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GoogleCloudPlatform/appengine-pipelines | python/src/pipeline/util.py | _get_task_target | def _get_task_target():
"""Get the default target for a pipeline task.
Current version id format is: user_defined_version.minor_version_number
Current module id is just the module's name. It could be "default"
Returns:
A complete target name is of format version.module. If module is the
default module, just version. None if target can not be determined.
"""
# Break circular dependency.
# pylint: disable=g-import-not-at-top
import pipeline
if pipeline._TEST_MODE:
return None
# Further protect against test cases that doesn't set env vars
# propertly.
if ("CURRENT_VERSION_ID" not in os.environ or
"CURRENT_MODULE_ID" not in os.environ):
logging.warning("Running Pipeline in non TEST_MODE but important "
"env vars are not set.")
return None
version = os.environ["CURRENT_VERSION_ID"].split(".")[0]
module = os.environ["CURRENT_MODULE_ID"]
return "%s.%s" % (version, module) | python | def _get_task_target():
"""Get the default target for a pipeline task.
Current version id format is: user_defined_version.minor_version_number
Current module id is just the module's name. It could be "default"
Returns:
A complete target name is of format version.module. If module is the
default module, just version. None if target can not be determined.
"""
# Break circular dependency.
# pylint: disable=g-import-not-at-top
import pipeline
if pipeline._TEST_MODE:
return None
# Further protect against test cases that doesn't set env vars
# propertly.
if ("CURRENT_VERSION_ID" not in os.environ or
"CURRENT_MODULE_ID" not in os.environ):
logging.warning("Running Pipeline in non TEST_MODE but important "
"env vars are not set.")
return None
version = os.environ["CURRENT_VERSION_ID"].split(".")[0]
module = os.environ["CURRENT_MODULE_ID"]
return "%s.%s" % (version, module) | [
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aws/sagemaker-python-sdk | src/sagemaker/estimator.py | _TrainingJob.start_new | def start_new(cls, estimator, inputs):
"""Create a new Amazon SageMaker training job from the estimator.
Args:
estimator (sagemaker.estimator.EstimatorBase): Estimator object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.estimator._TrainingJob: Constructed object that captures all information about the started
training job.
"""
local_mode = estimator.sagemaker_session.local_mode
model_uri = estimator.model_uri
# Allow file:// input only in local mode
if cls._is_local_channel(inputs) or cls._is_local_channel(model_uri):
if not local_mode:
raise ValueError('File URIs are supported in local mode only. Please use a S3 URI instead.')
config = _Job._load_config(inputs, estimator)
if estimator.hyperparameters() is not None:
hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
train_args = config.copy()
train_args['input_mode'] = estimator.input_mode
train_args['job_name'] = estimator._current_job_name
train_args['hyperparameters'] = hyperparameters
train_args['tags'] = estimator.tags
train_args['metric_definitions'] = estimator.metric_definitions
if estimator.enable_network_isolation():
train_args['enable_network_isolation'] = True
if estimator.encrypt_inter_container_traffic:
train_args['encrypt_inter_container_traffic'] = True
if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
train_args['algorithm_arn'] = estimator.algorithm_arn
else:
train_args['image'] = estimator.train_image()
estimator.sagemaker_session.train(**train_args)
return cls(estimator.sagemaker_session, estimator._current_job_name) | python | def start_new(cls, estimator, inputs):
"""Create a new Amazon SageMaker training job from the estimator.
Args:
estimator (sagemaker.estimator.EstimatorBase): Estimator object created by the user.
inputs (str): Parameters used when called :meth:`~sagemaker.estimator.EstimatorBase.fit`.
Returns:
sagemaker.estimator._TrainingJob: Constructed object that captures all information about the started
training job.
"""
local_mode = estimator.sagemaker_session.local_mode
model_uri = estimator.model_uri
# Allow file:// input only in local mode
if cls._is_local_channel(inputs) or cls._is_local_channel(model_uri):
if not local_mode:
raise ValueError('File URIs are supported in local mode only. Please use a S3 URI instead.')
config = _Job._load_config(inputs, estimator)
if estimator.hyperparameters() is not None:
hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
train_args = config.copy()
train_args['input_mode'] = estimator.input_mode
train_args['job_name'] = estimator._current_job_name
train_args['hyperparameters'] = hyperparameters
train_args['tags'] = estimator.tags
train_args['metric_definitions'] = estimator.metric_definitions
if estimator.enable_network_isolation():
train_args['enable_network_isolation'] = True
if estimator.encrypt_inter_container_traffic:
train_args['encrypt_inter_container_traffic'] = True
if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
train_args['algorithm_arn'] = estimator.algorithm_arn
else:
train_args['image'] = estimator.train_image()
estimator.sagemaker_session.train(**train_args)
return cls(estimator.sagemaker_session, estimator._current_job_name) | [
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python-xlib/python-xlib | Xlib/display.py | Display.no_operation | def no_operation(self, onerror = None):
"""Do nothing but send a request to the server."""
request.NoOperation(display = self.display,
onerror = onerror) | python | def no_operation(self, onerror = None):
"""Do nothing but send a request to the server."""
request.NoOperation(display = self.display,
onerror = onerror) | [
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PrefPy/prefpy | prefpy/stats.py | mse | def mse(mean, estimator):
"""
Description:
Calculates the Mean Squared Error (MSE) of
an estimation on flat numpy ndarrays.
Parameters:
mean: actual value (numpy ndarray)
estimator: estimated value of the mean (numpy ndarray)
"""
return np.mean((np.asarray(estimator) - np.asarray(mean)) ** 2, axis=0) | python | def mse(mean, estimator):
"""
Description:
Calculates the Mean Squared Error (MSE) of
an estimation on flat numpy ndarrays.
Parameters:
mean: actual value (numpy ndarray)
estimator: estimated value of the mean (numpy ndarray)
"""
return np.mean((np.asarray(estimator) - np.asarray(mean)) ** 2, axis=0) | [
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bukun/TorCMS | torcms/model/label_model.py | MLabel.get_id_by_name | def get_id_by_name(tag_name, kind='z'):
'''
Get ID by tag_name of the label.
'''
recs = TabTag.select().where(
(TabTag.name == tag_name) & (TabTag.kind == kind)
)
logger.info('tag count of {0}: {1} '.format(tag_name, recs.count()))
# the_id = ''
if recs.count() == 1:
the_id = recs.get().uid
elif recs.count() > 1:
rec0 = None
for rec in recs:
# Only keep one.
if rec0:
TabPost2Tag.delete().where(TabPost2Tag.tag_id == rec.uid).execute()
TabTag.delete().where(TabTag.uid == rec.uid).execute()
else:
rec0 = rec
the_id = rec0.uid
else:
the_id = MLabel.create_tag(tag_name)
return the_id | python | def get_id_by_name(tag_name, kind='z'):
'''
Get ID by tag_name of the label.
'''
recs = TabTag.select().where(
(TabTag.name == tag_name) & (TabTag.kind == kind)
)
logger.info('tag count of {0}: {1} '.format(tag_name, recs.count()))
# the_id = ''
if recs.count() == 1:
the_id = recs.get().uid
elif recs.count() > 1:
rec0 = None
for rec in recs:
# Only keep one.
if rec0:
TabPost2Tag.delete().where(TabPost2Tag.tag_id == rec.uid).execute()
TabTag.delete().where(TabTag.uid == rec.uid).execute()
else:
rec0 = rec
the_id = rec0.uid
else:
the_id = MLabel.create_tag(tag_name)
return the_id | [
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JarryShaw/PyPCAPKit | src/protocols/application/httpv2.py | HTTPv2.read_http | def read_http(self, length):
"""Read Hypertext Transfer Protocol (HTTP/2).
Structure of HTTP/2 packet [RFC 7540]:
+-----------------------------------------------+
| Length (24) |
+---------------+---------------+---------------+
| Type (8) | Flags (8) |
+-+-------------+---------------+-------------------------------+
|R| Stream Identifier (31) |
+=+=============================================================+
| Frame Payload (0...) ...
+---------------------------------------------------------------+
Octets Bits Name Description
0 0 http.length Length
3 24 http.type Type
4 32 http.flags Flags
5 40 - Reserved
5 41 http.sid Stream Identifier
9 72 http.payload Frame Payload
"""
if length is None:
length = len(self)
if length < 9:
raise ProtocolError(f'HTTP/2: invalid format', quiet=True)
_tlen = self._read_unpack(3)
_type = self._read_unpack(1)
_flag = self._read_binary(1)
_rsid = self._read_binary(4)
if _tlen != length:
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
if int(_rsid[0], base=2):
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
http = dict(
length=_tlen,
type=_HTTP_TYPE.get(_type),
sid=int(_rsid[1:], base=2),
packet=self._read_packet(_tlen),
)
if http['type'] is None:
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
if http['type'] in ('SETTINGS', 'PING') and http['sid'] != 0:
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
_http = _HTTP_FUNC[_type](self, _tlen, _type, _flag)
http.update(_http)
return http | python | def read_http(self, length):
"""Read Hypertext Transfer Protocol (HTTP/2).
Structure of HTTP/2 packet [RFC 7540]:
+-----------------------------------------------+
| Length (24) |
+---------------+---------------+---------------+
| Type (8) | Flags (8) |
+-+-------------+---------------+-------------------------------+
|R| Stream Identifier (31) |
+=+=============================================================+
| Frame Payload (0...) ...
+---------------------------------------------------------------+
Octets Bits Name Description
0 0 http.length Length
3 24 http.type Type
4 32 http.flags Flags
5 40 - Reserved
5 41 http.sid Stream Identifier
9 72 http.payload Frame Payload
"""
if length is None:
length = len(self)
if length < 9:
raise ProtocolError(f'HTTP/2: invalid format', quiet=True)
_tlen = self._read_unpack(3)
_type = self._read_unpack(1)
_flag = self._read_binary(1)
_rsid = self._read_binary(4)
if _tlen != length:
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
if int(_rsid[0], base=2):
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
http = dict(
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type=_HTTP_TYPE.get(_type),
sid=int(_rsid[1:], base=2),
packet=self._read_packet(_tlen),
)
if http['type'] is None:
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if http['type'] in ('SETTINGS', 'PING') and http['sid'] != 0:
raise ProtocolError(f'HTTP/2: [Type {_type}] invalid format', quiet=True)
_http = _HTTP_FUNC[_type](self, _tlen, _type, _flag)
http.update(_http)
return http | [
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datosgobar/pydatajson | pydatajson/validation.py | _catalog_validation_to_list | def _catalog_validation_to_list(response):
"""Formatea la validación de un catálogo a dos listas de errores.
Una lista de errores para "catalog" y otra para "dataset".
"""
# crea una lista de dicts para volcarse en una tabla (catalog)
rows_catalog = []
validation_result = {
"catalog_title": response["error"]["catalog"]["title"],
"catalog_status": response["error"]["catalog"]["status"],
}
for error in response["error"]["catalog"]["errors"]:
catalog_result = dict(validation_result)
catalog_result.update({
"catalog_error_message": error["message"],
"catalog_error_location": ", ".join(error["path"]),
})
rows_catalog.append(catalog_result)
if len(response["error"]["catalog"]["errors"]) == 0:
catalog_result = dict(validation_result)
catalog_result.update({
"catalog_error_message": None,
"catalog_error_location": None
})
rows_catalog.append(catalog_result)
# crea una lista de dicts para volcarse en una tabla (dataset)
rows_dataset = []
for dataset in response["error"]["dataset"]:
validation_result = {
"dataset_title": dataset["title"],
"dataset_identifier": dataset["identifier"],
"dataset_list_index": dataset["list_index"],
"dataset_status": dataset["status"]
}
for error in dataset["errors"]:
dataset_result = dict(validation_result)
dataset_result.update({
"dataset_error_message": error["message"],
"dataset_error_location": error["path"][-1]
})
rows_dataset.append(dataset_result)
if len(dataset["errors"]) == 0:
dataset_result = dict(validation_result)
dataset_result.update({
"dataset_error_message": None,
"dataset_error_location": None
})
rows_dataset.append(dataset_result)
return {"catalog": rows_catalog, "dataset": rows_dataset} | python | def _catalog_validation_to_list(response):
"""Formatea la validación de un catálogo a dos listas de errores.
Una lista de errores para "catalog" y otra para "dataset".
"""
# crea una lista de dicts para volcarse en una tabla (catalog)
rows_catalog = []
validation_result = {
"catalog_title": response["error"]["catalog"]["title"],
"catalog_status": response["error"]["catalog"]["status"],
}
for error in response["error"]["catalog"]["errors"]:
catalog_result = dict(validation_result)
catalog_result.update({
"catalog_error_message": error["message"],
"catalog_error_location": ", ".join(error["path"]),
})
rows_catalog.append(catalog_result)
if len(response["error"]["catalog"]["errors"]) == 0:
catalog_result = dict(validation_result)
catalog_result.update({
"catalog_error_message": None,
"catalog_error_location": None
})
rows_catalog.append(catalog_result)
# crea una lista de dicts para volcarse en una tabla (dataset)
rows_dataset = []
for dataset in response["error"]["dataset"]:
validation_result = {
"dataset_title": dataset["title"],
"dataset_identifier": dataset["identifier"],
"dataset_list_index": dataset["list_index"],
"dataset_status": dataset["status"]
}
for error in dataset["errors"]:
dataset_result = dict(validation_result)
dataset_result.update({
"dataset_error_message": error["message"],
"dataset_error_location": error["path"][-1]
})
rows_dataset.append(dataset_result)
if len(dataset["errors"]) == 0:
dataset_result = dict(validation_result)
dataset_result.update({
"dataset_error_message": None,
"dataset_error_location": None
})
rows_dataset.append(dataset_result)
return {"catalog": rows_catalog, "dataset": rows_dataset} | [
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SKA-ScienceDataProcessor/integration-prototype | sip/tango_control/tango_processing_block/app/processing_block_device.py | ProcessingBlockDevice.pb_id | def pb_id(self, pb_id: str):
"""Set the PB Id for this device."""
# FIXME(BMo) instead of creating the object to check if the PB exists
# use a method on PB List?
# ProcessingBlock(pb_id)
self.set_state(DevState.ON)
self._pb_id = pb_id | python | def pb_id(self, pb_id: str):
"""Set the PB Id for this device."""
# FIXME(BMo) instead of creating the object to check if the PB exists
# use a method on PB List?
# ProcessingBlock(pb_id)
self.set_state(DevState.ON)
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Yelp/kafka-utils | kafka_utils/util/__init__.py | format_to_json | def format_to_json(data):
"""Converts `data` into json
If stdout is a tty it performs a pretty print.
"""
if sys.stdout.isatty():
return json.dumps(data, indent=4, separators=(',', ': '))
else:
return json.dumps(data) | python | def format_to_json(data):
"""Converts `data` into json
If stdout is a tty it performs a pretty print.
"""
if sys.stdout.isatty():
return json.dumps(data, indent=4, separators=(',', ': '))
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Metatab/metatab | metatab/terms.py | SectionTerm.add_term | def add_term(self, t):
"""Add a term to this section and set it's ownership. Should only be used on root level terms"""
if t not in self.terms:
if t.parent_term_lc == 'root':
self.terms.append(t)
self.doc.add_term(t, add_section=False)
t.set_ownership()
else:
raise GenerateError("Can only add or move root-level terms. Term '{}' parent is '{}' "
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assert t.section or t.join_lc == 'root.root', t | python | def add_term(self, t):
"""Add a term to this section and set it's ownership. Should only be used on root level terms"""
if t not in self.terms:
if t.parent_term_lc == 'root':
self.terms.append(t)
self.doc.add_term(t, add_section=False)
t.set_ownership()
else:
raise GenerateError("Can only add or move root-level terms. Term '{}' parent is '{}' "
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assert t.section or t.join_lc == 'root.root', t | [
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rigetti/quantumflow | tools/benchmark.py | benchmark_gops | def benchmark_gops(N, gates, reps):
"""Return benchmark performance in GOPS (Gate operations per second)"""
t = timeit.timeit(lambda: benchmark(N, gates), number=reps)
gops = (GATES*REPS)/t
gops = int((gops * 100) + 0.5) / 100.0
return gops | python | def benchmark_gops(N, gates, reps):
"""Return benchmark performance in GOPS (Gate operations per second)"""
t = timeit.timeit(lambda: benchmark(N, gates), number=reps)
gops = (GATES*REPS)/t
gops = int((gops * 100) + 0.5) / 100.0
return gops | [
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ehansis/ozelot | ozelot/orm/base.py | render_diagram | def render_diagram(out_base):
"""Render a data model diagram
Included in the diagram are all classes from the model registry.
For your project, write a small script that imports all models that you would like to
have included and then calls this function.
.. note:: This function requires the 'dot' executable from the GraphViz package to be installed
and its location configured in your `project_config.py` variable :attr:`DOT_EXECUTABLE`.
Args:
out_base (str): output base path (file endings will be appended)
"""
import codecs
import subprocess
import sadisplay
# generate class descriptions
desc = sadisplay.describe(list(model_registry.values()),
show_methods=False,
show_properties=True,
show_indexes=True,
)
# write description in DOT format
with codecs.open(out_base + '.dot', 'w', encoding='utf-8') as f:
f.write(sadisplay.dot(desc))
# check existence of DOT_EXECUTABLE variable and file
if not hasattr(config, 'DOT_EXECUTABLE'):
raise RuntimeError("Please configure the 'DOT_EXECUTABLE' variable in your 'project_config.py'")
if not os.path.exists(config.DOT_EXECUTABLE):
raise IOError("Could not find file pointed to by 'DOT_EXECUTABLE': " + str(config.DOT_EXECUTABLE))
# render to image using DOT
# noinspection PyUnresolvedReferences
subprocess.check_call([
config.DOT_EXECUTABLE,
'-T', 'png',
'-o', out_base + '.png',
out_base + '.dot'
]) | python | def render_diagram(out_base):
"""Render a data model diagram
Included in the diagram are all classes from the model registry.
For your project, write a small script that imports all models that you would like to
have included and then calls this function.
.. note:: This function requires the 'dot' executable from the GraphViz package to be installed
and its location configured in your `project_config.py` variable :attr:`DOT_EXECUTABLE`.
Args:
out_base (str): output base path (file endings will be appended)
"""
import codecs
import subprocess
import sadisplay
# generate class descriptions
desc = sadisplay.describe(list(model_registry.values()),
show_methods=False,
show_properties=True,
show_indexes=True,
)
# write description in DOT format
with codecs.open(out_base + '.dot', 'w', encoding='utf-8') as f:
f.write(sadisplay.dot(desc))
# check existence of DOT_EXECUTABLE variable and file
if not hasattr(config, 'DOT_EXECUTABLE'):
raise RuntimeError("Please configure the 'DOT_EXECUTABLE' variable in your 'project_config.py'")
if not os.path.exists(config.DOT_EXECUTABLE):
raise IOError("Could not find file pointed to by 'DOT_EXECUTABLE': " + str(config.DOT_EXECUTABLE))
# render to image using DOT
# noinspection PyUnresolvedReferences
subprocess.check_call([
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Genida/dependenpy | src/dependenpy/node.py | LeafNode.root | def root(self):
"""
Property to return the root of this node.
