib.handlers import ProgressBar from ignite.contrib.metrics import ROC_AUC, AveragePrecision from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine, param_scheduler from ignite.metrics import Accuracy from ignite.utils import manual_seed, setup_logger from mmcv import Config from mmcv.runner import build_optimizer from torch.utils.data import WeightedRandomSampler from ..classifiers import build_classifier from ..datasets import build_dataset from ..losses import build_loss from .eval_hooks import MetricsTextLogger from .step_fn import get_eval_step_fn, get_train_step_fn from .train_hooks import TrainStatsTextLogger from .utils import logits_transform, prob_transform def train_classifier(local_rank: int, cfg: Config) -> None: rank = idist.get_rank() manual_seed(cfg.get('seed', 2022) + rank) device = idist.device() logger = setup_logger( 'imba-explain', filepath=osp.join(cfg.work_dir, f"{datetime.now().strftime("%Y%m%d_%H%M%S")}.log")) env_info_dict = mmcv.collect_env() env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) dash_line = '-' * 60 + '\n' logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line) logger.info(f'Config:\n{cfg.pretty_text}') train_set = build_dataset(cfg.data['train']) val_set = build_dataset(cfg.data['val']) logger.info(f'Training set size: {len(train_set)} samples. Validation set size: {len(val_set)} samples.') data_loader_cfg = deepcopy(cfg.data['data_loader']) use_weighted_sampler = data_loader_cfg.pop('weighted_sampler', False) if use_weighted_sampler: if not hasattr(train_set, 'imba_sampling_weights'): raise ValueError('The training dataset class must implement the method imba_sampling_weights, ' 'when weighted_sampler in data_loader config is True.') weights = train_set.imba_sampling_weights() sampler = WeightedRandomSampler(weights, num_samples=len(train_set)) data_loader_cfg.update({'sampler': sampler, 'shuffle': False}) train_loader = idist.auto_dataloader(train_set, **data_loader_cfg) data_loader_cfg.update({'shuffle': False, 'sampler': None}) val_loader = idist.auto_dataloader(val_set, **data_loader_cfg) epoch_length = len(train_loader) classifier = build_classifier(cfg.classifier) classifier.to(device) classifier = idist.auto_model(classifier, sync_bn=cfg.get('sync_bn', False)) # build trainer optimizer = build_optimizer(classifier, cfg.optimizer) criterion = build_loss(cfg.loss) # let the loss function receive the data distribution information if hasattr(criterion, 'receive_data_dist_info'): criterion.receive_data_dist_info(train_set.get_num_pos_neg()) criterion.to(device) try: has_parameter = next(criterion.parameters()) is not None except StopIteration: has_parameter = False if has_parameter: # in case where loss function contains learnable parameters, use DDP criterion = idist.auto_model(criterion) # when the loss function has learnable parameters, add them to the optimizer's parameter group optimizer.add_param_group({'params': criterion.parameters()}) optimizer = idist.auto_optim(optimizer) trainer = Engine(get_train_step_fn(classifier, criterion, optimizer, device)) trainer.logger = logger # built evaluator eval_step_fn = get_eval_step_fn(classifier, device) evaluator = Engine(eval_step_fn) evaluator.logger = logger # evaluator handlers pbar = ProgressBar(persist=True) pbar.attach(evaluator) # pred_select_inds is only used in cross-dataset test _prob_transform = partial(prob_transform, pred_select_inds=None, target_select_inds=None) _logits_transform = partial(logits_transform, pred_select_inds=None, target_select_inds=None) val_metrics = { 'accuracy': Accuracy(output_transform=_prob_transform, device=device, **cfg.class_metrics['accuracy']), 'roc_auc': ROC_AUC(output_transform=_logits_transform, device=device, **cfg.class_metrics['roc_auc']), 'ap': AveragePrecision(output_transform=_logits_transform, device=device, **cfg.class_metrics['ap']) } for name, metric in val_metrics.items(): metric.attach(engine=evaluator, name=name) metrics_logger = MetricsTextLogger(logger=logger) metrics_logger.attach(evaluator, trainer) # trainer handlers def run_validation(engine_train: Engine, engine_val: Engine) -> None: engine_val.run(val_loader) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=cfg.val_interval), run_validation, evaluator) if cfg.cosine_annealing: cycle_size = cfg.max_epochs * epoch_length lr = cfg.optimizer['lr'] lr_scheduler = param_scheduler.CosineAnnealingScheduler( optimizer=optimizer, param_name='lr', start_value=lr, end_value=lr * 0.01, cycle_size=cycle_size) lr_scheduler = param_scheduler.create_lr_scheduler_with_warmup( lr_scheduler, warmup_start_value=0.01 * lr, warmup_duration=1000, warmup_end_value=lr) trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler) to_save = {'classifier': classifier} save_handler = DiskSaver(osp.join(osp.join(cfg.work_dir, 'ckpts')), require_empty=False) score_fn = Checkpoint.get_default_score_fn('roc_auc') ckpt_handler = Checkpoint( to_save, save_handler, n_saved=cfg.get('n_saved', 1), score_name='roc_auc', score_function=score_fn, global_step_transform=global_step_from_engine(trainer, Events.EPOCH_COMPLETED), greater_or_equal=True) evaluator.add_event_handler(Events.COMPLETED, ckpt_handler) train_stats_logger = TrainStatsTextLogger(interval=cfg.log_interval, logger=logger) train_stats_logger.attach(trainer, optimizer) trainer.run(data=train_loader, max_epochs=cfg.max_epochs)

import os.path as osp from copy import deepcopy from datetime import datetime import ignite.distributed as idist import mmcv from functools import partial from ignite.contrib.handlers import ProgressBar from ignite.contrib.metrics import ROC_AUC, AveragePrecision from ignite.engine import Engine, Events from ignite.handlers import Checkpoint, DiskSaver, global_step_from_engine, param_scheduler from ignite.metrics import Accuracy from ignite.utils import manual_seed, setup_logger from mmcv import Config from mmcv.runner import build_optimizer from torch.utils.data import WeightedRandomSampler from ..classifiers import build_classifier from ..datasets import build_dataset from ..losses import build_loss from .eval_hooks import MetricsTextLogger from .step_fn import get_eval_step_fn, get_train_step_fn from .train_hooks import TrainStatsTextLogger from .utils import logits_transform, prob_transform def train_classifier(local_rank: int, cfg: Config) -> None: rank = idist.get_rank() manual_seed(cfg.get('seed', 2022) + rank) device = idist.device() logger = setup_logger( 'imba-explain', filepath=osp.join(cfg.work_dir, f"{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")) env_info_dict = mmcv.collect_env() env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) dash_line = '-' * 60 + '\n' logger.info('Environment info:\n' + dash_line + env_info + '\n' + dash_line) logger.info(f'Config:\n{cfg.pretty_text}') train_set = build_dataset(cfg.data['train']) val_set = build_dataset(cfg.data['val']) logger.info(f'Training set size: {len(train_set)} samples. Validation set size: {len(val_set)} samples.') data_loader_cfg = deepcopy(cfg.data['data_loader']) use_weighted_sampler = data_loader_cfg.pop('weighted_sampler', False) if use_weighted_sampler: if not hasattr(train_set, 'imba_sampling_weights'): raise ValueError('The training dataset class must implement the method imba_sampling_weights, ' 'when weighted_sampler in data_loader config is True.') weights = train_set.imba_sampling_weights() sampler = WeightedRandomSampler(weights, num_samples=len(train_set)) data_loader_cfg.update({'sampler': sampler, 'shuffle': False}) train_loader = idist.auto_dataloader(train_set, **data_loader_cfg) data_loader_cfg.update({'shuffle': False, 'sampler': None}) val_loader = idist.auto_dataloader(val_set, **data_loader_cfg) epoch_length = len(train_loader) classifier = build_classifier(cfg.classifier) classifier.to(device) classifier = idist.auto_model(classifier, sync_bn=cfg.get('sync_bn', False)) # build trainer optimizer = build_optimizer(classifier, cfg.optimizer) criterion = build_loss(cfg.loss) # let the loss function receive the data distribution information if hasattr(criterion, 'receive_data_dist_info'): criterion.receive_data_dist_info(train_set.get_num_pos_neg()) criterion.to(device) try: has_parameter = next(criterion.parameters()) is not None except StopIteration: has_parameter = False if has_parameter: # in case where loss function contains learnable parameters, use DDP criterion = idist.auto_model(criterion) # when the loss function has learnable parameters, add them to the optimizer's parameter group optimizer.add_param_group({'params': criterion.parameters()}) optimizer = idist.auto_optim(optimizer) trainer = Engine(get_train_step_fn(classifier, criterion, optimizer, device)) trainer.logger = logger # built evaluator eval_step_fn = get_eval_step_fn(classifier, device) evaluator = Engine(eval_step_fn) evaluator.logger = logger # evaluator handlers pbar = ProgressBar(persist=True) pbar.attach(evaluator) # pred_select_inds is only used in cross-dataset test _prob_transform = partial(prob_transform, pred_select_inds=None, target_select_inds=None) _logits_transform = partial(logits_transform, pred_select_inds=None, target_select_inds=None) val_metrics = { 'accuracy': Accuracy(output_transform=_prob_transform, device=device, **cfg.class_metrics['accuracy']), 'roc_auc': ROC_AUC(output_transform=_logits_transform, device=device, **cfg.class_metrics['roc_auc']), 'ap': AveragePrecision(output_transform=_logits_transform, device=device, **cfg.class_metrics['ap']) } for name, metric in val_metrics.items(): metric.attach(engine=evaluator, name=name) metrics_logger = MetricsTextLogger(logger=logger) metrics_logger.attach(evaluator, trainer) # trainer handlers def run_validation(engine_train: Engine, engine_val: Engine) -> None: engine_val.run(val_loader) trainer.add_event_handler(Events.EPOCH_COMPLETED(every=cfg.val_interval), run_validation, evaluator) if cfg.cosine_annealing: cycle_size = cfg.max_epochs * epoch_length lr = cfg.optimizer['lr'] lr_scheduler = param_scheduler.CosineAnnealingScheduler( optimizer=optimizer, param_name='lr', start_value=lr, end_value=lr * 0.01, cycle_size=cycle_size) lr_scheduler = param_scheduler.create_lr_scheduler_with_warmup( lr_scheduler, warmup_start_value=0.01 * lr, warmup_duration=1000, warmup_end_value=lr) trainer.add_event_handler(Events.ITERATION_STARTED, lr_scheduler) to_save = {'classifier': classifier} save_handler = DiskSaver(osp.join(osp.join(cfg.work_dir, 'ckpts')), require_empty=False) score_fn = Checkpoint.get_default_score_fn('roc_auc') ckpt_handler = Checkpoint( to_save, save_handler, n_saved=cfg.get('n_saved', 1), score_name='roc_auc', score_function=score_fn, global_step_transform=global_step_from_engine(trainer, Events.EPOCH_COMPLETED), greater_or_equal=True) evaluator.add_event_handler(Events.COMPLETED, ckpt_handler) train_stats_logger = TrainStatsTextLogger(interval=cfg.log_interval, logger=logger) train_stats_logger.attach(trainer, optimizer) trainer.run(data=train_loader, max_epochs=cfg.max_epochs)

"""Test Home Assistant template helper methods.""" from datetime import datetime import math import random import pytest import pytz from homeassistant.components import group from homeassistant.const import ( LENGTH_METERS, MASS_GRAMS, MATCH_ALL, PRESSURE_PA, TEMP_CELSIUS, VOLUME_LITERS, ) from homeassistant.exceptions import TemplateError from homeassistant.helpers import template import homeassistant.util.dt as dt_util from homeassistant.util.unit_system import UnitSystem from tests.async_mock import patch def _set_up_units(hass): """Set up the tests.""" hass.config.units = UnitSystem( "custom", TEMP_CELSIUS, LENGTH_METERS, VOLUME_LITERS, MASS_GRAMS, PRESSURE_PA ) def render_to_info(hass, template_str, variables=None): """Create render info from template.""" tmp = template.Template(template_str, hass) return tmp.async_render_to_info(variables) def extract_entities(hass, template_str, variables=None): """Extract entities from a template.""" info = render_to_info(hass, template_str, variables) # pylint: disable=protected-access assert not hasattr(info, "_domains") return info._entities def assert_result_info(info, result, entities=None, domains=None, all_states=False): """Check result info.""" assert info.result == result # pylint: disable=protected-access assert info._all_states == all_states assert info.filter_lifecycle("invalid_entity_name.somewhere") == all_states if entities is not None: assert info._entities == frozenset(entities) assert all([info.filter(entity) for entity in entities]) assert not info.filter("invalid_entity_name.somewhere") else: assert not info._entities if domains is not None: assert info._domains == frozenset(domains) assert all([info.filter_lifecycle(domain + ".entity") for domain in domains]) else: assert not hasattr(info, "_domains") def test_template_equality(): """Test template comparison and hashing.""" template_one = template.Template("{{ template_one }}") template_one_1 = template.Template("{{ template_one }}") template_two = template.Template("{{ template_two }}") assert template_one == template_one_1 assert template_one != template_two assert hash(template_one) == hash(template_one_1) assert hash(template_one) != hash(template_two) assert str(template_one_1) == 'Template("{{ template_one }}")' with pytest.raises(TypeError): template.Template(["{{ template_one }}"]) def test_invalid_template(hass): """Invalid template raises error.""" tmpl = template.Template("{{", hass) with pytest.raises(TemplateError): tmpl.ensure_valid() with pytest.raises(TemplateError): tmpl.async_render() info = tmpl.async_render_to_info() with pytest.raises(TemplateError): assert info.result == "impossible" tmpl = template.Template("{{states(keyword)}}", hass) tmpl.ensure_valid() with pytest.raises(TemplateError): tmpl.async_render() def test_referring_states_by_entity_id(hass): """Test referring states by entity id.""" hass.states.async_set("test.object", "happy") assert ( template.Template("{{ states.test.object.state }}", hass).async_render() == "happy" ) assert ( template.Template('{{ states["test.object"].state }}', hass).async_render() == "happy" ) assert ( template.Template('{{ states("test.object") }}', hass).async_render() == "happy" ) def test_invalid_entity_id(hass): """Test referring states by entity id.""" with pytest.raises(TemplateError): template.Template('{{ states["big.fat..."] }}', hass).async_render() with pytest.raises(TemplateError): template.Template('{{ states.test["big.fat..."] }}', hass).async_render() with pytest.raises(TemplateError): template.Template('{{ states["invalid/domain"] }}', hass).async_render() def test_raise_exception_on_error(hass): """Test raising an exception on error.""" with pytest.raises(TemplateError): template.Template("{{ invalid_syntax").ensure_valid() def test_iterating_all_states(hass): """Test iterating all states.""" tmpl_str = "{% for state in states %}{{ state.state }}{% endfor %}" info = render_to_info(hass, tmpl_str) assert_result_info(info, "", all_states=True) hass.states.async_set("test.object", "happy") hass.states.async_set("sensor.temperature", 10) info = render_to_info(hass, tmpl_str) assert_result_info( info, "10happy", entities=["test.object", "sensor.temperature"], all_states=True ) def test_iterating_domain_states(hass): """Test iterating domain states.""" tmpl_str = "{% for state in states.sensor %}{{ state.state }}{% endfor %}" info = render_to_info(hass, tmpl_str) assert_result_info(info, "", domains=["sensor"]) hass.states.async_set("test.object", "happy") hass.states.async_set("sensor.back_door", "open") hass.states.async_set("sensor.temperature", 10) info = render_to_info(hass, tmpl_str) assert_result_info( info, "open10", entities=["sensor.back_door", "sensor.temperature"], domains=["sensor"], ) def test_float(hass): """Test float.""" hass.states.async_set("sensor.temperature", "12") assert ( template.Template( "{{ float(states.sensor.temperature.state) }}", hass ).async_render() == "12.0" ) assert ( template.Template( "{{ float(states.sensor.temperature.state) > 11 }}", hass ).async_render() == "True" ) assert ( template.Template("{{ float('forgiving') }}", hass).async_render() == "forgiving" ) def test_rounding_value(hass): """Test rounding value.""" hass.states.async_set("sensor.temperature", 12.78) assert ( template.Template( "{{ states.sensor.temperature.state | round(1) }}", hass ).async_render() == "12.8" ) assert ( template.Template( "{{ states.sensor.temperature.state | multiply(10) | round }}", hass ).async_render() == "128" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "floor") }}', hass ).async_render() == "12.7" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "ceil") }}', hass ).async_render() == "12.8" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "half") }}', hass ).async_render() == "13.0" ) def test_rounding_value_get_original_value_on_error(hass): """Test rounding value get original value on error.""" assert template.Template("{{ None | round }}", hass).async_render() == "None" assert ( template.Template('{{ "no_number" | round }}', hass).async_render() == "no_number" ) def test_multiply(hass): """Test multiply.""" tests = {None: "None", 10: "100", '"abcd"': "abcd"} for inp, out in tests.items(): assert ( template.Template( "{{ %s | multiply(10) | round }}" % inp, hass ).async_render() == out ) def test_logarithm(hass): """Test logarithm.""" tests = [ (4, 2, "2.0"), (1000, 10, "3.0"), (math.e, "", "1.0"), ('"invalid"', "_", "invalid"), (10, '"invalid"', "10.0"), ] for value, base, expected in tests: assert ( template.Template( f"{{{{ {value} | log({base}) | round(1) }}}}", hass ).async_render() == expected ) assert ( template.Template( f"{{{{ log({value}, {base}) | round(1) }}}}", hass ).async_render() == expected ) def test_sine(hass): """Test sine.""" tests = [ (0, "0.0"), (math.pi / 2, "1.0"), (math.pi, "0.0"), (math.pi * 1.5, "-1.0"), (math.pi / 10, "0.309"), ('"duck"', "duck"), ] for value, expected in tests: assert ( template.Template("{{ %s | sin | round(3) }}" % value, hass).async_render() == expected ) def test_cos(hass): """Test cosine.""" tests = [ (0, "1.0"), (math.pi / 2, "0.0"), (math.pi, "-1.0"), (math.pi * 1.5, "-0.0"), (math.pi / 10, "0.951"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | cos | round(3) }}" % value, hass).async_render() == expected ) def test_tan(hass): """Test tangent.""" tests = [ (0, "0.0"), (math.pi, "-0.0"), (math.pi / 180 * 45, "1.0"), (math.pi / 180 * 90, "1.633123935319537e+16"), (math.pi / 180 * 135, "-1.0"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | tan | round(3) }}" % value, hass).async_render() == expected ) def test_sqrt(hass): """Test square root.""" tests = [ (0, "0.0"), (1, "1.0"), (2, "1.414"), (10, "3.162"), (100, "10.0"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | sqrt | round(3) }}" % value, hass).async_render() == expected ) def test_arc_sine(hass): """Test arcus sine.""" tests = [ (-2.0, "-2.0"), # value error (-1.0, "-1.571"), (-0.5, "-0.524"), (0.0, "0.0"), (0.5, "0.524"), (1.0, "1.571"), (2.0, "2.0"), # value error ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | asin | round(3) }}" % value, hass).async_render() == expected ) def test_arc_cos(hass): """Test arcus cosine.""" tests = [ (-2.0, "-2.0"), # value error (-1.0, "3.142"), (-0.5, "2.094"), (0.0, "1.571"), (0.5, "1.047"), (1.0, "0.0"), (2.0, "2.0"), # value error ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | acos | round(3) }}" % value, hass).async_render() == expected ) def test_arc_tan(hass): """Test arcus tangent.""" tests = [ (-10.0, "-1.471"), (-2.0, "-1.107"), (-1.0, "-0.785"), (-0.5, "-0.464"), (0.0, "0.0"), (0.5, "0.464"), (1.0, "0.785"), (2.0, "1.107"), (10.0, "1.471"), ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | atan | round(3) }}" % value, hass).async_render() == expected ) def test_arc_tan2(hass): """Test two parameter version of arcus tangent.""" tests = [ (-10.0, -10.0, "-2.356"), (-10.0, 0.0, "-1.571"), (-10.0, 10.0, "-0.785"), (0.0, -10.0, "3.142"), (0.0, 0.0, "0.0"), (0.0, 10.0, "0.0"), (10.0, -10.0, "2.356"), (10.0, 0.0, "1.571"), (10.0, 10.0, "0.785"), (-4.0, 3.0, "-0.927"), (-1.0, 2.0, "-0.464"), (2.0, 1.0, "1.107"), ('"duck"', '"goose"', "('duck', 'goose')"), ] for y, x, expected in tests: assert ( template.Template( f"{{{{ ({y}, {x}) | atan2 | round(3) }}}}", hass ).async_render() == expected ) assert ( template.Template( f"{{{{ atan2({y}, {x}) | round(3) }}}}", hass ).async_render() == expected ) def test_strptime(hass): """Test the parse timestamp method.""" tests = [ ("2016-10-19 15:22:05.588122 UTC", "%Y-%m-%d %H:%M:%S.%f %Z", None), ("2016-10-19 15:22:05.588122+0100", "%Y-%m-%d %H:%M:%S.%f%z", None), ("2016-10-19 15:22:05.588122", "%Y-%m-%d %H:%M:%S.%f", None), ("2016-10-19", "%Y-%m-%d", None), ("2016", "%Y", None), ("15:22:05", "%H:%M:%S", None), ("1469119144", "%Y", "1469119144"), ("invalid", "%Y", "invalid"), ] for inp, fmt, expected in tests: if expected is None: expected = datetime.strptime(inp, fmt) temp = f"{{{{ strptime("{inp}", "{fmt}') }}}}" assert template.Template(temp, hass).async_render() == str(expected) def test_timestamp_custom(hass): """Test the timestamps to custom filter.""" now = dt_util.utcnow() tests = [ (None, None, None, "None"), (1469119144, None, True, "2016-07-21 16:39:04"), (1469119144, "%Y", True, "2016"), (1469119144, "invalid", True, "invalid"), (dt_util.as_timestamp(now), None, False, now.strftime("%Y-%m-%d %H:%M:%S")), ] for inp, fmt, local, out in tests: if fmt: fil = f"timestamp_custom('{fmt}')" elif fmt and local: fil = f"timestamp_custom('{fmt}', {local})" else: fil = "timestamp_custom" assert template.Template(f"{{{{ {inp} | {fil} }}}}", hass).async_render() == out def test_timestamp_local(hass): """Test the timestamps to local filter.""" tests = {None: "None", 1469119144: "2016-07-21 16:39:04"} for inp, out in tests.items(): assert ( template.Template("{{ %s | timestamp_local }}" % inp, hass).async_render() == out ) def test_to_json(hass): """Test the object to JSON string filter.""" # Note that we're not testing the actual json.loads and json.dumps methods, # only the filters, so we don't need to be exhaustive with our sample JSON. expected_result = '{"Foo": "Bar"}' actual_result = template.Template( "{{ {'Foo': 'Bar'} | to_json }}", hass ).async_render() assert actual_result == expected_result def test_from_json(hass): """Test the JSON string to object filter.""" # Note that we're not testing the actual json.loads and json.dumps methods, # only the filters, so we don't need to be exhaustive with our sample JSON. expected_result = "Bar" actual_result = template.Template( '{{ (\'{"Foo": "Bar"}\' | from_json).Foo }}', hass ).async_render() assert actual_result == expected_result def test_min(hass): """Test the min filter.""" assert template.Template("{{ [1, 2, 3] | min }}", hass).async_render() == "1" def test_max(hass): """Test the max filter.""" assert template.Template("{{ [1, 2, 3] | max }}", hass).async_render() == "3" def test_ord(hass): """Test the ord filter.""" assert template.Template('{{ "d" | ord }}', hass).async_render() == "100" def test_base64_encode(hass): """Test the base64_encode filter.""" assert ( template.Template('{{ "homeassistant" | base64_encode }}', hass).async_render() == "aG9tZWFzc2lzdGFudA==" ) def test_base64_decode(hass): """Test the base64_decode filter.""" assert ( template.Template( '{{ "aG9tZWFzc2lzdGFudA==" | base64_decode }}', hass ).async_render() == "homeassistant" ) def test_ordinal(hass): """Test the ordinal filter.""" tests = [ (1, "1st"), (2, "2nd"), (3, "3rd"), (4, "4th"), (5, "5th"), (12, "12th"), (100, "100th"), (101, "101st"), ] for value, expected in tests: assert ( template.Template("{{ %s | ordinal }}" % value, hass).async_render() == expected ) def test_timestamp_utc(hass): """Test the timestamps to local filter.""" now = dt_util.utcnow() tests = { None: "None", 1469119144: "2016-07-21 16:39:04", dt_util.as_timestamp(now): now.strftime("%Y-%m-%d %H:%M:%S"), } for inp, out in tests.items(): assert ( template.Template("{{ %s | timestamp_utc }}" % inp, hass).async_render() == out ) def test_as_timestamp(hass): """Test the as_timestamp function.""" assert ( template.Template('{{ as_timestamp("invalid") }}', hass).async_render() == "None" ) hass.mock = None assert ( template.Template("{{ as_timestamp(states.mock) }}", hass).async_render() == "None" ) tpl = ( '{{ as_timestamp(strptime("2024-02-03T09:10:24+0000", ' '"%Y-%m-%dT%H:%M:%S%z")) }}' ) assert template.Template(tpl, hass).async_render() == "1706951424.0" @patch.object(random, "choice") def test_random_every_time(test_choice, hass): """Ensure the random filter runs every time, not just once.""" tpl = template.Template("{{ [1,2] | random }}", hass) test_choice.return_value = "foo" assert tpl.async_render() == "foo" test_choice.return_value = "bar" assert tpl.async_render() == "bar" def test_passing_vars_as_keywords(hass): """Test passing variables as keywords.""" assert template.Template("{{ hello }}", hass).async_render(hello=127) == "127" def test_passing_vars_as_vars(hass): """Test passing variables as variables.""" assert template.Template("{{ hello }}", hass).async_render({"hello": 127}) == "127" def test_passing_vars_as_list(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello }}", hass), {"hello": ["foo", "bar"]} ) == "['foo', 'bar']" ) def test_passing_vars_as_list_element(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello[1] }}", hass), {"hello": ["foo", "bar"]} ) == "bar" ) def test_passing_vars_as_dict_element(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello.foo }}", hass), {"hello": {"foo": "bar"}} ) == "bar" ) def test_passing_vars_as_dict(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello }}", hass), {"hello": {"foo": "bar"}} ) == "{'foo': 'bar'}" ) def test_render_with_possible_json_value_with_valid_json(hass): """Render with possible JSON value with valid JSON.""" tpl = template.Template("{{ value_json.hello }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "world" def test_render_with_possible_json_value_with_invalid_json(hass): """Render with possible JSON value with invalid JSON.""" tpl = template.Template("{{ value_json }}", hass) assert tpl.async_render_with_possible_json_value("{ I AM NOT JSON }") == "" def test_render_with_possible_json_value_with_template_error_value(hass): """Render with possible JSON value with template error value.""" tpl = template.Template("{{ non_existing.variable }}", hass) assert tpl.async_render_with_possible_json_value("hello", "-") == "-" def test_render_with_possible_json_value_with_missing_json_value(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.goodbye }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "" def test_render_with_possible_json_value_valid_with_is_defined(hass): """Render with possible JSON value with known JSON object.""" tpl = template.Template("{{ value_json.hello|is_defined }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "world" def test_render_with_possible_json_value_undefined_json(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.bye|is_defined }}", hass) assert ( tpl.async_render_with_possible_json_value('{"hello": "world"}') == '{"hello": "world"}' ) def test_render_with_possible_json_value_undefined_json_error_value(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.bye|is_defined }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}', "") == "" def test_render_with_possible_json_value_non_string_value(hass): """Render with possible JSON value with non-string value.""" tpl = template.Template( """ {{ strptime(value~'+0000', '%Y-%m-%d %H:%M:%S%z') }} """, hass, ) value = datetime(2019, 1, 18, 12, 13, 14) expected = str(pytz.utc.localize(value)) assert tpl.async_render_with_possible_json_value(value) == expected def test_if_state_exists(hass): """Test if state exists works.""" hass.states.async_set("test.object", "available") tpl = template.Template( "{% if states.test.object %}exists{% else %}not exists{% endif %}", hass ) assert tpl.async_render() == "exists" def test_is_state(hass): """Test is_state method.""" hass.states.async_set("test.object", "available") tpl = template.Template( """ {% if is_state("test.object", "available") %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ is_state("test.noobject", "available") }} """, hass, ) assert tpl.async_render() == "False" def test_is_state_attr(hass): """Test is_state_attr method.""" hass.states.async_set("test.object", "available", {"mode": "on"}) tpl = template.Template( """ {% if is_state_attr("test.object", "mode", "on") %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ is_state_attr("test.noobject", "mode", "on") }} """, hass, ) assert tpl.async_render() == "False" def test_state_attr(hass): """Test state_attr method.""" hass.states.async_set("test.object", "available", {"mode": "on"}) tpl = template.Template( """ {% if state_attr("test.object", "mode") == "on" %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ state_attr("test.noobject", "mode") == None }} """, hass, ) assert tpl.async_render() == "True" def test_states_function(hass): """Test using states as a function.""" hass.states.async_set("test.object", "available") tpl = template.Template('{{ states("test.object") }}', hass) assert tpl.async_render() == "available" tpl2 = template.Template('{{ states("test.object2") }}', hass) assert tpl2.async_render() == "unknown" @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_now(mock_is_safe, hass): """Test now method.""" now = dt_util.now() with patch("homeassistant.util.dt.now", return_value=now): assert ( now.isoformat() == template.Template("{{ now().isoformat() }}", hass).async_render() ) @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_relative_time(mock_is_safe, hass): """Test relative_time method.""" now = datetime.strptime("2000-01-01 10:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z") with patch("homeassistant.util.dt.now", return_value=now): assert ( "1 hour" == template.Template( '{{relative_time(strptime("2000-01-01 09:00:00", "%Y-%m-%d %H:%M:%S"))}}', hass, ).async_render() ) assert ( "2 hours" == template.Template( '{{relative_time(strptime("2000-01-01 09:00:00 +01:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( "1 hour" == template.Template( '{{relative_time(strptime("2000-01-01 03:00:00 -06:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( str(template.strptime("2000-01-01 11:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z")) == template.Template( '{{relative_time(strptime("2000-01-01 11:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( "string" == template.Template('{{relative_time("string")}}', hass,).async_render() ) @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_utcnow(mock_is_safe, hass): """Test utcnow method.""" now = dt_util.utcnow() with patch("homeassistant.util.dt.utcnow", return_value=now): assert ( now.isoformat() == template.Template("{{ utcnow().isoformat() }}", hass).async_render() ) def test_regex_match(hass): """Test regex_match method.""" tpl = template.Template( r""" {{ '123-456-7890' | regex_match('(\\d{3})-(\\d{3})-(\\d{4})') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Home Assistant test' | regex_match('home', True) }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Another Home Assistant test' | regex_match('Home') }} """, hass, ) assert tpl.async_render() == "False" tpl = template.Template( """ {{ ['Home Assistant test'] | regex_match('.*Assist') }} """, hass, ) assert tpl.async_render() == "True" def test_regex_search(hass): """Test regex_search method.""" tpl = template.Template( r""" {{ '123-456-7890' | regex_search('(\\d{3})-(\\d{3})-(\\d{4})') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Home Assistant test' | regex_search('home', True) }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Another Home Assistant test' | regex_search('Home') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ ['Home Assistant test'] | regex_search('Assist') }} """, hass, ) assert tpl.async_render() == "True" def test_regex_replace(hass): """Test regex_replace method.""" tpl = template.Template( r""" {{ 'Hello World' | regex_replace('(Hello\\s)',) }} """, hass, ) assert tpl.async_render() == "World" tpl = template.Template( """ {{ ['Home hinderant test'] | regex_replace('hinder', 'Assist') }} """, hass, ) assert tpl.async_render() == "['Home Assistant test']" def test_regex_findall_index(hass): """Test regex_findall_index method.""" tpl = template.Template( """ {{ 'Flight from JFK to LHR' | regex_findall_index('([A-Z]{3})', 0) }} """, hass, ) assert tpl.async_render() == "JFK" tpl = template.Template( """ {{ 'Flight from JFK to LHR' | regex_findall_index('([A-Z]{3})', 1) }} """, hass, ) assert tpl.async_render() == "LHR" tpl = template.Template( """ {{ ['JFK', 'LHR'] | regex_findall_index('([A-Z]{3})', 1) }} """, hass, ) assert tpl.async_render() == "LHR" def test_bitwise_and(hass): """Test bitwise_and method.""" tpl = template.Template( """ {{ 8 | bitwise_and(8) }} """, hass, ) assert tpl.async_render() == str(8 & 8) tpl = template.Template( """ {{ 10 | bitwise_and(2) }} """, hass, ) assert tpl.async_render() == str(10 & 2) tpl = template.Template( """ {{ 8 | bitwise_and(2) }} """, hass, ) assert tpl.async_render() == str(8 & 2) def test_bitwise_or(hass): """Test bitwise_or method.""" tpl = template.Template( """ {{ 8 | bitwise_or(8) }} """, hass, ) assert tpl.async_render() == str(8 | 8) tpl = template.Template( """ {{ 10 | bitwise_or(2) }} """, hass, ) assert tpl.async_render() == str(10 | 2) tpl = template.Template( """ {{ 8 | bitwise_or(2) }} """, hass, ) assert tpl.async_render() == str(8 | 2) def test_distance_function_with_1_state(hass): """Test distance function with 1 state.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) tpl = template.Template("{{ distance(states.test.object) | round }}", hass) assert tpl.async_render() == "187" def test_distance_function_with_2_states(hass): """Test distance function with 2 states.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( "{{ distance(states.test.object, states.test.object_2) | round }}", hass ) assert tpl.async_render() == "187" def test_distance_function_with_1_coord(hass): """Test distance function with 1 coord.""" _set_up_units(hass) tpl = template.Template('{{ distance("32.87336", "-117.22943") | round }}', hass) assert tpl.async_render() == "187" def test_distance_function_with_2_coords(hass): """Test distance function with 2 coords.""" _set_up_units(hass) assert ( template.Template( '{{ distance("32.87336", "-117.22943", %s, %s) | round }}' % (hass.config.latitude, hass.config.longitude), hass, ).async_render() == "187" ) def test_distance_function_with_1_state_1_coord(hass): """Test distance function with 1 state 1 coord.""" _set_up_units(hass) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("32.87336", "-117.22943", states.test.object_2) ' "| round }}", hass, ) assert tpl.async_render() == "187" tpl2 = template.Template( '{{ distance(states.test.object_2, "32.87336", "-117.22943") ' "| round }}", hass, ) assert tpl2.async_render() == "187" def test_distance_function_return_none_if_invalid_state(hass): """Test distance function return None if invalid state.""" hass.states.async_set("test.object_2", "happy", {"latitude": 10}) tpl = template.Template("{{ distance(states.test.object_2) | round }}", hass) assert tpl.async_render() == "None" def test_distance_function_return_none_if_invalid_coord(hass): """Test distance function return None if invalid coord.""" assert ( template.Template('{{ distance("123", "abc") }}', hass).async_render() == "None" ) assert template.Template('{{ distance("123") }}', hass).async_render() == "None" hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template('{{ distance("123", states.test_object_2) }}', hass) assert tpl.async_render() == "None" def test_distance_function_with_2_entity_ids(hass): """Test distance function with 2 entity ids.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("test.object", "test.object_2") | round }}', hass ) assert tpl.async_render() == "187" def test_distance_function_with_1_entity_1_coord(hass): """Test distance function with 1 entity_id and 1 coord.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("test.object", "32.87336", "-117.22943") | round }}', hass ) assert tpl.async_render() == "187" def test_closest_function_home_vs_domain(hass): """Test closest function home vs domain.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_test_domain.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) assert ( template.Template( "{{ closest(states.test_domain).entity_id }}", hass ).async_render() == "test_domain.object" ) assert ( template.Template( "{{ (states.test_domain | closest).entity_id }}", hass ).async_render() == "test_domain.object" ) def test_closest_function_home_vs_all_states(hass): """Test closest function home vs all states.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain_2.and_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) assert ( template.Template("{{ closest(states).entity_id }}", hass).async_render() == "test_domain_2.and_closer" ) assert ( template.Template("{{ (states | closest).entity_id }}", hass).async_render() == "test_domain_2.and_closer" ) async def test_closest_function_home_vs_group_entity_id(hass): """Test closest function home vs group entity id.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_in_group.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) await group.Group.async_create_group(hass, "location group", ["test_domain.object"]) info = render_to_info(hass, '{{ closest("group.location_group").entity_id }}') assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) async def test_closest_function_home_vs_group_state(hass): """Test closest function home vs group state.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_in_group.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) await group.Group.async_create_group(hass, "location group", ["test_domain.object"]) info = render_to_info(hass, '{{ closest("group.location_group").entity_id }}') assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) info = render_to_info(hass, "{{ closest(states.group.location_group).entity_id }}") assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) async def test_expand(hass): """Test expand function.""" info = render_to_info(hass, "{{ expand('test.object') }}") assert_result_info(info, "[]", ["test.object"]) info = render_to_info(hass, "{{ expand(56) }}") assert_result_info(info, "[]") hass.states.async_set("test.object", "happy") info = render_to_info( hass, "{{ expand('test.object') | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "test.object", []) info = render_to_info( hass, "{{ expand('group.new_group') | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "", ["group.new_group"]) info = render_to_info( hass, "{{ expand(states.group) | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "", [], ["group"]) await group.Group.async_create_group(hass, "new group", ["test.object"]) info = render_to_info( hass, "{{ expand('group.new_group') | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) info = render_to_info( hass, "{{ expand(states.group) | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "test.object", ["group.new_group"], ["group"]) info = render_to_info( hass, "{{ expand('group.new_group', 'test.object')" " | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) info = render_to_info( hass, "{{ ['group.new_group', 'test.object'] | expand" " | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) def test_closest_function_to_coord(hass): """Test closest function to coord.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) tpl = template.Template( '{{ closest("%s", %s, states.test_domain).entity_id }}' % (hass.config.latitude + 0.3, hass.config.longitude + 0.3), hass, ) assert tpl.async_render() == "test_domain.closest_zone" tpl = template.Template( '{{ (states.test_domain | closest("%s", %s)).entity_id }}' % (hass.config.latitude + 0.3, hass.config.longitude + 0.3), hass, ) assert tpl.async_render() == "test_domain.closest_zone" def test_closest_function_to_entity_id(hass): """Test closest function to entity id.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) info = render_to_info( hass, "{{ closest(zone, states.test_domain).entity_id }}", {"zone": "zone.far_away"}, ) assert_result_info( info, "test_domain.closest_zone", ["test_domain.closest_home", "test_domain.closest_zone", "zone.far_away"], ["test_domain"], ) info = render_to_info( hass, "{{ ([states.test_domain, 'test_domain.closest_zone'] " "| closest(zone)).entity_id }}", {"zone": "zone.far_away"}, ) assert_result_info( info, "test_domain.closest_zone", ["test_domain.closest_home", "test_domain.closest_zone", "zone.far_away"], ["test_domain"], ) def test_closest_function_to_state(hass): """Test closest function to state.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) assert ( template.Template( "{{ closest(states.zone.far_away, states.test_domain).entity_id }}", hass ).async_render() == "test_domain.closest_zone" ) def test_closest_function_invalid_state(hass): """Test closest function invalid state.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) for state in ("states.zone.non_existing", '"zone.non_existing"'): assert ( template.Template("{{ closest(%s, states) }}" % state, hass).async_render() == "None" ) def test_closest_function_state_with_invalid_location(hass): """Test closest function state with invalid location.""" hass.states.async_set( "test_domain.closest_home", "happy", {"latitude": "invalid latitude", "longitude": hass.config.longitude + 0.1}, ) assert ( template.Template( "{{ closest(states.test_domain.closest_home, states) }}", hass ).async_render() == "None" ) def test_closest_function_invalid_coordinates(hass): """Test closest function invalid coordinates.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) assert ( template.Template( '{{ closest("invalid", "coord", states) }}', hass ).async_render() == "None" ) assert ( template.Template( '{{ states | closest("invalid", "coord") }}', hass ).async_render() == "None" ) def test_closest_function_no_location_states(hass): """Test closest function without location states.""" assert ( template.Template("{{ closest(states).entity_id }}", hass).async_render() == "" ) def test_extract_entities_none_exclude_stuff(hass): """Test extract entities function with none or exclude stuff.""" assert template.extract_entities(hass, None) == [] assert template.extract_entities(hass, "mdi:water") == [] assert ( template.extract_entities( hass, "{{ closest(states.zone.far_away, states.test_domain).entity_id }}" ) == MATCH_ALL ) assert ( template.extract_entities(hass, '{{ distance("123", states.test_object_2) }}') == MATCH_ALL ) def test_extract_entities_no_match_entities(hass): """Test extract entities function with none entities stuff.""" assert ( template.extract_entities( hass, "{{ value_json.tst | timestamp_custom('%Y' True) }}" ) == MATCH_ALL ) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.state }},d {% endfor %} """, ) assert_result_info(info, "", domains=["sensor"]) def test_generate_filter_iterators(hass): """Test extract entities function with none entities stuff.""" info = render_to_info( hass, """ {% for state in states %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "", all_states=True) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "", domains=["sensor"]) hass.states.async_set("sensor.test_sensor", "off", {"attr": "value"}) # Don't need the entity because the state is not accessed info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "sensor.test_sensor", domains=["sensor"]) # But we do here because the state gets accessed info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.state }}, {% endfor %} """, ) assert_result_info( info, "sensor.test_sensor=off,", ["sensor.test_sensor"], ["sensor"] ) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.attributes.attr }}, {% endfor %} """, ) assert_result_info( info, "sensor.test_sensor=value,", ["sensor.test_sensor"], ["sensor"] ) def test_generate_select(hass): """Test extract entities function with none entities stuff.""" template_str = """ {{ states.sensor|selectattr("state","equalto","off") |join(",", attribute="entity_id") }} """ tmp = template.Template(template_str, hass) info = tmp.async_render_to_info() assert_result_info(info, "", [], ["sensor"]) hass.states.async_set("sensor.test_sensor", "off", {"attr": "value"}) hass.states.async_set("sensor.test_sensor_on", "on") info = tmp.async_render_to_info() assert_result_info( info, "sensor.test_sensor", ["sensor.test_sensor", "sensor.test_sensor_on"], ["sensor"], ) async def test_extract_entities_match_entities(hass): """Test extract entities function with entities stuff.""" assert ( template.extract_entities( hass, """ {% if is_state('device_tracker.phone_1', 'home') %} Ha, Hercules is home! {% else %} Hercules is at {{ states('device_tracker.phone_1') }}. {% endif %} """, ) == ["device_tracker.phone_1"] ) assert ( template.extract_entities( hass, """ {{ as_timestamp(states.binary_sensor.garage_door.last_changed) }} """, ) == ["binary_sensor.garage_door"] ) assert ( template.extract_entities( hass, """ {{ states("binary_sensor.garage_door") }} """, ) == ["binary_sensor.garage_door"] ) hass.states.async_set("device_tracker.phone_2", "not_home", {"battery": 20}) assert ( template.extract_entities( hass, """ {{ is_state_attr('device_tracker.phone_2', 'battery', 40) }} """, ) == ["device_tracker.phone_2"] ) assert sorted(["device_tracker.phone_1", "device_tracker.phone_2"]) == sorted( template.extract_entities( hass, """ {% if is_state('device_tracker.phone_1', 'home') %} Ha, Hercules is home! {% elif states.device_tracker.phone_2.attributes.battery < 40 %} Hercules you power goes done!. {% endif %} """, ) ) assert sorted(["sensor.pick_humidity", "sensor.pick_temperature"]) == sorted( template.extract_entities( hass, """ {{ states.sensor.pick_temperature.state ~ „°C (“ ~ states.sensor.pick_humidity.state ~ „ %“ }} """, ) ) assert sorted( ["sensor.luftfeuchtigkeit_mean", "input_number.luftfeuchtigkeit"] ) == sorted( template.extract_entities( hass, "{% if (states('sensor.luftfeuchtigkeit_mean') | int)" " > (states('input_number.luftfeuchtigkeit') | int +1.5)" " %}true{% endif %}", ) ) await group.Group.async_create_group(hass, "empty group", []) assert ["group.empty_group"] == template.extract_entities( hass, "{{ expand('group.empty_group') | list | length }}" ) hass.states.async_set("test_domain.object", "exists") await group.Group.async_create_group(hass, "expand group", ["test_domain.object"]) assert sorted(["group.expand_group", "test_domain.object"]) == sorted( template.extract_entities( hass, "{{ expand('group.expand_group') | list | length }}" ) ) assert ["test_domain.entity"] == template.Template( '{{ is_state("test_domain.entity", "on") }}', hass ).extract_entities() def test_extract_entities_with_variables(hass): """Test extract entities function with variables and entities stuff.""" hass.states.async_set("input_boolean.switch", "on") assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state('input_boolean.switch', 'off') }}", {} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(trigger.entity_id, 'off') }}", {"trigger": {"entity_id": "input_boolean.switch"}}, ) assert MATCH_ALL == template.extract_entities( hass, "{{ is_state(data, 'off') }}", {"data": "no_state"} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(data, 'off') }}", {"data": "input_boolean.switch"} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(trigger.entity_id, 'off') }}", {"trigger": {"entity_id": "input_boolean.switch"}}, ) hass.states.async_set("media_player.livingroom", "off") assert {"media_player.livingroom"} == extract_entities( hass, "{{ is_state('media_player.' ~ where , 'playing') }}", {"where": "livingroom"}, ) def test_jinja_namespace(hass): """Test Jinja's namespace command can be used.""" test_template = template.Template( ( "{% set ns = namespace(a_key='') %}" "{% set ns.a_key = states.sensor.dummy.state %}" "{{ ns.a_key }}" ), hass, ) hass.states.async_set("sensor.dummy", "a value") assert test_template.async_render() == "a value" hass.states.async_set("sensor.dummy", "another value") assert test_template.async_render() == "another value" def test_state_with_unit(hass): """Test the state_with_unit property helper.""" hass.states.async_set("sensor.test", "23", {"unit_of_measurement": "beers"}) hass.states.async_set("sensor.test2", "wow") tpl = template.Template("{{ states.sensor.test.state_with_unit }}", hass) assert tpl.async_render() == "23 beers" tpl = template.Template("{{ states.sensor.test2.state_with_unit }}", hass) assert tpl.async_render() == "wow" tpl = template.Template( "{% for state in states %}{{ state.state_with_unit }} {% endfor %}", hass ) assert tpl.async_render() == "23 beers wow" tpl = template.Template("{{ states.sensor.non_existing.state_with_unit }}", hass) assert tpl.async_render() == "" def test_length_of_states(hass): """Test fetching the length of states.""" hass.states.async_set("sensor.test", "23") hass.states.async_set("sensor.test2", "wow") hass.states.async_set("climate.test2", "cooling") tpl = template.Template("{{ states | length }}", hass) assert tpl.async_render() == "3" tpl = template.Template("{{ states.sensor | length }}", hass) assert tpl.async_render() == "2" def test_render_complex_handling_non_template_values(hass): """Test that we can render non-template fields.""" assert template.render_complex( {True: 1, False: template.Template("{{ hello }}", hass)}, {"hello": 2} ) == {True: 1, False: "2"} def test_urlencode(hass): """Test the urlencode method.""" tpl = template.Template( ("{% set dict = {'foo': 'x&y', 'bar': 42} %}" "{{ dict | urlencode }}"), hass, ) assert tpl.async_render() == "foo=x%26y&bar=42" tpl = template.Template( ("{% set string = 'the quick brown fox = true' %}" "{{ string | urlencode }}"), hass, ) assert tpl.async_render() == "the%20quick%20brown%20fox%20%3D%20true"

"""Test Home Assistant template helper methods.""" from datetime import datetime import math import random import pytest import pytz from homeassistant.components import group from homeassistant.const import ( LENGTH_METERS, MASS_GRAMS, MATCH_ALL, PRESSURE_PA, TEMP_CELSIUS, VOLUME_LITERS, ) from homeassistant.exceptions import TemplateError from homeassistant.helpers import template import homeassistant.util.dt as dt_util from homeassistant.util.unit_system import UnitSystem from tests.async_mock import patch def _set_up_units(hass): """Set up the tests.""" hass.config.units = UnitSystem( "custom", TEMP_CELSIUS, LENGTH_METERS, VOLUME_LITERS, MASS_GRAMS, PRESSURE_PA ) def render_to_info(hass, template_str, variables=None): """Create render info from template.""" tmp = template.Template(template_str, hass) return tmp.async_render_to_info(variables) def extract_entities(hass, template_str, variables=None): """Extract entities from a template.""" info = render_to_info(hass, template_str, variables) # pylint: disable=protected-access assert not hasattr(info, "_domains") return info._entities def assert_result_info(info, result, entities=None, domains=None, all_states=False): """Check result info.""" assert info.result == result # pylint: disable=protected-access assert info._all_states == all_states assert info.filter_lifecycle("invalid_entity_name.somewhere") == all_states if entities is not None: assert info._entities == frozenset(entities) assert all([info.filter(entity) for entity in entities]) assert not info.filter("invalid_entity_name.somewhere") else: assert not info._entities if domains is not None: assert info._domains == frozenset(domains) assert all([info.filter_lifecycle(domain + ".entity") for domain in domains]) else: assert not hasattr(info, "_domains") def test_template_equality(): """Test template comparison and hashing.""" template_one = template.Template("{{ template_one }}") template_one_1 = template.Template("{{ template_one }}") template_two = template.Template("{{ template_two }}") assert template_one == template_one_1 assert template_one != template_two assert hash(template_one) == hash(template_one_1) assert hash(template_one) != hash(template_two) assert str(template_one_1) == 'Template("{{ template_one }}")' with pytest.raises(TypeError): template.Template(["{{ template_one }}"]) def test_invalid_template(hass): """Invalid template raises error.""" tmpl = template.Template("{{", hass) with pytest.raises(TemplateError): tmpl.ensure_valid() with pytest.raises(TemplateError): tmpl.async_render() info = tmpl.async_render_to_info() with pytest.raises(TemplateError): assert info.result == "impossible" tmpl = template.Template("{{states(keyword)}}", hass) tmpl.ensure_valid() with pytest.raises(TemplateError): tmpl.async_render() def test_referring_states_by_entity_id(hass): """Test referring states by entity id.""" hass.states.async_set("test.object", "happy") assert ( template.Template("{{ states.test.object.state }}", hass).async_render() == "happy" ) assert ( template.Template('{{ states["test.object"].state }}', hass).async_render() == "happy" ) assert ( template.Template('{{ states("test.object") }}', hass).async_render() == "happy" ) def test_invalid_entity_id(hass): """Test referring states by entity id.""" with pytest.raises(TemplateError): template.Template('{{ states["big.fat..."] }}', hass).async_render() with pytest.raises(TemplateError): template.Template('{{ states.test["big.fat..."] }}', hass).async_render() with pytest.raises(TemplateError): template.Template('{{ states["invalid/domain"] }}', hass).async_render() def test_raise_exception_on_error(hass): """Test raising an exception on error.""" with pytest.raises(TemplateError): template.Template("{{ invalid_syntax").ensure_valid() def test_iterating_all_states(hass): """Test iterating all states.""" tmpl_str = "{% for state in states %}{{ state.state }}{% endfor %}" info = render_to_info(hass, tmpl_str) assert_result_info(info, "", all_states=True) hass.states.async_set("test.object", "happy") hass.states.async_set("sensor.temperature", 10) info = render_to_info(hass, tmpl_str) assert_result_info( info, "10happy", entities=["test.object", "sensor.temperature"], all_states=True ) def test_iterating_domain_states(hass): """Test iterating domain states.""" tmpl_str = "{% for state in states.sensor %}{{ state.state }}{% endfor %}" info = render_to_info(hass, tmpl_str) assert_result_info(info, "", domains=["sensor"]) hass.states.async_set("test.object", "happy") hass.states.async_set("sensor.back_door", "open") hass.states.async_set("sensor.temperature", 10) info = render_to_info(hass, tmpl_str) assert_result_info( info, "open10", entities=["sensor.back_door", "sensor.temperature"], domains=["sensor"], ) def test_float(hass): """Test float.""" hass.states.async_set("sensor.temperature", "12") assert ( template.Template( "{{ float(states.sensor.temperature.state) }}", hass ).async_render() == "12.0" ) assert ( template.Template( "{{ float(states.sensor.temperature.state) > 11 }}", hass ).async_render() == "True" ) assert ( template.Template("{{ float('forgiving') }}", hass).async_render() == "forgiving" ) def test_rounding_value(hass): """Test rounding value.""" hass.states.async_set("sensor.temperature", 12.78) assert ( template.Template( "{{ states.sensor.temperature.state | round(1) }}", hass ).async_render() == "12.8" ) assert ( template.Template( "{{ states.sensor.temperature.state | multiply(10) | round }}", hass ).async_render() == "128" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "floor") }}', hass ).async_render() == "12.7" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "ceil") }}', hass ).async_render() == "12.8" ) assert ( template.Template( '{{ states.sensor.temperature.state | round(1, "half") }}', hass ).async_render() == "13.0" ) def test_rounding_value_get_original_value_on_error(hass): """Test rounding value get original value on error.""" assert template.Template("{{ None | round }}", hass).async_render() == "None" assert ( template.Template('{{ "no_number" | round }}', hass).async_render() == "no_number" ) def test_multiply(hass): """Test multiply.""" tests = {None: "None", 10: "100", '"abcd"': "abcd"} for inp, out in tests.items(): assert ( template.Template( "{{ %s | multiply(10) | round }}" % inp, hass ).async_render() == out ) def test_logarithm(hass): """Test logarithm.""" tests = [ (4, 2, "2.0"), (1000, 10, "3.0"), (math.e, "", "1.0"), ('"invalid"', "_", "invalid"), (10, '"invalid"', "10.0"), ] for value, base, expected in tests: assert ( template.Template( f"{{{{ {value} | log({base}) | round(1) }}}}", hass ).async_render() == expected ) assert ( template.Template( f"{{{{ log({value}, {base}) | round(1) }}}}", hass ).async_render() == expected ) def test_sine(hass): """Test sine.""" tests = [ (0, "0.0"), (math.pi / 2, "1.0"), (math.pi, "0.0"), (math.pi * 1.5, "-1.0"), (math.pi / 10, "0.309"), ('"duck"', "duck"), ] for value, expected in tests: assert ( template.Template("{{ %s | sin | round(3) }}" % value, hass).async_render() == expected ) def test_cos(hass): """Test cosine.""" tests = [ (0, "1.0"), (math.pi / 2, "0.0"), (math.pi, "-1.0"), (math.pi * 1.5, "-0.0"), (math.pi / 10, "0.951"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | cos | round(3) }}" % value, hass).async_render() == expected ) def test_tan(hass): """Test tangent.""" tests = [ (0, "0.0"), (math.pi, "-0.0"), (math.pi / 180 * 45, "1.0"), (math.pi / 180 * 90, "1.633123935319537e+16"), (math.pi / 180 * 135, "-1.0"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | tan | round(3) }}" % value, hass).async_render() == expected ) def test_sqrt(hass): """Test square root.""" tests = [ (0, "0.0"), (1, "1.0"), (2, "1.414"), (10, "3.162"), (100, "10.0"), ("'error'", "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | sqrt | round(3) }}" % value, hass).async_render() == expected ) def test_arc_sine(hass): """Test arcus sine.""" tests = [ (-2.0, "-2.0"), # value error (-1.0, "-1.571"), (-0.5, "-0.524"), (0.0, "0.0"), (0.5, "0.524"), (1.0, "1.571"), (2.0, "2.0"), # value error ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | asin | round(3) }}" % value, hass).async_render() == expected ) def test_arc_cos(hass): """Test arcus cosine.""" tests = [ (-2.0, "-2.0"), # value error (-1.0, "3.142"), (-0.5, "2.094"), (0.0, "1.571"), (0.5, "1.047"), (1.0, "0.0"), (2.0, "2.0"), # value error ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | acos | round(3) }}" % value, hass).async_render() == expected ) def test_arc_tan(hass): """Test arcus tangent.""" tests = [ (-10.0, "-1.471"), (-2.0, "-1.107"), (-1.0, "-0.785"), (-0.5, "-0.464"), (0.0, "0.0"), (0.5, "0.464"), (1.0, "0.785"), (2.0, "1.107"), (10.0, "1.471"), ('"error"', "error"), ] for value, expected in tests: assert ( template.Template("{{ %s | atan | round(3) }}" % value, hass).async_render() == expected ) def test_arc_tan2(hass): """Test two parameter version of arcus tangent.""" tests = [ (-10.0, -10.0, "-2.356"), (-10.0, 0.0, "-1.571"), (-10.0, 10.0, "-0.785"), (0.0, -10.0, "3.142"), (0.0, 0.0, "0.0"), (0.0, 10.0, "0.0"), (10.0, -10.0, "2.356"), (10.0, 0.0, "1.571"), (10.0, 10.0, "0.785"), (-4.0, 3.0, "-0.927"), (-1.0, 2.0, "-0.464"), (2.0, 1.0, "1.107"), ('"duck"', '"goose"', "('duck', 'goose')"), ] for y, x, expected in tests: assert ( template.Template( f"{{{{ ({y}, {x}) | atan2 | round(3) }}}}", hass ).async_render() == expected ) assert ( template.Template( f"{{{{ atan2({y}, {x}) | round(3) }}}}", hass ).async_render() == expected ) def test_strptime(hass): """Test the parse timestamp method.""" tests = [ ("2016-10-19 15:22:05.588122 UTC", "%Y-%m-%d %H:%M:%S.%f %Z", None), ("2016-10-19 15:22:05.588122+0100", "%Y-%m-%d %H:%M:%S.%f%z", None), ("2016-10-19 15:22:05.588122", "%Y-%m-%d %H:%M:%S.%f", None), ("2016-10-19", "%Y-%m-%d", None), ("2016", "%Y", None), ("15:22:05", "%H:%M:%S", None), ("1469119144", "%Y", "1469119144"), ("invalid", "%Y", "invalid"), ] for inp, fmt, expected in tests: if expected is None: expected = datetime.strptime(inp, fmt) temp = f"{{{{ strptime('{inp}', '{fmt}') }}}}" assert template.Template(temp, hass).async_render() == str(expected) def test_timestamp_custom(hass): """Test the timestamps to custom filter.""" now = dt_util.utcnow() tests = [ (None, None, None, "None"), (1469119144, None, True, "2016-07-21 16:39:04"), (1469119144, "%Y", True, "2016"), (1469119144, "invalid", True, "invalid"), (dt_util.as_timestamp(now), None, False, now.strftime("%Y-%m-%d %H:%M:%S")), ] for inp, fmt, local, out in tests: if fmt: fil = f"timestamp_custom('{fmt}')" elif fmt and local: fil = f"timestamp_custom('{fmt}', {local})" else: fil = "timestamp_custom" assert template.Template(f"{{{{ {inp} | {fil} }}}}", hass).async_render() == out def test_timestamp_local(hass): """Test the timestamps to local filter.""" tests = {None: "None", 1469119144: "2016-07-21 16:39:04"} for inp, out in tests.items(): assert ( template.Template("{{ %s | timestamp_local }}" % inp, hass).async_render() == out ) def test_to_json(hass): """Test the object to JSON string filter.""" # Note that we're not testing the actual json.loads and json.dumps methods, # only the filters, so we don't need to be exhaustive with our sample JSON. expected_result = '{"Foo": "Bar"}' actual_result = template.Template( "{{ {'Foo': 'Bar'} | to_json }}", hass ).async_render() assert actual_result == expected_result def test_from_json(hass): """Test the JSON string to object filter.""" # Note that we're not testing the actual json.loads and json.dumps methods, # only the filters, so we don't need to be exhaustive with our sample JSON. expected_result = "Bar" actual_result = template.Template( '{{ (\'{"Foo": "Bar"}\' | from_json).Foo }}', hass ).async_render() assert actual_result == expected_result def test_min(hass): """Test the min filter.""" assert template.Template("{{ [1, 2, 3] | min }}", hass).async_render() == "1" def test_max(hass): """Test the max filter.""" assert template.Template("{{ [1, 2, 3] | max }}", hass).async_render() == "3" def test_ord(hass): """Test the ord filter.""" assert template.Template('{{ "d" | ord }}', hass).async_render() == "100" def test_base64_encode(hass): """Test the base64_encode filter.""" assert ( template.Template('{{ "homeassistant" | base64_encode }}', hass).async_render() == "aG9tZWFzc2lzdGFudA==" ) def test_base64_decode(hass): """Test the base64_decode filter.""" assert ( template.Template( '{{ "aG9tZWFzc2lzdGFudA==" | base64_decode }}', hass ).async_render() == "homeassistant" ) def test_ordinal(hass): """Test the ordinal filter.""" tests = [ (1, "1st"), (2, "2nd"), (3, "3rd"), (4, "4th"), (5, "5th"), (12, "12th"), (100, "100th"), (101, "101st"), ] for value, expected in tests: assert ( template.Template("{{ %s | ordinal }}" % value, hass).async_render() == expected ) def test_timestamp_utc(hass): """Test the timestamps to local filter.""" now = dt_util.utcnow() tests = { None: "None", 1469119144: "2016-07-21 16:39:04", dt_util.as_timestamp(now): now.strftime("%Y-%m-%d %H:%M:%S"), } for inp, out in tests.items(): assert ( template.Template("{{ %s | timestamp_utc }}" % inp, hass).async_render() == out ) def test_as_timestamp(hass): """Test the as_timestamp function.""" assert ( template.Template('{{ as_timestamp("invalid") }}', hass).async_render() == "None" ) hass.mock = None assert ( template.Template("{{ as_timestamp(states.mock) }}", hass).async_render() == "None" ) tpl = ( '{{ as_timestamp(strptime("2024-02-03T09:10:24+0000", ' '"%Y-%m-%dT%H:%M:%S%z")) }}' ) assert template.Template(tpl, hass).async_render() == "1706951424.0" @patch.object(random, "choice") def test_random_every_time(test_choice, hass): """Ensure the random filter runs every time, not just once.""" tpl = template.Template("{{ [1,2] | random }}", hass) test_choice.return_value = "foo" assert tpl.async_render() == "foo" test_choice.return_value = "bar" assert tpl.async_render() == "bar" def test_passing_vars_as_keywords(hass): """Test passing variables as keywords.""" assert template.Template("{{ hello }}", hass).async_render(hello=127) == "127" def test_passing_vars_as_vars(hass): """Test passing variables as variables.""" assert template.Template("{{ hello }}", hass).async_render({"hello": 127}) == "127" def test_passing_vars_as_list(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello }}", hass), {"hello": ["foo", "bar"]} ) == "['foo', 'bar']" ) def test_passing_vars_as_list_element(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello[1] }}", hass), {"hello": ["foo", "bar"]} ) == "bar" ) def test_passing_vars_as_dict_element(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello.foo }}", hass), {"hello": {"foo": "bar"}} ) == "bar" ) def test_passing_vars_as_dict(hass): """Test passing variables as list.""" assert ( template.render_complex( template.Template("{{ hello }}", hass), {"hello": {"foo": "bar"}} ) == "{'foo': 'bar'}" ) def test_render_with_possible_json_value_with_valid_json(hass): """Render with possible JSON value with valid JSON.""" tpl = template.Template("{{ value_json.hello }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "world" def test_render_with_possible_json_value_with_invalid_json(hass): """Render with possible JSON value with invalid JSON.""" tpl = template.Template("{{ value_json }}", hass) assert tpl.async_render_with_possible_json_value("{ I AM NOT JSON }") == "" def test_render_with_possible_json_value_with_template_error_value(hass): """Render with possible JSON value with template error value.""" tpl = template.Template("{{ non_existing.variable }}", hass) assert tpl.async_render_with_possible_json_value("hello", "-") == "-" def test_render_with_possible_json_value_with_missing_json_value(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.goodbye }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "" def test_render_with_possible_json_value_valid_with_is_defined(hass): """Render with possible JSON value with known JSON object.""" tpl = template.Template("{{ value_json.hello|is_defined }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}') == "world" def test_render_with_possible_json_value_undefined_json(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.bye|is_defined }}", hass) assert ( tpl.async_render_with_possible_json_value('{"hello": "world"}') == '{"hello": "world"}' ) def test_render_with_possible_json_value_undefined_json_error_value(hass): """Render with possible JSON value with unknown JSON object.""" tpl = template.Template("{{ value_json.bye|is_defined }}", hass) assert tpl.async_render_with_possible_json_value('{"hello": "world"}', "") == "" def test_render_with_possible_json_value_non_string_value(hass): """Render with possible JSON value with non-string value.""" tpl = template.Template( """ {{ strptime(value~'+0000', '%Y-%m-%d %H:%M:%S%z') }} """, hass, ) value = datetime(2019, 1, 18, 12, 13, 14) expected = str(pytz.utc.localize(value)) assert tpl.async_render_with_possible_json_value(value) == expected def test_if_state_exists(hass): """Test if state exists works.""" hass.states.async_set("test.object", "available") tpl = template.Template( "{% if states.test.object %}exists{% else %}not exists{% endif %}", hass ) assert tpl.async_render() == "exists" def test_is_state(hass): """Test is_state method.""" hass.states.async_set("test.object", "available") tpl = template.Template( """ {% if is_state("test.object", "available") %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ is_state("test.noobject", "available") }} """, hass, ) assert tpl.async_render() == "False" def test_is_state_attr(hass): """Test is_state_attr method.""" hass.states.async_set("test.object", "available", {"mode": "on"}) tpl = template.Template( """ {% if is_state_attr("test.object", "mode", "on") %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ is_state_attr("test.noobject", "mode", "on") }} """, hass, ) assert tpl.async_render() == "False" def test_state_attr(hass): """Test state_attr method.""" hass.states.async_set("test.object", "available", {"mode": "on"}) tpl = template.Template( """ {% if state_attr("test.object", "mode") == "on" %}yes{% else %}no{% endif %} """, hass, ) assert tpl.async_render() == "yes" tpl = template.Template( """ {{ state_attr("test.noobject", "mode") == None }} """, hass, ) assert tpl.async_render() == "True" def test_states_function(hass): """Test using states as a function.""" hass.states.async_set("test.object", "available") tpl = template.Template('{{ states("test.object") }}', hass) assert tpl.async_render() == "available" tpl2 = template.Template('{{ states("test.object2") }}', hass) assert tpl2.async_render() == "unknown" @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_now(mock_is_safe, hass): """Test now method.""" now = dt_util.now() with patch("homeassistant.util.dt.now", return_value=now): assert ( now.isoformat() == template.Template("{{ now().isoformat() }}", hass).async_render() ) @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_relative_time(mock_is_safe, hass): """Test relative_time method.""" now = datetime.strptime("2000-01-01 10:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z") with patch("homeassistant.util.dt.now", return_value=now): assert ( "1 hour" == template.Template( '{{relative_time(strptime("2000-01-01 09:00:00", "%Y-%m-%d %H:%M:%S"))}}', hass, ).async_render() ) assert ( "2 hours" == template.Template( '{{relative_time(strptime("2000-01-01 09:00:00 +01:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( "1 hour" == template.Template( '{{relative_time(strptime("2000-01-01 03:00:00 -06:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( str(template.strptime("2000-01-01 11:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z")) == template.Template( '{{relative_time(strptime("2000-01-01 11:00:00 +00:00", "%Y-%m-%d %H:%M:%S %z"))}}', hass, ).async_render() ) assert ( "string" == template.Template('{{relative_time("string")}}', hass,).async_render() ) @patch( "homeassistant.helpers.template.TemplateEnvironment.is_safe_callable", return_value=True, ) def test_utcnow(mock_is_safe, hass): """Test utcnow method.""" now = dt_util.utcnow() with patch("homeassistant.util.dt.utcnow", return_value=now): assert ( now.isoformat() == template.Template("{{ utcnow().isoformat() }}", hass).async_render() ) def test_regex_match(hass): """Test regex_match method.""" tpl = template.Template( r""" {{ '123-456-7890' | regex_match('(\\d{3})-(\\d{3})-(\\d{4})') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Home Assistant test' | regex_match('home', True) }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Another Home Assistant test' | regex_match('Home') }} """, hass, ) assert tpl.async_render() == "False" tpl = template.Template( """ {{ ['Home Assistant test'] | regex_match('.*Assist') }} """, hass, ) assert tpl.async_render() == "True" def test_regex_search(hass): """Test regex_search method.""" tpl = template.Template( r""" {{ '123-456-7890' | regex_search('(\\d{3})-(\\d{3})-(\\d{4})') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Home Assistant test' | regex_search('home', True) }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ 'Another Home Assistant test' | regex_search('Home') }} """, hass, ) assert tpl.async_render() == "True" tpl = template.Template( """ {{ ['Home Assistant test'] | regex_search('Assist') }} """, hass, ) assert tpl.async_render() == "True" def test_regex_replace(hass): """Test regex_replace method.""" tpl = template.Template( r""" {{ 'Hello World' | regex_replace('(Hello\\s)',) }} """, hass, ) assert tpl.async_render() == "World" tpl = template.Template( """ {{ ['Home hinderant test'] | regex_replace('hinder', 'Assist') }} """, hass, ) assert tpl.async_render() == "['Home Assistant test']" def test_regex_findall_index(hass): """Test regex_findall_index method.""" tpl = template.Template( """ {{ 'Flight from JFK to LHR' | regex_findall_index('([A-Z]{3})', 0) }} """, hass, ) assert tpl.async_render() == "JFK" tpl = template.Template( """ {{ 'Flight from JFK to LHR' | regex_findall_index('([A-Z]{3})', 1) }} """, hass, ) assert tpl.async_render() == "LHR" tpl = template.Template( """ {{ ['JFK', 'LHR'] | regex_findall_index('([A-Z]{3})', 1) }} """, hass, ) assert tpl.async_render() == "LHR" def test_bitwise_and(hass): """Test bitwise_and method.""" tpl = template.Template( """ {{ 8 | bitwise_and(8) }} """, hass, ) assert tpl.async_render() == str(8 & 8) tpl = template.Template( """ {{ 10 | bitwise_and(2) }} """, hass, ) assert tpl.async_render() == str(10 & 2) tpl = template.Template( """ {{ 8 | bitwise_and(2) }} """, hass, ) assert tpl.async_render() == str(8 & 2) def test_bitwise_or(hass): """Test bitwise_or method.""" tpl = template.Template( """ {{ 8 | bitwise_or(8) }} """, hass, ) assert tpl.async_render() == str(8 | 8) tpl = template.Template( """ {{ 10 | bitwise_or(2) }} """, hass, ) assert tpl.async_render() == str(10 | 2) tpl = template.Template( """ {{ 8 | bitwise_or(2) }} """, hass, ) assert tpl.async_render() == str(8 | 2) def test_distance_function_with_1_state(hass): """Test distance function with 1 state.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) tpl = template.Template("{{ distance(states.test.object) | round }}", hass) assert tpl.async_render() == "187" def test_distance_function_with_2_states(hass): """Test distance function with 2 states.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( "{{ distance(states.test.object, states.test.object_2) | round }}", hass ) assert tpl.async_render() == "187" def test_distance_function_with_1_coord(hass): """Test distance function with 1 coord.""" _set_up_units(hass) tpl = template.Template('{{ distance("32.87336", "-117.22943") | round }}', hass) assert tpl.async_render() == "187" def test_distance_function_with_2_coords(hass): """Test distance function with 2 coords.""" _set_up_units(hass) assert ( template.Template( '{{ distance("32.87336", "-117.22943", %s, %s) | round }}' % (hass.config.latitude, hass.config.longitude), hass, ).async_render() == "187" ) def test_distance_function_with_1_state_1_coord(hass): """Test distance function with 1 state 1 coord.""" _set_up_units(hass) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("32.87336", "-117.22943", states.test.object_2) ' "| round }}", hass, ) assert tpl.async_render() == "187" tpl2 = template.Template( '{{ distance(states.test.object_2, "32.87336", "-117.22943") ' "| round }}", hass, ) assert tpl2.async_render() == "187" def test_distance_function_return_none_if_invalid_state(hass): """Test distance function return None if invalid state.""" hass.states.async_set("test.object_2", "happy", {"latitude": 10}) tpl = template.Template("{{ distance(states.test.object_2) | round }}", hass) assert tpl.async_render() == "None" def test_distance_function_return_none_if_invalid_coord(hass): """Test distance function return None if invalid coord.""" assert ( template.Template('{{ distance("123", "abc") }}', hass).async_render() == "None" ) assert template.Template('{{ distance("123") }}', hass).async_render() == "None" hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template('{{ distance("123", states.test_object_2) }}', hass) assert tpl.async_render() == "None" def test_distance_function_with_2_entity_ids(hass): """Test distance function with 2 entity ids.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": 32.87336, "longitude": -117.22943} ) hass.states.async_set( "test.object_2", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("test.object", "test.object_2") | round }}', hass ) assert tpl.async_render() == "187" def test_distance_function_with_1_entity_1_coord(hass): """Test distance function with 1 entity_id and 1 coord.""" _set_up_units(hass) hass.states.async_set( "test.object", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) tpl = template.Template( '{{ distance("test.object", "32.87336", "-117.22943") | round }}', hass ) assert tpl.async_render() == "187" def test_closest_function_home_vs_domain(hass): """Test closest function home vs domain.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_test_domain.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) assert ( template.Template( "{{ closest(states.test_domain).entity_id }}", hass ).async_render() == "test_domain.object" ) assert ( template.Template( "{{ (states.test_domain | closest).entity_id }}", hass ).async_render() == "test_domain.object" ) def test_closest_function_home_vs_all_states(hass): """Test closest function home vs all states.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain_2.and_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) assert ( template.Template("{{ closest(states).entity_id }}", hass).async_render() == "test_domain_2.and_closer" ) assert ( template.Template("{{ (states | closest).entity_id }}", hass).async_render() == "test_domain_2.and_closer" ) async def test_closest_function_home_vs_group_entity_id(hass): """Test closest function home vs group entity id.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_in_group.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) await group.Group.async_create_group(hass, "location group", ["test_domain.object"]) info = render_to_info(hass, '{{ closest("group.location_group").entity_id }}') assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) async def test_closest_function_home_vs_group_state(hass): """Test closest function home vs group state.""" hass.states.async_set( "test_domain.object", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "not_in_group.but_closer", "happy", {"latitude": hass.config.latitude, "longitude": hass.config.longitude}, ) await group.Group.async_create_group(hass, "location group", ["test_domain.object"]) info = render_to_info(hass, '{{ closest("group.location_group").entity_id }}') assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) info = render_to_info(hass, "{{ closest(states.group.location_group).entity_id }}") assert_result_info( info, "test_domain.object", ["test_domain.object", "group.location_group"] ) async def test_expand(hass): """Test expand function.""" info = render_to_info(hass, "{{ expand('test.object') }}") assert_result_info(info, "[]", ["test.object"]) info = render_to_info(hass, "{{ expand(56) }}") assert_result_info(info, "[]") hass.states.async_set("test.object", "happy") info = render_to_info( hass, "{{ expand('test.object') | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "test.object", []) info = render_to_info( hass, "{{ expand('group.new_group') | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "", ["group.new_group"]) info = render_to_info( hass, "{{ expand(states.group) | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "", [], ["group"]) await group.Group.async_create_group(hass, "new group", ["test.object"]) info = render_to_info( hass, "{{ expand('group.new_group') | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) info = render_to_info( hass, "{{ expand(states.group) | map(attribute='entity_id') | join(', ') }}" ) assert_result_info(info, "test.object", ["group.new_group"], ["group"]) info = render_to_info( hass, "{{ expand('group.new_group', 'test.object')" " | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) info = render_to_info( hass, "{{ ['group.new_group', 'test.object'] | expand" " | map(attribute='entity_id') | join(', ') }}", ) assert_result_info(info, "test.object", ["group.new_group"]) def test_closest_function_to_coord(hass): """Test closest function to coord.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) tpl = template.Template( '{{ closest("%s", %s, states.test_domain).entity_id }}' % (hass.config.latitude + 0.3, hass.config.longitude + 0.3), hass, ) assert tpl.async_render() == "test_domain.closest_zone" tpl = template.Template( '{{ (states.test_domain | closest("%s", %s)).entity_id }}' % (hass.config.latitude + 0.3, hass.config.longitude + 0.3), hass, ) assert tpl.async_render() == "test_domain.closest_zone" def test_closest_function_to_entity_id(hass): """Test closest function to entity id.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) info = render_to_info( hass, "{{ closest(zone, states.test_domain).entity_id }}", {"zone": "zone.far_away"}, ) assert_result_info( info, "test_domain.closest_zone", ["test_domain.closest_home", "test_domain.closest_zone", "zone.far_away"], ["test_domain"], ) info = render_to_info( hass, "{{ ([states.test_domain, 'test_domain.closest_zone'] " "| closest(zone)).entity_id }}", {"zone": "zone.far_away"}, ) assert_result_info( info, "test_domain.closest_zone", ["test_domain.closest_home", "test_domain.closest_zone", "zone.far_away"], ["test_domain"], ) def test_closest_function_to_state(hass): """Test closest function to state.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) hass.states.async_set( "test_domain.closest_zone", "happy", { "latitude": hass.config.latitude + 0.2, "longitude": hass.config.longitude + 0.2, }, ) hass.states.async_set( "zone.far_away", "zoning", { "latitude": hass.config.latitude + 0.3, "longitude": hass.config.longitude + 0.3, }, ) assert ( template.Template( "{{ closest(states.zone.far_away, states.test_domain).entity_id }}", hass ).async_render() == "test_domain.closest_zone" ) def test_closest_function_invalid_state(hass): """Test closest function invalid state.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) for state in ("states.zone.non_existing", '"zone.non_existing"'): assert ( template.Template("{{ closest(%s, states) }}" % state, hass).async_render() == "None" ) def test_closest_function_state_with_invalid_location(hass): """Test closest function state with invalid location.""" hass.states.async_set( "test_domain.closest_home", "happy", {"latitude": "invalid latitude", "longitude": hass.config.longitude + 0.1}, ) assert ( template.Template( "{{ closest(states.test_domain.closest_home, states) }}", hass ).async_render() == "None" ) def test_closest_function_invalid_coordinates(hass): """Test closest function invalid coordinates.""" hass.states.async_set( "test_domain.closest_home", "happy", { "latitude": hass.config.latitude + 0.1, "longitude": hass.config.longitude + 0.1, }, ) assert ( template.Template( '{{ closest("invalid", "coord", states) }}', hass ).async_render() == "None" ) assert ( template.Template( '{{ states | closest("invalid", "coord") }}', hass ).async_render() == "None" ) def test_closest_function_no_location_states(hass): """Test closest function without location states.""" assert ( template.Template("{{ closest(states).entity_id }}", hass).async_render() == "" ) def test_extract_entities_none_exclude_stuff(hass): """Test extract entities function with none or exclude stuff.""" assert template.extract_entities(hass, None) == [] assert template.extract_entities(hass, "mdi:water") == [] assert ( template.extract_entities( hass, "{{ closest(states.zone.far_away, states.test_domain).entity_id }}" ) == MATCH_ALL ) assert ( template.extract_entities(hass, '{{ distance("123", states.test_object_2) }}') == MATCH_ALL ) def test_extract_entities_no_match_entities(hass): """Test extract entities function with none entities stuff.""" assert ( template.extract_entities( hass, "{{ value_json.tst | timestamp_custom('%Y' True) }}" ) == MATCH_ALL ) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.state }},d {% endfor %} """, ) assert_result_info(info, "", domains=["sensor"]) def test_generate_filter_iterators(hass): """Test extract entities function with none entities stuff.""" info = render_to_info( hass, """ {% for state in states %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "", all_states=True) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "", domains=["sensor"]) hass.states.async_set("sensor.test_sensor", "off", {"attr": "value"}) # Don't need the entity because the state is not accessed info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }} {% endfor %} """, ) assert_result_info(info, "sensor.test_sensor", domains=["sensor"]) # But we do here because the state gets accessed info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.state }}, {% endfor %} """, ) assert_result_info( info, "sensor.test_sensor=off,", ["sensor.test_sensor"], ["sensor"] ) info = render_to_info( hass, """ {% for state in states.sensor %} {{ state.entity_id }}={{ state.attributes.attr }}, {% endfor %} """, ) assert_result_info( info, "sensor.test_sensor=value,", ["sensor.test_sensor"], ["sensor"] ) def test_generate_select(hass): """Test extract entities function with none entities stuff.""" template_str = """ {{ states.sensor|selectattr("state","equalto","off") |join(",", attribute="entity_id") }} """ tmp = template.Template(template_str, hass) info = tmp.async_render_to_info() assert_result_info(info, "", [], ["sensor"]) hass.states.async_set("sensor.test_sensor", "off", {"attr": "value"}) hass.states.async_set("sensor.test_sensor_on", "on") info = tmp.async_render_to_info() assert_result_info( info, "sensor.test_sensor", ["sensor.test_sensor", "sensor.test_sensor_on"], ["sensor"], ) async def test_extract_entities_match_entities(hass): """Test extract entities function with entities stuff.""" assert ( template.extract_entities( hass, """ {% if is_state('device_tracker.phone_1', 'home') %} Ha, Hercules is home! {% else %} Hercules is at {{ states('device_tracker.phone_1') }}. {% endif %} """, ) == ["device_tracker.phone_1"] ) assert ( template.extract_entities( hass, """ {{ as_timestamp(states.binary_sensor.garage_door.last_changed) }} """, ) == ["binary_sensor.garage_door"] ) assert ( template.extract_entities( hass, """ {{ states("binary_sensor.garage_door") }} """, ) == ["binary_sensor.garage_door"] ) hass.states.async_set("device_tracker.phone_2", "not_home", {"battery": 20}) assert ( template.extract_entities( hass, """ {{ is_state_attr('device_tracker.phone_2', 'battery', 40) }} """, ) == ["device_tracker.phone_2"] ) assert sorted(["device_tracker.phone_1", "device_tracker.phone_2"]) == sorted( template.extract_entities( hass, """ {% if is_state('device_tracker.phone_1', 'home') %} Ha, Hercules is home! {% elif states.device_tracker.phone_2.attributes.battery < 40 %} Hercules you power goes done!. {% endif %} """, ) ) assert sorted(["sensor.pick_humidity", "sensor.pick_temperature"]) == sorted( template.extract_entities( hass, """ {{ states.sensor.pick_temperature.state ~ „°C (“ ~ states.sensor.pick_humidity.state ~ „ %“ }} """, ) ) assert sorted( ["sensor.luftfeuchtigkeit_mean", "input_number.luftfeuchtigkeit"] ) == sorted( template.extract_entities( hass, "{% if (states('sensor.luftfeuchtigkeit_mean') | int)" " > (states('input_number.luftfeuchtigkeit') | int +1.5)" " %}true{% endif %}", ) ) await group.Group.async_create_group(hass, "empty group", []) assert ["group.empty_group"] == template.extract_entities( hass, "{{ expand('group.empty_group') | list | length }}" ) hass.states.async_set("test_domain.object", "exists") await group.Group.async_create_group(hass, "expand group", ["test_domain.object"]) assert sorted(["group.expand_group", "test_domain.object"]) == sorted( template.extract_entities( hass, "{{ expand('group.expand_group') | list | length }}" ) ) assert ["test_domain.entity"] == template.Template( '{{ is_state("test_domain.entity", "on") }}', hass ).extract_entities() def test_extract_entities_with_variables(hass): """Test extract entities function with variables and entities stuff.""" hass.states.async_set("input_boolean.switch", "on") assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state('input_boolean.switch', 'off') }}", {} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(trigger.entity_id, 'off') }}", {"trigger": {"entity_id": "input_boolean.switch"}}, ) assert MATCH_ALL == template.extract_entities( hass, "{{ is_state(data, 'off') }}", {"data": "no_state"} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(data, 'off') }}", {"data": "input_boolean.switch"} ) assert ["input_boolean.switch"] == template.extract_entities( hass, "{{ is_state(trigger.entity_id, 'off') }}", {"trigger": {"entity_id": "input_boolean.switch"}}, ) hass.states.async_set("media_player.livingroom", "off") assert {"media_player.livingroom"} == extract_entities( hass, "{{ is_state('media_player.' ~ where , 'playing') }}", {"where": "livingroom"}, ) def test_jinja_namespace(hass): """Test Jinja's namespace command can be used.""" test_template = template.Template( ( "{% set ns = namespace(a_key='') %}" "{% set ns.a_key = states.sensor.dummy.state %}" "{{ ns.a_key }}" ), hass, ) hass.states.async_set("sensor.dummy", "a value") assert test_template.async_render() == "a value" hass.states.async_set("sensor.dummy", "another value") assert test_template.async_render() == "another value" def test_state_with_unit(hass): """Test the state_with_unit property helper.""" hass.states.async_set("sensor.test", "23", {"unit_of_measurement": "beers"}) hass.states.async_set("sensor.test2", "wow") tpl = template.Template("{{ states.sensor.test.state_with_unit }}", hass) assert tpl.async_render() == "23 beers" tpl = template.Template("{{ states.sensor.test2.state_with_unit }}", hass) assert tpl.async_render() == "wow" tpl = template.Template( "{% for state in states %}{{ state.state_with_unit }} {% endfor %}", hass ) assert tpl.async_render() == "23 beers wow" tpl = template.Template("{{ states.sensor.non_existing.state_with_unit }}", hass) assert tpl.async_render() == "" def test_length_of_states(hass): """Test fetching the length of states.""" hass.states.async_set("sensor.test", "23") hass.states.async_set("sensor.test2", "wow") hass.states.async_set("climate.test2", "cooling") tpl = template.Template("{{ states | length }}", hass) assert tpl.async_render() == "3" tpl = template.Template("{{ states.sensor | length }}", hass) assert tpl.async_render() == "2" def test_render_complex_handling_non_template_values(hass): """Test that we can render non-template fields.""" assert template.render_complex( {True: 1, False: template.Template("{{ hello }}", hass)}, {"hello": 2} ) == {True: 1, False: "2"} def test_urlencode(hass): """Test the urlencode method.""" tpl = template.Template( ("{% set dict = {'foo': 'x&y', 'bar': 42} %}" "{{ dict | urlencode }}"), hass, ) assert tpl.async_render() == "foo=x%26y&bar=42" tpl = template.Template( ("{% set string = 'the quick brown fox = true' %}" "{{ string | urlencode }}"), hass, ) assert tpl.async_render() == "the%20quick%20brown%20fox%20%3D%20true"

#!/usr/bin/env python # Copyright 2020 The Tekton Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This script helps synchronize contents from their respective sources of # truth (usually GitHub repositories of each Tekton # components, such as tektoncd/pipelines) to tektoncd/website. import copy import fnmatch import json import logging import markdown from multiprocessing import Pool import os import os.path import re import sys from urllib.error import URLError from urllib.parse import urlparse, urljoin, urlunparse from bs4 import BeautifulSoup import click import git from jinja2 import Environment from jinja2 import FileSystemLoader from ruamel.yaml import YAML CONTENT_DIR = './content/en/docs' VAULT_DIR = './content/en/vault' JS_ASSET_DIR = './assets/js' TEMPLATE_DIR = './templates' BASE_FOLDER = os.path.dirname(os.path.abspath(__file__)) DEFAULT_CONFIG_FOLDER = os.path.join(BASE_FOLDER, 'config') DEFAULT_CACHE_FOLDER = os.path.join(BASE_FOLDER, '.cache') jinja_env = Environment(loader=FileSystemLoader(TEMPLATE_DIR)) FM_BOUNDARY = re.compile(r"^(?:)?", re.MULTILINE) YAML_SEPARATOR = "---\n" FOLDER_INDEX = '_index.md' def doc_config(doc, folder_config, weight=None): """ Return the target name, folder and header for doc based on folder_config :param doc: the doc as a gitpython Blob :param folder_config: a dict with the configuration of the folder the doc was found in, as specified in the sync config file under `folders` :params weight: optional weight of the doc. When specified it's set in the returned header dict :returns: a tuple (target_filename, target_folder, header), which describes which files `doc` should be written to, in which folder, with which header """ index_file = folder_config.get('index', FOLDER_INDEX) target_folder = folder_config.get('target', '') # If the doc name is configured as index, rewrite it to FOLDER_INDEX target_filename = FOLDER_INDEX if doc.name == index_file else doc.name # If an header is specified, build it an return it header_dict = None if 'header' in folder_config: header_dict = copy.deepcopy(folder_config['header']) if weight is not None: header_dict['weight'] = weight return target_filename, target_folder, header_dict def docs_from_tree(tree, include=['*'], exclude=[]): """ Get matching docs (git blobs) from a git tree Filter all blobs directly under a tree based on include and exclude lists. Filters are specified as list of unix style filename pattern: (https://docs.python.org/3/library/fnmatch.html) """ return filter(lambda b: any(fnmatch.fnmatch(b.name, i) for i in include) and not any(fnmatch.fnmatch(b.name, e) for e in exclude), tree.blobs) def transform_docs(git_repo, tag, folders, site_folder, base_path, base_url): """ Transform all folders configured for a tag :param git_repo: a gitpython Repo object, that points to the source git repo :param tag: a string that represent the git tag to be used :param folders: a list of folder names with a dict config each, loaded from sync config file :param site_folder: the root folder on disk where files shall be written to :param base_path: used to rewrite relative links to sync'ed files :param base_url: used to rewrite relative links to unknown files """ # Get the root tree for the requested version from the repo try: tag = next(x for x in git_repo.tags if x.name == tag) except StopIteration: # When no tag is found try to match a branch (remote heads) try: tag = next(x for x in git_repo.remote().refs if x.remote_head == tag) except StopIteration: logging.error(f'No tag or branch {tag} found in {git_repo}') sys.exit(1) # List all relevant blobs based on the folder config files = [] for folder, folder_config in folders.items(): if folder == '.': root = tag.commit.tree else: root = tag.commit.tree.join(folder) docs = docs_from_tree( tree=root, include=folder_config.get('include', ['*']), exclude=folder_config.get('exclude', [])) # zip doc, folder, targer and header so we can process them in parallel later files.extend([(doc, folder, *doc_config(doc, folder_config, idx)) for idx, doc in enumerate(docs)]) # Build a dict of all valid local links # This is used by `transfor_line` to identify local links local_files = {doc.path: (target, target_folder) for doc, _, target, target_folder, _ in files} # Build a list of tuple of `transform_doc` parameters tranform_args = [ (*f, local_files, base_path, base_url, site_folder) for f in files] with Pool() as pool: results = pool.starmap(transform_doc, tranform_args) # Return the list of files transformed return results def safe_makedirs(path): try: os.makedirs(path, exist_ok=True) except FileExistsError: pass def transform_doc(doc, source_folder, target, target_folder, header, local_files, base_path, base_url, site_folder): """ Transform a single doc to the target file Read a doc (git blob), transform links in it and writes the results in to a target file :param doc: The source doc as gitpython Blob :param source_folder: the name of the folder in the source repo where the file comes from :param target: the name of the file the transformed doc shall be written to :param target_folder: the folder within `site_folder` where the transformed doc shall be written to :param header: a dict with the content of a header (if any) to be prepended in the transformed doc :param local_files: a dict source file -> target used to rewrite relative links to sync'ed files :param base_path: used to rewrite relative links to sync'ed files :param base_url: used to rewrite relative links to unknown files :param site_folder: the root folder on disk where files shall be written to """ site_target_folder = os.path.normpath(os.path.join(site_folder, target_folder)) safe_makedirs(site_target_folder) target = os.path.join(site_target_folder, target) # Look for markdown files. # Some machines seem to use text/plain (e.g. running on a mac) and some use # text/markdown (e.g. running in a fresh ubuntu container) if doc.mime_type == 'text/plain' or doc.mime_type == 'text/markdown': with open(target, 'w+') as target_doc: # If there is an header configured, write it (in YAML) doc_all = decode(doc.data_stream.read()) doc_markdown, fm = read_front_matter(doc_all) # Update the doc front matter with the configured one and write it write_front_matter(target_doc, fm, header) doc_markdown = transform_links_doc( doc_markdown, source_folder, local_files, base_path, base_url) target_doc.write(doc_markdown) return target # Pass-through for other mime types with open(target, 'bw+') as target_doc: logging.info(f'Pass-through {doc.mime_type} file {doc.path}') target_doc.write(doc.data_stream.read()) return target def decode(s, encodings=('utf8', 'latin1', 'ascii')): for encoding in encodings: try: return s.decode(encoding) except UnicodeDecodeError: pass return s.decode('ascii', 'ignore') def read_front_matter(text): """ returns a tuple text, frontmatter (as dict) """ if FM_BOUNDARY.match(text): try: _, fm, content = FM_BOUNDARY.split(text, 2) except ValueError: # Not enough values to unpack, boundary was matched once return text, None if content.startswith('\n'): content = content[1:] return content, YAML().load(fm) else: return text, None def write_front_matter(target_doc, fm_doc, fm_config): fm_doc = fm_doc or {} fm_config = fm_config or {} fm_doc.update(fm_config) if fm_doc: target_doc.write(YAML_SEPARATOR) YAML().dump(fm_doc, target_doc) target_doc.write(YAML_SEPARATOR) def transform_links_doc(text, base_path, local_files, rewrite_path, rewrite_url): """ transform all the links the text """ links = get_links(text) # Rewrite map, only use links with an href rewrite_map = {x.get("href"): transform_link(x.get("href"), base_path, local_files, rewrite_path, rewrite_url) for x in links if x.get("href")} for source, target in rewrite_map.items(): text = text.replace(f'({source})', f'({target})') return text def get_links(md): """ return a list of all the links in a string formatted in markdown """ md = markdown.markdown(md) soup = BeautifulSoup(md, 'html.parser') return soup.find_all("a") def transform_link(link, base_path, local_files, rewrite_path, rewrite_url): """ Transform hrefs to be valid URLs on the web-site Relative URLs are rewritten to `rewrite_path` when `link` points to a sync'ed file. Else they're rewritten to `rewrite_url`. Absolute URLs are not changed (they may be external) Fragments are relative to the page and do not need changes, except for lower() on local files because hugo generated anchors are always lower case. :param link: the link to be re-written :param base_path: the folder where the source document that contains the link lives :param local_files: a dict source file -> (target file, folder) that maps sync'ed files from their fully qualified source name into their filename in the site folder :param rewrite_path: the file local (sync'ed) files are rewritten to :param rewrite_url: the URL remote files are rewritten to :note: urlparse treats URLs without scheme like path only URLs, so 'github.com' will be rewritten to 'rewrite_url/github.com' """ # ignore empty links if not link: return link # urlparse returns a named tuple parsed = urlparse(link) if is_absolute_url(parsed): return link if is_fragment(parsed): # A fragment only link points to an .md file return urlunparse(parsed._replace(fragment=parsed.fragment.lower())) path = os.path.normpath(parsed.path) # The list if local_file includes paths based on the root of the git # repo, so we need join base_path and normalize to fq_path to find the # link in the list of local files fq_path = os.path.normpath(os.path.join(base_path, parsed.path)) if fq_path in local_files: target_file = local_files[fq_path][0] target_folder = local_files[fq_path][1] is_index = (target_file == FOLDER_INDEX) filename, ext = os.path.splitext(target_file) # Special handling for md files if ext == '.md': # Links to the index file are rendered as base_path/ if is_index: target_file = '' # links to md other files are rendered as .../[md filename]/ else: target_file = filename + '/' # for .md files, lower the case of fragments to match hugo's behaviour parsed = parsed._replace(fragment=parsed.fragment.lower()) if target_folder: new_path = [rewrite_path, target_folder, target_file] else: new_path = [rewrite_path, target_file] return parsed._replace(path="/".join(new_path)).geturl() # when not found on disk, append to the base_url return urljoin(rewrite_url, parsed._replace(path=fq_path).geturl()) def is_absolute_url(parsed_url): """ check if it is an absolute url """ return all([parsed_url.scheme, parsed_url.netloc]) def is_fragment(parsed_url): """ determine if the url is an a link """ return len(parsed_url.fragment) > 0 and not any(parsed_url[:-1]) def download_resources_to_project(yaml_list, clones): """ download the files from local clones based on a spec. The YAML sync spec can be found in sync/config/README.md """ for entry in yaml_list: component = entry['component'] repository = entry['repository'] local_clone = clones.get(repository) if not local_clone: logging.error(f'No git clone found for {repository} in {clones}') sys.exit(1) for index, tag in enumerate(entry['tags']): logging.info(f'Syncing {component}@{tag['name']}') link_base_url = f'{repository}/tree/{tag['name']}/' if index == 0: # first links belongs on the home page base_path = f'/docs/{component}'.lower() site_dir = f'{CONTENT_DIR}/{component}' os.makedirs(site_dir, exist_ok=True) else: # the other links belong in the other versions a.k.a vault base_path = f'/vault/{component}-{tag['displayName']}' site_dir = f'{VAULT_DIR}/{component}-{tag['displayName']}' os.makedirs(site_dir, exist_ok=True) results = transform_docs( git_repo=local_clone, tag=tag['name'], folders=tag['folders'], site_folder=site_dir, base_path=base_path, base_url=link_base_url) logging.debug(f'Finished syncing {component}@{tag['name']}: ') logging.debug(f'{results}') def get_files_in_path(path, file_type): """ return a list of all the files in path that match the file_type """ file_list = [] # walk through every file in directory and its sub directories for root, dirs, files in os.walk(path): for file in files: # append the file name to the list if is it the correct type if file.endswith(file_type): file_list.append(os.path.join(root, file)) return file_list def load_config(files): """ return a list of yaml files""" yaml = YAML() dic_list = [] for file in files: with open(file, 'r') as text: # get the paths from the config file dic_list.append({ "filename": file, "content": yaml.load(text) }) return dic_list def save_config(config): """ save config files back to yaml """ yaml = YAML() for c in config: with open(c['filename'], 'w') as out: yaml.dump(c['content'], out) def get_tags(sync_config): """ return a list of tags with, there name, and displayName """ tags = [] for tag in sync_config['tags']: tags.append({'name': tag['name'], 'displayName': tag['displayName']}) return tags def get_versions(sync_configs): """ return the list of all the versions and there tag, name, archive """ component_versions = [] for sync_config in sync_configs: component_versions.append({ 'name': sync_config['component'], 'tags': get_tags(sync_config), 'archive': sync_config['archive'] }) return component_versions def create_resource(dest_prefix, file, versions): """ create site resource based on the version and file """ resource_template = jinja_env.get_template(f'{file}.template') if file.endswith(".js"): serialize = json.dumps(versions) resource = resource_template.render(component_versions_json=serialize) elif file.endswith(".md"): resource = resource_template.render(component_versions=versions) else: logging.warning(f'Cannot create resource for {file}. Only .js and .md supported') return with open(f'{dest_prefix}/{file}', 'w') as f: f.write(resource) def clone_repo(repo, update): project = repo.split('/')[-1] clone_dir = os.path.join(DEFAULT_CACHE_FOLDER, project) if os.path.isdir(clone_dir): if not update: print(f'{project}: Cache folder {clone_dir} found, skipping clone.') return repo, git.Repo(clone_dir) # Cleanup and update via fetch --all print(f'{project}: updating started') cloned_repo = git.Repo(clone_dir) cloned_repo.git.reset('--hard') cloned_repo.git.clean('-xdf') cloned_repo.git.fetch('--all') print(f'{project}: updating completed') return repo, cloned_repo # Clone the repo print(f'{project}: cloning started') cloned_repo = git.Repo.clone_from(repo, clone_dir) print(f'{project}: cloning completed') return repo, cloned_repo def clone_repos(sync_configs, update): # Make sure the cache folder exists safe_makedirs(DEFAULT_CACHE_FOLDER) with Pool() as pool: results = pool.starmap(clone_repo, [(x['repository'], update) for x in sync_configs]) return {x: y for x, y in results} @click.command() @click.option('--config-folder', default=DEFAULT_CONFIG_FOLDER, help='the folder that contains the config files') @click.option('--update-cache/--no-update-cache', default=False, help='update clone caches. !! This will force cleanup caches !!') def sync(config_folder, update_cache): """ fetch all the files and sync it to the website """ # get the path of the urls needed config_files = get_files_in_path(config_folder, ".yaml") config = [x["content"] for x in load_config(config_files)] # clone all relevant repos clones = clone_repos(config, update_cache) # download resources from the clone cache download_resources_to_project(config, clones) versions = get_versions(config) # create version switcher script create_resource(JS_ASSET_DIR, "version-switcher.js", versions) # create index for vault create_resource(VAULT_DIR, FOLDER_INDEX, versions) if __name__ == '__main__': sync()

#!/usr/bin/env python # Copyright 2020 The Tekton Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This script helps synchronize contents from their respective sources of # truth (usually GitHub repositories of each Tekton # components, such as tektoncd/pipelines) to tektoncd/website. import copy import fnmatch import json import logging import markdown from multiprocessing import Pool import os import os.path import re import sys from urllib.error import URLError from urllib.parse import urlparse, urljoin, urlunparse from bs4 import BeautifulSoup import click import git from jinja2 import Environment from jinja2 import FileSystemLoader from ruamel.yaml import YAML CONTENT_DIR = './content/en/docs' VAULT_DIR = './content/en/vault' JS_ASSET_DIR = './assets/js' TEMPLATE_DIR = './templates' BASE_FOLDER = os.path.dirname(os.path.abspath(__file__)) DEFAULT_CONFIG_FOLDER = os.path.join(BASE_FOLDER, 'config') DEFAULT_CACHE_FOLDER = os.path.join(BASE_FOLDER, '.cache') jinja_env = Environment(loader=FileSystemLoader(TEMPLATE_DIR)) FM_BOUNDARY = re.compile(r"^(?:)?", re.MULTILINE) YAML_SEPARATOR = "---\n" FOLDER_INDEX = '_index.md' def doc_config(doc, folder_config, weight=None): """ Return the target name, folder and header for doc based on folder_config :param doc: the doc as a gitpython Blob :param folder_config: a dict with the configuration of the folder the doc was found in, as specified in the sync config file under `folders` :params weight: optional weight of the doc. When specified it's set in the returned header dict :returns: a tuple (target_filename, target_folder, header), which describes which files `doc` should be written to, in which folder, with which header """ index_file = folder_config.get('index', FOLDER_INDEX) target_folder = folder_config.get('target', '') # If the doc name is configured as index, rewrite it to FOLDER_INDEX target_filename = FOLDER_INDEX if doc.name == index_file else doc.name # If an header is specified, build it an return it header_dict = None if 'header' in folder_config: header_dict = copy.deepcopy(folder_config['header']) if weight is not None: header_dict['weight'] = weight return target_filename, target_folder, header_dict def docs_from_tree(tree, include=['*'], exclude=[]): """ Get matching docs (git blobs) from a git tree Filter all blobs directly under a tree based on include and exclude lists. Filters are specified as list of unix style filename pattern: (https://docs.python.org/3/library/fnmatch.html) """ return filter(lambda b: any(fnmatch.fnmatch(b.name, i) for i in include) and not any(fnmatch.fnmatch(b.name, e) for e in exclude), tree.blobs) def transform_docs(git_repo, tag, folders, site_folder, base_path, base_url): """ Transform all folders configured for a tag :param git_repo: a gitpython Repo object, that points to the source git repo :param tag: a string that represent the git tag to be used :param folders: a list of folder names with a dict config each, loaded from sync config file :param site_folder: the root folder on disk where files shall be written to :param base_path: used to rewrite relative links to sync'ed files :param base_url: used to rewrite relative links to unknown files """ # Get the root tree for the requested version from the repo try: tag = next(x for x in git_repo.tags if x.name == tag) except StopIteration: # When no tag is found try to match a branch (remote heads) try: tag = next(x for x in git_repo.remote().refs if x.remote_head == tag) except StopIteration: logging.error(f'No tag or branch {tag} found in {git_repo}') sys.exit(1) # List all relevant blobs based on the folder config files = [] for folder, folder_config in folders.items(): if folder == '.': root = tag.commit.tree else: root = tag.commit.tree.join(folder) docs = docs_from_tree( tree=root, include=folder_config.get('include', ['*']), exclude=folder_config.get('exclude', [])) # zip doc, folder, targer and header so we can process them in parallel later files.extend([(doc, folder, *doc_config(doc, folder_config, idx)) for idx, doc in enumerate(docs)]) # Build a dict of all valid local links # This is used by `transfor_line` to identify local links local_files = {doc.path: (target, target_folder) for doc, _, target, target_folder, _ in files} # Build a list of tuple of `transform_doc` parameters tranform_args = [ (*f, local_files, base_path, base_url, site_folder) for f in files] with Pool() as pool: results = pool.starmap(transform_doc, tranform_args) # Return the list of files transformed return results def safe_makedirs(path): try: os.makedirs(path, exist_ok=True) except FileExistsError: pass def transform_doc(doc, source_folder, target, target_folder, header, local_files, base_path, base_url, site_folder): """ Transform a single doc to the target file Read a doc (git blob), transform links in it and writes the results in to a target file :param doc: The source doc as gitpython Blob :param source_folder: the name of the folder in the source repo where the file comes from :param target: the name of the file the transformed doc shall be written to :param target_folder: the folder within `site_folder` where the transformed doc shall be written to :param header: a dict with the content of a header (if any) to be prepended in the transformed doc :param local_files: a dict source file -> target used to rewrite relative links to sync'ed files :param base_path: used to rewrite relative links to sync'ed files :param base_url: used to rewrite relative links to unknown files :param site_folder: the root folder on disk where files shall be written to """ site_target_folder = os.path.normpath(os.path.join(site_folder, target_folder)) safe_makedirs(site_target_folder) target = os.path.join(site_target_folder, target) # Look for markdown files. # Some machines seem to use text/plain (e.g. running on a mac) and some use # text/markdown (e.g. running in a fresh ubuntu container) if doc.mime_type == 'text/plain' or doc.mime_type == 'text/markdown': with open(target, 'w+') as target_doc: # If there is an header configured, write it (in YAML) doc_all = decode(doc.data_stream.read()) doc_markdown, fm = read_front_matter(doc_all) # Update the doc front matter with the configured one and write it write_front_matter(target_doc, fm, header) doc_markdown = transform_links_doc( doc_markdown, source_folder, local_files, base_path, base_url) target_doc.write(doc_markdown) return target # Pass-through for other mime types with open(target, 'bw+') as target_doc: logging.info(f'Pass-through {doc.mime_type} file {doc.path}') target_doc.write(doc.data_stream.read()) return target def decode(s, encodings=('utf8', 'latin1', 'ascii')): for encoding in encodings: try: return s.decode(encoding) except UnicodeDecodeError: pass return s.decode('ascii', 'ignore') def read_front_matter(text): """ returns a tuple text, frontmatter (as dict) """ if FM_BOUNDARY.match(text): try: _, fm, content = FM_BOUNDARY.split(text, 2) except ValueError: # Not enough values to unpack, boundary was matched once return text, None if content.startswith('\n'): content = content[1:] return content, YAML().load(fm) else: return text, None def write_front_matter(target_doc, fm_doc, fm_config): fm_doc = fm_doc or {} fm_config = fm_config or {} fm_doc.update(fm_config) if fm_doc: target_doc.write(YAML_SEPARATOR) YAML().dump(fm_doc, target_doc) target_doc.write(YAML_SEPARATOR) def transform_links_doc(text, base_path, local_files, rewrite_path, rewrite_url): """ transform all the links the text """ links = get_links(text) # Rewrite map, only use links with an href rewrite_map = {x.get("href"): transform_link(x.get("href"), base_path, local_files, rewrite_path, rewrite_url) for x in links if x.get("href")} for source, target in rewrite_map.items(): text = text.replace(f'({source})', f'({target})') return text def get_links(md): """ return a list of all the links in a string formatted in markdown """ md = markdown.markdown(md) soup = BeautifulSoup(md, 'html.parser') return soup.find_all("a") def transform_link(link, base_path, local_files, rewrite_path, rewrite_url): """ Transform hrefs to be valid URLs on the web-site Relative URLs are rewritten to `rewrite_path` when `link` points to a sync'ed file. Else they're rewritten to `rewrite_url`. Absolute URLs are not changed (they may be external) Fragments are relative to the page and do not need changes, except for lower() on local files because hugo generated anchors are always lower case. :param link: the link to be re-written :param base_path: the folder where the source document that contains the link lives :param local_files: a dict source file -> (target file, folder) that maps sync'ed files from their fully qualified source name into their filename in the site folder :param rewrite_path: the file local (sync'ed) files are rewritten to :param rewrite_url: the URL remote files are rewritten to :note: urlparse treats URLs without scheme like path only URLs, so 'github.com' will be rewritten to 'rewrite_url/github.com' """ # ignore empty links if not link: return link # urlparse returns a named tuple parsed = urlparse(link) if is_absolute_url(parsed): return link if is_fragment(parsed): # A fragment only link points to an .md file return urlunparse(parsed._replace(fragment=parsed.fragment.lower())) path = os.path.normpath(parsed.path) # The list if local_file includes paths based on the root of the git # repo, so we need join base_path and normalize to fq_path to find the # link in the list of local files fq_path = os.path.normpath(os.path.join(base_path, parsed.path)) if fq_path in local_files: target_file = local_files[fq_path][0] target_folder = local_files[fq_path][1] is_index = (target_file == FOLDER_INDEX) filename, ext = os.path.splitext(target_file) # Special handling for md files if ext == '.md': # Links to the index file are rendered as base_path/ if is_index: target_file = '' # links to md other files are rendered as .../[md filename]/ else: target_file = filename + '/' # for .md files, lower the case of fragments to match hugo's behaviour parsed = parsed._replace(fragment=parsed.fragment.lower()) if target_folder: new_path = [rewrite_path, target_folder, target_file] else: new_path = [rewrite_path, target_file] return parsed._replace(path="/".join(new_path)).geturl() # when not found on disk, append to the base_url return urljoin(rewrite_url, parsed._replace(path=fq_path).geturl()) def is_absolute_url(parsed_url): """ check if it is an absolute url """ return all([parsed_url.scheme, parsed_url.netloc]) def is_fragment(parsed_url): """ determine if the url is an a link """ return len(parsed_url.fragment) > 0 and not any(parsed_url[:-1]) def download_resources_to_project(yaml_list, clones): """ download the files from local clones based on a spec. The YAML sync spec can be found in sync/config/README.md """ for entry in yaml_list: component = entry['component'] repository = entry['repository'] local_clone = clones.get(repository) if not local_clone: logging.error(f'No git clone found for {repository} in {clones}') sys.exit(1) for index, tag in enumerate(entry['tags']): logging.info(f'Syncing {component}@{tag["name"]}') link_base_url = f'{repository}/tree/{tag["name"]}/' if index == 0: # first links belongs on the home page base_path = f'/docs/{component}'.lower() site_dir = f'{CONTENT_DIR}/{component}' os.makedirs(site_dir, exist_ok=True) else: # the other links belong in the other versions a.k.a vault base_path = f'/vault/{component}-{tag["displayName"]}' site_dir = f'{VAULT_DIR}/{component}-{tag["displayName"]}' os.makedirs(site_dir, exist_ok=True) results = transform_docs( git_repo=local_clone, tag=tag['name'], folders=tag['folders'], site_folder=site_dir, base_path=base_path, base_url=link_base_url) logging.debug(f'Finished syncing {component}@{tag["name"]}: ') logging.debug(f'{results}') def get_files_in_path(path, file_type): """ return a list of all the files in path that match the file_type """ file_list = [] # walk through every file in directory and its sub directories for root, dirs, files in os.walk(path): for file in files: # append the file name to the list if is it the correct type if file.endswith(file_type): file_list.append(os.path.join(root, file)) return file_list def load_config(files): """ return a list of yaml files""" yaml = YAML() dic_list = [] for file in files: with open(file, 'r') as text: # get the paths from the config file dic_list.append({ "filename": file, "content": yaml.load(text) }) return dic_list def save_config(config): """ save config files back to yaml """ yaml = YAML() for c in config: with open(c['filename'], 'w') as out: yaml.dump(c['content'], out) def get_tags(sync_config): """ return a list of tags with, there name, and displayName """ tags = [] for tag in sync_config['tags']: tags.append({'name': tag['name'], 'displayName': tag['displayName']}) return tags def get_versions(sync_configs): """ return the list of all the versions and there tag, name, archive """ component_versions = [] for sync_config in sync_configs: component_versions.append({ 'name': sync_config['component'], 'tags': get_tags(sync_config), 'archive': sync_config['archive'] }) return component_versions def create_resource(dest_prefix, file, versions): """ create site resource based on the version and file """ resource_template = jinja_env.get_template(f'{file}.template') if file.endswith(".js"): serialize = json.dumps(versions) resource = resource_template.render(component_versions_json=serialize) elif file.endswith(".md"): resource = resource_template.render(component_versions=versions) else: logging.warning(f'Cannot create resource for {file}. Only .js and .md supported') return with open(f'{dest_prefix}/{file}', 'w') as f: f.write(resource) def clone_repo(repo, update): project = repo.split('/')[-1] clone_dir = os.path.join(DEFAULT_CACHE_FOLDER, project) if os.path.isdir(clone_dir): if not update: print(f'{project}: Cache folder {clone_dir} found, skipping clone.') return repo, git.Repo(clone_dir) # Cleanup and update via fetch --all print(f'{project}: updating started') cloned_repo = git.Repo(clone_dir) cloned_repo.git.reset('--hard') cloned_repo.git.clean('-xdf') cloned_repo.git.fetch('--all') print(f'{project}: updating completed') return repo, cloned_repo # Clone the repo print(f'{project}: cloning started') cloned_repo = git.Repo.clone_from(repo, clone_dir) print(f'{project}: cloning completed') return repo, cloned_repo def clone_repos(sync_configs, update): # Make sure the cache folder exists safe_makedirs(DEFAULT_CACHE_FOLDER) with Pool() as pool: results = pool.starmap(clone_repo, [(x['repository'], update) for x in sync_configs]) return {x: y for x, y in results} @click.command() @click.option('--config-folder', default=DEFAULT_CONFIG_FOLDER, help='the folder that contains the config files') @click.option('--update-cache/--no-update-cache', default=False, help='update clone caches. !! This will force cleanup caches !!') def sync(config_folder, update_cache): """ fetch all the files and sync it to the website """ # get the path of the urls needed config_files = get_files_in_path(config_folder, ".yaml") config = [x["content"] for x in load_config(config_files)] # clone all relevant repos clones = clone_repos(config, update_cache) # download resources from the clone cache download_resources_to_project(config, clones) versions = get_versions(config) # create version switcher script create_resource(JS_ASSET_DIR, "version-switcher.js", versions) # create index for vault create_resource(VAULT_DIR, FOLDER_INDEX, versions) if __name__ == '__main__': sync()

import json import os from .config import API_KEY import requests from datetime import date from w3lib.html import remove_tags def save_data_to_json(file_name, data): """Save data to a json file creating it if it does not already exist :parameter: file_name -> 'example' do not add the '.json' :parameter: data -> json data with the following structure [{},{},...]""" # Save Data if os.path.exists(file_name+'.json') == False: with open(file_name+'.json', 'w', encoding='utf-8') as json_file: # json.dump(data, json_file, indent=0, ensure_ascii=False) for entry in data: json.dump(entry, json_file) json_file.write('\n') json_file.close() else: with open(file_name+'.json', 'a+', encoding='utf-8') as json_file: # json.dump(data, json_file, indent=0, ensure_ascii=False) for entry in data: json.dump(entry, json_file, ensure_ascii=False) json_file.write('\n') json_file.close() def call_api(): #json query params = { 'api_key': API_KEY, 'limit': 3890 } response = requests.get(url=f"https://api.itjobs.pt/job/list.json", params=params) # print(response) # Save results to json results = response.json() jobs = results['results'] return jobs def main(filename): jobs = call_api() job_offers = [] for j in jobs: title = j['title'] description = remove_tags(j['body']) post_date = j['publishedAt'] company = j['company']['name'] try: job_location = [i['name'] for i in j['locations']] ', '.join(job_location) except KeyError: job_location = '' try: salary = j['wage'] except AttributeError: salary = '' job_offer = { 'job_title': title, 'job_description': description, 'post_date': post_date, 'scrape_date': date.today().strftime("%d/%m/%Y"), 'company': company, 'job_location': job_location, 'job_category': '', 'job_ref': f"https://www.itjobs.pt/oferta/{j['id']}/{j['slug']}", 'salary': salary, } job_offers.append(job_offer) save_data_to_json(f"C:/Users/gilnr/OneDrive - NOVASBE/Work Project/Code/Data/{filename}", job_offers) if __name__ == '__main__': main('itjobs_jobs') print('Jobs Retrieved successefully')

# AutoTransform # Large scale, component based code modification library # # Licensed under the MIT License <http://opensource.org/licenses/MIT> # SPDX-License-Identifier: MIT # Copyright (c) 2022-present Nathan Rockenbach <http://github.com/nathro> # @black_format """The settings command is used to update AutoTransform settings, such as scheduler.json files, manager.json files, configs and imported components.""" import json import os from argparse import ArgumentParser, Namespace from typing import Dict, List from autotransform.config.config import Config from autotransform.config.default import DefaultConfigFetcher from autotransform.schema.schema import AutoTransformSchema from autotransform.util.component import ComponentFactory, ComponentImport from autotransform.util.console import choose_options_from_list, error, get_str, info from autotransform.util.manager import Manager from autotransform.util.package import get_config_dir from autotransform.util.scheduler import Scheduler def add_args(parser: ArgumentParser) -> None: """Adds the args to a subparser that are required to update/view settings. Args: parser (ArgumentParser): The parser for the command. """ setting_type_group = parser.add_mutually_exclusive_group(required=True) setting_type_group.add_argument( "--user-config", dest="setting_type", action="store_const", const="user_config", help="Update or view the user configuration for AutoTransform", ) setting_type_group.add_argument( "--repo-config", dest="setting_type", action="store_const", const="repo_config", help="Update or view the repo configuration for AutoTransform", ) setting_type_group.add_argument( "--cwd-config", dest="setting_type", action="store_const", const="cwd_config", help="Update or view the current working directory configuration for AutoTransform", ) setting_type_group.add_argument( "--custom-components", dest="setting_type", action="store_const", const="custom_components", help="Update or view custom components", ) setting_type_group.add_argument( "--manager", dest="setting_type", action="store_const", const="manager", help="Update or view manager settings", ) setting_type_group.add_argument( "--schema", type=str, help="The path to an existing or to be created JSON encoded schema.", ) parser.add_argument( "--update", dest="update_settings", action="store_true", help="Used to indicate updates are to be made to the settings.", ) parser.set_defaults(func=settings_command_main, update_settings=False) def settings_command_main(args: Namespace) -> None: """The main method for the settings command, handles the actual execution of updating and viewing settings. Args: args (Namespace): The arguments supplied to the settings command. """ if args.setting_type == "user_config": path = f"{get_config_dir}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "User", args.update_settings) elif args.setting_type == "repo_config": path = f"{DefaultConfigFetcher.get_repo_config_dir()}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "Repo", args.update_settings) elif args.setting_type == "cwd_config": path = f"{DefaultConfigFetcher.get_cwd_config_dir()}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "CWD", args.update_settings) elif args.setting_type == "custom_components": handle_custom_components(args.update_settings) elif args.setting_type == "manager": handle_manager(args.update_settings) else: handle_schema(args.update_settings, args.schema) def handle_config(path: str, config_type: str, update: bool) -> None: """Handles updating a config file. Args: path (str): The path to the file. config_type (str): The type of config being updated (i.e. user). update (bool): Whether to update the config. """ config = Config.read(path) info(f"Current {config_type} Config\n{config!r}") if not update: return config.from_console(config, user_config=config_type == "User")[0].write(path) def handle_custom_components(update: bool) -> None: """Handle updating/viewing custom components. Args: update (bool): Whether to apply updates to the custom components. """ component_file_name = choose_options_from_list( "Select a component type", [ ("input.json", "Inputs"), ("filter.json", "Filters"), ("batcher.json", "Batchers"), ("transformer.json", "Transformers"), ("validator.json", "Validators"), ("repo.json", "Repos"), ("schema_builder.json", "Schema Builders"), ("runner.json", "Runners"), ("item.json", "Items"), ("change.json", "Changes"), ("step.json", "Steps"), ("condition.json", "Conditions"), ], )[0] component_dict = ComponentFactory.get_custom_components_dict(component_file_name, strict=False) if component_dict: info("Custom components:") for name, component_import in component_dict.items(): info(f"\t{name.removeprefix("custom/")}: {component_import!r}") else: info("No existing custom components") if not update: return # Remove components components_to_remove = get_components_to_remove(component_dict) changed = bool(components_to_remove) for component in components_to_remove: del component_dict[component] # Add components components_to_add = get_components_to_add(component_dict) changed = changed or bool(components_to_add) component_dict = component_dict | components_to_add if changed: file_path = ComponentFactory.get_custom_components_path(component_file_name) os.makedirs(os.path.dirname(file_path), exist_ok=True) with open(file_path, "w+", encoding="UTF-8") as component_file: component_file.write( json.dumps( {k.removeprefix("custom/"): v.bundle() for k, v in component_dict.items()}, indent=4, ) ) component_file.flush() def get_components_to_remove(component_dict: Dict[str, ComponentImport]) -> List[str]: """Gets a list of components to remove from the dictionary using console input. Args: component_dict (Dict[str, ComponentImport]): The custom component dictionary. Returns: List[str]: The keys to remove from the dictionary. """ components = [] if component_dict: name = get_str("Enter a component name to remove(blank to skip): ") else: name = "" while name != "": if name.startswith("custom/"): name = name.removeprefix("custom/") if f"custom/{name}" not in component_dict: error(f"No component import with name: {name}") elif f"custom/{name}" in components: error(f"Already removing component import with name: {name}") else: components.append(f"custom/{name}") if len(component_dict) <= len(components): break name = get_str("Enter a component name to remove(blank to skip): ") return components def get_components_to_add(component_dict: Dict[str, ComponentImport]) -> Dict[str, ComponentImport]: """Gets a dictionary of new components to add to the custom component imports. Args: component_dict (Dict[str, ComponentImport]): The existing custom components. Returns: Dict[str, ComponentImport]: The components to add to the dictionary. """ components_to_add = {} name = get_str("Enter component name to add(blank to skip): ") while name != "": if name.startswith("custom/"): name = name.removeprefix("custom/") if f"custom/{name}" in component_dict: error(f"Component already exists with name: {name}") elif f"custom/{name}" in components_to_add: error(f"Already adding component with name: {name}") else: class_name = get_str("Enter the class representing this component: ") module = get_str("Enter the fully qualified name of the module for the class: ") components_to_add[f"custom/{name}"] = ComponentImport( class_name=class_name, module=module ) name = get_str("Enter component name to add(blank to skip): ") return components_to_add def handle_manager(update: bool) -> None: """Handle updating/viewing the Manager. Args: update (bool): Whether to apply updates to the Manager. """ path = f"{DefaultConfigFetcher.get_repo_config_dir()}/manager.json" manager = None try: manager = Manager.read(path) info(f"Current Manager\n{manager!r}") except FileNotFoundError: error("No Manager file found") except json.JSONDecodeError as err: error(f"Failed to decode Manager JSON\n{err}") except ValueError as err: error(f"Invalid Manager value\n{err}") except TypeError as err: error(f"Invalid Manager type\n{err}") if not update: return Manager.from_console(manager).write(path) def handle_scheduler(update: bool) -> None: """Handle updating/viewing the Scheduler. Args: update (bool): Whether to apply updates to the Scheduler. """ path = f"{DefaultConfigFetcher.get_repo_config_dir()}/scheduler.json" scheduler = None try: scheduler = Scheduler.read(path) info(f"Current Scheduler\n{scheduler!r}") except FileNotFoundError: error("No Manager file found") except json.JSONDecodeError as err: error(f"Failed to decode Manager JSON\n{err}") except ValueError as err: error(f"Invalid Manager value\n{err}") except TypeError as err: error(f"Invalid Manager type\n{err}") if not update: return Scheduler.from_console(scheduler).write(path) def handle_schema(update: bool, file_path: str) -> None: """Handle updating/viewing a Schema. Args: update (bool): Whether to apply updates to the Schema. file_path (str): The path to the Schema file. """ try: with open(file_path, "r", encoding="UTF-8") as schema_file: schema = AutoTransformSchema.from_data(json.loads(schema_file.read())) info(f"Existing schema:\n{schema!r}") except FileNotFoundError: info(f"No schema found at path: {file_path}") schema = None if update: schema = AutoTransformSchema.from_console(schema) os.makedirs(os.path.dirname(file_path), exist_ok=True) with open(file_path, "w+", encoding="UTF-8") as schema_file: schema_file.write(json.dumps(schema.bundle(), indent=4)) schema_file.flush()

# AutoTransform # Large scale, component based code modification library # # Licensed under the MIT License <http://opensource.org/licenses/MIT> # SPDX-License-Identifier: MIT # Copyright (c) 2022-present Nathan Rockenbach <http://github.com/nathro> # @black_format """The settings command is used to update AutoTransform settings, such as scheduler.json files, manager.json files, configs and imported components.""" import json import os from argparse import ArgumentParser, Namespace from typing import Dict, List from autotransform.config.config import Config from autotransform.config.default import DefaultConfigFetcher from autotransform.schema.schema import AutoTransformSchema from autotransform.util.component import ComponentFactory, ComponentImport from autotransform.util.console import choose_options_from_list, error, get_str, info from autotransform.util.manager import Manager from autotransform.util.package import get_config_dir from autotransform.util.scheduler import Scheduler def add_args(parser: ArgumentParser) -> None: """Adds the args to a subparser that are required to update/view settings. Args: parser (ArgumentParser): The parser for the command. """ setting_type_group = parser.add_mutually_exclusive_group(required=True) setting_type_group.add_argument( "--user-config", dest="setting_type", action="store_const", const="user_config", help="Update or view the user configuration for AutoTransform", ) setting_type_group.add_argument( "--repo-config", dest="setting_type", action="store_const", const="repo_config", help="Update or view the repo configuration for AutoTransform", ) setting_type_group.add_argument( "--cwd-config", dest="setting_type", action="store_const", const="cwd_config", help="Update or view the current working directory configuration for AutoTransform", ) setting_type_group.add_argument( "--custom-components", dest="setting_type", action="store_const", const="custom_components", help="Update or view custom components", ) setting_type_group.add_argument( "--manager", dest="setting_type", action="store_const", const="manager", help="Update or view manager settings", ) setting_type_group.add_argument( "--schema", type=str, help="The path to an existing or to be created JSON encoded schema.", ) parser.add_argument( "--update", dest="update_settings", action="store_true", help="Used to indicate updates are to be made to the settings.", ) parser.set_defaults(func=settings_command_main, update_settings=False) def settings_command_main(args: Namespace) -> None: """The main method for the settings command, handles the actual execution of updating and viewing settings. Args: args (Namespace): The arguments supplied to the settings command. """ if args.setting_type == "user_config": path = f"{get_config_dir}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "User", args.update_settings) elif args.setting_type == "repo_config": path = f"{DefaultConfigFetcher.get_repo_config_dir()}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "Repo", args.update_settings) elif args.setting_type == "cwd_config": path = f"{DefaultConfigFetcher.get_cwd_config_dir()}/{DefaultConfigFetcher.FILE_NAME}" handle_config(path, "CWD", args.update_settings) elif args.setting_type == "custom_components": handle_custom_components(args.update_settings) elif args.setting_type == "manager": handle_manager(args.update_settings) else: handle_schema(args.update_settings, args.schema) def handle_config(path: str, config_type: str, update: bool) -> None: """Handles updating a config file. Args: path (str): The path to the file. config_type (str): The type of config being updated (i.e. user). update (bool): Whether to update the config. """ config = Config.read(path) info(f"Current {config_type} Config\n{config!r}") if not update: return config.from_console(config, user_config=config_type == "User")[0].write(path) def handle_custom_components(update: bool) -> None: """Handle updating/viewing custom components. Args: update (bool): Whether to apply updates to the custom components. """ component_file_name = choose_options_from_list( "Select a component type", [ ("input.json", "Inputs"), ("filter.json", "Filters"), ("batcher.json", "Batchers"), ("transformer.json", "Transformers"), ("validator.json", "Validators"), ("repo.json", "Repos"), ("schema_builder.json", "Schema Builders"), ("runner.json", "Runners"), ("item.json", "Items"), ("change.json", "Changes"), ("step.json", "Steps"), ("condition.json", "Conditions"), ], )[0] component_dict = ComponentFactory.get_custom_components_dict(component_file_name, strict=False) if component_dict: info("Custom components:") for name, component_import in component_dict.items(): info(f"\t{name.removeprefix('custom/')}: {component_import!r}") else: info("No existing custom components") if not update: return # Remove components components_to_remove = get_components_to_remove(component_dict) changed = bool(components_to_remove) for component in components_to_remove: del component_dict[component] # Add components components_to_add = get_components_to_add(component_dict) changed = changed or bool(components_to_add) component_dict = component_dict | components_to_add if changed: file_path = ComponentFactory.get_custom_components_path(component_file_name) os.makedirs(os.path.dirname(file_path), exist_ok=True) with open(file_path, "w+", encoding="UTF-8") as component_file: component_file.write( json.dumps( {k.removeprefix("custom/"): v.bundle() for k, v in component_dict.items()}, indent=4, ) ) component_file.flush() def get_components_to_remove(component_dict: Dict[str, ComponentImport]) -> List[str]: """Gets a list of components to remove from the dictionary using console input. Args: component_dict (Dict[str, ComponentImport]): The custom component dictionary. Returns: List[str]: The keys to remove from the dictionary. """ components = [] if component_dict: name = get_str("Enter a component name to remove(blank to skip): ") else: name = "" while name != "": if name.startswith("custom/"): name = name.removeprefix("custom/") if f"custom/{name}" not in component_dict: error(f"No component import with name: {name}") elif f"custom/{name}" in components: error(f"Already removing component import with name: {name}") else: components.append(f"custom/{name}") if len(component_dict) <= len(components): break name = get_str("Enter a component name to remove(blank to skip): ") return components def get_components_to_add(component_dict: Dict[str, ComponentImport]) -> Dict[str, ComponentImport]: """Gets a dictionary of new components to add to the custom component imports. Args: component_dict (Dict[str, ComponentImport]): The existing custom components. Returns: Dict[str, ComponentImport]: The components to add to the dictionary. """ components_to_add = {} name = get_str("Enter component name to add(blank to skip): ") while name != "": if name.startswith("custom/"): name = name.removeprefix("custom/") if f"custom/{name}" in component_dict: error(f"Component already exists with name: {name}") elif f"custom/{name}" in components_to_add: error(f"Already adding component with name: {name}") else: class_name = get_str("Enter the class representing this component: ") module = get_str("Enter the fully qualified name of the module for the class: ") components_to_add[f"custom/{name}"] = ComponentImport( class_name=class_name, module=module ) name = get_str("Enter component name to add(blank to skip): ") return components_to_add def handle_manager(update: bool) -> None: """Handle updating/viewing the Manager. Args: update (bool): Whether to apply updates to the Manager. """ path = f"{DefaultConfigFetcher.get_repo_config_dir()}/manager.json" manager = None try: manager = Manager.read(path) info(f"Current Manager\n{manager!r}") except FileNotFoundError: error("No Manager file found") except json.JSONDecodeError as err: error(f"Failed to decode Manager JSON\n{err}") except ValueError as err: error(f"Invalid Manager value\n{err}") except TypeError as err: error(f"Invalid Manager type\n{err}") if not update: return Manager.from_console(manager).write(path) def handle_scheduler(update: bool) -> None: """Handle updating/viewing the Scheduler. Args: update (bool): Whether to apply updates to the Scheduler. """ path = f"{DefaultConfigFetcher.get_repo_config_dir()}/scheduler.json" scheduler = None try: scheduler = Scheduler.read(path) info(f"Current Scheduler\n{scheduler!r}") except FileNotFoundError: error("No Manager file found") except json.JSONDecodeError as err: error(f"Failed to decode Manager JSON\n{err}") except ValueError as err: error(f"Invalid Manager value\n{err}") except TypeError as err: error(f"Invalid Manager type\n{err}") if not update: return Scheduler.from_console(scheduler).write(path) def handle_schema(update: bool, file_path: str) -> None: """Handle updating/viewing a Schema. Args: update (bool): Whether to apply updates to the Schema. file_path (str): The path to the Schema file. """ try: with open(file_path, "r", encoding="UTF-8") as schema_file: schema = AutoTransformSchema.from_data(json.loads(schema_file.read())) info(f"Existing schema:\n{schema!r}") except FileNotFoundError: info(f"No schema found at path: {file_path}") schema = None if update: schema = AutoTransformSchema.from_console(schema) os.makedirs(os.path.dirname(file_path), exist_ok=True) with open(file_path, "w+", encoding="UTF-8") as schema_file: schema_file.write(json.dumps(schema.bundle(), indent=4)) schema_file.flush()

import boto3 from datetime import datetime, timedelta from os import environ from src.functions.csv_dump import send_sql rds_client = boto3.client("rds-data") ddb_resource = boto3.resource("dynamodb") shub_table = ddb_resource.Table(environ.get("TABLE_SHUB")) shub_index = environ.get("SHUB_INDEX") db_name = environ.get("DB_NAME") db_table = environ.get("DB_TABLE") db_cluster_arn = environ.get("DB_CLUSTER_ARN") db_secret_arn = environ.get("DB_SECRET_ARN") pipeline_version = environ.get("PIPELINE_VERSION") def handler(event, context): days_ago = [] for day in range(1,8): date = (datetime.now() - timedelta(day)).strftime("%Y-%m-%d") days_ago.append(date) print(f"Offloading fixtures from '{days_ago[-1]}' to '{days_ago[0]}'") for day in days_ago: fixtures = get_day_fixtures(day) print(f"Updating '{len(fixtures)}' fixtures on '{day}' into aurora ...") for fixture in fixtures: update_sql = f"update {db_table} set gameFtScore = :fts where gameID = :id" update_params = [ {"name": "id", "value":{"stringValue": fixture["fixture_id"]}}, {"name": "fts", "value":{"stringValue": fixture["ft_score"]}} ] response = send_sql(db_name, db_secret_arn, db_cluster_arn, update_sql, update_params) if response: print(f"Updated '{day}" fixture id: "{fixture["fixture_id"]}'") return { "query_dates": days_ago, "pipeline_version": pipeline_version } def get_day_fixtures(date): day_fixtures = shub_table.query( IndexName=shub_index, KeyConditionExpression="play_date = :date and ft_score > :fts", ExpressionAttributeValues={ ":date": date, ":fts": "-" }, ProjectionExpression="fixture_id, ft_score" ) return day_fixtures["Items"]

import boto3 from datetime import datetime, timedelta from os import environ from src.functions.csv_dump import send_sql rds_client = boto3.client("rds-data") ddb_resource = boto3.resource("dynamodb") shub_table = ddb_resource.Table(environ.get("TABLE_SHUB")) shub_index = environ.get("SHUB_INDEX") db_name = environ.get("DB_NAME") db_table = environ.get("DB_TABLE") db_cluster_arn = environ.get("DB_CLUSTER_ARN") db_secret_arn = environ.get("DB_SECRET_ARN") pipeline_version = environ.get("PIPELINE_VERSION") def handler(event, context): days_ago = [] for day in range(1,8): date = (datetime.now() - timedelta(day)).strftime("%Y-%m-%d") days_ago.append(date) print(f"Offloading fixtures from '{days_ago[-1]}' to '{days_ago[0]}'") for day in days_ago: fixtures = get_day_fixtures(day) print(f"Updating '{len(fixtures)}' fixtures on '{day}' into aurora ...") for fixture in fixtures: update_sql = f"update {db_table} set gameFtScore = :fts where gameID = :id" update_params = [ {"name": "id", "value":{"stringValue": fixture["fixture_id"]}}, {"name": "fts", "value":{"stringValue": fixture["ft_score"]}} ] response = send_sql(db_name, db_secret_arn, db_cluster_arn, update_sql, update_params) if response: print(f"Updated '{day}' fixture id: '{fixture['fixture_id']}'") return { "query_dates": days_ago, "pipeline_version": pipeline_version } def get_day_fixtures(date): day_fixtures = shub_table.query( IndexName=shub_index, KeyConditionExpression="play_date = :date and ft_score > :fts", ExpressionAttributeValues={ ":date": date, ":fts": "-" }, ProjectionExpression="fixture_id, ft_score" ) return day_fixtures["Items"]

#!/usr/bin/env python3 """ Common test run patterns """ from datetime import datetime from clusters import NullCluster from pre_tests import NullPreTest from ci_tests import NullTest from post_tests import NullPostTest class ClusterTestSetsRunner: """A cluster test runner that runs multiple sets of pre, test & post steps wrapped by a cluster provision and with similar semantics to ClusterTestRunner. Each test set will attempt to run regardless of the outcome of prior sets. This can be overriden on a set by set basis with 'always_run'""" def __init__( self, cluster=NullCluster(), final_post=NullPostTest(), sets=None, ): self.cluster = cluster self.final_post = final_post if sets is None: sets = [] self.sets = sets def run(self): hold = None try: self.log_event("About to provision") self.cluster.provision() self.log_event("provisioned") except Exception as err: self.log_event("ERROR: provision failed") hold = err if hold is None: for idx, test_set in enumerate(self.sets): test_set = { **{ "name": f"set {idx + 1}", "pre_test": NullPreTest(), "test": NullTest(), "post_test": NullPostTest(), "always_run": True, }, **test_set, } if hold is None or test_set["always_run"]: try: self.log_event("About to run", test_set) self.run_test_set(test_set) self.log_event("run completed", test_set) except Exception as err: self.log_event("ERROR: run failed", test_set) if hold is None: hold = err try: self.log_event("About to teardown") self.cluster.teardown() self.log_event("teardown completed") except Exception as err: self.log_event("ERROR: teardown failed") if hold is None: hold = err try: self.log_event("About to run final post") self.final_post.run() self.log_event("final post completed") except Exception as err: self.log_event("ERROR: final post failed") if hold is None: hold = err if hold is not None: raise hold def run_test_set(self, test_set): hold = None try: self.log_event("About to run pre test", test_set) test_set["pre_test"].run() self.log_event("pre test completed", test_set) except Exception as err: self.log_event("ERROR: pre test failed", test_set) hold = err if hold is None: try: self.log_event("About to run test", test_set) test_set["test"].run() self.log_event("test completed", test_set) except Exception as err: self.log_event("ERROR: test failed", test_set) hold = err try: self.log_event("About to run post test", test_set) test_set["post_test"].run( test_output_dirs=test_set["test"].test_output_dirs ) self.log_event("post test completed", test_set) except Exception as err: self.log_event("ERROR: post test failed", test_set) if hold is None: hold = err if hold is not None: raise hold def log_event(self, msg, test_set=None): now = datetime.now() time = now.strftime("%H:%M:%S") marker = "****" if test_set is not None and test_set["name"] is not None: msg = f"{msg} [{test_set["name"]}]" print(marker) print(f"{marker} {time}: {msg}") print(marker) # pylint: disable=too-many-arguments class ClusterTestRunner(ClusterTestSetsRunner): """A simple cluster test runner that: . provisions a cluster . runs any pre_test (if provision was successful) . runs the test (if provisioned and any pre_test was successful) . runs post_test (if the test ran) . tears down the cluster""" def __init__( self, cluster=NullCluster(), pre_test=NullPreTest(), test=NullTest(), post_test=NullPostTest(), final_post=NullPostTest(), ): super().__init__( cluster=cluster, final_post=final_post, sets=[ { "name": None, "pre_test": pre_test, "test": test, "post_test": post_test, } ], )

#!/usr/bin/env python3 """ Common test run patterns """ from datetime import datetime from clusters import NullCluster from pre_tests import NullPreTest from ci_tests import NullTest from post_tests import NullPostTest class ClusterTestSetsRunner: """A cluster test runner that runs multiple sets of pre, test & post steps wrapped by a cluster provision and with similar semantics to ClusterTestRunner. Each test set will attempt to run regardless of the outcome of prior sets. This can be overriden on a set by set basis with 'always_run'""" def __init__( self, cluster=NullCluster(), final_post=NullPostTest(), sets=None, ): self.cluster = cluster self.final_post = final_post if sets is None: sets = [] self.sets = sets def run(self): hold = None try: self.log_event("About to provision") self.cluster.provision() self.log_event("provisioned") except Exception as err: self.log_event("ERROR: provision failed") hold = err if hold is None: for idx, test_set in enumerate(self.sets): test_set = { **{ "name": f"set {idx + 1}", "pre_test": NullPreTest(), "test": NullTest(), "post_test": NullPostTest(), "always_run": True, }, **test_set, } if hold is None or test_set["always_run"]: try: self.log_event("About to run", test_set) self.run_test_set(test_set) self.log_event("run completed", test_set) except Exception as err: self.log_event("ERROR: run failed", test_set) if hold is None: hold = err try: self.log_event("About to teardown") self.cluster.teardown() self.log_event("teardown completed") except Exception as err: self.log_event("ERROR: teardown failed") if hold is None: hold = err try: self.log_event("About to run final post") self.final_post.run() self.log_event("final post completed") except Exception as err: self.log_event("ERROR: final post failed") if hold is None: hold = err if hold is not None: raise hold def run_test_set(self, test_set): hold = None try: self.log_event("About to run pre test", test_set) test_set["pre_test"].run() self.log_event("pre test completed", test_set) except Exception as err: self.log_event("ERROR: pre test failed", test_set) hold = err if hold is None: try: self.log_event("About to run test", test_set) test_set["test"].run() self.log_event("test completed", test_set) except Exception as err: self.log_event("ERROR: test failed", test_set) hold = err try: self.log_event("About to run post test", test_set) test_set["post_test"].run( test_output_dirs=test_set["test"].test_output_dirs ) self.log_event("post test completed", test_set) except Exception as err: self.log_event("ERROR: post test failed", test_set) if hold is None: hold = err if hold is not None: raise hold def log_event(self, msg, test_set=None): now = datetime.now() time = now.strftime("%H:%M:%S") marker = "****" if test_set is not None and test_set["name"] is not None: msg = f"{msg} [{test_set['name']}]" print(marker) print(f"{marker} {time}: {msg}") print(marker) # pylint: disable=too-many-arguments class ClusterTestRunner(ClusterTestSetsRunner): """A simple cluster test runner that: . provisions a cluster . runs any pre_test (if provision was successful) . runs the test (if provisioned and any pre_test was successful) . runs post_test (if the test ran) . tears down the cluster""" def __init__( self, cluster=NullCluster(), pre_test=NullPreTest(), test=NullTest(), post_test=NullPostTest(), final_post=NullPostTest(), ): super().__init__( cluster=cluster, final_post=final_post, sets=[ { "name": None, "pre_test": pre_test, "test": test, "post_test": post_test, } ], )

import collections import inspect import logging from typing import ( Any, Callable, Dict, Optional, Tuple, Union, overload, ) from fastapi import APIRouter, FastAPI from starlette.requests import Request from uvicorn.config import Config from uvicorn.lifespan.on import LifespanOn from ray.serve.common import DeploymentStatusInfo from ray.serve.config import ( AutoscalingConfig, DeploymentConfig, HTTPOptions, ) from ray.serve.constants import ( DEFAULT_CHECKPOINT_PATH, HTTP_PROXY_TIMEOUT, SERVE_CONTROLLER_NAME, CONTROLLER_MAX_CONCURRENCY, DEFAULT_HTTP_HOST, DEFAULT_HTTP_PORT, ) from ray.serve.controller import ServeController from ray.serve.deployment import Deployment from ray.serve.exceptions import RayServeException from ray.experimental.dag import DAGNode from ray.serve.handle import RayServeHandle from ray.serve.http_util import ASGIHTTPSender, make_fastapi_class_based_view from ray.serve.logging_utils import LoggingContext from ray.serve.utils import ( ensure_serialization_context, format_actor_name, get_current_node_resource_key, get_random_letters, in_interactive_shell, DEFAULT, ) from ray.util.annotations import PublicAPI import ray from ray import cloudpickle from ray.serve.deployment_graph import ClassNode, FunctionNode from ray.serve.application import Application from ray.serve.client import ServeControllerClient, get_controller_namespace from ray.serve.context import ( set_global_client, get_global_client, get_internal_replica_context, ReplicaContext, ) from ray._private.usage import usage_lib logger = logging.getLogger(__file__) @PublicAPI(stability="beta") def start( detached: bool = False, http_options: Optional[Union[dict, HTTPOptions]] = None, dedicated_cpu: bool = False, _checkpoint_path: str = DEFAULT_CHECKPOINT_PATH, _override_controller_namespace: Optional[str] = None, **kwargs, ) -> ServeControllerClient: """Initialize a serve instance. By default, the instance will be scoped to the lifetime of the returned Client object (or when the script exits). If detached is set to True, the instance will instead persist until serve.shutdown() is called. This is only relevant if connecting to a long-running Ray cluster (e.g., with ray.init(address="auto") or ray.init("ray://<remote_addr>")). Args: detached (bool): Whether not the instance should be detached from this script. If set, the instance will live on the Ray cluster until it is explicitly stopped with serve.shutdown(). http_options (Optional[Dict, serve.HTTPOptions]): Configuration options for HTTP proxy. You can pass in a dictionary or HTTPOptions object with fields: - host(str, None): Host for HTTP servers to listen on. Defaults to "127.0.0.1". To expose Serve publicly, you probably want to set this to "0.0.0.0". - port(int): Port for HTTP server. Defaults to 8000. - root_path(str): Root path to mount the serve application (for example, "/serve"). All deployment routes will be prefixed with this path. Defaults to "". - middlewares(list): A list of Starlette middlewares that will be applied to the HTTP servers in the cluster. Defaults to []. - location(str, serve.config.DeploymentMode): The deployment location of HTTP servers: - "HeadOnly": start one HTTP server on the head node. Serve assumes the head node is the node you executed serve.start on. This is the default. - "EveryNode": start one HTTP server per node. - "NoServer" or None: disable HTTP server. - num_cpus (int): The number of CPU cores to reserve for each internal Serve HTTP proxy actor. Defaults to 0. dedicated_cpu (bool): Whether to reserve a CPU core for the internal Serve controller actor. Defaults to False. """ usage_lib.record_library_usage("serve") http_deprecated_args = ["http_host", "http_port", "http_middlewares"] for key in http_deprecated_args: if key in kwargs: raise ValueError( f"{key} is deprecated, please use serve.start(http_options=" f'{{'{key}': {kwargs[key]}}}) instead.' ) # Initialize ray if needed. ray.worker.global_worker.filter_logs_by_job = False if not ray.is_initialized(): ray.init(namespace="serve") controller_namespace = get_controller_namespace( detached, _override_controller_namespace=_override_controller_namespace ) try: client = get_global_client( _override_controller_namespace=_override_controller_namespace, _health_check_controller=True, ) logger.info( "Connecting to existing Serve instance in namespace " f"'{controller_namespace}'." ) _check_http_and_checkpoint_options(client, http_options, _checkpoint_path) return client except RayServeException: pass if detached: controller_name = SERVE_CONTROLLER_NAME else: controller_name = format_actor_name(get_random_letters(), SERVE_CONTROLLER_NAME) if isinstance(http_options, dict): http_options = HTTPOptions.parse_obj(http_options) if http_options is None: http_options = HTTPOptions() controller = ServeController.options( num_cpus=1 if dedicated_cpu else 0, name=controller_name, lifetime="detached" if detached else None, max_restarts=-1, max_task_retries=-1, # Pin Serve controller on the head node. resources={get_current_node_resource_key(): 0.01}, namespace=controller_namespace, max_concurrency=CONTROLLER_MAX_CONCURRENCY, ).remote( controller_name, http_options, _checkpoint_path, detached=detached, _override_controller_namespace=_override_controller_namespace, ) proxy_handles = ray.get(controller.get_http_proxies.remote()) if len(proxy_handles) > 0: try: ray.get( [handle.ready.remote() for handle in proxy_handles.values()], timeout=HTTP_PROXY_TIMEOUT, ) except ray.exceptions.GetTimeoutError: raise TimeoutError( f"HTTP proxies not available after {HTTP_PROXY_TIMEOUT}s." ) client = ServeControllerClient( controller, controller_name, detached=detached, _override_controller_namespace=_override_controller_namespace, ) set_global_client(client) logger.info( f"Started{" detached " if detached else " "}Serve instance in " f"namespace '{controller_namespace}'." ) return client @PublicAPI def shutdown() -> None: """Completely shut down the connected Serve instance. Shuts down all processes and deletes all state associated with the instance. """ try: client = get_global_client() except RayServeException: logger.info( "Nothing to shut down. There's no Serve application " "running on this Ray cluster." ) return client.shutdown() set_global_client(None) @PublicAPI def get_replica_context() -> ReplicaContext: """If called from a deployment, returns the deployment and replica tag. A replica tag uniquely identifies a single replica for a Ray Serve deployment at runtime. Replica tags are of the form `<deployment_name>#<random letters>`. Raises: RayServeException: if not called from within a Ray Serve deployment. Example: >>> from ray import serve >>> # deployment_name >>> serve.get_replica_context().deployment # doctest: +SKIP >>> # deployment_name#krcwoa >>> serve.get_replica_context().replica_tag # doctest: +SKIP """ internal_replica_context = get_internal_replica_context() if internal_replica_context is None: raise RayServeException( "`serve.get_replica_context()` " "may only be called from within a " "Ray Serve deployment." ) return internal_replica_context @PublicAPI(stability="beta") def ingress(app: Union["FastAPI", "APIRouter", Callable]): """Mark an ASGI application ingress for Serve. Args: app (FastAPI,APIRouter,Starlette,etc): the app or router object serve as ingress for this deployment. It can be any ASGI compatible object. Example: >>> from fastapi import FastAPI >>> from ray import serve >>> app = FastAPI() # doctest: +SKIP >>> app = FastAPI() # doctest: +SKIP >>> @serve.deployment # doctest: +SKIP ... @serve.ingress(app) # doctest: +SKIP ... class App: # doctest: +SKIP ... pass # doctest: +SKIP >>> App.deploy() # doctest: +SKIP """ def decorator(cls): if not inspect.isclass(cls): raise ValueError("@serve.ingress must be used with a class.") if issubclass(cls, collections.abc.Callable): raise ValueError( "Class passed to @serve.ingress may not have __call__ method." ) # Sometimes there are decorators on the methods. We want to fix # the fast api routes here. if isinstance(app, (FastAPI, APIRouter)): make_fastapi_class_based_view(app, cls) # Free the state of the app so subsequent modification won't affect # this ingress deployment. We don't use copy.copy here to avoid # recursion issue. ensure_serialization_context() frozen_app = cloudpickle.loads(cloudpickle.dumps(app)) class ASGIAppWrapper(cls): async def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._serve_app = frozen_app # Use uvicorn's lifespan handling code to properly deal with # startup and shutdown event. self._serve_asgi_lifespan = LifespanOn( Config(self._serve_app, lifespan="on") ) # Replace uvicorn logger with our own. self._serve_asgi_lifespan.logger = logger # LifespanOn's logger logs in INFO level thus becomes spammy # Within this block we temporarily uplevel for cleaner logging with LoggingContext( self._serve_asgi_lifespan.logger, level=logging.WARNING ): await self._serve_asgi_lifespan.startup() async def __call__(self, request: Request): sender = ASGIHTTPSender() await self._serve_app( request.scope, request.receive, sender, ) return sender.build_asgi_response() # NOTE: __del__ must be async so that we can run asgi shutdown # in the same event loop. async def __del__(self): # LifespanOn's logger logs in INFO level thus becomes spammy # Within this block we temporarily uplevel for cleaner logging with LoggingContext( self._serve_asgi_lifespan.logger, level=logging.WARNING ): await self._serve_asgi_lifespan.shutdown() # Make sure to call user's del method as well. super_cls = super() if hasattr(super_cls, "__del__"): super_cls.__del__() ASGIAppWrapper.__name__ = cls.__name__ return ASGIAppWrapper return decorator @overload def deployment(func_or_class: Callable) -> Deployment: pass @overload def deployment( name: Optional[str] = None, version: Optional[str] = None, prev_version: Optional[str] = None, num_replicas: Optional[int] = None, init_args: Optional[Tuple[Any]] = None, init_kwargs: Optional[Dict[Any, Any]] = None, route_prefix: Union[str, None, DEFAULT] = DEFAULT.VALUE, ray_actor_options: Optional[Dict] = None, user_config: Optional[Any] = None, max_concurrent_queries: Optional[int] = None, _autoscaling_config: Optional[Union[Dict, AutoscalingConfig]] = None, _graceful_shutdown_wait_loop_s: Optional[float] = None, _graceful_shutdown_timeout_s: Optional[float] = None, _health_check_period_s: Optional[float] = None, _health_check_timeout_s: Optional[float] = None, ) -> Callable[[Callable], Deployment]: pass @PublicAPI def deployment( _func_or_class: Optional[Callable] = None, name: Optional[str] = None, version: Optional[str] = None, prev_version: Optional[str] = None, num_replicas: Optional[int] = None, init_args: Optional[Tuple[Any]] = None, init_kwargs: Optional[Dict[Any, Any]] = None, route_prefix: Union[str, None, DEFAULT] = DEFAULT.VALUE, ray_actor_options: Optional[Dict] = None, user_config: Optional[Any] = None, max_concurrent_queries: Optional[int] = None, _autoscaling_config: Optional[Union[Dict, AutoscalingConfig]] = None, _graceful_shutdown_wait_loop_s: Optional[float] = None, _graceful_shutdown_timeout_s: Optional[float] = None, _health_check_period_s: Optional[float] = None, _health_check_timeout_s: Optional[float] = None, ) -> Callable[[Callable], Deployment]: """Define a Serve deployment. Args: name (Optional[str]): Globally-unique name identifying this deployment. If not provided, the name of the class or function will be used. version (Optional[str]): Version of the deployment. This is used to indicate a code change for the deployment; when it is re-deployed with a version change, a rolling update of the replicas will be performed. If not provided, every deployment will be treated as a new version. prev_version (Optional[str]): Version of the existing deployment which is used as a precondition for the next deployment. If prev_version does not match with the existing deployment's version, the deployment will fail. If not provided, deployment procedure will not check the existing deployment's version. num_replicas (Optional[int]): The number of processes to start up that will handle requests to this deployment. Defaults to 1. init_args (Optional[Tuple]): Positional args to be passed to the class constructor when starting up deployment replicas. These can also be passed when you call `.deploy()` on the returned Deployment. init_kwargs (Optional[Dict]): Keyword args to be passed to the class constructor when starting up deployment replicas. These can also be passed when you call `.deploy()` on the returned Deployment. route_prefix (Optional[str]): Requests to paths under this HTTP path prefix will be routed to this deployment. Defaults to '/{name}'. When set to 'None', no HTTP endpoint will be created. Routing is done based on longest-prefix match, so if you have deployment A with a prefix of '/a' and deployment B with a prefix of '/a/b', requests to '/a', '/a/', and '/a/c' go to A and requests to '/a/b', '/a/b/', and '/a/b/c' go to B. Routes must not end with a '/' unless they're the root (just '/'), which acts as a catch-all. ray_actor_options (dict): Options to be passed to the Ray actor constructor such as resource requirements. user_config (Optional[Any]): [experimental] Config to pass to the reconfigure method of the deployment. This can be updated dynamically without changing the version of the deployment and restarting its replicas. The user_config needs to be hashable to keep track of updates, so it must only contain hashable types, or hashable types nested in lists and dictionaries. max_concurrent_queries (Optional[int]): The maximum number of queries that will be sent to a replica of this deployment without receiving a response. Defaults to 100. Example: >>> from ray import serve >>> @serve.deployment(name="deployment1", version="v1") # doctest: +SKIP ... class MyDeployment: # doctest: +SKIP ... pass # doctest: +SKIP >>> MyDeployment.deploy(*init_args) # doctest: +SKIP >>> MyDeployment.options( # doctest: +SKIP ... num_replicas=2, init_args=init_args).deploy() Returns: Deployment """ if num_replicas is not None and _autoscaling_config is not None: raise ValueError( "Manually setting num_replicas is not allowed when " "_autoscaling_config is provided." ) config = DeploymentConfig.from_default( ignore_none=True, num_replicas=num_replicas, user_config=user_config, max_concurrent_queries=max_concurrent_queries, autoscaling_config=_autoscaling_config, graceful_shutdown_wait_loop_s=_graceful_shutdown_wait_loop_s, graceful_shutdown_timeout_s=_graceful_shutdown_timeout_s, health_check_period_s=_health_check_period_s, health_check_timeout_s=_health_check_timeout_s, ) def decorator(_func_or_class): return Deployment( _func_or_class, name if name is not None else _func_or_class.__name__, config, version=version, prev_version=prev_version, init_args=init_args, init_kwargs=init_kwargs, route_prefix=route_prefix, ray_actor_options=ray_actor_options, _internal=True, ) # This handles both parametrized and non-parametrized usage of the # decorator. See the @serve.batch code for more details. return decorator(_func_or_class) if callable(_func_or_class) else decorator @PublicAPI def get_deployment(name: str) -> Deployment: """Dynamically fetch a handle to a Deployment object. This can be used to update and redeploy a deployment without access to the original definition. Example: >>> from ray import serve >>> MyDeployment = serve.get_deployment("name") # doctest: +SKIP >>> MyDeployment.options(num_replicas=10).deploy() # doctest: +SKIP Args: name(str): name of the deployment. This must have already been deployed. Returns: Deployment """ try: ( deployment_info, route_prefix, ) = get_global_client().get_deployment_info(name) except KeyError: raise KeyError( f"Deployment {name} was not found. Did you call Deployment.deploy()?" ) return Deployment( cloudpickle.loads(deployment_info.replica_config.serialized_deployment_def), name, deployment_info.deployment_config, version=deployment_info.version, init_args=deployment_info.replica_config.init_args, init_kwargs=deployment_info.replica_config.init_kwargs, route_prefix=route_prefix, ray_actor_options=deployment_info.replica_config.ray_actor_options, _internal=True, ) @PublicAPI def list_deployments() -> Dict[str, Deployment]: """Returns a dictionary of all active deployments. Dictionary maps deployment name to Deployment objects. """ infos = get_global_client().list_deployments() deployments = {} for name, (deployment_info, route_prefix) in infos.items(): deployments[name] = Deployment( cloudpickle.loads(deployment_info.replica_config.serialized_deployment_def), name, deployment_info.deployment_config, version=deployment_info.version, init_args=deployment_info.replica_config.init_args, init_kwargs=deployment_info.replica_config.init_kwargs, route_prefix=route_prefix, ray_actor_options=deployment_info.replica_config.ray_actor_options, _internal=True, ) return deployments def get_deployment_statuses() -> Dict[str, DeploymentStatusInfo]: """Returns a dictionary of deployment statuses. A deployment's status is one of {UPDATING, UNHEALTHY, and HEALTHY}. Example: >>> from ray.serve.api import get_deployment_statuses >>> statuses = get_deployment_statuses() # doctest: +SKIP >>> status_info = statuses["deployment_name"] # doctest: +SKIP >>> status = status_info.status # doctest: +SKIP >>> message = status_info.message # doctest: +SKIP Returns: Dict[str, DeploymentStatus]: This dictionary maps the running deployment's name to a DeploymentStatus object containing its status and a message explaining the status. """ return get_global_client().get_deployment_statuses() @PublicAPI(stability="alpha") def run( target: Union[ClassNode, FunctionNode], _blocking: bool = True, *, host: str = DEFAULT_HTTP_HOST, port: int = DEFAULT_HTTP_PORT, ) -> Optional[RayServeHandle]: """Run a Serve application and return a ServeHandle to the ingress. Either a ClassNode, FunctionNode, or a pre-built application can be passed in. If a node is passed in, all of the deployments it depends on will be deployed. If there is an ingress, its handle will be returned. Args: target (Union[ClassNode, FunctionNode, Application]): A user-built Serve Application or a ClassNode that acts as the root node of DAG. By default ClassNode is the Driver deployment unless user provides a customized one. host (str): The host passed into serve.start(). port (int): The port passed into serve.start(). Returns: RayServeHandle: A regular ray serve handle that can be called by user to execute the serve DAG. """ # TODO (jiaodong): Resolve circular reference in pipeline codebase and serve from ray.serve.pipeline.api import build as pipeline_build from ray.serve.pipeline.api import get_and_validate_ingress_deployment client = start(detached=True, http_options={"host": host, "port": port}) if isinstance(target, Application): deployments = list(target.deployments.values()) ingress = target.ingress # Each DAG should always provide a valid Driver ClassNode elif isinstance(target, ClassNode): deployments = pipeline_build(target) ingress = get_and_validate_ingress_deployment(deployments) # Special case where user is doing single function serve.run(func.bind()) elif isinstance(target, FunctionNode): deployments = pipeline_build(target) ingress = get_and_validate_ingress_deployment(deployments) if len(deployments) != 1: raise ValueError( "We only support single function node in serve.run, ex: " "serve.run(func.bind()). For more than one nodes in your DAG, " "Please provide a driver class and bind it as entrypoint to " "your Serve DAG." ) elif isinstance(target, DAGNode): raise ValueError( "Invalid DAGNode type as entry to serve.run(), " f"type: {type(target)}, accepted: ClassNode, " "FunctionNode please provide a driver class and bind it " "as entrypoint to your Serve DAG." ) else: raise TypeError( "Expected a ClassNode, FunctionNode, or Application as target. " f"Got unexpected type {type(target)} instead." ) parameter_group = [] for deployment in deployments: deployment_parameters = { "name": deployment._name, "func_or_class": deployment._func_or_class, "init_args": deployment.init_args, "init_kwargs": deployment.init_kwargs, "ray_actor_options": deployment._ray_actor_options, "config": deployment._config, "version": deployment._version, "prev_version": deployment._prev_version, "route_prefix": deployment.route_prefix, "url": deployment.url, } parameter_group.append(deployment_parameters) client.deploy_group( parameter_group, _blocking=_blocking, remove_past_deployments=True ) if ingress is not None: return ingress.get_handle() def build(target: Union[ClassNode, FunctionNode]) -> Application: """Builds a Serve application into a static application. Takes in a ClassNode or FunctionNode and converts it to a Serve application consisting of one or more deployments. This is intended to be used for production scenarios and deployed via the Serve REST API or CLI, so there are some restrictions placed on the deployments: 1) All of the deployments must be importable. That is, they cannot be defined in __main__ or inline defined. The deployments will be imported in production using the same import path they were here. 2) All arguments bound to the deployment must be JSON-serializable. The returned Application object can be exported to a dictionary or YAML config. """ # TODO (jiaodong): Resolve circular reference in pipeline codebase and serve from ray.serve.pipeline.api import build as pipeline_build if in_interactive_shell(): raise RuntimeError( "build cannot be called from an interactive shell like " "IPython or Jupyter because it requires all deployments to be " "importable to run the app after building." ) # TODO(edoakes): this should accept host and port, but we don't # currently support them in the REST API. return Application(pipeline_build(target)) def _check_http_and_checkpoint_options( client: ServeControllerClient, http_options: Union[dict, HTTPOptions], checkpoint_path: str, ) -> None: if checkpoint_path and checkpoint_path != client.checkpoint_path: logger.warning( f"The new client checkpoint path '{checkpoint_path}' " f"is different from the existing one '{client.checkpoint_path}'. " "The new checkpoint path is ignored." ) if http_options: client_http_options = client.http_config new_http_options = ( http_options if isinstance(http_options, HTTPOptions) else HTTPOptions.parse_obj(http_options) ) different_fields = [] all_http_option_fields = new_http_options.__dict__ for field in all_http_option_fields: if getattr(new_http_options, field) != getattr(client_http_options, field): different_fields.append(field) if len(different_fields): logger.warning( "The new client HTTP config differs from the existing one " f"in the following fields: {different_fields}. " "The new HTTP config is ignored." )

import collections import inspect import logging from typing import ( Any, Callable, Dict, Optional, Tuple, Union, overload, ) from fastapi import APIRouter, FastAPI from starlette.requests import Request from uvicorn.config import Config from uvicorn.lifespan.on import LifespanOn from ray.serve.common import DeploymentStatusInfo from ray.serve.config import ( AutoscalingConfig, DeploymentConfig, HTTPOptions, ) from ray.serve.constants import ( DEFAULT_CHECKPOINT_PATH, HTTP_PROXY_TIMEOUT, SERVE_CONTROLLER_NAME, CONTROLLER_MAX_CONCURRENCY, DEFAULT_HTTP_HOST, DEFAULT_HTTP_PORT, ) from ray.serve.controller import ServeController from ray.serve.deployment import Deployment from ray.serve.exceptions import RayServeException from ray.experimental.dag import DAGNode from ray.serve.handle import RayServeHandle from ray.serve.http_util import ASGIHTTPSender, make_fastapi_class_based_view from ray.serve.logging_utils import LoggingContext from ray.serve.utils import ( ensure_serialization_context, format_actor_name, get_current_node_resource_key, get_random_letters, in_interactive_shell, DEFAULT, ) from ray.util.annotations import PublicAPI import ray from ray import cloudpickle from ray.serve.deployment_graph import ClassNode, FunctionNode from ray.serve.application import Application from ray.serve.client import ServeControllerClient, get_controller_namespace from ray.serve.context import ( set_global_client, get_global_client, get_internal_replica_context, ReplicaContext, ) from ray._private.usage import usage_lib logger = logging.getLogger(__file__) @PublicAPI(stability="beta") def start( detached: bool = False, http_options: Optional[Union[dict, HTTPOptions]] = None, dedicated_cpu: bool = False, _checkpoint_path: str = DEFAULT_CHECKPOINT_PATH, _override_controller_namespace: Optional[str] = None, **kwargs, ) -> ServeControllerClient: """Initialize a serve instance. By default, the instance will be scoped to the lifetime of the returned Client object (or when the script exits). If detached is set to True, the instance will instead persist until serve.shutdown() is called. This is only relevant if connecting to a long-running Ray cluster (e.g., with ray.init(address="auto") or ray.init("ray://<remote_addr>")). Args: detached (bool): Whether not the instance should be detached from this script. If set, the instance will live on the Ray cluster until it is explicitly stopped with serve.shutdown(). http_options (Optional[Dict, serve.HTTPOptions]): Configuration options for HTTP proxy. You can pass in a dictionary or HTTPOptions object with fields: - host(str, None): Host for HTTP servers to listen on. Defaults to "127.0.0.1". To expose Serve publicly, you probably want to set this to "0.0.0.0". - port(int): Port for HTTP server. Defaults to 8000. - root_path(str): Root path to mount the serve application (for example, "/serve"). All deployment routes will be prefixed with this path. Defaults to "". - middlewares(list): A list of Starlette middlewares that will be applied to the HTTP servers in the cluster. Defaults to []. - location(str, serve.config.DeploymentMode): The deployment location of HTTP servers: - "HeadOnly": start one HTTP server on the head node. Serve assumes the head node is the node you executed serve.start on. This is the default. - "EveryNode": start one HTTP server per node. - "NoServer" or None: disable HTTP server. - num_cpus (int): The number of CPU cores to reserve for each internal Serve HTTP proxy actor. Defaults to 0. dedicated_cpu (bool): Whether to reserve a CPU core for the internal Serve controller actor. Defaults to False. """ usage_lib.record_library_usage("serve") http_deprecated_args = ["http_host", "http_port", "http_middlewares"] for key in http_deprecated_args: if key in kwargs: raise ValueError( f"{key} is deprecated, please use serve.start(http_options=" f'{{"{key}": {kwargs[key]}}}) instead.' ) # Initialize ray if needed. ray.worker.global_worker.filter_logs_by_job = False if not ray.is_initialized(): ray.init(namespace="serve") controller_namespace = get_controller_namespace( detached, _override_controller_namespace=_override_controller_namespace ) try: client = get_global_client( _override_controller_namespace=_override_controller_namespace, _health_check_controller=True, ) logger.info( "Connecting to existing Serve instance in namespace " f"'{controller_namespace}'." ) _check_http_and_checkpoint_options(client, http_options, _checkpoint_path) return client except RayServeException: pass if detached: controller_name = SERVE_CONTROLLER_NAME else: controller_name = format_actor_name(get_random_letters(), SERVE_CONTROLLER_NAME) if isinstance(http_options, dict): http_options = HTTPOptions.parse_obj(http_options) if http_options is None: http_options = HTTPOptions() controller = ServeController.options( num_cpus=1 if dedicated_cpu else 0, name=controller_name, lifetime="detached" if detached else None, max_restarts=-1, max_task_retries=-1, # Pin Serve controller on the head node. resources={get_current_node_resource_key(): 0.01}, namespace=controller_namespace, max_concurrency=CONTROLLER_MAX_CONCURRENCY, ).remote( controller_name, http_options, _checkpoint_path, detached=detached, _override_controller_namespace=_override_controller_namespace, ) proxy_handles = ray.get(controller.get_http_proxies.remote()) if len(proxy_handles) > 0: try: ray.get( [handle.ready.remote() for handle in proxy_handles.values()], timeout=HTTP_PROXY_TIMEOUT, ) except ray.exceptions.GetTimeoutError: raise TimeoutError( f"HTTP proxies not available after {HTTP_PROXY_TIMEOUT}s." ) client = ServeControllerClient( controller, controller_name, detached=detached, _override_controller_namespace=_override_controller_namespace, ) set_global_client(client) logger.info( f"Started{' detached ' if detached else ' '}Serve instance in " f"namespace '{controller_namespace}'." ) return client @PublicAPI def shutdown() -> None: """Completely shut down the connected Serve instance. Shuts down all processes and deletes all state associated with the instance. """ try: client = get_global_client() except RayServeException: logger.info( "Nothing to shut down. There's no Serve application " "running on this Ray cluster." ) return client.shutdown() set_global_client(None) @PublicAPI def get_replica_context() -> ReplicaContext: """If called from a deployment, returns the deployment and replica tag. A replica tag uniquely identifies a single replica for a Ray Serve deployment at runtime. Replica tags are of the form `<deployment_name>#<random letters>`. Raises: RayServeException: if not called from within a Ray Serve deployment. Example: >>> from ray import serve >>> # deployment_name >>> serve.get_replica_context().deployment # doctest: +SKIP >>> # deployment_name#krcwoa >>> serve.get_replica_context().replica_tag # doctest: +SKIP """ internal_replica_context = get_internal_replica_context() if internal_replica_context is None: raise RayServeException( "`serve.get_replica_context()` " "may only be called from within a " "Ray Serve deployment." ) return internal_replica_context @PublicAPI(stability="beta") def ingress(app: Union["FastAPI", "APIRouter", Callable]): """Mark an ASGI application ingress for Serve. Args: app (FastAPI,APIRouter,Starlette,etc): the app or router object serve as ingress for this deployment. It can be any ASGI compatible object. Example: >>> from fastapi import FastAPI >>> from ray import serve >>> app = FastAPI() # doctest: +SKIP >>> app = FastAPI() # doctest: +SKIP >>> @serve.deployment # doctest: +SKIP ... @serve.ingress(app) # doctest: +SKIP ... class App: # doctest: +SKIP ... pass # doctest: +SKIP >>> App.deploy() # doctest: +SKIP """ def decorator(cls): if not inspect.isclass(cls): raise ValueError("@serve.ingress must be used with a class.") if issubclass(cls, collections.abc.Callable): raise ValueError( "Class passed to @serve.ingress may not have __call__ method." ) # Sometimes there are decorators on the methods. We want to fix # the fast api routes here. if isinstance(app, (FastAPI, APIRouter)): make_fastapi_class_based_view(app, cls) # Free the state of the app so subsequent modification won't affect # this ingress deployment. We don't use copy.copy here to avoid # recursion issue. ensure_serialization_context() frozen_app = cloudpickle.loads(cloudpickle.dumps(app)) class ASGIAppWrapper(cls): async def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._serve_app = frozen_app # Use uvicorn's lifespan handling code to properly deal with # startup and shutdown event. self._serve_asgi_lifespan = LifespanOn( Config(self._serve_app, lifespan="on") ) # Replace uvicorn logger with our own. self._serve_asgi_lifespan.logger = logger # LifespanOn's logger logs in INFO level thus becomes spammy # Within this block we temporarily uplevel for cleaner logging with LoggingContext( self._serve_asgi_lifespan.logger, level=logging.WARNING ): await self._serve_asgi_lifespan.startup() async def __call__(self, request: Request): sender = ASGIHTTPSender() await self._serve_app( request.scope, request.receive, sender, ) return sender.build_asgi_response() # NOTE: __del__ must be async so that we can run asgi shutdown # in the same event loop. async def __del__(self): # LifespanOn's logger logs in INFO level thus becomes spammy # Within this block we temporarily uplevel for cleaner logging with LoggingContext( self._serve_asgi_lifespan.logger, level=logging.WARNING ): await self._serve_asgi_lifespan.shutdown() # Make sure to call user's del method as well. super_cls = super() if hasattr(super_cls, "__del__"): super_cls.__del__() ASGIAppWrapper.__name__ = cls.__name__ return ASGIAppWrapper return decorator @overload def deployment(func_or_class: Callable) -> Deployment: pass @overload def deployment( name: Optional[str] = None, version: Optional[str] = None, prev_version: Optional[str] = None, num_replicas: Optional[int] = None, init_args: Optional[Tuple[Any]] = None, init_kwargs: Optional[Dict[Any, Any]] = None, route_prefix: Union[str, None, DEFAULT] = DEFAULT.VALUE, ray_actor_options: Optional[Dict] = None, user_config: Optional[Any] = None, max_concurrent_queries: Optional[int] = None, _autoscaling_config: Optional[Union[Dict, AutoscalingConfig]] = None, _graceful_shutdown_wait_loop_s: Optional[float] = None, _graceful_shutdown_timeout_s: Optional[float] = None, _health_check_period_s: Optional[float] = None, _health_check_timeout_s: Optional[float] = None, ) -> Callable[[Callable], Deployment]: pass @PublicAPI def deployment( _func_or_class: Optional[Callable] = None, name: Optional[str] = None, version: Optional[str] = None, prev_version: Optional[str] = None, num_replicas: Optional[int] = None, init_args: Optional[Tuple[Any]] = None, init_kwargs: Optional[Dict[Any, Any]] = None, route_prefix: Union[str, None, DEFAULT] = DEFAULT.VALUE, ray_actor_options: Optional[Dict] = None, user_config: Optional[Any] = None, max_concurrent_queries: Optional[int] = None, _autoscaling_config: Optional[Union[Dict, AutoscalingConfig]] = None, _graceful_shutdown_wait_loop_s: Optional[float] = None, _graceful_shutdown_timeout_s: Optional[float] = None, _health_check_period_s: Optional[float] = None, _health_check_timeout_s: Optional[float] = None, ) -> Callable[[Callable], Deployment]: """Define a Serve deployment. Args: name (Optional[str]): Globally-unique name identifying this deployment. If not provided, the name of the class or function will be used. version (Optional[str]): Version of the deployment. This is used to indicate a code change for the deployment; when it is re-deployed with a version change, a rolling update of the replicas will be performed. If not provided, every deployment will be treated as a new version. prev_version (Optional[str]): Version of the existing deployment which is used as a precondition for the next deployment. If prev_version does not match with the existing deployment's version, the deployment will fail. If not provided, deployment procedure will not check the existing deployment's version. num_replicas (Optional[int]): The number of processes to start up that will handle requests to this deployment. Defaults to 1. init_args (Optional[Tuple]): Positional args to be passed to the class constructor when starting up deployment replicas. These can also be passed when you call `.deploy()` on the returned Deployment. init_kwargs (Optional[Dict]): Keyword args to be passed to the class constructor when starting up deployment replicas. These can also be passed when you call `.deploy()` on the returned Deployment. route_prefix (Optional[str]): Requests to paths under this HTTP path prefix will be routed to this deployment. Defaults to '/{name}'. When set to 'None', no HTTP endpoint will be created. Routing is done based on longest-prefix match, so if you have deployment A with a prefix of '/a' and deployment B with a prefix of '/a/b', requests to '/a', '/a/', and '/a/c' go to A and requests to '/a/b', '/a/b/', and '/a/b/c' go to B. Routes must not end with a '/' unless they're the root (just '/'), which acts as a catch-all. ray_actor_options (dict): Options to be passed to the Ray actor constructor such as resource requirements. user_config (Optional[Any]): [experimental] Config to pass to the reconfigure method of the deployment. This can be updated dynamically without changing the version of the deployment and restarting its replicas. The user_config needs to be hashable to keep track of updates, so it must only contain hashable types, or hashable types nested in lists and dictionaries. max_concurrent_queries (Optional[int]): The maximum number of queries that will be sent to a replica of this deployment without receiving a response. Defaults to 100. Example: >>> from ray import serve >>> @serve.deployment(name="deployment1", version="v1") # doctest: +SKIP ... class MyDeployment: # doctest: +SKIP ... pass # doctest: +SKIP >>> MyDeployment.deploy(*init_args) # doctest: +SKIP >>> MyDeployment.options( # doctest: +SKIP ... num_replicas=2, init_args=init_args).deploy() Returns: Deployment """ if num_replicas is not None and _autoscaling_config is not None: raise ValueError( "Manually setting num_replicas is not allowed when " "_autoscaling_config is provided." ) config = DeploymentConfig.from_default( ignore_none=True, num_replicas=num_replicas, user_config=user_config, max_concurrent_queries=max_concurrent_queries, autoscaling_config=_autoscaling_config, graceful_shutdown_wait_loop_s=_graceful_shutdown_wait_loop_s, graceful_shutdown_timeout_s=_graceful_shutdown_timeout_s, health_check_period_s=_health_check_period_s, health_check_timeout_s=_health_check_timeout_s, ) def decorator(_func_or_class): return Deployment( _func_or_class, name if name is not None else _func_or_class.__name__, config, version=version, prev_version=prev_version, init_args=init_args, init_kwargs=init_kwargs, route_prefix=route_prefix, ray_actor_options=ray_actor_options, _internal=True, ) # This handles both parametrized and non-parametrized usage of the # decorator. See the @serve.batch code for more details. return decorator(_func_or_class) if callable(_func_or_class) else decorator @PublicAPI def get_deployment(name: str) -> Deployment: """Dynamically fetch a handle to a Deployment object. This can be used to update and redeploy a deployment without access to the original definition. Example: >>> from ray import serve >>> MyDeployment = serve.get_deployment("name") # doctest: +SKIP >>> MyDeployment.options(num_replicas=10).deploy() # doctest: +SKIP Args: name(str): name of the deployment. This must have already been deployed. Returns: Deployment """ try: ( deployment_info, route_prefix, ) = get_global_client().get_deployment_info(name) except KeyError: raise KeyError( f"Deployment {name} was not found. Did you call Deployment.deploy()?" ) return Deployment( cloudpickle.loads(deployment_info.replica_config.serialized_deployment_def), name, deployment_info.deployment_config, version=deployment_info.version, init_args=deployment_info.replica_config.init_args, init_kwargs=deployment_info.replica_config.init_kwargs, route_prefix=route_prefix, ray_actor_options=deployment_info.replica_config.ray_actor_options, _internal=True, ) @PublicAPI def list_deployments() -> Dict[str, Deployment]: """Returns a dictionary of all active deployments. Dictionary maps deployment name to Deployment objects. """ infos = get_global_client().list_deployments() deployments = {} for name, (deployment_info, route_prefix) in infos.items(): deployments[name] = Deployment( cloudpickle.loads(deployment_info.replica_config.serialized_deployment_def), name, deployment_info.deployment_config, version=deployment_info.version, init_args=deployment_info.replica_config.init_args, init_kwargs=deployment_info.replica_config.init_kwargs, route_prefix=route_prefix, ray_actor_options=deployment_info.replica_config.ray_actor_options, _internal=True, ) return deployments def get_deployment_statuses() -> Dict[str, DeploymentStatusInfo]: """Returns a dictionary of deployment statuses. A deployment's status is one of {UPDATING, UNHEALTHY, and HEALTHY}. Example: >>> from ray.serve.api import get_deployment_statuses >>> statuses = get_deployment_statuses() # doctest: +SKIP >>> status_info = statuses["deployment_name"] # doctest: +SKIP >>> status = status_info.status # doctest: +SKIP >>> message = status_info.message # doctest: +SKIP Returns: Dict[str, DeploymentStatus]: This dictionary maps the running deployment's name to a DeploymentStatus object containing its status and a message explaining the status. """ return get_global_client().get_deployment_statuses() @PublicAPI(stability="alpha") def run( target: Union[ClassNode, FunctionNode], _blocking: bool = True, *, host: str = DEFAULT_HTTP_HOST, port: int = DEFAULT_HTTP_PORT, ) -> Optional[RayServeHandle]: """Run a Serve application and return a ServeHandle to the ingress. Either a ClassNode, FunctionNode, or a pre-built application can be passed in. If a node is passed in, all of the deployments it depends on will be deployed. If there is an ingress, its handle will be returned. Args: target (Union[ClassNode, FunctionNode, Application]): A user-built Serve Application or a ClassNode that acts as the root node of DAG. By default ClassNode is the Driver deployment unless user provides a customized one. host (str): The host passed into serve.start(). port (int): The port passed into serve.start(). Returns: RayServeHandle: A regular ray serve handle that can be called by user to execute the serve DAG. """ # TODO (jiaodong): Resolve circular reference in pipeline codebase and serve from ray.serve.pipeline.api import build as pipeline_build from ray.serve.pipeline.api import get_and_validate_ingress_deployment client = start(detached=True, http_options={"host": host, "port": port}) if isinstance(target, Application): deployments = list(target.deployments.values()) ingress = target.ingress # Each DAG should always provide a valid Driver ClassNode elif isinstance(target, ClassNode): deployments = pipeline_build(target) ingress = get_and_validate_ingress_deployment(deployments) # Special case where user is doing single function serve.run(func.bind()) elif isinstance(target, FunctionNode): deployments = pipeline_build(target) ingress = get_and_validate_ingress_deployment(deployments) if len(deployments) != 1: raise ValueError( "We only support single function node in serve.run, ex: " "serve.run(func.bind()). For more than one nodes in your DAG, " "Please provide a driver class and bind it as entrypoint to " "your Serve DAG." ) elif isinstance(target, DAGNode): raise ValueError( "Invalid DAGNode type as entry to serve.run(), " f"type: {type(target)}, accepted: ClassNode, " "FunctionNode please provide a driver class and bind it " "as entrypoint to your Serve DAG." ) else: raise TypeError( "Expected a ClassNode, FunctionNode, or Application as target. " f"Got unexpected type {type(target)} instead." ) parameter_group = [] for deployment in deployments: deployment_parameters = { "name": deployment._name, "func_or_class": deployment._func_or_class, "init_args": deployment.init_args, "init_kwargs": deployment.init_kwargs, "ray_actor_options": deployment._ray_actor_options, "config": deployment._config, "version": deployment._version, "prev_version": deployment._prev_version, "route_prefix": deployment.route_prefix, "url": deployment.url, } parameter_group.append(deployment_parameters) client.deploy_group( parameter_group, _blocking=_blocking, remove_past_deployments=True ) if ingress is not None: return ingress.get_handle() def build(target: Union[ClassNode, FunctionNode]) -> Application: """Builds a Serve application into a static application. Takes in a ClassNode or FunctionNode and converts it to a Serve application consisting of one or more deployments. This is intended to be used for production scenarios and deployed via the Serve REST API or CLI, so there are some restrictions placed on the deployments: 1) All of the deployments must be importable. That is, they cannot be defined in __main__ or inline defined. The deployments will be imported in production using the same import path they were here. 2) All arguments bound to the deployment must be JSON-serializable. The returned Application object can be exported to a dictionary or YAML config. """ # TODO (jiaodong): Resolve circular reference in pipeline codebase and serve from ray.serve.pipeline.api import build as pipeline_build if in_interactive_shell(): raise RuntimeError( "build cannot be called from an interactive shell like " "IPython or Jupyter because it requires all deployments to be " "importable to run the app after building." ) # TODO(edoakes): this should accept host and port, but we don't # currently support them in the REST API. return Application(pipeline_build(target)) def _check_http_and_checkpoint_options( client: ServeControllerClient, http_options: Union[dict, HTTPOptions], checkpoint_path: str, ) -> None: if checkpoint_path and checkpoint_path != client.checkpoint_path: logger.warning( f"The new client checkpoint path '{checkpoint_path}' " f"is different from the existing one '{client.checkpoint_path}'. " "The new checkpoint path is ignored." ) if http_options: client_http_options = client.http_config new_http_options = ( http_options if isinstance(http_options, HTTPOptions) else HTTPOptions.parse_obj(http_options) ) different_fields = [] all_http_option_fields = new_http_options.__dict__ for field in all_http_option_fields: if getattr(new_http_options, field) != getattr(client_http_options, field): different_fields.append(field) if len(different_fields): logger.warning( "The new client HTTP config differs from the existing one " f"in the following fields: {different_fields}. " "The new HTTP config is ignored." )

import tempfile import argparse import logging import datetime import threading import os import re from botocore.exceptions import ClientError from ocs_ci.framework import config from ocs_ci.ocs.constants import CLEANUP_YAML, TEMPLATE_CLEANUP_DIR from ocs_ci.ocs.exceptions import CommandFailed from ocs_ci.utility.utils import get_openshift_installer, destroy_cluster from ocs_ci.utility import templating from ocs_ci.utility.aws import ( AWS, delete_cluster_buckets, destroy_volumes, get_rhel_worker_instances, StackStatusError, terminate_rhel_workers ) from ocs_ci.cleanup.aws import defaults FORMAT = ( '%(asctime)s - %(threadName)s - %(name)s - %(levelname)s - %(message)s' ) logging.basicConfig(format=FORMAT, level=logging.DEBUG) logger = logging.getLogger(__name__) def cleanup(cluster_name, cluster_id, upi=False, failed_deletions=None): """ Cleanup existing cluster in AWS Args: cluster_name (str): Name of the cluster cluster_id (str): Cluster id to cleanup upi (bool): True for UPI cluster, False otherwise failed_deletions (list): list of clusters we failed to delete, used for reporting purposes """ data = {'cluster_name': cluster_name, 'cluster_id': cluster_id} template = templating.Templating(base_path=TEMPLATE_CLEANUP_DIR) cleanup_template = template.render_template(CLEANUP_YAML, data) cleanup_path = tempfile.mkdtemp(prefix='cleanup_') cleanup_file = os.path.join(cleanup_path, 'metadata.json') with open(cleanup_file, "w") as temp: temp.write(cleanup_template) bin_dir = os.path.expanduser(config.RUN['bin_dir']) oc_bin = os.path.join(bin_dir, "openshift-install") if upi: aws = AWS() rhel_workers = get_rhel_worker_instances(cleanup_path) logger.info(f"{cluster_name}'s RHEL workers: {rhel_workers}") if rhel_workers: terminate_rhel_workers(rhel_workers) # Destroy extra volumes destroy_volumes(cluster_name) stack_names = list() # Get master, bootstrap and security group stacks for stack_type in ['ma', 'bs', 'sg']: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-{stack_type}" )[0]['StackName'] ) except ClientError: continue # Get the worker stacks worker_index = 0 worker_stack_exists = True while worker_stack_exists: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-no{worker_index}" )[0]['StackName'] ) worker_index += 1 except ClientError: worker_stack_exists = False logger.info(f"Deleting stacks: {stack_names}") aws.delete_cloudformation_stacks(stack_names) # Destroy the cluster logger.info(f"cleaning up {cluster_id}") destroy_cluster(installer=oc_bin, cluster_path=cleanup_path) for stack_type in ['inf', 'vpc']: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-{stack_type}" )[0]['StackName'] ) except ClientError: continue try: aws.delete_cloudformation_stacks(stack_names) except StackStatusError: logger.error('Failed to fully destroy cluster %s', cluster_name) if failed_deletions: failed_deletions.append(cluster_name) raise else: logger.info(f"cleaning up {cluster_id}") try: destroy_cluster(installer=oc_bin, cluster_path=cleanup_path) except CommandFailed: logger.error('Failed to fully destroy cluster %s', cluster_name) if failed_deletions: failed_deletions.append(cluster_name) raise delete_cluster_buckets(cluster_name) def get_clusters(time_to_delete, region_name, prefixes_hours_to_spare): """ Get all cluster names that their EC2 instances running time is greater than the specified time to delete Args: time_to_delete (int): The maximum time in seconds that is allowed for clusters to continue running region_name (str): The name of the AWS region to delete the resources from prefixes_hours_to_spare (dict): Dictionaries of the cluster prefixes to spare along with the maximum time in hours that is allowed for spared clusters to continue running Returns: tuple: List of the cluster names (e.g ebenahar-cluster-gqtd4) to be provided to the ci-cleanup script, a list of VPCs that are part of cloudformation, and a list of remaining clusters """ def determine_cluster_deletion(ec2_instances, cluster_name): for instance in ec2_instances: allowed_running_time = time_to_delete do_not_delete = False if instance.state["Name"] == "running": for prefix, hours in prefixes_hours_to_spare.items(): # case insensitive 'startswith' if bool(re.match(prefix, cluster_name, re.I)): if hours == 'never': do_not_delete = True else: allowed_running_time = int(hours) * 60 * 60 break if do_not_delete: logger.info( "%s marked as 'do not delete' and will not be " "destroyed", cluster_name ) return False else: launch_time = instance.launch_time current_time = datetime.datetime.now(launch_time.tzinfo) running_time = current_time - launch_time logger.info( f"Instance {[tag["Value"] for tag in instance.tags if tag["Key"] == "Name"][0]} " f"(id: {instance.id}) running time is {running_time} hours while the allowed" f" running time for it is {allowed_running_time/3600} hours" ) if running_time.total_seconds() > allowed_running_time: return True return False aws = AWS(region_name=region_name) clusters_to_delete = list() remaining_clusters = list() cloudformation_vpc_names = list() vpcs = aws.ec2_client.describe_vpcs()['Vpcs'] vpc_ids = [vpc['VpcId'] for vpc in vpcs] vpc_objs = [aws.ec2_resource.Vpc(vpc_id) for vpc_id in vpc_ids] for vpc_obj in vpc_objs: vpc_tags = vpc_obj.tags if vpc_tags: cloudformation_vpc_name = [ tag['Value'] for tag in vpc_tags if tag['Key'] == defaults.AWS_CLOUDFORMATION_TAG ] if cloudformation_vpc_name: cloudformation_vpc_names.append(cloudformation_vpc_name[0]) continue vpc_name = [ tag['Value'] for tag in vpc_tags if tag['Key'] == 'Name' ][0] cluster_name = vpc_name.replace('-vpc', '') vpc_instances = vpc_obj.instances.all() if not vpc_instances: clusters_to_delete.append(cluster_name) continue # Append to clusters_to_delete if cluster should be deleted if determine_cluster_deletion(vpc_instances, cluster_name): clusters_to_delete.append(cluster_name) else: remaining_clusters.append(cluster_name) else: logger.info("No tags found for VPC") # Get all cloudformation based clusters to delete cf_clusters_to_delete = list() for vpc_name in cloudformation_vpc_names: instance_dicts = aws.get_instances_by_name_pattern(f"{vpc_name.replace("-vpc", "")}*") ec2_instances = [aws.get_ec2_instance(instance_dict['id']) for instance_dict in instance_dicts] if not ec2_instances: continue cluster_io_tag = None for instance in ec2_instances: cluster_io_tag = [ tag['Key'] for tag in instance.tags if 'kubernetes.io/cluster' in tag['Key'] ] if cluster_io_tag: break if not cluster_io_tag: logger.warning( "Unable to find valid cluster IO tag from ec2 instance tags " "for VPC %s. This is probably not an OCS cluster VPC!", vpc_name ) continue cluster_name = cluster_io_tag[0].replace('kubernetes.io/cluster/', '') if determine_cluster_deletion(ec2_instances, cluster_name): cf_clusters_to_delete.append(cluster_name) else: remaining_clusters.append(cluster_name) return clusters_to_delete, cf_clusters_to_delete, remaining_clusters def cluster_cleanup(): parser = argparse.ArgumentParser(description='Cleanup AWS Resource') parser.add_argument( '--cluster', nargs=1, action='append', required=True, help="Cluster name tag" ) parser.add_argument( '--upi', action='store_true', required=False, help="For UPI cluster deletion" ) logging.basicConfig(level=logging.DEBUG) args = parser.parse_args() procs = [] for id in args.cluster: cluster_name = id[0].rsplit('-', 1)[0] logger.info(f"cleaning up {id[0]}") proc = threading.Thread(target=cleanup, args=(cluster_name, id[0], args.upi)) proc.start() procs.append(proc) for p in procs: p.join() def aws_cleanup(): parser = argparse.ArgumentParser( description='AWS overall resources cleanup according to running time' ) parser.add_argument( '--hours', type=hour_valid, action='store', required=True, help=""" Maximum running time of the cluster (in hours). Clusters older than this will be deleted. The minimum is 10 hours """ ) parser.add_argument( '--region', action='store', required=False, help="The name of the AWS region to delete the resources from" ) parser.add_argument( '--prefix', action='append', required=False, type=prefix_hour_mapping, help=""" Additional prefix:hour combo to treat as a special rule. Clusters starting with this prefix will only be cleaned up if their runtime exceeds the provided hour(this takes precedence over the value provided to --hours). Note: if you want to skip cleanup of a cluster entirely you can use 'never' for the hour. Example: --prefix foo:24 --prefix bar:48 --prefix foobar:never """ ) parser.add_argument( '--force', action='store_true', required=False, help=""" Force cluster cleanup. User will not be prompted for confirmation. WARNING: this utility is destructive, only use this option if you know what you are doing. """ ) args = parser.parse_args() if not args.force: confirmation = input( 'Careful! This action could be highly destructive. ' 'Are you sure you want to proceed? ' ) assert confirmation == defaults.CONFIRMATION_ANSWER, ( "Wrong confirmation answer. Exiting" ) prefixes_hours_to_spare = defaults.CLUSTER_PREFIXES_SPECIAL_RULES if args.prefix: for prefix, hours in args.prefix: logger.info( "Adding special rule for prefix '%s' with hours %s", prefix, hours ) prefixes_hours_to_spare.update({prefix: hours}) time_to_delete = args.hours * 60 * 60 region = defaults.AWS_REGION if not args.region else args.region clusters_to_delete, cf_clusters_to_delete, remaining_clusters = ( get_clusters( time_to_delete=time_to_delete, region_name=region, prefixes_hours_to_spare=prefixes_hours_to_spare, ) ) if not clusters_to_delete: logger.info("No clusters to delete") else: logger.info("Deleting clusters: %s", clusters_to_delete) get_openshift_installer() procs = [] failed_deletions = [] for cluster in clusters_to_delete: cluster_name = cluster.rsplit('-', 1)[0] logger.info(f"Deleting cluster {cluster_name}") proc = threading.Thread( target=cleanup, args=(cluster_name, cluster, False, failed_deletions) ) proc.start() procs.append(proc) for p in procs: p.join() for cluster in cf_clusters_to_delete: cluster_name = cluster.rsplit('-', 1)[0] logger.info(f"Deleting UPI cluster {cluster_name}") proc = threading.Thread( target=cleanup, args=(cluster_name, cluster, True, failed_deletions) ) proc.start() procs.append(proc) for p in procs: p.join() logger.info("Remaining clusters: %s", remaining_clusters) filename = 'failed_cluster_deletions.txt' content = 'None\n' if failed_deletions: logger.error("Failed cluster deletions: %s", failed_deletions) content = "" for cluster in failed_deletions: content += f"{cluster}\n" with open(filename, 'w') as f: f.write(content) def prefix_hour_mapping(string): """ Validate that the string provided to --prefix is properly formatted Args: string (str): input provided to --prefix Raises: argparse.ArgumentTypeError: if the provided string is not correctly formatted Returns: str, str: prefix, hours """ msg = ( f'{string} is not a properly formatted prefix:hour combination. ' f'See the --help for more information.' ) try: prefix, hours = string.split(':') if not prefix or not hours: raise argparse.ArgumentTypeError(msg) # 'never' should be the only non-int value for hours if hours != 'never': int(hours) except ValueError: raise argparse.ArgumentTypeError(msg) return prefix, hours def hour_valid(string): """ Validate that the hour value provided is an int and not lower than the minimum allowed running time Args: string: input provided to --hours Raises: argparse.ArgumentTypeError: if the provided hours value is not an int or lower than the minimum allowed running time Returns: int: valid hour value """ try: hours = int(string) assert hours >= defaults.MINIMUM_CLUSTER_RUNNING_TIME except ValueError: msg = f'{string} is not an int, please provide an int value' raise argparse.ArgumentTypeError(msg) except AssertionError: msg = ( f"Number of hours ({hours}) is lower than the required minimum " f"({defaults.MINIMUM_CLUSTER_RUNNING_TIME})." ) raise argparse.ArgumentTypeError(msg) return hours

import tempfile import argparse import logging import datetime import threading import os import re from botocore.exceptions import ClientError from ocs_ci.framework import config from ocs_ci.ocs.constants import CLEANUP_YAML, TEMPLATE_CLEANUP_DIR from ocs_ci.ocs.exceptions import CommandFailed from ocs_ci.utility.utils import get_openshift_installer, destroy_cluster from ocs_ci.utility import templating from ocs_ci.utility.aws import ( AWS, delete_cluster_buckets, destroy_volumes, get_rhel_worker_instances, StackStatusError, terminate_rhel_workers ) from ocs_ci.cleanup.aws import defaults FORMAT = ( '%(asctime)s - %(threadName)s - %(name)s - %(levelname)s - %(message)s' ) logging.basicConfig(format=FORMAT, level=logging.DEBUG) logger = logging.getLogger(__name__) def cleanup(cluster_name, cluster_id, upi=False, failed_deletions=None): """ Cleanup existing cluster in AWS Args: cluster_name (str): Name of the cluster cluster_id (str): Cluster id to cleanup upi (bool): True for UPI cluster, False otherwise failed_deletions (list): list of clusters we failed to delete, used for reporting purposes """ data = {'cluster_name': cluster_name, 'cluster_id': cluster_id} template = templating.Templating(base_path=TEMPLATE_CLEANUP_DIR) cleanup_template = template.render_template(CLEANUP_YAML, data) cleanup_path = tempfile.mkdtemp(prefix='cleanup_') cleanup_file = os.path.join(cleanup_path, 'metadata.json') with open(cleanup_file, "w") as temp: temp.write(cleanup_template) bin_dir = os.path.expanduser(config.RUN['bin_dir']) oc_bin = os.path.join(bin_dir, "openshift-install") if upi: aws = AWS() rhel_workers = get_rhel_worker_instances(cleanup_path) logger.info(f"{cluster_name}'s RHEL workers: {rhel_workers}") if rhel_workers: terminate_rhel_workers(rhel_workers) # Destroy extra volumes destroy_volumes(cluster_name) stack_names = list() # Get master, bootstrap and security group stacks for stack_type in ['ma', 'bs', 'sg']: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-{stack_type}" )[0]['StackName'] ) except ClientError: continue # Get the worker stacks worker_index = 0 worker_stack_exists = True while worker_stack_exists: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-no{worker_index}" )[0]['StackName'] ) worker_index += 1 except ClientError: worker_stack_exists = False logger.info(f"Deleting stacks: {stack_names}") aws.delete_cloudformation_stacks(stack_names) # Destroy the cluster logger.info(f"cleaning up {cluster_id}") destroy_cluster(installer=oc_bin, cluster_path=cleanup_path) for stack_type in ['inf', 'vpc']: try: stack_names.append( aws.get_cloudformation_stacks( pattern=f"{cluster_name}-{stack_type}" )[0]['StackName'] ) except ClientError: continue try: aws.delete_cloudformation_stacks(stack_names) except StackStatusError: logger.error('Failed to fully destroy cluster %s', cluster_name) if failed_deletions: failed_deletions.append(cluster_name) raise else: logger.info(f"cleaning up {cluster_id}") try: destroy_cluster(installer=oc_bin, cluster_path=cleanup_path) except CommandFailed: logger.error('Failed to fully destroy cluster %s', cluster_name) if failed_deletions: failed_deletions.append(cluster_name) raise delete_cluster_buckets(cluster_name) def get_clusters(time_to_delete, region_name, prefixes_hours_to_spare): """ Get all cluster names that their EC2 instances running time is greater than the specified time to delete Args: time_to_delete (int): The maximum time in seconds that is allowed for clusters to continue running region_name (str): The name of the AWS region to delete the resources from prefixes_hours_to_spare (dict): Dictionaries of the cluster prefixes to spare along with the maximum time in hours that is allowed for spared clusters to continue running Returns: tuple: List of the cluster names (e.g ebenahar-cluster-gqtd4) to be provided to the ci-cleanup script, a list of VPCs that are part of cloudformation, and a list of remaining clusters """ def determine_cluster_deletion(ec2_instances, cluster_name): for instance in ec2_instances: allowed_running_time = time_to_delete do_not_delete = False if instance.state["Name"] == "running": for prefix, hours in prefixes_hours_to_spare.items(): # case insensitive 'startswith' if bool(re.match(prefix, cluster_name, re.I)): if hours == 'never': do_not_delete = True else: allowed_running_time = int(hours) * 60 * 60 break if do_not_delete: logger.info( "%s marked as 'do not delete' and will not be " "destroyed", cluster_name ) return False else: launch_time = instance.launch_time current_time = datetime.datetime.now(launch_time.tzinfo) running_time = current_time - launch_time logger.info( f"Instance {[tag['Value'] for tag in instance.tags if tag['Key'] == 'Name'][0]} " f"(id: {instance.id}) running time is {running_time} hours while the allowed" f" running time for it is {allowed_running_time/3600} hours" ) if running_time.total_seconds() > allowed_running_time: return True return False aws = AWS(region_name=region_name) clusters_to_delete = list() remaining_clusters = list() cloudformation_vpc_names = list() vpcs = aws.ec2_client.describe_vpcs()['Vpcs'] vpc_ids = [vpc['VpcId'] for vpc in vpcs] vpc_objs = [aws.ec2_resource.Vpc(vpc_id) for vpc_id in vpc_ids] for vpc_obj in vpc_objs: vpc_tags = vpc_obj.tags if vpc_tags: cloudformation_vpc_name = [ tag['Value'] for tag in vpc_tags if tag['Key'] == defaults.AWS_CLOUDFORMATION_TAG ] if cloudformation_vpc_name: cloudformation_vpc_names.append(cloudformation_vpc_name[0]) continue vpc_name = [ tag['Value'] for tag in vpc_tags if tag['Key'] == 'Name' ][0] cluster_name = vpc_name.replace('-vpc', '') vpc_instances = vpc_obj.instances.all() if not vpc_instances: clusters_to_delete.append(cluster_name) continue # Append to clusters_to_delete if cluster should be deleted if determine_cluster_deletion(vpc_instances, cluster_name): clusters_to_delete.append(cluster_name) else: remaining_clusters.append(cluster_name) else: logger.info("No tags found for VPC") # Get all cloudformation based clusters to delete cf_clusters_to_delete = list() for vpc_name in cloudformation_vpc_names: instance_dicts = aws.get_instances_by_name_pattern(f"{vpc_name.replace('-vpc', '')}*") ec2_instances = [aws.get_ec2_instance(instance_dict['id']) for instance_dict in instance_dicts] if not ec2_instances: continue cluster_io_tag = None for instance in ec2_instances: cluster_io_tag = [ tag['Key'] for tag in instance.tags if 'kubernetes.io/cluster' in tag['Key'] ] if cluster_io_tag: break if not cluster_io_tag: logger.warning( "Unable to find valid cluster IO tag from ec2 instance tags " "for VPC %s. This is probably not an OCS cluster VPC!", vpc_name ) continue cluster_name = cluster_io_tag[0].replace('kubernetes.io/cluster/', '') if determine_cluster_deletion(ec2_instances, cluster_name): cf_clusters_to_delete.append(cluster_name) else: remaining_clusters.append(cluster_name) return clusters_to_delete, cf_clusters_to_delete, remaining_clusters def cluster_cleanup(): parser = argparse.ArgumentParser(description='Cleanup AWS Resource') parser.add_argument( '--cluster', nargs=1, action='append', required=True, help="Cluster name tag" ) parser.add_argument( '--upi', action='store_true', required=False, help="For UPI cluster deletion" ) logging.basicConfig(level=logging.DEBUG) args = parser.parse_args() procs = [] for id in args.cluster: cluster_name = id[0].rsplit('-', 1)[0] logger.info(f"cleaning up {id[0]}") proc = threading.Thread(target=cleanup, args=(cluster_name, id[0], args.upi)) proc.start() procs.append(proc) for p in procs: p.join() def aws_cleanup(): parser = argparse.ArgumentParser( description='AWS overall resources cleanup according to running time' ) parser.add_argument( '--hours', type=hour_valid, action='store', required=True, help=""" Maximum running time of the cluster (in hours). Clusters older than this will be deleted. The minimum is 10 hours """ ) parser.add_argument( '--region', action='store', required=False, help="The name of the AWS region to delete the resources from" ) parser.add_argument( '--prefix', action='append', required=False, type=prefix_hour_mapping, help=""" Additional prefix:hour combo to treat as a special rule. Clusters starting with this prefix will only be cleaned up if their runtime exceeds the provided hour(this takes precedence over the value provided to --hours). Note: if you want to skip cleanup of a cluster entirely you can use 'never' for the hour. Example: --prefix foo:24 --prefix bar:48 --prefix foobar:never """ ) parser.add_argument( '--force', action='store_true', required=False, help=""" Force cluster cleanup. User will not be prompted for confirmation. WARNING: this utility is destructive, only use this option if you know what you are doing. """ ) args = parser.parse_args() if not args.force: confirmation = input( 'Careful! This action could be highly destructive. ' 'Are you sure you want to proceed? ' ) assert confirmation == defaults.CONFIRMATION_ANSWER, ( "Wrong confirmation answer. Exiting" ) prefixes_hours_to_spare = defaults.CLUSTER_PREFIXES_SPECIAL_RULES if args.prefix: for prefix, hours in args.prefix: logger.info( "Adding special rule for prefix '%s' with hours %s", prefix, hours ) prefixes_hours_to_spare.update({prefix: hours}) time_to_delete = args.hours * 60 * 60 region = defaults.AWS_REGION if not args.region else args.region clusters_to_delete, cf_clusters_to_delete, remaining_clusters = ( get_clusters( time_to_delete=time_to_delete, region_name=region, prefixes_hours_to_spare=prefixes_hours_to_spare, ) ) if not clusters_to_delete: logger.info("No clusters to delete") else: logger.info("Deleting clusters: %s", clusters_to_delete) get_openshift_installer() procs = [] failed_deletions = [] for cluster in clusters_to_delete: cluster_name = cluster.rsplit('-', 1)[0] logger.info(f"Deleting cluster {cluster_name}") proc = threading.Thread( target=cleanup, args=(cluster_name, cluster, False, failed_deletions) ) proc.start() procs.append(proc) for p in procs: p.join() for cluster in cf_clusters_to_delete: cluster_name = cluster.rsplit('-', 1)[0] logger.info(f"Deleting UPI cluster {cluster_name}") proc = threading.Thread( target=cleanup, args=(cluster_name, cluster, True, failed_deletions) ) proc.start() procs.append(proc) for p in procs: p.join() logger.info("Remaining clusters: %s", remaining_clusters) filename = 'failed_cluster_deletions.txt' content = 'None\n' if failed_deletions: logger.error("Failed cluster deletions: %s", failed_deletions) content = "" for cluster in failed_deletions: content += f"{cluster}\n" with open(filename, 'w') as f: f.write(content) def prefix_hour_mapping(string): """ Validate that the string provided to --prefix is properly formatted Args: string (str): input provided to --prefix Raises: argparse.ArgumentTypeError: if the provided string is not correctly formatted Returns: str, str: prefix, hours """ msg = ( f'{string} is not a properly formatted prefix:hour combination. ' f'See the --help for more information.' ) try: prefix, hours = string.split(':') if not prefix or not hours: raise argparse.ArgumentTypeError(msg) # 'never' should be the only non-int value for hours if hours != 'never': int(hours) except ValueError: raise argparse.ArgumentTypeError(msg) return prefix, hours def hour_valid(string): """ Validate that the hour value provided is an int and not lower than the minimum allowed running time Args: string: input provided to --hours Raises: argparse.ArgumentTypeError: if the provided hours value is not an int or lower than the minimum allowed running time Returns: int: valid hour value """ try: hours = int(string) assert hours >= defaults.MINIMUM_CLUSTER_RUNNING_TIME except ValueError: msg = f'{string} is not an int, please provide an int value' raise argparse.ArgumentTypeError(msg) except AssertionError: msg = ( f"Number of hours ({hours}) is lower than the required minimum " f"({defaults.MINIMUM_CLUSTER_RUNNING_TIME})." ) raise argparse.ArgumentTypeError(msg) return hours

from typing import List, Tuple import logging import pytest logging.basicConfig( level=logging.INFO, format="%(asctime)s test %(levelname)s: %(message)s", datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger("ambassador") from ambassador import Cache, IR from ambassador.compile import Compile def require_no_errors(ir: IR): assert ir.aconf.errors == {} def require_errors(ir: IR, errors: List[Tuple[str, str]]): flattened_ir_errors: List[str] = [] for key in ir.aconf.errors.keys(): for error in ir.aconf.errors[key]: flattened_ir_errors.append(f"{key}: {error["error"]}") flattened_wanted_errors: List[str] = [ f"{key}: {error}" for key, error in errors ] assert sorted(flattened_ir_errors) == sorted(flattened_wanted_errors) def test_valid_forward_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: forward_client_cert_details: SANITIZE_SET """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_no_errors(r1["ir"]) require_no_errors(r2["ir"]) def test_invalid_forward_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: forward_client_cert_details: SANITIZE_INVALID """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_errors(r1["ir"], [ ( "ambassador.default.1", "'forward_client_cert_details' may not be set to 'SANITIZE_INVALID'; it may only be set to one of: SANITIZE, FORWARD_ONLY, APPEND_FORWARD, SANITIZE_SET, ALWAYS_FORWARD_ONLY") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'forward_client_cert_details' may not be set to 'SANITIZE_INVALID'; it may only be set to one of: SANITIZE, FORWARD_ONLY, APPEND_FORWARD, SANITIZE_SET, ALWAYS_FORWARD_ONLY") ]) def test_valid_set_current_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: subject: true dns: true """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_no_errors(r1["ir"]) require_no_errors(r2["ir"]) def test_invalid_set_current_client_cert_details_key(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: invalid: true """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) logger.info("R1 IR: %s", r1["ir"].as_json()) require_errors(r1["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' may not contain key 'invalid'; it may only contain keys: subject, cert, chain, dns, uri") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' may not contain key 'invalid'; it may only contain keys: subject, cert, chain, dns, uri") ]) def test_invalid_set_current_client_cert_details_value(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: subject: invalid """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_errors(r1["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' value for key 'subject' may only be 'true' or 'false', not 'invalid'") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' value for key 'subject' may only be 'true' or 'false', not 'invalid'") ])

from typing import List, Tuple import logging import pytest logging.basicConfig( level=logging.INFO, format="%(asctime)s test %(levelname)s: %(message)s", datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger("ambassador") from ambassador import Cache, IR from ambassador.compile import Compile def require_no_errors(ir: IR): assert ir.aconf.errors == {} def require_errors(ir: IR, errors: List[Tuple[str, str]]): flattened_ir_errors: List[str] = [] for key in ir.aconf.errors.keys(): for error in ir.aconf.errors[key]: flattened_ir_errors.append(f"{key}: {error['error']}") flattened_wanted_errors: List[str] = [ f"{key}: {error}" for key, error in errors ] assert sorted(flattened_ir_errors) == sorted(flattened_wanted_errors) def test_valid_forward_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: forward_client_cert_details: SANITIZE_SET """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_no_errors(r1["ir"]) require_no_errors(r2["ir"]) def test_invalid_forward_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: forward_client_cert_details: SANITIZE_INVALID """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_errors(r1["ir"], [ ( "ambassador.default.1", "'forward_client_cert_details' may not be set to 'SANITIZE_INVALID'; it may only be set to one of: SANITIZE, FORWARD_ONLY, APPEND_FORWARD, SANITIZE_SET, ALWAYS_FORWARD_ONLY") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'forward_client_cert_details' may not be set to 'SANITIZE_INVALID'; it may only be set to one of: SANITIZE, FORWARD_ONLY, APPEND_FORWARD, SANITIZE_SET, ALWAYS_FORWARD_ONLY") ]) def test_valid_set_current_client_cert_details(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: subject: true dns: true """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_no_errors(r1["ir"]) require_no_errors(r2["ir"]) def test_invalid_set_current_client_cert_details_key(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: invalid: true """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) logger.info("R1 IR: %s", r1["ir"].as_json()) require_errors(r1["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' may not contain key 'invalid'; it may only contain keys: subject, cert, chain, dns, uri") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' may not contain key 'invalid'; it may only contain keys: subject, cert, chain, dns, uri") ]) def test_invalid_set_current_client_cert_details_value(): yaml = """ --- apiVersion: getambassador.io/v2 kind: Module metadata: name: ambassador namespace: default spec: config: set_current_client_cert_details: subject: invalid """ cache = Cache(logger) r1 = Compile(logger, yaml, k8s=True) r2 = Compile(logger, yaml, k8s=True, cache=cache) require_errors(r1["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' value for key 'subject' may only be 'true' or 'false', not 'invalid'") ]) require_errors(r2["ir"], [ ( "ambassador.default.1", "'set_current_client_cert_details' value for key 'subject' may only be 'true' or 'false', not 'invalid'") ])

from os import environ def get_program(environment_variable: str, backup: str) -> str: return environ.get(environment_variable, default=backup) def get_terminal_program(program: str) -> str: return f"{PROGRAMS["terminal"]} -e {program}" def get_site(url: str) -> str: return f'{PROGRAMS['browser']} {url}' # Set used programs and commands PROGRAMS = dict( editor=get_program("MY_EDITOR", "code"), terminal=get_program("MY_TERMINAL", "xterm"), browser=get_program("MY_BROWSER", "firefox"), launcher=get_program("MY_LAUNCHER", "rofi -show run"), file_explorer=get_program("MY_EXPLORER", "thunar"), email_client=get_program("MY_EMAIL_CLIENT", "thunderbird"), work_communication=get_program("MY_WORK_COMMUNICATION", "skypeforlinux"), screenshot=get_program("MY_SCREENSHOT", "flameshot gui"), volume_manager=get_program("MY_VOLUME_MANAGER", "pavucontrol"), system_monitor="psensor", volume_toggle="amixer set Master toggle", wallpaper_manager="variety", ) # Append terminal programs (requires PROGRAMS to be defined) PROGRAMS.update( dict( tech_news=get_terminal_program("daily-hn"), ) ) # Set commands used to open useful websites WEBSITES = dict( stack_overflow=get_site( "https://stackoverflow.com/questions/tagged/python?sort=Newest&filters=NoAnswers" "&uqlId=33538" ), github=get_site("https://github.com/Rolv-Apneseth"), youtube=get_site("https://www.youtube.com/"), netflix=get_site("https://www.netflix.com/"), ) if __name__ == "__main__": # Print out programs, for debugging from pprint import pprint pprint(PROGRAMS) pprint(WEBSITES)

from os import environ def get_program(environment_variable: str, backup: str) -> str: return environ.get(environment_variable, default=backup) def get_terminal_program(program: str) -> str: return f"{PROGRAMS['terminal']} -e {program}" def get_site(url: str) -> str: return f'{PROGRAMS["browser"]} {url}' # Set used programs and commands PROGRAMS = dict( editor=get_program("MY_EDITOR", "code"), terminal=get_program("MY_TERMINAL", "xterm"), browser=get_program("MY_BROWSER", "firefox"), launcher=get_program("MY_LAUNCHER", "rofi -show run"), file_explorer=get_program("MY_EXPLORER", "thunar"), email_client=get_program("MY_EMAIL_CLIENT", "thunderbird"), work_communication=get_program("MY_WORK_COMMUNICATION", "skypeforlinux"), screenshot=get_program("MY_SCREENSHOT", "flameshot gui"), volume_manager=get_program("MY_VOLUME_MANAGER", "pavucontrol"), system_monitor="psensor", volume_toggle="amixer set Master toggle", wallpaper_manager="variety", ) # Append terminal programs (requires PROGRAMS to be defined) PROGRAMS.update( dict( tech_news=get_terminal_program("daily-hn"), ) ) # Set commands used to open useful websites WEBSITES = dict( stack_overflow=get_site( "https://stackoverflow.com/questions/tagged/python?sort=Newest&filters=NoAnswers" "&uqlId=33538" ), github=get_site("https://github.com/Rolv-Apneseth"), youtube=get_site("https://www.youtube.com/"), netflix=get_site("https://www.netflix.com/"), ) if __name__ == "__main__": # Print out programs, for debugging from pprint import pprint pprint(PROGRAMS) pprint(WEBSITES)

import sqlite3 from util.constants import DATABASE class DBManager: def __init__(self): self.connection = None self.cursor = None def connect(self): self.connection = sqlite3.connect(DATABASE["path"]) self.cursor = self.connection.cursor() return self def create_tables(self): # Create table self.cursor.execute("CREATE TABLE user (password int, service int)") self.connection.commit() def insert_user(self, user) -> int: self.cursor.execute(f" INSERT INTO user VALUES ('{user["password"]}', '${user["service"]}');") self.connection.commit() return self.cursor.lastrowid def delete_user(self, _id=1): self.cursor.execute(f" delete from user where rowid={_id}") self.connection.commit() def get_user(self, _id=1): return self.cursor.execute(f"SELECT * FROM user WHERE rowid={_id}").fetchone() def get_all_users(self): return self.cursor.execute('SELECT * FROM user').fetchall() def close(self): self.connection.close() def drop_tables(self): for table in self.cursor.execute("select name from sqlite_master where type = 'table'"): self.cursor.execute(f"drop table {table[0]}") self.connection.commit() if __name__ == "__main__": db = DBManager().connect() db.drop_tables() db.create_tables() row_id = db.insert_user({"password": 123, "service": 1}) print("rowid =", row_id) print(db.get_user(row_id)) db.delete_user(row_id) print(db.get_all_users()) db.drop_tables() db.close()

import sqlite3 from util.constants import DATABASE class DBManager: def __init__(self): self.connection = None self.cursor = None def connect(self): self.connection = sqlite3.connect(DATABASE["path"]) self.cursor = self.connection.cursor() return self def create_tables(self): # Create table self.cursor.execute("CREATE TABLE user (password int, service int)") self.connection.commit() def insert_user(self, user) -> int: self.cursor.execute(f" INSERT INTO user VALUES ('{user['password']}', '${user['service']}');") self.connection.commit() return self.cursor.lastrowid def delete_user(self, _id=1): self.cursor.execute(f" delete from user where rowid={_id}") self.connection.commit() def get_user(self, _id=1): return self.cursor.execute(f"SELECT * FROM user WHERE rowid={_id}").fetchone() def get_all_users(self): return self.cursor.execute('SELECT * FROM user').fetchall() def close(self): self.connection.close() def drop_tables(self): for table in self.cursor.execute("select name from sqlite_master where type = 'table'"): self.cursor.execute(f"drop table {table[0]}") self.connection.commit() if __name__ == "__main__": db = DBManager().connect() db.drop_tables() db.create_tables() row_id = db.insert_user({"password": 123, "service": 1}) print("rowid =", row_id) print(db.get_user(row_id)) db.delete_user(row_id) print(db.get_all_users()) db.drop_tables() db.close()

# -*- coding: utf-8 -*- """ Run method, and save results. Run as: python main.py --dataset <ds> --method <met> where dataset name should be in UCI_Datasets folder and method is piven, qd, deep-ens, mid or only-rmse. """ import argparse import json import datetime import tensorflow as tf # import tensorflow.compat.v1 as tf import scipy.stats as stats import itertools import os import random import numpy as np import data_loader from DataGen import DataGenerator from DeepNetPI import TfNetwork from utils import * from sklearn.model_selection import train_test_split start_time = datetime.datetime.now() parser = argparse.ArgumentParser() parser.add_argument('--dataset', type=str, default='flight_delay', metavar='', help='dataset name, flight_delay') parser.add_argument('--method', type=str, help='piven, qd, mid, only-rmse, deep-ens', required=True) args = parser.parse_args() method = args.method ############# added code start ############# print(args) print(args.dataset) original_data_path = '../flight_delay_data/' ## flight delay data results_path = './Results/piven/'+args.dataset + '_PIVEN_UCI.txt' seed = 12345 neurons = [100] lambda_in = 15.0 sigma_in = 0.2 random.seed(seed) np.random.seed(seed) tf.compat.v1.random.set_random_seed(seed) # tf.random.set_random_seed(seed) h_size = neurons # n_runs = params['n_runs'] # number of runs n_runs = 1 n_epoch = 500 # number epochs to train for # h_size = params['h_size'] # number of hidden units in network: [50]=layer_1 of 50, [8,4]=layer_1 of 8, layer_2 of 4 l_rate = 0.01 # learning rate of optimizer decay_rate = 0.99 # learning rate decay soften = 160.0 # soften param in the loss patience = -1 # patience n_ensemble = 1 # number of individual NNs in ensemble # 5 alpha = 0.05 # data points captured = (1 - alpha) train_prop = 0.9 # % of data to use as training in_ddof = 1 if n_runs > 1 else 0 # this is for results over runs only is_early_stop = patience != -1 if args.dataset == 'YearPredictionMSD': n_batch = 1000 # batch size out_biases = [5., -5.] else: n_batch = 100 # batch size out_biases = [2., -2.] # chose biases for output layer (for deep_ens is overwritten to 0,1) results_runs = [] run = 0 fail_times = 0 for run in range(0, n_runs): ''' ######### flight delay data loading ###############''' xTrain, yTrain, test_data_list = data_loader.load_flight_delays(original_data_path) # y_train = np.reshape(y_train, (-1, 1)) # y_test = np.reshape(y_test, (-1, 1)) '''choose the train/test dataset ''' x_train = xTrain y_train = yTrain y_train = y_train.reshape(-1, 1) # y_scale = yTrain_scale test_idx = 0 # [0, 1, 2, 3] for test 1,2,3,4 X_test = test_data_list[test_idx][0] y_test = test_data_list[test_idx][1] y_test = y_test.reshape(-1, 1) X_val = X_test y_val = y_test.reshape(-1, 1) y_pred_all = [] y_pred_all_train = [] i = 0 while i < n_ensemble: is_failed_run = False tf.reset_default_graph() sess = tf.Session() print(f'\nrun number {run+1} of {n_runs} -- ensemble number {i+1} of {n_ensemble}') # create network NN = TfNetwork(x_size=x_train.shape[1], y_size=2, h_size=h_size, alpha=alpha, soften=soften, lambda_in=lambda_in, sigma_in=sigma_in, out_biases=out_biases, method=method, patience=patience, dataset=args.dataset, rnd_seed=seed) # train NN.train(sess, x_train, y_train, X_val, y_val, n_epoch=n_epoch, l_rate=l_rate, decay_rate=decay_rate, is_early_stop=is_early_stop, n_batch=n_batch) # predict y_loss, y_pred = NN.predict(sess, X_test=X_test, y_test=y_test) # prediction for training data y_loss_train, y_pred_train = NN.predict(sess, X_test=x_train, y_test=y_train) # check whether the run failed or not if np.abs(y_loss) > 20. and fail_times < 1: # jump out of some endless failures # if False: is_failed_run = True fail_times+=1 print('\n\n### one messed up! repeating ensemble ### failed {}/5 times!'.format(fail_times)) continue # without saving result else: i += 1 # continue to next # save prediction y_pred_all.append(y_pred) y_pred_all_train.append(y_pred_train) sess.close() y_pred_all = np.array(y_pred_all) y_pred_all_train = np.array(y_pred_all_train) if method == 'deep-ens': y_pred_gauss_mid_all = y_pred_all[:, :, 0] # occasionally may get -ves for std dev so need to do max y_pred_gauss_dev_all = np.sqrt(np.maximum(np.log(1. + np.exp(y_pred_all[:, :, 1])), 10e-6)) y_pred_gauss_mid, y_pred_gauss_dev, y_pred_U, \ y_pred_L = gauss_to_pi(y_pred_gauss_mid_all, y_pred_gauss_dev_all) else: ### for test data y_pred_gauss_mid, y_pred_gauss_dev, y_pred_U, y_pred_L, y_pred_v = pi_to_gauss(y_pred_all, method=method) ### for training data y_pred_gauss_mid_train, y_pred_gauss_dev_train, y_pred_U_train, y_pred_L_train, y_pred_v_train = pi_to_gauss(y_pred_all_train, method=method) ### calculate the confidence scores # for train y_U_cap_train = y_pred_U_train > y_train.reshape(-1) y_L_cap_train = y_pred_L_train < y_train.reshape(-1) MPIW_array_train = y_pred_U_train - y_pred_L_train MPIW_train = np.mean(MPIW_array_train) MPIW_array_test = y_pred_U - y_pred_L confidence_arr_test = [min(MPIW_train/test_width, 1.0) for test_width in MPIW_array_test] confidence_arr_train = [min(MPIW_train/train_width, 1.0) for train_width in MPIW_array_train] print('----------- OOD analysis --- confidence scores ----------------') print('--- Train conf_scores MEAN: {}, STD: {}'.format(np.mean(confidence_arr_train), np.std(confidence_arr_train))) print('--- Test: {} rank: {} conf_scores MEAN: {}, STD: {}'.format(test_idx+1, test_idx+1, np.mean(confidence_arr_test), np.std(confidence_arr_test))) dist_arr_train = np.sqrt(np.sum(x_train ** 2.0, axis=1)) dist_arr_test = np.sqrt(np.sum(X_val ** 2.0, axis=1)) confidence_arr_train = np.array(confidence_arr_train) confidence_arr_test = np.array(confidence_arr_test) PIVEN_OOD_train_np = np.hstack((dist_arr_train.reshape(-1, 1), confidence_arr_train.reshape(-1, 1))) PIVEN_OOD_test_np = np.hstack((dist_arr_test.reshape(-1, 1), confidence_arr_test.reshape(-1, 1))) np.savetxt('PIVEN_OOD_flight_delay_'+ str(test_idx+1) +'_train_np.txt', PIVEN_OOD_train_np, delimiter=',') np.savetxt('PIVEN_OOD_flight_delay_'+ str(test_idx+1) +'_test_np.txt', PIVEN_OOD_test_np, delimiter=',') # # work out metrics # y_U_cap = y_pred_U > y_test.reshape(-1) # y_L_cap = y_pred_L < y_test.reshape(-1) # y_all_cap = y_U_cap * y_L_cap # PICP = np.sum(y_all_cap) / y_L_cap.shape[0] # MPIW = np.mean(y_pred_U - y_pred_L) # y_pred_mid = np.mean((y_pred_U, y_pred_L), axis=0) # # MSE = np.mean(np.square(Gen.scale_c * (y_pred_mid - y_test[:, 0]))) # # RMSE = np.sqrt(MSE) # if method == 'qd' or method == 'deep-ens': # RMSE_ELI = 0.0 # RMSE_PIVEN # else: # if method == 'piven': # y_piven = y_pred_v * y_pred_U + (1 - y_pred_v) * y_pred_L # elif method == 'mid': # y_piven = 0.5 * y_pred_U + 0.5 * y_pred_L # elif method == 'only-rmse': # y_piven = y_pred_v # MSE_ELI = np.mean(np.square(Gen.scale_c * (y_piven - y_test[:, 0]))) # RMSE_ELI = np.sqrt(MSE_ELI) # RMSE_PIVEN # CWC = np_QD_loss(y_test, y_pred_L, y_pred_U, alpha, lambda_in) # from qd paper. # neg_log_like = gauss_neg_log_like(y_test, y_pred_gauss_mid, y_pred_gauss_dev, Gen.scale_c) # residuals = y_pred_mid - y_test[:, 0] # shapiro_W, shapiro_p = stats.shapiro(residuals[:]) # results_runs.append((PICP, MPIW, CWC, RMSE, RMSE_ELI, neg_log_like, shapiro_W, shapiro_p)) # # summarize results # results_path = f"./Results/{method}/" # results_path += f"{params["dataset"]}-{start_time.strftime("%d-%m-%H-%M")}-{method}.csv" # results = np.array(results_runs) # results_to_csv(results_path, results, params, n_runs, n_ensemble, in_ddof) # timing info end_time = datetime.datetime.now() total_time = end_time - start_time print('\n\nminutes taken:', round(total_time.total_seconds() / 60, 3), '\nstart_time:', start_time.strftime('%H:%M:%S'), 'end_time:', end_time.strftime('%H:%M:%S'))

# -*- coding: utf-8 -*- """ Run method, and save results. Run as: python main.py --dataset <ds> --method <met> where dataset name should be in UCI_Datasets folder and method is piven, qd, deep-ens, mid or only-rmse. """ import argparse import json import datetime import tensorflow as tf # import tensorflow.compat.v1 as tf import scipy.stats as stats import itertools import os import random import numpy as np import data_loader from DataGen import DataGenerator from DeepNetPI import TfNetwork from utils import * from sklearn.model_selection import train_test_split start_time = datetime.datetime.now() parser = argparse.ArgumentParser() parser.add_argument('--dataset', type=str, default='flight_delay', metavar='', help='dataset name, flight_delay') parser.add_argument('--method', type=str, help='piven, qd, mid, only-rmse, deep-ens', required=True) args = parser.parse_args() method = args.method ############# added code start ############# print(args) print(args.dataset) original_data_path = '../flight_delay_data/' ## flight delay data results_path = './Results/piven/'+args.dataset + '_PIVEN_UCI.txt' seed = 12345 neurons = [100] lambda_in = 15.0 sigma_in = 0.2 random.seed(seed) np.random.seed(seed) tf.compat.v1.random.set_random_seed(seed) # tf.random.set_random_seed(seed) h_size = neurons # n_runs = params['n_runs'] # number of runs n_runs = 1 n_epoch = 500 # number epochs to train for # h_size = params['h_size'] # number of hidden units in network: [50]=layer_1 of 50, [8,4]=layer_1 of 8, layer_2 of 4 l_rate = 0.01 # learning rate of optimizer decay_rate = 0.99 # learning rate decay soften = 160.0 # soften param in the loss patience = -1 # patience n_ensemble = 1 # number of individual NNs in ensemble # 5 alpha = 0.05 # data points captured = (1 - alpha) train_prop = 0.9 # % of data to use as training in_ddof = 1 if n_runs > 1 else 0 # this is for results over runs only is_early_stop = patience != -1 if args.dataset == 'YearPredictionMSD': n_batch = 1000 # batch size out_biases = [5., -5.] else: n_batch = 100 # batch size out_biases = [2., -2.] # chose biases for output layer (for deep_ens is overwritten to 0,1) results_runs = [] run = 0 fail_times = 0 for run in range(0, n_runs): ''' ######### flight delay data loading ###############''' xTrain, yTrain, test_data_list = data_loader.load_flight_delays(original_data_path) # y_train = np.reshape(y_train, (-1, 1)) # y_test = np.reshape(y_test, (-1, 1)) '''choose the train/test dataset ''' x_train = xTrain y_train = yTrain y_train = y_train.reshape(-1, 1) # y_scale = yTrain_scale test_idx = 0 # [0, 1, 2, 3] for test 1,2,3,4 X_test = test_data_list[test_idx][0] y_test = test_data_list[test_idx][1] y_test = y_test.reshape(-1, 1) X_val = X_test y_val = y_test.reshape(-1, 1) y_pred_all = [] y_pred_all_train = [] i = 0 while i < n_ensemble: is_failed_run = False tf.reset_default_graph() sess = tf.Session() print(f'\nrun number {run+1} of {n_runs} -- ensemble number {i+1} of {n_ensemble}') # create network NN = TfNetwork(x_size=x_train.shape[1], y_size=2, h_size=h_size, alpha=alpha, soften=soften, lambda_in=lambda_in, sigma_in=sigma_in, out_biases=out_biases, method=method, patience=patience, dataset=args.dataset, rnd_seed=seed) # train NN.train(sess, x_train, y_train, X_val, y_val, n_epoch=n_epoch, l_rate=l_rate, decay_rate=decay_rate, is_early_stop=is_early_stop, n_batch=n_batch) # predict y_loss, y_pred = NN.predict(sess, X_test=X_test, y_test=y_test) # prediction for training data y_loss_train, y_pred_train = NN.predict(sess, X_test=x_train, y_test=y_train) # check whether the run failed or not if np.abs(y_loss) > 20. and fail_times < 1: # jump out of some endless failures # if False: is_failed_run = True fail_times+=1 print('\n\n### one messed up! repeating ensemble ### failed {}/5 times!'.format(fail_times)) continue # without saving result else: i += 1 # continue to next # save prediction y_pred_all.append(y_pred) y_pred_all_train.append(y_pred_train) sess.close() y_pred_all = np.array(y_pred_all) y_pred_all_train = np.array(y_pred_all_train) if method == 'deep-ens': y_pred_gauss_mid_all = y_pred_all[:, :, 0] # occasionally may get -ves for std dev so need to do max y_pred_gauss_dev_all = np.sqrt(np.maximum(np.log(1. + np.exp(y_pred_all[:, :, 1])), 10e-6)) y_pred_gauss_mid, y_pred_gauss_dev, y_pred_U, \ y_pred_L = gauss_to_pi(y_pred_gauss_mid_all, y_pred_gauss_dev_all) else: ### for test data y_pred_gauss_mid, y_pred_gauss_dev, y_pred_U, y_pred_L, y_pred_v = pi_to_gauss(y_pred_all, method=method) ### for training data y_pred_gauss_mid_train, y_pred_gauss_dev_train, y_pred_U_train, y_pred_L_train, y_pred_v_train = pi_to_gauss(y_pred_all_train, method=method) ### calculate the confidence scores # for train y_U_cap_train = y_pred_U_train > y_train.reshape(-1) y_L_cap_train = y_pred_L_train < y_train.reshape(-1) MPIW_array_train = y_pred_U_train - y_pred_L_train MPIW_train = np.mean(MPIW_array_train) MPIW_array_test = y_pred_U - y_pred_L confidence_arr_test = [min(MPIW_train/test_width, 1.0) for test_width in MPIW_array_test] confidence_arr_train = [min(MPIW_train/train_width, 1.0) for train_width in MPIW_array_train] print('----------- OOD analysis --- confidence scores ----------------') print('--- Train conf_scores MEAN: {}, STD: {}'.format(np.mean(confidence_arr_train), np.std(confidence_arr_train))) print('--- Test: {} rank: {} conf_scores MEAN: {}, STD: {}'.format(test_idx+1, test_idx+1, np.mean(confidence_arr_test), np.std(confidence_arr_test))) dist_arr_train = np.sqrt(np.sum(x_train ** 2.0, axis=1)) dist_arr_test = np.sqrt(np.sum(X_val ** 2.0, axis=1)) confidence_arr_train = np.array(confidence_arr_train) confidence_arr_test = np.array(confidence_arr_test) PIVEN_OOD_train_np = np.hstack((dist_arr_train.reshape(-1, 1), confidence_arr_train.reshape(-1, 1))) PIVEN_OOD_test_np = np.hstack((dist_arr_test.reshape(-1, 1), confidence_arr_test.reshape(-1, 1))) np.savetxt('PIVEN_OOD_flight_delay_'+ str(test_idx+1) +'_train_np.txt', PIVEN_OOD_train_np, delimiter=',') np.savetxt('PIVEN_OOD_flight_delay_'+ str(test_idx+1) +'_test_np.txt', PIVEN_OOD_test_np, delimiter=',') # # work out metrics # y_U_cap = y_pred_U > y_test.reshape(-1) # y_L_cap = y_pred_L < y_test.reshape(-1) # y_all_cap = y_U_cap * y_L_cap # PICP = np.sum(y_all_cap) / y_L_cap.shape[0] # MPIW = np.mean(y_pred_U - y_pred_L) # y_pred_mid = np.mean((y_pred_U, y_pred_L), axis=0) # # MSE = np.mean(np.square(Gen.scale_c * (y_pred_mid - y_test[:, 0]))) # # RMSE = np.sqrt(MSE) # if method == 'qd' or method == 'deep-ens': # RMSE_ELI = 0.0 # RMSE_PIVEN # else: # if method == 'piven': # y_piven = y_pred_v * y_pred_U + (1 - y_pred_v) * y_pred_L # elif method == 'mid': # y_piven = 0.5 * y_pred_U + 0.5 * y_pred_L # elif method == 'only-rmse': # y_piven = y_pred_v # MSE_ELI = np.mean(np.square(Gen.scale_c * (y_piven - y_test[:, 0]))) # RMSE_ELI = np.sqrt(MSE_ELI) # RMSE_PIVEN # CWC = np_QD_loss(y_test, y_pred_L, y_pred_U, alpha, lambda_in) # from qd paper. # neg_log_like = gauss_neg_log_like(y_test, y_pred_gauss_mid, y_pred_gauss_dev, Gen.scale_c) # residuals = y_pred_mid - y_test[:, 0] # shapiro_W, shapiro_p = stats.shapiro(residuals[:]) # results_runs.append((PICP, MPIW, CWC, RMSE, RMSE_ELI, neg_log_like, shapiro_W, shapiro_p)) # # summarize results # results_path = f"./Results/{method}/" # results_path += f"{params['dataset']}-{start_time.strftime('%d-%m-%H-%M')}-{method}.csv" # results = np.array(results_runs) # results_to_csv(results_path, results, params, n_runs, n_ensemble, in_ddof) # timing info end_time = datetime.datetime.now() total_time = end_time - start_time print('\n\nminutes taken:', round(total_time.total_seconds() / 60, 3), '\nstart_time:', start_time.strftime('%H:%M:%S'), 'end_time:', end_time.strftime('%H:%M:%S'))

import os import logging import copy from tqdm import trange from datetime import datetime import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from torchvision.utils import save_image from utils import ema from lib.dataset import DataLooper from lib.sde import VPSDE from lib.model.ddpm import DDPM from lib.trainer import DiffusionTrainer from lib.sampler import DiffusionSampler def train(config, logdir, resume=True): """Running a training pipeline""" # Dataset setup datalooper = DataLooper( config, batch_size=config.train.batch_size, ) # Model setup if config.model.name.lower() == 'ddpm': net_model = DDPM( config.dataset.ch, config.model.ch, config.model.ch_mult, config.model.attn, config.model.num_res_blocks, config.model.dropout, ) else: raise ValueError ema_model = copy.deepcopy(net_model) if config.parallel: net_model = torch.nn.DataParallel(net_model) ema_model = torch.nn.DataParallel(ema_model) # SDE setup if config.sde.name == 'VPSDE': sde = VPSDE( config.sde.beta_min, config.sde.beta_max, config.sde.N, ) else: raise ValueError # Trainer setup trainer = DiffusionTrainer( sde, net_model, config.model.pred_type, ).to(config.device) trainer.train() # Optimizer setup optim = torch.optim.Adam( net_model.parameters(), lr=config.train.lr, ) warmup = config.train.warmup sched = torch.optim.lr_scheduler.LambdaLR( optim, lr_lambda=lambda step: min(step, warmup) / warmup, ) # Sampler setup sampler = DiffusionSampler( sde, ema_model, config.model.pred_type, ).to(config.device) sampler.eval() # Log setup sample_dir = os.path.join(logdir, 'samples') os.makedirs(sample_dir, exist_ok=True) writer = SummaryWriter(logdir) # Show model size model_size = sum(p.numel() for p in net_model.parameters()) logging.info(f'Model Params : {model_size / 1024 / 1024:.2f}M') # Load checkpoint (if exists) try: assert resume ckpt = torch.load(os.path.join(logdir, f'ckpt_latest.pt')) net_model.load_state_dict(ckpt['net_model']) ema_model.load_state_dict(ckpt['ema_model']) optim.load_state_dict(ckpt['optimizer']) sched.load_state_dict(ckpt['scheduler']) init_step = ckpt['step'] + 1 logging.info(f'Checkpoint loaded! Re-start from step {init_step}.') except: init_step = 0 logging.info(f'No checkpoint found. Start from step {init_step}.') # Start training with trange(init_step, config.train.total_steps, dynamic_ncols=True) as pbar: for step in pbar: # Train optim.zero_grad() x_0 = next(datalooper) x_0 = x_0.to(config.device) loss = trainer(x_0) loss = loss.mean() loss.backward() torch.nn.utils.clip_grad_norm_( net_model.parameters(), config.train.grad_clip, ) optim.step() sched.step() ema(net_model, ema_model, config.train.ema_decay) # Log writer.add_scalar('loss', loss, step) pbar.set_postfix(loss=f'{loss:.3f}') # Sample if config.train.sample_step > 0 and step % config.train.sample_step == 0: xs = [] total_steps = config.eval.sample_size // config.eval.batch_size for i in range(0, config.eval.sample_size, config.eval.batch_size): x_T = torch.randn_like(x_0) with torch.no_grad(): x = sampler( x_T, pbar, corrector_n_steps=1, corrector_langevin_snr=0.16, ) xs.append((x.detach().cpu() + 1.) / 2) pbar.set_postfix(option=f'({i+1}/{total_steps})') xs = torch.cat(xs, dim=0) save_image( xs[:64], os.path.join(sample_dir, f'sample_{step}.png'), nrow=8, ) # Save if config.train.save_step > 0 and step % config.train.save_step == 0: ckpt = { 'net_model': net_model.state_dict(), 'ema_model': ema_model.state_dict(), 'optimizer': optim.state_dict(), 'scheduler': sched.state_dict(), 'step': step, } torch.save(ckpt, os.path.join(logdir, f'ckpt_latest.pt')) # Archive if config.train.archive_step > 0 and step % config.train.archive_step == 0: ckpt = { 'net_model': net_model.state_dict(), 'ema_model': ema_model.state_dict(), 'optimizer': optim.state_dict(), 'scheduler': sched.state_dict(), 'step': step, } torch.save(ckpt, os.path.join(logdir, f'ckpt_{step}.pt')) writer.close() def eval(config, logdir): """Running an evaluation pipeline""" # Datalooper setup eval_datalooper = DataLooper( config, batch_size=config.eval.batch_size, ) sample_size = config.eval.sample_size batch_size = config.eval.batch_size # Model setup if config.model.name.lower() == 'ddpm': model = DDPM( config.dataset.ch, config.model.ch, config.model.ch_mult, config.model.attn, config.model.num_res_blocks, config.model.dropout, ) else: raise ValueError if config.parallel: model = torch.nn.DataParallel(model) # SDE setup if config.sde.name == 'VPSDE': sde = VPSDE( config.sde.beta_min, config.sde.beta_max, config.sde.N, ) else: raise ValueError # Sampler setup sampler = DiffusionSampler( sde, model, config.model.pred_type, ).to(config.device) sampler.eval() # Show model size model_size = sum(p.numel() for p in model.parameters()) logging.info(f'Model Params : {model_size / 1024 / 1024:.2f}M') # Load checkpoint ckpt = torch.load( os.path.join(logdir, f'ckpt_latest.pt'), map_location=config.device ) logging.info(f'Checkpoint step : {ckpt['step']}') model.load_state_dict(ckpt['ema_model']) # Directory setup eval_dir = os.path.join(logdir, 'eval') sample_dir = os.path.join(eval_dir, 'samples') os.makedirs(eval_dir, exist_ok=True) os.makedirs(sample_dir, exist_ok=True) xs = [] x_0 = next(eval_datalooper).to(config.device) with trange(0, sample_size, batch_size, dynamic_ncols=True) as pbar: for _ in pbar: x_T = torch.randn_like(x_0) with torch.no_grad(): x = sampler( x_T, pbar, corrector_n_steps=3, corrector_langevin_snr=0.16, ) xs.append((x.detach().cpu() + 1.) / 2) xs = torch.cat(xs, dim=0) now = datetime.now() save_image( xs[:64], os.path.join(sample_dir, f'samples_{now}.png'), nrow=8, )

import os import logging import copy from tqdm import trange from datetime import datetime import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from torchvision.utils import save_image from utils import ema from lib.dataset import DataLooper from lib.sde import VPSDE from lib.model.ddpm import DDPM from lib.trainer import DiffusionTrainer from lib.sampler import DiffusionSampler def train(config, logdir, resume=True): """Running a training pipeline""" # Dataset setup datalooper = DataLooper( config, batch_size=config.train.batch_size, ) # Model setup if config.model.name.lower() == 'ddpm': net_model = DDPM( config.dataset.ch, config.model.ch, config.model.ch_mult, config.model.attn, config.model.num_res_blocks, config.model.dropout, ) else: raise ValueError ema_model = copy.deepcopy(net_model) if config.parallel: net_model = torch.nn.DataParallel(net_model) ema_model = torch.nn.DataParallel(ema_model) # SDE setup if config.sde.name == 'VPSDE': sde = VPSDE( config.sde.beta_min, config.sde.beta_max, config.sde.N, ) else: raise ValueError # Trainer setup trainer = DiffusionTrainer( sde, net_model, config.model.pred_type, ).to(config.device) trainer.train() # Optimizer setup optim = torch.optim.Adam( net_model.parameters(), lr=config.train.lr, ) warmup = config.train.warmup sched = torch.optim.lr_scheduler.LambdaLR( optim, lr_lambda=lambda step: min(step, warmup) / warmup, ) # Sampler setup sampler = DiffusionSampler( sde, ema_model, config.model.pred_type, ).to(config.device) sampler.eval() # Log setup sample_dir = os.path.join(logdir, 'samples') os.makedirs(sample_dir, exist_ok=True) writer = SummaryWriter(logdir) # Show model size model_size = sum(p.numel() for p in net_model.parameters()) logging.info(f'Model Params : {model_size / 1024 / 1024:.2f}M') # Load checkpoint (if exists) try: assert resume ckpt = torch.load(os.path.join(logdir, f'ckpt_latest.pt')) net_model.load_state_dict(ckpt['net_model']) ema_model.load_state_dict(ckpt['ema_model']) optim.load_state_dict(ckpt['optimizer']) sched.load_state_dict(ckpt['scheduler']) init_step = ckpt['step'] + 1 logging.info(f'Checkpoint loaded! Re-start from step {init_step}.') except: init_step = 0 logging.info(f'No checkpoint found. Start from step {init_step}.') # Start training with trange(init_step, config.train.total_steps, dynamic_ncols=True) as pbar: for step in pbar: # Train optim.zero_grad() x_0 = next(datalooper) x_0 = x_0.to(config.device) loss = trainer(x_0) loss = loss.mean() loss.backward() torch.nn.utils.clip_grad_norm_( net_model.parameters(), config.train.grad_clip, ) optim.step() sched.step() ema(net_model, ema_model, config.train.ema_decay) # Log writer.add_scalar('loss', loss, step) pbar.set_postfix(loss=f'{loss:.3f}') # Sample if config.train.sample_step > 0 and step % config.train.sample_step == 0: xs = [] total_steps = config.eval.sample_size // config.eval.batch_size for i in range(0, config.eval.sample_size, config.eval.batch_size): x_T = torch.randn_like(x_0) with torch.no_grad(): x = sampler( x_T, pbar, corrector_n_steps=1, corrector_langevin_snr=0.16, ) xs.append((x.detach().cpu() + 1.) / 2) pbar.set_postfix(option=f'({i+1}/{total_steps})') xs = torch.cat(xs, dim=0) save_image( xs[:64], os.path.join(sample_dir, f'sample_{step}.png'), nrow=8, ) # Save if config.train.save_step > 0 and step % config.train.save_step == 0: ckpt = { 'net_model': net_model.state_dict(), 'ema_model': ema_model.state_dict(), 'optimizer': optim.state_dict(), 'scheduler': sched.state_dict(), 'step': step, } torch.save(ckpt, os.path.join(logdir, f'ckpt_latest.pt')) # Archive if config.train.archive_step > 0 and step % config.train.archive_step == 0: ckpt = { 'net_model': net_model.state_dict(), 'ema_model': ema_model.state_dict(), 'optimizer': optim.state_dict(), 'scheduler': sched.state_dict(), 'step': step, } torch.save(ckpt, os.path.join(logdir, f'ckpt_{step}.pt')) writer.close() def eval(config, logdir): """Running an evaluation pipeline""" # Datalooper setup eval_datalooper = DataLooper( config, batch_size=config.eval.batch_size, ) sample_size = config.eval.sample_size batch_size = config.eval.batch_size # Model setup if config.model.name.lower() == 'ddpm': model = DDPM( config.dataset.ch, config.model.ch, config.model.ch_mult, config.model.attn, config.model.num_res_blocks, config.model.dropout, ) else: raise ValueError if config.parallel: model = torch.nn.DataParallel(model) # SDE setup if config.sde.name == 'VPSDE': sde = VPSDE( config.sde.beta_min, config.sde.beta_max, config.sde.N, ) else: raise ValueError # Sampler setup sampler = DiffusionSampler( sde, model, config.model.pred_type, ).to(config.device) sampler.eval() # Show model size model_size = sum(p.numel() for p in model.parameters()) logging.info(f'Model Params : {model_size / 1024 / 1024:.2f}M') # Load checkpoint ckpt = torch.load( os.path.join(logdir, f'ckpt_latest.pt'), map_location=config.device ) logging.info(f'Checkpoint step : {ckpt["step"]}') model.load_state_dict(ckpt['ema_model']) # Directory setup eval_dir = os.path.join(logdir, 'eval') sample_dir = os.path.join(eval_dir, 'samples') os.makedirs(eval_dir, exist_ok=True) os.makedirs(sample_dir, exist_ok=True) xs = [] x_0 = next(eval_datalooper).to(config.device) with trange(0, sample_size, batch_size, dynamic_ncols=True) as pbar: for _ in pbar: x_T = torch.randn_like(x_0) with torch.no_grad(): x = sampler( x_T, pbar, corrector_n_steps=3, corrector_langevin_snr=0.16, ) xs.append((x.detach().cpu() + 1.) / 2) xs = torch.cat(xs, dim=0) now = datetime.now() save_image( xs[:64], os.path.join(sample_dir, f'samples_{now}.png'), nrow=8, )

import json import os import sys import argparse import shutil import uuid import prettytable import glob import requests import logging from datetime import datetime from zipfile import ZipFile from typing import Any, Tuple, Union from Tests.Marketplace.marketplace_services import init_storage_client, init_bigquery_client, Pack, PackStatus, \ GCPConfig, PACKS_FULL_PATH, IGNORED_FILES, PACKS_FOLDER, IGNORED_PATHS, Metadata, CONTENT_ROOT_PATH, \ get_packs_statistics_dataframe, BucketUploadFlow, load_json, get_content_git_client, get_recent_commits_data, \ store_successful_and_failed_packs_in_ci_artifacts from demisto_sdk.commands.common.tools import run_command, str2bool from Tests.scripts.utils.log_util import install_logging METADATA_TO_REMOVE = { 'IAM/metadata-1.1.0.json', 'IAM/metadata-1.2.0.json', 'IAM/metadata-1.0.0.json', 'IAM/metadata-1.3.0.json', 'HelloWorldPremium/metadata-1.0.0.json', 'HelloWorldPremium/metadata-1.1.0.json', 'HelloWorldPremium/metadata-1.0.8.json', 'HelloWorldPremium/metadata-1.0.9.json', } def get_packs_names(target_packs: str, previous_commit_hash: str = "HEAD^") -> set: """Detects and returns packs names to upload. In case that `Modified` is passed in target_packs input, checks the git difference between two commits, current and previous and greps only ones with prefix Packs/. By default this function will receive `All` as target_packs and will return all packs names from content repo. Args: target_packs (str): csv packs names or `All` for all available packs in content or `Modified` for only modified packs (currently not in use). previous_commit_hash (str): the previous commit to diff with. Returns: set: unique collection of packs names to upload. """ if target_packs.lower() == "all": if os.path.exists(PACKS_FULL_PATH): all_packs = {p for p in os.listdir(PACKS_FULL_PATH) if p not in IGNORED_FILES} logging.info(f"Number of selected packs to upload is: {len(all_packs)}") # return all available packs names return all_packs else: logging.error(f"Folder {PACKS_FOLDER} was not found at the following path: {PACKS_FULL_PATH}") sys.exit(1) elif target_packs.lower() == "modified": cmd = f"git diff --name-only HEAD..{previous_commit_hash} | grep 'Packs/'" modified_packs_path = run_command(cmd).splitlines() modified_packs = {p.split('/')[1] for p in modified_packs_path if p not in IGNORED_PATHS} logging.info(f"Number of modified packs is: {len(modified_packs)}") # return only modified packs between two commits return modified_packs elif target_packs and isinstance(target_packs, str): modified_packs = {p.strip() for p in target_packs.split(',') if p not in IGNORED_FILES} logging.info(f"Number of selected packs to upload is: {len(modified_packs)}") # return only packs from csv list return modified_packs else: logging.critical("Not correct usage of flag -p. Please check help section of upload packs script.") sys.exit(1) def extract_packs_artifacts(packs_artifacts_path: str, extract_destination_path: str): """Extracts all packs from content pack artifact zip. Args: packs_artifacts_path (str): full path to content artifacts zip file. extract_destination_path (str): full path to directory where to extract the packs. """ with ZipFile(packs_artifacts_path) as packs_artifacts: packs_artifacts.extractall(extract_destination_path) logging.info("Finished extracting packs artifacts") def download_and_extract_index(storage_bucket: Any, extract_destination_path: str) -> Tuple[str, Any, int]: """Downloads and extracts index zip from cloud storage. Args: storage_bucket (google.cloud.storage.bucket.Bucket): google storage bucket where index.zip is stored. extract_destination_path (str): the full path of extract folder. Returns: str: extracted index folder full path. Blob: google cloud storage object that represents index.zip blob. str: downloaded index generation. """ if storage_bucket.name == GCPConfig.PRODUCTION_PRIVATE_BUCKET: index_storage_path = os.path.join(GCPConfig.PRIVATE_BASE_PATH, f"{GCPConfig.INDEX_NAME}.zip") else: index_storage_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, f"{GCPConfig.INDEX_NAME}.zip") download_index_path = os.path.join(extract_destination_path, f"{GCPConfig.INDEX_NAME}.zip") index_blob = storage_bucket.blob(index_storage_path) index_folder_path = os.path.join(extract_destination_path, GCPConfig.INDEX_NAME) index_generation = 0 # Setting to 0 makes the operation succeed only if there are no live versions of the blob if not os.path.exists(extract_destination_path): os.mkdir(extract_destination_path) if not index_blob.exists(): os.mkdir(index_folder_path) logging.error(f"{storage_bucket.name} index blob does not exists") return index_folder_path, index_blob, index_generation index_blob.reload() index_generation = index_blob.generation index_blob.download_to_filename(download_index_path, if_generation_match=index_generation) if os.path.exists(download_index_path): with ZipFile(download_index_path, 'r') as index_zip: index_zip.extractall(extract_destination_path) if not os.path.exists(index_folder_path): logging.critical(f"Failed creating {GCPConfig.INDEX_NAME} folder with extracted data.") sys.exit(1) os.remove(download_index_path) logging.success(f"Finished downloading and extracting {GCPConfig.INDEX_NAME} file to " f"{extract_destination_path}") return index_folder_path, index_blob, index_generation else: logging.critical(f"Failed to download {GCPConfig.INDEX_NAME}.zip file from cloud storage.") sys.exit(1) def update_index_folder(index_folder_path: str, pack_name: str, pack_path: str, pack_version: str = '', hidden_pack: bool = False) -> bool: """ Copies pack folder into index folder. Args: index_folder_path (str): full path to index folder. pack_name (str): pack folder name to copy. pack_path (str): pack folder full path. pack_version (str): pack latest version. hidden_pack (bool): whether pack is hidden/internal or regular pack. Returns: bool: whether the operation succeeded. """ task_status = False try: index_folder_subdirectories = [d for d in os.listdir(index_folder_path) if os.path.isdir(os.path.join(index_folder_path, d))] index_pack_path = os.path.join(index_folder_path, pack_name) metadata_files_in_index = glob.glob(f"{index_pack_path}/metadata-*.json") new_metadata_path = os.path.join(index_pack_path, f"metadata-{pack_version}.json") if pack_version: # Update the latest metadata if new_metadata_path in metadata_files_in_index: metadata_files_in_index.remove(new_metadata_path) # Remove old files but keep metadata files if pack_name in index_folder_subdirectories: for d in os.scandir(index_pack_path): if d.path not in metadata_files_in_index: os.remove(d.path) # skipping index update in case hidden is set to True if hidden_pack: if os.path.exists(index_pack_path): shutil.rmtree(index_pack_path) # remove pack folder inside index in case that it exists logging.warning(f"Skipping updating {pack_name} pack files to index") task_status = True return True # Copy new files and add metadata for latest version for d in os.scandir(pack_path): if not os.path.exists(index_pack_path): os.mkdir(index_pack_path) logging.info(f"Created {pack_name} pack folder in {GCPConfig.INDEX_NAME}") shutil.copy(d.path, index_pack_path) if pack_version and Pack.METADATA == d.name: shutil.copy(d.path, new_metadata_path) task_status = True except Exception: logging.exception(f"Failed in updating index folder for {pack_name} pack.") finally: return task_status def clean_non_existing_packs(index_folder_path: str, private_packs: list, storage_bucket: Any) -> bool: """ Detects packs that are not part of content repo or from private packs bucket. In case such packs were detected, problematic pack is deleted from index and from content/packs/{target_pack} path. Args: index_folder_path (str): full path to downloaded index folder. private_packs (list): priced packs from private bucket. storage_bucket (google.cloud.storage.bucket.Bucket): google storage bucket where index.zip is stored. Returns: bool: whether cleanup was skipped or not. """ if ('CI' not in os.environ) or ( os.environ.get('CIRCLE_BRANCH') != 'master' and storage_bucket.name == GCPConfig.PRODUCTION_BUCKET) or ( os.environ.get('CIRCLE_BRANCH') == 'master' and storage_bucket.name not in (GCPConfig.PRODUCTION_BUCKET, GCPConfig.CI_BUILD_BUCKET)): logging.info("Skipping cleanup of packs in gcs.") # skipping execution of cleanup in gcs bucket return True public_packs_names = {p for p in os.listdir(PACKS_FULL_PATH) if p not in IGNORED_FILES} private_packs_names = {p.get('id', '') for p in private_packs} valid_packs_names = public_packs_names.union(private_packs_names) # search for invalid packs folder inside index invalid_packs_names = {(entry.name, entry.path) for entry in os.scandir(index_folder_path) if entry.name not in valid_packs_names and entry.is_dir()} if invalid_packs_names: try: logging.warning(f"Detected {len(invalid_packs_names)} non existing pack inside index, starting cleanup.") for invalid_pack in invalid_packs_names: invalid_pack_name = invalid_pack[0] invalid_pack_path = invalid_pack[1] # remove pack from index shutil.rmtree(invalid_pack_path) logging.warning(f"Deleted {invalid_pack_name} pack from {GCPConfig.INDEX_NAME} folder") # important to add trailing slash at the end of path in order to avoid packs with same prefix invalid_pack_gcs_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, invalid_pack_name, "") # by design for invalid_blob in [b for b in storage_bucket.list_blobs(prefix=invalid_pack_gcs_path)]: logging.warning(f"Deleted invalid {invalid_pack_name} pack under url {invalid_blob.public_url}") invalid_blob.delete() # delete invalid pack in gcs except Exception: logging.exception("Failed to cleanup non existing packs.") else: logging.info(f"No invalid packs detected inside {GCPConfig.INDEX_NAME} folder") return False def upload_index_to_storage(index_folder_path: str, extract_destination_path: str, index_blob: Any, build_number: str, private_packs: list, current_commit_hash: str, index_generation: int, is_private: bool = False, force_upload: bool = False, previous_commit_hash: str = None): """ Upload updated index zip to cloud storage. :param index_folder_path: index folder full path. :param extract_destination_path: extract folder full path. :param index_blob: google cloud storage object that represents index.zip blob. :param build_number: circleCI build number, used as an index revision. :param private_packs: List of private packs and their price. :param current_commit_hash: last commit hash of head. :param index_generation: downloaded index generation. :param is_private: Indicates if upload is private. :param force_upload: Indicates if force upload or not. :param previous_commit_hash: The previous commit hash to diff with. :returns None. """ if force_upload: # If we force upload we don't want to update the commit in the index.json file, # this is to be able to identify all changed packs in the next upload commit = previous_commit_hash logging.info('Force upload flow - Index commit hash shuould not be changed') else: # Otherwise, update the index with the current commit hash (the commit of the upload) commit = current_commit_hash logging.info('Updating production index commit hash to master last commit hash') logging.debug(f'commit hash is: {commit}') with open(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json"), "w+") as index_file: index = { 'revision': build_number, 'modified': datetime.utcnow().strftime(Metadata.DATE_FORMAT), 'packs': private_packs, 'commit': commit } json.dump(index, index_file, indent=4) index_zip_name = os.path.basename(index_folder_path) # REMOVE AFTER SUCCESSFUL RUN logging.info("Starting to remove old meta files") iam_metadata = glob.glob(f"{index_folder_path}/IAM/metadata-*.json") for iam_meta in iam_metadata: if any(x in iam_meta for x in METADATA_TO_REMOVE): logging.info(f"Removing - {iam_meta}") os.remove(iam_meta) hwp_metadata = glob.glob(f"{index_folder_path}/HelloWorldPremium/metadata-*.json") for hwp_meta in hwp_metadata: if any(x in hwp_meta for x in METADATA_TO_REMOVE): logging.info(f"Removing - {hwp_meta}") os.remove(hwp_meta) index_zip_path = shutil.make_archive(base_name=index_folder_path, format="zip", root_dir=extract_destination_path, base_dir=index_zip_name) try: index_blob.reload() current_index_generation = index_blob.generation index_blob.cache_control = "no-cache,max-age=0" # disabling caching for index blob if is_private or current_index_generation == index_generation: index_blob.upload_from_filename(index_zip_path) logging.success(f"Finished uploading {GCPConfig.INDEX_NAME}.zip to storage.") else: logging.critical(f"Failed in uploading {GCPConfig.INDEX_NAME}, mismatch in index file generation") logging.critical(f"Downloaded index generation: {index_generation}") logging.critical(f"Current index generation: {current_index_generation}") sys.exit(0) except Exception: logging.exception(f"Failed in uploading {GCPConfig.INDEX_NAME}.") sys.exit(1) finally: shutil.rmtree(index_folder_path) def upload_core_packs_config(storage_bucket: Any, build_number: str, index_folder_path: str): """Uploads corepacks.json file configuration to bucket. Corepacks file includes core packs for server installation. Args: storage_bucket (google.cloud.storage.bucket.Bucket): gcs bucket where core packs config is uploaded. build_number (str): circleCI build number. index_folder_path (str): The index folder path. """ core_packs_public_urls = [] found_core_packs = set() for pack in os.scandir(index_folder_path): if pack.is_dir() and pack.name in GCPConfig.CORE_PACKS_LIST: pack_metadata_path = os.path.join(index_folder_path, pack.name, Pack.METADATA) if not os.path.exists(pack_metadata_path): logging.critical(f"{pack.name} pack {Pack.METADATA} is missing in {GCPConfig.INDEX_NAME}") sys.exit(1) with open(pack_metadata_path, 'r') as metadata_file: metadata = json.load(metadata_file) pack_current_version = metadata.get('currentVersion', Pack.PACK_INITIAL_VERSION) core_pack_relative_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, pack.name, pack_current_version, f"{pack.name}.zip") core_pack_public_url = os.path.join(GCPConfig.GCS_PUBLIC_URL, storage_bucket.name, core_pack_relative_path) if not storage_bucket.blob(core_pack_relative_path).exists(): logging.critical(f"{pack.name} pack does not exist under {core_pack_relative_path} path") sys.exit(1) core_packs_public_urls.append(core_pack_public_url) found_core_packs.add(pack.name) if len(found_core_packs) != len(GCPConfig.CORE_PACKS_LIST): missing_core_packs = set(GCPConfig.CORE_PACKS_LIST) ^ found_core_packs logging.critical(f"Number of defined core packs are: {len(GCPConfig.CORE_PACKS_LIST)}") logging.critical(f"Actual number of found core packs are: {len(found_core_packs)}") logging.critical(f"Missing core packs are: {missing_core_packs}") sys.exit(1) # construct core pack data with public gcs urls core_packs_data = { 'corePacks': core_packs_public_urls, 'buildNumber': build_number } # upload core pack json file to gcs core_packs_config_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, GCPConfig.CORE_PACK_FILE_NAME) blob = storage_bucket.blob(core_packs_config_path) blob.upload_from_string(json.dumps(core_packs_data, indent=4)) logging.success(f"Finished uploading {GCPConfig.CORE_PACK_FILE_NAME} to storage.") def upload_id_set(storage_bucket: Any, id_set_local_path: str = None): """ Uploads the id_set.json artifact to the bucket. Args: storage_bucket (google.cloud.storage.bucket.Bucket): gcs bucket where core packs config is uploaded. id_set_local_path: path to the id_set.json file """ if not id_set_local_path: logging.info("Skipping upload of id set to gcs.") return id_set_gcs_path = os.path.join(os.path.dirname(GCPConfig.STORAGE_BASE_PATH), 'id_set.json') blob = storage_bucket.blob(id_set_gcs_path) with open(id_set_local_path, mode='r') as f: blob.upload_from_file(f) logging.success("Finished uploading id_set.json to storage.") def _build_summary_table(packs_input_list: list, include_pack_status: bool = False) -> Any: """Build summary table from pack list Args: packs_input_list (list): list of Packs include_pack_status (bool): whether pack includes status Returns: PrettyTable: table with upload result of packs. """ table_fields = ["Index", "Pack ID", "Pack Display Name", "Latest Version", "Aggregated Pack Versions"] if include_pack_status: table_fields.append("Status") table = prettytable.PrettyTable() table.field_names = table_fields for index, pack in enumerate(packs_input_list, start=1): pack_status_message = PackStatus[pack.status].value row = [index, pack.name, pack.display_name, pack.latest_version, pack.aggregation_str if pack.aggregated and pack.aggregation_str else "False"] if include_pack_status: row.append(pack_status_message) table.add_row(row) return table def build_summary_table_md(packs_input_list: list, include_pack_status: bool = False) -> str: """Build markdown summary table from pack list Args: packs_input_list (list): list of Packs include_pack_status (bool): whether pack includes status Returns: Markdown table: table with upload result of packs. """ table_fields = ["Index", "Pack ID", "Pack Display Name", "Latest Version", "Status"] if include_pack_status \ else ["Index", "Pack ID", "Pack Display Name", "Latest Version"] table = ['|', '|'] for key in table_fields: table[0] = f'{table[0]} {key} |' table[1] = f'{table[1]} :- |' for index, pack in enumerate(packs_input_list): pack_status_message = PackStatus[pack.status].value if include_pack_status else '' row = [index, pack.name, pack.display_name, pack.latest_version, pack_status_message] if include_pack_status \ else [index, pack.name, pack.display_name, pack.latest_version] row_hr = '|' for _value in row: row_hr = f'{row_hr} {_value}|' table.append(row_hr) return '\n'.join(table) def update_index_with_priced_packs(private_storage_bucket: Any, extract_destination_path: str, index_folder_path: str, pack_names: set) \ -> Tuple[Union[list, list], str, Any, list]: """ Updates index with priced packs and returns list of priced packs data. Args: private_storage_bucket (google.cloud.storage.bucket.Bucket): google storage private bucket. extract_destination_path (str): full path to extract directory. index_folder_path (str): downloaded index folder directory path. pack_names (set): Collection of pack names. Returns: list: priced packs from private bucket. """ private_index_path = "" private_packs = [] updated_private_packs = [] try: (private_index_path, private_index_blob, _) = \ download_and_extract_index(private_storage_bucket, os.path.join(extract_destination_path, 'private')) logging.info("get_private_packs") private_packs = get_private_packs(private_index_path, pack_names, extract_destination_path) logging.info("get_updated_private_packs") updated_private_packs = get_updated_private_packs(private_packs, index_folder_path) logging.info("add_private_packs_to_index") add_private_packs_to_index(index_folder_path, private_index_path) logging.info("Finished updating index with priced packs") except Exception: logging.exception('Could not add private packs to the index.') finally: shutil.rmtree(os.path.dirname(private_index_path), ignore_errors=True) return private_packs, private_index_path, private_index_blob, updated_private_packs def get_updated_private_packs(private_packs, index_folder_path): """ Checks for updated private packs by compering contentCommitHash between public index json and private pack metadata files. Args: private_packs (list): List of dicts containing pack metadata information. index_folder_path (str): The public index folder path. Returns: updated_private_packs (list) : a list of all private packs id's that were updated. """ updated_private_packs = [] public_index_file_path = os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json") public_index_json = load_json(public_index_file_path) private_packs_from_public_index = public_index_json.get("packs", {}) for pack in private_packs: private_pack_id = pack.get('id') private_commit_hash_from_metadata = pack.get('contentCommitHash', "") private_commit_hash_from_content_repo = "" for public_pack in private_packs_from_public_index: if public_pack.get('id') == private_pack_id: private_commit_hash_from_content_repo = public_pack.get('contentCommitHash', "") private_pack_was_updated = private_commit_hash_from_metadata != private_commit_hash_from_content_repo if private_pack_was_updated: updated_private_packs.append(private_pack_id) return updated_private_packs def get_private_packs(private_index_path: str, pack_names: set = set(), extract_destination_path: str = '') -> list: """ Gets a list of private packs. :param private_index_path: Path to where the private index is located. :param pack_names: Collection of pack names. :param extract_destination_path: Path to where the files should be extracted to. :return: List of dicts containing pack metadata information. """ try: metadata_files = glob.glob(f"{private_index_path}/**/metadata.json") except Exception: logging.exception(f'Could not find metadata files in {private_index_path}.') return [] if not metadata_files: logging.warning(f'No metadata files found in [{private_index_path}]') private_packs = [] for metadata_file_path in metadata_files: try: with open(metadata_file_path, "r") as metadata_file: metadata = json.load(metadata_file) pack_id = metadata.get('id') is_changed_private_pack = pack_id in pack_names if is_changed_private_pack: # Should take metadata from artifacts. with open(os.path.join(extract_destination_path, pack_id, "pack_metadata.json"), "r") as metadata_file: metadata = json.load(metadata_file) if metadata: private_packs.append({ 'id': metadata.get('id') if not is_changed_private_pack else metadata.get('name'), 'price': metadata.get('price'), 'vendorId': metadata.get('vendorId'), 'vendorName': metadata.get('vendorName'), 'contentCommitHash': metadata.get('contentCommitHash', "") }) except ValueError: logging.exception(f'Invalid JSON in the metadata file [{metadata_file_path}].') return private_packs def add_private_packs_to_index(index_folder_path: str, private_index_path: str): """ Add the private packs to the index folder. Args: index_folder_path: The index folder path. private_index_path: The path for the index of the private packs. """ for d in os.scandir(private_index_path): if os.path.isdir(d.path): update_index_folder(index_folder_path, d.name, d.path) def check_if_index_is_updated(index_folder_path: str, content_repo: Any, current_commit_hash: str, previous_commit_hash: str, storage_bucket: Any): """ Checks stored at index.json commit hash and compares it to current commit hash. In case no packs folders were added/modified/deleted, all other steps are not performed. Args: index_folder_path (str): index folder full path. content_repo (git.repo.base.Repo): content repo object. current_commit_hash (str): last commit hash of head. previous_commit_hash (str): the previous commit to diff with storage_bucket: public storage bucket. """ skipping_build_task_message = "Skipping Upload Packs To Marketplace Storage Step." try: if storage_bucket.name not in (GCPConfig.CI_BUILD_BUCKET, GCPConfig.PRODUCTION_BUCKET): logging.info("Skipping index update check in non production/build bucket") return if not os.path.exists(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json")): # will happen only in init bucket run logging.warning(f"{GCPConfig.INDEX_NAME}.json not found in {GCPConfig.INDEX_NAME} folder") return with open(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json")) as index_file: index_json = json.load(index_file) index_commit_hash = index_json.get('commit', previous_commit_hash) try: index_commit = content_repo.commit(index_commit_hash) except Exception: # not updated build will receive this exception because it is missing more updated commit logging.exception(f"Index is already updated. {skipping_build_task_message}") sys.exit() current_commit = content_repo.commit(current_commit_hash) if current_commit.committed_datetime <= index_commit.committed_datetime: logging.warning( f"Current commit {current_commit.hexsha} committed time: {current_commit.committed_datetime}") logging.warning(f"Index commit {index_commit.hexsha} committed time: {index_commit.committed_datetime}") logging.warning("Index is already updated.") logging.warning(skipping_build_task_message) sys.exit() for changed_file in current_commit.diff(index_commit): if changed_file.a_path.startswith(PACKS_FOLDER): logging.info( f"Found changed packs between index commit {index_commit.hexsha} and {current_commit.hexsha}") break else: logging.warning(f"No changes found between index commit {index_commit.hexsha} and {current_commit.hexsha}") logging.warning(skipping_build_task_message) sys.exit() except Exception: logging.exception("Failed in checking status of index") sys.exit(1) def print_packs_summary(successful_packs: list, skipped_packs: list, failed_packs: list, fail_build: bool = True): """Prints summary of packs uploaded to gcs. Args: successful_packs (list): list of packs that were successfully uploaded. skipped_packs (list): list of packs that were skipped during upload. failed_packs (list): list of packs that were failed during upload. fail_build (bool): indicates whether to fail the build upon failing pack to upload or not """ logging.info( f"""\n ------------------------------------------ Packs Upload Summary ------------------------------------------ Total number of packs: {len(successful_packs + skipped_packs + failed_packs)} ----------------------------------------------------------------------------------------------------------""") if successful_packs: successful_packs_table = _build_summary_table(successful_packs) logging.success(f"Number of successful uploaded packs: {len(successful_packs)}") logging.success(f"Uploaded packs:\n{successful_packs_table}") with open('pack_list.txt', 'w') as f: f.write(successful_packs_table.get_string()) if skipped_packs: skipped_packs_table = _build_summary_table(skipped_packs, include_pack_status=True) logging.warning(f"Number of skipped packs: {len(skipped_packs)}") logging.warning(f"Skipped packs:\n{skipped_packs_table}") if failed_packs: failed_packs_table = _build_summary_table(failed_packs, include_pack_status=True) logging.critical(f"Number of failed packs: {len(failed_packs)}") logging.critical(f"Failed packs:\n{failed_packs_table}") if fail_build: # We don't want the bucket upload flow to fail in Prepare Content step if a pack has failed to upload. sys.exit(1) # for external pull requests - when there is no failed packs, add the build summary to the pull request branch_name = os.environ.get('CIRCLE_BRANCH') if branch_name and branch_name.startswith('pull/'): successful_packs_table = build_summary_table_md(successful_packs) build_num = os.environ['CIRCLE_BUILD_NUM'] bucket_path = f'https://console.cloud.google.com/storage/browser/' \ f'marketplace-ci-build/content/builds/{branch_name}/{build_num}' pr_comment = f'Number of successful uploaded packs: {len(successful_packs)}\n' \ f'Uploaded packs:\n{successful_packs_table}\n\n' \ f'Browse to the build bucket with this address:\n{bucket_path}' add_pr_comment(pr_comment) def option_handler(): """Validates and parses script arguments. Returns: Namespace: Parsed arguments object. """ parser = argparse.ArgumentParser(description="Store packs in cloud storage.") # disable-secrets-detection-start parser.add_argument('-a', '--artifacts_path', help="The full path of packs artifacts", required=True) parser.add_argument('-e', '--extract_path', help="Full path of folder to extract wanted packs", required=True) parser.add_argument('-b', '--bucket_name', help="Storage bucket name", required=True) parser.add_argument('-s', '--service_account', help=("Path to gcloud service account, is for circleCI usage. " "For local development use your personal account and " "authenticate using Google Cloud SDK by running: " "`gcloud auth application-default login` and leave this parameter blank. " "For more information go to: " "https://googleapis.dev/python/google-api-core/latest/auth.html"), required=False) parser.add_argument('-i', '--id_set_path', help="The full path of id_set.json", required=False) parser.add_argument('-d', '--pack_dependencies', help="Full path to pack dependencies json file.", required=False) parser.add_argument('-p', '--pack_names', help=("Target packs to upload to gcs. Optional values are: `All`, " "`Modified` or csv list of packs " "Default is set to `All`"), required=False, default="All") parser.add_argument('-n', '--ci_build_number', help="CircleCi build number (will be used as hash revision at index file)", required=False) parser.add_argument('-o', '--override_all_packs', help="Override all existing packs in cloud storage", type=str2bool, default=False, required=True) parser.add_argument('-k', '--key_string', help="Base64 encoded signature key used for signing packs.", required=False) parser.add_argument('-sb', '--storage_base_path', help="Storage base path of the directory to upload to.", required=False) parser.add_argument('-rt', '--remove_test_playbooks', type=str2bool, help='Should remove test playbooks from content packs or not.', default=True) parser.add_argument('-bu', '--bucket_upload', help='is bucket upload build?', type=str2bool, required=True) parser.add_argument('-pb', '--private_bucket_name', help="Private storage bucket name", required=False) parser.add_argument('-c', '--circle_branch', help="CircleCi branch of current build", required=True) parser.add_argument('-f', '--force_upload', help="is force upload build?", type=str2bool, required=True) # disable-secrets-detection-end return parser.parse_args() def add_pr_comment(comment: str): """Add comment to the pull request. Args: comment (string): The comment text. """ token = os.environ['CONTENT_GITHUB_TOKEN'] branch_name = os.environ['CIRCLE_BRANCH'] sha1 = os.environ['CIRCLE_SHA1'] query = f'?q={sha1}+repo:demisto/content+is:pr+is:open+head:{branch_name}+is:open' url = 'https://api.github.com/search/issues' headers = {'Authorization': 'Bearer ' + token} try: res = requests.get(url + query, headers=headers, verify=False) res = handle_github_response(res) if res and res.get('total_count', 0) == 1: issue_url = res['items'][0].get('comments_url') if res.get('items', []) else None if issue_url: res = requests.post(issue_url, json={'body': comment}, headers=headers, verify=False) handle_github_response(res) else: logging.warning( f'Add pull request comment failed: There is more then one open pull request for branch {branch_name}.') except Exception: logging.exception('Add pull request comment failed.') def handle_github_response(response: json) -> dict: """ Handles the response from the GitHub server after making a request. :param response: Response from the server. :return: The returned response. """ res_dict = response.json() if not res_dict.get('ok'): logging.warning(f'Add pull request comment failed: {res_dict.get('message')}') return res_dict def get_packs_summary(packs_list): """ Returns the packs list divided into 3 lists by their status Args: packs_list (list): The full packs list Returns: 3 lists of packs - successful_packs, skipped_packs & failed_packs """ successful_packs = [pack for pack in packs_list if pack.status == PackStatus.SUCCESS.name] skipped_packs = [pack for pack in packs_list if pack.status == PackStatus.PACK_ALREADY_EXISTS.name or pack.status == PackStatus.PACK_IS_NOT_UPDATED_IN_RUNNING_BUILD.name] failed_packs = [pack for pack in packs_list if pack not in successful_packs and pack not in skipped_packs] return successful_packs, skipped_packs, failed_packs def main(): install_logging('Prepare_Content_Packs_For_Testing.log') option = option_handler() packs_artifacts_path = option.artifacts_path extract_destination_path = option.extract_path storage_bucket_name = option.bucket_name service_account = option.service_account target_packs = option.pack_names if option.pack_names else "" build_number = option.ci_build_number if option.ci_build_number else str(uuid.uuid4()) override_all_packs = option.override_all_packs signature_key = option.key_string id_set_path = option.id_set_path packs_dependencies_mapping = load_json(option.pack_dependencies) if option.pack_dependencies else {} storage_base_path = option.storage_base_path remove_test_playbooks = option.remove_test_playbooks is_bucket_upload_flow = option.bucket_upload private_bucket_name = option.private_bucket_name circle_branch = option.circle_branch force_upload = option.force_upload # google cloud storage client initialized storage_client = init_storage_client(service_account) storage_bucket = storage_client.bucket(storage_bucket_name) if storage_base_path: GCPConfig.STORAGE_BASE_PATH = storage_base_path # download and extract index from public bucket index_folder_path, index_blob, index_generation = download_and_extract_index(storage_bucket, extract_destination_path) # content repo client initialized content_repo = get_content_git_client(CONTENT_ROOT_PATH) current_commit_hash, previous_commit_hash = get_recent_commits_data(content_repo, index_folder_path, is_bucket_upload_flow, circle_branch) # detect packs to upload pack_names = get_packs_names(target_packs, previous_commit_hash) extract_packs_artifacts(packs_artifacts_path, extract_destination_path) packs_list = [Pack(pack_name, os.path.join(extract_destination_path, pack_name)) for pack_name in pack_names if os.path.exists(os.path.join(extract_destination_path, pack_name))] if not option.override_all_packs: check_if_index_is_updated(index_folder_path, content_repo, current_commit_hash, previous_commit_hash, storage_bucket) # google cloud bigquery client initialized bq_client = init_bigquery_client(service_account) packs_statistic_df = get_packs_statistics_dataframe(bq_client) updated_private_packs_ids = [] if private_bucket_name: # Add private packs to the index private_storage_bucket = storage_client.bucket(private_bucket_name) private_packs, _, _, updated_private_packs_ids = update_index_with_priced_packs(private_storage_bucket, extract_destination_path, index_folder_path, pack_names) else: # skipping private packs logging.debug("Skipping index update of priced packs") private_packs = [] # clean index and gcs from non existing or invalid packs clean_non_existing_packs(index_folder_path, private_packs, storage_bucket) # starting iteration over packs for pack in packs_list: task_status, user_metadata = pack.load_user_metadata() if not task_status: pack.status = PackStatus.FAILED_LOADING_USER_METADATA.value pack.cleanup() continue task_status, pack_content_items = pack.collect_content_items() if not task_status: pack.status = PackStatus.FAILED_COLLECT_ITEMS.name pack.cleanup() continue task_status, integration_images = pack.upload_integration_images(storage_bucket) if not task_status: pack.status = PackStatus.FAILED_IMAGES_UPLOAD.name pack.cleanup() continue task_status, author_image = pack.upload_author_image(storage_bucket) if not task_status: pack.status = PackStatus.FAILED_AUTHOR_IMAGE_UPLOAD.name pack.cleanup() continue task_status, pack_was_modified = pack.detect_modified(content_repo, index_folder_path, current_commit_hash, previous_commit_hash) if not task_status: pack.status = PackStatus.FAILED_DETECTING_MODIFIED_FILES.name pack.cleanup() continue task_status = pack.format_metadata(user_metadata=user_metadata, pack_content_items=pack_content_items, integration_images=integration_images, author_image=author_image, index_folder_path=index_folder_path, packs_dependencies_mapping=packs_dependencies_mapping, build_number=build_number, commit_hash=current_commit_hash, packs_statistic_df=packs_statistic_df, pack_was_modified=pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_METADATA_PARSING.name pack.cleanup() continue task_status, not_updated_build = pack.prepare_release_notes(index_folder_path, build_number, pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_RELEASE_NOTES.name pack.cleanup() continue if not_updated_build: pack.status = PackStatus.PACK_IS_NOT_UPDATED_IN_RUNNING_BUILD.name pack.cleanup() continue task_status = pack.remove_unwanted_files(remove_test_playbooks) if not task_status: pack.status = PackStatus.FAILED_REMOVING_PACK_SKIPPED_FOLDERS pack.cleanup() continue task_status = pack.sign_pack(signature_key) if not task_status: pack.status = PackStatus.FAILED_SIGNING_PACKS.name pack.cleanup() continue task_status, zip_pack_path = pack.zip_pack() if not task_status: pack.status = PackStatus.FAILED_ZIPPING_PACK_ARTIFACTS.name pack.cleanup() continue (task_status, skipped_pack_uploading, full_pack_path) = \ pack.upload_to_storage(zip_pack_path, pack.latest_version, storage_bucket, override_all_packs or pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_UPLOADING_PACK.name pack.cleanup() continue task_status, exists_in_index = pack.check_if_exists_in_index(index_folder_path) if not task_status: pack.status = PackStatus.FAILED_SEARCHING_PACK_IN_INDEX.name pack.cleanup() continue task_status = pack.prepare_for_index_upload() if not task_status: pack.status = PackStatus.FAILED_PREPARING_INDEX_FOLDER.name pack.cleanup() continue task_status = update_index_folder(index_folder_path=index_folder_path, pack_name=pack.name, pack_path=pack.path, pack_version=pack.latest_version, hidden_pack=pack.hidden) if not task_status: pack.status = PackStatus.FAILED_UPDATING_INDEX_FOLDER.name pack.cleanup() continue # in case that pack already exist at cloud storage path and in index, don't show that the pack was changed if skipped_pack_uploading and exists_in_index: pack.status = PackStatus.PACK_ALREADY_EXISTS.name pack.cleanup() continue pack.status = PackStatus.SUCCESS.name # upload core packs json to bucket upload_core_packs_config(storage_bucket, build_number, index_folder_path) # finished iteration over content packs upload_index_to_storage(index_folder_path=index_folder_path, extract_destination_path=extract_destination_path, index_blob=index_blob, build_number=build_number, private_packs=private_packs, current_commit_hash=current_commit_hash, index_generation=index_generation, force_upload=force_upload, previous_commit_hash=previous_commit_hash) # upload id_set.json to bucket upload_id_set(storage_bucket, id_set_path) # get the lists of packs divided by their status successful_packs, skipped_packs, failed_packs = get_packs_summary(packs_list) # Store successful and failed packs list in CircleCI artifacts - to be used in Upload Packs To Marketplace job packs_results_file_path = os.path.join(os.path.dirname(packs_artifacts_path), BucketUploadFlow.PACKS_RESULTS_FILE) store_successful_and_failed_packs_in_ci_artifacts( packs_results_file_path, BucketUploadFlow.PREPARE_CONTENT_FOR_TESTING, successful_packs, failed_packs, updated_private_packs_ids ) # summary of packs status print_packs_summary(successful_packs, skipped_packs, failed_packs, not is_bucket_upload_flow) if __name__ == '__main__': main()

import json import os import sys import argparse import shutil import uuid import prettytable import glob import requests import logging from datetime import datetime from zipfile import ZipFile from typing import Any, Tuple, Union from Tests.Marketplace.marketplace_services import init_storage_client, init_bigquery_client, Pack, PackStatus, \ GCPConfig, PACKS_FULL_PATH, IGNORED_FILES, PACKS_FOLDER, IGNORED_PATHS, Metadata, CONTENT_ROOT_PATH, \ get_packs_statistics_dataframe, BucketUploadFlow, load_json, get_content_git_client, get_recent_commits_data, \ store_successful_and_failed_packs_in_ci_artifacts from demisto_sdk.commands.common.tools import run_command, str2bool from Tests.scripts.utils.log_util import install_logging METADATA_TO_REMOVE = { 'IAM/metadata-1.1.0.json', 'IAM/metadata-1.2.0.json', 'IAM/metadata-1.0.0.json', 'IAM/metadata-1.3.0.json', 'HelloWorldPremium/metadata-1.0.0.json', 'HelloWorldPremium/metadata-1.1.0.json', 'HelloWorldPremium/metadata-1.0.8.json', 'HelloWorldPremium/metadata-1.0.9.json', } def get_packs_names(target_packs: str, previous_commit_hash: str = "HEAD^") -> set: """Detects and returns packs names to upload. In case that `Modified` is passed in target_packs input, checks the git difference between two commits, current and previous and greps only ones with prefix Packs/. By default this function will receive `All` as target_packs and will return all packs names from content repo. Args: target_packs (str): csv packs names or `All` for all available packs in content or `Modified` for only modified packs (currently not in use). previous_commit_hash (str): the previous commit to diff with. Returns: set: unique collection of packs names to upload. """ if target_packs.lower() == "all": if os.path.exists(PACKS_FULL_PATH): all_packs = {p for p in os.listdir(PACKS_FULL_PATH) if p not in IGNORED_FILES} logging.info(f"Number of selected packs to upload is: {len(all_packs)}") # return all available packs names return all_packs else: logging.error(f"Folder {PACKS_FOLDER} was not found at the following path: {PACKS_FULL_PATH}") sys.exit(1) elif target_packs.lower() == "modified": cmd = f"git diff --name-only HEAD..{previous_commit_hash} | grep 'Packs/'" modified_packs_path = run_command(cmd).splitlines() modified_packs = {p.split('/')[1] for p in modified_packs_path if p not in IGNORED_PATHS} logging.info(f"Number of modified packs is: {len(modified_packs)}") # return only modified packs between two commits return modified_packs elif target_packs and isinstance(target_packs, str): modified_packs = {p.strip() for p in target_packs.split(',') if p not in IGNORED_FILES} logging.info(f"Number of selected packs to upload is: {len(modified_packs)}") # return only packs from csv list return modified_packs else: logging.critical("Not correct usage of flag -p. Please check help section of upload packs script.") sys.exit(1) def extract_packs_artifacts(packs_artifacts_path: str, extract_destination_path: str): """Extracts all packs from content pack artifact zip. Args: packs_artifacts_path (str): full path to content artifacts zip file. extract_destination_path (str): full path to directory where to extract the packs. """ with ZipFile(packs_artifacts_path) as packs_artifacts: packs_artifacts.extractall(extract_destination_path) logging.info("Finished extracting packs artifacts") def download_and_extract_index(storage_bucket: Any, extract_destination_path: str) -> Tuple[str, Any, int]: """Downloads and extracts index zip from cloud storage. Args: storage_bucket (google.cloud.storage.bucket.Bucket): google storage bucket where index.zip is stored. extract_destination_path (str): the full path of extract folder. Returns: str: extracted index folder full path. Blob: google cloud storage object that represents index.zip blob. str: downloaded index generation. """ if storage_bucket.name == GCPConfig.PRODUCTION_PRIVATE_BUCKET: index_storage_path = os.path.join(GCPConfig.PRIVATE_BASE_PATH, f"{GCPConfig.INDEX_NAME}.zip") else: index_storage_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, f"{GCPConfig.INDEX_NAME}.zip") download_index_path = os.path.join(extract_destination_path, f"{GCPConfig.INDEX_NAME}.zip") index_blob = storage_bucket.blob(index_storage_path) index_folder_path = os.path.join(extract_destination_path, GCPConfig.INDEX_NAME) index_generation = 0 # Setting to 0 makes the operation succeed only if there are no live versions of the blob if not os.path.exists(extract_destination_path): os.mkdir(extract_destination_path) if not index_blob.exists(): os.mkdir(index_folder_path) logging.error(f"{storage_bucket.name} index blob does not exists") return index_folder_path, index_blob, index_generation index_blob.reload() index_generation = index_blob.generation index_blob.download_to_filename(download_index_path, if_generation_match=index_generation) if os.path.exists(download_index_path): with ZipFile(download_index_path, 'r') as index_zip: index_zip.extractall(extract_destination_path) if not os.path.exists(index_folder_path): logging.critical(f"Failed creating {GCPConfig.INDEX_NAME} folder with extracted data.") sys.exit(1) os.remove(download_index_path) logging.success(f"Finished downloading and extracting {GCPConfig.INDEX_NAME} file to " f"{extract_destination_path}") return index_folder_path, index_blob, index_generation else: logging.critical(f"Failed to download {GCPConfig.INDEX_NAME}.zip file from cloud storage.") sys.exit(1) def update_index_folder(index_folder_path: str, pack_name: str, pack_path: str, pack_version: str = '', hidden_pack: bool = False) -> bool: """ Copies pack folder into index folder. Args: index_folder_path (str): full path to index folder. pack_name (str): pack folder name to copy. pack_path (str): pack folder full path. pack_version (str): pack latest version. hidden_pack (bool): whether pack is hidden/internal or regular pack. Returns: bool: whether the operation succeeded. """ task_status = False try: index_folder_subdirectories = [d for d in os.listdir(index_folder_path) if os.path.isdir(os.path.join(index_folder_path, d))] index_pack_path = os.path.join(index_folder_path, pack_name) metadata_files_in_index = glob.glob(f"{index_pack_path}/metadata-*.json") new_metadata_path = os.path.join(index_pack_path, f"metadata-{pack_version}.json") if pack_version: # Update the latest metadata if new_metadata_path in metadata_files_in_index: metadata_files_in_index.remove(new_metadata_path) # Remove old files but keep metadata files if pack_name in index_folder_subdirectories: for d in os.scandir(index_pack_path): if d.path not in metadata_files_in_index: os.remove(d.path) # skipping index update in case hidden is set to True if hidden_pack: if os.path.exists(index_pack_path): shutil.rmtree(index_pack_path) # remove pack folder inside index in case that it exists logging.warning(f"Skipping updating {pack_name} pack files to index") task_status = True return True # Copy new files and add metadata for latest version for d in os.scandir(pack_path): if not os.path.exists(index_pack_path): os.mkdir(index_pack_path) logging.info(f"Created {pack_name} pack folder in {GCPConfig.INDEX_NAME}") shutil.copy(d.path, index_pack_path) if pack_version and Pack.METADATA == d.name: shutil.copy(d.path, new_metadata_path) task_status = True except Exception: logging.exception(f"Failed in updating index folder for {pack_name} pack.") finally: return task_status def clean_non_existing_packs(index_folder_path: str, private_packs: list, storage_bucket: Any) -> bool: """ Detects packs that are not part of content repo or from private packs bucket. In case such packs were detected, problematic pack is deleted from index and from content/packs/{target_pack} path. Args: index_folder_path (str): full path to downloaded index folder. private_packs (list): priced packs from private bucket. storage_bucket (google.cloud.storage.bucket.Bucket): google storage bucket where index.zip is stored. Returns: bool: whether cleanup was skipped or not. """ if ('CI' not in os.environ) or ( os.environ.get('CIRCLE_BRANCH') != 'master' and storage_bucket.name == GCPConfig.PRODUCTION_BUCKET) or ( os.environ.get('CIRCLE_BRANCH') == 'master' and storage_bucket.name not in (GCPConfig.PRODUCTION_BUCKET, GCPConfig.CI_BUILD_BUCKET)): logging.info("Skipping cleanup of packs in gcs.") # skipping execution of cleanup in gcs bucket return True public_packs_names = {p for p in os.listdir(PACKS_FULL_PATH) if p not in IGNORED_FILES} private_packs_names = {p.get('id', '') for p in private_packs} valid_packs_names = public_packs_names.union(private_packs_names) # search for invalid packs folder inside index invalid_packs_names = {(entry.name, entry.path) for entry in os.scandir(index_folder_path) if entry.name not in valid_packs_names and entry.is_dir()} if invalid_packs_names: try: logging.warning(f"Detected {len(invalid_packs_names)} non existing pack inside index, starting cleanup.") for invalid_pack in invalid_packs_names: invalid_pack_name = invalid_pack[0] invalid_pack_path = invalid_pack[1] # remove pack from index shutil.rmtree(invalid_pack_path) logging.warning(f"Deleted {invalid_pack_name} pack from {GCPConfig.INDEX_NAME} folder") # important to add trailing slash at the end of path in order to avoid packs with same prefix invalid_pack_gcs_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, invalid_pack_name, "") # by design for invalid_blob in [b for b in storage_bucket.list_blobs(prefix=invalid_pack_gcs_path)]: logging.warning(f"Deleted invalid {invalid_pack_name} pack under url {invalid_blob.public_url}") invalid_blob.delete() # delete invalid pack in gcs except Exception: logging.exception("Failed to cleanup non existing packs.") else: logging.info(f"No invalid packs detected inside {GCPConfig.INDEX_NAME} folder") return False def upload_index_to_storage(index_folder_path: str, extract_destination_path: str, index_blob: Any, build_number: str, private_packs: list, current_commit_hash: str, index_generation: int, is_private: bool = False, force_upload: bool = False, previous_commit_hash: str = None): """ Upload updated index zip to cloud storage. :param index_folder_path: index folder full path. :param extract_destination_path: extract folder full path. :param index_blob: google cloud storage object that represents index.zip blob. :param build_number: circleCI build number, used as an index revision. :param private_packs: List of private packs and their price. :param current_commit_hash: last commit hash of head. :param index_generation: downloaded index generation. :param is_private: Indicates if upload is private. :param force_upload: Indicates if force upload or not. :param previous_commit_hash: The previous commit hash to diff with. :returns None. """ if force_upload: # If we force upload we don't want to update the commit in the index.json file, # this is to be able to identify all changed packs in the next upload commit = previous_commit_hash logging.info('Force upload flow - Index commit hash shuould not be changed') else: # Otherwise, update the index with the current commit hash (the commit of the upload) commit = current_commit_hash logging.info('Updating production index commit hash to master last commit hash') logging.debug(f'commit hash is: {commit}') with open(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json"), "w+") as index_file: index = { 'revision': build_number, 'modified': datetime.utcnow().strftime(Metadata.DATE_FORMAT), 'packs': private_packs, 'commit': commit } json.dump(index, index_file, indent=4) index_zip_name = os.path.basename(index_folder_path) # REMOVE AFTER SUCCESSFUL RUN logging.info("Starting to remove old meta files") iam_metadata = glob.glob(f"{index_folder_path}/IAM/metadata-*.json") for iam_meta in iam_metadata: if any(x in iam_meta for x in METADATA_TO_REMOVE): logging.info(f"Removing - {iam_meta}") os.remove(iam_meta) hwp_metadata = glob.glob(f"{index_folder_path}/HelloWorldPremium/metadata-*.json") for hwp_meta in hwp_metadata: if any(x in hwp_meta for x in METADATA_TO_REMOVE): logging.info(f"Removing - {hwp_meta}") os.remove(hwp_meta) index_zip_path = shutil.make_archive(base_name=index_folder_path, format="zip", root_dir=extract_destination_path, base_dir=index_zip_name) try: index_blob.reload() current_index_generation = index_blob.generation index_blob.cache_control = "no-cache,max-age=0" # disabling caching for index blob if is_private or current_index_generation == index_generation: index_blob.upload_from_filename(index_zip_path) logging.success(f"Finished uploading {GCPConfig.INDEX_NAME}.zip to storage.") else: logging.critical(f"Failed in uploading {GCPConfig.INDEX_NAME}, mismatch in index file generation") logging.critical(f"Downloaded index generation: {index_generation}") logging.critical(f"Current index generation: {current_index_generation}") sys.exit(0) except Exception: logging.exception(f"Failed in uploading {GCPConfig.INDEX_NAME}.") sys.exit(1) finally: shutil.rmtree(index_folder_path) def upload_core_packs_config(storage_bucket: Any, build_number: str, index_folder_path: str): """Uploads corepacks.json file configuration to bucket. Corepacks file includes core packs for server installation. Args: storage_bucket (google.cloud.storage.bucket.Bucket): gcs bucket where core packs config is uploaded. build_number (str): circleCI build number. index_folder_path (str): The index folder path. """ core_packs_public_urls = [] found_core_packs = set() for pack in os.scandir(index_folder_path): if pack.is_dir() and pack.name in GCPConfig.CORE_PACKS_LIST: pack_metadata_path = os.path.join(index_folder_path, pack.name, Pack.METADATA) if not os.path.exists(pack_metadata_path): logging.critical(f"{pack.name} pack {Pack.METADATA} is missing in {GCPConfig.INDEX_NAME}") sys.exit(1) with open(pack_metadata_path, 'r') as metadata_file: metadata = json.load(metadata_file) pack_current_version = metadata.get('currentVersion', Pack.PACK_INITIAL_VERSION) core_pack_relative_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, pack.name, pack_current_version, f"{pack.name}.zip") core_pack_public_url = os.path.join(GCPConfig.GCS_PUBLIC_URL, storage_bucket.name, core_pack_relative_path) if not storage_bucket.blob(core_pack_relative_path).exists(): logging.critical(f"{pack.name} pack does not exist under {core_pack_relative_path} path") sys.exit(1) core_packs_public_urls.append(core_pack_public_url) found_core_packs.add(pack.name) if len(found_core_packs) != len(GCPConfig.CORE_PACKS_LIST): missing_core_packs = set(GCPConfig.CORE_PACKS_LIST) ^ found_core_packs logging.critical(f"Number of defined core packs are: {len(GCPConfig.CORE_PACKS_LIST)}") logging.critical(f"Actual number of found core packs are: {len(found_core_packs)}") logging.critical(f"Missing core packs are: {missing_core_packs}") sys.exit(1) # construct core pack data with public gcs urls core_packs_data = { 'corePacks': core_packs_public_urls, 'buildNumber': build_number } # upload core pack json file to gcs core_packs_config_path = os.path.join(GCPConfig.STORAGE_BASE_PATH, GCPConfig.CORE_PACK_FILE_NAME) blob = storage_bucket.blob(core_packs_config_path) blob.upload_from_string(json.dumps(core_packs_data, indent=4)) logging.success(f"Finished uploading {GCPConfig.CORE_PACK_FILE_NAME} to storage.") def upload_id_set(storage_bucket: Any, id_set_local_path: str = None): """ Uploads the id_set.json artifact to the bucket. Args: storage_bucket (google.cloud.storage.bucket.Bucket): gcs bucket where core packs config is uploaded. id_set_local_path: path to the id_set.json file """ if not id_set_local_path: logging.info("Skipping upload of id set to gcs.") return id_set_gcs_path = os.path.join(os.path.dirname(GCPConfig.STORAGE_BASE_PATH), 'id_set.json') blob = storage_bucket.blob(id_set_gcs_path) with open(id_set_local_path, mode='r') as f: blob.upload_from_file(f) logging.success("Finished uploading id_set.json to storage.") def _build_summary_table(packs_input_list: list, include_pack_status: bool = False) -> Any: """Build summary table from pack list Args: packs_input_list (list): list of Packs include_pack_status (bool): whether pack includes status Returns: PrettyTable: table with upload result of packs. """ table_fields = ["Index", "Pack ID", "Pack Display Name", "Latest Version", "Aggregated Pack Versions"] if include_pack_status: table_fields.append("Status") table = prettytable.PrettyTable() table.field_names = table_fields for index, pack in enumerate(packs_input_list, start=1): pack_status_message = PackStatus[pack.status].value row = [index, pack.name, pack.display_name, pack.latest_version, pack.aggregation_str if pack.aggregated and pack.aggregation_str else "False"] if include_pack_status: row.append(pack_status_message) table.add_row(row) return table def build_summary_table_md(packs_input_list: list, include_pack_status: bool = False) -> str: """Build markdown summary table from pack list Args: packs_input_list (list): list of Packs include_pack_status (bool): whether pack includes status Returns: Markdown table: table with upload result of packs. """ table_fields = ["Index", "Pack ID", "Pack Display Name", "Latest Version", "Status"] if include_pack_status \ else ["Index", "Pack ID", "Pack Display Name", "Latest Version"] table = ['|', '|'] for key in table_fields: table[0] = f'{table[0]} {key} |' table[1] = f'{table[1]} :- |' for index, pack in enumerate(packs_input_list): pack_status_message = PackStatus[pack.status].value if include_pack_status else '' row = [index, pack.name, pack.display_name, pack.latest_version, pack_status_message] if include_pack_status \ else [index, pack.name, pack.display_name, pack.latest_version] row_hr = '|' for _value in row: row_hr = f'{row_hr} {_value}|' table.append(row_hr) return '\n'.join(table) def update_index_with_priced_packs(private_storage_bucket: Any, extract_destination_path: str, index_folder_path: str, pack_names: set) \ -> Tuple[Union[list, list], str, Any, list]: """ Updates index with priced packs and returns list of priced packs data. Args: private_storage_bucket (google.cloud.storage.bucket.Bucket): google storage private bucket. extract_destination_path (str): full path to extract directory. index_folder_path (str): downloaded index folder directory path. pack_names (set): Collection of pack names. Returns: list: priced packs from private bucket. """ private_index_path = "" private_packs = [] updated_private_packs = [] try: (private_index_path, private_index_blob, _) = \ download_and_extract_index(private_storage_bucket, os.path.join(extract_destination_path, 'private')) logging.info("get_private_packs") private_packs = get_private_packs(private_index_path, pack_names, extract_destination_path) logging.info("get_updated_private_packs") updated_private_packs = get_updated_private_packs(private_packs, index_folder_path) logging.info("add_private_packs_to_index") add_private_packs_to_index(index_folder_path, private_index_path) logging.info("Finished updating index with priced packs") except Exception: logging.exception('Could not add private packs to the index.') finally: shutil.rmtree(os.path.dirname(private_index_path), ignore_errors=True) return private_packs, private_index_path, private_index_blob, updated_private_packs def get_updated_private_packs(private_packs, index_folder_path): """ Checks for updated private packs by compering contentCommitHash between public index json and private pack metadata files. Args: private_packs (list): List of dicts containing pack metadata information. index_folder_path (str): The public index folder path. Returns: updated_private_packs (list) : a list of all private packs id's that were updated. """ updated_private_packs = [] public_index_file_path = os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json") public_index_json = load_json(public_index_file_path) private_packs_from_public_index = public_index_json.get("packs", {}) for pack in private_packs: private_pack_id = pack.get('id') private_commit_hash_from_metadata = pack.get('contentCommitHash', "") private_commit_hash_from_content_repo = "" for public_pack in private_packs_from_public_index: if public_pack.get('id') == private_pack_id: private_commit_hash_from_content_repo = public_pack.get('contentCommitHash', "") private_pack_was_updated = private_commit_hash_from_metadata != private_commit_hash_from_content_repo if private_pack_was_updated: updated_private_packs.append(private_pack_id) return updated_private_packs def get_private_packs(private_index_path: str, pack_names: set = set(), extract_destination_path: str = '') -> list: """ Gets a list of private packs. :param private_index_path: Path to where the private index is located. :param pack_names: Collection of pack names. :param extract_destination_path: Path to where the files should be extracted to. :return: List of dicts containing pack metadata information. """ try: metadata_files = glob.glob(f"{private_index_path}/**/metadata.json") except Exception: logging.exception(f'Could not find metadata files in {private_index_path}.') return [] if not metadata_files: logging.warning(f'No metadata files found in [{private_index_path}]') private_packs = [] for metadata_file_path in metadata_files: try: with open(metadata_file_path, "r") as metadata_file: metadata = json.load(metadata_file) pack_id = metadata.get('id') is_changed_private_pack = pack_id in pack_names if is_changed_private_pack: # Should take metadata from artifacts. with open(os.path.join(extract_destination_path, pack_id, "pack_metadata.json"), "r") as metadata_file: metadata = json.load(metadata_file) if metadata: private_packs.append({ 'id': metadata.get('id') if not is_changed_private_pack else metadata.get('name'), 'price': metadata.get('price'), 'vendorId': metadata.get('vendorId'), 'vendorName': metadata.get('vendorName'), 'contentCommitHash': metadata.get('contentCommitHash', "") }) except ValueError: logging.exception(f'Invalid JSON in the metadata file [{metadata_file_path}].') return private_packs def add_private_packs_to_index(index_folder_path: str, private_index_path: str): """ Add the private packs to the index folder. Args: index_folder_path: The index folder path. private_index_path: The path for the index of the private packs. """ for d in os.scandir(private_index_path): if os.path.isdir(d.path): update_index_folder(index_folder_path, d.name, d.path) def check_if_index_is_updated(index_folder_path: str, content_repo: Any, current_commit_hash: str, previous_commit_hash: str, storage_bucket: Any): """ Checks stored at index.json commit hash and compares it to current commit hash. In case no packs folders were added/modified/deleted, all other steps are not performed. Args: index_folder_path (str): index folder full path. content_repo (git.repo.base.Repo): content repo object. current_commit_hash (str): last commit hash of head. previous_commit_hash (str): the previous commit to diff with storage_bucket: public storage bucket. """ skipping_build_task_message = "Skipping Upload Packs To Marketplace Storage Step." try: if storage_bucket.name not in (GCPConfig.CI_BUILD_BUCKET, GCPConfig.PRODUCTION_BUCKET): logging.info("Skipping index update check in non production/build bucket") return if not os.path.exists(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json")): # will happen only in init bucket run logging.warning(f"{GCPConfig.INDEX_NAME}.json not found in {GCPConfig.INDEX_NAME} folder") return with open(os.path.join(index_folder_path, f"{GCPConfig.INDEX_NAME}.json")) as index_file: index_json = json.load(index_file) index_commit_hash = index_json.get('commit', previous_commit_hash) try: index_commit = content_repo.commit(index_commit_hash) except Exception: # not updated build will receive this exception because it is missing more updated commit logging.exception(f"Index is already updated. {skipping_build_task_message}") sys.exit() current_commit = content_repo.commit(current_commit_hash) if current_commit.committed_datetime <= index_commit.committed_datetime: logging.warning( f"Current commit {current_commit.hexsha} committed time: {current_commit.committed_datetime}") logging.warning(f"Index commit {index_commit.hexsha} committed time: {index_commit.committed_datetime}") logging.warning("Index is already updated.") logging.warning(skipping_build_task_message) sys.exit() for changed_file in current_commit.diff(index_commit): if changed_file.a_path.startswith(PACKS_FOLDER): logging.info( f"Found changed packs between index commit {index_commit.hexsha} and {current_commit.hexsha}") break else: logging.warning(f"No changes found between index commit {index_commit.hexsha} and {current_commit.hexsha}") logging.warning(skipping_build_task_message) sys.exit() except Exception: logging.exception("Failed in checking status of index") sys.exit(1) def print_packs_summary(successful_packs: list, skipped_packs: list, failed_packs: list, fail_build: bool = True): """Prints summary of packs uploaded to gcs. Args: successful_packs (list): list of packs that were successfully uploaded. skipped_packs (list): list of packs that were skipped during upload. failed_packs (list): list of packs that were failed during upload. fail_build (bool): indicates whether to fail the build upon failing pack to upload or not """ logging.info( f"""\n ------------------------------------------ Packs Upload Summary ------------------------------------------ Total number of packs: {len(successful_packs + skipped_packs + failed_packs)} ----------------------------------------------------------------------------------------------------------""") if successful_packs: successful_packs_table = _build_summary_table(successful_packs) logging.success(f"Number of successful uploaded packs: {len(successful_packs)}") logging.success(f"Uploaded packs:\n{successful_packs_table}") with open('pack_list.txt', 'w') as f: f.write(successful_packs_table.get_string()) if skipped_packs: skipped_packs_table = _build_summary_table(skipped_packs, include_pack_status=True) logging.warning(f"Number of skipped packs: {len(skipped_packs)}") logging.warning(f"Skipped packs:\n{skipped_packs_table}") if failed_packs: failed_packs_table = _build_summary_table(failed_packs, include_pack_status=True) logging.critical(f"Number of failed packs: {len(failed_packs)}") logging.critical(f"Failed packs:\n{failed_packs_table}") if fail_build: # We don't want the bucket upload flow to fail in Prepare Content step if a pack has failed to upload. sys.exit(1) # for external pull requests - when there is no failed packs, add the build summary to the pull request branch_name = os.environ.get('CIRCLE_BRANCH') if branch_name and branch_name.startswith('pull/'): successful_packs_table = build_summary_table_md(successful_packs) build_num = os.environ['CIRCLE_BUILD_NUM'] bucket_path = f'https://console.cloud.google.com/storage/browser/' \ f'marketplace-ci-build/content/builds/{branch_name}/{build_num}' pr_comment = f'Number of successful uploaded packs: {len(successful_packs)}\n' \ f'Uploaded packs:\n{successful_packs_table}\n\n' \ f'Browse to the build bucket with this address:\n{bucket_path}' add_pr_comment(pr_comment) def option_handler(): """Validates and parses script arguments. Returns: Namespace: Parsed arguments object. """ parser = argparse.ArgumentParser(description="Store packs in cloud storage.") # disable-secrets-detection-start parser.add_argument('-a', '--artifacts_path', help="The full path of packs artifacts", required=True) parser.add_argument('-e', '--extract_path', help="Full path of folder to extract wanted packs", required=True) parser.add_argument('-b', '--bucket_name', help="Storage bucket name", required=True) parser.add_argument('-s', '--service_account', help=("Path to gcloud service account, is for circleCI usage. " "For local development use your personal account and " "authenticate using Google Cloud SDK by running: " "`gcloud auth application-default login` and leave this parameter blank. " "For more information go to: " "https://googleapis.dev/python/google-api-core/latest/auth.html"), required=False) parser.add_argument('-i', '--id_set_path', help="The full path of id_set.json", required=False) parser.add_argument('-d', '--pack_dependencies', help="Full path to pack dependencies json file.", required=False) parser.add_argument('-p', '--pack_names', help=("Target packs to upload to gcs. Optional values are: `All`, " "`Modified` or csv list of packs " "Default is set to `All`"), required=False, default="All") parser.add_argument('-n', '--ci_build_number', help="CircleCi build number (will be used as hash revision at index file)", required=False) parser.add_argument('-o', '--override_all_packs', help="Override all existing packs in cloud storage", type=str2bool, default=False, required=True) parser.add_argument('-k', '--key_string', help="Base64 encoded signature key used for signing packs.", required=False) parser.add_argument('-sb', '--storage_base_path', help="Storage base path of the directory to upload to.", required=False) parser.add_argument('-rt', '--remove_test_playbooks', type=str2bool, help='Should remove test playbooks from content packs or not.', default=True) parser.add_argument('-bu', '--bucket_upload', help='is bucket upload build?', type=str2bool, required=True) parser.add_argument('-pb', '--private_bucket_name', help="Private storage bucket name", required=False) parser.add_argument('-c', '--circle_branch', help="CircleCi branch of current build", required=True) parser.add_argument('-f', '--force_upload', help="is force upload build?", type=str2bool, required=True) # disable-secrets-detection-end return parser.parse_args() def add_pr_comment(comment: str): """Add comment to the pull request. Args: comment (string): The comment text. """ token = os.environ['CONTENT_GITHUB_TOKEN'] branch_name = os.environ['CIRCLE_BRANCH'] sha1 = os.environ['CIRCLE_SHA1'] query = f'?q={sha1}+repo:demisto/content+is:pr+is:open+head:{branch_name}+is:open' url = 'https://api.github.com/search/issues' headers = {'Authorization': 'Bearer ' + token} try: res = requests.get(url + query, headers=headers, verify=False) res = handle_github_response(res) if res and res.get('total_count', 0) == 1: issue_url = res['items'][0].get('comments_url') if res.get('items', []) else None if issue_url: res = requests.post(issue_url, json={'body': comment}, headers=headers, verify=False) handle_github_response(res) else: logging.warning( f'Add pull request comment failed: There is more then one open pull request for branch {branch_name}.') except Exception: logging.exception('Add pull request comment failed.') def handle_github_response(response: json) -> dict: """ Handles the response from the GitHub server after making a request. :param response: Response from the server. :return: The returned response. """ res_dict = response.json() if not res_dict.get('ok'): logging.warning(f'Add pull request comment failed: {res_dict.get("message")}') return res_dict def get_packs_summary(packs_list): """ Returns the packs list divided into 3 lists by their status Args: packs_list (list): The full packs list Returns: 3 lists of packs - successful_packs, skipped_packs & failed_packs """ successful_packs = [pack for pack in packs_list if pack.status == PackStatus.SUCCESS.name] skipped_packs = [pack for pack in packs_list if pack.status == PackStatus.PACK_ALREADY_EXISTS.name or pack.status == PackStatus.PACK_IS_NOT_UPDATED_IN_RUNNING_BUILD.name] failed_packs = [pack for pack in packs_list if pack not in successful_packs and pack not in skipped_packs] return successful_packs, skipped_packs, failed_packs def main(): install_logging('Prepare_Content_Packs_For_Testing.log') option = option_handler() packs_artifacts_path = option.artifacts_path extract_destination_path = option.extract_path storage_bucket_name = option.bucket_name service_account = option.service_account target_packs = option.pack_names if option.pack_names else "" build_number = option.ci_build_number if option.ci_build_number else str(uuid.uuid4()) override_all_packs = option.override_all_packs signature_key = option.key_string id_set_path = option.id_set_path packs_dependencies_mapping = load_json(option.pack_dependencies) if option.pack_dependencies else {} storage_base_path = option.storage_base_path remove_test_playbooks = option.remove_test_playbooks is_bucket_upload_flow = option.bucket_upload private_bucket_name = option.private_bucket_name circle_branch = option.circle_branch force_upload = option.force_upload # google cloud storage client initialized storage_client = init_storage_client(service_account) storage_bucket = storage_client.bucket(storage_bucket_name) if storage_base_path: GCPConfig.STORAGE_BASE_PATH = storage_base_path # download and extract index from public bucket index_folder_path, index_blob, index_generation = download_and_extract_index(storage_bucket, extract_destination_path) # content repo client initialized content_repo = get_content_git_client(CONTENT_ROOT_PATH) current_commit_hash, previous_commit_hash = get_recent_commits_data(content_repo, index_folder_path, is_bucket_upload_flow, circle_branch) # detect packs to upload pack_names = get_packs_names(target_packs, previous_commit_hash) extract_packs_artifacts(packs_artifacts_path, extract_destination_path) packs_list = [Pack(pack_name, os.path.join(extract_destination_path, pack_name)) for pack_name in pack_names if os.path.exists(os.path.join(extract_destination_path, pack_name))] if not option.override_all_packs: check_if_index_is_updated(index_folder_path, content_repo, current_commit_hash, previous_commit_hash, storage_bucket) # google cloud bigquery client initialized bq_client = init_bigquery_client(service_account) packs_statistic_df = get_packs_statistics_dataframe(bq_client) updated_private_packs_ids = [] if private_bucket_name: # Add private packs to the index private_storage_bucket = storage_client.bucket(private_bucket_name) private_packs, _, _, updated_private_packs_ids = update_index_with_priced_packs(private_storage_bucket, extract_destination_path, index_folder_path, pack_names) else: # skipping private packs logging.debug("Skipping index update of priced packs") private_packs = [] # clean index and gcs from non existing or invalid packs clean_non_existing_packs(index_folder_path, private_packs, storage_bucket) # starting iteration over packs for pack in packs_list: task_status, user_metadata = pack.load_user_metadata() if not task_status: pack.status = PackStatus.FAILED_LOADING_USER_METADATA.value pack.cleanup() continue task_status, pack_content_items = pack.collect_content_items() if not task_status: pack.status = PackStatus.FAILED_COLLECT_ITEMS.name pack.cleanup() continue task_status, integration_images = pack.upload_integration_images(storage_bucket) if not task_status: pack.status = PackStatus.FAILED_IMAGES_UPLOAD.name pack.cleanup() continue task_status, author_image = pack.upload_author_image(storage_bucket) if not task_status: pack.status = PackStatus.FAILED_AUTHOR_IMAGE_UPLOAD.name pack.cleanup() continue task_status, pack_was_modified = pack.detect_modified(content_repo, index_folder_path, current_commit_hash, previous_commit_hash) if not task_status: pack.status = PackStatus.FAILED_DETECTING_MODIFIED_FILES.name pack.cleanup() continue task_status = pack.format_metadata(user_metadata=user_metadata, pack_content_items=pack_content_items, integration_images=integration_images, author_image=author_image, index_folder_path=index_folder_path, packs_dependencies_mapping=packs_dependencies_mapping, build_number=build_number, commit_hash=current_commit_hash, packs_statistic_df=packs_statistic_df, pack_was_modified=pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_METADATA_PARSING.name pack.cleanup() continue task_status, not_updated_build = pack.prepare_release_notes(index_folder_path, build_number, pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_RELEASE_NOTES.name pack.cleanup() continue if not_updated_build: pack.status = PackStatus.PACK_IS_NOT_UPDATED_IN_RUNNING_BUILD.name pack.cleanup() continue task_status = pack.remove_unwanted_files(remove_test_playbooks) if not task_status: pack.status = PackStatus.FAILED_REMOVING_PACK_SKIPPED_FOLDERS pack.cleanup() continue task_status = pack.sign_pack(signature_key) if not task_status: pack.status = PackStatus.FAILED_SIGNING_PACKS.name pack.cleanup() continue task_status, zip_pack_path = pack.zip_pack() if not task_status: pack.status = PackStatus.FAILED_ZIPPING_PACK_ARTIFACTS.name pack.cleanup() continue (task_status, skipped_pack_uploading, full_pack_path) = \ pack.upload_to_storage(zip_pack_path, pack.latest_version, storage_bucket, override_all_packs or pack_was_modified) if not task_status: pack.status = PackStatus.FAILED_UPLOADING_PACK.name pack.cleanup() continue task_status, exists_in_index = pack.check_if_exists_in_index(index_folder_path) if not task_status: pack.status = PackStatus.FAILED_SEARCHING_PACK_IN_INDEX.name pack.cleanup() continue task_status = pack.prepare_for_index_upload() if not task_status: pack.status = PackStatus.FAILED_PREPARING_INDEX_FOLDER.name pack.cleanup() continue task_status = update_index_folder(index_folder_path=index_folder_path, pack_name=pack.name, pack_path=pack.path, pack_version=pack.latest_version, hidden_pack=pack.hidden) if not task_status: pack.status = PackStatus.FAILED_UPDATING_INDEX_FOLDER.name pack.cleanup() continue # in case that pack already exist at cloud storage path and in index, don't show that the pack was changed if skipped_pack_uploading and exists_in_index: pack.status = PackStatus.PACK_ALREADY_EXISTS.name pack.cleanup() continue pack.status = PackStatus.SUCCESS.name # upload core packs json to bucket upload_core_packs_config(storage_bucket, build_number, index_folder_path) # finished iteration over content packs upload_index_to_storage(index_folder_path=index_folder_path, extract_destination_path=extract_destination_path, index_blob=index_blob, build_number=build_number, private_packs=private_packs, current_commit_hash=current_commit_hash, index_generation=index_generation, force_upload=force_upload, previous_commit_hash=previous_commit_hash) # upload id_set.json to bucket upload_id_set(storage_bucket, id_set_path) # get the lists of packs divided by their status successful_packs, skipped_packs, failed_packs = get_packs_summary(packs_list) # Store successful and failed packs list in CircleCI artifacts - to be used in Upload Packs To Marketplace job packs_results_file_path = os.path.join(os.path.dirname(packs_artifacts_path), BucketUploadFlow.PACKS_RESULTS_FILE) store_successful_and_failed_packs_in_ci_artifacts( packs_results_file_path, BucketUploadFlow.PREPARE_CONTENT_FOR_TESTING, successful_packs, failed_packs, updated_private_packs_ids ) # summary of packs status print_packs_summary(successful_packs, skipped_packs, failed_packs, not is_bucket_upload_flow) if __name__ == '__main__': main()

import shutil from typing import Dict from covid_shared import workflow import covid_model_seiir_pipeline from covid_model_seiir_pipeline.pipeline.parameter_fit.specification import FIT_JOBS, FitScenario class BetaFitTaskTemplate(workflow.TaskTemplate): tool = workflow.get_jobmon_tool(covid_model_seiir_pipeline) task_name_template = f"{FIT_JOBS.fit}_{{scenario}}_{{draw_id}}" command_template = ( f"{shutil.which("stask")} " f"{FIT_JOBS.fit} " "--fit-version {fit_version} " "--scenario {scenario} " "--draw-id {draw_id} " "-vv" ) node_args = ['scenario', 'draw_id'] task_args = ['fit_version'] class FitWorkflow(workflow.WorkflowTemplate): tool = workflow.get_jobmon_tool(covid_model_seiir_pipeline) workflow_name_template = 'seiir-oos-fit-{version}' task_template_classes = { FIT_JOBS.fit: BetaFitTaskTemplate, } def attach_tasks(self, n_draws: int, scenarios: Dict[str, FitScenario]): fit_template = self.task_templates[FIT_JOBS.fit] for scenario_name, scenario_spec in scenarios.items(): for draw in range(n_draws): fit_task = fit_template.get_task( fit_version=self.version, draw_id=draw, scenario=scenario_name, ) self.workflow.add_task(fit_task)

import shutil from typing import Dict from covid_shared import workflow import covid_model_seiir_pipeline from covid_model_seiir_pipeline.pipeline.parameter_fit.specification import FIT_JOBS, FitScenario class BetaFitTaskTemplate(workflow.TaskTemplate): tool = workflow.get_jobmon_tool(covid_model_seiir_pipeline) task_name_template = f"{FIT_JOBS.fit}_{{scenario}}_{{draw_id}}" command_template = ( f"{shutil.which('stask')} " f"{FIT_JOBS.fit} " "--fit-version {fit_version} " "--scenario {scenario} " "--draw-id {draw_id} " "-vv" ) node_args = ['scenario', 'draw_id'] task_args = ['fit_version'] class FitWorkflow(workflow.WorkflowTemplate): tool = workflow.get_jobmon_tool(covid_model_seiir_pipeline) workflow_name_template = 'seiir-oos-fit-{version}' task_template_classes = { FIT_JOBS.fit: BetaFitTaskTemplate, } def attach_tasks(self, n_draws: int, scenarios: Dict[str, FitScenario]): fit_template = self.task_templates[FIT_JOBS.fit] for scenario_name, scenario_spec in scenarios.items(): for draw in range(n_draws): fit_task = fit_template.get_task( fit_version=self.version, draw_id=draw, scenario=scenario_name, ) self.workflow.add_task(fit_task)

