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LumaPictures/pymel | fa88a3f4fa18e09bb8aa9bdf4dab53d984bada72 | pymel/util/utilitytypes.py | python | metaReadOnlyAttr.__setattr__ | (cls, name, value) | overload __setattr__ to forbid modification of read only class info | overload __setattr__ to forbid modification of read only class info | [
"overload",
"__setattr__",
"to",
"forbid",
"modification",
"of",
"read",
"only",
"class",
"info"
] | def __setattr__(cls, name, value): # @NoSelf
""" overload __setattr__ to forbid modification of read only class info """
readonly = {}
for c in inspect.getmro(cls):
if hasattr(c, '__readonly__'):
readonly.update(c.__readonly__)
if name in readonly:
raise AttributeError("attribute %s is a read only class attribute and cannot be modified on class %s" % (name, cls.__name__))
else:
super(metaReadOnlyAttr, cls).__setattr__(name, value) | [
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||
whyliam/whyliam.workflows.youdao | 2dfa7f1de56419dab1c2e70c1a27e5e13ba25a5c | urllib3/util/retry.py | python | Retry.increment | (
self,
method=None,
url=None,
response=None,
error=None,
_pool=None,
_stacktrace=None,
) | return new_retry | Return a new Retry object with incremented retry counters.
:param response: A response object, or None, if the server did not
return a response.
:type response: :class:`~urllib3.response.HTTPResponse`
:param Exception error: An error encountered during the request, or
None if the response was received successfully.
:return: A new ``Retry`` object. | Return a new Retry object with incremented retry counters. | [
"Return",
"a",
"new",
"Retry",
"object",
"with",
"incremented",
"retry",
"counters",
"."
] | def increment(
self,
method=None,
url=None,
response=None,
error=None,
_pool=None,
_stacktrace=None,
):
""" Return a new Retry object with incremented retry counters.
:param response: A response object, or None, if the server did not
return a response.
:type response: :class:`~urllib3.response.HTTPResponse`
:param Exception error: An error encountered during the request, or
None if the response was received successfully.
:return: A new ``Retry`` object.
"""
if self.total is False and error:
# Disabled, indicate to re-raise the error.
raise six.reraise(type(error), error, _stacktrace)
total = self.total
if total is not None:
total -= 1
connect = self.connect
read = self.read
redirect = self.redirect
status_count = self.status
cause = "unknown"
status = None
redirect_location = None
if error and self._is_connection_error(error):
# Connect retry?
if connect is False:
raise six.reraise(type(error), error, _stacktrace)
elif connect is not None:
connect -= 1
elif error and self._is_read_error(error):
# Read retry?
if read is False or not self._is_method_retryable(method):
raise six.reraise(type(error), error, _stacktrace)
elif read is not None:
read -= 1
elif response and response.get_redirect_location():
# Redirect retry?
if redirect is not None:
redirect -= 1
cause = "too many redirects"
redirect_location = response.get_redirect_location()
status = response.status
else:
# Incrementing because of a server error like a 500 in
# status_forcelist and a the given method is in the whitelist
cause = ResponseError.GENERIC_ERROR
if response and response.status:
if status_count is not None:
status_count -= 1
cause = ResponseError.SPECIFIC_ERROR.format(status_code=response.status)
status = response.status
history = self.history + (
RequestHistory(method, url, error, status, redirect_location),
)
new_retry = self.new(
total=total,
connect=connect,
read=read,
redirect=redirect,
status=status_count,
history=history,
)
if new_retry.is_exhausted():
raise MaxRetryError(_pool, url, error or ResponseError(cause))
log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
return new_retry | [
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|
google/grr | 8ad8a4d2c5a93c92729206b7771af19d92d4f915 | grr/server/grr_response_server/flows/general/discovery.py | python | Interrogate.ClientInfo | (self, responses) | Obtain some information about the GRR client running. | Obtain some information about the GRR client running. | [
"Obtain",
"some",
"information",
"about",
"the",
"GRR",
"client",
"running",
"."
