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HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/lib/python2.7/site-packages/pip/_vendor/retrying.py | python | Retrying.fixed_sleep | (self, previous_attempt_number, delay_since_first_attempt_ms) | return self._wait_fixed | Sleep a fixed amount of time between each retry. | Sleep a fixed amount of time between each retry. | [
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] | def fixed_sleep(self, previous_attempt_number, delay_since_first_attempt_ms):
"""Sleep a fixed amount of time between each retry."""
return self._wait_fixed | [
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/rich/live.py | python | Live.refresh | (self) | Update the display of the Live Render. | Update the display of the Live Render. | [
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"""Update the display of the Live Render."""
with self._lock:
self._live_render.set_renderable(self.renderable)
if self.console.is_jupyter: # pragma: no cover
try:
from IPython.display import display
from ipywidgets import Output
except ImportError:
import warnings
warnings.warn('install "ipywidgets" for Jupyter support')
else:
if self.ipy_widget is None:
self.ipy_widget = Output()
display(self.ipy_widget)
with self.ipy_widget:
self.ipy_widget.clear_output(wait=True)
self.console.print(self._live_render.renderable)
elif self.console.is_terminal and not self.console.is_dumb_terminal:
with self.console:
self.console.print(Control())
elif (
not self._started and not self.transient
): # if it is finished allow files or dumb-terminals to see final result
with self.console:
self.console.print(Control()) | [
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mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pywin/Lib/subprocess.py | python | check_output | (*popenargs, **kwargs) | return output | r"""Run command with arguments and return its output as a byte string.
If the exit code was non-zero it raises a CalledProcessError. The
CalledProcessError object will have the return code in the returncode
attribute and output in the output attribute.
The arguments are the same as for the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument is not allowed as it is used internally.
To capture standard error in the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
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r"""Run command with arguments and return its output as a byte string.
If the exit code was non-zero it raises a CalledProcessError. The
CalledProcessError object will have the return code in the returncode
attribute and output in the output attribute.
The arguments are the same as for the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument is not allowed as it is used internally.
To capture standard error in the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
'ls: non_existent_file: No such file or directory\n'
"""
if 'stdout' in kwargs:
raise ValueError('stdout argument not allowed, it will be overridden.')
process = Popen(stdout=PIPE, *popenargs, **kwargs)
output, unused_err = process.communicate()
retcode = process.poll()
if retcode:
cmd = kwargs.get("args")
if cmd is None:
cmd = popenargs[0]
raise CalledProcessError(retcode, cmd, output=output)
return output | [
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hydroshare/hydroshare | 7ba563b55412f283047fb3ef6da367d41dec58c6 | hs_swat_modelinstance/receivers.py | python | _process_metadata_update_create | (**kwargs) | [] | def _process_metadata_update_create(**kwargs):
element_name = kwargs['element_name'].lower()
request = kwargs['request']
if element_name == "modeloutput":
element_form = ModelOutputValidationForm(request.POST)
elif element_name == 'executedby':
element_form = ExecutedByValidationForm(request.POST)
elif element_name == 'modelobjective':
element_form = ModelObjectiveValidationForm(request.POST)
elif element_name == 'simulationtype':
element_form = SimulationTypeValidationForm(request.POST)
elif element_name == 'modelmethod':
element_form = ModelMethodValidationForm(request.POST)
elif element_name == 'modelparameter':
element_form = ModelParameterValidationForm(request.POST)
elif element_name == 'modelinput':
element_form = ModelInputValidationForm(request.POST)
if element_form.is_valid():
return {'is_valid': True, 'element_data_dict': element_form.cleaned_data}
else:
return {'is_valid': False, 'element_data_dict': None, "errors": element_form.errors} | [
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21dotco/two1-python | 4e833300fd5a58363e3104ed4c097631e5d296d3 | two1/channels/database.py | python | DatabaseBase.create | (self, model) | Create a new payment channel in the database.
Args:
model (PaymentChannelModel): Payment channel state model. | Create a new payment channel in the database. | [
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"""Create a new payment channel in the database.
Args:
model (PaymentChannelModel): Payment channel state model.
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shiweibsw/Translation-Tools | 2fbbf902364e557fa7017f9a74a8797b7440c077 | venv/Lib/site-packages/pip-9.0.3-py3.6.egg/pip/_vendor/distlib/database.py | python | _Cache.clear | (self) | Clear the cache, setting it to its initial state. | Clear the cache, setting it to its initial state. | [
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"""
Clear the cache, setting it to its initial state.
"""
self.name.clear()
self.path.clear()
self.generated = False | [
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google/capirca | 679e3885e3a5e5e129dc2dfab204ec44d63b26a4 | capirca/lib/nacaddr.py | python | _InNetList | (adders, ip) | return False | Returns True if ip is contained in adders. | Returns True if ip is contained in adders. | [
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] | def _InNetList(adders, ip):
"""Returns True if ip is contained in adders."""
for addr in adders:
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return True
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awesto/django-shop | 13d9a77aff7eede74a5f363c1d540e005d88dbcd | shop/money/money_maker.py | python | AbstractMoney.__radd__ | (self, other, context=None) | return self.__add__(other, context) | [] | def __radd__(self, other, context=None):
return self.__add__(other, context) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/rpyc/core/brine.py | python | _load_none | (stream) | return None | [] | def _load_none(stream):
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mgedmin/check-manifest | 19f0a43ba00ecc536832f93801cf4446ea42e9b4 | check_manifest.py | python | _get_ignore_from_manifest_lines | (lines, ui) | return ignore | Gather the various ignore patterns from a MANIFEST.in.
'lines' should be a list of strings with comments removed
and continuation lines joined.
Returns an IgnoreList instance. | Gather the various ignore patterns from a MANIFEST.in. | [
"Gather",
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"various",
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"patterns",
"from",
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"MANIFEST",
".",
"in",
"."
] | def _get_ignore_from_manifest_lines(lines, ui):
"""Gather the various ignore patterns from a MANIFEST.in.
'lines' should be a list of strings with comments removed
and continuation lines joined.
Returns an IgnoreList instance.
"""
ignore = IgnoreList()
for line in lines:
try:
cmd, rest = line.split(None, 1)
except ValueError:
# no whitespace, so not interesting
continue
for part in rest.split():
# distutils enforces these warnings on Windows only
if part.startswith('/'):
ui.warning("ERROR: Leading slashes are not allowed in MANIFEST.in on Windows: %s" % part)
if part.endswith('/'):
ui.warning("ERROR: Trailing slashes are not allowed in MANIFEST.in on Windows: %s" % part)
if cmd == 'exclude':
ignore.exclude(*rest.split())
elif cmd == 'global-exclude':
ignore.global_exclude(*rest.split())
elif cmd == 'recursive-exclude':
try:
dirname, patterns = rest.split(None, 1)
except ValueError:
# Wrong MANIFEST.in line.
ui.warning(
"You have a wrong line in MANIFEST.in: %r\n"
"'recursive-exclude' expects <dir> <pattern1> <pattern2>..."
% line
)
continue
ignore.recursive_exclude(dirname, *patterns.split())
elif cmd == 'prune':
ignore.prune(rest)
# XXX: This ignores all 'include'/'global-include'/'recusive-include'/'graft' commands,
# which is wrong! Quoting the documentation:
#
# The order of commands in the manifest template matters: initially,
# we have the list of default files as described above, and each
# command in the template adds to or removes from that list of
# files.
# -- https://docs.python.org/3.8/distutils/sourcedist.html#specifying-the-files-to-distribute
return ignore | [
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compas-dev/compas | 0b33f8786481f710115fb1ae5fe79abc2a9a5175 | src/compas/numerical/ga/moga.py | python | MOGA.code_decoded | (self, decoded_pop) | return binary_pop | Returns a binary coded population from a decoded population
Parameters
----------
decoded_pop: dict
The decoded population dictionary to be coded
Returns
-------
binary_pop: dict
The binary population dictionary. | Returns a binary coded population from a decoded population | [
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Parameters
----------
decoded_pop: dict
The decoded population dictionary to be coded
Returns
-------
binary_pop: dict
The binary population dictionary.
"""
binary_pop = [[[]] * self.num_var for i in range(self.num_pop)]
for i in range(len(decoded_pop)):
binary_pop[i] = {}
for j in range(self.num_var):
bin_list = []
temp_bin = bin(decoded_pop[i][j])[2:]
temp_bin = temp_bin[::-1]
digit_dif = self.num_bin_dig[j] - len(temp_bin)
for h in range(digit_dif):
temp_bin = temp_bin + '0'
for k in range(self.num_bin_dig[j]):
bin_list.append(int(temp_bin[k]))
binary_pop[i][j] = bin_list
return binary_pop | [
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IntelLabs/coach | dea46ae0d22b0a0cd30b9fc138a4a2642e1b9d9d | rl_coach/architectures/tensorflow_components/architecture.py | python | TensorFlowArchitecture.apply_gradients | (self, gradients, scaler=1., additional_inputs=None) | Applies the given gradients to the network weights
:param gradients: The gradients to use for the update
:param scaler: A scaling factor that allows rescaling the gradients before applying them.
