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raiden-network/raiden | raiden/utils/filters.py | get_filter_args_for_all_events_from_channel | def get_filter_args_for_all_events_from_channel(
token_network_address: TokenNetworkAddress,
channel_identifier: ChannelID,
contract_manager: ContractManager,
from_block: BlockSpecification = GENESIS_BLOCK_NUMBER,
to_block: BlockSpecification = 'latest',
) -> Dict:
""" Return the filter params for all events of a given channel. """
event_filter_params = get_filter_args_for_specific_event_from_channel(
token_network_address=token_network_address,
channel_identifier=channel_identifier,
event_name=ChannelEvent.OPENED,
contract_manager=contract_manager,
from_block=from_block,
to_block=to_block,
)
# As we want to get all events for a certain channel we remove the event specific code here
# and filter just for the channel identifier
# We also have to remove the trailing topics to get all filters
event_filter_params['topics'] = [None, event_filter_params['topics'][1]]
return event_filter_params | python | def get_filter_args_for_all_events_from_channel(
token_network_address: TokenNetworkAddress,
channel_identifier: ChannelID,
contract_manager: ContractManager,
from_block: BlockSpecification = GENESIS_BLOCK_NUMBER,
to_block: BlockSpecification = 'latest',
) -> Dict:
""" Return the filter params for all events of a given channel. """
event_filter_params = get_filter_args_for_specific_event_from_channel(
token_network_address=token_network_address,
channel_identifier=channel_identifier,
event_name=ChannelEvent.OPENED,
contract_manager=contract_manager,
from_block=from_block,
to_block=to_block,
)
# As we want to get all events for a certain channel we remove the event specific code here
# and filter just for the channel identifier
# We also have to remove the trailing topics to get all filters
event_filter_params['topics'] = [None, event_filter_params['topics'][1]]
return event_filter_params | [
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pytorch/tnt | torchnet/meter/confusionmeter.py | ConfusionMeter.add | def add(self, predicted, target):
"""Computes the confusion matrix of K x K size where K is no of classes
Args:
predicted (tensor): Can be an N x K tensor of predicted scores obtained from
the model for N examples and K classes or an N-tensor of
integer values between 0 and K-1.
target (tensor): Can be a N-tensor of integer values assumed to be integer
values between 0 and K-1 or N x K tensor, where targets are
assumed to be provided as one-hot vectors
"""
predicted = predicted.cpu().numpy()
target = target.cpu().numpy()
assert predicted.shape[0] == target.shape[0], \
'number of targets and predicted outputs do not match'
if np.ndim(predicted) != 1:
assert predicted.shape[1] == self.k, \
'number of predictions does not match size of confusion matrix'
predicted = np.argmax(predicted, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 1 and k'
onehot_target = np.ndim(target) != 1
if onehot_target:
assert target.shape[1] == self.k, \
'Onehot target does not match size of confusion matrix'
assert (target >= 0).all() and (target <= 1).all(), \
'in one-hot encoding, target values should be 0 or 1'
assert (target.sum(1) == 1).all(), \
'multi-label setting is not supported'
target = np.argmax(target, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 0 and k-1'
# hack for bincounting 2 arrays together
x = predicted + self.k * target
bincount_2d = np.bincount(x.astype(np.int32),
minlength=self.k ** 2)
assert bincount_2d.size == self.k ** 2
conf = bincount_2d.reshape((self.k, self.k))
self.conf += conf | python | def add(self, predicted, target):
"""Computes the confusion matrix of K x K size where K is no of classes
Args:
predicted (tensor): Can be an N x K tensor of predicted scores obtained from
the model for N examples and K classes or an N-tensor of
integer values between 0 and K-1.
target (tensor): Can be a N-tensor of integer values assumed to be integer
values between 0 and K-1 or N x K tensor, where targets are
assumed to be provided as one-hot vectors
"""
predicted = predicted.cpu().numpy()
target = target.cpu().numpy()
assert predicted.shape[0] == target.shape[0], \
'number of targets and predicted outputs do not match'
if np.ndim(predicted) != 1:
assert predicted.shape[1] == self.k, \
'number of predictions does not match size of confusion matrix'
predicted = np.argmax(predicted, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 1 and k'
onehot_target = np.ndim(target) != 1
if onehot_target:
assert target.shape[1] == self.k, \
'Onehot target does not match size of confusion matrix'
assert (target >= 0).all() and (target <= 1).all(), \
'in one-hot encoding, target values should be 0 or 1'
assert (target.sum(1) == 1).all(), \
'multi-label setting is not supported'
target = np.argmax(target, 1)
else:
assert (predicted.max() < self.k) and (predicted.min() >= 0), \
'predicted values are not between 0 and k-1'
# hack for bincounting 2 arrays together
x = predicted + self.k * target
bincount_2d = np.bincount(x.astype(np.int32),
minlength=self.k ** 2)
assert bincount_2d.size == self.k ** 2
conf = bincount_2d.reshape((self.k, self.k))
self.conf += conf | [
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pytorch/tnt | torchnet/dataset/shuffledataset.py | ShuffleDataset.resample | def resample(self, seed=None):
"""Resample the dataset.
Args:
seed (int, optional): Seed for resampling. By default no seed is
used.
"""
if seed is not None:
gen = torch.manual_seed(seed)
else:
gen = torch.default_generator
if self.replacement:
self.perm = torch.LongTensor(len(self)).random_(
len(self.dataset), generator=gen)
else:
self.perm = torch.randperm(
len(self.dataset), generator=gen).narrow(0, 0, len(self)) | python | def resample(self, seed=None):
"""Resample the dataset.
Args:
seed (int, optional): Seed for resampling. By default no seed is
used.
"""
if seed is not None:
gen = torch.manual_seed(seed)
else:
gen = torch.default_generator
if self.replacement:
self.perm = torch.LongTensor(len(self)).random_(
len(self.dataset), generator=gen)
else:
self.perm = torch.randperm(
len(self.dataset), generator=gen).narrow(0, 0, len(self)) | [
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pytorch/tnt | torchnet/meter/apmeter.py | APMeter.reset | def reset(self):
"""Resets the meter with empty member variables"""
self.scores = torch.FloatTensor(torch.FloatStorage())
self.targets = torch.LongTensor(torch.LongStorage())
self.weights = torch.FloatTensor(torch.FloatStorage()) | python | def reset(self):
"""Resets the meter with empty member variables"""
self.scores = torch.FloatTensor(torch.FloatStorage())
self.targets = torch.LongTensor(torch.LongStorage())
self.weights = torch.FloatTensor(torch.FloatStorage()) | [
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pytorch/tnt | torchnet/meter/apmeter.py | APMeter.value | def value(self):
"""Returns the model's average precision for each class
Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k
"""
if self.scores.numel() == 0:
return 0
ap = torch.zeros(self.scores.size(1))
if hasattr(torch, "arange"):
rg = torch.arange(1, self.scores.size(0) + 1).float()
else:
rg = torch.range(1, self.scores.size(0)).float()
if self.weights.numel() > 0:
weight = self.weights.new(self.weights.size())
weighted_truth = self.weights.new(self.weights.size())
# compute average precision for each class
for k in range(self.scores.size(1)):
# sort scores
scores = self.scores[:, k]
targets = self.targets[:, k]
_, sortind = torch.sort(scores, 0, True)
truth = targets[sortind]
if self.weights.numel() > 0:
weight = self.weights[sortind]
weighted_truth = truth.float() * weight
rg = weight.cumsum(0)
# compute true positive sums
if self.weights.numel() > 0:
tp = weighted_truth.cumsum(0)
else:
tp = truth.float().cumsum(0)
# compute precision curve
precision = tp.div(rg)
# compute average precision
ap[k] = precision[truth.byte()].sum() / max(float(truth.sum()), 1)
return ap | python | def value(self):
"""Returns the model's average precision for each class
Return:
ap (FloatTensor): 1xK tensor, with avg precision for each class k
"""
if self.scores.numel() == 0:
return 0
ap = torch.zeros(self.scores.size(1))
if hasattr(torch, "arange"):
rg = torch.arange(1, self.scores.size(0) + 1).float()
else:
rg = torch.range(1, self.scores.size(0)).float()
if self.weights.numel() > 0:
weight = self.weights.new(self.weights.size())
weighted_truth = self.weights.new(self.weights.size())
# compute average precision for each class
for k in range(self.scores.size(1)):
# sort scores
scores = self.scores[:, k]
targets = self.targets[:, k]
_, sortind = torch.sort(scores, 0, True)
truth = targets[sortind]
if self.weights.numel() > 0:
weight = self.weights[sortind]
weighted_truth = truth.float() * weight
rg = weight.cumsum(0)
# compute true positive sums
if self.weights.numel() > 0:
tp = weighted_truth.cumsum(0)
else:
tp = truth.float().cumsum(0)
# compute precision curve
precision = tp.div(rg)
# compute average precision
ap[k] = precision[truth.byte()].sum() / max(float(truth.sum()), 1)
return ap | [
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pytorch/tnt | torchnet/engine/engine.py | Engine.hook | def hook(self, name, state):
r"""Registers a backward hook.
The hook will be called every time a gradient with respect to the
Tensor is computed. The hook should have the following signature::
hook (grad) -> Tensor or None
The hook should not modify its argument, but it can optionally return
a new gradient which will be used in place of :attr:`grad`.
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Example:
>>> v = torch.tensor([0., 0., 0.], requires_grad=True)
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>>> v.backward(torch.tensor([1., 2., 3.]))
>>> v.grad
2
4
6
[torch.FloatTensor of size (3,)]
>>> h.remove() # removes the hook
"""
if name in self.hooks:
self.hooks[name](state) | python | def hook(self, name, state):
r"""Registers a backward hook.
The hook will be called every time a gradient with respect to the
Tensor is computed. The hook should have the following signature::
hook (grad) -> Tensor or None
The hook should not modify its argument, but it can optionally return
a new gradient which will be used in place of :attr:`grad`.
This function returns a handle with a method ``handle.remove()``
that removes the hook from the module.
Example:
>>> v = torch.tensor([0., 0., 0.], requires_grad=True)
>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
>>> v.backward(torch.tensor([1., 2., 3.]))
>>> v.grad
2
4
6
[torch.FloatTensor of size (3,)]
>>> h.remove() # removes the hook
"""
if name in self.hooks:
self.hooks[name](state) | [
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pytorch/tnt | torchnet/logger/visdomlogger.py | BaseVisdomLogger._viz_prototype | def _viz_prototype(self, vis_fn):
''' Outputs a function which will log the arguments to Visdom in an appropriate way.
Args:
vis_fn: A function, such as self.vis.image
'''
def _viz_logger(*args, **kwargs):
self.win = vis_fn(*args,
win=self.win,
env=self.env,
opts=self.opts,
**kwargs)
return _viz_logger | python | def _viz_prototype(self, vis_fn):
''' Outputs a function which will log the arguments to Visdom in an appropriate way.
Args:
vis_fn: A function, such as self.vis.image
'''
def _viz_logger(*args, **kwargs):
self.win = vis_fn(*args,
win=self.win,
env=self.env,
opts=self.opts,
**kwargs)
return _viz_logger | [
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pytorch/tnt | torchnet/logger/visdomlogger.py | BaseVisdomLogger.log_state | def log_state(self, state):
""" Gathers the stats from self.trainer.stats and passes them into
self.log, as a list """
results = []
for field_idx, field in enumerate(self.fields):
parent, stat = None, state
for f in field:
parent, stat = stat, stat[f]
results.append(stat)
self.log(*results) | python | def log_state(self, state):
""" Gathers the stats from self.trainer.stats and passes them into
self.log, as a list """
results = []
for field_idx, field in enumerate(self.fields):
parent, stat = None, state
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pytorch/tnt | torchnet/logger/meterlogger.py | MeterLogger.peek_meter | def peek_meter(self):
'''Returns a dict of all meters and their values.'''
result = {}
for key in self.meter.keys():
val = self.meter[key].value()
val = val[0] if isinstance(val, (list, tuple)) else val
result[key] = val
return result | python | def peek_meter(self):
'''Returns a dict of all meters and their values.'''
result = {}
for key in self.meter.keys():
val = self.meter[key].value()
val = val[0] if isinstance(val, (list, tuple)) else val
result[key] = val
return result | [
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pytorch/tnt | torchnet/utils/resultswriter.py | ResultsWriter.update | def update(self, task_name, result):
''' Update the results file with new information.
Args:
task_name (str): Name of the currently running task. A previously unseen
``task_name`` will create a new entry in both :attr:`tasks`
and :attr:`results`.
result: This will be appended to the list in :attr:`results` which
corresponds to the ``task_name`` in ``task_name``:attr:`tasks`.
'''
with open(self.filepath, 'rb') as f:
existing_results = pickle.load(f)
if task_name not in self.tasks:
self._add_task(task_name)
existing_results['tasks'].append(task_name)
existing_results['results'].append([])
task_name_idx = existing_results['tasks'].index(task_name)
results = existing_results['results'][task_name_idx]
results.append(result)
with open(self.filepath, 'wb') as f:
pickle.dump(existing_results, f) | python | def update(self, task_name, result):
''' Update the results file with new information.
Args:
task_name (str): Name of the currently running task. A previously unseen
``task_name`` will create a new entry in both :attr:`tasks`
and :attr:`results`.
result: This will be appended to the list in :attr:`results` which
corresponds to the ``task_name`` in ``task_name``:attr:`tasks`.
'''
with open(self.filepath, 'rb') as f:
existing_results = pickle.load(f)
if task_name not in self.tasks:
self._add_task(task_name)
existing_results['tasks'].append(task_name)
existing_results['results'].append([])
task_name_idx = existing_results['tasks'].index(task_name)
results = existing_results['results'][task_name_idx]
results.append(result)
with open(self.filepath, 'wb') as f:
pickle.dump(existing_results, f) | [
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onelogin/python3-saml | src/onelogin/saml2/xml_utils.py | OneLogin_Saml2_XML.query | def query(dom, query, context=None, tagid=None):
"""
Extracts nodes that match the query from the Element
:param dom: The root of the lxml objet
:type: Element
:param query: Xpath Expresion
:type: string
:param context: Context Node
:type: DOMElement
:param tagid: Tag ID
:type query: String
:returns: The queried nodes
:rtype: list
"""
if context is None:
source = dom
else:
source = context
if tagid is None:
return source.xpath(query, namespaces=OneLogin_Saml2_Constants.NSMAP)
else:
return source.xpath(query, tagid=tagid, namespaces=OneLogin_Saml2_Constants.NSMAP) | python | def query(dom, query, context=None, tagid=None):
"""
Extracts nodes that match the query from the Element
:param dom: The root of the lxml objet
:type: Element
:param query: Xpath Expresion
:type: string
:param context: Context Node
:type: DOMElement
:param tagid: Tag ID
:type query: String
:returns: The queried nodes
:rtype: list
"""
if context is None:
source = dom
else:
source = context
if tagid is None:
return source.xpath(query, namespaces=OneLogin_Saml2_Constants.NSMAP)
else:
return source.xpath(query, tagid=tagid, namespaces=OneLogin_Saml2_Constants.NSMAP) | [
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onelogin/python3-saml | src/onelogin/saml2/idp_metadata_parser.py | dict_deep_merge | def dict_deep_merge(a, b, path=None):
"""Deep-merge dictionary `b` into dictionary `a`.
Kudos to http://stackoverflow.com/a/7205107/145400
"""
if path is None:
path = []
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"""Deep-merge dictionary `b` into dictionary `a`.
Kudos to http://stackoverflow.com/a/7205107/145400
"""
if path is None:
path = []
for key in b:
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a[key] = b[key]
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onelogin/python3-saml | src/onelogin/saml2/settings.py | OneLogin_Saml2_Settings.__load_paths | def __load_paths(self, base_path=None):
"""
Set the paths of the different folders
"""
if base_path is None:
base_path = dirname(dirname(dirname(__file__)))
if not base_path.endswith(sep):
base_path += sep
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'cert': base_path + 'certs' + sep,
'lib': base_path + 'lib' + sep,
'extlib': base_path + 'extlib' + sep,
} | python | def __load_paths(self, base_path=None):
"""
Set the paths of the different folders
"""
if base_path is None:
base_path = dirname(dirname(dirname(__file__)))
if not base_path.endswith(sep):
base_path += sep
self.__paths = {
'base': base_path,
'cert': base_path + 'certs' + sep,
'lib': base_path + 'lib' + sep,
'extlib': base_path + 'extlib' + sep,
} | [
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onelogin/python3-saml | src/onelogin/saml2/settings.py | OneLogin_Saml2_Settings.__update_paths | def __update_paths(self, settings):
"""
Set custom paths if necessary
"""
if not isinstance(settings, dict):
return
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base_path = settings['custom_base_path']
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"""
Set custom paths if necessary
"""
if not isinstance(settings, dict):
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base_path = settings['custom_base_path']
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onelogin/python3-saml | src/onelogin/saml2/settings.py | OneLogin_Saml2_Settings.__load_settings_from_dict | def __load_settings_from_dict(self, settings):
"""
Loads settings info from a settings Dict
:param settings: SAML Toolkit Settings
:type settings: dict
:returns: True if the settings info is valid
:rtype: boolean
"""
errors = self.check_settings(settings)
if len(errors) == 0:
self.__errors = []
self.__sp = settings['sp']
self.__idp = settings.get('idp', {})
self.__strict = settings.get('strict', False)
self.__debug = settings.get('debug', False)
self.__security = settings.get('security', {})
self.__contacts = settings.get('contactPerson', {})
self.__organization = settings.get('organization', {})
self.__add_default_values()
return True
self.__errors = errors
return False | python | def __load_settings_from_dict(self, settings):
"""
Loads settings info from a settings Dict
:param settings: SAML Toolkit Settings
:type settings: dict
:returns: True if the settings info is valid
:rtype: boolean
"""
errors = self.check_settings(settings)
if len(errors) == 0:
self.__errors = []
self.__sp = settings['sp']
self.__idp = settings.get('idp', {})
self.__strict = settings.get('strict', False)
self.__debug = settings.get('debug', False)
self.__security = settings.get('security', {})
self.__contacts = settings.get('contactPerson', {})
self.__organization = settings.get('organization', {})
self.__add_default_values()
return True
self.__errors = errors
return False | [
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onelogin/python3-saml | src/onelogin/saml2/settings.py | OneLogin_Saml2_Settings.check_settings | def check_settings(self, settings):
"""
Checks the settings info.
