body_hash
stringlengths 64
64
| body
stringlengths 23
109k
| docstring
stringlengths 1
57k
| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
111
| repository_stars
float64 0
191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
108k
| unified
stringlengths 45
133k
|
|---|---|---|---|---|---|---|---|---|---|
3e736577b913b53ec5201a82caf177a30b760629d4d95904f533b258b3f64586
|
def recursive_glob(rootdir='.', suffix=''):
'Performs recursive glob with given suffix and rootdir \n :param rootdir is the root directory\n :param suffix is the suffix to be searched\n '
return [os.path.join(looproot, filename) for (looproot, _, filenames) in os.walk(rootdir) for filename in filenames if filename.endswith(suffix)]
|
Performs recursive glob with given suffix and rootdir
:param rootdir is the root directory
:param suffix is the suffix to be searched
|
models/utils.py
|
recursive_glob
|
usama13o/codeServerEPI
| 0
|
python
|
def recursive_glob(rootdir='.', suffix=):
'Performs recursive glob with given suffix and rootdir \n :param rootdir is the root directory\n :param suffix is the suffix to be searched\n '
return [os.path.join(looproot, filename) for (looproot, _, filenames) in os.walk(rootdir) for filename in filenames if filename.endswith(suffix)]
|
def recursive_glob(rootdir='.', suffix=):
'Performs recursive glob with given suffix and rootdir \n :param rootdir is the root directory\n :param suffix is the suffix to be searched\n '
return [os.path.join(looproot, filename) for (looproot, _, filenames) in os.walk(rootdir) for filename in filenames if filename.endswith(suffix)]<|docstring|>Performs recursive glob with given suffix and rootdir
:param rootdir is the root directory
:param suffix is the suffix to be searched<|endoftext|>
|
185816d69287dc7267f7099b3455f1f4315b125c4f4ed0ffae0417ae3b57c7d1
|
def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=30000, power=0.9):
'Polynomial decay of learning rate\n :param init_lr is base learning rate\n :param iter is a current iteration\n :param lr_decay_iter how frequently decay occurs, default is 1\n :param max_iter is number of maximum iterations\n :param power is a polymomial power\n\n '
if ((iter % lr_decay_iter) or (iter > max_iter)):
return optimizer
for param_group in optimizer.param_groups:
param_group['lr'] = (init_lr * ((1 - (iter / max_iter)) ** power))
|
Polynomial decay of learning rate
:param init_lr is base learning rate
:param iter is a current iteration
:param lr_decay_iter how frequently decay occurs, default is 1
:param max_iter is number of maximum iterations
:param power is a polymomial power
|
models/utils.py
|
poly_lr_scheduler
|
usama13o/codeServerEPI
| 0
|
python
|
def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=30000, power=0.9):
'Polynomial decay of learning rate\n :param init_lr is base learning rate\n :param iter is a current iteration\n :param lr_decay_iter how frequently decay occurs, default is 1\n :param max_iter is number of maximum iterations\n :param power is a polymomial power\n\n '
if ((iter % lr_decay_iter) or (iter > max_iter)):
return optimizer
for param_group in optimizer.param_groups:
param_group['lr'] = (init_lr * ((1 - (iter / max_iter)) ** power))
|
def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=30000, power=0.9):
'Polynomial decay of learning rate\n :param init_lr is base learning rate\n :param iter is a current iteration\n :param lr_decay_iter how frequently decay occurs, default is 1\n :param max_iter is number of maximum iterations\n :param power is a polymomial power\n\n '
if ((iter % lr_decay_iter) or (iter > max_iter)):
return optimizer
for param_group in optimizer.param_groups:
param_group['lr'] = (init_lr * ((1 - (iter / max_iter)) ** power))<|docstring|>Polynomial decay of learning rate
:param init_lr is base learning rate
:param iter is a current iteration
:param lr_decay_iter how frequently decay occurs, default is 1
:param max_iter is number of maximum iterations
:param power is a polymomial power<|endoftext|>
|
06772256623a7832b8d01e4607928bfdc742c8b4605e2ba65b8f551a26c76c45
|
def adjust_learning_rate(optimizer, init_lr, epoch):
'Sets the learning rate to the initial LR decayed by 10 every 30 epochs'
lr = (init_lr * (0.1 ** (epoch // 30)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
|
Sets the learning rate to the initial LR decayed by 10 every 30 epochs
|
models/utils.py
|
adjust_learning_rate
|
usama13o/codeServerEPI
| 0
|
python
|
def adjust_learning_rate(optimizer, init_lr, epoch):
lr = (init_lr * (0.1 ** (epoch // 30)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
|
def adjust_learning_rate(optimizer, init_lr, epoch):
lr = (init_lr * (0.1 ** (epoch // 30)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr<|docstring|>Sets the learning rate to the initial LR decayed by 10 every 30 epochs<|endoftext|>
|
10fd54a872e7ed2e92faaaebedf9922c653a1d731e7aa0744b5b1de28e8566d1
|
def load_state_dict(module, state_dict, strict=False, logger=None):
"Load state_dict to a module.\n\n This method is modified from :meth:`torch.nn.Module.load_state_dict`.\n Default value for ``strict`` is set to ``False`` and the message for\n param mismatch will be shown even if strict is False.\n\n Args:\n module (Module): Module that receives the state_dict.\n state_dict (OrderedDict): Weights.\n strict (bool): whether to strictly enforce that the keys\n in :attr:`state_dict` match the keys returned by this module's\n :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.\n logger (:obj:`logging.Logger`, optional): Logger to log the error\n message. If not specified, print function will be used.\n "
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if (metadata is not None):
state_dict._metadata = metadata
def load(module, prefix=''):
if is_module_wrapper(module):
module = module.module
local_metadata = ({} if (metadata is None) else metadata.get(prefix[:(- 1)], {}))
module._load_from_state_dict(state_dict, prefix, local_metadata, True, all_missing_keys, unexpected_keys, err_msg)
for (name, child) in module._modules.items():
if (child is not None):
load(child, ((prefix + name) + '.'))
load(module)
load = None
missing_keys = [key for key in all_missing_keys if ('num_batches_tracked' not in key)]
if unexpected_keys:
err_msg.append(f'''unexpected key in source state_dict: {', '.join(unexpected_keys)}
''')
if missing_keys:
err_msg.append(f'''missing keys in source state_dict: {', '.join(missing_keys)}
''')
(rank, _) = get_dist_info()
if ((len(err_msg) > 0) and (rank == 0)):
err_msg.insert(0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif (logger is not None):
logger.warning(err_msg)
else:
print(err_msg)
|
Load state_dict to a module.
This method is modified from :meth:`torch.nn.Module.load_state_dict`.
Default value for ``strict`` is set to ``False`` and the message for
param mismatch will be shown even if strict is False.
Args:
module (Module): Module that receives the state_dict.
state_dict (OrderedDict): Weights.
strict (bool): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
logger (:obj:`logging.Logger`, optional): Logger to log the error
message. If not specified, print function will be used.
|
models/utils.py
|
load_state_dict
|
usama13o/codeServerEPI
| 0
|
python
|
def load_state_dict(module, state_dict, strict=False, logger=None):
"Load state_dict to a module.\n\n This method is modified from :meth:`torch.nn.Module.load_state_dict`.\n Default value for ``strict`` is set to ``False`` and the message for\n param mismatch will be shown even if strict is False.\n\n Args:\n module (Module): Module that receives the state_dict.\n state_dict (OrderedDict): Weights.\n strict (bool): whether to strictly enforce that the keys\n in :attr:`state_dict` match the keys returned by this module's\n :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.\n logger (:obj:`logging.Logger`, optional): Logger to log the error\n message. If not specified, print function will be used.\n "
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if (metadata is not None):
state_dict._metadata = metadata
def load(module, prefix=):
if is_module_wrapper(module):
module = module.module
local_metadata = ({} if (metadata is None) else metadata.get(prefix[:(- 1)], {}))
module._load_from_state_dict(state_dict, prefix, local_metadata, True, all_missing_keys, unexpected_keys, err_msg)
for (name, child) in module._modules.items():
if (child is not None):
load(child, ((prefix + name) + '.'))
load(module)
load = None
missing_keys = [key for key in all_missing_keys if ('num_batches_tracked' not in key)]
if unexpected_keys:
err_msg.append(f'unexpected key in source state_dict: {', '.join(unexpected_keys)}
')
if missing_keys:
err_msg.append(f'missing keys in source state_dict: {', '.join(missing_keys)}
')
(rank, _) = get_dist_info()
if ((len(err_msg) > 0) and (rank == 0)):
err_msg.insert(0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif (logger is not None):
logger.warning(err_msg)
else:
print(err_msg)
|
def load_state_dict(module, state_dict, strict=False, logger=None):
"Load state_dict to a module.\n\n This method is modified from :meth:`torch.nn.Module.load_state_dict`.\n Default value for ``strict`` is set to ``False`` and the message for\n param mismatch will be shown even if strict is False.\n\n Args:\n module (Module): Module that receives the state_dict.\n state_dict (OrderedDict): Weights.\n strict (bool): whether to strictly enforce that the keys\n in :attr:`state_dict` match the keys returned by this module's\n :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.\n logger (:obj:`logging.Logger`, optional): Logger to log the error\n message. If not specified, print function will be used.\n "
unexpected_keys = []
all_missing_keys = []
err_msg = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if (metadata is not None):
state_dict._metadata = metadata
def load(module, prefix=):
if is_module_wrapper(module):
module = module.module
local_metadata = ({} if (metadata is None) else metadata.get(prefix[:(- 1)], {}))
module._load_from_state_dict(state_dict, prefix, local_metadata, True, all_missing_keys, unexpected_keys, err_msg)
for (name, child) in module._modules.items():
if (child is not None):
load(child, ((prefix + name) + '.'))
load(module)
load = None
missing_keys = [key for key in all_missing_keys if ('num_batches_tracked' not in key)]
if unexpected_keys:
err_msg.append(f'unexpected key in source state_dict: {', '.join(unexpected_keys)}
')
if missing_keys:
err_msg.append(f'missing keys in source state_dict: {', '.join(missing_keys)}
')
(rank, _) = get_dist_info()
if ((len(err_msg) > 0) and (rank == 0)):
err_msg.insert(0, 'The model and loaded state dict do not match exactly\n')
err_msg = '\n'.join(err_msg)
if strict:
raise RuntimeError(err_msg)
elif (logger is not None):
logger.warning(err_msg)
else:
print(err_msg)<|docstring|>Load state_dict to a module.
This method is modified from :meth:`torch.nn.Module.load_state_dict`.
Default value for ``strict`` is set to ``False`` and the message for
param mismatch will be shown even if strict is False.
Args:
module (Module): Module that receives the state_dict.
state_dict (OrderedDict): Weights.
strict (bool): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
logger (:obj:`logging.Logger`, optional): Logger to log the error
message. If not specified, print function will be used.<|endoftext|>
|
43d18cc560813f6de8d8a5b1bcf154c3d01542750b3475f3af83bc145cf2ca4d
|
def load_url_dist(url, model_dir=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
return checkpoint
|
In distributed setting, this function only download checkpoint at local
rank 0.
|
models/utils.py
|
load_url_dist
|
usama13o/codeServerEPI
| 0
|
python
|
def load_url_dist(url, model_dir=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
return checkpoint
|
def load_url_dist(url, model_dir=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
return checkpoint<|docstring|>In distributed setting, this function only download checkpoint at local
rank 0.<|endoftext|>
|
99d9951478d7bc01a55be45bd97b0bc6d95136728623573157d27a8c219b934b
|
def load_pavimodel_dist(model_path, map_location=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
try:
from pavi import modelcloud
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
return checkpoint
|
In distributed setting, this function only download checkpoint at local
rank 0.
|
models/utils.py
|
load_pavimodel_dist
|
usama13o/codeServerEPI
| 0
|
python
|
def load_pavimodel_dist(model_path, map_location=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
try:
from pavi import modelcloud
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
return checkpoint
|
def load_pavimodel_dist(model_path, map_location=None):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
try:
from pavi import modelcloud
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
if (rank == 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
model = modelcloud.get(model_path)
with TemporaryDirectory() as tmp_dir:
downloaded_file = osp.join(tmp_dir, model.name)
model.download(downloaded_file)
checkpoint = torch.load(downloaded_file, map_location=map_location)
return checkpoint<|docstring|>In distributed setting, this function only download checkpoint at local
rank 0.<|endoftext|>
|
365bc3e3dae4ba1fcfebec2b12b423acae43bc97eac4ec17e4c544be3705ba0d
|
def load_fileclient_dist(filename, backend, map_location):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
allowed_backends = ['ceph']
if (backend not in allowed_backends):
raise ValueError(f'Load from Backend {backend} is not supported.')
if (rank == 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
return checkpoint
|
In distributed setting, this function only download checkpoint at local
rank 0.
|
models/utils.py
|
load_fileclient_dist
|
usama13o/codeServerEPI
| 0
|
python
|
def load_fileclient_dist(filename, backend, map_location):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
allowed_backends = ['ceph']
if (backend not in allowed_backends):
raise ValueError(f'Load from Backend {backend} is not supported.')
if (rank == 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
return checkpoint
|
def load_fileclient_dist(filename, backend, map_location):
'In distributed setting, this function only download checkpoint at local\n rank 0.'
(rank, world_size) = get_dist_info()
rank = int(os.environ.get('LOCAL_RANK', rank))
allowed_backends = ['ceph']
if (backend not in allowed_backends):
raise ValueError(f'Load from Backend {backend} is not supported.')
if (rank == 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
if (world_size > 1):
torch.distributed.barrier()
if (rank > 0):
fileclient = FileClient(backend=backend)
buffer = io.BytesIO(fileclient.get(filename))
checkpoint = torch.load(buffer, map_location=map_location)
return checkpoint<|docstring|>In distributed setting, this function only download checkpoint at local
rank 0.<|endoftext|>
|
8dd7876a7a198a8b3fd1f7ec3791a34066e7b06a3d273d1eb6274e2479c46043
|
def _load_checkpoint(filename, map_location=None):
'Load checkpoint from somewhere (modelzoo, file, url).\n\n Args:\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str | None): Same as :func:`torch.load`. Default: None.\n\n Returns:\n dict | OrderedDict: The loaded checkpoint. It can be either an\n OrderedDict storing model weights or a dict containing other\n information, which depends on the checkpoint.\n '
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please use "torchvision://" instead')
model_urls = get_torchvision_models()
model_name = filename[11:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('torchvision://'):
model_urls = get_torchvision_models()
model_name = filename[14:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_urls = get_external_models()
model_name = filename[13:]
deprecated_urls = get_deprecated_model_names()
if (model_name in deprecated_urls):
warnings.warn(f'open-mmlab://{model_name} is deprecated in favor of open-mmlab://{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
if model_url.startswith(('http://', 'https://')):
checkpoint = load_url_dist(model_url)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
elif filename.startswith('mmcls://'):
model_urls = get_mmcls_models()
model_name = filename[8:]
checkpoint = load_url_dist(model_urls[model_name])
checkpoint = _process_mmcls_checkpoint(checkpoint)
elif filename.startswith(('http://', 'https://')):
checkpoint = load_url_dist(filename)
elif filename.startswith('pavi://'):
model_path = filename[7:]
checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
elif filename.startswith('s3://'):
checkpoint = load_fileclient_dist(filename, backend='ceph', map_location=map_location)
else:
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint
|
Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`. Default: None.
Returns:
dict | OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.
|
models/utils.py
|
_load_checkpoint
|
usama13o/codeServerEPI
| 0
|
python
|
def _load_checkpoint(filename, map_location=None):
'Load checkpoint from somewhere (modelzoo, file, url).\n\n Args:\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str | None): Same as :func:`torch.load`. Default: None.\n\n Returns:\n dict | OrderedDict: The loaded checkpoint. It can be either an\n OrderedDict storing model weights or a dict containing other\n information, which depends on the checkpoint.\n '
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please use "torchvision://" instead')
model_urls = get_torchvision_models()
model_name = filename[11:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('torchvision://'):
model_urls = get_torchvision_models()
model_name = filename[14:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_urls = get_external_models()
model_name = filename[13:]
deprecated_urls = get_deprecated_model_names()
if (model_name in deprecated_urls):
warnings.warn(f'open-mmlab://{model_name} is deprecated in favor of open-mmlab://{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
if model_url.startswith(('http://', 'https://')):
checkpoint = load_url_dist(model_url)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
elif filename.startswith('mmcls://'):
model_urls = get_mmcls_models()
model_name = filename[8:]
checkpoint = load_url_dist(model_urls[model_name])
checkpoint = _process_mmcls_checkpoint(checkpoint)
elif filename.startswith(('http://', 'https://')):
checkpoint = load_url_dist(filename)
elif filename.startswith('pavi://'):
model_path = filename[7:]
checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
elif filename.startswith('s3://'):
checkpoint = load_fileclient_dist(filename, backend='ceph', map_location=map_location)
else:
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint
|
def _load_checkpoint(filename, map_location=None):
'Load checkpoint from somewhere (modelzoo, file, url).\n\n Args:\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str | None): Same as :func:`torch.load`. Default: None.\n\n Returns:\n dict | OrderedDict: The loaded checkpoint. It can be either an\n OrderedDict storing model weights or a dict containing other\n information, which depends on the checkpoint.\n '
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please use "torchvision://" instead')
model_urls = get_torchvision_models()
model_name = filename[11:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('torchvision://'):
model_urls = get_torchvision_models()
model_name = filename[14:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_urls = get_external_models()
model_name = filename[13:]
deprecated_urls = get_deprecated_model_names()
if (model_name in deprecated_urls):
warnings.warn(f'open-mmlab://{model_name} is deprecated in favor of open-mmlab://{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
if model_url.startswith(('http://', 'https://')):
checkpoint = load_url_dist(model_url)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
elif filename.startswith('mmcls://'):
model_urls = get_mmcls_models()
model_name = filename[8:]
checkpoint = load_url_dist(model_urls[model_name])
checkpoint = _process_mmcls_checkpoint(checkpoint)
elif filename.startswith(('http://', 'https://')):
checkpoint = load_url_dist(filename)
elif filename.startswith('pavi://'):
model_path = filename[7:]
checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
elif filename.startswith('s3://'):
checkpoint = load_fileclient_dist(filename, backend='ceph', map_location=map_location)
else:
if (not osp.isfile(filename)):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint<|docstring|>Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`. Default: None.
Returns:
dict | OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.<|endoftext|>
|
06cc6536b78aa0df804311b0189651755a3ac1aa2dc7efa6283c09eab968d447
|
def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None):
'Load checkpoint from a file or URI.\n\n Args:\n model (Module): Module to load checkpoint.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str): Same as :func:`torch.load`.\n strict (bool): Whether to allow different params for the model and\n checkpoint.\n logger (:mod:`logging.Logger` or None): The logger for error message.\n\n Returns:\n dict or OrderedDict: The loaded checkpoint.\n '
checkpoint = _load_checkpoint(filename, map_location)
if (not isinstance(checkpoint, dict)):
raise RuntimeError(f'No state_dict found in checkpoint file {filename}')
if ('state_dict' in checkpoint):
state_dict = checkpoint['state_dict']
elif ('model' in checkpoint):
state_dict = checkpoint['model']
else:
state_dict = checkpoint
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for (k, v) in state_dict.items()}
if sorted(list(state_dict.keys()))[0].startswith('encoder'):
state_dict = {k.replace('encoder.', ''): v for (k, v) in state_dict.items() if k.startswith('encoder.')}
if list(state_dict.keys())[0].startswith('backbone.'):
state_dict = {k[9:]: v for (k, v) in state_dict.items()}
if (state_dict.get('absolute_pos_embed') is not None):
absolute_pos_embed = state_dict['absolute_pos_embed']
(N1, L, C1) = absolute_pos_embed.size()
(N2, C2, H, W) = model.absolute_pos_embed.size()
if ((N1 != N2) or (C1 != C2) or (L != (H * W))):
logger.warning('Error in loading absolute_pos_embed, pass')
else:
state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
relative_position_bias_table_keys = [k for k in state_dict.keys() if ('relative_position_bias_table' in k)]
for table_key in relative_position_bias_table_keys:
table_pretrained = state_dict[table_key]
table_current = model.state_dict()[table_key]
(L1, nH1) = table_pretrained.size()
(L2, nH2) = table_current.size()
if (nH1 != nH2):
logger.warning(f'Error in loading {table_key}, pass')
elif (L1 != L2):
S1 = int((L1 ** 0.5))
S2 = int((L2 ** 0.5))
table_pretrained_resized = F.interpolate(table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(S2, S2), mode='bicubic')
state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
load_state_dict(model, state_dict, strict, logger)
return checkpoint
|
Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
Returns:
dict or OrderedDict: The loaded checkpoint.
|
models/utils.py
|
load_checkpoint
|
usama13o/codeServerEPI
| 0
|
python
|
def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None):
'Load checkpoint from a file or URI.\n\n Args:\n model (Module): Module to load checkpoint.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str): Same as :func:`torch.load`.\n strict (bool): Whether to allow different params for the model and\n checkpoint.\n logger (:mod:`logging.Logger` or None): The logger for error message.\n\n Returns:\n dict or OrderedDict: The loaded checkpoint.\n '
checkpoint = _load_checkpoint(filename, map_location)
if (not isinstance(checkpoint, dict)):
raise RuntimeError(f'No state_dict found in checkpoint file {filename}')
if ('state_dict' in checkpoint):
state_dict = checkpoint['state_dict']
elif ('model' in checkpoint):
state_dict = checkpoint['model']
else:
state_dict = checkpoint
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for (k, v) in state_dict.items()}
if sorted(list(state_dict.keys()))[0].startswith('encoder'):
state_dict = {k.replace('encoder.', ): v for (k, v) in state_dict.items() if k.startswith('encoder.')}
if list(state_dict.keys())[0].startswith('backbone.'):
state_dict = {k[9:]: v for (k, v) in state_dict.items()}
if (state_dict.get('absolute_pos_embed') is not None):
absolute_pos_embed = state_dict['absolute_pos_embed']
(N1, L, C1) = absolute_pos_embed.size()
(N2, C2, H, W) = model.absolute_pos_embed.size()
if ((N1 != N2) or (C1 != C2) or (L != (H * W))):
logger.warning('Error in loading absolute_pos_embed, pass')
else:
state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
relative_position_bias_table_keys = [k for k in state_dict.keys() if ('relative_position_bias_table' in k)]
for table_key in relative_position_bias_table_keys:
table_pretrained = state_dict[table_key]
table_current = model.state_dict()[table_key]
(L1, nH1) = table_pretrained.size()
(L2, nH2) = table_current.size()
if (nH1 != nH2):
logger.warning(f'Error in loading {table_key}, pass')
elif (L1 != L2):
S1 = int((L1 ** 0.5))
S2 = int((L2 ** 0.5))
table_pretrained_resized = F.interpolate(table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(S2, S2), mode='bicubic')
state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
load_state_dict(model, state_dict, strict, logger)
return checkpoint
|
def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None):
'Load checkpoint from a file or URI.\n\n Args:\n model (Module): Module to load checkpoint.\n filename (str): Accept local filepath, URL, ``torchvision://xxx``,\n ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for\n details.\n map_location (str): Same as :func:`torch.load`.\n strict (bool): Whether to allow different params for the model and\n checkpoint.\n logger (:mod:`logging.Logger` or None): The logger for error message.\n\n Returns:\n dict or OrderedDict: The loaded checkpoint.\n '
checkpoint = _load_checkpoint(filename, map_location)
if (not isinstance(checkpoint, dict)):
raise RuntimeError(f'No state_dict found in checkpoint file {filename}')
if ('state_dict' in checkpoint):
state_dict = checkpoint['state_dict']
elif ('model' in checkpoint):
state_dict = checkpoint['model']
else:
state_dict = checkpoint
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for (k, v) in state_dict.items()}
if sorted(list(state_dict.keys()))[0].startswith('encoder'):
state_dict = {k.replace('encoder.', ): v for (k, v) in state_dict.items() if k.startswith('encoder.')}
if list(state_dict.keys())[0].startswith('backbone.'):
state_dict = {k[9:]: v for (k, v) in state_dict.items()}
if (state_dict.get('absolute_pos_embed') is not None):
absolute_pos_embed = state_dict['absolute_pos_embed']
(N1, L, C1) = absolute_pos_embed.size()
(N2, C2, H, W) = model.absolute_pos_embed.size()
if ((N1 != N2) or (C1 != C2) or (L != (H * W))):
logger.warning('Error in loading absolute_pos_embed, pass')
else:
state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
relative_position_bias_table_keys = [k for k in state_dict.keys() if ('relative_position_bias_table' in k)]
for table_key in relative_position_bias_table_keys:
table_pretrained = state_dict[table_key]
table_current = model.state_dict()[table_key]
(L1, nH1) = table_pretrained.size()
(L2, nH2) = table_current.size()
if (nH1 != nH2):
logger.warning(f'Error in loading {table_key}, pass')
elif (L1 != L2):
S1 = int((L1 ** 0.5))
S2 = int((L2 ** 0.5))
table_pretrained_resized = F.interpolate(table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(S2, S2), mode='bicubic')
state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
load_state_dict(model, state_dict, strict, logger)
return checkpoint<|docstring|>Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
Returns:
dict or OrderedDict: The loaded checkpoint.<|endoftext|>
|
cfc1e86220859a9d379a4c434282f39c24d88a192e7f7d27d4fa17f741238906
|
def weights_to_cpu(state_dict):
'Copy a model state_dict to cpu.\n\n Args:\n state_dict (OrderedDict): Model weights on GPU.\n\n Returns:\n OrderedDict: Model weights on GPU.\n '
state_dict_cpu = OrderedDict()
for (key, val) in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu
|
Copy a model state_dict to cpu.
Args:
state_dict (OrderedDict): Model weights on GPU.
Returns:
OrderedDict: Model weights on GPU.
|
models/utils.py
|
weights_to_cpu
|
usama13o/codeServerEPI
| 0
|
python
|
def weights_to_cpu(state_dict):
'Copy a model state_dict to cpu.\n\n Args:\n state_dict (OrderedDict): Model weights on GPU.\n\n Returns:\n OrderedDict: Model weights on GPU.\n '
state_dict_cpu = OrderedDict()
for (key, val) in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu
|
def weights_to_cpu(state_dict):
'Copy a model state_dict to cpu.\n\n Args:\n state_dict (OrderedDict): Model weights on GPU.\n\n Returns:\n OrderedDict: Model weights on GPU.\n '
state_dict_cpu = OrderedDict()
for (key, val) in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu<|docstring|>Copy a model state_dict to cpu.
Args:
state_dict (OrderedDict): Model weights on GPU.
Returns:
OrderedDict: Model weights on GPU.<|endoftext|>
|
10c4c30e205454983941698550a53fdeb5e491338db4355f93e22913bfb4df65
|
def _save_to_state_dict(module, destination, prefix, keep_vars):
'Saves module state to `destination` dictionary.\n\n This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (dict): A dict where state will be stored.\n prefix (str): The prefix for parameters and buffers used in this\n module.\n '
for (name, param) in module._parameters.items():
if (param is not None):
destination[(prefix + name)] = (param if keep_vars else param.detach())
for (name, buf) in module._buffers.items():
if (buf is not None):
destination[(prefix + name)] = (buf if keep_vars else buf.detach())
|
Saves module state to `destination` dictionary.
This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
Args:
module (nn.Module): The module to generate state_dict.
destination (dict): A dict where state will be stored.
prefix (str): The prefix for parameters and buffers used in this
module.
|
models/utils.py
|
_save_to_state_dict
|
usama13o/codeServerEPI
| 0
|
python
|
def _save_to_state_dict(module, destination, prefix, keep_vars):
'Saves module state to `destination` dictionary.\n\n This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (dict): A dict where state will be stored.\n prefix (str): The prefix for parameters and buffers used in this\n module.\n '
for (name, param) in module._parameters.items():
if (param is not None):
destination[(prefix + name)] = (param if keep_vars else param.detach())
for (name, buf) in module._buffers.items():
if (buf is not None):
destination[(prefix + name)] = (buf if keep_vars else buf.detach())
|
def _save_to_state_dict(module, destination, prefix, keep_vars):
'Saves module state to `destination` dictionary.\n\n This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (dict): A dict where state will be stored.\n prefix (str): The prefix for parameters and buffers used in this\n module.\n '
for (name, param) in module._parameters.items():
if (param is not None):
destination[(prefix + name)] = (param if keep_vars else param.detach())
for (name, buf) in module._buffers.items():
if (buf is not None):
destination[(prefix + name)] = (buf if keep_vars else buf.detach())<|docstring|>Saves module state to `destination` dictionary.
This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
Args:
module (nn.Module): The module to generate state_dict.
destination (dict): A dict where state will be stored.
prefix (str): The prefix for parameters and buffers used in this
module.<|endoftext|>
|
b3904322dde7e35298c2be3cc0cc574400cd9d967ebaeb6c7b7d10aa2ff066ca
|
def get_state_dict(module, destination=None, prefix='', keep_vars=False):
'Returns a dictionary containing a whole state of the module.\n\n Both parameters and persistent buffers (e.g. running averages) are\n included. Keys are corresponding parameter and buffer names.\n\n This method is modified from :meth:`torch.nn.Module.state_dict` to\n recursively check parallel module in case that the model has a complicated\n structure, e.g., nn.Module(nn.Module(DDP)).\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (OrderedDict): Returned dict for the state of the\n module.\n prefix (str): Prefix of the key.\n keep_vars (bool): Whether to keep the variable property of the\n parameters. Default: False.\n\n Returns:\n dict: A dictionary containing a whole state of the module.\n '
if is_module_wrapper(module):
module = module.module
if (destination is None):
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:(- 1)]] = local_metadata = dict(version=module._version)
_save_to_state_dict(module, destination, prefix, keep_vars)
for (name, child) in module._modules.items():
if (child is not None):
get_state_dict(child, destination, ((prefix + name) + '.'), keep_vars=keep_vars)
for hook in module._state_dict_hooks.values():
hook_result = hook(module, destination, prefix, local_metadata)
if (hook_result is not None):
destination = hook_result
return destination
|
Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
This method is modified from :meth:`torch.nn.Module.state_dict` to
recursively check parallel module in case that the model has a complicated
structure, e.g., nn.Module(nn.Module(DDP)).
Args:
module (nn.Module): The module to generate state_dict.
destination (OrderedDict): Returned dict for the state of the
module.
prefix (str): Prefix of the key.
keep_vars (bool): Whether to keep the variable property of the
parameters. Default: False.