] | def ClientInfo(self, responses):
"""Obtain some information about the GRR client running."""
if not responses.success:
self.Log("Could not get ClientInfo.")
return
response = responses.First()
if fleetspeak_utils.IsFleetspeakEnabledClient(self.client_id):
# Fetch labels for the client from Fleetspeak. If Fleetspeak doesn't
# have any labels for the GRR client, fall back to labels reported by
# the client.
fleetspeak_labels = fleetspeak_utils.GetLabelsFromFleetspeak(
self.client_id)
if fleetspeak_labels:
response.labels = fleetspeak_labels
else:
FLEETSPEAK_UNLABELED_CLIENTS.Increment()
logging.warning("Failed to get labels for Fleetspeak client %s.",
self.client_id)
sanitized_labels = []
for label in response.labels:
try:
self._ValidateLabel(label)
sanitized_labels.append(label)
except ValueError:
self.Log("Got invalid label: %s", label)
response.labels = sanitized_labels
self.state.client.startup_info.client_info = response
metadata = data_store.REL_DB.ReadClientMetadata(self.client_id)
if metadata and metadata.last_fleetspeak_validation_info:
self.state.client.fleetspeak_validation_info = (
metadata.last_fleetspeak_validation_info) | [
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||
edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/esp8266_clients/venv/lib/python2.7/site-packages/pip/_vendor/distlib/database.py | python | _Cache.__init__ | (self) | Initialise an instance. There is normally one for each DistributionPath. | Initialise an instance. There is normally one for each DistributionPath. | [
"Initialise",
"an",
"instance",
".",
"There",
"is",
"normally",
"one",
"for",
"each",
"DistributionPath",
"."
] | def __init__(self):
"""
Initialise an instance. There is normally one for each DistributionPath.
"""
self.name = {}
self.path = {}
self.generated = False | [
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||
GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/linux_packages/tensorflow_serving.py | python | InstallTensorFlowServingAPI | (vm) | Installs TF Serving API on the vm.
Currently this is only useful so that the clients can run python
scripts that import tensorflow_serving. The server vms make no use
of it.
Args:
vm: VM to operate on. | Installs TF Serving API on the vm. | [
"Installs",
"TF",
"Serving",
"API",
"on",
"the",
"vm",
"."
] | def InstallTensorFlowServingAPI(vm):
"""Installs TF Serving API on the vm.
Currently this is only useful so that the clients can run python
scripts that import tensorflow_serving. The server vms make no use
of it.
Args:
vm: VM to operate on.
"""
pip_package_output_dir = posixpath.join(VM_TMP_DIR, 'tf_serving_pip_package')
pip_package = posixpath.join(pip_package_output_dir,
'tensorflow_serving_api*.whl')
vm.Install('pip3')
vm.RemoteCommand('sudo pip3 install --upgrade pip')
# Build the pip package from the same source as the serving binary
vm.RemoteCommand('sudo docker run --rm -v {0}:{0} '
'benchmarks/tensorflow-serving-devel '
'bash -c "bazel build --config=nativeopt '
'tensorflow_serving/tools/pip_package:build_pip_package && '
'bazel-bin/tensorflow_serving/tools/pip_package/'
'build_pip_package {0}"'.format(pip_package_output_dir))
vm.RemoteCommand('sudo pip3 install {0}'.format(pip_package)) | [
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||
juhakivekas/multidiff | b22b5e202b1ce1a93657f6549e893242f9302f5a | multidiff/Multidiffmodel.py | python | MultidiffModel.diff_baseline | (self, baseline=0) | Diff all objects against a common baseline | Diff all objects against a common baseline | [
"Diff",
"all",
"objects",
"against",
"a",
"common",
"baseline"
] | def diff_baseline(self, baseline=0):
"""Diff all objects against a common baseline"""
for i in range(len(self.objects)):
if i is baseline:
pass
self.diff(baseline, i) | [
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||
lkiesow/python-feedgen | ffe3e4d752ac76e23c879c35682c310c2b1ccb86 | feedgen/feed.py | python | FeedGenerator.register_extension | (self, namespace, extension_class_feed=None,
extension_class_entry=None, atom=True, rss=True) | Registers an extension by class.
:param namespace: namespace for the extension
:param extension_class_feed: Class of the feed extension to load.
:param extension_class_entry: Class of the entry extension to load
:param atom: If the extension should be used for ATOM feeds.
:param rss: If the extension should be used for RSS feeds. | Registers an extension by class. | [
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"extension",
"by",
"class",
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] | def register_extension(self, namespace, extension_class_feed=None,
extension_class_entry=None, atom=True, rss=True):
'''Registers an extension by class.
:param namespace: namespace for the extension
:param extension_class_feed: Class of the feed extension to load.
:param extension_class_entry: Class of the entry extension to load
:param atom: If the extension should be used for ATOM feeds.
:param rss: If the extension should be used for RSS feeds.