Returns:
Package: this node's root package.
"""
node = self
while node.package is not None:
node = node.package
return node | python | def root(self):
"""
Property to return the root of this node.
Returns:
Package: this node's root package.
"""
node = self
while node.package is not None:
node = node.package
return node | [
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esafak/mca | src/mca.py | MCA.cont_c | def cont_c(self, percent=0.9, N=None): # bug? check axis number 0 vs 1 here
"""Return the contribution of each column."""
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self.fs_c(N=self.rank) # generate G
return apply_along_axis(lambda _: _/self.L[:N], 1,
apply_along_axis(lambda _: _*self.c, 0, self.G[:, :N]**2)) | python | def cont_c(self, percent=0.9, N=None): # bug? check axis number 0 vs 1 here
"""Return the contribution of each column."""
if not hasattr(self, 'G'):
self.fs_c(N=self.rank) # generate G
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PmagPy/PmagPy | programs/demag_gui.py | Demag_GUI.get_DIR | def get_DIR(self):
"""
Dialog that allows user to choose a working directory
"""
dlg = wx.DirDialog(self, "Choose a directory:", defaultPath=self.currentDirectory,
style=wx.DD_DEFAULT_STYLE | wx.DD_NEW_DIR_BUTTON | wx.DD_CHANGE_DIR)
ok = self.show_dlg(dlg)
if ok == wx.ID_OK:
new_WD = dlg.GetPath()
dlg.Destroy()
else:
new_WD = os.getcwd()
dlg.Destroy()
return new_WD | python | def get_DIR(self):
"""
Dialog that allows user to choose a working directory
"""
dlg = wx.DirDialog(self, "Choose a directory:", defaultPath=self.currentDirectory,
style=wx.DD_DEFAULT_STYLE | wx.DD_NEW_DIR_BUTTON | wx.DD_CHANGE_DIR)
ok = self.show_dlg(dlg)
if ok == wx.ID_OK:
new_WD = dlg.GetPath()
dlg.Destroy()
else:
new_WD = os.getcwd()
dlg.Destroy()
return new_WD | [
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jmvrbanac/lplight | lplight/client.py | LaunchpadClient.get_project | def get_project(self, name):
""" Retrives project information by name
:param name: The formal project name in string form.
"""
uri = '{base}/{project}'.format(base=self.BASE_URI, project=name)
resp = self._client.get(uri, model=models.Project)
return resp | python | def get_project(self, name):
""" Retrives project information by name
:param name: The formal project name in string form.
"""
uri = '{base}/{project}'.format(base=self.BASE_URI, project=name)
resp = self._client.get(uri, model=models.Project)
return resp | [
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rvswift/EB | EB/builder/utilities/classification.py | make_score_structure | def make_score_structure(molecules, ensemble):
"""
puts data in the score_structure format for subsequent processing
:param molecules: list [mol_object_1, mol_object_2, .... ] mol_objects are instances of common_tools.molecules
:return score_structure: list [(id, best_score, best_query, status, net decoy count, net active count), ..., ]
"""
# sort molecules by their ensemble score
#sort_order = get_sort_order(molecules)
sort_order = 'asc'
sorted_molecules = screener.screener(molecules, ensemble, sort_order)
# initiate variables
score_structure = []
net_active_count = 0
net_decoy_count = 0
for mol in sorted_molecules:
# determine net active count & net decoy count
status = mol.GetProp('status')
if status == '1':
net_active_count += 1
elif status == '0':
net_decoy_count += 1
else:
continue
score_structure.append((mol.GetProp('id'), mol.GetProp('best_score'), mol.GetProp('best_query'), status,
net_decoy_count, net_active_count))
return score_structure | python | def make_score_structure(molecules, ensemble):
"""
puts data in the score_structure format for subsequent processing
:param molecules: list [mol_object_1, mol_object_2, .... ] mol_objects are instances of common_tools.molecules
:return score_structure: list [(id, best_score, best_query, status, net decoy count, net active count), ..., ]
"""
# sort molecules by their ensemble score
#sort_order = get_sort_order(molecules)
sort_order = 'asc'
sorted_molecules = screener.screener(molecules, ensemble, sort_order)
# initiate variables
score_structure = []
net_active_count = 0
net_decoy_count = 0
for mol in sorted_molecules:
# determine net active count & net decoy count
status = mol.GetProp('status')
if status == '1':
net_active_count += 1
elif status == '0':
net_decoy_count += 1
else:
continue
score_structure.append((mol.GetProp('id'), mol.GetProp('best_score'), mol.GetProp('best_query'), status,
net_decoy_count, net_active_count))
return score_structure | [
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adafruit/Adafruit_Python_SSD1306 | Adafruit_SSD1306/SSD1306.py | SSD1306Base.set_contrast | def set_contrast(self, contrast):
"""Sets the contrast of the display. Contrast should be a value between
0 and 255."""
if contrast < 0 or contrast > 255:
raise ValueError('Contrast must be a value from 0 to 255 (inclusive).')
self.command(SSD1306_SETCONTRAST)
self.command(contrast) | python | def set_contrast(self, contrast):
"""Sets the contrast of the display. Contrast should be a value between
0 and 255."""
if contrast < 0 or contrast > 255:
raise ValueError('Contrast must be a value from 0 to 255 (inclusive).')
self.command(SSD1306_SETCONTRAST)
self.command(contrast) | [
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DLR-RM/RAFCON | source/rafcon/gui/controllers/state_machine_tree.py | StateMachineTreeController.show_content | def show_content(self, state_model):
"""Check state machine tree specific show content flag.
Is returning true if the upper most library state of a state model has a enabled show content flag or if there
is no library root state above this state.
:param rafcon.gui.models.abstract_state.AbstractStateModel state_model: The state model to check
"""
upper_most_lib_state_m = None
if isinstance(state_model, LibraryStateModel):
uppermost_library_root_state = state_model.state.get_uppermost_library_root_state()
if uppermost_library_root_state is None:
upper_most_lib_state_m = state_model
else:
upper_lib_state = uppermost_library_root_state.parent
upper_most_lib_state_m = self._selected_sm_model.get_state_model_by_path(upper_lib_state.get_path())
if upper_most_lib_state_m:
return upper_most_lib_state_m.show_content()
else:
return True | python | def show_content(self, state_model):
"""Check state machine tree specific show content flag.
Is returning true if the upper most library state of a state model has a enabled show content flag or if there
is no library root state above this state.
:param rafcon.gui.models.abstract_state.AbstractStateModel state_model: The state model to check
"""
upper_most_lib_state_m = None
if isinstance(state_model, LibraryStateModel):
uppermost_library_root_state = state_model.state.get_uppermost_library_root_state()
if uppermost_library_root_state is None:
upper_most_lib_state_m = state_model
else:
upper_lib_state = uppermost_library_root_state.parent
upper_most_lib_state_m = self._selected_sm_model.get_state_model_by_path(upper_lib_state.get_path())
if upper_most_lib_state_m:
return upper_most_lib_state_m.show_content()
else:
return True | [
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apache/incubator-mxnet | python/mxnet/symbol/symbol.py | Symbol._set_attr | def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set
"""
for key, value in kwargs.items():
if not isinstance(value, string_types):
raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value)))) | python | def _set_attr(self, **kwargs):
"""Sets an attribute of the symbol.
For example. A._set_attr(foo="bar") adds the mapping ``"{foo: bar}"``
to the symbol's attribute dictionary.
Parameters
----------
**kwargs
The attributes to set
"""
for key, value in kwargs.items():
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raise ValueError("Set Attr only accepts string values")
check_call(_LIB.MXSymbolSetAttr(
self.handle, c_str(key), c_str(str(value)))) | [
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juanifioren/django-oidc-provider | oidc_provider/lib/utils/common.py | get_site_url | def get_site_url(site_url=None, request=None):
"""
Construct the site url.
Orders to decide site url:
1. valid `site_url` parameter
2. valid `SITE_URL` in settings
3. construct from `request` object
"""
site_url = site_url or settings.get('SITE_URL')
if site_url:
return site_url
elif request:
return '{}://{}'.format(request.scheme, request.get_host())
else:
raise Exception('Either pass `site_url`, '
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'or pass `request` object.') | python | def get_site_url(site_url=None, request=None):
"""
Construct the site url.
Orders to decide site url:
1. valid `site_url` parameter
2. valid `SITE_URL` in settings
3. construct from `request` object
"""
site_url = site_url or settings.get('SITE_URL')
if site_url:
return site_url
elif request:
return '{}://{}'.format(request.scheme, request.get_host())
else:
raise Exception('Either pass `site_url`, '
'or set `SITE_URL` in settings, '
'or pass `request` object.') | [
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pycontribs/pyrax | pyrax/base_identity.py | BaseIdentity.get_client | def get_client(self, service, region, public=True, cached=True,
client_class=None):
"""
Returns the client object for the specified service and region.
By default the public endpoint is used. If you wish to work with a
services internal endpoints, specify `public=False`.
By default, if a client has already been created for the given service,
region, and public values, that will be returned. To force a new client
to be created, pass 'cached=False'.
"""
if not self.authenticated:
raise exc.NotAuthenticated("You must authenticate before trying "
"to create clients.")
clt = ep = None
mapped_service = self.service_mapping.get(service) or service
svc = self.services.get(mapped_service)
if svc:
ep = svc.endpoints.get(region)
if ep:
clt = ep._get_client(public=public, cached=cached,
client_class=client_class)
if not clt:
raise exc.NoSuchClient("There is no client available for the "
"service '%s' in the region '%s'." % (service, region))
return clt | python | def get_client(self, service, region, public=True, cached=True,
client_class=None):
"""
Returns the client object for the specified service and region.
By default the public endpoint is used. If you wish to work with a
services internal endpoints, specify `public=False`.
By default, if a client has already been created for the given service,
region, and public values, that will be returned. To force a new client
to be created, pass 'cached=False'.
"""
if not self.authenticated:
raise exc.NotAuthenticated("You must authenticate before trying "
"to create clients.")
clt = ep = None
mapped_service = self.service_mapping.get(service) or service
svc = self.services.get(mapped_service)
if svc:
ep = svc.endpoints.get(region)
if ep:
clt = ep._get_client(public=public, cached=cached,
client_class=client_class)
if not clt:
raise exc.NoSuchClient("There is no client available for the "
"service '%s' in the region '%s'." % (service, region))
return clt | [
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billyoverton/tweetqueue | tweetqueue/__main__.py | queue | def queue(ctx, message):
"""Adds a message to your twitter queue"""
if not valid_tweet(message):
click.echo("Message is too long for twitter.")
click.echo("Message: " + message)
ctx.exit(2)
if ctx.obj['DRYRUN']:
click.echo("Message not queue due to dry-run mode.")
ctx.exit(0)
ctx.obj['TWEETLIST'].append(message) | python | def queue(ctx, message):
"""Adds a message to your twitter queue"""
if not valid_tweet(message):
click.echo("Message is too long for twitter.")
click.echo("Message: " + message)
ctx.exit(2)
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ctx.exit(0)
ctx.obj['TWEETLIST'].append(message) | [
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pjmark/NIMPA | niftypet/nimpa/prc/imio.py | orientnii | def orientnii(imfile):
'''Get the orientation from NIfTI sform. Not fully functional yet.'''
strorient = ['L-R', 'S-I', 'A-P']
niiorient = []
niixyz = np.zeros(3,dtype=np.int8)
if os.path.isfile(imfile):
nim = nib.load(imfile)
pct = nim.get_data()
A = nim.get_sform()
for i in range(3):
niixyz[i] = np.argmax(abs(A[i,:-1]))
niiorient.append( strorient[ niixyz[i] ] )
print niiorient | python | def orientnii(imfile):
'''Get the orientation from NIfTI sform. Not fully functional yet.'''
strorient = ['L-R', 'S-I', 'A-P']
niiorient = []
niixyz = np.zeros(3,dtype=np.int8)
if os.path.isfile(imfile):
nim = nib.load(imfile)
pct = nim.get_data()
A = nim.get_sform()
for i in range(3):
niixyz[i] = np.argmax(abs(A[i,:-1]))
niiorient.append( strorient[ niixyz[i] ] )
print niiorient | [
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HazyResearch/metal | metal/utils.py | convert_labels | def convert_labels(Y, source, dest):
"""Convert a matrix from one label type to another
Args:
Y: A np.ndarray or torch.Tensor of labels (ints)
source: The convention the labels are currently expressed in
dest: The convention to convert the labels to
Conventions:
'categorical': [0: abstain, 1: positive, 2: negative]
'plusminus': [0: abstain, 1: positive, -1: negative]
'onezero': [0: negative, 1: positive]
Note that converting to 'onezero' will combine abstain and negative labels.
"""
if Y is None:
return Y
if isinstance(Y, np.ndarray):
Y = Y.copy()
assert isinstance(Y, int)
elif isinstance(Y, torch.Tensor):
Y = Y.clone()
assert np.sum(Y.numpy() - Y.numpy().astype(int)) == 0.0
else:
raise ValueError("Unrecognized label data type.")
negative_map = {"categorical": 2, "plusminus": -1, "onezero": 0}
Y[Y == negative_map[source]] = negative_map[dest]
return Y | python | def convert_labels(Y, source, dest):
"""Convert a matrix from one label type to another
Args:
Y: A np.ndarray or torch.Tensor of labels (ints)
source: The convention the labels are currently expressed in
dest: The convention to convert the labels to
Conventions:
'categorical': [0: abstain, 1: positive, 2: negative]
'plusminus': [0: abstain, 1: positive, -1: negative]
'onezero': [0: negative, 1: positive]
Note that converting to 'onezero' will combine abstain and negative labels.
"""
if Y is None:
return Y
if isinstance(Y, np.ndarray):
Y = Y.copy()
assert isinstance(Y, int)
elif isinstance(Y, torch.Tensor):
Y = Y.clone()
assert np.sum(Y.numpy() - Y.numpy().astype(int)) == 0.0
else:
raise ValueError("Unrecognized label data type.")
negative_map = {"categorical": 2, "plusminus": -1, "onezero": 0}
Y[Y == negative_map[source]] = negative_map[dest]
return Y | [
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ethereum/web3.py | web3/gas_strategies/time_based.py | _compute_gas_price | def _compute_gas_price(probabilities, desired_probability):
"""
Given a sorted range of ``Probability`` named-tuples returns a gas price
computed based on where the ``desired_probability`` would fall within the
range.
:param probabilities: An iterable of `Probability` named-tuples sorted in reverse order.
:param desired_probability: An floating point representation of the desired
probability. (e.g. ``85% -> 0.85``)
"""
first = probabilities[0]
last = probabilities[-1]
if desired_probability >= first.prob:
return int(first.gas_price)
elif desired_probability <= last.prob:
return int(last.gas_price)
for left, right in sliding_window(2, probabilities):
if desired_probability < right.prob:
continue
elif desired_probability > left.prob:
# This code block should never be reachable as it would indicate
# that we already passed by the probability window in which our
# `desired_probability` is located.
raise Exception('Invariant')
adj_prob = desired_probability - right.prob
window_size = left.prob - right.prob
position = adj_prob / window_size
gas_window_size = left.gas_price - right.gas_price
gas_price = int(math.ceil(right.gas_price + gas_window_size * position))
return gas_price
else:
# The initial `if/else` clause in this function handles the case where
# the `desired_probability` is either above or below the min/max
# probability found in the `probabilities`.
#
# With these two cases handled, the only way this code block should be
# reachable would be if the `probabilities` were not sorted correctly.
# Otherwise, the `desired_probability` **must** fall between two of the
# values in the `probabilities``.
raise Exception('Invariant') | python | def _compute_gas_price(probabilities, desired_probability):
"""
Given a sorted range of ``Probability`` named-tuples returns a gas price
computed based on where the ``desired_probability`` would fall within the
range.
:param probabilities: An iterable of `Probability` named-tuples sorted in reverse order.
:param desired_probability: An floating point representation of the desired
probability. (e.g. ``85% -> 0.85``)
"""
first = probabilities[0]
last = probabilities[-1]
if desired_probability >= first.prob:
return int(first.gas_price)
elif desired_probability <= last.prob:
return int(last.gas_price)
for left, right in sliding_window(2, probabilities):
if desired_probability < right.prob:
continue
elif desired_probability > left.prob:
# This code block should never be reachable as it would indicate
# that we already passed by the probability window in which our
# `desired_probability` is located.
raise Exception('Invariant')
adj_prob = desired_probability - right.prob
window_size = left.prob - right.prob
position = adj_prob / window_size
gas_window_size = left.gas_price - right.gas_price
gas_price = int(math.ceil(right.gas_price + gas_window_size * position))
return gas_price
else:
# The initial `if/else` clause in this function handles the case where
# the `desired_probability` is either above or below the min/max
# probability found in the `probabilities`.
#
# With these two cases handled, the only way this code block should be
# reachable would be if the `probabilities` were not sorted correctly.
# Otherwise, the `desired_probability` **must** fall between two of the
# values in the `probabilities``.
raise Exception('Invariant') | [
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gabstopper/smc-python | smc-monitoring/smc_monitoring/models/filters.py | TranslatedFilter.within_ipv4_network | def within_ipv4_network(self, field, values):
"""
This filter adds specified networks to a filter to check
for inclusion.
:param str field: name of field to filter on. Taken from 'Show Filter
Expression' within SMC.
:param list values: network definitions, in cidr format, i.e: 1.1.1.0/24.
"""
v = ['ipv4_net("%s")' % net for net in values]
self.update_filter('{} IN union({})'.format(field, ','.join(v))) | python | def within_ipv4_network(self, field, values):
"""
This filter adds specified networks to a filter to check
for inclusion.
:param str field: name of field to filter on. Taken from 'Show Filter
Expression' within SMC.
:param list values: network definitions, in cidr format, i.e: 1.1.1.0/24.
"""
v = ['ipv4_net("%s")' % net for net in values]
self.update_filter('{} IN union({})'.format(field, ','.join(v))) | [
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szastupov/aiotg | aiotg/chat.py | Chat.kick_chat_member | def kick_chat_member(self, user_id):
"""
Use this method to kick a user from a group or a supergroup.
The bot must be an administrator in the group for this to work.
:param int user_id: Unique identifier of the target user
"""
return self.bot.api_call("kickChatMember", chat_id=self.id, user_id=user_id) | python | def kick_chat_member(self, user_id):
"""
Use this method to kick a user from a group or a supergroup.
The bot must be an administrator in the group for this to work.
:param int user_id: Unique identifier of the target user
"""
return self.bot.api_call("kickChatMember", chat_id=self.id, user_id=user_id) | [
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dw/mitogen | mitogen/parent.py | Context.call_no_reply | def call_no_reply(self, fn, *args, **kwargs):
"""
See :meth:`CallChain.call_no_reply`.