Returns:
dict: A dictionary containing a whole state of the module.
|
models/utils.py
|
get_state_dict
|
usama13o/codeServerEPI
| 0
|
python
|
def get_state_dict(module, destination=None, prefix=, keep_vars=False):
'Returns a dictionary containing a whole state of the module.\n\n Both parameters and persistent buffers (e.g. running averages) are\n included. Keys are corresponding parameter and buffer names.\n\n This method is modified from :meth:`torch.nn.Module.state_dict` to\n recursively check parallel module in case that the model has a complicated\n structure, e.g., nn.Module(nn.Module(DDP)).\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (OrderedDict): Returned dict for the state of the\n module.\n prefix (str): Prefix of the key.\n keep_vars (bool): Whether to keep the variable property of the\n parameters. Default: False.\n\n Returns:\n dict: A dictionary containing a whole state of the module.\n '
if is_module_wrapper(module):
module = module.module
if (destination is None):
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:(- 1)]] = local_metadata = dict(version=module._version)
_save_to_state_dict(module, destination, prefix, keep_vars)
for (name, child) in module._modules.items():
if (child is not None):
get_state_dict(child, destination, ((prefix + name) + '.'), keep_vars=keep_vars)
for hook in module._state_dict_hooks.values():
hook_result = hook(module, destination, prefix, local_metadata)
if (hook_result is not None):
destination = hook_result
return destination
|
def get_state_dict(module, destination=None, prefix=, keep_vars=False):
'Returns a dictionary containing a whole state of the module.\n\n Both parameters and persistent buffers (e.g. running averages) are\n included. Keys are corresponding parameter and buffer names.\n\n This method is modified from :meth:`torch.nn.Module.state_dict` to\n recursively check parallel module in case that the model has a complicated\n structure, e.g., nn.Module(nn.Module(DDP)).\n\n Args:\n module (nn.Module): The module to generate state_dict.\n destination (OrderedDict): Returned dict for the state of the\n module.\n prefix (str): Prefix of the key.\n keep_vars (bool): Whether to keep the variable property of the\n parameters. Default: False.\n\n Returns:\n dict: A dictionary containing a whole state of the module.\n '
if is_module_wrapper(module):
module = module.module
if (destination is None):
destination = OrderedDict()
destination._metadata = OrderedDict()
destination._metadata[prefix[:(- 1)]] = local_metadata = dict(version=module._version)
_save_to_state_dict(module, destination, prefix, keep_vars)
for (name, child) in module._modules.items():
if (child is not None):
get_state_dict(child, destination, ((prefix + name) + '.'), keep_vars=keep_vars)
for hook in module._state_dict_hooks.values():
hook_result = hook(module, destination, prefix, local_metadata)
if (hook_result is not None):
destination = hook_result
return destination<|docstring|>Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
This method is modified from :meth:`torch.nn.Module.state_dict` to
recursively check parallel module in case that the model has a complicated
structure, e.g., nn.Module(nn.Module(DDP)).
Args:
module (nn.Module): The module to generate state_dict.
destination (OrderedDict): Returned dict for the state of the
module.
prefix (str): Prefix of the key.
keep_vars (bool): Whether to keep the variable property of the
parameters. Default: False.
Returns:
dict: A dictionary containing a whole state of the module.<|endoftext|>
|
e1ac9cb9524b9fa647b9aa7e38dd743a642e15c7a3ada9430d11dba355f0226e
|
def save_checkpoint(model, filename, optimizer=None, meta=None):
'Save checkpoint to file.\n\n The checkpoint will have 3 fields: ``meta``, ``state_dict`` and\n ``optimizer``. By default ``meta`` will contain version and time info.\n\n Args:\n model (Module): Module whose params are to be saved.\n filename (str): Checkpoint filename.\n optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.\n meta (dict, optional): Metadata to be saved in checkpoint.\n '
if (meta is None):
meta = {}
elif (not isinstance(meta, dict)):
raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
if is_module_wrapper(model):
model = model.module
if (hasattr(model, 'CLASSES') and (model.CLASSES is not None)):
meta.update(CLASSES=model.CLASSES)
checkpoint = {'meta': meta, 'state_dict': weights_to_cpu(get_state_dict(model))}
if isinstance(optimizer, Optimizer):
checkpoint['optimizer'] = optimizer.state_dict()
elif isinstance(optimizer, dict):
checkpoint['optimizer'] = {}
for (name, optim) in optimizer.items():
checkpoint['optimizer'][name] = optim.state_dict()
if filename.startswith('pavi://'):
try:
from pavi import modelcloud
from pavi.exception import NodeNotFoundError
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
model_path = filename[7:]
root = modelcloud.Folder()
(model_dir, model_name) = osp.split(model_path)
try:
model = modelcloud.get(model_dir)
except NodeNotFoundError:
model = root.create_training_model(model_dir)
with TemporaryDirectory() as tmp_dir:
checkpoint_file = osp.join(tmp_dir, model_name)
with open(checkpoint_file, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
model.create_file(checkpoint_file, name=model_name)
else:
mmcv.mkdir_or_exist(osp.dirname(filename))
with open(filename, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
|
Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.
|
models/utils.py
|
save_checkpoint
|
usama13o/codeServerEPI
| 0
|
python
|
def save_checkpoint(model, filename, optimizer=None, meta=None):
'Save checkpoint to file.\n\n The checkpoint will have 3 fields: ``meta``, ``state_dict`` and\n ``optimizer``. By default ``meta`` will contain version and time info.\n\n Args:\n model (Module): Module whose params are to be saved.\n filename (str): Checkpoint filename.\n optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.\n meta (dict, optional): Metadata to be saved in checkpoint.\n '
if (meta is None):
meta = {}
elif (not isinstance(meta, dict)):
raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
if is_module_wrapper(model):
model = model.module
if (hasattr(model, 'CLASSES') and (model.CLASSES is not None)):
meta.update(CLASSES=model.CLASSES)
checkpoint = {'meta': meta, 'state_dict': weights_to_cpu(get_state_dict(model))}
if isinstance(optimizer, Optimizer):
checkpoint['optimizer'] = optimizer.state_dict()
elif isinstance(optimizer, dict):
checkpoint['optimizer'] = {}
for (name, optim) in optimizer.items():
checkpoint['optimizer'][name] = optim.state_dict()
if filename.startswith('pavi://'):
try:
from pavi import modelcloud
from pavi.exception import NodeNotFoundError
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
model_path = filename[7:]
root = modelcloud.Folder()
(model_dir, model_name) = osp.split(model_path)
try:
model = modelcloud.get(model_dir)
except NodeNotFoundError:
model = root.create_training_model(model_dir)
with TemporaryDirectory() as tmp_dir:
checkpoint_file = osp.join(tmp_dir, model_name)
with open(checkpoint_file, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
model.create_file(checkpoint_file, name=model_name)
else:
mmcv.mkdir_or_exist(osp.dirname(filename))
with open(filename, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
|
def save_checkpoint(model, filename, optimizer=None, meta=None):
'Save checkpoint to file.\n\n The checkpoint will have 3 fields: ``meta``, ``state_dict`` and\n ``optimizer``. By default ``meta`` will contain version and time info.\n\n Args:\n model (Module): Module whose params are to be saved.\n filename (str): Checkpoint filename.\n optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.\n meta (dict, optional): Metadata to be saved in checkpoint.\n '
if (meta is None):
meta = {}
elif (not isinstance(meta, dict)):
raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
if is_module_wrapper(model):
model = model.module
if (hasattr(model, 'CLASSES') and (model.CLASSES is not None)):
meta.update(CLASSES=model.CLASSES)
checkpoint = {'meta': meta, 'state_dict': weights_to_cpu(get_state_dict(model))}
if isinstance(optimizer, Optimizer):
checkpoint['optimizer'] = optimizer.state_dict()
elif isinstance(optimizer, dict):
checkpoint['optimizer'] = {}
for (name, optim) in optimizer.items():
checkpoint['optimizer'][name] = optim.state_dict()
if filename.startswith('pavi://'):
try:
from pavi import modelcloud
from pavi.exception import NodeNotFoundError
except ImportError:
raise ImportError('Please install pavi to load checkpoint from modelcloud.')
model_path = filename[7:]
root = modelcloud.Folder()
(model_dir, model_name) = osp.split(model_path)
try:
model = modelcloud.get(model_dir)
except NodeNotFoundError:
model = root.create_training_model(model_dir)
with TemporaryDirectory() as tmp_dir:
checkpoint_file = osp.join(tmp_dir, model_name)
with open(checkpoint_file, 'wb') as f:
torch.save(checkpoint, f)
f.flush()
model.create_file(checkpoint_file, name=model_name)
else:
mmcv.mkdir_or_exist(osp.dirname(filename))
with open(filename, 'wb') as f:
torch.save(checkpoint, f)
f.flush()<|docstring|>Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.<|endoftext|>
|
3df2cf27cbc8b1c92068b4aa9fbe60e56061808003e456bb11ec294ddbcdc945
|
def setUp(self):
'\n Perturb the normal import behavior subtly by installing an import\n hook. No custom behavior is provided, but this adds some extra\n frames to the call stack, which L{namedAny} must be able to account\n for.\n '
self.importer = ModuleImporter()
self.importer.install()
|
Perturb the normal import behavior subtly by installing an import
hook. No custom behavior is provided, but this adds some extra
frames to the call stack, which L{namedAny} must be able to account
for.
|
libraries/twisted/test/test_reflect.py
|
setUp
|
Heufneutje/DideRobot
| 0
|
python
|
def setUp(self):
'\n Perturb the normal import behavior subtly by installing an import\n hook. No custom behavior is provided, but this adds some extra\n frames to the call stack, which L{namedAny} must be able to account\n for.\n '
self.importer = ModuleImporter()
self.importer.install()
|
def setUp(self):
'\n Perturb the normal import behavior subtly by installing an import\n hook. No custom behavior is provided, but this adds some extra\n frames to the call stack, which L{namedAny} must be able to account\n for.\n '
self.importer = ModuleImporter()
self.importer.install()<|docstring|>Perturb the normal import behavior subtly by installing an import
hook. No custom behavior is provided, but this adds some extra
frames to the call stack, which L{namedAny} must be able to account
for.<|endoftext|>
|
66fb96ee2fef46d43a56ab648b82f2b27755ef1642981b70c9567e60aeddcba8
|
def tearDown(self):
'\n Uninstall the custom import hook.\n '
self.importer.uninstall()
|
Uninstall the custom import hook.
|
libraries/twisted/test/test_reflect.py
|
tearDown
|
Heufneutje/DideRobot
| 0
|
python
|
def tearDown(self):
'\n \n '
self.importer.uninstall()
|
def tearDown(self):
'\n \n '
self.importer.uninstall()<|docstring|>Uninstall the custom import hook.<|endoftext|>
|
9e0f8110eb8f2aa9bf074bb892c1fd5a23f0180a78a5e08e37762a85bcdafed8
|
def test_dictionary(self):
'\n Test references search through a dictionnary, as a key or as a value.\n '
o = object()
d1 = {None: o}
d2 = {o: None}
self.assertIn('[None]', reflect.objgrep(d1, o, reflect.isSame))
self.assertIn('{None}', reflect.objgrep(d2, o, reflect.isSame))
|
Test references search through a dictionnary, as a key or as a value.
|
libraries/twisted/test/test_reflect.py
|
test_dictionary
|
Heufneutje/DideRobot
| 0
|
python
|
def test_dictionary(self):
'\n \n '
o = object()
d1 = {None: o}
d2 = {o: None}
self.assertIn('[None]', reflect.objgrep(d1, o, reflect.isSame))
self.assertIn('{None}', reflect.objgrep(d2, o, reflect.isSame))
|
def test_dictionary(self):
'\n \n '
o = object()
d1 = {None: o}
d2 = {o: None}
self.assertIn('[None]', reflect.objgrep(d1, o, reflect.isSame))
self.assertIn('{None}', reflect.objgrep(d2, o, reflect.isSame))<|docstring|>Test references search through a dictionnary, as a key or as a value.<|endoftext|>
|
bc0549368d97a180619ede69382761a8884b85aba54cc6233a7a8b08fba624e8
|
def test_list(self):
'\n Test references search through a list.\n '
o = object()
L = [None, o]
self.assertIn('[1]', reflect.objgrep(L, o, reflect.isSame))
|
Test references search through a list.
|
libraries/twisted/test/test_reflect.py
|
test_list
|
Heufneutje/DideRobot
| 0
|
python
|
def test_list(self):
'\n \n '
o = object()
L = [None, o]
self.assertIn('[1]', reflect.objgrep(L, o, reflect.isSame))
|
def test_list(self):
'\n \n '
o = object()
L = [None, o]
self.assertIn('[1]', reflect.objgrep(L, o, reflect.isSame))<|docstring|>Test references search through a list.<|endoftext|>
|
d40bb2574f4ba0d9e644dbc8fbe10676bfd416622b1400e73c609bb0fc790aa8
|
def test_tuple(self):
'\n Test references search through a tuple.\n '
o = object()
T = (o, None)
self.assertIn('[0]', reflect.objgrep(T, o, reflect.isSame))
|
Test references search through a tuple.
|
libraries/twisted/test/test_reflect.py
|
test_tuple
|
Heufneutje/DideRobot
| 0
|
python
|
def test_tuple(self):
'\n \n '
o = object()
T = (o, None)
self.assertIn('[0]', reflect.objgrep(T, o, reflect.isSame))
|
def test_tuple(self):
'\n \n '
o = object()
T = (o, None)
self.assertIn('[0]', reflect.objgrep(T, o, reflect.isSame))<|docstring|>Test references search through a tuple.<|endoftext|>
|
1bb36a7a9dd81316ee8caab88cefe1f62bad31773acea34249eab4ddb1a9e1a1
|
def test_instance(self):
'\n Test references search through an object attribute.\n '
class Dummy():
pass
o = object()
d = Dummy()
d.o = o
self.assertIn('.o', reflect.objgrep(d, o, reflect.isSame))
|
Test references search through an object attribute.
|
libraries/twisted/test/test_reflect.py
|
test_instance
|
Heufneutje/DideRobot
| 0
|
python
|
def test_instance(self):
'\n \n '
class Dummy():
pass
o = object()
d = Dummy()
d.o = o
self.assertIn('.o', reflect.objgrep(d, o, reflect.isSame))
|
def test_instance(self):
'\n \n '
class Dummy():
pass
o = object()
d = Dummy()
d.o = o
self.assertIn('.o', reflect.objgrep(d, o, reflect.isSame))<|docstring|>Test references search through an object attribute.<|endoftext|>
|
f1b59ee40d2379927ddd63715c78b635b237e19b7ed8a063d5a9820c3fe8f240
|
def test_weakref(self):
'\n Test references search through a weakref object.\n '
class Dummy():
pass
o = Dummy()
w1 = weakref.ref(o)
self.assertIn('()', reflect.objgrep(w1, o, reflect.isSame))
|
Test references search through a weakref object.
|
libraries/twisted/test/test_reflect.py
|
test_weakref
|
Heufneutje/DideRobot
| 0
|
python
|
def test_weakref(self):
'\n \n '
class Dummy():
pass
o = Dummy()
w1 = weakref.ref(o)
self.assertIn('()', reflect.objgrep(w1, o, reflect.isSame))
|
def test_weakref(self):
'\n \n '
class Dummy():
pass
o = Dummy()
w1 = weakref.ref(o)
self.assertIn('()', reflect.objgrep(w1, o, reflect.isSame))<|docstring|>Test references search through a weakref object.<|endoftext|>
|
53f555fc1d13445813ebe3e0fa289a201d85ebbd370f6e2ad51a90c1c6be82fd
|
def test_boundMethod(self):
'\n Test references search through method special attributes.\n '
class Dummy():
def dummy(self):
pass
o = Dummy()
m = o.dummy
self.assertIn('.im_self', reflect.objgrep(m, m.im_self, reflect.isSame))
self.assertIn('.im_class', reflect.objgrep(m, m.im_class, reflect.isSame))
self.assertIn('.im_func', reflect.objgrep(m, m.im_func, reflect.isSame))
|
Test references search through method special attributes.
|
libraries/twisted/test/test_reflect.py
|
test_boundMethod
|
Heufneutje/DideRobot
| 0
|
python
|
def test_boundMethod(self):
'\n \n '
class Dummy():
def dummy(self):
pass
o = Dummy()
m = o.dummy
self.assertIn('.im_self', reflect.objgrep(m, m.im_self, reflect.isSame))
self.assertIn('.im_class', reflect.objgrep(m, m.im_class, reflect.isSame))
self.assertIn('.im_func', reflect.objgrep(m, m.im_func, reflect.isSame))
|
def test_boundMethod(self):
'\n \n '
class Dummy():
def dummy(self):
pass
o = Dummy()
m = o.dummy
self.assertIn('.im_self', reflect.objgrep(m, m.im_self, reflect.isSame))
self.assertIn('.im_class', reflect.objgrep(m, m.im_class, reflect.isSame))
self.assertIn('.im_func', reflect.objgrep(m, m.im_func, reflect.isSame))<|docstring|>Test references search through method special attributes.<|endoftext|>
|
826a872163a6884cbc353ea49c7a6909dd2d6e568daaeaba4936d3823ea872bf
|
def test_everything(self):
'\n Test references search using complex set of objects.\n '
class Dummy():
def method(self):
pass
o = Dummy()
D1 = {(): 'baz', None: 'Quux', o: 'Foosh'}
L = [None, (), D1, 3]
T = (L, {}, Dummy())
D2 = {0: 'foo', 1: 'bar', 2: T}
i = Dummy()
i.attr = D2
m = i.method
w = weakref.ref(m)
self.assertIn("().im_self.attr[2][0][2]{'Foosh'}", reflect.objgrep(w, o, reflect.isSame))
|
Test references search using complex set of objects.
|
libraries/twisted/test/test_reflect.py
|
test_everything
|
Heufneutje/DideRobot
| 0
|
python
|
def test_everything(self):
'\n \n '
class Dummy():
def method(self):
pass
o = Dummy()
D1 = {(): 'baz', None: 'Quux', o: 'Foosh'}
L = [None, (), D1, 3]
T = (L, {}, Dummy())
D2 = {0: 'foo', 1: 'bar', 2: T}
i = Dummy()
i.attr = D2
m = i.method
w = weakref.ref(m)
self.assertIn("().im_self.attr[2][0][2]{'Foosh'}", reflect.objgrep(w, o, reflect.isSame))
|
def test_everything(self):
'\n \n '
class Dummy():
def method(self):
pass
o = Dummy()
D1 = {(): 'baz', None: 'Quux', o: 'Foosh'}
L = [None, (), D1, 3]
T = (L, {}, Dummy())
D2 = {0: 'foo', 1: 'bar', 2: T}
i = Dummy()
i.attr = D2
m = i.method
w = weakref.ref(m)
self.assertIn("().im_self.attr[2][0][2]{'Foosh'}", reflect.objgrep(w, o, reflect.isSame))<|docstring|>Test references search using complex set of objects.<|endoftext|>
|
7a9a0d59e98a2c60a113a6018fb736948945a2a9e931754f5be67031306461ac
|
def test_depthLimit(self):
'\n Test the depth of references search.\n '
a = []
b = [a]
c = [a, b]
d = [a, c]
self.assertEqual(['[0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=1))
self.assertEqual(['[0]', '[1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=2))
self.assertEqual(['[0]', '[1][0]', '[1][1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=3))
|
Test the depth of references search.
|
libraries/twisted/test/test_reflect.py
|
test_depthLimit
|
Heufneutje/DideRobot
| 0
|
python
|
def test_depthLimit(self):
'\n \n '
a = []
b = [a]
c = [a, b]
d = [a, c]
self.assertEqual(['[0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=1))
self.assertEqual(['[0]', '[1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=2))
self.assertEqual(['[0]', '[1][0]', '[1][1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=3))
|
def test_depthLimit(self):
'\n \n '
a = []
b = [a]
c = [a, b]
d = [a, c]
self.assertEqual(['[0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=1))
self.assertEqual(['[0]', '[1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=2))
self.assertEqual(['[0]', '[1][0]', '[1][1][0]'], reflect.objgrep(d, a, reflect.isSame, maxDepth=3))<|docstring|>Test the depth of references search.<|endoftext|>
|
66b5acacec71ebd346d3f7a2f4a8ac6219c6abcccc9de3695ee7594940764ba8
|
def test_deque(self):
'\n Test references search through a deque object.\n '
o = object()
D = deque()
D.append(None)
D.append(o)
self.assertIn('[1]', reflect.objgrep(D, o, reflect.isSame))
|
Test references search through a deque object.
|
libraries/twisted/test/test_reflect.py
|
test_deque
|
Heufneutje/DideRobot
| 0
|
python
|
def test_deque(self):
'\n \n '
o = object()
D = deque()
D.append(None)
D.append(o)
self.assertIn('[1]', reflect.objgrep(D, o, reflect.isSame))
|
def test_deque(self):
'\n \n '
o = object()
D = deque()
D.append(None)
D.append(o)
self.assertIn('[1]', reflect.objgrep(D, o, reflect.isSame))<|docstring|>Test references search through a deque object.<|endoftext|>
|
653f53719c7d477ee2aeb3100c449e8581504e1733376eece08c2c9d817a80b7
|
def test_allYourBase(self):
'\n Test deprecation of L{reflect.allYourBase}. See #5481 for removal.\n '
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.allYourBase, DeprecationTestCase)
|
Test deprecation of L{reflect.allYourBase}. See #5481 for removal.
|
libraries/twisted/test/test_reflect.py
|
test_allYourBase
|
Heufneutje/DideRobot
| 0
|
python
|
def test_allYourBase(self):
'\n \n '
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.allYourBase, DeprecationTestCase)
|
def test_allYourBase(self):
'\n \n '
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.allYourBase, DeprecationTestCase)<|docstring|>Test deprecation of L{reflect.allYourBase}. See #5481 for removal.<|endoftext|>
|
ccc930f732b43c062cf4de9ba6e1d746003753a2655aa33dd81139bfa2077c01
|
def test_accumulateBases(self):
'\n Test deprecation of L{reflect.accumulateBases}. See #5481 for removal.\n '
l = []
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.accumulateBases, DeprecationTestCase, l, None)
|
Test deprecation of L{reflect.accumulateBases}. See #5481 for removal.
|
libraries/twisted/test/test_reflect.py
|
test_accumulateBases
|
Heufneutje/DideRobot
| 0
|
python
|
def test_accumulateBases(self):
'\n \n '
l = []
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.accumulateBases, DeprecationTestCase, l, None)
|
def test_accumulateBases(self):
'\n \n '
l = []
self.callDeprecated((Version('Twisted', 11, 0, 0), 'inspect.getmro'), reflect.accumulateBases, DeprecationTestCase, l, None)<|docstring|>Test deprecation of L{reflect.accumulateBases}. See #5481 for removal.<|endoftext|>
|
3117fa5fc7fbc3d9b605fc742796864d6f552bd9ddca8ca99b04c373e98b9ee8
|
def _hash_artifact(filepath, hash_algorithms=None, normalize_line_endings=False):
"Internal helper that takes a filename and hashes the respective file's\n contents using the passed hash_algorithms and returns a hashdict conformant\n with securesystemslib.formats.HASHDICT_SCHEMA. "
if (not hash_algorithms):
hash_algorithms = ['sha256']
securesystemslib.formats.HASHALGORITHMS_SCHEMA.check_match(hash_algorithms)
hash_dict = {}
for algorithm in hash_algorithms:
digest_object = securesystemslib.hash.digest_filename(filepath, algorithm, normalize_line_endings=normalize_line_endings)
hash_dict.update({algorithm: digest_object.hexdigest()})
securesystemslib.formats.HASHDICT_SCHEMA.check_match(hash_dict)
return hash_dict
|
Internal helper that takes a filename and hashes the respective file's
contents using the passed hash_algorithms and returns a hashdict conformant
with securesystemslib.formats.HASHDICT_SCHEMA.
|
in_toto/runlib.py
|
_hash_artifact
|
reeeeeeem/in-toto
| 507
|
python
|
def _hash_artifact(filepath, hash_algorithms=None, normalize_line_endings=False):
"Internal helper that takes a filename and hashes the respective file's\n contents using the passed hash_algorithms and returns a hashdict conformant\n with securesystemslib.formats.HASHDICT_SCHEMA. "
if (not hash_algorithms):
hash_algorithms = ['sha256']
securesystemslib.formats.HASHALGORITHMS_SCHEMA.check_match(hash_algorithms)
hash_dict = {}
for algorithm in hash_algorithms:
digest_object = securesystemslib.hash.digest_filename(filepath, algorithm, normalize_line_endings=normalize_line_endings)
hash_dict.update({algorithm: digest_object.hexdigest()})
securesystemslib.formats.HASHDICT_SCHEMA.check_match(hash_dict)
return hash_dict
|
def _hash_artifact(filepath, hash_algorithms=None, normalize_line_endings=False):
"Internal helper that takes a filename and hashes the respective file's\n contents using the passed hash_algorithms and returns a hashdict conformant\n with securesystemslib.formats.HASHDICT_SCHEMA. "
if (not hash_algorithms):
hash_algorithms = ['sha256']
securesystemslib.formats.HASHALGORITHMS_SCHEMA.check_match(hash_algorithms)
hash_dict = {}
for algorithm in hash_algorithms:
digest_object = securesystemslib.hash.digest_filename(filepath, algorithm, normalize_line_endings=normalize_line_endings)
hash_dict.update({algorithm: digest_object.hexdigest()})
securesystemslib.formats.HASHDICT_SCHEMA.check_match(hash_dict)
return hash_dict<|docstring|>Internal helper that takes a filename and hashes the respective file's
contents using the passed hash_algorithms and returns a hashdict conformant
with securesystemslib.formats.HASHDICT_SCHEMA.<|endoftext|>
|
c290a79c62ac0889d44bbe8cbffe0c2c75e46d8c9ab37a486aa37cf0ab6beafe
|
def _apply_exclude_patterns(names, exclude_filter):
'Exclude matched patterns from passed names.'
included = set(names)
if hasattr(exclude_filter, '__iter__'):
exclude_filter = PathSpec.from_lines('gitwildmatch', exclude_filter)
for excluded in exclude_filter.match_files(names):
included.discard(excluded)
return sorted(included)
|
Exclude matched patterns from passed names.
|
in_toto/runlib.py
|
_apply_exclude_patterns
|
reeeeeeem/in-toto
| 507
|
python
|
def _apply_exclude_patterns(names, exclude_filter):
included = set(names)
if hasattr(exclude_filter, '__iter__'):
exclude_filter = PathSpec.from_lines('gitwildmatch', exclude_filter)
for excluded in exclude_filter.match_files(names):
included.discard(excluded)
return sorted(included)
|
def _apply_exclude_patterns(names, exclude_filter):
included = set(names)
if hasattr(exclude_filter, '__iter__'):
exclude_filter = PathSpec.from_lines('gitwildmatch', exclude_filter)
for excluded in exclude_filter.match_files(names):
included.discard(excluded)
return sorted(included)<|docstring|>Exclude matched patterns from passed names.<|endoftext|>
|
06e1f730b14a9e790039ae5a3a1b41cea669c3c4623ce1d407843387e8f663ff
|
def _apply_left_strip(artifact_filepath, artifacts_dict, lstrip_paths=None):
' Internal helper function to left strip dictionary keys based on\n prefixes passed by the user. '
if lstrip_paths:
for prefix in lstrip_paths:
if artifact_filepath.startswith(prefix):
artifact_filepath = artifact_filepath[len(prefix):]
break
if (artifact_filepath in artifacts_dict):
raise in_toto.exceptions.PrefixError("Prefix selection has resulted in non unique dictionary key '{}'".format(artifact_filepath))
return artifact_filepath
|
Internal helper function to left strip dictionary keys based on
prefixes passed by the user.
|
in_toto/runlib.py
|
_apply_left_strip
|
reeeeeeem/in-toto
| 507
|
python
|
def _apply_left_strip(artifact_filepath, artifacts_dict, lstrip_paths=None):
' Internal helper function to left strip dictionary keys based on\n prefixes passed by the user. '
if lstrip_paths:
for prefix in lstrip_paths:
if artifact_filepath.startswith(prefix):
artifact_filepath = artifact_filepath[len(prefix):]
break
if (artifact_filepath in artifacts_dict):
raise in_toto.exceptions.PrefixError("Prefix selection has resulted in non unique dictionary key '{}'".format(artifact_filepath))
return artifact_filepath
|
def _apply_left_strip(artifact_filepath, artifacts_dict, lstrip_paths=None):
' Internal helper function to left strip dictionary keys based on\n prefixes passed by the user. '
if lstrip_paths:
for prefix in lstrip_paths:
if artifact_filepath.startswith(prefix):
artifact_filepath = artifact_filepath[len(prefix):]
break
if (artifact_filepath in artifacts_dict):
raise in_toto.exceptions.PrefixError("Prefix selection has resulted in non unique dictionary key '{}'".format(artifact_filepath))
return artifact_filepath<|docstring|>Internal helper function to left strip dictionary keys based on
prefixes passed by the user.<|endoftext|>
|
31b19156ab43bcfe60788355f39b0bb09d7f64feae29742fec1b329811f942c1
|
def record_artifacts_as_dict(artifacts, exclude_patterns=None, base_path=None, follow_symlink_dirs=False, normalize_line_endings=False, lstrip_paths=None):
'\n <Purpose>\n Hashes each file in the passed path list. If the path list contains\n paths to directories the directory tree(s) are traversed.\n\n The files a link command is executed on are called materials.\n The files that result form a link command execution are called\n products.\n\n Paths are normalized for matching and storing by left stripping "./"\n\n NOTE on exclude patterns:\n - Uses PathSpec to compile gitignore-style patterns, making use of the\n GitWildMatchPattern class (registered as \'gitwildmatch\')\n\n - Patterns are checked for match against the full path relative to each\n path passed in the artifacts list\n\n - If a directory is excluded, all its files and subdirectories are also\n excluded\n\n - How it differs from .gitignore\n - No need to escape #\n - No ignoring of trailing spaces\n - No general negation with exclamation mark !\n - No special treatment of slash /\n - No special treatment of consecutive asterisks **\n\n - Exclude patterns are likely to become command line arguments or part of\n a config file.\n\n <Arguments>\n artifacts:\n A list of file or directory paths used as materials or products for\n the link command.\n\n exclude_patterns: (optional)\n Artifacts matched by the pattern are excluded from the result.\n Exclude patterns can be passed as argument or specified via\n ARTIFACT_EXCLUDE_PATTERNS setting (see `in_toto.settings`) or\n via envvars or rcfiles (see `in_toto.user_settings`).\n If passed, patterns specified via settings are overriden.\n\n base_path: (optional)\n Change to base_path and record artifacts relative from there.\n If not passed, current working directory is used as base_path.\n NOTE: The base_path part of the recorded artifact is not included\n in the returned paths.\n\n follow_symlink_dirs: (optional)\n Follow symlinked dirs if the linked dir exists (default is False).\n The recorded path contains the symlink name, not the resolved name.\n NOTE: This parameter toggles following linked directories only,\n linked files are always recorded, independently of this parameter.\n NOTE: Beware of infinite recursions that can occur if a symlink\n points to a parent directory or itself.\n\n normalize_line_endings: (optional)\n If True, replaces windows and mac line endings with unix line\n endings before hashing the content of the passed files, for\n cross-platform support.\n\n lstrip_paths: (optional)\n If a prefix path is passed, the prefix is left stripped from\n the path of every artifact that contains the prefix.\n\n <Exceptions>\n in_toto.exceptions.ValueError,\n if we cannot change to base path directory\n\n in_toto.exceptions.FormatError,\n if the list of exlcude patterns does not match format\n securesystemslib.formats.NAMES_SCHEMA\n\n <Side Effects>\n Calls functions to generate cryptographic hashes.\n\n <Returns>\n A dictionary with file paths as keys and the files\' hashes as values.\n '
artifacts_dict = {}
if (not artifacts):
return artifacts_dict
if base_path:
LOG.info('Overriding setting ARTIFACT_BASE_PATH with passed base path.')
else:
base_path = in_toto.settings.ARTIFACT_BASE_PATH
if base_path:
original_cwd = os.getcwd()
try:
os.chdir(base_path)
except Exception as e:
raise ValueError("Could not use '{}' as base path: '{}'".format(base_path, e)) from e
norm_artifacts = []
for path in artifacts:
norm_artifacts.append(os.path.normpath(path))
if exclude_patterns:
LOG.info('Overriding setting ARTIFACT_EXCLUDE_PATTERNS with passed exclude patterns.')
else:
exclude_patterns = in_toto.settings.ARTIFACT_EXCLUDE_PATTERNS
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
norm_artifacts = _apply_exclude_patterns(norm_artifacts, exclude_patterns)
if lstrip_paths:
for (prefix_one, prefix_two) in itertools.combinations(lstrip_paths, 2):
if (prefix_one.startswith(prefix_two) or prefix_two.startswith(prefix_one)):
raise in_toto.exceptions.PrefixError("'{}' and '{}' triggered a left substring error".format(prefix_one, prefix_two))
exclude_filter = PathSpec.from_lines('gitwildmatch', (exclude_patterns or []))
for artifact in norm_artifacts:
if os.path.isfile(artifact):
artifact = artifact.replace('\\', '/')
key = _apply_left_strip(artifact, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(artifact, normalize_line_endings=normalize_line_endings)
elif os.path.isdir(artifact):
for (root, dirs, files) in os.walk(artifact, followlinks=follow_symlink_dirs):
dirpaths = []
for dirname in dirs:
norm_dirpath = os.path.normpath(os.path.join(root, dirname))
dirpaths.append(norm_dirpath)
if exclude_patterns:
dirpaths = _apply_exclude_patterns(dirpaths, exclude_filter)
dirs[:] = []
for dirpath in dirpaths:
name = os.path.basename(dirpath)
dirs.append(name)
filepaths = []
for filename in files:
norm_filepath = os.path.normpath(os.path.join(root, filename))
if os.path.isfile(norm_filepath):
filepaths.append(norm_filepath)
else:
LOG.info("File '{}' appears to be a broken symlink. Skipping...".format(norm_filepath))
if exclude_patterns:
filepaths = _apply_exclude_patterns(filepaths, exclude_filter)
for filepath in filepaths:
normalized_filepath = filepath.replace('\\', '/')
key = _apply_left_strip(normalized_filepath, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(filepath, normalize_line_endings=normalize_line_endings)
else:
LOG.info('path: {} does not exist, skipping..'.format(artifact))
if base_path:
os.chdir(original_cwd)
return artifacts_dict
|
<Purpose>
Hashes each file in the passed path list. If the path list contains
paths to directories the directory tree(s) are traversed.