# ___________________________________________________________________________ # # Prescient # Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # This software is distributed under the Revised BSD License. # ___________________________________________________________________________ from __future__ import annotations import os import math import logging import datetime import dateutil from pyomo.environ import value, Suffix from egret.common.log import logger as egret_logger from egret.data.model_data import ModelData from egret.parsers.prescient_dat_parser import get_uc_model, create_model_data_dict_params from egret.models.unit_commitment import _time_series_dict, _preallocated_list, _solve_unit_commitment, \ _save_uc_results, create_tight_unit_commitment_model, \ _get_uc_model from prescient.util import DEFAULT_MAX_LABEL_LENGTH from prescient.util.math_utils import round_small_values from prescient.simulator.data_manager import RucMarket from ..modeling_engine import ForecastErrorMethod from ..forecast_helper import get_forecastables from . import reporting from typing import TYPE_CHECKING if TYPE_CHECKING: from prescient.data.data_provider import DataProvider from prescient.data.simulation_state import SimulationState from typing import Optional from egret.data.model_data import ModelData as EgretModel uc_abstract_data_model = get_uc_model() ######################################################################################## # a utility to find the "nearest" - quantified via Euclidean distance - scenario among # # a candidate set relative to the input scenario, up through and including the # # specified simulation hour. # ######################################################################################## def call_solver(solver,instance,options,solver_options,relaxed=False, set_instance=True): tee = options.output_solver_logs if not tee: egret_logger.setLevel(logging.WARNING) symbolic_solver_labels = options.symbolic_solver_labels mipgap = options.ruc_mipgap solver_options_dict = dict() for s in solver_options: opts = s.split(' ') for opt in opts: option, val = opt.split('=') try: val = float(val) except: pass solver_options_dict[option] = val m, results, solver = _solve_unit_commitment(instance, solver, mipgap, None, tee, symbolic_solver_labels, solver_options_dict, None, relaxed, set_instance=set_instance) md = _save_uc_results(m, relaxed) if hasattr(results, 'egret_metasolver_status'): time = results.egret_metasolver_status['time'] else: time = results.solver.wallclock_time return md, time, solver def _zero_out_costs(sced_model, hours_in_objective): ''' zero out certain costs in a sced model ''' # establish the objective function for the hour to simulate - which is simply to # minimize production costs during this time period. no fixed costs to worry about. # however, we do need to penalize load shedding across all time periods - otherwise, # very bad things happen. m = sced_model hours_in = set() for idx,t in enumerate(m.TimePeriods): if idx < hours_in_objective: hours_in.add(t) else: break hours_out = set(m.TimePeriods) - hours_in for t in hours_out: for g in m.SingleFuelGenerators: m.ProductionCostConstr[g,t].deactivate() m.ProductionCost[g,t].value = 0. m.ProductionCost[g,t].fix() for g in m.DualFuelGenerators: m.DualFuelProductionCost[g,t].expr = 0. if m.regulation_service: for g in m.AGC_Generators: m.RegulationCostGeneration[g,t].expr = 0. if m.spinning_reserve: for g in m.ThermalGenerators: m.SpinningReserveCostGeneration[g,t].expr = 0. if m.non_spinning_reserve: for g in m.ThermalGenerators: m.NonSpinningReserveCostGeneration[g,t].expr = 0. if m.supplemental_reserve: for g in m.ThermalGenerators: m.SupplementalReserveCostGeneration[g,t].expr = 0. return # TBD - we probably want to grab topology from somewhere, even if the stochastic # RUC is not solved with the topology. def create_sced_instance(data_provider:DataProvider, current_state:SimulationState, options, sced_horizon, forecast_error_method = ForecastErrorMethod.PRESCIENT ): ''' Create a deterministic economic dispatch instance, given current forecasts and commitments. ''' assert current_state is not None sced_md = data_provider.get_initial_model(options, sced_horizon, current_state.minutes_per_step) # Set initial state _copy_initial_state_into_model(options, current_state, sced_md) ################################################################################ # initialize the demand and renewables data, based on the forecast error model # ################################################################################ if forecast_error_method is ForecastErrorMethod.PRESCIENT: # Warning: This method can see into the future! future_actuals = current_state.get_future_actuals() sced_forecastables, = get_forecastables(sced_md) for future,sced_data in zip(future_actuals, sced_actuals): for t in range(sced_horizon): sced_data[t] = future[t] else: # persistent forecast error: current_actuals = current_state.get_current_actuals() forecasts = current_state.get_forecasts() sced_forecastables = get_forecastables(sced_md) # Go through each time series that can be forecasted for current_actual, forecast, (sced_data,) in zip(current_actuals, forecasts, sced_forecastables): # the first value is, by definition, the actual. sced_data[0] = current_actual # Find how much the first forecast was off from the actual, as a fraction of # the forecast. For all subsequent times, adjust the forecast by the same fraction. current_forecast = forecast[0] if current_forecast == 0.0: forecast_error_ratio = 0.0 else: forecast_error_ratio = current_actual / forecast[0] for t in range(1, sced_horizon): sced_data[t] = forecast[t] * forecast_error_ratio _ensure_reserve_factor_honored(options, sced_md, range(sced_horizon)) # Set generator commitments & future state for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): # Start by preparing an empty array of the correct size for each generator fixed_commitment = [None]*sced_horizon g_dict['fixed_commitment'] = _time_series_dict(fixed_commitment) # Now fill it in with data for t in range(sced_horizon): fixed_commitment[t] = current_state.get_generator_commitment(g,t) # Look as far into the future as we can for future startups / shutdowns last_commitment = fixed_commitment[-1] for t in range(sced_horizon, current_state.timestep_count): this_commitment = current_state.get_generator_commitment(g,t) if (this_commitment - last_commitment) > 0.5: # future startup future_status_time_steps = ( t - sced_horizon + 1 ) break elif (last_commitment - this_commitment) > 0.5: # future shutdown future_status_time_steps = -( t - sced_horizon + 1 ) break else: # no break future_status_time_steps = 0 g_dict['future_status'] = (current_state.minutes_per_step/60.) * future_status_time_steps if not options.no_startup_shutdown_curves: minutes_per_step = current_state.minutes_per_step for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): if 'startup_curve' in g_dict: continue ramp_up_rate_sced = g_dict['ramp_up_60min'] * minutes_per_step/60. if 'startup_capacity' not in g_dict: sced_startup_capacity = _calculate_sced_startup_shutdown_capacity_from_none( g_dict['p_min'], ramp_up_rate_sced) else: sced_startup_capacity = _calculate_sced_startup_shutdown_capacity_from_existing( g_dict['startup_capacity'], g_dict['p_min'], minutes_per_step) g_dict['startup_curve'] = [ sced_startup_capacity - i*ramp_up_rate_sced \ for i in range(1,int(math.ceil(sced_startup_capacity/ramp_up_rate_sced))) ] for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): if 'shutdown_curve' in g_dict: continue ramp_down_rate_sced = g_dict['ramp_down_60min'] * minutes_per_step/60. # compute a new shutdown curve if we go from "on" to "off" if g_dict['initial_status'] > 0 and g_dict['fixed_commitment']['values'][0] == 0: power_t0 = g_dict['initial_p_output'] # if we end up using a historical curve, it's important # for the time-horizons to match, particularly since this # function is also used to create long-horizon look-ahead # SCEDs for the unit commitment process create_sced_instance.shutdown_curves[g, minutes_per_step] = \ [ power_t0 - i*ramp_down_rate_sced for i in range(1,int(math.ceil(power_t0/ramp_down_rate_sced))) ] if (g,minutes_per_step) in create_sced_instance.shutdown_curves: g_dict['shutdown_curve'] = create_sced_instance.shutdown_curves[g,minutes_per_step] else: if 'shutdown_capacity' not in g_dict: sced_shutdown_capacity = _calculate_sced_startup_shutdown_capacity_from_none( g_dict['p_min'], ramp_down_rate_sced) else: sced_shutdown_capacity = _calculate_sced_startup_shutdown_capacity_from_existing( g_dict['shutdown_capacity'], g_dict['p_min'], minutes_per_step) g_dict['shutdown_curve'] = [ sced_shutdown_capacity - i*ramp_down_rate_sced \ for i in range(1,int(math.ceil(sced_shutdown_capacity/ramp_down_rate_sced))) ] if not options.enforce_sced_shutdown_ramprate: for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): # make sure the generator can immediately turn off g_dict['shutdown_capacity'] = max(g_dict['shutdown_capacity'], (60./current_state.minutes_per_step)*g_dict['initial_p_output'] + 1.) return sced_md # cache for shutdown curves create_sced_instance.shutdown_curves = dict() def _calculate_sced_startup_shutdown_capacity_from_none(p_min, ramp_rate_sced): if isinstance(p_min, dict): sced_susd_capacity = [pm+ramp_rate_sced/2. for pm in p_min['values']] return sum(sced_susd_capacity)/len(sced_susd_capacity) else: return p_min + ramp_rate_sced/2. def _calculate_sced_startup_shutdown_capacity_from_existing(startup_shutdown, p_min, minutes_per_step): susd_capacity_time_varying = isinstance(startup_shutdown, dict) p_min_time_varying = isinstance(p_min, dict) if p_min_time_varying and susd_capacity_time_varying: sced_susd_capacity = [ (susd - pm)*(minutes_per_step/60.) + pm \ for pm, susd in zip(p_min['values'], startup_shutdown['values']) ] return sum(sced_susd_capacity)/len(sced_susd_capacity) elif p_min_time_varying: sced_susd_capacity = [ (startup_shutdown - pm)*(minutes_per_step/60.) + pm \ for pm in p_min['values'] ] return sum(sced_susd_capacity)/len(sced_susd_capacity) elif susd_capacity_time_varying: sced_susd_capacity = [ (susd - p_min)*(minutes_per_step/60.) + p_min \ for susd in startup_shutdown['values'] ] return sum(sced_susd_capacity)/len(sced_susd_capacity) else: return (startup_shutdown - p_min)*(minutes_per_step/60.) + p_min ##### BEGIN Deterministic RUC solvers and helper functions ######## ################################################################### # utility functions for computing various aspects # # of a deterministic unit commitment solution. # ################################################################### ## NOTE: in closure for deterministic_ruc_solver_plugin def create_solve_deterministic_ruc(deterministic_ruc_solver): def solve_deterministic_ruc(solver, options, ruc_instance_for_this_period, this_date, this_hour, ptdf_manager): ruc_instance_for_this_period = deterministic_ruc_solver(ruc_instance_for_this_period, solver, options, ptdf_manager) if options.write_deterministic_ruc_instances: current_ruc_filename = options.output_directory + os.sep + str(this_date) + \ os.sep + "ruc_hour_" + str(this_hour) + ".json" ruc_instance_for_this_period.write(current_ruc_filename) print("RUC instance written to file=" + current_ruc_filename) total_cost = ruc_instance_for_this_period.data['system']['total_cost'] print("") print("Deterministic RUC Cost: {0:.2f}".format(total_cost)) if options.output_ruc_solutions: print("") reporting.output_solution_for_deterministic_ruc( ruc_instance_for_this_period, options.ruc_every_hours) print("") reporting.report_fixed_costs_for_deterministic_ruc(ruc_instance_for_this_period) reporting.report_generation_costs_for_deterministic_ruc(ruc_instance_for_this_period) print("") reporting.report_load_generation_mismatch_for_deterministic_ruc(ruc_instance_for_this_period) print("") reporting.report_curtailment_for_deterministic_ruc(ruc_instance_for_this_period) return ruc_instance_for_this_period return solve_deterministic_ruc def _solve_deterministic_ruc(deterministic_ruc_instance, solver, options, ptdf_manager): ptdf_manager.mark_active(deterministic_ruc_instance) pyo_model = create_tight_unit_commitment_model(deterministic_ruc_instance, ptdf_options=ptdf_manager.ruc_ptdf_options, PTDF_matrix_dict=ptdf_manager.PTDF_matrix_dict) # update in case lines were taken out ptdf_manager.PTDF_matrix_dict = pyo_model._PTDFs try: ruc_results, pyo_results, _ = call_solver(solver, pyo_model, options, options.deterministic_ruc_solver_options) except: print("Failed to solve deterministic RUC instance - likely because no feasible solution exists!") output_filename = "bad_ruc.json" deterministic_ruc_instance.write(output_filename) print("Wrote failed RUC model to file=" + output_filename) raise ptdf_manager.update_active(ruc_results) return ruc_results ## create this function with default solver solve_deterministic_ruc = create_solve_deterministic_ruc(_solve_deterministic_ruc) # utilities for creating a deterministic RUC instance, and a standard way to solve them. def create_deterministic_ruc(options, data_provider:DataProvider, this_date, this_hour, current_state:SimulationState, ruc_horizon, use_next_day_in_ruc): ruc_every_hours = options.ruc_every_hours start_day = this_date start_time = datetime.datetime.combine(start_day, datetime.time(hour=this_hour)) # Create a new model md = data_provider.get_initial_model(options, ruc_horizon, 60) # Populate the T0 data if current_state is None or current_state.timestep_count == 0: data_provider.populate_initial_state_data(options, start_day, md) else: _copy_initial_state_into_model(options, current_state, md) # Populate forecasts copy_first_day = (not use_next_day_in_ruc) and (this_hour != 0) forecast_request_count = 24 if copy_first_day else ruc_horizon data_provider.populate_with_forecast_data(options, start_time, forecast_request_count, 60, md) # Make some near-term forecasts more accurate ruc_delay = -(options.ruc_execution_hour%(-options.ruc_every_hours)) if options.ruc_prescience_hour > ruc_delay + 1: improved_hour_count = options.ruc_prescience_hour - ruc_delay - 1 for forecast, actuals in zip(get_forecastables(md), current_state.get_future_actuals()): for t in range(0, improved_hour_count): forecast_portion = (ruc_delay+t)/options.ruc_prescience_hour actuals_portion = 1-forecast_portion forecast[t] = forecast_portion*forecast[t] + actuals_portion*actuals[t] # Ensure the reserve requirement is satisfied _ensure_reserve_factor_honored(options, md, range(forecast_request_count)) # Copy from first 24 to second 24, if necessary if copy_first_day: for vals, in get_forecastables(md): for t in range(24, ruc_horizon): vals[t] = vals[t-24] return md ###### END Deterministic RUC solvers and helper functions ######### def create_pricing_model(model_data, network_constraints='ptdf_power_flow', relaxed=True, **kwargs): ''' Create a model appropriate for pricing ''' formulation_list = [ 'garver_3bin_vars', 'garver_power_vars', 'MLR_reserve_vars', 'pan_guan_gentile_KOW_generation_limits', 'damcikurt_ramping', 'KOW_production_costs_tightened', 'rajan_takriti_UT_DT', 'KOW_startup_costs', network_constraints, ] return _get_uc_model(model_data, formulation_list, relaxed, **kwargs) def _get_fixed_if_off(cur_commit, cur_fixed): cur_commit = cur_commit['values'] if cur_fixed is None: cur_fixed = [None for _ in cur_commit] elif isinstance(cur_fixed, dict): cur_fixed = cur_fixed['values'] else: cur_fixed = [cur_fixed for _ in cur_commit] new_fixed = [None]*len(cur_commit) for idx, (fixed, committed) in enumerate(zip(cur_fixed, cur_commit)): if fixed is None: new_fixed[idx] = None if committed == 1 else 0 else: new_fixed[idx] = fixed return {'data_type':'time_series', 'values':new_fixed} def solve_deterministic_day_ahead_pricing_problem(solver, ruc_results, options, ptdf_manager): ## create a copy because we want to maintain the solution data ## in ruc_results pricing_type = options.day_ahead_pricing print("Computing day-ahead prices using method "+pricing_type+".") pricing_instance = ruc_results.clone() if pricing_type == "LMP": for g, g_dict in pricing_instance.elements(element_type='generator', generator_type='thermal'): g_dict['fixed_commitment'] = g_dict['commitment'] if 'reg_provider' in g_dict: g_dict['fixed_regulation'] = g_dict['reg_provider'] ## TODO: add fixings for storage; need hooks in EGRET elif pricing_type == "ELMP": ## for ELMP we fix all commitment binaries that were 0 in the RUC solve time_periods = pricing_instance.data['system']['time_keys'] for g, g_dict in pricing_instance.elements(element_type='generator', generator_type='thermal'): g_dict['fixed_commitment'] = _get_fixed_if_off(g_dict['commitment'], g_dict.get('fixed_commitment', None)) if 'reg_provider' in g_dict: g_dict['fixed_regulation'] = _get_fixed_if_off(g_dict['reg_provider'], g_dict.get('fixed_regulation', None)) ## TODO: add fixings for storage; need hooks in EGRET elif pricing_type == "aCHP": # don't do anything pass else: raise RuntimeError("Unknown pricing type "+pricing_type+".") ## change the penalty prices to the caps, if necessary reserve_requirement = ('reserve_requirement' in pricing_instance.data['system']) # In case of demand shortfall, the price skyrockets, so we threshold the value. if pricing_instance.data['system']['load_mismatch_cost'] > options.price_threshold: pricing_instance.data['system']['load_mismatch_cost'] = options.price_threshold # In case of reserve shortfall, the price skyrockets, so we threshold the value. if reserve_requirement: if pricing_instance.data['system']['reserve_shortfall_cost'] > options.reserve_price_threshold: pricing_instance.data['system']['reserve_shortfall_cost'] = options.reserve_price_threshold ptdf_manager.mark_active(pricing_instance) pyo_model = create_pricing_model(pricing_instance, relaxed=True, ptdf_options=ptdf_manager.damarket_ptdf_options, PTDF_matrix_dict=ptdf_manager.PTDF_matrix_dict) pyo_model.dual = Suffix(direction=Suffix.IMPORT) try: ## TODO: Should there be separate options for this run? pricing_results, _, _ = call_solver(solver, pyo_model, options, options.deterministic_ruc_solver_options, relaxed=True) except: print("Failed to solve pricing instance - likely because no feasible solution exists!") output_filename = "bad_pricing.json" pricing_instance.write(output_filename) print("Wrote failed RUC model to file=" + output_filename) raise ptdf_manager.update_active(pricing_results) ## Debugging if pricing_results.data['system']['total_cost'] > ruc_results.data['system']['total_cost']*(1.+1.e-06): print("The pricing run had a higher objective value than the MIP run. This is indicative of a bug.") print("Writing LP pricing_problem.json") output_filename = 'pricing_instance.json' pricing_results.write(output_filename) output_filename = 'ruc_results.json' ruc_results.write(output_filename) raise RuntimeError("Halting due to bug in pricing.") day_ahead_prices = {} for b, b_dict in pricing_results.elements(element_type='bus'): for t,lmp in enumerate(b_dict['lmp']['values']): day_ahead_prices[b,t] = lmp if reserve_requirement: day_ahead_reserve_prices = {} for t,price in enumerate(pricing_results.data['system']['reserve_price']['values']): # Thresholding the value of the reserve price to the passed in option day_ahead_reserve_prices[t] = price print("Recalculating RUC reserve procurement") ## scale the provided reserves by the amount over we are thermal_reserve_cleared_DA = {} g_reserve_values = { g : g_dict['rg']['values'] for g, g_dict in ruc_results.elements(element_type='generator', generator_type='thermal') } reserve_shortfall = ruc_results.data['system']['reserve_shortfall']['values'] reserve_requirement = ruc_results.data['system']['reserve_requirement']['values'] for t in range(0,options.ruc_every_hours): reserve_provided_t = sum(reserve_vals[t] for reserve_vals in g_reserve_values.values()) reserve_shortfall_t = reserve_shortfall[t] reserve_requirement_t = reserve_requirement[t] surplus_reserves_t = reserve_provided_t + reserve_shortfall_t - reserve_requirement_t ## if there's a shortfall, grab the full amount from the RUC solve ## or if there's no provided reserves this can safely be set to 1. if round_small_values(reserve_shortfall_t) > 0 or reserve_provided_t == 0: surplus_multiple_t = 1. else: ## scale the reserves from the RUC down by the same fraction ## so that they exactly meed the needed reserves surplus_multiple_t = reserve_requirement_t/reserve_provided_t for g, reserve_vals in g_reserve_values.items(): thermal_reserve_cleared_DA[g,t] = reserve_vals[t]*surplus_multiple_t else: day_ahead_reserve_prices = { t : 0. for t in enumerate(ruc_results.data['system']['time_keys']) } thermal_reserve_cleared_DA = { (g,t) : 0. \ for t in enumerate(ruc_results.data['system']['time_keys']) \ for g,_ in ruc_results.elements(element_type='generator', generator_type='thermal') } thermal_gen_cleared_DA = {} renewable_gen_cleared_DA = {} for g, g_dict in ruc_results.elements(element_type='generator'): pg = g_dict['pg']['values'] if g_dict['generator_type'] == 'thermal': store_dict = thermal_gen_cleared_DA elif g_dict['generator_type'] == 'renewable': store_dict = renewable_gen_cleared_DA else: raise RuntimeError(f"Unrecognized generator type {g_dict["generator_type"]}") for t in range(0,options.ruc_every_hours): store_dict[g,t] = pg[t] return RucMarket(day_ahead_prices=day_ahead_prices, day_ahead_reserve_prices=day_ahead_reserve_prices, thermal_gen_cleared_DA=thermal_gen_cleared_DA, thermal_reserve_cleared_DA=thermal_reserve_cleared_DA, renewable_gen_cleared_DA=renewable_gen_cleared_DA) def create_simulation_actuals( options:Options, data_provider:DataProvider, this_date:datetime.date, this_hour:int, step_size_minutes:int) -> EgretModel: ''' Get an Egret model consisting of data to be treated as actuals, starting at a given time. Parameters ---------- options:Options Global option values data_provider: DataProvider An object that can provide actual and/or forecast data for the requested days this_date: date The date of the first time step for which data should be retrieved this_hour: int 0-based index of the first hour of the day for which data should be retrieved step_size_minutes: int The number of minutes between each time step ''' # Convert time string to time start_time = datetime.datetime.combine(this_date, datetime.time(hour=this_hour)) # Pick whether we're getting actuals or forecasts if options.simulate_out_of_sample: get_data_func = data_provider.populate_with_actuals else: print("") print("***WARNING: Simulating the forecast scenario when running deterministic RUC - " "time consistency across midnight boundaries is not guaranteed, and may lead to threshold events.") get_data_func = data_provider.populate_with_forecast_data # Get a new model total_step_count = options.ruc_horizon * 60 // step_size_minutes md = data_provider.get_initial_model(options, total_step_count, step_size_minutes) # Fill it in with data data_provider.populate_initial_state_data(options, start_time.date(), md) if this_hour == 0: get_data_func(options, start_time, total_step_count, step_size_minutes, md) else: # only get up to 24 hours of data, then copy it timesteps_per_day = 24 * 60 / step_size_minutes steps_to_request = math.min(timesteps_per_day, total_step_count) get_data_func(options, start_time, steps_to_request, step_size_minutes, md) for vals, in get_forecastables(md): for t in range(timesteps_per_day, total_step_count): vals[t] = vals[t-timesteps_per_day] return md def _ensure_reserve_factor_honored(options:Options, md:EgretModel, time_periods:Iterable[int]) -> None: ''' Adjust reserve requirements to satisfy the reserve factor. For each time period in time_periods, ensure that the reserve requirement is no less than the total load for that time period multiplied by the reserve factor. If the reserve requirement for a time is too low it is raised, otherwise it is left alone. ''' if options.reserve_factor > 0: reserve_factor = options.reserve_factor reserve_reqs = md.data['system']['reserve_requirement']['values'] for t in time_periods: total_load = sum(bdata['p_load']['values'][t] for bus, bdata in md.elements('load')) min_reserve = reserve_factor*total_load if reserve_reqs[t] < min_reserve: reserve_reqs[t] = min_reserve def _copy_initial_state_into_model(options:Options, current_state:SimulationState, md:EgretModel): for g, g_dict in md.elements('generator', generator_type='thermal'): g_dict['initial_status'] = current_state.get_initial_generator_state(g) g_dict['initial_p_output'] = current_state.get_initial_power_generated(g) for s,s_dict in md.elements('storage'): s_dict['initial_state_of_charge'] = current_state.get_initial_state_of_charge(s)

# ___________________________________________________________________________ # # Prescient # Copyright 2020 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # This software is distributed under the Revised BSD License. # ___________________________________________________________________________ from __future__ import annotations import os import math import logging import datetime import dateutil from pyomo.environ import value, Suffix from egret.common.log import logger as egret_logger from egret.data.model_data import ModelData from egret.parsers.prescient_dat_parser import get_uc_model, create_model_data_dict_params from egret.models.unit_commitment import _time_series_dict, _preallocated_list, _solve_unit_commitment, \ _save_uc_results, create_tight_unit_commitment_model, \ _get_uc_model from prescient.util import DEFAULT_MAX_LABEL_LENGTH from prescient.util.math_utils import round_small_values from prescient.simulator.data_manager import RucMarket from ..modeling_engine import ForecastErrorMethod from ..forecast_helper import get_forecastables from . import reporting from typing import TYPE_CHECKING if TYPE_CHECKING: from prescient.data.data_provider import DataProvider from prescient.data.simulation_state import SimulationState from typing import Optional from egret.data.model_data import ModelData as EgretModel uc_abstract_data_model = get_uc_model() ######################################################################################## # a utility to find the "nearest" - quantified via Euclidean distance - scenario among # # a candidate set relative to the input scenario, up through and including the # # specified simulation hour. # ######################################################################################## def call_solver(solver,instance,options,solver_options,relaxed=False, set_instance=True): tee = options.output_solver_logs if not tee: egret_logger.setLevel(logging.WARNING) symbolic_solver_labels = options.symbolic_solver_labels mipgap = options.ruc_mipgap solver_options_dict = dict() for s in solver_options: opts = s.split(' ') for opt in opts: option, val = opt.split('=') try: val = float(val) except: pass solver_options_dict[option] = val m, results, solver = _solve_unit_commitment(instance, solver, mipgap, None, tee, symbolic_solver_labels, solver_options_dict, None, relaxed, set_instance=set_instance) md = _save_uc_results(m, relaxed) if hasattr(results, 'egret_metasolver_status'): time = results.egret_metasolver_status['time'] else: time = results.solver.wallclock_time return md, time, solver def _zero_out_costs(sced_model, hours_in_objective): ''' zero out certain costs in a sced model ''' # establish the objective function for the hour to simulate - which is simply to # minimize production costs during this time period. no fixed costs to worry about. # however, we do need to penalize load shedding across all time periods - otherwise, # very bad things happen. m = sced_model hours_in = set() for idx,t in enumerate(m.TimePeriods): if idx < hours_in_objective: hours_in.add(t) else: break hours_out = set(m.TimePeriods) - hours_in for t in hours_out: for g in m.SingleFuelGenerators: m.ProductionCostConstr[g,t].deactivate() m.ProductionCost[g,t].value = 0. m.ProductionCost[g,t].fix() for g in m.DualFuelGenerators: m.DualFuelProductionCost[g,t].expr = 0. if m.regulation_service: for g in m.AGC_Generators: m.RegulationCostGeneration[g,t].expr = 0. if m.spinning_reserve: for g in m.ThermalGenerators: m.SpinningReserveCostGeneration[g,t].expr = 0. if m.non_spinning_reserve: for g in m.ThermalGenerators: m.NonSpinningReserveCostGeneration[g,t].expr = 0. if m.supplemental_reserve: for g in m.ThermalGenerators: m.SupplementalReserveCostGeneration[g,t].expr = 0. return # TBD - we probably want to grab topology from somewhere, even if the stochastic # RUC is not solved with the topology. def create_sced_instance(data_provider:DataProvider, current_state:SimulationState, options, sced_horizon, forecast_error_method = ForecastErrorMethod.PRESCIENT ): ''' Create a deterministic economic dispatch instance, given current forecasts and commitments. ''' assert current_state is not None sced_md = data_provider.get_initial_model(options, sced_horizon, current_state.minutes_per_step) # Set initial state _copy_initial_state_into_model(options, current_state, sced_md) ################################################################################ # initialize the demand and renewables data, based on the forecast error model # ################################################################################ if forecast_error_method is ForecastErrorMethod.PRESCIENT: # Warning: This method can see into the future! future_actuals = current_state.get_future_actuals() sced_forecastables, = get_forecastables(sced_md) for future,sced_data in zip(future_actuals, sced_actuals): for t in range(sced_horizon): sced_data[t] = future[t] else: # persistent forecast error: current_actuals = current_state.get_current_actuals() forecasts = current_state.get_forecasts() sced_forecastables = get_forecastables(sced_md) # Go through each time series that can be forecasted for current_actual, forecast, (sced_data,) in zip(current_actuals, forecasts, sced_forecastables): # the first value is, by definition, the actual. sced_data[0] = current_actual # Find how much the first forecast was off from the actual, as a fraction of # the forecast. For all subsequent times, adjust the forecast by the same fraction. current_forecast = forecast[0] if current_forecast == 0.0: forecast_error_ratio = 0.0 else: forecast_error_ratio = current_actual / forecast[0] for t in range(1, sced_horizon): sced_data[t] = forecast[t] * forecast_error_ratio _ensure_reserve_factor_honored(options, sced_md, range(sced_horizon)) # Set generator commitments & future state for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): # Start by preparing an empty array of the correct size for each generator fixed_commitment = [None]*sced_horizon g_dict['fixed_commitment'] = _time_series_dict(fixed_commitment) # Now fill it in with data for t in range(sced_horizon): fixed_commitment[t] = current_state.get_generator_commitment(g,t) # Look as far into the future as we can for future startups / shutdowns last_commitment = fixed_commitment[-1] for t in range(sced_horizon, current_state.timestep_count): this_commitment = current_state.get_generator_commitment(g,t) if (this_commitment - last_commitment) > 0.5: # future startup future_status_time_steps = ( t - sced_horizon + 1 ) break elif (last_commitment - this_commitment) > 0.5: # future shutdown future_status_time_steps = -( t - sced_horizon + 1 ) break else: # no break future_status_time_steps = 0 g_dict['future_status'] = (current_state.minutes_per_step/60.) * future_status_time_steps if not options.no_startup_shutdown_curves: minutes_per_step = current_state.minutes_per_step for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): if 'startup_curve' in g_dict: continue ramp_up_rate_sced = g_dict['ramp_up_60min'] * minutes_per_step/60. if 'startup_capacity' not in g_dict: sced_startup_capacity = _calculate_sced_startup_shutdown_capacity_from_none( g_dict['p_min'], ramp_up_rate_sced) else: sced_startup_capacity = _calculate_sced_startup_shutdown_capacity_from_existing( g_dict['startup_capacity'], g_dict['p_min'], minutes_per_step) g_dict['startup_curve'] = [ sced_startup_capacity - i*ramp_up_rate_sced \ for i in range(1,int(math.ceil(sced_startup_capacity/ramp_up_rate_sced))) ] for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): if 'shutdown_curve' in g_dict: continue ramp_down_rate_sced = g_dict['ramp_down_60min'] * minutes_per_step/60. # compute a new shutdown curve if we go from "on" to "off" if g_dict['initial_status'] > 0 and g_dict['fixed_commitment']['values'][0] == 0: power_t0 = g_dict['initial_p_output'] # if we end up using a historical curve, it's important # for the time-horizons to match, particularly since this # function is also used to create long-horizon look-ahead # SCEDs for the unit commitment process create_sced_instance.shutdown_curves[g, minutes_per_step] = \ [ power_t0 - i*ramp_down_rate_sced for i in range(1,int(math.ceil(power_t0/ramp_down_rate_sced))) ] if (g,minutes_per_step) in create_sced_instance.shutdown_curves: g_dict['shutdown_curve'] = create_sced_instance.shutdown_curves[g,minutes_per_step] else: if 'shutdown_capacity' not in g_dict: sced_shutdown_capacity = _calculate_sced_startup_shutdown_capacity_from_none( g_dict['p_min'], ramp_down_rate_sced) else: sced_shutdown_capacity = _calculate_sced_startup_shutdown_capacity_from_existing( g_dict['shutdown_capacity'], g_dict['p_min'], minutes_per_step) g_dict['shutdown_curve'] = [ sced_shutdown_capacity - i*ramp_down_rate_sced \ for i in range(1,int(math.ceil(sced_shutdown_capacity/ramp_down_rate_sced))) ] if not options.enforce_sced_shutdown_ramprate: for g, g_dict in sced_md.elements(element_type='generator', generator_type='thermal'): # make sure the generator can immediately turn off g_dict['shutdown_capacity'] = max(g_dict['shutdown_capacity'], (60./current_state.minutes_per_step)*g_dict['initial_p_output'] + 1.) return sced_md # cache for shutdown curves create_sced_instance.shutdown_curves = dict() def _calculate_sced_startup_shutdown_capacity_from_none(p_min, ramp_rate_sced): if isinstance(p_min, dict): sced_susd_capacity = [pm+ramp_rate_sced/2. for pm in p_min['values']] return sum(sced_susd_capacity)/len(sced_susd_capacity) else: return p_min + ramp_rate_sced/2. def _calculate_sced_startup_shutdown_capacity_from_existing(startup_shutdown, p_min, minutes_per_step): susd_capacity_time_varying = isinstance(startup_shutdown, dict) p_min_time_varying = isinstance(p_min, dict) if p_min_time_varying and susd_capacity_time_varying: sced_susd_capacity = [ (susd - pm)*(minutes_per_step/60.) + pm \ for pm, susd in zip(p_min['values'], startup_shutdown['values']) ] return sum(sced_susd_capacity)/len(sced_susd_capacity) elif p_min_time_varying: sced_susd_capacity = [ (startup_shutdown - pm)*(minutes_per_step/60.) + pm \ for pm in p_min['values'] ] return sum(sced_susd_capacity)/len(sced_susd_capacity) elif susd_capacity_time_varying: sced_susd_capacity = [ (susd - p_min)*(minutes_per_step/60.) + p_min \ for susd in startup_shutdown['values'] ] return sum(sced_susd_capacity)/len(sced_susd_capacity) else: return (startup_shutdown - p_min)*(minutes_per_step/60.) + p_min ##### BEGIN Deterministic RUC solvers and helper functions ######## ################################################################### # utility functions for computing various aspects # # of a deterministic unit commitment solution. # ################################################################### ## NOTE: in closure for deterministic_ruc_solver_plugin def create_solve_deterministic_ruc(deterministic_ruc_solver): def solve_deterministic_ruc(solver, options, ruc_instance_for_this_period, this_date, this_hour, ptdf_manager): ruc_instance_for_this_period = deterministic_ruc_solver(ruc_instance_for_this_period, solver, options, ptdf_manager) if options.write_deterministic_ruc_instances: current_ruc_filename = options.output_directory + os.sep + str(this_date) + \ os.sep + "ruc_hour_" + str(this_hour) + ".json" ruc_instance_for_this_period.write(current_ruc_filename) print("RUC instance written to file=" + current_ruc_filename) total_cost = ruc_instance_for_this_period.data['system']['total_cost'] print("") print("Deterministic RUC Cost: {0:.2f}".format(total_cost)) if options.output_ruc_solutions: print("") reporting.output_solution_for_deterministic_ruc( ruc_instance_for_this_period, options.ruc_every_hours) print("") reporting.report_fixed_costs_for_deterministic_ruc(ruc_instance_for_this_period) reporting.report_generation_costs_for_deterministic_ruc(ruc_instance_for_this_period) print("") reporting.report_load_generation_mismatch_for_deterministic_ruc(ruc_instance_for_this_period) print("") reporting.report_curtailment_for_deterministic_ruc(ruc_instance_for_this_period) return ruc_instance_for_this_period return solve_deterministic_ruc def _solve_deterministic_ruc(deterministic_ruc_instance, solver, options, ptdf_manager): ptdf_manager.mark_active(deterministic_ruc_instance) pyo_model = create_tight_unit_commitment_model(deterministic_ruc_instance, ptdf_options=ptdf_manager.ruc_ptdf_options, PTDF_matrix_dict=ptdf_manager.PTDF_matrix_dict) # update in case lines were taken out ptdf_manager.PTDF_matrix_dict = pyo_model._PTDFs try: ruc_results, pyo_results, _ = call_solver(solver, pyo_model, options, options.deterministic_ruc_solver_options) except: print("Failed to solve deterministic RUC instance - likely because no feasible solution exists!") output_filename = "bad_ruc.json" deterministic_ruc_instance.write(output_filename) print("Wrote failed RUC model to file=" + output_filename) raise ptdf_manager.update_active(ruc_results) return ruc_results ## create this function with default solver solve_deterministic_ruc = create_solve_deterministic_ruc(_solve_deterministic_ruc) # utilities for creating a deterministic RUC instance, and a standard way to solve them. def create_deterministic_ruc(options, data_provider:DataProvider, this_date, this_hour, current_state:SimulationState, ruc_horizon, use_next_day_in_ruc): ruc_every_hours = options.ruc_every_hours start_day = this_date start_time = datetime.datetime.combine(start_day, datetime.time(hour=this_hour)) # Create a new model md = data_provider.get_initial_model(options, ruc_horizon, 60) # Populate the T0 data if current_state is None or current_state.timestep_count == 0: data_provider.populate_initial_state_data(options, start_day, md) else: _copy_initial_state_into_model(options, current_state, md) # Populate forecasts copy_first_day = (not use_next_day_in_ruc) and (this_hour != 0) forecast_request_count = 24 if copy_first_day else ruc_horizon data_provider.populate_with_forecast_data(options, start_time, forecast_request_count, 60, md) # Make some near-term forecasts more accurate ruc_delay = -(options.ruc_execution_hour%(-options.ruc_every_hours)) if options.ruc_prescience_hour > ruc_delay + 1: improved_hour_count = options.ruc_prescience_hour - ruc_delay - 1 for forecast, actuals in zip(get_forecastables(md), current_state.get_future_actuals()): for t in range(0, improved_hour_count): forecast_portion = (ruc_delay+t)/options.ruc_prescience_hour actuals_portion = 1-forecast_portion forecast[t] = forecast_portion*forecast[t] + actuals_portion*actuals[t] # Ensure the reserve requirement is satisfied _ensure_reserve_factor_honored(options, md, range(forecast_request_count)) # Copy from first 24 to second 24, if necessary if copy_first_day: for vals, in get_forecastables(md): for t in range(24, ruc_horizon): vals[t] = vals[t-24] return md ###### END Deterministic RUC solvers and helper functions ######### def create_pricing_model(model_data, network_constraints='ptdf_power_flow', relaxed=True, **kwargs): ''' Create a model appropriate for pricing ''' formulation_list = [ 'garver_3bin_vars', 'garver_power_vars', 'MLR_reserve_vars', 'pan_guan_gentile_KOW_generation_limits', 'damcikurt_ramping', 'KOW_production_costs_tightened', 'rajan_takriti_UT_DT', 'KOW_startup_costs', network_constraints, ] return _get_uc_model(model_data, formulation_list, relaxed, **kwargs) def _get_fixed_if_off(cur_commit, cur_fixed): cur_commit = cur_commit['values'] if cur_fixed is None: cur_fixed = [None for _ in cur_commit] elif isinstance(cur_fixed, dict): cur_fixed = cur_fixed['values'] else: cur_fixed = [cur_fixed for _ in cur_commit] new_fixed = [None]*len(cur_commit) for idx, (fixed, committed) in enumerate(zip(cur_fixed, cur_commit)): if fixed is None: new_fixed[idx] = None if committed == 1 else 0 else: new_fixed[idx] = fixed return {'data_type':'time_series', 'values':new_fixed} def solve_deterministic_day_ahead_pricing_problem(solver, ruc_results, options, ptdf_manager): ## create a copy because we want to maintain the solution data ## in ruc_results pricing_type = options.day_ahead_pricing print("Computing day-ahead prices using method "+pricing_type+".") pricing_instance = ruc_results.clone() if pricing_type == "LMP": for g, g_dict in pricing_instance.elements(element_type='generator', generator_type='thermal'): g_dict['fixed_commitment'] = g_dict['commitment'] if 'reg_provider' in g_dict: g_dict['fixed_regulation'] = g_dict['reg_provider'] ## TODO: add fixings for storage; need hooks in EGRET elif pricing_type == "ELMP": ## for ELMP we fix all commitment binaries that were 0 in the RUC solve time_periods = pricing_instance.data['system']['time_keys'] for g, g_dict in pricing_instance.elements(element_type='generator', generator_type='thermal'): g_dict['fixed_commitment'] = _get_fixed_if_off(g_dict['commitment'], g_dict.get('fixed_commitment', None)) if 'reg_provider' in g_dict: g_dict['fixed_regulation'] = _get_fixed_if_off(g_dict['reg_provider'], g_dict.get('fixed_regulation', None)) ## TODO: add fixings for storage; need hooks in EGRET elif pricing_type == "aCHP": # don't do anything pass else: raise RuntimeError("Unknown pricing type "+pricing_type+".") ## change the penalty prices to the caps, if necessary reserve_requirement = ('reserve_requirement' in pricing_instance.data['system']) # In case of demand shortfall, the price skyrockets, so we threshold the value. if pricing_instance.data['system']['load_mismatch_cost'] > options.price_threshold: pricing_instance.data['system']['load_mismatch_cost'] = options.price_threshold # In case of reserve shortfall, the price skyrockets, so we threshold the value. if reserve_requirement: if pricing_instance.data['system']['reserve_shortfall_cost'] > options.reserve_price_threshold: pricing_instance.data['system']['reserve_shortfall_cost'] = options.reserve_price_threshold ptdf_manager.mark_active(pricing_instance) pyo_model = create_pricing_model(pricing_instance, relaxed=True, ptdf_options=ptdf_manager.damarket_ptdf_options, PTDF_matrix_dict=ptdf_manager.PTDF_matrix_dict) pyo_model.dual = Suffix(direction=Suffix.IMPORT) try: ## TODO: Should there be separate options for this run? pricing_results, _, _ = call_solver(solver, pyo_model, options, options.deterministic_ruc_solver_options, relaxed=True) except: print("Failed to solve pricing instance - likely because no feasible solution exists!") output_filename = "bad_pricing.json" pricing_instance.write(output_filename) print("Wrote failed RUC model to file=" + output_filename) raise ptdf_manager.update_active(pricing_results) ## Debugging if pricing_results.data['system']['total_cost'] > ruc_results.data['system']['total_cost']*(1.+1.e-06): print("The pricing run had a higher objective value than the MIP run. This is indicative of a bug.") print("Writing LP pricing_problem.json") output_filename = 'pricing_instance.json' pricing_results.write(output_filename) output_filename = 'ruc_results.json' ruc_results.write(output_filename) raise RuntimeError("Halting due to bug in pricing.") day_ahead_prices = {} for b, b_dict in pricing_results.elements(element_type='bus'): for t,lmp in enumerate(b_dict['lmp']['values']): day_ahead_prices[b,t] = lmp if reserve_requirement: day_ahead_reserve_prices = {} for t,price in enumerate(pricing_results.data['system']['reserve_price']['values']): # Thresholding the value of the reserve price to the passed in option day_ahead_reserve_prices[t] = price print("Recalculating RUC reserve procurement") ## scale the provided reserves by the amount over we are thermal_reserve_cleared_DA = {} g_reserve_values = { g : g_dict['rg']['values'] for g, g_dict in ruc_results.elements(element_type='generator', generator_type='thermal') } reserve_shortfall = ruc_results.data['system']['reserve_shortfall']['values'] reserve_requirement = ruc_results.data['system']['reserve_requirement']['values'] for t in range(0,options.ruc_every_hours): reserve_provided_t = sum(reserve_vals[t] for reserve_vals in g_reserve_values.values()) reserve_shortfall_t = reserve_shortfall[t] reserve_requirement_t = reserve_requirement[t] surplus_reserves_t = reserve_provided_t + reserve_shortfall_t - reserve_requirement_t ## if there's a shortfall, grab the full amount from the RUC solve ## or if there's no provided reserves this can safely be set to 1. if round_small_values(reserve_shortfall_t) > 0 or reserve_provided_t == 0: surplus_multiple_t = 1. else: ## scale the reserves from the RUC down by the same fraction ## so that they exactly meed the needed reserves surplus_multiple_t = reserve_requirement_t/reserve_provided_t for g, reserve_vals in g_reserve_values.items(): thermal_reserve_cleared_DA[g,t] = reserve_vals[t]*surplus_multiple_t else: day_ahead_reserve_prices = { t : 0. for t in enumerate(ruc_results.data['system']['time_keys']) } thermal_reserve_cleared_DA = { (g,t) : 0. \ for t in enumerate(ruc_results.data['system']['time_keys']) \ for g,_ in ruc_results.elements(element_type='generator', generator_type='thermal') } thermal_gen_cleared_DA = {} renewable_gen_cleared_DA = {} for g, g_dict in ruc_results.elements(element_type='generator'): pg = g_dict['pg']['values'] if g_dict['generator_type'] == 'thermal': store_dict = thermal_gen_cleared_DA elif g_dict['generator_type'] == 'renewable': store_dict = renewable_gen_cleared_DA else: raise RuntimeError(f"Unrecognized generator type {g_dict['generator_type']}") for t in range(0,options.ruc_every_hours): store_dict[g,t] = pg[t] return RucMarket(day_ahead_prices=day_ahead_prices, day_ahead_reserve_prices=day_ahead_reserve_prices, thermal_gen_cleared_DA=thermal_gen_cleared_DA, thermal_reserve_cleared_DA=thermal_reserve_cleared_DA, renewable_gen_cleared_DA=renewable_gen_cleared_DA) def create_simulation_actuals( options:Options, data_provider:DataProvider, this_date:datetime.date, this_hour:int, step_size_minutes:int) -> EgretModel: ''' Get an Egret model consisting of data to be treated as actuals, starting at a given time. Parameters ---------- options:Options Global option values data_provider: DataProvider An object that can provide actual and/or forecast data for the requested days this_date: date The date of the first time step for which data should be retrieved this_hour: int 0-based index of the first hour of the day for which data should be retrieved step_size_minutes: int The number of minutes between each time step ''' # Convert time string to time start_time = datetime.datetime.combine(this_date, datetime.time(hour=this_hour)) # Pick whether we're getting actuals or forecasts if options.simulate_out_of_sample: get_data_func = data_provider.populate_with_actuals else: print("") print("***WARNING: Simulating the forecast scenario when running deterministic RUC - " "time consistency across midnight boundaries is not guaranteed, and may lead to threshold events.") get_data_func = data_provider.populate_with_forecast_data # Get a new model total_step_count = options.ruc_horizon * 60 // step_size_minutes md = data_provider.get_initial_model(options, total_step_count, step_size_minutes) # Fill it in with data data_provider.populate_initial_state_data(options, start_time.date(), md) if this_hour == 0: get_data_func(options, start_time, total_step_count, step_size_minutes, md) else: # only get up to 24 hours of data, then copy it timesteps_per_day = 24 * 60 / step_size_minutes steps_to_request = math.min(timesteps_per_day, total_step_count) get_data_func(options, start_time, steps_to_request, step_size_minutes, md) for vals, in get_forecastables(md): for t in range(timesteps_per_day, total_step_count): vals[t] = vals[t-timesteps_per_day] return md def _ensure_reserve_factor_honored(options:Options, md:EgretModel, time_periods:Iterable[int]) -> None: ''' Adjust reserve requirements to satisfy the reserve factor. For each time period in time_periods, ensure that the reserve requirement is no less than the total load for that time period multiplied by the reserve factor. If the reserve requirement for a time is too low it is raised, otherwise it is left alone. ''' if options.reserve_factor > 0: reserve_factor = options.reserve_factor reserve_reqs = md.data['system']['reserve_requirement']['values'] for t in time_periods: total_load = sum(bdata['p_load']['values'][t] for bus, bdata in md.elements('load')) min_reserve = reserve_factor*total_load if reserve_reqs[t] < min_reserve: reserve_reqs[t] = min_reserve def _copy_initial_state_into_model(options:Options, current_state:SimulationState, md:EgretModel): for g, g_dict in md.elements('generator', generator_type='thermal'): g_dict['initial_status'] = current_state.get_initial_generator_state(g) g_dict['initial_p_output'] = current_state.get_initial_power_generated(g) for s,s_dict in md.elements('storage'): s_dict['initial_state_of_charge'] = current_state.get_initial_state_of_charge(s)

"""A mixing that extends a HasDriver class with Galaxy-specific utilities. Implementer must provide a self.build_url method to target Galaxy. """ import collections import contextlib import random import string import time from abc import abstractmethod from functools import ( partial, wraps, ) from typing import ( Any, cast, Dict, Optional, Union, ) import requests import yaml from selenium.webdriver.common.keys import Keys from selenium.webdriver.remote.webdriver import WebDriver from galaxy.util import DEFAULT_SOCKET_TIMEOUT from . import sizzle from .components import ( Component, HasText, ) from .data import load_root_component from .has_driver import ( exception_indicates_click_intercepted, exception_indicates_not_clickable, exception_indicates_stale_element, HasDriver, TimeoutException, ) from .smart_components import SmartComponent # Test case data DEFAULT_PASSWORD = "123456" RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT = 0.1 RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT = 10 GALAXY_MAIN_FRAME_ID = "galaxy_main" WaitType = collections.namedtuple("WaitType", ["name", "default_length"]) # Default wait times should make sense for a development server under low # load. Wait times for production servers can be scaled up with a multiplier. class WAIT_TYPES: # Rendering a form and registering callbacks, etc... UX_RENDER = WaitType("ux_render", 1) # Fade in, fade out, etc... UX_TRANSITION = WaitType("ux_transition", 5) # Toastr popup and dismissal, etc... UX_POPUP = WaitType("ux_popup", 15) # Creating a new history and loading it into the panel. DATABASE_OPERATION = WaitType("database_operation", 10) # Wait time for jobs to complete in default environment. JOB_COMPLETION = WaitType("job_completion", 30) # Wait time for a GIE to spawn. GIE_SPAWN = WaitType("gie_spawn", 30) # Wait time for toolshed search SHED_SEARCH = WaitType("shed_search", 30) # Wait time for repository installation REPO_INSTALL = WaitType("repo_install", 60) # Beta history Polling Duration HISTORY_POLL = WaitType("history_poll", 3) # Choose a moderate wait type for operations that don't specify a type. DEFAULT_WAIT_TYPE = WAIT_TYPES.DATABASE_OPERATION class NullTourCallback: def handle_step(self, step, step_index): pass def exception_seems_to_indicate_transition(e): """True if exception seems to indicate the page state is transitioning. Galaxy features many different transition effects that change the page state over time. These transitions make it slightly more difficult to test Galaxy because atomic input actions take an indeterminate amount of time to be reflected on the screen. This method takes a Selenium assertion and tries to infer if such a transition could be the root cause of the exception. The methods that follow use it to allow retrying actions during transitions. Currently the two kinds of exceptions that we say may indicate a transition are StaleElement exceptions (a DOM element grabbed at one step is no longer available) and "not clickable" exceptions (so perhaps a popup modal is blocking a click). """ return ( exception_indicates_stale_element(e) or exception_indicates_not_clickable(e) or exception_indicates_click_intercepted(e) ) def retry_call_during_transitions( f, attempts=RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT, sleep=RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT, exception_check=exception_seems_to_indicate_transition, ): previous_attempts = 0 while True: try: return f() except Exception as e: if previous_attempts > attempts: raise if not exception_check(e): raise time.sleep(sleep) previous_attempts += 1 def retry_during_transitions( f, attempts=RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT, sleep=RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT, exception_check=exception_seems_to_indicate_transition, ): @wraps(f) def _retry(*args, **kwds): return retry_call_during_transitions( partial(f, *args, **kwds), attempts=attempts, sleep=sleep, exception_check=exception_check ) return _retry def edit_details(f, scope=".history-index"): """Open the editor, run the edits, hit the save button""" @wraps(f) def func_wrapper(self, *args, **kwds): # open editor if self.is_beta_history(): self.open_history_editor(scope=scope) # run edits result = f(self, *args, **kwds) # save edits if self.is_beta_history(): self.history_click_editor_save() return result return func_wrapper class NavigatesGalaxy(HasDriver): """Class with helpers methods for driving components of the Galaxy interface. In most cases, methods for interacting with Galaxy components that appear in multiple tests or applications should be refactored into this class for now. Keep in mind that this class is used outside the context of ``TestCase`` s as well - so some methods more explicitly related to test data or assertion checking may make more sense in SeleniumTestCase for instance. Some day this class will likely be split up into smaller mixins for particular components of Galaxy, but until that day the best practice is to prefix methods for driving or querying the interface with the name of the component or page the method operates on. These serve as pseudo-namespaces until we decompose this class. For instance, the method for clicking an option in the workflow editor is workflow_editor_click_option instead of click_workflow_editor_option. """ timeout_multiplier: float driver: WebDriver @abstractmethod def build_url(self, url: str, for_selenium: bool = True) -> str: """Build URL to the target Galaxy.""" default_password = DEFAULT_PASSWORD wait_types = WAIT_TYPES # set to True to reload each invocation (good for interactive test building) _interactive_components: bool = False _root_component: Component = load_root_component() def get(self, url: str = ""): """Expand supplied relative URL and navigate to page using Selenium driver.""" full_url = self.build_url(url) return self.driver.get(full_url) @property def navigation(self) -> Component: if self._interactive_components: return load_root_component() else: return self._root_component @property def components(self) -> SmartComponent: """Fetch root component describing the Galaxy DOM.""" return SmartComponent(self.navigation, self) def wait_length(self, wait_type: WaitType) -> float: """Return the wait time specified by wait_type after applying `timeout_multipler`. `timeout_multiplier` is used in production CI tests to reduce transient failures in a uniform way across test suites to expand waiting. """ return wait_type.default_length * self.timeout_multiplier def sleep_for(self, wait_type: WaitType) -> None: """Sleep on the Python client side for the specified wait_type. This method uses `wait_length` to apply any `timeout_multiplier`. """ self.sleep_for_seconds(self.wait_length(wait_type)) def sleep_for_seconds(self, duration: float) -> None: """Sleep in the local thread for specified number of seconds. Ideally, we would be sleeping on the Selenium server instead of in the local client (e.g. test) thread. """ time.sleep(duration) def timeout_for(self, wait_type: WaitType = DEFAULT_WAIT_TYPE, **kwd) -> float: return self.wait_length(wait_type) def home(self) -> None: """Return to root Galaxy page and wait for some basic widgets to appear.""" self.get() self.components.masthead._.wait_for_visible() def go_to_trs_search(self) -> None: self.driver.get(self.build_url("workflows/trs_search")) self.components.masthead._.wait_for_visible() def go_to_trs_by_id(self) -> None: self.driver.get(self.build_url("workflows/trs_import")) self.components.masthead._.wait_for_visible() def go_to_workflow_sharing(self, workflow_id: str) -> None: self.driver.get(self.build_url(f"workflows/sharing?id={workflow_id}")) def go_to_workflow_export(self, workflow_id: str) -> None: self.driver.get(self.build_url(f"workflow/export?id={workflow_id}")) def go_to_history_sharing(self, history_id: str) -> None: self.driver.get(self.build_url(f"histories/sharing?id={history_id}")) def switch_to_main_panel(self): self.driver.switch_to.frame(GALAXY_MAIN_FRAME_ID) @contextlib.contextmanager def local_storage(self, key: str, value: Union[float, str]): """Method decorator to modify localStorage for the scope of the supplied context.""" self.driver.execute_script(f"""window.localStorage.setItem("{key}", {value});""") try: yield finally: self.driver.execute_script(f"""window.localStorage.removeItem("{key}");""") @contextlib.contextmanager def main_panel(self): """Decorator to operate within the context of Galaxy's main frame.""" try: self.switch_to_main_panel() yield finally: self.driver.switch_to.default_content() def api_get(self, endpoint, data=None, raw=False): data = data or {} full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.get( full_url, data=data, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) if raw: return response else: return response.json() def api_post(self, endpoint, data=None): data = data or {} full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.post( full_url, data=data, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) return response.json() def api_delete(self, endpoint, raw=False): full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.delete( full_url, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) if raw: return response else: return response.json() def get_galaxy_session(self): for cookie in self.driver.get_cookies(): if cookie["name"] == "galaxysession": return cookie["value"] def selenium_to_requests_cookies(self): return {"galaxysession": self.get_galaxy_session()} def history_panel_name_element(self): if self.is_beta_history(): component = self.beta_history_element("name display") else: component = self.components.history_panel.name return component.wait_for_present() @retry_during_transitions def history_panel_name(self): return self.history_panel_name_element().text def history_panel_collection_rename(self, hid: int, new_name: str, assert_old_name: Optional[str] = None): collection_view = self.history_panel_expand_collection(hid) if self.is_beta_history(): self.__beta_rename_collection(new_name) self.sleep_for(WAIT_TYPES.UX_RENDER) else: title_element = collection_view.title.wait_for_visible() if assert_old_name is not None: assert title_element.text == assert_old_name title_element.click() title_rename_element = collection_view.title_input.wait_for_visible() title_rename_element.send_keys(new_name) self.send_enter(title_rename_element) def history_panel_expand_collection(self, collection_hid: int) -> SmartComponent: self.history_panel_click_item_title(collection_hid) collection_view = self.components.history_panel.collection_view collection_view._.wait_for_present() return collection_view @edit_details def __beta_rename_collection(self, new_name): title_element = self.beta_history_element("name input").wait_for_clickable() title_element.clear() title_element.send_keys(new_name) def history_panel_collection_name_element(self): if self.is_beta_history(): title_element = self.beta_history_element("collection name display").wait_for_present() else: collection_view = self.components.history_panel.collection_view collection_view._.wait_for_present() title_element = collection_view.title.wait_for_visible() return title_element def make_accessible_and_publishable(self): self.components.histories.sharing.make_accessible.wait_for_and_click() self.components.histories.sharing.make_publishable.wait_for_and_click() def history_contents(self, history_id=None, view="summary", datasets_only=True): if history_id is None: history_id = self.current_history_id() histories = self.api_get("histories?keys=id") if history_id not in [h["id"] for h in histories]: return {} if datasets_only: endpoint = f"histories/{history_id}/contents?view={view}" else: endpoint = f"histories/{history_id}?view={view}" return self.api_get(endpoint) def current_history(self): full_url = self.build_url("history/current_history_json", for_selenium=False) response = requests.get(full_url, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT) return response.json() def current_history_id(self): return self.current_history()["id"] def latest_history_item(self): history_contents = self.history_contents() assert len(history_contents) > 0 return history_contents[-1] def wait_for_history(self, assert_ok=True): def history_becomes_terminal(driver): current_history_id = self.current_history_id() state = self.api_get(f"histories/{current_history_id}")["state"] if state not in ["running", "queued", "new", "ready"]: return state else: return None timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) final_state = self.wait(timeout=timeout).until(history_becomes_terminal) if assert_ok: assert final_state == "ok", final_state return final_state def history_panel_create_new_with_name(self, name): self.history_panel_create_new() self.history_panel_rename(name) def history_panel_create_new(self): """Click create new and pause a bit for the history to begin to refresh.""" self.history_click_create_new() self.sleep_for(WAIT_TYPES.UX_RENDER) def history_panel_wait_for_hid_ok(self, hid, allowed_force_refreshes=0): return self.history_panel_wait_for_hid_state(hid, "ok", allowed_force_refreshes=allowed_force_refreshes) def history_panel_wait_for_hid_deferred(self, hid, allowed_force_refreshes=0): return self.history_panel_wait_for_hid_state(hid, "deferred", allowed_force_refreshes=allowed_force_refreshes) def history_panel_item_component(self, history_item=None, hid=None, multi_history_panel=False): if self.is_beta_history(): assert hid return self.content_item_by_attributes(hid=hid) if history_item is None: assert hid history_item = self.hid_to_history_item(hid) item = self.components.history_panel.item.selector if multi_history_panel: item = self.components.multi_history_view.item return item(history_content_type=history_item["history_content_type"], id=history_item["id"]) def wait_for_history_to_have_hid(self, history_id, hid): def get_hids(): contents = self.api_get(f"histories/{history_id}/contents") return [d["hid"] for d in contents] def history_has_hid(driver): hids = get_hids() return any(h == hid for h in hids) timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) try: self.wait(timeout).until(history_has_hid) except self.TimeoutException as e: hids = get_hids() message = f"Timeout waiting for history {history_id} to have hid {hid} - have hids {hids}" raise self.prepend_timeout_message(e, message) def history_panel_wait_for_hid_visible(self, hid, allowed_force_refreshes=0, multi_history_panel=False): current_history_id = self.current_history_id() self.wait_for_history_to_have_hid(current_history_id, hid) # TODO: just use HID and stop resolving history_item -or- place history item in DOM. # I think Mason thought the first was cleaner based on recent changes, but I think duplicated # HIDs due to conversions and such make using the actual history item ID more robust. history_item = self.hid_to_history_item(hid, current_history_id=current_history_id) history_item_selector = self.history_panel_item_component( history_item, hid=hid, multi_history_panel=multi_history_panel ) try: self.history_item_wait_for(history_item_selector, allowed_force_refreshes) except self.TimeoutException as e: selector = self.navigation.history_panel.selectors.contents if self.is_beta_history(): selector = self.navigation.history_panel.selectors.contents_beta contents_elements = self.find_elements(selector) div_ids = [f"#{d.get_attribute("id")}" for d in contents_elements] template = "Failed waiting on history item %d to become visible, visible datasets include [%s]." message = template % (hid, ",".join(div_ids)) raise self.prepend_timeout_message(e, message) return history_item_selector def hid_to_history_item(self, hid, current_history_id=None): if current_history_id is None: current_history_id = self.current_history_id() contents = self.api_get(f"histories/{current_history_id}/contents") history_item = [d for d in contents if d["hid"] == hid][0] return history_item def history_item_wait_for(self, history_item_selector, allowed_force_refreshes): attempt = 0 while True: try: rval = self.wait_for_visible(history_item_selector, wait_type=WAIT_TYPES.JOB_COMPLETION) break except self.TimeoutException: if attempt >= allowed_force_refreshes: raise attempt += 1 if not self.is_beta_history(): self.history_panel_refresh_click() return rval def history_panel_wait_for_history_loaded(self): # Verify that the history has been loaded and is empty. self.wait_for_visible( self.navigation.history_panel.selectors.empty_message, wait_type=WAIT_TYPES.DATABASE_OPERATION ) def history_panel_wait_for_hid_hidden(self, hid, multi_history_panel=False): history_item_selector = self.history_panel_item_component(hid=hid, multi_history_panel=multi_history_panel) self.wait_for_absent_or_hidden(history_item_selector, wait_type=WAIT_TYPES.JOB_COMPLETION) return history_item_selector def history_panel_wait_for_hid_state(self, hid, state, allowed_force_refreshes=0, multi_history_panel=False): history_item_selector = self.history_panel_wait_for_hid_visible( hid, allowed_force_refreshes=allowed_force_refreshes, multi_history_panel=multi_history_panel ) if self.is_beta_history(): history_item_selector_state = self.content_item_by_attributes(hid=hid, state=state) else: # history_item_selector_state = history_item_selector.with_class(f"state-{state}") history_item_selector_state = history_item_selector.with_data("state", state) try: self.history_item_wait_for(history_item_selector_state, allowed_force_refreshes) except self.TimeoutException as e: history_item = self.wait_for_visible(history_item_selector) current_state = "UNKNOWN" classes = history_item.get_attribute("class").split(" ") for clazz in classes: if clazz.startswith("state-"): current_state = clazz[len("state-") :] template = "Failed waiting on history item %d state to change to [%s] current state [%s]. " message = template % (hid, state, current_state) raise self.prepend_timeout_message(e, message) return history_item_selector_state def click_grid_popup_option(self, item_name, option_label): item_button = None grid = self.components.grids.body.wait_for_visible() for row in grid.find_elements_by_tag_name("tr"): name_cell = row.find_elements_by_tag_name("td")[1] if name_cell.text == item_name: item_button = name_cell break if item_button is None: raise AssertionError(f"Failed to find item with name [{item_name}]") popup_menu_button = item_button.find_element_by_css_selector(".dropdown-toggle") popup_menu_button.click() popup_option = self.driver.find_element_by_link_text(option_label) popup_option.click() def published_grid_search_for(self, search_term=None): return self._inline_search_for( self.navigation.grids.free_text_search, search_term, ) def get_logged_in_user(self): return self.api_get("users/current") def get_api_key(self, force=False): # If force is false, use the form inputs API and allow the key to be absent. if not force: return self.api_get(f"users/{self.get_user_id()}/api_key/inputs")["inputs"][0]["value"] else: return self.api_post(f"users/{self.get_user_id()}/api_key") def get_user_id(self): user = self.get_logged_in_user() return user["id"] def is_logged_in(self): return "email" in self.get_logged_in_user() @retry_during_transitions def _inline_search_for(self, selector, search_term=None, escape_to_clear=False): # Clear tooltip resulting from clicking on the masthead to get here. self.clear_tooltips() search_box = self.wait_for_and_click(selector) if escape_to_clear: # The combination of DebouncedInput+b-input doesn't seem to uniformly respect # .clear() below. We use escape handling a lot - and that does cauase to the input # to reset correctly and fire the required re-active events/handlers. self.send_escape(search_box) search_box.clear() if search_term is not None: search_box.send_keys(search_term) self.send_enter(search_box) return search_box def _get_random_name(self, prefix=None, suffix=None, len=10): return "{}{}{}".format( prefix or "", "".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(len)), suffix or "", ) def _get_random_email(self, username=None, domain=None): username = username or "test" domain = domain or "test.test" return self._get_random_name(prefix=username, suffix=f"@{domain}") # Creates a random password of length len by creating an array with all ASCII letters and the numbers 0 to 9, # then using the random number generator to pick one elemenent to concatinate it to the end of the password string until # we have a password of length len. def _get_random_password(self, len=6): return "".join(random.SystemRandom().choice(string.ascii_letters + string.digits) for _ in range(len)) def submit_login(self, email, password=None, assert_valid=True, retries=0): self.components.masthead.register_or_login.wait_for_and_click() self.sleep_for(WAIT_TYPES.UX_RENDER) self.fill_login_and_submit(email, password=password) if assert_valid: try: self.wait_for_logged_in() except NotLoggedInException: self.snapshot("login-failed") if retries > 0: self.submit_login(email, password, assert_valid, retries - 1) else: raise self.snapshot("logged-in") def fill_login_and_submit(self, email, password=None): if password is None: password = self.default_password login_info = { "login": email, "password": password, } form = self.wait_for_visible(self.navigation.login.selectors.form) self.fill(form, login_info) self.snapshot("logging-in") self.wait_for_and_click(self.navigation.login.selectors.submit) self.snapshot("login-submitted") def register(self, email=None, password=None, username=None, confirm=None, assert_valid=True): if email is None: email = self._get_random_email() if password is None: password = self.default_password if confirm is None: confirm = password if username is None: username = email.split("@")[0] self.home() self.components.masthead.register_or_login.wait_for_and_click() self.wait_for_and_click(self.navigation.registration.selectors.toggle) form = self.wait_for_visible(self.navigation.registration.selectors.form) self.fill(form, dict(email=email, password=password, username=username, confirm=confirm)) self.wait_for_and_click(self.navigation.registration.selectors.submit) if assert_valid is False: self.assert_error_message() elif assert_valid: self.wait_for_logged_in() # Code below previously was needed because there was a bug that would prevent the masthead from changing, # the bug seems maybe to be fixed though - so we could consider eliminating these extra checks to speed # up tests. self.home() self.wait_for_logged_in() self.click_masthead_user() # Make sure the user menu was dropped down user_menu = self.components.masthead.user_menu.wait_for_visible() try: username_element = self.components.masthead.username.wait_for_visible() except self.TimeoutException as e: menu_items = user_menu.find_elements_by_css_selector("li a") menu_text = [mi.text for mi in menu_items] message = f"Failed to find logged in message in menu items {", ".join(menu_text)}" raise self.prepend_timeout_message(e, message) text = username_element.text assert username in text assert self.get_logged_in_user()["email"] == email # clicking away no longer closes menu post Masthead -> VueJS self.click_masthead_user() def wait_for_logged_in(self): try: self.components.masthead.logged_in_only.wait_for_visible() except self.TimeoutException as e: ui_logged_out = self.components.masthead.logged_out_only.is_displayed if ui_logged_out: dom_message = ( "Element a.loggedout-only is present in DOM, indicating Login or Register button still in masthead." ) else: dom_message = "Element a.loggedout-only is *not* present in DOM." user_info = self.api_get("users/current") if "username" in user_info: template = "Failed waiting for masthead to update for login, but user API response indicates [%s] is logged in. This seems to be a bug in Galaxy. %s logged API response was [%s]. " message = template % (user_info["username"], dom_message, user_info) raise self.prepend_timeout_message(e, message) else: raise NotLoggedInException(e, user_info, dom_message) def click_center(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).click().perform() def perform_upload(self, test_path, **kwd): self._perform_upload(test_path=test_path, **kwd) def perform_upload_of_pasted_content(self, paste_data, **kwd): self._perform_upload(paste_data=paste_data, **kwd) def _perform_upload( self, test_path=None, paste_data=None, ext=None, genome=None, ext_all=None, genome_all=None, deferred=None ): self.home() self.upload_start_click() self.upload_set_footer_extension(ext_all) self.upload_set_footer_genome(genome_all) if test_path: self.upload_queue_local_file(test_path) else: assert paste_data is not None self.upload_paste_data(paste_data) if ext is not None: self.wait_for_selector_visible(".upload-extension") self.select2_set_value(".upload-extension", ext) if genome is not None: self.wait_for_selector_visible(".upload-genome") self.select2_set_value(".upload-genome", genome) if deferred is not None: upload = self.components.upload upload.settings_button(n=0).wait_for_and_click() upload.settings.wait_for_visible() setting = upload.setting_deferred.wait_for_visible() classes = setting.get_attribute("class").split(" ") if deferred is True and "fa-check-square-o" not in classes: setting.click() elif deferred is False and "fa-check-square-o" in classes: setting.click() self.upload_start() self.wait_for_and_click_selector("button#btn-close") def upload_list(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "List") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_set_name(name) self.collection_builder_create() def upload_pair(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "Pair") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_set_name(name) self.collection_builder_create() def upload_paired_list(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "List of Pairs") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_clear_filters() # TODO: generalize and loop these clicks so we don't need the assert assert len(test_paths) == 2 self.collection_builder_click_paired_item("forward", 0) self.collection_builder_click_paired_item("reverse", 1) self.collection_builder_set_name(name) self.collection_builder_create() def _collection_upload_start(self, test_paths, ext, genome, collection_type): # Perform upload of files and open the collection builder for specified # type. self.home() self.upload_start_click() self.upload_tab_click("collection") self.upload_set_footer_extension(ext, tab_id="collection") self.upload_set_footer_genome(genome, tab_id="collection") self.upload_set_collection_type(collection_type) for test_path in test_paths: self.upload_queue_local_file(test_path, tab_id="collection") self.upload_start(tab_id="collection") self.upload_build() def upload_tab_click(self, tab): self.components.upload.tab(tab=tab).wait_for_and_click() def upload_start_click(self): self.components.upload.start.wait_for_and_click() @retry_during_transitions def upload_set_footer_extension(self, ext, tab_id="regular"): if ext is not None: selector = f"div#{tab_id} .upload-footer-extension" self.wait_for_selector_visible(selector) self.select2_set_value(selector, ext) @retry_during_transitions def upload_set_footer_genome(self, genome, tab_id="regular"): if genome is not None: selector = f"div#{tab_id} .upload-footer-genome" self.wait_for_selector_visible(selector) self.select2_set_value(selector, genome) @retry_during_transitions def upload_set_collection_type(self, collection_type): self.wait_for_selector_visible(".upload-footer-collection-type") self.select2_set_value(".upload-footer-collection-type", collection_type) def upload_start(self, tab_id="regular"): self.wait_for_and_click_selector(f"div#{tab_id} button#btn-start") @retry_during_transitions def upload_build(self, tab="collection"): build_selector = f"div#{tab} button#btn-build" # Pause a bit to let the callback on the build button be registered. time.sleep(0.5) # Click the Build button and make sure it disappears. self.wait_for_and_click_selector(build_selector) try: self.wait_for_selector_absent_or_hidden(build_selector) except TimeoutException: # Sometimes the callback in the JS hasn't be registered by the # time that the build button is clicked. By the time the timeout # has been registered - it should have been. self.wait_for_and_click_selector(build_selector) self.wait_for_selector_absent_or_hidden(build_selector) def upload_queue_local_file(self, test_path, tab_id="regular"): self.wait_for_and_click_selector(f"div#{tab_id} button#btn-local") file_upload = self.wait_for_selector(f'div#{tab_id} input[type="file"]') file_upload.send_keys(test_path) def upload_paste_data(self, pasted_content, tab_id="regular"): tab_locator = f"div#{tab_id}" self.wait_for_and_click_selector(f"{tab_locator} button#btn-new") textarea = self.wait_for_selector(f"{tab_locator} .upload-text-content") textarea.send_keys(pasted_content) def upload_rule_start(self): self.upload_start_click() self.upload_tab_click("rule-based") def upload_rule_build(self): self.upload_build(tab="rule-based") def upload_rule_set_data_type(self, type_description): upload = self.components.upload data_type_element = upload.rule_select_data_type.wait_for_visible() self.select2_set_value(data_type_element, type_description) def upload_rule_set_input_type(self, input_description): upload = self.components.upload input_type_element = upload.rule_select_input_type.wait_for_visible() self.select2_set_value(input_type_element, input_description) def upload_rule_set_dataset(self, row=1): upload = self.components.upload upload.rule_dataset_selector.wait_for_visible() upload.rule_dataset_selector_row(rowindex=row).wait_for_and_click() def rule_builder_set_collection_name(self, name): rule_builder = self.components.rule_builder name_element = rule_builder.collection_name_input.wait_for_and_click() name_element.send_keys(name) self.sleep_for(WAIT_TYPES.UX_RENDER) def rule_builder_set_extension(self, extension): self.select2_set_value(self.navigation.rule_builder.selectors.extension_select, extension) def rule_builder_filter_count(self, count=1): rule_builder = self.components.rule_builder rule_builder.menu_button_filter.wait_for_and_click() with self.rule_builder_rule_editor("add-filter-count") as editor_element: filter_input = editor_element.find_element_by_css_selector("input[type='number']") filter_input.clear() filter_input.send_keys(f"{count}") def rule_builder_sort(self, column_label, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("sort") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) self.screenshot_if(screenshot_name) def rule_builder_add_regex_groups(self, column_label, group_count, regex, screenshot_name): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-regex") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) groups_elem = editor_element.find_element_by_css_selector("input[type='radio'][value='groups']") groups_elem.click() regex_elem = editor_element.find_element_by_css_selector("input.rule-regular-expression") regex_elem.clear() regex_elem.send_keys(regex) filter_input = editor_element.find_element_by_css_selector("input[type='number']") filter_input.clear() filter_input.send_keys(f"{group_count}") self.screenshot_if(screenshot_name) def rule_builder_add_regex_replacement(self, column_label, regex, replacement, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-regex") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) groups_elem = editor_element.find_element_by_css_selector("input[type='radio'][value='replacement']") groups_elem.click() regex_elem = editor_element.find_element_by_css_selector("input.rule-regular-expression") regex_elem.clear() regex_elem.send_keys(regex) filter_input = editor_element.find_element_by_css_selector("input.rule-replacement") filter_input.clear() filter_input.send_keys(f"{replacement}") self.screenshot_if(screenshot_name) def rule_builder_add_value(self, value, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-value") as editor_element: filter_input = editor_element.find_element_by_css_selector("input[type='text']") filter_input.clear() filter_input.send_keys(value) self.screenshot_if(screenshot_name) def rule_builder_remove_columns(self, column_labels, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("remove-columns") as filter_editor_element: column_elem = filter_editor_element.find_element_by_css_selector(".rule-column-selector") for column_label in column_labels: self.select2_set_value(column_elem, column_label) self.screenshot_if(screenshot_name) def rule_builder_concatenate_columns(self, column_label_1, column_label_2, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-concatenate") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[0], column_label_1) column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[1], column_label_2) self.screenshot_if(screenshot_name) def rule_builder_split_columns(self, column_labels_1, column_labels_2, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("split-columns") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") clear = True for column_label_1 in column_labels_1: self.select2_set_value(column_elems[0], column_label_1, clear_value=clear) clear = False column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") clear = True for column_label_2 in column_labels_2: self.select2_set_value(column_elems[1], column_label_2, clear_value=clear) clear = False self.screenshot_if(screenshot_name) def rule_builder_swap_columns(self, column_label_1, column_label_2, screenshot_name): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("swap-columns") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[0], column_label_1) column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[1], column_label_2) self.screenshot_if(screenshot_name) @contextlib.contextmanager def rule_builder_rule_editor(self, rule_type): rule_builder = self.components.rule_builder rule_builder.menu_item_rule_type(rule_type=rule_type).wait_for_and_click() filter_editor = rule_builder.rule_editor(rule_type=rule_type) filter_editor_element = filter_editor.wait_for_visible() yield filter_editor_element rule_builder.rule_editor_ok.wait_for_and_click() def rule_builder_set_mapping(self, mapping_type, column_label, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() rule_builder.menu_item_rule_type(rule_type="mapping").wait_for_and_click() rule_builder.add_mapping_menu.wait_for_and_click() rule_builder.add_mapping_button(mapping_type=mapping_type).wait_for_and_click() if mapping_type != "list-identifiers" or not isinstance(column_label, list): mapping_elem = rule_builder.mapping_edit(mapping_type=mapping_type).wait_for_visible() self.select2_set_value(mapping_elem, column_label) self.screenshot_if(screenshot_name) else: assert len(column_label) > 0 column_labels = column_label for i, column_label in enumerate(column_labels): if i > 0: rule_builder.mapping_add_column(mapping_type=mapping_type).wait_for_and_click() mapping_elem = rule_builder.mapping_edit(mapping_type=mapping_type).wait_for_visible() self.select2_set_value(mapping_elem, column_label) self.screenshot_if(screenshot_name) rule_builder.mapping_ok.wait_for_and_click() def rule_builder_set_source(self, json): rule_builder = self.components.rule_builder rule_builder.view_source.wait_for_and_click() self.rule_builder_enter_source_text(json) rule_builder.main_button_ok.wait_for_and_click() rule_builder.view_source.wait_for_visible() def rule_builder_enter_source_text(self, json): rule_builder = self.components.rule_builder text_area_elem = rule_builder.source.wait_for_visible() text_area_elem.clear() text_area_elem.send_keys(json) def workflow_editor_click_option(self, option_label): self.workflow_editor_click_options() menu_element = self.workflow_editor_options_menu_element() option_elements = menu_element.find_elements_by_css_selector("a") assert len(option_elements) > 0, "Failed to find workflow editor options" self.sleep_for(WAIT_TYPES.UX_RENDER) found_option = False for option_element in option_elements: if option_label in option_element.text: action_chains = self.action_chains() action_chains.move_to_element(option_element) action_chains.click() action_chains.perform() found_option = True break if not found_option: raise Exception(f"Failed to find workflow editor option with label [{option_label}]") def workflow_editor_click_options(self): return self.wait_for_and_click_selector("#workflow-options-button") def workflow_editor_options_menu_element(self): return self.wait_for_selector_visible("#workflow-options-button") def workflow_editor_click_run(self): return self.wait_for_and_click_selector("#workflow-run-button") def workflow_editor_click_save(self): self.wait_for_and_click_selector("#workflow-save-button") self.sleep_for(self.wait_types.DATABASE_OPERATION) def navigate_to_histories_page(self): self.home() self.click_masthead_user() self.components.masthead.histories.wait_for_and_click() def navigate_to_user_preferences(self): self.home() self.click_masthead_user() self.components.masthead.preferences.wait_for_and_click() def navigate_to_invocations(self): self.home() self.click_masthead_user() self.components.masthead.invocations.wait_for_and_click() def navigate_to_pages(self): self.home() self.click_masthead_user() self.components.masthead.pages.wait_for_and_click() def admin_open(self): self.components.masthead.admin.wait_for_and_click() def select_dataset_from_lib_import_modal(self, filenames): for name in filenames: self.components.libraries.folder.select_import_dir_item(name=name).wait_for_and_click() self.components.libraries.folder.import_dir_btn.wait_for_and_click() def create_new_library(self, login=True): if login: self.admin_login() self.libraries_open() self.name = self._get_random_name(prefix="testcontents") self.libraries_index_create(self.name) def libraries_open(self): self.home() self.click_masthead_shared_data() self.components.masthead.libraries.wait_for_and_click() self.components.libraries.selector.wait_for_visible() def libraries_open_with_name(self, name): self.libraries_open() self.libraries_index_search_for(name) self.libraries_index_table_elements()[0].find_element_by_css_selector("td a").click() @retry_during_transitions def libraries_index_table_elements(self): container = self.components.libraries._.wait_for_visible() elements = container.find_elements_by_css_selector("tbody") if not elements: return [] else: assert len(elements) == 1 element = elements[0] return element.find_elements_by_css_selector("tr") # [style='display: table-row'] def libraries_index_create(self, name): self.components.libraries.create_new_library_btn.wait_for_and_click() name_input_field = self.components.libraries.new_library_name_input.wait_for_visible() input_field = self.components.libraries.new_library_description_input.wait_for_visible() name_input_field.send_keys(name) input_field.send_keys(self._get_random_name(prefix="description")) self.components.libraries.save_new_library_btn.wait_for_and_click() def libraries_index_click_search(self): self.sleep_for(WAIT_TYPES.UX_RENDER) search_element = self.components.libraries.search_field.wait_for_visible() search_element.click() return search_element def libraries_index_sort_selector(self): return "th[aria-sort]" def libraries_index_sort_click(self): sort_element = self.wait_for_selector_clickable(self.libraries_index_sort_selector()) sort_element.click() return sort_element def libraries_index_search_for(self, text): self.wait_for_overlays_cleared() search_box = self.libraries_index_click_search() search_box.clear() search_box.send_keys(text) value = search_box.get_attribute("value") assert value == text, value def libraries_folder_create(self, name): self.components.libraries.folder.add_folder.wait_for_and_click() self.components.libraries.folder.input_folder_name.wait_for_and_send_keys(name) self.components.libraries.folder.save_folder_btn.wait_for_and_click() def libraries_click_dataset_import(self): self.wait_for_and_click(self.navigation.libraries.folder.selectors.add_items_button) self.wait_for_visible(self.navigation.libraries.folder.selectors.add_items_menu) def libraries_dataset_import(self, btn): self.libraries_click_dataset_import() self.wait_for_and_click(btn) def libraries_dataset_import_from_history_select(self, to_select_items): self.wait_for_visible(self.navigation.libraries.folder.selectors.import_history_content) history_elements = self.find_elements(self.navigation.libraries.folder.selectors.import_history_contents_items) for to_select_item in to_select_items: found = False for history_element in history_elements: if to_select_item in history_element.text: history_element.find_element_by_css_selector("input").click() found = True break if not found: raise Exception(f"Failed to find history item [{to_select_item}] to select") def libraries_dataset_import_from_history_click_ok(self, wait=True): self.wait_for_and_click(self.navigation.libraries.folder.selectors.import_datasets_ok_button) if wait: # Let the progress bar disappear... self.wait_for_absent_or_hidden(self.navigation.libraries.folder.selectors.import_progress_bar) def libraries_table_elements(self): tbody_element = self.wait_for_selector_visible("#folder_list_body > tbody") return tbody_element.find_elements_by_css_selector("tr:not(.b-table-empty-row)") def populate_library_folder_from_import_dir(self, library_name, filenames): self.libraries_open_with_name(library_name) self.libraries_dataset_import(self.navigation.libraries.folder.labels.from_import_dir) self.select_dataset_from_lib_import_modal(filenames) def navigate_to_new_library(self, login=True): self.create_new_library(login) self.libraries_open_with_name(self.name) def wait_for_overlays_cleared(self): """Wait for modals and Toast notifications to disappear.""" self.wait_for_selector_absent_or_hidden(".ui-modal", wait_type=WAIT_TYPES.UX_POPUP) self.wait_for_selector_absent_or_hidden(".toast", wait_type=WAIT_TYPES.UX_POPUP) def clear_tooltips(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).perform() self.wait_for_selector_absent_or_hidden(".b-tooltip", wait_type=WAIT_TYPES.UX_POPUP) def workflow_index_open(self): self.home() self.click_masthead_workflow() def workflow_index_table_elements(self): workflows = self.components.workflows workflows.workflow_table.wait_for_visible() return workflows.workflow_rows.all() def workflow_index_table_row(self, workflow_index=0): self.components.workflows.workflow_rows.wait_for_element_count_of_at_least(workflow_index + 1) return self.workflow_index_table_elements()[workflow_index] @retry_during_transitions def workflow_index_column_text(self, column_index, workflow_index=0): row_element = self.workflow_index_table_row(workflow_index=workflow_index) columns = row_element.find_elements_by_css_selector("td") return columns[column_index].text def workflow_index_click_search(self): return self.wait_for_and_click_selector("#workflow-search") def workflow_index_search_for(self, search_term=None): return self._inline_search_for( self.navigation.workflows.search_box, search_term, escape_to_clear=True, ) def workflow_index_click_import(self): return self.components.workflows.import_button.wait_for_and_click() def workflow_index_rename(self, new_name, workflow_index=0): self.workflow_index_click_option("Rename", workflow_index=workflow_index) alert = self.driver.switch_to.alert alert.send_keys(new_name) alert.accept() @retry_during_transitions def workflow_index_name(self, workflow_index=0): """Get workflow name for workflow_index'th row.""" row_element = self.workflow_index_table_row(workflow_index=workflow_index) workflow_button = row_element.find_element_by_css_selector(".workflow-dropdown") return workflow_button.text @retry_during_transitions def workflow_click_option(self, workflow_selector, workflow_index=0): workflow_row = self.workflow_index_table_row(workflow_index=workflow_index) workflow_button = workflow_row.find_element_by_css_selector(workflow_selector) workflow_button.click() def select_dropdown_item(self, option_title): menu_element = self.wait_for_selector_visible(".dropdown-menu.show") menu_options = menu_element.find_elements_by_css_selector("a.dropdown-item") for menu_option in menu_options: if option_title in menu_option.text: menu_option.click() return True def workflow_index_click_option(self, option_title, workflow_index=0): self.workflow_click_option(".workflow-dropdown", workflow_index) if not self.select_dropdown_item(option_title): raise AssertionError(f"Failed to find workflow action option with title [{option_title}]") def workflow_index_click_tag_display(self, workflow_index=0): workflow_row_element = self.workflow_index_table_row(workflow_index) tag_display = workflow_row_element.find_element_by_css_selector(".tags-display") tag_display.click() def workflow_index_add_tag(self, tag: str, workflow_index: int = 0): self.workflow_index_click_tag_display(workflow_index=workflow_index) self.tagging_add([tag]) @retry_during_transitions def workflow_index_tags(self, workflow_index=0): tag_spans = self.workflow_index_tag_elements(workflow_index=workflow_index) tags = [] for tag_span in tag_spans: tags.append(tag_span.text) return tags @retry_during_transitions def workflow_index_tag_elements(self, workflow_index=0): workflow_row_element = self.workflow_index_table_row(workflow_index) tag_display = workflow_row_element.find_element_by_css_selector(".tags-display") tag_spans = tag_display.find_elements_by_css_selector(".tag-name") return tag_spans @retry_during_transitions def workflow_index_click_tag(self, tag, workflow_index=0): tag_spans = self.workflow_index_tag_elements(workflow_index=workflow_index) clicked = False for tag_span in tag_spans: if tag_span.text == tag: tag_span.click() clicked = True break if not clicked: raise KeyError(f"Failed to find tag {tag} on workflow with index {workflow_index}") def workflow_import_submit_url(self, url): form_button = self.wait_for_selector_visible("#workflow-import-button") url_element = self.wait_for_selector_visible("#workflow-import-url-input") url_element.send_keys(url) form_button.click() def workflow_sharing_click_publish(self): self.wait_for_and_click_selector("input[name='make_accessible_and_publish']") def tagging_add(self, tags, auto_closes=True, parent_selector=""): for i, tag in enumerate(tags): if auto_closes or i == 0: tag_area = f"{parent_selector}.tags-input input[type='text']" tag_area = self.wait_for_selector_clickable(tag_area) tag_area.click() tag_area.send_keys(tag) self.send_enter(tag_area) def workflow_run_with_name(self, name: str): self.workflow_index_open() self.workflow_index_search_for(name) self.workflow_click_option(".workflow-run") def workflow_run_specify_inputs(self, inputs: Dict[str, Any]): workflow_run = self.components.workflow_run for label, value in inputs.items(): input_div_element = workflow_run.input_data_div(label=label).wait_for_visible() self.select2_set_value(input_div_element, "%d: " % value["hid"]) def workflow_run_submit(self): self.components.workflow_run.run_workflow.wait_for_and_click() def workflow_run_ensure_expanded(self): workflow_run = self.components.workflow_run if workflow_run.expanded_form.is_absent: workflow_run.expand_form_link.wait_for_and_click() workflow_run.expanded_form.wait_for_visible() def workflow_create_new(self, annotation=None, clear_placeholder=False): self.workflow_index_open() self.sleep_for(self.wait_types.UX_RENDER) self.click_button_new_workflow() self.sleep_for(self.wait_types.UX_RENDER) form_element = self.driver.find_element_by_id("submit") name = self._get_random_name() annotation = annotation or self._get_random_name() inputs = self.driver.find_elements_by_class_name("ui-input") if clear_placeholder: inputs[0].clear() inputs[0].send_keys(name) inputs[1].send_keys(annotation) form_element.click() return name def invocation_index_table_elements(self): invocations = self.components.invocations invocations.invocations_table.wait_for_visible() return invocations.invocations_table_rows.all() def tool_open(self, tool_id, outer=False): if outer: tool_link = self.components.tool_panel.outer_tool_link(tool_id=tool_id) else: tool_link = self.components.tool_panel.tool_link(tool_id=tool_id) tool_element = tool_link.wait_for_present() self.driver.execute_script("arguments[0].scrollIntoView(true);", tool_element) tool_link.wait_for_and_click() def create_page_and_edit(self, name=None, slug=None, content_format=None, screenshot_name=None): name = self.create_page(name=name, slug=slug, content_format=content_format, screenshot_name=screenshot_name) self.click_grid_popup_option(name, "Edit content") self.components.pages.editor.wym_iframe.wait_for_visible() return name def create_page(self, name=None, slug=None, content_format=None, screenshot_name=None): self.components.pages.create.wait_for_and_click() name = name or self._get_random_name(prefix="page") slug = slug = self._get_random_name(prefix="pageslug") content_format = content_format or "HTML" self.tool_set_value("title", name) self.tool_set_value("slug", slug) self.tool_set_value("content_format", content_format, expected_type="select") self.screenshot_if(screenshot_name) # Sometimes 'submit' button not yet hooked up? self.sleep_for(self.wait_types.UX_RENDER) self.components.pages.submit.wait_for_and_click() return name def tool_parameter_div(self, expanded_parameter_id): return self.components.tool_form.parameter_div(parameter=expanded_parameter_id).wait_for_clickable() def tool_parameter_edit_rules(self): rules_div_element = self.tool_parameter_div("rules") edit_button_element = rules_div_element.find_element_by_css_selector("i.fa-edit") edit_button_element.click() def tool_set_value(self, expanded_parameter_id, value, expected_type=None): div_element = self.tool_parameter_div(expanded_parameter_id) assert div_element if expected_type in ["select", "data", "data_collection"]: div_selector = f"div.ui-form-element[id$='form-element-{expanded_parameter_id}']" self.select2_set_value(div_selector, value) else: input_element = div_element.find_element_by_css_selector("input") # Clear default value input_element.clear() input_element.send_keys(value) def tool_form_generate_tour(self): self.components.tool_form.options.wait_for_and_click() self.components.tool_form.generate_tour.wait_for_and_click() def tool_form_execute(self): self.components.tool_form.execute.wait_for_and_click() def click_masthead_user(self): self.components.masthead.user.wait_for_and_click() def click_masthead_shared_data(self): self.components.masthead.shared_data.wait_for_and_click() def click_masthead_workflow(self): self.components.masthead.workflow.wait_for_and_click() def click_button_new_workflow(self): self.wait_for_and_click(self.navigation.workflows.selectors.new_button) def wait_for_sizzle_selector_clickable(self, selector): element = self._wait_on( sizzle.sizzle_selector_clickable(selector), f"sizzle/jQuery selector [{selector}] to become clickable", ) return element @retry_during_transitions def click_history_options(self): if self.is_beta_history(): component = self.components.history_panel.options_button_icon_beta else: component = self.components.history_panel.options_button_icon component.wait_for_and_click() def click_history_option_export_to_file(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="export to file", menu="history options") else: self.click_history_options() self.components.history_panel.options_show_export_history_to_file.wait_for_and_click() def click_history_option_sharing(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="share or publish", menu="history options") else: self.click_history_option("Share or Publish") def click_history_option(self, option_label_or_component): # Open menu self.click_history_options() if isinstance(option_label_or_component, str): option_label = option_label_or_component # Click labeled option self.wait_for_visible(self.navigation.history_panel.options_menu) menu_item_sizzle_selector = self.navigation.history_panel.options_menu_item( option_label=option_label ).selector menu_selection_element = self.wait_for_sizzle_selector_clickable(menu_item_sizzle_selector) menu_selection_element.click() else: option_component = option_label_or_component option_component.wait_for_and_click() def use_beta_history(self): if not self.is_beta_history(): self.click_history_option(self.components.history_panel.options_use_beta_history) self.components.history_panel.beta.wait_for_present() def is_beta_history(self): old_panel = self.components.history_panel.beta.is_absent new_panel = self.components.history_panel.new_history_button.is_absent if old_panel and new_panel: # both absent, let page render a bit... self.sleep_for(self.wait_types.UX_RENDER) else: return new_panel old_panel = self.components.history_panel.beta.is_absent return not old_panel # avoids problematic ID and classes on markup def beta_history_element(self, attribute_value, attribute_name="data-description", scope=".history-index"): return self.components._.by_attribute(name=attribute_name, value=attribute_value, scope=scope) # join list of attrs into css attribute selectors and append to base beta_item selector def content_item_by_attributes(self, **attrs): suffix_list = [f'[data-{k}="{v}"]' for (k, v) in attrs.items()] suffix = "".join(suffix_list) return self.components.history_panel.content_item.selector(suffix=suffix) def history_click_create_new(self): if not self.is_beta_history(): self.components.history_panel.new_history_button.wait_for_and_click() else: option = self.beta_history_element("create new history") option.wait_for_and_click() def history_click_editor_save(self): option = self.beta_history_element("editor save button") option.wait_for_and_click() self.sleep_for(self.wait_types.UX_RENDER) def history_panel_click_copy_elements(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="copy datasets", menu="history action menu") else: self.click_history_option("Copy Datasets") def use_bootstrap_dropdown(self, option=None, menu=None): """uses bootstrap dropdown by data-description attributes""" if option is None: raise TypeError if menu is None: raise TypeError toggle = self.beta_history_element(menu).descendant("button") self.wait_for_and_click(toggle) return self.beta_history_element(option).wait_for_and_click() @retry_during_transitions def histories_click_advanced_search(self): search_selector = "#standard-search .advanced-search-toggle" self.wait_for_and_click_selector(search_selector) @retry_during_transitions def histories_get_history_names(self): self.sleep_for(self.wait_types.UX_RENDER) names = [] grid = self.wait_for_selector("#grid-table-body") for row in grid.find_elements_by_tag_name("tr"): td = row.find_elements_by_tag_name("td") name = td[1].text if td[0].text == "" else td[0].text if name != "No items" and not name.startswith("No matching entries found"): names.append(name) return names @edit_details def history_panel_add_tags(self, tags): tag_icon = self.components.history_panel.tag_icon tag_area = self.components.history_panel.tag_area tag_area_input = self.components.history_panel.tag_area_input # if the tag editor is not present but the tag_icon is, then click it if not tag_icon.is_absent and (tag_area.is_absent or not tag_area.is_displayed): tag_icon.wait_for_and_click() input_element = tag_area_input.wait_for_and_click() self.sleep_for(self.wait_types.UX_RENDER) for tag in tags: input_element.send_keys(tag) self.send_enter(input_element) self.sleep_for(self.wait_types.UX_RENDER) @edit_details def history_panel_rename(self, new_name): editable_text_input_element = self.history_panel_name_input() if self.is_beta_history(): editable_text_input_element.clear() editable_text_input_element.send_keys(new_name) self.send_enter(editable_text_input_element) return editable_text_input_element def history_panel_name_input(self): if not self.is_beta_history(): editable_text_input_element = self.history_panel_click_to_rename() history_panel = self.components.history_panel edit = history_panel.name_edit_input editable_text_input_element = edit.wait_for_visible() return editable_text_input_element def history_panel_click_to_rename(self): history_panel = self.components.history_panel name = history_panel.name edit = history_panel.name_edit_input name.wait_for_and_click() return edit.wait_for_visible() def history_panel_refresh_click(self): self.wait_for_and_click(self.navigation.history_panel.selectors.refresh_button) def history_panel_multi_operations_show(self): return self.wait_for_and_click(self.navigation.history_panel.multi_operations.selectors.show_button) def history_panel_muli_operation_select_hid(self, hid): item_selector = self.history_panel_item_selector(hid, wait=True) operation_radio_selector = f"{item_selector} .selector" self.wait_for_and_click_selector(operation_radio_selector) def history_panel_multi_operation_action_click(self, action): # Maybe isn't needed? # self.sleep_for(WAIT_TYPES.UX_RENDER) self.wait_for_and_click(self.navigation.history_panel.multi_operations.selectors.action_button) @retry_during_transitions def _click_action_in_menu(): menu_element = self.wait_for_visible(self.navigation.history_panel.multi_operations.selectors.action_menu) menu_element.find_element_by_link_text(action.text).click() _click_action_in_menu() def open_history_multi_view(self): if self.is_beta_history(): self.components.history_panel.histories_operation_menu.wait_for_and_click() self.components.history_panel.multi_view_button_beta.wait_for_and_click() else: self.components.history_panel.multi_view_button.wait_for_and_click() def history_panel_show_structure(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="show structure", menu="history options") else: self.click_history_option(self.components.history_panel.options_show_history_structure) def history_multi_view_display_collection_contents(self, collection_hid, collection_type="list"): self.open_history_multi_view() selector = self.history_panel_wait_for_hid_state(collection_hid, "ok", multi_history_panel=True) self.click(selector) next_level_element_selector = selector for _ in range(len(collection_type.split(":")) - 1): next_level_element_selector = next_level_element_selector.descendant(".dataset-collection-element") self.wait_for_and_click(next_level_element_selector) dataset_selector = next_level_element_selector.descendant(".dataset") self.wait_for_and_click(dataset_selector) def history_panel_item_view_dataset_details(self, hid): if not self.is_beta_history(): self.history_panel_ensure_showing_item_details(hid) self.hda_click_details(hid) self.components.dataset_details._.wait_for_visible() else: item = self.history_panel_item_component(hid=hid) item.dataset_operations_dropdown.wait_for_and_click() item.info_button.wait_for_and_click() self.components.dataset_details._.wait_for_visible() def history_panel_item_click_visualization_menu(self, hid): viz_button_selector = f"{self.history_panel_item_selector(hid)} .visualizations-dropdown" self.wait_for_and_click_selector(viz_button_selector) self.wait_for_selector_visible(f"{viz_button_selector} .dropdown-menu") def history_panel_item_available_visualizations_elements(self, hid): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu viz_menu_selectors = f"{self.history_panel_item_selector(hid)} a.visualization-link" return self.driver.find_elements_by_css_selector(viz_menu_selectors) def history_panel_item_get_tags(self, hid): item_component = self.history_panel_item_component(hid=hid) item_component.wait_for_visible() return [e.text for e in item_component.alltags.all()] def history_panel_item_available_visualizations(self, hid): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu return [e.text for e in self.history_panel_item_available_visualizations_elements(hid)] def history_panel_item_click_visualization(self, hid, visualization_name): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu elements = self.history_panel_item_available_visualizations_elements(hid) for element in elements: if element.text == visualization_name: element.click() return element raise ValueError(f"No visualization [{visualization_name}] found.") def history_panel_item_selector(self, hid, wait=False): current_history_id = self.current_history_id() contents = self.api_get(f"histories/{current_history_id}/contents") try: history_item = [d for d in contents if d["hid"] == hid][0] except IndexError: raise Exception(f"Could not find history item with hid [{hid}] in contents [{contents}]") history_item_selector = f"#{history_item["history_content_type"]}-{history_item["id"]}" if wait: self.wait_for_selector_visible(history_item_selector) return history_item_selector def modal_body_selector(self): return ".modal-body" def history_panel_item_body_component(self, hid, wait=False): details_component = self.history_panel_item_component(hid=hid).details if wait: details_component.wait_for_visible() return details_component def hda_click_primary_action_button(self, hid: int, button_key: str): self.history_panel_ensure_showing_item_details(hid) item_component = self.history_panel_item_component(hid=hid) button_component = item_component[f"{button_key}_button"] button_component.wait_for_and_click() def hda_click_details(self, hid: int): self.hda_click_primary_action_button(hid, "info") def history_panel_click_item_title(self, hid, **kwds): item_component = self.history_panel_item_component(hid=hid) details_component = item_component.details details_displayed = not details_component.is_absent and details_component.is_displayed item_component.title.wait_for_and_click() if kwds.get("wait", False): if details_displayed: details_component.wait_for_absent_or_hidden() else: details_component.wait_for_visible() return item_component def history_panel_ensure_showing_item_details(self, hid): if not self.history_panel_item_showing_details(hid): self.history_panel_click_item_title(hid=hid, wait=True) def history_panel_item_showing_details(self, hid): item_component = self.history_panel_item_component(hid=hid) item_component.wait_for_present() if item_component.details.is_absent: return False return item_component.details.is_displayed def collection_builder_set_name(self, name): name_element = self.wait_for_selector_visible("input.collection-name") name_element.send_keys(name) def collection_builder_hide_originals(self): self.wait_for_and_click_selector("input.hide-originals") def collection_builder_create(self): self.wait_for_and_click_selector("button.create-collection") def collection_builder_clear_filters(self): self.wait_for_and_click_selector("a.clear-filters-link") def collection_builder_click_paired_item(self, forward_or_reverse, item): assert forward_or_reverse in ["forward", "reverse"] forward_column = self.wait_for_selector_visible(f".{forward_or_reverse}-column .column-datasets") first_datset_forward = forward_column.find_elements_by_css_selector("li")[item] first_datset_forward.click() def logout_if_needed(self): if self.is_logged_in(): self.home() self.logout() def logout(self): self.components.masthead.logged_in_only.wait_for_visible() self.click_masthead_user() self.components.masthead.logout.wait_for_and_click() try: self.components.masthead.logged_out_only.wait_for_visible() except self.TimeoutException as e: message = "Clicked logout button but waiting for 'Login or Registration' button failed, perhaps the logout button was clicked before the handler was setup?" raise self.prepend_timeout_message(e, message) assert ( not self.is_logged_in() ), "Clicked to logged out and UI reflects a logout, but API still thinks a user is logged in." def run_tour(self, path, skip_steps=None, sleep_on_steps=None, tour_callback=None): skip_steps = skip_steps or [] sleep_on_steps = sleep_on_steps or {} if tour_callback is None: tour_callback = NullTourCallback() self.home() with open(path) as f: tour_dict = yaml.safe_load(f) steps = tour_dict["steps"] for i, step in enumerate(steps): title = step.get("title", None) skip = False if skip_steps: for skip_step in skip_steps: if title == skip_step: skip = True if title in sleep_on_steps: time.sleep(sleep_on_steps[title]) if skip: continue self.run_tour_step(step, i, tour_callback) def tour_wait_for_clickable_element(self, selector): timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) wait = self.wait(timeout=timeout) timeout_message = self._timeout_message(f"sizzle (jQuery) selector [{selector}] to become clickable") element = wait.until( sizzle.sizzle_selector_clickable(selector), timeout_message, ) return element def tour_wait_for_element_present(self, selector): timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) wait = self.wait(timeout=timeout) timeout_message = self._timeout_message(f"sizzle (jQuery) selector [{selector}] to become present") element = wait.until( sizzle.sizzle_presence_of_selector(selector), timeout_message, ) return element def get_tooltip_text(self, element, sleep=0, click_away=True): tooltip_balloon = self.components._.tooltip_balloon tooltip_balloon.wait_for_absent() action_chains = self.action_chains() action_chains.move_to_element(element) action_chains.perform() if sleep > 0: time.sleep(sleep) tooltip_element = tooltip_balloon.wait_for_visible() text = tooltip_element.text if click_away: self.click_center() return text @retry_during_transitions def assert_selector_absent_or_hidden_after_transitions(self, selector): """Variant of assert_selector_absent_or_hidden that retries during transitions. In the parent method - the element is found and then it is checked to see if it is visible. It may disappear from the page in the middle there and cause a StaleElement error. For checks where we care about the final resting state after transitions - this method can be used to retry during those transitions. """ return self.assert_selector_absent_or_hidden(selector) @retry_during_transitions def assert_absent_or_hidden_after_transitions(self, selector): """Variant of assert_absent_or_hidden that retries during transitions. See details above for more information about this. """ return self.assert_absent_or_hidden(selector) def assert_tooltip_text(self, element, expected: Union[str, HasText], sleep: int = 0, click_away: bool = True): if hasattr(expected, "text"): expected = cast(HasText, expected).text text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert text == expected, f"Tooltip text [{text}] was not expected text [{expected}]." def assert_tooltip_text_contains( self, element, expected: Union[str, HasText], sleep: int = 0, click_away: bool = True ): if hasattr(expected, "text"): expected = cast(HasText, expected).text text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert expected in text, f"Tooltip text [{text}] was not expected text [{expected}]." def assert_error_message(self, contains=None): self.components._.messages.error.wait_for_visible() elements = self.find_elements(self.components._.messages.selectors.error) return self.assert_message(elements, contains=contains) def assert_warning_message(self, contains=None): element = self.components._.messages["warning"] return self.assert_message(element, contains=contains) def assert_message(self, element, contains=None): if contains is not None: if type(element) == list: assert any( contains in el.text for el in element ), f"{contains} was not found in {[el.text for el in element]}" return element = element.wait_for_visible() text = element.text if contains not in text: message = f"Text [{contains}] expected inside of [{text}] but not found." raise AssertionError(message) def assert_no_error_message(self): self.components._.messages.error.assert_absent_or_hidden() def run_tour_step(self, step, step_index, tour_callback): preclick = step.get("preclick", []) for preclick_selector in preclick: print(f"(Pre)Clicking {preclick_selector}") self._tour_wait_for_and_click_element(preclick_selector) element_str = step.get("element", None) if element_str is not None: print(f"Waiting for element {element_str}") element = self.tour_wait_for_element_present(element_str) assert element is not None textinsert = step.get("textinsert", None) if textinsert is not None: element.send_keys(textinsert) tour_callback.handle_step(step, step_index) postclick = step.get("postclick", []) for postclick_selector in postclick: print(f"(Post)Clicking {postclick_selector}") self._tour_wait_for_and_click_element(postclick_selector) @retry_during_transitions def _tour_wait_for_and_click_element(self, selector): element = self.tour_wait_for_clickable_element(selector) element.click() @retry_during_transitions def wait_for_and_click_selector(self, selector): element = self.wait_for_selector_clickable(selector) element.click() return element @retry_during_transitions def wait_for_and_click(self, selector_template): element = self.wait_for_clickable(selector_template) element.click() return element def set_history_annotation(self, annotation, clear_text=False): history_panel = self.components.history_panel if self.is_beta_history(): toggle = self.beta_history_element("editor toggle") toggle.wait_for_and_click() annotation_input = self.beta_history_element("annotation input").wait_for_visible() if clear_text: annotation_input.clear() annotation_input.send_keys(annotation) self.history_click_editor_save() else: self.ensure_history_annotation_area_displayed() editable = history_panel.annotation_editable_text edit = history_panel.annotation_edit editable.wait_for_and_click() edit_el = edit.wait_for_and_click() if clear_text: # previously this was just edit_el.clear() but # .clear() doesn't work with beta history panel action_chains = self.action_chains() for _ in range(40): action_chains.send_keys(Keys.BACKSPACE) action_chains.perform() edit_el.send_keys(annotation) history_panel.annotation_done.wait_for_and_click() def ensure_history_annotation_area_displayed(self): annotation_area = self.components.history_panel.annotation_area annotation_icon = self.components.history_panel.annotation_icon if annotation_area.is_absent or not annotation_area.is_displayed: annotation_icon.wait_for_and_click() def select2_set_value(self, container_selector_or_elem, value, with_click=True, clear_value=False): # There are two hacky was to select things from the select2 widget - # with_click=True: This simulates the mouse click after the suggestion contains # only the selected value. # with_click=False: This presses enter on the selection. Not sure # why. # with_click seems to work in all situtations - the enter methods # doesn't seem to work with the tool form for some reason. if hasattr(container_selector_or_elem, "selector"): container_selector_or_elem = container_selector_or_elem.selector if not hasattr(container_selector_or_elem, "find_element_by_css_selector"): container_elem = self.wait_for_selector(container_selector_or_elem) else: container_elem = container_selector_or_elem text_element = container_elem.find_element_by_css_selector("input[type='text']") if clear_value: self.send_backspace(text_element) self.send_backspace(text_element) text_element.send_keys(value) # Wait for select2 options to load and then click to add this one. drop_elem = self.wait_for_selector_visible("#select2-drop") # Sleep seems to be needed - at least for send_enter. time.sleep(0.5) if not with_click: # Wait for select2 options to load and then click to add this one. self.send_enter(text_element) else: select_elem = drop_elem.find_elements_by_css_selector(".select2-result-label")[0] action_chains = self.action_chains() action_chains.move_to_element(select_elem).click().perform() self.wait_for_selector_absent_or_hidden("#select2-drop") def snapshot(self, description): """Test case subclass overrides this to provide detailed logging.""" def open_history_editor(self, scope=".history-index"): if self.is_beta_history(): panel = self.components.history_panel.editor.selector(scope=scope) if panel.name_input.is_absent: toggle = panel.toggle toggle.wait_for_and_click() editor = panel.form editor.wait_for_present() def close_history_editor(self, scope=".history-index"): if self.is_beta_history(): toggle = self.components.history_panel.edit_toggle toggle.wait_for_and_click() editor = self.components.history_panel.editor.selector(scope=scope) self.assert_absent_or_hidden(editor) def share_ensure_by_user_available(self, sharing_component): collapse = sharing_component.share_with_collapse collapse.wait_for_visible() if collapse.has_class("collapsed"): collapse.wait_for_and_click() sharing_component.share_with_multiselect.wait_for_visible() def share_unshare_with_user(self, sharing_component, email): self.share_ensure_by_user_available(sharing_component) unshare_user_button = self.components.histories.sharing.unshare_with_user_button(email=email) unshare_user_button.wait_for_and_click() self.components.histories.sharing.submit_sharing_with.wait_for_and_click() unshare_user_button.wait_for_absent_or_hidden() def share_with_user( self, sharing_component, user_id=None, user_email=None, screenshot_before_submit=None, screenshot_after_submit=None, assert_valid=False, ): self.share_ensure_by_user_available(sharing_component) multiselect = sharing_component.share_with_multiselect.wait_for_and_click() sharing_component.share_with_input.wait_for_and_send_keys(user_id or user_email) self.send_enter(multiselect) self.screenshot_if(screenshot_before_submit) sharing_component.submit_sharing_with.wait_for_and_click() if assert_valid: self.assert_no_error_message() xpath = f'//span[contains(text(), "{user_email}")]' self.wait_for_xpath_visible(xpath) self.screenshot_if(screenshot_after_submit) class NotLoggedInException(TimeoutException): def __init__(self, timeout_exception, user_info, dom_message): template = "Waiting for UI to reflect user logged in but it did not occur. API indicates no user is currently logged in. %s API response was [%s]. %s" msg = template % (dom_message, user_info, timeout_exception.msg) super().__init__(msg=msg, screen=timeout_exception.screen, stacktrace=timeout_exception.stacktrace)

"""A mixing that extends a HasDriver class with Galaxy-specific utilities. Implementer must provide a self.build_url method to target Galaxy. """ import collections import contextlib import random import string import time from abc import abstractmethod from functools import ( partial, wraps, ) from typing import ( Any, cast, Dict, Optional, Union, ) import requests import yaml from selenium.webdriver.common.keys import Keys from selenium.webdriver.remote.webdriver import WebDriver from galaxy.util import DEFAULT_SOCKET_TIMEOUT from . import sizzle from .components import ( Component, HasText, ) from .data import load_root_component from .has_driver import ( exception_indicates_click_intercepted, exception_indicates_not_clickable, exception_indicates_stale_element, HasDriver, TimeoutException, ) from .smart_components import SmartComponent # Test case data DEFAULT_PASSWORD = "123456" RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT = 0.1 RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT = 10 GALAXY_MAIN_FRAME_ID = "galaxy_main" WaitType = collections.namedtuple("WaitType", ["name", "default_length"]) # Default wait times should make sense for a development server under low # load. Wait times for production servers can be scaled up with a multiplier. class WAIT_TYPES: # Rendering a form and registering callbacks, etc... UX_RENDER = WaitType("ux_render", 1) # Fade in, fade out, etc... UX_TRANSITION = WaitType("ux_transition", 5) # Toastr popup and dismissal, etc... UX_POPUP = WaitType("ux_popup", 15) # Creating a new history and loading it into the panel. DATABASE_OPERATION = WaitType("database_operation", 10) # Wait time for jobs to complete in default environment. JOB_COMPLETION = WaitType("job_completion", 30) # Wait time for a GIE to spawn. GIE_SPAWN = WaitType("gie_spawn", 30) # Wait time for toolshed search SHED_SEARCH = WaitType("shed_search", 30) # Wait time for repository installation REPO_INSTALL = WaitType("repo_install", 60) # Beta history Polling Duration HISTORY_POLL = WaitType("history_poll", 3) # Choose a moderate wait type for operations that don't specify a type. DEFAULT_WAIT_TYPE = WAIT_TYPES.DATABASE_OPERATION class NullTourCallback: def handle_step(self, step, step_index): pass def exception_seems_to_indicate_transition(e): """True if exception seems to indicate the page state is transitioning. Galaxy features many different transition effects that change the page state over time. These transitions make it slightly more difficult to test Galaxy because atomic input actions take an indeterminate amount of time to be reflected on the screen. This method takes a Selenium assertion and tries to infer if such a transition could be the root cause of the exception. The methods that follow use it to allow retrying actions during transitions. Currently the two kinds of exceptions that we say may indicate a transition are StaleElement exceptions (a DOM element grabbed at one step is no longer available) and "not clickable" exceptions (so perhaps a popup modal is blocking a click). """ return ( exception_indicates_stale_element(e) or exception_indicates_not_clickable(e) or exception_indicates_click_intercepted(e) ) def retry_call_during_transitions( f, attempts=RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT, sleep=RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT, exception_check=exception_seems_to_indicate_transition, ): previous_attempts = 0 while True: try: return f() except Exception as e: if previous_attempts > attempts: raise if not exception_check(e): raise time.sleep(sleep) previous_attempts += 1 def retry_during_transitions( f, attempts=RETRY_DURING_TRANSITIONS_ATTEMPTS_DEFAULT, sleep=RETRY_DURING_TRANSITIONS_SLEEP_DEFAULT, exception_check=exception_seems_to_indicate_transition, ): @wraps(f) def _retry(*args, **kwds): return retry_call_during_transitions( partial(f, *args, **kwds), attempts=attempts, sleep=sleep, exception_check=exception_check ) return _retry def edit_details(f, scope=".history-index"): """Open the editor, run the edits, hit the save button""" @wraps(f) def func_wrapper(self, *args, **kwds): # open editor if self.is_beta_history(): self.open_history_editor(scope=scope) # run edits result = f(self, *args, **kwds) # save edits if self.is_beta_history(): self.history_click_editor_save() return result return func_wrapper class NavigatesGalaxy(HasDriver): """Class with helpers methods for driving components of the Galaxy interface. In most cases, methods for interacting with Galaxy components that appear in multiple tests or applications should be refactored into this class for now. Keep in mind that this class is used outside the context of ``TestCase`` s as well - so some methods more explicitly related to test data or assertion checking may make more sense in SeleniumTestCase for instance. Some day this class will likely be split up into smaller mixins for particular components of Galaxy, but until that day the best practice is to prefix methods for driving or querying the interface with the name of the component or page the method operates on. These serve as pseudo-namespaces until we decompose this class. For instance, the method for clicking an option in the workflow editor is workflow_editor_click_option instead of click_workflow_editor_option. """ timeout_multiplier: float driver: WebDriver @abstractmethod def build_url(self, url: str, for_selenium: bool = True) -> str: """Build URL to the target Galaxy.""" default_password = DEFAULT_PASSWORD wait_types = WAIT_TYPES # set to True to reload each invocation (good for interactive test building) _interactive_components: bool = False _root_component: Component = load_root_component() def get(self, url: str = ""): """Expand supplied relative URL and navigate to page using Selenium driver.""" full_url = self.build_url(url) return self.driver.get(full_url) @property def navigation(self) -> Component: if self._interactive_components: return load_root_component() else: return self._root_component @property def components(self) -> SmartComponent: """Fetch root component describing the Galaxy DOM.""" return SmartComponent(self.navigation, self) def wait_length(self, wait_type: WaitType) -> float: """Return the wait time specified by wait_type after applying `timeout_multipler`. `timeout_multiplier` is used in production CI tests to reduce transient failures in a uniform way across test suites to expand waiting. """ return wait_type.default_length * self.timeout_multiplier def sleep_for(self, wait_type: WaitType) -> None: """Sleep on the Python client side for the specified wait_type. This method uses `wait_length` to apply any `timeout_multiplier`. """ self.sleep_for_seconds(self.wait_length(wait_type)) def sleep_for_seconds(self, duration: float) -> None: """Sleep in the local thread for specified number of seconds. Ideally, we would be sleeping on the Selenium server instead of in the local client (e.g. test) thread. """ time.sleep(duration) def timeout_for(self, wait_type: WaitType = DEFAULT_WAIT_TYPE, **kwd) -> float: return self.wait_length(wait_type) def home(self) -> None: """Return to root Galaxy page and wait for some basic widgets to appear.""" self.get() self.components.masthead._.wait_for_visible() def go_to_trs_search(self) -> None: self.driver.get(self.build_url("workflows/trs_search")) self.components.masthead._.wait_for_visible() def go_to_trs_by_id(self) -> None: self.driver.get(self.build_url("workflows/trs_import")) self.components.masthead._.wait_for_visible() def go_to_workflow_sharing(self, workflow_id: str) -> None: self.driver.get(self.build_url(f"workflows/sharing?id={workflow_id}")) def go_to_workflow_export(self, workflow_id: str) -> None: self.driver.get(self.build_url(f"workflow/export?id={workflow_id}")) def go_to_history_sharing(self, history_id: str) -> None: self.driver.get(self.build_url(f"histories/sharing?id={history_id}")) def switch_to_main_panel(self): self.driver.switch_to.frame(GALAXY_MAIN_FRAME_ID) @contextlib.contextmanager def local_storage(self, key: str, value: Union[float, str]): """Method decorator to modify localStorage for the scope of the supplied context.""" self.driver.execute_script(f"""window.localStorage.setItem("{key}", {value});""") try: yield finally: self.driver.execute_script(f"""window.localStorage.removeItem("{key}");""") @contextlib.contextmanager def main_panel(self): """Decorator to operate within the context of Galaxy's main frame.""" try: self.switch_to_main_panel() yield finally: self.driver.switch_to.default_content() def api_get(self, endpoint, data=None, raw=False): data = data or {} full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.get( full_url, data=data, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) if raw: return response else: return response.json() def api_post(self, endpoint, data=None): data = data or {} full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.post( full_url, data=data, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) return response.json() def api_delete(self, endpoint, raw=False): full_url = self.build_url(f"api/{endpoint}", for_selenium=False) response = requests.delete( full_url, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT ) if raw: return response else: return response.json() def get_galaxy_session(self): for cookie in self.driver.get_cookies(): if cookie["name"] == "galaxysession": return cookie["value"] def selenium_to_requests_cookies(self): return {"galaxysession": self.get_galaxy_session()} def history_panel_name_element(self): if self.is_beta_history(): component = self.beta_history_element("name display") else: component = self.components.history_panel.name return component.wait_for_present() @retry_during_transitions def history_panel_name(self): return self.history_panel_name_element().text def history_panel_collection_rename(self, hid: int, new_name: str, assert_old_name: Optional[str] = None): collection_view = self.history_panel_expand_collection(hid) if self.is_beta_history(): self.__beta_rename_collection(new_name) self.sleep_for(WAIT_TYPES.UX_RENDER) else: title_element = collection_view.title.wait_for_visible() if assert_old_name is not None: assert title_element.text == assert_old_name title_element.click() title_rename_element = collection_view.title_input.wait_for_visible() title_rename_element.send_keys(new_name) self.send_enter(title_rename_element) def history_panel_expand_collection(self, collection_hid: int) -> SmartComponent: self.history_panel_click_item_title(collection_hid) collection_view = self.components.history_panel.collection_view collection_view._.wait_for_present() return collection_view @edit_details def __beta_rename_collection(self, new_name): title_element = self.beta_history_element("name input").wait_for_clickable() title_element.clear() title_element.send_keys(new_name) def history_panel_collection_name_element(self): if self.is_beta_history(): title_element = self.beta_history_element("collection name display").wait_for_present() else: collection_view = self.components.history_panel.collection_view collection_view._.wait_for_present() title_element = collection_view.title.wait_for_visible() return title_element def make_accessible_and_publishable(self): self.components.histories.sharing.make_accessible.wait_for_and_click() self.components.histories.sharing.make_publishable.wait_for_and_click() def history_contents(self, history_id=None, view="summary", datasets_only=True): if history_id is None: history_id = self.current_history_id() histories = self.api_get("histories?keys=id") if history_id not in [h["id"] for h in histories]: return {} if datasets_only: endpoint = f"histories/{history_id}/contents?view={view}" else: endpoint = f"histories/{history_id}?view={view}" return self.api_get(endpoint) def current_history(self): full_url = self.build_url("history/current_history_json", for_selenium=False) response = requests.get(full_url, cookies=self.selenium_to_requests_cookies(), timeout=DEFAULT_SOCKET_TIMEOUT) return response.json() def current_history_id(self): return self.current_history()["id"] def latest_history_item(self): history_contents = self.history_contents() assert len(history_contents) > 0 return history_contents[-1] def wait_for_history(self, assert_ok=True): def history_becomes_terminal(driver): current_history_id = self.current_history_id() state = self.api_get(f"histories/{current_history_id}")["state"] if state not in ["running", "queued", "new", "ready"]: return state else: return None timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) final_state = self.wait(timeout=timeout).until(history_becomes_terminal) if assert_ok: assert final_state == "ok", final_state return final_state def history_panel_create_new_with_name(self, name): self.history_panel_create_new() self.history_panel_rename(name) def history_panel_create_new(self): """Click create new and pause a bit for the history to begin to refresh.""" self.history_click_create_new() self.sleep_for(WAIT_TYPES.UX_RENDER) def history_panel_wait_for_hid_ok(self, hid, allowed_force_refreshes=0): return self.history_panel_wait_for_hid_state(hid, "ok", allowed_force_refreshes=allowed_force_refreshes) def history_panel_wait_for_hid_deferred(self, hid, allowed_force_refreshes=0): return self.history_panel_wait_for_hid_state(hid, "deferred", allowed_force_refreshes=allowed_force_refreshes) def history_panel_item_component(self, history_item=None, hid=None, multi_history_panel=False): if self.is_beta_history(): assert hid return self.content_item_by_attributes(hid=hid) if history_item is None: assert hid history_item = self.hid_to_history_item(hid) item = self.components.history_panel.item.selector if multi_history_panel: item = self.components.multi_history_view.item return item(history_content_type=history_item["history_content_type"], id=history_item["id"]) def wait_for_history_to_have_hid(self, history_id, hid): def get_hids(): contents = self.api_get(f"histories/{history_id}/contents") return [d["hid"] for d in contents] def history_has_hid(driver): hids = get_hids() return any(h == hid for h in hids) timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) try: self.wait(timeout).until(history_has_hid) except self.TimeoutException as e: hids = get_hids() message = f"Timeout waiting for history {history_id} to have hid {hid} - have hids {hids}" raise self.prepend_timeout_message(e, message) def history_panel_wait_for_hid_visible(self, hid, allowed_force_refreshes=0, multi_history_panel=False): current_history_id = self.current_history_id() self.wait_for_history_to_have_hid(current_history_id, hid) # TODO: just use HID and stop resolving history_item -or- place history item in DOM. # I think Mason thought the first was cleaner based on recent changes, but I think duplicated # HIDs due to conversions and such make using the actual history item ID more robust. history_item = self.hid_to_history_item(hid, current_history_id=current_history_id) history_item_selector = self.history_panel_item_component( history_item, hid=hid, multi_history_panel=multi_history_panel ) try: self.history_item_wait_for(history_item_selector, allowed_force_refreshes) except self.TimeoutException as e: selector = self.navigation.history_panel.selectors.contents if self.is_beta_history(): selector = self.navigation.history_panel.selectors.contents_beta contents_elements = self.find_elements(selector) div_ids = [f"#{d.get_attribute('id')}" for d in contents_elements] template = "Failed waiting on history item %d to become visible, visible datasets include [%s]." message = template % (hid, ",".join(div_ids)) raise self.prepend_timeout_message(e, message) return history_item_selector def hid_to_history_item(self, hid, current_history_id=None): if current_history_id is None: current_history_id = self.current_history_id() contents = self.api_get(f"histories/{current_history_id}/contents") history_item = [d for d in contents if d["hid"] == hid][0] return history_item def history_item_wait_for(self, history_item_selector, allowed_force_refreshes): attempt = 0 while True: try: rval = self.wait_for_visible(history_item_selector, wait_type=WAIT_TYPES.JOB_COMPLETION) break except self.TimeoutException: if attempt >= allowed_force_refreshes: raise attempt += 1 if not self.is_beta_history(): self.history_panel_refresh_click() return rval def history_panel_wait_for_history_loaded(self): # Verify that the history has been loaded and is empty. self.wait_for_visible( self.navigation.history_panel.selectors.empty_message, wait_type=WAIT_TYPES.DATABASE_OPERATION ) def history_panel_wait_for_hid_hidden(self, hid, multi_history_panel=False): history_item_selector = self.history_panel_item_component(hid=hid, multi_history_panel=multi_history_panel) self.wait_for_absent_or_hidden(history_item_selector, wait_type=WAIT_TYPES.JOB_COMPLETION) return history_item_selector def history_panel_wait_for_hid_state(self, hid, state, allowed_force_refreshes=0, multi_history_panel=False): history_item_selector = self.history_panel_wait_for_hid_visible( hid, allowed_force_refreshes=allowed_force_refreshes, multi_history_panel=multi_history_panel ) if self.is_beta_history(): history_item_selector_state = self.content_item_by_attributes(hid=hid, state=state) else: # history_item_selector_state = history_item_selector.with_class(f"state-{state}") history_item_selector_state = history_item_selector.with_data("state", state) try: self.history_item_wait_for(history_item_selector_state, allowed_force_refreshes) except self.TimeoutException as e: history_item = self.wait_for_visible(history_item_selector) current_state = "UNKNOWN" classes = history_item.get_attribute("class").split(" ") for clazz in classes: if clazz.startswith("state-"): current_state = clazz[len("state-") :] template = "Failed waiting on history item %d state to change to [%s] current state [%s]. " message = template % (hid, state, current_state) raise self.prepend_timeout_message(e, message) return history_item_selector_state def click_grid_popup_option(self, item_name, option_label): item_button = None grid = self.components.grids.body.wait_for_visible() for row in grid.find_elements_by_tag_name("tr"): name_cell = row.find_elements_by_tag_name("td")[1] if name_cell.text == item_name: item_button = name_cell break if item_button is None: raise AssertionError(f"Failed to find item with name [{item_name}]") popup_menu_button = item_button.find_element_by_css_selector(".dropdown-toggle") popup_menu_button.click() popup_option = self.driver.find_element_by_link_text(option_label) popup_option.click() def published_grid_search_for(self, search_term=None): return self._inline_search_for( self.navigation.grids.free_text_search, search_term, ) def get_logged_in_user(self): return self.api_get("users/current") def get_api_key(self, force=False): # If force is false, use the form inputs API and allow the key to be absent. if not force: return self.api_get(f"users/{self.get_user_id()}/api_key/inputs")["inputs"][0]["value"] else: return self.api_post(f"users/{self.get_user_id()}/api_key") def get_user_id(self): user = self.get_logged_in_user() return user["id"] def is_logged_in(self): return "email" in self.get_logged_in_user() @retry_during_transitions def _inline_search_for(self, selector, search_term=None, escape_to_clear=False): # Clear tooltip resulting from clicking on the masthead to get here. self.clear_tooltips() search_box = self.wait_for_and_click(selector) if escape_to_clear: # The combination of DebouncedInput+b-input doesn't seem to uniformly respect # .clear() below. We use escape handling a lot - and that does cauase to the input # to reset correctly and fire the required re-active events/handlers. self.send_escape(search_box) search_box.clear() if search_term is not None: search_box.send_keys(search_term) self.send_enter(search_box) return search_box def _get_random_name(self, prefix=None, suffix=None, len=10): return "{}{}{}".format( prefix or "", "".join(random.choice(string.ascii_lowercase + string.digits) for _ in range(len)), suffix or "", ) def _get_random_email(self, username=None, domain=None): username = username or "test" domain = domain or "test.test" return self._get_random_name(prefix=username, suffix=f"@{domain}") # Creates a random password of length len by creating an array with all ASCII letters and the numbers 0 to 9, # then using the random number generator to pick one elemenent to concatinate it to the end of the password string until # we have a password of length len. def _get_random_password(self, len=6): return "".join(random.SystemRandom().choice(string.ascii_letters + string.digits) for _ in range(len)) def submit_login(self, email, password=None, assert_valid=True, retries=0): self.components.masthead.register_or_login.wait_for_and_click() self.sleep_for(WAIT_TYPES.UX_RENDER) self.fill_login_and_submit(email, password=password) if assert_valid: try: self.wait_for_logged_in() except NotLoggedInException: self.snapshot("login-failed") if retries > 0: self.submit_login(email, password, assert_valid, retries - 1) else: raise self.snapshot("logged-in") def fill_login_and_submit(self, email, password=None): if password is None: password = self.default_password login_info = { "login": email, "password": password, } form = self.wait_for_visible(self.navigation.login.selectors.form) self.fill(form, login_info) self.snapshot("logging-in") self.wait_for_and_click(self.navigation.login.selectors.submit) self.snapshot("login-submitted") def register(self, email=None, password=None, username=None, confirm=None, assert_valid=True): if email is None: email = self._get_random_email() if password is None: password = self.default_password if confirm is None: confirm = password if username is None: username = email.split("@")[0] self.home() self.components.masthead.register_or_login.wait_for_and_click() self.wait_for_and_click(self.navigation.registration.selectors.toggle) form = self.wait_for_visible(self.navigation.registration.selectors.form) self.fill(form, dict(email=email, password=password, username=username, confirm=confirm)) self.wait_for_and_click(self.navigation.registration.selectors.submit) if assert_valid is False: self.assert_error_message() elif assert_valid: self.wait_for_logged_in() # Code below previously was needed because there was a bug that would prevent the masthead from changing, # the bug seems maybe to be fixed though - so we could consider eliminating these extra checks to speed # up tests. self.home() self.wait_for_logged_in() self.click_masthead_user() # Make sure the user menu was dropped down user_menu = self.components.masthead.user_menu.wait_for_visible() try: username_element = self.components.masthead.username.wait_for_visible() except self.TimeoutException as e: menu_items = user_menu.find_elements_by_css_selector("li a") menu_text = [mi.text for mi in menu_items] message = f"Failed to find logged in message in menu items {', '.join(menu_text)}" raise self.prepend_timeout_message(e, message) text = username_element.text assert username in text assert self.get_logged_in_user()["email"] == email # clicking away no longer closes menu post Masthead -> VueJS self.click_masthead_user() def wait_for_logged_in(self): try: self.components.masthead.logged_in_only.wait_for_visible() except self.TimeoutException as e: ui_logged_out = self.components.masthead.logged_out_only.is_displayed if ui_logged_out: dom_message = ( "Element a.loggedout-only is present in DOM, indicating Login or Register button still in masthead." ) else: dom_message = "Element a.loggedout-only is *not* present in DOM." user_info = self.api_get("users/current") if "username" in user_info: template = "Failed waiting for masthead to update for login, but user API response indicates [%s] is logged in. This seems to be a bug in Galaxy. %s logged API response was [%s]. " message = template % (user_info["username"], dom_message, user_info) raise self.prepend_timeout_message(e, message) else: raise NotLoggedInException(e, user_info, dom_message) def click_center(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).click().perform() def perform_upload(self, test_path, **kwd): self._perform_upload(test_path=test_path, **kwd) def perform_upload_of_pasted_content(self, paste_data, **kwd): self._perform_upload(paste_data=paste_data, **kwd) def _perform_upload( self, test_path=None, paste_data=None, ext=None, genome=None, ext_all=None, genome_all=None, deferred=None ): self.home() self.upload_start_click() self.upload_set_footer_extension(ext_all) self.upload_set_footer_genome(genome_all) if test_path: self.upload_queue_local_file(test_path) else: assert paste_data is not None self.upload_paste_data(paste_data) if ext is not None: self.wait_for_selector_visible(".upload-extension") self.select2_set_value(".upload-extension", ext) if genome is not None: self.wait_for_selector_visible(".upload-genome") self.select2_set_value(".upload-genome", genome) if deferred is not None: upload = self.components.upload upload.settings_button(n=0).wait_for_and_click() upload.settings.wait_for_visible() setting = upload.setting_deferred.wait_for_visible() classes = setting.get_attribute("class").split(" ") if deferred is True and "fa-check-square-o" not in classes: setting.click() elif deferred is False and "fa-check-square-o" in classes: setting.click() self.upload_start() self.wait_for_and_click_selector("button#btn-close") def upload_list(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "List") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_set_name(name) self.collection_builder_create() def upload_pair(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "Pair") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_set_name(name) self.collection_builder_create() def upload_paired_list(self, test_paths, name="test", ext=None, genome=None, hide_source_items=True): self._collection_upload_start(test_paths, ext, genome, "List of Pairs") if not hide_source_items: self.collection_builder_hide_originals() self.collection_builder_clear_filters() # TODO: generalize and loop these clicks so we don't need the assert assert len(test_paths) == 2 self.collection_builder_click_paired_item("forward", 0) self.collection_builder_click_paired_item("reverse", 1) self.collection_builder_set_name(name) self.collection_builder_create() def _collection_upload_start(self, test_paths, ext, genome, collection_type): # Perform upload of files and open the collection builder for specified # type. self.home() self.upload_start_click() self.upload_tab_click("collection") self.upload_set_footer_extension(ext, tab_id="collection") self.upload_set_footer_genome(genome, tab_id="collection") self.upload_set_collection_type(collection_type) for test_path in test_paths: self.upload_queue_local_file(test_path, tab_id="collection") self.upload_start(tab_id="collection") self.upload_build() def upload_tab_click(self, tab): self.components.upload.tab(tab=tab).wait_for_and_click() def upload_start_click(self): self.components.upload.start.wait_for_and_click() @retry_during_transitions def upload_set_footer_extension(self, ext, tab_id="regular"): if ext is not None: selector = f"div#{tab_id} .upload-footer-extension" self.wait_for_selector_visible(selector) self.select2_set_value(selector, ext) @retry_during_transitions def upload_set_footer_genome(self, genome, tab_id="regular"): if genome is not None: selector = f"div#{tab_id} .upload-footer-genome" self.wait_for_selector_visible(selector) self.select2_set_value(selector, genome) @retry_during_transitions def upload_set_collection_type(self, collection_type): self.wait_for_selector_visible(".upload-footer-collection-type") self.select2_set_value(".upload-footer-collection-type", collection_type) def upload_start(self, tab_id="regular"): self.wait_for_and_click_selector(f"div#{tab_id} button#btn-start") @retry_during_transitions def upload_build(self, tab="collection"): build_selector = f"div#{tab} button#btn-build" # Pause a bit to let the callback on the build button be registered. time.sleep(0.5) # Click the Build button and make sure it disappears. self.wait_for_and_click_selector(build_selector) try: self.wait_for_selector_absent_or_hidden(build_selector) except TimeoutException: # Sometimes the callback in the JS hasn't be registered by the # time that the build button is clicked. By the time the timeout # has been registered - it should have been. self.wait_for_and_click_selector(build_selector) self.wait_for_selector_absent_or_hidden(build_selector) def upload_queue_local_file(self, test_path, tab_id="regular"): self.wait_for_and_click_selector(f"div#{tab_id} button#btn-local") file_upload = self.wait_for_selector(f'div#{tab_id} input[type="file"]') file_upload.send_keys(test_path) def upload_paste_data(self, pasted_content, tab_id="regular"): tab_locator = f"div#{tab_id}" self.wait_for_and_click_selector(f"{tab_locator} button#btn-new") textarea = self.wait_for_selector(f"{tab_locator} .upload-text-content") textarea.send_keys(pasted_content) def upload_rule_start(self): self.upload_start_click() self.upload_tab_click("rule-based") def upload_rule_build(self): self.upload_build(tab="rule-based") def upload_rule_set_data_type(self, type_description): upload = self.components.upload data_type_element = upload.rule_select_data_type.wait_for_visible() self.select2_set_value(data_type_element, type_description) def upload_rule_set_input_type(self, input_description): upload = self.components.upload input_type_element = upload.rule_select_input_type.wait_for_visible() self.select2_set_value(input_type_element, input_description) def upload_rule_set_dataset(self, row=1): upload = self.components.upload upload.rule_dataset_selector.wait_for_visible() upload.rule_dataset_selector_row(rowindex=row).wait_for_and_click() def rule_builder_set_collection_name(self, name): rule_builder = self.components.rule_builder name_element = rule_builder.collection_name_input.wait_for_and_click() name_element.send_keys(name) self.sleep_for(WAIT_TYPES.UX_RENDER) def rule_builder_set_extension(self, extension): self.select2_set_value(self.navigation.rule_builder.selectors.extension_select, extension) def rule_builder_filter_count(self, count=1): rule_builder = self.components.rule_builder rule_builder.menu_button_filter.wait_for_and_click() with self.rule_builder_rule_editor("add-filter-count") as editor_element: filter_input = editor_element.find_element_by_css_selector("input[type='number']") filter_input.clear() filter_input.send_keys(f"{count}") def rule_builder_sort(self, column_label, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("sort") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) self.screenshot_if(screenshot_name) def rule_builder_add_regex_groups(self, column_label, group_count, regex, screenshot_name): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-regex") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) groups_elem = editor_element.find_element_by_css_selector("input[type='radio'][value='groups']") groups_elem.click() regex_elem = editor_element.find_element_by_css_selector("input.rule-regular-expression") regex_elem.clear() regex_elem.send_keys(regex) filter_input = editor_element.find_element_by_css_selector("input[type='number']") filter_input.clear() filter_input.send_keys(f"{group_count}") self.screenshot_if(screenshot_name) def rule_builder_add_regex_replacement(self, column_label, regex, replacement, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-regex") as editor_element: column_elem = editor_element.find_element_by_css_selector(".rule-column-selector") self.select2_set_value(column_elem, column_label) groups_elem = editor_element.find_element_by_css_selector("input[type='radio'][value='replacement']") groups_elem.click() regex_elem = editor_element.find_element_by_css_selector("input.rule-regular-expression") regex_elem.clear() regex_elem.send_keys(regex) filter_input = editor_element.find_element_by_css_selector("input.rule-replacement") filter_input.clear() filter_input.send_keys(f"{replacement}") self.screenshot_if(screenshot_name) def rule_builder_add_value(self, value, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-value") as editor_element: filter_input = editor_element.find_element_by_css_selector("input[type='text']") filter_input.clear() filter_input.send_keys(value) self.screenshot_if(screenshot_name) def rule_builder_remove_columns(self, column_labels, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("remove-columns") as filter_editor_element: column_elem = filter_editor_element.find_element_by_css_selector(".rule-column-selector") for column_label in column_labels: self.select2_set_value(column_elem, column_label) self.screenshot_if(screenshot_name) def rule_builder_concatenate_columns(self, column_label_1, column_label_2, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_column.wait_for_and_click() with self.rule_builder_rule_editor("add-column-concatenate") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[0], column_label_1) column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[1], column_label_2) self.screenshot_if(screenshot_name) def rule_builder_split_columns(self, column_labels_1, column_labels_2, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("split-columns") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") clear = True for column_label_1 in column_labels_1: self.select2_set_value(column_elems[0], column_label_1, clear_value=clear) clear = False column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") clear = True for column_label_2 in column_labels_2: self.select2_set_value(column_elems[1], column_label_2, clear_value=clear) clear = False self.screenshot_if(screenshot_name) def rule_builder_swap_columns(self, column_label_1, column_label_2, screenshot_name): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() with self.rule_builder_rule_editor("swap-columns") as filter_editor_element: column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[0], column_label_1) column_elems = filter_editor_element.find_elements_by_css_selector(".rule-column-selector") self.select2_set_value(column_elems[1], column_label_2) self.screenshot_if(screenshot_name) @contextlib.contextmanager def rule_builder_rule_editor(self, rule_type): rule_builder = self.components.rule_builder rule_builder.menu_item_rule_type(rule_type=rule_type).wait_for_and_click() filter_editor = rule_builder.rule_editor(rule_type=rule_type) filter_editor_element = filter_editor.wait_for_visible() yield filter_editor_element rule_builder.rule_editor_ok.wait_for_and_click() def rule_builder_set_mapping(self, mapping_type, column_label, screenshot_name=None): rule_builder = self.components.rule_builder rule_builder.menu_button_rules.wait_for_and_click() rule_builder.menu_item_rule_type(rule_type="mapping").wait_for_and_click() rule_builder.add_mapping_menu.wait_for_and_click() rule_builder.add_mapping_button(mapping_type=mapping_type).wait_for_and_click() if mapping_type != "list-identifiers" or not isinstance(column_label, list): mapping_elem = rule_builder.mapping_edit(mapping_type=mapping_type).wait_for_visible() self.select2_set_value(mapping_elem, column_label) self.screenshot_if(screenshot_name) else: assert len(column_label) > 0 column_labels = column_label for i, column_label in enumerate(column_labels): if i > 0: rule_builder.mapping_add_column(mapping_type=mapping_type).wait_for_and_click() mapping_elem = rule_builder.mapping_edit(mapping_type=mapping_type).wait_for_visible() self.select2_set_value(mapping_elem, column_label) self.screenshot_if(screenshot_name) rule_builder.mapping_ok.wait_for_and_click() def rule_builder_set_source(self, json): rule_builder = self.components.rule_builder rule_builder.view_source.wait_for_and_click() self.rule_builder_enter_source_text(json) rule_builder.main_button_ok.wait_for_and_click() rule_builder.view_source.wait_for_visible() def rule_builder_enter_source_text(self, json): rule_builder = self.components.rule_builder text_area_elem = rule_builder.source.wait_for_visible() text_area_elem.clear() text_area_elem.send_keys(json) def workflow_editor_click_option(self, option_label): self.workflow_editor_click_options() menu_element = self.workflow_editor_options_menu_element() option_elements = menu_element.find_elements_by_css_selector("a") assert len(option_elements) > 0, "Failed to find workflow editor options" self.sleep_for(WAIT_TYPES.UX_RENDER) found_option = False for option_element in option_elements: if option_label in option_element.text: action_chains = self.action_chains() action_chains.move_to_element(option_element) action_chains.click() action_chains.perform() found_option = True break if not found_option: raise Exception(f"Failed to find workflow editor option with label [{option_label}]") def workflow_editor_click_options(self): return self.wait_for_and_click_selector("#workflow-options-button") def workflow_editor_options_menu_element(self): return self.wait_for_selector_visible("#workflow-options-button") def workflow_editor_click_run(self): return self.wait_for_and_click_selector("#workflow-run-button") def workflow_editor_click_save(self): self.wait_for_and_click_selector("#workflow-save-button") self.sleep_for(self.wait_types.DATABASE_OPERATION) def navigate_to_histories_page(self): self.home() self.click_masthead_user() self.components.masthead.histories.wait_for_and_click() def navigate_to_user_preferences(self): self.home() self.click_masthead_user() self.components.masthead.preferences.wait_for_and_click() def navigate_to_invocations(self): self.home() self.click_masthead_user() self.components.masthead.invocations.wait_for_and_click() def navigate_to_pages(self): self.home() self.click_masthead_user() self.components.masthead.pages.wait_for_and_click() def admin_open(self): self.components.masthead.admin.wait_for_and_click() def select_dataset_from_lib_import_modal(self, filenames): for name in filenames: self.components.libraries.folder.select_import_dir_item(name=name).wait_for_and_click() self.components.libraries.folder.import_dir_btn.wait_for_and_click() def create_new_library(self, login=True): if login: self.admin_login() self.libraries_open() self.name = self._get_random_name(prefix="testcontents") self.libraries_index_create(self.name) def libraries_open(self): self.home() self.click_masthead_shared_data() self.components.masthead.libraries.wait_for_and_click() self.components.libraries.selector.wait_for_visible() def libraries_open_with_name(self, name): self.libraries_open() self.libraries_index_search_for(name) self.libraries_index_table_elements()[0].find_element_by_css_selector("td a").click() @retry_during_transitions def libraries_index_table_elements(self): container = self.components.libraries._.wait_for_visible() elements = container.find_elements_by_css_selector("tbody") if not elements: return [] else: assert len(elements) == 1 element = elements[0] return element.find_elements_by_css_selector("tr") # [style='display: table-row'] def libraries_index_create(self, name): self.components.libraries.create_new_library_btn.wait_for_and_click() name_input_field = self.components.libraries.new_library_name_input.wait_for_visible() input_field = self.components.libraries.new_library_description_input.wait_for_visible() name_input_field.send_keys(name) input_field.send_keys(self._get_random_name(prefix="description")) self.components.libraries.save_new_library_btn.wait_for_and_click() def libraries_index_click_search(self): self.sleep_for(WAIT_TYPES.UX_RENDER) search_element = self.components.libraries.search_field.wait_for_visible() search_element.click() return search_element def libraries_index_sort_selector(self): return "th[aria-sort]" def libraries_index_sort_click(self): sort_element = self.wait_for_selector_clickable(self.libraries_index_sort_selector()) sort_element.click() return sort_element def libraries_index_search_for(self, text): self.wait_for_overlays_cleared() search_box = self.libraries_index_click_search() search_box.clear() search_box.send_keys(text) value = search_box.get_attribute("value") assert value == text, value def libraries_folder_create(self, name): self.components.libraries.folder.add_folder.wait_for_and_click() self.components.libraries.folder.input_folder_name.wait_for_and_send_keys(name) self.components.libraries.folder.save_folder_btn.wait_for_and_click() def libraries_click_dataset_import(self): self.wait_for_and_click(self.navigation.libraries.folder.selectors.add_items_button) self.wait_for_visible(self.navigation.libraries.folder.selectors.add_items_menu) def libraries_dataset_import(self, btn): self.libraries_click_dataset_import() self.wait_for_and_click(btn) def libraries_dataset_import_from_history_select(self, to_select_items): self.wait_for_visible(self.navigation.libraries.folder.selectors.import_history_content) history_elements = self.find_elements(self.navigation.libraries.folder.selectors.import_history_contents_items) for to_select_item in to_select_items: found = False for history_element in history_elements: if to_select_item in history_element.text: history_element.find_element_by_css_selector("input").click() found = True break if not found: raise Exception(f"Failed to find history item [{to_select_item}] to select") def libraries_dataset_import_from_history_click_ok(self, wait=True): self.wait_for_and_click(self.navigation.libraries.folder.selectors.import_datasets_ok_button) if wait: # Let the progress bar disappear... self.wait_for_absent_or_hidden(self.navigation.libraries.folder.selectors.import_progress_bar) def libraries_table_elements(self): tbody_element = self.wait_for_selector_visible("#folder_list_body > tbody") return tbody_element.find_elements_by_css_selector("tr:not(.b-table-empty-row)") def populate_library_folder_from_import_dir(self, library_name, filenames): self.libraries_open_with_name(library_name) self.libraries_dataset_import(self.navigation.libraries.folder.labels.from_import_dir) self.select_dataset_from_lib_import_modal(filenames) def navigate_to_new_library(self, login=True): self.create_new_library(login) self.libraries_open_with_name(self.name) def wait_for_overlays_cleared(self): """Wait for modals and Toast notifications to disappear.""" self.wait_for_selector_absent_or_hidden(".ui-modal", wait_type=WAIT_TYPES.UX_POPUP) self.wait_for_selector_absent_or_hidden(".toast", wait_type=WAIT_TYPES.UX_POPUP) def clear_tooltips(self): action_chains = self.action_chains() center_element = self.driver.find_element_by_css_selector("#center") action_chains.move_to_element(center_element).perform() self.wait_for_selector_absent_or_hidden(".b-tooltip", wait_type=WAIT_TYPES.UX_POPUP) def workflow_index_open(self): self.home() self.click_masthead_workflow() def workflow_index_table_elements(self): workflows = self.components.workflows workflows.workflow_table.wait_for_visible() return workflows.workflow_rows.all() def workflow_index_table_row(self, workflow_index=0): self.components.workflows.workflow_rows.wait_for_element_count_of_at_least(workflow_index + 1) return self.workflow_index_table_elements()[workflow_index] @retry_during_transitions def workflow_index_column_text(self, column_index, workflow_index=0): row_element = self.workflow_index_table_row(workflow_index=workflow_index) columns = row_element.find_elements_by_css_selector("td") return columns[column_index].text def workflow_index_click_search(self): return self.wait_for_and_click_selector("#workflow-search") def workflow_index_search_for(self, search_term=None): return self._inline_search_for( self.navigation.workflows.search_box, search_term, escape_to_clear=True, ) def workflow_index_click_import(self): return self.components.workflows.import_button.wait_for_and_click() def workflow_index_rename(self, new_name, workflow_index=0): self.workflow_index_click_option("Rename", workflow_index=workflow_index) alert = self.driver.switch_to.alert alert.send_keys(new_name) alert.accept() @retry_during_transitions def workflow_index_name(self, workflow_index=0): """Get workflow name for workflow_index'th row.""" row_element = self.workflow_index_table_row(workflow_index=workflow_index) workflow_button = row_element.find_element_by_css_selector(".workflow-dropdown") return workflow_button.text @retry_during_transitions def workflow_click_option(self, workflow_selector, workflow_index=0): workflow_row = self.workflow_index_table_row(workflow_index=workflow_index) workflow_button = workflow_row.find_element_by_css_selector(workflow_selector) workflow_button.click() def select_dropdown_item(self, option_title): menu_element = self.wait_for_selector_visible(".dropdown-menu.show") menu_options = menu_element.find_elements_by_css_selector("a.dropdown-item") for menu_option in menu_options: if option_title in menu_option.text: menu_option.click() return True def workflow_index_click_option(self, option_title, workflow_index=0): self.workflow_click_option(".workflow-dropdown", workflow_index) if not self.select_dropdown_item(option_title): raise AssertionError(f"Failed to find workflow action option with title [{option_title}]") def workflow_index_click_tag_display(self, workflow_index=0): workflow_row_element = self.workflow_index_table_row(workflow_index) tag_display = workflow_row_element.find_element_by_css_selector(".tags-display") tag_display.click() def workflow_index_add_tag(self, tag: str, workflow_index: int = 0): self.workflow_index_click_tag_display(workflow_index=workflow_index) self.tagging_add([tag]) @retry_during_transitions def workflow_index_tags(self, workflow_index=0): tag_spans = self.workflow_index_tag_elements(workflow_index=workflow_index) tags = [] for tag_span in tag_spans: tags.append(tag_span.text) return tags @retry_during_transitions def workflow_index_tag_elements(self, workflow_index=0): workflow_row_element = self.workflow_index_table_row(workflow_index) tag_display = workflow_row_element.find_element_by_css_selector(".tags-display") tag_spans = tag_display.find_elements_by_css_selector(".tag-name") return tag_spans @retry_during_transitions def workflow_index_click_tag(self, tag, workflow_index=0): tag_spans = self.workflow_index_tag_elements(workflow_index=workflow_index) clicked = False for tag_span in tag_spans: if tag_span.text == tag: tag_span.click() clicked = True break if not clicked: raise KeyError(f"Failed to find tag {tag} on workflow with index {workflow_index}") def workflow_import_submit_url(self, url): form_button = self.wait_for_selector_visible("#workflow-import-button") url_element = self.wait_for_selector_visible("#workflow-import-url-input") url_element.send_keys(url) form_button.click() def workflow_sharing_click_publish(self): self.wait_for_and_click_selector("input[name='make_accessible_and_publish']") def tagging_add(self, tags, auto_closes=True, parent_selector=""): for i, tag in enumerate(tags): if auto_closes or i == 0: tag_area = f"{parent_selector}.tags-input input[type='text']" tag_area = self.wait_for_selector_clickable(tag_area) tag_area.click() tag_area.send_keys(tag) self.send_enter(tag_area) def workflow_run_with_name(self, name: str): self.workflow_index_open() self.workflow_index_search_for(name) self.workflow_click_option(".workflow-run") def workflow_run_specify_inputs(self, inputs: Dict[str, Any]): workflow_run = self.components.workflow_run for label, value in inputs.items(): input_div_element = workflow_run.input_data_div(label=label).wait_for_visible() self.select2_set_value(input_div_element, "%d: " % value["hid"]) def workflow_run_submit(self): self.components.workflow_run.run_workflow.wait_for_and_click() def workflow_run_ensure_expanded(self): workflow_run = self.components.workflow_run if workflow_run.expanded_form.is_absent: workflow_run.expand_form_link.wait_for_and_click() workflow_run.expanded_form.wait_for_visible() def workflow_create_new(self, annotation=None, clear_placeholder=False): self.workflow_index_open() self.sleep_for(self.wait_types.UX_RENDER) self.click_button_new_workflow() self.sleep_for(self.wait_types.UX_RENDER) form_element = self.driver.find_element_by_id("submit") name = self._get_random_name() annotation = annotation or self._get_random_name() inputs = self.driver.find_elements_by_class_name("ui-input") if clear_placeholder: inputs[0].clear() inputs[0].send_keys(name) inputs[1].send_keys(annotation) form_element.click() return name def invocation_index_table_elements(self): invocations = self.components.invocations invocations.invocations_table.wait_for_visible() return invocations.invocations_table_rows.all() def tool_open(self, tool_id, outer=False): if outer: tool_link = self.components.tool_panel.outer_tool_link(tool_id=tool_id) else: tool_link = self.components.tool_panel.tool_link(tool_id=tool_id) tool_element = tool_link.wait_for_present() self.driver.execute_script("arguments[0].scrollIntoView(true);", tool_element) tool_link.wait_for_and_click() def create_page_and_edit(self, name=None, slug=None, content_format=None, screenshot_name=None): name = self.create_page(name=name, slug=slug, content_format=content_format, screenshot_name=screenshot_name) self.click_grid_popup_option(name, "Edit content") self.components.pages.editor.wym_iframe.wait_for_visible() return name def create_page(self, name=None, slug=None, content_format=None, screenshot_name=None): self.components.pages.create.wait_for_and_click() name = name or self._get_random_name(prefix="page") slug = slug = self._get_random_name(prefix="pageslug") content_format = content_format or "HTML" self.tool_set_value("title", name) self.tool_set_value("slug", slug) self.tool_set_value("content_format", content_format, expected_type="select") self.screenshot_if(screenshot_name) # Sometimes 'submit' button not yet hooked up? self.sleep_for(self.wait_types.UX_RENDER) self.components.pages.submit.wait_for_and_click() return name def tool_parameter_div(self, expanded_parameter_id): return self.components.tool_form.parameter_div(parameter=expanded_parameter_id).wait_for_clickable() def tool_parameter_edit_rules(self): rules_div_element = self.tool_parameter_div("rules") edit_button_element = rules_div_element.find_element_by_css_selector("i.fa-edit") edit_button_element.click() def tool_set_value(self, expanded_parameter_id, value, expected_type=None): div_element = self.tool_parameter_div(expanded_parameter_id) assert div_element if expected_type in ["select", "data", "data_collection"]: div_selector = f"div.ui-form-element[id$='form-element-{expanded_parameter_id}']" self.select2_set_value(div_selector, value) else: input_element = div_element.find_element_by_css_selector("input") # Clear default value input_element.clear() input_element.send_keys(value) def tool_form_generate_tour(self): self.components.tool_form.options.wait_for_and_click() self.components.tool_form.generate_tour.wait_for_and_click() def tool_form_execute(self): self.components.tool_form.execute.wait_for_and_click() def click_masthead_user(self): self.components.masthead.user.wait_for_and_click() def click_masthead_shared_data(self): self.components.masthead.shared_data.wait_for_and_click() def click_masthead_workflow(self): self.components.masthead.workflow.wait_for_and_click() def click_button_new_workflow(self): self.wait_for_and_click(self.navigation.workflows.selectors.new_button) def wait_for_sizzle_selector_clickable(self, selector): element = self._wait_on( sizzle.sizzle_selector_clickable(selector), f"sizzle/jQuery selector [{selector}] to become clickable", ) return element @retry_during_transitions def click_history_options(self): if self.is_beta_history(): component = self.components.history_panel.options_button_icon_beta else: component = self.components.history_panel.options_button_icon component.wait_for_and_click() def click_history_option_export_to_file(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="export to file", menu="history options") else: self.click_history_options() self.components.history_panel.options_show_export_history_to_file.wait_for_and_click() def click_history_option_sharing(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="share or publish", menu="history options") else: self.click_history_option("Share or Publish") def click_history_option(self, option_label_or_component): # Open menu self.click_history_options() if isinstance(option_label_or_component, str): option_label = option_label_or_component # Click labeled option self.wait_for_visible(self.navigation.history_panel.options_menu) menu_item_sizzle_selector = self.navigation.history_panel.options_menu_item( option_label=option_label ).selector menu_selection_element = self.wait_for_sizzle_selector_clickable(menu_item_sizzle_selector) menu_selection_element.click() else: option_component = option_label_or_component option_component.wait_for_and_click() def use_beta_history(self): if not self.is_beta_history(): self.click_history_option(self.components.history_panel.options_use_beta_history) self.components.history_panel.beta.wait_for_present() def is_beta_history(self): old_panel = self.components.history_panel.beta.is_absent new_panel = self.components.history_panel.new_history_button.is_absent if old_panel and new_panel: # both absent, let page render a bit... self.sleep_for(self.wait_types.UX_RENDER) else: return new_panel old_panel = self.components.history_panel.beta.is_absent return not old_panel # avoids problematic ID and classes on markup def beta_history_element(self, attribute_value, attribute_name="data-description", scope=".history-index"): return self.components._.by_attribute(name=attribute_name, value=attribute_value, scope=scope) # join list of attrs into css attribute selectors and append to base beta_item selector def content_item_by_attributes(self, **attrs): suffix_list = [f'[data-{k}="{v}"]' for (k, v) in attrs.items()] suffix = "".join(suffix_list) return self.components.history_panel.content_item.selector(suffix=suffix) def history_click_create_new(self): if not self.is_beta_history(): self.components.history_panel.new_history_button.wait_for_and_click() else: option = self.beta_history_element("create new history") option.wait_for_and_click() def history_click_editor_save(self): option = self.beta_history_element("editor save button") option.wait_for_and_click() self.sleep_for(self.wait_types.UX_RENDER) def history_panel_click_copy_elements(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="copy datasets", menu="history action menu") else: self.click_history_option("Copy Datasets") def use_bootstrap_dropdown(self, option=None, menu=None): """uses bootstrap dropdown by data-description attributes""" if option is None: raise TypeError if menu is None: raise TypeError toggle = self.beta_history_element(menu).descendant("button") self.wait_for_and_click(toggle) return self.beta_history_element(option).wait_for_and_click() @retry_during_transitions def histories_click_advanced_search(self): search_selector = "#standard-search .advanced-search-toggle" self.wait_for_and_click_selector(search_selector) @retry_during_transitions def histories_get_history_names(self): self.sleep_for(self.wait_types.UX_RENDER) names = [] grid = self.wait_for_selector("#grid-table-body") for row in grid.find_elements_by_tag_name("tr"): td = row.find_elements_by_tag_name("td") name = td[1].text if td[0].text == "" else td[0].text if name != "No items" and not name.startswith("No matching entries found"): names.append(name) return names @edit_details def history_panel_add_tags(self, tags): tag_icon = self.components.history_panel.tag_icon tag_area = self.components.history_panel.tag_area tag_area_input = self.components.history_panel.tag_area_input # if the tag editor is not present but the tag_icon is, then click it if not tag_icon.is_absent and (tag_area.is_absent or not tag_area.is_displayed): tag_icon.wait_for_and_click() input_element = tag_area_input.wait_for_and_click() self.sleep_for(self.wait_types.UX_RENDER) for tag in tags: input_element.send_keys(tag) self.send_enter(input_element) self.sleep_for(self.wait_types.UX_RENDER) @edit_details def history_panel_rename(self, new_name): editable_text_input_element = self.history_panel_name_input() if self.is_beta_history(): editable_text_input_element.clear() editable_text_input_element.send_keys(new_name) self.send_enter(editable_text_input_element) return editable_text_input_element def history_panel_name_input(self): if not self.is_beta_history(): editable_text_input_element = self.history_panel_click_to_rename() history_panel = self.components.history_panel edit = history_panel.name_edit_input editable_text_input_element = edit.wait_for_visible() return editable_text_input_element def history_panel_click_to_rename(self): history_panel = self.components.history_panel name = history_panel.name edit = history_panel.name_edit_input name.wait_for_and_click() return edit.wait_for_visible() def history_panel_refresh_click(self): self.wait_for_and_click(self.navigation.history_panel.selectors.refresh_button) def history_panel_multi_operations_show(self): return self.wait_for_and_click(self.navigation.history_panel.multi_operations.selectors.show_button) def history_panel_muli_operation_select_hid(self, hid): item_selector = self.history_panel_item_selector(hid, wait=True) operation_radio_selector = f"{item_selector} .selector" self.wait_for_and_click_selector(operation_radio_selector) def history_panel_multi_operation_action_click(self, action): # Maybe isn't needed? # self.sleep_for(WAIT_TYPES.UX_RENDER) self.wait_for_and_click(self.navigation.history_panel.multi_operations.selectors.action_button) @retry_during_transitions def _click_action_in_menu(): menu_element = self.wait_for_visible(self.navigation.history_panel.multi_operations.selectors.action_menu) menu_element.find_element_by_link_text(action.text).click() _click_action_in_menu() def open_history_multi_view(self): if self.is_beta_history(): self.components.history_panel.histories_operation_menu.wait_for_and_click() self.components.history_panel.multi_view_button_beta.wait_for_and_click() else: self.components.history_panel.multi_view_button.wait_for_and_click() def history_panel_show_structure(self): if self.is_beta_history(): self.use_bootstrap_dropdown(option="show structure", menu="history options") else: self.click_history_option(self.components.history_panel.options_show_history_structure) def history_multi_view_display_collection_contents(self, collection_hid, collection_type="list"): self.open_history_multi_view() selector = self.history_panel_wait_for_hid_state(collection_hid, "ok", multi_history_panel=True) self.click(selector) next_level_element_selector = selector for _ in range(len(collection_type.split(":")) - 1): next_level_element_selector = next_level_element_selector.descendant(".dataset-collection-element") self.wait_for_and_click(next_level_element_selector) dataset_selector = next_level_element_selector.descendant(".dataset") self.wait_for_and_click(dataset_selector) def history_panel_item_view_dataset_details(self, hid): if not self.is_beta_history(): self.history_panel_ensure_showing_item_details(hid) self.hda_click_details(hid) self.components.dataset_details._.wait_for_visible() else: item = self.history_panel_item_component(hid=hid) item.dataset_operations_dropdown.wait_for_and_click() item.info_button.wait_for_and_click() self.components.dataset_details._.wait_for_visible() def history_panel_item_click_visualization_menu(self, hid): viz_button_selector = f"{self.history_panel_item_selector(hid)} .visualizations-dropdown" self.wait_for_and_click_selector(viz_button_selector) self.wait_for_selector_visible(f"{viz_button_selector} .dropdown-menu") def history_panel_item_available_visualizations_elements(self, hid): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu viz_menu_selectors = f"{self.history_panel_item_selector(hid)} a.visualization-link" return self.driver.find_elements_by_css_selector(viz_menu_selectors) def history_panel_item_get_tags(self, hid): item_component = self.history_panel_item_component(hid=hid) item_component.wait_for_visible() return [e.text for e in item_component.alltags.all()] def history_panel_item_available_visualizations(self, hid): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu return [e.text for e in self.history_panel_item_available_visualizations_elements(hid)] def history_panel_item_click_visualization(self, hid, visualization_name): # Precondition: viz menu has been opened with history_panel_item_click_visualization_menu elements = self.history_panel_item_available_visualizations_elements(hid) for element in elements: if element.text == visualization_name: element.click() return element raise ValueError(f"No visualization [{visualization_name}] found.") def history_panel_item_selector(self, hid, wait=False): current_history_id = self.current_history_id() contents = self.api_get(f"histories/{current_history_id}/contents") try: history_item = [d for d in contents if d["hid"] == hid][0] except IndexError: raise Exception(f"Could not find history item with hid [{hid}] in contents [{contents}]") history_item_selector = f"#{history_item['history_content_type']}-{history_item['id']}" if wait: self.wait_for_selector_visible(history_item_selector) return history_item_selector def modal_body_selector(self): return ".modal-body" def history_panel_item_body_component(self, hid, wait=False): details_component = self.history_panel_item_component(hid=hid).details if wait: details_component.wait_for_visible() return details_component def hda_click_primary_action_button(self, hid: int, button_key: str): self.history_panel_ensure_showing_item_details(hid) item_component = self.history_panel_item_component(hid=hid) button_component = item_component[f"{button_key}_button"] button_component.wait_for_and_click() def hda_click_details(self, hid: int): self.hda_click_primary_action_button(hid, "info") def history_panel_click_item_title(self, hid, **kwds): item_component = self.history_panel_item_component(hid=hid) details_component = item_component.details details_displayed = not details_component.is_absent and details_component.is_displayed item_component.title.wait_for_and_click() if kwds.get("wait", False): if details_displayed: details_component.wait_for_absent_or_hidden() else: details_component.wait_for_visible() return item_component def history_panel_ensure_showing_item_details(self, hid): if not self.history_panel_item_showing_details(hid): self.history_panel_click_item_title(hid=hid, wait=True) def history_panel_item_showing_details(self, hid): item_component = self.history_panel_item_component(hid=hid) item_component.wait_for_present() if item_component.details.is_absent: return False return item_component.details.is_displayed def collection_builder_set_name(self, name): name_element = self.wait_for_selector_visible("input.collection-name") name_element.send_keys(name) def collection_builder_hide_originals(self): self.wait_for_and_click_selector("input.hide-originals") def collection_builder_create(self): self.wait_for_and_click_selector("button.create-collection") def collection_builder_clear_filters(self): self.wait_for_and_click_selector("a.clear-filters-link") def collection_builder_click_paired_item(self, forward_or_reverse, item): assert forward_or_reverse in ["forward", "reverse"] forward_column = self.wait_for_selector_visible(f".{forward_or_reverse}-column .column-datasets") first_datset_forward = forward_column.find_elements_by_css_selector("li")[item] first_datset_forward.click() def logout_if_needed(self): if self.is_logged_in(): self.home() self.logout() def logout(self): self.components.masthead.logged_in_only.wait_for_visible() self.click_masthead_user() self.components.masthead.logout.wait_for_and_click() try: self.components.masthead.logged_out_only.wait_for_visible() except self.TimeoutException as e: message = "Clicked logout button but waiting for 'Login or Registration' button failed, perhaps the logout button was clicked before the handler was setup?" raise self.prepend_timeout_message(e, message) assert ( not self.is_logged_in() ), "Clicked to logged out and UI reflects a logout, but API still thinks a user is logged in." def run_tour(self, path, skip_steps=None, sleep_on_steps=None, tour_callback=None): skip_steps = skip_steps or [] sleep_on_steps = sleep_on_steps or {} if tour_callback is None: tour_callback = NullTourCallback() self.home() with open(path) as f: tour_dict = yaml.safe_load(f) steps = tour_dict["steps"] for i, step in enumerate(steps): title = step.get("title", None) skip = False if skip_steps: for skip_step in skip_steps: if title == skip_step: skip = True if title in sleep_on_steps: time.sleep(sleep_on_steps[title]) if skip: continue self.run_tour_step(step, i, tour_callback) def tour_wait_for_clickable_element(self, selector): timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) wait = self.wait(timeout=timeout) timeout_message = self._timeout_message(f"sizzle (jQuery) selector [{selector}] to become clickable") element = wait.until( sizzle.sizzle_selector_clickable(selector), timeout_message, ) return element def tour_wait_for_element_present(self, selector): timeout = self.timeout_for(wait_type=WAIT_TYPES.JOB_COMPLETION) wait = self.wait(timeout=timeout) timeout_message = self._timeout_message(f"sizzle (jQuery) selector [{selector}] to become present") element = wait.until( sizzle.sizzle_presence_of_selector(selector), timeout_message, ) return element def get_tooltip_text(self, element, sleep=0, click_away=True): tooltip_balloon = self.components._.tooltip_balloon tooltip_balloon.wait_for_absent() action_chains = self.action_chains() action_chains.move_to_element(element) action_chains.perform() if sleep > 0: time.sleep(sleep) tooltip_element = tooltip_balloon.wait_for_visible() text = tooltip_element.text if click_away: self.click_center() return text @retry_during_transitions def assert_selector_absent_or_hidden_after_transitions(self, selector): """Variant of assert_selector_absent_or_hidden that retries during transitions. In the parent method - the element is found and then it is checked to see if it is visible. It may disappear from the page in the middle there and cause a StaleElement error. For checks where we care about the final resting state after transitions - this method can be used to retry during those transitions. """ return self.assert_selector_absent_or_hidden(selector) @retry_during_transitions def assert_absent_or_hidden_after_transitions(self, selector): """Variant of assert_absent_or_hidden that retries during transitions. See details above for more information about this. """ return self.assert_absent_or_hidden(selector) def assert_tooltip_text(self, element, expected: Union[str, HasText], sleep: int = 0, click_away: bool = True): if hasattr(expected, "text"): expected = cast(HasText, expected).text text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert text == expected, f"Tooltip text [{text}] was not expected text [{expected}]." def assert_tooltip_text_contains( self, element, expected: Union[str, HasText], sleep: int = 0, click_away: bool = True ): if hasattr(expected, "text"): expected = cast(HasText, expected).text text = self.get_tooltip_text(element, sleep=sleep, click_away=click_away) assert expected in text, f"Tooltip text [{text}] was not expected text [{expected}]." def assert_error_message(self, contains=None): self.components._.messages.error.wait_for_visible() elements = self.find_elements(self.components._.messages.selectors.error) return self.assert_message(elements, contains=contains) def assert_warning_message(self, contains=None): element = self.components._.messages["warning"] return self.assert_message(element, contains=contains) def assert_message(self, element, contains=None): if contains is not None: if type(element) == list: assert any( contains in el.text for el in element ), f"{contains} was not found in {[el.text for el in element]}" return element = element.wait_for_visible() text = element.text if contains not in text: message = f"Text [{contains}] expected inside of [{text}] but not found." raise AssertionError(message) def assert_no_error_message(self): self.components._.messages.error.assert_absent_or_hidden() def run_tour_step(self, step, step_index, tour_callback): preclick = step.get("preclick", []) for preclick_selector in preclick: print(f"(Pre)Clicking {preclick_selector}") self._tour_wait_for_and_click_element(preclick_selector) element_str = step.get("element", None) if element_str is not None: print(f"Waiting for element {element_str}") element = self.tour_wait_for_element_present(element_str) assert element is not None textinsert = step.get("textinsert", None) if textinsert is not None: element.send_keys(textinsert) tour_callback.handle_step(step, step_index) postclick = step.get("postclick", []) for postclick_selector in postclick: print(f"(Post)Clicking {postclick_selector}") self._tour_wait_for_and_click_element(postclick_selector) @retry_during_transitions def _tour_wait_for_and_click_element(self, selector): element = self.tour_wait_for_clickable_element(selector) element.click() @retry_during_transitions def wait_for_and_click_selector(self, selector): element = self.wait_for_selector_clickable(selector) element.click() return element @retry_during_transitions def wait_for_and_click(self, selector_template): element = self.wait_for_clickable(selector_template) element.click() return element def set_history_annotation(self, annotation, clear_text=False): history_panel = self.components.history_panel if self.is_beta_history(): toggle = self.beta_history_element("editor toggle") toggle.wait_for_and_click() annotation_input = self.beta_history_element("annotation input").wait_for_visible() if clear_text: annotation_input.clear() annotation_input.send_keys(annotation) self.history_click_editor_save() else: self.ensure_history_annotation_area_displayed() editable = history_panel.annotation_editable_text edit = history_panel.annotation_edit editable.wait_for_and_click() edit_el = edit.wait_for_and_click() if clear_text: # previously this was just edit_el.clear() but # .clear() doesn't work with beta history panel action_chains = self.action_chains() for _ in range(40): action_chains.send_keys(Keys.BACKSPACE) action_chains.perform() edit_el.send_keys(annotation) history_panel.annotation_done.wait_for_and_click() def ensure_history_annotation_area_displayed(self): annotation_area = self.components.history_panel.annotation_area annotation_icon = self.components.history_panel.annotation_icon if annotation_area.is_absent or not annotation_area.is_displayed: annotation_icon.wait_for_and_click() def select2_set_value(self, container_selector_or_elem, value, with_click=True, clear_value=False): # There are two hacky was to select things from the select2 widget - # with_click=True: This simulates the mouse click after the suggestion contains # only the selected value. # with_click=False: This presses enter on the selection. Not sure # why. # with_click seems to work in all situtations - the enter methods # doesn't seem to work with the tool form for some reason. if hasattr(container_selector_or_elem, "selector"): container_selector_or_elem = container_selector_or_elem.selector if not hasattr(container_selector_or_elem, "find_element_by_css_selector"): container_elem = self.wait_for_selector(container_selector_or_elem) else: container_elem = container_selector_or_elem text_element = container_elem.find_element_by_css_selector("input[type='text']") if clear_value: self.send_backspace(text_element) self.send_backspace(text_element) text_element.send_keys(value) # Wait for select2 options to load and then click to add this one. drop_elem = self.wait_for_selector_visible("#select2-drop") # Sleep seems to be needed - at least for send_enter. time.sleep(0.5) if not with_click: # Wait for select2 options to load and then click to add this one. self.send_enter(text_element) else: select_elem = drop_elem.find_elements_by_css_selector(".select2-result-label")[0] action_chains = self.action_chains() action_chains.move_to_element(select_elem).click().perform() self.wait_for_selector_absent_or_hidden("#select2-drop") def snapshot(self, description): """Test case subclass overrides this to provide detailed logging.""" def open_history_editor(self, scope=".history-index"): if self.is_beta_history(): panel = self.components.history_panel.editor.selector(scope=scope) if panel.name_input.is_absent: toggle = panel.toggle toggle.wait_for_and_click() editor = panel.form editor.wait_for_present() def close_history_editor(self, scope=".history-index"): if self.is_beta_history(): toggle = self.components.history_panel.edit_toggle toggle.wait_for_and_click() editor = self.components.history_panel.editor.selector(scope=scope) self.assert_absent_or_hidden(editor) def share_ensure_by_user_available(self, sharing_component): collapse = sharing_component.share_with_collapse collapse.wait_for_visible() if collapse.has_class("collapsed"): collapse.wait_for_and_click() sharing_component.share_with_multiselect.wait_for_visible() def share_unshare_with_user(self, sharing_component, email): self.share_ensure_by_user_available(sharing_component) unshare_user_button = self.components.histories.sharing.unshare_with_user_button(email=email) unshare_user_button.wait_for_and_click() self.components.histories.sharing.submit_sharing_with.wait_for_and_click() unshare_user_button.wait_for_absent_or_hidden() def share_with_user( self, sharing_component, user_id=None, user_email=None, screenshot_before_submit=None, screenshot_after_submit=None, assert_valid=False, ): self.share_ensure_by_user_available(sharing_component) multiselect = sharing_component.share_with_multiselect.wait_for_and_click() sharing_component.share_with_input.wait_for_and_send_keys(user_id or user_email) self.send_enter(multiselect) self.screenshot_if(screenshot_before_submit) sharing_component.submit_sharing_with.wait_for_and_click() if assert_valid: self.assert_no_error_message() xpath = f'//span[contains(text(), "{user_email}")]' self.wait_for_xpath_visible(xpath) self.screenshot_if(screenshot_after_submit) class NotLoggedInException(TimeoutException): def __init__(self, timeout_exception, user_info, dom_message): template = "Waiting for UI to reflect user logged in but it did not occur. API indicates no user is currently logged in. %s API response was [%s]. %s" msg = template % (dom_message, user_info, timeout_exception.msg) super().__init__(msg=msg, screen=timeout_exception.screen, stacktrace=timeout_exception.stacktrace)

import logging import os import warnings from abc import ABC, abstractmethod from collections import defaultdict from os.path import join from typing import Iterable, List, Optional, Tuple, Union import torch from torch import nn from .composition import AdapterCompositionBlock, Fuse, Stack, parse_composition from .configuration import AdapterConfig, AdapterConfigBase, AdapterFusionConfig, get_adapter_config_hash from .context import AdapterSetup, ForwardContext from .hub_mixin import PushAdapterToHubMixin from .layer import AdapterLayer, AdapterLayerBase from .loading import AdapterFusionLoader, AdapterLoader, PredictionHeadLoader, WeightsLoader from .modeling import Adapter, GLOWCouplingBlock, NICECouplingBlock from .prefix_tuning import PrefixTuningPool, PrefixTuningShim from .utils import EMBEDDING_FILE, TOKENIZER_PATH, inherit_doc from .wrappers.configuration import wrap_config logger = logging.getLogger(__name__) class InvertibleAdaptersMixin: """Mixin for Transformer models adding invertible adapters.""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.invertible_adapters = nn.ModuleDict(dict()) # Make sure config is wrapped self.config = wrap_config(self.config) def add_invertible_adapter(self, adapter_name: str): """ Adds an invertible adapter module for the adapter with the given name. If the given adapter does not specify an invertible adapter config, this method does nothing. Args: adapter_name (str): The name of the adapter for which to add an invertible adapter module. """ if adapter_name in self.invertible_adapters: raise ValueError(f"Model already contains an adapter module for '{adapter_name}'.") adapter_config = self.config.adapters.match( adapter_name, config_type=AdapterConfig, location_key="inv_adapter", ) if adapter_config and adapter_config["inv_adapter"]: if adapter_config["inv_adapter"] == "nice": inv_adap = NICECouplingBlock( [[self.config.hidden_size]], non_linearity=adapter_config["non_linearity"], reduction_factor=adapter_config["inv_adapter_reduction_factor"], ) elif adapter_config["inv_adapter"] == "glow": inv_adap = GLOWCouplingBlock( [[self.config.hidden_size]], non_linearity=adapter_config["non_linearity"], reduction_factor=adapter_config["inv_adapter_reduction_factor"], ) else: raise ValueError(f"Invalid invertible adapter type '{adapter_config["inv_adapter"]}'.") self.invertible_adapters[adapter_name] = inv_adap self.invertible_adapters[adapter_name].apply(Adapter.init_bert_weights) def delete_invertible_adapter(self, adapter_name: str): if adapter_name in self.invertible_adapters: del self.invertible_adapters[adapter_name] def get_invertible_adapter(self): # TODO: Currently no fusion over invertible adapters, takes only very first language adapter position if self.config.adapters.active_setup is not None and len(self.config.adapters.active_setup) > 0: first_adapter = self.config.adapters.active_setup.first() if first_adapter in self.invertible_adapters: return self.invertible_adapters[first_adapter] return None def enable_invertible_adapters(self, adapter_names): for adapter_name in adapter_names: if adapter_name in self.invertible_adapters: for param in self.invertible_adapters[adapter_name].parameters(): param.requires_grad = True def invertible_adapters_forward(self, hidden_states, rev=False): # TODO: Currently no fusion over invertible adapters, takes only very first language adapter position if self.config.adapters.active_setup is not None and len(self.config.adapters.active_setup) > 0: first_adapter = self.config.adapters.active_setup.first() if first_adapter in self.invertible_adapters: hidden_states = self.invertible_adapters[first_adapter](hidden_states, rev=rev) return hidden_states class ModelAdaptersMixin(PushAdapterToHubMixin, ABC): """Mixin for transformer models adding support for loading/ saving adapters.""" def __init__(self, config, *args, **kwargs): super().__init__(config, *args, **kwargs) if config.name_or_path and not os.path.exists(config.name_or_path): self.model_name = config.name_or_path else: self.model_name = None self.loaded_embeddings = {} self.shared_parameters = nn.ModuleDict() self._active_embedding = "default" # Make sure config is wrapped self.config = wrap_config(self.config) def _link_prefix_to_pool(self, layer): if isinstance(layer, PrefixTuningShim): layer.set_pool(self.base_model.prefix_tuning) def _init_adapter_modules(self, add_prefix_tuning_pool=True): """ This method initializes adapter modules and fusion modules from the model config. """ # Link all prefix tunings if add_prefix_tuning_pool: self.base_model.prefix_tuning = PrefixTuningPool(self.config) self.apply_to_adapter_layers(lambda i, layer: self._link_prefix_to_pool(layer)) # Initialize adapters from config for adapter_name in self.config.adapters: self.apply_to_adapter_layers(lambda i, layer: layer.add_adapter(adapter_name, i)) # Initialize fusion from config for fusion_name in self.config.adapters.fusions: self.apply_to_adapter_layers(lambda i, layer: layer.add_fusion_layer(fusion_name)) self.loaded_embeddings["default"] = self.get_input_embeddings() # These methods have to be implemented by every deriving class: @abstractmethod def iter_layers(self) -> Iterable[Tuple[int, nn.Module]]: """ Iterates over all layers of the model. This abstract method has to ne implemented by every implementing model. """ pass def apply_to_adapter_layers(self, fn): """ Applies a function to all adapter layers of the model. """ for i, layer in self.iter_layers(): for module in layer.modules(): if isinstance(module, AdapterLayerBase): fn(i, module) def train_adapter(self, adapter_setup: Union[list, AdapterCompositionBlock], train_embeddings=False): """Sets the model into mode for training the given adapters.""" self.train() self.freeze_model(True) adapter_setup = parse_composition(adapter_setup) self.apply_to_adapter_layers(lambda i, layer: layer.enable_adapters(adapter_setup, True, False)) for adapter_name in adapter_setup: if adapter_name in self.shared_parameters: for param in self.shared_parameters[adapter_name].values(): param.requires_grad = True if isinstance(self, InvertibleAdaptersMixin): self.enable_invertible_adapters(adapter_setup.flatten()) # use the adapters to be trained by default in every forward pass self.set_active_adapters(adapter_setup) if train_embeddings: self.get_input_embeddings().train() def train_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """Sets the model into mode for training of adapter fusion determined by a list of adapter names.""" warnings.warn( "add_fusion() has been deprecated in favor of add_adapter_fusion(). Please use the newer method instead.", FutureWarning, ) self.train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """Sets the model into mode for training of adapter fusion determined by a list of adapter names.""" self.train() self.freeze_model(True) adapter_setup = parse_composition(adapter_setup) self.apply_to_adapter_layers(lambda i, layer: layer.enable_adapters(adapter_setup, unfreeze_adapters, True)) # use the adapters to be trained by default in every forward pass self.set_active_adapters(adapter_setup) # TODO implement fusion for invertible adapters def has_adapters(self): if not getattr(self.config, "is_adaptable", None): return False return len(self.config.adapters.adapters) > 0 @property def has_parallel_adapters(self) -> bool: if self.config.adapters.active_setup: return self.config.adapters.active_setup.parallel_channels > 1 else: return False @property def active_adapters(self) -> AdapterCompositionBlock: return self.config.adapters.active_setup @active_adapters.setter def active_adapters(self, adapter_setup: Union[list, AdapterCompositionBlock]): self.set_active_adapters(adapter_setup) def set_shared_parameters(self, param): self.shared_parameters = param def set_active_adapters( self, adapter_setup: Union[list, AdapterCompositionBlock], skip_layers: Optional[List[int]] = None ): """ Sets the adapter modules to be used by default in every forward pass. If no adapter with the given name is found, no module of the respective type will be activated. Args: adapter_setup (list): The list of adapters to be activated by default. Can be a fusion or stacking configuration. """ adapter_setup = parse_composition(adapter_setup, model_type=self.config.model_type) if adapter_setup: for adapter_name in adapter_setup.flatten(): if adapter_name not in self.config.adapters.adapters: raise ValueError( f"No adapter with name '{adapter_name}' found. Please make sure that all specified adapters are correctly loaded." ) self.config.adapters.active_setup = adapter_setup self.config.adapters.skip_layers = skip_layers def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False, set_active: bool = False): """ Adds a new adapter module of the specified type to the model. Args: adapter_name (str): The name of the adapter module to be added. config (str or dict or AdapterConfigBase, optional): The adapter configuration, can be either: - the string identifier of a pre-defined configuration dictionary - a configuration dictionary specifying the full config - if not given, the default configuration for this adapter type will be used overwrite_ok (bool, optional): Overwrite an adapter with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Set the adapter to be the active one. By default (False), the adapter is added but not activated. """ if isinstance(config, dict): config = AdapterConfigBase.load(config) # ensure config is ok and up-to-date # In case adapter already exists and we allow overwriting, explicitly delete the existing one first if overwrite_ok and adapter_name in self.config.adapters: self.delete_adapter(adapter_name) self.config.adapters.add(adapter_name, config=config) try: self.apply_to_adapter_layers(lambda i, layer: layer.add_adapter(adapter_name, i)) # PHM Layer if self.config.adapters.match(adapter_name, AdapterConfig, location_key="phm_layer"): self._add_shared_parameters(adapter_name, config) # Prefix Tuning for module in self.modules(): if isinstance(module, PrefixTuningPool): module.confirm_prefix(adapter_name) if isinstance(self, InvertibleAdaptersMixin): self.add_invertible_adapter(adapter_name) except ValueError as ex: self.delete_adapter(adapter_name) raise ex if set_active: self.set_active_adapters(adapter_name) def _add_shared_parameters(self, adapter_name, adapter_config: AdapterConfig): self.shared_parameters[adapter_name] = ( list(self.get_adapter(adapter_name)[0].values())[0].adapter_down[0].init_shared_parameters() ) def add_fusion(self, adapter_names: Union[Fuse, list], adapter_fusion_config=None, override_kwargs=None): warnings.warn( "add_fusion() has been deprecated in favor of add_adapter_fusion(). Please use the newer method instead.", FutureWarning, ) adapter_fusion_config = AdapterFusionConfig.from_dict(adapter_fusion_config).replace(**override_kwargs) self.add_adapter_fusion(adapter_names, adapter_fusion_config) def add_adapter_fusion( self, adapter_names: Union[Fuse, list, str], config=None, overwrite_ok: bool = False, set_active: bool = False, ): """ Adds AdapterFusion to the model with alll the necessary configurations and weight initializations Args: adapter_names (Fuse or list or str): AdapterFusion layer to add. Can be either: - a ``Fuse`` composition block - a list of adapter names to fuse - a comma-separated string of adapter names to fuse config (str or dict): adapter fusion configuration, can be either: - a string identifying a pre-defined adapter fusion configuration - a dictionary representing the adapter fusion configuration - the path to a file containing the adapter fusion configuration overwrite_ok (bool, optional): Overwrite an AdapterFusion layer with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Activate the added AdapterFusion. By default (False), the AdapterFusion is added but not activated. """ if isinstance(adapter_names, Fuse): adapter_names = adapter_names.children elif isinstance(adapter_names, str): adapter_names = adapter_names.split(",") if isinstance(config, dict): config = AdapterFusionConfig.from_dict(config) # ensure config is ok and up-to-date # In case adapter already exists and we allow overwriting, explicitly delete the existing one first if overwrite_ok and self.config.adapters.get_fusion(adapter_names) is not None: self.delete_adapter_fusion(adapter_names) self.config.adapters.add_fusion(adapter_names, config=config) self.apply_to_adapter_layers(lambda i, layer: layer.add_fusion_layer(adapter_names)) if set_active: if not isinstance(adapter_names, list): adapter_names = adapter_names.split(",") self.set_active_adapters(Fuse(*adapter_names)) def delete_adapter(self, adapter_name: str): """ Deletes the adapter with the specified name from the model. Args: adapter_name (str): The name of the adapter. """ if adapter_name not in self.config.adapters: logger.info("No adapter '%s' found for deletion. Skipping.", adapter_name) return del self.config.adapters.adapters[adapter_name] self.apply_to_adapter_layers(lambda i, layer: layer.delete_adapter(adapter_name)) if isinstance(self, InvertibleAdaptersMixin): self.delete_invertible_adapter(adapter_name) # Reset active adapters if this was the only active adapter if self.active_adapters == Stack(adapter_name): self.active_adapters = None def delete_adapter_fusion(self, adapter_names: Union[Fuse, list, str]): """ Deletes the AdapterFusion layer of the specified adapters. Args: adapter_names (Union[Fuse, list, str]): AdapterFusion layer to delete. """ if isinstance(adapter_names, Fuse): adapter_fusion_name = ",".join(adapter_names.children) elif isinstance(adapter_names, list): adapter_fusion_name = ",".join(adapter_names) elif isinstance(adapter_names, str): adapter_fusion_name = adapter_names else: raise ValueError("Invalid AdapterFusion definition: {}".format(adapter_names)) if adapter_fusion_name not in self.config.adapters.fusions: logger.info("No AdapterFusion '%s' found for deletion. Skipping.", adapter_fusion_name) return del self.config.adapters.fusions[adapter_fusion_name] self.apply_to_adapter_layers(lambda i, layer: layer.delete_fusion_layer(adapter_fusion_name)) # Reset active adapters if this was the active setup if self.active_adapters == adapter_names: self.active_adapters = None def save_adapter( self, save_directory: str, adapter_name: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using `load_adapter()`. Args: save_directory (str): Path to a directory where the adapter should be saved. adapter_name (str): Name of the adapter to be saved. Raises: ValueError: If the given adapter name is invalid. """ loader = AdapterLoader(self) loader.save(save_directory, adapter_name, meta_dict) # save additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.save(save_directory, adapter_name) def save_adapter_fusion( self, save_directory: str, adapter_names: Union[Fuse, list, str], meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves an AdapterFusion layer and its configuration file to a directory so that it can be shared or reloaded using `load_adapter_fusion()`. Args: save_directory (str): Path to a directory where the AdapterFusion should be saved. adapter_names (Union[Fuse, list, str]): AdapterFusion to be saved. Raises: ValueError: If the given AdapterFusion name is invalid. """ if isinstance(adapter_names, Fuse): adapter_fusion_name = ",".join(adapter_names.children) elif isinstance(adapter_names, list): adapter_fusion_name = ",".join(adapter_names) elif isinstance(adapter_names, str): adapter_fusion_name = adapter_names else: raise ValueError("Invalid AdapterFusion definition: {}".format(adapter_names)) loader = AdapterFusionLoader(self) loader.save(save_directory, adapter_fusion_name, meta_dict) # save additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.save(save_directory, adapter_fusion_name) def load_adapter( self, adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, source: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, leave_out: Optional[List[int]] = None, id2label=None, set_active: bool = False, **kwargs ) -> str: """ Loads a pre-trained pytorch adapter module from the local file system or a remote location. Args: adapter_name_or_path (str): can be either: - the identifier of a pre-trained task adapter to be loaded from Adapter Hub - a path to a directory containing adapter weights saved using `model.saved_adapter()` - a URL pointing to a zip folder containing a saved adapter module config (dict or str, optional): The requested configuration of the adapter. If not specified, will be either: - the default adapter config for the requested adapter if specified - the global default adapter config version (str, optional): The version of the adapter to be loaded. model_name (str, optional): The string identifier of the pre-trained model. load_as (str, optional): Load the adapter using this name. By default, the name with which the adapter was saved will be used. source (str, optional): Identifier of the source(s) from where to load the adapter. Can be: - "ah" (default): search on AdapterHub. - "hf": search on HuggingFace model hub. - None: search on all sources leave_out: Dynamically drop adapter modules in the specified Transformer layers when loading the adapter. set_active (bool, optional): Set the loaded adapter to be the active one. By default (False), the adapter is loaded but not activated. Returns: str: The name with which the adapter was added to the model. """ loader = AdapterLoader(self) load_dir, load_name = loader.load( adapter_name_or_path, config, version, model_name, load_as, source=source, leave_out=leave_out, set_active=set_active, **kwargs, ) # load additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.load( load_dir, load_as=load_as, loading_info=kwargs.get("loading_info", None), main_load_name=load_name, id2label=id2label, set_active=set_active, ) return load_name def load_adapter_fusion( self, adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, set_active: bool = False, **kwargs ) -> str: """ Loads a pre-trained AdapterFusion layer from the local file system. Args: adapter_fusion_name_or_path (str): a path to a directory containing AdapterFusion weights saved using `model.save_adapter_fusion()`. load_as (str, optional): Load the AdapterFusion using this name. By default, the name with which the AdapterFusion layer was saved will be used. set_active (bool, optional): Activate the loaded AdapterFusion. By default (False), the AdapterFusion is loaded but not activated. Returns: str: The name with which the AdapterFusion was added to the model. """ loader = AdapterFusionLoader(self) load_dir, load_name = loader.load(adapter_fusion_name_or_path, load_as, set_active=set_active) # load additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.load( load_dir, load_as=load_as, loading_info=kwargs.get("loading_info", None), main_load_name=load_name, set_active=set_active, ) return load_name def save_all_adapters( self, save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves all adapters of this model together with their configuration to subfolders of the given location. Args: save_directory (str): Path to a directory where the adapters should be saved. """ for name in self.config.adapters: adapter_config = self.config.adapters.get(name) h = get_adapter_config_hash(adapter_config) save_path = join(save_directory, name) if meta_dict: meta_dict.update({"config_id": h}) else: meta_dict = {"config_id": h} self.save_adapter(save_path, name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders) def save_all_adapter_fusions( self, save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves all AdapterFusion layers of this model together with their configuration to subfolders of the given location. Args: save_directory (str): Path to a directory where the AdapterFusion layers should be saved. """ for name in self.config.adapters.fusions: adapter_fusion_config = self.config.adapters.get_fusion(name) h = get_adapter_config_hash(adapter_fusion_config) save_path = join(save_directory, name) if meta_dict: meta_dict.update({"config_id": h}) else: meta_dict = {"config_id": h} self.save_adapter_fusion( save_path, name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders ) def freeze_model(self, freeze=True): """Freezes all weights of the model.""" # first freeze/ unfreeze all model weights for param in self.base_model.parameters(): param.requires_grad = not freeze self.model_frozen = freeze def forward_context(self, context: ForwardContext, *args, **kwargs): """ This method is called by the ``ForwardContext`` at the beginning of the forward pass. """ # some warnings if we don't use available adapters active_adapters = getattr(self, "active_adapters", None) or AdapterSetup.get_context() if not active_adapters: if self.has_adapters(): logger.warning("There are adapters available but none are activated for the forward pass.") return context.adapters_parallelized = False # Add the shared parameters for the active adapters to the context context.shared_parameters = { name: param for name, param in self.shared_parameters.items() if name in active_adapters.flatten() } # Prefix tuning input_tensor = kwargs.get("input_ids", None) if input_tensor is None: input_tensor = kwargs.get("decoder_input_ids", None) if input_tensor is None: input_tensor = kwargs.get("attention_mask", None) if input_tensor is None: input_tensor = args[0] context.prefix_states = self.base_model.prefix_tuning(input_tensor.shape[0]) def load_embeddings(self, path: str, name: str): """ Load a saved embedding from the given path. If the embedding was saved with a tokenizer it is returned Args: path: the path to the saved embedding name: the name the embedding should be loaded as Returns: a tokenizer if it ws saved with the embedding otherwise None """ from ..models.auto.tokenization_auto import AutoTokenizer if name in self.loaded_embeddings: raise ValueError("An embedding with the name {} already exists".format(name)) tokenizer = None tokenizer_path = os.path.join(path, TOKENIZER_PATH) if os.path.isdir(tokenizer_path): tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) embedding_path = os.path.join(path, EMBEDDING_FILE) if not os.path.isfile(embedding_path): raise FileNotFoundError("No embeddings found at {}".format(embedding_path)) weights = torch.load(embedding_path) self.loaded_embeddings[name] = nn.Embedding.from_pretrained(weights) self.set_active_embeddings(name) return tokenizer def add_embeddings(self, name, tokenizer, reference_embedding=None, reference_tokenizer=None, embedding_dim=None): """ Add a new embedding to the model. If a reference embedding and reference tokenizer are provided tokens in the present in both tokenizers are initialized to the embedding in the reference_embedding. Args: name: the name of the embedding tokenizer: the tokenizer determining the vocab of the embedding reference_embedding: the reference embedding to use for initializing the embeddings of tokens present in the newly created embedding reference_tokenizer: the tokenizer providing the vocab for the reference embedding embedding_dim: the dimension of the embeddings (if None the hidden_size from the config is used) """ if name in self.loaded_embeddings: raise ValueError("An embedding with the name {} already exists".format(name)) if embedding_dim is None: embedding_dim = self.config.hidden_size embedding = nn.Embedding(tokenizer.vocab_size, embedding_dim) embedding.requires_grad_(False) if (reference_embedding is not None and reference_tokenizer is None) or ( reference_tokenizer is not None and reference_embedding is None ): raise KeyError( "Reference embedding and reference tokenizer are required to use initialize embeddings from reference embedding" ) if reference_embedding is not None and reference_tokenizer is not None: tokens = set(tokenizer.get_vocab().keys()) & set(reference_tokenizer.get_vocab().keys()) reference_vocab = reference_tokenizer.get_vocab() vocab = tokenizer.get_vocab() for t in tokens: idx_reference = reference_vocab[t] idx = vocab[t] embedding.weight[idx] = self.loaded_embeddings[reference_embedding].weight[idx_reference].clone() embedding.train(False) self.loaded_embeddings[name] = embedding self.set_active_embeddings(name) def delete_embeddings(self, name): """ Deletes the embedding with the given name Args: name: The name of the embedding that should be deleted """ if name not in self.loaded_embeddings: raise ValueError("No embedding with name {}".format(name)) if self.active_embeddings == name: logger.warning("The active embedding is deleted. Setting the default embedding as active.") self.set_active_embeddings("default") del self.loaded_embeddings[name] def save_embeddings(self, path, name, tokenizer=None): """ Saves the embedding with the given name. If a tokenizer is passed as well the tokenizer is saved together with the embedding. Args: path: The path where the embedding should be saved name: The name of the embedding that should be saved tokenizer: optionally a tokenizer to save with the embedding (default is None) """ if self.active_embeddings == name: self.loaded_embeddings[name] = self.get_input_embeddings() os.makedirs(path, exist_ok=True) embedding_path = os.path.join(path, EMBEDDING_FILE) torch.save(self.loaded_embeddings[name].weight, embedding_path) if tokenizer: tokenizer_path = os.path.join(path, TOKENIZER_PATH) tokenizer.save_pretrained(tokenizer_path) def set_active_embeddings(self, name): """ Sets the active embedding for the forward pass of the model Args: name: The name of the embedding that should be used """ self.loaded_embeddings[self.active_embeddings] = self.get_input_embeddings() self.set_input_embeddings(self.loaded_embeddings[name]) self._active_embedding = name @property def active_embeddings(self): return self._active_embedding def get_fusion_regularization_loss(self): reg_loss = 0.0 target = torch.zeros((self.config.hidden_size, self.config.hidden_size)).fill_diagonal_(1.0).to(self.device) for i, layer in self.iter_layers(): for module in layer.modules(): if isinstance(module, AdapterLayer): for _, layer_fusion in module.adapter_fusion_layer.items(): if hasattr(layer_fusion, "value"): reg_loss += 0.01 * (target - layer_fusion.value.weight).pow(2).sum() return reg_loss def get_adapter(self, name) -> dict: """ Returns a dictionary with all weights of the adapter with the specified name. Args: name (str): The adapter name. Returns: dict: A nested dictionary containing the weights of the adapter. The dictionary is structured as follow: {<layer id>: {<module location>: <nn.Module>}}. """ destination = defaultdict(dict) # use a custom index to ensure numbering is from 0 to N layers for i, (_, layer) in enumerate(self.iter_layers()): for module in layer.modules(): if isinstance(module, AdapterLayerBase): adapter_module = module.get_adapter(name) if adapter_module is not None: destination[i][module.location_key] = adapter_module return dict(destination) def eject_prefix_tuning(self, name: str): """ Converts the prefix tuning with the given name from the reparameterized form into the flat form. Args: name (str): The name of the prefix tuning. """ for module in self.modules(): if isinstance(module, PrefixTuningPool): if name in module.prefix_tunings: module.prefix_tunings[name].eject() @inherit_doc class ModelWithHeadsAdaptersMixin(ModelAdaptersMixin): """ Mixin adding support for loading/ saving adapters to transformer models with head(s). """ def __init__(self, config, *args, **kwargs): super().__init__(config, *args, **kwargs) self._convert_to_flex_head = False def set_shared_parameters(self, param): self.shared_parameters = param if self.base_model is not self: self.base_model.shared_parameters = self.shared_parameters def iter_layers(self) -> Iterable[Tuple[int, nn.Module]]: """ Iterates over all layers of the model. """ if self.base_model is self: return super().iter_layers() else: return self.base_model.iter_layers() def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False, set_active: bool = False): """ Adds a new adapter module of the specified type to the model. Args: adapter_name (str): The name of the adapter module to be added. config (str or dict, optional): The adapter configuration, can be either: - the string identifier of a pre-defined configuration dictionary - a configuration dictionary specifying the full config - if not given, the default configuration for this adapter type will be used overwrite_ok (bool, optional): Overwrite an adapter with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Set the adapter to be the active one. By default (False), the adapter is added but not activated. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().add_adapter(adapter_name, config, overwrite_ok=overwrite_ok, set_active=set_active) else: self.base_model.add_adapter(adapter_name, config, overwrite_ok=overwrite_ok, set_active=set_active) def train_adapter(self, adapter_setup: Union[list, AdapterCompositionBlock], train_embeddings=False): """ Sets the model into mode for training the given adapters. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().train_adapter(adapter_setup, train_embeddings) else: self.base_model.train_adapter(adapter_setup, train_embeddings) def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """ Sets the model into mode for training of adapter fusion determined by a list of adapter names. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) else: self.base_model.train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) def save_head(self, save_directory: str, head_name: str = None): loader = PredictionHeadLoader(self) loader.save(save_directory, name=head_name) def load_head(self, save_directory, load_as=None, id2label=None, **kwargs): loader = PredictionHeadLoader(self, convert_to_flex_head=self._convert_to_flex_head) return loader.load(save_directory, load_as=load_as, id2label=id2label, **kwargs) def save_adapter( self, save_directory: str, adapter_name: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().save_adapter( save_directory, adapter_name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders, ) def load_adapter( self, adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, source: str = None, with_head: bool = True, custom_weights_loaders: Optional[List[WeightsLoader]] = None, leave_out: Optional[List[int]] = None, id2label=None, set_active: bool = False, **kwargs ) -> str: if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append( PredictionHeadLoader( self, error_on_missing=False, convert_to_flex_head=self._convert_to_flex_head, ) ) # Support passing a num_labels for compatibility reasons. Convert to label map here. num_labels = kwargs.pop("num_labels", None) if num_labels is not None: id2label = {i: "LABEL_" + str(i) for i in range(num_labels)} return super().load_adapter( adapter_name_or_path, config=config, version=version, model_name=model_name, load_as=load_as, source=source, custom_weights_loaders=custom_weights_loaders, leave_out=leave_out, id2label=id2label, set_active=set_active, **kwargs, ) def save_all_adapters( self, save_directory: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().save_all_adapters( save_directory, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders, ) def save_adapter_fusion( self, save_directory: str, adapter_names: Union[Fuse, list, str], meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, with_head: Union[bool, str] = False, ): """ Saves an AdapterFusion layer and its configuration file to a directory so that it can be shared or reloaded using `load_adapter_fusion()`. Args: save_directory (str): Path to a directory where the AdapterFusion should be saved. adapter_names (Union[Fuse, list, str]): AdapterFusion to be saved. with_head (Union[bool, str]): If True, will save a head with the same name as the AdapterFusionLayer. If a string, this will be used as the name of the head to be saved. Raises: ValueError: If the given AdapterFusion name is invalid. """ super().save_adapter_fusion(save_directory, adapter_names, meta_dict, custom_weights_loaders) if with_head: # Make sure to cover the different options for adapter_names if isinstance(with_head, str): head_name = with_head elif isinstance(adapter_names, Fuse): head_name = adapter_names.name elif isinstance(adapter_names, list): head_name = ",".join(adapter_names) else: head_name = adapter_names if head_name not in self.heads: raise ValueError("No head with name {} found".format(head_name)) loader = PredictionHeadLoader(self) loader.save(save_directory, head_name) def load_adapter_fusion( self, adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, set_active: bool = False, with_head: bool = True, **kwargs ) -> str: if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().load_adapter_fusion(adapter_fusion_name_or_path, load_as, custom_weights_loaders, set_active) def save_all_heads(self, save_directory): for head_name in self.heads: save_path = join(save_directory, head_name) self.save_head(save_path, head_name) def get_labels(self): return list(self.config.id2label.values()) def get_labels_dict(self): return self.config.id2label def get_adapter(self, name): """ If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: return super().get_adapter(name) else: return self.base_model.get_adapter(name) def load_embeddings(self, path: str, name: str): if self.base_model is self: return super().load_embeddings(path, name) else: return self.base_model.load_embeddings(path, name) def save_embeddings(self, path, name, tokenizer=None): if self.base_model is self: return super().save_embeddings(path, name, tokenizer) else: return self.base_model.save_embeddings(path, name, tokenizer) def add_embeddings(self, name, tokenizer, reference_embedding=None, reference_tokenizer=None, embedding_dim=None): if self.base_model is None: return super().add_embeddings(name, tokenizer, reference_embedding, reference_tokenizer, embedding_dim) else: return self.base_model.add_embeddings( name, tokenizer, reference_embedding, reference_tokenizer, embedding_dim ) def set_active_embeddings(self, name): if self.base_model is None: return super().set_active_embeddings(name) else: return self.base_model.set_active_embeddings(name) def delete_embeddings(self, name): if self.base_model is None: return super().delete_embeddings(name) else: return self.base_model.delete_embeddings(name)

import logging import os import warnings from abc import ABC, abstractmethod from collections import defaultdict from os.path import join from typing import Iterable, List, Optional, Tuple, Union import torch from torch import nn from .composition import AdapterCompositionBlock, Fuse, Stack, parse_composition from .configuration import AdapterConfig, AdapterConfigBase, AdapterFusionConfig, get_adapter_config_hash from .context import AdapterSetup, ForwardContext from .hub_mixin import PushAdapterToHubMixin from .layer import AdapterLayer, AdapterLayerBase from .loading import AdapterFusionLoader, AdapterLoader, PredictionHeadLoader, WeightsLoader from .modeling import Adapter, GLOWCouplingBlock, NICECouplingBlock from .prefix_tuning import PrefixTuningPool, PrefixTuningShim from .utils import EMBEDDING_FILE, TOKENIZER_PATH, inherit_doc from .wrappers.configuration import wrap_config logger = logging.getLogger(__name__) class InvertibleAdaptersMixin: """Mixin for Transformer models adding invertible adapters.""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.invertible_adapters = nn.ModuleDict(dict()) # Make sure config is wrapped self.config = wrap_config(self.config) def add_invertible_adapter(self, adapter_name: str): """ Adds an invertible adapter module for the adapter with the given name. If the given adapter does not specify an invertible adapter config, this method does nothing. Args: adapter_name (str): The name of the adapter for which to add an invertible adapter module. """ if adapter_name in self.invertible_adapters: raise ValueError(f"Model already contains an adapter module for '{adapter_name}'.") adapter_config = self.config.adapters.match( adapter_name, config_type=AdapterConfig, location_key="inv_adapter", ) if adapter_config and adapter_config["inv_adapter"]: if adapter_config["inv_adapter"] == "nice": inv_adap = NICECouplingBlock( [[self.config.hidden_size]], non_linearity=adapter_config["non_linearity"], reduction_factor=adapter_config["inv_adapter_reduction_factor"], ) elif adapter_config["inv_adapter"] == "glow": inv_adap = GLOWCouplingBlock( [[self.config.hidden_size]], non_linearity=adapter_config["non_linearity"], reduction_factor=adapter_config["inv_adapter_reduction_factor"], ) else: raise ValueError(f"Invalid invertible adapter type '{adapter_config['inv_adapter']}'.") self.invertible_adapters[adapter_name] = inv_adap self.invertible_adapters[adapter_name].apply(Adapter.init_bert_weights) def delete_invertible_adapter(self, adapter_name: str): if adapter_name in self.invertible_adapters: del self.invertible_adapters[adapter_name] def get_invertible_adapter(self): # TODO: Currently no fusion over invertible adapters, takes only very first language adapter position if self.config.adapters.active_setup is not None and len(self.config.adapters.active_setup) > 0: first_adapter = self.config.adapters.active_setup.first() if first_adapter in self.invertible_adapters: return self.invertible_adapters[first_adapter] return None def enable_invertible_adapters(self, adapter_names): for adapter_name in adapter_names: if adapter_name in self.invertible_adapters: for param in self.invertible_adapters[adapter_name].parameters(): param.requires_grad = True def invertible_adapters_forward(self, hidden_states, rev=False): # TODO: Currently no fusion over invertible adapters, takes only very first language adapter position if self.config.adapters.active_setup is not None and len(self.config.adapters.active_setup) > 0: first_adapter = self.config.adapters.active_setup.first() if first_adapter in self.invertible_adapters: hidden_states = self.invertible_adapters[first_adapter](hidden_states, rev=rev) return hidden_states class ModelAdaptersMixin(PushAdapterToHubMixin, ABC): """Mixin for transformer models adding support for loading/ saving adapters.""" def __init__(self, config, *args, **kwargs): super().__init__(config, *args, **kwargs) if config.name_or_path and not os.path.exists(config.name_or_path): self.model_name = config.name_or_path else: self.model_name = None self.loaded_embeddings = {} self.shared_parameters = nn.ModuleDict() self._active_embedding = "default" # Make sure config is wrapped self.config = wrap_config(self.config) def _link_prefix_to_pool(self, layer): if isinstance(layer, PrefixTuningShim): layer.set_pool(self.base_model.prefix_tuning) def _init_adapter_modules(self, add_prefix_tuning_pool=True): """ This method initializes adapter modules and fusion modules from the model config. """ # Link all prefix tunings if add_prefix_tuning_pool: self.base_model.prefix_tuning = PrefixTuningPool(self.config) self.apply_to_adapter_layers(lambda i, layer: self._link_prefix_to_pool(layer)) # Initialize adapters from config for adapter_name in self.config.adapters: self.apply_to_adapter_layers(lambda i, layer: layer.add_adapter(adapter_name, i)) # Initialize fusion from config for fusion_name in self.config.adapters.fusions: self.apply_to_adapter_layers(lambda i, layer: layer.add_fusion_layer(fusion_name)) self.loaded_embeddings["default"] = self.get_input_embeddings() # These methods have to be implemented by every deriving class: @abstractmethod def iter_layers(self) -> Iterable[Tuple[int, nn.Module]]: """ Iterates over all layers of the model. This abstract method has to ne implemented by every implementing model. """ pass def apply_to_adapter_layers(self, fn): """ Applies a function to all adapter layers of the model. """ for i, layer in self.iter_layers(): for module in layer.modules(): if isinstance(module, AdapterLayerBase): fn(i, module) def train_adapter(self, adapter_setup: Union[list, AdapterCompositionBlock], train_embeddings=False): """Sets the model into mode for training the given adapters.""" self.train() self.freeze_model(True) adapter_setup = parse_composition(adapter_setup) self.apply_to_adapter_layers(lambda i, layer: layer.enable_adapters(adapter_setup, True, False)) for adapter_name in adapter_setup: if adapter_name in self.shared_parameters: for param in self.shared_parameters[adapter_name].values(): param.requires_grad = True if isinstance(self, InvertibleAdaptersMixin): self.enable_invertible_adapters(adapter_setup.flatten()) # use the adapters to be trained by default in every forward pass self.set_active_adapters(adapter_setup) if train_embeddings: self.get_input_embeddings().train() def train_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """Sets the model into mode for training of adapter fusion determined by a list of adapter names.""" warnings.warn( "add_fusion() has been deprecated in favor of add_adapter_fusion(). Please use the newer method instead.", FutureWarning, ) self.train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """Sets the model into mode for training of adapter fusion determined by a list of adapter names.""" self.train() self.freeze_model(True) adapter_setup = parse_composition(adapter_setup) self.apply_to_adapter_layers(lambda i, layer: layer.enable_adapters(adapter_setup, unfreeze_adapters, True)) # use the adapters to be trained by default in every forward pass self.set_active_adapters(adapter_setup) # TODO implement fusion for invertible adapters def has_adapters(self): if not getattr(self.config, "is_adaptable", None): return False return len(self.config.adapters.adapters) > 0 @property def has_parallel_adapters(self) -> bool: if self.config.adapters.active_setup: return self.config.adapters.active_setup.parallel_channels > 1 else: return False @property def active_adapters(self) -> AdapterCompositionBlock: return self.config.adapters.active_setup @active_adapters.setter def active_adapters(self, adapter_setup: Union[list, AdapterCompositionBlock]): self.set_active_adapters(adapter_setup) def set_shared_parameters(self, param): self.shared_parameters = param def set_active_adapters( self, adapter_setup: Union[list, AdapterCompositionBlock], skip_layers: Optional[List[int]] = None ): """ Sets the adapter modules to be used by default in every forward pass. If no adapter with the given name is found, no module of the respective type will be activated. Args: adapter_setup (list): The list of adapters to be activated by default. Can be a fusion or stacking configuration. """ adapter_setup = parse_composition(adapter_setup, model_type=self.config.model_type) if adapter_setup: for adapter_name in adapter_setup.flatten(): if adapter_name not in self.config.adapters.adapters: raise ValueError( f"No adapter with name '{adapter_name}' found. Please make sure that all specified adapters are correctly loaded." ) self.config.adapters.active_setup = adapter_setup self.config.adapters.skip_layers = skip_layers def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False, set_active: bool = False): """ Adds a new adapter module of the specified type to the model. Args: adapter_name (str): The name of the adapter module to be added. config (str or dict or AdapterConfigBase, optional): The adapter configuration, can be either: - the string identifier of a pre-defined configuration dictionary - a configuration dictionary specifying the full config - if not given, the default configuration for this adapter type will be used overwrite_ok (bool, optional): Overwrite an adapter with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Set the adapter to be the active one. By default (False), the adapter is added but not activated. """ if isinstance(config, dict): config = AdapterConfigBase.load(config) # ensure config is ok and up-to-date # In case adapter already exists and we allow overwriting, explicitly delete the existing one first if overwrite_ok and adapter_name in self.config.adapters: self.delete_adapter(adapter_name) self.config.adapters.add(adapter_name, config=config) try: self.apply_to_adapter_layers(lambda i, layer: layer.add_adapter(adapter_name, i)) # PHM Layer if self.config.adapters.match(adapter_name, AdapterConfig, location_key="phm_layer"): self._add_shared_parameters(adapter_name, config) # Prefix Tuning for module in self.modules(): if isinstance(module, PrefixTuningPool): module.confirm_prefix(adapter_name) if isinstance(self, InvertibleAdaptersMixin): self.add_invertible_adapter(adapter_name) except ValueError as ex: self.delete_adapter(adapter_name) raise ex if set_active: self.set_active_adapters(adapter_name) def _add_shared_parameters(self, adapter_name, adapter_config: AdapterConfig): self.shared_parameters[adapter_name] = ( list(self.get_adapter(adapter_name)[0].values())[0].adapter_down[0].init_shared_parameters() ) def add_fusion(self, adapter_names: Union[Fuse, list], adapter_fusion_config=None, override_kwargs=None): warnings.warn( "add_fusion() has been deprecated in favor of add_adapter_fusion(). Please use the newer method instead.", FutureWarning, ) adapter_fusion_config = AdapterFusionConfig.from_dict(adapter_fusion_config).replace(**override_kwargs) self.add_adapter_fusion(adapter_names, adapter_fusion_config) def add_adapter_fusion( self, adapter_names: Union[Fuse, list, str], config=None, overwrite_ok: bool = False, set_active: bool = False, ): """ Adds AdapterFusion to the model with alll the necessary configurations and weight initializations Args: adapter_names (Fuse or list or str): AdapterFusion layer to add. Can be either: - a ``Fuse`` composition block - a list of adapter names to fuse - a comma-separated string of adapter names to fuse config (str or dict): adapter fusion configuration, can be either: - a string identifying a pre-defined adapter fusion configuration - a dictionary representing the adapter fusion configuration - the path to a file containing the adapter fusion configuration overwrite_ok (bool, optional): Overwrite an AdapterFusion layer with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Activate the added AdapterFusion. By default (False), the AdapterFusion is added but not activated. """ if isinstance(adapter_names, Fuse): adapter_names = adapter_names.children elif isinstance(adapter_names, str): adapter_names = adapter_names.split(",") if isinstance(config, dict): config = AdapterFusionConfig.from_dict(config) # ensure config is ok and up-to-date # In case adapter already exists and we allow overwriting, explicitly delete the existing one first if overwrite_ok and self.config.adapters.get_fusion(adapter_names) is not None: self.delete_adapter_fusion(adapter_names) self.config.adapters.add_fusion(adapter_names, config=config) self.apply_to_adapter_layers(lambda i, layer: layer.add_fusion_layer(adapter_names)) if set_active: if not isinstance(adapter_names, list): adapter_names = adapter_names.split(",") self.set_active_adapters(Fuse(*adapter_names)) def delete_adapter(self, adapter_name: str): """ Deletes the adapter with the specified name from the model. Args: adapter_name (str): The name of the adapter. """ if adapter_name not in self.config.adapters: logger.info("No adapter '%s' found for deletion. Skipping.", adapter_name) return del self.config.adapters.adapters[adapter_name] self.apply_to_adapter_layers(lambda i, layer: layer.delete_adapter(adapter_name)) if isinstance(self, InvertibleAdaptersMixin): self.delete_invertible_adapter(adapter_name) # Reset active adapters if this was the only active adapter if self.active_adapters == Stack(adapter_name): self.active_adapters = None def delete_adapter_fusion(self, adapter_names: Union[Fuse, list, str]): """ Deletes the AdapterFusion layer of the specified adapters. Args: adapter_names (Union[Fuse, list, str]): AdapterFusion layer to delete. """ if isinstance(adapter_names, Fuse): adapter_fusion_name = ",".join(adapter_names.children) elif isinstance(adapter_names, list): adapter_fusion_name = ",".join(adapter_names) elif isinstance(adapter_names, str): adapter_fusion_name = adapter_names else: raise ValueError("Invalid AdapterFusion definition: {}".format(adapter_names)) if adapter_fusion_name not in self.config.adapters.fusions: logger.info("No AdapterFusion '%s' found for deletion. Skipping.", adapter_fusion_name) return del self.config.adapters.fusions[adapter_fusion_name] self.apply_to_adapter_layers(lambda i, layer: layer.delete_fusion_layer(adapter_fusion_name)) # Reset active adapters if this was the active setup if self.active_adapters == adapter_names: self.active_adapters = None def save_adapter( self, save_directory: str, adapter_name: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using `load_adapter()`. Args: save_directory (str): Path to a directory where the adapter should be saved. adapter_name (str): Name of the adapter to be saved. Raises: ValueError: If the given adapter name is invalid. """ loader = AdapterLoader(self) loader.save(save_directory, adapter_name, meta_dict) # save additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.save(save_directory, adapter_name) def save_adapter_fusion( self, save_directory: str, adapter_names: Union[Fuse, list, str], meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves an AdapterFusion layer and its configuration file to a directory so that it can be shared or reloaded using `load_adapter_fusion()`. Args: save_directory (str): Path to a directory where the AdapterFusion should be saved. adapter_names (Union[Fuse, list, str]): AdapterFusion to be saved. Raises: ValueError: If the given AdapterFusion name is invalid. """ if isinstance(adapter_names, Fuse): adapter_fusion_name = ",".join(adapter_names.children) elif isinstance(adapter_names, list): adapter_fusion_name = ",".join(adapter_names) elif isinstance(adapter_names, str): adapter_fusion_name = adapter_names else: raise ValueError("Invalid AdapterFusion definition: {}".format(adapter_names)) loader = AdapterFusionLoader(self) loader.save(save_directory, adapter_fusion_name, meta_dict) # save additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.save(save_directory, adapter_fusion_name) def load_adapter( self, adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, source: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, leave_out: Optional[List[int]] = None, id2label=None, set_active: bool = False, **kwargs ) -> str: """ Loads a pre-trained pytorch adapter module from the local file system or a remote location. Args: adapter_name_or_path (str): can be either: - the identifier of a pre-trained task adapter to be loaded from Adapter Hub - a path to a directory containing adapter weights saved using `model.saved_adapter()` - a URL pointing to a zip folder containing a saved adapter module config (dict or str, optional): The requested configuration of the adapter. If not specified, will be either: - the default adapter config for the requested adapter if specified - the global default adapter config version (str, optional): The version of the adapter to be loaded. model_name (str, optional): The string identifier of the pre-trained model. load_as (str, optional): Load the adapter using this name. By default, the name with which the adapter was saved will be used. source (str, optional): Identifier of the source(s) from where to load the adapter. Can be: - "ah" (default): search on AdapterHub. - "hf": search on HuggingFace model hub. - None: search on all sources leave_out: Dynamically drop adapter modules in the specified Transformer layers when loading the adapter. set_active (bool, optional): Set the loaded adapter to be the active one. By default (False), the adapter is loaded but not activated. Returns: str: The name with which the adapter was added to the model. """ loader = AdapterLoader(self) load_dir, load_name = loader.load( adapter_name_or_path, config, version, model_name, load_as, source=source, leave_out=leave_out, set_active=set_active, **kwargs, ) # load additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.load( load_dir, load_as=load_as, loading_info=kwargs.get("loading_info", None), main_load_name=load_name, id2label=id2label, set_active=set_active, ) return load_name def load_adapter_fusion( self, adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, set_active: bool = False, **kwargs ) -> str: """ Loads a pre-trained AdapterFusion layer from the local file system. Args: adapter_fusion_name_or_path (str): a path to a directory containing AdapterFusion weights saved using `model.save_adapter_fusion()`. load_as (str, optional): Load the AdapterFusion using this name. By default, the name with which the AdapterFusion layer was saved will be used. set_active (bool, optional): Activate the loaded AdapterFusion. By default (False), the AdapterFusion is loaded but not activated. Returns: str: The name with which the AdapterFusion was added to the model. """ loader = AdapterFusionLoader(self) load_dir, load_name = loader.load(adapter_fusion_name_or_path, load_as, set_active=set_active) # load additional custom weights if custom_weights_loaders: for weights_loader in custom_weights_loaders: weights_loader.load( load_dir, load_as=load_as, loading_info=kwargs.get("loading_info", None), main_load_name=load_name, set_active=set_active, ) return load_name def save_all_adapters( self, save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves all adapters of this model together with their configuration to subfolders of the given location. Args: save_directory (str): Path to a directory where the adapters should be saved. """ for name in self.config.adapters: adapter_config = self.config.adapters.get(name) h = get_adapter_config_hash(adapter_config) save_path = join(save_directory, name) if meta_dict: meta_dict.update({"config_id": h}) else: meta_dict = {"config_id": h} self.save_adapter(save_path, name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders) def save_all_adapter_fusions( self, save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): """ Saves all AdapterFusion layers of this model together with their configuration to subfolders of the given location. Args: save_directory (str): Path to a directory where the AdapterFusion layers should be saved. """ for name in self.config.adapters.fusions: adapter_fusion_config = self.config.adapters.get_fusion(name) h = get_adapter_config_hash(adapter_fusion_config) save_path = join(save_directory, name) if meta_dict: meta_dict.update({"config_id": h}) else: meta_dict = {"config_id": h} self.save_adapter_fusion( save_path, name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders ) def freeze_model(self, freeze=True): """Freezes all weights of the model.""" # first freeze/ unfreeze all model weights for param in self.base_model.parameters(): param.requires_grad = not freeze self.model_frozen = freeze def forward_context(self, context: ForwardContext, *args, **kwargs): """ This method is called by the ``ForwardContext`` at the beginning of the forward pass. """ # some warnings if we don't use available adapters active_adapters = getattr(self, "active_adapters", None) or AdapterSetup.get_context() if not active_adapters: if self.has_adapters(): logger.warning("There are adapters available but none are activated for the forward pass.") return context.adapters_parallelized = False # Add the shared parameters for the active adapters to the context context.shared_parameters = { name: param for name, param in self.shared_parameters.items() if name in active_adapters.flatten() } # Prefix tuning input_tensor = kwargs.get("input_ids", None) if input_tensor is None: input_tensor = kwargs.get("decoder_input_ids", None) if input_tensor is None: input_tensor = kwargs.get("attention_mask", None) if input_tensor is None: input_tensor = args[0] context.prefix_states = self.base_model.prefix_tuning(input_tensor.shape[0]) def load_embeddings(self, path: str, name: str): """ Load a saved embedding from the given path. If the embedding was saved with a tokenizer it is returned Args: path: the path to the saved embedding name: the name the embedding should be loaded as Returns: a tokenizer if it ws saved with the embedding otherwise None """ from ..models.auto.tokenization_auto import AutoTokenizer if name in self.loaded_embeddings: raise ValueError("An embedding with the name {} already exists".format(name)) tokenizer = None tokenizer_path = os.path.join(path, TOKENIZER_PATH) if os.path.isdir(tokenizer_path): tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) embedding_path = os.path.join(path, EMBEDDING_FILE) if not os.path.isfile(embedding_path): raise FileNotFoundError("No embeddings found at {}".format(embedding_path)) weights = torch.load(embedding_path) self.loaded_embeddings[name] = nn.Embedding.from_pretrained(weights) self.set_active_embeddings(name) return tokenizer def add_embeddings(self, name, tokenizer, reference_embedding=None, reference_tokenizer=None, embedding_dim=None): """ Add a new embedding to the model. If a reference embedding and reference tokenizer are provided tokens in the present in both tokenizers are initialized to the embedding in the reference_embedding. Args: name: the name of the embedding tokenizer: the tokenizer determining the vocab of the embedding reference_embedding: the reference embedding to use for initializing the embeddings of tokens present in the newly created embedding reference_tokenizer: the tokenizer providing the vocab for the reference embedding embedding_dim: the dimension of the embeddings (if None the hidden_size from the config is used) """ if name in self.loaded_embeddings: raise ValueError("An embedding with the name {} already exists".format(name)) if embedding_dim is None: embedding_dim = self.config.hidden_size embedding = nn.Embedding(tokenizer.vocab_size, embedding_dim) embedding.requires_grad_(False) if (reference_embedding is not None and reference_tokenizer is None) or ( reference_tokenizer is not None and reference_embedding is None ): raise KeyError( "Reference embedding and reference tokenizer are required to use initialize embeddings from reference embedding" ) if reference_embedding is not None and reference_tokenizer is not None: tokens = set(tokenizer.get_vocab().keys()) & set(reference_tokenizer.get_vocab().keys()) reference_vocab = reference_tokenizer.get_vocab() vocab = tokenizer.get_vocab() for t in tokens: idx_reference = reference_vocab[t] idx = vocab[t] embedding.weight[idx] = self.loaded_embeddings[reference_embedding].weight[idx_reference].clone() embedding.train(False) self.loaded_embeddings[name] = embedding self.set_active_embeddings(name) def delete_embeddings(self, name): """ Deletes the embedding with the given name Args: name: The name of the embedding that should be deleted """ if name not in self.loaded_embeddings: raise ValueError("No embedding with name {}".format(name)) if self.active_embeddings == name: logger.warning("The active embedding is deleted. Setting the default embedding as active.") self.set_active_embeddings("default") del self.loaded_embeddings[name] def save_embeddings(self, path, name, tokenizer=None): """ Saves the embedding with the given name. If a tokenizer is passed as well the tokenizer is saved together with the embedding. Args: path: The path where the embedding should be saved name: The name of the embedding that should be saved tokenizer: optionally a tokenizer to save with the embedding (default is None) """ if self.active_embeddings == name: self.loaded_embeddings[name] = self.get_input_embeddings() os.makedirs(path, exist_ok=True) embedding_path = os.path.join(path, EMBEDDING_FILE) torch.save(self.loaded_embeddings[name].weight, embedding_path) if tokenizer: tokenizer_path = os.path.join(path, TOKENIZER_PATH) tokenizer.save_pretrained(tokenizer_path) def set_active_embeddings(self, name): """ Sets the active embedding for the forward pass of the model Args: name: The name of the embedding that should be used """ self.loaded_embeddings[self.active_embeddings] = self.get_input_embeddings() self.set_input_embeddings(self.loaded_embeddings[name]) self._active_embedding = name @property def active_embeddings(self): return self._active_embedding def get_fusion_regularization_loss(self): reg_loss = 0.0 target = torch.zeros((self.config.hidden_size, self.config.hidden_size)).fill_diagonal_(1.0).to(self.device) for i, layer in self.iter_layers(): for module in layer.modules(): if isinstance(module, AdapterLayer): for _, layer_fusion in module.adapter_fusion_layer.items(): if hasattr(layer_fusion, "value"): reg_loss += 0.01 * (target - layer_fusion.value.weight).pow(2).sum() return reg_loss def get_adapter(self, name) -> dict: """ Returns a dictionary with all weights of the adapter with the specified name. Args: name (str): The adapter name. Returns: dict: A nested dictionary containing the weights of the adapter. The dictionary is structured as follow: {<layer id>: {<module location>: <nn.Module>}}. """ destination = defaultdict(dict) # use a custom index to ensure numbering is from 0 to N layers for i, (_, layer) in enumerate(self.iter_layers()): for module in layer.modules(): if isinstance(module, AdapterLayerBase): adapter_module = module.get_adapter(name) if adapter_module is not None: destination[i][module.location_key] = adapter_module return dict(destination) def eject_prefix_tuning(self, name: str): """ Converts the prefix tuning with the given name from the reparameterized form into the flat form. Args: name (str): The name of the prefix tuning. """ for module in self.modules(): if isinstance(module, PrefixTuningPool): if name in module.prefix_tunings: module.prefix_tunings[name].eject() @inherit_doc class ModelWithHeadsAdaptersMixin(ModelAdaptersMixin): """ Mixin adding support for loading/ saving adapters to transformer models with head(s). """ def __init__(self, config, *args, **kwargs): super().__init__(config, *args, **kwargs) self._convert_to_flex_head = False def set_shared_parameters(self, param): self.shared_parameters = param if self.base_model is not self: self.base_model.shared_parameters = self.shared_parameters def iter_layers(self) -> Iterable[Tuple[int, nn.Module]]: """ Iterates over all layers of the model. """ if self.base_model is self: return super().iter_layers() else: return self.base_model.iter_layers() def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False, set_active: bool = False): """ Adds a new adapter module of the specified type to the model. Args: adapter_name (str): The name of the adapter module to be added. config (str or dict, optional): The adapter configuration, can be either: - the string identifier of a pre-defined configuration dictionary - a configuration dictionary specifying the full config - if not given, the default configuration for this adapter type will be used overwrite_ok (bool, optional): Overwrite an adapter with the same name if it exists. By default (False), an exception is thrown. set_active (bool, optional): Set the adapter to be the active one. By default (False), the adapter is added but not activated. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().add_adapter(adapter_name, config, overwrite_ok=overwrite_ok, set_active=set_active) else: self.base_model.add_adapter(adapter_name, config, overwrite_ok=overwrite_ok, set_active=set_active) def train_adapter(self, adapter_setup: Union[list, AdapterCompositionBlock], train_embeddings=False): """ Sets the model into mode for training the given adapters. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().train_adapter(adapter_setup, train_embeddings) else: self.base_model.train_adapter(adapter_setup, train_embeddings) def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """ Sets the model into mode for training of adapter fusion determined by a list of adapter names. If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: super().train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) else: self.base_model.train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) def save_head(self, save_directory: str, head_name: str = None): loader = PredictionHeadLoader(self) loader.save(save_directory, name=head_name) def load_head(self, save_directory, load_as=None, id2label=None, **kwargs): loader = PredictionHeadLoader(self, convert_to_flex_head=self._convert_to_flex_head) return loader.load(save_directory, load_as=load_as, id2label=id2label, **kwargs) def save_adapter( self, save_directory: str, adapter_name: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().save_adapter( save_directory, adapter_name, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders, ) def load_adapter( self, adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, source: str = None, with_head: bool = True, custom_weights_loaders: Optional[List[WeightsLoader]] = None, leave_out: Optional[List[int]] = None, id2label=None, set_active: bool = False, **kwargs ) -> str: if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append( PredictionHeadLoader( self, error_on_missing=False, convert_to_flex_head=self._convert_to_flex_head, ) ) # Support passing a num_labels for compatibility reasons. Convert to label map here. num_labels = kwargs.pop("num_labels", None) if num_labels is not None: id2label = {i: "LABEL_" + str(i) for i in range(num_labels)} return super().load_adapter( adapter_name_or_path, config=config, version=version, model_name=model_name, load_as=load_as, source=source, custom_weights_loaders=custom_weights_loaders, leave_out=leave_out, id2label=id2label, set_active=set_active, **kwargs, ) def save_all_adapters( self, save_directory: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, ): if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().save_all_adapters( save_directory, meta_dict=meta_dict, custom_weights_loaders=custom_weights_loaders, ) def save_adapter_fusion( self, save_directory: str, adapter_names: Union[Fuse, list, str], meta_dict: dict = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, with_head: Union[bool, str] = False, ): """ Saves an AdapterFusion layer and its configuration file to a directory so that it can be shared or reloaded using `load_adapter_fusion()`. Args: save_directory (str): Path to a directory where the AdapterFusion should be saved. adapter_names (Union[Fuse, list, str]): AdapterFusion to be saved. with_head (Union[bool, str]): If True, will save a head with the same name as the AdapterFusionLayer. If a string, this will be used as the name of the head to be saved. Raises: ValueError: If the given AdapterFusion name is invalid. """ super().save_adapter_fusion(save_directory, adapter_names, meta_dict, custom_weights_loaders) if with_head: # Make sure to cover the different options for adapter_names if isinstance(with_head, str): head_name = with_head elif isinstance(adapter_names, Fuse): head_name = adapter_names.name elif isinstance(adapter_names, list): head_name = ",".join(adapter_names) else: head_name = adapter_names if head_name not in self.heads: raise ValueError("No head with name {} found".format(head_name)) loader = PredictionHeadLoader(self) loader.save(save_directory, head_name) def load_adapter_fusion( self, adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[WeightsLoader]] = None, set_active: bool = False, with_head: bool = True, **kwargs ) -> str: if with_head: if custom_weights_loaders is None: custom_weights_loaders = [] custom_weights_loaders.append(PredictionHeadLoader(self, error_on_missing=False)) super().load_adapter_fusion(adapter_fusion_name_or_path, load_as, custom_weights_loaders, set_active) def save_all_heads(self, save_directory): for head_name in self.heads: save_path = join(save_directory, head_name) self.save_head(save_path, head_name) def get_labels(self): return list(self.config.id2label.values()) def get_labels_dict(self): return self.config.id2label def get_adapter(self, name): """ If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion """ if self.base_model is self: return super().get_adapter(name) else: return self.base_model.get_adapter(name) def load_embeddings(self, path: str, name: str): if self.base_model is self: return super().load_embeddings(path, name) else: return self.base_model.load_embeddings(path, name) def save_embeddings(self, path, name, tokenizer=None): if self.base_model is self: return super().save_embeddings(path, name, tokenizer) else: return self.base_model.save_embeddings(path, name, tokenizer) def add_embeddings(self, name, tokenizer, reference_embedding=None, reference_tokenizer=None, embedding_dim=None): if self.base_model is None: return super().add_embeddings(name, tokenizer, reference_embedding, reference_tokenizer, embedding_dim) else: return self.base_model.add_embeddings( name, tokenizer, reference_embedding, reference_tokenizer, embedding_dim ) def set_active_embeddings(self, name): if self.base_model is None: return super().set_active_embeddings(name) else: return self.base_model.set_active_embeddings(name) def delete_embeddings(self, name): if self.base_model is None: return super().delete_embeddings(name) else: return self.base_model.delete_embeddings(name)

import numpy as np import torch from dataclasses import dataclass from typing import List from jiant.tasks.core import ( BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, Task, TaskTypes, ) from jiant.tasks.lib.templates.shared import double_sentence_featurize, labels_to_bimap from jiant.utils.python.io import read_jsonl @dataclass class Example(BaseExample): guid: str input_premise: str input_hypothesis: str label: str def tokenize(self, tokenizer): return TokenizedExample( guid=self.guid, input_premise=tokenizer.tokenize(self.input_premise), input_hypothesis=tokenizer.tokenize(self.input_hypothesis), label_id=CounterfactualNliTask.LABEL_TO_ID[self.label], ) @dataclass class TokenizedExample(BaseTokenizedExample): guid: str input_premise: List input_hypothesis: List label_id: int def featurize(self, tokenizer, feat_spec): return double_sentence_featurize( guid=self.guid, input_tokens_a=self.input_premise, input_tokens_b=self.input_hypothesis, label_id=self.label_id, tokenizer=tokenizer, feat_spec=feat_spec, data_row_class=DataRow, ) @dataclass class DataRow(BaseDataRow): guid: str input_ids: np.ndarray input_mask: np.ndarray segment_ids: np.ndarray label_id: int tokens: list @dataclass class Batch(BatchMixin): input_ids: torch.LongTensor input_mask: torch.LongTensor segment_ids: torch.LongTensor label_id: torch.LongTensor tokens: list class CounterfactualNliTask(Task): Example = Example TokenizedExample = Example DataRow = DataRow Batch = Batch TASK_TYPE = TaskTypes.CLASSIFICATION LABELS = ["contradiction", "entailment", "neutral"] LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS) def get_train_examples(self): return self._create_examples(lines=read_jsonl(self.train_path), set_type="train") def get_val_examples(self): return self._create_examples(lines=read_jsonl(self.val_path), set_type="val") def get_test_examples(self): return self._create_examples(lines=read_jsonl(self.test_path), set_type="test") @classmethod def _create_examples(cls, lines, set_type): examples = [] empty_labels = 0 dash_labels = 0 for (i, line) in enumerate(lines): if set_type != "test": if line["label"] == "-": dash_labels+=1 continue elif line["label"] == "": empty_labels+=1 continue else: assert line["label"] in cls.LABELS, f"Example {i} label not supported: {line["label"]}" examples.append( Example( guid="%s-%s" % (set_type, i), input_premise=line["premise"], input_hypothesis=line["hypothesis"], label=line["label"] if set_type != "test" else cls.LABELS[-1], ) ) return examples

import numpy as np import torch from dataclasses import dataclass from typing import List from jiant.tasks.core import ( BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, Task, TaskTypes, ) from jiant.tasks.lib.templates.shared import double_sentence_featurize, labels_to_bimap from jiant.utils.python.io import read_jsonl @dataclass class Example(BaseExample): guid: str input_premise: str input_hypothesis: str label: str def tokenize(self, tokenizer): return TokenizedExample( guid=self.guid, input_premise=tokenizer.tokenize(self.input_premise), input_hypothesis=tokenizer.tokenize(self.input_hypothesis), label_id=CounterfactualNliTask.LABEL_TO_ID[self.label], ) @dataclass class TokenizedExample(BaseTokenizedExample): guid: str input_premise: List input_hypothesis: List label_id: int def featurize(self, tokenizer, feat_spec): return double_sentence_featurize( guid=self.guid, input_tokens_a=self.input_premise, input_tokens_b=self.input_hypothesis, label_id=self.label_id, tokenizer=tokenizer, feat_spec=feat_spec, data_row_class=DataRow, ) @dataclass class DataRow(BaseDataRow): guid: str input_ids: np.ndarray input_mask: np.ndarray segment_ids: np.ndarray label_id: int tokens: list @dataclass class Batch(BatchMixin): input_ids: torch.LongTensor input_mask: torch.LongTensor segment_ids: torch.LongTensor label_id: torch.LongTensor tokens: list class CounterfactualNliTask(Task): Example = Example TokenizedExample = Example DataRow = DataRow Batch = Batch TASK_TYPE = TaskTypes.CLASSIFICATION LABELS = ["contradiction", "entailment", "neutral"] LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS) def get_train_examples(self): return self._create_examples(lines=read_jsonl(self.train_path), set_type="train") def get_val_examples(self): return self._create_examples(lines=read_jsonl(self.val_path), set_type="val") def get_test_examples(self): return self._create_examples(lines=read_jsonl(self.test_path), set_type="test") @classmethod def _create_examples(cls, lines, set_type): examples = [] empty_labels = 0 dash_labels = 0 for (i, line) in enumerate(lines): if set_type != "test": if line["label"] == "-": dash_labels+=1 continue elif line["label"] == "": empty_labels+=1 continue else: assert line["label"] in cls.LABELS, f"Example {i} label not supported: {line['label']}" examples.append( Example( guid="%s-%s" % (set_type, i), input_premise=line["premise"], input_hypothesis=line["hypothesis"], label=line["label"] if set_type != "test" else cls.LABELS[-1], ) ) return examples

import click import logging from os.path import basename, exists from shutil import rmtree from helper.aws import AwsApiHelper logging.getLogger().setLevel(logging.DEBUG) class Helper(AwsApiHelper): def __init__(self, sql_file): super().__init__() self._sql_file = sql_file with open(sql_file, "r") as f: self._sql_statement = f.read() def process_request(self, session, account_id, region, kwargs): output_filename = f'{account_id}_{region}_{basename(self._sql_file).replace('.sql', '')}.txt' if exists(output_filename): rmtree(output_filename) client = session.client("config", region_name=region) params = {"Expression": self._sql_statement} while True: resp = client.select_resource_config(**params) for item in resp["Results"]: dump(item, output_filename) if resp.get("NextToken") is None: break params["NextToken"] = resp.get("NextToken") def dump(data, output_filename, to_console=True): if to_console is True: print(data) with open(output_filename, "a") as f: f.write(f'{data}\n') @click.command() @click.option("--sqlfile", "-s", required=True, help="File containing the sql statement (.sql)") @click.option("--profile", "-p", help="AWS profile name. Use profiles in ~/.aws if not specified.") @click.option("--region", "-r", default="ap-southeast-2", show_default=True, help="AWS Region. Use 'all' for all regions.") def main(sqlfile, profile, region): Helper(sqlfile).start(profile, region, "config") if __name__ == "__main__": main()

import click import logging from os.path import basename, exists from shutil import rmtree from helper.aws import AwsApiHelper logging.getLogger().setLevel(logging.DEBUG) class Helper(AwsApiHelper): def __init__(self, sql_file): super().__init__() self._sql_file = sql_file with open(sql_file, "r") as f: self._sql_statement = f.read() def process_request(self, session, account_id, region, kwargs): output_filename = f'{account_id}_{region}_{basename(self._sql_file).replace(".sql", "")}.txt' if exists(output_filename): rmtree(output_filename) client = session.client("config", region_name=region) params = {"Expression": self._sql_statement} while True: resp = client.select_resource_config(**params) for item in resp["Results"]: dump(item, output_filename) if resp.get("NextToken") is None: break params["NextToken"] = resp.get("NextToken") def dump(data, output_filename, to_console=True): if to_console is True: print(data) with open(output_filename, "a") as f: f.write(f'{data}\n') @click.command() @click.option("--sqlfile", "-s", required=True, help="File containing the sql statement (.sql)") @click.option("--profile", "-p", help="AWS profile name. Use profiles in ~/.aws if not specified.") @click.option("--region", "-r", default="ap-southeast-2", show_default=True, help="AWS Region. Use 'all' for all regions.") def main(sqlfile, profile, region): Helper(sqlfile).start(profile, region, "config") if __name__ == "__main__": main()

import os import csv import genanki from gtts import gTTS from mnemocards import ASSETS_DIR from mnemocards.utils import get_hash_id, NoteID, generate_furigana from mnemocards.builders.vocabulary_builder import VocabularyBuilder from mnemocards.builders.vocabulary_builder import remove_parentheses, remove_spaces from mnemocards import maketsv as tsv css = open(f"{ASSETS_DIR}/css/autogenerate.css").read() CARD_MODEL = genanki.Model( get_hash_id("725b5570-eb22-4ca5-a2b2-817e04514cde"), "Autogenerated vocabulary model", fields=[ # Visible fields. {"name": "YourLanguageWord"}, {"name": "YourLanguageExplanation"}, {"name": "LanguageYouLearnWord"}, {"name": "LanguageYouLearnPronunciation"}, {"name": "LanguageYouLearnExplanation"}, # Configuration fields. {"name": "CardColor"}, {"name": "ShowPronunciationInReverse"}, ], templates=[ { "name": "Vocabulary card", "qfmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .synonyms .line_2 { color: #0000; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> <hr> <div class="destination word">{{LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', }, { "name": "Vocabulary card (reversed)", "qfmt": ''' <style> .card { background: {{CardColor}}; } .destination { color: black; } .definitions .line_1{ color: #0000 } .definitions .line_2{ color: #0000 } </style> <div class="destination word">{{LanguageYouLearnWord}}</div> {{#ShowPronunciationInReverse}} <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> {{/ShowPronunciationInReverse}} <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="destination word">{{LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> <hr> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', }, ], css=css, ) CARD_MODEL_JAPANESE = genanki.Model( get_hash_id("fcac015f-d9dc-4f62-a8f9-0a1ef7a621e2"), "Autogenerated vocabulary model (Japanese)", fields=[ # Visible fields. {"name": "YourLanguageWord"}, {"name": "YourLanguageExplanation"}, {"name": "LanguageYouLearnWord"}, {"name": "LanguageYouLearnPronunciation"}, {"name": "LanguageYouLearnExplanation"}, # Configuration fields. {"name": "CardColor"}, {"name": "ShowPronunciationInReverse"}, ], templates=[ { "name": "Vocabulary card", "qfmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .synonyms .line_2 { color: #0000; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> <hr> <div class="destination word">{{furigana:LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', }, { "name": "Vocabulary card (reversed)", "qfmt": ''' <style> .card { background: {{CardColor}}; } .destination { color: black; } .definitions .line_1{ color: #0000 } .definitions .line_2{ color: #0000 } </style> <div class="destination word">{{kanji:LanguageYouLearnWord}}</div> {{#ShowPronunciationInReverse}} <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> {{/ShowPronunciationInReverse}} <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="destination word">{{furigana:LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> <hr> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', }, ], css=css, ) class AutogenerateBuilder(VocabularyBuilder, object): def __init__(self): pass def parse_src_to_settings(self, data_dir, src): settings = {} settings["color"] = src.get("card_color", "#f5f5f5") settings["show_p"] = "true" if src.get( "pronunciation_in_reverse", False) else "" settings["tsv_as_source"] = src.get("use_tsv_for_generation", False) settings["filename"] = os.path.join(data_dir, src["file"]) settings["generate_audio"] = src.get("audio", False) settings["lang"] = src.get("lang", {}) if not settings["lang"]: settings["lang"]["original"] = "en" settings["lang"]["translation"] = "es" if settings["generate_audio"]: settings["media_dir"] = self.prepare_media_dir(data_dir, {}) settings["furigana"] = src.get("furigana", False) settings["furigana_type"] = src.get("furigana_type", "hira") settings["card_properties"] = src.get("card_properties", None) settings["tags"] = [] if src.get("card_properties", None): settings["tags"] = src["card_properties"].get("tags", []) return settings def build_cards_from_words(self, settings): words = tsv.scrape_words_from_file(settings["filename"]) translations = tsv.get_translation( words, settings["lang"]["original"], settings["lang"]["translation"]) cards = [tsv.prepare_card_fields(trans) for trans in translations] for card in cards: card["tags"] = settings["tags"] return cards def build_cards_from_tsv(self, settings): with open(settings["filename"], "r") as csvfile: reader = csv.reader(csvfile, delimiter="\t", quotechar='"') iterator = iter(reader) cards = [] # Skip header that is present in every autogenerated TSV. next(iterator) for i, row in enumerate(iterator): card_id, ylw, yle, lylw, lylp, lyle, row_tags = row tags = settings["tags"] tags.extend(row_tags.split(",")) card = {"card_id": card_id, "ylw": ylw, "yle": yle, "lylw": lylw, "lylp": lylp, "lyle": lyle, "tags": tags} cards.append(card) return cards def build_notes_and_media(self, settings, cards): notes, media = [], [] for card in cards: if settings["furigana"] and (settings["lang"]["original"] == "ja"): card["lylp"] = "" card["lylw"] = generate_furigana( card["lylw"], settings["furigana_type"]) if settings["generate_audio"]: clean_text = remove_parentheses(card["lylw"]) if settings["furigana"]: # If you leave those spaces you get wrong # pronunciations, like in `スペイン人`. # Instead of `supein jin` it pronounces it as # `supein hito` because the kanji `人` alone is # pronounced as `hito`. clean_text = remove_spaces(clean_text) hash_text = get_hash_id(clean_text, bytes=8) sound_file = f'{settings['media_dir']}/{hash_text}.mp3' if not os.path.exists(sound_file): print(f"Creating audio file {sound_file}") lang = settings["lang"]["original"] tts = gTTS(clean_text, lang=lang) tts.save(sound_file) card["lylp"] += f" [sound:{hash_text}.mp3]" media.append(sound_file) note = NoteID( card["card_id"], model=CARD_MODEL_JAPANESE if settings["furigana"] else CARD_MODEL, fields=[ card["ylw"], card["yle"], card["lylw"], card["lylp"], card["lyle"], settings["color"], settings["show_p"]], tags=card["tags"]) notes.append(note) return notes, media def build_cards(self, data_dir, src, deck_config): # Get data from config. settings = self.parse_src_to_settings(data_dir, src) # Choose what builder to use - from words or TSV. builder = self.build_cards_from_words if "tsv" in settings["filename"].split('.')[-1]: builder = self.build_cards_from_tsv cards = builder(settings) # notes, media = self.build_notes_and_media(settings, cards) return notes, media

import os import csv import genanki from gtts import gTTS from mnemocards import ASSETS_DIR from mnemocards.utils import get_hash_id, NoteID, generate_furigana from mnemocards.builders.vocabulary_builder import VocabularyBuilder from mnemocards.builders.vocabulary_builder import remove_parentheses, remove_spaces from mnemocards import maketsv as tsv css = open(f"{ASSETS_DIR}/css/autogenerate.css").read() CARD_MODEL = genanki.Model( get_hash_id("725b5570-eb22-4ca5-a2b2-817e04514cde"), "Autogenerated vocabulary model", fields=[ # Visible fields. {"name": "YourLanguageWord"}, {"name": "YourLanguageExplanation"}, {"name": "LanguageYouLearnWord"}, {"name": "LanguageYouLearnPronunciation"}, {"name": "LanguageYouLearnExplanation"}, # Configuration fields. {"name": "CardColor"}, {"name": "ShowPronunciationInReverse"}, ], templates=[ { "name": "Vocabulary card", "qfmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .synonyms .line_2 { color: #0000; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> <hr> <div class="destination word">{{LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', }, { "name": "Vocabulary card (reversed)", "qfmt": ''' <style> .card { background: {{CardColor}}; } .destination { color: black; } .definitions .line_1{ color: #0000 } .definitions .line_2{ color: #0000 } </style> <div class="destination word">{{LanguageYouLearnWord}}</div> {{#ShowPronunciationInReverse}} <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> {{/ShowPronunciationInReverse}} <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="destination word">{{LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> <hr> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', }, ], css=css, ) CARD_MODEL_JAPANESE = genanki.Model( get_hash_id("fcac015f-d9dc-4f62-a8f9-0a1ef7a621e2"), "Autogenerated vocabulary model (Japanese)", fields=[ # Visible fields. {"name": "YourLanguageWord"}, {"name": "YourLanguageExplanation"}, {"name": "LanguageYouLearnWord"}, {"name": "LanguageYouLearnPronunciation"}, {"name": "LanguageYouLearnExplanation"}, # Configuration fields. {"name": "CardColor"}, {"name": "ShowPronunciationInReverse"}, ], templates=[ { "name": "Vocabulary card", "qfmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .synonyms .line_2 { color: #0000; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> <hr> <div class="destination word">{{furigana:LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', }, { "name": "Vocabulary card (reversed)", "qfmt": ''' <style> .card { background: {{CardColor}}; } .destination { color: black; } .definitions .line_1{ color: #0000 } .definitions .line_2{ color: #0000 } </style> <div class="destination word">{{kanji:LanguageYouLearnWord}}</div> {{#ShowPronunciationInReverse}} <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> {{/ShowPronunciationInReverse}} <div class="destination comment">{{LanguageYouLearnExplanation}}</div> ''', "afmt": ''' <style> .card { background: {{CardColor}}; } .origin { color: black; } .destination { color: black; } </style> <div class="destination word">{{furigana:LanguageYouLearnWord}}</div> <div class="destination fonetic">{{LanguageYouLearnPronunciation}}</div> <div class="destination comment">{{LanguageYouLearnExplanation}}</div> <hr> <div class="origin word">{{YourLanguageWord}}</div> <div class="origin comment">{{YourLanguageExplanation}}</div> ''', }, ], css=css, ) class AutogenerateBuilder(VocabularyBuilder, object): def __init__(self): pass def parse_src_to_settings(self, data_dir, src): settings = {} settings["color"] = src.get("card_color", "#f5f5f5") settings["show_p"] = "true" if src.get( "pronunciation_in_reverse", False) else "" settings["tsv_as_source"] = src.get("use_tsv_for_generation", False) settings["filename"] = os.path.join(data_dir, src["file"]) settings["generate_audio"] = src.get("audio", False) settings["lang"] = src.get("lang", {}) if not settings["lang"]: settings["lang"]["original"] = "en" settings["lang"]["translation"] = "es" if settings["generate_audio"]: settings["media_dir"] = self.prepare_media_dir(data_dir, {}) settings["furigana"] = src.get("furigana", False) settings["furigana_type"] = src.get("furigana_type", "hira") settings["card_properties"] = src.get("card_properties", None) settings["tags"] = [] if src.get("card_properties", None): settings["tags"] = src["card_properties"].get("tags", []) return settings def build_cards_from_words(self, settings): words = tsv.scrape_words_from_file(settings["filename"]) translations = tsv.get_translation( words, settings["lang"]["original"], settings["lang"]["translation"]) cards = [tsv.prepare_card_fields(trans) for trans in translations] for card in cards: card["tags"] = settings["tags"] return cards def build_cards_from_tsv(self, settings): with open(settings["filename"], "r") as csvfile: reader = csv.reader(csvfile, delimiter="\t", quotechar='"') iterator = iter(reader) cards = [] # Skip header that is present in every autogenerated TSV. next(iterator) for i, row in enumerate(iterator): card_id, ylw, yle, lylw, lylp, lyle, row_tags = row tags = settings["tags"] tags.extend(row_tags.split(",")) card = {"card_id": card_id, "ylw": ylw, "yle": yle, "lylw": lylw, "lylp": lylp, "lyle": lyle, "tags": tags} cards.append(card) return cards def build_notes_and_media(self, settings, cards): notes, media = [], [] for card in cards: if settings["furigana"] and (settings["lang"]["original"] == "ja"): card["lylp"] = "" card["lylw"] = generate_furigana( card["lylw"], settings["furigana_type"]) if settings["generate_audio"]: clean_text = remove_parentheses(card["lylw"]) if settings["furigana"]: # If you leave those spaces you get wrong # pronunciations, like in `スペイン人`. # Instead of `supein jin` it pronounces it as # `supein hito` because the kanji `人` alone is # pronounced as `hito`. clean_text = remove_spaces(clean_text) hash_text = get_hash_id(clean_text, bytes=8) sound_file = f'{settings["media_dir"]}/{hash_text}.mp3' if not os.path.exists(sound_file): print(f"Creating audio file {sound_file}") lang = settings["lang"]["original"] tts = gTTS(clean_text, lang=lang) tts.save(sound_file) card["lylp"] += f" [sound:{hash_text}.mp3]" media.append(sound_file) note = NoteID( card["card_id"], model=CARD_MODEL_JAPANESE if settings["furigana"] else CARD_MODEL, fields=[ card["ylw"], card["yle"], card["lylw"], card["lylp"], card["lyle"], settings["color"], settings["show_p"]], tags=card["tags"]) notes.append(note) return notes, media def build_cards(self, data_dir, src, deck_config): # Get data from config. settings = self.parse_src_to_settings(data_dir, src) # Choose what builder to use - from words or TSV. builder = self.build_cards_from_words if "tsv" in settings["filename"].split('.')[-1]: builder = self.build_cards_from_tsv cards = builder(settings) # notes, media = self.build_notes_and_media(settings, cards) return notes, media

import time import logging from spaceone.inventory.libs.manager import GoogleCloudManager from spaceone.inventory.libs.schema.base import ReferenceModel from spaceone.inventory.connector.instance_group import InstanceGroupConnector from spaceone.inventory.model.instance_group.data import * from spaceone.inventory.model.instance_group.cloud_service import * from spaceone.inventory.model.instance_group.cloud_service_type import CLOUD_SERVICE_TYPES _LOGGER = logging.getLogger(__name__) class InstanceGroupManager(GoogleCloudManager): connector_name = 'InstanceGroupConnector' cloud_service_types = CLOUD_SERVICE_TYPES def collect_cloud_service(self, params): _LOGGER.debug(f'** Instance Group START **') start_time = time.time() """ Args: params: - options - schema - secret_data - filter - zones Response: CloudServiceResponse """ collected_cloud_services = [] error_responses = [] instance_group_id = "" secret_data = params['secret_data'] instance_group_conn: InstanceGroupConnector = self.locator.get_connector(self.connector_name, **params) # Get all Resources instance_groups = instance_group_conn.list_instance_groups() instance_group_managers = instance_group_conn.list_instance_group_managers() autoscalers = instance_group_conn.list_autoscalers() instance_templates = instance_group_conn.list_instance_templates() for instance_group in instance_groups: try: instance_group_id = instance_group.get('id') instance_group.update({ 'project': secret_data['project_id'] }) scheduler = {'type': 'zone'} if 'zone' in instance_group else {'type': 'region'} if match_instance_group_manager := \ self.match_instance_group_manager(instance_group_managers, instance_group.get('selfLink')): instance_group_type = self.get_instance_group_type(match_instance_group_manager) scheduler.update({'instance_group_type': instance_group_type}) # Managed match_instance_group_manager.update({ 'statefulPolicy': { 'preservedState': {'disks': self._get_stateful_policy(match_instance_group_manager)}} }) instance_group.update({ 'instance_group_type': instance_group_type, 'instance_group_manager': InstanceGroupManagers(match_instance_group_manager, strict=False) }) if match_auto_scaler := self.match_auto_scaler(autoscalers, match_instance_group_manager): self._get_auto_policy_for_scheduler(scheduler, match_auto_scaler) instance_group.update({ 'auto_scaler': AutoScaler(match_auto_scaler, strict=False), 'autoscaling_display': self._get_autoscaling_display(match_auto_scaler.get('autoscalingPolicy', {})) }) match_instance_template = \ self.match_instance_template(instance_templates, match_instance_group_manager.get('instanceTemplate')) if match_instance_template: instance_group.update({'template': InstanceTemplate(match_instance_template, strict=False)}) else: # Unmanaged instance_group.update({'instance_group_type': 'UNMANAGED'}) scheduler.update({'instance_group_type': 'UNMANAGED'}) loc_type, location = self.get_instance_group_loc(instance_group) region = self.generate_region_from_zone(location) if loc_type == 'zone' else location instances = instance_group_conn.list_instances(instance_group.get('name'), location, loc_type) display_loc = {'region': location, 'zone': ''} if loc_type == 'region' \ else {'region': location[:-2], 'zone': location} instance_group.update({'display_location': display_loc}) instance_group.update({ 'power_scheduler': scheduler, 'instances': self.get_instances(instances), 'instance_counts': len(instances) }) # No labels _name = instance_group.get('name', '') instance_group_data = InstanceGroup(instance_group, strict=False) instance_group_resource = InstanceGroupResource({ 'name': _name, 'data': instance_group_data, 'region_code': region, 'reference': ReferenceModel(instance_group_data.reference()) }) self.set_region_code(region) collected_cloud_services.append(InstanceGroupResponse({'resource': instance_group_resource})) except Exception as e: _LOGGER.error(f'[collect_cloud_service] => {e}', exc_info=True) error_response = self.generate_resource_error_response(e, 'ComputeEngine', 'InstanceGroup', instance_group_id) error_responses.append(error_response) _LOGGER.debug(f'** Instance Group Finished {time.time() - start_time} Seconds **') return collected_cloud_services, error_responses def get_instance_group_loc(self, instance_group): inst_type = 'zone' if 'zone' in instance_group else 'region' loc = self._get_last_target(instance_group, inst_type) return inst_type, loc def get_instances(self, instances): _instances = [] for instance in instances: instance.update({'name': self._get_last_target(instance, 'instance')}) _instances.append(instance) return _instances @staticmethod def match_instance_template(instance_templates, instance_template_self_link): for instance_template in instance_templates: if instance_template['selfLink'] == instance_template_self_link: return instance_template return None @staticmethod def match_instance_group_manager(instance_group_managers, instance_group_name): for instance_group_manager in instance_group_managers: if instance_group_manager['instanceGroup'] == instance_group_name: return instance_group_manager return None @staticmethod def match_auto_scaler(auto_scalers, instance_group_manager): match_auto_scaler_name = instance_group_manager.get('status', {}).get('autoscaler') if match_auto_scaler_name: for auto_scaler in auto_scalers: if match_auto_scaler_name == auto_scaler['selfLink']: return auto_scaler return None @staticmethod def _get_stateful_policy(match_instance_group_manager): disks_vos = [] stateful_policy = match_instance_group_manager.get('statefulPolicy') if stateful_policy: preserved_state = stateful_policy.get('preservedState') if preserved_state: for key, val in preserved_state.get('disks', {}).items(): disks_vos.append({'key': key, 'value': val}) return disks_vos @staticmethod def get_instance_group_type(instance_group_manager): if instance_group_manager.get('status', {}).get('stateful', {}).get('hasStatefulConfig'): return 'STATEFUL' else: return 'STATELESS' def _get_autoscaling_display(self, autoscaling_policy): display_string = f'{autoscaling_policy.get('mode')}: Target ' policy_display_list = [] if 'cpuUtilization' in autoscaling_policy: policy_display_list.append( f'CPU utilization {(autoscaling_policy.get('cpuUtilization', {}).get("utilizationTarget")) * 100}%') if 'loadBalancingUtilization' in autoscaling_policy: policy_display_list.append( f'LB capacity fraction {(autoscaling_policy.get('loadBalancingUtilization', {}).get("utilizationTarget")) * 100}%') for custom_metric in autoscaling_policy.get('customMetricUtilizations', []): policy_display_list.append( f'{self._get_custom_metric_target_name(custom_metric.get('metric', ''))} {custom_metric.get('utilizationTarget', '')}{self._get_custom_metric_target_type(custom_metric.get('utilizationTargetType'))}') if policy_display_list: policy_join_str = ', '.join(policy_display_list) return f'{display_string}{policy_join_str}' else: return '' @staticmethod def _get_custom_metric_target_name(util_target): try: target_name = util_target.split('/')[-1] return target_name except Exception as e: return '' @staticmethod def _get_custom_metric_target_type(util_target_type): if util_target_type == 'GAUGE': return '' elif util_target_type == 'DELTA_PER_SECOND': return '/s' elif util_target_type == 'DELTA_PER_MINUTE': return '/m' else: return '' @staticmethod def _get_last_target(target_vo, key): a = target_vo.get(key, '') return a[a.rfind('/') + 1:] @staticmethod def _get_auto_policy_for_scheduler(scheduler, matched_scheduler): auto_policy = matched_scheduler.get('autoscalingPolicy', {}) if auto_policy != {}: scheduler.update({ 'recommend_size': matched_scheduler.get('recommendedSize', 1), 'origin_min_size': auto_policy.get('minNumReplicas'), 'origin_max_size': auto_policy.get('maxNumReplicas'), 'mode': auto_policy.get('mode') })

import time import logging from spaceone.inventory.libs.manager import GoogleCloudManager from spaceone.inventory.libs.schema.base import ReferenceModel from spaceone.inventory.connector.instance_group import InstanceGroupConnector from spaceone.inventory.model.instance_group.data import * from spaceone.inventory.model.instance_group.cloud_service import * from spaceone.inventory.model.instance_group.cloud_service_type import CLOUD_SERVICE_TYPES _LOGGER = logging.getLogger(__name__) class InstanceGroupManager(GoogleCloudManager): connector_name = 'InstanceGroupConnector' cloud_service_types = CLOUD_SERVICE_TYPES def collect_cloud_service(self, params): _LOGGER.debug(f'** Instance Group START **') start_time = time.time() """ Args: params: - options - schema - secret_data - filter - zones Response: CloudServiceResponse """ collected_cloud_services = [] error_responses = [] instance_group_id = "" secret_data = params['secret_data'] instance_group_conn: InstanceGroupConnector = self.locator.get_connector(self.connector_name, **params) # Get all Resources instance_groups = instance_group_conn.list_instance_groups() instance_group_managers = instance_group_conn.list_instance_group_managers() autoscalers = instance_group_conn.list_autoscalers() instance_templates = instance_group_conn.list_instance_templates() for instance_group in instance_groups: try: instance_group_id = instance_group.get('id') instance_group.update({ 'project': secret_data['project_id'] }) scheduler = {'type': 'zone'} if 'zone' in instance_group else {'type': 'region'} if match_instance_group_manager := \ self.match_instance_group_manager(instance_group_managers, instance_group.get('selfLink')): instance_group_type = self.get_instance_group_type(match_instance_group_manager) scheduler.update({'instance_group_type': instance_group_type}) # Managed match_instance_group_manager.update({ 'statefulPolicy': { 'preservedState': {'disks': self._get_stateful_policy(match_instance_group_manager)}} }) instance_group.update({ 'instance_group_type': instance_group_type, 'instance_group_manager': InstanceGroupManagers(match_instance_group_manager, strict=False) }) if match_auto_scaler := self.match_auto_scaler(autoscalers, match_instance_group_manager): self._get_auto_policy_for_scheduler(scheduler, match_auto_scaler) instance_group.update({ 'auto_scaler': AutoScaler(match_auto_scaler, strict=False), 'autoscaling_display': self._get_autoscaling_display(match_auto_scaler.get('autoscalingPolicy', {})) }) match_instance_template = \ self.match_instance_template(instance_templates, match_instance_group_manager.get('instanceTemplate')) if match_instance_template: instance_group.update({'template': InstanceTemplate(match_instance_template, strict=False)}) else: # Unmanaged instance_group.update({'instance_group_type': 'UNMANAGED'}) scheduler.update({'instance_group_type': 'UNMANAGED'}) loc_type, location = self.get_instance_group_loc(instance_group) region = self.generate_region_from_zone(location) if loc_type == 'zone' else location instances = instance_group_conn.list_instances(instance_group.get('name'), location, loc_type) display_loc = {'region': location, 'zone': ''} if loc_type == 'region' \ else {'region': location[:-2], 'zone': location} instance_group.update({'display_location': display_loc}) instance_group.update({ 'power_scheduler': scheduler, 'instances': self.get_instances(instances), 'instance_counts': len(instances) }) # No labels _name = instance_group.get('name', '') instance_group_data = InstanceGroup(instance_group, strict=False) instance_group_resource = InstanceGroupResource({ 'name': _name, 'data': instance_group_data, 'region_code': region, 'reference': ReferenceModel(instance_group_data.reference()) }) self.set_region_code(region) collected_cloud_services.append(InstanceGroupResponse({'resource': instance_group_resource})) except Exception as e: _LOGGER.error(f'[collect_cloud_service] => {e}', exc_info=True) error_response = self.generate_resource_error_response(e, 'ComputeEngine', 'InstanceGroup', instance_group_id) error_responses.append(error_response) _LOGGER.debug(f'** Instance Group Finished {time.time() - start_time} Seconds **') return collected_cloud_services, error_responses def get_instance_group_loc(self, instance_group): inst_type = 'zone' if 'zone' in instance_group else 'region' loc = self._get_last_target(instance_group, inst_type) return inst_type, loc def get_instances(self, instances): _instances = [] for instance in instances: instance.update({'name': self._get_last_target(instance, 'instance')}) _instances.append(instance) return _instances @staticmethod def match_instance_template(instance_templates, instance_template_self_link): for instance_template in instance_templates: if instance_template['selfLink'] == instance_template_self_link: return instance_template return None @staticmethod def match_instance_group_manager(instance_group_managers, instance_group_name): for instance_group_manager in instance_group_managers: if instance_group_manager['instanceGroup'] == instance_group_name: return instance_group_manager return None @staticmethod def match_auto_scaler(auto_scalers, instance_group_manager): match_auto_scaler_name = instance_group_manager.get('status', {}).get('autoscaler') if match_auto_scaler_name: for auto_scaler in auto_scalers: if match_auto_scaler_name == auto_scaler['selfLink']: return auto_scaler return None @staticmethod def _get_stateful_policy(match_instance_group_manager): disks_vos = [] stateful_policy = match_instance_group_manager.get('statefulPolicy') if stateful_policy: preserved_state = stateful_policy.get('preservedState') if preserved_state: for key, val in preserved_state.get('disks', {}).items(): disks_vos.append({'key': key, 'value': val}) return disks_vos @staticmethod def get_instance_group_type(instance_group_manager): if instance_group_manager.get('status', {}).get('stateful', {}).get('hasStatefulConfig'): return 'STATEFUL' else: return 'STATELESS' def _get_autoscaling_display(self, autoscaling_policy): display_string = f'{autoscaling_policy.get("mode")}: Target ' policy_display_list = [] if 'cpuUtilization' in autoscaling_policy: policy_display_list.append( f'CPU utilization {(autoscaling_policy.get("cpuUtilization", {}).get("utilizationTarget")) * 100}%') if 'loadBalancingUtilization' in autoscaling_policy: policy_display_list.append( f'LB capacity fraction {(autoscaling_policy.get("loadBalancingUtilization", {}).get("utilizationTarget")) * 100}%') for custom_metric in autoscaling_policy.get('customMetricUtilizations', []): policy_display_list.append( f'{self._get_custom_metric_target_name(custom_metric.get("metric", ""))} {custom_metric.get("utilizationTarget", "")}{self._get_custom_metric_target_type(custom_metric.get("utilizationTargetType"))}') if policy_display_list: policy_join_str = ', '.join(policy_display_list) return f'{display_string}{policy_join_str}' else: return '' @staticmethod def _get_custom_metric_target_name(util_target): try: target_name = util_target.split('/')[-1] return target_name except Exception as e: return '' @staticmethod def _get_custom_metric_target_type(util_target_type): if util_target_type == 'GAUGE': return '' elif util_target_type == 'DELTA_PER_SECOND': return '/s' elif util_target_type == 'DELTA_PER_MINUTE': return '/m' else: return '' @staticmethod def _get_last_target(target_vo, key): a = target_vo.get(key, '') return a[a.rfind('/') + 1:] @staticmethod def _get_auto_policy_for_scheduler(scheduler, matched_scheduler): auto_policy = matched_scheduler.get('autoscalingPolicy', {}) if auto_policy != {}: scheduler.update({ 'recommend_size': matched_scheduler.get('recommendedSize', 1), 'origin_min_size': auto_policy.get('minNumReplicas'), 'origin_max_size': auto_policy.get('maxNumReplicas'), 'mode': auto_policy.get('mode') })

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import csv from tempfile import NamedTemporaryFile from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union from airflow.models import BaseOperator from airflow.providers.google.cloud.hooks.gcs import GCSHook from airflow.providers.google.suite.hooks.sheets import GSheetsHook if TYPE_CHECKING: from airflow.utils.context import Context class GoogleSheetsToGCSOperator(BaseOperator): """ Writes Google Sheet data into Google Cloud Storage. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:GoogleSheetsToGCSOperator` :param spreadsheet_id: The Google Sheet ID to interact with. :type spreadsheet_id: str :param sheet_filter: Default to None, if provided, Should be an array of the sheet titles to pull from. :type sheet_filter: List[str] :param destination_bucket: The destination Google cloud storage bucket where the report should be written to. (templated) :type destination_bucket: str :param destination_path: The Google cloud storage URI array for the object created by the operator. For example: ``path/to/my/files``. :type destination_path: str :param gcp_conn_id: The connection ID to use when fetching connection info. :type gcp_conn_id: str :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: str :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). :type impersonation_chain: Union[str, Sequence[str]] """ template_fields: Sequence[str] = ( "spreadsheet_id", "destination_bucket", "destination_path", "sheet_filter", "impersonation_chain", ) def __init__( self, *, spreadsheet_id: str, destination_bucket: str, sheet_filter: Optional[List[str]] = None, destination_path: Optional[str] = None, gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.gcp_conn_id = gcp_conn_id self.spreadsheet_id = spreadsheet_id self.sheet_filter = sheet_filter self.destination_bucket = destination_bucket self.destination_path = destination_path self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain def _upload_data( self, gcs_hook: GCSHook, hook: GSheetsHook, sheet_range: str, sheet_values: List[Any], ) -> str: # Construct destination file path sheet = hook.get_spreadsheet(self.spreadsheet_id) file_name = f"{sheet["properties"]["title"]}_{sheet_range}.csv".replace(" ", "_") dest_file_name = ( f"{self.destination_path.strip("/")}/{file_name}" if self.destination_path else file_name ) with NamedTemporaryFile("w+") as temp_file: # Write data writer = csv.writer(temp_file) writer.writerows(sheet_values) temp_file.flush() # Upload to GCS gcs_hook.upload( bucket_name=self.destination_bucket, object_name=dest_file_name, filename=temp_file.name, ) return dest_file_name def execute(self, context: 'Context'): sheet_hook = GSheetsHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) gcs_hook = GCSHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) # Pull data and upload destination_array: List[str] = [] sheet_titles = sheet_hook.get_sheet_titles( spreadsheet_id=self.spreadsheet_id, sheet_filter=self.sheet_filter ) for sheet_range in sheet_titles: data = sheet_hook.get_values(spreadsheet_id=self.spreadsheet_id, range_=sheet_range) gcs_path_to_file = self._upload_data(gcs_hook, sheet_hook, sheet_range, data) destination_array.append(gcs_path_to_file) self.xcom_push(context, "destination_objects", destination_array) return destination_array

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import csv from tempfile import NamedTemporaryFile from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union from airflow.models import BaseOperator from airflow.providers.google.cloud.hooks.gcs import GCSHook from airflow.providers.google.suite.hooks.sheets import GSheetsHook if TYPE_CHECKING: from airflow.utils.context import Context class GoogleSheetsToGCSOperator(BaseOperator): """ Writes Google Sheet data into Google Cloud Storage. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:GoogleSheetsToGCSOperator` :param spreadsheet_id: The Google Sheet ID to interact with. :type spreadsheet_id: str :param sheet_filter: Default to None, if provided, Should be an array of the sheet titles to pull from. :type sheet_filter: List[str] :param destination_bucket: The destination Google cloud storage bucket where the report should be written to. (templated) :type destination_bucket: str :param destination_path: The Google cloud storage URI array for the object created by the operator. For example: ``path/to/my/files``. :type destination_path: str :param gcp_conn_id: The connection ID to use when fetching connection info. :type gcp_conn_id: str :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :type delegate_to: str :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). :type impersonation_chain: Union[str, Sequence[str]] """ template_fields: Sequence[str] = ( "spreadsheet_id", "destination_bucket", "destination_path", "sheet_filter", "impersonation_chain", ) def __init__( self, *, spreadsheet_id: str, destination_bucket: str, sheet_filter: Optional[List[str]] = None, destination_path: Optional[str] = None, gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.gcp_conn_id = gcp_conn_id self.spreadsheet_id = spreadsheet_id self.sheet_filter = sheet_filter self.destination_bucket = destination_bucket self.destination_path = destination_path self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain def _upload_data( self, gcs_hook: GCSHook, hook: GSheetsHook, sheet_range: str, sheet_values: List[Any], ) -> str: # Construct destination file path sheet = hook.get_spreadsheet(self.spreadsheet_id) file_name = f"{sheet['properties']['title']}_{sheet_range}.csv".replace(" ", "_") dest_file_name = ( f"{self.destination_path.strip('/')}/{file_name}" if self.destination_path else file_name ) with NamedTemporaryFile("w+") as temp_file: # Write data writer = csv.writer(temp_file) writer.writerows(sheet_values) temp_file.flush() # Upload to GCS gcs_hook.upload( bucket_name=self.destination_bucket, object_name=dest_file_name, filename=temp_file.name, ) return dest_file_name def execute(self, context: 'Context'): sheet_hook = GSheetsHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) gcs_hook = GCSHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) # Pull data and upload destination_array: List[str] = [] sheet_titles = sheet_hook.get_sheet_titles( spreadsheet_id=self.spreadsheet_id, sheet_filter=self.sheet_filter ) for sheet_range in sheet_titles: data = sheet_hook.get_values(spreadsheet_id=self.spreadsheet_id, range_=sheet_range) gcs_path_to_file = self._upload_data(gcs_hook, sheet_hook, sheet_range, data) destination_array.append(gcs_path_to_file) self.xcom_push(context, "destination_objects", destination_array) return destination_array