The gradients will be MULTIPLIED by this factor
:param additional_inputs: optional additional inputs required for when applying the gradients (e.g. batchnorm's
update ops also requires the inputs) | Applies the given gradients to the network weights
:param gradients: The gradients to use for the update
:param scaler: A scaling factor that allows rescaling the gradients before applying them.
The gradients will be MULTIPLIED by this factor
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"""
Applies the given gradients to the network weights
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:param scaler: A scaling factor that allows rescaling the gradients before applying them.
The gradients will be MULTIPLIED by this factor
:param additional_inputs: optional additional inputs required for when applying the gradients (e.g. batchnorm's
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"""
if self.network_parameters.async_training or not isinstance(self.ap.task_parameters, DistributedTaskParameters):
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self.sess.run(self.inc_step)
if self.optimizer_type != 'LBFGS':
if self.distributed_training and not self.network_parameters.async_training:
# rescale the gradients so that they average out with the gradients from the other workers
if self.network_parameters.scale_down_gradients_by_number_of_workers_for_sync_training:
scaler /= float(self.ap.task_parameters.num_training_tasks)
# rescale the gradients
if scaler != 1.:
for gradient in gradients:
gradient *= scaler
# apply the gradients
feed_dict = dict(zip(self.weights_placeholders, gradients))
if self.distributed_training and self.network_parameters.shared_optimizer \
and not self.network_parameters.async_training:
# synchronous distributed training with shared optimizer:
# - each worker adds its gradients to the shared gradients accumulators
# - we wait for all the workers to add their gradients
# - the chief worker (worker with task index = 0) applies the gradients once and resets the accumulators
self.sess.run(self.accumulate_shared_gradients, feed_dict=feed_dict)
self.wait_for_all_workers_barrier(include_only_training_workers=True)
if self.is_chief:
self.sess.run(self.update_weights_from_shared_gradients)
self.sess.run(self.init_shared_accumulated_gradients)
else:
# async distributed training / distributed training with independent optimizer
# / non-distributed training - just apply the gradients
feed_dict = dict(zip(self.weights_placeholders, gradients))
if additional_inputs is not None:
feed_dict = {**feed_dict, **self.create_feed_dict(additional_inputs)}
self.sess.run(self.update_weights_from_batch_gradients, feed_dict=feed_dict)
# release barrier
if self.distributed_training and not self.network_parameters.async_training:
self.wait_for_all_workers_barrier(include_only_training_workers=True) | [
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openstack/taskflow | 38b9011094dbcfdd00e6446393816201e8256d38 | taskflow/engines/action_engine/compiler.py | python | _overlap_occurrence_detector | (to_graph, from_graph) | return iter_utils.count(node for node in from_graph.nodes
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/dialects/sybase/base.py | python | SybaseSQLCompiler.delete_extra_from_clause | (self, delete_stmt, from_table,
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return "FROM " + ', '.join(
t._compiler_dispatch(self, asfrom=True,
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ym2011/POC-EXP | 206b22d3a6b2a172359678df33bbc5b2ad04b6c3 | K8/Web-Exp/sqlmap/lib/core/common.py | python | showHttpErrorCodes | () | Shows all HTTP error codes raised till now | Shows all HTTP error codes raised till now | [
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Shows all HTTP error codes raised till now
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if kb.httpErrorCodes:
warnMsg = "HTTP error codes detected during run:\n"
warnMsg += ", ".join("%d (%s) - %d times" % (code, httplib.responses[code] \
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for code, count in kb.httpErrorCodes.items())
logger.warn(warnMsg)
if any((str(_).startswith('4') or str(_).startswith('5')) and _ != httplib.INTERNAL_SERVER_ERROR and _ != kb.originalCode for _ in kb.httpErrorCodes.keys()):
msg = "too many 4xx and/or 5xx HTTP error codes "
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zhangxiaosong18/FreeAnchor | afad43a7c48034301d981d831fa61e42d9b6ddad | maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py | python | FastRCNNLossComputation.__init__ | (self, proposal_matcher, fg_bg_sampler, box_coder) | Arguments:
proposal_matcher (Matcher)
fg_bg_sampler (BalancedPositiveNegativeSampler)
box_coder (BoxCoder) | Arguments:
proposal_matcher (Matcher)
fg_bg_sampler (BalancedPositiveNegativeSampler)
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williballenthin/python-registry | 11e857623469dd28ed14519a08d2db7c8228ca0c | samples/copyclean.py | python | Copy.pack_dword | (self, offset, *data) | write little-endian DWORDs (4 bytes) to the relative offset.
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edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/esp8266_clients/venv/lib/python2.7/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | TarInfo.create_ustar_header | (self, info, encoding, errors) | return self._create_header(info, USTAR_FORMAT, encoding, errors) | Return the object as a ustar header block. | Return the object as a ustar header block. | [
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brendano/tweetmotif | 1b0b1e3a941745cd5a26eba01f554688b7c4b27e | everything_else/djfrontend/django-1.0.2/contrib/gis/gdal/geometries.py | python | GeometryCollection.__len__ | (self) | return self.geom_count | The number of geometries in this Geometry Collection. | The number of geometries in this Geometry Collection. | [
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openstack/python-keystoneclient | 100253d52e0c62dffffddb6f046ad660a9bce1a9 | keystoneclient/v3/access_rules.py | python | AccessRuleManager.get | (self, access_rule, user=None) | return super(AccessRuleManager, self).get(
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user = user or self.client.user_id
self.base_url = '/users/%(user)s' % {'user': user}
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blue-oil/blueoil | 0c9160b524b17482d59ae48a0c11384f1d26dccc | blueoil/networks/object_detection/yolo_v1.py | python | YoloV1.offset_boxes | (self) | return offset_x, offset_y | Return yolo space offset of x and y.
Return:
offset_x: shape is [batch_size, cell_size, cell_size, boxes_per_cell]
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Return:
offset_x: shape is [batch_size, cell_size, cell_size, boxes_per_cell]
offset_y: shape is [batch_size, cell_size, cell_size, boxes_per_cell]
"""
offset_y = np.arange(self.cell_size)
offset_y = np.reshape(offset_y, (1, self.cell_size, 1, 1))
offset_y = np.tile(offset_y, [self.batch_size, 1, self.cell_size, self.boxes_per_cell])
offset_x = np.transpose(offset_y, (0, 2, 1, 3))
return offset_x, offset_y | [
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mne-tools/mne-python | f90b303ce66a8415e64edd4605b09ac0179c1ebf | mne/externals/tqdm/_tqdm/std.py | python | tqdm.format_dict | (self) | return dict(
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/github/RepositoryKey.py | python | RepositoryKey.title | (self) | return self._title.value | :type: string | :type: string | [
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ntoll/drogulus | d74b78d0bf0220b91f075dbd3f9a06c2663b474e | drogulus/dht/storage.py | python | DictDataStore.keys | (self) | return self._dict.keys() | Return a view object of the keys in this data store. | Return a view object of the keys in this data store. | [
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nipy/nipy | d16d268938dcd5c15748ca051532c21f57cf8a22 | nipy/algorithms/statistics/models/model.py | python | LikelihoodModelResults.Fcontrast | (self, matrix, dispersion=None, invcov=None) | return FContrastResults(
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matrix : 1D array-like
contrast matrix
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Known inverse of variance covariance matrix.
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f_res : ``FContrastResults`` instance
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Parameters
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matrix : 1D array-like
contrast matrix
dispersion : None or float, optional
If None, use ``self.dispersion``
invcov : None or array, optional
Known inverse of variance covariance matrix.
If None, calculate this matrix.
Returns
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f_res : ``FContrastResults`` instance
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matrix = np.asarray(matrix)
# 1D vectors assumed to be row vector
if matrix.ndim == 1:
matrix = matrix[None]
if matrix.shape[1] != self.theta.shape[0]:
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ctheta = np.dot(matrix, self.theta)
if matrix.ndim == 1:
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if dispersion is None:
dispersion = self.dispersion
q = matrix.shape[0]
if invcov is None:
invcov = inv(self.vcov(matrix=matrix, dispersion=1.0))
F = np.add.reduce(np.dot(invcov, ctheta) * ctheta, 0) *\
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F = np.squeeze(F)
return FContrastResults(
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SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions | 014c4ca27a70b5907a183e942228004c989dcbe4 | selim_sef/generate_polygons.py | python | _remove_interiors | (line) | return line | [] | def _remove_interiors(line):
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/pep425tags.py | python | get_config_var | (var) | [] | def get_config_var(var):
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ppizarror/pygame-menu | da5827a1ad0686e8ff2aa536b74bbfba73967bcf | pygame_menu/widgets/core/widget.py | python | Widget.get_frame_depth | (self) | return depth | Get frame depth (If frame is packed within another frame).
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astorfi/speechpy | 7ffaa4d6909eef0afeef2741a8cd0cd0a42c4a67 | speechpy/processing.py | python | cmvn | (vec, variance_normalization=False) | return output | This function is aimed to perform global cepstral mean and
variance normalization (CMVN) on input feature vector "vec".
The code assumes that there is one observation per row.