The files a link command is executed on are called materials.
The files that result form a link command execution are called
products.
Paths are normalized for matching and storing by left stripping "./"
NOTE on exclude patterns:
- Uses PathSpec to compile gitignore-style patterns, making use of the
GitWildMatchPattern class (registered as 'gitwildmatch')
- Patterns are checked for match against the full path relative to each
path passed in the artifacts list
- If a directory is excluded, all its files and subdirectories are also
excluded
- How it differs from .gitignore
- No need to escape #
- No ignoring of trailing spaces
- No general negation with exclamation mark !
- No special treatment of slash /
- No special treatment of consecutive asterisks **
- Exclude patterns are likely to become command line arguments or part of
a config file.
<Arguments>
artifacts:
A list of file or directory paths used as materials or products for
the link command.
exclude_patterns: (optional)
Artifacts matched by the pattern are excluded from the result.
Exclude patterns can be passed as argument or specified via
ARTIFACT_EXCLUDE_PATTERNS setting (see `in_toto.settings`) or
via envvars or rcfiles (see `in_toto.user_settings`).
If passed, patterns specified via settings are overriden.
base_path: (optional)
Change to base_path and record artifacts relative from there.
If not passed, current working directory is used as base_path.
NOTE: The base_path part of the recorded artifact is not included
in the returned paths.
follow_symlink_dirs: (optional)
Follow symlinked dirs if the linked dir exists (default is False).
The recorded path contains the symlink name, not the resolved name.
NOTE: This parameter toggles following linked directories only,
linked files are always recorded, independently of this parameter.
NOTE: Beware of infinite recursions that can occur if a symlink
points to a parent directory or itself.
normalize_line_endings: (optional)
If True, replaces windows and mac line endings with unix line
endings before hashing the content of the passed files, for
cross-platform support.
lstrip_paths: (optional)
If a prefix path is passed, the prefix is left stripped from
the path of every artifact that contains the prefix.
<Exceptions>
in_toto.exceptions.ValueError,
if we cannot change to base path directory
in_toto.exceptions.FormatError,
if the list of exlcude patterns does not match format
securesystemslib.formats.NAMES_SCHEMA
<Side Effects>
Calls functions to generate cryptographic hashes.
<Returns>
A dictionary with file paths as keys and the files' hashes as values.
|
in_toto/runlib.py
|
record_artifacts_as_dict
|
reeeeeeem/in-toto
| 507
|
python
|
def record_artifacts_as_dict(artifacts, exclude_patterns=None, base_path=None, follow_symlink_dirs=False, normalize_line_endings=False, lstrip_paths=None):
'\n <Purpose>\n Hashes each file in the passed path list. If the path list contains\n paths to directories the directory tree(s) are traversed.\n\n The files a link command is executed on are called materials.\n The files that result form a link command execution are called\n products.\n\n Paths are normalized for matching and storing by left stripping "./"\n\n NOTE on exclude patterns:\n - Uses PathSpec to compile gitignore-style patterns, making use of the\n GitWildMatchPattern class (registered as \'gitwildmatch\')\n\n - Patterns are checked for match against the full path relative to each\n path passed in the artifacts list\n\n - If a directory is excluded, all its files and subdirectories are also\n excluded\n\n - How it differs from .gitignore\n - No need to escape #\n - No ignoring of trailing spaces\n - No general negation with exclamation mark !\n - No special treatment of slash /\n - No special treatment of consecutive asterisks **\n\n - Exclude patterns are likely to become command line arguments or part of\n a config file.\n\n <Arguments>\n artifacts:\n A list of file or directory paths used as materials or products for\n the link command.\n\n exclude_patterns: (optional)\n Artifacts matched by the pattern are excluded from the result.\n Exclude patterns can be passed as argument or specified via\n ARTIFACT_EXCLUDE_PATTERNS setting (see `in_toto.settings`) or\n via envvars or rcfiles (see `in_toto.user_settings`).\n If passed, patterns specified via settings are overriden.\n\n base_path: (optional)\n Change to base_path and record artifacts relative from there.\n If not passed, current working directory is used as base_path.\n NOTE: The base_path part of the recorded artifact is not included\n in the returned paths.\n\n follow_symlink_dirs: (optional)\n Follow symlinked dirs if the linked dir exists (default is False).\n The recorded path contains the symlink name, not the resolved name.\n NOTE: This parameter toggles following linked directories only,\n linked files are always recorded, independently of this parameter.\n NOTE: Beware of infinite recursions that can occur if a symlink\n points to a parent directory or itself.\n\n normalize_line_endings: (optional)\n If True, replaces windows and mac line endings with unix line\n endings before hashing the content of the passed files, for\n cross-platform support.\n\n lstrip_paths: (optional)\n If a prefix path is passed, the prefix is left stripped from\n the path of every artifact that contains the prefix.\n\n <Exceptions>\n in_toto.exceptions.ValueError,\n if we cannot change to base path directory\n\n in_toto.exceptions.FormatError,\n if the list of exlcude patterns does not match format\n securesystemslib.formats.NAMES_SCHEMA\n\n <Side Effects>\n Calls functions to generate cryptographic hashes.\n\n <Returns>\n A dictionary with file paths as keys and the files\' hashes as values.\n '
artifacts_dict = {}
if (not artifacts):
return artifacts_dict
if base_path:
LOG.info('Overriding setting ARTIFACT_BASE_PATH with passed base path.')
else:
base_path = in_toto.settings.ARTIFACT_BASE_PATH
if base_path:
original_cwd = os.getcwd()
try:
os.chdir(base_path)
except Exception as e:
raise ValueError("Could not use '{}' as base path: '{}'".format(base_path, e)) from e
norm_artifacts = []
for path in artifacts:
norm_artifacts.append(os.path.normpath(path))
if exclude_patterns:
LOG.info('Overriding setting ARTIFACT_EXCLUDE_PATTERNS with passed exclude patterns.')
else:
exclude_patterns = in_toto.settings.ARTIFACT_EXCLUDE_PATTERNS
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
norm_artifacts = _apply_exclude_patterns(norm_artifacts, exclude_patterns)
if lstrip_paths:
for (prefix_one, prefix_two) in itertools.combinations(lstrip_paths, 2):
if (prefix_one.startswith(prefix_two) or prefix_two.startswith(prefix_one)):
raise in_toto.exceptions.PrefixError("'{}' and '{}' triggered a left substring error".format(prefix_one, prefix_two))
exclude_filter = PathSpec.from_lines('gitwildmatch', (exclude_patterns or []))
for artifact in norm_artifacts:
if os.path.isfile(artifact):
artifact = artifact.replace('\\', '/')
key = _apply_left_strip(artifact, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(artifact, normalize_line_endings=normalize_line_endings)
elif os.path.isdir(artifact):
for (root, dirs, files) in os.walk(artifact, followlinks=follow_symlink_dirs):
dirpaths = []
for dirname in dirs:
norm_dirpath = os.path.normpath(os.path.join(root, dirname))
dirpaths.append(norm_dirpath)
if exclude_patterns:
dirpaths = _apply_exclude_patterns(dirpaths, exclude_filter)
dirs[:] = []
for dirpath in dirpaths:
name = os.path.basename(dirpath)
dirs.append(name)
filepaths = []
for filename in files:
norm_filepath = os.path.normpath(os.path.join(root, filename))
if os.path.isfile(norm_filepath):
filepaths.append(norm_filepath)
else:
LOG.info("File '{}' appears to be a broken symlink. Skipping...".format(norm_filepath))
if exclude_patterns:
filepaths = _apply_exclude_patterns(filepaths, exclude_filter)
for filepath in filepaths:
normalized_filepath = filepath.replace('\\', '/')
key = _apply_left_strip(normalized_filepath, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(filepath, normalize_line_endings=normalize_line_endings)
else:
LOG.info('path: {} does not exist, skipping..'.format(artifact))
if base_path:
os.chdir(original_cwd)
return artifacts_dict
|
def record_artifacts_as_dict(artifacts, exclude_patterns=None, base_path=None, follow_symlink_dirs=False, normalize_line_endings=False, lstrip_paths=None):
'\n <Purpose>\n Hashes each file in the passed path list. If the path list contains\n paths to directories the directory tree(s) are traversed.\n\n The files a link command is executed on are called materials.\n The files that result form a link command execution are called\n products.\n\n Paths are normalized for matching and storing by left stripping "./"\n\n NOTE on exclude patterns:\n - Uses PathSpec to compile gitignore-style patterns, making use of the\n GitWildMatchPattern class (registered as \'gitwildmatch\')\n\n - Patterns are checked for match against the full path relative to each\n path passed in the artifacts list\n\n - If a directory is excluded, all its files and subdirectories are also\n excluded\n\n - How it differs from .gitignore\n - No need to escape #\n - No ignoring of trailing spaces\n - No general negation with exclamation mark !\n - No special treatment of slash /\n - No special treatment of consecutive asterisks **\n\n - Exclude patterns are likely to become command line arguments or part of\n a config file.\n\n <Arguments>\n artifacts:\n A list of file or directory paths used as materials or products for\n the link command.\n\n exclude_patterns: (optional)\n Artifacts matched by the pattern are excluded from the result.\n Exclude patterns can be passed as argument or specified via\n ARTIFACT_EXCLUDE_PATTERNS setting (see `in_toto.settings`) or\n via envvars or rcfiles (see `in_toto.user_settings`).\n If passed, patterns specified via settings are overriden.\n\n base_path: (optional)\n Change to base_path and record artifacts relative from there.\n If not passed, current working directory is used as base_path.\n NOTE: The base_path part of the recorded artifact is not included\n in the returned paths.\n\n follow_symlink_dirs: (optional)\n Follow symlinked dirs if the linked dir exists (default is False).\n The recorded path contains the symlink name, not the resolved name.\n NOTE: This parameter toggles following linked directories only,\n linked files are always recorded, independently of this parameter.\n NOTE: Beware of infinite recursions that can occur if a symlink\n points to a parent directory or itself.\n\n normalize_line_endings: (optional)\n If True, replaces windows and mac line endings with unix line\n endings before hashing the content of the passed files, for\n cross-platform support.\n\n lstrip_paths: (optional)\n If a prefix path is passed, the prefix is left stripped from\n the path of every artifact that contains the prefix.\n\n <Exceptions>\n in_toto.exceptions.ValueError,\n if we cannot change to base path directory\n\n in_toto.exceptions.FormatError,\n if the list of exlcude patterns does not match format\n securesystemslib.formats.NAMES_SCHEMA\n\n <Side Effects>\n Calls functions to generate cryptographic hashes.\n\n <Returns>\n A dictionary with file paths as keys and the files\' hashes as values.\n '
artifacts_dict = {}
if (not artifacts):
return artifacts_dict
if base_path:
LOG.info('Overriding setting ARTIFACT_BASE_PATH with passed base path.')
else:
base_path = in_toto.settings.ARTIFACT_BASE_PATH
if base_path:
original_cwd = os.getcwd()
try:
os.chdir(base_path)
except Exception as e:
raise ValueError("Could not use '{}' as base path: '{}'".format(base_path, e)) from e
norm_artifacts = []
for path in artifacts:
norm_artifacts.append(os.path.normpath(path))
if exclude_patterns:
LOG.info('Overriding setting ARTIFACT_EXCLUDE_PATTERNS with passed exclude patterns.')
else:
exclude_patterns = in_toto.settings.ARTIFACT_EXCLUDE_PATTERNS
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
norm_artifacts = _apply_exclude_patterns(norm_artifacts, exclude_patterns)
if lstrip_paths:
for (prefix_one, prefix_two) in itertools.combinations(lstrip_paths, 2):
if (prefix_one.startswith(prefix_two) or prefix_two.startswith(prefix_one)):
raise in_toto.exceptions.PrefixError("'{}' and '{}' triggered a left substring error".format(prefix_one, prefix_two))
exclude_filter = PathSpec.from_lines('gitwildmatch', (exclude_patterns or []))
for artifact in norm_artifacts:
if os.path.isfile(artifact):
artifact = artifact.replace('\\', '/')
key = _apply_left_strip(artifact, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(artifact, normalize_line_endings=normalize_line_endings)
elif os.path.isdir(artifact):
for (root, dirs, files) in os.walk(artifact, followlinks=follow_symlink_dirs):
dirpaths = []
for dirname in dirs:
norm_dirpath = os.path.normpath(os.path.join(root, dirname))
dirpaths.append(norm_dirpath)
if exclude_patterns:
dirpaths = _apply_exclude_patterns(dirpaths, exclude_filter)
dirs[:] = []
for dirpath in dirpaths:
name = os.path.basename(dirpath)
dirs.append(name)
filepaths = []
for filename in files:
norm_filepath = os.path.normpath(os.path.join(root, filename))
if os.path.isfile(norm_filepath):
filepaths.append(norm_filepath)
else:
LOG.info("File '{}' appears to be a broken symlink. Skipping...".format(norm_filepath))
if exclude_patterns:
filepaths = _apply_exclude_patterns(filepaths, exclude_filter)
for filepath in filepaths:
normalized_filepath = filepath.replace('\\', '/')
key = _apply_left_strip(normalized_filepath, artifacts_dict, lstrip_paths)
artifacts_dict[key] = _hash_artifact(filepath, normalize_line_endings=normalize_line_endings)
else:
LOG.info('path: {} does not exist, skipping..'.format(artifact))
if base_path:
os.chdir(original_cwd)
return artifacts_dict<|docstring|><Purpose>
Hashes each file in the passed path list. If the path list contains
paths to directories the directory tree(s) are traversed.
The files a link command is executed on are called materials.
The files that result form a link command execution are called
products.
Paths are normalized for matching and storing by left stripping "./"
NOTE on exclude patterns:
- Uses PathSpec to compile gitignore-style patterns, making use of the
GitWildMatchPattern class (registered as 'gitwildmatch')
- Patterns are checked for match against the full path relative to each
path passed in the artifacts list
- If a directory is excluded, all its files and subdirectories are also
excluded
- How it differs from .gitignore
- No need to escape #
- No ignoring of trailing spaces
- No general negation with exclamation mark !
- No special treatment of slash /
- No special treatment of consecutive asterisks **
- Exclude patterns are likely to become command line arguments or part of
a config file.
<Arguments>
artifacts:
A list of file or directory paths used as materials or products for
the link command.
exclude_patterns: (optional)
Artifacts matched by the pattern are excluded from the result.
Exclude patterns can be passed as argument or specified via
ARTIFACT_EXCLUDE_PATTERNS setting (see `in_toto.settings`) or
via envvars or rcfiles (see `in_toto.user_settings`).
If passed, patterns specified via settings are overriden.
base_path: (optional)
Change to base_path and record artifacts relative from there.
If not passed, current working directory is used as base_path.
NOTE: The base_path part of the recorded artifact is not included
in the returned paths.
follow_symlink_dirs: (optional)
Follow symlinked dirs if the linked dir exists (default is False).
The recorded path contains the symlink name, not the resolved name.
NOTE: This parameter toggles following linked directories only,
linked files are always recorded, independently of this parameter.
NOTE: Beware of infinite recursions that can occur if a symlink
points to a parent directory or itself.
normalize_line_endings: (optional)
If True, replaces windows and mac line endings with unix line
endings before hashing the content of the passed files, for
cross-platform support.
lstrip_paths: (optional)
If a prefix path is passed, the prefix is left stripped from
the path of every artifact that contains the prefix.
<Exceptions>
in_toto.exceptions.ValueError,
if we cannot change to base path directory
in_toto.exceptions.FormatError,
if the list of exlcude patterns does not match format
securesystemslib.formats.NAMES_SCHEMA
<Side Effects>
Calls functions to generate cryptographic hashes.
<Returns>
A dictionary with file paths as keys and the files' hashes as values.<|endoftext|>
|
6a24753a4968c6f2d3e9320521acf6e3ae2f1bfc44a3b91492a5a93123b62e0a
|
def execute_link(link_cmd_args, record_streams):
'\n <Purpose>\n Executes the passed command plus arguments in a subprocess and returns\n the return value of the executed command. If the specified standard output\n and standard error of the command are recorded and also returned to the\n caller.\n\n <Arguments>\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n record_streams:\n A bool that specifies whether to redirect standard output and\n and standard error to a temporary file which is returned to the\n caller (True) or not (False).\n\n <Exceptions>\n OSError:\n The given command is not present or non-executable\n\n securesystemslib.process.subprocess.TimeoutExpired:\n The execution of the given command times out. The default timeout\n is securesystemslib.settings.SUBPROCESS_TIMEOUT.\n\n\n <Side Effects>\n Executes passed command in a subprocess and redirects stdout and stderr\n if specified.\n\n <Returns>\n - A dictionary containing standard output and standard error of the\n executed command, called by-products.\n Note: If record_streams is False, the dict values are empty strings.\n - The return value of the executed command.\n '
if record_streams:
(return_code, stdout_str, stderr_str) = securesystemslib.process.run_duplicate_streams(link_cmd_args, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT))
else:
process = securesystemslib.process.run(link_cmd_args, check=False, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT), stdout=securesystemslib.process.DEVNULL, stderr=securesystemslib.process.DEVNULL)
stdout_str = stderr_str = ''
return_code = process.returncode
return {'stdout': stdout_str, 'stderr': stderr_str, 'return-value': return_code}
|
<Purpose>
Executes the passed command plus arguments in a subprocess and returns
the return value of the executed command. If the specified standard output
and standard error of the command are recorded and also returned to the
caller.
<Arguments>
link_cmd_args:
A list where the first element is a command and the remaining
elements are arguments passed to that command.
record_streams:
A bool that specifies whether to redirect standard output and
and standard error to a temporary file which is returned to the
caller (True) or not (False).
<Exceptions>
OSError:
The given command is not present or non-executable
securesystemslib.process.subprocess.TimeoutExpired:
The execution of the given command times out. The default timeout
is securesystemslib.settings.SUBPROCESS_TIMEOUT.
<Side Effects>
Executes passed command in a subprocess and redirects stdout and stderr
if specified.
<Returns>
- A dictionary containing standard output and standard error of the
executed command, called by-products.
Note: If record_streams is False, the dict values are empty strings.
- The return value of the executed command.
|
in_toto/runlib.py
|
execute_link
|
reeeeeeem/in-toto
| 507
|
python
|
def execute_link(link_cmd_args, record_streams):
'\n <Purpose>\n Executes the passed command plus arguments in a subprocess and returns\n the return value of the executed command. If the specified standard output\n and standard error of the command are recorded and also returned to the\n caller.\n\n <Arguments>\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n record_streams:\n A bool that specifies whether to redirect standard output and\n and standard error to a temporary file which is returned to the\n caller (True) or not (False).\n\n <Exceptions>\n OSError:\n The given command is not present or non-executable\n\n securesystemslib.process.subprocess.TimeoutExpired:\n The execution of the given command times out. The default timeout\n is securesystemslib.settings.SUBPROCESS_TIMEOUT.\n\n\n <Side Effects>\n Executes passed command in a subprocess and redirects stdout and stderr\n if specified.\n\n <Returns>\n - A dictionary containing standard output and standard error of the\n executed command, called by-products.\n Note: If record_streams is False, the dict values are empty strings.\n - The return value of the executed command.\n '
if record_streams:
(return_code, stdout_str, stderr_str) = securesystemslib.process.run_duplicate_streams(link_cmd_args, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT))
else:
process = securesystemslib.process.run(link_cmd_args, check=False, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT), stdout=securesystemslib.process.DEVNULL, stderr=securesystemslib.process.DEVNULL)
stdout_str = stderr_str =
return_code = process.returncode
return {'stdout': stdout_str, 'stderr': stderr_str, 'return-value': return_code}
|
def execute_link(link_cmd_args, record_streams):
'\n <Purpose>\n Executes the passed command plus arguments in a subprocess and returns\n the return value of the executed command. If the specified standard output\n and standard error of the command are recorded and also returned to the\n caller.\n\n <Arguments>\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n record_streams:\n A bool that specifies whether to redirect standard output and\n and standard error to a temporary file which is returned to the\n caller (True) or not (False).\n\n <Exceptions>\n OSError:\n The given command is not present or non-executable\n\n securesystemslib.process.subprocess.TimeoutExpired:\n The execution of the given command times out. The default timeout\n is securesystemslib.settings.SUBPROCESS_TIMEOUT.\n\n\n <Side Effects>\n Executes passed command in a subprocess and redirects stdout and stderr\n if specified.\n\n <Returns>\n - A dictionary containing standard output and standard error of the\n executed command, called by-products.\n Note: If record_streams is False, the dict values are empty strings.\n - The return value of the executed command.\n '
if record_streams:
(return_code, stdout_str, stderr_str) = securesystemslib.process.run_duplicate_streams(link_cmd_args, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT))
else:
process = securesystemslib.process.run(link_cmd_args, check=False, timeout=float(in_toto.settings.LINK_CMD_EXEC_TIMEOUT), stdout=securesystemslib.process.DEVNULL, stderr=securesystemslib.process.DEVNULL)
stdout_str = stderr_str =
return_code = process.returncode
return {'stdout': stdout_str, 'stderr': stderr_str, 'return-value': return_code}<|docstring|><Purpose>
Executes the passed command plus arguments in a subprocess and returns
the return value of the executed command. If the specified standard output
and standard error of the command are recorded and also returned to the
caller.
<Arguments>
link_cmd_args:
A list where the first element is a command and the remaining
elements are arguments passed to that command.
record_streams:
A bool that specifies whether to redirect standard output and
and standard error to a temporary file which is returned to the
caller (True) or not (False).
<Exceptions>
OSError:
The given command is not present or non-executable
securesystemslib.process.subprocess.TimeoutExpired:
The execution of the given command times out. The default timeout
is securesystemslib.settings.SUBPROCESS_TIMEOUT.
<Side Effects>
Executes passed command in a subprocess and redirects stdout and stderr
if specified.
<Returns>
- A dictionary containing standard output and standard error of the
executed command, called by-products.
Note: If record_streams is False, the dict values are empty strings.
- The return value of the executed command.<|endoftext|>
|
2576852086da7ef4b2499ccb2a81c2d3e08f51f85f550486de19ba3b84f40c3c
|
def in_toto_mock(name, link_cmd_args):
'\n <Purpose>\n in_toto_run with defaults\n - Records materials and products in current directory\n - Does not sign resulting link file\n - Stores resulting link file under "<name>.link"\n\n <Arguments>\n name:\n A unique name to relate mock link metadata with a step or\n inspection defined in the layout.\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n\n <Exceptions>\n None.\n\n <Side Effects>\n Writes newly created link metadata file to disk using the filename scheme\n from link.FILENAME_FORMAT_SHORT\n\n <Returns>\n Newly created Metablock object containing a Link object\n\n '
link_metadata = in_toto_run(name, ['.'], ['.'], link_cmd_args, record_streams=True)
filename = FILENAME_FORMAT_SHORT.format(step_name=name)
LOG.info("Storing unsigned link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata
|
<Purpose>
in_toto_run with defaults
- Records materials and products in current directory
- Does not sign resulting link file
- Stores resulting link file under "<name>.link"
<Arguments>
name:
A unique name to relate mock link metadata with a step or
inspection defined in the layout.
link_cmd_args:
A list where the first element is a command and the remaining
elements are arguments passed to that command.
<Exceptions>
None.
<Side Effects>
Writes newly created link metadata file to disk using the filename scheme
from link.FILENAME_FORMAT_SHORT
<Returns>
Newly created Metablock object containing a Link object
|
in_toto/runlib.py
|
in_toto_mock
|
reeeeeeem/in-toto
| 507
|
python
|
def in_toto_mock(name, link_cmd_args):
'\n <Purpose>\n in_toto_run with defaults\n - Records materials and products in current directory\n - Does not sign resulting link file\n - Stores resulting link file under "<name>.link"\n\n <Arguments>\n name:\n A unique name to relate mock link metadata with a step or\n inspection defined in the layout.\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n\n <Exceptions>\n None.\n\n <Side Effects>\n Writes newly created link metadata file to disk using the filename scheme\n from link.FILENAME_FORMAT_SHORT\n\n <Returns>\n Newly created Metablock object containing a Link object\n\n '
link_metadata = in_toto_run(name, ['.'], ['.'], link_cmd_args, record_streams=True)
filename = FILENAME_FORMAT_SHORT.format(step_name=name)
LOG.info("Storing unsigned link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata
|
def in_toto_mock(name, link_cmd_args):
'\n <Purpose>\n in_toto_run with defaults\n - Records materials and products in current directory\n - Does not sign resulting link file\n - Stores resulting link file under "<name>.link"\n\n <Arguments>\n name:\n A unique name to relate mock link metadata with a step or\n inspection defined in the layout.\n link_cmd_args:\n A list where the first element is a command and the remaining\n elements are arguments passed to that command.\n\n <Exceptions>\n None.\n\n <Side Effects>\n Writes newly created link metadata file to disk using the filename scheme\n from link.FILENAME_FORMAT_SHORT\n\n <Returns>\n Newly created Metablock object containing a Link object\n\n '
link_metadata = in_toto_run(name, ['.'], ['.'], link_cmd_args, record_streams=True)
filename = FILENAME_FORMAT_SHORT.format(step_name=name)
LOG.info("Storing unsigned link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata<|docstring|><Purpose>
in_toto_run with defaults
- Records materials and products in current directory
- Does not sign resulting link file
- Stores resulting link file under "<name>.link"
<Arguments>
name:
A unique name to relate mock link metadata with a step or
inspection defined in the layout.
link_cmd_args:
A list where the first element is a command and the remaining
elements are arguments passed to that command.
<Exceptions>
None.
<Side Effects>
Writes newly created link metadata file to disk using the filename scheme
from link.FILENAME_FORMAT_SHORT
<Returns>
Newly created Metablock object containing a Link object<|endoftext|>
|
c962dd0dc7c09f84701e32d9a2379ba6a11180b901b2806cab6ffa51c89f60da
|
def _check_match_signing_key(signing_key):
" Helper method to check if the signing_key has securesystemslib's\n KEY_SCHEMA and the private part is not empty.\n # FIXME: Add private key format check to formats\n "
securesystemslib.formats.KEY_SCHEMA.check_match(signing_key)
if (not signing_key['keyval'].get('private')):
raise securesystemslib.exceptions.FormatError('Signing key needs to be a private key.')
|
Helper method to check if the signing_key has securesystemslib's
KEY_SCHEMA and the private part is not empty.
# FIXME: Add private key format check to formats
|
in_toto/runlib.py
|
_check_match_signing_key
|
reeeeeeem/in-toto
| 507
|
python
|
def _check_match_signing_key(signing_key):
" Helper method to check if the signing_key has securesystemslib's\n KEY_SCHEMA and the private part is not empty.\n # FIXME: Add private key format check to formats\n "
securesystemslib.formats.KEY_SCHEMA.check_match(signing_key)
if (not signing_key['keyval'].get('private')):
raise securesystemslib.exceptions.FormatError('Signing key needs to be a private key.')
|
def _check_match_signing_key(signing_key):
" Helper method to check if the signing_key has securesystemslib's\n KEY_SCHEMA and the private part is not empty.\n # FIXME: Add private key format check to formats\n "
securesystemslib.formats.KEY_SCHEMA.check_match(signing_key)
if (not signing_key['keyval'].get('private')):
raise securesystemslib.exceptions.FormatError('Signing key needs to be a private key.')<|docstring|>Helper method to check if the signing_key has securesystemslib's
KEY_SCHEMA and the private part is not empty.