:param settings: Dict with settings data
:type settings: dict
:returns: Errors found on the settings data
:rtype: list
"""
assert isinstance(settings, dict)
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errors += sp_errors
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"""
Checks the settings info.
:param settings: Dict with settings data
:type settings: dict
:returns: Errors found on the settings data
:rtype: list
"""
assert isinstance(settings, dict)
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errors += sp_errors
return errors | [
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onelogin/python3-saml | src/onelogin/saml2/settings.py | OneLogin_Saml2_Settings.format_idp_cert_multi | def format_idp_cert_multi(self):
"""
Formats the Multple IdP certs.
"""
if 'x509certMulti' in self.__idp:
if 'signing' in self.__idp['x509certMulti']:
for idx in range(len(self.__idp['x509certMulti']['signing'])):
self.__idp['x509certMulti']['signing'][idx] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509certMulti']['signing'][idx])
if 'encryption' in self.__idp['x509certMulti']:
for idx in range(len(self.__idp['x509certMulti']['encryption'])):
self.__idp['x509certMulti']['encryption'][idx] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509certMulti']['encryption'][idx]) | python | def format_idp_cert_multi(self):
"""
Formats the Multple IdP certs.
"""
if 'x509certMulti' in self.__idp:
if 'signing' in self.__idp['x509certMulti']:
for idx in range(len(self.__idp['x509certMulti']['signing'])):
self.__idp['x509certMulti']['signing'][idx] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509certMulti']['signing'][idx])
if 'encryption' in self.__idp['x509certMulti']:
for idx in range(len(self.__idp['x509certMulti']['encryption'])):
self.__idp['x509certMulti']['encryption'][idx] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509certMulti']['encryption'][idx]) | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | return_false_on_exception | def return_false_on_exception(func):
"""
Decorator. When applied to a function, it will, by default, suppress any exceptions
raised by that function and return False. It may be overridden by passing a
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"""
@wraps(func)
def exceptfalse(*args, **kwargs):
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return exceptfalse | python | def return_false_on_exception(func):
"""
Decorator. When applied to a function, it will, by default, suppress any exceptions
raised by that function and return False. It may be overridden by passing a
"raise_exceptions" keyword argument when calling the wrapped function.
"""
@wraps(func)
def exceptfalse(*args, **kwargs):
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else:
return func(*args, **kwargs)
return exceptfalse | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.is_https | def is_https(request_data):
"""
Checks if https or http.
:param request_data: The request as a dict
:type: dict
:return: False if https is not active
:rtype: boolean
"""
is_https = 'https' in request_data and request_data['https'] != 'off'
is_https = is_https or ('server_port' in request_data and str(request_data['server_port']) == '443')
return is_https | python | def is_https(request_data):
"""
Checks if https or http.
:param request_data: The request as a dict
:type: dict
:return: False if https is not active
:rtype: boolean
"""
is_https = 'https' in request_data and request_data['https'] != 'off'
is_https = is_https or ('server_port' in request_data and str(request_data['server_port']) == '443')
return is_https | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.get_self_url_no_query | def get_self_url_no_query(request_data):
"""
Returns the URL of the current host + current view.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
script_name = request_data['script_name']
if script_name:
if script_name[0] != '/':
script_name = '/' + script_name
else:
script_name = ''
self_url_no_query = self_url_host + script_name
if 'path_info' in request_data:
self_url_no_query += request_data['path_info']
return self_url_no_query | python | def get_self_url_no_query(request_data):
"""
Returns the URL of the current host + current view.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
script_name = request_data['script_name']
if script_name:
if script_name[0] != '/':
script_name = '/' + script_name
else:
script_name = ''
self_url_no_query = self_url_host + script_name
if 'path_info' in request_data:
self_url_no_query += request_data['path_info']
return self_url_no_query | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.get_self_routed_url_no_query | def get_self_routed_url_no_query(request_data):
"""
Returns the routed URL of the current host + current view.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
route = ''
if 'request_uri' in request_data and request_data['request_uri']:
route = request_data['request_uri']
if 'query_string' in request_data and request_data['query_string']:
route = route.replace(request_data['query_string'], '')
return self_url_host + route | python | def get_self_routed_url_no_query(request_data):
"""
Returns the routed URL of the current host + current view.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
route = ''
if 'request_uri' in request_data and request_data['request_uri']:
route = request_data['request_uri']
if 'query_string' in request_data and request_data['query_string']:
route = route.replace(request_data['query_string'], '')
return self_url_host + route | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.get_self_url | def get_self_url(request_data):
"""
Returns the URL of the current host + current view + query.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view + query
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
request_uri = ''
if 'request_uri' in request_data:
request_uri = request_data['request_uri']
if not request_uri.startswith('/'):
match = re.search('^https?://[^/]*(/.*)', request_uri)
if match is not None:
request_uri = match.groups()[0]
return self_url_host + request_uri | python | def get_self_url(request_data):
"""
Returns the URL of the current host + current view + query.
:param request_data: The request as a dict
:type: dict
:return: The url of current host + current view + query
:rtype: string
"""
self_url_host = OneLogin_Saml2_Utils.get_self_url_host(request_data)
request_uri = ''
if 'request_uri' in request_data:
request_uri = request_data['request_uri']
if not request_uri.startswith('/'):
match = re.search('^https?://[^/]*(/.*)', request_uri)
if match is not None:
request_uri = match.groups()[0]
return self_url_host + request_uri | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.get_expire_time | def get_expire_time(cache_duration=None, valid_until=None):
"""
Compares 2 dates and returns the earliest.
:param cache_duration: The duration, as a string.
:type: string
:param valid_until: The valid until date, as a string or as a timestamp
:type: string
:return: The expiration time.
:rtype: int
"""
expire_time = None
if cache_duration is not None:
expire_time = OneLogin_Saml2_Utils.parse_duration(cache_duration)
if valid_until is not None:
if isinstance(valid_until, int):
valid_until_time = valid_until
else:
valid_until_time = OneLogin_Saml2_Utils.parse_SAML_to_time(valid_until)
if expire_time is None or expire_time > valid_until_time:
expire_time = valid_until_time
if expire_time is not None:
return '%d' % expire_time
return None | python | def get_expire_time(cache_duration=None, valid_until=None):
"""
Compares 2 dates and returns the earliest.
:param cache_duration: The duration, as a string.
:type: string
:param valid_until: The valid until date, as a string or as a timestamp
:type: string
:return: The expiration time.
:rtype: int
"""
expire_time = None
if cache_duration is not None:
expire_time = OneLogin_Saml2_Utils.parse_duration(cache_duration)
if valid_until is not None:
if isinstance(valid_until, int):
valid_until_time = valid_until
else:
valid_until_time = OneLogin_Saml2_Utils.parse_SAML_to_time(valid_until)
if expire_time is None or expire_time > valid_until_time:
expire_time = valid_until_time
if expire_time is not None:
return '%d' % expire_time
return None | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.calculate_x509_fingerprint | def calculate_x509_fingerprint(x509_cert, alg='sha1'):
"""
Calculates the fingerprint of a formatted x509cert.
:param x509_cert: x509 cert formatted
:type: string
:param alg: The algorithm to build the fingerprint
:type: string
:returns: fingerprint
:rtype: string
"""
assert isinstance(x509_cert, compat.str_type)
lines = x509_cert.split('\n')
data = ''
inData = False
for line in lines:
# Remove '\r' from end of line if present.
line = line.rstrip()
if not inData:
if line == '-----BEGIN CERTIFICATE-----':
inData = True
elif line == '-----BEGIN PUBLIC KEY-----' or line == '-----BEGIN RSA PRIVATE KEY-----':
# This isn't an X509 certificate.
return None
else:
if line == '-----END CERTIFICATE-----':
break
# Append the current line to the certificate data.
data += line
if not data:
return None
decoded_data = base64.b64decode(compat.to_bytes(data))
if alg == 'sha512':
fingerprint = sha512(decoded_data)
elif alg == 'sha384':
fingerprint = sha384(decoded_data)
elif alg == 'sha256':
fingerprint = sha256(decoded_data)
else:
fingerprint = sha1(decoded_data)
return fingerprint.hexdigest().lower() | python | def calculate_x509_fingerprint(x509_cert, alg='sha1'):
"""
Calculates the fingerprint of a formatted x509cert.
:param x509_cert: x509 cert formatted
:type: string
:param alg: The algorithm to build the fingerprint
:type: string
:returns: fingerprint
:rtype: string
"""
assert isinstance(x509_cert, compat.str_type)
lines = x509_cert.split('\n')
data = ''
inData = False
for line in lines:
# Remove '\r' from end of line if present.
line = line.rstrip()
if not inData:
if line == '-----BEGIN CERTIFICATE-----':
inData = True
elif line == '-----BEGIN PUBLIC KEY-----' or line == '-----BEGIN RSA PRIVATE KEY-----':
# This isn't an X509 certificate.
return None
else:
if line == '-----END CERTIFICATE-----':
break
# Append the current line to the certificate data.
data += line
if not data:
return None
decoded_data = base64.b64decode(compat.to_bytes(data))
if alg == 'sha512':
fingerprint = sha512(decoded_data)
elif alg == 'sha384':
fingerprint = sha384(decoded_data)
elif alg == 'sha256':
fingerprint = sha256(decoded_data)
else:
fingerprint = sha1(decoded_data)
return fingerprint.hexdigest().lower() | [
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onelogin/python3-saml | src/onelogin/saml2/utils.py | OneLogin_Saml2_Utils.sign_binary | def sign_binary(msg, key, algorithm=xmlsec.Transform.RSA_SHA1, debug=False):
"""
Sign binary message
:param msg: The element we should validate
:type: bytes
:param key: The private key
:type: string
:param debug: Activate the xmlsec debug
:type: bool
:return signed message
:rtype str
"""
if isinstance(msg, str):
msg = msg.encode('utf8')
xmlsec.enable_debug_trace(debug)
dsig_ctx = xmlsec.SignatureContext()
dsig_ctx.key = xmlsec.Key.from_memory(key, xmlsec.KeyFormat.PEM, None)
return dsig_ctx.sign_binary(compat.to_bytes(msg), algorithm) | python | def sign_binary(msg, key, algorithm=xmlsec.Transform.RSA_SHA1, debug=False):
"""
Sign binary message
:param msg: The element we should validate
:type: bytes
:param key: The private key
:type: string
:param debug: Activate the xmlsec debug
:type: bool
:return signed message
:rtype str
"""
if isinstance(msg, str):
msg = msg.encode('utf8')
xmlsec.enable_debug_trace(debug)
dsig_ctx = xmlsec.SignatureContext()
dsig_ctx.key = xmlsec.Key.from_memory(key, xmlsec.KeyFormat.PEM, None)
return dsig_ctx.sign_binary(compat.to_bytes(msg), algorithm) | [
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onelogin/python3-saml | src/onelogin/saml2/auth.py | OneLogin_Saml2_Auth.process_response | def process_response(self, request_id=None):
"""
Process the SAML Response sent by the IdP.
:param request_id: Is an optional argument. Is the ID of the AuthNRequest sent by this SP to the IdP.
:type request_id: string
:raises: OneLogin_Saml2_Error.SAML_RESPONSE_NOT_FOUND, when a POST with a SAMLResponse is not found
"""
self.__errors = []
self.__error_reason = None
if 'post_data' in self.__request_data and 'SAMLResponse' in self.__request_data['post_data']:
# AuthnResponse -- HTTP_POST Binding
response = OneLogin_Saml2_Response(self.__settings, self.__request_data['post_data']['SAMLResponse'])
self.__last_response = response.get_xml_document()
if response.is_valid(self.__request_data, request_id):
self.__attributes = response.get_attributes()
self.__nameid = response.get_nameid()
self.__nameid_format = response.get_nameid_format()
self.__session_index = response.get_session_index()
self.__session_expiration = response.get_session_not_on_or_after()
self.__last_message_id = response.get_id()
self.__last_assertion_id = response.get_assertion_id()
self.__last_authn_contexts = response.get_authn_contexts()
self.__authenticated = True
self.__last_assertion_not_on_or_after = response.get_assertion_not_on_or_after()
else:
self.__errors.append('invalid_response')
self.__error_reason = response.get_error()
else:
self.__errors.append('invalid_binding')
raise OneLogin_Saml2_Error(
'SAML Response not found, Only supported HTTP_POST Binding',
OneLogin_Saml2_Error.SAML_RESPONSE_NOT_FOUND
) | python | def process_response(self, request_id=None):
"""
Process the SAML Response sent by the IdP.
:param request_id: Is an optional argument. Is the ID of the AuthNRequest sent by this SP to the IdP.
:type request_id: string
:raises: OneLogin_Saml2_Error.SAML_RESPONSE_NOT_FOUND, when a POST with a SAMLResponse is not found
"""
self.__errors = []
self.__error_reason = None
if 'post_data' in self.__request_data and 'SAMLResponse' in self.__request_data['post_data']:
# AuthnResponse -- HTTP_POST Binding
response = OneLogin_Saml2_Response(self.__settings, self.__request_data['post_data']['SAMLResponse'])
self.__last_response = response.get_xml_document()
if response.is_valid(self.__request_data, request_id):
self.__attributes = response.get_attributes()
self.__nameid = response.get_nameid()
self.__nameid_format = response.get_nameid_format()
self.__session_index = response.get_session_index()
self.__session_expiration = response.get_session_not_on_or_after()
self.__last_message_id = response.get_id()
self.__last_assertion_id = response.get_assertion_id()
self.__last_authn_contexts = response.get_authn_contexts()
self.__authenticated = True
self.__last_assertion_not_on_or_after = response.get_assertion_not_on_or_after()
else:
self.__errors.append('invalid_response')
self.__error_reason = response.get_error()
else:
self.__errors.append('invalid_binding')
raise OneLogin_Saml2_Error(
'SAML Response not found, Only supported HTTP_POST Binding',
OneLogin_Saml2_Error.SAML_RESPONSE_NOT_FOUND
) | [
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onelogin/python3-saml | src/onelogin/saml2/auth.py | OneLogin_Saml2_Auth.redirect_to | def redirect_to(self, url=None, parameters={}):
"""
Redirects the user to the URL passed by parameter or to the URL that we defined in our SSO Request.
:param url: The target URL to redirect the user
:type url: string
:param parameters: Extra parameters to be passed as part of the URL
:type parameters: dict
:returns: Redirection URL
"""
if url is None and 'RelayState' in self.__request_data['get_data']:
url = self.__request_data['get_data']['RelayState']
return OneLogin_Saml2_Utils.redirect(url, parameters, request_data=self.__request_data) | python | def redirect_to(self, url=None, parameters={}):
"""
Redirects the user to the URL passed by parameter or to the URL that we defined in our SSO Request.