Args:
vec (array): input feature matrix
(size:(num_observation,num_features))
variance_normalization (bool): If the variance
normilization should be performed or not.
Return:
array: The mean(or mean+variance) normalized feature vector. | This function is aimed to perform global cepstral mean and
variance normalization (CMVN) on input feature vector "vec".
The code assumes that there is one observation per row. | [
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""" This function is aimed to perform global cepstral mean and
variance normalization (CMVN) on input feature vector "vec".
The code assumes that there is one observation per row.
Args:
vec (array): input feature matrix
(size:(num_observation,num_features))
variance_normalization (bool): If the variance
normilization should be performed or not.
Return:
array: The mean(or mean+variance) normalized feature vector.
"""
eps = 2**-30
rows, cols = vec.shape
# Mean calculation
norm = np.mean(vec, axis=0)
norm_vec = np.tile(norm, (rows, 1))
# Mean subtraction
mean_subtracted = vec - norm_vec
# Variance normalization
if variance_normalization:
stdev = np.std(mean_subtracted, axis=0)
stdev_vec = np.tile(stdev, (rows, 1))
output = mean_subtracted / (stdev_vec + eps)
else:
output = mean_subtracted
return output | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/asn1crypto/_errors.py | python | unwrap | (string, *params) | return output | Takes a multi-line string and does the following:
- dedents
- converts newlines with text before and after into a single line
- strips leading and trailing whitespace
:param string:
The string to format
:param *params:
Params to interpolate into the string
:return:
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- strips leading and trailing whitespace
:param string:
The string to format
:param *params:
Params to interpolate into the string
:return:
The formatted string
"""
output = textwrap.dedent(string)
# Unwrap lines, taking into account bulleted lists, ordered lists and
# underlines consisting of = signs
if output.find('\n') != -1:
output = re.sub('(?<=\\S)\n(?=[^ \n\t\\d\\*\\-=])', ' ', output)
if params:
output = output % params
output = output.strip()
return output | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pip/req/req_file.py | python | break_args_options | (line) | return ' '.join(args), ' '.join(options) | Break up the line into an args and options string. We only want to shlex
(and then optparse) the options, not the args. args can contain markers
which are corrupted by shlex. | Break up the line into an args and options string. We only want to shlex
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"""Break up the line into an args and options string. We only want to shlex
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which are corrupted by shlex.
"""
tokens = line.split(' ')
args = []
options = tokens[:]
for token in tokens:
if token.startswith('-') or token.startswith('--'):
break
else:
args.append(token)
options.pop(0)
return ' '.join(args), ' '.join(options) | [
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sokolovstas/SublimeWebInspector | e808d12956f115ea2b37bdf46fbfdb20b4e5ec13 | swi.py | python | SwiDebugCommand.command_selected | (self, index) | Called by Sublime when a quick panel entry is selected | Called by Sublime when a quick panel entry is selected | [
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""" Called by Sublime when a quick panel entry is selected """
utils.assert_main_thread()
if index == -1:
return
command = self.cmds[index]
if command == 'swi_dump_file_mappings':
# we wrap this command so we can use the correct view
print(command)
v = views.find_or_create_view('mapping')
v.run_command('swi_dump_file_mappings_internal')
return
self.window.run_command(command) | [
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awslabs/gluon-ts | 066ec3b7f47aa4ee4c061a28f35db7edbad05a98 | src/gluonts/nursery/SCott/pts/model/forecast.py | python | DistributionForecast.mean_ts | (self) | return pd.Series(data=self.mean, index=self.index) | Forecast mean, as a pandas.Series object. | Forecast mean, as a pandas.Series object. | [
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"""
Forecast mean, as a pandas.Series object.
"""
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/olefile/olefile.py | python | OleDirectoryEntry.dump | (self, tab = 0) | Dump this entry, and all its subentries (for debug purposes only) | Dump this entry, and all its subentries (for debug purposes only) | [
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"Dump this entry, and all its subentries (for debug purposes only)"
TYPES = ["(invalid)", "(storage)", "(stream)", "(lockbytes)",
"(property)", "(root)"]
print(" "*tab + repr(self.name), TYPES[self.entry_type], end=' ')
if self.entry_type in (STGTY_STREAM, STGTY_ROOT):
print(self.size, "bytes", end=' ')
print()
if self.entry_type in (STGTY_STORAGE, STGTY_ROOT) and self.clsid:
print(" "*tab + "{%s}" % self.clsid)
for kid in self.kids:
kid.dump(tab + 2) | [
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0xInfection/XSRFProbe | ce0411174fa2e438bea0712bdd2acf74f7f62d9a | xsrfprobe/modules/Debugger.py | python | getAllForms | (soup) | return soup.findAll('form', method=re.compile("post", re.IGNORECASE)) | [] | def getAllForms(soup): # get all forms
return soup.findAll('form', method=re.compile("post", re.IGNORECASE)) | [
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bit4woo/teemo | b7c1bca65c9e9e6b00f362284e74665e20cbf707 | searchengine/search_shodan.py | python | search_shodan.do_search | (self) | [] | def do_search(self):
try:
api = shodan.Shodan(self.apikey)
self.results = api.search(self.word)
self.totalresults +=str(self.results)
return True
except shodan.APIError, e:
logger.error("Error in {0}: {1}".format(__file__.split('/')[-1],e))
return False | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/packages/IronPython.StdLib.2.7.4/content/Lib/base64.py | python | urlsafe_b64encode | (s) | return b64encode(s, '-_') | Encode a string using a url-safe Base64 alphabet.
s is the string to encode. The encoded string is returned. The alphabet
uses '-' instead of '+' and '_' instead of '/'. | Encode a string using a url-safe Base64 alphabet. | [
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] | def urlsafe_b64encode(s):
"""Encode a string using a url-safe Base64 alphabet.
s is the string to encode. The encoded string is returned. The alphabet
uses '-' instead of '+' and '_' instead of '/'.
"""
return b64encode(s, '-_') | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/decision_tree.py | python | DecisionTree.print_leafs | (self) | [] | def print_leafs(self):
LOGGER.debug('printing tree')
if len(self.tree_node) == 0:
LOGGER.debug('this tree is empty')
else:
for node in self.tree_node:
LOGGER.debug(node) | [
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jaheyns/CfdOF | 97fab2347e421eaad7cb549006329425112b9e21 | CfdTools.py | python | convertMesh | (case, mesh_file, scale) | Convert gmsh created UNV mesh to FOAM. A scaling of 1e-3 is prescribed as the CAD is always in mm while FOAM
uses SI units (m). | Convert gmsh created UNV mesh to FOAM. A scaling of 1e-3 is prescribed as the CAD is always in mm while FOAM
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""" Convert gmsh created UNV mesh to FOAM. A scaling of 1e-3 is prescribed as the CAD is always in mm while FOAM
uses SI units (m). """
if mesh_file.find(".unv") > 0:
mesh_file = translatePath(mesh_file)
cmdline = ['ideasUnvToFoam', '"{}"'.format(mesh_file)]
runFoamApplication(cmdline, case)
# changeBoundaryType(case, 'defaultFaces', 'wall') # rename default boundary type to wall
# Set in the correct patch types
cmdline = ['changeDictionary']
runFoamApplication(cmdline, case)
else:
raise Exception("Error: Only supporting unv mesh files.")
if scale and isinstance(scale, numbers.Number):
cmdline = ['transformPoints', '-scale', '"({} {} {})"'.format(scale, scale, scale)]
runFoamApplication(cmdline, case)
else:
print("Error: mesh scaling ratio is must be a float or integer\n") | [
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evennia/evennia | fa79110ba6b219932f22297838e8ac72ebc0be0e | evennia/utils/utils.py | python | LimitedSizeOrderedDict.__init__ | (self, *args, **kwargs) | Limited-size ordered dict.
Keyword Args:
size_limit (int): Use this to limit the number of elements
alloweds to be in this list. By default the overshooting elements
will be removed in FIFO order.
fifo (bool, optional): Defaults to `True`. Remove overshooting elements
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"""
Limited-size ordered dict.
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size_limit (int): Use this to limit the number of elements
alloweds to be in this list. By default the overshooting elements
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fifo (bool, optional): Defaults to `True`. Remove overshooting elements
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"""
super().__init__()
self.size_limit = kwargs.get("size_limit", None)
self.filo = not kwargs.get("fifo", True) # FIFO inverse of FILO
self._check_size() | [
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EasonLiao/CudaTree | fcd9c7f4dbac00c105bbfbff9f55db1d29747fc7 | cudatree/util.py | python | compile_module | (module_file, params) | [] | def compile_module(module_file, params):
module_file = kernels_dir + module_file
key = (module_file, params)
if key in _module_cache:
return _module_cache[key]
with open(module_file) as code_file:
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code = code_file.read()
src = code % params
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end_timer("compile module")
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ivre/ivre | 5728855b51c0ae2e59450a1c3a782febcad2128b | ivre/db/mongo.py | python | MongoDBFlow.flt_from_query | (cls, query) | return flt | Returns a MongoDB filter from the given query object. | Returns a MongoDB filter from the given query object. | [
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"""
Returns a MongoDB filter from the given query object.