"""
self.default_call_chain.call_no_reply(fn, *args, **kwargs) | python | def call_no_reply(self, fn, *args, **kwargs):
"""
See :meth:`CallChain.call_no_reply`.
"""
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sebp/scikit-survival | sksurv/meta/stacking.py | Stacking.predict | def predict(self, X):
"""Perform prediction.
Only available of the meta estimator has a predict method.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data with samples to predict.
Returns
-------
prediction : array, shape = (n_samples, n_dim)
Prediction of meta estimator that combines
predictions of base estimators. `n_dim` depends
on the return value of meta estimator's `predict`
method.
"""
X = numpy.asarray(X)
Xt = self._predict_estimators(X)
return self.meta_estimator.predict(Xt) | python | def predict(self, X):
"""Perform prediction.
Only available of the meta estimator has a predict method.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
Data with samples to predict.
Returns
-------
prediction : array, shape = (n_samples, n_dim)
Prediction of meta estimator that combines
predictions of base estimators. `n_dim` depends
on the return value of meta estimator's `predict`
method.
"""
X = numpy.asarray(X)
Xt = self._predict_estimators(X)
return self.meta_estimator.predict(Xt) | [
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numberoverzero/bloop | bloop/search.py | Search.prepare | def prepare(self):
"""Constructs a :class:`~bloop.search.PreparedSearch`."""
p = PreparedSearch()
p.prepare(
engine=self.engine,
mode=self.mode,
model=self.model,
index=self.index,
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filter=self.filter,
projection=self.projection,
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forward=self.forward,
parallel=self.parallel
)
return p | python | def prepare(self):
"""Constructs a :class:`~bloop.search.PreparedSearch`."""
p = PreparedSearch()
p.prepare(
engine=self.engine,
mode=self.mode,
model=self.model,
index=self.index,
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allenai/allennlp | allennlp/semparse/domain_languages/domain_language.py | DomainLanguage.logical_form_to_action_sequence | def logical_form_to_action_sequence(self, logical_form: str) -> List[str]:
"""
Converts a logical form into a linearization of the production rules from its abstract
syntax tree. The linearization is top-down, depth-first.
Each production rule is formatted as "LHS -> RHS", where "LHS" is a single non-terminal
type, and RHS is either a terminal or a list of non-terminals (other possible values for
RHS in a more general context-free grammar are not produced by our grammar induction
logic).
Non-terminals are `types` in the grammar, either basic types (like ``int``, ``str``, or
some class that you define), or functional types, represented with angle brackets with a
colon separating arguments from the return type. Multi-argument functions have commas
separating their argument types. For example, ``<int:int>`` is a function that takes an
integer and returns an integer, and ``<int,int:int>`` is a function that takes two integer
arguments and returns an integer.
As an example translation from logical form to complete action sequence, the logical form
``(add 2 3)`` would be translated to ``['@start@ -> int', 'int -> [<int,int:int>, int, int]',
'<int,int:int> -> add', 'int -> 2', 'int -> 3']``.
"""
expression = util.lisp_to_nested_expression(logical_form)
try:
transitions, start_type = self._get_transitions(expression, expected_type=None)
if self._start_types and start_type not in self._start_types:
raise ParsingError(f"Expression had unallowed start type of {start_type}: {expression}")
except ParsingError:
logger.error(f'Error parsing logical form: {logical_form}')
raise
transitions.insert(0, f'@start@ -> {start_type}')
return transitions | python | def logical_form_to_action_sequence(self, logical_form: str) -> List[str]:
"""
Converts a logical form into a linearization of the production rules from its abstract
syntax tree. The linearization is top-down, depth-first.
Each production rule is formatted as "LHS -> RHS", where "LHS" is a single non-terminal
type, and RHS is either a terminal or a list of non-terminals (other possible values for
RHS in a more general context-free grammar are not produced by our grammar induction
logic).
Non-terminals are `types` in the grammar, either basic types (like ``int``, ``str``, or
some class that you define), or functional types, represented with angle brackets with a
colon separating arguments from the return type. Multi-argument functions have commas
separating their argument types. For example, ``<int:int>`` is a function that takes an
integer and returns an integer, and ``<int,int:int>`` is a function that takes two integer
arguments and returns an integer.
As an example translation from logical form to complete action sequence, the logical form
``(add 2 3)`` would be translated to ``['@start@ -> int', 'int -> [<int,int:int>, int, int]',
'<int,int:int> -> add', 'int -> 2', 'int -> 3']``.
"""
expression = util.lisp_to_nested_expression(logical_form)
try:
transitions, start_type = self._get_transitions(expression, expected_type=None)
if self._start_types and start_type not in self._start_types:
raise ParsingError(f"Expression had unallowed start type of {start_type}: {expression}")
except ParsingError:
logger.error(f'Error parsing logical form: {logical_form}')
raise
transitions.insert(0, f'@start@ -> {start_type}')
return transitions | [
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dh1tw/pyhamtools | pyhamtools/lookuplib.py | LookupLib._check_inv_operation_for_date | def _check_inv_operation_for_date(self, item, timestamp, data_dict, data_index_dict):
"""
Checks if the callsign is marked as an invalid operation for a given timestamp.
In case the operation is invalid, True is returned. Otherwise a KeyError is raised.
"""
if item in data_index_dict:
for item in data_index_dict[item]:
# startdate < timestamp
if const.START in data_dict[item] and not const.END in data_dict[item]:
if data_dict[item][const.START] < timestamp:
return True
# enddate > timestamp
elif not const.START in data_dict[item] and const.END in data_dict[item]:
if data_dict[item][const.END] > timestamp:
return True
# startdate > timestamp > enddate
elif const.START in data_dict[item] and const.END in data_dict[item]:
if data_dict[item][const.START] < timestamp \
and data_dict[item][const.END] > timestamp:
return True
# no startdate or enddate available
elif not const.START in data_dict[item] and not const.END in data_dict[item]:
return True
raise KeyError | python | def _check_inv_operation_for_date(self, item, timestamp, data_dict, data_index_dict):
"""
Checks if the callsign is marked as an invalid operation for a given timestamp.
In case the operation is invalid, True is returned. Otherwise a KeyError is raised.
"""
if item in data_index_dict:
for item in data_index_dict[item]:
# startdate < timestamp
if const.START in data_dict[item] and not const.END in data_dict[item]:
if data_dict[item][const.START] < timestamp:
return True
# enddate > timestamp
elif not const.START in data_dict[item] and const.END in data_dict[item]:
if data_dict[item][const.END] > timestamp:
return True
# startdate > timestamp > enddate
elif const.START in data_dict[item] and const.END in data_dict[item]:
if data_dict[item][const.START] < timestamp \
and data_dict[item][const.END] > timestamp:
return True
# no startdate or enddate available
elif not const.START in data_dict[item] and not const.END in data_dict[item]:
return True
raise KeyError | [
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neon-jungle/wagtailvideos | wagtailvideos/models.py | get_local_file | def get_local_file(file):
"""
Get a local version of the file, downloading it from the remote storage if
required. The returned value should be used as a context manager to
ensure any temporary files are cleaned up afterwards.
"""
try:
with open(file.path):
yield file.path
except NotImplementedError:
_, ext = os.path.splitext(file.name)
with NamedTemporaryFile(prefix='wagtailvideo-', suffix=ext) as tmp:
try:
file.open('rb')
for chunk in file.chunks():
tmp.write(chunk)
finally:
file.close()
tmp.flush()
yield tmp.name | python | def get_local_file(file):
"""
Get a local version of the file, downloading it from the remote storage if
required. The returned value should be used as a context manager to
ensure any temporary files are cleaned up afterwards.
"""
try:
with open(file.path):
yield file.path
except NotImplementedError:
_, ext = os.path.splitext(file.name)
with NamedTemporaryFile(prefix='wagtailvideo-', suffix=ext) as tmp:
try:
file.open('rb')
for chunk in file.chunks():
tmp.write(chunk)
finally:
file.close()
tmp.flush()
yield tmp.name | [
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tensorflow/tensorboard | tensorboard/plugins/hparams/summary.py | session_start_pb | def session_start_pb(hparams,
model_uri='',
monitor_url='',
group_name='',
start_time_secs=None):
"""Constructs a SessionStartInfo protobuffer.
Creates a summary that contains a training session metadata information.
One such summary per training session should be created. Each should have
a different run.
Args:
hparams: A dictionary with string keys. Describes the hyperparameter values
used in the session, mapping each hyperparameter name to its value.
Supported value types are `bool`, `int`, `float`, `str`, `list`,
`tuple`.
The type of value must correspond to the type of hyperparameter
(defined in the corresponding api_pb2.HParamInfo member of the
Experiment protobuf) as follows:
+-----------------+---------------------------------+
|Hyperparameter | Allowed (Python) value types |
|type | |
+-----------------+---------------------------------+
|DATA_TYPE_BOOL | bool |
|DATA_TYPE_FLOAT64| int, float |
|DATA_TYPE_STRING | six.string_types, tuple, list |
+-----------------+---------------------------------+
Tuple and list instances will be converted to their string
representation.
model_uri: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
monitor_url: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
group_name: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
start_time_secs: float. The time to use as the session start time.
Represented as seconds since the UNIX epoch. If None uses
the current time.
Returns:
The summary protobuffer mentioned above.
"""
if start_time_secs is None:
start_time_secs = time.time()
session_start_info = plugin_data_pb2.SessionStartInfo(
model_uri=model_uri,
monitor_url=monitor_url,
group_name=group_name,
start_time_secs=start_time_secs)
for (hp_name, hp_val) in six.iteritems(hparams):
if isinstance(hp_val, (float, int)):
session_start_info.hparams[hp_name].number_value = hp_val
elif isinstance(hp_val, six.string_types):
session_start_info.hparams[hp_name].string_value = hp_val
elif isinstance(hp_val, bool):
session_start_info.hparams[hp_name].bool_value = hp_val
elif isinstance(hp_val, (list, tuple)):
session_start_info.hparams[hp_name].string_value = str(hp_val)
else:
raise TypeError('hparams[%s]=%s has type: %s which is not supported' %
(hp_name, hp_val, type(hp_val)))
return _summary(metadata.SESSION_START_INFO_TAG,
plugin_data_pb2.HParamsPluginData(
session_start_info=session_start_info)) | python | def session_start_pb(hparams,
model_uri='',
monitor_url='',
group_name='',
start_time_secs=None):
"""Constructs a SessionStartInfo protobuffer.
Creates a summary that contains a training session metadata information.
One such summary per training session should be created. Each should have
a different run.
Args:
hparams: A dictionary with string keys. Describes the hyperparameter values
used in the session, mapping each hyperparameter name to its value.
Supported value types are `bool`, `int`, `float`, `str`, `list`,
`tuple`.
The type of value must correspond to the type of hyperparameter
(defined in the corresponding api_pb2.HParamInfo member of the
Experiment protobuf) as follows:
+-----------------+---------------------------------+
|Hyperparameter | Allowed (Python) value types |
|type | |
+-----------------+---------------------------------+
|DATA_TYPE_BOOL | bool |
|DATA_TYPE_FLOAT64| int, float |
|DATA_TYPE_STRING | six.string_types, tuple, list |
+-----------------+---------------------------------+
Tuple and list instances will be converted to their string
representation.
model_uri: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
monitor_url: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
group_name: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
start_time_secs: float. The time to use as the session start time.
Represented as seconds since the UNIX epoch. If None uses
the current time.
Returns:
The summary protobuffer mentioned above.
"""
if start_time_secs is None:
start_time_secs = time.time()
session_start_info = plugin_data_pb2.SessionStartInfo(
model_uri=model_uri,
monitor_url=monitor_url,
group_name=group_name,
start_time_secs=start_time_secs)
for (hp_name, hp_val) in six.iteritems(hparams):
if isinstance(hp_val, (float, int)):
session_start_info.hparams[hp_name].number_value = hp_val
elif isinstance(hp_val, six.string_types):
session_start_info.hparams[hp_name].string_value = hp_val
elif isinstance(hp_val, bool):
session_start_info.hparams[hp_name].bool_value = hp_val
elif isinstance(hp_val, (list, tuple)):
session_start_info.hparams[hp_name].string_value = str(hp_val)
else:
raise TypeError('hparams[%s]=%s has type: %s which is not supported' %
(hp_name, hp_val, type(hp_val)))
return _summary(metadata.SESSION_START_INFO_TAG,
plugin_data_pb2.HParamsPluginData(
session_start_info=session_start_info)) | [
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Creates a summary that contains a training session metadata information.
One such summary per training session should be created. Each should have
a different run.
Args:
hparams: A dictionary with string keys. Describes the hyperparameter values
used in the session, mapping each hyperparameter name to its value.
Supported value types are `bool`, `int`, `float`, `str`, `list`,
`tuple`.
The type of value must correspond to the type of hyperparameter
(defined in the corresponding api_pb2.HParamInfo member of the
Experiment protobuf) as follows:
+-----------------+---------------------------------+
|Hyperparameter | Allowed (Python) value types |
|type | |
+-----------------+---------------------------------+
|DATA_TYPE_BOOL | bool |
|DATA_TYPE_FLOAT64| int, float |
|DATA_TYPE_STRING | six.string_types, tuple, list |
+-----------------+---------------------------------+
Tuple and list instances will be converted to their string
representation.
model_uri: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
monitor_url: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
group_name: See the comment for the field with the same name of
plugin_data_pb2.SessionStartInfo.
start_time_secs: float. The time to use as the session start time.
Represented as seconds since the UNIX epoch. If None uses
the current time.
Returns:
The summary protobuffer mentioned above. | [
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] | train | https://github.com/tensorflow/tensorboard/blob/8e5f497b48e40f2a774f85416b8a35ac0693c35e/tensorboard/plugins/hparams/summary.py#L83-L147 | 0.003802 |
wglass/lighthouse | lighthouse/configs/monitor.py | ConfigFileMonitor.start | def start(self, on_add, on_update, on_delete):
"""
Starts monitoring the file path, passing along on_(add|update|delete)
callbacks to a watchdog observer.
Iterates over the files in the target path before starting the observer
and calls the on_created callback before starting the observer, so
that existing files aren't missed.
"""
handler = ConfigFileChangeHandler(
self.target_class, on_add, on_update, on_delete
)
for file_name in os.listdir(self.file_path):
if os.path.isdir(os.path.join(self.file_path, file_name)):
continue
if (
not self.target_class.config_subdirectory and
not (
file_name.endswith(".yaml") or file_name.endswith(".yml")
)
):
continue
handler.on_created(
events.FileCreatedEvent(
os.path.join(self.file_path, file_name)
)
)
observer = observers.Observer()
observer.schedule(handler, self.file_path)
observer.start()
return observer | python | def start(self, on_add, on_update, on_delete):
"""
Starts monitoring the file path, passing along on_(add|update|delete)
callbacks to a watchdog observer.
Iterates over the files in the target path before starting the observer
and calls the on_created callback before starting the observer, so
that existing files aren't missed.
"""
handler = ConfigFileChangeHandler(
self.target_class, on_add, on_update, on_delete
)
for file_name in os.listdir(self.file_path):
if os.path.isdir(os.path.join(self.file_path, file_name)):
continue
if (
not self.target_class.config_subdirectory and
not (
file_name.endswith(".yaml") or file_name.endswith(".yml")
)
):
continue
handler.on_created(
events.FileCreatedEvent(
os.path.join(self.file_path, file_name)
)
)
observer = observers.Observer()
observer.schedule(handler, self.file_path)
observer.start()
return observer | [
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GPflow/GPflow | gpflow/conditionals.py | _conditional | def _conditional(Xnew, X, kern, f, *, full_cov=False, q_sqrt=None, white=False, full_output_cov=None):
"""
Given f, representing the GP at the points X, produce the mean and
(co-)variance of the GP at the points Xnew.
Additionally, there may be Gaussian uncertainty about f as represented by
q_sqrt. In this case `f` represents the mean of the distribution and
q_sqrt the square-root of the covariance.
Additionally, the GP may have been centered (whitened) so that
p(v) = N(0, I)
f = L v
thus
p(f) = N(0, LL^T) = N(0, K).
In this case `f` represents the values taken by v.
The method can either return the diagonals of the covariance matrix for
each output (default) or the full covariance matrix (full_cov=True).
We assume R independent GPs, represented by the columns of f (and the
first dimension of q_sqrt).
:param Xnew: data matrix, size N x D. Evaluate the GP at these new points
:param X: data points, size M x D.
:param kern: GPflow kernel.
:param f: data matrix, M x R, representing the function values at X,
for R functions.
:param q_sqrt: matrix of standard-deviations or Cholesky matrices,
size M x R or R x M x M.
:param white: boolean of whether to use the whitened representation as
described above.
:return:
- mean: N x R
- variance: N x R (full_cov = False), R x N x N (full_cov = True)
"""
logger.debug("Conditional: Kernel")
num_data = tf.shape(X)[-2] # M
Kmm = kern.K(X) + tf.eye(num_data, dtype=settings.float_type) * settings.numerics.jitter_level # [..., M, M]
Kmn = kern.K(X, Xnew) # [M, ..., N]
if full_cov:
Knn = kern.K(Xnew) # [...,N,N]
else:
Knn = kern.Kdiag(Xnew) # [...,N]
mean, var = base_conditional(Kmn, Kmm, Knn, f, full_cov=full_cov, q_sqrt=q_sqrt, white=white)
return mean, var | python | def _conditional(Xnew, X, kern, f, *, full_cov=False, q_sqrt=None, white=False, full_output_cov=None):
"""
Given f, representing the GP at the points X, produce the mean and
(co-)variance of the GP at the points Xnew.
Additionally, there may be Gaussian uncertainty about f as represented by
q_sqrt. In this case `f` represents the mean of the distribution and
q_sqrt the square-root of the covariance.
Additionally, the GP may have been centered (whitened) so that
p(v) = N(0, I)
f = L v
thus
p(f) = N(0, LL^T) = N(0, K).
In this case `f` represents the values taken by v.
The method can either return the diagonals of the covariance matrix for
each output (default) or the full covariance matrix (full_cov=True).
We assume R independent GPs, represented by the columns of f (and the
first dimension of q_sqrt).
:param Xnew: data matrix, size N x D. Evaluate the GP at these new points
:param X: data points, size M x D.
:param kern: GPflow kernel.
:param f: data matrix, M x R, representing the function values at X,
for R functions.
:param q_sqrt: matrix of standard-deviations or Cholesky matrices,
size M x R or R x M x M.
:param white: boolean of whether to use the whitened representation as
described above.