:param url: The target URL to redirect the user
:type url: string
:param parameters: Extra parameters to be passed as part of the URL
:type parameters: dict
:returns: Redirection URL
"""
if url is None and 'RelayState' in self.__request_data['get_data']:
url = self.__request_data['get_data']['RelayState']
return OneLogin_Saml2_Utils.redirect(url, parameters, request_data=self.__request_data) | [
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onelogin/python3-saml | src/onelogin/saml2/auth.py | OneLogin_Saml2_Auth.__build_sign_query | def __build_sign_query(saml_data, relay_state, algorithm, saml_type, lowercase_urlencoding=False):
"""
Build sign query
:param saml_data: The Request data
:type saml_data: str
:param relay_state: The Relay State
:type relay_state: str
:param algorithm: The Signature Algorithm
:type algorithm: str
:param saml_type: The target URL the user should be redirected to
:type saml_type: string SAMLRequest | SAMLResponse
:param lowercase_urlencoding: lowercase or no
:type lowercase_urlencoding: boolean
"""
sign_data = ['%s=%s' % (saml_type, OneLogin_Saml2_Utils.escape_url(saml_data, lowercase_urlencoding))]
if relay_state is not None:
sign_data.append('RelayState=%s' % OneLogin_Saml2_Utils.escape_url(relay_state, lowercase_urlencoding))
sign_data.append('SigAlg=%s' % OneLogin_Saml2_Utils.escape_url(algorithm, lowercase_urlencoding))
return '&'.join(sign_data) | python | def __build_sign_query(saml_data, relay_state, algorithm, saml_type, lowercase_urlencoding=False):
"""
Build sign query
:param saml_data: The Request data
:type saml_data: str
:param relay_state: The Relay State
:type relay_state: str
:param algorithm: The Signature Algorithm
:type algorithm: str
:param saml_type: The target URL the user should be redirected to
:type saml_type: string SAMLRequest | SAMLResponse
:param lowercase_urlencoding: lowercase or no
:type lowercase_urlencoding: boolean
"""
sign_data = ['%s=%s' % (saml_type, OneLogin_Saml2_Utils.escape_url(saml_data, lowercase_urlencoding))]
if relay_state is not None:
sign_data.append('RelayState=%s' % OneLogin_Saml2_Utils.escape_url(relay_state, lowercase_urlencoding))
sign_data.append('SigAlg=%s' % OneLogin_Saml2_Utils.escape_url(algorithm, lowercase_urlencoding))
return '&'.join(sign_data) | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.check_status | def check_status(self):
"""
Check if the status of the response is success or not
:raises: Exception. If the status is not success
"""
status = OneLogin_Saml2_Utils.get_status(self.document)
code = status.get('code', None)
if code and code != OneLogin_Saml2_Constants.STATUS_SUCCESS:
splited_code = code.split(':')
printable_code = splited_code.pop()
status_exception_msg = 'The status code of the Response was not Success, was %s' % printable_code
status_msg = status.get('msg', None)
if status_msg:
status_exception_msg += ' -> ' + status_msg
raise OneLogin_Saml2_ValidationError(
status_exception_msg,
OneLogin_Saml2_ValidationError.STATUS_CODE_IS_NOT_SUCCESS
) | python | def check_status(self):
"""
Check if the status of the response is success or not
:raises: Exception. If the status is not success
"""
status = OneLogin_Saml2_Utils.get_status(self.document)
code = status.get('code', None)
if code and code != OneLogin_Saml2_Constants.STATUS_SUCCESS:
splited_code = code.split(':')
printable_code = splited_code.pop()
status_exception_msg = 'The status code of the Response was not Success, was %s' % printable_code
status_msg = status.get('msg', None)
if status_msg:
status_exception_msg += ' -> ' + status_msg
raise OneLogin_Saml2_ValidationError(
status_exception_msg,
OneLogin_Saml2_ValidationError.STATUS_CODE_IS_NOT_SUCCESS
) | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_authn_contexts | def get_authn_contexts(self):
"""
Gets the authentication contexts
:returns: The authentication classes for the SAML Response
:rtype: list
"""
authn_context_nodes = self.__query_assertion('/saml:AuthnStatement/saml:AuthnContext/saml:AuthnContextClassRef')
return [OneLogin_Saml2_XML.element_text(node) for node in authn_context_nodes] | python | def get_authn_contexts(self):
"""
Gets the authentication contexts
:returns: The authentication classes for the SAML Response
:rtype: list
"""
authn_context_nodes = self.__query_assertion('/saml:AuthnStatement/saml:AuthnContext/saml:AuthnContextClassRef')
return [OneLogin_Saml2_XML.element_text(node) for node in authn_context_nodes] | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_nameid | def get_nameid(self):
"""
Gets the NameID provided by the SAML Response from the IdP
:returns: NameID (value)
:rtype: string|None
"""
nameid_value = None
nameid_data = self.get_nameid_data()
if nameid_data and 'Value' in nameid_data.keys():
nameid_value = nameid_data['Value']
return nameid_value | python | def get_nameid(self):
"""
Gets the NameID provided by the SAML Response from the IdP
:returns: NameID (value)
:rtype: string|None
"""
nameid_value = None
nameid_data = self.get_nameid_data()
if nameid_data and 'Value' in nameid_data.keys():
nameid_value = nameid_data['Value']
return nameid_value | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_nameid_format | def get_nameid_format(self):
"""
Gets the NameID Format provided by the SAML Response from the IdP
:returns: NameID Format
:rtype: string|None
"""
nameid_format = None
nameid_data = self.get_nameid_data()
if nameid_data and 'Format' in nameid_data.keys():
nameid_format = nameid_data['Format']
return nameid_format | python | def get_nameid_format(self):
"""
Gets the NameID Format provided by the SAML Response from the IdP
:returns: NameID Format
:rtype: string|None
"""
nameid_format = None
nameid_data = self.get_nameid_data()
if nameid_data and 'Format' in nameid_data.keys():
nameid_format = nameid_data['Format']
return nameid_format | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_session_not_on_or_after | def get_session_not_on_or_after(self):
"""
Gets the SessionNotOnOrAfter from the AuthnStatement
Could be used to set the local session expiration
:returns: The SessionNotOnOrAfter value
:rtype: time|None
"""
not_on_or_after = None
authn_statement_nodes = self.__query_assertion('/saml:AuthnStatement[@SessionNotOnOrAfter]')
if authn_statement_nodes:
not_on_or_after = OneLogin_Saml2_Utils.parse_SAML_to_time(authn_statement_nodes[0].get('SessionNotOnOrAfter'))
return not_on_or_after | python | def get_session_not_on_or_after(self):
"""
Gets the SessionNotOnOrAfter from the AuthnStatement
Could be used to set the local session expiration
:returns: The SessionNotOnOrAfter value
:rtype: time|None
"""
not_on_or_after = None
authn_statement_nodes = self.__query_assertion('/saml:AuthnStatement[@SessionNotOnOrAfter]')
if authn_statement_nodes:
not_on_or_after = OneLogin_Saml2_Utils.parse_SAML_to_time(authn_statement_nodes[0].get('SessionNotOnOrAfter'))
return not_on_or_after | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_session_index | def get_session_index(self):
"""
Gets the SessionIndex from the AuthnStatement
Could be used to be stored in the local session in order
to be used in a future Logout Request that the SP could
send to the SP, to set what specific session must be deleted
:returns: The SessionIndex value
:rtype: string|None
"""
session_index = None
authn_statement_nodes = self.__query_assertion('/saml:AuthnStatement[@SessionIndex]')
if authn_statement_nodes:
session_index = authn_statement_nodes[0].get('SessionIndex')
return session_index | python | def get_session_index(self):
"""
Gets the SessionIndex from the AuthnStatement
Could be used to be stored in the local session in order
to be used in a future Logout Request that the SP could
send to the SP, to set what specific session must be deleted
:returns: The SessionIndex value
:rtype: string|None
"""
session_index = None
authn_statement_nodes = self.__query_assertion('/saml:AuthnStatement[@SessionIndex]')
if authn_statement_nodes:
session_index = authn_statement_nodes[0].get('SessionIndex')
return session_index | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.get_attributes | def get_attributes(self):
"""
Gets the Attributes from the AttributeStatement element.
EncryptedAttributes are not supported
"""
attributes = {}
attribute_nodes = self.__query_assertion('/saml:AttributeStatement/saml:Attribute')
for attribute_node in attribute_nodes:
attr_name = attribute_node.get('Name')
if attr_name in attributes.keys():
raise OneLogin_Saml2_ValidationError(
'Found an Attribute element with duplicated Name',
OneLogin_Saml2_ValidationError.DUPLICATED_ATTRIBUTE_NAME_FOUND
)
values = []
for attr in attribute_node.iterchildren('{%s}AttributeValue' % OneLogin_Saml2_Constants.NSMAP['saml']):
attr_text = OneLogin_Saml2_XML.element_text(attr)
if attr_text:
attr_text = attr_text.strip()
if attr_text:
values.append(attr_text)
# Parse any nested NameID children
for nameid in attr.iterchildren('{%s}NameID' % OneLogin_Saml2_Constants.NSMAP['saml']):
values.append({
'NameID': {
'Format': nameid.get('Format'),
'NameQualifier': nameid.get('NameQualifier'),
'value': nameid.text
}
})
attributes[attr_name] = values
return attributes | python | def get_attributes(self):
"""
Gets the Attributes from the AttributeStatement element.
EncryptedAttributes are not supported
"""
attributes = {}
attribute_nodes = self.__query_assertion('/saml:AttributeStatement/saml:Attribute')
for attribute_node in attribute_nodes:
attr_name = attribute_node.get('Name')
if attr_name in attributes.keys():
raise OneLogin_Saml2_ValidationError(
'Found an Attribute element with duplicated Name',
OneLogin_Saml2_ValidationError.DUPLICATED_ATTRIBUTE_NAME_FOUND
)
values = []
for attr in attribute_node.iterchildren('{%s}AttributeValue' % OneLogin_Saml2_Constants.NSMAP['saml']):
attr_text = OneLogin_Saml2_XML.element_text(attr)
if attr_text:
attr_text = attr_text.strip()
if attr_text:
values.append(attr_text)
# Parse any nested NameID children
for nameid in attr.iterchildren('{%s}NameID' % OneLogin_Saml2_Constants.NSMAP['saml']):
values.append({
'NameID': {
'Format': nameid.get('Format'),
'NameQualifier': nameid.get('NameQualifier'),
'value': nameid.text
}
})
attributes[attr_name] = values
return attributes | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.validate_timestamps | def validate_timestamps(self):
"""
Verifies that the document is valid according to Conditions Element
:returns: True if the condition is valid, False otherwise
:rtype: bool
"""
conditions_nodes = self.__query_assertion('/saml:Conditions')
for conditions_node in conditions_nodes:
nb_attr = conditions_node.get('NotBefore')
nooa_attr = conditions_node.get('NotOnOrAfter')
if nb_attr and OneLogin_Saml2_Utils.parse_SAML_to_time(nb_attr) > OneLogin_Saml2_Utils.now() + OneLogin_Saml2_Constants.ALLOWED_CLOCK_DRIFT:
raise OneLogin_Saml2_ValidationError(
'Could not validate timestamp: not yet valid. Check system clock.',
OneLogin_Saml2_ValidationError.ASSERTION_TOO_EARLY
)
if nooa_attr and OneLogin_Saml2_Utils.parse_SAML_to_time(nooa_attr) + OneLogin_Saml2_Constants.ALLOWED_CLOCK_DRIFT <= OneLogin_Saml2_Utils.now():
raise OneLogin_Saml2_ValidationError(
'Could not validate timestamp: expired. Check system clock.',
OneLogin_Saml2_ValidationError.ASSERTION_EXPIRED
)
return True | python | def validate_timestamps(self):
"""
Verifies that the document is valid according to Conditions Element
:returns: True if the condition is valid, False otherwise
:rtype: bool
"""
conditions_nodes = self.__query_assertion('/saml:Conditions')
for conditions_node in conditions_nodes:
nb_attr = conditions_node.get('NotBefore')
nooa_attr = conditions_node.get('NotOnOrAfter')
if nb_attr and OneLogin_Saml2_Utils.parse_SAML_to_time(nb_attr) > OneLogin_Saml2_Utils.now() + OneLogin_Saml2_Constants.ALLOWED_CLOCK_DRIFT:
raise OneLogin_Saml2_ValidationError(
'Could not validate timestamp: not yet valid. Check system clock.',
OneLogin_Saml2_ValidationError.ASSERTION_TOO_EARLY
)
if nooa_attr and OneLogin_Saml2_Utils.parse_SAML_to_time(nooa_attr) + OneLogin_Saml2_Constants.ALLOWED_CLOCK_DRIFT <= OneLogin_Saml2_Utils.now():
raise OneLogin_Saml2_ValidationError(
'Could not validate timestamp: expired. Check system clock.',
OneLogin_Saml2_ValidationError.ASSERTION_EXPIRED
)
return True | [
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onelogin/python3-saml | src/onelogin/saml2/response.py | OneLogin_Saml2_Response.__query_assertion | def __query_assertion(self, xpath_expr):
"""
Extracts nodes that match the query from the Assertion
:param xpath_expr: Xpath Expresion
:type xpath_expr: String
:returns: The queried nodes
:rtype: list
"""
assertion_expr = '/saml:Assertion'
signature_expr = '/ds:Signature/ds:SignedInfo/ds:Reference'
signed_assertion_query = '/samlp:Response' + assertion_expr + signature_expr
assertion_reference_nodes = self.__query(signed_assertion_query)
tagid = None
if not assertion_reference_nodes:
# Check if the message is signed
signed_message_query = '/samlp:Response' + signature_expr
message_reference_nodes = self.__query(signed_message_query)
if message_reference_nodes:
message_id = message_reference_nodes[0].get('URI')
final_query = "/samlp:Response[@ID=$tagid]/"
tagid = message_id[1:]
else:
final_query = "/samlp:Response"
final_query += assertion_expr
else:
assertion_id = assertion_reference_nodes[0].get('URI')
final_query = '/samlp:Response' + assertion_expr + "[@ID=$tagid]"
tagid = assertion_id[1:]
final_query += xpath_expr
return self.__query(final_query, tagid) | python | def __query_assertion(self, xpath_expr):
"""
Extracts nodes that match the query from the Assertion
:param xpath_expr: Xpath Expresion
:type xpath_expr: String
:returns: The queried nodes
:rtype: list
"""
assertion_expr = '/saml:Assertion'
signature_expr = '/ds:Signature/ds:SignedInfo/ds:Reference'
signed_assertion_query = '/samlp:Response' + assertion_expr + signature_expr
assertion_reference_nodes = self.__query(signed_assertion_query)
tagid = None
if not assertion_reference_nodes:
# Check if the message is signed
signed_message_query = '/samlp:Response' + signature_expr
message_reference_nodes = self.__query(signed_message_query)
if message_reference_nodes:
message_id = message_reference_nodes[0].get('URI')
final_query = "/samlp:Response[@ID=$tagid]/"
tagid = message_id[1:]
else:
final_query = "/samlp:Response"
final_query += assertion_expr
else:
assertion_id = assertion_reference_nodes[0].get('URI')
final_query = '/samlp:Response' + assertion_expr + "[@ID=$tagid]"
tagid = assertion_id[1:]
final_query += xpath_expr
return self.__query(final_query, tagid) | [
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automl/HpBandSter | hpbandster/optimizers/kde/mvkde.py | MultivariateKDE.pdf | def pdf(self, x_test):
"""
Computes the probability density function at all x_test
"""
N,D = self.data.shape
x_test = np.asfortranarray(x_test)
x_test = x_test.reshape([-1, D])
pdfs = self._individual_pdfs(x_test)
#import pdb; pdb.set_trace()
# combine values based on fully_dimensional!
if self.fully_dimensional:
# first the product of the individual pdfs for each point in the data across dimensions and then the average (factorized kernel)
pdfs = np.sum(np.prod(pdfs, axis=-1)*self.weights[None, :], axis=-1)
else:
# first the average over the 1d pdfs and the the product over dimensions (TPE like factorization of the pdf)
pdfs = np.prod(np.sum(pdfs*self.weights[None,:,None], axis=-2), axis=-1)
return(pdfs) | python | def pdf(self, x_test):
"""
Computes the probability density function at all x_test
"""
N,D = self.data.shape
x_test = np.asfortranarray(x_test)
x_test = x_test.reshape([-1, D])
pdfs = self._individual_pdfs(x_test)
#import pdb; pdb.set_trace()
# combine values based on fully_dimensional!
if self.fully_dimensional:
# first the product of the individual pdfs for each point in the data across dimensions and then the average (factorized kernel)
pdfs = np.sum(np.prod(pdfs, axis=-1)*self.weights[None, :], axis=-1)
else:
# first the average over the 1d pdfs and the the product over dimensions (TPE like factorization of the pdf)
pdfs = np.prod(np.sum(pdfs*self.weights[None,:,None], axis=-2), axis=-1)
return(pdfs) | [
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] | 841db4b827f342e5eb7f725723ea6461ac52d45a | https://github.com/automl/HpBandSter/blob/841db4b827f342e5eb7f725723ea6461ac52d45a/hpbandster/optimizers/kde/mvkde.py#L172-L189 | train | 216,737 |
automl/HpBandSter | hpbandster/examples/plot_example_7_interactive_plot.py | realtime_learning_curves | def realtime_learning_curves(runs):
"""
example how to extract a different kind of learning curve.
The x values are now the time the runs finished, not the budget anymore.
We no longer plot the validation loss on the y axis, but now the test accuracy.
This is just to show how to get different information into the interactive plot.
"""
sr = sorted(runs, key=lambda r: r.budget)
lc = list(filter(lambda t: not t[1] is None, [(r.time_stamps['finished'], r.info['test accuracy']) for r in sr]))
return([lc,]) | python | def realtime_learning_curves(runs):
"""
example how to extract a different kind of learning curve.
The x values are now the time the runs finished, not the budget anymore.
We no longer plot the validation loss on the y axis, but now the test accuracy.
This is just to show how to get different information into the interactive plot.