# FIXME: Add private key format check to formats<|endoftext|>
|
88c8f7f5c1deb5a17e9cbc57f76f2ddff584702b8823a35509d28bd2d0accc1b
|
def in_toto_run(name, material_list, product_list, link_cmd_args, record_streams=False, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, compact_json=False, record_environment=False, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Performs a supply chain step or inspection generating link metadata.\n\n Executes link_cmd_args, recording paths and hashes of files before and after\n command execution (aka. artifacts) in a link metadata file. The metadata is\n signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. The resulting link file is written to\n ``STEP-NAME.KEYID-PREFIX.link``. If no key argument is passed the link\n metadata is neither signed nor written to disk.\n\n Arguments:\n name: A unique name to associate link metadata with a step or inspection.\n\n material_list: A list of artifact paths to be recorded before command\n execution. Directories are traversed recursively.\n\n product_list: A list of artifact paths to be recorded after command\n execution. Directories are traversed recursively.\n\n link_cmd_args: A list where the first element is a command and the\n remaining elements are arguments passed to that command.\n\n record_streams (optional): A boolean indicating if standard output and\n standard error of the link command should be recorded in the link\n metadata in addition to being displayed while the command is executed.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n compact_json (optional): A boolean indicating if the resulting link\n metadata should be written in the most compact JSON representation.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: Cannot change to base path directory.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Runs link command in subprocess.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes link metadata file to disk, if any key argument is passed.\n\n Returns:\n A Metablock object that contains the resulting link object.\n\n '
LOG.info("Running '{}'...".format(name))
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
if link_cmd_args:
LOG.info("Running command '{}'...".format(' '.join(link_cmd_args)))
byproducts = execute_link(link_cmd_args, record_streams)
else:
byproducts = {}
if product_list:
securesystemslib.formats.PATHS_SCHEMA.check_match(product_list)
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
products_dict = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=name, materials=materials_dict, products=products_dict, command=link_cmd_args, byproducts=byproducts, environment=environment)
link_metadata = Metablock(signed=link, compact_json=compact_json)
signature = None
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
elif gpg_use_default:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
if signature:
signing_keyid = signature['keyid']
filename = FILENAME_FORMAT.format(step_name=name, keyid=signing_keyid)
if (metadata_directory is not None):
filename = os.path.join(metadata_directory, filename)
LOG.info("Storing link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata
|
Performs a supply chain step or inspection generating link metadata.
Executes link_cmd_args, recording paths and hashes of files before and after
command execution (aka. artifacts) in a link metadata file. The metadata is
signed with the passed signing_key, a gpg key identified by its ID, or the
default gpg key. If multiple key arguments are passed, only one key is used
in above order of precedence. The resulting link file is written to
``STEP-NAME.KEYID-PREFIX.link``. If no key argument is passed the link
metadata is neither signed nor written to disk.
Arguments:
name: A unique name to associate link metadata with a step or inspection.
material_list: A list of artifact paths to be recorded before command
execution. Directories are traversed recursively.
product_list: A list of artifact paths to be recorded after command
execution. Directories are traversed recursively.
link_cmd_args: A list where the first element is a command and the
remaining elements are arguments passed to that command.
record_streams (optional): A boolean indicating if standard output and
standard error of the link command should be recorded in the link
metadata in addition to being displayed while the command is executed.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts. See Config docs for details.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
compact_json (optional): A boolean indicating if the resulting link
metadata should be written in the most compact JSON representation.
record_environment (optional): A boolean indicating if information about
the environment should be added in the resulting link metadata.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
metadata_directory (optional): A directory path to write the resulting link
metadata file to. Default destination is the current working directory.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: Cannot change to base path directory.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, FileNotFoundError, NotADirectoryError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads artifact files from disk.
Runs link command in subprocess.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes link metadata file to disk, if any key argument is passed.
Returns:
A Metablock object that contains the resulting link object.
|
in_toto/runlib.py
|
in_toto_run
|
reeeeeeem/in-toto
| 507
|
python
|
def in_toto_run(name, material_list, product_list, link_cmd_args, record_streams=False, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, compact_json=False, record_environment=False, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Performs a supply chain step or inspection generating link metadata.\n\n Executes link_cmd_args, recording paths and hashes of files before and after\n command execution (aka. artifacts) in a link metadata file. The metadata is\n signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. The resulting link file is written to\n ``STEP-NAME.KEYID-PREFIX.link``. If no key argument is passed the link\n metadata is neither signed nor written to disk.\n\n Arguments:\n name: A unique name to associate link metadata with a step or inspection.\n\n material_list: A list of artifact paths to be recorded before command\n execution. Directories are traversed recursively.\n\n product_list: A list of artifact paths to be recorded after command\n execution. Directories are traversed recursively.\n\n link_cmd_args: A list where the first element is a command and the\n remaining elements are arguments passed to that command.\n\n record_streams (optional): A boolean indicating if standard output and\n standard error of the link command should be recorded in the link\n metadata in addition to being displayed while the command is executed.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n compact_json (optional): A boolean indicating if the resulting link\n metadata should be written in the most compact JSON representation.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: Cannot change to base path directory.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Runs link command in subprocess.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes link metadata file to disk, if any key argument is passed.\n\n Returns:\n A Metablock object that contains the resulting link object.\n\n '
LOG.info("Running '{}'...".format(name))
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
if link_cmd_args:
LOG.info("Running command '{}'...".format(' '.join(link_cmd_args)))
byproducts = execute_link(link_cmd_args, record_streams)
else:
byproducts = {}
if product_list:
securesystemslib.formats.PATHS_SCHEMA.check_match(product_list)
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
products_dict = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=name, materials=materials_dict, products=products_dict, command=link_cmd_args, byproducts=byproducts, environment=environment)
link_metadata = Metablock(signed=link, compact_json=compact_json)
signature = None
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
elif gpg_use_default:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
if signature:
signing_keyid = signature['keyid']
filename = FILENAME_FORMAT.format(step_name=name, keyid=signing_keyid)
if (metadata_directory is not None):
filename = os.path.join(metadata_directory, filename)
LOG.info("Storing link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata
|
def in_toto_run(name, material_list, product_list, link_cmd_args, record_streams=False, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, compact_json=False, record_environment=False, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Performs a supply chain step or inspection generating link metadata.\n\n Executes link_cmd_args, recording paths and hashes of files before and after\n command execution (aka. artifacts) in a link metadata file. The metadata is\n signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. The resulting link file is written to\n ``STEP-NAME.KEYID-PREFIX.link``. If no key argument is passed the link\n metadata is neither signed nor written to disk.\n\n Arguments:\n name: A unique name to associate link metadata with a step or inspection.\n\n material_list: A list of artifact paths to be recorded before command\n execution. Directories are traversed recursively.\n\n product_list: A list of artifact paths to be recorded after command\n execution. Directories are traversed recursively.\n\n link_cmd_args: A list where the first element is a command and the\n remaining elements are arguments passed to that command.\n\n record_streams (optional): A boolean indicating if standard output and\n standard error of the link command should be recorded in the link\n metadata in addition to being displayed while the command is executed.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n compact_json (optional): A boolean indicating if the resulting link\n metadata should be written in the most compact JSON representation.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: Cannot change to base path directory.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Runs link command in subprocess.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes link metadata file to disk, if any key argument is passed.\n\n Returns:\n A Metablock object that contains the resulting link object.\n\n '
LOG.info("Running '{}'...".format(name))
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
if link_cmd_args:
LOG.info("Running command '{}'...".format(' '.join(link_cmd_args)))
byproducts = execute_link(link_cmd_args, record_streams)
else:
byproducts = {}
if product_list:
securesystemslib.formats.PATHS_SCHEMA.check_match(product_list)
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
products_dict = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=name, materials=materials_dict, products=products_dict, command=link_cmd_args, byproducts=byproducts, environment=environment)
link_metadata = Metablock(signed=link, compact_json=compact_json)
signature = None
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
elif gpg_use_default:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
if signature:
signing_keyid = signature['keyid']
filename = FILENAME_FORMAT.format(step_name=name, keyid=signing_keyid)
if (metadata_directory is not None):
filename = os.path.join(metadata_directory, filename)
LOG.info("Storing link metadata to '{}'...".format(filename))
link_metadata.dump(filename)
return link_metadata<|docstring|>Performs a supply chain step or inspection generating link metadata.
Executes link_cmd_args, recording paths and hashes of files before and after
command execution (aka. artifacts) in a link metadata file. The metadata is
signed with the passed signing_key, a gpg key identified by its ID, or the
default gpg key. If multiple key arguments are passed, only one key is used
in above order of precedence. The resulting link file is written to
``STEP-NAME.KEYID-PREFIX.link``. If no key argument is passed the link
metadata is neither signed nor written to disk.
Arguments:
name: A unique name to associate link metadata with a step or inspection.
material_list: A list of artifact paths to be recorded before command
execution. Directories are traversed recursively.
product_list: A list of artifact paths to be recorded after command
execution. Directories are traversed recursively.
link_cmd_args: A list where the first element is a command and the
remaining elements are arguments passed to that command.
record_streams (optional): A boolean indicating if standard output and
standard error of the link command should be recorded in the link
metadata in addition to being displayed while the command is executed.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts. See Config docs for details.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
compact_json (optional): A boolean indicating if the resulting link
metadata should be written in the most compact JSON representation.
record_environment (optional): A boolean indicating if information about
the environment should be added in the resulting link metadata.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
metadata_directory (optional): A directory path to write the resulting link
metadata file to. Default destination is the current working directory.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: Cannot change to base path directory.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, FileNotFoundError, NotADirectoryError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads artifact files from disk.
Runs link command in subprocess.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes link metadata file to disk, if any key argument is passed.
Returns:
A Metablock object that contains the resulting link object.<|endoftext|>
|
4d69a919706bf2d4e580ecff31a9442772f8c7685bdc2efaf041566034627979
|
def in_toto_record_start(step_name, material_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, record_environment=False, normalize_line_endings=False, lstrip_paths=None):
'Generates preliminary link metadata.\n\n Records paths and hashes of materials in a preliminary link metadata file.\n The metadata is signed with the passed signing_key, a gpg key identified by\n its ID, or the default gpg key. If multiple key arguments are passed, only\n one key is used in above order of precedence. At least one key argument must\n be passed. The resulting link file is written to\n ``.STEP-NAME.KEYID-PREFIX.link-unfinished``.\n\n Use this function together with in_toto_record_stop as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n material_list: A list of artifact paths to be recorded as materials.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes preliminary link metadata file to disk.\n\n '
LOG.info("Start recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True!')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating preliminary link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=step_name, materials=materials_dict, products={}, command=[], byproducts={}, environment=environment)
link_metadata = Metablock(signed=link)
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
else:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
signing_keyid = signature['keyid']
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_keyid)
LOG.info("Storing preliminary link metadata to '{}'...".format(unfinished_fn))
link_metadata.dump(unfinished_fn)
|
Generates preliminary link metadata.
Records paths and hashes of materials in a preliminary link metadata file.
The metadata is signed with the passed signing_key, a gpg key identified by
its ID, or the default gpg key. If multiple key arguments are passed, only
one key is used in above order of precedence. At least one key argument must
be passed. The resulting link file is written to
``.STEP-NAME.KEYID-PREFIX.link-unfinished``.
Use this function together with in_toto_record_stop as an alternative to
in_toto_run, in order to provide evidence for supply chain steps that cannot
be carried out by a single command.
Arguments:
step_name: A unique name to associate link metadata with a step.
material_list: A list of artifact paths to be recorded as materials.
Directories are traversed recursively.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts. See Config docs for details.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
record_environment (optional): A boolean indicating if information about
the environment should be added in the resulting link metadata.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is
passed.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads artifact files from disk.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes preliminary link metadata file to disk.
|
in_toto/runlib.py
|
in_toto_record_start
|
reeeeeeem/in-toto
| 507
|
python
|
def in_toto_record_start(step_name, material_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, record_environment=False, normalize_line_endings=False, lstrip_paths=None):
'Generates preliminary link metadata.\n\n Records paths and hashes of materials in a preliminary link metadata file.\n The metadata is signed with the passed signing_key, a gpg key identified by\n its ID, or the default gpg key. If multiple key arguments are passed, only\n one key is used in above order of precedence. At least one key argument must\n be passed. The resulting link file is written to\n ``.STEP-NAME.KEYID-PREFIX.link-unfinished``.\n\n Use this function together with in_toto_record_stop as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n material_list: A list of artifact paths to be recorded as materials.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes preliminary link metadata file to disk.\n\n '
LOG.info("Start recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True!')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating preliminary link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=step_name, materials=materials_dict, products={}, command=[], byproducts={}, environment=environment)
link_metadata = Metablock(signed=link)
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
else:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
signing_keyid = signature['keyid']
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_keyid)
LOG.info("Storing preliminary link metadata to '{}'...".format(unfinished_fn))
link_metadata.dump(unfinished_fn)
|
def in_toto_record_start(step_name, material_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, record_environment=False, normalize_line_endings=False, lstrip_paths=None):
'Generates preliminary link metadata.\n\n Records paths and hashes of materials in a preliminary link metadata file.\n The metadata is signed with the passed signing_key, a gpg key identified by\n its ID, or the default gpg key. If multiple key arguments are passed, only\n one key is used in above order of precedence. At least one key argument must\n be passed. The resulting link file is written to\n ``.STEP-NAME.KEYID-PREFIX.link-unfinished``.\n\n Use this function together with in_toto_record_stop as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n material_list: A list of artifact paths to be recorded as materials.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts. See Config docs for details.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n record_environment (optional): A boolean indicating if information about\n the environment should be added in the resulting link metadata.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes preliminary link metadata file to disk.\n\n '
LOG.info("Start recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True!')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if material_list:
LOG.info("Recording materials '{}'...".format(', '.join(material_list)))
materials_dict = record_artifacts_as_dict(material_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
LOG.info('Creating preliminary link metadata...')
environment = {}
if record_environment:
environment['workdir'] = os.getcwd().replace('\\', '/')
link = in_toto.models.link.Link(name=step_name, materials=materials_dict, products={}, command=[], byproducts={}, environment=environment)
link_metadata = Metablock(signed=link)
if signing_key:
LOG.info('Signing link metadata using passed key...')
signature = link_metadata.sign(signing_key)
elif gpg_keyid:
LOG.info('Signing link metadata using passed GPG keyid...')
signature = link_metadata.sign_gpg(gpg_keyid, gpg_home=gpg_home)
else:
LOG.info('Signing link metadata using default GPG key ...')
signature = link_metadata.sign_gpg(gpg_keyid=None, gpg_home=gpg_home)
signing_keyid = signature['keyid']
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_keyid)
LOG.info("Storing preliminary link metadata to '{}'...".format(unfinished_fn))
link_metadata.dump(unfinished_fn)<|docstring|>Generates preliminary link metadata.
Records paths and hashes of materials in a preliminary link metadata file.
The metadata is signed with the passed signing_key, a gpg key identified by
its ID, or the default gpg key. If multiple key arguments are passed, only
one key is used in above order of precedence. At least one key argument must
be passed. The resulting link file is written to
``.STEP-NAME.KEYID-PREFIX.link-unfinished``.
Use this function together with in_toto_record_stop as an alternative to
in_toto_run, in order to provide evidence for supply chain steps that cannot
be carried out by a single command.
Arguments:
step_name: A unique name to associate link metadata with a step.
material_list: A list of artifact paths to be recorded as materials.
Directories are traversed recursively.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts. See Config docs for details.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
record_environment (optional): A boolean indicating if information about
the environment should be added in the resulting link metadata.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is
passed.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads artifact files from disk.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes preliminary link metadata file to disk.<|endoftext|>
|
716dd3a0f54efe8ea396da9780f67bb3ea3489e282c7733a51328cecd23461f0
|
def in_toto_record_stop(step_name, product_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Finalizes preliminary link metadata generated with in_toto_record_start.\n\n Loads preliminary link metadata file, verifies its signature, and records\n paths and hashes as products, thus finalizing the link metadata. The metadata\n is signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. At least one key argument must be passed and it\n must be the same as the one used to sign the preliminary link metadata file.\n The resulting link file is written to ``STEP-NAME.KEYID-PREFIX.link``.\n\n Use this function together with in_toto_record_start as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n product_list: A list of artifact paths to be recorded as products.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n LinkNotFoundError: No preliminary link metadata file found.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads preliminary link metadata file from disk.\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes resulting link metadata file to disk.\n Removes preliminary link metadata file from disk.\n\n '
LOG.info("Stop recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if signing_key:
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_key['keyid'])
else:
unfinished_fn_glob = UNFINISHED_FILENAME_FORMAT_GLOB.format(step_name=step_name, pattern='*')
unfinished_fn_list = glob.glob(unfinished_fn_glob)
if (not len(unfinished_fn_list)):
raise in_toto.exceptions.LinkNotFoundError("Could not find a preliminary link for step '{}' in the current working directory.".format(step_name))
if (len(unfinished_fn_list) > 1):
raise in_toto.exceptions.LinkNotFoundError("Found more than one preliminary links for step '{}' in the current working directory: {}. We need exactly one to stop recording.".format(step_name, ', '.join(unfinished_fn_list)))
unfinished_fn = unfinished_fn_list[0]
LOG.info("Loading preliminary link metadata '{}'...".format(unfinished_fn))
link_metadata = Metablock.load(unfinished_fn)
if signing_key:
LOG.info('Verifying preliminary link signature using passed signing key...')
keyid = signing_key['keyid']
verification_key = signing_key
elif gpg_keyid:
LOG.info('Verifying preliminary link signature using passed gpg key...')
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(gpg_keyid, gpg_home)
keyid = gpg_pubkey['keyid']
verification_key = gpg_pubkey
else:
LOG.info('Verifying preliminary link signature using default gpg key...')
keyid = link_metadata.signatures[0]['keyid']
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(keyid, gpg_home)
verification_key = gpg_pubkey
link_metadata.verify_signature(verification_key)
if product_list:
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
link_metadata.signed.products = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
link_metadata.signatures = []
if signing_key:
LOG.info("Updating signature with key '{:.8}...'...".format(keyid))
link_metadata.sign(signing_key)
else:
LOG.info("Updating signature with gpg key '{:.8}...'...".format(keyid))
link_metadata.sign_gpg(keyid, gpg_home)
fn = FILENAME_FORMAT.format(step_name=step_name, keyid=keyid)
if (metadata_directory is not None):
fn = os.path.join(metadata_directory, fn)
LOG.info("Storing link metadata to '{}'...".format(fn))
link_metadata.dump(fn)
LOG.info("Removing unfinished link metadata '{}'...".format(unfinished_fn))
os.remove(unfinished_fn)
|
Finalizes preliminary link metadata generated with in_toto_record_start.
Loads preliminary link metadata file, verifies its signature, and records
paths and hashes as products, thus finalizing the link metadata. The metadata
is signed with the passed signing_key, a gpg key identified by its ID, or the
default gpg key. If multiple key arguments are passed, only one key is used
in above order of precedence. At least one key argument must be passed and it
must be the same as the one used to sign the preliminary link metadata file.
The resulting link file is written to ``STEP-NAME.KEYID-PREFIX.link``.
Use this function together with in_toto_record_start as an alternative to
in_toto_run, in order to provide evidence for supply chain steps that cannot
be carried out by a single command.
Arguments:
step_name: A unique name to associate link metadata with a step.
product_list: A list of artifact paths to be recorded as products.
Directories are traversed recursively.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
metadata_directory (optional): A directory path to write the resulting link
metadata file to. Default destination is the current working directory.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is
passed.
LinkNotFoundError: No preliminary link metadata file found.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, FileNotFoundError, NotADirectoryError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads preliminary link metadata file from disk.
Reads artifact files from disk.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes resulting link metadata file to disk.
Removes preliminary link metadata file from disk.
|
in_toto/runlib.py
|
in_toto_record_stop
|
reeeeeeem/in-toto
| 507
|
python
|
def in_toto_record_stop(step_name, product_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Finalizes preliminary link metadata generated with in_toto_record_start.\n\n Loads preliminary link metadata file, verifies its signature, and records\n paths and hashes as products, thus finalizing the link metadata. The metadata\n is signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. At least one key argument must be passed and it\n must be the same as the one used to sign the preliminary link metadata file.\n The resulting link file is written to ``STEP-NAME.KEYID-PREFIX.link``.\n\n Use this function together with in_toto_record_start as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n product_list: A list of artifact paths to be recorded as products.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n LinkNotFoundError: No preliminary link metadata file found.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads preliminary link metadata file from disk.\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes resulting link metadata file to disk.\n Removes preliminary link metadata file from disk.\n\n '
LOG.info("Stop recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if signing_key:
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_key['keyid'])
else:
unfinished_fn_glob = UNFINISHED_FILENAME_FORMAT_GLOB.format(step_name=step_name, pattern='*')
unfinished_fn_list = glob.glob(unfinished_fn_glob)
if (not len(unfinished_fn_list)):
raise in_toto.exceptions.LinkNotFoundError("Could not find a preliminary link for step '{}' in the current working directory.".format(step_name))
if (len(unfinished_fn_list) > 1):
raise in_toto.exceptions.LinkNotFoundError("Found more than one preliminary links for step '{}' in the current working directory: {}. We need exactly one to stop recording.".format(step_name, ', '.join(unfinished_fn_list)))
unfinished_fn = unfinished_fn_list[0]
LOG.info("Loading preliminary link metadata '{}'...".format(unfinished_fn))
link_metadata = Metablock.load(unfinished_fn)
if signing_key:
LOG.info('Verifying preliminary link signature using passed signing key...')
keyid = signing_key['keyid']
verification_key = signing_key
elif gpg_keyid:
LOG.info('Verifying preliminary link signature using passed gpg key...')
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(gpg_keyid, gpg_home)
keyid = gpg_pubkey['keyid']
verification_key = gpg_pubkey
else:
LOG.info('Verifying preliminary link signature using default gpg key...')
keyid = link_metadata.signatures[0]['keyid']
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(keyid, gpg_home)
verification_key = gpg_pubkey
link_metadata.verify_signature(verification_key)
if product_list:
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
link_metadata.signed.products = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
link_metadata.signatures = []
if signing_key:
LOG.info("Updating signature with key '{:.8}...'...".format(keyid))
link_metadata.sign(signing_key)
else:
LOG.info("Updating signature with gpg key '{:.8}...'...".format(keyid))
link_metadata.sign_gpg(keyid, gpg_home)
fn = FILENAME_FORMAT.format(step_name=step_name, keyid=keyid)
if (metadata_directory is not None):
fn = os.path.join(metadata_directory, fn)
LOG.info("Storing link metadata to '{}'...".format(fn))
link_metadata.dump(fn)
LOG.info("Removing unfinished link metadata '{}'...".format(unfinished_fn))
os.remove(unfinished_fn)
|
def in_toto_record_stop(step_name, product_list, signing_key=None, gpg_keyid=None, gpg_use_default=False, gpg_home=None, exclude_patterns=None, base_path=None, normalize_line_endings=False, lstrip_paths=None, metadata_directory=None):
'Finalizes preliminary link metadata generated with in_toto_record_start.\n\n Loads preliminary link metadata file, verifies its signature, and records\n paths and hashes as products, thus finalizing the link metadata. The metadata\n is signed with the passed signing_key, a gpg key identified by its ID, or the\n default gpg key. If multiple key arguments are passed, only one key is used\n in above order of precedence. At least one key argument must be passed and it\n must be the same as the one used to sign the preliminary link metadata file.\n The resulting link file is written to ``STEP-NAME.KEYID-PREFIX.link``.\n\n Use this function together with in_toto_record_start as an alternative to\n in_toto_run, in order to provide evidence for supply chain steps that cannot\n be carried out by a single command.\n\n Arguments:\n step_name: A unique name to associate link metadata with a step.\n\n product_list: A list of artifact paths to be recorded as products.\n Directories are traversed recursively.\n\n signing_key (optional): A key used to sign the resulting link metadata. The\n format is securesystemslib.formats.KEY_SCHEMA.\n\n gpg_keyid (optional): A keyid used to identify a local gpg key used to sign\n the resulting link metadata.\n\n gpg_use_default (optional): A boolean indicating if the default gpg key\n should be used to sign the resulting link metadata.\n\n gpg_home (optional): A path to the gpg home directory. If not set the\n default gpg home directory is used.\n\n exclude_patterns (optional): A list of filename patterns to exclude certain\n files from being recorded as artifacts.\n\n base_path (optional): A path relative to which artifacts are recorded.\n Default is the current working directory.\n\n normalize_line_endings (optional): A boolean indicating if line endings of\n artifacts should be normalized before hashing for cross-platform\n support.\n\n lstrip_paths (optional): A list of path prefixes used to left-strip\n artifact paths before storing them in the resulting link metadata.\n\n metadata_directory (optional): A directory path to write the resulting link\n metadata file to. Default destination is the current working directory.\n\n Raises:\n securesystemslib.exceptions.FormatError: Passed arguments are malformed.\n\n ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is\n passed.\n\n LinkNotFoundError: No preliminary link metadata file found.\n\n securesystemslib.exceptions.StorageError: Cannot hash artifacts.\n\n PrefixError: Left-stripping artifact paths results in non-unique dict keys.\n\n securesystemslib.process.subprocess.TimeoutExpired: Link command times out.\n\n IOError, FileNotFoundError, NotADirectoryError, PermissionError:\n Cannot write link metadata.\n\n securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:\n Signing errors.\n\n ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:\n gpg signing errors.\n\n Side Effects:\n Reads preliminary link metadata file from disk.\n Reads artifact files from disk.\n Calls system gpg in a subprocess, if a gpg key argument is passed.\n Writes resulting link metadata file to disk.\n Removes preliminary link metadata file from disk.\n\n '
LOG.info("Stop recording '{}'...".format(step_name))
if ((not signing_key) and (not gpg_keyid) and (not gpg_use_default)):
raise ValueError('Pass either a signing key, a gpg keyid or set gpg_use_default to True')
if signing_key:
_check_match_signing_key(signing_key)
if gpg_keyid:
securesystemslib.formats.KEYID_SCHEMA.check_match(gpg_keyid)
if exclude_patterns:
securesystemslib.formats.NAMES_SCHEMA.check_match(exclude_patterns)
if base_path:
securesystemslib.formats.PATH_SCHEMA.check_match(base_path)
if metadata_directory:
securesystemslib.formats.PATH_SCHEMA.check_match(metadata_directory)
if signing_key:
unfinished_fn = UNFINISHED_FILENAME_FORMAT.format(step_name=step_name, keyid=signing_key['keyid'])
else:
unfinished_fn_glob = UNFINISHED_FILENAME_FORMAT_GLOB.format(step_name=step_name, pattern='*')
unfinished_fn_list = glob.glob(unfinished_fn_glob)
if (not len(unfinished_fn_list)):
raise in_toto.exceptions.LinkNotFoundError("Could not find a preliminary link for step '{}' in the current working directory.".format(step_name))
if (len(unfinished_fn_list) > 1):
raise in_toto.exceptions.LinkNotFoundError("Found more than one preliminary links for step '{}' in the current working directory: {}. We need exactly one to stop recording.".format(step_name, ', '.join(unfinished_fn_list)))
unfinished_fn = unfinished_fn_list[0]
LOG.info("Loading preliminary link metadata '{}'...".format(unfinished_fn))
link_metadata = Metablock.load(unfinished_fn)
if signing_key:
LOG.info('Verifying preliminary link signature using passed signing key...')
keyid = signing_key['keyid']
verification_key = signing_key
elif gpg_keyid:
LOG.info('Verifying preliminary link signature using passed gpg key...')
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(gpg_keyid, gpg_home)
keyid = gpg_pubkey['keyid']
verification_key = gpg_pubkey
else:
LOG.info('Verifying preliminary link signature using default gpg key...')
keyid = link_metadata.signatures[0]['keyid']
gpg_pubkey = securesystemslib.gpg.functions.export_pubkey(keyid, gpg_home)
verification_key = gpg_pubkey
link_metadata.verify_signature(verification_key)
if product_list:
LOG.info("Recording products '{}'...".format(', '.join(product_list)))
link_metadata.signed.products = record_artifacts_as_dict(product_list, exclude_patterns=exclude_patterns, base_path=base_path, follow_symlink_dirs=True, normalize_line_endings=normalize_line_endings, lstrip_paths=lstrip_paths)
link_metadata.signatures = []
if signing_key:
LOG.info("Updating signature with key '{:.8}...'...".format(keyid))
link_metadata.sign(signing_key)
else:
LOG.info("Updating signature with gpg key '{:.8}...'...".format(keyid))
link_metadata.sign_gpg(keyid, gpg_home)
fn = FILENAME_FORMAT.format(step_name=step_name, keyid=keyid)
if (metadata_directory is not None):
fn = os.path.join(metadata_directory, fn)
LOG.info("Storing link metadata to '{}'...".format(fn))
link_metadata.dump(fn)
LOG.info("Removing unfinished link metadata '{}'...".format(unfinished_fn))
os.remove(unfinished_fn)<|docstring|>Finalizes preliminary link metadata generated with in_toto_record_start.
Loads preliminary link metadata file, verifies its signature, and records
paths and hashes as products, thus finalizing the link metadata. The metadata
is signed with the passed signing_key, a gpg key identified by its ID, or the
default gpg key. If multiple key arguments are passed, only one key is used
in above order of precedence. At least one key argument must be passed and it
must be the same as the one used to sign the preliminary link metadata file.
The resulting link file is written to ``STEP-NAME.KEYID-PREFIX.link``.
Use this function together with in_toto_record_start as an alternative to
in_toto_run, in order to provide evidence for supply chain steps that cannot
be carried out by a single command.
Arguments:
step_name: A unique name to associate link metadata with a step.
product_list: A list of artifact paths to be recorded as products.
Directories are traversed recursively.
signing_key (optional): A key used to sign the resulting link metadata. The
format is securesystemslib.formats.KEY_SCHEMA.
gpg_keyid (optional): A keyid used to identify a local gpg key used to sign
the resulting link metadata.
gpg_use_default (optional): A boolean indicating if the default gpg key
should be used to sign the resulting link metadata.
gpg_home (optional): A path to the gpg home directory. If not set the
default gpg home directory is used.
exclude_patterns (optional): A list of filename patterns to exclude certain
files from being recorded as artifacts.
base_path (optional): A path relative to which artifacts are recorded.
Default is the current working directory.
normalize_line_endings (optional): A boolean indicating if line endings of
artifacts should be normalized before hashing for cross-platform
support.
lstrip_paths (optional): A list of path prefixes used to left-strip
artifact paths before storing them in the resulting link metadata.
metadata_directory (optional): A directory path to write the resulting link
metadata file to. Default destination is the current working directory.
Raises:
securesystemslib.exceptions.FormatError: Passed arguments are malformed.
ValueError: None of signing_key, gpg_keyid or gpg_use_default=True is
passed.
LinkNotFoundError: No preliminary link metadata file found.
securesystemslib.exceptions.StorageError: Cannot hash artifacts.
PrefixError: Left-stripping artifact paths results in non-unique dict keys.
securesystemslib.process.subprocess.TimeoutExpired: Link command times out.
IOError, FileNotFoundError, NotADirectoryError, PermissionError:
Cannot write link metadata.
securesystemslib.exceptions.CryptoError, securesystemslib.exceptions.UnsupportedAlgorithmError:
Signing errors.