'''
# Check loaded extensions
# `load_extension` ignores the "Extension" suffix.
if not isinstance(self.__extensions, dict):
self.__extensions = {}
if namespace in self.__extensions.keys():
raise ImportError('Extension already loaded')
# Load extension
extinst = extension_class_feed()
setattr(self, namespace, extinst)
# `load_extension` registry
self.__extensions[namespace] = {
'inst': extinst,
'extension_class_feed': extension_class_feed,
'extension_class_entry': extension_class_entry,
'atom': atom,
'rss': rss
}
# Try to load the extension for already existing entries:
for entry in self.__feed_entries:
try:
entry.register_extension(namespace,
extension_class_entry,
atom,
rss)
except ImportError:
pass | [
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||
cython/cython | 9db1fc39b31b7b3b2ed574a79f5f9fd980ee3be7 | Cython/Compiler/Optimize.py | python | FinalOptimizePhase.visit_PyTypeTestNode | (self, node) | return node | Remove tests for alternatively allowed None values from
type tests when we know that the argument cannot be None
anyway. | Remove tests for alternatively allowed None values from
type tests when we know that the argument cannot be None
anyway. | [
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] | def visit_PyTypeTestNode(self, node):
"""Remove tests for alternatively allowed None values from
type tests when we know that the argument cannot be None
anyway.
"""
self.visitchildren(node)
if not node.notnone:
if not node.arg.may_be_none():
node.notnone = True
return node | [
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|
DonnchaC/shadowbrokers-exploits | 42d8265db860b634717da4faa668b2670457cf7e | windows/fuzzbunch/log.py | python | log.socket_closed | (self, port, ip='0.0.0.0', project=None, is_tcp=None, is_udp=None, is_raw=None, **params) | Close a local socket and any open connections. | Close a local socket and any open connections. | [
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] | def socket_closed(self, port, ip='0.0.0.0', project=None, is_tcp=None, is_udp=None, is_raw=None, **params):
''' Close a local socket and any open connections. '''
if is_raw or is_tcp or is_udp: return self(event_type='socket closed', socket_port=port, socket_ip=ip, socket_project=project, socket_is_raw=is_raw, socket_is_tcp=is_tcp, socket_is_udp=is_udp, **params)
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||
giantbranch/python-hacker-code | addbc8c73e7e6fb9e4fcadcec022fa1d3da4b96d | 我手敲的代码(中文注释)/chapter9/pycrypto-2.6.1/build/lib.win32-2.7/Crypto/Random/random.py | python | StrongRandom.choice | (self, seq) | return seq[self.randrange(len(seq))] | Return a random element from a (non-empty) sequence.
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"""Return a random element from a (non-empty) sequence.
If the seqence is empty, raises IndexError.
"""
if len(seq) == 0:
raise IndexError("empty sequence")
return seq[self.randrange(len(seq))] | [
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|
DiligentPanda/Tencent_Ads_Algo_2018 | dc1aa2285e196e87d100b7e6769f3c97c19e394f | src/lib/lr_scheduler.py | python | adjust_learning_rate_custom | (base_lr, optimizer, cur_batch, func) | :param lr:
:param optimizer:
:param cur_batch:
:param func:
:return: | [] | def adjust_learning_rate_custom(base_lr, optimizer, cur_batch, func):
'''
:param lr:
:param optimizer:
:param cur_batch:
:param func:
:return:
'''
lr = func(base_lr,cur_batch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr * param_group['lr_mult'] | [
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|||
Asana/python-asana | 9b54ab99423208bd6aa87dbfaa628c069430b127 | asana/resources/gen/sections.py | python | _Sections.get_sections_for_project | (self, project_gid, params=None, **options) | return self.client.get_collection(path, params, **options) | Get sections in a project
:param str project_gid: (required) Globally unique identifier for the project.
:param Object params: Parameters for the request
:param **options
- offset {str}: Offset token. An offset to the next page returned by the API. A pagination request will return an offset token, which can be used as an input parameter to the next request. If an offset is not passed in, the API will return the first page of results. 'Note: You can only pass in an offset that was returned to you via a previously paginated request.'
- limit {int}: Results per page. The number of objects to return per page. The value must be between 1 and 100.
- opt_fields {list[str]}: Defines fields to return. Some requests return *compact* representations of objects in order to conserve resources and complete the request more efficiently. Other times requests return more information than you may need. This option allows you to list the exact set of fields that the API should be sure to return for the objects. The field names should be provided as paths, described below. The id of included objects will always be returned, regardless of the field options.