"""
sr = sorted(runs, key=lambda r: r.budget)
lc = list(filter(lambda t: not t[1] is None, [(r.time_stamps['finished'], r.info['test accuracy']) for r in sr]))
return([lc,]) | [
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automl/HpBandSter | hpbandster/core/worker.py | Worker.load_nameserver_credentials | def load_nameserver_credentials(self, working_directory, num_tries=60, interval=1):
"""
loads the nameserver credentials in cases where master and workers share a filesystem
Parameters
----------
working_directory: str
the working directory for the HPB run (see master)
num_tries: int
number of attempts to find the file (default 60)
interval: float
waiting period between the attempts
"""
fn = os.path.join(working_directory, 'HPB_run_%s_pyro.pkl'%self.run_id)
for i in range(num_tries):
try:
with open(fn, 'rb') as fh:
self.nameserver, self.nameserver_port = pickle.load(fh)
return
except FileNotFoundError:
self.logger.warning('config file %s not found (trail %i/%i)'%(fn, i+1, num_tries))
time.sleep(interval)
except:
raise
raise RuntimeError("Could not find the nameserver information, aborting!") | python | def load_nameserver_credentials(self, working_directory, num_tries=60, interval=1):
"""
loads the nameserver credentials in cases where master and workers share a filesystem
Parameters
----------
working_directory: str
the working directory for the HPB run (see master)
num_tries: int
number of attempts to find the file (default 60)
interval: float
waiting period between the attempts
"""
fn = os.path.join(working_directory, 'HPB_run_%s_pyro.pkl'%self.run_id)
for i in range(num_tries):
try:
with open(fn, 'rb') as fh:
self.nameserver, self.nameserver_port = pickle.load(fh)
return
except FileNotFoundError:
self.logger.warning('config file %s not found (trail %i/%i)'%(fn, i+1, num_tries))
time.sleep(interval)
except:
raise
raise RuntimeError("Could not find the nameserver information, aborting!") | [
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automl/HpBandSter | hpbandster/core/master.py | Master.wait_for_workers | def wait_for_workers(self, min_n_workers=1):
"""
helper function to hold execution until some workers are active
Parameters
----------
min_n_workers: int
minimum number of workers present before the run starts
"""
self.logger.debug('wait_for_workers trying to get the condition')
with self.thread_cond:
while (self.dispatcher.number_of_workers() < min_n_workers):
self.logger.debug('HBMASTER: only %i worker(s) available, waiting for at least %i.'%(self.dispatcher.number_of_workers(), min_n_workers))
self.thread_cond.wait(1)
self.dispatcher.trigger_discover_worker()
self.logger.debug('Enough workers to start this run!') | python | def wait_for_workers(self, min_n_workers=1):
"""
helper function to hold execution until some workers are active
Parameters
----------
min_n_workers: int
minimum number of workers present before the run starts
"""
self.logger.debug('wait_for_workers trying to get the condition')
with self.thread_cond:
while (self.dispatcher.number_of_workers() < min_n_workers):
self.logger.debug('HBMASTER: only %i worker(s) available, waiting for at least %i.'%(self.dispatcher.number_of_workers(), min_n_workers))
self.thread_cond.wait(1)
self.dispatcher.trigger_discover_worker()
self.logger.debug('Enough workers to start this run!') | [
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automl/HpBandSter | hpbandster/core/master.py | Master.run | def run(self, n_iterations=1, min_n_workers=1, iteration_kwargs = {},):
"""
run n_iterations of SuccessiveHalving
Parameters
----------
n_iterations: int
number of iterations to be performed in this run
min_n_workers: int
minimum number of workers before starting the run
"""
self.wait_for_workers(min_n_workers)
iteration_kwargs.update({'result_logger': self.result_logger})
if self.time_ref is None:
self.time_ref = time.time()
self.config['time_ref'] = self.time_ref
self.logger.info('HBMASTER: starting run at %s'%(str(self.time_ref)))
self.thread_cond.acquire()
while True:
self._queue_wait()
next_run = None
# find a new run to schedule
for i in self.active_iterations():
next_run = self.iterations[i].get_next_run()
if not next_run is None: break
if not next_run is None:
self.logger.debug('HBMASTER: schedule new run for iteration %i'%i)
self._submit_job(*next_run)
continue
else:
if n_iterations > 0: #we might be able to start the next iteration
self.iterations.append(self.get_next_iteration(len(self.iterations), iteration_kwargs))
n_iterations -= 1
continue
# at this point there is no imediate run that can be scheduled,
# so wait for some job to finish if there are active iterations
if self.active_iterations():
self.thread_cond.wait()
else:
break
self.thread_cond.release()
for i in self.warmstart_iteration:
i.fix_timestamps(self.time_ref)
ws_data = [i.data for i in self.warmstart_iteration]
return Result([copy.deepcopy(i.data) for i in self.iterations] + ws_data, self.config) | python | def run(self, n_iterations=1, min_n_workers=1, iteration_kwargs = {},):
"""
run n_iterations of SuccessiveHalving
Parameters
----------
n_iterations: int
number of iterations to be performed in this run
min_n_workers: int
minimum number of workers before starting the run
"""
self.wait_for_workers(min_n_workers)
iteration_kwargs.update({'result_logger': self.result_logger})
if self.time_ref is None:
self.time_ref = time.time()
self.config['time_ref'] = self.time_ref
self.logger.info('HBMASTER: starting run at %s'%(str(self.time_ref)))
self.thread_cond.acquire()
while True:
self._queue_wait()
next_run = None
# find a new run to schedule
for i in self.active_iterations():
next_run = self.iterations[i].get_next_run()
if not next_run is None: break
if not next_run is None:
self.logger.debug('HBMASTER: schedule new run for iteration %i'%i)
self._submit_job(*next_run)
continue
else:
if n_iterations > 0: #we might be able to start the next iteration
self.iterations.append(self.get_next_iteration(len(self.iterations), iteration_kwargs))
n_iterations -= 1
continue
# at this point there is no imediate run that can be scheduled,
# so wait for some job to finish if there are active iterations
if self.active_iterations():
self.thread_cond.wait()
else:
break
self.thread_cond.release()
for i in self.warmstart_iteration:
i.fix_timestamps(self.time_ref)
ws_data = [i.data for i in self.warmstart_iteration]
return Result([copy.deepcopy(i.data) for i in self.iterations] + ws_data, self.config) | [
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automl/HpBandSter | hpbandster/core/master.py | Master.job_callback | def job_callback(self, job):
"""
method to be called when a job has finished
this will do some book keeping and call the user defined
new_result_callback if one was specified
"""
self.logger.debug('job_callback for %s started'%str(job.id))
with self.thread_cond:
self.logger.debug('job_callback for %s got condition'%str(job.id))
self.num_running_jobs -= 1
if not self.result_logger is None:
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self.iterations[job.id[0]].register_result(job)
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if self.num_running_jobs <= self.job_queue_sizes[0]:
self.logger.debug("HBMASTER: Trying to run another job!")
self.thread_cond.notify()
self.logger.debug('job_callback for %s finished'%str(job.id)) | python | def job_callback(self, job):
"""
method to be called when a job has finished
this will do some book keeping and call the user defined
new_result_callback if one was specified
"""
self.logger.debug('job_callback for %s started'%str(job.id))
with self.thread_cond:
self.logger.debug('job_callback for %s got condition'%str(job.id))
self.num_running_jobs -= 1
if not self.result_logger is None:
self.result_logger(job)
self.iterations[job.id[0]].register_result(job)
self.config_generator.new_result(job)
if self.num_running_jobs <= self.job_queue_sizes[0]:
self.logger.debug("HBMASTER: Trying to run another job!")
self.thread_cond.notify()
self.logger.debug('job_callback for %s finished'%str(job.id)) | [
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automl/HpBandSter | hpbandster/core/master.py | Master._submit_job | def _submit_job(self, config_id, config, budget):
"""
hidden function to submit a new job to the dispatcher
This function handles the actual submission in a
(hopefully) thread save way
"""
self.logger.debug('HBMASTER: trying submitting job %s to dispatcher'%str(config_id))
with self.thread_cond:
self.logger.debug('HBMASTER: submitting job %s to dispatcher'%str(config_id))
self.dispatcher.submit_job(config_id, config=config, budget=budget, working_directory=self.working_directory)
self.num_running_jobs += 1
#shouldn't the next line be executed while holding the condition?
self.logger.debug("HBMASTER: job %s submitted to dispatcher"%str(config_id)) | python | def _submit_job(self, config_id, config, budget):
"""
hidden function to submit a new job to the dispatcher
This function handles the actual submission in a
(hopefully) thread save way
"""
self.logger.debug('HBMASTER: trying submitting job %s to dispatcher'%str(config_id))
with self.thread_cond:
self.logger.debug('HBMASTER: submitting job %s to dispatcher'%str(config_id))
self.dispatcher.submit_job(config_id, config=config, budget=budget, working_directory=self.working_directory)
self.num_running_jobs += 1
#shouldn't the next line be executed while holding the condition?
self.logger.debug("HBMASTER: job %s submitted to dispatcher"%str(config_id)) | [
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automl/HpBandSter | hpbandster/optimizers/learning_curve_models/lcnet.py | LCNetWrapper.fit | def fit(self, times, losses, configs=None):
"""
function to train the model on the observed data
Parameters:
-----------
times: list
list of numpy arrays of the timesteps for each curve
losses: list
list of numpy arrays of the loss (the actual learning curve)
configs: list or None
list of the configurations for each sample. Each element
has to be a numpy array. Set to None, if no configuration
information is available.
"""
assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
train = None
targets = None
for i in range(len(configs)):
t_idx = times[i] / self.max_num_epochs
x = np.repeat(np.array(configs[i])[None, :], t_idx.shape[0], axis=0)
x = np.concatenate((x, t_idx[:, None]), axis=1)
# LCNet assumes increasing curves, if we feed in losses here we have to flip the curves
lc = [1 - l for l in losses[i]]
if train is None:
train = x
targets = lc
else:
train = np.concatenate((train, x), 0)
targets = np.concatenate((targets, lc), 0)
self.model.train(train, targets) | python | def fit(self, times, losses, configs=None):
"""
function to train the model on the observed data
Parameters:
-----------
times: list
list of numpy arrays of the timesteps for each curve
losses: list
list of numpy arrays of the loss (the actual learning curve)
configs: list or None
list of the configurations for each sample. Each element
has to be a numpy array. Set to None, if no configuration
information is available.
"""
assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
train = None
targets = None
for i in range(len(configs)):
t_idx = times[i] / self.max_num_epochs
x = np.repeat(np.array(configs[i])[None, :], t_idx.shape[0], axis=0)
x = np.concatenate((x, t_idx[:, None]), axis=1)
# LCNet assumes increasing curves, if we feed in losses here we have to flip the curves
lc = [1 - l for l in losses[i]]
if train is None:
train = x
targets = lc
else:
train = np.concatenate((train, x), 0)
targets = np.concatenate((targets, lc), 0)
self.model.train(train, targets) | [
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losses: list
list of numpy arrays of the loss (the actual learning curve)
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list of the configurations for each sample. Each element
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automl/HpBandSter | hpbandster/optimizers/learning_curve_models/lcnet.py | LCNetWrapper.predict_unseen | def predict_unseen(self, times, config):
"""
predict the loss of an unseen configuration
Parameters:
-----------
times: numpy array
times where to predict the loss
config: numpy array
the numerical representation of the config
Returns:
--------
mean and variance prediction at input times for the given config
"""
assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
x = np.array(config)[None, :]
idx = times / self.max_num_epochs
x = np.repeat(x, idx.shape[0], axis=0)
x = np.concatenate((x, idx[:, None]), axis=1)
mean, var = self.model.predict(x)
return 1 - mean, var | python | def predict_unseen(self, times, config):
"""
predict the loss of an unseen configuration
Parameters:
-----------
times: numpy array
times where to predict the loss
config: numpy array
the numerical representation of the config
Returns:
--------
mean and variance prediction at input times for the given config
"""
assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
x = np.array(config)[None, :]
idx = times / self.max_num_epochs
x = np.repeat(x, idx.shape[0], axis=0)
x = np.concatenate((x, idx[:, None]), axis=1)
mean, var = self.model.predict(x)
return 1 - mean, var | [
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automl/HpBandSter | hpbandster/optimizers/learning_curve_models/lcnet.py | LCNetWrapper.extend_partial | def extend_partial(self, times, obs_times, obs_losses, config=None):
"""
extends a partially observed curve
Parameters:
-----------
times: numpy array
times where to predict the loss
obs_times: numpy array
times where the curve has already been observed
obs_losses: numpy array
corresponding observed losses
config: numpy array
numerical reperesentation of the config; None if no config
information is available
Returns:
--------
mean and variance prediction at input times
"""
return self.predict_unseen(times, config) | python | def extend_partial(self, times, obs_times, obs_losses, config=None):
"""
extends a partially observed curve
Parameters:
-----------
times: numpy array
times where to predict the loss
obs_times: numpy array
times where the curve has already been observed
obs_losses: numpy array
corresponding observed losses
config: numpy array
numerical reperesentation of the config; None if no config
information is available
Returns:
--------
mean and variance prediction at input times
"""
return self.predict_unseen(times, config) | [
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Parameters:
-----------
times: numpy array
times where to predict the loss
obs_times: numpy array
times where the curve has already been observed
obs_losses: numpy array
corresponding observed losses
config: numpy array
numerical reperesentation of the config; None if no config
information is available
Returns:
--------
mean and variance prediction at input times | [
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] | 841db4b827f342e5eb7f725723ea6461ac52d45a | https://github.com/automl/HpBandSter/blob/841db4b827f342e5eb7f725723ea6461ac52d45a/hpbandster/optimizers/learning_curve_models/lcnet.py#L95-L119 | train | 216,746 |
automl/HpBandSter | hpbandster/core/base_config_generator.py | base_config_generator.new_result | def new_result(self, job, update_model=True):
"""
registers finished runs
Every time a run has finished, this function should be called
to register it with the result logger. If overwritten, make
sure to call this method from the base class to ensure proper
logging.
Parameters
----------
job: instance of hpbandster.distributed.dispatcher.Job
contains all necessary information about the job
update_model: boolean
determines whether a model inside the config_generator should be updated
"""
if not job.exception is None:
self.logger.warning("job {} failed with exception\n{}".format(job.id, job.exception)) | python | def new_result(self, job, update_model=True):
"""
registers finished runs
Every time a run has finished, this function should be called
to register it with the result logger. If overwritten, make
sure to call this method from the base class to ensure proper
logging.
Parameters
----------
job: instance of hpbandster.distributed.dispatcher.Job
contains all necessary information about the job
update_model: boolean
determines whether a model inside the config_generator should be updated
"""
if not job.exception is None:
self.logger.warning("job {} failed with exception\n{}".format(job.id, job.exception)) | [
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automl/HpBandSter | hpbandster/optimizers/learning_curve_models/arif.py | ARIF.invert_differencing | def invert_differencing(self, initial_part, differenced_rest, order=None):
"""
function to invert the differencing
"""
if order is None: order = self.diff_order
# compute the differenced values of the initial part:
starting_points = [ self.apply_differencing(initial_part, order=order)[-1] for order in range(self.diff_order)]
actual_predictions = differenced_rest
import pdb
pdb.set_trace()
for s in starting_points[::-1]:
actual_predictions = np.cumsum(np.hstack([s, actual_predictions]))[1:]
return(actual_predictions) | python | def invert_differencing(self, initial_part, differenced_rest, order=None):
"""
function to invert the differencing
"""
if order is None: order = self.diff_order
# compute the differenced values of the initial part:
starting_points = [ self.apply_differencing(initial_part, order=order)[-1] for order in range(self.diff_order)]
actual_predictions = differenced_rest
import pdb
pdb.set_trace()
for s in starting_points[::-1]:
actual_predictions = np.cumsum(np.hstack([s, actual_predictions]))[1:]
return(actual_predictions) | [
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automl/HpBandSter | hpbandster/core/nameserver.py | nic_name_to_host | def nic_name_to_host(nic_name):
""" helper function to translate the name of a network card into a valid host name"""
from netifaces import ifaddresses, AF_INET
host = ifaddresses(nic_name).setdefault(AF_INET, [{'addr': 'No IP addr'}] )[0]['addr']
return(host) | python | def nic_name_to_host(nic_name):
""" helper function to translate the name of a network card into a valid host name"""
from netifaces import ifaddresses, AF_INET
host = ifaddresses(nic_name).setdefault(AF_INET, [{'addr': 'No IP addr'}] )[0]['addr']
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automl/HpBandSter | hpbandster/core/base_iteration.py | BaseIteration.register_result | def register_result(self, job, skip_sanity_checks=False):
"""
function to register the result of a job
This function is called from HB_master, don't call this from
your script.
"""
if self.is_finished:
raise RuntimeError("This HB iteration is finished, you can't register more results!")
config_id = job.id
config = job.kwargs['config']
budget = job.kwargs['budget']
timestamps = job.timestamps
result = job.result
exception = job.exception
d = self.data[config_id]
if not skip_sanity_checks:
assert d.config == config, 'Configurations differ!'
assert d.status == 'RUNNING', "Configuration wasn't scheduled for a run."
assert d.budget == budget, 'Budgets differ (%f != %f)!'%(self.data[config_id]['budget'], budget)
d.time_stamps[budget] = timestamps
d.results[budget] = result
if (not job.result is None) and np.isfinite(result['loss']):
d.status = 'REVIEW'
else:
d.status = 'CRASHED'
d.exceptions[budget] = exception
self.num_running -= 1 | python | def register_result(self, job, skip_sanity_checks=False):
"""
function to register the result of a job
This function is called from HB_master, don't call this from
your script.