"""
clauses = query.clauses
flt = {}
and_clauses = []
for and_clause in clauses:
or_clauses = []
for or_clause in and_clause:
or_clauses.append(cls.flt_from_clause(or_clause))
if len(or_clauses) > 1:
and_clauses.append({"$or": or_clauses})
elif len(or_clauses) == 1:
and_clauses.append(or_clauses[0])
if len(and_clauses) > 1:
flt = {"$and": and_clauses}
elif len(and_clauses) == 1:
flt = and_clauses[0]
return flt | [
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openstack/openstacksdk | 58384268487fa854f21c470b101641ab382c9897 | openstack/compute/v2/_proxy.py | python | Proxy.backup_server | (self, server, name, backup_type, rotation) | Backup a server
:param server: Either the ID of a server or a
:class:`~openstack.compute.v2.server.Server` instance.
:param name: The name of the backup image.
:param backup_type: The type of the backup, for example, daily.
:param rotation: The rotation of the back up image, the oldest
image will be removed when image count exceed
the rotation count.
:returns: None | Backup a server | [
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] | def backup_server(self, server, name, backup_type, rotation):
"""Backup a server
:param server: Either the ID of a server or a
:class:`~openstack.compute.v2.server.Server` instance.
:param name: The name of the backup image.
:param backup_type: The type of the backup, for example, daily.
:param rotation: The rotation of the back up image, the oldest
image will be removed when image count exceed
the rotation count.
:returns: None
"""
server = self._get_resource(_server.Server, server)
server.backup(self, name, backup_type, rotation) | [
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/analysis/ESP/NanoHiveUtils.py | python | NH_Connection.sendCommand | (self, command) | Sends a command to the N-H instance and returns the response.
Parameters:
command
The command to send to the N-H instance
Returns:
success indicator
A one if the command was successfully sent and a response was
received
response or error message
If all was successful, the encoded N-H instance response, or a
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"""
Sends a command to the N-H instance and returns the response.
Parameters:
command
The command to send to the N-H instance
Returns:
success indicator
A one if the command was successfully sent and a response was
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response or error message
If all was successful, the encoded N-H instance response, or a
description of the error if unsuccessful. The encoded response
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"""
# Send command
success = 1
try:
self.connectionHandle.send(command)
except socket.error, errorMessage:
success = 0
# Read response length
if success:
try:
responseLengthChars = self.connectionHandle.recv(4)
except socket.error, errorMessage:
success = 0
if success:
responseLength = ord(responseLengthChars[0])
responseLength |= ord(responseLengthChars[1]) << 8
responseLength |= ord(responseLengthChars[2]) << 16
responseLength |= ord(responseLengthChars[3]) << 24;
# Read response
try:
response = self.connectionHandle.recv(responseLength)
except socket.error, errorMessage:
success = 0
if success:
return 1, response
else:
return 0, errorMessage | [
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mozillazg/pypy | 2ff5cd960c075c991389f842c6d59e71cf0cb7d0 | lib-python/2.7/poplib.py | python | POP3.rpop | (self, user) | return self._shortcmd('RPOP %s' % user) | Not sure what this does. | Not sure what this does. | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models/albert/tokenization.py | python | FullTokenizer.tokenize | (self, text) | return split_tokens | [] | def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
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hqqasw/person-search-PPCC | 3ce82bffff3646291b9a631534e295a6aa749c4d | utils/metric.py | python | affmat2retdict | (affmat, pid_list) | return ret_dict | parse affinity matrix to resutl dict
ret_dict:
key: target person id
value: list of candidates | parse affinity matrix to resutl dict
ret_dict:
key: target person id
value: list of candidates | [
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"""
parse affinity matrix to resutl dict
ret_dict:
key: target person id
value: list of candidates
"""
num_cast = affmat.shape[0]
num_instance = affmat.shape[1]
index = np.argsort(-affmat, axis=1)
ret_dict = {}
for i in range(num_cast):
ret_dict[pid_list[i]] = index[i].tolist()
return ret_dict | [
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mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/interpreter/mesonmain.py | python | MesonMain.get_external_property_method | (self, args: T.Tuple[str, T.Optional[object]], kwargs: 'NativeKW') | return self.__get_external_property_impl(propname, fallback, kwargs['native']) | [] | def get_external_property_method(self, args: T.Tuple[str, T.Optional[object]], kwargs: 'NativeKW') -> object:
propname, fallback = args
return self.__get_external_property_impl(propname, fallback, kwargs['native']) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/wtforms/fields/core.py | python | StringField.process_formdata | (self, valuelist) | [] | def process_formdata(self, valuelist):
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self.data = valuelist[0]
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JimmXinu/FanFicFare | bc149a2deb2636320fe50a3e374af6eef8f61889 | included_dependencies/requests/utils.py | python | should_bypass_proxies | (url, no_proxy) | return False | Returns whether we should bypass proxies or not.
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"""
Returns whether we should bypass proxies or not.
:rtype: bool
"""
# Prioritize lowercase environment variables over uppercase
# to keep a consistent behaviour with other http projects (curl, wget).
get_proxy = lambda k: os.environ.get(k) or os.environ.get(k.upper())
# First check whether no_proxy is defined. If it is, check that the URL
# we're getting isn't in the no_proxy list.
no_proxy_arg = no_proxy
if no_proxy is None:
no_proxy = get_proxy('no_proxy')
parsed = urlparse(url)
if parsed.hostname is None:
# URLs don't always have hostnames, e.g. file:/// urls.
return True
if no_proxy:
# We need to check whether we match here. We need to see if we match
# the end of the hostname, both with and without the port.
no_proxy = (
host for host in no_proxy.replace(' ', '').split(',') if host
)
if is_ipv4_address(parsed.hostname):
for proxy_ip in no_proxy:
if is_valid_cidr(proxy_ip):
if address_in_network(parsed.hostname, proxy_ip):
return True
elif parsed.hostname == proxy_ip:
# If no_proxy ip was defined in plain IP notation instead of cidr notation &
# matches the IP of the index
return True
else:
host_with_port = parsed.hostname
if parsed.port:
host_with_port += ':{}'.format(parsed.port)
for host in no_proxy:
if parsed.hostname.endswith(host) or host_with_port.endswith(host):
# The URL does match something in no_proxy, so we don't want
# to apply the proxies on this URL.
return True
with set_environ('no_proxy', no_proxy_arg):
# parsed.hostname can be `None` in cases such as a file URI.
try:
bypass = proxy_bypass(parsed.hostname)
except (TypeError, socket.gaierror):
bypass = False
if bypass:
return True
return False | [
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otto-torino/django-baton | 8ae3b2ea1b875cf38031ee4a58a88e9478447193 | baton/views.py | python | GetAppListJsonView.get_default_voices | (self) | return voices | When no custom menu is defined in settings
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children = []
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voices.append(voice)
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oc_volume.py | python | Yedit.update | (self, path, value, index=None, curr_value=None) | return (False, self.yaml_dict) | put path, value into a dict | put path, value into a dict | [
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''' put path, value into a dict '''
try:
entry = Yedit.get_entry(self.yaml_dict, path, self.separator)
except KeyError:
entry = None
if isinstance(entry, dict):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
if not isinstance(value, dict):
raise YeditException('Cannot replace key, value entry in dict with non-dict type. ' +
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entry.update(value)
return (True, self.yaml_dict)
elif isinstance(entry, list):
# AUDIT:maybe-no-member makes sense due to fuzzy types
# pylint: disable=maybe-no-member
ind = None
if curr_value:
try:
ind = entry.index(curr_value)
except ValueError:
return (False, self.yaml_dict)
elif index is not None:
ind = index
if ind is not None and entry[ind] != value:
entry[ind] = value
return (True, self.yaml_dict)
# see if it exists in the list
try:
ind = entry.index(value)
except ValueError:
# doesn't exist, append it
entry.append(value)
return (True, self.yaml_dict)
# already exists, return
if ind is not None:
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_group.py | python | OpenShiftCLIConfig.to_option_list | (self, ascommalist='') | return self.stringify(ascommalist) | return all options as a string
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/asn1crypto/util.py | python | extended_date.isoformat | (self) | return self.strftime('0000-%m-%d') | Formats the date as %Y-%m-%d
:return:
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Formats the date as %Y-%m-%d
:return:
The date formatted to %Y-%m-%d as a unicode string in Python 3
and a byte string in Python 2
"""
return self.strftime('0000-%m-%d') | [
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heineman/python-algorithms | b8a0061b52ff15cbf4a390854cb537a3fa7e309e | 6. DepthFirstSearch/graph.py | python | DepthFirstTraversal.dfs_visit | (self, u) | Recursive traversal of graph using DFS | Recursive traversal of graph using DFS | [
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codelv/enaml-native | 04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc | src/enamlnative/core/hotswap/core.py | python | Hotswapper.find_best_matching_node | (self, new, old_nodes) | return matches[0] if matches else None | Find the node that best matches the new node given the old nodes. If no
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"""
name = new.__class__.__name__
#: TODO: We should pick the BEST one from this list
#: based on some "matching" criteria (such as matching ref name or params)
matches = [c for c in old_nodes if name == c.__class__.__name__]
if self.debug:
print("Found matches for {}: {} ".format(new, matches))
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OpenMDAO/OpenMDAO | f47eb5485a0bb5ea5d2ae5bd6da4b94dc6b296bd | openmdao/utils/indexer.py | python | ShapedSliceIndexer._check_bounds | (self) | Check that indices are within the bounds of the source shape. | Check that indices are within the bounds of the source shape. | [
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if self._src_shape is not None:
start = self._slice.start
stop = self._slice.stop
sz = np.product(self._dist_shape)
if (start is not None and (start >= sz or start < -sz)
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/setuptools/msvc.py | python | SystemInfo._find_dot_net_versions | (self, bits=32) | return frameworkver | Find Microsoft .NET Framework versions.