:return:
- mean: N x R
- variance: N x R (full_cov = False), R x N x N (full_cov = True)
"""
logger.debug("Conditional: Kernel")
num_data = tf.shape(X)[-2] # M
Kmm = kern.K(X) + tf.eye(num_data, dtype=settings.float_type) * settings.numerics.jitter_level # [..., M, M]
Kmn = kern.K(X, Xnew) # [M, ..., N]
if full_cov:
Knn = kern.K(Xnew) # [...,N,N]
else:
Knn = kern.Kdiag(Xnew) # [...,N]
mean, var = base_conditional(Kmn, Kmm, Knn, f, full_cov=full_cov, q_sqrt=q_sqrt, white=white)
return mean, var | [
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bwohlberg/sporco | sporco/admm/parcbpdn.py | par_y1step | def par_y1step(i):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}_{1,G_i}`, one of the disjoint problems of
optimizing :math:`\mathbf{y}_1`.
Parameters
----------
i : int
Index of grouping to update
"""
global mp_Y1
grpind = slice(mp_grp[i], mp_grp[i+1])
XU1 = mp_X[grpind] + 1/mp_alpha*mp_U1[grpind]
if mp_wl1.shape[mp_axisM] is 1:
gamma = mp_lmbda/(mp_alpha**2*mp_rho)*mp_wl1
else:
gamma = mp_lmbda/(mp_alpha**2*mp_rho)*mp_wl1[grpind]
Y1 = sp.prox_l1(XU1, gamma)
if mp_NonNegCoef:
Y1[Y1 < 0.0] = 0.0
if mp_NoBndryCross:
for n in range(len(mp_Nv)):
Y1[(slice(None),) + (slice(None),)*n +
(slice(1-mp_Dshp[n], None),)] = 0.0
mp_Y1[mp_grp[i]:mp_grp[i+1]] = Y1 | python | def par_y1step(i):
r"""Minimise Augmented Lagrangian with respect to
:math:`\mathbf{y}_{1,G_i}`, one of the disjoint problems of
optimizing :math:`\mathbf{y}_1`.
Parameters
----------
i : int
Index of grouping to update
"""
global mp_Y1
grpind = slice(mp_grp[i], mp_grp[i+1])
XU1 = mp_X[grpind] + 1/mp_alpha*mp_U1[grpind]
if mp_wl1.shape[mp_axisM] is 1:
gamma = mp_lmbda/(mp_alpha**2*mp_rho)*mp_wl1
else:
gamma = mp_lmbda/(mp_alpha**2*mp_rho)*mp_wl1[grpind]
Y1 = sp.prox_l1(XU1, gamma)
if mp_NonNegCoef:
Y1[Y1 < 0.0] = 0.0
if mp_NoBndryCross:
for n in range(len(mp_Nv)):
Y1[(slice(None),) + (slice(None),)*n +
(slice(1-mp_Dshp[n], None),)] = 0.0
mp_Y1[mp_grp[i]:mp_grp[i+1]] = Y1 | [
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Parameters
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i : int
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datajoint/datajoint-python | datajoint/heading.py | Heading.project | def project(self, attribute_list, named_attributes=None, force_primary_key=None):
"""
derive a new heading by selecting, renaming, or computing attributes.
In relational algebra these operators are known as project, rename, and extend.
:param attribute_list: the full list of existing attributes to include
:param force_primary_key: attributes to force to be converted to primary
:param named_attributes: dictionary of renamed attributes
"""
try: # check for missing attributes
raise DataJointError('Attribute `%s` is not found' % next(a for a in attribute_list if a not in self.names))
except StopIteration:
if named_attributes is None:
named_attributes = {}
if force_primary_key is None:
force_primary_key = set()
rename_map = {v: k for k, v in named_attributes.items() if v in self.attributes}
# copied and renamed attributes
copy_attrs = (dict(self.attributes[k].todict(),
in_key=self.attributes[k].in_key or k in force_primary_key,
**({'name': rename_map[k], 'sql_expression': '`%s`' % k} if k in rename_map else {}))
for k in self.attributes if k in rename_map or k in attribute_list)
compute_attrs = (dict(default_attribute_properties, name=new_name, sql_expression=expr)
for new_name, expr in named_attributes.items() if expr not in rename_map)
return Heading(chain(copy_attrs, compute_attrs)) | python | def project(self, attribute_list, named_attributes=None, force_primary_key=None):
"""
derive a new heading by selecting, renaming, or computing attributes.
In relational algebra these operators are known as project, rename, and extend.
:param attribute_list: the full list of existing attributes to include
:param force_primary_key: attributes to force to be converted to primary
:param named_attributes: dictionary of renamed attributes
"""
try: # check for missing attributes
raise DataJointError('Attribute `%s` is not found' % next(a for a in attribute_list if a not in self.names))
except StopIteration:
if named_attributes is None:
named_attributes = {}
if force_primary_key is None:
force_primary_key = set()
rename_map = {v: k for k, v in named_attributes.items() if v in self.attributes}
# copied and renamed attributes
copy_attrs = (dict(self.attributes[k].todict(),
in_key=self.attributes[k].in_key or k in force_primary_key,
**({'name': rename_map[k], 'sql_expression': '`%s`' % k} if k in rename_map else {}))
for k in self.attributes if k in rename_map or k in attribute_list)
compute_attrs = (dict(default_attribute_properties, name=new_name, sql_expression=expr)
for new_name, expr in named_attributes.items() if expr not in rename_map)
return Heading(chain(copy_attrs, compute_attrs)) | [
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In relational algebra these operators are known as project, rename, and extend.
:param attribute_list: the full list of existing attributes to include
:param force_primary_key: attributes to force to be converted to primary
:param named_attributes: dictionary of renamed attributes | [
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google/grr | grr/server/grr_response_server/data_stores/common.py | DatabaseDirectorySize | def DatabaseDirectorySize(root_path, extension):
"""Compute size (in bytes) and number of files of a file-based data store."""
directories = collections.deque([root_path])
total_size = 0
total_files = 0
while directories:
directory = directories.popleft()
try:
items = os.listdir(directory)
except OSError:
continue
for comp in items:
path = os.path.join(directory, comp)
try:
statinfo = os.lstat(path)
if stat.S_ISLNK(statinfo.st_mode):
continue
if stat.S_ISDIR(statinfo.st_mode):
directories.append(path)
elif stat.S_ISREG(statinfo.st_mode):
if comp.endswith(extension):
total_size += statinfo.st_size
total_files += 1
except OSError:
continue
return total_size, total_files | python | def DatabaseDirectorySize(root_path, extension):
"""Compute size (in bytes) and number of files of a file-based data store."""
directories = collections.deque([root_path])
total_size = 0
total_files = 0
while directories:
directory = directories.popleft()
try:
items = os.listdir(directory)
except OSError:
continue
for comp in items:
path = os.path.join(directory, comp)
try:
statinfo = os.lstat(path)
if stat.S_ISLNK(statinfo.st_mode):
continue
if stat.S_ISDIR(statinfo.st_mode):
directories.append(path)
elif stat.S_ISREG(statinfo.st_mode):
if comp.endswith(extension):
total_size += statinfo.st_size
total_files += 1
except OSError:
continue
return total_size, total_files | [
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openstack/networking-cisco | networking_cisco/apps/saf/server/services/firewall/native/fabric_setup_base.py | FabricBase.get_key_state | def get_key_state(self, status, state_dict):
"""Returns the key associated with the dict. """
for key, val in state_dict.items():
if val == status:
return key | python | def get_key_state(self, status, state_dict):
"""Returns the key associated with the dict. """
for key, val in state_dict.items():
if val == status:
return key | [
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castelao/oceansdb | oceansdb/cars.py | cars_profile | def cars_profile(filename, doy, latitude, longitude, depth):
"""
For now only the nearest value
For now only for one position, not an array of positions
longitude 0-360
"""
assert np.size(doy) == 1
assert np.size(latitude) == 1
assert np.size(longitude) == 1
#assert np.size(depth) == 1
assert (longitude >= 0) & (longitude <= 360)
assert depth >= 0
nc = netCDF4.Dataset(filename)
t = 2 * np.pi * doy/366
# Improve this. Optimize to get only necessary Z
Z = slice(0, nc['depth'].size)
I = np.absolute(nc['lat'][:] - latitude).argmin()
J = np.absolute(nc['lon'][:] - longitude).argmin()
# Not efficient, but it works
assert (nc['depth'][:64] == nc['depth_ann'][:]).all()
assert (nc['depth'][:55] == nc['depth_semiann'][:]).all()
value = nc['mean'][:, I, J]
value[:64] += nc['an_cos'][Z, I, J] * np.cos(t) + \
nc['an_sin'][:, I, J] * np.sin(t)
value[:55] += nc['sa_cos'][Z, I, J] * np.cos(2*t) + \
nc['sa_sin'][:, I, J] * np.sin(2*t)
value = value
output = {'depth': np.asanyarray(depth)}
from scipy.interpolate import griddata
output['value'] = griddata(nc['depth'][Z], value[Z], depth)
for v in ['std_dev']:
output[v] = griddata(nc['depth'][Z], nc[v][Z, I, J], depth)
return output | python | def cars_profile(filename, doy, latitude, longitude, depth):
"""
For now only the nearest value
For now only for one position, not an array of positions
longitude 0-360
"""
assert np.size(doy) == 1
assert np.size(latitude) == 1
assert np.size(longitude) == 1
#assert np.size(depth) == 1
assert (longitude >= 0) & (longitude <= 360)
assert depth >= 0
nc = netCDF4.Dataset(filename)
t = 2 * np.pi * doy/366
# Improve this. Optimize to get only necessary Z
Z = slice(0, nc['depth'].size)
I = np.absolute(nc['lat'][:] - latitude).argmin()
J = np.absolute(nc['lon'][:] - longitude).argmin()
# Not efficient, but it works
assert (nc['depth'][:64] == nc['depth_ann'][:]).all()
assert (nc['depth'][:55] == nc['depth_semiann'][:]).all()
value = nc['mean'][:, I, J]
value[:64] += nc['an_cos'][Z, I, J] * np.cos(t) + \
nc['an_sin'][:, I, J] * np.sin(t)
value[:55] += nc['sa_cos'][Z, I, J] * np.cos(2*t) + \
nc['sa_sin'][:, I, J] * np.sin(2*t)
value = value
output = {'depth': np.asanyarray(depth)}
from scipy.interpolate import griddata
output['value'] = griddata(nc['depth'][Z], value[Z], depth)
for v in ['std_dev']:
output[v] = griddata(nc['depth'][Z], nc[v][Z, I, J], depth)
return output | [
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lrq3000/pyFileFixity | pyFileFixity/lib/profilers/visual/pympler/classtracker.py | ClassTracker.track_change | def track_change(self, instance, resolution_level=0):
"""
Change tracking options for the already tracked object 'instance'.
If instance is not tracked, a KeyError will be raised.
"""
tobj = self.objects[id(instance)]
tobj.set_resolution_level(resolution_level) | python | def track_change(self, instance, resolution_level=0):
"""
Change tracking options for the already tracked object 'instance'.
If instance is not tracked, a KeyError will be raised.
"""
tobj = self.objects[id(instance)]
tobj.set_resolution_level(resolution_level) | [
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maljovec/topopy | topopy/MorseSmaleComplex.py | MorseSmaleComplex.save | def save(self, filename=None):
""" Saves a constructed Morse-Smale Complex in json file
@ In, filename, a filename for storing the hierarchical
merging of features and the base level partitions of the
data
"""
if filename is None:
filename = "morse_smale_complex.json"
with open(filename, "w") as fp:
fp.write(self.to_json()) | python | def save(self, filename=None):
""" Saves a constructed Morse-Smale Complex in json file
@ In, filename, a filename for storing the hierarchical
merging of features and the base level partitions of the
data
"""
if filename is None:
filename = "morse_smale_complex.json"
with open(filename, "w") as fp:
fp.write(self.to_json()) | [
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bukun/TorCMS | helper_scripts/script_meta_xlsx_import.py | update_category | def update_category(uid, postdata, kwargs):
'''
Update the category of the post.
'''
catid = kwargs['catid'] if ('catid' in kwargs and MCategory.get_by_uid(kwargs['catid'])) else None
post_data = postdata
current_infos = MPost2Catalog.query_by_entity_uid(uid, kind='').objects()
new_category_arr = []
# Used to update post2category, to keep order.
def_cate_arr = ['gcat{0}'.format(x) for x in range(10)]
# for old page.
def_cate_arr.append('def_cat_uid')
# Used to update post extinfo.
cat_dic = {}
for key in def_cate_arr:
if key not in post_data:
continue
if post_data[key] == '' or post_data[key] == '0':
continue
# 有可能选重复了。保留前面的
if post_data[key] in new_category_arr:
continue
new_category_arr.append(post_data[key] + ' ' * (4 - len(post_data[key])))
cat_dic[key] = post_data[key] + ' ' * (4 - len(post_data[key]))
if catid:
def_cat_id = catid
elif new_category_arr:
def_cat_id = new_category_arr[0]
else:
def_cat_id = None
if def_cat_id:
cat_dic['def_cat_uid'] = def_cat_id
cat_dic['def_cat_pid'] = MCategory.get_by_uid(def_cat_id).pid
print('=' * 40)
print(uid)
print(cat_dic)
MPost.update_jsonb(uid, cat_dic)
for index, catid in enumerate(new_category_arr):
MPost2Catalog.add_record(uid, catid, index)
# Delete the old category if not in post requests.
for cur_info in current_infos:
if cur_info.tag_id not in new_category_arr:
MPost2Catalog.remove_relation(uid, cur_info.tag_id) | python | def update_category(uid, postdata, kwargs):
'''
Update the category of the post.
'''
catid = kwargs['catid'] if ('catid' in kwargs and MCategory.get_by_uid(kwargs['catid'])) else None
post_data = postdata
current_infos = MPost2Catalog.query_by_entity_uid(uid, kind='').objects()
new_category_arr = []
# Used to update post2category, to keep order.
def_cate_arr = ['gcat{0}'.format(x) for x in range(10)]
# for old page.
def_cate_arr.append('def_cat_uid')
# Used to update post extinfo.
cat_dic = {}
for key in def_cate_arr:
if key not in post_data:
continue
if post_data[key] == '' or post_data[key] == '0':
continue
# 有可能选重复了。保留前面的
if post_data[key] in new_category_arr:
continue
new_category_arr.append(post_data[key] + ' ' * (4 - len(post_data[key])))
cat_dic[key] = post_data[key] + ' ' * (4 - len(post_data[key]))
if catid:
def_cat_id = catid
elif new_category_arr:
def_cat_id = new_category_arr[0]
else:
def_cat_id = None
if def_cat_id:
cat_dic['def_cat_uid'] = def_cat_id
cat_dic['def_cat_pid'] = MCategory.get_by_uid(def_cat_id).pid
print('=' * 40)
print(uid)
print(cat_dic)
MPost.update_jsonb(uid, cat_dic)
for index, catid in enumerate(new_category_arr):
MPost2Catalog.add_record(uid, catid, index)
# Delete the old category if not in post requests.
for cur_info in current_infos:
if cur_info.tag_id not in new_category_arr:
MPost2Catalog.remove_relation(uid, cur_info.tag_id) | [
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log2timeline/dfdatetime | dfdatetime/posix_time.py | PosixTime._GetNormalizedTimestamp | def _GetNormalizedTimestamp(self):
"""Retrieves the normalized timestamp.
Returns:
decimal.Decimal: normalized timestamp, which contains the number of
seconds since January 1, 1970 00:00:00 and a fraction of second used
for increased precision, or None if the normalized timestamp cannot be
determined.
"""
if self._normalized_timestamp is None:
if self._timestamp is not None:
self._normalized_timestamp = decimal.Decimal(self._timestamp)
return self._normalized_timestamp | python | def _GetNormalizedTimestamp(self):
"""Retrieves the normalized timestamp.
Returns:
decimal.Decimal: normalized timestamp, which contains the number of
seconds since January 1, 1970 00:00:00 and a fraction of second used
for increased precision, or None if the normalized timestamp cannot be
determined.
"""
if self._normalized_timestamp is None:
if self._timestamp is not None:
self._normalized_timestamp = decimal.Decimal(self._timestamp)
return self._normalized_timestamp | [
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JustinLovinger/optimal | optimal/optimize.py | _meta_fitness_func | def _meta_fitness_func(parameters,
_optimizer,
_problems,
_master_fitness_dict,
_runs=20):
"""Test a metaheuristic with parameters encoded in solution.
Our goal is to minimize number of evaluation runs until a solution is found,
while maximizing chance of finding solution to the underlying problem
NOTE: while meta optimization requires a 'known' solution, this solution
can be an estimate to provide the meta optimizer with a sense of progress.
"""
# Create the optimizer with parameters encoded in solution
optimizer = copy.deepcopy(_optimizer)
optimizer._set_hyperparameters(parameters)
optimizer.logging = False
# Preload fitness dictionary from master, and disable clearing dict
# NOTE: master_fitness_dict will be modified inline, and therefore,
# we do not need to take additional steps to update it
if _master_fitness_dict != None: # None means low memory mode
optimizer.clear_cache = False
optimizer._Optimizer__encoded_cache = _master_fitness_dict
# Because metaheuristics are stochastic, we run the optimizer multiple times,
# to obtain an average of performance
all_evaluation_runs = []
solutions_found = []
for _ in range(_runs):
for problem in _problems:
# Get performance for problem
optimizer.optimize(problem)
all_evaluation_runs.append(optimizer.fitness_runs)
if optimizer.solution_found:
solutions_found.append(1.0)
else:
solutions_found.append(0.0)
# Our main goal is to minimize time the optimizer takes
fitness = 1.0 / helpers.avg(all_evaluation_runs)
# Optimizer is heavily penalized for missing solutions
# To avoid 0 fitness
fitness = fitness * helpers.avg(solutions_found)**2 + 1e-19
return fitness | python | def _meta_fitness_func(parameters,
_optimizer,
_problems,
_master_fitness_dict,
_runs=20):
"""Test a metaheuristic with parameters encoded in solution.
Our goal is to minimize number of evaluation runs until a solution is found,
while maximizing chance of finding solution to the underlying problem
NOTE: while meta optimization requires a 'known' solution, this solution
can be an estimate to provide the meta optimizer with a sense of progress.