"""
if self.is_finished:
raise RuntimeError("This HB iteration is finished, you can't register more results!")
config_id = job.id
config = job.kwargs['config']
budget = job.kwargs['budget']
timestamps = job.timestamps
result = job.result
exception = job.exception
d = self.data[config_id]
if not skip_sanity_checks:
assert d.config == config, 'Configurations differ!'
assert d.status == 'RUNNING', "Configuration wasn't scheduled for a run."
assert d.budget == budget, 'Budgets differ (%f != %f)!'%(self.data[config_id]['budget'], budget)
d.time_stamps[budget] = timestamps
d.results[budget] = result
if (not job.result is None) and np.isfinite(result['loss']):
d.status = 'REVIEW'
else:
d.status = 'CRASHED'
d.exceptions[budget] = exception
self.num_running -= 1 | [
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automl/HpBandSter | hpbandster/core/base_iteration.py | BaseIteration.get_next_run | def get_next_run(self):
"""
function to return the next configuration and budget to run.
This function is called from HB_master, don't call this from
your script.
It returns None if this run of SH is finished or there are
pending jobs that need to finish to progress to the next stage.
If there are empty slots to be filled in the current SH stage
(which never happens in the original SH version), a new
configuration will be sampled and scheduled to run next.
"""
if self.is_finished:
return(None)
for k,v in self.data.items():
if v.status == 'QUEUED':
assert v.budget == self.budgets[self.stage], 'Configuration budget does not align with current stage!'
v.status = 'RUNNING'
self.num_running += 1
return(k, v.config, v.budget)
# check if there are still slots to fill in the current stage and return that
if (self.actual_num_configs[self.stage] < self.num_configs[self.stage]):
self.add_configuration()
return(self.get_next_run())
if self.num_running == 0:
# at this point a stage is completed
self.process_results()
return(self.get_next_run())
return(None) | python | def get_next_run(self):
"""
function to return the next configuration and budget to run.
This function is called from HB_master, don't call this from
your script.
It returns None if this run of SH is finished or there are
pending jobs that need to finish to progress to the next stage.
If there are empty slots to be filled in the current SH stage
(which never happens in the original SH version), a new
configuration will be sampled and scheduled to run next.
"""
if self.is_finished:
return(None)
for k,v in self.data.items():
if v.status == 'QUEUED':
assert v.budget == self.budgets[self.stage], 'Configuration budget does not align with current stage!'
v.status = 'RUNNING'
self.num_running += 1
return(k, v.config, v.budget)
# check if there are still slots to fill in the current stage and return that
if (self.actual_num_configs[self.stage] < self.num_configs[self.stage]):
self.add_configuration()
return(self.get_next_run())
if self.num_running == 0:
# at this point a stage is completed
self.process_results()
return(self.get_next_run())
return(None) | [
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automl/HpBandSter | hpbandster/core/base_iteration.py | BaseIteration.process_results | def process_results(self):
"""
function that is called when a stage is completed and
needs to be analyzed befor further computations.
The code here implements the original SH algorithms by
advancing the k-best (lowest loss) configurations at the current
budget. k is defined by the num_configs list (see __init__)
and the current stage value.
For more advanced methods like resampling after each stage,
overload this function only.
"""
self.stage += 1
# collect all config_ids that need to be compared
config_ids = list(filter(lambda cid: self.data[cid].status == 'REVIEW', self.data.keys()))
if (self.stage >= len(self.num_configs)):
self.finish_up()
return
budgets = [self.data[cid].budget for cid in config_ids]
if len(set(budgets)) > 1:
raise RuntimeError('Not all configurations have the same budget!')
budget = self.budgets[self.stage-1]
losses = np.array([self.data[cid].results[budget]['loss'] for cid in config_ids])
advance = self._advance_to_next_stage(config_ids, losses)
for i, a in enumerate(advance):
if a:
self.logger.debug('ITERATION: Advancing config %s to next budget %f'%(config_ids[i], self.budgets[self.stage]))
for i, cid in enumerate(config_ids):
if advance[i]:
self.data[cid].status = 'QUEUED'
self.data[cid].budget = self.budgets[self.stage]
self.actual_num_configs[self.stage] += 1
else:
self.data[cid].status = 'TERMINATED' | python | def process_results(self):
"""
function that is called when a stage is completed and
needs to be analyzed befor further computations.
The code here implements the original SH algorithms by
advancing the k-best (lowest loss) configurations at the current
budget. k is defined by the num_configs list (see __init__)
and the current stage value.
For more advanced methods like resampling after each stage,
overload this function only.
"""
self.stage += 1
# collect all config_ids that need to be compared
config_ids = list(filter(lambda cid: self.data[cid].status == 'REVIEW', self.data.keys()))
if (self.stage >= len(self.num_configs)):
self.finish_up()
return
budgets = [self.data[cid].budget for cid in config_ids]
if len(set(budgets)) > 1:
raise RuntimeError('Not all configurations have the same budget!')
budget = self.budgets[self.stage-1]
losses = np.array([self.data[cid].results[budget]['loss'] for cid in config_ids])
advance = self._advance_to_next_stage(config_ids, losses)
for i, a in enumerate(advance):
if a:
self.logger.debug('ITERATION: Advancing config %s to next budget %f'%(config_ids[i], self.budgets[self.stage]))
for i, cid in enumerate(config_ids):
if advance[i]:
self.data[cid].status = 'QUEUED'
self.data[cid].budget = self.budgets[self.stage]
self.actual_num_configs[self.stage] += 1
else:
self.data[cid].status = 'TERMINATED' | [
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automl/HpBandSter | hpbandster/core/base_iteration.py | WarmStartIteration.fix_timestamps | def fix_timestamps(self, time_ref):
"""
manipulates internal time stamps such that the last run ends at time 0
"""
for k,v in self.data.items():
for kk, vv in v.time_stamps.items():
for kkk,vvv in vv.items():
self.data[k].time_stamps[kk][kkk] += time_ref | python | def fix_timestamps(self, time_ref):
"""
manipulates internal time stamps such that the last run ends at time 0
"""
for k,v in self.data.items():
for kk, vv in v.time_stamps.items():
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automl/HpBandSter | hpbandster/optimizers/config_generators/lcnet.py | LCNetWrapper.get_config | def get_config(self, budget):
"""
function to sample a new configuration
This function is called inside Hyperband to query a new configuration
Parameters:
-----------
budget: float
the budget for which this configuration is scheduled
returns: config
should return a valid configuration
"""
self.lock.acquire()
if not self.is_trained:
c = self.config_space.sample_configuration().get_array()
else:
candidates = np.array([self.config_space.sample_configuration().get_array()
for _ in range(self.n_candidates)])
# We are only interested on the asymptotic value
projected_candidates = np.concatenate((candidates, np.ones([self.n_candidates, 1])), axis=1)
# Compute the upper confidence bound of the function at the asymptote
m, v = self.model.predict(projected_candidates)
ucb_values = m + self.delta * np.sqrt(v)
print(ucb_values)
# Sample a configuration based on the ucb values
p = np.ones(self.n_candidates) * (ucb_values / np.sum(ucb_values))
idx = np.random.choice(self.n_candidates, 1, False, p)
c = candidates[idx][0]
config = ConfigSpace.Configuration(self.config_space, vector=c)
self.lock.release()
return config.get_dictionary(), {} | python | def get_config(self, budget):
"""
function to sample a new configuration
This function is called inside Hyperband to query a new configuration
Parameters:
-----------
budget: float
the budget for which this configuration is scheduled
returns: config
should return a valid configuration
"""
self.lock.acquire()
if not self.is_trained:
c = self.config_space.sample_configuration().get_array()
else:
candidates = np.array([self.config_space.sample_configuration().get_array()
for _ in range(self.n_candidates)])
# We are only interested on the asymptotic value
projected_candidates = np.concatenate((candidates, np.ones([self.n_candidates, 1])), axis=1)
# Compute the upper confidence bound of the function at the asymptote
m, v = self.model.predict(projected_candidates)
ucb_values = m + self.delta * np.sqrt(v)
print(ucb_values)
# Sample a configuration based on the ucb values
p = np.ones(self.n_candidates) * (ucb_values / np.sum(ucb_values))
idx = np.random.choice(self.n_candidates, 1, False, p)
c = candidates[idx][0]
config = ConfigSpace.Configuration(self.config_space, vector=c)
self.lock.release()
return config.get_dictionary(), {} | [
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automl/HpBandSter | hpbandster/core/result.py | extract_HBS_learning_curves | def extract_HBS_learning_curves(runs):
"""
function to get the hyperband learning curves
This is an example function showing the interface to use the
HB_result.get_learning_curves method.
Parameters
----------
runs: list of HB_result.run objects
the performed runs for an unspecified config
Returns
-------
list of learning curves: list of lists of tuples
An individual learning curve is a list of (t, x_t) tuples.
This function must return a list of these. One could think
of cases where one could extract multiple learning curves
from these runs, e.g. if each run is an independent training
run of a neural network on the data.
"""
sr = sorted(runs, key=lambda r: r.budget)
lc = list(filter(lambda t: not t[1] is None, [(r.budget, r.loss) for r in sr]))
return([lc,]) | python | def extract_HBS_learning_curves(runs):
"""
function to get the hyperband learning curves
This is an example function showing the interface to use the
HB_result.get_learning_curves method.
Parameters
----------
runs: list of HB_result.run objects
the performed runs for an unspecified config
Returns
-------
list of learning curves: list of lists of tuples
An individual learning curve is a list of (t, x_t) tuples.
This function must return a list of these. One could think
of cases where one could extract multiple learning curves
from these runs, e.g. if each run is an independent training
run of a neural network on the data.
"""
sr = sorted(runs, key=lambda r: r.budget)
lc = list(filter(lambda t: not t[1] is None, [(r.budget, r.loss) for r in sr]))
return([lc,]) | [
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This is an example function showing the interface to use the
HB_result.get_learning_curves method.
Parameters
----------
runs: list of HB_result.run objects
the performed runs for an unspecified config
Returns
-------
list of learning curves: list of lists of tuples
An individual learning curve is a list of (t, x_t) tuples.
This function must return a list of these. One could think
of cases where one could extract multiple learning curves
from these runs, e.g. if each run is an independent training
run of a neural network on the data. | [
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automl/HpBandSter | hpbandster/core/result.py | logged_results_to_HBS_result | def logged_results_to_HBS_result(directory):
"""
function to import logged 'live-results' and return a HB_result object
You can load live run results with this function and the returned
HB_result object gives you access to the results the same way
a finished run would.
Parameters
----------
directory: str
the directory containing the results.json and config.json files
Returns
-------
hpbandster.core.result.Result: :object:
TODO
"""
data = {}
time_ref = float('inf')
budget_set = set()
with open(os.path.join(directory, 'configs.json')) as fh:
for line in fh:
line = json.loads(line)
if len(line) == 3:
config_id, config, config_info = line
if len(line) == 2:
config_id, config, = line
config_info = 'N/A'
data[tuple(config_id)] = Datum(config=config, config_info=config_info)
with open(os.path.join(directory, 'results.json')) as fh:
for line in fh:
config_id, budget,time_stamps, result, exception = json.loads(line)
id = tuple(config_id)
data[id].time_stamps[budget] = time_stamps
data[id].results[budget] = result
data[id].exceptions[budget] = exception
budget_set.add(budget)
time_ref = min(time_ref, time_stamps['submitted'])
# infer the hyperband configuration from the data
budget_list = sorted(list(budget_set))
HB_config = {
'eta' : None if len(budget_list) < 2 else budget_list[1]/budget_list[0],
'min_budget' : min(budget_set),
'max_budget' : max(budget_set),
'budgets' : budget_list,
'max_SH_iter': len(budget_set),
'time_ref' : time_ref
}
return(Result([data], HB_config)) | python | def logged_results_to_HBS_result(directory):
"""
function to import logged 'live-results' and return a HB_result object
You can load live run results with this function and the returned
HB_result object gives you access to the results the same way
a finished run would.
Parameters
----------
directory: str
the directory containing the results.json and config.json files
Returns
-------
hpbandster.core.result.Result: :object:
TODO
"""
data = {}
time_ref = float('inf')
budget_set = set()
with open(os.path.join(directory, 'configs.json')) as fh:
for line in fh:
line = json.loads(line)
if len(line) == 3:
config_id, config, config_info = line
if len(line) == 2:
config_id, config, = line
config_info = 'N/A'
data[tuple(config_id)] = Datum(config=config, config_info=config_info)
with open(os.path.join(directory, 'results.json')) as fh:
for line in fh:
config_id, budget,time_stamps, result, exception = json.loads(line)
id = tuple(config_id)
data[id].time_stamps[budget] = time_stamps
data[id].results[budget] = result
data[id].exceptions[budget] = exception
budget_set.add(budget)
time_ref = min(time_ref, time_stamps['submitted'])
# infer the hyperband configuration from the data
budget_list = sorted(list(budget_set))
HB_config = {
'eta' : None if len(budget_list) < 2 else budget_list[1]/budget_list[0],
'min_budget' : min(budget_set),
'max_budget' : max(budget_set),
'budgets' : budget_list,
'max_SH_iter': len(budget_set),
'time_ref' : time_ref
}
return(Result([data], HB_config)) | [
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You can load live run results with this function and the returned
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Parameters
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directory: str
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automl/HpBandSter | hpbandster/core/result.py | Result.get_incumbent_id | def get_incumbent_id(self):
"""
Find the config_id of the incumbent.
The incumbent here is the configuration with the smallest loss
among all runs on the maximum budget! If no run finishes on the
maximum budget, None is returned!
"""
tmp_list = []
for k,v in self.data.items():
try:
# only things run for the max budget are considered
res = v.results[self.HB_config['max_budget']]
if not res is None:
tmp_list.append((res['loss'], k))
except KeyError as e:
pass
except:
raise
if len(tmp_list) > 0:
return(min(tmp_list)[1])
return(None) | python | def get_incumbent_id(self):
"""
Find the config_id of the incumbent.
The incumbent here is the configuration with the smallest loss
among all runs on the maximum budget! If no run finishes on the
maximum budget, None is returned!
"""
tmp_list = []
for k,v in self.data.items():
try:
# only things run for the max budget are considered
res = v.results[self.HB_config['max_budget']]
if not res is None:
tmp_list.append((res['loss'], k))
except KeyError as e:
pass
except:
raise
if len(tmp_list) > 0:
return(min(tmp_list)[1])
return(None) | [
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automl/HpBandSter | hpbandster/core/result.py | Result.get_runs_by_id | def get_runs_by_id(self, config_id):
"""
returns a list of runs for a given config id
The runs are sorted by ascending budget, so '-1' will give
the longest run for this config.
"""
d = self.data[config_id]
runs = []
for b in d.results.keys():
try:
err_logs = d.exceptions.get(b, None)
if d.results[b] is None:
r = Run(config_id, b, None, None , d.time_stamps[b], err_logs)
else:
r = Run(config_id, b, d.results[b]['loss'], d.results[b]['info'] , d.time_stamps[b], err_logs)
runs.append(r)
except:
raise
runs.sort(key=lambda r: r.budget)
return(runs) | python | def get_runs_by_id(self, config_id):
"""
returns a list of runs for a given config id
The runs are sorted by ascending budget, so '-1' will give
the longest run for this config.