######################################################################################################################## #!/usr/bin/env python3 # -*- coding: utf-8 -*- import sys if sys.version_info[0] < 3: raise EnvironmentError("Hey, caveman, use Python 3.") __doc__ = \ """ PDB minimization for different GNB2 models. """ __author__ = "Matteo Ferla. [Github](https://github.com/matteoferla)" __email__ = "matteo.ferla@gmail.com" __date__ = "2019 A.D." __license__ = "MIT" __version__ = "1" __citation__ = "TBA" ######################################################################################################################## import pyrosetta pyrosetta.init() def relax(pose, cycles:int=2): scorefxn = pyrosetta.get_fa_scorefxn() relax = pyrosetta.rosetta.protocols.relax.FastRelax(scorefxn, cycles) relax.apply(pose) print('Done') ################### alpha beta gamma ###################################### native_alpha = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alpha, 'GNB2_alpha.pdb') relax(native_alpha, 15)#equivalent to -relax:thorough native_alpha.dump_pdb('GNB2_alpha.r.pdb') ################### GRK2 ###################################### native_alt = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alt, 'GNB2_GRK.pdb') relax(native_alt, 15) native_alt.dump_pdb('GNB2_alt.r.pdb') ################### WITHOUT alpha ###################################### import pymol2 with pymol2.PyMOL() as pymol: pymol.cmd.load('GNB2_alpha.pdb') pymol.cmd.remove('chain A') pymol.cmd.save('GNB2_alone.pdb') native_alone = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alone, 'GNB2_alone.pdb') relax(native_alone, 15) native_alone.dump_pdb('GNB2_alone.r.pdb') ################### phosphorylated ###################################### from Bio.SeqUtils import seq3 from michelanglo_protein import ProteinAnalyser, global_settings global_settings.startup(data_folder='/home/matteo/Coding/Michelanglo/protein-data') p = ProteinAnalyser(taxid=9606, uniprot='P62879').load() native_phospho = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_phospho, 'GNB2_alone.pdb') MutateResidue = pyrosetta.rosetta.protocols.simple_moves.MutateResidue # KinaseMover = pyrosetta.rosetta.protocols.enzymatic_movers.KinaseMover add_variant_type_to_residue = pyrosetta.rosetta.core.pose.add_variant_type_to_residue pose2pdb = native_phospho.pdb_info().pdb2pose for record in p.features['PSP_modified_residues']: change = record['from_residue'] + '-' + record['ptm'] if record['ptm'] == 'ub': continue elif record['ptm'] == 'p': patch = 'phosphorylated' elif record['ptm'] == 'ac': patch = 'acetylated' elif record['ptm'] == 'm1': patch = 'monomethylated' elif record['ptm'] == 'm2': patch = 'dimethylated' elif record['ptm'] == 'm3': patch = 'trimethylated' else: raise ValueError new_res = f'{seq3(p['from_residue'])}:{patch}' r = pose2pdb(res=int(record['residue_index']), chain='B') MutateResidue(target=r, new_res=new_res).apply(native_phospho) relax(native_phospho, 15) native_phospho.dump_pdb('GNB2_phospho.r.pdb')

######################################################################################################################## #!/usr/bin/env python3 # -*- coding: utf-8 -*- import sys if sys.version_info[0] < 3: raise EnvironmentError("Hey, caveman, use Python 3.") __doc__ = \ """ PDB minimization for different GNB2 models. """ __author__ = "Matteo Ferla. [Github](https://github.com/matteoferla)" __email__ = "matteo.ferla@gmail.com" __date__ = "2019 A.D." __license__ = "MIT" __version__ = "1" __citation__ = "TBA" ######################################################################################################################## import pyrosetta pyrosetta.init() def relax(pose, cycles:int=2): scorefxn = pyrosetta.get_fa_scorefxn() relax = pyrosetta.rosetta.protocols.relax.FastRelax(scorefxn, cycles) relax.apply(pose) print('Done') ################### alpha beta gamma ###################################### native_alpha = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alpha, 'GNB2_alpha.pdb') relax(native_alpha, 15)#equivalent to -relax:thorough native_alpha.dump_pdb('GNB2_alpha.r.pdb') ################### GRK2 ###################################### native_alt = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alt, 'GNB2_GRK.pdb') relax(native_alt, 15) native_alt.dump_pdb('GNB2_alt.r.pdb') ################### WITHOUT alpha ###################################### import pymol2 with pymol2.PyMOL() as pymol: pymol.cmd.load('GNB2_alpha.pdb') pymol.cmd.remove('chain A') pymol.cmd.save('GNB2_alone.pdb') native_alone = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_alone, 'GNB2_alone.pdb') relax(native_alone, 15) native_alone.dump_pdb('GNB2_alone.r.pdb') ################### phosphorylated ###################################### from Bio.SeqUtils import seq3 from michelanglo_protein import ProteinAnalyser, global_settings global_settings.startup(data_folder='/home/matteo/Coding/Michelanglo/protein-data') p = ProteinAnalyser(taxid=9606, uniprot='P62879').load() native_phospho = pyrosetta.rosetta.core.pose.Pose() pyrosetta.rosetta.core.import_pose.pose_from_file(native_phospho, 'GNB2_alone.pdb') MutateResidue = pyrosetta.rosetta.protocols.simple_moves.MutateResidue # KinaseMover = pyrosetta.rosetta.protocols.enzymatic_movers.KinaseMover add_variant_type_to_residue = pyrosetta.rosetta.core.pose.add_variant_type_to_residue pose2pdb = native_phospho.pdb_info().pdb2pose for record in p.features['PSP_modified_residues']: change = record['from_residue'] + '-' + record['ptm'] if record['ptm'] == 'ub': continue elif record['ptm'] == 'p': patch = 'phosphorylated' elif record['ptm'] == 'ac': patch = 'acetylated' elif record['ptm'] == 'm1': patch = 'monomethylated' elif record['ptm'] == 'm2': patch = 'dimethylated' elif record['ptm'] == 'm3': patch = 'trimethylated' else: raise ValueError new_res = f'{seq3(p["from_residue"])}:{patch}' r = pose2pdb(res=int(record['residue_index']), chain='B') MutateResidue(target=r, new_res=new_res).apply(native_phospho) relax(native_phospho, 15) native_phospho.dump_pdb('GNB2_phospho.r.pdb')

import requests import sys import re def get_merged_pull_reqs_since_last_release(token): """ Get all the merged pull requests since the last release. """ stopPattern = r"^(r|R)elease v" pull_reqs = [] found_last_release = False page = 1 print("Getting PRs since last release.") while not found_last_release: data = get_merged_pull_reqs(token, page) # assume we don't encounter it during the loop last_release_index = 101 for i in range(len(data)): if re.search(stopPattern, data[i]["title"]): found_last_release = True last_release_index = i break pull_reqs.extend(data[:last_release_index]) page += 1 # should contain all the PRs since last release return pull_reqs def get_merged_pull_reqs(token, page): """ Get the merged pull requests based on page. There are 100 results per page. See https://docs.github.com/en/rest/reference/pulls for more details on the parameters. :param token, a GitHub API token. :param page, the page number. """ queryPath = "https://api.github.com/repos/devicons/devicon/pulls" headers = { "Authorization": f"token {token}" } params = { "accept": "application/vnd.github.v3+json", "state": "closed", "per_page": 100, "page": page } print(f"Querying the GitHub API for requests page #{page}") response = requests.get(queryPath, headers=headers, params=params) if not response: print(f"Can't query the GitHub API. Status code is {response.status_code}. Message is {response.text}") sys.exit(1) closed_pull_reqs = response.json() return [merged_pull_req for merged_pull_req in closed_pull_reqs if merged_pull_req["merged_at"] is not None] def is_feature_icon(pull_req_data): """ Check whether the pullData is a feature:icon PR. :param pull_req_data - the data on a specific pull request from GitHub. :return true if the pullData has a label named "feature:icon" """ for label in pull_req_data["labels"]: if label["name"] == "feature:icon": return True return False def find_all_authors(pull_req_data, token): """ Find all the authors of a PR based on its commits. :param pull_req_data - the data on a specific pull request from GitHub. :param token - a GitHub API token. """ headers = { "Authorization": f"token {token}" } response = requests.get(pull_req_data["commits_url"], headers=headers) if not response: print(f"Can't query the GitHub API. Status code is {response.status_code}") print("Response is: ", response.text) return commits = response.json() authors = set() # want unique authors only for commit in commits: try: # this contains proper referenceable github name authors.add(commit["author"]["login"]) except TypeError: # special case authors.add(commit["commit"]["author"]["name"]) print(f"This URL didn't have an `author` attribute: {pull_req_data["commits_url"]}") return ", ".join(["@" + author for author in list(authors)])

import requests import sys import re def get_merged_pull_reqs_since_last_release(token): """ Get all the merged pull requests since the last release. """ stopPattern = r"^(r|R)elease v" pull_reqs = [] found_last_release = False page = 1 print("Getting PRs since last release.") while not found_last_release: data = get_merged_pull_reqs(token, page) # assume we don't encounter it during the loop last_release_index = 101 for i in range(len(data)): if re.search(stopPattern, data[i]["title"]): found_last_release = True last_release_index = i break pull_reqs.extend(data[:last_release_index]) page += 1 # should contain all the PRs since last release return pull_reqs def get_merged_pull_reqs(token, page): """ Get the merged pull requests based on page. There are 100 results per page. See https://docs.github.com/en/rest/reference/pulls for more details on the parameters. :param token, a GitHub API token. :param page, the page number. """ queryPath = "https://api.github.com/repos/devicons/devicon/pulls" headers = { "Authorization": f"token {token}" } params = { "accept": "application/vnd.github.v3+json", "state": "closed", "per_page": 100, "page": page } print(f"Querying the GitHub API for requests page #{page}") response = requests.get(queryPath, headers=headers, params=params) if not response: print(f"Can't query the GitHub API. Status code is {response.status_code}. Message is {response.text}") sys.exit(1) closed_pull_reqs = response.json() return [merged_pull_req for merged_pull_req in closed_pull_reqs if merged_pull_req["merged_at"] is not None] def is_feature_icon(pull_req_data): """ Check whether the pullData is a feature:icon PR. :param pull_req_data - the data on a specific pull request from GitHub. :return true if the pullData has a label named "feature:icon" """ for label in pull_req_data["labels"]: if label["name"] == "feature:icon": return True return False def find_all_authors(pull_req_data, token): """ Find all the authors of a PR based on its commits. :param pull_req_data - the data on a specific pull request from GitHub. :param token - a GitHub API token. """ headers = { "Authorization": f"token {token}" } response = requests.get(pull_req_data["commits_url"], headers=headers) if not response: print(f"Can't query the GitHub API. Status code is {response.status_code}") print("Response is: ", response.text) return commits = response.json() authors = set() # want unique authors only for commit in commits: try: # this contains proper referenceable github name authors.add(commit["author"]["login"]) except TypeError: # special case authors.add(commit["commit"]["author"]["name"]) print(f"This URL didn't have an `author` attribute: {pull_req_data['commits_url']}") return ", ".join(["@" + author for author in list(authors)])

# !/usr/bin/env python """ ExcisionFinder identifies allele-specific excision sites. Written in Python version 3.6.1. Kathleen Keough et al 2017-2018. Note: This version of the script is intended only for analysis of large cohorts, particularly the 1000 Genomes cohort. There is a more general purpose script for small cohorts and individuals with more options, such as outputting sgRNAs for pairs identified. Usage: ExcisionFinder.py [-vs] <annots> <gene> <targdir> <maxcut> <cas_list> <bcf> <outdir> [--window=<window_in_bp>] ExcisionFinder.py -h Arguments: annots Gene annotations file (gene_annots_wsize) filepath. gene Gene you would like to analyze. targdir Directory where the variant targetability HDF5 files are stored. maxcut Maximum distance between cut position pairs. cas_list Comma separated (no spaces!) list of Cas varieties to evaluate, options below. outdir Directory to which you would like to write the output files. Options: -h Show this screen. -v Run as verbose, print to stderr. -s Only consider sgRNA sites where variant is in a PAM (strict). --window=<window_in_bp> Window around the gene (in bp) to also consider [default: 0]. available Cas types = cpf1,SpCas9,SpCas9_VRER,SpCas9_EQR,SpCas9_VQR_1,SpCas9_VQR_2,StCas9,StCas9_2,SaCas9,SaCas9_KKH,nmCas9,cjCas9 """ import pandas as pd from pandas import HDFStore import numpy as np from functools import reduce from docopt import docopt import itertools import regex as re import logging import subprocess from io import StringIO import os import time __version__ = '0.0.0' def load_gene_annots(annots_path): """ Load gene annotation data (transcript data). :param annots_path: str, filepath for gene_annots_wsize (Part of ExcisionFinder package). :return: Refseq gene annotations file. """ gene_annots = pd.read_csv(annots_path, sep='\t', header=0, names=['name', 'chrom', 'txStart', 'txEnd', 'cdsStart', 'cdsEnd', 'exonCount', 'exonStarts', 'exonEnds', 'size']) return gene_annots def het(genotype): """ Determine whether a genotype in format A|G is het. :param genotype: genotype, str. :return: bool, True = het, False = hom. """ hap1, hap2 = re.split('/|\|',genotype) return hap1 != hap2 def next_exon(variant_position, coding_exon_starts): """ get location of next coding exon after variant :param coding_exon_starts: coding exon start positions, Pandas Series. :param variant_position: chromosomal position, int :return: chromosomal position of start of next coding exon, int """ greater_than_var = [x for x in coding_exon_starts if x > variant_position] if not greater_than_var: return False else: next_coding_exon_pos = min(greater_than_var) return next_coding_exon_pos def targ_pair(variant1, variant2, coding_positions, coding_exon_starts): """ Determine whether a pair of variants positions is targetable based on whether they might disrupt an exon. :param variant1: position of variant 1, int. :param variant2: position of variant 2, int. :param coding_positions: coding positions, set. :param coding_exon_starts: Start positions of coding exons, Pandas Series. :return: whether targetable or not, bool. """ low_var, high_var = sorted([variant1, variant2]) if low_var in coding_positions or high_var in coding_positions: return True else: # checks whether larger variant position occurs in or after next exon return bool(high_var >= next_exon(low_var, coding_exon_starts)) def translate_gene_name(gene_name): """ HDF5 throws all sort of errors when you have weird punctuation in the gene name, so this translates it to a less offensive form. """ repls = ('-', 'dash'), ('.', 'period') trans_gene_name = reduce(lambda a, kv: a.replace(*kv), repls, str(gene_name)) return trans_gene_name class Gene: """Holds information for the gene""" def __init__(self, official_gene_symbol, annots, window): self.official_gene_symbol = official_gene_symbol self.info = annots.query('index == @self.official_gene_symbol') self.n_exons = self.info['exonCount'].item() self.coding_start = self.info['cdsStart'].item() self.coding_end = self.info['cdsEnd'].item() self.coding_exons = [x for x in list(zip(list(map(int,self.info['exonStarts'].item().split(',')[:-1])), list(map(int,self.info['exonEnds'].item().split(',')[:-1])))) if x[0] >= self.coding_start and \ x[1] <= self.coding_end] self.n_coding_exons = len(self.coding_exons) self.start = self.info['txStart'].item() - window self.end = self.info['txEnd'].item() + window self.chrom = annots.query('index == @self.official_gene_symbol')['chrom'].item() def get_coding_positions_and_starts(self): coding_positions = [] coding_exon_starts = [] for start, stop in self.coding_exons: coding_positions.extend(list(range(start, stop+1))) coding_exon_starts.append(start) return coding_positions, coding_exon_starts def check_bcftools(): """ Checks bcftools version, and exits the program if the version is incorrect """ version = subprocess.run("bcftools -v | head -1 | cut -d ' ' -f2", shell=True,\ stdout=subprocess.PIPE).stdout.decode("utf-8").rstrip() if float(version) >= REQUIRED_BCFTOOLS_VER: print(f'bcftools version {version} running') else: print(f'Error: bcftools must be >=1.5. Current version: {version}') exit() class SafeHDFStore(HDFStore): # from https://stackoverflow.com/questions/22522551/pandas-hdf5-as-a-database/29014295#29014295 # due to parallel write issues with HDF5 def __init__(self, *args, **kwargs): probe_interval = kwargs.pop('probe_interval', 1) self._lock = '%s.lock' % args[0] while True: try: self._flock = os.open(self._lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY) break except FileExistsError: time.sleep(probe_interval) HDFStore.__init__(self, *args, **kwargs) def __exit__(self, *args, **kwargs): HDFStore.__exit__(self, *args, **kwargs) os.close(self._flock) os.remove(self._lock) def main(args): annots = load_gene_annots(args['<annots>']) gene = args['<gene>'] targ_df = args['<targdir>'] out_dir = args['<outdir>'] maxcut = int(args['<maxcut>']) cas_list_append = args['<cas_list>'].split(',') bcf = args['<bcf>'] window = int(args['--window']) cas_list = ['all'] + cas_list_append # define strictness level, which is whether or not variants near PAMs are considered # along with those that are in PAMs if args['-s']: logging.info('Running as strict.') strict_level = 'strict' else: strict_level = 'relaxed' logging.info('Running as relaxed.') logging.info('Now running ExcisionFinder on ' + gene + '.') # grab info about relevant gene w/ class MyGene = Gene(gene, annots, window) # get number of coding exons in gene, must have at least 1 to continue n_exons = MyGene.n_exons n_coding_exons = MyGene.n_coding_exons chrom = MyGene.chrom.replace('chr','') if n_coding_exons < 1: logging.error(f'{n_exons} total exons in this gene, {n_coding_exons} of which are coding.\ No coding exons in gene {gene}, exiting.') with open(f'{out_dir}no_coding_exons.txt','a+') as f: f.write(gene + '\n') exit() else: logging.info(f'{n_exons} total exons in this gene, {n_coding_exons} of which are coding.') # load targetability information for each variant targ_df = pd.read_hdf(f'{targ_df}{chrom}_targ.hdf5', 'all', where=f'pos >= {MyGene.start} and pos <= {MyGene.end}') # check whether there are annotated variants for this gene, abort otherwise if targ_df.empty: logging.error(f'No variants in 1KGP for gene {gene}') with open(f'{out_dir}not_enough_hets.txt', 'a+') as fout: fout.write(gene+'\n') exit() else: logging.info( f'Targetability data loaded, {targ_df.shape[0]} variants annotated in 1KGP for {gene}.') # import region of interest genotypes bcf = f'bcf = f'{bcf}ALL.chr{chrom}_GRCh38.genotypes.20170504.bcf' # this was for 1kgp' # this was for 1kgp bcl_v = f'bcftools view -g 'het' -r {chrom}:{MyGene.start}-{MyGene.end} -H {bcf}' samples_cmd = f'bcftools query -l {bcf}' bcl_samps = subprocess.Popen(samples_cmd, shell=True, stdout=subprocess.PIPE) samples=bcl_samps.communicate()[0].decode('utf-8').split('\n')[:-1] col_names = ['chrom','pos','rsid','ref','alt','score','random','info','gt'] + samples bcl_view = subprocess.Popen(bcl_v, shell=True, stdout=subprocess.PIPE) gens = pd.read_csv(StringIO(bcl_view.communicate()[0].decode('utf-8')),sep='\t', header=None, names=col_names, usecols=['chrom','pos','ref','alt']+samples) logging.info('1KGP genotypes loaded.') het_gens = gens[samples].applymap(het).copy() enough_hets = list(het_gens.sum(axis=0).loc[lambda s: s >= 2].index) logging.info(str(len(enough_hets)) + ' individuals have >= 2 het positions.') if len(enough_hets) < 1: logging.info('No individuals have at least 2 het sites, aborting analysis.') with open(f'{out_dir}not_enough_hets.txt', 'a+') as fout: fout.write(gene+'\n') exit() logging.info('Checking targetability of individuals with sufficient number of hets.') # get variants in region variants = sorted(gens.pos) # set up targetability analyses het_vars_per_ind = {} # get heterozygous variant positions for each individual for ind in enough_hets: het_vars_per_ind[ind] = gens.pos[het_gens[ind]].tolist() # get variant combinations and extract targetable pairs logging.info('Getting variant combos.') variant1 = [] variant2 = [] coding_positions, coding_exon_starts = MyGene.get_coding_positions_and_starts() for var1, var2 in itertools.product(variants, repeat=2): if (var1 != var2) and (max([var1,var2]) <= min([var1,var2])+10000) and (targ_pair(var1, var2, coding_positions, coding_exon_starts)): variant1.append(var1) variant2.append(var2) else: continue logging.info('Combos obtained.') # get rid of dups and make df targ_pairs_df = pd.DataFrame({'var1':variant1, 'var2':variant2}).query('var1 < var2') # check that each individual that has enough hets also has at least one of these pairs # and specify which pairs they have inds_w_targ_pair = {} for ind in het_vars_per_ind.keys(): ind_vars = het_vars_per_ind[ind] ind_targ_pairs = targ_pairs_df.loc[targ_pairs_df.isin(ind_vars).all(axis=1)].reset_index(drop=True).copy() if ind_targ_pairs.empty: continue else: inds_w_targ_pair[ind] = ind_targ_pairs logging.info(f'{len(inds_w_targ_pair.keys())} individuals have at least one targetable pair of variants.') if not inds_w_targ_pair: logging.info(f'No individuals in 1KGP have at least 1 targetable variant pair for {gene}.') with open(f'{out_dir}no_targetable_inds.txt', 'a+') as fout: fout.write(gene+'\n') exit() # check targetability for each type of Cas final_targ = pd.DataFrame({'sample':list(inds_w_targ_pair.keys())}) finaltargcols = [] # keeps track of columns for all cas types for later evaluating 'all' condition for cas in cas_list[1:]: # skip all because is handled below faster logging.info(f'Evaluating gene targetability for {cas}') if args['-s']: targ_vars_cas = targ_df.query(f'(makes_{cas}) or (breaks_{cas})').pos.tolist() else: targ_vars_cas = targ_df.query(f'(var_near_{cas}) or (makes_{cas}) or (breaks_{cas})').pos.tolist() targ_pairs_cas = targ_pairs_df.loc[targ_pairs_df.isin(targ_vars_cas).all(axis=1)].reset_index(drop=True).copy() # eliminate individuals that do not have at least one targetable pair for this specific cas ind_targ_cas = [] for ind in list(inds_w_targ_pair.keys()): ind_cas_targ_pairs = inds_w_targ_pair[ind].merge(targ_pairs_cas, how='inner').drop_duplicates() if ind_cas_targ_pairs.empty: ind_targ_cas.append(False) continue else: # check that at least one pair of allele-specific cut sites is on the same haplotype in individual if args['-s']: ind_cas_targ_pairs['both_make'] = ind_cas_targ_pairs[['var1_make_pam','var2_make_pam']].all(axis=1) ind_cas_targ_pairs['both_break'] = ind_cas_targ_pairs[['var1_break_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_1'] = ind_cas_targ_pairs[['var1_make_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_2'] = ind_cas_targ_pairs[['var2_make_pam','var1_break_pam']].all(axis=1) gens_replace = {'0|1':'hap2','0|2':'hap2','0|3':'hap2', '1|0':'hap1','2|0':'hap1','3|0':'hap1', '0|0':'not_het','1|1':'not_het'} ind_gens = gens[['pos',ind]].replace(gens_replace).copy() ind_cas_targ_pairs['var1_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var1', right_on='pos', how='left')[ind] ind_cas_targ_pairs['var2_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var2', right_on='pos', how='left')[ind] ind_cas_targ_pairs['same_hap'] = np.where(ind_cas_targ_pairs['var1_hap'] == ind_cas_targ_pairs['var2_hap'],True,False) ind_cas_targ_pairs['not_same_hap'] = ~ind_cas_targ_pairs['same_hap'] if ind_cas_targ_pairs[['both_make','same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['both_break','same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # check if pair where one makes, one breaks a PAM, and on different haplotypes elif ind_cas_targ_pairs[['one_make_one_break_1','not_same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['one_make_one_break_2','not_same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # all possibilities exhausted, this person just isn't targetable at this gene else: ind_targ_cas.append(False) continue else: # if both near PAM, haplotype doesn't have to be the same because both are allele-specific sgRNA sites ind_cas_targ_pairs['both_near_pam'] = ind_cas_targ_pairs[['var1','var2']].isin(targ_df.query(f'var_near_{cas}').pos.tolist()).all(axis=1) if ind_cas_targ_pairs['both_near_pam'].any(): # this doesn't work for 'strict' mode ind_targ_cas.append(True) continue else: # if none have both near a PAM, when both make or break, need to be same hap ind_cas_targ_pairs['var1_make_pam'] = ind_cas_targ_pairs['var1'].isin(targ_df.query(f'makes_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_make_pam'] = ind_cas_targ_pairs[['var2']].isin(targ_df.query(f'makes_{cas}').pos.tolist()) ind_cas_targ_pairs['var1_near_pam'] = ind_cas_targ_pairs['var1'].isin(targ_df.query(f'var_near_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_near_pam'] = ind_cas_targ_pairs['var2'].isin(targ_df.query(f'var_near_{cas}').pos.tolist()) ind_cas_targ_pairs['var1_break_pam'] = ind_cas_targ_pairs[['var1']].isin(targ_df.query(f'breaks_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_break_pam'] = ind_cas_targ_pairs[['var2']].isin(targ_df.query(f'breaks_{cas}').pos.tolist()) # if one var is near a pam and the other makes/breaks, haplotype doesn't matter if not ind_cas_targ_pairs.query('(var1_near_pam and var2_make_pam) or (var1_near_pam and var2_break_pam) or (var2_near_pam and var1_make_pam) or (var2_near_pam and var1_break_pam)').empty: ind_targ_cas.append(True) continue else: ind_cas_targ_pairs['both_make'] = ind_cas_targ_pairs[['var1_make_pam','var2_make_pam']].all(axis=1) ind_cas_targ_pairs['both_break'] = ind_cas_targ_pairs[['var1_break_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_1'] = ind_cas_targ_pairs[['var1_make_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_2'] = ind_cas_targ_pairs[['var2_make_pam','var1_break_pam']].all(axis=1) gens_replace = {'0|1':'hap2','0|2':'hap2','0|3':'hap2', '1|0':'hap1','2|0':'hap1','3|0':'hap1', '0|0':'not_het','1|1':'not_het'} ind_gens = gens[['pos',ind]].replace(gens_replace).copy() ind_cas_targ_pairs['var1_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var1', right_on='pos', how='left')[ind] ind_cas_targ_pairs['var2_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var2', right_on='pos', how='left')[ind] ind_cas_targ_pairs['same_hap'] = np.where(ind_cas_targ_pairs['var1_hap'] == ind_cas_targ_pairs['var2_hap'],True,False) ind_cas_targ_pairs['not_same_hap'] = ~ind_cas_targ_pairs['same_hap'] if ind_cas_targ_pairs[['both_make','same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['both_break','same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # check if pair where one makes, one breaks a PAM, and on different haplotypes elif ind_cas_targ_pairs[['one_make_one_break_1','not_same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['one_make_one_break_2','not_same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # all possibilities exhausted, this person just isn't targetable at this gene else: ind_targ_cas.append(False) continue finaltargcols.append(f'targ_{cas}') final_targ[f'targ_{cas}'] = ind_targ_cas # add column summarizing targetability across assessed Cas varieties final_targ['targ_all'] = final_targ[finaltargcols].any(axis=1) # HDF has issues with certain characters translated_gene_name = translate_gene_name(gene) # save to HDF # final_targ.to_hdf(f'{out_dir}chr{chrom}_gene_targ.h5',translated_gene_name, mode='a', comp_level=9, complib='blosc') # make list of genes that actually get written to HDF5 with open(f'{out_dir}genes_evaluated.txt','a+') as f: f.write(f'{translated_gene_name}\n') # write gene dat to file final_targ.to_hdf(f'{out_dir}{translated_gene_name}.h5', 'all', comp_level=9,complib='blosc') logging.info('Done!') if __name__ == '__main__': arguments = docopt(__doc__, version=__version__) if arguments['-v']: logging.basicConfig(level=logging.INFO, format='[%(asctime)s %(name)s:%(levelname)s ]%(message)s') else: logging.basicConfig(level=logging.ERROR, format='[%(asctime)s %(name)s:%(levelname)s ]%(message)s') logging.info(arguments) main(arguments)

# !/usr/bin/env python """ ExcisionFinder identifies allele-specific excision sites. Written in Python version 3.6.1. Kathleen Keough et al 2017-2018. Note: This version of the script is intended only for analysis of large cohorts, particularly the 1000 Genomes cohort. There is a more general purpose script for small cohorts and individuals with more options, such as outputting sgRNAs for pairs identified. Usage: ExcisionFinder.py [-vs] <annots> <gene> <targdir> <maxcut> <cas_list> <bcf> <outdir> [--window=<window_in_bp>] ExcisionFinder.py -h Arguments: annots Gene annotations file (gene_annots_wsize) filepath. gene Gene you would like to analyze. targdir Directory where the variant targetability HDF5 files are stored. maxcut Maximum distance between cut position pairs. cas_list Comma separated (no spaces!) list of Cas varieties to evaluate, options below. outdir Directory to which you would like to write the output files. Options: -h Show this screen. -v Run as verbose, print to stderr. -s Only consider sgRNA sites where variant is in a PAM (strict). --window=<window_in_bp> Window around the gene (in bp) to also consider [default: 0]. available Cas types = cpf1,SpCas9,SpCas9_VRER,SpCas9_EQR,SpCas9_VQR_1,SpCas9_VQR_2,StCas9,StCas9_2,SaCas9,SaCas9_KKH,nmCas9,cjCas9 """ import pandas as pd from pandas import HDFStore import numpy as np from functools import reduce from docopt import docopt import itertools import regex as re import logging import subprocess from io import StringIO import os import time __version__ = '0.0.0' def load_gene_annots(annots_path): """ Load gene annotation data (transcript data). :param annots_path: str, filepath for gene_annots_wsize (Part of ExcisionFinder package). :return: Refseq gene annotations file. """ gene_annots = pd.read_csv(annots_path, sep='\t', header=0, names=['name', 'chrom', 'txStart', 'txEnd', 'cdsStart', 'cdsEnd', 'exonCount', 'exonStarts', 'exonEnds', 'size']) return gene_annots def het(genotype): """ Determine whether a genotype in format A|G is het. :param genotype: genotype, str. :return: bool, True = het, False = hom. """ hap1, hap2 = re.split('/|\|',genotype) return hap1 != hap2 def next_exon(variant_position, coding_exon_starts): """ get location of next coding exon after variant :param coding_exon_starts: coding exon start positions, Pandas Series. :param variant_position: chromosomal position, int :return: chromosomal position of start of next coding exon, int """ greater_than_var = [x for x in coding_exon_starts if x > variant_position] if not greater_than_var: return False else: next_coding_exon_pos = min(greater_than_var) return next_coding_exon_pos def targ_pair(variant1, variant2, coding_positions, coding_exon_starts): """ Determine whether a pair of variants positions is targetable based on whether they might disrupt an exon. :param variant1: position of variant 1, int. :param variant2: position of variant 2, int. :param coding_positions: coding positions, set. :param coding_exon_starts: Start positions of coding exons, Pandas Series. :return: whether targetable or not, bool. """ low_var, high_var = sorted([variant1, variant2]) if low_var in coding_positions or high_var in coding_positions: return True else: # checks whether larger variant position occurs in or after next exon return bool(high_var >= next_exon(low_var, coding_exon_starts)) def translate_gene_name(gene_name): """ HDF5 throws all sort of errors when you have weird punctuation in the gene name, so this translates it to a less offensive form. """ repls = ('-', 'dash'), ('.', 'period') trans_gene_name = reduce(lambda a, kv: a.replace(*kv), repls, str(gene_name)) return trans_gene_name class Gene: """Holds information for the gene""" def __init__(self, official_gene_symbol, annots, window): self.official_gene_symbol = official_gene_symbol self.info = annots.query('index == @self.official_gene_symbol') self.n_exons = self.info['exonCount'].item() self.coding_start = self.info['cdsStart'].item() self.coding_end = self.info['cdsEnd'].item() self.coding_exons = [x for x in list(zip(list(map(int,self.info['exonStarts'].item().split(',')[:-1])), list(map(int,self.info['exonEnds'].item().split(',')[:-1])))) if x[0] >= self.coding_start and \ x[1] <= self.coding_end] self.n_coding_exons = len(self.coding_exons) self.start = self.info['txStart'].item() - window self.end = self.info['txEnd'].item() + window self.chrom = annots.query('index == @self.official_gene_symbol')['chrom'].item() def get_coding_positions_and_starts(self): coding_positions = [] coding_exon_starts = [] for start, stop in self.coding_exons: coding_positions.extend(list(range(start, stop+1))) coding_exon_starts.append(start) return coding_positions, coding_exon_starts def check_bcftools(): """ Checks bcftools version, and exits the program if the version is incorrect """ version = subprocess.run("bcftools -v | head -1 | cut -d ' ' -f2", shell=True,\ stdout=subprocess.PIPE).stdout.decode("utf-8").rstrip() if float(version) >= REQUIRED_BCFTOOLS_VER: print(f'bcftools version {version} running') else: print(f"Error: bcftools must be >=1.5. Current version: {version}") exit() class SafeHDFStore(HDFStore): # from https://stackoverflow.com/questions/22522551/pandas-hdf5-as-a-database/29014295#29014295 # due to parallel write issues with HDF5 def __init__(self, *args, **kwargs): probe_interval = kwargs.pop("probe_interval", 1) self._lock = "%s.lock" % args[0] while True: try: self._flock = os.open(self._lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY) break except FileExistsError: time.sleep(probe_interval) HDFStore.__init__(self, *args, **kwargs) def __exit__(self, *args, **kwargs): HDFStore.__exit__(self, *args, **kwargs) os.close(self._flock) os.remove(self._lock) def main(args): annots = load_gene_annots(args['<annots>']) gene = args['<gene>'] targ_df = args['<targdir>'] out_dir = args['<outdir>'] maxcut = int(args['<maxcut>']) cas_list_append = args['<cas_list>'].split(',') bcf = args['<bcf>'] window = int(args['--window']) cas_list = ['all'] + cas_list_append # define strictness level, which is whether or not variants near PAMs are considered # along with those that are in PAMs if args['-s']: logging.info('Running as strict.') strict_level = 'strict' else: strict_level = 'relaxed' logging.info('Running as relaxed.') logging.info('Now running ExcisionFinder on ' + gene + '.') # grab info about relevant gene w/ class MyGene = Gene(gene, annots, window) # get number of coding exons in gene, must have at least 1 to continue n_exons = MyGene.n_exons n_coding_exons = MyGene.n_coding_exons chrom = MyGene.chrom.replace('chr','') if n_coding_exons < 1: logging.error(f'{n_exons} total exons in this gene, {n_coding_exons} of which are coding.\ No coding exons in gene {gene}, exiting.') with open(f'{out_dir}no_coding_exons.txt','a+') as f: f.write(gene + '\n') exit() else: logging.info(f'{n_exons} total exons in this gene, {n_coding_exons} of which are coding.') # load targetability information for each variant targ_df = pd.read_hdf(f'{targ_df}{chrom}_targ.hdf5', 'all', where=f'pos >= {MyGene.start} and pos <= {MyGene.end}') # check whether there are annotated variants for this gene, abort otherwise if targ_df.empty: logging.error(f'No variants in 1KGP for gene {gene}') with open(f'{out_dir}not_enough_hets.txt', 'a+') as fout: fout.write(gene+'\n') exit() else: logging.info( f"Targetability data loaded, {targ_df.shape[0]} variants annotated in 1KGP for {gene}.") # import region of interest genotypes bcf = f'bcf = f'{bcf}ALL.chr{chrom}_GRCh38.genotypes.20170504.bcf' # this was for 1kgp' # this was for 1kgp bcl_v = f'bcftools view -g "het" -r {chrom}:{MyGene.start}-{MyGene.end} -H {bcf}' samples_cmd = f'bcftools query -l {bcf}' bcl_samps = subprocess.Popen(samples_cmd, shell=True, stdout=subprocess.PIPE) samples=bcl_samps.communicate()[0].decode("utf-8").split('\n')[:-1] col_names = ['chrom','pos','rsid','ref','alt','score','random','info','gt'] + samples bcl_view = subprocess.Popen(bcl_v, shell=True, stdout=subprocess.PIPE) gens = pd.read_csv(StringIO(bcl_view.communicate()[0].decode("utf-8")),sep='\t', header=None, names=col_names, usecols=['chrom','pos','ref','alt']+samples) logging.info("1KGP genotypes loaded.") het_gens = gens[samples].applymap(het).copy() enough_hets = list(het_gens.sum(axis=0).loc[lambda s: s >= 2].index) logging.info(str(len(enough_hets)) + ' individuals have >= 2 het positions.') if len(enough_hets) < 1: logging.info('No individuals have at least 2 het sites, aborting analysis.') with open(f'{out_dir}not_enough_hets.txt', 'a+') as fout: fout.write(gene+'\n') exit() logging.info('Checking targetability of individuals with sufficient number of hets.') # get variants in region variants = sorted(gens.pos) # set up targetability analyses het_vars_per_ind = {} # get heterozygous variant positions for each individual for ind in enough_hets: het_vars_per_ind[ind] = gens.pos[het_gens[ind]].tolist() # get variant combinations and extract targetable pairs logging.info('Getting variant combos.') variant1 = [] variant2 = [] coding_positions, coding_exon_starts = MyGene.get_coding_positions_and_starts() for var1, var2 in itertools.product(variants, repeat=2): if (var1 != var2) and (max([var1,var2]) <= min([var1,var2])+10000) and (targ_pair(var1, var2, coding_positions, coding_exon_starts)): variant1.append(var1) variant2.append(var2) else: continue logging.info('Combos obtained.') # get rid of dups and make df targ_pairs_df = pd.DataFrame({'var1':variant1, 'var2':variant2}).query('var1 < var2') # check that each individual that has enough hets also has at least one of these pairs # and specify which pairs they have inds_w_targ_pair = {} for ind in het_vars_per_ind.keys(): ind_vars = het_vars_per_ind[ind] ind_targ_pairs = targ_pairs_df.loc[targ_pairs_df.isin(ind_vars).all(axis=1)].reset_index(drop=True).copy() if ind_targ_pairs.empty: continue else: inds_w_targ_pair[ind] = ind_targ_pairs logging.info(f'{len(inds_w_targ_pair.keys())} individuals have at least one targetable pair of variants.') if not inds_w_targ_pair: logging.info(f'No individuals in 1KGP have at least 1 targetable variant pair for {gene}.') with open(f'{out_dir}no_targetable_inds.txt', 'a+') as fout: fout.write(gene+'\n') exit() # check targetability for each type of Cas final_targ = pd.DataFrame({'sample':list(inds_w_targ_pair.keys())}) finaltargcols = [] # keeps track of columns for all cas types for later evaluating "all" condition for cas in cas_list[1:]: # skip all because is handled below faster logging.info(f'Evaluating gene targetability for {cas}') if args['-s']: targ_vars_cas = targ_df.query(f'(makes_{cas}) or (breaks_{cas})').pos.tolist() else: targ_vars_cas = targ_df.query(f'(var_near_{cas}) or (makes_{cas}) or (breaks_{cas})').pos.tolist() targ_pairs_cas = targ_pairs_df.loc[targ_pairs_df.isin(targ_vars_cas).all(axis=1)].reset_index(drop=True).copy() # eliminate individuals that do not have at least one targetable pair for this specific cas ind_targ_cas = [] for ind in list(inds_w_targ_pair.keys()): ind_cas_targ_pairs = inds_w_targ_pair[ind].merge(targ_pairs_cas, how='inner').drop_duplicates() if ind_cas_targ_pairs.empty: ind_targ_cas.append(False) continue else: # check that at least one pair of allele-specific cut sites is on the same haplotype in individual if args['-s']: ind_cas_targ_pairs['both_make'] = ind_cas_targ_pairs[['var1_make_pam','var2_make_pam']].all(axis=1) ind_cas_targ_pairs['both_break'] = ind_cas_targ_pairs[['var1_break_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_1'] = ind_cas_targ_pairs[['var1_make_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_2'] = ind_cas_targ_pairs[['var2_make_pam','var1_break_pam']].all(axis=1) gens_replace = {'0|1':'hap2','0|2':'hap2','0|3':'hap2', '1|0':'hap1','2|0':'hap1','3|0':'hap1', '0|0':'not_het','1|1':'not_het'} ind_gens = gens[['pos',ind]].replace(gens_replace).copy() ind_cas_targ_pairs['var1_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var1', right_on='pos', how='left')[ind] ind_cas_targ_pairs['var2_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var2', right_on='pos', how='left')[ind] ind_cas_targ_pairs['same_hap'] = np.where(ind_cas_targ_pairs['var1_hap'] == ind_cas_targ_pairs['var2_hap'],True,False) ind_cas_targ_pairs['not_same_hap'] = ~ind_cas_targ_pairs['same_hap'] if ind_cas_targ_pairs[['both_make','same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['both_break','same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # check if pair where one makes, one breaks a PAM, and on different haplotypes elif ind_cas_targ_pairs[['one_make_one_break_1','not_same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['one_make_one_break_2','not_same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # all possibilities exhausted, this person just isn't targetable at this gene else: ind_targ_cas.append(False) continue else: # if both near PAM, haplotype doesn't have to be the same because both are allele-specific sgRNA sites ind_cas_targ_pairs['both_near_pam'] = ind_cas_targ_pairs[['var1','var2']].isin(targ_df.query(f'var_near_{cas}').pos.tolist()).all(axis=1) if ind_cas_targ_pairs['both_near_pam'].any(): # this doesn't work for "strict" mode ind_targ_cas.append(True) continue else: # if none have both near a PAM, when both make or break, need to be same hap ind_cas_targ_pairs['var1_make_pam'] = ind_cas_targ_pairs['var1'].isin(targ_df.query(f'makes_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_make_pam'] = ind_cas_targ_pairs[['var2']].isin(targ_df.query(f'makes_{cas}').pos.tolist()) ind_cas_targ_pairs['var1_near_pam'] = ind_cas_targ_pairs['var1'].isin(targ_df.query(f'var_near_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_near_pam'] = ind_cas_targ_pairs['var2'].isin(targ_df.query(f'var_near_{cas}').pos.tolist()) ind_cas_targ_pairs['var1_break_pam'] = ind_cas_targ_pairs[['var1']].isin(targ_df.query(f'breaks_{cas}').pos.tolist()) ind_cas_targ_pairs['var2_break_pam'] = ind_cas_targ_pairs[['var2']].isin(targ_df.query(f'breaks_{cas}').pos.tolist()) # if one var is near a pam and the other makes/breaks, haplotype doesn't matter if not ind_cas_targ_pairs.query('(var1_near_pam and var2_make_pam) or (var1_near_pam and var2_break_pam) or (var2_near_pam and var1_make_pam) or (var2_near_pam and var1_break_pam)').empty: ind_targ_cas.append(True) continue else: ind_cas_targ_pairs['both_make'] = ind_cas_targ_pairs[['var1_make_pam','var2_make_pam']].all(axis=1) ind_cas_targ_pairs['both_break'] = ind_cas_targ_pairs[['var1_break_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_1'] = ind_cas_targ_pairs[['var1_make_pam','var2_break_pam']].all(axis=1) ind_cas_targ_pairs['one_make_one_break_2'] = ind_cas_targ_pairs[['var2_make_pam','var1_break_pam']].all(axis=1) gens_replace = {'0|1':'hap2','0|2':'hap2','0|3':'hap2', '1|0':'hap1','2|0':'hap1','3|0':'hap1', '0|0':'not_het','1|1':'not_het'} ind_gens = gens[['pos',ind]].replace(gens_replace).copy() ind_cas_targ_pairs['var1_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var1', right_on='pos', how='left')[ind] ind_cas_targ_pairs['var2_hap'] = pd.merge(ind_cas_targ_pairs.copy(), ind_gens.copy(), left_on='var2', right_on='pos', how='left')[ind] ind_cas_targ_pairs['same_hap'] = np.where(ind_cas_targ_pairs['var1_hap'] == ind_cas_targ_pairs['var2_hap'],True,False) ind_cas_targ_pairs['not_same_hap'] = ~ind_cas_targ_pairs['same_hap'] if ind_cas_targ_pairs[['both_make','same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['both_break','same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # check if pair where one makes, one breaks a PAM, and on different haplotypes elif ind_cas_targ_pairs[['one_make_one_break_1','not_same_hap']].all(axis=1).any() or ind_cas_targ_pairs[['one_make_one_break_2','not_same_hap']].all(axis=1).any(): ind_targ_cas.append(True) continue # all possibilities exhausted, this person just isn't targetable at this gene else: ind_targ_cas.append(False) continue finaltargcols.append(f'targ_{cas}') final_targ[f'targ_{cas}'] = ind_targ_cas # add column summarizing targetability across assessed Cas varieties final_targ['targ_all'] = final_targ[finaltargcols].any(axis=1) # HDF has issues with certain characters translated_gene_name = translate_gene_name(gene) # save to HDF # final_targ.to_hdf(f'{out_dir}chr{chrom}_gene_targ.h5',translated_gene_name, mode='a', comp_level=9, complib='blosc') # make list of genes that actually get written to HDF5 with open(f'{out_dir}genes_evaluated.txt','a+') as f: f.write(f'{translated_gene_name}\n') # write gene dat to file final_targ.to_hdf(f'{out_dir}{translated_gene_name}.h5', 'all', comp_level=9,complib='blosc') logging.info('Done!') if __name__ == '__main__': arguments = docopt(__doc__, version=__version__) if arguments['-v']: logging.basicConfig(level=logging.INFO, format='[%(asctime)s %(name)s:%(levelname)s ]%(message)s') else: logging.basicConfig(level=logging.ERROR, format='[%(asctime)s %(name)s:%(levelname)s ]%(message)s') logging.info(arguments) main(arguments)

import sys from pathlib import Path import h5py import numpy as np import matplotlib.pyplot as plt TWOTHETA_KEYS = ["2th", "2theta", "twotheta"] Q_KEYS = ["q"] INTENSITY_KEYS = ["i", "intensity", "int"] STACK_INDICES_KEY = "stack_indices" DPI = 300 FIGSIZE = (12,4) FONTSIZE_LABELS = 20 FONTSIZE_TICKS = 14 LINEWIDTH = 1 COLORS = dict(bg_blue='#0B3C5D', bg_red='#B82601', bg_green='#1c6b0a', bg_lightblue='#328CC1', bg_darkblue='#062F4F', bg_yellow='#D9B310', bg_darkred='#984B43', bg_bordeaux='#76323F', bg_olivegreen='#626E60', bg_yellowgrey='#AB987A', bg_brownorange='#C09F80') COLOR = COLORS["bg_blue"] def h5_extract_to_dict(h5_file): f = h5py.File(h5_file, mode="r") d = {} fkeys = list(f.keys()) if "entry" in fkeys: fkeys = list(f["entry"].keys()) for k in fkeys: d[k.lower()] = np.array(f[k]) return d def dict_to_xy_write(d, fname): twotheta, q, intensity = None, None, None dkeys = d.keys() for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] for k in Q_KEYS: if k in dkeys: q = d[k] for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] if STACK_INDICES_KEY in dkeys: stack_indices = d[STACK_INDICES_KEY] if isinstance(twotheta, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = twotheta, intensity[i,:] else: x, y = twotheta, intensity xy = np.column_stack((x,y)) h = "2theta\tintensity" if STACK_INDICES_KEY in dkeys: np.savetxt(f"xy/{fname}_{stack_indices[i]}.xy", xy, encoding="utf-8", header=h) else: np.savetxt(f"xy/{fname}_{str(i).zfill(zfill)}.xy", xy, encoding="utf-8", header=h) elif isinstance(q, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = q, intensity[i,:] else: x, y = q, intensity xy = np.column_stack((x,y)) h = "q\tintensity" if STACK_INDICES_KEY in dkeys: np.savetxt(f"xy/{fname}_{stack_indices[i]}.xy", xy, encoding="utf-8", header=h) else: np.savetxt(f"xy/{fname}_{str(i).zfill(zfill)}.xy", xy, encoding="utf-8", header=h) return None def dict_to_plot(d, fname): twotheta, q, intensity = None, None, None dkeys = d.keys() for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] for k in Q_KEYS: if k in dkeys: q = d[k] for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] if STACK_INDICES_KEY in dkeys: stack_indices = d[STACK_INDICES_KEY] if isinstance(twotheta, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = twotheta, intensity[i,:] else: x, y = twotheta, intensity plt.figure(dpi=DPI, figsize=FIGSIZE) plt.plot(x, y, c=COLOR, lw=LINEWIDTH) plt.xlim(np.amin(x), np.amax(x)) plt.xlabel(r"$2\theta$ $[\degree]$", fontsize=FONTSIZE_LABELS) plt.ylabel(r"$I$ $[\mathrm{arb. u.}]$", fontsize=FONTSIZE_LABELS) plt.tick_params(axis='both', which='major', labelsize=FONTSIZE_LABELS) plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0)) if STACK_INDICES_KEY in dkeys: plt.savefig(f"png/{fname}_{stack_indices[i]}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{stack_indices[i]}.pdf", bbox_inches="tight") else: plt.savefig(f"png/{fname}_{str(i).zfill(zfill)}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{str(i).zfill(zfill)}.pdf", bbox_inches="tight") plt.close() if isinstance(q, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = q, intensity[i,:] else: x, y = q, intensity plt.figure(dpi=DPI, figsize=FIGSIZE) plt.plot(x, y, c=COLOR, lw=LINEWIDTH) plt.xlim(np.amin(x), np.amax(x)) if np.amax(q) > 40 : plt.xlabel(r"$Q$ $[\mathrm{nm}^{-1}]$", fontsize=FONTSIZE_LABELS) else: plt.xlabel(r"$Q$ $[\mathrm{\AA}^{-1}]$", fontsize=FONTSIZE_LABELS) plt.ylabel(r"$I$ $[\mathrm{arb. u.}]$", fontsize=FONTSIZE_LABELS) plt.tick_params(axis='both', which='major', labelsize=FONTSIZE_LABELS) plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0)) if STACK_INDICES_KEY in dkeys: plt.savefig(f"png/{fname}_{stack_indices[i]}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{stack_indices[i]}.pdf", bbox_inches="tight") else: plt.savefig(f"png/{fname}_{str(i).zfill(zfill)}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{str(i).zfill(zfill)}.pdf", bbox_inches="tight") plt.close() return None def merge_dict(d): twotheta, q, intensity = None, None, None dkeys = d.keys() d_merged = {} for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] d_merged[k] = twotheta for k in Q_KEYS: if k in dkeys: q = d[k] d_merged[k] = q for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] intensity_key = k if isinstance(intensity, np.ndarray): zfill = len(str(intensity.shape[0])) number_of_scans = intensity.shape[0] scans_to_stack = int(input("\t\t\tHow many scans should be stacked " "together?: ")) full_stacks = number_of_scans // scans_to_stack remainder_to_stack = number_of_scans % scans_to_stack stack_indices = [] for i in range(full_stacks): stack = intensity[i*scans_to_stack, :] stack_indices_str = str(i*scans_to_stack).zfill(zfill) for j in range(1, scans_to_stack): stack += intensity[i*scans_to_stack+j, :] stack_indices.append(f"{stack_indices_str}-" f"{str(i*scans_to_stack+j).zfill(zfill)}") if i == 0: d_merged[intensity_key] = stack else: d_merged[intensity_key] = np.vstack((d_merged[intensity_key], stack)) if remainder_to_stack != 0: stack = intensity[(full_stacks * scans_to_stack),:] stack_indices_str = str(full_stacks * scans_to_stack).zfill(zfill) for j in range(1, remainder_to_stack-1): stack = intensity[(full_stacks * scans_to_stack) + 1 + j,:] if remainder_to_stack == 1: stack_indices.append(f"{stack_indices_str}") else: last_scan = str((full_stacks*scans_to_stack)+1+j).zfill(zfill) stack_indices.append(f"{stack_indices_str}-{last_scan}") d_merged[intensity_key] = np.vstack((d_merged[intensity_key], stack)) d_merged[STACK_INDICES_KEY] = stack_indices return d_merged def main(): h5_path = Path.cwd() / "h5" if not h5_path.exists(): h5_path.mkdir() print(f"{80*"-"}\nA folder called 'h5' has been created. Please " f"place your .h5 files there and\nrerun the code.\n{80*"-"}") sys.exit() h5_files = list(h5_path.glob("*.h5")) if len(h5_files) == 0: print(f"{80*"-"}\nNo .h5 files were found in the 'h5' folder. Please " f"place your .h5 files there\nand rerun the code.\n{80*"-"}") sys.exit() output_paths = ["xy", "png", "pdf"] for e in output_paths: p = Path.cwd() / e if not p.exists(): p.mkdir() print("Working w. files...") for h5_file in h5_files: try: print(f"{80*"-"}\n\tFile: {h5_file.name}") fname = h5_file.stem d = h5_extract_to_dict(h5_file) for k in INTENSITY_KEYS: if k in d.keys(): print(f"\t\tNumber of scans: {d[k].shape[0]}") mergereq = input("\t\tDo you want to merge any of the scans? " "(y/n): ") while mergereq not in ["y", "n"]: mergereq = input("\t\tDo you want to merge any of the scans? " "(y/n): ") if mergereq == "y": writereq = input("\t\tDo you want to write .xy files for all " "merged scans? (y/n): ") while writereq not in ["y", "n"]: writereq = input("\t\tDo you want to write .xy files for " "all merged scans? (y/n): ") else: writereq = input("\t\tDo you want to write .xy files for all " "scans? (y/n): ") while writereq not in ["y", "n"]: writereq = input("\t\tDo you want to write .xy files for " "merged scans? (y/n): ") if mergereq == "y": plotreq = input("\t\tDo you want to plot all merged scans? " "(y/n): ") while plotreq not in ["y", "n"]: plotreq = input("\t\tDo you want to plot all merged scans? " "(y/n): ") else: plotreq = input("\t\tDo you want to plot all scans? (y/n): ") while plotreq not in ["y", "n"]: plotreq = input("\t\tDo you want to plot all scans? " "(y/n): ") if mergereq.lower() == "y": d_merged = merge_dict(d) if writereq == "y": print("\t\tWriting to two-column files of merged scans...") dict_to_xy_write(d_merged, fname) print("\t\tPlotting merged scans...") if plotreq == "y": dict_to_plot(d_merged, fname) else: if writereq == "y": print("\t\tWriting to two-column files for each scan...") dict_to_xy_write(d, fname) if plotreq == "y": print("\t\tPlotting each scan...") dict_to_plot(d, fname) except KeyError: print(f"\t\tThis file seems to contain non-integrated data. File " "skipped.") print(f"{80*"-"}\nDone working w. files.\n{80*"-"}") return None if __name__ == "__main__": main() # End of file.

import sys from pathlib import Path import h5py import numpy as np import matplotlib.pyplot as plt TWOTHETA_KEYS = ["2th", "2theta", "twotheta"] Q_KEYS = ["q"] INTENSITY_KEYS = ["i", "intensity", "int"] STACK_INDICES_KEY = "stack_indices" DPI = 300 FIGSIZE = (12,4) FONTSIZE_LABELS = 20 FONTSIZE_TICKS = 14 LINEWIDTH = 1 COLORS = dict(bg_blue='#0B3C5D', bg_red='#B82601', bg_green='#1c6b0a', bg_lightblue='#328CC1', bg_darkblue='#062F4F', bg_yellow='#D9B310', bg_darkred='#984B43', bg_bordeaux='#76323F', bg_olivegreen='#626E60', bg_yellowgrey='#AB987A', bg_brownorange='#C09F80') COLOR = COLORS["bg_blue"] def h5_extract_to_dict(h5_file): f = h5py.File(h5_file, mode="r") d = {} fkeys = list(f.keys()) if "entry" in fkeys: fkeys = list(f["entry"].keys()) for k in fkeys: d[k.lower()] = np.array(f[k]) return d def dict_to_xy_write(d, fname): twotheta, q, intensity = None, None, None dkeys = d.keys() for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] for k in Q_KEYS: if k in dkeys: q = d[k] for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] if STACK_INDICES_KEY in dkeys: stack_indices = d[STACK_INDICES_KEY] if isinstance(twotheta, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = twotheta, intensity[i,:] else: x, y = twotheta, intensity xy = np.column_stack((x,y)) h = "2theta\tintensity" if STACK_INDICES_KEY in dkeys: np.savetxt(f"xy/{fname}_{stack_indices[i]}.xy", xy, encoding="utf-8", header=h) else: np.savetxt(f"xy/{fname}_{str(i).zfill(zfill)}.xy", xy, encoding="utf-8", header=h) elif isinstance(q, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = q, intensity[i,:] else: x, y = q, intensity xy = np.column_stack((x,y)) h = "q\tintensity" if STACK_INDICES_KEY in dkeys: np.savetxt(f"xy/{fname}_{stack_indices[i]}.xy", xy, encoding="utf-8", header=h) else: np.savetxt(f"xy/{fname}_{str(i).zfill(zfill)}.xy", xy, encoding="utf-8", header=h) return None def dict_to_plot(d, fname): twotheta, q, intensity = None, None, None dkeys = d.keys() for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] for k in Q_KEYS: if k in dkeys: q = d[k] for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] if STACK_INDICES_KEY in dkeys: stack_indices = d[STACK_INDICES_KEY] if isinstance(twotheta, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = twotheta, intensity[i,:] else: x, y = twotheta, intensity plt.figure(dpi=DPI, figsize=FIGSIZE) plt.plot(x, y, c=COLOR, lw=LINEWIDTH) plt.xlim(np.amin(x), np.amax(x)) plt.xlabel(r"$2\theta$ $[\degree]$", fontsize=FONTSIZE_LABELS) plt.ylabel(r"$I$ $[\mathrm{arb. u.}]$", fontsize=FONTSIZE_LABELS) plt.tick_params(axis='both', which='major', labelsize=FONTSIZE_LABELS) plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0)) if STACK_INDICES_KEY in dkeys: plt.savefig(f"png/{fname}_{stack_indices[i]}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{stack_indices[i]}.pdf", bbox_inches="tight") else: plt.savefig(f"png/{fname}_{str(i).zfill(zfill)}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{str(i).zfill(zfill)}.pdf", bbox_inches="tight") plt.close() if isinstance(q, np.ndarray) and isinstance(intensity, np.ndarray): if intensity.ndim > 1: zfill = len(str(intensity.shape[0])) scans_index = intensity.shape[0] else: scans_index = 1 for i in range(scans_index): if STACK_INDICES_KEY in dkeys: print(f"\t\t\t{stack_indices[i]}") else: print(f"\t\t\t{i}") if intensity.ndim > 1: x, y = q, intensity[i,:] else: x, y = q, intensity plt.figure(dpi=DPI, figsize=FIGSIZE) plt.plot(x, y, c=COLOR, lw=LINEWIDTH) plt.xlim(np.amin(x), np.amax(x)) if np.amax(q) > 40 : plt.xlabel(r"$Q$ $[\mathrm{nm}^{-1}]$", fontsize=FONTSIZE_LABELS) else: plt.xlabel(r"$Q$ $[\mathrm{\AA}^{-1}]$", fontsize=FONTSIZE_LABELS) plt.ylabel(r"$I$ $[\mathrm{arb. u.}]$", fontsize=FONTSIZE_LABELS) plt.tick_params(axis='both', which='major', labelsize=FONTSIZE_LABELS) plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0)) if STACK_INDICES_KEY in dkeys: plt.savefig(f"png/{fname}_{stack_indices[i]}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{stack_indices[i]}.pdf", bbox_inches="tight") else: plt.savefig(f"png/{fname}_{str(i).zfill(zfill)}.png", bbox_inches="tight") plt.savefig(f"pdf/{fname}_{str(i).zfill(zfill)}.pdf", bbox_inches="tight") plt.close() return None def merge_dict(d): twotheta, q, intensity = None, None, None dkeys = d.keys() d_merged = {} for k in TWOTHETA_KEYS: if k in dkeys: twotheta = d[k] d_merged[k] = twotheta for k in Q_KEYS: if k in dkeys: q = d[k] d_merged[k] = q for k in INTENSITY_KEYS: if k in dkeys: intensity = d[k] intensity_key = k if isinstance(intensity, np.ndarray): zfill = len(str(intensity.shape[0])) number_of_scans = intensity.shape[0] scans_to_stack = int(input("\t\t\tHow many scans should be stacked " "together?: ")) full_stacks = number_of_scans // scans_to_stack remainder_to_stack = number_of_scans % scans_to_stack stack_indices = [] for i in range(full_stacks): stack = intensity[i*scans_to_stack, :] stack_indices_str = str(i*scans_to_stack).zfill(zfill) for j in range(1, scans_to_stack): stack += intensity[i*scans_to_stack+j, :] stack_indices.append(f"{stack_indices_str}-" f"{str(i*scans_to_stack+j).zfill(zfill)}") if i == 0: d_merged[intensity_key] = stack else: d_merged[intensity_key] = np.vstack((d_merged[intensity_key], stack)) if remainder_to_stack != 0: stack = intensity[(full_stacks * scans_to_stack),:] stack_indices_str = str(full_stacks * scans_to_stack).zfill(zfill) for j in range(1, remainder_to_stack-1): stack = intensity[(full_stacks * scans_to_stack) + 1 + j,:] if remainder_to_stack == 1: stack_indices.append(f"{stack_indices_str}") else: last_scan = str((full_stacks*scans_to_stack)+1+j).zfill(zfill) stack_indices.append(f"{stack_indices_str}-{last_scan}") d_merged[intensity_key] = np.vstack((d_merged[intensity_key], stack)) d_merged[STACK_INDICES_KEY] = stack_indices return d_merged def main(): h5_path = Path.cwd() / "h5" if not h5_path.exists(): h5_path.mkdir() print(f"{80*'-'}\nA folder called 'h5' has been created. Please " f"place your .h5 files there and\nrerun the code.\n{80*'-'}") sys.exit() h5_files = list(h5_path.glob("*.h5")) if len(h5_files) == 0: print(f"{80*'-'}\nNo .h5 files were found in the 'h5' folder. Please " f"place your .h5 files there\nand rerun the code.\n{80*'-'}") sys.exit() output_paths = ["xy", "png", "pdf"] for e in output_paths: p = Path.cwd() / e if not p.exists(): p.mkdir() print("Working w. files...") for h5_file in h5_files: try: print(f"{80*'-'}\n\tFile: {h5_file.name}") fname = h5_file.stem d = h5_extract_to_dict(h5_file) for k in INTENSITY_KEYS: if k in d.keys(): print(f"\t\tNumber of scans: {d[k].shape[0]}") mergereq = input("\t\tDo you want to merge any of the scans? " "(y/n): ") while mergereq not in ["y", "n"]: mergereq = input("\t\tDo you want to merge any of the scans? " "(y/n): ") if mergereq == "y": writereq = input("\t\tDo you want to write .xy files for all " "merged scans? (y/n): ") while writereq not in ["y", "n"]: writereq = input("\t\tDo you want to write .xy files for " "all merged scans? (y/n): ") else: writereq = input("\t\tDo you want to write .xy files for all " "scans? (y/n): ") while writereq not in ["y", "n"]: writereq = input("\t\tDo you want to write .xy files for " "merged scans? (y/n): ") if mergereq == "y": plotreq = input("\t\tDo you want to plot all merged scans? " "(y/n): ") while plotreq not in ["y", "n"]: plotreq = input("\t\tDo you want to plot all merged scans? " "(y/n): ") else: plotreq = input("\t\tDo you want to plot all scans? (y/n): ") while plotreq not in ["y", "n"]: plotreq = input("\t\tDo you want to plot all scans? " "(y/n): ") if mergereq.lower() == "y": d_merged = merge_dict(d) if writereq == "y": print("\t\tWriting to two-column files of merged scans...") dict_to_xy_write(d_merged, fname) print("\t\tPlotting merged scans...") if plotreq == "y": dict_to_plot(d_merged, fname) else: if writereq == "y": print("\t\tWriting to two-column files for each scan...") dict_to_xy_write(d, fname) if plotreq == "y": print("\t\tPlotting each scan...") dict_to_plot(d, fname) except KeyError: print(f"\t\tThis file seems to contain non-integrated data. File " "skipped.") print(f"{80*'-'}\nDone working w. files.\n{80*'-'}") return None if __name__ == "__main__": main() # End of file.

import json import os import time import uuid from copy import deepcopy from datetime import datetime, timedelta, timezone from random import randint from urllib.parse import parse_qs, urlparse, urlsplit import pystac from pydantic.datetime_parse import parse_datetime from pystac.utils import datetime_to_str from shapely.geometry import Polygon from stac_fastapi.sqlalchemy.core import CoreCrudClient from stac_fastapi.types.core import LandingPageMixin from stac_fastapi.types.rfc3339 import rfc3339_str_to_datetime def test_create_and_delete_item(app_client, load_test_data): """Test creation and deletion of a single item (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 resp = app_client.delete( f"/collections/{test_item["collection"]}/items/{resp.json()["id"]}" ) assert resp.status_code == 200 def test_create_item_conflict(app_client, load_test_data): """Test creation of an item which already exists (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 409 def test_create_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # add test_item to test-collection again, resource already exists test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 409 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2, posts successfully test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 def test_delete_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2 test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # delete test_item from test-collection test_item["collection"] = "test-collection" resp = app_client.delete( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert resp.status_code == 200 # test-item in test-collection has already been deleted resp = app_client.delete( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert resp.status_code == 404 # test-item in test-collection-2 still exists, was not deleted test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 409 def test_update_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2 test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # update gsd in test_item, test-collection-2 test_item["properties"]["gsd"] = 16 resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 updated_item = resp.json() assert updated_item["properties"]["gsd"] == 16 # update gsd in test_item, test-collection test_item["collection"] = "test-collection" test_item["properties"]["gsd"] = 17 resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 updated_item = resp.json() assert updated_item["properties"]["gsd"] == 17 # test_item in test-collection, updated gsd = 17 resp = app_client.get( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert resp.status_code == 200 item = resp.json() assert item["properties"]["gsd"] == 17 # test_item in test-collection-2, updated gsd = 16 test_item["collection"] = "test-collection-2" resp = app_client.get( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert resp.status_code == 200 item = resp.json() assert item["properties"]["gsd"] == 16 def test_delete_missing_item(app_client, load_test_data): """Test deletion of an item which does not exist (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.delete(f"/collections/{test_item["collection"]}/items/hijosh") assert resp.status_code == 404 def test_create_item_missing_collection(app_client, load_test_data): """Test creation of an item without a parent collection (transactions extension)""" test_item = load_test_data("test_item.json") test_item["collection"] = "stac is cool" resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 422 def test_update_item_already_exists(app_client, load_test_data): """Test updating an item which already exists (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 assert test_item["properties"]["gsd"] != 16 test_item["properties"]["gsd"] = 16 resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) updated_item = resp.json() assert updated_item["properties"]["gsd"] == 16 def test_update_new_item(app_client, load_test_data): """Test updating an item which does not exist (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 404 def test_update_item_missing_collection(app_client, load_test_data): """Test updating an item without a parent collection (transactions extension)""" test_item = load_test_data("test_item.json") # Create the item resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # Try to update collection of the item test_item["collection"] = "stac is cool" resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 404 def test_update_item_geometry(app_client, load_test_data): test_item = load_test_data("test_item.json") # Create the item resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # Update the geometry of the item test_item["geometry"]["coordinates"] = [[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]] resp = app_client.put( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # Fetch the updated item resp = app_client.get( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert resp.status_code == 200 assert resp.json()["geometry"]["coordinates"] == [ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]] ] def test_get_item(app_client, load_test_data): """Test read an item by id (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 get_item = app_client.get( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert get_item.status_code == 200 def test_returns_valid_item(app_client, load_test_data): """Test validates fetched item with jsonschema""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 get_item = app_client.get( f"/collections/{test_item["collection"]}/items/{test_item["id"]}" ) assert get_item.status_code == 200 item_dict = get_item.json() # Mock root to allow validation mock_root = pystac.Catalog( id="test", description="test desc", href="https://example.com" ) item = pystac.Item.from_dict(item_dict, preserve_dict=False, root=mock_root) item.validate() def test_get_item_collection(app_client, load_test_data): """Test read an item collection (core)""" item_count = randint(1, 4) test_item = load_test_data("test_item.json") for idx in range(item_count): _test_item = deepcopy(test_item) _test_item["id"] = test_item["id"] + str(idx) resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=_test_item ) assert resp.status_code == 200 resp = app_client.get(f"/collections/{test_item["collection"]}/items") assert resp.status_code == 200 item_collection = resp.json() assert item_collection["context"]["matched"] == len(range(item_count)) def test_pagination(app_client, load_test_data): """Test item collection pagination (paging extension)""" item_count = 10 test_item = load_test_data("test_item.json") for idx in range(item_count): _test_item = deepcopy(test_item) _test_item["id"] = test_item["id"] + str(idx) resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=_test_item ) assert resp.status_code == 200 resp = app_client.get( f"/collections/{test_item["collection"]}/items", params={"limit": 3} ) assert resp.status_code == 200 first_page = resp.json() assert first_page["context"]["returned"] == 3 url_components = urlsplit(first_page["links"][0]["href"]) resp = app_client.get(f"{url_components.path}?{url_components.query}") assert resp.status_code == 200 second_page = resp.json() assert second_page["context"]["returned"] == 3 def test_item_timestamps(app_client, load_test_data): """Test created and updated timestamps (common metadata)""" test_item = load_test_data("test_item.json") start_time = datetime.now(timezone.utc) time.sleep(2) # Confirm `created` timestamp resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) item = resp.json() created_dt = parse_datetime(item["properties"]["created"]) assert resp.status_code == 200 assert start_time < created_dt < datetime.now(timezone.utc) time.sleep(2) # Confirm `updated` timestamp item["properties"]["proj:epsg"] = 4326 resp = app_client.put(f"/collections/{test_item["collection"]}/items", json=item) assert resp.status_code == 200 updated_item = resp.json() # Created shouldn't change on update assert item["properties"]["created"] == updated_item["properties"]["created"] assert parse_datetime(updated_item["properties"]["updated"]) > created_dt def test_item_search_by_id_post(app_client, load_test_data): """Test POST search by item id (core)""" ids = ["test1", "test2", "test3"] for id in ids: test_item = load_test_data("test_item.json") test_item["id"] = id resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = {"collections": [test_item["collection"]], "ids": ids} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == len(ids) assert set([feat["id"] for feat in resp_json["features"]]) == set(ids) def test_item_search_spatial_query_post(app_client, load_test_data): """Test POST search with spatial query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_query_post(app_client, load_test_data): """Test POST search with single-tailed spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date = item_date + timedelta(seconds=1) params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], "datetime": f"../{datetime_to_str(item_date)}", } resp = app_client.post("/search", json=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_window_post(app_client, load_test_data): """Test POST search with two-tailed spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date_before = item_date - timedelta(seconds=1) item_date_after = item_date + timedelta(seconds=1) params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], "datetime": f"{datetime_to_str(item_date_before)}/{datetime_to_str(item_date_after)}", } resp = app_client.post("/search", json=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_open_window(app_client, load_test_data): """Test POST search with open spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 for dt in ["/", "../", "/..", "../.."]: resp = app_client.post("/search", json={"datetime": dt}) assert resp.status_code == 400 def test_item_search_sort_post(app_client, load_test_data): """Test POST search with sorting (sort extension)""" first_item = load_test_data("test_item.json") item_date = rfc3339_str_to_datetime(first_item["properties"]["datetime"]) resp = app_client.post( f"/collections/{first_item["collection"]}/items", json=first_item ) assert resp.status_code == 200 second_item = load_test_data("test_item.json") second_item["id"] = "another-item" another_item_date = item_date - timedelta(days=1) second_item["properties"]["datetime"] = datetime_to_str(another_item_date) resp = app_client.post( f"/collections/{second_item["collection"]}/items", json=second_item ) assert resp.status_code == 200 params = { "collections": [first_item["collection"]], "sortby": [{"field": "datetime", "direction": "desc"}], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == first_item["id"] assert resp_json["features"][1]["id"] == second_item["id"] def test_item_search_by_id_get(app_client, load_test_data): """Test GET search by item id (core)""" ids = ["test1", "test2", "test3"] for id in ids: test_item = load_test_data("test_item.json") test_item["id"] = id resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = {"collections": test_item["collection"], "ids": ",".join(ids)} resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == len(ids) assert set([feat["id"] for feat in resp_json["features"]]) == set(ids) def test_item_search_bbox_get(app_client, load_test_data): """Test GET search with spatial query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = { "collections": test_item["collection"], "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), } resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_get_without_collections(app_client, load_test_data): """Test GET search without specifying collections""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = { "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), } resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_window_get(app_client, load_test_data): """Test GET search with spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date_before = item_date - timedelta(seconds=1) item_date_after = item_date + timedelta(seconds=1) params = { "collections": test_item["collection"], "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), "datetime": f"{datetime_to_str(item_date_before)}/{datetime_to_str(item_date_after)}", } resp = app_client.get("/search", params=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_sort_get(app_client, load_test_data): """Test GET search with sorting (sort extension)""" first_item = load_test_data("test_item.json") item_date = rfc3339_str_to_datetime(first_item["properties"]["datetime"]) resp = app_client.post( f"/collections/{first_item["collection"]}/items", json=first_item ) assert resp.status_code == 200 second_item = load_test_data("test_item.json") second_item["id"] = "another-item" another_item_date = item_date - timedelta(days=1) second_item["properties"]["datetime"] = datetime_to_str(another_item_date) resp = app_client.post( f"/collections/{second_item["collection"]}/items", json=second_item ) assert resp.status_code == 200 params = {"collections": [first_item["collection"]], "sortby": "-datetime"} resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == first_item["id"] assert resp_json["features"][1]["id"] == second_item["id"] def test_item_search_post_without_collection(app_client, load_test_data): """Test POST search without specifying a collection""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = { "bbox": test_item["bbox"], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_properties_jsonb(app_client, load_test_data): """Test POST search with JSONB query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # EPSG is a JSONB key params = {"query": {"proj:epsg": {"gt": test_item["properties"]["proj:epsg"] + 1}}} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == 0 def test_item_search_properties_field(app_client, load_test_data): """Test POST search indexed field with query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # Orientation is an indexed field params = {"query": {"orientation": {"eq": "south"}}} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == 0 def test_item_search_get_query_extension(app_client, load_test_data): """Test GET search with JSONB query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 # EPSG is a JSONB key params = { "collections": [test_item["collection"]], "query": json.dumps( {"proj:epsg": {"gt": test_item["properties"]["proj:epsg"] + 1}} ), } resp = app_client.get("/search", params=params) assert resp.json()["context"]["returned"] == 0 params["query"] = json.dumps( {"proj:epsg": {"eq": test_item["properties"]["proj:epsg"]}} ) resp = app_client.get("/search", params=params) resp_json = resp.json() assert resp_json["context"]["returned"] == 1 assert ( resp_json["features"][0]["properties"]["proj:epsg"] == test_item["properties"]["proj:epsg"] ) def test_get_missing_item_collection(app_client): """Test reading a collection which does not exist""" resp = app_client.get("/collections/invalid-collection/items") assert resp.status_code == 200 def test_pagination_item_collection(app_client, load_test_data): """Test item collection pagination links (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) # Paginate through all 5 items with a limit of 1 (expecting 5 requests) page = app_client.get( f"/collections/{test_item["collection"]}/items", params={"limit": 1} ) idx = 0 item_ids = [] while True: idx += 1 page_data = page.json() item_ids.append(page_data["features"][0]["id"]) next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) if not next_link: break query_params = parse_qs(urlparse(next_link[0]["href"]).query) page = app_client.get( f"/collections/{test_item["collection"]}/items", params=query_params, ) # Our limit is 1 so we expect len(ids) number of requests before we run out of pages assert idx == len(ids) # Confirm we have paginated through all items assert not set(item_ids) - set(ids) def test_pagination_post(app_client, load_test_data): """Test POST pagination (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) # Paginate through all 5 items with a limit of 1 (expecting 5 requests) request_body = {"ids": ids, "limit": 1} page = app_client.post("/search", json=request_body) idx = 0 item_ids = [] while True: idx += 1 page_data = page.json() item_ids.append(page_data["features"][0]["id"]) next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) if not next_link: break # Merge request bodies request_body.update(next_link[0]["body"]) page = app_client.post("/search", json=request_body) # Our limit is 1 so we expect len(ids) number of requests before we run out of pages assert idx == len(ids) # Confirm we have paginated through all items assert not set(item_ids) - set(ids) def test_pagination_token_idempotent(app_client, load_test_data): """Test that pagination tokens are idempotent (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) page = app_client.get("/search", params={"ids": ",".join(ids), "limit": 3}) page_data = page.json() next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) # Confirm token is idempotent resp1 = app_client.get( "/search", params=parse_qs(urlparse(next_link[0]["href"]).query) ) resp2 = app_client.get( "/search", params=parse_qs(urlparse(next_link[0]["href"]).query) ) resp1_data = resp1.json() resp2_data = resp2.json() # Two different requests with the same pagination token should return the same items assert [item["id"] for item in resp1_data["features"]] == [ item["id"] for item in resp2_data["features"] ] def test_field_extension_get(app_client, load_test_data): """Test GET search with included fields (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 params = {"fields": "+properties.proj:epsg,+properties.gsd"} resp = app_client.get("/search", params=params) feat_properties = resp.json()["features"][0]["properties"] assert not set(feat_properties) - {"proj:epsg", "gsd", "datetime"} def test_field_extension_post(app_client, load_test_data): """Test POST search with included and excluded fields (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 body = { "fields": { "exclude": ["assets.B1"], "include": ["properties.eo:cloud_cover", "properties.orientation"], } } resp = app_client.post("/search", json=body) resp_json = resp.json() assert "B1" not in resp_json["features"][0]["assets"].keys() assert not set(resp_json["features"][0]["properties"]) - { "orientation", "eo:cloud_cover", "datetime", } def test_field_extension_exclude_and_include(app_client, load_test_data): """Test POST search including/excluding same field (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 body = { "fields": { "exclude": ["properties.eo:cloud_cover"], "include": ["properties.eo:cloud_cover"], } } resp = app_client.post("/search", json=body) resp_json = resp.json() assert "eo:cloud_cover" not in resp_json["features"][0]["properties"] def test_field_extension_exclude_default_includes(app_client, load_test_data): """Test POST search excluding a forbidden field (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item["collection"]}/items", json=test_item ) assert resp.status_code == 200 body = {"fields": {"exclude": ["geometry"]}} resp = app_client.post("/search", json=body) resp_json = resp.json() assert "geometry" not in resp_json["features"][0] def test_search_intersects_and_bbox(app_client): """Test POST search intersects and bbox are mutually exclusive (core)""" bbox = [-118, 34, -117, 35] geoj = Polygon.from_bounds(*bbox).__geo_interface__ params = {"bbox": bbox, "intersects": geoj} resp = app_client.post("/search", json=params) assert resp.status_code == 400 def test_get_missing_item(app_client, load_test_data): """Test read item which does not exist (transactions extension)""" test_coll = load_test_data("test_collection.json") resp = app_client.get(f"/collections/{test_coll["id"]}/items/invalid-item") assert resp.status_code == 404 def test_search_invalid_query_field(app_client): body = {"query": {"gsd": {"lt": 100}, "invalid-field": {"eq": 50}}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 def test_search_bbox_errors(app_client): body = {"query": {"bbox": [0]}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 body = {"query": {"bbox": [100.0, 0.0, 0.0, 105.0, 1.0, 1.0]}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 params = {"bbox": "100.0,0.0,0.0,105.0"} resp = app_client.get("/search", params=params) assert resp.status_code == 400 def test_conformance_classes_configurable(): """Test conformance class configurability""" landing = LandingPageMixin() landing_page = landing._landing_page( base_url="http://test/test", conformance_classes=["this is a test"], extension_schemas=[], ) assert landing_page["conformsTo"][0] == "this is a test" # Update environment to avoid key error on client instantiation os.environ["READER_CONN_STRING"] = "testing" os.environ["WRITER_CONN_STRING"] = "testing" client = CoreCrudClient(base_conformance_classes=["this is a test"]) assert client.conformance_classes()[0] == "this is a test" def test_search_datetime_validation_errors(app_client): bad_datetimes = [ "37-01-01T12:00:27.87Z", "1985-13-12T23:20:50.52Z", "1985-12-32T23:20:50.52Z", "1985-12-01T25:20:50.52Z", "1985-12-01T00:60:50.52Z", "1985-12-01T00:06:61.52Z", "1990-12-31T23:59:61Z", "1986-04-12T23:20:50.52Z/1985-04-12T23:20:50.52Z", ] for dt in bad_datetimes: body = {"query": {"datetime": dt}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 resp = app_client.get("/search?datetime={}".format(dt)) assert resp.status_code == 400

import json import os import time import uuid from copy import deepcopy from datetime import datetime, timedelta, timezone from random import randint from urllib.parse import parse_qs, urlparse, urlsplit import pystac from pydantic.datetime_parse import parse_datetime from pystac.utils import datetime_to_str from shapely.geometry import Polygon from stac_fastapi.sqlalchemy.core import CoreCrudClient from stac_fastapi.types.core import LandingPageMixin from stac_fastapi.types.rfc3339 import rfc3339_str_to_datetime def test_create_and_delete_item(app_client, load_test_data): """Test creation and deletion of a single item (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 resp = app_client.delete( f"/collections/{test_item['collection']}/items/{resp.json()['id']}" ) assert resp.status_code == 200 def test_create_item_conflict(app_client, load_test_data): """Test creation of an item which already exists (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 409 def test_create_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # add test_item to test-collection again, resource already exists test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 409 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2, posts successfully test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 def test_delete_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2 test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # delete test_item from test-collection test_item["collection"] = "test-collection" resp = app_client.delete( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert resp.status_code == 200 # test-item in test-collection has already been deleted resp = app_client.delete( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert resp.status_code == 404 # test-item in test-collection-2 still exists, was not deleted test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 409 def test_update_item_duplicate(app_client, load_test_data): """Test creation of an item id which already exists but in a different collection(transactions extension)""" # add test_item to test-collection test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # create "test-collection-2" collection_2 = load_test_data("test_collection.json") collection_2["id"] = "test-collection-2" resp = app_client.post("/collections", json=collection_2) assert resp.status_code == 200 # add test_item to test-collection-2 test_item["collection"] = "test-collection-2" resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # update gsd in test_item, test-collection-2 test_item["properties"]["gsd"] = 16 resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 updated_item = resp.json() assert updated_item["properties"]["gsd"] == 16 # update gsd in test_item, test-collection test_item["collection"] = "test-collection" test_item["properties"]["gsd"] = 17 resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 updated_item = resp.json() assert updated_item["properties"]["gsd"] == 17 # test_item in test-collection, updated gsd = 17 resp = app_client.get( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert resp.status_code == 200 item = resp.json() assert item["properties"]["gsd"] == 17 # test_item in test-collection-2, updated gsd = 16 test_item["collection"] = "test-collection-2" resp = app_client.get( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert resp.status_code == 200 item = resp.json() assert item["properties"]["gsd"] == 16 def test_delete_missing_item(app_client, load_test_data): """Test deletion of an item which does not exist (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.delete(f"/collections/{test_item['collection']}/items/hijosh") assert resp.status_code == 404 def test_create_item_missing_collection(app_client, load_test_data): """Test creation of an item without a parent collection (transactions extension)""" test_item = load_test_data("test_item.json") test_item["collection"] = "stac is cool" resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 422 def test_update_item_already_exists(app_client, load_test_data): """Test updating an item which already exists (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 assert test_item["properties"]["gsd"] != 16 test_item["properties"]["gsd"] = 16 resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) updated_item = resp.json() assert updated_item["properties"]["gsd"] == 16 def test_update_new_item(app_client, load_test_data): """Test updating an item which does not exist (transactions extension)""" test_item = load_test_data("test_item.json") resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 404 def test_update_item_missing_collection(app_client, load_test_data): """Test updating an item without a parent collection (transactions extension)""" test_item = load_test_data("test_item.json") # Create the item resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # Try to update collection of the item test_item["collection"] = "stac is cool" resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 404 def test_update_item_geometry(app_client, load_test_data): test_item = load_test_data("test_item.json") # Create the item resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # Update the geometry of the item test_item["geometry"]["coordinates"] = [[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]] resp = app_client.put( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # Fetch the updated item resp = app_client.get( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert resp.status_code == 200 assert resp.json()["geometry"]["coordinates"] == [ [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]] ] def test_get_item(app_client, load_test_data): """Test read an item by id (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 get_item = app_client.get( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert get_item.status_code == 200 def test_returns_valid_item(app_client, load_test_data): """Test validates fetched item with jsonschema""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 get_item = app_client.get( f"/collections/{test_item['collection']}/items/{test_item['id']}" ) assert get_item.status_code == 200 item_dict = get_item.json() # Mock root to allow validation mock_root = pystac.Catalog( id="test", description="test desc", href="https://example.com" ) item = pystac.Item.from_dict(item_dict, preserve_dict=False, root=mock_root) item.validate() def test_get_item_collection(app_client, load_test_data): """Test read an item collection (core)""" item_count = randint(1, 4) test_item = load_test_data("test_item.json") for idx in range(item_count): _test_item = deepcopy(test_item) _test_item["id"] = test_item["id"] + str(idx) resp = app_client.post( f"/collections/{test_item['collection']}/items", json=_test_item ) assert resp.status_code == 200 resp = app_client.get(f"/collections/{test_item['collection']}/items") assert resp.status_code == 200 item_collection = resp.json() assert item_collection["context"]["matched"] == len(range(item_count)) def test_pagination(app_client, load_test_data): """Test item collection pagination (paging extension)""" item_count = 10 test_item = load_test_data("test_item.json") for idx in range(item_count): _test_item = deepcopy(test_item) _test_item["id"] = test_item["id"] + str(idx) resp = app_client.post( f"/collections/{test_item['collection']}/items", json=_test_item ) assert resp.status_code == 200 resp = app_client.get( f"/collections/{test_item['collection']}/items", params={"limit": 3} ) assert resp.status_code == 200 first_page = resp.json() assert first_page["context"]["returned"] == 3 url_components = urlsplit(first_page["links"][0]["href"]) resp = app_client.get(f"{url_components.path}?{url_components.query}") assert resp.status_code == 200 second_page = resp.json() assert second_page["context"]["returned"] == 3 def test_item_timestamps(app_client, load_test_data): """Test created and updated timestamps (common metadata)""" test_item = load_test_data("test_item.json") start_time = datetime.now(timezone.utc) time.sleep(2) # Confirm `created` timestamp resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) item = resp.json() created_dt = parse_datetime(item["properties"]["created"]) assert resp.status_code == 200 assert start_time < created_dt < datetime.now(timezone.utc) time.sleep(2) # Confirm `updated` timestamp item["properties"]["proj:epsg"] = 4326 resp = app_client.put(f"/collections/{test_item['collection']}/items", json=item) assert resp.status_code == 200 updated_item = resp.json() # Created shouldn't change on update assert item["properties"]["created"] == updated_item["properties"]["created"] assert parse_datetime(updated_item["properties"]["updated"]) > created_dt def test_item_search_by_id_post(app_client, load_test_data): """Test POST search by item id (core)""" ids = ["test1", "test2", "test3"] for id in ids: test_item = load_test_data("test_item.json") test_item["id"] = id resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = {"collections": [test_item["collection"]], "ids": ids} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == len(ids) assert set([feat["id"] for feat in resp_json["features"]]) == set(ids) def test_item_search_spatial_query_post(app_client, load_test_data): """Test POST search with spatial query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_query_post(app_client, load_test_data): """Test POST search with single-tailed spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date = item_date + timedelta(seconds=1) params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], "datetime": f"../{datetime_to_str(item_date)}", } resp = app_client.post("/search", json=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_window_post(app_client, load_test_data): """Test POST search with two-tailed spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date_before = item_date - timedelta(seconds=1) item_date_after = item_date + timedelta(seconds=1) params = { "collections": [test_item["collection"]], "intersects": test_item["geometry"], "datetime": f"{datetime_to_str(item_date_before)}/{datetime_to_str(item_date_after)}", } resp = app_client.post("/search", json=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_open_window(app_client, load_test_data): """Test POST search with open spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 for dt in ["/", "../", "/..", "../.."]: resp = app_client.post("/search", json={"datetime": dt}) assert resp.status_code == 400 def test_item_search_sort_post(app_client, load_test_data): """Test POST search with sorting (sort extension)""" first_item = load_test_data("test_item.json") item_date = rfc3339_str_to_datetime(first_item["properties"]["datetime"]) resp = app_client.post( f"/collections/{first_item['collection']}/items", json=first_item ) assert resp.status_code == 200 second_item = load_test_data("test_item.json") second_item["id"] = "another-item" another_item_date = item_date - timedelta(days=1) second_item["properties"]["datetime"] = datetime_to_str(another_item_date) resp = app_client.post( f"/collections/{second_item['collection']}/items", json=second_item ) assert resp.status_code == 200 params = { "collections": [first_item["collection"]], "sortby": [{"field": "datetime", "direction": "desc"}], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == first_item["id"] assert resp_json["features"][1]["id"] == second_item["id"] def test_item_search_by_id_get(app_client, load_test_data): """Test GET search by item id (core)""" ids = ["test1", "test2", "test3"] for id in ids: test_item = load_test_data("test_item.json") test_item["id"] = id resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = {"collections": test_item["collection"], "ids": ",".join(ids)} resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == len(ids) assert set([feat["id"] for feat in resp_json["features"]]) == set(ids) def test_item_search_bbox_get(app_client, load_test_data): """Test GET search with spatial query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = { "collections": test_item["collection"], "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), } resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_get_without_collections(app_client, load_test_data): """Test GET search without specifying collections""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = { "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), } resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_temporal_window_get(app_client, load_test_data): """Test GET search with spatio-temporal query (core)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 item_date = rfc3339_str_to_datetime(test_item["properties"]["datetime"]) item_date_before = item_date - timedelta(seconds=1) item_date_after = item_date + timedelta(seconds=1) params = { "collections": test_item["collection"], "bbox": ",".join([str(coord) for coord in test_item["bbox"]]), "datetime": f"{datetime_to_str(item_date_before)}/{datetime_to_str(item_date_after)}", } resp = app_client.get("/search", params=params) resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_sort_get(app_client, load_test_data): """Test GET search with sorting (sort extension)""" first_item = load_test_data("test_item.json") item_date = rfc3339_str_to_datetime(first_item["properties"]["datetime"]) resp = app_client.post( f"/collections/{first_item['collection']}/items", json=first_item ) assert resp.status_code == 200 second_item = load_test_data("test_item.json") second_item["id"] = "another-item" another_item_date = item_date - timedelta(days=1) second_item["properties"]["datetime"] = datetime_to_str(another_item_date) resp = app_client.post( f"/collections/{second_item['collection']}/items", json=second_item ) assert resp.status_code == 200 params = {"collections": [first_item["collection"]], "sortby": "-datetime"} resp = app_client.get("/search", params=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == first_item["id"] assert resp_json["features"][1]["id"] == second_item["id"] def test_item_search_post_without_collection(app_client, load_test_data): """Test POST search without specifying a collection""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = { "bbox": test_item["bbox"], } resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert resp_json["features"][0]["id"] == test_item["id"] def test_item_search_properties_jsonb(app_client, load_test_data): """Test POST search with JSONB query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # EPSG is a JSONB key params = {"query": {"proj:epsg": {"gt": test_item["properties"]["proj:epsg"] + 1}}} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == 0 def test_item_search_properties_field(app_client, load_test_data): """Test POST search indexed field with query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # Orientation is an indexed field params = {"query": {"orientation": {"eq": "south"}}} resp = app_client.post("/search", json=params) assert resp.status_code == 200 resp_json = resp.json() assert len(resp_json["features"]) == 0 def test_item_search_get_query_extension(app_client, load_test_data): """Test GET search with JSONB query (query extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 # EPSG is a JSONB key params = { "collections": [test_item["collection"]], "query": json.dumps( {"proj:epsg": {"gt": test_item["properties"]["proj:epsg"] + 1}} ), } resp = app_client.get("/search", params=params) assert resp.json()["context"]["returned"] == 0 params["query"] = json.dumps( {"proj:epsg": {"eq": test_item["properties"]["proj:epsg"]}} ) resp = app_client.get("/search", params=params) resp_json = resp.json() assert resp_json["context"]["returned"] == 1 assert ( resp_json["features"][0]["properties"]["proj:epsg"] == test_item["properties"]["proj:epsg"] ) def test_get_missing_item_collection(app_client): """Test reading a collection which does not exist""" resp = app_client.get("/collections/invalid-collection/items") assert resp.status_code == 200 def test_pagination_item_collection(app_client, load_test_data): """Test item collection pagination links (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) # Paginate through all 5 items with a limit of 1 (expecting 5 requests) page = app_client.get( f"/collections/{test_item['collection']}/items", params={"limit": 1} ) idx = 0 item_ids = [] while True: idx += 1 page_data = page.json() item_ids.append(page_data["features"][0]["id"]) next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) if not next_link: break query_params = parse_qs(urlparse(next_link[0]["href"]).query) page = app_client.get( f"/collections/{test_item['collection']}/items", params=query_params, ) # Our limit is 1 so we expect len(ids) number of requests before we run out of pages assert idx == len(ids) # Confirm we have paginated through all items assert not set(item_ids) - set(ids) def test_pagination_post(app_client, load_test_data): """Test POST pagination (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) # Paginate through all 5 items with a limit of 1 (expecting 5 requests) request_body = {"ids": ids, "limit": 1} page = app_client.post("/search", json=request_body) idx = 0 item_ids = [] while True: idx += 1 page_data = page.json() item_ids.append(page_data["features"][0]["id"]) next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) if not next_link: break # Merge request bodies request_body.update(next_link[0]["body"]) page = app_client.post("/search", json=request_body) # Our limit is 1 so we expect len(ids) number of requests before we run out of pages assert idx == len(ids) # Confirm we have paginated through all items assert not set(item_ids) - set(ids) def test_pagination_token_idempotent(app_client, load_test_data): """Test that pagination tokens are idempotent (paging extension)""" test_item = load_test_data("test_item.json") ids = [] # Ingest 5 items for idx in range(5): uid = str(uuid.uuid4()) test_item["id"] = uid resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 ids.append(uid) page = app_client.get("/search", params={"ids": ",".join(ids), "limit": 3}) page_data = page.json() next_link = list(filter(lambda l: l["rel"] == "next", page_data["links"])) # Confirm token is idempotent resp1 = app_client.get( "/search", params=parse_qs(urlparse(next_link[0]["href"]).query) ) resp2 = app_client.get( "/search", params=parse_qs(urlparse(next_link[0]["href"]).query) ) resp1_data = resp1.json() resp2_data = resp2.json() # Two different requests with the same pagination token should return the same items assert [item["id"] for item in resp1_data["features"]] == [ item["id"] for item in resp2_data["features"] ] def test_field_extension_get(app_client, load_test_data): """Test GET search with included fields (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 params = {"fields": "+properties.proj:epsg,+properties.gsd"} resp = app_client.get("/search", params=params) feat_properties = resp.json()["features"][0]["properties"] assert not set(feat_properties) - {"proj:epsg", "gsd", "datetime"} def test_field_extension_post(app_client, load_test_data): """Test POST search with included and excluded fields (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 body = { "fields": { "exclude": ["assets.B1"], "include": ["properties.eo:cloud_cover", "properties.orientation"], } } resp = app_client.post("/search", json=body) resp_json = resp.json() assert "B1" not in resp_json["features"][0]["assets"].keys() assert not set(resp_json["features"][0]["properties"]) - { "orientation", "eo:cloud_cover", "datetime", } def test_field_extension_exclude_and_include(app_client, load_test_data): """Test POST search including/excluding same field (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 body = { "fields": { "exclude": ["properties.eo:cloud_cover"], "include": ["properties.eo:cloud_cover"], } } resp = app_client.post("/search", json=body) resp_json = resp.json() assert "eo:cloud_cover" not in resp_json["features"][0]["properties"] def test_field_extension_exclude_default_includes(app_client, load_test_data): """Test POST search excluding a forbidden field (fields extension)""" test_item = load_test_data("test_item.json") resp = app_client.post( f"/collections/{test_item['collection']}/items", json=test_item ) assert resp.status_code == 200 body = {"fields": {"exclude": ["geometry"]}} resp = app_client.post("/search", json=body) resp_json = resp.json() assert "geometry" not in resp_json["features"][0] def test_search_intersects_and_bbox(app_client): """Test POST search intersects and bbox are mutually exclusive (core)""" bbox = [-118, 34, -117, 35] geoj = Polygon.from_bounds(*bbox).__geo_interface__ params = {"bbox": bbox, "intersects": geoj} resp = app_client.post("/search", json=params) assert resp.status_code == 400 def test_get_missing_item(app_client, load_test_data): """Test read item which does not exist (transactions extension)""" test_coll = load_test_data("test_collection.json") resp = app_client.get(f"/collections/{test_coll['id']}/items/invalid-item") assert resp.status_code == 404 def test_search_invalid_query_field(app_client): body = {"query": {"gsd": {"lt": 100}, "invalid-field": {"eq": 50}}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 def test_search_bbox_errors(app_client): body = {"query": {"bbox": [0]}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 body = {"query": {"bbox": [100.0, 0.0, 0.0, 105.0, 1.0, 1.0]}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 params = {"bbox": "100.0,0.0,0.0,105.0"} resp = app_client.get("/search", params=params) assert resp.status_code == 400 def test_conformance_classes_configurable(): """Test conformance class configurability""" landing = LandingPageMixin() landing_page = landing._landing_page( base_url="http://test/test", conformance_classes=["this is a test"], extension_schemas=[], ) assert landing_page["conformsTo"][0] == "this is a test" # Update environment to avoid key error on client instantiation os.environ["READER_CONN_STRING"] = "testing" os.environ["WRITER_CONN_STRING"] = "testing" client = CoreCrudClient(base_conformance_classes=["this is a test"]) assert client.conformance_classes()[0] == "this is a test" def test_search_datetime_validation_errors(app_client): bad_datetimes = [ "37-01-01T12:00:27.87Z", "1985-13-12T23:20:50.52Z", "1985-12-32T23:20:50.52Z", "1985-12-01T25:20:50.52Z", "1985-12-01T00:60:50.52Z", "1985-12-01T00:06:61.52Z", "1990-12-31T23:59:61Z", "1986-04-12T23:20:50.52Z/1985-04-12T23:20:50.52Z", ] for dt in bad_datetimes: body = {"query": {"datetime": dt}} resp = app_client.post("/search", json=body) assert resp.status_code == 400 resp = app_client.get("/search?datetime={}".format(dt)) assert resp.status_code == 400

import copy import time from collections import OrderedDict import torch from data.dataloader import local_client_dataset, test_dataset from models.utils import * from utils.train_helper import validate_one_model from utils.sampling import * import numpy as np from multiprocessing import Process import time def return_state_dict(network): """ save model to state_dict """ feat_model = {k: v.cpu() for k, v in network["feat_model"].state_dict().items()} classifier = {k: v.cpu() for k, v in network["classifier"].state_dict().items()} return {"feat_model": feat_model, "classifier": classifier} def load_state_dict(network, state_dict): """ restore model from state_dict """ network["feat_model"].load_state_dict(state_dict["feat_model"]) network["classifier"].load_state_dict(state_dict["classifier"]) # for name, param in state_dict["feat_model"].items(): # print(name, "\t", param.size()) return network def check_status(status_list, selected_idx, target_status): """ 0. original status (1st FL round) 1. server finished sending: server_network --> mp_list 2. client received, and returned the model: mp_list --> networks[i] --> local_update --> mp_list 3. server received: mp_list --> networks[i] --> 1. aggregation finished. networks[i] --> aggregate --> server_network --> mp_list, the status change to 1 --- Return True: when all clients meet conditions, else False """ tmp = np.array(status_list) if (tmp[selected_idx] == target_status).all() == True: return True else: return False def set_status(status_list, selected_idx, target_status): """ see function: check_status """ if type(selected_idx) is int: selected_idx = [selected_idx] for i in selected_idx: status_list[i] = target_status # print(f"set_status {target_status}") def difference_models_norm_2(model_1, model_2): """ Return the norm 2 difference between the two model parameters. Used in FedProx. """ tensor_1_backbone = list(model_1["feat_model"].parameters()) tensor_1_classifier = list(model_1["classifier"].parameters()) tensor_2_backbone = list(model_2["feat_model"].parameters()) tensor_2_classifier = list(model_2["classifier"].parameters()) diff_list = [ torch.sum((tensor_1_backbone[i] - tensor_2_backbone[i]) ** 2) for i in range(len(tensor_1_backbone)) ] diff_list.extend( [ torch.sum((tensor_1_classifier[i] - tensor_2_classifier[i]) ** 2) for i in range(len(tensor_1_classifier)) ] ) norm = sum(diff_list) return norm class Fed_server(Process): """ Class for client updating and model aggregation """ def __init__( self, init_network, criterion, config, per_client_data, per_client_label, idx_per_client_train, test_data, test_label, state_list=None, state_dict_list=None, idx=None, ): super(Fed_server, self).__init__() self.local_bs = config["fl_opt"]["local_bs"] self.local_ep = config["fl_opt"]["local_ep"] self.num_clients = config["fl_opt"]["num_clients"] self.criterion = criterion self.networks, self.optimizers, self.optimizers_stage2, self.schedulers = ( [], [], [], [], ) self.train_loaders = [] # include dataloader or pre-loaded dataset self.train_loader_balanced = [] # balanced-sampling dataloader self.local_num_per_cls = [] # list to store local data number per class self.test_loaders = [] self.status_list = state_list self.state_dict_list = state_dict_list self.client_idx = idx # physical idx of clients (hardcoded) self.config = config self.prefetch = False self.feat_aug = config["fl_opt"]["feat_aug"] self.crt = config["fl_opt"]["crt"] self.client_weights = np.array([i for i in idx_per_client_train]) self.client_weights = self.client_weights / self.client_weights.sum() self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.server_network = copy.deepcopy(init_network) self.server_network["feat_model"].to(self.device) self.server_network["classifier"].to(self.device) # per-client accuracy and loss self.acc = [0 for i in range(self.num_clients)] self.losses_cls = [-1 for i in range(self.num_clients)] self.losses_kd = [-1 for i in range(self.num_clients)] print(f'=====> {config['metainfo']['optimizer']}, Server (fed.py)\n ') ######## init backbone, classifier, optimizer and dataloader ######## for client_i in range(self.num_clients): backbone = copy.deepcopy(self.server_network["feat_model"]) classifier = copy.deepcopy(self.server_network["classifier"]) self.networks.append({"feat_model": backbone, "classifier": classifier}) """ Server does not need # list of optimizer_dict. One optimizer for one network self.optimizers.append(init_optimizers(self.networks[client_i], config)) optim_params_dict = {'params': self.networks[client_i]["classifier"].parameters(), 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0} self.optimizers_stage2.append(torch.optim.SGD([optim_params_dict],)) # dataloader num_workers = 0 local_dataset = \ local_client_dataset(per_client_data[client_i], per_client_label[client_i], config) self.train_loaders.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, shuffle=True, num_workers=num_workers, pin_memory=False) ) self.train_loader_balanced.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, sampler=local_dataset.get_balanced_sampler(), num_workers=num_workers, pin_memory=False) ) self.local_num_per_cls.append(local_dataset.class_sample_count) """ # centralized train dataset train_data_all, train_label_all = [], [] for client_i in range(len(per_client_label)): train_data_all = train_data_all + per_client_data[client_i] train_label_all = train_label_all + per_client_label[client_i] self.train_dataset = local_client_dataset( train_data_all, train_label_all, config ) self.test_dataset = test_dataset(test_data, test_label, config) def local_train(self, selected_idx): """ server-side code """ # self.server_network --> mp_list for i in selected_idx: self.state_dict_list[i] = return_state_dict( self.server_network ) # model transfer set_status(self.status_list, selected_idx, 1) # wait until all clients returning the model while check_status(self.status_list, selected_idx, 2) is False: time.sleep(0.1) # mp_list --> self.networks (copys of client models on the server). Prepare for aggregation. for i in selected_idx: load_state_dict(self.networks[i], self.state_dict_list[i]) # model transfer print("===> Local training finished") def aggregation(self, selected_idx, mode): """ server-side code: aggregation """ if mode in ["fedavg", "fedavgm", "fedbn", "fedprox"]: self.aggregate_layers(selected_idx, mode, backbone_only=False) elif mode == "fedavg_fs": opt = self.config["fl_opt"] backbone_only, imprint, spread_out = ( opt["backbone_only"], opt["imprint"], opt["spread_out"], ) self.aggregate_layers(selected_idx, "fedavg", backbone_only=backbone_only) if imprint: self.imprint(selected_idx) if spread_out: self.spread_out() # model: self.server_network --> mp_list for i in selected_idx: self.state_dict_list[i] = return_state_dict( self.server_network ) # model transfer set_status(self.status_list, selected_idx, 0) # back to original print("===> Aggregation finished") def aggregate_layers(self, selected_idx, mode, backbone_only): """ backbone_only: choose to only aggregate backbone """ weights_sum = self.client_weights[selected_idx].sum() with torch.no_grad(): if mode in ["fedavg", "fedprox"]: for net_name, net in self.server_network.items(): if net_name == "classifier" and backbone_only: pass else: for key, layer in net.state_dict().items(): if "num_batches_tracked" in key: # num_batches_tracked is a non trainable LongTensor # and num_batches_tracked are the same for # all clients for the given datasets layer.data.copy_( self.networks[0][net_name].state_dict()[key] ) else: temp = torch.zeros_like(layer) # Fedavg for idx in selected_idx: weight = self.client_weights[idx] / weights_sum temp += ( weight * self.networks[idx][net_name].state_dict()[key] ) layer.data.copy_(temp) # update client models # for idx in selected_idx: # self.networks[idx][net_name].state_dict()[key].data.copy_(layer) elif mode == "fedbn": # https://openreview.net/pdf?id=6YEQUn0QICG for net_name, net in self.server_network.items(): if net_name == "classifier" and backbone_only: pass else: for key, layer in net.state_dict().items(): if "bn" not in key: temp = torch.zeros_like(layer) # Fedavg for idx in selected_idx: weight = self.client_weights[idx] / weights_sum temp += ( weight * self.networks[idx][net_name].state_dict()[key] ) layer.data.copy_(temp) # update client models # for idx in selected_idx: # self.networks[idx][net_name].state_dict()[key].data.copy_(layer) elif mode == "fedavgm": raise NotImplementedError def evaluate_global(self, train_dataset=None, test_dataset=None): """ Accuracy of the global model and all classes """ # evaluate on training set if train_dataset is None: train_dataset = self.train_dataset if test_dataset is None: test_dataset = self.test_dataset train_loss_per_cls, train_acc_per_cls = validate_one_model( self.server_network, train_dataset, self.device, per_cls_acc=True ) # evaluate on test set: per-class loss/acc test_loss_per_cls, test_acc_per_cls = validate_one_model( self.server_network, test_dataset, self.device, per_cls_acc=True ) print("===> Evaluation finished\n") return ( train_loss_per_cls, train_acc_per_cls, test_loss_per_cls, test_acc_per_cls, ) def evaluate_global_all(self, train_dataset=None, test_dataset=None): """ Accuracy of models of all nodes and all classes Return: all_results shape: (4, num_client, num_cls), 4 for (train_loss, train_acc, test_loss, test_acc) """ # evaluate on training set if train_dataset is None: train_dataset = self.train_dataset if test_dataset is None: test_dataset = self.test_dataset all_results = [None for i in range(self.num_clients)] for idx in range(self.num_clients): # evaluate on test set: per-class loss/acc train_loss_per_cls, train_acc_per_cls = validate_one_model( self.networks[idx], train_dataset, self.device, per_cls_acc=True ) # evaluate on test set: per-class loss/acc test_loss_per_cls, test_acc_per_cls = validate_one_model( self.networks[idx], test_dataset, self.device, per_cls_acc=True ) all_results[idx] = ( train_loss_per_cls, train_acc_per_cls, test_loss_per_cls, test_acc_per_cls, ) print(f"===> Evaluation finished{idx}\n") all_results = np.array(all_results).transpose(1, 0, 2) return all_results class Fed_client(Process): """ Class for client updating and model aggregation """ def __init__( self, init_network, criterion, config, per_client_data, per_client_label, idx_per_client_train, test_data, test_label, state_list=None, state_dict_list=None, idx=None, ): super(Fed_client, self).__init__() self.local_bs = config["fl_opt"]["local_bs"] self.local_ep = config["fl_opt"]["local_ep"] self.num_clients = config["fl_opt"]["num_clients"] self.criterion = criterion self.networks, self.optimizers, self.optimizers_stage2, self.schedulers = ( [], [], [], [], ) self.train_loaders = [] # include dataloader or pre-loaded dataset self.train_loader_balanced = [] # balanced-sampling dataloader self.local_num_per_cls = [] # list to store local data number per class self.test_loaders = [] self.status_list = state_list self.state_dict_list = state_dict_list self.client_idx = idx # physical idx of clients (hardcoded) self.config = config self.device = config["device_client"][idx] self.server_network = copy.deepcopy(init_network) self.balanced_loader = config["fl_opt"]["balanced_loader"] self.prefetch = False self.feat_aug = config["fl_opt"]["feat_aug"] self.crt = config["fl_opt"]["crt"] if config["fl_opt"]["aggregation"] == "fedprox": self.fedprox = True else: self.fedprox = False self.mu = 0.05 self.client_weights = np.array([i for i in idx_per_client_train]) self.client_weights = self.client_weights / self.client_weights.sum() # per-client accuracy and loss self.acc = [0 for i in range(self.num_clients)] self.losses_cls = [-1 for i in range(self.num_clients)] self.losses_kd = [-1 for i in range(self.num_clients)] print(f'=====> {config['metainfo']['optimizer']}, Client {idx} (fed.py)\n ') ######## init backbone, classifier, optimizer and dataloader ######## for client_i in range(self.num_clients): # list of network and optimizer_dict. One optimizer for one network. if client_i != self.client_idx: self.networks.append(None) self.optimizers.append(None) self.optimizers_stage2.append(None) else: backbone = copy.deepcopy(self.server_network["feat_model"]) classifier = copy.deepcopy(self.server_network["classifier"]) self.networks.append({"feat_model": backbone, "classifier": classifier}) self.optimizers.append(init_optimizers(self.networks[client_i], config)) optim_params_dict = { "params": self.networks[client_i]["classifier"].parameters(), "lr": 0.001, "momentum": 0.9, "weight_decay": 0, } self.optimizers_stage2.append(torch.optim.SGD([optim_params_dict],)) # dataloader num_workers = 0 local_dataset = local_client_dataset( per_client_data[client_i], per_client_label[client_i], config ) self.train_loaders.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, shuffle=True, num_workers=num_workers, pin_memory=False, ) ) self.train_loader_balanced.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, sampler=local_dataset.get_balanced_sampler(), num_workers=num_workers, pin_memory=False, ) ) self.local_num_per_cls.append(local_dataset.class_sample_count) """ clients do not need # centralized train dataset train_data_all, train_label_all = [], [] for client_i in range(len(per_client_label)): train_data_all = train_data_all + per_client_data[client_i] train_label_all = train_label_all + per_client_label[client_i] self.train_dataset = local_client_dataset(train_data_all, train_label_all, config) self.test_dataset = test_dataset(test_data, test_label, config) """ def run(self): """ client-side code """ self.server_network["feat_model"].to(self.device) self.server_network["classifier"].to(self.device) self.networks[self.client_idx]["feat_model"].to(self.device) self.networks[self.client_idx]["classifier"].to(self.device) while 1: while check_status(self.status_list, self.client_idx, 1) is False: time.sleep(0.1) # model: mp_list --> server_network load_state_dict( self.server_network, self.state_dict_list[self.client_idx] ) # model transfer self.train_lt(self.client_idx) # local model updating # self.networks[i] --> mp_list self.state_dict_list[self.client_idx] = return_state_dict( self.networks[self.client_idx] ) # model transfer set_status(self.status_list, self.client_idx, 2) def train_lt(self, idx): """ client-side code --- Argus: - idx: the index in all clients (e.g., 50) or selected clients (e.g., 10). If self.prefetch is true: the index in selected clients, If self.prefetch is true: the index in all clients """ idx_in_all = idx # server broadcast the model to clients """ # optimizer will not work if use this, because optimizer needs the params from the model # self.networks[idx_in_all] = copy.deepcopy(self.server_network) """ for net_name, net in self.server_network.items(): # feat_model, classifier state_dict = self.networks[idx_in_all][net_name].state_dict() for key, layer in net.state_dict().items(): state_dict[key].data.copy_(layer.data) for net in self.networks[idx_in_all].values(): net.train() for net in self.server_network.values(): net.train() teacher = self.server_network # torch.cuda.empty_cache() """ (Per-cls) Covariance Calculation """ if self.feat_aug: # probability for augmentation for every class max_num = max(self.local_num_per_cls[idx]) prob = torch.tensor( [1.0 - i / max_num for i in self.local_num_per_cls[idx]] ) # obtain features and labels under eval mode feat_list, label_list = [], [] # self.networks[idx_in_all]['feat_model'].eval() for (imgs, labels, indexs) in self.train_loaders[idx]: with torch.no_grad(): imgs = imgs.to(self.device) feat_list.append(teacher["feat_model"](imgs).cpu()) label_list.append(labels) feat_list = torch.cat(feat_list, 0) # self.networks[idx_in_all]['feat_model'].train() label_list = torch.cat(label_list, 0) unique_labels = list(np.unique(label_list)) # e.g., size (6, ) transformed_label_list = torch.tensor( [unique_labels.index(i) for i in label_list] ) # e.g., size (n, ) # per-cls features feats_per_cls = [[] for i in range(len(unique_labels))] for feats, label in zip(feat_list, transformed_label_list): feats_per_cls[label].append(feats) # calculate the variance sampled_data, sample_label = [], [] per_cls_cov = [] for feats in feats_per_cls: if len(feats) > 1: per_cls_cov.append(np.cov(torch.stack(feats, 1).numpy())) else: per_cls_cov.append(np.zeros((feats[0].shape[0], feats[0].shape[0]))) per_cls_cov = np.array(per_cls_cov) # per_cls_cov = np.array([np.cov(torch.stack(feats, 1).numpy()) for feats in feats_per_cls]) cov = np.average( per_cls_cov, axis=0, weights=self.local_num_per_cls[idx] ) # covariance for feature dimension, shape: e.g., (128, 128) # pre-generate deviation divider = 500 pointer = 0 augs = ( torch.from_numpy( np.random.multivariate_normal( mean=np.zeros(cov.shape[0]), cov=cov, # covariance for feature dimension, shape: e.g., (128, 128) size=divider, ) ) .float() .to(self.device) ) with torch.set_grad_enabled(True): losses_cls = 0 losses_kd = 0 ########################## #### stage 1 training #### ########################## for epoch in range(self.local_ep): """ model update """ if self.local_ep > 10: # locla training mode print(epoch, end=" ") if self.balanced_loader: tmp_loader = self.train_loader_balanced[idx] else: tmp_loader = self.train_loaders[idx] for (imgs, labels, indexs) in tmp_loader: # to device imgs = imgs.to(self.device) # forward feat = self.networks[idx_in_all]["feat_model"](imgs) logits = self.networks[idx_in_all]["classifier"](feat) # do feature space augmentation with a likelihood if self.feat_aug: # prob = torch.tensor([1.0 for i in self.local_num_per_cls[idx]]) rand_list = torch.rand(len(labels)) mask = ( rand_list < prob[ torch.tensor([unique_labels.index(i) for i in labels]) ] ) degree = 1 aug_num = sum(mask).item() if aug_num > 0: if pointer + aug_num >= divider: pointer = 0 feat_aug = feat.clone() feat_aug[mask] = ( feat[mask] + augs[pointer : pointer + aug_num] * degree ) logits_aug = self.networks[idx_in_all]["classifier"]( feat_aug ) pointer = pointer + aug_num # teacher with torch.no_grad(): feat_teacher = teacher["feat_model"](imgs) pred_teacher = teacher["classifier"](feat_teacher) # loss labels = labels.to(self.device) if self.config["criterions"]["def_file"].find("LwF") > 0: if self.feat_aug: if len(labels) != len(logits_aug): continue loss, loss_cls, loss_kd = self.criterion( labels, pred_teacher, logits, logits_aug ) else: loss, loss_cls, loss_kd = self.criterion( labels, pred_teacher, logits ) elif self.config["criterions"]["def_file"].find("KDLoss") > 0: loss, loss_cls, loss_kd = self.criterion( logits, labels, feat, feat_teacher, classfier_weight=self.networks[idx_in_all][ "classifier" ].fc.weight, ) # fedprox loss: https://epione.gitlabpages.inria.fr/flhd/federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.html#federated-training-with-fedprox if self.fedprox: prox_loss = difference_models_norm_2( self.networks[idx_in_all], teacher ) # print("FedProx Loss: ", prox_loss, loss) loss += self.mu / 2 * prox_loss # backward for optimizer in self.optimizers[idx_in_all].values(): optimizer.zero_grad() loss.backward() for optimizer in self.optimizers[idx_in_all].values(): optimizer.step() # classifier L2-norm if self.networks[idx_in_all]["classifier"].l2_norm: self.networks[idx_in_all]["classifier"].weight_norm() losses_cls += loss_cls.item() losses_kd += loss_kd.item() self.losses_cls[idx_in_all] = ( losses_cls / len(self.train_loaders[idx]) / self.local_ep ) self.losses_kd[idx_in_all] = ( losses_kd / len(self.train_loaders[idx]) / self.local_ep ) ########################## #### stage 2 training #### ########################## if self.crt: self.networks[idx_in_all]["feat_model"].eval() if self.feat_aug: # obtain features and labels feat_list = [] label_list = [] for (imgs, labels, indexs) in self.train_loaders[idx]: imgs = imgs.to(self.device) with torch.no_grad(): feat_list.append( self.networks[idx_in_all]["feat_model"](imgs).cpu() ) label_list.append(labels) feat_list = torch.cat(feat_list, 0) label_list = torch.cat(label_list, 0) unique_labels = list(np.unique(label_list)) # e.g., size (6, ) transformed_label_list = torch.tensor( [unique_labels.index(i) for i in label_list] ) # e.g., size (n, ) # per-cls features feats_per_cls = [[] for i in range(len(unique_labels))] for feat, label in zip(feat_list, transformed_label_list): feats_per_cls[label].append(feat) # determine the extra sample number for every existing samples num_per_cls = np.array( [len(np.where(label_list == t)[0]) for t in unique_labels] ) # e.g., size (6, ) max_num = max(num_per_cls) gen_nums = [ np.array( [max_num // num_per_cls[i] - 1 for _ in feats_per_cls[i]] ) for i in range(len(unique_labels)) ] for cls_i, nums in enumerate(gen_nums): nums[: max_num % num_per_cls[cls_i]] = ( nums[: max_num % num_per_cls[cls_i]] + 1 ) # generate samples sampled_data, sample_label = [], [] per_cls_cov = np.array( [ np.cov(torch.stack(feats, 1).numpy()) for feats in feats_per_cls ] ) cov = np.average(per_cls_cov, axis=0, weights=num_per_cls) # print([np.mean(i) for i in per_cls_cov]) for cls_i, nums in enumerate(gen_nums): for sample_i, num in enumerate(nums): if num > 0: sampled_data.append( torch.from_numpy( np.random.multivariate_normal( mean=feats_per_cls[cls_i][sample_i], cov=cov, # covariance for feature dimension, shape: e.g., (128, 128) size=num, ) ).float() ) sample_label.append(torch.full((num,), cls_i).long()) # add generated fetaures to training data feat_list = torch.cat([feat_list, *sampled_data], 0) label_list = torch.cat([transformed_label_list, *sample_label], 0) # build new dataloader feats_dataset = local_client_dataset( feat_list, label_list, self.config ) feats_loader = torch.utils.data.DataLoader( feats_dataset, batch_size=self.local_bs, shuffle=True, num_workers=0, pin_memory=False, ) # train classifier for epoch in range(5): for (feats, labels, indexs) in feats_loader: feats = feats.to(self.device) labels = labels.to(self.device) logits = self.networks[idx_in_all]["classifier"](feats) loss = torch.nn.CrossEntropyLoss()( logits[:, unique_labels], labels ) self.optimizers_stage2[idx_in_all].zero_grad() loss.backward() self.optimizers_stage2[idx_in_all].step() # print(loss) # re-sampling without feature augmentation else: for epoch in range(5): for (imgs, labels, indexs) in self.train_loader_balanced[idx]: # to device imgs = imgs.to(self.device) # forward with torch.no_grad(): feat = self.networks[idx_in_all]["feat_model"](imgs) logits = self.networks[idx_in_all]["classifier"](feat) pos_cls = torch.unique(labels).tolist() transformed_labels = torch.tensor( [pos_cls.index(i) for i in labels] ).to(self.device) loss = torch.nn.CrossEntropyLoss()( logits[:, pos_cls], transformed_labels ) self.optimizers_stage2[idx_in_all].zero_grad() loss.backward() self.optimizers_stage2[idx_in_all].step() # print(loss) print("=> ", end="") def fedavg(w): w_avg = copy.deepcopy(w[0]) for k in w_avg.keys(): for i in range(1, len(w)): w_avg[k] += w[i][k] w_avg[k] = torch.div(w_avg[k] * 1.0, len(w)) return w_avg # See: https://arxiv.org/abs/1909.06335 def fedavgm(new_ws, old_w, vel, args): """ fedavg + momentum - new_ws (list of OrderedDict): The new calculated global model - old_w (OrderedDict) : Initial state of the global model (which needs to be updated here) """ global_lr = 1 beta1 = 0 new_w = fedavg(new_ws) # For the first round: initialize old_w, create an Orderdict to store velocity if old_w is None: old_w = new_w new_v = OrderedDict() for key in old_w.keys(): new_v[key] = torch.zeros(old_w[key].shape, dtype=old_w[key].dtype).to( args.device ) else: new_v = copy.deepcopy(vel) for key in new_w.keys(): delta_w_tmp = old_w[key] - new_w[key] new_v[key] = beta1 * new_v[key] + torch.mul(delta_w_tmp, global_lr) old_w[key] -= new_v[key] return old_w, new_v def fedavgw(new_ws, old_w, args, round_i): """ fedavg + adaptive updating parameter - new_ws (list of OrderedDict): The new calculated global model - old_w (OrderedDict) : Initial state of the global model (which needs to be updated here) """ new_w = fedavg(new_ws) # For the first round: initialize old_w if old_w is None: old_w = new_w for key in new_w.keys(): old_w[key] = new_w[key] * (1 / (round_i + 1)) + old_w[key] * ( round_i / (round_i + 1) ) # for key in new_w.keys(): # if key == "classifier.fc.weight": # old_w[key] = new_w[key]*(1/(round_i+1)) + old_w[key]*(round_i/(round_i+1)) # else: # old_w[key] = new_w[key] return old_w

import copy import time from collections import OrderedDict import torch from data.dataloader import local_client_dataset, test_dataset from models.utils import * from utils.train_helper import validate_one_model from utils.sampling import * import numpy as np from multiprocessing import Process import time def return_state_dict(network): """ save model to state_dict """ feat_model = {k: v.cpu() for k, v in network["feat_model"].state_dict().items()} classifier = {k: v.cpu() for k, v in network["classifier"].state_dict().items()} return {"feat_model": feat_model, "classifier": classifier} def load_state_dict(network, state_dict): """ restore model from state_dict """ network["feat_model"].load_state_dict(state_dict["feat_model"]) network["classifier"].load_state_dict(state_dict["classifier"]) # for name, param in state_dict["feat_model"].items(): # print(name, "\t", param.size()) return network def check_status(status_list, selected_idx, target_status): """ 0. original status (1st FL round) 1. server finished sending: server_network --> mp_list 2. client received, and returned the model: mp_list --> networks[i] --> local_update --> mp_list 3. server received: mp_list --> networks[i] --> 1. aggregation finished. networks[i] --> aggregate --> server_network --> mp_list, the status change to 1 --- Return True: when all clients meet conditions, else False """ tmp = np.array(status_list) if (tmp[selected_idx] == target_status).all() == True: return True else: return False def set_status(status_list, selected_idx, target_status): """ see function: check_status """ if type(selected_idx) is int: selected_idx = [selected_idx] for i in selected_idx: status_list[i] = target_status # print(f"set_status {target_status}") def difference_models_norm_2(model_1, model_2): """ Return the norm 2 difference between the two model parameters. Used in FedProx. """ tensor_1_backbone = list(model_1["feat_model"].parameters()) tensor_1_classifier = list(model_1["classifier"].parameters()) tensor_2_backbone = list(model_2["feat_model"].parameters()) tensor_2_classifier = list(model_2["classifier"].parameters()) diff_list = [ torch.sum((tensor_1_backbone[i] - tensor_2_backbone[i]) ** 2) for i in range(len(tensor_1_backbone)) ] diff_list.extend( [ torch.sum((tensor_1_classifier[i] - tensor_2_classifier[i]) ** 2) for i in range(len(tensor_1_classifier)) ] ) norm = sum(diff_list) return norm class Fed_server(Process): """ Class for client updating and model aggregation """ def __init__( self, init_network, criterion, config, per_client_data, per_client_label, idx_per_client_train, test_data, test_label, state_list=None, state_dict_list=None, idx=None, ): super(Fed_server, self).__init__() self.local_bs = config["fl_opt"]["local_bs"] self.local_ep = config["fl_opt"]["local_ep"] self.num_clients = config["fl_opt"]["num_clients"] self.criterion = criterion self.networks, self.optimizers, self.optimizers_stage2, self.schedulers = ( [], [], [], [], ) self.train_loaders = [] # include dataloader or pre-loaded dataset self.train_loader_balanced = [] # balanced-sampling dataloader self.local_num_per_cls = [] # list to store local data number per class self.test_loaders = [] self.status_list = state_list self.state_dict_list = state_dict_list self.client_idx = idx # physical idx of clients (hardcoded) self.config = config self.prefetch = False self.feat_aug = config["fl_opt"]["feat_aug"] self.crt = config["fl_opt"]["crt"] self.client_weights = np.array([i for i in idx_per_client_train]) self.client_weights = self.client_weights / self.client_weights.sum() self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.server_network = copy.deepcopy(init_network) self.server_network["feat_model"].to(self.device) self.server_network["classifier"].to(self.device) # per-client accuracy and loss self.acc = [0 for i in range(self.num_clients)] self.losses_cls = [-1 for i in range(self.num_clients)] self.losses_kd = [-1 for i in range(self.num_clients)] print(f'=====> {config["metainfo"]["optimizer"]}, Server (fed.py)\n ') ######## init backbone, classifier, optimizer and dataloader ######## for client_i in range(self.num_clients): backbone = copy.deepcopy(self.server_network["feat_model"]) classifier = copy.deepcopy(self.server_network["classifier"]) self.networks.append({"feat_model": backbone, "classifier": classifier}) """ Server does not need # list of optimizer_dict. One optimizer for one network self.optimizers.append(init_optimizers(self.networks[client_i], config)) optim_params_dict = {'params': self.networks[client_i]["classifier"].parameters(), 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0} self.optimizers_stage2.append(torch.optim.SGD([optim_params_dict],)) # dataloader num_workers = 0 local_dataset = \ local_client_dataset(per_client_data[client_i], per_client_label[client_i], config) self.train_loaders.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, shuffle=True, num_workers=num_workers, pin_memory=False) ) self.train_loader_balanced.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, sampler=local_dataset.get_balanced_sampler(), num_workers=num_workers, pin_memory=False) ) self.local_num_per_cls.append(local_dataset.class_sample_count) """ # centralized train dataset train_data_all, train_label_all = [], [] for client_i in range(len(per_client_label)): train_data_all = train_data_all + per_client_data[client_i] train_label_all = train_label_all + per_client_label[client_i] self.train_dataset = local_client_dataset( train_data_all, train_label_all, config ) self.test_dataset = test_dataset(test_data, test_label, config) def local_train(self, selected_idx): """ server-side code """ # self.server_network --> mp_list for i in selected_idx: self.state_dict_list[i] = return_state_dict( self.server_network ) # model transfer set_status(self.status_list, selected_idx, 1) # wait until all clients returning the model while check_status(self.status_list, selected_idx, 2) is False: time.sleep(0.1) # mp_list --> self.networks (copys of client models on the server). Prepare for aggregation. for i in selected_idx: load_state_dict(self.networks[i], self.state_dict_list[i]) # model transfer print("===> Local training finished") def aggregation(self, selected_idx, mode): """ server-side code: aggregation """ if mode in ["fedavg", "fedavgm", "fedbn", "fedprox"]: self.aggregate_layers(selected_idx, mode, backbone_only=False) elif mode == "fedavg_fs": opt = self.config["fl_opt"] backbone_only, imprint, spread_out = ( opt["backbone_only"], opt["imprint"], opt["spread_out"], ) self.aggregate_layers(selected_idx, "fedavg", backbone_only=backbone_only) if imprint: self.imprint(selected_idx) if spread_out: self.spread_out() # model: self.server_network --> mp_list for i in selected_idx: self.state_dict_list[i] = return_state_dict( self.server_network ) # model transfer set_status(self.status_list, selected_idx, 0) # back to original print("===> Aggregation finished") def aggregate_layers(self, selected_idx, mode, backbone_only): """ backbone_only: choose to only aggregate backbone """ weights_sum = self.client_weights[selected_idx].sum() with torch.no_grad(): if mode in ["fedavg", "fedprox"]: for net_name, net in self.server_network.items(): if net_name == "classifier" and backbone_only: pass else: for key, layer in net.state_dict().items(): if "num_batches_tracked" in key: # num_batches_tracked is a non trainable LongTensor # and num_batches_tracked are the same for # all clients for the given datasets layer.data.copy_( self.networks[0][net_name].state_dict()[key] ) else: temp = torch.zeros_like(layer) # Fedavg for idx in selected_idx: weight = self.client_weights[idx] / weights_sum temp += ( weight * self.networks[idx][net_name].state_dict()[key] ) layer.data.copy_(temp) # update client models # for idx in selected_idx: # self.networks[idx][net_name].state_dict()[key].data.copy_(layer) elif mode == "fedbn": # https://openreview.net/pdf?id=6YEQUn0QICG for net_name, net in self.server_network.items(): if net_name == "classifier" and backbone_only: pass else: for key, layer in net.state_dict().items(): if "bn" not in key: temp = torch.zeros_like(layer) # Fedavg for idx in selected_idx: weight = self.client_weights[idx] / weights_sum temp += ( weight * self.networks[idx][net_name].state_dict()[key] ) layer.data.copy_(temp) # update client models # for idx in selected_idx: # self.networks[idx][net_name].state_dict()[key].data.copy_(layer) elif mode == "fedavgm": raise NotImplementedError def evaluate_global(self, train_dataset=None, test_dataset=None): """ Accuracy of the global model and all classes """ # evaluate on training set if train_dataset is None: train_dataset = self.train_dataset if test_dataset is None: test_dataset = self.test_dataset train_loss_per_cls, train_acc_per_cls = validate_one_model( self.server_network, train_dataset, self.device, per_cls_acc=True ) # evaluate on test set: per-class loss/acc test_loss_per_cls, test_acc_per_cls = validate_one_model( self.server_network, test_dataset, self.device, per_cls_acc=True ) print("===> Evaluation finished\n") return ( train_loss_per_cls, train_acc_per_cls, test_loss_per_cls, test_acc_per_cls, ) def evaluate_global_all(self, train_dataset=None, test_dataset=None): """ Accuracy of models of all nodes and all classes Return: all_results shape: (4, num_client, num_cls), 4 for (train_loss, train_acc, test_loss, test_acc) """ # evaluate on training set if train_dataset is None: train_dataset = self.train_dataset if test_dataset is None: test_dataset = self.test_dataset all_results = [None for i in range(self.num_clients)] for idx in range(self.num_clients): # evaluate on test set: per-class loss/acc train_loss_per_cls, train_acc_per_cls = validate_one_model( self.networks[idx], train_dataset, self.device, per_cls_acc=True ) # evaluate on test set: per-class loss/acc test_loss_per_cls, test_acc_per_cls = validate_one_model( self.networks[idx], test_dataset, self.device, per_cls_acc=True ) all_results[idx] = ( train_loss_per_cls, train_acc_per_cls, test_loss_per_cls, test_acc_per_cls, ) print(f"===> Evaluation finished{idx}\n") all_results = np.array(all_results).transpose(1, 0, 2) return all_results class Fed_client(Process): """ Class for client updating and model aggregation """ def __init__( self, init_network, criterion, config, per_client_data, per_client_label, idx_per_client_train, test_data, test_label, state_list=None, state_dict_list=None, idx=None, ): super(Fed_client, self).__init__() self.local_bs = config["fl_opt"]["local_bs"] self.local_ep = config["fl_opt"]["local_ep"] self.num_clients = config["fl_opt"]["num_clients"] self.criterion = criterion self.networks, self.optimizers, self.optimizers_stage2, self.schedulers = ( [], [], [], [], ) self.train_loaders = [] # include dataloader or pre-loaded dataset self.train_loader_balanced = [] # balanced-sampling dataloader self.local_num_per_cls = [] # list to store local data number per class self.test_loaders = [] self.status_list = state_list self.state_dict_list = state_dict_list self.client_idx = idx # physical idx of clients (hardcoded) self.config = config self.device = config["device_client"][idx] self.server_network = copy.deepcopy(init_network) self.balanced_loader = config["fl_opt"]["balanced_loader"] self.prefetch = False self.feat_aug = config["fl_opt"]["feat_aug"] self.crt = config["fl_opt"]["crt"] if config["fl_opt"]["aggregation"] == "fedprox": self.fedprox = True else: self.fedprox = False self.mu = 0.05 self.client_weights = np.array([i for i in idx_per_client_train]) self.client_weights = self.client_weights / self.client_weights.sum() # per-client accuracy and loss self.acc = [0 for i in range(self.num_clients)] self.losses_cls = [-1 for i in range(self.num_clients)] self.losses_kd = [-1 for i in range(self.num_clients)] print(f'=====> {config["metainfo"]["optimizer"]}, Client {idx} (fed.py)\n ') ######## init backbone, classifier, optimizer and dataloader ######## for client_i in range(self.num_clients): # list of network and optimizer_dict. One optimizer for one network. if client_i != self.client_idx: self.networks.append(None) self.optimizers.append(None) self.optimizers_stage2.append(None) else: backbone = copy.deepcopy(self.server_network["feat_model"]) classifier = copy.deepcopy(self.server_network["classifier"]) self.networks.append({"feat_model": backbone, "classifier": classifier}) self.optimizers.append(init_optimizers(self.networks[client_i], config)) optim_params_dict = { "params": self.networks[client_i]["classifier"].parameters(), "lr": 0.001, "momentum": 0.9, "weight_decay": 0, } self.optimizers_stage2.append(torch.optim.SGD([optim_params_dict],)) # dataloader num_workers = 0 local_dataset = local_client_dataset( per_client_data[client_i], per_client_label[client_i], config ) self.train_loaders.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, shuffle=True, num_workers=num_workers, pin_memory=False, ) ) self.train_loader_balanced.append( torch.utils.data.DataLoader( local_dataset, batch_size=self.local_bs, sampler=local_dataset.get_balanced_sampler(), num_workers=num_workers, pin_memory=False, ) ) self.local_num_per_cls.append(local_dataset.class_sample_count) """ clients do not need # centralized train dataset train_data_all, train_label_all = [], [] for client_i in range(len(per_client_label)): train_data_all = train_data_all + per_client_data[client_i] train_label_all = train_label_all + per_client_label[client_i] self.train_dataset = local_client_dataset(train_data_all, train_label_all, config) self.test_dataset = test_dataset(test_data, test_label, config) """ def run(self): """ client-side code """ self.server_network["feat_model"].to(self.device) self.server_network["classifier"].to(self.device) self.networks[self.client_idx]["feat_model"].to(self.device) self.networks[self.client_idx]["classifier"].to(self.device) while 1: while check_status(self.status_list, self.client_idx, 1) is False: time.sleep(0.1) # model: mp_list --> server_network load_state_dict( self.server_network, self.state_dict_list[self.client_idx] ) # model transfer self.train_lt(self.client_idx) # local model updating # self.networks[i] --> mp_list self.state_dict_list[self.client_idx] = return_state_dict( self.networks[self.client_idx] ) # model transfer set_status(self.status_list, self.client_idx, 2) def train_lt(self, idx): """ client-side code --- Argus: - idx: the index in all clients (e.g., 50) or selected clients (e.g., 10). If self.prefetch is true: the index in selected clients, If self.prefetch is true: the index in all clients """ idx_in_all = idx # server broadcast the model to clients """ # optimizer will not work if use this, because optimizer needs the params from the model # self.networks[idx_in_all] = copy.deepcopy(self.server_network) """ for net_name, net in self.server_network.items(): # feat_model, classifier state_dict = self.networks[idx_in_all][net_name].state_dict() for key, layer in net.state_dict().items(): state_dict[key].data.copy_(layer.data) for net in self.networks[idx_in_all].values(): net.train() for net in self.server_network.values(): net.train() teacher = self.server_network # torch.cuda.empty_cache() """ (Per-cls) Covariance Calculation """ if self.feat_aug: # probability for augmentation for every class max_num = max(self.local_num_per_cls[idx]) prob = torch.tensor( [1.0 - i / max_num for i in self.local_num_per_cls[idx]] ) # obtain features and labels under eval mode feat_list, label_list = [], [] # self.networks[idx_in_all]['feat_model'].eval() for (imgs, labels, indexs) in self.train_loaders[idx]: with torch.no_grad(): imgs = imgs.to(self.device) feat_list.append(teacher["feat_model"](imgs).cpu()) label_list.append(labels) feat_list = torch.cat(feat_list, 0) # self.networks[idx_in_all]['feat_model'].train() label_list = torch.cat(label_list, 0) unique_labels = list(np.unique(label_list)) # e.g., size (6, ) transformed_label_list = torch.tensor( [unique_labels.index(i) for i in label_list] ) # e.g., size (n, ) # per-cls features feats_per_cls = [[] for i in range(len(unique_labels))] for feats, label in zip(feat_list, transformed_label_list): feats_per_cls[label].append(feats) # calculate the variance sampled_data, sample_label = [], [] per_cls_cov = [] for feats in feats_per_cls: if len(feats) > 1: per_cls_cov.append(np.cov(torch.stack(feats, 1).numpy())) else: per_cls_cov.append(np.zeros((feats[0].shape[0], feats[0].shape[0]))) per_cls_cov = np.array(per_cls_cov) # per_cls_cov = np.array([np.cov(torch.stack(feats, 1).numpy()) for feats in feats_per_cls]) cov = np.average( per_cls_cov, axis=0, weights=self.local_num_per_cls[idx] ) # covariance for feature dimension, shape: e.g., (128, 128) # pre-generate deviation divider = 500 pointer = 0 augs = ( torch.from_numpy( np.random.multivariate_normal( mean=np.zeros(cov.shape[0]), cov=cov, # covariance for feature dimension, shape: e.g., (128, 128) size=divider, ) ) .float() .to(self.device) ) with torch.set_grad_enabled(True): losses_cls = 0 losses_kd = 0 ########################## #### stage 1 training #### ########################## for epoch in range(self.local_ep): """ model update """ if self.local_ep > 10: # locla training mode print(epoch, end=" ") if self.balanced_loader: tmp_loader = self.train_loader_balanced[idx] else: tmp_loader = self.train_loaders[idx] for (imgs, labels, indexs) in tmp_loader: # to device imgs = imgs.to(self.device) # forward feat = self.networks[idx_in_all]["feat_model"](imgs) logits = self.networks[idx_in_all]["classifier"](feat) # do feature space augmentation with a likelihood if self.feat_aug: # prob = torch.tensor([1.0 for i in self.local_num_per_cls[idx]]) rand_list = torch.rand(len(labels)) mask = ( rand_list < prob[ torch.tensor([unique_labels.index(i) for i in labels]) ] ) degree = 1 aug_num = sum(mask).item() if aug_num > 0: if pointer + aug_num >= divider: pointer = 0 feat_aug = feat.clone() feat_aug[mask] = ( feat[mask] + augs[pointer : pointer + aug_num] * degree ) logits_aug = self.networks[idx_in_all]["classifier"]( feat_aug ) pointer = pointer + aug_num # teacher with torch.no_grad(): feat_teacher = teacher["feat_model"](imgs) pred_teacher = teacher["classifier"](feat_teacher) # loss labels = labels.to(self.device) if self.config["criterions"]["def_file"].find("LwF") > 0: if self.feat_aug: if len(labels) != len(logits_aug): continue loss, loss_cls, loss_kd = self.criterion( labels, pred_teacher, logits, logits_aug ) else: loss, loss_cls, loss_kd = self.criterion( labels, pred_teacher, logits ) elif self.config["criterions"]["def_file"].find("KDLoss") > 0: loss, loss_cls, loss_kd = self.criterion( logits, labels, feat, feat_teacher, classfier_weight=self.networks[idx_in_all][ "classifier" ].fc.weight, ) # fedprox loss: https://epione.gitlabpages.inria.fr/flhd/federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.html#federated-training-with-fedprox if self.fedprox: prox_loss = difference_models_norm_2( self.networks[idx_in_all], teacher ) # print("FedProx Loss: ", prox_loss, loss) loss += self.mu / 2 * prox_loss # backward for optimizer in self.optimizers[idx_in_all].values(): optimizer.zero_grad() loss.backward() for optimizer in self.optimizers[idx_in_all].values(): optimizer.step() # classifier L2-norm if self.networks[idx_in_all]["classifier"].l2_norm: self.networks[idx_in_all]["classifier"].weight_norm() losses_cls += loss_cls.item() losses_kd += loss_kd.item() self.losses_cls[idx_in_all] = ( losses_cls / len(self.train_loaders[idx]) / self.local_ep ) self.losses_kd[idx_in_all] = ( losses_kd / len(self.train_loaders[idx]) / self.local_ep ) ########################## #### stage 2 training #### ########################## if self.crt: self.networks[idx_in_all]["feat_model"].eval() if self.feat_aug: # obtain features and labels feat_list = [] label_list = [] for (imgs, labels, indexs) in self.train_loaders[idx]: imgs = imgs.to(self.device) with torch.no_grad(): feat_list.append( self.networks[idx_in_all]["feat_model"](imgs).cpu() ) label_list.append(labels) feat_list = torch.cat(feat_list, 0) label_list = torch.cat(label_list, 0) unique_labels = list(np.unique(label_list)) # e.g., size (6, ) transformed_label_list = torch.tensor( [unique_labels.index(i) for i in label_list] ) # e.g., size (n, ) # per-cls features feats_per_cls = [[] for i in range(len(unique_labels))] for feat, label in zip(feat_list, transformed_label_list): feats_per_cls[label].append(feat) # determine the extra sample number for every existing samples num_per_cls = np.array( [len(np.where(label_list == t)[0]) for t in unique_labels] ) # e.g., size (6, ) max_num = max(num_per_cls) gen_nums = [ np.array( [max_num // num_per_cls[i] - 1 for _ in feats_per_cls[i]] ) for i in range(len(unique_labels)) ] for cls_i, nums in enumerate(gen_nums): nums[: max_num % num_per_cls[cls_i]] = ( nums[: max_num % num_per_cls[cls_i]] + 1 ) # generate samples sampled_data, sample_label = [], [] per_cls_cov = np.array( [ np.cov(torch.stack(feats, 1).numpy()) for feats in feats_per_cls ] ) cov = np.average(per_cls_cov, axis=0, weights=num_per_cls) # print([np.mean(i) for i in per_cls_cov]) for cls_i, nums in enumerate(gen_nums): for sample_i, num in enumerate(nums): if num > 0: sampled_data.append( torch.from_numpy( np.random.multivariate_normal( mean=feats_per_cls[cls_i][sample_i], cov=cov, # covariance for feature dimension, shape: e.g., (128, 128) size=num, ) ).float() ) sample_label.append(torch.full((num,), cls_i).long()) # add generated fetaures to training data feat_list = torch.cat([feat_list, *sampled_data], 0) label_list = torch.cat([transformed_label_list, *sample_label], 0) # build new dataloader feats_dataset = local_client_dataset( feat_list, label_list, self.config ) feats_loader = torch.utils.data.DataLoader( feats_dataset, batch_size=self.local_bs, shuffle=True, num_workers=0, pin_memory=False, ) # train classifier for epoch in range(5): for (feats, labels, indexs) in feats_loader: feats = feats.to(self.device) labels = labels.to(self.device) logits = self.networks[idx_in_all]["classifier"](feats) loss = torch.nn.CrossEntropyLoss()( logits[:, unique_labels], labels ) self.optimizers_stage2[idx_in_all].zero_grad() loss.backward() self.optimizers_stage2[idx_in_all].step() # print(loss) # re-sampling without feature augmentation else: for epoch in range(5): for (imgs, labels, indexs) in self.train_loader_balanced[idx]: # to device imgs = imgs.to(self.device) # forward with torch.no_grad(): feat = self.networks[idx_in_all]["feat_model"](imgs) logits = self.networks[idx_in_all]["classifier"](feat) pos_cls = torch.unique(labels).tolist() transformed_labels = torch.tensor( [pos_cls.index(i) for i in labels] ).to(self.device) loss = torch.nn.CrossEntropyLoss()( logits[:, pos_cls], transformed_labels ) self.optimizers_stage2[idx_in_all].zero_grad() loss.backward() self.optimizers_stage2[idx_in_all].step() # print(loss) print("=> ", end="") def fedavg(w): w_avg = copy.deepcopy(w[0]) for k in w_avg.keys(): for i in range(1, len(w)): w_avg[k] += w[i][k] w_avg[k] = torch.div(w_avg[k] * 1.0, len(w)) return w_avg # See: https://arxiv.org/abs/1909.06335 def fedavgm(new_ws, old_w, vel, args): """ fedavg + momentum - new_ws (list of OrderedDict): The new calculated global model - old_w (OrderedDict) : Initial state of the global model (which needs to be updated here) """ global_lr = 1 beta1 = 0 new_w = fedavg(new_ws) # For the first round: initialize old_w, create an Orderdict to store velocity if old_w is None: old_w = new_w new_v = OrderedDict() for key in old_w.keys(): new_v[key] = torch.zeros(old_w[key].shape, dtype=old_w[key].dtype).to( args.device ) else: new_v = copy.deepcopy(vel) for key in new_w.keys(): delta_w_tmp = old_w[key] - new_w[key] new_v[key] = beta1 * new_v[key] + torch.mul(delta_w_tmp, global_lr) old_w[key] -= new_v[key] return old_w, new_v def fedavgw(new_ws, old_w, args, round_i): """ fedavg + adaptive updating parameter - new_ws (list of OrderedDict): The new calculated global model - old_w (OrderedDict) : Initial state of the global model (which needs to be updated here) """ new_w = fedavg(new_ws) # For the first round: initialize old_w if old_w is None: old_w = new_w for key in new_w.keys(): old_w[key] = new_w[key] * (1 / (round_i + 1)) + old_w[key] * ( round_i / (round_i + 1) ) # for key in new_w.keys(): # if key == "classifier.fc.weight": # old_w[key] = new_w[key]*(1/(round_i+1)) + old_w[key]*(round_i/(round_i+1)) # else: # old_w[key] = new_w[key] return old_w

class BatmanQuotes(object): def get_quote(quotes, hero): return f"{("Batman", "Joker", "Robin")["BJR".index(hero[0])]}: {quotes[int(min(hero))]}"

class BatmanQuotes(object): def get_quote(quotes, hero): return f"{('Batman', 'Joker', 'Robin')['BJR'.index(hero[0])]}: {quotes[int(min(hero))]}"

# Concord # # Copyright (c) 2020-2021 VMware, Inc. All Rights Reserved. # # This product is licensed to you under the Apache 2.0 license (the "License"). # You may not use this product except in compliance with the Apache 2.0 License. # # This product may include a number of subcomponents with separate copyright # notices and license terms. Your use of these subcomponents is subject to the # terms and conditions of the subcomponent's license, as noted in the LICENSE # file. from visitor import Visitor def struct_start(name, id): return f""" struct {name} {{ static constexpr uint32_t id = {id}; """ serialize_byte_buffer_fn = "void serialize(std::vector<uint8_t>& output, const {name}& t)" def serialize_byte_buffer_declaration(name): return serialize_byte_buffer_fn.format(name=name) + ";\n" def serialize_byte_buffer_start(name): return serialize_byte_buffer_fn.format(name=name) + " {\n" serialize_string_fn = "void serialize(std::string& output, const {name}& t)" def serialize_string_declaration(name): return serialize_string_fn.format(name=name) + ";\n" def serialize_string_start(name): return serialize_string_fn.format(name=name) + " {\n" deserialize_fn = "void deserialize(const uint8_t*& input, const uint8_t* end, {name}& t)" def deserialize_declaration(name): return deserialize_fn.format(name=name) + ";\n" def deserialize_start(name): return deserialize_fn.format(name=name) + " {\n" deserialize_byte_buffer_fn = "void deserialize(const std::vector<uint8_t>& input, {name}& t)" def deserialize_byte_buffer_declaration(name): return deserialize_byte_buffer_fn.format(name=name) + ";\n" def deserialize_byte_buffer(name): return deserialize_byte_buffer_fn.format(name=name) + f""" {{ auto begin = input.data(); deserialize(begin, begin + input.size(), t); }} """ deserialize_string_fn = "void deserialize(const std::string& input, {name}& t)" def deserialize_string_declaration(name): return deserialize_string_fn.format(name=name) + ";\n" def deserialize_string(name): return deserialize_string_fn.format(name=name) + f""" {{ auto begin = reinterpret_cast<const unsigned char*>(input.data()); deserialize(begin, begin + input.size(), t); }} """ def serialize_field(name, type): # All messages except oneofs and messages exist in the cmf namespace, and are provided in # serialize.h if type in ["oneof", "msg"]: return f" serialize(output, t.{name});\n" return f" cmf::serialize(output, t.{name});\n" def deserialize_field(name, type): # All messages except oneofs and messages exist in the cmf namespace, and are provided in # serialize.h if type in ["oneof", "msg"]: return f" deserialize(input, end, t.{name});\n" return f" cmf::deserialize(input, end, t.{name});\n" variant_serialize_byte_buffer_fn = "void serialize(std::vector<uint8_t>& output, const {variant}& val)" def variant_serialize_byte_buffer_declaration(variant): return variant_serialize_byte_buffer_fn.format(variant=variant) + ";\n" def variant_serialize_byte_buffer(variant): return variant_serialize_byte_buffer_fn.format(variant=variant) + """ { std::visit([&output](auto&& arg){ cmf::serialize(output, arg.id); serialize(output, arg); }, val); }""" variant_serialize_string_fn = "void serialize(std::string& output, const {variant}& val)" def variant_serialize_string_declaration(variant): return variant_serialize_string_fn.format(variant=variant) + ";\n" def variant_serialize_string(variant): return variant_serialize_string_fn.format(variant=variant) + """ { std::visit([&output](auto&& arg){ cmf::serialize(output, arg.id); serialize(output, arg); }, val); }""" variant_deserialize_fn = "void deserialize(const uint8_t*& start, const uint8_t* end, {variant}& val)" def variant_deserialize_declaration(variant): return variant_deserialize_fn.format(variant=variant) + ";\n" def variant_deserialize(variant, msgs): s = variant_deserialize_fn.format(variant=variant) + """ { uint32_t id; cmf::deserialize(start, end, id); """ for (name, id) in msgs.items(): s += f""" if (id == {id}) {{ {name} value; deserialize(start, end, value); val = value; return; }} """ s += """ throw cmf::DeserializeError(std::string("Invalid Message id in variant: ") + std::to_string(id)); } """ return s equalop_str_fn = "bool operator==(const {msg_name}& l, const {msg_name}& r)" def equalop_str_declaration(msg_name): return equalop_str_fn.format(msg_name=msg_name) + ";\n" def equalop_str(msg_name, fields): """ Create an 'operator==' function for the current message struct """ comparison = "true" if fields: comparison = " && ".join([f"l.{f} == r.{f}" for f in fields]) return equalop_str_fn.format(msg_name=msg_name) + f""" {{ return {comparison}; }}""" class CppVisitor(Visitor): """ A visitor that generates C++ code. """ def __init__(self): # All output currently constructed self.output = "" # All output declarations currently constructed self.output_declaration = "" # The current message being processed self.msg_name = '' # The current field being processed for a message self.field = {'type': '', 'name': ''} # All fields currently seen for the given message self.fields_seen = [] # The struct being created for the current message. This includes the fields of the struct. self.struct = "" # The 'serialize' function for the current message self.serialize_byte_buffer = "" self.serialize_string = "" # The 'deserialize' function for the current message self.deserialize = "" # Each oneof in a message corresponds to a variant. Since we don't need duplicate # serialization functions, in case there are multiple messages or fields with the same # variants, we only generate a single serialization and deserialization function for each # variant. # # This set keeps track of which variants already had their serialization and # deserialization functions generated. self.oneofs_seen = set() # The `serialize` member functions for all oneofs in the current message self.oneof_serialize_byte_buffer = "" self.oneof_serialize_byte_buffer_declaration = "" self.oneof_serialize_string = "" self.oneof_serialize_string_declaration = "" # The `deserialize` member functions for all oneofs in the current message self.oneof_deserialize = "" self.oneof_deserialize_declaration = "" def _reset(self): # output and oneofs_seen accumulate across messages output = self.output output_declaration = self.output_declaration oneofs = self.oneofs_seen self.__init__() self.output = output self.output_declaration = output_declaration self.oneofs_seen = oneofs def create_enum(self, name, tags): enumstr = 'enum class {name} : uint8_t {{ {tagstr} }};\n' enumsize_decl = 'uint8_t enumSize({name} _);\n' enumsize_def = 'uint8_t enumSize({name} _) {{ (void)_; return {num_tags}; }}\n' self.output_declaration += enumstr.format(name=name, tagstr=", ".join(tags)) self.output_declaration += enumsize_decl.format(name=name) self.output += enumsize_def.format(name=name, num_tags = len(tags)) def msg_start(self, name, id): self.msg_name = name self.struct = struct_start(name, id) self.serialize_byte_buffer = serialize_byte_buffer_start(name) self.serialize_string = serialize_string_start(name) self.deserialize = deserialize_start(name) def msg_end(self): self.struct += "};\n" self.serialize_byte_buffer += "}" self.serialize_string += "}" self.deserialize += "}\n" self.deserialize += deserialize_byte_buffer(self.msg_name) self.deserialize += "\n" self.deserialize += deserialize_string(self.msg_name) self.output += "\n".join([ s for s in [ self.oneof_serialize_byte_buffer, self.oneof_serialize_string, self.oneof_deserialize, equalop_str(self.msg_name, self.fields_seen), self.serialize_byte_buffer, self.serialize_string, self.deserialize, ] if s != '' ]) + "\n" self.output_declaration += "".join([ s for s in [ self.struct, "\n", serialize_byte_buffer_declaration(self.msg_name), serialize_string_declaration(self.msg_name), deserialize_declaration(self.msg_name), deserialize_byte_buffer_declaration(self.msg_name), deserialize_string_declaration(self.msg_name), self.oneof_serialize_byte_buffer_declaration, self.oneof_serialize_string_declaration, self.oneof_deserialize_declaration, equalop_str_declaration(self.msg_name), ] if s != '' ]) + "\n" self._reset() def field_start(self, name, type): self.struct += " " # Indent fields self.field['name'] = name self.fields_seen.append(name) self.serialize_byte_buffer += serialize_field(name, type) self.serialize_string += serialize_field(name, type) self.deserialize += deserialize_field(name, type) def field_end(self): # The field is preceeded by the type in the struct definition. Close it with the name and # necessary syntax. self.struct += f" {self.field["name"]}{{}};\n" ### The following callbacks generate types for struct fields, recursively when necessary. def bool(self): self.struct += "bool" def uint8(self): self.struct += "uint8_t" def uint16(self): self.struct += "uint16_t" def uint32(self): self.struct += "uint32_t" def uint64(self): self.struct += "uint64_t" def int8(self): self.struct += "int8_t" def int16(self): self.struct += "int16_t" def int32(self): self.struct += "int32_t" def int64(self): self.struct += "int64_t" def string(self): self.struct += "std::string" def bytes(self): self.struct += "std::vector<uint8_t>" def msgname_ref(self, name): self.struct += name def kvpair_start(self): self.struct += "std::pair<" def kvpair_key_end(self): self.struct += ", " def kvpair_end(self): self.struct += ">" def list_start(self): self.struct += "std::vector<" def list_end(self): self.struct += ">" def fixedlist_start(self): self.struct += "std::array<" def fixedlist_type_end(self): self.struct += ", " def fixedlist_end(self, size): self.struct += f"{size}>" def map_start(self): self.struct += "std::map<" def map_key_end(self): self.struct += ", " def map_end(self): self.struct += ">" def optional_start(self): self.struct += "std::optional<" def optional_end(self): self.struct += ">" def oneof(self, msgs): variant = "std::variant<" + ", ".join(msgs.keys()) + ">" self.struct += variant oneof = frozenset(msgs.keys()) if oneof in self.oneofs_seen: return self.oneofs_seen.add(oneof) self.oneof_serialize_byte_buffer += variant_serialize_byte_buffer(variant) self.oneof_serialize_string += variant_serialize_string(variant) self.oneof_serialize_byte_buffer_declaration += \ variant_serialize_byte_buffer_declaration(variant) self.oneof_serialize_string_declaration += \ variant_serialize_string_declaration(variant) self.oneof_deserialize += variant_deserialize(variant, msgs) self.oneof_deserialize_declaration += variant_deserialize_declaration(variant) def enum(self, type_name): self.struct += type_name

# Concord # # Copyright (c) 2020-2021 VMware, Inc. All Rights Reserved. # # This product is licensed to you under the Apache 2.0 license (the "License"). # You may not use this product except in compliance with the Apache 2.0 License. # # This product may include a number of subcomponents with separate copyright # notices and license terms. Your use of these subcomponents is subject to the # terms and conditions of the subcomponent's license, as noted in the LICENSE # file. from visitor import Visitor def struct_start(name, id): return f""" struct {name} {{ static constexpr uint32_t id = {id}; """ serialize_byte_buffer_fn = "void serialize(std::vector<uint8_t>& output, const {name}& t)" def serialize_byte_buffer_declaration(name): return serialize_byte_buffer_fn.format(name=name) + ";\n" def serialize_byte_buffer_start(name): return serialize_byte_buffer_fn.format(name=name) + " {\n" serialize_string_fn = "void serialize(std::string& output, const {name}& t)" def serialize_string_declaration(name): return serialize_string_fn.format(name=name) + ";\n" def serialize_string_start(name): return serialize_string_fn.format(name=name) + " {\n" deserialize_fn = "void deserialize(const uint8_t*& input, const uint8_t* end, {name}& t)" def deserialize_declaration(name): return deserialize_fn.format(name=name) + ";\n" def deserialize_start(name): return deserialize_fn.format(name=name) + " {\n" deserialize_byte_buffer_fn = "void deserialize(const std::vector<uint8_t>& input, {name}& t)" def deserialize_byte_buffer_declaration(name): return deserialize_byte_buffer_fn.format(name=name) + ";\n" def deserialize_byte_buffer(name): return deserialize_byte_buffer_fn.format(name=name) + f""" {{ auto begin = input.data(); deserialize(begin, begin + input.size(), t); }} """ deserialize_string_fn = "void deserialize(const std::string& input, {name}& t)" def deserialize_string_declaration(name): return deserialize_string_fn.format(name=name) + ";\n" def deserialize_string(name): return deserialize_string_fn.format(name=name) + f""" {{ auto begin = reinterpret_cast<const unsigned char*>(input.data()); deserialize(begin, begin + input.size(), t); }} """ def serialize_field(name, type): # All messages except oneofs and messages exist in the cmf namespace, and are provided in # serialize.h if type in ["oneof", "msg"]: return f" serialize(output, t.{name});\n" return f" cmf::serialize(output, t.{name});\n" def deserialize_field(name, type): # All messages except oneofs and messages exist in the cmf namespace, and are provided in # serialize.h if type in ["oneof", "msg"]: return f" deserialize(input, end, t.{name});\n" return f" cmf::deserialize(input, end, t.{name});\n" variant_serialize_byte_buffer_fn = "void serialize(std::vector<uint8_t>& output, const {variant}& val)" def variant_serialize_byte_buffer_declaration(variant): return variant_serialize_byte_buffer_fn.format(variant=variant) + ";\n" def variant_serialize_byte_buffer(variant): return variant_serialize_byte_buffer_fn.format(variant=variant) + """ { std::visit([&output](auto&& arg){ cmf::serialize(output, arg.id); serialize(output, arg); }, val); }""" variant_serialize_string_fn = "void serialize(std::string& output, const {variant}& val)" def variant_serialize_string_declaration(variant): return variant_serialize_string_fn.format(variant=variant) + ";\n" def variant_serialize_string(variant): return variant_serialize_string_fn.format(variant=variant) + """ { std::visit([&output](auto&& arg){ cmf::serialize(output, arg.id); serialize(output, arg); }, val); }""" variant_deserialize_fn = "void deserialize(const uint8_t*& start, const uint8_t* end, {variant}& val)" def variant_deserialize_declaration(variant): return variant_deserialize_fn.format(variant=variant) + ";\n" def variant_deserialize(variant, msgs): s = variant_deserialize_fn.format(variant=variant) + """ { uint32_t id; cmf::deserialize(start, end, id); """ for (name, id) in msgs.items(): s += f""" if (id == {id}) {{ {name} value; deserialize(start, end, value); val = value; return; }} """ s += """ throw cmf::DeserializeError(std::string("Invalid Message id in variant: ") + std::to_string(id)); } """ return s equalop_str_fn = "bool operator==(const {msg_name}& l, const {msg_name}& r)" def equalop_str_declaration(msg_name): return equalop_str_fn.format(msg_name=msg_name) + ";\n" def equalop_str(msg_name, fields): """ Create an 'operator==' function for the current message struct """ comparison = "true" if fields: comparison = " && ".join([f"l.{f} == r.{f}" for f in fields]) return equalop_str_fn.format(msg_name=msg_name) + f""" {{ return {comparison}; }}""" class CppVisitor(Visitor): """ A visitor that generates C++ code. """ def __init__(self): # All output currently constructed self.output = "" # All output declarations currently constructed self.output_declaration = "" # The current message being processed self.msg_name = '' # The current field being processed for a message self.field = {'type': '', 'name': ''} # All fields currently seen for the given message self.fields_seen = [] # The struct being created for the current message. This includes the fields of the struct. self.struct = "" # The 'serialize' function for the current message self.serialize_byte_buffer = "" self.serialize_string = "" # The 'deserialize' function for the current message self.deserialize = "" # Each oneof in a message corresponds to a variant. Since we don't need duplicate # serialization functions, in case there are multiple messages or fields with the same # variants, we only generate a single serialization and deserialization function for each # variant. # # This set keeps track of which variants already had their serialization and # deserialization functions generated. self.oneofs_seen = set() # The `serialize` member functions for all oneofs in the current message self.oneof_serialize_byte_buffer = "" self.oneof_serialize_byte_buffer_declaration = "" self.oneof_serialize_string = "" self.oneof_serialize_string_declaration = "" # The `deserialize` member functions for all oneofs in the current message self.oneof_deserialize = "" self.oneof_deserialize_declaration = "" def _reset(self): # output and oneofs_seen accumulate across messages output = self.output output_declaration = self.output_declaration oneofs = self.oneofs_seen self.__init__() self.output = output self.output_declaration = output_declaration self.oneofs_seen = oneofs def create_enum(self, name, tags): enumstr = 'enum class {name} : uint8_t {{ {tagstr} }};\n' enumsize_decl = 'uint8_t enumSize({name} _);\n' enumsize_def = 'uint8_t enumSize({name} _) {{ (void)_; return {num_tags}; }}\n' self.output_declaration += enumstr.format(name=name, tagstr=", ".join(tags)) self.output_declaration += enumsize_decl.format(name=name) self.output += enumsize_def.format(name=name, num_tags = len(tags)) def msg_start(self, name, id): self.msg_name = name self.struct = struct_start(name, id) self.serialize_byte_buffer = serialize_byte_buffer_start(name) self.serialize_string = serialize_string_start(name) self.deserialize = deserialize_start(name) def msg_end(self): self.struct += "};\n" self.serialize_byte_buffer += "}" self.serialize_string += "}" self.deserialize += "}\n" self.deserialize += deserialize_byte_buffer(self.msg_name) self.deserialize += "\n" self.deserialize += deserialize_string(self.msg_name) self.output += "\n".join([ s for s in [ self.oneof_serialize_byte_buffer, self.oneof_serialize_string, self.oneof_deserialize, equalop_str(self.msg_name, self.fields_seen), self.serialize_byte_buffer, self.serialize_string, self.deserialize, ] if s != '' ]) + "\n" self.output_declaration += "".join([ s for s in [ self.struct, "\n", serialize_byte_buffer_declaration(self.msg_name), serialize_string_declaration(self.msg_name), deserialize_declaration(self.msg_name), deserialize_byte_buffer_declaration(self.msg_name), deserialize_string_declaration(self.msg_name), self.oneof_serialize_byte_buffer_declaration, self.oneof_serialize_string_declaration, self.oneof_deserialize_declaration, equalop_str_declaration(self.msg_name), ] if s != '' ]) + "\n" self._reset() def field_start(self, name, type): self.struct += " " # Indent fields self.field['name'] = name self.fields_seen.append(name) self.serialize_byte_buffer += serialize_field(name, type) self.serialize_string += serialize_field(name, type) self.deserialize += deserialize_field(name, type) def field_end(self): # The field is preceeded by the type in the struct definition. Close it with the name and # necessary syntax. self.struct += f" {self.field['name']}{{}};\n" ### The following callbacks generate types for struct fields, recursively when necessary. def bool(self): self.struct += "bool" def uint8(self): self.struct += "uint8_t" def uint16(self): self.struct += "uint16_t" def uint32(self): self.struct += "uint32_t" def uint64(self): self.struct += "uint64_t" def int8(self): self.struct += "int8_t" def int16(self): self.struct += "int16_t" def int32(self): self.struct += "int32_t" def int64(self): self.struct += "int64_t" def string(self): self.struct += "std::string" def bytes(self): self.struct += "std::vector<uint8_t>" def msgname_ref(self, name): self.struct += name def kvpair_start(self): self.struct += "std::pair<" def kvpair_key_end(self): self.struct += ", " def kvpair_end(self): self.struct += ">" def list_start(self): self.struct += "std::vector<" def list_end(self): self.struct += ">" def fixedlist_start(self): self.struct += "std::array<" def fixedlist_type_end(self): self.struct += ", " def fixedlist_end(self, size): self.struct += f"{size}>" def map_start(self): self.struct += "std::map<" def map_key_end(self): self.struct += ", " def map_end(self): self.struct += ">" def optional_start(self): self.struct += "std::optional<" def optional_end(self): self.struct += ">" def oneof(self, msgs): variant = "std::variant<" + ", ".join(msgs.keys()) + ">" self.struct += variant oneof = frozenset(msgs.keys()) if oneof in self.oneofs_seen: return self.oneofs_seen.add(oneof) self.oneof_serialize_byte_buffer += variant_serialize_byte_buffer(variant) self.oneof_serialize_string += variant_serialize_string(variant) self.oneof_serialize_byte_buffer_declaration += \ variant_serialize_byte_buffer_declaration(variant) self.oneof_serialize_string_declaration += \ variant_serialize_string_declaration(variant) self.oneof_deserialize += variant_deserialize(variant, msgs) self.oneof_deserialize_declaration += variant_deserialize_declaration(variant) def enum(self, type_name): self.struct += type_name

import os import dataset from stuf import stuf '''HELPER FUNCTIONS''' def init_local_db(local_db = os.path.expanduser(r'~/scripts/leavesdb.db'), src_db = r'/media/data_cifs/irodri15/data/db/leavesdb.db'): ''' Whenever working on a new machine, run this function in order to make sure the main leavesdb.db file is stored locally to avoid CIFS permissions issues. usage: init_local_db() ''' if not os.path.isfile(local_db): print(f'Copying sql db file from {src_db} to {local_db}') shutil.copyfile(src_db, local_db) print(f'Proceeding with sql db at location {local_db}') return local_db def __get_family_names_per_dataset(db): ''' Helper function that returns dataset_families, a list of tuples: [(,),(,),...] db = dataset.connect(f'sqlite:///{db_path}', row_type=stuf) dataset_families contains tuples of len == 2, where item 0 is a dataset name, and item 1 is a list of strings, one for each family name in the dataset. e.g. [('Fossil',['Adoxaceae', 'Anacardiaceae',...]), ('PNAS',['Apocynaceae','Betulaceae',...]), ...] ''' dataset_families = [] for dataset in db['dataset'].distinct('dataset'): dataset_name = dataset.dataset distinct_families = db['dataset'].distinct('family', dataset=dataset_name) dataset_families.append((dataset_name, [fam.family for fam in distinct_families])) return dataset_families def __get_num_families_per_dataset(db): ''' Helper function similar to __get_family_names_per_dataset, but instead of tuple containing (dataset_name, list(family names)), returns the total number of unique families for each dataset. Arguments: db : open connection to database e.g. db = dataset.connect(f'sqlite:///{db_path}', row_type=stuf) Return: num_families_per_dataset : list(tuples(str,int)) e.g. [('Fossil',27), ('PNAS',19), ...] ''' num_families_per_dataset = [] dataset_families = __get_family_names_per_dataset(db) for dataset in dataset_families: num_families_per_dataset.append((dataset[0], len(dataset[1]))) return num_families_per_dataset def summarize_db(db): ''' Combines helper functions to summarize key info about the data in opened database, db. ''' print('Database column keys:\n', db['dataset'].columns) print('Number of distinct families:\n', __get_num_families_per_dataset(db)) print(f"Number of rows in db:\n {len(db["dataset"])}")

import os import dataset from stuf import stuf '''HELPER FUNCTIONS''' def init_local_db(local_db = os.path.expanduser(r'~/scripts/leavesdb.db'), src_db = r'/media/data_cifs/irodri15/data/db/leavesdb.db'): ''' Whenever working on a new machine, run this function in order to make sure the main leavesdb.db file is stored locally to avoid CIFS permissions issues. usage: init_local_db() ''' if not os.path.isfile(local_db): print(f'Copying sql db file from {src_db} to {local_db}') shutil.copyfile(src_db, local_db) print(f'Proceeding with sql db at location {local_db}') return local_db def __get_family_names_per_dataset(db): ''' Helper function that returns dataset_families, a list of tuples: [(,),(,),...] db = dataset.connect(f'sqlite:///{db_path}', row_type=stuf) dataset_families contains tuples of len == 2, where item 0 is a dataset name, and item 1 is a list of strings, one for each family name in the dataset. e.g. [('Fossil',['Adoxaceae', 'Anacardiaceae',...]), ('PNAS',['Apocynaceae','Betulaceae',...]), ...] ''' dataset_families = [] for dataset in db['dataset'].distinct('dataset'): dataset_name = dataset.dataset distinct_families = db['dataset'].distinct('family', dataset=dataset_name) dataset_families.append((dataset_name, [fam.family for fam in distinct_families])) return dataset_families def __get_num_families_per_dataset(db): ''' Helper function similar to __get_family_names_per_dataset, but instead of tuple containing (dataset_name, list(family names)), returns the total number of unique families for each dataset. Arguments: db : open connection to database e.g. db = dataset.connect(f'sqlite:///{db_path}', row_type=stuf) Return: num_families_per_dataset : list(tuples(str,int)) e.g. [('Fossil',27), ('PNAS',19), ...] ''' num_families_per_dataset = [] dataset_families = __get_family_names_per_dataset(db) for dataset in dataset_families: num_families_per_dataset.append((dataset[0], len(dataset[1]))) return num_families_per_dataset def summarize_db(db): ''' Combines helper functions to summarize key info about the data in opened database, db. ''' print('Database column keys:\n', db['dataset'].columns) print('Number of distinct families:\n', __get_num_families_per_dataset(db)) print(f"Number of rows in db:\n {len(db['dataset'])}")

from functools import reduce import io import json import logging import os import platform import random import re import shlex import smtplib import string import subprocess import time import traceback import stat from copy import deepcopy from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from scipy.stats import tmean, scoreatpercentile from shutil import which, move, rmtree import hcl2 import requests import yaml import git from bs4 import BeautifulSoup from paramiko import SSHClient, AutoAddPolicy from paramiko.auth_handler import AuthenticationException, SSHException from semantic_version import Version from tempfile import NamedTemporaryFile, mkdtemp from ocs_ci.framework import config from ocs_ci.ocs import constants, defaults from ocs_ci.ocs.exceptions import ( CephHealthException, ClientDownloadError, CommandFailed, TagNotFoundException, TimeoutException, TimeoutExpiredError, UnavailableBuildException, UnexpectedImage, UnsupportedOSType, ) from ocs_ci.utility import version as version_module from ocs_ci.utility.flexy import load_cluster_info from ocs_ci.utility.retry import retry log = logging.getLogger(__name__) # variables mounting_dir = "/mnt/cephfs/" clients = [] md5sum_list1 = [] md5sum_list2 = [] fuse_clients = [] kernel_clients = [] mon_node = "" mon_node_ip = "" mds_nodes = [] md5sum_file_lock = [] active_mdss = [] RC = [] failure = {} output = [] unique_test_names = [] # function for getting the clients def get_client_info(ceph_nodes, clients): log.info("Getting Clients") for node in ceph_nodes: if node.role == "client": clients.append(node) # Identifying MON node for node in ceph_nodes: if node.role == "mon": mon_node = node out, err = mon_node.exec_command(cmd="sudo hostname -I") mon_node_ip = out.read().decode().rstrip("\n") break for node in ceph_nodes: if node.role == "mds": mds_nodes.append(node) for node in clients: node.exec_command(cmd="sudo yum install -y attr") fuse_clients = clients[0:2] # seperating clients for fuse and kernel kernel_clients = clients[2:4] return ( fuse_clients, kernel_clients, mon_node, mounting_dir, mds_nodes, md5sum_file_lock, mon_node_ip, ) # function for providing authorization to the clients from MON ndoe def auth_list(clients, mon_node): for node in clients: log.info("Giving required permissions for clients from MON node:") mon_node.exec_command( cmd="sudo ceph auth get-or-create client.%s mon 'allow *' mds 'allow *, allow rw path=/' " "osd 'allow rw pool=cephfs_data' -o /etc/ceph/ceph.client.%s.keyring" % (node.hostname, node.hostname) ) out, err = mon_node.exec_command( sudo=True, cmd="cat /etc/ceph/ceph.client.%s.keyring" % (node.hostname) ) keyring = out.read().decode() key_file = node.write_file( sudo=True, file_name="/etc/ceph/ceph.client.%s.keyring" % (node.hostname), file_mode="w", ) key_file.write(keyring) key_file.flush() node.exec_command( cmd="sudo chmod 644 /etc/ceph/ceph.client.%s.keyring" % (node.hostname) ) # creating mounting directory node.exec_command(cmd="sudo mkdir %s" % (mounting_dir)) # MOunting single FS with ceph-fuse def fuse_mount(fuse_clients, mounting_dir): try: for client in fuse_clients: log.info("Creating mounting dir:") log.info("Mounting fs with ceph-fuse on client %s:" % (client.hostname)) client.exec_command( cmd="sudo ceph-fuse -n client.%s %s" % (client.hostname, mounting_dir) ) out, err = client.exec_command(cmd="mount") mount_output = out.read().decode() mount_output.split() log.info("Checking if fuse mount is is passed of failed:") if "fuse" in mount_output: log.info("ceph-fuse mounting passed") else: log.error("ceph-fuse mounting failed") return md5sum_list1 except Exception as e: log.error(e) def kernel_mount(mounting_dir, mon_node_ip, kernel_clients): try: for client in kernel_clients: out, err = client.exec_command( cmd="sudo ceph auth get-key client.%s" % (client.hostname) ) secret_key = out.read().decode().rstrip("\n") mon_node_ip = mon_node_ip.replace(" ", "") client.exec_command( cmd="sudo mount -t ceph %s:6789:/ %s -o name=%s,secret=%s" % (mon_node_ip, mounting_dir, client.hostname, secret_key) ) out, err = client.exec_command(cmd="mount") mount_output = out.read().decode() mount_output.split() log.info("Checking if kernel mount is is passed of failed:") if "%s:6789:/" % (mon_node_ip) in mount_output: log.info("kernel mount passed") else: log.error("kernel mount failed") return md5sum_list2 except Exception as e: log.error(e) def fuse_client_io(client, mounting_dir): try: rand_count = random.randint(1, 5) rand_bs = random.randint(100, 300) log.info("Performing IOs on fuse-clients") client.exec_command( cmd="sudo dd if=/dev/zero of=%snewfile_%s bs=%dM count=%d" % (mounting_dir, client.hostname, rand_bs, rand_count), long_running=True, ) except Exception as e: log.error(e) def kernel_client_io(client, mounting_dir): try: rand_count = random.randint(1, 6) rand_bs = random.randint(100, 500) log.info("Performing IOs on kernel-clients") client.exec_command( cmd="sudo dd if=/dev/zero of=%snewfile_%s bs=%dM count=%d" % (mounting_dir, client.hostname, rand_bs, rand_count), long_running=True, ) except Exception as e: log.error(e) def fuse_client_md5(fuse_clients, md5sum_list1): try: log.info("Calculating MD5 sums of files in fuse-clients:") for client in fuse_clients: md5sum_list1.append( client.exec_command( cmd="sudo md5sum %s* | awk '{print $1}' " % (mounting_dir), long_running=True, ) ) except Exception as e: log.error(e) def kernel_client_md5(kernel_clients, md5sum_list2): try: log.info("Calculating MD5 sums of files in kernel-clients:") for client in kernel_clients: md5sum_list2.append( client.exec_command( cmd="sudo md5sum %s* | awk '{print $1}' " % (mounting_dir), long_running=True, ) ) except Exception as e: log.error(e) # checking file locking mechanism def file_locking(client): try: to_lock_file = """ import fcntl import subprocess import time try: f = open('/mnt/cephfs/to_test_file_lock', 'w+') fcntl.lockf(f, fcntl.LOCK_EX | fcntl.LOCK_NB) print "locking file:--------------------------------" subprocess.check_output(["sudo","dd","if=/dev/zero","of=/mnt/cephfs/to_test_file_lock","bs=1M","count=2"]) except IOError as e: print e finally: print "Unlocking file:------------------------------" fcntl.lockf(f,fcntl.LOCK_UN) """ to_lock_code = client.write_file( sudo=True, file_name="/home/cephuser/file_lock.py", file_mode="w" ) to_lock_code.write(to_lock_file) to_lock_code.flush() out, err = client.exec_command(cmd="sudo python /home/cephuser/file_lock.py") output = out.read().decode() output.split() if "Errno 11" in output: log.info("File locking achieved, data is not corrupted") elif "locking" in output: log.info("File locking achieved, data is not corrupted") else: log.error("Data is corrupted") out, err = client.exec_command( cmd="sudo md5sum %sto_test_file_lock | awk '{print $1}'" % (mounting_dir) ) md5sum_file_lock.append(out.read().decode()) except Exception as e: log.error(e) def activate_multiple_mdss(mds_nodes): try: log.info("Activating Multiple MDSs") for node in mds_nodes: out1, err = node.exec_command( cmd="sudo ceph fs set cephfs allow_multimds true --yes-i-really-mean-it" ) out2, err = node.exec_command(cmd="sudo ceph fs set cephfs max_mds 2") break except Exception as e: log.error(e) def mkdir_pinning(clients, range1, range2, dir_name, pin_val): try: log.info("Creating Directories and Pinning to MDS %s" % (pin_val)) for client in clients: for num in range(range1, range2): out, err = client.exec_command( cmd="sudo mkdir %s%s_%d" % (mounting_dir, dir_name, num) ) if pin_val != "": client.exec_command( cmd="sudo setfattr -n ceph.dir.pin -v %s %s%s_%d" % (pin_val, mounting_dir, dir_name, num) ) else: print("Pin val not given") print(out.read().decode()) print(time.time()) break except Exception as e: log.error(e) def allow_dir_fragmentation(mds_nodes): try: log.info("Allowing directorty fragmenation for splitting") for node in mds_nodes: node.exec_command(cmd="sudo ceph fs set cephfs allow_dirfrags 1") break except Exception as e: log.error(e) def mds_fail_over(mds_nodes): try: rand = random.randint(0, 1) for node in mds_nodes: log.info("Failing MDS %d" % (rand)) node.exec_command(cmd="sudo ceph mds fail %d" % (rand)) break except Exception as e: log.error(e) def pinned_dir_io(clients, mds_fail_over, num_of_files, range1, range2): try: log.info("Performing IOs and MDSfailovers on clients") for client in clients: client.exec_command(cmd="sudo pip install crefi") for num in range(range1, range2): if mds_fail_over != "": mds_fail_over(mds_nodes) out, err = client.exec_command( cmd="sudo crefi -n %d %sdir_%d" % (num_of_files, mounting_dir, num) ) rc = out.channel.recv_exit_status() print(out.read().decode()) RC.append(rc) print(time.time()) if rc == 0: log.info("Client IO is going on,success") else: log.error("Client IO got interrupted") failure.update({client: out}) break break except Exception as e: log.error(e) def custom_ceph_config(suite_config, custom_config, custom_config_file): """ Combines and returns custom configuration overrides for ceph. Hierarchy is as follows:: custom_config > custom_config_file > suite_config Args: suite_config: ceph_conf_overrides that currently exist in the test suite custom_config: custom config args provided by the cli (these all go to the global scope) custom_config_file: path to custom config yaml file provided by the cli Returns New value to be used for ceph_conf_overrides in test config """ log.debug("Suite config: {}".format(suite_config)) log.debug("Custom config: {}".format(custom_config)) log.debug("Custom config file: {}".format(custom_config_file)) full_custom_config = suite_config or {} cli_config_dict = {} custom_config_dict = {} # retrieve custom config from file if custom_config_file: with open(custom_config_file) as f: custom_config_dict = yaml.safe_load(f) log.info("File contents: {}".format(custom_config_dict)) # format cli configs into dict if custom_config: cli_config_dict = dict(item.split("=") for item in custom_config) # combine file and cli configs if cli_config_dict: if not custom_config_dict.get("global"): custom_config_dict["global"] = {} for key, value in cli_config_dict.items(): custom_config_dict["global"][key] = value # combine file and suite configs for key, value in custom_config_dict.items(): subsection = {} if full_custom_config.get(key): subsection.update(full_custom_config[key]) subsection.update(value) full_custom_config[key] = subsection log.info("Full custom config: {}".format(full_custom_config)) return full_custom_config def mask_secrets(plaintext, secrets): """ Replace secrets in plaintext with asterisks Args: plaintext (str or list): The plaintext to remove the secrets from or list of strings to remove secrets from secrets (list): List of secret strings to replace in the plaintext Returns: str: The censored version of plaintext """ if secrets: for secret in secrets: if isinstance(plaintext, list): plaintext = [string.replace(secret, "*" * 5) for string in plaintext] else: plaintext = plaintext.replace(secret, "*" * 5) return plaintext def run_cmd(cmd, secrets=None, timeout=600, ignore_error=False, **kwargs): """ *The deprecated form of exec_cmd.* Run an arbitrary command locally Args: cmd (str): command to run secrets (list): A list of secrets to be masked with asterisks This kwarg is popped in order to not interfere with subprocess.run(``**kwargs``) timeout (int): Timeout for the command, defaults to 600 seconds. ignore_error (bool): True if ignore non zero return code and do not raise the exception. Raises: CommandFailed: In case the command execution fails Returns: (str) Decoded stdout of command """ completed_process = exec_cmd(cmd, secrets, timeout, ignore_error, **kwargs) return mask_secrets(completed_process.stdout.decode(), secrets) def exec_cmd(cmd, secrets=None, timeout=600, ignore_error=False, **kwargs): """ Run an arbitrary command locally Args: cmd (str): command to run secrets (list): A list of secrets to be masked with asterisks This kwarg is popped in order to not interfere with subprocess.run(``**kwargs``) timeout (int): Timeout for the command, defaults to 600 seconds. ignore_error (bool): True if ignore non zero return code and do not raise the exception. Raises: CommandFailed: In case the command execution fails Returns: (CompletedProcess) A CompletedProcess object of the command that was executed CompletedProcess attributes: args: The list or str args passed to run(). returncode (str): The exit code of the process, negative for signals. stdout (str): The standard output (None if not captured). stderr (str): The standard error (None if not captured). """ masked_cmd = mask_secrets(cmd, secrets) log.info(f"Executing command: {masked_cmd}") if isinstance(cmd, str): cmd = shlex.split(cmd) completed_process = subprocess.run( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, timeout=timeout, **kwargs, ) masked_stdout = mask_secrets(completed_process.stdout.decode(), secrets) if len(completed_process.stdout) > 0: log.debug(f"Command stdout: {masked_stdout}") else: log.debug("Command stdout is empty") masked_stderr = mask_secrets(completed_process.stderr.decode(), secrets) if len(completed_process.stderr) > 0: log.warning(f"Command stderr: {masked_stderr}") else: log.debug("Command stderr is empty") log.debug(f"Command return code: {completed_process.returncode}") if completed_process.returncode and not ignore_error: raise CommandFailed( f"Error during execution of command: {masked_cmd}." f"\nError is {masked_stderr}" ) return completed_process def download_file(url, filename, **kwargs): """ Download a file from a specified url Args: url (str): URL of the file to download filename (str): Name of the file to write the download to kwargs (dict): additional keyword arguments passed to requests.get(...) """ log.debug(f"Download '{url}' to '{filename}'.") with open(filename, "wb") as f: r = requests.get(url, **kwargs) assert r.ok, f"The URL {url} is not available! Status: {r.status_code}." f.write(r.content) def get_url_content(url, **kwargs): """ Return URL content Args: url (str): URL address to return kwargs (dict): additional keyword arguments passed to requests.get(...) Returns: str: Content of URL Raises: AssertionError: When couldn't load URL """ log.debug(f"Download '{url}' content.") r = requests.get(url, **kwargs) assert r.ok, f"Couldn't load URL: {url} content! Status: {r.status_code}." return r.content def expose_ocp_version(version): """ This helper function exposes latest nightly version or GA version of OCP. When the version string ends with .nightly (e.g. 4.2.0-0.nightly) it will expose the version to latest accepted OCP build (e.g. 4.2.0-0.nightly-2019-08-08-103722) If the version ends with -ga than it will find the latest GA OCP version and will expose 4.2-ga to for example 4.2.22. Args: version (str): Verison of OCP Returns: str: Version of OCP exposed to full version if latest nighly passed """ if version.endswith(".nightly"): latest_nightly_url = ( f"https://amd64.ocp.releases.ci.openshift.org/api/v1/" f"releasestream/{version}/latest" ) version_url_content = get_url_content(latest_nightly_url) version_json = json.loads(version_url_content) return version_json["name"] if version.endswith("-ga"): channel = config.DEPLOYMENT.get("ocp_channel", "stable") ocp_version = version.rstrip("-ga") index = config.DEPLOYMENT.get("ocp_version_index", -1) return get_latest_ocp_version(f"{channel}-{ocp_version}", index) else: return version def get_openshift_installer( version=None, bin_dir=None, force_download=False, ): """ Download the OpenShift installer binary, if not already present. Update env. PATH and get path of the openshift installer binary. Args: version (str): Version of the installer to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force installer download even if already present Returns: str: Path to the installer binary """ version = version or config.DEPLOYMENT["installer_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) installer_filename = "openshift-install" installer_binary_path = os.path.join(bin_dir, installer_filename) if os.path.isfile(installer_binary_path) and force_download: delete_file(installer_binary_path) if os.path.isfile(installer_binary_path): log.debug(f"Installer exists ({installer_binary_path}), skipping download.") # TODO: check installer version else: version = expose_ocp_version(version) log.info(f"Downloading openshift installer ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) tarball = f"{installer_filename}.tar.gz" url = get_openshift_mirror_url(installer_filename, version) download_file(url, tarball) run_cmd(f"tar xzvf {tarball} {installer_filename}") delete_file(tarball) # return to the previous working directory os.chdir(previous_dir) installer_version = run_cmd(f"{installer_binary_path} version") log.info(f"OpenShift Installer version: {installer_version}") return installer_binary_path def get_ocm_cli( version=None, bin_dir=None, force_download=False, ): """ Download the OCM binary, if not already present. Update env. PATH and get path of the OCM binary. Args: version (str): Version of the OCM to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force OCM download even if already present Returns: str: Path to the OCM binary """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) ocm_filename = "ocm" ocm_binary_path = os.path.join(bin_dir, ocm_filename) if os.path.isfile(ocm_binary_path) and force_download: delete_file(ocm_binary_path) if os.path.isfile(ocm_binary_path): log.debug(f"ocm exists ({ocm_binary_path}), skipping download.") else: log.info(f"Downloading ocm cli ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://github.com/openshift-online/ocm-cli/releases/download/v{version}/ocm-linux-amd64" download_file(url, ocm_filename) # return to the previous working directory os.chdir(previous_dir) current_file_permissions = os.stat(ocm_binary_path) os.chmod( ocm_binary_path, current_file_permissions.st_mode | stat.S_IEXEC, ) ocm_version = run_cmd(f"{ocm_binary_path} version") log.info(f"OCM version: {ocm_version}") return ocm_binary_path def get_rosa_cli( version=None, bin_dir=None, force_download=False, ): """ Download the ROSA binary, if not already present. Update env. PATH and get path of the ROSA binary. Args: version (str): Version of the ROSA to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force ROSA download even if already present Returns: str: Path to the rosa binary """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) rosa_filename = "rosa" rosa_binary_path = os.path.join(bin_dir, rosa_filename) if os.path.isfile(rosa_binary_path) and force_download: delete_file(rosa_binary_path) if os.path.isfile(rosa_binary_path): log.debug(f"rosa exists ({rosa_binary_path}), skipping download.") else: log.info(f"Downloading rosa cli ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://github.com/openshift/rosa/releases/download/v{version}/rosa-linux-amd64" download_file(url, rosa_filename) # return to the previous working directory os.chdir(previous_dir) current_file_permissions = os.stat(rosa_binary_path) os.chmod( rosa_binary_path, current_file_permissions.st_mode | stat.S_IEXEC, ) rosa_version = run_cmd(f"{rosa_binary_path} version") log.info(f"rosa version: {rosa_version}") return rosa_binary_path def get_openshift_client( version=None, bin_dir=None, force_download=False, skip_comparison=False ): """ Download the OpenShift client binary, if not already present. Update env. PATH and get path of the oc binary. Args: version (str): Version of the client to download (default: config.RUN['client_version']) bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force client download even if already present skip_comparison (bool): Skip the comparison between the existing OCP client version and the configured one. Returns: str: Path to the client binary """ version = version or config.RUN["client_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) client_binary_path = os.path.join(bin_dir, "oc") kubectl_binary_path = os.path.join(bin_dir, "kubectl") download_client = True client_version = None try: version = expose_ocp_version(version) except Exception: log.exception("Unable to expose OCP version, skipping client download.") skip_comparison = True download_client = False force_download = False if force_download: log.info("Forcing client download.") elif os.path.isfile(client_binary_path) and not skip_comparison: current_client_version = get_client_version(client_binary_path) if current_client_version != version: log.info( f"Existing client version ({current_client_version}) does not match " f"configured version ({version})." ) else: log.debug( f"Client exists ({client_binary_path}) and matches configured version, " f"skipping download." ) download_client = False if download_client: # Move existing client binaries to backup location client_binary_backup = f"{client_binary_path}.bak" kubectl_binary_backup = f"{kubectl_binary_path}.bak" try: os.rename(client_binary_path, client_binary_backup) os.rename(kubectl_binary_path, kubectl_binary_backup) except FileNotFoundError: pass # Download the client log.info(f"Downloading openshift client ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = get_openshift_mirror_url("openshift-client", version) tarball = "openshift-client.tar.gz" download_file(url, tarball) run_cmd(f"tar xzvf {tarball} oc kubectl") delete_file(tarball) try: client_version = run_cmd(f"{client_binary_path} version --client") except CommandFailed: log.error("Unable to get version from downloaded client.") if client_version: try: delete_file(client_binary_backup) delete_file(kubectl_binary_backup) log.info("Deleted backup binaries.") except FileNotFoundError: pass else: try: os.rename(client_binary_backup, client_binary_path) os.rename(kubectl_binary_backup, kubectl_binary_path) log.info("Restored backup binaries to their original location.") except FileNotFoundError: raise ClientDownloadError( "No backups exist and new binary was unable to be verified." ) # return to the previous working directory os.chdir(previous_dir) log.info(f"OpenShift Client version: {client_version}") return client_binary_path def get_vault_cli(bind_dir=None, force_download=False): """ Download vault based on platform basically for CLI purpose. Binary will be directly put into ocs_ci/bin/ directory Args: bind_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force vault cli download even if already present """ res = requests.get(constants.VAULT_VERSION_INFO_URL) version = res.url.split("/")[-1].lstrip("v") bin_dir = os.path.expanduser(bind_dir or config.RUN["bin_dir"]) system = platform.system() if "Darwin" not in system and "Linux" not in system: raise UnsupportedOSType("Not a supported platform for vault") system = system.lower() zip_file = f"vault_{version}_{system}_amd64.zip" vault_cli_filename = "vault" vault_binary_path = os.path.join(bin_dir, vault_cli_filename) if os.path.isfile(vault_binary_path) and force_download: delete_file(vault_binary_path) if os.path.isfile(vault_binary_path): log.debug( f"Vault CLI binary already exists {vault_binary_path}, skipping download." ) else: log.info(f"Downloading vault cli {version}") prepare_bin_dir() previous_dir = os.getcwd() os.chdir(bin_dir) url = f"{constants.VAULT_DOWNLOAD_BASE_URL}/{version}/{zip_file}" download_file(url, zip_file) run_cmd(f"unzip {zip_file}") delete_file(zip_file) os.chdir(previous_dir) vault_ver = run_cmd(f"{vault_binary_path} version") log.info(f"Vault cli version:{vault_ver}") def ensure_nightly_build_availability(build_url): base_build_url = build_url.rsplit("/", 1)[0] r = requests.get(base_build_url) extracting_condition = b"Extracting" in r.content if extracting_condition: log.info("Build is extracting now, may take up to a minute.") return r.ok and not extracting_condition def get_openshift_mirror_url(file_name, version): """ Format url to OpenShift mirror (for client and installer download). Args: file_name (str): Name of file version (str): Version of the installer or client to download Returns: str: Url of the desired file (installer or client) Raises: UnsupportedOSType: In case the OS type is not supported UnavailableBuildException: In case the build url is not reachable """ if platform.system() == "Darwin": os_type = "mac" elif platform.system() == "Linux": os_type = "linux" else: raise UnsupportedOSType url_template = config.DEPLOYMENT.get( "ocp_url_template", "https://openshift-release-artifacts.apps.ci.l2s4.p1.openshiftapps.com/" "{version}/{file_name}-{os_type}-{version}.tar.gz", ) url = url_template.format( version=version, file_name=file_name, os_type=os_type, ) sample = TimeoutSampler( timeout=540, sleep=5, func=ensure_nightly_build_availability, build_url=url, ) if not sample.wait_for_func_status(result=True): raise UnavailableBuildException(f"The build url {url} is not reachable") return url def prepare_bin_dir(bin_dir=None): """ Prepare bin directory for OpenShift client and installer Args: bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) try: os.mkdir(bin_dir) log.info(f"Directory '{bin_dir}' successfully created.") except FileExistsError: log.debug(f"Directory '{bin_dir}' already exists.") def add_path_to_env_path(path): """ Add path to the PATH environment variable (if not already there). Args: path (str): Path which should be added to the PATH env. variable """ env_path = os.environ["PATH"].split(os.pathsep) if path not in env_path: os.environ["PATH"] = os.pathsep.join([path] + env_path) log.info(f"Path '{path}' added to the PATH environment variable.") log.debug(f"PATH: {os.environ["PATH"]}") def delete_file(file_name): """ Delete file_name Args: file_name (str): Path to the file you want to delete """ os.remove(file_name) def delete_dir(dir_name): """ Deletes the directory Args: dir_name (str): Directory path to delete """ try: rmtree(dir_name) except OSError as e: log.error(f"Failed to delete the directory {dir_name}. Error: {e.strerror}") class TimeoutSampler(object): """ Samples the function output. This is a generator object that at first yields the output of function `func`. After the yield, it either raises instance of `timeout_exc_cls` or sleeps `sleep` seconds. Yielding the output allows you to handle every value as you wish. Feel free to set the instance variables. Args: timeout (int): Timeout in seconds sleep (int): Sleep interval in seconds func (function): The function to sample func_args: Arguments for the function func_kwargs: Keyword arguments for the function """ def __init__(self, timeout, sleep, func, *func_args, **func_kwargs): self.timeout = timeout self.sleep = sleep # check that given timeout and sleep values makes sense if self.timeout < self.sleep: raise ValueError("timeout should be larger than sleep time") self.func = func self.func_args = func_args self.func_kwargs = func_kwargs # Timestamps of the first and most recent samples self.start_time = None self.last_sample_time = None # The exception to raise self.timeout_exc_cls = TimeoutExpiredError # Arguments that will be passed to the exception self.timeout_exc_args = [self.timeout] try: self.timeout_exc_args.append( f"Timed out after {timeout}s running {self._build_call_string()}" ) except Exception: log.exception( "Failed to assemble call string. Not necessarily a test failure." ) def _build_call_string(self): def stringify(value): if isinstance(value, str): return f'"{value}"' return str(value) args = list(map(stringify, self.func_args)) kwargs = [f"{stringify(k)}={stringify(v)}" for k, v in self.func_kwargs.items()] all_args_string = ", ".join(args + kwargs) return f"{self.func.__name__}({all_args_string})" def __iter__(self): if self.start_time is None: self.start_time = time.time() while True: self.last_sample_time = time.time() if self.timeout <= (self.last_sample_time - self.start_time): raise self.timeout_exc_cls(*self.timeout_exc_args) try: yield self.func(*self.func_args, **self.func_kwargs) except Exception as ex: msg = f"Exception raised during iteration: {ex}" log.exception(msg) if self.timeout <= (time.time() - self.start_time): raise self.timeout_exc_cls(*self.timeout_exc_args) log.info("Going to sleep for %d seconds before next iteration", self.sleep) time.sleep(self.sleep) def wait_for_func_value(self, value): """ Implements common usecase of TimeoutSampler: waiting until func (given function) returns a given value. Args: value: Expected return value of func we are waiting for. """ try: for i_value in self: if i_value == value: break except self.timeout_exc_cls: log.error( "function %s failed to return expected value %s " "after multiple retries during %d second timeout", self.func.__name__, value, self.timeout, ) raise def wait_for_func_status(self, result): """ Get function and run it for given time until success or timeout. (using __iter__ function) Args: result (bool): Expected result from func. Examples:: sample = TimeoutSampler( timeout=60, sleep=1, func=some_func, func_arg1="1", func_arg2="2" ) if not sample.wait_for_func_status(result=True): raise Exception """ try: self.wait_for_func_value(result) return True except self.timeout_exc_cls: return False class TimeoutIterator(TimeoutSampler): """ Wrapper of TimeoutSampler which separates parameters of the class itself and func arguments in __init__ method. Such way of passing function with parameters is used in python standard library. This allows more explicit usage, which improves readability, eg.:: t1 = TimeoutIterator(timeout=60, sleep=5, func=foo, func_args=[bar]) t2 = TimeoutIterator(3600, sleep=10, func=foo, func_args=[bar]) """ def __init__(self, timeout, sleep, func, func_args=None, func_kwargs=None): if func_args is None: func_args = [] if func_kwargs is None: func_kwargs = {} super().__init__(timeout, sleep, func, *func_args, **func_kwargs) def get_random_str(size=13): """ generates the random string of given size Args: size (int): number of random characters to generate Returns: str : string of random characters of given size """ chars = string.ascii_lowercase + string.digits return "".join(random.choice(chars) for _ in range(size)) def run_async(command): """ Run command locally and return without waiting for completion Args: command (str): The command to run. Returns: An open descriptor to be used by the calling function. Example: command = 'oc delete pvc pvc1' proc = run_async(command) ret, out, err = proc.async_communicate() """ log.info(f"Executing command: {command}") popen_obj = subprocess.Popen( command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True, encoding="utf-8", ) def async_communicate(): """ Wait for command to complete and fetch the result Returns: retcode, stdout, stderr of the command """ stdout, stderr = popen_obj.communicate() retcode = popen_obj.returncode return retcode, stdout, stderr popen_obj.async_communicate = async_communicate return popen_obj def is_cluster_running(cluster_path): from ocs_ci.ocs.openshift_ops import OCP return config.RUN["cli_params"].get("cluster_path") and OCP.set_kubeconfig( os.path.join(cluster_path, config.RUN.get("kubeconfig_location")) ) def decompose_html_attributes(soup, attributes): """ Decomposes the given html attributes Args: soup (obj): BeautifulSoup object attributes (list): attributes to decompose Returns: None """ for attribute in attributes: tg = soup.find_all(attrs={"class": attribute}) for each in tg: each.decompose() def parse_html_for_email(soup): """ Parses the html and filters out the unnecessary data/tags/attributes for email reporting Args: soup (obj): BeautifulSoup object """ attributes_to_decompose = ["extra"] if not config.RUN.get("logs_url"): attributes_to_decompose.append("col-links") decompose_html_attributes(soup, attributes_to_decompose) soup.find(id="not-found-message").decompose() if not config.RUN.get("logs_url"): for tr in soup.find_all("tr"): for th in tr.find_all("th"): if "Links" in th.text: th.decompose() for p in soup.find_all("p"): if "(Un)check the boxes to filter the results." in p.text: p.decompose() if "pytest-html" in p.text: data = p.text.split("by")[0] p.string = data for ip in soup.find_all("input"): if not ip.has_attr("disabled"): ip["disabled"] = "true" for td in soup.find_all("td"): if "pytest" in td.text or "html" in td.text: data = td.text.replace("&apos", "") td.string = data main_header = soup.find("h1") main_header.string.replace_with("OCS-CI RESULTS") def add_squad_analysis_to_email(session, soup): """ Add squad analysis to the html test results used in email reporting Args: session (obj): Pytest session object soup (obj): BeautifulSoup object of HTML Report data """ failed = {} skipped = {} # sort out failed and skipped test cases to failed and skipped dicts for result in session.results.values(): if result.failed or result.skipped: unassigned = True for squad, res in constants.SQUADS.items(): for item in res: if item in result.nodeid: if result.failed: if squad not in failed: failed[squad] = [] failed[squad].append(result.nodeid) unassigned = False if result.skipped: if squad not in skipped: skipped[squad] = [] try: skipped_message = result.longrepr[2][8:] except TypeError: skipped_message = "--unknown--" skipped[squad].append((result.nodeid, skipped_message)) unassigned = False if unassigned: if result.failed: if "UNASSIGNED" not in failed: failed["UNASSIGNED"] = [] failed["UNASSIGNED"].append(result.nodeid) if result.skipped: if "UNASSIGNED" not in skipped: skipped["UNASSIGNED"] = [] try: skipped_message = result.longrepr[2][8:] except TypeError: skipped_message = "--unknown--" skipped["UNASSIGNED"].append((result.nodeid, skipped_message)) # no failed or skipped tests - exit the function if not failed and not skipped: return # add CSS for the Squad Analysis report style = soup.find("style") # use colors for squad names from squad names style.string += "\n".join( [ f"h4.squad-{color.lower()} {{\n color: {color.lower()};\n}}" for color in constants.SQUADS ] ) # few additional styles style.string += """ .squad-analysis { color: black; font-family: monospace; background-color: #eee; padding: 5px; margin-top: 10px; } .squad-analysis h2 { margin: 0px; } .squad-analysis h3 { margin: 0px; margin-top: 10px; } .squad-analysis h4 { margin: 0px; } .squad-analysis ul { margin: 0px; } .squad-analysis ul li em { margin-left: 1em; } .squad-unassigned { background-color: #FFBA88; } h4.squad-yellow { color: black; background-color: yellow; display: inline; } """ # prepare place for the Squad Analysis in the email squad_analysis_div = soup.new_tag("div") squad_analysis_div["class"] = "squad-analysis" main_header = soup.find("h1") main_header.insert_after(squad_analysis_div) failed_h2_tag = soup.new_tag("h2") failed_h2_tag.string = "Squad Analysis - please analyze:" squad_analysis_div.append(failed_h2_tag) if failed: # print failed testcases peer squad failed_div_tag = soup.new_tag("div") squad_analysis_div.append(failed_div_tag) failed_h3_tag = soup.new_tag("h3") failed_h3_tag.string = "Failures:" failed_div_tag.append(failed_h3_tag) for squad in failed: failed_h4_tag = soup.new_tag("h4") failed_h4_tag.string = f"{squad} squad" failed_h4_tag["class"] = f"squad-{squad.lower()}" failed_div_tag.append(failed_h4_tag) failed_ul_tag = soup.new_tag("ul") failed_ul_tag["class"] = f"squad-{squad.lower()}" failed_div_tag.append(failed_ul_tag) for test in failed[squad]: failed_li_tag = soup.new_tag("li") failed_li_tag.string = test failed_ul_tag.append(failed_li_tag) if skipped: # print skipped testcases with reason peer squad skips_div_tag = soup.new_tag("div") squad_analysis_div.append(skips_div_tag) skips_h3_tag = soup.new_tag("h3") skips_h3_tag.string = "Skips:" skips_div_tag.append(skips_h3_tag) for squad in skipped: skips_h4_tag = soup.new_tag("h4") skips_h4_tag.string = f"{squad} squad" skips_h4_tag["class"] = f"squad-{squad.lower()}" skips_div_tag.append(skips_h4_tag) skips_ul_tag = soup.new_tag("ul") skips_ul_tag["class"] = f"squad-{squad.lower()}" skips_div_tag.append(skips_ul_tag) for test in skipped[squad]: skips_li_tag = soup.new_tag("li") skips_test_span_tag = soup.new_tag("span") skips_test_span_tag.string = test[0] skips_li_tag.append(skips_test_span_tag) skips_li_tag.append(soup.new_tag("br")) skips_reason_em_tag = soup.new_tag("em") skips_reason_em_tag.string = f"Reason: {test[1]}" skips_li_tag.append(skips_reason_em_tag) skips_ul_tag.append(skips_li_tag) def move_summary_to_top(soup): """ Move summary to the top of the eamil report """ summary = [] summary.append(soup.find("h2", text="Summary")) for tag in summary[0].next_siblings: if tag.name == "h2": break else: summary.append(tag) for tag in summary: tag.extract() main_header = soup.find("h1") # because we are inserting the tags just after the header one by one, we # have to insert them in reverse order summary.reverse() for tag in summary: main_header.insert_after(tag) def email_reports(session): """ Email results of test run """ # calculate percentage pass # reporter = session.config.pluginmanager.get_plugin("terminalreporter") # passed = len(reporter.stats.get("passed", [])) # failed = len(reporter.stats.get("failed", [])) # error = len(reporter.stats.get("error", [])) # total = passed + failed + error # percentage_passed = (passed / total) * 100 try: build_id = get_ocs_build_number() except Exception: build_id = "" log.exception("Getting OCS operator build number failed!") build_str = f"BUILD ID: {build_id} " if build_id else "" mailids = config.RUN["cli_params"]["email"] recipients = [] [recipients.append(mailid) for mailid in mailids.split(",")] sender = "ocs-ci@redhat.com" msg = MIMEMultipart("alternative") msg["Subject"] = ( f"ocs-ci results for {get_testrun_name()} " f"({build_str}" f"RUN ID: {config.RUN["run_id"]}) " # f"Passed: {percentage_passed:.0f}%" ) msg["From"] = sender msg["To"] = ", ".join(recipients) html = config.RUN["cli_params"]["--html"] with open(os.path.expanduser(html)) as fd: html_data = fd.read() soup = BeautifulSoup(html_data, "html.parser") parse_html_for_email(soup) if config.RUN["cli_params"].get("squad_analysis"): add_squad_analysis_to_email(session, soup) move_summary_to_top(soup) part1 = MIMEText(soup, "html") msg.attach(part1) try: s = smtplib.SMTP(config.REPORTING["email"]["smtp_server"]) s.sendmail(sender, recipients, msg.as_string()) s.quit() log.info(f"Results have been emailed to {recipients}") except Exception: log.exception("Sending email with results failed!") def get_cluster_version_info(): """ Gets the complete cluster version information Returns: dict: cluster version information """ # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="clusterversion") cluster_version_info = ocp.get("version") return cluster_version_info def get_ocs_build_number(): """ Gets the build number for ocs operator Return: str: build number for ocs operator version """ # Importing here to avoid circular dependency from ocs_ci.ocs.resources.csv import get_csvs_start_with_prefix from ocs_ci.ocs.resources.catalog_source import CatalogSource from ocs_ci.ocs.resources.packagemanifest import get_selector_for_ocs_operator build_num = "" if ( version_module.get_semantic_ocs_version_from_config() >= version_module.VERSION_4_9 ): operator_name = defaults.ODF_OPERATOR_NAME else: operator_name = defaults.OCS_OPERATOR_NAME ocs_csvs = get_csvs_start_with_prefix( operator_name, defaults.ROOK_CLUSTER_NAMESPACE, ) try: ocs_csv = ocs_csvs[0] csv_labels = ocs_csv["metadata"]["labels"] if "full_version" in csv_labels: return csv_labels["full_version"] build_num = ocs_csv["spec"]["version"] operator_selector = get_selector_for_ocs_operator() # This is a temporary solution how to get the build id from the registry image. # Because we are now missing build ID in the CSV. If catalog source with our # internal label exists, we will be getting build id from the tag of the image # in catalog source. Boris is working on better way how to populate the internal # build version in the CSV. if operator_selector: catalog_source = CatalogSource( resource_name=constants.OPERATOR_CATALOG_SOURCE_NAME, namespace=constants.MARKETPLACE_NAMESPACE, selector=operator_selector, ) cs_data = catalog_source.get()["items"][0] cs_image = cs_data["spec"]["image"] image_tag = cs_image.split(":")[1] if "-" in image_tag: build_id = image_tag.split("-")[1] build_num += f"-{build_id}" except (IndexError, AttributeError, CommandFailed, KeyError): log.exception("No version info found for OCS operator") return build_num def get_cluster_version(): """ Gets the cluster version Returns: str: cluster version """ return get_cluster_version_info()["status"]["desired"]["version"] def get_cluster_image(): """ Gets the cluster image Returns: str: cluster image """ return get_cluster_version_info()["status"]["desired"]["image"] def get_ceph_version(): """ Gets the ceph version Returns: str: ceph version """ # importing here to avoid circular imports from ocs_ci.ocs.resources import pod ct_pod = pod.get_ceph_tools_pod() ceph_version = ct_pod.exec_ceph_cmd("ceph version") return re.split(r"ceph version ", ceph_version["version"])[1] def get_rook_version(): """ Gets the rook version Returns: str: rook version """ # importing here to avoid circular imports from ocs_ci.ocs.resources import pod ct_pod = pod.get_ceph_tools_pod() rook_versions = ct_pod.exec_ceph_cmd("rook version", format="") return rook_versions["rook"] def get_csi_versions(): """ Gets the CSI related version information Returns: dict: CSI related version information """ csi_versions = {} # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp_pod_obj = OCP( kind=constants.POD, namespace=config.ENV_DATA["cluster_namespace"] ) csi_provisioners = ["csi-cephfsplugin-provisioner", "csi-rbdplugin-provisioner"] for provisioner in csi_provisioners: csi_provisioner_pod = run_cmd( f"oc -n {config.ENV_DATA["cluster_namespace"]} get pod -l " f"'app={provisioner}' -o jsonpath='{{.items[0].metadata.name}}'" ) desc = ocp_pod_obj.get(csi_provisioner_pod) for container in desc["spec"]["containers"]: name = container["name"] version = container["image"].split("/")[-1].split(":")[1] csi_versions[name] = version return csi_versions def get_ocp_version(seperator=None): """ Get current ocp version Args: seperator (str): String that would seperate major and minor version nubers Returns: string : If seperator is 'None', version string will be returned as is eg: '4.2', '4.3'. If seperator is provided then '.' in the version string would be replaced by seperator and resulting string will be returned. eg: If seperator is '_' then string returned would be '4_2' """ char = seperator if seperator else "." if config.ENV_DATA.get("skip_ocp_deployment"): raw_version = json.loads(run_cmd("oc version -o json"))["openshiftVersion"] else: raw_version = config.DEPLOYMENT["installer_version"] version = Version.coerce(raw_version) return char.join([str(version.major), str(version.minor)]) def get_running_ocp_version(separator=None): """ Get current running ocp version Args: separator (str): String that would separate major and minor version numbers Returns: string : If separator is 'None', version string will be returned as is eg: '4.2', '4.3'. If separator is provided then '.' in the version string would be replaced by separator and resulting string will be returned. eg: If separator is '_' then string returned would be '4_2' """ char = separator if separator else "." namespace = config.ENV_DATA["cluster_namespace"] try: # if the cluster exist, this part will be run results = run_cmd(f"oc get clusterversion -n {namespace} -o yaml") build = yaml.safe_load(results)["items"][0]["status"]["desired"]["version"] return char.join(build.split(".")[0:2]) except Exception: # this part will return version from the config file in case # cluster is not exists. return get_ocp_version(seperator=char) def get_ocp_repo(): """ Get ocp repo file, name will be generated dynamically based on ocp version. Returns: string : Path to ocp repo file """ repo_path = os.path.join(constants.REPO_DIR, f"ocp_{get_ocp_version("_")}.repo") path = os.path.expanduser(repo_path) assert os.path.exists(path), f"OCP repo file {path} doesn't exists!" return path def parse_pgsql_logs(data): """ Parse the pgsql benchmark data from ripsaw and return the data in list format Args: data (str): log data from pgsql bench run Returns: list_data (list): data digestable by scripts with below format e.g.: [ {1: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, {2: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, {3: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, ] where keys{1,2,3} are run-IDs """ match = data.split("PGBench Results") list_data = [] for i in range(2, len(match)): log = "".join(match[i].split("\n")) pgsql_data = dict() pgsql_data[i - 1] = {} clients = re.search(r"scaling_factor\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["scaling_factor"] = clients.group(1) clients = re.search(r"number_of_clients\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["num_clients"] = clients.group(1) threads = re.search(r"number_of_threads\':\s+(\d+)", log) if threads and threads.group(1): pgsql_data[i - 1]["num_threads"] = threads.group(1) clients = re.search(r"number_of_transactions_per_client\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["number_of_transactions_per_client"] = clients.group(1) clients = re.search( r"number_of_transactions_actually_processed\':\s+(\d+),", log ) if clients and clients.group(1): pgsql_data[i - 1][ "number_of_transactions_actually_processed" ] = clients.group(1) lat_avg = re.search(r"latency_average_ms\':\s+(\d+)", log) if lat_avg and lat_avg.group(1): pgsql_data[i - 1]["latency_avg"] = lat_avg.group(1) lat_stddev = re.search(r"latency_stddev_ms\':\s+(\d+)", log) if lat_stddev and lat_stddev.group(1): pgsql_data[i - 1]["lat_stddev"] = lat_stddev.group(1) tps_incl = re.search(r"tps_incl_con_est\':\s+(\w+)", log) if tps_incl and tps_incl.group(1): pgsql_data[i - 1]["tps_incl"] = tps_incl.group(1) tps_excl = re.search(r"tps_excl_con_est\':\s+(\w+)", log) if tps_excl and tps_excl.group(1): pgsql_data[i - 1]["tps_excl"] = tps_excl.group(1) list_data.append(pgsql_data) return list_data def create_directory_path(path): """ Creates directory if path doesn't exists """ path = os.path.expanduser(path) if not os.path.exists(path): os.makedirs(path) else: log.debug(f"{path} already exists") def ocsci_log_path(): """ Construct the full path for the log directory. Returns: str: full path for ocs-ci log directory """ return os.path.expanduser( os.path.join(config.RUN["log_dir"], f"ocs-ci-logs-{config.RUN["run_id"]}") ) def get_testrun_name(): """ Prepare testrun ID for Polarion (and other reports). Returns: str: String containing testrun name """ markers = config.RUN["cli_params"].get("-m", "").replace(" ", "-") us_ds = config.REPORTING.get("us_ds") if us_ds.upper() == "US": us_ds = "Upstream" elif us_ds.upper() == "DS": us_ds = "Downstream" ocp_version = ".".join(config.DEPLOYMENT.get("installer_version").split(".")[:-2]) ocp_version_string = f"OCP{ocp_version}" if ocp_version else "" ocs_version = config.ENV_DATA.get("ocs_version") ocs_version_string = f"OCS{ocs_version}" if ocs_version else "" worker_os = "RHEL" if config.ENV_DATA.get("rhel_workers") else "RHCOS" build_user = None baremetal_config = None if config.ENV_DATA.get("mon_type"): baremetal_config = ( f"MON {config.ENV_DATA.get("mon_type").upper()} " f"OSD {config.ENV_DATA.get("osd_type").upper()}" ) lso_deployment = "" if not baremetal_config and config.DEPLOYMENT.get("local_storage"): lso_deployment = "LSO " if config.REPORTING.get("display_name"): testrun_name = config.REPORTING.get("display_name") else: build_user = config.REPORTING.get("build_user") testrun_name = ( f"{config.ENV_DATA.get("platform", "").upper()} " f"{config.ENV_DATA.get("deployment_type", "").upper()} " ) if baremetal_config: testrun_name = f"LSO {baremetal_config} {testrun_name}" testrun_name = ( f"{testrun_name}" f"{get_az_count()}AZ " f"{worker_os} " f"{lso_deployment}" f"{config.ENV_DATA.get("master_replicas")}M " f"{config.ENV_DATA.get("worker_replicas")}W " f"{markers}" ) testrun_name = ( f"{ocs_version_string} {us_ds} {ocp_version_string} " f"{testrun_name}" ) if build_user: testrun_name = f"{build_user} {testrun_name}" # replace invalid character(s) by '-' testrun_name = testrun_name.translate( str.maketrans({key: "-" for key in """ \\/.:*"<>|~!@#$?%^&'*(){}+`,=\t"""}) ) log.info("testrun_name: %s", testrun_name) return testrun_name def get_az_count(): """ Using a number of different configuration attributes, determine how many availability zones the cluster is configured for. Returns: int: number of availability zones """ if config.ENV_DATA.get("availability_zone_count"): return int(config.ENV_DATA.get("availability_zone_count")) elif config.ENV_DATA.get("worker_availability_zones"): return len(config.ENV_DATA.get("worker_availability_zones")) elif config.ENV_DATA.get("platform") == "vsphere": return 1 else: return 1 def ceph_health_check(namespace=None, tries=20, delay=30): """ Args: namespace (str): Namespace of OCS (default: config.ENV_DATA['cluster_namespace']) tries (int): Number of retries delay (int): Delay in seconds between retries Returns: bool: ceph_health_check_base return value with default retries of 20, delay of 30 seconds if default values are not changed via args. """ if config.ENV_DATA["platform"].lower() == constants.IBM_POWER_PLATFORM: delay = 60 return retry( (CephHealthException, CommandFailed, subprocess.TimeoutExpired), tries=tries, delay=delay, backoff=1, )(ceph_health_check_base)(namespace) def ceph_health_check_base(namespace=None): """ Exec `ceph health` cmd on tools pod to determine health of cluster. Args: namespace (str): Namespace of OCS (default: config.ENV_DATA['cluster_namespace']) Raises: CephHealthException: If the ceph health returned is not HEALTH_OK CommandFailed: If the command to retrieve the tools pod name or the command to get ceph health returns a non-zero exit code Returns: boolean: True if HEALTH_OK """ namespace = namespace or config.ENV_DATA["cluster_namespace"] run_cmd( f"oc wait --for condition=ready pod " f"-l app=rook-ceph-tools " f"-n {namespace} " f"--timeout=120s" ) tools_pod = run_cmd( f"oc -n {namespace} get pod -l 'app=rook-ceph-tools' " f"-o jsonpath='{{.items[0].metadata.name}}'", timeout=60, ) health = run_cmd(f"oc -n {namespace} exec {tools_pod} -- ceph health") if health.strip() == "HEALTH_OK": log.info("Ceph cluster health is HEALTH_OK.") return True else: raise CephHealthException(f"Ceph cluster health is not OK. Health: {health}") def get_rook_repo(branch="master", to_checkout=None): """ Clone and checkout the rook repository to specific branch/commit. Args: branch (str): Branch name to checkout to_checkout (str): Commit id or tag to checkout """ cwd = constants.ROOK_REPO_DIR if not os.path.isdir(cwd): log.info(f"Cloning rook repository into {cwd}.") run_cmd(f"git clone {constants.ROOK_REPOSITORY} {cwd}") else: log.info( f"The rook directory {cwd} already exists, ocs-ci will skip the " f"clone of rook repository." ) log.info("Fetching latest changes from rook repository.") run_cmd("git fetch --all", cwd=cwd) log.info(f"Checkout rook repository to specific branch: {branch}") run_cmd(f"git checkout {branch}", cwd=cwd) log.info(f"Reset branch: {branch} with latest changes") run_cmd(f"git reset --hard origin/{branch}", cwd=cwd) if to_checkout: run_cmd(f"git checkout {to_checkout}", cwd=cwd) def clone_repo(url, location, branch="master", to_checkout=None): """ Clone a repository or checkout latest changes if it already exists at specified location. Args: url (str): location of the repository to clone location (str): path where the repository will be cloned to branch (str): branch name to checkout to_checkout (str): commit id or tag to checkout """ if not os.path.isdir(location): log.info("Cloning repository into %s", location) run_cmd(f"git clone {url} {location}") else: log.info("Repository already cloned at %s, skipping clone", location) log.info("Fetching latest changes from repository") run_cmd("git fetch --all", cwd=location) log.info("Checking out repository to specific branch: %s", branch) run_cmd(f"git checkout {branch}", cwd=location) log.info("Reset branch: %s with latest changes", branch) run_cmd(f"git reset --hard origin/{branch}", cwd=location) if to_checkout: run_cmd(f"git checkout {to_checkout}", cwd=location) def get_latest_ds_olm_tag(upgrade=False, latest_tag=None): """ This function returns latest tag of OCS downstream registry or one before latest if upgrade parameter is True Args: upgrade (str): If True then it returns one version of the build before the latest. latest_tag (str): Tag of the latest build. If not specified config.DEPLOYMENT['default_latest_tag'] or 'latest' will be used. Returns: str: latest tag for downstream image from quay registry Raises: TagNotFoundException: In case no tag found """ latest_tag = latest_tag or config.DEPLOYMENT.get("default_latest_tag", "latest") tags = get_ocs_olm_operator_tags() latest_image = None ocs_version = config.ENV_DATA["ocs_version"] upgrade_ocs_version = config.UPGRADE.get("upgrade_ocs_version") use_rc_build = config.UPGRADE.get("use_rc_build") previous_rc_build = config.UPGRADE.get("previous_rc_build") upgrade_version_change = upgrade_ocs_version and ocs_version != upgrade_ocs_version if upgrade and use_rc_build and previous_rc_build and not upgrade_version_change: latest_tag = previous_rc_build if upgrade_version_change: upgrade = False for tag in tags: if tag["name"] == latest_tag: latest_image = tag["manifest_digest"] break if not latest_image: raise TagNotFoundException("Couldn't find latest tag!") latest_tag_found = False for tag in tags: if not upgrade: if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and tag["manifest_digest"] == latest_image ): return tag["name"] if upgrade: if not latest_tag_found and tag["name"] == latest_tag: latest_tag_found = True continue if not latest_tag_found: continue if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and tag["manifest_digest"] != latest_image and ocs_version in tag["name"] ): if config.UPGRADE.get("use_rc_build") and "rc" not in tag["name"]: continue return tag["name"] raise TagNotFoundException("Couldn't find any desired tag!") def get_next_version_available_for_upgrade(current_tag): """ This function returns the tag built after the current_version Args: current_tag (str): Current build tag from which to search the next one build tag. Returns: str: tag for downstream image from quay registry built after the current_tag. Raises: TagNotFoundException: In case no tag suitable for upgrade found """ tags = get_ocs_olm_operator_tags() if any(t in current_tag for t in constants.LATEST_TAGS): return current_tag current_tag_index = None for index, tag in enumerate(tags): if tag["name"] == current_tag: if index < 2: raise TagNotFoundException("Couldn't find tag for upgrade!") current_tag_index = index break sliced_reversed_tags = tags[:current_tag_index] sliced_reversed_tags.reverse() ocs_version = config.ENV_DATA["ocs_version"] for tag in sliced_reversed_tags: if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and ocs_version in tag["name"] ): if config.UPGRADE.get("use_rc_build") and "rc" not in tag["name"]: continue return tag["name"] raise TagNotFoundException("Couldn't find any tag!") def load_auth_config(): """ Load the authentication config YAML from /data/auth.yaml Raises: FileNotFoundError: if the auth config is not found Returns: dict: A dictionary reprensenting the YAML file """ log.info("Retrieving the authentication config dictionary") auth_file = os.path.join(constants.TOP_DIR, "data", constants.AUTHYAML) try: with open(auth_file) as f: return yaml.safe_load(f) except FileNotFoundError: log.warning( f"Unable to find the authentication configuration at {auth_file}, " f"please refer to the getting started guide ({constants.AUTH_CONFIG_DOCS})" ) return {} def get_ocs_olm_operator_tags(limit=100): """ Query the OCS OLM Operator repo and retrieve a list of tags. Since we are limited to 100 tags per page, we end up making several API calls and combining the results into a single list of tags. Args: limit: the number of tags to limit the request to Raises: KeyError: if the auth config isn't setup properly requests.RequestException: if the response return code is not ok Returns: list: OCS OLM Operator tags """ try: quay_access_token = load_auth_config()["quay"]["access_token"] except (KeyError, TypeError): log.error( "Unable to retrieve the access token for quay, please refer to " f"the getting started guide ({constants.AUTH_CONFIG_DOCS}) " "to properly setup your authentication configuration" ) raise headers = {"Authorization": f"Bearer {quay_access_token}"} image = "ocs-registry" try: ocs_version = float(config.ENV_DATA.get("ocs_version")) if ocs_version < 4.5: image = "ocs-olm-operator" except (ValueError, TypeError): log.warning("Invalid ocs_version given, defaulting to ocs-registry image") pass all_tags = [] page = 1 while True: log.info(f"Retrieving OCS OLM Operator tags (limit {limit}, page {page})") resp = requests.get( constants.OPERATOR_CS_QUAY_API_QUERY.format( tag_limit=limit, image=image, page=page, ), headers=headers, ) if not resp.ok: raise requests.RequestException(resp.json()) tags = resp.json()["tags"] if len(tags) == 0: log.info("No more tags to retrieve") break log.debug(tags) all_tags.extend(tags) page += 1 return all_tags def check_if_executable_in_path(exec_name): """ Checks whether an executable can be found in the $PATH Args: exec_name: Name of executable to look for Returns: Boolean: Whether the executable was found """ return which(exec_name) is not None def upload_file(server, localpath, remotepath, user=None, password=None, key_file=None): """ Upload a file to remote server Args: server (str): Name of the server to upload localpath (str): Local file to upload remotepath (str): Target path on the remote server. filename should be included user (str): User to use for the remote connection """ if not user: user = "root" try: ssh = SSHClient() ssh.set_missing_host_key_policy(AutoAddPolicy()) if password: ssh.connect(hostname=server, username=user, password=password) else: log.info(key_file) ssh.connect(hostname=server, username=user, key_filename=key_file) sftp = ssh.open_sftp() log.info(f"uploading {localpath} to {user}@{server}:{remotepath}") sftp.put(localpath, remotepath) sftp.close() ssh.close() except AuthenticationException as authException: log.error(f"Authentication failed: {authException}") raise authException except SSHException as sshException: log.error(f"SSH connection failed: {sshException}") raise sshException def read_file_as_str(filepath): """ Reads the file content Args: filepath (str): File to read Returns: str : File contents in string """ with open(rf"{filepath}") as fd: content = fd.read() return content def replace_content_in_file(file, old, new, match_and_replace_line=False): """ Replaces contents in file, if old value is not found, it adds new value to the file Args: file (str): Name of the file in which contents will be replaced old (str): Data to search for new (str): Data to replace the old value match_and_replace_line (bool): If True, it will match a line if `old` pattern is found in the line. The whole line will be replaced with `new` content. Otherwise it will replace only `old` string with `new` string but the rest of the line will be intact. This is the default option. """ # Read the file with open(rf"{file}", "r") as fd: file_data = [line.rstrip("\n") for line in fd.readlines()] if match_and_replace_line: # Replace the whole line with `new` string if the line contains `old` # string pattern. file_data = [new if old in line else line for line in file_data] else: # Replace the old string by new file_data = [ line.replace(old, new) if old in line else line for line in file_data ] updated_data = [line for line in file_data if new in line] # In case the old pattern wasn't found it will be added as first line if not updated_data: file_data.insert(0, new) file_data = [f"{line}\n" for line in file_data] # Write the file out again with open(rf"{file}", "w") as fd: fd.writelines(file_data) @retry((CommandFailed), tries=100, delay=10, backoff=1) def wait_for_co(operator): """ Waits for ClusterOperator to created Args: operator (str): Name of the ClusterOperator """ from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="ClusterOperator") ocp.get(operator) def censor_values(data_to_censor): """ This function censor string and numeric values in dictionary based on keys that match pattern defined in config_keys_patterns_to_censor in constants. It is performed recursively for nested dictionaries. Args: data_to_censor (dict): Data to censor. Returns: dict: filtered data """ for key in data_to_censor: if isinstance(data_to_censor[key], dict): censor_values(data_to_censor[key]) elif isinstance(data_to_censor[key], (str, int, float)): for pattern in constants.config_keys_patterns_to_censor: if pattern in key.lower(): data_to_censor[key] = "*" * 5 return data_to_censor def dump_config_to_file(file_path): """ Dump the config to the yaml file with censored secret values. Args: file_path (str): Path to file where to write the configuration. """ config_copy = deepcopy(config.to_dict()) censor_values(config_copy) with open(file_path, "w+") as fs: yaml.safe_dump(config_copy, fs) def create_rhelpod(namespace, pod_name, timeout=300): """ Creates the RHEL pod Args: namespace (str): Namespace to create RHEL pod pod_name (str): Pod name timeout (int): wait time for RHEL pod to be in Running state Returns: pod: Pod instance for RHEL """ # importing here to avoid dependencies from ocs_ci.helpers import helpers rhelpod_obj = helpers.create_pod( namespace=namespace, pod_name=pod_name, pod_dict_path=constants.RHEL_7_7_POD_YAML, ) helpers.wait_for_resource_state(rhelpod_obj, constants.STATUS_RUNNING, timeout) return rhelpod_obj def check_timeout_reached(start_time, timeout, err_msg=None): """ Check if timeout reached and if so raise the exception. Args: start_time (time): Star time of the operation. timeout (int): Timeout in seconds. err_msg (str): Error message for the exception. Raises: TimeoutException: In case the timeout reached. """ msg = f"Timeout {timeout} reached!" if err_msg: msg += " Error: {err_msg}" if timeout < (time.time() - start_time): raise TimeoutException(msg) def convert_yaml2tfvars(yaml): """ Converts yaml file to tfvars. It creates the tfvars with the same filename in the required format which is used for deployment. Args: yaml (str): File path to yaml Returns: str: File path to tfvars """ # importing here to avoid dependencies from ocs_ci.utility.templating import load_yaml data = load_yaml(yaml) tfvars_file = os.path.splitext(yaml)[0] log.debug(f"Converting {yaml} to {tfvars_file}") with open(tfvars_file, "w+") as fd: for key, val in data.items(): if key == "control_plane_ignition": fd.write("control_plane_ignition = <<END_OF_MASTER_IGNITION\n") fd.write(f"{val}\n") fd.write("END_OF_MASTER_IGNITION\n") continue if key == "compute_ignition": fd.write("compute_ignition = <<END_OF_WORKER_IGNITION\n") fd.write(f"{val}\n") fd.write("END_OF_WORKER_IGNITION\n") continue if key == "vm_dns_addresses": fd.write(f'vm_dns_addresses = ["{val}"]\n') continue fd.write(key) fd.write(" = ") fd.write('"') fd.write(f"{val}") fd.write('"\n') return tfvars_file def remove_keys_from_tf_variable_file(tf_file, keys): """ Removes the keys from the tf files and convert to json format Args: tf_file (str): path to tf file keys (list): list of keys to remove """ # importing here to avoid dependencies from ocs_ci.utility.templating import dump_data_to_json with open(tf_file, "r") as fd: obj = hcl2.load(fd) for key in keys: obj["variable"].pop(key) dump_data_to_json(obj, f"{tf_file}.json") os.rename(tf_file, f"{tf_file}.backup") def get_kubeadmin_password(): filename = os.path.join( config.ENV_DATA["cluster_path"], config.RUN["password_location"] ) with open(filename) as f: return f.read() def get_infra_id(cluster_path): """ Get infraID from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['infraID'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["infraID"] def get_cluster_name(cluster_path): """ Get clusterName from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['clusterName'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["clusterName"] def skipif_ocp_version(expressions): """ This function evaluates the condition for test skip based on expression Args: expressions (str OR list): condition for which we need to check, eg: A single expression string '>=4.2' OR A list of expressions like ['<4.3', '>4.2'], ['<=4.3', '>=4.2'] Return: 'True' if test needs to be skipped else 'False' """ skip_this = True ocp_version = get_running_ocp_version() expr_list = [expressions] if isinstance(expressions, str) else expressions for expr in expr_list: comparision_str = ocp_version + expr skip_this = skip_this and eval(comparision_str) # skip_this will be either True or False after eval return skip_this def skipif_ocs_version(expressions): """ This function evaluates the condition for test skip based on expression Args: expressions (str OR list): condition for which we need to check, eg: A single expression string '>=4.2' OR A list of expressions like ['<4.3', '>4.2'], ['<=4.3', '>=4.2'] Return: 'True' if test needs to be skipped else 'False' """ expr_list = [expressions] if isinstance(expressions, str) else expressions return any(eval(config.ENV_DATA["ocs_version"] + expr) for expr in expr_list) def skipif_ui_not_support(ui_test): """ This function evaluates the condition for ui test skip based on ui_test expression Args: ui_test (str): condition for which we need to check, Return: 'True' if test needs to be skipped else 'False' """ from ocs_ci.ocs.ui.views import locators ocp_version = get_running_ocp_version() if ( config.ENV_DATA["platform"].lower() == constants.IBMCLOUD_PLATFORM or config.ENV_DATA["platform"].lower() == constants.OPENSHIFT_DEDICATED_PLATFORM or config.ENV_DATA["platform"].lower() == constants.ROSA_PLATFORM ): return True try: locators[ocp_version][ui_test] except KeyError: return True return False def get_ocs_version_from_image(image): """ Parse major.minor version from OCS image tag. Args: image (str): image in format url:tag Returns str: Version in x.y format Raises: ValueError: In case of the tag which we cannot parse to version. """ try: version = image.rsplit(":", 1)[1].lstrip("latest-").lstrip("stable-") version = Version.coerce(version) return "{major}.{minor}".format(major=version.major, minor=version.minor) except ValueError: log.error(f"The version: {version} couldn't be parsed!") raise def get_available_ocp_versions(channel): """ Find all available OCP versions for specific channel. Args: channel (str): Channel of OCP (e.g. stable-4.2 or fast-4.2) Returns list: Sorted list with OCP versions for specified channel. """ headers = {"Accept": "application/json"} req = requests.get( constants.OPENSHIFT_UPGRADE_INFO_API.format(channel=channel), headers=headers ) data = req.json() versions = [Version(node["version"]) for node in data["nodes"]] versions.sort() return versions def get_latest_ocp_version(channel, index=-1): """ Find latest OCP version for specific channel. Args: channel (str): Channel of OCP (e.g. stable-4.2 or fast-4.2) index (int): Index to get from all available versions list e.g. default -1 is latest version (version[-1]). If you want to get previous version pass index -2 and so on. Returns str: Latest OCP version for specified channel. """ versions = get_available_ocp_versions(channel) return str(versions[index]) def load_config_file(config_file): """ Loads config file to the ocs-ci config Args: config_file (str): Path to yaml config file. Raises: FileNotFoundError: In the case the config file not found. """ config_file = os.path.expanduser(config_file) assert os.path.exists(config_file), f"Config file {config_file} doesn't exist!" with open(os.path.abspath(os.path.expanduser(config_file)), "r") as file_stream: custom_config_data = yaml.safe_load(file_stream) config.update(custom_config_data) def destroy_cluster(installer, cluster_path, log_level="DEBUG"): """ Destroy OCP cluster specific Args: installer (str): The path to the installer binary cluster_path (str): The path of the cluster log_level (str): log level openshift-installer (default: DEBUG) """ destroy_cmd = ( f"{installer} destroy cluster " f"--dir {cluster_path} " f"--log-level {log_level}" ) try: # Execute destroy cluster using OpenShift installer log.info(f"Destroying cluster defined in {cluster_path}") run_cmd(destroy_cmd, timeout=1200) except CommandFailed: log.error(traceback.format_exc()) raise except Exception: log.error(traceback.format_exc()) def config_to_string(config): """ Convert ConfigParser object to string in INI format. Args: config (obj): ConfigParser object Returns: str: Config in one string """ strio = io.StringIO() config.write(strio, space_around_delimiters=False) return strio.getvalue() class AZInfo(object): """ A class for getting different az numbers across calls """ zone_number = 0 def get_zone_number(self): """ Increment current zone_number and perform modulus op to roll-on to next available number Returns: int: zone number index """ prev = AZInfo.zone_number AZInfo.zone_number += 1 AZInfo.zone_number %= get_az_count() return prev def convert_device_size(unformatted_size, units_to_covert_to): """ Convert a string representing a size to an int according to the given units to convert to Args: unformatted_size (str): The size to convert (i.e, '1Gi'/'100Mi') units_to_covert_to (str): The units to convert the size to (i.e, TB/GB/MB) Returns: int: The converted size """ units = unformatted_size[-2:] abso = int(unformatted_size[:-2]) conversion = { "TB": {"Ti": abso, "Gi": abso / 1000, "Mi": abso / 1e6, "Ki": abso / 1e9}, "GB": {"Ti": abso * 1000, "Gi": abso, "Mi": abso / 1000, "Ki": abso / 1e6}, "MB": {"Ti": abso * 1e6, "Gi": abso * 1000, "Mi": abso, "Ki": abso / 1000}, "KB": {"Ti": abso * 1e9, "Gi": abso * 1e6, "Mi": abso * 1000, "Ki": abso}, "B": {"Ti": abso * 1e12, "Gi": abso * 1e9, "Mi": abso * 1e6, "Ki": abso * 1000}, } return conversion[units_to_covert_to][units] def prepare_customized_pull_secret(images=None): """ Prepare customized pull-secret containing auth section related to given image(s). If image(s) not defined or no related section is found, it will use whole content of pull-secret. Args: images (str, list): image (or images) to match with auth section Returns: NamedTemporaryFile: prepared pull-secret """ log.debug(f"Prepare customized pull-secret for images: {images}") if type(images) == str: images = [images] # load pull-secret file to pull_secret dict pull_secret_path = os.path.join(constants.TOP_DIR, "data", "pull-secret") with open(pull_secret_path) as pull_secret_fo: pull_secret = json.load(pull_secret_fo) authfile_content = {"auths": {}} # if images defined, try to find auth section related to specified images if images: for image in images: # find all auths which might be related to the specified image tmp_auths = [auth for auth in pull_secret["auths"] if auth in image] # get the most specific auth for particular image tmp_auths = sorted(tmp_auths, key=len, reverse=True) if tmp_auths: # if there is match to particular auth, prepare authfile just with the # matching auth auth = tmp_auths[0] # as key use only server name, without namespace authfile_content["auths"][auth.split("/", 1)[0]] = pull_secret["auths"][ auth ] if not authfile_content["auths"]: authfile_content = pull_secret # create temporary auth file authfile_fo = NamedTemporaryFile(mode="w", prefix="authfile_") json.dump(authfile_content, authfile_fo) # ensure the content will be saved into the file authfile_fo.flush() return authfile_fo def inspect_image(image, authfile_fo): """ Inspect image Args: image (str): image to inspect authfile_fo (NamedTemporaryFile): pull-secret required for pulling the given image Returns: dict: json object of the inspected image """ # pull original image (to be able to inspect it) exec_cmd(f"podman image pull {image} --authfile {authfile_fo.name}") # inspect the image cmd_result = exec_cmd(f"podman image inspect {image}") image_inspect = json.loads(cmd_result.stdout) return image_inspect def get_image_with_digest(image): """ Return image with sha256 digest for usage in disconnected environment Args: image (str): image Raises: UnexpectedImage: In case the image information is unexpected Returns: str: image with sha256 digest specification """ if "@sha256:" in image: return image with prepare_customized_pull_secret(image) as authfile_fo: image_inspect = inspect_image(image, authfile_fo) # we expect, that 'Digest' will match one of the images in 'RepoDigests', # if not, raise UnexpectedImage for image in image_inspect[0]["RepoDigests"]: if image_inspect[0]["Digest"] in image: return image else: raise UnexpectedImage( f"Image digest ({image_inspect[0]["Digest"]}) doesn't match with " f"any image from RepoDigests ({image_inspect[0]["RepoDigests"]})." ) def login_to_mirror_registry(authfile): """ Login to mirror registry Args: authfile (str): authfile (pull-secret) path """ # load cluster info load_cluster_info() mirror_registry = config.DEPLOYMENT["mirror_registry"] mirror_registry_user = config.DEPLOYMENT["mirror_registry_user"] mirror_registry_password = config.DEPLOYMENT["mirror_registry_password"] login_cmd = ( f"podman login --authfile {authfile} " f"{mirror_registry} -u {mirror_registry_user} " f"-p {mirror_registry_password} --tls-verify=false" ) exec_cmd(login_cmd, (mirror_registry_user, mirror_registry_password)) def mirror_image(image): """ Mirror image to mirror image registry. Args: image (str): image to be mirrored, can be defined just with name or with full url, with or without tag or digest Returns: str: the mirrored image link """ with prepare_customized_pull_secret(image) as authfile_fo: # login to mirror registry login_to_mirror_registry(authfile_fo.name) # if there is any tag specified, use it in the full image url, # otherwise use url with digest image_inspect = inspect_image(image, authfile_fo) if image_inspect[0].get("RepoTags"): orig_image_full = image_inspect[0]["RepoTags"][0] else: orig_image_full = image_inspect[0]["RepoDigests"][0] # prepare mirrored image url mirror_registry = config.DEPLOYMENT["mirror_registry"] mirrored_image = mirror_registry + re.sub(r"^[^/]*", "", orig_image_full) # mirror the image log.info( f"Mirroring image '{image}' ('{orig_image_full}') to '{mirrored_image}'" ) exec_cmd( f"oc image mirror --insecure --registry-config" f" {authfile_fo.name} {orig_image_full} {mirrored_image}" ) return mirrored_image def update_container_with_mirrored_image(job_pod_dict): """ Update Job or Pod configuration dict with mirrored image (required for disconnected installation). Args: job_pod_dict (dict): dictionary with Job or Pod configuration Returns: dict: for disconnected installation, returns updated Job or Pod dict, for normal installation return unchanged job_pod_dict """ if config.DEPLOYMENT.get("disconnected"): if "containers" in job_pod_dict["spec"]: container = job_pod_dict["spec"]["containers"][0] else: container = job_pod_dict["spec"]["template"]["spec"]["containers"][0] container["image"] = mirror_image(container["image"]) return job_pod_dict def get_trim_mean(values, percentage=20): """ Get the trimmed mean of a list of values. Explanation: This function finds the arithmetic mean of given values, ignoring values outside the given limits. Args: values (list): The list of values percentage (int): The percentage to be trimmed Returns: float: Trimmed mean. In case trimmed mean calculation fails, the regular mean average is returned """ lower_limit = scoreatpercentile(values, percentage) upper_limit = scoreatpercentile(values, 100 - percentage) try: return tmean(values, limits=(lower_limit, upper_limit)) except ValueError: log.warning( f"Failed to calculate the trimmed mean of {values}. The " f"Regular mean average will be calculated instead" ) return sum(values) / len(values) def set_selinux_permissions(workers=None): """ Workaround for #1777384 - enable container_use_cephfs on RHEL workers Ticket: RHSTOR-787, see more details in the issue: #1151 Args: workers (list): List of worker nodes to set selinux permissions """ log.info("Running WA for ticket: RHSTOR-787") from ocs_ci.ocs import ocp ocp_obj = ocp.OCP() cmd = ["/usr/sbin/setsebool -P container_use_cephfs on"] cmd_list = cmd.copy() if not workers: from ocs_ci.ocs.node import get_typed_worker_nodes worker_nodes = get_typed_worker_nodes(os_id="rhel") else: worker_nodes = workers for worker in worker_nodes: node = worker.get().get("metadata").get("name") if not workers else worker log.info(f"{node} is a RHEL based worker - applying '{cmd_list}'") if config.ENV_DATA["platform"] == constants.IBMCLOUD_PLATFORM: retry(CommandFailed, tries=10, delay=3, backoff=2)( ocp_obj.exec_oc_debug_cmd )(node=node, cmd_list=cmd_list) else: retry(CommandFailed)(ocp_obj.exec_oc_debug_cmd)( node=node, cmd_list=cmd_list ) def set_registry_to_managed_state(): """ In order to be able to deploy from stage we need to change image registry config to Managed state. More described in BZs: https://bugzilla.redhat.com/show_bug.cgi?id=1806593 https://bugzilla.redhat.com/show_bug.cgi?id=1807471#c3 We need to change to managed state as described here: https://github.com/red-hat-storage/ocs-ci/issues/1436 So this is not suppose to be deleted as WA case we really need to do this operation for OCS deployment as was originally done here: https://github.com/red-hat-storage/ocs-ci/pull/1437 Currently it has to be moved here to enable CA certificate to be properly propagated for the stage deployment as mentioned in BZ. """ # In RHV platform config is already set to Managed and storage pre-configured on_prem_platform_to_exclude = [constants.RHV_PLATFORM] platform_list_to_exclude = constants.CLOUD_PLATFORMS + on_prem_platform_to_exclude if config.ENV_DATA["platform"] not in platform_list_to_exclude: cluster_config = yaml.safe_load( exec_cmd(f"oc get {constants.IMAGE_REGISTRY_CONFIG} -o yaml").stdout ) if "emptyDir" not in cluster_config["spec"].get("storage", {}).keys(): run_cmd( f"oc patch {constants.IMAGE_REGISTRY_CONFIG} --type merge -p " f'\'{{'spec':{{'storage': {{'emptyDir':{{}}}}}}}}\'' ) if cluster_config["spec"].get("managementState") != "Managed": run_cmd( f"oc patch {constants.IMAGE_REGISTRY_CONFIG} --type merge -p " f'\'{{'spec':{{'managementState': 'Managed'}}}}\'' ) def add_stage_cert(): """ Deploy stage certificate to the cluster. """ log.info("Create configmap stage-registry-config with stage CA.") run_cmd( f"oc -n openshift-config create configmap stage-registry-config" f" --from-file=registry.stage.redhat.io={constants.STAGE_CA_FILE}" ) log.info("Add stage-registry-config to additionalTrustedCA.") additional_trusted_ca_patch = ( '{"spec":{"additionalTrustedCA":{"name":"stage-registry-config"}}}' ) run_cmd( f"oc patch image.config.openshift.io cluster --type=merge" f" -p '{additional_trusted_ca_patch}'" ) def get_terraform(version=None, bin_dir=None): """ Downloads the terraform binary Args: version (str): Version of the terraform to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) Returns: str: Path to the terraform binary """ if platform.system() == "Darwin": os_type = "darwin" elif platform.system() == "Linux": os_type = "linux" else: raise UnsupportedOSType version = version or config.DEPLOYMENT["terraform_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) terraform_zip_file = f"terraform_{version}_{os_type}_amd64.zip" terraform_filename = "terraform" terraform_binary_path = os.path.join(bin_dir, terraform_filename) log.info(f"Downloading terraform version {version}") previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://releases.hashicorp.com/terraform/{version}/" f"{terraform_zip_file}" download_file(url, terraform_zip_file) run_cmd(f"unzip -o {terraform_zip_file}") delete_file(terraform_zip_file) # return to the previous working directory os.chdir(previous_dir) return terraform_binary_path def get_terraform_ignition_provider(terraform_dir, version=None): """ Downloads the terraform ignition provider Args: terraform_dir (str): Path to terraform working directory version (str): Version of the terraform ignition provider to download """ version = version or constants.TERRAFORM_IGNITION_PROVIDER_VERSION terraform_ignition_provider_zip_file = ( f"terraform-provider-ignition-{version}-linux-amd64.tar.gz" ) terraform_ignition_provider_dir = ( f"terraform-provider-ignition-{version}-linux-amd64" ) terraform_plugins_path = ".terraform/plugins/linux_amd64/" log.info(f"Downloading terraform ignition proivider version {version}") previous_dir = os.getcwd() os.chdir(terraform_dir) url = ( "https://github.com/community-terraform-providers/" f"terraform-provider-ignition/releases/download/{version}/" f"{terraform_ignition_provider_zip_file}" ) # Download and untar download_file(url, terraform_ignition_provider_zip_file) run_cmd(f"tar xzf {terraform_ignition_provider_zip_file}") # move the ignition provider binary to plugins path create_directory_path(terraform_plugins_path) move( f"{terraform_ignition_provider_dir}/terraform-provider-ignition", terraform_plugins_path, ) # delete the downloaded files delete_file(terraform_ignition_provider_zip_file) delete_dir(terraform_ignition_provider_dir) # return to the previous working directory os.chdir(previous_dir) def get_module_ip(terraform_state_file, module): """ Gets the node IP from terraform.tfstate file Args: terraform_state_file (str): Path to terraform state file module (str): Module name in terraform.tfstate file e.g: constants.LOAD_BALANCER_MODULE Returns: list: IP of the node """ ips = [] with open(terraform_state_file) as fd: obj = json.loads(fd.read()) if config.ENV_DATA.get("folder_structure"): resources = obj["resources"] log.debug(f"Extracting module information for {module}") log.debug(f"Resource in {terraform_state_file}: {resources}") for resource in resources: if resource.get("module") == module and resource.get("mode") == "data": for each_resource in resource["instances"]: resource_body = each_resource["attributes"]["body"] ips.append(resource_body.split('"')[3]) else: modules = obj["modules"] target_module = module.split("_")[1] log.debug(f"Extracting module information for {module}") log.debug(f"Modules in {terraform_state_file}: {modules}") for each_module in modules: if target_module in each_module["path"]: return each_module["outputs"]["ip_addresses"]["value"] return ips def set_aws_region(region=None): """ Exports environment variable AWS_REGION Args: region (str): AWS region to export """ log.debug("Exporting environment variable AWS_REGION") region = region or config.ENV_DATA["region"] os.environ["AWS_REGION"] = region def get_system_architecture(): """ Get output from 'uname -m' command run on first worker node. Returns: str: Architecture of system """ from ocs_ci.ocs.node import get_nodes log.info("Checking architecture of system") node = get_nodes(node_type=constants.WORKER_MACHINE)[0] return node.ocp.exec_oc_debug_cmd(node.data["metadata"]["name"], ["uname -m"]) def wait_for_machineconfigpool_status(node_type, timeout=900): """ Check for Machineconfigpool status Args: node_type (str): The node type to check machineconfigpool status is updated. e.g: worker, master and all if we want to check for all nodes timeout (int): Time in seconds to wait """ # importing here to avoid dependencies from ocs_ci.ocs import ocp node_types = [node_type] if node_type == "all": node_types = [f"{constants.WORKER_MACHINE}", f"{constants.MASTER_MACHINE}"] for role in node_types: log.info(f"Checking machineconfigpool status for {role} nodes") ocp_obj = ocp.OCP(kind=constants.MACHINECONFIGPOOL, resource_name=role) machine_count = ocp_obj.get()["status"]["machineCount"] assert ocp_obj.wait_for_resource( condition=str(machine_count), column="READYMACHINECOUNT", timeout=timeout, sleep=5, ) def configure_chrony_and_wait_for_machineconfig_status( node_type=constants.WORKER_MACHINE, timeout=900 ): """ Configure chrony on the nodes Args: node_type (str): The node type to configure chrony e.g: worker, master and all if we want to configure on all nodes timeout (int): Time in seconds to wait """ # importing here to avoid dependencies from ocs_ci.utility.templating import load_yaml from ocs_ci.ocs.resources.ocs import OCS chrony_data = load_yaml(constants.NTP_CHRONY_CONF) node_types = [node_type] if node_type == "all": node_types = [f"{constants.WORKER_MACHINE}", f"{constants.MASTER_MACHINE}"] for role in node_types: log.info(f"Creating chrony for {role} nodes") chrony_data["metadata"]["labels"][ "machineconfiguration.openshift.io/role" ] = role chrony_data["metadata"]["name"] = f"{role}-chrony-configuration" chrony_obj = OCS(**chrony_data) chrony_obj.create() # sleep here to start update machineconfigpool status time.sleep(60) wait_for_machineconfigpool_status(role, timeout=timeout) def modify_csv(csv, replace_from, replace_to): """ Modify the CSV Args: csv (str): The CSV name replace_from (str): The pattern to replace from in the CSV replace_to (str): The pattern to replace to in the CSV """ data = ( f"oc -n openshift-storage get csv {csv} -o yaml | sed" f" 's,{replace_from},{replace_to},g' | oc replace -f -" ) log.info( f"CSV {csv} will be modified: {replace_from} will be replaced " f"with {replace_to}.\nThe command that will be used for that is:\n{data}" ) temp_file = NamedTemporaryFile(mode="w+", prefix="csv_modification", suffix=".sh") with open(temp_file.name, "w") as t_file: t_file.writelines(data) run_cmd(f"chmod 777 {temp_file.name}") run_cmd(f"sh {temp_file.name}") def check_for_rhcos_images(url): """ Check for rhcos images are present in given location Args: url (str): rhcos_images url Returns: (bool): True if images present if not false """ r = requests.head(url) return r.status_code == requests.codes.ok def download_file_from_git_repo(git_repo_url, path_to_file_in_git, filename): """ Download a file from a specified git repository Args: git_repo_url (str): The git repository url path_to_file_in_git (str): Path to the file to download in git repository filename (str): Name of the file to write the download to """ log.debug( f"Download file '{path_to_file_in_git}' from " f"git repository {git_repo_url} to local file '{filename}'." ) temp_dir = mkdtemp() git.Repo.clone_from(git_repo_url, temp_dir, branch="master", depth=1) move(os.path.join(temp_dir, path_to_file_in_git), filename) rmtree(temp_dir) def skipif_upgraded_from(version_list): """ This function evaluates the condition to skip a test if the cluster is upgraded from a particular OCS version Args: version_list (list): List of versions to check Return: (bool): True if test needs to be skipped else False """ try: from ocs_ci.ocs.resources.ocs import get_ocs_csv skip_this = False version_list = [version_list] if isinstance(version_list, str) else version_list ocs_csv = get_ocs_csv() csv_info = ocs_csv.get() prev_version = csv_info.get("spec").get("replaces", "") for version in version_list: if f".v{version}" in prev_version: skip_this = True break return skip_this except Exception as err: log.error(str(err)) return False def get_cluster_id(cluster_path): """ Get ClusterID from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['clusterID'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["clusterID"] def get_running_cluster_id(): """ Get cluster UUID Not relying on metadata.json as user sometimes want to run only with kubeconfig for some tests. For this function to work cluster has to be in running state Returns: str: cluster UUID """ cluster_id = run_cmd( "oc get clusterversion version -o jsonpath='{.spec.clusterID}'" ) return cluster_id def get_ocp_upgrade_history(): """ Gets the OCP upgrade history for the cluster Returns: list: List of OCP upgrade paths. Latest version in the beginning of the list """ # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="clusterversion") cluster_version_info = ocp.get("version") upgrade_history_info = cluster_version_info["status"]["history"] upgrade_history = [each_upgrade["version"] for each_upgrade in upgrade_history_info] return upgrade_history def get_attr_chain(obj, attr_chain): """ Attempt to retrieve object attributes when uncertain about the existence of the attribute or a different attribute in a given attribute chain. If the retrieval fails, None is returned. The function can be used to retrieve a direct attribute, or a chain of attributes. i.e. - obj.attr_a, obj_attr_a.sub_attr Another example - trying to access "sub_attr_b" in object.attr.sub_attr_a.sub_attr_b - get_attr_chain(object, "attr.sub_attr_a.sub_attr_b") The function can be used to try and retrieve "sub_attribute_b" without an exception, even in cases where "attr" or "sub_attr_a" might not exist. In those cases, the function will return None. Args: obj: An object attr_chain (str): A string containing one attribute or several sub-attributes separated by dots (i.e. - "attr.sub_attr_a.sub_attr_b") Returns: The requested attribute if found, otherwise None """ return reduce( lambda _obj, _attr: getattr(_obj, _attr, None), attr_chain.split("."), obj ) def get_default_if_keyval_empty(dictionary, key, default_val): """ if Key has an empty value OR key doesn't exist then return default value Args: dictionary (dict): Dictionary where we have to lookup key (str): key to lookup default_val (str): If key doesn't have value then return this default_val Returns: dictionary[key] if value is present else default_val """ if not dictionary.get(key): return default_val return dictionary.get(key) def get_client_version(client_binary_path): """ Get version reported by `oc version`. Args: client_binary_path (str): path to `oc` binary Returns: str: version reported by `oc version`. None if the client does not exist at the provided path. """ if os.path.isfile(client_binary_path): cmd = f"{client_binary_path} version --client -o json" resp = exec_cmd(cmd) stdout = json.loads(resp.stdout.decode()) return stdout["releaseClientVersion"] def clone_notify(): """ Repository contains the source code of notify tool, which is a python3 based tool wrapped by a container used to configure Ceph Bucket Notifications Returns: notify_path (str): Path location of the notify code """ notify_dir = mkdtemp(prefix="notify_") log.info(f"cloning repo notify in {notify_dir}") git_clone_cmd = f"git clone {constants.RGW_KAFKA_NOTIFY}" subprocess.run(git_clone_cmd, shell=True, cwd=notify_dir, check=True) notify_path = f"{notify_dir}/notify/notify.py" return notify_path def add_chrony_to_ocp_deployment(): """ Create and Add necessary chrony resources """ for role in ["master", "worker"]: log.info(f"Creating and Adding Chrony file for {role}") with open(constants.CHRONY_TEMPLATE) as file_stream: chrony_template_obj = yaml.safe_load(file_stream) chrony_template_obj["metadata"]["labels"][ "machineconfiguration.openshift.io/role" ] = role chrony_template_obj["metadata"]["name"] = f"99-{role}-chrony-configuration" ignition_version = config.DEPLOYMENT["ignition_version"] chrony_template_obj["spec"]["config"]["ignition"]["version"] = ignition_version if Version.coerce(ignition_version) < Version.coerce("3.0"): chrony_template_obj["spec"]["config"]["storage"]["files"][0][ "filesystem" ] = "root" chrony_template_str = yaml.safe_dump(chrony_template_obj) chrony_file = os.path.join( config.ENV_DATA["cluster_path"], "openshift", f"99-{role}-chrony-configuration.yaml", ) with open(chrony_file, "w") as f: f.write(chrony_template_str) def enable_huge_pages(): log.info("Enabling huge pages.") exec_cmd(f"oc apply -f {constants.HUGE_PAGES_TEMPLATE}") time.sleep(10) log.info("Waiting for machine config will be applied with huge pages") wait_for_machineconfigpool_status(node_type=constants.WORKER_MACHINE)