ValueError, OSError, securesystemslib.gpg.exceptions.CommandError, securesystemslib.gpg.exceptions.KeyNotFoundError:
gpg signing errors.
Side Effects:
Reads preliminary link metadata file from disk.
Reads artifact files from disk.
Calls system gpg in a subprocess, if a gpg key argument is passed.
Writes resulting link metadata file to disk.
Removes preliminary link metadata file from disk.<|endoftext|>
|
af46b1de7d497263550fef07bae567313a87626c1757ffe292bf3ecdb277d59d
|
def is_good_license(detected_license):
'\n Return True if a `detected license` mapping is consider to a high quality\n conclusive match.\n '
score = detected_license['score']
rule = detected_license['matched_rule']
coverage = (rule.get('match_coverage') or 0)
relevance = (rule.get('rule_relevance') or 0)
match_types = dict([('is_license_text', rule['is_license_text']), ('is_license_notice', rule['is_license_notice']), ('is_license_reference', rule['is_license_reference']), ('is_license_tag', rule['is_license_tag']), ('is_license_intro', rule['is_license_intro'])])
matched = False
for (match_type, mval) in match_types.items():
if mval:
matched = True
break
if (not matched):
return False
thresholds = FILTERS[match_type]
if ((not coverage) or (not relevance)):
if (score >= thresholds.min_score):
return True
elif ((score >= thresholds.min_score) and (coverage >= thresholds.min_coverage) and (relevance >= thresholds.min_relevance)):
return True
return False
|
Return True if a `detected license` mapping is consider to a high quality
conclusive match.
|
src/summarycode/score.py
|
is_good_license
|
alec-z/zhangfei
| 1,511
|
python
|
def is_good_license(detected_license):
'\n Return True if a `detected license` mapping is consider to a high quality\n conclusive match.\n '
score = detected_license['score']
rule = detected_license['matched_rule']
coverage = (rule.get('match_coverage') or 0)
relevance = (rule.get('rule_relevance') or 0)
match_types = dict([('is_license_text', rule['is_license_text']), ('is_license_notice', rule['is_license_notice']), ('is_license_reference', rule['is_license_reference']), ('is_license_tag', rule['is_license_tag']), ('is_license_intro', rule['is_license_intro'])])
matched = False
for (match_type, mval) in match_types.items():
if mval:
matched = True
break
if (not matched):
return False
thresholds = FILTERS[match_type]
if ((not coverage) or (not relevance)):
if (score >= thresholds.min_score):
return True
elif ((score >= thresholds.min_score) and (coverage >= thresholds.min_coverage) and (relevance >= thresholds.min_relevance)):
return True
return False
|
def is_good_license(detected_license):
'\n Return True if a `detected license` mapping is consider to a high quality\n conclusive match.\n '
score = detected_license['score']
rule = detected_license['matched_rule']
coverage = (rule.get('match_coverage') or 0)
relevance = (rule.get('rule_relevance') or 0)
match_types = dict([('is_license_text', rule['is_license_text']), ('is_license_notice', rule['is_license_notice']), ('is_license_reference', rule['is_license_reference']), ('is_license_tag', rule['is_license_tag']), ('is_license_intro', rule['is_license_intro'])])
matched = False
for (match_type, mval) in match_types.items():
if mval:
matched = True
break
if (not matched):
return False
thresholds = FILTERS[match_type]
if ((not coverage) or (not relevance)):
if (score >= thresholds.min_score):
return True
elif ((score >= thresholds.min_score) and (coverage >= thresholds.min_coverage) and (relevance >= thresholds.min_relevance)):
return True
return False<|docstring|>Return True if a `detected license` mapping is consider to a high quality
conclusive match.<|endoftext|>
|
b0673290ca73811c77e545f0b78c064a441bd9201cae6133b7bb44e2561cf927
|
def compute_license_score(codebase):
'\n Return a mapping of scoring elements and a license clarity score computed at\n the codebase level.\n '
score = 0
scoring_elements = dict(score=score)
for element in SCORING_ELEMENTS:
element_score = element.scorer(codebase)
if element.is_binary:
scoring_elements[element.name] = bool(element_score)
element_score = (1 if element_score else 0)
else:
scoring_elements[element.name] = (round(element_score, 2) or 0)
score += int((element_score * element.weight))
if TRACE:
logger_debug('compute_license_score: element:', element, 'element_score: ', element_score, ' new score:', score)
scoring_elements['score'] = (score or 0)
return scoring_elements
|
Return a mapping of scoring elements and a license clarity score computed at
the codebase level.
|
src/summarycode/score.py
|
compute_license_score
|
alec-z/zhangfei
| 1,511
|
python
|
def compute_license_score(codebase):
'\n Return a mapping of scoring elements and a license clarity score computed at\n the codebase level.\n '
score = 0
scoring_elements = dict(score=score)
for element in SCORING_ELEMENTS:
element_score = element.scorer(codebase)
if element.is_binary:
scoring_elements[element.name] = bool(element_score)
element_score = (1 if element_score else 0)
else:
scoring_elements[element.name] = (round(element_score, 2) or 0)
score += int((element_score * element.weight))
if TRACE:
logger_debug('compute_license_score: element:', element, 'element_score: ', element_score, ' new score:', score)
scoring_elements['score'] = (score or 0)
return scoring_elements
|
def compute_license_score(codebase):
'\n Return a mapping of scoring elements and a license clarity score computed at\n the codebase level.\n '
score = 0
scoring_elements = dict(score=score)
for element in SCORING_ELEMENTS:
element_score = element.scorer(codebase)
if element.is_binary:
scoring_elements[element.name] = bool(element_score)
element_score = (1 if element_score else 0)
else:
scoring_elements[element.name] = (round(element_score, 2) or 0)
score += int((element_score * element.weight))
if TRACE:
logger_debug('compute_license_score: element:', element, 'element_score: ', element_score, ' new score:', score)
scoring_elements['score'] = (score or 0)
return scoring_elements<|docstring|>Return a mapping of scoring elements and a license clarity score computed at
the codebase level.<|endoftext|>
|
8fbcc1266910069f1115f0ac1e51e67c15ddbc1f1c775f9d18cf0704ec7c3b50
|
def get_declared_license_keys(codebase):
'\n Return a list of declared license keys found in packages and key files.\n '
return (get_declared_license_keys_in_key_files(codebase) + get_declared_license_keys_in_packages(codebase))
|
Return a list of declared license keys found in packages and key files.
|
src/summarycode/score.py
|
get_declared_license_keys
|
alec-z/zhangfei
| 1,511
|
python
|
def get_declared_license_keys(codebase):
'\n \n '
return (get_declared_license_keys_in_key_files(codebase) + get_declared_license_keys_in_packages(codebase))
|
def get_declared_license_keys(codebase):
'\n \n '
return (get_declared_license_keys_in_key_files(codebase) + get_declared_license_keys_in_packages(codebase))<|docstring|>Return a list of declared license keys found in packages and key files.<|endoftext|>
|
11d737c81053dbd01b7658dd3757701d197a6cad7b8b5d756c367f7dcfa13586
|
def get_declared_license_keys_in_packages(codebase):
'\n Return a list of declared license keys found in packages.\n\n A package manifest (such as Maven POM file or an npm package.json file)\n contains structured declared license information. This is further normalized\n as a license_expression. We extract the list of licenses from the normalized\n license expressions.\n '
packages = chain.from_iterable(((getattr(res, 'packages', []) or []) for res in codebase.walk(topdown=True)))
licensing = Licensing()
detected_good_licenses = []
for package in packages:
expression = package.get('license_expression')
if expression:
exp = licensing.parse(expression, validate=False, strict=False, simple=True)
keys = licensing.license_keys(exp, unique=True)
detected_good_licenses.extend(keys)
return detected_good_licenses
|
Return a list of declared license keys found in packages.
A package manifest (such as Maven POM file or an npm package.json file)
contains structured declared license information. This is further normalized
as a license_expression. We extract the list of licenses from the normalized
license expressions.
|
src/summarycode/score.py
|
get_declared_license_keys_in_packages
|
alec-z/zhangfei
| 1,511
|
python
|
def get_declared_license_keys_in_packages(codebase):
'\n Return a list of declared license keys found in packages.\n\n A package manifest (such as Maven POM file or an npm package.json file)\n contains structured declared license information. This is further normalized\n as a license_expression. We extract the list of licenses from the normalized\n license expressions.\n '
packages = chain.from_iterable(((getattr(res, 'packages', []) or []) for res in codebase.walk(topdown=True)))
licensing = Licensing()
detected_good_licenses = []
for package in packages:
expression = package.get('license_expression')
if expression:
exp = licensing.parse(expression, validate=False, strict=False, simple=True)
keys = licensing.license_keys(exp, unique=True)
detected_good_licenses.extend(keys)
return detected_good_licenses
|
def get_declared_license_keys_in_packages(codebase):
'\n Return a list of declared license keys found in packages.\n\n A package manifest (such as Maven POM file or an npm package.json file)\n contains structured declared license information. This is further normalized\n as a license_expression. We extract the list of licenses from the normalized\n license expressions.\n '
packages = chain.from_iterable(((getattr(res, 'packages', []) or []) for res in codebase.walk(topdown=True)))
licensing = Licensing()
detected_good_licenses = []
for package in packages:
expression = package.get('license_expression')
if expression:
exp = licensing.parse(expression, validate=False, strict=False, simple=True)
keys = licensing.license_keys(exp, unique=True)
detected_good_licenses.extend(keys)
return detected_good_licenses<|docstring|>Return a list of declared license keys found in packages.
A package manifest (such as Maven POM file or an npm package.json file)
contains structured declared license information. This is further normalized
as a license_expression. We extract the list of licenses from the normalized
license expressions.<|endoftext|>
|
f806372b745aa0a836e5b7b0f3a8adc69760cfc96c28abbaa2ae288aea96be5d
|
def get_declared_license_keys_in_key_files(codebase):
'\n Return a list of "declared" license keys from the expressions as detected in\n key files.\n\n A project has specific key file(s) at the top level of its code hierarchy\n such as LICENSE, NOTICE or similar (and/or a package manifest) containing\n structured license information such as an SPDX license expression or SPDX\n license identifier: when such a file contains "clearly defined" declared\n license information, we return this.\n\n Note: this ignores facets.\n '
declared = []
for resource in codebase.walk(topdown=True):
if (not resource.is_key_file):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (not is_good_license(detected_license)):
declared.append('unknown')
else:
declared.append(detected_license['key'])
return declared
|
Return a list of "declared" license keys from the expressions as detected in
key files.
A project has specific key file(s) at the top level of its code hierarchy
such as LICENSE, NOTICE or similar (and/or a package manifest) containing
structured license information such as an SPDX license expression or SPDX
license identifier: when such a file contains "clearly defined" declared
license information, we return this.
Note: this ignores facets.
|
src/summarycode/score.py
|
get_declared_license_keys_in_key_files
|
alec-z/zhangfei
| 1,511
|
python
|
def get_declared_license_keys_in_key_files(codebase):
'\n Return a list of "declared" license keys from the expressions as detected in\n key files.\n\n A project has specific key file(s) at the top level of its code hierarchy\n such as LICENSE, NOTICE or similar (and/or a package manifest) containing\n structured license information such as an SPDX license expression or SPDX\n license identifier: when such a file contains "clearly defined" declared\n license information, we return this.\n\n Note: this ignores facets.\n '
declared = []
for resource in codebase.walk(topdown=True):
if (not resource.is_key_file):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (not is_good_license(detected_license)):
declared.append('unknown')
else:
declared.append(detected_license['key'])
return declared
|
def get_declared_license_keys_in_key_files(codebase):
'\n Return a list of "declared" license keys from the expressions as detected in\n key files.\n\n A project has specific key file(s) at the top level of its code hierarchy\n such as LICENSE, NOTICE or similar (and/or a package manifest) containing\n structured license information such as an SPDX license expression or SPDX\n license identifier: when such a file contains "clearly defined" declared\n license information, we return this.\n\n Note: this ignores facets.\n '
declared = []
for resource in codebase.walk(topdown=True):
if (not resource.is_key_file):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (not is_good_license(detected_license)):
declared.append('unknown')
else:
declared.append(detected_license['key'])
return declared<|docstring|>Return a list of "declared" license keys from the expressions as detected in
key files.
A project has specific key file(s) at the top level of its code hierarchy
such as LICENSE, NOTICE or similar (and/or a package manifest) containing
structured license information such as an SPDX license expression or SPDX
license identifier: when such a file contains "clearly defined" declared
license information, we return this.
Note: this ignores facets.<|endoftext|>
|
cd62a2ea6d9d22683e6cb6a7957db496d44e95954a0c21b37be673a43983c0b7
|
def is_core_facet(resource, core_facet=facet.FACET_CORE):
'\n Return True if the resource is in the core facet.\n If we do not have facets, everything is considered as being core by default.\n '
has_facets = hasattr(resource, 'facets')
if (not has_facets):
return True
return ((not resource.facets) or (core_facet in resource.facets))
|
Return True if the resource is in the core facet.
If we do not have facets, everything is considered as being core by default.
|
src/summarycode/score.py
|
is_core_facet
|
alec-z/zhangfei
| 1,511
|
python
|
def is_core_facet(resource, core_facet=facet.FACET_CORE):
'\n Return True if the resource is in the core facet.\n If we do not have facets, everything is considered as being core by default.\n '
has_facets = hasattr(resource, 'facets')
if (not has_facets):
return True
return ((not resource.facets) or (core_facet in resource.facets))
|
def is_core_facet(resource, core_facet=facet.FACET_CORE):
'\n Return True if the resource is in the core facet.\n If we do not have facets, everything is considered as being core by default.\n '
has_facets = hasattr(resource, 'facets')
if (not has_facets):
return True
return ((not resource.facets) or (core_facet in resource.facets))<|docstring|>Return True if the resource is in the core facet.
If we do not have facets, everything is considered as being core by default.<|endoftext|>
|
d01d33bc9b8166bd10a9f9dc19bce68e78dd9d2c861f25f1f1d5eb7c6b2be25d
|
def has_good_licenses(resource):
'\n Return True if a Resource licenses are all detected as a "good license"\n detection-wise.\n '
licenses = (getattr(resource, 'licenses', []) or [])
if (not licenses):
return False
for detected_license in licenses:
if (not is_good_license(detected_license)):
return False
if is_unknown_license(detected_license['key']):
return False
return True
|
Return True if a Resource licenses are all detected as a "good license"
detection-wise.
|
src/summarycode/score.py
|
has_good_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def has_good_licenses(resource):
'\n Return True if a Resource licenses are all detected as a "good license"\n detection-wise.\n '
licenses = (getattr(resource, 'licenses', []) or [])
if (not licenses):
return False
for detected_license in licenses:
if (not is_good_license(detected_license)):
return False
if is_unknown_license(detected_license['key']):
return False
return True
|
def has_good_licenses(resource):
'\n Return True if a Resource licenses are all detected as a "good license"\n detection-wise.\n '
licenses = (getattr(resource, 'licenses', []) or [])
if (not licenses):
return False
for detected_license in licenses:
if (not is_good_license(detected_license)):
return False
if is_unknown_license(detected_license['key']):
return False
return True<|docstring|>Return True if a Resource licenses are all detected as a "good license"
detection-wise.<|endoftext|>
|
615e8c95e88e25f77fc3430c183aa565ccd96cf4ab9c383c50f2f8314631658f
|
def is_unknown_license(lic_key):
'\n Return True if a license key is for some lesser known or unknown license.\n '
return (lic_key.startswith(('unknown', 'other-')) or ('unknown' in lic_key))
|
Return True if a license key is for some lesser known or unknown license.
|
src/summarycode/score.py
|
is_unknown_license
|
alec-z/zhangfei
| 1,511
|
python
|
def is_unknown_license(lic_key):
'\n \n '
return (lic_key.startswith(('unknown', 'other-')) or ('unknown' in lic_key))
|
def is_unknown_license(lic_key):
'\n \n '
return (lic_key.startswith(('unknown', 'other-')) or ('unknown' in lic_key))<|docstring|>Return True if a license key is for some lesser known or unknown license.<|endoftext|>
|
77755f5950ebd70a4e3fcd742db041300c18d0b3943ad5b7a793354b5a203f1f
|
def has_unkown_licenses(resource):
'\n Return True if some Resource licenses are unknown.\n '
return (not any((is_unknown_license(lic['key']) for lic in (getattr(resource, 'licenses', []) or []))))
|
Return True if some Resource licenses are unknown.
|
src/summarycode/score.py
|
has_unkown_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def has_unkown_licenses(resource):
'\n \n '
return (not any((is_unknown_license(lic['key']) for lic in (getattr(resource, 'licenses', []) or []))))
|
def has_unkown_licenses(resource):
'\n \n '
return (not any((is_unknown_license(lic['key']) for lic in (getattr(resource, 'licenses', []) or []))))<|docstring|>Return True if some Resource licenses are unknown.<|endoftext|>
|
adfdc1bf3d09ea227fff8ca0e926914978c322d94a2ab0a38a4ab44aa39ebc9f
|
def get_spdx_keys():
'\n Return a set of ScanCode license keys for licenses that are listed in SPDX.\n '
global _spdx_keys
if (not _spdx_keys):
_spdx_keys = frozenset(models.get_all_spdx_keys(get_licenses_db()))
return _spdx_keys
|
Return a set of ScanCode license keys for licenses that are listed in SPDX.
|
src/summarycode/score.py
|
get_spdx_keys
|
alec-z/zhangfei
| 1,511
|
python
|
def get_spdx_keys():
'\n \n '
global _spdx_keys
if (not _spdx_keys):
_spdx_keys = frozenset(models.get_all_spdx_keys(get_licenses_db()))
return _spdx_keys
|
def get_spdx_keys():
'\n \n '
global _spdx_keys
if (not _spdx_keys):
_spdx_keys = frozenset(models.get_all_spdx_keys(get_licenses_db()))
return _spdx_keys<|docstring|>Return a set of ScanCode license keys for licenses that are listed in SPDX.<|endoftext|>
|
4bb0f39bc4c38dbd369852193f624beb0e287a1f3e920e771d74f65fbd63f701
|
def is_using_only_spdx_licenses(codebase):
'\n Return True if all file-level detected licenses are SPDX licenses.\n '
licenses = chain.from_iterable((res.licenses for res in codebase.walk() if res.is_file))
keys = set((l['key'] for l in licenses))
spdx_keys = get_spdx_keys()
return (keys and spdx_keys and all(((k in spdx_keys) for k in keys)))
|
Return True if all file-level detected licenses are SPDX licenses.
|
src/summarycode/score.py
|
is_using_only_spdx_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def is_using_only_spdx_licenses(codebase):
'\n \n '
licenses = chain.from_iterable((res.licenses for res in codebase.walk() if res.is_file))
keys = set((l['key'] for l in licenses))
spdx_keys = get_spdx_keys()
return (keys and spdx_keys and all(((k in spdx_keys) for k in keys)))
|
def is_using_only_spdx_licenses(codebase):
'\n \n '
licenses = chain.from_iterable((res.licenses for res in codebase.walk() if res.is_file))
keys = set((l['key'] for l in licenses))
spdx_keys = get_spdx_keys()
return (keys and spdx_keys and all(((k in spdx_keys) for k in keys)))<|docstring|>Return True if all file-level detected licenses are SPDX licenses.<|endoftext|>
|
f6c62a2345f0f6e24bbf9f2f839d82024e0578425915487e29676781ea2f0990
|
def has_consistent_key_and_file_level_licenses(codebase):
'\n Return True if the file-level licenses are consistent with top level key\n files licenses.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase)
if (key_files_licenses and (key_files_licenses == other_files_licenses) and (not any((is_unknown_license(l) for l in key_files_licenses)))):
return True
else:
return False
|
Return True if the file-level licenses are consistent with top level key
files licenses.
|
src/summarycode/score.py
|
has_consistent_key_and_file_level_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def has_consistent_key_and_file_level_licenses(codebase):
'\n Return True if the file-level licenses are consistent with top level key\n files licenses.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase)
if (key_files_licenses and (key_files_licenses == other_files_licenses) and (not any((is_unknown_license(l) for l in key_files_licenses)))):
return True
else:
return False
|
def has_consistent_key_and_file_level_licenses(codebase):
'\n Return True if the file-level licenses are consistent with top level key\n files licenses.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase)
if (key_files_licenses and (key_files_licenses == other_files_licenses) and (not any((is_unknown_license(l) for l in key_files_licenses)))):
return True
else:
return False<|docstring|>Return True if the file-level licenses are consistent with top level key
files licenses.<|endoftext|>
|
338513f3ab8eb7cdcecc1883bbab4dec2ff2c45bb84c916aebbf5e2a3b075d68
|
def get_unique_licenses(codebase, good_only=True):
'\n Return a tuple of two sets of license keys found in the codebase:\n - the set license found in key files\n - the set license found in non-key files\n\n This is only for files in the core facet.\n '
key_license_keys = set()
other_license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (not (resource.is_key_file or is_core_facet(resource))):
continue
if resource.is_key_file:
license_keys = key_license_keys
else:
license_keys = other_license_keys
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
license_keys.add('unknown')
else:
license_keys.add(detected_license['key'])
return (key_license_keys, other_license_keys)
|
Return a tuple of two sets of license keys found in the codebase:
- the set license found in key files
- the set license found in non-key files
This is only for files in the core facet.
|
src/summarycode/score.py
|
get_unique_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def get_unique_licenses(codebase, good_only=True):
'\n Return a tuple of two sets of license keys found in the codebase:\n - the set license found in key files\n - the set license found in non-key files\n\n This is only for files in the core facet.\n '
key_license_keys = set()
other_license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (not (resource.is_key_file or is_core_facet(resource))):
continue
if resource.is_key_file:
license_keys = key_license_keys
else:
license_keys = other_license_keys
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
license_keys.add('unknown')
else:
license_keys.add(detected_license['key'])
return (key_license_keys, other_license_keys)
|
def get_unique_licenses(codebase, good_only=True):
'\n Return a tuple of two sets of license keys found in the codebase:\n - the set license found in key files\n - the set license found in non-key files\n\n This is only for files in the core facet.\n '
key_license_keys = set()
other_license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (not (resource.is_key_file or is_core_facet(resource))):
continue
if resource.is_key_file:
license_keys = key_license_keys
else:
license_keys = other_license_keys
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
license_keys.add('unknown')
else:
license_keys.add(detected_license['key'])
return (key_license_keys, other_license_keys)<|docstring|>Return a tuple of two sets of license keys found in the codebase:
- the set license found in key files
- the set license found in non-key files
This is only for files in the core facet.<|endoftext|>
|
e364ea57f3f7a763a22632276e1146d7da353a7acafad3c2d6a22c6bac181a4b
|
def get_detected_license_keys_with_full_text(codebase, key_files_only=False, good_only=True):
'\n Return a set of license keys for which at least one detection includes the\n full license text.\n\n This is for any files in the core facet or not.\n '
license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (key_files_only and (not resource.is_key_file)):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
continue
if detected_license['matched_rule']['is_license_text']:
license_keys.add(detected_license['key'])
return license_keys
|
Return a set of license keys for which at least one detection includes the
full license text.
This is for any files in the core facet or not.
|
src/summarycode/score.py
|
get_detected_license_keys_with_full_text
|
alec-z/zhangfei
| 1,511
|
python
|
def get_detected_license_keys_with_full_text(codebase, key_files_only=False, good_only=True):
'\n Return a set of license keys for which at least one detection includes the\n full license text.\n\n This is for any files in the core facet or not.\n '
license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (key_files_only and (not resource.is_key_file)):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
continue
if detected_license['matched_rule']['is_license_text']:
license_keys.add(detected_license['key'])
return license_keys
|
def get_detected_license_keys_with_full_text(codebase, key_files_only=False, good_only=True):
'\n Return a set of license keys for which at least one detection includes the\n full license text.\n\n This is for any files in the core facet or not.\n '
license_keys = set()
for resource in codebase.walk():
if (not resource.is_file):
continue
if (key_files_only and (not resource.is_key_file)):
continue
for detected_license in (getattr(resource, 'licenses', []) or []):
if (good_only and (not is_good_license(detected_license))):
continue
if detected_license['matched_rule']['is_license_text']:
license_keys.add(detected_license['key'])
return license_keys<|docstring|>Return a set of license keys for which at least one detection includes the
full license text.
This is for any files in the core facet or not.<|endoftext|>
|
398e111ab26af22da8fb6a72532addbb23c0d51c8aabc63a83d54698c95b3394
|
def has_full_text_in_key_files_for_all_licenses(codebase):
'\n Return True if the full text of all licenses is preset in the codebase key,\n top level files.\n '
return _has_full_text(codebase, key_files_only=True)
|
Return True if the full text of all licenses is preset in the codebase key,
top level files.
|
src/summarycode/score.py
|
has_full_text_in_key_files_for_all_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def has_full_text_in_key_files_for_all_licenses(codebase):
'\n Return True if the full text of all licenses is preset in the codebase key,\n top level files.\n '
return _has_full_text(codebase, key_files_only=True)
|
def has_full_text_in_key_files_for_all_licenses(codebase):
'\n Return True if the full text of all licenses is preset in the codebase key,\n top level files.\n '
return _has_full_text(codebase, key_files_only=True)<|docstring|>Return True if the full text of all licenses is preset in the codebase key,
top level files.<|endoftext|>
|
545b30f3ecec088ae89336afa3fa051436c82b72beafd2d9a35a7e1bc348c041
|
def has_full_text_for_all_licenses(codebase):
'\n Return True if the full text of all licenses is preset in the codebase.\n '
return _has_full_text(codebase, key_files_only=False)
|
Return True if the full text of all licenses is preset in the codebase.
|
src/summarycode/score.py
|
has_full_text_for_all_licenses
|
alec-z/zhangfei
| 1,511
|
python
|
def has_full_text_for_all_licenses(codebase):
'\n \n '
return _has_full_text(codebase, key_files_only=False)
|
def has_full_text_for_all_licenses(codebase):
'\n \n '
return _has_full_text(codebase, key_files_only=False)<|docstring|>Return True if the full text of all licenses is preset in the codebase.<|endoftext|>
|
4f957e6720871ff8d748195edc2a5748aad1e535908cb84fbee921cb05cea8fc
|
def _has_full_text(codebase, key_files_only=False):
'\n Return True if the full text of all licenses is preset in the codebase.\n Consider only key files if key_files_only is True.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase, good_only=False)
if TRACE:
logger_debug('_has_full_text: key_files_licenses:', key_files_licenses, 'other_files_licenses:', other_files_licenses)
all_keys = (key_files_licenses | other_files_licenses)
if (not all_keys):
return False
if TRACE:
logger_debug('_has_full_text: all_keys:', all_keys)
keys_with_license_text = get_detected_license_keys_with_full_text(codebase, key_files_only, good_only=False)
if TRACE:
logger_debug('_has_full_text: keys_with_license_text:', keys_with_license_text)
logger_debug('_has_full_text: all_keys == keys_with_license_text:', (all_keys == keys_with_license_text))
return (all_keys == keys_with_license_text)
|
Return True if the full text of all licenses is preset in the codebase.
Consider only key files if key_files_only is True.
|
src/summarycode/score.py
|
_has_full_text
|
alec-z/zhangfei
| 1,511
|
python
|
def _has_full_text(codebase, key_files_only=False):
'\n Return True if the full text of all licenses is preset in the codebase.\n Consider only key files if key_files_only is True.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase, good_only=False)
if TRACE:
logger_debug('_has_full_text: key_files_licenses:', key_files_licenses, 'other_files_licenses:', other_files_licenses)
all_keys = (key_files_licenses | other_files_licenses)
if (not all_keys):
return False
if TRACE:
logger_debug('_has_full_text: all_keys:', all_keys)
keys_with_license_text = get_detected_license_keys_with_full_text(codebase, key_files_only, good_only=False)
if TRACE:
logger_debug('_has_full_text: keys_with_license_text:', keys_with_license_text)
logger_debug('_has_full_text: all_keys == keys_with_license_text:', (all_keys == keys_with_license_text))
return (all_keys == keys_with_license_text)
|
def _has_full_text(codebase, key_files_only=False):
'\n Return True if the full text of all licenses is preset in the codebase.\n Consider only key files if key_files_only is True.\n '
(key_files_licenses, other_files_licenses) = get_unique_licenses(codebase, good_only=False)
if TRACE:
logger_debug('_has_full_text: key_files_licenses:', key_files_licenses, 'other_files_licenses:', other_files_licenses)
all_keys = (key_files_licenses | other_files_licenses)
if (not all_keys):
return False
if TRACE:
logger_debug('_has_full_text: all_keys:', all_keys)
keys_with_license_text = get_detected_license_keys_with_full_text(codebase, key_files_only, good_only=False)
if TRACE:
logger_debug('_has_full_text: keys_with_license_text:', keys_with_license_text)
logger_debug('_has_full_text: all_keys == keys_with_license_text:', (all_keys == keys_with_license_text))
return (all_keys == keys_with_license_text)<|docstring|>Return True if the full text of all licenses is preset in the codebase.
Consider only key files if key_files_only is True.<|endoftext|>
|
6b3ac6b60797b713c730928f32fd08d36d3c761e1b3c32cb69cbecb2557f27db
|
def get_file_level_license_and_copyright_coverage(codebase):
'\n Return a float between 0 and 1 that represent the proportions of files that\n have a license and a copyright vs. all files.\n '
scoring_element = 0
(covered_files, files_count) = get_other_licenses_and_copyrights_counts(codebase)
if TRACE:
logger_debug('compute_license_score:covered_files:', covered_files, 'files_count:', files_count)
if files_count:
scoring_element = ((covered_files / files_count) or 0)
if TRACE:
logger_debug('compute_license_score:scoring_element:', scoring_element)
return scoring_element
|
Return a float between 0 and 1 that represent the proportions of files that
have a license and a copyright vs. all files.
|
src/summarycode/score.py
|
get_file_level_license_and_copyright_coverage
|
alec-z/zhangfei
| 1,511
|
python
|
def get_file_level_license_and_copyright_coverage(codebase):
'\n Return a float between 0 and 1 that represent the proportions of files that\n have a license and a copyright vs. all files.\n '
scoring_element = 0
(covered_files, files_count) = get_other_licenses_and_copyrights_counts(codebase)
if TRACE:
logger_debug('compute_license_score:covered_files:', covered_files, 'files_count:', files_count)
if files_count:
scoring_element = ((covered_files / files_count) or 0)
if TRACE:
logger_debug('compute_license_score:scoring_element:', scoring_element)
return scoring_element
|
def get_file_level_license_and_copyright_coverage(codebase):
'\n Return a float between 0 and 1 that represent the proportions of files that\n have a license and a copyright vs. all files.\n '
scoring_element = 0
(covered_files, files_count) = get_other_licenses_and_copyrights_counts(codebase)
if TRACE:
logger_debug('compute_license_score:covered_files:', covered_files, 'files_count:', files_count)
if files_count:
scoring_element = ((covered_files / files_count) or 0)
if TRACE:
logger_debug('compute_license_score:scoring_element:', scoring_element)
return scoring_element<|docstring|>Return a float between 0 and 1 that represent the proportions of files that
have a license and a copyright vs. all files.<|endoftext|>
|
274e68c2840a1e177110cd99d012536548a416c8dbeb19509af0cb67df77e8f5
|
def get_other_licenses_and_copyrights_counts(codebase):
'\n Return a tuple of (count of files with a license/copyright, total count of\n files).\n\n Do files that can contain licensing and copyright information reliably carry\n such information? This is based on a percentage of files in the core facet\n of the project that have both:\n\n - A license text, notice or an SPDX-License-Identifier and,\n - A copyright statement in standard (e.g. recognized) format.\n\n Here "reliably" means that these are reliably detected by tool(s) with a\n high level of confidence This is a progressive element that is computed\n based on:\n\n - LICCOP: the number of files with a license notice and copyright statement\n - TOT: the total number of files\n\n '
total_files_count = 0
files_with_good_license_and_copyright_count = 0
files_with_a_license_count = 0
files_with_a_good_license_count = 0
files_with_a_copyright_count = 0
for resource in codebase.walk():
if (resource.is_key_file or (not resource.is_file)):
continue
if (not is_core_facet(resource)):
continue
total_files_count += 1
licenses = (getattr(resource, 'licenses', []) or [])
if licenses:
files_with_a_license_count += 1
is_public_domain = ([l['key'] for l in licenses] == 'public-domain')
copyrights = (getattr(resource, 'copyrights', []) or [])
if (copyrights or ((not copyrights) and is_public_domain)):
files_with_a_copyright_count += 1
if has_good_licenses(resource):
files_with_a_good_license_count += 1
if copyrights:
files_with_good_license_and_copyright_count += 1
return (files_with_good_license_and_copyright_count, total_files_count)
|
Return a tuple of (count of files with a license/copyright, total count of
files).