"""
d = self.data[config_id]
runs = []
for b in d.results.keys():
try:
err_logs = d.exceptions.get(b, None)
if d.results[b] is None:
r = Run(config_id, b, None, None , d.time_stamps[b], err_logs)
else:
r = Run(config_id, b, d.results[b]['loss'], d.results[b]['info'] , d.time_stamps[b], err_logs)
runs.append(r)
except:
raise
runs.sort(key=lambda r: r.budget)
return(runs) | [
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automl/HpBandSter | hpbandster/core/result.py | Result.get_learning_curves | def get_learning_curves(self, lc_extractor=extract_HBS_learning_curves, config_ids=None):
"""
extracts all learning curves from all run configurations
Parameters
----------
lc_extractor: callable
a function to return a list of learning_curves.
defaults to hpbanster.HB_result.extract_HP_learning_curves
config_ids: list of valid config ids
if only a subset of the config ids is wanted
Returns
-------
dict
a dictionary with the config_ids as keys and the
learning curves as values
"""
config_ids = self.data.keys() if config_ids is None else config_ids
lc_dict = {}
for id in config_ids:
runs = self.get_runs_by_id(id)
lc_dict[id] = lc_extractor(runs)
return(lc_dict) | python | def get_learning_curves(self, lc_extractor=extract_HBS_learning_curves, config_ids=None):
"""
extracts all learning curves from all run configurations
Parameters
----------
lc_extractor: callable
a function to return a list of learning_curves.
defaults to hpbanster.HB_result.extract_HP_learning_curves
config_ids: list of valid config ids
if only a subset of the config ids is wanted
Returns
-------
dict
a dictionary with the config_ids as keys and the
learning curves as values
"""
config_ids = self.data.keys() if config_ids is None else config_ids
lc_dict = {}
for id in config_ids:
runs = self.get_runs_by_id(id)
lc_dict[id] = lc_extractor(runs)
return(lc_dict) | [
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lc_extractor: callable
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config_ids: list of valid config ids
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automl/HpBandSter | hpbandster/core/result.py | Result.get_all_runs | def get_all_runs(self, only_largest_budget=False):
"""
returns all runs performed
Parameters
----------
only_largest_budget: boolean
if True, only the largest budget for each configuration
is returned. This makes sense if the runs are continued
across budgets and the info field contains the information
you care about. If False, all runs of a configuration
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"""
all_runs = []
for k in self.data.keys():
runs = self.get_runs_by_id(k)
if len(runs) > 0:
if only_largest_budget:
all_runs.append(runs[-1])
else:
all_runs.extend(runs)
return(all_runs) | python | def get_all_runs(self, only_largest_budget=False):
"""
returns all runs performed
Parameters
----------
only_largest_budget: boolean
if True, only the largest budget for each configuration
is returned. This makes sense if the runs are continued
across budgets and the info field contains the information
you care about. If False, all runs of a configuration
are returned
"""
all_runs = []
for k in self.data.keys():
runs = self.get_runs_by_id(k)
if len(runs) > 0:
if only_largest_budget:
all_runs.append(runs[-1])
else:
all_runs.extend(runs)
return(all_runs) | [
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automl/HpBandSter | hpbandster/core/result.py | Result.get_id2config_mapping | def get_id2config_mapping(self):
"""
returns a dict where the keys are the config_ids and the values
are the actual configurations
"""
new_dict = {}
for k, v in self.data.items():
new_dict[k] = {}
new_dict[k]['config'] = copy.deepcopy(v.config)
try:
new_dict[k]['config_info'] = copy.deepcopy(v.config_info)
except:
pass
return(new_dict) | python | def get_id2config_mapping(self):
"""
returns a dict where the keys are the config_ids and the values
are the actual configurations
"""
new_dict = {}
for k, v in self.data.items():
new_dict[k] = {}
new_dict[k]['config'] = copy.deepcopy(v.config)
try:
new_dict[k]['config_info'] = copy.deepcopy(v.config_info)
except:
pass
return(new_dict) | [
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automl/HpBandSter | hpbandster/core/result.py | Result._merge_results | def _merge_results(self):
"""
hidden function to merge the list of results into one
dictionary and 'normalize' the time stamps
"""
new_dict = {}
for it in self.data:
new_dict.update(it)
for k,v in new_dict.items():
for kk, vv in v.time_stamps.items():
for kkk,vvv in vv.items():
new_dict[k].time_stamps[kk][kkk] = vvv - self.HB_config['time_ref']
self.data = new_dict | python | def _merge_results(self):
"""
hidden function to merge the list of results into one
dictionary and 'normalize' the time stamps
"""
new_dict = {}
for it in self.data:
new_dict.update(it)
for k,v in new_dict.items():
for kk, vv in v.time_stamps.items():
for kkk,vvv in vv.items():
new_dict[k].time_stamps[kk][kkk] = vvv - self.HB_config['time_ref']
self.data = new_dict | [
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Koed00/django-q | django_q/core_signing.py | TimestampSigner.unsign | def unsign(self, value, max_age=None):
"""
Retrieve original value and check it wasn't signed more
than max_age seconds ago.
"""
result = super(TimestampSigner, self).unsign(value)
value, timestamp = result.rsplit(self.sep, 1)
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# Check timestamp is not older than max_age
age = time.time() - timestamp
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return value | python | def unsign(self, value, max_age=None):
"""
Retrieve original value and check it wasn't signed more
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"""
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value, timestamp = result.rsplit(self.sep, 1)
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Koed00/django-q | django_q/humanhash.py | HumanHasher.humanize | def humanize(self, hexdigest, words=4, separator='-'):
"""
Humanize a given hexadecimal digest.
Change the number of words output by specifying `words`. Change the
word separator with `separator`.
>>> digest = '60ad8d0d871b6095808297'
>>> HumanHasher().humanize(digest)
'sodium-magnesium-nineteen-hydrogen'
"""
# Gets a list of byte values between 0-255.
bytes = [int(x, 16) for x in list(map(''.join, list(zip(hexdigest[::2], hexdigest[1::2]))))]
# Compress an arbitrary number of bytes to `words`.
compressed = self.compress(bytes, words)
# Map the compressed byte values through the word list.
return separator.join(self.wordlist[byte] for byte in compressed) | python | def humanize(self, hexdigest, words=4, separator='-'):
"""
Humanize a given hexadecimal digest.
Change the number of words output by specifying `words`. Change the
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>>> digest = '60ad8d0d871b6095808297'
>>> HumanHasher().humanize(digest)
'sodium-magnesium-nineteen-hydrogen'
"""
# Gets a list of byte values between 0-255.
bytes = [int(x, 16) for x in list(map(''.join, list(zip(hexdigest[::2], hexdigest[1::2]))))]
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compressed = self.compress(bytes, words)
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>>> digest = '60ad8d0d871b6095808297'
>>> HumanHasher().humanize(digest)
'sodium-magnesium-nineteen-hydrogen' | [
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] | c84fd11a67c9a47d821786dfcdc189bb258c6f54 | https://github.com/Koed00/django-q/blob/c84fd11a67c9a47d821786dfcdc189bb258c6f54/django_q/humanhash.py#L73-L91 | train | 216,764 |
Koed00/django-q | django_q/humanhash.py | HumanHasher.compress | def compress(bytes, target):
"""
Compress a list of byte values to a fixed target length.
>>> bytes = [96, 173, 141, 13, 135, 27, 96, 149, 128, 130, 151]
>>> HumanHasher.compress(bytes, 4)
[205, 128, 156, 96]
Attempting to compress a smaller number of bytes to a larger number is
an error:
>>> HumanHasher.compress(bytes, 15) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Fewer input bytes than requested output
"""
length = len(bytes)
if target > length:
raise ValueError("Fewer input bytes than requested output")
# Split `bytes` into `target` segments.
seg_size = length // target
segments = [bytes[i * seg_size:(i + 1) * seg_size]
for i in range(target)]
# Catch any left-over bytes in the last segment.
segments[-1].extend(bytes[target * seg_size:])
# Use a simple XOR checksum-like function for compression.
checksum = lambda bytes: reduce(operator.xor, bytes, 0)
checksums = list(map(checksum, segments))
return checksums | python | def compress(bytes, target):
"""
Compress a list of byte values to a fixed target length.
>>> bytes = [96, 173, 141, 13, 135, 27, 96, 149, 128, 130, 151]
>>> HumanHasher.compress(bytes, 4)
[205, 128, 156, 96]
Attempting to compress a smaller number of bytes to a larger number is
an error:
>>> HumanHasher.compress(bytes, 15) # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Fewer input bytes than requested output
"""
length = len(bytes)
if target > length:
raise ValueError("Fewer input bytes than requested output")
# Split `bytes` into `target` segments.
seg_size = length // target
segments = [bytes[i * seg_size:(i + 1) * seg_size]
for i in range(target)]
# Catch any left-over bytes in the last segment.
segments[-1].extend(bytes[target * seg_size:])
# Use a simple XOR checksum-like function for compression.
checksum = lambda bytes: reduce(operator.xor, bytes, 0)
checksums = list(map(checksum, segments))
return checksums | [
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ValueError: Fewer input bytes than requested output | [
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Koed00/django-q | django_q/humanhash.py | HumanHasher.uuid | def uuid(self, **params):
"""
Generate a UUID with a human-readable representation.
Returns `(human_repr, full_digest)`. Accepts the same keyword arguments
as :meth:`humanize` (they'll be passed straight through).
"""
digest = str(uuidlib.uuid4()).replace('-', '')
return self.humanize(digest, **params), digest | python | def uuid(self, **params):
"""
Generate a UUID with a human-readable representation.
Returns `(human_repr, full_digest)`. Accepts the same keyword arguments
as :meth:`humanize` (they'll be passed straight through).
"""
digest = str(uuidlib.uuid4()).replace('-', '')
return self.humanize(digest, **params), digest | [
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Koed00/django-q | django_q/tasks.py | async_task | def async_task(func, *args, **kwargs):
"""Queue a task for the cluster."""
keywords = kwargs.copy()
opt_keys = (
'hook', 'group', 'save', 'sync', 'cached', 'ack_failure', 'iter_count', 'iter_cached', 'chain', 'broker')
q_options = keywords.pop('q_options', {})
# get an id
tag = uuid()
# build the task package
task = {'id': tag[1],
'name': keywords.pop('task_name', None) or q_options.pop('task_name', None) or tag[0],
'func': func,
'args': args}
# push optionals
for key in opt_keys:
if q_options and key in q_options:
task[key] = q_options[key]
elif key in keywords:
task[key] = keywords.pop(key)
# don't serialize the broker
broker = task.pop('broker', get_broker())
# overrides
if 'cached' not in task and Conf.CACHED:
task['cached'] = Conf.CACHED
if 'sync' not in task and Conf.SYNC:
task['sync'] = Conf.SYNC
if 'ack_failure' not in task and Conf.ACK_FAILURES:
task['ack_failure'] = Conf.ACK_FAILURES
# finalize
task['kwargs'] = keywords
task['started'] = timezone.now()
# signal it
pre_enqueue.send(sender="django_q", task=task)
# sign it
pack = SignedPackage.dumps(task)
if task.get('sync', False):
return _sync(pack)
# push it
enqueue_id = broker.enqueue(pack)
logger.info('Enqueued {}'.format(enqueue_id))
logger.debug('Pushed {}'.format(tag))
return task['id'] | python | def async_task(func, *args, **kwargs):
"""Queue a task for the cluster."""
keywords = kwargs.copy()
opt_keys = (
'hook', 'group', 'save', 'sync', 'cached', 'ack_failure', 'iter_count', 'iter_cached', 'chain', 'broker')
q_options = keywords.pop('q_options', {})
# get an id
tag = uuid()
# build the task package
task = {'id': tag[1],
'name': keywords.pop('task_name', None) or q_options.pop('task_name', None) or tag[0],
'func': func,
'args': args}
# push optionals
for key in opt_keys:
if q_options and key in q_options:
task[key] = q_options[key]
elif key in keywords:
task[key] = keywords.pop(key)
# don't serialize the broker
broker = task.pop('broker', get_broker())
# overrides
if 'cached' not in task and Conf.CACHED:
task['cached'] = Conf.CACHED
if 'sync' not in task and Conf.SYNC:
task['sync'] = Conf.SYNC
if 'ack_failure' not in task and Conf.ACK_FAILURES:
task['ack_failure'] = Conf.ACK_FAILURES
# finalize
task['kwargs'] = keywords
task['started'] = timezone.now()
# signal it
pre_enqueue.send(sender="django_q", task=task)
# sign it
pack = SignedPackage.dumps(task)
if task.get('sync', False):
return _sync(pack)
# push it
enqueue_id = broker.enqueue(pack)
logger.info('Enqueued {}'.format(enqueue_id))
logger.debug('Pushed {}'.format(tag))
return task['id'] | [
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Koed00/django-q | django_q/tasks.py | schedule | def schedule(func, *args, **kwargs):
"""
Create a schedule.
:param func: function to schedule.
:param args: function arguments.
:param name: optional name for the schedule.
:param hook: optional result hook function.
:type schedule_type: Schedule.TYPE
:param repeats: how many times to repeat. 0=never, -1=always.
:param next_run: Next scheduled run.
:type next_run: datetime.datetime
:param kwargs: function keyword arguments.
:return: the schedule object.
:rtype: Schedule
"""
name = kwargs.pop('name', None)
hook = kwargs.pop('hook', None)
schedule_type = kwargs.pop('schedule_type', Schedule.ONCE)
minutes = kwargs.pop('minutes', None)
repeats = kwargs.pop('repeats', -1)
next_run = kwargs.pop('next_run', timezone.now())
# check for name duplicates instead of am unique constraint
if name and Schedule.objects.filter(name=name).exists():
raise IntegrityError("A schedule with the same name already exists.")
# create and return the schedule
return Schedule.objects.create(name=name,
func=func,
hook=hook,
args=args,
kwargs=kwargs,
schedule_type=schedule_type,
minutes=minutes,
repeats=repeats,
next_run=next_run
) | python | def schedule(func, *args, **kwargs):
"""
Create a schedule.
:param func: function to schedule.
:param args: function arguments.
:param name: optional name for the schedule.
:param hook: optional result hook function.
:type schedule_type: Schedule.TYPE
:param repeats: how many times to repeat. 0=never, -1=always.
:param next_run: Next scheduled run.
:type next_run: datetime.datetime
:param kwargs: function keyword arguments.
:return: the schedule object.
:rtype: Schedule
"""
name = kwargs.pop('name', None)
hook = kwargs.pop('hook', None)
schedule_type = kwargs.pop('schedule_type', Schedule.ONCE)
minutes = kwargs.pop('minutes', None)
repeats = kwargs.pop('repeats', -1)
next_run = kwargs.pop('next_run', timezone.now())
# check for name duplicates instead of am unique constraint
if name and Schedule.objects.filter(name=name).exists():
raise IntegrityError("A schedule with the same name already exists.")
# create and return the schedule
return Schedule.objects.create(name=name,
func=func,
hook=hook,
args=args,
kwargs=kwargs,
schedule_type=schedule_type,
minutes=minutes,
repeats=repeats,
next_run=next_run
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Koed00/django-q | django_q/tasks.py | result | def result(task_id, wait=0, cached=Conf.CACHED):
"""
Return the result of the named task.
:type task_id: str or uuid
:param task_id: the task name or uuid
:type wait: int
:param wait: number of milliseconds to wait for a result
:param bool cached: run this against the cache backend
:return: the result object of this task
:rtype: object
"""
if cached:
return result_cached(task_id, wait)
start = time()
while True:
r = Task.get_result(task_id)
if r:
return r
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def result(task_id, wait=0, cached=Conf.CACHED):
"""
Return the result of the named task.
:type task_id: str or uuid
:param task_id: the task name or uuid
:type wait: int
:param wait: number of milliseconds to wait for a result
:param bool cached: run this against the cache backend
:return: the result object of this task
:rtype: object
"""
if cached:
return result_cached(task_id, wait)
start = time()
while True:
r = Task.get_result(task_id)
if r:
return r
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | result_cached | def result_cached(task_id, wait=0, broker=None):
"""
Return the result from the cache backend
"""
if not broker:
broker = get_broker()
start = time()
while True:
r = broker.cache.get('{}:{}'.format(broker.list_key, task_id))
if r:
return SignedPackage.loads(r)['result']
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def result_cached(task_id, wait=0, broker=None):
"""
Return the result from the cache backend
"""
if not broker:
broker = get_broker()
start = time()
while True:
r = broker.cache.get('{}:{}'.format(broker.list_key, task_id))
if r:
return SignedPackage.loads(r)['result']
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | result_group | def result_group(group_id, failures=False, wait=0, count=None, cached=Conf.CACHED):
"""
Return a list of results for a task group.
:param str group_id: the group id
:param bool failures: set to True to include failures
:param int count: Block until there are this many results in the group
:param bool cached: run this against the cache backend
:return: list or results
"""
if cached:
return result_group_cached(group_id, failures, wait, count)
start = time()
if count:
while True:
if count_group(group_id) == count or wait and (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01)
while True:
r = Task.get_result_group(group_id, failures)
if r:
return r
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def result_group(group_id, failures=False, wait=0, count=None, cached=Conf.CACHED):
"""
Return a list of results for a task group.
:param str group_id: the group id
:param bool failures: set to True to include failures
:param int count: Block until there are this many results in the group
:param bool cached: run this against the cache backend
:return: list or results
"""
if cached:
return result_group_cached(group_id, failures, wait, count)
start = time()
if count:
while True:
if count_group(group_id) == count or wait and (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01)
while True:
r = Task.get_result_group(group_id, failures)
if r:
return r
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | result_group_cached | def result_group_cached(group_id, failures=False, wait=0, count=None, broker=None):
"""
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"""
if not broker:
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sleep(0.01)
while True:
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if group_list:
result_list = []
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if task['success'] or failures:
result_list.append(task['result'])
return result_list
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def result_group_cached(group_id, failures=False, wait=0, count=None, broker=None):
"""
Return a list of results for a task group from the cache backend
"""
if not broker:
broker = get_broker()
start = time()
if count:
while True:
if count_group_cached(group_id) == count or wait and (time() - start) * 1000 >= wait > 0:
break
sleep(0.01)
while True:
group_list = broker.cache.get('{}:{}:keys'.format(broker.list_key, group_id))
if group_list:
result_list = []
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if task['success'] or failures:
result_list.append(task['result'])
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break
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Koed00/django-q | django_q/tasks.py | fetch | def fetch(task_id, wait=0, cached=Conf.CACHED):
"""
Return the processed task.
:param task_id: the task name or uuid
:type task_id: str or uuid
:param wait: the number of milliseconds to wait for a result
:type wait: int
:param bool cached: run this against the cache backend
:return: the full task object
:rtype: Task
"""
if cached:
return fetch_cached(task_id, wait)
start = time()
while True:
t = Task.get_task(task_id)
if t:
return t
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def fetch(task_id, wait=0, cached=Conf.CACHED):
"""
Return the processed task.