from functools import reduce import io import json import logging import os import platform import random import re import shlex import smtplib import string import subprocess import time import traceback import stat from copy import deepcopy from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from scipy.stats import tmean, scoreatpercentile from shutil import which, move, rmtree import hcl2 import requests import yaml import git from bs4 import BeautifulSoup from paramiko import SSHClient, AutoAddPolicy from paramiko.auth_handler import AuthenticationException, SSHException from semantic_version import Version from tempfile import NamedTemporaryFile, mkdtemp from ocs_ci.framework import config from ocs_ci.ocs import constants, defaults from ocs_ci.ocs.exceptions import ( CephHealthException, ClientDownloadError, CommandFailed, TagNotFoundException, TimeoutException, TimeoutExpiredError, UnavailableBuildException, UnexpectedImage, UnsupportedOSType, ) from ocs_ci.utility import version as version_module from ocs_ci.utility.flexy import load_cluster_info from ocs_ci.utility.retry import retry log = logging.getLogger(__name__) # variables mounting_dir = "/mnt/cephfs/" clients = [] md5sum_list1 = [] md5sum_list2 = [] fuse_clients = [] kernel_clients = [] mon_node = "" mon_node_ip = "" mds_nodes = [] md5sum_file_lock = [] active_mdss = [] RC = [] failure = {} output = [] unique_test_names = [] # function for getting the clients def get_client_info(ceph_nodes, clients): log.info("Getting Clients") for node in ceph_nodes: if node.role == "client": clients.append(node) # Identifying MON node for node in ceph_nodes: if node.role == "mon": mon_node = node out, err = mon_node.exec_command(cmd="sudo hostname -I") mon_node_ip = out.read().decode().rstrip("\n") break for node in ceph_nodes: if node.role == "mds": mds_nodes.append(node) for node in clients: node.exec_command(cmd="sudo yum install -y attr") fuse_clients = clients[0:2] # seperating clients for fuse and kernel kernel_clients = clients[2:4] return ( fuse_clients, kernel_clients, mon_node, mounting_dir, mds_nodes, md5sum_file_lock, mon_node_ip, ) # function for providing authorization to the clients from MON ndoe def auth_list(clients, mon_node): for node in clients: log.info("Giving required permissions for clients from MON node:") mon_node.exec_command( cmd="sudo ceph auth get-or-create client.%s mon 'allow *' mds 'allow *, allow rw path=/' " "osd 'allow rw pool=cephfs_data' -o /etc/ceph/ceph.client.%s.keyring" % (node.hostname, node.hostname) ) out, err = mon_node.exec_command( sudo=True, cmd="cat /etc/ceph/ceph.client.%s.keyring" % (node.hostname) ) keyring = out.read().decode() key_file = node.write_file( sudo=True, file_name="/etc/ceph/ceph.client.%s.keyring" % (node.hostname), file_mode="w", ) key_file.write(keyring) key_file.flush() node.exec_command( cmd="sudo chmod 644 /etc/ceph/ceph.client.%s.keyring" % (node.hostname) ) # creating mounting directory node.exec_command(cmd="sudo mkdir %s" % (mounting_dir)) # MOunting single FS with ceph-fuse def fuse_mount(fuse_clients, mounting_dir): try: for client in fuse_clients: log.info("Creating mounting dir:") log.info("Mounting fs with ceph-fuse on client %s:" % (client.hostname)) client.exec_command( cmd="sudo ceph-fuse -n client.%s %s" % (client.hostname, mounting_dir) ) out, err = client.exec_command(cmd="mount") mount_output = out.read().decode() mount_output.split() log.info("Checking if fuse mount is is passed of failed:") if "fuse" in mount_output: log.info("ceph-fuse mounting passed") else: log.error("ceph-fuse mounting failed") return md5sum_list1 except Exception as e: log.error(e) def kernel_mount(mounting_dir, mon_node_ip, kernel_clients): try: for client in kernel_clients: out, err = client.exec_command( cmd="sudo ceph auth get-key client.%s" % (client.hostname) ) secret_key = out.read().decode().rstrip("\n") mon_node_ip = mon_node_ip.replace(" ", "") client.exec_command( cmd="sudo mount -t ceph %s:6789:/ %s -o name=%s,secret=%s" % (mon_node_ip, mounting_dir, client.hostname, secret_key) ) out, err = client.exec_command(cmd="mount") mount_output = out.read().decode() mount_output.split() log.info("Checking if kernel mount is is passed of failed:") if "%s:6789:/" % (mon_node_ip) in mount_output: log.info("kernel mount passed") else: log.error("kernel mount failed") return md5sum_list2 except Exception as e: log.error(e) def fuse_client_io(client, mounting_dir): try: rand_count = random.randint(1, 5) rand_bs = random.randint(100, 300) log.info("Performing IOs on fuse-clients") client.exec_command( cmd="sudo dd if=/dev/zero of=%snewfile_%s bs=%dM count=%d" % (mounting_dir, client.hostname, rand_bs, rand_count), long_running=True, ) except Exception as e: log.error(e) def kernel_client_io(client, mounting_dir): try: rand_count = random.randint(1, 6) rand_bs = random.randint(100, 500) log.info("Performing IOs on kernel-clients") client.exec_command( cmd="sudo dd if=/dev/zero of=%snewfile_%s bs=%dM count=%d" % (mounting_dir, client.hostname, rand_bs, rand_count), long_running=True, ) except Exception as e: log.error(e) def fuse_client_md5(fuse_clients, md5sum_list1): try: log.info("Calculating MD5 sums of files in fuse-clients:") for client in fuse_clients: md5sum_list1.append( client.exec_command( cmd="sudo md5sum %s* | awk '{print $1}' " % (mounting_dir), long_running=True, ) ) except Exception as e: log.error(e) def kernel_client_md5(kernel_clients, md5sum_list2): try: log.info("Calculating MD5 sums of files in kernel-clients:") for client in kernel_clients: md5sum_list2.append( client.exec_command( cmd="sudo md5sum %s* | awk '{print $1}' " % (mounting_dir), long_running=True, ) ) except Exception as e: log.error(e) # checking file locking mechanism def file_locking(client): try: to_lock_file = """ import fcntl import subprocess import time try: f = open('/mnt/cephfs/to_test_file_lock', 'w+') fcntl.lockf(f, fcntl.LOCK_EX | fcntl.LOCK_NB) print "locking file:--------------------------------" subprocess.check_output(["sudo","dd","if=/dev/zero","of=/mnt/cephfs/to_test_file_lock","bs=1M","count=2"]) except IOError as e: print e finally: print "Unlocking file:------------------------------" fcntl.lockf(f,fcntl.LOCK_UN) """ to_lock_code = client.write_file( sudo=True, file_name="/home/cephuser/file_lock.py", file_mode="w" ) to_lock_code.write(to_lock_file) to_lock_code.flush() out, err = client.exec_command(cmd="sudo python /home/cephuser/file_lock.py") output = out.read().decode() output.split() if "Errno 11" in output: log.info("File locking achieved, data is not corrupted") elif "locking" in output: log.info("File locking achieved, data is not corrupted") else: log.error("Data is corrupted") out, err = client.exec_command( cmd="sudo md5sum %sto_test_file_lock | awk '{print $1}'" % (mounting_dir) ) md5sum_file_lock.append(out.read().decode()) except Exception as e: log.error(e) def activate_multiple_mdss(mds_nodes): try: log.info("Activating Multiple MDSs") for node in mds_nodes: out1, err = node.exec_command( cmd="sudo ceph fs set cephfs allow_multimds true --yes-i-really-mean-it" ) out2, err = node.exec_command(cmd="sudo ceph fs set cephfs max_mds 2") break except Exception as e: log.error(e) def mkdir_pinning(clients, range1, range2, dir_name, pin_val): try: log.info("Creating Directories and Pinning to MDS %s" % (pin_val)) for client in clients: for num in range(range1, range2): out, err = client.exec_command( cmd="sudo mkdir %s%s_%d" % (mounting_dir, dir_name, num) ) if pin_val != "": client.exec_command( cmd="sudo setfattr -n ceph.dir.pin -v %s %s%s_%d" % (pin_val, mounting_dir, dir_name, num) ) else: print("Pin val not given") print(out.read().decode()) print(time.time()) break except Exception as e: log.error(e) def allow_dir_fragmentation(mds_nodes): try: log.info("Allowing directorty fragmenation for splitting") for node in mds_nodes: node.exec_command(cmd="sudo ceph fs set cephfs allow_dirfrags 1") break except Exception as e: log.error(e) def mds_fail_over(mds_nodes): try: rand = random.randint(0, 1) for node in mds_nodes: log.info("Failing MDS %d" % (rand)) node.exec_command(cmd="sudo ceph mds fail %d" % (rand)) break except Exception as e: log.error(e) def pinned_dir_io(clients, mds_fail_over, num_of_files, range1, range2): try: log.info("Performing IOs and MDSfailovers on clients") for client in clients: client.exec_command(cmd="sudo pip install crefi") for num in range(range1, range2): if mds_fail_over != "": mds_fail_over(mds_nodes) out, err = client.exec_command( cmd="sudo crefi -n %d %sdir_%d" % (num_of_files, mounting_dir, num) ) rc = out.channel.recv_exit_status() print(out.read().decode()) RC.append(rc) print(time.time()) if rc == 0: log.info("Client IO is going on,success") else: log.error("Client IO got interrupted") failure.update({client: out}) break break except Exception as e: log.error(e) def custom_ceph_config(suite_config, custom_config, custom_config_file): """ Combines and returns custom configuration overrides for ceph. Hierarchy is as follows:: custom_config > custom_config_file > suite_config Args: suite_config: ceph_conf_overrides that currently exist in the test suite custom_config: custom config args provided by the cli (these all go to the global scope) custom_config_file: path to custom config yaml file provided by the cli Returns New value to be used for ceph_conf_overrides in test config """ log.debug("Suite config: {}".format(suite_config)) log.debug("Custom config: {}".format(custom_config)) log.debug("Custom config file: {}".format(custom_config_file)) full_custom_config = suite_config or {} cli_config_dict = {} custom_config_dict = {} # retrieve custom config from file if custom_config_file: with open(custom_config_file) as f: custom_config_dict = yaml.safe_load(f) log.info("File contents: {}".format(custom_config_dict)) # format cli configs into dict if custom_config: cli_config_dict = dict(item.split("=") for item in custom_config) # combine file and cli configs if cli_config_dict: if not custom_config_dict.get("global"): custom_config_dict["global"] = {} for key, value in cli_config_dict.items(): custom_config_dict["global"][key] = value # combine file and suite configs for key, value in custom_config_dict.items(): subsection = {} if full_custom_config.get(key): subsection.update(full_custom_config[key]) subsection.update(value) full_custom_config[key] = subsection log.info("Full custom config: {}".format(full_custom_config)) return full_custom_config def mask_secrets(plaintext, secrets): """ Replace secrets in plaintext with asterisks Args: plaintext (str or list): The plaintext to remove the secrets from or list of strings to remove secrets from secrets (list): List of secret strings to replace in the plaintext Returns: str: The censored version of plaintext """ if secrets: for secret in secrets: if isinstance(plaintext, list): plaintext = [string.replace(secret, "*" * 5) for string in plaintext] else: plaintext = plaintext.replace(secret, "*" * 5) return plaintext def run_cmd(cmd, secrets=None, timeout=600, ignore_error=False, **kwargs): """ *The deprecated form of exec_cmd.* Run an arbitrary command locally Args: cmd (str): command to run secrets (list): A list of secrets to be masked with asterisks This kwarg is popped in order to not interfere with subprocess.run(``**kwargs``) timeout (int): Timeout for the command, defaults to 600 seconds. ignore_error (bool): True if ignore non zero return code and do not raise the exception. Raises: CommandFailed: In case the command execution fails Returns: (str) Decoded stdout of command """ completed_process = exec_cmd(cmd, secrets, timeout, ignore_error, **kwargs) return mask_secrets(completed_process.stdout.decode(), secrets) def exec_cmd(cmd, secrets=None, timeout=600, ignore_error=False, **kwargs): """ Run an arbitrary command locally Args: cmd (str): command to run secrets (list): A list of secrets to be masked with asterisks This kwarg is popped in order to not interfere with subprocess.run(``**kwargs``) timeout (int): Timeout for the command, defaults to 600 seconds. ignore_error (bool): True if ignore non zero return code and do not raise the exception. Raises: CommandFailed: In case the command execution fails Returns: (CompletedProcess) A CompletedProcess object of the command that was executed CompletedProcess attributes: args: The list or str args passed to run(). returncode (str): The exit code of the process, negative for signals. stdout (str): The standard output (None if not captured). stderr (str): The standard error (None if not captured). """ masked_cmd = mask_secrets(cmd, secrets) log.info(f"Executing command: {masked_cmd}") if isinstance(cmd, str): cmd = shlex.split(cmd) completed_process = subprocess.run( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, timeout=timeout, **kwargs, ) masked_stdout = mask_secrets(completed_process.stdout.decode(), secrets) if len(completed_process.stdout) > 0: log.debug(f"Command stdout: {masked_stdout}") else: log.debug("Command stdout is empty") masked_stderr = mask_secrets(completed_process.stderr.decode(), secrets) if len(completed_process.stderr) > 0: log.warning(f"Command stderr: {masked_stderr}") else: log.debug("Command stderr is empty") log.debug(f"Command return code: {completed_process.returncode}") if completed_process.returncode and not ignore_error: raise CommandFailed( f"Error during execution of command: {masked_cmd}." f"\nError is {masked_stderr}" ) return completed_process def download_file(url, filename, **kwargs): """ Download a file from a specified url Args: url (str): URL of the file to download filename (str): Name of the file to write the download to kwargs (dict): additional keyword arguments passed to requests.get(...) """ log.debug(f"Download '{url}' to '{filename}'.") with open(filename, "wb") as f: r = requests.get(url, **kwargs) assert r.ok, f"The URL {url} is not available! Status: {r.status_code}." f.write(r.content) def get_url_content(url, **kwargs): """ Return URL content Args: url (str): URL address to return kwargs (dict): additional keyword arguments passed to requests.get(...) Returns: str: Content of URL Raises: AssertionError: When couldn't load URL """ log.debug(f"Download '{url}' content.") r = requests.get(url, **kwargs) assert r.ok, f"Couldn't load URL: {url} content! Status: {r.status_code}." return r.content def expose_ocp_version(version): """ This helper function exposes latest nightly version or GA version of OCP. When the version string ends with .nightly (e.g. 4.2.0-0.nightly) it will expose the version to latest accepted OCP build (e.g. 4.2.0-0.nightly-2019-08-08-103722) If the version ends with -ga than it will find the latest GA OCP version and will expose 4.2-ga to for example 4.2.22. Args: version (str): Verison of OCP Returns: str: Version of OCP exposed to full version if latest nighly passed """ if version.endswith(".nightly"): latest_nightly_url = ( f"https://amd64.ocp.releases.ci.openshift.org/api/v1/" f"releasestream/{version}/latest" ) version_url_content = get_url_content(latest_nightly_url) version_json = json.loads(version_url_content) return version_json["name"] if version.endswith("-ga"): channel = config.DEPLOYMENT.get("ocp_channel", "stable") ocp_version = version.rstrip("-ga") index = config.DEPLOYMENT.get("ocp_version_index", -1) return get_latest_ocp_version(f"{channel}-{ocp_version}", index) else: return version def get_openshift_installer( version=None, bin_dir=None, force_download=False, ): """ Download the OpenShift installer binary, if not already present. Update env. PATH and get path of the openshift installer binary. Args: version (str): Version of the installer to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force installer download even if already present Returns: str: Path to the installer binary """ version = version or config.DEPLOYMENT["installer_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) installer_filename = "openshift-install" installer_binary_path = os.path.join(bin_dir, installer_filename) if os.path.isfile(installer_binary_path) and force_download: delete_file(installer_binary_path) if os.path.isfile(installer_binary_path): log.debug(f"Installer exists ({installer_binary_path}), skipping download.") # TODO: check installer version else: version = expose_ocp_version(version) log.info(f"Downloading openshift installer ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) tarball = f"{installer_filename}.tar.gz" url = get_openshift_mirror_url(installer_filename, version) download_file(url, tarball) run_cmd(f"tar xzvf {tarball} {installer_filename}") delete_file(tarball) # return to the previous working directory os.chdir(previous_dir) installer_version = run_cmd(f"{installer_binary_path} version") log.info(f"OpenShift Installer version: {installer_version}") return installer_binary_path def get_ocm_cli( version=None, bin_dir=None, force_download=False, ): """ Download the OCM binary, if not already present. Update env. PATH and get path of the OCM binary. Args: version (str): Version of the OCM to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force OCM download even if already present Returns: str: Path to the OCM binary """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) ocm_filename = "ocm" ocm_binary_path = os.path.join(bin_dir, ocm_filename) if os.path.isfile(ocm_binary_path) and force_download: delete_file(ocm_binary_path) if os.path.isfile(ocm_binary_path): log.debug(f"ocm exists ({ocm_binary_path}), skipping download.") else: log.info(f"Downloading ocm cli ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://github.com/openshift-online/ocm-cli/releases/download/v{version}/ocm-linux-amd64" download_file(url, ocm_filename) # return to the previous working directory os.chdir(previous_dir) current_file_permissions = os.stat(ocm_binary_path) os.chmod( ocm_binary_path, current_file_permissions.st_mode | stat.S_IEXEC, ) ocm_version = run_cmd(f"{ocm_binary_path} version") log.info(f"OCM version: {ocm_version}") return ocm_binary_path def get_rosa_cli( version=None, bin_dir=None, force_download=False, ): """ Download the ROSA binary, if not already present. Update env. PATH and get path of the ROSA binary. Args: version (str): Version of the ROSA to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force ROSA download even if already present Returns: str: Path to the rosa binary """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) rosa_filename = "rosa" rosa_binary_path = os.path.join(bin_dir, rosa_filename) if os.path.isfile(rosa_binary_path) and force_download: delete_file(rosa_binary_path) if os.path.isfile(rosa_binary_path): log.debug(f"rosa exists ({rosa_binary_path}), skipping download.") else: log.info(f"Downloading rosa cli ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://github.com/openshift/rosa/releases/download/v{version}/rosa-linux-amd64" download_file(url, rosa_filename) # return to the previous working directory os.chdir(previous_dir) current_file_permissions = os.stat(rosa_binary_path) os.chmod( rosa_binary_path, current_file_permissions.st_mode | stat.S_IEXEC, ) rosa_version = run_cmd(f"{rosa_binary_path} version") log.info(f"rosa version: {rosa_version}") return rosa_binary_path def get_openshift_client( version=None, bin_dir=None, force_download=False, skip_comparison=False ): """ Download the OpenShift client binary, if not already present. Update env. PATH and get path of the oc binary. Args: version (str): Version of the client to download (default: config.RUN['client_version']) bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force client download even if already present skip_comparison (bool): Skip the comparison between the existing OCP client version and the configured one. Returns: str: Path to the client binary """ version = version or config.RUN["client_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) client_binary_path = os.path.join(bin_dir, "oc") kubectl_binary_path = os.path.join(bin_dir, "kubectl") download_client = True client_version = None try: version = expose_ocp_version(version) except Exception: log.exception("Unable to expose OCP version, skipping client download.") skip_comparison = True download_client = False force_download = False if force_download: log.info("Forcing client download.") elif os.path.isfile(client_binary_path) and not skip_comparison: current_client_version = get_client_version(client_binary_path) if current_client_version != version: log.info( f"Existing client version ({current_client_version}) does not match " f"configured version ({version})." ) else: log.debug( f"Client exists ({client_binary_path}) and matches configured version, " f"skipping download." ) download_client = False if download_client: # Move existing client binaries to backup location client_binary_backup = f"{client_binary_path}.bak" kubectl_binary_backup = f"{kubectl_binary_path}.bak" try: os.rename(client_binary_path, client_binary_backup) os.rename(kubectl_binary_path, kubectl_binary_backup) except FileNotFoundError: pass # Download the client log.info(f"Downloading openshift client ({version}).") prepare_bin_dir() # record current working directory and switch to BIN_DIR previous_dir = os.getcwd() os.chdir(bin_dir) url = get_openshift_mirror_url("openshift-client", version) tarball = "openshift-client.tar.gz" download_file(url, tarball) run_cmd(f"tar xzvf {tarball} oc kubectl") delete_file(tarball) try: client_version = run_cmd(f"{client_binary_path} version --client") except CommandFailed: log.error("Unable to get version from downloaded client.") if client_version: try: delete_file(client_binary_backup) delete_file(kubectl_binary_backup) log.info("Deleted backup binaries.") except FileNotFoundError: pass else: try: os.rename(client_binary_backup, client_binary_path) os.rename(kubectl_binary_backup, kubectl_binary_path) log.info("Restored backup binaries to their original location.") except FileNotFoundError: raise ClientDownloadError( "No backups exist and new binary was unable to be verified." ) # return to the previous working directory os.chdir(previous_dir) log.info(f"OpenShift Client version: {client_version}") return client_binary_path def get_vault_cli(bind_dir=None, force_download=False): """ Download vault based on platform basically for CLI purpose. Binary will be directly put into ocs_ci/bin/ directory Args: bind_dir (str): Path to bin directory (default: config.RUN['bin_dir']) force_download (bool): Force vault cli download even if already present """ res = requests.get(constants.VAULT_VERSION_INFO_URL) version = res.url.split("/")[-1].lstrip("v") bin_dir = os.path.expanduser(bind_dir or config.RUN["bin_dir"]) system = platform.system() if "Darwin" not in system and "Linux" not in system: raise UnsupportedOSType("Not a supported platform for vault") system = system.lower() zip_file = f"vault_{version}_{system}_amd64.zip" vault_cli_filename = "vault" vault_binary_path = os.path.join(bin_dir, vault_cli_filename) if os.path.isfile(vault_binary_path) and force_download: delete_file(vault_binary_path) if os.path.isfile(vault_binary_path): log.debug( f"Vault CLI binary already exists {vault_binary_path}, skipping download." ) else: log.info(f"Downloading vault cli {version}") prepare_bin_dir() previous_dir = os.getcwd() os.chdir(bin_dir) url = f"{constants.VAULT_DOWNLOAD_BASE_URL}/{version}/{zip_file}" download_file(url, zip_file) run_cmd(f"unzip {zip_file}") delete_file(zip_file) os.chdir(previous_dir) vault_ver = run_cmd(f"{vault_binary_path} version") log.info(f"Vault cli version:{vault_ver}") def ensure_nightly_build_availability(build_url): base_build_url = build_url.rsplit("/", 1)[0] r = requests.get(base_build_url) extracting_condition = b"Extracting" in r.content if extracting_condition: log.info("Build is extracting now, may take up to a minute.") return r.ok and not extracting_condition def get_openshift_mirror_url(file_name, version): """ Format url to OpenShift mirror (for client and installer download). Args: file_name (str): Name of file version (str): Version of the installer or client to download Returns: str: Url of the desired file (installer or client) Raises: UnsupportedOSType: In case the OS type is not supported UnavailableBuildException: In case the build url is not reachable """ if platform.system() == "Darwin": os_type = "mac" elif platform.system() == "Linux": os_type = "linux" else: raise UnsupportedOSType url_template = config.DEPLOYMENT.get( "ocp_url_template", "https://openshift-release-artifacts.apps.ci.l2s4.p1.openshiftapps.com/" "{version}/{file_name}-{os_type}-{version}.tar.gz", ) url = url_template.format( version=version, file_name=file_name, os_type=os_type, ) sample = TimeoutSampler( timeout=540, sleep=5, func=ensure_nightly_build_availability, build_url=url, ) if not sample.wait_for_func_status(result=True): raise UnavailableBuildException(f"The build url {url} is not reachable") return url def prepare_bin_dir(bin_dir=None): """ Prepare bin directory for OpenShift client and installer Args: bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) """ bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) try: os.mkdir(bin_dir) log.info(f"Directory '{bin_dir}' successfully created.") except FileExistsError: log.debug(f"Directory '{bin_dir}' already exists.") def add_path_to_env_path(path): """ Add path to the PATH environment variable (if not already there). Args: path (str): Path which should be added to the PATH env. variable """ env_path = os.environ["PATH"].split(os.pathsep) if path not in env_path: os.environ["PATH"] = os.pathsep.join([path] + env_path) log.info(f"Path '{path}' added to the PATH environment variable.") log.debug(f"PATH: {os.environ['PATH']}") def delete_file(file_name): """ Delete file_name Args: file_name (str): Path to the file you want to delete """ os.remove(file_name) def delete_dir(dir_name): """ Deletes the directory Args: dir_name (str): Directory path to delete """ try: rmtree(dir_name) except OSError as e: log.error(f"Failed to delete the directory {dir_name}. Error: {e.strerror}") class TimeoutSampler(object): """ Samples the function output. This is a generator object that at first yields the output of function `func`. After the yield, it either raises instance of `timeout_exc_cls` or sleeps `sleep` seconds. Yielding the output allows you to handle every value as you wish. Feel free to set the instance variables. Args: timeout (int): Timeout in seconds sleep (int): Sleep interval in seconds func (function): The function to sample func_args: Arguments for the function func_kwargs: Keyword arguments for the function """ def __init__(self, timeout, sleep, func, *func_args, **func_kwargs): self.timeout = timeout self.sleep = sleep # check that given timeout and sleep values makes sense if self.timeout < self.sleep: raise ValueError("timeout should be larger than sleep time") self.func = func self.func_args = func_args self.func_kwargs = func_kwargs # Timestamps of the first and most recent samples self.start_time = None self.last_sample_time = None # The exception to raise self.timeout_exc_cls = TimeoutExpiredError # Arguments that will be passed to the exception self.timeout_exc_args = [self.timeout] try: self.timeout_exc_args.append( f"Timed out after {timeout}s running {self._build_call_string()}" ) except Exception: log.exception( "Failed to assemble call string. Not necessarily a test failure." ) def _build_call_string(self): def stringify(value): if isinstance(value, str): return f'"{value}"' return str(value) args = list(map(stringify, self.func_args)) kwargs = [f"{stringify(k)}={stringify(v)}" for k, v in self.func_kwargs.items()] all_args_string = ", ".join(args + kwargs) return f"{self.func.__name__}({all_args_string})" def __iter__(self): if self.start_time is None: self.start_time = time.time() while True: self.last_sample_time = time.time() if self.timeout <= (self.last_sample_time - self.start_time): raise self.timeout_exc_cls(*self.timeout_exc_args) try: yield self.func(*self.func_args, **self.func_kwargs) except Exception as ex: msg = f"Exception raised during iteration: {ex}" log.exception(msg) if self.timeout <= (time.time() - self.start_time): raise self.timeout_exc_cls(*self.timeout_exc_args) log.info("Going to sleep for %d seconds before next iteration", self.sleep) time.sleep(self.sleep) def wait_for_func_value(self, value): """ Implements common usecase of TimeoutSampler: waiting until func (given function) returns a given value. Args: value: Expected return value of func we are waiting for. """ try: for i_value in self: if i_value == value: break except self.timeout_exc_cls: log.error( "function %s failed to return expected value %s " "after multiple retries during %d second timeout", self.func.__name__, value, self.timeout, ) raise def wait_for_func_status(self, result): """ Get function and run it for given time until success or timeout. (using __iter__ function) Args: result (bool): Expected result from func. Examples:: sample = TimeoutSampler( timeout=60, sleep=1, func=some_func, func_arg1="1", func_arg2="2" ) if not sample.wait_for_func_status(result=True): raise Exception """ try: self.wait_for_func_value(result) return True except self.timeout_exc_cls: return False class TimeoutIterator(TimeoutSampler): """ Wrapper of TimeoutSampler which separates parameters of the class itself and func arguments in __init__ method. Such way of passing function with parameters is used in python standard library. This allows more explicit usage, which improves readability, eg.:: t1 = TimeoutIterator(timeout=60, sleep=5, func=foo, func_args=[bar]) t2 = TimeoutIterator(3600, sleep=10, func=foo, func_args=[bar]) """ def __init__(self, timeout, sleep, func, func_args=None, func_kwargs=None): if func_args is None: func_args = [] if func_kwargs is None: func_kwargs = {} super().__init__(timeout, sleep, func, *func_args, **func_kwargs) def get_random_str(size=13): """ generates the random string of given size Args: size (int): number of random characters to generate Returns: str : string of random characters of given size """ chars = string.ascii_lowercase + string.digits return "".join(random.choice(chars) for _ in range(size)) def run_async(command): """ Run command locally and return without waiting for completion Args: command (str): The command to run. Returns: An open descriptor to be used by the calling function. Example: command = 'oc delete pvc pvc1' proc = run_async(command) ret, out, err = proc.async_communicate() """ log.info(f"Executing command: {command}") popen_obj = subprocess.Popen( command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True, encoding="utf-8", ) def async_communicate(): """ Wait for command to complete and fetch the result Returns: retcode, stdout, stderr of the command """ stdout, stderr = popen_obj.communicate() retcode = popen_obj.returncode return retcode, stdout, stderr popen_obj.async_communicate = async_communicate return popen_obj def is_cluster_running(cluster_path): from ocs_ci.ocs.openshift_ops import OCP return config.RUN["cli_params"].get("cluster_path") and OCP.set_kubeconfig( os.path.join(cluster_path, config.RUN.get("kubeconfig_location")) ) def decompose_html_attributes(soup, attributes): """ Decomposes the given html attributes Args: soup (obj): BeautifulSoup object attributes (list): attributes to decompose Returns: None """ for attribute in attributes: tg = soup.find_all(attrs={"class": attribute}) for each in tg: each.decompose() def parse_html_for_email(soup): """ Parses the html and filters out the unnecessary data/tags/attributes for email reporting Args: soup (obj): BeautifulSoup object """ attributes_to_decompose = ["extra"] if not config.RUN.get("logs_url"): attributes_to_decompose.append("col-links") decompose_html_attributes(soup, attributes_to_decompose) soup.find(id="not-found-message").decompose() if not config.RUN.get("logs_url"): for tr in soup.find_all("tr"): for th in tr.find_all("th"): if "Links" in th.text: th.decompose() for p in soup.find_all("p"): if "(Un)check the boxes to filter the results." in p.text: p.decompose() if "pytest-html" in p.text: data = p.text.split("by")[0] p.string = data for ip in soup.find_all("input"): if not ip.has_attr("disabled"): ip["disabled"] = "true" for td in soup.find_all("td"): if "pytest" in td.text or "html" in td.text: data = td.text.replace("&apos", "") td.string = data main_header = soup.find("h1") main_header.string.replace_with("OCS-CI RESULTS") def add_squad_analysis_to_email(session, soup): """ Add squad analysis to the html test results used in email reporting Args: session (obj): Pytest session object soup (obj): BeautifulSoup object of HTML Report data """ failed = {} skipped = {} # sort out failed and skipped test cases to failed and skipped dicts for result in session.results.values(): if result.failed or result.skipped: unassigned = True for squad, res in constants.SQUADS.items(): for item in res: if item in result.nodeid: if result.failed: if squad not in failed: failed[squad] = [] failed[squad].append(result.nodeid) unassigned = False if result.skipped: if squad not in skipped: skipped[squad] = [] try: skipped_message = result.longrepr[2][8:] except TypeError: skipped_message = "--unknown--" skipped[squad].append((result.nodeid, skipped_message)) unassigned = False if unassigned: if result.failed: if "UNASSIGNED" not in failed: failed["UNASSIGNED"] = [] failed["UNASSIGNED"].append(result.nodeid) if result.skipped: if "UNASSIGNED" not in skipped: skipped["UNASSIGNED"] = [] try: skipped_message = result.longrepr[2][8:] except TypeError: skipped_message = "--unknown--" skipped["UNASSIGNED"].append((result.nodeid, skipped_message)) # no failed or skipped tests - exit the function if not failed and not skipped: return # add CSS for the Squad Analysis report style = soup.find("style") # use colors for squad names from squad names style.string += "\n".join( [ f"h4.squad-{color.lower()} {{\n color: {color.lower()};\n}}" for color in constants.SQUADS ] ) # few additional styles style.string += """ .squad-analysis { color: black; font-family: monospace; background-color: #eee; padding: 5px; margin-top: 10px; } .squad-analysis h2 { margin: 0px; } .squad-analysis h3 { margin: 0px; margin-top: 10px; } .squad-analysis h4 { margin: 0px; } .squad-analysis ul { margin: 0px; } .squad-analysis ul li em { margin-left: 1em; } .squad-unassigned { background-color: #FFBA88; } h4.squad-yellow { color: black; background-color: yellow; display: inline; } """ # prepare place for the Squad Analysis in the email squad_analysis_div = soup.new_tag("div") squad_analysis_div["class"] = "squad-analysis" main_header = soup.find("h1") main_header.insert_after(squad_analysis_div) failed_h2_tag = soup.new_tag("h2") failed_h2_tag.string = "Squad Analysis - please analyze:" squad_analysis_div.append(failed_h2_tag) if failed: # print failed testcases peer squad failed_div_tag = soup.new_tag("div") squad_analysis_div.append(failed_div_tag) failed_h3_tag = soup.new_tag("h3") failed_h3_tag.string = "Failures:" failed_div_tag.append(failed_h3_tag) for squad in failed: failed_h4_tag = soup.new_tag("h4") failed_h4_tag.string = f"{squad} squad" failed_h4_tag["class"] = f"squad-{squad.lower()}" failed_div_tag.append(failed_h4_tag) failed_ul_tag = soup.new_tag("ul") failed_ul_tag["class"] = f"squad-{squad.lower()}" failed_div_tag.append(failed_ul_tag) for test in failed[squad]: failed_li_tag = soup.new_tag("li") failed_li_tag.string = test failed_ul_tag.append(failed_li_tag) if skipped: # print skipped testcases with reason peer squad skips_div_tag = soup.new_tag("div") squad_analysis_div.append(skips_div_tag) skips_h3_tag = soup.new_tag("h3") skips_h3_tag.string = "Skips:" skips_div_tag.append(skips_h3_tag) for squad in skipped: skips_h4_tag = soup.new_tag("h4") skips_h4_tag.string = f"{squad} squad" skips_h4_tag["class"] = f"squad-{squad.lower()}" skips_div_tag.append(skips_h4_tag) skips_ul_tag = soup.new_tag("ul") skips_ul_tag["class"] = f"squad-{squad.lower()}" skips_div_tag.append(skips_ul_tag) for test in skipped[squad]: skips_li_tag = soup.new_tag("li") skips_test_span_tag = soup.new_tag("span") skips_test_span_tag.string = test[0] skips_li_tag.append(skips_test_span_tag) skips_li_tag.append(soup.new_tag("br")) skips_reason_em_tag = soup.new_tag("em") skips_reason_em_tag.string = f"Reason: {test[1]}" skips_li_tag.append(skips_reason_em_tag) skips_ul_tag.append(skips_li_tag) def move_summary_to_top(soup): """ Move summary to the top of the eamil report """ summary = [] summary.append(soup.find("h2", text="Summary")) for tag in summary[0].next_siblings: if tag.name == "h2": break else: summary.append(tag) for tag in summary: tag.extract() main_header = soup.find("h1") # because we are inserting the tags just after the header one by one, we # have to insert them in reverse order summary.reverse() for tag in summary: main_header.insert_after(tag) def email_reports(session): """ Email results of test run """ # calculate percentage pass # reporter = session.config.pluginmanager.get_plugin("terminalreporter") # passed = len(reporter.stats.get("passed", [])) # failed = len(reporter.stats.get("failed", [])) # error = len(reporter.stats.get("error", [])) # total = passed + failed + error # percentage_passed = (passed / total) * 100 try: build_id = get_ocs_build_number() except Exception: build_id = "" log.exception("Getting OCS operator build number failed!") build_str = f"BUILD ID: {build_id} " if build_id else "" mailids = config.RUN["cli_params"]["email"] recipients = [] [recipients.append(mailid) for mailid in mailids.split(",")] sender = "ocs-ci@redhat.com" msg = MIMEMultipart("alternative") msg["Subject"] = ( f"ocs-ci results for {get_testrun_name()} " f"({build_str}" f"RUN ID: {config.RUN['run_id']}) " # f"Passed: {percentage_passed:.0f}%" ) msg["From"] = sender msg["To"] = ", ".join(recipients) html = config.RUN["cli_params"]["--html"] with open(os.path.expanduser(html)) as fd: html_data = fd.read() soup = BeautifulSoup(html_data, "html.parser") parse_html_for_email(soup) if config.RUN["cli_params"].get("squad_analysis"): add_squad_analysis_to_email(session, soup) move_summary_to_top(soup) part1 = MIMEText(soup, "html") msg.attach(part1) try: s = smtplib.SMTP(config.REPORTING["email"]["smtp_server"]) s.sendmail(sender, recipients, msg.as_string()) s.quit() log.info(f"Results have been emailed to {recipients}") except Exception: log.exception("Sending email with results failed!") def get_cluster_version_info(): """ Gets the complete cluster version information Returns: dict: cluster version information """ # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="clusterversion") cluster_version_info = ocp.get("version") return cluster_version_info def get_ocs_build_number(): """ Gets the build number for ocs operator Return: str: build number for ocs operator version """ # Importing here to avoid circular dependency from ocs_ci.ocs.resources.csv import get_csvs_start_with_prefix from ocs_ci.ocs.resources.catalog_source import CatalogSource from ocs_ci.ocs.resources.packagemanifest import get_selector_for_ocs_operator build_num = "" if ( version_module.get_semantic_ocs_version_from_config() >= version_module.VERSION_4_9 ): operator_name = defaults.ODF_OPERATOR_NAME else: operator_name = defaults.OCS_OPERATOR_NAME ocs_csvs = get_csvs_start_with_prefix( operator_name, defaults.ROOK_CLUSTER_NAMESPACE, ) try: ocs_csv = ocs_csvs[0] csv_labels = ocs_csv["metadata"]["labels"] if "full_version" in csv_labels: return csv_labels["full_version"] build_num = ocs_csv["spec"]["version"] operator_selector = get_selector_for_ocs_operator() # This is a temporary solution how to get the build id from the registry image. # Because we are now missing build ID in the CSV. If catalog source with our # internal label exists, we will be getting build id from the tag of the image # in catalog source. Boris is working on better way how to populate the internal # build version in the CSV. if operator_selector: catalog_source = CatalogSource( resource_name=constants.OPERATOR_CATALOG_SOURCE_NAME, namespace=constants.MARKETPLACE_NAMESPACE, selector=operator_selector, ) cs_data = catalog_source.get()["items"][0] cs_image = cs_data["spec"]["image"] image_tag = cs_image.split(":")[1] if "-" in image_tag: build_id = image_tag.split("-")[1] build_num += f"-{build_id}" except (IndexError, AttributeError, CommandFailed, KeyError): log.exception("No version info found for OCS operator") return build_num def get_cluster_version(): """ Gets the cluster version Returns: str: cluster version """ return get_cluster_version_info()["status"]["desired"]["version"] def get_cluster_image(): """ Gets the cluster image Returns: str: cluster image """ return get_cluster_version_info()["status"]["desired"]["image"] def get_ceph_version(): """ Gets the ceph version Returns: str: ceph version """ # importing here to avoid circular imports from ocs_ci.ocs.resources import pod ct_pod = pod.get_ceph_tools_pod() ceph_version = ct_pod.exec_ceph_cmd("ceph version") return re.split(r"ceph version ", ceph_version["version"])[1] def get_rook_version(): """ Gets the rook version Returns: str: rook version """ # importing here to avoid circular imports from ocs_ci.ocs.resources import pod ct_pod = pod.get_ceph_tools_pod() rook_versions = ct_pod.exec_ceph_cmd("rook version", format="") return rook_versions["rook"] def get_csi_versions(): """ Gets the CSI related version information Returns: dict: CSI related version information """ csi_versions = {} # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp_pod_obj = OCP( kind=constants.POD, namespace=config.ENV_DATA["cluster_namespace"] ) csi_provisioners = ["csi-cephfsplugin-provisioner", "csi-rbdplugin-provisioner"] for provisioner in csi_provisioners: csi_provisioner_pod = run_cmd( f"oc -n {config.ENV_DATA['cluster_namespace']} get pod -l " f"'app={provisioner}' -o jsonpath='{{.items[0].metadata.name}}'" ) desc = ocp_pod_obj.get(csi_provisioner_pod) for container in desc["spec"]["containers"]: name = container["name"] version = container["image"].split("/")[-1].split(":")[1] csi_versions[name] = version return csi_versions def get_ocp_version(seperator=None): """ Get current ocp version Args: seperator (str): String that would seperate major and minor version nubers Returns: string : If seperator is 'None', version string will be returned as is eg: '4.2', '4.3'. If seperator is provided then '.' in the version string would be replaced by seperator and resulting string will be returned. eg: If seperator is '_' then string returned would be '4_2' """ char = seperator if seperator else "." if config.ENV_DATA.get("skip_ocp_deployment"): raw_version = json.loads(run_cmd("oc version -o json"))["openshiftVersion"] else: raw_version = config.DEPLOYMENT["installer_version"] version = Version.coerce(raw_version) return char.join([str(version.major), str(version.minor)]) def get_running_ocp_version(separator=None): """ Get current running ocp version Args: separator (str): String that would separate major and minor version numbers Returns: string : If separator is 'None', version string will be returned as is eg: '4.2', '4.3'. If separator is provided then '.' in the version string would be replaced by separator and resulting string will be returned. eg: If separator is '_' then string returned would be '4_2' """ char = separator if separator else "." namespace = config.ENV_DATA["cluster_namespace"] try: # if the cluster exist, this part will be run results = run_cmd(f"oc get clusterversion -n {namespace} -o yaml") build = yaml.safe_load(results)["items"][0]["status"]["desired"]["version"] return char.join(build.split(".")[0:2]) except Exception: # this part will return version from the config file in case # cluster is not exists. return get_ocp_version(seperator=char) def get_ocp_repo(): """ Get ocp repo file, name will be generated dynamically based on ocp version. Returns: string : Path to ocp repo file """ repo_path = os.path.join(constants.REPO_DIR, f"ocp_{get_ocp_version('_')}.repo") path = os.path.expanduser(repo_path) assert os.path.exists(path), f"OCP repo file {path} doesn't exists!" return path def parse_pgsql_logs(data): """ Parse the pgsql benchmark data from ripsaw and return the data in list format Args: data (str): log data from pgsql bench run Returns: list_data (list): data digestable by scripts with below format e.g.: [ {1: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, {2: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, {3: {'num_clients': '2','num_threads': '7','latency_avg': '7', 'lat_stddev': '0', 'tps_incl': '234', 'tps_excl': '243'}, ] where keys{1,2,3} are run-IDs """ match = data.split("PGBench Results") list_data = [] for i in range(2, len(match)): log = "".join(match[i].split("\n")) pgsql_data = dict() pgsql_data[i - 1] = {} clients = re.search(r"scaling_factor\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["scaling_factor"] = clients.group(1) clients = re.search(r"number_of_clients\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["num_clients"] = clients.group(1) threads = re.search(r"number_of_threads\':\s+(\d+)", log) if threads and threads.group(1): pgsql_data[i - 1]["num_threads"] = threads.group(1) clients = re.search(r"number_of_transactions_per_client\':\s+(\d+),", log) if clients and clients.group(1): pgsql_data[i - 1]["number_of_transactions_per_client"] = clients.group(1) clients = re.search( r"number_of_transactions_actually_processed\':\s+(\d+),", log ) if clients and clients.group(1): pgsql_data[i - 1][ "number_of_transactions_actually_processed" ] = clients.group(1) lat_avg = re.search(r"latency_average_ms\':\s+(\d+)", log) if lat_avg and lat_avg.group(1): pgsql_data[i - 1]["latency_avg"] = lat_avg.group(1) lat_stddev = re.search(r"latency_stddev_ms\':\s+(\d+)", log) if lat_stddev and lat_stddev.group(1): pgsql_data[i - 1]["lat_stddev"] = lat_stddev.group(1) tps_incl = re.search(r"tps_incl_con_est\':\s+(\w+)", log) if tps_incl and tps_incl.group(1): pgsql_data[i - 1]["tps_incl"] = tps_incl.group(1) tps_excl = re.search(r"tps_excl_con_est\':\s+(\w+)", log) if tps_excl and tps_excl.group(1): pgsql_data[i - 1]["tps_excl"] = tps_excl.group(1) list_data.append(pgsql_data) return list_data def create_directory_path(path): """ Creates directory if path doesn't exists """ path = os.path.expanduser(path) if not os.path.exists(path): os.makedirs(path) else: log.debug(f"{path} already exists") def ocsci_log_path(): """ Construct the full path for the log directory. Returns: str: full path for ocs-ci log directory """ return os.path.expanduser( os.path.join(config.RUN["log_dir"], f"ocs-ci-logs-{config.RUN['run_id']}") ) def get_testrun_name(): """ Prepare testrun ID for Polarion (and other reports). Returns: str: String containing testrun name """ markers = config.RUN["cli_params"].get("-m", "").replace(" ", "-") us_ds = config.REPORTING.get("us_ds") if us_ds.upper() == "US": us_ds = "Upstream" elif us_ds.upper() == "DS": us_ds = "Downstream" ocp_version = ".".join(config.DEPLOYMENT.get("installer_version").split(".")[:-2]) ocp_version_string = f"OCP{ocp_version}" if ocp_version else "" ocs_version = config.ENV_DATA.get("ocs_version") ocs_version_string = f"OCS{ocs_version}" if ocs_version else "" worker_os = "RHEL" if config.ENV_DATA.get("rhel_workers") else "RHCOS" build_user = None baremetal_config = None if config.ENV_DATA.get("mon_type"): baremetal_config = ( f"MON {config.ENV_DATA.get('mon_type').upper()} " f"OSD {config.ENV_DATA.get('osd_type').upper()}" ) lso_deployment = "" if not baremetal_config and config.DEPLOYMENT.get("local_storage"): lso_deployment = "LSO " if config.REPORTING.get("display_name"): testrun_name = config.REPORTING.get("display_name") else: build_user = config.REPORTING.get("build_user") testrun_name = ( f"{config.ENV_DATA.get('platform', '').upper()} " f"{config.ENV_DATA.get('deployment_type', '').upper()} " ) if baremetal_config: testrun_name = f"LSO {baremetal_config} {testrun_name}" testrun_name = ( f"{testrun_name}" f"{get_az_count()}AZ " f"{worker_os} " f"{lso_deployment}" f"{config.ENV_DATA.get('master_replicas')}M " f"{config.ENV_DATA.get('worker_replicas')}W " f"{markers}" ) testrun_name = ( f"{ocs_version_string} {us_ds} {ocp_version_string} " f"{testrun_name}" ) if build_user: testrun_name = f"{build_user} {testrun_name}" # replace invalid character(s) by '-' testrun_name = testrun_name.translate( str.maketrans({key: "-" for key in """ \\/.:*"<>|~!@#$?%^&'*(){}+`,=\t"""}) ) log.info("testrun_name: %s", testrun_name) return testrun_name def get_az_count(): """ Using a number of different configuration attributes, determine how many availability zones the cluster is configured for. Returns: int: number of availability zones """ if config.ENV_DATA.get("availability_zone_count"): return int(config.ENV_DATA.get("availability_zone_count")) elif config.ENV_DATA.get("worker_availability_zones"): return len(config.ENV_DATA.get("worker_availability_zones")) elif config.ENV_DATA.get("platform") == "vsphere": return 1 else: return 1 def ceph_health_check(namespace=None, tries=20, delay=30): """ Args: namespace (str): Namespace of OCS (default: config.ENV_DATA['cluster_namespace']) tries (int): Number of retries delay (int): Delay in seconds between retries Returns: bool: ceph_health_check_base return value with default retries of 20, delay of 30 seconds if default values are not changed via args. """ if config.ENV_DATA["platform"].lower() == constants.IBM_POWER_PLATFORM: delay = 60 return retry( (CephHealthException, CommandFailed, subprocess.TimeoutExpired), tries=tries, delay=delay, backoff=1, )(ceph_health_check_base)(namespace) def ceph_health_check_base(namespace=None): """ Exec `ceph health` cmd on tools pod to determine health of cluster. Args: namespace (str): Namespace of OCS (default: config.ENV_DATA['cluster_namespace']) Raises: CephHealthException: If the ceph health returned is not HEALTH_OK CommandFailed: If the command to retrieve the tools pod name or the command to get ceph health returns a non-zero exit code Returns: boolean: True if HEALTH_OK """ namespace = namespace or config.ENV_DATA["cluster_namespace"] run_cmd( f"oc wait --for condition=ready pod " f"-l app=rook-ceph-tools " f"-n {namespace} " f"--timeout=120s" ) tools_pod = run_cmd( f"oc -n {namespace} get pod -l 'app=rook-ceph-tools' " f"-o jsonpath='{{.items[0].metadata.name}}'", timeout=60, ) health = run_cmd(f"oc -n {namespace} exec {tools_pod} -- ceph health") if health.strip() == "HEALTH_OK": log.info("Ceph cluster health is HEALTH_OK.") return True else: raise CephHealthException(f"Ceph cluster health is not OK. Health: {health}") def get_rook_repo(branch="master", to_checkout=None): """ Clone and checkout the rook repository to specific branch/commit. Args: branch (str): Branch name to checkout to_checkout (str): Commit id or tag to checkout """ cwd = constants.ROOK_REPO_DIR if not os.path.isdir(cwd): log.info(f"Cloning rook repository into {cwd}.") run_cmd(f"git clone {constants.ROOK_REPOSITORY} {cwd}") else: log.info( f"The rook directory {cwd} already exists, ocs-ci will skip the " f"clone of rook repository." ) log.info("Fetching latest changes from rook repository.") run_cmd("git fetch --all", cwd=cwd) log.info(f"Checkout rook repository to specific branch: {branch}") run_cmd(f"git checkout {branch}", cwd=cwd) log.info(f"Reset branch: {branch} with latest changes") run_cmd(f"git reset --hard origin/{branch}", cwd=cwd) if to_checkout: run_cmd(f"git checkout {to_checkout}", cwd=cwd) def clone_repo(url, location, branch="master", to_checkout=None): """ Clone a repository or checkout latest changes if it already exists at specified location. Args: url (str): location of the repository to clone location (str): path where the repository will be cloned to branch (str): branch name to checkout to_checkout (str): commit id or tag to checkout """ if not os.path.isdir(location): log.info("Cloning repository into %s", location) run_cmd(f"git clone {url} {location}") else: log.info("Repository already cloned at %s, skipping clone", location) log.info("Fetching latest changes from repository") run_cmd("git fetch --all", cwd=location) log.info("Checking out repository to specific branch: %s", branch) run_cmd(f"git checkout {branch}", cwd=location) log.info("Reset branch: %s with latest changes", branch) run_cmd(f"git reset --hard origin/{branch}", cwd=location) if to_checkout: run_cmd(f"git checkout {to_checkout}", cwd=location) def get_latest_ds_olm_tag(upgrade=False, latest_tag=None): """ This function returns latest tag of OCS downstream registry or one before latest if upgrade parameter is True Args: upgrade (str): If True then it returns one version of the build before the latest. latest_tag (str): Tag of the latest build. If not specified config.DEPLOYMENT['default_latest_tag'] or 'latest' will be used. Returns: str: latest tag for downstream image from quay registry Raises: TagNotFoundException: In case no tag found """ latest_tag = latest_tag or config.DEPLOYMENT.get("default_latest_tag", "latest") tags = get_ocs_olm_operator_tags() latest_image = None ocs_version = config.ENV_DATA["ocs_version"] upgrade_ocs_version = config.UPGRADE.get("upgrade_ocs_version") use_rc_build = config.UPGRADE.get("use_rc_build") previous_rc_build = config.UPGRADE.get("previous_rc_build") upgrade_version_change = upgrade_ocs_version and ocs_version != upgrade_ocs_version if upgrade and use_rc_build and previous_rc_build and not upgrade_version_change: latest_tag = previous_rc_build if upgrade_version_change: upgrade = False for tag in tags: if tag["name"] == latest_tag: latest_image = tag["manifest_digest"] break if not latest_image: raise TagNotFoundException("Couldn't find latest tag!") latest_tag_found = False for tag in tags: if not upgrade: if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and tag["manifest_digest"] == latest_image ): return tag["name"] if upgrade: if not latest_tag_found and tag["name"] == latest_tag: latest_tag_found = True continue if not latest_tag_found: continue if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and tag["manifest_digest"] != latest_image and ocs_version in tag["name"] ): if config.UPGRADE.get("use_rc_build") and "rc" not in tag["name"]: continue return tag["name"] raise TagNotFoundException("Couldn't find any desired tag!") def get_next_version_available_for_upgrade(current_tag): """ This function returns the tag built after the current_version Args: current_tag (str): Current build tag from which to search the next one build tag. Returns: str: tag for downstream image from quay registry built after the current_tag. Raises: TagNotFoundException: In case no tag suitable for upgrade found """ tags = get_ocs_olm_operator_tags() if any(t in current_tag for t in constants.LATEST_TAGS): return current_tag current_tag_index = None for index, tag in enumerate(tags): if tag["name"] == current_tag: if index < 2: raise TagNotFoundException("Couldn't find tag for upgrade!") current_tag_index = index break sliced_reversed_tags = tags[:current_tag_index] sliced_reversed_tags.reverse() ocs_version = config.ENV_DATA["ocs_version"] for tag in sliced_reversed_tags: if ( not any(t in tag["name"] for t in constants.LATEST_TAGS) and ocs_version in tag["name"] ): if config.UPGRADE.get("use_rc_build") and "rc" not in tag["name"]: continue return tag["name"] raise TagNotFoundException("Couldn't find any tag!") def load_auth_config(): """ Load the authentication config YAML from /data/auth.yaml Raises: FileNotFoundError: if the auth config is not found Returns: dict: A dictionary reprensenting the YAML file """ log.info("Retrieving the authentication config dictionary") auth_file = os.path.join(constants.TOP_DIR, "data", constants.AUTHYAML) try: with open(auth_file) as f: return yaml.safe_load(f) except FileNotFoundError: log.warning( f"Unable to find the authentication configuration at {auth_file}, " f"please refer to the getting started guide ({constants.AUTH_CONFIG_DOCS})" ) return {} def get_ocs_olm_operator_tags(limit=100): """ Query the OCS OLM Operator repo and retrieve a list of tags. Since we are limited to 100 tags per page, we end up making several API calls and combining the results into a single list of tags. Args: limit: the number of tags to limit the request to Raises: KeyError: if the auth config isn't setup properly requests.RequestException: if the response return code is not ok Returns: list: OCS OLM Operator tags """ try: quay_access_token = load_auth_config()["quay"]["access_token"] except (KeyError, TypeError): log.error( "Unable to retrieve the access token for quay, please refer to " f"the getting started guide ({constants.AUTH_CONFIG_DOCS}) " "to properly setup your authentication configuration" ) raise headers = {"Authorization": f"Bearer {quay_access_token}"} image = "ocs-registry" try: ocs_version = float(config.ENV_DATA.get("ocs_version")) if ocs_version < 4.5: image = "ocs-olm-operator" except (ValueError, TypeError): log.warning("Invalid ocs_version given, defaulting to ocs-registry image") pass all_tags = [] page = 1 while True: log.info(f"Retrieving OCS OLM Operator tags (limit {limit}, page {page})") resp = requests.get( constants.OPERATOR_CS_QUAY_API_QUERY.format( tag_limit=limit, image=image, page=page, ), headers=headers, ) if not resp.ok: raise requests.RequestException(resp.json()) tags = resp.json()["tags"] if len(tags) == 0: log.info("No more tags to retrieve") break log.debug(tags) all_tags.extend(tags) page += 1 return all_tags def check_if_executable_in_path(exec_name): """ Checks whether an executable can be found in the $PATH Args: exec_name: Name of executable to look for Returns: Boolean: Whether the executable was found """ return which(exec_name) is not None def upload_file(server, localpath, remotepath, user=None, password=None, key_file=None): """ Upload a file to remote server Args: server (str): Name of the server to upload localpath (str): Local file to upload remotepath (str): Target path on the remote server. filename should be included user (str): User to use for the remote connection """ if not user: user = "root" try: ssh = SSHClient() ssh.set_missing_host_key_policy(AutoAddPolicy()) if password: ssh.connect(hostname=server, username=user, password=password) else: log.info(key_file) ssh.connect(hostname=server, username=user, key_filename=key_file) sftp = ssh.open_sftp() log.info(f"uploading {localpath} to {user}@{server}:{remotepath}") sftp.put(localpath, remotepath) sftp.close() ssh.close() except AuthenticationException as authException: log.error(f"Authentication failed: {authException}") raise authException except SSHException as sshException: log.error(f"SSH connection failed: {sshException}") raise sshException def read_file_as_str(filepath): """ Reads the file content Args: filepath (str): File to read Returns: str : File contents in string """ with open(rf"{filepath}") as fd: content = fd.read() return content def replace_content_in_file(file, old, new, match_and_replace_line=False): """ Replaces contents in file, if old value is not found, it adds new value to the file Args: file (str): Name of the file in which contents will be replaced old (str): Data to search for new (str): Data to replace the old value match_and_replace_line (bool): If True, it will match a line if `old` pattern is found in the line. The whole line will be replaced with `new` content. Otherwise it will replace only `old` string with `new` string but the rest of the line will be intact. This is the default option. """ # Read the file with open(rf"{file}", "r") as fd: file_data = [line.rstrip("\n") for line in fd.readlines()] if match_and_replace_line: # Replace the whole line with `new` string if the line contains `old` # string pattern. file_data = [new if old in line else line for line in file_data] else: # Replace the old string by new file_data = [ line.replace(old, new) if old in line else line for line in file_data ] updated_data = [line for line in file_data if new in line] # In case the old pattern wasn't found it will be added as first line if not updated_data: file_data.insert(0, new) file_data = [f"{line}\n" for line in file_data] # Write the file out again with open(rf"{file}", "w") as fd: fd.writelines(file_data) @retry((CommandFailed), tries=100, delay=10, backoff=1) def wait_for_co(operator): """ Waits for ClusterOperator to created Args: operator (str): Name of the ClusterOperator """ from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="ClusterOperator") ocp.get(operator) def censor_values(data_to_censor): """ This function censor string and numeric values in dictionary based on keys that match pattern defined in config_keys_patterns_to_censor in constants. It is performed recursively for nested dictionaries. Args: data_to_censor (dict): Data to censor. Returns: dict: filtered data """ for key in data_to_censor: if isinstance(data_to_censor[key], dict): censor_values(data_to_censor[key]) elif isinstance(data_to_censor[key], (str, int, float)): for pattern in constants.config_keys_patterns_to_censor: if pattern in key.lower(): data_to_censor[key] = "*" * 5 return data_to_censor def dump_config_to_file(file_path): """ Dump the config to the yaml file with censored secret values. Args: file_path (str): Path to file where to write the configuration. """ config_copy = deepcopy(config.to_dict()) censor_values(config_copy) with open(file_path, "w+") as fs: yaml.safe_dump(config_copy, fs) def create_rhelpod(namespace, pod_name, timeout=300): """ Creates the RHEL pod Args: namespace (str): Namespace to create RHEL pod pod_name (str): Pod name timeout (int): wait time for RHEL pod to be in Running state Returns: pod: Pod instance for RHEL """ # importing here to avoid dependencies from ocs_ci.helpers import helpers rhelpod_obj = helpers.create_pod( namespace=namespace, pod_name=pod_name, pod_dict_path=constants.RHEL_7_7_POD_YAML, ) helpers.wait_for_resource_state(rhelpod_obj, constants.STATUS_RUNNING, timeout) return rhelpod_obj def check_timeout_reached(start_time, timeout, err_msg=None): """ Check if timeout reached and if so raise the exception. Args: start_time (time): Star time of the operation. timeout (int): Timeout in seconds. err_msg (str): Error message for the exception. Raises: TimeoutException: In case the timeout reached. """ msg = f"Timeout {timeout} reached!" if err_msg: msg += " Error: {err_msg}" if timeout < (time.time() - start_time): raise TimeoutException(msg) def convert_yaml2tfvars(yaml): """ Converts yaml file to tfvars. It creates the tfvars with the same filename in the required format which is used for deployment. Args: yaml (str): File path to yaml Returns: str: File path to tfvars """ # importing here to avoid dependencies from ocs_ci.utility.templating import load_yaml data = load_yaml(yaml) tfvars_file = os.path.splitext(yaml)[0] log.debug(f"Converting {yaml} to {tfvars_file}") with open(tfvars_file, "w+") as fd: for key, val in data.items(): if key == "control_plane_ignition": fd.write("control_plane_ignition = <<END_OF_MASTER_IGNITION\n") fd.write(f"{val}\n") fd.write("END_OF_MASTER_IGNITION\n") continue if key == "compute_ignition": fd.write("compute_ignition = <<END_OF_WORKER_IGNITION\n") fd.write(f"{val}\n") fd.write("END_OF_WORKER_IGNITION\n") continue if key == "vm_dns_addresses": fd.write(f'vm_dns_addresses = ["{val}"]\n') continue fd.write(key) fd.write(" = ") fd.write('"') fd.write(f"{val}") fd.write('"\n') return tfvars_file def remove_keys_from_tf_variable_file(tf_file, keys): """ Removes the keys from the tf files and convert to json format Args: tf_file (str): path to tf file keys (list): list of keys to remove """ # importing here to avoid dependencies from ocs_ci.utility.templating import dump_data_to_json with open(tf_file, "r") as fd: obj = hcl2.load(fd) for key in keys: obj["variable"].pop(key) dump_data_to_json(obj, f"{tf_file}.json") os.rename(tf_file, f"{tf_file}.backup") def get_kubeadmin_password(): filename = os.path.join( config.ENV_DATA["cluster_path"], config.RUN["password_location"] ) with open(filename) as f: return f.read() def get_infra_id(cluster_path): """ Get infraID from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['infraID'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["infraID"] def get_cluster_name(cluster_path): """ Get clusterName from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['clusterName'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["clusterName"] def skipif_ocp_version(expressions): """ This function evaluates the condition for test skip based on expression Args: expressions (str OR list): condition for which we need to check, eg: A single expression string '>=4.2' OR A list of expressions like ['<4.3', '>4.2'], ['<=4.3', '>=4.2'] Return: 'True' if test needs to be skipped else 'False' """ skip_this = True ocp_version = get_running_ocp_version() expr_list = [expressions] if isinstance(expressions, str) else expressions for expr in expr_list: comparision_str = ocp_version + expr skip_this = skip_this and eval(comparision_str) # skip_this will be either True or False after eval return skip_this def skipif_ocs_version(expressions): """ This function evaluates the condition for test skip based on expression Args: expressions (str OR list): condition for which we need to check, eg: A single expression string '>=4.2' OR A list of expressions like ['<4.3', '>4.2'], ['<=4.3', '>=4.2'] Return: 'True' if test needs to be skipped else 'False' """ expr_list = [expressions] if isinstance(expressions, str) else expressions return any(eval(config.ENV_DATA["ocs_version"] + expr) for expr in expr_list) def skipif_ui_not_support(ui_test): """ This function evaluates the condition for ui test skip based on ui_test expression Args: ui_test (str): condition for which we need to check, Return: 'True' if test needs to be skipped else 'False' """ from ocs_ci.ocs.ui.views import locators ocp_version = get_running_ocp_version() if ( config.ENV_DATA["platform"].lower() == constants.IBMCLOUD_PLATFORM or config.ENV_DATA["platform"].lower() == constants.OPENSHIFT_DEDICATED_PLATFORM or config.ENV_DATA["platform"].lower() == constants.ROSA_PLATFORM ): return True try: locators[ocp_version][ui_test] except KeyError: return True return False def get_ocs_version_from_image(image): """ Parse major.minor version from OCS image tag. Args: image (str): image in format url:tag Returns str: Version in x.y format Raises: ValueError: In case of the tag which we cannot parse to version. """ try: version = image.rsplit(":", 1)[1].lstrip("latest-").lstrip("stable-") version = Version.coerce(version) return "{major}.{minor}".format(major=version.major, minor=version.minor) except ValueError: log.error(f"The version: {version} couldn't be parsed!") raise def get_available_ocp_versions(channel): """ Find all available OCP versions for specific channel. Args: channel (str): Channel of OCP (e.g. stable-4.2 or fast-4.2) Returns list: Sorted list with OCP versions for specified channel. """ headers = {"Accept": "application/json"} req = requests.get( constants.OPENSHIFT_UPGRADE_INFO_API.format(channel=channel), headers=headers ) data = req.json() versions = [Version(node["version"]) for node in data["nodes"]] versions.sort() return versions def get_latest_ocp_version(channel, index=-1): """ Find latest OCP version for specific channel. Args: channel (str): Channel of OCP (e.g. stable-4.2 or fast-4.2) index (int): Index to get from all available versions list e.g. default -1 is latest version (version[-1]). If you want to get previous version pass index -2 and so on. Returns str: Latest OCP version for specified channel. """ versions = get_available_ocp_versions(channel) return str(versions[index]) def load_config_file(config_file): """ Loads config file to the ocs-ci config Args: config_file (str): Path to yaml config file. Raises: FileNotFoundError: In the case the config file not found. """ config_file = os.path.expanduser(config_file) assert os.path.exists(config_file), f"Config file {config_file} doesn't exist!" with open(os.path.abspath(os.path.expanduser(config_file)), "r") as file_stream: custom_config_data = yaml.safe_load(file_stream) config.update(custom_config_data) def destroy_cluster(installer, cluster_path, log_level="DEBUG"): """ Destroy OCP cluster specific Args: installer (str): The path to the installer binary cluster_path (str): The path of the cluster log_level (str): log level openshift-installer (default: DEBUG) """ destroy_cmd = ( f"{installer} destroy cluster " f"--dir {cluster_path} " f"--log-level {log_level}" ) try: # Execute destroy cluster using OpenShift installer log.info(f"Destroying cluster defined in {cluster_path}") run_cmd(destroy_cmd, timeout=1200) except CommandFailed: log.error(traceback.format_exc()) raise except Exception: log.error(traceback.format_exc()) def config_to_string(config): """ Convert ConfigParser object to string in INI format. Args: config (obj): ConfigParser object Returns: str: Config in one string """ strio = io.StringIO() config.write(strio, space_around_delimiters=False) return strio.getvalue() class AZInfo(object): """ A class for getting different az numbers across calls """ zone_number = 0 def get_zone_number(self): """ Increment current zone_number and perform modulus op to roll-on to next available number Returns: int: zone number index """ prev = AZInfo.zone_number AZInfo.zone_number += 1 AZInfo.zone_number %= get_az_count() return prev def convert_device_size(unformatted_size, units_to_covert_to): """ Convert a string representing a size to an int according to the given units to convert to Args: unformatted_size (str): The size to convert (i.e, '1Gi'/'100Mi') units_to_covert_to (str): The units to convert the size to (i.e, TB/GB/MB) Returns: int: The converted size """ units = unformatted_size[-2:] abso = int(unformatted_size[:-2]) conversion = { "TB": {"Ti": abso, "Gi": abso / 1000, "Mi": abso / 1e6, "Ki": abso / 1e9}, "GB": {"Ti": abso * 1000, "Gi": abso, "Mi": abso / 1000, "Ki": abso / 1e6}, "MB": {"Ti": abso * 1e6, "Gi": abso * 1000, "Mi": abso, "Ki": abso / 1000}, "KB": {"Ti": abso * 1e9, "Gi": abso * 1e6, "Mi": abso * 1000, "Ki": abso}, "B": {"Ti": abso * 1e12, "Gi": abso * 1e9, "Mi": abso * 1e6, "Ki": abso * 1000}, } return conversion[units_to_covert_to][units] def prepare_customized_pull_secret(images=None): """ Prepare customized pull-secret containing auth section related to given image(s). If image(s) not defined or no related section is found, it will use whole content of pull-secret. Args: images (str, list): image (or images) to match with auth section Returns: NamedTemporaryFile: prepared pull-secret """ log.debug(f"Prepare customized pull-secret for images: {images}") if type(images) == str: images = [images] # load pull-secret file to pull_secret dict pull_secret_path = os.path.join(constants.TOP_DIR, "data", "pull-secret") with open(pull_secret_path) as pull_secret_fo: pull_secret = json.load(pull_secret_fo) authfile_content = {"auths": {}} # if images defined, try to find auth section related to specified images if images: for image in images: # find all auths which might be related to the specified image tmp_auths = [auth for auth in pull_secret["auths"] if auth in image] # get the most specific auth for particular image tmp_auths = sorted(tmp_auths, key=len, reverse=True) if tmp_auths: # if there is match to particular auth, prepare authfile just with the # matching auth auth = tmp_auths[0] # as key use only server name, without namespace authfile_content["auths"][auth.split("/", 1)[0]] = pull_secret["auths"][ auth ] if not authfile_content["auths"]: authfile_content = pull_secret # create temporary auth file authfile_fo = NamedTemporaryFile(mode="w", prefix="authfile_") json.dump(authfile_content, authfile_fo) # ensure the content will be saved into the file authfile_fo.flush() return authfile_fo def inspect_image(image, authfile_fo): """ Inspect image Args: image (str): image to inspect authfile_fo (NamedTemporaryFile): pull-secret required for pulling the given image Returns: dict: json object of the inspected image """ # pull original image (to be able to inspect it) exec_cmd(f"podman image pull {image} --authfile {authfile_fo.name}") # inspect the image cmd_result = exec_cmd(f"podman image inspect {image}") image_inspect = json.loads(cmd_result.stdout) return image_inspect def get_image_with_digest(image): """ Return image with sha256 digest for usage in disconnected environment Args: image (str): image Raises: UnexpectedImage: In case the image information is unexpected Returns: str: image with sha256 digest specification """ if "@sha256:" in image: return image with prepare_customized_pull_secret(image) as authfile_fo: image_inspect = inspect_image(image, authfile_fo) # we expect, that 'Digest' will match one of the images in 'RepoDigests', # if not, raise UnexpectedImage for image in image_inspect[0]["RepoDigests"]: if image_inspect[0]["Digest"] in image: return image else: raise UnexpectedImage( f"Image digest ({image_inspect[0]['Digest']}) doesn't match with " f"any image from RepoDigests ({image_inspect[0]['RepoDigests']})." ) def login_to_mirror_registry(authfile): """ Login to mirror registry Args: authfile (str): authfile (pull-secret) path """ # load cluster info load_cluster_info() mirror_registry = config.DEPLOYMENT["mirror_registry"] mirror_registry_user = config.DEPLOYMENT["mirror_registry_user"] mirror_registry_password = config.DEPLOYMENT["mirror_registry_password"] login_cmd = ( f"podman login --authfile {authfile} " f"{mirror_registry} -u {mirror_registry_user} " f"-p {mirror_registry_password} --tls-verify=false" ) exec_cmd(login_cmd, (mirror_registry_user, mirror_registry_password)) def mirror_image(image): """ Mirror image to mirror image registry. Args: image (str): image to be mirrored, can be defined just with name or with full url, with or without tag or digest Returns: str: the mirrored image link """ with prepare_customized_pull_secret(image) as authfile_fo: # login to mirror registry login_to_mirror_registry(authfile_fo.name) # if there is any tag specified, use it in the full image url, # otherwise use url with digest image_inspect = inspect_image(image, authfile_fo) if image_inspect[0].get("RepoTags"): orig_image_full = image_inspect[0]["RepoTags"][0] else: orig_image_full = image_inspect[0]["RepoDigests"][0] # prepare mirrored image url mirror_registry = config.DEPLOYMENT["mirror_registry"] mirrored_image = mirror_registry + re.sub(r"^[^/]*", "", orig_image_full) # mirror the image log.info( f"Mirroring image '{image}' ('{orig_image_full}') to '{mirrored_image}'" ) exec_cmd( f"oc image mirror --insecure --registry-config" f" {authfile_fo.name} {orig_image_full} {mirrored_image}" ) return mirrored_image def update_container_with_mirrored_image(job_pod_dict): """ Update Job or Pod configuration dict with mirrored image (required for disconnected installation). Args: job_pod_dict (dict): dictionary with Job or Pod configuration Returns: dict: for disconnected installation, returns updated Job or Pod dict, for normal installation return unchanged job_pod_dict """ if config.DEPLOYMENT.get("disconnected"): if "containers" in job_pod_dict["spec"]: container = job_pod_dict["spec"]["containers"][0] else: container = job_pod_dict["spec"]["template"]["spec"]["containers"][0] container["image"] = mirror_image(container["image"]) return job_pod_dict def get_trim_mean(values, percentage=20): """ Get the trimmed mean of a list of values. Explanation: This function finds the arithmetic mean of given values, ignoring values outside the given limits. Args: values (list): The list of values percentage (int): The percentage to be trimmed Returns: float: Trimmed mean. In case trimmed mean calculation fails, the regular mean average is returned """ lower_limit = scoreatpercentile(values, percentage) upper_limit = scoreatpercentile(values, 100 - percentage) try: return tmean(values, limits=(lower_limit, upper_limit)) except ValueError: log.warning( f"Failed to calculate the trimmed mean of {values}. The " f"Regular mean average will be calculated instead" ) return sum(values) / len(values) def set_selinux_permissions(workers=None): """ Workaround for #1777384 - enable container_use_cephfs on RHEL workers Ticket: RHSTOR-787, see more details in the issue: #1151 Args: workers (list): List of worker nodes to set selinux permissions """ log.info("Running WA for ticket: RHSTOR-787") from ocs_ci.ocs import ocp ocp_obj = ocp.OCP() cmd = ["/usr/sbin/setsebool -P container_use_cephfs on"] cmd_list = cmd.copy() if not workers: from ocs_ci.ocs.node import get_typed_worker_nodes worker_nodes = get_typed_worker_nodes(os_id="rhel") else: worker_nodes = workers for worker in worker_nodes: node = worker.get().get("metadata").get("name") if not workers else worker log.info(f"{node} is a RHEL based worker - applying '{cmd_list}'") if config.ENV_DATA["platform"] == constants.IBMCLOUD_PLATFORM: retry(CommandFailed, tries=10, delay=3, backoff=2)( ocp_obj.exec_oc_debug_cmd )(node=node, cmd_list=cmd_list) else: retry(CommandFailed)(ocp_obj.exec_oc_debug_cmd)( node=node, cmd_list=cmd_list ) def set_registry_to_managed_state(): """ In order to be able to deploy from stage we need to change image registry config to Managed state. More described in BZs: https://bugzilla.redhat.com/show_bug.cgi?id=1806593 https://bugzilla.redhat.com/show_bug.cgi?id=1807471#c3 We need to change to managed state as described here: https://github.com/red-hat-storage/ocs-ci/issues/1436 So this is not suppose to be deleted as WA case we really need to do this operation for OCS deployment as was originally done here: https://github.com/red-hat-storage/ocs-ci/pull/1437 Currently it has to be moved here to enable CA certificate to be properly propagated for the stage deployment as mentioned in BZ. """ # In RHV platform config is already set to Managed and storage pre-configured on_prem_platform_to_exclude = [constants.RHV_PLATFORM] platform_list_to_exclude = constants.CLOUD_PLATFORMS + on_prem_platform_to_exclude if config.ENV_DATA["platform"] not in platform_list_to_exclude: cluster_config = yaml.safe_load( exec_cmd(f"oc get {constants.IMAGE_REGISTRY_CONFIG} -o yaml").stdout ) if "emptyDir" not in cluster_config["spec"].get("storage", {}).keys(): run_cmd( f"oc patch {constants.IMAGE_REGISTRY_CONFIG} --type merge -p " f'\'{{"spec":{{"storage": {{"emptyDir":{{}}}}}}}}\'' ) if cluster_config["spec"].get("managementState") != "Managed": run_cmd( f"oc patch {constants.IMAGE_REGISTRY_CONFIG} --type merge -p " f'\'{{"spec":{{"managementState": "Managed"}}}}\'' ) def add_stage_cert(): """ Deploy stage certificate to the cluster. """ log.info("Create configmap stage-registry-config with stage CA.") run_cmd( f"oc -n openshift-config create configmap stage-registry-config" f" --from-file=registry.stage.redhat.io={constants.STAGE_CA_FILE}" ) log.info("Add stage-registry-config to additionalTrustedCA.") additional_trusted_ca_patch = ( '{"spec":{"additionalTrustedCA":{"name":"stage-registry-config"}}}' ) run_cmd( f"oc patch image.config.openshift.io cluster --type=merge" f" -p '{additional_trusted_ca_patch}'" ) def get_terraform(version=None, bin_dir=None): """ Downloads the terraform binary Args: version (str): Version of the terraform to download bin_dir (str): Path to bin directory (default: config.RUN['bin_dir']) Returns: str: Path to the terraform binary """ if platform.system() == "Darwin": os_type = "darwin" elif platform.system() == "Linux": os_type = "linux" else: raise UnsupportedOSType version = version or config.DEPLOYMENT["terraform_version"] bin_dir = os.path.expanduser(bin_dir or config.RUN["bin_dir"]) terraform_zip_file = f"terraform_{version}_{os_type}_amd64.zip" terraform_filename = "terraform" terraform_binary_path = os.path.join(bin_dir, terraform_filename) log.info(f"Downloading terraform version {version}") previous_dir = os.getcwd() os.chdir(bin_dir) url = f"https://releases.hashicorp.com/terraform/{version}/" f"{terraform_zip_file}" download_file(url, terraform_zip_file) run_cmd(f"unzip -o {terraform_zip_file}") delete_file(terraform_zip_file) # return to the previous working directory os.chdir(previous_dir) return terraform_binary_path def get_terraform_ignition_provider(terraform_dir, version=None): """ Downloads the terraform ignition provider Args: terraform_dir (str): Path to terraform working directory version (str): Version of the terraform ignition provider to download """ version = version or constants.TERRAFORM_IGNITION_PROVIDER_VERSION terraform_ignition_provider_zip_file = ( f"terraform-provider-ignition-{version}-linux-amd64.tar.gz" ) terraform_ignition_provider_dir = ( f"terraform-provider-ignition-{version}-linux-amd64" ) terraform_plugins_path = ".terraform/plugins/linux_amd64/" log.info(f"Downloading terraform ignition proivider version {version}") previous_dir = os.getcwd() os.chdir(terraform_dir) url = ( "https://github.com/community-terraform-providers/" f"terraform-provider-ignition/releases/download/{version}/" f"{terraform_ignition_provider_zip_file}" ) # Download and untar download_file(url, terraform_ignition_provider_zip_file) run_cmd(f"tar xzf {terraform_ignition_provider_zip_file}") # move the ignition provider binary to plugins path create_directory_path(terraform_plugins_path) move( f"{terraform_ignition_provider_dir}/terraform-provider-ignition", terraform_plugins_path, ) # delete the downloaded files delete_file(terraform_ignition_provider_zip_file) delete_dir(terraform_ignition_provider_dir) # return to the previous working directory os.chdir(previous_dir) def get_module_ip(terraform_state_file, module): """ Gets the node IP from terraform.tfstate file Args: terraform_state_file (str): Path to terraform state file module (str): Module name in terraform.tfstate file e.g: constants.LOAD_BALANCER_MODULE Returns: list: IP of the node """ ips = [] with open(terraform_state_file) as fd: obj = json.loads(fd.read()) if config.ENV_DATA.get("folder_structure"): resources = obj["resources"] log.debug(f"Extracting module information for {module}") log.debug(f"Resource in {terraform_state_file}: {resources}") for resource in resources: if resource.get("module") == module and resource.get("mode") == "data": for each_resource in resource["instances"]: resource_body = each_resource["attributes"]["body"] ips.append(resource_body.split('"')[3]) else: modules = obj["modules"] target_module = module.split("_")[1] log.debug(f"Extracting module information for {module}") log.debug(f"Modules in {terraform_state_file}: {modules}") for each_module in modules: if target_module in each_module["path"]: return each_module["outputs"]["ip_addresses"]["value"] return ips def set_aws_region(region=None): """ Exports environment variable AWS_REGION Args: region (str): AWS region to export """ log.debug("Exporting environment variable AWS_REGION") region = region or config.ENV_DATA["region"] os.environ["AWS_REGION"] = region def get_system_architecture(): """ Get output from 'uname -m' command run on first worker node. Returns: str: Architecture of system """ from ocs_ci.ocs.node import get_nodes log.info("Checking architecture of system") node = get_nodes(node_type=constants.WORKER_MACHINE)[0] return node.ocp.exec_oc_debug_cmd(node.data["metadata"]["name"], ["uname -m"]) def wait_for_machineconfigpool_status(node_type, timeout=900): """ Check for Machineconfigpool status Args: node_type (str): The node type to check machineconfigpool status is updated. e.g: worker, master and all if we want to check for all nodes timeout (int): Time in seconds to wait """ # importing here to avoid dependencies from ocs_ci.ocs import ocp node_types = [node_type] if node_type == "all": node_types = [f"{constants.WORKER_MACHINE}", f"{constants.MASTER_MACHINE}"] for role in node_types: log.info(f"Checking machineconfigpool status for {role} nodes") ocp_obj = ocp.OCP(kind=constants.MACHINECONFIGPOOL, resource_name=role) machine_count = ocp_obj.get()["status"]["machineCount"] assert ocp_obj.wait_for_resource( condition=str(machine_count), column="READYMACHINECOUNT", timeout=timeout, sleep=5, ) def configure_chrony_and_wait_for_machineconfig_status( node_type=constants.WORKER_MACHINE, timeout=900 ): """ Configure chrony on the nodes Args: node_type (str): The node type to configure chrony e.g: worker, master and all if we want to configure on all nodes timeout (int): Time in seconds to wait """ # importing here to avoid dependencies from ocs_ci.utility.templating import load_yaml from ocs_ci.ocs.resources.ocs import OCS chrony_data = load_yaml(constants.NTP_CHRONY_CONF) node_types = [node_type] if node_type == "all": node_types = [f"{constants.WORKER_MACHINE}", f"{constants.MASTER_MACHINE}"] for role in node_types: log.info(f"Creating chrony for {role} nodes") chrony_data["metadata"]["labels"][ "machineconfiguration.openshift.io/role" ] = role chrony_data["metadata"]["name"] = f"{role}-chrony-configuration" chrony_obj = OCS(**chrony_data) chrony_obj.create() # sleep here to start update machineconfigpool status time.sleep(60) wait_for_machineconfigpool_status(role, timeout=timeout) def modify_csv(csv, replace_from, replace_to): """ Modify the CSV Args: csv (str): The CSV name replace_from (str): The pattern to replace from in the CSV replace_to (str): The pattern to replace to in the CSV """ data = ( f"oc -n openshift-storage get csv {csv} -o yaml | sed" f" 's,{replace_from},{replace_to},g' | oc replace -f -" ) log.info( f"CSV {csv} will be modified: {replace_from} will be replaced " f"with {replace_to}.\nThe command that will be used for that is:\n{data}" ) temp_file = NamedTemporaryFile(mode="w+", prefix="csv_modification", suffix=".sh") with open(temp_file.name, "w") as t_file: t_file.writelines(data) run_cmd(f"chmod 777 {temp_file.name}") run_cmd(f"sh {temp_file.name}") def check_for_rhcos_images(url): """ Check for rhcos images are present in given location Args: url (str): rhcos_images url Returns: (bool): True if images present if not false """ r = requests.head(url) return r.status_code == requests.codes.ok def download_file_from_git_repo(git_repo_url, path_to_file_in_git, filename): """ Download a file from a specified git repository Args: git_repo_url (str): The git repository url path_to_file_in_git (str): Path to the file to download in git repository filename (str): Name of the file to write the download to """ log.debug( f"Download file '{path_to_file_in_git}' from " f"git repository {git_repo_url} to local file '{filename}'." ) temp_dir = mkdtemp() git.Repo.clone_from(git_repo_url, temp_dir, branch="master", depth=1) move(os.path.join(temp_dir, path_to_file_in_git), filename) rmtree(temp_dir) def skipif_upgraded_from(version_list): """ This function evaluates the condition to skip a test if the cluster is upgraded from a particular OCS version Args: version_list (list): List of versions to check Return: (bool): True if test needs to be skipped else False """ try: from ocs_ci.ocs.resources.ocs import get_ocs_csv skip_this = False version_list = [version_list] if isinstance(version_list, str) else version_list ocs_csv = get_ocs_csv() csv_info = ocs_csv.get() prev_version = csv_info.get("spec").get("replaces", "") for version in version_list: if f".v{version}" in prev_version: skip_this = True break return skip_this except Exception as err: log.error(str(err)) return False def get_cluster_id(cluster_path): """ Get ClusterID from metadata.json in given cluster_path Args: cluster_path: path to cluster install directory Returns: str: metadata.json['clusterID'] """ metadata_file = os.path.join(cluster_path, "metadata.json") with open(metadata_file) as f: metadata = json.load(f) return metadata["clusterID"] def get_running_cluster_id(): """ Get cluster UUID Not relying on metadata.json as user sometimes want to run only with kubeconfig for some tests. For this function to work cluster has to be in running state Returns: str: cluster UUID """ cluster_id = run_cmd( "oc get clusterversion version -o jsonpath='{.spec.clusterID}'" ) return cluster_id def get_ocp_upgrade_history(): """ Gets the OCP upgrade history for the cluster Returns: list: List of OCP upgrade paths. Latest version in the beginning of the list """ # importing here to avoid circular imports from ocs_ci.ocs.ocp import OCP ocp = OCP(kind="clusterversion") cluster_version_info = ocp.get("version") upgrade_history_info = cluster_version_info["status"]["history"] upgrade_history = [each_upgrade["version"] for each_upgrade in upgrade_history_info] return upgrade_history def get_attr_chain(obj, attr_chain): """ Attempt to retrieve object attributes when uncertain about the existence of the attribute or a different attribute in a given attribute chain. If the retrieval fails, None is returned. The function can be used to retrieve a direct attribute, or a chain of attributes. i.e. - obj.attr_a, obj_attr_a.sub_attr Another example - trying to access "sub_attr_b" in object.attr.sub_attr_a.sub_attr_b - get_attr_chain(object, "attr.sub_attr_a.sub_attr_b") The function can be used to try and retrieve "sub_attribute_b" without an exception, even in cases where "attr" or "sub_attr_a" might not exist. In those cases, the function will return None. Args: obj: An object attr_chain (str): A string containing one attribute or several sub-attributes separated by dots (i.e. - "attr.sub_attr_a.sub_attr_b") Returns: The requested attribute if found, otherwise None """ return reduce( lambda _obj, _attr: getattr(_obj, _attr, None), attr_chain.split("."), obj ) def get_default_if_keyval_empty(dictionary, key, default_val): """ if Key has an empty value OR key doesn't exist then return default value Args: dictionary (dict): Dictionary where we have to lookup key (str): key to lookup default_val (str): If key doesn't have value then return this default_val Returns: dictionary[key] if value is present else default_val """ if not dictionary.get(key): return default_val return dictionary.get(key) def get_client_version(client_binary_path): """ Get version reported by `oc version`. Args: client_binary_path (str): path to `oc` binary Returns: str: version reported by `oc version`. None if the client does not exist at the provided path. """ if os.path.isfile(client_binary_path): cmd = f"{client_binary_path} version --client -o json" resp = exec_cmd(cmd) stdout = json.loads(resp.stdout.decode()) return stdout["releaseClientVersion"] def clone_notify(): """ Repository contains the source code of notify tool, which is a python3 based tool wrapped by a container used to configure Ceph Bucket Notifications Returns: notify_path (str): Path location of the notify code """ notify_dir = mkdtemp(prefix="notify_") log.info(f"cloning repo notify in {notify_dir}") git_clone_cmd = f"git clone {constants.RGW_KAFKA_NOTIFY}" subprocess.run(git_clone_cmd, shell=True, cwd=notify_dir, check=True) notify_path = f"{notify_dir}/notify/notify.py" return notify_path def add_chrony_to_ocp_deployment(): """ Create and Add necessary chrony resources """ for role in ["master", "worker"]: log.info(f"Creating and Adding Chrony file for {role}") with open(constants.CHRONY_TEMPLATE) as file_stream: chrony_template_obj = yaml.safe_load(file_stream) chrony_template_obj["metadata"]["labels"][ "machineconfiguration.openshift.io/role" ] = role chrony_template_obj["metadata"]["name"] = f"99-{role}-chrony-configuration" ignition_version = config.DEPLOYMENT["ignition_version"] chrony_template_obj["spec"]["config"]["ignition"]["version"] = ignition_version if Version.coerce(ignition_version) < Version.coerce("3.0"): chrony_template_obj["spec"]["config"]["storage"]["files"][0][ "filesystem" ] = "root" chrony_template_str = yaml.safe_dump(chrony_template_obj) chrony_file = os.path.join( config.ENV_DATA["cluster_path"], "openshift", f"99-{role}-chrony-configuration.yaml", ) with open(chrony_file, "w") as f: f.write(chrony_template_str) def enable_huge_pages(): log.info("Enabling huge pages.") exec_cmd(f"oc apply -f {constants.HUGE_PAGES_TEMPLATE}") time.sleep(10) log.info("Waiting for machine config will be applied with huge pages") wait_for_machineconfigpool_status(node_type=constants.WORKER_MACHINE)

# (C) Datadog, Inc. 2020-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import json import re from ....trello import TrelloClient from ...console import abort, echo_info class RCBuildCardsUpdater: version_regex = r'(\d*)\.(\d*)\.(\d*)([-~])rc([\.-])(\d*)' def __init__(self, trello: TrelloClient, release_version: str): self.__trello = trello match = re.fullmatch(self.version_regex, release_version) if not match: abort( f'Cannot update cards in RC builds columnn. ' f'`{release_version}` is an invalid release candidate version. ' f'A valid version is for example `7.21.0-rc.3`. ' f'You can disable the update of cards in RC builds column by removing --update-rc-builds-cards' ) else: groups = match.groups() if len(groups) != 6: raise Exception('Regex in RCBuildCardsUpdater is not correct') (_, self.__minor, self.__patch, _, _, self.__rc) = groups def update_cards(self): rc_build_cards = [ 'Rrn1Y0yU', # [A7] Windows + Docker + Chocolatey 'DyjjKkZD', # [A6] Windows 'BOvSs9Le', # [IOT] Linux 'hu1JXJ18', # [A7] Linux + Docker 'E7bHwa14', # [A6] Linux + Docker 'dYrSpOLW', # MacOS ] for card_id in rc_build_cards: card = self.__trello.get_card(card_id) description = card['desc'] new_version = f'\\g<1>.{self.__minor}.{self.__patch}\\g<4>rc\\g<5>{self.__rc}' new_description = re.sub(self.version_regex, new_version, description) updated_card = {'desc': new_description} echo_info(f'updating release version for the card {card['name']}') self.__trello.update_card(card_id, json.dumps(updated_card))

# (C) Datadog, Inc. 2020-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import json import re from ....trello import TrelloClient from ...console import abort, echo_info class RCBuildCardsUpdater: version_regex = r'(\d*)\.(\d*)\.(\d*)([-~])rc([\.-])(\d*)' def __init__(self, trello: TrelloClient, release_version: str): self.__trello = trello match = re.fullmatch(self.version_regex, release_version) if not match: abort( f'Cannot update cards in RC builds columnn. ' f'`{release_version}` is an invalid release candidate version. ' f'A valid version is for example `7.21.0-rc.3`. ' f'You can disable the update of cards in RC builds column by removing --update-rc-builds-cards' ) else: groups = match.groups() if len(groups) != 6: raise Exception('Regex in RCBuildCardsUpdater is not correct') (_, self.__minor, self.__patch, _, _, self.__rc) = groups def update_cards(self): rc_build_cards = [ 'Rrn1Y0yU', # [A7] Windows + Docker + Chocolatey 'DyjjKkZD', # [A6] Windows 'BOvSs9Le', # [IOT] Linux 'hu1JXJ18', # [A7] Linux + Docker 'E7bHwa14', # [A6] Linux + Docker 'dYrSpOLW', # MacOS ] for card_id in rc_build_cards: card = self.__trello.get_card(card_id) description = card['desc'] new_version = f'\\g<1>.{self.__minor}.{self.__patch}\\g<4>rc\\g<5>{self.__rc}' new_description = re.sub(self.version_regex, new_version, description) updated_card = {'desc': new_description} echo_info(f'updating release version for the card {card["name"]}') self.__trello.update_card(card_id, json.dumps(updated_card))

from requests import get from re import findall import os import glob from rubika.client import Bot import requests from rubika.tools import Tools from rubika.encryption import encryption from gtts import gTTS from mutagen.mp3 import MP3 import time import random import urllib import io bot = Bot("ssnakxcydoxdtheauejqujhwujbctupo") target = "g0B4OLc066ebaf44f890d98bf8f5d156" # created By HiBye & ShayanHeidari(Snipe4Kill)(TG GAMES)(libs for Bahman Ahmadi) def hasAds(msg): links = ["http://","https://",".ir",".com",".org",".net",".me"] for i in links: if i in msg: return True def hasInsult(msg): swData = [False,None] for i in open("dontReadMe.txt").read().split("\n"): if i in msg: swData = [True, i] break else: continue return swData # static variable answered, sleeped, retries = [], False, {} alerts, blacklist = [] , [] def alert(guid,user,link=False): alerts.append(guid) coun = int(alerts.count(guid)) haslink = "" if link : haslink = "گزاشتن لینک در گروه ممنوع میباشد .\n\n" if coun == 1: bot.sendMessage(target, "💢 اخطار [ @"+user+" ] \n"+haslink+" شما (1/3) اخطار دریافت کرده اید .\n\nپس از دریافت 3 اخطار از گروه حذف خواهید شد !\nجهت اطلاع از قوانین کلمه (قوانین) را ارسال کنید .") elif coun == 2: bot.sendMessage(target, "💢 اخطار [ @"+user+" ] \n"+haslink+" شما (2/3) اخطار دریافت کرده اید .\n\nپس از دریافت 3 اخطار از گروه حذف خواهید شد !\nجهت اطلاع از قوانین کلمه (قوانین) را ارسال کنید .") elif coun == 3: blacklist.append(guid) bot.sendMessage(target, "🚫 کاربر [ @"+user+" ] \n (3/3) اخطار دریافت کرد ، بنابراین اکنون اخراج میشود .") bot.banGroupMember(target, guid) while True: # time.sleep(15) try: admins = [i["member_guid"] for i in bot.getGroupAdmins(target)["data"]["in_chat_members"]] min_id = bot.getGroupInfo(target)["data"]["chat"]["last_message_id"] while True: try: messages = bot.getMessages(target,min_id) break except: continue for msg in messages: try: if msg["type"]=="Text" and not msg.get("message_id") in answered: if not sleeped: if hasAds(msg.get("text")) and not msg.get("author_object_guid") in admins : guid = msg.get("author_object_guid") user = bot.getUserInfo(guid)["data"]["user"]["username"] bot.deleteMessages(target, [msg.get("message_id")]) alert(guid,user,True) elif msg.get("text") == "!stop" or msg.get("text") == "/stop" and msg.get("author_object_guid") in admins : try: sleeped = True bot.sendMessage(target, "✅ ربات اکنون خاموش است", message_id=msg.get("message_id")) except: print("err off bot") elif msg.get("text") == "!restart" or msg.get("text") == "/restart" and msg.get("author_object_guid") in admins : try: sleeped = True bot.sendMessage(target, "در حال راه اندازی مجدد...", message_id=msg.get("message_id")) sleeped = False bot.sendMessage(target, "ربا‌ت با موفقیت مجددا راه اندازی شد!", message_id=msg.get("message_id")) except: print("err Restart bot") elif msg.get("text").startswith("حذف") and msg.get("author_object_guid") in admins : try: number = int(msg.get("text").split(" ")[1]) answered.reverse() bot.deleteMessages(target, answered[0:number]) bot.sendMessage(target, "✅ "+ str(number) +" پیام اخیر با موفقیت حذف شد", message_id=msg.get("message_id")) answered.reverse() except IndexError: bot.deleteMessages(target, [msg.get("reply_to_message_id")]) bot.sendMessage(target, "✅ پیام با موفقیت حذف شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text").startswith("اخراج") and msg.get("author_object_guid") in admins : try: guid = bot.getInfoByUsername(msg.get("text").split(" ")[1][1:])["data"]["chat"]["abs_object"]["object_guid"] if not guid in admins : bot.banGroupMember(target, guid) # bot.sendMessage(target, "✅ کاربر با موفقیت از گروه اخراج شد", message_id=msg.get("message_id")) else : bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except IndexError: bot.banGroupMember(target, bot.getMessagesInfo(target, [msg.get("reply_to_message_id")])[0]["author_object_guid"]) # bot.sendMessage(target, "✅ کاربر با موفقیت از گروه اخراج شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ دستور اشتباه", message_id=msg.get("message_id")) elif msg.get("text").startswith("افزودن") or msg.get("text").startswith("!add") : try: guid = bot.getInfoByUsername(msg.get("text").split(" ")[1][1:])["data"]["chat"]["object_guid"] if guid in blacklist: if msg.get("author_object_guid") in admins: alerts.remove(guid) alerts.remove(guid) alerts.remove(guid) blacklist.remove(guid) bot.invite(target, [guid]) else: bot.sendMessage(target, "❌ کاربر محدود میباشد", message_id=msg.get("message_id")) else: bot.invite(target, [guid]) # bot.sendMessage(target, "✅ کاربر اکنون عضو گروه است", message_id=msg.get("message_id")) except IndexError: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ دستور اشتباه", message_id=msg.get("message_id")) elif msg.get("text") == "دستورات": try: rules = open("help.txt","r",encoding='utf-8').read() bot.sendMessage(target, str(rules), message_id=msg.get("message_id")) except: print("err dastorat") elif msg.get("text").startswith("آپدیت دستورات") and msg.get("author_object_guid") in admins: try: rules = open("help.txt","w",encoding='utf-8').write(str(msg.get("text").strip("آپدیت قوانین"))) bot.sendMessage(target, "دستو‌رات ربا‌ت به‌روزرسانی شد!", message_id=msg.get("message_id")) # rules.close() except: bot.sendMessage(target, "مشکلی پیش اومد مجددا تلاش کنید!", message_id=msg.get("message_id")) elif msg["text"].startswith("!number") or msg["text"].startswith("بشمار"): try: response = get(f"http://api.codebazan.ir/adad/?text={msg["text"].split()[1]}").json() bot.sendMessage(msg["author_object_guid"], "\n".join(list(response["result"].values())[:20])).text bot.sendMessage(target, "نتیجه بزودی برای شما ارسال خواهد شد...", message_id=msg["message_id"]) except: bot.sendMessage(target, "متاسفانه نتیجه‌ای موجود نبود!", message_id=msg["message_id"]) elif msg.get("text").startswith("زمان"): try: response = get("https://api.codebazan.ir/time-date/?td=all").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: print("err answer time") elif msg.get("text") == "ساعت": try: bot.sendMessage(target, f"Time : {time.localtime().tm_hour} : {time.localtime().tm_min} : {time.localtime().tm_sec}", message_id=msg.get("message_id")) except: print("err time answer") elif msg.get("text") == "!date": try: bot.sendMessage(target, f"Date: {time.localtime().tm_year} / {time.localtime().tm_mon} / {time.localtime().tm_mday}", message_id=msg.get("message_id")) except: print("err date") elif msg.get("text") == "پاک" and msg.get("author_object_guid") in admins : try: bot.deleteMessages(target, [msg.get("reply_to_message_id")]) bot.sendMessage(target, "پیام مورد نظر پاک شد...", message_id=msg.get("message_id")) except: print("err pak") elif msg.get("text").startswith("!cal") or msg.get("text").startswith("حساب"): msd = msg.get("text") if plus == True: try: call = [msd.split(" ")[1], msd.split(" ")[2], msd.split(" ")[3]] if call[1] == "+": try: am = float(call[0]) + float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) plus = False except: print("err answer +") elif call[1] == "-": try: am = float(call[0]) - float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer -") elif call[1] == "*": try: am = float(call[0]) * float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer *") elif call[1] == "/": try: am = float(call[0]) / float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer /") except IndexError: bot.sendMessage(target, "متاسفانه دستور شما اشتباه میباشد!" ,message_id=msg.get("message_id")) plus= True elif hasInsult(msg.get("text"))[0] and not msg.get("author_object_guid") in admins : try: print("yek ahmagh fohsh dad") bot.deleteMessages(target, [str(msg.get("message_id"))]) print("fohsh pak shod") except: print("err del fohsh Bug") elif msg.get("text").startswith("سلام") or msg.get("text").startswith("سلم") or msg.get("text").startswith("صلام") or msg.get("text").startswith("صلم") or msg.get("text").startswith("سیلام") or msg.get("text").startswith("صیلام"): try: bot.sendMessage(target,'سلا‌م' ,message_id=msg.get("message_id")) except: print("err hello") elif msg.get("text").startswith("خوبی") or msg.get("text").startswith("خبی"): try: bot.sendMessage(target, "تو چطوری؟🤪", message_id=msg.get("message_id")) except: print("err answer hay") elif msg.get("text").startswith("چه خبر") or msg.get("text").startswith("چخبر"): try: bot.sendMessage(target, "ســلامـتیت😍♥", message_id=msg.get("message_id")) except: print("err CheKhabar") elif msg.get("text").startswith("ربات") or msg.get("text").startswith("بات"): try: bot.sendMessage(target, "جــونـم😁💋", message_id=msg.get("message_id")) except: print("err bot answer") elif msg.get("text").startswith("😂") or msg.get("text").startswith("🤣"): try: bot.sendMessage(target, "جــون تـو فــقط بخـند😍", message_id=msg.get("message_id")) except: print("err luagh") elif msg.get("text") == "😐": try: bot.sendMessage(target, "😑😐", message_id=msg.get("message_id")) except: print("err poker answer") elif msg.get("text") == "سنجاق" and msg.get("author_object_guid") in admins : try: bot.pin(target, msg["reply_to_message_id"]) bot.sendMessage(target, "پیام مورد نظر با موفقیت سنجاق شد!", message_id=msg.get("message_id")) except: print("err pin") elif msg.get("text") == "برداشتن سنجاق" and msg.get("author_object_guid") in admins : try: bot.unpin(target, msg["reply_to_message_id"]) bot.sendMessage(target, "پیام مورد نظر از سنجاق برداشته شد!", message_id=msg.get("message_id")) except: print("err unpin") elif msg.get("text").startswith("!trans"): try: responser = get(f"https://api.codebazan.ir/translate/?type=json&from=en&to=fa&text={msg.get("text").split()[1:]}").json() al = [responser["result"]] bot.sendMessage(msg.get("author_object_guid"), "پاسخ به ترجمه:\n"+"".join(al)).text bot.sendMessage(target, "نتیجه رو برات ارسال کردم😘", message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif msg.get("text").startswith("!font"): try: response = get(f"https://api.codebazan.ir/font/?text={msg.get("text").split()[1]}").json() bot.sendMessage(msg.get("author_object_guid"), "\n".join(list(response["result"].values())[:110])).text bot.sendMessage(target, "نتیجه رو برات ارسال کردم😘", message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif msg.get("text").startswith("جوک") or msg.get("text").startswith("jok") or msg.get("text").startswith("!jok"): try: response = get("https://api.codebazan.ir/jok/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "دستورت رو اشتباه وارد کردی", message_id=msg["message_id"]) elif msg.get("text").startswith("ذکر") or msg.get("text").startswith("zekr") or msg.get("text").startswith("!zekr"): try: response = get("http://api.codebazan.ir/zekr/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("حدیث") or msg.get("text").startswith("hadis") or msg.get("text").startswith("!hadis"): try: response = get("http://api.codebazan.ir/hadis/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی تو ارسال پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("بیو") or msg.get("text").startswith("bio") or msg.get("text").startswith("!bio"): try: response = get("https://api.codebazan.ir/bio/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی تو ارسال پیش اومد!", message_id=msg["message_id"]) elif msg["text"].startswith("!weather"): try: response = get(f"https://api.codebazan.ir/weather/?city={msg["text"].split()[1]}").json() bot.sendMessage(msg["author_object_guid"], "\n".join(list(response["result"].values())[:20])).text bot.sendMessage(target, "نتیجه بزودی برای شما ارسال خواهد شد...", message_id=msg["message_id"]) except: bot.sendMessage(target, "متاسفانه نتیجه‌ای موجود نبود!", message_id=msg["message_id"]) elif msg.get("text").startswith("دیالوگ"): try: response = get("http://api.codebazan.ir/dialog/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "متاسفانه تو ارسال مشکلی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("دانستنی"): try: response = get("http://api.codebazan.ir/danestani/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "دستورت رو اشتباه وارد کردی", message_id=msg["message_id"]) elif msg.get("text").startswith("پ ن پ") or msg.get("text").startswith("!pa-na-pa") or msg.get("text").startswith("په نه په"): try: response = get("http://api.codebazan.ir/jok/pa-na-pa/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "شرمنده نتونستم بفرستم!", message_id=msg["message_id"]) elif msg.get("text").startswith("الکی مثلا") or msg.get("text").startswith("!alaki-masalan"): try: response = get("http://api.codebazan.ir/jok/alaki-masalan/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "نشد بفرستم:(", message_id=msg["message_id"]) elif msg.get("text").startswith("داستان") or msg.get("text").startswith("!dastan"): try: response = get("http://api.codebazan.ir/dastan/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "مشکلی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("!ping"): try: responser = get(f"https://api.codebazan.ir/ping/?url={msg.get("text").split()[1]}").text bot.sendMessage(target, responser,message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif "forwarded_from" in msg.keys() and bot.getMessagesInfo(target, [msg.get("message_id")])[0]["forwarded_from"]["type_from"] == "Channel" and not msg.get("author_object_guid") in admins : try: print("Yek ahmagh forwared Zad") bot.deleteMessages(target, [str(msg.get("message_id"))]) print("tabligh forearedi pak shod") except: print("err delete forwared") elif msg.get("text") == "قوانین": try: rules = open("rules.txt","r",encoding='utf-8').read() bot.sendMessage(target, str(rules), message_id=msg.get("message_id")) except: print("err ghanon") elif msg.get("text").startswith("آپدیت قوانین") and msg.get("author_object_guid") in admins: try: rules = open("rules.txt","w",encoding='utf-8').write(str(msg.get("text").strip("آپدیت قوانین"))) bot.sendMessage(target, "✅ قوانین بروزرسانی شد", message_id=msg.get("message_id")) # rules.close() except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "حالت آرام" and msg.get("author_object_guid") in admins: try: number = 10 bot.setGroupTimer(target,number) bot.sendMessage(target, "✅ حالت آرام برای "+str(number)+"ثانیه فعال شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "!speak" or msg.get("text") == "speak" or msg.get("text") == "Speak" or msg.get("text") == "بگو": try: if msg.get('reply_to_message_id') != None: msg_reply_info = bot.getMessagesInfo(target, [msg.get('reply_to_message_id')])[0] if msg_reply_info['text'] != None: text = msg_reply_info['text'] speech = gTTS(text) changed_voice = io.BytesIO() speech.write_to_fp(changed_voice) b2 = changed_voice.getvalue() changed_voice.seek(0) audio = MP3(changed_voice) dur = audio.info.length dur = dur * 1000 f = open('sound.ogg','wb') f.write(b2) f.close() bot.sendVoice(target , 'sound.ogg', dur,message_id=msg["message_id"]) os.remove('sound.ogg') print('sended voice') else: bot.sendMessage(target, 'پیام شما متن یا کپشن ندارد',message_id=msg["message_id"]) except: print('server gtts bug') elif msg.get("text") == "برداشتن حالت آرام" and msg.get("author_object_guid") in admins: try: number = 0 bot.setGroupTimer(target,number) bot.sendMessage(target, "✅ حالت آرام غیرفعال شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "لطفا دستور رو صحیح وارد کنید!", message_id=msg.get("message_id")) elif msg.get("text").startswith("اخطار") and msg.get("author_object_guid") in admins: try: user = msg.get("text").split(" ")[1][1:] guid = bot.getInfoByUsername(user)["data"]["chat"]["abs_object"]["object_guid"] if not guid in admins : alert(guid,user) else : bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except IndexError: guid = bot.getMessagesInfo(target, [msg.get("reply_to_message_id")])[0]["author_object_guid"] user = bot.getUserInfo(guid)["data"]["user"]["username"] if not guid in admins: alert(guid,user) else: bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "قفل گروه" and msg.get("author_object_guid") in admins : try: bot.setMembersAccess(target, ["AddMember"]) bot.sendMessage(target, "🔒 گروه قفل شد", message_id=msg.get("message_id")) except: print("err lock GP") elif msg.get("text") == "بازکردن گروه" or msg.get("text") == "باز کردن گروه" and msg.get("author_object_guid") in admins : try: bot.setMembersAccess(target, ["SendMessages","AddMember"]) bot.sendMessage(target, "🔓 گروه اکنون باز است", message_id=msg.get("message_id")) except: print("err unlock GP") else: if msg.get("text") == "!start" or msg.get("text") == "/start" and msg.get("author_object_guid") in admins : try: sleeped = False bot.sendMessage(target, "ربا‌ت با موفقیت روشن شد!", message_id=msg.get("message_id")) except: print("err on bot") elif msg["type"]=="Event" and not msg.get("message_id") in answered and not sleeped: name = bot.getGroupInfo(target)["data"]["group"]["group_title"] data = msg['event_data'] if data["type"]=="RemoveGroupMembers": try: user = bot.getUserInfo(data['peer_objects'][0]['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"‼️ کاربر {user} با موفقیت از گروه حذف شد .", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err rm member answer") elif data["type"]=="AddedGroupMembers": try: user = bot.getUserInfo(data['peer_objects'][0]['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"هــای {user} عزیز 😘🌹 \n • به گـروه {name} خیـلی خوش اومدی 😍❤️ \nلطفا قوانین رو رعایت کن .\n 💎 برای مشاهده قوانین کافیه کلمه (قوانین) رو ارسال کنی!\n", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err add member answer") elif data["type"]=="LeaveGroup": try: user = bot.getUserInfo(data['performer_object']['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"خدانگهدار {user} 👋 ", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err Leave member Answer") elif data["type"]=="JoinedGroupByLink": try: user = bot.getUserInfo(data['performer_object']['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"هــای {user} عزیز 😘🌹 \n • به گـروه {name} خیـلی خوش اومدی 😍❤️ \nلطفا قوانین رو رعایت کن .\n 💎 برای مشاهده قوانین کافیه کلمه (قوانین) رو ارسال کنی!\nدوست داری ربات بسازی؟ بیا اینجا😍👇\nt.me/RubikaBotCreate", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err Joined member Answer") else: if "forwarded_from" in msg.keys() and bot.getMessagesInfo(target, [msg.get("message_id")])[0]["forwarded_from"]["type_from"] == "Channel" and not msg.get("author_object_guid") in admins : bot.deleteMessages(target, [msg.get("message_id")]) guid = msg.get("author_object_guid") user = bot.getUserInfo(guid)["data"]["user"]["username"] bot.deleteMessages(target, [msg.get("message_id")]) alert(guid,user,True) continue except: continue answered.append(msg.get("message_id")) print("[" + msg.get("message_id")+ "] >>> " + msg.get("text") + "\n") except KeyboardInterrupt: exit() except Exception as e: if type(e) in list(retries.keys()): if retries[type(e)] < 3: retries[type(e)] += 1 continue else: retries.pop(type(e)) else: retries[type(e)] = 1 continue

from requests import get from re import findall import os import glob from rubika.client import Bot import requests from rubika.tools import Tools from rubika.encryption import encryption from gtts import gTTS from mutagen.mp3 import MP3 import time import random import urllib import io bot = Bot("ssnakxcydoxdtheauejqujhwujbctupo") target = "g0B4OLc066ebaf44f890d98bf8f5d156" # created By HiBye & ShayanHeidari(Snipe4Kill)(TG GAMES)(libs for Bahman Ahmadi) def hasAds(msg): links = ["http://","https://",".ir",".com",".org",".net",".me"] for i in links: if i in msg: return True def hasInsult(msg): swData = [False,None] for i in open("dontReadMe.txt").read().split("\n"): if i in msg: swData = [True, i] break else: continue return swData # static variable answered, sleeped, retries = [], False, {} alerts, blacklist = [] , [] def alert(guid,user,link=False): alerts.append(guid) coun = int(alerts.count(guid)) haslink = "" if link : haslink = "گزاشتن لینک در گروه ممنوع میباشد .\n\n" if coun == 1: bot.sendMessage(target, "💢 اخطار [ @"+user+" ] \n"+haslink+" شما (1/3) اخطار دریافت کرده اید .\n\nپس از دریافت 3 اخطار از گروه حذف خواهید شد !\nجهت اطلاع از قوانین کلمه (قوانین) را ارسال کنید .") elif coun == 2: bot.sendMessage(target, "💢 اخطار [ @"+user+" ] \n"+haslink+" شما (2/3) اخطار دریافت کرده اید .\n\nپس از دریافت 3 اخطار از گروه حذف خواهید شد !\nجهت اطلاع از قوانین کلمه (قوانین) را ارسال کنید .") elif coun == 3: blacklist.append(guid) bot.sendMessage(target, "🚫 کاربر [ @"+user+" ] \n (3/3) اخطار دریافت کرد ، بنابراین اکنون اخراج میشود .") bot.banGroupMember(target, guid) while True: # time.sleep(15) try: admins = [i["member_guid"] for i in bot.getGroupAdmins(target)["data"]["in_chat_members"]] min_id = bot.getGroupInfo(target)["data"]["chat"]["last_message_id"] while True: try: messages = bot.getMessages(target,min_id) break except: continue for msg in messages: try: if msg["type"]=="Text" and not msg.get("message_id") in answered: if not sleeped: if hasAds(msg.get("text")) and not msg.get("author_object_guid") in admins : guid = msg.get("author_object_guid") user = bot.getUserInfo(guid)["data"]["user"]["username"] bot.deleteMessages(target, [msg.get("message_id")]) alert(guid,user,True) elif msg.get("text") == "!stop" or msg.get("text") == "/stop" and msg.get("author_object_guid") in admins : try: sleeped = True bot.sendMessage(target, "✅ ربات اکنون خاموش است", message_id=msg.get("message_id")) except: print("err off bot") elif msg.get("text") == "!restart" or msg.get("text") == "/restart" and msg.get("author_object_guid") in admins : try: sleeped = True bot.sendMessage(target, "در حال راه اندازی مجدد...", message_id=msg.get("message_id")) sleeped = False bot.sendMessage(target, "ربا‌ت با موفقیت مجددا راه اندازی شد!", message_id=msg.get("message_id")) except: print("err Restart bot") elif msg.get("text").startswith("حذف") and msg.get("author_object_guid") in admins : try: number = int(msg.get("text").split(" ")[1]) answered.reverse() bot.deleteMessages(target, answered[0:number]) bot.sendMessage(target, "✅ "+ str(number) +" پیام اخیر با موفقیت حذف شد", message_id=msg.get("message_id")) answered.reverse() except IndexError: bot.deleteMessages(target, [msg.get("reply_to_message_id")]) bot.sendMessage(target, "✅ پیام با موفقیت حذف شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text").startswith("اخراج") and msg.get("author_object_guid") in admins : try: guid = bot.getInfoByUsername(msg.get("text").split(" ")[1][1:])["data"]["chat"]["abs_object"]["object_guid"] if not guid in admins : bot.banGroupMember(target, guid) # bot.sendMessage(target, "✅ کاربر با موفقیت از گروه اخراج شد", message_id=msg.get("message_id")) else : bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except IndexError: bot.banGroupMember(target, bot.getMessagesInfo(target, [msg.get("reply_to_message_id")])[0]["author_object_guid"]) # bot.sendMessage(target, "✅ کاربر با موفقیت از گروه اخراج شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ دستور اشتباه", message_id=msg.get("message_id")) elif msg.get("text").startswith("افزودن") or msg.get("text").startswith("!add") : try: guid = bot.getInfoByUsername(msg.get("text").split(" ")[1][1:])["data"]["chat"]["object_guid"] if guid in blacklist: if msg.get("author_object_guid") in admins: alerts.remove(guid) alerts.remove(guid) alerts.remove(guid) blacklist.remove(guid) bot.invite(target, [guid]) else: bot.sendMessage(target, "❌ کاربر محدود میباشد", message_id=msg.get("message_id")) else: bot.invite(target, [guid]) # bot.sendMessage(target, "✅ کاربر اکنون عضو گروه است", message_id=msg.get("message_id")) except IndexError: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ دستور اشتباه", message_id=msg.get("message_id")) elif msg.get("text") == "دستورات": try: rules = open("help.txt","r",encoding='utf-8').read() bot.sendMessage(target, str(rules), message_id=msg.get("message_id")) except: print("err dastorat") elif msg.get("text").startswith("آپدیت دستورات") and msg.get("author_object_guid") in admins: try: rules = open("help.txt","w",encoding='utf-8').write(str(msg.get("text").strip("آپدیت قوانین"))) bot.sendMessage(target, "دستو‌رات ربا‌ت به‌روزرسانی شد!", message_id=msg.get("message_id")) # rules.close() except: bot.sendMessage(target, "مشکلی پیش اومد مجددا تلاش کنید!", message_id=msg.get("message_id")) elif msg["text"].startswith("!number") or msg["text"].startswith("بشمار"): try: response = get(f"http://api.codebazan.ir/adad/?text={msg['text'].split()[1]}").json() bot.sendMessage(msg["author_object_guid"], "\n".join(list(response["result"].values())[:20])).text bot.sendMessage(target, "نتیجه بزودی برای شما ارسال خواهد شد...", message_id=msg["message_id"]) except: bot.sendMessage(target, "متاسفانه نتیجه‌ای موجود نبود!", message_id=msg["message_id"]) elif msg.get("text").startswith("زمان"): try: response = get("https://api.codebazan.ir/time-date/?td=all").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: print("err answer time") elif msg.get("text") == "ساعت": try: bot.sendMessage(target, f"Time : {time.localtime().tm_hour} : {time.localtime().tm_min} : {time.localtime().tm_sec}", message_id=msg.get("message_id")) except: print("err time answer") elif msg.get("text") == "!date": try: bot.sendMessage(target, f"Date: {time.localtime().tm_year} / {time.localtime().tm_mon} / {time.localtime().tm_mday}", message_id=msg.get("message_id")) except: print("err date") elif msg.get("text") == "پاک" and msg.get("author_object_guid") in admins : try: bot.deleteMessages(target, [msg.get("reply_to_message_id")]) bot.sendMessage(target, "پیام مورد نظر پاک شد...", message_id=msg.get("message_id")) except: print("err pak") elif msg.get("text").startswith("!cal") or msg.get("text").startswith("حساب"): msd = msg.get("text") if plus == True: try: call = [msd.split(" ")[1], msd.split(" ")[2], msd.split(" ")[3]] if call[1] == "+": try: am = float(call[0]) + float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) plus = False except: print("err answer +") elif call[1] == "-": try: am = float(call[0]) - float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer -") elif call[1] == "*": try: am = float(call[0]) * float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer *") elif call[1] == "/": try: am = float(call[0]) / float(call[2]) bot.sendMessage(target, "حاصل :\n"+"".join(str(am)), message_id=msg.get("message_id")) except: print("err answer /") except IndexError: bot.sendMessage(target, "متاسفانه دستور شما اشتباه میباشد!" ,message_id=msg.get("message_id")) plus= True elif hasInsult(msg.get("text"))[0] and not msg.get("author_object_guid") in admins : try: print("yek ahmagh fohsh dad") bot.deleteMessages(target, [str(msg.get("message_id"))]) print("fohsh pak shod") except: print("err del fohsh Bug") elif msg.get("text").startswith("سلام") or msg.get("text").startswith("سلم") or msg.get("text").startswith("صلام") or msg.get("text").startswith("صلم") or msg.get("text").startswith("سیلام") or msg.get("text").startswith("صیلام"): try: bot.sendMessage(target,'سلا‌م' ,message_id=msg.get("message_id")) except: print("err hello") elif msg.get("text").startswith("خوبی") or msg.get("text").startswith("خبی"): try: bot.sendMessage(target, "تو چطوری؟🤪", message_id=msg.get("message_id")) except: print("err answer hay") elif msg.get("text").startswith("چه خبر") or msg.get("text").startswith("چخبر"): try: bot.sendMessage(target, "ســلامـتیت😍♥", message_id=msg.get("message_id")) except: print("err CheKhabar") elif msg.get("text").startswith("ربات") or msg.get("text").startswith("بات"): try: bot.sendMessage(target, "جــونـم😁💋", message_id=msg.get("message_id")) except: print("err bot answer") elif msg.get("text").startswith("😂") or msg.get("text").startswith("🤣"): try: bot.sendMessage(target, "جــون تـو فــقط بخـند😍", message_id=msg.get("message_id")) except: print("err luagh") elif msg.get("text") == "😐": try: bot.sendMessage(target, "😑😐", message_id=msg.get("message_id")) except: print("err poker answer") elif msg.get("text") == "سنجاق" and msg.get("author_object_guid") in admins : try: bot.pin(target, msg["reply_to_message_id"]) bot.sendMessage(target, "پیام مورد نظر با موفقیت سنجاق شد!", message_id=msg.get("message_id")) except: print("err pin") elif msg.get("text") == "برداشتن سنجاق" and msg.get("author_object_guid") in admins : try: bot.unpin(target, msg["reply_to_message_id"]) bot.sendMessage(target, "پیام مورد نظر از سنجاق برداشته شد!", message_id=msg.get("message_id")) except: print("err unpin") elif msg.get("text").startswith("!trans"): try: responser = get(f"https://api.codebazan.ir/translate/?type=json&from=en&to=fa&text={msg.get('text').split()[1:]}").json() al = [responser["result"]] bot.sendMessage(msg.get("author_object_guid"), "پاسخ به ترجمه:\n"+"".join(al)).text bot.sendMessage(target, "نتیجه رو برات ارسال کردم😘", message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif msg.get("text").startswith("!font"): try: response = get(f"https://api.codebazan.ir/font/?text={msg.get('text').split()[1]}").json() bot.sendMessage(msg.get("author_object_guid"), "\n".join(list(response["result"].values())[:110])).text bot.sendMessage(target, "نتیجه رو برات ارسال کردم😘", message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif msg.get("text").startswith("جوک") or msg.get("text").startswith("jok") or msg.get("text").startswith("!jok"): try: response = get("https://api.codebazan.ir/jok/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "دستورت رو اشتباه وارد کردی", message_id=msg["message_id"]) elif msg.get("text").startswith("ذکر") or msg.get("text").startswith("zekr") or msg.get("text").startswith("!zekr"): try: response = get("http://api.codebazan.ir/zekr/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("حدیث") or msg.get("text").startswith("hadis") or msg.get("text").startswith("!hadis"): try: response = get("http://api.codebazan.ir/hadis/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی تو ارسال پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("بیو") or msg.get("text").startswith("bio") or msg.get("text").startswith("!bio"): try: response = get("https://api.codebazan.ir/bio/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "ببخشید، خطایی تو ارسال پیش اومد!", message_id=msg["message_id"]) elif msg["text"].startswith("!weather"): try: response = get(f"https://api.codebazan.ir/weather/?city={msg['text'].split()[1]}").json() bot.sendMessage(msg["author_object_guid"], "\n".join(list(response["result"].values())[:20])).text bot.sendMessage(target, "نتیجه بزودی برای شما ارسال خواهد شد...", message_id=msg["message_id"]) except: bot.sendMessage(target, "متاسفانه نتیجه‌ای موجود نبود!", message_id=msg["message_id"]) elif msg.get("text").startswith("دیالوگ"): try: response = get("http://api.codebazan.ir/dialog/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "متاسفانه تو ارسال مشکلی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("دانستنی"): try: response = get("http://api.codebazan.ir/danestani/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "دستورت رو اشتباه وارد کردی", message_id=msg["message_id"]) elif msg.get("text").startswith("پ ن پ") or msg.get("text").startswith("!pa-na-pa") or msg.get("text").startswith("په نه په"): try: response = get("http://api.codebazan.ir/jok/pa-na-pa/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "شرمنده نتونستم بفرستم!", message_id=msg["message_id"]) elif msg.get("text").startswith("الکی مثلا") or msg.get("text").startswith("!alaki-masalan"): try: response = get("http://api.codebazan.ir/jok/alaki-masalan/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "نشد بفرستم:(", message_id=msg["message_id"]) elif msg.get("text").startswith("داستان") or msg.get("text").startswith("!dastan"): try: response = get("http://api.codebazan.ir/dastan/").text bot.sendMessage(target, response,message_id=msg.get("message_id")) except: bot.sendMessage(target, "مشکلی پیش اومد!", message_id=msg["message_id"]) elif msg.get("text").startswith("!ping"): try: responser = get(f"https://api.codebazan.ir/ping/?url={msg.get('text').split()[1]}").text bot.sendMessage(target, responser,message_id=msg["message_id"]) except: bot.sendMessage(target, "دستور رو درست وارد کن دیگه😁", message_id=msg["message_id"]) elif "forwarded_from" in msg.keys() and bot.getMessagesInfo(target, [msg.get("message_id")])[0]["forwarded_from"]["type_from"] == "Channel" and not msg.get("author_object_guid") in admins : try: print("Yek ahmagh forwared Zad") bot.deleteMessages(target, [str(msg.get("message_id"))]) print("tabligh forearedi pak shod") except: print("err delete forwared") elif msg.get("text") == "قوانین": try: rules = open("rules.txt","r",encoding='utf-8').read() bot.sendMessage(target, str(rules), message_id=msg.get("message_id")) except: print("err ghanon") elif msg.get("text").startswith("آپدیت قوانین") and msg.get("author_object_guid") in admins: try: rules = open("rules.txt","w",encoding='utf-8').write(str(msg.get("text").strip("آپدیت قوانین"))) bot.sendMessage(target, "✅ قوانین بروزرسانی شد", message_id=msg.get("message_id")) # rules.close() except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "حالت آرام" and msg.get("author_object_guid") in admins: try: number = 10 bot.setGroupTimer(target,number) bot.sendMessage(target, "✅ حالت آرام برای "+str(number)+"ثانیه فعال شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "!speak" or msg.get("text") == "speak" or msg.get("text") == "Speak" or msg.get("text") == "بگو": try: if msg.get('reply_to_message_id') != None: msg_reply_info = bot.getMessagesInfo(target, [msg.get('reply_to_message_id')])[0] if msg_reply_info['text'] != None: text = msg_reply_info['text'] speech = gTTS(text) changed_voice = io.BytesIO() speech.write_to_fp(changed_voice) b2 = changed_voice.getvalue() changed_voice.seek(0) audio = MP3(changed_voice) dur = audio.info.length dur = dur * 1000 f = open('sound.ogg','wb') f.write(b2) f.close() bot.sendVoice(target , 'sound.ogg', dur,message_id=msg["message_id"]) os.remove('sound.ogg') print('sended voice') else: bot.sendMessage(target, 'پیام شما متن یا کپشن ندارد',message_id=msg["message_id"]) except: print('server gtts bug') elif msg.get("text") == "برداشتن حالت آرام" and msg.get("author_object_guid") in admins: try: number = 0 bot.setGroupTimer(target,number) bot.sendMessage(target, "✅ حالت آرام غیرفعال شد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "لطفا دستور رو صحیح وارد کنید!", message_id=msg.get("message_id")) elif msg.get("text").startswith("اخطار") and msg.get("author_object_guid") in admins: try: user = msg.get("text").split(" ")[1][1:] guid = bot.getInfoByUsername(user)["data"]["chat"]["abs_object"]["object_guid"] if not guid in admins : alert(guid,user) else : bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except IndexError: guid = bot.getMessagesInfo(target, [msg.get("reply_to_message_id")])[0]["author_object_guid"] user = bot.getUserInfo(guid)["data"]["user"]["username"] if not guid in admins: alert(guid,user) else: bot.sendMessage(target, "❌ کاربر ادمین میباشد", message_id=msg.get("message_id")) except: bot.sendMessage(target, "❌ لطفا دستور را به درستی وارد کنید", message_id=msg.get("message_id")) elif msg.get("text") == "قفل گروه" and msg.get("author_object_guid") in admins : try: bot.setMembersAccess(target, ["AddMember"]) bot.sendMessage(target, "🔒 گروه قفل شد", message_id=msg.get("message_id")) except: print("err lock GP") elif msg.get("text") == "بازکردن گروه" or msg.get("text") == "باز کردن گروه" and msg.get("author_object_guid") in admins : try: bot.setMembersAccess(target, ["SendMessages","AddMember"]) bot.sendMessage(target, "🔓 گروه اکنون باز است", message_id=msg.get("message_id")) except: print("err unlock GP") else: if msg.get("text") == "!start" or msg.get("text") == "/start" and msg.get("author_object_guid") in admins : try: sleeped = False bot.sendMessage(target, "ربا‌ت با موفقیت روشن شد!", message_id=msg.get("message_id")) except: print("err on bot") elif msg["type"]=="Event" and not msg.get("message_id") in answered and not sleeped: name = bot.getGroupInfo(target)["data"]["group"]["group_title"] data = msg['event_data'] if data["type"]=="RemoveGroupMembers": try: user = bot.getUserInfo(data['peer_objects'][0]['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"‼️ کاربر {user} با موفقیت از گروه حذف شد .", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err rm member answer") elif data["type"]=="AddedGroupMembers": try: user = bot.getUserInfo(data['peer_objects'][0]['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"هــای {user} عزیز 😘🌹 \n • به گـروه {name} خیـلی خوش اومدی 😍❤️ \nلطفا قوانین رو رعایت کن .\n 💎 برای مشاهده قوانین کافیه کلمه (قوانین) رو ارسال کنی!\n", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err add member answer") elif data["type"]=="LeaveGroup": try: user = bot.getUserInfo(data['performer_object']['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"خدانگهدار {user} 👋 ", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err Leave member Answer") elif data["type"]=="JoinedGroupByLink": try: user = bot.getUserInfo(data['performer_object']['object_guid'])["data"]["user"]["first_name"] bot.sendMessage(target, f"هــای {user} عزیز 😘🌹 \n • به گـروه {name} خیـلی خوش اومدی 😍❤️ \nلطفا قوانین رو رعایت کن .\n 💎 برای مشاهده قوانین کافیه کلمه (قوانین) رو ارسال کنی!\nدوست داری ربات بسازی؟ بیا اینجا😍👇\nt.me/RubikaBotCreate", message_id=msg["message_id"]) # bot.deleteMessages(target, [msg["message_id"]]) except: print("err Joined member Answer") else: if "forwarded_from" in msg.keys() and bot.getMessagesInfo(target, [msg.get("message_id")])[0]["forwarded_from"]["type_from"] == "Channel" and not msg.get("author_object_guid") in admins : bot.deleteMessages(target, [msg.get("message_id")]) guid = msg.get("author_object_guid") user = bot.getUserInfo(guid)["data"]["user"]["username"] bot.deleteMessages(target, [msg.get("message_id")]) alert(guid,user,True) continue except: continue answered.append(msg.get("message_id")) print("[" + msg.get("message_id")+ "] >>> " + msg.get("text") + "\n") except KeyboardInterrupt: exit() except Exception as e: if type(e) in list(retries.keys()): if retries[type(e)] < 3: retries[type(e)] += 1 continue else: retries.pop(type(e)) else: retries[type(e)] = 1 continue

import dash from dash.dependencies import Input, Output from dash import dash_table from dash import dcc from dash import html import pandas as pd # Import data into pandas df = pd.read_csv("data.csv") df["Condition"] = df["Condition Category"] df = df.drop(["Condition Category", "Missed Prices", "Index", "SKU"], axis=1) df = df[ [ "Brand", "Model", "Reference", "Year", "Condition", "Papers", "Box", "Movement", "Dimensions", "Gender", "Case", "Bracelet", "Crystal", "Dial Color", "Price", "Features", "Link", ] ] app = dash.Dash(__name__) money = dash_table.FormatTemplate.money(0) # App Layout app.layout = html.Div( [ # Title html.H1("Watch Data", style={"text-align": "center"}), # Dropdowns html.Div( className="row", children=[ # First dropdown html.Div( children=[ html.Label(["Brand"], style={"text-align": "center"},), dcc.Dropdown( id="brand_dropdown", options=[ {"label": i, "value": i} for i in df["Brand"].sort_values().unique() ], value=None, clearable=True, searchable=True, ), ], style=dict(width="50%"), ), # Second dropdown html.Div( children=[ html.Label(["Model"], style={"text-align": "center"},), dcc.Dropdown( id="model_dropdown", value=None, # [![enter image description here][1]][1] clearable=True, searchable=True, ), ], style=dict(width="50%"), ), html.Div( children=[ html.Label(["Price"], style={"text-align": "center"},), dcc.RangeSlider( id="range_slider", tooltip={"placement": "bottom", "always_visible": True}, ), ], style=dict(width="50%"), ), ], style=dict(display="flex"), ), html.Br(), html.Div( [ dash_table.DataTable( id="table", filter_action="native", sort_action="native", style_cell={"textAlign": "left", "minWidth": 110, "width": 110}, style_table={"minWidth": "100%"}, style_cell_conditional=[ {"if": {"column_id": "Features"}, "textAlign": "right",}, {"if": {"column_id": "Link"}, "textAlign": "right"}, ], style_data_conditional=[ { "if": {"row_index": "odd"}, "backgroundColor": "rgb(220, 220, 220)", } ], style_header={ "backgroundColor": "rgb(210, 210, 210)", "color": "black", "fontWeight": "bold", }, ) ] ), ] ) # Connecting Dash Components @app.callback( [Output(component_id="model_dropdown", component_property="options")], [Input(component_id="brand_dropdown", component_property="value")], ) def update_model(brand_selected): dff = df[df["Brand"] == brand_selected] return [[{"label": i, "value": i} for i in dff["Model"].sort_values().unique()]] @app.callback( [ Output(component_id="range_slider", component_property="min"), Output(component_id="range_slider", component_property="max"), Output(component_id="range_slider", component_property="value"), ], [ Input(component_id="brand_dropdown", component_property="value"), Input(component_id="model_dropdown", component_property="value"), ], ) def update_slider(brand_selected, model_selected): dff = df[(df["Brand"] == brand_selected) & (df["Model"] == model_selected)] return ( dff["Price"].min(), dff["Price"].max(), [dff["Price"].min(), dff["Price"].max()], ) @app.callback( [ Output(component_id="table", component_property="columns"), Output(component_id="table", component_property="data"), ], [ Input(component_id="brand_dropdown", component_property="value"), Input(component_id="model_dropdown", component_property="value"), Input(component_id="range_slider", component_property="value"), ], ) def update_table(brand_selected, model_selected, range): if brand_selected is None and model_selected is None: dff = df elif model_selected is None: dff = df[df["Brand"] == brand_selected] else: dff = df[ (df["Brand"] == brand_selected) & (df["Model"] == model_selected) & (df["Price"] >= range[0]) & (df["Price"] <= range[1]) ] return ( [ {"name": i, "id": i, "hideable": True, "type": "numeric", "format": money} if i == "Price" else {"name": i, "id": i, "hideable": True} for i in dff.columns ], dff.to_dict("records"), ) if __name__ == "__main__": app.run_server(debug=True)

#!/usr/bin/env python3 # # Copyright (c) 2019 Nordic Semiconductor ASA # # SPDX-License-Identifier: LicenseRef-BSD-5-Clause-Nordic import argparse import yaml from os import path import sys from pprint import pformat PERMITTED_STR_KEYS = ['size', 'region'] END_TO_START = 'end_to_start' START_TO_END = 'start_to_end' COMPLEX = 'complex' def remove_item_not_in_list(list_to_remove_from, list_to_check): to_remove = [x for x in list_to_remove_from.copy() if x not in list_to_check and x != 'app'] list(map(list_to_remove_from.remove, to_remove)) def item_is_placed(d, item, after_or_before): assert after_or_before in ['after', 'before'] return after_or_before in d['placement'] and d['placement'][after_or_before][0] == item def resolve_one_of(reqs, partitions): def empty_one_of(one_of_list): return RuntimeError("'one_of' dict did not evaluate to any partition. " "Available partitions {}, one_of {}".format(partitions, one_of_list)) for k, v in reqs.items(): if isinstance(v, dict): if 'one_of' in v.keys(): if len(v.keys()) != 1: raise RuntimeError("'one_of' must be the only key in its dict") # Now fetch the first existing partition. Note that the value must be a list even if there is only # one entry. reqs[k] = [partition for partition in v['one_of'] if partition in partitions][:1] if len(reqs[k]) == 0: raise empty_one_of(v['one_of']) else: resolve_one_of(v, partitions) # 'one_of' dicts can occur inside lists of partitions. # dicts with 'one_of' key is the only dict supported inside lists elif isinstance(v, list): # Resolve all 'one-of' dicts inside the list to_remove = list() to_add = list() for i in v: if isinstance(i, dict): if 'one_of' not in i.keys(): raise RuntimeError("Found illegal dict inside list. Only 'one_of' dicts are allowed") try: to_add.append([partition for partition in i['one_of'] if partition in partitions][0]) except IndexError: raise empty_one_of(i['one_of']) to_remove.append(i) if to_add: reqs[k] = [i if i not in to_remove else to_add.pop(0) for i in v] def remove_all_zero_sized_partitions(reqs, to_delete=None): first = False if to_delete is None: to_delete = list() first = True for k, v in reqs.items(): if 'size' in v and v['size'] == 0: to_delete.append(k) remove_all_zero_sized_partitions({k: v for k, v in reqs.items() if k not in to_delete}, to_delete) if 'share_size' in v.keys(): non_zero_partitions = [p for p in reqs if 'size' not in reqs[p] or reqs[p]['size'] != 0] actual_partitions = v['share_size'] if not isinstance(v['share_size'], dict) else v['share_size']['one_of'] remove_item_not_in_list(actual_partitions, non_zero_partitions) if not v['share_size'] or ('one_of' in v['share_size'] and len(v['share_size']['one_of']) == 0): del v['share_size'] if 'size' not in v.keys(): # The partition has no size, delete it, and rerun this function with the new reqs. to_delete.append(k) remove_all_zero_sized_partitions({k: v for k, v in reqs.items() if k not in to_delete}, to_delete) if first and to_delete: for k in list(set(to_delete)): print (f"Dropping partition '{k}' since its size is 0.") del reqs[k] def remove_irrelevant_requirements(reqs): remove_all_zero_sized_partitions(reqs) # Verify that no partitions define an empty 'placement' for k, v in reqs.items(): if 'placement' in v.keys() and len(v['placement']) == 0: raise RuntimeError("Found empty 'placement' property for partition '{}'".format(k)) # Exchange all occurrences of 'one_of' list, with the first existing partition in the 'one_of' list. # Extend the keys given as input with 'end' and 'start' as these are also valid references. resolve_one_of(reqs, list(reqs.keys()) + ['end', 'start']) # Remove dependencies to partitions which are not present for k, v in reqs.items(): for before_after in ['before', 'after']: if 'placement' in v.keys() and before_after in v['placement'].keys(): remove_item_not_in_list(v['placement'][before_after], [*reqs.keys(), 'start', 'end']) if not v['placement'][before_after]: del v['placement'][before_after] if 'span' in v.keys(): remove_item_not_in_list(v['span'], reqs.keys()) if 'inside' in v.keys(): remove_item_not_in_list(v['inside'], reqs.keys()) if not v['inside']: del v['inside'] def get_images_which_need_resolving(reqs, sub_partitions): # Get candidates which have placement specs. unsorted = {x for x in reqs.keys() if 'placement' in reqs[x].keys() and ('before' in reqs[x]['placement'].keys() or 'after' in reqs[x]['placement'].keys())} # Sort sub_partitions by whether they are inside other sub_partitions. Innermost first. sorted_subs = sorted(sub_partitions.values(), key=lambda x: len(x['span'])) # Sort candidates by whether they are part of a sub_partitions. # sub_partition parts come last in the result list so they are more likely # to end up being placed next to each other, since they are inserted last. result = [] for sub in sorted_subs: result = [part for part in sub['span'] if part in unsorted and part not in result] + result # Lastly, place non-partitioned parts at the front. result = [part for part in unsorted if part not in result] + result return result def solve_direction(reqs, sub_partitions, unsolved, solution, ab): assert ab in ['after', 'before'] current_index = 0 pool = solution + list(sub_partitions.keys()) current = pool[current_index] while current: depends = [x for x in unsolved if item_is_placed(reqs[x], current, ab)] if depends: # Place based on current, or based on the first/last element in the span of current. if ab == 'before': anchor = current if current in solution else next(solved for solved in solution if solved in sub_partitions[current]['span']) solution.insert(solution.index(anchor), depends[0]) else: anchor = current if current in solution else next(solved for solved in reversed(solution) if solved in sub_partitions[current]['span']) solution.insert(solution.index(anchor) + 1, depends[0]) unsolved.remove(depends[0]) current = depends[0] else: current_index += 1 if current_index >= len(pool): break current = pool[current_index] def solve_first_last(reqs, unsolved, solution): for fl in [('after', 'start', lambda x: solution.insert(0, x)), ('before', 'end', solution.append)]: first_or_last = [x for x in reqs.keys() if 'placement' in reqs[x] and fl[0] in reqs[x]['placement'].keys() and fl[1] in reqs[x]['placement'][fl[0]]] if first_or_last: fl[2](first_or_last[0]) if first_or_last[0] in unsolved: unsolved.remove(first_or_last[0]) def solve_inside(reqs, sub_partitions): for key, value in reqs.items(): if 'inside' in value.keys(): sub_partitions[value['inside'][0]]['span'].append(key) def clean_sub_partitions(reqs, sub_partitions): keys_to_delete = list() new_deletion = True # Remove empty partitions and partitions containing only empty partitions. while new_deletion: new_deletion = False for key, value in sub_partitions.items(): if (len(value['span']) == 0) or all(x in keys_to_delete for x in value['span']): if key not in keys_to_delete: keys_to_delete.append(key) new_deletion = True for key in keys_to_delete: print (f"Dropping partition '{key}' since it is empty.") del sub_partitions[key] # "Flatten" by changing all span lists to contain the innermost partitions. done = False while not done: done = True for key, value in sub_partitions.items(): assert len(value['span']) > 0, "partition {} is empty".format(key) value['orig_span'] = value['span'].copy() # Take a "backup" of the span. for part in (part for part in value['span'] if part in sub_partitions): value['span'].extend(sub_partitions[part]['span']) value['span'].remove(part) value['span'] = list(set(value['span'])) # remove duplicates done = False for part in (part for part in value['span'] if part not in sub_partitions and part not in reqs): value['span'].remove(part) def convert_str_to_list(with_str): for k, v in with_str.items(): if isinstance(v, dict): convert_str_to_list(v) elif isinstance(v, str) and k not in PERMITTED_STR_KEYS: with_str[k] = list() with_str[k].append(v) def resolve(reqs): convert_str_to_list(reqs) solution = list(['app']) remove_irrelevant_requirements(reqs) sub_partitions = {k: v for k, v in reqs.items() if 'span' in v} reqs = {k: v for k, v in reqs.items() if 'span' not in v} solve_inside(reqs, sub_partitions) clean_sub_partitions(reqs, sub_partitions) unsolved = get_images_which_need_resolving(reqs, sub_partitions) solve_first_last(reqs, unsolved, solution) while unsolved: solve_direction(reqs, sub_partitions, unsolved, solution, 'before') solve_direction(reqs, sub_partitions, unsolved, solution, 'after') # Validate partition spanning. for sub in sub_partitions: indices = [solution.index(part) for part in sub_partitions[sub]['span']] assert ((not indices) or (max(indices) - min(indices) + 1 == len(indices))), \ "partition {} ({}) does not span over consecutive parts." \ " Solution: {}".format(sub, str(sub_partitions[sub]['span']), str(solution)) for part in sub_partitions[sub]['span']: assert (part in solution), "Some or all parts of partition {} have not been placed.".format(part) return solution, sub_partitions def shared_size(reqs, share_with, total_size): sharer_count = reqs[share_with]['sharers'] size = sizeof(reqs, share_with, total_size) if share_with == 'app' or ('span' in reqs[share_with].keys() and 'app' in reqs[share_with]['span']): size /= (sharer_count + 1) return int(size) def get_size_source(reqs, sharer): size_source = sharer while 'share_size' in reqs[size_source].keys(): # Find "original" source. size_source = reqs[size_source]['share_size'][0] return size_source def set_shared_size(all_reqs, total_size): for req in all_reqs.keys(): if 'share_size' in all_reqs[req].keys(): size_source = get_size_source(all_reqs, req) if 'sharers' not in all_reqs[size_source].keys(): all_reqs[size_source]['sharers'] = 0 all_reqs[size_source]['sharers'] += 1 all_reqs[req]['share_size'] = [size_source] new_sizes = dict() # Find all partitions which share size with 'app' or a container partition which spans 'app'. dynamic_size_sharers = get_dependent_partitions(all_reqs, 'app') static_size_sharers = [k for k, v in all_reqs.items() if 'share_size' in v.keys() and k not in dynamic_size_sharers] for req in static_size_sharers: all_reqs[req]['size'] = shared_size(all_reqs, all_reqs[req]['share_size'][0], total_size) for req in dynamic_size_sharers: new_sizes[req] = shared_size(all_reqs, all_reqs[req]['share_size'][0], total_size) # Update all sizes after-the-fact or else the calculation will be messed up. for key, value in new_sizes.items(): all_reqs[key]['size'] = value def get_dependent_partitions(all_reqs, target): return [k for k, v in all_reqs.items() if 'share_size' in v.keys() and (v['share_size'][0] == target or ('span' in all_reqs[v['share_size'][0]].keys() and target in all_reqs[v['share_size'][0]]['span']))] def app_size(reqs, total_size): size = total_size - sum([req['size'] for name, req in reqs.items() if 'size' in req.keys() and name != 'app']) return size def verify_layout(reqs, solution, total_size, flash_start): # Verify no overlap, that all flash is assigned, and that the total amount of flash # assigned corresponds to the total size available. expected_address = flash_start + reqs[solution[0]]['size'] for p in solution[1:]: actual_address = reqs[p]['address'] if actual_address != expected_address: raise RuntimeError("Error when inspecting {}, invalid address {}".format(p, actual_address)) expected_address += reqs[p]['size'] last = reqs[solution[-1]] assert last['address'] + last['size'] == flash_start + total_size def set_addresses_and_align(reqs, sub_partitions, solution, size, start=0): all_reqs = dict(reqs, **sub_partitions) set_shared_size(all_reqs, size) dynamic_partitions = ['app'] dynamic_partitions += get_dependent_partitions(all_reqs, 'app') reqs['app']['size'] = app_size(reqs, size) reqs[solution[0]]['address'] = start if len(reqs) > 1: _set_addresses_and_align(reqs, sub_partitions, solution, size, start, dynamic_partitions) verify_layout(reqs, solution, size, start) def first_partition_has_been_aligned(first, solution): return 'placement' in first and 'align' in first['placement'] and 'end' in first['placement']['align'] \ and solution[1] == 'EMPTY_0' def _set_addresses_and_align(reqs, sub_partitions, solution, size, start, dynamic_partitions): # Perform address assignment and alignment in two steps, first from start to app, then from end to app. for i in range(0, solution.index('app') + 1): current = solution[i] if i != 0: previous = solution[i - 1] reqs[current]['address'] = reqs[previous]['address'] + reqs[previous]['size'] # To avoid messing with vector table, don't store empty partition as the first. insert_empty_partition_before = i != 0 # Special handling is needed when aligning the first partition if i == 0 and first_partition_has_been_aligned(reqs[current], solution): continue if align_if_required(i, dynamic_partitions, insert_empty_partition_before, reqs, solution): _set_addresses_and_align(reqs, sub_partitions, solution, size, start, dynamic_partitions) for i in range(len(solution) - 1, solution.index('app'), -1): current = solution[i] if i == len(solution) - 1: reqs[current]['address'] = (start + size) - reqs[current]['size'] else: higher_partition = solution[i + 1] reqs[current]['address'] = reqs[higher_partition]['address'] - reqs[current]['size'] if align_if_required(i, dynamic_partitions, False, reqs, solution): _set_addresses_and_align(reqs, sub_partitions, solution, size, start, dynamic_partitions) def align_if_required(i, dynamic_partitions, move_up, reqs, solution): current = solution[i] if 'placement' in reqs[current] and 'align' in reqs[current]['placement']: required_offset = align_partition(current, reqs, move_up, dynamic_partitions) if required_offset: solution_index = i if move_up else i + 1 solution.insert(solution_index, required_offset) return True return False def align_partition(current, reqs, move_up, dynamic_partitions): required_offset = get_required_offset(align=reqs[current]['placement']['align'], start=reqs[current]['address'], size=reqs[current]['size'], move_up=move_up) if not required_offset: return None empty_partition_size = required_offset if current not in dynamic_partitions: if move_up: empty_partition_address = reqs[current]['address'] reqs[current]['address'] += required_offset else: empty_partition_address = reqs[current]['address'] + reqs[current]['size'] if reqs[current]['address'] != 0: # Special handling for the first partition as it cannot be moved down reqs[current]['address'] -= required_offset elif not move_up: empty_partition_address, empty_partition_size = \ align_dynamic_partition(dynamic_partitions, current, reqs, required_offset) else: raise RuntimeError("Invalid combination, can not have dynamic partition in front of app with alignment") e = 'EMPTY_{}'.format(len([x for x in reqs.keys() if 'EMPTY' in x])) reqs[e] = {'address': empty_partition_address, 'size': empty_partition_size, 'placement': {'before' if move_up else 'after': [current]}} if current not in dynamic_partitions: # We have stolen space from the 'app' partition. Hence, all partitions which share size with 'app' partition # must have their sizes reduced. Note that the total amount of 'stealing' is divided between the partitions # sharing size with app (including 'app' itself). for p in dynamic_partitions: reqs[p]['size'] = reqs[p]['size'] - (reqs[e]['size'] // len(dynamic_partitions)) return e def align_dynamic_partition(app_dep_parts, current, reqs, required_offset): # Since this is a dynamic partition, the introduced empty partition will take space from the 'app' partition # and the partition being aligned. Take special care to ensure the offset becomes correct. required_offset *= 2 for p in app_dep_parts: reqs[p]['size'] -= required_offset // 2 reqs[current]['address'] -= required_offset empty_partition_address = reqs[current]['address'] + reqs[current]['size'] empty_partition_size = required_offset return empty_partition_address, empty_partition_size def get_required_offset(align, start, size, move_up): if len(align) != 1 or ('start' not in align and 'end' not in align): raise RuntimeError("Invalid alignment requirement {}".format(align)) end = start + size align_start = 'start' in align try: if (align_start and start % align['start'] == 0) or (not align_start and end % align['end'] == 0): return 0 if move_up: return align['start'] - (start % align['start']) if align_start else align['end'] - (end % align['end']) else: if align_start: return start % align['start'] else: # Special handling is needed if start is 0 since this partition can not be moved down return end % align['end'] if start != 0 else align['end'] - (end % align['end']) except TypeError as err: keyword = 'start' if align_start else 'end' raise TypeError(f"elements in align: {{{keyword}:{align[keyword]}}} is not type of \'int\'") from err def set_size_addr(entry, size, address): entry['size'] = size entry['address'] = address def set_sub_partition_address_and_size(reqs, sub_partitions): for sp_name, sp_value in sub_partitions.items(): size = sum([reqs[part]['size'] for part in sp_value['span']]) if size == 0: raise RuntimeError("No compatible parent partition found for {}".format(sp_name)) address = min([reqs[part]['address'] for part in sp_value['span']]) reqs[sp_name] = sp_value reqs[sp_name]['span'] = reqs[sp_name]['orig_span'] # Restore "backup". set_size_addr(reqs[sp_name], size, address) def sizeof(reqs, req, total_size): if req == 'app': size = app_size(reqs, total_size) elif 'span' not in reqs[req].keys(): size = reqs[req]['size'] if 'size' in reqs[req].keys() else 0 else: size = sum([sizeof(reqs, part, total_size) for part in reqs[req]['span']]) return size def load_reqs(input_config): reqs = dict() for ymlpath in input_config: if path.exists(ymlpath): with open(ymlpath, 'r') as f: loaded_reqs = yaml.safe_load(f) if loaded_reqs is None: continue for key in loaded_reqs.keys(): if key in reqs.keys() and loaded_reqs[key] != reqs[key]: raise RuntimeError("Conflicting configuration found for '{}' value for key '{}' differs." "val1: {} val2: {} ".format(f.name, key, loaded_reqs[key], reqs[key])) reqs.update(loaded_reqs) return reqs def get_dynamic_area_start_and_size(static_config, flash_size): # Remove app from this dict to simplify the case where partitions before and after are removed. proper_partitions = [config for name, config in static_config.items() if 'span' not in config.keys() and name != 'app'] starts = {flash_size} | {config['address'] for config in proper_partitions} ends = {0} | {config['address'] + config['size'] for config in proper_partitions} gaps = list(zip(sorted(ends - starts), sorted(starts - ends))) assert len(gaps) == 1, "Incorrect amount of gaps found" start, end = gaps[0] return start, end - start def get_region_config(pm_config, region_config, static_conf=None): start = region_config['base_address'] size = region_config['size'] placement_strategy = region_config['placement_strategy'] region_name = region_config['name'] device = region_config['device'] if placement_strategy in [END_TO_START, START_TO_END]: solve_simple_region(pm_config, start, size, placement_strategy, region_name, device, static_conf) else: solve_complex_region(pm_config, start, size, placement_strategy, region_name, device, static_conf) def solve_simple_region(pm_config, start, size, placement_strategy, region_name, device, static_conf): reserved = 0 if static_conf: verify_static_conf(size, start, placement_strategy, static_conf) reserved = sum([config['size'] for name, config in static_conf.items() if 'region' in config.keys() and config['region'] == region_name and name != 'app']) if placement_strategy == END_TO_START: address = start + size - reserved else: address = start + reserved for partition_name in pm_config: if placement_strategy == END_TO_START: address -= pm_config[partition_name]['size'] pm_config[partition_name]['address'] = address if placement_strategy == START_TO_END: address += pm_config[partition_name]['size'] if device: pm_config[partition_name]['device'] = device # Generate the region partition containing the non-reserved memory. # But first, verify that the user hasn't created a partition with the name of the region. if region_name in pm_config: raise RuntimeError(f"Found partition named {region_name}, this is the name of a region, and is a reserved name") pm_config[region_name] = dict() pm_config[region_name]['region'] = region_name if placement_strategy == END_TO_START: pm_config[region_name]['address'] = start pm_config[region_name]['size'] = address - start else: pm_config[region_name]['address'] = address pm_config[region_name]['size'] = (start + size) - address def verify_static_conf(size, start, placement_strategy, static_conf): # Verify that all statically defined partitions has given address, # and that they are packed at the end/start of the region. starts = {start + size} | {c['address'] for c in static_conf.values() if 'size' in c} ends = {start} | {c['address'] + c['size'] for c in static_conf.values() if 'size' in c} gaps = list(zip(sorted(ends - starts), sorted(starts - ends))) if placement_strategy == START_TO_END: start_end_correct = gaps[0][0] == start + size else: start_end_correct = gaps[0][0] == start if len(gaps) != 1 or not start_end_correct: raise RuntimeError("Statically defined partitions are not packed at the start/end of region") def solve_complex_region(pm_config, start, size, placement_strategy, region_name, device, static_conf): free_size = size if static_conf: start, free_size = get_dynamic_area_start_and_size(static_conf, free_size) # If nothing is unresolved (only app remaining), simply return the pre defined config with 'app' if len(pm_config) == 1: pm_config.update(static_conf) pm_config['app']['address'] = start pm_config['app']['size'] = free_size return solution, sub_partitions = resolve(pm_config) set_addresses_and_align(pm_config, sub_partitions, solution, free_size, start) set_sub_partition_address_and_size(pm_config, sub_partitions) if static_conf: # Merge the results, take the new 'app' as that has the correct size. pm_config.update({name: config for name, config in static_conf.items() if name != 'app'}) def write_yaml_out_file(pm_config, out_path): def hexint_presenter(dumper, data): return dumper.represent_int(hex(data)) yaml.add_representer(int, hexint_presenter) yamldump = yaml.dump(pm_config) with open(out_path, 'w') as out_file: out_file.write(yamldump) def parse_args(): parser = argparse.ArgumentParser( description='''Parse given 'pm.yml' partition manager configuration files to deduce the placement of partitions. The partitions and their relative placement is defined in the 'pm.yml' files. The path to the 'pm.yml' files are used to locate 'autoconf.h' files. These are used to find the partition sizes, as well as the total flash size. This script generates a file for each partition - "pm_config.h". This file contains all addresses and sizes of all partitions. "pm_config.h" is in the same folder as the given 'pm.yml' file.''', formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument("--input-files", required=True, type=str, nargs="+", help="List of paths to input yaml files. ") parser.add_argument("--output-partitions", required=True, type=str, help="Path to output partition configuration file.") parser.add_argument("--output-regions", required=True, type=str, help="Path to output regions configuration file.") parser.add_argument("--static-config", required=False, type=argparse.FileType(mode='r'), help="Path static configuration.") parser.add_argument("--regions", required=False, type=str, nargs='*', help="Space separated list of regions. For each region specified here, one must specify" "--{region_name}-base-addr and --{region_name}-size. If the region is associated" "with a driver, the device name must be given in --{region_name}-device (e.g. an " "external flash driver. For regions with placement strategy 'complex' it is possible" "to specify the --{region_name}-dynamic-partition to set the name of the dynamic partition" "which occupies all non-used area.") main_args, region_args = parser.parse_known_args() # Create new instance to parse regions parser = argparse.ArgumentParser() for x in main_args.regions: # Generate arguments for each region dynamically parser.add_argument(f'--{x}-size', required=True, type=lambda z: int(z, 0)) parser.add_argument(f'--{x}-base-address', required=False, type=lambda z: int(z, 0), default=0) parser.add_argument(f'--{x}-placement-strategy', required=False, type=str, choices=[START_TO_END, END_TO_START, COMPLEX], default=START_TO_END) parser.add_argument(f'--{x}-device', required=False, type=str, default='') parser.add_argument(f'--{x}-dynamic-partition', required=False, type=str, help="Name of dynamic partition") ranges_configuration = parser.parse_args(region_args) return main_args, ranges_configuration def replace_app_with_dynamic_partition(d, dynamic_partition_name): for k, v in d.items(): if isinstance(v, dict): replace_app_with_dynamic_partition(v, dynamic_partition_name) elif isinstance(v, list) and "app" in v: d[k] = [o if o != "app" else dynamic_partition_name for o in v] elif isinstance(v, str) and v == "app": v = dynamic_partition_name def set_flash_primary_region(pm_config): for v in pm_config.values(): if 'region' not in v: v['region'] = 'flash_primary' def fix_syntactic_sugar(pm_config): set_flash_primary_region(pm_config) def get_region_config_from_args(args, ranges_configuration): regions = {x: {k.replace(f'{x}_', ''): v for k, v in vars(ranges_configuration).items() if k.startswith(x)} for x in [y.replace('-', '_') for y in args.regions]} # Replace - with _ to match argparse namespace return regions def solve_region(pm_config, region, region_config, static_config): solution = dict() region_config['name'] = region partitions = {k: v for k, v in pm_config.items() if region in v['region']} static_partitions = {k: v for k, v in static_config.items() if region in v['region']} get_region_config(partitions, region_config, static_partitions) solution.update(partitions) if region_config['dynamic_partition']: solution[region_config['dynamic_partition'].strip()] = solution['app'] del solution['app'] replace_app_with_dynamic_partition(solution, region_config['dynamic_partition'].strip()) return solution def load_static_configuration(args, pm_config): static_config = yaml.safe_load(args.static_config) fix_syntactic_sugar(static_config) # Delete all statically defined partitions from the pm_config dict. # This is done since all partitions in pm_config will be resolved. for statically_defined_image in static_config: if statically_defined_image in pm_config and statically_defined_image: print (f"Dropping partition '{statically_defined_image}' since it is statically defined.") del pm_config[statically_defined_image] return static_config def main(): args, ranges_configuration = parse_args() pm_config = load_reqs(args.input_files) static_config = load_static_configuration(args, pm_config) if args.static_config else dict() pm_config['app'] = dict() fix_syntactic_sugar(pm_config) regions = get_region_config_from_args(args, ranges_configuration) solution = dict() for region, region_config in regions.items(): solution.update(solve_region(pm_config, region, region_config, static_config)) write_yaml_out_file(solution, args.output_partitions) write_yaml_out_file(regions, args.output_regions) def expect_addr_size(td, name, expected_address, expected_size): if expected_size: assert td[name]['size'] == expected_size, \ "Size of {} was {}, expected {}.\ntd:{}".format(name, td[name]['size'], expected_size, pformat(td)) if expected_address: assert td[name]['address'] == expected_address, \ "Address of {} was {}, expected {}.\ntd:{}".format(name, td[name]['address'], expected_address, pformat(td)) def expect_list(expected, actual): expected_list = list(sorted(expected)) actual_list = list(sorted(actual)) assert sorted(expected_list) == sorted(actual_list), "Expected list %s, was %s" % (str(expected_list), str(actual_list)) def test(): list_one = [1, 2, 3, 4] items_to_check = [4] remove_item_not_in_list(list_one, items_to_check) assert list_one[0] == 4 assert len(list_one) == 1 test_config = { 'first': {'address': 0, 'size': 10}, # Gap from deleted partition. 'app': {'address': 20, 'size': 10}, # Gap from deleted partition. 'fourth': {'address': 40, 'size': 60}} start, size = get_dynamic_area_start_and_size(test_config, 100) assert start == 10 assert size == 40-10 test_config = { 'first': {'address': 0, 'size': 10}, 'second': {'address': 10, 'size': 10}, 'app': {'address': 20, 'size': 80} # Gap from deleted partition. } start, size = get_dynamic_area_start_and_size(test_config, 100) assert start == 20 assert size == 80 test_config = { 'app': {'address': 0, 'size': 10}, # Gap from deleted partition. 'second': {'address': 40, 'size': 60}} start, size = get_dynamic_area_start_and_size(test_config, 100) assert start == 0 assert size == 40 test_config = { 'first': {'address': 0, 'size': 10}, # Gap from deleted partition. 'app': {'address': 20, 'size': 10}} start, size = get_dynamic_area_start_and_size(test_config, 100) assert start == 10 assert size == 100 - 10 # Verify that all 'end' and 'start' are valid references in 'one_of' dicts td = { 'a': {'placement': {'after': {'one_of': ['x0', 'x1', 'start']}}, 'size': 100}, 'b': {'placement': {'before': {'one_of': ['x0', 'x1', 'end']}}, 'size': 200}, 'app': {}, } s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'a', 0, 100) expect_addr_size(td, 'app', 100, 700) expect_addr_size(td, 'b', 800, 200) # Verify that START_TO_END region configuration is correct td = {'b': {'size': 100, 'region': 'extflash'}} test_region = {'name': 'extflash', 'size': 1000, 'base_address': 2000, 'placement_strategy': START_TO_END, 'device': 'some-driver-device'} get_region_config(td, test_region) assert td['b']['size'] == 100 assert td['extflash']['address'] == 2100 assert td['extflash']['size'] == 900 # Verify that RAM configuration is correct td = {'b': {'size': 100, 'region': 'ram'}} test_region = {'name': 'ram', 'size': 1000, 'base_address': 2000, 'placement_strategy': END_TO_START, 'device': None} get_region_config(td, test_region) assert td['b']['size'] == 100 assert td['ram']['address'] == 2000 assert td['ram']['size'] == 900 # Verify that RAM configuration is correct td = { 'b': {'size': 100, 'region': 'ram'}, 'c': {'size': 200, 'region': 'ram'}, 'd': {'size': 300, 'region': 'ram'} } test_region = {'name': 'ram', 'size': 1000, 'base_address': 2000, 'placement_strategy': END_TO_START, 'device': None} get_region_config(td, test_region) assert td['ram']['address'] == 2000 assert td['ram']['size'] == 400 # Can not verify the placement, as this is random assert td['b']['size'] == 100 assert td['c']['size'] == 200 assert td['d']['size'] == 300 # Verify that RAM configuration with given static configuration is correct test_region = {'name': 'ram', 'size': 1000, 'base_address': 2000, 'placement_strategy': END_TO_START, 'device': None} td = { 'b': {'size': 100, 'region': 'ram'}, 'c': {'size': 200, 'region': 'ram'}, 'd': {'size': 300, 'region': 'ram'}, } get_region_config(td, test_region, static_conf={'s1': {'size': 100, 'address': (1000+2000)-100, 'region': 'ram'}, 's2': {'size': 200, 'address': (1000+2000)-100-200, 'region': 'ram'}}) assert td['ram']['address'] == 2000 assert td['ram']['size'] == 100 # Can not verify the placement, as this is random assert td['b']['size'] == 100 assert td['c']['size'] == 200 assert td['d']['size'] == 300 # Verify that RAM configuration with given static configuration fails if static RAM partitions are not # packed at the end of flash, here there is a space between the two regions test_region = {'name': 'ram', 'size': 1000, 'base_address': 2000, 'placement_strategy': END_TO_START, 'device': None} failed = False td = { 'a': {'placement': {'after': 'start'}, 'size': 100}, 'b': {'size': 100, 'region': 'ram'}, 'c': {'size': 200, 'region': 'ram'}, 'd': {'size': 300, 'region': 'ram'}, 'app': {} } try: get_region_config(td, test_region, static_conf={'s1': {'size': 100, 'address': (1000+2000)-100, 'region': 'ram'}, 's2': {'size': 200, 'address': (1000+2000)-100-300, 'region': 'ram'}}) # Note 300 not 200 except RuntimeError: failed = True assert failed # Verify that RAM configuration with given static configuration fails if static RAM partitions are not # packed at the end of flash, here the partitions are packed, but does not go to the end of RAM failed = False test_region = {'name': 'ram', 'size': 1000, 'base_address': 2000, 'placement_strategy': END_TO_START, 'device': None} td = { 'a': {'placement': {'after': 'start'}, 'size': 100}, 'b': {'size': 100, 'region': 'ram'}, 'c': {'size': 200, 'region': 'ram'}, 'd': {'size': 300, 'region': 'ram'}, 'app': {} } try: get_region_config(td, test_region, static_conf={'s1': {'size': 100, 'address': (1000+2000-50)-100, 'region': 'ram'}, # Note - 50 's2': {'size': 200, 'address': (1000+2000-50)-100-200, 'region': 'ram'}}) # Note - 50 except RuntimeError: failed = True assert failed # Verify that all 'one_of' dicts are replaced with the first entry which corresponds to an existing partition td = { 'a': {'placement': {'after': 'start'}, 'size': 100}, 'b': {'placement': {'after': {'one_of': ['x0', 'x1', 'a', 'x2']}}, 'size': 200}, 'c': {'placement': {'after': 'b'}, 'share_size': {'one_of': ['x0', 'x1', 'b', 'a']}}, 'd': {'placement': {'after': 'c'}, 'share_size': {'one_of': ['a', 'b']}}, # Should take first existing # We can use several 'one_of' - dicts inside lists 's': {'span': ['a', {'one_of': ['x0', 'b', 'd']}, {'one_of': ['x2', 'c', 'a']}]}, 'app': {}, 'e': {'placement': {'after': 'app'}, 'share_size': {'one_of': ['x0', 'app']}}, # app always exists } s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'a', 0, 100) # b is after a expect_addr_size(td, 'b', 100, 200) # b is after a expect_addr_size(td, 'c', 300, 200) # c shares size with b expect_addr_size(td, 'd', 500, 100) # d shares size with a expect_addr_size(td, 's', 0, 500) # s spans a, b and c expect_addr_size(td, 'app', 600, 200) # s spans a, b and c expect_addr_size(td, 'e', 800, 200) # s spans a, b and c # Verify that all 'share_size' with value partition that has size 0 is compatible with 'one_of' dicts td = { 'a': {'placement': {'after': 'start'}, 'size': 0}, 'b': {'placement': {'after': {'one_of': ['a', 'start']}}, 'share_size': ['a']}, 'c': {'placement': {'after': {'one_of': ['a', 'b', 'start']}}, 'share_size': {'one_of': ['a', 'b']}}, 'd': {'placement': {'after': {'one_of': ['a', 'b', 'c', 'start']}}, 'share_size': {'one_of': ['a', 'b', 'c']}}, # You get the point 'e': {'placement': {'after': {'one_of': ['a', 'b', 'c', 'd', 'start']}}, 'size': 100} } remove_all_zero_sized_partitions(td) assert 'a' not in td assert 'b' not in td assert 'c' not in td assert 'd' not in td # Verify that all 'share_size' with value partition that has size 0 is compatible withe 'one_of' dicts. td = { 'a': {'placement': {'after': 'start'}, 'size': 0}, 'b': {'placement': {'after': {'one_of': ['a', 'start']}}, 'share_size': ['a']}, 'c': {'placement': {'after': {'one_of': ['a', 'b', 'start']}}, 'share_size': {'one_of': ['a', 'b']}}, 'd': {'placement': {'after': {'one_of': ['a', 'b', 'c', 'start']}}, 'share_size': {'one_of': ['a', 'b', 'c']}}, # You get the point 'e': {'placement': {'after': {'one_of': ['a', 'b', 'c', 'd', 'start']}}, 'size': 100}, 'app': {} } # Perform the same test as above, but run it through the 'resolve' function this time. s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) assert 'a' not in td assert 'b' not in td assert 'c' not in td assert 'd' not in td expect_addr_size(td, 'e', 0, 100) # Verify that an error is raised when no partition inside 'one_of' dicts exist as dict value failed = False td = { 'a': {'placement': {'after': {'one_of': ['x0', 'x1']}}, 'size': 100}, 'app': {} } try: resolve(td) except RuntimeError: failed = True assert failed # Verify that an error is raised when no partition inside 'one_of' dicts exist as list item failed = False td = { 'app': {}, 'a': {'placement': {'after': 'app'}, 'size': 100}, 's': {'span': ['a', {'one_of': ['x0', 'x1']}]}, } try: resolve(td) except RuntimeError: failed = True assert failed # Verify that empty placement property throws error td = {'spm': {'placement': {'before': ['app']}, 'size': 100, 'inside': ['mcuboot_slot0']}, 'mcuboot': {'placement': {'before': ['spm', 'app']}, 'size': 200}, 'mcuboot_slot0': {'span': ['app']}, 'invalid': {'placement': {}}, 'app': {}} failed = False try: s, sub_partitions = resolve(td) except RuntimeError: failed = True assert failed # Verify that offset is correct when aligning partition not at address 0 offset = get_required_offset(align={'end': 800}, start=1400, size=100, move_up=False) assert offset == 700 # Verify that offset is correct when aligning partition at address 0 offset = get_required_offset(align={'end': 800}, start=0, size=100, move_up=False) assert offset == 700 # Verify that offset is correct when aligning partition at address 0 # and end of first partition is larger than the required alignment. offset = get_required_offset(align={'end': 800}, start=0, size=1000, move_up=False) assert offset == 600 for l in [ lambda : get_required_offset(align={'end': ["CONFIG_VAR"]}, start=0, size=1000, move_up=False), lambda : get_required_offset(align={'start': ["CONFIG_VAR"]}, start=0, size=1000, move_up=False), lambda : get_required_offset(align={'start': [[2]]},start=0, size=1000, move_up=False) ]: failed = False try: l() except TypeError: failed = True assert failed, "Should have received a TypeError." # Verify that the first partition can be aligned, and that the inserted empty partition is placed behind it. td = {'first': {'placement': {'before': 'app', 'align': {'end': 800}}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'EMPTY_0', 100, 700) expect_addr_size(td, 'app', 800, 200) # Verify that providing a static configuration with nothing unresolved gives a valid configuration with 'app'. static_config = {'spm': {'address': 0, 'placement': None, 'before': ['app'], 'size': 400}} test_config = {'app': dict()} flash_region = { 'name': 'flash_primary', 'placement_strategy': COMPLEX, 'size': 1000, 'base_address': 0, 'device': 'nordic_flash_stuff' } get_region_config(test_config, flash_region, static_config) assert 'app' in test_config assert test_config['app']['address'] == 400 assert test_config['app']['size'] == 600 assert 'spm' in test_config assert test_config['spm']['address'] == 0 # Test a single partition with alignment where the address is smaller than the alignment value. td = {'without_alignment': {'placement': {'before': 'with_alignment'}, 'size': 100}, 'with_alignment': {'placement': {'before': 'app', 'align': {'start': 200}}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'EMPTY_0', 100, 100) expect_addr_size(td, 'with_alignment', 200, 100) # Test alignment after 'app' td = {'without_alignment': {'placement': {'after': 'app'}, 'size': 100}, 'with_alignment': {'placement': {'after': 'without_alignment', 'align': {'start': 400}}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'app', 0, 700) expect_addr_size(td, 'with_alignment', 800, 100) expect_addr_size(td, 'EMPTY_0', 900, 100) # Test two partitions with alignment where the address is smaller than the alignment value. td = {'without_alignment': {'placement': {'before': 'with_alignment'}, 'size': 100}, 'with_alignment': {'placement': {'before': 'with_alignment_2', 'align': {'end': 400}}, 'size': 100}, 'with_alignment_2': {'placement': {'before': 'app', 'align': {'start': 1000}}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 10000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'EMPTY_0', 100, 200) expect_addr_size(td, 'with_alignment', 300, 100) expect_addr_size(td, 'EMPTY_1', 400, 600) expect_addr_size(td, 'with_alignment_2', 1000, 100) # Test three partitions with alignment where the address is BIGGER than the alignment value. td = {'without_alignment': {'placement': {'before': 'with_alignment'}, 'size': 10000}, 'with_alignment': {'placement': {'before': 'with_alignment_2', 'align': {'end': 400}}, 'size': 100}, 'with_alignment_2': {'placement': {'before': 'app', 'align': {'start': 1000}}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 10000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'EMPTY_0', 10000, 300) expect_addr_size(td, 'with_alignment', 10300, 100) expect_addr_size(td, 'EMPTY_1', 10400, 600) expect_addr_size(td, 'with_alignment_2', 11000, 100) # FLASH (0x100000): # +------------------------------------------+ # | 0x0: b0 (0x8000) | # +---0x8000: s0 (0xc200)--------------------+ # | 0x8000: s0_pad (0x200) | # +---0x8200: s0_image (0xc000)--------------+ # | 0x8200: mcuboot (0xc000) | # | 0x14200: EMPTY_0 (0xe00) | # +---0x15000: s1 (0xc200)-------------------+ # | 0x15000: s1_pad (0x200) | # | 0x15200: s1_image (0xc000) | # | 0x21200: EMPTY_1 (0xe00) | # +---0x22000: mcuboot_primary (0x5d000)-----+ # | 0x22000: mcuboot_pad (0x200) | # +---0x22200: mcuboot_primary_app (0x5ce00)-+ # | 0x22200: app (0x5ce00) | # | 0x7f000: mcuboot_secondary (0x5d000) | # | 0xdc000: EMPTY_2 (0x1000) | # | 0xdd000: mcuboot_scratch (0x1e000) | # | 0xfb000: mcuboot_storage (0x4000) | # | 0xff000: provision (0x1000) | # +------------------------------------------+ # Verify that alignment works with partition which shares size with app. td = {'b0': {'placement': {'after': 'start'}, 'size': 0x8000}, 's0': {'span': ['s0_pad', 's0_image']}, 's0_pad': {'placement': {'after': 'b0', 'align': {'start': 0x1000}}, 'share_size': 'mcuboot_pad'}, 's0_image': {'span': {'one_of': ['mcuboot', 'spm', 'app']}}, 'mcuboot': {'placement': {'before': 'mcuboot_primary'}, 'size': 0xc000}, 's1': {'span': ['s1_pad', 's1_image']}, 's1_pad': {'placement': {'after': 's0', 'align': {'start': 0x1000}}, 'share_size': 'mcuboot_pad'}, 's1_image': {'placement': {'after': 's1_pad'}, 'share_size': 'mcuboot'}, 'mcuboot_primary': {'span': ['mcuboot_pad', 'mcuboot_primary_app']}, 'mcuboot_pad': {'placement': {'before': 'mcuboot_primary_app', 'align': {'start': 0x1000}}, 'size': 0x200}, 'mcuboot_primary_app': {'span': ['app']}, 'app': {}, 'mcuboot_secondary': {'placement': {'after': 'mcuboot_primary', 'align': {'start': 0x1000}}, 'share_size': 'mcuboot_primary'}, 'mcuboot_scratch': {'placement': {'after': 'app', 'align': {'start': 0x1000}}, 'size': 0x1e000}, 'mcuboot_storage': {'placement': {'after': 'mcuboot_scratch', 'align': {'start': 0x1000}}, 'size': 0x4000}, 'provision': {'placement': {'before': 'end', 'align': {'start': 0x1000}}, 'size': 0x1000}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 0x100000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'EMPTY_0', 0x14200, 0xe00) expect_addr_size(td, 'EMPTY_1', 0x21200, 0xe00) expect_addr_size(td, 'EMPTY_2', 0xdc000, 0x1000) assert td['mcuboot_secondary']['size'] == td['mcuboot_primary']['size'] # Verify that if a partition X uses 'share_size' with a non-existing partition, then partition X is given size 0, # and is hence not created. td = {'should_not_exist': {'placement': {'before': 'exists'}, 'share_size': 'does_not_exist'}, 'exists': {'placement': {'before': 'app'}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) assert 'should_not_exist' not in td.keys() # Verify that if a partition X uses 'share_size' with a non-existing partition, but has set a default size, # then partition X is created with the default size. td = {'should_exist': {'placement': {'before': 'exists'}, 'share_size': 'does_not_exist', 'size': 200}, 'exists': {'placement': {'before': 'app'}, 'size': 100}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'should_exist', 0, 200) td = {'spm': {'placement': {'before': ['app']}, 'size': 100}, 'mcuboot': {'placement': {'before': ['spm', 'app']}, 'size': 200}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'mcuboot', 0, None) expect_addr_size(td, 'spm', 200, None) expect_addr_size(td, 'app', 300, 700) td = {'spm': {'placement': {'before': ['app']}, 'size': 100, 'inside': ['mcuboot_slot0']}, 'mcuboot': {'placement': {'before': ['spm', 'app']}, 'size': 200}, 'mcuboot_slot0': {'span': ['app']}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'mcuboot', 0, None) expect_addr_size(td, 'spm', 200, 100) expect_addr_size(td, 'app', 300, 700) expect_addr_size(td, 'mcuboot_slot0', 200, 800) td = {'spm': {'placement': {'before': 'app'}, 'size': 100, 'inside': 'mcuboot_slot0'}, 'mcuboot': {'placement': {'before': 'app'}, 'size': 200}, 'mcuboot_pad': {'placement': {'after': 'mcuboot'}, 'inside': 'mcuboot_slot0', 'size': 10}, 'app_partition': {'span': ['spm', 'app'], 'inside': 'mcuboot_slot0'}, 'mcuboot_slot0': {'span': ['app', 'foo']}, 'mcuboot_data': {'placement': {'after': ['mcuboot_slot0']}, 'size': 200}, 'mcuboot_slot1': {'share_size': 'mcuboot_slot0', 'placement': {'after': 'mcuboot_data'}}, 'mcuboot_slot2': {'share_size': 'mcuboot_slot1', 'placement': {'after': 'mcuboot_slot1'}}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'mcuboot', 0, None) expect_addr_size(td, 'spm', 210, None) expect_addr_size(td, 'mcuboot_slot0', 200, 200) expect_addr_size(td, 'mcuboot_slot1', 600, 200) expect_addr_size(td, 'mcuboot_slot2', 800, 200) expect_addr_size(td, 'app', 310, 90) expect_addr_size(td, 'mcuboot_pad', 200, 10) expect_addr_size(td, 'mcuboot_data', 400, 200) td = {'spm': {'placement': {'before': ['app']}, 'size': 100, 'inside': ['mcuboot_slot0']}, 'mcuboot': {'placement': {'before': ['app']}, 'size': 200}, 'mcuboot_pad': {'placement': {'after': ['mcuboot']}, 'inside': ['mcuboot_slot0'], 'size': 10}, 'app_partition': {'span': ['spm', 'app'], 'inside': ['mcuboot_slot0']}, 'mcuboot_slot0': {'span': 'app'}, 'mcuboot_data': {'placement': {'after': ['mcuboot_slot0']}, 'size': 200}, 'mcuboot_slot1': {'share_size': ['mcuboot_slot0'], 'placement': {'after': ['mcuboot_data']}}, 'mcuboot_slot2': {'share_size': ['mcuboot_slot1'], 'placement': {'after': ['mcuboot_slot1']}}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, sub_partitions, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'mcuboot', 0, None) expect_addr_size(td, 'spm', 210, None) expect_addr_size(td, 'mcuboot_slot0', 200, 200) expect_addr_size(td, 'mcuboot_slot1', 600, 200) expect_addr_size(td, 'mcuboot_slot2', 800, 200) expect_addr_size(td, 'app', 310, 90) expect_addr_size(td, 'mcuboot_pad', 200, 10) expect_addr_size(td, 'mcuboot_data', 400, 200) td = { 'e': {'placement': {'before': ['app']}, 'size': 100}, 'a': {'placement': {'before': ['b']}, 'size': 100}, 'd': {'placement': {'before': ['e']}, 'size': 100}, 'c': {'placement': {'before': ['d']}, 'share_size': ['z', 'a', 'g']}, 'j': {'placement': {'before': ['end']}, 'inside': ['k'], 'size': 20}, 'i': {'placement': {'before': ['j']}, 'inside': ['k'], 'size': 20}, 'h': {'placement': {'before': ['i']}, 'size': 20}, 'f': {'placement': {'after': ['app']}, 'size': 20}, 'g': {'placement': {'after': ['f']}, 'size': 20}, 'b': {'placement': {'before': ['c']}, 'size': 20}, 'k': {'span': []}, 'app': {}} s, sub_partitions = resolve(td) set_addresses_and_align(td, {}, s, 1000) set_sub_partition_address_and_size(td, sub_partitions) expect_addr_size(td, 'a', 0, None) expect_addr_size(td, 'b', 100, None) expect_addr_size(td, 'c', 120, None) expect_addr_size(td, 'd', 220, None) expect_addr_size(td, 'e', 320, None) expect_addr_size(td, 'app', 420, 480) expect_addr_size(td, 'f', 900, None) expect_addr_size(td, 'g', 920, None) expect_addr_size(td, 'h', 940, None) expect_addr_size(td, 'i', 960, None) expect_addr_size(td, 'j', 980, None) expect_addr_size(td, 'k', 960, 40) td = {'mcuboot': {'placement': {'before': ['app', 'spu']}, 'size': 200}, 'b0': {'placement': {'before': ['mcuboot', 'app']}, 'size': 100}, 'app': {}} s, _ = resolve(td) set_addresses_and_align(td, {}, s, 1000) expect_addr_size(td, 'b0', 0, None) expect_addr_size(td, 'mcuboot', 100, None) expect_addr_size(td, 'app', 300, 700) td = {'b0': {'placement': {'before': ['mcuboot', 'app']}, 'size': 100}, 'app': {}} s, _ = resolve(td) set_addresses_and_align(td, {}, s, 1000) expect_addr_size(td, 'b0', 0, None) expect_addr_size(td, 'app', 100, 900) td = {'spu': {'placement': {'before': ['app']}, 'size': 100}, 'mcuboot': {'placement': {'before': ['spu', 'app']}, 'size': 200}, 'app': {}} s, _ = resolve(td) set_addresses_and_align(td, {}, s, 1000) expect_addr_size(td, 'mcuboot', 0, None) expect_addr_size(td, 'spu', 200, None) expect_addr_size(td, 'app', 300, 700) td = {'provision': {'placement': {'before': ['end']}, 'size': 100}, 'mcuboot': {'placement': {'before': ['spu', 'app']}, 'size': 100}, 'b0': {'placement': {'before': ['mcuboot', 'app']}, 'size': 50}, 'spu': {'placement': {'before': ['app']}, 'size': 100}, 'app': {}} s, _ = resolve(td) set_addresses_and_align(td, {}, s, 1000) expect_addr_size(td, 'b0', 0, None) expect_addr_size(td, 'mcuboot', 50, None) expect_addr_size(td, 'spu', 150, None) expect_addr_size(td, 'app', 250, 650) expect_addr_size(td, 'provision', 900, None) # Test #1 for removal of empty container partitions. td = {'a': {'share_size': 'does_not_exist'}, # a should be removed 'b': {'span': 'a'}, # b through d should be removed because a is removed 'c': {'span': 'b'}, 'd': {'span': 'c'}, 'e': {'placement': {'before': ['end']}}} s, sub = resolve(td) expect_list(['e', 'app'], s) expect_list([], sub) # Test #2 for removal of empty container partitions. td = {'a': {'share_size': 'does_not_exist'}, # a should be removed 'b': {'span': 'a'}, # b should not be removed, since d is placed inside it. 'c': {'placement': {'after': ['start']}}, 'd': {'inside': ['does_not_exist', 'b'], 'placement': {'after': ['c']}}} s, sub = resolve(td) expect_list(['c', 'd', 'app'], s) expect_list(['b'], sub) expect_list(['d'], sub['b']['orig_span']) # Backup must contain edits. print("All tests passed!") if __name__ == "__main__": if len(sys.argv) > 1: main() else: print("No input, running tests.") test()

import pandas as pd from pathlib import Path from collections import Counter import datetime from collections import defaultdict from faker import Factory import faker from preprocessing.nfeProvider import Invoice import csv def convert_to_numeric(num): """ Converte strings que representam valores monetários em Reais (R$) para o padrão americano. """ num = num.strip() if num != "": num = num.replace(',', '.') count_dot = num.count('.') if count_dot >= 2: while count_dot >= 2: # armazena o index da primeira ocorrência da string ponto slice_index = num.index('.') # faz um slice baseado na localizacao desse index new_str = num[0:slice_index] + num[slice_index + 1:] num = new_str count_dot = num.count('.') return float(num) else: return float(num) else: return 0.0 def identify_encoding(filename: str) -> str: """ Identifica o encoding do arquivo filename retornando uma string com o nome do encoding. Atributos: filename: é o path (full ou relativo) do arquivo a ser analisado. """ try: encoding = 'utf8' with open(filename, "r", encoding=encoding) as file: _ = file.readlines() except UnicodeDecodeError: encoding = 'latin1' with open(filename, "r", encoding=encoding) as file: _ = file.readlines() finally: return encoding def report_pkl_into_csv(filename, foldername, logger): """ Produz um relatório do status do arquivo tabular gerado a partir dos arquivos .pkl Atributos: filename: é o nome do arquivo .csv que será analisado. foldername: é o nome da sub pasta dos arquivos pkl dentro de ./data-storage/validacao/ """ # VERIFICA SE ALGUM ARQUIVO .pkl NÃO FORAM PROCESSADOS. df = pd.read_csv(f"./tabular-data/{filename}.csv", sep=';', encoding='latin1') lista_chaves_processadas = set(df['nf_chave'].unique()) pkl_folder = Path(f"./data-storage/validacao/{foldername}") pkl_folder = set(pkl_folder.rglob("*.pkl")) pkl_folder = set([f.name[:-4][-44:] for f in pkl_folder]) num_arquivos_diff = lista_chaves_processadas.difference(pkl_folder) if len(num_arquivos_diff) == 0: logger.debug(f"Todos os arquivos .pkl foram processados. Ao todo foram processados {df["nf_chave"].nunique()} notas fiscais.\n") else: logger.critical(f"Não foram processados {len(num_arquivos_diff)} arquivos.\n") for f in num_arquivos_diff: logger.critical(f"Arquivo {f} não foi processado.\n") # VALIDAÇÃO SE HÁ ARQUIVOS DUPLICADOS files_check = Path(f"./data-storage/validacao/{foldername}") files_check = list(files_check.rglob("*.pkl")) files_check = [f.name[:-4][-44:] for f in files_check] a = Counter() for f in files_check: a[f] += 1 for chave, count in a.items(): if count > 1: logger.critical(f"CHAVE REPETIDA: {chave} # {count}") # VERIFICA SE HÁ ALGUMA INCONSISTÊNCIA NOS VALORES DOS PRODUTOS E DA NOTA FISCAL df['prod_valor_liquido'] = df.apply(lambda x: x['prod_valor'] - x['prod_valor_desconto'], axis='columns') check_valor_nota_valores = df.groupby("nf_chave")['prod_valor_liquido'].sum().sort_values(ascending=False) inconsistencia_count = 0 container = {} for chave, valor in zip(check_valor_nota_valores.index, check_valor_nota_valores.values): validacao = df.loc[df['nf_chave'] == chave, 'nf_valor'].values[0] valor = round(valor, 2) chave = chave.replace("-", "").replace(".", "").replace("/", "") if validacao != valor: inconsistencia_count += 1 diff_produtos = round(valor - validacao, 2) container[chave] = diff_produtos logger.critical(f"{chave} => Valor Nota: R${validacao} @ Valor Produtos: R${valor} @ Diferença: R${diff_produtos}\n") def normalize_ncm(ncm: str) -> str: """ Normaliza a string que representa o código NCM Atributos: ncm : string que representa o código NCM """ if len(ncm) != 8: ncm = "0" + ncm return ncm def get_ncm_values(): """ Função que retorna um dicionário contendo uma lista de macro categorias e os codigos NCM associados a cada um deles. """ sheet_names = [ 'CARNES E OVOS', 'HORTIFRUTI', 'LIMPEZA', 'HIGIENE', 'LATICINIOS E DERIVADOS', 'BEBIDAS', 'PET', 'PADARIA', 'CEREAIS_GRAOS_SEMENTES', 'DOCES', 'VESTUARIO', 'FARINACEOS', 'MASSAS', 'TEMPEROS_MOLHOS', 'OUTROS' ] categorias_ncm = {} for sheet in sheet_names: df = pd.read_excel("./data/others/compilado_ncm_mercado_mod.xlsx", sheet_name=sheet, dtype={'cod_ncm': str}) df['cod_ncm'] = df['cod_ncm'].astype(str) df['cod_ncm'] = df['cod_ncm'].apply(normalize_ncm) categorias_ncm[sheet] = df['cod_ncm'].unique().tolist() return categorias_ncm def get_weekday(value: int): """ Recebe um INT representando um datetime e retorna uma string com o dia da semana. Atributos: value: Inteiro representando um timestamp """ convert_int_to_day = { 0: 'Segunda-Feira', 1: 'Terça-Feira', 2: 'Quarta-Feira', 3: 'Quinta-Feira', 4: 'Sexta-Feira', 5: 'Sábado', 6: 'Domingo' } weekday = datetime.datetime.utcfromtimestamp(value / 1e9).weekday() return convert_int_to_day[weekday] def logging_report(report, list_required_fields, logger): f = Path(report['tables'][0]['source']) map_columns_to_number = report['tables'][0]['headers'] map_columns_to_number = {i: col for col, i in zip(map_columns_to_number, range(1, len(map_columns_to_number)))} fields_required_error = {f: False for f in list_required_fields} num_errors = report['error-count'] if report['valid']: logger.debug(f"Arquivo: {f.name} válido pelo schema.\n") return True, num_errors else: lista_errors = report['tables'][0]['errors'] if 0 < num_errors < 1000: logger.debug(f"Arquivo {f.name} não validado com {num_errors} erros.") for erro in lista_errors: for feature, valor in erro.items(): if feature == 'code': if valor == 'required-constraint': # identify which column is null col = map_columns_to_number[erro['column-number']] # change validation status of this feature if not fields_required_error[col]: fields_required_error[col] = True line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} possui {col} sem valor atribuído.") elif valor == 'enumerable-constraint': col = map_columns_to_number[erro['column-number']] line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} e Coluna {col} erro: {erro["message"]} ") else: try: col = map_columns_to_number[erro['column-number']] except: # o erro associado não é referente a uma coluna try: line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} : {erro["message"]}") except KeyError: logger.critical(f"{f.name} @ {erro["message"]}") return False, num_errors def anonymize_rows(rows): """ Rows is an iterable of dictionaries that contain name and email fields that need to be anonymized. """ # Load the faker and its providers faker = Factory.create("pt_BR") faker.add_provider(Invoice) # Create mappings of names & emails to faked names & emails. # https://stackoverflow.com/questions/18066837/passing-a-parameter-to-objects-created-by-defaultdict nfecod = defaultdict(lambda: faker.nfce(**{'uf_code': 'DF'})) cpf = defaultdict(faker.cpf) nome = defaultdict(faker.name) endereco = defaultdict(faker.address) bairro = defaultdict(faker.bairro) municipio = defaultdict(faker.city) telefone = defaultdict(faker.phone_number) uf = defaultdict(faker.state_abbr) pais = defaultdict(faker.country) email = defaultdict(faker.email) # Iterate over the rows and yield anonymized rows. for row in rows: # Replace the name and email fields with faked fields. row['nf_chave'] = nfecod[row['nf_chave']] row['dest_cpf'] = cpf[row['dest_cpf']] row['dest_rz'] = nome[row['dest_rz']] row['dest_endereco'] = endereco[row['dest_endereco']] row['dest_bairro'] = bairro[row['dest_bairro']] row['dest_municipio'] = municipio[row['dest_municipio']] row['dest_telefone'] = telefone[row['dest_telefone']] row['dest_uf'] = uf[row['dest_uf']] row['dest_pais'] = pais[row['dest_pais']] row['dest_email'] = email[row['dest_email']] # Yield the row back to the caller yield row def anonymize(source, target): """ The source argument is a path to a CSV file containing data to anonymize, while target is a path to write the anonymized CSV data to. """ # https://pymotw.com/2/csv/ PARTIAL_SOURCE_DATA = Path("./tabular-data/") / f"{source}" PARTIAL_DEST_DATA = Path("./tabular-data/") / f"{target}" csv.register_dialect('semicolon', delimiter=';') with open(PARTIAL_SOURCE_DATA, 'r') as f: with open(PARTIAL_DEST_DATA, 'w') as o: # Use the DictReader to easily extract fields reader = csv.DictReader(f, dialect='semicolon') writer = csv.DictWriter(o, reader.fieldnames, dialect='semicolon') # write col names writer.writeheader() # Read and anonymize data, writing to target file. for row in anonymize_rows(reader): writer.writerow(row) def subseting_data(dataframe: pd.core.frame.DataFrame, rootname: str): """ Salva um arquivo .csv com um subset das features originais """ dataframe = dataframe[['nf_dia_semana', 'nf_chave', 'nf_valor', 'em_rz', 'em_nomeFantasia', 'em_cnpj', 'em_endereco', 'em_bairro', 'em_cep', 'em_municipio', 'em_telefone', 'em_uf', 'em_pais', 'em_inscricao_estadual', 'em_inscricao_municipal', 'em_cnae_fiscal', 'dest_rz', 'dest_cpf', 'dest_endereco', 'dest_bairro', 'dest_municipio', 'dest_telefone', 'dest_uf', 'dest_pais', 'dest_inscricao_estadual', 'dest_email', 'prod_nome', 'prod_quantidade', 'prod_unidade', 'prod_valor', 'prod_codigo_produto', 'prod_codigo_ncm', 'prod_categoria_ncm', 'prod_cfop', 'prod_valor_desconto', 'prod_valor_tributos', 'prod_codigo_ean_cmc', 'prod_valor_unitario_cmc', 'prod_valor_unitario_trib', 'prod_unidade_trib']] dataframe.to_csv(f"./tabular-data/PRE_ANONY_{rootname}.csv", sep=';', encoding='latin1', index=True)

import pandas as pd from pathlib import Path from collections import Counter import datetime from collections import defaultdict from faker import Factory import faker from preprocessing.nfeProvider import Invoice import csv def convert_to_numeric(num): """ Converte strings que representam valores monetários em Reais (R$) para o padrão americano. """ num = num.strip() if num != "": num = num.replace(',', '.') count_dot = num.count('.') if count_dot >= 2: while count_dot >= 2: # armazena o index da primeira ocorrência da string ponto slice_index = num.index('.') # faz um slice baseado na localizacao desse index new_str = num[0:slice_index] + num[slice_index + 1:] num = new_str count_dot = num.count('.') return float(num) else: return float(num) else: return 0.0 def identify_encoding(filename: str) -> str: """ Identifica o encoding do arquivo filename retornando uma string com o nome do encoding. Atributos: filename: é o path (full ou relativo) do arquivo a ser analisado. """ try: encoding = 'utf8' with open(filename, "r", encoding=encoding) as file: _ = file.readlines() except UnicodeDecodeError: encoding = 'latin1' with open(filename, "r", encoding=encoding) as file: _ = file.readlines() finally: return encoding def report_pkl_into_csv(filename, foldername, logger): """ Produz um relatório do status do arquivo tabular gerado a partir dos arquivos .pkl Atributos: filename: é o nome do arquivo .csv que será analisado. foldername: é o nome da sub pasta dos arquivos pkl dentro de ./data-storage/validacao/ """ # VERIFICA SE ALGUM ARQUIVO .pkl NÃO FORAM PROCESSADOS. df = pd.read_csv(f"./tabular-data/{filename}.csv", sep=';', encoding='latin1') lista_chaves_processadas = set(df['nf_chave'].unique()) pkl_folder = Path(f"./data-storage/validacao/{foldername}") pkl_folder = set(pkl_folder.rglob("*.pkl")) pkl_folder = set([f.name[:-4][-44:] for f in pkl_folder]) num_arquivos_diff = lista_chaves_processadas.difference(pkl_folder) if len(num_arquivos_diff) == 0: logger.debug(f"Todos os arquivos .pkl foram processados. Ao todo foram processados {df['nf_chave'].nunique()} notas fiscais.\n") else: logger.critical(f"Não foram processados {len(num_arquivos_diff)} arquivos.\n") for f in num_arquivos_diff: logger.critical(f"Arquivo {f} não foi processado.\n") # VALIDAÇÃO SE HÁ ARQUIVOS DUPLICADOS files_check = Path(f"./data-storage/validacao/{foldername}") files_check = list(files_check.rglob("*.pkl")) files_check = [f.name[:-4][-44:] for f in files_check] a = Counter() for f in files_check: a[f] += 1 for chave, count in a.items(): if count > 1: logger.critical(f"CHAVE REPETIDA: {chave} # {count}") # VERIFICA SE HÁ ALGUMA INCONSISTÊNCIA NOS VALORES DOS PRODUTOS E DA NOTA FISCAL df['prod_valor_liquido'] = df.apply(lambda x: x['prod_valor'] - x['prod_valor_desconto'], axis='columns') check_valor_nota_valores = df.groupby("nf_chave")['prod_valor_liquido'].sum().sort_values(ascending=False) inconsistencia_count = 0 container = {} for chave, valor in zip(check_valor_nota_valores.index, check_valor_nota_valores.values): validacao = df.loc[df['nf_chave'] == chave, 'nf_valor'].values[0] valor = round(valor, 2) chave = chave.replace("-", "").replace(".", "").replace("/", "") if validacao != valor: inconsistencia_count += 1 diff_produtos = round(valor - validacao, 2) container[chave] = diff_produtos logger.critical(f"{chave} => Valor Nota: R${validacao} @ Valor Produtos: R${valor} @ Diferença: R${diff_produtos}\n") def normalize_ncm(ncm: str) -> str: """ Normaliza a string que representa o código NCM Atributos: ncm : string que representa o código NCM """ if len(ncm) != 8: ncm = "0" + ncm return ncm def get_ncm_values(): """ Função que retorna um dicionário contendo uma lista de macro categorias e os codigos NCM associados a cada um deles. """ sheet_names = [ 'CARNES E OVOS', 'HORTIFRUTI', 'LIMPEZA', 'HIGIENE', 'LATICINIOS E DERIVADOS', 'BEBIDAS', 'PET', 'PADARIA', 'CEREAIS_GRAOS_SEMENTES', 'DOCES', 'VESTUARIO', 'FARINACEOS', 'MASSAS', 'TEMPEROS_MOLHOS', 'OUTROS' ] categorias_ncm = {} for sheet in sheet_names: df = pd.read_excel("./data/others/compilado_ncm_mercado_mod.xlsx", sheet_name=sheet, dtype={'cod_ncm': str}) df['cod_ncm'] = df['cod_ncm'].astype(str) df['cod_ncm'] = df['cod_ncm'].apply(normalize_ncm) categorias_ncm[sheet] = df['cod_ncm'].unique().tolist() return categorias_ncm def get_weekday(value: int): """ Recebe um INT representando um datetime e retorna uma string com o dia da semana. Atributos: value: Inteiro representando um timestamp """ convert_int_to_day = { 0: 'Segunda-Feira', 1: 'Terça-Feira', 2: 'Quarta-Feira', 3: 'Quinta-Feira', 4: 'Sexta-Feira', 5: 'Sábado', 6: 'Domingo' } weekday = datetime.datetime.utcfromtimestamp(value / 1e9).weekday() return convert_int_to_day[weekday] def logging_report(report, list_required_fields, logger): f = Path(report['tables'][0]['source']) map_columns_to_number = report['tables'][0]['headers'] map_columns_to_number = {i: col for col, i in zip(map_columns_to_number, range(1, len(map_columns_to_number)))} fields_required_error = {f: False for f in list_required_fields} num_errors = report['error-count'] if report['valid']: logger.debug(f"Arquivo: {f.name} válido pelo schema.\n") return True, num_errors else: lista_errors = report['tables'][0]['errors'] if 0 < num_errors < 1000: logger.debug(f"Arquivo {f.name} não validado com {num_errors} erros.") for erro in lista_errors: for feature, valor in erro.items(): if feature == 'code': if valor == 'required-constraint': # identify which column is null col = map_columns_to_number[erro['column-number']] # change validation status of this feature if not fields_required_error[col]: fields_required_error[col] = True line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} possui {col} sem valor atribuído.") elif valor == 'enumerable-constraint': col = map_columns_to_number[erro['column-number']] line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} e Coluna {col} erro: {erro['message']} ") else: try: col = map_columns_to_number[erro['column-number']] except: # o erro associado não é referente a uma coluna try: line = erro['row-number'] logger.critical(f"{f.name} @ Linha {line} : {erro['message']}") except KeyError: logger.critical(f"{f.name} @ {erro['message']}") return False, num_errors def anonymize_rows(rows): """ Rows is an iterable of dictionaries that contain name and email fields that need to be anonymized. """ # Load the faker and its providers faker = Factory.create("pt_BR") faker.add_provider(Invoice) # Create mappings of names & emails to faked names & emails. # https://stackoverflow.com/questions/18066837/passing-a-parameter-to-objects-created-by-defaultdict nfecod = defaultdict(lambda: faker.nfce(**{'uf_code': 'DF'})) cpf = defaultdict(faker.cpf) nome = defaultdict(faker.name) endereco = defaultdict(faker.address) bairro = defaultdict(faker.bairro) municipio = defaultdict(faker.city) telefone = defaultdict(faker.phone_number) uf = defaultdict(faker.state_abbr) pais = defaultdict(faker.country) email = defaultdict(faker.email) # Iterate over the rows and yield anonymized rows. for row in rows: # Replace the name and email fields with faked fields. row['nf_chave'] = nfecod[row['nf_chave']] row['dest_cpf'] = cpf[row['dest_cpf']] row['dest_rz'] = nome[row['dest_rz']] row['dest_endereco'] = endereco[row['dest_endereco']] row['dest_bairro'] = bairro[row['dest_bairro']] row['dest_municipio'] = municipio[row['dest_municipio']] row['dest_telefone'] = telefone[row['dest_telefone']] row['dest_uf'] = uf[row['dest_uf']] row['dest_pais'] = pais[row['dest_pais']] row['dest_email'] = email[row['dest_email']] # Yield the row back to the caller yield row def anonymize(source, target): """ The source argument is a path to a CSV file containing data to anonymize, while target is a path to write the anonymized CSV data to. """ # https://pymotw.com/2/csv/ PARTIAL_SOURCE_DATA = Path("./tabular-data/") / f"{source}" PARTIAL_DEST_DATA = Path("./tabular-data/") / f"{target}" csv.register_dialect('semicolon', delimiter=';') with open(PARTIAL_SOURCE_DATA, 'r') as f: with open(PARTIAL_DEST_DATA, 'w') as o: # Use the DictReader to easily extract fields reader = csv.DictReader(f, dialect='semicolon') writer = csv.DictWriter(o, reader.fieldnames, dialect='semicolon') # write col names writer.writeheader() # Read and anonymize data, writing to target file. for row in anonymize_rows(reader): writer.writerow(row) def subseting_data(dataframe: pd.core.frame.DataFrame, rootname: str): """ Salva um arquivo .csv com um subset das features originais """ dataframe = dataframe[['nf_dia_semana', 'nf_chave', 'nf_valor', 'em_rz', 'em_nomeFantasia', 'em_cnpj', 'em_endereco', 'em_bairro', 'em_cep', 'em_municipio', 'em_telefone', 'em_uf', 'em_pais', 'em_inscricao_estadual', 'em_inscricao_municipal', 'em_cnae_fiscal', 'dest_rz', 'dest_cpf', 'dest_endereco', 'dest_bairro', 'dest_municipio', 'dest_telefone', 'dest_uf', 'dest_pais', 'dest_inscricao_estadual', 'dest_email', 'prod_nome', 'prod_quantidade', 'prod_unidade', 'prod_valor', 'prod_codigo_produto', 'prod_codigo_ncm', 'prod_categoria_ncm', 'prod_cfop', 'prod_valor_desconto', 'prod_valor_tributos', 'prod_codigo_ean_cmc', 'prod_valor_unitario_cmc', 'prod_valor_unitario_trib', 'prod_unidade_trib']] dataframe.to_csv(f"./tabular-data/PRE_ANONY_{rootname}.csv", sep=';', encoding='latin1', index=True)

from typing import Any, Dict, Iterable, cast from openslides_backend.action.actions.meeting.shared_meeting import ( meeting_projector_default_replacements, ) from openslides_backend.permissions.management_levels import ( CommitteeManagementLevel, OrganizationManagementLevel, ) from tests.system.action.base import BaseActionTestCase class MeetingCreateActionTest(BaseActionTestCase): def basic_test(self, datapart: Dict[str, Any]) -> Dict[str, Any]: self.set_models( { "organization/1": {"limit_of_meetings": 0, "active_meeting_ids": []}, "committee/1": { "name": "test_committee", "user_ids": [2], "organization_id": 1, }, "group/1": {}, "user/2": {}, "organization_tag/3": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, "organization_tag_ids": [3], **datapart, }, ) self.assert_status_code(response, 200) return self.get_model("meeting/1") def test_create_simple_and_complex_workflow(self) -> None: meeting = self.basic_test(dict()) self.assertCountEqual( cast(Iterable[Any], meeting.get("default_projector_$_id")), meeting_projector_default_replacements, ) self.assert_model_exists( "meeting/1", { "name": "test_name", "committee_id": 1, "group_ids": [2, 3, 4, 5, 6], "default_group_id": 2, "admin_group_id": 3, "motion_workflow_ids": [1, 2], "motions_default_workflow_id": 1, "motions_default_amendment_workflow_id": 1, "motions_default_statute_amendment_workflow_id": 1, "motion_state_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "list_of_speakers_countdown_id": 1, "poll_countdown_id": 2, "projector_countdown_warning_time": 0, "organization_tag_ids": [3], "is_active_in_organization_id": 1, "assignment_poll_default_group_ids": [4], "motion_poll_default_group_ids": [4], **{ f"default_projector_${name}_id": 1 for name in meeting_projector_default_replacements }, }, ) self.assert_model_exists("organization/1", {"active_meeting_ids": [1]}) self.assert_model_exists("group/2", {"name": "Default"}) self.assert_model_exists("group/3", {"name": "Admin"}) self.assert_model_exists("group/4", {"name": "Delegates"}) self.assert_model_exists("group/5", {"name": "Staff"}) self.assert_model_exists("group/6", {"name": "Committees"}) self.assert_model_exists( "motion_workflow/1", { "name": "Simple Workflow", "meeting_id": 1, "default_workflow_meeting_id": 1, "default_amendment_workflow_meeting_id": 1, "default_statute_amendment_workflow_meeting_id": 1, "state_ids": [1, 2, 3, 4], "first_state_id": 1, }, ) self.assert_model_exists( "motion_state/1", {"name": "submitted", "next_state_ids": [2, 3, 4]} ) self.assert_model_exists( "motion_state/2", { "name": "accepted", "previous_state_ids": [1], "meeting_id": 1, "workflow_id": 1, }, ) self.assert_model_exists( "motion_state/3", {"name": "rejected", "previous_state_ids": [1]} ) self.assert_model_exists( "motion_state/4", {"name": "not_decided", "previous_state_ids": [1]} ) self.assert_model_exists( "motion_workflow/2", { "name": "Complex Workflow", "meeting_id": 1, "state_ids": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "first_state_id": 5, }, ) self.assert_model_exists( "motion_state/5", {"name": "in progress", "next_state_ids": [6, 10]} ) self.assert_model_exists( "motion_state/6", {"name": "submitted", "previous_state_ids": [5]} ) self.assert_model_exists( "motion_state/7", {"name": "permitted", "previous_state_ids": [6]} ) self.assert_model_exists( "motion_state/8", {"name": "accepted", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/9", {"name": "rejected", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/10", {"name": "withdrawn", "previous_state_ids": [5, 6, 7]} ) self.assert_model_exists( "motion_state/11", {"name": "adjourned", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/12", {"name": "not concerned", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/13", {"name": "referred to committee", "previous_state_ids": [7]}, ) self.assert_model_exists( "motion_state/14", {"name": "needs review", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/15", {"name": "rejected (not authorized)", "previous_state_ids": [6]}, ) projector1 = self.get_model("projector/1") self.assertCountEqual( cast(Iterable[Any], projector1.get("used_as_default_$_in_meeting_id")), meeting_projector_default_replacements, ) self.assert_model_exists( "projector/1", { "name": "Default projector", "meeting_id": 1, "used_as_reference_projector_meeting_id": 1, **{ f"used_as_default_${name}_in_meeting_id": 1 for name in meeting_projector_default_replacements }, }, ) self.assert_model_exists( "projector_countdown/1", { "title": "List of speakers countdown", "meeting_id": 1, "used_as_list_of_speaker_countdown_meeting_id": 1, "default_time": 60, "countdown_time": 60, }, ) self.assert_model_exists( "projector_countdown/2", { "title": "Voting countdown", "meeting_id": 1, "used_as_poll_countdown_meeting_id": 1, "default_time": 60, "countdown_time": 60, }, ) def test_check_action_data_fields(self) -> None: meeting = self.basic_test( { "description": "RRfnzxHA", "location": "LSFHPTgE", "start_time": 1608120653, "end_time": 1608121653, "url_name": "JWdYZqDX", } ) assert meeting.get("description") == "RRfnzxHA" assert meeting.get("location") == "LSFHPTgE" assert meeting.get("start_time") == 1608120653 assert meeting.get("end_time") == 1608121653 assert meeting.get("url_name") == "JWdYZqDX" # check two defaults: assert meeting.get("assignment_poll_default_type") == "analog" assert meeting.get("assignment_poll_default_method") == "Y" def test_create_check_users(self) -> None: meeting = self.basic_test({"user_ids": [2]}) assert meeting.get("user_ids") == [2] default_group_id = meeting.get("default_group_id") self.assert_model_exists( "user/2", {f"group_${meeting["id"]}_ids": [default_group_id]} ) def test_create_check_admins(self) -> None: meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting["id"]}_ids": [admin_group_id]} ) def test_create_with_same_user_in_users_and_admins(self) -> None: meeting = self.basic_test({"user_ids": [2], "admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting["id"]}_ids": [admin_group_id]} ) def test_create_multiple_users(self) -> None: self.set_models( { "organization/1": {"limit_of_meetings": 0, "active_meeting_ids": []}, "committee/1": {"organization_id": 1}, "user/2": {}, "user/3": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, "user_ids": [2, 3], "admin_ids": [1], }, ) self.assert_status_code(response, 200) meeting = self.get_model("meeting/1") default_group_id = meeting.get("default_group_id") self.assert_model_exists( "user/2", {"group_$1_ids": [default_group_id], "committee_ids": [1]} ) self.assert_model_exists( "user/3", {"group_$1_ids": [default_group_id], "committee_ids": [1]} ) admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/1", {"group_$1_ids": [admin_group_id], "committee_ids": [1]} ) self.assertCountEqual(meeting.get("user_ids"), [1, 2, 3]) committee = self.get_model("committee/1") self.assertCountEqual(committee.get("user_ids"), [1, 2, 3]) def test_create_with_admins_empty_array(self) -> None: meeting = self.basic_test({"admin_ids": []}) assert "admin_ids" not in meeting def test_create_no_permissions(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_USERS }, "committee/1": {"name": "test_committee", "user_ids": [1, 2]}, "group/1": {}, "user/2": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, }, ) self.assert_status_code(response, 403) assert ( "Missing CommitteeManagementLevel: can_manage" in response.json["message"] ) def test_create_permissions(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_USERS, "committee_$1_management_level": CommitteeManagementLevel.CAN_MANAGE, } } ) self.basic_test({}) def test_create_with_admin_ids_and_permissions_cml(self) -> None: self.set_models( { "user/1": { "organization_management_level": None, "committee_$1_management_level": CommitteeManagementLevel.CAN_MANAGE, } } ) meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting["id"]}_ids": [admin_group_id]} ) def test_create_with_admin_ids_and_permissions_oml(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_ORGANIZATION, "committee_$1_management_level": None, } } ) meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting["id"]}_ids": [admin_group_id]} ) def test_create_limit_of_meetings_reached(self) -> None: self.set_models( { "organization/1": {"limit_of_meetings": 1, "active_meeting_ids": [1]}, "committee/1": {"organization_id": 1}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, }, ) self.assert_status_code(response, 400) self.assertIn( "You cannot create a new meeting, because you reached your limit of 1 active meetings.", response.json["message"], )

from typing import Any, Dict, Iterable, cast from openslides_backend.action.actions.meeting.shared_meeting import ( meeting_projector_default_replacements, ) from openslides_backend.permissions.management_levels import ( CommitteeManagementLevel, OrganizationManagementLevel, ) from tests.system.action.base import BaseActionTestCase class MeetingCreateActionTest(BaseActionTestCase): def basic_test(self, datapart: Dict[str, Any]) -> Dict[str, Any]: self.set_models( { "organization/1": {"limit_of_meetings": 0, "active_meeting_ids": []}, "committee/1": { "name": "test_committee", "user_ids": [2], "organization_id": 1, }, "group/1": {}, "user/2": {}, "organization_tag/3": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, "organization_tag_ids": [3], **datapart, }, ) self.assert_status_code(response, 200) return self.get_model("meeting/1") def test_create_simple_and_complex_workflow(self) -> None: meeting = self.basic_test(dict()) self.assertCountEqual( cast(Iterable[Any], meeting.get("default_projector_$_id")), meeting_projector_default_replacements, ) self.assert_model_exists( "meeting/1", { "name": "test_name", "committee_id": 1, "group_ids": [2, 3, 4, 5, 6], "default_group_id": 2, "admin_group_id": 3, "motion_workflow_ids": [1, 2], "motions_default_workflow_id": 1, "motions_default_amendment_workflow_id": 1, "motions_default_statute_amendment_workflow_id": 1, "motion_state_ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "list_of_speakers_countdown_id": 1, "poll_countdown_id": 2, "projector_countdown_warning_time": 0, "organization_tag_ids": [3], "is_active_in_organization_id": 1, "assignment_poll_default_group_ids": [4], "motion_poll_default_group_ids": [4], **{ f"default_projector_${name}_id": 1 for name in meeting_projector_default_replacements }, }, ) self.assert_model_exists("organization/1", {"active_meeting_ids": [1]}) self.assert_model_exists("group/2", {"name": "Default"}) self.assert_model_exists("group/3", {"name": "Admin"}) self.assert_model_exists("group/4", {"name": "Delegates"}) self.assert_model_exists("group/5", {"name": "Staff"}) self.assert_model_exists("group/6", {"name": "Committees"}) self.assert_model_exists( "motion_workflow/1", { "name": "Simple Workflow", "meeting_id": 1, "default_workflow_meeting_id": 1, "default_amendment_workflow_meeting_id": 1, "default_statute_amendment_workflow_meeting_id": 1, "state_ids": [1, 2, 3, 4], "first_state_id": 1, }, ) self.assert_model_exists( "motion_state/1", {"name": "submitted", "next_state_ids": [2, 3, 4]} ) self.assert_model_exists( "motion_state/2", { "name": "accepted", "previous_state_ids": [1], "meeting_id": 1, "workflow_id": 1, }, ) self.assert_model_exists( "motion_state/3", {"name": "rejected", "previous_state_ids": [1]} ) self.assert_model_exists( "motion_state/4", {"name": "not_decided", "previous_state_ids": [1]} ) self.assert_model_exists( "motion_workflow/2", { "name": "Complex Workflow", "meeting_id": 1, "state_ids": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "first_state_id": 5, }, ) self.assert_model_exists( "motion_state/5", {"name": "in progress", "next_state_ids": [6, 10]} ) self.assert_model_exists( "motion_state/6", {"name": "submitted", "previous_state_ids": [5]} ) self.assert_model_exists( "motion_state/7", {"name": "permitted", "previous_state_ids": [6]} ) self.assert_model_exists( "motion_state/8", {"name": "accepted", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/9", {"name": "rejected", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/10", {"name": "withdrawn", "previous_state_ids": [5, 6, 7]} ) self.assert_model_exists( "motion_state/11", {"name": "adjourned", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/12", {"name": "not concerned", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/13", {"name": "referred to committee", "previous_state_ids": [7]}, ) self.assert_model_exists( "motion_state/14", {"name": "needs review", "previous_state_ids": [7]} ) self.assert_model_exists( "motion_state/15", {"name": "rejected (not authorized)", "previous_state_ids": [6]}, ) projector1 = self.get_model("projector/1") self.assertCountEqual( cast(Iterable[Any], projector1.get("used_as_default_$_in_meeting_id")), meeting_projector_default_replacements, ) self.assert_model_exists( "projector/1", { "name": "Default projector", "meeting_id": 1, "used_as_reference_projector_meeting_id": 1, **{ f"used_as_default_${name}_in_meeting_id": 1 for name in meeting_projector_default_replacements }, }, ) self.assert_model_exists( "projector_countdown/1", { "title": "List of speakers countdown", "meeting_id": 1, "used_as_list_of_speaker_countdown_meeting_id": 1, "default_time": 60, "countdown_time": 60, }, ) self.assert_model_exists( "projector_countdown/2", { "title": "Voting countdown", "meeting_id": 1, "used_as_poll_countdown_meeting_id": 1, "default_time": 60, "countdown_time": 60, }, ) def test_check_action_data_fields(self) -> None: meeting = self.basic_test( { "description": "RRfnzxHA", "location": "LSFHPTgE", "start_time": 1608120653, "end_time": 1608121653, "url_name": "JWdYZqDX", } ) assert meeting.get("description") == "RRfnzxHA" assert meeting.get("location") == "LSFHPTgE" assert meeting.get("start_time") == 1608120653 assert meeting.get("end_time") == 1608121653 assert meeting.get("url_name") == "JWdYZqDX" # check two defaults: assert meeting.get("assignment_poll_default_type") == "analog" assert meeting.get("assignment_poll_default_method") == "Y" def test_create_check_users(self) -> None: meeting = self.basic_test({"user_ids": [2]}) assert meeting.get("user_ids") == [2] default_group_id = meeting.get("default_group_id") self.assert_model_exists( "user/2", {f"group_${meeting['id']}_ids": [default_group_id]} ) def test_create_check_admins(self) -> None: meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting['id']}_ids": [admin_group_id]} ) def test_create_with_same_user_in_users_and_admins(self) -> None: meeting = self.basic_test({"user_ids": [2], "admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting['id']}_ids": [admin_group_id]} ) def test_create_multiple_users(self) -> None: self.set_models( { "organization/1": {"limit_of_meetings": 0, "active_meeting_ids": []}, "committee/1": {"organization_id": 1}, "user/2": {}, "user/3": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, "user_ids": [2, 3], "admin_ids": [1], }, ) self.assert_status_code(response, 200) meeting = self.get_model("meeting/1") default_group_id = meeting.get("default_group_id") self.assert_model_exists( "user/2", {"group_$1_ids": [default_group_id], "committee_ids": [1]} ) self.assert_model_exists( "user/3", {"group_$1_ids": [default_group_id], "committee_ids": [1]} ) admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/1", {"group_$1_ids": [admin_group_id], "committee_ids": [1]} ) self.assertCountEqual(meeting.get("user_ids"), [1, 2, 3]) committee = self.get_model("committee/1") self.assertCountEqual(committee.get("user_ids"), [1, 2, 3]) def test_create_with_admins_empty_array(self) -> None: meeting = self.basic_test({"admin_ids": []}) assert "admin_ids" not in meeting def test_create_no_permissions(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_USERS }, "committee/1": {"name": "test_committee", "user_ids": [1, 2]}, "group/1": {}, "user/2": {}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, }, ) self.assert_status_code(response, 403) assert ( "Missing CommitteeManagementLevel: can_manage" in response.json["message"] ) def test_create_permissions(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_USERS, "committee_$1_management_level": CommitteeManagementLevel.CAN_MANAGE, } } ) self.basic_test({}) def test_create_with_admin_ids_and_permissions_cml(self) -> None: self.set_models( { "user/1": { "organization_management_level": None, "committee_$1_management_level": CommitteeManagementLevel.CAN_MANAGE, } } ) meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting['id']}_ids": [admin_group_id]} ) def test_create_with_admin_ids_and_permissions_oml(self) -> None: self.set_models( { "user/1": { "organization_management_level": OrganizationManagementLevel.CAN_MANAGE_ORGANIZATION, "committee_$1_management_level": None, } } ) meeting = self.basic_test({"admin_ids": [2]}) assert meeting.get("user_ids") == [2] admin_group_id = meeting.get("admin_group_id") self.assert_model_exists( "user/2", {f"group_${meeting['id']}_ids": [admin_group_id]} ) def test_create_limit_of_meetings_reached(self) -> None: self.set_models( { "organization/1": {"limit_of_meetings": 1, "active_meeting_ids": [1]}, "committee/1": {"organization_id": 1}, } ) response = self.request( "meeting.create", { "name": "test_name", "committee_id": 1, }, ) self.assert_status_code(response, 400) self.assertIn( "You cannot create a new meeting, because you reached your limit of 1 active meetings.", response.json["message"], )

import os import sqlite3 import traceback class GeneralDB: ''' basic sqlite3 db which can be used for many purposes this isnt very safe probably but i dont really care''' def __init__(self, sessionName): self.connection = None self.cursor = None self.sessionName = sessionName # unique String to each instance of this object (The DB name, not the table name) def initCursor(self): ''' create and set the cursor''' os.makedirs("DBSessions", exist_ok=True) self.connection = sqlite3.connect("DBSessions/"+self.sessionName+".db") self.cursor = self.connection.cursor() def closeCursor(self, save=True): ''' close the connection and maybe save the changes''' if self.connection is None: return if save: self.connection.commit() self.connection.close() self.connection = None self.cursor = None def checkCursor(self): ''' shorten the cursor init even more''' if self.connection is None: self.initCursor() def rawExecute(self, statement): ''' directly execute a command to the db note: does not print anything. this meant for ddl related queries''' self.checkCursor() try: self.cursor.execute(statement) print("A statement was run: "+str(statement)) except: traceback.print_exc() self.closeCursor() def rawExecuteAndPrint(self, statement): ''' directly execute a select statement''' self.checkCursor() g = "" try: self.cursor.execute(statement) g = self.cursor.fetchall() print(g) except: traceback.print_exc() self.closeCursor() return g def getItem(self, table, column, rowID): ''' get a specific item from a table given a row ID and a column''' self.checkCursor() try: self.cursor.execute("select ? from {} where id=?".format(table), (column, rowID,)) output = self.cursor.fetchone() except: traceback.print_exc() output = None self.closeCursor() return output def getRow(self, table, rowID): ''' get a row from a table given a row ID returns None if row doesnt exist or a tuple if it does''' self.checkCursor() try: self.cursor.execute("select * from {} where id=?".format(table), (rowID,)) output = self.cursor.fetchone() except: traceback.print_exc() output = None self.closeCursor() return output def getColumn(self, table, column): ''' get a column from a table as a list''' self.checkCursor() try: self.cursor.execute("select {} from {}".format(column, table)) output = self.cursor.fetchall() except: traceback.print_exc() output = None self.closeCursor() return output def getTable(self, table): ''' get a whole table''' self.checkCursor() try: self.cursor.execute("select * from {}".format(table)) output = self.cursor.fetchall() except: traceback.print_exc() output = None self.closeCursor() return output def addRow(self, table, values): ''' make a new row in a table with the given list of values''' self.checkCursor() try: self.cursor.execute("insert into {} values ({})".format(table, ",".join(values))) except: traceback.print_exc() self.closeCursor() def addRows(self, table, values): ''' insert many rows into a table it is expected that values is a list of rows matching the columns exactly''' self.checkCursor() try: self.cursor.executemany(f"insert into {table} values ({",".join(["?" for _ in values[0]])})", values ) except: traceback.print_exc() self.closeCursor() def emptyTable(self, table): ''' delete the contents of a table''' self.checkCursor() try: self.cursor.execute("delete from {}".format(table)) except: traceback.print_exc() self.closeCursor() def delRow(self, table, rowID): ''' delete a row from a table with the given row ID''' self.checkCursor() try: self.cursor.execute("delete from {} where id = ?".format(table), (rowID,)) except: traceback.print_exc() self.closeCursor() def editItem(self, table, rowID, column, newValue): ''' edit a value in a table with a given column and row ID''' self.checkCursor() try: self.cursor.execute("update {} set {} = ? where id = ?".format(table, column), (newValue, rowID,)) except: traceback.print_exc() self.closeCursor() def replaceRow(self, table, rowID, newRowValues): ''' edit a row in a table to replace all of its values with new info newRowValues should be a list of new values not including the row ID''' self.checkCursor() try: self.delRow(table, rowID) self.addRow(table, ['"'+rowID+'"']+newRowValues) except: traceback.print_exc() self.closeCursor() def createTable(self, name, columns, suppress=False): ''' make a new table with these column names''' self.checkCursor() try: self.cursor.execute("create table {} ({})".format(name, ",".join(columns))) except: if not suppress: traceback.print_exc() else: pass self.closeCursor() def verifyTableExists(self, table): ''' check to see if a table exists and return true or false''' self.checkCursor() try: self.cursor.execute("select * from {}".format(table)) output = True except: output = False self.closeCursor() return output def verifyTableExistsWithRows(self, table, rowIDs): ''' check to see if a table exists with the given row IDs return a list of row IDs that are missing''' missingRows = [] if not(self.verifyTableExists(table)): return rowIDs self.checkCursor() for rowID in rowIDs: try: self.cursor.execute("select * from {} where id = {}".format(table, rowID)) if len(self.cursor.fetchone()) == 0: missingRows.append(rowID) except: missingRows.append(rowID) self.closeCursor() return missingRows

import os import sqlite3 import traceback class GeneralDB: ''' basic sqlite3 db which can be used for many purposes this isnt very safe probably but i dont really care''' def __init__(self, sessionName): self.connection = None self.cursor = None self.sessionName = sessionName # unique String to each instance of this object (The DB name, not the table name) def initCursor(self): ''' create and set the cursor''' os.makedirs("DBSessions", exist_ok=True) self.connection = sqlite3.connect("DBSessions/"+self.sessionName+".db") self.cursor = self.connection.cursor() def closeCursor(self, save=True): ''' close the connection and maybe save the changes''' if self.connection is None: return if save: self.connection.commit() self.connection.close() self.connection = None self.cursor = None def checkCursor(self): ''' shorten the cursor init even more''' if self.connection is None: self.initCursor() def rawExecute(self, statement): ''' directly execute a command to the db note: does not print anything. this meant for ddl related queries''' self.checkCursor() try: self.cursor.execute(statement) print("A statement was run: "+str(statement)) except: traceback.print_exc() self.closeCursor() def rawExecuteAndPrint(self, statement): ''' directly execute a select statement''' self.checkCursor() g = "" try: self.cursor.execute(statement) g = self.cursor.fetchall() print(g) except: traceback.print_exc() self.closeCursor() return g def getItem(self, table, column, rowID): ''' get a specific item from a table given a row ID and a column''' self.checkCursor() try: self.cursor.execute("select ? from {} where id=?".format(table), (column, rowID,)) output = self.cursor.fetchone() except: traceback.print_exc() output = None self.closeCursor() return output def getRow(self, table, rowID): ''' get a row from a table given a row ID returns None if row doesnt exist or a tuple if it does''' self.checkCursor() try: self.cursor.execute("select * from {} where id=?".format(table), (rowID,)) output = self.cursor.fetchone() except: traceback.print_exc() output = None self.closeCursor() return output def getColumn(self, table, column): ''' get a column from a table as a list''' self.checkCursor() try: self.cursor.execute("select {} from {}".format(column, table)) output = self.cursor.fetchall() except: traceback.print_exc() output = None self.closeCursor() return output def getTable(self, table): ''' get a whole table''' self.checkCursor() try: self.cursor.execute("select * from {}".format(table)) output = self.cursor.fetchall() except: traceback.print_exc() output = None self.closeCursor() return output def addRow(self, table, values): ''' make a new row in a table with the given list of values''' self.checkCursor() try: self.cursor.execute("insert into {} values ({})".format(table, ",".join(values))) except: traceback.print_exc() self.closeCursor() def addRows(self, table, values): ''' insert many rows into a table it is expected that values is a list of rows matching the columns exactly''' self.checkCursor() try: self.cursor.executemany(f"insert into {table} values ({','.join(['?' for _ in values[0]])})", values ) except: traceback.print_exc() self.closeCursor() def emptyTable(self, table): ''' delete the contents of a table''' self.checkCursor() try: self.cursor.execute("delete from {}".format(table)) except: traceback.print_exc() self.closeCursor() def delRow(self, table, rowID): ''' delete a row from a table with the given row ID''' self.checkCursor() try: self.cursor.execute("delete from {} where id = ?".format(table), (rowID,)) except: traceback.print_exc() self.closeCursor() def editItem(self, table, rowID, column, newValue): ''' edit a value in a table with a given column and row ID''' self.checkCursor() try: self.cursor.execute("update {} set {} = ? where id = ?".format(table, column), (newValue, rowID,)) except: traceback.print_exc() self.closeCursor() def replaceRow(self, table, rowID, newRowValues): ''' edit a row in a table to replace all of its values with new info newRowValues should be a list of new values not including the row ID''' self.checkCursor() try: self.delRow(table, rowID) self.addRow(table, ['"'+rowID+'"']+newRowValues) except: traceback.print_exc() self.closeCursor() def createTable(self, name, columns, suppress=False): ''' make a new table with these column names''' self.checkCursor() try: self.cursor.execute("create table {} ({})".format(name, ",".join(columns))) except: if not suppress: traceback.print_exc() else: pass self.closeCursor() def verifyTableExists(self, table): ''' check to see if a table exists and return true or false''' self.checkCursor() try: self.cursor.execute("select * from {}".format(table)) output = True except: output = False self.closeCursor() return output def verifyTableExistsWithRows(self, table, rowIDs): ''' check to see if a table exists with the given row IDs return a list of row IDs that are missing''' missingRows = [] if not(self.verifyTableExists(table)): return rowIDs self.checkCursor() for rowID in rowIDs: try: self.cursor.execute("select * from {} where id = {}".format(table, rowID)) if len(self.cursor.fetchone()) == 0: missingRows.append(rowID) except: missingRows.append(rowID) self.closeCursor() return missingRows

#!/usr/bin/env python ''' Advent of Code 2021 - Day 9: Smoke Basin (Part 1) https://adventofcode.com/2021/day/9 ''' import numpy as np class HeightMap(): def __init__(self) -> None: self._grid = np.array([]) def add_row(self, row): np_row = np.array(row) if self._grid.size != 0: self._grid = np.vstack([self._grid, np_row]) else: self._grid = np_row def find_low_points(self, radius=1): low_points = [] for index, point in np.ndenumerate(self._grid): neighbor_points = self._neighbors(radius, coordinates=index) if point < min(neighbor_points): low_points.append(point) return low_points def _neighbors(self, radius, coordinates=(0, 0)): neighbors = [] row = coordinates[0] column = coordinates[1] # Get UP neighbor value if row >= 1: neighbors.append(self._grid[row - radius, column]) # Get LEFT neighbor value if column >= 1: neighbors.append(self._grid[row, column - radius]) # Get RIGHT neighbor value if column < len(self._grid[0]) - radius: neighbors.append(self._grid[row, column + radius]) # Get DOWN neighbor value if row < len(self._grid) - radius: neighbors.append(self._grid[row + radius, column]) return neighbors def __str__(self) -> str: output = "" for row in self._grid: for elem in row: output = output + f"{elem:>3}" output = output + "\n" return output def calculate_risk(heights): # Risk is 1 plus the height return sum([height + 1 for height in heights]) def main(): filename = input("What is the input file name? ") try: with open(filename, "r") as file: # Create a new board area = HeightMap() # Read the rows and setup the HeightMap for line in file: line = line.strip() input_row = [int(x) for x in str(line)] area.add_row(input_row) print("The input grid: ") print(area) low_points = area.find_low_points() sum_risk_levels = calculate_risk( low_points) if low_points else None if sum_risk_levels: low_points_str = [str(point) for point in low_points] print(f"Number of low points: {len(low_points)}") print(f"Low points: {", ".join(low_points_str)}") print( f"\nThe sum of the risk levels of all low points is: {sum_risk_levels}\n") else: print("The sum of the risk levels of all low points not found.\n") except FileNotFoundError: print(f"No such file or directory: '{filename}'") if __name__ == "__main__": main()

#!/usr/bin/env python ''' Advent of Code 2021 - Day 9: Smoke Basin (Part 1) https://adventofcode.com/2021/day/9 ''' import numpy as np class HeightMap(): def __init__(self) -> None: self._grid = np.array([]) def add_row(self, row): np_row = np.array(row) if self._grid.size != 0: self._grid = np.vstack([self._grid, np_row]) else: self._grid = np_row def find_low_points(self, radius=1): low_points = [] for index, point in np.ndenumerate(self._grid): neighbor_points = self._neighbors(radius, coordinates=index) if point < min(neighbor_points): low_points.append(point) return low_points def _neighbors(self, radius, coordinates=(0, 0)): neighbors = [] row = coordinates[0] column = coordinates[1] # Get UP neighbor value if row >= 1: neighbors.append(self._grid[row - radius, column]) # Get LEFT neighbor value if column >= 1: neighbors.append(self._grid[row, column - radius]) # Get RIGHT neighbor value if column < len(self._grid[0]) - radius: neighbors.append(self._grid[row, column + radius]) # Get DOWN neighbor value if row < len(self._grid) - radius: neighbors.append(self._grid[row + radius, column]) return neighbors def __str__(self) -> str: output = "" for row in self._grid: for elem in row: output = output + f"{elem:>3}" output = output + "\n" return output def calculate_risk(heights): # Risk is 1 plus the height return sum([height + 1 for height in heights]) def main(): filename = input("What is the input file name? ") try: with open(filename, "r") as file: # Create a new board area = HeightMap() # Read the rows and setup the HeightMap for line in file: line = line.strip() input_row = [int(x) for x in str(line)] area.add_row(input_row) print("The input grid: ") print(area) low_points = area.find_low_points() sum_risk_levels = calculate_risk( low_points) if low_points else None if sum_risk_levels: low_points_str = [str(point) for point in low_points] print(f"Number of low points: {len(low_points)}") print(f"Low points: {', '.join(low_points_str)}") print( f"\nThe sum of the risk levels of all low points is: {sum_risk_levels}\n") else: print("The sum of the risk levels of all low points not found.\n") except FileNotFoundError: print(f"No such file or directory: '{filename}'") if __name__ == "__main__": main()

import argparse from ast import literal_eval from astropy.io import fits from astropy.visualization import ( AsymmetricPercentileInterval, LinearStretch, LogStretch, ImageNormalize, ) import base64 from bson.json_util import loads import confluent_kafka from copy import deepcopy import dask.distributed import datetime import fastavro import gzip import io import matplotlib.pyplot as plt import multiprocessing import numpy as np import os import pandas as pd import pathlib import requests from requests.packages.urllib3.util.retry import Retry import subprocess import sys import tensorflow as tf from tensorflow.keras.models import load_model import threading import time import traceback from typing import Mapping, Optional, Sequence from utils import ( deg2dms, deg2hms, great_circle_distance, in_ellipse, init_db_sync, load_config, log, memoize, Mongo, radec2lb, time_stamp, timer, TimeoutHTTPAdapter, ZTFAlert, ) tf.config.optimizer.set_jit(True) """ load config and secrets """ config = load_config(config_file="config.yaml")["kowalski"] def read_schema_data(bytes_io): """ Read data that already has an Avro schema. :param bytes_io: `_io.BytesIO` Data to be decoded. :return: `dict` Decoded data. """ bytes_io.seek(0) message = fastavro.reader(bytes_io) return message class EopError(Exception): """ Exception raised when reaching end of a Kafka topic partition. """ def __init__(self, msg): """ :param msg: The Kafka message result from consumer.poll() """ message = ( f"{time_stamp()}: topic:{msg.topic()}, partition:{msg.partition()}, " f"status:end, offset:{msg.offset()}, key:{str(msg.key())}\n" ) self.message = message def __str__(self): return self.message def make_photometry(alert: dict, jd_start: float = None): """ Make a de-duplicated pandas.DataFrame with photometry of alert['objectId'] :param alert: ZTF alert packet/dict :param jd_start: :return: """ alert = deepcopy(alert) df_candidate = pd.DataFrame(alert["candidate"], index=[0]) df_prv_candidates = pd.DataFrame(alert["prv_candidates"]) df_light_curve = pd.concat( [df_candidate, df_prv_candidates], ignore_index=True, sort=False ) ztf_filters = {1: "ztfg", 2: "ztfr", 3: "ztfi"} df_light_curve["ztf_filter"] = df_light_curve["fid"].apply(lambda x: ztf_filters[x]) df_light_curve["magsys"] = "ab" df_light_curve["mjd"] = df_light_curve["jd"] - 2400000.5 df_light_curve["mjd"] = df_light_curve["mjd"].apply(lambda x: np.float64(x)) df_light_curve["magpsf"] = df_light_curve["magpsf"].apply(lambda x: np.float32(x)) df_light_curve["sigmapsf"] = df_light_curve["sigmapsf"].apply( lambda x: np.float32(x) ) df_light_curve = ( df_light_curve.drop_duplicates(subset=["mjd", "magpsf"]) .reset_index(drop=True) .sort_values(by=["mjd"]) ) # filter out bad data: mask_good_diffmaglim = df_light_curve["diffmaglim"] > 0 df_light_curve = df_light_curve.loc[mask_good_diffmaglim] # convert from mag to flux # step 1: calculate the coefficient that determines whether the # flux should be negative or positive coeff = df_light_curve["isdiffpos"].apply( lambda x: 1.0 if x in [True, 1, "y", "Y", "t", "1"] else -1.0 ) # step 2: calculate the flux normalized to an arbitrary AB zeropoint of # 23.9 (results in flux in uJy) df_light_curve["flux"] = coeff * 10 ** (-0.4 * (df_light_curve["magpsf"] - 23.9)) # step 3: separate detections from non detections detected = np.isfinite(df_light_curve["magpsf"]) undetected = ~detected # step 4: calculate the flux error df_light_curve["fluxerr"] = None # initialize the column # step 4a: calculate fluxerr for detections using sigmapsf df_light_curve.loc[detected, "fluxerr"] = np.abs( df_light_curve.loc[detected, "sigmapsf"] * df_light_curve.loc[detected, "flux"] * np.log(10) / 2.5 ) # step 4b: calculate fluxerr for non detections using diffmaglim df_light_curve.loc[undetected, "fluxerr"] = ( 10 ** (-0.4 * (df_light_curve.loc[undetected, "diffmaglim"] - 23.9)) / 5.0 ) # as diffmaglim is the 5-sigma depth # step 5: set the zeropoint and magnitude system df_light_curve["zp"] = 23.9 df_light_curve["zpsys"] = "ab" # only "new" photometry requested? if jd_start is not None: w_after_jd = df_light_curve["jd"] > jd_start df_light_curve = df_light_curve.loc[w_after_jd] return df_light_curve def make_thumbnail(alert, ttype: str, ztftype: str): """ Convert lossless FITS cutouts from ZTF alerts into PNGs :param alert: ZTF alert packet/dict :param ttype: <new|ref|sub> :param ztftype: <Science|Template|Difference> :return: """ alert = deepcopy(alert) cutout_data = alert[f"cutout{ztftype}"]["stampData"] with gzip.open(io.BytesIO(cutout_data), "rb") as f: with fits.open(io.BytesIO(f.read())) as hdu: # header = hdu[0].header data_flipped_y = np.flipud(hdu[0].data) # fixme: png, switch to fits eventually buff = io.BytesIO() plt.close("all") fig = plt.figure() fig.set_size_inches(4, 4, forward=False) ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) ax.set_axis_off() fig.add_axes(ax) # replace nans with median: img = np.array(data_flipped_y) # replace dubiously large values xl = np.greater(np.abs(img), 1e20, where=~np.isnan(img)) if img[xl].any(): img[xl] = np.nan if np.isnan(img).any(): median = float(np.nanmean(img.flatten())) img = np.nan_to_num(img, nan=median) norm = ImageNormalize( img, stretch=LinearStretch() if ztftype == "Difference" else LogStretch() ) img_norm = norm(img) normalizer = AsymmetricPercentileInterval(lower_percentile=1, upper_percentile=100) vmin, vmax = normalizer.get_limits(img_norm) ax.imshow(img_norm, cmap="bone", origin="lower", vmin=vmin, vmax=vmax) plt.savefig(buff, dpi=42) buff.seek(0) plt.close("all") thumb = { "obj_id": alert["objectId"], "data": base64.b64encode(buff.read()).decode("utf-8"), "ttype": ttype, } return thumb """ Alert filters """ def make_triplet(alert, to_tpu: bool = False): """ Make an L2-normalized cutout triplet out of a ZTF alert :param alert: :param to_tpu: :return: """ cutout_dict = dict() for cutout in ("science", "template", "difference"): cutout_data = alert[f"cutout{cutout.capitalize()}"]["stampData"] # unzip with gzip.open(io.BytesIO(cutout_data), "rb") as f: with fits.open(io.BytesIO(f.read())) as hdu: data = hdu[0].data # replace nans with zeros cutout_dict[cutout] = np.nan_to_num(data) # L2-normalize cutout_dict[cutout] /= np.linalg.norm(cutout_dict[cutout]) # pad to 63x63 if smaller shape = cutout_dict[cutout].shape if shape != (63, 63): # print(f'Shape of {candid}/{cutout}: {shape}, padding to (63, 63)') cutout_dict[cutout] = np.pad( cutout_dict[cutout], [(0, 63 - shape[0]), (0, 63 - shape[1])], mode="constant", constant_values=1e-9, ) triplet = np.zeros((63, 63, 3)) triplet[:, :, 0] = cutout_dict["science"] triplet[:, :, 1] = cutout_dict["template"] triplet[:, :, 2] = cutout_dict["difference"] if to_tpu: # Edge TPUs require additional processing triplet = np.rint(triplet * 128 + 128).astype(np.uint8).flatten() return triplet def alert_filter__ml(alert, ml_models: dict = None) -> dict: """Execute ML models on a ZTF alert :param alert: :param ml_models: :return: """ scores = dict() if ml_models is not None and len(ml_models) > 0: try: with timer("ZTFAlert(alert)"): ztf_alert = ZTFAlert(alert) with timer("Prepping features"): features = np.expand_dims(ztf_alert.data["features"], axis=[0, -1]) triplet = np.expand_dims(ztf_alert.data["triplet"], axis=[0]) # braai if "braai" in ml_models.keys(): with timer("braai"): braai = ml_models["braai"]["model"].predict(x=triplet)[0] scores["braai"] = float(braai) scores["braai_version"] = ml_models["braai"]["version"] # acai for model_name in ("acai_h", "acai_v", "acai_o", "acai_n", "acai_b"): if model_name in ml_models.keys(): with timer(model_name): score = ml_models[model_name]["model"].predict( [features, triplet] )[0] scores[model_name] = float(score) scores[f"{model_name}_version"] = ml_models[model_name][ "version" ] except Exception as e: log(str(e)) return scores # cone search radius: cone_search_radius = float(config["database"]["xmatch"]["cone_search_radius"]) # convert to rad: if config["database"]["xmatch"]["cone_search_unit"] == "arcsec": cone_search_radius *= np.pi / 180.0 / 3600.0 elif config["database"]["xmatch"]["cone_search_unit"] == "arcmin": cone_search_radius *= np.pi / 180.0 / 60.0 elif config["database"]["xmatch"]["cone_search_unit"] == "deg": cone_search_radius *= np.pi / 180.0 elif config["database"]["xmatch"]["cone_search_unit"] == "rad": cone_search_radius *= 1 else: raise Exception("Unknown cone search unit. Must be in [deg, rad, arcsec, arcmin]") def alert_filter__xmatch(database, alert) -> dict: """ Cross-match alerts """ xmatches = dict() try: ra_geojson = float(alert["candidate"]["ra"]) # geojson-friendly ra: ra_geojson -= 180.0 dec_geojson = float(alert["candidate"]["dec"]) """ catalogs """ for catalog in config["database"]["xmatch"]["catalogs"]: catalog_filter = config["database"]["xmatch"]["catalogs"][catalog]["filter"] catalog_projection = config["database"]["xmatch"]["catalogs"][catalog][ "projection" ] object_position_query = dict() object_position_query["coordinates.radec_geojson"] = { "$geoWithin": { "$centerSphere": [[ra_geojson, dec_geojson], cone_search_radius] } } s = database[catalog].find( {**object_position_query, **catalog_filter}, {**catalog_projection} ) xmatches[catalog] = list(s) except Exception as e: log(str(e)) return xmatches # cone search radius in deg: cone_search_radius_clu = 3.0 # convert deg to rad: cone_search_radius_clu *= np.pi / 180.0 def alert_filter__xmatch_clu( database, alert, size_margin=3, clu_version="CLU_20190625" ) -> dict: """ Run cross-match with the CLU catalog :param database: :param alert: :param size_margin: multiply galaxy size by this much before looking for a match :param clu_version: CLU catalog version :return: """ xmatches = dict() try: ra = float(alert["candidate"]["ra"]) dec = float(alert["candidate"]["dec"]) # geojson-friendly ra: ra_geojson = float(alert["candidate"]["ra"]) - 180.0 dec_geojson = dec catalog_filter = {} catalog_projection = { "_id": 1, "name": 1, "ra": 1, "dec": 1, "a": 1, "b2a": 1, "pa": 1, "z": 1, "sfr_fuv": 1, "mstar": 1, "sfr_ha": 1, "coordinates.radec_str": 1, } # first do a coarse search of everything that is around object_position_query = dict() object_position_query["coordinates.radec_geojson"] = { "$geoWithin": { "$centerSphere": [[ra_geojson, dec_geojson], cone_search_radius_clu] } } galaxies = list( database[clu_version].find( {**object_position_query, **catalog_filter}, {**catalog_projection} ) ) # these guys are very big, so check them separately M31 = { "_id": 596900, "name": "PGC2557", "ra": 10.6847, "dec": 41.26901, "a": 6.35156, "b2a": 0.32, "pa": 35.0, "z": -0.00100100006, "sfr_fuv": None, "mstar": 253816876.412914, "sfr_ha": 0, "coordinates": {"radec_str": ["00:42:44.3503", "41:16:08.634"]}, } M33 = { "_id": 597543, "name": "PGC5818", "ra": 23.46204, "dec": 30.66022, "a": 2.35983, "b2a": 0.59, "pa": 23.0, "z": -0.000597000006, "sfr_fuv": None, "mstar": 4502777.420493, "sfr_ha": 0, "coordinates": {"radec_str": ["01:33:50.8900", "30:39:36.800"]}, } # do elliptical matches matches = [] for galaxy in galaxies + [M31, M33]: alpha1, delta01 = galaxy["ra"], galaxy["dec"] redshift = galaxy["z"] # By default, set the cross-match radius to 50 kpc at the redshift of the host galaxy cm_radius = 50.0 * (0.05 / redshift) / 3600 if redshift < 0.01: # for nearby galaxies and galaxies with negative redshifts, do a 5 arc-minute cross-match # (cross-match radius would otherwise get un-physically large for nearby galaxies) cm_radius = 300.0 / 3600 in_galaxy = in_ellipse(ra, dec, alpha1, delta01, cm_radius, 1, 0) if in_galaxy: match = galaxy distance_arcsec = round( great_circle_distance(ra, dec, alpha1, delta01) * 3600, 2 ) # also add a physical distance parameter for redshifts in the Hubble flow if redshift > 0.005: distance_kpc = round( great_circle_distance(ra, dec, alpha1, delta01) * 3600 * (redshift / 0.05), 2, ) else: distance_kpc = -1 match["coordinates"]["distance_arcsec"] = distance_arcsec match["coordinates"]["distance_kpc"] = distance_kpc matches.append(match) xmatches[clu_version] = matches except Exception as e: log(str(e)) return xmatches def alert_filter__user_defined( database, filter_templates, alert, catalog: str = "ZTF_alerts", max_time_ms: int = 500, ) -> list: """ Evaluate user-defined filters :param database: :param filter_templates: :param alert: :param catalog: :param max_time_ms: :return: """ passed_filters = [] for filter_template in filter_templates: try: _filter = deepcopy(filter_template) # match candid _filter["pipeline"][0]["$match"]["candid"] = alert["candid"] filtered_data = list( database[catalog].aggregate( _filter["pipeline"], allowDiskUse=False, maxTimeMS=max_time_ms ) ) # passed filter? then len(passed_filter) must be = 1 if len(filtered_data) == 1: log( f'{alert['objectId']} {alert['candid']} passed filter {_filter['fid']}' ) passed_filters.append( { "group_id": _filter["group_id"], "filter_id": _filter["filter_id"], "group_name": _filter["group_name"], "filter_name": _filter["filter_name"], "fid": _filter["fid"], "permissions": _filter["permissions"], "autosave": _filter["autosave"], "update_annotations": _filter["update_annotations"], "data": filtered_data[0], } ) except Exception as e: log( f'Filter {filter_template['fid']} execution failed on alert {alert['candid']}: {e}' ) continue return passed_filters def process_alert(record, topic): """Alert brokering task run by dask.distributed workers :param record: decoded alert from IPAC's Kafka stream :param topic: Kafka stream topic name for bookkeeping :return: """ candid = record["candid"] objectId = record["objectId"] # get worker running current task worker = dask.distributed.get_worker() alert_worker = worker.plugins["worker-init"].alert_worker log(f"{topic} {objectId} {candid} {worker.address}") # return if this alert packet has already been processed and ingested into collection_alerts: if ( alert_worker.mongo.db[alert_worker.collection_alerts].count_documents( {"candid": candid}, limit=1 ) == 1 ): return # candid not in db, ingest decoded avro packet into db # todo: ?? restructure alerts even further? # move cutouts to ZTF_alerts_cutouts? reduce the main db size for performance # group by objectId similar to prv_candidates?? maybe this is too much with timer(f"Mongification of {objectId} {candid}", alert_worker.verbose > 1): alert, prv_candidates = alert_worker.alert_mongify(record) # ML models: with timer(f"MLing of {objectId} {candid}", alert_worker.verbose > 1): scores = alert_filter__ml(record, ml_models=alert_worker.ml_models) alert["classifications"] = scores with timer(f"Ingesting {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.insert_one( collection=alert_worker.collection_alerts, document=alert ) # prv_candidates: pop nulls - save space prv_candidates = [ {kk: vv for kk, vv in prv_candidate.items() if vv is not None} for prv_candidate in prv_candidates ] # cross-match with external catalogs if objectId not in collection_alerts_aux: if ( alert_worker.mongo.db[alert_worker.collection_alerts_aux].count_documents( {"_id": objectId}, limit=1 ) == 0 ): with timer(f"Cross-match of {objectId} {candid}", alert_worker.verbose > 1): xmatches = alert_filter__xmatch(alert_worker.mongo.db, alert) # CLU cross-match: with timer(f"CLU cross-match {objectId} {candid}", alert_worker.verbose > 1): xmatches = { **xmatches, **alert_filter__xmatch_clu(alert_worker.mongo.db, alert), } alert_aux = { "_id": objectId, "cross_matches": xmatches, "prv_candidates": prv_candidates, } with timer(f"Aux ingesting {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.insert_one( collection=alert_worker.collection_alerts_aux, document=alert_aux ) else: with timer(f"Aux updating of {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.db[alert_worker.collection_alerts_aux].update_one( {"_id": objectId}, {"$addToSet": {"prv_candidates": {"$each": prv_candidates}}}, upsert=True, ) if config["misc"]["broker"]: # execute user-defined alert filters with timer(f"Filtering of {objectId} {candid}", alert_worker.verbose > 1): passed_filters = alert_filter__user_defined( alert_worker.mongo.db, alert_worker.filter_templates, alert ) if alert_worker.verbose > 1: log(f"{objectId} {candid} number of filters passed: {len(passed_filters)}") # post to SkyPortal alert_worker.alert_sentinel_skyportal(alert, prv_candidates, passed_filters) # clean up after thyself del record, alert, prv_candidates class AlertConsumer: """ Creates an alert stream Kafka consumer for a given topic. """ def __init__(self, topic, dask_client, **kwargs): self.verbose = kwargs.get("verbose", 2) self.dask_client = dask_client # keep track of disconnected partitions self.num_disconnected_partitions = 0 self.topic = topic def error_cb(err, _self=self): log(f"error_cb --------> {err}") # print(err.code()) if err.code() == -195: _self.num_disconnected_partitions += 1 if _self.num_disconnected_partitions == _self.num_partitions: log("All partitions got disconnected, killing thread") sys.exit() else: log( f"{_self.topic}: disconnected from partition. total: {_self.num_disconnected_partitions}" ) # 'error_cb': error_cb kwargs["error_cb"] = error_cb self.consumer = confluent_kafka.Consumer(**kwargs) self.num_partitions = 0 def on_assign(consumer, partitions, _self=self): # force-reset offsets when subscribing to a topic: for part in partitions: # -2 stands for beginning and -1 for end part.offset = -2 # keep number of partitions. # when reaching end of last partition, kill thread and start from beginning _self.num_partitions += 1 log(consumer.get_watermark_offsets(part)) self.consumer.subscribe([topic], on_assign=on_assign) # set up own mongo client self.collection_alerts = config["database"]["collections"]["alerts_ztf"] self.mongo = Mongo( host=config["database"]["host"], port=config["database"]["port"], replica_set=config["database"]["replica_set"], username=config["database"]["username"], password=config["database"]["password"], db=config["database"]["db"], verbose=self.verbose, ) # create indexes if config["database"]["build_indexes"]: for index in config["database"]["indexes"][self.collection_alerts]: try: ind = [tuple(ii) for ii in index["fields"]] self.mongo.db[self.collection_alerts].create_index( keys=ind, name=index["name"], background=True, unique=index["unique"], ) except Exception as e: log(e) @staticmethod def decode_message(msg): """ Decode Avro message according to a schema. :param msg: The Kafka message result from consumer.poll() :return: """ message = msg.value() decoded_msg = message try: bytes_io = io.BytesIO(message) decoded_msg = read_schema_data(bytes_io) except AssertionError: decoded_msg = None except IndexError: literal_msg = literal_eval( str(message, encoding="utf-8") ) # works to give bytes bytes_io = io.BytesIO(literal_msg) # works to give <class '_io.BytesIO'> decoded_msg = read_schema_data(bytes_io) # yields reader except Exception: decoded_msg = message finally: return decoded_msg def poll(self): """ Polls Kafka broker to consume a topic. :return: """ msg = self.consumer.poll() if msg is None: log("Caught error: msg is None") if msg.error(): log(f"Caught error: {msg.error()}") raise EopError(msg) elif msg is not None: try: # decode avro packet with timer("Decoding alert", self.verbose > 1): msg_decoded = self.decode_message(msg) for record in msg_decoded: # submit only unprocessed alerts: if ( self.mongo.db[self.collection_alerts].count_documents( {"candid": record["candid"]}, limit=1 ) == 0 ): with timer( f"Submitting alert {record["objectId"]} {record["candid"]} for processing", self.verbose > 1, ): future = self.dask_client.submit( process_alert, record, self.topic, pure=True ) dask.distributed.fire_and_forget(future) future.release() del future except Exception as e: log(e) _err = traceback.format_exc() log(_err) class AlertWorker: """Tools to handle alert processing: database ingestion, filtering, ml'ing, cross-matches, reporting to SP""" def __init__(self, **kwargs): self.verbose = kwargs.get("verbose", 2) self.config = config # Kowalski version path_version_file = pathlib.Path(__file__).parent.absolute() / "version.txt" version = f"v{self.config["server"]["version"]}" if path_version_file.exists(): with open( pathlib.Path(__file__).parent.absolute() / "version.txt", "r" ) as version_file: version = version_file.read().strip() # MongoDB collections to store the alerts: self.collection_alerts = self.config["database"]["collections"]["alerts_ztf"] self.collection_alerts_aux = self.config["database"]["collections"][ "alerts_ztf_aux" ] self.collection_alerts_filter = self.config["database"]["collections"][ "alerts_ztf_filter" ] self.mongo = Mongo( host=config["database"]["host"], port=config["database"]["port"], replica_set=config["database"]["replica_set"], username=config["database"]["username"], password=config["database"]["password"], db=config["database"]["db"], verbose=self.verbose, ) # ML models self.ml_models = dict() for model in config["ml_models"]: try: model_version = config["ml_models"][model]["version"] # todo: allow other formats such as SavedModel model_filepath = os.path.join( config["path"]["ml_models"], f"{model}.{model_version}.h5" ) self.ml_models[model] = { "model": load_model(model_filepath), "version": model_version, } except Exception as e: log(f"Error loading ML model {model}: {str(e)}") _err = traceback.format_exc() log(_err) continue # talking to SkyPortal? if not config["misc"]["broker"]: return # session to talk to SkyPortal self.session = requests.Session() self.session_headers = { "Authorization": f"token {config["skyportal"]["token"]}", "User-Agent": f"Kowalski {version}", } retries = Retry( total=5, backoff_factor=2, status_forcelist=[405, 429, 500, 502, 503, 504], method_whitelist=["HEAD", "GET", "PUT", "POST", "PATCH"], ) adapter = TimeoutHTTPAdapter(timeout=5, max_retries=retries) self.session.mount("https://", adapter) self.session.mount("http://", adapter) # get ZTF instrument id self.instrument_id = 1 with timer("Getting ZTF instrument_id from SkyPortal", self.verbose > 1): response = self.api_skyportal("GET", "/api/instrument", {"name": "ZTF"}) if response.json()["status"] == "success" and len(response.json()["data"]) > 0: self.instrument_id = response.json()["data"][0]["id"] log(f"Got ZTF instrument_id from SkyPortal: {self.instrument_id}") else: log("Failed to get ZTF instrument_id from SkyPortal") raise ValueError("Failed to get ZTF instrument_id from SkyPortal") # get ZTF alert stream ids to program ids mapping self.ztf_program_id_to_stream_id = dict() with timer("Getting ZTF alert stream ids from SkyPortal", self.verbose > 1): response = self.api_skyportal("GET", "/api/streams") if response.json()["status"] == "success" and len(response.json()["data"]) > 0: for stream in response.json()["data"]: if stream.get("name") == "ZTF Public": self.ztf_program_id_to_stream_id[1] = stream["id"] if stream.get("name") == "ZTF Public+Partnership": self.ztf_program_id_to_stream_id[2] = stream["id"] if stream.get("name") == "ZTF Public+Partnership+Caltech": # programid=0 is engineering data self.ztf_program_id_to_stream_id[0] = stream["id"] self.ztf_program_id_to_stream_id[3] = stream["id"] if len(self.ztf_program_id_to_stream_id) != 4: log("Failed to map ZTF alert stream ids from SkyPortal to program ids") raise ValueError( "Failed to map ZTF alert stream ids from SkyPortal to program ids" ) log( f"Got ZTF program id to SP stream id mapping: {self.ztf_program_id_to_stream_id}" ) else: log("Failed to get ZTF alert stream ids from SkyPortal") raise ValueError("Failed to get ZTF alert stream ids from SkyPortal") # filter pipeline upstream: select current alert, ditch cutouts, and merge with aux data # including archival photometry and cross-matches: self.filter_pipeline_upstream = config["database"]["filters"][ self.collection_alerts ] log("Upstream filtering pipeline:") log(self.filter_pipeline_upstream) # load *active* user-defined alert filter templates and pre-populate them active_filters = self.get_active_filters() self.filter_templates = self.make_filter_templates(active_filters) # set up watchdog for periodic refresh of the filter templates, in case those change self.filter_monitor = threading.Thread(target=self.reload_filters) self.filter_monitor.start() log("Loaded user-defined filters:") log(self.filter_templates) def api_skyportal(self, method: str, endpoint: str, data: Optional[Mapping] = None): """Make an API call to a SkyPortal instance :param method: :param endpoint: :param data: :return: """ method = method.lower() methods = { "head": self.session.head, "get": self.session.get, "post": self.session.post, "put": self.session.put, "patch": self.session.patch, "delete": self.session.delete, } if endpoint is None: raise ValueError("Endpoint not specified") if method not in ["head", "get", "post", "put", "patch", "delete"]: raise ValueError(f"Unsupported method: {method}") if method == "get": response = methods[method]( f"{config["skyportal"]["protocol"]}://" f"{config["skyportal"]["host"]}:{config["skyportal"]["port"]}" f"{endpoint}", params=data, headers=self.session_headers, ) else: response = methods[method]( f"{config["skyportal"]["protocol"]}://" f"{config["skyportal"]["host"]}:{config["skyportal"]["port"]}" f"{endpoint}", json=data, headers=self.session_headers, ) return response @memoize def api_skyportal_get_group(self, group_id): return self.api_skyportal( "GET", f"/api/groups/{group_id}?includeGroupUsers=False" ) def get_active_filters(self): """ Fetch user-defined filters from own db marked as active :return: """ # todo: query SP to make sure the filters still exist there and we're not out of sync; # clean up if necessary return list( self.mongo.db[config["database"]["collections"]["filters"]].aggregate( [ { "$match": { "catalog": config["database"]["collections"]["alerts_ztf"], "active": True, } }, { "$project": { "group_id": 1, "filter_id": 1, "permissions": 1, "autosave": 1, "update_annotations": 1, "fv": { "$arrayElemAt": [ { "$filter": { "input": "$fv", "as": "fvv", "cond": { "$eq": ["$$fvv.fid", "$active_fid"] }, } }, 0, ] }, } }, ] ) ) def make_filter_templates(self, active_filters: Sequence): """ Make filter templates by adding metadata, prepending upstream aggregation stages and setting permissions :param active_filters: :return: """ filter_templates = [] for active_filter in active_filters: try: # collect additional info from SkyPortal with timer( f"Getting info on group id={active_filter["group_id"]} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal_get_group(active_filter["group_id"]) if self.verbose > 1: log(response.json()) if response.json()["status"] == "success": group_name = ( response.json()["data"]["nickname"] if response.json()["data"]["nickname"] is not None else response.json()["data"]["name"] ) filter_name = [ filtr["name"] for filtr in response.json()["data"]["filters"] if filtr["id"] == active_filter["filter_id"] ][0] else: log( f"Failed to get info on group id={active_filter["group_id"]} from SkyPortal" ) group_name, filter_name = None, None # raise ValueError(f"Failed to get info on group id={active_filter["group_id"]} from SkyPortal") log(f"Group name: {group_name}, filter name: {filter_name}") # prepend upstream aggregation stages: pipeline = deepcopy(self.filter_pipeline_upstream) + loads( active_filter["fv"]["pipeline"] ) # set permissions pipeline[0]["$match"]["candidate.programid"]["$in"] = active_filter[ "permissions" ] pipeline[3]["$project"]["prv_candidates"]["$filter"]["cond"]["$and"][0][ "$in" ][1] = active_filter["permissions"] filter_template = { "group_id": active_filter["group_id"], "filter_id": active_filter["filter_id"], "group_name": group_name, "filter_name": filter_name, "fid": active_filter["fv"]["fid"], "permissions": active_filter["permissions"], "autosave": active_filter["autosave"], "update_annotations": active_filter["update_annotations"], "pipeline": deepcopy(pipeline), } filter_templates.append(filter_template) except Exception as e: log( "Failed to generate filter template for " f"group_id={active_filter["group_id"]} filter_id={active_filter["filter_id"]}: {e}" ) continue return filter_templates def reload_filters(self): """ Helper function to periodically reload filters from SkyPortal :return: """ while True: time.sleep(60 * 5) active_filters = self.get_active_filters() self.filter_templates = self.make_filter_templates(active_filters) @staticmethod def alert_mongify(alert: Mapping): """ Prepare a raw ZTF alert for ingestion into MongoDB: - add a placeholder for ML-based classifications - add coordinates for 2D spherical indexing and compute Galactic coordinates - cut off the prv_candidates section :param alert: :return: """ doc = dict(alert) # let mongo create a unique _id # placeholders for classifications doc["classifications"] = dict() # GeoJSON for 2D indexing doc["coordinates"] = {} _ra = doc["candidate"]["ra"] _dec = doc["candidate"]["dec"] # string format: H:M:S, D:M:S _radec_str = [deg2hms(_ra), deg2dms(_dec)] doc["coordinates"]["radec_str"] = _radec_str # for GeoJSON, must be lon:[-180, 180], lat:[-90, 90] (i.e. in deg) _radec_geojson = [_ra - 180.0, _dec] doc["coordinates"]["radec_geojson"] = { "type": "Point", "coordinates": _radec_geojson, } # Galactic coordinates l and b l, b = radec2lb(doc["candidate"]["ra"], doc["candidate"]["dec"]) doc["coordinates"]["l"] = l doc["coordinates"]["b"] = b prv_candidates = deepcopy(doc["prv_candidates"]) doc.pop("prv_candidates", None) if prv_candidates is None: prv_candidates = [] return doc, prv_candidates def alert_post_candidate(self, alert: Mapping, filter_ids: Sequence): """ Post a ZTF alert as a candidate for filters on SkyPortal :param alert: :param filter_ids: :return: """ # post metadata with all filter_ids in single call to /api/candidates alert_thin = { "id": alert["objectId"], "ra": alert["candidate"].get("ra"), "dec": alert["candidate"].get("dec"), "score": alert["candidate"].get("drb", alert["candidate"]["rb"]), "filter_ids": filter_ids, "passing_alert_id": alert["candid"], "passed_at": datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S.%f"), "origin": "Kowalski", } if self.verbose > 1: log(alert_thin) with timer( f"Posting metadata of {alert["objectId"]} {alert["candid"]} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/candidates", alert_thin) if response.json()["status"] == "success": log(f"Posted {alert["objectId"]} {alert["candid"]} metadata to SkyPortal") else: log( f"Failed to post {alert["objectId"]} {alert["candid"]} metadata to SkyPortal" ) log(response.json()) def alert_post_source(self, alert: Mapping, group_ids: Sequence): """ Save a ZTF alert as a source to groups on SkyPortal :param alert: :param group_ids: :return: """ # save source alert_thin = { "id": alert["objectId"], "group_ids": group_ids, "origin": "Kowalski", } if self.verbose > 1: log(alert_thin) with timer( f"Saving {alert["objectId"]} {alert["candid"]} as a Source on SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/sources", alert_thin) if response.json()["status"] == "success": log(f"Saved {alert["objectId"]} {alert["candid"]} as a Source on SkyPortal") else: log( f"Failed to save {alert["objectId"]} {alert["candid"]} as a Source on SkyPortal" ) log(response.json()) def alert_post_annotations(self, alert: Mapping, passed_filters: Sequence): """ Post annotations to SkyPortal for an alert that passed user-defined filters :param alert: :param passed_filters: :return: """ for passed_filter in passed_filters: annotations = { "obj_id": alert["objectId"], "origin": f"{passed_filter.get("group_name")}:{passed_filter.get("filter_name")}", "data": passed_filter.get("data", dict()).get("annotations", dict()), "group_ids": [passed_filter.get("group_id")], } if len(annotations["data"]) > 0: with timer( f"Posting annotation for {alert["objectId"]} {alert["candid"]} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "POST", "/api/annotation", annotations ) if response.json()["status"] == "success": log(f"Posted {alert["objectId"]} annotation to SkyPortal") else: log(f"Failed to post {alert["objectId"]} annotation to SkyPortal") log(response.json()) def alert_put_annotations(self, alert: Mapping, passed_filters: Sequence): """ Update annotations on SkyPortal for an alert that passed user-defined filters :param alert: :param passed_filters: :return: """ # first need to learn existing annotation id's and corresponding author id's to use with the PUT call with timer( f"Getting annotations for {alert["objectId"]} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "GET", f"/api/sources/{alert["objectId"]}/annotations" ) if response.json()["status"] == "success": log(f"Got {alert["objectId"]} annotations from SkyPortal") else: log(f"Failed to get {alert["objectId"]} annotations from SkyPortal") log(response.json()) return False existing_annotations = { annotation["origin"]: { "annotation_id": annotation["id"], "author_id": annotation["author_id"], } for annotation in response.json()["data"] } for passed_filter in passed_filters: origin = ( f"{passed_filter.get("group_name")}:{passed_filter.get("filter_name")}" ) # no annotation exists on SkyPortal for this object? just post then if origin not in existing_annotations: self.alert_post_annotations(alert, [passed_filter]) continue annotations = { "author_id": existing_annotations[origin]["author_id"], "obj_id": alert["objectId"], "origin": origin, "data": passed_filter.get("data", dict()).get("annotations", dict()), "group_ids": [passed_filter.get("group_id")], } if len(annotations["data"]) > 0 and passed_filter.get( "update_annotations", False ): with timer( f"Putting annotation for {alert["objectId"]} {alert["candid"]} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "PUT", f"/api/annotation/{existing_annotations[origin]["annotation_id"]}", annotations, ) if response.json()["status"] == "success": log(f"Posted {alert["objectId"]} annotation to SkyPortal") else: log(f"Failed to post {alert["objectId"]} annotation to SkyPortal") log(response.json()) def alert_post_thumbnails(self, alert: Mapping): """ Post ZTF alert thumbnails to SkyPortal :param alert: :return: """ for ttype, ztftype in [ ("new", "Science"), ("ref", "Template"), ("sub", "Difference"), ]: with timer( f"Making {ztftype} thumbnail for {alert["objectId"]} {alert["candid"]}", self.verbose > 1, ): thumb = make_thumbnail(alert, ttype, ztftype) with timer( f"Posting {ztftype} thumbnail for {alert["objectId"]} {alert["candid"]} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/thumbnail", thumb) if response.json()["status"] == "success": log( f"Posted {alert["objectId"]} {alert["candid"]} {ztftype} cutout to SkyPortal" ) else: log( f"Failed to post {alert["objectId"]} {alert["candid"]} {ztftype} cutout to SkyPortal" ) log(response.json()) def alert_put_photometry(self, alert): """PUT photometry to SkyPortal :param alert: :return: """ with timer( f"Making alert photometry of {alert["objectId"]} {alert["candid"]}", self.verbose > 1, ): df_photometry = make_photometry(alert) df_photometry["stream_id"] = df_photometry["programid"].apply( lambda programid: self.ztf_program_id_to_stream_id[programid] ) # post photometry by stream_id for stream_id in set(df_photometry.stream_id.unique()): stream_id_mask = df_photometry.stream_id == int(stream_id) photometry = { "obj_id": alert["objectId"], "stream_ids": [int(stream_id)], "instrument_id": self.instrument_id, "mjd": df_photometry.loc[stream_id_mask, "mjd"].tolist(), "flux": df_photometry.loc[stream_id_mask, "flux"].tolist(), "fluxerr": df_photometry.loc[stream_id_mask, "fluxerr"].tolist(), "zp": df_photometry.loc[stream_id_mask, "zp"].tolist(), "magsys": df_photometry.loc[stream_id_mask, "zpsys"].tolist(), "filter": df_photometry.loc[stream_id_mask, "ztf_filter"].tolist(), "ra": df_photometry.loc[stream_id_mask, "ra"].tolist(), "dec": df_photometry.loc[stream_id_mask, "dec"].tolist(), } if (len(photometry.get("flux", ())) > 0) or ( len(photometry.get("fluxerr", ())) > 0 ): with timer( f"Posting photometry of {alert["objectId"]} {alert["candid"]}, " f"stream_id={stream_id} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("PUT", "/api/photometry", photometry) if response.json()["status"] == "success": log( f"Posted {alert["objectId"]} photometry stream_id={stream_id} to SkyPortal" ) else: log( f"Failed to post {alert["objectId"]} photometry stream_id={stream_id} to SkyPortal" ) log(response.json()) def alert_sentinel_skyportal(self, alert, prv_candidates, passed_filters): """ Post alerts to SkyPortal, if need be. Logic: - check if candidate/source exist on SP - if candidate does not exist and len(passed_filters) > 0 - post metadata with all filter_ids in single call to /api/candidates - post full light curve with all group_ids in single call to /api/photometry - post thumbnails - if candidate exists: - get filter_ids of saved candidate from SP - post to /api/candidates with new_filter_ids, if any - post alert light curve in single PUT call to /api/photometry specifying stream_ids - if source exists: - get groups and check stream access - decide which points to post to what groups based on permissions - post alert light curve in single PUT call to /api/photometry specifying stream_ids :param alert: ZTF_alert with a stripped-off prv_candidates section :param prv_candidates: could be plain prv_candidates section of an alert, or extended alert history :param passed_filters: list of filters that alert passed, with their output :return: """ # check if candidate/source exist in SP: with timer( f"Checking if {alert["objectId"]} is Candidate in SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "HEAD", f"/api/candidates/{alert["objectId"]}" ) is_candidate = response.status_code == 200 if self.verbose > 1: log( f"{alert["objectId"]} {"is" if is_candidate else "is not"} Candidate in SkyPortal" ) with timer( f"Checking if {alert["objectId"]} is Source in SkyPortal", self.verbose > 1 ): response = self.api_skyportal("HEAD", f"/api/sources/{alert["objectId"]}") is_source = response.status_code == 200 if self.verbose > 1: log( f"{alert["objectId"]} {"is" if is_source else "is not"} Source in SkyPortal" ) # obj does not exit in SP: if (not is_candidate) and (not is_source): # passed at least one filter? if len(passed_filters) > 0: # post candidate filter_ids = [f.get("filter_id") for f in passed_filters] self.alert_post_candidate(alert, filter_ids) # post annotations self.alert_post_annotations(alert, passed_filters) # post full light curve try: alert["prv_candidates"] = list( self.mongo.db[self.collection_alerts_aux].find( {"_id": alert["objectId"]}, {"prv_candidates": 1}, limit=1 ) )[0]["prv_candidates"] except Exception as e: # this should never happen, but just in case log(e) alert["prv_candidates"] = prv_candidates self.alert_put_photometry(alert) # post thumbnails self.alert_post_thumbnails(alert) # post source if autosave=True autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) # obj exists in SP: else: if len(passed_filters) > 0: filter_ids = [f.get("filter_id") for f in passed_filters] # post candidate with new filter ids self.alert_post_candidate(alert, filter_ids) # put annotations self.alert_put_annotations(alert, passed_filters) # already saved as a source? if is_source: # get info on the corresponding groups: with timer( f"Getting source groups info on {alert["objectId"]} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "GET", f"/api/sources/{alert["objectId"]}/groups" ) if response.json()["status"] == "success": existing_groups = response.json()["data"] existing_group_ids = [g["id"] for g in existing_groups] # post source if autosave=True and not already saved autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) and (f.get("group_id") not in existing_group_ids) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) else: log(f"Failed to get source groups info on {alert["objectId"]}") else: # post source if autosave=True and not is_source autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) # post alert photometry in single call to /api/photometry alert["prv_candidates"] = prv_candidates self.alert_put_photometry(alert) class WorkerInitializer(dask.distributed.WorkerPlugin): def __init__(self, *args, **kwargs): self.alert_worker = None def setup(self, worker: dask.distributed.Worker): self.alert_worker = AlertWorker() def topic_listener( topic, bootstrap_servers: str, offset_reset: str = "earliest", group: str = None, test: bool = False, ): """ Listen to a topic :param topic: :param bootstrap_servers: :param offset_reset: :param group: :param test: when testing, terminate once reached end of partition :return: """ # Configure dask client dask_client = dask.distributed.Client( address=f"{config["dask"]["host"]}:{config["dask"]["scheduler_port"]}" ) # init each worker with AlertWorker instance worker_initializer = WorkerInitializer() dask_client.register_worker_plugin(worker_initializer, name="worker-init") # Configure consumer connection to Kafka broker conf = { "bootstrap.servers": bootstrap_servers, "default.topic.config": {"auto.offset.reset": offset_reset}, } if group is not None: conf["group.id"] = group else: conf["group.id"] = os.environ.get("HOSTNAME", "kowalski") # make it unique: conf[ "group.id" ] = f"{conf["group.id"]}_{datetime.datetime.utcnow().strftime("%Y-%m-%d_%H:%M:%S.%f")}" # Start alert stream consumer stream_reader = AlertConsumer(topic, dask_client, **conf) while True: try: # poll! stream_reader.poll() except EopError as e: # Write when reaching end of partition log(e.message) if test: # when testing, terminate once reached end of partition: sys.exit() except IndexError: log("Data cannot be decoded\n") except UnicodeDecodeError: log("Unexpected data format received\n") except KeyboardInterrupt: log("Aborted by user\n") sys.exit() except Exception as e: log(str(e)) _err = traceback.format_exc() log(_err) sys.exit() def watchdog(obs_date: str = None, test: bool = False): """ Watchdog for topic listeners :param obs_date: observing date: YYYYMMDD :param test: test mode :return: """ init_db_sync(config=config, verbose=True) topics_on_watch = dict() while True: try: # get kafka topic names with kafka-topics command if not test: # Production Kafka stream at IPAC kafka_cmd = [ os.path.join(config["path"]["kafka"], "bin", "kafka-topics.sh"), "--zookeeper", config["kafka"]["zookeeper"], "-list", ] else: # Local test stream kafka_cmd = [ os.path.join(config["path"]["kafka"], "bin", "kafka-topics.sh"), "--zookeeper", config["kafka"]["zookeeper.test"], "-list", ] topics = ( subprocess.run(kafka_cmd, stdout=subprocess.PIPE) .stdout.decode("utf-8") .split("\n")[:-1] ) if obs_date is None: datestr = datetime.datetime.utcnow().strftime("%Y%m%d") else: datestr = obs_date # as of 20180403, the naming convention is ztf_%Y%m%d_programidN # exclude ZUDS, ingest separately topics_tonight = [ t for t in topics if (datestr in t) and ("programid" in t) and ("zuds" not in t) ] log(f"Topics: {topics_tonight}") for t in topics_tonight: if t not in topics_on_watch: log(f"Starting listener thread for {t}") offset_reset = config["kafka"]["default.topic.config"][ "auto.offset.reset" ] if not test: bootstrap_servers = config["kafka"]["bootstrap.servers"] else: bootstrap_servers = config["kafka"]["bootstrap.test.servers"] group = config["kafka"]["group"] topics_on_watch[t] = multiprocessing.Process( target=topic_listener, args=(t, bootstrap_servers, offset_reset, group, test), ) topics_on_watch[t].daemon = True topics_on_watch[t].start() else: log(f"Performing thread health check for {t}") try: if not topics_on_watch[t].is_alive(): log(f"Thread {t} died, removing") # topics_on_watch[t].terminate() topics_on_watch.pop(t, None) else: log(f"Thread {t} appears normal") except Exception as _e: log(f"Failed to perform health check: {_e}") pass if test: time.sleep(120) # when testing, wait for topic listeners to pull all the data, then break # fixme: do this more gracefully for t in topics_on_watch: topics_on_watch[t].kill() break except Exception as e: log(str(e)) _err = traceback.format_exc() log(str(_err)) time.sleep(60) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Kowalski's ZTF Alert Broker") parser.add_argument("--obsdate", help="observing date YYYYMMDD") parser.add_argument("--test", help="listen to the test stream", action="store_true") args = parser.parse_args() watchdog(obs_date=args.obsdate, test=args.test)

import argparse from ast import literal_eval from astropy.io import fits from astropy.visualization import ( AsymmetricPercentileInterval, LinearStretch, LogStretch, ImageNormalize, ) import base64 from bson.json_util import loads import confluent_kafka from copy import deepcopy import dask.distributed import datetime import fastavro import gzip import io import matplotlib.pyplot as plt import multiprocessing import numpy as np import os import pandas as pd import pathlib import requests from requests.packages.urllib3.util.retry import Retry import subprocess import sys import tensorflow as tf from tensorflow.keras.models import load_model import threading import time import traceback from typing import Mapping, Optional, Sequence from utils import ( deg2dms, deg2hms, great_circle_distance, in_ellipse, init_db_sync, load_config, log, memoize, Mongo, radec2lb, time_stamp, timer, TimeoutHTTPAdapter, ZTFAlert, ) tf.config.optimizer.set_jit(True) """ load config and secrets """ config = load_config(config_file="config.yaml")["kowalski"] def read_schema_data(bytes_io): """ Read data that already has an Avro schema. :param bytes_io: `_io.BytesIO` Data to be decoded. :return: `dict` Decoded data. """ bytes_io.seek(0) message = fastavro.reader(bytes_io) return message class EopError(Exception): """ Exception raised when reaching end of a Kafka topic partition. """ def __init__(self, msg): """ :param msg: The Kafka message result from consumer.poll() """ message = ( f"{time_stamp()}: topic:{msg.topic()}, partition:{msg.partition()}, " f"status:end, offset:{msg.offset()}, key:{str(msg.key())}\n" ) self.message = message def __str__(self): return self.message def make_photometry(alert: dict, jd_start: float = None): """ Make a de-duplicated pandas.DataFrame with photometry of alert['objectId'] :param alert: ZTF alert packet/dict :param jd_start: :return: """ alert = deepcopy(alert) df_candidate = pd.DataFrame(alert["candidate"], index=[0]) df_prv_candidates = pd.DataFrame(alert["prv_candidates"]) df_light_curve = pd.concat( [df_candidate, df_prv_candidates], ignore_index=True, sort=False ) ztf_filters = {1: "ztfg", 2: "ztfr", 3: "ztfi"} df_light_curve["ztf_filter"] = df_light_curve["fid"].apply(lambda x: ztf_filters[x]) df_light_curve["magsys"] = "ab" df_light_curve["mjd"] = df_light_curve["jd"] - 2400000.5 df_light_curve["mjd"] = df_light_curve["mjd"].apply(lambda x: np.float64(x)) df_light_curve["magpsf"] = df_light_curve["magpsf"].apply(lambda x: np.float32(x)) df_light_curve["sigmapsf"] = df_light_curve["sigmapsf"].apply( lambda x: np.float32(x) ) df_light_curve = ( df_light_curve.drop_duplicates(subset=["mjd", "magpsf"]) .reset_index(drop=True) .sort_values(by=["mjd"]) ) # filter out bad data: mask_good_diffmaglim = df_light_curve["diffmaglim"] > 0 df_light_curve = df_light_curve.loc[mask_good_diffmaglim] # convert from mag to flux # step 1: calculate the coefficient that determines whether the # flux should be negative or positive coeff = df_light_curve["isdiffpos"].apply( lambda x: 1.0 if x in [True, 1, "y", "Y", "t", "1"] else -1.0 ) # step 2: calculate the flux normalized to an arbitrary AB zeropoint of # 23.9 (results in flux in uJy) df_light_curve["flux"] = coeff * 10 ** (-0.4 * (df_light_curve["magpsf"] - 23.9)) # step 3: separate detections from non detections detected = np.isfinite(df_light_curve["magpsf"]) undetected = ~detected # step 4: calculate the flux error df_light_curve["fluxerr"] = None # initialize the column # step 4a: calculate fluxerr for detections using sigmapsf df_light_curve.loc[detected, "fluxerr"] = np.abs( df_light_curve.loc[detected, "sigmapsf"] * df_light_curve.loc[detected, "flux"] * np.log(10) / 2.5 ) # step 4b: calculate fluxerr for non detections using diffmaglim df_light_curve.loc[undetected, "fluxerr"] = ( 10 ** (-0.4 * (df_light_curve.loc[undetected, "diffmaglim"] - 23.9)) / 5.0 ) # as diffmaglim is the 5-sigma depth # step 5: set the zeropoint and magnitude system df_light_curve["zp"] = 23.9 df_light_curve["zpsys"] = "ab" # only "new" photometry requested? if jd_start is not None: w_after_jd = df_light_curve["jd"] > jd_start df_light_curve = df_light_curve.loc[w_after_jd] return df_light_curve def make_thumbnail(alert, ttype: str, ztftype: str): """ Convert lossless FITS cutouts from ZTF alerts into PNGs :param alert: ZTF alert packet/dict :param ttype: <new|ref|sub> :param ztftype: <Science|Template|Difference> :return: """ alert = deepcopy(alert) cutout_data = alert[f"cutout{ztftype}"]["stampData"] with gzip.open(io.BytesIO(cutout_data), "rb") as f: with fits.open(io.BytesIO(f.read())) as hdu: # header = hdu[0].header data_flipped_y = np.flipud(hdu[0].data) # fixme: png, switch to fits eventually buff = io.BytesIO() plt.close("all") fig = plt.figure() fig.set_size_inches(4, 4, forward=False) ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0]) ax.set_axis_off() fig.add_axes(ax) # replace nans with median: img = np.array(data_flipped_y) # replace dubiously large values xl = np.greater(np.abs(img), 1e20, where=~np.isnan(img)) if img[xl].any(): img[xl] = np.nan if np.isnan(img).any(): median = float(np.nanmean(img.flatten())) img = np.nan_to_num(img, nan=median) norm = ImageNormalize( img, stretch=LinearStretch() if ztftype == "Difference" else LogStretch() ) img_norm = norm(img) normalizer = AsymmetricPercentileInterval(lower_percentile=1, upper_percentile=100) vmin, vmax = normalizer.get_limits(img_norm) ax.imshow(img_norm, cmap="bone", origin="lower", vmin=vmin, vmax=vmax) plt.savefig(buff, dpi=42) buff.seek(0) plt.close("all") thumb = { "obj_id": alert["objectId"], "data": base64.b64encode(buff.read()).decode("utf-8"), "ttype": ttype, } return thumb """ Alert filters """ def make_triplet(alert, to_tpu: bool = False): """ Make an L2-normalized cutout triplet out of a ZTF alert :param alert: :param to_tpu: :return: """ cutout_dict = dict() for cutout in ("science", "template", "difference"): cutout_data = alert[f"cutout{cutout.capitalize()}"]["stampData"] # unzip with gzip.open(io.BytesIO(cutout_data), "rb") as f: with fits.open(io.BytesIO(f.read())) as hdu: data = hdu[0].data # replace nans with zeros cutout_dict[cutout] = np.nan_to_num(data) # L2-normalize cutout_dict[cutout] /= np.linalg.norm(cutout_dict[cutout]) # pad to 63x63 if smaller shape = cutout_dict[cutout].shape if shape != (63, 63): # print(f'Shape of {candid}/{cutout}: {shape}, padding to (63, 63)') cutout_dict[cutout] = np.pad( cutout_dict[cutout], [(0, 63 - shape[0]), (0, 63 - shape[1])], mode="constant", constant_values=1e-9, ) triplet = np.zeros((63, 63, 3)) triplet[:, :, 0] = cutout_dict["science"] triplet[:, :, 1] = cutout_dict["template"] triplet[:, :, 2] = cutout_dict["difference"] if to_tpu: # Edge TPUs require additional processing triplet = np.rint(triplet * 128 + 128).astype(np.uint8).flatten() return triplet def alert_filter__ml(alert, ml_models: dict = None) -> dict: """Execute ML models on a ZTF alert :param alert: :param ml_models: :return: """ scores = dict() if ml_models is not None and len(ml_models) > 0: try: with timer("ZTFAlert(alert)"): ztf_alert = ZTFAlert(alert) with timer("Prepping features"): features = np.expand_dims(ztf_alert.data["features"], axis=[0, -1]) triplet = np.expand_dims(ztf_alert.data["triplet"], axis=[0]) # braai if "braai" in ml_models.keys(): with timer("braai"): braai = ml_models["braai"]["model"].predict(x=triplet)[0] scores["braai"] = float(braai) scores["braai_version"] = ml_models["braai"]["version"] # acai for model_name in ("acai_h", "acai_v", "acai_o", "acai_n", "acai_b"): if model_name in ml_models.keys(): with timer(model_name): score = ml_models[model_name]["model"].predict( [features, triplet] )[0] scores[model_name] = float(score) scores[f"{model_name}_version"] = ml_models[model_name][ "version" ] except Exception as e: log(str(e)) return scores # cone search radius: cone_search_radius = float(config["database"]["xmatch"]["cone_search_radius"]) # convert to rad: if config["database"]["xmatch"]["cone_search_unit"] == "arcsec": cone_search_radius *= np.pi / 180.0 / 3600.0 elif config["database"]["xmatch"]["cone_search_unit"] == "arcmin": cone_search_radius *= np.pi / 180.0 / 60.0 elif config["database"]["xmatch"]["cone_search_unit"] == "deg": cone_search_radius *= np.pi / 180.0 elif config["database"]["xmatch"]["cone_search_unit"] == "rad": cone_search_radius *= 1 else: raise Exception("Unknown cone search unit. Must be in [deg, rad, arcsec, arcmin]") def alert_filter__xmatch(database, alert) -> dict: """ Cross-match alerts """ xmatches = dict() try: ra_geojson = float(alert["candidate"]["ra"]) # geojson-friendly ra: ra_geojson -= 180.0 dec_geojson = float(alert["candidate"]["dec"]) """ catalogs """ for catalog in config["database"]["xmatch"]["catalogs"]: catalog_filter = config["database"]["xmatch"]["catalogs"][catalog]["filter"] catalog_projection = config["database"]["xmatch"]["catalogs"][catalog][ "projection" ] object_position_query = dict() object_position_query["coordinates.radec_geojson"] = { "$geoWithin": { "$centerSphere": [[ra_geojson, dec_geojson], cone_search_radius] } } s = database[catalog].find( {**object_position_query, **catalog_filter}, {**catalog_projection} ) xmatches[catalog] = list(s) except Exception as e: log(str(e)) return xmatches # cone search radius in deg: cone_search_radius_clu = 3.0 # convert deg to rad: cone_search_radius_clu *= np.pi / 180.0 def alert_filter__xmatch_clu( database, alert, size_margin=3, clu_version="CLU_20190625" ) -> dict: """ Run cross-match with the CLU catalog :param database: :param alert: :param size_margin: multiply galaxy size by this much before looking for a match :param clu_version: CLU catalog version :return: """ xmatches = dict() try: ra = float(alert["candidate"]["ra"]) dec = float(alert["candidate"]["dec"]) # geojson-friendly ra: ra_geojson = float(alert["candidate"]["ra"]) - 180.0 dec_geojson = dec catalog_filter = {} catalog_projection = { "_id": 1, "name": 1, "ra": 1, "dec": 1, "a": 1, "b2a": 1, "pa": 1, "z": 1, "sfr_fuv": 1, "mstar": 1, "sfr_ha": 1, "coordinates.radec_str": 1, } # first do a coarse search of everything that is around object_position_query = dict() object_position_query["coordinates.radec_geojson"] = { "$geoWithin": { "$centerSphere": [[ra_geojson, dec_geojson], cone_search_radius_clu] } } galaxies = list( database[clu_version].find( {**object_position_query, **catalog_filter}, {**catalog_projection} ) ) # these guys are very big, so check them separately M31 = { "_id": 596900, "name": "PGC2557", "ra": 10.6847, "dec": 41.26901, "a": 6.35156, "b2a": 0.32, "pa": 35.0, "z": -0.00100100006, "sfr_fuv": None, "mstar": 253816876.412914, "sfr_ha": 0, "coordinates": {"radec_str": ["00:42:44.3503", "41:16:08.634"]}, } M33 = { "_id": 597543, "name": "PGC5818", "ra": 23.46204, "dec": 30.66022, "a": 2.35983, "b2a": 0.59, "pa": 23.0, "z": -0.000597000006, "sfr_fuv": None, "mstar": 4502777.420493, "sfr_ha": 0, "coordinates": {"radec_str": ["01:33:50.8900", "30:39:36.800"]}, } # do elliptical matches matches = [] for galaxy in galaxies + [M31, M33]: alpha1, delta01 = galaxy["ra"], galaxy["dec"] redshift = galaxy["z"] # By default, set the cross-match radius to 50 kpc at the redshift of the host galaxy cm_radius = 50.0 * (0.05 / redshift) / 3600 if redshift < 0.01: # for nearby galaxies and galaxies with negative redshifts, do a 5 arc-minute cross-match # (cross-match radius would otherwise get un-physically large for nearby galaxies) cm_radius = 300.0 / 3600 in_galaxy = in_ellipse(ra, dec, alpha1, delta01, cm_radius, 1, 0) if in_galaxy: match = galaxy distance_arcsec = round( great_circle_distance(ra, dec, alpha1, delta01) * 3600, 2 ) # also add a physical distance parameter for redshifts in the Hubble flow if redshift > 0.005: distance_kpc = round( great_circle_distance(ra, dec, alpha1, delta01) * 3600 * (redshift / 0.05), 2, ) else: distance_kpc = -1 match["coordinates"]["distance_arcsec"] = distance_arcsec match["coordinates"]["distance_kpc"] = distance_kpc matches.append(match) xmatches[clu_version] = matches except Exception as e: log(str(e)) return xmatches def alert_filter__user_defined( database, filter_templates, alert, catalog: str = "ZTF_alerts", max_time_ms: int = 500, ) -> list: """ Evaluate user-defined filters :param database: :param filter_templates: :param alert: :param catalog: :param max_time_ms: :return: """ passed_filters = [] for filter_template in filter_templates: try: _filter = deepcopy(filter_template) # match candid _filter["pipeline"][0]["$match"]["candid"] = alert["candid"] filtered_data = list( database[catalog].aggregate( _filter["pipeline"], allowDiskUse=False, maxTimeMS=max_time_ms ) ) # passed filter? then len(passed_filter) must be = 1 if len(filtered_data) == 1: log( f'{alert["objectId"]} {alert["candid"]} passed filter {_filter["fid"]}' ) passed_filters.append( { "group_id": _filter["group_id"], "filter_id": _filter["filter_id"], "group_name": _filter["group_name"], "filter_name": _filter["filter_name"], "fid": _filter["fid"], "permissions": _filter["permissions"], "autosave": _filter["autosave"], "update_annotations": _filter["update_annotations"], "data": filtered_data[0], } ) except Exception as e: log( f'Filter {filter_template["fid"]} execution failed on alert {alert["candid"]}: {e}' ) continue return passed_filters def process_alert(record, topic): """Alert brokering task run by dask.distributed workers :param record: decoded alert from IPAC's Kafka stream :param topic: Kafka stream topic name for bookkeeping :return: """ candid = record["candid"] objectId = record["objectId"] # get worker running current task worker = dask.distributed.get_worker() alert_worker = worker.plugins["worker-init"].alert_worker log(f"{topic} {objectId} {candid} {worker.address}") # return if this alert packet has already been processed and ingested into collection_alerts: if ( alert_worker.mongo.db[alert_worker.collection_alerts].count_documents( {"candid": candid}, limit=1 ) == 1 ): return # candid not in db, ingest decoded avro packet into db # todo: ?? restructure alerts even further? # move cutouts to ZTF_alerts_cutouts? reduce the main db size for performance # group by objectId similar to prv_candidates?? maybe this is too much with timer(f"Mongification of {objectId} {candid}", alert_worker.verbose > 1): alert, prv_candidates = alert_worker.alert_mongify(record) # ML models: with timer(f"MLing of {objectId} {candid}", alert_worker.verbose > 1): scores = alert_filter__ml(record, ml_models=alert_worker.ml_models) alert["classifications"] = scores with timer(f"Ingesting {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.insert_one( collection=alert_worker.collection_alerts, document=alert ) # prv_candidates: pop nulls - save space prv_candidates = [ {kk: vv for kk, vv in prv_candidate.items() if vv is not None} for prv_candidate in prv_candidates ] # cross-match with external catalogs if objectId not in collection_alerts_aux: if ( alert_worker.mongo.db[alert_worker.collection_alerts_aux].count_documents( {"_id": objectId}, limit=1 ) == 0 ): with timer(f"Cross-match of {objectId} {candid}", alert_worker.verbose > 1): xmatches = alert_filter__xmatch(alert_worker.mongo.db, alert) # CLU cross-match: with timer(f"CLU cross-match {objectId} {candid}", alert_worker.verbose > 1): xmatches = { **xmatches, **alert_filter__xmatch_clu(alert_worker.mongo.db, alert), } alert_aux = { "_id": objectId, "cross_matches": xmatches, "prv_candidates": prv_candidates, } with timer(f"Aux ingesting {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.insert_one( collection=alert_worker.collection_alerts_aux, document=alert_aux ) else: with timer(f"Aux updating of {objectId} {candid}", alert_worker.verbose > 1): alert_worker.mongo.db[alert_worker.collection_alerts_aux].update_one( {"_id": objectId}, {"$addToSet": {"prv_candidates": {"$each": prv_candidates}}}, upsert=True, ) if config["misc"]["broker"]: # execute user-defined alert filters with timer(f"Filtering of {objectId} {candid}", alert_worker.verbose > 1): passed_filters = alert_filter__user_defined( alert_worker.mongo.db, alert_worker.filter_templates, alert ) if alert_worker.verbose > 1: log(f"{objectId} {candid} number of filters passed: {len(passed_filters)}") # post to SkyPortal alert_worker.alert_sentinel_skyportal(alert, prv_candidates, passed_filters) # clean up after thyself del record, alert, prv_candidates class AlertConsumer: """ Creates an alert stream Kafka consumer for a given topic. """ def __init__(self, topic, dask_client, **kwargs): self.verbose = kwargs.get("verbose", 2) self.dask_client = dask_client # keep track of disconnected partitions self.num_disconnected_partitions = 0 self.topic = topic def error_cb(err, _self=self): log(f"error_cb --------> {err}") # print(err.code()) if err.code() == -195: _self.num_disconnected_partitions += 1 if _self.num_disconnected_partitions == _self.num_partitions: log("All partitions got disconnected, killing thread") sys.exit() else: log( f"{_self.topic}: disconnected from partition. total: {_self.num_disconnected_partitions}" ) # 'error_cb': error_cb kwargs["error_cb"] = error_cb self.consumer = confluent_kafka.Consumer(**kwargs) self.num_partitions = 0 def on_assign(consumer, partitions, _self=self): # force-reset offsets when subscribing to a topic: for part in partitions: # -2 stands for beginning and -1 for end part.offset = -2 # keep number of partitions. # when reaching end of last partition, kill thread and start from beginning _self.num_partitions += 1 log(consumer.get_watermark_offsets(part)) self.consumer.subscribe([topic], on_assign=on_assign) # set up own mongo client self.collection_alerts = config["database"]["collections"]["alerts_ztf"] self.mongo = Mongo( host=config["database"]["host"], port=config["database"]["port"], replica_set=config["database"]["replica_set"], username=config["database"]["username"], password=config["database"]["password"], db=config["database"]["db"], verbose=self.verbose, ) # create indexes if config["database"]["build_indexes"]: for index in config["database"]["indexes"][self.collection_alerts]: try: ind = [tuple(ii) for ii in index["fields"]] self.mongo.db[self.collection_alerts].create_index( keys=ind, name=index["name"], background=True, unique=index["unique"], ) except Exception as e: log(e) @staticmethod def decode_message(msg): """ Decode Avro message according to a schema. :param msg: The Kafka message result from consumer.poll() :return: """ message = msg.value() decoded_msg = message try: bytes_io = io.BytesIO(message) decoded_msg = read_schema_data(bytes_io) except AssertionError: decoded_msg = None except IndexError: literal_msg = literal_eval( str(message, encoding="utf-8") ) # works to give bytes bytes_io = io.BytesIO(literal_msg) # works to give <class '_io.BytesIO'> decoded_msg = read_schema_data(bytes_io) # yields reader except Exception: decoded_msg = message finally: return decoded_msg def poll(self): """ Polls Kafka broker to consume a topic. :return: """ msg = self.consumer.poll() if msg is None: log("Caught error: msg is None") if msg.error(): log(f"Caught error: {msg.error()}") raise EopError(msg) elif msg is not None: try: # decode avro packet with timer("Decoding alert", self.verbose > 1): msg_decoded = self.decode_message(msg) for record in msg_decoded: # submit only unprocessed alerts: if ( self.mongo.db[self.collection_alerts].count_documents( {"candid": record["candid"]}, limit=1 ) == 0 ): with timer( f"Submitting alert {record['objectId']} {record['candid']} for processing", self.verbose > 1, ): future = self.dask_client.submit( process_alert, record, self.topic, pure=True ) dask.distributed.fire_and_forget(future) future.release() del future except Exception as e: log(e) _err = traceback.format_exc() log(_err) class AlertWorker: """Tools to handle alert processing: database ingestion, filtering, ml'ing, cross-matches, reporting to SP""" def __init__(self, **kwargs): self.verbose = kwargs.get("verbose", 2) self.config = config # Kowalski version path_version_file = pathlib.Path(__file__).parent.absolute() / "version.txt" version = f"v{self.config['server']['version']}" if path_version_file.exists(): with open( pathlib.Path(__file__).parent.absolute() / "version.txt", "r" ) as version_file: version = version_file.read().strip() # MongoDB collections to store the alerts: self.collection_alerts = self.config["database"]["collections"]["alerts_ztf"] self.collection_alerts_aux = self.config["database"]["collections"][ "alerts_ztf_aux" ] self.collection_alerts_filter = self.config["database"]["collections"][ "alerts_ztf_filter" ] self.mongo = Mongo( host=config["database"]["host"], port=config["database"]["port"], replica_set=config["database"]["replica_set"], username=config["database"]["username"], password=config["database"]["password"], db=config["database"]["db"], verbose=self.verbose, ) # ML models self.ml_models = dict() for model in config["ml_models"]: try: model_version = config["ml_models"][model]["version"] # todo: allow other formats such as SavedModel model_filepath = os.path.join( config["path"]["ml_models"], f"{model}.{model_version}.h5" ) self.ml_models[model] = { "model": load_model(model_filepath), "version": model_version, } except Exception as e: log(f"Error loading ML model {model}: {str(e)}") _err = traceback.format_exc() log(_err) continue # talking to SkyPortal? if not config["misc"]["broker"]: return # session to talk to SkyPortal self.session = requests.Session() self.session_headers = { "Authorization": f"token {config['skyportal']['token']}", "User-Agent": f"Kowalski {version}", } retries = Retry( total=5, backoff_factor=2, status_forcelist=[405, 429, 500, 502, 503, 504], method_whitelist=["HEAD", "GET", "PUT", "POST", "PATCH"], ) adapter = TimeoutHTTPAdapter(timeout=5, max_retries=retries) self.session.mount("https://", adapter) self.session.mount("http://", adapter) # get ZTF instrument id self.instrument_id = 1 with timer("Getting ZTF instrument_id from SkyPortal", self.verbose > 1): response = self.api_skyportal("GET", "/api/instrument", {"name": "ZTF"}) if response.json()["status"] == "success" and len(response.json()["data"]) > 0: self.instrument_id = response.json()["data"][0]["id"] log(f"Got ZTF instrument_id from SkyPortal: {self.instrument_id}") else: log("Failed to get ZTF instrument_id from SkyPortal") raise ValueError("Failed to get ZTF instrument_id from SkyPortal") # get ZTF alert stream ids to program ids mapping self.ztf_program_id_to_stream_id = dict() with timer("Getting ZTF alert stream ids from SkyPortal", self.verbose > 1): response = self.api_skyportal("GET", "/api/streams") if response.json()["status"] == "success" and len(response.json()["data"]) > 0: for stream in response.json()["data"]: if stream.get("name") == "ZTF Public": self.ztf_program_id_to_stream_id[1] = stream["id"] if stream.get("name") == "ZTF Public+Partnership": self.ztf_program_id_to_stream_id[2] = stream["id"] if stream.get("name") == "ZTF Public+Partnership+Caltech": # programid=0 is engineering data self.ztf_program_id_to_stream_id[0] = stream["id"] self.ztf_program_id_to_stream_id[3] = stream["id"] if len(self.ztf_program_id_to_stream_id) != 4: log("Failed to map ZTF alert stream ids from SkyPortal to program ids") raise ValueError( "Failed to map ZTF alert stream ids from SkyPortal to program ids" ) log( f"Got ZTF program id to SP stream id mapping: {self.ztf_program_id_to_stream_id}" ) else: log("Failed to get ZTF alert stream ids from SkyPortal") raise ValueError("Failed to get ZTF alert stream ids from SkyPortal") # filter pipeline upstream: select current alert, ditch cutouts, and merge with aux data # including archival photometry and cross-matches: self.filter_pipeline_upstream = config["database"]["filters"][ self.collection_alerts ] log("Upstream filtering pipeline:") log(self.filter_pipeline_upstream) # load *active* user-defined alert filter templates and pre-populate them active_filters = self.get_active_filters() self.filter_templates = self.make_filter_templates(active_filters) # set up watchdog for periodic refresh of the filter templates, in case those change self.filter_monitor = threading.Thread(target=self.reload_filters) self.filter_monitor.start() log("Loaded user-defined filters:") log(self.filter_templates) def api_skyportal(self, method: str, endpoint: str, data: Optional[Mapping] = None): """Make an API call to a SkyPortal instance :param method: :param endpoint: :param data: :return: """ method = method.lower() methods = { "head": self.session.head, "get": self.session.get, "post": self.session.post, "put": self.session.put, "patch": self.session.patch, "delete": self.session.delete, } if endpoint is None: raise ValueError("Endpoint not specified") if method not in ["head", "get", "post", "put", "patch", "delete"]: raise ValueError(f"Unsupported method: {method}") if method == "get": response = methods[method]( f"{config['skyportal']['protocol']}://" f"{config['skyportal']['host']}:{config['skyportal']['port']}" f"{endpoint}", params=data, headers=self.session_headers, ) else: response = methods[method]( f"{config['skyportal']['protocol']}://" f"{config['skyportal']['host']}:{config['skyportal']['port']}" f"{endpoint}", json=data, headers=self.session_headers, ) return response @memoize def api_skyportal_get_group(self, group_id): return self.api_skyportal( "GET", f"/api/groups/{group_id}?includeGroupUsers=False" ) def get_active_filters(self): """ Fetch user-defined filters from own db marked as active :return: """ # todo: query SP to make sure the filters still exist there and we're not out of sync; # clean up if necessary return list( self.mongo.db[config["database"]["collections"]["filters"]].aggregate( [ { "$match": { "catalog": config["database"]["collections"]["alerts_ztf"], "active": True, } }, { "$project": { "group_id": 1, "filter_id": 1, "permissions": 1, "autosave": 1, "update_annotations": 1, "fv": { "$arrayElemAt": [ { "$filter": { "input": "$fv", "as": "fvv", "cond": { "$eq": ["$$fvv.fid", "$active_fid"] }, } }, 0, ] }, } }, ] ) ) def make_filter_templates(self, active_filters: Sequence): """ Make filter templates by adding metadata, prepending upstream aggregation stages and setting permissions :param active_filters: :return: """ filter_templates = [] for active_filter in active_filters: try: # collect additional info from SkyPortal with timer( f"Getting info on group id={active_filter['group_id']} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal_get_group(active_filter["group_id"]) if self.verbose > 1: log(response.json()) if response.json()["status"] == "success": group_name = ( response.json()["data"]["nickname"] if response.json()["data"]["nickname"] is not None else response.json()["data"]["name"] ) filter_name = [ filtr["name"] for filtr in response.json()["data"]["filters"] if filtr["id"] == active_filter["filter_id"] ][0] else: log( f"Failed to get info on group id={active_filter['group_id']} from SkyPortal" ) group_name, filter_name = None, None # raise ValueError(f"Failed to get info on group id={active_filter['group_id']} from SkyPortal") log(f"Group name: {group_name}, filter name: {filter_name}") # prepend upstream aggregation stages: pipeline = deepcopy(self.filter_pipeline_upstream) + loads( active_filter["fv"]["pipeline"] ) # set permissions pipeline[0]["$match"]["candidate.programid"]["$in"] = active_filter[ "permissions" ] pipeline[3]["$project"]["prv_candidates"]["$filter"]["cond"]["$and"][0][ "$in" ][1] = active_filter["permissions"] filter_template = { "group_id": active_filter["group_id"], "filter_id": active_filter["filter_id"], "group_name": group_name, "filter_name": filter_name, "fid": active_filter["fv"]["fid"], "permissions": active_filter["permissions"], "autosave": active_filter["autosave"], "update_annotations": active_filter["update_annotations"], "pipeline": deepcopy(pipeline), } filter_templates.append(filter_template) except Exception as e: log( "Failed to generate filter template for " f"group_id={active_filter['group_id']} filter_id={active_filter['filter_id']}: {e}" ) continue return filter_templates def reload_filters(self): """ Helper function to periodically reload filters from SkyPortal :return: """ while True: time.sleep(60 * 5) active_filters = self.get_active_filters() self.filter_templates = self.make_filter_templates(active_filters) @staticmethod def alert_mongify(alert: Mapping): """ Prepare a raw ZTF alert for ingestion into MongoDB: - add a placeholder for ML-based classifications - add coordinates for 2D spherical indexing and compute Galactic coordinates - cut off the prv_candidates section :param alert: :return: """ doc = dict(alert) # let mongo create a unique _id # placeholders for classifications doc["classifications"] = dict() # GeoJSON for 2D indexing doc["coordinates"] = {} _ra = doc["candidate"]["ra"] _dec = doc["candidate"]["dec"] # string format: H:M:S, D:M:S _radec_str = [deg2hms(_ra), deg2dms(_dec)] doc["coordinates"]["radec_str"] = _radec_str # for GeoJSON, must be lon:[-180, 180], lat:[-90, 90] (i.e. in deg) _radec_geojson = [_ra - 180.0, _dec] doc["coordinates"]["radec_geojson"] = { "type": "Point", "coordinates": _radec_geojson, } # Galactic coordinates l and b l, b = radec2lb(doc["candidate"]["ra"], doc["candidate"]["dec"]) doc["coordinates"]["l"] = l doc["coordinates"]["b"] = b prv_candidates = deepcopy(doc["prv_candidates"]) doc.pop("prv_candidates", None) if prv_candidates is None: prv_candidates = [] return doc, prv_candidates def alert_post_candidate(self, alert: Mapping, filter_ids: Sequence): """ Post a ZTF alert as a candidate for filters on SkyPortal :param alert: :param filter_ids: :return: """ # post metadata with all filter_ids in single call to /api/candidates alert_thin = { "id": alert["objectId"], "ra": alert["candidate"].get("ra"), "dec": alert["candidate"].get("dec"), "score": alert["candidate"].get("drb", alert["candidate"]["rb"]), "filter_ids": filter_ids, "passing_alert_id": alert["candid"], "passed_at": datetime.datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S.%f"), "origin": "Kowalski", } if self.verbose > 1: log(alert_thin) with timer( f"Posting metadata of {alert['objectId']} {alert['candid']} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/candidates", alert_thin) if response.json()["status"] == "success": log(f"Posted {alert['objectId']} {alert['candid']} metadata to SkyPortal") else: log( f"Failed to post {alert['objectId']} {alert['candid']} metadata to SkyPortal" ) log(response.json()) def alert_post_source(self, alert: Mapping, group_ids: Sequence): """ Save a ZTF alert as a source to groups on SkyPortal :param alert: :param group_ids: :return: """ # save source alert_thin = { "id": alert["objectId"], "group_ids": group_ids, "origin": "Kowalski", } if self.verbose > 1: log(alert_thin) with timer( f"Saving {alert['objectId']} {alert['candid']} as a Source on SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/sources", alert_thin) if response.json()["status"] == "success": log(f"Saved {alert['objectId']} {alert['candid']} as a Source on SkyPortal") else: log( f"Failed to save {alert['objectId']} {alert['candid']} as a Source on SkyPortal" ) log(response.json()) def alert_post_annotations(self, alert: Mapping, passed_filters: Sequence): """ Post annotations to SkyPortal for an alert that passed user-defined filters :param alert: :param passed_filters: :return: """ for passed_filter in passed_filters: annotations = { "obj_id": alert["objectId"], "origin": f"{passed_filter.get('group_name')}:{passed_filter.get('filter_name')}", "data": passed_filter.get("data", dict()).get("annotations", dict()), "group_ids": [passed_filter.get("group_id")], } if len(annotations["data"]) > 0: with timer( f"Posting annotation for {alert['objectId']} {alert['candid']} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "POST", "/api/annotation", annotations ) if response.json()["status"] == "success": log(f"Posted {alert['objectId']} annotation to SkyPortal") else: log(f"Failed to post {alert['objectId']} annotation to SkyPortal") log(response.json()) def alert_put_annotations(self, alert: Mapping, passed_filters: Sequence): """ Update annotations on SkyPortal for an alert that passed user-defined filters :param alert: :param passed_filters: :return: """ # first need to learn existing annotation id's and corresponding author id's to use with the PUT call with timer( f"Getting annotations for {alert['objectId']} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "GET", f"/api/sources/{alert['objectId']}/annotations" ) if response.json()["status"] == "success": log(f"Got {alert['objectId']} annotations from SkyPortal") else: log(f"Failed to get {alert['objectId']} annotations from SkyPortal") log(response.json()) return False existing_annotations = { annotation["origin"]: { "annotation_id": annotation["id"], "author_id": annotation["author_id"], } for annotation in response.json()["data"] } for passed_filter in passed_filters: origin = ( f"{passed_filter.get('group_name')}:{passed_filter.get('filter_name')}" ) # no annotation exists on SkyPortal for this object? just post then if origin not in existing_annotations: self.alert_post_annotations(alert, [passed_filter]) continue annotations = { "author_id": existing_annotations[origin]["author_id"], "obj_id": alert["objectId"], "origin": origin, "data": passed_filter.get("data", dict()).get("annotations", dict()), "group_ids": [passed_filter.get("group_id")], } if len(annotations["data"]) > 0 and passed_filter.get( "update_annotations", False ): with timer( f"Putting annotation for {alert['objectId']} {alert['candid']} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "PUT", f"/api/annotation/{existing_annotations[origin]['annotation_id']}", annotations, ) if response.json()["status"] == "success": log(f"Posted {alert['objectId']} annotation to SkyPortal") else: log(f"Failed to post {alert['objectId']} annotation to SkyPortal") log(response.json()) def alert_post_thumbnails(self, alert: Mapping): """ Post ZTF alert thumbnails to SkyPortal :param alert: :return: """ for ttype, ztftype in [ ("new", "Science"), ("ref", "Template"), ("sub", "Difference"), ]: with timer( f"Making {ztftype} thumbnail for {alert['objectId']} {alert['candid']}", self.verbose > 1, ): thumb = make_thumbnail(alert, ttype, ztftype) with timer( f"Posting {ztftype} thumbnail for {alert['objectId']} {alert['candid']} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("POST", "/api/thumbnail", thumb) if response.json()["status"] == "success": log( f"Posted {alert['objectId']} {alert['candid']} {ztftype} cutout to SkyPortal" ) else: log( f"Failed to post {alert['objectId']} {alert['candid']} {ztftype} cutout to SkyPortal" ) log(response.json()) def alert_put_photometry(self, alert): """PUT photometry to SkyPortal :param alert: :return: """ with timer( f"Making alert photometry of {alert['objectId']} {alert['candid']}", self.verbose > 1, ): df_photometry = make_photometry(alert) df_photometry["stream_id"] = df_photometry["programid"].apply( lambda programid: self.ztf_program_id_to_stream_id[programid] ) # post photometry by stream_id for stream_id in set(df_photometry.stream_id.unique()): stream_id_mask = df_photometry.stream_id == int(stream_id) photometry = { "obj_id": alert["objectId"], "stream_ids": [int(stream_id)], "instrument_id": self.instrument_id, "mjd": df_photometry.loc[stream_id_mask, "mjd"].tolist(), "flux": df_photometry.loc[stream_id_mask, "flux"].tolist(), "fluxerr": df_photometry.loc[stream_id_mask, "fluxerr"].tolist(), "zp": df_photometry.loc[stream_id_mask, "zp"].tolist(), "magsys": df_photometry.loc[stream_id_mask, "zpsys"].tolist(), "filter": df_photometry.loc[stream_id_mask, "ztf_filter"].tolist(), "ra": df_photometry.loc[stream_id_mask, "ra"].tolist(), "dec": df_photometry.loc[stream_id_mask, "dec"].tolist(), } if (len(photometry.get("flux", ())) > 0) or ( len(photometry.get("fluxerr", ())) > 0 ): with timer( f"Posting photometry of {alert['objectId']} {alert['candid']}, " f"stream_id={stream_id} to SkyPortal", self.verbose > 1, ): response = self.api_skyportal("PUT", "/api/photometry", photometry) if response.json()["status"] == "success": log( f"Posted {alert['objectId']} photometry stream_id={stream_id} to SkyPortal" ) else: log( f"Failed to post {alert['objectId']} photometry stream_id={stream_id} to SkyPortal" ) log(response.json()) def alert_sentinel_skyportal(self, alert, prv_candidates, passed_filters): """ Post alerts to SkyPortal, if need be. Logic: - check if candidate/source exist on SP - if candidate does not exist and len(passed_filters) > 0 - post metadata with all filter_ids in single call to /api/candidates - post full light curve with all group_ids in single call to /api/photometry - post thumbnails - if candidate exists: - get filter_ids of saved candidate from SP - post to /api/candidates with new_filter_ids, if any - post alert light curve in single PUT call to /api/photometry specifying stream_ids - if source exists: - get groups and check stream access - decide which points to post to what groups based on permissions - post alert light curve in single PUT call to /api/photometry specifying stream_ids :param alert: ZTF_alert with a stripped-off prv_candidates section :param prv_candidates: could be plain prv_candidates section of an alert, or extended alert history :param passed_filters: list of filters that alert passed, with their output :return: """ # check if candidate/source exist in SP: with timer( f"Checking if {alert['objectId']} is Candidate in SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "HEAD", f"/api/candidates/{alert['objectId']}" ) is_candidate = response.status_code == 200 if self.verbose > 1: log( f"{alert['objectId']} {'is' if is_candidate else 'is not'} Candidate in SkyPortal" ) with timer( f"Checking if {alert['objectId']} is Source in SkyPortal", self.verbose > 1 ): response = self.api_skyportal("HEAD", f"/api/sources/{alert['objectId']}") is_source = response.status_code == 200 if self.verbose > 1: log( f"{alert['objectId']} {'is' if is_source else 'is not'} Source in SkyPortal" ) # obj does not exit in SP: if (not is_candidate) and (not is_source): # passed at least one filter? if len(passed_filters) > 0: # post candidate filter_ids = [f.get("filter_id") for f in passed_filters] self.alert_post_candidate(alert, filter_ids) # post annotations self.alert_post_annotations(alert, passed_filters) # post full light curve try: alert["prv_candidates"] = list( self.mongo.db[self.collection_alerts_aux].find( {"_id": alert["objectId"]}, {"prv_candidates": 1}, limit=1 ) )[0]["prv_candidates"] except Exception as e: # this should never happen, but just in case log(e) alert["prv_candidates"] = prv_candidates self.alert_put_photometry(alert) # post thumbnails self.alert_post_thumbnails(alert) # post source if autosave=True autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) # obj exists in SP: else: if len(passed_filters) > 0: filter_ids = [f.get("filter_id") for f in passed_filters] # post candidate with new filter ids self.alert_post_candidate(alert, filter_ids) # put annotations self.alert_put_annotations(alert, passed_filters) # already saved as a source? if is_source: # get info on the corresponding groups: with timer( f"Getting source groups info on {alert['objectId']} from SkyPortal", self.verbose > 1, ): response = self.api_skyportal( "GET", f"/api/sources/{alert['objectId']}/groups" ) if response.json()["status"] == "success": existing_groups = response.json()["data"] existing_group_ids = [g["id"] for g in existing_groups] # post source if autosave=True and not already saved autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) and (f.get("group_id") not in existing_group_ids) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) else: log(f"Failed to get source groups info on {alert['objectId']}") else: # post source if autosave=True and not is_source autosave_group_ids = [ f.get("group_id") for f in passed_filters if f.get("autosave", False) ] if len(autosave_group_ids) > 0: self.alert_post_source(alert, autosave_group_ids) # post alert photometry in single call to /api/photometry alert["prv_candidates"] = prv_candidates self.alert_put_photometry(alert) class WorkerInitializer(dask.distributed.WorkerPlugin): def __init__(self, *args, **kwargs): self.alert_worker = None def setup(self, worker: dask.distributed.Worker): self.alert_worker = AlertWorker() def topic_listener( topic, bootstrap_servers: str, offset_reset: str = "earliest", group: str = None, test: bool = False, ): """ Listen to a topic :param topic: :param bootstrap_servers: :param offset_reset: :param group: :param test: when testing, terminate once reached end of partition :return: """ # Configure dask client dask_client = dask.distributed.Client( address=f"{config['dask']['host']}:{config['dask']['scheduler_port']}" ) # init each worker with AlertWorker instance worker_initializer = WorkerInitializer() dask_client.register_worker_plugin(worker_initializer, name="worker-init") # Configure consumer connection to Kafka broker conf = { "bootstrap.servers": bootstrap_servers, "default.topic.config": {"auto.offset.reset": offset_reset}, } if group is not None: conf["group.id"] = group else: conf["group.id"] = os.environ.get("HOSTNAME", "kowalski") # make it unique: conf[ "group.id" ] = f"{conf['group.id']}_{datetime.datetime.utcnow().strftime('%Y-%m-%d_%H:%M:%S.%f')}" # Start alert stream consumer stream_reader = AlertConsumer(topic, dask_client, **conf) while True: try: # poll! stream_reader.poll() except EopError as e: # Write when reaching end of partition log(e.message) if test: # when testing, terminate once reached end of partition: sys.exit() except IndexError: log("Data cannot be decoded\n") except UnicodeDecodeError: log("Unexpected data format received\n") except KeyboardInterrupt: log("Aborted by user\n") sys.exit() except Exception as e: log(str(e)) _err = traceback.format_exc() log(_err) sys.exit() def watchdog(obs_date: str = None, test: bool = False): """ Watchdog for topic listeners :param obs_date: observing date: YYYYMMDD :param test: test mode :return: """ init_db_sync(config=config, verbose=True) topics_on_watch = dict() while True: try: # get kafka topic names with kafka-topics command if not test: # Production Kafka stream at IPAC kafka_cmd = [ os.path.join(config["path"]["kafka"], "bin", "kafka-topics.sh"), "--zookeeper", config["kafka"]["zookeeper"], "-list", ] else: # Local test stream kafka_cmd = [ os.path.join(config["path"]["kafka"], "bin", "kafka-topics.sh"), "--zookeeper", config["kafka"]["zookeeper.test"], "-list", ] topics = ( subprocess.run(kafka_cmd, stdout=subprocess.PIPE) .stdout.decode("utf-8") .split("\n")[:-1] ) if obs_date is None: datestr = datetime.datetime.utcnow().strftime("%Y%m%d") else: datestr = obs_date # as of 20180403, the naming convention is ztf_%Y%m%d_programidN # exclude ZUDS, ingest separately topics_tonight = [ t for t in topics if (datestr in t) and ("programid" in t) and ("zuds" not in t) ] log(f"Topics: {topics_tonight}") for t in topics_tonight: if t not in topics_on_watch: log(f"Starting listener thread for {t}") offset_reset = config["kafka"]["default.topic.config"][ "auto.offset.reset" ] if not test: bootstrap_servers = config["kafka"]["bootstrap.servers"] else: bootstrap_servers = config["kafka"]["bootstrap.test.servers"] group = config["kafka"]["group"] topics_on_watch[t] = multiprocessing.Process( target=topic_listener, args=(t, bootstrap_servers, offset_reset, group, test), ) topics_on_watch[t].daemon = True topics_on_watch[t].start() else: log(f"Performing thread health check for {t}") try: if not topics_on_watch[t].is_alive(): log(f"Thread {t} died, removing") # topics_on_watch[t].terminate() topics_on_watch.pop(t, None) else: log(f"Thread {t} appears normal") except Exception as _e: log(f"Failed to perform health check: {_e}") pass if test: time.sleep(120) # when testing, wait for topic listeners to pull all the data, then break # fixme: do this more gracefully for t in topics_on_watch: topics_on_watch[t].kill() break except Exception as e: log(str(e)) _err = traceback.format_exc() log(str(_err)) time.sleep(60) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Kowalski's ZTF Alert Broker") parser.add_argument("--obsdate", help="observing date YYYYMMDD") parser.add_argument("--test", help="listen to the test stream", action="store_true") args = parser.parse_args() watchdog(obs_date=args.obsdate, test=args.test)

import os import copy import ray import argparse import tqdm import logging import pandas as pd from pathlib import Path from datetime import datetime import shutil import uuid as uu_id from typing import ClassVar, Dict, Generator, Tuple, List, Any from proseco.evaluator.remote import run from proseco.evaluator.progressBar import ProgressBar import proseco.utility.io as io import proseco.utility.ui as ui from proseco.dashboard.model import ( get_run_directories, is_successful_result, load_result, load_options, load_scenario, load_uuid, ) from proseco.evaluator.util import ( flatten_dictionary, nest_dictionary, hash_dict, get_permutations, ) class Head: """Head node of the Evaluation.""" pack_path: str args: argparse.Namespace bulk_config: Dict[str, Any] time_string: str number_evaluations: int iteration_index: int logger: logging.Logger c_error: ClassVar[str] = "\033[91m\033[1m" c_warning: ClassVar[str] = "\33[33m\033[" c_okay: ClassVar[str] = "\033[92m\033[1m" c_reset: ClassVar[str] = "\033[0m" def __init__( self, args: argparse.Namespace, time_string: str = None, init_ray: bool = True ): """Constructor. Parameters ---------- args : argparse.Namespace Command line arguments for starting the evaluation. time_string : str Optional time stamp when this head node was started. init_ray : bool When True the evaluation will connect the local ray client to a cluster (default: True). It is used to prevent multiple ray-inits when running evaluations in sequence, else ray init will throw an exception. """ self.logger = ui.get_logger("ProSeCo Evaluator") # Arguments with flags self.args = args # Openend config directory self.bulk_config = self.get_file(file_type="evaluator", file=args.config) # Timestamp of the head node if time_string == None: self.time_string = io.create_timestamp() else: self.time_string = time_string # How many evaluations this config will generate self.number_evaluations = self.determine_number_evaluations() # The current iteration index the evaluation is at self.iteration_index = 0 self.path = self.create_evaluation_directory() # The time in seconds, when a task is considered to be timed out. self.task_timeout = 120 self.isolate_binary() self.init_ray() def __del__(self): """Destructor. Removes the temporary binary for the evaluation. """ self.remove_binary() def determine_number_evaluations(self) -> int: """Determines the number of MCTS-Evaluations to be started. Returns ------- int Total number of scenario evaluations. """ len_options = len(self.bulk_config["options"]) len_scenarios = len(self.bulk_config["scenarios"]) number_runs = self.bulk_config["number_runs"] alterations = flatten_dictionary(self.bulk_config["options_alterations"]) scenario_alterations = flatten_dictionary( self.bulk_config["scenario_alterations"] ) alterations.update(scenario_alterations) len_alter = 1 for _, v in alterations.items(): if type(v) is list: len_alter = len_alter * len(v) else: pass number_evaluations = len_options * len_scenarios * len_alter * number_runs return number_evaluations def create_evaluation_directory(self) -> Path: """Creates a directory for the evaluation. Returns ------- Path The path to the evaluation directory. """ path = ( io.get_user_temp_dir() / f"proseco_evaluator_output/{self.time_string}_{self.bulk_config["evaluation_name"]}" ) path.mkdir(parents=True) self.logger.info(f"Creating evaluation directory {path}") return path def isolate_binary(self) -> None: """Moves the binary to a unique directory, so that rebuilding the binary does not affect the evaluation.""" devel_binary_path = (Path(io.get_ros_pack_path()).parents[1]).joinpath( "devel_isolated/ros_proseco_planning/lib/ros_proseco_planning" ) temp_binary_path = Path(f"~/evaluator_bin_{self.time_string}").expanduser() shutil.copytree(devel_binary_path, temp_binary_path) self.logger.debug(f"Created temporary binary path {temp_binary_path}") self.binary_path = temp_binary_path def remove_binary(self) -> None: """Removes the temporary binary for the evaluation.""" self.logger.debug(f"Removed temporary binary path {self.binary_path}") shutil.rmtree(self.binary_path) def init_ray(self) -> None: """Initializes the ray cluster, determines the maximum number of workers and creates the workers.""" self.logger.debug(f"Initializing ray cluster") ray.init( address=self.args.address or self.bulk_config["ray_cluster"]["address"], include_dashboard=not self.args.no_dashboard, dashboard_host="0.0.0.0", log_to_driver=self.args.debug, _temp_dir=str(io.get_user_temp_dir() / "ray"), ) def print_evaluation_scenarios(self) -> None: """Prints the different combinations of settings for the evaluation.""" self.logger.debug(f"EVALUATOR CONFIGURATION: {self.bulk_config}") scenarios = ",".join(self.bulk_config["scenarios"]) self.logger.info(f"Evaluating Scenarios: [{scenarios}]") @staticmethod def permute_dictionary( dictionary: Dict[str, any], permutation: Dict[str, any] ) -> Dict[str, any]: """Creates a new dictionary with the given permutation applied. Parameters ---------- dictionary : Dict[str, any] The dictionary to apply the permutation to. permutation : Dict[str, any] The permutation to apply. Returns ------- Dict[str, any] The new options dictionary. """ dictionary = flatten_dictionary(dictionary) dictionary.update(permutation) return nest_dictionary(dictionary) @staticmethod def remove_random_seed(options: Dict[str, any]) -> Dict[str, any]: """Removes the random seed from the options. Parameters ---------- options : Dict[str, any] The options to remove the random seed from. Returns ------- Dict[str, any] The options without the random seed. """ options = copy.deepcopy(options) options["compute_options"].pop("random_seed") return options @staticmethod def hash_options_and_scenario( options: Dict[str, Any], scenario: Dict[str, Any] ) -> str: """Hashes the options and the scenario. Parameters ---------- options : Dict[str, Any] The options to hash. scenario : Dict[str, Any] The scenario to hash. Returns ------- str The combination of the options and the scenario. """ options_scenario = {} options_scenario.update(options) options_scenario.update(scenario) return hash_dict(options_scenario) def get_file(self, file_type: str, file: str) -> Dict[str, Any]: """Returns a scenario, config or options dictionary. The files are loaded via json. Parameters ---------- file_type : str String indicating whether the file to load contains options, a scenario or a config for the evaluator. file : str Name of the file to load. Returns ------- Dict[str, Any] Loaded options dictionary. """ if isinstance(file, dict): data = file elif type(file) is str: if not file.endswith(".json"): file += ".json" if ( file_type == "options" or file_type == "scenarios" or file_type == "evaluator" ): path = io.get_ros_pack_path() / "config" / file_type / file else: raise Exception(f"Unknown file type {file_type}") data = io.load_data(path) return data def options_iterator( self, ) -> Generator[ Tuple[Dict[str, Dict[str, Any]], Dict[str, Any], List[str], Dict[str, Any]], None, None, ]: """Defines an iterator for the option permutations. The iterator returns the necessary files for evaluating a scenario with a worker node. Additionally, it also returns an info dict used to update the progress bar. Yields ------- options: Tuple[Dict[str, Dict[str, Any]], Dict[str, Any], List[str], Dict[str, Any]] Tuple containing all the necessary files to initiate an evaluation run. Contains: - new_options : Dictionary containing compute options. - new_scenario : Loaded scenario configuration. - uuid : Unique IDs and scenario-options hashes. - info_dict: Information to update the progress bar. """ # Iterate over all provided compute option files for options in self.bulk_config["options"]: options = io.load_options(options) flat_options = flatten_dictionary(self.bulk_config["options_alterations"]) options_permutations = get_permutations(flat_options) # Iterate over all permutations of options alterations for options_permutation in options_permutations: self.logger.debug(f"Permuting options with: {options_permutation}") new_options = self.permute_dictionary(options, options_permutation) # Unique ID for the options file uuid_options = str(uu_id.uuid4()) new_options_wo_random_seed = self.remove_random_seed(new_options) options_hash = hash_dict(new_options_wo_random_seed) # Iterate over every scenario for scenario in self.bulk_config["scenarios"]: scenario = io.load_scenario(scenario) flat_scenario = flatten_dictionary( self.bulk_config["scenario_alterations"] ) scenarios_permutations = get_permutations(flat_scenario) # Iterate over all scenario permutations of the scenario alterations for scenarios_permutation in scenarios_permutations: self.logger.debug( f"Permuting scenario with: {scenarios_permutation}" ) new_scenario = self.permute_dictionary( scenario, scenarios_permutation ) # Unique ID for the options and scenario tuple uuid_scenario = str(uu_id.uuid4()) options_scenario_hash = self.hash_options_and_scenario( new_options_wo_random_seed, new_scenario ) uuids = { "options_uuid": uuid_options, "options_scenario_uuid": uuid_scenario, "options_hash": options_hash, "options_scenario_hash": options_scenario_hash, } # Iterate over number of runs for _ in range(self.bulk_config["number_runs"]): yield new_options, new_scenario, uuids def run_tasks(self): """Starts the tasks and waits for them to finish. Parameters ---------- results : List[List[Any]] The list of results. """ pb = ProgressBar(self.number_evaluations) results_ref = [] for options, scenario, uuids in self.options_iterator(): results_ref.append( run.remote( options, scenario, uuids, self.binary_path, self.args.debug, pb.actor, ) ) pb.print_until_done_or_timeout(self.task_timeout) results = ray.get(results_ref) return results def start(self) -> Tuple[List[List[Any]], bool]: """Creates all the ray futures according to the configuration file and evaluates them. Main method for running the evaluation and delegating tasks to workers. Notes ----- If the result list becomes larger than 5GB, the result list returned by this method does not contain all results. The return value `complete` indicates whether all results are contained. Despite the limitation of the returned list, all results are always persisted to disk in the evaluation directory. Returns ------- Tuple[List[List[Any]], bool] Tuple containing the results of the evaluation. The first element is a list with the current results. The second element is a boolean flag indicating whether the list contains ALL results. """ self.print_evaluation_scenarios() # Starting time of the evaluation beginning = datetime.now() results = self.run_tasks() self.save_results(results) # create summary json if specified if not self.args.no_summary: _ = self.create_summary(True) self.logger.info( f"{self.c_okay}The {self.iteration_index}-th evaluation terminated successfully in {datetime.now()-beginning}{self.c_reset}" ) self.iteration_index += 1 return results def save_results(self, results: List[List[Any]]) -> None: """Saves the results to disk. Parameters ---------- results : List[List[Any]] The list containing the results of all runs. """ for result_index in range(len(results)): self.save_result(results[result_index], result_index) def save_result(self, result: List[Any], result_index: int) -> None: """Saves a single result to disk. Parameters ---------- result : List[Any] The list containing the partial results of a single run. result_index : int The index of the result. """ result_path = ( self.path / f"iteration_{self.iteration_index}" / f"result_{str(result_index).zfill(io.get_number_digits(self.number_evaluations-1))}of{str(self.number_evaluations-1).zfill(io.get_number_digits(self.number_evaluations-1))}" ) result_path.mkdir(parents=True) for file_name, data in result: io.save_data(data, result_path / file_name) # append the path to the result result.append(["%PATH%", str(result_path)]) @staticmethod def _check_alterations_key(key: str, value: Any) -> bool: """Checks whether the specified key is being iterated over in the evaluator bulk config. Parameters ---------- key : str String dictionary key of an options file. value : Any Dictionary value. Returns ------- bool True if the key is a compute_options alteration, False else. """ return ( isinstance(value, list) and key.startswith("options_alterations") and not key.endswith("seed") and not key.split("/")[1] == "output_options" ) def create_summary(self, save_to_disk: bool = False) -> List[Dict]: """Returns a summary of the results and saves it in the evaluation directory if the flag argument is True. Parameters ---------- save_to_disk : bool = False Flag for saving the summary to disk. Returns ------- List[Dict] List containing summary dicts for each run. The summary contains: Scenario name, options_uuid, path to the results folder, success flag. """ self.logger.info("Summarizing results") results = [] # Get the config keys that define alterations from the bulk config co_keys = [ key.lstrip("options_alterations/") for key, value in (flatten_dictionary(self.bulk_config)).items() if self._check_alterations_key(key, value) ] for run_dir in get_run_directories(self.path): data = load_result(run_dir) options = load_options(run_dir) scenario = load_scenario(run_dir) uuid = load_uuid(run_dir) data["scenario"] = scenario["name"] # Add uuids and hashes to the results file data.update(uuid) # added the path to the results so that the folder can be found for the visualization data["path"] = str(run_dir.resolve()) # Load the altered options from the options file and append them to the results if co_keys: flat_opt = flatten_dictionary(options) for key in co_keys: name = key.split("/")[-1] data[name] = flat_opt[key] # determine whether the run was successful or not data["success"] = is_successful_result(data) results.append(data) # persist summary to disk if save_to_disk: path = self.path / "results.json" io.save_data(results, path) return results def create_result_dataframe(self, save_to_disk: bool = False) -> pd.DataFrame: """Returns detailed result information including the compute options and saves them in the evaluation directory if the flag argument is True. Parameters ---------- save_to_disk : bool = False Flag for saving the summary to disk. Returns ------- pandas.DataFrame DataFrame generated from the individual run results. Contains for each run: Complete options, detailed result, uuid, scenario name. """ all_data = [] for result_path, options_path, scenario_path, uuid_path in tqdm( zip( self.path.rglob("result.json"), self.path.rglob("options_output.json"), self.path.rglob("scenario_output.json"), self.path.rglob("uuid.json"), ), total=self.number_evaluations, ascii=True, desc="Generating DataFrame", ): # open all files and extract the information options = pd.json_normalize(io.load_data(options_path)) results = pd.json_normalize(io.load_data(result_path)) uuid = pd.json_normalize(io.load_data(uuid_path)) run_data = pd.concat([options, uuid, results], axis=1) run_data["scenario"] = io.load_data(scenario_path)["name"] all_data.append(run_data) df_all = pd.concat(all_data, axis=0) df_all.reset_index(drop=True, inplace=True) if save_to_disk: df_all.to_pickle(self.path / "run_data.gz", compression="gzip") return df_all

import os import copy import ray import argparse import tqdm import logging import pandas as pd from pathlib import Path from datetime import datetime import shutil import uuid as uu_id from typing import ClassVar, Dict, Generator, Tuple, List, Any from proseco.evaluator.remote import run from proseco.evaluator.progressBar import ProgressBar import proseco.utility.io as io import proseco.utility.ui as ui from proseco.dashboard.model import ( get_run_directories, is_successful_result, load_result, load_options, load_scenario, load_uuid, ) from proseco.evaluator.util import ( flatten_dictionary, nest_dictionary, hash_dict, get_permutations, ) class Head: """Head node of the Evaluation.""" pack_path: str args: argparse.Namespace bulk_config: Dict[str, Any] time_string: str number_evaluations: int iteration_index: int logger: logging.Logger c_error: ClassVar[str] = "\033[91m\033[1m" c_warning: ClassVar[str] = "\33[33m\033[" c_okay: ClassVar[str] = "\033[92m\033[1m" c_reset: ClassVar[str] = "\033[0m" def __init__( self, args: argparse.Namespace, time_string: str = None, init_ray: bool = True ): """Constructor. Parameters ---------- args : argparse.Namespace Command line arguments for starting the evaluation. time_string : str Optional time stamp when this head node was started. init_ray : bool When True the evaluation will connect the local ray client to a cluster (default: True). It is used to prevent multiple ray-inits when running evaluations in sequence, else ray init will throw an exception. """ self.logger = ui.get_logger("ProSeCo Evaluator") # Arguments with flags self.args = args # Openend config directory self.bulk_config = self.get_file(file_type="evaluator", file=args.config) # Timestamp of the head node if time_string == None: self.time_string = io.create_timestamp() else: self.time_string = time_string # How many evaluations this config will generate self.number_evaluations = self.determine_number_evaluations() # The current iteration index the evaluation is at self.iteration_index = 0 self.path = self.create_evaluation_directory() # The time in seconds, when a task is considered to be timed out. self.task_timeout = 120 self.isolate_binary() self.init_ray() def __del__(self): """Destructor. Removes the temporary binary for the evaluation. """ self.remove_binary() def determine_number_evaluations(self) -> int: """Determines the number of MCTS-Evaluations to be started. Returns ------- int Total number of scenario evaluations. """ len_options = len(self.bulk_config["options"]) len_scenarios = len(self.bulk_config["scenarios"]) number_runs = self.bulk_config["number_runs"] alterations = flatten_dictionary(self.bulk_config["options_alterations"]) scenario_alterations = flatten_dictionary( self.bulk_config["scenario_alterations"] ) alterations.update(scenario_alterations) len_alter = 1 for _, v in alterations.items(): if type(v) is list: len_alter = len_alter * len(v) else: pass number_evaluations = len_options * len_scenarios * len_alter * number_runs return number_evaluations def create_evaluation_directory(self) -> Path: """Creates a directory for the evaluation. Returns ------- Path The path to the evaluation directory. """ path = ( io.get_user_temp_dir() / f"proseco_evaluator_output/{self.time_string}_{self.bulk_config['evaluation_name']}" ) path.mkdir(parents=True) self.logger.info(f"Creating evaluation directory {path}") return path def isolate_binary(self) -> None: """Moves the binary to a unique directory, so that rebuilding the binary does not affect the evaluation.""" devel_binary_path = (Path(io.get_ros_pack_path()).parents[1]).joinpath( "devel_isolated/ros_proseco_planning/lib/ros_proseco_planning" ) temp_binary_path = Path(f"~/evaluator_bin_{self.time_string}").expanduser() shutil.copytree(devel_binary_path, temp_binary_path) self.logger.debug(f"Created temporary binary path {temp_binary_path}") self.binary_path = temp_binary_path def remove_binary(self) -> None: """Removes the temporary binary for the evaluation.""" self.logger.debug(f"Removed temporary binary path {self.binary_path}") shutil.rmtree(self.binary_path) def init_ray(self) -> None: """Initializes the ray cluster, determines the maximum number of workers and creates the workers.""" self.logger.debug(f"Initializing ray cluster") ray.init( address=self.args.address or self.bulk_config["ray_cluster"]["address"], include_dashboard=not self.args.no_dashboard, dashboard_host="0.0.0.0", log_to_driver=self.args.debug, _temp_dir=str(io.get_user_temp_dir() / "ray"), ) def print_evaluation_scenarios(self) -> None: """Prints the different combinations of settings for the evaluation.""" self.logger.debug(f"EVALUATOR CONFIGURATION: {self.bulk_config}") scenarios = ",".join(self.bulk_config["scenarios"]) self.logger.info(f"Evaluating Scenarios: [{scenarios}]") @staticmethod def permute_dictionary( dictionary: Dict[str, any], permutation: Dict[str, any] ) -> Dict[str, any]: """Creates a new dictionary with the given permutation applied. Parameters ---------- dictionary : Dict[str, any] The dictionary to apply the permutation to. permutation : Dict[str, any] The permutation to apply. Returns ------- Dict[str, any] The new options dictionary. """ dictionary = flatten_dictionary(dictionary) dictionary.update(permutation) return nest_dictionary(dictionary) @staticmethod def remove_random_seed(options: Dict[str, any]) -> Dict[str, any]: """Removes the random seed from the options. Parameters ---------- options : Dict[str, any] The options to remove the random seed from. Returns ------- Dict[str, any] The options without the random seed. """ options = copy.deepcopy(options) options["compute_options"].pop("random_seed") return options @staticmethod def hash_options_and_scenario( options: Dict[str, Any], scenario: Dict[str, Any] ) -> str: """Hashes the options and the scenario. Parameters ---------- options : Dict[str, Any] The options to hash. scenario : Dict[str, Any] The scenario to hash. Returns ------- str The combination of the options and the scenario. """ options_scenario = {} options_scenario.update(options) options_scenario.update(scenario) return hash_dict(options_scenario) def get_file(self, file_type: str, file: str) -> Dict[str, Any]: """Returns a scenario, config or options dictionary. The files are loaded via json. Parameters ---------- file_type : str String indicating whether the file to load contains options, a scenario or a config for the evaluator. file : str Name of the file to load. Returns ------- Dict[str, Any] Loaded options dictionary. """ if isinstance(file, dict): data = file elif type(file) is str: if not file.endswith(".json"): file += ".json" if ( file_type == "options" or file_type == "scenarios" or file_type == "evaluator" ): path = io.get_ros_pack_path() / "config" / file_type / file else: raise Exception(f"Unknown file type {file_type}") data = io.load_data(path) return data def options_iterator( self, ) -> Generator[ Tuple[Dict[str, Dict[str, Any]], Dict[str, Any], List[str], Dict[str, Any]], None, None, ]: """Defines an iterator for the option permutations. The iterator returns the necessary files for evaluating a scenario with a worker node. Additionally, it also returns an info dict used to update the progress bar. Yields ------- options: Tuple[Dict[str, Dict[str, Any]], Dict[str, Any], List[str], Dict[str, Any]] Tuple containing all the necessary files to initiate an evaluation run. Contains: - new_options : Dictionary containing compute options. - new_scenario : Loaded scenario configuration. - uuid : Unique IDs and scenario-options hashes. - info_dict: Information to update the progress bar. """ # Iterate over all provided compute option files for options in self.bulk_config["options"]: options = io.load_options(options) flat_options = flatten_dictionary(self.bulk_config["options_alterations"]) options_permutations = get_permutations(flat_options) # Iterate over all permutations of options alterations for options_permutation in options_permutations: self.logger.debug(f"Permuting options with: {options_permutation}") new_options = self.permute_dictionary(options, options_permutation) # Unique ID for the options file uuid_options = str(uu_id.uuid4()) new_options_wo_random_seed = self.remove_random_seed(new_options) options_hash = hash_dict(new_options_wo_random_seed) # Iterate over every scenario for scenario in self.bulk_config["scenarios"]: scenario = io.load_scenario(scenario) flat_scenario = flatten_dictionary( self.bulk_config["scenario_alterations"] ) scenarios_permutations = get_permutations(flat_scenario) # Iterate over all scenario permutations of the scenario alterations for scenarios_permutation in scenarios_permutations: self.logger.debug( f"Permuting scenario with: {scenarios_permutation}" ) new_scenario = self.permute_dictionary( scenario, scenarios_permutation ) # Unique ID for the options and scenario tuple uuid_scenario = str(uu_id.uuid4()) options_scenario_hash = self.hash_options_and_scenario( new_options_wo_random_seed, new_scenario ) uuids = { "options_uuid": uuid_options, "options_scenario_uuid": uuid_scenario, "options_hash": options_hash, "options_scenario_hash": options_scenario_hash, } # Iterate over number of runs for _ in range(self.bulk_config["number_runs"]): yield new_options, new_scenario, uuids def run_tasks(self): """Starts the tasks and waits for them to finish. Parameters ---------- results : List[List[Any]] The list of results. """ pb = ProgressBar(self.number_evaluations) results_ref = [] for options, scenario, uuids in self.options_iterator(): results_ref.append( run.remote( options, scenario, uuids, self.binary_path, self.args.debug, pb.actor, ) ) pb.print_until_done_or_timeout(self.task_timeout) results = ray.get(results_ref) return results def start(self) -> Tuple[List[List[Any]], bool]: """Creates all the ray futures according to the configuration file and evaluates them. Main method for running the evaluation and delegating tasks to workers. Notes ----- If the result list becomes larger than 5GB, the result list returned by this method does not contain all results. The return value `complete` indicates whether all results are contained. Despite the limitation of the returned list, all results are always persisted to disk in the evaluation directory. Returns ------- Tuple[List[List[Any]], bool] Tuple containing the results of the evaluation. The first element is a list with the current results. The second element is a boolean flag indicating whether the list contains ALL results. """ self.print_evaluation_scenarios() # Starting time of the evaluation beginning = datetime.now() results = self.run_tasks() self.save_results(results) # create summary json if specified if not self.args.no_summary: _ = self.create_summary(True) self.logger.info( f"{self.c_okay}The {self.iteration_index}-th evaluation terminated successfully in {datetime.now()-beginning}{self.c_reset}" ) self.iteration_index += 1 return results def save_results(self, results: List[List[Any]]) -> None: """Saves the results to disk. Parameters ---------- results : List[List[Any]] The list containing the results of all runs. """ for result_index in range(len(results)): self.save_result(results[result_index], result_index) def save_result(self, result: List[Any], result_index: int) -> None: """Saves a single result to disk. Parameters ---------- result : List[Any] The list containing the partial results of a single run. result_index : int The index of the result. """ result_path = ( self.path / f"iteration_{self.iteration_index}" / f"result_{str(result_index).zfill(io.get_number_digits(self.number_evaluations-1))}of{str(self.number_evaluations-1).zfill(io.get_number_digits(self.number_evaluations-1))}" ) result_path.mkdir(parents=True) for file_name, data in result: io.save_data(data, result_path / file_name) # append the path to the result result.append(["%PATH%", str(result_path)]) @staticmethod def _check_alterations_key(key: str, value: Any) -> bool: """Checks whether the specified key is being iterated over in the evaluator bulk config. Parameters ---------- key : str String dictionary key of an options file. value : Any Dictionary value. Returns ------- bool True if the key is a compute_options alteration, False else. """ return ( isinstance(value, list) and key.startswith("options_alterations") and not key.endswith("seed") and not key.split("/")[1] == "output_options" ) def create_summary(self, save_to_disk: bool = False) -> List[Dict]: """Returns a summary of the results and saves it in the evaluation directory if the flag argument is True. Parameters ---------- save_to_disk : bool = False Flag for saving the summary to disk. Returns ------- List[Dict] List containing summary dicts for each run. The summary contains: Scenario name, options_uuid, path to the results folder, success flag. """ self.logger.info("Summarizing results") results = [] # Get the config keys that define alterations from the bulk config co_keys = [ key.lstrip("options_alterations/") for key, value in (flatten_dictionary(self.bulk_config)).items() if self._check_alterations_key(key, value) ] for run_dir in get_run_directories(self.path): data = load_result(run_dir) options = load_options(run_dir) scenario = load_scenario(run_dir) uuid = load_uuid(run_dir) data["scenario"] = scenario["name"] # Add uuids and hashes to the results file data.update(uuid) # added the path to the results so that the folder can be found for the visualization data["path"] = str(run_dir.resolve()) # Load the altered options from the options file and append them to the results if co_keys: flat_opt = flatten_dictionary(options) for key in co_keys: name = key.split("/")[-1] data[name] = flat_opt[key] # determine whether the run was successful or not data["success"] = is_successful_result(data) results.append(data) # persist summary to disk if save_to_disk: path = self.path / "results.json" io.save_data(results, path) return results def create_result_dataframe(self, save_to_disk: bool = False) -> pd.DataFrame: """Returns detailed result information including the compute options and saves them in the evaluation directory if the flag argument is True. Parameters ---------- save_to_disk : bool = False Flag for saving the summary to disk. Returns ------- pandas.DataFrame DataFrame generated from the individual run results. Contains for each run: Complete options, detailed result, uuid, scenario name. """ all_data = [] for result_path, options_path, scenario_path, uuid_path in tqdm( zip( self.path.rglob("result.json"), self.path.rglob("options_output.json"), self.path.rglob("scenario_output.json"), self.path.rglob("uuid.json"), ), total=self.number_evaluations, ascii=True, desc="Generating DataFrame", ): # open all files and extract the information options = pd.json_normalize(io.load_data(options_path)) results = pd.json_normalize(io.load_data(result_path)) uuid = pd.json_normalize(io.load_data(uuid_path)) run_data = pd.concat([options, uuid, results], axis=1) run_data["scenario"] = io.load_data(scenario_path)["name"] all_data.append(run_data) df_all = pd.concat(all_data, axis=0) df_all.reset_index(drop=True, inplace=True) if save_to_disk: df_all.to_pickle(self.path / "run_data.gz", compression="gzip") return df_all