Parameters
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bits: int
Platform number of bits: 32 or 64. | Find Microsoft .NET Framework versions. | [
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Find Microsoft .NET Framework versions.
Parameters
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Platform number of bits: 32 or 64.
"""
# Find actual .NET version
ver = self.ri.lookup(self.ri.vc, 'frameworkver%d' % bits) or ''
# Set .NET versions for specified MSVC++ version
if self.vc_ver >= 12.0:
frameworkver = (ver, 'v4.0')
elif self.vc_ver >= 10.0:
frameworkver = ('v4.0.30319' if ver.lower()[:2] != 'v4' else ver,
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elif self.vc_ver == 9.0:
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/lib-tk/tkSimpleDialog.py | python | Dialog.body | (self, master) | create dialog body.
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vivisect/vivisect | 37b0b655d8dedfcf322e86b0f144b096e48d547e | vtrace/breakpoints.py | python | Breakpoint.notify | (self, event, trace) | Breakpoints may also extend and implement "notify" which will be
called whenever they are hit. If you want to continue the ability
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | openedx/core/djangoapps/content_libraries/auth.py | python | LtiAuthenticationBackend.authenticate | (self, request, iss=None, aud=None, sub=None, **kwargs) | return None | Authenticate if the user in the request has an LTI profile. | Authenticate if the user in the request has an LTI profile. | [
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log.info('LTI 1.3 authentication: iss=%s, sub=%s', iss, sub)
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IntelLabs/nlp-architect | 60afd0dd1bfd74f01b4ac8f613cb484777b80284 | nlp_architect/data/sequential_tagging.py | python | SequentialTaggingDataset.char_vocab | (self) | return self.vocabs["char"] | characters vocabulary | characters vocabulary | [
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lisa-lab/pylearn2 | af81e5c362f0df4df85c3e54e23b2adeec026055 | pylearn2/datasets/retina.py | python | RetinaCodingViewConverter.design_mat_to_topo_view | (self, X) | return self.decoder.perform(X) | .. todo::
WRITEME | .. todo:: | [
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.. todo::
WRITEME
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/contextlib.py | python | AbstractContextManager.__exit__ | (self, exc_type, exc_value, traceback) | return None | Raise any exception triggered within the runtime context. | Raise any exception triggered within the runtime context. | [
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tensorflow/tensor2tensor | 2a33b152d7835af66a6d20afe7961751047e28dd | tensor2tensor/models/research/autoencoders.py | python | autoencoder_discrete_pong_range | (rhp) | Narrow tuning grid. | Narrow tuning grid. | [
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"""Narrow tuning grid."""
rhp.set_float("dropout", 0.0, 0.2)
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dragonfly/dragonfly | a579b5eadf452e23b07d4caf27b402703b0012b7 | dragonfly/utils/graph_utils.py | python | apsp_dijkstra | (A) | return np.array(vertex_dists) | Runs Dijkstra's on all nodes to compute the all pairs shortest paths. | Runs Dijkstra's on all nodes to compute the all pairs shortest paths. | [
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google/python-fire | ed44d8b801fc24e40729abef11b2dcbf6588d361 | fire/console/console_pager.py | python | Pager._Help | (self) | Print command help and wait for any character to continue. | Print command help and wait for any character to continue. | [
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Abjad/abjad | d0646dfbe83db3dc5ab268f76a0950712b87b7fd | abjad/score.py | python | Container.__delitem__ | (self, i) | r"""
Deletes components(s) at index ``i`` in container.
.. container:: example
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>>> voice = abjad.Voice()
>>> voice.append(abjad.Tuplet((4, 6), "c'4 d'4 e'4"))
>>> voice.append(abjad.Tuplet((2, 3), "e'4 d'4 c'4"))
>>> leaves = abjad.select(voice).leaves()
>>> abjad.slur(leaves)
>>> abjad.show(voice) # doctest: +SKIP
.. docs::
>>> string = abjad.lilypond(voice)
>>> print(string)
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>>> del(voice[0])
>>> start_slur = abjad.StartSlur()
>>> leaf = abjad.select(voice).leaf(0)
>>> abjad.attach(start_slur, leaf)
First tuplet no longer appears in voice:
>>> abjad.show(voice) # doctest: +SKIP
.. docs::
>>> string = abjad.lilypond(voice)
>>> print(string)
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.. docs::
>>> string = abjad.lilypond(tuplet_1)
>>> print(string)
\times 4/6
{
c'4
d'4
e'4
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>>> abjad.wf.wellformed(tuplet_1)
True
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Deletes components(s) at index ``i`` in container.
.. container:: example
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>>> voice = abjad.Voice()
>>> voice.append(abjad.Tuplet((4, 6), "c'4 d'4 e'4"))
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>>> leaves = abjad.select(voice).leaves()
>>> abjad.slur(leaves)
>>> abjad.show(voice) # doctest: +SKIP
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c'4
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True
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.. docs::
>>> string = abjad.lilypond(tuplet_1)
>>> print(string)
\times 4/6
{
c'4
d'4
e'4
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>>> abjad.wf.wellformed(tuplet_1)
True
Returns none.
"""
result = self[i]
if isinstance(result, Component):
result._set_parent(None)
return
for component in result:
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brainiak/brainiak | ee093597c6c11597b0a59e95b48d2118e40394a5 | brainiak/hyperparamopt/hpo.py | python | gmm_1d_distribution.__call__ | (self, x) | Return the GMM likelihood for given point(s).
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x : scalar (or) 1D array of reals
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Point(s) at which likelihood needs to be computed
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/google_ads_service/client.py | python | GoogleAdsServiceClient.gender_view_path | (
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) | return "customers/{customer_id}/genderViews/{ad_group_id}~{criterion_id}".format(
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return "customers/{customer_id}/genderViews/{ad_group_id}~{criterion_id}".format(
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/zmq/auth/base.py | python | Authenticator._send_zap_reply | (self, request_id, status_code, status_text, user_id='user') | Send a ZAP reply to finish the authentication. | Send a ZAP reply to finish the authentication. | [
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user_id = user_id if status_code == b'200' else b''
if isinstance(user_id, unicode):
user_id = user_id.encode(self.encoding, 'replace')
metadata = b'' # not currently used
self.log.debug("ZAP reply code=%s text=%s", status_code, status_text)
reply = [VERSION, request_id, status_code, status_text, user_id, metadata]
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fizyr/keras-maskrcnn | 5c4b3610775d2a32beb9ed9c0d04eb6b16b114f2 | keras_maskrcnn/preprocessing/generator.py | python | Generator.preprocess_group_entry | (self, image, annotations) | return image, annotations | Preprocess image and its annotations. | Preprocess image and its annotations. | [
"Preprocess",
"image",
"and",
"its",
"annotations",
"."
] | def preprocess_group_entry(self, image, annotations):
""" Preprocess image and its annotations.
"""
# preprocess the image
image = self.preprocess_image(image)
# randomly transform image and annotations
image, annotations = self.random_transform_group_entry(image, annotations)
# resize image
image, image_scale = self.resize_image(image)
# resize masks
for i in range(len(annotations['masks'])):
annotations['masks'][i], _ = self.resize_image(annotations['masks'][i])
# apply resizing to annotations too
annotations['bboxes'] *= image_scale
# convert to the wanted keras floatx
image = keras.backend.cast_to_floatx(image)
return image, annotations | [
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504ensicsLabs/DAMM | 60e7ec7dacd6087cd6320b3615becca9b4cf9b24 | volatility/dwarf.py | python | DWARFParser.resolve | (self, memb) | Lookup anonymous member and replace it with a well known one. | Lookup anonymous member and replace it with a well known one. | [
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"""Lookup anonymous member and replace it with a well known one."""
# Reference to another type
if isinstance(memb, str) and memb.startswith('<'):
if memb[1:3] == "0x":
memb = "<0x" + memb[3:].lstrip('0')
resolved = self.id_to_name[memb[1:]]
return self.resolve(resolved)
elif isinstance(memb, list):
return [self.resolve(r) for r in memb]
else:
# Literal
return memb | [
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NTMC-Community/MatchZoo | 8a487ee5a574356fc91e4f48e219253dc11bcff2 | matchzoo/layers/matching_layer.py | python | MatchingLayer.call | (self, inputs: list, **kwargs) | The computation logic of MatchingLayer.
:param inputs: two input tensors. | The computation logic of MatchingLayer. | [
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] | def call(self, inputs: list, **kwargs) -> typing.Any:
"""
The computation logic of MatchingLayer.