"""
# Create the optimizer with parameters encoded in solution
optimizer = copy.deepcopy(_optimizer)
optimizer._set_hyperparameters(parameters)
optimizer.logging = False
# Preload fitness dictionary from master, and disable clearing dict
# NOTE: master_fitness_dict will be modified inline, and therefore,
# we do not need to take additional steps to update it
if _master_fitness_dict != None: # None means low memory mode
optimizer.clear_cache = False
optimizer._Optimizer__encoded_cache = _master_fitness_dict
# Because metaheuristics are stochastic, we run the optimizer multiple times,
# to obtain an average of performance
all_evaluation_runs = []
solutions_found = []
for _ in range(_runs):
for problem in _problems:
# Get performance for problem
optimizer.optimize(problem)
all_evaluation_runs.append(optimizer.fitness_runs)
if optimizer.solution_found:
solutions_found.append(1.0)
else:
solutions_found.append(0.0)
# Our main goal is to minimize time the optimizer takes
fitness = 1.0 / helpers.avg(all_evaluation_runs)
# Optimizer is heavily penalized for missing solutions
# To avoid 0 fitness
fitness = fitness * helpers.avg(solutions_found)**2 + 1e-19
return fitness | [
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lextoumbourou/txstripe | txstripe/resource.py | APIResource.retrieve | def retrieve(cls, id, api_key=None, **params):
"""Return a deferred."""
instance = cls(id, api_key, **params)
d = instance.refresh()
return d.addCallback(lambda _: instance) | python | def retrieve(cls, id, api_key=None, **params):
"""Return a deferred."""
instance = cls(id, api_key, **params)
d = instance.refresh()
return d.addCallback(lambda _: instance) | [
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ska-sa/hypercube | hypercube/util/__init__.py | array_bytes | def array_bytes(array):
""" Estimates the memory of the supplied array in bytes """
return np.product(array.shape)*np.dtype(array.dtype).itemsize | python | def array_bytes(array):
""" Estimates the memory of the supplied array in bytes """
return np.product(array.shape)*np.dtype(array.dtype).itemsize | [
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ska-sa/katcp-python | katcp/core.py | MessageParser._unescape_match | def _unescape_match(self, match):
"""Given an re.Match, unescape the escape code it represents."""
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if char in self.ESCAPE_LOOKUP:
return self.ESCAPE_LOOKUP[char]
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raise KatcpSyntaxError("Invalid escape character %r." % (char,)) | python | def _unescape_match(self, match):
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char = match.group(1)
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objectrocket/python-client | objectrocket/acls.py | Acls.create | def create(self, instance, cidr_mask, description, **kwargs):
"""Create an ACL entry for the specified instance.
:param str instance: The name of the instance to associate the new ACL entry with.
:param str cidr_mask: The IPv4 CIDR mask for the new ACL entry.
:param str description: A short description for the new ACL entry.
:param collector kwargs: (optional) Additional key=value pairs to be supplied to the
creation payload. **Caution:** fields unrecognized by the API will cause this request
to fail with a 400 from the API.
"""
# Build up request data.
url = self._url.format(instance=instance)
request_data = {
'cidr_mask': cidr_mask,
'description': description
}
request_data.update(kwargs)
# Call to create an instance.
response = requests.post(
url,
data=json.dumps(request_data),
**self._default_request_kwargs
)
# Log outcome of instance creation request.
if response.status_code == 200:
logger.info('Successfully created a new ACL for instance {} with: {}.'
.format(instance, request_data))
else:
logger.info('Failed to create a new ACL for instance {} with: {}.'
.format(instance, request_data))
data = self._get_response_data(response)
return self._concrete_acl(data) | python | def create(self, instance, cidr_mask, description, **kwargs):
"""Create an ACL entry for the specified instance.
:param str instance: The name of the instance to associate the new ACL entry with.
:param str cidr_mask: The IPv4 CIDR mask for the new ACL entry.
:param str description: A short description for the new ACL entry.
:param collector kwargs: (optional) Additional key=value pairs to be supplied to the
creation payload. **Caution:** fields unrecognized by the API will cause this request
to fail with a 400 from the API.
"""
# Build up request data.
url = self._url.format(instance=instance)
request_data = {
'cidr_mask': cidr_mask,
'description': description
}
request_data.update(kwargs)
# Call to create an instance.
response = requests.post(
url,
data=json.dumps(request_data),
**self._default_request_kwargs
)
# Log outcome of instance creation request.
if response.status_code == 200:
logger.info('Successfully created a new ACL for instance {} with: {}.'
.format(instance, request_data))
else:
logger.info('Failed to create a new ACL for instance {} with: {}.'
.format(instance, request_data))
data = self._get_response_data(response)
return self._concrete_acl(data) | [
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softlayer/softlayer-python | SoftLayer/managers/network.py | NetworkManager.edit_rwhois | def edit_rwhois(self, abuse_email=None, address1=None, address2=None,
city=None, company_name=None, country=None,
first_name=None, last_name=None, postal_code=None,
private_residence=None, state=None):
"""Edit rwhois record."""
update = {}
for key, value in [('abuseEmail', abuse_email),
('address1', address1),
('address2', address2),
('city', city),
('companyName', company_name),
('country', country),
('firstName', first_name),
('lastName', last_name),
('privateResidenceFlag', private_residence),
('state', state),
('postalCode', postal_code)]:
if value is not None:
update[key] = value
# If there's anything to update, update it
if update:
rwhois = self.get_rwhois()
return self.client['Network_Subnet_Rwhois_Data'].editObject(
update, id=rwhois['id'])
return True | python | def edit_rwhois(self, abuse_email=None, address1=None, address2=None,
city=None, company_name=None, country=None,
first_name=None, last_name=None, postal_code=None,
private_residence=None, state=None):
"""Edit rwhois record."""
update = {}
for key, value in [('abuseEmail', abuse_email),
('address1', address1),
('address2', address2),
('city', city),
('companyName', company_name),
('country', country),
('firstName', first_name),
('lastName', last_name),
('privateResidenceFlag', private_residence),
('state', state),
('postalCode', postal_code)]:
if value is not None:
update[key] = value
# If there's anything to update, update it
if update:
rwhois = self.get_rwhois()
return self.client['Network_Subnet_Rwhois_Data'].editObject(
update, id=rwhois['id'])
return True | [
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ray-project/ray | python/ray/tune/examples/tune_mnist_async_hyperband.py | bias_variable | def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial) | python | def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial) | [
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bitly/asyncmongo | asyncmongo/cursor.py | Cursor.insert | def insert(self, doc_or_docs,
manipulate=True, safe=True, check_keys=True, callback=None, **kwargs):
"""Insert a document(s) into this collection.
If `manipulate` is set, the document(s) are manipulated using
any :class:`~pymongo.son_manipulator.SONManipulator` instances
that have been added to this
:class:`~pymongo.database.Database`. Returns the ``"_id"`` of
the inserted document or a list of ``"_id"`` values of the
inserted documents. If the document(s) does not already
contain an ``"_id"`` one will be added.
If `safe` is ``True`` then the insert will be checked for
errors, raising :class:`~pymongo.errors.OperationFailure` if
one occurred. Safe inserts wait for a response from the
database, while normal inserts do not.
Any additional keyword arguments imply ``safe=True``, and
will be used as options for the resultant `getLastError`
command. For example, to wait for replication to 3 nodes, pass
``w=3``.
:Parameters:
- `doc_or_docs`: a document or list of documents to be
inserted
- `manipulate` (optional): manipulate the documents before
inserting?
- `safe` (optional): check that the insert succeeded?
- `check_keys` (optional): check if keys start with '$' or
contain '.', raising :class:`~pymongo.errors.InvalidName`
in either case
- `**kwargs` (optional): any additional arguments imply
``safe=True``, and will be used as options for the
`getLastError` command
.. mongodoc:: insert
"""
if not isinstance(safe, bool):
raise TypeError("safe must be an instance of bool")
docs = doc_or_docs
# return_one = False
if isinstance(docs, dict):
# return_one = True
docs = [docs]
# if manipulate:
# docs = [self.__database._fix_incoming(doc, self) for doc in docs]
self.__limit = None
if kwargs:
safe = True
if safe and not callable(callback):
raise TypeError("callback must be callable")
if not safe and callback is not None:
raise TypeError("callback can not be used with safe=False")
if callback:
callback = functools.partial(self._handle_response, orig_callback=callback)
connection = self.__pool.connection()
try:
connection.send_message(
message.insert(self.full_collection_name, docs,
check_keys, safe, kwargs), callback=callback)
except:
connection.close()
raise | python | def insert(self, doc_or_docs,
manipulate=True, safe=True, check_keys=True, callback=None, **kwargs):
"""Insert a document(s) into this collection.
If `manipulate` is set, the document(s) are manipulated using
any :class:`~pymongo.son_manipulator.SONManipulator` instances
that have been added to this
:class:`~pymongo.database.Database`. Returns the ``"_id"`` of
the inserted document or a list of ``"_id"`` values of the
inserted documents. If the document(s) does not already
contain an ``"_id"`` one will be added.
If `safe` is ``True`` then the insert will be checked for
errors, raising :class:`~pymongo.errors.OperationFailure` if
one occurred. Safe inserts wait for a response from the
database, while normal inserts do not.
Any additional keyword arguments imply ``safe=True``, and
will be used as options for the resultant `getLastError`
command. For example, to wait for replication to 3 nodes, pass
``w=3``.
:Parameters:
- `doc_or_docs`: a document or list of documents to be
inserted
- `manipulate` (optional): manipulate the documents before
inserting?
- `safe` (optional): check that the insert succeeded?
- `check_keys` (optional): check if keys start with '$' or
contain '.', raising :class:`~pymongo.errors.InvalidName`
in either case
- `**kwargs` (optional): any additional arguments imply
``safe=True``, and will be used as options for the
`getLastError` command
.. mongodoc:: insert
"""
if not isinstance(safe, bool):
raise TypeError("safe must be an instance of bool")
docs = doc_or_docs
# return_one = False
if isinstance(docs, dict):
# return_one = True
docs = [docs]
# if manipulate:
# docs = [self.__database._fix_incoming(doc, self) for doc in docs]
self.__limit = None
if kwargs:
safe = True
if safe and not callable(callback):
raise TypeError("callback must be callable")
if not safe and callback is not None:
raise TypeError("callback can not be used with safe=False")
if callback:
callback = functools.partial(self._handle_response, orig_callback=callback)
connection = self.__pool.connection()
try:
connection.send_message(
message.insert(self.full_collection_name, docs,
check_keys, safe, kwargs), callback=callback)
except:
connection.close()
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- `manipulate` (optional): manipulate the documents before
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- `safe` (optional): check that the insert succeeded?
- `check_keys` (optional): check if keys start with '$' or
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- `**kwargs` (optional): any additional arguments imply
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Telefonica/toolium | toolium/utils.py | Utils.wait_until_element_not_contain_text | def wait_until_element_not_contain_text(self, element, text, timeout=None):
"""Search element and wait until it does not contain the expected text
:param element: PageElement or element locator as a tuple (locator_type, locator_value) to be found
:param text: text expected to be contained into the element
:param timeout: max time to wait
:returns: the web element if it does not contain the given text
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
:raises TimeoutException: If the element contains the expected text after the timeout
"""
return self._wait_until(self._expected_condition_find_element_not_containing_text, (element, text), timeout) | python | def wait_until_element_not_contain_text(self, element, text, timeout=None):
"""Search element and wait until it does not contain the expected text
:param element: PageElement or element locator as a tuple (locator_type, locator_value) to be found
:param text: text expected to be contained into the element
:param timeout: max time to wait
:returns: the web element if it does not contain the given text
:rtype: selenium.webdriver.remote.webelement.WebElement or appium.webdriver.webelement.WebElement
:raises TimeoutException: If the element contains the expected text after the timeout
"""
return self._wait_until(self._expected_condition_find_element_not_containing_text, (element, text), timeout) | [
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log2timeline/plaso | plaso/filters/file_entry.py | SignaturesFileEntryFilter.Print | def Print(self, output_writer):
"""Prints a human readable version of the filter.
Args:
output_writer (CLIOutputWriter): output writer.
"""
if self._file_scanner:
output_writer.Write('\tsignature identifiers: {0:s}\n'.format(
', '.join(self._signature_identifiers))) | python | def Print(self, output_writer):
"""Prints a human readable version of the filter.
Args:
output_writer (CLIOutputWriter): output writer.
"""
if self._file_scanner:
output_writer.Write('\tsignature identifiers: {0:s}\n'.format(
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apache/incubator-mxnet | example/svrg_module/api_usage_example/example_api_train.py | create_network | def create_network(batch_size, update_freq):
"""Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients
Returns
----------
di: mx.io.NDArrayIter
Data iterator
update_freq: SVRGModule
An instance of SVRGModule for performing SVRG optimization
"""
import logging
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.INFO, format=head)
train_data = np.random.randint(1, 5, [1000, 2])
weights = np.array([1.0, 2.0])
train_label = train_data.dot(weights)
di = mx.io.NDArrayIter(train_data, train_label, batch_size=batch_size, shuffle=True, label_name='lin_reg_label')
X = mx.sym.Variable('data')
Y = mx.symbol.Variable('lin_reg_label')
fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1)
lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
mod = SVRGModule(
symbol=lro,
data_names=['data'],
label_names=['lin_reg_label'], update_freq=update_freq, logger=logging
)
return di, mod | python | def create_network(batch_size, update_freq):
"""Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients
Returns
----------
di: mx.io.NDArrayIter
Data iterator
update_freq: SVRGModule
An instance of SVRGModule for performing SVRG optimization
"""
import logging
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.INFO, format=head)
train_data = np.random.randint(1, 5, [1000, 2])
weights = np.array([1.0, 2.0])
train_label = train_data.dot(weights)
di = mx.io.NDArrayIter(train_data, train_label, batch_size=batch_size, shuffle=True, label_name='lin_reg_label')
X = mx.sym.Variable('data')
Y = mx.symbol.Variable('lin_reg_label')
fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1)
lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
mod = SVRGModule(
symbol=lro,
data_names=['data'],
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10gen/mongo-orchestration | mongo_orchestration/process.py | PortPool.change_range | def change_range(self, min_port=1025, max_port=2000, port_sequence=None):
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"""change Pool port range"""
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aouyar/healthgraph-api | healthgraph/authmgr.py | AuthManager.revoke_access_token | def revoke_access_token(self, access_token):
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@param access_token: Access Token for querying Health Graph API.
"""
payload = {'access_token': access_token,}
req = requests.post(settings.API_DEAUTHORIZATION_URL, data=payload) | python | def revoke_access_token(self, access_token):
"""Revokes the Access Token by accessing the De-authorization Endpoint
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@param access_token: Access Token for querying Health Graph API.
"""
payload = {'access_token': access_token,}
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Rapptz/discord.py | discord/guild.py | Guild.unban | async def unban(self, user, *, reason=None):
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You must have the :attr:`~Permissions.ban_members` permission to
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The user to unban.
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You do not have the proper permissions to unban.
HTTPException
Unbanning failed.
"""
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Unbans a user from the guild.
The user must meet the :class:`abc.Snowflake` abc.
You must have the :attr:`~Permissions.ban_members` permission to
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user: :class:`abc.Snowflake`
The user to unban.
reason: Optional[:class:`str`]
The reason for doing this action. Shows up on the audit log.
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You do not have the proper permissions to unban.
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Unbanning failed.
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sbg/sevenbridges-python | sevenbridges/models/project.py | Project.add_files | def add_files(self, files):
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Adds files to this project.
:param files: List of files or a Collection object.
"""
for file in files:
file.copy(project=self.id) | python | def add_files(self, files):
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Adds files to this project.
:param files: List of files or a Collection object.
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PmagPy/PmagPy | programs/thellier_gui.py | Arai_GUI.on_menu_exit | def on_menu_exit(self, event):
"""
Runs whenever Thellier GUI exits
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self.Destroy()
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if self.evt_quit:
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self.GetEventHandler().ProcessEvent(event)
if self.standalone:
sys.exit()
else:
self.GUI_log.close()
self.Destroy()
# if a custom quit event is specified, fire it
if self.evt_quit:
event = self.evt_quit(self.GetId())
self.GetEventHandler().ProcessEvent(event)
if self.standalone:
sys.exit() | python | def on_menu_exit(self, event):
"""
Runs whenever Thellier GUI exits
"""
if self.close_warning:
TEXT = "Data is not saved to a file yet!\nTo properly save your data:\n1) Analysis --> Save current interpretations to a redo file.\nor\n1) File --> Save MagIC tables.\n\n Press OK to exit without saving."
dlg1 = wx.MessageDialog(
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if self.show_dlg(dlg1) == wx.ID_OK:
dlg1.Destroy()
self.GUI_log.close()
self.Destroy()
# if a custom quit event is specified, fire it
if self.evt_quit:
event = self.evt_quit(self.GetId())
self.GetEventHandler().ProcessEvent(event)
if self.standalone:
sys.exit()
else:
self.GUI_log.close()
self.Destroy()
# if a custom quit event is specified, fire it
if self.evt_quit:
event = self.evt_quit(self.GetId())
self.GetEventHandler().ProcessEvent(event)
if self.standalone:
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materialsproject/pymatgen | pymatgen/io/lammps/data.py | LammpsData.structure | def structure(self):
"""
Exports a periodic structure object representing the simulation
box.
Return:
Structure
"""
masses = self.masses
atoms = self.atoms.copy()
if "nx" in atoms.columns:
atoms.drop(["nx", "ny", "nz"], axis=1, inplace=True)
atoms["molecule-ID"] = 1
ld_copy = self.__class__(self.box, masses, atoms)
topologies = ld_copy.disassemble()[-1]
molecule = topologies[0].sites
coords = molecule.cart_coords - np.array(self.box.bounds)[:, 0]
species = molecule.species
latt = self.box.to_lattice()
site_properties = {}
if "q" in atoms:
site_properties["charge"] = atoms["q"].values
if self.velocities is not None:
site_properties["velocities"] = self.velocities.values
return Structure(latt, species, coords, coords_are_cartesian=True,
site_properties=site_properties) | python | def structure(self):
"""
Exports a periodic structure object representing the simulation
box.
Return:
Structure
"""
masses = self.masses
atoms = self.atoms.copy()
if "nx" in atoms.columns:
atoms.drop(["nx", "ny", "nz"], axis=1, inplace=True)
atoms["molecule-ID"] = 1
ld_copy = self.__class__(self.box, masses, atoms)
topologies = ld_copy.disassemble()[-1]
molecule = topologies[0].sites
coords = molecule.cart_coords - np.array(self.box.bounds)[:, 0]
species = molecule.species
latt = self.box.to_lattice()
site_properties = {}
if "q" in atoms:
site_properties["charge"] = atoms["q"].values
if self.velocities is not None:
site_properties["velocities"] = self.velocities.values
return Structure(latt, species, coords, coords_are_cartesian=True,
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OpenTreeOfLife/peyotl | peyotl/git_storage/git_action.py | get_HEAD_SHA1 | def get_HEAD_SHA1(git_dir):
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head_file = os.path.join(git_dir, 'HEAD')
with open(head_file, 'r') as hf:
head_contents = hf.read().strip()
assert head_contents.startswith('ref: ')
ref_filename = head_contents[5:] # strip off "ref: "
real_ref = os.path.join(git_dir, ref_filename)
with open(real_ref, 'r') as rf:
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"""Not locked!
"""
head_file = os.path.join(git_dir, 'HEAD')
with open(head_file, 'r') as hf:
head_contents = hf.read().strip()
assert head_contents.startswith('ref: ')
ref_filename = head_contents[5:] # strip off "ref: "
real_ref = os.path.join(git_dir, ref_filename)
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aio-libs/aioredis | aioredis/pool.py | ConnectionsPool.get_connection | def get_connection(self, command, args=()):
"""Get free connection from pool.