"""Support for the Netatmo cameras.""" import logging import aiohttp import pyatmo import voluptuous as vol from openpeerpower.components.camera import SUPPORT_STREAM, Camera from openpeerpower.core import callback from openpeerpower.exceptions import PlatformNotReady from openpeerpower.helpers import config_validation as cv, entity_platform from openpeerpower.helpers.dispatcher import async_dispatcher_connect from .const import ( ATTR_CAMERA_LIGHT_MODE, ATTR_PERSON, ATTR_PERSONS, ATTR_PSEUDO, CAMERA_LIGHT_MODES, DATA_CAMERAS, DATA_EVENTS, DATA_HANDLER, DATA_PERSONS, DOMAIN, EVENT_TYPE_LIGHT_MODE, EVENT_TYPE_OFF, EVENT_TYPE_ON, MANUFACTURER, MODELS, SERVICE_SET_CAMERA_LIGHT, SERVICE_SET_PERSON_AWAY, SERVICE_SET_PERSONS_HOME, SIGNAL_NAME, WEBHOOK_LIGHT_MODE, WEBHOOK_NACAMERA_CONNECTION, WEBHOOK_PUSH_TYPE, ) from .data_handler import CAMERA_DATA_CLASS_NAME from .netatmo_entity_base import NetatmoBase _LOGGER = logging.getLogger(__name__) DEFAULT_QUALITY = "high" async def async_setup_entry(opp, entry, async_add_entities): """Set up the Netatmo camera platform.""" if "access_camera" not in entry.data["token"]["scope"]: _LOGGER.info( "Cameras are currently not supported with this authentication method" ) data_handler = opp.data[DOMAIN][entry.entry_id][DATA_HANDLER] await data_handler.register_data_class( CAMERA_DATA_CLASS_NAME, CAMERA_DATA_CLASS_NAME, None ) data_class = data_handler.data.get(CAMERA_DATA_CLASS_NAME) if not data_class or not data_class.raw_data: raise PlatformNotReady all_cameras = [] for home in data_class.cameras.values(): for camera in home.values(): all_cameras.append(camera) entities = [ NetatmoCamera( data_handler, camera["id"], camera["type"], camera["home_id"], DEFAULT_QUALITY, ) for camera in all_cameras ] for person_id, person_data in data_handler.data[ CAMERA_DATA_CLASS_NAME ].persons.items(): opp.data[DOMAIN][DATA_PERSONS][person_id] = person_data.get(ATTR_PSEUDO) _LOGGER.debug("Adding cameras %s", entities) async_add_entities(entities, True) platform = entity_platform.async_get_current_platform() platform.async_register_entity_service( SERVICE_SET_PERSONS_HOME, {vol.Required(ATTR_PERSONS): vol.All(cv.ensure_list, [cv.string])}, "_service_set_persons_home", ) platform.async_register_entity_service( SERVICE_SET_PERSON_AWAY, {vol.Optional(ATTR_PERSON): cv.string}, "_service_set_person_away", ) platform.async_register_entity_service( SERVICE_SET_CAMERA_LIGHT, {vol.Required(ATTR_CAMERA_LIGHT_MODE): vol.In(CAMERA_LIGHT_MODES)}, "_service_set_camera_light", ) class NetatmoCamera(NetatmoBase, Camera): """Representation of a Netatmo camera.""" def __init__( self, data_handler, camera_id, camera_type, home_id, quality, ): """Set up for access to the Netatmo camera images.""" Camera.__init__(self) super().__init__(data_handler) self._data_classes.append( {"name": CAMERA_DATA_CLASS_NAME, SIGNAL_NAME: CAMERA_DATA_CLASS_NAME} ) self._id = camera_id self._home_id = home_id self._device_name = self._data.get_camera(camera_id=camera_id).get("name") self._name = f"{MANUFACTURER} {self._device_name}" self._model = camera_type self._unique_id = f"{self._id}-{self._model}" self._quality = quality self._vpnurl = None self._localurl = None self._status = None self._sd_status = None self._alim_status = None self._is_local = None self._light_state = None async def async_added_to_opp(self) -> None: """Entity created.""" await super().async_added_to_opp() for event_type in (EVENT_TYPE_LIGHT_MODE, EVENT_TYPE_OFF, EVENT_TYPE_ON): self._listeners.append( async_dispatcher_connect( self.opp, f"signal-{DOMAIN}-webhook-{event_type}", self.handle_event, ) ) self.opp.data[DOMAIN][DATA_CAMERAS][self._id] = self._device_name @callback def handle_event(self, event): """Handle webhook events.""" data = event["data"] if not data.get("camera_id"): return if data["home_id"] == self._home_id and data["camera_id"] == self._id: if data[WEBHOOK_PUSH_TYPE] in ["NACamera-off", "NACamera-disconnection"]: self.is_streaming = False self._status = "off" elif data[WEBHOOK_PUSH_TYPE] in [ "NACamera-on", WEBHOOK_NACAMERA_CONNECTION, ]: self.is_streaming = True self._status = "on" elif data[WEBHOOK_PUSH_TYPE] == WEBHOOK_LIGHT_MODE: self._light_state = data["sub_type"] self.async_write_op_state() return async def async_camera_image(self): """Return a still image response from the camera.""" try: return await self._data.async_get_live_snapshot(camera_id=self._id) except ( aiohttp.ClientPayloadError, aiohttp.ContentTypeError, aiohttp.ServerDisconnectedError, aiohttp.ClientConnectorError, pyatmo.exceptions.ApiError, ) as err: _LOGGER.debug("Could not fetch live camera image (%s)", err) return None @property def extra_state_attributes(self): """Return the Netatmo-specific camera state attributes.""" return { "id": self._id, "status": self._status, "sd_status": self._sd_status, "alim_status": self._alim_status, "is_local": self._is_local, "vpn_url": self._vpnurl, "local_url": self._localurl, "light_state": self._light_state, } @property def available(self): """Return True if entity is available.""" return bool(self._alim_status == "on" or self._status == "disconnected") @property def supported_features(self): """Return supported features.""" return SUPPORT_STREAM @property def brand(self): """Return the camera brand.""" return MANUFACTURER @property def motion_detection_enabled(self): """Return the camera motion detection status.""" return bool(self._status == "on") @property def is_on(self): """Return true if on.""" return self.is_streaming async def async_turn_off(self): """Turn off camera.""" await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, monitoring="off" ) async def async_turn_on(self): """Turn on camera.""" await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, monitoring="on" ) async def stream_source(self): """Return the stream source.""" url = "{0}/live/files/{1}/index.m3u8" if self._localurl: return url.format(self._localurl, self._quality) return url.format(self._vpnurl, self._quality) @property def model(self): """Return the camera model.""" return MODELS[self._model] @callback def async_update_callback(self): """Update the entity's state.""" camera = self._data.get_camera(self._id) self._vpnurl, self._localurl = self._data.camera_urls(self._id) self._status = camera.get("status") self._sd_status = camera.get("sd_status") self._alim_status = camera.get("alim_status") self._is_local = camera.get("is_local") self.is_streaming = bool(self._status == "on") if self._model == "NACamera": # Smart Indoor Camera self.opp.data[DOMAIN][DATA_EVENTS][self._id] = self.process_events( self._data.events.get(self._id, {}) ) elif self._model == "NOC": # Smart Outdoor Camera self.opp.data[DOMAIN][DATA_EVENTS][self._id] = self.process_events( self._data.outdoor_events.get(self._id, {}) ) def process_events(self, events): """Add meta data to events.""" for event in events.values(): if "video_id" not in event: continue if self._is_local: event[ "media_url" ] = f"{self._localurl}/vod/{event["video_id"]}/files/{self._quality}/index.m3u8" else: event[ "media_url" ] = f"{self._vpnurl}/vod/{event["video_id"]}/files/{self._quality}/index.m3u8" return events async def _service_set_persons_home(self, **kwargs): """Service to change current home schedule.""" persons = kwargs.get(ATTR_PERSONS) person_ids = [] for person in persons: for pid, data in self._data.persons.items(): if data.get("pseudo") == person: person_ids.append(pid) await self._data.async_set_persons_home( person_ids=person_ids, home_id=self._home_id ) _LOGGER.debug("Set %s as at home", persons) async def _service_set_person_away(self, **kwargs): """Service to mark a person as away or set the home as empty.""" person = kwargs.get(ATTR_PERSON) person_id = None if person: for pid, data in self._data.persons.items(): if data.get("pseudo") == person: person_id = pid if person_id: await self._data.async_set_persons_away( person_id=person_id, home_id=self._home_id, ) _LOGGER.debug("Set %s as away", person) else: await self._data.async_set_persons_away( person_id=person_id, home_id=self._home_id, ) _LOGGER.debug("Set home as empty") async def _service_set_camera_light(self, **kwargs): """Service to set light mode.""" mode = kwargs.get(ATTR_CAMERA_LIGHT_MODE) _LOGGER.debug("Turn %s camera light for '%s'", mode, self._name) await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, floodlight=mode, )

"""Support for the Netatmo cameras.""" import logging import aiohttp import pyatmo import voluptuous as vol from openpeerpower.components.camera import SUPPORT_STREAM, Camera from openpeerpower.core import callback from openpeerpower.exceptions import PlatformNotReady from openpeerpower.helpers import config_validation as cv, entity_platform from openpeerpower.helpers.dispatcher import async_dispatcher_connect from .const import ( ATTR_CAMERA_LIGHT_MODE, ATTR_PERSON, ATTR_PERSONS, ATTR_PSEUDO, CAMERA_LIGHT_MODES, DATA_CAMERAS, DATA_EVENTS, DATA_HANDLER, DATA_PERSONS, DOMAIN, EVENT_TYPE_LIGHT_MODE, EVENT_TYPE_OFF, EVENT_TYPE_ON, MANUFACTURER, MODELS, SERVICE_SET_CAMERA_LIGHT, SERVICE_SET_PERSON_AWAY, SERVICE_SET_PERSONS_HOME, SIGNAL_NAME, WEBHOOK_LIGHT_MODE, WEBHOOK_NACAMERA_CONNECTION, WEBHOOK_PUSH_TYPE, ) from .data_handler import CAMERA_DATA_CLASS_NAME from .netatmo_entity_base import NetatmoBase _LOGGER = logging.getLogger(__name__) DEFAULT_QUALITY = "high" async def async_setup_entry(opp, entry, async_add_entities): """Set up the Netatmo camera platform.""" if "access_camera" not in entry.data["token"]["scope"]: _LOGGER.info( "Cameras are currently not supported with this authentication method" ) data_handler = opp.data[DOMAIN][entry.entry_id][DATA_HANDLER] await data_handler.register_data_class( CAMERA_DATA_CLASS_NAME, CAMERA_DATA_CLASS_NAME, None ) data_class = data_handler.data.get(CAMERA_DATA_CLASS_NAME) if not data_class or not data_class.raw_data: raise PlatformNotReady all_cameras = [] for home in data_class.cameras.values(): for camera in home.values(): all_cameras.append(camera) entities = [ NetatmoCamera( data_handler, camera["id"], camera["type"], camera["home_id"], DEFAULT_QUALITY, ) for camera in all_cameras ] for person_id, person_data in data_handler.data[ CAMERA_DATA_CLASS_NAME ].persons.items(): opp.data[DOMAIN][DATA_PERSONS][person_id] = person_data.get(ATTR_PSEUDO) _LOGGER.debug("Adding cameras %s", entities) async_add_entities(entities, True) platform = entity_platform.async_get_current_platform() platform.async_register_entity_service( SERVICE_SET_PERSONS_HOME, {vol.Required(ATTR_PERSONS): vol.All(cv.ensure_list, [cv.string])}, "_service_set_persons_home", ) platform.async_register_entity_service( SERVICE_SET_PERSON_AWAY, {vol.Optional(ATTR_PERSON): cv.string}, "_service_set_person_away", ) platform.async_register_entity_service( SERVICE_SET_CAMERA_LIGHT, {vol.Required(ATTR_CAMERA_LIGHT_MODE): vol.In(CAMERA_LIGHT_MODES)}, "_service_set_camera_light", ) class NetatmoCamera(NetatmoBase, Camera): """Representation of a Netatmo camera.""" def __init__( self, data_handler, camera_id, camera_type, home_id, quality, ): """Set up for access to the Netatmo camera images.""" Camera.__init__(self) super().__init__(data_handler) self._data_classes.append( {"name": CAMERA_DATA_CLASS_NAME, SIGNAL_NAME: CAMERA_DATA_CLASS_NAME} ) self._id = camera_id self._home_id = home_id self._device_name = self._data.get_camera(camera_id=camera_id).get("name") self._name = f"{MANUFACTURER} {self._device_name}" self._model = camera_type self._unique_id = f"{self._id}-{self._model}" self._quality = quality self._vpnurl = None self._localurl = None self._status = None self._sd_status = None self._alim_status = None self._is_local = None self._light_state = None async def async_added_to_opp(self) -> None: """Entity created.""" await super().async_added_to_opp() for event_type in (EVENT_TYPE_LIGHT_MODE, EVENT_TYPE_OFF, EVENT_TYPE_ON): self._listeners.append( async_dispatcher_connect( self.opp, f"signal-{DOMAIN}-webhook-{event_type}", self.handle_event, ) ) self.opp.data[DOMAIN][DATA_CAMERAS][self._id] = self._device_name @callback def handle_event(self, event): """Handle webhook events.""" data = event["data"] if not data.get("camera_id"): return if data["home_id"] == self._home_id and data["camera_id"] == self._id: if data[WEBHOOK_PUSH_TYPE] in ["NACamera-off", "NACamera-disconnection"]: self.is_streaming = False self._status = "off" elif data[WEBHOOK_PUSH_TYPE] in [ "NACamera-on", WEBHOOK_NACAMERA_CONNECTION, ]: self.is_streaming = True self._status = "on" elif data[WEBHOOK_PUSH_TYPE] == WEBHOOK_LIGHT_MODE: self._light_state = data["sub_type"] self.async_write_op_state() return async def async_camera_image(self): """Return a still image response from the camera.""" try: return await self._data.async_get_live_snapshot(camera_id=self._id) except ( aiohttp.ClientPayloadError, aiohttp.ContentTypeError, aiohttp.ServerDisconnectedError, aiohttp.ClientConnectorError, pyatmo.exceptions.ApiError, ) as err: _LOGGER.debug("Could not fetch live camera image (%s)", err) return None @property def extra_state_attributes(self): """Return the Netatmo-specific camera state attributes.""" return { "id": self._id, "status": self._status, "sd_status": self._sd_status, "alim_status": self._alim_status, "is_local": self._is_local, "vpn_url": self._vpnurl, "local_url": self._localurl, "light_state": self._light_state, } @property def available(self): """Return True if entity is available.""" return bool(self._alim_status == "on" or self._status == "disconnected") @property def supported_features(self): """Return supported features.""" return SUPPORT_STREAM @property def brand(self): """Return the camera brand.""" return MANUFACTURER @property def motion_detection_enabled(self): """Return the camera motion detection status.""" return bool(self._status == "on") @property def is_on(self): """Return true if on.""" return self.is_streaming async def async_turn_off(self): """Turn off camera.""" await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, monitoring="off" ) async def async_turn_on(self): """Turn on camera.""" await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, monitoring="on" ) async def stream_source(self): """Return the stream source.""" url = "{0}/live/files/{1}/index.m3u8" if self._localurl: return url.format(self._localurl, self._quality) return url.format(self._vpnurl, self._quality) @property def model(self): """Return the camera model.""" return MODELS[self._model] @callback def async_update_callback(self): """Update the entity's state.""" camera = self._data.get_camera(self._id) self._vpnurl, self._localurl = self._data.camera_urls(self._id) self._status = camera.get("status") self._sd_status = camera.get("sd_status") self._alim_status = camera.get("alim_status") self._is_local = camera.get("is_local") self.is_streaming = bool(self._status == "on") if self._model == "NACamera": # Smart Indoor Camera self.opp.data[DOMAIN][DATA_EVENTS][self._id] = self.process_events( self._data.events.get(self._id, {}) ) elif self._model == "NOC": # Smart Outdoor Camera self.opp.data[DOMAIN][DATA_EVENTS][self._id] = self.process_events( self._data.outdoor_events.get(self._id, {}) ) def process_events(self, events): """Add meta data to events.""" for event in events.values(): if "video_id" not in event: continue if self._is_local: event[ "media_url" ] = f"{self._localurl}/vod/{event['video_id']}/files/{self._quality}/index.m3u8" else: event[ "media_url" ] = f"{self._vpnurl}/vod/{event['video_id']}/files/{self._quality}/index.m3u8" return events async def _service_set_persons_home(self, **kwargs): """Service to change current home schedule.""" persons = kwargs.get(ATTR_PERSONS) person_ids = [] for person in persons: for pid, data in self._data.persons.items(): if data.get("pseudo") == person: person_ids.append(pid) await self._data.async_set_persons_home( person_ids=person_ids, home_id=self._home_id ) _LOGGER.debug("Set %s as at home", persons) async def _service_set_person_away(self, **kwargs): """Service to mark a person as away or set the home as empty.""" person = kwargs.get(ATTR_PERSON) person_id = None if person: for pid, data in self._data.persons.items(): if data.get("pseudo") == person: person_id = pid if person_id: await self._data.async_set_persons_away( person_id=person_id, home_id=self._home_id, ) _LOGGER.debug("Set %s as away", person) else: await self._data.async_set_persons_away( person_id=person_id, home_id=self._home_id, ) _LOGGER.debug("Set home as empty") async def _service_set_camera_light(self, **kwargs): """Service to set light mode.""" mode = kwargs.get(ATTR_CAMERA_LIGHT_MODE) _LOGGER.debug("Turn %s camera light for '%s'", mode, self._name) await self._data.async_set_state( home_id=self._home_id, camera_id=self._id, floodlight=mode, )

import os import re import math import json import tempfile import zipfile import bagit import asyncio from datetime import datetime from asyncio import events from ratelimit import sleep_and_retry from ratelimit.exception import RateLimitException from aiohttp import ClientSession, http_exceptions import internetarchive from datacite import DataCiteMDSClient from datacite.errors import DataCiteNotFoundError from osf_pigeon import settings async def stream_files_to_dir(from_url, to_dir, name): async with ClientSession() as session: async with session.get(from_url) as resp: with open(os.path.join(to_dir, name), "wb") as fp: async for chunk in resp.content.iter_any(): fp.write(chunk) async def dump_json_to_dir(from_url, to_dir, name, parse_json=None): pages = await get_paginated_data(from_url, parse_json) with open(os.path.join(to_dir, name), "w") as fp: json.dump(pages, fp) return pages def create_zip(temp_dir): with zipfile.ZipFile(os.path.join(temp_dir, "bag.zip"), "w") as fp: for root, dirs, files in os.walk(os.path.join(temp_dir, "bag")): for file in files: file_path = os.path.join(root, file) file_name = re.sub(f"^{temp_dir}", "", file_path) fp.write(file_path, arcname=file_name) async def get_relationship_attribute(key, url, func): data = await get_paginated_data(url) if "data" in data: return {key: list(map(func, data["data"]))} return {key: list(map(func, data))} async def get_metadata_for_ia_item(json_metadata): """ This is meant to take the response JSON metadata and format it for IA buckets, this is not used to generate JSON to be uploaded as raw data into the buckets. :param json_metadata: metadata from OSF registration view contains attributes and relationship urls. Note: Internet Archive advises that all metadata that points to internal OSF features should have a specific `osf_` prefix. Example: `registry` should be `osf_registry`, however metadata such as affiliated_institutions is self-explanatory and doesn't need a prefix. :return: ia_metadata the metadata for an IA bucket. Should include the following if they are not null: - publisher - title - description - date - osf_category - osf_subjects - osf_tags - osf_registration_doi - osf_registry - osf_registration_schema - creator (biblographic contributors, IA recommended this keyword) - article_doi - parent - children - source - affiliated_institutions - license """ relationship_data = [ get_relationship_attribute( "creator", f'{settings.OSF_API_URL}v2/registrations/{json_metadata['data']['id']}/contributors/' f"?filter[bibliographic]=true&", lambda contrib: contrib["embeds"]["users"]["data"]["attributes"][ "full_name" ], ), get_relationship_attribute( "affiliated_institutions", f'{settings.OSF_API_URL}v2/registrations/{json_metadata['data']['id']}/institutions/', lambda institution: institution["attributes"]["name"], ), get_relationship_attribute( "osf_subjects", f'{settings.OSF_API_URL}v2/registrations/{json_metadata['data']['id']}/subjects/', lambda subject: subject["attributes"]["text"], ), get_relationship_attribute( "children", f'{settings.OSF_API_URL}v2/registrations/{json_metadata['data']['id']}/children/', lambda child: f"https://archive.org/details/" f'{settings.REG_ID_TEMPLATE.format(guid=child['id'])}', ), ] relationship_data = { k: v for pair in await asyncio.gather(*relationship_data) for k, v in pair.items() } # merge all the pairs parent = json_metadata["data"]["relationships"]["parent"]["data"] if parent: relationship_data[ "parent" ] = f"https://archive.org/details/{settings.REG_ID_TEMPLATE.format(guid=parent["id"])}" embeds = json_metadata["data"]["embeds"] if not embeds["license"].get( "errors" ): # The reported error here is just a 404, so ignore if no license relationship_data["license"] = embeds["license"]["data"]["attributes"]["url"] doi = next( ( identifier["attributes"]["value"] for identifier in embeds["identifiers"]["data"] if identifier["attributes"]["category"] == "doi" ), None, ) osf_url = "/".join(json_metadata["data"]["links"]["html"].split("/")[:3]) + "/" attributes = json_metadata["data"]["attributes"] article_doi = json_metadata["data"]["attributes"]["article_doi"] ia_metadata = { "publisher": "Center for Open Science", "osf_registration_doi": doi, "title": attributes["title"], "description": attributes["description"], "osf_category": attributes["category"], "osf_tags": attributes["tags"], "date": str( datetime.strptime( attributes["date_created"], "%Y-%m-%dT%H:%M:%S.%fZ" ).date() ), "article_doi": f"urn:doi:{article_doi}" if article_doi else "", "osf_registry": embeds["provider"]["data"]["attributes"]["name"], "osf_registration_schema": embeds["registration_schema"]["data"]["attributes"][ "name" ], "source": osf_url + json_metadata["data"]["relationships"]["registered_from"]["data"]["id"], **relationship_data, } return ia_metadata async def write_datacite_metadata(guid, temp_dir, metadata): try: doi = next( ( identifier["attributes"]["value"] for identifier in metadata["data"]["embeds"]["identifiers"]["data"] if identifier["attributes"]["category"] == "doi" ) ) except StopIteration: raise DataCiteNotFoundError( f"Datacite DOI not found for registration {guid} on OSF server." ) client = DataCiteMDSClient( url=settings.DATACITE_URL, username=settings.DATACITE_USERNAME, password=settings.DATACITE_PASSWORD, prefix=settings.DATACITE_PREFIX, ) try: xml_metadata = client.metadata_get(doi) except DataCiteNotFoundError: raise DataCiteNotFoundError( f"Datacite DOI {doi} not found for registration {guid} on Datacite server." ) with open(os.path.join(temp_dir, "datacite.xml"), "w") as fp: fp.write(xml_metadata) return xml_metadata @sleep_and_retry async def get_with_retry(url, retry_on=(), sleep_period=None, headers=None): if not headers: headers = {} if settings.OSF_BEARER_TOKEN: headers["Authorization"] = f"Bearer {settings.OSF_BEARER_TOKEN}" async with ClientSession() as session: async with session.get(url, headers=headers) as resp: if resp.status in retry_on: raise RateLimitException( message="Too many requests, sleeping.", period_remaining=sleep_period or int(resp.headers.get("Retry-After") or 0), ) # This will be caught by @sleep_and_retry and retried resp.raise_for_status() return await resp.json() async def get_pages(url, page, result={}, parse_json=None): url = f"{url}?page={page}&page={page}" data = await get_with_retry(url, retry_on=(429,)) result[page] = data["data"] if parse_json: result[page] = parse_json(data)["data"] return result async def get_additional_contributor_info(response): contributor_data_list = [] for contributor in response["data"]: contributor_data = {} embed_data = contributor["embeds"]["users"]["data"] institution_url = embed_data["relationships"]["institutions"]["links"][ "related" ]["href"] data = await get_with_retry(institution_url) institution_data = data["data"] institution_list = [ institution["attributes"]["name"] for institution in institution_data ] contributor_data["affiliated_institutions"] = institution_list contributor.update(contributor_data) contributor_data_list.append(contributor) response["data"] = contributor_data_list return response async def get_paginated_data(url, parse_json=None): data = await get_with_retry(url, retry_on=(429,)) tasks = [] is_paginated = data.get("links", {}).get("next") if parse_json: data = await parse_json(data) if is_paginated: result = {1: data["data"]} total = data["links"].get("meta", {}).get("total") or data["meta"].get("total") per_page = data["links"].get("meta", {}).get("per_page") or data["meta"].get( "per_page" ) pages = math.ceil(int(total) / int(per_page)) for i in range(1, pages): task = get_pages(url, i + 1, result) tasks.append(task) await asyncio.gather(*tasks) pages_as_list = [] # through the magic of async all our pages have loaded. for page in list(result.values()): pages_as_list += page return pages_as_list else: return data def get_ia_item(guid): session = internetarchive.get_session( config={ "s3": {"access": settings.IA_ACCESS_KEY, "secret": settings.IA_SECRET_KEY}, }, ) return session.get_item(guid) def sync_metadata(guid, metadata): """ This is used to sync the metadata of archive.org items with OSF Registrations. synced is as follows: - title - description - date - category - subjects - tags - affiliated_institutions - license - article_doi `moderation_state` is an allowable key, but only to determine a withdrawal status of a registration. :param guid: :param metadata: :return: """ if not metadata: raise http_exceptions.PayloadEncodingError( "Metadata Payload not included in request" ) valid_updatable_metadata_keys = [ "title", "description", "date", "modified", "osf_category", "osf_subjects", "osf_tags", "article_doi", "affiliated_institutions", "license", "withdrawal_justification", ] invalid_keys = set(metadata.keys()).difference(set(valid_updatable_metadata_keys)) if invalid_keys: raise http_exceptions.PayloadEncodingError( f"Metadata payload contained invalid tag(s): `{", ".join(list(invalid_keys))}`" f" not included in valid keys: `{", ".join(valid_updatable_metadata_keys)}`.", ) item_name = settings.REG_ID_TEMPLATE.format(guid=guid) ia_item = get_ia_item(item_name) if not metadata.get("withdrawal_justification"): # withdrawn == not searchable ia_item.modify_metadata(metadata) else: description = ia_item.metadata.get("description") if description: metadata[ "description" ] = f"Note this registration has been withdrawn: \n{description}" else: metadata["description"] = "This registration has been withdrawn" ia_item.modify_metadata(metadata) ia_item.modify_metadata({"noindex": True}) return ia_item, list(metadata.keys()) async def upload(item_name, temp_dir, metadata): ia_item = get_ia_item(item_name) ia_metadata = await get_metadata_for_ia_item(metadata) provider_id = metadata["data"]["embeds"]["provider"]["data"]["id"] ia_item.upload( os.path.join(temp_dir, "bag.zip"), metadata={ "collection": settings.PROVIDER_ID_TEMPLATE.format(provider_id=provider_id), **ia_metadata, }, access_key=settings.IA_ACCESS_KEY, secret_key=settings.IA_SECRET_KEY, ) return ia_item async def get_registration_metadata(guid, temp_dir, filename): metadata = await get_paginated_data( f"{settings.OSF_API_URL}v2/registrations/{guid}/" f"?embed=parent" f"&embed=children" f"&embed=provider" f"&embed=identifiers" f"&embed=license" f"&embed=registration_schema" f"&related_counts=true" f"&version=2.20" ) if metadata["data"]["attributes"]["withdrawn"]: raise PermissionError(f"Registration {guid} is withdrawn") with open(os.path.join(temp_dir, filename), "w") as fp: json.dump(metadata, fp) return metadata async def archive(guid): with tempfile.TemporaryDirectory( dir=settings.PIGEON_TEMP_DIR, prefix=settings.REG_ID_TEMPLATE.format(guid=guid) ) as temp_dir: os.mkdir(os.path.join(temp_dir, "bag")) # await first to check if withdrawn metadata = await get_registration_metadata(guid, os.path.join(temp_dir, "bag"), "registration.json") tasks = [ write_datacite_metadata(guid, temp_dir, metadata), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/wikis/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="wikis.json", ), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/logs/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="logs.json", ), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/contributors/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="contributors.json", parse_json=get_additional_contributor_info, ), ] # only download archived data if there are files file_count = metadata["data"]["relationships"]["files"]["links"]["related"][ "meta" ]["count"] if file_count: tasks.append( stream_files_to_dir( f"{settings.OSF_FILES_URL}v1/resources/{guid}/providers/osfstorage/?zip=", os.path.join(temp_dir, "bag"), "archived_files.zip", ) ) await asyncio.gather(*tasks) bagit.make_bag(os.path.join(temp_dir, "bag")) bag = bagit.Bag(os.path.join(temp_dir, "bag")) assert bag.is_valid() create_zip(temp_dir) ia_item = await upload( settings.REG_ID_TEMPLATE.format(guid=guid), temp_dir, metadata ) return ia_item, guid def run(coroutine): loop = events.new_event_loop() try: events.set_event_loop(loop) return loop.run_until_complete(coroutine) finally: try: loop.run_until_complete(loop.shutdown_asyncgens()) finally: events.set_event_loop(None) loop.close()

import os import re import math import json import tempfile import zipfile import bagit import asyncio from datetime import datetime from asyncio import events from ratelimit import sleep_and_retry from ratelimit.exception import RateLimitException from aiohttp import ClientSession, http_exceptions import internetarchive from datacite import DataCiteMDSClient from datacite.errors import DataCiteNotFoundError from osf_pigeon import settings async def stream_files_to_dir(from_url, to_dir, name): async with ClientSession() as session: async with session.get(from_url) as resp: with open(os.path.join(to_dir, name), "wb") as fp: async for chunk in resp.content.iter_any(): fp.write(chunk) async def dump_json_to_dir(from_url, to_dir, name, parse_json=None): pages = await get_paginated_data(from_url, parse_json) with open(os.path.join(to_dir, name), "w") as fp: json.dump(pages, fp) return pages def create_zip(temp_dir): with zipfile.ZipFile(os.path.join(temp_dir, "bag.zip"), "w") as fp: for root, dirs, files in os.walk(os.path.join(temp_dir, "bag")): for file in files: file_path = os.path.join(root, file) file_name = re.sub(f"^{temp_dir}", "", file_path) fp.write(file_path, arcname=file_name) async def get_relationship_attribute(key, url, func): data = await get_paginated_data(url) if "data" in data: return {key: list(map(func, data["data"]))} return {key: list(map(func, data))} async def get_metadata_for_ia_item(json_metadata): """ This is meant to take the response JSON metadata and format it for IA buckets, this is not used to generate JSON to be uploaded as raw data into the buckets. :param json_metadata: metadata from OSF registration view contains attributes and relationship urls. Note: Internet Archive advises that all metadata that points to internal OSF features should have a specific `osf_` prefix. Example: `registry` should be `osf_registry`, however metadata such as affiliated_institutions is self-explanatory and doesn't need a prefix. :return: ia_metadata the metadata for an IA bucket. Should include the following if they are not null: - publisher - title - description - date - osf_category - osf_subjects - osf_tags - osf_registration_doi - osf_registry - osf_registration_schema - creator (biblographic contributors, IA recommended this keyword) - article_doi - parent - children - source - affiliated_institutions - license """ relationship_data = [ get_relationship_attribute( "creator", f'{settings.OSF_API_URL}v2/registrations/{json_metadata["data"]["id"]}/contributors/' f"?filter[bibliographic]=true&", lambda contrib: contrib["embeds"]["users"]["data"]["attributes"][ "full_name" ], ), get_relationship_attribute( "affiliated_institutions", f'{settings.OSF_API_URL}v2/registrations/{json_metadata["data"]["id"]}/institutions/', lambda institution: institution["attributes"]["name"], ), get_relationship_attribute( "osf_subjects", f'{settings.OSF_API_URL}v2/registrations/{json_metadata["data"]["id"]}/subjects/', lambda subject: subject["attributes"]["text"], ), get_relationship_attribute( "children", f'{settings.OSF_API_URL}v2/registrations/{json_metadata["data"]["id"]}/children/', lambda child: f"https://archive.org/details/" f'{settings.REG_ID_TEMPLATE.format(guid=child["id"])}', ), ] relationship_data = { k: v for pair in await asyncio.gather(*relationship_data) for k, v in pair.items() } # merge all the pairs parent = json_metadata["data"]["relationships"]["parent"]["data"] if parent: relationship_data[ "parent" ] = f"https://archive.org/details/{settings.REG_ID_TEMPLATE.format(guid=parent['id'])}" embeds = json_metadata["data"]["embeds"] if not embeds["license"].get( "errors" ): # The reported error here is just a 404, so ignore if no license relationship_data["license"] = embeds["license"]["data"]["attributes"]["url"] doi = next( ( identifier["attributes"]["value"] for identifier in embeds["identifiers"]["data"] if identifier["attributes"]["category"] == "doi" ), None, ) osf_url = "/".join(json_metadata["data"]["links"]["html"].split("/")[:3]) + "/" attributes = json_metadata["data"]["attributes"] article_doi = json_metadata["data"]["attributes"]["article_doi"] ia_metadata = { "publisher": "Center for Open Science", "osf_registration_doi": doi, "title": attributes["title"], "description": attributes["description"], "osf_category": attributes["category"], "osf_tags": attributes["tags"], "date": str( datetime.strptime( attributes["date_created"], "%Y-%m-%dT%H:%M:%S.%fZ" ).date() ), "article_doi": f"urn:doi:{article_doi}" if article_doi else "", "osf_registry": embeds["provider"]["data"]["attributes"]["name"], "osf_registration_schema": embeds["registration_schema"]["data"]["attributes"][ "name" ], "source": osf_url + json_metadata["data"]["relationships"]["registered_from"]["data"]["id"], **relationship_data, } return ia_metadata async def write_datacite_metadata(guid, temp_dir, metadata): try: doi = next( ( identifier["attributes"]["value"] for identifier in metadata["data"]["embeds"]["identifiers"]["data"] if identifier["attributes"]["category"] == "doi" ) ) except StopIteration: raise DataCiteNotFoundError( f"Datacite DOI not found for registration {guid} on OSF server." ) client = DataCiteMDSClient( url=settings.DATACITE_URL, username=settings.DATACITE_USERNAME, password=settings.DATACITE_PASSWORD, prefix=settings.DATACITE_PREFIX, ) try: xml_metadata = client.metadata_get(doi) except DataCiteNotFoundError: raise DataCiteNotFoundError( f"Datacite DOI {doi} not found for registration {guid} on Datacite server." ) with open(os.path.join(temp_dir, "datacite.xml"), "w") as fp: fp.write(xml_metadata) return xml_metadata @sleep_and_retry async def get_with_retry(url, retry_on=(), sleep_period=None, headers=None): if not headers: headers = {} if settings.OSF_BEARER_TOKEN: headers["Authorization"] = f"Bearer {settings.OSF_BEARER_TOKEN}" async with ClientSession() as session: async with session.get(url, headers=headers) as resp: if resp.status in retry_on: raise RateLimitException( message="Too many requests, sleeping.", period_remaining=sleep_period or int(resp.headers.get("Retry-After") or 0), ) # This will be caught by @sleep_and_retry and retried resp.raise_for_status() return await resp.json() async def get_pages(url, page, result={}, parse_json=None): url = f"{url}?page={page}&page={page}" data = await get_with_retry(url, retry_on=(429,)) result[page] = data["data"] if parse_json: result[page] = parse_json(data)["data"] return result async def get_additional_contributor_info(response): contributor_data_list = [] for contributor in response["data"]: contributor_data = {} embed_data = contributor["embeds"]["users"]["data"] institution_url = embed_data["relationships"]["institutions"]["links"][ "related" ]["href"] data = await get_with_retry(institution_url) institution_data = data["data"] institution_list = [ institution["attributes"]["name"] for institution in institution_data ] contributor_data["affiliated_institutions"] = institution_list contributor.update(contributor_data) contributor_data_list.append(contributor) response["data"] = contributor_data_list return response async def get_paginated_data(url, parse_json=None): data = await get_with_retry(url, retry_on=(429,)) tasks = [] is_paginated = data.get("links", {}).get("next") if parse_json: data = await parse_json(data) if is_paginated: result = {1: data["data"]} total = data["links"].get("meta", {}).get("total") or data["meta"].get("total") per_page = data["links"].get("meta", {}).get("per_page") or data["meta"].get( "per_page" ) pages = math.ceil(int(total) / int(per_page)) for i in range(1, pages): task = get_pages(url, i + 1, result) tasks.append(task) await asyncio.gather(*tasks) pages_as_list = [] # through the magic of async all our pages have loaded. for page in list(result.values()): pages_as_list += page return pages_as_list else: return data def get_ia_item(guid): session = internetarchive.get_session( config={ "s3": {"access": settings.IA_ACCESS_KEY, "secret": settings.IA_SECRET_KEY}, }, ) return session.get_item(guid) def sync_metadata(guid, metadata): """ This is used to sync the metadata of archive.org items with OSF Registrations. synced is as follows: - title - description - date - category - subjects - tags - affiliated_institutions - license - article_doi `moderation_state` is an allowable key, but only to determine a withdrawal status of a registration. :param guid: :param metadata: :return: """ if not metadata: raise http_exceptions.PayloadEncodingError( "Metadata Payload not included in request" ) valid_updatable_metadata_keys = [ "title", "description", "date", "modified", "osf_category", "osf_subjects", "osf_tags", "article_doi", "affiliated_institutions", "license", "withdrawal_justification", ] invalid_keys = set(metadata.keys()).difference(set(valid_updatable_metadata_keys)) if invalid_keys: raise http_exceptions.PayloadEncodingError( f"Metadata payload contained invalid tag(s): `{', '.join(list(invalid_keys))}`" f" not included in valid keys: `{', '.join(valid_updatable_metadata_keys)}`.", ) item_name = settings.REG_ID_TEMPLATE.format(guid=guid) ia_item = get_ia_item(item_name) if not metadata.get("withdrawal_justification"): # withdrawn == not searchable ia_item.modify_metadata(metadata) else: description = ia_item.metadata.get("description") if description: metadata[ "description" ] = f"Note this registration has been withdrawn: \n{description}" else: metadata["description"] = "This registration has been withdrawn" ia_item.modify_metadata(metadata) ia_item.modify_metadata({"noindex": True}) return ia_item, list(metadata.keys()) async def upload(item_name, temp_dir, metadata): ia_item = get_ia_item(item_name) ia_metadata = await get_metadata_for_ia_item(metadata) provider_id = metadata["data"]["embeds"]["provider"]["data"]["id"] ia_item.upload( os.path.join(temp_dir, "bag.zip"), metadata={ "collection": settings.PROVIDER_ID_TEMPLATE.format(provider_id=provider_id), **ia_metadata, }, access_key=settings.IA_ACCESS_KEY, secret_key=settings.IA_SECRET_KEY, ) return ia_item async def get_registration_metadata(guid, temp_dir, filename): metadata = await get_paginated_data( f"{settings.OSF_API_URL}v2/registrations/{guid}/" f"?embed=parent" f"&embed=children" f"&embed=provider" f"&embed=identifiers" f"&embed=license" f"&embed=registration_schema" f"&related_counts=true" f"&version=2.20" ) if metadata["data"]["attributes"]["withdrawn"]: raise PermissionError(f"Registration {guid} is withdrawn") with open(os.path.join(temp_dir, filename), "w") as fp: json.dump(metadata, fp) return metadata async def archive(guid): with tempfile.TemporaryDirectory( dir=settings.PIGEON_TEMP_DIR, prefix=settings.REG_ID_TEMPLATE.format(guid=guid) ) as temp_dir: os.mkdir(os.path.join(temp_dir, "bag")) # await first to check if withdrawn metadata = await get_registration_metadata(guid, os.path.join(temp_dir, "bag"), "registration.json") tasks = [ write_datacite_metadata(guid, temp_dir, metadata), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/wikis/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="wikis.json", ), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/logs/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="logs.json", ), dump_json_to_dir( from_url=f"{settings.OSF_API_URL}v2/registrations/{guid}/contributors/" f"?page[size]=100", to_dir=os.path.join(temp_dir, "bag"), name="contributors.json", parse_json=get_additional_contributor_info, ), ] # only download archived data if there are files file_count = metadata["data"]["relationships"]["files"]["links"]["related"][ "meta" ]["count"] if file_count: tasks.append( stream_files_to_dir( f"{settings.OSF_FILES_URL}v1/resources/{guid}/providers/osfstorage/?zip=", os.path.join(temp_dir, "bag"), "archived_files.zip", ) ) await asyncio.gather(*tasks) bagit.make_bag(os.path.join(temp_dir, "bag")) bag = bagit.Bag(os.path.join(temp_dir, "bag")) assert bag.is_valid() create_zip(temp_dir) ia_item = await upload( settings.REG_ID_TEMPLATE.format(guid=guid), temp_dir, metadata ) return ia_item, guid def run(coroutine): loop = events.new_event_loop() try: events.set_event_loop(loop) return loop.run_until_complete(coroutine) finally: try: loop.run_until_complete(loop.shutdown_asyncgens()) finally: events.set_event_loop(None) loop.close()

import re import smtplib from email.message import EmailMessage from collections import defaultdict from datetime import datetime, timedelta from typing import Union, List, Dict # for 3.7 we need typing.List, typing.Dict, etc... RebuiltLogLines = List[str] ResultData = Union[int, RebuiltLogLines] PerDayDict = Dict[str, ResultData] ResultDict = Dict[str, PerDayDict] FILE_LOCATION = "/home/yman/gandi_logs/access.log" JUNK_QUERIES = frozenset(["robots.txt", "detectproxy", "wp-", "preview=true", "internal dummy connection", "xmlrpc", "favicon"]) JUNK_AGENTS = frozenset(["wordpress", "bytespider", "barkrowler", "go-http-client", "scaninfo@paloaltonetworks.com", "go http package", "netsystemsresearch.com", "favicon"]) LOG_REGEX = re.compile(r"""(?:\S+)\s # server name (?P<clip>[0-9a-f:.]+) # remote IP \s-\s-\s # remote logname + user \[(?P<ts>.+?)\]\s # timestamp $\d+\ss$\s # processing time \"(?P<req>.+?)\"\s # request (?P<status>\d+)\s # status code (?:-|\d+)\s # response size \".+?\"\s # referer \"(?P<uagent>.+)\" # user-agent""", re.VERBOSE) SENDER = "parser@yman.site" RECEIVER = "yman@protonmail.ch" def is_junk_request(request: str, agent: str) -> bool: """Filter user-agents that contain line from 'junk_agents' list, i.e. bots and some misc stuff.""" request = request.lower() agent = agent.lower() if any(word in request for word in JUNK_QUERIES): return True if re.search(r"bot\W", agent): return True if any(word in agent for word in JUNK_AGENTS): return True return False def parse_access_log(file_location: str) -> ResultDict: """Parse httpd access log (Gandi simple hosting instance config), filter out uninteresting requests, bogus, bots, etc. Good lines will be packed into per-day dict with hits count and altered log lines.""" result = defaultdict(lambda: {"hits": 0, "lines": []}) with open(file_location, "r", encoding="utf-8") as logfile: for line in logfile: match_result = LOG_REGEX.match(line) if not match_result: print("Can not parse line:", line) continue (clip, timestamp, request, status_code, uagent) = match_result.groups() status_code = int(status_code) # filter bots and maintenance requests if is_junk_request(request, uagent): continue # filter not successful status codes if status_code < 200 or status_code > 299: continue full_ts = datetime.strptime(timestamp, "%d/%b/%Y:%H:%M:%S %z") day = datetime.strftime(full_ts, "%d-%m-%Y") result[day]["hits"] += 1 rebuilt_line = f"{clip} : {status_code} : {request} : {uagent}" result[day]["lines"].append(rebuilt_line) return result def email_results(date: str, one_day_data: PerDayDict) -> None: """Email results of one day.""" report_string = f"Results for {date}\nTotal hits: {one_day_data["hits"]}\n" for line in one_day_data["lines"]: report_string = f"{report_string}{line}\n" msg = EmailMessage() msg.set_content(report_string[:-1]) msg["Subject"] = "Daily dvjourney access.log parse results" msg["From"] = SENDER msg["To"] = RECEIVER server = smtplib.SMTP("localhost") server.set_debuglevel(1) server.send_message(msg) server.quit() if __name__ == "__main__": res = parse_access_log(FILE_LOCATION) yesterday = datetime.now() - timedelta(days=1) yesterday_formatted = datetime.strftime(yesterday, "%d-%m-%Y") email_results(yesterday_formatted, res[yesterday_formatted])

import re import smtplib from email.message import EmailMessage from collections import defaultdict from datetime import datetime, timedelta from typing import Union, List, Dict # for 3.7 we need typing.List, typing.Dict, etc... RebuiltLogLines = List[str] ResultData = Union[int, RebuiltLogLines] PerDayDict = Dict[str, ResultData] ResultDict = Dict[str, PerDayDict] FILE_LOCATION = "/home/yman/gandi_logs/access.log" JUNK_QUERIES = frozenset(["robots.txt", "detectproxy", "wp-", "preview=true", "internal dummy connection", "xmlrpc", "favicon"]) JUNK_AGENTS = frozenset(["wordpress", "bytespider", "barkrowler", "go-http-client", "scaninfo@paloaltonetworks.com", "go http package", "netsystemsresearch.com", "favicon"]) LOG_REGEX = re.compile(r"""(?:\S+)\s # server name (?P<clip>[0-9a-f:.]+) # remote IP \s-\s-\s # remote logname + user \[(?P<ts>.+?)\]\s # timestamp $\d+\ss$\s # processing time \"(?P<req>.+?)\"\s # request (?P<status>\d+)\s # status code (?:-|\d+)\s # response size \".+?\"\s # referer \"(?P<uagent>.+)\" # user-agent""", re.VERBOSE) SENDER = "parser@yman.site" RECEIVER = "yman@protonmail.ch" def is_junk_request(request: str, agent: str) -> bool: """Filter user-agents that contain line from 'junk_agents' list, i.e. bots and some misc stuff.""" request = request.lower() agent = agent.lower() if any(word in request for word in JUNK_QUERIES): return True if re.search(r"bot\W", agent): return True if any(word in agent for word in JUNK_AGENTS): return True return False def parse_access_log(file_location: str) -> ResultDict: """Parse httpd access log (Gandi simple hosting instance config), filter out uninteresting requests, bogus, bots, etc. Good lines will be packed into per-day dict with hits count and altered log lines.""" result = defaultdict(lambda: {"hits": 0, "lines": []}) with open(file_location, "r", encoding="utf-8") as logfile: for line in logfile: match_result = LOG_REGEX.match(line) if not match_result: print("Can not parse line:", line) continue (clip, timestamp, request, status_code, uagent) = match_result.groups() status_code = int(status_code) # filter bots and maintenance requests if is_junk_request(request, uagent): continue # filter not successful status codes if status_code < 200 or status_code > 299: continue full_ts = datetime.strptime(timestamp, "%d/%b/%Y:%H:%M:%S %z") day = datetime.strftime(full_ts, "%d-%m-%Y") result[day]["hits"] += 1 rebuilt_line = f"{clip} : {status_code} : {request} : {uagent}" result[day]["lines"].append(rebuilt_line) return result def email_results(date: str, one_day_data: PerDayDict) -> None: """Email results of one day.""" report_string = f"Results for {date}\nTotal hits: {one_day_data['hits']}\n" for line in one_day_data["lines"]: report_string = f"{report_string}{line}\n" msg = EmailMessage() msg.set_content(report_string[:-1]) msg["Subject"] = "Daily dvjourney access.log parse results" msg["From"] = SENDER msg["To"] = RECEIVER server = smtplib.SMTP("localhost") server.set_debuglevel(1) server.send_message(msg) server.quit() if __name__ == "__main__": res = parse_access_log(FILE_LOCATION) yesterday = datetime.now() - timedelta(days=1) yesterday_formatted = datetime.strftime(yesterday, "%d-%m-%Y") email_results(yesterday_formatted, res[yesterday_formatted])

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @NetflixMovieslk import re import pyrogram from pyrogram import ( filters, Client ) from pyrogram.types import ( InlineKeyboardButton, InlineKeyboardMarkup, Message, CallbackQuery ) from bot import Bot from script import script from database.mdb import searchquery from plugins.channel import deleteallfilters from config import AUTH_USERS BUTTONS = {} @Client.on_message(filters.group & filters.text) async def filter(client: Bot, message: Message): if re.findall("((^\/|^,|^!|^\.|^[\U0001F600-\U000E007F]).*)", message.text): return if 2 < len(message.text) < 50: btn = [] group_id = message.chat.id name = message.text filenames, links = await searchquery(group_id, name) if filenames and links: for filename, link in zip(filenames, links): btn.append( [InlineKeyboardButton(text=f"{filename}",url=f"{link}")] ) else: return if not btn: return if len(btn) > 10: btns = list(split_list(btn, 10)) keyword = f"{message.chat.id}-{message.message_id}" BUTTONS[keyword] = { "total" : len(btns), "buttons" : btns } else: buttons = btn buttons.append( [InlineKeyboardButton(text="📃 Pages 1/1",callback_data="pages")] ) await message.reply_text( f"<b> Here is the result for {message.text}</b>", reply_markup=InlineKeyboardMarkup(buttons) ) return data = BUTTONS[keyword] buttons = data['buttons'][0].copy() buttons.append( [InlineKeyboardButton(text="NEXT ⏩",callback_data=f"next_0_{keyword}")] ) buttons.append( [InlineKeyboardButton(text=f"📃 Pages 1/{data["total"]}",callback_data="pages")] ) await message.reply_text( f"<b> Here is the result for {message.text}</b>", reply_markup=InlineKeyboardMarkup(buttons) ) @Client.on_callback_query() async def cb_handler(client: Bot, query: CallbackQuery): clicked = query.from_user.id typed = query.message.reply_to_message.from_user.id if (clicked == typed) or (clicked in AUTH_USERS): if query.data.startswith("next"): await query.answer() ident, index, keyword = query.data.split("_") data = BUTTONS[keyword] if int(index) == int(data["total"]) - 2: buttons = data['buttons'][int(index)+1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)+1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return else: buttons = data['buttons'][int(index)+1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)+1}_{keyword}"),InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)+1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data["total"]}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return elif query.data.startswith("back"): await query.answer() ident, index, keyword = query.data.split("_") data = BUTTONS[keyword] if int(index) == 1: buttons = data['buttons'][int(index)-1].copy() buttons.append( [InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)-1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return else: buttons = data['buttons'][int(index)-1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)-1}_{keyword}"),InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)-1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)}/{data["total"]}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return elif query.data == "pages": await query.answer() elif query.data == "start_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("HELP", callback_data="help_data"), InlineKeyboardButton("ABOUT", callback_data="about_data")], [InlineKeyboardButton("⭕️ JOIN OUR CHANNEL ⭕️", url="https://t.me/NetflixMovieslk")] ]) await query.message.edit_text( script.START_MSG.format(query.from_user.mention), reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "help_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("BACK", callback_data="start_data"), InlineKeyboardButton("ABOUT", callback_data="about_data")], [InlineKeyboardButton("⭕️ SUPPORT ⭕️", url="https://t.me/NetflixMovieslk")] ]) await query.message.edit_text( script.HELP_MSG, reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "about_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("BACK", callback_data="help_data"), InlineKeyboardButton("START", callback_data="start_data")], [InlineKeyboardButton("SOURCE CODE", url="https://github.com/TroJanzHEX/Auto-Filter-Bot-V2")] ]) await query.message.edit_text( script.ABOUT_MSG, reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "delallconfirm": await query.message.delete() await deleteallfilters(client, query.message) elif query.data == "delallcancel": await query.message.reply_to_message.delete() await query.message.delete() else: await query.answer("Thats not for you!!",show_alert=True) def split_list(l, n): for i in range(0, len(l), n): yield l[i:i + n]

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @NetflixMovieslk import re import pyrogram from pyrogram import ( filters, Client ) from pyrogram.types import ( InlineKeyboardButton, InlineKeyboardMarkup, Message, CallbackQuery ) from bot import Bot from script import script from database.mdb import searchquery from plugins.channel import deleteallfilters from config import AUTH_USERS BUTTONS = {} @Client.on_message(filters.group & filters.text) async def filter(client: Bot, message: Message): if re.findall("((^\/|^,|^!|^\.|^[\U0001F600-\U000E007F]).*)", message.text): return if 2 < len(message.text) < 50: btn = [] group_id = message.chat.id name = message.text filenames, links = await searchquery(group_id, name) if filenames and links: for filename, link in zip(filenames, links): btn.append( [InlineKeyboardButton(text=f"{filename}",url=f"{link}")] ) else: return if not btn: return if len(btn) > 10: btns = list(split_list(btn, 10)) keyword = f"{message.chat.id}-{message.message_id}" BUTTONS[keyword] = { "total" : len(btns), "buttons" : btns } else: buttons = btn buttons.append( [InlineKeyboardButton(text="📃 Pages 1/1",callback_data="pages")] ) await message.reply_text( f"<b> Here is the result for {message.text}</b>", reply_markup=InlineKeyboardMarkup(buttons) ) return data = BUTTONS[keyword] buttons = data['buttons'][0].copy() buttons.append( [InlineKeyboardButton(text="NEXT ⏩",callback_data=f"next_0_{keyword}")] ) buttons.append( [InlineKeyboardButton(text=f"📃 Pages 1/{data['total']}",callback_data="pages")] ) await message.reply_text( f"<b> Here is the result for {message.text}</b>", reply_markup=InlineKeyboardMarkup(buttons) ) @Client.on_callback_query() async def cb_handler(client: Bot, query: CallbackQuery): clicked = query.from_user.id typed = query.message.reply_to_message.from_user.id if (clicked == typed) or (clicked in AUTH_USERS): if query.data.startswith("next"): await query.answer() ident, index, keyword = query.data.split("_") data = BUTTONS[keyword] if int(index) == int(data["total"]) - 2: buttons = data['buttons'][int(index)+1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)+1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return else: buttons = data['buttons'][int(index)+1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)+1}_{keyword}"),InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)+1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)+2}/{data['total']}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return elif query.data.startswith("back"): await query.answer() ident, index, keyword = query.data.split("_") data = BUTTONS[keyword] if int(index) == 1: buttons = data['buttons'][int(index)-1].copy() buttons.append( [InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)-1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return else: buttons = data['buttons'][int(index)-1].copy() buttons.append( [InlineKeyboardButton("⏪ BACK", callback_data=f"back_{int(index)-1}_{keyword}"),InlineKeyboardButton("NEXT ⏩", callback_data=f"next_{int(index)-1}_{keyword}")] ) buttons.append( [InlineKeyboardButton(f"📃 Pages {int(index)}/{data['total']}", callback_data="pages")] ) await query.edit_message_reply_markup( reply_markup=InlineKeyboardMarkup(buttons) ) return elif query.data == "pages": await query.answer() elif query.data == "start_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("HELP", callback_data="help_data"), InlineKeyboardButton("ABOUT", callback_data="about_data")], [InlineKeyboardButton("⭕️ JOIN OUR CHANNEL ⭕️", url="https://t.me/NetflixMovieslk")] ]) await query.message.edit_text( script.START_MSG.format(query.from_user.mention), reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "help_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("BACK", callback_data="start_data"), InlineKeyboardButton("ABOUT", callback_data="about_data")], [InlineKeyboardButton("⭕️ SUPPORT ⭕️", url="https://t.me/NetflixMovieslk")] ]) await query.message.edit_text( script.HELP_MSG, reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "about_data": await query.answer() keyboard = InlineKeyboardMarkup([ [InlineKeyboardButton("BACK", callback_data="help_data"), InlineKeyboardButton("START", callback_data="start_data")], [InlineKeyboardButton("SOURCE CODE", url="https://github.com/TroJanzHEX/Auto-Filter-Bot-V2")] ]) await query.message.edit_text( script.ABOUT_MSG, reply_markup=keyboard, disable_web_page_preview=True ) elif query.data == "delallconfirm": await query.message.delete() await deleteallfilters(client, query.message) elif query.data == "delallcancel": await query.message.reply_to_message.delete() await query.message.delete() else: await query.answer("Thats not for you!!",show_alert=True) def split_list(l, n): for i in range(0, len(l), n): yield l[i:i + n]

import json import discord from discord.ext import commands, tasks from utils import * class HelpCmd(commands.HelpCommand): async def send_bot_help(self, mapping): ctx = self.context bot = ctx.bot commands = bot.commands result = [] for cmd in commands: sign = self.get_command_signature(cmd) result.append(f"`{sign.strip()}`: {cmd.help}") await ctx.send("\n".join(result)) send_cog_help = send_command_help = send_group_help = send_bot_help class MsgLeaderBot(commands.Bot): def __init__(self): helpattr = {"usage": ""} super().__init__( command_prefix="-", help_command=HelpCmd(command_attrs=helpattr), allowed_mentions=discord.AllowedMentions.none(), ) # start json updater and file saver self.json_updater.start() self.save.start() async def on_ready(self): # launch everytime bot is online (not only first boot) # just a way to know if the bot is online print("Bot online!") @tasks.loop(hours=8) async def json_updater(self): # update json every 8 hours print("Updated!") update_json(bot.msg_dic) @tasks.loop(hours=24) async def save(self): # create/update json for every server every 24 hours saver() @json_updater.before_loop async def before_update(self): await bot.wait_until_ready() @save.before_loop async def before_save(self): await bot.wait_until_ready() bot = MsgLeaderBot() try: with open("settings.json", "r") as a: bot.settings = json.loads(a.read()) bot.settings["token"] except (FileNotFoundError, KeyError, json.decoder.JSONDecodeError): token = input("input bot token: ") bot.settings = {"token": token} with open("settings.json", "w+") as a: json.dump(bot.settings, a, indent=4) try: with open("messages.json", "r") as b: bot.msg_dic = json.loads(b.read()) except (FileNotFoundError, json.decoder.JSONDecodeError): bot.msg_dic = {} @bot.command() @commands.has_guild_permissions(manage_channels=True) async def autoupdate(ctx): """turns on/off automatic addition of new users to the leaderboard""" server = str(ctx.message.guild.id) if bot.settings[server]["listen_to_all"]: bot.settings[server]["listen_to_all"] = False update_settings(bot.settings) return await ctx.send( "New users **will not** get added to the leaderboard anymore" ) else: bot.settings[server]["listen_to_all"] = True update_settings(bot.settings) return await ctx.send("New users **will** get added to the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def edit(ctx, user: discord.User, message_number: int): """update a user's message number""" name = user.name server = str(ctx.message.guild.id) if str(user.id) not in bot.msg_dic[server]: bot.msg_dic[server][str(user.id)] = { "messages": message_number, "name": name, "alt": None, "is_alt": False, "is_bot": False, } else: bot.msg_dic[server][str(user.id)]["messages"] = message_number update_json(bot.msg_dic) await ctx.send(f"{name} was saved with {message_number} messages") @edit.error async def edit_err(ctx, error): # error handler for edit command if isinstance(error, commands.BadArgument): return await ctx.send("Error: you must input a valid number of messages") await on_command_error(ctx, error, bypass_check=True) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def alt(ctx, user: discord.User, alt: discord.User): """adds up the alt's messages to the user's messages""" await ctx.send(alt_handler(bot, ctx, user, alt)) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def removealt(ctx, user: discord.User, alt: discord.User): """removes alt from user""" await ctx.send(alt_handler(bot, ctx, user, alt, add=False)) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def addbot(ctx, user: discord.User): """saves a user as a bot (displayed on the bottom of the leaderboard)""" server = str(ctx.message.guild.id) try: if bot.msg_dic[server][str(user.id)]["is_bot"]: await ctx.send(f"{user} is already a bot") else: bot.msg_dic[server][str(user.id)]["is_bot"] = True update_json(bot.msg_dic) await ctx.send(f"{user} is now a bot") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def rmvbot(ctx, user: discord.User): """removes bot tag from a user""" server = str(ctx.message.guild.id) try: if not bot.msg_dic[server][str(user.id)]["is_bot"]: await ctx.send(f"{user} is already not a bot") else: bot.msg_dic[server][str(user.id)]["is_bot"] = False update_json(bot.msg_dic) await ctx.send(f"{user} is no longer a bot") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def delete(ctx, user: discord.User): """delete a user from the leaderboard""" server = str(ctx.message.guild.id) try: bot.msg_dic[server].pop(str(user.id)) update_json(bot.msg_dic) await ctx.send(f"{user} was deleted") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def minimum(ctx, value: int): """change the minimum amount of messages necessary to appear on the leaderboard (defaults to 20000)""" server = str(ctx.message.guild.id) bot.settings[server]["minimum"] = value update_settings(bot.settings) if value == 1: await ctx.send( f"Every user with more than {value} message will now be displayed on the leadeboard" ) else: await ctx.send( f"Every user with more than {value} messages will now be displayed on the leadeboard" ) @minimum.error async def minimum_err(ctx, error): # error handler for minimum command if isinstance(error, commands.BadArgument): return await ctx.send("Error: invalid value") await on_command_error(ctx, error, bypass_check=True) @bot.command() async def source(ctx): """prints the source code link""" await ctx.send("https://github.com/RafaeISilva/Message_LeaderBot") @bot.command() async def ping(ctx): """Tells the ping of the bot to the discord servers""" update_json(bot.msg_dic) # because why not await ctx.send(f"Pong! {round(bot.latency*1000)}ms") @bot.command() async def minfo(ctx): """prints the current minimum value to appear on the leaderboard""" await ctx.send( f"The current minimum is {bot.settings[str(ctx.message.guild.id)]["minimum"]} messages" ) @bot.command() async def name(ctx): """updates author's name on the leadeboard""" author = ctx.author msg_dic = bot.msg_dic[str(ctx.message.guild.id)] if str(author.id) not in msg_dic: return name = author.name if name == msg_dic[str(author.id)]["name"]: return await ctx.send("Your name is already up to date") else: msg_dic[str(author.id)]["name"] = name await ctx.send(f"Name updated to {name}") @bot.command() async def msglb(ctx): """prints the message leaderboard""" update_json(bot.msg_dic) server = str(ctx.message.guild.id) author = str(ctx.author.id) smgs_dic = {} msg_lb = "" bots_lb = "" top_users = [] msg_dic = bot.msg_dic[server] if author in msg_dic and msg_dic[author]["is_alt"]: for id in msg_dic: if msg_dic[id]["alt"] is not None and author in msg_dic[id]["alt"]: author = id break for id in msg_dic: # excludes alt users from the leadeboard if not msg_dic[id]["is_alt"]: if not msg_dic[id]["alt"]: smgs_dic[id] = msg_dic[id]["messages"] # sums the number of messages of users with alts to its respective alts if msg_dic[id]["alt"]: messages = 0 for alt in msg_dic[id]["alt"]: messages += msg_dic[alt]["messages"] smgs_dic[id] = msg_dic[id]["messages"] + messages # sorts the leaderboard smgs_dic = dict(sorted(smgs_dic.items(), key=lambda item: item[1], reverse=True)) # restricts the leaderboard to only users with more than a certain minimum for user in smgs_dic: if int(smgs_dic[user]) >= bot.settings[server]["minimum"]: top_users.append(user) # prevents bots from being on the top if msg_dic[user]["is_bot"]: bots_lb += f"{smgs_dic[user]}: {msg_dic[user]["name"]}\n" elif msg_dic[user]["alt"] is not None: if author == user: msg_lb += "**" if len(msg_dic[user]["alt"]) == 1: msg_lb += f"{smgs_dic[user]}: {msg_dic[user]["name"]} + alt\n" else: alts = len(msg_dic[user]["alt"]) msg_lb += ( f"{smgs_dic[user]}: {msg_dic[user]["name"]} +{alts} alts\n" ) if author == user: msg_lb += "**" else: if author == user: msg_lb += "**" msg_lb += f"{smgs_dic[user]}: {msg_dic[user]["name"]}\n" if author == user: msg_lb += "**" # adds bots to the end msg_lb += "\n" + bots_lb # adds message author to the end if not already on the leaderboard if author in msg_dic and author not in top_users: if msg_dic[author]["alt"]: if len(msg_dic[author]["alt"]) == 1: msg_lb += f"**{smgs_dic[author]}: {msg_dic[author]["name"]} + alt**" else: alts = len(msg_dic[author]["alt"]) msg_lb += ( f"**{smgs_dic[author]}: {msg_dic[author]["name"]} +{alts} alts**" ) else: msg_lb += f"**{smgs_dic[author]}: {msg_dic[author]["name"]}**" embed = discord.Embed( title="Message Leaderboard", color=7419530, description=msg_lb ) await ctx.send(embed=embed) @bot.command() async def msg(ctx, username: str = ""): """check how many messages a user has""" msg_dic = bot.msg_dic[str(ctx.message.guild.id)] success = False if not username: username = str(ctx.author.id) # checks if input is a user's id on the leadeboard if username.isdecimal(): try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a user mention and if user's id is on the leaderboard elif "<@" in username: if "!" in username: username = username.replace("!", "") username = username.replace("<@", "").replace(">", "") try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a username on the leaderboard else: for id in msg_dic: if msg_dic[id]["name"].lower() == username.lower(): username = id success = True break try: msg_dic[username] except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) if success: name = msg_dic[username]["name"] messages = msg_dic[username]["messages"] if msg_dic[username]["alt"] is None: await ctx.send( discord.utils.escape_mentions(f"{name} has {messages} messages") ) else: alt_messages = 0 for alt in msg_dic[username]["alt"]: alt_messages += msg_dic[alt]["messages"] await ctx.send( discord.utils.escape_mentions( f"{name} has {messages} (+{alt_messages}) messages" ) ) @bot.command() async def altinfo(ctx, username: str): """check the name of a user's alt or vice versa""" msg_dic = bot.msg_dic[str(ctx.message.guild.id)] result = "" success = False # checks if input is a user's id on the leadeboard if username.isdecimal(): try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a user mention and if user's id is on the leaderboard elif "<@" in username: if "!" in username: username = username.replace("!", "") username = username.replace("<@", "").replace(">", "") try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a username on the leaderboard else: for id in msg_dic: if msg_dic[id]["name"].lower() == username.lower(): username = id success = True break try: msg_dic[username] except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) if success: # checks if username is an alt and gets its name if msg_dic[username]["is_alt"]: for id in msg_dic: if msg_dic[id]["alt"] is not None and username in msg_dic[id]["alt"]: result = f"{msg_dic[username]["name"]} is an alt of {msg_dic[id]["name"]}" break # checks if username has an alt and gets its name elif msg_dic[username]["alt"] is not None: if len(msg_dic[username]["alt"]) == 1: result = f"{msg_dic[msg_dic[username]["alt"][0]]["name"]} is an alt of {msg_dic[username]["name"]}" else: alt_list = msg_dic[username]["alt"] result = ", ".join(msg_dic[alt]["name"] for alt in alt_list[0:-1]) result += f" and {msg_dic[alt_list[-1]]["name"]} are alts of {msg_dic[username]["name"]}" else: result = f"{msg_dic[username]["name"]} has no alts/is not an alt" await ctx.send(result) @bot.event async def on_message(message): user = message.author if user == bot.user: return try: msg_dic = bot.msg_dic[str(message.guild.id)] except KeyError: msg_dic = bot.msg_dic[str(message.guild.id)] = {} try: bot.settings[str(message.guild.id)]["minimum"] bot.settings[str(message.guild.id)]["listen_to_all"] except KeyError: bot.settings[str(message.guild.id)] = {"minimum": 20000, "listen_to_all": True} update_settings(bot.settings) settings = bot.settings[str(message.guild.id)] # adds a point to the author everytime a message is sent if str(user.id) not in msg_dic and settings["listen_to_all"]: if user.bot: msg_dic[str(user.id)] = { "messages": 1, "name": user.name, "alt": None, "is_alt": False, "is_bot": True, } else: msg_dic[str(user.id)] = { "messages": 1, "name": user.name, "alt": None, "is_alt": False, "is_bot": False, } elif str(user.id) in msg_dic: msg_dic[str(user.id)]["messages"] += 1 # process a command (if valid) await bot.process_commands(message) @bot.event async def on_message_delete(message): user = str(message.author.id) msg_dic = bot.msg_dic[str(message.guild.id)] if user in msg_dic: msg_dic[user]["messages"] -= 1 @bot.event async def on_command_error( ctx, error: commands.CommandError, *, bypass_check: bool = False ): # handles command error if ctx.command and ctx.command.has_error_handler() and not bypass_check: # already have error handler return # get the "real" error error = getattr(error, "original", error) if isinstance(error, commands.CommandNotFound): # command not found is annoying for most bot users, so just return nothing return if isinstance(error, commands.UserNotFound): return await ctx.send(f"Error: user '{error.argument}' not found") if isinstance(error, commands.MissingRequiredArgument): return await ctx.send(f"Error: you must input a valid `{error.param.name}`") if isinstance(error, commands.MissingPermissions): # probably i made it too over-complicated, # but its so that the message stays consistent with the other error messages error = str(error) return await ctx.send(f"Error: {error[0].lower()}{error[1:-1]}") try: raise error except discord.errors.Forbidden: await ctx.author.send(f"```\n{error}\n```") if __name__ == "__main__": bot.run(bot.settings["token"])

import json import discord from discord.ext import commands, tasks from utils import * class HelpCmd(commands.HelpCommand): async def send_bot_help(self, mapping): ctx = self.context bot = ctx.bot commands = bot.commands result = [] for cmd in commands: sign = self.get_command_signature(cmd) result.append(f"`{sign.strip()}`: {cmd.help}") await ctx.send("\n".join(result)) send_cog_help = send_command_help = send_group_help = send_bot_help class MsgLeaderBot(commands.Bot): def __init__(self): helpattr = {"usage": ""} super().__init__( command_prefix="-", help_command=HelpCmd(command_attrs=helpattr), allowed_mentions=discord.AllowedMentions.none(), ) # start json updater and file saver self.json_updater.start() self.save.start() async def on_ready(self): # launch everytime bot is online (not only first boot) # just a way to know if the bot is online print("Bot online!") @tasks.loop(hours=8) async def json_updater(self): # update json every 8 hours print("Updated!") update_json(bot.msg_dic) @tasks.loop(hours=24) async def save(self): # create/update json for every server every 24 hours saver() @json_updater.before_loop async def before_update(self): await bot.wait_until_ready() @save.before_loop async def before_save(self): await bot.wait_until_ready() bot = MsgLeaderBot() try: with open("settings.json", "r") as a: bot.settings = json.loads(a.read()) bot.settings["token"] except (FileNotFoundError, KeyError, json.decoder.JSONDecodeError): token = input("input bot token: ") bot.settings = {"token": token} with open("settings.json", "w+") as a: json.dump(bot.settings, a, indent=4) try: with open("messages.json", "r") as b: bot.msg_dic = json.loads(b.read()) except (FileNotFoundError, json.decoder.JSONDecodeError): bot.msg_dic = {} @bot.command() @commands.has_guild_permissions(manage_channels=True) async def autoupdate(ctx): """turns on/off automatic addition of new users to the leaderboard""" server = str(ctx.message.guild.id) if bot.settings[server]["listen_to_all"]: bot.settings[server]["listen_to_all"] = False update_settings(bot.settings) return await ctx.send( "New users **will not** get added to the leaderboard anymore" ) else: bot.settings[server]["listen_to_all"] = True update_settings(bot.settings) return await ctx.send("New users **will** get added to the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def edit(ctx, user: discord.User, message_number: int): """update a user's message number""" name = user.name server = str(ctx.message.guild.id) if str(user.id) not in bot.msg_dic[server]: bot.msg_dic[server][str(user.id)] = { "messages": message_number, "name": name, "alt": None, "is_alt": False, "is_bot": False, } else: bot.msg_dic[server][str(user.id)]["messages"] = message_number update_json(bot.msg_dic) await ctx.send(f"{name} was saved with {message_number} messages") @edit.error async def edit_err(ctx, error): # error handler for edit command if isinstance(error, commands.BadArgument): return await ctx.send("Error: you must input a valid number of messages") await on_command_error(ctx, error, bypass_check=True) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def alt(ctx, user: discord.User, alt: discord.User): """adds up the alt's messages to the user's messages""" await ctx.send(alt_handler(bot, ctx, user, alt)) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def removealt(ctx, user: discord.User, alt: discord.User): """removes alt from user""" await ctx.send(alt_handler(bot, ctx, user, alt, add=False)) @bot.command() @commands.has_guild_permissions(manage_channels=True) async def addbot(ctx, user: discord.User): """saves a user as a bot (displayed on the bottom of the leaderboard)""" server = str(ctx.message.guild.id) try: if bot.msg_dic[server][str(user.id)]["is_bot"]: await ctx.send(f"{user} is already a bot") else: bot.msg_dic[server][str(user.id)]["is_bot"] = True update_json(bot.msg_dic) await ctx.send(f"{user} is now a bot") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def rmvbot(ctx, user: discord.User): """removes bot tag from a user""" server = str(ctx.message.guild.id) try: if not bot.msg_dic[server][str(user.id)]["is_bot"]: await ctx.send(f"{user} is already not a bot") else: bot.msg_dic[server][str(user.id)]["is_bot"] = False update_json(bot.msg_dic) await ctx.send(f"{user} is no longer a bot") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def delete(ctx, user: discord.User): """delete a user from the leaderboard""" server = str(ctx.message.guild.id) try: bot.msg_dic[server].pop(str(user.id)) update_json(bot.msg_dic) await ctx.send(f"{user} was deleted") except KeyError: await ctx.send(f"Error: {user} is not listed in the leaderboard") @bot.command() @commands.has_guild_permissions(manage_channels=True) async def minimum(ctx, value: int): """change the minimum amount of messages necessary to appear on the leaderboard (defaults to 20000)""" server = str(ctx.message.guild.id) bot.settings[server]["minimum"] = value update_settings(bot.settings) if value == 1: await ctx.send( f"Every user with more than {value} message will now be displayed on the leadeboard" ) else: await ctx.send( f"Every user with more than {value} messages will now be displayed on the leadeboard" ) @minimum.error async def minimum_err(ctx, error): # error handler for minimum command if isinstance(error, commands.BadArgument): return await ctx.send("Error: invalid value") await on_command_error(ctx, error, bypass_check=True) @bot.command() async def source(ctx): """prints the source code link""" await ctx.send("https://github.com/RafaeISilva/Message_LeaderBot") @bot.command() async def ping(ctx): """Tells the ping of the bot to the discord servers""" update_json(bot.msg_dic) # because why not await ctx.send(f"Pong! {round(bot.latency*1000)}ms") @bot.command() async def minfo(ctx): """prints the current minimum value to appear on the leaderboard""" await ctx.send( f"The current minimum is {bot.settings[str(ctx.message.guild.id)]['minimum']} messages" ) @bot.command() async def name(ctx): """updates author's name on the leadeboard""" author = ctx.author msg_dic = bot.msg_dic[str(ctx.message.guild.id)] if str(author.id) not in msg_dic: return name = author.name if name == msg_dic[str(author.id)]["name"]: return await ctx.send("Your name is already up to date") else: msg_dic[str(author.id)]["name"] = name await ctx.send(f"Name updated to {name}") @bot.command() async def msglb(ctx): """prints the message leaderboard""" update_json(bot.msg_dic) server = str(ctx.message.guild.id) author = str(ctx.author.id) smgs_dic = {} msg_lb = "" bots_lb = "" top_users = [] msg_dic = bot.msg_dic[server] if author in msg_dic and msg_dic[author]["is_alt"]: for id in msg_dic: if msg_dic[id]["alt"] is not None and author in msg_dic[id]["alt"]: author = id break for id in msg_dic: # excludes alt users from the leadeboard if not msg_dic[id]["is_alt"]: if not msg_dic[id]["alt"]: smgs_dic[id] = msg_dic[id]["messages"] # sums the number of messages of users with alts to its respective alts if msg_dic[id]["alt"]: messages = 0 for alt in msg_dic[id]["alt"]: messages += msg_dic[alt]["messages"] smgs_dic[id] = msg_dic[id]["messages"] + messages # sorts the leaderboard smgs_dic = dict(sorted(smgs_dic.items(), key=lambda item: item[1], reverse=True)) # restricts the leaderboard to only users with more than a certain minimum for user in smgs_dic: if int(smgs_dic[user]) >= bot.settings[server]["minimum"]: top_users.append(user) # prevents bots from being on the top if msg_dic[user]["is_bot"]: bots_lb += f"{smgs_dic[user]}: {msg_dic[user]['name']}\n" elif msg_dic[user]["alt"] is not None: if author == user: msg_lb += "**" if len(msg_dic[user]["alt"]) == 1: msg_lb += f"{smgs_dic[user]}: {msg_dic[user]['name']} + alt\n" else: alts = len(msg_dic[user]["alt"]) msg_lb += ( f"{smgs_dic[user]}: {msg_dic[user]['name']} +{alts} alts\n" ) if author == user: msg_lb += "**" else: if author == user: msg_lb += "**" msg_lb += f"{smgs_dic[user]}: {msg_dic[user]['name']}\n" if author == user: msg_lb += "**" # adds bots to the end msg_lb += "\n" + bots_lb # adds message author to the end if not already on the leaderboard if author in msg_dic and author not in top_users: if msg_dic[author]["alt"]: if len(msg_dic[author]["alt"]) == 1: msg_lb += f"**{smgs_dic[author]}: {msg_dic[author]['name']} + alt**" else: alts = len(msg_dic[author]["alt"]) msg_lb += ( f"**{smgs_dic[author]}: {msg_dic[author]['name']} +{alts} alts**" ) else: msg_lb += f"**{smgs_dic[author]}: {msg_dic[author]['name']}**" embed = discord.Embed( title="Message Leaderboard", color=7419530, description=msg_lb ) await ctx.send(embed=embed) @bot.command() async def msg(ctx, username: str = ""): """check how many messages a user has""" msg_dic = bot.msg_dic[str(ctx.message.guild.id)] success = False if not username: username = str(ctx.author.id) # checks if input is a user's id on the leadeboard if username.isdecimal(): try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a user mention and if user's id is on the leaderboard elif "<@" in username: if "!" in username: username = username.replace("!", "") username = username.replace("<@", "").replace(">", "") try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a username on the leaderboard else: for id in msg_dic: if msg_dic[id]["name"].lower() == username.lower(): username = id success = True break try: msg_dic[username] except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) if success: name = msg_dic[username]["name"] messages = msg_dic[username]["messages"] if msg_dic[username]["alt"] is None: await ctx.send( discord.utils.escape_mentions(f"{name} has {messages} messages") ) else: alt_messages = 0 for alt in msg_dic[username]["alt"]: alt_messages += msg_dic[alt]["messages"] await ctx.send( discord.utils.escape_mentions( f"{name} has {messages} (+{alt_messages}) messages" ) ) @bot.command() async def altinfo(ctx, username: str): """check the name of a user's alt or vice versa""" msg_dic = bot.msg_dic[str(ctx.message.guild.id)] result = "" success = False # checks if input is a user's id on the leadeboard if username.isdecimal(): try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a user mention and if user's id is on the leaderboard elif "<@" in username: if "!" in username: username = username.replace("!", "") username = username.replace("<@", "").replace(">", "") try: msg_dic[username] success = True except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) # checks if input is a username on the leaderboard else: for id in msg_dic: if msg_dic[id]["name"].lower() == username.lower(): username = id success = True break try: msg_dic[username] except KeyError: await ctx.send( discord.utils.escape_mentions(f"Error: {username} not found") ) if success: # checks if username is an alt and gets its name if msg_dic[username]["is_alt"]: for id in msg_dic: if msg_dic[id]["alt"] is not None and username in msg_dic[id]["alt"]: result = f"{msg_dic[username]['name']} is an alt of {msg_dic[id]['name']}" break # checks if username has an alt and gets its name elif msg_dic[username]["alt"] is not None: if len(msg_dic[username]["alt"]) == 1: result = f"{msg_dic[msg_dic[username]['alt'][0]]['name']} is an alt of {msg_dic[username]['name']}" else: alt_list = msg_dic[username]["alt"] result = ", ".join(msg_dic[alt]["name"] for alt in alt_list[0:-1]) result += f" and {msg_dic[alt_list[-1]]['name']} are alts of {msg_dic[username]['name']}" else: result = f"{msg_dic[username]['name']} has no alts/is not an alt" await ctx.send(result) @bot.event async def on_message(message): user = message.author if user == bot.user: return try: msg_dic = bot.msg_dic[str(message.guild.id)] except KeyError: msg_dic = bot.msg_dic[str(message.guild.id)] = {} try: bot.settings[str(message.guild.id)]["minimum"] bot.settings[str(message.guild.id)]["listen_to_all"] except KeyError: bot.settings[str(message.guild.id)] = {"minimum": 20000, "listen_to_all": True} update_settings(bot.settings) settings = bot.settings[str(message.guild.id)] # adds a point to the author everytime a message is sent if str(user.id) not in msg_dic and settings["listen_to_all"]: if user.bot: msg_dic[str(user.id)] = { "messages": 1, "name": user.name, "alt": None, "is_alt": False, "is_bot": True, } else: msg_dic[str(user.id)] = { "messages": 1, "name": user.name, "alt": None, "is_alt": False, "is_bot": False, } elif str(user.id) in msg_dic: msg_dic[str(user.id)]["messages"] += 1 # process a command (if valid) await bot.process_commands(message) @bot.event async def on_message_delete(message): user = str(message.author.id) msg_dic = bot.msg_dic[str(message.guild.id)] if user in msg_dic: msg_dic[user]["messages"] -= 1 @bot.event async def on_command_error( ctx, error: commands.CommandError, *, bypass_check: bool = False ): # handles command error if ctx.command and ctx.command.has_error_handler() and not bypass_check: # already have error handler return # get the "real" error error = getattr(error, "original", error) if isinstance(error, commands.CommandNotFound): # command not found is annoying for most bot users, so just return nothing return if isinstance(error, commands.UserNotFound): return await ctx.send(f"Error: user '{error.argument}' not found") if isinstance(error, commands.MissingRequiredArgument): return await ctx.send(f"Error: you must input a valid `{error.param.name}`") if isinstance(error, commands.MissingPermissions): # probably i made it too over-complicated, # but its so that the message stays consistent with the other error messages error = str(error) return await ctx.send(f"Error: {error[0].lower()}{error[1:-1]}") try: raise error except discord.errors.Forbidden: await ctx.author.send(f"```\n{error}\n```") if __name__ == "__main__": bot.run(bot.settings["token"])

import requests from urllib.parse import urlparse from bs4 import BeautifulSoup as bs from presscontrol.utils import tprint, read_cookies from presscontrol.config import config import pandas as pd import random import urllib import newspaper from urllib.parse import urlparse import datetime from googlesearch import search def process_link(link): link = get_direct_link(link) if link != '': try: d = process_outer(link) except requests.exceptions.ConnectionError: error = '[-] Connection Error '+link tprint(error, important=False) d = {'error': 1, 'info': 'ConnectionError'} except Exception as exc: error = '[-] Error General '+link+' :' + str(exc) error = error[:275] tprint(error, important=False) d = {'error': 1, 'info': error} else: # Mark for deletion if tweet does not contain any links. error = '[-] Link Vacío en process_link' tprint(error, important=False) d = {'error': 1, 'borrar': 1, 'info': error} return d def get_direct_link(twitter_link): ''' Extract direct link from tweet. ''' if 'https://twitter.com' in twitter_link: indirect_links = [] try: page = requests.get(twitter_link) lxml = bs(page.content, 'lxml') box = lxml.find_all('p', {"class": "TweetTextSize--jumbo"})[0] links = [l for l in box.find_all('a') if 'pic.twitter' not in l] for l in links: try: indirect_links.append(l['data-expanded-url']) except Exception as exc: pass if not links: try: indirect_links += [x for x in box.text.split() if 'http' in x] except: pass except Exception as exc: pass #tprint(str(exc)[:60], important=False) return indirect_links[0] if len(indirect_links)!=0 else '' else: return twitter_link def process_inner(requests_page): art = newspaper.Article('') art.set_html(requests_page.content) art.parse() d = {} d['title'] = art.title d['description'] = art.meta_description d['body'] = art.text d['authors'] = ', '.join(art.authors) d['date'] = art.publish_date d['link'] = requests_page.url d['source'] = urlparse(requests_page.url).netloc.split('.')[-2].capitalize() d['image'] = art.top_image d['error'] = 0 try: d['year'] = art.date_publish.year except: pass try: d['date'] = d['date'].replace(tzinfo=None) except: pass return d def process_outer(link): page = requests.get(link) source = urlparse(page.url).netloc.split('.')[-2].capitalize() # Personalized treatment by source action = { 'Df': process_Df, 'Emol': process_Emol, 'Latercera': process_Latercera, 'Cooperativa': process_Cooperativa, 'Elmostrador': process_Elmostrador, 'Biobiochile': process_Biobiochile, } if source in action.keys(): d = action[source](page) else: d = process_inner(page) # Add year try: d['year'] = d['date'].year except: pass if d['error'] == 0: # Mark for deletion if link isn't from any source in config.txt. try: if d['source'] not in config['SOURCES']: d['borrar'] = 1 d['info'] = f"Borrar, no pertenece a los dominios buscados. ({str(d["link"])[:25]}...)" except: pass # Restrict text field to 50.000 characters try: d['body'] = d['body'][:50000] except: pass # Encode Links try: d['image'] = urllib.parse.quote(d['image']).replace('%3A', ':') except: pass try: d['link'] = urllib.parse.quote(d['link']).replace('%3A', ':') except: pass # Encode content and description and title try: d['body'] = d['body'].encode('latin1', 'ignore').decode('latin1') #d['body'] = d['body'].replace('\x80', '') except: pass try: d['description'] = d['description'].encode('latin1', 'ignore').decode('latin1') d['description'] = d['description'].replace('\x80', '') except: pass try: d['title'] = d['title'].encode('latin1', 'ignore').decode('latin1') d['title'] = d['title'].replace('\x80', '') except: pass try: if d['title']==None or d['body']==None or d['title']=='' or d['body']=='': d['error'] = 1 d['borrar'] = 1 d['info'] = 'Title and content blank' except: pass return d # Add aditional steps by source: def process_Df(page): cookies = read_cookies() page = requests.get(page.url, cookies=cookies['df']) if '¡Página no encontrada!' in page.text: try: tprint('[·] df.cl page not found. Searching for title...', important=False) title_ = page.url.split('/')[3].replace('-', '+') search_ = f'https://www.df.cl/cgi-bin/prontus_search.cgi?search_texto="{title_}"&search_prontus=noticias&search_tmp=search.html&search_idx=ALL&search_modo=and&search_form=yes' soup = bs(page.content, 'lxml') page = requests.get(search_) soup = bs(page.content, 'lxml') box = soup.find('div', {'id': 'wrap-noticias'}) new_url = 'https://www.df.cl'+box.find('article').h2.a['href'] tprint('[+] df.cl page found!', important=False) page = requests.get(new_url, cookies=cookies['df']) except Exception as exc: tprint('[-] df.cl page not found', important=False) d = process_inner(page) soup = bs(page.content, 'lxml') try: d['section'] = soup.find('meta', {'name': 'keywords'})['content'].strip() except Exception as exc: tprint('[-] Error parsing section (Df) - ', exc, important=False) try: d['body'] = '\n'.join([p for p in d['body'].split('\n') if len(p.split())>4 and p!=d['description']]) except Exception as exc: tprint('[-] Error parsing body (Df) - ', exc, important=False) return d def process_Emol(page): d = process_inner(page) soup = bs(page.content, 'lxml') try: d['section'] = d['link'].split('/')[4].capitalize() except Exception as exc: tprint('[-] Error parsing section (Emol) - ', exc, important=False) try: d['authors'] = soup.find('div', {'class', 'info-notaemol-porfecha'}).text.split('|')[-1].strip().replace('Por ','').replace('Redactado por ', '') except Exception as exc: tprint('[-] Error parsing section (Emol) - ', exc, important=False) return d def process_Latercera(page): d = {} if 'Lo sentimos, estamos actualizando el sitio' not in page.text: d = process_inner(page) else: ### Buscar noticia en google, si es necesario. scraped_link = page.url.strip('/') tprint('[-] Link Latercera no encontrado', page.url, important=False) new_link = 'https://www.latercera.com/noticia/'+'-'.join([p for p in scraped_link.split('/')[-1].split('.')[0].split('-') if not p.isdigit()]) #print(new_link) page = requests.get(new_link) if 'Lo sentimos, estamos actualizando el sitio' not in page.text: d = process_inner(page) tprint('[+] Link Latercera encontrado (intento:1): ', new_link, important=False) else: try: tprint('[·] Google Searching...', important=False) buscar = ' '.join([p for p in scraped_link.split('/')[-1].split('.')[0].split('-') if not p.isdigit()]) + ' site:latercera.com' results = search(buscar, stop=5) rs = [] for r in results: rs.append(r) result = [r for r in rs if 'sitemap' not in r][0] if 'sitemap' not in result: tprint('[+] Resultado en Google (intento:2):',result, important=False) page = requests.get(result) d = process_inner(page) else: d['error'] = 1 d['info'] = 'Link Latercera no encontrado en google' except Exception as exc: tprint('[-] Link Latercera no encontrado', important=False) d['error'] = 1 d['info'] = 'Link Latercera no encontrado en google' soup = bs(page.content, 'lxml') ### Recuperar Image. try: d['image'] = soup.find('figure').find('img')['src'] except Exception as exc: tprint('[-] Error parsing image (Latercera) - ', exc, important=False) ### Recuperar Autor try: d['authors'] = [h.text for h in soup.find_all('h4') if 'Autor' in h.text][0].replace('Autor: ', '') except Exception as exc: tprint('[-] Error parsing authors (Latercera) - ', exc, important=False) try: if d['description'] == None: d['description'] = soup.find('div', {'class': 'bajada-art'}).text except Exception as exc: tprint('[-] Error parsing description (Latercera) - ', exc, important=False) try: if d['date'] == None: date = ' '.join(soup.find('span', {'class': 'time-ago'}).text.replace('|', '').split()) d['date'] = datetime.datetime.strptime(date, '%d/%m/%Y %I:%M %p') except Exception as exc: tprint('[-] Error parsing date (Latercera) - ', exc, important=False) try: d['section'] = soup.find('meta', property='article:section')['content'] except: try: d['section'] = [x.find('a').text for x in soup.find_all('h4') if x.find('a')!=None and 'canal' in x.find('a')['href']][0] except Exception as exc: tprint('[-] Error parsing section (Latercera) - ', exc, important=False) d['tags'] = ', '.join([x['content'] for x in soup.find_all('meta', property='article:tag')]) if not d['tags']: try: d['tags'] = ', '.join([x.text for x in soup.find('div', {'class': 'tags-interior'}).find_all('a')]) except Exception as exc: tprint('[-] Error parsing tags (Latercera) - ', exc, important=False) return d def process_Cooperativa(page): d = process_inner(page) try: if 'al aire libre' in d['title'].lower(): d = {'borrar':1, info:'Borrar, Al aire libre'} except: pass soup = bs(page.content, 'lxml') try: d['authors'] = soup.find('div', {'class': 'fecha-publicacion'}).find('span').text except Exception as exc: tprint('[-] Error parsing authors (Cooperativa) - ', exc, important=False) try: d['section'] = soup.find('a', {'id': 'linkactivo'}).text except Exception as exc: tprint('[-] Error parsing section (Cooperativa) - ', exc, important=False) try: d['tags'] = soup.find('meta', {'name': 'keywords'})['content'].strip() except Exception as exc: tprint('[-] Error parsing tags (Cooperativa) - ', exc, important=False) try: d['link'] = soup.find('meta', property='og:url')['content'] except Exception as exc: tprint('[-] Error parsing link (Cooperativa) - ', exc, important=False) if not d['date']: try: date = [x for x in d['link'].split('/') if '-' in x][-1].split('-') d['date'] = datetime.datetime(*map(int,date)) except Exception as exc: tprint('[-] Error parsing date (Cooperativa) - ', exc, important=False) try: if 'www.cooperativa.cl' not in d['image'] and d['image']: d['image'] = 'https://www.cooperativa.cl'+d['image'] except Exception as exc: tprint('[-] Error fixing image (Cooperativa) - ', exc, important=False) return d def process_Elmostrador(page): d = process_inner(page) soup = bs(page.content, 'lxml') d['description'] = None try: d['description'] = soup.find('figcaption').text except Exception as exc: tprint('[-] Error parsing description (Elmostrador) - ',exc, important=False) try: d['authors'] = soup.find('p', {'class': 'autor-y-fecha'}).find('a').text except Exception as exc: tprint('[-] Error parsing authors (Elmostrador) - ',exc, important=False) try: if 'www.elmostrador.cl' not in d['image'] and d['image']: d['image'] = 'https://www.elmostrador.cl'+d['image'] except Exception as exc: tprint('[-] Error fixing image (Elmostrador) - ',exc, important=False) if not d['date']: try: date = [s for s in d['link'].split('/') if s.isdigit()][:3] d['date'] = datetime.datetime(*map(int,date)) except Exception as exc: tprint('[-] Error parsing date (Elmostrador) - ',exc, important=False) try: d['section'] = ' '.join([x for x in soup.find_all('h2') if x.find('i')!=None][0].text.split()) except Exception as exc: tprint('[-] Error parsing section (Elmostrador) - ',exc, important=False) try: d['body'] = d['body'].split('__________________')[0] except Exception as exc: tprint('[-] Error fixing body (Elmostrador) - ',exc, important=False) return d def process_Biobiochile(page): d = process_inner(page) soup = bs(page.content, 'lxml') try: d['authors'] = soup.find('div', {'class': 'nota-autor'}).find('a').text except Exception as exc: tprint('[-] Error parsing authors (Biobiochile) - ',exc, important=False) try: d['section'] = ' '.join(soup.find('div', {'class': 'categoria-titulo-nota'}).text.split()) except Exception as exc: tprint('[-] Error parsing section (Biobiochile) - ',exc, important=False) try: d['body'] = soup.find('div', {'class': 'nota-body'}).text d['body'] = d['body'].replace('Etiquetas de esta nota:', '') except Exception as exc: tprint('[-] Error parsing body (Biobiochile) - ',exc, important=False) try: d['description'] = None except Exception as exc: tprint('[-] Error parsing description (Biobiochile) - ',exc, important=False) return d

import requests from urllib.parse import urlparse from bs4 import BeautifulSoup as bs from presscontrol.utils import tprint, read_cookies from presscontrol.config import config import pandas as pd import random import urllib import newspaper from urllib.parse import urlparse import datetime from googlesearch import search def process_link(link): link = get_direct_link(link) if link != '': try: d = process_outer(link) except requests.exceptions.ConnectionError: error = '[-] Connection Error '+link tprint(error, important=False) d = {'error': 1, 'info': 'ConnectionError'} except Exception as exc: error = '[-] Error General '+link+' :' + str(exc) error = error[:275] tprint(error, important=False) d = {'error': 1, 'info': error} else: # Mark for deletion if tweet does not contain any links. error = '[-] Link Vacío en process_link' tprint(error, important=False) d = {'error': 1, 'borrar': 1, 'info': error} return d def get_direct_link(twitter_link): ''' Extract direct link from tweet. ''' if 'https://twitter.com' in twitter_link: indirect_links = [] try: page = requests.get(twitter_link) lxml = bs(page.content, 'lxml') box = lxml.find_all('p', {"class": "TweetTextSize--jumbo"})[0] links = [l for l in box.find_all('a') if 'pic.twitter' not in l] for l in links: try: indirect_links.append(l['data-expanded-url']) except Exception as exc: pass if not links: try: indirect_links += [x for x in box.text.split() if 'http' in x] except: pass except Exception as exc: pass #tprint(str(exc)[:60], important=False) return indirect_links[0] if len(indirect_links)!=0 else '' else: return twitter_link def process_inner(requests_page): art = newspaper.Article('') art.set_html(requests_page.content) art.parse() d = {} d['title'] = art.title d['description'] = art.meta_description d['body'] = art.text d['authors'] = ', '.join(art.authors) d['date'] = art.publish_date d['link'] = requests_page.url d['source'] = urlparse(requests_page.url).netloc.split('.')[-2].capitalize() d['image'] = art.top_image d['error'] = 0 try: d['year'] = art.date_publish.year except: pass try: d['date'] = d['date'].replace(tzinfo=None) except: pass return d def process_outer(link): page = requests.get(link) source = urlparse(page.url).netloc.split('.')[-2].capitalize() # Personalized treatment by source action = { 'Df': process_Df, 'Emol': process_Emol, 'Latercera': process_Latercera, 'Cooperativa': process_Cooperativa, 'Elmostrador': process_Elmostrador, 'Biobiochile': process_Biobiochile, } if source in action.keys(): d = action[source](page) else: d = process_inner(page) # Add year try: d['year'] = d['date'].year except: pass if d['error'] == 0: # Mark for deletion if link isn't from any source in config.txt. try: if d['source'] not in config['SOURCES']: d['borrar'] = 1 d['info'] = f"Borrar, no pertenece a los dominios buscados. ({str(d['link'])[:25]}...)" except: pass # Restrict text field to 50.000 characters try: d['body'] = d['body'][:50000] except: pass # Encode Links try: d['image'] = urllib.parse.quote(d['image']).replace('%3A', ':') except: pass try: d['link'] = urllib.parse.quote(d['link']).replace('%3A', ':') except: pass # Encode content and description and title try: d['body'] = d['body'].encode('latin1', 'ignore').decode('latin1') #d['body'] = d['body'].replace('\x80', '') except: pass try: d['description'] = d['description'].encode('latin1', 'ignore').decode('latin1') d['description'] = d['description'].replace('\x80', '') except: pass try: d['title'] = d['title'].encode('latin1', 'ignore').decode('latin1') d['title'] = d['title'].replace('\x80', '') except: pass try: if d['title']==None or d['body']==None or d['title']=='' or d['body']=='': d['error'] = 1 d['borrar'] = 1 d['info'] = 'Title and content blank' except: pass return d # Add aditional steps by source: def process_Df(page): cookies = read_cookies() page = requests.get(page.url, cookies=cookies['df']) if '¡Página no encontrada!' in page.text: try: tprint('[·] df.cl page not found. Searching for title...', important=False) title_ = page.url.split('/')[3].replace('-', '+') search_ = f'https://www.df.cl/cgi-bin/prontus_search.cgi?search_texto="{title_}"&search_prontus=noticias&search_tmp=search.html&search_idx=ALL&search_modo=and&search_form=yes' soup = bs(page.content, 'lxml') page = requests.get(search_) soup = bs(page.content, 'lxml') box = soup.find('div', {'id': 'wrap-noticias'}) new_url = 'https://www.df.cl'+box.find('article').h2.a['href'] tprint('[+] df.cl page found!', important=False) page = requests.get(new_url, cookies=cookies['df']) except Exception as exc: tprint('[-] df.cl page not found', important=False) d = process_inner(page) soup = bs(page.content, 'lxml') try: d['section'] = soup.find('meta', {'name': 'keywords'})['content'].strip() except Exception as exc: tprint('[-] Error parsing section (Df) - ', exc, important=False) try: d['body'] = '\n'.join([p for p in d['body'].split('\n') if len(p.split())>4 and p!=d['description']]) except Exception as exc: tprint('[-] Error parsing body (Df) - ', exc, important=False) return d def process_Emol(page): d = process_inner(page) soup = bs(page.content, 'lxml') try: d['section'] = d['link'].split('/')[4].capitalize() except Exception as exc: tprint('[-] Error parsing section (Emol) - ', exc, important=False) try: d['authors'] = soup.find('div', {'class', 'info-notaemol-porfecha'}).text.split('|')[-1].strip().replace('Por ','').replace('Redactado por ', '') except Exception as exc: tprint('[-] Error parsing section (Emol) - ', exc, important=False) return d def process_Latercera(page): d = {} if 'Lo sentimos, estamos actualizando el sitio' not in page.text: d = process_inner(page) else: ### Buscar noticia en google, si es necesario. scraped_link = page.url.strip('/') tprint('[-] Link Latercera no encontrado', page.url, important=False) new_link = 'https://www.latercera.com/noticia/'+'-'.join([p for p in scraped_link.split('/')[-1].split('.')[0].split('-') if not p.isdigit()]) #print(new_link) page = requests.get(new_link) if 'Lo sentimos, estamos actualizando el sitio' not in page.text: d = process_inner(page) tprint('[+] Link Latercera encontrado (intento:1): ', new_link, important=False) else: try: tprint('[·] Google Searching...', important=False) buscar = ' '.join([p for p in scraped_link.split('/')[-1].split('.')[0].split('-') if not p.isdigit()]) + ' site:latercera.com' results = search(buscar, stop=5) rs = [] for r in results: rs.append(r) result = [r for r in rs if 'sitemap' not in r][0] if 'sitemap' not in result: tprint('[+] Resultado en Google (intento:2):',result, important=False) page = requests.get(result) d = process_inner(page) else: d['error'] = 1 d['info'] = 'Link Latercera no encontrado en google' except Exception as exc: tprint('[-] Link Latercera no encontrado', important=False) d['error'] = 1 d['info'] = 'Link Latercera no encontrado en google' soup = bs(page.content, 'lxml') ### Recuperar Image. try: d['image'] = soup.find('figure').find('img')['src'] except Exception as exc: tprint('[-] Error parsing image (Latercera) - ', exc, important=False) ### Recuperar Autor try: d['authors'] = [h.text for h in soup.find_all('h4') if 'Autor' in h.text][0].replace('Autor: ', '') except Exception as exc: tprint('[-] Error parsing authors (Latercera) - ', exc, important=False) try: if d['description'] == None: d['description'] = soup.find('div', {'class': 'bajada-art'}).text except Exception as exc: tprint('[-] Error parsing description (Latercera) - ', exc, important=False) try: if d['date'] == None: date = ' '.join(soup.find('span', {'class': 'time-ago'}).text.replace('|', '').split()) d['date'] = datetime.datetime.strptime(date, '%d/%m/%Y %I:%M %p') except Exception as exc: tprint('[-] Error parsing date (Latercera) - ', exc, important=False) try: d['section'] = soup.find('meta', property='article:section')['content'] except: try: d['section'] = [x.find('a').text for x in soup.find_all('h4') if x.find('a')!=None and 'canal' in x.find('a')['href']][0] except Exception as exc: tprint('[-] Error parsing section (Latercera) - ', exc, important=False) d['tags'] = ', '.join([x['content'] for x in soup.find_all('meta', property='article:tag')]) if not d['tags']: try: d['tags'] = ', '.join([x.text for x in soup.find('div', {'class': 'tags-interior'}).find_all('a')]) except Exception as exc: tprint('[-] Error parsing tags (Latercera) - ', exc, important=False) return d def process_Cooperativa(page): d = process_inner(page) try: if 'al aire libre' in d['title'].lower(): d = {'borrar':1, info:'Borrar, Al aire libre'} except: pass soup = bs(page.content, 'lxml') try: d['authors'] = soup.find('div', {'class': 'fecha-publicacion'}).find('span').text except Exception as exc: tprint('[-] Error parsing authors (Cooperativa) - ', exc, important=False) try: d['section'] = soup.find('a', {'id': 'linkactivo'}).text except Exception as exc: tprint('[-] Error parsing section (Cooperativa) - ', exc, important=False) try: d['tags'] = soup.find('meta', {'name': 'keywords'})['content'].strip() except Exception as exc: tprint('[-] Error parsing tags (Cooperativa) - ', exc, important=False) try: d['link'] = soup.find('meta', property='og:url')['content'] except Exception as exc: tprint('[-] Error parsing link (Cooperativa) - ', exc, important=False) if not d['date']: try: date = [x for x in d['link'].split('/') if '-' in x][-1].split('-') d['date'] = datetime.datetime(*map(int,date)) except Exception as exc: tprint('[-] Error parsing date (Cooperativa) - ', exc, important=False) try: if 'www.cooperativa.cl' not in d['image'] and d['image']: d['image'] = 'https://www.cooperativa.cl'+d['image'] except Exception as exc: tprint('[-] Error fixing image (Cooperativa) - ', exc, important=False) return d def process_Elmostrador(page): d = process_inner(page) soup = bs(page.content, 'lxml') d['description'] = None try: d['description'] = soup.find('figcaption').text except Exception as exc: tprint('[-] Error parsing description (Elmostrador) - ',exc, important=False) try: d['authors'] = soup.find('p', {'class': 'autor-y-fecha'}).find('a').text except Exception as exc: tprint('[-] Error parsing authors (Elmostrador) - ',exc, important=False) try: if 'www.elmostrador.cl' not in d['image'] and d['image']: d['image'] = 'https://www.elmostrador.cl'+d['image'] except Exception as exc: tprint('[-] Error fixing image (Elmostrador) - ',exc, important=False) if not d['date']: try: date = [s for s in d['link'].split('/') if s.isdigit()][:3] d['date'] = datetime.datetime(*map(int,date)) except Exception as exc: tprint('[-] Error parsing date (Elmostrador) - ',exc, important=False) try: d['section'] = ' '.join([x for x in soup.find_all('h2') if x.find('i')!=None][0].text.split()) except Exception as exc: tprint('[-] Error parsing section (Elmostrador) - ',exc, important=False) try: d['body'] = d['body'].split('__________________')[0] except Exception as exc: tprint('[-] Error fixing body (Elmostrador) - ',exc, important=False) return d def process_Biobiochile(page): d = process_inner(page) soup = bs(page.content, 'lxml') try: d['authors'] = soup.find('div', {'class': 'nota-autor'}).find('a').text except Exception as exc: tprint('[-] Error parsing authors (Biobiochile) - ',exc, important=False) try: d['section'] = ' '.join(soup.find('div', {'class': 'categoria-titulo-nota'}).text.split()) except Exception as exc: tprint('[-] Error parsing section (Biobiochile) - ',exc, important=False) try: d['body'] = soup.find('div', {'class': 'nota-body'}).text d['body'] = d['body'].replace('Etiquetas de esta nota:', '') except Exception as exc: tprint('[-] Error parsing body (Biobiochile) - ',exc, important=False) try: d['description'] = None except Exception as exc: tprint('[-] Error parsing description (Biobiochile) - ',exc, important=False) return d

"""Exercício Python 088: Faça um programa que ajude um jogador da MEGA SENA a criar palpites. O programa vai perguntar quantos jogos serão gerados e vai sortear 6 números entre 1 e 60 para cada jogo, cadastrando tudo em uma lista composta.""" print('-=' * 25) print(f'{'JOGO DA MEGA SENA':^50}') print('-=' * 25) total_jogos = int(input('Quantos jogos você quer que eu sorteie? ')) print('-' * 50) print(' ' * 15, f'Sorteando {total_jogos} jogos', ' ' * 15) print('-' * 50) from time import sleep from random import randint c = 0 jogo = list() jogos = list() for i in range(0, total_jogos): while True: n = randint(1, 61) if n not in jogo: jogo.append(n) c += 1 if c >= 6: break c = 0 jogo.sort() jogos.append(jogo[:]) jogo.clear() for i, jogo in enumerate(jogos): print(f'Jogo {i+1}: {jogos[i]}') sleep(0.5) print('-' * 50) print(f'{'Boa sorte!':^50}')

"""Exercício Python 088: Faça um programa que ajude um jogador da MEGA SENA a criar palpites. O programa vai perguntar quantos jogos serão gerados e vai sortear 6 números entre 1 e 60 para cada jogo, cadastrando tudo em uma lista composta.""" print('-=' * 25) print(f'{"JOGO DA MEGA SENA":^50}') print('-=' * 25) total_jogos = int(input('Quantos jogos você quer que eu sorteie? ')) print('-' * 50) print(' ' * 15, f'Sorteando {total_jogos} jogos', ' ' * 15) print('-' * 50) from time import sleep from random import randint c = 0 jogo = list() jogos = list() for i in range(0, total_jogos): while True: n = randint(1, 61) if n not in jogo: jogo.append(n) c += 1 if c >= 6: break c = 0 jogo.sort() jogos.append(jogo[:]) jogo.clear() for i, jogo in enumerate(jogos): print(f'Jogo {i+1}: {jogos[i]}') sleep(0.5) print('-' * 50) print(f'{"Boa sorte!":^50}')

#!/usr/bin/env python3 import os import subprocess import json # Text styling class class Text: HEADER = '\033[1;34m' SUCCESS = '\033[1;32m' FAIL = '\033[1;21m' ENDC = '\033[0m' # Shell commands class class Commands: INSTALL_PY_DEPS = 'sudo apt-get install -y python3 python3-distutils python3-pip python3-setuptools python3-venv' CLONE_PIIRBLASTER = 'git clone https://github.com/Electronya/PiirBlaster.git' CREATE_PIIRBLASTER_SVC = 'sudo cp ./PiirBlaster/scripts/services/piirblaster.service /etc/systemd/system' ENABLE_PIIRBLASTER_SVC = 'sudo systemctl enable piirblaster.service' START_PIIRBLASTER_SVC = 'sudo systemctl start piirblaster.service' CREATE_VRITUAL_ENV = 'python3 -m venv venv' INSTALL_DEPENDENCIES = 'venv/bin/pip install -r requirements.txt' DWNLD_PIGPIO = 'wget https://github.com/joan2937/pigpio/archive/master.zip' UNZIP_PIGPIO = 'unzip master.zip' BUILD_PIGPIO = 'make' INSTALL_PIGPIO = 'sudo make install' CREATE_PIGPIO_SVC = 'sudo cp ./PiirBlaster/scripts/services/pigpiod.service /etc/systemd/system' ENABLE_PIGPIO_SVC = 'sudo systemctl enable pigpiod.service' START_PIGPIO_SVC = 'sudo systemctl start pigpiod.service' # Execute shell command def execCommand(command): process = subprocess.run(command.split(' ')) return process.returncode # Download PIGPIO def downloadPIGPIO(): print(f"{Text.HEADER}*** DOWNLOADING PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.DWNLD_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO DOWNLOAD FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO DOWNLOAD DONE{Text.ENDC}") return True # Unzip PIGPIO def unzipPIGPIO(): print(f"{Text.HEADER}*** UNZIPPNG PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.UNZIP_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO UNZIP FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO UNZIP DONE{Text.ENDC}") return True # Building PIGPIO def buildPIGPIO(): print(f"{Text.HEADER}*** BUILDING PIGPIO ***{Text.ENDC}") os.chdir('pigpio-master') cmdResult = execCommand(Commands.BUILD_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO BUILD FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO BUILD DONE{Text.ENDC}") return True # Install PIGPIO def installPIGPIO(): print(f"{Text.HEADER}*** INSTALLING PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_PIGPIO) if cmdResult !=0: print(f"{Text.FAIL}PIGPIO INSTALL FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO INSTALL DONE{Text.ENDC}") return True # Creating PIGPIO service def createPigpioSvc(): print(f"{Text.HEADER}*** CREATING PIGPIO SERVICE ***{Text.ENDC}") os.chdir('..') cmdResult = execCommand(Commands.CREATE_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}CREATING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING PIGPIO SERVICE DONE{Text.ENDC}") return True # Enabling PIGPIO service def enablePigpioSvc(): print(f"{Text.HEADER}*** ENABLING PIGPIO SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.ENABLE_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}ENABLING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}ENABLING PIGPIO SERVICE DONE{Text.ENDC}") return True # Starting PIGPIO service def startPigpioSvc(): print(f"{Text.HEADER}*** STARTING PIGPIO SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.START_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}STARTING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}STARTING PIGPIO SERVICE DONE{Text.ENDC}") return True # Setup PIGPIO service def setupPigpioSvc(): # TODO: Check if sevice is already installed & Split in multiple functions print(f"{Text.HEADER}*** SETTING UP PIGPIO SERVICE ***{Text.ENDC}") if (downloadPIGPIO() and unzipPIGPIO() and buildPIGPIO() and installPIGPIO() and createPigpioSvc() and enablePigpioSvc() and startPigpioSvc()): print(f"{Text.SUCCESS}SETTING UP PIGPIO SERVICE DONE{Text.ENDC}") return True print(f"{Text.FAIL}SETTING UP PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False # Install Python dependencies def installPythonDeps(): print(f"{Text.HEADER}*** INSTALLING PYTHON DEPENDENCIES ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_PY_DEPS) if cmdResult != 0: print(f"{Text.FAIL}INSTALLING PYTHON DEPS FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}INTALLING PYTHON DEPS DONE{Text.ENDC}") return True # Clone PiirBlaster repo def clonePiirBlaster(): print(f"{Text.HEADER}*** CLONING PiirBlaster REPO ***{Text.ENDC}") cmdResult = execCommand(Commands.CLONE_PIIRBLASTER) if cmdResult != 0: print(f"{Text.FAIL}CLONING PiirBlaster FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CLONING PiirBlaster DONE{Text.ENDC}") return True # Ask for MQTT configuration def getMqttConfig(): print(f"{Text.HEADER}*** GATTERING MQTT CONFIGURATION INFO ***{Text.ENDC}") os.chdir('PiirBlaster') with open('config/components/mqtt.json', 'r+') as mqttConfigFile: mqttConfig = json.loads(mqttConfigFile.read()) mqttConfig['broker']['hostname'] = input('Please enter the hostname/ip of the broker: ') mqttConfig['broker']['port'] = int(input(f"Please enter the broker port [{mqttConfig["broker"]["port"]}]: ") or mqttConfig['broker']['port']) mqttConfig['user']['name'] = input('Please enter the service username: ') mqttConfig['user']['password'] = input('Please enter the service password: ') newContent = json.dumps(mqttConfig, sort_keys=True, indent=2) mqttConfigFile.write(newContent) os.chdir('..') # Creating virtual environment def createVirtualEnv(): print(f"{Text.HEADER}*** CREATING VIRTUAL ENVIRONMENT ***{Text.ENDC}") os.chdir('PiirBlaster') cmdResult = execCommand(Commands.CREATE_VRITUAL_ENV) if cmdResult != 0: print(f"{Text.FAIL}CREATING VIRTUAL ENVIRONEMENT FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING VIRTUAL ENVIRONMENT DONE{Text.ENDC}") return True # Install dependencies def installDependencies(): print(f"{Text.HEADER}*** INSTALLING PiirBlaster DEPENDENCIES ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_DEPENDENCIES) if cmdResult != 0: print(f"{Text.FAIL}INSTALLING PiirBlaster DEPENDENCIES FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}INSTALLING PiirBlaster DEPENDENCIES DONE{Text.ENDC}") os.chdir('..') return True # Create PiirBlaster service def createPiirBlasterSvc(): print(f"{Text.HEADER}*** CREATING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.CREATE_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}CREATING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Enabling PiirBlaster Service def enablePiirBlasterSvc(): print(f"{Text.HEADER}*** ENABLING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.ENABLE_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}ENALBING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}ENABLING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Start PiirBlaster Service def startPiirBlasterSvc(): print(f"{Text.HEADER}*** STARTING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.START_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}STARTING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}STARTING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Setup PiirBlaster service def setupPiirBlasterSvc(): # TODO: Check if sevice is already installed getMqttConfig() print(f"{Text.HEADER}*** SETTING UP PiirBlaster SERVICE ***{Text.ENDC}") if (createVirtualEnv() and installDependencies() and createPiirBlasterSvc() and enablePiirBlasterSvc() and startPiirBlasterSvc()): print(f"{Text.SUCCESS}SETTING UP PiirBlaster SERVICE DONE{Text.ENDC}") return True print(f"{Text.FAIL}SETTING UP PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False # print(f"{Text.HEADER}*** SERVICE CONFIGURATION ***{Text.ENDC}") # Ask for the hostname the service will use for advertising # hostname = input(f"Please enter the hostname that the service will use for advertising:") # Install PiirBlaster def install(): if (installPythonDeps() and clonePiirBlaster() and setupPigpioSvc() and setupPiirBlasterSvc()): print(f"{Text.SUCCESS}INSATALLING PiirBlaster SERVICE DONE{Text.ENDC}") exit() print(f"{Text.FAIL}INTALLING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") if __name__ == '__main__': install()

#!/usr/bin/env python3 import os import subprocess import json # Text styling class class Text: HEADER = '\033[1;34m' SUCCESS = '\033[1;32m' FAIL = '\033[1;21m' ENDC = '\033[0m' # Shell commands class class Commands: INSTALL_PY_DEPS = 'sudo apt-get install -y python3 python3-distutils python3-pip python3-setuptools python3-venv' CLONE_PIIRBLASTER = 'git clone https://github.com/Electronya/PiirBlaster.git' CREATE_PIIRBLASTER_SVC = 'sudo cp ./PiirBlaster/scripts/services/piirblaster.service /etc/systemd/system' ENABLE_PIIRBLASTER_SVC = 'sudo systemctl enable piirblaster.service' START_PIIRBLASTER_SVC = 'sudo systemctl start piirblaster.service' CREATE_VRITUAL_ENV = 'python3 -m venv venv' INSTALL_DEPENDENCIES = 'venv/bin/pip install -r requirements.txt' DWNLD_PIGPIO = 'wget https://github.com/joan2937/pigpio/archive/master.zip' UNZIP_PIGPIO = 'unzip master.zip' BUILD_PIGPIO = 'make' INSTALL_PIGPIO = 'sudo make install' CREATE_PIGPIO_SVC = 'sudo cp ./PiirBlaster/scripts/services/pigpiod.service /etc/systemd/system' ENABLE_PIGPIO_SVC = 'sudo systemctl enable pigpiod.service' START_PIGPIO_SVC = 'sudo systemctl start pigpiod.service' # Execute shell command def execCommand(command): process = subprocess.run(command.split(' ')) return process.returncode # Download PIGPIO def downloadPIGPIO(): print(f"{Text.HEADER}*** DOWNLOADING PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.DWNLD_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO DOWNLOAD FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO DOWNLOAD DONE{Text.ENDC}") return True # Unzip PIGPIO def unzipPIGPIO(): print(f"{Text.HEADER}*** UNZIPPNG PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.UNZIP_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO UNZIP FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO UNZIP DONE{Text.ENDC}") return True # Building PIGPIO def buildPIGPIO(): print(f"{Text.HEADER}*** BUILDING PIGPIO ***{Text.ENDC}") os.chdir('pigpio-master') cmdResult = execCommand(Commands.BUILD_PIGPIO) if cmdResult != 0: print(f"{Text.FAIL}PIGPIO BUILD FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO BUILD DONE{Text.ENDC}") return True # Install PIGPIO def installPIGPIO(): print(f"{Text.HEADER}*** INSTALLING PIGPIO ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_PIGPIO) if cmdResult !=0: print(f"{Text.FAIL}PIGPIO INSTALL FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}PIGPIO INSTALL DONE{Text.ENDC}") return True # Creating PIGPIO service def createPigpioSvc(): print(f"{Text.HEADER}*** CREATING PIGPIO SERVICE ***{Text.ENDC}") os.chdir('..') cmdResult = execCommand(Commands.CREATE_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}CREATING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING PIGPIO SERVICE DONE{Text.ENDC}") return True # Enabling PIGPIO service def enablePigpioSvc(): print(f"{Text.HEADER}*** ENABLING PIGPIO SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.ENABLE_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}ENABLING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}ENABLING PIGPIO SERVICE DONE{Text.ENDC}") return True # Starting PIGPIO service def startPigpioSvc(): print(f"{Text.HEADER}*** STARTING PIGPIO SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.START_PIGPIO_SVC) if cmdResult != 0: print(f"{Text.FAIL}STARTING PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}STARTING PIGPIO SERVICE DONE{Text.ENDC}") return True # Setup PIGPIO service def setupPigpioSvc(): # TODO: Check if sevice is already installed & Split in multiple functions print(f"{Text.HEADER}*** SETTING UP PIGPIO SERVICE ***{Text.ENDC}") if (downloadPIGPIO() and unzipPIGPIO() and buildPIGPIO() and installPIGPIO() and createPigpioSvc() and enablePigpioSvc() and startPigpioSvc()): print(f"{Text.SUCCESS}SETTING UP PIGPIO SERVICE DONE{Text.ENDC}") return True print(f"{Text.FAIL}SETTING UP PIGPIO SERVICE FAILED!!!{Text.ENDC}") return False # Install Python dependencies def installPythonDeps(): print(f"{Text.HEADER}*** INSTALLING PYTHON DEPENDENCIES ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_PY_DEPS) if cmdResult != 0: print(f"{Text.FAIL}INSTALLING PYTHON DEPS FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}INTALLING PYTHON DEPS DONE{Text.ENDC}") return True # Clone PiirBlaster repo def clonePiirBlaster(): print(f"{Text.HEADER}*** CLONING PiirBlaster REPO ***{Text.ENDC}") cmdResult = execCommand(Commands.CLONE_PIIRBLASTER) if cmdResult != 0: print(f"{Text.FAIL}CLONING PiirBlaster FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CLONING PiirBlaster DONE{Text.ENDC}") return True # Ask for MQTT configuration def getMqttConfig(): print(f"{Text.HEADER}*** GATTERING MQTT CONFIGURATION INFO ***{Text.ENDC}") os.chdir('PiirBlaster') with open('config/components/mqtt.json', 'r+') as mqttConfigFile: mqttConfig = json.loads(mqttConfigFile.read()) mqttConfig['broker']['hostname'] = input('Please enter the hostname/ip of the broker: ') mqttConfig['broker']['port'] = int(input(f"Please enter the broker port [{mqttConfig['broker']['port']}]: ") or mqttConfig['broker']['port']) mqttConfig['user']['name'] = input('Please enter the service username: ') mqttConfig['user']['password'] = input('Please enter the service password: ') newContent = json.dumps(mqttConfig, sort_keys=True, indent=2) mqttConfigFile.write(newContent) os.chdir('..') # Creating virtual environment def createVirtualEnv(): print(f"{Text.HEADER}*** CREATING VIRTUAL ENVIRONMENT ***{Text.ENDC}") os.chdir('PiirBlaster') cmdResult = execCommand(Commands.CREATE_VRITUAL_ENV) if cmdResult != 0: print(f"{Text.FAIL}CREATING VIRTUAL ENVIRONEMENT FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING VIRTUAL ENVIRONMENT DONE{Text.ENDC}") return True # Install dependencies def installDependencies(): print(f"{Text.HEADER}*** INSTALLING PiirBlaster DEPENDENCIES ***{Text.ENDC}") cmdResult = execCommand(Commands.INSTALL_DEPENDENCIES) if cmdResult != 0: print(f"{Text.FAIL}INSTALLING PiirBlaster DEPENDENCIES FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}INSTALLING PiirBlaster DEPENDENCIES DONE{Text.ENDC}") os.chdir('..') return True # Create PiirBlaster service def createPiirBlasterSvc(): print(f"{Text.HEADER}*** CREATING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.CREATE_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}CREATING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}CREATING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Enabling PiirBlaster Service def enablePiirBlasterSvc(): print(f"{Text.HEADER}*** ENABLING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.ENABLE_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}ENALBING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}ENABLING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Start PiirBlaster Service def startPiirBlasterSvc(): print(f"{Text.HEADER}*** STARTING PiirBlaster SERVICE ***{Text.ENDC}") cmdResult = execCommand(Commands.START_PIIRBLASTER_SVC) if cmdResult != 0: print(f"{Text.FAIL}STARTING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False print(f"{Text.SUCCESS}STARTING PiirBlaster SERVICE DONE{Text.ENDC}") return True # Setup PiirBlaster service def setupPiirBlasterSvc(): # TODO: Check if sevice is already installed getMqttConfig() print(f"{Text.HEADER}*** SETTING UP PiirBlaster SERVICE ***{Text.ENDC}") if (createVirtualEnv() and installDependencies() and createPiirBlasterSvc() and enablePiirBlasterSvc() and startPiirBlasterSvc()): print(f"{Text.SUCCESS}SETTING UP PiirBlaster SERVICE DONE{Text.ENDC}") return True print(f"{Text.FAIL}SETTING UP PiirBlaster SERVICE FAILED!!!{Text.ENDC}") return False # print(f"{Text.HEADER}*** SERVICE CONFIGURATION ***{Text.ENDC}") # Ask for the hostname the service will use for advertising # hostname = input(f"Please enter the hostname that the service will use for advertising:") # Install PiirBlaster def install(): if (installPythonDeps() and clonePiirBlaster() and setupPigpioSvc() and setupPiirBlasterSvc()): print(f"{Text.SUCCESS}INSATALLING PiirBlaster SERVICE DONE{Text.ENDC}") exit() print(f"{Text.FAIL}INTALLING PiirBlaster SERVICE FAILED!!!{Text.ENDC}") if __name__ == '__main__': install()

# Copyright (C) 2019 The Raphielscape Company LLC. # # Licensed under the Raphielscape Public License, Version 1.d (the "License"); # you may not use this file except in compliance with the License. # # ReCode by @mrismanaziz # FROM Man-Userbot <https://github.com/mrismanaziz/Man-Userbot> # t.me/SharingUserbot & t.me/Lunatic0de import random import time from datetime import datetime from speedtest import Speedtest from userbot import CMD_HANDLER as cmd from userbot import CMD_HELP, StartTime from userbot.events import register from userbot.utils import edit_or_reply, humanbytes, ayiin_cmd from time import sleep absen = [ "**𝙃𝙖𝙙𝙞𝙧 𝙙𝙤𝙣𝙜 𝙏𝙤𝙙** 😁", "**𝙃𝙖𝙙𝙞𝙧 𝙆𝙖𝙠𝙖 𝙂𝙖𝙣𝙩𝙚𝙣𝙜** 😉", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝘾𝙤𝙣𝙩𝙤𝙡** 😁", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝙂𝙖𝙣𝙩𝙚𝙣𝙜** 🥵", "**𝙃𝙖𝙙𝙞𝙧 𝙉𝙜𝙖𝙗** 😎", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝘼𝙗𝙖𝙣𝙜** 🥺", "**𝙎𝙞 𝘾𝙖𝙠𝙚𝙥 𝙃𝙖𝙙𝙞𝙧 𝘽𝙖𝙣𝙜** 😎", ] ayiincakep = [ "**𝙄𝙮𝙖 𝘼𝙮𝙞𝙞𝙣 𝙂𝙖𝙣𝙩𝙚𝙣𝙜 𝘽𝙖𝙣𝙜𝙚𝙩** 😍", "**𝙂𝙖𝙣𝙩𝙚𝙣𝙜𝙣𝙮𝙖 𝙂𝙖𝙠 𝘼𝙙𝙖 𝙇𝙖𝙬𝙖𝙣** 😚", "**𝘼𝙮𝙞𝙞𝙣 𝙂𝙖𝙣𝙩𝙚𝙣𝙜𝙣𝙮𝙖 𝘼𝙠𝙪 𝙆𝙖𝙣** 😍", "**𝙂𝙖𝙠 𝘼𝙙𝙖 𝙎𝙖𝙞𝙣𝙜 𝙔𝙞𝙣𝙨** 😎", "**𝘼𝙮𝙞𝙞𝙣 𝙅𝙖𝙢𝙚𝙩 𝙏𝙖𝙥𝙞 𝘽𝙤𝙤𝙣𝙜** 😚", ] async def get_readable_time(seconds: int) -> str: count = 0 up_time = "" time_list = [] time_suffix_list = ["s", "m", "Jam", "Hari"] while count < 4: count += 1 remainder, result = divmod( seconds, 60) if count < 3 else divmod( seconds, 24) if seconds == 0 and remainder == 0: break time_list.append(int(result)) seconds = int(remainder) for x in range(len(time_list)): time_list[x] = str(time_list[x]) + time_suffix_list[x] if len(time_list) == 4: up_time += time_list.pop() + ", " time_list.reverse() up_time += ":".join(time_list) return up_time @ayiin_cmd(pattern="ping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() xx = await edit_or_reply(ping, "**✧**") await xx.edit("**✧✧**") await xx.edit("**✧✧✧**") await xx.edit("**✧✧✧✧**") await xx.edit("**✧✧✧✧✧**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await ping.client.get_me() await xx.edit("⚡") sleep(3) await xx.edit( f"**✧ 𝚉𝙴𝚃-𝚄𝚂𝙴𝚁𝙱𝙾𝚃 ✧**\n" f"**𝙿𝙾𝙽𝙶!!!**\n" f"✧ **𝙿𝙸𝙽𝙶𝙴𝚁 :** `%sms`\n" f"✧ **𝚄𝙿𝚃𝙸𝙼𝙴 :** `{uptime}` \n" f"✧ **𝙾𝚆𝙽𝙴𝚁 :** [{user.first_name}](tg://user?id={user.id})" % (duration) ) @ayiin_cmd(pattern="xping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() xping = await edit_or_reply(ping, "`Pinging....`") end = datetime.now() duration = (end - start).microseconds / 1000 await xping.edit( f"**PONG!! 🍭**\n**Pinger** : %sms\n**Bot Uptime** : {uptime}🕛" % (duration) ) @ayiin_cmd(pattern="lping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() lping = await edit_or_reply(ping, "**★ PING ★**") await lping.edit("**★★ PING ★★**") await lping.edit("**★★★ PING ★★★**") await lping.edit("**★★★★ PING ★★★★**") await lping.edit("**✦҈͜͡➳ PONG!**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await ping.client.get_me() await lping.edit( f"❃ **Ping !!** " f"`%sms` \n" f"❃ **Uptime -** " f"`{uptime}` \n" f"**✦҈͜͡➳ Master :** [{user.first_name}](tg://user?id={user.id})" % (duration) ) @ayiin_cmd(pattern="keping$") async def _(pong): await get_readable_time((time.time() - StartTime)) start = datetime.now() kopong = await edit_or_reply(pong, "**『⍟𝐊𝐎𝐍𝐓𝐎𝐋』**") await kopong.edit("**◆◈𝐊𝐀𝐌𝐏𝐀𝐍𝐆◈◆**") await kopong.edit("**𝐏𝐄𝐂𝐀𝐇𝐊𝐀𝐍 𝐁𝐈𝐉𝐈 𝐊𝐀𝐔 𝐀𝐒𝐔**") await kopong.edit("**☬𝐒𝐈𝐀𝐏 𝐊𝐀𝐌𝐏𝐀𝐍𝐆 𝐌𝐄𝐍𝐔𝐌𝐁𝐔𝐊 𝐀𝐒𝐔☬**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await pong.client.get_me() await kopong.edit( f"**✲ 𝙺𝙾𝙽𝚃𝙾𝙻 𝙼𝙴𝙻𝙴𝙳𝚄𝙶** " f"\n ⫸ 𝙺𝙾𝙽𝚃𝙾𝙻 `%sms` \n" f"**✲ 𝙱𝙸𝙹𝙸 𝙿𝙴𝙻𝙴𝚁** " f"\n ⫸ 𝙺𝙰𝙼𝙿𝙰𝙽𝙶『[{user.first_name}](tg://user?id={user.id})』 \n" % (duration) ) # .keping & kping Coded by Koala @ayiin_cmd(pattern=r"kping$") async def _(pong): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() kping = await edit_or_reply(pong, "8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8==✊=D") await kping.edit("8=✊==D") await kping.edit("8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8==✊=D") await kping.edit("8=✊==D") await kping.edit("8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8===✊D💦") await kping.edit("8====D💦💦") await kping.edit("**CROOTTTT**") await kping.edit("**CROOTTTT AAAHHH.....**") end = datetime.now() duration = (end - start).microseconds / 1000 await kping.edit("🥵") sleep(3) await kping.edit( f"**𝙽𝙶𝙴𝙽𝚃𝙾𝚃 𝙰𝙷𝙷!! 🥵**\n**𝙺𝚄𝚃𝙰𝙽𝙶** : %sms\n**𝙱𝙾𝚃 𝚄𝙿𝚃𝙸𝙼𝙴** : {uptime}🕛" % (duration) ) @ayiin_cmd(pattern="speedtest$") async def _(speed): xxnx = await edit_or_reply(speed, "`Running speed test...`") test = Speedtest() test.get_best_server() test.download() test.upload() test.results.share() result = test.results.dict() msg = ( f"**Started at {result["timestamp"]}**\n\n" "**Client**\n" f"**ISP :** `{result["client"]["isp"]}`\n" f"**Country :** `{result["client"]["country"]}`\n\n" "**Server**\n" f"**Name :** `{result["server"]["name"]}`\n" f"**Country :** `{result["server"]["country"]}`\n" f"**Sponsor :** `{result["server"]["sponsor"]}`\n\n" f"**Ping :** `{result["ping"]}`\n" f"**Upload :** `{humanbytes(result["upload"])}/s`\n" f"**Download :** `{humanbytes(result["download"])}/s`" ) await xxnx.delete() await speed.client.send_file( speed.chat_id, result["share"], caption=msg, force_document=False, ) @ayiin_cmd(pattern="pong$") async def _(pong): start = datetime.now() xx = await edit_or_reply(pong, "`Sepong`") await xx.edit("Sepong Sayang.....") end = datetime.now() duration = (end - start).microseconds / 9000 await xx.edit("🥵") sleep(3) await xx.edit("**𝙿𝙸𝙽𝙶!**\n`%sms`" % (duration)) # KALO NGEFORK absen ini GA USAH DI HAPUS YA GOBLOK 😡 @register(incoming=True, from_users=1700405732, pattern=r"^Absen$") async def ayiinabsen(ganteng): await ganteng.reply(random.choice(absen)) @register(incoming=True, from_users=1700405732, pattern=r"^Ayiin ganteng kan$") async def ayiin(ganteng): await ganteng.reply(random.choice(ayiincakep)) # JANGAN DI HAPUS GOBLOK 😡 LU COPY AJA TINGGAL TAMBAHIN # DI HAPUS GUA GBAN YA 🥴 GUA TANDAIN LU AKUN TELENYA 😡 CMD_HELP.update( { "ping": f"**Plugin : **`ping`\ \n\n • **Syntax :** `{cmd}ping` ; `{cmd}lping` ; `{cmd}xping` ; `{cmd}kping`\ \n • **Function : **Untuk menunjukkan ping userbot.\ \n\n • **Syntax :** `{cmd}pong`\ \n • **Function : **Sama seperti perintah ping\ " } ) CMD_HELP.update( { "speedtest": f"**Plugin : **`speedtest`\ \n\n • **Syntax :** `{cmd}speedtest`\ \n • **Function : **Untuk Mengetes kecepatan server userbot.\ " } )

# Copyright (C) 2019 The Raphielscape Company LLC. # # Licensed under the Raphielscape Public License, Version 1.d (the "License"); # you may not use this file except in compliance with the License. # # ReCode by @mrismanaziz # FROM Man-Userbot <https://github.com/mrismanaziz/Man-Userbot> # t.me/SharingUserbot & t.me/Lunatic0de import random import time from datetime import datetime from speedtest import Speedtest from userbot import CMD_HANDLER as cmd from userbot import CMD_HELP, StartTime from userbot.events import register from userbot.utils import edit_or_reply, humanbytes, ayiin_cmd from time import sleep absen = [ "**𝙃𝙖𝙙𝙞𝙧 𝙙𝙤𝙣𝙜 𝙏𝙤𝙙** 😁", "**𝙃𝙖𝙙𝙞𝙧 𝙆𝙖𝙠𝙖 𝙂𝙖𝙣𝙩𝙚𝙣𝙜** 😉", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝘾𝙤𝙣𝙩𝙤𝙡** 😁", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝙂𝙖𝙣𝙩𝙚𝙣𝙜** 🥵", "**𝙃𝙖𝙙𝙞𝙧 𝙉𝙜𝙖𝙗** 😎", "**𝙂𝙪𝙖 𝙃𝙖𝙙𝙞𝙧 𝘼𝙗𝙖𝙣𝙜** 🥺", "**𝙎𝙞 𝘾𝙖𝙠𝙚𝙥 𝙃𝙖𝙙𝙞𝙧 𝘽𝙖𝙣𝙜** 😎", ] ayiincakep = [ "**𝙄𝙮𝙖 𝘼𝙮𝙞𝙞𝙣 𝙂𝙖𝙣𝙩𝙚𝙣𝙜 𝘽𝙖𝙣𝙜𝙚𝙩** 😍", "**𝙂𝙖𝙣𝙩𝙚𝙣𝙜𝙣𝙮𝙖 𝙂𝙖𝙠 𝘼𝙙𝙖 𝙇𝙖𝙬𝙖𝙣** 😚", "**𝘼𝙮𝙞𝙞𝙣 𝙂𝙖𝙣𝙩𝙚𝙣𝙜𝙣𝙮𝙖 𝘼𝙠𝙪 𝙆𝙖𝙣** 😍", "**𝙂𝙖𝙠 𝘼𝙙𝙖 𝙎𝙖𝙞𝙣𝙜 𝙔𝙞𝙣𝙨** 😎", "**𝘼𝙮𝙞𝙞𝙣 𝙅𝙖𝙢𝙚𝙩 𝙏𝙖𝙥𝙞 𝘽𝙤𝙤𝙣𝙜** 😚", ] async def get_readable_time(seconds: int) -> str: count = 0 up_time = "" time_list = [] time_suffix_list = ["s", "m", "Jam", "Hari"] while count < 4: count += 1 remainder, result = divmod( seconds, 60) if count < 3 else divmod( seconds, 24) if seconds == 0 and remainder == 0: break time_list.append(int(result)) seconds = int(remainder) for x in range(len(time_list)): time_list[x] = str(time_list[x]) + time_suffix_list[x] if len(time_list) == 4: up_time += time_list.pop() + ", " time_list.reverse() up_time += ":".join(time_list) return up_time @ayiin_cmd(pattern="ping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() xx = await edit_or_reply(ping, "**✧**") await xx.edit("**✧✧**") await xx.edit("**✧✧✧**") await xx.edit("**✧✧✧✧**") await xx.edit("**✧✧✧✧✧**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await ping.client.get_me() await xx.edit("⚡") sleep(3) await xx.edit( f"**✧ 𝚉𝙴𝚃-𝚄𝚂𝙴𝚁𝙱𝙾𝚃 ✧**\n" f"**𝙿𝙾𝙽𝙶!!!**\n" f"✧ **𝙿𝙸𝙽𝙶𝙴𝚁 :** `%sms`\n" f"✧ **𝚄𝙿𝚃𝙸𝙼𝙴 :** `{uptime}` \n" f"✧ **𝙾𝚆𝙽𝙴𝚁 :** [{user.first_name}](tg://user?id={user.id})" % (duration) ) @ayiin_cmd(pattern="xping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() xping = await edit_or_reply(ping, "`Pinging....`") end = datetime.now() duration = (end - start).microseconds / 1000 await xping.edit( f"**PONG!! 🍭**\n**Pinger** : %sms\n**Bot Uptime** : {uptime}🕛" % (duration) ) @ayiin_cmd(pattern="lping$") async def _(ping): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() lping = await edit_or_reply(ping, "**★ PING ★**") await lping.edit("**★★ PING ★★**") await lping.edit("**★★★ PING ★★★**") await lping.edit("**★★★★ PING ★★★★**") await lping.edit("**✦҈͜͡➳ PONG!**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await ping.client.get_me() await lping.edit( f"❃ **Ping !!** " f"`%sms` \n" f"❃ **Uptime -** " f"`{uptime}` \n" f"**✦҈͜͡➳ Master :** [{user.first_name}](tg://user?id={user.id})" % (duration) ) @ayiin_cmd(pattern="keping$") async def _(pong): await get_readable_time((time.time() - StartTime)) start = datetime.now() kopong = await edit_or_reply(pong, "**『⍟𝐊𝐎𝐍𝐓𝐎𝐋』**") await kopong.edit("**◆◈𝐊𝐀𝐌𝐏𝐀𝐍𝐆◈◆**") await kopong.edit("**𝐏𝐄𝐂𝐀𝐇𝐊𝐀𝐍 𝐁𝐈𝐉𝐈 𝐊𝐀𝐔 𝐀𝐒𝐔**") await kopong.edit("**☬𝐒𝐈𝐀𝐏 𝐊𝐀𝐌𝐏𝐀𝐍𝐆 𝐌𝐄𝐍𝐔𝐌𝐁𝐔𝐊 𝐀𝐒𝐔☬**") end = datetime.now() duration = (end - start).microseconds / 1000 user = await pong.client.get_me() await kopong.edit( f"**✲ 𝙺𝙾𝙽𝚃𝙾𝙻 𝙼𝙴𝙻𝙴𝙳𝚄𝙶** " f"\n ⫸ 𝙺𝙾𝙽𝚃𝙾𝙻 `%sms` \n" f"**✲ 𝙱𝙸𝙹𝙸 𝙿𝙴𝙻𝙴𝚁** " f"\n ⫸ 𝙺𝙰𝙼𝙿𝙰𝙽𝙶『[{user.first_name}](tg://user?id={user.id})』 \n" % (duration) ) # .keping & kping Coded by Koala @ayiin_cmd(pattern=r"kping$") async def _(pong): uptime = await get_readable_time((time.time() - StartTime)) start = datetime.now() kping = await edit_or_reply(pong, "8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8==✊=D") await kping.edit("8=✊==D") await kping.edit("8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8==✊=D") await kping.edit("8=✊==D") await kping.edit("8✊===D") await kping.edit("8=✊==D") await kping.edit("8==✊=D") await kping.edit("8===✊D") await kping.edit("8===✊D💦") await kping.edit("8====D💦💦") await kping.edit("**CROOTTTT**") await kping.edit("**CROOTTTT AAAHHH.....**") end = datetime.now() duration = (end - start).microseconds / 1000 await kping.edit("🥵") sleep(3) await kping.edit( f"**𝙽𝙶𝙴𝙽𝚃𝙾𝚃 𝙰𝙷𝙷!! 🥵**\n**𝙺𝚄𝚃𝙰𝙽𝙶** : %sms\n**𝙱𝙾𝚃 𝚄𝙿𝚃𝙸𝙼𝙴** : {uptime}🕛" % (duration) ) @ayiin_cmd(pattern="speedtest$") async def _(speed): xxnx = await edit_or_reply(speed, "`Running speed test...`") test = Speedtest() test.get_best_server() test.download() test.upload() test.results.share() result = test.results.dict() msg = ( f"**Started at {result['timestamp']}**\n\n" "**Client**\n" f"**ISP :** `{result['client']['isp']}`\n" f"**Country :** `{result['client']['country']}`\n\n" "**Server**\n" f"**Name :** `{result['server']['name']}`\n" f"**Country :** `{result['server']['country']}`\n" f"**Sponsor :** `{result['server']['sponsor']}`\n\n" f"**Ping :** `{result['ping']}`\n" f"**Upload :** `{humanbytes(result['upload'])}/s`\n" f"**Download :** `{humanbytes(result['download'])}/s`" ) await xxnx.delete() await speed.client.send_file( speed.chat_id, result["share"], caption=msg, force_document=False, ) @ayiin_cmd(pattern="pong$") async def _(pong): start = datetime.now() xx = await edit_or_reply(pong, "`Sepong`") await xx.edit("Sepong Sayang.....") end = datetime.now() duration = (end - start).microseconds / 9000 await xx.edit("🥵") sleep(3) await xx.edit("**𝙿𝙸𝙽𝙶!**\n`%sms`" % (duration)) # KALO NGEFORK absen ini GA USAH DI HAPUS YA GOBLOK 😡 @register(incoming=True, from_users=1700405732, pattern=r"^Absen$") async def ayiinabsen(ganteng): await ganteng.reply(random.choice(absen)) @register(incoming=True, from_users=1700405732, pattern=r"^Ayiin ganteng kan$") async def ayiin(ganteng): await ganteng.reply(random.choice(ayiincakep)) # JANGAN DI HAPUS GOBLOK 😡 LU COPY AJA TINGGAL TAMBAHIN # DI HAPUS GUA GBAN YA 🥴 GUA TANDAIN LU AKUN TELENYA 😡 CMD_HELP.update( { "ping": f"**Plugin : **`ping`\ \n\n • **Syntax :** `{cmd}ping` ; `{cmd}lping` ; `{cmd}xping` ; `{cmd}kping`\ \n • **Function : **Untuk menunjukkan ping userbot.\ \n\n • **Syntax :** `{cmd}pong`\ \n • **Function : **Sama seperti perintah ping\ " } ) CMD_HELP.update( { "speedtest": f"**Plugin : **`speedtest`\ \n\n • **Syntax :** `{cmd}speedtest`\ \n • **Function : **Untuk Mengetes kecepatan server userbot.\ " } )

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import math import os import pickle import sys from argparse import Namespace from copy import deepcopy from pathlib import Path from unittest.mock import ANY, call, patch import cloudpickle import pytest import torch from omegaconf import OmegaConf from torch.optim import SGD from torch.utils.data import DataLoader import tests.helpers.utils as tutils from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv from pytorch_lightning.loggers import TensorBoardLogger from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper, UnrepeatedDistributedSampler from pytorch_lightning.plugins import DDPSpawnPlugin from pytorch_lightning.trainer.states import TrainerFn from pytorch_lightning.utilities import DeviceType, DistributedType from pytorch_lightning.utilities.cloud_io import load as pl_load from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning.utilities.seed import seed_everything from tests.base import EvalModelTemplate from tests.helpers import BoringModel, RandomDataset from tests.helpers.runif import RunIf @pytest.mark.parametrize("url_ckpt", [True, False]) def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", str(tmpdir)) model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) # fit model trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # assert ckpt has hparams ckpt = torch.load(new_weights_path) assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in ckpt.keys(), "hyper_parameters missing from checkpoints" # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) model_2 = EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) model_2.eval() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) model_2 = EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) model_2.eval() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_strict_model_load(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) model = EvalModelTemplate() # Extra layer model.c_d3 = torch.nn.Linear(model.hidden_dim, model.hidden_dim) # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) try: EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) # todo: specify the possible exception except Exception: failed = True else: failed = False assert failed, "Model should not been loaded since the extra layer added." failed = False try: EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False, ) # todo: specify the possible exception except Exception: failed = True assert not failed, "Model should be loaded due to strict=False." @pytest.mark.parametrize("accumulate_grad_batches", (1, 2, 3)) def test_trainer_accumulate_grad_batches_zero_grad(tmpdir, accumulate_grad_batches): with patch("torch.optim.SGD.zero_grad") as sgd_zero_grad: model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, limit_train_batches=20, limit_val_batches=1, max_epochs=1, weights_summary=None, accumulate_grad_batches=accumulate_grad_batches, ) trainer.fit(model) assert sgd_zero_grad.call_count == math.ceil(trainer.limit_train_batches / accumulate_grad_batches) @pytest.mark.parametrize( ["accumulate_grad_batches", "limit_train_batches"], [ ({ 1: 2, 3: 4 }, 1.0), ({ 1: 2, 3: 4 }, 0.5), # not to be divisible by accumulate_grad_batches on purpose (3, 1.0), (3, 0.8), # not to be divisible by accumulate_grad_batches on purpose (4, 1.0), (4, 0.7), # not to be divisible by accumulate_grad_batches on purpose ], ) def test_gradient_accumulation_scheduling_last_batch(tmpdir, accumulate_grad_batches, limit_train_batches): """ Verify optimizer.step() applied to last batch while grad accumulation """ class TestModel(BoringModel): def state_dict(self, *args, **kwargs): return deepcopy(super().state_dict(*args, **kwargs)) def check(self, d1, d2, equal=True): keys = d1.keys() | d2.keys() values = [torch.equal(d1[k], d2[k]) for k in keys] return all(values) if equal else not any(values) def backward(self, *args, **kwargs) -> None: pre_bwd_state_dict = self.state_dict() assert self.check(self.start_state_dict, pre_bwd_state_dict) out = super().backward(*args, **kwargs) # state dict is equal, just the gradients changed assert self.check(pre_bwd_state_dict, self.state_dict()) return out def optimizer_step(self, *args, **kwargs): pre_opt_step_state_dict = self.state_dict() assert self.check(self.start_state_dict, pre_opt_step_state_dict) # this calls `backward` and `on_after_backward` inside the closure out = super().optimizer_step(*args, **kwargs) # the state dict changed assert self.check(pre_opt_step_state_dict, self.state_dict(), equal=False) self.opt_step_called = True return out def on_train_batch_start(self, *_): self.start_state_dict = self.state_dict() self.opt_step_called = False def on_train_batch_end(self, outputs, batch, batch_idx, *_): end_state_dict = self.state_dict() is_last_batch = (batch_idx + 1) == self.trainer.num_training_batches if is_last_batch or self.opt_step_called: assert self.check(self.start_state_dict, end_state_dict, equal=False) else: assert self.check(self.start_state_dict, end_state_dict) model = TestModel() trainer = Trainer( accumulate_grad_batches=accumulate_grad_batches, max_epochs=2, limit_train_batches=limit_train_batches, limit_val_batches=0, default_root_dir=tmpdir, progress_bar_refresh_rate=0, ) trainer.fit(model) def test_loading_meta_tags(tmpdir): """ test for backward compatibility to meta_tags.csv """ tutils.reset_seed() hparams = EvalModelTemplate.get_default_hparams() # save tags logger = tutils.get_default_logger(tmpdir) logger.log_hyperparams(Namespace(some_str="a_str", an_int=1, a_float=2.0)) logger.log_hyperparams(hparams) logger.save() # load hparams path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(path_expt_dir, TensorBoardLogger.NAME_HPARAMS_FILE) hparams = load_hparams_from_yaml(hparams_path) # save as legacy meta_tags.csv tags_path = os.path.join(path_expt_dir, "meta_tags.csv") save_hparams_to_tags_csv(tags_path, hparams) tags = load_hparams_from_tags_csv(tags_path) assert hparams == tags def test_loading_yaml(tmpdir): tutils.reset_seed() hparams = EvalModelTemplate.get_default_hparams() # save tags logger = tutils.get_default_logger(tmpdir) logger.log_hyperparams(Namespace(some_str="a_str", an_int=1, a_float=2.0)) logger.log_hyperparams(hparams) logger.save() # load hparams path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(path_expt_dir, "hparams.yaml") tags = load_hparams_from_yaml(hparams_path) assert tags["batch_size"] == 32 and tags["hidden_dim"] == 1000 @pytest.mark.parametrize( "save_top_k,save_last,expected_files", [ pytest.param(-1, False, [f"epoch={i}.ckpt" for i in range(5)], id="CASE K=-1 (all)"), pytest.param(1, False, {"epoch=4.ckpt"}, id="CASE K=1 (2.5, epoch 4)"), pytest.param(2, False, [f"epoch={i}.ckpt" for i in (2, 4)], id="CASE K=2 (2.5 epoch 4, 2.8 epoch 2)"), pytest.param(4, False, [f"epoch={i}.ckpt" for i in range(1, 5)], id="CASE K=4 (save all 4 base)"), pytest.param(3, False, [f"epoch={i}.ckpt" for i in range(2, 5)], id="CASE K=3 (save the 2nd, 3rd, 4th model)"), pytest.param(1, True, {"epoch=4.ckpt", "last.ckpt"}, id="CASE K=1 (save the 4th model and the last model)"), ], ) def test_model_checkpoint_options(tmpdir, save_top_k, save_last, expected_files): """Test ModelCheckpoint options.""" def mock_save_function(filepath, *args): open(filepath, "a").close() # simulated losses losses = [10, 9, 2.8, 5, 2.5] checkpoint_callback = ModelCheckpoint( dirpath=tmpdir, filename='{epoch}', monitor='checkpoint_on', save_top_k=save_top_k, save_last=save_last, verbose=True ) trainer = Trainer() trainer.state.fn = TrainerFn.FITTING trainer.save_checkpoint = mock_save_function # emulate callback's calls during the training for i, loss in enumerate(losses): trainer.fit_loop.current_epoch = i trainer.fit_loop.global_step = i trainer.logger_connector.callback_metrics.update({"checkpoint_on": loss}) checkpoint_callback.on_validation_end(trainer, trainer.lightning_module) file_lists = set(os.listdir(tmpdir)) assert len(file_lists) == len( expected_files ), f"Should save {len(expected_files)} models when save_top_k={save_top_k} but found={file_lists}" # verify correct naming for fname in expected_files: assert fname in file_lists def test_model_checkpoint_only_weights(tmpdir): """Tests use case where ModelCheckpoint is configured to save only model weights, and user tries to load checkpoint to resume training. """ model = EvalModelTemplate() trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_weights_only=True)], ) # fit model trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0] # assert saved checkpoint has no trainer data checkpoint = torch.load(checkpoint_path) assert "optimizer_states" not in checkpoint, "checkpoint should contain only model weights" assert "lr_schedulers" not in checkpoint, "checkpoint should contain only model weights" # assert loading model works when checkpoint has only weights assert EvalModelTemplate.load_from_checkpoint(checkpoint_path=checkpoint_path) # directly save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path, weights_only=True) # assert saved checkpoint has no trainer data checkpoint = torch.load(new_weights_path) assert "optimizer_states" not in checkpoint, "checkpoint should contain only model weights" assert "lr_schedulers" not in checkpoint, "checkpoint should contain only model weights" # assert restoring train state fails with pytest.raises(KeyError, match="checkpoint contains only the model"): trainer.checkpoint_connector.restore(new_weights_path) def test_model_freeze_unfreeze(): model = EvalModelTemplate() model.freeze() model.unfreeze() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_resume_from_checkpoint_epoch_restored(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Verify resuming from checkpoint runs the right number of epochs""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) class TestModel(BoringModel): # Model that tracks epochs and batches seen num_epochs_end_seen = 0 num_batches_seen = 0 num_on_load_checkpoint_called = 0 def on_epoch_end(self): self.num_epochs_end_seen += 1 def on_train_batch_start(self, *_): self.num_batches_seen += 1 def on_load_checkpoint(self, _): self.num_on_load_checkpoint_called += 1 model = TestModel() trainer = Trainer( max_epochs=2, limit_train_batches=0.65, limit_val_batches=1, callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1)], default_root_dir=tmpdir, val_check_interval=1.0, progress_bar_refresh_rate=0, logger=False, weights_summary=None, ) trainer.fit(model) # `on_epoch_end` will be called once for val_sanity, twice for train, twice for val assert model.num_epochs_end_seen == 1 + 2 + 2 assert model.num_batches_seen == trainer.num_training_batches * 2 assert model.num_on_load_checkpoint_called == 0 # Other checkpoints can be uncommented if/when resuming mid-epoch is supported checkpoints = Path(trainer.checkpoint_callback.dirpath).glob("*.ckpt") if url_ckpt: # transform local paths into url checkpoints ip, port = tmpdir_server checkpoints = [f"http://{ip}:{port}/" + ckpt.name for ckpt in checkpoints] for ckpt in checkpoints: next_model = TestModel() state = pl_load(ckpt) # Resume training new_trainer = Trainer( default_root_dir=tmpdir, resume_from_checkpoint=ckpt, max_epochs=2, ) new_trainer.fit(next_model) assert state["global_step"] + next_model.num_batches_seen == trainer.num_training_batches * trainer.max_epochs assert next_model.num_on_load_checkpoint_called == 1 def test_trainer_max_steps_and_epochs(tmpdir): """Verify model trains according to specified max steps""" model = BoringModel() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) # define less train steps than epochs trainer_kwargs = { 'limit_train_batches': 0.5, 'default_root_dir': tmpdir, 'max_epochs': 3, 'max_steps': num_train_samples + 10, 'logger': False, 'weights_summary': None, 'progress_bar_refresh_rate': 0, } trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == trainer.max_steps, "Model did not stop at max_steps" # define less train epochs than steps trainer_kwargs['max_epochs'] = 2 trainer_kwargs['max_steps'] = 3 * 2 * num_train_samples trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == num_train_samples * trainer.max_epochs assert trainer.current_epoch == trainer.max_epochs - 1, "Model did not stop at max_epochs" def test_trainer_min_steps_and_epochs(tmpdir): """Verify model trains according to specified min steps""" model = EvalModelTemplate() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) trainer_kwargs = { 'limit_train_batches': 0.5, 'default_root_dir': tmpdir, # define callback for stopping the model 'callbacks': [EarlyStopping(monitor="early_stop_on", min_delta=1.0)], 'val_check_interval': 2, 'min_epochs': 1, 'max_epochs': 7, # define less min steps than 1 epoch 'min_steps': num_train_samples // 2, 'logger': False, 'weights_summary': None, 'progress_bar_refresh_rate': 0, } trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch > 0 assert trainer.global_step >= num_train_samples, "Model did not train for at least min_epochs" # define less epochs than min_steps trainer_kwargs["min_steps"] = math.floor(num_train_samples * 1.5) trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch > 0 assert trainer.global_step >= math.floor(num_train_samples * 1.5), "Model did not train for at least min_steps" def test_trainer_min_steps_and_min_epochs_not_reached(tmpdir, caplog): """ Test that min_epochs/min_steps in Trainer are enforced even if EarlyStopping is triggered. """ class TestModel(BoringModel): training_step_invoked = 0 def training_step(self, batch, batch_idx): output = super().training_step(batch, batch_idx) output["loss"] = output["loss"] * 0.0 # force minimal loss to trigger early stopping self.log("loss", output["loss"]) self.training_step_invoked += 1 assert not self.trainer.should_stop return output model = TestModel() early_stop = EarlyStopping(monitor="loss", patience=0, check_on_train_epoch_end=True) min_epochs = 5 trainer = Trainer( default_root_dir=tmpdir, progress_bar_refresh_rate=0, min_epochs=min_epochs, limit_val_batches=0, limit_train_batches=2, callbacks=[early_stop] ) with caplog.at_level(logging.INFO, logger="pytorch_lightning.trainer.trainer"): trainer.fit(model) message = f"minimum epochs ({min_epochs}) or minimum steps (None) has not been met. Training will continue" num_messages = len([record.message for record in caplog.records if message in record.message]) assert num_messages == min_epochs - 2 assert model.training_step_invoked == min_epochs * 2 def test_trainer_max_steps_accumulate_batches(tmpdir): """Verify model trains according to specified max steps with grad accumulated batches""" model = BoringModel() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) # define less train steps than epochs trainer = Trainer( limit_train_batches=0.5, default_root_dir=tmpdir, max_steps=num_train_samples + 10, accumulate_grad_batches=10, logger=False, weights_summary=None, progress_bar_refresh_rate=0, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == trainer.max_steps, "Model did not stop at max_steps" def test_benchmark_option(tmpdir): """Verify benchmark option.""" model = EvalModelTemplate() model.val_dataloader = model.val_dataloader__multiple # verify torch.backends.cudnn.benchmark is not turned on assert not torch.backends.cudnn.benchmark # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, benchmark=True, ) trainer.fit(model) # verify training completed assert trainer.state.finished, f"Training failed with {trainer.state}" # verify torch.backends.cudnn.benchmark is not turned off assert torch.backends.cudnn.benchmark @pytest.mark.parametrize("ckpt_path", (None, "best", "specific")) @pytest.mark.parametrize("save_top_k", (-1, 0, 1, 2)) @pytest.mark.parametrize("fn", ("validate", "test", "predict")) def test_tested_checkpoint_path(tmpdir, ckpt_path, save_top_k, fn): class TestModel(BoringModel): def validation_step(self, batch, batch_idx): self.log("foo", -batch_idx) return super().validation_step(batch, batch_idx) def test_step(self, *args): return self.validation_step(*args) def predict_step(self, batch, *_): return self(batch) model = TestModel() model.test_epoch_end = None trainer = Trainer( max_epochs=2, limit_val_batches=1, limit_test_batches=1, limit_predict_batches=1, progress_bar_refresh_rate=0, default_root_dir=tmpdir, callbacks=[ModelCheckpoint(monitor="foo", save_top_k=save_top_k)], ) trainer.fit(model) trainer_fn = getattr(trainer, fn) path_attr = f"{fn}{"d" if fn == "validate" else "ed"}_ckpt_path" assert getattr(trainer, path_attr) is None if ckpt_path == "best": # ckpt_path is 'best', meaning we load the best weights if save_top_k == 0: with pytest.raises(MisconfigurationException, match=".*is not configured to save the best.*"): trainer_fn(ckpt_path=ckpt_path) else: trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) == trainer.checkpoint_callback.best_model_path elif ckpt_path is None: # ckpt_path is None, meaning we don't load any checkpoints and # use the weights from the end of training trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) is None else: # specific checkpoint, pick one from saved ones if save_top_k == 0: with pytest.raises(FileNotFoundError): trainer_fn(ckpt_path="random.ckpt") else: ckpt_path = str( list((Path(tmpdir) / f"lightning_logs/version_{trainer.logger.version}/checkpoints").iterdir() )[0].absolute() ) trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) == ckpt_path def test_disabled_training(tmpdir): """Verify that `limit_train_batches=0` disables the training loop unless `fast_dev_run=True`.""" class CurrentModel(BoringModel): training_step_invoked = False training_epoch_end_invoked = False def training_step(self, *args, **kwargs): self.training_step_invoked = True return super().training_step(*args, **kwargs) def training_epoch_end(self, *args, **kwargs): self.training_epoch_end_invoked = True return super().training_epoch_end(*args, **kwargs) model = CurrentModel() trainer_options = dict( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=0.0, limit_val_batches=0.2, fast_dev_run=False, ) before_state_dict = deepcopy(model.state_dict()) trainer = Trainer(**trainer_options) trainer.fit(model) after_state_dict = model.state_dict() for key in before_state_dict.keys(): assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])) # check that limit_train_batches=0 turns off training assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert not model.training_step_invoked, "`training_step` should not run when `limit_train_batches=0`" assert not model.training_epoch_end_invoked, "`training_epoch_end` should not run when `limit_train_batches=0`" # check that limit_train_batches has no influence when fast_dev_run is turned on model = CurrentModel() trainer_options.update(fast_dev_run=True) before_state_dict = deepcopy(model.state_dict()) trainer = Trainer(**trainer_options) trainer.fit(model) after_state_dict = model.state_dict() for key in before_state_dict.keys(): assert not torch.all(torch.eq(before_state_dict[key], after_state_dict[key])) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert model.training_step_invoked, "did not run `training_step` with `fast_dev_run=True`" assert model.training_epoch_end_invoked, "did not run `training_epoch_end` with `fast_dev_run=True`" def test_disabled_validation(tmpdir): """Verify that `limit_val_batches=0` disables the validation loop unless `fast_dev_run=True`.""" class CurrentModel(EvalModelTemplate): validation_step_invoked = False validation_epoch_end_invoked = False def validation_step(self, *args, **kwargs): self.validation_step_invoked = True return super().validation_step(*args, **kwargs) def validation_epoch_end(self, *args, **kwargs): self.validation_epoch_end_invoked = True return super().validation_epoch_end(*args, **kwargs) hparams = EvalModelTemplate.get_default_hparams() model = CurrentModel(**hparams) trainer_options = dict( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=0.4, limit_val_batches=0.0, fast_dev_run=False, ) trainer = Trainer(**trainer_options) trainer.fit(model) # check that limit_val_batches=0 turns off validation assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 1 assert not model.validation_step_invoked, "`validation_step` should not run when `limit_val_batches=0`" assert not model.validation_epoch_end_invoked, "`validation_epoch_end` should not run when `limit_val_batches=0`" # check that limit_val_batches has no influence when fast_dev_run is turned on model = CurrentModel(**hparams) trainer_options.update(fast_dev_run=True) trainer = Trainer(**trainer_options) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert model.validation_step_invoked, "did not run `validation_step` with `fast_dev_run=True`" assert model.validation_epoch_end_invoked, "did not run `validation_epoch_end` with `fast_dev_run=True`" def test_nan_loss_detection(tmpdir): class CurrentModel(BoringModel): test_batch_inf = 3 def training_step(self, batch, batch_idx): output = super().training_step(batch, batch_idx) if batch_idx == self.test_batch_inf: if isinstance(output, dict): output["loss"] *= torch.tensor(math.inf) # make loss infinite else: output /= 0 return output model = CurrentModel() # fit model trainer = Trainer( default_root_dir=tmpdir, max_steps=(model.test_batch_inf + 1), terminate_on_nan=True, ) with pytest.raises(ValueError, match=r".*The loss returned in `training_step` is.*"): trainer.fit(model) assert trainer.global_step == model.test_batch_inf for param in model.parameters(): assert torch.isfinite(param).all() def test_nan_params_detection(tmpdir): class CurrentModel(BoringModel): test_batch_nan = 3 def on_after_backward(self): if self.global_step == self.test_batch_nan: # simulate parameter that became nan torch.nn.init.constant_(self.layer.bias, math.nan) model = CurrentModel() trainer = Trainer( default_root_dir=tmpdir, max_steps=(model.test_batch_nan + 1), terminate_on_nan=True, ) with pytest.raises(ValueError, match=r".*Detected nan and/or inf values in `layer.bias`.*"): trainer.fit(model) assert trainer.global_step == model.test_batch_nan # after aborting the training loop, model still has nan-valued params params = torch.cat([param.view(-1) for param in model.parameters()]) assert not torch.isfinite(params).all() def test_trainer_interrupted_flag(tmpdir): """Test the flag denoting that a user interrupted training.""" model = EvalModelTemplate() class InterruptCallback(Callback): def __init__(self): super().__init__() def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx): raise KeyboardInterrupt class HandleInterruptCallback(Callback): def __init__(self): super().__init__() self.exc_info = None def on_keyboard_interrupt(self, trainer, pl_module): self.exc_info = sys.exc_info() interrupt_callback = InterruptCallback() handle_interrupt_callback = HandleInterruptCallback() trainer = Trainer( callbacks=[interrupt_callback, handle_interrupt_callback], max_epochs=1, limit_val_batches=0.1, limit_train_batches=0.2, progress_bar_refresh_rate=0, logger=False, default_root_dir=tmpdir, ) assert not trainer.interrupted assert handle_interrupt_callback.exc_info is None trainer.fit(model) assert trainer.interrupted assert isinstance(handle_interrupt_callback.exc_info[1], KeyboardInterrupt) def test_gradient_clipping(tmpdir): """ Test gradient clipping """ tutils.reset_seed() model = EvalModelTemplate() trainer = Trainer( max_steps=1, max_epochs=1, gradient_clip_val=1.0, default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2) assert (grad_norm - 1.0).abs() < 0.01, "Gradient norm != 1.0: {grad_norm}".format(grad_norm=grad_norm) return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward # for the test model.prev_called_batch_idx = 0 trainer.fit(model) def test_gradient_clipping_by_value(tmpdir): """ Test gradient clipping by value """ tutils.reset_seed() model = BoringModel() grad_clip_val = 1e-10 trainer = Trainer( max_steps=1, max_epochs=1, gradient_clip_val=grad_clip_val, gradient_clip_algorithm='value', default_root_dir=tmpdir ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_max_list = [torch.max(p.grad.detach().abs()) for p in parameters] grad_max = torch.max(torch.stack(grad_max_list)) assert abs(grad_max.item() - grad_clip_val) < 1e-11, \ f"Gradient max value {grad_max} != grad_clip_val {grad_clip_val} ." return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward # for the test model.prev_called_batch_idx = 0 trainer.fit(model) @RunIf(min_gpus=1, amp_native=True) def test_gradient_clipping_fp16(tmpdir): """ Test gradient clipping with fp16 """ tutils.reset_seed() model = EvalModelTemplate() trainer = Trainer( max_steps=1, max_epochs=1, precision=16, gpus=1, gradient_clip_val=1.0, default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2) assert (grad_norm - 1.0).abs() < 0.01, "Gradient norm != 1.0: {grad_norm}".format(grad_norm=grad_norm) return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward model.prev_called_batch_idx = 0 trainer.fit(model) @RunIf(min_gpus=1, amp_native=True) def test_gradient_clipping_by_value_fp16(tmpdir): """ Test gradient clipping by value with fp16 """ tutils.reset_seed() model = BoringModel() grad_clip_val = 1e-10 trainer = Trainer( max_steps=1, max_epochs=1, precision=16, gpus=1, gradient_clip_val=grad_clip_val, gradient_clip_algorithm='value', default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_max_list = [torch.max(p.grad.detach().abs()) for p in parameters] grad_max = torch.max(torch.stack(grad_max_list)) assert abs(grad_max.item() - grad_clip_val) < 1e-11, \ f"Gradient max value {grad_max} != grad_clip_val {grad_clip_val} ." return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward model.prev_called_batch_idx = 0 trainer.fit(model) def test_gpu_choice(tmpdir): trainer_options = dict(default_root_dir=tmpdir) # Only run if CUDA is available if not torch.cuda.is_available(): return num_gpus = torch.cuda.device_count() Trainer(**trainer_options, gpus=num_gpus, auto_select_gpus=True) with pytest.raises(RuntimeError, match=r".*No GPUs available.*"): Trainer(**trainer_options, gpus=num_gpus + 1, auto_select_gpus=True) @pytest.mark.parametrize( "limit_val_batches", [0.0, 1, 1.0, 0.5, 5], ) def test_num_sanity_val_steps(tmpdir, limit_val_batches): """ Test that the number of sanity check batches is clipped to `limit_val_batches`. """ model = EvalModelTemplate() model.validation_step = model.validation_step__multiple_dataloaders model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders num_sanity_val_steps = 4 trainer = Trainer( default_root_dir=tmpdir, num_sanity_val_steps=num_sanity_val_steps, limit_val_batches=limit_val_batches, max_steps=1, ) assert trainer.num_sanity_val_steps == num_sanity_val_steps with patch.object( trainer.fit_loop.validation_loop.epoch_loop, "evaluation_step", wraps=trainer.fit_loop.validation_loop.epoch_loop.evaluation_step ) as mocked: val_dataloaders = model.val_dataloader__multiple_mixed_length() trainer.fit(model, val_dataloaders=val_dataloaders) assert mocked.call_count == sum( min(num_sanity_val_steps, num_batches) for num_batches in trainer.num_val_batches ) @pytest.mark.parametrize("limit_val_batches", [0.0, 1, 1.0, 0.3]) def test_num_sanity_val_steps_neg_one(tmpdir, limit_val_batches): """ Test that `num_sanity_val_steps=-1` runs through all validation data once, and as many batches as limited by `limit_val_batches` Trainer argument. """ model = EvalModelTemplate() model.validation_step = model.validation_step__multiple_dataloaders model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders trainer = Trainer( default_root_dir=tmpdir, num_sanity_val_steps=-1, limit_val_batches=limit_val_batches, max_steps=1, ) assert trainer.num_sanity_val_steps == float("inf") with patch.object( trainer.fit_loop.validation_loop.epoch_loop, "evaluation_step", wraps=trainer.fit_loop.validation_loop.epoch_loop.evaluation_step ) as mocked: val_dataloaders = model.val_dataloader__multiple() trainer.fit(model, val_dataloaders=val_dataloaders) assert mocked.call_count == sum(trainer.num_val_batches) @pytest.mark.parametrize( "trainer_kwargs,expected", [ ( dict(accelerator=None, gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="dp", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp", num_processes=2, gpus=None), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp", num_nodes=2, gpus=None), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp_cpu", num_processes=2, gpus=None), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp2", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator=None, gpus=1), dict(_distrib_type=None, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="dp", gpus=1), dict(_distrib_type=DistributedType.DP, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="ddp", gpus=1), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="ddp_cpu", num_processes=2, gpus=1), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp2", gpus=1), dict(_distrib_type=DistributedType.DDP2, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator=None, gpus=2), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.GPU, num_gpus=2, num_processes=2), ), ( dict(accelerator="dp", gpus=2), dict(_distrib_type=DistributedType.DP, _device_type=DeviceType.GPU, num_gpus=2, num_processes=1), ), ( dict(accelerator="ddp", gpus=2), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.GPU, num_gpus=2, num_processes=2), ), ( dict(accelerator="ddp2", gpus=2), dict(_distrib_type=DistributedType.DDP2, _device_type=DeviceType.GPU, num_gpus=2, num_processes=1), ), ], ) def test_trainer_config(trainer_kwargs, expected, monkeypatch): if trainer_kwargs["gpus"] is not None: monkeypatch.setattr(torch.cuda, "is_available", lambda: True) monkeypatch.setattr(torch.cuda, "device_count", lambda: trainer_kwargs["gpus"]) trainer = Trainer(**trainer_kwargs) assert len(expected) == 4 for k, v in expected.items(): assert getattr(trainer, k) == v, f"Failed {k}: {v}" def test_trainer_subclassing(): model = EvalModelTemplate() # First way of pulling out args from signature is to list them class TrainerSubclass(Trainer): def __init__(self, custom_arg, *args, custom_kwarg="test", **kwargs): super().__init__(*args, **kwargs) self.custom_arg = custom_arg self.custom_kwarg = custom_kwarg trainer = TrainerSubclass(123, custom_kwarg="custom", fast_dev_run=True) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.custom_arg == 123 assert trainer.custom_kwarg == "custom" assert trainer.fast_dev_run # Second way is to pop from the dict # It's a special case because Trainer does not have any positional args class TrainerSubclass(Trainer): def __init__(self, **kwargs): self.custom_arg = kwargs.pop("custom_arg", 0) self.custom_kwarg = kwargs.pop("custom_kwarg", "test") super().__init__(**kwargs) trainer = TrainerSubclass(custom_kwarg="custom", fast_dev_run=True) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.custom_kwarg == "custom" assert trainer.fast_dev_run # when we pass in an unknown arg, the base class should complain with pytest.raises(TypeError, match=r"__init__ got an unexpected keyword argument 'abcdefg'"): TrainerSubclass(abcdefg="unknown_arg") @pytest.mark.parametrize( "trainer_params", [ OmegaConf.create(dict(max_epochs=1, gpus=1)), OmegaConf.create(dict(max_epochs=1, gpus=[0])), ] ) @RunIf(min_gpus=1) def test_trainer_omegaconf(trainer_params): Trainer(**trainer_params) def test_trainer_pickle(tmpdir): trainer = Trainer( max_epochs=1, default_root_dir=tmpdir, ) pickle.dumps(trainer) cloudpickle.dumps(trainer) @pytest.mark.parametrize("stage", ("fit", "validate", "test")) def test_trainer_setup_call(tmpdir, stage): """Test setup call gets the correct stage""" class CurrentModel(BoringModel): def setup(self, stage): self.stage = stage class TrainerSubclass(Trainer): def setup(self, model, stage): assert model is not None self.stage = stage model = CurrentModel() # fit model trainer = TrainerSubclass(default_root_dir=tmpdir, max_epochs=1, checkpoint_callback=False) if stage == "fit": trainer.fit(model) elif stage == "validate": trainer.validate(model, ckpt_path=None) else: trainer.test(model, ckpt_path=None) assert trainer.stage == stage assert trainer.lightning_module.stage == stage @pytest.mark.parametrize( "train_batches, max_steps, log_interval", [ (10, 10, 1), (3, 10, 1), (3, 10, 5), ], ) @patch("pytorch_lightning.loggers.tensorboard.TensorBoardLogger.log_metrics") def test_log_every_n_steps(log_metrics_mock, tmpdir, train_batches, max_steps, log_interval): class TestModel(BoringModel): def training_step(self, *args, **kwargs): self.log("foo", -1) return super().training_step(*args, **kwargs) model = TestModel() trainer = Trainer( default_root_dir=tmpdir, log_every_n_steps=log_interval, flush_logs_every_n_steps=log_interval, limit_train_batches=train_batches, limit_val_batches=0, max_steps=max_steps, ) trainer.fit(model) expected_calls = [call(metrics=ANY, step=s) for s in range(log_interval - 1, max_steps, log_interval)] log_metrics_mock.assert_has_calls(expected_calls) class TestLightningDataModule(LightningDataModule): def __init__(self, dataloaders): super().__init__() self._dataloaders = dataloaders def test_dataloader(self): return self._dataloaders def predict_dataloader(self): return self._dataloaders class CustomPredictionWriter(BasePredictionWriter): write_on_batch_end_called = False write_on_epoch_end_called = False def __init__(self, output_dir: str, *args, **kwargs): super().__init__(*args, **kwargs) self.output_dir = output_dir def write_on_batch_end(self, trainer, pl_module, prediction, batch_indices, *args, **kwargs): assert prediction.shape == torch.Size([1, 2]) if trainer.accelerator_connector.is_distributed: assert len(batch_indices) == 1 else: assert batch_indices is None self.write_on_batch_end_called = True def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices): expected = 1 if trainer.accelerator_connector.is_distributed else 2 assert len(predictions) == 2 assert len(predictions[0]) == expected if trainer.accelerator_connector.is_distributed: assert len(batch_indices) == 2 assert len(batch_indices[0]) == expected else: assert batch_indices is None self.write_on_epoch_end_called = True def on_predict_epoch_end(self, trainer, pl_module, outputs): if trainer.accelerator_connector.is_distributed: for idx in range(2): assert isinstance(trainer.predict_dataloaders[idx].batch_sampler.sampler, UnrepeatedDistributedSampler) assert isinstance(trainer.predict_dataloaders[idx].batch_sampler, IndexBatchSamplerWrapper) super().on_predict_epoch_end(trainer, pl_module, outputs) def predict( tmpdir, accelerator, gpus, num_processes, model=None, plugins=None, datamodule=True, pbrr=None, use_callbacks=True ): dataloaders = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))] model = model or BoringModel() dm = TestLightningDataModule(dataloaders) cb = CustomPredictionWriter(tmpdir, write_interval="batch") cb_1 = CustomPredictionWriter(tmpdir, write_interval="epoch") trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, log_every_n_steps=1, weights_summary=None, accelerator=accelerator, gpus=gpus, num_processes=num_processes, plugins=plugins, progress_bar_refresh_rate=pbrr, callbacks=[cb, cb_1] if use_callbacks else [] ) if accelerator == "ddp_spawn": with pytest.raises(MisconfigurationException): trainer.predict(model, datamodule=dm, return_predictions=True) if datamodule: results = trainer.predict(model, datamodule=dm) else: results = trainer.predict(model, dataloaders=dataloaders) if not isinstance(trainer.training_type_plugin, DDPSpawnPlugin): if use_callbacks: assert cb.write_on_batch_end_called assert not cb.write_on_epoch_end_called assert not cb_1.write_on_batch_end_called assert cb_1.write_on_epoch_end_called num_samples = 1 if accelerator == "ddp" else 2 assert len(results) == 2 assert len(results[0]) == num_samples assert results[0][0].shape == torch.Size([1, 2]) def test_trainer_predict_no_return(tmpdir): """ Test trainer.predict warns when nothing is returned """ class CustomBoringModel(BoringModel): def predict_step(self, batch, batch_idx, dataloader_idx=None): if (batch_idx + 1) % 2 == 0: return return super().predict_step(batch, batch_idx, dataloader_idx) with pytest.warns(UserWarning, match='predict returned None'): predict(tmpdir, None, None, 1, model=CustomBoringModel(), use_callbacks=False) def test_trainer_predict_grad(tmpdir): class CustomBoringModel(BoringModel): def predict_step(self, batch, batch_idx, dataloader_idx=None): assert batch.expand_as(batch).grad_fn is None return super().predict_step(batch, batch_idx, dataloader_idx) predict(tmpdir, None, None, 1, model=CustomBoringModel(), use_callbacks=False) x = torch.zeros(1, requires_grad=True) assert x.expand_as(x).grad_fn is not None @pytest.mark.parametrize('progress_bar_refresh_rate', [0, 5, None]) @pytest.mark.parametrize('datamodule', [False, True]) def test_trainer_predict_cpu(tmpdir, datamodule, progress_bar_refresh_rate): predict(tmpdir, None, None, 1, datamodule=datamodule, pbrr=progress_bar_refresh_rate) @RunIf(min_gpus=2, special=True) @pytest.mark.parametrize('num_gpus', [1, 2]) def test_trainer_predict_dp(tmpdir, num_gpus): predict(tmpdir, "dp", num_gpus, None) @RunIf(min_gpus=2, special=True, fairscale=True) def test_trainer_predict_ddp(tmpdir): predict(tmpdir, "ddp", 2, None) @RunIf(min_gpus=2, skip_windows=True, special=True) def test_trainer_predict_ddp_spawn(tmpdir): predict(tmpdir, "ddp_spawn", 2, None) @RunIf(min_gpus=2, special=True) def test_trainer_predict_1_gpu(tmpdir): predict(tmpdir, None, 1, None) @RunIf(skip_windows=True) def test_trainer_predict_ddp_cpu(tmpdir): predict(tmpdir, "ddp_cpu", 0, 2) @patch('torch.cuda.device_count', return_value=2) @patch('torch.cuda.is_available', return_value=True) def test_spawn_predict_return_predictions(*_): """ Test that `return_predictions=True` raise a MisconfigurationException with spawn training type plugins. """ model = BoringModel() def run(expected_plugin, **trainer_kwargs): trainer = Trainer(**trainer_kwargs, fast_dev_run=True) assert isinstance(trainer.training_type_plugin, expected_plugin) with pytest.raises(MisconfigurationException, match="`return_predictions` should be set to `False`"): trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=True) run(DDPSpawnPlugin, accelerator="ddp_spawn", gpus=2) run(DDPSpawnPlugin, accelerator="ddp_cpu", num_processes=2) @pytest.mark.parametrize("return_predictions", [None, False, True]) @pytest.mark.parametrize("precision", [32, 64]) def test_predict_return_predictions_cpu(return_predictions, precision, tmpdir): """ Test that `return_predictions=True`. """ seed_everything(42) model = BoringModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, precision=precision) preds = trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=return_predictions) if return_predictions or return_predictions is None: assert len(preds) == 1 assert preds[0].shape == torch.Size([1, 2]) assert preds[0].dtype == (torch.float64 if precision == 64 else torch.float32) @pytest.mark.parametrize( ["limit_train_batches", "global_step", "num_training_batches", "current_epoch", "should_train"], [(0.2, 0, 0, 0, False), (0.5, 10, 2, 4, True)], ) def test_disabled_training_for_insufficient_limit_train_batches( tmpdir, limit_train_batches, global_step, num_training_batches, current_epoch, should_train ): """ Verify when `limit_train_batches` is float & between [0.0, 1.0] and `int(self.num_training_batches * self.limit_train_batches) == 0`, the training loop is disabled. """ class CurrentModel(BoringModel): training_step_invoked = False training_epoch_end_invoked = False def training_step(self, *args, **kwargs): self.training_step_invoked = True return super().training_step(*args, **kwargs) def training_epoch_end(self, *args, **kwargs): self.training_epoch_end_invoked = True return super().training_epoch_end(*args, **kwargs) dataset_len = 100 batch_size = 25 train = RandomDataset(32, length=dataset_len) train_loader = DataLoader(train, batch_size=batch_size) model = CurrentModel() trainer = Trainer( default_root_dir=tmpdir, max_epochs=5, limit_train_batches=limit_train_batches, ) trainer.fit(model, train_loader) params_string = f"""`limit_train_batches={limit_train_batches}`, `dataset_len={dataset_len}` & `batch_size={batch_size}` as `num_training_batches={num_training_batches}`""" if should_train: error_string = f"should run with {params_string}" else: error_string = f"should not run with {params_string}" assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == global_step assert trainer.num_training_batches == num_training_batches assert trainer.current_epoch == current_epoch assert model.training_step_invoked == should_train, f"`training_step` {error_string}" assert model.training_epoch_end_invoked == should_train, f"`training_epoch_end` {error_string}" @pytest.mark.parametrize(["max_steps", "max_epochs", "global_step"], [(10, 5, 10), (20, None, 20)]) def test_repeated_fit_calls_with_max_epochs_and_steps(tmpdir, max_steps, max_epochs, global_step): """ Ensure that the training loop is bound by `max_steps` and `max_epochs` for repeated calls of `trainer.fit`, and disabled if the limit is reached """ dataset_len = 200 batch_size = 10 train_data = DataLoader(RandomDataset(32, dataset_len), batch_size=batch_size) model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, max_steps=max_steps, max_epochs=max_epochs, ) trainer.fit(model, train_data) assert trainer.global_step == global_step trainer.fit(model, train_data) assert trainer.global_step == global_step def test_trainer_access_in_configure_optimizers(tmpdir): """ Verify that the configure optimizer function can reference the trainer. """ class TestModel(BoringModel): def configure_optimizers(self): assert self.trainer is not None, "Expect to have access to the trainer within `configure_optimizers`" train_data = torch.utils.data.DataLoader(RandomDataset(32, 64)) model = TestModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True) trainer.fit(model, train_data) @RunIf(min_gpus=1) def test_setup_hook_move_to_device_correctly(tmpdir): """ Verify that if a user defines a layer in the setup hook function, this is moved to the correct device. """ class TestModel(BoringModel): def setup(self, stage: str) -> None: self.new_layer = torch.nn.Linear(2, 2) def training_step(self, batch, batch_idx): output = self.layer(batch) # will crash if not moved to correct device output = self.new_layer(output) loss = self.loss(batch, output) return {"loss": loss} # fake data train_data = torch.utils.data.DataLoader(RandomDataset(32, 64)) # model model = TestModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=1) trainer.fit(model, train_data) def test_train_loop_system(tmpdir): """ Test the following methods are called in the order in automatic optimization. 1. optimizer.step (skip when gradient accumulation) 2. model.training_step 3. optimizer.zero_grad (run when the first batch of gradient accumulation) 4. model.backward Note that the order is NOT `training_step`->`zero_grad`->`backward`->`step`. This is because `optimizer.step(closure)` calls `closure()` which then calls the three remaining methods `training_step`, `zero_grad` and `backward` inside. """ called_methods = [] trainer_options = dict( default_root_dir=tmpdir, max_epochs=1, limit_train_batches=5, limit_val_batches=1, limit_test_batches=1, progress_bar_refresh_rate=0, ) class TestOptimizer(SGD): def step(self, *args, **kwargs): called_methods.append("step") return super().step(*args, **kwargs) def zero_grad(self, *args, **kwargs): called_methods.append("zero_grad") return super().zero_grad(*args, **kwargs) class TestModel(BoringModel): def configure_optimizers(self): return TestOptimizer(self.parameters(), lr=0.1) def training_step(self, *args, **kwargs): called_methods.append("training_step") return super().training_step(*args, **kwargs) def backward(self, *args, **kwargs): called_methods.append("backward") return super().backward(*args, **kwargs) model = TestModel() trainer = Trainer(**trainer_options) # No methods are called yet. assert called_methods == [] trainer.fit(model) assert called_methods == [ "step", "training_step", "zero_grad", "backward", ] * trainer.limit_train_batches called_methods.clear() trainer = Trainer(**trainer_options, accumulate_grad_batches=3) # No methods are called yet. assert called_methods == [] trainer.fit(model) assert called_methods == [ # 0 "training_step", "zero_grad", "backward", # 1 "training_step", "backward", # 2 "step", "training_step", "backward", # 3 "training_step", "zero_grad", "backward", # 4 "step", "training_step", "backward", ] def test_init_optimizers_resets_lightning_optimizers(tmpdir): """ Test that the Trainer resets the `lightning_optimizers` list everytime new optimizers get initialized. """ def compare_optimizers(): assert trainer.lightning_optimizers[0].optimizer is trainer.optimizers[0] model = BoringModel() model.lr = 0.2 trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, auto_lr_find=True, ) trainer.tune(model) compare_optimizers() trainer.fit(model) compare_optimizers() trainer.fit_loop.max_epochs = 2 # simulate multiple fit calls trainer.fit(model) compare_optimizers() def test_check_val_every_n_epoch_exception(tmpdir): with pytest.raises(MisconfigurationException, match="should be an integer."): Trainer( default_root_dir=tmpdir, max_epochs=1, check_val_every_n_epoch=1.2, ) def test_trainer_attach_data_pipeline_to_model(tmpdir): class DataPipeline: pass class TestDataModule(LightningDataModule): data_pipeline = DataPipeline() def train_dataloader(self): return DataLoader(RandomDataset(32, 64)) def val_dataloader(self): return DataLoader(RandomDataset(32, 64)) def test_dataloader(self): return DataLoader(RandomDataset(32, 64)) class TestCallback(Callback): def on_fit_start(self, trainer, pl_module: LightningModule) -> None: """Called when fit begins""" assert isinstance(pl_module.data_pipeline, DataPipeline) model = BoringModel() dm = TestDataModule() trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, callbacks=[TestCallback()]) trainer.fit(model, datamodule=dm) def test_exception_when_testing_or_validating_with_fast_dev_run(tmpdir): trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True) model = BoringModel() trainer.fit(model) with pytest.raises(MisconfigurationException, match=r"\.validate` with `fast_dev_run=True"): trainer.validate() with pytest.raises(MisconfigurationException, match=r"\.test` with `fast_dev_run=True"): trainer.test() class TrainerStagesModel(BoringModel): def on_train_start(self) -> None: assert self.trainer.model.training assert self.training def on_validation_start(self) -> None: assert not self.trainer.model.training assert not self.training def on_test_start(self) -> None: assert not self.trainer.model.training assert not self.training def on_predict_start(self) -> None: assert not self.trainer.model.training assert not self.training @pytest.mark.parametrize( 'accelerator,num_processes', [(None, 1), pytest.param('ddp', 2, marks=RunIf(skip_windows=True))] ) def test_model_in_correct_mode_during_stages(tmpdir, accelerator, num_processes): model = TrainerStagesModel() trainer = Trainer(default_root_dir=tmpdir, accelerator=accelerator, num_processes=num_processes, fast_dev_run=True) trainer.fit(model) trainer.validate(model) trainer.test(model) trainer.predict(model, model.val_dataloader()) class TestDummyModelForCheckpoint(BoringModel): def validation_step(self, batch, batch_idx): output = self.layer(batch) loss = self.loss(batch, output) self.log('x', loss) def validation_epoch_end(self, outputs) -> None: pass @RunIf(skip_windows=True) def test_fit_test_synchronization(tmpdir): """Test that the trainer synchronizes processes before returning control back to the caller. """ tutils.set_random_master_port() model = TestDummyModelForCheckpoint() checkpoint = ModelCheckpoint(dirpath=tmpdir, monitor='x', mode='min', save_top_k=1) trainer = Trainer( default_root_dir=tmpdir, max_epochs=2, accelerator='ddp_cpu', num_processes=2, callbacks=[checkpoint], ) trainer.fit(model) assert os.path.exists(checkpoint.best_model_path), f'Could not find checkpoint at rank {trainer.global_rank}' trainer.test() class CustomCallbackOnLoadCheckpoint(Callback): def on_save_checkpoint(self, trainer, pl_module, checkpoint) -> dict: return {"a": None} def test_on_load_checkpoint_missing_callbacks(tmpdir): """ Test a warning appears when callbacks in the checkpoint don't match callbacks provided when resuming. """ model = BoringModel() chk = ModelCheckpoint(dirpath=tmpdir, save_last=True) trainer = Trainer(default_root_dir=tmpdir, max_epochs=3, callbacks=[chk, CustomCallbackOnLoadCheckpoint()]) trainer.fit(model) trainer = Trainer( default_root_dir=tmpdir, max_epochs=5, resume_from_checkpoint=chk.last_model_path, progress_bar_refresh_rate=1 ) with pytest.warns(UserWarning, match="CustomCallbackOnLoadCheckpoint"): trainer.fit(model) def test_module_current_fx_attributes_reset(tmpdir): """ Ensure that lightning module's attributes related to current fx are reset at the end of execution. """ model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, fast_dev_run=1, checkpoint_callback=False, logger=False, ) trainer.fit(model) assert model._current_fx_name is None assert model._current_dataloader_idx is None trainer.test(model) assert model._current_fx_name is None assert model._current_dataloader_idx is None def test_exception_when_lightning_module_is_not_set_on_trainer(): trainer = Trainer() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*validate"): trainer.validate() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*test"): trainer.test() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*predict"): trainer.predict()

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import math import os import pickle import sys from argparse import Namespace from copy import deepcopy from pathlib import Path from unittest.mock import ANY, call, patch import cloudpickle import pytest import torch from omegaconf import OmegaConf from torch.optim import SGD from torch.utils.data import DataLoader import tests.helpers.utils as tutils from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.callbacks.prediction_writer import BasePredictionWriter from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv from pytorch_lightning.loggers import TensorBoardLogger from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper, UnrepeatedDistributedSampler from pytorch_lightning.plugins import DDPSpawnPlugin from pytorch_lightning.trainer.states import TrainerFn from pytorch_lightning.utilities import DeviceType, DistributedType from pytorch_lightning.utilities.cloud_io import load as pl_load from pytorch_lightning.utilities.exceptions import MisconfigurationException from pytorch_lightning.utilities.seed import seed_everything from tests.base import EvalModelTemplate from tests.helpers import BoringModel, RandomDataset from tests.helpers.runif import RunIf @pytest.mark.parametrize("url_ckpt", [True, False]) def test_no_val_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", str(tmpdir)) model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) # fit model trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # assert ckpt has hparams ckpt = torch.load(new_weights_path) assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in ckpt.keys(), "hyper_parameters missing from checkpoints" # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) model_2 = EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) model_2.eval() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_no_val_end_module(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) model = EvalModelTemplate() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) model_2 = EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) model_2.eval() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_strict_model_load(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) model = EvalModelTemplate() # Extra layer model.c_d3 = torch.nn.Linear(model.hidden_dim, model.hidden_dim) # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, "hparams.yaml") ckpt_path = ( f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" if url_ckpt else new_weights_path ) try: EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, ) # todo: specify the possible exception except Exception: failed = True else: failed = False assert failed, "Model should not been loaded since the extra layer added." failed = False try: EvalModelTemplate.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False, ) # todo: specify the possible exception except Exception: failed = True assert not failed, "Model should be loaded due to strict=False." @pytest.mark.parametrize("accumulate_grad_batches", (1, 2, 3)) def test_trainer_accumulate_grad_batches_zero_grad(tmpdir, accumulate_grad_batches): with patch("torch.optim.SGD.zero_grad") as sgd_zero_grad: model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, limit_train_batches=20, limit_val_batches=1, max_epochs=1, weights_summary=None, accumulate_grad_batches=accumulate_grad_batches, ) trainer.fit(model) assert sgd_zero_grad.call_count == math.ceil(trainer.limit_train_batches / accumulate_grad_batches) @pytest.mark.parametrize( ["accumulate_grad_batches", "limit_train_batches"], [ ({ 1: 2, 3: 4 }, 1.0), ({ 1: 2, 3: 4 }, 0.5), # not to be divisible by accumulate_grad_batches on purpose (3, 1.0), (3, 0.8), # not to be divisible by accumulate_grad_batches on purpose (4, 1.0), (4, 0.7), # not to be divisible by accumulate_grad_batches on purpose ], ) def test_gradient_accumulation_scheduling_last_batch(tmpdir, accumulate_grad_batches, limit_train_batches): """ Verify optimizer.step() applied to last batch while grad accumulation """ class TestModel(BoringModel): def state_dict(self, *args, **kwargs): return deepcopy(super().state_dict(*args, **kwargs)) def check(self, d1, d2, equal=True): keys = d1.keys() | d2.keys() values = [torch.equal(d1[k], d2[k]) for k in keys] return all(values) if equal else not any(values) def backward(self, *args, **kwargs) -> None: pre_bwd_state_dict = self.state_dict() assert self.check(self.start_state_dict, pre_bwd_state_dict) out = super().backward(*args, **kwargs) # state dict is equal, just the gradients changed assert self.check(pre_bwd_state_dict, self.state_dict()) return out def optimizer_step(self, *args, **kwargs): pre_opt_step_state_dict = self.state_dict() assert self.check(self.start_state_dict, pre_opt_step_state_dict) # this calls `backward` and `on_after_backward` inside the closure out = super().optimizer_step(*args, **kwargs) # the state dict changed assert self.check(pre_opt_step_state_dict, self.state_dict(), equal=False) self.opt_step_called = True return out def on_train_batch_start(self, *_): self.start_state_dict = self.state_dict() self.opt_step_called = False def on_train_batch_end(self, outputs, batch, batch_idx, *_): end_state_dict = self.state_dict() is_last_batch = (batch_idx + 1) == self.trainer.num_training_batches if is_last_batch or self.opt_step_called: assert self.check(self.start_state_dict, end_state_dict, equal=False) else: assert self.check(self.start_state_dict, end_state_dict) model = TestModel() trainer = Trainer( accumulate_grad_batches=accumulate_grad_batches, max_epochs=2, limit_train_batches=limit_train_batches, limit_val_batches=0, default_root_dir=tmpdir, progress_bar_refresh_rate=0, ) trainer.fit(model) def test_loading_meta_tags(tmpdir): """ test for backward compatibility to meta_tags.csv """ tutils.reset_seed() hparams = EvalModelTemplate.get_default_hparams() # save tags logger = tutils.get_default_logger(tmpdir) logger.log_hyperparams(Namespace(some_str="a_str", an_int=1, a_float=2.0)) logger.log_hyperparams(hparams) logger.save() # load hparams path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(path_expt_dir, TensorBoardLogger.NAME_HPARAMS_FILE) hparams = load_hparams_from_yaml(hparams_path) # save as legacy meta_tags.csv tags_path = os.path.join(path_expt_dir, "meta_tags.csv") save_hparams_to_tags_csv(tags_path, hparams) tags = load_hparams_from_tags_csv(tags_path) assert hparams == tags def test_loading_yaml(tmpdir): tutils.reset_seed() hparams = EvalModelTemplate.get_default_hparams() # save tags logger = tutils.get_default_logger(tmpdir) logger.log_hyperparams(Namespace(some_str="a_str", an_int=1, a_float=2.0)) logger.log_hyperparams(hparams) logger.save() # load hparams path_expt_dir = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(path_expt_dir, "hparams.yaml") tags = load_hparams_from_yaml(hparams_path) assert tags["batch_size"] == 32 and tags["hidden_dim"] == 1000 @pytest.mark.parametrize( "save_top_k,save_last,expected_files", [ pytest.param(-1, False, [f"epoch={i}.ckpt" for i in range(5)], id="CASE K=-1 (all)"), pytest.param(1, False, {"epoch=4.ckpt"}, id="CASE K=1 (2.5, epoch 4)"), pytest.param(2, False, [f"epoch={i}.ckpt" for i in (2, 4)], id="CASE K=2 (2.5 epoch 4, 2.8 epoch 2)"), pytest.param(4, False, [f"epoch={i}.ckpt" for i in range(1, 5)], id="CASE K=4 (save all 4 base)"), pytest.param(3, False, [f"epoch={i}.ckpt" for i in range(2, 5)], id="CASE K=3 (save the 2nd, 3rd, 4th model)"), pytest.param(1, True, {"epoch=4.ckpt", "last.ckpt"}, id="CASE K=1 (save the 4th model and the last model)"), ], ) def test_model_checkpoint_options(tmpdir, save_top_k, save_last, expected_files): """Test ModelCheckpoint options.""" def mock_save_function(filepath, *args): open(filepath, "a").close() # simulated losses losses = [10, 9, 2.8, 5, 2.5] checkpoint_callback = ModelCheckpoint( dirpath=tmpdir, filename='{epoch}', monitor='checkpoint_on', save_top_k=save_top_k, save_last=save_last, verbose=True ) trainer = Trainer() trainer.state.fn = TrainerFn.FITTING trainer.save_checkpoint = mock_save_function # emulate callback's calls during the training for i, loss in enumerate(losses): trainer.fit_loop.current_epoch = i trainer.fit_loop.global_step = i trainer.logger_connector.callback_metrics.update({"checkpoint_on": loss}) checkpoint_callback.on_validation_end(trainer, trainer.lightning_module) file_lists = set(os.listdir(tmpdir)) assert len(file_lists) == len( expected_files ), f"Should save {len(expected_files)} models when save_top_k={save_top_k} but found={file_lists}" # verify correct naming for fname in expected_files: assert fname in file_lists def test_model_checkpoint_only_weights(tmpdir): """Tests use case where ModelCheckpoint is configured to save only model weights, and user tries to load checkpoint to resume training. """ model = EvalModelTemplate() trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_weights_only=True)], ) # fit model trainer.fit(model) # training complete assert trainer.state.finished, f"Training failed with {trainer.state}" checkpoint_path = list(trainer.checkpoint_callback.best_k_models.keys())[0] # assert saved checkpoint has no trainer data checkpoint = torch.load(checkpoint_path) assert "optimizer_states" not in checkpoint, "checkpoint should contain only model weights" assert "lr_schedulers" not in checkpoint, "checkpoint should contain only model weights" # assert loading model works when checkpoint has only weights assert EvalModelTemplate.load_from_checkpoint(checkpoint_path=checkpoint_path) # directly save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path, weights_only=True) # assert saved checkpoint has no trainer data checkpoint = torch.load(new_weights_path) assert "optimizer_states" not in checkpoint, "checkpoint should contain only model weights" assert "lr_schedulers" not in checkpoint, "checkpoint should contain only model weights" # assert restoring train state fails with pytest.raises(KeyError, match="checkpoint contains only the model"): trainer.checkpoint_connector.restore(new_weights_path) def test_model_freeze_unfreeze(): model = EvalModelTemplate() model.freeze() model.unfreeze() @pytest.mark.parametrize("url_ckpt", [True, False]) def test_resume_from_checkpoint_epoch_restored(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Verify resuming from checkpoint runs the right number of epochs""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) class TestModel(BoringModel): # Model that tracks epochs and batches seen num_epochs_end_seen = 0 num_batches_seen = 0 num_on_load_checkpoint_called = 0 def on_epoch_end(self): self.num_epochs_end_seen += 1 def on_train_batch_start(self, *_): self.num_batches_seen += 1 def on_load_checkpoint(self, _): self.num_on_load_checkpoint_called += 1 model = TestModel() trainer = Trainer( max_epochs=2, limit_train_batches=0.65, limit_val_batches=1, callbacks=[ModelCheckpoint(dirpath=tmpdir, monitor='early_stop_on', save_top_k=-1)], default_root_dir=tmpdir, val_check_interval=1.0, progress_bar_refresh_rate=0, logger=False, weights_summary=None, ) trainer.fit(model) # `on_epoch_end` will be called once for val_sanity, twice for train, twice for val assert model.num_epochs_end_seen == 1 + 2 + 2 assert model.num_batches_seen == trainer.num_training_batches * 2 assert model.num_on_load_checkpoint_called == 0 # Other checkpoints can be uncommented if/when resuming mid-epoch is supported checkpoints = Path(trainer.checkpoint_callback.dirpath).glob("*.ckpt") if url_ckpt: # transform local paths into url checkpoints ip, port = tmpdir_server checkpoints = [f"http://{ip}:{port}/" + ckpt.name for ckpt in checkpoints] for ckpt in checkpoints: next_model = TestModel() state = pl_load(ckpt) # Resume training new_trainer = Trainer( default_root_dir=tmpdir, resume_from_checkpoint=ckpt, max_epochs=2, ) new_trainer.fit(next_model) assert state["global_step"] + next_model.num_batches_seen == trainer.num_training_batches * trainer.max_epochs assert next_model.num_on_load_checkpoint_called == 1 def test_trainer_max_steps_and_epochs(tmpdir): """Verify model trains according to specified max steps""" model = BoringModel() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) # define less train steps than epochs trainer_kwargs = { 'limit_train_batches': 0.5, 'default_root_dir': tmpdir, 'max_epochs': 3, 'max_steps': num_train_samples + 10, 'logger': False, 'weights_summary': None, 'progress_bar_refresh_rate': 0, } trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == trainer.max_steps, "Model did not stop at max_steps" # define less train epochs than steps trainer_kwargs['max_epochs'] = 2 trainer_kwargs['max_steps'] = 3 * 2 * num_train_samples trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == num_train_samples * trainer.max_epochs assert trainer.current_epoch == trainer.max_epochs - 1, "Model did not stop at max_epochs" def test_trainer_min_steps_and_epochs(tmpdir): """Verify model trains according to specified min steps""" model = EvalModelTemplate() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) trainer_kwargs = { 'limit_train_batches': 0.5, 'default_root_dir': tmpdir, # define callback for stopping the model 'callbacks': [EarlyStopping(monitor="early_stop_on", min_delta=1.0)], 'val_check_interval': 2, 'min_epochs': 1, 'max_epochs': 7, # define less min steps than 1 epoch 'min_steps': num_train_samples // 2, 'logger': False, 'weights_summary': None, 'progress_bar_refresh_rate': 0, } trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch > 0 assert trainer.global_step >= num_train_samples, "Model did not train for at least min_epochs" # define less epochs than min_steps trainer_kwargs["min_steps"] = math.floor(num_train_samples * 1.5) trainer = Trainer(**trainer_kwargs) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch > 0 assert trainer.global_step >= math.floor(num_train_samples * 1.5), "Model did not train for at least min_steps" def test_trainer_min_steps_and_min_epochs_not_reached(tmpdir, caplog): """ Test that min_epochs/min_steps in Trainer are enforced even if EarlyStopping is triggered. """ class TestModel(BoringModel): training_step_invoked = 0 def training_step(self, batch, batch_idx): output = super().training_step(batch, batch_idx) output["loss"] = output["loss"] * 0.0 # force minimal loss to trigger early stopping self.log("loss", output["loss"]) self.training_step_invoked += 1 assert not self.trainer.should_stop return output model = TestModel() early_stop = EarlyStopping(monitor="loss", patience=0, check_on_train_epoch_end=True) min_epochs = 5 trainer = Trainer( default_root_dir=tmpdir, progress_bar_refresh_rate=0, min_epochs=min_epochs, limit_val_batches=0, limit_train_batches=2, callbacks=[early_stop] ) with caplog.at_level(logging.INFO, logger="pytorch_lightning.trainer.trainer"): trainer.fit(model) message = f"minimum epochs ({min_epochs}) or minimum steps (None) has not been met. Training will continue" num_messages = len([record.message for record in caplog.records if message in record.message]) assert num_messages == min_epochs - 2 assert model.training_step_invoked == min_epochs * 2 def test_trainer_max_steps_accumulate_batches(tmpdir): """Verify model trains according to specified max steps with grad accumulated batches""" model = BoringModel() num_train_samples = math.floor(len(model.train_dataloader()) * 0.5) # define less train steps than epochs trainer = Trainer( limit_train_batches=0.5, default_root_dir=tmpdir, max_steps=num_train_samples + 10, accumulate_grad_batches=10, logger=False, weights_summary=None, progress_bar_refresh_rate=0, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == trainer.max_steps, "Model did not stop at max_steps" def test_benchmark_option(tmpdir): """Verify benchmark option.""" model = EvalModelTemplate() model.val_dataloader = model.val_dataloader__multiple # verify torch.backends.cudnn.benchmark is not turned on assert not torch.backends.cudnn.benchmark # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, benchmark=True, ) trainer.fit(model) # verify training completed assert trainer.state.finished, f"Training failed with {trainer.state}" # verify torch.backends.cudnn.benchmark is not turned off assert torch.backends.cudnn.benchmark @pytest.mark.parametrize("ckpt_path", (None, "best", "specific")) @pytest.mark.parametrize("save_top_k", (-1, 0, 1, 2)) @pytest.mark.parametrize("fn", ("validate", "test", "predict")) def test_tested_checkpoint_path(tmpdir, ckpt_path, save_top_k, fn): class TestModel(BoringModel): def validation_step(self, batch, batch_idx): self.log("foo", -batch_idx) return super().validation_step(batch, batch_idx) def test_step(self, *args): return self.validation_step(*args) def predict_step(self, batch, *_): return self(batch) model = TestModel() model.test_epoch_end = None trainer = Trainer( max_epochs=2, limit_val_batches=1, limit_test_batches=1, limit_predict_batches=1, progress_bar_refresh_rate=0, default_root_dir=tmpdir, callbacks=[ModelCheckpoint(monitor="foo", save_top_k=save_top_k)], ) trainer.fit(model) trainer_fn = getattr(trainer, fn) path_attr = f"{fn}{'d' if fn == 'validate' else 'ed'}_ckpt_path" assert getattr(trainer, path_attr) is None if ckpt_path == "best": # ckpt_path is 'best', meaning we load the best weights if save_top_k == 0: with pytest.raises(MisconfigurationException, match=".*is not configured to save the best.*"): trainer_fn(ckpt_path=ckpt_path) else: trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) == trainer.checkpoint_callback.best_model_path elif ckpt_path is None: # ckpt_path is None, meaning we don't load any checkpoints and # use the weights from the end of training trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) is None else: # specific checkpoint, pick one from saved ones if save_top_k == 0: with pytest.raises(FileNotFoundError): trainer_fn(ckpt_path="random.ckpt") else: ckpt_path = str( list((Path(tmpdir) / f"lightning_logs/version_{trainer.logger.version}/checkpoints").iterdir() )[0].absolute() ) trainer_fn(ckpt_path=ckpt_path) assert getattr(trainer, path_attr) == ckpt_path def test_disabled_training(tmpdir): """Verify that `limit_train_batches=0` disables the training loop unless `fast_dev_run=True`.""" class CurrentModel(BoringModel): training_step_invoked = False training_epoch_end_invoked = False def training_step(self, *args, **kwargs): self.training_step_invoked = True return super().training_step(*args, **kwargs) def training_epoch_end(self, *args, **kwargs): self.training_epoch_end_invoked = True return super().training_epoch_end(*args, **kwargs) model = CurrentModel() trainer_options = dict( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=0.0, limit_val_batches=0.2, fast_dev_run=False, ) before_state_dict = deepcopy(model.state_dict()) trainer = Trainer(**trainer_options) trainer.fit(model) after_state_dict = model.state_dict() for key in before_state_dict.keys(): assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])) # check that limit_train_batches=0 turns off training assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert not model.training_step_invoked, "`training_step` should not run when `limit_train_batches=0`" assert not model.training_epoch_end_invoked, "`training_epoch_end` should not run when `limit_train_batches=0`" # check that limit_train_batches has no influence when fast_dev_run is turned on model = CurrentModel() trainer_options.update(fast_dev_run=True) before_state_dict = deepcopy(model.state_dict()) trainer = Trainer(**trainer_options) trainer.fit(model) after_state_dict = model.state_dict() for key in before_state_dict.keys(): assert not torch.all(torch.eq(before_state_dict[key], after_state_dict[key])) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert model.training_step_invoked, "did not run `training_step` with `fast_dev_run=True`" assert model.training_epoch_end_invoked, "did not run `training_epoch_end` with `fast_dev_run=True`" def test_disabled_validation(tmpdir): """Verify that `limit_val_batches=0` disables the validation loop unless `fast_dev_run=True`.""" class CurrentModel(EvalModelTemplate): validation_step_invoked = False validation_epoch_end_invoked = False def validation_step(self, *args, **kwargs): self.validation_step_invoked = True return super().validation_step(*args, **kwargs) def validation_epoch_end(self, *args, **kwargs): self.validation_epoch_end_invoked = True return super().validation_epoch_end(*args, **kwargs) hparams = EvalModelTemplate.get_default_hparams() model = CurrentModel(**hparams) trainer_options = dict( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=0.4, limit_val_batches=0.0, fast_dev_run=False, ) trainer = Trainer(**trainer_options) trainer.fit(model) # check that limit_val_batches=0 turns off validation assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 1 assert not model.validation_step_invoked, "`validation_step` should not run when `limit_val_batches=0`" assert not model.validation_epoch_end_invoked, "`validation_epoch_end` should not run when `limit_val_batches=0`" # check that limit_val_batches has no influence when fast_dev_run is turned on model = CurrentModel(**hparams) trainer_options.update(fast_dev_run=True) trainer = Trainer(**trainer_options) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.current_epoch == 0 assert model.validation_step_invoked, "did not run `validation_step` with `fast_dev_run=True`" assert model.validation_epoch_end_invoked, "did not run `validation_epoch_end` with `fast_dev_run=True`" def test_nan_loss_detection(tmpdir): class CurrentModel(BoringModel): test_batch_inf = 3 def training_step(self, batch, batch_idx): output = super().training_step(batch, batch_idx) if batch_idx == self.test_batch_inf: if isinstance(output, dict): output["loss"] *= torch.tensor(math.inf) # make loss infinite else: output /= 0 return output model = CurrentModel() # fit model trainer = Trainer( default_root_dir=tmpdir, max_steps=(model.test_batch_inf + 1), terminate_on_nan=True, ) with pytest.raises(ValueError, match=r".*The loss returned in `training_step` is.*"): trainer.fit(model) assert trainer.global_step == model.test_batch_inf for param in model.parameters(): assert torch.isfinite(param).all() def test_nan_params_detection(tmpdir): class CurrentModel(BoringModel): test_batch_nan = 3 def on_after_backward(self): if self.global_step == self.test_batch_nan: # simulate parameter that became nan torch.nn.init.constant_(self.layer.bias, math.nan) model = CurrentModel() trainer = Trainer( default_root_dir=tmpdir, max_steps=(model.test_batch_nan + 1), terminate_on_nan=True, ) with pytest.raises(ValueError, match=r".*Detected nan and/or inf values in `layer.bias`.*"): trainer.fit(model) assert trainer.global_step == model.test_batch_nan # after aborting the training loop, model still has nan-valued params params = torch.cat([param.view(-1) for param in model.parameters()]) assert not torch.isfinite(params).all() def test_trainer_interrupted_flag(tmpdir): """Test the flag denoting that a user interrupted training.""" model = EvalModelTemplate() class InterruptCallback(Callback): def __init__(self): super().__init__() def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx): raise KeyboardInterrupt class HandleInterruptCallback(Callback): def __init__(self): super().__init__() self.exc_info = None def on_keyboard_interrupt(self, trainer, pl_module): self.exc_info = sys.exc_info() interrupt_callback = InterruptCallback() handle_interrupt_callback = HandleInterruptCallback() trainer = Trainer( callbacks=[interrupt_callback, handle_interrupt_callback], max_epochs=1, limit_val_batches=0.1, limit_train_batches=0.2, progress_bar_refresh_rate=0, logger=False, default_root_dir=tmpdir, ) assert not trainer.interrupted assert handle_interrupt_callback.exc_info is None trainer.fit(model) assert trainer.interrupted assert isinstance(handle_interrupt_callback.exc_info[1], KeyboardInterrupt) def test_gradient_clipping(tmpdir): """ Test gradient clipping """ tutils.reset_seed() model = EvalModelTemplate() trainer = Trainer( max_steps=1, max_epochs=1, gradient_clip_val=1.0, default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2) assert (grad_norm - 1.0).abs() < 0.01, "Gradient norm != 1.0: {grad_norm}".format(grad_norm=grad_norm) return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward # for the test model.prev_called_batch_idx = 0 trainer.fit(model) def test_gradient_clipping_by_value(tmpdir): """ Test gradient clipping by value """ tutils.reset_seed() model = BoringModel() grad_clip_val = 1e-10 trainer = Trainer( max_steps=1, max_epochs=1, gradient_clip_val=grad_clip_val, gradient_clip_algorithm='value', default_root_dir=tmpdir ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_max_list = [torch.max(p.grad.detach().abs()) for p in parameters] grad_max = torch.max(torch.stack(grad_max_list)) assert abs(grad_max.item() - grad_clip_val) < 1e-11, \ f"Gradient max value {grad_max} != grad_clip_val {grad_clip_val} ." return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward # for the test model.prev_called_batch_idx = 0 trainer.fit(model) @RunIf(min_gpus=1, amp_native=True) def test_gradient_clipping_fp16(tmpdir): """ Test gradient clipping with fp16 """ tutils.reset_seed() model = EvalModelTemplate() trainer = Trainer( max_steps=1, max_epochs=1, precision=16, gpus=1, gradient_clip_val=1.0, default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2) assert (grad_norm - 1.0).abs() < 0.01, "Gradient norm != 1.0: {grad_norm}".format(grad_norm=grad_norm) return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward model.prev_called_batch_idx = 0 trainer.fit(model) @RunIf(min_gpus=1, amp_native=True) def test_gradient_clipping_by_value_fp16(tmpdir): """ Test gradient clipping by value with fp16 """ tutils.reset_seed() model = BoringModel() grad_clip_val = 1e-10 trainer = Trainer( max_steps=1, max_epochs=1, precision=16, gpus=1, gradient_clip_val=grad_clip_val, gradient_clip_algorithm='value', default_root_dir=tmpdir, ) old_training_step_and_backward = trainer.fit_loop.training_loop.batch_loop.training_step_and_backward def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens): """ wrap the forward step in a closure so second order methods work """ # test that gradient is clipped correctly ret_val = old_training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens) parameters = model.parameters() grad_max_list = [torch.max(p.grad.detach().abs()) for p in parameters] grad_max = torch.max(torch.stack(grad_max_list)) assert abs(grad_max.item() - grad_clip_val) < 1e-11, \ f"Gradient max value {grad_max} != grad_clip_val {grad_clip_val} ." return ret_val trainer.fit_loop.training_loop.batch_loop.training_step_and_backward = training_step_and_backward model.prev_called_batch_idx = 0 trainer.fit(model) def test_gpu_choice(tmpdir): trainer_options = dict(default_root_dir=tmpdir) # Only run if CUDA is available if not torch.cuda.is_available(): return num_gpus = torch.cuda.device_count() Trainer(**trainer_options, gpus=num_gpus, auto_select_gpus=True) with pytest.raises(RuntimeError, match=r".*No GPUs available.*"): Trainer(**trainer_options, gpus=num_gpus + 1, auto_select_gpus=True) @pytest.mark.parametrize( "limit_val_batches", [0.0, 1, 1.0, 0.5, 5], ) def test_num_sanity_val_steps(tmpdir, limit_val_batches): """ Test that the number of sanity check batches is clipped to `limit_val_batches`. """ model = EvalModelTemplate() model.validation_step = model.validation_step__multiple_dataloaders model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders num_sanity_val_steps = 4 trainer = Trainer( default_root_dir=tmpdir, num_sanity_val_steps=num_sanity_val_steps, limit_val_batches=limit_val_batches, max_steps=1, ) assert trainer.num_sanity_val_steps == num_sanity_val_steps with patch.object( trainer.fit_loop.validation_loop.epoch_loop, "evaluation_step", wraps=trainer.fit_loop.validation_loop.epoch_loop.evaluation_step ) as mocked: val_dataloaders = model.val_dataloader__multiple_mixed_length() trainer.fit(model, val_dataloaders=val_dataloaders) assert mocked.call_count == sum( min(num_sanity_val_steps, num_batches) for num_batches in trainer.num_val_batches ) @pytest.mark.parametrize("limit_val_batches", [0.0, 1, 1.0, 0.3]) def test_num_sanity_val_steps_neg_one(tmpdir, limit_val_batches): """ Test that `num_sanity_val_steps=-1` runs through all validation data once, and as many batches as limited by `limit_val_batches` Trainer argument. """ model = EvalModelTemplate() model.validation_step = model.validation_step__multiple_dataloaders model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders trainer = Trainer( default_root_dir=tmpdir, num_sanity_val_steps=-1, limit_val_batches=limit_val_batches, max_steps=1, ) assert trainer.num_sanity_val_steps == float("inf") with patch.object( trainer.fit_loop.validation_loop.epoch_loop, "evaluation_step", wraps=trainer.fit_loop.validation_loop.epoch_loop.evaluation_step ) as mocked: val_dataloaders = model.val_dataloader__multiple() trainer.fit(model, val_dataloaders=val_dataloaders) assert mocked.call_count == sum(trainer.num_val_batches) @pytest.mark.parametrize( "trainer_kwargs,expected", [ ( dict(accelerator=None, gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="dp", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp", num_processes=2, gpus=None), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp", num_nodes=2, gpus=None), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator="ddp_cpu", num_processes=2, gpus=None), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp2", gpus=None), dict(_distrib_type=None, _device_type=DeviceType.CPU, num_gpus=0, num_processes=1), ), ( dict(accelerator=None, gpus=1), dict(_distrib_type=None, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="dp", gpus=1), dict(_distrib_type=DistributedType.DP, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="ddp", gpus=1), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator="ddp_cpu", num_processes=2, gpus=1), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.CPU, num_gpus=0, num_processes=2), ), ( dict(accelerator="ddp2", gpus=1), dict(_distrib_type=DistributedType.DDP2, _device_type=DeviceType.GPU, num_gpus=1, num_processes=1), ), ( dict(accelerator=None, gpus=2), dict(_distrib_type=DistributedType.DDP_SPAWN, _device_type=DeviceType.GPU, num_gpus=2, num_processes=2), ), ( dict(accelerator="dp", gpus=2), dict(_distrib_type=DistributedType.DP, _device_type=DeviceType.GPU, num_gpus=2, num_processes=1), ), ( dict(accelerator="ddp", gpus=2), dict(_distrib_type=DistributedType.DDP, _device_type=DeviceType.GPU, num_gpus=2, num_processes=2), ), ( dict(accelerator="ddp2", gpus=2), dict(_distrib_type=DistributedType.DDP2, _device_type=DeviceType.GPU, num_gpus=2, num_processes=1), ), ], ) def test_trainer_config(trainer_kwargs, expected, monkeypatch): if trainer_kwargs["gpus"] is not None: monkeypatch.setattr(torch.cuda, "is_available", lambda: True) monkeypatch.setattr(torch.cuda, "device_count", lambda: trainer_kwargs["gpus"]) trainer = Trainer(**trainer_kwargs) assert len(expected) == 4 for k, v in expected.items(): assert getattr(trainer, k) == v, f"Failed {k}: {v}" def test_trainer_subclassing(): model = EvalModelTemplate() # First way of pulling out args from signature is to list them class TrainerSubclass(Trainer): def __init__(self, custom_arg, *args, custom_kwarg="test", **kwargs): super().__init__(*args, **kwargs) self.custom_arg = custom_arg self.custom_kwarg = custom_kwarg trainer = TrainerSubclass(123, custom_kwarg="custom", fast_dev_run=True) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.custom_arg == 123 assert trainer.custom_kwarg == "custom" assert trainer.fast_dev_run # Second way is to pop from the dict # It's a special case because Trainer does not have any positional args class TrainerSubclass(Trainer): def __init__(self, **kwargs): self.custom_arg = kwargs.pop("custom_arg", 0) self.custom_kwarg = kwargs.pop("custom_kwarg", "test") super().__init__(**kwargs) trainer = TrainerSubclass(custom_kwarg="custom", fast_dev_run=True) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.custom_kwarg == "custom" assert trainer.fast_dev_run # when we pass in an unknown arg, the base class should complain with pytest.raises(TypeError, match=r"__init__ got an unexpected keyword argument 'abcdefg'"): TrainerSubclass(abcdefg="unknown_arg") @pytest.mark.parametrize( "trainer_params", [ OmegaConf.create(dict(max_epochs=1, gpus=1)), OmegaConf.create(dict(max_epochs=1, gpus=[0])), ] ) @RunIf(min_gpus=1) def test_trainer_omegaconf(trainer_params): Trainer(**trainer_params) def test_trainer_pickle(tmpdir): trainer = Trainer( max_epochs=1, default_root_dir=tmpdir, ) pickle.dumps(trainer) cloudpickle.dumps(trainer) @pytest.mark.parametrize("stage", ("fit", "validate", "test")) def test_trainer_setup_call(tmpdir, stage): """Test setup call gets the correct stage""" class CurrentModel(BoringModel): def setup(self, stage): self.stage = stage class TrainerSubclass(Trainer): def setup(self, model, stage): assert model is not None self.stage = stage model = CurrentModel() # fit model trainer = TrainerSubclass(default_root_dir=tmpdir, max_epochs=1, checkpoint_callback=False) if stage == "fit": trainer.fit(model) elif stage == "validate": trainer.validate(model, ckpt_path=None) else: trainer.test(model, ckpt_path=None) assert trainer.stage == stage assert trainer.lightning_module.stage == stage @pytest.mark.parametrize( "train_batches, max_steps, log_interval", [ (10, 10, 1), (3, 10, 1), (3, 10, 5), ], ) @patch("pytorch_lightning.loggers.tensorboard.TensorBoardLogger.log_metrics") def test_log_every_n_steps(log_metrics_mock, tmpdir, train_batches, max_steps, log_interval): class TestModel(BoringModel): def training_step(self, *args, **kwargs): self.log("foo", -1) return super().training_step(*args, **kwargs) model = TestModel() trainer = Trainer( default_root_dir=tmpdir, log_every_n_steps=log_interval, flush_logs_every_n_steps=log_interval, limit_train_batches=train_batches, limit_val_batches=0, max_steps=max_steps, ) trainer.fit(model) expected_calls = [call(metrics=ANY, step=s) for s in range(log_interval - 1, max_steps, log_interval)] log_metrics_mock.assert_has_calls(expected_calls) class TestLightningDataModule(LightningDataModule): def __init__(self, dataloaders): super().__init__() self._dataloaders = dataloaders def test_dataloader(self): return self._dataloaders def predict_dataloader(self): return self._dataloaders class CustomPredictionWriter(BasePredictionWriter): write_on_batch_end_called = False write_on_epoch_end_called = False def __init__(self, output_dir: str, *args, **kwargs): super().__init__(*args, **kwargs) self.output_dir = output_dir def write_on_batch_end(self, trainer, pl_module, prediction, batch_indices, *args, **kwargs): assert prediction.shape == torch.Size([1, 2]) if trainer.accelerator_connector.is_distributed: assert len(batch_indices) == 1 else: assert batch_indices is None self.write_on_batch_end_called = True def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices): expected = 1 if trainer.accelerator_connector.is_distributed else 2 assert len(predictions) == 2 assert len(predictions[0]) == expected if trainer.accelerator_connector.is_distributed: assert len(batch_indices) == 2 assert len(batch_indices[0]) == expected else: assert batch_indices is None self.write_on_epoch_end_called = True def on_predict_epoch_end(self, trainer, pl_module, outputs): if trainer.accelerator_connector.is_distributed: for idx in range(2): assert isinstance(trainer.predict_dataloaders[idx].batch_sampler.sampler, UnrepeatedDistributedSampler) assert isinstance(trainer.predict_dataloaders[idx].batch_sampler, IndexBatchSamplerWrapper) super().on_predict_epoch_end(trainer, pl_module, outputs) def predict( tmpdir, accelerator, gpus, num_processes, model=None, plugins=None, datamodule=True, pbrr=None, use_callbacks=True ): dataloaders = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))] model = model or BoringModel() dm = TestLightningDataModule(dataloaders) cb = CustomPredictionWriter(tmpdir, write_interval="batch") cb_1 = CustomPredictionWriter(tmpdir, write_interval="epoch") trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, log_every_n_steps=1, weights_summary=None, accelerator=accelerator, gpus=gpus, num_processes=num_processes, plugins=plugins, progress_bar_refresh_rate=pbrr, callbacks=[cb, cb_1] if use_callbacks else [] ) if accelerator == "ddp_spawn": with pytest.raises(MisconfigurationException): trainer.predict(model, datamodule=dm, return_predictions=True) if datamodule: results = trainer.predict(model, datamodule=dm) else: results = trainer.predict(model, dataloaders=dataloaders) if not isinstance(trainer.training_type_plugin, DDPSpawnPlugin): if use_callbacks: assert cb.write_on_batch_end_called assert not cb.write_on_epoch_end_called assert not cb_1.write_on_batch_end_called assert cb_1.write_on_epoch_end_called num_samples = 1 if accelerator == "ddp" else 2 assert len(results) == 2 assert len(results[0]) == num_samples assert results[0][0].shape == torch.Size([1, 2]) def test_trainer_predict_no_return(tmpdir): """ Test trainer.predict warns when nothing is returned """ class CustomBoringModel(BoringModel): def predict_step(self, batch, batch_idx, dataloader_idx=None): if (batch_idx + 1) % 2 == 0: return return super().predict_step(batch, batch_idx, dataloader_idx) with pytest.warns(UserWarning, match='predict returned None'): predict(tmpdir, None, None, 1, model=CustomBoringModel(), use_callbacks=False) def test_trainer_predict_grad(tmpdir): class CustomBoringModel(BoringModel): def predict_step(self, batch, batch_idx, dataloader_idx=None): assert batch.expand_as(batch).grad_fn is None return super().predict_step(batch, batch_idx, dataloader_idx) predict(tmpdir, None, None, 1, model=CustomBoringModel(), use_callbacks=False) x = torch.zeros(1, requires_grad=True) assert x.expand_as(x).grad_fn is not None @pytest.mark.parametrize('progress_bar_refresh_rate', [0, 5, None]) @pytest.mark.parametrize('datamodule', [False, True]) def test_trainer_predict_cpu(tmpdir, datamodule, progress_bar_refresh_rate): predict(tmpdir, None, None, 1, datamodule=datamodule, pbrr=progress_bar_refresh_rate) @RunIf(min_gpus=2, special=True) @pytest.mark.parametrize('num_gpus', [1, 2]) def test_trainer_predict_dp(tmpdir, num_gpus): predict(tmpdir, "dp", num_gpus, None) @RunIf(min_gpus=2, special=True, fairscale=True) def test_trainer_predict_ddp(tmpdir): predict(tmpdir, "ddp", 2, None) @RunIf(min_gpus=2, skip_windows=True, special=True) def test_trainer_predict_ddp_spawn(tmpdir): predict(tmpdir, "ddp_spawn", 2, None) @RunIf(min_gpus=2, special=True) def test_trainer_predict_1_gpu(tmpdir): predict(tmpdir, None, 1, None) @RunIf(skip_windows=True) def test_trainer_predict_ddp_cpu(tmpdir): predict(tmpdir, "ddp_cpu", 0, 2) @patch('torch.cuda.device_count', return_value=2) @patch('torch.cuda.is_available', return_value=True) def test_spawn_predict_return_predictions(*_): """ Test that `return_predictions=True` raise a MisconfigurationException with spawn training type plugins. """ model = BoringModel() def run(expected_plugin, **trainer_kwargs): trainer = Trainer(**trainer_kwargs, fast_dev_run=True) assert isinstance(trainer.training_type_plugin, expected_plugin) with pytest.raises(MisconfigurationException, match="`return_predictions` should be set to `False`"): trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=True) run(DDPSpawnPlugin, accelerator="ddp_spawn", gpus=2) run(DDPSpawnPlugin, accelerator="ddp_cpu", num_processes=2) @pytest.mark.parametrize("return_predictions", [None, False, True]) @pytest.mark.parametrize("precision", [32, 64]) def test_predict_return_predictions_cpu(return_predictions, precision, tmpdir): """ Test that `return_predictions=True`. """ seed_everything(42) model = BoringModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, precision=precision) preds = trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=return_predictions) if return_predictions or return_predictions is None: assert len(preds) == 1 assert preds[0].shape == torch.Size([1, 2]) assert preds[0].dtype == (torch.float64 if precision == 64 else torch.float32) @pytest.mark.parametrize( ["limit_train_batches", "global_step", "num_training_batches", "current_epoch", "should_train"], [(0.2, 0, 0, 0, False), (0.5, 10, 2, 4, True)], ) def test_disabled_training_for_insufficient_limit_train_batches( tmpdir, limit_train_batches, global_step, num_training_batches, current_epoch, should_train ): """ Verify when `limit_train_batches` is float & between [0.0, 1.0] and `int(self.num_training_batches * self.limit_train_batches) == 0`, the training loop is disabled. """ class CurrentModel(BoringModel): training_step_invoked = False training_epoch_end_invoked = False def training_step(self, *args, **kwargs): self.training_step_invoked = True return super().training_step(*args, **kwargs) def training_epoch_end(self, *args, **kwargs): self.training_epoch_end_invoked = True return super().training_epoch_end(*args, **kwargs) dataset_len = 100 batch_size = 25 train = RandomDataset(32, length=dataset_len) train_loader = DataLoader(train, batch_size=batch_size) model = CurrentModel() trainer = Trainer( default_root_dir=tmpdir, max_epochs=5, limit_train_batches=limit_train_batches, ) trainer.fit(model, train_loader) params_string = f"""`limit_train_batches={limit_train_batches}`, `dataset_len={dataset_len}` & `batch_size={batch_size}` as `num_training_batches={num_training_batches}`""" if should_train: error_string = f"should run with {params_string}" else: error_string = f"should not run with {params_string}" assert trainer.state.finished, f"Training failed with {trainer.state}" assert trainer.global_step == global_step assert trainer.num_training_batches == num_training_batches assert trainer.current_epoch == current_epoch assert model.training_step_invoked == should_train, f"`training_step` {error_string}" assert model.training_epoch_end_invoked == should_train, f"`training_epoch_end` {error_string}" @pytest.mark.parametrize(["max_steps", "max_epochs", "global_step"], [(10, 5, 10), (20, None, 20)]) def test_repeated_fit_calls_with_max_epochs_and_steps(tmpdir, max_steps, max_epochs, global_step): """ Ensure that the training loop is bound by `max_steps` and `max_epochs` for repeated calls of `trainer.fit`, and disabled if the limit is reached """ dataset_len = 200 batch_size = 10 train_data = DataLoader(RandomDataset(32, dataset_len), batch_size=batch_size) model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, max_steps=max_steps, max_epochs=max_epochs, ) trainer.fit(model, train_data) assert trainer.global_step == global_step trainer.fit(model, train_data) assert trainer.global_step == global_step def test_trainer_access_in_configure_optimizers(tmpdir): """ Verify that the configure optimizer function can reference the trainer. """ class TestModel(BoringModel): def configure_optimizers(self): assert self.trainer is not None, "Expect to have access to the trainer within `configure_optimizers`" train_data = torch.utils.data.DataLoader(RandomDataset(32, 64)) model = TestModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True) trainer.fit(model, train_data) @RunIf(min_gpus=1) def test_setup_hook_move_to_device_correctly(tmpdir): """ Verify that if a user defines a layer in the setup hook function, this is moved to the correct device. """ class TestModel(BoringModel): def setup(self, stage: str) -> None: self.new_layer = torch.nn.Linear(2, 2) def training_step(self, batch, batch_idx): output = self.layer(batch) # will crash if not moved to correct device output = self.new_layer(output) loss = self.loss(batch, output) return {"loss": loss} # fake data train_data = torch.utils.data.DataLoader(RandomDataset(32, 64)) # model model = TestModel() trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=1) trainer.fit(model, train_data) def test_train_loop_system(tmpdir): """ Test the following methods are called in the order in automatic optimization. 1. optimizer.step (skip when gradient accumulation) 2. model.training_step 3. optimizer.zero_grad (run when the first batch of gradient accumulation) 4. model.backward Note that the order is NOT `training_step`->`zero_grad`->`backward`->`step`. This is because `optimizer.step(closure)` calls `closure()` which then calls the three remaining methods `training_step`, `zero_grad` and `backward` inside. """ called_methods = [] trainer_options = dict( default_root_dir=tmpdir, max_epochs=1, limit_train_batches=5, limit_val_batches=1, limit_test_batches=1, progress_bar_refresh_rate=0, ) class TestOptimizer(SGD): def step(self, *args, **kwargs): called_methods.append("step") return super().step(*args, **kwargs) def zero_grad(self, *args, **kwargs): called_methods.append("zero_grad") return super().zero_grad(*args, **kwargs) class TestModel(BoringModel): def configure_optimizers(self): return TestOptimizer(self.parameters(), lr=0.1) def training_step(self, *args, **kwargs): called_methods.append("training_step") return super().training_step(*args, **kwargs) def backward(self, *args, **kwargs): called_methods.append("backward") return super().backward(*args, **kwargs) model = TestModel() trainer = Trainer(**trainer_options) # No methods are called yet. assert called_methods == [] trainer.fit(model) assert called_methods == [ "step", "training_step", "zero_grad", "backward", ] * trainer.limit_train_batches called_methods.clear() trainer = Trainer(**trainer_options, accumulate_grad_batches=3) # No methods are called yet. assert called_methods == [] trainer.fit(model) assert called_methods == [ # 0 "training_step", "zero_grad", "backward", # 1 "training_step", "backward", # 2 "step", "training_step", "backward", # 3 "training_step", "zero_grad", "backward", # 4 "step", "training_step", "backward", ] def test_init_optimizers_resets_lightning_optimizers(tmpdir): """ Test that the Trainer resets the `lightning_optimizers` list everytime new optimizers get initialized. """ def compare_optimizers(): assert trainer.lightning_optimizers[0].optimizer is trainer.optimizers[0] model = BoringModel() model.lr = 0.2 trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, auto_lr_find=True, ) trainer.tune(model) compare_optimizers() trainer.fit(model) compare_optimizers() trainer.fit_loop.max_epochs = 2 # simulate multiple fit calls trainer.fit(model) compare_optimizers() def test_check_val_every_n_epoch_exception(tmpdir): with pytest.raises(MisconfigurationException, match="should be an integer."): Trainer( default_root_dir=tmpdir, max_epochs=1, check_val_every_n_epoch=1.2, ) def test_trainer_attach_data_pipeline_to_model(tmpdir): class DataPipeline: pass class TestDataModule(LightningDataModule): data_pipeline = DataPipeline() def train_dataloader(self): return DataLoader(RandomDataset(32, 64)) def val_dataloader(self): return DataLoader(RandomDataset(32, 64)) def test_dataloader(self): return DataLoader(RandomDataset(32, 64)) class TestCallback(Callback): def on_fit_start(self, trainer, pl_module: LightningModule) -> None: """Called when fit begins""" assert isinstance(pl_module.data_pipeline, DataPipeline) model = BoringModel() dm = TestDataModule() trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, callbacks=[TestCallback()]) trainer.fit(model, datamodule=dm) def test_exception_when_testing_or_validating_with_fast_dev_run(tmpdir): trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True) model = BoringModel() trainer.fit(model) with pytest.raises(MisconfigurationException, match=r"\.validate` with `fast_dev_run=True"): trainer.validate() with pytest.raises(MisconfigurationException, match=r"\.test` with `fast_dev_run=True"): trainer.test() class TrainerStagesModel(BoringModel): def on_train_start(self) -> None: assert self.trainer.model.training assert self.training def on_validation_start(self) -> None: assert not self.trainer.model.training assert not self.training def on_test_start(self) -> None: assert not self.trainer.model.training assert not self.training def on_predict_start(self) -> None: assert not self.trainer.model.training assert not self.training @pytest.mark.parametrize( 'accelerator,num_processes', [(None, 1), pytest.param('ddp', 2, marks=RunIf(skip_windows=True))] ) def test_model_in_correct_mode_during_stages(tmpdir, accelerator, num_processes): model = TrainerStagesModel() trainer = Trainer(default_root_dir=tmpdir, accelerator=accelerator, num_processes=num_processes, fast_dev_run=True) trainer.fit(model) trainer.validate(model) trainer.test(model) trainer.predict(model, model.val_dataloader()) class TestDummyModelForCheckpoint(BoringModel): def validation_step(self, batch, batch_idx): output = self.layer(batch) loss = self.loss(batch, output) self.log('x', loss) def validation_epoch_end(self, outputs) -> None: pass @RunIf(skip_windows=True) def test_fit_test_synchronization(tmpdir): """Test that the trainer synchronizes processes before returning control back to the caller. """ tutils.set_random_master_port() model = TestDummyModelForCheckpoint() checkpoint = ModelCheckpoint(dirpath=tmpdir, monitor='x', mode='min', save_top_k=1) trainer = Trainer( default_root_dir=tmpdir, max_epochs=2, accelerator='ddp_cpu', num_processes=2, callbacks=[checkpoint], ) trainer.fit(model) assert os.path.exists(checkpoint.best_model_path), f'Could not find checkpoint at rank {trainer.global_rank}' trainer.test() class CustomCallbackOnLoadCheckpoint(Callback): def on_save_checkpoint(self, trainer, pl_module, checkpoint) -> dict: return {"a": None} def test_on_load_checkpoint_missing_callbacks(tmpdir): """ Test a warning appears when callbacks in the checkpoint don't match callbacks provided when resuming. """ model = BoringModel() chk = ModelCheckpoint(dirpath=tmpdir, save_last=True) trainer = Trainer(default_root_dir=tmpdir, max_epochs=3, callbacks=[chk, CustomCallbackOnLoadCheckpoint()]) trainer.fit(model) trainer = Trainer( default_root_dir=tmpdir, max_epochs=5, resume_from_checkpoint=chk.last_model_path, progress_bar_refresh_rate=1 ) with pytest.warns(UserWarning, match="CustomCallbackOnLoadCheckpoint"): trainer.fit(model) def test_module_current_fx_attributes_reset(tmpdir): """ Ensure that lightning module's attributes related to current fx are reset at the end of execution. """ model = BoringModel() trainer = Trainer( default_root_dir=tmpdir, fast_dev_run=1, checkpoint_callback=False, logger=False, ) trainer.fit(model) assert model._current_fx_name is None assert model._current_dataloader_idx is None trainer.test(model) assert model._current_fx_name is None assert model._current_dataloader_idx is None def test_exception_when_lightning_module_is_not_set_on_trainer(): trainer = Trainer() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*validate"): trainer.validate() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*test"): trainer.test() with pytest.raises(MisconfigurationException, match=r"`model` must be provided.*predict"): trainer.predict()

"""OData service implementation Details regarding batch requests and changesets: http://www.odata.org/documentation/odata-version-2-0/batch-processing/ """ # pylint: disable=too-many-lines import logging from functools import partial import json import random from email.parser import Parser from http.client import HTTPResponse from io import BytesIO from urllib.parse import urlencode from pyodata.exceptions import HttpError, PyODataException, ExpressionError, ProgramError from . import model LOGGER_NAME = 'pyodata.service' HTTP_CODE_OK = 200 HTTP_CODE_CREATED = 201 def urljoin(*path): """Joins the passed string parts into a one string url""" return '/'.join((part.strip('/') for part in path)) def encode_multipart(boundary, http_requests): """Encode list of requests into multipart body""" lines = [] lines.append('') for req in http_requests: lines.append(f'--{boundary}') if not isinstance(req, MultipartRequest): lines.extend(('Content-Type: application/http ', 'Content-Transfer-Encoding:binary')) lines.append('') # request line (method + path + query params) line = f'{req.get_method()} {req.get_path()}' query_params = urlencode(req.get_query_params()) if query_params: line += '?' + query_params line += ' HTTP/1.1' lines.append(line) # request specific headers for hdr, hdr_val in req.get_headers().items(): lines.append(f'{hdr}: {hdr_val}') lines.append('') body = req.get_body() if body is not None: lines.append(req.get_body()) else: # this is very important since SAP gateway rejected request witout this line. It seems # blank line must be provided as a representation of emtpy body, else we are getting # 400 Bad fromat from SAP gateway lines.append('') lines.append(f'--{boundary}--') return '\r\n'.join(lines) def decode_multipart(data, content_type): """Decode parts of the multipart mime content""" def decode(message): """Decode tree of messages for specific message""" messages = [] for i, part in enumerate(message.walk()): # pylint: disable=unused-variable if part.get_content_type() == 'multipart/mixed': for submessage in part.get_payload(): messages.append(decode(submessage)) break messages.append(part.get_payload()) return messages data = f"Content-Type: {content_type}\n" + data parser = Parser() parsed = parser.parsestr(data) decoded = decode(parsed) return decoded class ODataHttpResponse: """Representation of http response""" def __init__(self, headers, status_code, content=None): self.headers = headers self.status_code = status_code self.content = content @staticmethod def from_string(data): """Parse http response to status code, headers and body Based on: https://stackoverflow.com/questions/24728088/python-parse-http-response-string """ class FakeSocket: """Fake socket to simulate received http response content""" def __init__(self, response_str): self._file = BytesIO(response_str.encode('utf-8')) def makefile(self, *args, **kwargs): """Fake file that provides string content""" # pylint: disable=unused-argument return self._file source = FakeSocket(data) response = HTTPResponse(source) response.begin() response.length = response.fp.__sizeof__() return ODataHttpResponse( dict(response.getheaders()), response.status, response.read(len(data)) # the len here will give a 'big enough' value to read the whole content ) def json(self): """Return response as decoded json""" # TODO: see implementation in python requests, our simple # approach can bring issues with encoding # https://github.com/requests/requests/blob/master/requests/models.py#L868 if self.content: return json.loads(self.content.decode('utf-8')) return None class EntityKey: """An immutable entity-key, made up of either a single value (single) or multiple key-value pairs (complex). Every entity must have an entity-key. The entity-key must be unique within the entity-set, and thus defines an entity's identity. The string representation of an entity-key is wrapped with parentheses, such as (2), ('foo') or (a=1,foo='bar'). Entity-keys are equal if their string representations are equal. """ TYPE_SINGLE = 0 TYPE_COMPLEX = 1 def __init__(self, entity_type, single_key=None, **args): self._logger = logging.getLogger(LOGGER_NAME) self._proprties = args self._entity_type = entity_type self._key = entity_type.key_proprties # single key does not need property name if single_key is not None: # check that entity type key consists of exactly one property if len(self._key) != 1: raise PyODataException(('Key of entity type {} consists of multiple properties {} ' 'and cannot be initialized by single value').format( self._entity_type.name, ', '.join([prop.name for prop in self._key]))) # get single key property and format key string key_prop = self._key[0] args[key_prop.name] = single_key self._type = EntityKey.TYPE_SINGLE self._logger.debug(('Detected single property key, adding pair %s->%s to key' 'properties'), key_prop.name, single_key) else: for key_prop in self._key: if key_prop.name not in args: raise PyODataException(f'Missing value for key property {key_prop.name}') self._type = EntityKey.TYPE_COMPLEX @property def key_properties(self): """Key properties""" return self._key def to_key_string_without_parentheses(self): """Gets the string representation of the key without parentheses""" if self._type == EntityKey.TYPE_SINGLE: # first property is the key property key_prop = self._key[0] return key_prop.to_literal(self._proprties[key_prop.name]) key_pairs = [] for key_prop in self._key: # if key_prop.name not in self.__dict__['_cache']: # raise RuntimeError('Entity key is not complete, missing value of property: {0}'.format(key_prop.name)) key_pairs.append( f'{key_prop.name}={key_prop.to_literal(self._proprties[key_prop.name])}') return ','.join(key_pairs) def to_key_string(self): """Gets the string representation of the key, including parentheses""" return f'({self.to_key_string_without_parentheses()})' def __repr__(self): return self.to_key_string() class ODataHttpRequest: """Deferred HTTP Request""" def __init__(self, url, connection, handler, headers=None): self._connection = connection self._url = url self._handler = handler self._headers = headers or dict() self._logger = logging.getLogger(LOGGER_NAME) self._customs = {} # string -> string hash self._next_url = None @property def handler(self): """Getter for handler""" return self._handler def get_path(self): """Get path of the HTTP request""" # pylint: disable=no-self-use return '' def get_query_params(self): """Get query params""" # pylint: disable=no-self-use return dict(self._customs) def get_method(self): """Get HTTP method""" # pylint: disable=no-self-use return 'GET' def get_body(self): """Get HTTP body or None if not applicable""" # pylint: disable=no-self-use return None def get_default_headers(self): """Get dict of Child specific HTTP headers""" # pylint: disable=no-self-use return dict() def get_headers(self): """Get dict of HTTP headers which is union of return value of the method get_default_headers() and the headers added via the method add_headers() where the latter headers have priority - same keys get value of the latter. """ headers = self.get_default_headers() headers.update(self._headers) return headers def add_headers(self, value): """Add the give dictionary of HTTP headers to HTTP request sent by this ODataHttpRequest instance. """ if not isinstance(value, dict): raise TypeError(f"Headers must be of type 'dict' not {type(value)}") self._headers.update(value) def execute(self): """Fetches HTTP response and returns processed result Sends the query-request to the OData service, returning a client-side Enumerable for subsequent in-memory operations. Fetches HTTP response and returns processed result""" if self._next_url: url = self._next_url else: url = urljoin(self._url, self.get_path()) # pylint: disable=assignment-from-none body = self.get_body() headers = self.get_headers() self._logger.debug('Send (execute) %s request to %s', self.get_method(), url) self._logger.debug(' query params: %s', self.get_query_params()) self._logger.debug(' headers: %s', headers) if body: self._logger.debug(' body: %s', body) params = urlencode(self.get_query_params()) response = self._connection.request( self.get_method(), url, headers=headers, params=params, data=body) self._logger.debug('Received response') self._logger.debug(' url: %s', response.url) self._logger.debug(' headers: %s', response.headers) self._logger.debug(' status code: %d', response.status_code) try: self._logger.debug(' body: %s', response.content.decode('utf-8')) except UnicodeDecodeError: self._logger.debug(' body: <cannot be decoded>') return self._handler(response) def custom(self, name, value): """Adds a custom name-value pair.""" # returns QueryRequest self._customs[name] = value return self class EntityGetRequest(ODataHttpRequest): """Used for GET operations of a single entity""" def __init__(self, handler, entity_key, entity_set_proxy): super(EntityGetRequest, self).__init__(entity_set_proxy.service.url, entity_set_proxy.service.connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_key = entity_key self._entity_set_proxy = entity_set_proxy self._select = None self._expand = None self._logger.debug('New instance of EntityGetRequest for last segment: %s', self._entity_set_proxy.last_segment) def nav(self, nav_property): """Navigates to given navigation property and returns the EntitySetProxy""" return self._entity_set_proxy.nav(nav_property, self._entity_key) def select(self, select): """Specifies a subset of properties to return. @param select a comma-separated list of selection clauses """ self._select = select return self def expand(self, expand): """Specifies related entities to expand inline as part of the response. @param expand a comma-separated list of navigation properties """ self._expand = expand return self def get_path(self): return self._entity_set_proxy.last_segment + self._entity_key.to_key_string() def get_default_headers(self): return {'Accept': 'application/json'} def get_query_params(self): qparams = super(EntityGetRequest, self).get_query_params() if self._select is not None: qparams['$select'] = self._select if self._expand is not None: qparams['$expand'] = self._expand return qparams def get_value(self, connection=None): """Returns Value of Media EntityTypes also known as the $value URL suffix.""" if connection is None: connection = self._connection def stream_handler(response): """Returns $value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for $value failed with status code {}' .format(response.status_code), response) return response return ODataHttpRequest( urljoin(self._url, self.get_path(), '/$value'), connection, stream_handler) class NavEntityGetRequest(EntityGetRequest): """Used for GET operations of a single entity accessed via a Navigation property""" def __init__(self, handler, master_key, entity_set_proxy, nav_property): super(NavEntityGetRequest, self).__init__(handler, master_key, entity_set_proxy) self._nav_property = nav_property def get_path(self): return f"{super(NavEntityGetRequest, self).get_path()}/{self._nav_property}" class EntityCreateRequest(ODataHttpRequest): """Used for creating entities (POST operations of a single entity) Call execute() to send the create-request to the OData service and get the newly created entity.""" def __init__(self, url, connection, handler, entity_set, last_segment=None): super(EntityCreateRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_type = entity_set.entity_type if last_segment is None: self._last_segment = self._entity_set.name else: self._last_segment = last_segment self._values = {} # get all properties declared by entity type self._type_props = self._entity_type.proprties() self._logger.debug('New instance of EntityCreateRequest for entity type: %s on path %s', self._entity_type.name, self._last_segment) def get_path(self): return self._last_segment def get_method(self): # pylint: disable=no-self-use return 'POST' def _get_body(self): """Recursively builds a dictionary of values where some of the values might be another entities. """ body = {} for key, val in self._values.items(): # The value is either an entity or a scalar if isinstance(val, EntityProxy): body[key] = val._get_body() # pylint: disable=protected-access else: body[key] = val return body def get_body(self): return json.dumps(self._get_body()) def get_default_headers(self): return {'Accept': 'application/json', 'Content-Type': 'application/json', 'X-Requested-With': 'X'} @staticmethod def _build_values(entity_type, entity): """Recursively converts a dictionary of values where some of the values might be another entities (navigation properties) into the internal representation. """ if isinstance(entity, list): return [EntityCreateRequest._build_values(entity_type, item) for item in entity] values = {} for key, val in entity.items(): try: val = entity_type.proprty(key).to_json(val) except KeyError: try: nav_prop = entity_type.nav_proprty(key) val = EntityCreateRequest._build_values(nav_prop.typ, val) except KeyError: raise PyODataException('Property {} is not declared in {} entity type'.format( key, entity_type.name)) values[key] = val return values def set(self, **kwargs): """Set properties on the new entity.""" self._logger.info(kwargs) # TODO: consider use of attset for setting properties self._values = EntityCreateRequest._build_values(self._entity_type, kwargs) return self class EntityDeleteRequest(ODataHttpRequest): """Used for deleting entity (DELETE operations on a single entity)""" def __init__(self, url, connection, handler, entity_set, entity_key): super(EntityDeleteRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_key = entity_key self._logger.debug('New instance of EntityDeleteRequest for entity type: %s', entity_set.entity_type.name) def get_path(self): return self._entity_set.name + self._entity_key.to_key_string() def get_method(self): # pylint: disable=no-self-use return 'DELETE' class EntityModifyRequest(ODataHttpRequest): """Used for modyfing entities (UPDATE/MERGE operations on a single entity) Call execute() to send the update-request to the OData service and get the modified entity.""" ALLOWED_HTTP_METHODS = ['PATCH', 'PUT', 'MERGE'] def __init__(self, url, connection, handler, entity_set, entity_key, method="PATCH"): super(EntityModifyRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_type = entity_set.entity_type self._entity_key = entity_key self._method = method.upper() if self._method not in EntityModifyRequest.ALLOWED_HTTP_METHODS: raise ValueError('The value "{}" is not on the list of allowed Entity Update HTTP Methods: {}' .format(method, ', '.join(EntityModifyRequest.ALLOWED_HTTP_METHODS))) self._values = {} # get all properties declared by entity type self._type_props = self._entity_type.proprties() self._logger.debug('New instance of EntityModifyRequest for entity type: %s', self._entity_type.name) def get_path(self): return self._entity_set.name + self._entity_key.to_key_string() def get_method(self): # pylint: disable=no-self-use return self._method def get_body(self): # pylint: disable=no-self-use body = {} for key, val in self._values.items(): body[key] = val return json.dumps(body) def get_default_headers(self): return {'Accept': 'application/json', 'Content-Type': 'application/json'} def set(self, **kwargs): """Set properties to be changed.""" self._logger.info(kwargs) for key, val in kwargs.items(): try: val = self._entity_type.proprty(key).to_json(val) except KeyError: raise PyODataException( f'Property {key} is not declared in {self._entity_type.name} entity type') self._values[key] = val return self class QueryRequest(ODataHttpRequest): """INTERFACE A consumer-side query-request builder. Call execute() to issue the request.""" # pylint: disable=too-many-instance-attributes def __init__(self, url, connection, handler, last_segment): super(QueryRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._count = None self._inlinecount = None self._top = None self._skip = None self._order_by = None self._filter = None self._select = None self._expand = None self._last_segment = last_segment self._logger.debug('New instance of QueryRequest for last segment: %s', self._last_segment) def count(self, inline=False): """Sets a flag to return the number of items. Can be inline with results or just the count.""" if inline: self._inlinecount = True else: self._count = True return self def next_url(self, next_url): """ Sets URL which identifies the next partial set of entities from the originally identified complete set. Once set, this URL takes precedence over all query parameters. For details, see section "6. Representing Collections of Entries" on https://www.odata.org/documentation/odata-version-2-0/json-format/ """ self._next_url = next_url return self def expand(self, expand): """Sets the expand expressions.""" self._expand = expand return self def filter(self, filter_val): """Sets the filter expression.""" # returns QueryRequest self._filter = filter_val return self # def nav(self, key_value, nav_property): # """Navigates to a referenced collection using a collection-valued navigation property.""" # # returns QueryRequest # raise NotImplementedError def order_by(self, order_by): """Sets the ordering expressions.""" self._order_by = order_by return self def select(self, select): """Sets the selection clauses.""" self._select = select return self def skip(self, skip): """Sets the number of items to skip.""" self._skip = skip return self def top(self, top): """Sets the number of items to return.""" self._top = top return self def get_path(self): if self._count: return urljoin(self._last_segment, '/$count') return self._last_segment def get_default_headers(self): if self._count: return {} return { 'Accept': 'application/json', } def get_query_params(self): if self._next_url: return {} qparams = super(QueryRequest, self).get_query_params() if self._top is not None: qparams['$top'] = self._top if self._skip is not None: qparams['$skip'] = self._skip if self._order_by is not None: qparams['$orderby'] = self._order_by if self._filter is not None: qparams['$filter'] = self._filter if self._select is not None: qparams['$select'] = self._select if self._expand is not None: qparams['$expand'] = self._expand if self._inlinecount: qparams['$inlinecount'] = 'allpages' return qparams class FunctionRequest(QueryRequest): """Function import request (Service call)""" def __init__(self, url, connection, handler, function_import): super(FunctionRequest, self).__init__(url, connection, handler, function_import.name) self._function_import = function_import self._logger.debug('New instance of FunctionRequest for %s', self._function_import.name) def parameter(self, name, value): '''Sets value of parameter.''' # check if param is valid (is declared in metadata) try: param = self._function_import.get_parameter(name) # add parameter as custom query argument self.custom(param.name, param.to_literal(value)) except KeyError: raise PyODataException('Function import {0} does not have pararmeter {1}' .format(self._function_import.name, name)) return self def get_method(self): return self._function_import.http_method def get_default_headers(self): return { 'Accept': 'application/json' } # pylint: disable=too-many-instance-attributes class EntityProxy: """An immutable OData entity instance, consisting of an identity (an entity-set and a unique entity-key within that set), properties (typed, named values), and links (references to other entities). """ # pylint: disable=too-many-branches,too-many-nested-blocks,too-many-statements def __init__(self, service, entity_set, entity_type, proprties=None, entity_key=None, etag=None): self._logger = logging.getLogger(LOGGER_NAME) self._service = service self._entity_set = entity_set self._entity_type = entity_type self._key_props = entity_type.key_proprties self._cache = dict() self._entity_key = entity_key self._etag = etag self._logger.debug('New entity proxy instance of type %s from properties: %s', entity_type.name, proprties) # cache values of individual properties if provided if proprties is not None: etag_body = proprties.get('__metadata', dict()).get('etag', None) if etag is not None and etag_body is not None and etag_body != etag: raise PyODataException('Etag from header does not match the Etag from response body') if etag_body is not None: self._etag = etag_body # first, cache values of direct properties for type_proprty in self._entity_type.proprties(): if type_proprty.name in proprties: # Property value available if proprties[type_proprty.name] is not None: self._cache[type_proprty.name] = type_proprty.from_json(proprties[type_proprty.name]) continue # Property value missing and user wants a type specific default value filled in if not self._service.retain_null: # null value is in literal form for now, convert it to python representation self._cache[type_proprty.name] = type_proprty.from_literal(type_proprty.typ.null_value) continue # Property is nullable - save it as such if type_proprty.nullable: self._cache[type_proprty.name] = None continue raise PyODataException(f'Value of non-nullable Property {type_proprty.name} is null') # then, assign all navigation properties for prop in self._entity_type.nav_proprties: if prop.name in proprties: # entity type of navigation property prop_etype = prop.to_role.entity_type # cache value according to multiplicity if prop.to_role.multiplicity in \ [model.EndRole.MULTIPLICITY_ONE, model.EndRole.MULTIPLICITY_ZERO_OR_ONE]: # cache None in case we receive nothing (null) instead of entity data if proprties[prop.name] is None: self._cache[prop.name] = None else: self._cache[prop.name] = EntityProxy(service, None, prop_etype, proprties[prop.name]) elif prop.to_role.multiplicity == model.EndRole.MULTIPLICITY_ZERO_OR_MORE: # default value is empty array self._cache[prop.name] = [] # if there are no entities available, received data consists of # metadata properties only. if 'results' in proprties[prop.name]: # available entities are serialized in results array for entity in proprties[prop.name]['results']: self._cache[prop.name].append(EntityProxy(service, None, prop_etype, entity)) else: raise PyODataException('Unknown multiplicity {0} of association role {1}' .format(prop.to_role.multiplicity, prop.to_role.name)) # build entity key if not provided if self._entity_key is None: # try to build key from available property values try: # if key seems to be simple (consists of single property) if len(self._key_props) == 1: self._entity_key = EntityKey(entity_type, self._cache[self._key_props[0].name]) else: # build complex key self._entity_key = EntityKey(entity_type, **self._cache) except KeyError: pass except PyODataException: pass def __repr__(self): return self._entity_key.to_key_string() def __getattr__(self, attr): try: return self._cache[attr] except KeyError: try: value = self.get_proprty(attr).execute() self._cache[attr] = value return value except KeyError as ex: raise AttributeError('EntityType {0} does not have Property {1}: {2}' .format(self._entity_type.name, attr, str(ex))) def nav(self, nav_property): """Navigates to given navigation property and returns the EntitySetProxy""" # for now duplicated with simillar method in entity set proxy class try: navigation_property = self._entity_type.nav_proprty(nav_property) except KeyError: raise PyODataException('Navigation property {} is not declared in {} entity type'.format( nav_property, self._entity_type)) # Get entity set of navigation property association_info = navigation_property.association_info association_set = self._service.schema.association_set_by_association( association_info.name, association_info.namespace) navigation_entity_set = None for end in association_set.end_roles: if association_set.end_by_entity_set(end.entity_set_name).role == navigation_property.to_role.role: navigation_entity_set = self._service.schema.entity_set(end.entity_set_name, association_info.namespace) if not navigation_entity_set: raise PyODataException(f'No association set for role {navigation_property.to_role}') roles = navigation_property.association.end_roles if all((role.multiplicity != model.EndRole.MULTIPLICITY_ZERO_OR_MORE for role in roles)): return NavEntityProxy(self, nav_property, navigation_entity_set.entity_type, {}) return EntitySetProxy( self._service, self._service.schema.entity_set(navigation_entity_set.name), nav_property, self._entity_set.name + self._entity_key.to_key_string()) def get_path(self): """Returns this entity's relative path - e.g. EntitySet(KEY)""" return self._entity_set._name + self._entity_key.to_key_string() # pylint: disable=protected-access def get_proprty(self, name, connection=None): """Returns value of the property""" self._logger.info('Initiating property request for %s', name) def proprty_get_handler(key, proprty, response): """Gets property value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Attribute {0} of Entity {1} failed with status code {2}' .format(proprty.name, key, response.status_code), response) data = response.json()['d'] return proprty.from_json(data[proprty.name]) path = urljoin(self.get_path(), name) return self._service.http_get_odata( path, partial(proprty_get_handler, path, self._entity_type.proprty(name)), connection=connection) def get_value(self, connection=None): "Returns $value of Stream entities" def value_get_handler(key, response): """Gets property value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for $value of Entity {0} failed with status code {1}' .format(key, response.status_code), response) return response path = urljoin(self.get_path(), '/$value') return self._service.http_get_odata(path, partial(value_get_handler, self.entity_key), connection=connection) @property def entity_set(self): """Entity set related to this entity""" return self._entity_set @property def entity_key(self): """Key of entity""" return self._entity_key @property def url(self): """URL of the real entity""" service_url = self._service.url.rstrip('/') entity_path = self.get_path() return urljoin(service_url, entity_path) @property def etag(self): """ETag generated by service""" return self._etag def equals(self, other): """Returns true if the self and the other contains the same data""" # pylint: disable=W0212 return self._cache == other._cache class NavEntityProxy(EntityProxy): """Special case of an Entity access via 1 to 1 Navigation property""" def __init__(self, parent_entity, prop_name, entity_type, entity): # pylint: disable=protected-access super(NavEntityProxy, self).__init__(parent_entity._service, parent_entity._entity_set, entity_type, entity) self._parent_entity = parent_entity self._prop_name = prop_name def get_path(self): """Returns URL of the entity""" return urljoin(self._parent_entity.get_path(), self._prop_name) class GetEntitySetFilter: """Create filters for humans""" def __init__(self, proprty): self._proprty = proprty @staticmethod def build_expression(operator, operands): """Creates a expression by joining the operands with the operator""" if len(operands) < 2: raise ExpressionError('The $filter operator \'{}\' needs at least two operands'.format(operator)) return f"({" {} ".format(operator).join(operands)})" @staticmethod def and_(*operands): """Creates logical AND expression from the operands""" return GetEntitySetFilter.build_expression('and', operands) @staticmethod def or_(*operands): """Creates logical OR expression from the operands""" return GetEntitySetFilter.build_expression('or', operands) @staticmethod def format_filter(proprty, operator, value): """Creates a filter expression """ return f'{proprty.name} {operator} {proprty.to_literal(value)}' def __eq__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'eq', value) def __ne__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'ne', value) def __lt__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'lt', value) def __le__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'le', value) def __ge__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'ge', value) def __gt__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'gt', value) class FilterExpression: """A class representing named expression of OData $filter""" def __init__(self, **kwargs): self._expressions = kwargs self._other = None self._operator = None @property def expressions(self): """Get expressions where key is property name with the operator suffix and value is the left hand side operand. """ return self._expressions.items() @property def other(self): """Get an instance of the other operand""" return self._other @property def operator(self): """The other operand""" return self._operator def __or__(self, other): if self._other is not None: raise RuntimeError('The FilterExpression already initialized') self._other = other self._operator = "or" return self def __and__(self, other): if self._other is not None: raise RuntimeError('The FilterExpression already initialized') self._other = other self._operator = "and" return self class GetEntitySetFilterChainable: """ Example expressions FirstName='Tim' FirstName__contains='Tim' Age__gt=56 Age__gte=6 Age__lt=78 Age__lte=90 Age__range=(5,9) FirstName__in=['Tim', 'Bob', 'Sam'] FirstName__startswith='Tim' FirstName__endswith='mothy' Addresses__Suburb='Chatswood' Addresses__Suburb__contains='wood' """ OPERATORS = [ 'startswith', 'endswith', 'lt', 'lte', 'gt', 'gte', 'contains', 'range', 'in', 'length', 'eq' ] def __init__(self, entity_type, filter_expressions, exprs): self._entity_type = entity_type self._filter_expressions = filter_expressions self._expressions = exprs @property def expressions(self): """Get expressions as a list of tuples where the first item is a property name with the operator suffix and the second item is a left hand side value. """ return self._expressions.items() def proprty_obj(self, name): """Returns a model property for a particular property""" return self._entity_type.proprty(name) def _decode_and_combine_filter_expression(self, filter_expression): filter_expressions = [self._decode_expression(expr, val) for expr, val in filter_expression.expressions] return self._combine_expressions(filter_expressions) def _process_query_objects(self): """Processes FilterExpression objects to OData lookups""" filter_expressions = [] for expr in self._filter_expressions: lhs_expressions = self._decode_and_combine_filter_expression(expr) if expr.other is not None: rhs_expressions = self._decode_and_combine_filter_expression(expr.other) filter_expressions.append(f'({lhs_expressions}) {expr.operator} ({rhs_expressions})') else: filter_expressions.append(lhs_expressions) return filter_expressions def _process_expressions(self): filter_expressions = [self._decode_expression(expr, val) for expr, val in self.expressions] filter_expressions.extend(self._process_query_objects()) return filter_expressions def _decode_expression(self, expr, val): field = None # field_heirarchy = [] operator = 'eq' exprs = expr.split('__') for part in exprs: if self._entity_type.has_proprty(part): field = part # field_heirarchy.append(part) elif part in self.__class__.OPERATORS: operator = part else: raise ValueError(f'"{part}" is not a valid property or operator') # field = '/'.join(field_heirarchy) # target_field = self.proprty_obj(field_heirarchy[-1]) expression = self._build_expression(field, operator, val) return expression # pylint: disable=no-self-use def _combine_expressions(self, expressions): return ' and '.join(expressions) # pylint: disable=too-many-return-statements, too-many-branches def _build_expression(self, field_name, operator, value): target_field = self.proprty_obj(field_name) if operator not in ['length', 'in', 'range']: value = target_field.to_literal(value) if operator == 'lt': return f'{field_name} lt {value}' if operator == 'lte': return f'{field_name} le {value}' if operator == 'gte': return f'{field_name} ge {value}' if operator == 'gt': return f'{field_name} gt {value}' if operator == 'startswith': return f'startswith({field_name}, {value}) eq true' if operator == 'endswith': return f'endswith({field_name}, {value}) eq true' if operator == 'length': value = int(value) return f'length({field_name}) eq {value}' if operator in ['contains']: return f'substringof({value}, {field_name}) eq true' if operator == 'range': if not isinstance(value, (tuple, list)): raise TypeError(f'Range must be tuple or list not {type(value)}') if len(value) != 2: raise ValueError('Only two items can be passed in a range.') low_bound = target_field.to_literal(value[0]) high_bound = target_field.to_literal(value[1]) return f'{field_name} gte {low_bound} and {field_name} lte {high_bound}' if operator == 'in': literal_values = (f'{field_name} eq {target_field.to_literal(item)}' for item in value) return ' or '.join(literal_values) if operator == 'eq': return f'{field_name} eq {value}' raise ValueError(f'Invalid expression {operator}') def __str__(self): expressions = self._process_expressions() result = self._combine_expressions(expressions) return result class GetEntitySetRequest(QueryRequest): """GET on EntitySet""" def __init__(self, url, connection, handler, last_segment, entity_type): super(GetEntitySetRequest, self).__init__(url, connection, handler, last_segment) self._entity_type = entity_type def __getattr__(self, name): proprty = self._entity_type.proprty(name) return GetEntitySetFilter(proprty) def _set_filter(self, filter_val): filter_text = self._filter + ' and ' if self._filter else '' filter_text += filter_val self._filter = filter_text def filter(self, *args, **kwargs): if args and len(args) == 1 and isinstance(args[0], str): self._filter = args[0] else: self._set_filter(str(GetEntitySetFilterChainable(self._entity_type, args, kwargs))) return self class ListWithTotalCount(list): """ A list with the additional property total_count and next_url. If set, use next_url to fetch the next batch of entities. """ def __init__(self, total_count, next_url): super(ListWithTotalCount, self).__init__() self._total_count = total_count self._next_url = next_url @property def next_url(self): """ URL which identifies the next partial set of entities from the originally identified complete set. None if no entities remaining. """ return self._next_url @property def total_count(self): """Count of all entities""" if self._total_count is None: raise ProgramError('The collection does not include Total Count ' 'of items because the request was made without ' 'specifying "count(inline=True)".') return self._total_count class EntitySetProxy: """EntitySet Proxy""" def __init__(self, service, entity_set, alias=None, parent_last_segment=None): """Creates new Entity Set object @param alias in case the entity set is access via assossiation @param parent_last_segment in case of association also parent key must be used """ self._service = service self._entity_set = entity_set self._alias = alias if parent_last_segment is None: self._parent_last_segment = '' else: if parent_last_segment.endswith('/'): self._parent_last_segment = parent_last_segment else: self._parent_last_segment = parent_last_segment + '/' self._name = entity_set.name self._key = entity_set.entity_type.key_proprties self._logger = logging.getLogger(LOGGER_NAME) self._logger.debug('New entity set proxy instance for %s', self._name) @property def service(self): """Return service""" return self._service @property def last_segment(self): """Return last segment of url""" entity_set_name = self._alias if self._alias is not None else self._entity_set.name return self._parent_last_segment + entity_set_name def nav(self, nav_property, key): """Navigates to given navigation property and returns the EntitySetProxy""" try: navigation_property = self._entity_set.entity_type.nav_proprty(nav_property) except KeyError: raise PyODataException('Navigation property {} is not declared in {} entity type'.format( nav_property, self._entity_set.entity_type)) # Get entity set of navigation property association_info = navigation_property.association_info association_set = self._service.schema.association_set_by_association( association_info.name) navigation_entity_set = None for end in association_set.end_roles: if association_set.end_by_entity_set(end.entity_set_name).role == navigation_property.to_role.role: navigation_entity_set = self._service.schema.entity_set(end.entity_set_name) if not navigation_entity_set: raise PyODataException( f'No association set for role {navigation_property.to_role} {association_set.end_roles}') roles = navigation_property.association.end_roles if all((role.multiplicity != model.EndRole.MULTIPLICITY_ZERO_OR_MORE for role in roles)): return self._get_nav_entity(key, nav_property, navigation_entity_set) return EntitySetProxy( self._service, navigation_entity_set, nav_property, self._entity_set.name + key.to_key_string()) def _get_nav_entity(self, master_key, nav_property, navigation_entity_set): """Get entity based on provided key of the master and Navigation property name""" def get_entity_handler(parent, nav_property, navigation_entity_set, response): """Gets entity from HTTP response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity {0} failed with status code {1}' .format(self._name, response.status_code), response) entity = response.json()['d'] return NavEntityProxy(parent, nav_property, navigation_entity_set.entity_type, entity) self._logger.info( 'Getting the nav property %s of the entity %s for the key %s', nav_property, self._entity_set.entity_type.name, master_key) parent = EntityProxy(self._service, self, self._entity_set.entity_type, entity_key=master_key) return NavEntityGetRequest( partial(get_entity_handler, parent, nav_property, navigation_entity_set), master_key, self, nav_property) def get_entity(self, key=None, **args): """Get entity based on provided key properties""" def get_entity_handler(response): """Gets entity from HTTP response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity {0} failed with status code {1}' .format(self._name, response.status_code), response) entity = response.json()['d'] etag = response.headers.get('ETag', None) return EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, entity, etag=etag) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **args) self._logger.info('Getting entity %s for key %s and args %s', self._entity_set.entity_type.name, key, args) return EntityGetRequest(get_entity_handler, entity_key, self) def get_entities(self): """Get some, potentially all entities""" def get_entities_handler(response): """Gets entity set from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity Set {0} failed with status code {1}' .format(self._name, response.status_code), response) content = response.json() if isinstance(content, int): return content entities = content['d'] total_count = None next_url = None if isinstance(entities, dict): if '__count' in entities: total_count = int(entities['__count']) if '__next' in entities: next_url = entities['__next'] entities = entities['results'] self._logger.info('Fetched %d entities', len(entities)) result = ListWithTotalCount(total_count, next_url) for props in entities: entity = EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, props) result.append(entity) return result entity_set_name = self._alias if self._alias is not None else self._entity_set.name return GetEntitySetRequest(self._service.url, self._service.connection, get_entities_handler, self._parent_last_segment + entity_set_name, self._entity_set.entity_type) def create_entity(self, return_code=HTTP_CODE_CREATED): """Creates a new entity in the given entity-set.""" def create_entity_handler(response): """Gets newly created entity encoded in HTTP Response""" if response.status_code != return_code: raise HttpError('HTTP POST for Entity Set {0} failed with status code {1}' .format(self._name, response.status_code), response) entity_props = response.json()['d'] etag = response.headers.get('ETag', None) return EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, entity_props, etag=etag) return EntityCreateRequest(self._service.url, self._service.connection, create_entity_handler, self._entity_set, self.last_segment) def update_entity(self, key=None, method=None, **kwargs): """Updates an existing entity in the given entity-set.""" def update_entity_handler(response): """Gets modified entity encoded in HTTP Response""" if response.status_code != 204: raise HttpError('HTTP modify request for Entity Set {} failed with status code {}' .format(self._name, response.status_code), response) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **kwargs) self._logger.info('Updating entity %s for key %s and args %s', self._entity_set.entity_type.name, key, kwargs) if method is None: method = self._service.config['http']['update_method'] return EntityModifyRequest(self._service.url, self._service.connection, update_entity_handler, self._entity_set, entity_key, method=method) def delete_entity(self, key: EntityKey = None, **kwargs): """Delete the entity""" def delete_entity_handler(response): """Check if entity deletion was successful""" if response.status_code != 204: raise HttpError(f'HTTP POST for Entity delete {self._name} ' f'failed with status code {response.status_code}', response) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **kwargs) return EntityDeleteRequest(self._service.url, self._service.connection, delete_entity_handler, self._entity_set, entity_key) # pylint: disable=too-few-public-methods class EntityContainer: """Set of EntitSet proxies""" def __init__(self, service): self._service = service self._entity_sets = dict() for entity_set in self._service.schema.entity_sets: self._entity_sets[entity_set.name] = EntitySetProxy(self._service, entity_set) def __getattr__(self, name): try: return self._entity_sets[name] except KeyError: raise AttributeError( f"EntitySet {name} not defined in {",".join(list(self._entity_sets.keys()))}.") class FunctionContainer: """Set of Function proxies Call a server-side functions (also known as a service operation). """ def __init__(self, service): self._service = service self._functions = dict() for fimport in self._service.schema.function_imports: self._functions[fimport.name] = fimport def __getattr__(self, name): if name not in self._functions: raise AttributeError( f"Function {name} not defined in {",".join(list(self._functions.keys()))}.") fimport = self._service.schema.function_import(name) def function_import_handler(fimport, response): """Get function call response from HTTP Response""" if 300 <= response.status_code < 400: raise HttpError(f'Function Import {fimport.name} requires Redirection which is not supported', response) if response.status_code == 401: raise HttpError(f'Not authorized to call Function Import {fimport.name}', response) if response.status_code == 403: raise HttpError(f'Missing privileges to call Function Import {fimport.name}', response) if response.status_code == 405: raise HttpError( f'Despite definition Function Import {fimport.name} does not support HTTP {fimport.http_method}', response) if 400 <= response.status_code < 500: raise HttpError( f'Function Import {fimport.name} call has failed with status code {response.status_code}', response) if response.status_code >= 500: raise HttpError(f'Server has encountered an error while processing Function Import {fimport.name}', response) if fimport.return_type is None: if response.status_code != 204: logging.getLogger(LOGGER_NAME).warning( 'The No Return Function Import %s has replied with HTTP Status Code %d instead of 204', fimport.name, response.status_code) if response.text: logging.getLogger(LOGGER_NAME).warning( 'The No Return Function Import %s has returned content:\n%s', fimport.name, response.text) return None if response.status_code != 200: logging.getLogger(LOGGER_NAME).warning( 'The Function Import %s has replied with HTTP Status Code %d instead of 200', fimport.name, response.status_code) response_data = response.json()['d'] # 1. if return types is "entity type", return instance of appropriate entity proxy if isinstance(fimport.return_type, model.EntityType): entity_set = self._service.schema.entity_set(fimport.entity_set_name) return EntityProxy(self._service, entity_set, fimport.return_type, response_data) # 2. return raw data for all other return types (primitives, complex types encoded in dicts, etc.) return response_data return FunctionRequest(self._service.url, self._service.connection, partial(function_import_handler, fimport), fimport) class Service: """OData service""" def __init__(self, url, schema, connection, config=None): self._url = url self._schema = schema self._connection = connection self._retain_null = config.retain_null if config else False self._entity_container = EntityContainer(self) self._function_container = FunctionContainer(self) self._config = {'http': {'update_method': 'PATCH'}} @property def schema(self): """Parsed metadata""" return self._schema @property def url(self): """Service url""" return self._url @property def connection(self): """Service connection""" return self._connection @property def retain_null(self): """Whether to respect null-ed values or to substitute them with type specific default values""" return self._retain_null @property def entity_sets(self): """EntitySet proxy""" return self._entity_container @property def functions(self): """Functions proxy""" return self._function_container @property def config(self): """Service specific configuration""" return self._config def http_get(self, path, connection=None): """HTTP GET response for the passed path in the service""" conn = connection if conn is None: conn = self._connection return conn.get(urljoin(self._url, path)) def http_get_odata(self, path, handler, connection=None): """HTTP GET request proxy for the passed path in the service""" conn = connection if conn is None: conn = self._connection return ODataHttpRequest( urljoin(self._url, path), conn, handler, headers={'Accept': 'application/json'}) def create_batch(self, batch_id=None): """Create instance of OData batch request""" def batch_handler(batch, parts): """Process parsed multipart request (parts)""" logging.getLogger(LOGGER_NAME).debug('Batch handler called for batch %s', batch.id) result = [] for part, req in zip(parts, batch.requests): logging.getLogger(LOGGER_NAME).debug('Batch handler is processing part %s for request %s', part, req) # if part represents multiple requests, dont' parse body and # process parts by appropriate reuqest instance if isinstance(req, MultipartRequest): result.append(req.handler(req, part)) else: # part represents single request, we have to parse # content (without checking Content type for binary/http) response = ODataHttpResponse.from_string(part[0]) result.append(req.handler(response)) return result return BatchRequest(self._url, self._connection, batch_handler, batch_id) def create_changeset(self, changeset_id=None): """Create instance of OData changeset""" def changeset_handler(changeset, parts): """Gets changeset response from HTTP response""" logging.getLogger(LOGGER_NAME).debug('Changeset handler called for changeset %s', changeset.id) result = [] # check if changeset response consists of parts, this is important # to distinguish cases when server responds with single HTTP response # for whole request if not isinstance(parts[0], list): # raise error (even for successfull status codes) since such changeset response # always means something wrong happened on server response = ODataHttpResponse.from_string(parts[0]) raise HttpError('Changeset cannot be processed due to single response received, status code: {}'.format( response.status_code), response) for part, req in zip(parts, changeset.requests): logging.getLogger(LOGGER_NAME).debug('Changeset handler is processing part %s for request %s', part, req) if isinstance(req, MultipartRequest): raise PyODataException('Changeset cannot contain nested multipart content') # part represents single request, we have to parse # content (without checking Content type for binary/http) response = ODataHttpResponse.from_string(part[0]) result.append(req.handler(response)) return result return Changeset(self._url, self._connection, changeset_handler, changeset_id) class MultipartRequest(ODataHttpRequest): """HTTP Batch request""" def __init__(self, url, connection, handler, request_id=None): super(MultipartRequest, self).__init__(url, connection, partial(MultipartRequest.http_response_handler, self)) self.requests = [] self._handler_decoded = handler # generate random id of form dddd-dddd-dddd # pylint: disable=invalid-name self.id = request_id if request_id is not None else '{}_{}_{}'.format( random.randint(1000, 9999), random.randint(1000, 9999), random.randint(1000, 9999)) self._logger.debug('New multipart %s request initialized, id=%s', self.__class__.__name__, self.id) @property def handler(self): return self._handler_decoded def get_boundary(self): """Get boundary used for request parts""" return self.id def get_default_headers(self): # pylint: disable=no-self-use return {'Content-Type': f'multipart/mixed;boundary={self.get_boundary()}'} def get_body(self): return encode_multipart(self.get_boundary(), self.requests) def add_request(self, request): """Add request to be sent in batch""" self.requests.append(request) self._logger.debug('New %s request added to multipart request %s', request.get_method(), self.id) @staticmethod def http_response_handler(request, response): """Process HTTP response to mutipart HTTP request""" if response.status_code != 202: # 202 Accepted raise HttpError('HTTP POST for multipart request {0} failed with status code {1}' .format(request.id, response.status_code), response) logging.getLogger(LOGGER_NAME).debug('Generic multipart http response request handler called') # get list of all parts (headers + body) decoded = decode_multipart(response.content.decode('utf-8'), response.headers['Content-Type']) return request.handler(request, decoded) class BatchRequest(MultipartRequest): """HTTP Batch request""" def get_boundary(self): return 'batch_' + self.id def get_path(self): # pylint: disable=no-self-use return '$batch' def get_method(self): # pylint: disable=no-self-use return 'POST' class Changeset(MultipartRequest): """Representation of changeset (unsorted group of requests)""" def get_boundary(self): return 'changeset_' + self.id