Do files that can contain licensing and copyright information reliably carry
such information? This is based on a percentage of files in the core facet
of the project that have both:
- A license text, notice or an SPDX-License-Identifier and,
- A copyright statement in standard (e.g. recognized) format.
Here "reliably" means that these are reliably detected by tool(s) with a
high level of confidence This is a progressive element that is computed
based on:
- LICCOP: the number of files with a license notice and copyright statement
- TOT: the total number of files
|
src/summarycode/score.py
|
get_other_licenses_and_copyrights_counts
|
alec-z/zhangfei
| 1,511
|
python
|
def get_other_licenses_and_copyrights_counts(codebase):
'\n Return a tuple of (count of files with a license/copyright, total count of\n files).\n\n Do files that can contain licensing and copyright information reliably carry\n such information? This is based on a percentage of files in the core facet\n of the project that have both:\n\n - A license text, notice or an SPDX-License-Identifier and,\n - A copyright statement in standard (e.g. recognized) format.\n\n Here "reliably" means that these are reliably detected by tool(s) with a\n high level of confidence This is a progressive element that is computed\n based on:\n\n - LICCOP: the number of files with a license notice and copyright statement\n - TOT: the total number of files\n\n '
total_files_count = 0
files_with_good_license_and_copyright_count = 0
files_with_a_license_count = 0
files_with_a_good_license_count = 0
files_with_a_copyright_count = 0
for resource in codebase.walk():
if (resource.is_key_file or (not resource.is_file)):
continue
if (not is_core_facet(resource)):
continue
total_files_count += 1
licenses = (getattr(resource, 'licenses', []) or [])
if licenses:
files_with_a_license_count += 1
is_public_domain = ([l['key'] for l in licenses] == 'public-domain')
copyrights = (getattr(resource, 'copyrights', []) or [])
if (copyrights or ((not copyrights) and is_public_domain)):
files_with_a_copyright_count += 1
if has_good_licenses(resource):
files_with_a_good_license_count += 1
if copyrights:
files_with_good_license_and_copyright_count += 1
return (files_with_good_license_and_copyright_count, total_files_count)
|
def get_other_licenses_and_copyrights_counts(codebase):
'\n Return a tuple of (count of files with a license/copyright, total count of\n files).\n\n Do files that can contain licensing and copyright information reliably carry\n such information? This is based on a percentage of files in the core facet\n of the project that have both:\n\n - A license text, notice or an SPDX-License-Identifier and,\n - A copyright statement in standard (e.g. recognized) format.\n\n Here "reliably" means that these are reliably detected by tool(s) with a\n high level of confidence This is a progressive element that is computed\n based on:\n\n - LICCOP: the number of files with a license notice and copyright statement\n - TOT: the total number of files\n\n '
total_files_count = 0
files_with_good_license_and_copyright_count = 0
files_with_a_license_count = 0
files_with_a_good_license_count = 0
files_with_a_copyright_count = 0
for resource in codebase.walk():
if (resource.is_key_file or (not resource.is_file)):
continue
if (not is_core_facet(resource)):
continue
total_files_count += 1
licenses = (getattr(resource, 'licenses', []) or [])
if licenses:
files_with_a_license_count += 1
is_public_domain = ([l['key'] for l in licenses] == 'public-domain')
copyrights = (getattr(resource, 'copyrights', []) or [])
if (copyrights or ((not copyrights) and is_public_domain)):
files_with_a_copyright_count += 1
if has_good_licenses(resource):
files_with_a_good_license_count += 1
if copyrights:
files_with_good_license_and_copyright_count += 1
return (files_with_good_license_and_copyright_count, total_files_count)<|docstring|>Return a tuple of (count of files with a license/copyright, total count of
files).
Do files that can contain licensing and copyright information reliably carry
such information? This is based on a percentage of files in the core facet
of the project that have both:
- A license text, notice or an SPDX-License-Identifier and,
- A copyright statement in standard (e.g. recognized) format.
Here "reliably" means that these are reliably detected by tool(s) with a
high level of confidence This is a progressive element that is computed
based on:
- LICCOP: the number of files with a license notice and copyright statement
- TOT: the total number of files<|endoftext|>
|
3e4aab1cdae4f1163ecaaa5461dd697d4ad73fa521ab288c3e99381fbca5a93f
|
def get(self, x: int, y: int):
'Coordinate system starts top left.'
return self._storage[((y * self._width) + x)]
|
Coordinate system starts top left.
|
submitted_algorithms/mentoren/nils.py
|
get
|
coderdojoka/algo-battle
| 1
|
python
|
def get(self, x: int, y: int):
return self._storage[((y * self._width) + x)]
|
def get(self, x: int, y: int):
return self._storage[((y * self._width) + x)]<|docstring|>Coordinate system starts top left.<|endoftext|>
|
c0d85087823da77bd2df39cf7115581c159c2c3a05711bca080887d8b2b64ec6
|
@ComponentGetter(QRTComponentType.ComponentAnalog, RTAnalogComponent)
def get_analog(self, component_info=None, data=None, component_position=None):
'Get analog data.'
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDevice, data, component_position)
if (device.sample_count > 0):
(component_position, sample_number) = QRTPacket._get_exact(RTSampleNumber, data, component_position)
RTAnalogChannel.format = struct.Struct((RTAnalogChannel.format_str % device.sample_count))
for _ in range(device.channel_count):
(component_position, channel) = QRTPacket._get_tuple(RTAnalogChannel, data, component_position)
append_components((device, sample_number, channel))
return components
|
Get analog data.
|
qtm/packet.py
|
get_analog
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentAnalog, RTAnalogComponent)
def get_analog(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDevice, data, component_position)
if (device.sample_count > 0):
(component_position, sample_number) = QRTPacket._get_exact(RTSampleNumber, data, component_position)
RTAnalogChannel.format = struct.Struct((RTAnalogChannel.format_str % device.sample_count))
for _ in range(device.channel_count):
(component_position, channel) = QRTPacket._get_tuple(RTAnalogChannel, data, component_position)
append_components((device, sample_number, channel))
return components
|
@ComponentGetter(QRTComponentType.ComponentAnalog, RTAnalogComponent)
def get_analog(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDevice, data, component_position)
if (device.sample_count > 0):
(component_position, sample_number) = QRTPacket._get_exact(RTSampleNumber, data, component_position)
RTAnalogChannel.format = struct.Struct((RTAnalogChannel.format_str % device.sample_count))
for _ in range(device.channel_count):
(component_position, channel) = QRTPacket._get_tuple(RTAnalogChannel, data, component_position)
append_components((device, sample_number, channel))
return components<|docstring|>Get analog data.<|endoftext|>
|
d7fb4fd81096f80919ce717f24e1584c79c891494a23651244cc9e11bae5ff63
|
@ComponentGetter(QRTComponentType.ComponentAnalogSingle, RTAnalogComponent)
def get_analog_single(self, component_info=None, data=None, component_position=None):
'Get a single analog data channel.'
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDeviceSingle, data, component_position)
RTAnalogDeviceSamples.format = struct.Struct((RTAnalogDeviceSamples.format_str % device.channel_count))
(component_position, sample) = QRTPacket._get_tuple(RTAnalogDeviceSamples, data, component_position)
append_components((device, sample))
return components
|
Get a single analog data channel.
|
qtm/packet.py
|
get_analog_single
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentAnalogSingle, RTAnalogComponent)
def get_analog_single(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDeviceSingle, data, component_position)
RTAnalogDeviceSamples.format = struct.Struct((RTAnalogDeviceSamples.format_str % device.channel_count))
(component_position, sample) = QRTPacket._get_tuple(RTAnalogDeviceSamples, data, component_position)
append_components((device, sample))
return components
|
@ComponentGetter(QRTComponentType.ComponentAnalogSingle, RTAnalogComponent)
def get_analog_single(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.device_count):
(component_position, device) = QRTPacket._get_exact(RTAnalogDeviceSingle, data, component_position)
RTAnalogDeviceSamples.format = struct.Struct((RTAnalogDeviceSamples.format_str % device.channel_count))
(component_position, sample) = QRTPacket._get_tuple(RTAnalogDeviceSamples, data, component_position)
append_components((device, sample))
return components<|docstring|>Get a single analog data channel.<|endoftext|>
|
2da3f46195f541e9da7c07d1f54f852384851ee1b0e4a89a6ad0e4ecab809ad2
|
@ComponentGetter(QRTComponentType.ComponentForce, RTForceComponent)
def get_force(self, component_info=None, data=None, component_position=None):
'Get force data.'
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlate, data, component_position)
force_list = []
for _ in range(plate.force_count):
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
force_list.append(force)
append_components((plate, force_list))
return components
|
Get force data.
|
qtm/packet.py
|
get_force
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentForce, RTForceComponent)
def get_force(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlate, data, component_position)
force_list = []
for _ in range(plate.force_count):
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
force_list.append(force)
append_components((plate, force_list))
return components
|
@ComponentGetter(QRTComponentType.ComponentForce, RTForceComponent)
def get_force(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlate, data, component_position)
force_list = []
for _ in range(plate.force_count):
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
force_list.append(force)
append_components((plate, force_list))
return components<|docstring|>Get force data.<|endoftext|>
|
da0c189efa86637659560e81b3832ffbc2a7db720eebaa156d59a4d04f346cc9
|
@ComponentGetter(QRTComponentType.ComponentForceSingle, RTForceComponent)
def get_force_single(self, component_info=None, data=None, component_position=None):
'Get a single force data channel.'
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlateSingle, data, component_position)
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
append_components((plate, force))
return components
|
Get a single force data channel.
|
qtm/packet.py
|
get_force_single
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentForceSingle, RTForceComponent)
def get_force_single(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlateSingle, data, component_position)
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
append_components((plate, force))
return components
|
@ComponentGetter(QRTComponentType.ComponentForceSingle, RTForceComponent)
def get_force_single(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.plate_count):
(component_position, plate) = QRTPacket._get_exact(RTForcePlateSingle, data, component_position)
(component_position, force) = QRTPacket._get_exact(RTForce, data, component_position)
append_components((plate, force))
return components<|docstring|>Get a single force data channel.<|endoftext|>
|
2fb20efcdee4453629576331573ec0ed6b5c116e3401975ba937f51168d480aa
|
@ComponentGetter(QRTComponentType.Component6d, RT6DComponent)
def get_6d(self, component_info=None, data=None, component_position=None):
'Get 6D data.'
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
append_components((position, matrix))
return components
|
Get 6D data.
|
qtm/packet.py
|
get_6d
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component6d, RT6DComponent)
def get_6d(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
append_components((position, matrix))
return components
|
@ComponentGetter(QRTComponentType.Component6d, RT6DComponent)
def get_6d(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
append_components((position, matrix))
return components<|docstring|>Get 6D data.<|endoftext|>
|
1d6bd7e47f13a5d067369345ecc41f0b000118b9bae4f4619a4008de3b5b3ad5
|
@ComponentGetter(QRTComponentType.Component6dRes, RT6DComponent)
def get_6d_residual(self, component_info=None, data=None, component_position=None):
'Get 6D data with residual.'
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, matrix, residual))
return components
|
Get 6D data with residual.
|
qtm/packet.py
|
get_6d_residual
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component6dRes, RT6DComponent)
def get_6d_residual(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, matrix, residual))
return components
|
@ComponentGetter(QRTComponentType.Component6dRes, RT6DComponent)
def get_6d_residual(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, matrix) = QRTPacket._get_tuple(RT6DBodyRotation, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, matrix, residual))
return components<|docstring|>Get 6D data with residual.<|endoftext|>
|
0a6cfd367c90ba0e883fe1ca071fab22250ddc4a8d5f3483b81e0623e3d4f812
|
@ComponentGetter(QRTComponentType.Component6dEuler, RT6DComponent)
def get_6d_euler(self, component_info=None, data=None, component_position=None):
'Get 6D data with euler rotations.'
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
append_components((position, euler))
return components
|
Get 6D data with euler rotations.
|
qtm/packet.py
|
get_6d_euler
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component6dEuler, RT6DComponent)
def get_6d_euler(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
append_components((position, euler))
return components
|
@ComponentGetter(QRTComponentType.Component6dEuler, RT6DComponent)
def get_6d_euler(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
append_components((position, euler))
return components<|docstring|>Get 6D data with euler rotations.<|endoftext|>
|
4d93d223fb3a061be3ef5f7b3b4dd562a381c5d15e1be4bf3531de96f24623fd
|
@ComponentGetter(QRTComponentType.Component6dEulerRes, RT6DComponent)
def get_6d_euler_residual(self, component_info=None, data=None, component_position=None):
'Get 6D data with residuals and euler rotations.'
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, euler, residual))
return components
|
Get 6D data with residuals and euler rotations.
|
qtm/packet.py
|
get_6d_euler_residual
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component6dEulerRes, RT6DComponent)
def get_6d_euler_residual(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, euler, residual))
return components
|
@ComponentGetter(QRTComponentType.Component6dEulerRes, RT6DComponent)
def get_6d_euler_residual(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.body_count):
(component_position, position) = QRTPacket._get_exact(RT6DBodyPosition, data, component_position)
(component_position, euler) = QRTPacket._get_exact(RT6DBodyEuler, data, component_position)
(component_position, residual) = QRTPacket._get_exact(RT6DBodyResidual, data, component_position)
append_components((position, euler, residual))
return components<|docstring|>Get 6D data with residuals and euler rotations.<|endoftext|>
|
88dd8cbebf329ff2db4f8cfac393131e501b4bf2e5ba24c000d8aab9d36ecfa9
|
@ComponentGetter(QRTComponentType.ComponentImage, RTImageComponent)
def get_image(self, component_info=None, data=None, component_position=None):
'Get image.'
components = []
append_components = components.append
for _ in range(component_info.image_count):
(component_position, image_info) = QRTPacket._get_exact(RTImage, data, component_position)
append_components((image_info, data[component_position:(component_position + image_info.image_size)]))
component_position += image_info.image_size
return components
|
Get image.
|
qtm/packet.py
|
get_image
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentImage, RTImageComponent)
def get_image(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.image_count):
(component_position, image_info) = QRTPacket._get_exact(RTImage, data, component_position)
append_components((image_info, data[component_position:(component_position + image_info.image_size)]))
component_position += image_info.image_size
return components
|
@ComponentGetter(QRTComponentType.ComponentImage, RTImageComponent)
def get_image(self, component_info=None, data=None, component_position=None):
components = []
append_components = components.append
for _ in range(component_info.image_count):
(component_position, image_info) = QRTPacket._get_exact(RTImage, data, component_position)
append_components((image_info, data[component_position:(component_position + image_info.image_size)]))
component_position += image_info.image_size
return components<|docstring|>Get image.<|endoftext|>
|
c9debb4854e543fb4adf33e4cba10a68ad2881b68874f7ba62603b67397904b4
|
@ComponentGetter(QRTComponentType.Component3d, RT3DComponent)
def get_3d_markers(self, component_info=None, data=None, component_position=None):
'Get 3D markers.'
return self._get_3d_markers(RT3DMarkerPosition, component_info, data, component_position)
|
Get 3D markers.
|
qtm/packet.py
|
get_3d_markers
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component3d, RT3DComponent)
def get_3d_markers(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPosition, component_info, data, component_position)
|
@ComponentGetter(QRTComponentType.Component3d, RT3DComponent)
def get_3d_markers(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPosition, component_info, data, component_position)<|docstring|>Get 3D markers.<|endoftext|>
|
989c655f1f067617c2348c04cea5cbeb18aaea52472c36922fb0b082bc7c7809
|
@ComponentGetter(QRTComponentType.Component3dRes, RT3DComponent)
def get_3d_markers_residual(self, component_info=None, data=None, component_position=None):
'Get 3D markers with residual.'
return self._get_3d_markers(RT3DMarkerPositionResidual, component_info, data, component_position)
|
Get 3D markers with residual.
|
qtm/packet.py
|
get_3d_markers_residual
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component3dRes, RT3DComponent)
def get_3d_markers_residual(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionResidual, component_info, data, component_position)
|
@ComponentGetter(QRTComponentType.Component3dRes, RT3DComponent)
def get_3d_markers_residual(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionResidual, component_info, data, component_position)<|docstring|>Get 3D markers with residual.<|endoftext|>
|
02561f6ef946d5ca361f35fda9d1e64b80f31e8553f0a0ff152402c53a95a667
|
@ComponentGetter(QRTComponentType.Component3dNoLabels, RT3DComponent)
def get_3d_markers_no_label(self, component_info=None, data=None, component_position=None):
'Get 3D markers without label.'
return self._get_3d_markers(RT3DMarkerPositionNoLabel, component_info, data, component_position)
|
Get 3D markers without label.
|
qtm/packet.py
|
get_3d_markers_no_label
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component3dNoLabels, RT3DComponent)
def get_3d_markers_no_label(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionNoLabel, component_info, data, component_position)
|
@ComponentGetter(QRTComponentType.Component3dNoLabels, RT3DComponent)
def get_3d_markers_no_label(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionNoLabel, component_info, data, component_position)<|docstring|>Get 3D markers without label.<|endoftext|>
|
8685680e80a41f80d567d8944fbd2bf6b2308eecd4bf12912806d0d526903592
|
@ComponentGetter(QRTComponentType.Component3dNoLabelsRes, RT3DComponent)
def get_3d_markers_no_label_residual(self, component_info=None, data=None, component_position=None):
'Get 3D markers without label with residual.'
return self._get_3d_markers(RT3DMarkerPositionNoLabelResidual, component_info, data, component_position)
|
Get 3D markers without label with residual.
|
qtm/packet.py
|
get_3d_markers_no_label_residual
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component3dNoLabelsRes, RT3DComponent)
def get_3d_markers_no_label_residual(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionNoLabelResidual, component_info, data, component_position)
|
@ComponentGetter(QRTComponentType.Component3dNoLabelsRes, RT3DComponent)
def get_3d_markers_no_label_residual(self, component_info=None, data=None, component_position=None):
return self._get_3d_markers(RT3DMarkerPositionNoLabelResidual, component_info, data, component_position)<|docstring|>Get 3D markers without label with residual.<|endoftext|>
|
66b10634c1c8d7851fb4891717c905c1f7ccc959c47d6ddb1af86b9d34013126
|
@ComponentGetter(QRTComponentType.Component2d, RT2DComponent)
def get_2d_markers(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)
|
Get 2D markers.
:param index: Specify which camera to get 2D from, will be returned as
first entry in the returned array.
|
qtm/packet.py
|
get_2d_markers
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component2d, RT2DComponent)
def get_2d_markers(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)
|
@ComponentGetter(QRTComponentType.Component2d, RT2DComponent)
def get_2d_markers(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)<|docstring|>Get 2D markers.
:param index: Specify which camera to get 2D from, will be returned as
first entry in the returned array.<|endoftext|>
|
2b50094dd9fcb237b0f6e6aa8f41f3bff696c88f4aa96f412976a4c6ecb0158b
|
@ComponentGetter(QRTComponentType.Component2dLin, RT2DComponent)
def get_2d_markers_linearized(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D linearized markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)
|
Get 2D linearized markers.
:param index: Specify which camera to get 2D from, will be returned as
first entry in the returned array.
|
qtm/packet.py
|
get_2d_markers_linearized
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.Component2dLin, RT2DComponent)
def get_2d_markers_linearized(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D linearized markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)
|
@ComponentGetter(QRTComponentType.Component2dLin, RT2DComponent)
def get_2d_markers_linearized(self, component_info=None, data=None, component_position=None, index=None):
'Get 2D linearized markers.\n\n :param index: Specify which camera to get 2D from, will be returned as\n first entry in the returned array.\n '
return self._get_2d_markers(data, component_info, component_position, index=index)<|docstring|>Get 2D linearized markers.
:param index: Specify which camera to get 2D from, will be returned as
first entry in the returned array.<|endoftext|>
|
23a6a611acc946a289a2e561fec942a7015b6e2435917d6b43a35853c36825e4
|
@ComponentGetter(QRTComponentType.ComponentSkeleton, RTSkeletonComponent)
def get_skeletons(self, component_info=None, data=None, component_position=None):
'Get skeletons\n '
components = []
append_components = components.append
for _ in range(component_info.skeleton_count):
(component_position, info) = QRTPacket._get_exact(RTSegmentCount, data, component_position)
segments = []
for __ in range(info.segment_count):
(component_position, segment) = QRTPacket._get_exact(RTSegmentId, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTSegmentPosition, data, component_position)
(component_position, rotation) = QRTPacket._get_exact(RTSegmentRotation, data, component_position)
segments.append((segment.id, position, rotation))
append_components(segments)
return components
|
Get skeletons
|
qtm/packet.py
|
get_skeletons
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentSkeleton, RTSkeletonComponent)
def get_skeletons(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.skeleton_count):
(component_position, info) = QRTPacket._get_exact(RTSegmentCount, data, component_position)
segments = []
for __ in range(info.segment_count):
(component_position, segment) = QRTPacket._get_exact(RTSegmentId, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTSegmentPosition, data, component_position)
(component_position, rotation) = QRTPacket._get_exact(RTSegmentRotation, data, component_position)
segments.append((segment.id, position, rotation))
append_components(segments)
return components
|
@ComponentGetter(QRTComponentType.ComponentSkeleton, RTSkeletonComponent)
def get_skeletons(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.skeleton_count):
(component_position, info) = QRTPacket._get_exact(RTSegmentCount, data, component_position)
segments = []
for __ in range(info.segment_count):
(component_position, segment) = QRTPacket._get_exact(RTSegmentId, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTSegmentPosition, data, component_position)
(component_position, rotation) = QRTPacket._get_exact(RTSegmentRotation, data, component_position)
segments.append((segment.id, position, rotation))
append_components(segments)
return components<|docstring|>Get skeletons<|endoftext|>
|
0492bdec4a14e9aaf3d4f49f66d35bb5b505265ef5292b8f0fde0fc26d3c9656
|
@ComponentGetter(QRTComponentType.ComponentGazeVector, RTGazeVectorComponent)
def get_gaze_vectors(self, component_info=None, data=None, component_position=None):
'Get gaze vectors\n '
components = []
append_components = components.append
for _ in range(component_info.vector_count):
(component_position, info) = QRTPacket._get_exact(RTGazeVectorInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, unit_vector) = QRTPacket._get_exact(RTGazeVectorUnitVector, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTGazeVectorPosition, data, component_position)
samples.append((unit_vector, position))
append_components((info, samples))
return components
|
Get gaze vectors
|
qtm/packet.py
|
get_gaze_vectors
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentGazeVector, RTGazeVectorComponent)
def get_gaze_vectors(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.vector_count):
(component_position, info) = QRTPacket._get_exact(RTGazeVectorInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, unit_vector) = QRTPacket._get_exact(RTGazeVectorUnitVector, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTGazeVectorPosition, data, component_position)
samples.append((unit_vector, position))
append_components((info, samples))
return components
|
@ComponentGetter(QRTComponentType.ComponentGazeVector, RTGazeVectorComponent)
def get_gaze_vectors(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.vector_count):
(component_position, info) = QRTPacket._get_exact(RTGazeVectorInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, unit_vector) = QRTPacket._get_exact(RTGazeVectorUnitVector, data, component_position)
(component_position, position) = QRTPacket._get_exact(RTGazeVectorPosition, data, component_position)
samples.append((unit_vector, position))
append_components((info, samples))
return components<|docstring|>Get gaze vectors<|endoftext|>
|
2adf42218cd6a81f292c150a53ffc27405f5dfc6770c879521b0737e1daadce8
|
@ComponentGetter(QRTComponentType.ComponentEyeTracker, RTEyeTrackerComponent)
def get_eye_trackers(self, component_info=None, data=None, component_position=None):
'Get eye trackers\n '
components = []
append_components = components.append
for _ in range(component_info.eye_tracker_count):
(component_position, info) = QRTPacket._get_exact(RTEyeTrackerInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, diameter) = QRTPacket._get_exact(RTEyeTrackerDiameter, data, component_position)
samples.append(diameter)
append_components((info, samples))
return components
|
Get eye trackers
|
qtm/packet.py
|
get_eye_trackers
|
qualisys/qualisys_python_sdk
| 24
|
python
|
@ComponentGetter(QRTComponentType.ComponentEyeTracker, RTEyeTrackerComponent)
def get_eye_trackers(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.eye_tracker_count):
(component_position, info) = QRTPacket._get_exact(RTEyeTrackerInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, diameter) = QRTPacket._get_exact(RTEyeTrackerDiameter, data, component_position)
samples.append(diameter)
append_components((info, samples))
return components
|
@ComponentGetter(QRTComponentType.ComponentEyeTracker, RTEyeTrackerComponent)
def get_eye_trackers(self, component_info=None, data=None, component_position=None):
'\n '
components = []
append_components = components.append
for _ in range(component_info.eye_tracker_count):
(component_position, info) = QRTPacket._get_exact(RTEyeTrackerInfo, data, component_position)
samples = []
if (info.sample_count > 0):
for _ in range(info.sample_count):
(component_position, diameter) = QRTPacket._get_exact(RTEyeTrackerDiameter, data, component_position)
samples.append(diameter)
append_components((info, samples))
return components<|docstring|>Get eye trackers<|endoftext|>
|
6f7c8dec5961b1b7757bc0fb9bfda6ecf24cf5087ea0ff919527aba7a47a0b0f
|
def to_categorical(y, nb_classes=None):
'Converts a class vector (integers) to binary class matrix.\n\n E.g. for use with categorical_crossentropy.\n\n # Arguments\n y: class vector to be converted into a matrix\n (integers from 0 to nb_classes).\n nb_classes: total number of classes.\n\n # Returns\n A binary matrix representation of the input.\n '
y = np.array(y, dtype='int').ravel()
if (not nb_classes):
nb_classes = (np.max(y) + 1)
n = y.shape[0]
categorical = np.zeros((n, nb_classes))
categorical[(np.arange(n), y)] = 1
return categorical
|
Converts a class vector (integers) to binary class matrix.
E.g. for use with categorical_crossentropy.
# Arguments
y: class vector to be converted into a matrix
(integers from 0 to nb_classes).
nb_classes: total number of classes.
# Returns
A binary matrix representation of the input.
|
keras/utils/np_utils.py
|
to_categorical
|
digimatronics/keras
| 150
|
python
|
def to_categorical(y, nb_classes=None):
'Converts a class vector (integers) to binary class matrix.\n\n E.g. for use with categorical_crossentropy.\n\n # Arguments\n y: class vector to be converted into a matrix\n (integers from 0 to nb_classes).\n nb_classes: total number of classes.\n\n # Returns\n A binary matrix representation of the input.\n '
y = np.array(y, dtype='int').ravel()
if (not nb_classes):
nb_classes = (np.max(y) + 1)
n = y.shape[0]
categorical = np.zeros((n, nb_classes))
categorical[(np.arange(n), y)] = 1
return categorical
|
def to_categorical(y, nb_classes=None):
'Converts a class vector (integers) to binary class matrix.\n\n E.g. for use with categorical_crossentropy.\n\n # Arguments\n y: class vector to be converted into a matrix\n (integers from 0 to nb_classes).\n nb_classes: total number of classes.\n\n # Returns\n A binary matrix representation of the input.\n '
y = np.array(y, dtype='int').ravel()
if (not nb_classes):
nb_classes = (np.max(y) + 1)
n = y.shape[0]
categorical = np.zeros((n, nb_classes))
categorical[(np.arange(n), y)] = 1
return categorical<|docstring|>Converts a class vector (integers) to binary class matrix.
E.g. for use with categorical_crossentropy.
# Arguments
y: class vector to be converted into a matrix
(integers from 0 to nb_classes).
nb_classes: total number of classes.
# Returns
A binary matrix representation of the input.<|endoftext|>
|
8211c605f6880ddc8008244157c6e09d0c377d7f6c4a0ebed57793c1a8ca615c
|
def convert_kernel(kernel, dim_ordering=None):
'Converts a Numpy kernel matrix from Theano format to TensorFlow format.\n\n Also works reciprocally, since the transformation is its own inverse.\n\n # Arguments\n kernel: Numpy array (4D or 5D).\n dim_ordering: the data format.\n\n # Returns\n The converted kernel.\n\n # Raises\n ValueError: in case of invalid kernel shape or invalid dim_ordering.\n '
if (dim_ordering is None):
dim_ordering = K.image_dim_ordering()
if (not (4 <= kernel.ndim <= 5)):
raise ValueError('Invalid kernel shape:', kernel.shape)
slices = [slice(None, None, (- 1)) for _ in range(kernel.ndim)]
no_flip = (slice(None, None), slice(None, None))
if (dim_ordering == 'th'):
slices[:2] = no_flip
elif (dim_ordering == 'tf'):
slices[(- 2):] = no_flip
else:
raise ValueError('Invalid dim_ordering:', dim_ordering)
return np.copy(kernel[slices])
|
Converts a Numpy kernel matrix from Theano format to TensorFlow format.
Also works reciprocally, since the transformation is its own inverse.
# Arguments
kernel: Numpy array (4D or 5D).
dim_ordering: the data format.
# Returns
The converted kernel.