- opt_pretty {bool}: Provides “pretty” output. Provides the response in a “pretty” format. In the case of JSON this means doing proper line breaking and indentation to make it readable. This will take extra time and increase the response size so it is advisable only to use this during debugging.
:return: Object | Get sections in a project
:param str project_gid: (required) Globally unique identifier for the project.
:param Object params: Parameters for the request
:param **options
- offset {str}: Offset token. An offset to the next page returned by the API. A pagination request will return an offset token, which can be used as an input parameter to the next request. If an offset is not passed in, the API will return the first page of results. 'Note: You can only pass in an offset that was returned to you via a previously paginated request.'
- limit {int}: Results per page. The number of objects to return per page. The value must be between 1 and 100.
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- opt_pretty {bool}: Provides “pretty” output. Provides the response in a “pretty” format. In the case of JSON this means doing proper line breaking and indentation to make it readable. This will take extra time and increase the response size so it is advisable only to use this during debugging.
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:param Object params: Parameters for the request
:param **options
- offset {str}: Offset token. An offset to the next page returned by the API. A pagination request will return an offset token, which can be used as an input parameter to the next request. If an offset is not passed in, the API will return the first page of results. 'Note: You can only pass in an offset that was returned to you via a previously paginated request.'
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:return: Object
"""
if params is None:
params = {}
path = "/projects/{project_gid}/sections".replace("{project_gid}", project_gid)
return self.client.get_collection(path, params, **options) | [
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|
Staffjoy/suite | 14ed49b21cf8296d2e0696a7f50f91f8e4b65072 | app/limiters.py | python | BaseNotificationLimiter.allowed_to_send | (cls, user) | determines if a notification is able to be sent | determines if a notification is able to be sent | [
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"notification",
"is",
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"be",
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] | def allowed_to_send(cls, user):
"""
determines if a notification is able to be sent
"""
try:
last_reminder = cls.get(user.id)
last_reminder = iso8601.parse_date(last_reminder).replace(
tzinfo=None)
except:
last_reminder = None
if not last_reminder:
return True
if cls.COMPARE_LAST_SEEN:
# Still allowed to send if user has been active
# since last notification.
if user.last_seen > last_reminder:
cls.delete(user.id)
return True
# redis doesn't guarantee that the key isn't expired exactly at expiration time
# check if it *should* have been expired
if last_reminder + timedelta(seconds=cls.EXPIRY) < datetime.utcnow():
cls.delete(user.id)
return True
if cls.WRITE_LOG:
current_app.logger.info(
"Not sending %s notification to user %s because it was sent recently"
% (cls.NAME, user.id))
return False | [
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||
JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/base_types/nodes/base_node.py | python | AnimationNode.getBakeCode | (self) | return [] | [] | def getBakeCode(self):
return [] | [
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|||
ansible/ansible-modules-extras | f216ba8e0616bc8ad8794c22d4b48e1ab18886cf | packaging/os/zypper.py | python | package_present | (m, name, want_latest) | return result, retvals | install and update (if want_latest) the packages in name_install, while removing the packages in name_remove | install and update (if want_latest) the packages in name_install, while removing the packages in name_remove | [
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] | def package_present(m, name, want_latest):
"install and update (if want_latest) the packages in name_install, while removing the packages in name_remove"
retvals = {'rc': 0, 'stdout': '', 'stderr': ''}
name_install, name_remove, urls = get_want_state(m, name)
# if a version string is given, pass it to zypper
install_version = [p+name_install[p] for p in name_install if name_install[p]]
remove_version = [p+name_remove[p] for p in name_remove if name_remove[p]]
# add oldpackage flag when a version is given to allow downgrades
if install_version or remove_version:
m.params['oldpackage'] = True
if not want_latest:
# for state=present: filter out already installed packages
install_and_remove = name_install.copy()
install_and_remove.update(name_remove)
prerun_state = get_installed_state(m, install_and_remove)
# generate lists of packages to install or remove
name_install = [p for p in name_install if p not in prerun_state]
name_remove = [p for p in name_remove if p in prerun_state]
if not any((name_install, name_remove, urls, install_version, remove_version)):
# nothing to install/remove and nothing to update
return None, retvals
# zypper install also updates packages
cmd = get_cmd(m, 'install')
cmd.append('--')
cmd.extend(urls)
# pass packages with version information
cmd.extend(install_version)
cmd.extend(['-%s' % p for p in remove_version])
# allow for + or - prefixes in install/remove lists
# do this in one zypper run to allow for dependency-resolution
# for example "-exim postfix" runs without removing packages depending on mailserver
cmd.extend(name_install)
cmd.extend(['-%s' % p for p in name_remove])
retvals['cmd'] = cmd
result, retvals['rc'], retvals['stdout'], retvals['stderr'] = parse_zypper_xml(m, cmd)
return result, retvals | [
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|
avocado-framework/avocado | 1f9b3192e8ba47d029c33fe21266bd113d17811f | avocado/utils/archive.py | python | ArchiveFile.close | (self) | Close archive. | Close archive. | [
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"""
Close archive.