Returns connection.
"""
# TODO: find a better way to determine if connection is free
# and not havily used.
command = command.upper().strip()
is_pubsub = command in _PUBSUB_COMMANDS
if is_pubsub and self._pubsub_conn:
if not self._pubsub_conn.closed:
return self._pubsub_conn, self._pubsub_conn.address
self._pubsub_conn = None
for i in range(self.freesize):
conn = self._pool[0]
self._pool.rotate(1)
if conn.closed: # or conn._waiters: (eg: busy connection)
continue
if conn.in_pubsub:
continue
if is_pubsub:
self._pubsub_conn = conn
self._pool.remove(conn)
self._used.add(conn)
return conn, conn.address
return None, self._address | python | def get_connection(self, command, args=()):
"""Get free connection from pool.
Returns connection.
"""
# TODO: find a better way to determine if connection is free
# and not havily used.
command = command.upper().strip()
is_pubsub = command in _PUBSUB_COMMANDS
if is_pubsub and self._pubsub_conn:
if not self._pubsub_conn.closed:
return self._pubsub_conn, self._pubsub_conn.address
self._pubsub_conn = None
for i in range(self.freesize):
conn = self._pool[0]
self._pool.rotate(1)
if conn.closed: # or conn._waiters: (eg: busy connection)
continue
if conn.in_pubsub:
continue
if is_pubsub:
self._pubsub_conn = conn
self._pool.remove(conn)
self._used.add(conn)
return conn, conn.address
return None, self._address | [
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albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _fit_and_score | def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
fit_params, return_train_score=False,
return_parameters=False):
"""Fit estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like, optional, default: None
The target variable to try to predict in the case of
supervised learning.
scoring : callable
A scorer callable object / function with signature
``scorer(estimator, X, y)``.
train : array-like, shape = (n_train_samples,)
Indices of training samples.
test : array-like, shape = (n_test_samples,)
Indices of test samples.
verbose : integer
The verbosity level.
parameters : dict or None
Parameters to be set on the estimator.
fit_params : dict or None
Parameters that will be passed to ``estimator.fit``.
return_train_score : boolean, optional, default: False
Compute and return score on training set.
return_parameters : boolean, optional, default: False
Return parameters that has been used for the estimator.
Returns
-------
train_score : float, optional
Score on training set, returned only if `return_train_score` is `True`.
test_score : float
Score on test set.
n_test_samples : int
Number of test samples.
scoring_time : float
Time spent for fitting and scoring in seconds.
parameters : dict or None, optional
The parameters that have been evaluated.
"""
if verbose > 1:
if parameters is None:
msg = "no parameters to be set"
else:
msg = '%s' % (', '.join('%s=%s' % (k, v)
for k, v in list(parameters.items())))
print("[CV] %s %s" % (msg, (64 - len(msg)) * '.'))
# Adjust lenght of sample weights
n_samples = _num_samples(X)
fit_params = fit_params if fit_params is not None else {}
fit_params = dict([(k, np.asarray(v)[train]
if hasattr(v, '__len__') and len(v) == n_samples else v)
for k, v in list(fit_params.items())])
if parameters is not None:
estimator.set_params(**parameters)
start_time = time.time()
X_train, y_train = _safe_split(estimator, X, y, train)
X_test, y_test = _safe_split(estimator, X, y, test, train)
if y_train is None:
estimator.fit(X_train, **fit_params)
else:
estimator.fit(X_train, y_train, **fit_params)
test_score = _score(estimator, X_test, y_test, scorer)
if return_train_score:
train_score = _score(estimator, X_train, y_train, scorer)
scoring_time = time.time() - start_time
if verbose > 2:
msg += ", score=%f" % test_score
if verbose > 1:
end_msg = "%s -%s" % (msg, logger.short_format_time(scoring_time))
print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg))
ret = [train_score] if return_train_score else []
ret.extend([test_score, _num_samples(X_test), scoring_time])
if return_parameters:
ret.append(parameters)
return ret | python | def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
fit_params, return_train_score=False,
return_parameters=False):
"""Fit estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like, optional, default: None
The target variable to try to predict in the case of
supervised learning.
scoring : callable
A scorer callable object / function with signature
``scorer(estimator, X, y)``.
train : array-like, shape = (n_train_samples,)
Indices of training samples.
test : array-like, shape = (n_test_samples,)
Indices of test samples.
verbose : integer
The verbosity level.
parameters : dict or None
Parameters to be set on the estimator.
fit_params : dict or None
Parameters that will be passed to ``estimator.fit``.
return_train_score : boolean, optional, default: False
Compute and return score on training set.
return_parameters : boolean, optional, default: False
Return parameters that has been used for the estimator.
Returns
-------
train_score : float, optional
Score on training set, returned only if `return_train_score` is `True`.
test_score : float
Score on test set.
n_test_samples : int
Number of test samples.
scoring_time : float
Time spent for fitting and scoring in seconds.
parameters : dict or None, optional
The parameters that have been evaluated.
"""
if verbose > 1:
if parameters is None:
msg = "no parameters to be set"
else:
msg = '%s' % (', '.join('%s=%s' % (k, v)
for k, v in list(parameters.items())))
print("[CV] %s %s" % (msg, (64 - len(msg)) * '.'))
# Adjust lenght of sample weights
n_samples = _num_samples(X)
fit_params = fit_params if fit_params is not None else {}
fit_params = dict([(k, np.asarray(v)[train]
if hasattr(v, '__len__') and len(v) == n_samples else v)
for k, v in list(fit_params.items())])
if parameters is not None:
estimator.set_params(**parameters)
start_time = time.time()
X_train, y_train = _safe_split(estimator, X, y, train)
X_test, y_test = _safe_split(estimator, X, y, test, train)
if y_train is None:
estimator.fit(X_train, **fit_params)
else:
estimator.fit(X_train, y_train, **fit_params)
test_score = _score(estimator, X_test, y_test, scorer)
if return_train_score:
train_score = _score(estimator, X_train, y_train, scorer)
scoring_time = time.time() - start_time
if verbose > 2:
msg += ", score=%f" % test_score
if verbose > 1:
end_msg = "%s -%s" % (msg, logger.short_format_time(scoring_time))
print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg))
ret = [train_score] if return_train_score else []
ret.extend([test_score, _num_samples(X_test), scoring_time])
if return_parameters:
ret.append(parameters)
return ret | [
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Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like, optional, default: None
The target variable to try to predict in the case of
supervised learning.
scoring : callable
A scorer callable object / function with signature
``scorer(estimator, X, y)``.
train : array-like, shape = (n_train_samples,)
Indices of training samples.
test : array-like, shape = (n_test_samples,)
Indices of test samples.
verbose : integer
The verbosity level.
parameters : dict or None
Parameters to be set on the estimator.
fit_params : dict or None
Parameters that will be passed to ``estimator.fit``.
return_train_score : boolean, optional, default: False
Compute and return score on training set.
return_parameters : boolean, optional, default: False
Return parameters that has been used for the estimator.
Returns
-------
train_score : float, optional
Score on training set, returned only if `return_train_score` is `True`.
test_score : float
Score on test set.
n_test_samples : int
Number of test samples.
scoring_time : float
Time spent for fitting and scoring in seconds.
parameters : dict or None, optional
The parameters that have been evaluated. | [
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CityOfZion/neo-boa | boa/code/module.py | Module.link_methods | def link_methods(self):
"""
Perform linkage of addresses between methods.
"""
from ..compiler import Compiler
for method in self.methods:
method.prepare()
self.all_vm_tokens = OrderedDict()
address = 0
for method in self.orderered_methods:
if not method.is_interop:
# print("ADDING METHOD %s " % method.full_name)
method.address = address
for key, vmtoken in method.vm_tokens.items():
self.all_vm_tokens[address] = vmtoken
address += 1
if vmtoken.data is not None and vmtoken.vm_op != VMOp.NOP:
address += len(vmtoken.data)
vmtoken.addr = vmtoken.addr + method.address
for key, vmtoken in self.all_vm_tokens.items():
if vmtoken.src_method is not None:
target_method = self.method_by_name(vmtoken.target_method)
if target_method:
jump_len = target_method.address - vmtoken.addr
param_ret_counts = bytearray()
if Compiler.instance().nep8:
param_ret_counts = vmtoken.data[0:2]
jump_len -= 2
if jump_len > -32767 and jump_len < 32767:
vmtoken.data = param_ret_counts + jump_len.to_bytes(2, 'little', signed=True)
else:
vmtoken.data = param_ret_counts + jump_len.to_bytes(4, 'little', signed=True)
else:
raise Exception("Target method %s not found" % vmtoken.target_method) | python | def link_methods(self):
"""
Perform linkage of addresses between methods.
"""
from ..compiler import Compiler
for method in self.methods:
method.prepare()
self.all_vm_tokens = OrderedDict()
address = 0
for method in self.orderered_methods:
if not method.is_interop:
# print("ADDING METHOD %s " % method.full_name)
method.address = address
for key, vmtoken in method.vm_tokens.items():
self.all_vm_tokens[address] = vmtoken
address += 1
if vmtoken.data is not None and vmtoken.vm_op != VMOp.NOP:
address += len(vmtoken.data)
vmtoken.addr = vmtoken.addr + method.address
for key, vmtoken in self.all_vm_tokens.items():
if vmtoken.src_method is not None:
target_method = self.method_by_name(vmtoken.target_method)
if target_method:
jump_len = target_method.address - vmtoken.addr
param_ret_counts = bytearray()
if Compiler.instance().nep8:
param_ret_counts = vmtoken.data[0:2]
jump_len -= 2
if jump_len > -32767 and jump_len < 32767:
vmtoken.data = param_ret_counts + jump_len.to_bytes(2, 'little', signed=True)
else:
vmtoken.data = param_ret_counts + jump_len.to_bytes(4, 'little', signed=True)
else:
raise Exception("Target method %s not found" % vmtoken.target_method) | [
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iotile/coretools | iotileemulate/iotile/emulate/reference/controller_features/sensor_graph.py | SensorGraphSubsystem.persist | def persist(self):
"""Trigger saving the current sensorgraph to persistent storage."""
self.persisted_nodes = self.graph.dump_nodes()
self.persisted_streamers = self.graph.dump_streamers()
self.persisted_exists = True
self.persisted_constants = self._sensor_log.dump_constants() | python | def persist(self):
"""Trigger saving the current sensorgraph to persistent storage."""
self.persisted_nodes = self.graph.dump_nodes()
self.persisted_streamers = self.graph.dump_streamers()
self.persisted_exists = True
self.persisted_constants = self._sensor_log.dump_constants() | [
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uber/tchannel-python | tchannel/_queue.py | Queue.get_nowait | def get_nowait(self):
"""Returns a value from the queue without waiting.
Raises ``QueueEmpty`` if no values are available right now.
"""
new_get = Future()
with self._lock:
if not self._get.done():
raise QueueEmpty
get, self._get = self._get, new_get
hole = get.result()
if not hole.done():
# Restore the unfinished hole.
new_get.set_result(hole)
raise QueueEmpty
node = hole.result()
value = node.value
new_hole, node.next = node.next, None
new_get.set_result(new_hole)
return value | python | def get_nowait(self):
"""Returns a value from the queue without waiting.
Raises ``QueueEmpty`` if no values are available right now.
"""
new_get = Future()
with self._lock:
if not self._get.done():
raise QueueEmpty
get, self._get = self._get, new_get
hole = get.result()
if not hole.done():
# Restore the unfinished hole.
new_get.set_result(hole)
raise QueueEmpty
node = hole.result()
value = node.value
new_hole, node.next = node.next, None
new_get.set_result(new_hole)
return value | [
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phaethon/kamene | kamene/fields.py | Field.randval | def randval(self):
"""Return a volatile object whose value is both random and suitable for this field"""
fmtt = self.fmt[-1]
if fmtt in "BHIQ":
return {"B":RandByte,"H":RandShort,"I":RandInt, "Q":RandLong}[fmtt]()
elif fmtt == "s":
if self.fmt[0] in "0123456789":
l = int(self.fmt[:-1])
else:
l = int(self.fmt[1:-1])
return RandBin(l)
else:
warning("no random class for [%s] (fmt=%s)." % (self.name, self.fmt)) | python | def randval(self):
"""Return a volatile object whose value is both random and suitable for this field"""
fmtt = self.fmt[-1]
if fmtt in "BHIQ":
return {"B":RandByte,"H":RandShort,"I":RandInt, "Q":RandLong}[fmtt]()
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l = int(self.fmt[:-1])
else:
l = int(self.fmt[1:-1])
return RandBin(l)
else:
warning("no random class for [%s] (fmt=%s)." % (self.name, self.fmt)) | [
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ultrabug/py3status | py3status/py3.py | Py3.trigger_event | def trigger_event(self, module_name, event):
"""
Trigger an event on a named module.
"""
if module_name:
self._py3_wrapper.events_thread.process_event(module_name, event) | python | def trigger_event(self, module_name, event):
"""
Trigger an event on a named module.
"""
if module_name:
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trustar/trustar-python | trustar/indicator_client.py | IndicatorClient.get_indicators_metadata | def get_indicators_metadata(self, indicators):
"""
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lastSeen, enclaveIds, and tags. The metadata is determined based on the enclaves the user making the request has
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:return: A list of |Indicator| objects. The following attributes of the objects will be returned:
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"""
data = [{
'value': i.value,
'indicatorType': i.type
} for i in indicators]
resp = self._client.post("indicators/metadata", data=json.dumps(data))
return [Indicator.from_dict(x) for x in resp.json()] | python | def get_indicators_metadata(self, indicators):
"""
Provide metadata associated with an list of indicators, including value, indicatorType, noteCount, sightings,
lastSeen, enclaveIds, and tags. The metadata is determined based on the enclaves the user making the request has
READ access to.
:param indicators: a list of |Indicator| objects to query. Values are required, types are optional. Types
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:return: A list of |Indicator| objects. The following attributes of the objects will be returned:
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objects will have Null values.
"""
data = [{
'value': i.value,
'indicatorType': i.type
} for i in indicators]
resp = self._client.post("indicators/metadata", data=json.dumps(data))
return [Indicator.from_dict(x) for x in resp.json()] | [
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poppy-project/pypot | pypot/vrep/remoteApiBindings/vrep.py | simxGetObjectOrientation | def simxGetObjectOrientation(clientID, objectHandle, relativeToObjectHandle, operationMode):
'''
Please have a look at the function description/documentation in the V-REP user manual
'''
eulerAngles = (ct.c_float*3)()
ret = c_GetObjectOrientation(clientID, objectHandle, relativeToObjectHandle, eulerAngles, operationMode)
arr = []
for i in range(3):
arr.append(eulerAngles[i])
return ret, arr | python | def simxGetObjectOrientation(clientID, objectHandle, relativeToObjectHandle, operationMode):
'''
Please have a look at the function description/documentation in the V-REP user manual
'''
eulerAngles = (ct.c_float*3)()
ret = c_GetObjectOrientation(clientID, objectHandle, relativeToObjectHandle, eulerAngles, operationMode)
arr = []
for i in range(3):
arr.append(eulerAngles[i])
return ret, arr | [
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Chilipp/model-organization | docs/square_full.py | SquareModelOrganizer.run | def run(self, **kwargs):
"""
Run the model
Parameters
----------
``**kwargs``
Any other parameter for the
:meth:`model_organization.ModelOrganizer.app_main` method
"""
from calculate import compute
self.app_main(**kwargs)
# get the default output name
output = osp.join(self.exp_config['expdir'], 'output.dat')
# save the paths in the configuration
self.exp_config['output'] = output
# run the model
data = np.loadtxt(self.exp_config['infile'])
out = compute(data)
# save the output
self.logger.info('Saving output data to %s', osp.relpath(output))
np.savetxt(output, out)
# store some additional information in the configuration of the
# experiment
self.exp_config['mean'] = mean = float(out.mean())
self.exp_config['std'] = std = float(out.std())
self.logger.debug('Mean: %s, Standard deviation: %s', mean, std) | python | def run(self, **kwargs):
"""
Run the model
Parameters
----------
``**kwargs``
Any other parameter for the
:meth:`model_organization.ModelOrganizer.app_main` method
"""
from calculate import compute
self.app_main(**kwargs)
# get the default output name
output = osp.join(self.exp_config['expdir'], 'output.dat')
# save the paths in the configuration
self.exp_config['output'] = output
# run the model
data = np.loadtxt(self.exp_config['infile'])
out = compute(data)
# save the output
self.logger.info('Saving output data to %s', osp.relpath(output))
np.savetxt(output, out)
# store some additional information in the configuration of the
# experiment
self.exp_config['mean'] = mean = float(out.mean())
self.exp_config['std'] = std = float(out.std())
self.logger.debug('Mean: %s, Standard deviation: %s', mean, std) | [
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nicolargo/glances | glances/plugins/glances_irq.py | Plugin.update | def update(self):
"""Update the IRQ stats."""
# Init new stats
stats = self.get_init_value()
# IRQ plugin only available on GNU/Linux
if not LINUX:
return self.stats
if self.input_method == 'local':
# Grab the stats
stats = self.irq.get()
elif self.input_method == 'snmp':
# not available
pass
# Get the TOP 5 (by rate/s)
stats = sorted(stats,
key=operator.itemgetter('irq_rate'),
reverse=True)[:5]
# Update the stats
self.stats = stats
return self.stats | python | def update(self):
"""Update the IRQ stats."""
# Init new stats
stats = self.get_init_value()
# IRQ plugin only available on GNU/Linux
if not LINUX:
return self.stats
if self.input_method == 'local':
# Grab the stats
stats = self.irq.get()
elif self.input_method == 'snmp':
# not available
pass
# Get the TOP 5 (by rate/s)
stats = sorted(stats,
key=operator.itemgetter('irq_rate'),
reverse=True)[:5]
# Update the stats
self.stats = stats
return self.stats | [
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okpy/ok-client | client/sources/common/pyconsole.py | PythonConsole.load | def load(self, code, setup='', teardown=''):
"""Prepares a set of setup, test, and teardown code to be
run in the console.
PARAMETERS:
code -- list; processed lines of code. Elements in the list are
either strings (input) or CodeAnswer objects (output)
setup -- str; raw setup code
teardown -- str; raw teardown code
"""
super().load(code, setup, teardown)
self._frame = self._original_frame.copy() | python | def load(self, code, setup='', teardown=''):
"""Prepares a set of setup, test, and teardown code to be
run in the console.
PARAMETERS:
code -- list; processed lines of code. Elements in the list are
either strings (input) or CodeAnswer objects (output)
setup -- str; raw setup code
teardown -- str; raw teardown code
"""
super().load(code, setup, teardown)
self._frame = self._original_frame.copy() | [
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log2timeline/plaso | plaso/parsers/opera.py | OperaGlobalHistoryParser._ParseAndValidateRecord | def _ParseAndValidateRecord(self, parser_mediator, text_file_object):
"""Parses and validates an Opera global history record.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
text_file_object (dfvfs.TextFile): text file.