:param inputs: two input tensors.
"""
x1 = inputs[0]
x2 = inputs[1]
if self._matching_type == 'dot':
if self._normalize:
x1 = tf.math.l2_normalize(x1, axis=2)
x2 = tf.math.l2_normalize(x2, axis=2)
return tf.expand_dims(tf.einsum('abd,acd->abc', x1, x2), 3)
else:
if self._matching_type == 'mul':
def func(x, y):
return x * y
elif self._matching_type == 'plus':
def func(x, y):
return x + y
elif self._matching_type == 'minus':
def func(x, y):
return x - y
elif self._matching_type == 'concat':
def func(x, y):
return tf.concat([x, y], axis=3)
else:
raise ValueError(f"Invalid matching type."
f"{self._matching_type} received."
f"Mut be in `dot`, `mul`, `plus`, "
f"`minus` and `concat`.")
x1_exp = tf.stack([x1] * self._shape2[1], 2)
x2_exp = tf.stack([x2] * self._shape1[1], 1)
return func(x1_exp, x2_exp) | [
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3/s3model.py | python | S3Model.model | (self) | return None | Defines all tables in this model, to be implemented by
subclasses | Defines all tables in this model, to be implemented by
subclasses | [
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] | def model(self):
"""
Defines all tables in this model, to be implemented by
subclasses
"""
return None | [
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nlloyd/SubliminalCollaborator | 5c619e17ddbe8acb9eea8996ec038169ddcd50a1 | libs/twisted/mail/imap4.py | python | IMAP4Client.registerAuthenticator | (self, auth) | Register a new form of authentication
When invoking the authenticate() method of IMAP4Client, the first
matching authentication scheme found will be used. The ordering is
that in which the server lists support authentication schemes.
@type auth: Implementor of C{IClientAuthentication}
@param auth: The object to use to perform the client
side of this authentication scheme. | Register a new form of authentication | [
"Register",
"a",
"new",
"form",
"of",
"authentication"
] | def registerAuthenticator(self, auth):
"""Register a new form of authentication
When invoking the authenticate() method of IMAP4Client, the first
matching authentication scheme found will be used. The ordering is
that in which the server lists support authentication schemes.
@type auth: Implementor of C{IClientAuthentication}
@param auth: The object to use to perform the client
side of this authentication scheme.
"""
self.authenticators[auth.getName().upper()] = auth | [
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/gae_proxy/server/lib/google/appengine/api/images/__init__.py | python | vertical_flip_async | (image_data, output_encoding=PNG, quality=None,
correct_orientation=UNCHANGED_ORIENTATION, rpc=None,
transparent_substitution_rgb=None) | return image.execute_transforms_async(output_encoding=output_encoding,
quality=quality,
rpc=rpc,
transparent_substitution_rgb=transparent_substitution_rgb) | Flip the image vertically - async version.
Args:
image_data: str, source image data.
output_encoding: a value from OUTPUT_ENCODING_TYPES.
quality: A value between 1 and 100 to specify the quality of the
encoding. This value is only used for JPEG quality control.
correct_orientation: one of ORIENTATION_CORRECTION_TYPE, to indicate if
orientation correction should be performed during the transformation.
rpc: An Optional UserRPC object
transparent_substition_rgb: When transparent pixels are not support in the
destination image format then transparent pixels will be substituted
for the specified color, which must be 32 bit rgb format.
Returns:
A UserRPC object, call get_result to complete the RPC and obtain the crop
result.
Raises:
Error when something went wrong with the call. See Image.ExecuteTransforms
for more details. | Flip the image vertically - async version. | [
"Flip",
"the",
"image",
"vertically",
"-",
"async",
"version",
"."
] | def vertical_flip_async(image_data, output_encoding=PNG, quality=None,
correct_orientation=UNCHANGED_ORIENTATION, rpc=None,
transparent_substitution_rgb=None):
"""Flip the image vertically - async version.
Args:
image_data: str, source image data.
output_encoding: a value from OUTPUT_ENCODING_TYPES.
quality: A value between 1 and 100 to specify the quality of the
encoding. This value is only used for JPEG quality control.
correct_orientation: one of ORIENTATION_CORRECTION_TYPE, to indicate if
orientation correction should be performed during the transformation.
rpc: An Optional UserRPC object
transparent_substition_rgb: When transparent pixels are not support in the
destination image format then transparent pixels will be substituted
for the specified color, which must be 32 bit rgb format.
Returns:
A UserRPC object, call get_result to complete the RPC and obtain the crop
result.
Raises:
Error when something went wrong with the call. See Image.ExecuteTransforms
for more details.
"""
image = Image(image_data)
image.vertical_flip()
image.set_correct_orientation(correct_orientation)
return image.execute_transforms_async(output_encoding=output_encoding,
quality=quality,
rpc=rpc,
transparent_substitution_rgb=transparent_substitution_rgb) | [
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datamllab/rlcard | c21ea82519c453a42e3bdc6848bd3356e9b6ac43 | rlcard/games/nolimitholdem/game.py | python | NolimitholdemGame.step | (self, action) | return state, self.game_pointer | Get the next state
Args:
action (str): a specific action. (call, raise, fold, or check)
Returns:
(tuple): Tuple containing:
(dict): next player's state
(int): next player id | Get the next state | [
"Get",
"the",
"next",
"state"
] | def step(self, action):
"""
Get the next state
Args:
action (str): a specific action. (call, raise, fold, or check)
Returns:
(tuple): Tuple containing:
(dict): next player's state
(int): next player id
"""
if action not in self.get_legal_actions():
print(action, self.get_legal_actions())
print(self.get_state(self.game_pointer))
raise Exception('Action not allowed')
if self.allow_step_back:
# First snapshot the current state
r = deepcopy(self.round)
b = self.game_pointer
r_c = self.round_counter
d = deepcopy(self.dealer)
p = deepcopy(self.public_cards)
ps = deepcopy(self.players)
self.history.append((r, b, r_c, d, p, ps))
# Then we proceed to the next round
self.game_pointer = self.round.proceed_round(self.players, action)
players_in_bypass = [1 if player.status in (PlayerStatus.FOLDED, PlayerStatus.ALLIN) else 0 for player in self.players]
if self.num_players - sum(players_in_bypass) == 1:
last_player = players_in_bypass.index(0)
if self.round.raised[last_player] >= max(self.round.raised):
# If the last player has put enough chips, he is also bypassed
players_in_bypass[last_player] = 1
# If a round is over, we deal more public cards
if self.round.is_over():
# Game pointer goes to the first player not in bypass after the dealer, if there is one
self.game_pointer = (self.dealer_id + 1) % self.num_players
if sum(players_in_bypass) < self.num_players:
while players_in_bypass[self.game_pointer]:
self.game_pointer = (self.game_pointer + 1) % self.num_players
# For the first round, we deal 3 cards
if self.round_counter == 0:
self.stage = Stage.FLOP
self.public_cards.append(self.dealer.deal_card())
self.public_cards.append(self.dealer.deal_card())
self.public_cards.append(self.dealer.deal_card())
if len(self.players) == np.sum(players_in_bypass):
self.round_counter += 1
# For the following rounds, we deal only 1 card
if self.round_counter == 1:
self.stage = Stage.TURN
self.public_cards.append(self.dealer.deal_card())
if len(self.players) == np.sum(players_in_bypass):
self.round_counter += 1
if self.round_counter == 2:
self.stage = Stage.RIVER
self.public_cards.append(self.dealer.deal_card())
if len(self.players) == np.sum(players_in_bypass):
self.round_counter += 1
self.round_counter += 1
self.round.start_new_round(self.game_pointer)
state = self.get_state(self.game_pointer)
return state, self.game_pointer | [
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jobovy/galpy | 8e6a230bbe24ce16938db10053f92eb17fe4bb52 | galpy/actionAngle/actionAngleInverse.py | python | actionAngleInverse.__call__ | (self,*args,**kwargs) | NAME:
evaluate the phase-space coordinates (x,v) for a number of angles on a single torus
INPUT:
jr - radial action (scalar)
jphi - azimuthal action (scalar)
jz - vertical action (scalar)
angler - radial angle (array [N])
anglephi - azimuthal angle (array [N])
anglez - vertical angle (array [N])
Some sub-classes (like actionAngleTorus) have additional parameters:
actionAngleTorus:
tol= (object-wide value) goal for |dJ|/|J| along the torus
OUTPUT:
[R,vR,vT,z,vz,phi]
HISTORY:
2017-11-14 - Written - Bovy (UofT) | NAME: | [
"NAME",
":"
] | def __call__(self,*args,**kwargs):
"""
NAME:
evaluate the phase-space coordinates (x,v) for a number of angles on a single torus
INPUT:
jr - radial action (scalar)
jphi - azimuthal action (scalar)
jz - vertical action (scalar)
angler - radial angle (array [N])
anglephi - azimuthal angle (array [N])
anglez - vertical angle (array [N])
Some sub-classes (like actionAngleTorus) have additional parameters:
actionAngleTorus:
tol= (object-wide value) goal for |dJ|/|J| along the torus
OUTPUT:
[R,vR,vT,z,vz,phi]
HISTORY:
2017-11-14 - Written - Bovy (UofT)
"""
try:
return self._evaluate(*args,**kwargs)
except AttributeError: #pragma: no cover
raise NotImplementedError("'__call__' method not implemented for this actionAngle module") | [
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panchunguang/ccks_baidu_entity_link | f6eb5298620b460f2f1222a3b182d15c938c4ea2 | code/entity_vec_extract.py | python | extract | (max_len=512) | :param max_len: 文本最大长度