"""
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bridgecrewio/checkov | f4f8caead6aa2f1824ae1cc88cd1816b12211629 | checkov/terraform/module_loading/content.py | python | ModuleContent.cleanup | (self) | Clean up any temporary resources, if applicable. | Clean up any temporary resources, if applicable. | [
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Delta-ML/delta | 31dfebc8f20b7cb282b62f291ff25a87e403cc86 | delta/data/feat/speech_feature.py | python | load_wav | (wavpath, sr=8000) | return sample_rate, audio | audio:
np.float32, shape [None], sample in [-1, 1], using librosa.load
np.int16, shape [None], sample in [-32768, 32767], using scipy.io.wavfile
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return
sr: sample rate
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'''
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return
sr: sample rate
audio: [-1, 1], same to tf.DecodeWav
'''
#from scipy.io import wavfile
#sample_rate, audio = wavfile.read(wavpath)
#samples, sample_rate = librosa.load(wavpath, sr=sr)
feat_name = 'load_wav'
graph = None
# get session
if feat_name not in _global_sess:
graph = tf.Graph()
with graph.as_default():
params = speech_ops.speech_params(sr=sr, audio_desired_samples=-1)
t_wavpath = tf.placeholder(dtype=tf.string, name="wavpath")
t_audio, t_sample_rate = speech_ops.read_wav(t_wavpath, params)
t_audio = tf.identity(t_audio, name="audio")
t_sample_rate = tf.identity(t_sample_rate, name="sample_rate")
sess = _get_session(feat_name, graph)
audio, sample_rate = sess.run([
_get_out_tensor_name('audio', 0),
_get_out_tensor_name('sample_rate', 0)
],
feed_dict={"wavpath:0": wavpath})
audio = audio[:, 0]
assert sample_rate == sr, 'sampling rate must be {}Hz, get {}Hz'.format(
sr, sample_rate)
return sample_rate, audio | [
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iopsgroup/imoocc | de810eb6d4c1697b7139305925a5b0ba21225f3f | scanhosts/modules/paramiko1_9/sftp_handle.py | python | SFTPHandle.stat | (self) | return SFTP_OP_UNSUPPORTED | Return an L{SFTPAttributes} object referring to this open file, or an
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|
FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/federatedml/ensemble/basic_algorithms/decision_tree/tree_core/splitter.py | python | Splitter.find_split_single_histogram_guest | (self, histogram, valid_features, sitename, use_missing, zero_as_missing,
reshape_tuple=None) | return splitinfo | [] | def find_split_single_histogram_guest(self, histogram, valid_features, sitename, use_missing, zero_as_missing,
reshape_tuple=None):
if reshape_tuple:
histogram = histogram.reshape(reshape_tuple)
# default values
best_fid = None
best_gain = self.min_impurity_split - consts.FLOAT_ZERO
best_bid = None
best_sum_grad_l = None
best_sum_hess_l = None
missing_bin = 0
if use_missing:
missing_bin = 1
# in default, missing value going to right
missing_dir = 1
for fid in range(len(histogram)):
if valid_features[fid] is False:
continue
bin_num = len(histogram[fid])
if bin_num == 0 + missing_bin:
continue
# last bin contains sum values (cumsum from left)
sum_grad = histogram[fid][bin_num - 1][0]
sum_hess = histogram[fid][bin_num - 1][1]
node_cnt = histogram[fid][bin_num - 1][2]
if node_cnt < self.min_sample_split:
break
# last bin will not participate in split find, so bin_num - 1
for bid in range(bin_num - missing_bin - 1):
# left gh
sum_grad_l = histogram[fid][bid][0]
sum_hess_l = histogram[fid][bid][1]
node_cnt_l = histogram[fid][bid][2]
# right gh
sum_grad_r = sum_grad - sum_grad_l