:param task_id: the task name or uuid
:type task_id: str or uuid
:param wait: the number of milliseconds to wait for a result
:type wait: int
:param bool cached: run this against the cache backend
:return: the full task object
:rtype: Task
"""
if cached:
return fetch_cached(task_id, wait)
start = time()
while True:
t = Task.get_task(task_id)
if t:
return t
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | fetch_cached | def fetch_cached(task_id, wait=0, broker=None):
"""
Return the processed task from the cache backend
"""
if not broker:
broker = get_broker()
start = time()
while True:
r = broker.cache.get('{}:{}'.format(broker.list_key, task_id))
if r:
task = SignedPackage.loads(r)
t = Task(id=task['id'],
name=task['name'],
func=task['func'],
hook=task.get('hook'),
args=task['args'],
kwargs=task['kwargs'],
started=task['started'],
stopped=task['stopped'],
result=task['result'],
success=task['success'])
return t
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def fetch_cached(task_id, wait=0, broker=None):
"""
Return the processed task from the cache backend
"""
if not broker:
broker = get_broker()
start = time()
while True:
r = broker.cache.get('{}:{}'.format(broker.list_key, task_id))
if r:
task = SignedPackage.loads(r)
t = Task(id=task['id'],
name=task['name'],
func=task['func'],
hook=task.get('hook'),
args=task['args'],
kwargs=task['kwargs'],
started=task['started'],
stopped=task['stopped'],
result=task['result'],
success=task['success'])
return t
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | fetch_group | def fetch_group(group_id, failures=True, wait=0, count=None, cached=Conf.CACHED):
"""
Return a list of Tasks for a task group.
:param str group_id: the group id
:param bool failures: set to False to exclude failures
:param bool cached: run this against the cache backend
:return: list of Tasks
"""
if cached:
return fetch_group_cached(group_id, failures, wait, count)
start = time()
if count:
while True:
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break
sleep(0.01)
while True:
r = Task.get_task_group(group_id, failures)
if r:
return r
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sleep(0.01) | python | def fetch_group(group_id, failures=True, wait=0, count=None, cached=Conf.CACHED):
"""
Return a list of Tasks for a task group.
:param str group_id: the group id
:param bool failures: set to False to exclude failures
:param bool cached: run this against the cache backend
:return: list of Tasks
"""
if cached:
return fetch_group_cached(group_id, failures, wait, count)
start = time()
if count:
while True:
if count_group(group_id) == count or wait and (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01)
while True:
r = Task.get_task_group(group_id, failures)
if r:
return r
if (time() - start) * 1000 >= wait >= 0:
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Koed00/django-q | django_q/tasks.py | fetch_group_cached | def fetch_group_cached(group_id, failures=True, wait=0, count=None, broker=None):
"""
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"""
if not broker:
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break
sleep(0.01)
while True:
group_list = broker.cache.get('{}:{}:keys'.format(broker.list_key, group_id))
if group_list:
task_list = []
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if task['success'] or failures:
t = Task(id=task['id'],
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success=task['success'])
task_list.append(t)
return task_list
if (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01) | python | def fetch_group_cached(group_id, failures=True, wait=0, count=None, broker=None):
"""
Return a list of Tasks for a task group in the cache backend
"""
if not broker:
broker = get_broker()
start = time()
if count:
while True:
if count_group_cached(group_id) == count or wait and (time() - start) * 1000 >= wait >= 0:
break
sleep(0.01)
while True:
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if group_list:
task_list = []
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if task['success'] or failures:
t = Task(id=task['id'],
name=task['name'],
func=task['func'],
hook=task.get('hook'),
args=task['args'],
kwargs=task['kwargs'],
started=task['started'],
stopped=task['stopped'],
result=task['result'],
group=task.get('group'),
success=task['success'])
task_list.append(t)
return task_list
if (time() - start) * 1000 >= wait >= 0:
break
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Koed00/django-q | django_q/tasks.py | count_group | def count_group(group_id, failures=False, cached=Conf.CACHED):
"""
Count the results in a group.
:param str group_id: the group id
:param bool failures: Returns failure count if True
:param bool cached: run this against the cache backend
:return: the number of tasks/results in a group
:rtype: int
"""
if cached:
return count_group_cached(group_id, failures)
return Task.get_group_count(group_id, failures) | python | def count_group(group_id, failures=False, cached=Conf.CACHED):
"""
Count the results in a group.
:param str group_id: the group id
:param bool failures: Returns failure count if True
:param bool cached: run this against the cache backend
:return: the number of tasks/results in a group
:rtype: int
"""
if cached:
return count_group_cached(group_id, failures)
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Koed00/django-q | django_q/tasks.py | count_group_cached | def count_group_cached(group_id, failures=False, broker=None):
"""
Count the results in a group in the cache backend
"""
if not broker:
broker = get_broker()
group_list = broker.cache.get('{}:{}:keys'.format(broker.list_key, group_id))
if group_list:
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failure_count = 0
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if not task['success']:
failure_count += 1
return failure_count | python | def count_group_cached(group_id, failures=False, broker=None):
"""
Count the results in a group in the cache backend
"""
if not broker:
broker = get_broker()
group_list = broker.cache.get('{}:{}:keys'.format(broker.list_key, group_id))
if group_list:
if not failures:
return len(group_list)
failure_count = 0
for task_key in group_list:
task = SignedPackage.loads(broker.cache.get(task_key))
if not task['success']:
failure_count += 1
return failure_count | [
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Koed00/django-q | django_q/tasks.py | delete_group_cached | def delete_group_cached(group_id, broker=None):
"""
Delete a group from the cache backend
"""
if not broker:
broker = get_broker()
group_key = '{}:{}:keys'.format(broker.list_key, group_id)
group_list = broker.cache.get(group_key)
broker.cache.delete_many(group_list)
broker.cache.delete(group_key) | python | def delete_group_cached(group_id, broker=None):
"""
Delete a group from the cache backend
"""
if not broker:
broker = get_broker()
group_key = '{}:{}:keys'.format(broker.list_key, group_id)
group_list = broker.cache.get(group_key)
broker.cache.delete_many(group_list)
broker.cache.delete(group_key) | [
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Koed00/django-q | django_q/tasks.py | delete_cached | def delete_cached(task_id, broker=None):
"""
Delete a task from the cache backend
"""
if not broker:
broker = get_broker()
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"""
Delete a task from the cache backend
"""
if not broker:
broker = get_broker()
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Koed00/django-q | django_q/tasks.py | async_iter | def async_iter(func, args_iter, **kwargs):
"""
enqueues a function with iterable arguments
"""
iter_count = len(args_iter)
iter_group = uuid()[1]
# clean up the kwargs
options = kwargs.get('q_options', kwargs)
options.pop('hook', None)
options['broker'] = options.get('broker', get_broker())
options['group'] = iter_group
options['iter_count'] = iter_count
if options.get('cached', None):
options['iter_cached'] = options['cached']
options['cached'] = True
# save the original arguments
broker = options['broker']
broker.cache.set('{}:{}:args'.format(broker.list_key, iter_group), SignedPackage.dumps(args_iter))
for args in args_iter:
if not isinstance(args, tuple):
args = (args,)
async_task(func, *args, **options)
return iter_group | python | def async_iter(func, args_iter, **kwargs):
"""
enqueues a function with iterable arguments
"""
iter_count = len(args_iter)
iter_group = uuid()[1]
# clean up the kwargs
options = kwargs.get('q_options', kwargs)
options.pop('hook', None)
options['broker'] = options.get('broker', get_broker())
options['group'] = iter_group
options['iter_count'] = iter_count
if options.get('cached', None):
options['iter_cached'] = options['cached']
options['cached'] = True
# save the original arguments
broker = options['broker']
broker.cache.set('{}:{}:args'.format(broker.list_key, iter_group), SignedPackage.dumps(args_iter))
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Koed00/django-q | django_q/tasks.py | _sync | def _sync(pack):
"""Simulate a package travelling through the cluster."""
task_queue = Queue()
result_queue = Queue()
task = SignedPackage.loads(pack)
task_queue.put(task)
task_queue.put('STOP')
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result_queue.put('STOP')
monitor(result_queue)
task_queue.close()
task_queue.join_thread()
result_queue.close()
result_queue.join_thread()
return task['id'] | python | def _sync(pack):
"""Simulate a package travelling through the cluster."""
task_queue = Queue()
result_queue = Queue()
task = SignedPackage.loads(pack)
task_queue.put(task)
task_queue.put('STOP')
worker(task_queue, result_queue, Value('f', -1))
result_queue.put('STOP')
monitor(result_queue)
task_queue.close()
task_queue.join_thread()
result_queue.close()
result_queue.join_thread()
return task['id'] | [
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Koed00/django-q | django_q/tasks.py | Iter.append | def append(self, *args):
"""
add arguments to the set
"""
self.args.append(args)
if self.started:
self.started = False
return self.length() | python | def append(self, *args):
"""
add arguments to the set
"""
self.args.append(args)
if self.started:
self.started = False
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Koed00/django-q | django_q/admin.py | retry_failed | def retry_failed(FailAdmin, request, queryset):
"""Submit selected tasks back to the queue."""
for task in queryset:
async_task(task.func, *task.args or (), hook=task.hook, **task.kwargs or {})
task.delete() | python | def retry_failed(FailAdmin, request, queryset):
"""Submit selected tasks back to the queue."""
for task in queryset:
async_task(task.func, *task.args or (), hook=task.hook, **task.kwargs or {})
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Koed00/django-q | django_q/admin.py | TaskAdmin.get_queryset | def get_queryset(self, request):
"""Only show successes."""
qs = super(TaskAdmin, self).get_queryset(request)
return qs.filter(success=True) | python | def get_queryset(self, request):
"""Only show successes."""
qs = super(TaskAdmin, self).get_queryset(request)
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Koed00/django-q | django_q/admin.py | TaskAdmin.get_readonly_fields | def get_readonly_fields(self, request, obj=None):
"""Set all fields readonly."""
return list(self.readonly_fields) + [field.name for field in obj._meta.fields] | python | def get_readonly_fields(self, request, obj=None):
"""Set all fields readonly."""
return list(self.readonly_fields) + [field.name for field in obj._meta.fields] | [
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Koed00/django-q | django_q/cluster.py | save_task | def save_task(task, broker):
"""
Saves the task package to Django or the cache
"""
# SAVE LIMIT < 0 : Don't save success
if not task.get('save', Conf.SAVE_LIMIT >= 0) and task['success']:
return
# enqueues next in a chain
if task.get('chain', None):
django_q.tasks.async_chain(task['chain'], group=task['group'], cached=task['cached'], sync=task['sync'], broker=broker)
# SAVE LIMIT > 0: Prune database, SAVE_LIMIT 0: No pruning
db.close_old_connections()
try:
if task['success'] and 0 < Conf.SAVE_LIMIT <= Success.objects.count():
Success.objects.last().delete()
# check if this task has previous results
if Task.objects.filter(id=task['id'], name=task['name']).exists():
existing_task = Task.objects.get(id=task['id'], name=task['name'])
# only update the result if it hasn't succeeded yet
if not existing_task.success:
existing_task.stopped = task['stopped']
existing_task.result = task['result']
existing_task.success = task['success']
existing_task.save()
else:
Task.objects.create(id=task['id'],
name=task['name'],
func=task['func'],
hook=task.get('hook'),
args=task['args'],
kwargs=task['kwargs'],
started=task['started'],
stopped=task['stopped'],
result=task['result'],
group=task.get('group'),
success=task['success']
)
except Exception as e:
logger.error(e) | python | def save_task(task, broker):
"""
Saves the task package to Django or the cache
"""
# SAVE LIMIT < 0 : Don't save success
if not task.get('save', Conf.SAVE_LIMIT >= 0) and task['success']:
return
# enqueues next in a chain
if task.get('chain', None):
django_q.tasks.async_chain(task['chain'], group=task['group'], cached=task['cached'], sync=task['sync'], broker=broker)
# SAVE LIMIT > 0: Prune database, SAVE_LIMIT 0: No pruning
db.close_old_connections()
try:
if task['success'] and 0 < Conf.SAVE_LIMIT <= Success.objects.count():
Success.objects.last().delete()
# check if this task has previous results
if Task.objects.filter(id=task['id'], name=task['name']).exists():
existing_task = Task.objects.get(id=task['id'], name=task['name'])
# only update the result if it hasn't succeeded yet
if not existing_task.success:
existing_task.stopped = task['stopped']
existing_task.result = task['result']
existing_task.success = task['success']
existing_task.save()
else:
Task.objects.create(id=task['id'],
name=task['name'],
func=task['func'],
hook=task.get('hook'),
args=task['args'],
kwargs=task['kwargs'],
started=task['started'],
stopped=task['stopped'],
result=task['result'],
group=task.get('group'),
success=task['success']
)
except Exception as e:
logger.error(e) | [
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Koed00/django-q | django_q/cluster.py | scheduler | def scheduler(broker=None):
"""
Creates a task from a schedule at the scheduled time and schedules next run
"""
if not broker:
broker = get_broker()
db.close_old_connections()
try:
for s in Schedule.objects.exclude(repeats=0).filter(next_run__lt=timezone.now()):
args = ()
kwargs = {}
# get args, kwargs and hook
if s.kwargs:
try:
# eval should be safe here because dict()
kwargs = eval('dict({})'.format(s.kwargs))
except SyntaxError:
kwargs = {}
if s.args:
args = ast.literal_eval(s.args)
# single value won't eval to tuple, so:
if type(args) != tuple:
args = (args,)
q_options = kwargs.get('q_options', {})
if s.hook:
q_options['hook'] = s.hook
# set up the next run time
if not s.schedule_type == s.ONCE:
next_run = arrow.get(s.next_run)
while True:
if s.schedule_type == s.MINUTES:
next_run = next_run.replace(minutes=+(s.minutes or 1))
elif s.schedule_type == s.HOURLY:
next_run = next_run.replace(hours=+1)
elif s.schedule_type == s.DAILY:
next_run = next_run.replace(days=+1)
elif s.schedule_type == s.WEEKLY:
next_run = next_run.replace(weeks=+1)
elif s.schedule_type == s.MONTHLY:
next_run = next_run.replace(months=+1)
elif s.schedule_type == s.QUARTERLY:
next_run = next_run.replace(months=+3)
elif s.schedule_type == s.YEARLY:
next_run = next_run.replace(years=+1)
if Conf.CATCH_UP or next_run > arrow.utcnow():
break
s.next_run = next_run.datetime
s.repeats += -1
# send it to the cluster
q_options['broker'] = broker
q_options['group'] = q_options.get('group', s.name or s.id)
kwargs['q_options'] = q_options
s.task = django_q.tasks.async_task(s.func, *args, **kwargs)
# log it
if not s.task:
logger.error(
_('{} failed to create a task from schedule [{}]').format(current_process().name,
s.name or s.id))
else:
logger.info(
_('{} created a task from schedule [{}]').format(current_process().name, s.name or s.id))
# default behavior is to delete a ONCE schedule
if s.schedule_type == s.ONCE:
if s.repeats < 0:
s.delete()
continue
# but not if it has a positive repeats
s.repeats = 0
# save the schedule
s.save()
except Exception as e:
logger.error(e) | python | def scheduler(broker=None):
"""
Creates a task from a schedule at the scheduled time and schedules next run
"""
if not broker:
broker = get_broker()
db.close_old_connections()
try:
for s in Schedule.objects.exclude(repeats=0).filter(next_run__lt=timezone.now()):
args = ()
kwargs = {}
# get args, kwargs and hook
if s.kwargs:
try:
# eval should be safe here because dict()
kwargs = eval('dict({})'.format(s.kwargs))
except SyntaxError:
kwargs = {}
if s.args:
args = ast.literal_eval(s.args)
# single value won't eval to tuple, so:
if type(args) != tuple:
args = (args,)
q_options = kwargs.get('q_options', {})
if s.hook:
q_options['hook'] = s.hook
# set up the next run time
if not s.schedule_type == s.ONCE:
next_run = arrow.get(s.next_run)
while True:
if s.schedule_type == s.MINUTES:
next_run = next_run.replace(minutes=+(s.minutes or 1))
elif s.schedule_type == s.HOURLY:
next_run = next_run.replace(hours=+1)
elif s.schedule_type == s.DAILY:
next_run = next_run.replace(days=+1)
elif s.schedule_type == s.WEEKLY:
next_run = next_run.replace(weeks=+1)
elif s.schedule_type == s.MONTHLY:
next_run = next_run.replace(months=+1)
elif s.schedule_type == s.QUARTERLY:
next_run = next_run.replace(months=+3)
elif s.schedule_type == s.YEARLY:
next_run = next_run.replace(years=+1)
if Conf.CATCH_UP or next_run > arrow.utcnow():
break
s.next_run = next_run.datetime
s.repeats += -1
# send it to the cluster
q_options['broker'] = broker
q_options['group'] = q_options.get('group', s.name or s.id)
kwargs['q_options'] = q_options
s.task = django_q.tasks.async_task(s.func, *args, **kwargs)
# log it
if not s.task:
logger.error(
_('{} failed to create a task from schedule [{}]').format(current_process().name,
s.name or s.id))
else:
logger.info(
_('{} created a task from schedule [{}]').format(current_process().name, s.name or s.id))
# default behavior is to delete a ONCE schedule
if s.schedule_type == s.ONCE:
if s.repeats < 0:
s.delete()
continue
# but not if it has a positive repeats
s.repeats = 0
# save the schedule
s.save()
except Exception as e:
logger.error(e) | [
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google/python-adb | adb/adb_commands.py | AdbCommands._get_service_connection | def _get_service_connection(self, service, service_command=None, create=True, timeout_ms=None):
"""
Based on the service, get the AdbConnection for that service or create one if it doesnt exist
:param service:
:param service_command: Additional service parameters to append
:param create: If False, dont create a connection if it does not exist
:return:
"""
connection = self._service_connections.get(service, None)
if connection:
return connection
if not connection and not create:
return None
if service_command:
destination_str = b'%s:%s' % (service, service_command)
else:
destination_str = service
connection = self.protocol_handler.Open(
self._handle, destination=destination_str, timeout_ms=timeout_ms)
self._service_connections.update({service: connection})
return connection | python | def _get_service_connection(self, service, service_command=None, create=True, timeout_ms=None):
"""
Based on the service, get the AdbConnection for that service or create one if it doesnt exist
:param service:
:param service_command: Additional service parameters to append
:param create: If False, dont create a connection if it does not exist
:return:
"""
connection = self._service_connections.get(service, None)
if connection:
return connection
if not connection and not create:
return None
if service_command:
destination_str = b'%s:%s' % (service, service_command)
else:
destination_str = service
connection = self.protocol_handler.Open(
self._handle, destination=destination_str, timeout_ms=timeout_ms)
self._service_connections.update({service: connection})
return connection | [
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google/python-adb | adb/adb_commands.py | AdbCommands.ConnectDevice | def ConnectDevice(self, port_path=None, serial=None, default_timeout_ms=None, **kwargs):
"""Convenience function to setup a transport handle for the adb device from
usb path or serial then connect to it.