# Raises
ValueError: in case of invalid kernel shape or invalid dim_ordering.
|
keras/utils/np_utils.py
|
convert_kernel
|
digimatronics/keras
| 150
|
python
|
def convert_kernel(kernel, dim_ordering=None):
'Converts a Numpy kernel matrix from Theano format to TensorFlow format.\n\n Also works reciprocally, since the transformation is its own inverse.\n\n # Arguments\n kernel: Numpy array (4D or 5D).\n dim_ordering: the data format.\n\n # Returns\n The converted kernel.\n\n # Raises\n ValueError: in case of invalid kernel shape or invalid dim_ordering.\n '
if (dim_ordering is None):
dim_ordering = K.image_dim_ordering()
if (not (4 <= kernel.ndim <= 5)):
raise ValueError('Invalid kernel shape:', kernel.shape)
slices = [slice(None, None, (- 1)) for _ in range(kernel.ndim)]
no_flip = (slice(None, None), slice(None, None))
if (dim_ordering == 'th'):
slices[:2] = no_flip
elif (dim_ordering == 'tf'):
slices[(- 2):] = no_flip
else:
raise ValueError('Invalid dim_ordering:', dim_ordering)
return np.copy(kernel[slices])
|
def convert_kernel(kernel, dim_ordering=None):
'Converts a Numpy kernel matrix from Theano format to TensorFlow format.\n\n Also works reciprocally, since the transformation is its own inverse.\n\n # Arguments\n kernel: Numpy array (4D or 5D).\n dim_ordering: the data format.\n\n # Returns\n The converted kernel.\n\n # Raises\n ValueError: in case of invalid kernel shape or invalid dim_ordering.\n '
if (dim_ordering is None):
dim_ordering = K.image_dim_ordering()
if (not (4 <= kernel.ndim <= 5)):
raise ValueError('Invalid kernel shape:', kernel.shape)
slices = [slice(None, None, (- 1)) for _ in range(kernel.ndim)]
no_flip = (slice(None, None), slice(None, None))
if (dim_ordering == 'th'):
slices[:2] = no_flip
elif (dim_ordering == 'tf'):
slices[(- 2):] = no_flip
else:
raise ValueError('Invalid dim_ordering:', dim_ordering)
return np.copy(kernel[slices])<|docstring|>Converts a Numpy kernel matrix from Theano format to TensorFlow format.
Also works reciprocally, since the transformation is its own inverse.
# Arguments
kernel: Numpy array (4D or 5D).
dim_ordering: the data format.
# Returns
The converted kernel.
# Raises
ValueError: in case of invalid kernel shape or invalid dim_ordering.<|endoftext|>
|
ebb843798b7ad1655688609c27c947dcefe46c6ff3e899349eda06db516baead
|
def conv_output_length(input_length, filter_size, border_mode, stride, dilation=1):
'Determines output length of a convolution given input length.\n\n # Arguments\n input_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n dilation: dilation rate, integer.\n\n # Returns\n The output length (integer).\n '
if (input_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
dilated_filter_size = (filter_size + ((filter_size - 1) * (dilation - 1)))
if (border_mode == 'same'):
output_length = input_length
elif (border_mode == 'valid'):
output_length = ((input_length - dilated_filter_size) + 1)
elif (border_mode == 'full'):
output_length = ((input_length + dilated_filter_size) - 1)
return (((output_length + stride) - 1) // stride)
|
Determines output length of a convolution given input length.
# Arguments
input_length: integer.
filter_size: integer.
border_mode: one of "same", "valid", "full".
stride: integer.
dilation: dilation rate, integer.
# Returns
The output length (integer).
|
keras/utils/np_utils.py
|
conv_output_length
|
digimatronics/keras
| 150
|
python
|
def conv_output_length(input_length, filter_size, border_mode, stride, dilation=1):
'Determines output length of a convolution given input length.\n\n # Arguments\n input_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n dilation: dilation rate, integer.\n\n # Returns\n The output length (integer).\n '
if (input_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
dilated_filter_size = (filter_size + ((filter_size - 1) * (dilation - 1)))
if (border_mode == 'same'):
output_length = input_length
elif (border_mode == 'valid'):
output_length = ((input_length - dilated_filter_size) + 1)
elif (border_mode == 'full'):
output_length = ((input_length + dilated_filter_size) - 1)
return (((output_length + stride) - 1) // stride)
|
def conv_output_length(input_length, filter_size, border_mode, stride, dilation=1):
'Determines output length of a convolution given input length.\n\n # Arguments\n input_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n dilation: dilation rate, integer.\n\n # Returns\n The output length (integer).\n '
if (input_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
dilated_filter_size = (filter_size + ((filter_size - 1) * (dilation - 1)))
if (border_mode == 'same'):
output_length = input_length
elif (border_mode == 'valid'):
output_length = ((input_length - dilated_filter_size) + 1)
elif (border_mode == 'full'):
output_length = ((input_length + dilated_filter_size) - 1)
return (((output_length + stride) - 1) // stride)<|docstring|>Determines output length of a convolution given input length.
# Arguments
input_length: integer.
filter_size: integer.
border_mode: one of "same", "valid", "full".
stride: integer.
dilation: dilation rate, integer.
# Returns
The output length (integer).<|endoftext|>
|
295c4f31a829dede1fc1e84e0cf5531c4c3fd1d528291816ce7de78074610a20
|
def conv_input_length(output_length, filter_size, border_mode, stride):
'Determines input length of a convolution given output length.\n\n # Arguments\n output_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n\n # Returns\n The input length (integer).\n '
if (output_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
if (border_mode == 'same'):
pad = (filter_size // 2)
elif (border_mode == 'valid'):
pad = 0
elif (border_mode == 'full'):
pad = (filter_size - 1)
return ((((output_length - 1) * stride) - (2 * pad)) + filter_size)
|
Determines input length of a convolution given output length.
# Arguments
output_length: integer.
filter_size: integer.
border_mode: one of "same", "valid", "full".
stride: integer.
# Returns
The input length (integer).
|
keras/utils/np_utils.py
|
conv_input_length
|
digimatronics/keras
| 150
|
python
|
def conv_input_length(output_length, filter_size, border_mode, stride):
'Determines input length of a convolution given output length.\n\n # Arguments\n output_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n\n # Returns\n The input length (integer).\n '
if (output_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
if (border_mode == 'same'):
pad = (filter_size // 2)
elif (border_mode == 'valid'):
pad = 0
elif (border_mode == 'full'):
pad = (filter_size - 1)
return ((((output_length - 1) * stride) - (2 * pad)) + filter_size)
|
def conv_input_length(output_length, filter_size, border_mode, stride):
'Determines input length of a convolution given output length.\n\n # Arguments\n output_length: integer.\n filter_size: integer.\n border_mode: one of "same", "valid", "full".\n stride: integer.\n\n # Returns\n The input length (integer).\n '
if (output_length is None):
return None
assert (border_mode in {'same', 'valid', 'full'})
if (border_mode == 'same'):
pad = (filter_size // 2)
elif (border_mode == 'valid'):
pad = 0
elif (border_mode == 'full'):
pad = (filter_size - 1)
return ((((output_length - 1) * stride) - (2 * pad)) + filter_size)<|docstring|>Determines input length of a convolution given output length.
# Arguments
output_length: integer.
filter_size: integer.
border_mode: one of "same", "valid", "full".
stride: integer.
# Returns
The input length (integer).<|endoftext|>
|
78a630ce9696d3694ac4f6e99eba784984f33fda3bb004b73794424b5afce1db
|
def _find_puzzle_section(self, thr_img):
'\n Finds the biggest square in the image (that should be the puzzle)\n After the coordinates of the corners are found (top-lef, top-right,\n bottom-left, bottom-right), the perspective is warped so that\n puzzle_img contains only the puzzle.\n :param thr_img:\n :return:\n '
(img, contours, h) = cv2.findContours(thr_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
biggest = []
max_area = (- 1)
for i in contours:
area = cv2.contourArea(i)
if (area > 100):
perimeter = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, (0.02 * perimeter), True)
if ((area > max_area) and (len(approx) == 4)):
biggest = approx
max_area = area
if (not len(biggest)):
raise PuzzleNotFound
(p1, p2, p3, p4) = uniformize_points(biggest[0][0], biggest[1][0], biggest[2][0], biggest[3][0])
self._puzzle_coords.extend([p1, p2, p3, p4])
pts1 = np.float32([[p1, p2, p3, p4]])
pts2 = np.float32([[0, 0], [(self.PUZZLE_SIZE - 1), 0], [0, (self.PUZZLE_SIZE - 1)], [(self.PUZZLE_SIZE - 1), (self.PUZZLE_SIZE - 1)]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
puzzle_img = cv2.warpPerspective(self.img_gray, matrix, (self.PUZZLE_SIZE, self.PUZZLE_SIZE))
if (max_area > (self.PUZZLE_SIZE ** 2)):
puzzle_img = cv2.GaussianBlur(puzzle_img, (5, 5), 0)
if self.debug_mode:
color_white = (255, 255, 255)
poly_points = np.array([[p1[0], p1[1]], [p2[0], p2[1]], [p4[0], p4[1]], [p3[0], p3[1]]], dtype=np.int32)
cv2.polylines(img, [poly_points], isClosed=True, color=color_white, thickness=3)
img_show(thr_img)
return puzzle_img
|
Finds the biggest square in the image (that should be the puzzle)
After the coordinates of the corners are found (top-lef, top-right,
bottom-left, bottom-right), the perspective is warped so that
puzzle_img contains only the puzzle.
:param thr_img:
:return:
|
sudoku_solver/finder.py
|
_find_puzzle_section
|
bbuhai/sudoku-solver
| 0
|
python
|
def _find_puzzle_section(self, thr_img):
'\n Finds the biggest square in the image (that should be the puzzle)\n After the coordinates of the corners are found (top-lef, top-right,\n bottom-left, bottom-right), the perspective is warped so that\n puzzle_img contains only the puzzle.\n :param thr_img:\n :return:\n '
(img, contours, h) = cv2.findContours(thr_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
biggest = []
max_area = (- 1)
for i in contours:
area = cv2.contourArea(i)
if (area > 100):
perimeter = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, (0.02 * perimeter), True)
if ((area > max_area) and (len(approx) == 4)):
biggest = approx
max_area = area
if (not len(biggest)):
raise PuzzleNotFound
(p1, p2, p3, p4) = uniformize_points(biggest[0][0], biggest[1][0], biggest[2][0], biggest[3][0])
self._puzzle_coords.extend([p1, p2, p3, p4])
pts1 = np.float32([[p1, p2, p3, p4]])
pts2 = np.float32([[0, 0], [(self.PUZZLE_SIZE - 1), 0], [0, (self.PUZZLE_SIZE - 1)], [(self.PUZZLE_SIZE - 1), (self.PUZZLE_SIZE - 1)]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
puzzle_img = cv2.warpPerspective(self.img_gray, matrix, (self.PUZZLE_SIZE, self.PUZZLE_SIZE))
if (max_area > (self.PUZZLE_SIZE ** 2)):
puzzle_img = cv2.GaussianBlur(puzzle_img, (5, 5), 0)
if self.debug_mode:
color_white = (255, 255, 255)
poly_points = np.array([[p1[0], p1[1]], [p2[0], p2[1]], [p4[0], p4[1]], [p3[0], p3[1]]], dtype=np.int32)
cv2.polylines(img, [poly_points], isClosed=True, color=color_white, thickness=3)
img_show(thr_img)
return puzzle_img
|
def _find_puzzle_section(self, thr_img):
'\n Finds the biggest square in the image (that should be the puzzle)\n After the coordinates of the corners are found (top-lef, top-right,\n bottom-left, bottom-right), the perspective is warped so that\n puzzle_img contains only the puzzle.\n :param thr_img:\n :return:\n '
(img, contours, h) = cv2.findContours(thr_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
biggest = []
max_area = (- 1)
for i in contours:
area = cv2.contourArea(i)
if (area > 100):
perimeter = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, (0.02 * perimeter), True)
if ((area > max_area) and (len(approx) == 4)):
biggest = approx
max_area = area
if (not len(biggest)):
raise PuzzleNotFound
(p1, p2, p3, p4) = uniformize_points(biggest[0][0], biggest[1][0], biggest[2][0], biggest[3][0])
self._puzzle_coords.extend([p1, p2, p3, p4])
pts1 = np.float32([[p1, p2, p3, p4]])
pts2 = np.float32([[0, 0], [(self.PUZZLE_SIZE - 1), 0], [0, (self.PUZZLE_SIZE - 1)], [(self.PUZZLE_SIZE - 1), (self.PUZZLE_SIZE - 1)]])
matrix = cv2.getPerspectiveTransform(pts1, pts2)
puzzle_img = cv2.warpPerspective(self.img_gray, matrix, (self.PUZZLE_SIZE, self.PUZZLE_SIZE))
if (max_area > (self.PUZZLE_SIZE ** 2)):
puzzle_img = cv2.GaussianBlur(puzzle_img, (5, 5), 0)
if self.debug_mode:
color_white = (255, 255, 255)
poly_points = np.array([[p1[0], p1[1]], [p2[0], p2[1]], [p4[0], p4[1]], [p3[0], p3[1]]], dtype=np.int32)
cv2.polylines(img, [poly_points], isClosed=True, color=color_white, thickness=3)
img_show(thr_img)
return puzzle_img<|docstring|>Finds the biggest square in the image (that should be the puzzle)
After the coordinates of the corners are found (top-lef, top-right,
bottom-left, bottom-right), the perspective is warped so that
puzzle_img contains only the puzzle.
:param thr_img:
:return:<|endoftext|>
|
55f2e6b96cecb17000936d40c7b1d35d4f1be55828490c9be547abc21d2f62a7
|
def _get_puzzle_matrix(self, puzzle_img):
'\n Returns a 9x9 matrix with the found digits.\n\n Puzzles may have different font sizes and because of that multiple iterations\n are required to find the best width / height.\n\n Another idea is to remove the sudoku grid first and then use the hierarchy\n from findContours to retrieve only the top-level contours.\n (http://answers.opencv.org/question/53293/how-to-remove-line-on-music-sheet/)\n :param img:\n :param original:\n :return:\n '
img_copy = np.copy(puzzle_img)
img_color = cv2.cvtColor(img_copy, cv2.COLOR_GRAY2BGR)
(i, contours, _) = cv2.findContours(puzzle_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
erode = cv2.erode(img_copy, kernel)
img_dilate = cv2.dilate(erode, kernel)
trained_knn = get_trained_knn()
sudoku_matrix_final = []
(min_w, max_w) = (5, 40)
(min_h, max_h) = (44, 59)
step = 0
coords_digits_fun = []
coords_digits_final = []
for _ in xrange(33):
coords_digits = []
sudoku_matrix = np.empty((9, 9), dtype=np.object)
step += 1
i = 0
for cnt in contours:
if (cv2.contourArea(cnt) < 20):
continue
(x, y, w, h) = cv2.boundingRect(cnt)
if ((min_w < w < max_w) and ((min_h - step) < h < (max_h - step))):
a = (y / 50)
b = (x / 50)
if ((a, b) in coords_digits):
break
roi = img_dilate[(y:(y + h), x:(x + w))]
digit = cv2.resize(roi, (self.DIGIT_RESIZE_W, self.DIGIT_RESIZE_H))
num_total_px = (self.DIGIT_RESIZE_H * self.DIGIT_RESIZE_W)
test_data = digit.reshape(((- 1), num_total_px)).astype(np.float32)
(_, result, _, _) = trained_knn.findNearest(test_data, k=1)
coords_digits.append((a, b))
coords_digits_fun.append(((x + 3), (y + 3), int(result[(0, 0)]), i))
sudoku_matrix[(a, b)] = int(result[(0, 0)])
i += 1
if (len(coords_digits) > 16):
if (not len(sudoku_matrix_final)):
sudoku_matrix_final = np.copy(sudoku_matrix)
coords_digits_final = deepcopy(coords_digits_fun)
else:
len_matrix = len(sudoku_matrix[np.where((sudoku_matrix > 0))])
len_final = len(sudoku_matrix_final[np.where((sudoku_matrix_final > 0))])
if (len_matrix > len_final):
coords_digits_final = deepcopy(coords_digits_fun)
sudoku_matrix_final = np.copy(sudoku_matrix)
if self.debug_mode:
for (x, y, digit, idx) in coords_digits_final:
idx = str(idx)
digit = str(digit)
cv2.putText(img_color, digit, (x, y), cv2.FONT_HERSHEY_PLAIN, fontScale=1.5, color=(0, 0, 240), thickness=1, lineType=cv2.LINE_AA)
img_show(img_color)
return sudoku_matrix_final
|
Returns a 9x9 matrix with the found digits.
Puzzles may have different font sizes and because of that multiple iterations
are required to find the best width / height.
Another idea is to remove the sudoku grid first and then use the hierarchy
from findContours to retrieve only the top-level contours.
(http://answers.opencv.org/question/53293/how-to-remove-line-on-music-sheet/)
:param img:
:param original:
:return:
|
sudoku_solver/finder.py
|
_get_puzzle_matrix
|
bbuhai/sudoku-solver
| 0
|
python
|
def _get_puzzle_matrix(self, puzzle_img):
'\n Returns a 9x9 matrix with the found digits.\n\n Puzzles may have different font sizes and because of that multiple iterations\n are required to find the best width / height.\n\n Another idea is to remove the sudoku grid first and then use the hierarchy\n from findContours to retrieve only the top-level contours.\n (http://answers.opencv.org/question/53293/how-to-remove-line-on-music-sheet/)\n :param img:\n :param original:\n :return:\n '
img_copy = np.copy(puzzle_img)
img_color = cv2.cvtColor(img_copy, cv2.COLOR_GRAY2BGR)
(i, contours, _) = cv2.findContours(puzzle_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
erode = cv2.erode(img_copy, kernel)
img_dilate = cv2.dilate(erode, kernel)
trained_knn = get_trained_knn()
sudoku_matrix_final = []
(min_w, max_w) = (5, 40)
(min_h, max_h) = (44, 59)
step = 0
coords_digits_fun = []
coords_digits_final = []
for _ in xrange(33):
coords_digits = []
sudoku_matrix = np.empty((9, 9), dtype=np.object)
step += 1
i = 0
for cnt in contours:
if (cv2.contourArea(cnt) < 20):
continue
(x, y, w, h) = cv2.boundingRect(cnt)
if ((min_w < w < max_w) and ((min_h - step) < h < (max_h - step))):
a = (y / 50)
b = (x / 50)
if ((a, b) in coords_digits):
break
roi = img_dilate[(y:(y + h), x:(x + w))]
digit = cv2.resize(roi, (self.DIGIT_RESIZE_W, self.DIGIT_RESIZE_H))
num_total_px = (self.DIGIT_RESIZE_H * self.DIGIT_RESIZE_W)
test_data = digit.reshape(((- 1), num_total_px)).astype(np.float32)
(_, result, _, _) = trained_knn.findNearest(test_data, k=1)
coords_digits.append((a, b))
coords_digits_fun.append(((x + 3), (y + 3), int(result[(0, 0)]), i))
sudoku_matrix[(a, b)] = int(result[(0, 0)])
i += 1
if (len(coords_digits) > 16):
if (not len(sudoku_matrix_final)):
sudoku_matrix_final = np.copy(sudoku_matrix)
coords_digits_final = deepcopy(coords_digits_fun)
else:
len_matrix = len(sudoku_matrix[np.where((sudoku_matrix > 0))])
len_final = len(sudoku_matrix_final[np.where((sudoku_matrix_final > 0))])
if (len_matrix > len_final):
coords_digits_final = deepcopy(coords_digits_fun)
sudoku_matrix_final = np.copy(sudoku_matrix)
if self.debug_mode:
for (x, y, digit, idx) in coords_digits_final:
idx = str(idx)
digit = str(digit)
cv2.putText(img_color, digit, (x, y), cv2.FONT_HERSHEY_PLAIN, fontScale=1.5, color=(0, 0, 240), thickness=1, lineType=cv2.LINE_AA)
img_show(img_color)
return sudoku_matrix_final
|
def _get_puzzle_matrix(self, puzzle_img):
'\n Returns a 9x9 matrix with the found digits.\n\n Puzzles may have different font sizes and because of that multiple iterations\n are required to find the best width / height.\n\n Another idea is to remove the sudoku grid first and then use the hierarchy\n from findContours to retrieve only the top-level contours.\n (http://answers.opencv.org/question/53293/how-to-remove-line-on-music-sheet/)\n :param img:\n :param original:\n :return:\n '
img_copy = np.copy(puzzle_img)
img_color = cv2.cvtColor(img_copy, cv2.COLOR_GRAY2BGR)
(i, contours, _) = cv2.findContours(puzzle_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
erode = cv2.erode(img_copy, kernel)
img_dilate = cv2.dilate(erode, kernel)
trained_knn = get_trained_knn()
sudoku_matrix_final = []
(min_w, max_w) = (5, 40)
(min_h, max_h) = (44, 59)
step = 0
coords_digits_fun = []
coords_digits_final = []
for _ in xrange(33):
coords_digits = []
sudoku_matrix = np.empty((9, 9), dtype=np.object)
step += 1
i = 0
for cnt in contours:
if (cv2.contourArea(cnt) < 20):
continue
(x, y, w, h) = cv2.boundingRect(cnt)
if ((min_w < w < max_w) and ((min_h - step) < h < (max_h - step))):
a = (y / 50)
b = (x / 50)
if ((a, b) in coords_digits):
break
roi = img_dilate[(y:(y + h), x:(x + w))]
digit = cv2.resize(roi, (self.DIGIT_RESIZE_W, self.DIGIT_RESIZE_H))
num_total_px = (self.DIGIT_RESIZE_H * self.DIGIT_RESIZE_W)
test_data = digit.reshape(((- 1), num_total_px)).astype(np.float32)
(_, result, _, _) = trained_knn.findNearest(test_data, k=1)
coords_digits.append((a, b))
coords_digits_fun.append(((x + 3), (y + 3), int(result[(0, 0)]), i))
sudoku_matrix[(a, b)] = int(result[(0, 0)])
i += 1
if (len(coords_digits) > 16):
if (not len(sudoku_matrix_final)):
sudoku_matrix_final = np.copy(sudoku_matrix)
coords_digits_final = deepcopy(coords_digits_fun)
else:
len_matrix = len(sudoku_matrix[np.where((sudoku_matrix > 0))])
len_final = len(sudoku_matrix_final[np.where((sudoku_matrix_final > 0))])
if (len_matrix > len_final):
coords_digits_final = deepcopy(coords_digits_fun)
sudoku_matrix_final = np.copy(sudoku_matrix)
if self.debug_mode:
for (x, y, digit, idx) in coords_digits_final:
idx = str(idx)
digit = str(digit)
cv2.putText(img_color, digit, (x, y), cv2.FONT_HERSHEY_PLAIN, fontScale=1.5, color=(0, 0, 240), thickness=1, lineType=cv2.LINE_AA)
img_show(img_color)
return sudoku_matrix_final<|docstring|>Returns a 9x9 matrix with the found digits.
Puzzles may have different font sizes and because of that multiple iterations
are required to find the best width / height.
Another idea is to remove the sudoku grid first and then use the hierarchy
from findContours to retrieve only the top-level contours.
(http://answers.opencv.org/question/53293/how-to-remove-line-on-music-sheet/)
:param img:
:param original:
:return:<|endoftext|>
|
c348578b1225b6dcc7edd0065b264f39aa8c0b991323016e8e849e5135f1c83d
|
@ix.ContainerProperty(timfest_bands)
def appearances(self, key):
'key get blah blah blah'
self._cb.gets += 1
return self._dict[key]
|
key get blah blah blah
|
indexedproperty/test/test_cp.py
|
appearances
|
NJDFan/indexedproperty
| 4
|
python
|
@ix.ContainerProperty(timfest_bands)
def appearances(self, key):
self._cb.gets += 1
return self._dict[key]
|
@ix.ContainerProperty(timfest_bands)
def appearances(self, key):
self._cb.gets += 1
return self._dict[key]<|docstring|>key get blah blah blah<|endoftext|>
|
e07c6456cf09f23ebbc68dfacf14be7e35474f0b3673399a02bf9e686dd576af
|
@appearances.setter
def appearances(self, key, value):
'key set blah blah blah'
self._cb.sets += 1
self._dict[key] = value
|
key set blah blah blah
|
indexedproperty/test/test_cp.py
|
appearances
|
NJDFan/indexedproperty
| 4
|
python
|
@appearances.setter
def appearances(self, key, value):
self._cb.sets += 1
self._dict[key] = value
|
@appearances.setter
def appearances(self, key, value):
self._cb.sets += 1
self._dict[key] = value<|docstring|>key set blah blah blah<|endoftext|>
|
c9973eca9c3d64f07187df8b98f8e7dc86ef93089d7284575e9f8cd49f09ba9b
|
@appearances.deleter
def appearances(self, key):
'key del blah blah blah'
self._cb.dels += 1
self._dict[key] = 0
|
key del blah blah blah
|
indexedproperty/test/test_cp.py
|
appearances
|
NJDFan/indexedproperty
| 4
|
python
|
@appearances.deleter
def appearances(self, key):
self._cb.dels += 1
self._dict[key] = 0
|
@appearances.deleter
def appearances(self, key):
self._cb.dels += 1
self._dict[key] = 0<|docstring|>key del blah blah blah<|endoftext|>
|
d3736467544822cc9101a5716fa1cb1856b8d5fc6c05676e2011f0b773619c2d
|
def plot_venn(lit_vocab, lex_vocab, file='venn_plot.png'):
'The function takes two sets as arguments and draws a Venn diagram \n that shows the intersection between the two sets.\n The legend includes the size of each set and the size \n of the intersection with the other set as a percentage.\n '
plt.figure(figsize=(8, 8))
lit_abs = len(lit_vocab)
lex_abs = len(lex_vocab)
inter_abs = len(lit_vocab.intersection(lex_vocab))
lit_per = '{:.0%}'.format((inter_abs / lit_abs))
lex_per = '{:.0%}'.format((inter_abs / lex_abs))
lit_legend = f'literary ({str(lit_abs)}) {lit_per} overlap'
lex_legend = f'lexical ({str(lex_abs)}) {lex_per} overlap'
c = venn2([lit_vocab, lex_vocab], (lit_legend, lex_legend))
c.get_patch_by_id('10').set_color('#fdb515')
c.get_patch_by_id('01').set_color('#003262')
c.get_patch_by_id('11').set_color('#bc9b6a')
plt.savefig(f'viz/{file}', bbox_inches='tight')
return
|
The function takes two sets as arguments and draws a Venn diagram
that shows the intersection between the two sets.
The legend includes the size of each set and the size
of the intersection with the other set as a percentage.
|
_build/jupyter_execute/3_Vocabularies/3_1_Lit_Lex_Vocab.py
|
plot_venn
|
niekveldhuis/compass
| 8
|
python
|
def plot_venn(lit_vocab, lex_vocab, file='venn_plot.png'):
'The function takes two sets as arguments and draws a Venn diagram \n that shows the intersection between the two sets.\n The legend includes the size of each set and the size \n of the intersection with the other set as a percentage.\n '
plt.figure(figsize=(8, 8))
lit_abs = len(lit_vocab)
lex_abs = len(lex_vocab)
inter_abs = len(lit_vocab.intersection(lex_vocab))
lit_per = '{:.0%}'.format((inter_abs / lit_abs))
lex_per = '{:.0%}'.format((inter_abs / lex_abs))
lit_legend = f'literary ({str(lit_abs)}) {lit_per} overlap'
lex_legend = f'lexical ({str(lex_abs)}) {lex_per} overlap'
c = venn2([lit_vocab, lex_vocab], (lit_legend, lex_legend))
c.get_patch_by_id('10').set_color('#fdb515')
c.get_patch_by_id('01').set_color('#003262')
c.get_patch_by_id('11').set_color('#bc9b6a')
plt.savefig(f'viz/{file}', bbox_inches='tight')
return
|
def plot_venn(lit_vocab, lex_vocab, file='venn_plot.png'):
'The function takes two sets as arguments and draws a Venn diagram \n that shows the intersection between the two sets.\n The legend includes the size of each set and the size \n of the intersection with the other set as a percentage.\n '
plt.figure(figsize=(8, 8))
lit_abs = len(lit_vocab)
lex_abs = len(lex_vocab)
inter_abs = len(lit_vocab.intersection(lex_vocab))
lit_per = '{:.0%}'.format((inter_abs / lit_abs))
lex_per = '{:.0%}'.format((inter_abs / lex_abs))
lit_legend = f'literary ({str(lit_abs)}) {lit_per} overlap'
lex_legend = f'lexical ({str(lex_abs)}) {lex_per} overlap'
c = venn2([lit_vocab, lex_vocab], (lit_legend, lex_legend))
c.get_patch_by_id('10').set_color('#fdb515')
c.get_patch_by_id('01').set_color('#003262')
c.get_patch_by_id('11').set_color('#bc9b6a')
plt.savefig(f'viz/{file}', bbox_inches='tight')
return<|docstring|>The function takes two sets as arguments and draws a Venn diagram
that shows the intersection between the two sets.