sum_hess_r = sum_hess - sum_hess_l
node_cnt_r = node_cnt - node_cnt_l
if self._check_sample_num(node_cnt_l, node_cnt_r) and self._check_min_child_weight(sum_hess_l, sum_hess_r):
gain = self.criterion.split_gain([sum_grad, sum_hess],
[sum_grad_l, sum_hess_l], [sum_grad_r, sum_hess_r])
if gain > self.min_impurity_split and gain > best_gain + consts.FLOAT_ZERO:
best_gain = gain
best_fid = fid
best_bid = bid
best_sum_grad_l = sum_grad_l
best_sum_hess_l = sum_hess_l
missing_dir = 1
""" missing value handle: dispatch to left child"""
if use_missing:
# add sum of samples with missing features to left
sum_grad_l += histogram[fid][-1][0] - histogram[fid][-2][0]
sum_hess_l += histogram[fid][-1][1] - histogram[fid][-2][1]
node_cnt_l += histogram[fid][-1][2] - histogram[fid][-2][2]
sum_grad_r -= histogram[fid][-1][0] - histogram[fid][-2][0]
sum_hess_r -= histogram[fid][-1][1] - histogram[fid][-2][1]
node_cnt_r -= histogram[fid][-1][2] - histogram[fid][-2][2]
# if have a better gain value, missing dir is left
if self._check_sample_num(node_cnt_l, node_cnt_r) and self._check_min_child_weight(sum_hess_l, sum_hess_r):
gain = self.criterion.split_gain([sum_grad, sum_hess],
[sum_grad_l, sum_hess_l], [sum_grad_r, sum_hess_r])
if gain > self.min_impurity_split and gain > best_gain + consts.FLOAT_ZERO:
best_gain = gain
best_fid = fid
best_bid = bid
best_sum_grad_l = sum_grad_l
best_sum_hess_l = sum_hess_l
missing_dir = -1
splitinfo = SplitInfo(sitename=sitename, best_fid=best_fid, best_bid=best_bid,
gain=best_gain, sum_grad=best_sum_grad_l, sum_hess=best_sum_hess_l,
missing_dir=missing_dir)
return splitinfo | [
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|||
Nitrate/Nitrate | 7eacef697a15dcbb7ae90c8a1dbf769cba6d1bb1 | src/tcms/xmlrpc/serializer.py | python | XMLRPCSerializer.__init__ | (self, queryset=None, model=None) | Initial the class | Initial the class | [
"Initial",
"the",
"class"
] | def __init__(self, queryset=None, model=None):
"""Initial the class"""
if hasattr(queryset, "__iter__"):
self.queryset = queryset
return
elif hasattr(model, "__dict__"):
self.model = model
return
raise TypeError("QuerySet(list) or Models(dictionary) is required") | [
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||
zalando/spilo | 72b447fac1fc9b9e6e2b519dc520a50a5e0fdd25 | postgres-appliance/scripts/configure_spilo.py | python | write_clone_pgpass | (placeholders, overwrite) | [] | def write_clone_pgpass(placeholders, overwrite):
pgpassfile = placeholders['CLONE_PGPASS']
# pgpass is host:port:database:user:password
r = {'host': escape_pgpass_value(placeholders['CLONE_HOST']),
'port': placeholders['CLONE_PORT'],
'database': '*',
'user': escape_pgpass_value(placeholders['CLONE_USER']),
'password': escape_pgpass_value(placeholders['CLONE_PASSWORD'])}
pgpass_string = "{host}:{port}:{database}:{user}:{password}".format(**r)
write_file(pgpass_string, pgpassfile, overwrite)
os.chmod(pgpassfile, 0o600)
adjust_owner(pgpassfile, gid=-1) | [
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||||
hoangminhle/hierarchical_IL_RL | f97263dbc646ea0f2bfbc602d130d68b01bda8ca | hybrid_imitation_reinforcement_Montezuma/replay_buffer.py | python | PrioritizedReplayBuffer.__init__ | (self, size, alpha) | Create Prioritized Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
alpha: float
how much prioritization is used
(0 - no prioritization, 1 - full prioritization)
See Also
--------
ReplayBuffer.__init__ | Create Prioritized Replay buffer. | [
"Create",
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"Replay",
"buffer",
"."