Args:
port_path: The filename of usb port to use.
serial: The serial number of the device to use.
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kwargs: handle: Device handle to use (instance of common.TcpHandle or common.UsbHandle)
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If serial specifies a TCP address:port, then a TCP connection is
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"""
# If there isnt a handle override (used by tests), build one here
if 'handle' in kwargs:
self._handle = kwargs.pop('handle')
else:
# if necessary, convert serial to a unicode string
if isinstance(serial, (bytes, bytearray)):
serial = serial.decode('utf-8')
if serial and ':' in serial:
self._handle = common.TcpHandle(serial, timeout_ms=default_timeout_ms)
else:
self._handle = common.UsbHandle.FindAndOpen(
DeviceIsAvailable, port_path=port_path, serial=serial,
timeout_ms=default_timeout_ms)
self._Connect(**kwargs)
return self | python | def ConnectDevice(self, port_path=None, serial=None, default_timeout_ms=None, **kwargs):
"""Convenience function to setup a transport handle for the adb device from
usb path or serial then connect to it.
Args:
port_path: The filename of usb port to use.
serial: The serial number of the device to use.
default_timeout_ms: The default timeout in milliseconds to use.
kwargs: handle: Device handle to use (instance of common.TcpHandle or common.UsbHandle)
banner: Connection banner to pass to the remote device
rsa_keys: List of AuthSigner subclass instances to be used for
authentication. The device can either accept one of these via the Sign
method, or we will send the result of GetPublicKey from the first one
if the device doesn't accept any of them.
auth_timeout_ms: Timeout to wait for when sending a new public key. This
is only relevant when we send a new public key. The device shows a
dialog and this timeout is how long to wait for that dialog. If used
in automation, this should be low to catch such a case as a failure
quickly; while in interactive settings it should be high to allow
users to accept the dialog. We default to automation here, so it's low
by default.
If serial specifies a TCP address:port, then a TCP connection is
used instead of a USB connection.
"""
# If there isnt a handle override (used by tests), build one here
if 'handle' in kwargs:
self._handle = kwargs.pop('handle')
else:
# if necessary, convert serial to a unicode string
if isinstance(serial, (bytes, bytearray)):
serial = serial.decode('utf-8')
if serial and ':' in serial:
self._handle = common.TcpHandle(serial, timeout_ms=default_timeout_ms)
else:
self._handle = common.UsbHandle.FindAndOpen(
DeviceIsAvailable, port_path=port_path, serial=serial,
timeout_ms=default_timeout_ms)
self._Connect(**kwargs)
return self | [
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google/python-adb | adb/adb_commands.py | AdbCommands.Install | def Install(self, apk_path, destination_dir='', replace_existing=True,
grant_permissions=False, timeout_ms=None, transfer_progress_callback=None):
"""Install an apk to the device.
Doesn't support verifier file, instead allows destination directory to be
overridden.
Args:
apk_path: Local path to apk to install.
destination_dir: Optional destination directory. Use /system/app/ for
persistent applications.
replace_existing: whether to replace existing application
grant_permissions: If True, grant all permissions to the app specified in its manifest
timeout_ms: Expected timeout for pushing and installing.
transfer_progress_callback: callback method that accepts filename, bytes_written and total_bytes of APK transfer
Returns:
The pm install output.
"""
if not destination_dir:
destination_dir = '/data/local/tmp/'
basename = os.path.basename(apk_path)
destination_path = posixpath.join(destination_dir, basename)
self.Push(apk_path, destination_path, timeout_ms=timeout_ms, progress_callback=transfer_progress_callback)
cmd = ['pm install']
if grant_permissions:
cmd.append('-g')
if replace_existing:
cmd.append('-r')
cmd.append('"{}"'.format(destination_path))
ret = self.Shell(' '.join(cmd), timeout_ms=timeout_ms)
# Remove the apk
rm_cmd = ['rm', destination_path]
rmret = self.Shell(' '.join(rm_cmd), timeout_ms=timeout_ms)
return ret | python | def Install(self, apk_path, destination_dir='', replace_existing=True,
grant_permissions=False, timeout_ms=None, transfer_progress_callback=None):
"""Install an apk to the device.
Doesn't support verifier file, instead allows destination directory to be
overridden.
Args:
apk_path: Local path to apk to install.
destination_dir: Optional destination directory. Use /system/app/ for
persistent applications.
replace_existing: whether to replace existing application
grant_permissions: If True, grant all permissions to the app specified in its manifest
timeout_ms: Expected timeout for pushing and installing.
transfer_progress_callback: callback method that accepts filename, bytes_written and total_bytes of APK transfer
Returns:
The pm install output.
"""
if not destination_dir:
destination_dir = '/data/local/tmp/'
basename = os.path.basename(apk_path)
destination_path = posixpath.join(destination_dir, basename)
self.Push(apk_path, destination_path, timeout_ms=timeout_ms, progress_callback=transfer_progress_callback)
cmd = ['pm install']
if grant_permissions:
cmd.append('-g')
if replace_existing:
cmd.append('-r')
cmd.append('"{}"'.format(destination_path))
ret = self.Shell(' '.join(cmd), timeout_ms=timeout_ms)
# Remove the apk
rm_cmd = ['rm', destination_path]
rmret = self.Shell(' '.join(rm_cmd), timeout_ms=timeout_ms)
return ret | [
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google/python-adb | adb/adb_commands.py | AdbCommands.Uninstall | def Uninstall(self, package_name, keep_data=False, timeout_ms=None):
"""Removes a package from the device.
Args:
package_name: Package name of target package.
keep_data: whether to keep the data and cache directories
timeout_ms: Expected timeout for pushing and installing.
Returns:
The pm uninstall output.
"""
cmd = ['pm uninstall']
if keep_data:
cmd.append('-k')
cmd.append('"%s"' % package_name)
return self.Shell(' '.join(cmd), timeout_ms=timeout_ms) | python | def Uninstall(self, package_name, keep_data=False, timeout_ms=None):
"""Removes a package from the device.
Args:
package_name: Package name of target package.
keep_data: whether to keep the data and cache directories
timeout_ms: Expected timeout for pushing and installing.
Returns:
The pm uninstall output.
"""
cmd = ['pm uninstall']
if keep_data:
cmd.append('-k')
cmd.append('"%s"' % package_name)
return self.Shell(' '.join(cmd), timeout_ms=timeout_ms) | [
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] | d9b94b2dda555c14674c19806debb8449c0e9652 | https://github.com/google/python-adb/blob/d9b94b2dda555c14674c19806debb8449c0e9652/adb/adb_commands.py#L232-L248 | train | 216,792 |
google/python-adb | adb/adb_commands.py | AdbCommands.Push | def Push(self, source_file, device_filename, mtime='0', timeout_ms=None, progress_callback=None, st_mode=None):
"""Push a file or directory to the device.
Args:
source_file: Either a filename, a directory or file-like object to push to
the device.
device_filename: Destination on the device to write to.
mtime: Optional, modification time to set on the file.
timeout_ms: Expected timeout for any part of the push.
st_mode: stat mode for filename
progress_callback: callback method that accepts filename, bytes_written and total_bytes,
total_bytes will be -1 for file-like objects
"""
if isinstance(source_file, str):
if os.path.isdir(source_file):
self.Shell("mkdir " + device_filename)
for f in os.listdir(source_file):
self.Push(os.path.join(source_file, f), device_filename + '/' + f,
progress_callback=progress_callback)
return
source_file = open(source_file, "rb")
with source_file:
connection = self.protocol_handler.Open(
self._handle, destination=b'sync:', timeout_ms=timeout_ms)
kwargs={}
if st_mode is not None:
kwargs['st_mode'] = st_mode
self.filesync_handler.Push(connection, source_file, device_filename,
mtime=int(mtime), progress_callback=progress_callback, **kwargs)
connection.Close() | python | def Push(self, source_file, device_filename, mtime='0', timeout_ms=None, progress_callback=None, st_mode=None):
"""Push a file or directory to the device.
Args:
source_file: Either a filename, a directory or file-like object to push to
the device.
device_filename: Destination on the device to write to.
mtime: Optional, modification time to set on the file.
timeout_ms: Expected timeout for any part of the push.
st_mode: stat mode for filename
progress_callback: callback method that accepts filename, bytes_written and total_bytes,
total_bytes will be -1 for file-like objects
"""
if isinstance(source_file, str):
if os.path.isdir(source_file):
self.Shell("mkdir " + device_filename)
for f in os.listdir(source_file):
self.Push(os.path.join(source_file, f), device_filename + '/' + f,
progress_callback=progress_callback)
return
source_file = open(source_file, "rb")
with source_file:
connection = self.protocol_handler.Open(
self._handle, destination=b'sync:', timeout_ms=timeout_ms)
kwargs={}
if st_mode is not None:
kwargs['st_mode'] = st_mode
self.filesync_handler.Push(connection, source_file, device_filename,
mtime=int(mtime), progress_callback=progress_callback, **kwargs)
connection.Close() | [
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google/python-adb | adb/adb_commands.py | AdbCommands.Pull | def Pull(self, device_filename, dest_file=None, timeout_ms=None, progress_callback=None):
"""Pull a file from the device.
Args:
device_filename: Filename on the device to pull.
dest_file: If set, a filename or writable file-like object.
timeout_ms: Expected timeout for any part of the pull.
progress_callback: callback method that accepts filename, bytes_written and total_bytes,
total_bytes will be -1 for file-like objects
Returns:
The file data if dest_file is not set. Otherwise, True if the destination file exists
"""
if not dest_file:
dest_file = io.BytesIO()
elif isinstance(dest_file, str):
dest_file = open(dest_file, 'wb')
elif isinstance(dest_file, file):
pass
else:
raise ValueError("destfile is of unknown type")
conn = self.protocol_handler.Open(
self._handle, destination=b'sync:', timeout_ms=timeout_ms)
self.filesync_handler.Pull(conn, device_filename, dest_file, progress_callback)
conn.Close()
if isinstance(dest_file, io.BytesIO):
return dest_file.getvalue()
else:
dest_file.close()
if hasattr(dest_file, 'name'):
return os.path.exists(dest_file.name)
# We don't know what the path is, so we just assume it exists.
return True | python | def Pull(self, device_filename, dest_file=None, timeout_ms=None, progress_callback=None):
"""Pull a file from the device.
Args:
device_filename: Filename on the device to pull.
dest_file: If set, a filename or writable file-like object.
timeout_ms: Expected timeout for any part of the pull.
progress_callback: callback method that accepts filename, bytes_written and total_bytes,
total_bytes will be -1 for file-like objects
Returns:
The file data if dest_file is not set. Otherwise, True if the destination file exists
"""
if not dest_file:
dest_file = io.BytesIO()
elif isinstance(dest_file, str):
dest_file = open(dest_file, 'wb')
elif isinstance(dest_file, file):
pass
else:
raise ValueError("destfile is of unknown type")
conn = self.protocol_handler.Open(
self._handle, destination=b'sync:', timeout_ms=timeout_ms)
self.filesync_handler.Pull(conn, device_filename, dest_file, progress_callback)
conn.Close()
if isinstance(dest_file, io.BytesIO):
return dest_file.getvalue()
else:
dest_file.close()
if hasattr(dest_file, 'name'):
return os.path.exists(dest_file.name)
# We don't know what the path is, so we just assume it exists.
return True | [
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google/python-adb | adb/adb_commands.py | AdbCommands.List | def List(self, device_path):
"""Return a directory listing of the given path.
Args:
device_path: Directory to list.
"""
connection = self.protocol_handler.Open(self._handle, destination=b'sync:')
listing = self.filesync_handler.List(connection, device_path)
connection.Close()
return listing | python | def List(self, device_path):
"""Return a directory listing of the given path.
Args:
device_path: Directory to list.
"""
connection = self.protocol_handler.Open(self._handle, destination=b'sync:')
listing = self.filesync_handler.List(connection, device_path)
connection.Close()
return listing | [
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google/python-adb | adb/adb_commands.py | AdbCommands.Shell | def Shell(self, command, timeout_ms=None):
"""Run command on the device, returning the output.
Args:
command: Shell command to run
timeout_ms: Maximum time to allow the command to run.
"""
return self.protocol_handler.Command(
self._handle, service=b'shell', command=command,
timeout_ms=timeout_ms) | python | def Shell(self, command, timeout_ms=None):
"""Run command on the device, returning the output.
Args:
command: Shell command to run
timeout_ms: Maximum time to allow the command to run.
"""
return self.protocol_handler.Command(
self._handle, service=b'shell', command=command,
timeout_ms=timeout_ms) | [
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google/python-adb | adb/adb_commands.py | AdbCommands.StreamingShell | def StreamingShell(self, command, timeout_ms=None):
"""Run command on the device, yielding each line of output.
Args:
command: Command to run on the target.
timeout_ms: Maximum time to allow the command to run.
Yields:
The responses from the shell command.
"""
return self.protocol_handler.StreamingCommand(
self._handle, service=b'shell', command=command,
timeout_ms=timeout_ms) | python | def StreamingShell(self, command, timeout_ms=None):
"""Run command on the device, yielding each line of output.
Args:
command: Command to run on the target.
timeout_ms: Maximum time to allow the command to run.
Yields:
The responses from the shell command.
"""
return self.protocol_handler.StreamingCommand(
self._handle, service=b'shell', command=command,
timeout_ms=timeout_ms) | [
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google/python-adb | adb/adb_commands.py | AdbCommands.InteractiveShell | def InteractiveShell(self, cmd=None, strip_cmd=True, delim=None, strip_delim=True):
"""Get stdout from the currently open interactive shell and optionally run a command
on the device, returning all output.
Args:
cmd: Optional. Command to run on the target.
strip_cmd: Optional (default True). Strip command name from stdout.
delim: Optional. Delimiter to look for in the output to know when to stop expecting more output
(usually the shell prompt)
strip_delim: Optional (default True): Strip the provided delimiter from the output
Returns:
The stdout from the shell command.
"""
conn = self._get_service_connection(b'shell:')
return self.protocol_handler.InteractiveShellCommand(
conn, cmd=cmd, strip_cmd=strip_cmd,
delim=delim, strip_delim=strip_delim) | python | def InteractiveShell(self, cmd=None, strip_cmd=True, delim=None, strip_delim=True):
"""Get stdout from the currently open interactive shell and optionally run a command
on the device, returning all output.
Args:
cmd: Optional. Command to run on the target.
strip_cmd: Optional (default True). Strip command name from stdout.
delim: Optional. Delimiter to look for in the output to know when to stop expecting more output
(usually the shell prompt)
strip_delim: Optional (default True): Strip the provided delimiter from the output
Returns:
The stdout from the shell command.
"""
conn = self._get_service_connection(b'shell:')
return self.protocol_handler.InteractiveShellCommand(
conn, cmd=cmd, strip_cmd=strip_cmd,
delim=delim, strip_delim=strip_delim) | [
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google/python-adb | adb/common.py | InterfaceMatcher | def InterfaceMatcher(clazz, subclass, protocol):
"""Returns a matcher that returns the setting with the given interface."""
interface = (clazz, subclass, protocol)
def Matcher(device):
for setting in device.iterSettings():
if GetInterface(setting) == interface:
return setting
return Matcher | python | def InterfaceMatcher(clazz, subclass, protocol):
"""Returns a matcher that returns the setting with the given interface."""
interface = (clazz, subclass, protocol)
def Matcher(device):
for setting in device.iterSettings():
if GetInterface(setting) == interface:
return setting
return Matcher | [
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