Returns:
bool: True if the record was successfully parsed.
"""
try:
title = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
url = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
timestamp = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
popularity_index = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
except UnicodeDecodeError:
return False
if len(title) == self._MAXIMUM_LINE_SIZE and title[-1] != '\n':
return False
if len(url) == self._MAXIMUM_LINE_SIZE and url[-1] != '\n':
return False
if len(timestamp) == self._MAXIMUM_LINE_SIZE and timestamp[-1] != '\n':
return False
if (len(popularity_index) == self._MAXIMUM_LINE_SIZE and
popularity_index[-1] != '\n'):
return False
title = title.strip()
url = url.strip()
timestamp = timestamp.strip()
popularity_index = popularity_index.strip()
if not title or not url or not timestamp or not popularity_index:
return False
event_data = OperaGlobalHistoryEventData()
if not self._IsValidUrl(url):
return False
event_data.url = url
if title != url:
event_data.title = title
try:
event_data.popularity_index = int(popularity_index, 10)
timestamp = int(timestamp, 10)
except ValueError:
return False
if event_data.popularity_index < 0:
event_data.description = 'First and Only Visit'
else:
event_data.description = 'Last Visit'
date_time = dfdatetime_posix_time.PosixTime(timestamp=timestamp)
event = time_events.DateTimeValuesEvent(
date_time, definitions.TIME_DESCRIPTION_LAST_VISITED)
parser_mediator.ProduceEventWithEventData(event, event_data)
return True | python | def _ParseAndValidateRecord(self, parser_mediator, text_file_object):
"""Parses and validates an Opera global history record.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
text_file_object (dfvfs.TextFile): text file.
Returns:
bool: True if the record was successfully parsed.
"""
try:
title = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
url = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
timestamp = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
popularity_index = text_file_object.readline(size=self._MAXIMUM_LINE_SIZE)
except UnicodeDecodeError:
return False
if len(title) == self._MAXIMUM_LINE_SIZE and title[-1] != '\n':
return False
if len(url) == self._MAXIMUM_LINE_SIZE and url[-1] != '\n':
return False
if len(timestamp) == self._MAXIMUM_LINE_SIZE and timestamp[-1] != '\n':
return False
if (len(popularity_index) == self._MAXIMUM_LINE_SIZE and
popularity_index[-1] != '\n'):
return False
title = title.strip()
url = url.strip()
timestamp = timestamp.strip()
popularity_index = popularity_index.strip()
if not title or not url or not timestamp or not popularity_index:
return False
event_data = OperaGlobalHistoryEventData()
if not self._IsValidUrl(url):
return False
event_data.url = url
if title != url:
event_data.title = title
try:
event_data.popularity_index = int(popularity_index, 10)
timestamp = int(timestamp, 10)
except ValueError:
return False
if event_data.popularity_index < 0:
event_data.description = 'First and Only Visit'
else:
event_data.description = 'Last Visit'
date_time = dfdatetime_posix_time.PosixTime(timestamp=timestamp)
event = time_events.DateTimeValuesEvent(
date_time, definitions.TIME_DESCRIPTION_LAST_VISITED)
parser_mediator.ProduceEventWithEventData(event, event_data)
return True | [
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text_file_object (dfvfs.TextFile): text file.
Returns:
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python-openxml/python-docx | docx/document.py | Document.add_heading | def add_heading(self, text="", level=1):
"""Return a heading paragraph newly added to the end of the document.
The heading paragraph will contain *text* and have its paragraph style
determined by *level*. If *level* is 0, the style is set to `Title`. If *level*
is 1 (or omitted), `Heading 1` is used. Otherwise the style is set to `Heading
{level}`. Raises |ValueError| if *level* is outside the range 0-9.
"""
if not 0 <= level <= 9:
raise ValueError("level must be in range 0-9, got %d" % level)
style = "Title" if level == 0 else "Heading %d" % level
return self.add_paragraph(text, style) | python | def add_heading(self, text="", level=1):
"""Return a heading paragraph newly added to the end of the document.
The heading paragraph will contain *text* and have its paragraph style
determined by *level*. If *level* is 0, the style is set to `Title`. If *level*
is 1 (or omitted), `Heading 1` is used. Otherwise the style is set to `Heading
{level}`. Raises |ValueError| if *level* is outside the range 0-9.
"""
if not 0 <= level <= 9:
raise ValueError("level must be in range 0-9, got %d" % level)
style = "Title" if level == 0 else "Heading %d" % level
return self.add_paragraph(text, style) | [
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faxir/faxir-python | faxir/api/outbox_api.py | OutboxApi.get_outbox_fax | def get_outbox_fax(self, outbox_fax_id, **kwargs): # noqa: E501
"""Get an outbox record # noqa: E501
Get an outbox fax record information # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_outbox_fax(outbox_fax_id, async=True)
>>> result = thread.get()
:param async bool
:param str outbox_fax_id: (required)
:return: Outbox
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_outbox_fax_with_http_info(outbox_fax_id, **kwargs) # noqa: E501
else:
(data) = self.get_outbox_fax_with_http_info(outbox_fax_id, **kwargs) # noqa: E501
return data | python | def get_outbox_fax(self, outbox_fax_id, **kwargs): # noqa: E501
"""Get an outbox record # noqa: E501
Get an outbox fax record information # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_outbox_fax(outbox_fax_id, async=True)
>>> result = thread.get()
:param async bool
:param str outbox_fax_id: (required)
:return: Outbox
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_outbox_fax_with_http_info(outbox_fax_id, **kwargs) # noqa: E501
else:
(data) = self.get_outbox_fax_with_http_info(outbox_fax_id, **kwargs) # noqa: E501
return data | [
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>>> thread = api.get_outbox_fax(outbox_fax_id, async=True)
>>> result = thread.get()
:param async bool
:param str outbox_fax_id: (required)
:return: Outbox
If the method is called asynchronously,
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thelabnyc/wagtail_blog | blog/management/commands/wordpress_to_wagtail.py | Command.lookup_comment_by_wordpress_id | def lookup_comment_by_wordpress_id(self, comment_id, comments):
""" Returns Django comment object with this wordpress id """
for comment in comments:
if comment.wordpress_id == comment_id:
return comment | python | def lookup_comment_by_wordpress_id(self, comment_id, comments):
""" Returns Django comment object with this wordpress id """
for comment in comments:
if comment.wordpress_id == comment_id:
return comment | [
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sunlightlabs/django-locksmith | locksmith/hub/views.py | confirm_registration | def confirm_registration(request, key, template="locksmith/confirmed.html"):
'''
API key confirmation
visiting this URL marks a Key as ready for use
'''
context = {'LOCKSMITH_BASE_TEMPLATE': settings.LOCKSMITH_BASE_TEMPLATE}
try:
context['key'] = key_obj = Key.objects.get(key=key)
if key_obj.status != 'U':
context['error'] = 'Key Already Activated'
else:
key_obj.status = 'A'
key_obj.save()
key_obj.mark_for_update()
except Key.DoesNotExist:
context['error'] = 'Invalid Key'
return render_to_response(template, context,
context_instance=RequestContext(request)) | python | def confirm_registration(request, key, template="locksmith/confirmed.html"):
'''
API key confirmation
visiting this URL marks a Key as ready for use
'''
context = {'LOCKSMITH_BASE_TEMPLATE': settings.LOCKSMITH_BASE_TEMPLATE}
try:
context['key'] = key_obj = Key.objects.get(key=key)
if key_obj.status != 'U':
context['error'] = 'Key Already Activated'
else:
key_obj.status = 'A'
key_obj.save()
key_obj.mark_for_update()
except Key.DoesNotExist:
context['error'] = 'Invalid Key'
return render_to_response(template, context,
context_instance=RequestContext(request)) | [
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pandas-dev/pandas | pandas/core/algorithms.py | _ensure_data | def _ensure_data(values, dtype=None):
"""
routine to ensure that our data is of the correct
input dtype for lower-level routines
This will coerce:
- ints -> int64
- uint -> uint64
- bool -> uint64 (TODO this should be uint8)
- datetimelike -> i8
- datetime64tz -> i8 (in local tz)
- categorical -> codes
Parameters
----------
values : array-like
dtype : pandas_dtype, optional
coerce to this dtype
Returns
-------
(ndarray, pandas_dtype, algo dtype as a string)
"""
# we check some simple dtypes first
try:
if is_object_dtype(dtype):
return ensure_object(np.asarray(values)), 'object', 'object'
if is_bool_dtype(values) or is_bool_dtype(dtype):
# we are actually coercing to uint64
# until our algos support uint8 directly (see TODO)
return np.asarray(values).astype('uint64'), 'bool', 'uint64'
elif is_signed_integer_dtype(values) or is_signed_integer_dtype(dtype):
return ensure_int64(values), 'int64', 'int64'
elif (is_unsigned_integer_dtype(values) or
is_unsigned_integer_dtype(dtype)):
return ensure_uint64(values), 'uint64', 'uint64'
elif is_float_dtype(values) or is_float_dtype(dtype):
return ensure_float64(values), 'float64', 'float64'
elif is_object_dtype(values) and dtype is None:
return ensure_object(np.asarray(values)), 'object', 'object'
elif is_complex_dtype(values) or is_complex_dtype(dtype):
# ignore the fact that we are casting to float
# which discards complex parts
with catch_warnings():
simplefilter("ignore", np.ComplexWarning)
values = ensure_float64(values)
return values, 'float64', 'float64'
except (TypeError, ValueError, OverflowError):
# if we are trying to coerce to a dtype
# and it is incompat this will fall thru to here
return ensure_object(values), 'object', 'object'
# datetimelike
if (needs_i8_conversion(values) or
is_period_dtype(dtype) or
is_datetime64_any_dtype(dtype) or
is_timedelta64_dtype(dtype)):
if is_period_dtype(values) or is_period_dtype(dtype):
from pandas import PeriodIndex
values = PeriodIndex(values)
dtype = values.dtype
elif is_timedelta64_dtype(values) or is_timedelta64_dtype(dtype):
from pandas import TimedeltaIndex
values = TimedeltaIndex(values)
dtype = values.dtype
else:
# Datetime
from pandas import DatetimeIndex
values = DatetimeIndex(values)
dtype = values.dtype
return values.asi8, dtype, 'int64'
elif (is_categorical_dtype(values) and
(is_categorical_dtype(dtype) or dtype is None)):
values = getattr(values, 'values', values)
values = values.codes
dtype = 'category'
# we are actually coercing to int64
# until our algos support int* directly (not all do)
values = ensure_int64(values)
return values, dtype, 'int64'
# we have failed, return object
values = np.asarray(values, dtype=np.object)
return ensure_object(values), 'object', 'object' | python | def _ensure_data(values, dtype=None):
"""
routine to ensure that our data is of the correct
input dtype for lower-level routines
This will coerce:
- ints -> int64
- uint -> uint64
- bool -> uint64 (TODO this should be uint8)
- datetimelike -> i8
- datetime64tz -> i8 (in local tz)
- categorical -> codes
Parameters
----------
values : array-like
dtype : pandas_dtype, optional
coerce to this dtype
Returns
-------
(ndarray, pandas_dtype, algo dtype as a string)
"""
# we check some simple dtypes first
try:
if is_object_dtype(dtype):
return ensure_object(np.asarray(values)), 'object', 'object'
if is_bool_dtype(values) or is_bool_dtype(dtype):
# we are actually coercing to uint64
# until our algos support uint8 directly (see TODO)
return np.asarray(values).astype('uint64'), 'bool', 'uint64'
elif is_signed_integer_dtype(values) or is_signed_integer_dtype(dtype):
return ensure_int64(values), 'int64', 'int64'
elif (is_unsigned_integer_dtype(values) or
is_unsigned_integer_dtype(dtype)):
return ensure_uint64(values), 'uint64', 'uint64'
elif is_float_dtype(values) or is_float_dtype(dtype):
return ensure_float64(values), 'float64', 'float64'
elif is_object_dtype(values) and dtype is None:
return ensure_object(np.asarray(values)), 'object', 'object'
elif is_complex_dtype(values) or is_complex_dtype(dtype):
# ignore the fact that we are casting to float
# which discards complex parts
with catch_warnings():
simplefilter("ignore", np.ComplexWarning)
values = ensure_float64(values)
return values, 'float64', 'float64'
except (TypeError, ValueError, OverflowError):
# if we are trying to coerce to a dtype
# and it is incompat this will fall thru to here
return ensure_object(values), 'object', 'object'
# datetimelike
if (needs_i8_conversion(values) or
is_period_dtype(dtype) or
is_datetime64_any_dtype(dtype) or
is_timedelta64_dtype(dtype)):
if is_period_dtype(values) or is_period_dtype(dtype):
from pandas import PeriodIndex
values = PeriodIndex(values)
dtype = values.dtype
elif is_timedelta64_dtype(values) or is_timedelta64_dtype(dtype):
from pandas import TimedeltaIndex
values = TimedeltaIndex(values)
dtype = values.dtype
else:
# Datetime
from pandas import DatetimeIndex
values = DatetimeIndex(values)
dtype = values.dtype
return values.asi8, dtype, 'int64'
elif (is_categorical_dtype(values) and
(is_categorical_dtype(dtype) or dtype is None)):
values = getattr(values, 'values', values)
values = values.codes
dtype = 'category'
# we are actually coercing to int64
# until our algos support int* directly (not all do)
values = ensure_int64(values)
return values, dtype, 'int64'
# we have failed, return object
values = np.asarray(values, dtype=np.object)
return ensure_object(values), 'object', 'object' | [
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- uint -> uint64
- bool -> uint64 (TODO this should be uint8)
- datetimelike -> i8
- datetime64tz -> i8 (in local tz)
- categorical -> codes
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values : array-like
dtype : pandas_dtype, optional
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childsish/lhc-python | lhc/misc/performance_measures.py | confusion_matrix | def confusion_matrix(exp, obs):
"""Create a confusion matrix
In each axis of the resulting confusion matrix the negative case is
0-index and the positive case 1-index. The labels get sorted, in a
True/False scenario true positives will occur at (1,1). The first dimension
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:param exp: expected values
:type exp: list of float
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:type obs: list of float
:rtype: tuple of square matrix and sorted labels
"""
assert len(exp) == len(obs)
# Expected in the first dimension (0;rows), observed in the second (1;cols)
lbls = sorted(set(exp))
res = numpy.zeros(shape=(len(lbls), len(lbls)))
for i in range(len(exp)):
res[lbls.index(exp[i]), lbls.index(obs[i])] += 1
return res, lbls | python | def confusion_matrix(exp, obs):
"""Create a confusion matrix
In each axis of the resulting confusion matrix the negative case is
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True/False scenario true positives will occur at (1,1). The first dimension
(rows) of the resulting matrix is the expected class and the second
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:param exp: expected values
:type exp: list of float
:param obs: observed values
:type obs: list of float
:rtype: tuple of square matrix and sorted labels
"""
assert len(exp) == len(obs)
# Expected in the first dimension (0;rows), observed in the second (1;cols)
lbls = sorted(set(exp))
res = numpy.zeros(shape=(len(lbls), len(lbls)))
for i in range(len(exp)):
res[lbls.index(exp[i]), lbls.index(obs[i])] += 1
return res, lbls | [
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rigetti/pyquil | pyquil/wavefunction.py | Wavefunction.get_outcome_probs | def get_outcome_probs(self):
"""
Parses a wavefunction (array of complex amplitudes) and returns a dictionary of
outcomes and associated probabilities.
:return: A dict with outcomes as keys and probabilities as values.
:rtype: dict
"""
outcome_dict = {}
qubit_num = len(self)
for index, amplitude in enumerate(self.amplitudes):
outcome = get_bitstring_from_index(index, qubit_num)
outcome_dict[outcome] = abs(amplitude) ** 2
return outcome_dict | python | def get_outcome_probs(self):
"""
Parses a wavefunction (array of complex amplitudes) and returns a dictionary of
outcomes and associated probabilities.
:return: A dict with outcomes as keys and probabilities as values.
:rtype: dict
"""
outcome_dict = {}
qubit_num = len(self)
for index, amplitude in enumerate(self.amplitudes):
outcome = get_bitstring_from_index(index, qubit_num)
outcome_dict[outcome] = abs(amplitude) ** 2
return outcome_dict | [
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Kortemme-Lab/klab | klab/bio/fragments/generate_fragments.py | reformat | def reformat(found_sequences):
'''Truncate the FASTA headers so that the first field is a 4-character ID.'''
for (pdb_id, chain, file_name), sequence in sorted(found_sequences.iteritems()):
header = sequence[0]
assert(header[0] == '>')
tokens = header.split('|')
tokens[0] = tokens[0][:5]
assert(len(tokens[0]) == 5)
sequence[0] = "|".join(tokens) | python | def reformat(found_sequences):
'''Truncate the FASTA headers so that the first field is a 4-character ID.'''
for (pdb_id, chain, file_name), sequence in sorted(found_sequences.iteritems()):
header = sequence[0]
assert(header[0] == '>')
tokens = header.split('|')
tokens[0] = tokens[0][:5]
assert(len(tokens[0]) == 5)
sequence[0] = "|".join(tokens) | [
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improbable-research/keanu | keanu-python/keanu/vertex/generated.py | BooleanTake | def BooleanTake(input_vertex: vertex_constructor_param_types, index: Collection[int], label: Optional[str]=None) -> Vertex:
"""
A vertex that extracts a scalar at a given index
:param input_vertex: the input vertex to extract from
:param index: the index to extract at
"""
return Boolean(context.jvm_view().BooleanTakeVertex, label, cast_to_vertex(input_vertex), cast_to_long_array(index)) | python | def BooleanTake(input_vertex: vertex_constructor_param_types, index: Collection[int], label: Optional[str]=None) -> Vertex:
"""
A vertex that extracts a scalar at a given index
:param input_vertex: the input vertex to extract from
:param index: the index to extract at
"""
return Boolean(context.jvm_view().BooleanTakeVertex, label, cast_to_vertex(input_vertex), cast_to_long_array(index)) | [
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python-diamond/Diamond | src/collectors/netapp/netapp.py | NetAppCollector._gen_delta_depend | def _gen_delta_depend(self, path, derivative, multiplier, prettyname,
device):
"""
For some metrics we need to divide the delta for one metric
with the delta of another.
Publishes a metric if the convertion goes well.
"""
primary_delta = derivative[path]
shortpath = ".".join(path.split(".")[:-1])
basename = path.split(".")[-1]
secondary_delta = None
if basename in self.DIVIDERS.keys():
mateKey = ".".join([shortpath, self.DIVIDERS[basename]])
else:
return
if mateKey in derivative.keys():
secondary_delta = derivative[mateKey]
else:
return
# If we find a corresponding secondary_delta, publish a metric
if primary_delta > 0 and secondary_delta > 0:
value = (float(primary_delta) / secondary_delta) * multiplier
self._replace_and_publish(path, prettyname, value, device) | python | def _gen_delta_depend(self, path, derivative, multiplier, prettyname,
device):
"""
For some metrics we need to divide the delta for one metric
with the delta of another.