"""OData service implementation Details regarding batch requests and changesets: http://www.odata.org/documentation/odata-version-2-0/batch-processing/ """ # pylint: disable=too-many-lines import logging from functools import partial import json import random from email.parser import Parser from http.client import HTTPResponse from io import BytesIO from urllib.parse import urlencode from pyodata.exceptions import HttpError, PyODataException, ExpressionError, ProgramError from . import model LOGGER_NAME = 'pyodata.service' HTTP_CODE_OK = 200 HTTP_CODE_CREATED = 201 def urljoin(*path): """Joins the passed string parts into a one string url""" return '/'.join((part.strip('/') for part in path)) def encode_multipart(boundary, http_requests): """Encode list of requests into multipart body""" lines = [] lines.append('') for req in http_requests: lines.append(f'--{boundary}') if not isinstance(req, MultipartRequest): lines.extend(('Content-Type: application/http ', 'Content-Transfer-Encoding:binary')) lines.append('') # request line (method + path + query params) line = f'{req.get_method()} {req.get_path()}' query_params = urlencode(req.get_query_params()) if query_params: line += '?' + query_params line += ' HTTP/1.1' lines.append(line) # request specific headers for hdr, hdr_val in req.get_headers().items(): lines.append(f'{hdr}: {hdr_val}') lines.append('') body = req.get_body() if body is not None: lines.append(req.get_body()) else: # this is very important since SAP gateway rejected request witout this line. It seems # blank line must be provided as a representation of emtpy body, else we are getting # 400 Bad fromat from SAP gateway lines.append('') lines.append(f'--{boundary}--') return '\r\n'.join(lines) def decode_multipart(data, content_type): """Decode parts of the multipart mime content""" def decode(message): """Decode tree of messages for specific message""" messages = [] for i, part in enumerate(message.walk()): # pylint: disable=unused-variable if part.get_content_type() == 'multipart/mixed': for submessage in part.get_payload(): messages.append(decode(submessage)) break messages.append(part.get_payload()) return messages data = f"Content-Type: {content_type}\n" + data parser = Parser() parsed = parser.parsestr(data) decoded = decode(parsed) return decoded class ODataHttpResponse: """Representation of http response""" def __init__(self, headers, status_code, content=None): self.headers = headers self.status_code = status_code self.content = content @staticmethod def from_string(data): """Parse http response to status code, headers and body Based on: https://stackoverflow.com/questions/24728088/python-parse-http-response-string """ class FakeSocket: """Fake socket to simulate received http response content""" def __init__(self, response_str): self._file = BytesIO(response_str.encode('utf-8')) def makefile(self, *args, **kwargs): """Fake file that provides string content""" # pylint: disable=unused-argument return self._file source = FakeSocket(data) response = HTTPResponse(source) response.begin() response.length = response.fp.__sizeof__() return ODataHttpResponse( dict(response.getheaders()), response.status, response.read(len(data)) # the len here will give a 'big enough' value to read the whole content ) def json(self): """Return response as decoded json""" # TODO: see implementation in python requests, our simple # approach can bring issues with encoding # https://github.com/requests/requests/blob/master/requests/models.py#L868 if self.content: return json.loads(self.content.decode('utf-8')) return None class EntityKey: """An immutable entity-key, made up of either a single value (single) or multiple key-value pairs (complex). Every entity must have an entity-key. The entity-key must be unique within the entity-set, and thus defines an entity's identity. The string representation of an entity-key is wrapped with parentheses, such as (2), ('foo') or (a=1,foo='bar'). Entity-keys are equal if their string representations are equal. """ TYPE_SINGLE = 0 TYPE_COMPLEX = 1 def __init__(self, entity_type, single_key=None, **args): self._logger = logging.getLogger(LOGGER_NAME) self._proprties = args self._entity_type = entity_type self._key = entity_type.key_proprties # single key does not need property name if single_key is not None: # check that entity type key consists of exactly one property if len(self._key) != 1: raise PyODataException(('Key of entity type {} consists of multiple properties {} ' 'and cannot be initialized by single value').format( self._entity_type.name, ', '.join([prop.name for prop in self._key]))) # get single key property and format key string key_prop = self._key[0] args[key_prop.name] = single_key self._type = EntityKey.TYPE_SINGLE self._logger.debug(('Detected single property key, adding pair %s->%s to key' 'properties'), key_prop.name, single_key) else: for key_prop in self._key: if key_prop.name not in args: raise PyODataException(f'Missing value for key property {key_prop.name}') self._type = EntityKey.TYPE_COMPLEX @property def key_properties(self): """Key properties""" return self._key def to_key_string_without_parentheses(self): """Gets the string representation of the key without parentheses""" if self._type == EntityKey.TYPE_SINGLE: # first property is the key property key_prop = self._key[0] return key_prop.to_literal(self._proprties[key_prop.name]) key_pairs = [] for key_prop in self._key: # if key_prop.name not in self.__dict__['_cache']: # raise RuntimeError('Entity key is not complete, missing value of property: {0}'.format(key_prop.name)) key_pairs.append( f'{key_prop.name}={key_prop.to_literal(self._proprties[key_prop.name])}') return ','.join(key_pairs) def to_key_string(self): """Gets the string representation of the key, including parentheses""" return f'({self.to_key_string_without_parentheses()})' def __repr__(self): return self.to_key_string() class ODataHttpRequest: """Deferred HTTP Request""" def __init__(self, url, connection, handler, headers=None): self._connection = connection self._url = url self._handler = handler self._headers = headers or dict() self._logger = logging.getLogger(LOGGER_NAME) self._customs = {} # string -> string hash self._next_url = None @property def handler(self): """Getter for handler""" return self._handler def get_path(self): """Get path of the HTTP request""" # pylint: disable=no-self-use return '' def get_query_params(self): """Get query params""" # pylint: disable=no-self-use return dict(self._customs) def get_method(self): """Get HTTP method""" # pylint: disable=no-self-use return 'GET' def get_body(self): """Get HTTP body or None if not applicable""" # pylint: disable=no-self-use return None def get_default_headers(self): """Get dict of Child specific HTTP headers""" # pylint: disable=no-self-use return dict() def get_headers(self): """Get dict of HTTP headers which is union of return value of the method get_default_headers() and the headers added via the method add_headers() where the latter headers have priority - same keys get value of the latter. """ headers = self.get_default_headers() headers.update(self._headers) return headers def add_headers(self, value): """Add the give dictionary of HTTP headers to HTTP request sent by this ODataHttpRequest instance. """ if not isinstance(value, dict): raise TypeError(f"Headers must be of type 'dict' not {type(value)}") self._headers.update(value) def execute(self): """Fetches HTTP response and returns processed result Sends the query-request to the OData service, returning a client-side Enumerable for subsequent in-memory operations. Fetches HTTP response and returns processed result""" if self._next_url: url = self._next_url else: url = urljoin(self._url, self.get_path()) # pylint: disable=assignment-from-none body = self.get_body() headers = self.get_headers() self._logger.debug('Send (execute) %s request to %s', self.get_method(), url) self._logger.debug(' query params: %s', self.get_query_params()) self._logger.debug(' headers: %s', headers) if body: self._logger.debug(' body: %s', body) params = urlencode(self.get_query_params()) response = self._connection.request( self.get_method(), url, headers=headers, params=params, data=body) self._logger.debug('Received response') self._logger.debug(' url: %s', response.url) self._logger.debug(' headers: %s', response.headers) self._logger.debug(' status code: %d', response.status_code) try: self._logger.debug(' body: %s', response.content.decode('utf-8')) except UnicodeDecodeError: self._logger.debug(' body: <cannot be decoded>') return self._handler(response) def custom(self, name, value): """Adds a custom name-value pair.""" # returns QueryRequest self._customs[name] = value return self class EntityGetRequest(ODataHttpRequest): """Used for GET operations of a single entity""" def __init__(self, handler, entity_key, entity_set_proxy): super(EntityGetRequest, self).__init__(entity_set_proxy.service.url, entity_set_proxy.service.connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_key = entity_key self._entity_set_proxy = entity_set_proxy self._select = None self._expand = None self._logger.debug('New instance of EntityGetRequest for last segment: %s', self._entity_set_proxy.last_segment) def nav(self, nav_property): """Navigates to given navigation property and returns the EntitySetProxy""" return self._entity_set_proxy.nav(nav_property, self._entity_key) def select(self, select): """Specifies a subset of properties to return. @param select a comma-separated list of selection clauses """ self._select = select return self def expand(self, expand): """Specifies related entities to expand inline as part of the response. @param expand a comma-separated list of navigation properties """ self._expand = expand return self def get_path(self): return self._entity_set_proxy.last_segment + self._entity_key.to_key_string() def get_default_headers(self): return {'Accept': 'application/json'} def get_query_params(self): qparams = super(EntityGetRequest, self).get_query_params() if self._select is not None: qparams['$select'] = self._select if self._expand is not None: qparams['$expand'] = self._expand return qparams def get_value(self, connection=None): """Returns Value of Media EntityTypes also known as the $value URL suffix.""" if connection is None: connection = self._connection def stream_handler(response): """Returns $value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for $value failed with status code {}' .format(response.status_code), response) return response return ODataHttpRequest( urljoin(self._url, self.get_path(), '/$value'), connection, stream_handler) class NavEntityGetRequest(EntityGetRequest): """Used for GET operations of a single entity accessed via a Navigation property""" def __init__(self, handler, master_key, entity_set_proxy, nav_property): super(NavEntityGetRequest, self).__init__(handler, master_key, entity_set_proxy) self._nav_property = nav_property def get_path(self): return f"{super(NavEntityGetRequest, self).get_path()}/{self._nav_property}" class EntityCreateRequest(ODataHttpRequest): """Used for creating entities (POST operations of a single entity) Call execute() to send the create-request to the OData service and get the newly created entity.""" def __init__(self, url, connection, handler, entity_set, last_segment=None): super(EntityCreateRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_type = entity_set.entity_type if last_segment is None: self._last_segment = self._entity_set.name else: self._last_segment = last_segment self._values = {} # get all properties declared by entity type self._type_props = self._entity_type.proprties() self._logger.debug('New instance of EntityCreateRequest for entity type: %s on path %s', self._entity_type.name, self._last_segment) def get_path(self): return self._last_segment def get_method(self): # pylint: disable=no-self-use return 'POST' def _get_body(self): """Recursively builds a dictionary of values where some of the values might be another entities. """ body = {} for key, val in self._values.items(): # The value is either an entity or a scalar if isinstance(val, EntityProxy): body[key] = val._get_body() # pylint: disable=protected-access else: body[key] = val return body def get_body(self): return json.dumps(self._get_body()) def get_default_headers(self): return {'Accept': 'application/json', 'Content-Type': 'application/json', 'X-Requested-With': 'X'} @staticmethod def _build_values(entity_type, entity): """Recursively converts a dictionary of values where some of the values might be another entities (navigation properties) into the internal representation. """ if isinstance(entity, list): return [EntityCreateRequest._build_values(entity_type, item) for item in entity] values = {} for key, val in entity.items(): try: val = entity_type.proprty(key).to_json(val) except KeyError: try: nav_prop = entity_type.nav_proprty(key) val = EntityCreateRequest._build_values(nav_prop.typ, val) except KeyError: raise PyODataException('Property {} is not declared in {} entity type'.format( key, entity_type.name)) values[key] = val return values def set(self, **kwargs): """Set properties on the new entity.""" self._logger.info(kwargs) # TODO: consider use of attset for setting properties self._values = EntityCreateRequest._build_values(self._entity_type, kwargs) return self class EntityDeleteRequest(ODataHttpRequest): """Used for deleting entity (DELETE operations on a single entity)""" def __init__(self, url, connection, handler, entity_set, entity_key): super(EntityDeleteRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_key = entity_key self._logger.debug('New instance of EntityDeleteRequest for entity type: %s', entity_set.entity_type.name) def get_path(self): return self._entity_set.name + self._entity_key.to_key_string() def get_method(self): # pylint: disable=no-self-use return 'DELETE' class EntityModifyRequest(ODataHttpRequest): """Used for modyfing entities (UPDATE/MERGE operations on a single entity) Call execute() to send the update-request to the OData service and get the modified entity.""" ALLOWED_HTTP_METHODS = ['PATCH', 'PUT', 'MERGE'] def __init__(self, url, connection, handler, entity_set, entity_key, method="PATCH"): super(EntityModifyRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._entity_set = entity_set self._entity_type = entity_set.entity_type self._entity_key = entity_key self._method = method.upper() if self._method not in EntityModifyRequest.ALLOWED_HTTP_METHODS: raise ValueError('The value "{}" is not on the list of allowed Entity Update HTTP Methods: {}' .format(method, ', '.join(EntityModifyRequest.ALLOWED_HTTP_METHODS))) self._values = {} # get all properties declared by entity type self._type_props = self._entity_type.proprties() self._logger.debug('New instance of EntityModifyRequest for entity type: %s', self._entity_type.name) def get_path(self): return self._entity_set.name + self._entity_key.to_key_string() def get_method(self): # pylint: disable=no-self-use return self._method def get_body(self): # pylint: disable=no-self-use body = {} for key, val in self._values.items(): body[key] = val return json.dumps(body) def get_default_headers(self): return {'Accept': 'application/json', 'Content-Type': 'application/json'} def set(self, **kwargs): """Set properties to be changed.""" self._logger.info(kwargs) for key, val in kwargs.items(): try: val = self._entity_type.proprty(key).to_json(val) except KeyError: raise PyODataException( f'Property {key} is not declared in {self._entity_type.name} entity type') self._values[key] = val return self class QueryRequest(ODataHttpRequest): """INTERFACE A consumer-side query-request builder. Call execute() to issue the request.""" # pylint: disable=too-many-instance-attributes def __init__(self, url, connection, handler, last_segment): super(QueryRequest, self).__init__(url, connection, handler) self._logger = logging.getLogger(LOGGER_NAME) self._count = None self._inlinecount = None self._top = None self._skip = None self._order_by = None self._filter = None self._select = None self._expand = None self._last_segment = last_segment self._logger.debug('New instance of QueryRequest for last segment: %s', self._last_segment) def count(self, inline=False): """Sets a flag to return the number of items. Can be inline with results or just the count.""" if inline: self._inlinecount = True else: self._count = True return self def next_url(self, next_url): """ Sets URL which identifies the next partial set of entities from the originally identified complete set. Once set, this URL takes precedence over all query parameters. For details, see section "6. Representing Collections of Entries" on https://www.odata.org/documentation/odata-version-2-0/json-format/ """ self._next_url = next_url return self def expand(self, expand): """Sets the expand expressions.""" self._expand = expand return self def filter(self, filter_val): """Sets the filter expression.""" # returns QueryRequest self._filter = filter_val return self # def nav(self, key_value, nav_property): # """Navigates to a referenced collection using a collection-valued navigation property.""" # # returns QueryRequest # raise NotImplementedError def order_by(self, order_by): """Sets the ordering expressions.""" self._order_by = order_by return self def select(self, select): """Sets the selection clauses.""" self._select = select return self def skip(self, skip): """Sets the number of items to skip.""" self._skip = skip return self def top(self, top): """Sets the number of items to return.""" self._top = top return self def get_path(self): if self._count: return urljoin(self._last_segment, '/$count') return self._last_segment def get_default_headers(self): if self._count: return {} return { 'Accept': 'application/json', } def get_query_params(self): if self._next_url: return {} qparams = super(QueryRequest, self).get_query_params() if self._top is not None: qparams['$top'] = self._top if self._skip is not None: qparams['$skip'] = self._skip if self._order_by is not None: qparams['$orderby'] = self._order_by if self._filter is not None: qparams['$filter'] = self._filter if self._select is not None: qparams['$select'] = self._select if self._expand is not None: qparams['$expand'] = self._expand if self._inlinecount: qparams['$inlinecount'] = 'allpages' return qparams class FunctionRequest(QueryRequest): """Function import request (Service call)""" def __init__(self, url, connection, handler, function_import): super(FunctionRequest, self).__init__(url, connection, handler, function_import.name) self._function_import = function_import self._logger.debug('New instance of FunctionRequest for %s', self._function_import.name) def parameter(self, name, value): '''Sets value of parameter.''' # check if param is valid (is declared in metadata) try: param = self._function_import.get_parameter(name) # add parameter as custom query argument self.custom(param.name, param.to_literal(value)) except KeyError: raise PyODataException('Function import {0} does not have pararmeter {1}' .format(self._function_import.name, name)) return self def get_method(self): return self._function_import.http_method def get_default_headers(self): return { 'Accept': 'application/json' } # pylint: disable=too-many-instance-attributes class EntityProxy: """An immutable OData entity instance, consisting of an identity (an entity-set and a unique entity-key within that set), properties (typed, named values), and links (references to other entities). """ # pylint: disable=too-many-branches,too-many-nested-blocks,too-many-statements def __init__(self, service, entity_set, entity_type, proprties=None, entity_key=None, etag=None): self._logger = logging.getLogger(LOGGER_NAME) self._service = service self._entity_set = entity_set self._entity_type = entity_type self._key_props = entity_type.key_proprties self._cache = dict() self._entity_key = entity_key self._etag = etag self._logger.debug('New entity proxy instance of type %s from properties: %s', entity_type.name, proprties) # cache values of individual properties if provided if proprties is not None: etag_body = proprties.get('__metadata', dict()).get('etag', None) if etag is not None and etag_body is not None and etag_body != etag: raise PyODataException('Etag from header does not match the Etag from response body') if etag_body is not None: self._etag = etag_body # first, cache values of direct properties for type_proprty in self._entity_type.proprties(): if type_proprty.name in proprties: # Property value available if proprties[type_proprty.name] is not None: self._cache[type_proprty.name] = type_proprty.from_json(proprties[type_proprty.name]) continue # Property value missing and user wants a type specific default value filled in if not self._service.retain_null: # null value is in literal form for now, convert it to python representation self._cache[type_proprty.name] = type_proprty.from_literal(type_proprty.typ.null_value) continue # Property is nullable - save it as such if type_proprty.nullable: self._cache[type_proprty.name] = None continue raise PyODataException(f'Value of non-nullable Property {type_proprty.name} is null') # then, assign all navigation properties for prop in self._entity_type.nav_proprties: if prop.name in proprties: # entity type of navigation property prop_etype = prop.to_role.entity_type # cache value according to multiplicity if prop.to_role.multiplicity in \ [model.EndRole.MULTIPLICITY_ONE, model.EndRole.MULTIPLICITY_ZERO_OR_ONE]: # cache None in case we receive nothing (null) instead of entity data if proprties[prop.name] is None: self._cache[prop.name] = None else: self._cache[prop.name] = EntityProxy(service, None, prop_etype, proprties[prop.name]) elif prop.to_role.multiplicity == model.EndRole.MULTIPLICITY_ZERO_OR_MORE: # default value is empty array self._cache[prop.name] = [] # if there are no entities available, received data consists of # metadata properties only. if 'results' in proprties[prop.name]: # available entities are serialized in results array for entity in proprties[prop.name]['results']: self._cache[prop.name].append(EntityProxy(service, None, prop_etype, entity)) else: raise PyODataException('Unknown multiplicity {0} of association role {1}' .format(prop.to_role.multiplicity, prop.to_role.name)) # build entity key if not provided if self._entity_key is None: # try to build key from available property values try: # if key seems to be simple (consists of single property) if len(self._key_props) == 1: self._entity_key = EntityKey(entity_type, self._cache[self._key_props[0].name]) else: # build complex key self._entity_key = EntityKey(entity_type, **self._cache) except KeyError: pass except PyODataException: pass def __repr__(self): return self._entity_key.to_key_string() def __getattr__(self, attr): try: return self._cache[attr] except KeyError: try: value = self.get_proprty(attr).execute() self._cache[attr] = value return value except KeyError as ex: raise AttributeError('EntityType {0} does not have Property {1}: {2}' .format(self._entity_type.name, attr, str(ex))) def nav(self, nav_property): """Navigates to given navigation property and returns the EntitySetProxy""" # for now duplicated with simillar method in entity set proxy class try: navigation_property = self._entity_type.nav_proprty(nav_property) except KeyError: raise PyODataException('Navigation property {} is not declared in {} entity type'.format( nav_property, self._entity_type)) # Get entity set of navigation property association_info = navigation_property.association_info association_set = self._service.schema.association_set_by_association( association_info.name, association_info.namespace) navigation_entity_set = None for end in association_set.end_roles: if association_set.end_by_entity_set(end.entity_set_name).role == navigation_property.to_role.role: navigation_entity_set = self._service.schema.entity_set(end.entity_set_name, association_info.namespace) if not navigation_entity_set: raise PyODataException(f'No association set for role {navigation_property.to_role}') roles = navigation_property.association.end_roles if all((role.multiplicity != model.EndRole.MULTIPLICITY_ZERO_OR_MORE for role in roles)): return NavEntityProxy(self, nav_property, navigation_entity_set.entity_type, {}) return EntitySetProxy( self._service, self._service.schema.entity_set(navigation_entity_set.name), nav_property, self._entity_set.name + self._entity_key.to_key_string()) def get_path(self): """Returns this entity's relative path - e.g. EntitySet(KEY)""" return self._entity_set._name + self._entity_key.to_key_string() # pylint: disable=protected-access def get_proprty(self, name, connection=None): """Returns value of the property""" self._logger.info('Initiating property request for %s', name) def proprty_get_handler(key, proprty, response): """Gets property value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Attribute {0} of Entity {1} failed with status code {2}' .format(proprty.name, key, response.status_code), response) data = response.json()['d'] return proprty.from_json(data[proprty.name]) path = urljoin(self.get_path(), name) return self._service.http_get_odata( path, partial(proprty_get_handler, path, self._entity_type.proprty(name)), connection=connection) def get_value(self, connection=None): "Returns $value of Stream entities" def value_get_handler(key, response): """Gets property value from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for $value of Entity {0} failed with status code {1}' .format(key, response.status_code), response) return response path = urljoin(self.get_path(), '/$value') return self._service.http_get_odata(path, partial(value_get_handler, self.entity_key), connection=connection) @property def entity_set(self): """Entity set related to this entity""" return self._entity_set @property def entity_key(self): """Key of entity""" return self._entity_key @property def url(self): """URL of the real entity""" service_url = self._service.url.rstrip('/') entity_path = self.get_path() return urljoin(service_url, entity_path) @property def etag(self): """ETag generated by service""" return self._etag def equals(self, other): """Returns true if the self and the other contains the same data""" # pylint: disable=W0212 return self._cache == other._cache class NavEntityProxy(EntityProxy): """Special case of an Entity access via 1 to 1 Navigation property""" def __init__(self, parent_entity, prop_name, entity_type, entity): # pylint: disable=protected-access super(NavEntityProxy, self).__init__(parent_entity._service, parent_entity._entity_set, entity_type, entity) self._parent_entity = parent_entity self._prop_name = prop_name def get_path(self): """Returns URL of the entity""" return urljoin(self._parent_entity.get_path(), self._prop_name) class GetEntitySetFilter: """Create filters for humans""" def __init__(self, proprty): self._proprty = proprty @staticmethod def build_expression(operator, operands): """Creates a expression by joining the operands with the operator""" if len(operands) < 2: raise ExpressionError('The $filter operator \'{}\' needs at least two operands'.format(operator)) return f"({' {} '.format(operator).join(operands)})" @staticmethod def and_(*operands): """Creates logical AND expression from the operands""" return GetEntitySetFilter.build_expression('and', operands) @staticmethod def or_(*operands): """Creates logical OR expression from the operands""" return GetEntitySetFilter.build_expression('or', operands) @staticmethod def format_filter(proprty, operator, value): """Creates a filter expression """ return f'{proprty.name} {operator} {proprty.to_literal(value)}' def __eq__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'eq', value) def __ne__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'ne', value) def __lt__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'lt', value) def __le__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'le', value) def __ge__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'ge', value) def __gt__(self, value): return GetEntitySetFilter.format_filter(self._proprty, 'gt', value) class FilterExpression: """A class representing named expression of OData $filter""" def __init__(self, **kwargs): self._expressions = kwargs self._other = None self._operator = None @property def expressions(self): """Get expressions where key is property name with the operator suffix and value is the left hand side operand. """ return self._expressions.items() @property def other(self): """Get an instance of the other operand""" return self._other @property def operator(self): """The other operand""" return self._operator def __or__(self, other): if self._other is not None: raise RuntimeError('The FilterExpression already initialized') self._other = other self._operator = "or" return self def __and__(self, other): if self._other is not None: raise RuntimeError('The FilterExpression already initialized') self._other = other self._operator = "and" return self class GetEntitySetFilterChainable: """ Example expressions FirstName='Tim' FirstName__contains='Tim' Age__gt=56 Age__gte=6 Age__lt=78 Age__lte=90 Age__range=(5,9) FirstName__in=['Tim', 'Bob', 'Sam'] FirstName__startswith='Tim' FirstName__endswith='mothy' Addresses__Suburb='Chatswood' Addresses__Suburb__contains='wood' """ OPERATORS = [ 'startswith', 'endswith', 'lt', 'lte', 'gt', 'gte', 'contains', 'range', 'in', 'length', 'eq' ] def __init__(self, entity_type, filter_expressions, exprs): self._entity_type = entity_type self._filter_expressions = filter_expressions self._expressions = exprs @property def expressions(self): """Get expressions as a list of tuples where the first item is a property name with the operator suffix and the second item is a left hand side value. """ return self._expressions.items() def proprty_obj(self, name): """Returns a model property for a particular property""" return self._entity_type.proprty(name) def _decode_and_combine_filter_expression(self, filter_expression): filter_expressions = [self._decode_expression(expr, val) for expr, val in filter_expression.expressions] return self._combine_expressions(filter_expressions) def _process_query_objects(self): """Processes FilterExpression objects to OData lookups""" filter_expressions = [] for expr in self._filter_expressions: lhs_expressions = self._decode_and_combine_filter_expression(expr) if expr.other is not None: rhs_expressions = self._decode_and_combine_filter_expression(expr.other) filter_expressions.append(f'({lhs_expressions}) {expr.operator} ({rhs_expressions})') else: filter_expressions.append(lhs_expressions) return filter_expressions def _process_expressions(self): filter_expressions = [self._decode_expression(expr, val) for expr, val in self.expressions] filter_expressions.extend(self._process_query_objects()) return filter_expressions def _decode_expression(self, expr, val): field = None # field_heirarchy = [] operator = 'eq' exprs = expr.split('__') for part in exprs: if self._entity_type.has_proprty(part): field = part # field_heirarchy.append(part) elif part in self.__class__.OPERATORS: operator = part else: raise ValueError(f'"{part}" is not a valid property or operator') # field = '/'.join(field_heirarchy) # target_field = self.proprty_obj(field_heirarchy[-1]) expression = self._build_expression(field, operator, val) return expression # pylint: disable=no-self-use def _combine_expressions(self, expressions): return ' and '.join(expressions) # pylint: disable=too-many-return-statements, too-many-branches def _build_expression(self, field_name, operator, value): target_field = self.proprty_obj(field_name) if operator not in ['length', 'in', 'range']: value = target_field.to_literal(value) if operator == 'lt': return f'{field_name} lt {value}' if operator == 'lte': return f'{field_name} le {value}' if operator == 'gte': return f'{field_name} ge {value}' if operator == 'gt': return f'{field_name} gt {value}' if operator == 'startswith': return f'startswith({field_name}, {value}) eq true' if operator == 'endswith': return f'endswith({field_name}, {value}) eq true' if operator == 'length': value = int(value) return f'length({field_name}) eq {value}' if operator in ['contains']: return f'substringof({value}, {field_name}) eq true' if operator == 'range': if not isinstance(value, (tuple, list)): raise TypeError(f'Range must be tuple or list not {type(value)}') if len(value) != 2: raise ValueError('Only two items can be passed in a range.') low_bound = target_field.to_literal(value[0]) high_bound = target_field.to_literal(value[1]) return f'{field_name} gte {low_bound} and {field_name} lte {high_bound}' if operator == 'in': literal_values = (f'{field_name} eq {target_field.to_literal(item)}' for item in value) return ' or '.join(literal_values) if operator == 'eq': return f'{field_name} eq {value}' raise ValueError(f'Invalid expression {operator}') def __str__(self): expressions = self._process_expressions() result = self._combine_expressions(expressions) return result class GetEntitySetRequest(QueryRequest): """GET on EntitySet""" def __init__(self, url, connection, handler, last_segment, entity_type): super(GetEntitySetRequest, self).__init__(url, connection, handler, last_segment) self._entity_type = entity_type def __getattr__(self, name): proprty = self._entity_type.proprty(name) return GetEntitySetFilter(proprty) def _set_filter(self, filter_val): filter_text = self._filter + ' and ' if self._filter else '' filter_text += filter_val self._filter = filter_text def filter(self, *args, **kwargs): if args and len(args) == 1 and isinstance(args[0], str): self._filter = args[0] else: self._set_filter(str(GetEntitySetFilterChainable(self._entity_type, args, kwargs))) return self class ListWithTotalCount(list): """ A list with the additional property total_count and next_url. If set, use next_url to fetch the next batch of entities. """ def __init__(self, total_count, next_url): super(ListWithTotalCount, self).__init__() self._total_count = total_count self._next_url = next_url @property def next_url(self): """ URL which identifies the next partial set of entities from the originally identified complete set. None if no entities remaining. """ return self._next_url @property def total_count(self): """Count of all entities""" if self._total_count is None: raise ProgramError('The collection does not include Total Count ' 'of items because the request was made without ' 'specifying "count(inline=True)".') return self._total_count class EntitySetProxy: """EntitySet Proxy""" def __init__(self, service, entity_set, alias=None, parent_last_segment=None): """Creates new Entity Set object @param alias in case the entity set is access via assossiation @param parent_last_segment in case of association also parent key must be used """ self._service = service self._entity_set = entity_set self._alias = alias if parent_last_segment is None: self._parent_last_segment = '' else: if parent_last_segment.endswith('/'): self._parent_last_segment = parent_last_segment else: self._parent_last_segment = parent_last_segment + '/' self._name = entity_set.name self._key = entity_set.entity_type.key_proprties self._logger = logging.getLogger(LOGGER_NAME) self._logger.debug('New entity set proxy instance for %s', self._name) @property def service(self): """Return service""" return self._service @property def last_segment(self): """Return last segment of url""" entity_set_name = self._alias if self._alias is not None else self._entity_set.name return self._parent_last_segment + entity_set_name def nav(self, nav_property, key): """Navigates to given navigation property and returns the EntitySetProxy""" try: navigation_property = self._entity_set.entity_type.nav_proprty(nav_property) except KeyError: raise PyODataException('Navigation property {} is not declared in {} entity type'.format( nav_property, self._entity_set.entity_type)) # Get entity set of navigation property association_info = navigation_property.association_info association_set = self._service.schema.association_set_by_association( association_info.name) navigation_entity_set = None for end in association_set.end_roles: if association_set.end_by_entity_set(end.entity_set_name).role == navigation_property.to_role.role: navigation_entity_set = self._service.schema.entity_set(end.entity_set_name) if not navigation_entity_set: raise PyODataException( f'No association set for role {navigation_property.to_role} {association_set.end_roles}') roles = navigation_property.association.end_roles if all((role.multiplicity != model.EndRole.MULTIPLICITY_ZERO_OR_MORE for role in roles)): return self._get_nav_entity(key, nav_property, navigation_entity_set) return EntitySetProxy( self._service, navigation_entity_set, nav_property, self._entity_set.name + key.to_key_string()) def _get_nav_entity(self, master_key, nav_property, navigation_entity_set): """Get entity based on provided key of the master and Navigation property name""" def get_entity_handler(parent, nav_property, navigation_entity_set, response): """Gets entity from HTTP response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity {0} failed with status code {1}' .format(self._name, response.status_code), response) entity = response.json()['d'] return NavEntityProxy(parent, nav_property, navigation_entity_set.entity_type, entity) self._logger.info( 'Getting the nav property %s of the entity %s for the key %s', nav_property, self._entity_set.entity_type.name, master_key) parent = EntityProxy(self._service, self, self._entity_set.entity_type, entity_key=master_key) return NavEntityGetRequest( partial(get_entity_handler, parent, nav_property, navigation_entity_set), master_key, self, nav_property) def get_entity(self, key=None, **args): """Get entity based on provided key properties""" def get_entity_handler(response): """Gets entity from HTTP response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity {0} failed with status code {1}' .format(self._name, response.status_code), response) entity = response.json()['d'] etag = response.headers.get('ETag', None) return EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, entity, etag=etag) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **args) self._logger.info('Getting entity %s for key %s and args %s', self._entity_set.entity_type.name, key, args) return EntityGetRequest(get_entity_handler, entity_key, self) def get_entities(self): """Get some, potentially all entities""" def get_entities_handler(response): """Gets entity set from HTTP Response""" if response.status_code != HTTP_CODE_OK: raise HttpError('HTTP GET for Entity Set {0} failed with status code {1}' .format(self._name, response.status_code), response) content = response.json() if isinstance(content, int): return content entities = content['d'] total_count = None next_url = None if isinstance(entities, dict): if '__count' in entities: total_count = int(entities['__count']) if '__next' in entities: next_url = entities['__next'] entities = entities['results'] self._logger.info('Fetched %d entities', len(entities)) result = ListWithTotalCount(total_count, next_url) for props in entities: entity = EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, props) result.append(entity) return result entity_set_name = self._alias if self._alias is not None else self._entity_set.name return GetEntitySetRequest(self._service.url, self._service.connection, get_entities_handler, self._parent_last_segment + entity_set_name, self._entity_set.entity_type) def create_entity(self, return_code=HTTP_CODE_CREATED): """Creates a new entity in the given entity-set.""" def create_entity_handler(response): """Gets newly created entity encoded in HTTP Response""" if response.status_code != return_code: raise HttpError('HTTP POST for Entity Set {0} failed with status code {1}' .format(self._name, response.status_code), response) entity_props = response.json()['d'] etag = response.headers.get('ETag', None) return EntityProxy(self._service, self._entity_set, self._entity_set.entity_type, entity_props, etag=etag) return EntityCreateRequest(self._service.url, self._service.connection, create_entity_handler, self._entity_set, self.last_segment) def update_entity(self, key=None, method=None, **kwargs): """Updates an existing entity in the given entity-set.""" def update_entity_handler(response): """Gets modified entity encoded in HTTP Response""" if response.status_code != 204: raise HttpError('HTTP modify request for Entity Set {} failed with status code {}' .format(self._name, response.status_code), response) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **kwargs) self._logger.info('Updating entity %s for key %s and args %s', self._entity_set.entity_type.name, key, kwargs) if method is None: method = self._service.config['http']['update_method'] return EntityModifyRequest(self._service.url, self._service.connection, update_entity_handler, self._entity_set, entity_key, method=method) def delete_entity(self, key: EntityKey = None, **kwargs): """Delete the entity""" def delete_entity_handler(response): """Check if entity deletion was successful""" if response.status_code != 204: raise HttpError(f'HTTP POST for Entity delete {self._name} ' f'failed with status code {response.status_code}', response) if key is not None and isinstance(key, EntityKey): entity_key = key else: entity_key = EntityKey(self._entity_set.entity_type, key, **kwargs) return EntityDeleteRequest(self._service.url, self._service.connection, delete_entity_handler, self._entity_set, entity_key) # pylint: disable=too-few-public-methods class EntityContainer: """Set of EntitSet proxies""" def __init__(self, service): self._service = service self._entity_sets = dict() for entity_set in self._service.schema.entity_sets: self._entity_sets[entity_set.name] = EntitySetProxy(self._service, entity_set) def __getattr__(self, name): try: return self._entity_sets[name] except KeyError: raise AttributeError( f"EntitySet {name} not defined in {','.join(list(self._entity_sets.keys()))}.") class FunctionContainer: """Set of Function proxies Call a server-side functions (also known as a service operation). """ def __init__(self, service): self._service = service self._functions = dict() for fimport in self._service.schema.function_imports: self._functions[fimport.name] = fimport def __getattr__(self, name): if name not in self._functions: raise AttributeError( f"Function {name} not defined in {','.join(list(self._functions.keys()))}.") fimport = self._service.schema.function_import(name) def function_import_handler(fimport, response): """Get function call response from HTTP Response""" if 300 <= response.status_code < 400: raise HttpError(f'Function Import {fimport.name} requires Redirection which is not supported', response) if response.status_code == 401: raise HttpError(f'Not authorized to call Function Import {fimport.name}', response) if response.status_code == 403: raise HttpError(f'Missing privileges to call Function Import {fimport.name}', response) if response.status_code == 405: raise HttpError( f'Despite definition Function Import {fimport.name} does not support HTTP {fimport.http_method}', response) if 400 <= response.status_code < 500: raise HttpError( f'Function Import {fimport.name} call has failed with status code {response.status_code}', response) if response.status_code >= 500: raise HttpError(f'Server has encountered an error while processing Function Import {fimport.name}', response) if fimport.return_type is None: if response.status_code != 204: logging.getLogger(LOGGER_NAME).warning( 'The No Return Function Import %s has replied with HTTP Status Code %d instead of 204', fimport.name, response.status_code) if response.text: logging.getLogger(LOGGER_NAME).warning( 'The No Return Function Import %s has returned content:\n%s', fimport.name, response.text) return None if response.status_code != 200: logging.getLogger(LOGGER_NAME).warning( 'The Function Import %s has replied with HTTP Status Code %d instead of 200', fimport.name, response.status_code) response_data = response.json()['d'] # 1. if return types is "entity type", return instance of appropriate entity proxy if isinstance(fimport.return_type, model.EntityType): entity_set = self._service.schema.entity_set(fimport.entity_set_name) return EntityProxy(self._service, entity_set, fimport.return_type, response_data) # 2. return raw data for all other return types (primitives, complex types encoded in dicts, etc.) return response_data return FunctionRequest(self._service.url, self._service.connection, partial(function_import_handler, fimport), fimport) class Service: """OData service""" def __init__(self, url, schema, connection, config=None): self._url = url self._schema = schema self._connection = connection self._retain_null = config.retain_null if config else False self._entity_container = EntityContainer(self) self._function_container = FunctionContainer(self) self._config = {'http': {'update_method': 'PATCH'}} @property def schema(self): """Parsed metadata""" return self._schema @property def url(self): """Service url""" return self._url @property def connection(self): """Service connection""" return self._connection @property def retain_null(self): """Whether to respect null-ed values or to substitute them with type specific default values""" return self._retain_null @property def entity_sets(self): """EntitySet proxy""" return self._entity_container @property def functions(self): """Functions proxy""" return self._function_container @property def config(self): """Service specific configuration""" return self._config def http_get(self, path, connection=None): """HTTP GET response for the passed path in the service""" conn = connection if conn is None: conn = self._connection return conn.get(urljoin(self._url, path)) def http_get_odata(self, path, handler, connection=None): """HTTP GET request proxy for the passed path in the service""" conn = connection if conn is None: conn = self._connection return ODataHttpRequest( urljoin(self._url, path), conn, handler, headers={'Accept': 'application/json'}) def create_batch(self, batch_id=None): """Create instance of OData batch request""" def batch_handler(batch, parts): """Process parsed multipart request (parts)""" logging.getLogger(LOGGER_NAME).debug('Batch handler called for batch %s', batch.id) result = [] for part, req in zip(parts, batch.requests): logging.getLogger(LOGGER_NAME).debug('Batch handler is processing part %s for request %s', part, req) # if part represents multiple requests, dont' parse body and # process parts by appropriate reuqest instance if isinstance(req, MultipartRequest): result.append(req.handler(req, part)) else: # part represents single request, we have to parse # content (without checking Content type for binary/http) response = ODataHttpResponse.from_string(part[0]) result.append(req.handler(response)) return result return BatchRequest(self._url, self._connection, batch_handler, batch_id) def create_changeset(self, changeset_id=None): """Create instance of OData changeset""" def changeset_handler(changeset, parts): """Gets changeset response from HTTP response""" logging.getLogger(LOGGER_NAME).debug('Changeset handler called for changeset %s', changeset.id) result = [] # check if changeset response consists of parts, this is important # to distinguish cases when server responds with single HTTP response # for whole request if not isinstance(parts[0], list): # raise error (even for successfull status codes) since such changeset response # always means something wrong happened on server response = ODataHttpResponse.from_string(parts[0]) raise HttpError('Changeset cannot be processed due to single response received, status code: {}'.format( response.status_code), response) for part, req in zip(parts, changeset.requests): logging.getLogger(LOGGER_NAME).debug('Changeset handler is processing part %s for request %s', part, req) if isinstance(req, MultipartRequest): raise PyODataException('Changeset cannot contain nested multipart content') # part represents single request, we have to parse # content (without checking Content type for binary/http) response = ODataHttpResponse.from_string(part[0]) result.append(req.handler(response)) return result return Changeset(self._url, self._connection, changeset_handler, changeset_id) class MultipartRequest(ODataHttpRequest): """HTTP Batch request""" def __init__(self, url, connection, handler, request_id=None): super(MultipartRequest, self).__init__(url, connection, partial(MultipartRequest.http_response_handler, self)) self.requests = [] self._handler_decoded = handler # generate random id of form dddd-dddd-dddd # pylint: disable=invalid-name self.id = request_id if request_id is not None else '{}_{}_{}'.format( random.randint(1000, 9999), random.randint(1000, 9999), random.randint(1000, 9999)) self._logger.debug('New multipart %s request initialized, id=%s', self.__class__.__name__, self.id) @property def handler(self): return self._handler_decoded def get_boundary(self): """Get boundary used for request parts""" return self.id def get_default_headers(self): # pylint: disable=no-self-use return {'Content-Type': f'multipart/mixed;boundary={self.get_boundary()}'} def get_body(self): return encode_multipart(self.get_boundary(), self.requests) def add_request(self, request): """Add request to be sent in batch""" self.requests.append(request) self._logger.debug('New %s request added to multipart request %s', request.get_method(), self.id) @staticmethod def http_response_handler(request, response): """Process HTTP response to mutipart HTTP request""" if response.status_code != 202: # 202 Accepted raise HttpError('HTTP POST for multipart request {0} failed with status code {1}' .format(request.id, response.status_code), response) logging.getLogger(LOGGER_NAME).debug('Generic multipart http response request handler called') # get list of all parts (headers + body) decoded = decode_multipart(response.content.decode('utf-8'), response.headers['Content-Type']) return request.handler(request, decoded) class BatchRequest(MultipartRequest): """HTTP Batch request""" def get_boundary(self): return 'batch_' + self.id def get_path(self): # pylint: disable=no-self-use return '$batch' def get_method(self): # pylint: disable=no-self-use return 'POST' class Changeset(MultipartRequest): """Representation of changeset (unsorted group of requests)""" def get_boundary(self): return 'changeset_' + self.id

from dataclasses import dataclass, field, asdict from django.core.management.base import BaseCommand, CommandError from django.conf import settings @dataclass class ComposeFile: version: str = "3.9" services: dict[str, dict] = field(default_factory=dict) volumes: dict[str, None] = field(default_factory=dict) _use_env = False def add_service(self, name, **kwargs): if self._use_env: kwargs["env_file"] = [".env"] for volume in kwargs.get("volumes", []): volume = volume.split(":", 0) if volume[0].isalpha(): self.volumes[volume[0]] = None self.services[name] = kwargs def add_django(self, name="web", **kwargs): self._django = name defaults = { "build": ".", "image": name, # "command": "sh -c 'python manage.py migrate && gunicorn debt_admin.wsgi:application --bind 0.0.0.0:8000'", "command": "sh -c 'python manage.py migrate && python manage.py runserver 0.0.0.0:8000'", "volumes": [".:/app"], # "expose": [8000], "ports": ["8000:8000"], "depends_on": getattr(self, "_databases", []), } defaults.update(kwargs) return self.add_service(name, **defaults) def add_database(self, name, **kwargs): setattr(self, "_databases", getattr(self, "_databases", []) + [name]) if hasattr(self, "_django"): self._django["depends_on"].append(name) return self.add_service(name, **kwargs) def save(self, filename): with open(filename, "w") as f: f.write(yaml.dump(asdict(self), Dumper=yaml.dumper.Dumper)) def from_settings(self, settings): db = settings["DATABASES"]["default"] if "postgres" in db["ENGINE"]: db = { "image": "postgres", "volumes": ["postgres:/var/lib/postgresql/data"], "environment": [ f'POSTGRES_DB={db['NAME']}', f'POSTGRES_USER={db['USER']}', f'POSTGRES_PASSWORD={db['PASSWORD']}', ], } self.add_database(settings["DATABASES"]["default"]["NAME"], **db) if "redis" in settings.CACHES["default"]["BACKEND"].lower() or hasattr( settings, "RQ_QUEUES" ): self.add_database("redis", image="redis:alpine") class Command(BaseCommand): help = "Generate docker-compose files for django project" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.compose = { "version": "3.9", "services": { "db": { "image": "postgres", "volumes": ["postgres:/var/lib/postgresql/data"], "environment": [ "POSTGRES_DB=postgres", "POSTGRES_USER=postgres", "POSTGRES_PASSWORD=postgres", ], }, "redis": {"image": "redis:alpine"}, "admin": { "build": ".", "image": "web", "command": "sh -c 'python manage.py migrate && gunicorn debt_admin.wsgi:application --bind 0.0.0.0:8000'", "volumes": [".:/app"], "expose": [8000], "depends_on": ["db", "redis"], "env_file": [".env"], }, "bot": { "build": ".", "env_file": [".env"], "image": "web", "command": "python manage.py bot", "volumes": [".:/app"], "depends_on": ["db", "redis"], }, "worker": { "build": ".", "env_file": [".env"], "image": "web", "command": "python manage.py rqworker default", "volumes": [".:/app", "static_volume:/app/staticfiles"], "depends_on": ["db", "redis"], }, "nginx": { "build": "./nginx", "volumes": ["static_volume:/app/staticfiles"], "ports": ["80:80"], "depends_on": ["admin"], }, }, "volumes": {"postgres": None, "static_volume": None}, } def add_arguments(self, parser): parser.add_argument("key", help="name of the key to get", metavar="KEY") def handle(self, *args, **options): from bot import bot bot.bot.polling()

from dataclasses import dataclass, field, asdict from django.core.management.base import BaseCommand, CommandError from django.conf import settings @dataclass class ComposeFile: version: str = "3.9" services: dict[str, dict] = field(default_factory=dict) volumes: dict[str, None] = field(default_factory=dict) _use_env = False def add_service(self, name, **kwargs): if self._use_env: kwargs["env_file"] = [".env"] for volume in kwargs.get("volumes", []): volume = volume.split(":", 0) if volume[0].isalpha(): self.volumes[volume[0]] = None self.services[name] = kwargs def add_django(self, name="web", **kwargs): self._django = name defaults = { "build": ".", "image": name, # "command": "sh -c 'python manage.py migrate && gunicorn debt_admin.wsgi:application --bind 0.0.0.0:8000'", "command": "sh -c 'python manage.py migrate && python manage.py runserver 0.0.0.0:8000'", "volumes": [".:/app"], # "expose": [8000], "ports": ["8000:8000"], "depends_on": getattr(self, "_databases", []), } defaults.update(kwargs) return self.add_service(name, **defaults) def add_database(self, name, **kwargs): setattr(self, "_databases", getattr(self, "_databases", []) + [name]) if hasattr(self, "_django"): self._django["depends_on"].append(name) return self.add_service(name, **kwargs) def save(self, filename): with open(filename, "w") as f: f.write(yaml.dump(asdict(self), Dumper=yaml.dumper.Dumper)) def from_settings(self, settings): db = settings["DATABASES"]["default"] if "postgres" in db["ENGINE"]: db = { "image": "postgres", "volumes": ["postgres:/var/lib/postgresql/data"], "environment": [ f'POSTGRES_DB={db["NAME"]}', f'POSTGRES_USER={db["USER"]}', f'POSTGRES_PASSWORD={db["PASSWORD"]}', ], } self.add_database(settings["DATABASES"]["default"]["NAME"], **db) if "redis" in settings.CACHES["default"]["BACKEND"].lower() or hasattr( settings, "RQ_QUEUES" ): self.add_database("redis", image="redis:alpine") class Command(BaseCommand): help = "Generate docker-compose files for django project" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.compose = { "version": "3.9", "services": { "db": { "image": "postgres", "volumes": ["postgres:/var/lib/postgresql/data"], "environment": [ "POSTGRES_DB=postgres", "POSTGRES_USER=postgres", "POSTGRES_PASSWORD=postgres", ], }, "redis": {"image": "redis:alpine"}, "admin": { "build": ".", "image": "web", "command": "sh -c 'python manage.py migrate && gunicorn debt_admin.wsgi:application --bind 0.0.0.0:8000'", "volumes": [".:/app"], "expose": [8000], "depends_on": ["db", "redis"], "env_file": [".env"], }, "bot": { "build": ".", "env_file": [".env"], "image": "web", "command": "python manage.py bot", "volumes": [".:/app"], "depends_on": ["db", "redis"], }, "worker": { "build": ".", "env_file": [".env"], "image": "web", "command": "python manage.py rqworker default", "volumes": [".:/app", "static_volume:/app/staticfiles"], "depends_on": ["db", "redis"], }, "nginx": { "build": "./nginx", "volumes": ["static_volume:/app/staticfiles"], "ports": ["80:80"], "depends_on": ["admin"], }, }, "volumes": {"postgres": None, "static_volume": None}, } def add_arguments(self, parser): parser.add_argument("key", help="name of the key to get", metavar="KEY") def handle(self, *args, **options): from bot import bot bot.bot.polling()

import discord import os import sys import random import sqlite3 from requests import get from discord.ext.commands import Cog, command from time import sleep class Fun(Cog): def __init__(self, bot): self.bot = bot @command(aliases=['dankmeme']) async def meme(self, ctx): await ctx.send("Memed you HAHAHAHA!") @command(aliases=["8ball","ball"]) async def _8ball(self,ctx,*args): responses = ["It is certain.", "It is decidedly so.", "Without a doubt.", "Yes - definitely.", "You may rely on it.", "As I see it, yes.", "Most likely.", "Outlook good.", "Yes.", "Signs point to yes.", "Reply hazy, try again.", "Ask again later.", "Better not tell you now.", "Cannot predict now.", "Concentrate and ask again.", "Don't count on it.", "My reply is no.", "My sources say no.", "Outlook not so good.", "Very doubtful." ] await ctx.send(random.choice(responses)) @command(aliases=['jokes']) async def joke(self,ctx): data = get("https://official-joke-api.appspot.com/random_joke") rand_joke = data.json() str = rand_joke embed=discord.Embed(title="Random joke",color=random.randint(0,0xffffff)) embed.add_field(name=f"Category: {str["type"]}", value="\u200b", inline=False) embed.add_field(name=f"Joke: {str["setup"]}", value=f"{str["punchline"]}", inline=True) await ctx.send(embed=embed) @command() async def choose(self,ctx,*,choices): choices = choices.split(" ") choice = random.choice(choices).strip() embed=discord.Embed(title="Choose command", color=random.randint(0, 0xffffff)) embed.add_field(name="Choices:", value=f"`{choices}`", inline=False) embed.add_field(name="Choice:", value=f"`{choice}`", inline=True) await ctx.send(embed=embed) @command() async def twans(self,ctx,*,arg): def replaceMultiple(mainString, toBeReplaces, newString): for elem in toBeReplaces : if elem in mainString : # Replace the string mainString = mainString.replace(elem, newString) return mainString trans = replaceMultiple(arg, ['l', 'r'] , "w") await ctx.send(trans) def setup(bot): bot.add_cog(Fun(bot))

import discord import os import sys import random import sqlite3 from requests import get from discord.ext.commands import Cog, command from time import sleep class Fun(Cog): def __init__(self, bot): self.bot = bot @command(aliases=['dankmeme']) async def meme(self, ctx): await ctx.send("Memed you HAHAHAHA!") @command(aliases=["8ball","ball"]) async def _8ball(self,ctx,*args): responses = ["It is certain.", "It is decidedly so.", "Without a doubt.", "Yes - definitely.", "You may rely on it.", "As I see it, yes.", "Most likely.", "Outlook good.", "Yes.", "Signs point to yes.", "Reply hazy, try again.", "Ask again later.", "Better not tell you now.", "Cannot predict now.", "Concentrate and ask again.", "Don't count on it.", "My reply is no.", "My sources say no.", "Outlook not so good.", "Very doubtful." ] await ctx.send(random.choice(responses)) @command(aliases=['jokes']) async def joke(self,ctx): data = get("https://official-joke-api.appspot.com/random_joke") rand_joke = data.json() str = rand_joke embed=discord.Embed(title="Random joke",color=random.randint(0,0xffffff)) embed.add_field(name=f"Category: {str['type']}", value="\u200b", inline=False) embed.add_field(name=f"Joke: {str['setup']}", value=f"{str['punchline']}", inline=True) await ctx.send(embed=embed) @command() async def choose(self,ctx,*,choices): choices = choices.split(" ") choice = random.choice(choices).strip() embed=discord.Embed(title="Choose command", color=random.randint(0, 0xffffff)) embed.add_field(name="Choices:", value=f"`{choices}`", inline=False) embed.add_field(name="Choice:", value=f"`{choice}`", inline=True) await ctx.send(embed=embed) @command() async def twans(self,ctx,*,arg): def replaceMultiple(mainString, toBeReplaces, newString): for elem in toBeReplaces : if elem in mainString : # Replace the string mainString = mainString.replace(elem, newString) return mainString trans = replaceMultiple(arg, ['l', 'r'] , "w") await ctx.send(trans) def setup(bot): bot.add_cog(Fun(bot))

from fsspec import AbstractFileSystem from fsspec.callbacks import _DEFAULT_CALLBACK import io import natsort import flywheel class FlywheelFileSystem(AbstractFileSystem): cachable = True _cached = False protocol = "flywheel" async_impl = False root_marker = "/" def __init__(self, hostname, apikey, *args, **kwargs): super().__init__(*args, **kwargs) self._hostname = hostname self._client = flywheel.Client(f"{self._hostname}:{apikey.split(":")[-1]}") def _strip_hostname(self, x): x = self._strip_protocol(x) if x.startswith(self._hostname): return x[len(self._hostname):].lstrip(self.sep) return x def mkdir(self, path, create_parents=True, **kwargs): raise NotImplementedError def makedirs(self, path, exist_ok=False): raise NotImplementedError def rmdir(self, path): raise NotImplementedError def ls(self, path, detail=False, **kwargs): path = self._strip_hostname(path).rstrip(self.sep).lstrip(self.root_marker) if len(path.strip()) == 0: node = None items = [i.id for i in self._client.groups()] else: try: parent, file = path.rsplit(self.sep, 1) except ValueError: parent = path file = None if file in ("analyses", "files"): # List analyses and files if path ends on "/analyses" or "/files" node = self._client.lookup(parent) items = getattr(node, file) else: node = self._client.lookup(path) items = getattr(node, node.child_types[0]) try: items = items() except TypeError: pass # List analysis files only on ".../analyses/name/files" if node is not None and self._type(node) == "analysis" and not file == "files": items = list(filter(lambda x: not self.isfile(x), items)) try: items.sort(key=lambda x: x.timestamp) except (AttributeError, TypeError): items = natsort.natsorted(items, key=self._ls_name, alg=natsort.IGNORECASE) # Add "analyses" and "files" entries if needed, to top of list, after sorting. for field in ("analyses", "files")[::-1]: try: if len(getattr(node, field)) > 0 and not (len(getattr(node, field)) == 1 and getattr(node, field)[0] == "files") and file != field: items.insert(0, field) except AttributeError: continue paths = [self.root_marker + path + self.sep + i for i in map(self._ls_name, items)] if not detail: return paths else: items = list(map(self.info, items)) for i, n in zip(items, paths): i["name"] = n return items def _ls_name(self, x): if not isinstance(x, str) and self._type(x) == "group": return x.id for field in ("label", "name"): try: return getattr(x, field) except AttributeError: continue return x def walk(self, path, maxdepth=None, **kwargs): full_dirs = {} dirs = {} files = {} try: detail = kwargs.pop("detail") or False except KeyError: detail = False for item in self.ls(path, detail=True, **kwargs): pathname = item["name"] itemname = pathname.rstrip(self.sep).rsplit(self.sep, 1)[-1] if not self.isfile(item) and pathname != path: if itemname in ("analyses", "files"): item = {} full_dirs[pathname] = item dirs[itemname] = item elif pathname == path: files[""] = item else: files[itemname] = item if detail: yield path, dirs, files else: yield path, list(dirs), list(files) if maxdepth is not None: maxdepth -= 1 if maxdepth < 1: return for d in full_dirs: yield from self.walk(d, maxdepth=maxdepth, detail=detail, **kwargs) def info(self, path, **kwargs): out = {} if not isinstance(path, str): node = path out["name"] = [self._ls_name(node)] parent = node while hasattr(parent, "parent"): if self._type(parent) == "analysis": out["name"].insert(0, "analyses") elif self._type(parent) == "file": out["name"].insert(0, "files") parent = self._client.get(getattr(parent, "parent")["id"]) out["name"].insert(0, self._ls_name(parent)) try: parents = parent["parents"] for field in ("acquisition", "session", "subject", "project", "group"): id = parents.get(field) or None if id is not None: out["name"].insert(0, self._ls_name(self._client.get(id))) except KeyError: pass out["name"] = self.sep.join(out["name"]) out["type"] = self._type(node) else: out["name"] = self._strip_hostname(path).rstrip(self.sep) out["type"] = self._type(out["name"]) node = self._client.lookup(out["name"]) out["size"] = self.size(node) out["created"] = self.created(node) out["modified"] = self.modified(node) out["data"] = node return out def _type(self, path): if self.isfile(path): return "file" if isinstance(path, str): path = self._strip_hostname(path).rstrip(self.sep).split(self.sep) if path[-1] in ("analyses", "files"): return "directory" if len(path) > 1 and path[-2] == "analyses": return "analysis" if len(path) == 1: return "group" elif len(path) == 2: return "project" elif len(path) == 3: return "subject" elif len(path) == 4: return "session" elif len(path) == 5: return "acquisition" else: raise ValueError(f'Unknown type at path "{self.sep.join(path)}"') else: kind = str(type(path)).lower() for i in ("group", "project", "subject", "session", "acquisition", "analysis"): if i in kind: return i raise ValueError(f'Unknown type "{type(path)}".') def size(self, path): if not isinstance(path, str): return path.get("size") or None if path.rstrip(self.sep).rsplit(self.sep, 1)[-1] in ("analyses", "files"): return None return self.size(self.info(path)) def isdir(self, path): return not self.isfile(path) def isfile(self, path): if not isinstance(path, str): return "file" in str(type(path)).lower() try: return path.rstrip(self.sep).rsplit(self.sep, 2)[-2] == "files" except IndexError: return False def cat_file(self, path, start=None, end=None, **kwargs): raise NotImplementedError def pipe_file(self, path, value, **kwargs): raise NotImplementedError def get_file(self, rpath, lpath, **kwargs): _rpath, fname = rpath.rsplit(self.sep, 1) info = self.info(_rpath) while "files" not in info["data"]: _rpath = rpath.rsplit(self.sep, 1)[0] info = self.info(_rpath) info["data"].download_file(fname, lpath) def get(self, rpath, lpath, recursive=False, callback=_DEFAULT_CALLBACK, **kwargs): self.get_file(rpath, lpath, **kwargs) def put_file(self, lpath, rpath, **kwargs): raise NotImplementedError def head(self, path, size=1024): raise NotImplementedError def tail(self, path, size=1024): raise NotImplementedError def cp_file(self, path1, path2, **kwargs): raise NotImplementedError def expand_path(self, path, recursive=False, maxdepth=None): raise NotImplementedError def rm_file(self, path): raise NotImplementedError @classmethod def _parent(cls, path): path = cls._strip_protocol(path.rstrip("/")) if "/" in path: parent = path.rsplit("/", 1)[0].lstrip(cls.root_marker) return cls.root_marker + parent else: return cls.root_marker def _open( self, path, mode="rb", block_size=None, autocommit=True, cache_options=None, **kwargs, ): # return AbstractBufferedFile( # self, # path, # mode, # block_size, # autocommit, # cache_options=cache_options, # **kwargs, # ) container, file = path.split("/files/") container = self.info(container) file = io.BytesIO(container["data"].read_file(file)) return io.BufferedRandom(file) def open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs): path = self._strip_hostname(path) if "b" not in mode: mode = mode.replace("t", "") + "b" text_kwargs = { k: kwargs.pop(k) for k in ["encoding", "errors", "newline"] if k in kwargs } return io.TextIOWrapper( self.open(path, mode, block_size, **kwargs), **text_kwargs ) else: ac = kwargs.pop("autocommit", not self._intrans) f = self._open( path, mode=mode, block_size=block_size, autocommit=ac, cache_options=cache_options, **kwargs, ) if not ac and "r" not in mode: self.transaction.files.append(f) return f def touch(self, path, truncate=True, **kwargs): raise NotImplementedError def created(self, path): if not isinstance(path, str): return path.get("created") or None return self.info(path).get("created") or None def modified(self, path): if not isinstance(path, str): return path.get("modified") or None return self.info(path).get("modified") or None

from fsspec import AbstractFileSystem from fsspec.callbacks import _DEFAULT_CALLBACK import io import natsort import flywheel class FlywheelFileSystem(AbstractFileSystem): cachable = True _cached = False protocol = "flywheel" async_impl = False root_marker = "/" def __init__(self, hostname, apikey, *args, **kwargs): super().__init__(*args, **kwargs) self._hostname = hostname self._client = flywheel.Client(f"{self._hostname}:{apikey.split(':')[-1]}") def _strip_hostname(self, x): x = self._strip_protocol(x) if x.startswith(self._hostname): return x[len(self._hostname):].lstrip(self.sep) return x def mkdir(self, path, create_parents=True, **kwargs): raise NotImplementedError def makedirs(self, path, exist_ok=False): raise NotImplementedError def rmdir(self, path): raise NotImplementedError def ls(self, path, detail=False, **kwargs): path = self._strip_hostname(path).rstrip(self.sep).lstrip(self.root_marker) if len(path.strip()) == 0: node = None items = [i.id for i in self._client.groups()] else: try: parent, file = path.rsplit(self.sep, 1) except ValueError: parent = path file = None if file in ("analyses", "files"): # List analyses and files if path ends on "/analyses" or "/files" node = self._client.lookup(parent) items = getattr(node, file) else: node = self._client.lookup(path) items = getattr(node, node.child_types[0]) try: items = items() except TypeError: pass # List analysis files only on ".../analyses/name/files" if node is not None and self._type(node) == "analysis" and not file == "files": items = list(filter(lambda x: not self.isfile(x), items)) try: items.sort(key=lambda x: x.timestamp) except (AttributeError, TypeError): items = natsort.natsorted(items, key=self._ls_name, alg=natsort.IGNORECASE) # Add "analyses" and "files" entries if needed, to top of list, after sorting. for field in ("analyses", "files")[::-1]: try: if len(getattr(node, field)) > 0 and not (len(getattr(node, field)) == 1 and getattr(node, field)[0] == "files") and file != field: items.insert(0, field) except AttributeError: continue paths = [self.root_marker + path + self.sep + i for i in map(self._ls_name, items)] if not detail: return paths else: items = list(map(self.info, items)) for i, n in zip(items, paths): i["name"] = n return items def _ls_name(self, x): if not isinstance(x, str) and self._type(x) == "group": return x.id for field in ("label", "name"): try: return getattr(x, field) except AttributeError: continue return x def walk(self, path, maxdepth=None, **kwargs): full_dirs = {} dirs = {} files = {} try: detail = kwargs.pop("detail") or False except KeyError: detail = False for item in self.ls(path, detail=True, **kwargs): pathname = item["name"] itemname = pathname.rstrip(self.sep).rsplit(self.sep, 1)[-1] if not self.isfile(item) and pathname != path: if itemname in ("analyses", "files"): item = {} full_dirs[pathname] = item dirs[itemname] = item elif pathname == path: files[""] = item else: files[itemname] = item if detail: yield path, dirs, files else: yield path, list(dirs), list(files) if maxdepth is not None: maxdepth -= 1 if maxdepth < 1: return for d in full_dirs: yield from self.walk(d, maxdepth=maxdepth, detail=detail, **kwargs) def info(self, path, **kwargs): out = {} if not isinstance(path, str): node = path out["name"] = [self._ls_name(node)] parent = node while hasattr(parent, "parent"): if self._type(parent) == "analysis": out["name"].insert(0, "analyses") elif self._type(parent) == "file": out["name"].insert(0, "files") parent = self._client.get(getattr(parent, "parent")["id"]) out["name"].insert(0, self._ls_name(parent)) try: parents = parent["parents"] for field in ("acquisition", "session", "subject", "project", "group"): id = parents.get(field) or None if id is not None: out["name"].insert(0, self._ls_name(self._client.get(id))) except KeyError: pass out["name"] = self.sep.join(out["name"]) out["type"] = self._type(node) else: out["name"] = self._strip_hostname(path).rstrip(self.sep) out["type"] = self._type(out["name"]) node = self._client.lookup(out["name"]) out["size"] = self.size(node) out["created"] = self.created(node) out["modified"] = self.modified(node) out["data"] = node return out def _type(self, path): if self.isfile(path): return "file" if isinstance(path, str): path = self._strip_hostname(path).rstrip(self.sep).split(self.sep) if path[-1] in ("analyses", "files"): return "directory" if len(path) > 1 and path[-2] == "analyses": return "analysis" if len(path) == 1: return "group" elif len(path) == 2: return "project" elif len(path) == 3: return "subject" elif len(path) == 4: return "session" elif len(path) == 5: return "acquisition" else: raise ValueError(f'Unknown type at path "{self.sep.join(path)}"') else: kind = str(type(path)).lower() for i in ("group", "project", "subject", "session", "acquisition", "analysis"): if i in kind: return i raise ValueError(f'Unknown type "{type(path)}".') def size(self, path): if not isinstance(path, str): return path.get("size") or None if path.rstrip(self.sep).rsplit(self.sep, 1)[-1] in ("analyses", "files"): return None return self.size(self.info(path)) def isdir(self, path): return not self.isfile(path) def isfile(self, path): if not isinstance(path, str): return "file" in str(type(path)).lower() try: return path.rstrip(self.sep).rsplit(self.sep, 2)[-2] == "files" except IndexError: return False def cat_file(self, path, start=None, end=None, **kwargs): raise NotImplementedError def pipe_file(self, path, value, **kwargs): raise NotImplementedError def get_file(self, rpath, lpath, **kwargs): _rpath, fname = rpath.rsplit(self.sep, 1) info = self.info(_rpath) while "files" not in info["data"]: _rpath = rpath.rsplit(self.sep, 1)[0] info = self.info(_rpath) info["data"].download_file(fname, lpath) def get(self, rpath, lpath, recursive=False, callback=_DEFAULT_CALLBACK, **kwargs): self.get_file(rpath, lpath, **kwargs) def put_file(self, lpath, rpath, **kwargs): raise NotImplementedError def head(self, path, size=1024): raise NotImplementedError def tail(self, path, size=1024): raise NotImplementedError def cp_file(self, path1, path2, **kwargs): raise NotImplementedError def expand_path(self, path, recursive=False, maxdepth=None): raise NotImplementedError def rm_file(self, path): raise NotImplementedError @classmethod def _parent(cls, path): path = cls._strip_protocol(path.rstrip("/")) if "/" in path: parent = path.rsplit("/", 1)[0].lstrip(cls.root_marker) return cls.root_marker + parent else: return cls.root_marker def _open( self, path, mode="rb", block_size=None, autocommit=True, cache_options=None, **kwargs, ): # return AbstractBufferedFile( # self, # path, # mode, # block_size, # autocommit, # cache_options=cache_options, # **kwargs, # ) container, file = path.split("/files/") container = self.info(container) file = io.BytesIO(container["data"].read_file(file)) return io.BufferedRandom(file) def open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs): path = self._strip_hostname(path) if "b" not in mode: mode = mode.replace("t", "") + "b" text_kwargs = { k: kwargs.pop(k) for k in ["encoding", "errors", "newline"] if k in kwargs } return io.TextIOWrapper( self.open(path, mode, block_size, **kwargs), **text_kwargs ) else: ac = kwargs.pop("autocommit", not self._intrans) f = self._open( path, mode=mode, block_size=block_size, autocommit=ac, cache_options=cache_options, **kwargs, ) if not ac and "r" not in mode: self.transaction.files.append(f) return f def touch(self, path, truncate=True, **kwargs): raise NotImplementedError def created(self, path): if not isinstance(path, str): return path.get("created") or None return self.info(path).get("created") or None def modified(self, path): if not isinstance(path, str): return path.get("modified") or None return self.info(path).get("modified") or None

"""Alembic generated code to run database migrations.""" from logging.config import fileConfig from os import environ from alembic import context from sqlalchemy import engine_from_config, pool # this is the Alembic Config object, which provides # access to the values within the .ini file in use. config = context.config config.set_main_option("sqlalchemy.url", f"postgresql://{environ.get("OEO_DB_USER")}:{environ.get("OEO_DB_PASSWORD")}" f"@{environ.get("OEO_DB_HOST")}:{environ.get("OEO_DB_PORT")}" f"/{environ.get("OEO_DB_NAME")}") # Interpret the config file for Python logging. # This line sets up loggers basically. fileConfig(config.config_file_name) # add your model's MetaData object here # for 'autogenerate' support target_metadata = None def run_migrations_offline() -> None: """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = config.get_main_option("sqlalchemy.url") context.configure( url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}, ) with context.begin_transaction(): context.run_migrations() def run_migrations_online() -> None: """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ connectable = engine_from_config( config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

"""Alembic generated code to run database migrations.""" from logging.config import fileConfig from os import environ from alembic import context from sqlalchemy import engine_from_config, pool # this is the Alembic Config object, which provides # access to the values within the .ini file in use. config = context.config config.set_main_option("sqlalchemy.url", f"postgresql://{environ.get('OEO_DB_USER')}:{environ.get('OEO_DB_PASSWORD')}" f"@{environ.get('OEO_DB_HOST')}:{environ.get('OEO_DB_PORT')}" f"/{environ.get('OEO_DB_NAME')}") # Interpret the config file for Python logging. # This line sets up loggers basically. fileConfig(config.config_file_name) # add your model's MetaData object here # for 'autogenerate' support target_metadata = None def run_migrations_offline() -> None: """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = config.get_main_option("sqlalchemy.url") context.configure( url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={"paramstyle": "named"}, ) with context.begin_transaction(): context.run_migrations() def run_migrations_online() -> None: """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ connectable = engine_from_config( config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

from datetime import datetime, timedelta from typing import Dict from discord import Embed from discord.ext import commands from .debug import generate_debug_embed from .hyperion import ( currency_details, hyperion_base_url, hyperion_session, resolve_account_id, ) for name, id_ in [("Reoccuring Payout", "reoccuring-payout"), ("Gambling", "gamble")]: hyperion_session.post( f"{hyperion_base_url}/accounts", json={"id": id_, "display_name": name, "system_account": True}, ) class Currency(commands.Cog): """Exposes basic Hyperion functionality via Discord.""" def __init__(self, bot: commands.Bot): self.bot = bot self.payouts: Dict[str, datetime] = {} @commands.command() async def openaccount(self, ctx: commands.Context): """Open a new account.""" resp = hyperion_session.post( f"{hyperion_base_url}/accounts", json={ "id": ctx.author.id, "display_name": ctx.author.name, }, ) if resp.status_code == 409: await ctx.reply( "You already have an account, so you cannot open a new one." ) return await ctx.reply("Opened new account.") @commands.command() async def payout(self, ctx: commands.Context): """Receive a reoccuring payout.""" if ( ctx.author.id in self.payouts and datetime.now() < self.payouts[ctx.author.id] ): await ctx.reply( "You've already received your scheduled payout - check back later!" ) return transaction_create_resp = hyperion_session.post( f"{hyperion_base_url}/transactions", json={ "source_account_id": "reoccuring-payout", "dest_account_id": ctx.author.id, "amount": 10, }, ) if transaction_create_resp.status_code == 404: await ctx.reply( "You don't have an account! Use `hyp!openaccount` to create one first." ) return transaction_id = transaction_create_resp.json()["id"] exec_resp = hyperion_session.post( f"{hyperion_base_url}/transactions/{transaction_id}/execute" ) exec_resp.raise_for_status() await ctx.reply( f"You've received a payout of 10 {currency_details["plural_form"]}." ) self.payouts[ctx.author.id] = datetime.now() + timedelta(days=1) @commands.command() async def ledger(self, ctx: commands.Context): """Get a list of all transactions for this currency.""" transactions_resp = hyperion_session.get(f"{hyperion_base_url}/transactions") resp_lines = [] for transaction in transactions_resp.json()[-10:]: source_account = resolve_account_id(transaction["source_account_id"]) source_name = source_account["display_name"] or source_account["id"] dest_account = resolve_account_id(transaction["dest_account_id"]) dest_name = dest_account["display_name"] or dest_account["id"] status_message = transaction["state"].capitalize() if transaction["state_reason"] is not None: status_message += f" ({transaction["state_reason"]})" resp_lines.append( f"{source_name} -> {dest_name}: {transaction["amount"]} {currency_details["shortcode"]} - {status_message}" ) resp = "\n".join(resp_lines) await ctx.reply(f"```\n{resp}\n```")

from datetime import datetime, timedelta from typing import Dict from discord import Embed from discord.ext import commands from .debug import generate_debug_embed from .hyperion import ( currency_details, hyperion_base_url, hyperion_session, resolve_account_id, ) for name, id_ in [("Reoccuring Payout", "reoccuring-payout"), ("Gambling", "gamble")]: hyperion_session.post( f"{hyperion_base_url}/accounts", json={"id": id_, "display_name": name, "system_account": True}, ) class Currency(commands.Cog): """Exposes basic Hyperion functionality via Discord.""" def __init__(self, bot: commands.Bot): self.bot = bot self.payouts: Dict[str, datetime] = {} @commands.command() async def openaccount(self, ctx: commands.Context): """Open a new account.""" resp = hyperion_session.post( f"{hyperion_base_url}/accounts", json={ "id": ctx.author.id, "display_name": ctx.author.name, }, ) if resp.status_code == 409: await ctx.reply( "You already have an account, so you cannot open a new one." ) return await ctx.reply("Opened new account.") @commands.command() async def payout(self, ctx: commands.Context): """Receive a reoccuring payout.""" if ( ctx.author.id in self.payouts and datetime.now() < self.payouts[ctx.author.id] ): await ctx.reply( "You've already received your scheduled payout - check back later!" ) return transaction_create_resp = hyperion_session.post( f"{hyperion_base_url}/transactions", json={ "source_account_id": "reoccuring-payout", "dest_account_id": ctx.author.id, "amount": 10, }, ) if transaction_create_resp.status_code == 404: await ctx.reply( "You don't have an account! Use `hyp!openaccount` to create one first." ) return transaction_id = transaction_create_resp.json()["id"] exec_resp = hyperion_session.post( f"{hyperion_base_url}/transactions/{transaction_id}/execute" ) exec_resp.raise_for_status() await ctx.reply( f"You've received a payout of 10 {currency_details['plural_form']}." ) self.payouts[ctx.author.id] = datetime.now() + timedelta(days=1) @commands.command() async def ledger(self, ctx: commands.Context): """Get a list of all transactions for this currency.""" transactions_resp = hyperion_session.get(f"{hyperion_base_url}/transactions") resp_lines = [] for transaction in transactions_resp.json()[-10:]: source_account = resolve_account_id(transaction["source_account_id"]) source_name = source_account["display_name"] or source_account["id"] dest_account = resolve_account_id(transaction["dest_account_id"]) dest_name = dest_account["display_name"] or dest_account["id"] status_message = transaction["state"].capitalize() if transaction["state_reason"] is not None: status_message += f" ({transaction['state_reason']})" resp_lines.append( f"{source_name} -> {dest_name}: {transaction['amount']} {currency_details['shortcode']} - {status_message}" ) resp = "\n".join(resp_lines) await ctx.reply(f"```\n{resp}\n```")

from logging import error from dotenv import dotenv_values from main import father import json import discord config = dotenv_values(".env") # SECRET_KEY = os.getenv("TOKEN") SECRET_KEY = config["TOKEN"] params = None countries = None # with open("./../../params.json", "r") as read_file: with open("params.json", "r") as read_file: params = json.load(read_file) async def make_child(): details = { "os": params["OS"], "name": params["name"], "region": params["region"], "provider": params["provider"] } if params["provider"] == "DigitalOcean": if params["Package"] == 1: details["memory"] = "1" details["processor"] = "1" elif params["Package"] == 2: details["memory"] = "2" details["processor"] = "1" elif params["Package"] == 3: details["memory"] = "2" details["processor"] = "2" elif params["Package"] == 4: details["memory"] = "4" details["processor"] = "2" elif params["Package"] == 5: details["memory"] = "8" details["processor"] = "4" elif params["provider"] == "AWS": if params["Package"] == 1: details["memory"] = "1" details["processor"] = "1" if await father(details): return True else: return False print(details) class MyClient(discord.Client): help_message = """```To get started, enter ‘~create <InstanceName>’. Certain necessary questions pop up which will help set up the necessary VM. To stop the process at any stage please enter ‘~cancel’. Follow the instructions prompted by the bot to finish the set-up.```""" # 0 is normal mode # 1 is create mode # in create mode, the bot starts interrogating you mode = 0 regions_string = "\n1. USA\n2. UK\n3. IN" current_prompt = -1 OS_string = "\n1. Fedora\n2. Ubuntu 16" packages_list = { "DigitalOcean": [ "1. 1 CPU, 1 GB RAM, 25 GB SSD", "2. 1 CPU, 2GB RAM, 50GB SSD", "3. 2 CPU, 2GB RAM, 60GB SSD", "4. 2 CPU, 4GB RAM, 80GB SSD", "5. 4 CPU, 8GB RAM, 160GB SSD" ], "AWS": [ "1. 1 CPU, 1 GB RAM [nano]", ] } async def find(self, queries, string): for q in queries: if q in string: return True return False async def on_ready(self): print(f"Logged on as {self.user}!") async def send_error(self, message): await message.channel.send("Sorry couldn't get that, please try again") async def handle_provider(self, message): success = False if (await self.find(["google", "gcp", "3"], message.content.lower())): params["provider"] = "Google Cloud Platform" success = True elif (await self.find(["amazon", "web", "services", "aws", "2"], message.content.lower())): params["provider"] = "AWS" success = True elif (await self.find(["digital", "ocean", "1"], message.content.lower())): params["provider"] = "DigitalOcean" success = True if success: await message.channel.send(f"You have selected {params["provider"]} as your provider") self.current_prompt = 1 await message.channel.send("Where would your VM like to live?" + self.regions_string) return True return False async def handle_region(self, message): success = False if (await self.find(["us", "states", "unitedstates", "united states", "america", "1"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "nyc3" elif (params["provider"] == "AWS"): params["region"] = "us-west-2" success = True elif (await self.find(["uk", "kingdom", "unitedkingdom", "united kingdom", "england", "britian", "2"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "eu-west-2" elif (params["provider"] == "AWS"): params["region"] = "us-west-2" success = True elif (await self.find(["india", "in", "bharat", "3"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "blr1" elif (params["provider"] == "AWS"): params["region"] = "ap-south-1" success = True if success: await message.channel.send(f"You have selected {params["region"]} as your region") self.current_prompt = 2 await message.channel.send("What OS would you like to use" + self.OS_string) return True async def handle_os(self, message): success = False if (await self.find(["ubuntu", "2"], message.content.lower())): params["OS"] = "ubuntu-16-04-x64" success = True elif (await self.find(["fedora", "1"], message.content.lower())): params["OS"] = "fedora-34-x64" success = True if success: await message.channel.send(f"You have selected {params["OS"]} as your operating system") self.current_prompt = 3 await message.channel.send("What package would you like to use?\n" + "\n".join(self.packages_list[params['provider']])) return True return False async def handle_package(self, message): success = False try: number = int(message.content.lower()[1:]) if params["provider"] == "DigitalOcean" and 0 < number <= 5: success = True elif params["provider"] == "AWS" and number == 1: success = True elif params["provider"] == "AWS" and number != 1: await message.channel.send("We only support the micro package cause its free and we don't wanna rack up bills") return 69 else: await message.channel.send("Invalid package selected") return 69 except: await message.channel.send("Couldn't parse the package number, are you sure you entered a number (eg: ~55)") return 70 if success: params["Package"] = number if(params["provider"] == 'AWS'): await message.channel.send(f"You have selected package {self.packages_list["AWS"][number-1]}, seems like you have a lot of money") else: await message.channel.send(f"You have selected package {self.packages_list["DigitalOcean"][number-1]}, seems like you have a lot of money") self.current_prompt = 4 await message.channel.send(f"Looks like things are done! Have a cup of coffee, your VM, {params["name"]}, will be ready in about a minute!") self.mode = 0 if await make_child(): return True else: await message.channel.send("Sorry an error occured") return 60 return False async def create_mode(self, message): if message.content == "~cancel": await message.channel.send("All settings have been discarded, returning to normal mode") self.mode = 0 return if message.content.startswith("~create "): await message.channel.send("You are already in create mode") return if self.current_prompt == 0: if not await self.handle_provider(message): await self.send_error(message) return elif self.current_prompt == 1: if not await self.handle_region(message): await self.send_error(message) return elif self.current_prompt == 2: if not await self.handle_os(message): await self.send_error(message) return elif self.current_prompt == 3: if not await self.handle_package(message): await self.send_error(message) return async def on_message(self, message): if (message.author == self.user or not message.content.startswith("~")): return contents = message.content.split(" ") # split the message by spaces # the first thing iz command command = contents[0][1:] # discard the first character if self.mode == 1: await self.create_mode(message) return if (command == 'cancel'): await message.channel.send("Too late!") return if (command != 'create'): await message.channel.send(self.help_message) return if (command == 'create'): try: params["name"] = contents[1] except: params["name"] = "myVM" self.mode = 1 first_message = f"""You will now be prompted with questions to select the specs for {params['name']} Send ~cancel to stop anytime and discard the changes Remember to prefix your replies with ~ Please select one of the following providers:\n1. DigitalOcean\n2. AWS\n3. GoogleCloudPlatform""" await message.channel.send(first_message) self.current_prompt = 0 client = MyClient() client.run(SECRET_KEY)

from logging import error from dotenv import dotenv_values from main import father import json import discord config = dotenv_values(".env") # SECRET_KEY = os.getenv("TOKEN") SECRET_KEY = config["TOKEN"] params = None countries = None # with open("./../../params.json", "r") as read_file: with open("params.json", "r") as read_file: params = json.load(read_file) async def make_child(): details = { "os": params["OS"], "name": params["name"], "region": params["region"], "provider": params["provider"] } if params["provider"] == "DigitalOcean": if params["Package"] == 1: details["memory"] = "1" details["processor"] = "1" elif params["Package"] == 2: details["memory"] = "2" details["processor"] = "1" elif params["Package"] == 3: details["memory"] = "2" details["processor"] = "2" elif params["Package"] == 4: details["memory"] = "4" details["processor"] = "2" elif params["Package"] == 5: details["memory"] = "8" details["processor"] = "4" elif params["provider"] == "AWS": if params["Package"] == 1: details["memory"] = "1" details["processor"] = "1" if await father(details): return True else: return False print(details) class MyClient(discord.Client): help_message = """```To get started, enter ‘~create <InstanceName>’. Certain necessary questions pop up which will help set up the necessary VM. To stop the process at any stage please enter ‘~cancel’. Follow the instructions prompted by the bot to finish the set-up.```""" # 0 is normal mode # 1 is create mode # in create mode, the bot starts interrogating you mode = 0 regions_string = "\n1. USA\n2. UK\n3. IN" current_prompt = -1 OS_string = "\n1. Fedora\n2. Ubuntu 16" packages_list = { "DigitalOcean": [ "1. 1 CPU, 1 GB RAM, 25 GB SSD", "2. 1 CPU, 2GB RAM, 50GB SSD", "3. 2 CPU, 2GB RAM, 60GB SSD", "4. 2 CPU, 4GB RAM, 80GB SSD", "5. 4 CPU, 8GB RAM, 160GB SSD" ], "AWS": [ "1. 1 CPU, 1 GB RAM [nano]", ] } async def find(self, queries, string): for q in queries: if q in string: return True return False async def on_ready(self): print(f"Logged on as {self.user}!") async def send_error(self, message): await message.channel.send("Sorry couldn't get that, please try again") async def handle_provider(self, message): success = False if (await self.find(["google", "gcp", "3"], message.content.lower())): params["provider"] = "Google Cloud Platform" success = True elif (await self.find(["amazon", "web", "services", "aws", "2"], message.content.lower())): params["provider"] = "AWS" success = True elif (await self.find(["digital", "ocean", "1"], message.content.lower())): params["provider"] = "DigitalOcean" success = True if success: await message.channel.send(f"You have selected {params['provider']} as your provider") self.current_prompt = 1 await message.channel.send("Where would your VM like to live?" + self.regions_string) return True return False async def handle_region(self, message): success = False if (await self.find(["us", "states", "unitedstates", "united states", "america", "1"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "nyc3" elif (params["provider"] == "AWS"): params["region"] = "us-west-2" success = True elif (await self.find(["uk", "kingdom", "unitedkingdom", "united kingdom", "england", "britian", "2"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "eu-west-2" elif (params["provider"] == "AWS"): params["region"] = "us-west-2" success = True elif (await self.find(["india", "in", "bharat", "3"], message.content.lower())): if (params["provider"] == "DigitalOcean"): params["region"] = "blr1" elif (params["provider"] == "AWS"): params["region"] = "ap-south-1" success = True if success: await message.channel.send(f"You have selected {params['region']} as your region") self.current_prompt = 2 await message.channel.send("What OS would you like to use" + self.OS_string) return True async def handle_os(self, message): success = False if (await self.find(["ubuntu", "2"], message.content.lower())): params["OS"] = "ubuntu-16-04-x64" success = True elif (await self.find(["fedora", "1"], message.content.lower())): params["OS"] = "fedora-34-x64" success = True if success: await message.channel.send(f"You have selected {params['OS']} as your operating system") self.current_prompt = 3 await message.channel.send("What package would you like to use?\n" + "\n".join(self.packages_list[params['provider']])) return True return False async def handle_package(self, message): success = False try: number = int(message.content.lower()[1:]) if params["provider"] == "DigitalOcean" and 0 < number <= 5: success = True elif params["provider"] == "AWS" and number == 1: success = True elif params["provider"] == "AWS" and number != 1: await message.channel.send("We only support the micro package cause its free and we don't wanna rack up bills") return 69 else: await message.channel.send("Invalid package selected") return 69 except: await message.channel.send("Couldn't parse the package number, are you sure you entered a number (eg: ~55)") return 70 if success: params["Package"] = number if(params["provider"] == 'AWS'): await message.channel.send(f"You have selected package {self.packages_list['AWS'][number-1]}, seems like you have a lot of money") else: await message.channel.send(f"You have selected package {self.packages_list['DigitalOcean'][number-1]}, seems like you have a lot of money") self.current_prompt = 4 await message.channel.send(f"Looks like things are done! Have a cup of coffee, your VM, {params['name']}, will be ready in about a minute!") self.mode = 0 if await make_child(): return True else: await message.channel.send("Sorry an error occured") return 60 return False async def create_mode(self, message): if message.content == "~cancel": await message.channel.send("All settings have been discarded, returning to normal mode") self.mode = 0 return if message.content.startswith("~create "): await message.channel.send("You are already in create mode") return if self.current_prompt == 0: if not await self.handle_provider(message): await self.send_error(message) return elif self.current_prompt == 1: if not await self.handle_region(message): await self.send_error(message) return elif self.current_prompt == 2: if not await self.handle_os(message): await self.send_error(message) return elif self.current_prompt == 3: if not await self.handle_package(message): await self.send_error(message) return async def on_message(self, message): if (message.author == self.user or not message.content.startswith("~")): return contents = message.content.split(" ") # split the message by spaces # the first thing iz command command = contents[0][1:] # discard the first character if self.mode == 1: await self.create_mode(message) return if (command == 'cancel'): await message.channel.send("Too late!") return if (command != 'create'): await message.channel.send(self.help_message) return if (command == 'create'): try: params["name"] = contents[1] except: params["name"] = "myVM" self.mode = 1 first_message = f"""You will now be prompted with questions to select the specs for {params['name']} Send ~cancel to stop anytime and discard the changes Remember to prefix your replies with ~ Please select one of the following providers:\n1. DigitalOcean\n2. AWS\n3. GoogleCloudPlatform""" await message.channel.send(first_message) self.current_prompt = 0 client = MyClient() client.run(SECRET_KEY)

# Copyright 2021 Elshan Agaev # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Everything related to teachers""" from bot.base import db from bot.teachers_vocabulary import * cur = db.cursor() # TODO Filter for groupId. Or not... def add_teacher(req: str, user_id: int): """ Add teacher to database :param user_id: User id, who is adding :param req: Request :return: Result """ if req.lower() == "имя=предмет": return "🤬 Чел, ты... [Имя=Предмет это лишь пример]" # We create list, where 0 is Teacher, and 1 is Class try: req = req.split("=") req = (req[0], req[1], req[1].lower(), user_id) # Teacher, Class, Searchable(we search in this column), User except IndexError: return r_teacher_add_help sql = "INSERT INTO teachers (teacherName, teacherClass, teacherClassSearchable, userId) VALUES (?, ?, ?, ?)" db.cursor().execute(sql, req) db.commit() return r_teacher_add_success.format(req[0], req[1]) def delete_teacher(req: int, user_id: int): """ Delete teacher from database :param req: Request, class name :param user_id: User id :return: Result (success or fail) """ sql = db.cursor().execute("DELETE FROM teachers WHERE teacherId = ? AND userId = ?", (req, user_id)).rowcount db.commit() return r_teacher_delete_success if (sql != 0) else r_teacher_delete_fail # Not 0 means deleted def find_teacher(req: str): """ Find teacher :param req: Request :return: Search result as string """ if len(req) < 3: return r_teacher_find_symbols req_f = f"%{req.lower()}%" res = cur.execute("SELECT * FROM teachers WHERE teacherClassSearchable LIKE ? LIMIT 5", (req_f,)).fetchall() out = "" for i in res: out += f"{i["teacherId"]}. {i["teacherName"]}\n[{i["teacherClass"]}]\n" if not out: return r_teacher_find_fail.format(req) return r_teacher_find_success.format(out)

# Copyright 2021 Elshan Agaev # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Everything related to teachers""" from bot.base import db from bot.teachers_vocabulary import * cur = db.cursor() # TODO Filter for groupId. Or not... def add_teacher(req: str, user_id: int): """ Add teacher to database :param user_id: User id, who is adding :param req: Request :return: Result """ if req.lower() == "имя=предмет": return "🤬 Чел, ты... [Имя=Предмет это лишь пример]" # We create list, where 0 is Teacher, and 1 is Class try: req = req.split("=") req = (req[0], req[1], req[1].lower(), user_id) # Teacher, Class, Searchable(we search in this column), User except IndexError: return r_teacher_add_help sql = "INSERT INTO teachers (teacherName, teacherClass, teacherClassSearchable, userId) VALUES (?, ?, ?, ?)" db.cursor().execute(sql, req) db.commit() return r_teacher_add_success.format(req[0], req[1]) def delete_teacher(req: int, user_id: int): """ Delete teacher from database :param req: Request, class name :param user_id: User id :return: Result (success or fail) """ sql = db.cursor().execute("DELETE FROM teachers WHERE teacherId = ? AND userId = ?", (req, user_id)).rowcount db.commit() return r_teacher_delete_success if (sql != 0) else r_teacher_delete_fail # Not 0 means deleted def find_teacher(req: str): """ Find teacher :param req: Request :return: Search result as string """ if len(req) < 3: return r_teacher_find_symbols req_f = f"%{req.lower()}%" res = cur.execute("SELECT * FROM teachers WHERE teacherClassSearchable LIKE ? LIMIT 5", (req_f,)).fetchall() out = "" for i in res: out += f"{i['teacherId']}. {i['teacherName']}\n[{i['teacherClass']}]\n" if not out: return r_teacher_find_fail.format(req) return r_teacher_find_success.format(out)

import argparse import io import logging import sys from collections import OrderedDict from dataclasses import dataclass from pathlib import Path from typing import List, Dict, Set, Union, Optional, TextIO import pandas as pd from jinja2 import Template, Environment, FileSystemLoader from kyoto_reader import KyotoReader, Document, Argument, SpecialArgument, BaseArgument, Predicate, Mention, BasePhrase from pyknp import BList from utils.constants import CASE2YOMI from utils.util import is_pas_target, is_bridging_target, is_coreference_target logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) class Scorer: """A class to evaluate system output. To evaluate system output with this class, you have to prepare gold data and system prediction data as instances of :class:`kyoto_reader.Document` Args: documents_pred (List[Document]): システム予測文書集合 documents_gold (List[Document]): 正解文書集合 target_cases (List[str]): 評価の対象とする格 (kyoto_reader.ALL_CASES を参照) target_exophors (List[str]): 評価の対象とする外界照応の照応先 (kyoto_reader.ALL_EXOPHORS を参照) bridging (bool): 橋渡し照応の評価を行うかどうか (default: False) coreference (bool): 共参照の評価を行うかどうか (default: False) pas_target (str): 述語項構造解析において述語として扱う対象 ('pred': 用言, 'noun': 体言, 'all': 両方, '': 述語なし (default: pred)) Attributes: cases (List[str]): 評価の対象となる格 doc_ids: (List[str]): 評価の対象となる文書の文書ID集合 did2document_pred (Dict[str, Document]): 文書IDからシステム予測文書を引くための辞書 did2document_gold (Dict[str, Document]): 文書IDから正解文書を引くための辞書 bridging (bool): 橋渡し照応の評価を行うかどうか coreference (bool): 共参照の評価を行うかどうか pas_target (str): 述語項構造解析において述語として扱う対象 comp_result (Dict[tuple, str]): 正解と予測を比較した結果を格納するための辞書 sub_scorers (List[SubScorer]): 文書ごとの評価を行うオブジェクトのリスト relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ """ DEPTYPE2ANALYSIS = OrderedDict([('overt', 'overt'), ('dep', 'dep'), ('intra', 'zero_intra'), ('inter', 'zero_inter'), ('exo', 'zero_exophora')]) def __init__(self, documents_pred: List[Document], documents_gold: List[Document], target_cases: List[str], target_exophors: List[str], bridging: bool = False, coreference: bool = False, pas_target: str = 'pred'): # long document may have been ignored assert set(doc.doc_id for doc in documents_pred) <= set(doc.doc_id for doc in documents_gold) self.cases: List[str] = target_cases if pas_target != '' else [] self.doc_ids: List[str] = [doc.doc_id for doc in documents_pred] self.did2document_pred: Dict[str, Document] = {doc.doc_id: doc for doc in documents_pred} self.did2document_gold: Dict[str, Document] = {doc.doc_id: doc for doc in documents_gold} self.bridging: bool = bridging self.coreference: bool = coreference self.pas_target: str = pas_target self.comp_result: Dict[tuple, str] = {} self.sub_scorers: List[SubScorer] = [] self.relax_exophors: Dict[str, str] = {} for exophor in target_exophors: self.relax_exophors[exophor] = exophor if exophor in ('不特定:人', '不特定:物', '不特定:状況'): for n in ('１', '２', '３', '４', '５', '６', '７', '８', '９', '１０', '１１'): self.relax_exophors[exophor + n] = exophor def run(self) -> 'ScoreResult': """読み込んだ正解文書集合とシステム予測文書集合に対して評価を行う Returns: ScoreResult: 評価結果のスコア """ self.comp_result = {} self.sub_scorers = [] all_result = None for doc_id in self.doc_ids: sub_scorer = SubScorer(self.did2document_pred[doc_id], self.did2document_gold[doc_id], cases=self.cases, bridging=self.bridging, coreference=self.coreference, relax_exophors=self.relax_exophors, pas_target=self.pas_target) if all_result is None: all_result = sub_scorer.run() else: all_result += sub_scorer.run() self.sub_scorers.append(sub_scorer) self.comp_result.update({(doc_id, *key): val for key, val in sub_scorer.comp_result.items()}) return all_result def write_html(self, output_file: Union[str, Path]) -> None: """正解データとシステム予測の比較をHTML形式で書き出し Args: output_file (Union[str, Path]): 出力先ファイル """ data: List[tuple] = [] for sub_scorer in self.sub_scorers: gold_tree = '' for sid in sub_scorer.document_gold.sid2sentence.keys(): with io.StringIO() as string: self._draw_tree(sid, sub_scorer.predicates_gold, sub_scorer.mentions_gold, sub_scorer.bridgings_gold, sub_scorer.document_gold, fh=string) gold_tree += string.getvalue() pred_tree = '' for sid in sub_scorer.document_pred.sid2sentence.keys(): with io.StringIO() as string: self._draw_tree(sid, sub_scorer.predicates_pred, sub_scorer.mentions_pred, sub_scorer.bridgings_pred, sub_scorer.document_pred, fh=string) pred_tree += string.getvalue() data.append((sub_scorer.document_gold.sentences, gold_tree, pred_tree)) env = Environment(loader=FileSystemLoader(str(Path(__file__).parent))) template: Template = env.get_template('template.html') with Path(output_file).open('wt') as f: f.write(template.render({'data': data})) def _draw_tree(self, sid: str, predicates: List[BasePhrase], mentions: List[BasePhrase], anaphors: List[BasePhrase], document: Document, fh: Optional[TextIO] = None, html: bool = True ) -> None: """Write the predicate-argument structures, coreference relations, and bridging anaphora relations of the specified sentence in tree format. Args: sid (str): 出力対象の文ID predicates (List[BasePhrase]): documentに含まれる全ての述語 mentions (List[BasePhrase]): documentに含まれる全てのメンション anaphors (List[BasePhrase]): documentに含まれる全ての橋渡し照応詞 document (Document): 出力対象の文が含まれる文書 fh (Optional[TextIO]): 出力ストリーム html (bool): HTML形式で出力するかどうか """ result2color = {anal: 'blue' for anal in Scorer.DEPTYPE2ANALYSIS.values()} result2color.update({'overt': 'green', 'wrong': 'red', None: 'gray'}) result2color_coref = {'correct': 'blue', 'wrong': 'red', None: 'gray'} blist: BList = document.sid2sentence[sid].blist with io.StringIO() as string: blist.draw_tag_tree(fh=string, show_pos=False) tree_strings = string.getvalue().rstrip('\n').split('\n') assert len(tree_strings) == len(blist.tag_list()) all_targets = [m.core for m in document.mentions.values()] tid2predicate: Dict[int, BasePhrase] = {predicate.tid: predicate for predicate in predicates if predicate.sid == sid} tid2mention: Dict[int, BasePhrase] = {mention.tid: mention for mention in mentions if mention.sid == sid} tid2bridging: Dict[int, BasePhrase] = {anaphor.tid: anaphor for anaphor in anaphors if anaphor.sid == sid} for tid in range(len(tree_strings)): tree_strings[tid] += ' ' if tid in tid2predicate: predicate = tid2predicate[tid] arguments = document.get_arguments(predicate) for case in self.cases: args = arguments[case] if case == 'ガ': args += arguments['判ガ'] targets = set() for arg in args: target = str(arg) if all_targets.count(str(arg)) > 1 and isinstance(arg, Argument): target += str(arg.dtid) targets.add(target) result = self.comp_result.get((document.doc_id, predicate.dtid, case), None) if html: tree_strings[tid] += f'<font color="{result2color[result]}'>{case}:{','.join(targets)}</font> ' else: tree_strings[tid] += f'{case}:{','.join(targets)} ' if self.bridging and tid in tid2bridging: anaphor = tid2bridging[tid] arguments = document.get_arguments(anaphor) args = arguments['ノ'] + arguments['ノ？'] targets = set() for arg in args: target = str(arg) if all_targets.count(str(arg)) > 1 and isinstance(arg, Argument): target += str(arg.dtid) targets.add(target) result = self.comp_result.get((document.doc_id, anaphor.dtid, 'ノ'), None) if html: tree_strings[tid] += f'<font color="{result2color[result]}'>ノ:{','.join(targets)}</font> ' else: tree_strings[tid] += f'ノ:{','.join(targets)} ' if self.coreference and tid in tid2mention: targets = set() src_dtid = tid2mention[tid].dtid if src_dtid in document.mentions: src_mention = document.mentions[src_dtid] tgt_mentions_relaxed = SubScorer.filter_mentions( document.get_siblings(src_mention, relax=True), src_mention) for tgt_mention in tgt_mentions_relaxed: target: str = tgt_mention.core if all_targets.count(target) > 1: target += str(tgt_mention.dtid) targets.add(target) for eid in src_mention.eids: entity = document.entities[eid] if entity.exophor in self.relax_exophors: targets.add(entity.exophor) result = self.comp_result.get((document.doc_id, src_dtid, '='), None) if html: tree_strings[tid] += f'<font color="{result2color_coref[result]}'>＝:{','.join(targets)}</font>' else: tree_strings[tid] += '＝:' + ','.join(targets) print('\n'.join(tree_strings), file=fh) class SubScorer: """Scorer for single document pair. Args: document_pred (Document): システム予測文書 document_gold (Document): 正解文書 cases (List[str]): 評価の対象とする格 bridging (bool): 橋渡し照応の評価を行うかどうか (default: False) coreference (bool): 共参照の評価を行うかどうか (default: False) relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ pas_target (str): 述語項構造解析において述語として扱う対象 Attributes: doc_id (str): 対象の文書ID document_pred (Document): システム予測文書 document_gold (Document): 正解文書 cases (List[str]): 評価の対象となる格 pas (bool): 述語項構造の評価を行うかどうか bridging (bool): 橋渡し照応の評価を行うかどうか coreference (bool): 共参照の評価を行うかどうか comp_result (Dict[tuple, str]): 正解と予測を比較した結果を格納するための辞書 relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ predicates_pred: (List[BasePhrase]): システム予測文書に含まれる述語 bridgings_pred: (List[BasePhrase]): システム予測文書に含まれる橋渡し照応詞 mentions_pred: (List[BasePhrase]): システム予測文書に含まれるメンション predicates_gold: (List[BasePhrase]): 正解文書に含まれる述語 bridgings_gold: (List[BasePhrase]): 正解文書に含まれる橋渡し照応詞 mentions_gold: (List[BasePhrase]): 正解文書に含まれるメンション """ def __init__(self, document_pred: Document, document_gold: Document, cases: List[str], bridging: bool, coreference: bool, relax_exophors: Dict[str, str], pas_target: str): assert document_pred.doc_id == document_gold.doc_id self.doc_id: str = document_gold.doc_id self.document_pred: Document = document_pred self.document_gold: Document = document_gold self.cases: List[str] = cases self.pas: bool = pas_target != '' self.bridging: bool = bridging self.coreference: bool = coreference self.comp_result: Dict[tuple, str] = {} self.relax_exophors: Dict[str, str] = relax_exophors self.predicates_pred: List[BasePhrase] = [] self.bridgings_pred: List[BasePhrase] = [] self.mentions_pred: List[BasePhrase] = [] for bp in document_pred.bp_list(): if is_pas_target(bp, verbal=(pas_target in ('pred', 'all')), nominal=(pas_target in ('noun', 'all'))): self.predicates_pred.append(bp) if self.bridging and is_bridging_target(bp): self.bridgings_pred.append(bp) if self.coreference and is_coreference_target(bp): self.mentions_pred.append(bp) self.predicates_gold: List[BasePhrase] = [] self.bridgings_gold: List[BasePhrase] = [] self.mentions_gold: List[BasePhrase] = [] for bp in document_gold.bp_list(): if is_pas_target(bp, verbal=(pas_target in ('pred', 'all')), nominal=(pas_target in ('noun', 'all'))): self.predicates_gold.append(bp) if self.bridging and is_bridging_target(bp): self.bridgings_gold.append(bp) if self.coreference and is_coreference_target(bp): self.mentions_gold.append(bp) def run(self) -> 'ScoreResult': """Perform evaluation for the given gold document and system prediction document. Returns: ScoreResult: 評価結果のスコア """ self.comp_result = {} measures_pas = self._evaluate_pas() if self.pas else None measures_bridging = self._evaluate_bridging() if self.bridging else None measure_coref = self._evaluate_coref() if self.coreference else None return ScoreResult(measures_pas, measures_bridging, measure_coref) def _evaluate_pas(self) -> pd.DataFrame: """calculate predicate-argument structure analysis scores""" # measures: Dict[str, Dict[str, Measure]] = OrderedDict( # (case, OrderedDict((anal, Measure()) for anal in Scorer.DEPTYPE2ANALYSIS.values())) # for case in self.cases) measures = pd.DataFrame([[Measure() for _ in Scorer.DEPTYPE2ANALYSIS.values()] for _ in self.cases], index=self.cases, columns=Scorer.DEPTYPE2ANALYSIS.values()) dtid2predicate_pred: Dict[int, Predicate] = {pred.dtid: pred for pred in self.predicates_pred} dtid2predicate_gold: Dict[int, Predicate] = {pred.dtid: pred for pred in self.predicates_gold} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2predicate_pred: predicate_pred = dtid2predicate_pred[dtid] arguments_pred = self.document_pred.get_arguments(predicate_pred, relax=False) else: arguments_pred = None if dtid in dtid2predicate_gold: predicate_gold = dtid2predicate_gold[dtid] arguments_gold = self.document_gold.get_arguments(predicate_gold, relax=False) arguments_gold_relaxed = self.document_gold.get_arguments(predicate_gold, relax=True) else: predicate_gold = arguments_gold = arguments_gold_relaxed = None for case in self.cases: args_pred: List[BaseArgument] = arguments_pred[case] if arguments_pred is not None else [] assert len(args_pred) in (0, 1) # Our analyzer predicts one argument for one predicate if predicate_gold is not None: args_gold = self._filter_args(arguments_gold[case], predicate_gold) args_gold_relaxed = self._filter_args( arguments_gold_relaxed[case] + (arguments_gold_relaxed['判ガ'] if case == 'ガ' else []), predicate_gold) else: args_gold = args_gold_relaxed = [] key = (dtid, case) # calculate precision if args_pred: arg = args_pred[0] if arg in args_gold_relaxed: # use dep_type of gold argument if possible arg_gold = args_gold_relaxed[args_gold_relaxed.index(arg)] analysis = Scorer.DEPTYPE2ANALYSIS[arg_gold.dep_type] self.comp_result[key] = analysis measures.at[case, analysis].correct += 1 else: # system出力のdep_typeはgoldのものと違うので不整合が起きるかもしれない analysis = Scorer.DEPTYPE2ANALYSIS[arg.dep_type] self.comp_result[key] = 'wrong' # precision が下がる measures.at[case, analysis].denom_pred += 1 # calculate recall # 正解が複数ある場合、そのうち一つが当てられていればそれを正解に採用 # いずれも当てられていなければ、relax されていない項から一つを選び正解に採用 if args_gold or (self.comp_result.get(key, None) in Scorer.DEPTYPE2ANALYSIS.values()): arg_gold = None for arg in args_gold_relaxed: if arg in args_pred: arg_gold = arg # 予測されている項を優先して正解の項に採用 break if arg_gold is not None: analysis = Scorer.DEPTYPE2ANALYSIS[arg_gold.dep_type] assert self.comp_result[key] == analysis else: analysis = Scorer.DEPTYPE2ANALYSIS[args_gold[0].dep_type] if args_pred: assert self.comp_result[key] == 'wrong' else: self.comp_result[key] = 'wrong' # recall が下がる measures.at[case, analysis].denom_gold += 1 return measures def _filter_args(self, args: List[BaseArgument], predicate: Predicate, ) -> List[BaseArgument]: filtered_args = [] for arg in args: if isinstance(arg, SpecialArgument): if arg.exophor not in self.relax_exophors: # filter out non-target exophors continue arg.exophor = self.relax_exophors[arg.exophor] # 「不特定:人１」なども「不特定:人」として扱う else: assert isinstance(arg, Argument) # filter out self-anaphora and cataphoras if predicate.dtid == arg.dtid or (predicate.dtid < arg.dtid and arg.sid != predicate.sid): continue filtered_args.append(arg) return filtered_args def _evaluate_bridging(self) -> pd.Series: """calculate bridging anaphora resolution scores""" measures: Dict[str, Measure] = OrderedDict((anal, Measure()) for anal in Scorer.DEPTYPE2ANALYSIS.values()) dtid2anaphor_pred: Dict[int, Predicate] = {pred.dtid: pred for pred in self.bridgings_pred} dtid2anaphor_gold: Dict[int, Predicate] = {pred.dtid: pred for pred in self.bridgings_gold} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2anaphor_pred: anaphor_pred = dtid2anaphor_pred[dtid] antecedents_pred: List[BaseArgument] = \ self._filter_args(self.document_pred.get_arguments(anaphor_pred, relax=False)['ノ'], anaphor_pred) else: antecedents_pred = [] assert len(antecedents_pred) in (0, 1) # in bert_pas_analysis, predict one argument for one predicate if dtid in dtid2anaphor_gold: anaphor_gold: Predicate = dtid2anaphor_gold[dtid] antecedents_gold: List[BaseArgument] = \ self._filter_args(self.document_gold.get_arguments(anaphor_gold, relax=False)['ノ'], anaphor_gold) arguments: Dict[str, List[BaseArgument]] = self.document_gold.get_arguments(anaphor_gold, relax=True) antecedents_gold_relaxed: List[BaseArgument] = \ self._filter_args(arguments['ノ'] + arguments['ノ？'], anaphor_gold) else: antecedents_gold = antecedents_gold_relaxed = [] key = (dtid, 'ノ') # calculate precision if antecedents_pred: antecedent_pred = antecedents_pred[0] if antecedent_pred in antecedents_gold_relaxed: # use dep_type of gold antecedent if possible antecedent_gold = antecedents_gold_relaxed[antecedents_gold_relaxed.index(antecedent_pred)] analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_gold.dep_type] if analysis == 'overt': analysis = 'dep' self.comp_result[key] = analysis measures[analysis].correct += 1 else: analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_pred.dep_type] if analysis == 'overt': analysis = 'dep' self.comp_result[key] = 'wrong' measures[analysis].denom_pred += 1 # calculate recall if antecedents_gold or (self.comp_result.get(key, None) in Scorer.DEPTYPE2ANALYSIS.values()): antecedent_gold = None for ant in antecedents_gold_relaxed: if ant in antecedents_pred: antecedent_gold = ant # 予測されている先行詞を優先して正解の先行詞に採用 break if antecedent_gold is not None: analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_gold.dep_type] if analysis == 'overt': analysis = 'dep' assert self.comp_result[key] == analysis else: analysis = Scorer.DEPTYPE2ANALYSIS[antecedents_gold[0].dep_type] if analysis == 'overt': analysis = 'dep' if antecedents_pred: assert self.comp_result[key] == 'wrong' else: self.comp_result[key] = 'wrong' measures[analysis].denom_gold += 1 return pd.Series(measures) def _evaluate_coref(self) -> pd.Series: """calculate coreference resolution scores""" measure = Measure() dtid2mention_pred: Dict[int, Mention] = {bp.dtid: self.document_pred.mentions[bp.dtid] for bp in self.mentions_pred if bp.dtid in self.document_pred.mentions} dtid2mention_gold: Dict[int, Mention] = {bp.dtid: self.document_gold.mentions[bp.dtid] for bp in self.mentions_gold if bp.dtid in self.document_gold.mentions} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2mention_pred: src_mention_pred = dtid2mention_pred[dtid] tgt_mentions_pred = \ self.filter_mentions(self.document_pred.get_siblings(src_mention_pred), src_mention_pred) exophors_pred = {e.exophor for e in map(self.document_pred.entities.get, src_mention_pred.eids) if e.is_special} else: tgt_mentions_pred = exophors_pred = set() if dtid in dtid2mention_gold: src_mention_gold = dtid2mention_gold[dtid] tgt_mentions_gold = self.filter_mentions(self.document_gold.get_siblings(src_mention_gold, relax=False), src_mention_gold) tgt_mentions_gold_relaxed = self.filter_mentions( self.document_gold.get_siblings(src_mention_gold, relax=True), src_mention_gold) exophors_gold = {self.relax_exophors[e.exophor] for e in map(self.document_gold.entities.get, src_mention_gold.eids) if e.is_special and e.exophor in self.relax_exophors} exophors_gold_relaxed = {self.relax_exophors[e.exophor] for e in map(self.document_gold.entities.get, src_mention_gold.all_eids) if e.is_special and e.exophor in self.relax_exophors} else: tgt_mentions_gold = tgt_mentions_gold_relaxed = exophors_gold = exophors_gold_relaxed = set() key = (dtid, '=') # calculate precision if tgt_mentions_pred or exophors_pred: if (tgt_mentions_pred & tgt_mentions_gold_relaxed) or (exophors_pred & exophors_gold_relaxed): self.comp_result[key] = 'correct' measure.correct += 1 else: self.comp_result[key] = 'wrong' measure.denom_pred += 1 # calculate recall if tgt_mentions_gold or exophors_gold or (self.comp_result.get(key, None) == 'correct'): if (tgt_mentions_pred & tgt_mentions_gold_relaxed) or (exophors_pred & exophors_gold_relaxed): assert self.comp_result[key] == 'correct' else: self.comp_result[key] = 'wrong' measure.denom_gold += 1 return pd.Series([measure], index=['all']) @staticmethod def filter_mentions(tgt_mentions: Set[Mention], src_mention: Mention) -> Set[Mention]: """filter out cataphors""" return {tgt_mention for tgt_mention in tgt_mentions if tgt_mention.dtid < src_mention.dtid} @dataclass(frozen=True) class ScoreResult: """A data class for storing the numerical result of an evaluation""" measures_pas: Optional[pd.DataFrame] measures_bridging: Optional[pd.Series] measure_coref: Optional[pd.Series] def to_dict(self) -> Dict[str, Dict[str, 'Measure']]: """convert data to dictionary""" df_all = pd.DataFrame(index=['all_case']) if self.pas: df_pas: pd.DataFrame = self.measures_pas.copy() df_pas['zero'] = df_pas['zero_intra'] + df_pas['zero_inter'] + df_pas['zero_exophora'] df_pas['dep_zero'] = df_pas['zero'] + df_pas['dep'] df_pas['all'] = df_pas['dep_zero'] + df_pas['overt'] df_all = pd.concat([df_pas, df_all]) df_all.loc['all_case'] = df_pas.sum(axis=0) if self.bridging: df_bar = self.measures_bridging.copy() df_bar['zero'] = df_bar['zero_intra'] + df_bar['zero_inter'] + df_bar['zero_exophora'] df_bar['dep_zero'] = df_bar['zero'] + df_bar['dep'] assert df_bar['overt'] == Measure() # No overt in BAR df_bar['all'] = df_bar['dep_zero'] df_all.at['all_case', 'bridging'] = df_bar['all'] if self.coreference: df_all.at['all_case', 'coreference'] = self.measure_coref['all'] return {k1: {k2: v2 for k2, v2 in v1.items() if pd.notnull(v2)} for k1, v1 in df_all.to_dict(orient='index').items()} def export_txt(self, destination: Union[str, Path, TextIO] ) -> None: """Export the evaluation results in a text format. Args: destination (Union[str, Path, TextIO]): 書き出す先 """ lines = [] for key, ms in self.to_dict().items(): lines.append(f'{key}格' if self.pas and key in self.measures_pas.index else key) for analysis, measure in ms.items(): lines.append(f' {analysis}') lines.append(f' precision: {measure.precision:.4f} ({measure.correct}/{measure.denom_pred})') lines.append(f' recall : {measure.recall:.4f} ({measure.correct}/{measure.denom_gold})') lines.append(f' F : {measure.f1:.4f}') text = '\n'.join(lines) + '\n' if isinstance(destination, str) or isinstance(destination, Path): with Path(destination).open('wt') as writer: writer.write(text) elif isinstance(destination, io.TextIOBase): destination.write(text) def export_csv(self, destination: Union[str, Path, TextIO], sep: str = ',' ) -> None: """Export the evaluation results in a csv format. Args: destination (Union[str, Path, TextIO]): 書き出す先 sep (str): 区切り文字 (default: ',') """ text = '' result_dict = self.to_dict() text += 'case' + sep text += sep.join(result_dict['all_case'].keys()) + '\n' for case, measures in result_dict.items(): text += CASE2YOMI.get(case, case) + sep text += sep.join(f'{measure.f1:.6}' for measure in measures.values()) text += '\n' if isinstance(destination, str) or isinstance(destination, Path): with Path(destination).open('wt') as writer: writer.write(text) elif isinstance(destination, io.TextIOBase): destination.write(text) @property def pas(self): """Whether self includes the score of predicate-argument structure analysis.""" return self.measures_pas is not None @property def bridging(self): """Whether self includes the score of bridging anaphora resolution.""" return self.measures_bridging is not None @property def coreference(self): """Whether self includes the score of coreference resolution.""" return self.measure_coref is not None def __add__(self, other: 'ScoreResult') -> 'ScoreResult': measures_pas = self.measures_pas + other.measures_pas if self.pas else None measures_bridging = self.measures_bridging + other.measures_bridging if self.bridging else None measure_coref = self.measure_coref + other.measure_coref if self.coreference else None return ScoreResult(measures_pas, measures_bridging, measure_coref) @dataclass class Measure: """A data class to calculate and represent F-measure""" denom_pred: int = 0 denom_gold: int = 0 correct: int = 0 def __add__(self, other: 'Measure'): return Measure(self.denom_pred + other.denom_pred, self.denom_gold + other.denom_gold, self.correct + other.correct) def __eq__(self, other: 'Measure'): return self.denom_pred == other.denom_pred and \ self.denom_gold == other.denom_gold and \ self.correct == other.correct @property def precision(self) -> float: if self.denom_pred == 0: return .0 return self.correct / self.denom_pred @property def recall(self) -> float: if self.denom_gold == 0: return .0 return self.correct / self.denom_gold @property def f1(self) -> float: if self.denom_pred + self.denom_gold == 0: return .0 return 2 * self.correct / (self.denom_pred + self.denom_gold) def main(): parser = argparse.ArgumentParser() parser.add_argument('--prediction-dir', default=None, type=str, help='path to directory where system output KWDLC files exist (default: None)') parser.add_argument('--gold-dir', default=None, type=str, help='path to directory where gold KWDLC files exist (default: None)') parser.add_argument('--coreference', '--coref', '--cr', action='store_true', default=False, help='perform coreference resolution') parser.add_argument('--bridging', '--brg', '--bar', action='store_true', default=False, help='perform bridging anaphora resolution') parser.add_argument('--case-string', type=str, default='ガ,ヲ,ニ,ガ２', help='case strings separated by ","') parser.add_argument('--exophors', '--exo', type=str, default='著者,読者,不特定:人,不特定:物', help='exophor strings separated by ","') parser.add_argument('--read-prediction-from-pas-tag', action='store_true', default=False, help='use <述語項構造:> tag instead of <rel > tag in prediction files') parser.add_argument('--pas-target', choices=['', 'pred', 'noun', 'all'], default='pred', help='PAS analysis evaluation target (pred: verbal predicates, noun: nominal predicates)') parser.add_argument('--result-html', default=None, type=str, help='path to html file which prediction result is exported (default: None)') parser.add_argument('--result-csv', default=None, type=str, help='path to csv file which prediction result is exported (default: None)') args = parser.parse_args() reader_gold = KyotoReader(Path(args.gold_dir), extract_nes=False, use_pas_tag=False) reader_pred = KyotoReader( Path(args.prediction_dir), extract_nes=False, use_pas_tag=args.read_prediction_from_pas_tag, ) documents_pred = reader_pred.process_all_documents() documents_gold = reader_gold.process_all_documents() assert set(args.case_string.split(',')) <= set(CASE2YOMI.keys()) msg = '"ノ" found in case string. If you want to perform bridging anaphora resolution, specify "--bridging" ' \ 'option instead' assert 'ノ' not in args.case_string.split(','), msg scorer = Scorer(documents_pred, documents_gold, target_cases=args.case_string.split(','), target_exophors=args.exophors.split(','), coreference=args.coreference, bridging=args.bridging, pas_target=args.pas_target) result = scorer.run() if args.result_html: scorer.write_html(Path(args.result_html)) if args.result_csv: result.export_csv(args.result_csv) result.export_txt(sys.stdout) if __name__ == '__main__': main()

import argparse import io import logging import sys from collections import OrderedDict from dataclasses import dataclass from pathlib import Path from typing import List, Dict, Set, Union, Optional, TextIO import pandas as pd from jinja2 import Template, Environment, FileSystemLoader from kyoto_reader import KyotoReader, Document, Argument, SpecialArgument, BaseArgument, Predicate, Mention, BasePhrase from pyknp import BList from utils.constants import CASE2YOMI from utils.util import is_pas_target, is_bridging_target, is_coreference_target logger = logging.getLogger(__name__) logger.setLevel(logging.WARNING) class Scorer: """A class to evaluate system output. To evaluate system output with this class, you have to prepare gold data and system prediction data as instances of :class:`kyoto_reader.Document` Args: documents_pred (List[Document]): システム予測文書集合 documents_gold (List[Document]): 正解文書集合 target_cases (List[str]): 評価の対象とする格 (kyoto_reader.ALL_CASES を参照) target_exophors (List[str]): 評価の対象とする外界照応の照応先 (kyoto_reader.ALL_EXOPHORS を参照) bridging (bool): 橋渡し照応の評価を行うかどうか (default: False) coreference (bool): 共参照の評価を行うかどうか (default: False) pas_target (str): 述語項構造解析において述語として扱う対象 ('pred': 用言, 'noun': 体言, 'all': 両方, '': 述語なし (default: pred)) Attributes: cases (List[str]): 評価の対象となる格 doc_ids: (List[str]): 評価の対象となる文書の文書ID集合 did2document_pred (Dict[str, Document]): 文書IDからシステム予測文書を引くための辞書 did2document_gold (Dict[str, Document]): 文書IDから正解文書を引くための辞書 bridging (bool): 橋渡し照応の評価を行うかどうか coreference (bool): 共参照の評価を行うかどうか pas_target (str): 述語項構造解析において述語として扱う対象 comp_result (Dict[tuple, str]): 正解と予測を比較した結果を格納するための辞書 sub_scorers (List[SubScorer]): 文書ごとの評価を行うオブジェクトのリスト relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ """ DEPTYPE2ANALYSIS = OrderedDict([('overt', 'overt'), ('dep', 'dep'), ('intra', 'zero_intra'), ('inter', 'zero_inter'), ('exo', 'zero_exophora')]) def __init__(self, documents_pred: List[Document], documents_gold: List[Document], target_cases: List[str], target_exophors: List[str], bridging: bool = False, coreference: bool = False, pas_target: str = 'pred'): # long document may have been ignored assert set(doc.doc_id for doc in documents_pred) <= set(doc.doc_id for doc in documents_gold) self.cases: List[str] = target_cases if pas_target != '' else [] self.doc_ids: List[str] = [doc.doc_id for doc in documents_pred] self.did2document_pred: Dict[str, Document] = {doc.doc_id: doc for doc in documents_pred} self.did2document_gold: Dict[str, Document] = {doc.doc_id: doc for doc in documents_gold} self.bridging: bool = bridging self.coreference: bool = coreference self.pas_target: str = pas_target self.comp_result: Dict[tuple, str] = {} self.sub_scorers: List[SubScorer] = [] self.relax_exophors: Dict[str, str] = {} for exophor in target_exophors: self.relax_exophors[exophor] = exophor if exophor in ('不特定:人', '不特定:物', '不特定:状況'): for n in ('１', '２', '３', '４', '５', '６', '７', '８', '９', '１０', '１１'): self.relax_exophors[exophor + n] = exophor def run(self) -> 'ScoreResult': """読み込んだ正解文書集合とシステム予測文書集合に対して評価を行う Returns: ScoreResult: 評価結果のスコア """ self.comp_result = {} self.sub_scorers = [] all_result = None for doc_id in self.doc_ids: sub_scorer = SubScorer(self.did2document_pred[doc_id], self.did2document_gold[doc_id], cases=self.cases, bridging=self.bridging, coreference=self.coreference, relax_exophors=self.relax_exophors, pas_target=self.pas_target) if all_result is None: all_result = sub_scorer.run() else: all_result += sub_scorer.run() self.sub_scorers.append(sub_scorer) self.comp_result.update({(doc_id, *key): val for key, val in sub_scorer.comp_result.items()}) return all_result def write_html(self, output_file: Union[str, Path]) -> None: """正解データとシステム予測の比較をHTML形式で書き出し Args: output_file (Union[str, Path]): 出力先ファイル """ data: List[tuple] = [] for sub_scorer in self.sub_scorers: gold_tree = '' for sid in sub_scorer.document_gold.sid2sentence.keys(): with io.StringIO() as string: self._draw_tree(sid, sub_scorer.predicates_gold, sub_scorer.mentions_gold, sub_scorer.bridgings_gold, sub_scorer.document_gold, fh=string) gold_tree += string.getvalue() pred_tree = '' for sid in sub_scorer.document_pred.sid2sentence.keys(): with io.StringIO() as string: self._draw_tree(sid, sub_scorer.predicates_pred, sub_scorer.mentions_pred, sub_scorer.bridgings_pred, sub_scorer.document_pred, fh=string) pred_tree += string.getvalue() data.append((sub_scorer.document_gold.sentences, gold_tree, pred_tree)) env = Environment(loader=FileSystemLoader(str(Path(__file__).parent))) template: Template = env.get_template('template.html') with Path(output_file).open('wt') as f: f.write(template.render({'data': data})) def _draw_tree(self, sid: str, predicates: List[BasePhrase], mentions: List[BasePhrase], anaphors: List[BasePhrase], document: Document, fh: Optional[TextIO] = None, html: bool = True ) -> None: """Write the predicate-argument structures, coreference relations, and bridging anaphora relations of the specified sentence in tree format. Args: sid (str): 出力対象の文ID predicates (List[BasePhrase]): documentに含まれる全ての述語 mentions (List[BasePhrase]): documentに含まれる全てのメンション anaphors (List[BasePhrase]): documentに含まれる全ての橋渡し照応詞 document (Document): 出力対象の文が含まれる文書 fh (Optional[TextIO]): 出力ストリーム html (bool): HTML形式で出力するかどうか """ result2color = {anal: 'blue' for anal in Scorer.DEPTYPE2ANALYSIS.values()} result2color.update({'overt': 'green', 'wrong': 'red', None: 'gray'}) result2color_coref = {'correct': 'blue', 'wrong': 'red', None: 'gray'} blist: BList = document.sid2sentence[sid].blist with io.StringIO() as string: blist.draw_tag_tree(fh=string, show_pos=False) tree_strings = string.getvalue().rstrip('\n').split('\n') assert len(tree_strings) == len(blist.tag_list()) all_targets = [m.core for m in document.mentions.values()] tid2predicate: Dict[int, BasePhrase] = {predicate.tid: predicate for predicate in predicates if predicate.sid == sid} tid2mention: Dict[int, BasePhrase] = {mention.tid: mention for mention in mentions if mention.sid == sid} tid2bridging: Dict[int, BasePhrase] = {anaphor.tid: anaphor for anaphor in anaphors if anaphor.sid == sid} for tid in range(len(tree_strings)): tree_strings[tid] += ' ' if tid in tid2predicate: predicate = tid2predicate[tid] arguments = document.get_arguments(predicate) for case in self.cases: args = arguments[case] if case == 'ガ': args += arguments['判ガ'] targets = set() for arg in args: target = str(arg) if all_targets.count(str(arg)) > 1 and isinstance(arg, Argument): target += str(arg.dtid) targets.add(target) result = self.comp_result.get((document.doc_id, predicate.dtid, case), None) if html: tree_strings[tid] += f'<font color="{result2color[result]}">{case}:{",".join(targets)}</font> ' else: tree_strings[tid] += f'{case}:{",".join(targets)} ' if self.bridging and tid in tid2bridging: anaphor = tid2bridging[tid] arguments = document.get_arguments(anaphor) args = arguments['ノ'] + arguments['ノ？'] targets = set() for arg in args: target = str(arg) if all_targets.count(str(arg)) > 1 and isinstance(arg, Argument): target += str(arg.dtid) targets.add(target) result = self.comp_result.get((document.doc_id, anaphor.dtid, 'ノ'), None) if html: tree_strings[tid] += f'<font color="{result2color[result]}">ノ:{",".join(targets)}</font> ' else: tree_strings[tid] += f'ノ:{",".join(targets)} ' if self.coreference and tid in tid2mention: targets = set() src_dtid = tid2mention[tid].dtid if src_dtid in document.mentions: src_mention = document.mentions[src_dtid] tgt_mentions_relaxed = SubScorer.filter_mentions( document.get_siblings(src_mention, relax=True), src_mention) for tgt_mention in tgt_mentions_relaxed: target: str = tgt_mention.core if all_targets.count(target) > 1: target += str(tgt_mention.dtid) targets.add(target) for eid in src_mention.eids: entity = document.entities[eid] if entity.exophor in self.relax_exophors: targets.add(entity.exophor) result = self.comp_result.get((document.doc_id, src_dtid, '='), None) if html: tree_strings[tid] += f'<font color="{result2color_coref[result]}">＝:{",".join(targets)}</font>' else: tree_strings[tid] += '＝:' + ','.join(targets) print('\n'.join(tree_strings), file=fh) class SubScorer: """Scorer for single document pair. Args: document_pred (Document): システム予測文書 document_gold (Document): 正解文書 cases (List[str]): 評価の対象とする格 bridging (bool): 橋渡し照応の評価を行うかどうか (default: False) coreference (bool): 共参照の評価を行うかどうか (default: False) relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ pas_target (str): 述語項構造解析において述語として扱う対象 Attributes: doc_id (str): 対象の文書ID document_pred (Document): システム予測文書 document_gold (Document): 正解文書 cases (List[str]): 評価の対象となる格 pas (bool): 述語項構造の評価を行うかどうか bridging (bool): 橋渡し照応の評価を行うかどうか coreference (bool): 共参照の評価を行うかどうか comp_result (Dict[tuple, str]): 正解と予測を比較した結果を格納するための辞書 relax_exophors (Dict[str, str]): 「不特定:人１」などを「不特定:人」として評価するためのマップ predicates_pred: (List[BasePhrase]): システム予測文書に含まれる述語 bridgings_pred: (List[BasePhrase]): システム予測文書に含まれる橋渡し照応詞 mentions_pred: (List[BasePhrase]): システム予測文書に含まれるメンション predicates_gold: (List[BasePhrase]): 正解文書に含まれる述語 bridgings_gold: (List[BasePhrase]): 正解文書に含まれる橋渡し照応詞 mentions_gold: (List[BasePhrase]): 正解文書に含まれるメンション """ def __init__(self, document_pred: Document, document_gold: Document, cases: List[str], bridging: bool, coreference: bool, relax_exophors: Dict[str, str], pas_target: str): assert document_pred.doc_id == document_gold.doc_id self.doc_id: str = document_gold.doc_id self.document_pred: Document = document_pred self.document_gold: Document = document_gold self.cases: List[str] = cases self.pas: bool = pas_target != '' self.bridging: bool = bridging self.coreference: bool = coreference self.comp_result: Dict[tuple, str] = {} self.relax_exophors: Dict[str, str] = relax_exophors self.predicates_pred: List[BasePhrase] = [] self.bridgings_pred: List[BasePhrase] = [] self.mentions_pred: List[BasePhrase] = [] for bp in document_pred.bp_list(): if is_pas_target(bp, verbal=(pas_target in ('pred', 'all')), nominal=(pas_target in ('noun', 'all'))): self.predicates_pred.append(bp) if self.bridging and is_bridging_target(bp): self.bridgings_pred.append(bp) if self.coreference and is_coreference_target(bp): self.mentions_pred.append(bp) self.predicates_gold: List[BasePhrase] = [] self.bridgings_gold: List[BasePhrase] = [] self.mentions_gold: List[BasePhrase] = [] for bp in document_gold.bp_list(): if is_pas_target(bp, verbal=(pas_target in ('pred', 'all')), nominal=(pas_target in ('noun', 'all'))): self.predicates_gold.append(bp) if self.bridging and is_bridging_target(bp): self.bridgings_gold.append(bp) if self.coreference and is_coreference_target(bp): self.mentions_gold.append(bp) def run(self) -> 'ScoreResult': """Perform evaluation for the given gold document and system prediction document. Returns: ScoreResult: 評価結果のスコア """ self.comp_result = {} measures_pas = self._evaluate_pas() if self.pas else None measures_bridging = self._evaluate_bridging() if self.bridging else None measure_coref = self._evaluate_coref() if self.coreference else None return ScoreResult(measures_pas, measures_bridging, measure_coref) def _evaluate_pas(self) -> pd.DataFrame: """calculate predicate-argument structure analysis scores""" # measures: Dict[str, Dict[str, Measure]] = OrderedDict( # (case, OrderedDict((anal, Measure()) for anal in Scorer.DEPTYPE2ANALYSIS.values())) # for case in self.cases) measures = pd.DataFrame([[Measure() for _ in Scorer.DEPTYPE2ANALYSIS.values()] for _ in self.cases], index=self.cases, columns=Scorer.DEPTYPE2ANALYSIS.values()) dtid2predicate_pred: Dict[int, Predicate] = {pred.dtid: pred for pred in self.predicates_pred} dtid2predicate_gold: Dict[int, Predicate] = {pred.dtid: pred for pred in self.predicates_gold} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2predicate_pred: predicate_pred = dtid2predicate_pred[dtid] arguments_pred = self.document_pred.get_arguments(predicate_pred, relax=False) else: arguments_pred = None if dtid in dtid2predicate_gold: predicate_gold = dtid2predicate_gold[dtid] arguments_gold = self.document_gold.get_arguments(predicate_gold, relax=False) arguments_gold_relaxed = self.document_gold.get_arguments(predicate_gold, relax=True) else: predicate_gold = arguments_gold = arguments_gold_relaxed = None for case in self.cases: args_pred: List[BaseArgument] = arguments_pred[case] if arguments_pred is not None else [] assert len(args_pred) in (0, 1) # Our analyzer predicts one argument for one predicate if predicate_gold is not None: args_gold = self._filter_args(arguments_gold[case], predicate_gold) args_gold_relaxed = self._filter_args( arguments_gold_relaxed[case] + (arguments_gold_relaxed['判ガ'] if case == 'ガ' else []), predicate_gold) else: args_gold = args_gold_relaxed = [] key = (dtid, case) # calculate precision if args_pred: arg = args_pred[0] if arg in args_gold_relaxed: # use dep_type of gold argument if possible arg_gold = args_gold_relaxed[args_gold_relaxed.index(arg)] analysis = Scorer.DEPTYPE2ANALYSIS[arg_gold.dep_type] self.comp_result[key] = analysis measures.at[case, analysis].correct += 1 else: # system出力のdep_typeはgoldのものと違うので不整合が起きるかもしれない analysis = Scorer.DEPTYPE2ANALYSIS[arg.dep_type] self.comp_result[key] = 'wrong' # precision が下がる measures.at[case, analysis].denom_pred += 1 # calculate recall # 正解が複数ある場合、そのうち一つが当てられていればそれを正解に採用 # いずれも当てられていなければ、relax されていない項から一つを選び正解に採用 if args_gold or (self.comp_result.get(key, None) in Scorer.DEPTYPE2ANALYSIS.values()): arg_gold = None for arg in args_gold_relaxed: if arg in args_pred: arg_gold = arg # 予測されている項を優先して正解の項に採用 break if arg_gold is not None: analysis = Scorer.DEPTYPE2ANALYSIS[arg_gold.dep_type] assert self.comp_result[key] == analysis else: analysis = Scorer.DEPTYPE2ANALYSIS[args_gold[0].dep_type] if args_pred: assert self.comp_result[key] == 'wrong' else: self.comp_result[key] = 'wrong' # recall が下がる measures.at[case, analysis].denom_gold += 1 return measures def _filter_args(self, args: List[BaseArgument], predicate: Predicate, ) -> List[BaseArgument]: filtered_args = [] for arg in args: if isinstance(arg, SpecialArgument): if arg.exophor not in self.relax_exophors: # filter out non-target exophors continue arg.exophor = self.relax_exophors[arg.exophor] # 「不特定:人１」なども「不特定:人」として扱う else: assert isinstance(arg, Argument) # filter out self-anaphora and cataphoras if predicate.dtid == arg.dtid or (predicate.dtid < arg.dtid and arg.sid != predicate.sid): continue filtered_args.append(arg) return filtered_args def _evaluate_bridging(self) -> pd.Series: """calculate bridging anaphora resolution scores""" measures: Dict[str, Measure] = OrderedDict((anal, Measure()) for anal in Scorer.DEPTYPE2ANALYSIS.values()) dtid2anaphor_pred: Dict[int, Predicate] = {pred.dtid: pred for pred in self.bridgings_pred} dtid2anaphor_gold: Dict[int, Predicate] = {pred.dtid: pred for pred in self.bridgings_gold} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2anaphor_pred: anaphor_pred = dtid2anaphor_pred[dtid] antecedents_pred: List[BaseArgument] = \ self._filter_args(self.document_pred.get_arguments(anaphor_pred, relax=False)['ノ'], anaphor_pred) else: antecedents_pred = [] assert len(antecedents_pred) in (0, 1) # in bert_pas_analysis, predict one argument for one predicate if dtid in dtid2anaphor_gold: anaphor_gold: Predicate = dtid2anaphor_gold[dtid] antecedents_gold: List[BaseArgument] = \ self._filter_args(self.document_gold.get_arguments(anaphor_gold, relax=False)['ノ'], anaphor_gold) arguments: Dict[str, List[BaseArgument]] = self.document_gold.get_arguments(anaphor_gold, relax=True) antecedents_gold_relaxed: List[BaseArgument] = \ self._filter_args(arguments['ノ'] + arguments['ノ？'], anaphor_gold) else: antecedents_gold = antecedents_gold_relaxed = [] key = (dtid, 'ノ') # calculate precision if antecedents_pred: antecedent_pred = antecedents_pred[0] if antecedent_pred in antecedents_gold_relaxed: # use dep_type of gold antecedent if possible antecedent_gold = antecedents_gold_relaxed[antecedents_gold_relaxed.index(antecedent_pred)] analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_gold.dep_type] if analysis == 'overt': analysis = 'dep' self.comp_result[key] = analysis measures[analysis].correct += 1 else: analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_pred.dep_type] if analysis == 'overt': analysis = 'dep' self.comp_result[key] = 'wrong' measures[analysis].denom_pred += 1 # calculate recall if antecedents_gold or (self.comp_result.get(key, None) in Scorer.DEPTYPE2ANALYSIS.values()): antecedent_gold = None for ant in antecedents_gold_relaxed: if ant in antecedents_pred: antecedent_gold = ant # 予測されている先行詞を優先して正解の先行詞に採用 break if antecedent_gold is not None: analysis = Scorer.DEPTYPE2ANALYSIS[antecedent_gold.dep_type] if analysis == 'overt': analysis = 'dep' assert self.comp_result[key] == analysis else: analysis = Scorer.DEPTYPE2ANALYSIS[antecedents_gold[0].dep_type] if analysis == 'overt': analysis = 'dep' if antecedents_pred: assert self.comp_result[key] == 'wrong' else: self.comp_result[key] = 'wrong' measures[analysis].denom_gold += 1 return pd.Series(measures) def _evaluate_coref(self) -> pd.Series: """calculate coreference resolution scores""" measure = Measure() dtid2mention_pred: Dict[int, Mention] = {bp.dtid: self.document_pred.mentions[bp.dtid] for bp in self.mentions_pred if bp.dtid in self.document_pred.mentions} dtid2mention_gold: Dict[int, Mention] = {bp.dtid: self.document_gold.mentions[bp.dtid] for bp in self.mentions_gold if bp.dtid in self.document_gold.mentions} for dtid in range(len(self.document_pred.bp_list())): if dtid in dtid2mention_pred: src_mention_pred = dtid2mention_pred[dtid] tgt_mentions_pred = \ self.filter_mentions(self.document_pred.get_siblings(src_mention_pred), src_mention_pred) exophors_pred = {e.exophor for e in map(self.document_pred.entities.get, src_mention_pred.eids) if e.is_special} else: tgt_mentions_pred = exophors_pred = set() if dtid in dtid2mention_gold: src_mention_gold = dtid2mention_gold[dtid] tgt_mentions_gold = self.filter_mentions(self.document_gold.get_siblings(src_mention_gold, relax=False), src_mention_gold) tgt_mentions_gold_relaxed = self.filter_mentions( self.document_gold.get_siblings(src_mention_gold, relax=True), src_mention_gold) exophors_gold = {self.relax_exophors[e.exophor] for e in map(self.document_gold.entities.get, src_mention_gold.eids) if e.is_special and e.exophor in self.relax_exophors} exophors_gold_relaxed = {self.relax_exophors[e.exophor] for e in map(self.document_gold.entities.get, src_mention_gold.all_eids) if e.is_special and e.exophor in self.relax_exophors} else: tgt_mentions_gold = tgt_mentions_gold_relaxed = exophors_gold = exophors_gold_relaxed = set() key = (dtid, '=') # calculate precision if tgt_mentions_pred or exophors_pred: if (tgt_mentions_pred & tgt_mentions_gold_relaxed) or (exophors_pred & exophors_gold_relaxed): self.comp_result[key] = 'correct' measure.correct += 1 else: self.comp_result[key] = 'wrong' measure.denom_pred += 1 # calculate recall if tgt_mentions_gold or exophors_gold or (self.comp_result.get(key, None) == 'correct'): if (tgt_mentions_pred & tgt_mentions_gold_relaxed) or (exophors_pred & exophors_gold_relaxed): assert self.comp_result[key] == 'correct' else: self.comp_result[key] = 'wrong' measure.denom_gold += 1 return pd.Series([measure], index=['all']) @staticmethod def filter_mentions(tgt_mentions: Set[Mention], src_mention: Mention) -> Set[Mention]: """filter out cataphors""" return {tgt_mention for tgt_mention in tgt_mentions if tgt_mention.dtid < src_mention.dtid} @dataclass(frozen=True) class ScoreResult: """A data class for storing the numerical result of an evaluation""" measures_pas: Optional[pd.DataFrame] measures_bridging: Optional[pd.Series] measure_coref: Optional[pd.Series] def to_dict(self) -> Dict[str, Dict[str, 'Measure']]: """convert data to dictionary""" df_all = pd.DataFrame(index=['all_case']) if self.pas: df_pas: pd.DataFrame = self.measures_pas.copy() df_pas['zero'] = df_pas['zero_intra'] + df_pas['zero_inter'] + df_pas['zero_exophora'] df_pas['dep_zero'] = df_pas['zero'] + df_pas['dep'] df_pas['all'] = df_pas['dep_zero'] + df_pas['overt'] df_all = pd.concat([df_pas, df_all]) df_all.loc['all_case'] = df_pas.sum(axis=0) if self.bridging: df_bar = self.measures_bridging.copy() df_bar['zero'] = df_bar['zero_intra'] + df_bar['zero_inter'] + df_bar['zero_exophora'] df_bar['dep_zero'] = df_bar['zero'] + df_bar['dep'] assert df_bar['overt'] == Measure() # No overt in BAR df_bar['all'] = df_bar['dep_zero'] df_all.at['all_case', 'bridging'] = df_bar['all'] if self.coreference: df_all.at['all_case', 'coreference'] = self.measure_coref['all'] return {k1: {k2: v2 for k2, v2 in v1.items() if pd.notnull(v2)} for k1, v1 in df_all.to_dict(orient='index').items()} def export_txt(self, destination: Union[str, Path, TextIO] ) -> None: """Export the evaluation results in a text format. Args: destination (Union[str, Path, TextIO]): 書き出す先 """ lines = [] for key, ms in self.to_dict().items(): lines.append(f'{key}格' if self.pas and key in self.measures_pas.index else key) for analysis, measure in ms.items(): lines.append(f' {analysis}') lines.append(f' precision: {measure.precision:.4f} ({measure.correct}/{measure.denom_pred})') lines.append(f' recall : {measure.recall:.4f} ({measure.correct}/{measure.denom_gold})') lines.append(f' F : {measure.f1:.4f}') text = '\n'.join(lines) + '\n' if isinstance(destination, str) or isinstance(destination, Path): with Path(destination).open('wt') as writer: writer.write(text) elif isinstance(destination, io.TextIOBase): destination.write(text) def export_csv(self, destination: Union[str, Path, TextIO], sep: str = ',' ) -> None: """Export the evaluation results in a csv format. Args: destination (Union[str, Path, TextIO]): 書き出す先 sep (str): 区切り文字 (default: ',') """ text = '' result_dict = self.to_dict() text += 'case' + sep text += sep.join(result_dict['all_case'].keys()) + '\n' for case, measures in result_dict.items(): text += CASE2YOMI.get(case, case) + sep text += sep.join(f'{measure.f1:.6}' for measure in measures.values()) text += '\n' if isinstance(destination, str) or isinstance(destination, Path): with Path(destination).open('wt') as writer: writer.write(text) elif isinstance(destination, io.TextIOBase): destination.write(text) @property def pas(self): """Whether self includes the score of predicate-argument structure analysis.""" return self.measures_pas is not None @property def bridging(self): """Whether self includes the score of bridging anaphora resolution.""" return self.measures_bridging is not None @property def coreference(self): """Whether self includes the score of coreference resolution.""" return self.measure_coref is not None def __add__(self, other: 'ScoreResult') -> 'ScoreResult': measures_pas = self.measures_pas + other.measures_pas if self.pas else None measures_bridging = self.measures_bridging + other.measures_bridging if self.bridging else None measure_coref = self.measure_coref + other.measure_coref if self.coreference else None return ScoreResult(measures_pas, measures_bridging, measure_coref) @dataclass class Measure: """A data class to calculate and represent F-measure""" denom_pred: int = 0 denom_gold: int = 0 correct: int = 0 def __add__(self, other: 'Measure'): return Measure(self.denom_pred + other.denom_pred, self.denom_gold + other.denom_gold, self.correct + other.correct) def __eq__(self, other: 'Measure'): return self.denom_pred == other.denom_pred and \ self.denom_gold == other.denom_gold and \ self.correct == other.correct @property def precision(self) -> float: if self.denom_pred == 0: return .0 return self.correct / self.denom_pred @property def recall(self) -> float: if self.denom_gold == 0: return .0 return self.correct / self.denom_gold @property def f1(self) -> float: if self.denom_pred + self.denom_gold == 0: return .0 return 2 * self.correct / (self.denom_pred + self.denom_gold) def main(): parser = argparse.ArgumentParser() parser.add_argument('--prediction-dir', default=None, type=str, help='path to directory where system output KWDLC files exist (default: None)') parser.add_argument('--gold-dir', default=None, type=str, help='path to directory where gold KWDLC files exist (default: None)') parser.add_argument('--coreference', '--coref', '--cr', action='store_true', default=False, help='perform coreference resolution') parser.add_argument('--bridging', '--brg', '--bar', action='store_true', default=False, help='perform bridging anaphora resolution') parser.add_argument('--case-string', type=str, default='ガ,ヲ,ニ,ガ２', help='case strings separated by ","') parser.add_argument('--exophors', '--exo', type=str, default='著者,読者,不特定:人,不特定:物', help='exophor strings separated by ","') parser.add_argument('--read-prediction-from-pas-tag', action='store_true', default=False, help='use <述語項構造:> tag instead of <rel > tag in prediction files') parser.add_argument('--pas-target', choices=['', 'pred', 'noun', 'all'], default='pred', help='PAS analysis evaluation target (pred: verbal predicates, noun: nominal predicates)') parser.add_argument('--result-html', default=None, type=str, help='path to html file which prediction result is exported (default: None)') parser.add_argument('--result-csv', default=None, type=str, help='path to csv file which prediction result is exported (default: None)') args = parser.parse_args() reader_gold = KyotoReader(Path(args.gold_dir), extract_nes=False, use_pas_tag=False) reader_pred = KyotoReader( Path(args.prediction_dir), extract_nes=False, use_pas_tag=args.read_prediction_from_pas_tag, ) documents_pred = reader_pred.process_all_documents() documents_gold = reader_gold.process_all_documents() assert set(args.case_string.split(',')) <= set(CASE2YOMI.keys()) msg = '"ノ" found in case string. If you want to perform bridging anaphora resolution, specify "--bridging" ' \ 'option instead' assert 'ノ' not in args.case_string.split(','), msg scorer = Scorer(documents_pred, documents_gold, target_cases=args.case_string.split(','), target_exophors=args.exophors.split(','), coreference=args.coreference, bridging=args.bridging, pas_target=args.pas_target) result = scorer.run() if args.result_html: scorer.write_html(Path(args.result_html)) if args.result_csv: result.export_csv(args.result_csv) result.export_txt(sys.stdout) if __name__ == '__main__': main()

import logging from typing import Dict, List, Iterable import asyncio import discord from discord.ext import commands import emoji from .game import RideTheBus, GameState from .result import Result from utils import playingcards logger = logging.getLogger(__name__) COLOR = 0xFFFF00 ROUND_RULES = { GameState.RED_OR_BLACK: "Answer the questions to build your hand, drinking along the way.", GameState.PYRAMID: "Play your matching cards to force others to drink.", GameState.RIDE_THE_BUS: "The loser has to ride the bus, and drink. A lot.", } ROUND_MESSAGES = { GameState.RED_OR_BLACK: { "prompt": emoji.emojize(":red_square: Red or :black_large_square: Black?"), "reactions": { emoji.emojize(":red_square:"): "red", emoji.emojize(":black_large_square:"): "black", }, }, GameState.HIGHER_OR_LOWER: { "prompt": emoji.emojize(":arrow_up: Higher or :arrow_down: Lower?"), "reactions": { "⬆️": "higher", "⬇️": "lower", }, }, GameState.INSIDE_OR_OUTSIDE: { "prompt": emoji.emojize(":thumbsup: Inside or :thumbsdown: Outside?"), "reactions": { "👍": "inside", "👎": "outside", }, }, GameState.SUIT: { "prompt": emoji.emojize( ":clubs: Club, :diamonds: Diamond, :hearts: Heart, or :spades: Spade?" ), "reactions": { "♣️": "clubs", "♦️": "diamonds", "♥️": "hearts", "♠️": "spades", }, }, } class RideTheBusCog(commands.Cog): def __init__(self, bot: commands.Bot): super().__init__() self.bot = bot self.keys: Dict[str, RideTheBus] = {} self.channels: Dict[discord.abc.Messageable, RideTheBus] = {} self.players: Dict[discord.User, RideTheBus] = {} self.context: Dict[str, discord.Context] = {} self.msg_refs: Dict[discord.Message, RideTheBus] = {} @commands.Cog.listener() async def on_reaction_add(self, reaction: discord.Reaction, user: discord.User): # Ignore reactions from the bot if user.bot: return # Try to get the game this reaction is for try: game = self.msg_refs[reaction.message] except: return # Validate the reaction if not self._validate_reaction(game, reaction, user): # Remove the invalid reaction await reaction.message.remove_reaction(reaction, user) return logger.info(f"Processing {reaction} by {user} in {game.key}") result = game.guess( user.id, ROUND_MESSAGES[game.state]["reactions"][reaction.emoji] ) embed = self._build_result(game, result) ctx = self.context[game.key] await ctx.send(embed=embed) self.msg_refs.pop(reaction.message) await asyncio.sleep(3) await self._handle_state(game) def _build_result(self, game: RideTheBus, result: Result): user = self.bot.get_user(result.player.id) card = result.player.cards[-1] result = "WON" if result.successful else "LOST" embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"Drew {card} and {result}" ) embed.set_author(name=user.display_name, icon_url=user.avatar_url) embed.set_image(url=playingcards.get_card_image_url(card)) return embed def _validate_reaction( self, game: RideTheBus, reaction: discord.Reaction, user: discord.User ): if user.id is not game.current_player.id: return False try: if reaction.emoji not in ROUND_MESSAGES[game.state]["reactions"]: return False except: return False return True async def _handle_state(self, game: RideTheBus): if game.state in [GameState.INIT, GameState.COMPLETE]: return await self._send_prompt(game) @commands.group(brief="Overview of Ride The Bus") async def bus(self, ctx: commands.Context): if ctx.invoked_subcommand is not None: return embed = discord.Embed( color=COLOR, title="Ride The Bus", description="A card based drinking game.", ) await ctx.send(embed=embed) @bus.command(brief="Create a game of Ride The Bus") async def create(self, ctx: commands.Context): if ctx.channel in self.channels: await ctx.reply( f"There is already a Ride The Bus game in progress here!\nUse `{self.bot.command_prefix}bus join {self.channels[ctx.channel].key}` to join." ) return game = RideTheBus() self.channels[ctx.channel] = game self.keys[game.key] = game self.context[game.key] = ctx embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"Ride The Bus game created.\nUse `{self.bot.command_prefix}bus join {self.channels[ctx.channel].key}` to join.", ) await ctx.send(embed=embed) @bus.command(brief="Join an existing game of Ride The Bus") async def join(self, ctx: commands.Context, key: str): if key not in self.keys: await ctx.reply(f"Sorry, there is no game with the key {key}...") return if key in self.players: game = self.players[ctx.author] await ctx.reply(f"Sorry, you're already in a game {game.key}.") return user = ctx.author game = self.keys[key] game.add_player(user.id, user.name) self.players[user] = game await ctx.reply(f"You have joined the game {game.key}!") @bus.command(brief="Leave your game of Ride The Bus") async def leave(self, ctx: commands.Context): if ctx.author not in self.players: await ctx.reply(f"Sorry, you're not in any games you can leave...") return # Get a reference to the game game = self.players[ctx.author] # Remove the player from the game game.remove_player(ctx.author.id) # Remove the player from the player list self.players.pop(ctx.author) await ctx.reply(f"You have left the game {game.key}.") @bus.command(brief="Start a game of Ride The Bus") async def start(self, ctx: commands.Context): if ctx.author not in self.players: await ctx.reply(f"Sorry, you're not in any games you can start...") game = self.players[ctx.author] game.start() embed = self._build_round_start(game) await ctx.send(embed=embed) await asyncio.sleep(5) await self._send_prompt(game) def _build_round_start(self, game: RideTheBus): player_list = self._build_player_list(game) embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"{ROUND_RULES.get(game.state, "")}\n\nPlayers:\n{player_list}", ) return embed async def _send_prompt(self, game: RideTheBus): ctx = self.context[game.key] user = self.bot.get_user(game.current_player.id) embed = self._build_prompt(game, user) msg = await ctx.send(embed=embed) await self._add_reactions(msg, ROUND_MESSAGES[game.state]["reactions"].keys()) self.msg_refs[msg] = game def _build_prompt(self, game: RideTheBus, user: discord.User): prompt = ROUND_MESSAGES[game.state]["prompt"] embed = discord.Embed(color=COLOR, title="Ride The Bus", description=prompt) embed.set_author(name=user.display_name, icon_url=user.avatar_url) return embed def _build_player_list(self, game: RideTheBus): s = "" for i, player in enumerate(game.player_list): s += f"{i+1}: {player.name}\n" return s async def _add_reactions(self, msg: discord.Message, reactions: Iterable[str]): for reaction in reactions: await msg.add_reaction(reaction)

import logging from typing import Dict, List, Iterable import asyncio import discord from discord.ext import commands import emoji from .game import RideTheBus, GameState from .result import Result from utils import playingcards logger = logging.getLogger(__name__) COLOR = 0xFFFF00 ROUND_RULES = { GameState.RED_OR_BLACK: "Answer the questions to build your hand, drinking along the way.", GameState.PYRAMID: "Play your matching cards to force others to drink.", GameState.RIDE_THE_BUS: "The loser has to ride the bus, and drink. A lot.", } ROUND_MESSAGES = { GameState.RED_OR_BLACK: { "prompt": emoji.emojize(":red_square: Red or :black_large_square: Black?"), "reactions": { emoji.emojize(":red_square:"): "red", emoji.emojize(":black_large_square:"): "black", }, }, GameState.HIGHER_OR_LOWER: { "prompt": emoji.emojize(":arrow_up: Higher or :arrow_down: Lower?"), "reactions": { "⬆️": "higher", "⬇️": "lower", }, }, GameState.INSIDE_OR_OUTSIDE: { "prompt": emoji.emojize(":thumbsup: Inside or :thumbsdown: Outside?"), "reactions": { "👍": "inside", "👎": "outside", }, }, GameState.SUIT: { "prompt": emoji.emojize( ":clubs: Club, :diamonds: Diamond, :hearts: Heart, or :spades: Spade?" ), "reactions": { "♣️": "clubs", "♦️": "diamonds", "♥️": "hearts", "♠️": "spades", }, }, } class RideTheBusCog(commands.Cog): def __init__(self, bot: commands.Bot): super().__init__() self.bot = bot self.keys: Dict[str, RideTheBus] = {} self.channels: Dict[discord.abc.Messageable, RideTheBus] = {} self.players: Dict[discord.User, RideTheBus] = {} self.context: Dict[str, discord.Context] = {} self.msg_refs: Dict[discord.Message, RideTheBus] = {} @commands.Cog.listener() async def on_reaction_add(self, reaction: discord.Reaction, user: discord.User): # Ignore reactions from the bot if user.bot: return # Try to get the game this reaction is for try: game = self.msg_refs[reaction.message] except: return # Validate the reaction if not self._validate_reaction(game, reaction, user): # Remove the invalid reaction await reaction.message.remove_reaction(reaction, user) return logger.info(f"Processing {reaction} by {user} in {game.key}") result = game.guess( user.id, ROUND_MESSAGES[game.state]["reactions"][reaction.emoji] ) embed = self._build_result(game, result) ctx = self.context[game.key] await ctx.send(embed=embed) self.msg_refs.pop(reaction.message) await asyncio.sleep(3) await self._handle_state(game) def _build_result(self, game: RideTheBus, result: Result): user = self.bot.get_user(result.player.id) card = result.player.cards[-1] result = "WON" if result.successful else "LOST" embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"Drew {card} and {result}" ) embed.set_author(name=user.display_name, icon_url=user.avatar_url) embed.set_image(url=playingcards.get_card_image_url(card)) return embed def _validate_reaction( self, game: RideTheBus, reaction: discord.Reaction, user: discord.User ): if user.id is not game.current_player.id: return False try: if reaction.emoji not in ROUND_MESSAGES[game.state]["reactions"]: return False except: return False return True async def _handle_state(self, game: RideTheBus): if game.state in [GameState.INIT, GameState.COMPLETE]: return await self._send_prompt(game) @commands.group(brief="Overview of Ride The Bus") async def bus(self, ctx: commands.Context): if ctx.invoked_subcommand is not None: return embed = discord.Embed( color=COLOR, title="Ride The Bus", description="A card based drinking game.", ) await ctx.send(embed=embed) @bus.command(brief="Create a game of Ride The Bus") async def create(self, ctx: commands.Context): if ctx.channel in self.channels: await ctx.reply( f"There is already a Ride The Bus game in progress here!\nUse `{self.bot.command_prefix}bus join {self.channels[ctx.channel].key}` to join." ) return game = RideTheBus() self.channels[ctx.channel] = game self.keys[game.key] = game self.context[game.key] = ctx embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"Ride The Bus game created.\nUse `{self.bot.command_prefix}bus join {self.channels[ctx.channel].key}` to join.", ) await ctx.send(embed=embed) @bus.command(brief="Join an existing game of Ride The Bus") async def join(self, ctx: commands.Context, key: str): if key not in self.keys: await ctx.reply(f"Sorry, there is no game with the key {key}...") return if key in self.players: game = self.players[ctx.author] await ctx.reply(f"Sorry, you're already in a game {game.key}.") return user = ctx.author game = self.keys[key] game.add_player(user.id, user.name) self.players[user] = game await ctx.reply(f"You have joined the game {game.key}!") @bus.command(brief="Leave your game of Ride The Bus") async def leave(self, ctx: commands.Context): if ctx.author not in self.players: await ctx.reply(f"Sorry, you're not in any games you can leave...") return # Get a reference to the game game = self.players[ctx.author] # Remove the player from the game game.remove_player(ctx.author.id) # Remove the player from the player list self.players.pop(ctx.author) await ctx.reply(f"You have left the game {game.key}.") @bus.command(brief="Start a game of Ride The Bus") async def start(self, ctx: commands.Context): if ctx.author not in self.players: await ctx.reply(f"Sorry, you're not in any games you can start...") game = self.players[ctx.author] game.start() embed = self._build_round_start(game) await ctx.send(embed=embed) await asyncio.sleep(5) await self._send_prompt(game) def _build_round_start(self, game: RideTheBus): player_list = self._build_player_list(game) embed = discord.Embed( color=COLOR, title="Ride The Bus", description=f"{ROUND_RULES.get(game.state, '')}\n\nPlayers:\n{player_list}", ) return embed async def _send_prompt(self, game: RideTheBus): ctx = self.context[game.key] user = self.bot.get_user(game.current_player.id) embed = self._build_prompt(game, user) msg = await ctx.send(embed=embed) await self._add_reactions(msg, ROUND_MESSAGES[game.state]["reactions"].keys()) self.msg_refs[msg] = game def _build_prompt(self, game: RideTheBus, user: discord.User): prompt = ROUND_MESSAGES[game.state]["prompt"] embed = discord.Embed(color=COLOR, title="Ride The Bus", description=prompt) embed.set_author(name=user.display_name, icon_url=user.avatar_url) return embed def _build_player_list(self, game: RideTheBus): s = "" for i, player in enumerate(game.player_list): s += f"{i+1}: {player.name}\n" return s async def _add_reactions(self, msg: discord.Message, reactions: Iterable[str]): for reaction in reactions: await msg.add_reaction(reaction)

# Tweepy # Copyright 2009-2022 Joshua Roesslein # See LICENSE for details. import requests class TweepyException(Exception): """Base exception for Tweepy .. versionadded:: 4.0 """ pass class HTTPException(TweepyException): """HTTPException() Exception raised when an HTTP request fails .. versionadded:: 4.0 Attributes ---------- response : requests.Response Requests Response from the Twitter API api_errors : List[dict[str, Union[int, str]]] The errors the Twitter API responded with, if any api_codes : List[int] The error codes the Twitter API responded with, if any api_messages : List[str] The error messages the Twitter API responded with, if any """ def __init__(self, response): self.response = response self.api_errors = [] self.api_codes = [] self.api_messages = [] try: response_json = response.json() except requests.JSONDecodeError: super().__init__(f"{response.status_code} {response.reason}") else: errors = response_json.get("errors", []) # Use := when support for Python 3.7 is dropped if "error" in response_json: errors.append(response_json["error"]) error_text = "" for error in errors: self.api_errors.append(error) if "code" in error: self.api_codes.append(error["code"]) if "message" in error: self.api_messages.append(error["message"]) if "code" in error and "message" in error: error_text += f"\n{error["code"]} - {error["message"]}" elif "message" in error: error_text += '\n' + error["message"] super().__init__( f"{response.status_code} {response.reason}{error_text}" ) class BadRequest(HTTPException): """BadRequest() Exception raised for a 400 HTTP status code .. versionadded:: 4.0 """ pass class Unauthorized(HTTPException): """Unauthorized() Exception raised for a 401 HTTP status code .. versionadded:: 4.0 """ pass class Forbidden(HTTPException): """Forbidden() Exception raised for a 403 HTTP status code .. versionadded:: 4.0 """ pass class NotFound(HTTPException): """NotFound() Exception raised for a 404 HTTP status code .. versionadded:: 4.0 """ pass class TooManyRequests(HTTPException): """TooManyRequests() Exception raised for a 429 HTTP status code .. versionadded:: 4.0 """ pass class TwitterServerError(HTTPException): """TwitterServerError() Exception raised for a 5xx HTTP status code .. versionadded:: 4.0 """ pass

# Tweepy # Copyright 2009-2022 Joshua Roesslein # See LICENSE for details. import requests class TweepyException(Exception): """Base exception for Tweepy .. versionadded:: 4.0 """ pass class HTTPException(TweepyException): """HTTPException() Exception raised when an HTTP request fails .. versionadded:: 4.0 Attributes ---------- response : requests.Response Requests Response from the Twitter API api_errors : List[dict[str, Union[int, str]]] The errors the Twitter API responded with, if any api_codes : List[int] The error codes the Twitter API responded with, if any api_messages : List[str] The error messages the Twitter API responded with, if any """ def __init__(self, response): self.response = response self.api_errors = [] self.api_codes = [] self.api_messages = [] try: response_json = response.json() except requests.JSONDecodeError: super().__init__(f"{response.status_code} {response.reason}") else: errors = response_json.get("errors", []) # Use := when support for Python 3.7 is dropped if "error" in response_json: errors.append(response_json["error"]) error_text = "" for error in errors: self.api_errors.append(error) if "code" in error: self.api_codes.append(error["code"]) if "message" in error: self.api_messages.append(error["message"]) if "code" in error and "message" in error: error_text += f"\n{error['code']} - {error['message']}" elif "message" in error: error_text += '\n' + error["message"] super().__init__( f"{response.status_code} {response.reason}{error_text}" ) class BadRequest(HTTPException): """BadRequest() Exception raised for a 400 HTTP status code .. versionadded:: 4.0 """ pass class Unauthorized(HTTPException): """Unauthorized() Exception raised for a 401 HTTP status code .. versionadded:: 4.0 """ pass class Forbidden(HTTPException): """Forbidden() Exception raised for a 403 HTTP status code .. versionadded:: 4.0 """ pass class NotFound(HTTPException): """NotFound() Exception raised for a 404 HTTP status code .. versionadded:: 4.0 """ pass class TooManyRequests(HTTPException): """TooManyRequests() Exception raised for a 429 HTTP status code .. versionadded:: 4.0 """ pass class TwitterServerError(HTTPException): """TwitterServerError() Exception raised for a 5xx HTTP status code .. versionadded:: 4.0 """ pass

from collections import defaultdict import csv from logging import Logger import logging import os import sys from typing import Callable, Dict, List, Tuple import numpy as np import pandas as pd from .run_training import run_training from chemprop.args import TrainArgs from chemprop.constants import TEST_SCORES_FILE_NAME, TRAIN_LOGGER_NAME from chemprop.data import get_data, get_task_names, MoleculeDataset, validate_dataset_type from chemprop.utils import create_logger, makedirs, timeit from chemprop.features import set_extra_atom_fdim, set_extra_bond_fdim from chemprop.models import MoleculeModel @timeit(logger_name=TRAIN_LOGGER_NAME) def cross_validate(args: TrainArgs, train_func: Callable[[TrainArgs, MoleculeDataset, Logger], Dict[str, List[float]]], model_list: List[MoleculeModel] = None) -> Tuple[float, float]: """ Runs k-fold cross-validation. For each of k splits (folds) of the data, trains and tests a model on that split and aggregates the performance across folds. :param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for loading data and training the Chemprop model. :param train_func: Function which runs training. :param model_list: A list of :class:`~chemprop.models.model.MoleculeModel`. :return: A tuple containing the mean and standard deviation performance across folds. """ logging.root.manager.loggerDict.pop(TRAIN_LOGGER_NAME,None) logger = create_logger(name=TRAIN_LOGGER_NAME, save_dir=args.save_dir, quiet=args.quiet) if logger is not None: debug, info = logger.debug, logger.info else: debug = info = print # Initialize relevant variables init_seed = args.seed save_dir = args.save_dir args.task_names = get_task_names(path=args.data_path, smiles_columns=args.smiles_columns, target_columns=args.target_columns, ignore_columns=args.ignore_columns) # Print command line debug('Command line') debug(f'python {' '.join(sys.argv)}') # Print args debug('Args') debug(args) # Save args makedirs(args.save_dir) args.save(os.path.join(args.save_dir, 'args.json')) # Get data debug('Loading data') data = get_data( path=args.data_path, args=args, smiles_columns=args.smiles_columns, logger=logger, skip_none_targets=True ) validate_dataset_type(data, dataset_type=args.dataset_type) args.features_size = data.features_size() if args.atom_descriptors == 'descriptor': args.atom_descriptors_size = data.atom_descriptors_size() args.ffn_hidden_size += args.atom_descriptors_size elif args.atom_descriptors == 'feature': args.atom_features_size = data.atom_features_size() set_extra_atom_fdim(args.atom_features_size) if args.bond_features_path is not None: args.bond_features_size = data.bond_features_size() set_extra_bond_fdim(args.bond_features_size) debug(f'Number of tasks = {args.num_tasks}') # Run training on different random seeds for each fold all_scores = defaultdict(list) for fold_num in range(args.num_folds): info(f'Fold {fold_num}') args.seed = init_seed + fold_num args.save_dir = os.path.join(save_dir, f'fold_{fold_num}') makedirs(args.save_dir) data.reset_features_and_targets() model_scores = train_func(args, data, logger, model_list) for metric, scores in model_scores.items(): all_scores[metric].append(scores) all_scores = dict(all_scores) # Convert scores to numpy arrays for metric, scores in all_scores.items(): all_scores[metric] = np.array(scores) # Report results info(f'{args.num_folds}-fold cross validation') # Report scores for each fold for fold_num in range(args.num_folds): for metric, scores in all_scores.items(): info(f'\tSeed {init_seed + fold_num} ==> test {metric} = {np.nanmean(scores[fold_num]):.6f}') if args.show_individual_scores: for task_name, score in zip(args.task_names, scores[fold_num]): info(f'\t\tSeed {init_seed + fold_num} ==> test {task_name} {metric} = {score:.6f}') # Report scores across folds for metric, scores in all_scores.items(): avg_scores = np.nanmean(scores, axis=1) # average score for each model across tasks mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores) info(f'Overall test {metric} = {mean_score:.6f} +/- {std_score:.6f}') if args.show_individual_scores: for task_num, task_name in enumerate(args.task_names): info(f'\tOverall test {task_name} {metric} = ' f'{np.nanmean(scores[:, task_num]):.6f} +/- {np.nanstd(scores[:, task_num]):.6f}') # Save scores with open(os.path.join(save_dir, TEST_SCORES_FILE_NAME), 'w') as f: writer = csv.writer(f) header = ['Task'] for metric in args.metrics: header += [f'Mean {metric}', f'Standard deviation {metric}'] + \ [f'Fold {i} {metric}' for i in range(args.num_folds)] writer.writerow(header) for task_num, task_name in enumerate(args.task_names): row = [task_name] for metric, scores in all_scores.items(): task_scores = scores[:, task_num] mean, std = np.nanmean(task_scores), np.nanstd(task_scores) row += [mean, std] + task_scores.tolist() writer.writerow(row) # Determine mean and std score of main metric avg_scores = np.nanmean(all_scores[args.metric], axis=1) mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores) # Optionally merge and save test preds if args.save_preds: all_preds = pd.concat([pd.read_csv(os.path.join(save_dir, f'fold_{fold_num}', 'test_preds.csv')) for fold_num in range(args.num_folds)]) all_preds.to_csv(os.path.join(save_dir, 'test_preds.csv'), index=False) for handler in logger.handlers[:]: handler.close() logger.removeHandler(handler) del logger return mean_score, std_score def chemprop_train() -> None: """Parses Chemprop training arguments and trains (cross-validates) a Chemprop model. This is the entry point for the command line command :code:`chemprop_train`. """ cross_validate(args=TrainArgs().parse_args(), train_func=run_training)

from collections import defaultdict import csv from logging import Logger import logging import os import sys from typing import Callable, Dict, List, Tuple import numpy as np import pandas as pd from .run_training import run_training from chemprop.args import TrainArgs from chemprop.constants import TEST_SCORES_FILE_NAME, TRAIN_LOGGER_NAME from chemprop.data import get_data, get_task_names, MoleculeDataset, validate_dataset_type from chemprop.utils import create_logger, makedirs, timeit from chemprop.features import set_extra_atom_fdim, set_extra_bond_fdim from chemprop.models import MoleculeModel @timeit(logger_name=TRAIN_LOGGER_NAME) def cross_validate(args: TrainArgs, train_func: Callable[[TrainArgs, MoleculeDataset, Logger], Dict[str, List[float]]], model_list: List[MoleculeModel] = None) -> Tuple[float, float]: """ Runs k-fold cross-validation. For each of k splits (folds) of the data, trains and tests a model on that split and aggregates the performance across folds. :param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for loading data and training the Chemprop model. :param train_func: Function which runs training. :param model_list: A list of :class:`~chemprop.models.model.MoleculeModel`. :return: A tuple containing the mean and standard deviation performance across folds. """ logging.root.manager.loggerDict.pop(TRAIN_LOGGER_NAME,None) logger = create_logger(name=TRAIN_LOGGER_NAME, save_dir=args.save_dir, quiet=args.quiet) if logger is not None: debug, info = logger.debug, logger.info else: debug = info = print # Initialize relevant variables init_seed = args.seed save_dir = args.save_dir args.task_names = get_task_names(path=args.data_path, smiles_columns=args.smiles_columns, target_columns=args.target_columns, ignore_columns=args.ignore_columns) # Print command line debug('Command line') debug(f'python {" ".join(sys.argv)}') # Print args debug('Args') debug(args) # Save args makedirs(args.save_dir) args.save(os.path.join(args.save_dir, 'args.json')) # Get data debug('Loading data') data = get_data( path=args.data_path, args=args, smiles_columns=args.smiles_columns, logger=logger, skip_none_targets=True ) validate_dataset_type(data, dataset_type=args.dataset_type) args.features_size = data.features_size() if args.atom_descriptors == 'descriptor': args.atom_descriptors_size = data.atom_descriptors_size() args.ffn_hidden_size += args.atom_descriptors_size elif args.atom_descriptors == 'feature': args.atom_features_size = data.atom_features_size() set_extra_atom_fdim(args.atom_features_size) if args.bond_features_path is not None: args.bond_features_size = data.bond_features_size() set_extra_bond_fdim(args.bond_features_size) debug(f'Number of tasks = {args.num_tasks}') # Run training on different random seeds for each fold all_scores = defaultdict(list) for fold_num in range(args.num_folds): info(f'Fold {fold_num}') args.seed = init_seed + fold_num args.save_dir = os.path.join(save_dir, f'fold_{fold_num}') makedirs(args.save_dir) data.reset_features_and_targets() model_scores = train_func(args, data, logger, model_list) for metric, scores in model_scores.items(): all_scores[metric].append(scores) all_scores = dict(all_scores) # Convert scores to numpy arrays for metric, scores in all_scores.items(): all_scores[metric] = np.array(scores) # Report results info(f'{args.num_folds}-fold cross validation') # Report scores for each fold for fold_num in range(args.num_folds): for metric, scores in all_scores.items(): info(f'\tSeed {init_seed + fold_num} ==> test {metric} = {np.nanmean(scores[fold_num]):.6f}') if args.show_individual_scores: for task_name, score in zip(args.task_names, scores[fold_num]): info(f'\t\tSeed {init_seed + fold_num} ==> test {task_name} {metric} = {score:.6f}') # Report scores across folds for metric, scores in all_scores.items(): avg_scores = np.nanmean(scores, axis=1) # average score for each model across tasks mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores) info(f'Overall test {metric} = {mean_score:.6f} +/- {std_score:.6f}') if args.show_individual_scores: for task_num, task_name in enumerate(args.task_names): info(f'\tOverall test {task_name} {metric} = ' f'{np.nanmean(scores[:, task_num]):.6f} +/- {np.nanstd(scores[:, task_num]):.6f}') # Save scores with open(os.path.join(save_dir, TEST_SCORES_FILE_NAME), 'w') as f: writer = csv.writer(f) header = ['Task'] for metric in args.metrics: header += [f'Mean {metric}', f'Standard deviation {metric}'] + \ [f'Fold {i} {metric}' for i in range(args.num_folds)] writer.writerow(header) for task_num, task_name in enumerate(args.task_names): row = [task_name] for metric, scores in all_scores.items(): task_scores = scores[:, task_num] mean, std = np.nanmean(task_scores), np.nanstd(task_scores) row += [mean, std] + task_scores.tolist() writer.writerow(row) # Determine mean and std score of main metric avg_scores = np.nanmean(all_scores[args.metric], axis=1) mean_score, std_score = np.nanmean(avg_scores), np.nanstd(avg_scores) # Optionally merge and save test preds if args.save_preds: all_preds = pd.concat([pd.read_csv(os.path.join(save_dir, f'fold_{fold_num}', 'test_preds.csv')) for fold_num in range(args.num_folds)]) all_preds.to_csv(os.path.join(save_dir, 'test_preds.csv'), index=False) for handler in logger.handlers[:]: handler.close() logger.removeHandler(handler) del logger return mean_score, std_score def chemprop_train() -> None: """Parses Chemprop training arguments and trains (cross-validates) a Chemprop model. This is the entry point for the command line command :code:`chemprop_train`. """ cross_validate(args=TrainArgs().parse_args(), train_func=run_training)

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GetCodeSigningConfigResult', 'AwaitableGetCodeSigningConfigResult', 'get_code_signing_config', 'get_code_signing_config_output', ] @pulumi.output_type class GetCodeSigningConfigResult: """ A collection of values returned by getCodeSigningConfig. """ def __init__(__self__, allowed_publishers=None, arn=None, config_id=None, description=None, id=None, last_modified=None, policies=None): if allowed_publishers and not isinstance(allowed_publishers, list): raise TypeError("Expected argument 'allowed_publishers' to be a list") pulumi.set(__self__, "allowed_publishers", allowed_publishers) if arn and not isinstance(arn, str): raise TypeError("Expected argument 'arn' to be a str") pulumi.set(__self__, "arn", arn) if config_id and not isinstance(config_id, str): raise TypeError("Expected argument 'config_id' to be a str") pulumi.set(__self__, "config_id", config_id) if description and not isinstance(description, str): raise TypeError("Expected argument 'description' to be a str") pulumi.set(__self__, "description", description) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_modified and not isinstance(last_modified, str): raise TypeError("Expected argument 'last_modified' to be a str") pulumi.set(__self__, "last_modified", last_modified) if policies and not isinstance(policies, list): raise TypeError("Expected argument 'policies' to be a list") pulumi.set(__self__, "policies", policies) @property @pulumi.getter(name="allowedPublishers") def allowed_publishers(self) -> Sequence['outputs.GetCodeSigningConfigAllowedPublisherResult']: """ List of allowed publishers as signing profiles for this code signing configuration. """ return pulumi.get(self, "allowed_publishers") @property @pulumi.getter def arn(self) -> str: return pulumi.get(self, "arn") @property @pulumi.getter(name="configId") def config_id(self) -> str: """ Unique identifier for the code signing configuration. """ return pulumi.get(self, "config_id") @property @pulumi.getter def description(self) -> str: """ Code signing configuration description. """ return pulumi.get(self, "description") @property @pulumi.getter def id(self) -> str: """ The provider-assigned unique ID for this managed resource. """ return pulumi.get(self, "id") @property @pulumi.getter(name="lastModified") def last_modified(self) -> str: """ The date and time that the code signing configuration was last modified. """ return pulumi.get(self, "last_modified") @property @pulumi.getter def policies(self) -> Sequence['outputs.GetCodeSigningConfigPolicyResult']: """ List of code signing policies that control the validation failure action for signature mismatch or expiry. """ return pulumi.get(self, "policies") class AwaitableGetCodeSigningConfigResult(GetCodeSigningConfigResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetCodeSigningConfigResult( allowed_publishers=self.allowed_publishers, arn=self.arn, config_id=self.config_id, description=self.description, id=self.id, last_modified=self.last_modified, policies=self.policies) def get_code_signing_config(arn: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetCodeSigningConfigResult: """ Provides information about a Lambda Code Signing Config. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail). For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html) ## Example Usage ```python import pulumi import pulumi_aws as aws existing_csc = aws.lambda.get_code_signing_config(arn=f"arn:aws:lambda:{var["aws_region"]}:{var["aws_account"]}:code-signing-config:csc-0f6c334abcdea4d8b") ``` :param str arn: The Amazon Resource Name (ARN) of the code signing configuration. """ __args__ = dict() __args__['arn'] = arn if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws:lambda/getCodeSigningConfig:getCodeSigningConfig', __args__, opts=opts, typ=GetCodeSigningConfigResult).value return AwaitableGetCodeSigningConfigResult( allowed_publishers=__ret__.allowed_publishers, arn=__ret__.arn, config_id=__ret__.config_id, description=__ret__.description, id=__ret__.id, last_modified=__ret__.last_modified, policies=__ret__.policies) @_utilities.lift_output_func(get_code_signing_config) def get_code_signing_config_output(arn: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetCodeSigningConfigResult]: """ Provides information about a Lambda Code Signing Config. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail). For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html) ## Example Usage ```python import pulumi import pulumi_aws as aws existing_csc = aws.lambda.get_code_signing_config(arn=f"arn:aws:lambda:{var["aws_region"]}:{var["aws_account"]}:code-signing-config:csc-0f6c334abcdea4d8b") ``` :param str arn: The Amazon Resource Name (ARN) of the code signing configuration. """ ...

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GetCodeSigningConfigResult', 'AwaitableGetCodeSigningConfigResult', 'get_code_signing_config', 'get_code_signing_config_output', ] @pulumi.output_type class GetCodeSigningConfigResult: """ A collection of values returned by getCodeSigningConfig. """ def __init__(__self__, allowed_publishers=None, arn=None, config_id=None, description=None, id=None, last_modified=None, policies=None): if allowed_publishers and not isinstance(allowed_publishers, list): raise TypeError("Expected argument 'allowed_publishers' to be a list") pulumi.set(__self__, "allowed_publishers", allowed_publishers) if arn and not isinstance(arn, str): raise TypeError("Expected argument 'arn' to be a str") pulumi.set(__self__, "arn", arn) if config_id and not isinstance(config_id, str): raise TypeError("Expected argument 'config_id' to be a str") pulumi.set(__self__, "config_id", config_id) if description and not isinstance(description, str): raise TypeError("Expected argument 'description' to be a str") pulumi.set(__self__, "description", description) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_modified and not isinstance(last_modified, str): raise TypeError("Expected argument 'last_modified' to be a str") pulumi.set(__self__, "last_modified", last_modified) if policies and not isinstance(policies, list): raise TypeError("Expected argument 'policies' to be a list") pulumi.set(__self__, "policies", policies) @property @pulumi.getter(name="allowedPublishers") def allowed_publishers(self) -> Sequence['outputs.GetCodeSigningConfigAllowedPublisherResult']: """ List of allowed publishers as signing profiles for this code signing configuration. """ return pulumi.get(self, "allowed_publishers") @property @pulumi.getter def arn(self) -> str: return pulumi.get(self, "arn") @property @pulumi.getter(name="configId") def config_id(self) -> str: """ Unique identifier for the code signing configuration. """ return pulumi.get(self, "config_id") @property @pulumi.getter def description(self) -> str: """ Code signing configuration description. """ return pulumi.get(self, "description") @property @pulumi.getter def id(self) -> str: """ The provider-assigned unique ID for this managed resource. """ return pulumi.get(self, "id") @property @pulumi.getter(name="lastModified") def last_modified(self) -> str: """ The date and time that the code signing configuration was last modified. """ return pulumi.get(self, "last_modified") @property @pulumi.getter def policies(self) -> Sequence['outputs.GetCodeSigningConfigPolicyResult']: """ List of code signing policies that control the validation failure action for signature mismatch or expiry. """ return pulumi.get(self, "policies") class AwaitableGetCodeSigningConfigResult(GetCodeSigningConfigResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetCodeSigningConfigResult( allowed_publishers=self.allowed_publishers, arn=self.arn, config_id=self.config_id, description=self.description, id=self.id, last_modified=self.last_modified, policies=self.policies) def get_code_signing_config(arn: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetCodeSigningConfigResult: """ Provides information about a Lambda Code Signing Config. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail). For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html) ## Example Usage ```python import pulumi import pulumi_aws as aws existing_csc = aws.lambda.get_code_signing_config(arn=f"arn:aws:lambda:{var['aws_region']}:{var['aws_account']}:code-signing-config:csc-0f6c334abcdea4d8b") ``` :param str arn: The Amazon Resource Name (ARN) of the code signing configuration. """ __args__ = dict() __args__['arn'] = arn if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws:lambda/getCodeSigningConfig:getCodeSigningConfig', __args__, opts=opts, typ=GetCodeSigningConfigResult).value return AwaitableGetCodeSigningConfigResult( allowed_publishers=__ret__.allowed_publishers, arn=__ret__.arn, config_id=__ret__.config_id, description=__ret__.description, id=__ret__.id, last_modified=__ret__.last_modified, policies=__ret__.policies) @_utilities.lift_output_func(get_code_signing_config) def get_code_signing_config_output(arn: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetCodeSigningConfigResult]: """ Provides information about a Lambda Code Signing Config. A code signing configuration defines a list of allowed signing profiles and defines the code-signing validation policy (action to be taken if deployment validation checks fail). For information about Lambda code signing configurations and how to use them, see [configuring code signing for Lambda functions](https://docs.aws.amazon.com/lambda/latest/dg/configuration-codesigning.html) ## Example Usage ```python import pulumi import pulumi_aws as aws existing_csc = aws.lambda.get_code_signing_config(arn=f"arn:aws:lambda:{var['aws_region']}:{var['aws_account']}:code-signing-config:csc-0f6c334abcdea4d8b") ``` :param str arn: The Amazon Resource Name (ARN) of the code signing configuration. """ ...

import timeit import pickle import os import errno import datetime import shutil import warnings import traceback import pstats import io import sys import gc import inspect import importlib import re import pathlib import types import operator import subprocess import shlex import json import contextlib import stat import itertools import ast import builtins import signal from collections import OrderedDict from string import Formatter import numpy as np from matplotlib import pyplot from IPython.utils.capture import capture_output from swutil import sys_info, np_tools, plots, misc from swutil.validation import Positive, Integer, String, List, Tuple,Iterable from swutil.logs import Log from swutil.hpc import Locker from swutil.misc import string_dialog, no_context, random_word,\ string_from_seconds,input_with_prefill,is_identifier,smart_range from swutil.files import append_text, delete_empty_files,\ delete_empty_directories, find_directories, path_from_keywords from swutil.decorators import print_peak_memory, add_runtime from swutil.collections import unique class GitError(Exception): def __init__(self, message, git_log): super(GitError, self).__init__(message) self.git_log = git_log GRP_WARN = 'Warning' GRP_ERROR = 'Error' FILE_DEBUG = '.debug' FILE_OUTPUT = 'output.pkl' FILE_INPUT = 'input.pkl' FILE_INFO = 'summary.txt' FILE_AUX = 'aux_data.pkl' FILE_RUNTIME = 'runtime.txt' FILE_MEMORY = 'memory.txt' FILE_LOAD = 'load.sh' FILE_EXP_ERR = 'stderr.txt' FILE_EXP_OUT = 'stdout.txt' FILE_LOG = 'log.txt' FILE_GITLOG = 'git.txt' FILE_ERR = 'err.txt' FILE_SOURCE = 'source.txt' FILE_WD = 'working_directory' FILE_ANALYSIS = 'analysis' FILE_EXP = lambda i: f'experiment{i}' FILE_RANDOMSTATE = 'randomstate.pkl' STR_GIT_TAG = lambda ID: f'scilog_{ID}' STR_GIT_LOG = lambda sha1, log: f'#Created git commit {sha1} as snapshot of current state of git repository using the following commands:\n{log}' STR_GIT_COMMIT_TITLE = lambda branch: f'Snapshot of working directory of branch {branch}' STR_GIT_COMMIT_BODY = lambda name, ID, directory: f'Created for scilog entry {ID} in {directory}' STR_LOADSCRIPT = ('#!/bin/sh \n ' + f' xterm -e {sys.executable} -i -c ' + r'''" print('>>> import scilog'); import scilog; print('>>> entry = scilog.load()'); entry = scilog.load(); try: import pandas as pd; print('>>> import pandas as pd'); print('>>> experiments = pd.DataFrame(entry[\'experiments\'])'); experiments = pd.DataFrame(entry['experiments']); print(experiments); except: pass;"''') STR_MEMFILE = lambda value,memory_profile: value + ( '' if memory_profile == 'detail' else 'MB (Use `memory_profile==\'detail\'` for a more detailed breakdown)' ) STR_SOURCE = lambda n, func, module,source: (('#Experiments were conducted with' if n != 1 else '#Experiment was conducted with ') + ('class' if inspect.isclass(func) else (f'{func.__class__.__name__}' if isinstance(func,(types.MethodType,types.FunctionType)) else f'instance of {func.__class__.__name__}')) + (f' called {func.__name__}' if hasattr(func, '__name__') else '') + f' from the module {module} whose source code is given below:\n{source}') STR_TIME = '%y-%m-%d %H:%M:%S' def STR_PARAMETERS_PROMPT(func,external,current_parameters,known_parameters,allow_variables,class_instance,allow_all_keys): if class_instance: why= f'to pass to instance of {func.__class__.__name__}' else: if external: why = f'to fill in `{external}`' else: name = _get_name(func) if inspect.isclass(func): why= f'to initialize class {name}' else: why = f'to pass to {name}' parameters_string = ', '.join(f'{key}={value!r}' for key,value in current_parameters.items()) require_parameters=[key for key in known_parameters if key not in current_parameters] if require_parameters: parameters_string += (', ' if parameters_string else '') + ', '.join(f'{key}=' for key in require_parameters) if allow_all_keys: parameters_string += '[, <kwarg>=<value>]*' return f'>> Specify {'variables or ' if allow_variables else ''}parameters {why} ({parameters_string}):\n\t' def STR_PARAMETERS_ALLOWED(passed_keys,known_parameters): forbidden = [key for key in passed_keys if key not in known_parameters] if len(forbidden)>1: out = '!! Cannot specify parameters'+', '.join(f'`{key}`' for key in forbidden[:-1]) + f', and `{forbidden[-1]}`' else: out = '!! Cannot specify parameter '+f'`{forbidden[0]}`' return out STR_PARAMETERS_FORMAT = '!! Input must have form `<key>=<value>[,<key>=<value>]*`\n!! Enter `help` for more information' STR_PARAMETERS_HELP = lambda allow_variables: ( '?? Parameters are specified by `<key>=<value>` with <key> a Python identifier and <value> a Python expression.' +( ( '\n?? Variables have the same syntax, except <value> has the form var(<iterable>).\n' '?? Variables are used to specify arguments that are varied in a specified range.\n' '?? Note the difference between <key>=[0,1] and <key>=var([0,1]):\n' '?? In the first case, `[0,1]` is passed at once; in the second case it is iterated over.' ) if allow_variables else '' ) ) MSG_DEBUG = 'Debug mode. Entry is not stored permanently, stdout and stderr are not captured, no git commit is created' MSG_NOGIT = 'Could not find git repository. No snapshot commit will be created' MSG_START_ANALYSIS = 'Updating analysis' MSG_START_EXPERIMENT = lambda i,n_experiments,inp: (f'Running experiment {i}' + (' with variable values {}{}'.format('\n\t' if '\n' in repr(inp) else '',repr(inp)) if inp != {} else '')) MSG_START_GIT = lambda repo:'Creating snapshot of current working tree of repository \'{}\'. Check {}'.format(repo,FILE_GITLOG) def MSG_START_EXPERIMENTS(name,variables,parameters): msg = f'Will call `{name}`' extend='' new_line=False if parameters: new_line='\n' in str(parameters) extend= ' with parameters {}{}'.format("\n\t" if new_line else "",parameters) if variables: s_var = 'variables' if len(variables)>1 else 'variable' variable_strings = [(variable[0],str(variable[1])) for variable in variables] newline_in_vs = any('\n' in vs[1] for vs in variable_strings) sep = '\n\t' if (len(variables)>1 or newline_in_vs) else ', ' strings = [('' if sep == ', ' else '-') +f'`{vs[0]}`'+ (f' varying in `{vs[1]}`' if not newline_in_vs else '') for vs in variable_strings] extend += (" \n" if new_line else " ") +(f'and {s_var}' if extend else f' with {s_var}')+(' ' if sep==', ' else sep)+sep.join(strings) if not extend: extend =' once' return msg + extend MSG_START_ENTRY = lambda directory: f'Created scilog entry {directory}' MSG_FINISH_EXPERIMENT = lambda i,n_experiments,runtime,result,external: 'Finished experiment {} in {}{}'.format(i,string_from_seconds(runtime), '' if ('\n' in f'{result}') else (f'. Check {os.path.join(FILE_EXP(i),FILE_EXP_OUT)}' if external else f'. Output: {result}')) MSG_FINISH_ENTRY=lambda directory: f'Completed scilog entry {directory}' MSG_SUCCESS = 'All experiments finished successfully' MSG_FAIL = 'Some experiments failed' MSG_FINISH_GIT = lambda sha1: f'Successfully created git commit {sha1}' MSG_ERROR_NOMATCH = 'Could not find matching scilog entry' MSG_ERROR_MULTIMATCH = lambda entries:'Multiple matching scilog entries (to iterate through all use need_unique=False):\n{}'.format('\n'.join(entries)) MSG_ERROR_LOAD = lambda name: f'Error loading {name}. Are all required modules in the Python path?' MSG_ERROR_INSTANTIATION = lambda name:f'Could not instantiate class {name} with given parameters' MSG_ERROR_PARALLEL = 'Error during parallel execution. Try running with `parallel=False`' MSG_ERROR_BASH_ANALYSIS = 'Cannot analyze output in bash mode' MSG_ERROR_GIT = lambda file:f'Error during git snapshot creation. Check {file}' MSG_ERROR_EXPERIMENT = lambda i:f'Experiment {i} failed. Check {os.path.join(FILE_EXP(i),FILE_EXP_ERR)}' MSG_ERROR_ANALYSIS = lambda file: f'Analysis could not be completed. Check {file}' MSG_ERROR_DIR = 'Could not create scilog entry directory' MSG_EXCEPTION_STORE = lambda file: f'Could not store {file}' MSG_EXCEPTION_ANALYSIS = 'Exception during online analysis' MSG_EXCEPTION_EXPERIMENT = lambda i: f'Exception during handling of experiment {i}. Check {FILE_ERR}' MSG_WARN_SOURCE = 'Could not find source code' MSG_WARN_LOADSCRIPT = 'Error during load script creation' MSG_WARN_PARALLEL = ('Could not find pathos. This might cause problems with parallel execution.' + 'Install pathos via `pip install pathos`.') MSG_WARN_MEMPROF = 'Could not find memory_profiler. Install memory_profiler via `pip install memory_profiler`.' MSG_WARN_DILL = ('Could not find dill. Some items might not be storable. ' + 'Storage of numpy arrays will be slow' + 'Install dill via `pip install dill`.') MSG_INTERRUPT = f'Kill signal received. Stored {FILE_INFO}, closing now.' LEN_ID = 8 #TODO(low,high) think about using inspect.formatargspec(inspect.getargspec(func)) to directly parse args and kwargs of user input even without named argument #TODO(med,high) understand ellipses in variable input: Do this at the string input level, so 2**3,...,2**6 can be understood. #TODO(med,low) make scilog --show work for all scilog entries in current git repo even outside of cwd #TODO(high,low) make scilog --show smarter: if FUNC doesn't match any scilog entry path, try if it matches a scilog entry ID #TODO(high,high) remove analysis functionality from scilog.py and add scilog --analyze working as follows: provide a list of entry identifiers (ID or paths) as well as a function that accepts scilog entries (i.e. the summary dicts). the source code file (foo.py) of that function (foo.func) is copied in the analysis subsubdirectories `foo_x` of each scilog entry #TODO technically: `scilog --analyze X3DH,UH1X --parameters [...] --variables [...] foo.func` starts a scilog run with arguments func=foo.func, analysis = True[effects that git=False, base_directory =tempfile.mktemp(), func is called with parameters={**parameters, entries=[scilog.load(identifier) for identifier in analzsis]} log does not say 'created scilog entry' but instead says which entries will be analyzed with what, and finishes with "added analysis <classification_x to X3DH and <classification>_y to UH1X, and entry is copied into subdireoctry analyze/<classificatoin>_x of X3DH and UH1X with x possibly being adapted to what is already in the analysis of X3DH and UH1X ] #TODO make load ignore subdirectories of scilog entries (to avoid listing analysis entries) #TODO(?,?) comunicate to plots.save #TODO(low,med) understand <param>=<string> without quotes around <string> (simple and stupid: fail, add unrecognized variable to locals, repeat...) #TODO(low,med) understand scilog foo(a=1)(b=var()) by defining foo in locals() and have it return another function that takes yet more arguments #TODO(med,low) if copy_output is a path, try to copy that path and only terminate when succeeded (e.g. when it starts existing) also, add argument check_done and if it is provided only try copying as soon as it returns True #TODO(low,low) store completion flag #TODO(high,low) make scilog --show show [log, current-stdout,current-stderr] if entry not completed, (so you can avoid screen -r and navigation to the filesystem directory) #TODO(low,low) make scilog --show show scilog-stderr if it exists and, if all experiments failed, also show current-stderr of last experiment in that case (if at least one succeeded leave it to user to navigate to the failed experiment) #TODO(med,low) make scilog --show default to no-git (add --git) #TODO(?,?) make scilog --show first look through screen sessions #TODO(low,med) Store final note in notes.txt, add --update <REASON> to scilog which then flags all previous runs with same config as outdated in their notes.txt #TODO(low,low) add --note flag #TODO(med,low) extend scilog ls output by scilog status (running, terminated, error,n_experiments) (store log files) #TODO(med,low) include no-shutdown script def record(func, variables=None, name=None, base_directory=None, aux_data=None, analysis=None, runtime_profile=False, memory_profile=False, git=True, no_date=False, parallel=False, copy_output = None, parameters=None,debug = None,classification= None,dry_run=False): ''' Call :code:`func` once or multiple times and store results along with auxiliary information about runtime and memory usage, installed modules, source code, hardware, etc. code:`func` is called once for each combination of variable values as specified by the variable ranges in :code:`variables`. For example, :code:`func` can be a numerical algorithm and :code:`variables` can be used to specify different mesh resolutions as follow `variables = {h:[2**(-l) for l in range(10)}` with the goal to assess the rate of convergence. Another example would be to specify a list of subroutines with the goal to find the best subroutine in terms of runtime/memory consumption/.... In the following, each call of :code:`func` is called an 'experiment'. Scilog creates a directory -- specified by :code:`directory`, :code:`name`, and optional parameters or a randomly generated ID -- with the following content: *summary.txt: *name: Name of scilog entry *ID: Alphanumeric string identifying the entry *modules: Module versions *time: Time of execution *experiments: For each experiment *string representation of input, *string representation of output, *runtime *status *(optional)memory usage *(optional)parameters: Parameters that are equal for all experiments *(optional)git_commit: SHA1 of git commit *(optional)aux_data: Argument :code:`aux_data` *log.txt *(optional)err.txt *(optional)git.txt: stdout of git snapshot creation *source.txt: Source code of the module containing :code:`func` *For each experiment a subdirectory 'experiment<i>' with: *output.pkl: Output of :code:`func` *(optional)input.pkl: Argument passed to :code:`func` *(optional)working_directory/: Working directory for call of :code:`func`, unless parameter :code:`copy_output` is specified *(optional)stderr.txt: *(optional)stdout.txt: *(optional)runtime_profile.txt: Extensive runtime information for each experiment *(optional)memory_profile.txt: Memory usage information for each experiment *(optional) analysis/: output of function :code:`analysis` *(optional)stderr.txt *(optional)stdout.txt *(optional)working_directory/: Working directory for call of :code:`analysis` To load a scilog entry, use the function :code:`scilog.load`. This function loads summary.txt and replaces the string representations of outputs and inputs by the actual Python objects. :param func: Function to be called with different experiment configurations :type func: function :param variables: Arguments for call of :code:`func` that are varied. If not specified, then :code:`func` is called once, without arguments :type variables: List(-like) if single variable or dictionary of lists :param name: Name of scilog entry. If not specified, :code:`func.__name__` is used :type name: String :param base_directory: Root directory for storage :type base_directory: String :param aux_data: Auxiliary data that should be stored along with the results :type aux_data: Any :param analysis: Function that is called after each experiment Can be used, e.g., for plotting :param runtime_profile: Store extensive runtime information May slow down execution :type runtime_profile: Boolean :param memory_profile: Track memory usage May slow down execution :type memory_profile: Boolean :param git: Create git snapshot commit The resulting commit is tagged with the entry ID and resides outside the branch history The repository path may be specified, else it will be automatically detected Add 'scilog' to your .gitignore to avoid storing the scilog entries in each snapshot. (Should you ever want get rid of the snapshots, use `git tag --list 'scilog_*'|xargs -I % git tag -d %` to remove all scilog commits or use function `clean_git_repository` to remove all scilog commits whose scilog entry does not reside in repository anymore) :type git: Boolean or String :param no_date: Do not store outputs in sub-directories grouped by calendar week :type no_date: Boolean :param parameters: Parameters that are equal for all experiments If :code:`func` is a class, these are used to instantiate this class :type parameters: Dictionary :param debug: Force debug mode (otherwise detected automatically) :param debug: Boolean :param copy_output: The contents of this directory will be copied into the scilog entry directory :type copy_output: String :param classification: Short, human readable description of entry :type classification: String :param dry_run: Only setup directory and experiments, don't execute anything :type dry_run: Boolean :return: Path of scilog entry :rtype: String ''' ########################### FIX ARGUMENTS ########################### variables,parameters,func_initialized,classification_t = _setup_experiments(variables,parameters,func) classification = classification or classification_t name = name or _get_name(func) if dry_run: return variables,parameters,classification,name external = _external(func) debug = debug if debug is not None else aux.isdebugging() if debug: git = False log_nogit = False if git is not False: if git is True: git = _get_func_directory(func) if not _has_git(git): log_nogit = True git = False ########################### SETUP INPUTS ############################## if len(variables)!=1:#Will result in infinite loop if one variable is infinite. t = itertools.product(*[variable[1] for variable in variables]) else: t = ([x] for x in variables[0][1]) inputs = ({variable[0]:tt[i] for i,variable in enumerate(variables)} for tt in t) try: n_experiments = int(np.prod([len(variable[1]) for variable in variables])) except TypeError: n_experiments = None ########################### CREATE SCILOG ENTRY ######################## entry_directory,ID = _get_directory(base_directory,func,name,no_date,debug,git,classification) log_file = os.path.join(entry_directory, FILE_LOG) err_file = os.path.join(entry_directory, FILE_ERR) info_file = os.path.join(entry_directory, FILE_INFO) load_file = os.path.join(entry_directory, FILE_LOAD) aux_data_file = os.path.join(entry_directory, FILE_AUX) source_file_name = os.path.join(entry_directory, FILE_SOURCE) git_file = os.path.join(entry_directory, FILE_GITLOG) locker = Locker() _log = Log(write_filter=True, print_filter=True, file_name=log_file,lock = locker.get_lock()) # Logging strategy: 1) Redirect out and err of user functions (analysis and experiment) to their own files _err = Log(write_filter=True, print_filter=False, file_name=err_file,lock = locker.get_lock()) # 2) Log errors outside user functions in _err 3) Log everything (user-err and _err, as well as other info) in _log _log.log(MSG_START_ENTRY(entry_directory)) if log_nogit: _log.log(group = GRP_WARN,message = MSG_NOGIT) if debug: _log.log(group =GRP_WARN,message = MSG_DEBUG) info = { 'parameters' : {key:repr(parameters[key]) for key in parameters}, 'variables' : [repr(variable) for variable in variables], 'name' : name, 'ID' : ID, 'time' : datetime.datetime.now().strftime(STR_TIME), 'func' : external or repr(func), 'parallel' : parallel, 'hardware' : sys_info.hardware(), 'gitcommit' : None, 'modules' : None, 'note': None, 'experiments' : { 'runtime':[], 'memory':[], 'status':[], 'input':[], 'output':[] } } if not external: info['modules'] = sys_info.modules() try: source = STR_SOURCE(n_experiments,func,sys.modules[func.__module__].__file__, ''.join(inspect.getsourcelines(sys.modules[func.__module__])[0])) append_text(source_file_name, source) except Exception: # TypeError only? _err.log(traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_SOURCE) if memory_profile is not False: if memory_profile == 'detail': try: import memory_profiler # @UnusedImport, just to check if this will be possible in _run_single_experiment except ImportError: _log.log(group=GRP_WARN, message=MSG_WARN_MEMPROF) memory_profile = True else: memory_profile = True try: with open(load_file, 'w') as fp: fp.write(STR_LOADSCRIPT) st = os.stat(load_file) os.chmod(load_file, st.st_mode | stat.S_IEXEC) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_LOADSCRIPT) if git: try: _log.log(message=MSG_START_GIT(os.path.basename(os.path.normpath(git)))) with (capture_output() if not debug else no_context()) as c: snapshot_id, git_log, _ = _git_snapshot(path=git,commit_body=STR_GIT_COMMIT_BODY(name, ID, entry_directory), ID=ID) append_text(git_file, STR_GIT_LOG(snapshot_id, git_log)) _log.log(message=MSG_FINISH_GIT(snapshot_id)) info['gitcommit'] = snapshot_id except GitError as e: _log.log(group=GRP_ERROR, message=MSG_ERROR_GIT(git_file)) _err.log(message=str(e)+'\n'+c.stderr) append_text(git_file, e.git_log) raise try: import dill serializer = dill except ImportError: serializer = pickle _log.log(group=GRP_WARN, message=MSG_WARN_DILL) _try_store(aux_data,serializer,aux_data_file,_log,_err) def _update_info(i, runtime, status, memory, input_str, output_str): for (key,val) in [('runtime',runtime), ('memory',memory if memory_profile is not False else None), ('status',status), ('input',input_str), ('output',output_str), ]: info['experiments'][key].append(val) store_info() def store_info(): with open(info_file,'w') as fp: json.dump(info,fp,indent = 1,separators = (',\n', ': ')) store_info() old_wd = os.getcwd() ########################### RUN EXPERIMENTS ############################### args = ( ( i, input, entry_directory, func_initialized, memory_profile, runtime_profile, _log,_err, 'pickle' if serializer == pickle else 'dill', external, debug, copy_output,n_experiments ) for i, input in enumerate(inputs) ) _log.log(message=MSG_START_EXPERIMENTS(name,variables,parameters)) def close_entry(): try: os.chdir(old_wd) except Exception: pass success = all(s=='finished' for s in info['experiments']['status']) try: _log.log(MSG_FINISH_ENTRY(entry_directory)) if not success: _log.log(MSG_FAIL) except Exception: pass if not debug: note = input('You may add a short note to this entry or simply press Enter to exit:') if note: info['note'] = note store_info() return entry_directory if parallel and not debug: try: from pathos.multiprocessing import ProcessingPool as Pool pool = Pool(nodes=n_experiments) except ImportError: _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_PARALLEL) from multiprocessing import Pool pool = Pool(processes=n_experiments) try: outputs = pool.map(_run_single_experiment, args) except pickle.PicklingError: # @UndefinedVariable _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_ERROR_PARALLEL) raise for output in outputs: _update_info(*output) pool.close() pool.join() else: for arg in args: try: output = _run_single_experiment(arg) except Exception:#These come from errors in the code of _run_single_experiments. The user function errors are caught within there _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_EXPERIMENT(arg[0])) else: _update_info(*output) if analysis: try: _log.log(message=MSG_START_ANALYSIS) except BrokenPipeError:#locks raise BrokenPipeError when experiments are terminated using <C-c> sys.exit(1) try: with capture_output(): entry = load(path=entry_directory, need_unique=True, no_objects=False) analyze(func=analysis, entry=entry, _log=_log, _err=_err, debug=debug) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_ANALYSIS) return close_entry() def _has_git(git): cwd = os.getcwd() try: os.chdir(git) #subprocess.check_call(['git','status'],stdout = subprocess.PIPE,stderr=subprocess.PIPE) subprocess.check_call(['git','rev-parse','HEAD',],stdout = subprocess.PIPE,stderr=subprocess.PIPE)#Sometimes git status works but rev-parse, which is used later, fails; e.g. on repos without initial commit return True except subprocess.CalledProcessError: return False finally: os.chdir(cwd) def _external(func): return func if isinstance(func,str) else False def _get_func_directory(func): return os.getcwd() if _external(func) else os.path.dirname(sys.modules[func.__module__].__file__) def _get_base_directory(directory,func,name,no_date): directory = directory or _get_func_directory(func) directory = os.path.join(directory,'scilog') if no_date: basepath = os.path.join(directory, name) else: date = datetime.date.today() basepath = os.path.join(directory, date.strftime('w%Wy%y'), name) return os.path.abspath(basepath) def _get_name(func): if _external(func): nowhite = re.compile('\S*') path = nowhite.match(func).group(0) name = os.path.basename(path) else: try:#func is a function or a class name = func.__name__ except AttributeError:#func is an object with __call__ method name = func.__class__.__name__ return name def _evaluator(what,locals_dict = None): locals_dict = locals_dict or {} return eval(f'(lambda **kwargs: kwargs)({what})',{'range':range,'count':itertools.count,'np':np,'__builtins__':{}},locals_dict) class _var: def __init__(self,*obj): if len(obj)>1:#e.g. var('ab','cd','ef') self.obj = obj elif len(obj)==1:#e.g. var(range(3)) if Iterable.valid(obj[0]): self.obj = list(obj[0])#turn into list so that numpy arrays go through later on (if you leave them as arrays, they will make problems in = comparison, for example) else:#Allows for --variables p=3 instead of --variables p=[3] self.obj = [obj[0]] elif len(obj)==0: raise ValueError() def __repr__(self): return 'var('+repr(self.obj)+')' def _setup_experiments(variables,parameters,func): ''' Note: input and output `variables` have iterator type, not _var. _var only occurs in the processing ''' external = _external(func) def _get_kwargs(func,external,variables,parameters,class_instance=False): if inspect.isclass(func): allow_variables =False variables = None else: allow_variables=True parameters_passed = parameters is not None variables_passed = variables is not None if parameters is None: parameters = {} if variables is None: variables = {} if external: field_names = [fname for _, fname, _, _ in Formatter().parse(external) if fname is not None] new_var_n = 0 new_names = [] for i,fname in enumerate(field_names): if fname == '': while True: if f'arg{new_var_n}' not in field_names: new_names.append(f'arg{new_var_n}') break else: new_var_n+=1 external = external.format( *[f'{{{new_name}}}' for new_name in new_names], **{fname:f'{{{fname}}}' for fname in field_names if fname !='' } ) known_parameters = OrderedDict((fname,inspect._empty) for _, fname, _, _ in Formatter().parse(external) if fname is not None) if len(known_parameters)==1 and list(known_parameters.keys())[0] == None: known_parameters = [] allow_all_keys = False default_parameters = {} else: func_parameters = inspect.signature(func).parameters default_parameters = { key:value.default for key,value in func_parameters.items() if (value.default != inspect._empty) } allow_all_keys = any(value.kind ==4 for key,value in func_parameters.items()) known_parameters = OrderedDict( (key,value.default) for key,value in func_parameters.items() if (value.kind not in [2,4]) ) kwargs=default_parameters.copy() free_keys=lambda : allow_all_keys or any(key not in variables and key not in parameters for key in known_parameters) required_keys=lambda : [key for key in known_parameters if key not in kwargs] is_allowed_key=lambda key: allow_all_keys or key in known_parameters if variables: if not isinstance(variables,dict):#allow for single range instead of var dictionary non_default_parameters = [key for key in known_parameters if key not in default_parameters] if len(non_default_parameters) == 1: variables={non_default_parameters[0]:variables} elif len(known_parameters) ==1: variables = {list(known_parameters.keys())[0]:variables} else: raise ValueError(f'Must specify name for variable {variables}') if any(not is_allowed_key(key) or not is_identifier(key) for key in variables): raise ValueError('Invalid variable names for function {}: {}'.format(external or func,{key for key in variables if not is_allowed_key(key)})) variables_update = {key:_var(value) for key,value in variables.items()} else: variables_update = {} if parameters: if any(key in variables_update for key in parameters): raise ValueError('Parameter names already defined as variables: {}'.format({key for key in parameters if key in kwargs})) if any(not is_allowed_key(key) or not is_identifier(key) for key in parameters): raise ValueError('Invalid parameter names for function {}: {}'.format(external or func,{key for key in parameters if not is_allowed_key(key)})) parameters_update = parameters else: parameters_update = parameters kwargs.update(**variables_update,**parameters_update) if (((not parameters_passed and not class_instance) or (not variables_passed and allow_variables)) and free_keys()) or required_keys(): while True: prefill=', '.join([key+'=' for key in required_keys()]) parameters_string = input_with_prefill(STR_PARAMETERS_PROMPT(func,external,kwargs,known_parameters,allow_variables,class_instance,allow_all_keys),prefill) if parameters_string in ['?','help','--help','??']: print(STR_PARAMETERS_HELP(allow_variables)) continue try: update_kwargs = _evaluator(parameters_string,{'var':_var} if allow_variables else {}) except Exception:#(ValueError,SyntaxError): if '=help' in parameters_string: print(STR_PARAMETERS_HELP(allow_variables)) else: print(STR_PARAMETERS_FORMAT) else: kwargs.update({key: value for key,value in update_kwargs.items() if is_allowed_key(key)}) done = True if not all(key in kwargs for key in known_parameters): if parameters_string =='': print(STR_PARAMETERS_FORMAT) done = False if any(not is_allowed_key(key) for key in update_kwargs): print(STR_PARAMETERS_ALLOWED(update_kwargs,known_parameters)) done = False if done: break return kwargs,external,default_parameters,known_parameters if external: def func(**kwargs): subprocess.check_call(external.format(**kwargs), stdout=sys.stdout, stderr=sys.stderr, shell=True) classification_variables = {}#variables.copy() if isinstance(variables,dict) else {}#can be None or a single unnamed iterable whose name will be found out only later classification_parameters = {}#parameters.copy() if isinstance(parameters,dict) else {}# can be None if inspect.isclass(func):# in this case, parameters are for initialization and variables for function call parameters,_,default_parameters,_ = _get_kwargs(func,False,None,parameters) func_initialized=func(**parameters) variables,_,default_parameters_2,known_parameters_2 = _get_kwargs(func_initialized,False,variables,None,class_instance=True) real_variables = {key:value for key,value in variables.items() if isinstance(value,_var)} classification_parameters.update({key:value for key,value in parameters.items() if key not in default_parameters or (key in default_parameters and value !=default_parameters[key])}) if len(variables)<=1:#nothing possibly interesting can be said if there is only one variable except if variable was not known (i.e. keyword argument) if not classification_parameters:#If not any classification yet take what you have classification_variables.update({key:value for key,value in variables.items()}) else: classification_variables.update({key:value for key,value in variables.items() if key not in known_parameters_2}) else: classification_variables.update({key:value for key,value in variables.items() if key not in known_parameters_2 or (key in default_parameters_2 and value!=default_parameters_2[key])}) if any(key not in real_variables for key in variables if not key in default_parameters_2):#Not all nondefault parameters actually vary, so list those that do classification_variables.update({key:value for key,value in real_variables.items() if key not in default_parameters_2}) variables = {key:(value.obj if isinstance(value,_var) else [value]) for key,value in variables.items()}#Users are prompeted vor variables or parameters, but if they enter parameters, i.e. a single value, the willy still be handled as variables taking only one value else: kwargs,external,default_parameters,_ =_get_kwargs(func,external,variables,parameters)#use all as name, first params as usual (all hand selected, fill to 5), then __var_l_h variables = {key:value.obj for key,value in kwargs.items() if isinstance(value,_var)} parameters ={key:value for key,value in kwargs.items() if not isinstance(value,_var)} #use classification even if only one known parameter, this helps if the braces in a bash command string are changed and suddenly control something very different classification_parameters.update({key:value for key,value in parameters.items() if key not in default_parameters or (key in default_parameters and value!=default_parameters[key])}) classification_variables.update({key:value for key,value in variables.items() if key not in default_parameters or (key in default_parameters and value!=default_parameters[key])}) def func_initialized(**experiment): return func(**experiment,**parameters) variables = list(variables.items()) classification_p = path_from_keywords(classification_parameters,into='file') classification_v = '_'.join(s.replace('_','') for s in classification_variables.keys()) classification = classification_p+('+' if classification_v else '') +classification_v for j,variable in enumerate(variables): if (List|Tuple).valid(variable[1]) and Ellipsis in variable[1]: variables[j] = (variable[0],smart_range(*[e for e in variable[1] if e != Ellipsis])) return variables,parameters,func_initialized,classification def _run_single_experiment(arg): (i, input, directory, func, memory_profile, runtime_profile, _log,_err, serializer, external, debug, copy_output,n_experiments) = arg experiment_directory = os.path.join(directory, FILE_EXP(i)) stderr_file = os.path.join(experiment_directory, FILE_EXP_ERR) stdout_file = os.path.join(experiment_directory, FILE_EXP_OUT) input_file = os.path.join(experiment_directory, FILE_INPUT) output_file = os.path.join(experiment_directory, FILE_OUTPUT) randomstate_file = os.path.join(experiment_directory, FILE_RANDOMSTATE) runtime_profile_file = os.path.join(experiment_directory, FILE_RUNTIME) memory_profile_file = os.path.join(experiment_directory, FILE_MEMORY) experiment_working_directory = os.path.join(experiment_directory, FILE_WD) if serializer == 'pickle': serializer = pickle else: import dill serializer = dill _log.log(MSG_START_EXPERIMENT(i,n_experiments,input)) runtime = None output = None input_str = repr(input) memory = None status = 'failed' randomstate = None if not external: randomstate = np.random.get_state() if hasattr(func, '__name__'):#func is function temp_func = func else:#func is object temp_func = func.__call__ if copy_output is None: os.makedirs(experiment_working_directory) os.chdir(experiment_working_directory) else: os.makedirs(experiment_directory) try: _try_store(input,serializer,input_file,_log,_err) _try_store(randomstate,serializer,randomstate_file,_log,_err) if memory_profile == 'detail':#Needs to be before runtime decorator so it gets the full view (otherwise it will profile the runtime decorator) m = io.StringIO() import memory_profiler temp_func = memory_profiler.profile(func = temp_func,stream =m ,precision = 4) if runtime_profile: temp_func = add_runtime(temp_func) if memory_profile is True:#Needs to be after runtime decorator so runtime gets the full view (since peak_memory is threaded) m = io.StringIO() temp_func = print_peak_memory(func=temp_func, stream=m) stderr_append = '' with open(stderr_file, 'a', 1) as err: with open(stdout_file, 'a', 1) as out: with contextlib.redirect_stdout(out) if not debug else no_context(): with contextlib.redirect_stderr(err) if not debug else no_context(): tic = timeit.default_timer() try: output = temp_func(**input) except Exception: status = 'failed' if debug: traceback.print_exc() try: import ipdb as debugger except ModuleNotFoundError: import pdb as debugger debugger.post_mortem(sys.exc_info()[2]) stderr_append = traceback.format_exc() else: status = 'finished' runtime = timeit.default_timer() - tic delete_empty_files([stderr_file, stdout_file]) if status == 'failed': append_text(stderr_file, stderr_append) _log.log(group=GRP_ERROR, message=MSG_ERROR_EXPERIMENT(i),use_lock = False)#locks are often broken already which leads to ugly printouts, also errors don't matter at this point anyway else: if runtime_profile: profile, output = output s = io.StringIO() ps = pstats.Stats(profile, stream=s) ps.sort_stats('cumulative') ps.print_stats() append_text(runtime_profile_file, s.getvalue()) s.close() if memory_profile: append_text(memory_profile_file,STR_MEMFILE(m.getvalue(),memory_profile)) memory = _max_mem(m.getvalue(), type=memory_profile) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_EXPERIMENT(i)) if copy_output is None: os.chdir(directory) else: shutil.copytree(copy_output, experiment_working_directory, symlinks=False, ignore_dangling_symlinks=True) delete_empty_directories([experiment_working_directory]) output_str = str(output) _try_store(output,serializer,output_file,_log,_err) del output if status == 'finished': _log.log(MSG_FINISH_EXPERIMENT(i, n_experiments, runtime, output_str,external)) gc.collect() return (i, runtime, status, memory, input_str, output_str) class ConvergencePlotter(): def __init__(self, *qois, cumulative=False, work=None, extrapolate=0,reference = 'self'): ''' Create convergence plots (of given quantities of interest (qois)) This is an auxiliary class that may be used as argument for parameter `analyze` of scilog.record or parameter `func` of scilog.analyze :param qois: List of functions that can be applied to the outputs of an experiment :param cumulative: Specify whether work is cumulative across the experiments :param work: If a measure other than runtime should be used, this must be a function taking integers and returning reals :param extrapolate: Degree of Richardson extrapolation extrapolate = -1 uses exponential extrapolation, whereas positive values merely improve algebraic convergence orders ''' self.qois = qois self.cumulative = cumulative self.work = work self.extrapolate = extrapolate self.reference = reference def __call__(self, entry): experiments = entry['experiments'] single_reference = (self.reference == 'self') ind_finished = [j for (j, s) in enumerate(experiments['status']) if s == 'finished'] if len(ind_finished) > 2 + (self.extrapolate if self.extrapolate >= 0 else 0): if self.work is None: times = experiments['runtime'][ind_finished] else: times = [self.work(i) for i in ind_finished] results = experiments[i]['output'][ind_finished] if self.cumulative: times = np.cumsum(times) if not self.qois: if hasattr(results[0], '__len__') and not isinstance(results[0], np.ndarray): self.qois = [lambda x,k = k: x[k] for k in range(len(results[0]))] else: self.qois = [lambda x:x] single_reference = True if single_reference: self.reference = [self.reference]*len(self.qois) for (k, qoi) in enumerate(self.qois): try: pyplot.figure(k).clf() qoi_values = np.array([qoi(result) for result in results]) qoi_times = np.array(times) if self.extrapolate: qoi_values, qoi_times = np_tools.extrapolate(qoi_values, qoi_times, self.extrapolate) plots.plot_convergence(qoi_times, qoi_values,reference = self.reference[k]) plots.save('convergence') except Exception: traceback.print_exc() def _try_store(what,serializer,file,_log,_err): if what is not None: try: with open(file, 'wb') as fp: serializer.dump(what, fp) except (TypeError, pickle.PicklingError): _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_EXCEPTION_STORE(os.path.split(file)[-1])) def clean_git_repository(directory=None,dry_run = True): ''' Delete all commits in repository specified by :code:`directory` which do not have matching scilog entry in repository directory. :param directory: Path that is under version control :type directory: String :param dry_run: Actually go ahead and delete commits, else just list them :type dry_run: Bool ''' directory = directory or os.getcwd() os.chdir(directory) scilog_tag = re.compile('scilog_.*') tags = [tag for tag in _git_command('tag --list',add_input = False).splitlines() if scilog_tag.match(tag)] git_directory = _git_command('rev-parse --show-toplevel', add_input=False).rstrip() os.chdir(git_directory) entries = load(need_unique=False,no_objects=True) IDs = [entry['ID'] for entry in entries] unmatched = [tag for tag in tags if tag[7:] not in IDs] if unmatched: print(f'The following scilog git commits have no matching scilog entry in {directory}:') [print(tag) for tag in unmatched] if dry_run: print('Specify `dry_run=False` to remove unmatched commits') else: print('Removing unmatched commits...',end='') [_git_command(f'tag -d {tag}') for tag in unmatched] print('done') else: print(f'All scilog git commits have matching scilog entries in {directory}') def analyze(entry,func, _log=None, _err=None, debug=False): ''' Add analysis to scilog entry or entries :param func: Function that performs analysis :param entry: scilog entry or entries (as returned by scilog.load) :param _log: Log object to be used instead of writing to standard stdout :param _err: Log object to be used instead of writing to standard stderr :param debug: If True, output is printed instead of being redirected into files ''' if not _log: _log = Log(print_filter=True) if not _err: _err = Log(print_filter=True) try: import dill serializer = dill except ImportError: serializer = pickle _log.log(group=GRP_WARN, message=MSG_WARN_DILL) cwd = os.getcwd() try: if not inspect.isgenerator(entry): entries = [entry] else: entries = entry for entry in entries: analysis_directory_tmp = os.path.join(entry['path'], 'tmp',FILE_ANALYSIS) working_directory = os.path.join(analysis_directory_tmp, FILE_WD) stderr_file = os.path.join(analysis_directory_tmp, FILE_EXP_ERR) stdout_file = os.path.join(analysis_directory_tmp, FILE_EXP_OUT) output_file = os.path.join(analysis_directory_tmp, FILE_OUTPUT) os.makedirs(analysis_directory_tmp) os.mkdir(working_directory) os.chdir(working_directory) output = None stderr_append = '' with open(stderr_file, 'a', 1) as err: with open(stdout_file, 'a', 1) as out: with contextlib.redirect_stdout(out) if not debug else no_context(): with contextlib.redirect_stderr(err) if not debug else no_context(): try: output = func(entry) except Exception: stderr_append = traceback.format_exc() delete_empty_files([stderr_file, stdout_file]) delete_empty_directories([working_directory]) if stderr_append: append_text(stderr_file, stderr_append) _log.log(group=GRP_ERROR, message=MSG_ERROR_ANALYSIS(stderr_file)) _try_store(output,serializer,output_file,_log,_err) os.chdir(cwd) analysis_directory = os.path.join(entry['path'], FILE_ANALYSIS) shutil.rmtree(analysis_directory, ignore_errors=True) shutil.move(analysis_directory_tmp, entry['path']) shutil.rmtree(os.path.split(analysis_directory_tmp)[0], ignore_errors=True) except Exception: os.chdir(cwd) class RE: def __init__(self,s): self.s=s def load(search_pattern='*', path='', ID=None, no_objects=False, need_unique=True,include_modules=False,parameters=None,fix_broken_summary_txt=False):#TODO remove fix_borken_summary_txt ''' Load scilog entry/entries. :param search_pattern: Shell-style glob/search pattern using wildcards If there are multiple entries of the same name (those are stored as <name>/v0 <name>/v1 ... in the filesystem) and they should all be returned, use `search_pattern=<name>/v*` and `need_unique=False` :type search_pattern: String, e.g. search_pattern='foo*' matches `foobar` :param path: Path of exact location if known (possibly only partially), relative or absolute :type path: String, e.g. '/home/username/<project>' or '<project>' :param no_objects: To save time, only load information about scilog entry, not results :type no_objects: Boolean :param need_unique: Require unique identification of scilog entry. :type need_unique: Boolean :param parameters: Search pattern that is applied to the scilog parameters :type parameters: Dictionary of regular expression strings or objects (which will be converted to strings) :return: Scilog entry :rtype: If need_unique=True, a single Namespace obejct If need_unique=False, a generator of such objects ''' deserializer = pickle try: import dill deserializer = dill except ImportError: warnings.warn(MSG_WARN_DILL) if os.sep in search_pattern and path == '':#Use absolute path part of search pattern as path, if existent temp_path, temp_search_pattern = search_pattern.rsplit(os.sep, 1) if os.path.isabs(temp_path): path, search_pattern = temp_path, temp_search_pattern if search_pattern[-1]!='*': search_pattern = search_pattern+'*' entries = [] entries.extend(find_directories(search_pattern, path=path)) entries.extend(find_directories('*/' + search_pattern, path=path)) entries = [entry for entry in entries if _is_experiment_directory(entry)] def get_output(entry, no_objects): file_name = os.path.join(entry, FILE_INFO) with open(file_name, 'r') as fp: try: info = json.load(fp) except Exception: raise ValueError(f'Problem with {file_name}') info['path'] = entry if not include_modules: del info['modules'] if not no_objects: #if isinstance(info['experiments'],dict):#Old version of scilog: # DL = info['experiments'] # info['experiments'] = [dict(zip(DL,t)) for t in zip(*DL.values())] for j,s in enumerate(info['experiments']['status'] if not fix_broken_summary_txt else ('finished' for i in itertools.count())): if s == 'finished': try: output_file_name = os.path.join(entry, FILE_EXP(j), FILE_OUTPUT) with open(output_file_name, 'rb') as fp: output = deserializer.load(fp) if fix_broken_summary_txt: info['experiments']['output'].append(output) else: info['experiments']['output'][j] = output except Exception: warnings.warn(MSG_ERROR_LOAD('file ' + output_file_name)) if fix_broken_summary_txt: break traceback.print_exc() if not fix_broken_summary_txt: try: input_file_name = os.path.join(entry,FILE_EXP(j),FILE_INPUT) with open(input_file_name,'rb') as fp: input = deserializer.load(fp) info['experiments']['input'][j] = input except Exception: warnings.warn(MSG_ERROR_LOAD('file ' + input_file_name)) traceback.print_exc() for key in info['experiments']: try: info['experiments'][key] = np.array(info['experiments'][key]) except Exception: pass return info if ID: partial_id = re.compile(ID) entries = [entry for entry in entries if partial_id.match(get_output(entry, no_objects = True)['ID'])] if parameters: parameters = {key:(repr(value) if not isinstance(value,RE) else value) for (key,value) in parameters.items()} def matches_parameters(entry): out = get_output(entry,no_objects=True) if not 'parameters' in out: return False else: test = out['parameters'] for key,value in parameters.items(): if key not in test: return False if isinstance(value,RE): if not re.match(value.s,test[key]): return False else: if not value == test[key]: return False return True entries = [entry for entry in entries if matches_parameters(entry)] if len(entries)>1 and need_unique: basenames = [os.path.basename(get_output(entry,no_objects=True)['path']).rsplit('_',1) for entry in entries] if len(set(bn[0] for bn in basenames))==1: entries = [max(entries,key = lambda entry: get_output(entry,no_objects=True)['time'])] entries = unique(entries) if not need_unique: return (get_output(entry, no_objects=no_objects) for entry in entries) else: if len(entries) == 0: raise ValueError(MSG_ERROR_NOMATCH) if len(entries) > 1: raise ValueError(MSG_ERROR_MULTIMATCH(entries)) return get_output(entries[0], no_objects=no_objects) def _is_experiment_directory(directory): return os.path.isfile(os.path.join(directory, FILE_INFO)) def _max_mem(m, type): # @ReservedAssignment if m == '': return -1 if type == 'detail': # Output of memory_profiler package find = re.compile('.*?(\d{1,}\.\d{4}) MiB.*') matches = [find.match(line) for line in m.splitlines()] values = [float(match.groups()[0]) for match in matches if match is not None] return max(values) - min(values) else: # Output of print_peak_memory return float(m) def _get_directory(directory,func,name,no_date, debug, git, classification): basepath = _get_base_directory(directory,func,name,no_date) if debug: directory = os.path.join(basepath, FILE_DEBUG) try: shutil.rmtree(directory) except FileNotFoundError: pass os.makedirs(directory) return directory, FILE_DEBUG try_classification_based_directory = 1 if classification else 0 for attempt in range(20): # Try keyword format, then random words, fail if cannot find unused ID = random_word(length = LEN_ID,dictionary = (attempt<10)) if try_classification_based_directory: directory = os.path.join(basepath,classification+f'_{try_classification_based_directory-1}') else: directory = os.path.join(basepath,ID) try: os.makedirs(directory) if git and _git_has_tag(git,STR_GIT_TAG(ID)): delete_empty_directories([directory]) else: return directory,ID except OSError as exc: if exc.errno == errno.EEXIST: if try_classification_based_directory: try_classification_based_directory += 1 else: if try_classification_based_directory:#There was a problem creating a directory with the keyword format try_classification_based_directory = 0#Maybe illegal characters in parameters, try it with random words else:#Already tried random words, something else is wrong raise raise ValueError(MSG_ERROR_DIR) def _git_command(string, add_input=True): string = 'git ' + string output = '$ ' + string + '\n' if add_input else '' args = shlex.split(string) output += subprocess.check_output(args, stderr=subprocess.STDOUT).decode('UTF8') return output def _git_id(): return _git_command('log --format="%H" -n 1', add_input=False).rstrip() def _git_has_tag(path,tag): initial_directory = os.getcwd() os.chdir(path) try: out = _git_command('tag --list',add_input = False) return tag in out.splitlines() except subprocess.CalledProcessError: os.chdir(initial_directory) raise def _git_snapshot(path, commit_body, ID): initial_directory = os.getcwd() os.chdir(path) git_directory = _git_command('rev-parse --show-toplevel', add_input=False).rstrip() os.chdir(git_directory) active_branch = _git_command('rev-parse --abbrev-ref HEAD', add_input=False) try: out = '' out += _git_command('add --all') out += _git_command('rm -r --cached .') out += _git_command('add --all') out += _git_command('commit --allow-empty -m "{0} \n {1}"'.format(STR_GIT_COMMIT_TITLE(active_branch), commit_body)) out += _git_command('tag {}'.format(STR_GIT_TAG(ID))) snap_id = _git_id() out += _git_command('reset HEAD~1') except subprocess.CalledProcessError as e: raise GitError(traceback.format_exc(), out + '\n' + str(e.output)) except Exception: raise GitError(traceback.format_exc(), out) os.chdir(initial_directory) return snap_id, out, git_directory

import timeit import pickle import os import errno import datetime import shutil import warnings import traceback import pstats import io import sys import gc import inspect import importlib import re import pathlib import types import operator import subprocess import shlex import json import contextlib import stat import itertools import ast import builtins import signal from collections import OrderedDict from string import Formatter import numpy as np from matplotlib import pyplot from IPython.utils.capture import capture_output from swutil import sys_info, np_tools, plots, misc from swutil.validation import Positive, Integer, String, List, Tuple,Iterable from swutil.logs import Log from swutil.hpc import Locker from swutil.misc import string_dialog, no_context, random_word,\ string_from_seconds,input_with_prefill,is_identifier,smart_range from swutil.files import append_text, delete_empty_files,\ delete_empty_directories, find_directories, path_from_keywords from swutil.decorators import print_peak_memory, add_runtime from swutil.collections import unique class GitError(Exception): def __init__(self, message, git_log): super(GitError, self).__init__(message) self.git_log = git_log GRP_WARN = 'Warning' GRP_ERROR = 'Error' FILE_DEBUG = '.debug' FILE_OUTPUT = 'output.pkl' FILE_INPUT = 'input.pkl' FILE_INFO = 'summary.txt' FILE_AUX = 'aux_data.pkl' FILE_RUNTIME = 'runtime.txt' FILE_MEMORY = 'memory.txt' FILE_LOAD = 'load.sh' FILE_EXP_ERR = 'stderr.txt' FILE_EXP_OUT = 'stdout.txt' FILE_LOG = 'log.txt' FILE_GITLOG = 'git.txt' FILE_ERR = 'err.txt' FILE_SOURCE = 'source.txt' FILE_WD = 'working_directory' FILE_ANALYSIS = 'analysis' FILE_EXP = lambda i: f'experiment{i}' FILE_RANDOMSTATE = 'randomstate.pkl' STR_GIT_TAG = lambda ID: f'scilog_{ID}' STR_GIT_LOG = lambda sha1, log: f'#Created git commit {sha1} as snapshot of current state of git repository using the following commands:\n{log}' STR_GIT_COMMIT_TITLE = lambda branch: f'Snapshot of working directory of branch {branch}' STR_GIT_COMMIT_BODY = lambda name, ID, directory: f'Created for scilog entry {ID} in {directory}' STR_LOADSCRIPT = ('#!/bin/sh \n ' + f' xterm -e {sys.executable} -i -c ' + r'''" print('>>> import scilog'); import scilog; print('>>> entry = scilog.load()'); entry = scilog.load(); try: import pandas as pd; print('>>> import pandas as pd'); print('>>> experiments = pd.DataFrame(entry[\'experiments\'])'); experiments = pd.DataFrame(entry['experiments']); print(experiments); except: pass;"''') STR_MEMFILE = lambda value,memory_profile: value + ( '' if memory_profile == 'detail' else 'MB (Use `memory_profile==\'detail\'` for a more detailed breakdown)' ) STR_SOURCE = lambda n, func, module,source: (('#Experiments were conducted with' if n != 1 else '#Experiment was conducted with ') + ('class' if inspect.isclass(func) else (f'{func.__class__.__name__}' if isinstance(func,(types.MethodType,types.FunctionType)) else f'instance of {func.__class__.__name__}')) + (f' called {func.__name__}' if hasattr(func, '__name__') else '') + f' from the module {module} whose source code is given below:\n{source}') STR_TIME = '%y-%m-%d %H:%M:%S' def STR_PARAMETERS_PROMPT(func,external,current_parameters,known_parameters,allow_variables,class_instance,allow_all_keys): if class_instance: why= f'to pass to instance of {func.__class__.__name__}' else: if external: why = f'to fill in `{external}`' else: name = _get_name(func) if inspect.isclass(func): why= f'to initialize class {name}' else: why = f'to pass to {name}' parameters_string = ', '.join(f'{key}={value!r}' for key,value in current_parameters.items()) require_parameters=[key for key in known_parameters if key not in current_parameters] if require_parameters: parameters_string += (', ' if parameters_string else '') + ', '.join(f'{key}=' for key in require_parameters) if allow_all_keys: parameters_string += '[, <kwarg>=<value>]*' return f'>> Specify {"variables or " if allow_variables else ""}parameters {why} ({parameters_string}):\n\t' def STR_PARAMETERS_ALLOWED(passed_keys,known_parameters): forbidden = [key for key in passed_keys if key not in known_parameters] if len(forbidden)>1: out = '!! Cannot specify parameters'+', '.join(f'`{key}`' for key in forbidden[:-1]) + f', and `{forbidden[-1]}`' else: out = '!! Cannot specify parameter '+f'`{forbidden[0]}`' return out STR_PARAMETERS_FORMAT = '!! Input must have form `<key>=<value>[,<key>=<value>]*`\n!! Enter `help` for more information' STR_PARAMETERS_HELP = lambda allow_variables: ( '?? Parameters are specified by `<key>=<value>` with <key> a Python identifier and <value> a Python expression.' +( ( '\n?? Variables have the same syntax, except <value> has the form var(<iterable>).\n' '?? Variables are used to specify arguments that are varied in a specified range.\n' '?? Note the difference between <key>=[0,1] and <key>=var([0,1]):\n' '?? In the first case, `[0,1]` is passed at once; in the second case it is iterated over.' ) if allow_variables else '' ) ) MSG_DEBUG = 'Debug mode. Entry is not stored permanently, stdout and stderr are not captured, no git commit is created' MSG_NOGIT = 'Could not find git repository. No snapshot commit will be created' MSG_START_ANALYSIS = 'Updating analysis' MSG_START_EXPERIMENT = lambda i,n_experiments,inp: (f'Running experiment {i}' + (' with variable values {}{}'.format('\n\t' if '\n' in repr(inp) else '',repr(inp)) if inp != {} else '')) MSG_START_GIT = lambda repo:'Creating snapshot of current working tree of repository \'{}\'. Check {}'.format(repo,FILE_GITLOG) def MSG_START_EXPERIMENTS(name,variables,parameters): msg = f'Will call `{name}`' extend='' new_line=False if parameters: new_line='\n' in str(parameters) extend= ' with parameters {}{}'.format("\n\t" if new_line else "",parameters) if variables: s_var = 'variables' if len(variables)>1 else 'variable' variable_strings = [(variable[0],str(variable[1])) for variable in variables] newline_in_vs = any('\n' in vs[1] for vs in variable_strings) sep = '\n\t' if (len(variables)>1 or newline_in_vs) else ', ' strings = [('' if sep == ', ' else '-') +f'`{vs[0]}`'+ (f' varying in `{vs[1]}`' if not newline_in_vs else '') for vs in variable_strings] extend += (" \n" if new_line else " ") +(f'and {s_var}' if extend else f' with {s_var}')+(' ' if sep==', ' else sep)+sep.join(strings) if not extend: extend =' once' return msg + extend MSG_START_ENTRY = lambda directory: f'Created scilog entry {directory}' MSG_FINISH_EXPERIMENT = lambda i,n_experiments,runtime,result,external: 'Finished experiment {} in {}{}'.format(i,string_from_seconds(runtime), '' if ('\n' in f'{result}') else (f'. Check {os.path.join(FILE_EXP(i),FILE_EXP_OUT)}' if external else f'. Output: {result}')) MSG_FINISH_ENTRY=lambda directory: f'Completed scilog entry {directory}' MSG_SUCCESS = 'All experiments finished successfully' MSG_FAIL = 'Some experiments failed' MSG_FINISH_GIT = lambda sha1: f'Successfully created git commit {sha1}' MSG_ERROR_NOMATCH = 'Could not find matching scilog entry' MSG_ERROR_MULTIMATCH = lambda entries:'Multiple matching scilog entries (to iterate through all use need_unique=False):\n{}'.format('\n'.join(entries)) MSG_ERROR_LOAD = lambda name: f'Error loading {name}. Are all required modules in the Python path?' MSG_ERROR_INSTANTIATION = lambda name:f'Could not instantiate class {name} with given parameters' MSG_ERROR_PARALLEL = 'Error during parallel execution. Try running with `parallel=False`' MSG_ERROR_BASH_ANALYSIS = 'Cannot analyze output in bash mode' MSG_ERROR_GIT = lambda file:f'Error during git snapshot creation. Check {file}' MSG_ERROR_EXPERIMENT = lambda i:f'Experiment {i} failed. Check {os.path.join(FILE_EXP(i),FILE_EXP_ERR)}' MSG_ERROR_ANALYSIS = lambda file: f'Analysis could not be completed. Check {file}' MSG_ERROR_DIR = 'Could not create scilog entry directory' MSG_EXCEPTION_STORE = lambda file: f'Could not store {file}' MSG_EXCEPTION_ANALYSIS = 'Exception during online analysis' MSG_EXCEPTION_EXPERIMENT = lambda i: f'Exception during handling of experiment {i}. Check {FILE_ERR}' MSG_WARN_SOURCE = 'Could not find source code' MSG_WARN_LOADSCRIPT = 'Error during load script creation' MSG_WARN_PARALLEL = ('Could not find pathos. This might cause problems with parallel execution.' + 'Install pathos via `pip install pathos`.') MSG_WARN_MEMPROF = 'Could not find memory_profiler. Install memory_profiler via `pip install memory_profiler`.' MSG_WARN_DILL = ('Could not find dill. Some items might not be storable. ' + 'Storage of numpy arrays will be slow' + 'Install dill via `pip install dill`.') MSG_INTERRUPT = f'Kill signal received. Stored {FILE_INFO}, closing now.' LEN_ID = 8 #TODO(low,high) think about using inspect.formatargspec(inspect.getargspec(func)) to directly parse args and kwargs of user input even without named argument #TODO(med,high) understand ellipses in variable input: Do this at the string input level, so 2**3,...,2**6 can be understood. #TODO(med,low) make scilog --show work for all scilog entries in current git repo even outside of cwd #TODO(high,low) make scilog --show smarter: if FUNC doesn't match any scilog entry path, try if it matches a scilog entry ID #TODO(high,high) remove analysis functionality from scilog.py and add scilog --analyze working as follows: provide a list of entry identifiers (ID or paths) as well as a function that accepts scilog entries (i.e. the summary dicts). the source code file (foo.py) of that function (foo.func) is copied in the analysis subsubdirectories `foo_x` of each scilog entry #TODO technically: `scilog --analyze X3DH,UH1X --parameters [...] --variables [...] foo.func` starts a scilog run with arguments func=foo.func, analysis = True[effects that git=False, base_directory =tempfile.mktemp(), func is called with parameters={**parameters, entries=[scilog.load(identifier) for identifier in analzsis]} log does not say 'created scilog entry' but instead says which entries will be analyzed with what, and finishes with "added analysis <classification_x to X3DH and <classification>_y to UH1X, and entry is copied into subdireoctry analyze/<classificatoin>_x of X3DH and UH1X with x possibly being adapted to what is already in the analysis of X3DH and UH1X ] #TODO make load ignore subdirectories of scilog entries (to avoid listing analysis entries) #TODO(?,?) comunicate to plots.save #TODO(low,med) understand <param>=<string> without quotes around <string> (simple and stupid: fail, add unrecognized variable to locals, repeat...) #TODO(low,med) understand scilog foo(a=1)(b=var()) by defining foo in locals() and have it return another function that takes yet more arguments #TODO(med,low) if copy_output is a path, try to copy that path and only terminate when succeeded (e.g. when it starts existing) also, add argument check_done and if it is provided only try copying as soon as it returns True #TODO(low,low) store completion flag #TODO(high,low) make scilog --show show [log, current-stdout,current-stderr] if entry not completed, (so you can avoid screen -r and navigation to the filesystem directory) #TODO(low,low) make scilog --show show scilog-stderr if it exists and, if all experiments failed, also show current-stderr of last experiment in that case (if at least one succeeded leave it to user to navigate to the failed experiment) #TODO(med,low) make scilog --show default to no-git (add --git) #TODO(?,?) make scilog --show first look through screen sessions #TODO(low,med) Store final note in notes.txt, add --update <REASON> to scilog which then flags all previous runs with same config as outdated in their notes.txt #TODO(low,low) add --note flag #TODO(med,low) extend scilog ls output by scilog status (running, terminated, error,n_experiments) (store log files) #TODO(med,low) include no-shutdown script def record(func, variables=None, name=None, base_directory=None, aux_data=None, analysis=None, runtime_profile=False, memory_profile=False, git=True, no_date=False, parallel=False, copy_output = None, parameters=None,debug = None,classification= None,dry_run=False): ''' Call :code:`func` once or multiple times and store results along with auxiliary information about runtime and memory usage, installed modules, source code, hardware, etc. code:`func` is called once for each combination of variable values as specified by the variable ranges in :code:`variables`. For example, :code:`func` can be a numerical algorithm and :code:`variables` can be used to specify different mesh resolutions as follow `variables = {h:[2**(-l) for l in range(10)}` with the goal to assess the rate of convergence. Another example would be to specify a list of subroutines with the goal to find the best subroutine in terms of runtime/memory consumption/.... In the following, each call of :code:`func` is called an 'experiment'. Scilog creates a directory -- specified by :code:`directory`, :code:`name`, and optional parameters or a randomly generated ID -- with the following content: *summary.txt: *name: Name of scilog entry *ID: Alphanumeric string identifying the entry *modules: Module versions *time: Time of execution *experiments: For each experiment *string representation of input, *string representation of output, *runtime *status *(optional)memory usage *(optional)parameters: Parameters that are equal for all experiments *(optional)git_commit: SHA1 of git commit *(optional)aux_data: Argument :code:`aux_data` *log.txt *(optional)err.txt *(optional)git.txt: stdout of git snapshot creation *source.txt: Source code of the module containing :code:`func` *For each experiment a subdirectory 'experiment<i>' with: *output.pkl: Output of :code:`func` *(optional)input.pkl: Argument passed to :code:`func` *(optional)working_directory/: Working directory for call of :code:`func`, unless parameter :code:`copy_output` is specified *(optional)stderr.txt: *(optional)stdout.txt: *(optional)runtime_profile.txt: Extensive runtime information for each experiment *(optional)memory_profile.txt: Memory usage information for each experiment *(optional) analysis/: output of function :code:`analysis` *(optional)stderr.txt *(optional)stdout.txt *(optional)working_directory/: Working directory for call of :code:`analysis` To load a scilog entry, use the function :code:`scilog.load`. This function loads summary.txt and replaces the string representations of outputs and inputs by the actual Python objects. :param func: Function to be called with different experiment configurations :type func: function :param variables: Arguments for call of :code:`func` that are varied. If not specified, then :code:`func` is called once, without arguments :type variables: List(-like) if single variable or dictionary of lists :param name: Name of scilog entry. If not specified, :code:`func.__name__` is used :type name: String :param base_directory: Root directory for storage :type base_directory: String :param aux_data: Auxiliary data that should be stored along with the results :type aux_data: Any :param analysis: Function that is called after each experiment Can be used, e.g., for plotting :param runtime_profile: Store extensive runtime information May slow down execution :type runtime_profile: Boolean :param memory_profile: Track memory usage May slow down execution :type memory_profile: Boolean :param git: Create git snapshot commit The resulting commit is tagged with the entry ID and resides outside the branch history The repository path may be specified, else it will be automatically detected Add 'scilog' to your .gitignore to avoid storing the scilog entries in each snapshot. (Should you ever want get rid of the snapshots, use `git tag --list 'scilog_*'|xargs -I % git tag -d %` to remove all scilog commits or use function `clean_git_repository` to remove all scilog commits whose scilog entry does not reside in repository anymore) :type git: Boolean or String :param no_date: Do not store outputs in sub-directories grouped by calendar week :type no_date: Boolean :param parameters: Parameters that are equal for all experiments If :code:`func` is a class, these are used to instantiate this class :type parameters: Dictionary :param debug: Force debug mode (otherwise detected automatically) :param debug: Boolean :param copy_output: The contents of this directory will be copied into the scilog entry directory :type copy_output: String :param classification: Short, human readable description of entry :type classification: String :param dry_run: Only setup directory and experiments, don't execute anything :type dry_run: Boolean :return: Path of scilog entry :rtype: String ''' ########################### FIX ARGUMENTS ########################### variables,parameters,func_initialized,classification_t = _setup_experiments(variables,parameters,func) classification = classification or classification_t name = name or _get_name(func) if dry_run: return variables,parameters,classification,name external = _external(func) debug = debug if debug is not None else aux.isdebugging() if debug: git = False log_nogit = False if git is not False: if git is True: git = _get_func_directory(func) if not _has_git(git): log_nogit = True git = False ########################### SETUP INPUTS ############################## if len(variables)!=1:#Will result in infinite loop if one variable is infinite. t = itertools.product(*[variable[1] for variable in variables]) else: t = ([x] for x in variables[0][1]) inputs = ({variable[0]:tt[i] for i,variable in enumerate(variables)} for tt in t) try: n_experiments = int(np.prod([len(variable[1]) for variable in variables])) except TypeError: n_experiments = None ########################### CREATE SCILOG ENTRY ######################## entry_directory,ID = _get_directory(base_directory,func,name,no_date,debug,git,classification) log_file = os.path.join(entry_directory, FILE_LOG) err_file = os.path.join(entry_directory, FILE_ERR) info_file = os.path.join(entry_directory, FILE_INFO) load_file = os.path.join(entry_directory, FILE_LOAD) aux_data_file = os.path.join(entry_directory, FILE_AUX) source_file_name = os.path.join(entry_directory, FILE_SOURCE) git_file = os.path.join(entry_directory, FILE_GITLOG) locker = Locker() _log = Log(write_filter=True, print_filter=True, file_name=log_file,lock = locker.get_lock()) # Logging strategy: 1) Redirect out and err of user functions (analysis and experiment) to their own files _err = Log(write_filter=True, print_filter=False, file_name=err_file,lock = locker.get_lock()) # 2) Log errors outside user functions in _err 3) Log everything (user-err and _err, as well as other info) in _log _log.log(MSG_START_ENTRY(entry_directory)) if log_nogit: _log.log(group = GRP_WARN,message = MSG_NOGIT) if debug: _log.log(group =GRP_WARN,message = MSG_DEBUG) info = { 'parameters' : {key:repr(parameters[key]) for key in parameters}, 'variables' : [repr(variable) for variable in variables], 'name' : name, 'ID' : ID, 'time' : datetime.datetime.now().strftime(STR_TIME), 'func' : external or repr(func), 'parallel' : parallel, 'hardware' : sys_info.hardware(), 'gitcommit' : None, 'modules' : None, 'note': None, 'experiments' : { 'runtime':[], 'memory':[], 'status':[], 'input':[], 'output':[] } } if not external: info['modules'] = sys_info.modules() try: source = STR_SOURCE(n_experiments,func,sys.modules[func.__module__].__file__, ''.join(inspect.getsourcelines(sys.modules[func.__module__])[0])) append_text(source_file_name, source) except Exception: # TypeError only? _err.log(traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_SOURCE) if memory_profile is not False: if memory_profile == 'detail': try: import memory_profiler # @UnusedImport, just to check if this will be possible in _run_single_experiment except ImportError: _log.log(group=GRP_WARN, message=MSG_WARN_MEMPROF) memory_profile = True else: memory_profile = True try: with open(load_file, 'w') as fp: fp.write(STR_LOADSCRIPT) st = os.stat(load_file) os.chmod(load_file, st.st_mode | stat.S_IEXEC) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_LOADSCRIPT) if git: try: _log.log(message=MSG_START_GIT(os.path.basename(os.path.normpath(git)))) with (capture_output() if not debug else no_context()) as c: snapshot_id, git_log, _ = _git_snapshot(path=git,commit_body=STR_GIT_COMMIT_BODY(name, ID, entry_directory), ID=ID) append_text(git_file, STR_GIT_LOG(snapshot_id, git_log)) _log.log(message=MSG_FINISH_GIT(snapshot_id)) info['gitcommit'] = snapshot_id except GitError as e: _log.log(group=GRP_ERROR, message=MSG_ERROR_GIT(git_file)) _err.log(message=str(e)+'\n'+c.stderr) append_text(git_file, e.git_log) raise try: import dill serializer = dill except ImportError: serializer = pickle _log.log(group=GRP_WARN, message=MSG_WARN_DILL) _try_store(aux_data,serializer,aux_data_file,_log,_err) def _update_info(i, runtime, status, memory, input_str, output_str): for (key,val) in [('runtime',runtime), ('memory',memory if memory_profile is not False else None), ('status',status), ('input',input_str), ('output',output_str), ]: info['experiments'][key].append(val) store_info() def store_info(): with open(info_file,'w') as fp: json.dump(info,fp,indent = 1,separators = (',\n', ': ')) store_info() old_wd = os.getcwd() ########################### RUN EXPERIMENTS ############################### args = ( ( i, input, entry_directory, func_initialized, memory_profile, runtime_profile, _log,_err, 'pickle' if serializer == pickle else 'dill', external, debug, copy_output,n_experiments ) for i, input in enumerate(inputs) ) _log.log(message=MSG_START_EXPERIMENTS(name,variables,parameters)) def close_entry(): try: os.chdir(old_wd) except Exception: pass success = all(s=='finished' for s in info['experiments']['status']) try: _log.log(MSG_FINISH_ENTRY(entry_directory)) if not success: _log.log(MSG_FAIL) except Exception: pass if not debug: note = input('You may add a short note to this entry or simply press Enter to exit:') if note: info['note'] = note store_info() return entry_directory if parallel and not debug: try: from pathos.multiprocessing import ProcessingPool as Pool pool = Pool(nodes=n_experiments) except ImportError: _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_WARN_PARALLEL) from multiprocessing import Pool pool = Pool(processes=n_experiments) try: outputs = pool.map(_run_single_experiment, args) except pickle.PicklingError: # @UndefinedVariable _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_ERROR_PARALLEL) raise for output in outputs: _update_info(*output) pool.close() pool.join() else: for arg in args: try: output = _run_single_experiment(arg) except Exception:#These come from errors in the code of _run_single_experiments. The user function errors are caught within there _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_EXPERIMENT(arg[0])) else: _update_info(*output) if analysis: try: _log.log(message=MSG_START_ANALYSIS) except BrokenPipeError:#locks raise BrokenPipeError when experiments are terminated using <C-c> sys.exit(1) try: with capture_output(): entry = load(path=entry_directory, need_unique=True, no_objects=False) analyze(func=analysis, entry=entry, _log=_log, _err=_err, debug=debug) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_ANALYSIS) return close_entry() def _has_git(git): cwd = os.getcwd() try: os.chdir(git) #subprocess.check_call(['git','status'],stdout = subprocess.PIPE,stderr=subprocess.PIPE) subprocess.check_call(['git','rev-parse','HEAD',],stdout = subprocess.PIPE,stderr=subprocess.PIPE)#Sometimes git status works but rev-parse, which is used later, fails; e.g. on repos without initial commit return True except subprocess.CalledProcessError: return False finally: os.chdir(cwd) def _external(func): return func if isinstance(func,str) else False def _get_func_directory(func): return os.getcwd() if _external(func) else os.path.dirname(sys.modules[func.__module__].__file__) def _get_base_directory(directory,func,name,no_date): directory = directory or _get_func_directory(func) directory = os.path.join(directory,'scilog') if no_date: basepath = os.path.join(directory, name) else: date = datetime.date.today() basepath = os.path.join(directory, date.strftime('w%Wy%y'), name) return os.path.abspath(basepath) def _get_name(func): if _external(func): nowhite = re.compile('\S*') path = nowhite.match(func).group(0) name = os.path.basename(path) else: try:#func is a function or a class name = func.__name__ except AttributeError:#func is an object with __call__ method name = func.__class__.__name__ return name def _evaluator(what,locals_dict = None): locals_dict = locals_dict or {} return eval(f'(lambda **kwargs: kwargs)({what})',{'range':range,'count':itertools.count,'np':np,'__builtins__':{}},locals_dict) class _var: def __init__(self,*obj): if len(obj)>1:#e.g. var('ab','cd','ef') self.obj = obj elif len(obj)==1:#e.g. var(range(3)) if Iterable.valid(obj[0]): self.obj = list(obj[0])#turn into list so that numpy arrays go through later on (if you leave them as arrays, they will make problems in = comparison, for example) else:#Allows for --variables p=3 instead of --variables p=[3] self.obj = [obj[0]] elif len(obj)==0: raise ValueError() def __repr__(self): return 'var('+repr(self.obj)+')' def _setup_experiments(variables,parameters,func): ''' Note: input and output `variables` have iterator type, not _var. _var only occurs in the processing ''' external = _external(func) def _get_kwargs(func,external,variables,parameters,class_instance=False): if inspect.isclass(func): allow_variables =False variables = None else: allow_variables=True parameters_passed = parameters is not None variables_passed = variables is not None if parameters is None: parameters = {} if variables is None: variables = {} if external: field_names = [fname for _, fname, _, _ in Formatter().parse(external) if fname is not None] new_var_n = 0 new_names = [] for i,fname in enumerate(field_names): if fname == '': while True: if f'arg{new_var_n}' not in field_names: new_names.append(f'arg{new_var_n}') break else: new_var_n+=1 external = external.format( *[f'{{{new_name}}}' for new_name in new_names], **{fname:f'{{{fname}}}' for fname in field_names if fname !='' } ) known_parameters = OrderedDict((fname,inspect._empty) for _, fname, _, _ in Formatter().parse(external) if fname is not None) if len(known_parameters)==1 and list(known_parameters.keys())[0] == None: known_parameters = [] allow_all_keys = False default_parameters = {} else: func_parameters = inspect.signature(func).parameters default_parameters = { key:value.default for key,value in func_parameters.items() if (value.default != inspect._empty) } allow_all_keys = any(value.kind ==4 for key,value in func_parameters.items()) known_parameters = OrderedDict( (key,value.default) for key,value in func_parameters.items() if (value.kind not in [2,4]) ) kwargs=default_parameters.copy() free_keys=lambda : allow_all_keys or any(key not in variables and key not in parameters for key in known_parameters) required_keys=lambda : [key for key in known_parameters if key not in kwargs] is_allowed_key=lambda key: allow_all_keys or key in known_parameters if variables: if not isinstance(variables,dict):#allow for single range instead of var dictionary non_default_parameters = [key for key in known_parameters if key not in default_parameters] if len(non_default_parameters) == 1: variables={non_default_parameters[0]:variables} elif len(known_parameters) ==1: variables = {list(known_parameters.keys())[0]:variables} else: raise ValueError(f'Must specify name for variable {variables}') if any(not is_allowed_key(key) or not is_identifier(key) for key in variables): raise ValueError('Invalid variable names for function {}: {}'.format(external or func,{key for key in variables if not is_allowed_key(key)})) variables_update = {key:_var(value) for key,value in variables.items()} else: variables_update = {} if parameters: if any(key in variables_update for key in parameters): raise ValueError('Parameter names already defined as variables: {}'.format({key for key in parameters if key in kwargs})) if any(not is_allowed_key(key) or not is_identifier(key) for key in parameters): raise ValueError('Invalid parameter names for function {}: {}'.format(external or func,{key for key in parameters if not is_allowed_key(key)})) parameters_update = parameters else: parameters_update = parameters kwargs.update(**variables_update,**parameters_update) if (((not parameters_passed and not class_instance) or (not variables_passed and allow_variables)) and free_keys()) or required_keys(): while True: prefill=', '.join([key+'=' for key in required_keys()]) parameters_string = input_with_prefill(STR_PARAMETERS_PROMPT(func,external,kwargs,known_parameters,allow_variables,class_instance,allow_all_keys),prefill) if parameters_string in ['?','help','--help','??']: print(STR_PARAMETERS_HELP(allow_variables)) continue try: update_kwargs = _evaluator(parameters_string,{'var':_var} if allow_variables else {}) except Exception:#(ValueError,SyntaxError): if '=help' in parameters_string: print(STR_PARAMETERS_HELP(allow_variables)) else: print(STR_PARAMETERS_FORMAT) else: kwargs.update({key: value for key,value in update_kwargs.items() if is_allowed_key(key)}) done = True if not all(key in kwargs for key in known_parameters): if parameters_string =='': print(STR_PARAMETERS_FORMAT) done = False if any(not is_allowed_key(key) for key in update_kwargs): print(STR_PARAMETERS_ALLOWED(update_kwargs,known_parameters)) done = False if done: break return kwargs,external,default_parameters,known_parameters if external: def func(**kwargs): subprocess.check_call(external.format(**kwargs), stdout=sys.stdout, stderr=sys.stderr, shell=True) classification_variables = {}#variables.copy() if isinstance(variables,dict) else {}#can be None or a single unnamed iterable whose name will be found out only later classification_parameters = {}#parameters.copy() if isinstance(parameters,dict) else {}# can be None if inspect.isclass(func):# in this case, parameters are for initialization and variables for function call parameters,_,default_parameters,_ = _get_kwargs(func,False,None,parameters) func_initialized=func(**parameters) variables,_,default_parameters_2,known_parameters_2 = _get_kwargs(func_initialized,False,variables,None,class_instance=True) real_variables = {key:value for key,value in variables.items() if isinstance(value,_var)} classification_parameters.update({key:value for key,value in parameters.items() if key not in default_parameters or (key in default_parameters and value !=default_parameters[key])}) if len(variables)<=1:#nothing possibly interesting can be said if there is only one variable except if variable was not known (i.e. keyword argument) if not classification_parameters:#If not any classification yet take what you have classification_variables.update({key:value for key,value in variables.items()}) else: classification_variables.update({key:value for key,value in variables.items() if key not in known_parameters_2}) else: classification_variables.update({key:value for key,value in variables.items() if key not in known_parameters_2 or (key in default_parameters_2 and value!=default_parameters_2[key])}) if any(key not in real_variables for key in variables if not key in default_parameters_2):#Not all nondefault parameters actually vary, so list those that do classification_variables.update({key:value for key,value in real_variables.items() if key not in default_parameters_2}) variables = {key:(value.obj if isinstance(value,_var) else [value]) for key,value in variables.items()}#Users are prompeted vor variables or parameters, but if they enter parameters, i.e. a single value, the willy still be handled as variables taking only one value else: kwargs,external,default_parameters,_ =_get_kwargs(func,external,variables,parameters)#use all as name, first params as usual (all hand selected, fill to 5), then __var_l_h variables = {key:value.obj for key,value in kwargs.items() if isinstance(value,_var)} parameters ={key:value for key,value in kwargs.items() if not isinstance(value,_var)} #use classification even if only one known parameter, this helps if the braces in a bash command string are changed and suddenly control something very different classification_parameters.update({key:value for key,value in parameters.items() if key not in default_parameters or (key in default_parameters and value!=default_parameters[key])}) classification_variables.update({key:value for key,value in variables.items() if key not in default_parameters or (key in default_parameters and value!=default_parameters[key])}) def func_initialized(**experiment): return func(**experiment,**parameters) variables = list(variables.items()) classification_p = path_from_keywords(classification_parameters,into='file') classification_v = '_'.join(s.replace('_','') for s in classification_variables.keys()) classification = classification_p+('+' if classification_v else '') +classification_v for j,variable in enumerate(variables): if (List|Tuple).valid(variable[1]) and Ellipsis in variable[1]: variables[j] = (variable[0],smart_range(*[e for e in variable[1] if e != Ellipsis])) return variables,parameters,func_initialized,classification def _run_single_experiment(arg): (i, input, directory, func, memory_profile, runtime_profile, _log,_err, serializer, external, debug, copy_output,n_experiments) = arg experiment_directory = os.path.join(directory, FILE_EXP(i)) stderr_file = os.path.join(experiment_directory, FILE_EXP_ERR) stdout_file = os.path.join(experiment_directory, FILE_EXP_OUT) input_file = os.path.join(experiment_directory, FILE_INPUT) output_file = os.path.join(experiment_directory, FILE_OUTPUT) randomstate_file = os.path.join(experiment_directory, FILE_RANDOMSTATE) runtime_profile_file = os.path.join(experiment_directory, FILE_RUNTIME) memory_profile_file = os.path.join(experiment_directory, FILE_MEMORY) experiment_working_directory = os.path.join(experiment_directory, FILE_WD) if serializer == 'pickle': serializer = pickle else: import dill serializer = dill _log.log(MSG_START_EXPERIMENT(i,n_experiments,input)) runtime = None output = None input_str = repr(input) memory = None status = 'failed' randomstate = None if not external: randomstate = np.random.get_state() if hasattr(func, '__name__'):#func is function temp_func = func else:#func is object temp_func = func.__call__ if copy_output is None: os.makedirs(experiment_working_directory) os.chdir(experiment_working_directory) else: os.makedirs(experiment_directory) try: _try_store(input,serializer,input_file,_log,_err) _try_store(randomstate,serializer,randomstate_file,_log,_err) if memory_profile == 'detail':#Needs to be before runtime decorator so it gets the full view (otherwise it will profile the runtime decorator) m = io.StringIO() import memory_profiler temp_func = memory_profiler.profile(func = temp_func,stream =m ,precision = 4) if runtime_profile: temp_func = add_runtime(temp_func) if memory_profile is True:#Needs to be after runtime decorator so runtime gets the full view (since peak_memory is threaded) m = io.StringIO() temp_func = print_peak_memory(func=temp_func, stream=m) stderr_append = '' with open(stderr_file, 'a', 1) as err: with open(stdout_file, 'a', 1) as out: with contextlib.redirect_stdout(out) if not debug else no_context(): with contextlib.redirect_stderr(err) if not debug else no_context(): tic = timeit.default_timer() try: output = temp_func(**input) except Exception: status = 'failed' if debug: traceback.print_exc() try: import ipdb as debugger except ModuleNotFoundError: import pdb as debugger debugger.post_mortem(sys.exc_info()[2]) stderr_append = traceback.format_exc() else: status = 'finished' runtime = timeit.default_timer() - tic delete_empty_files([stderr_file, stdout_file]) if status == 'failed': append_text(stderr_file, stderr_append) _log.log(group=GRP_ERROR, message=MSG_ERROR_EXPERIMENT(i),use_lock = False)#locks are often broken already which leads to ugly printouts, also errors don't matter at this point anyway else: if runtime_profile: profile, output = output s = io.StringIO() ps = pstats.Stats(profile, stream=s) ps.sort_stats('cumulative') ps.print_stats() append_text(runtime_profile_file, s.getvalue()) s.close() if memory_profile: append_text(memory_profile_file,STR_MEMFILE(m.getvalue(),memory_profile)) memory = _max_mem(m.getvalue(), type=memory_profile) except Exception: _err.log(message=traceback.format_exc()) _log.log(group=GRP_ERROR, message=MSG_EXCEPTION_EXPERIMENT(i)) if copy_output is None: os.chdir(directory) else: shutil.copytree(copy_output, experiment_working_directory, symlinks=False, ignore_dangling_symlinks=True) delete_empty_directories([experiment_working_directory]) output_str = str(output) _try_store(output,serializer,output_file,_log,_err) del output if status == 'finished': _log.log(MSG_FINISH_EXPERIMENT(i, n_experiments, runtime, output_str,external)) gc.collect() return (i, runtime, status, memory, input_str, output_str) class ConvergencePlotter(): def __init__(self, *qois, cumulative=False, work=None, extrapolate=0,reference = 'self'): ''' Create convergence plots (of given quantities of interest (qois)) This is an auxiliary class that may be used as argument for parameter `analyze` of scilog.record or parameter `func` of scilog.analyze :param qois: List of functions that can be applied to the outputs of an experiment :param cumulative: Specify whether work is cumulative across the experiments :param work: If a measure other than runtime should be used, this must be a function taking integers and returning reals :param extrapolate: Degree of Richardson extrapolation extrapolate = -1 uses exponential extrapolation, whereas positive values merely improve algebraic convergence orders ''' self.qois = qois self.cumulative = cumulative self.work = work self.extrapolate = extrapolate self.reference = reference def __call__(self, entry): experiments = entry['experiments'] single_reference = (self.reference == 'self') ind_finished = [j for (j, s) in enumerate(experiments['status']) if s == 'finished'] if len(ind_finished) > 2 + (self.extrapolate if self.extrapolate >= 0 else 0): if self.work is None: times = experiments['runtime'][ind_finished] else: times = [self.work(i) for i in ind_finished] results = experiments[i]['output'][ind_finished] if self.cumulative: times = np.cumsum(times) if not self.qois: if hasattr(results[0], '__len__') and not isinstance(results[0], np.ndarray): self.qois = [lambda x,k = k: x[k] for k in range(len(results[0]))] else: self.qois = [lambda x:x] single_reference = True if single_reference: self.reference = [self.reference]*len(self.qois) for (k, qoi) in enumerate(self.qois): try: pyplot.figure(k).clf() qoi_values = np.array([qoi(result) for result in results]) qoi_times = np.array(times) if self.extrapolate: qoi_values, qoi_times = np_tools.extrapolate(qoi_values, qoi_times, self.extrapolate) plots.plot_convergence(qoi_times, qoi_values,reference = self.reference[k]) plots.save('convergence') except Exception: traceback.print_exc() def _try_store(what,serializer,file,_log,_err): if what is not None: try: with open(file, 'wb') as fp: serializer.dump(what, fp) except (TypeError, pickle.PicklingError): _err.log(message=traceback.format_exc()) _log.log(group=GRP_WARN, message=MSG_EXCEPTION_STORE(os.path.split(file)[-1])) def clean_git_repository(directory=None,dry_run = True): ''' Delete all commits in repository specified by :code:`directory` which do not have matching scilog entry in repository directory. :param directory: Path that is under version control :type directory: String :param dry_run: Actually go ahead and delete commits, else just list them :type dry_run: Bool ''' directory = directory or os.getcwd() os.chdir(directory) scilog_tag = re.compile('scilog_.*') tags = [tag for tag in _git_command('tag --list',add_input = False).splitlines() if scilog_tag.match(tag)] git_directory = _git_command('rev-parse --show-toplevel', add_input=False).rstrip() os.chdir(git_directory) entries = load(need_unique=False,no_objects=True) IDs = [entry['ID'] for entry in entries] unmatched = [tag for tag in tags if tag[7:] not in IDs] if unmatched: print(f'The following scilog git commits have no matching scilog entry in {directory}:') [print(tag) for tag in unmatched] if dry_run: print('Specify `dry_run=False` to remove unmatched commits') else: print('Removing unmatched commits...',end='') [_git_command(f'tag -d {tag}') for tag in unmatched] print('done') else: print(f'All scilog git commits have matching scilog entries in {directory}') def analyze(entry,func, _log=None, _err=None, debug=False): ''' Add analysis to scilog entry or entries :param func: Function that performs analysis :param entry: scilog entry or entries (as returned by scilog.load) :param _log: Log object to be used instead of writing to standard stdout :param _err: Log object to be used instead of writing to standard stderr :param debug: If True, output is printed instead of being redirected into files ''' if not _log: _log = Log(print_filter=True) if not _err: _err = Log(print_filter=True) try: import dill serializer = dill except ImportError: serializer = pickle _log.log(group=GRP_WARN, message=MSG_WARN_DILL) cwd = os.getcwd() try: if not inspect.isgenerator(entry): entries = [entry] else: entries = entry for entry in entries: analysis_directory_tmp = os.path.join(entry['path'], 'tmp',FILE_ANALYSIS) working_directory = os.path.join(analysis_directory_tmp, FILE_WD) stderr_file = os.path.join(analysis_directory_tmp, FILE_EXP_ERR) stdout_file = os.path.join(analysis_directory_tmp, FILE_EXP_OUT) output_file = os.path.join(analysis_directory_tmp, FILE_OUTPUT) os.makedirs(analysis_directory_tmp) os.mkdir(working_directory) os.chdir(working_directory) output = None stderr_append = '' with open(stderr_file, 'a', 1) as err: with open(stdout_file, 'a', 1) as out: with contextlib.redirect_stdout(out) if not debug else no_context(): with contextlib.redirect_stderr(err) if not debug else no_context(): try: output = func(entry) except Exception: stderr_append = traceback.format_exc() delete_empty_files([stderr_file, stdout_file]) delete_empty_directories([working_directory]) if stderr_append: append_text(stderr_file, stderr_append) _log.log(group=GRP_ERROR, message=MSG_ERROR_ANALYSIS(stderr_file)) _try_store(output,serializer,output_file,_log,_err) os.chdir(cwd) analysis_directory = os.path.join(entry['path'], FILE_ANALYSIS) shutil.rmtree(analysis_directory, ignore_errors=True) shutil.move(analysis_directory_tmp, entry['path']) shutil.rmtree(os.path.split(analysis_directory_tmp)[0], ignore_errors=True) except Exception: os.chdir(cwd) class RE: def __init__(self,s): self.s=s def load(search_pattern='*', path='', ID=None, no_objects=False, need_unique=True,include_modules=False,parameters=None,fix_broken_summary_txt=False):#TODO remove fix_borken_summary_txt ''' Load scilog entry/entries. :param search_pattern: Shell-style glob/search pattern using wildcards If there are multiple entries of the same name (those are stored as <name>/v0 <name>/v1 ... in the filesystem) and they should all be returned, use `search_pattern=<name>/v*` and `need_unique=False` :type search_pattern: String, e.g. search_pattern='foo*' matches `foobar` :param path: Path of exact location if known (possibly only partially), relative or absolute :type path: String, e.g. '/home/username/<project>' or '<project>' :param no_objects: To save time, only load information about scilog entry, not results :type no_objects: Boolean :param need_unique: Require unique identification of scilog entry. :type need_unique: Boolean :param parameters: Search pattern that is applied to the scilog parameters :type parameters: Dictionary of regular expression strings or objects (which will be converted to strings) :return: Scilog entry :rtype: If need_unique=True, a single Namespace obejct If need_unique=False, a generator of such objects ''' deserializer = pickle try: import dill deserializer = dill except ImportError: warnings.warn(MSG_WARN_DILL) if os.sep in search_pattern and path == '':#Use absolute path part of search pattern as path, if existent temp_path, temp_search_pattern = search_pattern.rsplit(os.sep, 1) if os.path.isabs(temp_path): path, search_pattern = temp_path, temp_search_pattern if search_pattern[-1]!='*': search_pattern = search_pattern+'*' entries = [] entries.extend(find_directories(search_pattern, path=path)) entries.extend(find_directories('*/' + search_pattern, path=path)) entries = [entry for entry in entries if _is_experiment_directory(entry)] def get_output(entry, no_objects): file_name = os.path.join(entry, FILE_INFO) with open(file_name, 'r') as fp: try: info = json.load(fp) except Exception: raise ValueError(f'Problem with {file_name}') info['path'] = entry if not include_modules: del info['modules'] if not no_objects: #if isinstance(info['experiments'],dict):#Old version of scilog: # DL = info['experiments'] # info['experiments'] = [dict(zip(DL,t)) for t in zip(*DL.values())] for j,s in enumerate(info['experiments']['status'] if not fix_broken_summary_txt else ('finished' for i in itertools.count())): if s == 'finished': try: output_file_name = os.path.join(entry, FILE_EXP(j), FILE_OUTPUT) with open(output_file_name, 'rb') as fp: output = deserializer.load(fp) if fix_broken_summary_txt: info['experiments']['output'].append(output) else: info['experiments']['output'][j] = output except Exception: warnings.warn(MSG_ERROR_LOAD('file ' + output_file_name)) if fix_broken_summary_txt: break traceback.print_exc() if not fix_broken_summary_txt: try: input_file_name = os.path.join(entry,FILE_EXP(j),FILE_INPUT) with open(input_file_name,'rb') as fp: input = deserializer.load(fp) info['experiments']['input'][j] = input except Exception: warnings.warn(MSG_ERROR_LOAD('file ' + input_file_name)) traceback.print_exc() for key in info['experiments']: try: info['experiments'][key] = np.array(info['experiments'][key]) except Exception: pass return info if ID: partial_id = re.compile(ID) entries = [entry for entry in entries if partial_id.match(get_output(entry, no_objects = True)['ID'])] if parameters: parameters = {key:(repr(value) if not isinstance(value,RE) else value) for (key,value) in parameters.items()} def matches_parameters(entry): out = get_output(entry,no_objects=True) if not 'parameters' in out: return False else: test = out['parameters'] for key,value in parameters.items(): if key not in test: return False if isinstance(value,RE): if not re.match(value.s,test[key]): return False else: if not value == test[key]: return False return True entries = [entry for entry in entries if matches_parameters(entry)] if len(entries)>1 and need_unique: basenames = [os.path.basename(get_output(entry,no_objects=True)['path']).rsplit('_',1) for entry in entries] if len(set(bn[0] for bn in basenames))==1: entries = [max(entries,key = lambda entry: get_output(entry,no_objects=True)['time'])] entries = unique(entries) if not need_unique: return (get_output(entry, no_objects=no_objects) for entry in entries) else: if len(entries) == 0: raise ValueError(MSG_ERROR_NOMATCH) if len(entries) > 1: raise ValueError(MSG_ERROR_MULTIMATCH(entries)) return get_output(entries[0], no_objects=no_objects) def _is_experiment_directory(directory): return os.path.isfile(os.path.join(directory, FILE_INFO)) def _max_mem(m, type): # @ReservedAssignment if m == '': return -1 if type == 'detail': # Output of memory_profiler package find = re.compile('.*?(\d{1,}\.\d{4}) MiB.*') matches = [find.match(line) for line in m.splitlines()] values = [float(match.groups()[0]) for match in matches if match is not None] return max(values) - min(values) else: # Output of print_peak_memory return float(m) def _get_directory(directory,func,name,no_date, debug, git, classification): basepath = _get_base_directory(directory,func,name,no_date) if debug: directory = os.path.join(basepath, FILE_DEBUG) try: shutil.rmtree(directory) except FileNotFoundError: pass os.makedirs(directory) return directory, FILE_DEBUG try_classification_based_directory = 1 if classification else 0 for attempt in range(20): # Try keyword format, then random words, fail if cannot find unused ID = random_word(length = LEN_ID,dictionary = (attempt<10)) if try_classification_based_directory: directory = os.path.join(basepath,classification+f'_{try_classification_based_directory-1}') else: directory = os.path.join(basepath,ID) try: os.makedirs(directory) if git and _git_has_tag(git,STR_GIT_TAG(ID)): delete_empty_directories([directory]) else: return directory,ID except OSError as exc: if exc.errno == errno.EEXIST: if try_classification_based_directory: try_classification_based_directory += 1 else: if try_classification_based_directory:#There was a problem creating a directory with the keyword format try_classification_based_directory = 0#Maybe illegal characters in parameters, try it with random words else:#Already tried random words, something else is wrong raise raise ValueError(MSG_ERROR_DIR) def _git_command(string, add_input=True): string = 'git ' + string output = '$ ' + string + '\n' if add_input else '' args = shlex.split(string) output += subprocess.check_output(args, stderr=subprocess.STDOUT).decode('UTF8') return output def _git_id(): return _git_command('log --format="%H" -n 1', add_input=False).rstrip() def _git_has_tag(path,tag): initial_directory = os.getcwd() os.chdir(path) try: out = _git_command('tag --list',add_input = False) return tag in out.splitlines() except subprocess.CalledProcessError: os.chdir(initial_directory) raise def _git_snapshot(path, commit_body, ID): initial_directory = os.getcwd() os.chdir(path) git_directory = _git_command('rev-parse --show-toplevel', add_input=False).rstrip() os.chdir(git_directory) active_branch = _git_command('rev-parse --abbrev-ref HEAD', add_input=False) try: out = '' out += _git_command('add --all') out += _git_command('rm -r --cached .') out += _git_command('add --all') out += _git_command('commit --allow-empty -m "{0} \n {1}"'.format(STR_GIT_COMMIT_TITLE(active_branch), commit_body)) out += _git_command('tag {}'.format(STR_GIT_TAG(ID))) snap_id = _git_id() out += _git_command('reset HEAD~1') except subprocess.CalledProcessError as e: raise GitError(traceback.format_exc(), out + '\n' + str(e.output)) except Exception: raise GitError(traceback.format_exc(), out) os.chdir(initial_directory) return snap_id, out, git_directory

#!/usr/bin/env python3 import sys from functools import reduce tree_encounter_check = lambda pos: 1 if pos == "#" else 0 def main(forest): slope_mode = [ (1, 1), (3, 1), (5, 1), (7, 1), (1, 2), ] mode_to_result = [(mode, resolve_encounters(forest, *mode)) for mode in slope_mode] for mode, result in mode_to_result: print(f"For {mode}, encounters={result}") results = [r for _, r in mode_to_result] result = reduce(lambda x, y: x * y, results) print(f"product({",".join(str(r) for r in results)}) = {result}") def resolve_encounters(forest, shift_right, shift_bottom, verbose = "-v" in sys.argv) -> int: map_length = len(forest[0]) # Resolve the initial position as the first encounter n_tree_encounter = tree_encounter_check(forest[0][0]) column = shift_right # Start from initial position + shift_right if verbose: print(f">> For Right {shift_right}, Down {shift_bottom}") print(("X" if forest[0][0] == "#" else "O") + forest[0][1:]) for row, line in enumerate(forest[1:]): if (row + 1) % shift_bottom != 0: if verbose: print(line) continue # Consider this new tile column_rel = column % map_length n_tree_encounter += tree_encounter_check(line[column_rel]) if verbose: marked_line = line[0:column_rel] + ("X" if line[column_rel] == "#" else "O") + line[column_rel+1:] print(marked_line) # And continue to the next slope column += shift_right return n_tree_encounter if __name__ == '__main__': main([i.strip() for i in open(sys.argv[1]).readlines()])