:return: 字典形式，key: kb_id value: kb_id对应描述文本形成的向量 | :param max_len: 文本最大长度
:return: 字典形式，key: kb_id value: kb_id对应描述文本形成的向量 | [
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"字典形式，key",
":",
"kb_id",
"value",
":",
"kb_id对应描述文本形成的向量"
] | def extract(max_len=512):
'''
:param max_len: 文本最大长度
:return: 字典形式，key: kb_id value: kb_id对应描述文本形成的向量
'''
model = get_model(max_len)
token_dict = get_token_dict()
tokenizer = Tokenizer(token_dict)
id_text=pd.read_pickle('data/id_text.pkl')
id_embedding={}
for id in id_text:
if int(id)%10000==0:
print(id)
text=id_text[id]
indices, segments = tokenizer.encode(first=text,max_len=512)
predicts = model.predict([[indices], [segments]], verbose=2)
id_embedding[id]=predicts[0]
pd.to_pickle(id_embedding,'data/id_embedding.pkl') | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_vendor/colorama/ansi.py | python | clear_line | (mode=2) | return CSI + str(mode) + 'K' | [] | def clear_line(mode=2):
return CSI + str(mode) + 'K' | [
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bowenbaker/metaqnn | a25847f635e9545455f83405453e740646038f7a | libs/misc/clear_trained_models.py | python | clear_redundant_logs_caffe | (ckpt_dir, replay) | Deletes uneeded log files and model saves:
args:
replay - with standard replay dic columns. Deletes only model saves that aren't for the first iteration,
the best iteration, and the last iteration
ckpt_dir - where the models are saved, it must have a filemap | Deletes uneeded log files and model saves:
args:
replay - with standard replay dic columns. Deletes only model saves that aren't for the first iteration,
the best iteration, and the last iteration
ckpt_dir - where the models are saved, it must have a filemap | [
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''' Deletes uneeded log files and model saves:
args:
replay - with standard replay dic columns. Deletes only model saves that aren't for the first iteration,
the best iteration, and the last iteration
ckpt_dir - where the models are saved, it must have a filemap
'''
file_map = pd.read_csv(os.path.join(ckpt_dir, 'file_map.csv'))
for i in range(len(file_map)):
net = file_map.net.values[i]
# First check that the model was run on this computer
if net not in replay.net.values:
continue
else:
folder_path = os.path.join(ckpt_dir, str(int(file_map[file_map.net == net].file_number.values[0])))
if not os.path.isdir(folder_path):
continue
best_iter = replay[replay.net == net].iter_best_val.values[0]
last_iter = replay[replay.net == net].iter_last_val.values[0]
model_saves = [f for f in os.listdir(folder_path) if f.find('modelsave_iter') >= 0]
#make sure that the model actual ran
if not model_saves:
continue
first_iter = min([int(re.split('_|\.', f)[2]) for f in model_saves])
model_saves_to_keep = ['modelsave_iter_%i.solverstate' % iteration for iteration in [best_iter, last_iter, first_iter]] + \
['modelsave_iter_%i.caffemodel' % iteration for iteration in [best_iter, last_iter, first_iter]]
# make sure all of the files are there
if not np.all([os.path.isfile(os.path.join(folder_path, savefile)) for savefile in model_saves_to_keep]):
continue
# Delete extraneous files
for f in model_saves:
if f not in model_saves_to_keep:
os.remove(os.path.join(folder_path, f)) | [
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JosephLai241/URS | 9f8cf3a3adb9aa5079dfc7bfd7832b53358ee40f | urs/utils/Tools.py | python | Run.__init__ | (self, reddit) | Initialize variables used in instance methods:
self._reddit: Reddit instance
self._args: argparse Namespace object
self._parser: argparse ArgumentParser object
Calls a private method:
self._introduce_then_args()
Parameters
----------
reddit: PRAW Reddit object
Returns
-------
None | Initialize variables used in instance methods: | [
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":"
] | def __init__(self, reddit):
"""
Initialize variables used in instance methods:
self._reddit: Reddit instance
self._args: argparse Namespace object
self._parser: argparse ArgumentParser object
Calls a private method:
self._introduce_then_args()
Parameters
----------
reddit: PRAW Reddit object
Returns
-------
None
"""
self._reddit = reddit
self._args, self._parser = self._introduce_then_args() | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/string.py | python | Template.__init__ | (self, template) | [] | def __init__(self, template):
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awesto/django-shop | 13d9a77aff7eede74a5f363c1d540e005d88dbcd | shop/models/customer.py | python | CustomerManager.create | (self, *args, **kwargs) | return customer | [] | def create(self, *args, **kwargs):
if 'user' in kwargs and kwargs['user'].is_authenticated:
kwargs.setdefault('recognized', CustomerState.REGISTERED)
customer = super().create(*args, **kwargs)
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hasegaw/IkaLog | bd476da541fcc296f792d4db76a6b9174c4777ad | ikalog/scenes/game/ranked_battle_events.py | python | GameRankedBattleEvents.on_game_start | (self, context) | [] | def on_game_start(self, context):
rule_id = context['game']['rule']
masks_active = self._masks_ranked.copy()
if rule_id == 'area':
masks_active.update(self._masks_splatzone)
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masks_active.update(self._masks_rainmaker)
elif rule_id == 'yagura':
masks_active.update(self._masks_towercontrol)
else:
masks_active = {}
self._masks_active = masks_active
# Initialize Multi-Class IkaMatcher
self._masks_active2 = MultiClassIkaMatcher()
for mask in masks_active.keys():
self._masks_active2.add_mask(mask) | [
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/metagoofil/hachoir_parser/image/tiff.py | python | TiffFile.validate | (self) | return True | [] | def validate(self):
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cleverhans-lab/cleverhans | e5d00e537ce7ad6119ed5a8db1f0e9736d1f6e1d | cleverhans/tf2/attacks/carlini_wagner_l2.py | python | CarliniWagnerL2.__init__ | (
self,
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targeted=False,
batch_size=128,
clip_min=0.0,
clip_max=1.0,
binary_search_steps=5,
max_iterations=1_000,
abort_early=True,
confidence=0.0,
initial_const=1e-2,
learning_rate=5e-3,
) | This attack was originally proposed by Carlini and Wagner. It is an
iterative attack that finds adversarial examples on many defenses that
are robust to other attacks.
Paper link: https://arxiv.org/abs/1608.04644
At a high level, this attack is an iterative attack using Adam and
a specially-chosen loss function to find adversarial examples with
lower distortion than other attacks. This comes at the cost of speed,
as this attack is often much slower than others.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param y: (optional) Tensor with target labels.
:param targeted: (optional) Targeted attack?
:param batch_size (optional): Number of attacks to run simultaneously.
:param clip_min: (optional) float. Minimum float values for adversarial example components.
:param clip_max: (optional) float. Maximum float value for adversarial example components.
:param binary_search_steps (optional): The number of times we perform binary
search to find the optimal tradeoff-
constant between norm of the purturbation
and confidence of the classification.
:param max_iterations (optional): The maximum number of iterations. Setting this
to a larger value will produce lower distortion
results. Using only a few iterations requires
a larger learning rate, and will produce larger
distortion results.
:param abort_early (optional): If true, allows early aborts if gradient descent
is unable to make progress (i.e., gets stuck in
a local minimum).
:param confidence (optional): Confidence of adversarial examples: higher produces
examples with larger l2 distortion, but more
strongly classified as adversarial.
:param initial_const (optional): The initial tradeoff-constant used to tune the
relative importance of the size of the perturbation
and confidence of classification.
If binary_search_steps is large, the initial
constant is not important. A smaller value of
this constant gives lower distortion results.
:param learning_rate (optional): The learning rate for the attack algorithm.
Smaller values produce better results but are
slower to converge. | This attack was originally proposed by Carlini and Wagner. It is an
iterative attack that finds adversarial examples on many defenses that
are robust to other attacks.
Paper link: https://arxiv.org/abs/1608.04644
At a high level, this attack is an iterative attack using Adam and
a specially-chosen loss function to find adversarial examples with
lower distortion than other attacks. This comes at the cost of speed,
as this attack is often much slower than others. | [
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iterative attack that finds adversarial examples on many defenses that
are robust to other attacks.
Paper link: https://arxiv.org/abs/1608.04644
At a high level, this attack is an iterative attack using Adam and
a specially-chosen loss function to find adversarial examples with
lower distortion than other attacks. This comes at the cost of speed,
as this attack is often much slower than others.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param y: (optional) Tensor with target labels.
:param targeted: (optional) Targeted attack?
:param batch_size (optional): Number of attacks to run simultaneously.