Publishes a metric if the convertion goes well.
"""
primary_delta = derivative[path]
shortpath = ".".join(path.split(".")[:-1])
basename = path.split(".")[-1]
secondary_delta = None
if basename in self.DIVIDERS.keys():
mateKey = ".".join([shortpath, self.DIVIDERS[basename]])
else:
return
if mateKey in derivative.keys():
secondary_delta = derivative[mateKey]
else:
return
# If we find a corresponding secondary_delta, publish a metric
if primary_delta > 0 and secondary_delta > 0:
value = (float(primary_delta) / secondary_delta) * multiplier
self._replace_and_publish(path, prettyname, value, device) | [
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django-danceschool/django-danceschool | danceschool/core/models.py | Invoice.calculateTaxes | def calculateTaxes(self):
'''
Updates the tax field to reflect the amount of taxes depending on
the local rate as well as whether the buyer or seller pays sales tax.
'''
tax_rate = (getConstant('registration__salesTaxRate') or 0) / 100
if tax_rate > 0:
if self.buyerPaysSalesTax:
# If the buyer pays taxes, then taxes are just added as a fraction of the price
self.taxes = self.total * tax_rate
else:
# If the seller pays sales taxes, then adjusted_total will be their net revenue,
# and under this calculation adjusted_total + taxes = the price charged
adjusted_total = self.total / (1 + tax_rate)
self.taxes = adjusted_total * tax_rate | python | def calculateTaxes(self):
'''
Updates the tax field to reflect the amount of taxes depending on
the local rate as well as whether the buyer or seller pays sales tax.
'''
tax_rate = (getConstant('registration__salesTaxRate') or 0) / 100
if tax_rate > 0:
if self.buyerPaysSalesTax:
# If the buyer pays taxes, then taxes are just added as a fraction of the price
self.taxes = self.total * tax_rate
else:
# If the seller pays sales taxes, then adjusted_total will be their net revenue,
# and under this calculation adjusted_total + taxes = the price charged
adjusted_total = self.total / (1 + tax_rate)
self.taxes = adjusted_total * tax_rate | [
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zabuldon/teslajsonpy | teslajsonpy/gps.py | GPS.update | def update(self):
"""Update the current GPS location."""
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_drive_params(self._id)
if data:
self.__longitude = data['longitude']
self.__latitude = data['latitude']
self.__heading = data['heading']
if self.__longitude and self.__latitude and self.__heading:
self.__location = {'longitude': self.__longitude,
'latitude': self.__latitude,
'heading': self.__heading} | python | def update(self):
"""Update the current GPS location."""
self._controller.update(self._id, wake_if_asleep=False)
data = self._controller.get_drive_params(self._id)
if data:
self.__longitude = data['longitude']
self.__latitude = data['latitude']
self.__heading = data['heading']
if self.__longitude and self.__latitude and self.__heading:
self.__location = {'longitude': self.__longitude,
'latitude': self.__latitude,
'heading': self.__heading} | [
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titusjan/argos | argos/repo/filesytemrtis.py | createRtiFromFileName | def createRtiFromFileName(fileName):
""" Determines the type of RepoTreeItem to use given a file name and creates it.
Uses a DirectoryRti for directories and an UnknownFileRti if the file
extension doesn't match one of the registered RTI extensions.
"""
cls, rtiRegItem = detectRtiFromFileName(fileName)
if cls is None:
logger.warn("Unable to import plugin {}: {}"
.format(rtiRegItem.fullName, rtiRegItem.exception))
rti = UnknownFileRti.createFromFileName(fileName)
rti.setException(rtiRegItem.exception)
else:
rti = cls.createFromFileName(fileName)
assert rti, "Sanity check failed (createRtiFromFileName). Please report this bug."
return rti | python | def createRtiFromFileName(fileName):
""" Determines the type of RepoTreeItem to use given a file name and creates it.
Uses a DirectoryRti for directories and an UnknownFileRti if the file
extension doesn't match one of the registered RTI extensions.
"""
cls, rtiRegItem = detectRtiFromFileName(fileName)
if cls is None:
logger.warn("Unable to import plugin {}: {}"
.format(rtiRegItem.fullName, rtiRegItem.exception))
rti = UnknownFileRti.createFromFileName(fileName)
rti.setException(rtiRegItem.exception)
else:
rti = cls.createFromFileName(fileName)
assert rti, "Sanity check failed (createRtiFromFileName). Please report this bug."
return rti | [
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line/line-bot-sdk-python | linebot/utils.py | safe_compare_digest | def safe_compare_digest(val1, val2):
"""safe_compare_digest method.
:param val1: string or bytes for compare
:type val1: str | bytes
:param val2: string or bytes for compare
:type val2: str | bytes
"""
if len(val1) != len(val2):
return False
result = 0
if PY3 and isinstance(val1, bytes) and isinstance(val2, bytes):
for i, j in zip(val1, val2):
result |= i ^ j
else:
for i, j in zip(val1, val2):
result |= (ord(i) ^ ord(j))
return result == 0 | python | def safe_compare_digest(val1, val2):
"""safe_compare_digest method.
:param val1: string or bytes for compare
:type val1: str | bytes
:param val2: string or bytes for compare
:type val2: str | bytes
"""
if len(val1) != len(val2):
return False
result = 0
if PY3 and isinstance(val1, bytes) and isinstance(val2, bytes):
for i, j in zip(val1, val2):
result |= i ^ j
else:
for i, j in zip(val1, val2):
result |= (ord(i) ^ ord(j))
return result == 0 | [
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apache/incubator-mxnet | python/mxnet/gluon/model_zoo/vision/__init__.py | get_model | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
Returns
-------
HybridBlock
The model.
"""
models = {'resnet18_v1': resnet18_v1,
'resnet34_v1': resnet34_v1,
'resnet50_v1': resnet50_v1,
'resnet101_v1': resnet101_v1,
'resnet152_v1': resnet152_v1,
'resnet18_v2': resnet18_v2,
'resnet34_v2': resnet34_v2,
'resnet50_v2': resnet50_v2,
'resnet101_v2': resnet101_v2,
'resnet152_v2': resnet152_v2,
'vgg11': vgg11,
'vgg13': vgg13,
'vgg16': vgg16,
'vgg19': vgg19,
'vgg11_bn': vgg11_bn,
'vgg13_bn': vgg13_bn,
'vgg16_bn': vgg16_bn,
'vgg19_bn': vgg19_bn,
'alexnet': alexnet,
'densenet121': densenet121,
'densenet161': densenet161,
'densenet169': densenet169,
'densenet201': densenet201,
'squeezenet1.0': squeezenet1_0,
'squeezenet1.1': squeezenet1_1,
'inceptionv3': inception_v3,
'mobilenet1.0': mobilenet1_0,
'mobilenet0.75': mobilenet0_75,
'mobilenet0.5': mobilenet0_5,
'mobilenet0.25': mobilenet0_25,
'mobilenetv2_1.0': mobilenet_v2_1_0,
'mobilenetv2_0.75': mobilenet_v2_0_75,
'mobilenetv2_0.5': mobilenet_v2_0_5,
'mobilenetv2_0.25': mobilenet_v2_0_25
}
name = name.lower()
if name not in models:
raise ValueError(
'Model %s is not supported. Available options are\n\t%s' % (
name, '\n\t'.join(sorted(models.keys()))))
return models[name](**kwargs) | python | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
Returns
-------
HybridBlock
The model.
"""
models = {'resnet18_v1': resnet18_v1,
'resnet34_v1': resnet34_v1,
'resnet50_v1': resnet50_v1,
'resnet101_v1': resnet101_v1,
'resnet152_v1': resnet152_v1,
'resnet18_v2': resnet18_v2,
'resnet34_v2': resnet34_v2,
'resnet50_v2': resnet50_v2,
'resnet101_v2': resnet101_v2,
'resnet152_v2': resnet152_v2,
'vgg11': vgg11,
'vgg13': vgg13,
'vgg16': vgg16,
'vgg19': vgg19,
'vgg11_bn': vgg11_bn,
'vgg13_bn': vgg13_bn,
'vgg16_bn': vgg16_bn,
'vgg19_bn': vgg19_bn,
'alexnet': alexnet,
'densenet121': densenet121,
'densenet161': densenet161,
'densenet169': densenet169,
'densenet201': densenet201,
'squeezenet1.0': squeezenet1_0,
'squeezenet1.1': squeezenet1_1,
'inceptionv3': inception_v3,
'mobilenet1.0': mobilenet1_0,
'mobilenet0.75': mobilenet0_75,
'mobilenet0.5': mobilenet0_5,
'mobilenet0.25': mobilenet0_25,
'mobilenetv2_1.0': mobilenet_v2_1_0,
'mobilenetv2_0.75': mobilenet_v2_0_75,
'mobilenetv2_0.5': mobilenet_v2_0_5,
'mobilenetv2_0.25': mobilenet_v2_0_25
}
name = name.lower()
if name not in models:
raise ValueError(
'Model %s is not supported. Available options are\n\t%s' % (
name, '\n\t'.join(sorted(models.keys()))))
return models[name](**kwargs) | [
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cenkalti/kuyruk | kuyruk/worker.py | Worker.run | def run(self) -> None:
"""Runs the worker and consumes messages from RabbitMQ.
Returns only after `shutdown()` is called.
"""
if self._logging_level:
logging.basicConfig(
level=getattr(logging, self._logging_level.upper()),
format="%(levelname).1s %(name)s.%(funcName)s:%(lineno)d - %(message)s")
signal.signal(signal.SIGINT, self._handle_sigint)
signal.signal(signal.SIGTERM, self._handle_sigterm)
if platform.system() != 'Windows':
# These features will not be available on Windows, but that is OK.
# Read this issue for more details:
# https://github.com/cenkalti/kuyruk/issues/54
signal.signal(signal.SIGHUP, self._handle_sighup)
signal.signal(signal.SIGUSR1, self._handle_sigusr1)
signal.signal(signal.SIGUSR2, self._handle_sigusr2)
self._started_at = os.times().elapsed
for t in self._threads:
t.start()
try:
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self._consume_messages()
signals.worker_shutdown.send(self.kuyruk, worker=self)
finally:
self.shutdown_pending.set()
for t in self._threads:
t.join()
logger.debug("End run worker") | python | def run(self) -> None:
"""Runs the worker and consumes messages from RabbitMQ.
Returns only after `shutdown()` is called.
"""
if self._logging_level:
logging.basicConfig(
level=getattr(logging, self._logging_level.upper()),
format="%(levelname).1s %(name)s.%(funcName)s:%(lineno)d - %(message)s")
signal.signal(signal.SIGINT, self._handle_sigint)
signal.signal(signal.SIGTERM, self._handle_sigterm)
if platform.system() != 'Windows':
# These features will not be available on Windows, but that is OK.
# Read this issue for more details:
# https://github.com/cenkalti/kuyruk/issues/54
signal.signal(signal.SIGHUP, self._handle_sighup)
signal.signal(signal.SIGUSR1, self._handle_sigusr1)
signal.signal(signal.SIGUSR2, self._handle_sigusr2)
self._started_at = os.times().elapsed
for t in self._threads:
t.start()
try:
signals.worker_start.send(self.kuyruk, worker=self)
self._consume_messages()
signals.worker_shutdown.send(self.kuyruk, worker=self)
finally:
self.shutdown_pending.set()
for t in self._threads:
t.join()
logger.debug("End run worker") | [
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ourway/auth | auth/CAS/authorization.py | Authorization.user_has_permission | def user_has_permission(self, user, name):
""" verify user has permission """
targetRecord = AuthMembership.objects(creator=self.client, user=user).first()
if not targetRecord:
return False
for group in targetRecord.groups:
if self.has_permission(group.role, name):
return True
return False | python | def user_has_permission(self, user, name):
""" verify user has permission """
targetRecord = AuthMembership.objects(creator=self.client, user=user).first()
if not targetRecord:
return False
for group in targetRecord.groups:
if self.has_permission(group.role, name):
return True
return False | [
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dwavesystems/dimod | dimod/decorators.py | bqm_index_labels | def bqm_index_labels(f):
"""Decorator to convert a bqm to index-labels and relabel the sample set
output.
Designed to be applied to :meth:`.Sampler.sample`. Expects the wrapped
function or method to accept a :obj:`.BinaryQuadraticModel` as the second
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"""
@wraps(f)
def _index_label(sampler, bqm, **kwargs):
if not hasattr(bqm, 'linear'):
raise TypeError('expected input to be a BinaryQuadraticModel')
linear = bqm.linear
# if already index-labelled, just continue
if all(v in linear for v in range(len(bqm))):
return f(sampler, bqm, **kwargs)
try:
inverse_mapping = dict(enumerate(sorted(linear)))
except TypeError:
# in python3 unlike types cannot be sorted
inverse_mapping = dict(enumerate(linear))
mapping = {v: i for i, v in iteritems(inverse_mapping)}
response = f(sampler, bqm.relabel_variables(mapping, inplace=False), **kwargs)
# unapply the relabeling
return response.relabel_variables(inverse_mapping, inplace=True)
return _index_label | python | def bqm_index_labels(f):
"""Decorator to convert a bqm to index-labels and relabel the sample set
output.
Designed to be applied to :meth:`.Sampler.sample`. Expects the wrapped
function or method to accept a :obj:`.BinaryQuadraticModel` as the second
input and to return a :obj:`.SampleSet`.
"""
@wraps(f)
def _index_label(sampler, bqm, **kwargs):
if not hasattr(bqm, 'linear'):
raise TypeError('expected input to be a BinaryQuadraticModel')
linear = bqm.linear
# if already index-labelled, just continue
if all(v in linear for v in range(len(bqm))):
return f(sampler, bqm, **kwargs)
try:
inverse_mapping = dict(enumerate(sorted(linear)))
except TypeError:
# in python3 unlike types cannot be sorted
inverse_mapping = dict(enumerate(linear))
mapping = {v: i for i, v in iteritems(inverse_mapping)}
response = f(sampler, bqm.relabel_variables(mapping, inplace=False), **kwargs)
# unapply the relabeling
return response.relabel_variables(inverse_mapping, inplace=True)
return _index_label | [
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woolfson-group/isambard | isambard/ampal/protein.py | Polypeptide.tag_torsion_angles | def tag_torsion_angles(self, force=False):
"""Tags each Monomer of the Polymer with its omega, phi and psi torsion angle.
Parameters
----------
force : bool, optional
If `True` the tag will be run even if `Residues` are
already tagged.
"""
tagged = ['omega' in x.tags.keys() for x in self._monomers]
if (not all(tagged)) or force:
tas = measure_torsion_angles(self._monomers)
for monomer, (omega, phi, psi) in zip(self._monomers, tas):
monomer.tags['omega'] = omega
monomer.tags['phi'] = phi
monomer.tags['psi'] = psi
monomer.tags['tas'] = (omega, phi, psi)
return | python | def tag_torsion_angles(self, force=False):
"""Tags each Monomer of the Polymer with its omega, phi and psi torsion angle.
Parameters
----------
force : bool, optional
If `True` the tag will be run even if `Residues` are
already tagged.
"""
tagged = ['omega' in x.tags.keys() for x in self._monomers]
if (not all(tagged)) or force:
tas = measure_torsion_angles(self._monomers)
for monomer, (omega, phi, psi) in zip(self._monomers, tas):
monomer.tags['omega'] = omega
monomer.tags['phi'] = phi
monomer.tags['psi'] = psi
monomer.tags['tas'] = (omega, phi, psi)
return | [
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mitsei/dlkit | dlkit/handcar/repository/managers.py | RepositoryManager.get_repository_query_session | def get_repository_query_session(self):
"""Gets the repository query session.
return: (osid.repository.RepositoryQuerySession) - a
RepositoryQuerySession
raise: OperationFailed - unable to complete request
raise: Unimplemented - supports_repository_query() is false
compliance: optional - This method must be implemented if
supports_repository_query() is true.
"""
if not self.supports_repository_query():
raise Unimplemented()
try:
from . import sessions
except ImportError:
raise # OperationFailed()
try:
session = sessions.RepositoryQuerySession(proxy=self._proxy,
runtime=self._runtime)
except AttributeError:
raise # OperationFailed()
return session | python | def get_repository_query_session(self):
"""Gets the repository query session.
return: (osid.repository.RepositoryQuerySession) - a
RepositoryQuerySession
raise: OperationFailed - unable to complete request
raise: Unimplemented - supports_repository_query() is false
compliance: optional - This method must be implemented if
supports_repository_query() is true.
"""
if not self.supports_repository_query():
raise Unimplemented()
try:
from . import sessions
except ImportError:
raise # OperationFailed()
try:
session = sessions.RepositoryQuerySession(proxy=self._proxy,
runtime=self._runtime)
except AttributeError:
raise # OperationFailed()
return session | [
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django-danceschool/django-danceschool | danceschool/core/utils/requests.py | getDateTimeFromGet | def getDateTimeFromGet(request,key):
'''
This function just parses the request GET data for the requested key,
and returns it in datetime format, returning none if the key is not
available or is in incorrect format.
'''
if request.GET.get(key,''):
try:
return ensure_timezone(datetime.strptime(unquote(request.GET.get(key,'')),'%Y-%m-%d'))
except (ValueError, TypeError):
pass
return None | python | def getDateTimeFromGet(request,key):
'''
This function just parses the request GET data for the requested key,
and returns it in datetime format, returning none if the key is not
available or is in incorrect format.
'''
if request.GET.get(key,''):
try:
return ensure_timezone(datetime.strptime(unquote(request.GET.get(key,'')),'%Y-%m-%d'))
except (ValueError, TypeError):
pass
return None | [
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google/mobly | mobly/controllers/monsoon.py | MonsoonData.update_offset | def update_offset(self, new_offset):
"""Updates how many data points to skip in caculations.
Always use this function to update offset instead of directly setting
self.offset.
Args:
new_offset: The new offset.
"""
self.offset = new_offset
self.data_points = self._data_points[self.offset:]
self.timestamps = self._timestamps[self.offset:] | python | def update_offset(self, new_offset):
"""Updates how many data points to skip in caculations.
Always use this function to update offset instead of directly setting
self.offset.
Args:
new_offset: The new offset.
"""
self.offset = new_offset
self.data_points = self._data_points[self.offset:]
self.timestamps = self._timestamps[self.offset:] | [
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basho/riak-python-client | riak/transports/http/__init__.py | NoNagleHTTPConnection.connect | def connect(self):
"""
Set TCP_NODELAY on socket
"""
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self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) | python | def connect(self):
"""
Set TCP_NODELAY on socket
"""
HTTPConnection.connect(self)
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