#!/usr/bin/env python3 import sys from functools import reduce tree_encounter_check = lambda pos: 1 if pos == "#" else 0 def main(forest): slope_mode = [ (1, 1), (3, 1), (5, 1), (7, 1), (1, 2), ] mode_to_result = [(mode, resolve_encounters(forest, *mode)) for mode in slope_mode] for mode, result in mode_to_result: print(f"For {mode}, encounters={result}") results = [r for _, r in mode_to_result] result = reduce(lambda x, y: x * y, results) print(f"product({','.join(str(r) for r in results)}) = {result}") def resolve_encounters(forest, shift_right, shift_bottom, verbose = "-v" in sys.argv) -> int: map_length = len(forest[0]) # Resolve the initial position as the first encounter n_tree_encounter = tree_encounter_check(forest[0][0]) column = shift_right # Start from initial position + shift_right if verbose: print(f">> For Right {shift_right}, Down {shift_bottom}") print(("X" if forest[0][0] == "#" else "O") + forest[0][1:]) for row, line in enumerate(forest[1:]): if (row + 1) % shift_bottom != 0: if verbose: print(line) continue # Consider this new tile column_rel = column % map_length n_tree_encounter += tree_encounter_check(line[column_rel]) if verbose: marked_line = line[0:column_rel] + ("X" if line[column_rel] == "#" else "O") + line[column_rel+1:] print(marked_line) # And continue to the next slope column += shift_right return n_tree_encounter if __name__ == '__main__': main([i.strip() for i in open(sys.argv[1]).readlines()])

from collections import defaultdict from datetime import date, datetime, timedelta from dateutil.relativedelta import relativedelta from typing import Callable from itertools import product from functools import lru_cache from time import time import platform import multiprocessing #empyrical风险指标计算模块 from empyrical import (sortino_ratio,omega_ratio,annual_volatility,cagr,conditional_value_at_risk,downside_risk,stability_of_timeseries,tail_ratio,value_at_risk) import random import os import traceback import numpy as np np.seterr(divide='ignore',invalid='ignore') import matplotlib.pyplot as plt #matplotlib 美化样式：bmh，ggplot plt.style.use("ggplot") import scipy.stats as scs import seaborn as sns from pandas import DataFrame from deap import creator, base, tools, algorithms import redis import zlib import pickle REDIS_CLIENT =redis.Redis("localhost",12580) from vnpy.trader.constant import (Direction, Offset, Exchange, Interval, Status,OrderType) from vnpy.trader.database import database_manager from vnpy.trader.object import OrderData, TradeData, BarData, TickData from vnpy.trader.utility import (extract_vt_symbol,round_to) from vnpy.app.cta_strategy.base import (BacktestingMode, EngineType, STOPORDER_PREFIX, StopOrder, StopOrderStatus) from vnpy.app.cta_strategy.template import CtaTemplate sns.set_style('whitegrid') creator.create('FitnessMax', base.Fitness, weights=(1.0,)) #优化方向1求最大值，-1求最小值 creator.create('Individual', list, fitness=creator.FitnessMax) #年总交易日 TRADING_DAY = 365#365，252 class OptimizationSetting: ''' 回测优化设置 ''' def __init__(self): '''''' self.params = {} self.target_name = '' def add_parameter(self, name: str, start: float, end: float = None, step: float = None ): """ 设置优化参数 """ if not end and not step: self.params[name] = [start] return if start >= end: print('参数优化起始点必须小于终止点') return if step <= 0: print('参数优化步进必须大于0') return value = start value_list = [] while value <= end: value_list.append(value) value += step self.params[name] = value_list def set_target(self, target_name: str): """设置优化目标""" self.target_name = target_name def generate_setting(self): keys = self.params.keys() values = self.params.values() products = list(product(*values)) settings = [] for p in products: setting = dict(zip(keys, p)) settings.append(setting) return settings def generate_setting_ga(self): '''''' settings_ga = [] settings = self.generate_setting() for d in settings: param = [tuple(i) for i in d.items()] settings_ga.append(param) return settings_ga class BacktestingEngine: """ 回测引擎 """ engine_type = EngineType.BACKTESTING gateway_name = 'BACKTESTING' def __init__(self): self.vt_symbol = '' self.symbol = '' self.exchange = None self.start = None self.end = None self.rate = 0 self.slippage = 0 self.size = 1 self.price_tick = 0 self.capital = 100000 self.strategy_class = None self.strategy = None self.tick: TickData self.bar: BarData self.datetime = None self.interval = None self.days = 0 self.callback = None self.history_data = [] self.stop_order_count = 0 self.stop_orders = {} self.active_stop_orders = {} self.limit_order_count = 0 self.limit_orders = {} self.active_limit_orders = {} self.trade_count = 0 self.trades = {} self.logs = [] self.daily_results = {} self.daily_df = None #保存回测结果，优化结果路径 self.result_path = None # 持仓盁亏初始化 self.long_avg_cost = 0 #多头持仓均价 self.short_avg_cost = 0 #空头持仓均价 self.long_pos = 0 #多头仓位 self.short_pos = 0 #空头仓位 self.long_profit_total = 0 #多头总盈亏 self.short_profit_total = 0 #空头总盈亏 #净值指标 self.net_value = 0 self.net_value_list = [] #月度盈亏参数 self.last_month_date = None self.month_pnl = 0 self.month_dict = {} def set_capital(self,capital): """设置初始资金""" self.capital = capital def clear_data(self): ''' Clear all data of last backtesting. ''' self.strategy = None self.tick = None self.bar = None self.datetime = None self.stop_order_count = 0 self.stop_orders.clear() self.active_stop_orders.clear() self.limit_order_count = 0 self.limit_orders.clear() self.active_limit_orders.clear() self.trade_count = 0 self.trades.clear() self.logs.clear() self.daily_results.clear() def set_parameters(self, vt_symbol: str, start: datetime, rate: float, slippage: float, size: float, price_tick: float, capital: int = 0, end: datetime = None, mode: BacktestingMode = BacktestingMode.BAR, ): '''''' self.mode = mode self.vt_symbol = vt_symbol if self.mode == BacktestingMode.BAR: self.interval = Interval.MINUTE self.rate = rate self.slippage = slippage self.size = size self.price_tick = price_tick self.start = start self.symbol, exchange,gateway_name = extract_vt_symbol(vt_symbol) self.exchange = Exchange(exchange) if capital: self.capital = capital if end: self.end = end if mode: self.mode = mode def add_strategy(self, strategy_class: type, setting: dict): '''''' self.strategy_class = strategy_class self.strategy = strategy_class(self, strategy_class.__name__, self.vt_symbol, setting ) #初始化策略盈亏参数 self.strategy.capital = 0 #初始资金 self.strategy.balance = self.capital #总资金 self.strategy.long_pos = 0 #多头仓位 self.strategy.short_pos = 0 #空头仓位 self.strategy.long_profit = 0 #多头收益 self.strategy.short_profit = 0 #空头收益 self.strategy.size = self.size #每手乘数 self.strategy.price_tick = self.price_tick #最小价格变动 self.strategy.active_limit_orders = self.active_limit_orders #未成交限价单 self.strategy.active_stop_orders = self.active_stop_orders #未成交停止单 if setting: unactive_param = [loss_param for loss_param in list(setting.keys()) if loss_param not in self.strategy.parameters] assert not unactive_param,f"不在策略参数列表内的回测参数:{unactive_param}" def load_data(self): """加载历史数据""" self.output("开始加载历史数据") if not self.end: self.end = datetime.now() self.history_data.clear() #载入数据前清除历史数据 assert self.start < self.end,"回测开始时间必须小于结束时间，请核实！" if self.mode == BacktestingMode.BAR: self.history_data = load_bar_data(self.symbol, self.exchange, self.interval, self.start, self.end) else: self.history_data = load_tick_data(self.symbol, self.exchange, self.start, self.end) self.output(f"历史数据加载完成，数据量：{len(self.history_data)}") def run_backtesting(self): """回放历史数据""" if self.mode == BacktestingMode.BAR: func = self.new_bar else: func = self.new_tick self.strategy.on_init() # Use the first [days] of history data for initializing strategy day_count = 0 ix = 0 for ix, data in enumerate(self.history_data): if self.datetime and data.datetime.day != self.datetime.day: day_count += 1 if day_count >= self.days: break self.datetime = data.datetime try: self.callback(data) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.strategy.inited = True self.output('策略初始化完成') self.strategy.on_start() self.strategy.trading = True self.output('开始回放历史数据') #回放history_data数据到on_tick/on_bar for data in self.history_data[ix:]: try: func(data) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.output('历史数据回放结束') def calculate_result(self): """ 返回daily_df:DataFrame """ self.output('开始计算逐日盯市盈亏') if not self.trades: self.output('成交记录为空，无法计算') return # Add trade data into daily reuslt. for trade in self.trades.values(): trade_date = trade.datetime.date() daily_result = self.daily_results[trade_date] daily_result.add_trade(trade) # Calculate daily result by iteration. pre_close = 0 start_pos = 0 for daily_result in self.daily_results.values(): daily_result.calculate_pnl(pre_close, start_pos, self.size, self.rate, self.slippage ) pre_close = daily_result.close_price start_pos = daily_result.end_pos # Generate dataframe results = defaultdict(list) for daily_result in self.daily_results.values(): for key, value in daily_result.__dict__.items(): results[key].append(value) self.daily_df = DataFrame.from_dict(results).set_index('date') self.output('逐日盯市盈亏计算完成') return self.daily_df #---------------------------------------------------------------------- def statistics_status(self,array): """返回array均值，标准差，偏度，峰度""" stats = scs.describe(array) return stats[2],np.sqrt(stats[3]),stats[4],stats[5] #---------------------------------------------------------------------- def calculate_statistics(self, df: DataFrame = None,strategy_name=None,write_result=True): """计算回测结果""" from pyecharts.charts import (Bar,Line,Graph,Gauge,Page)#柱状图，折线图，关系图，仪表盘,多图同表 from pyecharts import options as opts self.output('开始计算策略统计指标') if df is None: #初始化统计变量 start_date = '' end_date = '' total_days = 0 profit_days = 0 loss_days = 0 end_balance = 0 max_drawdown = 0 max_drawdown_percent = 0 max_drawdown_duration = 0 total_net_pnl = 0 daily_net_pnl = 0 total_commission = 0 daily_commission = 0 total_slippage = 0 daily_slippage = 0 total_turnover = 0 daily_turnover = 0 total_trade_count = 0 daily_trade_count = 0 total_return = 0 annual_return = 0 return_mean = 0 return_std = 0 return_skew = 0 return_kurt = 0 sharpe_ratio = 0 calmar_ratio = 0 return_drawdown = 0 return_drawdown_ratio = 0 sortino_info = 0 omega_info = 0 annual_volatility_info = 0 cagr_info = 0 annual_downside_risk = 0 c_var = 0 var_info = 0 calmar_ratio = 0 stability_return = 0 tail_ratio_info = 0 else: # Calculate balance related time series data trades_list =[] #成交明细列表 df['balance'] = df['net_pnl'].cumsum() + self.capital #总资金 df['return'] = (np.log(df['balance']) - np.log(df['balance'].shift(1))).fillna(0) #净收益率 df['highlevel'] = (df['balance'].rolling( min_periods=1, window=len(df), center=False).max()) #净值高点 df['drawdown'] = df['balance'] - df['highlevel'] df['ddpercent'] = df['drawdown'] / df['highlevel'] * 100 #回撤百分比 # Calculate statistics value start_date = df.index[0] end_date = df.index[-1] total_days = len(df) profit_days = len(df[df['net_pnl'] > 0]) loss_days = len(df[df['net_pnl'] < 0]) end_balance = df['balance'].iloc[-1] #最终收益 max_drawdown = df['drawdown'].min() #最大回撤 max_drawdown_percent = df['ddpercent'].min() #最大回撤率 #最大回撤期，优化时max_drawdown_end可能为NAN需要做异常处理 max_drawdown_end = df["drawdown"].idxmin() if isinstance(max_drawdown_end,date): max_drawdown_start = df["balance"][:max_drawdown_end].idxmax() max_drawdown_duration = (max_drawdown_end - max_drawdown_start).days else: max_drawdown_start = "" max_drawdown_end = "" max_drawdown_duration = 0 total_net_pnl = df['net_pnl'].sum() #总净值 daily_net_pnl = total_net_pnl / total_days #日净值 total_commission = df['commission'].sum() #总手续费 daily_commission = total_commission / total_days total_slippage = df['slippage'].sum() #总滑点 daily_slippage = total_slippage / total_days total_turnover = df['turnover'].sum() daily_turnover = total_turnover / total_days total_trade_count = df['trade_count'].sum() #总交易次数 daily_trade_count = total_trade_count / total_days total_return = (end_balance / self.capital - 1) * 100 #总收益率 annual_return = total_return / total_days * TRADING_DAY #年化收益率 #收益率均值，标准差，偏度，峰度 return_mean,return_std,return_skew, return_kurt = self.statistics_status(df['return'].values) #sortino_info sortino_info = sortino_ratio(df['return']) omega_info = omega_ratio(df['return']) #年化波动率 annual_volatility_info = annual_volatility(df['return']) #年化复合增长率 cagr_info = cagr(df['return']) #年化下行风险率 annual_downside_risk = downside_risk(df['return']) """CVaR即条件风险价值，其含义为在投资组合的损失超过某个给定VaR值的条件下，该投资组合的平均损失值。""" c_var = conditional_value_at_risk(df['return']) """风险价值（VaR）是对投资损失风险的一种度量。它估计在正常的市场条件下，在设定的时间段（例如一天）中，一组投资可能（以给定的概率）损失多少。金融业中的公司和监管机构通常使用VaR来衡量弥补可能损失所需的资产数量""" var_info = value_at_risk(df['return']) #calmar_ratio:年化收益率与历史最大回撤率之间的比率 calmar_ratio = annual_return / abs(max_drawdown_percent) #收益稳定率 stability_return = stability_of_timeseries(df['return']) #尾部比率0.25 == 1/4,收益1，风险4 tail_ratio_info = tail_ratio(df['return']) if return_std: sharpe_ratio = return_mean / return_std * np.sqrt(TRADING_DAY) else: sharpe_ratio = 0 #收益回撤比 return_drawdown = -total_net_pnl/max_drawdown #收益率回撤率比 return_drawdown_ratio = -total_return / max_drawdown_percent for index in range(len(df['balance'])): if index == 0: nets_pnl = 1 else: nets_pnl = df['balance'][index]/df['balance'][index-1]-1 self.net_value += nets_pnl self.net_value_list.append(round(float(self.net_value),3)) #---------------------------------------------------------------------- if write_result: self.output('-' * 70) if hasattr(self.strategy,'strategy_name'): self.output(f"策略名称：{self.strategy.strategy_name},交易标的：{self.vt_symbol}") else: self.output(f"策略名称：{strategy_name},交易标的：{self.vt_symbol}") self.output(f"首个交易日：\t{start_date}，最后交易日：\t{end_date}，总交易日：\t{total_days}") self.output(f"盈利交易日：\t{profit_days}，亏损交易日：\t{loss_days}") self.output(f"起始资金：\t{self.capital:,.3f}，结束资金：\t{end_balance:,.3f}") self.output(f"总盈亏：\t{total_net_pnl:,.3f}") self.output(f"总收益率：\t{total_return:,.3f}%,复利净值：\t{self.net_value_list[-1]:,.3f}") self.output(f"收益回撤比：\t{return_drawdown:,.3f}") self.output(f"收益率回撤率比：\t{return_drawdown_ratio:,.3f}") self.output(f"最大回撤资金: \t{max_drawdown:,.3f},最大回撤日期:\t{max_drawdown_start}至{max_drawdown_end},最大回撤天数: \t{max_drawdown_duration}") self.output(f"最大回撤率: {max_drawdown_percent:,.3f}%") self.output(f"总手续费：\t{total_commission:,.3f}") self.output(f"总滑点：\t{total_slippage:,.3f}") self.output(f"总成交金额：\t{total_turnover:,.3f}") self.output(f"总成交笔数：\t{total_trade_count}") self.output(f"日均盈亏：\t{daily_net_pnl:,.3f}") self.output(f"日均手续费：\t{daily_commission:,.3f}") self.output(f"日均滑点：\t{daily_slippage:,.3f}") self.output(f"日均成交金额：\t{daily_turnover:,.3f}") self.output(f"日均成交笔数：\t{daily_trade_count:,.3f}") self.output(f"年化收益率：\t{annual_return:,.3f}%") self.output(f"日均收益率：\t{return_mean*100:,.3f}%，收益率标准差：\t{return_std*100:,.3f}%，收益率偏度：\t{return_skew:,.3f}，收益率峰度：\t{return_kurt:,.3f}") self.output(f"sharpe_ratio：\t{sharpe_ratio:,.3f}") self.output(f"calmar_ratio：\t{calmar_ratio:,.3f}") self.output(f"sortino_info：\t{sortino_info:,.3f}") self.output(f"omega_info：\t{omega_info:,.3f}") self.output(f"年化波动率：\t{annual_volatility_info:,.3f}") self.output(f"年化复合增长率：\t{cagr_info:,.3f}") self.output(f"年化下行风险率：\t{annual_downside_risk:,.3f}") self.output(f"c_var：\t{c_var:,.3f}") self.output(f"var_info：\t{var_info:,.3f}") self.output(f"收益稳定率：\t{stability_return:,.3f}") self.output(f"尾部比率：\t{tail_ratio_info:,.3f}") #回测统计结果和交易明细保存到backtesting_result文件夹 if hasattr(self.strategy,'strategy_name'): symbol,exchange,gateway_name = extract_vt_symbol(self.vt_symbol) path_symbol = f"{symbol}_{exchange.value}" if platform.uname().system == "Windows": self.result_path = f"C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\vnpy-2.1.0-py3.7.egg\\vnpy\\app\\cta_strategy\\backtesting_result\\{datetime.now().date()}_bcaktesting_{path_symbol}_{self.strategy.strategy_name}.csv" elif platform.uname().system == "Linux": self.result_path = f"/home/xldistance/anaconda3/lib/python3.7/site-packages/vnpy/app/cta_strategy/backtesting_result/{datetime.now().date()}_bcaktesting_{path_symbol}_{self.strategy.strategy_name}.csv" else: if platform.uname().system == "Windows": self.result_path = f"C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\vnpy-2.1.0-py3.7.egg\\vnpy\\app\\cta_strategy\\backtesting_result\\{datetime.now().date()}_bcaktesting_{strategy_name}.csv" elif platform.uname().system == "Linux": self.result_path = f"/home/xldistance/anaconda3/lib/python3.7/site-packages/vnpy/app/cta_strategy/backtesting_result/{datetime.now().date()}_bcaktesting_{strategy_name}.csv" df.to_csv(self.result_path,encoding='utf_8_sig') #保存回测统计数据到CSV #交易类转化为可读字典存到本地csv for trade_class in df['trades']: if trade_class: for trade in trade_class: trades_list.append(trade.__dict__) DataFrame(trades_list).to_csv(self.result_path.replace('_bcaktesting_','_trade_dict_'),encoding='utf_8_sig') #---------------------------------------------------------------------- #pyecharts绘图写入html,mark_point标记点，mark_point_symbol标记点图形'circle', 'diamond', 'rounddiamond', 'triangle','pin', 'arrow'可选 bar_1 = Bar() bar_1.add_xaxis(df['balance'].index.tolist()) if hasattr(self.strategy,'strategy_name'): bar_1.add_yaxis(f"策略:{self.vt_symbol}_{self.strategy.strategy_name}\n\n总资金\n\n起止时间：{df["balance"].index[0]}至{df["balance"].index[-1]}",df['balance'].tolist()) #主标题 else: bar_1.add_yaxis(f"策略:{self.vt_symbol}_{strategy_name}\n\n总资金\n\n起止时间：{df["balance"].index[0]}至{df["balance"].index[-1]}",df['balance'].tolist()) #主标题 bar_1.set_global_opts(opts.TitleOpts(title=f"资金\n\n总收益率：{total_return:,.3f}%"),toolbox_opts=opts.ToolboxOpts()) #副标题，ToolboxOpts设置工具箱配置项 bar_1.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 #成交记录画图 trade_datetime = [] trade_price = [] for trade in trades_list: trade_datetime.append(trade["datetime"]) trade_price.append(trade["price"]) trades_opts_data = [opts.MarkPointItem( name = f"orderid:{trade["orderid"]}，标的：{trade["vt_symbol"]}，方向：{trade["direction"].value}，{trade["offset"].value}，价格：{trade["price"]}，成交量：{trade["volume"]}", #成交详细信息添加到name itemstyle_opts = opts.ItemStyleOpts(color= "#ec0000" if trade["direction"].value == "多" else "#00da3c"), coord = [trade["datetime"],trade["price"] * random.randrange(1000,1010) / 1000], #标注的坐标 value = trade["direction"].value + trade["offset"].value ) for trade in trades_list] bar_2 = Line() bar_2.add_xaxis(trade_datetime) bar_2.add_yaxis(f"交易价格：交易时间：{trade_datetime[0]}至{trade_datetime[-1]}\n\n成交笔数：{len(trades_list)}",trade_price) #主标题 bar_2.set_global_opts(opts.TitleOpts(title="交易记录"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_2.set_series_opts(label_opts=opts.LabelOpts(is_show=False), #标签配置项 markpoint_opts = opts.MarkPointOpts(data = trades_opts_data, #标记的图形圆形："circle'"，方形："rect'"，圆角方形："roundRect'"，三角形："triangle'"，菱形："diamond'"，水滴："pin'"，箭头：'arrow' symbol = "pin" ), itemstyle_opts = opts.ItemStyleOpts(color = "#ec0000",color0 = "#00da3c"), ) #系列配置项 bar_3 = Bar() bar_3.add_xaxis(df['balance'].index.tolist()) bar_3.add_yaxis(f"复利净值最高点：{max(self.net_value_list)}\t复利净值最低点：{min(self.net_value_list)}",self.net_value_list) bar_3.set_global_opts(opts.TitleOpts(title="复利净值"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_3.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_4 = Bar() bar_4.add_xaxis(df['drawdown'].index.tolist()) bar_4.add_yaxis(f"回撤资金\n\n最大回撤资金：{max_drawdown:,.3f}\n最大回撤日期: \t{max_drawdown_start}至{max_drawdown_end},最大回撤天数: \t{max_drawdown_duration}",df['drawdown'].tolist()) bar_4.set_global_opts(opts.TitleOpts(title="资金"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_4.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_5 = Bar() bar_5.add_xaxis(df['ddpercent'].index.tolist()) bar_5.add_yaxis(f"回撤百分比\n\n最大回撤率：{max_drawdown_percent:,.3f}%",df['ddpercent'].tolist()) bar_5.set_global_opts(opts.TitleOpts(title="回撤百分比"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_5.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_6 = Bar() bar_6.add_xaxis(df['net_pnl'].index.tolist()) bar_6.add_yaxis(f"日盈亏\n\n最大日盈利：{df["net_pnl"].max():,.3f}\n\n最大日亏损：{df["net_pnl"].min():,.3f}",df['net_pnl'].tolist()) bar_6.set_global_opts(opts.TitleOpts(title="日盈亏"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_6.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 for pnl_index in df['net_pnl'].index: month_date = f"{pnl_index.year}-{pnl_index.month}" if month_date == self.last_month_date: self.month_pnl += df['net_pnl'][pnl_index] else: #月份减一保存实际月份收益 self.month_dict.update({month_date:self.month_pnl}) for key,value in list(self.month_dict.items()): if isinstance(key,datetime): continue key = datetime.strptime(key,"%Y-%m") - relativedelta(months = 1) self.month_dict.update({key:value}) #month_dict删除原始的str键值对 for key,value in list(self.month_dict.items()): if isinstance(key,str): self.month_dict.pop(key) self.month_pnl = df['net_pnl'][pnl_index] self.last_month_date = month_date self.month_dict.pop(list(self.month_dict.keys())[0]) max_month_pnl = max(self.month_dict.values()) min_month_pnl = min(self.month_dict.values()) bar_7 = Bar() bar_7.add_xaxis(list(self.month_dict.keys())) bar_7.add_yaxis(f"月盈亏\n\n最大月盈利：{max_month_pnl:,.3f}\n\n最大月亏损：{min_month_pnl:,.3f}",list(self.month_dict.values())) bar_7.set_global_opts(opts.TitleOpts(title="月盈亏"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_7.set_series_opts(label_opts=opts.LabelOpts(is_show=False) ) #系列配置项 hist,bin_edges= np.histogram(df['net_pnl'], bins=50) bar_8 = Bar() bar_8.add_xaxis(bin_edges[1:].tolist()) bar_8.add_yaxis("盈亏分布直方图",hist.tolist()) bar_8.set_global_opts(opts.TitleOpts(title="频数"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_8.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_9 = Bar() bar_9.add_xaxis(df['commission'].index.tolist()) bar_9.add_yaxis(f"每日手续费\n\n日最高手续费:{df["commission"].max():,.3f}",df['commission'].tolist()) bar_9.set_global_opts(opts.TitleOpts(title="手续费"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_9.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 page = Page() page.add(bar_1) page.add(bar_2) page.add(bar_3) page.add(bar_4) page.add(bar_5) page.add(bar_6) page.add(bar_7) page.add(bar_8) page.add(bar_9) #图表结果保存为html page.render(self.result_path.replace('.csv','.html')) #---------------------------------------------------------------------- statistics = { 'start_date': start_date, 'end_date': end_date, 'total_days': total_days, 'profit_days': profit_days, 'loss_days': loss_days, 'capital': self.capital, 'end_balance': end_balance, 'max_drawdown': max_drawdown, 'max_drawdown_percent': max_drawdown_percent, "max_drawdown_duration": max_drawdown_duration, 'total_net_pnl': total_net_pnl, 'daily_net_pnl': daily_net_pnl, 'total_commission': total_commission, 'daily_commission': daily_commission, 'total_slippage': total_slippage, 'daily_slippage': daily_slippage, 'total_turnover': total_turnover, 'daily_turnover': daily_turnover, 'total_trade_count': total_trade_count, 'daily_trade_count': daily_trade_count, 'total_return': total_return, 'annual_return': annual_return, 'return_mean': return_mean, 'return_std': return_std, 'return_skew': return_skew, 'return_kurt': return_kurt, 'sharpe_ratio': sharpe_ratio, 'calmar_ratio': calmar_ratio, 'sortino_info': sortino_info, 'omega_info': omega_info, 'annual_volatility_info': annual_volatility_info, 'cagr_info': cagr_info, 'annual_downside_risk': annual_downside_risk, 'c_var': c_var, 'var_info': var_info, 'stability_return': stability_return, 'tail_ratio_info': tail_ratio_info, 'return_drawdown': return_drawdown, 'return_drawdown_ratio': return_drawdown_ratio, } for key,value in statistics.items(): if value in (np.inf,-np.inf): value = 0 statistics[key] = np.nan_to_num(value) self.output("策略统计指标计算完成") return statistics #---------------------------------------------------------------------- def get_information_ratio(self,returns,benchmark=0.00008): #benchmark基准收益率 diff = returns - benchmark return np.mean(diff) / np.std(diff) * np.sqrt(TRADING_DAY) #---------------------------------------------------------------------- def show_chart(self, df: DataFrame = None): """matplotlib画图""" if df is None: return plt.figure(figsize=(10, 16)) balance_plot = plt.subplot(5, 1, 1) balance_plot.set_title('Balance') df['balance'].plot(legend=True) drawdown_plot = plt.subplot(5, 1, 2) drawdown_plot.set_title('Drawdown') drawdown_plot.fill_between(range(len(df)), df['drawdown'].values) drawdown_percent = plt.subplot(5, 1, 3) drawdown_percent.set_title('DrawdownPercent') drawdown_percent.fill_between(range(len(df)), df['ddpercent'].values) pnl_plot = plt.subplot(5, 1, 4) pnl_plot.set_title('Daily Pnl') df['net_pnl'].plot(kind='bar', legend=False, grid=False, xticks=[]) distribution_plot = plt.subplot(5, 1, 5) distribution_plot.set_title('Daily Pnl Distribution') df['net_pnl'].hist(bins=50) plt.show() def run_optimization(self, optimization_setting: OptimizationSetting,target_reverse =True): """多进程优化""" # Get optimization setting and target settings = optimization_setting.generate_setting() target_name = optimization_setting.target_name if not settings: self.output('优化参数组合为空，请检查') return if not target_name: self.output('优化目标未设置，请检查') return # Use multiprocessing pool for running backtesting with different setting pool = multiprocessing.Pool(multiprocessing.cpu_count(), maxtasksperchild=1) results = [] for setting in settings: result = (pool.apply_async(optimize, ( target_name, self.strategy_class, setting, self.vt_symbol, self.start, self.rate, self.slippage, self.size, self.price_tick, self.capital, self.end, self.mode ))) results.append(result) pool.close() pool.join() # Sort results and output result_values = [result.get() for result in results] result_values.sort(reverse=target_reverse, key=lambda result: result[1]) for value in result_values: msg = f'参数：{value[0]}, 目标：{value[1]}' self.output(msg) return result_values def run_ga_optimization(self, optimization_setting: OptimizationSetting, population_size=200, ngen_size=30): """遗传算法优化""" # Get optimization setting and target settings = optimization_setting.generate_setting_ga() target_name = optimization_setting.target_name if not settings: self.output('优化参数组合为空，请检查') return if not target_name: self.output('优化目标未设置，请检查') return # Define parameter generation function def generate_parameter(): '''''' return random.choice(settings) def mutate_individual(individual, indpb): '''''' size = len(individual) paramlist = generate_parameter() for i in range(size): if random.random() < indpb: individual[i] = paramlist[i] return individual, # Create ga object function global ga_target_name global ga_strategy_class global ga_setting global ga_vt_symbol global ga_interval global ga_start global ga_rate global ga_slippage global ga_size global ga_price_tick global ga_capital global ga_end global ga_mode ga_target_name = target_name ga_strategy_class = self.strategy_class ga_setting = settings[0] ga_vt_symbol = self.vt_symbol ga_interval = self.interval ga_start = self.start ga_rate = self.rate ga_slippage = self.slippage ga_size = self.size ga_price_tick = self.price_tick ga_capital = self.capital ga_end = self.end ga_mode = self.mode # Set up genetic algorithem toolbox = base.Toolbox() toolbox.register('individual', tools.initIterate, creator.Individual, generate_parameter) toolbox.register('population', tools.initRepeat, list, toolbox.individual) toolbox.register('mate', tools.cxTwoPoint) toolbox.register('mutate', mutate_individual, indpb=1) toolbox.register('evaluate', ga_optimize) toolbox.register('select', tools.selNSGA2) total_size = len(settings) pop_size = population_size #族群里面的个体数量 lambda_ = int(pop_size * 0.5) #每一代产生的子女数 mu = int(pop_size * 0.25) #每一代选择的个体数 cxpb = 0.5 #种群内部个体的交叉概率 mutpb = 1 - cxpb #种群内部个体的变异概率 ngen = ngen_size #产生种群代数,NGEN = 10要跑10个轮回 pop = toolbox.population(pop_size) hof = tools.ParetoFront() # end result of pareto front stats = tools.Statistics(lambda ind: ind.fitness.values) np.set_printoptions(suppress=True) stats.register('mean', np.mean, axis=0) stats.register('std', np.std, axis=0) stats.register('min', np.min, axis=0) stats.register('max', np.max, axis=0) # Multiprocessing is not supported yet. # pool = multiprocessing.Pool(multiprocessing.cpu_count()) # toolbox.register('map', pool.map) # Run ga optimization self.output(f'参数优化空间：{total_size}') self.output(f'每代族群总数：{pop_size}') self.output(f'优良筛选个数：{mu}') self.output(f'迭代次数：{ngen}') self.output(f'交叉概率：{cxpb:.0%}') self.output(f'突变概率：{mutpb:.0%}') start = time() algorithms.eaMuPlusLambda( pop, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats, halloffame=hof ) end = time() cost = int((end - start)) self.output(f'遗传算法优化完成，耗时{cost}秒') # Return result list results = [] for parameter_values in hof: setting = dict(parameter_values) target_value = ga_optimize(parameter_values)[0] results.append((setting, target_value, {})) self.output(results) return results def update_daily_close(self, price: float): '''''' d = self.datetime.date() daily_result = self.daily_results.get(d, None) if daily_result: daily_result.close_price = price else: self.daily_results[d] = DailyResult(d, price) def new_bar(self, bar: BarData): '''''' self.bar = bar self.datetime = bar.datetime self.cross_limit_order() #先撮合限价单 self.cross_stop_order() #再撮合停止单 self.strategy.on_bar(bar) #推送K线到策略中 self.update_postion() #更新持仓数据 self.update_daily_close(bar.close_price) def new_tick(self, tick: TickData): '''''' self.tick = tick self.datetime = tick.datetime self.cross_limit_order() self.cross_stop_order() self.strategy.on_tick(tick) self.update_postion() #更新持仓数据 self.update_daily_close(tick.last_price) def cross_limit_order(self): ''' Cross limit order with last bar/tick data. ''' if self.mode == BacktestingMode.BAR: long_cross_price = self.bar.low_price short_cross_price = self.bar.high_price long_best_price = self.bar.open_price short_best_price = self.bar.open_price else: long_cross_price = self.tick.ask_price_1 short_cross_price = self.tick.bid_price_1 long_best_price = long_cross_price short_best_price = short_cross_price for order in list(self.active_limit_orders.values()): is_submitting = False # Push order update with status 'not traded' (pending). if order.status == Status.SUBMITTING: is_submitting = True order.status = Status.NOTTRADED self.strategy.on_order(order) # Check whether limit orders can be filled. long_cross = ( order.direction == Direction.LONG and order.price >= long_cross_price and 0 < long_cross_price < 9999999 ) short_cross = ( order.direction == Direction.SHORT and order.price <= short_cross_price and 0 < short_cross_price < 9999999 ) if not long_cross and not short_cross: continue # Push order udpate with status 'all traded' (filled). order.traded = order.volume order.status = Status.ALLTRADED self.active_limit_orders.pop(order.vt_orderid) self.strategy.on_order(order) # Push trade update self.trade_count += 1 #直接成交使用order.price作为交易价 trade_price = order.price #计算挂单成交价 if long_cross: if is_submitting: trade_price = min(order.price, long_best_price) pos_change = order.volume elif short_cross: if is_submitting: trade_price = max(order.price, short_best_price) pos_change = -order.volume trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, date=self.datetime.strftime('%Y%m%d'), time=self.datetime.strftime('%H:%M:%S'), gateway_name=self.gateway_name, ) trade.datetime = self.datetime self.strategy.pos += pos_change self.strategy.on_trade(trade) self.trades[trade.vt_tradeid] = trade # 更新持仓数据 self.update_postion(trade=trade) def cross_stop_order(self): ''' Cross stop order with last bar/tick data. ''' if self.mode == BacktestingMode.BAR: long_cross_price = self.bar.high_price short_cross_price = self.bar.low_price long_best_price = self.bar.open_price short_best_price = self.bar.open_price else: long_cross_price = self.tick.last_price short_cross_price = self.tick.last_price long_best_price = long_cross_price short_best_price = short_cross_price for stop_order in list(self.active_stop_orders.values()): # Check whether stop order can be triggered. long_cross = ( stop_order.direction == Direction.LONG and stop_order.price <= long_cross_price ) short_cross = ( stop_order.direction == Direction.SHORT and stop_order.price >= short_cross_price ) if not long_cross and not short_cross: continue # Create order data. self.limit_order_count += 1 order = OrderData( symbol=self.symbol, exchange=self.exchange, orderid=str(self.limit_order_count), direction=stop_order.direction, offset=stop_order.offset, price=stop_order.price, volume=stop_order.volume, traded=stop_order.volume, status=Status.ALLTRADED, gateway_name=self.gateway_name, ) order.datetime = self.datetime self.limit_orders[order.vt_orderid] = order # Create trade data. if long_cross: trade_price = max(stop_order.price, long_best_price) pos_change = order.volume else: trade_price = min(stop_order.price, short_best_price) pos_change = -order.volume self.trade_count += 1 trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, date=self.datetime.strftime('%Y%m%d'), time=self.datetime.strftime('%H:%M:%S'), gateway_name=self.gateway_name, ) trade.datetime = self.datetime self.trades[trade.vt_tradeid] = trade # Update stop order. stop_order.vt_orderids.append(order.vt_orderid) stop_order.status = StopOrderStatus.TRIGGERED if stop_order.stop_orderid in self.active_stop_orders: self.active_stop_orders.pop(stop_order.stop_orderid) # Push update to strategy. self.strategy.on_stop_order(stop_order) self.strategy.on_order(order) self.strategy.pos += pos_change self.strategy.on_trade(trade) # 更新持仓数据 self.update_postion(trade=trade) #---------------------------------------------------------------------- def update_postion(self, trade =None): """持仓监控""" if trade: if trade.direction == Direction.LONG: # 做多单 if trade.offset == Offset.OPEN: long_cost = self.long_avg_cost * self.long_pos long_cost += trade.price * trade.volume # 平均成本 self.long_pos += trade.volume if self.long_pos > 0: self.long_avg_cost = round(long_cost / float(self.long_pos), 3) else: self.short_pos -= trade.volume else: # 做空单 if trade.offset == Offset.OPEN: short_cost = self.short_avg_cost * self.short_pos short_cost += trade.price * trade.volume # 平均成本 self.short_pos += trade.volume if self.short_pos > 0: self.short_avg_cost = round(short_cost / float(self.short_pos), 3) else: self.long_pos -= trade.volume # 多/空仓收益 if self.mode == BacktestingMode.BAR: last_price = self.bar.close_price else: last_price = self.tick.last_price long_profit = (last_price - self.long_avg_cost) * self.long_pos * self.size short_profit = (self.short_avg_cost - last_price) * self.short_pos * self.size if trade: if trade.direction == Direction.LONG: self.long_profit_total += long_profit if trade.direction == Direction.SHORT: self.short_profit_total += short_profit self.strategy.long_pos = self.long_pos self.strategy.short_pos = self.short_pos self.strategy.long_profit = long_profit self.strategy.short_profit = short_profit self.strategy.balance = self.capital + self.long_profit_total + self.short_profit_total def load_bar( self, vt_symbol: str, days: int, interval: Interval, callback: Callable ): '''''' self.days = days self.callback = callback def load_tick(self, vt_symbol: str, days: int, callback: Callable): '''''' self.days = days self.callback = callback def send_order(self,vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, stop: bool,line:bool, lock: bool,strategy:CtaTemplate,order_type:OrderType): """ 发送委托单 """ #价格，发单量取整到最小变动 price = round_to(price, self.price_tick) volume = round_to(volume, 1) #过滤非正常下单价格与委托量 if not price or not volume: return [] #平仓时仓位为0直接返回 if offset == Offset.CLOSE: if self.strategy.pos == 0: return if stop: vt_orderid = self.send_stop_order(vt_symbol,direction, offset, price, volume,self,OrderType.STOP) else: vt_orderid = self.send_limit_order(vt_symbol,direction, offset, price, volume,self,OrderType.LIMIT) return [vt_orderid] def send_stop_order(self, vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, strategy: CtaTemplate, order_type: OrderType, ): """ 发送本地停止单 """ self.stop_order_count += 1 stop_order = StopOrder( vt_symbol=self.vt_symbol, direction=direction, offset=offset, price=price, volume=volume, stop_orderid=f'{STOPORDER_PREFIX}.{self.stop_order_count}', strategy_name=self.strategy.strategy_name, ) self.strategy.on_stop_order(stop_order) self.active_stop_orders[stop_order.stop_orderid] = stop_order self.stop_orders[stop_order.stop_orderid] = stop_order return stop_order.stop_orderid def send_limit_order(self, vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, strategy: CtaTemplate, order_type: OrderType, ): '''''' self.limit_order_count += 1 order = OrderData( symbol=self.symbol, exchange=self.exchange, orderid=str(self.limit_order_count), direction=direction, offset=offset, price=price, volume=volume, traded=volume, status=Status.NOTTRADED, gateway_name=self.gateway_name, ) order.datetime = self.datetime self.active_limit_orders[order.vt_orderid] = order self.limit_orders[order.vt_orderid] = order return order.vt_orderid def cancel_order(self, strategy: CtaTemplate, vt_orderid: str): """ 用vt_orderid撤销委托单 """ if vt_orderid.startswith(STOPORDER_PREFIX): self.cancel_stop_order(strategy, vt_orderid) else: self.cancel_limit_order(strategy, vt_orderid) def cancel_stop_order(self, strategy: CtaTemplate, vt_orderid: str): '''''' if vt_orderid not in self.active_stop_orders: return stop_order = self.active_stop_orders.pop(vt_orderid) stop_order.status = StopOrderStatus.CANCELLED self.strategy.on_stop_order(stop_order) def cancel_limit_order(self, strategy: CtaTemplate, vt_orderid: str): '''''' if vt_orderid not in self.active_limit_orders: return order = self.active_limit_orders.pop(vt_orderid) order.status = Status.CANCELLED self.strategy.on_order(order) def cancel_all(self, strategy: CtaTemplate): ''' Cancel all orders, both limit and stop. ''' vt_orderids = list(self.active_limit_orders.keys()) for vt_orderid in vt_orderids: self.cancel_limit_order(strategy, vt_orderid) stop_orderids = list(self.active_stop_orders.keys()) for vt_orderid in stop_orderids: self.cancel_stop_order(strategy, vt_orderid) def write_log(self, msg: str, strategy: CtaTemplate = None): """ Write log message. """ msg = '{0}\t{1}'.format(self.datetime,msg) self.logs.append(msg) def send_email(self, msg: str, strategy: CtaTemplate = None): ''' Send email to default receiver. ''' pass def sync_strategy_data(self, strategy: CtaTemplate = None): pass def get_engine_type(self): ''' Return engine type. ''' return self.engine_type def put_strategy_event(self, strategy: CtaTemplate): ''' Put an event to update strategy status. ''' pass def output(self, msg): ''' Output message of backtesting engine. ''' print(f'{datetime.now()}\t{msg}') def get_all_trades(self): """ Return all trade data of current backtesting result. """ return list(self.trades.values()) def get_all_orders(self): """ Return all limit order data of current backtesting result. """ return list(self.limit_orders.values()) def get_all_daily_results(self): """ Return all daily result data. """ return list(self.daily_results.values()) class DailyResult: '''''' def __init__(self, date: date, close_price: float): '''''' self.date = date self.close_price = close_price self.pre_close = 0 self.trades = [] self.trade_count = 0 self.start_pos = 0 self.end_pos = 0 self.turnover = 0 self.commission = 0 self.slippage = 0 self.trading_pnl = 0 self.holding_pnl = 0 self.total_pnl = 0 self.net_pnl = 0 def add_trade(self, trade: TradeData): '''''' self.trades.append(trade) def calculate_pnl( self, pre_close: float, start_pos: float, size: int, rate: float, slippage: float, ): '''''' self.pre_close = pre_close # Holding pnl is the pnl from holding position at day start self.start_pos = start_pos self.end_pos = start_pos self.holding_pnl = self.start_pos * (self.close_price - self.pre_close) * size # Trading pnl is the pnl from new trade during the day self.trade_count = len(self.trades) for trade in self.trades: if trade.direction == Direction.LONG: pos_change = trade.volume else: pos_change = -trade.volume turnover = trade.price * trade.volume * size self.trading_pnl += pos_change * (self.close_price - trade.price) * size self.end_pos += pos_change self.turnover += turnover self.commission += turnover * rate self.slippage += trade.volume * size * slippage # Net pnl takes account of commission and slippage cost self.total_pnl = self.trading_pnl + self.holding_pnl self.net_pnl = self.total_pnl - self.commission - self.slippage def optimize( target_name: str, strategy_class: CtaTemplate, setting: dict, vt_symbol: str, start: datetime, rate: float, slippage: float, size: float, price_tick: float, capital: int, end: datetime, mode: BacktestingMode ): ''' Function for running in multiprocessing.pool ''' engine = BacktestingEngine() engine.clear_data() engine.set_parameters( vt_symbol=vt_symbol, start=start, rate=rate, slippage=slippage, size=size, price_tick=price_tick, capital=capital, end=end, mode=mode ) engine.add_strategy(strategy_class, setting) engine.load_data() engine.run_backtesting() daily_df = engine.calculate_result() statistics = engine.calculate_statistics(daily_df,write_result=False) target_value = statistics[target_name] return (str(setting), target_value, statistics) @lru_cache(maxsize=1000000) def _ga_optimize(parameter_values: tuple): '''''' setting = dict(parameter_values) result = optimize( ga_target_name, ga_strategy_class, setting, ga_vt_symbol, ga_start, ga_rate, ga_slippage, ga_size, ga_price_tick, ga_capital, ga_end, ga_mode ) return (result[1],) def ga_optimize(parameter_values: list): '''''' return _ga_optimize(tuple(parameter_values)) @lru_cache(maxsize=10) def load_bar_data( symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime ): """bar数据redis序列化存取""" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_bar" redis_data = REDIS_CLIENT.hget(file_name, file_name) if not redis_data: bar_data = database_manager.load_bar_data( symbol, exchange, interval, start, end ) REDIS_CLIENT.hset(file_name, file_name, zlib.compress(pickle.dumps(bar_data), 5)) else: bar_data = pickle.loads(zlib.decompress(redis_data)) return bar_data """数据缓存为pkl格式到本地硬盘""" """ dir_path = f"H:\\pickle_data\\" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_bar" pickle_path = dir_path + file_name + ".pkl" data_size =0 if not os.path.exists(pickle_path): bar_data = database_manager.load_bar_data( symbol, exchange, interval, start, end ) pickle_file = open(pickle_path,'wb') pickle.dump(bar_data,pickle_file) pickle_file.close() else: pickle_file = open(pickle_path,'rb') bar_data =pickle.load(pickle_file) pickle_file.close() #pickle_data文件夹大于50G删除缓存数据 for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: #当前目录所有文件名 data_size += os.path.getsize(dirpath + file_name) if data_size / (1024 ** 3) > 50: for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: os.remove(dirpath + file_name) return bar_data""" @lru_cache(maxsize=10) def load_tick_data( symbol: str, exchange: Exchange, start: datetime, end: datetime ): """tick数据redis序列化存取""" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_tick" redis_data = REDIS_CLIENT.hget(file_name, file_name) if not redis_data: tick_data = database_manager.load_tick_data( symbol, exchange, start, end ) REDIS_CLIENT.hset(file_name, file_name, zlib.compress(pickle.dumps(tick_data), 5)) else: tick_data = pickle.loads(zlib.decompress(redis_data)) return tick_data """数据缓存为pkl格式到本地硬盘""" """ dir_path = f"H:\\pickle_data\\" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_tick" pickle_path = dir_path + file_name + ".pkl" data_size =0 if not os.path.exists(pickle_path): tick_data = database_manager.load_tick_data( symbol, exchange, start, end ) pickle_file = open(pickle_path,'wb') pickle.dump(tick_data,pickle_file) pickle_file.close() else: pickle_file = open(pickle_path,'rb') tick_data =pickle.load(pickle_file) pickle_file.close() #pickle_data文件夹大于50G删除缓存数据 for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: #当前目录所有文件名 data_size += os.path.getsize(dirpath + file_name) if data_size / (1024 ** 3) > 50: for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: os.remove(dirpath + file_name) return tick_data""" # GA related global value ga_end = None ga_mode = None ga_target_name = None ga_strategy_class = None ga_setting = None ga_vt_symbol = None ga_interval = None ga_start = None ga_rate = None ga_slippage = None ga_size = None ga_price_tick = None ga_capital = None

from collections import defaultdict from datetime import date, datetime, timedelta from dateutil.relativedelta import relativedelta from typing import Callable from itertools import product from functools import lru_cache from time import time import platform import multiprocessing #empyrical风险指标计算模块 from empyrical import (sortino_ratio,omega_ratio,annual_volatility,cagr,conditional_value_at_risk,downside_risk,stability_of_timeseries,tail_ratio,value_at_risk) import random import os import traceback import numpy as np np.seterr(divide='ignore',invalid='ignore') import matplotlib.pyplot as plt #matplotlib 美化样式：bmh，ggplot plt.style.use("ggplot") import scipy.stats as scs import seaborn as sns from pandas import DataFrame from deap import creator, base, tools, algorithms import redis import zlib import pickle REDIS_CLIENT =redis.Redis("localhost",12580) from vnpy.trader.constant import (Direction, Offset, Exchange, Interval, Status,OrderType) from vnpy.trader.database import database_manager from vnpy.trader.object import OrderData, TradeData, BarData, TickData from vnpy.trader.utility import (extract_vt_symbol,round_to) from vnpy.app.cta_strategy.base import (BacktestingMode, EngineType, STOPORDER_PREFIX, StopOrder, StopOrderStatus) from vnpy.app.cta_strategy.template import CtaTemplate sns.set_style('whitegrid') creator.create('FitnessMax', base.Fitness, weights=(1.0,)) #优化方向1求最大值，-1求最小值 creator.create('Individual', list, fitness=creator.FitnessMax) #年总交易日 TRADING_DAY = 365#365，252 class OptimizationSetting: ''' 回测优化设置 ''' def __init__(self): '''''' self.params = {} self.target_name = '' def add_parameter(self, name: str, start: float, end: float = None, step: float = None ): """ 设置优化参数 """ if not end and not step: self.params[name] = [start] return if start >= end: print('参数优化起始点必须小于终止点') return if step <= 0: print('参数优化步进必须大于0') return value = start value_list = [] while value <= end: value_list.append(value) value += step self.params[name] = value_list def set_target(self, target_name: str): """设置优化目标""" self.target_name = target_name def generate_setting(self): keys = self.params.keys() values = self.params.values() products = list(product(*values)) settings = [] for p in products: setting = dict(zip(keys, p)) settings.append(setting) return settings def generate_setting_ga(self): '''''' settings_ga = [] settings = self.generate_setting() for d in settings: param = [tuple(i) for i in d.items()] settings_ga.append(param) return settings_ga class BacktestingEngine: """ 回测引擎 """ engine_type = EngineType.BACKTESTING gateway_name = 'BACKTESTING' def __init__(self): self.vt_symbol = '' self.symbol = '' self.exchange = None self.start = None self.end = None self.rate = 0 self.slippage = 0 self.size = 1 self.price_tick = 0 self.capital = 100000 self.strategy_class = None self.strategy = None self.tick: TickData self.bar: BarData self.datetime = None self.interval = None self.days = 0 self.callback = None self.history_data = [] self.stop_order_count = 0 self.stop_orders = {} self.active_stop_orders = {} self.limit_order_count = 0 self.limit_orders = {} self.active_limit_orders = {} self.trade_count = 0 self.trades = {} self.logs = [] self.daily_results = {} self.daily_df = None #保存回测结果，优化结果路径 self.result_path = None # 持仓盁亏初始化 self.long_avg_cost = 0 #多头持仓均价 self.short_avg_cost = 0 #空头持仓均价 self.long_pos = 0 #多头仓位 self.short_pos = 0 #空头仓位 self.long_profit_total = 0 #多头总盈亏 self.short_profit_total = 0 #空头总盈亏 #净值指标 self.net_value = 0 self.net_value_list = [] #月度盈亏参数 self.last_month_date = None self.month_pnl = 0 self.month_dict = {} def set_capital(self,capital): """设置初始资金""" self.capital = capital def clear_data(self): ''' Clear all data of last backtesting. ''' self.strategy = None self.tick = None self.bar = None self.datetime = None self.stop_order_count = 0 self.stop_orders.clear() self.active_stop_orders.clear() self.limit_order_count = 0 self.limit_orders.clear() self.active_limit_orders.clear() self.trade_count = 0 self.trades.clear() self.logs.clear() self.daily_results.clear() def set_parameters(self, vt_symbol: str, start: datetime, rate: float, slippage: float, size: float, price_tick: float, capital: int = 0, end: datetime = None, mode: BacktestingMode = BacktestingMode.BAR, ): '''''' self.mode = mode self.vt_symbol = vt_symbol if self.mode == BacktestingMode.BAR: self.interval = Interval.MINUTE self.rate = rate self.slippage = slippage self.size = size self.price_tick = price_tick self.start = start self.symbol, exchange,gateway_name = extract_vt_symbol(vt_symbol) self.exchange = Exchange(exchange) if capital: self.capital = capital if end: self.end = end if mode: self.mode = mode def add_strategy(self, strategy_class: type, setting: dict): '''''' self.strategy_class = strategy_class self.strategy = strategy_class(self, strategy_class.__name__, self.vt_symbol, setting ) #初始化策略盈亏参数 self.strategy.capital = 0 #初始资金 self.strategy.balance = self.capital #总资金 self.strategy.long_pos = 0 #多头仓位 self.strategy.short_pos = 0 #空头仓位 self.strategy.long_profit = 0 #多头收益 self.strategy.short_profit = 0 #空头收益 self.strategy.size = self.size #每手乘数 self.strategy.price_tick = self.price_tick #最小价格变动 self.strategy.active_limit_orders = self.active_limit_orders #未成交限价单 self.strategy.active_stop_orders = self.active_stop_orders #未成交停止单 if setting: unactive_param = [loss_param for loss_param in list(setting.keys()) if loss_param not in self.strategy.parameters] assert not unactive_param,f"不在策略参数列表内的回测参数:{unactive_param}" def load_data(self): """加载历史数据""" self.output("开始加载历史数据") if not self.end: self.end = datetime.now() self.history_data.clear() #载入数据前清除历史数据 assert self.start < self.end,"回测开始时间必须小于结束时间，请核实！" if self.mode == BacktestingMode.BAR: self.history_data = load_bar_data(self.symbol, self.exchange, self.interval, self.start, self.end) else: self.history_data = load_tick_data(self.symbol, self.exchange, self.start, self.end) self.output(f"历史数据加载完成，数据量：{len(self.history_data)}") def run_backtesting(self): """回放历史数据""" if self.mode == BacktestingMode.BAR: func = self.new_bar else: func = self.new_tick self.strategy.on_init() # Use the first [days] of history data for initializing strategy day_count = 0 ix = 0 for ix, data in enumerate(self.history_data): if self.datetime and data.datetime.day != self.datetime.day: day_count += 1 if day_count >= self.days: break self.datetime = data.datetime try: self.callback(data) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.strategy.inited = True self.output('策略初始化完成') self.strategy.on_start() self.strategy.trading = True self.output('开始回放历史数据') #回放history_data数据到on_tick/on_bar for data in self.history_data[ix:]: try: func(data) except Exception: self.output("触发异常，回测终止") self.output(traceback.format_exc()) return self.output('历史数据回放结束') def calculate_result(self): """ 返回daily_df:DataFrame """ self.output('开始计算逐日盯市盈亏') if not self.trades: self.output('成交记录为空，无法计算') return # Add trade data into daily reuslt. for trade in self.trades.values(): trade_date = trade.datetime.date() daily_result = self.daily_results[trade_date] daily_result.add_trade(trade) # Calculate daily result by iteration. pre_close = 0 start_pos = 0 for daily_result in self.daily_results.values(): daily_result.calculate_pnl(pre_close, start_pos, self.size, self.rate, self.slippage ) pre_close = daily_result.close_price start_pos = daily_result.end_pos # Generate dataframe results = defaultdict(list) for daily_result in self.daily_results.values(): for key, value in daily_result.__dict__.items(): results[key].append(value) self.daily_df = DataFrame.from_dict(results).set_index('date') self.output('逐日盯市盈亏计算完成') return self.daily_df #---------------------------------------------------------------------- def statistics_status(self,array): """返回array均值，标准差，偏度，峰度""" stats = scs.describe(array) return stats[2],np.sqrt(stats[3]),stats[4],stats[5] #---------------------------------------------------------------------- def calculate_statistics(self, df: DataFrame = None,strategy_name=None,write_result=True): """计算回测结果""" from pyecharts.charts import (Bar,Line,Graph,Gauge,Page)#柱状图，折线图，关系图，仪表盘,多图同表 from pyecharts import options as opts self.output('开始计算策略统计指标') if df is None: #初始化统计变量 start_date = '' end_date = '' total_days = 0 profit_days = 0 loss_days = 0 end_balance = 0 max_drawdown = 0 max_drawdown_percent = 0 max_drawdown_duration = 0 total_net_pnl = 0 daily_net_pnl = 0 total_commission = 0 daily_commission = 0 total_slippage = 0 daily_slippage = 0 total_turnover = 0 daily_turnover = 0 total_trade_count = 0 daily_trade_count = 0 total_return = 0 annual_return = 0 return_mean = 0 return_std = 0 return_skew = 0 return_kurt = 0 sharpe_ratio = 0 calmar_ratio = 0 return_drawdown = 0 return_drawdown_ratio = 0 sortino_info = 0 omega_info = 0 annual_volatility_info = 0 cagr_info = 0 annual_downside_risk = 0 c_var = 0 var_info = 0 calmar_ratio = 0 stability_return = 0 tail_ratio_info = 0 else: # Calculate balance related time series data trades_list =[] #成交明细列表 df['balance'] = df['net_pnl'].cumsum() + self.capital #总资金 df['return'] = (np.log(df['balance']) - np.log(df['balance'].shift(1))).fillna(0) #净收益率 df['highlevel'] = (df['balance'].rolling( min_periods=1, window=len(df), center=False).max()) #净值高点 df['drawdown'] = df['balance'] - df['highlevel'] df['ddpercent'] = df['drawdown'] / df['highlevel'] * 100 #回撤百分比 # Calculate statistics value start_date = df.index[0] end_date = df.index[-1] total_days = len(df) profit_days = len(df[df['net_pnl'] > 0]) loss_days = len(df[df['net_pnl'] < 0]) end_balance = df['balance'].iloc[-1] #最终收益 max_drawdown = df['drawdown'].min() #最大回撤 max_drawdown_percent = df['ddpercent'].min() #最大回撤率 #最大回撤期，优化时max_drawdown_end可能为NAN需要做异常处理 max_drawdown_end = df["drawdown"].idxmin() if isinstance(max_drawdown_end,date): max_drawdown_start = df["balance"][:max_drawdown_end].idxmax() max_drawdown_duration = (max_drawdown_end - max_drawdown_start).days else: max_drawdown_start = "" max_drawdown_end = "" max_drawdown_duration = 0 total_net_pnl = df['net_pnl'].sum() #总净值 daily_net_pnl = total_net_pnl / total_days #日净值 total_commission = df['commission'].sum() #总手续费 daily_commission = total_commission / total_days total_slippage = df['slippage'].sum() #总滑点 daily_slippage = total_slippage / total_days total_turnover = df['turnover'].sum() daily_turnover = total_turnover / total_days total_trade_count = df['trade_count'].sum() #总交易次数 daily_trade_count = total_trade_count / total_days total_return = (end_balance / self.capital - 1) * 100 #总收益率 annual_return = total_return / total_days * TRADING_DAY #年化收益率 #收益率均值，标准差，偏度，峰度 return_mean,return_std,return_skew, return_kurt = self.statistics_status(df['return'].values) #sortino_info sortino_info = sortino_ratio(df['return']) omega_info = omega_ratio(df['return']) #年化波动率 annual_volatility_info = annual_volatility(df['return']) #年化复合增长率 cagr_info = cagr(df['return']) #年化下行风险率 annual_downside_risk = downside_risk(df['return']) """CVaR即条件风险价值，其含义为在投资组合的损失超过某个给定VaR值的条件下，该投资组合的平均损失值。""" c_var = conditional_value_at_risk(df['return']) """风险价值（VaR）是对投资损失风险的一种度量。它估计在正常的市场条件下，在设定的时间段（例如一天）中，一组投资可能（以给定的概率）损失多少。金融业中的公司和监管机构通常使用VaR来衡量弥补可能损失所需的资产数量""" var_info = value_at_risk(df['return']) #calmar_ratio:年化收益率与历史最大回撤率之间的比率 calmar_ratio = annual_return / abs(max_drawdown_percent) #收益稳定率 stability_return = stability_of_timeseries(df['return']) #尾部比率0.25 == 1/4,收益1，风险4 tail_ratio_info = tail_ratio(df['return']) if return_std: sharpe_ratio = return_mean / return_std * np.sqrt(TRADING_DAY) else: sharpe_ratio = 0 #收益回撤比 return_drawdown = -total_net_pnl/max_drawdown #收益率回撤率比 return_drawdown_ratio = -total_return / max_drawdown_percent for index in range(len(df['balance'])): if index == 0: nets_pnl = 1 else: nets_pnl = df['balance'][index]/df['balance'][index-1]-1 self.net_value += nets_pnl self.net_value_list.append(round(float(self.net_value),3)) #---------------------------------------------------------------------- if write_result: self.output('-' * 70) if hasattr(self.strategy,'strategy_name'): self.output(f"策略名称：{self.strategy.strategy_name},交易标的：{self.vt_symbol}") else: self.output(f"策略名称：{strategy_name},交易标的：{self.vt_symbol}") self.output(f"首个交易日：\t{start_date}，最后交易日：\t{end_date}，总交易日：\t{total_days}") self.output(f"盈利交易日：\t{profit_days}，亏损交易日：\t{loss_days}") self.output(f"起始资金：\t{self.capital:,.3f}，结束资金：\t{end_balance:,.3f}") self.output(f"总盈亏：\t{total_net_pnl:,.3f}") self.output(f"总收益率：\t{total_return:,.3f}%,复利净值：\t{self.net_value_list[-1]:,.3f}") self.output(f"收益回撤比：\t{return_drawdown:,.3f}") self.output(f"收益率回撤率比：\t{return_drawdown_ratio:,.3f}") self.output(f"最大回撤资金: \t{max_drawdown:,.3f},最大回撤日期:\t{max_drawdown_start}至{max_drawdown_end},最大回撤天数: \t{max_drawdown_duration}") self.output(f"最大回撤率: {max_drawdown_percent:,.3f}%") self.output(f"总手续费：\t{total_commission:,.3f}") self.output(f"总滑点：\t{total_slippage:,.3f}") self.output(f"总成交金额：\t{total_turnover:,.3f}") self.output(f"总成交笔数：\t{total_trade_count}") self.output(f"日均盈亏：\t{daily_net_pnl:,.3f}") self.output(f"日均手续费：\t{daily_commission:,.3f}") self.output(f"日均滑点：\t{daily_slippage:,.3f}") self.output(f"日均成交金额：\t{daily_turnover:,.3f}") self.output(f"日均成交笔数：\t{daily_trade_count:,.3f}") self.output(f"年化收益率：\t{annual_return:,.3f}%") self.output(f"日均收益率：\t{return_mean*100:,.3f}%，收益率标准差：\t{return_std*100:,.3f}%，收益率偏度：\t{return_skew:,.3f}，收益率峰度：\t{return_kurt:,.3f}") self.output(f"sharpe_ratio：\t{sharpe_ratio:,.3f}") self.output(f"calmar_ratio：\t{calmar_ratio:,.3f}") self.output(f"sortino_info：\t{sortino_info:,.3f}") self.output(f"omega_info：\t{omega_info:,.3f}") self.output(f"年化波动率：\t{annual_volatility_info:,.3f}") self.output(f"年化复合增长率：\t{cagr_info:,.3f}") self.output(f"年化下行风险率：\t{annual_downside_risk:,.3f}") self.output(f"c_var：\t{c_var:,.3f}") self.output(f"var_info：\t{var_info:,.3f}") self.output(f"收益稳定率：\t{stability_return:,.3f}") self.output(f"尾部比率：\t{tail_ratio_info:,.3f}") #回测统计结果和交易明细保存到backtesting_result文件夹 if hasattr(self.strategy,'strategy_name'): symbol,exchange,gateway_name = extract_vt_symbol(self.vt_symbol) path_symbol = f"{symbol}_{exchange.value}" if platform.uname().system == "Windows": self.result_path = f"C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\vnpy-2.1.0-py3.7.egg\\vnpy\\app\\cta_strategy\\backtesting_result\\{datetime.now().date()}_bcaktesting_{path_symbol}_{self.strategy.strategy_name}.csv" elif platform.uname().system == "Linux": self.result_path = f"/home/xldistance/anaconda3/lib/python3.7/site-packages/vnpy/app/cta_strategy/backtesting_result/{datetime.now().date()}_bcaktesting_{path_symbol}_{self.strategy.strategy_name}.csv" else: if platform.uname().system == "Windows": self.result_path = f"C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\vnpy-2.1.0-py3.7.egg\\vnpy\\app\\cta_strategy\\backtesting_result\\{datetime.now().date()}_bcaktesting_{strategy_name}.csv" elif platform.uname().system == "Linux": self.result_path = f"/home/xldistance/anaconda3/lib/python3.7/site-packages/vnpy/app/cta_strategy/backtesting_result/{datetime.now().date()}_bcaktesting_{strategy_name}.csv" df.to_csv(self.result_path,encoding='utf_8_sig') #保存回测统计数据到CSV #交易类转化为可读字典存到本地csv for trade_class in df['trades']: if trade_class: for trade in trade_class: trades_list.append(trade.__dict__) DataFrame(trades_list).to_csv(self.result_path.replace('_bcaktesting_','_trade_dict_'),encoding='utf_8_sig') #---------------------------------------------------------------------- #pyecharts绘图写入html,mark_point标记点，mark_point_symbol标记点图形'circle', 'diamond', 'rounddiamond', 'triangle','pin', 'arrow'可选 bar_1 = Bar() bar_1.add_xaxis(df['balance'].index.tolist()) if hasattr(self.strategy,'strategy_name'): bar_1.add_yaxis(f"策略:{self.vt_symbol}_{self.strategy.strategy_name}\n\n总资金\n\n起止时间：{df['balance'].index[0]}至{df['balance'].index[-1]}",df['balance'].tolist()) #主标题 else: bar_1.add_yaxis(f"策略:{self.vt_symbol}_{strategy_name}\n\n总资金\n\n起止时间：{df['balance'].index[0]}至{df['balance'].index[-1]}",df['balance'].tolist()) #主标题 bar_1.set_global_opts(opts.TitleOpts(title=f"资金\n\n总收益率：{total_return:,.3f}%"),toolbox_opts=opts.ToolboxOpts()) #副标题，ToolboxOpts设置工具箱配置项 bar_1.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 #成交记录画图 trade_datetime = [] trade_price = [] for trade in trades_list: trade_datetime.append(trade["datetime"]) trade_price.append(trade["price"]) trades_opts_data = [opts.MarkPointItem( name = f"orderid:{trade['orderid']}，标的：{trade['vt_symbol']}，方向：{trade['direction'].value}，{trade['offset'].value}，价格：{trade['price']}，成交量：{trade['volume']}", #成交详细信息添加到name itemstyle_opts = opts.ItemStyleOpts(color= "#ec0000" if trade["direction"].value == "多" else "#00da3c"), coord = [trade["datetime"],trade["price"] * random.randrange(1000,1010) / 1000], #标注的坐标 value = trade["direction"].value + trade["offset"].value ) for trade in trades_list] bar_2 = Line() bar_2.add_xaxis(trade_datetime) bar_2.add_yaxis(f"交易价格：交易时间：{trade_datetime[0]}至{trade_datetime[-1]}\n\n成交笔数：{len(trades_list)}",trade_price) #主标题 bar_2.set_global_opts(opts.TitleOpts(title="交易记录"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_2.set_series_opts(label_opts=opts.LabelOpts(is_show=False), #标签配置项 markpoint_opts = opts.MarkPointOpts(data = trades_opts_data, #标记的图形圆形："circle'"，方形："rect'"，圆角方形："roundRect'"，三角形："triangle'"，菱形："diamond'"，水滴："pin'"，箭头：'arrow' symbol = "pin" ), itemstyle_opts = opts.ItemStyleOpts(color = "#ec0000",color0 = "#00da3c"), ) #系列配置项 bar_3 = Bar() bar_3.add_xaxis(df['balance'].index.tolist()) bar_3.add_yaxis(f"复利净值最高点：{max(self.net_value_list)}\t复利净值最低点：{min(self.net_value_list)}",self.net_value_list) bar_3.set_global_opts(opts.TitleOpts(title="复利净值"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_3.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_4 = Bar() bar_4.add_xaxis(df['drawdown'].index.tolist()) bar_4.add_yaxis(f"回撤资金\n\n最大回撤资金：{max_drawdown:,.3f}\n最大回撤日期: \t{max_drawdown_start}至{max_drawdown_end},最大回撤天数: \t{max_drawdown_duration}",df['drawdown'].tolist()) bar_4.set_global_opts(opts.TitleOpts(title="资金"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_4.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_5 = Bar() bar_5.add_xaxis(df['ddpercent'].index.tolist()) bar_5.add_yaxis(f"回撤百分比\n\n最大回撤率：{max_drawdown_percent:,.3f}%",df['ddpercent'].tolist()) bar_5.set_global_opts(opts.TitleOpts(title="回撤百分比"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_5.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_6 = Bar() bar_6.add_xaxis(df['net_pnl'].index.tolist()) bar_6.add_yaxis(f"日盈亏\n\n最大日盈利：{df['net_pnl'].max():,.3f}\n\n最大日亏损：{df['net_pnl'].min():,.3f}",df['net_pnl'].tolist()) bar_6.set_global_opts(opts.TitleOpts(title="日盈亏"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_6.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 for pnl_index in df['net_pnl'].index: month_date = f"{pnl_index.year}-{pnl_index.month}" if month_date == self.last_month_date: self.month_pnl += df['net_pnl'][pnl_index] else: #月份减一保存实际月份收益 self.month_dict.update({month_date:self.month_pnl}) for key,value in list(self.month_dict.items()): if isinstance(key,datetime): continue key = datetime.strptime(key,"%Y-%m") - relativedelta(months = 1) self.month_dict.update({key:value}) #month_dict删除原始的str键值对 for key,value in list(self.month_dict.items()): if isinstance(key,str): self.month_dict.pop(key) self.month_pnl = df['net_pnl'][pnl_index] self.last_month_date = month_date self.month_dict.pop(list(self.month_dict.keys())[0]) max_month_pnl = max(self.month_dict.values()) min_month_pnl = min(self.month_dict.values()) bar_7 = Bar() bar_7.add_xaxis(list(self.month_dict.keys())) bar_7.add_yaxis(f"月盈亏\n\n最大月盈利：{max_month_pnl:,.3f}\n\n最大月亏损：{min_month_pnl:,.3f}",list(self.month_dict.values())) bar_7.set_global_opts(opts.TitleOpts(title="月盈亏"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_7.set_series_opts(label_opts=opts.LabelOpts(is_show=False) ) #系列配置项 hist,bin_edges= np.histogram(df['net_pnl'], bins=50) bar_8 = Bar() bar_8.add_xaxis(bin_edges[1:].tolist()) bar_8.add_yaxis("盈亏分布直方图",hist.tolist()) bar_8.set_global_opts(opts.TitleOpts(title="频数"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_8.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 bar_9 = Bar() bar_9.add_xaxis(df['commission'].index.tolist()) bar_9.add_yaxis(f"每日手续费\n\n日最高手续费:{df['commission'].max():,.3f}",df['commission'].tolist()) bar_9.set_global_opts(opts.TitleOpts(title="手续费"),toolbox_opts=opts.ToolboxOpts()) #设置工具箱配置项 bar_9.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #系列配置项 page = Page() page.add(bar_1) page.add(bar_2) page.add(bar_3) page.add(bar_4) page.add(bar_5) page.add(bar_6) page.add(bar_7) page.add(bar_8) page.add(bar_9) #图表结果保存为html page.render(self.result_path.replace('.csv','.html')) #---------------------------------------------------------------------- statistics = { 'start_date': start_date, 'end_date': end_date, 'total_days': total_days, 'profit_days': profit_days, 'loss_days': loss_days, 'capital': self.capital, 'end_balance': end_balance, 'max_drawdown': max_drawdown, 'max_drawdown_percent': max_drawdown_percent, "max_drawdown_duration": max_drawdown_duration, 'total_net_pnl': total_net_pnl, 'daily_net_pnl': daily_net_pnl, 'total_commission': total_commission, 'daily_commission': daily_commission, 'total_slippage': total_slippage, 'daily_slippage': daily_slippage, 'total_turnover': total_turnover, 'daily_turnover': daily_turnover, 'total_trade_count': total_trade_count, 'daily_trade_count': daily_trade_count, 'total_return': total_return, 'annual_return': annual_return, 'return_mean': return_mean, 'return_std': return_std, 'return_skew': return_skew, 'return_kurt': return_kurt, 'sharpe_ratio': sharpe_ratio, 'calmar_ratio': calmar_ratio, 'sortino_info': sortino_info, 'omega_info': omega_info, 'annual_volatility_info': annual_volatility_info, 'cagr_info': cagr_info, 'annual_downside_risk': annual_downside_risk, 'c_var': c_var, 'var_info': var_info, 'stability_return': stability_return, 'tail_ratio_info': tail_ratio_info, 'return_drawdown': return_drawdown, 'return_drawdown_ratio': return_drawdown_ratio, } for key,value in statistics.items(): if value in (np.inf,-np.inf): value = 0 statistics[key] = np.nan_to_num(value) self.output("策略统计指标计算完成") return statistics #---------------------------------------------------------------------- def get_information_ratio(self,returns,benchmark=0.00008): #benchmark基准收益率 diff = returns - benchmark return np.mean(diff) / np.std(diff) * np.sqrt(TRADING_DAY) #---------------------------------------------------------------------- def show_chart(self, df: DataFrame = None): """matplotlib画图""" if df is None: return plt.figure(figsize=(10, 16)) balance_plot = plt.subplot(5, 1, 1) balance_plot.set_title('Balance') df['balance'].plot(legend=True) drawdown_plot = plt.subplot(5, 1, 2) drawdown_plot.set_title('Drawdown') drawdown_plot.fill_between(range(len(df)), df['drawdown'].values) drawdown_percent = plt.subplot(5, 1, 3) drawdown_percent.set_title('DrawdownPercent') drawdown_percent.fill_between(range(len(df)), df['ddpercent'].values) pnl_plot = plt.subplot(5, 1, 4) pnl_plot.set_title('Daily Pnl') df['net_pnl'].plot(kind='bar', legend=False, grid=False, xticks=[]) distribution_plot = plt.subplot(5, 1, 5) distribution_plot.set_title('Daily Pnl Distribution') df['net_pnl'].hist(bins=50) plt.show() def run_optimization(self, optimization_setting: OptimizationSetting,target_reverse =True): """多进程优化""" # Get optimization setting and target settings = optimization_setting.generate_setting() target_name = optimization_setting.target_name if not settings: self.output('优化参数组合为空，请检查') return if not target_name: self.output('优化目标未设置，请检查') return # Use multiprocessing pool for running backtesting with different setting pool = multiprocessing.Pool(multiprocessing.cpu_count(), maxtasksperchild=1) results = [] for setting in settings: result = (pool.apply_async(optimize, ( target_name, self.strategy_class, setting, self.vt_symbol, self.start, self.rate, self.slippage, self.size, self.price_tick, self.capital, self.end, self.mode ))) results.append(result) pool.close() pool.join() # Sort results and output result_values = [result.get() for result in results] result_values.sort(reverse=target_reverse, key=lambda result: result[1]) for value in result_values: msg = f'参数：{value[0]}, 目标：{value[1]}' self.output(msg) return result_values def run_ga_optimization(self, optimization_setting: OptimizationSetting, population_size=200, ngen_size=30): """遗传算法优化""" # Get optimization setting and target settings = optimization_setting.generate_setting_ga() target_name = optimization_setting.target_name if not settings: self.output('优化参数组合为空，请检查') return if not target_name: self.output('优化目标未设置，请检查') return # Define parameter generation function def generate_parameter(): '''''' return random.choice(settings) def mutate_individual(individual, indpb): '''''' size = len(individual) paramlist = generate_parameter() for i in range(size): if random.random() < indpb: individual[i] = paramlist[i] return individual, # Create ga object function global ga_target_name global ga_strategy_class global ga_setting global ga_vt_symbol global ga_interval global ga_start global ga_rate global ga_slippage global ga_size global ga_price_tick global ga_capital global ga_end global ga_mode ga_target_name = target_name ga_strategy_class = self.strategy_class ga_setting = settings[0] ga_vt_symbol = self.vt_symbol ga_interval = self.interval ga_start = self.start ga_rate = self.rate ga_slippage = self.slippage ga_size = self.size ga_price_tick = self.price_tick ga_capital = self.capital ga_end = self.end ga_mode = self.mode # Set up genetic algorithem toolbox = base.Toolbox() toolbox.register('individual', tools.initIterate, creator.Individual, generate_parameter) toolbox.register('population', tools.initRepeat, list, toolbox.individual) toolbox.register('mate', tools.cxTwoPoint) toolbox.register('mutate', mutate_individual, indpb=1) toolbox.register('evaluate', ga_optimize) toolbox.register('select', tools.selNSGA2) total_size = len(settings) pop_size = population_size #族群里面的个体数量 lambda_ = int(pop_size * 0.5) #每一代产生的子女数 mu = int(pop_size * 0.25) #每一代选择的个体数 cxpb = 0.5 #种群内部个体的交叉概率 mutpb = 1 - cxpb #种群内部个体的变异概率 ngen = ngen_size #产生种群代数,NGEN = 10要跑10个轮回 pop = toolbox.population(pop_size) hof = tools.ParetoFront() # end result of pareto front stats = tools.Statistics(lambda ind: ind.fitness.values) np.set_printoptions(suppress=True) stats.register('mean', np.mean, axis=0) stats.register('std', np.std, axis=0) stats.register('min', np.min, axis=0) stats.register('max', np.max, axis=0) # Multiprocessing is not supported yet. # pool = multiprocessing.Pool(multiprocessing.cpu_count()) # toolbox.register('map', pool.map) # Run ga optimization self.output(f'参数优化空间：{total_size}') self.output(f'每代族群总数：{pop_size}') self.output(f'优良筛选个数：{mu}') self.output(f'迭代次数：{ngen}') self.output(f'交叉概率：{cxpb:.0%}') self.output(f'突变概率：{mutpb:.0%}') start = time() algorithms.eaMuPlusLambda( pop, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats, halloffame=hof ) end = time() cost = int((end - start)) self.output(f'遗传算法优化完成，耗时{cost}秒') # Return result list results = [] for parameter_values in hof: setting = dict(parameter_values) target_value = ga_optimize(parameter_values)[0] results.append((setting, target_value, {})) self.output(results) return results def update_daily_close(self, price: float): '''''' d = self.datetime.date() daily_result = self.daily_results.get(d, None) if daily_result: daily_result.close_price = price else: self.daily_results[d] = DailyResult(d, price) def new_bar(self, bar: BarData): '''''' self.bar = bar self.datetime = bar.datetime self.cross_limit_order() #先撮合限价单 self.cross_stop_order() #再撮合停止单 self.strategy.on_bar(bar) #推送K线到策略中 self.update_postion() #更新持仓数据 self.update_daily_close(bar.close_price) def new_tick(self, tick: TickData): '''''' self.tick = tick self.datetime = tick.datetime self.cross_limit_order() self.cross_stop_order() self.strategy.on_tick(tick) self.update_postion() #更新持仓数据 self.update_daily_close(tick.last_price) def cross_limit_order(self): ''' Cross limit order with last bar/tick data. ''' if self.mode == BacktestingMode.BAR: long_cross_price = self.bar.low_price short_cross_price = self.bar.high_price long_best_price = self.bar.open_price short_best_price = self.bar.open_price else: long_cross_price = self.tick.ask_price_1 short_cross_price = self.tick.bid_price_1 long_best_price = long_cross_price short_best_price = short_cross_price for order in list(self.active_limit_orders.values()): is_submitting = False # Push order update with status 'not traded' (pending). if order.status == Status.SUBMITTING: is_submitting = True order.status = Status.NOTTRADED self.strategy.on_order(order) # Check whether limit orders can be filled. long_cross = ( order.direction == Direction.LONG and order.price >= long_cross_price and 0 < long_cross_price < 9999999 ) short_cross = ( order.direction == Direction.SHORT and order.price <= short_cross_price and 0 < short_cross_price < 9999999 ) if not long_cross and not short_cross: continue # Push order udpate with status 'all traded' (filled). order.traded = order.volume order.status = Status.ALLTRADED self.active_limit_orders.pop(order.vt_orderid) self.strategy.on_order(order) # Push trade update self.trade_count += 1 #直接成交使用order.price作为交易价 trade_price = order.price #计算挂单成交价 if long_cross: if is_submitting: trade_price = min(order.price, long_best_price) pos_change = order.volume elif short_cross: if is_submitting: trade_price = max(order.price, short_best_price) pos_change = -order.volume trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, date=self.datetime.strftime('%Y%m%d'), time=self.datetime.strftime('%H:%M:%S'), gateway_name=self.gateway_name, ) trade.datetime = self.datetime self.strategy.pos += pos_change self.strategy.on_trade(trade) self.trades[trade.vt_tradeid] = trade # 更新持仓数据 self.update_postion(trade=trade) def cross_stop_order(self): ''' Cross stop order with last bar/tick data. ''' if self.mode == BacktestingMode.BAR: long_cross_price = self.bar.high_price short_cross_price = self.bar.low_price long_best_price = self.bar.open_price short_best_price = self.bar.open_price else: long_cross_price = self.tick.last_price short_cross_price = self.tick.last_price long_best_price = long_cross_price short_best_price = short_cross_price for stop_order in list(self.active_stop_orders.values()): # Check whether stop order can be triggered. long_cross = ( stop_order.direction == Direction.LONG and stop_order.price <= long_cross_price ) short_cross = ( stop_order.direction == Direction.SHORT and stop_order.price >= short_cross_price ) if not long_cross and not short_cross: continue # Create order data. self.limit_order_count += 1 order = OrderData( symbol=self.symbol, exchange=self.exchange, orderid=str(self.limit_order_count), direction=stop_order.direction, offset=stop_order.offset, price=stop_order.price, volume=stop_order.volume, traded=stop_order.volume, status=Status.ALLTRADED, gateway_name=self.gateway_name, ) order.datetime = self.datetime self.limit_orders[order.vt_orderid] = order # Create trade data. if long_cross: trade_price = max(stop_order.price, long_best_price) pos_change = order.volume else: trade_price = min(stop_order.price, short_best_price) pos_change = -order.volume self.trade_count += 1 trade = TradeData( symbol=order.symbol, exchange=order.exchange, orderid=order.orderid, tradeid=str(self.trade_count), direction=order.direction, offset=order.offset, price=trade_price, volume=order.volume, date=self.datetime.strftime('%Y%m%d'), time=self.datetime.strftime('%H:%M:%S'), gateway_name=self.gateway_name, ) trade.datetime = self.datetime self.trades[trade.vt_tradeid] = trade # Update stop order. stop_order.vt_orderids.append(order.vt_orderid) stop_order.status = StopOrderStatus.TRIGGERED if stop_order.stop_orderid in self.active_stop_orders: self.active_stop_orders.pop(stop_order.stop_orderid) # Push update to strategy. self.strategy.on_stop_order(stop_order) self.strategy.on_order(order) self.strategy.pos += pos_change self.strategy.on_trade(trade) # 更新持仓数据 self.update_postion(trade=trade) #---------------------------------------------------------------------- def update_postion(self, trade =None): """持仓监控""" if trade: if trade.direction == Direction.LONG: # 做多单 if trade.offset == Offset.OPEN: long_cost = self.long_avg_cost * self.long_pos long_cost += trade.price * trade.volume # 平均成本 self.long_pos += trade.volume if self.long_pos > 0: self.long_avg_cost = round(long_cost / float(self.long_pos), 3) else: self.short_pos -= trade.volume else: # 做空单 if trade.offset == Offset.OPEN: short_cost = self.short_avg_cost * self.short_pos short_cost += trade.price * trade.volume # 平均成本 self.short_pos += trade.volume if self.short_pos > 0: self.short_avg_cost = round(short_cost / float(self.short_pos), 3) else: self.long_pos -= trade.volume # 多/空仓收益 if self.mode == BacktestingMode.BAR: last_price = self.bar.close_price else: last_price = self.tick.last_price long_profit = (last_price - self.long_avg_cost) * self.long_pos * self.size short_profit = (self.short_avg_cost - last_price) * self.short_pos * self.size if trade: if trade.direction == Direction.LONG: self.long_profit_total += long_profit if trade.direction == Direction.SHORT: self.short_profit_total += short_profit self.strategy.long_pos = self.long_pos self.strategy.short_pos = self.short_pos self.strategy.long_profit = long_profit self.strategy.short_profit = short_profit self.strategy.balance = self.capital + self.long_profit_total + self.short_profit_total def load_bar( self, vt_symbol: str, days: int, interval: Interval, callback: Callable ): '''''' self.days = days self.callback = callback def load_tick(self, vt_symbol: str, days: int, callback: Callable): '''''' self.days = days self.callback = callback def send_order(self,vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, stop: bool,line:bool, lock: bool,strategy:CtaTemplate,order_type:OrderType): """ 发送委托单 """ #价格，发单量取整到最小变动 price = round_to(price, self.price_tick) volume = round_to(volume, 1) #过滤非正常下单价格与委托量 if not price or not volume: return [] #平仓时仓位为0直接返回 if offset == Offset.CLOSE: if self.strategy.pos == 0: return if stop: vt_orderid = self.send_stop_order(vt_symbol,direction, offset, price, volume,self,OrderType.STOP) else: vt_orderid = self.send_limit_order(vt_symbol,direction, offset, price, volume,self,OrderType.LIMIT) return [vt_orderid] def send_stop_order(self, vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, strategy: CtaTemplate, order_type: OrderType, ): """ 发送本地停止单 """ self.stop_order_count += 1 stop_order = StopOrder( vt_symbol=self.vt_symbol, direction=direction, offset=offset, price=price, volume=volume, stop_orderid=f'{STOPORDER_PREFIX}.{self.stop_order_count}', strategy_name=self.strategy.strategy_name, ) self.strategy.on_stop_order(stop_order) self.active_stop_orders[stop_order.stop_orderid] = stop_order self.stop_orders[stop_order.stop_orderid] = stop_order return stop_order.stop_orderid def send_limit_order(self, vt_symbol, direction: Direction, offset: Offset, price: float, volume: float, strategy: CtaTemplate, order_type: OrderType, ): '''''' self.limit_order_count += 1 order = OrderData( symbol=self.symbol, exchange=self.exchange, orderid=str(self.limit_order_count), direction=direction, offset=offset, price=price, volume=volume, traded=volume, status=Status.NOTTRADED, gateway_name=self.gateway_name, ) order.datetime = self.datetime self.active_limit_orders[order.vt_orderid] = order self.limit_orders[order.vt_orderid] = order return order.vt_orderid def cancel_order(self, strategy: CtaTemplate, vt_orderid: str): """ 用vt_orderid撤销委托单 """ if vt_orderid.startswith(STOPORDER_PREFIX): self.cancel_stop_order(strategy, vt_orderid) else: self.cancel_limit_order(strategy, vt_orderid) def cancel_stop_order(self, strategy: CtaTemplate, vt_orderid: str): '''''' if vt_orderid not in self.active_stop_orders: return stop_order = self.active_stop_orders.pop(vt_orderid) stop_order.status = StopOrderStatus.CANCELLED self.strategy.on_stop_order(stop_order) def cancel_limit_order(self, strategy: CtaTemplate, vt_orderid: str): '''''' if vt_orderid not in self.active_limit_orders: return order = self.active_limit_orders.pop(vt_orderid) order.status = Status.CANCELLED self.strategy.on_order(order) def cancel_all(self, strategy: CtaTemplate): ''' Cancel all orders, both limit and stop. ''' vt_orderids = list(self.active_limit_orders.keys()) for vt_orderid in vt_orderids: self.cancel_limit_order(strategy, vt_orderid) stop_orderids = list(self.active_stop_orders.keys()) for vt_orderid in stop_orderids: self.cancel_stop_order(strategy, vt_orderid) def write_log(self, msg: str, strategy: CtaTemplate = None): """ Write log message. """ msg = '{0}\t{1}'.format(self.datetime,msg) self.logs.append(msg) def send_email(self, msg: str, strategy: CtaTemplate = None): ''' Send email to default receiver. ''' pass def sync_strategy_data(self, strategy: CtaTemplate = None): pass def get_engine_type(self): ''' Return engine type. ''' return self.engine_type def put_strategy_event(self, strategy: CtaTemplate): ''' Put an event to update strategy status. ''' pass def output(self, msg): ''' Output message of backtesting engine. ''' print(f'{datetime.now()}\t{msg}') def get_all_trades(self): """ Return all trade data of current backtesting result. """ return list(self.trades.values()) def get_all_orders(self): """ Return all limit order data of current backtesting result. """ return list(self.limit_orders.values()) def get_all_daily_results(self): """ Return all daily result data. """ return list(self.daily_results.values()) class DailyResult: '''''' def __init__(self, date: date, close_price: float): '''''' self.date = date self.close_price = close_price self.pre_close = 0 self.trades = [] self.trade_count = 0 self.start_pos = 0 self.end_pos = 0 self.turnover = 0 self.commission = 0 self.slippage = 0 self.trading_pnl = 0 self.holding_pnl = 0 self.total_pnl = 0 self.net_pnl = 0 def add_trade(self, trade: TradeData): '''''' self.trades.append(trade) def calculate_pnl( self, pre_close: float, start_pos: float, size: int, rate: float, slippage: float, ): '''''' self.pre_close = pre_close # Holding pnl is the pnl from holding position at day start self.start_pos = start_pos self.end_pos = start_pos self.holding_pnl = self.start_pos * (self.close_price - self.pre_close) * size # Trading pnl is the pnl from new trade during the day self.trade_count = len(self.trades) for trade in self.trades: if trade.direction == Direction.LONG: pos_change = trade.volume else: pos_change = -trade.volume turnover = trade.price * trade.volume * size self.trading_pnl += pos_change * (self.close_price - trade.price) * size self.end_pos += pos_change self.turnover += turnover self.commission += turnover * rate self.slippage += trade.volume * size * slippage # Net pnl takes account of commission and slippage cost self.total_pnl = self.trading_pnl + self.holding_pnl self.net_pnl = self.total_pnl - self.commission - self.slippage def optimize( target_name: str, strategy_class: CtaTemplate, setting: dict, vt_symbol: str, start: datetime, rate: float, slippage: float, size: float, price_tick: float, capital: int, end: datetime, mode: BacktestingMode ): ''' Function for running in multiprocessing.pool ''' engine = BacktestingEngine() engine.clear_data() engine.set_parameters( vt_symbol=vt_symbol, start=start, rate=rate, slippage=slippage, size=size, price_tick=price_tick, capital=capital, end=end, mode=mode ) engine.add_strategy(strategy_class, setting) engine.load_data() engine.run_backtesting() daily_df = engine.calculate_result() statistics = engine.calculate_statistics(daily_df,write_result=False) target_value = statistics[target_name] return (str(setting), target_value, statistics) @lru_cache(maxsize=1000000) def _ga_optimize(parameter_values: tuple): '''''' setting = dict(parameter_values) result = optimize( ga_target_name, ga_strategy_class, setting, ga_vt_symbol, ga_start, ga_rate, ga_slippage, ga_size, ga_price_tick, ga_capital, ga_end, ga_mode ) return (result[1],) def ga_optimize(parameter_values: list): '''''' return _ga_optimize(tuple(parameter_values)) @lru_cache(maxsize=10) def load_bar_data( symbol: str, exchange: Exchange, interval: Interval, start: datetime, end: datetime ): """bar数据redis序列化存取""" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_bar" redis_data = REDIS_CLIENT.hget(file_name, file_name) if not redis_data: bar_data = database_manager.load_bar_data( symbol, exchange, interval, start, end ) REDIS_CLIENT.hset(file_name, file_name, zlib.compress(pickle.dumps(bar_data), 5)) else: bar_data = pickle.loads(zlib.decompress(redis_data)) return bar_data """数据缓存为pkl格式到本地硬盘""" """ dir_path = f"H:\\pickle_data\\" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_bar" pickle_path = dir_path + file_name + ".pkl" data_size =0 if not os.path.exists(pickle_path): bar_data = database_manager.load_bar_data( symbol, exchange, interval, start, end ) pickle_file = open(pickle_path,'wb') pickle.dump(bar_data,pickle_file) pickle_file.close() else: pickle_file = open(pickle_path,'rb') bar_data =pickle.load(pickle_file) pickle_file.close() #pickle_data文件夹大于50G删除缓存数据 for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: #当前目录所有文件名 data_size += os.path.getsize(dirpath + file_name) if data_size / (1024 ** 3) > 50: for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: os.remove(dirpath + file_name) return bar_data""" @lru_cache(maxsize=10) def load_tick_data( symbol: str, exchange: Exchange, start: datetime, end: datetime ): """tick数据redis序列化存取""" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_tick" redis_data = REDIS_CLIENT.hget(file_name, file_name) if not redis_data: tick_data = database_manager.load_tick_data( symbol, exchange, start, end ) REDIS_CLIENT.hset(file_name, file_name, zlib.compress(pickle.dumps(tick_data), 5)) else: tick_data = pickle.loads(zlib.decompress(redis_data)) return tick_data """数据缓存为pkl格式到本地硬盘""" """ dir_path = f"H:\\pickle_data\\" file_name = f"{symbol}_{exchange.value}_{start.date()}_{end.date()}_tick" pickle_path = dir_path + file_name + ".pkl" data_size =0 if not os.path.exists(pickle_path): tick_data = database_manager.load_tick_data( symbol, exchange, start, end ) pickle_file = open(pickle_path,'wb') pickle.dump(tick_data,pickle_file) pickle_file.close() else: pickle_file = open(pickle_path,'rb') tick_data =pickle.load(pickle_file) pickle_file.close() #pickle_data文件夹大于50G删除缓存数据 for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: #当前目录所有文件名 data_size += os.path.getsize(dirpath + file_name) if data_size / (1024 ** 3) > 50: for dirpath, dirnames, filenames in os.walk(dir_path): for file_name in filenames: os.remove(dirpath + file_name) return tick_data""" # GA related global value ga_end = None ga_mode = None ga_target_name = None ga_strategy_class = None ga_setting = None ga_vt_symbol = None ga_interval = None ga_start = None ga_rate = None ga_slippage = None ga_size = None ga_price_tick = None ga_capital = None

# %% Imports import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier from sklearn.metrics import r2_score, roc_curve, roc_auc_score from itertools import product # Import to add project folder to sys path import sys utils_path = '/Users/Ryan/Documents/projects/ncaa-basketball/code/utils' if utils_path not in sys.path: sys.path.append(utils_path) from api import opponentAdjust from db import get_db from misc import getRelativeFp # %% Declare parameters metrics = [ 'PF', 'Margin', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FG2M', 'FG2A', 'FTA', 'FTM', 'Ast', 'ORB', 'DRB', 'TRB', 'TO', 'Stl', 'Blk', 'Foul' ] prefixes = ['Tm', 'Opp'] attrCols = [ 'Season', 'TmName', 'dataPriorTo', 'isRegularSeason', '' ] # %% Read in raw data def getTd(): """Reads in local csv of team-date data as of specified dates Returns: DataFrame: team-date data """ firstYr = True for yr in range(2003, 2021): print(f"Reading in {yr}'s data") fp = getRelativeFp(__file__, f'../data/predate_games/{yr}.csv') if firstYr: raw = pd.read_csv(fp) firstYr = False else: raw = raw.append(pd.read_csv(fp)) return raw # %% Add additional columns to team-date data def addColsToTd(_td, _metrics=metrics, _prefixes=prefixes): """Add additional columns to the team-date DataFrame Args: _td (DataFrame): Team-date DataFrame _metrics (list, optional): list of metrics to pass to opponent- adjusting function. Defaults to metrics. _prefixes (list, optional): list of prefixes to pass to opponent- adjusting function. Defaults to prefixes. Returns: DataFrame: Team-date data with additional columns """ _td = opponentAdjust( _td, _prefixes, _metrics, includeOARankFields=False, includeNormRankFields=False, includeNormFields=True ) _td['TmWinPct'] = _td['TmWin'] / _td['TmGame'] _td['TmPossPerGame'] = _td['TmPoss'] / _td['TmGame'] return _td # %% Clean team-date data def cleanTd(_td, minGames=10, _metrics=metrics, _prefixes=prefixes): """Removes extra columns, removes data prior to specified number of games, changes datetime data type Args: _td (DataFrame): team-date data minGames (int, optional): Minimum games played to keep data. Defaults to 10. _metrics (list, optional): Metrics to drop raw data of. Defaults to metrics. _prefixes (list, optional): Prefixes to drop raw data of. Defaults to prefixes. Returns: DataFrame: Cleaned data """ # Make list of columns to ignore colsToDrop = [ 'OppGame', 'GameOT' ] for metr in _metrics + ['Mins', 'Win', 'Poss']: for pref in _prefixes: colsToDrop.append(f'{pref}{metr}') colsToDrop.append(f'OppSum_{pref}{metr}') keptCols = [ col for col in _td.columns if (col not in colsToDrop) & (col[:7] != 'OppSum_') ] _td = _td[keptCols] # Limit team-dates to only those with >= minGames _td = _td.loc[_td['TmGame'] >= minGames] # Change field to datetime _td['dataPriorTo'] = pd.to_datetime(_td['dataPriorTo']) return _td # %% Get game data for analysis def getGames(): """Get 2003+ game data from database Returns: DataFrame: Game data """ db = get_db() q = {'Season': {'$gte': 2003}} fields = { '_id': 0, 'TmName': 1, 'OppName': 1, 'Season': 1, 'GameDate': 1, 'TmMargin': 1, 'GameVegasLine': 1, 'TmLoc': 1 } raw = pd.DataFrame( list( db.games.find( q, fields ) ) ) return raw # %% De-dupe, remove outliers def cleanGames(_games): """De-dupes game records, keeping only home teams and lower-name neutral games, rename a few columns, remove outlier lines, and change datatypes. Args: _games (DataFrame): game data Returns: DataFrame: Cleaned game data """ # _games = _games.loc[( # (_games['TmLoc'] == 'H') | # ((_games['TmLoc'] == 'N') & (_games['TmName'] < _games['OppName'])) # )] _games = _games.loc[np.abs(_games['GameVegasLine']) <= 50] _games.rename(inplace=True, columns={ 'GameDate': 'dataPriorTo' }) _games['dataPriorTo'] = pd.to_datetime(_games['dataPriorTo']) _games['Tm_isHome'] = np.where(_games['TmLoc'] == 'H', 1, 0) return _games # %% Add add'l columns to games def addColsToGames(_games): # GameVegasLine is the expected Tm margin. Positive means vegas favored Tm. # If TmVegasMargin > 0: TmMargin > GameVegasLine: Team outperformed vegas - bet on team. # If TmVegasMargin < 0: TmMargin < GameVegasLine, team did worse than vegas expected - bet on Opp _games['tmVegasMargin'] = _games['TmMargin'] - _games['GameVegasLine'] _games['tmAtsWinner'] = (_games['tmVegasMargin'] > 0) * 1 return _games # %% Merge in team-date data to game records def addTdToGames(_td, _games): tdCols = ['TmName', 'Season', 'dataPriorTo'] dfTm = _td.copy() dfTm.columns = [ f'Tm_{x}' if x not in tdCols else x for x in dfTm.columns ] dfOpp = _td.copy() dfOpp.columns = [ f'Opp_{x}' if x not in tdCols else x for x in dfOpp.columns ] dfOpp.rename(inplace=True, columns={'TmName': 'OppName'}) _games = pd.merge( left=_games, right=dfTm, on=['TmName', 'Season', 'dataPriorTo'], how='inner' ) _games = pd.merge( left=_games, right=dfOpp, on=['OppName', 'Season', 'dataPriorTo'], how='inner' ) return _games # %% Eval margin predictions def evalModel(predMargin, actualMargin, methodName, verbose=False): print(f"{methodName}:") # Accuracy of margin R2 = r2_score( y_true=actualMargin, y_pred=predMargin ) MAE = np.mean(np.abs(actualMargin - predMargin)) # Correctly picking winner sumWins = np.sum( (predMargin * actualMargin > 0)*1 ) sumLosses = np.sum( (predMargin * actualMargin < 0)*1 ) sumTies = np.sum( (predMargin * actualMargin == 0)*1 ) if verbose: print(f"MAE: {MAE:.2f}") print(f"R^2: {R2:.4f}") print(f"Correct Winner Record: {sumWins:,.0f} - {sumLosses:,.0f} - {sumTies:,.0f}") print(f"Win Pct: {sumWins/len(predMargin):.3%}") print(f"Win Pct excluding pushes: {sumWins/(sumWins + sumLosses):.3%}") print('\n') return R2, MAE # %% Create function for classification model def evalClassificationModel(predClass, actualClass, isPush, methodName, predProb, showCurve=False, verbose=False): print(f"{methodName}:") predWin = ((predClass == actualClass) & (isPush == 0))*1 predLoss = ((predClass != actualClass) & (isPush == 0))*1 predPush = (isPush == 1)*1 w = np.sum(predWin)/(np.sum(predWin)+np.sum(predLoss)) b_auc = roc_auc_score(actualClass, predClass) p_auc = roc_auc_score(actualClass, predProb) if verbose: print(f"Record: {np.sum(predWin)} - {np.sum(predLoss)} - {np.sum(predPush)}") print(f"Net wins: {np.sum(predWin) - np.sum(predLoss)}") print(f"Win Percent: {w:.2%}") print(f"Binary AUC: {b_auc:.2%}") print(f"Probability AUC: {p_auc:.2%}") if showCurve: fpr, tpr, thresholds = roc_curve(actualClass, predProb) fig = go.Figure(go.Scatter(x=fpr, y=tpr)) fig.show() return w, b_auc, p_auc # %% Main function if __name__ == '__main__': # Get team-date data td = addColsToTd(getTd()) td = cleanTd(td) # Get games data, add team details games = cleanGames(getGames()) games = addColsToGames(games) gamesCols = games.columns games = addTdToGames(td, games) gamesX = games[[col for col in games.columns if col not in gamesCols]] gamesy = games[gamesCols] #################################### ## Predicting Game Margin #################################### # Set baseline: Vegas lines to predict margin evalModel( predMargin=gamesy['GameVegasLine'], actualMargin=gamesy['TmMargin'], methodName='Vegas', verbose=True ) # Now, using Marginper40 diffs evalModel( predMargin=gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'], actualMargin=gamesy['TmMargin'], methodName='Margin per 40 Difference', verbose=True ) # OA Margin per 40 evalModel( predMargin=gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'], actualMargin=gamesy['TmMargin'], methodName='OA Margin per 40 Difference', verbose=True ) # Now, adding a few different home court advantages to margin and OAM buffs = [a/2 for a in range(-5,16)] mov_r= [] mov_m = [] oa_r = [] oa_m = [] for b in buffs: # print(a/2) r, m = evalModel( predMargin=gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'] + b* gamesy['Tm_isHome'], actualMargin=gamesy['TmMargin'], methodName=f'Margin per 40 Difference + {b}' ) mov_r.append(r) mov_m.append(m) r, m = evalModel( predMargin=gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'] + b* gamesy['Tm_isHome'], actualMargin=gamesy['TmMargin'], methodName=f'OA Margin per 40 Difference + {b}' ) oa_r.append(r) oa_m.append(m) fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=('R2 by Home Court Advantage', 'MAE by Home Court Advantage')) fig.add_trace( go.Scatter(x=buffs,y=mov_r, name='MoV - R2', legendgroup='MOV', line_color='steelblue'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_r, name='OA MoV - R2', legendgroup='OA', line_color='crimson'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_m, name='MoV - MAE', legendgroup='MOV', line_color='steelblue'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_m, name='OA MoV - MAE', legendgroup='OA', line_color='crimson'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs, y=[0.3504]*len(buffs), name='Vegas - R2', legendgroup='v', line_color='black'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs, y=[8.31]*len(buffs), name='Vegas - MAE', legendgroup='v', line_color='black'), row=2, col=1 ) fig.update_xaxes( title_text='Home Court Advantage Value', row=2 ) fig.update_yaxes(title_text='R2', row=1) fig.update_yaxes(title_text='MAE', row=2) fig.show() # Machine learning section rf = RandomForestRegressor( n_estimators=500, n_jobs=-1, max_depth=10, random_state=114492, min_samples_split=6, max_features='sqrt', bootstrap=True, oob_score=False, max_samples=0.8, # verbose=True ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") rf.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'TmMargin']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predMargin': rf.predict(gamesX.loc[gamesy['Season'] == s]), 'actlMargin': gamesy.loc[gamesy['Season'] == s, 'TmMargin'], 'season': s }) temp = temp.append(t) evalModel( predMargin=t['predMargin'], actualMargin=t['actlMargin'], methodName=f'Season {s} RF Results', verbose=True ) evalModel( predMargin=temp['predMargin'], actualMargin=temp['actlMargin'], methodName=f'Total RF Results', verbose=True ) df = temp.groupby(['Season']) ############## ## ATS ############## # Now, adding a few different home court advantages to margin and OAM buffs = [a/2 for a in range(-50,50)] mov_w = [] mov_bauc = [] mov_pauc = [] oa_w = [] oa_bauc = [] oa_pauc = [] for b in buffs: # print(a/2) # Check versus vegas # vegasMinusModel (+) when vegas likes Tm better than model (model bet on Opp) # vegasMinusModel (-) when vegas likes Tm worse than model (model bet on Tm) predMargin = gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'] + b* gamesy['Tm_isHome'] vegasMinusModel = gamesy['GameVegasLine'] - predMargin w, bauc, pauc = evalClassificationModel( predClass=(vegasMinusModel <= 0) * 1, actualClass=gamesy['tmAtsWinner'], isPush=(gamesy['tmVegasMargin'] == 0)*1, predProb=vegasMinusModel, methodName=f'Margin per 40 Difference + {b}' # showCurve=True ) mov_w.append(w) mov_bauc.append(bauc) mov_pauc.append(pauc) predMargin = gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'] + b* gamesy['Tm_isHome'] vegasMinusModel = gamesy['GameVegasLine'] - predMargin w, bauc, pauc = evalClassificationModel( predClass=(vegasMinusModel <= 0) * 1, actualClass=gamesy['tmAtsWinner'], isPush=(gamesy['tmVegasMargin'] == 0)*1, predProb=vegasMinusModel, methodName=f'Margin per 40 Difference + {b}' # showCurve=True ) oa_w.append(w) oa_bauc.append(bauc) oa_pauc.append(pauc) fig = make_subplots(rows=3, cols=1, shared_xaxes=True, subplot_titles=('Win Pct by Home Court Advantage', 'Binary AUC by HCA', 'Probability AUC by HCA')) fig.add_trace( go.Scatter(x=buffs,y=mov_w, name='MoV - Win Pct', legendgroup='MOV', line_color='steelblue'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_w, name='OA MoV - Win Pct', legendgroup='OA', line_color='crimson'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_bauc, name='MoV - Binary AUC', legendgroup='MOV', line_color='steelblue'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_bauc, name='OA MoV - Binary AUC', legendgroup='OA', line_color='crimson'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_pauc, name='MoV - Probability AUC', legendgroup='MOV', line_color='steelblue'), row=3, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_pauc, name='OA MoV - Probability AUC', legendgroup='OA', line_color='crimson'), row=3, col=1 ) fig.update_xaxes( title_text='Home Court Advantage Value', row=3 ) fig.update_yaxes(title_text='Win Pct', row=1) fig.update_yaxes(title_text='AUC', row=2) fig.update_yaxes(title_text='AUC', row=3) fig.show() # Try Classification based on if Tm wins ATS gamesX['GameVegasLine'] = gamesy['GameVegasLine'] # Train the winning parameters: 10 depth, 6 min samples rfc = RandomForestClassifier( n_estimators=500, criterion='gini', max_depth=5, min_samples_split=2, max_features='sqrt', bootstrap=True, # oob_score=True, n_jobs=-1, random_state=114492, # verbose=True, max_samples=.8 ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") rfc.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'tmAtsWinner']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predClass': rfc.predict(gamesX.loc[gamesy['Season'] == s]), 'predProb': rfc.predict_proba(gamesX.loc[gamesy['Season'] == s])[:,1], 'actlClass': gamesy.loc[gamesy['Season'] == s, 'tmAtsWinner'], 'isPush': ((gamesy.loc[gamesy['Season'] == s, 'tmVegasMargin'])==0)*1 }) temp = temp.append(t) evalClassificationModel( predClass=t['predClass'], actualClass=t['actlClass'], isPush=t['isPush'], predProb=t['predProb'], methodName=f'{s} Season Results', # showCurve=True ) evalClassificationModel( predClass=temp['predClass'], actualClass=temp['actlClass'], isPush=temp['isPush'], predProb=temp['predProb'], methodName=f'rfc All Season Results', showCurve=True ) # Gradient Boosting gb = GradientBoostingClassifier( n_estimators=20, max_depth=3, random_state=114492, verbose=True ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") gb.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'tmAtsWinner']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predClass': gb.predict(gamesX.loc[gamesy['Season'] == s]), 'predProb': gb.predict_proba(gamesX.loc[gamesy['Season'] == s])[:,1], 'actlClass': gamesy.loc[gamesy['Season'] == s, 'tmAtsWinner'], 'isPush': ((gamesy.loc[gamesy['Season'] == s, 'tmVegasMargin'])==0)*1 }) temp = temp.append(t) evalClassificationModel( predClass=t['predClass'], actualClass=t['actlClass'], isPush=t['isPush'], predProb=t['predProb'], methodName=f'{s} Season Results', # showCurve=True ) evalClassificationModel( predClass=temp['predClass'], actualClass=temp['actlClass'], isPush=temp['isPush'], predProb=temp['predProb'], methodName=f'GB All Season Results', showCurve=True, verbose=True ) # print(f"RF Classifier Correct: {np.mean(gamesy["rfc_Correct"]):.2%}") # games['rfc_correctCumSum'] = np.where( # games['rfc_betOnTm'] == games['tmAtsWinner'], # 1, # -1 # ) # games.sort_values(inplace=True, by=['dataPriorTo']) # games['modelDateCumSum'] = games['rfc_correctCumSum'].cumsum() # fig = px.line(games, x='dataPriorTo', y='modelDateCumSum') # fig.show() # gamesy['probRounded'] = np.round(rfc.oob_decision_function_[:,1],3) # temp2 = gamesy.groupby(['probRounded'])['rfc_correctCumSum'].agg({'rfc_correctCumSum': ['sum', 'count']}).reset_index() # temp2.columns = ['probRounded', 'rfc_correctCumSum', 'recordCnt'] # fig = px.bar(temp2, x='probRounded', y='rfc_correctCumSum') # fig.update_layout( # title_text=f"Performance of {predictionCol} by {col}" # ) # gamesy['placeBet'] = (gamesy['probRounded']<= 0.5)*1 # games['smartWinner'] = games['placeBet']*games['rfc_correctCumSum'] # print(f"Smart logic correct: {np.sum(games["smartWinner"]):.0f}, win pct = {np.sum(games["smartWinner"]==1)/np.sum(games["placeBet"]):.2%}") # temp = games[['tmAtsWinner', 'rfc_tmAtsWinner_1','rfc_betOnTm']] # %% gb = GradientBoostingClassifier( n_estimators=150, max_depth=3, random_state=114492, verbose=True ) gb.fit(gamesX, games['tmAtsWinner']) gamesy['rfc_betOnTm'] = gb.predict(gamesX) gamesy['rfc_Win'] = ((gamesy['rfc_betOnTm'] == gamesy['tmAtsWinner']) & (gamesy['tmVegasMargin'] != 0))*1 gamesy['rfc_Loss'] = ((gamesy['rfc_betOnTm'] != gamesy['tmAtsWinner']) & (gamesy['tmVegasMargin'] != 0))*1 gamesy['rfc_Push'] = (gamesy['tmVegasMargin'] == 0)*1 print(f"Record: {np.sum(gamesy["rfc_Win"])} - {np.sum(gamesy["rfc_Loss"])} - {np.sum(gamesy["rfc_Push"])}") print(f"Net wins: {np.sum(gamesy["rfc_Win"]) - np.sum(gamesy["rfc_Loss"])}") print(f"Win Percent: {np.sum(gamesy["rfc_Win"])/(np.sum(gamesy["rfc_Win"])+np.sum(gamesy["rfc_Loss"])):.2%}") # %%

# %% Imports import pandas as pd import numpy as np import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier from sklearn.metrics import r2_score, roc_curve, roc_auc_score from itertools import product # Import to add project folder to sys path import sys utils_path = '/Users/Ryan/Documents/projects/ncaa-basketball/code/utils' if utils_path not in sys.path: sys.path.append(utils_path) from api import opponentAdjust from db import get_db from misc import getRelativeFp # %% Declare parameters metrics = [ 'PF', 'Margin', 'FGM', 'FGA', 'FG3M', 'FG3A', 'FG2M', 'FG2A', 'FTA', 'FTM', 'Ast', 'ORB', 'DRB', 'TRB', 'TO', 'Stl', 'Blk', 'Foul' ] prefixes = ['Tm', 'Opp'] attrCols = [ 'Season', 'TmName', 'dataPriorTo', 'isRegularSeason', '' ] # %% Read in raw data def getTd(): """Reads in local csv of team-date data as of specified dates Returns: DataFrame: team-date data """ firstYr = True for yr in range(2003, 2021): print(f"Reading in {yr}'s data") fp = getRelativeFp(__file__, f'../data/predate_games/{yr}.csv') if firstYr: raw = pd.read_csv(fp) firstYr = False else: raw = raw.append(pd.read_csv(fp)) return raw # %% Add additional columns to team-date data def addColsToTd(_td, _metrics=metrics, _prefixes=prefixes): """Add additional columns to the team-date DataFrame Args: _td (DataFrame): Team-date DataFrame _metrics (list, optional): list of metrics to pass to opponent- adjusting function. Defaults to metrics. _prefixes (list, optional): list of prefixes to pass to opponent- adjusting function. Defaults to prefixes. Returns: DataFrame: Team-date data with additional columns """ _td = opponentAdjust( _td, _prefixes, _metrics, includeOARankFields=False, includeNormRankFields=False, includeNormFields=True ) _td['TmWinPct'] = _td['TmWin'] / _td['TmGame'] _td['TmPossPerGame'] = _td['TmPoss'] / _td['TmGame'] return _td # %% Clean team-date data def cleanTd(_td, minGames=10, _metrics=metrics, _prefixes=prefixes): """Removes extra columns, removes data prior to specified number of games, changes datetime data type Args: _td (DataFrame): team-date data minGames (int, optional): Minimum games played to keep data. Defaults to 10. _metrics (list, optional): Metrics to drop raw data of. Defaults to metrics. _prefixes (list, optional): Prefixes to drop raw data of. Defaults to prefixes. Returns: DataFrame: Cleaned data """ # Make list of columns to ignore colsToDrop = [ 'OppGame', 'GameOT' ] for metr in _metrics + ['Mins', 'Win', 'Poss']: for pref in _prefixes: colsToDrop.append(f'{pref}{metr}') colsToDrop.append(f'OppSum_{pref}{metr}') keptCols = [ col for col in _td.columns if (col not in colsToDrop) & (col[:7] != 'OppSum_') ] _td = _td[keptCols] # Limit team-dates to only those with >= minGames _td = _td.loc[_td['TmGame'] >= minGames] # Change field to datetime _td['dataPriorTo'] = pd.to_datetime(_td['dataPriorTo']) return _td # %% Get game data for analysis def getGames(): """Get 2003+ game data from database Returns: DataFrame: Game data """ db = get_db() q = {'Season': {'$gte': 2003}} fields = { '_id': 0, 'TmName': 1, 'OppName': 1, 'Season': 1, 'GameDate': 1, 'TmMargin': 1, 'GameVegasLine': 1, 'TmLoc': 1 } raw = pd.DataFrame( list( db.games.find( q, fields ) ) ) return raw # %% De-dupe, remove outliers def cleanGames(_games): """De-dupes game records, keeping only home teams and lower-name neutral games, rename a few columns, remove outlier lines, and change datatypes. Args: _games (DataFrame): game data Returns: DataFrame: Cleaned game data """ # _games = _games.loc[( # (_games['TmLoc'] == 'H') | # ((_games['TmLoc'] == 'N') & (_games['TmName'] < _games['OppName'])) # )] _games = _games.loc[np.abs(_games['GameVegasLine']) <= 50] _games.rename(inplace=True, columns={ 'GameDate': 'dataPriorTo' }) _games['dataPriorTo'] = pd.to_datetime(_games['dataPriorTo']) _games['Tm_isHome'] = np.where(_games['TmLoc'] == 'H', 1, 0) return _games # %% Add add'l columns to games def addColsToGames(_games): # GameVegasLine is the expected Tm margin. Positive means vegas favored Tm. # If TmVegasMargin > 0: TmMargin > GameVegasLine: Team outperformed vegas - bet on team. # If TmVegasMargin < 0: TmMargin < GameVegasLine, team did worse than vegas expected - bet on Opp _games['tmVegasMargin'] = _games['TmMargin'] - _games['GameVegasLine'] _games['tmAtsWinner'] = (_games['tmVegasMargin'] > 0) * 1 return _games # %% Merge in team-date data to game records def addTdToGames(_td, _games): tdCols = ['TmName', 'Season', 'dataPriorTo'] dfTm = _td.copy() dfTm.columns = [ f'Tm_{x}' if x not in tdCols else x for x in dfTm.columns ] dfOpp = _td.copy() dfOpp.columns = [ f'Opp_{x}' if x not in tdCols else x for x in dfOpp.columns ] dfOpp.rename(inplace=True, columns={'TmName': 'OppName'}) _games = pd.merge( left=_games, right=dfTm, on=['TmName', 'Season', 'dataPriorTo'], how='inner' ) _games = pd.merge( left=_games, right=dfOpp, on=['OppName', 'Season', 'dataPriorTo'], how='inner' ) return _games # %% Eval margin predictions def evalModel(predMargin, actualMargin, methodName, verbose=False): print(f"{methodName}:") # Accuracy of margin R2 = r2_score( y_true=actualMargin, y_pred=predMargin ) MAE = np.mean(np.abs(actualMargin - predMargin)) # Correctly picking winner sumWins = np.sum( (predMargin * actualMargin > 0)*1 ) sumLosses = np.sum( (predMargin * actualMargin < 0)*1 ) sumTies = np.sum( (predMargin * actualMargin == 0)*1 ) if verbose: print(f"MAE: {MAE:.2f}") print(f"R^2: {R2:.4f}") print(f"Correct Winner Record: {sumWins:,.0f} - {sumLosses:,.0f} - {sumTies:,.0f}") print(f"Win Pct: {sumWins/len(predMargin):.3%}") print(f"Win Pct excluding pushes: {sumWins/(sumWins + sumLosses):.3%}") print('\n') return R2, MAE # %% Create function for classification model def evalClassificationModel(predClass, actualClass, isPush, methodName, predProb, showCurve=False, verbose=False): print(f"{methodName}:") predWin = ((predClass == actualClass) & (isPush == 0))*1 predLoss = ((predClass != actualClass) & (isPush == 0))*1 predPush = (isPush == 1)*1 w = np.sum(predWin)/(np.sum(predWin)+np.sum(predLoss)) b_auc = roc_auc_score(actualClass, predClass) p_auc = roc_auc_score(actualClass, predProb) if verbose: print(f"Record: {np.sum(predWin)} - {np.sum(predLoss)} - {np.sum(predPush)}") print(f"Net wins: {np.sum(predWin) - np.sum(predLoss)}") print(f"Win Percent: {w:.2%}") print(f"Binary AUC: {b_auc:.2%}") print(f"Probability AUC: {p_auc:.2%}") if showCurve: fpr, tpr, thresholds = roc_curve(actualClass, predProb) fig = go.Figure(go.Scatter(x=fpr, y=tpr)) fig.show() return w, b_auc, p_auc # %% Main function if __name__ == '__main__': # Get team-date data td = addColsToTd(getTd()) td = cleanTd(td) # Get games data, add team details games = cleanGames(getGames()) games = addColsToGames(games) gamesCols = games.columns games = addTdToGames(td, games) gamesX = games[[col for col in games.columns if col not in gamesCols]] gamesy = games[gamesCols] #################################### ## Predicting Game Margin #################################### # Set baseline: Vegas lines to predict margin evalModel( predMargin=gamesy['GameVegasLine'], actualMargin=gamesy['TmMargin'], methodName='Vegas', verbose=True ) # Now, using Marginper40 diffs evalModel( predMargin=gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'], actualMargin=gamesy['TmMargin'], methodName='Margin per 40 Difference', verbose=True ) # OA Margin per 40 evalModel( predMargin=gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'], actualMargin=gamesy['TmMargin'], methodName='OA Margin per 40 Difference', verbose=True ) # Now, adding a few different home court advantages to margin and OAM buffs = [a/2 for a in range(-5,16)] mov_r= [] mov_m = [] oa_r = [] oa_m = [] for b in buffs: # print(a/2) r, m = evalModel( predMargin=gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'] + b* gamesy['Tm_isHome'], actualMargin=gamesy['TmMargin'], methodName=f'Margin per 40 Difference + {b}' ) mov_r.append(r) mov_m.append(m) r, m = evalModel( predMargin=gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'] + b* gamesy['Tm_isHome'], actualMargin=gamesy['TmMargin'], methodName=f'OA Margin per 40 Difference + {b}' ) oa_r.append(r) oa_m.append(m) fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=('R2 by Home Court Advantage', 'MAE by Home Court Advantage')) fig.add_trace( go.Scatter(x=buffs,y=mov_r, name='MoV - R2', legendgroup='MOV', line_color='steelblue'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_r, name='OA MoV - R2', legendgroup='OA', line_color='crimson'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_m, name='MoV - MAE', legendgroup='MOV', line_color='steelblue'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_m, name='OA MoV - MAE', legendgroup='OA', line_color='crimson'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs, y=[0.3504]*len(buffs), name='Vegas - R2', legendgroup='v', line_color='black'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs, y=[8.31]*len(buffs), name='Vegas - MAE', legendgroup='v', line_color='black'), row=2, col=1 ) fig.update_xaxes( title_text='Home Court Advantage Value', row=2 ) fig.update_yaxes(title_text='R2', row=1) fig.update_yaxes(title_text='MAE', row=2) fig.show() # Machine learning section rf = RandomForestRegressor( n_estimators=500, n_jobs=-1, max_depth=10, random_state=114492, min_samples_split=6, max_features='sqrt', bootstrap=True, oob_score=False, max_samples=0.8, # verbose=True ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") rf.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'TmMargin']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predMargin': rf.predict(gamesX.loc[gamesy['Season'] == s]), 'actlMargin': gamesy.loc[gamesy['Season'] == s, 'TmMargin'], 'season': s }) temp = temp.append(t) evalModel( predMargin=t['predMargin'], actualMargin=t['actlMargin'], methodName=f'Season {s} RF Results', verbose=True ) evalModel( predMargin=temp['predMargin'], actualMargin=temp['actlMargin'], methodName=f'Total RF Results', verbose=True ) df = temp.groupby(['Season']) ############## ## ATS ############## # Now, adding a few different home court advantages to margin and OAM buffs = [a/2 for a in range(-50,50)] mov_w = [] mov_bauc = [] mov_pauc = [] oa_w = [] oa_bauc = [] oa_pauc = [] for b in buffs: # print(a/2) # Check versus vegas # vegasMinusModel (+) when vegas likes Tm better than model (model bet on Opp) # vegasMinusModel (-) when vegas likes Tm worse than model (model bet on Tm) predMargin = gamesX['Tm_TmMarginper40'] - gamesX['Opp_TmMarginper40'] + b* gamesy['Tm_isHome'] vegasMinusModel = gamesy['GameVegasLine'] - predMargin w, bauc, pauc = evalClassificationModel( predClass=(vegasMinusModel <= 0) * 1, actualClass=gamesy['tmAtsWinner'], isPush=(gamesy['tmVegasMargin'] == 0)*1, predProb=vegasMinusModel, methodName=f'Margin per 40 Difference + {b}' # showCurve=True ) mov_w.append(w) mov_bauc.append(bauc) mov_pauc.append(pauc) predMargin = gamesX['Tm_OA_TmMarginper40'] - gamesX['Opp_OA_TmMarginper40'] + b* gamesy['Tm_isHome'] vegasMinusModel = gamesy['GameVegasLine'] - predMargin w, bauc, pauc = evalClassificationModel( predClass=(vegasMinusModel <= 0) * 1, actualClass=gamesy['tmAtsWinner'], isPush=(gamesy['tmVegasMargin'] == 0)*1, predProb=vegasMinusModel, methodName=f'Margin per 40 Difference + {b}' # showCurve=True ) oa_w.append(w) oa_bauc.append(bauc) oa_pauc.append(pauc) fig = make_subplots(rows=3, cols=1, shared_xaxes=True, subplot_titles=('Win Pct by Home Court Advantage', 'Binary AUC by HCA', 'Probability AUC by HCA')) fig.add_trace( go.Scatter(x=buffs,y=mov_w, name='MoV - Win Pct', legendgroup='MOV', line_color='steelblue'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_w, name='OA MoV - Win Pct', legendgroup='OA', line_color='crimson'), row=1, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_bauc, name='MoV - Binary AUC', legendgroup='MOV', line_color='steelblue'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_bauc, name='OA MoV - Binary AUC', legendgroup='OA', line_color='crimson'), row=2, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=mov_pauc, name='MoV - Probability AUC', legendgroup='MOV', line_color='steelblue'), row=3, col=1 ) fig.add_trace( go.Scatter(x=buffs,y=oa_pauc, name='OA MoV - Probability AUC', legendgroup='OA', line_color='crimson'), row=3, col=1 ) fig.update_xaxes( title_text='Home Court Advantage Value', row=3 ) fig.update_yaxes(title_text='Win Pct', row=1) fig.update_yaxes(title_text='AUC', row=2) fig.update_yaxes(title_text='AUC', row=3) fig.show() # Try Classification based on if Tm wins ATS gamesX['GameVegasLine'] = gamesy['GameVegasLine'] # Train the winning parameters: 10 depth, 6 min samples rfc = RandomForestClassifier( n_estimators=500, criterion='gini', max_depth=5, min_samples_split=2, max_features='sqrt', bootstrap=True, # oob_score=True, n_jobs=-1, random_state=114492, # verbose=True, max_samples=.8 ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") rfc.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'tmAtsWinner']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predClass': rfc.predict(gamesX.loc[gamesy['Season'] == s]), 'predProb': rfc.predict_proba(gamesX.loc[gamesy['Season'] == s])[:,1], 'actlClass': gamesy.loc[gamesy['Season'] == s, 'tmAtsWinner'], 'isPush': ((gamesy.loc[gamesy['Season'] == s, 'tmVegasMargin'])==0)*1 }) temp = temp.append(t) evalClassificationModel( predClass=t['predClass'], actualClass=t['actlClass'], isPush=t['isPush'], predProb=t['predProb'], methodName=f'{s} Season Results', # showCurve=True ) evalClassificationModel( predClass=temp['predClass'], actualClass=temp['actlClass'], isPush=temp['isPush'], predProb=temp['predProb'], methodName=f'rfc All Season Results', showCurve=True ) # Gradient Boosting gb = GradientBoostingClassifier( n_estimators=20, max_depth=3, random_state=114492, verbose=True ) temp = pd.DataFrame() for s in sorted(gamesy['Season'].unique()): print(f"Season {s}") gb.fit(gamesX.loc[gamesy['Season'] != s], gamesy.loc[gamesy['Season'] != s, 'tmAtsWinner']) # gb.predict(gamesX.loc[gamesy['Season'] == s) t = pd.DataFrame(data={ 'predClass': gb.predict(gamesX.loc[gamesy['Season'] == s]), 'predProb': gb.predict_proba(gamesX.loc[gamesy['Season'] == s])[:,1], 'actlClass': gamesy.loc[gamesy['Season'] == s, 'tmAtsWinner'], 'isPush': ((gamesy.loc[gamesy['Season'] == s, 'tmVegasMargin'])==0)*1 }) temp = temp.append(t) evalClassificationModel( predClass=t['predClass'], actualClass=t['actlClass'], isPush=t['isPush'], predProb=t['predProb'], methodName=f'{s} Season Results', # showCurve=True ) evalClassificationModel( predClass=temp['predClass'], actualClass=temp['actlClass'], isPush=temp['isPush'], predProb=temp['predProb'], methodName=f'GB All Season Results', showCurve=True, verbose=True ) # print(f"RF Classifier Correct: {np.mean(gamesy['rfc_Correct']):.2%}") # games['rfc_correctCumSum'] = np.where( # games['rfc_betOnTm'] == games['tmAtsWinner'], # 1, # -1 # ) # games.sort_values(inplace=True, by=['dataPriorTo']) # games['modelDateCumSum'] = games['rfc_correctCumSum'].cumsum() # fig = px.line(games, x='dataPriorTo', y='modelDateCumSum') # fig.show() # gamesy['probRounded'] = np.round(rfc.oob_decision_function_[:,1],3) # temp2 = gamesy.groupby(['probRounded'])['rfc_correctCumSum'].agg({'rfc_correctCumSum': ['sum', 'count']}).reset_index() # temp2.columns = ['probRounded', 'rfc_correctCumSum', 'recordCnt'] # fig = px.bar(temp2, x='probRounded', y='rfc_correctCumSum') # fig.update_layout( # title_text=f"Performance of {predictionCol} by {col}" # ) # gamesy['placeBet'] = (gamesy['probRounded']<= 0.5)*1 # games['smartWinner'] = games['placeBet']*games['rfc_correctCumSum'] # print(f"Smart logic correct: {np.sum(games['smartWinner']):.0f}, win pct = {np.sum(games['smartWinner']==1)/np.sum(games['placeBet']):.2%}") # temp = games[['tmAtsWinner', 'rfc_tmAtsWinner_1','rfc_betOnTm']] # %% gb = GradientBoostingClassifier( n_estimators=150, max_depth=3, random_state=114492, verbose=True ) gb.fit(gamesX, games['tmAtsWinner']) gamesy['rfc_betOnTm'] = gb.predict(gamesX) gamesy['rfc_Win'] = ((gamesy['rfc_betOnTm'] == gamesy['tmAtsWinner']) & (gamesy['tmVegasMargin'] != 0))*1 gamesy['rfc_Loss'] = ((gamesy['rfc_betOnTm'] != gamesy['tmAtsWinner']) & (gamesy['tmVegasMargin'] != 0))*1 gamesy['rfc_Push'] = (gamesy['tmVegasMargin'] == 0)*1 print(f"Record: {np.sum(gamesy['rfc_Win'])} - {np.sum(gamesy['rfc_Loss'])} - {np.sum(gamesy['rfc_Push'])}") print(f"Net wins: {np.sum(gamesy['rfc_Win']) - np.sum(gamesy['rfc_Loss'])}") print(f"Win Percent: {np.sum(gamesy['rfc_Win'])/(np.sum(gamesy['rfc_Win'])+np.sum(gamesy['rfc_Loss'])):.2%}") # %%

__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved." __license__ = "Apache-2.0" import click import os import string import random from jina.flow import Flow RANDOM_SEED = 10 # 5 os.environ['PARALLEL'] = str(2) os.environ['SHARDS'] = str(2) def get_random_ws(workspace_path, length=8): random.seed(RANDOM_SEED) letters = string.ascii_lowercase dn = ''.join(random.choice(letters) for i in range(length)) return os.path.join(workspace_path, dn) def print_topk(resp, word): for d in resp.search.docs: print(f'Ta-Dah🔮, here are what we found for: {word}') for idx, match in enumerate(d.matches): score = match.score.value if score <= 0.0: continue word_def = match.chunks[0].text word = match.meta_info.decode() print('> {:>2d}({:.2f}). {}: "{}"'.format(idx, score, word, word_def.strip())) @click.command() @click.option('--task', '-t') @click.option('--num_docs', '-n', default=50) @click.option('--top_k', '-k', default=5) def main(task, num_docs, top_k): workspace_path = '/tmp/jina/urbandict' os.environ['TMP_WORKSPACE'] = get_random_ws(workspace_path) print(f'{os.environ['TMP_WORKSPACE']}') data_fn = os.environ.get('WASHED_DATA_DIR', os.path.join(workspace_path, 'urbandict-word-defs.csv')) if task == 'index': f = Flow().load_config('flow-index.yml') with f: f.index_lines(filepath=data_fn, size=num_docs, batch_size=16) elif task == 'query': f = Flow().load_config('flow-query.yml') with f: while True: text = input('word definition: ') if not text: break ppr = lambda x: print_topk(x, text) f.search_lines(lines=[text, ], output_fn=ppr, topk=top_k) elif task == 'query_restful': f = Flow().load_config('flow-query.yml') f.use_rest_gateway() with f: f.block() else: raise NotImplementedError( f'unknown task: {task}. A valid task is `index` or `query` or `query_restful`.') if __name__ == '__main__': main()

__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved." __license__ = "Apache-2.0" import click import os import string import random from jina.flow import Flow RANDOM_SEED = 10 # 5 os.environ['PARALLEL'] = str(2) os.environ['SHARDS'] = str(2) def get_random_ws(workspace_path, length=8): random.seed(RANDOM_SEED) letters = string.ascii_lowercase dn = ''.join(random.choice(letters) for i in range(length)) return os.path.join(workspace_path, dn) def print_topk(resp, word): for d in resp.search.docs: print(f'Ta-Dah🔮, here are what we found for: {word}') for idx, match in enumerate(d.matches): score = match.score.value if score <= 0.0: continue word_def = match.chunks[0].text word = match.meta_info.decode() print('> {:>2d}({:.2f}). {}: "{}"'.format(idx, score, word, word_def.strip())) @click.command() @click.option('--task', '-t') @click.option('--num_docs', '-n', default=50) @click.option('--top_k', '-k', default=5) def main(task, num_docs, top_k): workspace_path = '/tmp/jina/urbandict' os.environ['TMP_WORKSPACE'] = get_random_ws(workspace_path) print(f'{os.environ["TMP_WORKSPACE"]}') data_fn = os.environ.get('WASHED_DATA_DIR', os.path.join(workspace_path, 'urbandict-word-defs.csv')) if task == 'index': f = Flow().load_config('flow-index.yml') with f: f.index_lines(filepath=data_fn, size=num_docs, batch_size=16) elif task == 'query': f = Flow().load_config('flow-query.yml') with f: while True: text = input('word definition: ') if not text: break ppr = lambda x: print_topk(x, text) f.search_lines(lines=[text, ], output_fn=ppr, topk=top_k) elif task == 'query_restful': f = Flow().load_config('flow-query.yml') f.use_rest_gateway() with f: f.block() else: raise NotImplementedError( f'unknown task: {task}. A valid task is `index` or `query` or `query_restful`.') if __name__ == '__main__': main()

import itertools as itt import pathlib as pl import matplotlib.pyplot as plt import numpy as np import pandas as pd from scipy.io import loadmat import src.data.rasters from src.data import dPCA as cdPCA from src.metrics import dprime as cDP from src.data.load import load, get_site_ids from src.data.cache import make_cache, get_cache from src.metrics.reliability import signal_reliability from src.utils.tools import shuffle_along_axis as shuffle from src.utils import fits as fit from src.visualization import fancy_plots as fplt ''' since applying the dprime CPN analysis toe the NTI data was unsuccessfull, the next alternative to compare Sam and my approach is to perform the CPN and NTI analysis to their respective datasets on recording sites that have both data ''' # 1. list sites with both datasets # list all NTI sites this have to be done manually # list all CPN sites, this should be trivial # check the intersection # 2. Calculates the dPrime for each site and all possible probes, context pairs and cells (?). This is the difficult part # to summarize the outcome of all the def cell_dprime(site, probe, meta): # recs = load(site, remote=True, rasterfs=meta['raster_fs'], recache=False) recs = load(site, rasterfs=meta['raster_fs'], recache=rec_recache) if len(recs) > 2: print(f'\n\n{recs.keys()}\n\n') rec = recs['trip0'] sig = rec['resp'] # calculates response realiability and select only good cells to improve analysis r_vals, goodcells = signal_reliability(sig, r'\ASTIM_*', threshold=meta['reliability']) goodcells = goodcells.tolist() # get the full data raster Context x Probe x Rep x Neuron x Time raster = src.data.rasters.raster_from_sig(sig, probe, channels=goodcells, contexts=meta['transitions'], smooth_window=meta['smoothing_window'], raster_fs=meta['raster_fs'], zscore=meta['zscore'], part='probe') # trialR shape: Trial x Cell x Context x Probe x Time; R shape: Cell x Context x Probe x Time trialR, R, _ = cdPCA.format_raster(raster) trialR, R = trialR.squeeze(axis=3), R.squeeze(axis=2) # squeezes out probe rep, chn, ctx, tme = trialR.shape trans_pairs = [f'{x}_{y}' for x, y in itt.combinations(meta['transitions'], 2)] dprime = cDP.pairwise_dprimes(trialR, observation_axis=0, condition_axis=2) # shape CellPair x Cell x Time # Shuffles the rasters n times and organizes in an array with the same shape the raster plus one dimension # with size n containing each shuffle shuffled = list() # pbar = ProgressBar() print(f"\nshuffling {meta["montecarlo"]} times") for tp in trans_pairs: shuf_trialR = np.empty([meta['montecarlo'], rep, chn, 2, tme]) shuf_trialR[:] = np.nan tran_idx = np.array([meta['transitions'].index(t) for t in tp.split('_')]) ctx_shuffle = trialR[:, :, tran_idx, :].copy() for rr in range(meta['montecarlo']): shuf_trialR[rr, ...] = shuffle(ctx_shuffle, shuffle_axis=2, indie_axis=0) shuffled.append(cDP.pairwise_dprimes(shuf_trialR, observation_axis=1, condition_axis=3)) shuffled = np.stack(shuffled, axis=1).squeeze(axis=0).swapaxes(0, 1) # shape Montecarlo x ContextPair x Cell x Time return dprime, shuffled, goodcells, trans_pairs def dPCA_fourway_analysis(site, probe, meta): # recs = load(site, remote=True, rasterfs=meta['raster_fs'], recache=False) recs = load(site, rasterfs=meta['raster_fs'], recache=rec_recache) if len(recs) > 2: print(f'\n\n{recs.keys()}\n\n') rec = recs['trip0'] sig = rec['resp'] # calculates response realiability and select only good cells to improve analysis r_vals, goodcells = signal_reliability(sig, r'\ASTIM_*', threshold=meta['reliability']) goodcells = goodcells.tolist() # get the full data raster Context x Probe x Rep x Neuron x Time raster = src.data.rasters.raster_from_sig(sig, probe, channels=goodcells, contexts=meta['transitions'], smooth_window=meta['smoothing_window'], raster_fs=meta['raster_fs'], zscore=meta['zscore']) # trialR shape: Trial x Cell x Context x Probe x Time; R shape: Cell x Context x Probe x Time trialR, R, _ = cdPCA.format_raster(raster) trialR, R = trialR.squeeze(axis=3), R.squeeze(axis=2) # squeezes out probe Re, C, S, T = trialR.shape # calculates full dPCA. i.e. considering all 4 categories dPCA_projection, dPCA_transformation = cdPCA.fit_transform(R, trialR) dprime = cDP.pairwise_dprimes(dPCA_projection, observation_axis=0, condition_axis=1) # calculates floor (ctx shuffle) and ceiling (simulated data) sim_dprime = np.empty([meta['montecarlo']] + list(dprime.shape)) shuf_dprime = np.empty([meta['montecarlo']] + list(dprime.shape)) ctx_shuffle = trialR.copy() # pbar = ProgressBar() for rr in range(meta['montecarlo']): # ceiling: simulates data, calculates dprimes sim_trial = np.random.normal(np.mean(trialR, axis=0), np.std(trialR, axis=0), size=[Re, C, S, T]) sim_projection = cdPCA.transform(sim_trial, dPCA_transformation) sim_dprime[rr, ...] = cDP.pairwise_dprimes(sim_projection, observation_axis=0, condition_axis=1) ctx_shuffle = shuffle(ctx_shuffle, shuffle_axis=2, indie_axis=0) shuf_projection = cdPCA.transform(ctx_shuffle, dPCA_transformation) shuf_dprime[rr, ...] = cDP.pairwise_dprimes(shuf_projection, observation_axis=0, condition_axis=1) return dprime, shuf_dprime, sim_dprime, goodcells # transferable plotting parameters plt.rcParams['svg.fonttype'] = 'none' sup_title_size = 30 sub_title_size = 20 ax_lab_size = 15 ax_val_size = 11 meta = {'reliability': 0.1, # r value 'smoothing_window': 0, # ms 'raster_fs': 30, 'transitions': ['silence', 'continuous', 'similar', 'sharp'], 'montecarlo': 1000, 'zscore': False} dprime_recache = False rec_recache = False analysis_name = 'NTI_singel_cell_dprime' analysis_parameters = '_'.join(['{}-{}'.format(key, str(val)) for key, val in meta.items()]) code_to_name = {'t': 'Probe', 'ct': 'Context'} full_screen = [19.2, 9.83] all_probes = [2, 3, 5, 6] sites = ['ley070a', # good site. A1 'ley072b', # Primary looking responses with strong contextual effects 'AMT028b', # good site 'AMT029a', # Strong response, somehow visible contextual effects 'AMT030a', # low responses, Ok but not as good # 'AMT031a', # low response, bad 'AMT032a'] # great site. PEG sites = list(get_site_ids(316).keys()) # problem sites: # sites = ['AMT031a'] # for site, probe in zip(['AMT029a', 'ley070a'],[5,2]): # all_sites = ['AMT029a'] # all_sites = ['AMT032a'] # all_probes = [5] bad_sites = list() all_pvalues = dict() all_reals = dict() all_shuffled = dict() for site in sites: this_site_reals = list() this_site_shuffled = list() this_site_pvalues = list() for pp, probe in enumerate(all_probes): # single cell analysis object_name = f'200221_{site}_P{probe}_single_cell_dprime' analysis_parameters = '_'.join(['{}-{}'.format(key, str(val)) for key, val in meta.items()]) analysis_name = 'CPN_singel_cell_dprime' cache_folder = pl.Path('C:\\', 'users', 'mateo', 'mycache', analysis_name, analysis_parameters) SC_cache = make_cache(function=cell_dprime, func_args={'site': site, 'probe': probe, 'meta': meta}, classobj_name=object_name, cache_folder=cache_folder, recache=dprime_recache) dprime, shuf_dprime, cell_names, trans_pairs = get_cache(SC_cache) this_site_reals.append(dprime) this_site_shuffled.append(shuf_dprime) # single tailed p value base on the montecarlo shuffling SC_pvalues = np.sum((shuf_dprime >= dprime), axis=0) / meta['montecarlo'] this_site_pvalues.append(SC_pvalues) this_site_reals = np.stack(this_site_reals, axis=0) this_site_shuffled = np.stack(this_site_shuffled, axis=0) this_site_pvalues = np.stack(this_site_pvalues, axis=0) # reorders date in dictionary of cells for cc, cell in enumerate(cell_names): all_reals[cell] = this_site_reals[:, :, cc, :] all_shuffled[cell] = this_site_shuffled[:, :, :, cc, :].swapaxes(0, 1) all_pvalues[cell] = this_site_pvalues[:, :, cc, :] # stacks the site individual arrays along a new site dimension. since the sites have disimilar cell number, pads all_cells = np.array(list(all_pvalues.keys())) threshold = 0.05 all_signif = {key: (val <= threshold) for key, val in all_pvalues.items()} # stacks arrays, with different time dimentions, padding with NAN shape = np.insert(np.max(np.stack([arr.shape for arr in all_signif.values()], axis=0), axis=0), 0, len(all_signif)) signif_array = np.empty(shape) signif_array[:] = np.nan for cc, arr in enumerate(all_signif.values()): t = arr.shape[-1] signif_array[cc, :, :, :t] = arr # sig_array = np.stack(list(all_signif.values()), axis=0) # dimensions: Cell x Probe x trans_pair x time # calculates exponential decay for each cell, collapsing across all probes and transisions nbin = signif_array.shape[-1] fs = meta['raster_fs'] times = np.linspace(0, nbin / fs, nbin, endpoint=False) * 1000 # units in ms!!!! collapsed = signif_array.mean(axis=(1, 2)) # organizes in a dataframe with columns r0: y intercept, decay: eponential valaue and tau: Time to a 36% amplitude df = list() for cellid, data in zip(all_cells, collapsed): popt, _, _ = fit.exp_decay(times, data) df.append({'cellid': cellid, 'r0_au': popt[0], 'decay_ms': popt[1]}) context_fits = pd.DataFrame(df) context_fits['tau_ms'] = -1/context_fits['decay_ms'] context_fits.set_index(['cellid'], inplace=True) # 3. import and parse matlab results for Sam's NTI analysis. These results are in a cell by cell format, then it makes # sense to calculate the dprimes idividually forP each cell file = pl.Path('C:\\', 'Users', 'Mateo', 'Documents', 'Science', 'code', 'integration_quilt', 'scrambling-ferrets', 'analysis', 'model_fit_pop_summary').with_suffix('.mat') best_fits = loadmat(file)['best_fits'].squeeze() # orders the data in DF df = list() for row in best_fits: df.append({'cellid': row[2][0], 'intper_ms': row[0][0][0], 'delay_ms': row[1][0][0]}) integration_fits = pd.DataFrame(df) integration_fits.set_index(['cellid'], inplace=True) # 4. pools together both approache, selects only common cell, plots relationships # join='inner' keeps only the intersection between the two Dfs, i.e. the cells that have both approaches def savefig(fig, root, name): root = pl.Path(f'C:\\users\\mateo\\Pictures\\{root}') if not root.exists(): root.mkdir(parents=True, exist_ok=True) png = root.joinpath(name).with_suffix('.png') fig.savefig(png, transparent=False, dpi=100) # svg = root.joinpath(name).with_suffix('.svg') # fig.savefig(svg, transparent=True) DF = pd.concat([context_fits, integration_fits], axis=1, join='inner') # filter out anomalous outliers i.e taus over 1 second due to poor fiting ff_good = DF['tau_ms'] < 1000 filtered = DF.loc[ff_good, :] fig_root = 'sam_vs_mat' x = filtered['tau_ms'] y = filtered['intper_ms'] fig, ax = plt.subplots(figsize=full_screen) ax.scatter(x, y) _ = fplt.lin_reg(x,y, ax=ax) ax.set_xlabel(x.name) ax.set_ylabel(y.name) ax.legend() fig.tight_layout(rect=(0, 0, 1, 0.95)) title = 'Sam integration vs Mateo Tau' fig.suptitle(title) savefig(fig, fig_root, title)

import itertools as itt import pathlib as pl import matplotlib.pyplot as plt import numpy as np import pandas as pd from scipy.io import loadmat import src.data.rasters from src.data import dPCA as cdPCA from src.metrics import dprime as cDP from src.data.load import load, get_site_ids from src.data.cache import make_cache, get_cache from src.metrics.reliability import signal_reliability from src.utils.tools import shuffle_along_axis as shuffle from src.utils import fits as fit from src.visualization import fancy_plots as fplt ''' since applying the dprime CPN analysis toe the NTI data was unsuccessfull, the next alternative to compare Sam and my approach is to perform the CPN and NTI analysis to their respective datasets on recording sites that have both data ''' # 1. list sites with both datasets # list all NTI sites this have to be done manually # list all CPN sites, this should be trivial # check the intersection # 2. Calculates the dPrime for each site and all possible probes, context pairs and cells (?). This is the difficult part # to summarize the outcome of all the def cell_dprime(site, probe, meta): # recs = load(site, remote=True, rasterfs=meta['raster_fs'], recache=False) recs = load(site, rasterfs=meta['raster_fs'], recache=rec_recache) if len(recs) > 2: print(f'\n\n{recs.keys()}\n\n') rec = recs['trip0'] sig = rec['resp'] # calculates response realiability and select only good cells to improve analysis r_vals, goodcells = signal_reliability(sig, r'\ASTIM_*', threshold=meta['reliability']) goodcells = goodcells.tolist() # get the full data raster Context x Probe x Rep x Neuron x Time raster = src.data.rasters.raster_from_sig(sig, probe, channels=goodcells, contexts=meta['transitions'], smooth_window=meta['smoothing_window'], raster_fs=meta['raster_fs'], zscore=meta['zscore'], part='probe') # trialR shape: Trial x Cell x Context x Probe x Time; R shape: Cell x Context x Probe x Time trialR, R, _ = cdPCA.format_raster(raster) trialR, R = trialR.squeeze(axis=3), R.squeeze(axis=2) # squeezes out probe rep, chn, ctx, tme = trialR.shape trans_pairs = [f'{x}_{y}' for x, y in itt.combinations(meta['transitions'], 2)] dprime = cDP.pairwise_dprimes(trialR, observation_axis=0, condition_axis=2) # shape CellPair x Cell x Time # Shuffles the rasters n times and organizes in an array with the same shape the raster plus one dimension # with size n containing each shuffle shuffled = list() # pbar = ProgressBar() print(f"\nshuffling {meta['montecarlo']} times") for tp in trans_pairs: shuf_trialR = np.empty([meta['montecarlo'], rep, chn, 2, tme]) shuf_trialR[:] = np.nan tran_idx = np.array([meta['transitions'].index(t) for t in tp.split('_')]) ctx_shuffle = trialR[:, :, tran_idx, :].copy() for rr in range(meta['montecarlo']): shuf_trialR[rr, ...] = shuffle(ctx_shuffle, shuffle_axis=2, indie_axis=0) shuffled.append(cDP.pairwise_dprimes(shuf_trialR, observation_axis=1, condition_axis=3)) shuffled = np.stack(shuffled, axis=1).squeeze(axis=0).swapaxes(0, 1) # shape Montecarlo x ContextPair x Cell x Time return dprime, shuffled, goodcells, trans_pairs def dPCA_fourway_analysis(site, probe, meta): # recs = load(site, remote=True, rasterfs=meta['raster_fs'], recache=False) recs = load(site, rasterfs=meta['raster_fs'], recache=rec_recache) if len(recs) > 2: print(f'\n\n{recs.keys()}\n\n') rec = recs['trip0'] sig = rec['resp'] # calculates response realiability and select only good cells to improve analysis r_vals, goodcells = signal_reliability(sig, r'\ASTIM_*', threshold=meta['reliability']) goodcells = goodcells.tolist() # get the full data raster Context x Probe x Rep x Neuron x Time raster = src.data.rasters.raster_from_sig(sig, probe, channels=goodcells, contexts=meta['transitions'], smooth_window=meta['smoothing_window'], raster_fs=meta['raster_fs'], zscore=meta['zscore']) # trialR shape: Trial x Cell x Context x Probe x Time; R shape: Cell x Context x Probe x Time trialR, R, _ = cdPCA.format_raster(raster) trialR, R = trialR.squeeze(axis=3), R.squeeze(axis=2) # squeezes out probe Re, C, S, T = trialR.shape # calculates full dPCA. i.e. considering all 4 categories dPCA_projection, dPCA_transformation = cdPCA.fit_transform(R, trialR) dprime = cDP.pairwise_dprimes(dPCA_projection, observation_axis=0, condition_axis=1) # calculates floor (ctx shuffle) and ceiling (simulated data) sim_dprime = np.empty([meta['montecarlo']] + list(dprime.shape)) shuf_dprime = np.empty([meta['montecarlo']] + list(dprime.shape)) ctx_shuffle = trialR.copy() # pbar = ProgressBar() for rr in range(meta['montecarlo']): # ceiling: simulates data, calculates dprimes sim_trial = np.random.normal(np.mean(trialR, axis=0), np.std(trialR, axis=0), size=[Re, C, S, T]) sim_projection = cdPCA.transform(sim_trial, dPCA_transformation) sim_dprime[rr, ...] = cDP.pairwise_dprimes(sim_projection, observation_axis=0, condition_axis=1) ctx_shuffle = shuffle(ctx_shuffle, shuffle_axis=2, indie_axis=0) shuf_projection = cdPCA.transform(ctx_shuffle, dPCA_transformation) shuf_dprime[rr, ...] = cDP.pairwise_dprimes(shuf_projection, observation_axis=0, condition_axis=1) return dprime, shuf_dprime, sim_dprime, goodcells # transferable plotting parameters plt.rcParams['svg.fonttype'] = 'none' sup_title_size = 30 sub_title_size = 20 ax_lab_size = 15 ax_val_size = 11 meta = {'reliability': 0.1, # r value 'smoothing_window': 0, # ms 'raster_fs': 30, 'transitions': ['silence', 'continuous', 'similar', 'sharp'], 'montecarlo': 1000, 'zscore': False} dprime_recache = False rec_recache = False analysis_name = 'NTI_singel_cell_dprime' analysis_parameters = '_'.join(['{}-{}'.format(key, str(val)) for key, val in meta.items()]) code_to_name = {'t': 'Probe', 'ct': 'Context'} full_screen = [19.2, 9.83] all_probes = [2, 3, 5, 6] sites = ['ley070a', # good site. A1 'ley072b', # Primary looking responses with strong contextual effects 'AMT028b', # good site 'AMT029a', # Strong response, somehow visible contextual effects 'AMT030a', # low responses, Ok but not as good # 'AMT031a', # low response, bad 'AMT032a'] # great site. PEG sites = list(get_site_ids(316).keys()) # problem sites: # sites = ['AMT031a'] # for site, probe in zip(['AMT029a', 'ley070a'],[5,2]): # all_sites = ['AMT029a'] # all_sites = ['AMT032a'] # all_probes = [5] bad_sites = list() all_pvalues = dict() all_reals = dict() all_shuffled = dict() for site in sites: this_site_reals = list() this_site_shuffled = list() this_site_pvalues = list() for pp, probe in enumerate(all_probes): # single cell analysis object_name = f'200221_{site}_P{probe}_single_cell_dprime' analysis_parameters = '_'.join(['{}-{}'.format(key, str(val)) for key, val in meta.items()]) analysis_name = 'CPN_singel_cell_dprime' cache_folder = pl.Path('C:\\', 'users', 'mateo', 'mycache', analysis_name, analysis_parameters) SC_cache = make_cache(function=cell_dprime, func_args={'site': site, 'probe': probe, 'meta': meta}, classobj_name=object_name, cache_folder=cache_folder, recache=dprime_recache) dprime, shuf_dprime, cell_names, trans_pairs = get_cache(SC_cache) this_site_reals.append(dprime) this_site_shuffled.append(shuf_dprime) # single tailed p value base on the montecarlo shuffling SC_pvalues = np.sum((shuf_dprime >= dprime), axis=0) / meta['montecarlo'] this_site_pvalues.append(SC_pvalues) this_site_reals = np.stack(this_site_reals, axis=0) this_site_shuffled = np.stack(this_site_shuffled, axis=0) this_site_pvalues = np.stack(this_site_pvalues, axis=0) # reorders date in dictionary of cells for cc, cell in enumerate(cell_names): all_reals[cell] = this_site_reals[:, :, cc, :] all_shuffled[cell] = this_site_shuffled[:, :, :, cc, :].swapaxes(0, 1) all_pvalues[cell] = this_site_pvalues[:, :, cc, :] # stacks the site individual arrays along a new site dimension. since the sites have disimilar cell number, pads all_cells = np.array(list(all_pvalues.keys())) threshold = 0.05 all_signif = {key: (val <= threshold) for key, val in all_pvalues.items()} # stacks arrays, with different time dimentions, padding with NAN shape = np.insert(np.max(np.stack([arr.shape for arr in all_signif.values()], axis=0), axis=0), 0, len(all_signif)) signif_array = np.empty(shape) signif_array[:] = np.nan for cc, arr in enumerate(all_signif.values()): t = arr.shape[-1] signif_array[cc, :, :, :t] = arr # sig_array = np.stack(list(all_signif.values()), axis=0) # dimensions: Cell x Probe x trans_pair x time # calculates exponential decay for each cell, collapsing across all probes and transisions nbin = signif_array.shape[-1] fs = meta['raster_fs'] times = np.linspace(0, nbin / fs, nbin, endpoint=False) * 1000 # units in ms!!!! collapsed = signif_array.mean(axis=(1, 2)) # organizes in a dataframe with columns r0: y intercept, decay: eponential valaue and tau: Time to a 36% amplitude df = list() for cellid, data in zip(all_cells, collapsed): popt, _, _ = fit.exp_decay(times, data) df.append({'cellid': cellid, 'r0_au': popt[0], 'decay_ms': popt[1]}) context_fits = pd.DataFrame(df) context_fits['tau_ms'] = -1/context_fits['decay_ms'] context_fits.set_index(['cellid'], inplace=True) # 3. import and parse matlab results for Sam's NTI analysis. These results are in a cell by cell format, then it makes # sense to calculate the dprimes idividually forP each cell file = pl.Path('C:\\', 'Users', 'Mateo', 'Documents', 'Science', 'code', 'integration_quilt', 'scrambling-ferrets', 'analysis', 'model_fit_pop_summary').with_suffix('.mat') best_fits = loadmat(file)['best_fits'].squeeze() # orders the data in DF df = list() for row in best_fits: df.append({'cellid': row[2][0], 'intper_ms': row[0][0][0], 'delay_ms': row[1][0][0]}) integration_fits = pd.DataFrame(df) integration_fits.set_index(['cellid'], inplace=True) # 4. pools together both approache, selects only common cell, plots relationships # join='inner' keeps only the intersection between the two Dfs, i.e. the cells that have both approaches def savefig(fig, root, name): root = pl.Path(f'C:\\users\\mateo\\Pictures\\{root}') if not root.exists(): root.mkdir(parents=True, exist_ok=True) png = root.joinpath(name).with_suffix('.png') fig.savefig(png, transparent=False, dpi=100) # svg = root.joinpath(name).with_suffix('.svg') # fig.savefig(svg, transparent=True) DF = pd.concat([context_fits, integration_fits], axis=1, join='inner') # filter out anomalous outliers i.e taus over 1 second due to poor fiting ff_good = DF['tau_ms'] < 1000 filtered = DF.loc[ff_good, :] fig_root = 'sam_vs_mat' x = filtered['tau_ms'] y = filtered['intper_ms'] fig, ax = plt.subplots(figsize=full_screen) ax.scatter(x, y) _ = fplt.lin_reg(x,y, ax=ax) ax.set_xlabel(x.name) ax.set_ylabel(y.name) ax.legend() fig.tight_layout(rect=(0, 0, 1, 0.95)) title = 'Sam integration vs Mateo Tau' fig.suptitle(title) savefig(fig, fig_root, title)

import argparse import logging import os import re import statistics import sys import time from datetime import timedelta, datetime from threading import Thread from typing import Dict, Tuple, Optional, List, Iterable from tqdm import tqdm from .env import H2TestEnv, H2Conf from pyhttpd.result import ExecResult log = logging.getLogger(__name__) class LoadTestException(Exception): pass class H2LoadLogSummary: @staticmethod def from_file(fpath: str, title: str, duration: timedelta) -> 'H2LoadLogSummary': with open(fpath) as fd: return H2LoadLogSummary.from_lines(fd.readlines(), title=title, duration=duration) @staticmethod def from_lines(lines: Iterable[str], title: str, duration: timedelta) -> 'H2LoadLogSummary': stati = {} count = 0 durations = list() all_durations = timedelta(milliseconds=0) for line in lines: parts = re.split(r'\s+', line) # start(us), status(int), duration(ms), tbd. if len(parts) >= 3 and parts[0] and parts[1] and parts[2]: count += 1 status = int(parts[1]) if status in stati: stati[status] += 1 else: stati[status] = 1 durations.append(int(parts[2])) all_durations += timedelta(microseconds=int(parts[2])) else: sys.stderr.write("unrecognize log line: {0}".format(line)) mean_duration = statistics.mean(durations) return H2LoadLogSummary(title=title, total=count, stati=stati, duration=duration, all_durations=all_durations, mean_duration=mean_duration) def __init__(self, title: str, total: int, stati: Dict[int, int], duration: timedelta, all_durations: timedelta, mean_duration: timedelta): self._title = title self._total = total self._stati = stati self._duration = duration self._all_durations = all_durations self._mean_duration = mean_duration self._transfered_mb = 0.0 self._exec_result = None self._expected_responses = 0 @property def title(self) -> str: return self._title @property def response_count(self) -> int: return self._total @property def duration(self) -> timedelta: return self._duration @property def mean_duration_ms(self) -> float: return self._mean_duration / 1000.0 @property def response_durations(self) -> timedelta: return self._all_durations @property def response_stati(self) -> Dict[int, int]: return self._stati @property def expected_responses(self) -> int: return self._expected_responses @property def execution(self) -> ExecResult: return self._exec_result def all_200(self) -> bool: non_200s = [n for n in self._stati.keys() if n != 200] return len(non_200s) == 0 @property def throughput_mb(self) -> float: if self._transfered_mb > 0.0: return self._transfered_mb / self.duration.total_seconds() return 0.0 def set_transfered_mb(self, mb: float) -> None: self._transfered_mb = mb def set_exec_result(self, result: ExecResult): self._exec_result = result def set_expected_responses(self, n: int): self._expected_responses = n def get_footnote(self) -> Optional[str]: note = "" if 0 < self.expected_responses != self.response_count: note += "{0}/{1} missing".format( self.expected_responses - self.response_count, self.expected_responses ) if not self.all_200(): note += ", non 200s:" for status in [n for n in self.response_stati.keys() if n != 200]: note += " {0}={1}".format(status, self.response_stati[status]) return note if len(note) else None class H2LoadMonitor: def __init__(self, fpath: str, expected: int, title: str): self._fpath = fpath self._expected = expected self._title = title self._tqdm = tqdm(desc=title, total=expected, unit="request", leave=False) self._running = False self._lines = () self._tail = None def start(self): self._tail = Thread(target=self._collect, kwargs={'self': self}) self._running = True self._tail.start() def get_summary(self, duration: timedelta) -> H2LoadLogSummary: self._running = False self._tail.join() return H2LoadLogSummary.from_file(self._fpath, title=self._title, duration=duration) def stop(self): self._running = False @staticmethod def _collect(self) -> None: first_call = True while self._running: try: with open(self._fpath) as fd: if first_call: fd.seek(0, 2) first_call = False latest_data = fd.read() while self._running: if '\n' not in latest_data: latest_data += fd.read() if '\n' not in latest_data: if not os.path.isfile(self._fpath): break time.sleep(0.1) continue lines = latest_data.split('\n') if lines[-1] != '\n': latest_data = lines[-1] lines = lines[:-1] else: latest_data = None self._tqdm.update(n=len(lines)) if latest_data is None: latest_data = fd.read() except IOError: time.sleep(0.1) self._tqdm.close() def mk_text_file(fpath: str, lines: int): t110 = "" for _ in range(11): t110 += "0123456789" with open(fpath, "w") as fd: for i in range(lines): fd.write("{0:015d}: ".format(i)) # total 128 bytes per line fd.write(t110) fd.write("\n") class LoadTestCase: @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': raise NotImplemented def run(self) -> H2LoadLogSummary: raise NotImplemented def format_result(self, summary: H2LoadLogSummary) -> str: raise NotImplemented def shutdown(self): raise NotImplemented @staticmethod def setup_base_conf(env: H2TestEnv, worker_count: int = 5000) -> H2Conf: conf = H2Conf(env=env) # ylavic's formula process_count = int(max(10, min(100, int(worker_count / 100)))) thread_count = int(max(25, int(worker_count / process_count))) conf.add(f""" StartServers 1 ServerLimit {int(process_count * 2.5)} ThreadLimit {thread_count} ThreadsPerChild {thread_count} MinSpareThreads {thread_count} MaxSpareThreads {int(worker_count / 2)} MaxRequestWorkers {worker_count} MaxConnectionsPerChild 0 KeepAliveTimeout 60 MaxKeepAliveRequests 0 """) return conf @staticmethod def start_server(env: H2TestEnv, cd: timedelta = None): if cd: with tqdm(desc="connection cooldown", total=int(cd.total_seconds()), unit="s", leave=False) as t: end = datetime.now() + cd while datetime.now() < end: time.sleep(1) t.update() assert env.apache_restart() == 0 @staticmethod def server_setup(env: H2TestEnv, extras: Dict = None): conf = LoadTestCase.setup_base_conf(env=env) if not extras: extras = { 'base': """ LogLevel ssl:warn Protocols h2 http/1.1 H2MinWorkers 32 H2MaxWorkers 256 """ } extras['base'] += f""" ProxyPreserveHost on SSLProxyVerify require SSLProxyCACertificateFile {env.ca.cert_file} <Proxy https://127.0.0.1:{env.https_port}/> SSLProxyEngine on </Proxy> <Proxy h2://127.0.0.1:{env.https_port}/> SSLProxyEngine on </Proxy> """ extras[env.domain_test1] = f""" Protocols h2 http/1.1 ProxyPass /proxy-h1/ https://127.0.0.1:{env.https_port}/ ProxyPass /proxy-h2/ h2://127.0.0.1:{env.https_port}/ """ conf.add_vhost_test1(extras=extras) conf.install() class UrlsLoadTest(LoadTestCase): SETUP_DONE = False def __init__(self, env: H2TestEnv, location: str, clients: int, requests: int, file_count: int, file_sizes: List[int], measure: str, protocol: str = 'h2', max_parallel: int = 1, threads: int = None, warmup: bool = False): self.env = env self._location = location self._clients = clients self._measure = measure self._requests = requests self._file_count = file_count self._file_sizes = file_sizes self._protocol = protocol self._max_parallel = max_parallel self._threads = threads if threads is not None else min(2, self._clients) self._url_file = "{gen_dir}/h2load-urls.txt".format(gen_dir=self.env.gen_dir) self._warmup = warmup @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': return UrlsLoadTest( env=env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], warmup=scenario['warmup'], measure=scenario['measure'] ) def next_scenario(self, scenario: Dict) -> 'UrlsLoadTest': return UrlsLoadTest( env=self.env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], warmup=scenario['warmup'], measure=scenario['measure'] ) def _setup(self, cls, extras: Dict = None): LoadTestCase.server_setup(env=self.env, extras=extras) docs_a = os.path.join(self.env.server_docs_dir, "test1") uris = [] for i in range(self._file_count): fsize = self._file_sizes[i % len(self._file_sizes)] if fsize is None: raise Exception("file sizes?: {0} {1}".format(i, fsize)) fname = "{0}-{1}k.txt".format(i, fsize) fpath = os.path.join(docs_a, fname) if not os.path.isfile(fpath): mk_text_file(os.path.join(docs_a, fname), 8 * fsize) uris.append(f"{self._location}{fname}") with open(self._url_file, 'w') as fd: fd.write("\n".join(uris)) fd.write("\n") self.start_server(env=self.env) def _teardown(self): # we shutdown apache at program exit pass def shutdown(self): self._teardown() def run_test(self, mode: str, path: str) -> H2LoadLogSummary: monitor = None try: log_file = "{gen_dir}/h2load.log".format(gen_dir=self.env.gen_dir) if os.path.isfile(log_file): os.remove(log_file) monitor = H2LoadMonitor(log_file, expected=self._requests, title=f"{self._protocol}/" f"{self._file_count / 1024}f/{self._clients}c[{mode}]") monitor.start() args = [ 'h2load', '--clients={0}'.format(self._clients), '--threads={0}'.format(self._threads), '--requests={0}'.format(self._requests), '--input-file={0}'.format(self._url_file), '--log-file={0}'.format(log_file), '--connect-to=localhost:{0}'.format(self.env.https_port) ] if self._protocol == 'h1' or self._protocol == 'http/1.1': args.append('--h1') elif self._protocol == 'h2': args.extend(['-m', str(self._max_parallel)]) else: raise Exception(f"unknown protocol: {self._protocol}") r = self.env.run(args + [ f'--base-uri=https://{self.env.domain_test1}:{self.env.https_port}{self._location}' ]) if r.exit_code != 0: raise LoadTestException("h2load returned {0}: {1}".format(r.exit_code, r.stderr)) summary = monitor.get_summary(duration=r.duration) summary.set_expected_responses(self._requests) summary.set_exec_result(r) return summary finally: if monitor is not None: monitor.stop() def run(self) -> H2LoadLogSummary: path = self._setup(self.__class__) try: if self._warmup: self.run_test(mode="warmup", path=path) r = self.run_test(mode="measure", path=path) # time.sleep(300) return r finally: self._teardown() def format_result(self, summary: H2LoadLogSummary) -> Tuple[str, Optional[List[str]]]: if self._measure == 'req/s': r = "{0:d}".format(round(summary.response_count / summary.duration.total_seconds())) elif self._measure == 'mean ms/req': r = "{0:.1f}".format(summary.mean_duration_ms) elif self._measure == 'mb/s': reqs = summary.response_count / summary.duration.total_seconds() mean_size = statistics.mean(self._file_sizes) r = "{0:d}".format(round(reqs * mean_size / 1024.0)) else: raise Exception(f"measure '{self._measure}' not defined") return r, summary.get_footnote() class StressTest(LoadTestCase): SETUP_DONE = False def __init__(self, env: H2TestEnv, location: str, clients: int, requests: int, file_count: int, file_sizes: List[int], protocol: str = 'h2', max_parallel: int = 1, cooldown: timedelta = None, threads: int = None, ): self.env = env self._location = location self._clients = clients self._requests = requests self._file_count = file_count self._file_sizes = file_sizes self._protocol = protocol self._max_parallel = max_parallel self._cooldown = cooldown if cooldown else timedelta(seconds=0) self._threads = threads if threads is not None else min(2, self._clients) self._url_file = "{gen_dir}/h2load-urls.txt".format(gen_dir=self.env.gen_dir) self._is_setup = False @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': return StressTest( env=env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], cooldown=scenario['cooldown'] ) def next_scenario(self, scenario: Dict) -> 'UrlsLoadTest': self._location = scenario['location'] self._clients = scenario['clients'] self._requests = scenario['requests'] self._file_sizes = scenario['file_sizes'] self._file_count = scenario['file_count'] self._protocol = scenario['protocol'] self._max_parallel = scenario['max_parallel'] return self def _setup(self, cls): LoadTestCase.server_setup(env=self.env, extras={ 'base': f""" H2MinWorkers 32 H2MaxWorkers 128 H2MaxWorkerIdleSeconds 5 """ }) if not cls.SETUP_DONE: with tqdm(desc="setup resources", total=self._file_count, unit="file", leave=False) as t: docs_a = os.path.join(self.env.server_docs_dir, "test1") uris = [] for i in range(self._file_count): fsize = self._file_sizes[i % len(self._file_sizes)] if fsize is None: raise Exception("file sizes?: {0} {1}".format(i, fsize)) fname = "{0}-{1}k.txt".format(i, fsize) mk_text_file(os.path.join(docs_a, fname), 8 * fsize) uris.append(f"{self._location}{fname}") t.update() with open(self._url_file, 'w') as fd: fd.write("\n".join(uris)) fd.write("\n") cls.SETUP_DONE = True self.start_server(env=self.env) self._is_setup = True def shutdown(self): # we shutdown apache at program exit pass def run_test(self, mode: str) -> H2LoadLogSummary: monitor = None try: log_file = "{gen_dir}/h2load.log".format(gen_dir=self.env.gen_dir) if os.path.isfile(log_file): os.remove(log_file) monitor = H2LoadMonitor(log_file, expected=self._requests, title=f"{self._protocol}/" f"{self._file_count / 1024}f/{self._clients}c[{mode}]") monitor.start() args = [ 'h2load', '--clients={0}'.format(self._clients), '--threads={0}'.format(min(self._clients, 2)), '--requests={0}'.format(self._requests), '--input-file={0}'.format(self._url_file), '--log-file={0}'.format(log_file), '--connect-to=localhost:{0}'.format(self.env.https_port) ] if self._protocol == 'h1' or self._protocol == 'http/1.1': args.append('--h1') elif self._protocol == 'h2': args.extend(['-m', str(self._max_parallel)]) else: raise Exception(f"unknown protocol: {self._protocol}") r = self.env.run(args + [ f'--base-uri=https://{self.env.domain_test1}:{self.env.https_port}{self._location}' ]) if r.exit_code != 0: raise LoadTestException("h2load returned {0}: {1}".format(r.exit_code, r.stderr)) summary = monitor.get_summary(duration=r.duration) summary.set_expected_responses(self._requests) summary.set_exec_result(r) return summary finally: if monitor is not None: monitor.stop() def run(self) -> H2LoadLogSummary: if not self._is_setup: self._setup(self.__class__) elif self._cooldown.total_seconds() > 0: with tqdm(desc="worker cooldown", total=int(self._cooldown.total_seconds()), unit="s", leave=False) as t: end = datetime.now() + self._cooldown while datetime.now() < end: time.sleep(1) t.update() return self.run_test(mode="measure") def format_result(self, summary: H2LoadLogSummary) -> Tuple[str, Optional[List[str]]]: return "{0:.1f}".format( summary.response_count / summary.duration.total_seconds() ), summary.get_footnote() class LoadTest: @staticmethod def print_table(table: List[List[str]], foot_notes: List[str] = None): col_widths = [] col_sep = " " for row in table[1:]: for idx, cell in enumerate(row): if idx >= len(col_widths): col_widths.append(len(cell)) else: col_widths[idx] = max(len(cell), col_widths[idx]) row_len = sum(col_widths) + (len(col_widths) * len(col_sep)) print(f"{" ".join(table[0]):^{row_len}}") for row in table[1:]: line = "" for idx, cell in enumerate(row): line += f"{col_sep if idx > 0 else ""}{cell:>{col_widths[idx]}}" print(line) if foot_notes is not None: for idx, note in enumerate(foot_notes): print("{0:3d}) {1}".format(idx+1, note)) @classmethod def main(cls): parser = argparse.ArgumentParser(prog='load_h1', description=""" Run a range of load tests against the test Apache setup. """) parser.add_argument("-p", "--protocol", type=str, default=None, help="which protocols to test, defaults to all") parser.add_argument("-v", "--verbose", action='count', default=0, help="log more output on stderr") parser.add_argument("names", nargs='*', help="Name(s) of scenarios to run") args = parser.parse_args() if args.verbose > 0: console = logging.StreamHandler() console.setLevel(logging.INFO) console.setFormatter(logging.Formatter(logging.BASIC_FORMAT)) logging.getLogger('').addHandler(console) scenarios = { "1k-files": { "title": "1k files, 1k-10MB, *conn, 10k req ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 10000, "warmup": True, "measure": "req/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 1}, {"protocol": 'h2', "max_parallel": 2}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 20}, {"protocol": 'h2', "max_parallel": 50}, {"protocol": 'h1', "max_parallel": 1}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1}, {"clients": 4}, {"clients": 8}, {"clients": 16}, {"clients": 32}, ], }, "long": { "title": "1k files, 10k size, *conn, 100k req, {protocol} ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 100, "file_sizes": [1], "requests": 100000, "warmup": False, "measure": "req/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "max requests", "row_title": "{max_parallel:3d} {requests}", "rows": [ {"max_parallel": 1, "requests": 100000}, {"max_parallel": 2, "requests": 100000}, #{"max_parallel": 6, "requests": 250000}, #{"max_parallel": 20, "requests": 500000}, #{"max_parallel": 50, "requests": 750000}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1}, ], }, "durations": { "title": "1k files, 64k size, 10k req/conn ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [64], "requests": 10000, "warmup": False, "measure": "mean ms/req", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 1}, {"protocol": 'h2', "max_parallel": 2}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 20}, {"protocol": 'h2', "max_parallel": 50}, {"protocol": 'h1', "max_parallel": 1}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1, "requests": 10000}, {"clients": 4, "requests": 40000}, {"clients": 8, "requests": 80000}, {"clients": 16, "requests": 160000}, {"clients": 32, "requests": 320000}, ], }, "transfers": { "title": "net transfer speed, by KB body size, (MB/s)", "class": UrlsLoadTest, "location": "/", "file_count": 1, "file_sizes": [10, 100, 1000, 10000], "requests": 10000, "clients": 1, "warmup": False, "measure": "mb/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol c/parallel", "row_title": "{protocol} {clients}/{max_parallel}", "rows": [ {"protocol": 'h1', "max_parallel": 1, "clients": 1}, {"protocol": 'h2', "max_parallel": 1, "clients": 1}, {"protocol": 'h2', "max_parallel": 2, "clients": 1}, {"protocol": 'h2', "max_parallel": 6, "clients": 1}, {"protocol": 'h1', "max_parallel": 1, "clients": 2}, {"protocol": 'h2', "max_parallel": 1, "clients": 2}, {"protocol": 'h2', "max_parallel": 2, "clients": 2}, {"protocol": 'h2', "max_parallel": 6, "clients": 2}, {"protocol": 'h1', "max_parallel": 1, "clients": 6}, {"protocol": 'h2', "max_parallel": 1, "clients": 6}, {"protocol": 'h2', "max_parallel": 2, "clients": 6}, {"protocol": 'h2', "max_parallel": 6, "clients": 6}, ], "col_title": "{file_sizes}", "clients": 1, "columns": [ {"file_sizes": [10], "requests": 100000}, {"file_sizes": [100], "requests": 50000}, {"file_sizes": [1000], "requests": 20000}, {"file_sizes": [10000], "requests": 5000}, ], }, "bursty": { "title": "1k files, {clients} clients, {requests} request, (req/s)", "class": StressTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 20000, "protocol": "h2", "max_parallel": 50, "clients": 32, "cooldown": timedelta(seconds=20), "row0_title": "protocol", "row_title": "{protocol}", "rows": [ {"protocol": 'h2', }, ], "col_title": "{run}", "columns": [ {"run": 1}, {"run": 2}, {"run": 3}, {"run": 4}, {"run": 5}, {"run": 6}, {"run": 7}, {"run": 8}, {"run": 9}, {"run": 10}, {"run": 11}, {"run": 12}, {"run": 13}, {"run": 14}, {"run": 15}, {"run": 16}, {"run": 17}, {"run": 18}, {"run": 19}, {"run": 20}, ], }, "m6": { "title": "1k files, 1k-10MB, *conn, 10k req ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 5000, "warmup": True, "measure": "req/s", "protocol": 'h2', "max_parallel": 6, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1, "requests": 1000}, {"clients": 32, "requests": 16000}, {"clients": 64, "requests": 32000}, {"clients": 128, "requests": 64000}, {"clients": 192, "requests": 96000}, ], }, } env = H2TestEnv() rv = 0 try: log.debug("starting tests") names = args.names if len(args.names) else sorted(scenarios.keys()) for name in names: if name not in scenarios: raise LoadTestException(f"unknown test scenario: {name}") scenario = scenarios[name] table = [ [scenario['title'].format(**scenario)], ] foot_notes = [] headers = [scenario['row0_title']] for col in scenario['columns']: headers.append(scenario['col_title'].format(**col)) table.append(headers) cls.print_table(table) test = scenario['class'].from_scenario(scenario, env=env) for row in scenario['rows']: if args.protocol is not None and row['protocol'] != args.protocol: continue row_line = [scenario['row_title'].format(**row)] table.append(row_line) for col in scenario['columns']: t = scenario.copy() t.update(row) t.update(col) test = test.next_scenario(t) env.apache_error_log_clear() summary = test.run() result, fnote = test.format_result(summary) if fnote: foot_notes.append(fnote) row_line.append("{0}{1}".format(result, f"[{len(foot_notes)}]" if fnote else "")) cls.print_table(table, foot_notes) test.shutdown() except KeyboardInterrupt: log.warning("aborted") rv = 1 except LoadTestException as ex: sys.stderr.write(f"ERROR: {str(ex)}\n") rv = 1 env.apache_stop() sys.exit(rv) if __name__ == "__main__": LoadTest.main()

import argparse import logging import os import re import statistics import sys import time from datetime import timedelta, datetime from threading import Thread from typing import Dict, Tuple, Optional, List, Iterable from tqdm import tqdm from .env import H2TestEnv, H2Conf from pyhttpd.result import ExecResult log = logging.getLogger(__name__) class LoadTestException(Exception): pass class H2LoadLogSummary: @staticmethod def from_file(fpath: str, title: str, duration: timedelta) -> 'H2LoadLogSummary': with open(fpath) as fd: return H2LoadLogSummary.from_lines(fd.readlines(), title=title, duration=duration) @staticmethod def from_lines(lines: Iterable[str], title: str, duration: timedelta) -> 'H2LoadLogSummary': stati = {} count = 0 durations = list() all_durations = timedelta(milliseconds=0) for line in lines: parts = re.split(r'\s+', line) # start(us), status(int), duration(ms), tbd. if len(parts) >= 3 and parts[0] and parts[1] and parts[2]: count += 1 status = int(parts[1]) if status in stati: stati[status] += 1 else: stati[status] = 1 durations.append(int(parts[2])) all_durations += timedelta(microseconds=int(parts[2])) else: sys.stderr.write("unrecognize log line: {0}".format(line)) mean_duration = statistics.mean(durations) return H2LoadLogSummary(title=title, total=count, stati=stati, duration=duration, all_durations=all_durations, mean_duration=mean_duration) def __init__(self, title: str, total: int, stati: Dict[int, int], duration: timedelta, all_durations: timedelta, mean_duration: timedelta): self._title = title self._total = total self._stati = stati self._duration = duration self._all_durations = all_durations self._mean_duration = mean_duration self._transfered_mb = 0.0 self._exec_result = None self._expected_responses = 0 @property def title(self) -> str: return self._title @property def response_count(self) -> int: return self._total @property def duration(self) -> timedelta: return self._duration @property def mean_duration_ms(self) -> float: return self._mean_duration / 1000.0 @property def response_durations(self) -> timedelta: return self._all_durations @property def response_stati(self) -> Dict[int, int]: return self._stati @property def expected_responses(self) -> int: return self._expected_responses @property def execution(self) -> ExecResult: return self._exec_result def all_200(self) -> bool: non_200s = [n for n in self._stati.keys() if n != 200] return len(non_200s) == 0 @property def throughput_mb(self) -> float: if self._transfered_mb > 0.0: return self._transfered_mb / self.duration.total_seconds() return 0.0 def set_transfered_mb(self, mb: float) -> None: self._transfered_mb = mb def set_exec_result(self, result: ExecResult): self._exec_result = result def set_expected_responses(self, n: int): self._expected_responses = n def get_footnote(self) -> Optional[str]: note = "" if 0 < self.expected_responses != self.response_count: note += "{0}/{1} missing".format( self.expected_responses - self.response_count, self.expected_responses ) if not self.all_200(): note += ", non 200s:" for status in [n for n in self.response_stati.keys() if n != 200]: note += " {0}={1}".format(status, self.response_stati[status]) return note if len(note) else None class H2LoadMonitor: def __init__(self, fpath: str, expected: int, title: str): self._fpath = fpath self._expected = expected self._title = title self._tqdm = tqdm(desc=title, total=expected, unit="request", leave=False) self._running = False self._lines = () self._tail = None def start(self): self._tail = Thread(target=self._collect, kwargs={'self': self}) self._running = True self._tail.start() def get_summary(self, duration: timedelta) -> H2LoadLogSummary: self._running = False self._tail.join() return H2LoadLogSummary.from_file(self._fpath, title=self._title, duration=duration) def stop(self): self._running = False @staticmethod def _collect(self) -> None: first_call = True while self._running: try: with open(self._fpath) as fd: if first_call: fd.seek(0, 2) first_call = False latest_data = fd.read() while self._running: if '\n' not in latest_data: latest_data += fd.read() if '\n' not in latest_data: if not os.path.isfile(self._fpath): break time.sleep(0.1) continue lines = latest_data.split('\n') if lines[-1] != '\n': latest_data = lines[-1] lines = lines[:-1] else: latest_data = None self._tqdm.update(n=len(lines)) if latest_data is None: latest_data = fd.read() except IOError: time.sleep(0.1) self._tqdm.close() def mk_text_file(fpath: str, lines: int): t110 = "" for _ in range(11): t110 += "0123456789" with open(fpath, "w") as fd: for i in range(lines): fd.write("{0:015d}: ".format(i)) # total 128 bytes per line fd.write(t110) fd.write("\n") class LoadTestCase: @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': raise NotImplemented def run(self) -> H2LoadLogSummary: raise NotImplemented def format_result(self, summary: H2LoadLogSummary) -> str: raise NotImplemented def shutdown(self): raise NotImplemented @staticmethod def setup_base_conf(env: H2TestEnv, worker_count: int = 5000) -> H2Conf: conf = H2Conf(env=env) # ylavic's formula process_count = int(max(10, min(100, int(worker_count / 100)))) thread_count = int(max(25, int(worker_count / process_count))) conf.add(f""" StartServers 1 ServerLimit {int(process_count * 2.5)} ThreadLimit {thread_count} ThreadsPerChild {thread_count} MinSpareThreads {thread_count} MaxSpareThreads {int(worker_count / 2)} MaxRequestWorkers {worker_count} MaxConnectionsPerChild 0 KeepAliveTimeout 60 MaxKeepAliveRequests 0 """) return conf @staticmethod def start_server(env: H2TestEnv, cd: timedelta = None): if cd: with tqdm(desc="connection cooldown", total=int(cd.total_seconds()), unit="s", leave=False) as t: end = datetime.now() + cd while datetime.now() < end: time.sleep(1) t.update() assert env.apache_restart() == 0 @staticmethod def server_setup(env: H2TestEnv, extras: Dict = None): conf = LoadTestCase.setup_base_conf(env=env) if not extras: extras = { 'base': """ LogLevel ssl:warn Protocols h2 http/1.1 H2MinWorkers 32 H2MaxWorkers 256 """ } extras['base'] += f""" ProxyPreserveHost on SSLProxyVerify require SSLProxyCACertificateFile {env.ca.cert_file} <Proxy https://127.0.0.1:{env.https_port}/> SSLProxyEngine on </Proxy> <Proxy h2://127.0.0.1:{env.https_port}/> SSLProxyEngine on </Proxy> """ extras[env.domain_test1] = f""" Protocols h2 http/1.1 ProxyPass /proxy-h1/ https://127.0.0.1:{env.https_port}/ ProxyPass /proxy-h2/ h2://127.0.0.1:{env.https_port}/ """ conf.add_vhost_test1(extras=extras) conf.install() class UrlsLoadTest(LoadTestCase): SETUP_DONE = False def __init__(self, env: H2TestEnv, location: str, clients: int, requests: int, file_count: int, file_sizes: List[int], measure: str, protocol: str = 'h2', max_parallel: int = 1, threads: int = None, warmup: bool = False): self.env = env self._location = location self._clients = clients self._measure = measure self._requests = requests self._file_count = file_count self._file_sizes = file_sizes self._protocol = protocol self._max_parallel = max_parallel self._threads = threads if threads is not None else min(2, self._clients) self._url_file = "{gen_dir}/h2load-urls.txt".format(gen_dir=self.env.gen_dir) self._warmup = warmup @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': return UrlsLoadTest( env=env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], warmup=scenario['warmup'], measure=scenario['measure'] ) def next_scenario(self, scenario: Dict) -> 'UrlsLoadTest': return UrlsLoadTest( env=self.env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], warmup=scenario['warmup'], measure=scenario['measure'] ) def _setup(self, cls, extras: Dict = None): LoadTestCase.server_setup(env=self.env, extras=extras) docs_a = os.path.join(self.env.server_docs_dir, "test1") uris = [] for i in range(self._file_count): fsize = self._file_sizes[i % len(self._file_sizes)] if fsize is None: raise Exception("file sizes?: {0} {1}".format(i, fsize)) fname = "{0}-{1}k.txt".format(i, fsize) fpath = os.path.join(docs_a, fname) if not os.path.isfile(fpath): mk_text_file(os.path.join(docs_a, fname), 8 * fsize) uris.append(f"{self._location}{fname}") with open(self._url_file, 'w') as fd: fd.write("\n".join(uris)) fd.write("\n") self.start_server(env=self.env) def _teardown(self): # we shutdown apache at program exit pass def shutdown(self): self._teardown() def run_test(self, mode: str, path: str) -> H2LoadLogSummary: monitor = None try: log_file = "{gen_dir}/h2load.log".format(gen_dir=self.env.gen_dir) if os.path.isfile(log_file): os.remove(log_file) monitor = H2LoadMonitor(log_file, expected=self._requests, title=f"{self._protocol}/" f"{self._file_count / 1024}f/{self._clients}c[{mode}]") monitor.start() args = [ 'h2load', '--clients={0}'.format(self._clients), '--threads={0}'.format(self._threads), '--requests={0}'.format(self._requests), '--input-file={0}'.format(self._url_file), '--log-file={0}'.format(log_file), '--connect-to=localhost:{0}'.format(self.env.https_port) ] if self._protocol == 'h1' or self._protocol == 'http/1.1': args.append('--h1') elif self._protocol == 'h2': args.extend(['-m', str(self._max_parallel)]) else: raise Exception(f"unknown protocol: {self._protocol}") r = self.env.run(args + [ f'--base-uri=https://{self.env.domain_test1}:{self.env.https_port}{self._location}' ]) if r.exit_code != 0: raise LoadTestException("h2load returned {0}: {1}".format(r.exit_code, r.stderr)) summary = monitor.get_summary(duration=r.duration) summary.set_expected_responses(self._requests) summary.set_exec_result(r) return summary finally: if monitor is not None: monitor.stop() def run(self) -> H2LoadLogSummary: path = self._setup(self.__class__) try: if self._warmup: self.run_test(mode="warmup", path=path) r = self.run_test(mode="measure", path=path) # time.sleep(300) return r finally: self._teardown() def format_result(self, summary: H2LoadLogSummary) -> Tuple[str, Optional[List[str]]]: if self._measure == 'req/s': r = "{0:d}".format(round(summary.response_count / summary.duration.total_seconds())) elif self._measure == 'mean ms/req': r = "{0:.1f}".format(summary.mean_duration_ms) elif self._measure == 'mb/s': reqs = summary.response_count / summary.duration.total_seconds() mean_size = statistics.mean(self._file_sizes) r = "{0:d}".format(round(reqs * mean_size / 1024.0)) else: raise Exception(f"measure '{self._measure}' not defined") return r, summary.get_footnote() class StressTest(LoadTestCase): SETUP_DONE = False def __init__(self, env: H2TestEnv, location: str, clients: int, requests: int, file_count: int, file_sizes: List[int], protocol: str = 'h2', max_parallel: int = 1, cooldown: timedelta = None, threads: int = None, ): self.env = env self._location = location self._clients = clients self._requests = requests self._file_count = file_count self._file_sizes = file_sizes self._protocol = protocol self._max_parallel = max_parallel self._cooldown = cooldown if cooldown else timedelta(seconds=0) self._threads = threads if threads is not None else min(2, self._clients) self._url_file = "{gen_dir}/h2load-urls.txt".format(gen_dir=self.env.gen_dir) self._is_setup = False @staticmethod def from_scenario(scenario: Dict, env: H2TestEnv) -> 'UrlsLoadTest': return StressTest( env=env, location=scenario['location'], clients=scenario['clients'], requests=scenario['requests'], file_sizes=scenario['file_sizes'], file_count=scenario['file_count'], protocol=scenario['protocol'], max_parallel=scenario['max_parallel'], cooldown=scenario['cooldown'] ) def next_scenario(self, scenario: Dict) -> 'UrlsLoadTest': self._location = scenario['location'] self._clients = scenario['clients'] self._requests = scenario['requests'] self._file_sizes = scenario['file_sizes'] self._file_count = scenario['file_count'] self._protocol = scenario['protocol'] self._max_parallel = scenario['max_parallel'] return self def _setup(self, cls): LoadTestCase.server_setup(env=self.env, extras={ 'base': f""" H2MinWorkers 32 H2MaxWorkers 128 H2MaxWorkerIdleSeconds 5 """ }) if not cls.SETUP_DONE: with tqdm(desc="setup resources", total=self._file_count, unit="file", leave=False) as t: docs_a = os.path.join(self.env.server_docs_dir, "test1") uris = [] for i in range(self._file_count): fsize = self._file_sizes[i % len(self._file_sizes)] if fsize is None: raise Exception("file sizes?: {0} {1}".format(i, fsize)) fname = "{0}-{1}k.txt".format(i, fsize) mk_text_file(os.path.join(docs_a, fname), 8 * fsize) uris.append(f"{self._location}{fname}") t.update() with open(self._url_file, 'w') as fd: fd.write("\n".join(uris)) fd.write("\n") cls.SETUP_DONE = True self.start_server(env=self.env) self._is_setup = True def shutdown(self): # we shutdown apache at program exit pass def run_test(self, mode: str) -> H2LoadLogSummary: monitor = None try: log_file = "{gen_dir}/h2load.log".format(gen_dir=self.env.gen_dir) if os.path.isfile(log_file): os.remove(log_file) monitor = H2LoadMonitor(log_file, expected=self._requests, title=f"{self._protocol}/" f"{self._file_count / 1024}f/{self._clients}c[{mode}]") monitor.start() args = [ 'h2load', '--clients={0}'.format(self._clients), '--threads={0}'.format(min(self._clients, 2)), '--requests={0}'.format(self._requests), '--input-file={0}'.format(self._url_file), '--log-file={0}'.format(log_file), '--connect-to=localhost:{0}'.format(self.env.https_port) ] if self._protocol == 'h1' or self._protocol == 'http/1.1': args.append('--h1') elif self._protocol == 'h2': args.extend(['-m', str(self._max_parallel)]) else: raise Exception(f"unknown protocol: {self._protocol}") r = self.env.run(args + [ f'--base-uri=https://{self.env.domain_test1}:{self.env.https_port}{self._location}' ]) if r.exit_code != 0: raise LoadTestException("h2load returned {0}: {1}".format(r.exit_code, r.stderr)) summary = monitor.get_summary(duration=r.duration) summary.set_expected_responses(self._requests) summary.set_exec_result(r) return summary finally: if monitor is not None: monitor.stop() def run(self) -> H2LoadLogSummary: if not self._is_setup: self._setup(self.__class__) elif self._cooldown.total_seconds() > 0: with tqdm(desc="worker cooldown", total=int(self._cooldown.total_seconds()), unit="s", leave=False) as t: end = datetime.now() + self._cooldown while datetime.now() < end: time.sleep(1) t.update() return self.run_test(mode="measure") def format_result(self, summary: H2LoadLogSummary) -> Tuple[str, Optional[List[str]]]: return "{0:.1f}".format( summary.response_count / summary.duration.total_seconds() ), summary.get_footnote() class LoadTest: @staticmethod def print_table(table: List[List[str]], foot_notes: List[str] = None): col_widths = [] col_sep = " " for row in table[1:]: for idx, cell in enumerate(row): if idx >= len(col_widths): col_widths.append(len(cell)) else: col_widths[idx] = max(len(cell), col_widths[idx]) row_len = sum(col_widths) + (len(col_widths) * len(col_sep)) print(f"{' '.join(table[0]):^{row_len}}") for row in table[1:]: line = "" for idx, cell in enumerate(row): line += f"{col_sep if idx > 0 else ''}{cell:>{col_widths[idx]}}" print(line) if foot_notes is not None: for idx, note in enumerate(foot_notes): print("{0:3d}) {1}".format(idx+1, note)) @classmethod def main(cls): parser = argparse.ArgumentParser(prog='load_h1', description=""" Run a range of load tests against the test Apache setup. """) parser.add_argument("-p", "--protocol", type=str, default=None, help="which protocols to test, defaults to all") parser.add_argument("-v", "--verbose", action='count', default=0, help="log more output on stderr") parser.add_argument("names", nargs='*', help="Name(s) of scenarios to run") args = parser.parse_args() if args.verbose > 0: console = logging.StreamHandler() console.setLevel(logging.INFO) console.setFormatter(logging.Formatter(logging.BASIC_FORMAT)) logging.getLogger('').addHandler(console) scenarios = { "1k-files": { "title": "1k files, 1k-10MB, *conn, 10k req ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 10000, "warmup": True, "measure": "req/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 1}, {"protocol": 'h2', "max_parallel": 2}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 20}, {"protocol": 'h2', "max_parallel": 50}, {"protocol": 'h1', "max_parallel": 1}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1}, {"clients": 4}, {"clients": 8}, {"clients": 16}, {"clients": 32}, ], }, "long": { "title": "1k files, 10k size, *conn, 100k req, {protocol} ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 100, "file_sizes": [1], "requests": 100000, "warmup": False, "measure": "req/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "max requests", "row_title": "{max_parallel:3d} {requests}", "rows": [ {"max_parallel": 1, "requests": 100000}, {"max_parallel": 2, "requests": 100000}, #{"max_parallel": 6, "requests": 250000}, #{"max_parallel": 20, "requests": 500000}, #{"max_parallel": 50, "requests": 750000}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1}, ], }, "durations": { "title": "1k files, 64k size, 10k req/conn ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [64], "requests": 10000, "warmup": False, "measure": "mean ms/req", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 1}, {"protocol": 'h2', "max_parallel": 2}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 20}, {"protocol": 'h2', "max_parallel": 50}, {"protocol": 'h1', "max_parallel": 1}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1, "requests": 10000}, {"clients": 4, "requests": 40000}, {"clients": 8, "requests": 80000}, {"clients": 16, "requests": 160000}, {"clients": 32, "requests": 320000}, ], }, "transfers": { "title": "net transfer speed, by KB body size, (MB/s)", "class": UrlsLoadTest, "location": "/", "file_count": 1, "file_sizes": [10, 100, 1000, 10000], "requests": 10000, "clients": 1, "warmup": False, "measure": "mb/s", "protocol": 'h2', "max_parallel": 1, "row0_title": "protocol c/parallel", "row_title": "{protocol} {clients}/{max_parallel}", "rows": [ {"protocol": 'h1', "max_parallel": 1, "clients": 1}, {"protocol": 'h2', "max_parallel": 1, "clients": 1}, {"protocol": 'h2', "max_parallel": 2, "clients": 1}, {"protocol": 'h2', "max_parallel": 6, "clients": 1}, {"protocol": 'h1', "max_parallel": 1, "clients": 2}, {"protocol": 'h2', "max_parallel": 1, "clients": 2}, {"protocol": 'h2', "max_parallel": 2, "clients": 2}, {"protocol": 'h2', "max_parallel": 6, "clients": 2}, {"protocol": 'h1', "max_parallel": 1, "clients": 6}, {"protocol": 'h2', "max_parallel": 1, "clients": 6}, {"protocol": 'h2', "max_parallel": 2, "clients": 6}, {"protocol": 'h2', "max_parallel": 6, "clients": 6}, ], "col_title": "{file_sizes}", "clients": 1, "columns": [ {"file_sizes": [10], "requests": 100000}, {"file_sizes": [100], "requests": 50000}, {"file_sizes": [1000], "requests": 20000}, {"file_sizes": [10000], "requests": 5000}, ], }, "bursty": { "title": "1k files, {clients} clients, {requests} request, (req/s)", "class": StressTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 20000, "protocol": "h2", "max_parallel": 50, "clients": 32, "cooldown": timedelta(seconds=20), "row0_title": "protocol", "row_title": "{protocol}", "rows": [ {"protocol": 'h2', }, ], "col_title": "{run}", "columns": [ {"run": 1}, {"run": 2}, {"run": 3}, {"run": 4}, {"run": 5}, {"run": 6}, {"run": 7}, {"run": 8}, {"run": 9}, {"run": 10}, {"run": 11}, {"run": 12}, {"run": 13}, {"run": 14}, {"run": 15}, {"run": 16}, {"run": 17}, {"run": 18}, {"run": 19}, {"run": 20}, ], }, "m6": { "title": "1k files, 1k-10MB, *conn, 10k req ({measure})", "class": UrlsLoadTest, "location": "/", "file_count": 1024, "file_sizes": [1, 2, 3, 4, 5, 10, 20, 30, 40, 50, 100, 10000], "requests": 5000, "warmup": True, "measure": "req/s", "protocol": 'h2', "max_parallel": 6, "row0_title": "protocol max", "row_title": "{protocol} {max_parallel:3d}", "rows": [ {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, {"protocol": 'h2', "max_parallel": 6}, ], "col_title": "{clients}c", "clients": 1, "columns": [ {"clients": 1, "requests": 1000}, {"clients": 32, "requests": 16000}, {"clients": 64, "requests": 32000}, {"clients": 128, "requests": 64000}, {"clients": 192, "requests": 96000}, ], }, } env = H2TestEnv() rv = 0 try: log.debug("starting tests") names = args.names if len(args.names) else sorted(scenarios.keys()) for name in names: if name not in scenarios: raise LoadTestException(f"unknown test scenario: {name}") scenario = scenarios[name] table = [ [scenario['title'].format(**scenario)], ] foot_notes = [] headers = [scenario['row0_title']] for col in scenario['columns']: headers.append(scenario['col_title'].format(**col)) table.append(headers) cls.print_table(table) test = scenario['class'].from_scenario(scenario, env=env) for row in scenario['rows']: if args.protocol is not None and row['protocol'] != args.protocol: continue row_line = [scenario['row_title'].format(**row)] table.append(row_line) for col in scenario['columns']: t = scenario.copy() t.update(row) t.update(col) test = test.next_scenario(t) env.apache_error_log_clear() summary = test.run() result, fnote = test.format_result(summary) if fnote: foot_notes.append(fnote) row_line.append("{0}{1}".format(result, f"[{len(foot_notes)}]" if fnote else "")) cls.print_table(table, foot_notes) test.shutdown() except KeyboardInterrupt: log.warning("aborted") rv = 1 except LoadTestException as ex: sys.stderr.write(f"ERROR: {str(ex)}\n") rv = 1 env.apache_stop() sys.exit(rv) if __name__ == "__main__": LoadTest.main()

# ------------------------------------------------------------------------ # Copyright (c) 2021 megvii-model. All Rights Reserved. # ------------------------------------------------------------------------ # Modified from BasicSR (https://github.com/xinntao/BasicSR) # Copyright 2018-2020 BasicSR Authors # ------------------------------------------------------------------------ import random from pathlib import Path import numpy as np import torch from torch.utils import data as data from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.flow_util import dequantize_flow class REDSDataset(data.Dataset): """REDS dataset for training. The keys are generated from a meta info txt file. basicsr/data/meta_info/meta_info_REDS_GT.txt Each line contains: 1. subfolder (clip) name; 2. frame number; 3. image shape, seperated by a white space. Examples: 000 100 (720,1280,3) 001 100 (720,1280,3) ... Key examples: "000/00000000" GT (gt): Ground-Truth; LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. Args: opt (dict): Config for train dataset. It contains the following keys: dataroot_gt (str): Data root path for gt. dataroot_lq (str): Data root path for lq. dataroot_flow (str, optional): Data root path for flow. meta_info_file (str): Path for meta information file. val_partition (str): Validation partition types. 'REDS4' or 'official'. io_backend (dict): IO backend type and other kwarg. num_frame (int): Window size for input frames. gt_size (int): Cropped patched size for gt patches. interval_list (list): Interval list for temporal augmentation. random_reverse (bool): Random reverse input frames. use_flip (bool): Use horizontal flips. use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). scale (bool): Scale, which will be added automatically. """ def __init__(self, opt): super(REDSDataset, self).__init__() self.opt = opt self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path( opt['dataroot_lq']) self.flow_root = Path( opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None assert opt['num_frame'] % 2 == 1, ( f'num_frame should be odd number, but got {opt['num_frame']}') self.num_frame = opt['num_frame'] self.num_half_frames = opt['num_frame'] // 2 self.keys = [] with open(opt['meta_info_file'], 'r') as fin: for line in fin: folder, frame_num, _ = line.split(' ') self.keys.extend( [f'{folder}/{i:08d}' for i in range(int(frame_num))]) # remove the video clips used in validation if opt['val_partition'] == 'REDS4': val_partition = ['000', '011', '015', '020'] elif opt['val_partition'] == 'official': val_partition = [f'{v:03d}' for v in range(240, 270)] else: raise ValueError( f'Wrong validation partition {opt['val_partition']}.' f"Supported ones are ['official', 'REDS4'].") self.keys = [ v for v in self.keys if v.split('/')[0] not in val_partition ] # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.is_lmdb = False if self.io_backend_opt['type'] == 'lmdb': self.is_lmdb = True if self.flow_root is not None: self.io_backend_opt['db_paths'] = [ self.lq_root, self.gt_root, self.flow_root ] self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] else: self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] self.io_backend_opt['client_keys'] = ['lq', 'gt'] # temporal augmentation configs self.interval_list = opt['interval_list'] self.random_reverse = opt['random_reverse'] interval_str = ','.join(str(x) for x in opt['interval_list']) logger = get_root_logger() logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' f'random reverse is {self.random_reverse}.') def __getitem__(self, index): if self.file_client is None: self.file_client = FileClient( self.io_backend_opt.pop('type'), **self.io_backend_opt) scale = self.opt['scale'] gt_size = self.opt['gt_size'] key = self.keys[index] clip_name, frame_name = key.split('/') # key example: 000/00000000 center_frame_idx = int(frame_name) # determine the neighboring frames interval = random.choice(self.interval_list) # ensure not exceeding the borders start_frame_idx = center_frame_idx - self.num_half_frames * interval end_frame_idx = center_frame_idx + self.num_half_frames * interval # each clip has 100 frames starting from 0 to 99 while (start_frame_idx < 0) or (end_frame_idx > 99): center_frame_idx = random.randint(0, 99) start_frame_idx = ( center_frame_idx - self.num_half_frames * interval) end_frame_idx = center_frame_idx + self.num_half_frames * interval frame_name = f'{center_frame_idx:08d}' neighbor_list = list( range(center_frame_idx - self.num_half_frames * interval, center_frame_idx + self.num_half_frames * interval + 1, interval)) # random reverse if self.random_reverse and random.random() < 0.5: neighbor_list.reverse() assert len(neighbor_list) == self.num_frame, ( f'Wrong length of neighbor list: {len(neighbor_list)}') # get the GT frame (as the center frame) if self.is_lmdb: img_gt_path = f'{clip_name}/{frame_name}' else: img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' img_bytes = self.file_client.get(img_gt_path, 'gt') img_gt = imfrombytes(img_bytes, float32=True) # get the neighboring LQ frames img_lqs = [] for neighbor in neighbor_list: if self.is_lmdb: img_lq_path = f'{clip_name}/{neighbor:08d}' else: img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' img_bytes = self.file_client.get(img_lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) img_lqs.append(img_lq) # get flows if self.flow_root is not None: img_flows = [] # read previous flows for i in range(self.num_half_frames, 0, -1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_p{i}' else: flow_path = ( self.flow_root / clip_name / f'{frame_name}_p{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes( img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow( dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # read next flows for i in range(1, self.num_half_frames + 1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_n{i}' else: flow_path = ( self.flow_root / clip_name / f'{frame_name}_n{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes( img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow( dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # for random crop, here, img_flows and img_lqs have the same # spatial size img_lqs.extend(img_flows) # randomly crop img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) if self.flow_root is not None: img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self. num_frame:] # augmentation - flip, rotate img_lqs.append(img_gt) if self.flow_root is not None: img_results, img_flows = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'], img_flows) else: img_results = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot']) img_results = img2tensor(img_results) img_lqs = torch.stack(img_results[0:-1], dim=0) img_gt = img_results[-1] if self.flow_root is not None: img_flows = img2tensor(img_flows) # add the zero center flow img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) img_flows = torch.stack(img_flows, dim=0) # img_lqs: (t, c, h, w) # img_flows: (t, 2, h, w) # img_gt: (c, h, w) # key: str if self.flow_root is not None: return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} else: return {'lq': img_lqs, 'gt': img_gt, 'key': key} def __len__(self): return len(self.keys)

# ------------------------------------------------------------------------ # Copyright (c) 2021 megvii-model. All Rights Reserved. # ------------------------------------------------------------------------ # Modified from BasicSR (https://github.com/xinntao/BasicSR) # Copyright 2018-2020 BasicSR Authors # ------------------------------------------------------------------------ import random from pathlib import Path import numpy as np import torch from torch.utils import data as data from basicsr.data.transforms import augment, paired_random_crop from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor from basicsr.utils.flow_util import dequantize_flow class REDSDataset(data.Dataset): """REDS dataset for training. The keys are generated from a meta info txt file. basicsr/data/meta_info/meta_info_REDS_GT.txt Each line contains: 1. subfolder (clip) name; 2. frame number; 3. image shape, seperated by a white space. Examples: 000 100 (720,1280,3) 001 100 (720,1280,3) ... Key examples: "000/00000000" GT (gt): Ground-Truth; LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. Args: opt (dict): Config for train dataset. It contains the following keys: dataroot_gt (str): Data root path for gt. dataroot_lq (str): Data root path for lq. dataroot_flow (str, optional): Data root path for flow. meta_info_file (str): Path for meta information file. val_partition (str): Validation partition types. 'REDS4' or 'official'. io_backend (dict): IO backend type and other kwarg. num_frame (int): Window size for input frames. gt_size (int): Cropped patched size for gt patches. interval_list (list): Interval list for temporal augmentation. random_reverse (bool): Random reverse input frames. use_flip (bool): Use horizontal flips. use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). scale (bool): Scale, which will be added automatically. """ def __init__(self, opt): super(REDSDataset, self).__init__() self.opt = opt self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path( opt['dataroot_lq']) self.flow_root = Path( opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None assert opt['num_frame'] % 2 == 1, ( f'num_frame should be odd number, but got {opt["num_frame"]}') self.num_frame = opt['num_frame'] self.num_half_frames = opt['num_frame'] // 2 self.keys = [] with open(opt['meta_info_file'], 'r') as fin: for line in fin: folder, frame_num, _ = line.split(' ') self.keys.extend( [f'{folder}/{i:08d}' for i in range(int(frame_num))]) # remove the video clips used in validation if opt['val_partition'] == 'REDS4': val_partition = ['000', '011', '015', '020'] elif opt['val_partition'] == 'official': val_partition = [f'{v:03d}' for v in range(240, 270)] else: raise ValueError( f'Wrong validation partition {opt["val_partition"]}.' f"Supported ones are ['official', 'REDS4'].") self.keys = [ v for v in self.keys if v.split('/')[0] not in val_partition ] # file client (io backend) self.file_client = None self.io_backend_opt = opt['io_backend'] self.is_lmdb = False if self.io_backend_opt['type'] == 'lmdb': self.is_lmdb = True if self.flow_root is not None: self.io_backend_opt['db_paths'] = [ self.lq_root, self.gt_root, self.flow_root ] self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] else: self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] self.io_backend_opt['client_keys'] = ['lq', 'gt'] # temporal augmentation configs self.interval_list = opt['interval_list'] self.random_reverse = opt['random_reverse'] interval_str = ','.join(str(x) for x in opt['interval_list']) logger = get_root_logger() logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' f'random reverse is {self.random_reverse}.') def __getitem__(self, index): if self.file_client is None: self.file_client = FileClient( self.io_backend_opt.pop('type'), **self.io_backend_opt) scale = self.opt['scale'] gt_size = self.opt['gt_size'] key = self.keys[index] clip_name, frame_name = key.split('/') # key example: 000/00000000 center_frame_idx = int(frame_name) # determine the neighboring frames interval = random.choice(self.interval_list) # ensure not exceeding the borders start_frame_idx = center_frame_idx - self.num_half_frames * interval end_frame_idx = center_frame_idx + self.num_half_frames * interval # each clip has 100 frames starting from 0 to 99 while (start_frame_idx < 0) or (end_frame_idx > 99): center_frame_idx = random.randint(0, 99) start_frame_idx = ( center_frame_idx - self.num_half_frames * interval) end_frame_idx = center_frame_idx + self.num_half_frames * interval frame_name = f'{center_frame_idx:08d}' neighbor_list = list( range(center_frame_idx - self.num_half_frames * interval, center_frame_idx + self.num_half_frames * interval + 1, interval)) # random reverse if self.random_reverse and random.random() < 0.5: neighbor_list.reverse() assert len(neighbor_list) == self.num_frame, ( f'Wrong length of neighbor list: {len(neighbor_list)}') # get the GT frame (as the center frame) if self.is_lmdb: img_gt_path = f'{clip_name}/{frame_name}' else: img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' img_bytes = self.file_client.get(img_gt_path, 'gt') img_gt = imfrombytes(img_bytes, float32=True) # get the neighboring LQ frames img_lqs = [] for neighbor in neighbor_list: if self.is_lmdb: img_lq_path = f'{clip_name}/{neighbor:08d}' else: img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' img_bytes = self.file_client.get(img_lq_path, 'lq') img_lq = imfrombytes(img_bytes, float32=True) img_lqs.append(img_lq) # get flows if self.flow_root is not None: img_flows = [] # read previous flows for i in range(self.num_half_frames, 0, -1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_p{i}' else: flow_path = ( self.flow_root / clip_name / f'{frame_name}_p{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes( img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow( dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # read next flows for i in range(1, self.num_half_frames + 1): if self.is_lmdb: flow_path = f'{clip_name}/{frame_name}_n{i}' else: flow_path = ( self.flow_root / clip_name / f'{frame_name}_n{i}.png') img_bytes = self.file_client.get(flow_path, 'flow') cat_flow = imfrombytes( img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] dx, dy = np.split(cat_flow, 2, axis=0) flow = dequantize_flow( dx, dy, max_val=20, denorm=False) # we use max_val 20 here. img_flows.append(flow) # for random crop, here, img_flows and img_lqs have the same # spatial size img_lqs.extend(img_flows) # randomly crop img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) if self.flow_root is not None: img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self. num_frame:] # augmentation - flip, rotate img_lqs.append(img_gt) if self.flow_root is not None: img_results, img_flows = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot'], img_flows) else: img_results = augment(img_lqs, self.opt['use_flip'], self.opt['use_rot']) img_results = img2tensor(img_results) img_lqs = torch.stack(img_results[0:-1], dim=0) img_gt = img_results[-1] if self.flow_root is not None: img_flows = img2tensor(img_flows) # add the zero center flow img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) img_flows = torch.stack(img_flows, dim=0) # img_lqs: (t, c, h, w) # img_flows: (t, 2, h, w) # img_gt: (c, h, w) # key: str if self.flow_root is not None: return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} else: return {'lq': img_lqs, 'gt': img_gt, 'key': key} def __len__(self): return len(self.keys)

import os import sys import re import json import platform try: from pathlib import Path from colorclass import Color from terminaltables import SingleTable import semver except ImportError: print("ERROR: Need to install required modules.") print("python3 -m pip install colorclass terminaltables semver") sys.exit(1) VERSION_REGEX = re.compile(r'^(\d+)\.(\d+)\.(\d+)$') VERSION_REGEX_NOCAP = r'\d+\.\d+\.\d+' COMPARATOR_REGEX = r'(?:<|>)=?' RANGE = f'({VERSION_REGEX_NOCAP})\\s+' + \ f'({COMPARATOR_REGEX})\\s+' + \ 'v\\s+' + \ f'({COMPARATOR_REGEX})\\s+' + \ f'({VERSION_REGEX_NOCAP})' VRANGE_REGEX = re.compile(RANGE) CMP = { "<": [-1], "<=": [-1, 0], ">": [1], ">=": [0, 1] } # will handle all dependencies PROJECT_DEPS = {} def get_versions_from_home(elm_home): dirs = filter( lambda d: os.path.isdir(os.path.join(elm_home, d)), [dir for dir in os.listdir(elm_home)] ) return [v for v in dirs if re.match(VERSION_REGEX_NOCAP, v)] def version_in_range(low, low_op, version, high_op, high): compare_low = semver.compare(low, version) in CMP[low_op] compare_high = semver.compare(version, high) in CMP[high_op] return compare_low and compare_high def get_highest_version_from_dir(dir, cmp_version): low, low_op, high_op, high = VRANGE_REGEX.findall(cmp_version)[0] all_versions = [v for v in get_versions_from_home(dir)] return max(list(filter( lambda v: version_in_range(low, low_op, v, high_op, high), all_versions ))) def add_dep_to_dict(pkg_home, who, what, pkg, version, type): with open( os.path.join(pkg_home, who, what, version, "elm.json"), "r" ) as dep_file: license = json.load(dep_file)["license"] PROJECT_DEPS[pkg] = { "version": version, "license": license, "type": type } def get_project_dependencies(json_directory): json_path = os.path.join( json_directory if json_directory else os.getcwd(), "elm.json" ) if platform.system() == "Windows": ELM_HOME = os.path.join(str(Path.home()), "AppData", "Roaming", "elm") else: ELM_HOME = os.path.join(str(Path.home()), ".elm") ELM_HOME = os.getenv("ELM_HOME", ELM_HOME) with open(json_path, "r") as elm_file: json_data = json.load(elm_file) dependencies = json_data["dependencies"] type = json_data["type"] elm_version = json_data["elm-version"] if type == "package": elm_version = get_highest_version_from_dir(ELM_HOME, elm_version) package_home = os.path.join(ELM_HOME, elm_version, "packages") if not os.path.exists(package_home): print(f"I'm unable to find your package home: {package_home}") raise if type == "application": for type in ["direct", "indirect"]: deps = dependencies[type] for pkg, ver in deps.items(): who, what = pkg.split("/") add_dep_to_dict(package_home, who, what, pkg, ver) elif type == "package": for pkg, ver in dependencies.items(): who, what = pkg.split("/") high_ver = get_highest_version_from_dir( os.path.join(package_home, who, what), ver ) add_dep_to_dict(package_home, who, what, pkg, high_ver, "direct") else: print(f"""Unknown Elm project type of {type}. Expected your elm.json to have either: \"type\": \"application\" or \"type\": \"package\" """) raise return PROJECT_DEPS def output_tables(deps): # Build Table Headers lsc_count_data = [ [Color("{red}License{/red}"), Color("{red}Count{/red}")] ] large_table_data = [ [ Color("{red}Package{/red}"), Color("{red}Version{/red}"), Color("{red}License{/red}"), Color("{red}Type{/red}") ] ] # Build table bodies packages = list(deps.keys()) lsc_count_data = {"total": 0, "direct": 0, "indirect": 0} for pkg in packages: pkg_data = deps[pkg] license = pkg_data["license"] if license not in lsc_count_data.keys(): lsc_count_data[license] = 0 lsc_count_data[license] += 1 lsc_count_data["total"] += 1 lsc_count_data[pkg_data["type"]] += 1 large_table_data.append( [pkg, pkg_data["version"], license, pkg_data["type"]] ) for l, c in lsc_count_data.items(): if l not in ["total", "direct", "indirect"]: lsc_count_data.append([l, str(c)]) # Format Tables lsc_table = SingleTable(lsc_count_data) lsc_table.inner_row_border = True lsc_table.justify_columns = {0: 'center', 1: 'center'} print("Dependencies:") print(f"Total: {lsc_count_data["total"]}") print(f"Direct: {lsc_count_data["direct"]}") print(f"Indirect: {lsc_count_data["indirect"]}") print(lsc_table.table) large_table = SingleTable(large_table_data) large_table.inner_row_border = True large_table.justify_columns = { 0: 'center', 1: 'center', 2: 'center', 3: 'center' } print(large_table.table)

import os import sys import re import json import platform try: from pathlib import Path from colorclass import Color from terminaltables import SingleTable import semver except ImportError: print("ERROR: Need to install required modules.") print("python3 -m pip install colorclass terminaltables semver") sys.exit(1) VERSION_REGEX = re.compile(r'^(\d+)\.(\d+)\.(\d+)$') VERSION_REGEX_NOCAP = r'\d+\.\d+\.\d+' COMPARATOR_REGEX = r'(?:<|>)=?' RANGE = f'({VERSION_REGEX_NOCAP})\\s+' + \ f'({COMPARATOR_REGEX})\\s+' + \ 'v\\s+' + \ f'({COMPARATOR_REGEX})\\s+' + \ f'({VERSION_REGEX_NOCAP})' VRANGE_REGEX = re.compile(RANGE) CMP = { "<": [-1], "<=": [-1, 0], ">": [1], ">=": [0, 1] } # will handle all dependencies PROJECT_DEPS = {} def get_versions_from_home(elm_home): dirs = filter( lambda d: os.path.isdir(os.path.join(elm_home, d)), [dir for dir in os.listdir(elm_home)] ) return [v for v in dirs if re.match(VERSION_REGEX_NOCAP, v)] def version_in_range(low, low_op, version, high_op, high): compare_low = semver.compare(low, version) in CMP[low_op] compare_high = semver.compare(version, high) in CMP[high_op] return compare_low and compare_high def get_highest_version_from_dir(dir, cmp_version): low, low_op, high_op, high = VRANGE_REGEX.findall(cmp_version)[0] all_versions = [v for v in get_versions_from_home(dir)] return max(list(filter( lambda v: version_in_range(low, low_op, v, high_op, high), all_versions ))) def add_dep_to_dict(pkg_home, who, what, pkg, version, type): with open( os.path.join(pkg_home, who, what, version, "elm.json"), "r" ) as dep_file: license = json.load(dep_file)["license"] PROJECT_DEPS[pkg] = { "version": version, "license": license, "type": type } def get_project_dependencies(json_directory): json_path = os.path.join( json_directory if json_directory else os.getcwd(), "elm.json" ) if platform.system() == "Windows": ELM_HOME = os.path.join(str(Path.home()), "AppData", "Roaming", "elm") else: ELM_HOME = os.path.join(str(Path.home()), ".elm") ELM_HOME = os.getenv("ELM_HOME", ELM_HOME) with open(json_path, "r") as elm_file: json_data = json.load(elm_file) dependencies = json_data["dependencies"] type = json_data["type"] elm_version = json_data["elm-version"] if type == "package": elm_version = get_highest_version_from_dir(ELM_HOME, elm_version) package_home = os.path.join(ELM_HOME, elm_version, "packages") if not os.path.exists(package_home): print(f"I'm unable to find your package home: {package_home}") raise if type == "application": for type in ["direct", "indirect"]: deps = dependencies[type] for pkg, ver in deps.items(): who, what = pkg.split("/") add_dep_to_dict(package_home, who, what, pkg, ver) elif type == "package": for pkg, ver in dependencies.items(): who, what = pkg.split("/") high_ver = get_highest_version_from_dir( os.path.join(package_home, who, what), ver ) add_dep_to_dict(package_home, who, what, pkg, high_ver, "direct") else: print(f"""Unknown Elm project type of {type}. Expected your elm.json to have either: \"type\": \"application\" or \"type\": \"package\" """) raise return PROJECT_DEPS def output_tables(deps): # Build Table Headers lsc_count_data = [ [Color("{red}License{/red}"), Color("{red}Count{/red}")] ] large_table_data = [ [ Color("{red}Package{/red}"), Color("{red}Version{/red}"), Color("{red}License{/red}"), Color("{red}Type{/red}") ] ] # Build table bodies packages = list(deps.keys()) lsc_count_data = {"total": 0, "direct": 0, "indirect": 0} for pkg in packages: pkg_data = deps[pkg] license = pkg_data["license"] if license not in lsc_count_data.keys(): lsc_count_data[license] = 0 lsc_count_data[license] += 1 lsc_count_data["total"] += 1 lsc_count_data[pkg_data["type"]] += 1 large_table_data.append( [pkg, pkg_data["version"], license, pkg_data["type"]] ) for l, c in lsc_count_data.items(): if l not in ["total", "direct", "indirect"]: lsc_count_data.append([l, str(c)]) # Format Tables lsc_table = SingleTable(lsc_count_data) lsc_table.inner_row_border = True lsc_table.justify_columns = {0: 'center', 1: 'center'} print("Dependencies:") print(f"Total: {lsc_count_data['total']}") print(f"Direct: {lsc_count_data['direct']}") print(f"Indirect: {lsc_count_data['indirect']}") print(lsc_table.table) large_table = SingleTable(large_table_data) large_table.inner_row_border = True large_table.justify_columns = { 0: 'center', 1: 'center', 2: 'center', 3: 'center' } print(large_table.table)

""" Merges the 3 Tesla Dashcam and Sentry camera video files into 1 video. If then further concatenates the files together to make 1 movie. """ import argparse import logging import os import sys from datetime import datetime, timedelta, timezone from fnmatch import fnmatch from glob import glob from pathlib import Path from re import search from shlex import split as shlex_split from shutil import which from subprocess import CalledProcessError, run from tempfile import mkstemp from time import sleep, time as timestamp from typing import List, Optional import requests from dateutil.parser import isoparse from psutil import disk_partitions from tzlocal import get_localzone _LOGGER = logging.getLogger(__name__) # TODO: Move everything into classes and separate files. For example, # update class, font class (for timestamp), folder class, clip class ( # combining front, left, and right info), file class (for individual file). # Clip class would then have to merge the camera clips, folder class would # have to concatenate the merged clips. Settings class to take in all settings # TODO: Create kind of logger or output classes for output. That then allows # different ones to be created based on where it should go to (stdout, # log file, ...). VERSION = {"major": 0, "minor": 1, "patch": 16, "beta": -1} VERSION_STR = "v{major}.{minor}.{patch}".format( major=VERSION["major"], minor=VERSION["minor"], patch=VERSION["patch"] ) if VERSION["beta"] > -1: VERSION_STR = VERSION_STR + "b{beta}".format(beta=VERSION["beta"]) MONITOR_SLEEP_TIME = 5 GITHUB = { "URL": "https://api.github.com", "owner": "ehendrix23", "repo": "tesla_dashcam", } FFMPEG = { "darwin": "ffmpeg", "win32": "ffmpeg.exe", "cygwin": "ffmpeg", "linux": "ffmpeg", "freebsd11": "ffmpeg", } # noinspection PyPep8 MOVIE_HOMEDIR = { "darwin": "Movies/Tesla_Dashcam", "win32": "Videos\Tesla_Dashcam", "cygwin": "Videos/Tesla_Dashcam", "linux": "Videos/Tesla_Dashcam", "freebsd11": "Videos/Tesla_Dashcam", } DEFAULT_CLIP_HEIGHT = 960 DEFAULT_CLIP_WIDTH = 1280 MOVIE_QUALITY = { "HIGH": "18", "MEDIUM": "20", "LOW": "23", "LOWER": "28", "LOWEST": "33", } MOVIE_ENCODING = { "x264": "libx264", "x264_nvidia": "h264_nvenc", "x264_mac": "h264_videotoolbox", "x264_intel": "h264_qsv", "x264_RPi": "h264_omx", "x265": "libx265", "x265_nvidia": "hevc_nvenc", "x265_mac": "hevc_videotoolbox", "x265_intel": "hevc_qsv", "x265_RPi": "h265", } DEFAULT_FONT = { "darwin": "/Library/Fonts/Arial Unicode.ttf", "win32": "/Windows/Fonts/arial.ttf", "cygwin": "/cygdrive/c/Windows/Fonts/arial.ttf", "linux": "/usr/share/fonts/truetype/freefont/FreeSans.ttf", "freebsd11": "/usr/share/local/fonts/freefont-ttf/FreeSans.ttf", } HALIGN = {"LEFT": "10", "CENTER": "(w/2-text_w/2)", "RIGHT": "(w-text_w)"} VALIGN = {"TOP": "10", "MIDDLE": "(h/2-(text_h/2))", "BOTTOM": "(h-(text_h*2))"} TOASTER_INSTANCE = None class Font(object): """ Font Class """ def __init__(self, layout, font=None, size=None, color=None): self._layout = layout self._font = font self._size = size self._color = color self._halign = None self._valign = None self._xpos = None self._ypos = None @property def font(self): return self._font @font.setter def font(self, value): self._font = value @property def size(self): if hasattr(self._layout, "_font_size"): return getattr(self._layout, "_font_size")() return ( int(max(16, 16 * self._layout.scale)) if self._size is None else self._size ) @size.setter def size(self, value): self._size = value @property def color(self): return self._color @color.setter def color(self, value): self._color = value @property def halign(self): if hasattr(self._layout, "_font_halign"): return getattr(self._layout, "_font_halign")() return HALIGN.get(self._halign, self._halign) @halign.setter def halign(self, value): self._halign = value @property def valign(self): if hasattr(self._layout, "_font_valign"): return getattr(self._layout, "_font_valign")() return VALIGN.get(self._valign, self._valign) @valign.setter def valign(self, value): self._valign = value @property def xpos(self): return self._xpos @xpos.setter def xpos(self, value): self._xpos = value @property def ypos(self): return self._ypos @ypos.setter def ypos(self, value): self._ypos = value class Camera(object): """ Camera Class """ def __init__(self, layout, camera): self._layout = layout self._camera = camera self._include = True self._width = 1280 self._height = 960 self._xpos = 0 self._ypos = 0 self._scale = 0 self._options = "" @property def camera(self): return self._camera @camera.setter def camera(self, value): self._camera = value @property def include(self): return self._include @include.setter def include(self, value): self._include = value @property def width(self): return ( getattr(self._layout, "_" + self._camera + "_width")() if hasattr(self._layout, "_" + self._camera + "_width") else int(self._width * self.scale * self.include) ) @width.setter def width(self, value): self._width = value @property def height(self): return ( getattr(self._layout, "_" + self._camera + "_height")() if hasattr(self._layout, "_" + self._camera + "_height") else int(self._height * self.scale * self.include) ) @height.setter def height(self, value): self._height = value @property def xpos(self): if hasattr(self._layout, "_" + self._camera + "_xpos"): return getattr(self._layout, "_" + self._camera + "_xpos")() * self.include return self._xpos * self.include @xpos.setter def xpos(self, value): self._xpos = value @property def ypos(self): if hasattr(self._layout, "_" + self._camera + "_ypos"): return getattr(self._layout, "_" + self._camera + "_ypos")() * self.include return self._ypos * self.include @ypos.setter def ypos(self, value): self._ypos = value @property def scale(self): return self._scale @scale.setter def scale(self, value): if value is None: self._scale = None elif len(str(value).split("x")) == 1: # Scale provided is a multiplier self._scale = float(str(value).split("x")[0]) else: # Scale is a resolution. self.width = int(str(value).split("x")[0]) self.height = int(str(value).split("x")[1]) self._scale = 1 @property def options(self): return self._options @options.setter def options(self, value): self._options = value class MovieLayout(object): """ Main Layout class """ def __init__(self): self._cameras = { "Front": Camera(layout=self, camera="front"), "Left": Camera(layout=self, camera="left"), "Right": Camera(layout=self, camera="right"), "Rear": Camera(layout=self, camera="rear"), } self._font = Font(layout=self) self._swap_left_right = False self._swap_front_rear = False self._perspective = False self._font.halign = "CENTER" self._font.valign = "BOTTOM" def cameras(self, camera): return self._cameras.get(camera, self._cameras) @property def font(self): return self._font @font.setter def font(self, value): self._font = value @property def swap_left_right(self): return self._swap_left_right @swap_left_right.setter def swap_left_right(self, value): self._swap_left_right = value @property def swap_front_rear(self): return self._swap_front_rear @swap_front_rear.setter def swap_front_rear(self, value): self._swap_front_rear = value @property def perspective(self): return self._perspective @perspective.setter def perspective(self, new_perspective): self._perspective = new_perspective if self._perspective: self.cameras("Left").options = ( ", pad=iw+4:3/2*ih:-1:ih/8:0x00000000, " "perspective=x0=0:y0=1*H/5:x1=W:y1=-3/44*H:" "x2=0:y2=6*H/5:x3=7/8*W:y3=5*H/6:sense=destination" ) self.cameras("Right").options = ( ", pad=iw+4:3/2*ih:-1:ih/8:0x00000000," "perspective=x0=0:y1=1*H/5:x1=W:y0=-3/44*H:" "x2=1/8*W:y3=6*H/5:x3=W:y2=5*H/6:sense=destination" ) else: self.cameras("Left").options = "" self.cameras("Right").options = "" @property def scale(self): # Return scale of new video based on 1280x960 video = scale:1 return (self.video_height * self.video_width) / (1280 * 960) @scale.setter def scale(self, scale): self.cameras("Front").scale = scale self.cameras("Left").scale = scale self.cameras("Right").scale = scale self.cameras("Rear").scale = scale @property def video_width(self): return int( max( self.cameras("Front").xpos + self.cameras("Front").width, self.cameras("Left").xpos + self.cameras("Left").width, self.cameras("Right").xpos + self.cameras("Right").width, self.cameras("Rear").xpos + self.cameras("Rear").width, ) ) @property def video_height(self): perspective_adjustement = 3 / 2 if self.perspective else 1 return int( max( self.cameras("Front").ypos + self.cameras("Front").height, perspective_adjustement * self.cameras("Left").ypos + self.cameras("Left").height, perspective_adjustement * self.cameras("Right").ypos + self.cameras("Right").height, self.cameras("Rear").ypos + self.cameras("Rear").height, ) ) @property def center_xpos(self): return int(self.video_width / 2) @property def center_ypos(self): return int(self.video_height / 2) class FullScreen(MovieLayout): """ FullScreen Movie Layout [FRONT_CAMERA] [LEFT_CAMERA][REAR_CAMERA][RIGHT_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 @property def video_width(self): return int( max( self.cameras("Front").width, self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width, ) ) @property def video_height(self): perspective_adjustement = 3 / 2 if self.perspective else 1 return int( self.cameras("Front").height + max( perspective_adjustement * self.cameras("Left").height, self.cameras("Rear").height, perspective_adjustement * self.cameras("Right").height, ) ) def _front_height(self): # For height keep same ratio of 4/3 return int(self.cameras("Front").width / 4 * 3) def _front_xpos(self): # Make sure that front is placed in the middle return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Front").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width, ) * self.cameras("Front").include ) def _left_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ), ) * self.cameras("Left").include ) def _left_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Left").include def _rear_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width, ) * self.cameras("Rear").include ) def _rear_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Rear").include def _right_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width + self.cameras("Rear").width, ) * self.cameras("Right").include ) def _right_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Right").include # noinspection PyProtectedMember class WideScreen(FullScreen): """ WideScreen Movie Layout [ FRONT_CAMERA ] [LEFT_CAMERA][REAR_CAMERA][RIGHT_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 # Set front scale to None so we know if it was overriden or not. self.cameras("Front").scale = None # Only front_width has to be adjusted as by default width would be left+rear+right instead of normal scale. def _front_width(self): return ( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) * self.cameras("Front").include if self.cameras("Front").scale is None else int( ( self.cameras("Front")._width * self.cameras("Front").scale * self.cameras("Front").include ) ) ) class Cross(FullScreen): """ Cross Movie Layout [FRONT_CAMERA] [LEFT_CAMERA][RIGHT_CAMERA] [REAR_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 @property def video_width(self): return max( self.cameras("Front").width, self.cameras("Left").width + self.cameras("Right").width, self.cameras("Rear").width, ) @property def video_height(self): if self.perspective: height = int( max( 3 / 2 * self.cameras("Left").height, 3 / 2 * self.cameras("Right").height, ) ) if ( self.cameras("Left").include and self.cameras("Left").scale >= self.cameras("Rear").scale and self.cameras("Right").include and self.cameras("Right").scale >= self.cameras("Rear").scale and self.cameras("Rear").include ): height = int(height / 3 * 2) height += self.cameras("Rear").height else: height = ( max(self.cameras("Left").height, self.cameras("Right").height) + self.cameras("Rear").height ) return int(height + self.cameras("Front").height) def _front_xpos(self): return ( int(max(0, self.center_xpos - (self.cameras("Front").width / 2))) * self.cameras("Front").include ) def _left_xpos(self): return ( max( 0, self.center_xpos - int((self.cameras("Left").width + self.cameras("Right").width) / 2), ) * self.cameras("Left").include ) def _left_ypos(self): return ( self.cameras("Front").height + int( ( max(self.cameras("Left").height, self.cameras("Right").height) - self.cameras("Left").height ) / 2 ) ) * self.cameras("Left").include def _right_xpos(self): return ( max( 0, self.center_xpos - int((self.cameras("Left").width + self.cameras("Right").width) / 2) + self.cameras("Left").width, ) * self.cameras("Right").include ) def _right_ypos(self): return ( self.cameras("Front").height + int( ( max(self.cameras("Left").height, self.cameras("Right").height) - self.cameras("Right").height ) / 2 ) ) * self.cameras("Right").include def _rear_xpos(self): return ( int(max(0, self.center_xpos - (self.cameras("Rear").width / 2))) * self.cameras("Rear").include ) def _rear_ypos(self): return int(max(0, self.video_height - self.cameras("Rear").height)) # noinspection PyProtectedMember class Diamond(Cross): """ Diamond Movie Layout [FRONT_CAMERA] [LEFT_CAMERA] [RIGHT_CAMERA] [REAR_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 self._font.valign = "MIDDLE" def _font_halign(self): if self._font._halign == "CENTER": # Change alignment to left or right if one of the left/right cameras is excluded. if (self.cameras("Left").include and not self.cameras("Right").include) or ( self.cameras("Right").include and not self.cameras("Left").include ): x_pos = int( max( self.cameras("Front").xpos + self.cameras("Front").width / 2, self.cameras("Rear").xpos + self.cameras("Rear").width / 2, ) ) return f"({x_pos} - text_w / 2)" return HALIGN.get(self._font._halign, self._font._halign) def _font_valign(self): if self._font._valign == "MIDDLE": if self.cameras("Front").include: return ( f'({self.cameras('Front').ypos + self.cameras('Front').height} + 5)' ) elif self.cameras("Rear").include: return f'({self.cameras('Rear').ypos} - 5 - text_h)' return VALIGN.get(self._font._valign, self._font._valign) def _font_size(self): # For this layout the video height has to include font size. But default for calculating # font size is based on video height. # Thus overriding font size to get video height without font size to figure our scaling. if self.font._size is None: scale = ( self._video_height(include_fontsize=False) * self.video_width / (1280 * 960) ) return int(max(16, 16 * scale)) else: return self.font.size @property def video_width(self): return ( max(self.cameras("Front").width, self.cameras("Rear").width) + self.cameras("Left").width + self.cameras("Right").width ) def _video_height(self, include_fontsize=True): perspective = 3 / 2 if self.perspective else 1 fontsize = self.font.size if include_fontsize else 0 return int( max( perspective * max(self.cameras("Left").height, self.cameras("Right").height), self.cameras("Front").height + self.cameras("Rear").height + fontsize, ) ) @property def video_height(self): return self._video_height(include_fontsize=True) def _front_xpos(self): return ( self.cameras("Left").width + int( ( max(self.cameras("Front").width, self.cameras("Rear").width) - self.cameras("Front").width ) / 2 ) ) * self.cameras("Front").include def _left_xpos(self): return 0 def _left_ypos(self): return max(0, self.center_ypos - int(self.cameras("Left").height / 2)) def _right_xpos(self): return max( self.cameras("Front").xpos + self.cameras("Front").width, self.cameras("Rear").xpos + self.cameras("Rear").width, ) def _right_ypos(self): return max(0, self.center_ypos - int(self.cameras("Right").height / 2)) def _rear_xpos(self): return ( self.cameras("Left").width + int( ( max(self.cameras("Front").width, self.cameras("Rear").width) - self.cameras("Rear").width ) / 2 ) ) * self.cameras("Rear").include class MyArgumentParser(argparse.ArgumentParser): def convert_arg_line_to_args(self, arg_line): # Remove comments. return shlex_split(arg_line, comments=True) def args_to_dict(self, arguments, default): argument_list = [] if arguments is None: return argument_list for argument in arguments: argument_dict = {} for argument_value in argument: if "=" in argument_value: key = argument_value.split("=")[0].lower() value = ( argument_value.split("=")[1].strip() if argument_value.split("=")[1].strip() != "" else None ) else: key = default value = argument_value argument_dict.update({key: value}) argument_list.append(argument_dict) return argument_list # noinspection PyCallByClass,PyProtectedMember class SmartFormatter(argparse.HelpFormatter): """ Formatter for argument help. """ def _split_lines(self, text, width): """ Provide raw output allowing for prettier help output """ if text.startswith("R|"): return text[2:].splitlines() # this is the RawTextHelpFormatter._split_lines return argparse.HelpFormatter._split_lines(self, text, width) def _get_help_string(self, action): """ Call default help string """ return argparse.ArgumentDefaultsHelpFormatter._get_help_string(self, action) def search_dict( match_value: object = None, key: str = None, search_list: List[dict] = None ) -> Optional[dict]: """ Returns the 1st element in a list containing dictionaries where the value of key provided matches the value provided. :param match_value: value to match upon (search for) :type match_value: object :param key: dictionary key to use for the match :type key: str :param search_list: List containing dictionary objects in which to search :type search_list: List[dict] :return: Dictionary object that matches :rtype: dict """ if key is None or search_list is None: return None if match_value is None: return next( (element for element in search_list if element.get(key) is None), None ) return next( (element for element in search_list if element.get(key) == match_value), None ) def check_latest_release(include_beta): """ Checks GitHub for latest release """ url = "{url}/repos/{owner}/{repo}/releases".format( url=GITHUB["URL"], owner=GITHUB["owner"], repo=GITHUB["repo"] ) if not include_beta: url = url + "/latest" try: releases = requests.get(url) except requests.exceptions.RequestException as exc: print("Unable to check for latest release: {exc}".format(exc=exc)) return None release_data = releases.json() # If we include betas then we would have received a list, thus get 1st # element as that is the latest release. if include_beta: release_data = release_data[0] return release_data def get_tesladashcam_folder(): """ Check if there is a drive mounted with the Tesla DashCam folder.""" for partition in disk_partitions(all=False): if "cdrom" in partition.opts or partition.fstype == "": continue teslacamfolder = os.path.join(partition.mountpoint, "TeslaCam") if os.path.isdir(teslacamfolder): _LOGGER.debug(f"Folder TeslaCam found on partition {partition.mountpoint}.") return teslacamfolder, partition.mountpoint _LOGGER.debug(f"No TeslaCam folder on partition {partition.mountpoint}.") return None, None def get_movie_files(source_folder, exclude_subdirs, video_settings): """ Find all the clip files within folder (and subfolder if requested) """ folder_list = {} total_folders = 0 for pathname in source_folder: if os.path.isdir(pathname): isfile = False if exclude_subdirs: # Retrieve all the video files in current path: search_path = os.path.join(pathname, "*.mp4") files = [ filename for filename in glob(search_path) if not os.path.basename(filename).startswith(".") ] print(f"Discovered {len(files)} files in {pathname}") else: # Search all sub folder. files = [] for folder, _, filenames in os.walk(pathname, followlinks=True): total_folders = total_folders + 1 for filename in ( filename for filename in filenames if not os.path.basename(filename).startswith(".") and fnmatch(filename, "*.mp4") ): files.append(os.path.join(folder, filename)) print( f"Discovered {total_folders} folders containing total of {len(files)} files in {pathname}" ) else: files = [pathname] isfile = True # Now go through and get timestamps etc.. for file in sorted(files): # Strip path so that we just have the filename. movie_folder, movie_filename = os.path.split(file) # And now get the timestamp of the filename. filename_timestamp = movie_filename.rsplit("-", 1)[0] movie_file_list = folder_list.get(movie_folder, {}) # Check if we already processed this timestamp. if movie_file_list.get(filename_timestamp) is not None: # Already processed this timestamp, moving on. continue _LOGGER.debug( f"Checking camera files in folder {movie_folder} with timestamp {filename_timestamp}" ) video_info = { "front_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "left_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "right_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "rear_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, } front_filename = str(filename_timestamp) + "-front.mp4" front_path = os.path.join(movie_folder, front_filename) left_filename = str(filename_timestamp) + "-left_repeater.mp4" left_path = os.path.join(movie_folder, left_filename) right_filename = str(filename_timestamp) + "-right_repeater.mp4" right_path = os.path.join(movie_folder, right_filename) rear_filename = str(filename_timestamp) + "-back.mp4" rear_path = os.path.join(movie_folder, rear_filename) # Get meta data for each video to determine creation time and duration. metadata = get_metadata( video_settings["ffmpeg_exec"], [front_path, left_path, right_path, rear_path], ) # Move on to next one if nothing received. if not metadata: continue # Get the longest duration: duration = 0 video_timestamp = None for item in metadata: _, filename = os.path.split(item["filename"]) if filename == front_filename: camera = "front_camera" video_filename = front_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Front").include else False ) elif filename == left_filename: camera = "left_camera" video_filename = left_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Left").include else False ) elif filename == right_filename: camera = "right_camera" video_filename = right_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Right").include else False ) elif filename == rear_filename: camera = "rear_camera" video_filename = rear_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Rear").include else False ) else: continue # Store duration and timestamp video_info[camera].update( filename=video_filename, duration=item["duration"], timestamp=item["timestamp"], include=include_clip, ) # Only check duration and timestamp if this file is not corrupt and if we include this camera # in our output. if include_clip: # Figure out which one has the longest duration duration = ( item["duration"] if item["duration"] > duration else duration ) # Figure out starting timestamp if video_timestamp is None: video_timestamp = item["timestamp"] else: video_timestamp = ( item["timestamp"] if item["timestamp"] < video_timestamp else video_timestamp ) if video_timestamp is None: # Firmware version 2019.16 changed filename timestamp format. if len(filename_timestamp) == 16: # This is for before version 2019.16 video_timestamp = datetime.strptime( filename_timestamp, "%Y-%m-%d_%H-%M" ) video_timestamp = video_timestamp.astimezone(get_localzone()) else: # This is for version 2019.16 and later video_timestamp = datetime.strptime( filename_timestamp, "%Y-%m-%d_%H-%M-%S" ) video_timestamp = video_timestamp.astimezone(timezone.utc) movie_info = { "movie_folder": movie_folder, "timestamp": video_timestamp, "duration": duration, "video_info": video_info, "file_only": isfile, } movie_file_list.update({filename_timestamp: movie_info}) folder_list.update({movie_folder: movie_file_list}) return folder_list def get_metadata(ffmpeg, filenames): """ Retrieve the meta data for the clip (i.e. timestamp, duration) """ # Get meta data for each video to determine creation time and duration. ffmpeg_command = [ffmpeg] metadata = [] for camera_file in filenames: if os.path.isfile(camera_file): ffmpeg_command.append("-i") ffmpeg_command.append(camera_file) metadata.append( { "filename": camera_file, "timestamp": None, "duration": 0, "include": False, } ) else: _LOGGER.debug(f"File {camera_file} does not exist, skipping.") # Don't run ffmpeg if nothing to check for. if not metadata: return metadata ffmpeg_command.append("-hide_banner") command_result = run(ffmpeg_command, capture_output=True, text=True) metadata_iterator = iter(metadata) input_counter = 0 video_timestamp = None wait_for_input_line = True metadata_item = {} for line in command_result.stderr.splitlines(): if search("^Input #", line) is not None: # If filename was not yet appended then it means it is a corrupt file, in that case just add to list for # but identify not to include for processing metadata_item = next(metadata_iterator) input_counter += 1 video_timestamp = None wait_for_input_line = False continue if wait_for_input_line: continue if search("^ *creation_time ", line) is not None: line_split = line.split(":", 1) video_timestamp = datetime.strptime( line_split[1].strip(), "%Y-%m-%dT%H:%M:%S.%f%z" ) continue if search("^ *Duration: ", line) is not None: line_split = line.split(",") line_split = line_split[0].split(":", 1) duration_list = line_split[1].split(":") duration = ( int(duration_list[0]) * 60 * 60 + int(duration_list[1]) * 60 + int(duration_list[2].split(".")[0]) + (float(duration_list[2].split(".")[1]) / 100) ) # File will only be processed if duration is greater then 0 include = duration > 0 metadata_item.update( {"timestamp": video_timestamp, "duration": duration, "include": include} ) wait_for_input_line = True return metadata def create_intermediate_movie( filename_timestamp, video, folder_timestamps, video_settings, clip_number, total_clips, ): """ Create intermediate movie files. This is the merging of the 3 camera video files into 1 video file. """ # We first stack (combine the 3 different camera video files into 1 # and then we concatenate. front_camera = ( os.path.join( video["movie_folder"], video["video_info"]["front_camera"]["filename"] ) if ( video["video_info"]["front_camera"]["filename"] is not None and video["video_info"]["front_camera"]["include"] ) else None ) left_camera = ( os.path.join( video["movie_folder"], video["video_info"]["left_camera"]["filename"] ) if ( video["video_info"]["left_camera"]["filename"] is not None and video["video_info"]["left_camera"]["include"] ) else None ) right_camera = ( os.path.join( video["movie_folder"], video["video_info"]["right_camera"]["filename"] ) if ( video["video_info"]["right_camera"]["filename"] is not None and video["video_info"]["right_camera"]["include"] ) else None ) rear_camera = ( os.path.join( video["movie_folder"], video["video_info"]["rear_camera"]["filename"] ) if ( video["video_info"]["rear_camera"]["filename"] is not None and video["video_info"]["rear_camera"]["include"] ) else None ) if ( front_camera is None and left_camera is None and right_camera is None and rear_camera is None ): _LOGGER.debug( f'No front, left, right, and rear camera clip exist for {video['timestamp']}' ) return None, 0, True if video_settings["video_layout"].swap_left_right: left_camera, right_camera = right_camera, left_camera if video_settings["video_layout"].swap_front_rear: front_camera, rear_camera = rear_camera, front_camera # Determine if this clip is to be included based on potential start and end timestamp/offsets that were provided. # Clip starting time is between the start&end times we're looking for # or Clip end time is between the start&end time we're looking for. # or Starting time is between start&end clip time # or End time is between start&end clip time starting_timestmp = video["timestamp"] ending_timestmp = starting_timestmp + timedelta(seconds=video["duration"]) if not ( folder_timestamps[0] <= starting_timestmp <= folder_timestamps[1] or folder_timestamps[0] <= ending_timestmp <= folder_timestamps[1] or starting_timestmp <= folder_timestamps[0] <= ending_timestmp or starting_timestmp <= folder_timestamps[1] <= ending_timestmp ): # This clip is not in-between the timestamps we want, skip it. _LOGGER.debug( f"Clip timestamp from {starting_timestmp} to {ending_timestmp} not " f"between {folder_timestamps[0]} and {folder_timestamps[1]}" ) return None, 0, True # Determine if we need to do an offset of the starting timestamp starting_offset = 0 ffmpeg_offset_command = [] clip_duration = video["duration"] # This clip falls in between the start and end timestamps to include. # Set offsets if required if video["timestamp"] < folder_timestamps[0]: # Starting timestamp is withing this clip. starting_offset = (folder_timestamps[0] - video["timestamp"]).total_seconds() starting_timestmp = folder_timestamps[0] ffmpeg_offset_command = ["-ss", str(starting_offset)] clip_duration = video["duration"] - starting_offset # Adjust duration if end of clip's timestamp is after ending timestamp we need. if video["timestamp"] + timedelta(seconds=video["duration"]) > folder_timestamps[1]: # Duration has to be cut. clip_duration = ( folder_timestamps[1] - (video["timestamp"] + timedelta(seconds=starting_offset)) ).total_seconds() ffmpeg_offset_command += ["-t", str(clip_duration)] # Confirm if files exist, if not replace with nullsrc input_count = 0 if left_camera is not None and os.path.isfile(left_camera): ffmpeg_left_command = ffmpeg_offset_command + ["-i", left_camera] ffmpeg_left_camera = ";[0:v] " + video_settings["left_camera"] input_count += 1 else: ffmpeg_left_command = [] ffmpeg_left_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Left").width, height=video_settings["video_layout"].cameras("Left").height, ) + "[left]" if video_settings["video_layout"].cameras("Left").include else "" ) if front_camera is not None and os.path.isfile(front_camera): ffmpeg_front_command = ffmpeg_offset_command + ["-i", front_camera] ffmpeg_front_camera = ( ";[" + str(input_count) + ":v] " + video_settings["front_camera"] ) input_count += 1 else: ffmpeg_front_command = [] ffmpeg_front_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Front").width, height=video_settings["video_layout"].cameras("Front").height, ) + "[front]" if video_settings["video_layout"].cameras("Front").include else "" ) if right_camera is not None and os.path.isfile(right_camera): ffmpeg_right_command = ffmpeg_offset_command + ["-i", right_camera] ffmpeg_right_camera = ( ";[" + str(input_count) + ":v] " + video_settings["right_camera"] ) input_count += 1 else: ffmpeg_right_command = [] ffmpeg_right_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Right").width, height=video_settings["video_layout"].cameras("Right").height, ) + "[right]" if video_settings["video_layout"].cameras("Right").include else "" ) if rear_camera is not None and os.path.isfile(rear_camera): ffmpeg_rear_command = ffmpeg_offset_command + ["-i", rear_camera] ffmpeg_rear_camera = ( ";[" + str(input_count) + ":v] " + video_settings["rear_camera"] ) input_count += 1 else: ffmpeg_rear_command = [] ffmpeg_rear_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Rear").width, height=video_settings["video_layout"].cameras("Rear").height, ) + "[rear]" if video_settings["video_layout"].cameras("Rear").include else "" ) local_timestamp = video["timestamp"].astimezone(get_localzone()) # Check if target video file exist if skip existing. file_already_exist = False if video_settings["skip_existing"]: temp_movie_name = ( os.path.join(video_settings["target_folder"], filename_timestamp) + ".mp4" ) if os.path.isfile(temp_movie_name): file_already_exist = True elif ( not video_settings["keep_intermediate"] and video_settings["temp_dir"] is not None ): temp_movie_name = ( os.path.join(video_settings["temp_dir"], filename_timestamp) + ".mp4" ) if os.path.isfile(temp_movie_name): file_already_exist = True if file_already_exist: print( "\t\tSkipping clip {clip_number}/{total_clips} from {timestamp} " "and {duration} seconds as it already exist.".format( clip_number=clip_number + 1, total_clips=total_clips, timestamp=local_timestamp.strftime("%x %X"), duration=int(clip_duration), ) ) # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [temp_movie_name]) duration = metadata[0]["duration"] if metadata else video["duration"] return temp_movie_name, duration, True else: target_folder = ( video_settings["temp_dir"] if not video_settings["keep_intermediate"] and video_settings["temp_dir"] is not None else video_settings["target_folder"] ) temp_movie_name = os.path.join(target_folder, filename_timestamp) + ".mp4" print( "\t\tProcessing clip {clip_number}/{total_clips} from {timestamp} " "and {duration} seconds long.".format( clip_number=clip_number + 1, total_clips=total_clips, timestamp=local_timestamp.strftime("%x %X"), duration=int(clip_duration), ) ) epoch_timestamp = int(starting_timestmp.timestamp()) ffmpeg_filter = ( video_settings["base"].format( duration=clip_duration, speed=video_settings["movie_speed"] ) + ffmpeg_left_camera + ffmpeg_front_camera + ffmpeg_right_camera + ffmpeg_rear_camera + video_settings["clip_positions"] + video_settings["timestamp_text"].format(epoch_time=epoch_timestamp) + video_settings["ffmpeg_speed"] + video_settings["ffmpeg_motiononly"] ) ffmpeg_command = ( [video_settings["ffmpeg_exec"]] + ["-loglevel", "error"] + ffmpeg_left_command + ffmpeg_front_command + ffmpeg_right_command + ffmpeg_rear_command + ["-filter_complex", ffmpeg_filter] + ["-map", f"[{video_settings["input_clip"]}]"] + video_settings["other_params"] ) ffmpeg_command = ffmpeg_command + ["-y", temp_movie_name] _LOGGER.debug(f"FFMPEG Command: {ffmpeg_command}") # Run the command. try: run(ffmpeg_command, capture_output=True, check=True) except CalledProcessError as exc: print( "\t\t\tError trying to create clip for {base_name}. RC: {rc}\n" "\t\t\tCommand: {command}\n" "\t\t\tError: {stderr}\n\n".format( base_name=os.path.join(video["movie_folder"], filename_timestamp), rc=exc.returncode, command=exc.cmd, stderr=exc.stderr, ) ) return None, 0, False # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [temp_movie_name]) duration = metadata[0]["duration"] if metadata else video["duration"] return temp_movie_name, duration, True def create_movie(clips_list, movie_filename, video_settings, chapter_offset): """ Concatenate provided movie files into 1.""" # Just return if there are no clips. if not clips_list: _LOGGER.debug("Clip list is empty") return None, None # Go through the list of clips to create the command and content for chapter meta file. ffmpeg_join_filehandle, ffmpeg_join_filename = mkstemp(suffix=".txt", text=True) total_clips = 0 meta_content = "" meta_start = 0 total_videoduration = 0 chapter_offset = chapter_offset * 1000000000 with os.fdopen(ffmpeg_join_filehandle, "w") as fp: # Loop through the list sorted by video timestamp. for video_clip in sorted( clips_list, key=lambda video: video["video_timestamp"] ): if not os.path.isfile(video_clip["video_filename"]): print( "\t\tFile {} does not exist anymore, skipping.".format( video_clip["video_filename"] ) ) continue # Add this file in our join list. fp.write( "file '" + video_clip["video_filename"] + "'{linesep}".format(linesep=os.linesep) ) total_clips = total_clips + 1 title = video_clip["video_timestamp"].astimezone(get_localzone()) # For duration need to also calculate if video was sped-up or slowed down. video_duration = int(video_clip["video_duration"] * 1000000000) total_videoduration += video_duration chapter_start = meta_start if video_duration > abs(chapter_offset): if chapter_offset < 0: chapter_start = meta_start + video_duration + chapter_offset elif chapter_offset > 0: chapter_start = chapter_start + chapter_offset # We need to add an initial chapter if our "1st" chapter is not at the beginning of the movie. if total_clips == 1 and chapter_start > 0: meta_content = ( "[CHAPTER]{linesep}" "TIMEBASE=1/1000000000{linesep}" "START={start}{linesep}" "END={end}{linesep}" "title={title}{linesep}".format( linesep=os.linesep, start=0, end=chapter_start - 1, title="Start", ) ) meta_content = ( meta_content + "[CHAPTER]{linesep}" "TIMEBASE=1/1000000000{linesep}" "START={start}{linesep}" "END={end}{linesep}" "title={title}{linesep}".format( linesep=os.linesep, start=chapter_start, end=meta_start + video_duration, title=title.strftime("%x %X"), ) ) meta_start = meta_start + 1 + video_duration if total_clips == 0: print("\t\tError: No valid clips to merge found.") return None, None # Write out the meta data file. meta_content = ";FFMETADATA1" + os.linesep + meta_content ffmpeg_meta_filehandle, ffmpeg_meta_filename = mkstemp(suffix=".txt", text=True) with os.fdopen(ffmpeg_meta_filehandle, "w") as fp: fp.write(meta_content) ffmpeg_params = [ "-f", "concat", "-safe", "0", "-i", ffmpeg_join_filename, "-i", ffmpeg_meta_filename, "-map_metadata", "1", "-map_chapters", "1", ] if video_settings["movflags_faststart"]: ffmpeg_params = ffmpeg_params + ["-movflags", "+faststart"] ffmpeg_params = ffmpeg_params + ["-c", "copy"] ffmpeg_params = ffmpeg_params + [ "-metadata", f"description=Created by tesla_dashcam {VERSION_STR}", ] ffmpeg_command = ( [video_settings["ffmpeg_exec"]] + ["-loglevel", "error"] + ffmpeg_params + ["-y", movie_filename] ) _LOGGER.debug(f"FFMPEG Command: {ffmpeg_command}") try: run(ffmpeg_command, capture_output=True, check=True) except CalledProcessError as exc: print( "\t\tError trying to create movie {base_name}. RC: {rc}\n" "\t\tCommand: {command}\n" "\t\tError: {stderr}\n\n".format( base_name=movie_filename, rc=exc.returncode, command=exc.cmd, stderr=exc.stderr, ) ) movie_filename = None duration = 0 else: # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [movie_filename]) duration = metadata[0]["duration"] if metadata else total_videoduration # Remove temp join file. # noinspection PyBroadException,PyPep8 try: os.remove(ffmpeg_join_filename) except: _LOGGER.debug(f"Failed to remove {ffmpeg_join_filename}") pass # Remove temp join file. # noinspection PyBroadException,PyPep8 try: os.remove(ffmpeg_meta_filename) except: _LOGGER.debug(f"Failed to remove {ffmpeg_meta_filename}") pass return movie_filename, duration def make_folder(parameter, folder): # Create folder if not already existing. if not os.path.isdir(folder): current_path, add_folder = os.path.split(folder) if add_folder == "": current_path, add_folder = os.path.split(current_path) # If path does not exist in which to create folder then exit. if not os.path.isdir(current_path): print( f"Path {current_path} for parameter {parameter} does not exist, please provide a valid path." ) return False try: os.mkdir(folder) except OSError: print( f"Error creating folder {add_folder} at location {current_path} for parameter {parameter}" ) return False return True def delete_intermediate(movie_files): """ Delete the files provided in list """ for file in movie_files: if file is not None: if os.path.isfile(file): try: os.remove(file) except OSError as exc: print("\t\tError trying to remove file {}: {}".format(file, exc)) elif os.path.isdir(file): # This is more specific for Mac but won't hurt on other platforms. if os.path.exists(os.path.join(file, ".DS_Store")): # noinspection PyBroadException,PyPep8 try: os.remove(os.path.join(file, ".DS_Store")) except: _LOGGER.debug(f"Failed to remove .DS_Store from {file}") pass try: os.rmdir(file) except OSError as exc: print("\t\tError trying to remove folder {}: {}".format(file, exc)) def process_folders(folders, video_settings, delete_source): """ Process all clips found within folders. """ start_time = timestamp() total_clips = 0 for folder_number, folder_name in enumerate(sorted(folders)): total_clips = total_clips + len(folders[folder_name]) print( "There are {total_folders} folders with {total_clips} clips to " "process.".format(total_folders=len(folders), total_clips=total_clips) ) # Loop through all the folders. dashcam_clips = [] for folder_number, folder_name in enumerate(sorted(folders)): files = folders[folder_name] # Ensure the clips are sorted based on video timestamp. sorted_video_clips = sorted(files, key=lambda video: files[video]["timestamp"]) # Get the start and ending timestamps, we add duration to # last timestamp to get true ending. first_clip_tmstp = files[sorted_video_clips[0]]["timestamp"] last_clip_tmstp = files[sorted_video_clips[-1]]["timestamp"] + timedelta( seconds=files[sorted_video_clips[-1]]["duration"] ) # Skip this folder if we it does not fall within provided timestamps. if ( video_settings["start_timestamp"] is not None and last_clip_tmstp < video_settings["start_timestamp"] ): # Clips from this folder are from before start timestamp requested. _LOGGER.debug( f"Clips in folder end at {last_clip_tmstp} which is still before " f'start timestamp {video_settings['start_timestamp']}' ) continue if ( video_settings["end_timestamp"] is not None and first_clip_tmstp > video_settings["end_timestamp"] ): # Clips from this folder are from after end timestamp requested. _LOGGER.debug( f"Clips in folder start at {first_clip_tmstp} which is after " f'end timestamp {video_settings['end_timestamp']}' ) continue # Determine the starting and ending timestamps for the clips in this folder based on start/end timestamps # provided and offsets. folder_start_timestmp = ( timedelta(seconds=video_settings["start_offset"]) + first_clip_tmstp ) # Use provided start timestamp if it is after folder timestamp + offset folder_start_timestmp = ( video_settings["start_timestamp"] if video_settings["start_timestamp"] is not None and video_settings["start_timestamp"] > folder_start_timestmp else folder_start_timestmp ) # Figure out potential end timestamp for clip based on offset and end timestamp. folder_end_timestmp = last_clip_tmstp - timedelta( seconds=video_settings["end_offset"] ) # Use provided end timestamp if it is before folder timestamp - offset folder_end_timestmp = ( video_settings["end_timestamp"] if video_settings["end_timestamp"] is not None and video_settings["end_timestamp"] < folder_end_timestmp else folder_end_timestmp ) # Put them together to create the filename for the folder. movie_filename = ( folder_start_timestmp.astimezone(get_localzone()).strftime( "%Y-%m-%dT%H-%M-%S" ) + "_" + folder_end_timestmp.astimezone(get_localzone()).strftime( "%Y-%m-%dT%H-%M-%S" ) ) # Now add full path to it. movie_filename = ( os.path.join(video_settings["target_folder"], movie_filename) + ".mp4" ) # Do not process the files from this folder if we're to skip it if # the target movie file already exist. if video_settings["skip_existing"] and os.path.isfile(movie_filename): print( "\tSkipping folder {folder} as {filename} is already " "created ({folder_number}/{total_folders})".format( folder=folder_name, filename=movie_filename, folder_number=folder_number + 1, total_folders=len(folders), ) ) # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [movie_filename]) movie_duration = metadata[0]["duration"] if metadata else 0 dashcam_clips.append( { "video_timestamp": first_clip_tmstp, "video_filename": movie_filename, "video_duration": movie_duration, } ) continue print( "\tProcessing {total_clips} clips in folder {folder} " "({folder_number}/{total_folders})".format( total_clips=len(files), folder=folder_name, folder_number=folder_number + 1, total_folders=len(folders), ) ) # Loop through all the files within the folder. folder_clips = [] delete_folder_clips = [] delete_folder_files = delete_source delete_file_list = [] folder_timestamp = None for clip_number, filename_timestamp in enumerate(sorted_video_clips): video_timestamp_info = files[filename_timestamp] folder_timestamp = ( video_timestamp_info["timestamp"] if folder_timestamp is None else folder_timestamp ) clip_name, clip_duration, files_processed = create_intermediate_movie( filename_timestamp, video_timestamp_info, (folder_start_timestmp, folder_end_timestmp), video_settings, clip_number, len(files), ) if clip_name is not None: if video_timestamp_info["file_only"]: # When file only there is no concatenation at the folder # level, will only happen at the higher level if requested. dashcam_clips.append( { "video_timestamp": video_timestamp_info["timestamp"], "video_filename": clip_name, "video_duration": clip_duration, } ) else: # Movie was created, store name for concatenation. folder_clips.append( { "video_timestamp": video_timestamp_info["timestamp"], "video_filename": clip_name, "video_duration": clip_duration, } ) # Add clip for deletion only if it's name is not the # same as the resulting movie filename if clip_name != movie_filename: delete_folder_clips.append(clip_name) elif not files_processed: delete_folder_files = False if files_processed: # Add the files to our list for removal. video_info = video_timestamp_info["video_info"] if video_info["front_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["front_camera"]["filename"], ) ) if video_info["left_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["left_camera"]["filename"], ) ) if video_info["right_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["right_camera"]["filename"], ) ) if video_info["rear_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["rear_camera"]["filename"], ) ) # All clips in folder have been processed, merge those clips # together now. movie_name = None movie_duration = 0 if folder_clips: print("\t\tCreating movie {}, please be patient.".format(movie_filename)) movie_name, movie_duration = create_movie( folder_clips, movie_filename, video_settings, 0 ) # Delete the source files if stated to delete. # We only do so if there were no issues in processing the clips if delete_folder_files and ( (folder_clips and movie_name is not None) or not folder_clips ): print( "\t\tDeleting files and folder {folder_name}".format( folder_name=folder_name ) ) delete_intermediate(delete_file_list) # And delete the folder delete_intermediate([folder_name]) # Add this one to our list for final concatenation if movie_name is not None: dashcam_clips.append( { "video_timestamp": folder_timestamp, "video_filename": movie_name, "video_duration": movie_duration, } ) # Delete the intermediate files we created. if not video_settings["keep_intermediate"]: delete_intermediate(delete_folder_clips) print( "\tMovie {base_name} for folder {folder_name} with duration {duration} is " "ready.".format( base_name=movie_name, folder_name=folder_name, duration=str(timedelta(seconds=int(movie_duration))), ) ) # Now that we have gone through all the folders merge. # We only do this if merge is enabled OR if we only have 1 clip and for # output a specific filename was provided. movie_name = None movie_duration = 0 if dashcam_clips: if video_settings["merge_subdirs"] or ( len(folders) == 1 and video_settings["target_filename"] is not None ): if video_settings["movie_filename"] is not None: movie_filename = video_settings["movie_filename"] elif video_settings["target_filename"] is not None: movie_filename = video_settings["target_filename"] else: folder, movie_filename = os.path.split(video_settings["target_folder"]) # If there was a trailing separator provided then it will be # empty, redo split then. if movie_filename == "": movie_filename = os.path.split(folder)[1] movie_filename = os.path.join( video_settings["target_folder"], movie_filename ) # Make sure it ends in .mp4 if os.path.splitext(movie_filename)[1] != ".mp4": movie_filename = movie_filename + ".mp4" print("\tCreating movie {}, please be patient.".format(movie_filename)) movie_name, movie_duration = create_movie( dashcam_clips, movie_filename, video_settings, video_settings["chapter_offset"], ) if movie_name is not None: print( "Movie {base_name} with duration {duration} has been created, enjoy.".format( base_name=movie_name, duration=str(timedelta(seconds=int(movie_duration))), ) ) else: print( "All folders have been processed, resulting movie files are " "located in {target_folder}".format( target_folder=video_settings["target_folder"] ) ) else: print("No clips found.") end_time = timestamp() real = int((end_time - start_time)) print("Total processing time: {real}".format(real=str(timedelta(seconds=real)))) if video_settings["notification"]: if movie_name is not None: notify( "TeslaCam", "Completed", "{total_folders} folder{folders} with {total_clips} " "clip{clips} have been processed, movie {movie_name} has " "been created.".format( folders="" if len(folders) < 2 else "s", total_folders=len(folders), clips="" if total_clips < 2 else "s", total_clips=total_clips, movie_name=video_settings["target_folder"], ), ) else: notify( "TeslaCam", "Completed", "{total_folders} folder{folders} with {total_clips} " "clip{clips} have been processed, {target_folder} contains " "resulting files.".format( folders="" if len(folders) < 2 else "s", total_folders=len(folders), clips="" if total_clips < 2 else "s", total_clips=total_clips, target_folder=video_settings["target_folder"], ), ) print() def resource_path(relative_path): """ Return absolute path for provided relative item based on location of program. """ # If compiled with pyinstaller then sys._MEIPASS points to the location # of the bundle. Otherwise path of python script is used. base_path = getattr(sys, "_MEIPASS", str(Path(__file__).parent)) return os.path.join(base_path, relative_path) def notify_macos(title, subtitle, message): """ Notification on MacOS """ try: run( [ "osascript", '-e display notification "{message}" with title "{title}" ' 'subtitle "{subtitle}"' "".format(message=message, title=title, subtitle=subtitle), ] ) except Exception as exc: print("Failed in notifification: ", exc) def notify_windows(title, subtitle, message): """ Notification on Windows """ # Section commented out, waiting to see if it really does not work on Windows 7 # This works only on Windows 10 9r Windows Server 2016/2019. Skipping for everything else # from platform import win32_ver # if win32_ver()[0] != 10: # return global TOASTER_INSTANCE # noinspection PyBroadException try: # noinspection PyUnresolvedReferences,PyPackageRequirements from win10toast import ToastNotifier if TOASTER_INSTANCE is None: TOASTER_INSTANCE = ToastNotifier() TOASTER_INSTANCE.show_toast( threaded=True, title="{} {}".format(title, subtitle), msg=message, duration=5, icon_path=resource_path("tesla_dashcam.ico"), ) run( [ "notify-send", '"{title} {subtitle}"'.format(title=title, subtitle=subtitle), '"{}"'.format(message), ] ) except Exception: pass def notify_linux(title, subtitle, message): """ Notification on Linux """ try: run( [ "notify-send", '"{title} {subtitle}"'.format(title=title, subtitle=subtitle), '"{}"'.format(message), ] ) except Exception as exc: print("Failed in notifification: ", exc) def notify(title, subtitle, message): """ Call function to send notification based on OS """ if sys.platform == "darwin": notify_macos(title, subtitle, message) elif sys.platform == "win32": notify_windows(title, subtitle, message) elif sys.platform == "linux": notify_linux(title, subtitle, message) def main() -> None: """ Main function """ loglevels = dict( (logging.getLevelName(level), level) for level in [10, 20, 30, 40, 50] ) internal_ffmpeg = getattr(sys, "frozen", None) is not None ffmpeg_default = resource_path(FFMPEG.get(sys.platform, "ffmpeg")) movie_folder = os.path.join(str(Path.home()), MOVIE_HOMEDIR.get(sys.platform), "") # Check if ffmpeg exist, if not then hope it is in default path or # provided. if not os.path.isfile(ffmpeg_default): internal_ffmpeg = False ffmpeg_default = FFMPEG.get(sys.platform, "ffmpeg") epilog = ( "This program leverages ffmpeg which is included. See https://ffmpeg.org/ for more information on ffmpeg" if internal_ffmpeg else "This program requires ffmpeg which can be downloaded from: https://ffmpeg.org/download.html" ) parser = MyArgumentParser( description="tesla_dashcam - Tesla DashCam & Sentry Video Creator", epilog=epilog, formatter_class=SmartFormatter, fromfile_prefix_chars="@", ) parser.add_argument( "source", type=str, nargs="*", help="Folder(s) (events) containing the saved camera files. Filenames can be provided as well to manage " "individual clips.", ) parser.add_argument( "--version", action="version", version=" %(prog)s " + VERSION_STR ) parser.add_argument( "--loglevel", default="INFO", choices=list(loglevels.keys()), help="Logging level.", ) parser.add_argument( "--temp_dir", required=False, type=str, help="R|Path to store temporary files." ) parser.add_argument( "--no-notification", dest="system_notification", action="store_false", help="Do not create a notification upon " "completion.", ) input_group = parser.add_argument_group( title="Video Input", description="Options related to what clips and events to process.", ) input_group.add_argument( "--skip_existing", dest="skip_existing", action="store_true", help="Skip creating encoded video file if it already exist. Note that only existence is checked, not if " "layout etc. are the same.", ) input_group.add_argument( "--delete_source", dest="delete_source", action="store_true", help="Delete the processed files upon completion.", ) input_group.add_argument( "--exclude_subdirs", dest="exclude_subdirs", action="store_true", help="Do not search sub folders (events) for video files to process.", ) monitor_group = parser.add_argument_group( title="Trigger Monitor", description="Parameters for monitoring of insertion of TeslaCam drive, folder, or file existence.", ) monitor_group.add_argument( "--monitor", dest="monitor", action="store_true", help="Enable monitoring for drive to be attached with TeslaCam folder.", ) monitor_group.add_argument( "--monitor_once", dest="monitor_once", action="store_true", help="Enable monitoring and exit once drive with TeslaCam folder has been attached and files processed.", ) monitor_group.add_argument( "--monitor_trigger", required=False, type=str, help="Trigger file to look for instead of waiting for drive to be attached. Once file is discovered then " "processing will start, file will be deleted when processing has been completed. If source is not " "provided then folder where file is located will be used as source.", ) layout_group = parser.add_argument_group( title="Video Layout", description="Set what the layout of the resulting video should be", ) layout_group.add_argument( "--layout", required=False, choices=["WIDESCREEN", "FULLSCREEN", "PERSPECTIVE", "CROSS", "DIAMOND"], default="FULLSCREEN", help="R|Layout of the created video.\n" " FULLSCREEN: Front camera center top, " "side cameras underneath it with rear camera between side camera.\n" " WIDESCREEN: Front camera on top with side and rear cameras smaller underneath it.\n" " PERSPECTIVE: Similar to FULLSCREEN but then with side cameras in perspective.\n" " CROSS: Front camera center top, side cameras underneath, and rear camera center bottom.\n" " DIAMOND: Front camera center top, side cameras below front camera left and right of front, " "and rear camera center bottom.\n", ) layout_group.add_argument( "--perspective", dest="perspective", action="store_true", help="Show side cameras in perspective.", ) layout_group.set_defaults(perspective=False) layout_group.add_argument( "--scale", dest="clip_scale", type=str, nargs="+", action="append", help="R|Set camera clip scale for all clips, scale of 1 is 1280x960 camera clip.\n" "If provided with value then it is default for all cameras, to set the scale for a specific " "camera provide camera=<front, left, right,rear> <scale>\n" "for example:\n" " --scale 0.5 all are 640x480\n" " --scale 640x480 all are 640x480\n" " --scale 0.5 --scale camera=front 1 all are 640x480 except front at 1280x960\n" " --scale camera=left .25 --scale camera=right 320x240 left and right are set to 320x240\n" "Defaults:\n" " WIDESCREEN: 1/2 (front 1280x960, others 640x480, video is 1920x1920)\n" " FULLSCREEN: 1/2 (640x480, video is 1920x960)\n" " CROSS: 1/2 (640x480, video is 1280x1440)\n" " DIAMOND: 1/2 (640x480, video is 1920x976)\n", ) layout_group.add_argument( "--mirror", dest="rear_or_mirror", action="store_const", const=1, help="Video from side and rear cameras as if being viewed through the mirror. Default when not providing " "parameter --no-front. Cannot be used in combination with --rear.", ) layout_group.add_argument( "--rear", dest="rear_or_mirror", action="store_const", const=0, help="Video from side and rear cameras as if looking backwards. Default when providing parameter --no-front. " "Cannot be used in combination with --mirror.", ) layout_group.add_argument( "--swap", dest="swap_leftright", action="store_const", const=1, help="Swap left and right cameras in output, default when side and rear cameras are as if looking backwards. " "See --rear parameter.", ) layout_group.add_argument( "--no-swap", dest="swap_leftright", action="store_const", const=0, help="Do not swap left and right cameras, default when side and rear cameras are as if looking through a " "mirror. Also see --mirror parameter", ) layout_group.add_argument( "--swap_frontrear", dest="swap_frontrear", action="store_true", help="Swap front and rear cameras in output.", ) layout_group.add_argument( "--background", dest="background", default="black", help="Background color for video. Can be a color string or RGB value. Also see --fontcolor.", ) camera_group = parser.add_argument_group( title="Camera Exclusion", description="Exclude one or more cameras:" ) camera_group.add_argument( "--no-front", dest="no_front", action="store_true", help="Exclude front camera from video.", ) camera_group.add_argument( "--no-left", dest="no_left", action="store_true", help="Exclude left camera from video.", ) camera_group.add_argument( "--no-right", dest="no_right", action="store_true", help="Exclude right camera from video.", ) camera_group.add_argument( "--no-rear", dest="no_rear", action="store_true", help="Exclude rear camera from video.", ) timestamp_group = parser.add_argument_group( title="Timestamp", description="Options on how to show date/time in resulting video:", ) timestamp_group.add_argument( "--no-timestamp", dest="no_timestamp", action="store_true", help="Do not show timestamp in video", ) timestamp_group.add_argument( "--halign", required=False, choices=["LEFT", "CENTER", "RIGHT"], help="Horizontal alignment for timestamp", ) timestamp_group.add_argument( "--valign", required=False, choices=["TOP", "MIDDLE", "BOTTOM"], help="Vertical Alignment for timestamp", ) timestamp_group.add_argument( "--font", required=False, type=str, default=DEFAULT_FONT.get(sys.platform, None), help="Fully qualified filename (.ttf) to the font to be chosen for timestamp.", ) timestamp_group.add_argument( "--fontsize", required=False, type=int, help="Font size for timestamp. Default is scaled based on resulting video size.", ) timestamp_group.add_argument( "--fontcolor", required=False, type=str, default="white", help="R|Font color for timestamp. Any color is accepted as a color string or RGB value.\n" "Some potential values are:\n" " white\n" " yellowgreen\n" " yellowgreen@0.9\n" " Red\n:" " 0x2E8B57\n" "For more information on this see ffmpeg documentation for color: https://ffmpeg.org/ffmpeg-utils.html#Color", ) filter_group = parser.add_argument_group( title="Timestamp Restriction", description="Restrict video to be between start and/or end timestamps. Timestamp to be provided in a ISO-8601 " "format (see https://fits.gsfc.nasa.gov/iso-time.html for examples)", ) filter_group.add_argument( "--start_timestamp", dest="start_timestamp", type=str, help="Starting timestamp" ) filter_group.add_argument( "--end_timestamp", dest="end_timestamp", type=str, # type=lambda d: datetime.strptime(d, "%Y-%m-%d_%H-%M-%S").datetime(), help="Ending timestamp", ) offset_group = parser.add_argument_group( title="Event offsets", description="Start and/or end offsets for events" ) offset_group.add_argument( "--start_offset", dest="start_offset", type=int, help="Skip x number of seconds from start of event for resulting video.", ) offset_group.add_argument( "--end_offset", dest="end_offset", type=int, help="Ignore the last x seconds of the event for resulting video", ) output_group = parser.add_argument_group( title="Video Output", description="Options related to resulting video creation." ) output_group.add_argument( "--output", required=False, default=movie_folder, type=str, help="R|Path/Filename for the new movie file. Event files will be stored in same folder." + os.linesep, ) output_group.add_argument( "--motion_only", dest="motion_only", action="store_true", help="Fast-forward through video when there is no motion.", ) output_group.add_argument( "--slowdown", dest="slow_down", type=float, default=argparse.SUPPRESS, help="Slow down video output. Accepts a number that is then used as multiplier, providing 2 means half the " "speed.", ) output_group.add_argument( "--speedup", dest="speed_up", type=float, default=argparse.SUPPRESS, help="Speed up the video. Accepts a number that is then used as a multiplier, providing 2 means " "twice the speed.", ) output_group.add_argument( "--chapter_offset", dest="chapter_offset", type=int, default=0, help="Offset in seconds for chapters in merged video. Negative offset is # of seconds before the end of the " "subdir video, positive offset if # of seconds after the start of the subdir video.", ) output_group.add_argument( "--merge", dest="merge_subdirs", action="store_true", help="Merge the video files from different folders (events) into 1 big video file.", ) output_group.add_argument( "--keep-intermediate", dest="keep_intermediate", action="store_true", help="Do not remove the clip video files that are created", ) advancedencoding_group = parser.add_argument_group( title="Advanced encoding settings", description="Advanced options for encoding" ) gpu_help = ( "R|Use GPU acceleration, only enable if supported by hardware.\n" " MAC: All MACs with Haswell CPU or later support this (Macs after 2013).\n" " See following link as well: \n" " https://en.wikipedia.org/wiki/List_of_Macintosh_models_grouped_by_CPU_type#Haswell\n" ) if sys.platform == "darwin": advancedencoding_group.add_argument( "--no-gpu", dest="gpu", action="store_true", help=gpu_help ) else: advancedencoding_group.add_argument( "--gpu", dest="gpu", action="store_true", help=gpu_help ) advancedencoding_group.add_argument( "--gpu_type", choices=["nvidia", "intel", "RPi"], help="Type of graphics card (GPU) in the system. This determines the encoder that will be used." "This parameter is mandatory if --gpu is provided.", ) advancedencoding_group.add_argument( "--no-faststart", dest="faststart", action="store_true", help="Do not enable flag faststart on the resulting video files. Use this when using a network share and " "errors occur during encoding.", ) advancedencoding_group.add_argument( "--quality", required=False, choices=["LOWEST", "LOWER", "LOW", "MEDIUM", "HIGH"], default="LOWER", help="Define the quality setting for the video, higher quality means bigger file size but might " "not be noticeable.", ) advancedencoding_group.add_argument( "--compression", required=False, choices=[ "ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow", ], default="medium", help="Speed to optimize video. Faster speed results in a bigger file. This does not impact the quality of " "the video, just how much time is used to compress it.", ) advancedencoding_group.add_argument( "--fps", required=False, type=int, default=24, help="Frames per second for resulting video. Tesla records at about 33fps hence going higher wouldn't do " "much as frames would just be duplicated. Default is 24fps which is the standard for movies and TV shows", ) if internal_ffmpeg: advancedencoding_group.add_argument( "--ffmpeg", required=False, type=str, help="Full path and filename for alternative " "ffmpeg.", ) else: advancedencoding_group.add_argument( "--ffmpeg", required=False, type=str, default=ffmpeg_default, help="Path and filename for ffmpeg. Specify if ffmpeg is not within path.", ) advancedencoding_group.add_argument( "--encoding", required=False, choices=["x264", "x265"], default=argparse.SUPPRESS, help="R|Encoding to use for video creation.\n" " x264: standard encoding, can be viewed on most devices but results in bigger file.\n" " x265: newer encoding standard but not all devices support this yet.\n", ) advancedencoding_group.add_argument( "--enc", required=False, type=str, default=argparse.SUPPRESS, help="R|Provide a custom encoder for video creation. Cannot be used in combination with --encoding.\n" "Note: when using this option the --gpu option is ignored. To use GPU hardware acceleration specify an " "encoding that provides this.", ) update_check_group = parser.add_argument_group( title="Update Check", description="Check for updates" ) update_check_group.add_argument( "--check_for_update", dest="check_for_updates", action="store_true", help="Check for update and exit.", ) update_check_group.add_argument( "--no-check_for_update", dest="no_check_for_updates", action="store_true", help="A check for new updates is performed every time. With this parameter that can be disabled", ) update_check_group.add_argument( "--include_test", dest="include_beta", action="store_true", help="Include test (beta) releases when checking for updates.", ) args = parser.parse_args() logging.basicConfig( level=loglevels[args.loglevel], format="%(asctime)s:%(levelname)s:\t%(name)s\t%(message)s", ) _LOGGER.debug(f"Arguments : {args}") # Check that any mutual exclusive items are not both provided. if "speed_up" in args and "slow_down" in args: print( "Option --speed_up and option --slow_down cannot be used together, only use one of them." ) return 1 if "enc" in args and "encoding" in args: print( "Option --enc and option --encoding cannot be used together, only use one of them." ) return 1 if not args.no_check_for_updates or args.check_for_updates: release_info = check_latest_release(args.include_beta) if release_info is not None: new_version = False if release_info.get("tag_name") is not None: github_version = release_info.get("tag_name").split(".") if len(github_version) == 3: # Release tags normally start with v. If that is the case # then strip the v. try: major_version = int(github_version[0]) except ValueError: major_version = int(github_version[0][1:]) minor_version = int(github_version[1]) if release_info.get("prerelease"): # Drafts will have b and then beta number. patch_version = int(github_version[2].split("b")[0]) beta_version = int(github_version[2].split("b")[1]) else: patch_version = int(github_version[2]) beta_version = -1 if major_version == VERSION["major"]: if minor_version == VERSION["minor"]: if patch_version == VERSION["patch"]: if beta_version > VERSION["beta"] or ( beta_version == -1 and VERSION["beta"] != -1 ): new_version = True elif patch_version > VERSION["patch"]: new_version = True elif minor_version > VERSION["minor"]: new_version = True elif major_version > VERSION["major"]: new_version = True if new_version: beta = "" if release_info.get("prerelease"): beta = "beta " release_notes = "" if not args.check_for_updates: if args.system_notification: notify( "TeslaCam", "Update available", "New {beta}release {release} is available. You are " "on version {version}".format( beta=beta, release=release_info.get("tag_name"), version=VERSION_STR, ), ) release_notes = ( "Use --check_for_update to get latest " "release notes." ) print( "New {beta}release {release} is available for download " "({url}). You are currently on {version}. {rel_note}".format( beta=beta, release=release_info.get("tag_name"), url=release_info.get("html_url"), version=VERSION_STR, rel_note=release_notes, ) ) if args.check_for_updates: print( "You can download the new release from: {url}".format( url=release_info.get("html_url") ) ) print( "Release Notes:\n {release_notes}".format( release_notes=release_info.get("body") ) ) return else: if args.check_for_updates: print( "{version} is the latest release available.".format( version=VERSION_STR ) ) return else: print("Did not retrieve latest version info.") ffmpeg = ffmpeg_default if getattr(args, "ffmpeg", None) is None else args.ffmpeg if which(ffmpeg) is None: print( f"ffmpeg is a requirement, unable to find {ffmpeg} executable. Please ensure it exist and is located" f"within PATH environment or provide full path using parameter --ffmpeg." ) if args.layout == "PERSPECTIVE": layout_settings = FullScreen() layout_settings.perspective = True else: if args.layout == "WIDESCREEN": layout_settings = WideScreen() elif args.layout == "FULLSCREEN": layout_settings = FullScreen() elif args.layout == "CROSS": layout_settings = Cross() elif args.layout == "DIAMOND": layout_settings = Diamond() else: layout_settings = FullScreen() layout_settings.perspective = args.perspective layout_settings.cameras("Front").include = not args.no_front layout_settings.cameras("Left").include = not args.no_left layout_settings.cameras("Right").include = not args.no_right layout_settings.cameras("Rear").include = not args.no_rear # Check if either rear or mirror argument has been provided. # If front camera then default to mirror, if no front camera then default to rear. side_camera_as_mirror = ( layout_settings.cameras("Front").include if args.rear_or_mirror is None else args.rear_or_mirror ) mirror_sides = ", hflip" if side_camera_as_mirror else "" # For scale first set the main clip one if provided, this than allows camera specific ones to override for # that camera. scaling = parser.args_to_dict(args.clip_scale, "scale") main_scale = search_dict(None, "camera", scaling) if main_scale is not None: layout_settings.scale = main_scale.get("scale", layout_settings.scale) for scale in scaling: if scale.get("camera", "").lower() in ["front", "left", "right", "rear"]: camera_scale = scale.get("scale") if camera_scale is not None: layout_settings.cameras( scale["camera"].lower().capitalize() ).scale = camera_scale layout_settings.font.halign = ( args.halign if args.halign is not None else layout_settings.font.halign ) layout_settings.font.valign = ( args.valign if args.valign is not None else layout_settings.font.valign ) # Determine if left and right cameras should be swapped or not. # No more arguments related to cameras (i.e .scale, include or not) can be processed from now on. # Up till now Left means left camera and Right means Right camera. # From this point forward Left can mean Right camera if we're swapping output. layout_settings.swap_left_right = ( not side_camera_as_mirror if args.swap_leftright is None else args.swap_leftright ) layout_settings.swap_front_rear = args.swap_frontrear layout_settings.font.font = args.font layout_settings.font.color = args.fontcolor if args.fontsize is not None and args.fontsize > 0: layout_settings.font.size = args.fontsize black_base = "color=duration={duration}:" black_size = f"s={{width}}x{{height}}:c={args.background}, fps={args.fps} " ffmpeg_base = ( black_base + black_size.format( width=layout_settings.video_width, height=layout_settings.video_height ) + "[base]" ) ffmpeg_black_video = ";" + black_base + black_size input_clip = "base" ffmpeg_video_position = "" ffmpeg_left_camera = "" camera = "Left" if layout_settings.cameras(camera).include: ffmpeg_left_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {mirror}{options}" " [left]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][left] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [left1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "left1" ffmpeg_front_camera = "" camera = "Front" if layout_settings.cameras(camera).include: ffmpeg_front_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {options}" " [front]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][front] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [front1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "front1" ffmpeg_right_camera = "" camera = "Right" if layout_settings.cameras(camera).include: ffmpeg_right_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {mirror}{options}" " [right]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][right] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [right1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "right1" ffmpeg_rear_camera = "" camera = "Rear" if layout_settings.cameras(camera).include: ffmpeg_rear_camera = ( "setpts=PTS-STARTPTS, " # "crop=512:798:225:26, " "scale={clip_width}x{clip_height} {mirror}{options}" " [rear]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][rear] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [rear1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "rear1" filter_counter = 0 filter_string = ";[{input_clip}] {filter} [tmp{filter_counter}]" ffmpeg_timestamp = "" if not args.no_timestamp: if layout_settings.font.font is None: print( f"Unable to get a font file for platform {sys.platform}. Please provide valid font file using " f"--font or disable timestamp using --no-timestamp." ) return # noinspection PyPep8 temp_font_file = ( f"c:\{layout_settings.font.font}" if sys.platform == "win32" else layout_settings.font.font ) if not os.path.isfile(temp_font_file): print( f"Font file {temp_font_file} does not exist. Provide a valid font file using --font or" f" disable timestamp using --no-timestamp" ) if sys.platform == "linux": print( "You can also install the fonts using for example: apt-get install ttf-freefont" ) return # noinspection PyPep8,PyPep8,PyPep8 ffmpeg_timestamp = ( ffmpeg_timestamp + f"drawtext=fontfile={layout_settings.font.font}:" f"fontcolor={layout_settings.font.color}:fontsize={layout_settings.font.size}:" "borderw=2:bordercolor=black@1.0:" f"x={layout_settings.font.halign}:y={layout_settings.font.valign}:" "text='%{{pts\:localtime\:{epoch_time}\:%x %X}}'" ) ffmpeg_timestamp = filter_string.format( input_clip=input_clip, filter=ffmpeg_timestamp, filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 speed = args.slow_down if "slow_down" in args else "" speed = round(1 / args.speed_up, 4) if "speed_up" in args else speed ffmpeg_speed = "" if speed != "": ffmpeg_speed = filter_string.format( input_clip=input_clip, filter=f"setpts={speed}*PTS", filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 ffmpeg_motiononly = "" if args.motion_only: ffmpeg_motiononly = filter_string.format( input_clip=input_clip, filter=f"mpdecimate=hi=64*48, setpts=N/FRAME_RATE/TB", filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 ffmpeg_params = ["-preset", args.compression, "-crf", MOVIE_QUALITY[args.quality]] use_gpu = not args.gpu if sys.platform == "darwin" else args.gpu video_encoding = [] if not "enc" in args: encoding = args.encoding if "encoding" in args else "x264" # GPU acceleration enabled if use_gpu: print("GPU acceleration is enabled") if sys.platform == "darwin": video_encoding = video_encoding + ["-allow_sw", "1"] encoding = encoding + "_mac" else: if args.gpu_type is None: print( "Parameter --gpu_type is mandatory when parameter --use_gpu is used." ) return encoding = encoding + "_" + args.gpu_type bit_rate = str(int(10000 * layout_settings.scale)) + "K" video_encoding = video_encoding + ["-b:v", bit_rate] video_encoding = video_encoding + ["-c:v", MOVIE_ENCODING[encoding]] else: video_encoding = video_encoding + ["-c:v", args.enc] ffmpeg_params = ffmpeg_params + video_encoding # Set metadata ffmpeg_params = ffmpeg_params + [ "-metadata", f"description=Created by tesla_dashcam {VERSION_STR}", ] # Determine the target folder and filename. # If no extension then assume it is a folder. if ( os.path.splitext(args.output)[1] is not None and os.path.splitext(args.output)[1] != "" ): target_folder, target_filename = os.path.split(args.output) if target_folder is None or target_folder == "": # If nothing in target_filename then no folder was given, # setting default movie folder target_folder = movie_folder target_filename = args.output else: # Folder only provided. target_folder = args.output target_filename = None # Convert target folder to absolute path if relative path has been provided. target_folder = os.path.abspath(target_folder) # Ensure folder if not already exist and if not can be created if not make_folder("--output", target_folder): return temp_folder = args.temp_dir if temp_folder is not None: # Convert temp folder to absolute path if relative path has been provided temp_folder = os.path.abspath(args.temp_dir) if not make_folder("--temp_dir", temp_folder): return # Set the run type based on arguments. runtype = "RUN" if args.monitor: runtype = "MONITOR" elif args.monitor_once: runtype = "MONITOR_ONCE" monitor_file = args.monitor_trigger # If no source provided then set to MONITOR_ONCE and we're only going to # take SavedClips and SentryClips source_list = args.source if not source_list: source_list = ["SavedClips", "SentryClips"] if runtype == "RUN": runtype = "MONITOR_ONCE" start_timestamp = None if args.start_timestamp is not None: start_timestamp = isoparse(args.start_timestamp) if start_timestamp.tzinfo is None: start_timestamp = start_timestamp.astimezone(get_localzone()) end_timestamp = None if args.end_timestamp is not None: end_timestamp = isoparse(args.end_timestamp) if end_timestamp.tzinfo is None: end_timestamp = end_timestamp.astimezone(get_localzone()) start_offset = abs(args.start_offset) if args.start_offset is not None else 0 end_offset = abs(args.end_offset) if args.end_offset is not None else 0 video_settings = { "source_folder": source_list, "output": args.output, "target_folder": target_folder, "target_filename": target_filename, "temp_dir": temp_folder, "run_type": runtype, "merge_subdirs": args.merge_subdirs, "chapter_offset": args.chapter_offset, "movie_filename": None, "keep_intermediate": args.keep_intermediate, "notification": args.system_notification, "movie_layout": args.layout, "movie_speed": speed, "video_encoding": video_encoding, "movie_encoding": args.encoding if "encoding" in args else "x264", "movie_compression": args.compression, "movie_quality": args.quality, "background": ffmpeg_black_video, "ffmpeg_exec": ffmpeg, "base": ffmpeg_base, "video_layout": layout_settings, "clip_positions": ffmpeg_video_position, "timestamp_text": ffmpeg_timestamp, "ffmpeg_speed": ffmpeg_speed, "ffmpeg_motiononly": ffmpeg_motiononly, "movflags_faststart": not args.faststart, "input_clip": input_clip, "other_params": ffmpeg_params, "left_camera": ffmpeg_left_camera, "front_camera": ffmpeg_front_camera, "right_camera": ffmpeg_right_camera, "rear_camera": ffmpeg_rear_camera, "start_timestamp": start_timestamp, "start_offset": start_offset, "end_timestamp": end_timestamp, "end_offset": end_offset, "skip_existing": args.skip_existing, } _LOGGER.debug(f"Video Settings {video_settings}") _LOGGER.debug(f"Layout Settings {layout_settings}") # If we constantly run and monitor for drive added or not. if video_settings["run_type"] in ["MONITOR", "MONITOR_ONCE"]: video_settings.update({"skip_existing": True}) trigger_exist = False if monitor_file is None: print("Monitoring for TeslaCam Drive to be inserted. Press CTRL-C to stop") else: print( "Monitoring for trigger {} to exist. Press CTRL-C to stop".format( monitor_file ) ) while True: try: # Monitoring for disk to be inserted and not for a file. if monitor_file is None: source_folder, source_partition = get_tesladashcam_folder() if source_folder is None: # Nothing found, sleep for 1 minute and check again. if trigger_exist: print("TeslaCam drive has been ejected.") print( "Monitoring for TeslaCam Drive to be inserted. " "Press CTRL-C to stop" ) sleep(MONITOR_SLEEP_TIME) trigger_exist = False continue # As long as TeslaCam drive is still attached we're going to # keep on waiting. if trigger_exist: _LOGGER.debug(f"TeslaCam Drive still attached") sleep(MONITOR_SLEEP_TIME) continue # Got a folder, append what was provided as source unless # . was provided in which case everything is done. source_folder_list = [] for folder in video_settings["source_folder"]: if folder == ".": source_folder_list.append(folder) else: source_folder_list.append( os.path.join(source_folder, folder) ) message = "TeslaCam folder found on {partition}.".format( partition=source_partition ) else: # Wait till trigger file exist (can also be folder). if not os.path.exists(monitor_file): _LOGGER.debug(f"Trigger file {monitor_file} does not exist.") sleep(MONITOR_SLEEP_TIME) trigger_exist = False continue if trigger_exist: sleep(MONITOR_SLEEP_TIME) continue message = "Trigger {} exist.".format(monitor_file) trigger_exist = True # Set monitor path, make sure what was provided is a file first otherwise get path. monitor_path = monitor_file if os.path.isfile(monitor_file): monitor_path, _ = os.path.split(monitor_file) # If . is provided then source folder is path where monitor file exist. source_folder_list = [] for folder in video_settings["source_folder"]: if folder == ".": source_folder_list.append(monitor_path) else: # If source path provided is absolute then use that for source path if os.path.isabs(folder): source_folder_list.append(folder) else: # Path provided is relative, hence based on path of trigger file. source_folder_list.append( os.path.join(monitor_path, folder) ) print(message) if args.system_notification: notify("TeslaCam", "Started", message) if len(source_folder_list) == 1: print(f"Retrieving all files from {source_folder_list[0]}") else: print(f"Retrieving all files from: ") for folder in source_folder_list: print(f" {folder}") folders = get_movie_files( source_folder_list, args.exclude_subdirs, video_settings ) if video_settings["run_type"] == "MONITOR": # We will continue to monitor hence we need to # ensure we always have a unique final movie name. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else os.path.splitext(video_settings["target_filename"])[0] + "_" + datetime.today().strftime("%Y-%m-%d_%H_%M") + os.path.splitext(video_settings["target_filename"])[1] ) video_settings.update({"movie_filename": movie_filename}) else: # Set filename to right now if no filename provided. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else video_settings["target_filename"] ) video_settings.update({"movie_filename": movie_filename}) process_folders(folders, video_settings, args.delete_source) print("Processing of movies has completed.") if args.system_notification: notify( "TeslaCam", "Completed", "Processing of movies has completed." ) # Stop if we're only to monitor once and then exit. if video_settings["run_type"] == "MONITOR_ONCE": if monitor_file is not None: if os.path.isfile(monitor_file): try: os.remove(monitor_file) except OSError as exc: print( "Error trying to remove trigger file {}: {}".format( monitor_file, exc ) ) print("Exiting monitoring as asked process once.") break if monitor_file is None: trigger_exist = True print( "Waiting for TeslaCam Drive to be ejected. Press " "CTRL-C to stop" ) else: if os.path.isfile(monitor_file): try: os.remove(monitor_file) except OSError as exc: print( "Error trying to remove trigger file {}: {}".format( monitor_file, exc ) ) break trigger_exist = False print( "Monitoring for trigger {}. Press CTRL-C to stop".format( monitor_file ) ) else: print( "Waiting for trigger {} to be removed. Press CTRL-C to stop".format( monitor_file ) ) except KeyboardInterrupt: print("Monitoring stopped due to CTRL-C.") break else: folders = get_movie_files( video_settings["source_folder"], args.exclude_subdirs, video_settings ) # Set filename to right now if no filename provided. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else video_settings["target_filename"] ) video_settings.update({"movie_filename": movie_filename}) process_folders(folders, video_settings, args.delete_source) if sys.version_info < (3, 7): print( f"Python version 3.7 or higher is required, you have: {sys.version}. Please update your Python version." ) sys.exit(1) if __name__ == "__main__": sys.exit(main())