] | def __init__(self, size, alpha):
"""Create Prioritized Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
alpha: float
how much prioritization is used
(0 - no prioritization, 1 - full prioritization)
See Also
--------
ReplayBuffer.__init__
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0 | [
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||
joosthoeks/jhTAlib | 4931a34829d966ccc973fb29d767a359d6e94b44 | jhtalib/overlap_studies/overlap_studies.py | python | WWMA | (df, n, price='Close') | return wwma_list | Welles Wilder Moving Average
Returns: list of floats = jhta.WWMA(df, n, price='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=WellesMA.htm | Welles Wilder Moving Average
Returns: list of floats = jhta.WWMA(df, n, price='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=WellesMA.htm | [
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"""
Welles Wilder Moving Average
Returns: list of floats = jhta.WWMA(df, n, price='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=WellesMA.htm
"""
wwma_list = []
for i in range(len(df[price])):
if i + 1 < n:
wwma = float('NaN')
wwma_list.append(wwma)
wwma = df[price][i]
else:
wwma = (wwma * (n - 1) + df[price][i]) / n
wwma_list.append(wwma)
return wwma_list | [
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|
pm4py/pm4py-core | 7807b09a088b02199cd0149d724d0e28793971bf | pm4py/algo/discovery/footprints/tree/variants/bottomup.py | python | get_footprints | (node, footprints_dictio) | Gets the footprints for a node (having the history of the child nodes)
Parameters
--------------
node
Node of the tree
footprints_dictio
Dictionary of footprints of the process tree
Returns
--------------
footprints
Footprints of the node (having the history of the child nodes) | Gets the footprints for a node (having the history of the child nodes) | [
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] | def get_footprints(node, footprints_dictio):
"""
Gets the footprints for a node (having the history of the child nodes)
Parameters
--------------
node
Node of the tree
footprints_dictio
Dictionary of footprints of the process tree
Returns
--------------
footprints
Footprints of the node (having the history of the child nodes)
"""
if len(node.children) == 0:
return get_footprints_leaf(node, footprints_dictio)
elif node.operator == Operator.XOR:
return get_footprints_xor(node, footprints_dictio)
elif node.operator == Operator.PARALLEL or node.operator == Operator.OR:
return get_footprints_parallel(node, footprints_dictio)
elif node.operator == Operator.SEQUENCE:
return get_footprints_sequence(node, footprints_dictio)
elif node.operator == Operator.LOOP:
return get_footprints_loop(node, footprints_dictio) | [
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||
avalonstrel/GatedConvolution | 407210b6ee91c536cda7ed9cc92ba73a0bf7024b | inpaint_ops.py | python | local_patch | (x, bbox) | return x | Crop local patch according to bbox.
Args:
x: input
bbox: (top, left, height, width)
Returns:
tf.Tensor: local patch | Crop local patch according to bbox. | [
"Crop",
"local",
"patch",
"according",
"to",
"bbox",
"."
] | def local_patch(x, bbox):
"""Crop local patch according to bbox.
Args:
x: input
bbox: (top, left, height, width)
Returns:
tf.Tensor: local patch
"""
x = tf.image.crop_to_bounding_box(x, bbox[0], bbox[1], bbox[2], bbox[3])
return x | [
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|
mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pylibs/win32/pyproj/__init__.py | python | transform | (p1, p2, x, y, z=None, radians=False) | x2, y2, z2 = transform(p1, p2, x1, y1, z1, radians=False)
Transform points between two coordinate systems defined by the
Proj instances p1 and p2.
The points x1,y1,z1 in the coordinate system defined by p1 are
transformed to x2,y2,z2 in the coordinate system defined by p2.
z1 is optional, if it is not set it is assumed to be zero (and
only x2 and y2 are returned).
In addition to converting between cartographic and geographic
projection coordinates, this function can take care of datum
shifts (which cannot be done using the __call__ method of the
Proj instances). It also allows for one of the coordinate
systems to be geographic (proj = 'latlong').
If optional keyword 'radians' is True (default is False) and p1
is defined in geographic coordinate (pj.is_latlong() is True),
x1,y1 is interpreted as radians instead of the default degrees.
Similarly, if p2 is defined in geographic coordinates and
radians=True, x2, y2 are returned in radians instead of degrees.
if p1.is_latlong() and p2.is_latlong() both are False, the
radians keyword has no effect.
x,y and z can be numpy or regular python arrays, python
lists/tuples or scalars. Arrays are fastest. For projections in
geocentric coordinates, values of x and y are given in meters.
z is always meters.
Example usage:
>>> # projection 1: UTM zone 15, grs80 ellipse, NAD83 datum
>>> # (defined by epsg code 26915)
>>> p1 = Proj(init='epsg:26915')
>>> # projection 2: UTM zone 15, clrk66 ellipse, NAD27 datum
>>> p2 = Proj(init='epsg:26715')
>>> # find x,y of Jefferson City, MO.
>>> x1, y1 = p1(-92.199881,38.56694)
>>> # transform this point to projection 2 coordinates.