The legend includes the size of each set and the size
of the intersection with the other set as a percentage.<|endoftext|>
|
fc55366d0471d4a7c7f4634d2d8da57fc55b2a51f00d0917e2d845fa758b5fae
|
def generate_launch_description():
'\n Returns ROS2 LaunchDescription object.\n '
gzclient = True
realSpeed = False
multiInstance = False
port = 11345
urdf = 'reinforcement_learning/mara_robot_gripper_140_train.urdf'
return ut_launch.generateLaunchDescriptionMara(gzclient, realSpeed, multiInstance, port, urdf)
|
Returns ROS2 LaunchDescription object.
|
examples/PHANTOMX/debug/mara_debug.launch.py
|
generate_launch_description
|
kkonen/gym-gazebo2
| 0
|
python
|
def generate_launch_description():
'\n \n '
gzclient = True
realSpeed = False
multiInstance = False
port = 11345
urdf = 'reinforcement_learning/mara_robot_gripper_140_train.urdf'
return ut_launch.generateLaunchDescriptionMara(gzclient, realSpeed, multiInstance, port, urdf)
|
def generate_launch_description():
'\n \n '
gzclient = True
realSpeed = False
multiInstance = False
port = 11345
urdf = 'reinforcement_learning/mara_robot_gripper_140_train.urdf'
return ut_launch.generateLaunchDescriptionMara(gzclient, realSpeed, multiInstance, port, urdf)<|docstring|>Returns ROS2 LaunchDescription object.<|endoftext|>
|
04dba9022e00413eb1c88d4ee5f3198c07b33312a401b26c8a6fa24e4cb94d61
|
def test_optimization_validation():
'Test that evaluator targets must have at least on calculation\n layer set.'
target = EvaluatorTarget(id='name', data_set_ids=['data-set-1'], denominators={'Density': '1.0 * g / mL'})
with pytest.raises(ValidationError):
EvaluatorTarget(**{**target.dict(), 'allow_direct_simulation': False})
|
Test that evaluator targets must have at least on calculation
layer set.
|
nonbonded/tests/library/models/test_targets.py
|
test_optimization_validation
|
SimonBoothroyd/nonbonded
| 5
|
python
|
def test_optimization_validation():
'Test that evaluator targets must have at least on calculation\n layer set.'
target = EvaluatorTarget(id='name', data_set_ids=['data-set-1'], denominators={'Density': '1.0 * g / mL'})
with pytest.raises(ValidationError):
EvaluatorTarget(**{**target.dict(), 'allow_direct_simulation': False})
|
def test_optimization_validation():
'Test that evaluator targets must have at least on calculation\n layer set.'
target = EvaluatorTarget(id='name', data_set_ids=['data-set-1'], denominators={'Density': '1.0 * g / mL'})
with pytest.raises(ValidationError):
EvaluatorTarget(**{**target.dict(), 'allow_direct_simulation': False})<|docstring|>Test that evaluator targets must have at least on calculation
layer set.<|endoftext|>
|
614fd419c841f1081c26f7bb91731861d4eb9519c1f4fcd7b459ef67feb89bda
|
def make_patches(boundaries, patch_length=5):
" \n 'tile' the boundaries of a city into patches, like a patchwork quilt\n "
longitude_factor = 0.00898
longitude_factor_m = (0.00898 / 1000)
latitude_factor = (math.cos((abs(boundaries.bounds[1]) * 0.0174533)) / 111.319)
latitude_factor_m = (latitude_factor / 1000)
bbox = boundaries.bounds
height_degrees = abs((bbox[3] - bbox[1]))
height_km = (height_degrees / latitude_factor)
width_degrees = abs((bbox[2] - bbox[0]))
width_km = (width_degrees / longitude_factor)
n_hslicers = math.floor((height_km / patch_length))
n_vslicers = math.floor((width_km / patch_length))
hslicers = []
vslicers = []
for i in range(1, (n_hslicers + 1)):
h_increment = ((bbox[3] - bbox[1]) / (n_hslicers + 1))
lat = (bbox[1] + (i * h_increment))
slicer = shapely.geometry.LineString([(bbox[0], lat), (bbox[2], lat)])
hslicers.append(slicer)
for i in range(1, (n_vslicers + 1)):
v_increment = ((bbox[2] - bbox[0]) / (n_vslicers + 1))
lon = (bbox[0] + (i * v_increment))
slicer = shapely.geometry.LineString([(lon, bbox[1]), (lon, bbox[3])])
hslicers.append(slicer)
patches = shapely.geometry.MultiPolygon(polygons=[boundaries])
for slicer in (hslicers + vslicers):
patches = shapely.geometry.MultiPolygon(polygons=shapely.ops.split(patches, slicer))
print((('cut' + str(len(list(patches)))) + 'patches'))
return list(patches)
|
'tile' the boundaries of a city into patches, like a patchwork quilt
|
pedestriansfirst.py
|
make_patches
|
ITDP/pedestriansfirst
| 8
|
python
|
def make_patches(boundaries, patch_length=5):
" \n \n "
longitude_factor = 0.00898
longitude_factor_m = (0.00898 / 1000)
latitude_factor = (math.cos((abs(boundaries.bounds[1]) * 0.0174533)) / 111.319)
latitude_factor_m = (latitude_factor / 1000)
bbox = boundaries.bounds
height_degrees = abs((bbox[3] - bbox[1]))
height_km = (height_degrees / latitude_factor)
width_degrees = abs((bbox[2] - bbox[0]))
width_km = (width_degrees / longitude_factor)
n_hslicers = math.floor((height_km / patch_length))
n_vslicers = math.floor((width_km / patch_length))
hslicers = []
vslicers = []
for i in range(1, (n_hslicers + 1)):
h_increment = ((bbox[3] - bbox[1]) / (n_hslicers + 1))
lat = (bbox[1] + (i * h_increment))
slicer = shapely.geometry.LineString([(bbox[0], lat), (bbox[2], lat)])
hslicers.append(slicer)
for i in range(1, (n_vslicers + 1)):
v_increment = ((bbox[2] - bbox[0]) / (n_vslicers + 1))
lon = (bbox[0] + (i * v_increment))
slicer = shapely.geometry.LineString([(lon, bbox[1]), (lon, bbox[3])])
hslicers.append(slicer)
patches = shapely.geometry.MultiPolygon(polygons=[boundaries])
for slicer in (hslicers + vslicers):
patches = shapely.geometry.MultiPolygon(polygons=shapely.ops.split(patches, slicer))
print((('cut' + str(len(list(patches)))) + 'patches'))
return list(patches)
|
def make_patches(boundaries, patch_length=5):
" \n \n "
longitude_factor = 0.00898
longitude_factor_m = (0.00898 / 1000)
latitude_factor = (math.cos((abs(boundaries.bounds[1]) * 0.0174533)) / 111.319)
latitude_factor_m = (latitude_factor / 1000)
bbox = boundaries.bounds
height_degrees = abs((bbox[3] - bbox[1]))
height_km = (height_degrees / latitude_factor)
width_degrees = abs((bbox[2] - bbox[0]))
width_km = (width_degrees / longitude_factor)
n_hslicers = math.floor((height_km / patch_length))
n_vslicers = math.floor((width_km / patch_length))
hslicers = []
vslicers = []
for i in range(1, (n_hslicers + 1)):
h_increment = ((bbox[3] - bbox[1]) / (n_hslicers + 1))
lat = (bbox[1] + (i * h_increment))
slicer = shapely.geometry.LineString([(bbox[0], lat), (bbox[2], lat)])
hslicers.append(slicer)
for i in range(1, (n_vslicers + 1)):
v_increment = ((bbox[2] - bbox[0]) / (n_vslicers + 1))
lon = (bbox[0] + (i * v_increment))
slicer = shapely.geometry.LineString([(lon, bbox[1]), (lon, bbox[3])])
hslicers.append(slicer)
patches = shapely.geometry.MultiPolygon(polygons=[boundaries])
for slicer in (hslicers + vslicers):
patches = shapely.geometry.MultiPolygon(polygons=shapely.ops.split(patches, slicer))
print((('cut' + str(len(list(patches)))) + 'patches'))
return list(patches)<|docstring|>'tile' the boundaries of a city into patches, like a patchwork quilt<|endoftext|>
|
dd37891c82db3257c2674155ff63149eddf937dd51fc0b6e5d7627e6025ccc39
|
def line_to_tensor(line, all_letters):
'\n Turn a line into a <line_length x 1 x n_letters>,\n or an array of one-hot letter vectors\n '
tensor = torch.zeros(len(line), 1, len(all_letters))
for (li, letter) in enumerate(line):
tensor[li][0][letter_to_index(letter, all_letters)] = 1
return tensor.unsqueeze(0)
|
Turn a line into a <line_length x 1 x n_letters>,
or an array of one-hot letter vectors
|
src/trw/datasets/name_nationality.py
|
line_to_tensor
|
civodlu/trw
| 3
|
python
|
def line_to_tensor(line, all_letters):
'\n Turn a line into a <line_length x 1 x n_letters>,\n or an array of one-hot letter vectors\n '
tensor = torch.zeros(len(line), 1, len(all_letters))
for (li, letter) in enumerate(line):
tensor[li][0][letter_to_index(letter, all_letters)] = 1
return tensor.unsqueeze(0)
|
def line_to_tensor(line, all_letters):
'\n Turn a line into a <line_length x 1 x n_letters>,\n or an array of one-hot letter vectors\n '
tensor = torch.zeros(len(line), 1, len(all_letters))
for (li, letter) in enumerate(line):
tensor[li][0][letter_to_index(letter, all_letters)] = 1
return tensor.unsqueeze(0)<|docstring|>Turn a line into a <line_length x 1 x n_letters>,
or an array of one-hot letter vectors<|endoftext|>
|
8fe955a955b30c3102df1b840cd6e7c8cef4a670792f042399611b44bd08924e
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_init(cc, req, command, runner_config):
'Test commands using a runner created by constructor.'
runner = BlackMagicProbeRunner(runner_config, TEST_GDB_SERIAL)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])
|
Test commands using a runner created by constructor.
|
scripts/west_commands/tests/test_blackmagicprobe.py
|
test_blackmagicprobe_init
|
TeckKitty/zephyr
| 70
|
python
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_init(cc, req, command, runner_config):
runner = BlackMagicProbeRunner(runner_config, TEST_GDB_SERIAL)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_init(cc, req, command, runner_config):
runner = BlackMagicProbeRunner(runner_config, TEST_GDB_SERIAL)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])<|docstring|>Test commands using a runner created by constructor.<|endoftext|>
|
615626823251f2c70efe967426c0055c92bd64048fce9fc5d7bfadc07c1687c1
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_create(cc, req, command, runner_config):
'Test commands using a runner created from command line parameters.'
args = ['--gdb-serial', TEST_GDB_SERIAL]
parser = argparse.ArgumentParser()
BlackMagicProbeRunner.add_parser(parser)
arg_namespace = parser.parse_args(args)
runner = BlackMagicProbeRunner.create(runner_config, arg_namespace)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])
|
Test commands using a runner created from command line parameters.
|
scripts/west_commands/tests/test_blackmagicprobe.py
|
test_blackmagicprobe_create
|
TeckKitty/zephyr
| 70
|
python
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_create(cc, req, command, runner_config):
args = ['--gdb-serial', TEST_GDB_SERIAL]
parser = argparse.ArgumentParser()
BlackMagicProbeRunner.add_parser(parser)
arg_namespace = parser.parse_args(args)
runner = BlackMagicProbeRunner.create(runner_config, arg_namespace)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])
|
@pytest.mark.parametrize('command', EXPECTED_COMMANDS)
@patch('runners.core.ZephyrBinaryRunner.require', side_effect=require_patch)
@patch('runners.core.ZephyrBinaryRunner.check_call')
def test_blackmagicprobe_create(cc, req, command, runner_config):
args = ['--gdb-serial', TEST_GDB_SERIAL]
parser = argparse.ArgumentParser()
BlackMagicProbeRunner.add_parser(parser)
arg_namespace = parser.parse_args(args)
runner = BlackMagicProbeRunner.create(runner_config, arg_namespace)
runner.run(command)
assert (cc.call_args_list == [call(x) for x in EXPECTED_COMMANDS[command]])<|docstring|>Test commands using a runner created from command line parameters.<|endoftext|>
|
eaf3c6d68c2dd102ca18c5b9e3e6bc407361c7193069cf8596b6ddf4487cbd8f
|
def parse_args():
'\n Parse input arguments\n '
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--dataset', dest='dataset', help='training dataset', default='pascal_voc', type=str)
parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='cfgs/res18.yml', type=str)
parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir', help='directory to load models', default='models', type=str)
parser.add_argument('--cuda', dest='cuda', help='whether use CUDA', action='store_true')
parser.add_argument('--ls', dest='large_scale', help='whether use large imag scale', action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs', help='whether use multiple GPUs', action='store_true')
parser.add_argument('--cag', dest='class_agnostic', help='whether perform class_agnostic bbox regression', action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type', help='which part of model to parallel, 0: all, 1: model before roi pooling', default=0, type=int)
parser.add_argument('--checksession', dest='checksession', help='checksession to load model', default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch', help='checkepoch to load network', default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint', help='checkpoint to load network', default=10021, type=int)
parser.add_argument('--vis', dest='vis', help='visualization mode', action='store_true')
parser.add_argument('--refine', dest='refine', help='whether use refine anchor', action='store_true')
args = parser.parse_args()
return args
|
Parse input arguments
|
test_net.py
|
parse_args
|
kevincao91/kevin.ai.vehicle_detection
| 2
|
python
|
def parse_args():
'\n \n '
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--dataset', dest='dataset', help='training dataset', default='pascal_voc', type=str)
parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='cfgs/res18.yml', type=str)
parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir', help='directory to load models', default='models', type=str)
parser.add_argument('--cuda', dest='cuda', help='whether use CUDA', action='store_true')
parser.add_argument('--ls', dest='large_scale', help='whether use large imag scale', action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs', help='whether use multiple GPUs', action='store_true')
parser.add_argument('--cag', dest='class_agnostic', help='whether perform class_agnostic bbox regression', action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type', help='which part of model to parallel, 0: all, 1: model before roi pooling', default=0, type=int)
parser.add_argument('--checksession', dest='checksession', help='checksession to load model', default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch', help='checkepoch to load network', default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint', help='checkpoint to load network', default=10021, type=int)
parser.add_argument('--vis', dest='vis', help='visualization mode', action='store_true')
parser.add_argument('--refine', dest='refine', help='whether use refine anchor', action='store_true')
args = parser.parse_args()
return args
|
def parse_args():
'\n \n '
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--dataset', dest='dataset', help='training dataset', default='pascal_voc', type=str)
parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='cfgs/res18.yml', type=str)
parser.add_argument('--net', dest='net', help='vgg16, res50, res101, res152', default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs', help='set config keys', default=None, nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir', help='directory to load models', default='models', type=str)
parser.add_argument('--cuda', dest='cuda', help='whether use CUDA', action='store_true')
parser.add_argument('--ls', dest='large_scale', help='whether use large imag scale', action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs', help='whether use multiple GPUs', action='store_true')
parser.add_argument('--cag', dest='class_agnostic', help='whether perform class_agnostic bbox regression', action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type', help='which part of model to parallel, 0: all, 1: model before roi pooling', default=0, type=int)
parser.add_argument('--checksession', dest='checksession', help='checksession to load model', default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch', help='checkepoch to load network', default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint', help='checkpoint to load network', default=10021, type=int)
parser.add_argument('--vis', dest='vis', help='visualization mode', action='store_true')
parser.add_argument('--refine', dest='refine', help='whether use refine anchor', action='store_true')
args = parser.parse_args()
return args<|docstring|>Parse input arguments<|endoftext|>
|
99f50d577b15253e6008317d7e085e79e36e8feacf2c8e39b9beaa3bb4e3ac80
|
def progress_bar(value, max_value, width=15):
'Print progress bar.\n\n Print a progress bar (utilising the carriage return function).\n\n Parameters\n ----------\n value : :obj:`float` or :obj:`int`\n Number representing the current progress of process.\n\n max_value : :obj:`float` or :obj:`int`\n Maximum possible value in process.\n\n width : :obj:`int`, optional\n Number of characters in the progress bar. Default is 15.\n\n '
progress = round(((value / max_value) * width))
remaining = (width - progress)
print((('\rOptimisation Progress: ' + ('+' * progress)) + ('-' * remaining)), end='')
|
Print progress bar.
Print a progress bar (utilising the carriage return function).
Parameters
----------
value : :obj:`float` or :obj:`int`
Number representing the current progress of process.
max_value : :obj:`float` or :obj:`int`
Maximum possible value in process.
width : :obj:`int`, optional
Number of characters in the progress bar. Default is 15.
|
pracopt/utils.py
|
progress_bar
|
RobSumner/pracopt
| 1
|
python
|
def progress_bar(value, max_value, width=15):
'Print progress bar.\n\n Print a progress bar (utilising the carriage return function).\n\n Parameters\n ----------\n value : :obj:`float` or :obj:`int`\n Number representing the current progress of process.\n\n max_value : :obj:`float` or :obj:`int`\n Maximum possible value in process.\n\n width : :obj:`int`, optional\n Number of characters in the progress bar. Default is 15.\n\n '
progress = round(((value / max_value) * width))
remaining = (width - progress)
print((('\rOptimisation Progress: ' + ('+' * progress)) + ('-' * remaining)), end=)
|
def progress_bar(value, max_value, width=15):
'Print progress bar.\n\n Print a progress bar (utilising the carriage return function).\n\n Parameters\n ----------\n value : :obj:`float` or :obj:`int`\n Number representing the current progress of process.\n\n max_value : :obj:`float` or :obj:`int`\n Maximum possible value in process.\n\n width : :obj:`int`, optional\n Number of characters in the progress bar. Default is 15.\n\n '
progress = round(((value / max_value) * width))
remaining = (width - progress)
print((('\rOptimisation Progress: ' + ('+' * progress)) + ('-' * remaining)), end=)<|docstring|>Print progress bar.
Print a progress bar (utilising the carriage return function).
Parameters
----------
value : :obj:`float` or :obj:`int`
Number representing the current progress of process.
max_value : :obj:`float` or :obj:`int`
Maximum possible value in process.
width : :obj:`int`, optional
Number of characters in the progress bar. Default is 15.<|endoftext|>
|
9eb5b54907a28d8832c391971ac2a8b5c4c9bad69ec03a4db1014e8103f03e73
|
def evaluate(algorithm, runs=5, filepath=None, description=None):
'Evaluate optimiser performance.\n\n Evaluate the performance of an optimiser class. Because of the random\n search nature of optimiser classes, sevveral evaulation runs are\n performed and results are averaged.\n\n Parameters\n ----------\n algorithms : :class:`pracopt.optimiser.Optimiser`\n The optimiser algorithm class to run.\n\n runs : :obj:`int`, optional.\n The number of runs to use when evaluating optimiser.\n\n filepath : :obj:`str`, Optional\n File path to save results to (as .mat file). If None, file is not\n saved.\n\n description : :obj:`str`, optional.\n Description string to save with results.\n\n Returns\n -------\n results : :class:`numpy.array`\n Result array. Each row consists of [optimiser step, average time,\n average value].\n I.e. the results are averaged across "runs" by objective function\n step.\n\n '
max_evals = algorithm._max_evaluations
f_data = np.zeros((max_evals, runs))
time_data = np.zeros((max_evals, runs))
for i in range(runs):
algorithm.reset()
print('Analysis run: ', i)
algorithm.run()
data = algorithm.archive.objective_data(max_evals)
f_data[(:, i)] = data[(:, 2)]
time_data[(:, i)] = data[(:, 1)]
f_average = np.reshape(np.mean(f_data, axis=1), (max_evals, 1))
t_average = np.reshape(np.mean(time_data, axis=1), (max_evals, 1))
iters = np.reshape(np.linspace(1, max_evals, max_evals), (max_evals, 1))
if (filepath is not None):
data_dict = {'f_average': f_average, 't_average': t_average, 'iters': iters}
if (description is not None):
data_dict['description'] = description
scipy.io.savemat((filepath + '.mat'), data_dict)
return np.concatenate((iters, t_average, f_average), axis=1)
|
Evaluate optimiser performance.
Evaluate the performance of an optimiser class. Because of the random
search nature of optimiser classes, sevveral evaulation runs are
performed and results are averaged.
Parameters
----------
algorithms : :class:`pracopt.optimiser.Optimiser`
The optimiser algorithm class to run.
runs : :obj:`int`, optional.
The number of runs to use when evaluating optimiser.
filepath : :obj:`str`, Optional
File path to save results to (as .mat file). If None, file is not
saved.
description : :obj:`str`, optional.
Description string to save with results.
Returns
-------
results : :class:`numpy.array`
Result array. Each row consists of [optimiser step, average time,
average value].
I.e. the results are averaged across "runs" by objective function
step.
|
pracopt/utils.py
|
evaluate
|
RobSumner/pracopt
| 1
|
python
|
def evaluate(algorithm, runs=5, filepath=None, description=None):
'Evaluate optimiser performance.\n\n Evaluate the performance of an optimiser class. Because of the random\n search nature of optimiser classes, sevveral evaulation runs are\n performed and results are averaged.\n\n Parameters\n ----------\n algorithms : :class:`pracopt.optimiser.Optimiser`\n The optimiser algorithm class to run.\n\n runs : :obj:`int`, optional.\n The number of runs to use when evaluating optimiser.\n\n filepath : :obj:`str`, Optional\n File path to save results to (as .mat file). If None, file is not\n saved.\n\n description : :obj:`str`, optional.\n Description string to save with results.\n\n Returns\n -------\n results : :class:`numpy.array`\n Result array. Each row consists of [optimiser step, average time,\n average value].\n I.e. the results are averaged across "runs" by objective function\n step.\n\n '
max_evals = algorithm._max_evaluations
f_data = np.zeros((max_evals, runs))
time_data = np.zeros((max_evals, runs))
for i in range(runs):
algorithm.reset()
print('Analysis run: ', i)
algorithm.run()
data = algorithm.archive.objective_data(max_evals)
f_data[(:, i)] = data[(:, 2)]
time_data[(:, i)] = data[(:, 1)]
f_average = np.reshape(np.mean(f_data, axis=1), (max_evals, 1))
t_average = np.reshape(np.mean(time_data, axis=1), (max_evals, 1))
iters = np.reshape(np.linspace(1, max_evals, max_evals), (max_evals, 1))
if (filepath is not None):
data_dict = {'f_average': f_average, 't_average': t_average, 'iters': iters}
if (description is not None):
data_dict['description'] = description
scipy.io.savemat((filepath + '.mat'), data_dict)
return np.concatenate((iters, t_average, f_average), axis=1)
|
def evaluate(algorithm, runs=5, filepath=None, description=None):
'Evaluate optimiser performance.\n\n Evaluate the performance of an optimiser class. Because of the random\n search nature of optimiser classes, sevveral evaulation runs are\n performed and results are averaged.\n\n Parameters\n ----------\n algorithms : :class:`pracopt.optimiser.Optimiser`\n The optimiser algorithm class to run.\n\n runs : :obj:`int`, optional.\n The number of runs to use when evaluating optimiser.\n\n filepath : :obj:`str`, Optional\n File path to save results to (as .mat file). If None, file is not\n saved.\n\n description : :obj:`str`, optional.\n Description string to save with results.\n\n Returns\n -------\n results : :class:`numpy.array`\n Result array. Each row consists of [optimiser step, average time,\n average value].\n I.e. the results are averaged across "runs" by objective function\n step.\n\n '
max_evals = algorithm._max_evaluations
f_data = np.zeros((max_evals, runs))
time_data = np.zeros((max_evals, runs))
for i in range(runs):
algorithm.reset()
print('Analysis run: ', i)
algorithm.run()
data = algorithm.archive.objective_data(max_evals)
f_data[(:, i)] = data[(:, 2)]
time_data[(:, i)] = data[(:, 1)]
f_average = np.reshape(np.mean(f_data, axis=1), (max_evals, 1))
t_average = np.reshape(np.mean(time_data, axis=1), (max_evals, 1))
iters = np.reshape(np.linspace(1, max_evals, max_evals), (max_evals, 1))
if (filepath is not None):
data_dict = {'f_average': f_average, 't_average': t_average, 'iters': iters}
if (description is not None):
data_dict['description'] = description
scipy.io.savemat((filepath + '.mat'), data_dict)
return np.concatenate((iters, t_average, f_average), axis=1)<|docstring|>Evaluate optimiser performance.
Evaluate the performance of an optimiser class. Because of the random
search nature of optimiser classes, sevveral evaulation runs are
performed and results are averaged.
Parameters
----------
algorithms : :class:`pracopt.optimiser.Optimiser`
The optimiser algorithm class to run.
runs : :obj:`int`, optional.
The number of runs to use when evaluating optimiser.
filepath : :obj:`str`, Optional
File path to save results to (as .mat file). If None, file is not
saved.
description : :obj:`str`, optional.
Description string to save with results.
Returns
-------
results : :class:`numpy.array`
Result array. Each row consists of [optimiser step, average time,
average value].
I.e. the results are averaged across "runs" by objective function
step.<|endoftext|>
|
237ce1ab8e0c038e8917ed26cff22c33b9cd71a9e456f14af7700bc74690d137
|
def input_email(self, email):
"Make webdriver set 'E-Mail' value."
self.driver.find_element(*LoginPageLocators.EMAIL_INPUT_FIELD).send_keys(email)
return self
|
Make webdriver set 'E-Mail' value.
|
pages/login.py
|
input_email
|
testsibirtsv/opncrt_taqc
| 0
|
python
|
def input_email(self, email):
self.driver.find_element(*LoginPageLocators.EMAIL_INPUT_FIELD).send_keys(email)
return self
|
def input_email(self, email):
self.driver.find_element(*LoginPageLocators.EMAIL_INPUT_FIELD).send_keys(email)
return self<|docstring|>Make webdriver set 'E-Mail' value.<|endoftext|>
|
e83d46583965ece3a782f917b5a26b3786d1bf37e7ae57fc13b70d03c88e4a4f
|
def input_password(self, password):
"Make webdriver set 'Password' value."
self.driver.find_element(*LoginPageLocators.PASSWORD_INPUT_FIELD).send_keys(password)
return self
|
Make webdriver set 'Password' value.
|
pages/login.py
|
input_password
|
testsibirtsv/opncrt_taqc
| 0
|
python
|
def input_password(self, password):
self.driver.find_element(*LoginPageLocators.PASSWORD_INPUT_FIELD).send_keys(password)
return self
|
def input_password(self, password):
self.driver.find_element(*LoginPageLocators.PASSWORD_INPUT_FIELD).send_keys(password)
return self<|docstring|>Make webdriver set 'Password' value.<|endoftext|>
|
43d5af57a088e4cea86733c4d4f5a9221601ab95078cf17d5107d4aa11bc80fe
|
def login(self):
"Make webdriver initiate login by click 'Login' Button"
self.driver.find_element(*LoginPageLocators.LOGIN_BUTTON).click()
return AccountPage(self.driver)
|
Make webdriver initiate login by click 'Login' Button
|
pages/login.py
|
login
|
testsibirtsv/opncrt_taqc
| 0
|
python
|
def login(self):
self.driver.find_element(*LoginPageLocators.LOGIN_BUTTON).click()
return AccountPage(self.driver)
|
def login(self):
self.driver.find_element(*LoginPageLocators.LOGIN_BUTTON).click()
return AccountPage(self.driver)<|docstring|>Make webdriver initiate login by click 'Login' Button<|endoftext|>
|
9437f83140dc15981e3c3c2cc48ef0a2c0074e1c08f122f2ccc9e76bcb4d227c
|
def put(self, key: Any, value: Any):
'\n Store the pair in the hash map\n :param key: key of of the the pair to store\n :param value: value to store\n '
position: int = self._position(self._hash(key))
if (self.entries[position] is None):
self.entries[position] = LinkedList()
self.entries[position].add(HashNode(key=key, value=value))
else:
current = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
if (current is None):
self.entries[position].add(HashNode(key=key, value=value))
else:
current.value.value = value
|
Store the pair in the hash map
:param key: key of of the the pair to store
:param value: value to store
|
helpers/hash_map.py
|
put
|
mvgiacomello/leetcode-solutions
| 0
|
python
|
def put(self, key: Any, value: Any):
'\n Store the pair in the hash map\n :param key: key of of the the pair to store\n :param value: value to store\n '
position: int = self._position(self._hash(key))
if (self.entries[position] is None):
self.entries[position] = LinkedList()
self.entries[position].add(HashNode(key=key, value=value))
else:
current = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
if (current is None):
self.entries[position].add(HashNode(key=key, value=value))
else:
current.value.value = value
|
def put(self, key: Any, value: Any):
'\n Store the pair in the hash map\n :param key: key of of the the pair to store\n :param value: value to store\n '
position: int = self._position(self._hash(key))
if (self.entries[position] is None):
self.entries[position] = LinkedList()
self.entries[position].add(HashNode(key=key, value=value))
else:
current = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
if (current is None):
self.entries[position].add(HashNode(key=key, value=value))
else:
current.value.value = value<|docstring|>Store the pair in the hash map
:param key: key of of the the pair to store
:param value: value to store<|endoftext|>
|
b4718cf6678239b1e3b5019f3a111a2cb341731b1f87074ab2697ba30c8d3907
|
def get(self, key: Any) -> Any:
'\n Retrieve the value of a certain key\n :param key: the key to retrieve its value\n :return: the value of the key or None if key does not exist\n '
position = self._position(self._hash(key))
if (self.entries[position] is None):
return None
else:
current: LinkedListNode = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
return (None if (current is None) else current.value.value)
|
Retrieve the value of a certain key
:param key: the key to retrieve its value
:return: the value of the key or None if key does not exist
|
helpers/hash_map.py
|
get
|
mvgiacomello/leetcode-solutions
| 0
|
python
|
def get(self, key: Any) -> Any:
'\n Retrieve the value of a certain key\n :param key: the key to retrieve its value\n :return: the value of the key or None if key does not exist\n '
position = self._position(self._hash(key))
if (self.entries[position] is None):
return None
else:
current: LinkedListNode = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
return (None if (current is None) else current.value.value)
|
def get(self, key: Any) -> Any:
'\n Retrieve the value of a certain key\n :param key: the key to retrieve its value\n :return: the value of the key or None if key does not exist\n '
position = self._position(self._hash(key))
if (self.entries[position] is None):
return None
else:
current: LinkedListNode = self.entries[position].head
while ((current is not None) and (current.value.key != key)):
current = current.next
return (None if (current is None) else current.value.value)<|docstring|>Retrieve the value of a certain key
:param key: the key to retrieve its value
:return: the value of the key or None if key does not exist<|endoftext|>
|
ba1cde3962df8f53236e3286ccc62e2ac02004ebb71c15eac5081f79ed4727e8
|
def _hash(self, key: Any) -> int:
'\n :param key: the value to get the hash from. Must be able to cast to string\n :return: hash (integer) representative of the key\n '
sum = 1
string = str(key)
for char in string:
sum = ((sum * self.seed) + ord(char))
return sum
|
:param key: the value to get the hash from. Must be able to cast to string
:return: hash (integer) representative of the key
|
helpers/hash_map.py
|
_hash
|
mvgiacomello/leetcode-solutions
| 0
|
python
|
def _hash(self, key: Any) -> int:
'\n :param key: the value to get the hash from. Must be able to cast to string\n :return: hash (integer) representative of the key\n '
sum = 1
string = str(key)
for char in string:
sum = ((sum * self.seed) + ord(char))
return sum
|
def _hash(self, key: Any) -> int:
'\n :param key: the value to get the hash from. Must be able to cast to string\n :return: hash (integer) representative of the key\n '
sum = 1
string = str(key)
for char in string:
sum = ((sum * self.seed) + ord(char))
return sum<|docstring|>:param key: the value to get the hash from. Must be able to cast to string
:return: hash (integer) representative of the key<|endoftext|>
|
8e9ac288b51a698c9b73ba3a75a198d279630e80fea89c07837d382d06b9ec4c
|
def _position(self, hash: int) -> int:
'\n :param hash: the hashed value of the key\n :return: the position in the list where value should be stored\n '
if (not isinstance(hash, int)):
raise ValueError('hash provided should be an integer')
position = (hash % self.size)
return position
|
:param hash: the hashed value of the key
:return: the position in the list where value should be stored
|
helpers/hash_map.py
|
_position
|
mvgiacomello/leetcode-solutions
| 0
|
python
|
def _position(self, hash: int) -> int:
'\n :param hash: the hashed value of the key\n :return: the position in the list where value should be stored\n '
if (not isinstance(hash, int)):
raise ValueError('hash provided should be an integer')
position = (hash % self.size)
return position
|
def _position(self, hash: int) -> int:
'\n :param hash: the hashed value of the key\n :return: the position in the list where value should be stored\n '
if (not isinstance(hash, int)):
raise ValueError('hash provided should be an integer')
position = (hash % self.size)
return position<|docstring|>:param hash: the hashed value of the key
:return: the position in the list where value should be stored<|endoftext|>
|
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