:param clip_min: (optional) float. Minimum float values for adversarial example components.
:param clip_max: (optional) float. Maximum float value for adversarial example components.
:param binary_search_steps (optional): The number of times we perform binary
search to find the optimal tradeoff-
constant between norm of the purturbation
and confidence of the classification.
:param max_iterations (optional): The maximum number of iterations. Setting this
to a larger value will produce lower distortion
results. Using only a few iterations requires
a larger learning rate, and will produce larger
distortion results.
:param abort_early (optional): If true, allows early aborts if gradient descent
is unable to make progress (i.e., gets stuck in
a local minimum).
:param confidence (optional): Confidence of adversarial examples: higher produces
examples with larger l2 distortion, but more
strongly classified as adversarial.
:param initial_const (optional): The initial tradeoff-constant used to tune the
relative importance of the size of the perturbation
and confidence of classification.
If binary_search_steps is large, the initial
constant is not important. A smaller value of
this constant gives lower distortion results.
:param learning_rate (optional): The learning rate for the attack algorithm.
Smaller values produce better results but are
slower to converge.
"""
self.model_fn = model_fn
self.batch_size = batch_size
self.y = y
self.targeted = y is not None
self.clip_min = clip_min
self.clip_max = clip_max
self.binary_search_steps = binary_search_steps
self.max_iterations = max_iterations
self.abort_early = abort_early
self.learning_rate = learning_rate
self.confidence = confidence
self.initial_const = initial_const
# the optimizer
self.optimizer = tf.keras.optimizers.Adam(self.learning_rate)
super(CarliniWagnerL2, self).__init__() | [
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ProjectQ-Framework/ProjectQ | 0d32c1610ba4e9aefd7f19eb52dadb4fbe5f9005 | projectq/ops/_gates.py | python | TGate.__str__ | (self) | return "T" | Return a string representation of the object. | Return a string representation of the object. | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | torch_geometric/datasets/elliptic.py | python | EllipticBitcoinDataset.download | (self) | [] | def download(self):
download_url('https://tinyurl.com/9b7f8efe', self.raw_dir)
os.rename(osp.join(self.raw_dir, '9b7f8efe'), self.raw_paths[0])
download_url('https://tinyurl.com/mr3v9d3f', self.raw_dir)
os.rename(osp.join(self.raw_dir, 'mr3v9d3f'), self.raw_paths[1])
download_url('https://tinyurl.com/2p8up25z', self.raw_dir)
os.rename(osp.join(self.raw_dir, '2p8up25z'), self.raw_paths[2]) | [
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galaxyproject/galaxy | 4c03520f05062e0f4a1b3655dc0b7452fda69943 | lib/galaxy/web/framework/helpers/grids.py | python | DateTimeColumn.sort | (self, trans, query, ascending, column_name=None) | return GridColumn.sort(self, trans, query, ascending, column_name=column_name) | Sort query using this column. | Sort query using this column. | [
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espnet/espnet | ea411f3f627b8f101c211e107d0ff7053344ac80 | espnet/nets/chainer_backend/rnn/encoders.py | python | encoder_for | (args, idim, subsample) | return Encoder(
args.etype,
idim,
args.elayers,
args.eunits,
args.eprojs,
subsample,
args.dropout_rate,
) | Return the Encoder module.
Args:
idim (int): Dimension of input array.
subsample (numpy.array): Subsample number. egs).1_2_2_2_1
Return
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"""Return the Encoder module.
Args:
idim (int): Dimension of input array.
subsample (numpy.array): Subsample number. egs).1_2_2_2_1
Return
chainer.nn.Module: Encoder module.
"""
return Encoder(
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PyTorchLightning/pytorch-lightning | 5914fb748fb53d826ab337fc2484feab9883a104 | pytorch_lightning/callbacks/prediction_writer.py | python | BasePredictionWriter.write_on_batch_end | (
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trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
prediction: Any,
batch_indices: Optional[Sequence[int]],
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dataloader_idx: int,
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pl_module: "pl.LightningModule",
prediction: Any,
batch_indices: Optional[Sequence[int]],
batch: Any,
batch_idx: int,
dataloader_idx: int,
) -> None:
"""Override with the logic to write a single batch."""
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pyrocko/pyrocko | b6baefb7540fb7fce6ed9b856ec0c413961a4320 | src/obspy_compat/base.py | python | to_obspy_stream | (pile) | return stream | Convert Pyrocko pile to ObsPy stream.
:param pile:
:py:class:`pyrocko.pile.Pile` object
:returns:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` object | Convert Pyrocko pile to ObsPy stream. | [
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'''
Convert Pyrocko pile to ObsPy stream.
:param pile:
:py:class:`pyrocko.pile.Pile` object
:returns:
:py:class:`obspy.Stream <obspy.core.stream.Stream>` object
'''
pyrocko_pile = pile
import obspy
stream = obspy.Stream()
stream.extend([to_obspy_trace(tr) for tr in pyrocko_pile.iter_all()])
return stream | [
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mjpost/sacrebleu | 65a8a9eeccd8c0c7875e875e12edf10db33ab0ba | sacrebleu/tokenizers/tokenizer_re.py | python | TokenizerRegexp.__call__ | (self, line) | return ' '.join(line.split()) | Common post-processing tokenizer for `13a` and `zh` tokenizers.
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:param line: a segment to tokenize
:return: the tokenized line
"""
for (_re, repl) in self._re:
line = _re.sub(repl, line)
# no leading or trailing spaces, single space within words
return ' '.join(line.split()) | [
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SavinaRoja/PyUserInput | 8a93a4e32430fd9da68590897e06e0be9a2ad26e | reference_materials/virtual_keystroke_example.py | python | press | (*args) | one press, one release.
accepts as many arguments as you want. e.g. press('left_arrow', 'a','b'). | one press, one release.
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'''
one press, one release.
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'''
for i in args:
win32api.keybd_event(VK_CODE[i], 0,0,0)
time.sleep(.05)
win32api.keybd_event(VK_CODE[i],0 ,win32con.KEYEVENTF_KEYUP ,0) | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/redis_model/models/base.py | python | Model.save | (self,pfields=None,new = False) | return True | Saves the instance to the datastore Redis!
params :
pfields for partial fields save
pfields = ("username","gender")
param:
pfields:object the default is None
new:True or False
return:
True or False | Saves the instance to the datastore Redis!
params :
pfields for partial fields save
pfields = ("username","gender")
param:
pfields:object the default is None
new:True or False
return:
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":"... | def save(self,pfields=None,new = False):
n = datetime.now()
"""
Saves the instance to the datastore Redis!
params :
pfields for partial fields save
pfields = ("username","gender")
param:
pfields:object the default is None
new:True or False
return:
True or False
"""
#清空错误列表
self._errors = []
#检查字段的有效性
if pfields:
if not isinstance(pfields,tuple):
self._errors.append("params must tuple list!")
return False
for p in pfields:
if not self.attributes.has_key(p):
self._errors.append("%s field not exists!" % p)
return False
#检查每个字段的有效性
if not self.is_valid():
return False
#这个如果不是New 要生成一个ID
if new:
_new = True
else:
_new = self.is_new()
if _new and not new and not self.id:
self._initialize_id()
#这里可以应用到分布式
#with Mutex(self):
self._write(_new,pfields)
logger.info("save type:%s, id:%s ,use: %s" % (self.__class__.__name__,self.id,datetime.now() - n))
return True | [
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riffnshred/nhl-led-scoreboard | 14baa7f0691ca507e4c6f7f2ec02e50ccd1ed9e1 | src/sbio/motionsensor.py | python | Motion.run | (self) | [] | def run(self):
if self.ms_run:
pause() # wait forever
else:
pass | [
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hyperspy/hyperspy | 1ffb3fab33e607045a37f30c1463350b72617e10 | hyperspy/io_plugins/_hierarchical.py | python | HierarchicalReader.get_format_version | (self) | return Version(version) | Return the format version. | Return the format version. | [
"Return",
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"format",
"version",
"."
] | def get_format_version(self):
"""Return the format version."""
if "file_format_version" in self.file.attrs:
version = self.file.attrs["file_format_version"]
if isinstance(version, bytes):
version = version.decode()
if isinstance(version, float):
version = str(round(version, 2))
elif "Experiments" in self.file:
# Chances are that this is a HSpy hdf5 file version 1.0
version = "1.0"
elif "Analysis" in self.file:
# Starting version 2.0 we have "Analysis" field as well
version = "2.0"
else:
raise IOError(not_valid_format)
return Version(version) | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/lore/tree.py | python | getHeaders | (document) | return domhelpers.findElements(
document,
lambda n, m=re.compile('h[23]$').match: m(n.nodeName)) | Return all H2 and H3 nodes in the given document.
@type document: A DOM Node or Document
@rtype: C{list} | Return all H2 and H3 nodes in the given document. | [
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] | def getHeaders(document):
"""
Return all H2 and H3 nodes in the given document.
@type document: A DOM Node or Document
@rtype: C{list}
"""
return domhelpers.findElements(
document,
lambda n, m=re.compile('h[23]$').match: m(n.nodeName)) | [
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