""" Merges the 3 Tesla Dashcam and Sentry camera video files into 1 video. If then further concatenates the files together to make 1 movie. """ import argparse import logging import os import sys from datetime import datetime, timedelta, timezone from fnmatch import fnmatch from glob import glob from pathlib import Path from re import search from shlex import split as shlex_split from shutil import which from subprocess import CalledProcessError, run from tempfile import mkstemp from time import sleep, time as timestamp from typing import List, Optional import requests from dateutil.parser import isoparse from psutil import disk_partitions from tzlocal import get_localzone _LOGGER = logging.getLogger(__name__) # TODO: Move everything into classes and separate files. For example, # update class, font class (for timestamp), folder class, clip class ( # combining front, left, and right info), file class (for individual file). # Clip class would then have to merge the camera clips, folder class would # have to concatenate the merged clips. Settings class to take in all settings # TODO: Create kind of logger or output classes for output. That then allows # different ones to be created based on where it should go to (stdout, # log file, ...). VERSION = {"major": 0, "minor": 1, "patch": 16, "beta": -1} VERSION_STR = "v{major}.{minor}.{patch}".format( major=VERSION["major"], minor=VERSION["minor"], patch=VERSION["patch"] ) if VERSION["beta"] > -1: VERSION_STR = VERSION_STR + "b{beta}".format(beta=VERSION["beta"]) MONITOR_SLEEP_TIME = 5 GITHUB = { "URL": "https://api.github.com", "owner": "ehendrix23", "repo": "tesla_dashcam", } FFMPEG = { "darwin": "ffmpeg", "win32": "ffmpeg.exe", "cygwin": "ffmpeg", "linux": "ffmpeg", "freebsd11": "ffmpeg", } # noinspection PyPep8 MOVIE_HOMEDIR = { "darwin": "Movies/Tesla_Dashcam", "win32": "Videos\Tesla_Dashcam", "cygwin": "Videos/Tesla_Dashcam", "linux": "Videos/Tesla_Dashcam", "freebsd11": "Videos/Tesla_Dashcam", } DEFAULT_CLIP_HEIGHT = 960 DEFAULT_CLIP_WIDTH = 1280 MOVIE_QUALITY = { "HIGH": "18", "MEDIUM": "20", "LOW": "23", "LOWER": "28", "LOWEST": "33", } MOVIE_ENCODING = { "x264": "libx264", "x264_nvidia": "h264_nvenc", "x264_mac": "h264_videotoolbox", "x264_intel": "h264_qsv", "x264_RPi": "h264_omx", "x265": "libx265", "x265_nvidia": "hevc_nvenc", "x265_mac": "hevc_videotoolbox", "x265_intel": "hevc_qsv", "x265_RPi": "h265", } DEFAULT_FONT = { "darwin": "/Library/Fonts/Arial Unicode.ttf", "win32": "/Windows/Fonts/arial.ttf", "cygwin": "/cygdrive/c/Windows/Fonts/arial.ttf", "linux": "/usr/share/fonts/truetype/freefont/FreeSans.ttf", "freebsd11": "/usr/share/local/fonts/freefont-ttf/FreeSans.ttf", } HALIGN = {"LEFT": "10", "CENTER": "(w/2-text_w/2)", "RIGHT": "(w-text_w)"} VALIGN = {"TOP": "10", "MIDDLE": "(h/2-(text_h/2))", "BOTTOM": "(h-(text_h*2))"} TOASTER_INSTANCE = None class Font(object): """ Font Class """ def __init__(self, layout, font=None, size=None, color=None): self._layout = layout self._font = font self._size = size self._color = color self._halign = None self._valign = None self._xpos = None self._ypos = None @property def font(self): return self._font @font.setter def font(self, value): self._font = value @property def size(self): if hasattr(self._layout, "_font_size"): return getattr(self._layout, "_font_size")() return ( int(max(16, 16 * self._layout.scale)) if self._size is None else self._size ) @size.setter def size(self, value): self._size = value @property def color(self): return self._color @color.setter def color(self, value): self._color = value @property def halign(self): if hasattr(self._layout, "_font_halign"): return getattr(self._layout, "_font_halign")() return HALIGN.get(self._halign, self._halign) @halign.setter def halign(self, value): self._halign = value @property def valign(self): if hasattr(self._layout, "_font_valign"): return getattr(self._layout, "_font_valign")() return VALIGN.get(self._valign, self._valign) @valign.setter def valign(self, value): self._valign = value @property def xpos(self): return self._xpos @xpos.setter def xpos(self, value): self._xpos = value @property def ypos(self): return self._ypos @ypos.setter def ypos(self, value): self._ypos = value class Camera(object): """ Camera Class """ def __init__(self, layout, camera): self._layout = layout self._camera = camera self._include = True self._width = 1280 self._height = 960 self._xpos = 0 self._ypos = 0 self._scale = 0 self._options = "" @property def camera(self): return self._camera @camera.setter def camera(self, value): self._camera = value @property def include(self): return self._include @include.setter def include(self, value): self._include = value @property def width(self): return ( getattr(self._layout, "_" + self._camera + "_width")() if hasattr(self._layout, "_" + self._camera + "_width") else int(self._width * self.scale * self.include) ) @width.setter def width(self, value): self._width = value @property def height(self): return ( getattr(self._layout, "_" + self._camera + "_height")() if hasattr(self._layout, "_" + self._camera + "_height") else int(self._height * self.scale * self.include) ) @height.setter def height(self, value): self._height = value @property def xpos(self): if hasattr(self._layout, "_" + self._camera + "_xpos"): return getattr(self._layout, "_" + self._camera + "_xpos")() * self.include return self._xpos * self.include @xpos.setter def xpos(self, value): self._xpos = value @property def ypos(self): if hasattr(self._layout, "_" + self._camera + "_ypos"): return getattr(self._layout, "_" + self._camera + "_ypos")() * self.include return self._ypos * self.include @ypos.setter def ypos(self, value): self._ypos = value @property def scale(self): return self._scale @scale.setter def scale(self, value): if value is None: self._scale = None elif len(str(value).split("x")) == 1: # Scale provided is a multiplier self._scale = float(str(value).split("x")[0]) else: # Scale is a resolution. self.width = int(str(value).split("x")[0]) self.height = int(str(value).split("x")[1]) self._scale = 1 @property def options(self): return self._options @options.setter def options(self, value): self._options = value class MovieLayout(object): """ Main Layout class """ def __init__(self): self._cameras = { "Front": Camera(layout=self, camera="front"), "Left": Camera(layout=self, camera="left"), "Right": Camera(layout=self, camera="right"), "Rear": Camera(layout=self, camera="rear"), } self._font = Font(layout=self) self._swap_left_right = False self._swap_front_rear = False self._perspective = False self._font.halign = "CENTER" self._font.valign = "BOTTOM" def cameras(self, camera): return self._cameras.get(camera, self._cameras) @property def font(self): return self._font @font.setter def font(self, value): self._font = value @property def swap_left_right(self): return self._swap_left_right @swap_left_right.setter def swap_left_right(self, value): self._swap_left_right = value @property def swap_front_rear(self): return self._swap_front_rear @swap_front_rear.setter def swap_front_rear(self, value): self._swap_front_rear = value @property def perspective(self): return self._perspective @perspective.setter def perspective(self, new_perspective): self._perspective = new_perspective if self._perspective: self.cameras("Left").options = ( ", pad=iw+4:3/2*ih:-1:ih/8:0x00000000, " "perspective=x0=0:y0=1*H/5:x1=W:y1=-3/44*H:" "x2=0:y2=6*H/5:x3=7/8*W:y3=5*H/6:sense=destination" ) self.cameras("Right").options = ( ", pad=iw+4:3/2*ih:-1:ih/8:0x00000000," "perspective=x0=0:y1=1*H/5:x1=W:y0=-3/44*H:" "x2=1/8*W:y3=6*H/5:x3=W:y2=5*H/6:sense=destination" ) else: self.cameras("Left").options = "" self.cameras("Right").options = "" @property def scale(self): # Return scale of new video based on 1280x960 video = scale:1 return (self.video_height * self.video_width) / (1280 * 960) @scale.setter def scale(self, scale): self.cameras("Front").scale = scale self.cameras("Left").scale = scale self.cameras("Right").scale = scale self.cameras("Rear").scale = scale @property def video_width(self): return int( max( self.cameras("Front").xpos + self.cameras("Front").width, self.cameras("Left").xpos + self.cameras("Left").width, self.cameras("Right").xpos + self.cameras("Right").width, self.cameras("Rear").xpos + self.cameras("Rear").width, ) ) @property def video_height(self): perspective_adjustement = 3 / 2 if self.perspective else 1 return int( max( self.cameras("Front").ypos + self.cameras("Front").height, perspective_adjustement * self.cameras("Left").ypos + self.cameras("Left").height, perspective_adjustement * self.cameras("Right").ypos + self.cameras("Right").height, self.cameras("Rear").ypos + self.cameras("Rear").height, ) ) @property def center_xpos(self): return int(self.video_width / 2) @property def center_ypos(self): return int(self.video_height / 2) class FullScreen(MovieLayout): """ FullScreen Movie Layout [FRONT_CAMERA] [LEFT_CAMERA][REAR_CAMERA][RIGHT_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 @property def video_width(self): return int( max( self.cameras("Front").width, self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width, ) ) @property def video_height(self): perspective_adjustement = 3 / 2 if self.perspective else 1 return int( self.cameras("Front").height + max( perspective_adjustement * self.cameras("Left").height, self.cameras("Rear").height, perspective_adjustement * self.cameras("Right").height, ) ) def _front_height(self): # For height keep same ratio of 4/3 return int(self.cameras("Front").width / 4 * 3) def _front_xpos(self): # Make sure that front is placed in the middle return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Front").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width, ) * self.cameras("Front").include ) def _left_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ), ) * self.cameras("Left").include ) def _left_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Left").include def _rear_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width, ) * self.cameras("Rear").include ) def _rear_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Rear").include def _right_xpos(self): return ( max( 0, self.center_xpos - int( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) / 2 ) + self.cameras("Left").width + self.cameras("Rear").width, ) * self.cameras("Right").include ) def _right_ypos(self): return ( self.cameras("Front").ypos + self.cameras("Front").height ) * self.cameras("Right").include # noinspection PyProtectedMember class WideScreen(FullScreen): """ WideScreen Movie Layout [ FRONT_CAMERA ] [LEFT_CAMERA][REAR_CAMERA][RIGHT_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 # Set front scale to None so we know if it was overriden or not. self.cameras("Front").scale = None # Only front_width has to be adjusted as by default width would be left+rear+right instead of normal scale. def _front_width(self): return ( ( self.cameras("Left").width + self.cameras("Rear").width + self.cameras("Right").width ) * self.cameras("Front").include if self.cameras("Front").scale is None else int( ( self.cameras("Front")._width * self.cameras("Front").scale * self.cameras("Front").include ) ) ) class Cross(FullScreen): """ Cross Movie Layout [FRONT_CAMERA] [LEFT_CAMERA][RIGHT_CAMERA] [REAR_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 @property def video_width(self): return max( self.cameras("Front").width, self.cameras("Left").width + self.cameras("Right").width, self.cameras("Rear").width, ) @property def video_height(self): if self.perspective: height = int( max( 3 / 2 * self.cameras("Left").height, 3 / 2 * self.cameras("Right").height, ) ) if ( self.cameras("Left").include and self.cameras("Left").scale >= self.cameras("Rear").scale and self.cameras("Right").include and self.cameras("Right").scale >= self.cameras("Rear").scale and self.cameras("Rear").include ): height = int(height / 3 * 2) height += self.cameras("Rear").height else: height = ( max(self.cameras("Left").height, self.cameras("Right").height) + self.cameras("Rear").height ) return int(height + self.cameras("Front").height) def _front_xpos(self): return ( int(max(0, self.center_xpos - (self.cameras("Front").width / 2))) * self.cameras("Front").include ) def _left_xpos(self): return ( max( 0, self.center_xpos - int((self.cameras("Left").width + self.cameras("Right").width) / 2), ) * self.cameras("Left").include ) def _left_ypos(self): return ( self.cameras("Front").height + int( ( max(self.cameras("Left").height, self.cameras("Right").height) - self.cameras("Left").height ) / 2 ) ) * self.cameras("Left").include def _right_xpos(self): return ( max( 0, self.center_xpos - int((self.cameras("Left").width + self.cameras("Right").width) / 2) + self.cameras("Left").width, ) * self.cameras("Right").include ) def _right_ypos(self): return ( self.cameras("Front").height + int( ( max(self.cameras("Left").height, self.cameras("Right").height) - self.cameras("Right").height ) / 2 ) ) * self.cameras("Right").include def _rear_xpos(self): return ( int(max(0, self.center_xpos - (self.cameras("Rear").width / 2))) * self.cameras("Rear").include ) def _rear_ypos(self): return int(max(0, self.video_height - self.cameras("Rear").height)) # noinspection PyProtectedMember class Diamond(Cross): """ Diamond Movie Layout [FRONT_CAMERA] [LEFT_CAMERA] [RIGHT_CAMERA] [REAR_CAMERA] """ def __init__(self): super().__init__() self.scale = 1 / 2 self._font.valign = "MIDDLE" def _font_halign(self): if self._font._halign == "CENTER": # Change alignment to left or right if one of the left/right cameras is excluded. if (self.cameras("Left").include and not self.cameras("Right").include) or ( self.cameras("Right").include and not self.cameras("Left").include ): x_pos = int( max( self.cameras("Front").xpos + self.cameras("Front").width / 2, self.cameras("Rear").xpos + self.cameras("Rear").width / 2, ) ) return f"({x_pos} - text_w / 2)" return HALIGN.get(self._font._halign, self._font._halign) def _font_valign(self): if self._font._valign == "MIDDLE": if self.cameras("Front").include: return ( f'({self.cameras("Front").ypos + self.cameras("Front").height} + 5)' ) elif self.cameras("Rear").include: return f'({self.cameras("Rear").ypos} - 5 - text_h)' return VALIGN.get(self._font._valign, self._font._valign) def _font_size(self): # For this layout the video height has to include font size. But default for calculating # font size is based on video height. # Thus overriding font size to get video height without font size to figure our scaling. if self.font._size is None: scale = ( self._video_height(include_fontsize=False) * self.video_width / (1280 * 960) ) return int(max(16, 16 * scale)) else: return self.font.size @property def video_width(self): return ( max(self.cameras("Front").width, self.cameras("Rear").width) + self.cameras("Left").width + self.cameras("Right").width ) def _video_height(self, include_fontsize=True): perspective = 3 / 2 if self.perspective else 1 fontsize = self.font.size if include_fontsize else 0 return int( max( perspective * max(self.cameras("Left").height, self.cameras("Right").height), self.cameras("Front").height + self.cameras("Rear").height + fontsize, ) ) @property def video_height(self): return self._video_height(include_fontsize=True) def _front_xpos(self): return ( self.cameras("Left").width + int( ( max(self.cameras("Front").width, self.cameras("Rear").width) - self.cameras("Front").width ) / 2 ) ) * self.cameras("Front").include def _left_xpos(self): return 0 def _left_ypos(self): return max(0, self.center_ypos - int(self.cameras("Left").height / 2)) def _right_xpos(self): return max( self.cameras("Front").xpos + self.cameras("Front").width, self.cameras("Rear").xpos + self.cameras("Rear").width, ) def _right_ypos(self): return max(0, self.center_ypos - int(self.cameras("Right").height / 2)) def _rear_xpos(self): return ( self.cameras("Left").width + int( ( max(self.cameras("Front").width, self.cameras("Rear").width) - self.cameras("Rear").width ) / 2 ) ) * self.cameras("Rear").include class MyArgumentParser(argparse.ArgumentParser): def convert_arg_line_to_args(self, arg_line): # Remove comments. return shlex_split(arg_line, comments=True) def args_to_dict(self, arguments, default): argument_list = [] if arguments is None: return argument_list for argument in arguments: argument_dict = {} for argument_value in argument: if "=" in argument_value: key = argument_value.split("=")[0].lower() value = ( argument_value.split("=")[1].strip() if argument_value.split("=")[1].strip() != "" else None ) else: key = default value = argument_value argument_dict.update({key: value}) argument_list.append(argument_dict) return argument_list # noinspection PyCallByClass,PyProtectedMember class SmartFormatter(argparse.HelpFormatter): """ Formatter for argument help. """ def _split_lines(self, text, width): """ Provide raw output allowing for prettier help output """ if text.startswith("R|"): return text[2:].splitlines() # this is the RawTextHelpFormatter._split_lines return argparse.HelpFormatter._split_lines(self, text, width) def _get_help_string(self, action): """ Call default help string """ return argparse.ArgumentDefaultsHelpFormatter._get_help_string(self, action) def search_dict( match_value: object = None, key: str = None, search_list: List[dict] = None ) -> Optional[dict]: """ Returns the 1st element in a list containing dictionaries where the value of key provided matches the value provided. :param match_value: value to match upon (search for) :type match_value: object :param key: dictionary key to use for the match :type key: str :param search_list: List containing dictionary objects in which to search :type search_list: List[dict] :return: Dictionary object that matches :rtype: dict """ if key is None or search_list is None: return None if match_value is None: return next( (element for element in search_list if element.get(key) is None), None ) return next( (element for element in search_list if element.get(key) == match_value), None ) def check_latest_release(include_beta): """ Checks GitHub for latest release """ url = "{url}/repos/{owner}/{repo}/releases".format( url=GITHUB["URL"], owner=GITHUB["owner"], repo=GITHUB["repo"] ) if not include_beta: url = url + "/latest" try: releases = requests.get(url) except requests.exceptions.RequestException as exc: print("Unable to check for latest release: {exc}".format(exc=exc)) return None release_data = releases.json() # If we include betas then we would have received a list, thus get 1st # element as that is the latest release. if include_beta: release_data = release_data[0] return release_data def get_tesladashcam_folder(): """ Check if there is a drive mounted with the Tesla DashCam folder.""" for partition in disk_partitions(all=False): if "cdrom" in partition.opts or partition.fstype == "": continue teslacamfolder = os.path.join(partition.mountpoint, "TeslaCam") if os.path.isdir(teslacamfolder): _LOGGER.debug(f"Folder TeslaCam found on partition {partition.mountpoint}.") return teslacamfolder, partition.mountpoint _LOGGER.debug(f"No TeslaCam folder on partition {partition.mountpoint}.") return None, None def get_movie_files(source_folder, exclude_subdirs, video_settings): """ Find all the clip files within folder (and subfolder if requested) """ folder_list = {} total_folders = 0 for pathname in source_folder: if os.path.isdir(pathname): isfile = False if exclude_subdirs: # Retrieve all the video files in current path: search_path = os.path.join(pathname, "*.mp4") files = [ filename for filename in glob(search_path) if not os.path.basename(filename).startswith(".") ] print(f"Discovered {len(files)} files in {pathname}") else: # Search all sub folder. files = [] for folder, _, filenames in os.walk(pathname, followlinks=True): total_folders = total_folders + 1 for filename in ( filename for filename in filenames if not os.path.basename(filename).startswith(".") and fnmatch(filename, "*.mp4") ): files.append(os.path.join(folder, filename)) print( f"Discovered {total_folders} folders containing total of {len(files)} files in {pathname}" ) else: files = [pathname] isfile = True # Now go through and get timestamps etc.. for file in sorted(files): # Strip path so that we just have the filename. movie_folder, movie_filename = os.path.split(file) # And now get the timestamp of the filename. filename_timestamp = movie_filename.rsplit("-", 1)[0] movie_file_list = folder_list.get(movie_folder, {}) # Check if we already processed this timestamp. if movie_file_list.get(filename_timestamp) is not None: # Already processed this timestamp, moving on. continue _LOGGER.debug( f"Checking camera files in folder {movie_folder} with timestamp {filename_timestamp}" ) video_info = { "front_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "left_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "right_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, "rear_camera": { "filename": None, "duration": None, "timestamp": None, "include": False, }, } front_filename = str(filename_timestamp) + "-front.mp4" front_path = os.path.join(movie_folder, front_filename) left_filename = str(filename_timestamp) + "-left_repeater.mp4" left_path = os.path.join(movie_folder, left_filename) right_filename = str(filename_timestamp) + "-right_repeater.mp4" right_path = os.path.join(movie_folder, right_filename) rear_filename = str(filename_timestamp) + "-back.mp4" rear_path = os.path.join(movie_folder, rear_filename) # Get meta data for each video to determine creation time and duration. metadata = get_metadata( video_settings["ffmpeg_exec"], [front_path, left_path, right_path, rear_path], ) # Move on to next one if nothing received. if not metadata: continue # Get the longest duration: duration = 0 video_timestamp = None for item in metadata: _, filename = os.path.split(item["filename"]) if filename == front_filename: camera = "front_camera" video_filename = front_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Front").include else False ) elif filename == left_filename: camera = "left_camera" video_filename = left_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Left").include else False ) elif filename == right_filename: camera = "right_camera" video_filename = right_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Right").include else False ) elif filename == rear_filename: camera = "rear_camera" video_filename = rear_filename include_clip = ( item["include"] if video_settings["video_layout"].cameras("Rear").include else False ) else: continue # Store duration and timestamp video_info[camera].update( filename=video_filename, duration=item["duration"], timestamp=item["timestamp"], include=include_clip, ) # Only check duration and timestamp if this file is not corrupt and if we include this camera # in our output. if include_clip: # Figure out which one has the longest duration duration = ( item["duration"] if item["duration"] > duration else duration ) # Figure out starting timestamp if video_timestamp is None: video_timestamp = item["timestamp"] else: video_timestamp = ( item["timestamp"] if item["timestamp"] < video_timestamp else video_timestamp ) if video_timestamp is None: # Firmware version 2019.16 changed filename timestamp format. if len(filename_timestamp) == 16: # This is for before version 2019.16 video_timestamp = datetime.strptime( filename_timestamp, "%Y-%m-%d_%H-%M" ) video_timestamp = video_timestamp.astimezone(get_localzone()) else: # This is for version 2019.16 and later video_timestamp = datetime.strptime( filename_timestamp, "%Y-%m-%d_%H-%M-%S" ) video_timestamp = video_timestamp.astimezone(timezone.utc) movie_info = { "movie_folder": movie_folder, "timestamp": video_timestamp, "duration": duration, "video_info": video_info, "file_only": isfile, } movie_file_list.update({filename_timestamp: movie_info}) folder_list.update({movie_folder: movie_file_list}) return folder_list def get_metadata(ffmpeg, filenames): """ Retrieve the meta data for the clip (i.e. timestamp, duration) """ # Get meta data for each video to determine creation time and duration. ffmpeg_command = [ffmpeg] metadata = [] for camera_file in filenames: if os.path.isfile(camera_file): ffmpeg_command.append("-i") ffmpeg_command.append(camera_file) metadata.append( { "filename": camera_file, "timestamp": None, "duration": 0, "include": False, } ) else: _LOGGER.debug(f"File {camera_file} does not exist, skipping.") # Don't run ffmpeg if nothing to check for. if not metadata: return metadata ffmpeg_command.append("-hide_banner") command_result = run(ffmpeg_command, capture_output=True, text=True) metadata_iterator = iter(metadata) input_counter = 0 video_timestamp = None wait_for_input_line = True metadata_item = {} for line in command_result.stderr.splitlines(): if search("^Input #", line) is not None: # If filename was not yet appended then it means it is a corrupt file, in that case just add to list for # but identify not to include for processing metadata_item = next(metadata_iterator) input_counter += 1 video_timestamp = None wait_for_input_line = False continue if wait_for_input_line: continue if search("^ *creation_time ", line) is not None: line_split = line.split(":", 1) video_timestamp = datetime.strptime( line_split[1].strip(), "%Y-%m-%dT%H:%M:%S.%f%z" ) continue if search("^ *Duration: ", line) is not None: line_split = line.split(",") line_split = line_split[0].split(":", 1) duration_list = line_split[1].split(":") duration = ( int(duration_list[0]) * 60 * 60 + int(duration_list[1]) * 60 + int(duration_list[2].split(".")[0]) + (float(duration_list[2].split(".")[1]) / 100) ) # File will only be processed if duration is greater then 0 include = duration > 0 metadata_item.update( {"timestamp": video_timestamp, "duration": duration, "include": include} ) wait_for_input_line = True return metadata def create_intermediate_movie( filename_timestamp, video, folder_timestamps, video_settings, clip_number, total_clips, ): """ Create intermediate movie files. This is the merging of the 3 camera video files into 1 video file. """ # We first stack (combine the 3 different camera video files into 1 # and then we concatenate. front_camera = ( os.path.join( video["movie_folder"], video["video_info"]["front_camera"]["filename"] ) if ( video["video_info"]["front_camera"]["filename"] is not None and video["video_info"]["front_camera"]["include"] ) else None ) left_camera = ( os.path.join( video["movie_folder"], video["video_info"]["left_camera"]["filename"] ) if ( video["video_info"]["left_camera"]["filename"] is not None and video["video_info"]["left_camera"]["include"] ) else None ) right_camera = ( os.path.join( video["movie_folder"], video["video_info"]["right_camera"]["filename"] ) if ( video["video_info"]["right_camera"]["filename"] is not None and video["video_info"]["right_camera"]["include"] ) else None ) rear_camera = ( os.path.join( video["movie_folder"], video["video_info"]["rear_camera"]["filename"] ) if ( video["video_info"]["rear_camera"]["filename"] is not None and video["video_info"]["rear_camera"]["include"] ) else None ) if ( front_camera is None and left_camera is None and right_camera is None and rear_camera is None ): _LOGGER.debug( f'No front, left, right, and rear camera clip exist for {video["timestamp"]}' ) return None, 0, True if video_settings["video_layout"].swap_left_right: left_camera, right_camera = right_camera, left_camera if video_settings["video_layout"].swap_front_rear: front_camera, rear_camera = rear_camera, front_camera # Determine if this clip is to be included based on potential start and end timestamp/offsets that were provided. # Clip starting time is between the start&end times we're looking for # or Clip end time is between the start&end time we're looking for. # or Starting time is between start&end clip time # or End time is between start&end clip time starting_timestmp = video["timestamp"] ending_timestmp = starting_timestmp + timedelta(seconds=video["duration"]) if not ( folder_timestamps[0] <= starting_timestmp <= folder_timestamps[1] or folder_timestamps[0] <= ending_timestmp <= folder_timestamps[1] or starting_timestmp <= folder_timestamps[0] <= ending_timestmp or starting_timestmp <= folder_timestamps[1] <= ending_timestmp ): # This clip is not in-between the timestamps we want, skip it. _LOGGER.debug( f"Clip timestamp from {starting_timestmp} to {ending_timestmp} not " f"between {folder_timestamps[0]} and {folder_timestamps[1]}" ) return None, 0, True # Determine if we need to do an offset of the starting timestamp starting_offset = 0 ffmpeg_offset_command = [] clip_duration = video["duration"] # This clip falls in between the start and end timestamps to include. # Set offsets if required if video["timestamp"] < folder_timestamps[0]: # Starting timestamp is withing this clip. starting_offset = (folder_timestamps[0] - video["timestamp"]).total_seconds() starting_timestmp = folder_timestamps[0] ffmpeg_offset_command = ["-ss", str(starting_offset)] clip_duration = video["duration"] - starting_offset # Adjust duration if end of clip's timestamp is after ending timestamp we need. if video["timestamp"] + timedelta(seconds=video["duration"]) > folder_timestamps[1]: # Duration has to be cut. clip_duration = ( folder_timestamps[1] - (video["timestamp"] + timedelta(seconds=starting_offset)) ).total_seconds() ffmpeg_offset_command += ["-t", str(clip_duration)] # Confirm if files exist, if not replace with nullsrc input_count = 0 if left_camera is not None and os.path.isfile(left_camera): ffmpeg_left_command = ffmpeg_offset_command + ["-i", left_camera] ffmpeg_left_camera = ";[0:v] " + video_settings["left_camera"] input_count += 1 else: ffmpeg_left_command = [] ffmpeg_left_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Left").width, height=video_settings["video_layout"].cameras("Left").height, ) + "[left]" if video_settings["video_layout"].cameras("Left").include else "" ) if front_camera is not None and os.path.isfile(front_camera): ffmpeg_front_command = ffmpeg_offset_command + ["-i", front_camera] ffmpeg_front_camera = ( ";[" + str(input_count) + ":v] " + video_settings["front_camera"] ) input_count += 1 else: ffmpeg_front_command = [] ffmpeg_front_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Front").width, height=video_settings["video_layout"].cameras("Front").height, ) + "[front]" if video_settings["video_layout"].cameras("Front").include else "" ) if right_camera is not None and os.path.isfile(right_camera): ffmpeg_right_command = ffmpeg_offset_command + ["-i", right_camera] ffmpeg_right_camera = ( ";[" + str(input_count) + ":v] " + video_settings["right_camera"] ) input_count += 1 else: ffmpeg_right_command = [] ffmpeg_right_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Right").width, height=video_settings["video_layout"].cameras("Right").height, ) + "[right]" if video_settings["video_layout"].cameras("Right").include else "" ) if rear_camera is not None and os.path.isfile(rear_camera): ffmpeg_rear_command = ffmpeg_offset_command + ["-i", rear_camera] ffmpeg_rear_camera = ( ";[" + str(input_count) + ":v] " + video_settings["rear_camera"] ) input_count += 1 else: ffmpeg_rear_command = [] ffmpeg_rear_camera = ( video_settings["background"].format( duration=clip_duration, speed=video_settings["movie_speed"], width=video_settings["video_layout"].cameras("Rear").width, height=video_settings["video_layout"].cameras("Rear").height, ) + "[rear]" if video_settings["video_layout"].cameras("Rear").include else "" ) local_timestamp = video["timestamp"].astimezone(get_localzone()) # Check if target video file exist if skip existing. file_already_exist = False if video_settings["skip_existing"]: temp_movie_name = ( os.path.join(video_settings["target_folder"], filename_timestamp) + ".mp4" ) if os.path.isfile(temp_movie_name): file_already_exist = True elif ( not video_settings["keep_intermediate"] and video_settings["temp_dir"] is not None ): temp_movie_name = ( os.path.join(video_settings["temp_dir"], filename_timestamp) + ".mp4" ) if os.path.isfile(temp_movie_name): file_already_exist = True if file_already_exist: print( "\t\tSkipping clip {clip_number}/{total_clips} from {timestamp} " "and {duration} seconds as it already exist.".format( clip_number=clip_number + 1, total_clips=total_clips, timestamp=local_timestamp.strftime("%x %X"), duration=int(clip_duration), ) ) # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [temp_movie_name]) duration = metadata[0]["duration"] if metadata else video["duration"] return temp_movie_name, duration, True else: target_folder = ( video_settings["temp_dir"] if not video_settings["keep_intermediate"] and video_settings["temp_dir"] is not None else video_settings["target_folder"] ) temp_movie_name = os.path.join(target_folder, filename_timestamp) + ".mp4" print( "\t\tProcessing clip {clip_number}/{total_clips} from {timestamp} " "and {duration} seconds long.".format( clip_number=clip_number + 1, total_clips=total_clips, timestamp=local_timestamp.strftime("%x %X"), duration=int(clip_duration), ) ) epoch_timestamp = int(starting_timestmp.timestamp()) ffmpeg_filter = ( video_settings["base"].format( duration=clip_duration, speed=video_settings["movie_speed"] ) + ffmpeg_left_camera + ffmpeg_front_camera + ffmpeg_right_camera + ffmpeg_rear_camera + video_settings["clip_positions"] + video_settings["timestamp_text"].format(epoch_time=epoch_timestamp) + video_settings["ffmpeg_speed"] + video_settings["ffmpeg_motiononly"] ) ffmpeg_command = ( [video_settings["ffmpeg_exec"]] + ["-loglevel", "error"] + ffmpeg_left_command + ffmpeg_front_command + ffmpeg_right_command + ffmpeg_rear_command + ["-filter_complex", ffmpeg_filter] + ["-map", f"[{video_settings['input_clip']}]"] + video_settings["other_params"] ) ffmpeg_command = ffmpeg_command + ["-y", temp_movie_name] _LOGGER.debug(f"FFMPEG Command: {ffmpeg_command}") # Run the command. try: run(ffmpeg_command, capture_output=True, check=True) except CalledProcessError as exc: print( "\t\t\tError trying to create clip for {base_name}. RC: {rc}\n" "\t\t\tCommand: {command}\n" "\t\t\tError: {stderr}\n\n".format( base_name=os.path.join(video["movie_folder"], filename_timestamp), rc=exc.returncode, command=exc.cmd, stderr=exc.stderr, ) ) return None, 0, False # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [temp_movie_name]) duration = metadata[0]["duration"] if metadata else video["duration"] return temp_movie_name, duration, True def create_movie(clips_list, movie_filename, video_settings, chapter_offset): """ Concatenate provided movie files into 1.""" # Just return if there are no clips. if not clips_list: _LOGGER.debug("Clip list is empty") return None, None # Go through the list of clips to create the command and content for chapter meta file. ffmpeg_join_filehandle, ffmpeg_join_filename = mkstemp(suffix=".txt", text=True) total_clips = 0 meta_content = "" meta_start = 0 total_videoduration = 0 chapter_offset = chapter_offset * 1000000000 with os.fdopen(ffmpeg_join_filehandle, "w") as fp: # Loop through the list sorted by video timestamp. for video_clip in sorted( clips_list, key=lambda video: video["video_timestamp"] ): if not os.path.isfile(video_clip["video_filename"]): print( "\t\tFile {} does not exist anymore, skipping.".format( video_clip["video_filename"] ) ) continue # Add this file in our join list. fp.write( "file '" + video_clip["video_filename"] + "'{linesep}".format(linesep=os.linesep) ) total_clips = total_clips + 1 title = video_clip["video_timestamp"].astimezone(get_localzone()) # For duration need to also calculate if video was sped-up or slowed down. video_duration = int(video_clip["video_duration"] * 1000000000) total_videoduration += video_duration chapter_start = meta_start if video_duration > abs(chapter_offset): if chapter_offset < 0: chapter_start = meta_start + video_duration + chapter_offset elif chapter_offset > 0: chapter_start = chapter_start + chapter_offset # We need to add an initial chapter if our "1st" chapter is not at the beginning of the movie. if total_clips == 1 and chapter_start > 0: meta_content = ( "[CHAPTER]{linesep}" "TIMEBASE=1/1000000000{linesep}" "START={start}{linesep}" "END={end}{linesep}" "title={title}{linesep}".format( linesep=os.linesep, start=0, end=chapter_start - 1, title="Start", ) ) meta_content = ( meta_content + "[CHAPTER]{linesep}" "TIMEBASE=1/1000000000{linesep}" "START={start}{linesep}" "END={end}{linesep}" "title={title}{linesep}".format( linesep=os.linesep, start=chapter_start, end=meta_start + video_duration, title=title.strftime("%x %X"), ) ) meta_start = meta_start + 1 + video_duration if total_clips == 0: print("\t\tError: No valid clips to merge found.") return None, None # Write out the meta data file. meta_content = ";FFMETADATA1" + os.linesep + meta_content ffmpeg_meta_filehandle, ffmpeg_meta_filename = mkstemp(suffix=".txt", text=True) with os.fdopen(ffmpeg_meta_filehandle, "w") as fp: fp.write(meta_content) ffmpeg_params = [ "-f", "concat", "-safe", "0", "-i", ffmpeg_join_filename, "-i", ffmpeg_meta_filename, "-map_metadata", "1", "-map_chapters", "1", ] if video_settings["movflags_faststart"]: ffmpeg_params = ffmpeg_params + ["-movflags", "+faststart"] ffmpeg_params = ffmpeg_params + ["-c", "copy"] ffmpeg_params = ffmpeg_params + [ "-metadata", f"description=Created by tesla_dashcam {VERSION_STR}", ] ffmpeg_command = ( [video_settings["ffmpeg_exec"]] + ["-loglevel", "error"] + ffmpeg_params + ["-y", movie_filename] ) _LOGGER.debug(f"FFMPEG Command: {ffmpeg_command}") try: run(ffmpeg_command, capture_output=True, check=True) except CalledProcessError as exc: print( "\t\tError trying to create movie {base_name}. RC: {rc}\n" "\t\tCommand: {command}\n" "\t\tError: {stderr}\n\n".format( base_name=movie_filename, rc=exc.returncode, command=exc.cmd, stderr=exc.stderr, ) ) movie_filename = None duration = 0 else: # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [movie_filename]) duration = metadata[0]["duration"] if metadata else total_videoduration # Remove temp join file. # noinspection PyBroadException,PyPep8 try: os.remove(ffmpeg_join_filename) except: _LOGGER.debug(f"Failed to remove {ffmpeg_join_filename}") pass # Remove temp join file. # noinspection PyBroadException,PyPep8 try: os.remove(ffmpeg_meta_filename) except: _LOGGER.debug(f"Failed to remove {ffmpeg_meta_filename}") pass return movie_filename, duration def make_folder(parameter, folder): # Create folder if not already existing. if not os.path.isdir(folder): current_path, add_folder = os.path.split(folder) if add_folder == "": current_path, add_folder = os.path.split(current_path) # If path does not exist in which to create folder then exit. if not os.path.isdir(current_path): print( f"Path {current_path} for parameter {parameter} does not exist, please provide a valid path." ) return False try: os.mkdir(folder) except OSError: print( f"Error creating folder {add_folder} at location {current_path} for parameter {parameter}" ) return False return True def delete_intermediate(movie_files): """ Delete the files provided in list """ for file in movie_files: if file is not None: if os.path.isfile(file): try: os.remove(file) except OSError as exc: print("\t\tError trying to remove file {}: {}".format(file, exc)) elif os.path.isdir(file): # This is more specific for Mac but won't hurt on other platforms. if os.path.exists(os.path.join(file, ".DS_Store")): # noinspection PyBroadException,PyPep8 try: os.remove(os.path.join(file, ".DS_Store")) except: _LOGGER.debug(f"Failed to remove .DS_Store from {file}") pass try: os.rmdir(file) except OSError as exc: print("\t\tError trying to remove folder {}: {}".format(file, exc)) def process_folders(folders, video_settings, delete_source): """ Process all clips found within folders. """ start_time = timestamp() total_clips = 0 for folder_number, folder_name in enumerate(sorted(folders)): total_clips = total_clips + len(folders[folder_name]) print( "There are {total_folders} folders with {total_clips} clips to " "process.".format(total_folders=len(folders), total_clips=total_clips) ) # Loop through all the folders. dashcam_clips = [] for folder_number, folder_name in enumerate(sorted(folders)): files = folders[folder_name] # Ensure the clips are sorted based on video timestamp. sorted_video_clips = sorted(files, key=lambda video: files[video]["timestamp"]) # Get the start and ending timestamps, we add duration to # last timestamp to get true ending. first_clip_tmstp = files[sorted_video_clips[0]]["timestamp"] last_clip_tmstp = files[sorted_video_clips[-1]]["timestamp"] + timedelta( seconds=files[sorted_video_clips[-1]]["duration"] ) # Skip this folder if we it does not fall within provided timestamps. if ( video_settings["start_timestamp"] is not None and last_clip_tmstp < video_settings["start_timestamp"] ): # Clips from this folder are from before start timestamp requested. _LOGGER.debug( f"Clips in folder end at {last_clip_tmstp} which is still before " f'start timestamp {video_settings["start_timestamp"]}' ) continue if ( video_settings["end_timestamp"] is not None and first_clip_tmstp > video_settings["end_timestamp"] ): # Clips from this folder are from after end timestamp requested. _LOGGER.debug( f"Clips in folder start at {first_clip_tmstp} which is after " f'end timestamp {video_settings["end_timestamp"]}' ) continue # Determine the starting and ending timestamps for the clips in this folder based on start/end timestamps # provided and offsets. folder_start_timestmp = ( timedelta(seconds=video_settings["start_offset"]) + first_clip_tmstp ) # Use provided start timestamp if it is after folder timestamp + offset folder_start_timestmp = ( video_settings["start_timestamp"] if video_settings["start_timestamp"] is not None and video_settings["start_timestamp"] > folder_start_timestmp else folder_start_timestmp ) # Figure out potential end timestamp for clip based on offset and end timestamp. folder_end_timestmp = last_clip_tmstp - timedelta( seconds=video_settings["end_offset"] ) # Use provided end timestamp if it is before folder timestamp - offset folder_end_timestmp = ( video_settings["end_timestamp"] if video_settings["end_timestamp"] is not None and video_settings["end_timestamp"] < folder_end_timestmp else folder_end_timestmp ) # Put them together to create the filename for the folder. movie_filename = ( folder_start_timestmp.astimezone(get_localzone()).strftime( "%Y-%m-%dT%H-%M-%S" ) + "_" + folder_end_timestmp.astimezone(get_localzone()).strftime( "%Y-%m-%dT%H-%M-%S" ) ) # Now add full path to it. movie_filename = ( os.path.join(video_settings["target_folder"], movie_filename) + ".mp4" ) # Do not process the files from this folder if we're to skip it if # the target movie file already exist. if video_settings["skip_existing"] and os.path.isfile(movie_filename): print( "\tSkipping folder {folder} as {filename} is already " "created ({folder_number}/{total_folders})".format( folder=folder_name, filename=movie_filename, folder_number=folder_number + 1, total_folders=len(folders), ) ) # Get actual duration of our new video, required for chapters when concatenating. metadata = get_metadata(video_settings["ffmpeg_exec"], [movie_filename]) movie_duration = metadata[0]["duration"] if metadata else 0 dashcam_clips.append( { "video_timestamp": first_clip_tmstp, "video_filename": movie_filename, "video_duration": movie_duration, } ) continue print( "\tProcessing {total_clips} clips in folder {folder} " "({folder_number}/{total_folders})".format( total_clips=len(files), folder=folder_name, folder_number=folder_number + 1, total_folders=len(folders), ) ) # Loop through all the files within the folder. folder_clips = [] delete_folder_clips = [] delete_folder_files = delete_source delete_file_list = [] folder_timestamp = None for clip_number, filename_timestamp in enumerate(sorted_video_clips): video_timestamp_info = files[filename_timestamp] folder_timestamp = ( video_timestamp_info["timestamp"] if folder_timestamp is None else folder_timestamp ) clip_name, clip_duration, files_processed = create_intermediate_movie( filename_timestamp, video_timestamp_info, (folder_start_timestmp, folder_end_timestmp), video_settings, clip_number, len(files), ) if clip_name is not None: if video_timestamp_info["file_only"]: # When file only there is no concatenation at the folder # level, will only happen at the higher level if requested. dashcam_clips.append( { "video_timestamp": video_timestamp_info["timestamp"], "video_filename": clip_name, "video_duration": clip_duration, } ) else: # Movie was created, store name for concatenation. folder_clips.append( { "video_timestamp": video_timestamp_info["timestamp"], "video_filename": clip_name, "video_duration": clip_duration, } ) # Add clip for deletion only if it's name is not the # same as the resulting movie filename if clip_name != movie_filename: delete_folder_clips.append(clip_name) elif not files_processed: delete_folder_files = False if files_processed: # Add the files to our list for removal. video_info = video_timestamp_info["video_info"] if video_info["front_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["front_camera"]["filename"], ) ) if video_info["left_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["left_camera"]["filename"], ) ) if video_info["right_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["right_camera"]["filename"], ) ) if video_info["rear_camera"]["filename"] is not None: delete_file_list.append( os.path.join( video_timestamp_info["movie_folder"], video_info["rear_camera"]["filename"], ) ) # All clips in folder have been processed, merge those clips # together now. movie_name = None movie_duration = 0 if folder_clips: print("\t\tCreating movie {}, please be patient.".format(movie_filename)) movie_name, movie_duration = create_movie( folder_clips, movie_filename, video_settings, 0 ) # Delete the source files if stated to delete. # We only do so if there were no issues in processing the clips if delete_folder_files and ( (folder_clips and movie_name is not None) or not folder_clips ): print( "\t\tDeleting files and folder {folder_name}".format( folder_name=folder_name ) ) delete_intermediate(delete_file_list) # And delete the folder delete_intermediate([folder_name]) # Add this one to our list for final concatenation if movie_name is not None: dashcam_clips.append( { "video_timestamp": folder_timestamp, "video_filename": movie_name, "video_duration": movie_duration, } ) # Delete the intermediate files we created. if not video_settings["keep_intermediate"]: delete_intermediate(delete_folder_clips) print( "\tMovie {base_name} for folder {folder_name} with duration {duration} is " "ready.".format( base_name=movie_name, folder_name=folder_name, duration=str(timedelta(seconds=int(movie_duration))), ) ) # Now that we have gone through all the folders merge. # We only do this if merge is enabled OR if we only have 1 clip and for # output a specific filename was provided. movie_name = None movie_duration = 0 if dashcam_clips: if video_settings["merge_subdirs"] or ( len(folders) == 1 and video_settings["target_filename"] is not None ): if video_settings["movie_filename"] is not None: movie_filename = video_settings["movie_filename"] elif video_settings["target_filename"] is not None: movie_filename = video_settings["target_filename"] else: folder, movie_filename = os.path.split(video_settings["target_folder"]) # If there was a trailing separator provided then it will be # empty, redo split then. if movie_filename == "": movie_filename = os.path.split(folder)[1] movie_filename = os.path.join( video_settings["target_folder"], movie_filename ) # Make sure it ends in .mp4 if os.path.splitext(movie_filename)[1] != ".mp4": movie_filename = movie_filename + ".mp4" print("\tCreating movie {}, please be patient.".format(movie_filename)) movie_name, movie_duration = create_movie( dashcam_clips, movie_filename, video_settings, video_settings["chapter_offset"], ) if movie_name is not None: print( "Movie {base_name} with duration {duration} has been created, enjoy.".format( base_name=movie_name, duration=str(timedelta(seconds=int(movie_duration))), ) ) else: print( "All folders have been processed, resulting movie files are " "located in {target_folder}".format( target_folder=video_settings["target_folder"] ) ) else: print("No clips found.") end_time = timestamp() real = int((end_time - start_time)) print("Total processing time: {real}".format(real=str(timedelta(seconds=real)))) if video_settings["notification"]: if movie_name is not None: notify( "TeslaCam", "Completed", "{total_folders} folder{folders} with {total_clips} " "clip{clips} have been processed, movie {movie_name} has " "been created.".format( folders="" if len(folders) < 2 else "s", total_folders=len(folders), clips="" if total_clips < 2 else "s", total_clips=total_clips, movie_name=video_settings["target_folder"], ), ) else: notify( "TeslaCam", "Completed", "{total_folders} folder{folders} with {total_clips} " "clip{clips} have been processed, {target_folder} contains " "resulting files.".format( folders="" if len(folders) < 2 else "s", total_folders=len(folders), clips="" if total_clips < 2 else "s", total_clips=total_clips, target_folder=video_settings["target_folder"], ), ) print() def resource_path(relative_path): """ Return absolute path for provided relative item based on location of program. """ # If compiled with pyinstaller then sys._MEIPASS points to the location # of the bundle. Otherwise path of python script is used. base_path = getattr(sys, "_MEIPASS", str(Path(__file__).parent)) return os.path.join(base_path, relative_path) def notify_macos(title, subtitle, message): """ Notification on MacOS """ try: run( [ "osascript", '-e display notification "{message}" with title "{title}" ' 'subtitle "{subtitle}"' "".format(message=message, title=title, subtitle=subtitle), ] ) except Exception as exc: print("Failed in notifification: ", exc) def notify_windows(title, subtitle, message): """ Notification on Windows """ # Section commented out, waiting to see if it really does not work on Windows 7 # This works only on Windows 10 9r Windows Server 2016/2019. Skipping for everything else # from platform import win32_ver # if win32_ver()[0] != 10: # return global TOASTER_INSTANCE # noinspection PyBroadException try: # noinspection PyUnresolvedReferences,PyPackageRequirements from win10toast import ToastNotifier if TOASTER_INSTANCE is None: TOASTER_INSTANCE = ToastNotifier() TOASTER_INSTANCE.show_toast( threaded=True, title="{} {}".format(title, subtitle), msg=message, duration=5, icon_path=resource_path("tesla_dashcam.ico"), ) run( [ "notify-send", '"{title} {subtitle}"'.format(title=title, subtitle=subtitle), '"{}"'.format(message), ] ) except Exception: pass def notify_linux(title, subtitle, message): """ Notification on Linux """ try: run( [ "notify-send", '"{title} {subtitle}"'.format(title=title, subtitle=subtitle), '"{}"'.format(message), ] ) except Exception as exc: print("Failed in notifification: ", exc) def notify(title, subtitle, message): """ Call function to send notification based on OS """ if sys.platform == "darwin": notify_macos(title, subtitle, message) elif sys.platform == "win32": notify_windows(title, subtitle, message) elif sys.platform == "linux": notify_linux(title, subtitle, message) def main() -> None: """ Main function """ loglevels = dict( (logging.getLevelName(level), level) for level in [10, 20, 30, 40, 50] ) internal_ffmpeg = getattr(sys, "frozen", None) is not None ffmpeg_default = resource_path(FFMPEG.get(sys.platform, "ffmpeg")) movie_folder = os.path.join(str(Path.home()), MOVIE_HOMEDIR.get(sys.platform), "") # Check if ffmpeg exist, if not then hope it is in default path or # provided. if not os.path.isfile(ffmpeg_default): internal_ffmpeg = False ffmpeg_default = FFMPEG.get(sys.platform, "ffmpeg") epilog = ( "This program leverages ffmpeg which is included. See https://ffmpeg.org/ for more information on ffmpeg" if internal_ffmpeg else "This program requires ffmpeg which can be downloaded from: https://ffmpeg.org/download.html" ) parser = MyArgumentParser( description="tesla_dashcam - Tesla DashCam & Sentry Video Creator", epilog=epilog, formatter_class=SmartFormatter, fromfile_prefix_chars="@", ) parser.add_argument( "source", type=str, nargs="*", help="Folder(s) (events) containing the saved camera files. Filenames can be provided as well to manage " "individual clips.", ) parser.add_argument( "--version", action="version", version=" %(prog)s " + VERSION_STR ) parser.add_argument( "--loglevel", default="INFO", choices=list(loglevels.keys()), help="Logging level.", ) parser.add_argument( "--temp_dir", required=False, type=str, help="R|Path to store temporary files." ) parser.add_argument( "--no-notification", dest="system_notification", action="store_false", help="Do not create a notification upon " "completion.", ) input_group = parser.add_argument_group( title="Video Input", description="Options related to what clips and events to process.", ) input_group.add_argument( "--skip_existing", dest="skip_existing", action="store_true", help="Skip creating encoded video file if it already exist. Note that only existence is checked, not if " "layout etc. are the same.", ) input_group.add_argument( "--delete_source", dest="delete_source", action="store_true", help="Delete the processed files upon completion.", ) input_group.add_argument( "--exclude_subdirs", dest="exclude_subdirs", action="store_true", help="Do not search sub folders (events) for video files to process.", ) monitor_group = parser.add_argument_group( title="Trigger Monitor", description="Parameters for monitoring of insertion of TeslaCam drive, folder, or file existence.", ) monitor_group.add_argument( "--monitor", dest="monitor", action="store_true", help="Enable monitoring for drive to be attached with TeslaCam folder.", ) monitor_group.add_argument( "--monitor_once", dest="monitor_once", action="store_true", help="Enable monitoring and exit once drive with TeslaCam folder has been attached and files processed.", ) monitor_group.add_argument( "--monitor_trigger", required=False, type=str, help="Trigger file to look for instead of waiting for drive to be attached. Once file is discovered then " "processing will start, file will be deleted when processing has been completed. If source is not " "provided then folder where file is located will be used as source.", ) layout_group = parser.add_argument_group( title="Video Layout", description="Set what the layout of the resulting video should be", ) layout_group.add_argument( "--layout", required=False, choices=["WIDESCREEN", "FULLSCREEN", "PERSPECTIVE", "CROSS", "DIAMOND"], default="FULLSCREEN", help="R|Layout of the created video.\n" " FULLSCREEN: Front camera center top, " "side cameras underneath it with rear camera between side camera.\n" " WIDESCREEN: Front camera on top with side and rear cameras smaller underneath it.\n" " PERSPECTIVE: Similar to FULLSCREEN but then with side cameras in perspective.\n" " CROSS: Front camera center top, side cameras underneath, and rear camera center bottom.\n" " DIAMOND: Front camera center top, side cameras below front camera left and right of front, " "and rear camera center bottom.\n", ) layout_group.add_argument( "--perspective", dest="perspective", action="store_true", help="Show side cameras in perspective.", ) layout_group.set_defaults(perspective=False) layout_group.add_argument( "--scale", dest="clip_scale", type=str, nargs="+", action="append", help="R|Set camera clip scale for all clips, scale of 1 is 1280x960 camera clip.\n" "If provided with value then it is default for all cameras, to set the scale for a specific " "camera provide camera=<front, left, right,rear> <scale>\n" "for example:\n" " --scale 0.5 all are 640x480\n" " --scale 640x480 all are 640x480\n" " --scale 0.5 --scale camera=front 1 all are 640x480 except front at 1280x960\n" " --scale camera=left .25 --scale camera=right 320x240 left and right are set to 320x240\n" "Defaults:\n" " WIDESCREEN: 1/2 (front 1280x960, others 640x480, video is 1920x1920)\n" " FULLSCREEN: 1/2 (640x480, video is 1920x960)\n" " CROSS: 1/2 (640x480, video is 1280x1440)\n" " DIAMOND: 1/2 (640x480, video is 1920x976)\n", ) layout_group.add_argument( "--mirror", dest="rear_or_mirror", action="store_const", const=1, help="Video from side and rear cameras as if being viewed through the mirror. Default when not providing " "parameter --no-front. Cannot be used in combination with --rear.", ) layout_group.add_argument( "--rear", dest="rear_or_mirror", action="store_const", const=0, help="Video from side and rear cameras as if looking backwards. Default when providing parameter --no-front. " "Cannot be used in combination with --mirror.", ) layout_group.add_argument( "--swap", dest="swap_leftright", action="store_const", const=1, help="Swap left and right cameras in output, default when side and rear cameras are as if looking backwards. " "See --rear parameter.", ) layout_group.add_argument( "--no-swap", dest="swap_leftright", action="store_const", const=0, help="Do not swap left and right cameras, default when side and rear cameras are as if looking through a " "mirror. Also see --mirror parameter", ) layout_group.add_argument( "--swap_frontrear", dest="swap_frontrear", action="store_true", help="Swap front and rear cameras in output.", ) layout_group.add_argument( "--background", dest="background", default="black", help="Background color for video. Can be a color string or RGB value. Also see --fontcolor.", ) camera_group = parser.add_argument_group( title="Camera Exclusion", description="Exclude one or more cameras:" ) camera_group.add_argument( "--no-front", dest="no_front", action="store_true", help="Exclude front camera from video.", ) camera_group.add_argument( "--no-left", dest="no_left", action="store_true", help="Exclude left camera from video.", ) camera_group.add_argument( "--no-right", dest="no_right", action="store_true", help="Exclude right camera from video.", ) camera_group.add_argument( "--no-rear", dest="no_rear", action="store_true", help="Exclude rear camera from video.", ) timestamp_group = parser.add_argument_group( title="Timestamp", description="Options on how to show date/time in resulting video:", ) timestamp_group.add_argument( "--no-timestamp", dest="no_timestamp", action="store_true", help="Do not show timestamp in video", ) timestamp_group.add_argument( "--halign", required=False, choices=["LEFT", "CENTER", "RIGHT"], help="Horizontal alignment for timestamp", ) timestamp_group.add_argument( "--valign", required=False, choices=["TOP", "MIDDLE", "BOTTOM"], help="Vertical Alignment for timestamp", ) timestamp_group.add_argument( "--font", required=False, type=str, default=DEFAULT_FONT.get(sys.platform, None), help="Fully qualified filename (.ttf) to the font to be chosen for timestamp.", ) timestamp_group.add_argument( "--fontsize", required=False, type=int, help="Font size for timestamp. Default is scaled based on resulting video size.", ) timestamp_group.add_argument( "--fontcolor", required=False, type=str, default="white", help="R|Font color for timestamp. Any color is accepted as a color string or RGB value.\n" "Some potential values are:\n" " white\n" " yellowgreen\n" " yellowgreen@0.9\n" " Red\n:" " 0x2E8B57\n" "For more information on this see ffmpeg documentation for color: https://ffmpeg.org/ffmpeg-utils.html#Color", ) filter_group = parser.add_argument_group( title="Timestamp Restriction", description="Restrict video to be between start and/or end timestamps. Timestamp to be provided in a ISO-8601 " "format (see https://fits.gsfc.nasa.gov/iso-time.html for examples)", ) filter_group.add_argument( "--start_timestamp", dest="start_timestamp", type=str, help="Starting timestamp" ) filter_group.add_argument( "--end_timestamp", dest="end_timestamp", type=str, # type=lambda d: datetime.strptime(d, "%Y-%m-%d_%H-%M-%S").datetime(), help="Ending timestamp", ) offset_group = parser.add_argument_group( title="Event offsets", description="Start and/or end offsets for events" ) offset_group.add_argument( "--start_offset", dest="start_offset", type=int, help="Skip x number of seconds from start of event for resulting video.", ) offset_group.add_argument( "--end_offset", dest="end_offset", type=int, help="Ignore the last x seconds of the event for resulting video", ) output_group = parser.add_argument_group( title="Video Output", description="Options related to resulting video creation." ) output_group.add_argument( "--output", required=False, default=movie_folder, type=str, help="R|Path/Filename for the new movie file. Event files will be stored in same folder." + os.linesep, ) output_group.add_argument( "--motion_only", dest="motion_only", action="store_true", help="Fast-forward through video when there is no motion.", ) output_group.add_argument( "--slowdown", dest="slow_down", type=float, default=argparse.SUPPRESS, help="Slow down video output. Accepts a number that is then used as multiplier, providing 2 means half the " "speed.", ) output_group.add_argument( "--speedup", dest="speed_up", type=float, default=argparse.SUPPRESS, help="Speed up the video. Accepts a number that is then used as a multiplier, providing 2 means " "twice the speed.", ) output_group.add_argument( "--chapter_offset", dest="chapter_offset", type=int, default=0, help="Offset in seconds for chapters in merged video. Negative offset is # of seconds before the end of the " "subdir video, positive offset if # of seconds after the start of the subdir video.", ) output_group.add_argument( "--merge", dest="merge_subdirs", action="store_true", help="Merge the video files from different folders (events) into 1 big video file.", ) output_group.add_argument( "--keep-intermediate", dest="keep_intermediate", action="store_true", help="Do not remove the clip video files that are created", ) advancedencoding_group = parser.add_argument_group( title="Advanced encoding settings", description="Advanced options for encoding" ) gpu_help = ( "R|Use GPU acceleration, only enable if supported by hardware.\n" " MAC: All MACs with Haswell CPU or later support this (Macs after 2013).\n" " See following link as well: \n" " https://en.wikipedia.org/wiki/List_of_Macintosh_models_grouped_by_CPU_type#Haswell\n" ) if sys.platform == "darwin": advancedencoding_group.add_argument( "--no-gpu", dest="gpu", action="store_true", help=gpu_help ) else: advancedencoding_group.add_argument( "--gpu", dest="gpu", action="store_true", help=gpu_help ) advancedencoding_group.add_argument( "--gpu_type", choices=["nvidia", "intel", "RPi"], help="Type of graphics card (GPU) in the system. This determines the encoder that will be used." "This parameter is mandatory if --gpu is provided.", ) advancedencoding_group.add_argument( "--no-faststart", dest="faststart", action="store_true", help="Do not enable flag faststart on the resulting video files. Use this when using a network share and " "errors occur during encoding.", ) advancedencoding_group.add_argument( "--quality", required=False, choices=["LOWEST", "LOWER", "LOW", "MEDIUM", "HIGH"], default="LOWER", help="Define the quality setting for the video, higher quality means bigger file size but might " "not be noticeable.", ) advancedencoding_group.add_argument( "--compression", required=False, choices=[ "ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow", ], default="medium", help="Speed to optimize video. Faster speed results in a bigger file. This does not impact the quality of " "the video, just how much time is used to compress it.", ) advancedencoding_group.add_argument( "--fps", required=False, type=int, default=24, help="Frames per second for resulting video. Tesla records at about 33fps hence going higher wouldn't do " "much as frames would just be duplicated. Default is 24fps which is the standard for movies and TV shows", ) if internal_ffmpeg: advancedencoding_group.add_argument( "--ffmpeg", required=False, type=str, help="Full path and filename for alternative " "ffmpeg.", ) else: advancedencoding_group.add_argument( "--ffmpeg", required=False, type=str, default=ffmpeg_default, help="Path and filename for ffmpeg. Specify if ffmpeg is not within path.", ) advancedencoding_group.add_argument( "--encoding", required=False, choices=["x264", "x265"], default=argparse.SUPPRESS, help="R|Encoding to use for video creation.\n" " x264: standard encoding, can be viewed on most devices but results in bigger file.\n" " x265: newer encoding standard but not all devices support this yet.\n", ) advancedencoding_group.add_argument( "--enc", required=False, type=str, default=argparse.SUPPRESS, help="R|Provide a custom encoder for video creation. Cannot be used in combination with --encoding.\n" "Note: when using this option the --gpu option is ignored. To use GPU hardware acceleration specify an " "encoding that provides this.", ) update_check_group = parser.add_argument_group( title="Update Check", description="Check for updates" ) update_check_group.add_argument( "--check_for_update", dest="check_for_updates", action="store_true", help="Check for update and exit.", ) update_check_group.add_argument( "--no-check_for_update", dest="no_check_for_updates", action="store_true", help="A check for new updates is performed every time. With this parameter that can be disabled", ) update_check_group.add_argument( "--include_test", dest="include_beta", action="store_true", help="Include test (beta) releases when checking for updates.", ) args = parser.parse_args() logging.basicConfig( level=loglevels[args.loglevel], format="%(asctime)s:%(levelname)s:\t%(name)s\t%(message)s", ) _LOGGER.debug(f"Arguments : {args}") # Check that any mutual exclusive items are not both provided. if "speed_up" in args and "slow_down" in args: print( "Option --speed_up and option --slow_down cannot be used together, only use one of them." ) return 1 if "enc" in args and "encoding" in args: print( "Option --enc and option --encoding cannot be used together, only use one of them." ) return 1 if not args.no_check_for_updates or args.check_for_updates: release_info = check_latest_release(args.include_beta) if release_info is not None: new_version = False if release_info.get("tag_name") is not None: github_version = release_info.get("tag_name").split(".") if len(github_version) == 3: # Release tags normally start with v. If that is the case # then strip the v. try: major_version = int(github_version[0]) except ValueError: major_version = int(github_version[0][1:]) minor_version = int(github_version[1]) if release_info.get("prerelease"): # Drafts will have b and then beta number. patch_version = int(github_version[2].split("b")[0]) beta_version = int(github_version[2].split("b")[1]) else: patch_version = int(github_version[2]) beta_version = -1 if major_version == VERSION["major"]: if minor_version == VERSION["minor"]: if patch_version == VERSION["patch"]: if beta_version > VERSION["beta"] or ( beta_version == -1 and VERSION["beta"] != -1 ): new_version = True elif patch_version > VERSION["patch"]: new_version = True elif minor_version > VERSION["minor"]: new_version = True elif major_version > VERSION["major"]: new_version = True if new_version: beta = "" if release_info.get("prerelease"): beta = "beta " release_notes = "" if not args.check_for_updates: if args.system_notification: notify( "TeslaCam", "Update available", "New {beta}release {release} is available. You are " "on version {version}".format( beta=beta, release=release_info.get("tag_name"), version=VERSION_STR, ), ) release_notes = ( "Use --check_for_update to get latest " "release notes." ) print( "New {beta}release {release} is available for download " "({url}). You are currently on {version}. {rel_note}".format( beta=beta, release=release_info.get("tag_name"), url=release_info.get("html_url"), version=VERSION_STR, rel_note=release_notes, ) ) if args.check_for_updates: print( "You can download the new release from: {url}".format( url=release_info.get("html_url") ) ) print( "Release Notes:\n {release_notes}".format( release_notes=release_info.get("body") ) ) return else: if args.check_for_updates: print( "{version} is the latest release available.".format( version=VERSION_STR ) ) return else: print("Did not retrieve latest version info.") ffmpeg = ffmpeg_default if getattr(args, "ffmpeg", None) is None else args.ffmpeg if which(ffmpeg) is None: print( f"ffmpeg is a requirement, unable to find {ffmpeg} executable. Please ensure it exist and is located" f"within PATH environment or provide full path using parameter --ffmpeg." ) if args.layout == "PERSPECTIVE": layout_settings = FullScreen() layout_settings.perspective = True else: if args.layout == "WIDESCREEN": layout_settings = WideScreen() elif args.layout == "FULLSCREEN": layout_settings = FullScreen() elif args.layout == "CROSS": layout_settings = Cross() elif args.layout == "DIAMOND": layout_settings = Diamond() else: layout_settings = FullScreen() layout_settings.perspective = args.perspective layout_settings.cameras("Front").include = not args.no_front layout_settings.cameras("Left").include = not args.no_left layout_settings.cameras("Right").include = not args.no_right layout_settings.cameras("Rear").include = not args.no_rear # Check if either rear or mirror argument has been provided. # If front camera then default to mirror, if no front camera then default to rear. side_camera_as_mirror = ( layout_settings.cameras("Front").include if args.rear_or_mirror is None else args.rear_or_mirror ) mirror_sides = ", hflip" if side_camera_as_mirror else "" # For scale first set the main clip one if provided, this than allows camera specific ones to override for # that camera. scaling = parser.args_to_dict(args.clip_scale, "scale") main_scale = search_dict(None, "camera", scaling) if main_scale is not None: layout_settings.scale = main_scale.get("scale", layout_settings.scale) for scale in scaling: if scale.get("camera", "").lower() in ["front", "left", "right", "rear"]: camera_scale = scale.get("scale") if camera_scale is not None: layout_settings.cameras( scale["camera"].lower().capitalize() ).scale = camera_scale layout_settings.font.halign = ( args.halign if args.halign is not None else layout_settings.font.halign ) layout_settings.font.valign = ( args.valign if args.valign is not None else layout_settings.font.valign ) # Determine if left and right cameras should be swapped or not. # No more arguments related to cameras (i.e .scale, include or not) can be processed from now on. # Up till now Left means left camera and Right means Right camera. # From this point forward Left can mean Right camera if we're swapping output. layout_settings.swap_left_right = ( not side_camera_as_mirror if args.swap_leftright is None else args.swap_leftright ) layout_settings.swap_front_rear = args.swap_frontrear layout_settings.font.font = args.font layout_settings.font.color = args.fontcolor if args.fontsize is not None and args.fontsize > 0: layout_settings.font.size = args.fontsize black_base = "color=duration={duration}:" black_size = f"s={{width}}x{{height}}:c={args.background}, fps={args.fps} " ffmpeg_base = ( black_base + black_size.format( width=layout_settings.video_width, height=layout_settings.video_height ) + "[base]" ) ffmpeg_black_video = ";" + black_base + black_size input_clip = "base" ffmpeg_video_position = "" ffmpeg_left_camera = "" camera = "Left" if layout_settings.cameras(camera).include: ffmpeg_left_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {mirror}{options}" " [left]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][left] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [left1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "left1" ffmpeg_front_camera = "" camera = "Front" if layout_settings.cameras(camera).include: ffmpeg_front_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {options}" " [front]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][front] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [front1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "front1" ffmpeg_right_camera = "" camera = "Right" if layout_settings.cameras(camera).include: ffmpeg_right_camera = ( "setpts=PTS-STARTPTS, " "scale={clip_width}x{clip_height} {mirror}{options}" " [right]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][right] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [right1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "right1" ffmpeg_rear_camera = "" camera = "Rear" if layout_settings.cameras(camera).include: ffmpeg_rear_camera = ( "setpts=PTS-STARTPTS, " # "crop=512:798:225:26, " "scale={clip_width}x{clip_height} {mirror}{options}" " [rear]".format( clip_width=layout_settings.cameras(camera).width, clip_height=layout_settings.cameras(camera).height, mirror=mirror_sides, options=layout_settings.cameras(camera).options, ) ) ffmpeg_video_position = ( ffmpeg_video_position + ";[{input_clip}][rear] overlay=eof_action=pass:repeatlast=0:" "x={x_pos}:y={y_pos} [rear1]".format( input_clip=input_clip, x_pos=layout_settings.cameras(camera).xpos, y_pos=layout_settings.cameras(camera).ypos, ) ) input_clip = "rear1" filter_counter = 0 filter_string = ";[{input_clip}] {filter} [tmp{filter_counter}]" ffmpeg_timestamp = "" if not args.no_timestamp: if layout_settings.font.font is None: print( f"Unable to get a font file for platform {sys.platform}. Please provide valid font file using " f"--font or disable timestamp using --no-timestamp." ) return # noinspection PyPep8 temp_font_file = ( f"c:\{layout_settings.font.font}" if sys.platform == "win32" else layout_settings.font.font ) if not os.path.isfile(temp_font_file): print( f"Font file {temp_font_file} does not exist. Provide a valid font file using --font or" f" disable timestamp using --no-timestamp" ) if sys.platform == "linux": print( "You can also install the fonts using for example: apt-get install ttf-freefont" ) return # noinspection PyPep8,PyPep8,PyPep8 ffmpeg_timestamp = ( ffmpeg_timestamp + f"drawtext=fontfile={layout_settings.font.font}:" f"fontcolor={layout_settings.font.color}:fontsize={layout_settings.font.size}:" "borderw=2:bordercolor=black@1.0:" f"x={layout_settings.font.halign}:y={layout_settings.font.valign}:" "text='%{{pts\:localtime\:{epoch_time}\:%x %X}}'" ) ffmpeg_timestamp = filter_string.format( input_clip=input_clip, filter=ffmpeg_timestamp, filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 speed = args.slow_down if "slow_down" in args else "" speed = round(1 / args.speed_up, 4) if "speed_up" in args else speed ffmpeg_speed = "" if speed != "": ffmpeg_speed = filter_string.format( input_clip=input_clip, filter=f"setpts={speed}*PTS", filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 ffmpeg_motiononly = "" if args.motion_only: ffmpeg_motiononly = filter_string.format( input_clip=input_clip, filter=f"mpdecimate=hi=64*48, setpts=N/FRAME_RATE/TB", filter_counter=filter_counter, ) input_clip = f"tmp{filter_counter}" filter_counter += 1 ffmpeg_params = ["-preset", args.compression, "-crf", MOVIE_QUALITY[args.quality]] use_gpu = not args.gpu if sys.platform == "darwin" else args.gpu video_encoding = [] if not "enc" in args: encoding = args.encoding if "encoding" in args else "x264" # GPU acceleration enabled if use_gpu: print("GPU acceleration is enabled") if sys.platform == "darwin": video_encoding = video_encoding + ["-allow_sw", "1"] encoding = encoding + "_mac" else: if args.gpu_type is None: print( "Parameter --gpu_type is mandatory when parameter --use_gpu is used." ) return encoding = encoding + "_" + args.gpu_type bit_rate = str(int(10000 * layout_settings.scale)) + "K" video_encoding = video_encoding + ["-b:v", bit_rate] video_encoding = video_encoding + ["-c:v", MOVIE_ENCODING[encoding]] else: video_encoding = video_encoding + ["-c:v", args.enc] ffmpeg_params = ffmpeg_params + video_encoding # Set metadata ffmpeg_params = ffmpeg_params + [ "-metadata", f"description=Created by tesla_dashcam {VERSION_STR}", ] # Determine the target folder and filename. # If no extension then assume it is a folder. if ( os.path.splitext(args.output)[1] is not None and os.path.splitext(args.output)[1] != "" ): target_folder, target_filename = os.path.split(args.output) if target_folder is None or target_folder == "": # If nothing in target_filename then no folder was given, # setting default movie folder target_folder = movie_folder target_filename = args.output else: # Folder only provided. target_folder = args.output target_filename = None # Convert target folder to absolute path if relative path has been provided. target_folder = os.path.abspath(target_folder) # Ensure folder if not already exist and if not can be created if not make_folder("--output", target_folder): return temp_folder = args.temp_dir if temp_folder is not None: # Convert temp folder to absolute path if relative path has been provided temp_folder = os.path.abspath(args.temp_dir) if not make_folder("--temp_dir", temp_folder): return # Set the run type based on arguments. runtype = "RUN" if args.monitor: runtype = "MONITOR" elif args.monitor_once: runtype = "MONITOR_ONCE" monitor_file = args.monitor_trigger # If no source provided then set to MONITOR_ONCE and we're only going to # take SavedClips and SentryClips source_list = args.source if not source_list: source_list = ["SavedClips", "SentryClips"] if runtype == "RUN": runtype = "MONITOR_ONCE" start_timestamp = None if args.start_timestamp is not None: start_timestamp = isoparse(args.start_timestamp) if start_timestamp.tzinfo is None: start_timestamp = start_timestamp.astimezone(get_localzone()) end_timestamp = None if args.end_timestamp is not None: end_timestamp = isoparse(args.end_timestamp) if end_timestamp.tzinfo is None: end_timestamp = end_timestamp.astimezone(get_localzone()) start_offset = abs(args.start_offset) if args.start_offset is not None else 0 end_offset = abs(args.end_offset) if args.end_offset is not None else 0 video_settings = { "source_folder": source_list, "output": args.output, "target_folder": target_folder, "target_filename": target_filename, "temp_dir": temp_folder, "run_type": runtype, "merge_subdirs": args.merge_subdirs, "chapter_offset": args.chapter_offset, "movie_filename": None, "keep_intermediate": args.keep_intermediate, "notification": args.system_notification, "movie_layout": args.layout, "movie_speed": speed, "video_encoding": video_encoding, "movie_encoding": args.encoding if "encoding" in args else "x264", "movie_compression": args.compression, "movie_quality": args.quality, "background": ffmpeg_black_video, "ffmpeg_exec": ffmpeg, "base": ffmpeg_base, "video_layout": layout_settings, "clip_positions": ffmpeg_video_position, "timestamp_text": ffmpeg_timestamp, "ffmpeg_speed": ffmpeg_speed, "ffmpeg_motiononly": ffmpeg_motiononly, "movflags_faststart": not args.faststart, "input_clip": input_clip, "other_params": ffmpeg_params, "left_camera": ffmpeg_left_camera, "front_camera": ffmpeg_front_camera, "right_camera": ffmpeg_right_camera, "rear_camera": ffmpeg_rear_camera, "start_timestamp": start_timestamp, "start_offset": start_offset, "end_timestamp": end_timestamp, "end_offset": end_offset, "skip_existing": args.skip_existing, } _LOGGER.debug(f"Video Settings {video_settings}") _LOGGER.debug(f"Layout Settings {layout_settings}") # If we constantly run and monitor for drive added or not. if video_settings["run_type"] in ["MONITOR", "MONITOR_ONCE"]: video_settings.update({"skip_existing": True}) trigger_exist = False if monitor_file is None: print("Monitoring for TeslaCam Drive to be inserted. Press CTRL-C to stop") else: print( "Monitoring for trigger {} to exist. Press CTRL-C to stop".format( monitor_file ) ) while True: try: # Monitoring for disk to be inserted and not for a file. if monitor_file is None: source_folder, source_partition = get_tesladashcam_folder() if source_folder is None: # Nothing found, sleep for 1 minute and check again. if trigger_exist: print("TeslaCam drive has been ejected.") print( "Monitoring for TeslaCam Drive to be inserted. " "Press CTRL-C to stop" ) sleep(MONITOR_SLEEP_TIME) trigger_exist = False continue # As long as TeslaCam drive is still attached we're going to # keep on waiting. if trigger_exist: _LOGGER.debug(f"TeslaCam Drive still attached") sleep(MONITOR_SLEEP_TIME) continue # Got a folder, append what was provided as source unless # . was provided in which case everything is done. source_folder_list = [] for folder in video_settings["source_folder"]: if folder == ".": source_folder_list.append(folder) else: source_folder_list.append( os.path.join(source_folder, folder) ) message = "TeslaCam folder found on {partition}.".format( partition=source_partition ) else: # Wait till trigger file exist (can also be folder). if not os.path.exists(monitor_file): _LOGGER.debug(f"Trigger file {monitor_file} does not exist.") sleep(MONITOR_SLEEP_TIME) trigger_exist = False continue if trigger_exist: sleep(MONITOR_SLEEP_TIME) continue message = "Trigger {} exist.".format(monitor_file) trigger_exist = True # Set monitor path, make sure what was provided is a file first otherwise get path. monitor_path = monitor_file if os.path.isfile(monitor_file): monitor_path, _ = os.path.split(monitor_file) # If . is provided then source folder is path where monitor file exist. source_folder_list = [] for folder in video_settings["source_folder"]: if folder == ".": source_folder_list.append(monitor_path) else: # If source path provided is absolute then use that for source path if os.path.isabs(folder): source_folder_list.append(folder) else: # Path provided is relative, hence based on path of trigger file. source_folder_list.append( os.path.join(monitor_path, folder) ) print(message) if args.system_notification: notify("TeslaCam", "Started", message) if len(source_folder_list) == 1: print(f"Retrieving all files from {source_folder_list[0]}") else: print(f"Retrieving all files from: ") for folder in source_folder_list: print(f" {folder}") folders = get_movie_files( source_folder_list, args.exclude_subdirs, video_settings ) if video_settings["run_type"] == "MONITOR": # We will continue to monitor hence we need to # ensure we always have a unique final movie name. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else os.path.splitext(video_settings["target_filename"])[0] + "_" + datetime.today().strftime("%Y-%m-%d_%H_%M") + os.path.splitext(video_settings["target_filename"])[1] ) video_settings.update({"movie_filename": movie_filename}) else: # Set filename to right now if no filename provided. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else video_settings["target_filename"] ) video_settings.update({"movie_filename": movie_filename}) process_folders(folders, video_settings, args.delete_source) print("Processing of movies has completed.") if args.system_notification: notify( "TeslaCam", "Completed", "Processing of movies has completed." ) # Stop if we're only to monitor once and then exit. if video_settings["run_type"] == "MONITOR_ONCE": if monitor_file is not None: if os.path.isfile(monitor_file): try: os.remove(monitor_file) except OSError as exc: print( "Error trying to remove trigger file {}: {}".format( monitor_file, exc ) ) print("Exiting monitoring as asked process once.") break if monitor_file is None: trigger_exist = True print( "Waiting for TeslaCam Drive to be ejected. Press " "CTRL-C to stop" ) else: if os.path.isfile(monitor_file): try: os.remove(monitor_file) except OSError as exc: print( "Error trying to remove trigger file {}: {}".format( monitor_file, exc ) ) break trigger_exist = False print( "Monitoring for trigger {}. Press CTRL-C to stop".format( monitor_file ) ) else: print( "Waiting for trigger {} to be removed. Press CTRL-C to stop".format( monitor_file ) ) except KeyboardInterrupt: print("Monitoring stopped due to CTRL-C.") break else: folders = get_movie_files( video_settings["source_folder"], args.exclude_subdirs, video_settings ) # Set filename to right now if no filename provided. movie_filename = ( datetime.today().strftime("%Y-%m-%d_%H_%M") if video_settings["target_filename"] is None else video_settings["target_filename"] ) video_settings.update({"movie_filename": movie_filename}) process_folders(folders, video_settings, args.delete_source) if sys.version_info < (3, 7): print( f"Python version 3.7 or higher is required, you have: {sys.version}. Please update your Python version." ) sys.exit(1) if __name__ == "__main__": sys.exit(main())

# coding=utf-8 from time import sleep import readchar import math import numpy import json import pigpio from turtle_draw import BrachioGraphTurtle try: pigpio.exceptions = False rpi = pigpio.pi() rpi.set_PWM_frequency(18, 50) pigpio.exceptions = True force_virtual = False except: print("pigpio daemon is not available; running in virtual mode") force_virtual = True import tqdm class BrachioGraph: def __init__( self, # ----------------- geometry of the plotter ----------------- inner_arm=8, # the lengths of the arms outer_arm=8, bounds=[-8, 4, 6, 13], # the maximum rectangular drawing area # ----------------- naive calculation values ----------------- servo_1_parked_pw=1500, # pulse-widths when parked servo_2_parked_pw=1500, servo_1_degree_ms=-10, # milliseconds pulse-width per degree servo_2_degree_ms=10, # reversed for the mounting of the shoulder servo servo_1_parked_angle=-90, # the arm angle in the parked position servo_2_parked_angle=90, # ----------------- hysteresis ----------------- hysteresis_correction_1=0, # hardware error compensation hysteresis_correction_2=0, # ----------------- servo angles and pulse-widths in lists ----------------- servo_1_angle_pws=[], # pulse-widths for various angles servo_2_angle_pws=[], # ----------------- servo angles and pulse-widths in lists (bi-directional) ------ servo_1_angle_pws_bidi = [], # bi-directional pulse-widths for various angles servo_2_angle_pws_bidi = [], # ----------------- the pen ----------------- pw_up=1500, # pulse-widths for pen up/down pw_down=1100, # ----------------- misc ----------------- wait=None, # default wait time between operations virtual = False, # run in virtual mode turtle = False ): # set the geometry self.inner_arm = inner_arm self.outer_arm = outer_arm self.virtual = virtual or force_virtual self.turtle = turtle if self.turtle: self.reset_turtle() # the box bounds describe a rectangle that we can safely draw in self.bounds = bounds # if pulse-widths to angles are supplied for each servo, we will feed them to # numpy.polyfit(), to produce a function for each one. Otherwise, we will use a simple # approximation based on a centre of travel of 1500µS and 10µS per degree self.servo_1_parked_pw = servo_1_parked_pw self.servo_1_degree_ms = servo_1_degree_ms self.servo_1_parked_angle = servo_1_parked_angle self.hysteresis_correction_1 = hysteresis_correction_1 self.servo_2_parked_pw = servo_2_parked_pw self.servo_2_degree_ms = servo_2_degree_ms self.servo_2_parked_angle = servo_2_parked_angle self.hysteresis_correction_2 = hysteresis_correction_2 # set some initial values required for moving methods self.previous_pw_1 = self.previous_pw_2 = 0 self.active_hysteresis_correction_1 = self.active_hysteresis_correction_2 = 0 self.reset_report() # Set the x and y position state, so it knows its current x/y position. self.x = -self.inner_arm self.y = self.outer_arm if servo_1_angle_pws_bidi: servo_1_angle_pws = [] differences = [] for angle, pws in servo_1_angle_pws_bidi.items(): pw = (pws['acw'] + pws['cw']) / 2 servo_1_angle_pws.append([angle, pw]) differences.append((pws['acw'] - pws['cw']) / 2) self.hysteresis_correction_1 = numpy.mean(differences) if servo_1_angle_pws: servo_1_array = numpy.array(servo_1_angle_pws) self.angles_to_pw_1 = numpy.poly1d( numpy.polyfit( servo_1_array[:,0], servo_1_array[:,1], 3 ) ) else: self.angles_to_pw_1 = self.naive_angles_to_pulse_widths_1 if servo_2_angle_pws_bidi: servo_2_angle_pws = [] differences = [] for angle, pws in servo_2_angle_pws_bidi.items(): pw = (pws['acw'] + pws['cw']) / 2 servo_2_angle_pws.append([angle, pw]) differences.append((pws['acw'] - pws['cw']) / 2) self.hysteresis_correction_2 = numpy.mean(differences) print(servo_2_angle_pws) if servo_2_angle_pws: servo_2_array = numpy.array(servo_2_angle_pws) self.angles_to_pw_2 = numpy.poly1d( numpy.polyfit( servo_2_array[:,0], servo_2_array[:,1], 3 ) ) else: self.angles_to_pw_2 = self.naive_angles_to_pulse_widths_2 # create the pen object self.pen = Pen(bg=self, pw_up=pw_up, pw_down=pw_down, virtual=self.virtual) if self.virtual: print("Initialising virtual BrachioGraph") self.virtual_pw_1 = self.angles_to_pw_1(-90) self.virtual_pw_2 = self.angles_to_pw_2(90) # by default in virtual mode, we use a wait factor of 0 for speed self.wait = wait or 0 else: # instantiate this Raspberry Pi as a pigpio.pi() instance self.rpi = pigpio.pi() # the pulse frequency should be no higher than 100Hz - higher values could (supposedly) damage the servos self.rpi.set_PWM_frequency(14, 50) self.rpi.set_PWM_frequency(15, 50) # by default we use a wait factor of 0.1 for accuracy self.wait = wait or .1 self.set_angles(-90, 90) if self.turtle: self.turtle.showturtle() self.status() # methods in this class: # drawing # line-processing # test patterns # pen-moving # angles-to-pulse-widths # hardware-related # trigonometric # calibration # manual driving # reporting # ----------------- drawing methods ----------------- def plot_file(self, filename="", wait=0, interpolate=10, bounds=None): """Passes the lines in the supplied JSON file to ``plot_lines()``""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "File plotting is only possible when BrachioGraph.bounds is set." with open(filename, "r") as line_file: lines = json.load(line_file) self.plot_lines(lines=lines, wait=wait, interpolate=interpolate, bounds=bounds, flip=True) def plot_lines(self, lines=[], wait=0, interpolate=10, rotate=False, flip=False, bounds=None): """Passes each segment of each line in lines to ``draw_line()``""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Line plotting is only possible when BrachioGraph.bounds is set." lines = self.rotate_and_scale_lines(lines=lines, bounds=bounds, flip=True) for line in tqdm.tqdm(lines, desc="Lines", leave=False): x, y = line[0] # only if we are not within 1mm of the start of the line, lift pen and go there if (round(self.x, 1), round(self.y, 1)) != (round(x, 1), round(y, 1)): self.xy(x, y, wait=wait, interpolate=interpolate) for point in tqdm.tqdm(line[1:], desc="Segments", leave=False): x, y = point self.xy(x, y, wait=wait, interpolate=interpolate, draw=True) self.park() def draw_line(self, start=(0, 0), end=(0, 0), wait=0, interpolate=10, both=False): """Draws a straight line between two points""" wait = wait or self.wait start_x, start_y = start end_x, end_y = end self.xy(x=start_x, y=start_y, wait=wait, interpolate=interpolate) self.xy(x=end_x, y=end_y, wait=wait, interpolate=interpolate, draw=True) if both: self.xy(x=start_x, y=start_y, wait=wait, interpolate=interpolate, draw=True) # ----------------- line-processing methods ----------------- def rotate_and_scale_lines(self, lines=[], rotate=False, flip=False, bounds=None): rotate, x_mid_point, y_mid_point, box_x_mid_point, box_y_mid_point, divider = self.analyse_lines( lines=lines, rotate=rotate, bounds=bounds ) for line in lines: for point in line: if rotate: point[0], point[1] = point[1], point[0] x = point[0] x = x - x_mid_point # shift x values so that they have zero as their mid-point x = x / divider # scale x values to fit in our box width if flip ^ rotate: # flip before moving back into drwaing pane x = -x x = x + box_x_mid_point # shift x values so that they have the box x midpoint as their endpoint y = point[1] y = y - y_mid_point y = y / divider y = y + box_y_mid_point point[0], point[1] = x, y return lines def analyse_lines(self, lines=[], rotate=False, bounds=None): # lines is a tuple itself containing a number of tuples, each of which contains a number of 2-tuples # # [ # | # [ # | # [3, 4], # | # | # [2, 4], # | # | # [1, 5], # a single point in a line # | a list of points defining a line # | # [3, 5], # | # | # [3, 7], # | # | # ], # | # [ # | all the lines # [...], # | # [...], # | # ], # | # [ # | # [...], # | # [...], # | # ], # | # ] # | # First, we create a pair of empty sets for all the x and y values in all of the lines of the plot data. x_values_in_lines = set() y_values_in_lines = set() # Loop over each line and all the points in each line, to get sets of all the x and y values: for line in lines: x_values_in_line, y_values_in_line = zip(*line) x_values_in_lines.update(x_values_in_line) y_values_in_lines.update(y_values_in_line) # Identify the minimum and maximum values. min_x, max_x = min(x_values_in_lines), max(x_values_in_lines) min_y, max_y = min(y_values_in_lines), max(y_values_in_lines) # Identify the range they span. x_range, y_range = max_x - min_x, max_y - min_y box_x_range, box_y_range = bounds[2] - bounds[0], bounds[3] - bounds[1] # And their mid-points. x_mid_point, y_mid_point = (max_x + min_x) / 2, (max_y + min_y) / 2 box_x_mid_point, box_y_mid_point = (bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2 # Get a 'divider' value for each range - the value by which we must divide all x and y so that they will # fit safely inside the drawing range of the plotter. # If both image and box are in portrait orientation, or both in landscape, we don't need to rotate the plot. if (x_range >= y_range and box_x_range >= box_y_range) or (x_range <= y_range and box_x_range <= box_y_range): divider = max((x_range / box_x_range), (y_range / box_y_range)) rotate = False else: divider = max((x_range / box_y_range), (y_range / box_x_range)) rotate = True x_mid_point, y_mid_point = y_mid_point, x_mid_point return rotate, x_mid_point, y_mid_point, box_x_mid_point, box_y_mid_point, divider # ----------------- test pattern methods ----------------- def test_pattern(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): self.vertical_lines( bounds=bounds, lines=lines, wait=wait, interpolate=interpolate, repeat=repeat, reverse=reverse, both=both ) self.horizontal_lines( bounds=bounds, lines=lines, wait=wait, interpolate=interpolate, repeat=repeat, reverse=reverse, both=both ) def vertical_lines(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Plotting a test pattern is only possible when BrachioGraph.bounds is set." if not reverse: top_y = self.bounds[1] bottom_y = self.bounds[3] else: bottom_y = self.bounds[1] top_y = self.bounds[3] for n in range(repeat): step = (self.bounds[2] - self.bounds[0]) / lines x = self.bounds[0] while x <= self.bounds[2]: self.draw_line((x, top_y), (x, bottom_y), interpolate=interpolate, both=both) x = x + step self.park() def horizontal_lines(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Plotting a test pattern is only possible when BrachioGraph.bounds is set." if not reverse: min_x = self.bounds[0] max_x = self.bounds[2] else: max_x = self.bounds[0] min_x = self.bounds[2] for n in range(repeat): step = (self.bounds[3] - self.bounds[1]) / lines y = self.bounds[1] while y <= self.bounds[3]: self.draw_line((min_x, y), (max_x, y), interpolate=interpolate, both=both) y = y + step self.park() def box(self, bounds=None, wait=0, interpolate=10, repeat=1, reverse=False): """Draw a box marked out by the ``bounds``.""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Box drawing is only possible when BrachioGraph.bounds is set." self.xy(bounds[0], bounds[1], wait, interpolate) for r in tqdm.tqdm(tqdm.trange(repeat), desc='Iteration', leave=False): if not reverse: self.xy(bounds[2], bounds[1], wait, interpolate, draw=True) self.xy(bounds[2], bounds[3], wait, interpolate, draw=True) self.xy(bounds[0], bounds[3], wait, interpolate, draw=True) self.xy(bounds[0], bounds[1], wait, interpolate, draw=True) else: self.xy(bounds[0], bounds[3], wait, interpolate, draw=True) self.xy(bounds[2], bounds[3], wait, interpolate, draw=True) self.xy(bounds[2], bounds[1], wait, interpolate, draw=True) self.xy(bounds[0], bounds[1], wait, interpolate, draw=True) self.park() def test_arcs(self): self.park() elbow_angle = 120 self.move_angles(angle_2=elbow_angle) for angle_1 in range(-135, 15, 15): self.move_angles(angle_1=angle_1, draw=True) for angle_2 in range(elbow_angle, elbow_angle+16): self.move_angles(angle_2=angle_2, draw=True) for angle_2 in range(elbow_angle+16, elbow_angle-16, -1): self.move_angles(angle_2=angle_2, draw=True) for angle_2 in range(elbow_angle-16, elbow_angle+1): self.move_angles(angle_2=angle_2, draw=True) # ----------------- pen-moving methods ----------------- def xy(self, x=None, y=None, wait=0, interpolate=10, draw=False): """Moves the pen to the xy position; optionally draws while doing it.""" wait = wait or self.wait if draw: self.pen.down() else: self.pen.up() x = x or self.x y = y or self.y (angle_1, angle_2) = self.xy_to_angles(x, y) # calculate how many steps we need for this move, and the x/y length of each (x_length, y_length) = (x - self.x, y - self.y) length = math.sqrt(x_length ** 2 + y_length **2) no_of_steps = int(length * interpolate) or 1 if no_of_steps < 100: disable_tqdm = True else: disable_tqdm = False (length_of_step_x, length_of_step_y) = (x_length/no_of_steps, y_length/no_of_steps) for step in tqdm.tqdm(range(no_of_steps), desc='Interpolation', leave=False, disable=disable_tqdm): self.x = self.x + length_of_step_x self.y = self.y + length_of_step_y angle_1, angle_2 = self.xy_to_angles(self.x, self.y) self.set_angles(angle_1, angle_2) if step + 1 < no_of_steps: sleep(length * wait/no_of_steps) sleep(length * wait/10) def move_angles(self, angle_1=None, angle_2=None, wait=0, interpolate=10, draw=False): """Moves the servo motors to the specified angles step-by-step, calling set_angles() for each step.""" wait = wait or self.wait if draw: self.pen.down() else: self.pen.up() diff_1 = diff_2 = 0 if angle_1 is not None: diff_1 = angle_1 - self.angle_1 if angle_2 is not None: diff_2 = angle_2 - self.angle_2 length = math.sqrt(diff_1 ** 2 + diff_2 **2) no_of_steps = int(length * interpolate) or 1 if no_of_steps < 100: disable_tqdm = True else: disable_tqdm = False (length_of_step_1, length_of_step_2) = (diff_1/no_of_steps, diff_2/no_of_steps) for step in tqdm.tqdm(range(no_of_steps), desc='Interpolation', leave=False, disable=disable_tqdm): self.angle_1 = self.angle_1 + length_of_step_1 self.angle_2 = self.angle_2 + length_of_step_2 self.set_angles(self.angle_1, self.angle_2) if step + 1 < no_of_steps: sleep(length * wait/no_of_steps) sleep(length * wait/10) def set_angles(self, angle_1=None, angle_2=None): """Moves the servo motors to the specified angles immediately. Relies upon getting accurate pulse-width values. Calls set_pulse_widths(). Sets current_x, current_y. """ pw_1 = pw_2 = None if angle_1 is not None: pw_1 = self.angles_to_pw_1(angle_1) if pw_1 > self.previous_pw_1: self.active_hysteresis_correction_1 = self.hysteresis_correction_1 elif pw_1 < self.previous_pw_1: self.active_hysteresis_correction_1 = - self.hysteresis_correction_1 self.previous_pw_1 = pw_1 pw_1 = pw_1 + self.active_hysteresis_correction_1 self.angle_1 = angle_1 self.angles_used_1.add(int(angle_1)) self.pulse_widths_used_1.add(int(pw_1)) if angle_2 is not None: pw_2 = self.angles_to_pw_2(angle_2) if pw_2 > self.previous_pw_2: self.active_hysteresis_correction_2 = self.hysteresis_correction_2 elif pw_2 < self.previous_pw_2: self.active_hysteresis_correction_2 = - self.hysteresis_correction_2 self.previous_pw_2 = pw_2 pw_2 = pw_2 + self.active_hysteresis_correction_2 self.angle_2 = angle_2 self.angles_used_2.add(int(angle_2)) self.pulse_widths_used_2.add(int(pw_2)) if self.turtle: x, y = self.angles_to_xy(self.angle_1, self.angle_2) self.turtle.setx(x * self.turtle.multiplier) self.turtle.sety(y * self.turtle.multiplier) self.set_pulse_widths(pw_1, pw_2) self.x, self.y = self.angles_to_xy(self.angle_1, self.angle_2) # ----------------- angles-to-pulse-widths methods ----------------- def naive_angles_to_pulse_widths_1(self, angle): return (angle - self.servo_1_parked_angle) * self.servo_1_degree_ms + self.servo_1_parked_pw def naive_angles_to_pulse_widths_2(self, angle): return (angle - self.servo_2_parked_angle) * self.servo_2_degree_ms + self.servo_2_parked_pw # ----------------- hardware-related methods ----------------- def set_pulse_widths(self, pw_1=None, pw_2=None): """Applies the supplied pulse-width values to the servos, or pretends to, if we're in virtual mode.""" if self.virtual: if pw_1: if 500 < pw_1 < 2500: self.virtual_pw_1 = pw_1 else: raise ValueError if pw_2: if 500 < pw_2 < 2500: self.virtual_pw_2 = pw_2 else: raise ValueError else: if pw_1: self.rpi.set_servo_pulsewidth(14, pw_1) if pw_2: self.rpi.set_servo_pulsewidth(15, pw_2) def get_pulse_widths(self): """Returns the actual pulse-widths values; if in virtual mode, returns the nominal values - i.e. the values that they might be. """ if self.virtual: actual_pulse_width_1 = self.virtual_pw_1 actual_pulse_width_2 = self.virtual_pw_2 else: actual_pulse_width_1 = self.rpi.get_servo_pulsewidth(14) actual_pulse_width_2 = self.rpi.get_servo_pulsewidth(15) return (actual_pulse_width_1, actual_pulse_width_2) def park(self): """Park the plotter with the inner arm at -90˚ and the outer arm at 90˚ to it. This corresponds to an x/y position: * x: ``-inner_arm`` * y: ``outer_arm`` """ if self.virtual: print("Parking") self.pen.up() self.xy(-self.inner_arm, self.outer_arm) sleep(1) def quiet(self, servos=[14, 15, 18]): """Stop sending pulses to the servos, so that they are no longer energised (and so that they stop buzzing). """ if self.virtual: print("Going quiet") else: for servo in servos: self.rpi.set_servo_pulsewidth(servo, 0) # ----------------- trigonometric methods ----------------- # Every x/y position of the plotter corresponds to a pair of angles of the arms. These methods # calculate: # # the angles required to reach any x/y position # the x/y position represented by any pair of angles def xy_to_angles(self, x=0, y=0): """Return the servo angles required to reach any x/y position.""" hypotenuse = math.sqrt(x**2+y**2) if hypotenuse > self.inner_arm + self.outer_arm: raise Exception(f"Cannot reach {hypotenuse}; total arm length is {self.inner_arm + self.outer_arm}") hypotenuse_angle = math.asin(x/hypotenuse) inner_angle = math.acos( (hypotenuse**2+self.inner_arm**2-self.outer_arm**2)/(2*hypotenuse*self.inner_arm) ) outer_angle = math.acos( (self.inner_arm**2+self.outer_arm**2-hypotenuse**2)/(2*self.inner_arm*self.outer_arm) ) shoulder_motor_angle = hypotenuse_angle - inner_angle elbow_motor_angle = math.pi - outer_angle return (math.degrees(shoulder_motor_angle), math.degrees(elbow_motor_angle)) def angles_to_xy(self, shoulder_motor_angle, elbow_motor_angle): """Return the x/y co-ordinates represented by a pair of servo angles.""" elbow_motor_angle = math.radians(elbow_motor_angle) shoulder_motor_angle = math.radians(shoulder_motor_angle) hypotenuse = math.sqrt( (self.inner_arm ** 2 + self.outer_arm ** 2 - 2 * self.inner_arm * self.outer_arm * math.cos( math.pi - elbow_motor_angle) ) ) base_angle = math.acos( (hypotenuse ** 2 + self.inner_arm ** 2 - self.outer_arm ** 2) / (2 * hypotenuse * self.inner_arm) ) inner_angle = base_angle + shoulder_motor_angle x = math.sin(inner_angle) * hypotenuse y = math.cos(inner_angle) * hypotenuse return(x, y) # ----------------- calibration ----------------- def auto_calibrate(self): self.park() for elbow in range(90, 136): self.set_angles(None, elbow) sleep(.01) for shoulder in range(-90, -140, -1): self.set_angles(shoulder, None) sleep(.01) def calibrate(self, servo=1): pin = {1: 14, 2: 15}[servo] servo_centre = {1: self.servo_1_parked_pw, 2: self.servo_2_parked_pw}.get(servo) servo_angle_pws = [] texts = { "arm-name": {1: "inner", 2: "outer"}, "nominal-centre": {1: 0, 2: 90}, "mount-arm": { 1: "(straight ahead)", 2: "(i.e. to the right) to the inner arm)" }, "safe-guess": {1: -60, 2: 90} } pw = servo_centre print(f"Calibrating servo {servo}, for the {texts["arm-name"][servo]} arm.") print(f"See https://brachiograph.art/how-to/calibrate.html") print() self.rpi.set_servo_pulsewidth(pin, pw) print(f"The servo is now at {pw}µS, in the centre of its range of movement.") print("Attach the protractor to the base, with its centre at the axis of the servo.") print(f"Mount the arm at a position as close as possible to {texts["nominal-centre"][servo]}˚ {texts["mount-arm"][servo]}.") print("Now drive the arm to a known angle, as marked on the protractor.") print("When the arm reaches the angle, press 1 and record the angle. Do this for as many angles as possible.") print() print("When you have done all the angles, press 2.") print("Press 0 to exit at any time.") while True: key = readchar.readchar() if key == "0": return elif key == "1": angle = float(input("Enter the angle: ")) servo_angle_pws.append([angle, pw]) elif key == "2": break elif key=="a": pw = pw - 10 elif key=="s": pw = pw + 10 elif key=="A": pw = pw - 1 elif key=="S": pw = pw + 1 else: continue print(pw) self.rpi.set_servo_pulsewidth(pin, pw) print(f"------------------------") print(f"Recorded angles servo {servo}") print(f"------------------------") print(f" angle | pulse-width ") print(f"---------+--------------") servo_angle_pws.sort() for [angle, pw] in servo_angle_pws: print(f" {angle:>6.1f} | {pw:>4.0f}") servo_array = numpy.array(servo_angle_pws) pw = int(numpy.poly1d( numpy.polyfit( servo_array[:,0], servo_array[:,1], 3 ) )(0)) self.rpi.set_servo_pulsewidth(pin, pw) print() print(f"The servo is now at {int(pw)}µS, which should correspond to {texts["nominal-centre"][servo]}˚.") print("If necessary, remount the arm at the centre of its optimal sweep for your drawing area.") print() print(f"Alternatively as a rule of thumb, if the arms are of equal length, use the position closest to {texts["safe-guess"][servo]}˚.") print("Carefully count how many spline positions you had to move the arm by to get it there.") print("Multiply that by the number of degrees for each spline to get the angle by which you moved it.") offset = float(input("Enter the angle by which you moved the arm (anti-clockwise is negative): ")) print(f"---------------------------") print(f"Calculated angles {texts["arm-name"][servo]} arm") print(f"---------------------------") print(f" angle | pulse-width ") print(f"----------+----------------") servo_angle_including_offset_pws = [] for [angle, pw] in servo_angle_pws: angle_including_offset = round(angle + offset, 1) servo_angle_including_offset_pws.append([angle_including_offset, pw]) print(f" {angle:>6.1f} | {pw:>4.0f}") print() print("Use this list of angles and pulse-widths in your BrachioGraph definition:") print() print(f"servo_{servo}_angle_pws={servo_angle_including_offset_pws}") # ----------------- manual driving methods ----------------- def drive(self): # adjust the pulse-widths using the keyboard pw_1, pw_2 = self.get_pulse_widths() self.set_pulse_widths(pw_1, pw_2) while True: key = readchar.readchar() if key == "0": return elif key=="a": pw_1 = pw_1 - 10 elif key=="s": pw_1 = pw_1 + 10 elif key=="A": pw_1 = pw_1 - 2 elif key=="S": pw_1 = pw_1 + 2 elif key=="k": pw_2 = pw_2 - 10 elif key=="l": pw_2 = pw_2 + 10 elif key=="K": pw_2 = pw_2 - 2 elif key=="L": pw_2 = pw_2 + 2 print(pw_1, pw_2) self.set_pulse_widths(pw_1, pw_2) def drive_xy(self): # move the pen up/down and left/right using the keyboard while True: key = readchar.readchar() if key == "0": return elif key=="a": self.x = self.x - 1 elif key=="s": self.x = self.x + 1 elif key=="A": self.x = self.x - .1 elif key=="S": self.x = self.x + .1 elif key=="k": self.y = self.y - 1 elif key=="l": self.y = self.y + 1 elif key=="K": self.y = self.y - .1 elif key=="L": self.y = self.y + .1 print(self.x, self.y) self.xy(self.x, self.y) # ----------------- reporting methods ----------------- def status(self): print("------------------------------------------") print(" | Servo 1 | Servo 2 ") print(" | Shoulder| Elbow ") print("----------------------|---------|---------") pw_1, pw_2 = self.get_pulse_widths() print(f"{"pulse-width |":>23}", f"{pw_1:>7.0f}", "|", f"{pw_2:>7.0f}") angle_1, angle_2 = self.angle_1, self.angle_2 print(f"{"angle |":>23}", f"{angle_1:>7.0f}", "|", f"{angle_2:>7.0f}") h1, h2 = self.hysteresis_correction_1, self.hysteresis_correction_2 print(f"{"hysteresis correction |":>23}", f"{h1:>7.1f}", "|", f"{h2:>7.1f}") print("------------------------------------------") print(f"{"x/y location |":>23}", f"{self.x:>7.1f}", "|", f"{self.y:>7.1f}") print() print("------------------------------------------") print("pen:", self.pen.position) bl = self.bounds[0], self.bounds[1] tr = self.bounds[2], self.bounds[3] print("------------------------------------------") print("bottom left:", bl, "top right:", tr) print("------------------------------------------") def report(self): print(f" -----------------|-----------------") print(f" Servo 1 | Servo 2 ") print(f" -----------------|-----------------") h1, h2 = self.hysteresis_correction_1, self.hysteresis_correction_2 print(f"hysteresis {h1:>2.1f} | {h2:>2.1f}") pw_1, pw_2 = self.get_pulse_widths() print(f"pulse-width {pw_1:<4.0f} | {pw_2:<4.0f}") angle_1, angle_2 = self.angle_1, self.angle_2 if angle_1 and angle_2: print(f" angle {angle_1:>4.0f} | {angle_2:>4.0f}") print(f" -----------------|-----------------") print(f" min max mid | min max mid") print(f" -----------------|-----------------") if self.angles_used_1 and self.angles_used_2 and self.pulse_widths_used_1 and self.pulse_widths_used_2: min1 = min(self.pulse_widths_used_1) max1 = max(self.pulse_widths_used_1) mid1 = (min1 + max1) / 2 min2 = min(self.pulse_widths_used_2) max2 = max(self.pulse_widths_used_2) mid2 = (min2 + max2) / 2 print(f"pulse-widths {min1:>4.0f} {max1:>4.0f} {mid1:>4.0f} | {min2:>4.0f} {max2:>4.0f} {mid2:>4.0f}") min1 = min(self.angles_used_1) max1 = max(self.angles_used_1) mid1 = (min1 + max1) / 2 min2 = min(self.angles_used_2) max2 = max(self.angles_used_2) mid2 = (min2 + max2) / 2 print(f" angles {min1:>4.0f} {max1:>4.0f} {mid1:>4.0f} | {min2:>4.0f} {max2:>4.0f} {mid2:>4.0f}") else: print("No data recorded yet. Try calling the BrachioGraph.box() method first.") def reset_report(self): self.angle_1 = self.angle_2 = None # Create sets for recording movement of the plotter. self.angles_used_1 = set() self.angles_used_2 = set() self.pulse_widths_used_1 = set() self.pulse_widths_used_2 = set() @property def bl(self): return (self.bounds[0], self.bounds[1]) @property def tl(self): return (self.bounds[0], self.bounds[3]) @property def tr(self): return (self.bounds[2], self.bounds[3]) @property def br(self): return (self.bounds[2], self.bounds[1]) def reset_turtle(self): self.turtle = BrachioGraphTurtle( inner_arm=self.inner_arm, # the length of the inner arm (blue) shoulder_centre_angle=-90, # the starting angle of the inner arm, relative to straight ahead shoulder_sweep=180, # the arc covered by the shoulder motor outer_arm=self.outer_arm, # the length of the outer arm (red) elbow_centre_angle=90, # the centre of the outer arm relative to the inner arm elbow_sweep=180, # the arc covered by the elbow motor window_size=800, # width and height of the turtle canvas speed=0, # how fast to draw ) self.turtle.draw_grid() class Pen: def __init__(self, bg, pw_up=1700, pw_down=1300, pin=18, transition_time=0.25, virtual=False): self.bg = bg self.pin = pin self.pw_up = pw_up self.pw_down = pw_down self.transition_time = transition_time self.virtual = virtual if self.virtual: print("Initialising virtual Pen") else: self.rpi = pigpio.pi() self.rpi.set_PWM_frequency(self.pin, 50) self.up() sleep(0.3) self.down() sleep(0.3) self.up() sleep(0.3) def down(self): if self.virtual: self.virtual_pw = self.pw_down else: self.rpi.set_servo_pulsewidth(self.pin, self.pw_down) sleep(self.transition_time) if self.bg.turtle: self.bg.turtle.down() self.bg.turtle.color('blue') self.bg.turtle.width(1) self.position = "down" def up(self): if self.virtual: self.virtual_pw = self.pw_up else: self.rpi.set_servo_pulsewidth(self.pin, self.pw_up) sleep(self.transition_time) if self.bg.turtle: self.bg.turtle.up() self.position = "up" # for convenience, a quick way to set pen motor pulse-widths def pw(self, pulse_width): if self.virtual: self.virtual_pw = pulse_width else: self.rpi.set_servo_pulsewidth(self.pin, pulse_width) def calibrate(self): print(f"Calibrating the pen-lifting servo.") print(f"See https://brachiograph.art/how-to/calibrate.html") pw_1, pw_2 = self.bg.get_pulse_widths() pw_3 = self.pw_up while True: self.bg.set_pulse_widths(pw_1, pw_2) self.pw(pw_3) key = readchar.readchar() if key == "0": break elif key=="a": pw_1 = pw_1 - 10 continue elif key=="s": pw_1 = pw_1 + 10 continue elif key=="k": pw_2 = pw_2 - 10 continue elif key=="l": pw_2 = pw_2 + 10 continue elif key=="t": if pw_3 == self.pw_up: pw_3 = self.pw_down else: pw_3 = self.pw_up continue elif key=="z": pw_3 = pw_3 - 10 print(pw_3) continue elif key=="x": pw_3 = pw_3 + 10 print(pw_3) continue elif key=="u": self.pw_up = pw_3 elif key=="d": self.pw_down = pw_3 else: continue mid = (self.pw_up + self.pw_down) / 2 print(f"Pen-up pulse-width: {self.pw_up}µS, pen-down pulse-width: {self.pw_down}µS, mid-point: {mid}") print() print("Use these values in your BrachioGraph definition:") print() print(f"pen_up={self.pw_up}, pen_down={self.pw_down}")

# coding=utf-8 from time import sleep import readchar import math import numpy import json import pigpio from turtle_draw import BrachioGraphTurtle try: pigpio.exceptions = False rpi = pigpio.pi() rpi.set_PWM_frequency(18, 50) pigpio.exceptions = True force_virtual = False except: print("pigpio daemon is not available; running in virtual mode") force_virtual = True import tqdm class BrachioGraph: def __init__( self, # ----------------- geometry of the plotter ----------------- inner_arm=8, # the lengths of the arms outer_arm=8, bounds=[-8, 4, 6, 13], # the maximum rectangular drawing area # ----------------- naive calculation values ----------------- servo_1_parked_pw=1500, # pulse-widths when parked servo_2_parked_pw=1500, servo_1_degree_ms=-10, # milliseconds pulse-width per degree servo_2_degree_ms=10, # reversed for the mounting of the shoulder servo servo_1_parked_angle=-90, # the arm angle in the parked position servo_2_parked_angle=90, # ----------------- hysteresis ----------------- hysteresis_correction_1=0, # hardware error compensation hysteresis_correction_2=0, # ----------------- servo angles and pulse-widths in lists ----------------- servo_1_angle_pws=[], # pulse-widths for various angles servo_2_angle_pws=[], # ----------------- servo angles and pulse-widths in lists (bi-directional) ------ servo_1_angle_pws_bidi = [], # bi-directional pulse-widths for various angles servo_2_angle_pws_bidi = [], # ----------------- the pen ----------------- pw_up=1500, # pulse-widths for pen up/down pw_down=1100, # ----------------- misc ----------------- wait=None, # default wait time between operations virtual = False, # run in virtual mode turtle = False ): # set the geometry self.inner_arm = inner_arm self.outer_arm = outer_arm self.virtual = virtual or force_virtual self.turtle = turtle if self.turtle: self.reset_turtle() # the box bounds describe a rectangle that we can safely draw in self.bounds = bounds # if pulse-widths to angles are supplied for each servo, we will feed them to # numpy.polyfit(), to produce a function for each one. Otherwise, we will use a simple # approximation based on a centre of travel of 1500µS and 10µS per degree self.servo_1_parked_pw = servo_1_parked_pw self.servo_1_degree_ms = servo_1_degree_ms self.servo_1_parked_angle = servo_1_parked_angle self.hysteresis_correction_1 = hysteresis_correction_1 self.servo_2_parked_pw = servo_2_parked_pw self.servo_2_degree_ms = servo_2_degree_ms self.servo_2_parked_angle = servo_2_parked_angle self.hysteresis_correction_2 = hysteresis_correction_2 # set some initial values required for moving methods self.previous_pw_1 = self.previous_pw_2 = 0 self.active_hysteresis_correction_1 = self.active_hysteresis_correction_2 = 0 self.reset_report() # Set the x and y position state, so it knows its current x/y position. self.x = -self.inner_arm self.y = self.outer_arm if servo_1_angle_pws_bidi: servo_1_angle_pws = [] differences = [] for angle, pws in servo_1_angle_pws_bidi.items(): pw = (pws['acw'] + pws['cw']) / 2 servo_1_angle_pws.append([angle, pw]) differences.append((pws['acw'] - pws['cw']) / 2) self.hysteresis_correction_1 = numpy.mean(differences) if servo_1_angle_pws: servo_1_array = numpy.array(servo_1_angle_pws) self.angles_to_pw_1 = numpy.poly1d( numpy.polyfit( servo_1_array[:,0], servo_1_array[:,1], 3 ) ) else: self.angles_to_pw_1 = self.naive_angles_to_pulse_widths_1 if servo_2_angle_pws_bidi: servo_2_angle_pws = [] differences = [] for angle, pws in servo_2_angle_pws_bidi.items(): pw = (pws['acw'] + pws['cw']) / 2 servo_2_angle_pws.append([angle, pw]) differences.append((pws['acw'] - pws['cw']) / 2) self.hysteresis_correction_2 = numpy.mean(differences) print(servo_2_angle_pws) if servo_2_angle_pws: servo_2_array = numpy.array(servo_2_angle_pws) self.angles_to_pw_2 = numpy.poly1d( numpy.polyfit( servo_2_array[:,0], servo_2_array[:,1], 3 ) ) else: self.angles_to_pw_2 = self.naive_angles_to_pulse_widths_2 # create the pen object self.pen = Pen(bg=self, pw_up=pw_up, pw_down=pw_down, virtual=self.virtual) if self.virtual: print("Initialising virtual BrachioGraph") self.virtual_pw_1 = self.angles_to_pw_1(-90) self.virtual_pw_2 = self.angles_to_pw_2(90) # by default in virtual mode, we use a wait factor of 0 for speed self.wait = wait or 0 else: # instantiate this Raspberry Pi as a pigpio.pi() instance self.rpi = pigpio.pi() # the pulse frequency should be no higher than 100Hz - higher values could (supposedly) damage the servos self.rpi.set_PWM_frequency(14, 50) self.rpi.set_PWM_frequency(15, 50) # by default we use a wait factor of 0.1 for accuracy self.wait = wait or .1 self.set_angles(-90, 90) if self.turtle: self.turtle.showturtle() self.status() # methods in this class: # drawing # line-processing # test patterns # pen-moving # angles-to-pulse-widths # hardware-related # trigonometric # calibration # manual driving # reporting # ----------------- drawing methods ----------------- def plot_file(self, filename="", wait=0, interpolate=10, bounds=None): """Passes the lines in the supplied JSON file to ``plot_lines()``""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "File plotting is only possible when BrachioGraph.bounds is set." with open(filename, "r") as line_file: lines = json.load(line_file) self.plot_lines(lines=lines, wait=wait, interpolate=interpolate, bounds=bounds, flip=True) def plot_lines(self, lines=[], wait=0, interpolate=10, rotate=False, flip=False, bounds=None): """Passes each segment of each line in lines to ``draw_line()``""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Line plotting is only possible when BrachioGraph.bounds is set." lines = self.rotate_and_scale_lines(lines=lines, bounds=bounds, flip=True) for line in tqdm.tqdm(lines, desc="Lines", leave=False): x, y = line[0] # only if we are not within 1mm of the start of the line, lift pen and go there if (round(self.x, 1), round(self.y, 1)) != (round(x, 1), round(y, 1)): self.xy(x, y, wait=wait, interpolate=interpolate) for point in tqdm.tqdm(line[1:], desc="Segments", leave=False): x, y = point self.xy(x, y, wait=wait, interpolate=interpolate, draw=True) self.park() def draw_line(self, start=(0, 0), end=(0, 0), wait=0, interpolate=10, both=False): """Draws a straight line between two points""" wait = wait or self.wait start_x, start_y = start end_x, end_y = end self.xy(x=start_x, y=start_y, wait=wait, interpolate=interpolate) self.xy(x=end_x, y=end_y, wait=wait, interpolate=interpolate, draw=True) if both: self.xy(x=start_x, y=start_y, wait=wait, interpolate=interpolate, draw=True) # ----------------- line-processing methods ----------------- def rotate_and_scale_lines(self, lines=[], rotate=False, flip=False, bounds=None): rotate, x_mid_point, y_mid_point, box_x_mid_point, box_y_mid_point, divider = self.analyse_lines( lines=lines, rotate=rotate, bounds=bounds ) for line in lines: for point in line: if rotate: point[0], point[1] = point[1], point[0] x = point[0] x = x - x_mid_point # shift x values so that they have zero as their mid-point x = x / divider # scale x values to fit in our box width if flip ^ rotate: # flip before moving back into drwaing pane x = -x x = x + box_x_mid_point # shift x values so that they have the box x midpoint as their endpoint y = point[1] y = y - y_mid_point y = y / divider y = y + box_y_mid_point point[0], point[1] = x, y return lines def analyse_lines(self, lines=[], rotate=False, bounds=None): # lines is a tuple itself containing a number of tuples, each of which contains a number of 2-tuples # # [ # | # [ # | # [3, 4], # | # | # [2, 4], # | # | # [1, 5], # a single point in a line # | a list of points defining a line # | # [3, 5], # | # | # [3, 7], # | # | # ], # | # [ # | all the lines # [...], # | # [...], # | # ], # | # [ # | # [...], # | # [...], # | # ], # | # ] # | # First, we create a pair of empty sets for all the x and y values in all of the lines of the plot data. x_values_in_lines = set() y_values_in_lines = set() # Loop over each line and all the points in each line, to get sets of all the x and y values: for line in lines: x_values_in_line, y_values_in_line = zip(*line) x_values_in_lines.update(x_values_in_line) y_values_in_lines.update(y_values_in_line) # Identify the minimum and maximum values. min_x, max_x = min(x_values_in_lines), max(x_values_in_lines) min_y, max_y = min(y_values_in_lines), max(y_values_in_lines) # Identify the range they span. x_range, y_range = max_x - min_x, max_y - min_y box_x_range, box_y_range = bounds[2] - bounds[0], bounds[3] - bounds[1] # And their mid-points. x_mid_point, y_mid_point = (max_x + min_x) / 2, (max_y + min_y) / 2 box_x_mid_point, box_y_mid_point = (bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2 # Get a 'divider' value for each range - the value by which we must divide all x and y so that they will # fit safely inside the drawing range of the plotter. # If both image and box are in portrait orientation, or both in landscape, we don't need to rotate the plot. if (x_range >= y_range and box_x_range >= box_y_range) or (x_range <= y_range and box_x_range <= box_y_range): divider = max((x_range / box_x_range), (y_range / box_y_range)) rotate = False else: divider = max((x_range / box_y_range), (y_range / box_x_range)) rotate = True x_mid_point, y_mid_point = y_mid_point, x_mid_point return rotate, x_mid_point, y_mid_point, box_x_mid_point, box_y_mid_point, divider # ----------------- test pattern methods ----------------- def test_pattern(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): self.vertical_lines( bounds=bounds, lines=lines, wait=wait, interpolate=interpolate, repeat=repeat, reverse=reverse, both=both ) self.horizontal_lines( bounds=bounds, lines=lines, wait=wait, interpolate=interpolate, repeat=repeat, reverse=reverse, both=both ) def vertical_lines(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Plotting a test pattern is only possible when BrachioGraph.bounds is set." if not reverse: top_y = self.bounds[1] bottom_y = self.bounds[3] else: bottom_y = self.bounds[1] top_y = self.bounds[3] for n in range(repeat): step = (self.bounds[2] - self.bounds[0]) / lines x = self.bounds[0] while x <= self.bounds[2]: self.draw_line((x, top_y), (x, bottom_y), interpolate=interpolate, both=both) x = x + step self.park() def horizontal_lines(self, bounds=None, lines=4, wait=0, interpolate=10, repeat=1, reverse=False, both=False): wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Plotting a test pattern is only possible when BrachioGraph.bounds is set." if not reverse: min_x = self.bounds[0] max_x = self.bounds[2] else: max_x = self.bounds[0] min_x = self.bounds[2] for n in range(repeat): step = (self.bounds[3] - self.bounds[1]) / lines y = self.bounds[1] while y <= self.bounds[3]: self.draw_line((min_x, y), (max_x, y), interpolate=interpolate, both=both) y = y + step self.park() def box(self, bounds=None, wait=0, interpolate=10, repeat=1, reverse=False): """Draw a box marked out by the ``bounds``.""" wait = wait or self.wait bounds = bounds or self.bounds if not bounds: return "Box drawing is only possible when BrachioGraph.bounds is set." self.xy(bounds[0], bounds[1], wait, interpolate) for r in tqdm.tqdm(tqdm.trange(repeat), desc='Iteration', leave=False): if not reverse: self.xy(bounds[2], bounds[1], wait, interpolate, draw=True) self.xy(bounds[2], bounds[3], wait, interpolate, draw=True) self.xy(bounds[0], bounds[3], wait, interpolate, draw=True) self.xy(bounds[0], bounds[1], wait, interpolate, draw=True) else: self.xy(bounds[0], bounds[3], wait, interpolate, draw=True) self.xy(bounds[2], bounds[3], wait, interpolate, draw=True) self.xy(bounds[2], bounds[1], wait, interpolate, draw=True) self.xy(bounds[0], bounds[1], wait, interpolate, draw=True) self.park() def test_arcs(self): self.park() elbow_angle = 120 self.move_angles(angle_2=elbow_angle) for angle_1 in range(-135, 15, 15): self.move_angles(angle_1=angle_1, draw=True) for angle_2 in range(elbow_angle, elbow_angle+16): self.move_angles(angle_2=angle_2, draw=True) for angle_2 in range(elbow_angle+16, elbow_angle-16, -1): self.move_angles(angle_2=angle_2, draw=True) for angle_2 in range(elbow_angle-16, elbow_angle+1): self.move_angles(angle_2=angle_2, draw=True) # ----------------- pen-moving methods ----------------- def xy(self, x=None, y=None, wait=0, interpolate=10, draw=False): """Moves the pen to the xy position; optionally draws while doing it.""" wait = wait or self.wait if draw: self.pen.down() else: self.pen.up() x = x or self.x y = y or self.y (angle_1, angle_2) = self.xy_to_angles(x, y) # calculate how many steps we need for this move, and the x/y length of each (x_length, y_length) = (x - self.x, y - self.y) length = math.sqrt(x_length ** 2 + y_length **2) no_of_steps = int(length * interpolate) or 1 if no_of_steps < 100: disable_tqdm = True else: disable_tqdm = False (length_of_step_x, length_of_step_y) = (x_length/no_of_steps, y_length/no_of_steps) for step in tqdm.tqdm(range(no_of_steps), desc='Interpolation', leave=False, disable=disable_tqdm): self.x = self.x + length_of_step_x self.y = self.y + length_of_step_y angle_1, angle_2 = self.xy_to_angles(self.x, self.y) self.set_angles(angle_1, angle_2) if step + 1 < no_of_steps: sleep(length * wait/no_of_steps) sleep(length * wait/10) def move_angles(self, angle_1=None, angle_2=None, wait=0, interpolate=10, draw=False): """Moves the servo motors to the specified angles step-by-step, calling set_angles() for each step.""" wait = wait or self.wait if draw: self.pen.down() else: self.pen.up() diff_1 = diff_2 = 0 if angle_1 is not None: diff_1 = angle_1 - self.angle_1 if angle_2 is not None: diff_2 = angle_2 - self.angle_2 length = math.sqrt(diff_1 ** 2 + diff_2 **2) no_of_steps = int(length * interpolate) or 1 if no_of_steps < 100: disable_tqdm = True else: disable_tqdm = False (length_of_step_1, length_of_step_2) = (diff_1/no_of_steps, diff_2/no_of_steps) for step in tqdm.tqdm(range(no_of_steps), desc='Interpolation', leave=False, disable=disable_tqdm): self.angle_1 = self.angle_1 + length_of_step_1 self.angle_2 = self.angle_2 + length_of_step_2 self.set_angles(self.angle_1, self.angle_2) if step + 1 < no_of_steps: sleep(length * wait/no_of_steps) sleep(length * wait/10) def set_angles(self, angle_1=None, angle_2=None): """Moves the servo motors to the specified angles immediately. Relies upon getting accurate pulse-width values. Calls set_pulse_widths(). Sets current_x, current_y. """ pw_1 = pw_2 = None if angle_1 is not None: pw_1 = self.angles_to_pw_1(angle_1) if pw_1 > self.previous_pw_1: self.active_hysteresis_correction_1 = self.hysteresis_correction_1 elif pw_1 < self.previous_pw_1: self.active_hysteresis_correction_1 = - self.hysteresis_correction_1 self.previous_pw_1 = pw_1 pw_1 = pw_1 + self.active_hysteresis_correction_1 self.angle_1 = angle_1 self.angles_used_1.add(int(angle_1)) self.pulse_widths_used_1.add(int(pw_1)) if angle_2 is not None: pw_2 = self.angles_to_pw_2(angle_2) if pw_2 > self.previous_pw_2: self.active_hysteresis_correction_2 = self.hysteresis_correction_2 elif pw_2 < self.previous_pw_2: self.active_hysteresis_correction_2 = - self.hysteresis_correction_2 self.previous_pw_2 = pw_2 pw_2 = pw_2 + self.active_hysteresis_correction_2 self.angle_2 = angle_2 self.angles_used_2.add(int(angle_2)) self.pulse_widths_used_2.add(int(pw_2)) if self.turtle: x, y = self.angles_to_xy(self.angle_1, self.angle_2) self.turtle.setx(x * self.turtle.multiplier) self.turtle.sety(y * self.turtle.multiplier) self.set_pulse_widths(pw_1, pw_2) self.x, self.y = self.angles_to_xy(self.angle_1, self.angle_2) # ----------------- angles-to-pulse-widths methods ----------------- def naive_angles_to_pulse_widths_1(self, angle): return (angle - self.servo_1_parked_angle) * self.servo_1_degree_ms + self.servo_1_parked_pw def naive_angles_to_pulse_widths_2(self, angle): return (angle - self.servo_2_parked_angle) * self.servo_2_degree_ms + self.servo_2_parked_pw # ----------------- hardware-related methods ----------------- def set_pulse_widths(self, pw_1=None, pw_2=None): """Applies the supplied pulse-width values to the servos, or pretends to, if we're in virtual mode.""" if self.virtual: if pw_1: if 500 < pw_1 < 2500: self.virtual_pw_1 = pw_1 else: raise ValueError if pw_2: if 500 < pw_2 < 2500: self.virtual_pw_2 = pw_2 else: raise ValueError else: if pw_1: self.rpi.set_servo_pulsewidth(14, pw_1) if pw_2: self.rpi.set_servo_pulsewidth(15, pw_2) def get_pulse_widths(self): """Returns the actual pulse-widths values; if in virtual mode, returns the nominal values - i.e. the values that they might be. """ if self.virtual: actual_pulse_width_1 = self.virtual_pw_1 actual_pulse_width_2 = self.virtual_pw_2 else: actual_pulse_width_1 = self.rpi.get_servo_pulsewidth(14) actual_pulse_width_2 = self.rpi.get_servo_pulsewidth(15) return (actual_pulse_width_1, actual_pulse_width_2) def park(self): """Park the plotter with the inner arm at -90˚ and the outer arm at 90˚ to it. This corresponds to an x/y position: * x: ``-inner_arm`` * y: ``outer_arm`` """ if self.virtual: print("Parking") self.pen.up() self.xy(-self.inner_arm, self.outer_arm) sleep(1) def quiet(self, servos=[14, 15, 18]): """Stop sending pulses to the servos, so that they are no longer energised (and so that they stop buzzing). """ if self.virtual: print("Going quiet") else: for servo in servos: self.rpi.set_servo_pulsewidth(servo, 0) # ----------------- trigonometric methods ----------------- # Every x/y position of the plotter corresponds to a pair of angles of the arms. These methods # calculate: # # the angles required to reach any x/y position # the x/y position represented by any pair of angles def xy_to_angles(self, x=0, y=0): """Return the servo angles required to reach any x/y position.""" hypotenuse = math.sqrt(x**2+y**2) if hypotenuse > self.inner_arm + self.outer_arm: raise Exception(f"Cannot reach {hypotenuse}; total arm length is {self.inner_arm + self.outer_arm}") hypotenuse_angle = math.asin(x/hypotenuse) inner_angle = math.acos( (hypotenuse**2+self.inner_arm**2-self.outer_arm**2)/(2*hypotenuse*self.inner_arm) ) outer_angle = math.acos( (self.inner_arm**2+self.outer_arm**2-hypotenuse**2)/(2*self.inner_arm*self.outer_arm) ) shoulder_motor_angle = hypotenuse_angle - inner_angle elbow_motor_angle = math.pi - outer_angle return (math.degrees(shoulder_motor_angle), math.degrees(elbow_motor_angle)) def angles_to_xy(self, shoulder_motor_angle, elbow_motor_angle): """Return the x/y co-ordinates represented by a pair of servo angles.""" elbow_motor_angle = math.radians(elbow_motor_angle) shoulder_motor_angle = math.radians(shoulder_motor_angle) hypotenuse = math.sqrt( (self.inner_arm ** 2 + self.outer_arm ** 2 - 2 * self.inner_arm * self.outer_arm * math.cos( math.pi - elbow_motor_angle) ) ) base_angle = math.acos( (hypotenuse ** 2 + self.inner_arm ** 2 - self.outer_arm ** 2) / (2 * hypotenuse * self.inner_arm) ) inner_angle = base_angle + shoulder_motor_angle x = math.sin(inner_angle) * hypotenuse y = math.cos(inner_angle) * hypotenuse return(x, y) # ----------------- calibration ----------------- def auto_calibrate(self): self.park() for elbow in range(90, 136): self.set_angles(None, elbow) sleep(.01) for shoulder in range(-90, -140, -1): self.set_angles(shoulder, None) sleep(.01) def calibrate(self, servo=1): pin = {1: 14, 2: 15}[servo] servo_centre = {1: self.servo_1_parked_pw, 2: self.servo_2_parked_pw}.get(servo) servo_angle_pws = [] texts = { "arm-name": {1: "inner", 2: "outer"}, "nominal-centre": {1: 0, 2: 90}, "mount-arm": { 1: "(straight ahead)", 2: "(i.e. to the right) to the inner arm)" }, "safe-guess": {1: -60, 2: 90} } pw = servo_centre print(f"Calibrating servo {servo}, for the {texts['arm-name'][servo]} arm.") print(f"See https://brachiograph.art/how-to/calibrate.html") print() self.rpi.set_servo_pulsewidth(pin, pw) print(f"The servo is now at {pw}µS, in the centre of its range of movement.") print("Attach the protractor to the base, with its centre at the axis of the servo.") print(f"Mount the arm at a position as close as possible to {texts['nominal-centre'][servo]}˚ {texts['mount-arm'][servo]}.") print("Now drive the arm to a known angle, as marked on the protractor.") print("When the arm reaches the angle, press 1 and record the angle. Do this for as many angles as possible.") print() print("When you have done all the angles, press 2.") print("Press 0 to exit at any time.") while True: key = readchar.readchar() if key == "0": return elif key == "1": angle = float(input("Enter the angle: ")) servo_angle_pws.append([angle, pw]) elif key == "2": break elif key=="a": pw = pw - 10 elif key=="s": pw = pw + 10 elif key=="A": pw = pw - 1 elif key=="S": pw = pw + 1 else: continue print(pw) self.rpi.set_servo_pulsewidth(pin, pw) print(f"------------------------") print(f"Recorded angles servo {servo}") print(f"------------------------") print(f" angle | pulse-width ") print(f"---------+--------------") servo_angle_pws.sort() for [angle, pw] in servo_angle_pws: print(f" {angle:>6.1f} | {pw:>4.0f}") servo_array = numpy.array(servo_angle_pws) pw = int(numpy.poly1d( numpy.polyfit( servo_array[:,0], servo_array[:,1], 3 ) )(0)) self.rpi.set_servo_pulsewidth(pin, pw) print() print(f"The servo is now at {int(pw)}µS, which should correspond to {texts['nominal-centre'][servo]}˚.") print("If necessary, remount the arm at the centre of its optimal sweep for your drawing area.") print() print(f"Alternatively as a rule of thumb, if the arms are of equal length, use the position closest to {texts['safe-guess'][servo]}˚.") print("Carefully count how many spline positions you had to move the arm by to get it there.") print("Multiply that by the number of degrees for each spline to get the angle by which you moved it.") offset = float(input("Enter the angle by which you moved the arm (anti-clockwise is negative): ")) print(f"---------------------------") print(f"Calculated angles {texts['arm-name'][servo]} arm") print(f"---------------------------") print(f" angle | pulse-width ") print(f"----------+----------------") servo_angle_including_offset_pws = [] for [angle, pw] in servo_angle_pws: angle_including_offset = round(angle + offset, 1) servo_angle_including_offset_pws.append([angle_including_offset, pw]) print(f" {angle:>6.1f} | {pw:>4.0f}") print() print("Use this list of angles and pulse-widths in your BrachioGraph definition:") print() print(f"servo_{servo}_angle_pws={servo_angle_including_offset_pws}") # ----------------- manual driving methods ----------------- def drive(self): # adjust the pulse-widths using the keyboard pw_1, pw_2 = self.get_pulse_widths() self.set_pulse_widths(pw_1, pw_2) while True: key = readchar.readchar() if key == "0": return elif key=="a": pw_1 = pw_1 - 10 elif key=="s": pw_1 = pw_1 + 10 elif key=="A": pw_1 = pw_1 - 2 elif key=="S": pw_1 = pw_1 + 2 elif key=="k": pw_2 = pw_2 - 10 elif key=="l": pw_2 = pw_2 + 10 elif key=="K": pw_2 = pw_2 - 2 elif key=="L": pw_2 = pw_2 + 2 print(pw_1, pw_2) self.set_pulse_widths(pw_1, pw_2) def drive_xy(self): # move the pen up/down and left/right using the keyboard while True: key = readchar.readchar() if key == "0": return elif key=="a": self.x = self.x - 1 elif key=="s": self.x = self.x + 1 elif key=="A": self.x = self.x - .1 elif key=="S": self.x = self.x + .1 elif key=="k": self.y = self.y - 1 elif key=="l": self.y = self.y + 1 elif key=="K": self.y = self.y - .1 elif key=="L": self.y = self.y + .1 print(self.x, self.y) self.xy(self.x, self.y) # ----------------- reporting methods ----------------- def status(self): print("------------------------------------------") print(" | Servo 1 | Servo 2 ") print(" | Shoulder| Elbow ") print("----------------------|---------|---------") pw_1, pw_2 = self.get_pulse_widths() print(f"{'pulse-width |':>23}", f"{pw_1:>7.0f}", "|", f"{pw_2:>7.0f}") angle_1, angle_2 = self.angle_1, self.angle_2 print(f"{'angle |':>23}", f"{angle_1:>7.0f}", "|", f"{angle_2:>7.0f}") h1, h2 = self.hysteresis_correction_1, self.hysteresis_correction_2 print(f"{'hysteresis correction |':>23}", f"{h1:>7.1f}", "|", f"{h2:>7.1f}") print("------------------------------------------") print(f"{'x/y location |':>23}", f"{self.x:>7.1f}", "|", f"{self.y:>7.1f}") print() print("------------------------------------------") print("pen:", self.pen.position) bl = self.bounds[0], self.bounds[1] tr = self.bounds[2], self.bounds[3] print("------------------------------------------") print("bottom left:", bl, "top right:", tr) print("------------------------------------------") def report(self): print(f" -----------------|-----------------") print(f" Servo 1 | Servo 2 ") print(f" -----------------|-----------------") h1, h2 = self.hysteresis_correction_1, self.hysteresis_correction_2 print(f"hysteresis {h1:>2.1f} | {h2:>2.1f}") pw_1, pw_2 = self.get_pulse_widths() print(f"pulse-width {pw_1:<4.0f} | {pw_2:<4.0f}") angle_1, angle_2 = self.angle_1, self.angle_2 if angle_1 and angle_2: print(f" angle {angle_1:>4.0f} | {angle_2:>4.0f}") print(f" -----------------|-----------------") print(f" min max mid | min max mid") print(f" -----------------|-----------------") if self.angles_used_1 and self.angles_used_2 and self.pulse_widths_used_1 and self.pulse_widths_used_2: min1 = min(self.pulse_widths_used_1) max1 = max(self.pulse_widths_used_1) mid1 = (min1 + max1) / 2 min2 = min(self.pulse_widths_used_2) max2 = max(self.pulse_widths_used_2) mid2 = (min2 + max2) / 2 print(f"pulse-widths {min1:>4.0f} {max1:>4.0f} {mid1:>4.0f} | {min2:>4.0f} {max2:>4.0f} {mid2:>4.0f}") min1 = min(self.angles_used_1) max1 = max(self.angles_used_1) mid1 = (min1 + max1) / 2 min2 = min(self.angles_used_2) max2 = max(self.angles_used_2) mid2 = (min2 + max2) / 2 print(f" angles {min1:>4.0f} {max1:>4.0f} {mid1:>4.0f} | {min2:>4.0f} {max2:>4.0f} {mid2:>4.0f}") else: print("No data recorded yet. Try calling the BrachioGraph.box() method first.") def reset_report(self): self.angle_1 = self.angle_2 = None # Create sets for recording movement of the plotter. self.angles_used_1 = set() self.angles_used_2 = set() self.pulse_widths_used_1 = set() self.pulse_widths_used_2 = set() @property def bl(self): return (self.bounds[0], self.bounds[1]) @property def tl(self): return (self.bounds[0], self.bounds[3]) @property def tr(self): return (self.bounds[2], self.bounds[3]) @property def br(self): return (self.bounds[2], self.bounds[1]) def reset_turtle(self): self.turtle = BrachioGraphTurtle( inner_arm=self.inner_arm, # the length of the inner arm (blue) shoulder_centre_angle=-90, # the starting angle of the inner arm, relative to straight ahead shoulder_sweep=180, # the arc covered by the shoulder motor outer_arm=self.outer_arm, # the length of the outer arm (red) elbow_centre_angle=90, # the centre of the outer arm relative to the inner arm elbow_sweep=180, # the arc covered by the elbow motor window_size=800, # width and height of the turtle canvas speed=0, # how fast to draw ) self.turtle.draw_grid() class Pen: def __init__(self, bg, pw_up=1700, pw_down=1300, pin=18, transition_time=0.25, virtual=False): self.bg = bg self.pin = pin self.pw_up = pw_up self.pw_down = pw_down self.transition_time = transition_time self.virtual = virtual if self.virtual: print("Initialising virtual Pen") else: self.rpi = pigpio.pi() self.rpi.set_PWM_frequency(self.pin, 50) self.up() sleep(0.3) self.down() sleep(0.3) self.up() sleep(0.3) def down(self): if self.virtual: self.virtual_pw = self.pw_down else: self.rpi.set_servo_pulsewidth(self.pin, self.pw_down) sleep(self.transition_time) if self.bg.turtle: self.bg.turtle.down() self.bg.turtle.color('blue') self.bg.turtle.width(1) self.position = "down" def up(self): if self.virtual: self.virtual_pw = self.pw_up else: self.rpi.set_servo_pulsewidth(self.pin, self.pw_up) sleep(self.transition_time) if self.bg.turtle: self.bg.turtle.up() self.position = "up" # for convenience, a quick way to set pen motor pulse-widths def pw(self, pulse_width): if self.virtual: self.virtual_pw = pulse_width else: self.rpi.set_servo_pulsewidth(self.pin, pulse_width) def calibrate(self): print(f"Calibrating the pen-lifting servo.") print(f"See https://brachiograph.art/how-to/calibrate.html") pw_1, pw_2 = self.bg.get_pulse_widths() pw_3 = self.pw_up while True: self.bg.set_pulse_widths(pw_1, pw_2) self.pw(pw_3) key = readchar.readchar() if key == "0": break elif key=="a": pw_1 = pw_1 - 10 continue elif key=="s": pw_1 = pw_1 + 10 continue elif key=="k": pw_2 = pw_2 - 10 continue elif key=="l": pw_2 = pw_2 + 10 continue elif key=="t": if pw_3 == self.pw_up: pw_3 = self.pw_down else: pw_3 = self.pw_up continue elif key=="z": pw_3 = pw_3 - 10 print(pw_3) continue elif key=="x": pw_3 = pw_3 + 10 print(pw_3) continue elif key=="u": self.pw_up = pw_3 elif key=="d": self.pw_down = pw_3 else: continue mid = (self.pw_up + self.pw_down) / 2 print(f"Pen-up pulse-width: {self.pw_up}µS, pen-down pulse-width: {self.pw_down}µS, mid-point: {mid}") print() print("Use these values in your BrachioGraph definition:") print() print(f"pen_up={self.pw_up}, pen_down={self.pw_down}")

# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import annotations import itertools import logging from dataclasses import dataclass from typing import Iterable from pants.backend.python.goals import lockfile from pants.backend.python.goals.export import ExportPythonTool, ExportPythonToolSentinel from pants.backend.python.goals.lockfile import GeneratePythonLockfile from pants.backend.python.subsystems.python_tool_base import PythonToolBase from pants.backend.python.subsystems.setup import PythonSetup from pants.backend.python.target_types import ( ConsoleScript, PythonRequirementsField, PythonSourceField, ) from pants.backend.python.typecheck.mypy.skip_field import SkipMyPyField from pants.backend.python.util_rules.interpreter_constraints import InterpreterConstraints from pants.backend.python.util_rules.pex_requirements import PexRequirements from pants.backend.python.util_rules.python_sources import ( PythonSourceFiles, PythonSourceFilesRequest, ) from pants.core.goals.generate_lockfiles import GenerateToolLockfileSentinel from pants.core.util_rules.config_files import ConfigFiles, ConfigFilesRequest from pants.engine.addresses import Addresses, UnparsedAddressInputs from pants.engine.fs import EMPTY_DIGEST, Digest, DigestContents, FileContent from pants.engine.rules import Get, MultiGet, collect_rules, rule, rule_helper from pants.engine.target import ( AllTargets, AllTargetsRequest, FieldSet, Target, TransitiveTargets, TransitiveTargetsRequest, ) from pants.engine.unions import UnionRule from pants.option.option_types import ( ArgsListOption, BoolOption, FileOption, SkipOption, StrListOption, TargetListOption, ) from pants.util.docutil import doc_url, git_url from pants.util.logging import LogLevel from pants.util.ordered_set import FrozenOrderedSet from pants.util.strutil import softwrap logger = logging.getLogger(__name__) @dataclass(frozen=True) class MyPyFieldSet(FieldSet): required_fields = (PythonSourceField,) sources: PythonSourceField @classmethod def opt_out(cls, tgt: Target) -> bool: return tgt.get(SkipMyPyField).value # -------------------------------------------------------------------------------------- # Subsystem # -------------------------------------------------------------------------------------- class MyPy(PythonToolBase): options_scope = "mypy" name = "MyPy" help = "The MyPy Python type checker (http://mypy-lang.org/)." default_version = "mypy==0.910" default_main = ConsoleScript("mypy") # See `mypy/rules.py`. We only use these default constraints in some situations. register_interpreter_constraints = True default_interpreter_constraints = ["CPython>=3.7,<4"] register_lockfile = True default_lockfile_resource = ("pants.backend.python.typecheck.mypy", "mypy.lock") default_lockfile_path = "src/python/pants/backend/python/typecheck/mypy/mypy.lock" default_lockfile_url = git_url(default_lockfile_path) uses_requirements_from_source_plugins = True skip = SkipOption("check") args = ArgsListOption(example="--python-version 3.7 --disallow-any-expr") config = FileOption( "--config", default=None, advanced=True, help=lambda cls: softwrap( f""" Path to a config file understood by MyPy (https://mypy.readthedocs.io/en/stable/config_file.html). Setting this option will disable `[{cls.options_scope}].config_discovery`. Use this option if the config is located in a non-standard location. """ ), ) config_discovery = BoolOption( "--config-discovery", default=True, advanced=True, help=lambda cls: softwrap( f""" If true, Pants will include any relevant config files during runs (`mypy.ini`, `.mypy.ini`, and `setup.cfg`). Use `[{cls.options_scope}].config` instead if your config is in a non-standard location. """ ), ) _source_plugins = TargetListOption( "--source-plugins", advanced=True, help=softwrap( """ An optional list of `python_sources` target addresses to load first-party plugins. You must also set `plugins = path.to.module` in your `mypy.ini`, and set the `[mypy].config` option in your `pants.toml`. To instead load third-party plugins, set the option `[mypy].extra_requirements` and set the `plugins` option in `mypy.ini`. Tip: it's often helpful to define a dedicated 'resolve' via `[python].resolves` for your MyPy plugins such as 'mypy-plugins' so that the third-party requirements used by your plugin, like `mypy`, do not mix with the rest of your project. Read that option's help message for more info on resolves. """ ), ) extra_type_stubs = StrListOption( "--extra-type-stubs", advanced=True, help=softwrap( """ Extra type stub requirements to install when running MyPy. Normally, type stubs can be installed as typical requirements, such as putting them in `requirements.txt` or using a `python_requirement` target. Alternatively, you can use this option so that the dependencies are solely used when running MyPy and are not runtime dependencies. Expects a list of pip-style requirement strings, like `['types-requests==2.25.9']`. """ ), ) @property def config_request(self) -> ConfigFilesRequest: # Refer to https://mypy.readthedocs.io/en/stable/config_file.html. return ConfigFilesRequest( specified=self.config, specified_option_name=f"{self.options_scope}.config", discovery=self.config_discovery, check_existence=["mypy.ini", ".mypy.ini"], check_content={"setup.cfg": b"[mypy", "pyproject.toml": b"[tool.mypy"}, ) @property def source_plugins(self) -> UnparsedAddressInputs: return UnparsedAddressInputs(self._source_plugins, owning_address=None) def check_and_warn_if_python_version_configured(self, config: FileContent | None) -> bool: """Determine if we can dynamically set `--python-version` and warn if not.""" configured = [] if config and b"python_version" in config.content: configured.append( f"`python_version` in {config.path} (which is used because of either config " "discovery or the `[mypy].config` option)" ) if "--py2" in self.args: configured.append("`--py2` in the `--mypy-args` option") if any(arg.startswith("--python-version") for arg in self.args): configured.append("`--python-version` in the `--mypy-args` option") if configured: formatted_configured = " and you set ".join(configured) logger.warning( f"You set {formatted_configured}. Normally, Pants would automatically set this " "for you based on your code's interpreter constraints " f"({doc_url("python-interpreter-compatibility")}). Instead, it will " "use what you set.\n\n" "(Automatically setting the option allows Pants to partition your targets by their " "constraints, so that, for example, you can run MyPy on Python 2-only code and " "Python 3-only code at the same time. This feature may no longer work.)" ) return bool(configured) # -------------------------------------------------------------------------------------- # Config files # -------------------------------------------------------------------------------------- @dataclass(frozen=True) class MyPyConfigFile: digest: Digest _python_version_configured: bool def python_version_to_autoset( self, interpreter_constraints: InterpreterConstraints, interpreter_universe: Iterable[str] ) -> str | None: """If the user did not already set `--python-version`, return the major.minor version to use.""" if self._python_version_configured: return None return interpreter_constraints.minimum_python_version(interpreter_universe) @rule async def setup_mypy_config(mypy: MyPy) -> MyPyConfigFile: config_files = await Get(ConfigFiles, ConfigFilesRequest, mypy.config_request) digest_contents = await Get(DigestContents, Digest, config_files.snapshot.digest) python_version_configured = mypy.check_and_warn_if_python_version_configured( digest_contents[0] if digest_contents else None ) return MyPyConfigFile(config_files.snapshot.digest, python_version_configured) # -------------------------------------------------------------------------------------- # First party plugins # -------------------------------------------------------------------------------------- @dataclass(frozen=True) class MyPyFirstPartyPlugins: requirement_strings: FrozenOrderedSet[str] sources_digest: Digest source_roots: tuple[str, ...] @rule("Prepare [mypy].source_plugins", level=LogLevel.DEBUG) async def mypy_first_party_plugins( mypy: MyPy, ) -> MyPyFirstPartyPlugins: if not mypy.source_plugins: return MyPyFirstPartyPlugins(FrozenOrderedSet(), EMPTY_DIGEST, ()) plugin_target_addresses = await Get(Addresses, UnparsedAddressInputs, mypy.source_plugins) transitive_targets = await Get( TransitiveTargets, TransitiveTargetsRequest(plugin_target_addresses) ) requirements = PexRequirements.create_from_requirement_fields( ( plugin_tgt[PythonRequirementsField] for plugin_tgt in transitive_targets.closure if plugin_tgt.has_field(PythonRequirementsField) ), constraints_strings=(), ) sources = await Get(PythonSourceFiles, PythonSourceFilesRequest(transitive_targets.closure)) return MyPyFirstPartyPlugins( requirement_strings=requirements.req_strings, sources_digest=sources.source_files.snapshot.digest, source_roots=sources.source_roots, ) # -------------------------------------------------------------------------------------- # Interpreter constraints # -------------------------------------------------------------------------------------- @rule_helper async def _mypy_interpreter_constraints( mypy: MyPy, python_setup: PythonSetup ) -> InterpreterConstraints: constraints = mypy.interpreter_constraints if mypy.options.is_default("interpreter_constraints"): all_tgts = await Get(AllTargets, AllTargetsRequest()) all_transitive_targets = await MultiGet( Get(TransitiveTargets, TransitiveTargetsRequest([tgt.address])) for tgt in all_tgts if MyPyFieldSet.is_applicable(tgt) ) unique_constraints = { InterpreterConstraints.create_from_targets(transitive_targets.closure, python_setup) for transitive_targets in all_transitive_targets } code_constraints = InterpreterConstraints(itertools.chain.from_iterable(unique_constraints)) if code_constraints.requires_python38_or_newer(python_setup.interpreter_universe): constraints = code_constraints return constraints # -------------------------------------------------------------------------------------- # Lockfile # -------------------------------------------------------------------------------------- class MyPyLockfileSentinel(GenerateToolLockfileSentinel): resolve_name = MyPy.options_scope @rule( desc="Determine MyPy interpreter constraints (for lockfile generation)", level=LogLevel.DEBUG, ) async def setup_mypy_lockfile( _: MyPyLockfileSentinel, first_party_plugins: MyPyFirstPartyPlugins, mypy: MyPy, python_setup: PythonSetup, ) -> GeneratePythonLockfile: if not mypy.uses_custom_lockfile: return GeneratePythonLockfile.from_tool( mypy, use_pex=python_setup.generate_lockfiles_with_pex ) constraints = await _mypy_interpreter_constraints(mypy, python_setup) return GeneratePythonLockfile.from_tool( mypy, constraints, extra_requirements=first_party_plugins.requirement_strings, use_pex=python_setup.generate_lockfiles_with_pex, ) # -------------------------------------------------------------------------------------- # Export # -------------------------------------------------------------------------------------- class MyPyExportSentinel(ExportPythonToolSentinel): pass @rule(desc="Determine MyPy interpreter constraints (for `export` goal)", level=LogLevel.DEBUG) async def mypy_export( _: MyPyExportSentinel, mypy: MyPy, python_setup: PythonSetup, first_party_plugins: MyPyFirstPartyPlugins, ) -> ExportPythonTool: constraints = await _mypy_interpreter_constraints(mypy, python_setup) return ExportPythonTool( resolve_name=mypy.options_scope, pex_request=mypy.to_pex_request( interpreter_constraints=constraints, extra_requirements=first_party_plugins.requirement_strings, ), ) def rules(): return ( *collect_rules(), *lockfile.rules(), UnionRule(GenerateToolLockfileSentinel, MyPyLockfileSentinel), UnionRule(ExportPythonToolSentinel, MyPyExportSentinel), )

# Copyright 2019 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import annotations import itertools import logging from dataclasses import dataclass from typing import Iterable from pants.backend.python.goals import lockfile from pants.backend.python.goals.export import ExportPythonTool, ExportPythonToolSentinel from pants.backend.python.goals.lockfile import GeneratePythonLockfile from pants.backend.python.subsystems.python_tool_base import PythonToolBase from pants.backend.python.subsystems.setup import PythonSetup from pants.backend.python.target_types import ( ConsoleScript, PythonRequirementsField, PythonSourceField, ) from pants.backend.python.typecheck.mypy.skip_field import SkipMyPyField from pants.backend.python.util_rules.interpreter_constraints import InterpreterConstraints from pants.backend.python.util_rules.pex_requirements import PexRequirements from pants.backend.python.util_rules.python_sources import ( PythonSourceFiles, PythonSourceFilesRequest, ) from pants.core.goals.generate_lockfiles import GenerateToolLockfileSentinel from pants.core.util_rules.config_files import ConfigFiles, ConfigFilesRequest from pants.engine.addresses import Addresses, UnparsedAddressInputs from pants.engine.fs import EMPTY_DIGEST, Digest, DigestContents, FileContent from pants.engine.rules import Get, MultiGet, collect_rules, rule, rule_helper from pants.engine.target import ( AllTargets, AllTargetsRequest, FieldSet, Target, TransitiveTargets, TransitiveTargetsRequest, ) from pants.engine.unions import UnionRule from pants.option.option_types import ( ArgsListOption, BoolOption, FileOption, SkipOption, StrListOption, TargetListOption, ) from pants.util.docutil import doc_url, git_url from pants.util.logging import LogLevel from pants.util.ordered_set import FrozenOrderedSet from pants.util.strutil import softwrap logger = logging.getLogger(__name__) @dataclass(frozen=True) class MyPyFieldSet(FieldSet): required_fields = (PythonSourceField,) sources: PythonSourceField @classmethod def opt_out(cls, tgt: Target) -> bool: return tgt.get(SkipMyPyField).value # -------------------------------------------------------------------------------------- # Subsystem # -------------------------------------------------------------------------------------- class MyPy(PythonToolBase): options_scope = "mypy" name = "MyPy" help = "The MyPy Python type checker (http://mypy-lang.org/)." default_version = "mypy==0.910" default_main = ConsoleScript("mypy") # See `mypy/rules.py`. We only use these default constraints in some situations. register_interpreter_constraints = True default_interpreter_constraints = ["CPython>=3.7,<4"] register_lockfile = True default_lockfile_resource = ("pants.backend.python.typecheck.mypy", "mypy.lock") default_lockfile_path = "src/python/pants/backend/python/typecheck/mypy/mypy.lock" default_lockfile_url = git_url(default_lockfile_path) uses_requirements_from_source_plugins = True skip = SkipOption("check") args = ArgsListOption(example="--python-version 3.7 --disallow-any-expr") config = FileOption( "--config", default=None, advanced=True, help=lambda cls: softwrap( f""" Path to a config file understood by MyPy (https://mypy.readthedocs.io/en/stable/config_file.html). Setting this option will disable `[{cls.options_scope}].config_discovery`. Use this option if the config is located in a non-standard location. """ ), ) config_discovery = BoolOption( "--config-discovery", default=True, advanced=True, help=lambda cls: softwrap( f""" If true, Pants will include any relevant config files during runs (`mypy.ini`, `.mypy.ini`, and `setup.cfg`). Use `[{cls.options_scope}].config` instead if your config is in a non-standard location. """ ), ) _source_plugins = TargetListOption( "--source-plugins", advanced=True, help=softwrap( """ An optional list of `python_sources` target addresses to load first-party plugins. You must also set `plugins = path.to.module` in your `mypy.ini`, and set the `[mypy].config` option in your `pants.toml`. To instead load third-party plugins, set the option `[mypy].extra_requirements` and set the `plugins` option in `mypy.ini`. Tip: it's often helpful to define a dedicated 'resolve' via `[python].resolves` for your MyPy plugins such as 'mypy-plugins' so that the third-party requirements used by your plugin, like `mypy`, do not mix with the rest of your project. Read that option's help message for more info on resolves. """ ), ) extra_type_stubs = StrListOption( "--extra-type-stubs", advanced=True, help=softwrap( """ Extra type stub requirements to install when running MyPy. Normally, type stubs can be installed as typical requirements, such as putting them in `requirements.txt` or using a `python_requirement` target. Alternatively, you can use this option so that the dependencies are solely used when running MyPy and are not runtime dependencies. Expects a list of pip-style requirement strings, like `['types-requests==2.25.9']`. """ ), ) @property def config_request(self) -> ConfigFilesRequest: # Refer to https://mypy.readthedocs.io/en/stable/config_file.html. return ConfigFilesRequest( specified=self.config, specified_option_name=f"{self.options_scope}.config", discovery=self.config_discovery, check_existence=["mypy.ini", ".mypy.ini"], check_content={"setup.cfg": b"[mypy", "pyproject.toml": b"[tool.mypy"}, ) @property def source_plugins(self) -> UnparsedAddressInputs: return UnparsedAddressInputs(self._source_plugins, owning_address=None) def check_and_warn_if_python_version_configured(self, config: FileContent | None) -> bool: """Determine if we can dynamically set `--python-version` and warn if not.""" configured = [] if config and b"python_version" in config.content: configured.append( f"`python_version` in {config.path} (which is used because of either config " "discovery or the `[mypy].config` option)" ) if "--py2" in self.args: configured.append("`--py2` in the `--mypy-args` option") if any(arg.startswith("--python-version") for arg in self.args): configured.append("`--python-version` in the `--mypy-args` option") if configured: formatted_configured = " and you set ".join(configured) logger.warning( f"You set {formatted_configured}. Normally, Pants would automatically set this " "for you based on your code's interpreter constraints " f"({doc_url('python-interpreter-compatibility')}). Instead, it will " "use what you set.\n\n" "(Automatically setting the option allows Pants to partition your targets by their " "constraints, so that, for example, you can run MyPy on Python 2-only code and " "Python 3-only code at the same time. This feature may no longer work.)" ) return bool(configured) # -------------------------------------------------------------------------------------- # Config files # -------------------------------------------------------------------------------------- @dataclass(frozen=True) class MyPyConfigFile: digest: Digest _python_version_configured: bool def python_version_to_autoset( self, interpreter_constraints: InterpreterConstraints, interpreter_universe: Iterable[str] ) -> str | None: """If the user did not already set `--python-version`, return the major.minor version to use.""" if self._python_version_configured: return None return interpreter_constraints.minimum_python_version(interpreter_universe) @rule async def setup_mypy_config(mypy: MyPy) -> MyPyConfigFile: config_files = await Get(ConfigFiles, ConfigFilesRequest, mypy.config_request) digest_contents = await Get(DigestContents, Digest, config_files.snapshot.digest) python_version_configured = mypy.check_and_warn_if_python_version_configured( digest_contents[0] if digest_contents else None ) return MyPyConfigFile(config_files.snapshot.digest, python_version_configured) # -------------------------------------------------------------------------------------- # First party plugins # -------------------------------------------------------------------------------------- @dataclass(frozen=True) class MyPyFirstPartyPlugins: requirement_strings: FrozenOrderedSet[str] sources_digest: Digest source_roots: tuple[str, ...] @rule("Prepare [mypy].source_plugins", level=LogLevel.DEBUG) async def mypy_first_party_plugins( mypy: MyPy, ) -> MyPyFirstPartyPlugins: if not mypy.source_plugins: return MyPyFirstPartyPlugins(FrozenOrderedSet(), EMPTY_DIGEST, ()) plugin_target_addresses = await Get(Addresses, UnparsedAddressInputs, mypy.source_plugins) transitive_targets = await Get( TransitiveTargets, TransitiveTargetsRequest(plugin_target_addresses) ) requirements = PexRequirements.create_from_requirement_fields( ( plugin_tgt[PythonRequirementsField] for plugin_tgt in transitive_targets.closure if plugin_tgt.has_field(PythonRequirementsField) ), constraints_strings=(), ) sources = await Get(PythonSourceFiles, PythonSourceFilesRequest(transitive_targets.closure)) return MyPyFirstPartyPlugins( requirement_strings=requirements.req_strings, sources_digest=sources.source_files.snapshot.digest, source_roots=sources.source_roots, ) # -------------------------------------------------------------------------------------- # Interpreter constraints # -------------------------------------------------------------------------------------- @rule_helper async def _mypy_interpreter_constraints( mypy: MyPy, python_setup: PythonSetup ) -> InterpreterConstraints: constraints = mypy.interpreter_constraints if mypy.options.is_default("interpreter_constraints"): all_tgts = await Get(AllTargets, AllTargetsRequest()) all_transitive_targets = await MultiGet( Get(TransitiveTargets, TransitiveTargetsRequest([tgt.address])) for tgt in all_tgts if MyPyFieldSet.is_applicable(tgt) ) unique_constraints = { InterpreterConstraints.create_from_targets(transitive_targets.closure, python_setup) for transitive_targets in all_transitive_targets } code_constraints = InterpreterConstraints(itertools.chain.from_iterable(unique_constraints)) if code_constraints.requires_python38_or_newer(python_setup.interpreter_universe): constraints = code_constraints return constraints # -------------------------------------------------------------------------------------- # Lockfile # -------------------------------------------------------------------------------------- class MyPyLockfileSentinel(GenerateToolLockfileSentinel): resolve_name = MyPy.options_scope @rule( desc="Determine MyPy interpreter constraints (for lockfile generation)", level=LogLevel.DEBUG, ) async def setup_mypy_lockfile( _: MyPyLockfileSentinel, first_party_plugins: MyPyFirstPartyPlugins, mypy: MyPy, python_setup: PythonSetup, ) -> GeneratePythonLockfile: if not mypy.uses_custom_lockfile: return GeneratePythonLockfile.from_tool( mypy, use_pex=python_setup.generate_lockfiles_with_pex ) constraints = await _mypy_interpreter_constraints(mypy, python_setup) return GeneratePythonLockfile.from_tool( mypy, constraints, extra_requirements=first_party_plugins.requirement_strings, use_pex=python_setup.generate_lockfiles_with_pex, ) # -------------------------------------------------------------------------------------- # Export # -------------------------------------------------------------------------------------- class MyPyExportSentinel(ExportPythonToolSentinel): pass @rule(desc="Determine MyPy interpreter constraints (for `export` goal)", level=LogLevel.DEBUG) async def mypy_export( _: MyPyExportSentinel, mypy: MyPy, python_setup: PythonSetup, first_party_plugins: MyPyFirstPartyPlugins, ) -> ExportPythonTool: constraints = await _mypy_interpreter_constraints(mypy, python_setup) return ExportPythonTool( resolve_name=mypy.options_scope, pex_request=mypy.to_pex_request( interpreter_constraints=constraints, extra_requirements=first_party_plugins.requirement_strings, ), ) def rules(): return ( *collect_rules(), *lockfile.rules(), UnionRule(GenerateToolLockfileSentinel, MyPyLockfileSentinel), UnionRule(ExportPythonToolSentinel, MyPyExportSentinel), )

import click from . import cli from .params import project from meltano.core.db import project_engine from meltano.core.project import Project from meltano.core.config_service import ConfigService from meltano.core.plugin.settings_service import PluginSettingsService @cli.group(invoke_without_command=True) @click.argument("plugin_name") @click.option("--format", default="json") @project(migrate=True) @click.pass_context def config(ctx, project, plugin_name, format): config = ConfigService(project) plugin = config.find_plugin(plugin_name) _, Session = project_engine(project) session = Session() settings = PluginSettingsService(project) ctx.obj["settings"] = settings ctx.obj["plugin"] = plugin ctx.obj["session"] = session if ctx.invoked_subcommand is None: if format == "json": print(settings.as_config(session, plugin)) if format == "env": for env, value in settings.as_env(session, plugin).items(): print(f"{env}={value}") @config.command() @click.argument("setting_name") @click.argument("value") @click.pass_context def set(ctx, setting_name, value): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] settings.set(session, plugin, setting_name, value) @config.command() @click.argument("setting_name") @click.pass_context def unset(ctx, setting_name): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] settings.unset(session, plugin, setting_name) @config.command() @click.pass_context def reset(ctx): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] for setting in settings.definitions(plugin): settings.unset(session, plugin, setting.name) @config.command() @click.pass_context def list(ctx): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] plugin_def = settings.get_definition(plugin) for setting_def in settings.definitions(plugin): env_key = settings.setting_env(setting_def, plugin_def) description_marker = ( f": {setting_def["description"]}" if setting_def.get("description") else "" ) click.secho(f"{setting_def["name"]} [{env_key}]{description_marker}")

import click from . import cli from .params import project from meltano.core.db import project_engine from meltano.core.project import Project from meltano.core.config_service import ConfigService from meltano.core.plugin.settings_service import PluginSettingsService @cli.group(invoke_without_command=True) @click.argument("plugin_name") @click.option("--format", default="json") @project(migrate=True) @click.pass_context def config(ctx, project, plugin_name, format): config = ConfigService(project) plugin = config.find_plugin(plugin_name) _, Session = project_engine(project) session = Session() settings = PluginSettingsService(project) ctx.obj["settings"] = settings ctx.obj["plugin"] = plugin ctx.obj["session"] = session if ctx.invoked_subcommand is None: if format == "json": print(settings.as_config(session, plugin)) if format == "env": for env, value in settings.as_env(session, plugin).items(): print(f"{env}={value}") @config.command() @click.argument("setting_name") @click.argument("value") @click.pass_context def set(ctx, setting_name, value): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] settings.set(session, plugin, setting_name, value) @config.command() @click.argument("setting_name") @click.pass_context def unset(ctx, setting_name): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] settings.unset(session, plugin, setting_name) @config.command() @click.pass_context def reset(ctx): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] session = ctx.obj["session"] for setting in settings.definitions(plugin): settings.unset(session, plugin, setting.name) @config.command() @click.pass_context def list(ctx): settings = ctx.obj["settings"] plugin = ctx.obj["plugin"] plugin_def = settings.get_definition(plugin) for setting_def in settings.definitions(plugin): env_key = settings.setting_env(setting_def, plugin_def) description_marker = ( f": {setting_def['description']}" if setting_def.get("description") else "" ) click.secho(f"{setting_def['name']} [{env_key}]{description_marker}")

import argparse import os import collections from os import system import re from collections import defaultdict parser = argparse.ArgumentParser() parser.add_argument( "-f", "--folder", default=".", help="Specifies which folder to make the naviagtable bat file", ) parser.add_argument( "-n", "--name", default="jump", help="Specifies what is the name of the bat file, other wise it adds to the jump.bat", ) parser.add_argument( "-r", "--recursive", default=0, help="Specifies if we want: to make navigation recursive to all the folders recursively", ) parser.add_argument( "-j", "--injump", default=0, help="Specifies if we want: to make navigation inside jump command", ) parser.add_argument( "-c", "--code", default=0, help="Specifies if we want: to add open in code options for the base folder ", ) parser.add_argument( "-s", "--start", default=0, help="Specifies if we want: to add open in file manager options for the base folder ", ) args = parser.parse_args() __name__ = args.name _name = f"C:\\nable-bin\{args.name}.bat" # shotcut batch file _folder = args.folder _r = args.recursive _code = int(args.code) _jump = args.injump _start = int(args.start) _fPath = os.path.abspath(_folder) __help__ = f""" if "%{2 if args.name == "jump" else 1:d}" == "-h" (\n \necho Shortcut for the Folder are\necho {"":=<70}""" __taken__ = defaultdict(bool) # <-- name is taken __initial__ = defaultdict(int) """ ./ds/hon/stat py scikit ./ds/tf/dl/relu conv /ml/lr /mr /stat/ # --> ds .honstat ds ..py ds ..scikit ..dl ..ml .tfstat # --> ds ...relu # so for conflict resolution we expand the dot """ def check(): if _jump: if __name__ == "jump": raise Exception( "Warning: Jump Flag raised but no name specified!\n----------> -j 1\nYou must specifie name option as\n----------> -j 1 -n XXXX" ) """---------------------------Code generator---------------------------""" """________________folder extraction _______________""" def __explore(r: int) -> list: if r: for root, _, _ in os.walk(_folder): if not root.startswith(".\."): yield f"{os.getcwd()}\{root.lstrip(".")}".replace("\\\\", "\\") else: for entry in os.scandir(_folder): if not entry.is_file() and not entry.name.startswith("."): yield f"{os.getcwd()}\{entry.name}" """________________folder processing_______________""" def __process(fPath: str): _rel = os.path.relpath(fPath) _lvl = __gtLvl(_rel) _ret = "" if _lvl == ".": _ret = __gt(fPath) elif _lvl < "..." and _lvl > ".": _ret = __gt(fPath, 0) return _ret """________________code gen_______________""" def __gt(fPath: str, lvl1=True): _name = __gtName(fPath) _rel = os.path.relpath(fPath) _lvl = __gtLvl(_rel) if not lvl1 else "" _ret = f""" if "%{__gtScope():d}" == "{_lvl}{_name}" ( cd /d "{fPath}" {__code(__gtScope()+1,fPath)} {__start(__gtScope()+1,fPath)} ) """ global __help__ __help__ += f"\necho {_lvl}{_name:-<50}{_rel}" return _ret def __gtScope() -> int: return 2 if args.name == "jump" else 1 def __gtLvl(relPath: str) -> str: _l = len(relPath.split("\\")) _lvl = "" for _ in range(_l): _lvl += "." return _lvl """---------------------------Options---------------------------""" """________________code_______________""" def __gtCode(path: str) -> str: _ret = f""" code "{os.path.abspath(path)}" """ return _ret def __code(scope: int, path: str = _fPath, rflg: int = 0) -> str: _ret = "" if _code == 1: _ret = f""" if "%{scope}" == "c" ( cd /d "{path}" {__gtCode(path)} ) """ _ret = _ret if not rflg == 1 else "" elif _code == 2: _ret = __gtCode(path) _ret = _ret if not rflg == 2 else "" return _ret """________________start_______________""" def __gtStart(path: str) -> str: _ret = f""" start . """ return _ret def __start(scope: int, path: str = _fPath, rflg=0) -> str: _ret = "" if _start == 1: _ret = f""" if "%{scope}" == "s" ( cd /d "{path}" {__gtStart(path)} ) """ _ret = _ret if not rflg == 1 else "" elif _start == 2: _ret = __gtStart(path) _ret = _ret if not rflg == 2 else "" return _ret """---------------------------Process name---------------------------""" def __gtName(relPath: str) -> str: global __taken__ _items = relPath.rsplit("\\", 1) name = _items[1] if (len(_items) > 1) else relPath _commons = defaultdict(str) for k, v in { "argument": "arg", "git": "git", "arguments": "args", "basics": "bsc", "utilities": "utils", "fuctions": "funcs", "database": "db", "numpy": "np", "io": "io", "string": "str", "function": "fucn", "utility": "util", "oop": "oop", "networking": "net", }.items(): _commons[k] = v name = name.lstrip(".") _ret = _commons[name.lower()] _pat = r"^[A-Za-z0-9]|\s[A-Za-z]|[A-Z]|[0-9]{,2}$|_[a-zA-Z]" _mtchs = re.findall(_pat, name) _ret = ( "".join(_mtchs[:5]).replace(" ", "").replace("_", "").lower() if _ret == "" else _ret ) """ ___Note:For Name collision___ """ if __taken__[_ret]: __initial__[_ret] += 1 _ret = f"{_ret}{__initial__[_ret]}" else: __taken__[_ret] = True return _ret """---------------------------Final draft---------------------------""" """________________basics root folder and option_______________""" def __bsc_op(iflg: int = 0) -> str: _ret = "" _ret += f""" cd /d "{_fPath}" {__start(2, _fPath, 1)} {__code(2, _fPath, 1)} """ _ret = ( f""" if "%2" == "" ( {_ret} ) """ if iflg else f""" if "%1" == "" ( {_ret} ) """ ) _ret = f""" {_ret} {__start(1, _fPath, 2)} {__code(1, _fPath, 2)} """ return _ret def __gtWrite(_data: str): global __help__ _write = "" if __name__ == "jump": _write += f""" \n\n \rREM {'':-<30} {__gtName(_fPath)} {'':-<30} if "%1" == "-{__gtName(_fPath) if not _jump else __name__ }" ( {__bsc_op(1)} {_data} {__help__} ) """ else: _write += f""" \r@echo off\n {__bsc_op()} {_data} {__help__} """ return _write """---------------------------Execution code---------------------------""" def __cached(data: str) -> None: if os.path.exists(_name) and __name__ == "jump": with open(_name, "a") as file: file.write(data) else: with open(_name, "w") as file: file.write(data) def __main__(): check() _data = "" global __main__ global __help__ for pth in __explore(_r): _data += __process(pth) + "\n" __help__ += f"\necho {"":=<70} \n)" __cached(__gtWrite(_data)) try: __main__() except Exception as e: print(e)

import argparse import os import collections from os import system import re from collections import defaultdict parser = argparse.ArgumentParser() parser.add_argument( "-f", "--folder", default=".", help="Specifies which folder to make the naviagtable bat file", ) parser.add_argument( "-n", "--name", default="jump", help="Specifies what is the name of the bat file, other wise it adds to the jump.bat", ) parser.add_argument( "-r", "--recursive", default=0, help="Specifies if we want: to make navigation recursive to all the folders recursively", ) parser.add_argument( "-j", "--injump", default=0, help="Specifies if we want: to make navigation inside jump command", ) parser.add_argument( "-c", "--code", default=0, help="Specifies if we want: to add open in code options for the base folder ", ) parser.add_argument( "-s", "--start", default=0, help="Specifies if we want: to add open in file manager options for the base folder ", ) args = parser.parse_args() __name__ = args.name _name = f"C:\\nable-bin\{args.name}.bat" # shotcut batch file _folder = args.folder _r = args.recursive _code = int(args.code) _jump = args.injump _start = int(args.start) _fPath = os.path.abspath(_folder) __help__ = f""" if "%{2 if args.name == "jump" else 1:d}" == "-h" (\n \necho Shortcut for the Folder are\necho {"":=<70}""" __taken__ = defaultdict(bool) # <-- name is taken __initial__ = defaultdict(int) """ ./ds/hon/stat py scikit ./ds/tf/dl/relu conv /ml/lr /mr /stat/ # --> ds .honstat ds ..py ds ..scikit ..dl ..ml .tfstat # --> ds ...relu # so for conflict resolution we expand the dot """ def check(): if _jump: if __name__ == "jump": raise Exception( "Warning: Jump Flag raised but no name specified!\n----------> -j 1\nYou must specifie name option as\n----------> -j 1 -n XXXX" ) """---------------------------Code generator---------------------------""" """________________folder extraction _______________""" def __explore(r: int) -> list: if r: for root, _, _ in os.walk(_folder): if not root.startswith(".\."): yield f"{os.getcwd()}\{root.lstrip('.')}".replace("\\\\", "\\") else: for entry in os.scandir(_folder): if not entry.is_file() and not entry.name.startswith("."): yield f"{os.getcwd()}\{entry.name}" """________________folder processing_______________""" def __process(fPath: str): _rel = os.path.relpath(fPath) _lvl = __gtLvl(_rel) _ret = "" if _lvl == ".": _ret = __gt(fPath) elif _lvl < "..." and _lvl > ".": _ret = __gt(fPath, 0) return _ret """________________code gen_______________""" def __gt(fPath: str, lvl1=True): _name = __gtName(fPath) _rel = os.path.relpath(fPath) _lvl = __gtLvl(_rel) if not lvl1 else "" _ret = f""" if "%{__gtScope():d}" == "{_lvl}{_name}" ( cd /d "{fPath}" {__code(__gtScope()+1,fPath)} {__start(__gtScope()+1,fPath)} ) """ global __help__ __help__ += f"\necho {_lvl}{_name:-<50}{_rel}" return _ret def __gtScope() -> int: return 2 if args.name == "jump" else 1 def __gtLvl(relPath: str) -> str: _l = len(relPath.split("\\")) _lvl = "" for _ in range(_l): _lvl += "." return _lvl """---------------------------Options---------------------------""" """________________code_______________""" def __gtCode(path: str) -> str: _ret = f""" code "{os.path.abspath(path)}" """ return _ret def __code(scope: int, path: str = _fPath, rflg: int = 0) -> str: _ret = "" if _code == 1: _ret = f""" if "%{scope}" == "c" ( cd /d "{path}" {__gtCode(path)} ) """ _ret = _ret if not rflg == 1 else "" elif _code == 2: _ret = __gtCode(path) _ret = _ret if not rflg == 2 else "" return _ret """________________start_______________""" def __gtStart(path: str) -> str: _ret = f""" start . """ return _ret def __start(scope: int, path: str = _fPath, rflg=0) -> str: _ret = "" if _start == 1: _ret = f""" if "%{scope}" == "s" ( cd /d "{path}" {__gtStart(path)} ) """ _ret = _ret if not rflg == 1 else "" elif _start == 2: _ret = __gtStart(path) _ret = _ret if not rflg == 2 else "" return _ret """---------------------------Process name---------------------------""" def __gtName(relPath: str) -> str: global __taken__ _items = relPath.rsplit("\\", 1) name = _items[1] if (len(_items) > 1) else relPath _commons = defaultdict(str) for k, v in { "argument": "arg", "git": "git", "arguments": "args", "basics": "bsc", "utilities": "utils", "fuctions": "funcs", "database": "db", "numpy": "np", "io": "io", "string": "str", "function": "fucn", "utility": "util", "oop": "oop", "networking": "net", }.items(): _commons[k] = v name = name.lstrip(".") _ret = _commons[name.lower()] _pat = r"^[A-Za-z0-9]|\s[A-Za-z]|[A-Z]|[0-9]{,2}$|_[a-zA-Z]" _mtchs = re.findall(_pat, name) _ret = ( "".join(_mtchs[:5]).replace(" ", "").replace("_", "").lower() if _ret == "" else _ret ) """ ___Note:For Name collision___ """ if __taken__[_ret]: __initial__[_ret] += 1 _ret = f"{_ret}{__initial__[_ret]}" else: __taken__[_ret] = True return _ret """---------------------------Final draft---------------------------""" """________________basics root folder and option_______________""" def __bsc_op(iflg: int = 0) -> str: _ret = "" _ret += f""" cd /d "{_fPath}" {__start(2, _fPath, 1)} {__code(2, _fPath, 1)} """ _ret = ( f""" if "%2" == "" ( {_ret} ) """ if iflg else f""" if "%1" == "" ( {_ret} ) """ ) _ret = f""" {_ret} {__start(1, _fPath, 2)} {__code(1, _fPath, 2)} """ return _ret def __gtWrite(_data: str): global __help__ _write = "" if __name__ == "jump": _write += f""" \n\n \rREM {'':-<30} {__gtName(_fPath)} {'':-<30} if "%1" == "-{__gtName(_fPath) if not _jump else __name__ }" ( {__bsc_op(1)} {_data} {__help__} ) """ else: _write += f""" \r@echo off\n {__bsc_op()} {_data} {__help__} """ return _write """---------------------------Execution code---------------------------""" def __cached(data: str) -> None: if os.path.exists(_name) and __name__ == "jump": with open(_name, "a") as file: file.write(data) else: with open(_name, "w") as file: file.write(data) def __main__(): check() _data = "" global __main__ global __help__ for pth in __explore(_r): _data += __process(pth) + "\n" __help__ += f"\necho {'':=<70} \n)" __cached(__gtWrite(_data)) try: __main__() except Exception as e: print(e)

# -*- coding: utf-8 -*- # Copyright © 2022, Neuroethology Lab Uni Tuebingen # # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted under the terms of the BSD License. See # LICENSE file in the root of the Project. import re import os import glob import odml import logging import subprocess import numpy as np import nixio as nix from .config import ConfigFile from .traces import EventTrace, RawTrace from .stimuli import StimuliDat from .util import parse_value, odml2nix, only_number from .stimdescription import parse_stimulus_description from IPython import embed class Converter(object): def __init__(self, folder_name, output_name, force=False) -> None: if not os.path.exists(folder_name): logging.error(f"{folder_name} does not exist!") raise ValueError("File not found error!") self._folder = folder_name self._output = output_name self._event_traces = None self._raw_traces = None self._raw_data_arrays = {} self._event_data_arrays = {} self._stimuli_dat = None self._force = force self._nixfile = None self._block = None self._repro_tags = {} self._stimulus_mtags = {} self.preflight() def preflight(self): logging.debug(f"Pre-checking folder {self._folder}!") self.check_output() self.check_folder() logging.debug("Pre-checking done.") def check_output(self): logging.debug(f"Checking output name: {self._output}!") if os.path.exists(self._output): logging.warn(f"Output file name {self._output} already exists!") if self._force: logging.warn(f"... force flag is set {self._force}, going to overwrite!") else: logging.error(f"Force flag is not set ({self._force}), abort!") raise ValueError("Output file {self._output} already exists! If you want to overwrite it use the --force flag.") logging.debug(f"... ok!") return True def unzip(self, tracename): if os.path.exists(tracename): logging.debug(f"\tunzip: {tracename}") subprocess.check_call(["gunzip", tracename]) def find_traces(self): event_traces = [] raw_traces = [] configuration = self.find_config_file() for et in self.find_event_traces(): event_traces.append(EventTrace(et, configuration)) for rt in self.find_raw_traces(): raw_traces.append(RawTrace(rt, configuration)) return raw_traces, event_traces def find_raw_traces(self): logging.debug(f"Checking for raw traces!") traces = sorted(glob.glob(os.path.join(self._folder, "trace-*.raw*"))) for rt in traces: if rt.endswith(".gz") and rt.split(".gz")[0] not in traces: self.unzip(os.path.split(rt)[-1]) traces = sorted(glob.glob(os.path.join(self._folder, "trace-*.raw"))) logging.debug(f"Found {len(traces)} raw traces. {[os.path.split(t)[-1] for t in traces]}") return traces def find_event_traces(self): logging.debug("Discovering event traces!") traces = sorted(glob.glob(os.path.join(self._folder, "*-events.dat"))) logging.debug(f"Found {len(traces)} event traces. {[os.path.split(t)[-1] for t in traces]}") return traces def find_config_file(self): if not os.path.exists(os.path.join(self._folder, "relacs.cfg")): logging.error("Found no info file!") raise ValueError(f"No relacs.cfg file found in {self._folder}!") configuration = ConfigFile(os.path.join(self._folder, "relacs.cfg")) return configuration def find_info(self): filename = os.path.join(self._folder, "info.dat") if not os.path.exists(filename): logging.error("Found no info file!") raise ValueError(f"No info file found in {self._folder}!") return True def read_info_file(self): def looks_like_oldstyle(filename): with open(filename, 'r') as f: for l in f: if "# Recording" in l: oldtyle = not l.strip().endswith(":") break return oldtyle filename = os.path.join(self._folder, "info.dat") oldstyle = looks_like_oldstyle(filename) info = {} logging.info("Reading info file....") try: with open(filename, 'r') as f: lines = f.readlines() except UnicodeDecodeError: logging.debug("Replacing experimenter...") command = r"sudo sed -i '/Experimenter/c\# Experimenter: Anna Stoeckl' %s" % filename subprocess.check_call(command, shell=True) with open(filename, 'r') as f: lines = f.readlines() for l in lines: if not l.startswith("#"): continue l = l.strip("#").strip() if len(l) == 0: continue if oldstyle: if not ":" in l: # subsection sec = {} info[l[:-1] if l.endswith(":") else l] = sec else: parts = l.split(':') sec[parts[0].strip()] = parts[1].strip('"').strip() if len(parts) > 1 else "" else: if l.endswith(":"): # subsection sec = {} info[l[:-1] if l.endswith(":") else l] = sec else: parts = l.split(': ') sec[parts[0].strip()] = parts[1].strip('"').strip() if len(parts) > 1 else "" return info def read_channel_config(self): logging.info("Reading channel configuration ...") ids = [f"identifier{i}" for i in range(1, len(self._raw_traces)+1)] units = [f"unit{i}" for i in range(1, len(self._raw_traces)+1)] sampling_intervals = [f"sample interval{i}" for i in range(1, len(self._raw_traces)+1)] sampling_rates = [f"sampling rate{i}" for i in range(1, len(self._raw_traces)+1)] channel_config = {} for i in range(1, len(self._raw_traces)+1): channel_config[i] = {} with open(os.path.join(self._folder, "stimuli.dat")) as f: for line in f: if "#" in line: line = line[1:] prop = line.strip().split(":")[0].strip() value = line.strip().split(":")[-1].strip() if prop in ids: index = int(prop[-1]) channel_config[index]["identifier"] = value if prop in units: index = int(prop[-1]) channel_config[index]["unit"] = value if prop in sampling_intervals: index = int(prop[-1]) channel_config[index]["sampling interval"] = value if prop in sampling_rates: index = int(prop[-1]) channel_config[index]["sampling rates"] = value if "analog output traces" in line: # end of channel configuration, we are done here break return channel_config def find_stimulus_info(self): logging.debug("Scanning stimuli.dat file!") if not os.path.exists(os.path.join(self._folder, "stimuli.dat")): logging.error("Found no stimuli.dat file! Abort!") raise ValueError("No stimuli.dat file found!") def find_stimulus_descriptions(self): logging.debug("Scanning stimulus-descriptions.dat!") filename = os.path.join(self._folder, "stimulus-descriptions.dat") if not os.path.exists(filename): logging.warning("Stimulus descriptions file {filename} does not exist!") return False return True def check_folder(self): logging.debug("Checking folder structure: ...") self._raw_traces, self._event_traces = self.find_traces() self.find_info() logging.debug("Found info file!") self.find_stimulus_info() logging.debug("Found stimulus information!") stim_descriptions_found = self.find_stimulus_descriptions() if stim_descriptions_found: logging.debug("Found stimulus descriptions!") else: logging.debug("Did not find stimulus descriptions!") return True def convert_dataset_info(self, metadata, parent_section=None): def split_list(value_str): results = None if len(value_str) == 0: return " " if "|" in value_str: results = list(map(str.strip, value_str.split("|"))) elif value_str[0] == "[" and "]" in value_str: results = list(map(str.strip, value_str[1:value_str.index("]")].split(', '))) else: results = value_str return results if parent_section is not None: for k in metadata.keys(): if isinstance(metadata[k], dict): sec = parent_section.create_section(k, k.lower()) self.convert_dataset_info(metadata[k], sec) else: # is property value, unit = parse_value(metadata[k]) if value is None: continue if isinstance(value, str): value = split_list(value) p = parent_section.create_property(k, value) if unit is not None: p.unit = unit def open_nix_file(self): info = self.read_info_file() logging.info(f"Creating output file {self._output} ...") self._nixfile = nix.File.open(self._output, nix.FileMode.Overwrite) dataset_name = os.path.split(self._output)[-1].strip(".nix") self._block = self._nixfile.create_block(dataset_name, "relacs.recording") sec = self._nixfile.create_section(dataset_name, "relacs.recording") self._block.metadata = sec sec.create_property("relacs-nix version", 1.1) self.convert_dataset_info(info, sec) def convert_raw_traces(self, channel_config): logging.info("Converting raw traces, this may take a little while...") for rt in self._raw_traces: logging.info(f"... trace {rt._trace_no}: {rt.name}") data = np.fromfile(os.path.join(self._folder, rt.filename), dtype=np.float32) da = self._block.create_data_array(rt.name, f"relacs.data.sampled.{rt.name}", dtype=nix.DataType.Float, data=data) da.unit = channel_config[rt._trace_no]["unit"] si = float(channel_config[rt._trace_no]["sampling interval"][:-2]) / 1000. da.append_sampled_dimension(si, unit="s") self._raw_data_arrays[rt] = da def convert_event_traces(self): def read_event_data(filename): logging.info(f"... reading event times from file {filename}...") times = [] with open(filename, 'r') as f: for l in f: if len(l.strip()) == 0 or "#" in l: continue times.append(float(l.strip().split()[0].strip())) return np.array(times) logging.info("Converting event traces...") for et in self._event_traces: logging.info(f"... trace {et.name}") event_times = read_event_data(et._filename) da = self._block.create_data_array(et.name, f"relacs.data.events.{et.name}", data=event_times) da.unit = "s" da.append_range_dimension_using_self() da.definition = f"Events detected in {et.inputtrace}" self._event_data_arrays[et] = da def convert_stimuli(self): def stimulus_descriptions(repro_name, reprorun, sampleinterval): def skip_first_index(signals): skip = True for s in signals: skip = skip and s.data[0].strip() == "-" return skip def find_active_signal(signals, stimulus_no): for i, s in enumerate(signals): if s.data[stimulus_no].strip() != "-": return i def parse_parameter(parameter_str): props = [] if parameter_str.strip().startswith("\""): parameter_str = parameter_str[1:-1] parts = parameter_str.split(",") for p in parts: name = p.split(":")[0].strip() value_str = p.split(":")[-1].strip() value, unit = parse_value(value_str) props.append(odml.Property(name=name, value=value, unit=unit)) return props stimuli = [] stimulus_columns = reprorun.table["stimulus"] signals = stimulus_columns.columns_by_name("signal") skip_first = skip_first_index(signals) index_col = reprorun.table.find_column(1) abstimes = stimulus_columns.columns_by_name("time")[0] delays = stimulus_columns.columns_by_name("delay")[0] durations = stimulus_columns.columns_by_name("duration") amplitudes = stimulus_columns.columns_by_name("amplitude") if len(amplitudes) == 0: # this is an attempt for very old pre 2011 files. amplitudes = stimulus_columns.columns_by_name("%6.3f") parameters = stimulus_columns.columns_by_name("parameter") for i in range(0 if not skip_first else 1, len(index_col)): start_time = index_col[i] * sampleinterval active = find_active_signal(signals, i) characteristics = odml.Section(f"{repro_name}_{i}") characteristics.create_property("signal", signals[active].data[i]) p = characteristics.create_property("start_time", start_time) p.unit = "s" dur = float(durations[active].data[i]) / (1000 if durations[active].type_or_unit == "ms" else 1) p = characteristics.create_property("duration", dur) p.unit = "s" p = characteristics.create_property("amplitude", float(amplitudes[active].data[i])) p.unit = amplitudes[active].type_or_unit d = float(delays.data[i]) / (1000 if delays.type_or_unit == "ms" else 1) p = characteristics.create_property("delay", d) p.unit = "s" at = float(abstimes.data[i]) / (1000 if abstimes.type_or_unit == "ms" else 1) p = characteristics.create_property("abs_time", at) p.unit = "s" characteristics.create_property("repro_tag_id", self._repro_tags[repro_name].id) if len(parameters) > 0: params = parse_parameter(parameters[active].data[i]) for p in params: characteristics.append(p) stimuli.append(characteristics) return stimuli def stimuli(sampleinterval): stims = {} counter = {} stim_metadata = parse_stimulus_description(os.path.join(self._folder, "stimulus-descriptions.dat")) for rr in self._stimuli_dat.repro_runs: if rr is None or rr.name is None: print(rr) continue if rr.name in counter: counter[rr.name] += 1 else: counter[rr.name] = 1 if not rr.valid: continue if "BaselineActivity" in rr.name: continue # there are no stimulus presented during baseline repro_name = f"{rr.name}_{counter[rr.name]}" stims[repro_name] = stimulus_descriptions(repro_name, rr, sampleinterval) return stims, stim_metadata def store_stimuli(stims, stim_metadata): def store_features(signal, features): excluded_feats = ["start_time", "duration", "signal"] fixed_feats = ["abs_time", "amplitude", "repro_tag_id"] feats = {} for i, feat in enumerate(features): for p in feat: if p.name in excluded_feats: continue if p.name not in feats: if p.dtype == "string": feats[p.name] = np.empty(len(features), dtype=object) feats[p.name][i] = p.values[0] else: feats[p.name] = np.empty(len(features)) else: feats[p.name][i] = p.values[0] for key in feats.keys(): feat_name = f"{signal}_{key}" feat_type = f"relacs.feature.{key if key in fixed_feats else "mutable"}" mtag = self._stimulus_mtags[signal] shape = (len(feats[key]), 1) data = np.reshape(feats[key], shape) dtype = nix.DataType.String if data.dtype == object else nix.DataType.Float feature_da = self._block.create_data_array(feat_name, feat_type, shape= shape, dtype=dtype, data=data) feature_da.append_set_dimension() mtag.create_feature(feature_da, nix.LinkType.Indexed) return None unique_signals = [] signal_counts = {} signal_starts = {} signal_durations = {} signal_features = {} for repro_run in stims: for stim in stims[repro_run]: signal = stim.props["signal"].values[0] if signal not in unique_signals: unique_signals.append(signal) signal_counts[signal] = 1 signal_starts[signal] = [stim.props["start_time"].values[0]] signal_durations[signal] = [stim.props["duration"].values[0]] signal_features[signal] = [stim] else: signal_starts[signal].append(stim.props["start_time"].values[0]) signal_durations[signal].append(stim.props["duration"].values[0]) signal_counts[signal] += 1 signal_features[signal].append(stim) excluded_refs = ["restart", "recording", "stimulus"] for signal in unique_signals: positions = self._block.create_data_array(f"{signal}_onset_times", "relacs.stimulus.onset", data=np.atleast_2d(signal_starts[signal]).T) positions.append_set_dimension() extents = self._block.create_data_array(f"{signal}_durations", "relacs.stimulus.duration", data=np.atleast_2d(signal_durations[signal]).T) extents.append_set_dimension() mtag = self._block.create_multi_tag(signal, "relacs.stimulus.segment", positions=positions, extents=extents) self._stimulus_mtags[signal] = mtag for et in self._event_data_arrays: if et not in excluded_refs: mtag.references.append(self._event_data_arrays[et]) for rt in self._raw_data_arrays: mtag.references.append(self._raw_data_arrays[rt]) if stim_metadata is not None and signal in stim_metadata.sections: metadata = stim_metadata[signal] mtag.metadata = self._nixfile.create_section(mtag.name, "relacs.stimulus") odml2nix(metadata, mtag.metadata) store_features(signal, signal_features[signal]) return None sampleinterval = self._stimuli_dat.input_settings.props["sample interval1"].values[0] /1000 stims, metadata = stimuli(sampleinterval) store_stimuli(stims, metadata) return def convert_repro_runs(self): def repro_times(reprorun, sampleinterval): if reprorun.name is None: return None, None if not reprorun.valid: return None, None index_col = reprorun.table.find_column(1) if len(index_col) == 0: return None, None stimulus_grp = reprorun.table["stimulus"] signals = stimulus_grp.columns_by_name("signal") is_init = np.any(np.array([s[0] for s in signals], dtype=object) == "init") delay_cols = stimulus_grp.columns_by_name("delay") delay = 0.0 if (len(delay_cols) == 0 or is_init) else delay_cols[0][0] start_time = index_col[0] * sampleinterval - delay / 1000. duration_cols = stimulus_grp.columns_by_name("duration") duration = 0.0 if "BaselineActivity" in reprorun.name: duration = 0.0 end_time = start_time else: for d in duration_cols: dur = d[-1] if isinstance(dur, (int, float)): duration = dur / 1000 break elif isinstance(dur, str) and only_number.search(dur) is not None: duration = float(dur) / 1000 break end_time = index_col[-1] * sampleinterval + duration logging.debug(f"Repro {reprorun.name} from {start_time} to {end_time}s") return start_time, end_time def repro_runs(): repro_names = [] repro_starts = [] repro_ends = [] repro_durations = [] repro_metadata = [] sampleinterval = self._stimuli_dat.input_settings.props["sample interval1"].values[0] /1000 counter = {} for i, rr in enumerate(self._stimuli_dat.repro_runs): if rr.name in counter: counter[rr.name] += 1 else: counter[rr.name] = 1 if not rr.valid: continue start, end = repro_times(rr, sampleinterval) if start is None: logging.error(f"RePro run: {rr.name} has no start/stop entries! It is ignored!") continue repro_names.append(f"{rr.name}_{counter[rr.name]}") repro_starts.append(start) repro_durations.append(end - start) repro_ends.append(end) repro_metadata.append(rr.metadata) for i, (start, end , duration) in enumerate(zip(repro_starts, repro_ends, repro_durations)): logging.debug(f"Duration {duration} for repro {repro_names[i]} and {i} < {len(repro_starts) - 1}") if duration < sampleinterval and i < len(repro_starts) -1: repro_durations[i] = repro_starts[i+1] - start logging.debug(f"\t new duration: {repro_durations[i]}") repro_ends[i] = repro_starts[i+1] return repro_names, repro_metadata, repro_starts, repro_durations def store_repro_runs(repro_names, repro_metadata, start_times, durations): excluded_refs = ["restart", "recording", "stimulus"] for name, metadata, start, duration in zip(repro_names, repro_metadata, start_times, durations): logging.debug(f"... storing {name} which ran from {start} to {start + duration}.") tag = self._block.create_tag(name, "relacs.repro_run", position=[start]) tag.extent = [duration] for et in self._event_data_arrays: if et not in excluded_refs: tag.references.append(self._event_data_arrays[et]) for rt in self._raw_data_arrays: tag.references.append(self._raw_data_arrays[rt]) tag.metadata = self._nixfile.create_section(name, "relacs.repro") odml2nix(metadata, tag.metadata) self._repro_tags[name] = tag names, metadata, starts, durations = repro_runs() logging.info("Converting RePro runs...") store_repro_runs(names, metadata, starts, durations) def convert(self): logging.info(f"Converting dataset {self._folder} to nix file {self._output}!") channel_config = self.read_channel_config() self.open_nix_file() self.convert_raw_traces(channel_config) self.convert_event_traces() self._stimuli_dat = StimuliDat(os.path.join(self._folder, "stimuli.dat")) self.convert_repro_runs() self.convert_stimuli() self._nixfile.close()

# -*- coding: utf-8 -*- # Copyright © 2022, Neuroethology Lab Uni Tuebingen # # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted under the terms of the BSD License. See # LICENSE file in the root of the Project. import re import os import glob import odml import logging import subprocess import numpy as np import nixio as nix from .config import ConfigFile from .traces import EventTrace, RawTrace from .stimuli import StimuliDat from .util import parse_value, odml2nix, only_number from .stimdescription import parse_stimulus_description from IPython import embed class Converter(object): def __init__(self, folder_name, output_name, force=False) -> None: if not os.path.exists(folder_name): logging.error(f"{folder_name} does not exist!") raise ValueError("File not found error!") self._folder = folder_name self._output = output_name self._event_traces = None self._raw_traces = None self._raw_data_arrays = {} self._event_data_arrays = {} self._stimuli_dat = None self._force = force self._nixfile = None self._block = None self._repro_tags = {} self._stimulus_mtags = {} self.preflight() def preflight(self): logging.debug(f"Pre-checking folder {self._folder}!") self.check_output() self.check_folder() logging.debug("Pre-checking done.") def check_output(self): logging.debug(f"Checking output name: {self._output}!") if os.path.exists(self._output): logging.warn(f"Output file name {self._output} already exists!") if self._force: logging.warn(f"... force flag is set {self._force}, going to overwrite!") else: logging.error(f"Force flag is not set ({self._force}), abort!") raise ValueError("Output file {self._output} already exists! If you want to overwrite it use the --force flag.") logging.debug(f"... ok!") return True def unzip(self, tracename): if os.path.exists(tracename): logging.debug(f"\tunzip: {tracename}") subprocess.check_call(["gunzip", tracename]) def find_traces(self): event_traces = [] raw_traces = [] configuration = self.find_config_file() for et in self.find_event_traces(): event_traces.append(EventTrace(et, configuration)) for rt in self.find_raw_traces(): raw_traces.append(RawTrace(rt, configuration)) return raw_traces, event_traces def find_raw_traces(self): logging.debug(f"Checking for raw traces!") traces = sorted(glob.glob(os.path.join(self._folder, "trace-*.raw*"))) for rt in traces: if rt.endswith(".gz") and rt.split(".gz")[0] not in traces: self.unzip(os.path.split(rt)[-1]) traces = sorted(glob.glob(os.path.join(self._folder, "trace-*.raw"))) logging.debug(f"Found {len(traces)} raw traces. {[os.path.split(t)[-1] for t in traces]}") return traces def find_event_traces(self): logging.debug("Discovering event traces!") traces = sorted(glob.glob(os.path.join(self._folder, "*-events.dat"))) logging.debug(f"Found {len(traces)} event traces. {[os.path.split(t)[-1] for t in traces]}") return traces def find_config_file(self): if not os.path.exists(os.path.join(self._folder, "relacs.cfg")): logging.error("Found no info file!") raise ValueError(f"No relacs.cfg file found in {self._folder}!") configuration = ConfigFile(os.path.join(self._folder, "relacs.cfg")) return configuration def find_info(self): filename = os.path.join(self._folder, "info.dat") if not os.path.exists(filename): logging.error("Found no info file!") raise ValueError(f"No info file found in {self._folder}!") return True def read_info_file(self): def looks_like_oldstyle(filename): with open(filename, 'r') as f: for l in f: if "# Recording" in l: oldtyle = not l.strip().endswith(":") break return oldtyle filename = os.path.join(self._folder, "info.dat") oldstyle = looks_like_oldstyle(filename) info = {} logging.info("Reading info file....") try: with open(filename, 'r') as f: lines = f.readlines() except UnicodeDecodeError: logging.debug("Replacing experimenter...") command = r"sudo sed -i '/Experimenter/c\# Experimenter: Anna Stoeckl' %s" % filename subprocess.check_call(command, shell=True) with open(filename, 'r') as f: lines = f.readlines() for l in lines: if not l.startswith("#"): continue l = l.strip("#").strip() if len(l) == 0: continue if oldstyle: if not ":" in l: # subsection sec = {} info[l[:-1] if l.endswith(":") else l] = sec else: parts = l.split(':') sec[parts[0].strip()] = parts[1].strip('"').strip() if len(parts) > 1 else "" else: if l.endswith(":"): # subsection sec = {} info[l[:-1] if l.endswith(":") else l] = sec else: parts = l.split(': ') sec[parts[0].strip()] = parts[1].strip('"').strip() if len(parts) > 1 else "" return info def read_channel_config(self): logging.info("Reading channel configuration ...") ids = [f"identifier{i}" for i in range(1, len(self._raw_traces)+1)] units = [f"unit{i}" for i in range(1, len(self._raw_traces)+1)] sampling_intervals = [f"sample interval{i}" for i in range(1, len(self._raw_traces)+1)] sampling_rates = [f"sampling rate{i}" for i in range(1, len(self._raw_traces)+1)] channel_config = {} for i in range(1, len(self._raw_traces)+1): channel_config[i] = {} with open(os.path.join(self._folder, "stimuli.dat")) as f: for line in f: if "#" in line: line = line[1:] prop = line.strip().split(":")[0].strip() value = line.strip().split(":")[-1].strip() if prop in ids: index = int(prop[-1]) channel_config[index]["identifier"] = value if prop in units: index = int(prop[-1]) channel_config[index]["unit"] = value if prop in sampling_intervals: index = int(prop[-1]) channel_config[index]["sampling interval"] = value if prop in sampling_rates: index = int(prop[-1]) channel_config[index]["sampling rates"] = value if "analog output traces" in line: # end of channel configuration, we are done here break return channel_config def find_stimulus_info(self): logging.debug("Scanning stimuli.dat file!") if not os.path.exists(os.path.join(self._folder, "stimuli.dat")): logging.error("Found no stimuli.dat file! Abort!") raise ValueError("No stimuli.dat file found!") def find_stimulus_descriptions(self): logging.debug("Scanning stimulus-descriptions.dat!") filename = os.path.join(self._folder, "stimulus-descriptions.dat") if not os.path.exists(filename): logging.warning("Stimulus descriptions file {filename} does not exist!") return False return True def check_folder(self): logging.debug("Checking folder structure: ...") self._raw_traces, self._event_traces = self.find_traces() self.find_info() logging.debug("Found info file!") self.find_stimulus_info() logging.debug("Found stimulus information!") stim_descriptions_found = self.find_stimulus_descriptions() if stim_descriptions_found: logging.debug("Found stimulus descriptions!") else: logging.debug("Did not find stimulus descriptions!") return True def convert_dataset_info(self, metadata, parent_section=None): def split_list(value_str): results = None if len(value_str) == 0: return " " if "|" in value_str: results = list(map(str.strip, value_str.split("|"))) elif value_str[0] == "[" and "]" in value_str: results = list(map(str.strip, value_str[1:value_str.index("]")].split(', '))) else: results = value_str return results if parent_section is not None: for k in metadata.keys(): if isinstance(metadata[k], dict): sec = parent_section.create_section(k, k.lower()) self.convert_dataset_info(metadata[k], sec) else: # is property value, unit = parse_value(metadata[k]) if value is None: continue if isinstance(value, str): value = split_list(value) p = parent_section.create_property(k, value) if unit is not None: p.unit = unit def open_nix_file(self): info = self.read_info_file() logging.info(f"Creating output file {self._output} ...") self._nixfile = nix.File.open(self._output, nix.FileMode.Overwrite) dataset_name = os.path.split(self._output)[-1].strip(".nix") self._block = self._nixfile.create_block(dataset_name, "relacs.recording") sec = self._nixfile.create_section(dataset_name, "relacs.recording") self._block.metadata = sec sec.create_property("relacs-nix version", 1.1) self.convert_dataset_info(info, sec) def convert_raw_traces(self, channel_config): logging.info("Converting raw traces, this may take a little while...") for rt in self._raw_traces: logging.info(f"... trace {rt._trace_no}: {rt.name}") data = np.fromfile(os.path.join(self._folder, rt.filename), dtype=np.float32) da = self._block.create_data_array(rt.name, f"relacs.data.sampled.{rt.name}", dtype=nix.DataType.Float, data=data) da.unit = channel_config[rt._trace_no]["unit"] si = float(channel_config[rt._trace_no]["sampling interval"][:-2]) / 1000. da.append_sampled_dimension(si, unit="s") self._raw_data_arrays[rt] = da def convert_event_traces(self): def read_event_data(filename): logging.info(f"... reading event times from file {filename}...") times = [] with open(filename, 'r') as f: for l in f: if len(l.strip()) == 0 or "#" in l: continue times.append(float(l.strip().split()[0].strip())) return np.array(times) logging.info("Converting event traces...") for et in self._event_traces: logging.info(f"... trace {et.name}") event_times = read_event_data(et._filename) da = self._block.create_data_array(et.name, f"relacs.data.events.{et.name}", data=event_times) da.unit = "s" da.append_range_dimension_using_self() da.definition = f"Events detected in {et.inputtrace}" self._event_data_arrays[et] = da def convert_stimuli(self): def stimulus_descriptions(repro_name, reprorun, sampleinterval): def skip_first_index(signals): skip = True for s in signals: skip = skip and s.data[0].strip() == "-" return skip def find_active_signal(signals, stimulus_no): for i, s in enumerate(signals): if s.data[stimulus_no].strip() != "-": return i def parse_parameter(parameter_str): props = [] if parameter_str.strip().startswith("\""): parameter_str = parameter_str[1:-1] parts = parameter_str.split(",") for p in parts: name = p.split(":")[0].strip() value_str = p.split(":")[-1].strip() value, unit = parse_value(value_str) props.append(odml.Property(name=name, value=value, unit=unit)) return props stimuli = [] stimulus_columns = reprorun.table["stimulus"] signals = stimulus_columns.columns_by_name("signal") skip_first = skip_first_index(signals) index_col = reprorun.table.find_column(1) abstimes = stimulus_columns.columns_by_name("time")[0] delays = stimulus_columns.columns_by_name("delay")[0] durations = stimulus_columns.columns_by_name("duration") amplitudes = stimulus_columns.columns_by_name("amplitude") if len(amplitudes) == 0: # this is an attempt for very old pre 2011 files. amplitudes = stimulus_columns.columns_by_name("%6.3f") parameters = stimulus_columns.columns_by_name("parameter") for i in range(0 if not skip_first else 1, len(index_col)): start_time = index_col[i] * sampleinterval active = find_active_signal(signals, i) characteristics = odml.Section(f"{repro_name}_{i}") characteristics.create_property("signal", signals[active].data[i]) p = characteristics.create_property("start_time", start_time) p.unit = "s" dur = float(durations[active].data[i]) / (1000 if durations[active].type_or_unit == "ms" else 1) p = characteristics.create_property("duration", dur) p.unit = "s" p = characteristics.create_property("amplitude", float(amplitudes[active].data[i])) p.unit = amplitudes[active].type_or_unit d = float(delays.data[i]) / (1000 if delays.type_or_unit == "ms" else 1) p = characteristics.create_property("delay", d) p.unit = "s" at = float(abstimes.data[i]) / (1000 if abstimes.type_or_unit == "ms" else 1) p = characteristics.create_property("abs_time", at) p.unit = "s" characteristics.create_property("repro_tag_id", self._repro_tags[repro_name].id) if len(parameters) > 0: params = parse_parameter(parameters[active].data[i]) for p in params: characteristics.append(p) stimuli.append(characteristics) return stimuli def stimuli(sampleinterval): stims = {} counter = {} stim_metadata = parse_stimulus_description(os.path.join(self._folder, "stimulus-descriptions.dat")) for rr in self._stimuli_dat.repro_runs: if rr is None or rr.name is None: print(rr) continue if rr.name in counter: counter[rr.name] += 1 else: counter[rr.name] = 1 if not rr.valid: continue if "BaselineActivity" in rr.name: continue # there are no stimulus presented during baseline repro_name = f"{rr.name}_{counter[rr.name]}" stims[repro_name] = stimulus_descriptions(repro_name, rr, sampleinterval) return stims, stim_metadata def store_stimuli(stims, stim_metadata): def store_features(signal, features): excluded_feats = ["start_time", "duration", "signal"] fixed_feats = ["abs_time", "amplitude", "repro_tag_id"] feats = {} for i, feat in enumerate(features): for p in feat: if p.name in excluded_feats: continue if p.name not in feats: if p.dtype == "string": feats[p.name] = np.empty(len(features), dtype=object) feats[p.name][i] = p.values[0] else: feats[p.name] = np.empty(len(features)) else: feats[p.name][i] = p.values[0] for key in feats.keys(): feat_name = f"{signal}_{key}" feat_type = f"relacs.feature.{key if key in fixed_feats else 'mutable'}" mtag = self._stimulus_mtags[signal] shape = (len(feats[key]), 1) data = np.reshape(feats[key], shape) dtype = nix.DataType.String if data.dtype == object else nix.DataType.Float feature_da = self._block.create_data_array(feat_name, feat_type, shape= shape, dtype=dtype, data=data) feature_da.append_set_dimension() mtag.create_feature(feature_da, nix.LinkType.Indexed) return None unique_signals = [] signal_counts = {} signal_starts = {} signal_durations = {} signal_features = {} for repro_run in stims: for stim in stims[repro_run]: signal = stim.props["signal"].values[0] if signal not in unique_signals: unique_signals.append(signal) signal_counts[signal] = 1 signal_starts[signal] = [stim.props["start_time"].values[0]] signal_durations[signal] = [stim.props["duration"].values[0]] signal_features[signal] = [stim] else: signal_starts[signal].append(stim.props["start_time"].values[0]) signal_durations[signal].append(stim.props["duration"].values[0]) signal_counts[signal] += 1 signal_features[signal].append(stim) excluded_refs = ["restart", "recording", "stimulus"] for signal in unique_signals: positions = self._block.create_data_array(f"{signal}_onset_times", "relacs.stimulus.onset", data=np.atleast_2d(signal_starts[signal]).T) positions.append_set_dimension() extents = self._block.create_data_array(f"{signal}_durations", "relacs.stimulus.duration", data=np.atleast_2d(signal_durations[signal]).T) extents.append_set_dimension() mtag = self._block.create_multi_tag(signal, "relacs.stimulus.segment", positions=positions, extents=extents) self._stimulus_mtags[signal] = mtag for et in self._event_data_arrays: if et not in excluded_refs: mtag.references.append(self._event_data_arrays[et]) for rt in self._raw_data_arrays: mtag.references.append(self._raw_data_arrays[rt]) if stim_metadata is not None and signal in stim_metadata.sections: metadata = stim_metadata[signal] mtag.metadata = self._nixfile.create_section(mtag.name, "relacs.stimulus") odml2nix(metadata, mtag.metadata) store_features(signal, signal_features[signal]) return None sampleinterval = self._stimuli_dat.input_settings.props["sample interval1"].values[0] /1000 stims, metadata = stimuli(sampleinterval) store_stimuli(stims, metadata) return def convert_repro_runs(self): def repro_times(reprorun, sampleinterval): if reprorun.name is None: return None, None if not reprorun.valid: return None, None index_col = reprorun.table.find_column(1) if len(index_col) == 0: return None, None stimulus_grp = reprorun.table["stimulus"] signals = stimulus_grp.columns_by_name("signal") is_init = np.any(np.array([s[0] for s in signals], dtype=object) == "init") delay_cols = stimulus_grp.columns_by_name("delay") delay = 0.0 if (len(delay_cols) == 0 or is_init) else delay_cols[0][0] start_time = index_col[0] * sampleinterval - delay / 1000. duration_cols = stimulus_grp.columns_by_name("duration") duration = 0.0 if "BaselineActivity" in reprorun.name: duration = 0.0 end_time = start_time else: for d in duration_cols: dur = d[-1] if isinstance(dur, (int, float)): duration = dur / 1000 break elif isinstance(dur, str) and only_number.search(dur) is not None: duration = float(dur) / 1000 break end_time = index_col[-1] * sampleinterval + duration logging.debug(f"Repro {reprorun.name} from {start_time} to {end_time}s") return start_time, end_time def repro_runs(): repro_names = [] repro_starts = [] repro_ends = [] repro_durations = [] repro_metadata = [] sampleinterval = self._stimuli_dat.input_settings.props["sample interval1"].values[0] /1000 counter = {} for i, rr in enumerate(self._stimuli_dat.repro_runs): if rr.name in counter: counter[rr.name] += 1 else: counter[rr.name] = 1 if not rr.valid: continue start, end = repro_times(rr, sampleinterval) if start is None: logging.error(f"RePro run: {rr.name} has no start/stop entries! It is ignored!") continue repro_names.append(f"{rr.name}_{counter[rr.name]}") repro_starts.append(start) repro_durations.append(end - start) repro_ends.append(end) repro_metadata.append(rr.metadata) for i, (start, end , duration) in enumerate(zip(repro_starts, repro_ends, repro_durations)): logging.debug(f"Duration {duration} for repro {repro_names[i]} and {i} < {len(repro_starts) - 1}") if duration < sampleinterval and i < len(repro_starts) -1: repro_durations[i] = repro_starts[i+1] - start logging.debug(f"\t new duration: {repro_durations[i]}") repro_ends[i] = repro_starts[i+1] return repro_names, repro_metadata, repro_starts, repro_durations def store_repro_runs(repro_names, repro_metadata, start_times, durations): excluded_refs = ["restart", "recording", "stimulus"] for name, metadata, start, duration in zip(repro_names, repro_metadata, start_times, durations): logging.debug(f"... storing {name} which ran from {start} to {start + duration}.") tag = self._block.create_tag(name, "relacs.repro_run", position=[start]) tag.extent = [duration] for et in self._event_data_arrays: if et not in excluded_refs: tag.references.append(self._event_data_arrays[et]) for rt in self._raw_data_arrays: tag.references.append(self._raw_data_arrays[rt]) tag.metadata = self._nixfile.create_section(name, "relacs.repro") odml2nix(metadata, tag.metadata) self._repro_tags[name] = tag names, metadata, starts, durations = repro_runs() logging.info("Converting RePro runs...") store_repro_runs(names, metadata, starts, durations) def convert(self): logging.info(f"Converting dataset {self._folder} to nix file {self._output}!") channel_config = self.read_channel_config() self.open_nix_file() self.convert_raw_traces(channel_config) self.convert_event_traces() self._stimuli_dat = StimuliDat(os.path.join(self._folder, "stimuli.dat")) self.convert_repro_runs() self.convert_stimuli() self._nixfile.close()

import bs4 import requests from typing import Union, List from .Infopage import info, InfoPage class SearchResult: def __init__(self, urls: dict): self.names = tuple(urls.keys()) self.urls = urls def __getitem__(self, item): if type(item) == str: return self.urls[item] elif type(item) == int: return self.names[item] else: raise TypeError def __len__(self): return len(self.names) def __iter__(self): self.__index = 0 return self def __next__(self): if self.__index < len(self.names): index = self.__index self.__index += 1 return self.names[index] else: raise StopIteration def __str__(self): return str(self.names) def get(self, x: Union[int, str]) -> InfoPage: if type(x) == int: return info(self.urls[self.names[x]]) elif type(x) == str: return info(self.urls[x]) def get_all(self, limit: int = 20) -> List[InfoPage]: lst = [] try: if limit > 20: limit = 20 except TypeError: limit = 20 for item in list(self.urls.keys())[:limit]: print('Getting:', item) lst.append(info(self.urls[item])) return lst def search(name: str, page: int = 1, style: str = None, year=None, eps: int = None, score: str = None, match_all: bool = True, max_results: int = 20) -> Union[SearchResult, None]: urls = {} if max_results > 20: print("Cannot have more than 20 Results!") max_results = 20 filters_given = any([style, year, eps, score]) url = f"https://mydramalist.com/search?q={name.replace(" ", "+")}&page={page}" base = requests.get(url) soup = bs4.BeautifulSoup(base.text, 'lxml') results_box = soup.find('div', class_='col-lg-8 col-md-8').find_all('div', class_='box') for item in results_box: # Get Title try: curr_title = item.find("h6").find('a').text except AttributeError: return None # Get Category curr_cateory = item.find('span', class_='text-muted') # Check if Ctegory Exists if curr_cateory: curr_cateory = curr_cateory.text else: continue # Get URL curr_url = item.find("h6").find('a')['href'] # Apply filters if filters_given: if match_all: filter_check = 15 # Has to match all filters given else: filter_check = 0 # Has to match atleast one of the filters given # In Binary from MSB [0] is style, [1] is year, [2] is eps, [3] is score # Check for Score curr_score = item.find('span', class_='score').text if score: if curr_score: if score.endswith('+'): if not float(curr_score) >= float(score.rstrip('+')): filter_check &= 0b1110 else: filter_check |= 0b0001 elif score.endswith('-'): if not float(curr_score) <= float(score.rstrip('-')): filter_check &= 0b1110 else: filter_check |= 0b0001 else: if not curr_score == score: filter_check &= 0b1110 else: filter_check |= 0b0001 else: filter_check &= 0b1110 # Check for Episodes Filter if eps: if not ((curr_cateory.split(',')[-1]).startswith(f" {eps} episode")): filter_check &= 0b1101 else: filter_check |= 0b0010 # Check for Year Filter if year: if not curr_cateory.split(',')[0].split('-')[-1].strip() == str(year): filter_check &= 0b1011 else: filter_check |= 0b0100 # Check for Style Filter if style: if curr_cateory.find(style) == -1: filter_check &= 0b0111 else: filter_check |= 0b1000 # Add it to list if checks pass elif match_all and filter_check == 15: urls[curr_title] = curr_url elif (not match_all) and filter_check != 0: urls[curr_title] = curr_url else: # Directly add if no filters are given urls[curr_title] = curr_url if len(urls) >= max_results: break if len(urls) > 0: return SearchResult(urls) else: return None

import bs4 import requests from typing import Union, List from .Infopage import info, InfoPage class SearchResult: def __init__(self, urls: dict): self.names = tuple(urls.keys()) self.urls = urls def __getitem__(self, item): if type(item) == str: return self.urls[item] elif type(item) == int: return self.names[item] else: raise TypeError def __len__(self): return len(self.names) def __iter__(self): self.__index = 0 return self def __next__(self): if self.__index < len(self.names): index = self.__index self.__index += 1 return self.names[index] else: raise StopIteration def __str__(self): return str(self.names) def get(self, x: Union[int, str]) -> InfoPage: if type(x) == int: return info(self.urls[self.names[x]]) elif type(x) == str: return info(self.urls[x]) def get_all(self, limit: int = 20) -> List[InfoPage]: lst = [] try: if limit > 20: limit = 20 except TypeError: limit = 20 for item in list(self.urls.keys())[:limit]: print('Getting:', item) lst.append(info(self.urls[item])) return lst def search(name: str, page: int = 1, style: str = None, year=None, eps: int = None, score: str = None, match_all: bool = True, max_results: int = 20) -> Union[SearchResult, None]: urls = {} if max_results > 20: print("Cannot have more than 20 Results!") max_results = 20 filters_given = any([style, year, eps, score]) url = f"https://mydramalist.com/search?q={name.replace(' ', '+')}&page={page}" base = requests.get(url) soup = bs4.BeautifulSoup(base.text, 'lxml') results_box = soup.find('div', class_='col-lg-8 col-md-8').find_all('div', class_='box') for item in results_box: # Get Title try: curr_title = item.find("h6").find('a').text except AttributeError: return None # Get Category curr_cateory = item.find('span', class_='text-muted') # Check if Ctegory Exists if curr_cateory: curr_cateory = curr_cateory.text else: continue # Get URL curr_url = item.find("h6").find('a')['href'] # Apply filters if filters_given: if match_all: filter_check = 15 # Has to match all filters given else: filter_check = 0 # Has to match atleast one of the filters given # In Binary from MSB [0] is style, [1] is year, [2] is eps, [3] is score # Check for Score curr_score = item.find('span', class_='score').text if score: if curr_score: if score.endswith('+'): if not float(curr_score) >= float(score.rstrip('+')): filter_check &= 0b1110 else: filter_check |= 0b0001 elif score.endswith('-'): if not float(curr_score) <= float(score.rstrip('-')): filter_check &= 0b1110 else: filter_check |= 0b0001 else: if not curr_score == score: filter_check &= 0b1110 else: filter_check |= 0b0001 else: filter_check &= 0b1110 # Check for Episodes Filter if eps: if not ((curr_cateory.split(',')[-1]).startswith(f" {eps} episode")): filter_check &= 0b1101 else: filter_check |= 0b0010 # Check for Year Filter if year: if not curr_cateory.split(',')[0].split('-')[-1].strip() == str(year): filter_check &= 0b1011 else: filter_check |= 0b0100 # Check for Style Filter if style: if curr_cateory.find(style) == -1: filter_check &= 0b0111 else: filter_check |= 0b1000 # Add it to list if checks pass elif match_all and filter_check == 15: urls[curr_title] = curr_url elif (not match_all) and filter_check != 0: urls[curr_title] = curr_url else: # Directly add if no filters are given urls[curr_title] = curr_url if len(urls) >= max_results: break if len(urls) > 0: return SearchResult(urls) else: return None

'''Contains helper function for this project filenames and example band dictionary and list''' def dirname(measurement): '''Returns directory of .asc file in CMIP5 dataset''' _dirname_prefix = 'bcc_csm1_1_m_rcp8_5_2080s' _dirname_postfix = '10min_r1i1p1_no_tile_asc' _dirname_global = './data/CMIP5' return f'{_dirname_global}/{_dirname_prefix}_{measurement}_{_dirname_postfix}' TEMPLATE = '#' BANDS = [ # `range`: parameters to substitute template with # len(`range`) == 1 if no template was required, dummy str { 'path': f'{dirname('cons')}/cons_mths.asc', 'range': ['-999'], 'desc': 'consecutive_dry_months' }, { 'path': f'{dirname('prec')}/prec_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'precipitation' }, { 'path': f'{dirname('tmax')}/tmax_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'maximum_monthly_temperature' }, { 'path': f'{dirname('tmean')}/tmean_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'mean_monthly_temperature' }, { 'path': f'{dirname('tmin')}/tmin_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'min_monthly_temperature' }, ]

'''Contains helper function for this project filenames and example band dictionary and list''' def dirname(measurement): '''Returns directory of .asc file in CMIP5 dataset''' _dirname_prefix = 'bcc_csm1_1_m_rcp8_5_2080s' _dirname_postfix = '10min_r1i1p1_no_tile_asc' _dirname_global = './data/CMIP5' return f'{_dirname_global}/{_dirname_prefix}_{measurement}_{_dirname_postfix}' TEMPLATE = '#' BANDS = [ # `range`: parameters to substitute template with # len(`range`) == 1 if no template was required, dummy str { 'path': f'{dirname("cons")}/cons_mths.asc', 'range': ['-999'], 'desc': 'consecutive_dry_months' }, { 'path': f'{dirname("prec")}/prec_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'precipitation' }, { 'path': f'{dirname("tmax")}/tmax_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'maximum_monthly_temperature' }, { 'path': f'{dirname("tmean")}/tmean_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'mean_monthly_temperature' }, { 'path': f'{dirname("tmin")}/tmin_{TEMPLATE}.asc', 'range': [str(i) for i in range(1, 13)], 'desc': 'min_monthly_temperature' }, ]

import datetime from email.headerregistry import Address from typing import Any, Dict, Iterable, List, Mapping, Optional, TypeVar, Union from unittest import mock import orjson from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.core.exceptions import ValidationError from django.test import override_settings from django.utils.timezone import now as timezone_now from zerver.lib.actions import ( change_user_is_active, create_users, do_change_can_create_users, do_change_user_role, do_create_user, do_deactivate_user, do_delete_user, do_invite_users, do_reactivate_user, do_set_realm_property, get_emails_from_user_ids, get_recipient_info, ) from zerver.lib.avatar import avatar_url, get_gravatar_url from zerver.lib.create_user import copy_user_settings from zerver.lib.events import do_events_register from zerver.lib.exceptions import JsonableError from zerver.lib.send_email import ( clear_scheduled_emails, deliver_scheduled_emails, send_future_email, ) from zerver.lib.stream_topic import StreamTopicTarget from zerver.lib.test_classes import ZulipTestCase from zerver.lib.test_helpers import ( cache_tries_captured, get_subscription, get_test_image_file, queries_captured, reset_emails_in_zulip_realm, simulated_empty_cache, tornado_redirected_to_list, ) from zerver.lib.topic_mutes import add_topic_mute from zerver.lib.upload import upload_avatar_image from zerver.lib.users import Accounts, access_user_by_id, get_accounts_for_email, user_ids_to_users from zerver.models import ( CustomProfileField, InvalidFakeEmailDomain, Message, PreregistrationUser, Realm, RealmDomain, Recipient, ScheduledEmail, Stream, Subscription, UserHotspot, UserProfile, check_valid_user_ids, get_client, get_fake_email_domain, get_realm, get_source_profile, get_stream, get_system_bot, get_user, get_user_by_delivery_email, get_user_by_id_in_realm_including_cross_realm, ) K = TypeVar("K") V = TypeVar("V") def find_dict(lst: Iterable[Dict[K, V]], k: K, v: V) -> Dict[K, V]: for dct in lst: if dct[k] == v: return dct raise AssertionError(f"Cannot find element in list where key {k} == {v}") class PermissionTest(ZulipTestCase): def test_role_setters(self) -> None: user_profile = self.example_user("hamlet") user_profile.is_realm_admin = True self.assertEqual(user_profile.is_realm_admin, True) self.assertEqual(user_profile.role, UserProfile.ROLE_REALM_ADMINISTRATOR) user_profile.is_guest = False self.assertEqual(user_profile.is_guest, False) self.assertEqual(user_profile.role, UserProfile.ROLE_REALM_ADMINISTRATOR) user_profile.is_realm_admin = False self.assertEqual(user_profile.is_realm_admin, False) self.assertEqual(user_profile.role, UserProfile.ROLE_MEMBER) user_profile.is_guest = True self.assertEqual(user_profile.is_guest, True) self.assertEqual(user_profile.role, UserProfile.ROLE_GUEST) user_profile.is_realm_admin = False self.assertEqual(user_profile.is_guest, True) self.assertEqual(user_profile.role, UserProfile.ROLE_GUEST) user_profile.is_guest = False self.assertEqual(user_profile.is_guest, False) self.assertEqual(user_profile.role, UserProfile.ROLE_MEMBER) def test_get_admin_users(self) -> None: user_profile = self.example_user("hamlet") do_change_user_role(user_profile, UserProfile.ROLE_MEMBER, acting_user=None) self.assertFalse(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertFalse(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertFalse(user_profile in admin_users) do_change_user_role(user_profile, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.assertFalse(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertTrue(user_profile in admin_users) do_change_user_role(user_profile, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.assertTrue(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_human_admin_users(include_realm_owners=False) self.assertFalse(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots(include_realm_owners=False) self.assertFalse(user_profile in admin_users) def test_get_first_human_user(self) -> None: realm = get_realm("zulip") UserProfile.objects.filter(realm=realm).delete() UserProfile.objects.create( realm=realm, email="bot1@zulip.com", delivery_email="bot1@zulip.com", is_bot=True ) first_human_user = UserProfile.objects.create( realm=realm, email="user1@zulip.com", delivery_email="user1@zulip.com", is_bot=False ) UserProfile.objects.create( realm=realm, email="user2@zulip.com", delivery_email="user2@zulip.com", is_bot=False ) UserProfile.objects.create( realm=realm, email="bot2@zulip.com", delivery_email="bot2@zulip.com", is_bot=True ) self.assertEqual(first_human_user, realm.get_first_human_user()) def test_updating_non_existent_user(self) -> None: self.login("hamlet") admin = self.example_user("hamlet") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) invalid_user_id = 1000 result = self.client_patch(f"/json/users/{invalid_user_id}", {}) self.assert_json_error(result, "No such user") def test_owner_api(self) -> None: self.login("iago") desdemona = self.example_user("desdemona") othello = self.example_user("othello") iago = self.example_user("iago") realm = iago.realm do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) result = self.client_get("/json/users") self.assert_json_success(result) members = result.json()["members"] iago_dict = find_dict(members, "email", iago.email) self.assertTrue(iago_dict["is_owner"]) othello_dict = find_dict(members, "email", othello.email) self.assertFalse(othello_dict["is_owner"]) req = dict(role=UserProfile.ROLE_REALM_OWNER) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertTrue(othello in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_REALM_OWNER) req = dict(role=UserProfile.ROLE_MEMBER) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertFalse(othello in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) # Cannot take away from last owner self.login("desdemona") req = dict(role=UserProfile.ROLE_MEMBER) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{iago.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertFalse(iago in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], iago.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) with tornado_redirected_to_list([]): result = self.client_patch(f"/json/users/{desdemona.id}", req) self.assert_json_error( result, "The owner permission cannot be removed from the only organization owner." ) do_change_user_role(iago, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.login("iago") with tornado_redirected_to_list([]): result = self.client_patch(f"/json/users/{desdemona.id}", req) self.assert_json_error(result, "Must be an organization owner") def test_admin_api(self) -> None: self.login("desdemona") hamlet = self.example_user("hamlet") othello = self.example_user("othello") desdemona = self.example_user("desdemona") realm = hamlet.realm # Make sure we see is_admin flag in /json/users result = self.client_get("/json/users") self.assert_json_success(result) members = result.json()["members"] desdemona_dict = find_dict(members, "email", desdemona.email) self.assertTrue(desdemona_dict["is_admin"]) othello_dict = find_dict(members, "email", othello.email) self.assertFalse(othello_dict["is_admin"]) # Giveth req = dict(role=orjson.dumps(UserProfile.ROLE_REALM_ADMINISTRATOR).decode()) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) admin_users = realm.get_human_admin_users() self.assertTrue(othello in admin_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_REALM_ADMINISTRATOR) # Taketh away req = dict(role=orjson.dumps(UserProfile.ROLE_MEMBER).decode()) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) admin_users = realm.get_human_admin_users() self.assertFalse(othello in admin_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) # Make sure only admins can patch other user's info. self.login("othello") result = self.client_patch(f"/json/users/{hamlet.id}", req) self.assert_json_error(result, "Insufficient permission") def test_admin_api_hide_emails(self) -> None: reset_emails_in_zulip_realm() user = self.example_user("hamlet") admin = self.example_user("iago") self.login_user(user) # First, verify client_gravatar works normally result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], user.email) self.assertIsNone(hamlet["avatar_url"]) self.assertNotIn("delivery_email", hamlet) # Also verify the /events code path. This is a bit hacky, but # we need to verify client_gravatar is not being overridden. with mock.patch( "zerver.lib.events.request_event_queue", return_value=None ) as mock_request_event_queue: with self.assertRaises(JsonableError): result = do_events_register(user, get_client("website"), client_gravatar=True) self.assertEqual(mock_request_event_queue.call_args_list[0][0][3], True) ############################################################# # Now, switch email address visibility, check client_gravatar # is automatically disabled for the user. do_set_realm_property( user.realm, "email_address_visibility", Realm.EMAIL_ADDRESS_VISIBILITY_ADMINS, acting_user=None, ) result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], f"user{user.id}@zulip.testserver") # Note that the Gravatar URL should still be computed from the # `delivery_email`; otherwise, we won't be able to serve the # user's Gravatar. self.assertEqual(hamlet["avatar_url"], get_gravatar_url(user.delivery_email, 1)) self.assertNotIn("delivery_email", hamlet) # Also verify the /events code path. This is a bit hacky, but # basically we want to verify client_gravatar is being # overridden. with mock.patch( "zerver.lib.events.request_event_queue", return_value=None ) as mock_request_event_queue: with self.assertRaises(JsonableError): result = do_events_register(user, get_client("website"), client_gravatar=True) self.assertEqual(mock_request_event_queue.call_args_list[0][0][3], False) # client_gravatar is still turned off for admins. In theory, # it doesn't need to be, but client-side changes would be # required in apps like the mobile apps. # delivery_email is sent for admins. admin.refresh_from_db() self.login_user(admin) result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], f"user{user.id}@zulip.testserver") self.assertEqual(hamlet["avatar_url"], get_gravatar_url(user.email, 1)) self.assertEqual(hamlet["delivery_email"], self.example_email("hamlet")) def test_user_cannot_promote_to_admin(self) -> None: self.login("hamlet") req = dict(role=orjson.dumps(UserProfile.ROLE_REALM_ADMINISTRATOR).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Insufficient permission") def test_admin_user_can_change_full_name(self) -> None: new_name = "new name" self.login("iago") hamlet = self.example_user("hamlet") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch(f"/json/users/{hamlet.id}", req) self.assert_json_success(result) hamlet = self.example_user("hamlet") self.assertEqual(hamlet.full_name, new_name) def test_non_admin_cannot_change_full_name(self) -> None: self.login("hamlet") req = dict(full_name=orjson.dumps("new name").decode()) result = self.client_patch("/json/users/{}".format(self.example_user("othello").id), req) self.assert_json_error(result, "Insufficient permission") def test_admin_cannot_set_long_full_name(self) -> None: new_name = "a" * (UserProfile.MAX_NAME_LENGTH + 1) self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Name too long!") def test_admin_cannot_set_short_full_name(self) -> None: new_name = "a" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Name too short!") def test_not_allowed_format(self) -> None: # Name of format "Alice|999" breaks in Markdown new_name = "iago|72" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid format!") def test_allowed_format_complex(self) -> None: # Adding characters after r'|d+' doesn't break Markdown new_name = "Hello- 12iago|72k" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_success(result) def test_not_allowed_format_complex(self) -> None: new_name = "Hello- 12iago|72" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid format!") def test_admin_cannot_set_full_name_with_invalid_characters(self) -> None: new_name = "Opheli*" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid characters in name!") def test_access_user_by_id(self) -> None: iago = self.example_user("iago") # Must be a valid user ID in the realm with self.assertRaises(JsonableError): access_user_by_id(iago, 1234, for_admin=False) with self.assertRaises(JsonableError): access_user_by_id(iago, self.mit_user("sipbtest").id, for_admin=False) # Can only access bot users if allow_bots is passed bot = self.example_user("default_bot") access_user_by_id(iago, bot.id, allow_bots=True, for_admin=True) with self.assertRaises(JsonableError): access_user_by_id(iago, bot.id, for_admin=True) # Can only access deactivated users if allow_deactivated is passed hamlet = self.example_user("hamlet") do_deactivate_user(hamlet, acting_user=None) with self.assertRaises(JsonableError): access_user_by_id(iago, hamlet.id, for_admin=False) with self.assertRaises(JsonableError): access_user_by_id(iago, hamlet.id, for_admin=True) access_user_by_id(iago, hamlet.id, allow_deactivated=True, for_admin=True) # Non-admin user can't admin another user with self.assertRaises(JsonableError): access_user_by_id( self.example_user("cordelia"), self.example_user("aaron").id, for_admin=True ) # But does have read-only access to it. access_user_by_id( self.example_user("cordelia"), self.example_user("aaron").id, for_admin=False ) def check_property_for_role(self, user_profile: UserProfile, role: int) -> bool: if role == UserProfile.ROLE_REALM_ADMINISTRATOR: return ( user_profile.is_realm_admin and not user_profile.is_guest and not user_profile.is_realm_owner and not user_profile.is_moderator ) elif role == UserProfile.ROLE_REALM_OWNER: return ( user_profile.is_realm_owner and user_profile.is_realm_admin and not user_profile.is_moderator and not user_profile.is_guest ) elif role == UserProfile.ROLE_MODERATOR: return ( user_profile.is_moderator and not user_profile.is_realm_owner and not user_profile.is_realm_admin and not user_profile.is_guest ) if role == UserProfile.ROLE_MEMBER: return ( not user_profile.is_guest and not user_profile.is_moderator and not user_profile.is_realm_admin and not user_profile.is_realm_owner ) assert role == UserProfile.ROLE_GUEST return ( user_profile.is_guest and not user_profile.is_moderator and not user_profile.is_realm_admin and not user_profile.is_realm_owner ) def check_user_role_change( self, user_email: str, new_role: int, ) -> None: self.login("desdemona") user_profile = self.example_user(user_email) old_role = user_profile.role self.assertTrue(self.check_property_for_role(user_profile, old_role)) req = dict(role=orjson.dumps(new_role).decode()) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{user_profile.id}", req) self.assert_json_success(result) user_profile = self.example_user(user_email) self.assertTrue(self.check_property_for_role(user_profile, new_role)) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], user_profile.id) self.assertTrue(person["role"], new_role) def test_change_regular_member_to_guest(self) -> None: self.check_user_role_change("hamlet", UserProfile.ROLE_GUEST) def test_change_guest_to_regular_member(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_MEMBER) def test_change_admin_to_guest(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_GUEST) def test_change_guest_to_admin(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_owner_to_guest(self) -> None: self.login("desdemona") iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_GUEST) def test_change_guest_to_owner(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_REALM_OWNER) def test_change_admin_to_owner(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_REALM_OWNER) def test_change_owner_to_admin(self) -> None: self.login("desdemona") iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_owner_to_moderator(self) -> None: iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_owner(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_REALM_OWNER) def test_change_admin_to_moderator(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_admin(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_guest_to_moderator(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_guest(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_GUEST) def test_admin_user_can_change_profile_data(self) -> None: realm = get_realm("zulip") self.login("iago") new_profile_data = [] cordelia = self.example_user("cordelia") # Test for all type of data fields = { "Phone number": "short text data", "Biography": "long text data", "Favorite food": "short text data", "Favorite editor": "vim", "Birthday": "1909-03-05", "Favorite website": "https://zulip.com", "Mentor": [cordelia.id], "GitHub": "timabbott", } for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) new_profile_data.append( { "id": field.id, "value": fields[field_name], } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_success(result) cordelia = self.example_user("cordelia") for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], fields[field_dict["name"]]) # Test admin user cannot set invalid profile data invalid_fields = [ ( "Favorite editor", "invalid choice", "'invalid choice' is not a valid choice for 'Favorite editor'.", ), ("Birthday", "1909-34-55", "Birthday is not a date"), ("Favorite website", "not url", "Favorite website is not a URL"), ("Mentor", "not list of user ids", "User IDs is not a list"), ] for field_name, field_value, error_msg in invalid_fields: new_profile_data = [] field = CustomProfileField.objects.get(name=field_name, realm=realm) new_profile_data.append( { "id": field.id, "value": field_value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()}, ) self.assert_json_error(result, error_msg) # non-existent field and no data invalid_profile_data = [ { "id": 9001, "value": "", } ] result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(invalid_profile_data).decode()}, ) self.assert_json_error(result, "Field id 9001 not found.") # non-existent field and data invalid_profile_data = [ { "id": 9001, "value": "some data", } ] result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(invalid_profile_data).decode()}, ) self.assert_json_error(result, "Field id 9001 not found.") # Test for clearing/resetting field values. empty_profile_data = [] for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) value: Union[str, None, List[Any]] = "" if field.field_type == CustomProfileField.USER: value = [] empty_profile_data.append( { "id": field.id, "value": value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(empty_profile_data).decode()}, ) self.assert_json_success(result) for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], None) # Test adding some of the field values after removing all. hamlet = self.example_user("hamlet") new_fields = { "Phone number": None, "Biography": "A test user", "Favorite food": None, "Favorite editor": None, "Birthday": None, "Favorite website": "https://zulip.github.io", "Mentor": [hamlet.id], "GitHub": "timabbott", } new_profile_data = [] for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) value = None if new_fields[field_name]: value = new_fields[field_name] new_profile_data.append( { "id": field.id, "value": value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_success(result) for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], new_fields[str(field_dict["name"])]) def test_non_admin_user_cannot_change_profile_data(self) -> None: self.login("cordelia") hamlet = self.example_user("hamlet") realm = get_realm("zulip") new_profile_data = [] field = CustomProfileField.objects.get(name="Biography", realm=realm) new_profile_data.append( { "id": field.id, "value": "New hamlet Biography", } ) result = self.client_patch( f"/json/users/{hamlet.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_error(result, "Insufficient permission") result = self.client_patch( "/json/users/{}".format(self.example_user("cordelia").id), {"profile_data": orjson.dumps(new_profile_data).decode()}, ) self.assert_json_error(result, "Insufficient permission") class QueryCountTest(ZulipTestCase): def test_create_user_with_multiple_streams(self) -> None: # add_new_user_history needs messages to be current Message.objects.all().update(date_sent=timezone_now()) ContentType.objects.clear_cache() # This just focuses on making sure we don't too many # queries/cache tries or send too many events. realm = get_realm("zulip") self.make_stream("private_stream1", invite_only=True) self.make_stream("private_stream2", invite_only=True) stream_names = [ "Denmark", "Scotland", "Verona", "private_stream1", "private_stream2", ] streams = [get_stream(stream_name, realm) for stream_name in stream_names] do_invite_users( user_profile=self.example_user("hamlet"), invitee_emails=["fred@zulip.com"], streams=streams, ) prereg_user = PreregistrationUser.objects.get(email="fred@zulip.com") events: List[Mapping[str, Any]] = [] with queries_captured() as queries: with cache_tries_captured() as cache_tries: with tornado_redirected_to_list(events): fred = do_create_user( email="fred@zulip.com", password="password", realm=realm, full_name="Fred Flintstone", prereg_user=prereg_user, acting_user=None, ) self.assert_length(queries, 70) self.assert_length(cache_tries, 22) self.assert_length(events, 7) peer_add_events = [event for event in events if event["event"].get("op") == "peer_add"] notifications = set() for event in peer_add_events: stream_ids = event["event"]["stream_ids"] stream_names = sorted(Stream.objects.get(id=stream_id).name for stream_id in stream_ids) self.assertTrue(event["event"]["user_ids"], {fred.id}) notifications.add(",".join(stream_names)) self.assertEqual( notifications, {"Denmark,Scotland,Verona", "private_stream1", "private_stream2"} ) class BulkCreateUserTest(ZulipTestCase): def test_create_users(self) -> None: realm = get_realm("zulip") realm.email_address_visibility = Realm.EMAIL_ADDRESS_VISIBILITY_ADMINS realm.save() name_list = [ ("Fred Flintstone", "fred@zulip.com"), ("Lisa Simpson", "lisa@zulip.com"), ] create_users(realm, name_list) fred = get_user_by_delivery_email("fred@zulip.com", realm) self.assertEqual( fred.email, f"user{fred.id}@zulip.testserver", ) lisa = get_user_by_delivery_email("lisa@zulip.com", realm) self.assertEqual(lisa.full_name, "Lisa Simpson") self.assertEqual(lisa.is_bot, False) self.assertEqual(lisa.bot_type, None) realm.email_address_visibility = Realm.EMAIL_ADDRESS_VISIBILITY_EVERYONE realm.save() name_list = [ ("Bono", "bono@zulip.com"), ("Cher", "cher@zulip.com"), ] create_users(realm, name_list) bono = get_user_by_delivery_email("bono@zulip.com", realm) self.assertEqual(bono.email, "bono@zulip.com") self.assertEqual(bono.delivery_email, "bono@zulip.com") cher = get_user_by_delivery_email("cher@zulip.com", realm) self.assertEqual(cher.full_name, "Cher") class AdminCreateUserTest(ZulipTestCase): def test_create_user_backend(self) -> None: # This test should give us complete coverage on # create_user_backend. It mostly exercises error # conditions, and it also does a basic test of the success # path. admin = self.example_user("hamlet") realm = admin.realm self.login_user(admin) do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) valid_params = dict( email="romeo@zulip.net", password="xxxx", full_name="Romeo Montague", ) self.assertEqual(admin.can_create_users, False) result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "User not authorized for this query") do_change_can_create_users(admin, True) # can_create_users is insufficient without being a realm administrator: do_change_user_role(admin, UserProfile.ROLE_MEMBER, acting_user=None) result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Must be an organization administrator") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) result = self.client_post("/json/users", {}) self.assert_json_error(result, "Missing 'email' argument") result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", ), ) self.assert_json_error(result, "Missing 'password' argument") result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", password="xxxx", ), ) self.assert_json_error(result, "Missing 'full_name' argument") # Test short_name gets properly ignored result = self.client_post( "/json/users", dict( email="romeo@zulip.com", password="xxxx", full_name="Romeo Montague", short_name="DEPRECATED", ), ) self.assert_json_success(result) result = self.client_post( "/json/users", dict( email="broken", password="xxxx", full_name="Romeo Montague", ), ) self.assert_json_error(result, "Bad name or username") do_set_realm_property(realm, "emails_restricted_to_domains", True, acting_user=None) result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", password="xxxx", full_name="Romeo Montague", ), ) self.assert_json_error( result, "Email 'romeo@not-zulip.com' not allowed in this organization" ) RealmDomain.objects.create(realm=get_realm("zulip"), domain="zulip.net") # Check can't use a bad password with zxcvbn enabled with self.settings(PASSWORD_MIN_LENGTH=6, PASSWORD_MIN_GUESSES=1000): result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "The password is too weak.") result = self.client_post("/json/users", valid_params) self.assert_json_success(result) # Romeo is a newly registered user new_user = get_user_by_delivery_email("romeo@zulip.net", get_realm("zulip")) result = orjson.loads(result.content) self.assertEqual(new_user.full_name, "Romeo Montague") self.assertEqual(new_user.id, result["user_id"]) # Make sure the recipient field is set correctly. self.assertEqual( new_user.recipient, Recipient.objects.get(type=Recipient.PERSONAL, type_id=new_user.id) ) # we can't create the same user twice. result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Email 'romeo@zulip.net' already in use") # Don't allow user to sign up with disposable email. realm.emails_restricted_to_domains = False realm.disallow_disposable_email_addresses = True realm.save() valid_params["email"] = "abc@mailnator.com" result = self.client_post("/json/users", valid_params) self.assert_json_error( result, "Disposable email addresses are not allowed in this organization" ) # Don't allow creating a user with + in their email address when realm # is restricted to a domain. realm.emails_restricted_to_domains = True realm.save() valid_params["email"] = "iago+label@zulip.com" result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Email addresses containing + are not allowed.") # Users can be created with + in their email address when realm # is not restricted to a domain. realm.emails_restricted_to_domains = False realm.save() valid_params["email"] = "iago+label@zulip.com" result = self.client_post("/json/users", valid_params) self.assert_json_success(result) class UserProfileTest(ZulipTestCase): def test_get_emails_from_user_ids(self) -> None: hamlet = self.example_user("hamlet") othello = self.example_user("othello") dct = get_emails_from_user_ids([hamlet.id, othello.id]) self.assertEqual(dct[hamlet.id], hamlet.email) self.assertEqual(dct[othello.id], othello.email) def test_valid_user_id(self) -> None: realm = get_realm("zulip") hamlet = self.example_user("hamlet") othello = self.example_user("othello") bot = self.example_user("default_bot") # Invalid user ID invalid_uid: object = 1000 with self.assertRaisesRegex(ValidationError, r"User IDs is not a list"): check_valid_user_ids(realm.id, invalid_uid) with self.assertRaisesRegex(ValidationError, rf"Invalid user ID: {invalid_uid}"): check_valid_user_ids(realm.id, [invalid_uid]) invalid_uid = "abc" with self.assertRaisesRegex(ValidationError, r"User IDs\[0\] is not an integer"): check_valid_user_ids(realm.id, [invalid_uid]) invalid_uid = str(othello.id) with self.assertRaisesRegex(ValidationError, r"User IDs\[0\] is not an integer"): check_valid_user_ids(realm.id, [invalid_uid]) # User is in different realm with self.assertRaisesRegex(ValidationError, rf"Invalid user ID: {hamlet.id}"): check_valid_user_ids(get_realm("zephyr").id, [hamlet.id]) # User is not active change_user_is_active(hamlet, False) with self.assertRaisesRegex(ValidationError, rf"User with ID {hamlet.id} is deactivated"): check_valid_user_ids(realm.id, [hamlet.id]) check_valid_user_ids(realm.id, [hamlet.id], allow_deactivated=True) # User is a bot with self.assertRaisesRegex(ValidationError, rf"User with ID {bot.id} is a bot"): check_valid_user_ids(realm.id, [bot.id]) # Successfully get non-bot, active user belong to your realm check_valid_user_ids(realm.id, [othello.id]) def test_cache_invalidation(self) -> None: hamlet = self.example_user("hamlet") with mock.patch("zerver.lib.cache.delete_display_recipient_cache") as m: hamlet.full_name = "Hamlet Junior" hamlet.save(update_fields=["full_name"]) self.assertTrue(m.called) with mock.patch("zerver.lib.cache.delete_display_recipient_cache") as m: hamlet.long_term_idle = True hamlet.save(update_fields=["long_term_idle"]) self.assertFalse(m.called) def test_user_ids_to_users(self) -> None: real_user_ids = [ self.example_user("hamlet").id, self.example_user("cordelia").id, ] self.assertEqual(user_ids_to_users([], get_realm("zulip")), []) self.assertEqual( { user_profile.id for user_profile in user_ids_to_users(real_user_ids, get_realm("zulip")) }, set(real_user_ids), ) with self.assertRaises(JsonableError): user_ids_to_users([1234], get_realm("zephyr")) with self.assertRaises(JsonableError): user_ids_to_users(real_user_ids, get_realm("zephyr")) def test_bulk_get_users(self) -> None: from zerver.lib.users import bulk_get_users hamlet = self.example_user("hamlet") cordelia = self.example_user("cordelia") webhook_bot = self.example_user("webhook_bot") result = bulk_get_users( [hamlet.email, cordelia.email], get_realm("zulip"), ) self.assertEqual(result[hamlet.email].email, hamlet.email) self.assertEqual(result[cordelia.email].email, cordelia.email) result = bulk_get_users( [hamlet.email, cordelia.email, webhook_bot.email], None, base_query=UserProfile.objects.all(), ) self.assertEqual(result[hamlet.email].email, hamlet.email) self.assertEqual(result[cordelia.email].email, cordelia.email) self.assertEqual(result[webhook_bot.email].email, webhook_bot.email) def test_get_accounts_for_email(self) -> None: reset_emails_in_zulip_realm() def check_account_present_in_accounts(user: UserProfile, accounts: List[Accounts]) -> None: for account in accounts: realm = user.realm if ( account["avatar"] == avatar_url(user) and account["full_name"] == user.full_name and account["realm_name"] == realm.name and account["realm_id"] == realm.id ): return raise AssertionError("Account not found") lear_realm = get_realm("lear") cordelia_in_zulip = self.example_user("cordelia") cordelia_in_lear = get_user_by_delivery_email("cordelia@zulip.com", lear_realm) email = "cordelia@zulip.com" accounts = get_accounts_for_email(email) self.assert_length(accounts, 2) check_account_present_in_accounts(cordelia_in_zulip, accounts) check_account_present_in_accounts(cordelia_in_lear, accounts) email = "CORDelia@zulip.com" accounts = get_accounts_for_email(email) self.assert_length(accounts, 2) check_account_present_in_accounts(cordelia_in_zulip, accounts) check_account_present_in_accounts(cordelia_in_lear, accounts) email = "IAGO@ZULIP.COM" accounts = get_accounts_for_email(email) self.assert_length(accounts, 1) check_account_present_in_accounts(self.example_user("iago"), accounts) # We verify that get_accounts_for_email don't return deactivated users accounts user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) email = self.example_email("hamlet") accounts = get_accounts_for_email(email) with self.assertRaises(AssertionError): check_account_present_in_accounts(user, accounts) def test_get_source_profile(self) -> None: reset_emails_in_zulip_realm() zulip_realm_id = get_realm("zulip").id iago = get_source_profile("iago@zulip.com", zulip_realm_id) assert iago is not None self.assertEqual(iago.email, "iago@zulip.com") self.assertEqual(iago.realm, get_realm("zulip")) iago = get_source_profile("IAGO@ZULIP.com", zulip_realm_id) assert iago is not None self.assertEqual(iago.email, "iago@zulip.com") lear_realm_id = get_realm("lear").id cordelia = get_source_profile("cordelia@zulip.com", lear_realm_id) assert cordelia is not None self.assertEqual(cordelia.email, "cordelia@zulip.com") self.assertIsNone(get_source_profile("iagod@zulip.com", zulip_realm_id)) self.assertIsNone(get_source_profile("iago@zulip.com", 0)) self.assertIsNone(get_source_profile("iago@zulip.com", lear_realm_id)) def test_copy_user_settings(self) -> None: iago = self.example_user("iago") cordelia = self.example_user("cordelia") hamlet = self.example_user("hamlet") hamlet.color_scheme = UserProfile.COLOR_SCHEME_LIGHT cordelia.default_language = "de" cordelia.default_view = "all_messages" cordelia.emojiset = "twitter" cordelia.timezone = "America/Phoenix" cordelia.color_scheme = UserProfile.COLOR_SCHEME_NIGHT cordelia.enable_offline_email_notifications = False cordelia.enable_stream_push_notifications = True cordelia.enter_sends = False cordelia.avatar_source = UserProfile.AVATAR_FROM_USER cordelia.save() # Upload cordelia's avatar with get_test_image_file("img.png") as image_file: upload_avatar_image(image_file, cordelia, cordelia) UserHotspot.objects.filter(user=cordelia).delete() UserHotspot.objects.filter(user=iago).delete() hotspots_completed = ["intro_reply", "intro_streams", "intro_topics"] for hotspot in hotspots_completed: UserHotspot.objects.create(user=cordelia, hotspot=hotspot) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): copy_user_settings(cordelia, iago) # Check that we didn't send an realm_user update events to # users; this work is happening before the user account is # created, so any changes will be reflected in the "add" event # introducing the user to clients. self.assertEqual(len(events), 0) # We verify that cordelia and iago match, but hamlet has the defaults. self.assertEqual(iago.full_name, "Cordelia, Lear's daughter") self.assertEqual(cordelia.full_name, "Cordelia, Lear's daughter") self.assertEqual(hamlet.full_name, "King Hamlet") self.assertEqual(iago.default_language, "de") self.assertEqual(cordelia.default_language, "de") self.assertEqual(hamlet.default_language, "en") self.assertEqual(iago.emojiset, "twitter") self.assertEqual(cordelia.emojiset, "twitter") self.assertEqual(hamlet.emojiset, "google-blob") self.assertEqual(iago.timezone, "America/Phoenix") self.assertEqual(cordelia.timezone, "America/Phoenix") self.assertEqual(hamlet.timezone, "") self.assertEqual(iago.color_scheme, UserProfile.COLOR_SCHEME_NIGHT) self.assertEqual(cordelia.color_scheme, UserProfile.COLOR_SCHEME_NIGHT) self.assertEqual(hamlet.color_scheme, UserProfile.COLOR_SCHEME_LIGHT) self.assertEqual(iago.enable_offline_email_notifications, False) self.assertEqual(cordelia.enable_offline_email_notifications, False) self.assertEqual(hamlet.enable_offline_email_notifications, True) self.assertEqual(iago.enable_stream_push_notifications, True) self.assertEqual(cordelia.enable_stream_push_notifications, True) self.assertEqual(hamlet.enable_stream_push_notifications, False) self.assertEqual(iago.enter_sends, False) self.assertEqual(cordelia.enter_sends, False) self.assertEqual(hamlet.enter_sends, True) hotspots = list(UserHotspot.objects.filter(user=iago).values_list("hotspot", flat=True)) self.assertEqual(hotspots, hotspots_completed) def test_get_user_by_id_in_realm_including_cross_realm(self) -> None: realm = get_realm("zulip") hamlet = self.example_user("hamlet") othello = self.example_user("othello") bot = get_system_bot(settings.WELCOME_BOT) # Pass in the ID of a cross-realm bot and a valid realm cross_realm_bot = get_user_by_id_in_realm_including_cross_realm(bot.id, realm) self.assertEqual(cross_realm_bot.email, bot.email) self.assertEqual(cross_realm_bot.id, bot.id) # Pass in the ID of a cross-realm bot but with a invalid realm, # note that the realm should be irrelevant here cross_realm_bot = get_user_by_id_in_realm_including_cross_realm(bot.id, None) self.assertEqual(cross_realm_bot.email, bot.email) self.assertEqual(cross_realm_bot.id, bot.id) # Pass in the ID of a non-cross-realm user with a realm user_profile = get_user_by_id_in_realm_including_cross_realm(othello.id, realm) self.assertEqual(user_profile.email, othello.email) self.assertEqual(user_profile.id, othello.id) # If the realm doesn't match, or if the ID is not that of a # cross-realm bot, UserProfile.DoesNotExist is raised with self.assertRaises(UserProfile.DoesNotExist): get_user_by_id_in_realm_including_cross_realm(hamlet.id, None) def test_get_user_subscription_status(self) -> None: self.login("hamlet") iago = self.example_user("iago") stream = get_stream("Rome", iago.realm) # Invalid user ID. result = self.client_get(f"/json/users/25/subscriptions/{stream.id}") self.assert_json_error(result, "No such user") # Invalid stream ID. result = self.client_get(f"/json/users/{iago.id}/subscriptions/25") self.assert_json_error(result, "Invalid stream id") result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assertFalse(result["is_subscribed"]) # Subscribe to the stream. self.subscribe(iago, stream.name) with queries_captured() as queries: result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assert_length(queries, 6) self.assertTrue(result["is_subscribed"]) # Logging in with a Guest user. polonius = self.example_user("polonius") self.login("polonius") self.assertTrue(polonius.is_guest) self.assertTrue(stream.is_web_public) result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assertTrue(result["is_subscribed"]) class ActivateTest(ZulipTestCase): def test_basics(self) -> None: user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) self.assertFalse(user.is_active) do_reactivate_user(user, acting_user=None) self.assertTrue(user.is_active) def test_subscriptions_is_user_active(self) -> None: user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) self.assertFalse(user.is_active) self.assertTrue(Subscription.objects.filter(user_profile=user).exists()) self.assertFalse( Subscription.objects.filter(user_profile=user, is_user_active=True).exists() ) do_reactivate_user(user, acting_user=None) self.assertTrue(user.is_active) self.assertTrue(Subscription.objects.filter(user_profile=user).exists()) self.assertFalse( Subscription.objects.filter(user_profile=user, is_user_active=False).exists() ) def test_api(self) -> None: admin = self.example_user("othello") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.login("othello") user = self.example_user("hamlet") self.assertTrue(user.is_active) result = self.client_delete(f"/json/users/{user.id}") self.assert_json_success(result) user = self.example_user("hamlet") self.assertFalse(user.is_active) result = self.client_post(f"/json/users/{user.id}/reactivate") self.assert_json_success(result) user = self.example_user("hamlet") self.assertTrue(user.is_active) def test_api_with_nonexistent_user(self) -> None: self.login("iago") # Organization administrator cannot deactivate organization owner. result = self.client_delete(f'/json/users/{self.example_user('desdemona').id}') self.assert_json_error(result, "Must be an organization owner") iago = self.example_user("iago") desdemona = self.example_user("desdemona") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) # Cannot deactivate a user with the bot api result = self.client_delete("/json/bots/{}".format(self.example_user("hamlet").id)) self.assert_json_error(result, "No such bot") # Cannot deactivate a nonexistent user. invalid_user_id = 1000 result = self.client_delete(f"/json/users/{invalid_user_id}") self.assert_json_error(result, "No such user") result = self.client_delete("/json/users/{}".format(self.example_user("webhook_bot").id)) self.assert_json_error(result, "No such user") result = self.client_delete(f"/json/users/{desdemona.id}") self.assert_json_success(result) result = self.client_delete(f"/json/users/{iago.id}") self.assert_json_error(result, "Cannot deactivate the only organization owner") # Cannot reactivate a nonexistent user. invalid_user_id = 1000 result = self.client_post(f"/json/users/{invalid_user_id}/reactivate") self.assert_json_error(result, "No such user") def test_api_with_insufficient_permissions(self) -> None: non_admin = self.example_user("othello") do_change_user_role(non_admin, UserProfile.ROLE_MEMBER, acting_user=None) self.login("othello") # Cannot deactivate a user with the users api result = self.client_delete("/json/users/{}".format(self.example_user("hamlet").id)) self.assert_json_error(result, "Insufficient permission") # Cannot reactivate a user result = self.client_post( "/json/users/{}/reactivate".format(self.example_user("hamlet").id) ) self.assert_json_error(result, "Insufficient permission") def test_clear_scheduled_jobs(self) -> None: user = self.example_user("hamlet") send_future_email( "zerver/emails/followup_day1", user.realm, to_user_ids=[user.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) do_deactivate_user(user, acting_user=None) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_send_future_email_with_multiple_recipients(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual( ScheduledEmail.objects.filter(users__in=[hamlet, iago]).distinct().count(), 1 ) email = ScheduledEmail.objects.all().first() self.assertEqual(email.users.count(), 2) def test_clear_scheduled_emails_with_multiple_user_ids(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) clear_scheduled_emails([hamlet.id, iago.id]) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_clear_schedule_emails_with_one_user_id(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) clear_scheduled_emails([hamlet.id]) self.assertEqual(ScheduledEmail.objects.count(), 1) self.assertEqual(ScheduledEmail.objects.filter(users=hamlet).count(), 0) self.assertEqual(ScheduledEmail.objects.filter(users=iago).count(), 1) def test_deliver_scheduled_emails(self) -> None: iago = self.example_user("iago") hamlet = self.example_user("hamlet") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) email = ScheduledEmail.objects.all().first() deliver_scheduled_emails(email) from django.core.mail import outbox self.assertEqual(len(outbox), 1) for message in outbox: self.assertEqual( set(message.to), { str(Address(display_name=hamlet.full_name, addr_spec=hamlet.delivery_email)), str(Address(display_name=iago.full_name, addr_spec=iago.delivery_email)), }, ) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_deliver_scheduled_emails_no_addressees(self) -> None: iago = self.example_user("iago") hamlet = self.example_user("hamlet") to_user_ids = [hamlet.id, iago.id] send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=to_user_ids, delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) email = ScheduledEmail.objects.all().first() email.users.remove(*to_user_ids) with self.assertLogs("zulip.send_email", level="INFO") as info_log: deliver_scheduled_emails(email) from django.core.mail import outbox self.assertEqual(len(outbox), 0) self.assertEqual(ScheduledEmail.objects.count(), 1) self.assertEqual( info_log.output, [ f"WARNING:zulip.send_email:ScheduledEmail id {email.id} has empty users and address attributes." ], ) class RecipientInfoTest(ZulipTestCase): def test_stream_recipient_info(self) -> None: hamlet = self.example_user("hamlet") cordelia = self.example_user("cordelia") othello = self.example_user("othello") # These tests were written with the old default for # enable_online_push_notifications; that default is better for # testing the full code path anyway. hamlet.enable_online_push_notifications = False cordelia.enable_online_push_notifications = False othello.enable_online_push_notifications = False hamlet.save() cordelia.save() othello.save() realm = hamlet.realm stream_name = "Test stream" topic_name = "test topic" for user in [hamlet, cordelia, othello]: self.subscribe(user, stream_name) stream = get_stream(stream_name, realm) recipient = stream.recipient stream_topic = StreamTopicTarget( stream_id=stream.id, topic_name=topic_name, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) all_user_ids = {hamlet.id, cordelia.id, othello.id} expected_info = dict( active_user_ids=all_user_ids, push_notify_user_ids=set(), stream_push_user_ids=set(), stream_email_user_ids=set(), wildcard_mention_user_ids=set(), um_eligible_user_ids=all_user_ids, long_term_idle_user_ids=set(), default_bot_user_ids=set(), service_bot_tuples=[], ) self.assertEqual(info, expected_info) cordelia.wildcard_mentions_notify = False cordelia.save() hamlet.enable_stream_push_notifications = True hamlet.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) self.assertEqual(info["stream_push_user_ids"], {hamlet.id}) self.assertEqual(info["wildcard_mention_user_ids"], set()) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["wildcard_mention_user_ids"], {hamlet.id, othello.id}) sub = get_subscription(stream_name, hamlet) sub.push_notifications = False sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) self.assertEqual(info["stream_push_user_ids"], set()) hamlet.enable_stream_push_notifications = False hamlet.save() sub = get_subscription(stream_name, hamlet) sub.push_notifications = True sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) self.assertEqual(info["stream_push_user_ids"], {hamlet.id}) # Now mute Hamlet to omit him from stream_push_user_ids. add_topic_mute( user_profile=hamlet, stream_id=stream.id, recipient_id=recipient.id, topic_name=topic_name, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) self.assertEqual(info["stream_push_user_ids"], set()) self.assertEqual(info["wildcard_mention_user_ids"], set()) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) # Since Hamlet has muted the stream and Cordelia has disabled # wildcard notifications, it should just be Othello here. self.assertEqual(info["wildcard_mention_user_ids"], {othello.id}) sub = get_subscription(stream_name, othello) sub.wildcard_mentions_notify = False sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) # Verify that stream-level wildcard_mentions_notify=False works correctly. self.assertEqual(info["wildcard_mention_user_ids"], set()) # Verify that True works as expected as well sub = get_subscription(stream_name, othello) sub.wildcard_mentions_notify = True sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) self.assertEqual(info["wildcard_mention_user_ids"], {othello.id}) # Add a service bot. service_bot = do_create_user( email="service-bot@zulip.com", password="", realm=realm, full_name="", bot_type=UserProfile.EMBEDDED_BOT, acting_user=None, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possibly_mentioned_user_ids={service_bot.id}, ) self.assertEqual( info["service_bot_tuples"], [ (service_bot.id, UserProfile.EMBEDDED_BOT), ], ) # Add a normal bot. normal_bot = do_create_user( email="normal-bot@zulip.com", password="", realm=realm, full_name="", bot_type=UserProfile.DEFAULT_BOT, acting_user=None, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possibly_mentioned_user_ids={service_bot.id, normal_bot.id}, ) self.assertEqual(info["default_bot_user_ids"], {normal_bot.id}) def test_get_recipient_info_invalid_recipient_type(self) -> None: hamlet = self.example_user("hamlet") realm = hamlet.realm stream = get_stream("Rome", realm) stream_topic = StreamTopicTarget( stream_id=stream.id, topic_name="test topic", ) # Make sure get_recipient_info asserts on invalid recipient types with self.assertRaisesRegex(ValueError, "Bad recipient type"): invalid_recipient = Recipient(type=999) # 999 is not a valid type get_recipient_info( recipient=invalid_recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) class BulkUsersTest(ZulipTestCase): def test_client_gravatar_option(self) -> None: reset_emails_in_zulip_realm() self.login("cordelia") hamlet = self.example_user("hamlet") def get_hamlet_avatar(client_gravatar: bool) -> Optional[str]: data = dict(client_gravatar=orjson.dumps(client_gravatar).decode()) result = self.client_get("/json/users", data) self.assert_json_success(result) rows = result.json()["members"] hamlet_data = [row for row in rows if row["user_id"] == hamlet.id][0] return hamlet_data["avatar_url"] self.assertEqual( get_hamlet_avatar(client_gravatar=True), None, ) """ The main purpose of this test is to make sure we return None for avatar_url when client_gravatar is set to True. And we do a sanity check for when it's False, but we leave it to other tests to validate the specific URL. """ self.assertIn( "gravatar.com", get_hamlet_avatar(client_gravatar=False), ) class GetProfileTest(ZulipTestCase): def test_cache_behavior(self) -> None: """Tests whether fetching a user object the normal way, with `get_user`, makes 1 cache query and 1 database query. """ realm = get_realm("zulip") email = self.example_user("hamlet").email with queries_captured() as queries: with simulated_empty_cache() as cache_queries: user_profile = get_user(email, realm) self.assert_length(queries, 1) self.assert_length(cache_queries, 1) self.assertEqual(user_profile.email, email) def test_get_user_profile(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") desdemona = self.example_user("desdemona") shiva = self.example_user("shiva") self.login("hamlet") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], hamlet.email) self.assertEqual(result["full_name"], "King Hamlet") self.assertIn("user_id", result) self.assertFalse(result["is_bot"]) self.assertFalse(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_MEMBER) self.assertFalse("delivery_email" in result) self.login("iago") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], iago.email) self.assertEqual(result["full_name"], "Iago") self.assertFalse(result["is_bot"]) self.assertTrue(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_REALM_ADMINISTRATOR) self.login("desdemona") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], desdemona.email) self.assertFalse(result["is_bot"]) self.assertTrue(result["is_admin"]) self.assertTrue(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_REALM_OWNER) self.login("shiva") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], shiva.email) self.assertFalse(result["is_bot"]) self.assertFalse(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_MODERATOR) # Tests the GET ../users/{id} API endpoint. user = self.example_user("hamlet") result = orjson.loads(self.client_get(f"/json/users/{user.id}").content) self.assertEqual(result["user"]["email"], user.email) self.assertEqual(result["user"]["full_name"], user.full_name) self.assertIn("user_id", result["user"]) self.assertNotIn("profile_data", result["user"]) self.assertFalse(result["user"]["is_bot"]) self.assertFalse(result["user"]["is_admin"]) self.assertFalse(result["user"]["is_owner"]) result = orjson.loads( self.client_get( f"/json/users/{user.id}", {"include_custom_profile_fields": "true"} ).content ) self.assertIn("profile_data", result["user"]) result = self.client_get("/json/users/30") self.assert_json_error(result, "No such user") bot = self.example_user("default_bot") result = orjson.loads(self.client_get(f"/json/users/{bot.id}").content) self.assertEqual(result["user"]["email"], bot.email) self.assertTrue(result["user"]["is_bot"]) def test_get_user_by_email(self) -> None: user = self.example_user("hamlet") self.login("hamlet") result = orjson.loads(self.client_get(f"/json/users/{user.email}").content) self.assertEqual(result["user"]["email"], user.email) self.assertEqual(result["user"]["full_name"], user.full_name) self.assertIn("user_id", result["user"]) self.assertNotIn("profile_data", result["user"]) self.assertFalse(result["user"]["is_bot"]) self.assertFalse(result["user"]["is_admin"]) self.assertFalse(result["user"]["is_owner"]) result = orjson.loads( self.client_get( f"/json/users/{user.email}", {"include_custom_profile_fields": "true"} ).content ) self.assertIn("profile_data", result["user"]) result = self.client_get("/json/users/invalid") self.assert_json_error(result, "No such user") bot = self.example_user("default_bot") result = orjson.loads(self.client_get(f"/json/users/{bot.email}").content) self.assertEqual(result["user"]["email"], bot.email) self.assertTrue(result["user"]["is_bot"]) def test_get_all_profiles_avatar_urls(self) -> None: hamlet = self.example_user("hamlet") result = self.api_get(hamlet, "/api/v1/users") self.assert_json_success(result) (my_user,) = [user for user in result.json()["members"] if user["email"] == hamlet.email] self.assertEqual( my_user["avatar_url"], avatar_url(hamlet), ) class DeleteUserTest(ZulipTestCase): def test_do_delete_user(self) -> None: realm = get_realm("zulip") cordelia = self.example_user("cordelia") othello = self.example_user("othello") hamlet = self.example_user("hamlet") hamlet_personal_recipient = hamlet.recipient hamlet_user_id = hamlet.id self.send_personal_message(cordelia, hamlet) self.send_personal_message(hamlet, cordelia) personal_message_ids_to_hamlet = Message.objects.filter( recipient=hamlet_personal_recipient ).values_list("id", flat=True) self.assertTrue(len(personal_message_ids_to_hamlet) > 0) self.assertTrue(Message.objects.filter(sender=hamlet).exists()) huddle_message_ids_from_cordelia = [ self.send_huddle_message(cordelia, [hamlet, othello]) for i in range(3) ] huddle_message_ids_from_hamlet = [ self.send_huddle_message(hamlet, [cordelia, othello]) for i in range(3) ] huddle_with_hamlet_recipient_ids = list( Subscription.objects.filter( user_profile=hamlet, recipient__type=Recipient.HUDDLE ).values_list("recipient_id", flat=True) ) self.assertTrue(len(huddle_with_hamlet_recipient_ids) > 0) do_delete_user(hamlet) replacement_dummy_user = UserProfile.objects.get(id=hamlet_user_id, realm=realm) self.assertEqual( replacement_dummy_user.delivery_email, f"deleteduser{hamlet_user_id}@{realm.uri}" ) self.assertEqual(replacement_dummy_user.is_mirror_dummy, True) self.assertEqual(Message.objects.filter(id__in=personal_message_ids_to_hamlet).count(), 0) # Huddle messages from hamlet should have been deleted, but messages of other participants should # be kept. self.assertEqual(Message.objects.filter(id__in=huddle_message_ids_from_hamlet).count(), 0) self.assertEqual(Message.objects.filter(id__in=huddle_message_ids_from_cordelia).count(), 3) self.assertEqual(Message.objects.filter(sender_id=hamlet_user_id).count(), 0) # Verify that the dummy user is subscribed to the deleted user's huddles, to keep huddle data # in a correct state. for recipient_id in huddle_with_hamlet_recipient_ids: self.assertTrue( Subscription.objects.filter( user_profile=replacement_dummy_user, recipient_id=recipient_id ).exists() ) class FakeEmailDomainTest(ZulipTestCase): def test_get_fake_email_domain(self) -> None: realm = get_realm("zulip") self.assertEqual("zulip.testserver", get_fake_email_domain(realm)) with self.settings(EXTERNAL_HOST="example.com"): self.assertEqual("zulip.example.com", get_fake_email_domain(realm)) @override_settings(FAKE_EMAIL_DOMAIN="fakedomain.com", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_get_fake_email_domain_realm_host_is_ip_addr(self) -> None: realm = get_realm("zulip") self.assertEqual("fakedomain.com", get_fake_email_domain(realm)) @override_settings(FAKE_EMAIL_DOMAIN="invaliddomain", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_invalid_fake_email_domain(self) -> None: realm = get_realm("zulip") with self.assertRaises(InvalidFakeEmailDomain): get_fake_email_domain(realm) @override_settings(FAKE_EMAIL_DOMAIN="127.0.0.1", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_invalid_fake_email_domain_ip(self) -> None: with self.assertRaises(InvalidFakeEmailDomain): realm = get_realm("zulip") get_fake_email_domain(realm)

import datetime from email.headerregistry import Address from typing import Any, Dict, Iterable, List, Mapping, Optional, TypeVar, Union from unittest import mock import orjson from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.core.exceptions import ValidationError from django.test import override_settings from django.utils.timezone import now as timezone_now from zerver.lib.actions import ( change_user_is_active, create_users, do_change_can_create_users, do_change_user_role, do_create_user, do_deactivate_user, do_delete_user, do_invite_users, do_reactivate_user, do_set_realm_property, get_emails_from_user_ids, get_recipient_info, ) from zerver.lib.avatar import avatar_url, get_gravatar_url from zerver.lib.create_user import copy_user_settings from zerver.lib.events import do_events_register from zerver.lib.exceptions import JsonableError from zerver.lib.send_email import ( clear_scheduled_emails, deliver_scheduled_emails, send_future_email, ) from zerver.lib.stream_topic import StreamTopicTarget from zerver.lib.test_classes import ZulipTestCase from zerver.lib.test_helpers import ( cache_tries_captured, get_subscription, get_test_image_file, queries_captured, reset_emails_in_zulip_realm, simulated_empty_cache, tornado_redirected_to_list, ) from zerver.lib.topic_mutes import add_topic_mute from zerver.lib.upload import upload_avatar_image from zerver.lib.users import Accounts, access_user_by_id, get_accounts_for_email, user_ids_to_users from zerver.models import ( CustomProfileField, InvalidFakeEmailDomain, Message, PreregistrationUser, Realm, RealmDomain, Recipient, ScheduledEmail, Stream, Subscription, UserHotspot, UserProfile, check_valid_user_ids, get_client, get_fake_email_domain, get_realm, get_source_profile, get_stream, get_system_bot, get_user, get_user_by_delivery_email, get_user_by_id_in_realm_including_cross_realm, ) K = TypeVar("K") V = TypeVar("V") def find_dict(lst: Iterable[Dict[K, V]], k: K, v: V) -> Dict[K, V]: for dct in lst: if dct[k] == v: return dct raise AssertionError(f"Cannot find element in list where key {k} == {v}") class PermissionTest(ZulipTestCase): def test_role_setters(self) -> None: user_profile = self.example_user("hamlet") user_profile.is_realm_admin = True self.assertEqual(user_profile.is_realm_admin, True) self.assertEqual(user_profile.role, UserProfile.ROLE_REALM_ADMINISTRATOR) user_profile.is_guest = False self.assertEqual(user_profile.is_guest, False) self.assertEqual(user_profile.role, UserProfile.ROLE_REALM_ADMINISTRATOR) user_profile.is_realm_admin = False self.assertEqual(user_profile.is_realm_admin, False) self.assertEqual(user_profile.role, UserProfile.ROLE_MEMBER) user_profile.is_guest = True self.assertEqual(user_profile.is_guest, True) self.assertEqual(user_profile.role, UserProfile.ROLE_GUEST) user_profile.is_realm_admin = False self.assertEqual(user_profile.is_guest, True) self.assertEqual(user_profile.role, UserProfile.ROLE_GUEST) user_profile.is_guest = False self.assertEqual(user_profile.is_guest, False) self.assertEqual(user_profile.role, UserProfile.ROLE_MEMBER) def test_get_admin_users(self) -> None: user_profile = self.example_user("hamlet") do_change_user_role(user_profile, UserProfile.ROLE_MEMBER, acting_user=None) self.assertFalse(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertFalse(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertFalse(user_profile in admin_users) do_change_user_role(user_profile, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.assertFalse(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertTrue(user_profile in admin_users) do_change_user_role(user_profile, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.assertTrue(user_profile.is_realm_owner) admin_users = user_profile.realm.get_human_admin_users() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_human_admin_users(include_realm_owners=False) self.assertFalse(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots() self.assertTrue(user_profile in admin_users) admin_users = user_profile.realm.get_admin_users_and_bots(include_realm_owners=False) self.assertFalse(user_profile in admin_users) def test_get_first_human_user(self) -> None: realm = get_realm("zulip") UserProfile.objects.filter(realm=realm).delete() UserProfile.objects.create( realm=realm, email="bot1@zulip.com", delivery_email="bot1@zulip.com", is_bot=True ) first_human_user = UserProfile.objects.create( realm=realm, email="user1@zulip.com", delivery_email="user1@zulip.com", is_bot=False ) UserProfile.objects.create( realm=realm, email="user2@zulip.com", delivery_email="user2@zulip.com", is_bot=False ) UserProfile.objects.create( realm=realm, email="bot2@zulip.com", delivery_email="bot2@zulip.com", is_bot=True ) self.assertEqual(first_human_user, realm.get_first_human_user()) def test_updating_non_existent_user(self) -> None: self.login("hamlet") admin = self.example_user("hamlet") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) invalid_user_id = 1000 result = self.client_patch(f"/json/users/{invalid_user_id}", {}) self.assert_json_error(result, "No such user") def test_owner_api(self) -> None: self.login("iago") desdemona = self.example_user("desdemona") othello = self.example_user("othello") iago = self.example_user("iago") realm = iago.realm do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) result = self.client_get("/json/users") self.assert_json_success(result) members = result.json()["members"] iago_dict = find_dict(members, "email", iago.email) self.assertTrue(iago_dict["is_owner"]) othello_dict = find_dict(members, "email", othello.email) self.assertFalse(othello_dict["is_owner"]) req = dict(role=UserProfile.ROLE_REALM_OWNER) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertTrue(othello in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_REALM_OWNER) req = dict(role=UserProfile.ROLE_MEMBER) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertFalse(othello in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) # Cannot take away from last owner self.login("desdemona") req = dict(role=UserProfile.ROLE_MEMBER) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{iago.id}", req) self.assert_json_success(result) owner_users = realm.get_human_owner_users() self.assertFalse(iago in owner_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], iago.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) with tornado_redirected_to_list([]): result = self.client_patch(f"/json/users/{desdemona.id}", req) self.assert_json_error( result, "The owner permission cannot be removed from the only organization owner." ) do_change_user_role(iago, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.login("iago") with tornado_redirected_to_list([]): result = self.client_patch(f"/json/users/{desdemona.id}", req) self.assert_json_error(result, "Must be an organization owner") def test_admin_api(self) -> None: self.login("desdemona") hamlet = self.example_user("hamlet") othello = self.example_user("othello") desdemona = self.example_user("desdemona") realm = hamlet.realm # Make sure we see is_admin flag in /json/users result = self.client_get("/json/users") self.assert_json_success(result) members = result.json()["members"] desdemona_dict = find_dict(members, "email", desdemona.email) self.assertTrue(desdemona_dict["is_admin"]) othello_dict = find_dict(members, "email", othello.email) self.assertFalse(othello_dict["is_admin"]) # Giveth req = dict(role=orjson.dumps(UserProfile.ROLE_REALM_ADMINISTRATOR).decode()) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) admin_users = realm.get_human_admin_users() self.assertTrue(othello in admin_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_REALM_ADMINISTRATOR) # Taketh away req = dict(role=orjson.dumps(UserProfile.ROLE_MEMBER).decode()) events = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{othello.id}", req) self.assert_json_success(result) admin_users = realm.get_human_admin_users() self.assertFalse(othello in admin_users) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], othello.id) self.assertEqual(person["role"], UserProfile.ROLE_MEMBER) # Make sure only admins can patch other user's info. self.login("othello") result = self.client_patch(f"/json/users/{hamlet.id}", req) self.assert_json_error(result, "Insufficient permission") def test_admin_api_hide_emails(self) -> None: reset_emails_in_zulip_realm() user = self.example_user("hamlet") admin = self.example_user("iago") self.login_user(user) # First, verify client_gravatar works normally result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], user.email) self.assertIsNone(hamlet["avatar_url"]) self.assertNotIn("delivery_email", hamlet) # Also verify the /events code path. This is a bit hacky, but # we need to verify client_gravatar is not being overridden. with mock.patch( "zerver.lib.events.request_event_queue", return_value=None ) as mock_request_event_queue: with self.assertRaises(JsonableError): result = do_events_register(user, get_client("website"), client_gravatar=True) self.assertEqual(mock_request_event_queue.call_args_list[0][0][3], True) ############################################################# # Now, switch email address visibility, check client_gravatar # is automatically disabled for the user. do_set_realm_property( user.realm, "email_address_visibility", Realm.EMAIL_ADDRESS_VISIBILITY_ADMINS, acting_user=None, ) result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], f"user{user.id}@zulip.testserver") # Note that the Gravatar URL should still be computed from the # `delivery_email`; otherwise, we won't be able to serve the # user's Gravatar. self.assertEqual(hamlet["avatar_url"], get_gravatar_url(user.delivery_email, 1)) self.assertNotIn("delivery_email", hamlet) # Also verify the /events code path. This is a bit hacky, but # basically we want to verify client_gravatar is being # overridden. with mock.patch( "zerver.lib.events.request_event_queue", return_value=None ) as mock_request_event_queue: with self.assertRaises(JsonableError): result = do_events_register(user, get_client("website"), client_gravatar=True) self.assertEqual(mock_request_event_queue.call_args_list[0][0][3], False) # client_gravatar is still turned off for admins. In theory, # it doesn't need to be, but client-side changes would be # required in apps like the mobile apps. # delivery_email is sent for admins. admin.refresh_from_db() self.login_user(admin) result = self.client_get("/json/users", {"client_gravatar": "true"}) self.assert_json_success(result) members = result.json()["members"] hamlet = find_dict(members, "user_id", user.id) self.assertEqual(hamlet["email"], f"user{user.id}@zulip.testserver") self.assertEqual(hamlet["avatar_url"], get_gravatar_url(user.email, 1)) self.assertEqual(hamlet["delivery_email"], self.example_email("hamlet")) def test_user_cannot_promote_to_admin(self) -> None: self.login("hamlet") req = dict(role=orjson.dumps(UserProfile.ROLE_REALM_ADMINISTRATOR).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Insufficient permission") def test_admin_user_can_change_full_name(self) -> None: new_name = "new name" self.login("iago") hamlet = self.example_user("hamlet") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch(f"/json/users/{hamlet.id}", req) self.assert_json_success(result) hamlet = self.example_user("hamlet") self.assertEqual(hamlet.full_name, new_name) def test_non_admin_cannot_change_full_name(self) -> None: self.login("hamlet") req = dict(full_name=orjson.dumps("new name").decode()) result = self.client_patch("/json/users/{}".format(self.example_user("othello").id), req) self.assert_json_error(result, "Insufficient permission") def test_admin_cannot_set_long_full_name(self) -> None: new_name = "a" * (UserProfile.MAX_NAME_LENGTH + 1) self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Name too long!") def test_admin_cannot_set_short_full_name(self) -> None: new_name = "a" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Name too short!") def test_not_allowed_format(self) -> None: # Name of format "Alice|999" breaks in Markdown new_name = "iago|72" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid format!") def test_allowed_format_complex(self) -> None: # Adding characters after r'|d+' doesn't break Markdown new_name = "Hello- 12iago|72k" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_success(result) def test_not_allowed_format_complex(self) -> None: new_name = "Hello- 12iago|72" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid format!") def test_admin_cannot_set_full_name_with_invalid_characters(self) -> None: new_name = "Opheli*" self.login("iago") req = dict(full_name=orjson.dumps(new_name).decode()) result = self.client_patch("/json/users/{}".format(self.example_user("hamlet").id), req) self.assert_json_error(result, "Invalid characters in name!") def test_access_user_by_id(self) -> None: iago = self.example_user("iago") # Must be a valid user ID in the realm with self.assertRaises(JsonableError): access_user_by_id(iago, 1234, for_admin=False) with self.assertRaises(JsonableError): access_user_by_id(iago, self.mit_user("sipbtest").id, for_admin=False) # Can only access bot users if allow_bots is passed bot = self.example_user("default_bot") access_user_by_id(iago, bot.id, allow_bots=True, for_admin=True) with self.assertRaises(JsonableError): access_user_by_id(iago, bot.id, for_admin=True) # Can only access deactivated users if allow_deactivated is passed hamlet = self.example_user("hamlet") do_deactivate_user(hamlet, acting_user=None) with self.assertRaises(JsonableError): access_user_by_id(iago, hamlet.id, for_admin=False) with self.assertRaises(JsonableError): access_user_by_id(iago, hamlet.id, for_admin=True) access_user_by_id(iago, hamlet.id, allow_deactivated=True, for_admin=True) # Non-admin user can't admin another user with self.assertRaises(JsonableError): access_user_by_id( self.example_user("cordelia"), self.example_user("aaron").id, for_admin=True ) # But does have read-only access to it. access_user_by_id( self.example_user("cordelia"), self.example_user("aaron").id, for_admin=False ) def check_property_for_role(self, user_profile: UserProfile, role: int) -> bool: if role == UserProfile.ROLE_REALM_ADMINISTRATOR: return ( user_profile.is_realm_admin and not user_profile.is_guest and not user_profile.is_realm_owner and not user_profile.is_moderator ) elif role == UserProfile.ROLE_REALM_OWNER: return ( user_profile.is_realm_owner and user_profile.is_realm_admin and not user_profile.is_moderator and not user_profile.is_guest ) elif role == UserProfile.ROLE_MODERATOR: return ( user_profile.is_moderator and not user_profile.is_realm_owner and not user_profile.is_realm_admin and not user_profile.is_guest ) if role == UserProfile.ROLE_MEMBER: return ( not user_profile.is_guest and not user_profile.is_moderator and not user_profile.is_realm_admin and not user_profile.is_realm_owner ) assert role == UserProfile.ROLE_GUEST return ( user_profile.is_guest and not user_profile.is_moderator and not user_profile.is_realm_admin and not user_profile.is_realm_owner ) def check_user_role_change( self, user_email: str, new_role: int, ) -> None: self.login("desdemona") user_profile = self.example_user(user_email) old_role = user_profile.role self.assertTrue(self.check_property_for_role(user_profile, old_role)) req = dict(role=orjson.dumps(new_role).decode()) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): result = self.client_patch(f"/json/users/{user_profile.id}", req) self.assert_json_success(result) user_profile = self.example_user(user_email) self.assertTrue(self.check_property_for_role(user_profile, new_role)) person = events[0]["event"]["person"] self.assertEqual(person["user_id"], user_profile.id) self.assertTrue(person["role"], new_role) def test_change_regular_member_to_guest(self) -> None: self.check_user_role_change("hamlet", UserProfile.ROLE_GUEST) def test_change_guest_to_regular_member(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_MEMBER) def test_change_admin_to_guest(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_GUEST) def test_change_guest_to_admin(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_owner_to_guest(self) -> None: self.login("desdemona") iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_GUEST) def test_change_guest_to_owner(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_REALM_OWNER) def test_change_admin_to_owner(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_REALM_OWNER) def test_change_owner_to_admin(self) -> None: self.login("desdemona") iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_owner_to_moderator(self) -> None: iago = self.example_user("iago") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) self.check_user_role_change("iago", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_owner(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_REALM_OWNER) def test_change_admin_to_moderator(self) -> None: self.check_user_role_change("iago", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_admin(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_REALM_ADMINISTRATOR) def test_change_guest_to_moderator(self) -> None: self.check_user_role_change("polonius", UserProfile.ROLE_MODERATOR) def test_change_moderator_to_guest(self) -> None: self.check_user_role_change("shiva", UserProfile.ROLE_GUEST) def test_admin_user_can_change_profile_data(self) -> None: realm = get_realm("zulip") self.login("iago") new_profile_data = [] cordelia = self.example_user("cordelia") # Test for all type of data fields = { "Phone number": "short text data", "Biography": "long text data", "Favorite food": "short text data", "Favorite editor": "vim", "Birthday": "1909-03-05", "Favorite website": "https://zulip.com", "Mentor": [cordelia.id], "GitHub": "timabbott", } for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) new_profile_data.append( { "id": field.id, "value": fields[field_name], } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_success(result) cordelia = self.example_user("cordelia") for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], fields[field_dict["name"]]) # Test admin user cannot set invalid profile data invalid_fields = [ ( "Favorite editor", "invalid choice", "'invalid choice' is not a valid choice for 'Favorite editor'.", ), ("Birthday", "1909-34-55", "Birthday is not a date"), ("Favorite website", "not url", "Favorite website is not a URL"), ("Mentor", "not list of user ids", "User IDs is not a list"), ] for field_name, field_value, error_msg in invalid_fields: new_profile_data = [] field = CustomProfileField.objects.get(name=field_name, realm=realm) new_profile_data.append( { "id": field.id, "value": field_value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()}, ) self.assert_json_error(result, error_msg) # non-existent field and no data invalid_profile_data = [ { "id": 9001, "value": "", } ] result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(invalid_profile_data).decode()}, ) self.assert_json_error(result, "Field id 9001 not found.") # non-existent field and data invalid_profile_data = [ { "id": 9001, "value": "some data", } ] result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(invalid_profile_data).decode()}, ) self.assert_json_error(result, "Field id 9001 not found.") # Test for clearing/resetting field values. empty_profile_data = [] for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) value: Union[str, None, List[Any]] = "" if field.field_type == CustomProfileField.USER: value = [] empty_profile_data.append( { "id": field.id, "value": value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(empty_profile_data).decode()}, ) self.assert_json_success(result) for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], None) # Test adding some of the field values after removing all. hamlet = self.example_user("hamlet") new_fields = { "Phone number": None, "Biography": "A test user", "Favorite food": None, "Favorite editor": None, "Birthday": None, "Favorite website": "https://zulip.github.io", "Mentor": [hamlet.id], "GitHub": "timabbott", } new_profile_data = [] for field_name in fields: field = CustomProfileField.objects.get(name=field_name, realm=realm) value = None if new_fields[field_name]: value = new_fields[field_name] new_profile_data.append( { "id": field.id, "value": value, } ) result = self.client_patch( f"/json/users/{cordelia.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_success(result) for field_dict in cordelia.profile_data: with self.subTest(field_name=field_dict["name"]): self.assertEqual(field_dict["value"], new_fields[str(field_dict["name"])]) def test_non_admin_user_cannot_change_profile_data(self) -> None: self.login("cordelia") hamlet = self.example_user("hamlet") realm = get_realm("zulip") new_profile_data = [] field = CustomProfileField.objects.get(name="Biography", realm=realm) new_profile_data.append( { "id": field.id, "value": "New hamlet Biography", } ) result = self.client_patch( f"/json/users/{hamlet.id}", {"profile_data": orjson.dumps(new_profile_data).decode()} ) self.assert_json_error(result, "Insufficient permission") result = self.client_patch( "/json/users/{}".format(self.example_user("cordelia").id), {"profile_data": orjson.dumps(new_profile_data).decode()}, ) self.assert_json_error(result, "Insufficient permission") class QueryCountTest(ZulipTestCase): def test_create_user_with_multiple_streams(self) -> None: # add_new_user_history needs messages to be current Message.objects.all().update(date_sent=timezone_now()) ContentType.objects.clear_cache() # This just focuses on making sure we don't too many # queries/cache tries or send too many events. realm = get_realm("zulip") self.make_stream("private_stream1", invite_only=True) self.make_stream("private_stream2", invite_only=True) stream_names = [ "Denmark", "Scotland", "Verona", "private_stream1", "private_stream2", ] streams = [get_stream(stream_name, realm) for stream_name in stream_names] do_invite_users( user_profile=self.example_user("hamlet"), invitee_emails=["fred@zulip.com"], streams=streams, ) prereg_user = PreregistrationUser.objects.get(email="fred@zulip.com") events: List[Mapping[str, Any]] = [] with queries_captured() as queries: with cache_tries_captured() as cache_tries: with tornado_redirected_to_list(events): fred = do_create_user( email="fred@zulip.com", password="password", realm=realm, full_name="Fred Flintstone", prereg_user=prereg_user, acting_user=None, ) self.assert_length(queries, 70) self.assert_length(cache_tries, 22) self.assert_length(events, 7) peer_add_events = [event for event in events if event["event"].get("op") == "peer_add"] notifications = set() for event in peer_add_events: stream_ids = event["event"]["stream_ids"] stream_names = sorted(Stream.objects.get(id=stream_id).name for stream_id in stream_ids) self.assertTrue(event["event"]["user_ids"], {fred.id}) notifications.add(",".join(stream_names)) self.assertEqual( notifications, {"Denmark,Scotland,Verona", "private_stream1", "private_stream2"} ) class BulkCreateUserTest(ZulipTestCase): def test_create_users(self) -> None: realm = get_realm("zulip") realm.email_address_visibility = Realm.EMAIL_ADDRESS_VISIBILITY_ADMINS realm.save() name_list = [ ("Fred Flintstone", "fred@zulip.com"), ("Lisa Simpson", "lisa@zulip.com"), ] create_users(realm, name_list) fred = get_user_by_delivery_email("fred@zulip.com", realm) self.assertEqual( fred.email, f"user{fred.id}@zulip.testserver", ) lisa = get_user_by_delivery_email("lisa@zulip.com", realm) self.assertEqual(lisa.full_name, "Lisa Simpson") self.assertEqual(lisa.is_bot, False) self.assertEqual(lisa.bot_type, None) realm.email_address_visibility = Realm.EMAIL_ADDRESS_VISIBILITY_EVERYONE realm.save() name_list = [ ("Bono", "bono@zulip.com"), ("Cher", "cher@zulip.com"), ] create_users(realm, name_list) bono = get_user_by_delivery_email("bono@zulip.com", realm) self.assertEqual(bono.email, "bono@zulip.com") self.assertEqual(bono.delivery_email, "bono@zulip.com") cher = get_user_by_delivery_email("cher@zulip.com", realm) self.assertEqual(cher.full_name, "Cher") class AdminCreateUserTest(ZulipTestCase): def test_create_user_backend(self) -> None: # This test should give us complete coverage on # create_user_backend. It mostly exercises error # conditions, and it also does a basic test of the success # path. admin = self.example_user("hamlet") realm = admin.realm self.login_user(admin) do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) valid_params = dict( email="romeo@zulip.net", password="xxxx", full_name="Romeo Montague", ) self.assertEqual(admin.can_create_users, False) result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "User not authorized for this query") do_change_can_create_users(admin, True) # can_create_users is insufficient without being a realm administrator: do_change_user_role(admin, UserProfile.ROLE_MEMBER, acting_user=None) result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Must be an organization administrator") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) result = self.client_post("/json/users", {}) self.assert_json_error(result, "Missing 'email' argument") result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", ), ) self.assert_json_error(result, "Missing 'password' argument") result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", password="xxxx", ), ) self.assert_json_error(result, "Missing 'full_name' argument") # Test short_name gets properly ignored result = self.client_post( "/json/users", dict( email="romeo@zulip.com", password="xxxx", full_name="Romeo Montague", short_name="DEPRECATED", ), ) self.assert_json_success(result) result = self.client_post( "/json/users", dict( email="broken", password="xxxx", full_name="Romeo Montague", ), ) self.assert_json_error(result, "Bad name or username") do_set_realm_property(realm, "emails_restricted_to_domains", True, acting_user=None) result = self.client_post( "/json/users", dict( email="romeo@not-zulip.com", password="xxxx", full_name="Romeo Montague", ), ) self.assert_json_error( result, "Email 'romeo@not-zulip.com' not allowed in this organization" ) RealmDomain.objects.create(realm=get_realm("zulip"), domain="zulip.net") # Check can't use a bad password with zxcvbn enabled with self.settings(PASSWORD_MIN_LENGTH=6, PASSWORD_MIN_GUESSES=1000): result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "The password is too weak.") result = self.client_post("/json/users", valid_params) self.assert_json_success(result) # Romeo is a newly registered user new_user = get_user_by_delivery_email("romeo@zulip.net", get_realm("zulip")) result = orjson.loads(result.content) self.assertEqual(new_user.full_name, "Romeo Montague") self.assertEqual(new_user.id, result["user_id"]) # Make sure the recipient field is set correctly. self.assertEqual( new_user.recipient, Recipient.objects.get(type=Recipient.PERSONAL, type_id=new_user.id) ) # we can't create the same user twice. result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Email 'romeo@zulip.net' already in use") # Don't allow user to sign up with disposable email. realm.emails_restricted_to_domains = False realm.disallow_disposable_email_addresses = True realm.save() valid_params["email"] = "abc@mailnator.com" result = self.client_post("/json/users", valid_params) self.assert_json_error( result, "Disposable email addresses are not allowed in this organization" ) # Don't allow creating a user with + in their email address when realm # is restricted to a domain. realm.emails_restricted_to_domains = True realm.save() valid_params["email"] = "iago+label@zulip.com" result = self.client_post("/json/users", valid_params) self.assert_json_error(result, "Email addresses containing + are not allowed.") # Users can be created with + in their email address when realm # is not restricted to a domain. realm.emails_restricted_to_domains = False realm.save() valid_params["email"] = "iago+label@zulip.com" result = self.client_post("/json/users", valid_params) self.assert_json_success(result) class UserProfileTest(ZulipTestCase): def test_get_emails_from_user_ids(self) -> None: hamlet = self.example_user("hamlet") othello = self.example_user("othello") dct = get_emails_from_user_ids([hamlet.id, othello.id]) self.assertEqual(dct[hamlet.id], hamlet.email) self.assertEqual(dct[othello.id], othello.email) def test_valid_user_id(self) -> None: realm = get_realm("zulip") hamlet = self.example_user("hamlet") othello = self.example_user("othello") bot = self.example_user("default_bot") # Invalid user ID invalid_uid: object = 1000 with self.assertRaisesRegex(ValidationError, r"User IDs is not a list"): check_valid_user_ids(realm.id, invalid_uid) with self.assertRaisesRegex(ValidationError, rf"Invalid user ID: {invalid_uid}"): check_valid_user_ids(realm.id, [invalid_uid]) invalid_uid = "abc" with self.assertRaisesRegex(ValidationError, r"User IDs\[0\] is not an integer"): check_valid_user_ids(realm.id, [invalid_uid]) invalid_uid = str(othello.id) with self.assertRaisesRegex(ValidationError, r"User IDs\[0\] is not an integer"): check_valid_user_ids(realm.id, [invalid_uid]) # User is in different realm with self.assertRaisesRegex(ValidationError, rf"Invalid user ID: {hamlet.id}"): check_valid_user_ids(get_realm("zephyr").id, [hamlet.id]) # User is not active change_user_is_active(hamlet, False) with self.assertRaisesRegex(ValidationError, rf"User with ID {hamlet.id} is deactivated"): check_valid_user_ids(realm.id, [hamlet.id]) check_valid_user_ids(realm.id, [hamlet.id], allow_deactivated=True) # User is a bot with self.assertRaisesRegex(ValidationError, rf"User with ID {bot.id} is a bot"): check_valid_user_ids(realm.id, [bot.id]) # Successfully get non-bot, active user belong to your realm check_valid_user_ids(realm.id, [othello.id]) def test_cache_invalidation(self) -> None: hamlet = self.example_user("hamlet") with mock.patch("zerver.lib.cache.delete_display_recipient_cache") as m: hamlet.full_name = "Hamlet Junior" hamlet.save(update_fields=["full_name"]) self.assertTrue(m.called) with mock.patch("zerver.lib.cache.delete_display_recipient_cache") as m: hamlet.long_term_idle = True hamlet.save(update_fields=["long_term_idle"]) self.assertFalse(m.called) def test_user_ids_to_users(self) -> None: real_user_ids = [ self.example_user("hamlet").id, self.example_user("cordelia").id, ] self.assertEqual(user_ids_to_users([], get_realm("zulip")), []) self.assertEqual( { user_profile.id for user_profile in user_ids_to_users(real_user_ids, get_realm("zulip")) }, set(real_user_ids), ) with self.assertRaises(JsonableError): user_ids_to_users([1234], get_realm("zephyr")) with self.assertRaises(JsonableError): user_ids_to_users(real_user_ids, get_realm("zephyr")) def test_bulk_get_users(self) -> None: from zerver.lib.users import bulk_get_users hamlet = self.example_user("hamlet") cordelia = self.example_user("cordelia") webhook_bot = self.example_user("webhook_bot") result = bulk_get_users( [hamlet.email, cordelia.email], get_realm("zulip"), ) self.assertEqual(result[hamlet.email].email, hamlet.email) self.assertEqual(result[cordelia.email].email, cordelia.email) result = bulk_get_users( [hamlet.email, cordelia.email, webhook_bot.email], None, base_query=UserProfile.objects.all(), ) self.assertEqual(result[hamlet.email].email, hamlet.email) self.assertEqual(result[cordelia.email].email, cordelia.email) self.assertEqual(result[webhook_bot.email].email, webhook_bot.email) def test_get_accounts_for_email(self) -> None: reset_emails_in_zulip_realm() def check_account_present_in_accounts(user: UserProfile, accounts: List[Accounts]) -> None: for account in accounts: realm = user.realm if ( account["avatar"] == avatar_url(user) and account["full_name"] == user.full_name and account["realm_name"] == realm.name and account["realm_id"] == realm.id ): return raise AssertionError("Account not found") lear_realm = get_realm("lear") cordelia_in_zulip = self.example_user("cordelia") cordelia_in_lear = get_user_by_delivery_email("cordelia@zulip.com", lear_realm) email = "cordelia@zulip.com" accounts = get_accounts_for_email(email) self.assert_length(accounts, 2) check_account_present_in_accounts(cordelia_in_zulip, accounts) check_account_present_in_accounts(cordelia_in_lear, accounts) email = "CORDelia@zulip.com" accounts = get_accounts_for_email(email) self.assert_length(accounts, 2) check_account_present_in_accounts(cordelia_in_zulip, accounts) check_account_present_in_accounts(cordelia_in_lear, accounts) email = "IAGO@ZULIP.COM" accounts = get_accounts_for_email(email) self.assert_length(accounts, 1) check_account_present_in_accounts(self.example_user("iago"), accounts) # We verify that get_accounts_for_email don't return deactivated users accounts user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) email = self.example_email("hamlet") accounts = get_accounts_for_email(email) with self.assertRaises(AssertionError): check_account_present_in_accounts(user, accounts) def test_get_source_profile(self) -> None: reset_emails_in_zulip_realm() zulip_realm_id = get_realm("zulip").id iago = get_source_profile("iago@zulip.com", zulip_realm_id) assert iago is not None self.assertEqual(iago.email, "iago@zulip.com") self.assertEqual(iago.realm, get_realm("zulip")) iago = get_source_profile("IAGO@ZULIP.com", zulip_realm_id) assert iago is not None self.assertEqual(iago.email, "iago@zulip.com") lear_realm_id = get_realm("lear").id cordelia = get_source_profile("cordelia@zulip.com", lear_realm_id) assert cordelia is not None self.assertEqual(cordelia.email, "cordelia@zulip.com") self.assertIsNone(get_source_profile("iagod@zulip.com", zulip_realm_id)) self.assertIsNone(get_source_profile("iago@zulip.com", 0)) self.assertIsNone(get_source_profile("iago@zulip.com", lear_realm_id)) def test_copy_user_settings(self) -> None: iago = self.example_user("iago") cordelia = self.example_user("cordelia") hamlet = self.example_user("hamlet") hamlet.color_scheme = UserProfile.COLOR_SCHEME_LIGHT cordelia.default_language = "de" cordelia.default_view = "all_messages" cordelia.emojiset = "twitter" cordelia.timezone = "America/Phoenix" cordelia.color_scheme = UserProfile.COLOR_SCHEME_NIGHT cordelia.enable_offline_email_notifications = False cordelia.enable_stream_push_notifications = True cordelia.enter_sends = False cordelia.avatar_source = UserProfile.AVATAR_FROM_USER cordelia.save() # Upload cordelia's avatar with get_test_image_file("img.png") as image_file: upload_avatar_image(image_file, cordelia, cordelia) UserHotspot.objects.filter(user=cordelia).delete() UserHotspot.objects.filter(user=iago).delete() hotspots_completed = ["intro_reply", "intro_streams", "intro_topics"] for hotspot in hotspots_completed: UserHotspot.objects.create(user=cordelia, hotspot=hotspot) events: List[Mapping[str, Any]] = [] with tornado_redirected_to_list(events): copy_user_settings(cordelia, iago) # Check that we didn't send an realm_user update events to # users; this work is happening before the user account is # created, so any changes will be reflected in the "add" event # introducing the user to clients. self.assertEqual(len(events), 0) # We verify that cordelia and iago match, but hamlet has the defaults. self.assertEqual(iago.full_name, "Cordelia, Lear's daughter") self.assertEqual(cordelia.full_name, "Cordelia, Lear's daughter") self.assertEqual(hamlet.full_name, "King Hamlet") self.assertEqual(iago.default_language, "de") self.assertEqual(cordelia.default_language, "de") self.assertEqual(hamlet.default_language, "en") self.assertEqual(iago.emojiset, "twitter") self.assertEqual(cordelia.emojiset, "twitter") self.assertEqual(hamlet.emojiset, "google-blob") self.assertEqual(iago.timezone, "America/Phoenix") self.assertEqual(cordelia.timezone, "America/Phoenix") self.assertEqual(hamlet.timezone, "") self.assertEqual(iago.color_scheme, UserProfile.COLOR_SCHEME_NIGHT) self.assertEqual(cordelia.color_scheme, UserProfile.COLOR_SCHEME_NIGHT) self.assertEqual(hamlet.color_scheme, UserProfile.COLOR_SCHEME_LIGHT) self.assertEqual(iago.enable_offline_email_notifications, False) self.assertEqual(cordelia.enable_offline_email_notifications, False) self.assertEqual(hamlet.enable_offline_email_notifications, True) self.assertEqual(iago.enable_stream_push_notifications, True) self.assertEqual(cordelia.enable_stream_push_notifications, True) self.assertEqual(hamlet.enable_stream_push_notifications, False) self.assertEqual(iago.enter_sends, False) self.assertEqual(cordelia.enter_sends, False) self.assertEqual(hamlet.enter_sends, True) hotspots = list(UserHotspot.objects.filter(user=iago).values_list("hotspot", flat=True)) self.assertEqual(hotspots, hotspots_completed) def test_get_user_by_id_in_realm_including_cross_realm(self) -> None: realm = get_realm("zulip") hamlet = self.example_user("hamlet") othello = self.example_user("othello") bot = get_system_bot(settings.WELCOME_BOT) # Pass in the ID of a cross-realm bot and a valid realm cross_realm_bot = get_user_by_id_in_realm_including_cross_realm(bot.id, realm) self.assertEqual(cross_realm_bot.email, bot.email) self.assertEqual(cross_realm_bot.id, bot.id) # Pass in the ID of a cross-realm bot but with a invalid realm, # note that the realm should be irrelevant here cross_realm_bot = get_user_by_id_in_realm_including_cross_realm(bot.id, None) self.assertEqual(cross_realm_bot.email, bot.email) self.assertEqual(cross_realm_bot.id, bot.id) # Pass in the ID of a non-cross-realm user with a realm user_profile = get_user_by_id_in_realm_including_cross_realm(othello.id, realm) self.assertEqual(user_profile.email, othello.email) self.assertEqual(user_profile.id, othello.id) # If the realm doesn't match, or if the ID is not that of a # cross-realm bot, UserProfile.DoesNotExist is raised with self.assertRaises(UserProfile.DoesNotExist): get_user_by_id_in_realm_including_cross_realm(hamlet.id, None) def test_get_user_subscription_status(self) -> None: self.login("hamlet") iago = self.example_user("iago") stream = get_stream("Rome", iago.realm) # Invalid user ID. result = self.client_get(f"/json/users/25/subscriptions/{stream.id}") self.assert_json_error(result, "No such user") # Invalid stream ID. result = self.client_get(f"/json/users/{iago.id}/subscriptions/25") self.assert_json_error(result, "Invalid stream id") result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assertFalse(result["is_subscribed"]) # Subscribe to the stream. self.subscribe(iago, stream.name) with queries_captured() as queries: result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assert_length(queries, 6) self.assertTrue(result["is_subscribed"]) # Logging in with a Guest user. polonius = self.example_user("polonius") self.login("polonius") self.assertTrue(polonius.is_guest) self.assertTrue(stream.is_web_public) result = orjson.loads( self.client_get(f"/json/users/{iago.id}/subscriptions/{stream.id}").content ) self.assertTrue(result["is_subscribed"]) class ActivateTest(ZulipTestCase): def test_basics(self) -> None: user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) self.assertFalse(user.is_active) do_reactivate_user(user, acting_user=None) self.assertTrue(user.is_active) def test_subscriptions_is_user_active(self) -> None: user = self.example_user("hamlet") do_deactivate_user(user, acting_user=None) self.assertFalse(user.is_active) self.assertTrue(Subscription.objects.filter(user_profile=user).exists()) self.assertFalse( Subscription.objects.filter(user_profile=user, is_user_active=True).exists() ) do_reactivate_user(user, acting_user=None) self.assertTrue(user.is_active) self.assertTrue(Subscription.objects.filter(user_profile=user).exists()) self.assertFalse( Subscription.objects.filter(user_profile=user, is_user_active=False).exists() ) def test_api(self) -> None: admin = self.example_user("othello") do_change_user_role(admin, UserProfile.ROLE_REALM_ADMINISTRATOR, acting_user=None) self.login("othello") user = self.example_user("hamlet") self.assertTrue(user.is_active) result = self.client_delete(f"/json/users/{user.id}") self.assert_json_success(result) user = self.example_user("hamlet") self.assertFalse(user.is_active) result = self.client_post(f"/json/users/{user.id}/reactivate") self.assert_json_success(result) user = self.example_user("hamlet") self.assertTrue(user.is_active) def test_api_with_nonexistent_user(self) -> None: self.login("iago") # Organization administrator cannot deactivate organization owner. result = self.client_delete(f'/json/users/{self.example_user("desdemona").id}') self.assert_json_error(result, "Must be an organization owner") iago = self.example_user("iago") desdemona = self.example_user("desdemona") do_change_user_role(iago, UserProfile.ROLE_REALM_OWNER, acting_user=None) # Cannot deactivate a user with the bot api result = self.client_delete("/json/bots/{}".format(self.example_user("hamlet").id)) self.assert_json_error(result, "No such bot") # Cannot deactivate a nonexistent user. invalid_user_id = 1000 result = self.client_delete(f"/json/users/{invalid_user_id}") self.assert_json_error(result, "No such user") result = self.client_delete("/json/users/{}".format(self.example_user("webhook_bot").id)) self.assert_json_error(result, "No such user") result = self.client_delete(f"/json/users/{desdemona.id}") self.assert_json_success(result) result = self.client_delete(f"/json/users/{iago.id}") self.assert_json_error(result, "Cannot deactivate the only organization owner") # Cannot reactivate a nonexistent user. invalid_user_id = 1000 result = self.client_post(f"/json/users/{invalid_user_id}/reactivate") self.assert_json_error(result, "No such user") def test_api_with_insufficient_permissions(self) -> None: non_admin = self.example_user("othello") do_change_user_role(non_admin, UserProfile.ROLE_MEMBER, acting_user=None) self.login("othello") # Cannot deactivate a user with the users api result = self.client_delete("/json/users/{}".format(self.example_user("hamlet").id)) self.assert_json_error(result, "Insufficient permission") # Cannot reactivate a user result = self.client_post( "/json/users/{}/reactivate".format(self.example_user("hamlet").id) ) self.assert_json_error(result, "Insufficient permission") def test_clear_scheduled_jobs(self) -> None: user = self.example_user("hamlet") send_future_email( "zerver/emails/followup_day1", user.realm, to_user_ids=[user.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) do_deactivate_user(user, acting_user=None) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_send_future_email_with_multiple_recipients(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual( ScheduledEmail.objects.filter(users__in=[hamlet, iago]).distinct().count(), 1 ) email = ScheduledEmail.objects.all().first() self.assertEqual(email.users.count(), 2) def test_clear_scheduled_emails_with_multiple_user_ids(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) clear_scheduled_emails([hamlet.id, iago.id]) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_clear_schedule_emails_with_one_user_id(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) clear_scheduled_emails([hamlet.id]) self.assertEqual(ScheduledEmail.objects.count(), 1) self.assertEqual(ScheduledEmail.objects.filter(users=hamlet).count(), 0) self.assertEqual(ScheduledEmail.objects.filter(users=iago).count(), 1) def test_deliver_scheduled_emails(self) -> None: iago = self.example_user("iago") hamlet = self.example_user("hamlet") send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=[hamlet.id, iago.id], delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) email = ScheduledEmail.objects.all().first() deliver_scheduled_emails(email) from django.core.mail import outbox self.assertEqual(len(outbox), 1) for message in outbox: self.assertEqual( set(message.to), { str(Address(display_name=hamlet.full_name, addr_spec=hamlet.delivery_email)), str(Address(display_name=iago.full_name, addr_spec=iago.delivery_email)), }, ) self.assertEqual(ScheduledEmail.objects.count(), 0) def test_deliver_scheduled_emails_no_addressees(self) -> None: iago = self.example_user("iago") hamlet = self.example_user("hamlet") to_user_ids = [hamlet.id, iago.id] send_future_email( "zerver/emails/followup_day1", iago.realm, to_user_ids=to_user_ids, delay=datetime.timedelta(hours=1), ) self.assertEqual(ScheduledEmail.objects.count(), 1) email = ScheduledEmail.objects.all().first() email.users.remove(*to_user_ids) with self.assertLogs("zulip.send_email", level="INFO") as info_log: deliver_scheduled_emails(email) from django.core.mail import outbox self.assertEqual(len(outbox), 0) self.assertEqual(ScheduledEmail.objects.count(), 1) self.assertEqual( info_log.output, [ f"WARNING:zulip.send_email:ScheduledEmail id {email.id} has empty users and address attributes." ], ) class RecipientInfoTest(ZulipTestCase): def test_stream_recipient_info(self) -> None: hamlet = self.example_user("hamlet") cordelia = self.example_user("cordelia") othello = self.example_user("othello") # These tests were written with the old default for # enable_online_push_notifications; that default is better for # testing the full code path anyway. hamlet.enable_online_push_notifications = False cordelia.enable_online_push_notifications = False othello.enable_online_push_notifications = False hamlet.save() cordelia.save() othello.save() realm = hamlet.realm stream_name = "Test stream" topic_name = "test topic" for user in [hamlet, cordelia, othello]: self.subscribe(user, stream_name) stream = get_stream(stream_name, realm) recipient = stream.recipient stream_topic = StreamTopicTarget( stream_id=stream.id, topic_name=topic_name, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) all_user_ids = {hamlet.id, cordelia.id, othello.id} expected_info = dict( active_user_ids=all_user_ids, push_notify_user_ids=set(), stream_push_user_ids=set(), stream_email_user_ids=set(), wildcard_mention_user_ids=set(), um_eligible_user_ids=all_user_ids, long_term_idle_user_ids=set(), default_bot_user_ids=set(), service_bot_tuples=[], ) self.assertEqual(info, expected_info) cordelia.wildcard_mentions_notify = False cordelia.save() hamlet.enable_stream_push_notifications = True hamlet.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) self.assertEqual(info["stream_push_user_ids"], {hamlet.id}) self.assertEqual(info["wildcard_mention_user_ids"], set()) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["wildcard_mention_user_ids"], {hamlet.id, othello.id}) sub = get_subscription(stream_name, hamlet) sub.push_notifications = False sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) self.assertEqual(info["stream_push_user_ids"], set()) hamlet.enable_stream_push_notifications = False hamlet.save() sub = get_subscription(stream_name, hamlet) sub.push_notifications = True sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) self.assertEqual(info["stream_push_user_ids"], {hamlet.id}) # Now mute Hamlet to omit him from stream_push_user_ids. add_topic_mute( user_profile=hamlet, stream_id=stream.id, recipient_id=recipient.id, topic_name=topic_name, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=False, ) self.assertEqual(info["stream_push_user_ids"], set()) self.assertEqual(info["wildcard_mention_user_ids"], set()) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) # Since Hamlet has muted the stream and Cordelia has disabled # wildcard notifications, it should just be Othello here. self.assertEqual(info["wildcard_mention_user_ids"], {othello.id}) sub = get_subscription(stream_name, othello) sub.wildcard_mentions_notify = False sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) # Verify that stream-level wildcard_mentions_notify=False works correctly. self.assertEqual(info["wildcard_mention_user_ids"], set()) # Verify that True works as expected as well sub = get_subscription(stream_name, othello) sub.wildcard_mentions_notify = True sub.save() info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possible_wildcard_mention=True, ) self.assertEqual(info["stream_push_user_ids"], set()) self.assertEqual(info["wildcard_mention_user_ids"], {othello.id}) # Add a service bot. service_bot = do_create_user( email="service-bot@zulip.com", password="", realm=realm, full_name="", bot_type=UserProfile.EMBEDDED_BOT, acting_user=None, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possibly_mentioned_user_ids={service_bot.id}, ) self.assertEqual( info["service_bot_tuples"], [ (service_bot.id, UserProfile.EMBEDDED_BOT), ], ) # Add a normal bot. normal_bot = do_create_user( email="normal-bot@zulip.com", password="", realm=realm, full_name="", bot_type=UserProfile.DEFAULT_BOT, acting_user=None, ) info = get_recipient_info( recipient=recipient, sender_id=hamlet.id, stream_topic=stream_topic, possibly_mentioned_user_ids={service_bot.id, normal_bot.id}, ) self.assertEqual(info["default_bot_user_ids"], {normal_bot.id}) def test_get_recipient_info_invalid_recipient_type(self) -> None: hamlet = self.example_user("hamlet") realm = hamlet.realm stream = get_stream("Rome", realm) stream_topic = StreamTopicTarget( stream_id=stream.id, topic_name="test topic", ) # Make sure get_recipient_info asserts on invalid recipient types with self.assertRaisesRegex(ValueError, "Bad recipient type"): invalid_recipient = Recipient(type=999) # 999 is not a valid type get_recipient_info( recipient=invalid_recipient, sender_id=hamlet.id, stream_topic=stream_topic, ) class BulkUsersTest(ZulipTestCase): def test_client_gravatar_option(self) -> None: reset_emails_in_zulip_realm() self.login("cordelia") hamlet = self.example_user("hamlet") def get_hamlet_avatar(client_gravatar: bool) -> Optional[str]: data = dict(client_gravatar=orjson.dumps(client_gravatar).decode()) result = self.client_get("/json/users", data) self.assert_json_success(result) rows = result.json()["members"] hamlet_data = [row for row in rows if row["user_id"] == hamlet.id][0] return hamlet_data["avatar_url"] self.assertEqual( get_hamlet_avatar(client_gravatar=True), None, ) """ The main purpose of this test is to make sure we return None for avatar_url when client_gravatar is set to True. And we do a sanity check for when it's False, but we leave it to other tests to validate the specific URL. """ self.assertIn( "gravatar.com", get_hamlet_avatar(client_gravatar=False), ) class GetProfileTest(ZulipTestCase): def test_cache_behavior(self) -> None: """Tests whether fetching a user object the normal way, with `get_user`, makes 1 cache query and 1 database query. """ realm = get_realm("zulip") email = self.example_user("hamlet").email with queries_captured() as queries: with simulated_empty_cache() as cache_queries: user_profile = get_user(email, realm) self.assert_length(queries, 1) self.assert_length(cache_queries, 1) self.assertEqual(user_profile.email, email) def test_get_user_profile(self) -> None: hamlet = self.example_user("hamlet") iago = self.example_user("iago") desdemona = self.example_user("desdemona") shiva = self.example_user("shiva") self.login("hamlet") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], hamlet.email) self.assertEqual(result["full_name"], "King Hamlet") self.assertIn("user_id", result) self.assertFalse(result["is_bot"]) self.assertFalse(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_MEMBER) self.assertFalse("delivery_email" in result) self.login("iago") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], iago.email) self.assertEqual(result["full_name"], "Iago") self.assertFalse(result["is_bot"]) self.assertTrue(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_REALM_ADMINISTRATOR) self.login("desdemona") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], desdemona.email) self.assertFalse(result["is_bot"]) self.assertTrue(result["is_admin"]) self.assertTrue(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_REALM_OWNER) self.login("shiva") result = orjson.loads(self.client_get("/json/users/me").content) self.assertEqual(result["email"], shiva.email) self.assertFalse(result["is_bot"]) self.assertFalse(result["is_admin"]) self.assertFalse(result["is_owner"]) self.assertFalse(result["is_guest"]) self.assertEqual(result["role"], UserProfile.ROLE_MODERATOR) # Tests the GET ../users/{id} API endpoint. user = self.example_user("hamlet") result = orjson.loads(self.client_get(f"/json/users/{user.id}").content) self.assertEqual(result["user"]["email"], user.email) self.assertEqual(result["user"]["full_name"], user.full_name) self.assertIn("user_id", result["user"]) self.assertNotIn("profile_data", result["user"]) self.assertFalse(result["user"]["is_bot"]) self.assertFalse(result["user"]["is_admin"]) self.assertFalse(result["user"]["is_owner"]) result = orjson.loads( self.client_get( f"/json/users/{user.id}", {"include_custom_profile_fields": "true"} ).content ) self.assertIn("profile_data", result["user"]) result = self.client_get("/json/users/30") self.assert_json_error(result, "No such user") bot = self.example_user("default_bot") result = orjson.loads(self.client_get(f"/json/users/{bot.id}").content) self.assertEqual(result["user"]["email"], bot.email) self.assertTrue(result["user"]["is_bot"]) def test_get_user_by_email(self) -> None: user = self.example_user("hamlet") self.login("hamlet") result = orjson.loads(self.client_get(f"/json/users/{user.email}").content) self.assertEqual(result["user"]["email"], user.email) self.assertEqual(result["user"]["full_name"], user.full_name) self.assertIn("user_id", result["user"]) self.assertNotIn("profile_data", result["user"]) self.assertFalse(result["user"]["is_bot"]) self.assertFalse(result["user"]["is_admin"]) self.assertFalse(result["user"]["is_owner"]) result = orjson.loads( self.client_get( f"/json/users/{user.email}", {"include_custom_profile_fields": "true"} ).content ) self.assertIn("profile_data", result["user"]) result = self.client_get("/json/users/invalid") self.assert_json_error(result, "No such user") bot = self.example_user("default_bot") result = orjson.loads(self.client_get(f"/json/users/{bot.email}").content) self.assertEqual(result["user"]["email"], bot.email) self.assertTrue(result["user"]["is_bot"]) def test_get_all_profiles_avatar_urls(self) -> None: hamlet = self.example_user("hamlet") result = self.api_get(hamlet, "/api/v1/users") self.assert_json_success(result) (my_user,) = [user for user in result.json()["members"] if user["email"] == hamlet.email] self.assertEqual( my_user["avatar_url"], avatar_url(hamlet), ) class DeleteUserTest(ZulipTestCase): def test_do_delete_user(self) -> None: realm = get_realm("zulip") cordelia = self.example_user("cordelia") othello = self.example_user("othello") hamlet = self.example_user("hamlet") hamlet_personal_recipient = hamlet.recipient hamlet_user_id = hamlet.id self.send_personal_message(cordelia, hamlet) self.send_personal_message(hamlet, cordelia) personal_message_ids_to_hamlet = Message.objects.filter( recipient=hamlet_personal_recipient ).values_list("id", flat=True) self.assertTrue(len(personal_message_ids_to_hamlet) > 0) self.assertTrue(Message.objects.filter(sender=hamlet).exists()) huddle_message_ids_from_cordelia = [ self.send_huddle_message(cordelia, [hamlet, othello]) for i in range(3) ] huddle_message_ids_from_hamlet = [ self.send_huddle_message(hamlet, [cordelia, othello]) for i in range(3) ] huddle_with_hamlet_recipient_ids = list( Subscription.objects.filter( user_profile=hamlet, recipient__type=Recipient.HUDDLE ).values_list("recipient_id", flat=True) ) self.assertTrue(len(huddle_with_hamlet_recipient_ids) > 0) do_delete_user(hamlet) replacement_dummy_user = UserProfile.objects.get(id=hamlet_user_id, realm=realm) self.assertEqual( replacement_dummy_user.delivery_email, f"deleteduser{hamlet_user_id}@{realm.uri}" ) self.assertEqual(replacement_dummy_user.is_mirror_dummy, True) self.assertEqual(Message.objects.filter(id__in=personal_message_ids_to_hamlet).count(), 0) # Huddle messages from hamlet should have been deleted, but messages of other participants should # be kept. self.assertEqual(Message.objects.filter(id__in=huddle_message_ids_from_hamlet).count(), 0) self.assertEqual(Message.objects.filter(id__in=huddle_message_ids_from_cordelia).count(), 3) self.assertEqual(Message.objects.filter(sender_id=hamlet_user_id).count(), 0) # Verify that the dummy user is subscribed to the deleted user's huddles, to keep huddle data # in a correct state. for recipient_id in huddle_with_hamlet_recipient_ids: self.assertTrue( Subscription.objects.filter( user_profile=replacement_dummy_user, recipient_id=recipient_id ).exists() ) class FakeEmailDomainTest(ZulipTestCase): def test_get_fake_email_domain(self) -> None: realm = get_realm("zulip") self.assertEqual("zulip.testserver", get_fake_email_domain(realm)) with self.settings(EXTERNAL_HOST="example.com"): self.assertEqual("zulip.example.com", get_fake_email_domain(realm)) @override_settings(FAKE_EMAIL_DOMAIN="fakedomain.com", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_get_fake_email_domain_realm_host_is_ip_addr(self) -> None: realm = get_realm("zulip") self.assertEqual("fakedomain.com", get_fake_email_domain(realm)) @override_settings(FAKE_EMAIL_DOMAIN="invaliddomain", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_invalid_fake_email_domain(self) -> None: realm = get_realm("zulip") with self.assertRaises(InvalidFakeEmailDomain): get_fake_email_domain(realm) @override_settings(FAKE_EMAIL_DOMAIN="127.0.0.1", REALM_HOSTS={"zulip": "127.0.0.1"}) def test_invalid_fake_email_domain_ip(self) -> None: with self.assertRaises(InvalidFakeEmailDomain): realm = get_realm("zulip") get_fake_email_domain(realm)

import subprocess import argparse import re from datetime import datetime, timedelta from time import sleep import socket import requests import scapy.all as scapy from concurrent.futures import ThreadPoolExecutor MONITOR_INTERVAL = 60 DISCOVERY_INTERVAL = 300 parser = argparse.ArgumentParser(description="Host Monitor") parser.add_argument('--poolsize', default=10, help='Size of the threadpool') parser.add_argument('--quokka', default="localhost:5001", help='Hostname/IP and port of the quokka server') args = parser.parse_args() threadpool_size = int(args.poolsize) quokka = args.quokka def get_hosts(): global quokka print("\n\n----> Retrieving hosts ...", end="") try: response = requests.get("http://"+quokka+"/hosts") except requests.exceptions.ConnectionError as e: print(f" !!! Exception trying to get hosts via REST API: {e}") return {} if response.status_code != 200: print(f" !!! Failed to retrieve hosts from server: {response.reason}") return {} print(" Hosts successfully retrieved") return response.json() def discovery(): # DISCOVER HOSTS ON NETWORK USING ARPING FUNCTION print( "\n\n----- Discovery hosts on network using arping() function ---------------------" ) ans, unans = scapy.arping("192.168.254.0/24") ans.summary() for res in ans.res: print(f"oooo> IP address discovered: {res[0].payload.pdst}") ip_addr = res[1].payload.psrc mac_addr = res[1].payload.hwsrc try: hostname = socket.gethostbyaddr(str(ip_addr)) except (socket.error, socket.gaierror): hostname = (str(ip_addr), [], [str(ip_addr)]) last_heard = str(datetime.now())[:-3] host = { "ip_address": ip_addr, "mac_address": mac_addr, "hostname": hostname[0], "last_heard": last_heard, "availability": True, "response_time": 0 } update_host(host) def update_host(host): global quokka print(f"----> Updating host status via REST API: {host["hostname"]}", end="") try: rsp = requests.put("http://"+quokka+"/hosts", params={"hostname": host["hostname"]}, json=host) except requests.exceptions.ConnectionError as e: print(f" !!! Exception trying to update host {host["hostname"]} via REST API: {e}") return if rsp.status_code != 204: print( f"{str(datetime.now())[:-3]}: Error posting to /hosts, response: {rsp.status_code}, {rsp.content}" ) print(f" !!! Unsuccessful attempt to update host status via REST API: {host["hostname"]}") else: print(f" Successfully updated host status via REST API: {host["hostname"]}") def get_response_time(ping_output): m = re.search(r"time=([0-9]*)", ping_output) if m.group(1).isnumeric(): return str(float(m.group(1))/1000) else: return 0 def ping_host(host): try: print(f"----> Pinging host: {host["hostname"]}", end="") ping_output = subprocess.check_output( ["ping", "-c3", "-n", "-i0.5", "-W2", host["ip_address"]] ) host["availability"] = True host["response_time"] = get_response_time(str(ping_output)) host["last_heard"] = str(datetime.now())[:-3] print(f" Host ping successful: {host["hostname"]}") except subprocess.CalledProcessError: host["availability"] = False print(f" !!! Host ping failed: {host["hostname"]}") update_host(host) def main(): global threadpool_size last_discovery = datetime.now()-timedelta(days=1) while True: if (datetime.now() - last_discovery).total_seconds() > DISCOVERY_INTERVAL: discovery() last_discovery = datetime.now() hosts = get_hosts() with ThreadPoolExecutor(max_workers=threadpool_size) as executor: executor.map(ping_host, hosts.values()) sleep(MONITOR_INTERVAL) if __name__ == "__main__": try: main() except KeyboardInterrupt: print("\n\nExiting host-monitor") exit()

import subprocess import argparse import re from datetime import datetime, timedelta from time import sleep import socket import requests import scapy.all as scapy from concurrent.futures import ThreadPoolExecutor MONITOR_INTERVAL = 60 DISCOVERY_INTERVAL = 300 parser = argparse.ArgumentParser(description="Host Monitor") parser.add_argument('--poolsize', default=10, help='Size of the threadpool') parser.add_argument('--quokka', default="localhost:5001", help='Hostname/IP and port of the quokka server') args = parser.parse_args() threadpool_size = int(args.poolsize) quokka = args.quokka def get_hosts(): global quokka print("\n\n----> Retrieving hosts ...", end="") try: response = requests.get("http://"+quokka+"/hosts") except requests.exceptions.ConnectionError as e: print(f" !!! Exception trying to get hosts via REST API: {e}") return {} if response.status_code != 200: print(f" !!! Failed to retrieve hosts from server: {response.reason}") return {} print(" Hosts successfully retrieved") return response.json() def discovery(): # DISCOVER HOSTS ON NETWORK USING ARPING FUNCTION print( "\n\n----- Discovery hosts on network using arping() function ---------------------" ) ans, unans = scapy.arping("192.168.254.0/24") ans.summary() for res in ans.res: print(f"oooo> IP address discovered: {res[0].payload.pdst}") ip_addr = res[1].payload.psrc mac_addr = res[1].payload.hwsrc try: hostname = socket.gethostbyaddr(str(ip_addr)) except (socket.error, socket.gaierror): hostname = (str(ip_addr), [], [str(ip_addr)]) last_heard = str(datetime.now())[:-3] host = { "ip_address": ip_addr, "mac_address": mac_addr, "hostname": hostname[0], "last_heard": last_heard, "availability": True, "response_time": 0 } update_host(host) def update_host(host): global quokka print(f"----> Updating host status via REST API: {host['hostname']}", end="") try: rsp = requests.put("http://"+quokka+"/hosts", params={"hostname": host["hostname"]}, json=host) except requests.exceptions.ConnectionError as e: print(f" !!! Exception trying to update host {host['hostname']} via REST API: {e}") return if rsp.status_code != 204: print( f"{str(datetime.now())[:-3]}: Error posting to /hosts, response: {rsp.status_code}, {rsp.content}" ) print(f" !!! Unsuccessful attempt to update host status via REST API: {host['hostname']}") else: print(f" Successfully updated host status via REST API: {host['hostname']}") def get_response_time(ping_output): m = re.search(r"time=([0-9]*)", ping_output) if m.group(1).isnumeric(): return str(float(m.group(1))/1000) else: return 0 def ping_host(host): try: print(f"----> Pinging host: {host['hostname']}", end="") ping_output = subprocess.check_output( ["ping", "-c3", "-n", "-i0.5", "-W2", host["ip_address"]] ) host["availability"] = True host["response_time"] = get_response_time(str(ping_output)) host["last_heard"] = str(datetime.now())[:-3] print(f" Host ping successful: {host['hostname']}") except subprocess.CalledProcessError: host["availability"] = False print(f" !!! Host ping failed: {host['hostname']}") update_host(host) def main(): global threadpool_size last_discovery = datetime.now()-timedelta(days=1) while True: if (datetime.now() - last_discovery).total_seconds() > DISCOVERY_INTERVAL: discovery() last_discovery = datetime.now() hosts = get_hosts() with ThreadPoolExecutor(max_workers=threadpool_size) as executor: executor.map(ping_host, hosts.values()) sleep(MONITOR_INTERVAL) if __name__ == "__main__": try: main() except KeyboardInterrupt: print("\n\nExiting host-monitor") exit()