>>> x2, y2 = transform(p1,p2,x1,y1)
>>> '%9.3f %11.3f' % (x1,y1)
'569704.566 4269024.671'
>>> '%9.3f %11.3f' % (x2,y2)
'569722.342 4268814.027'
>>> '%8.3f %5.3f' % p2(x2,y2,inverse=True)
' -92.200 38.567'
>>> # process 3 points at a time in a tuple
>>> lats = (38.83,39.32,38.75) # Columbia, KC and StL Missouri
>>> lons = (-92.22,-94.72,-90.37)
>>> x1, y1 = p1(lons,lats)
>>> x2, y2 = transform(p1,p2,x1,y1)
>>> xy = x1+y1
>>> '%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy
'567703.344 351730.944 728553.093 4298200.739 4353698.725 4292319.005'
>>> xy = x2+y2
>>> '%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy
'567721.149 351747.558 728569.133 4297989.112 4353489.644 4292106.305'
>>> lons, lats = p2(x2,y2,inverse=True)
>>> xy = lons+lats
>>> '%8.3f %8.3f %8.3f %5.3f %5.3f %5.3f' % xy
' -92.220 -94.720 -90.370 38.830 39.320 38.750'
>>> # test datum shifting, installation of extra datum grid files.
>>> p1 = Proj(proj='latlong',datum='WGS84')
>>> x1 = -111.5; y1 = 45.25919444444
>>> p2 = Proj(proj="utm",zone=10,datum='NAD27')
>>> x2, y2 = transform(p1, p2, x1, y1)
>>> "%s %s" % (str(x2)[:9],str(y2)[:9])
'1402285.9 5076292.4' | x2, y2, z2 = transform(p1, p2, x1, y1, z1, radians=False) | [
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"(",
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"p2",
"x1",
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] | def transform(p1, p2, x, y, z=None, radians=False):
"""
x2, y2, z2 = transform(p1, p2, x1, y1, z1, radians=False)
Transform points between two coordinate systems defined by the
Proj instances p1 and p2.
The points x1,y1,z1 in the coordinate system defined by p1 are
transformed to x2,y2,z2 in the coordinate system defined by p2.
z1 is optional, if it is not set it is assumed to be zero (and
only x2 and y2 are returned).
In addition to converting between cartographic and geographic
projection coordinates, this function can take care of datum
shifts (which cannot be done using the __call__ method of the
Proj instances). It also allows for one of the coordinate
systems to be geographic (proj = 'latlong').
If optional keyword 'radians' is True (default is False) and p1
is defined in geographic coordinate (pj.is_latlong() is True),
x1,y1 is interpreted as radians instead of the default degrees.
Similarly, if p2 is defined in geographic coordinates and
radians=True, x2, y2 are returned in radians instead of degrees.
if p1.is_latlong() and p2.is_latlong() both are False, the
radians keyword has no effect.
x,y and z can be numpy or regular python arrays, python
lists/tuples or scalars. Arrays are fastest. For projections in
geocentric coordinates, values of x and y are given in meters.
z is always meters.
Example usage:
>>> # projection 1: UTM zone 15, grs80 ellipse, NAD83 datum
>>> # (defined by epsg code 26915)
>>> p1 = Proj(init='epsg:26915')
>>> # projection 2: UTM zone 15, clrk66 ellipse, NAD27 datum
>>> p2 = Proj(init='epsg:26715')
>>> # find x,y of Jefferson City, MO.
>>> x1, y1 = p1(-92.199881,38.56694)
>>> # transform this point to projection 2 coordinates.
>>> x2, y2 = transform(p1,p2,x1,y1)
>>> '%9.3f %11.3f' % (x1,y1)
'569704.566 4269024.671'
>>> '%9.3f %11.3f' % (x2,y2)
'569722.342 4268814.027'
>>> '%8.3f %5.3f' % p2(x2,y2,inverse=True)
' -92.200 38.567'
>>> # process 3 points at a time in a tuple
>>> lats = (38.83,39.32,38.75) # Columbia, KC and StL Missouri
>>> lons = (-92.22,-94.72,-90.37)
>>> x1, y1 = p1(lons,lats)
>>> x2, y2 = transform(p1,p2,x1,y1)
>>> xy = x1+y1
>>> '%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy
'567703.344 351730.944 728553.093 4298200.739 4353698.725 4292319.005'
>>> xy = x2+y2
>>> '%9.3f %9.3f %9.3f %11.3f %11.3f %11.3f' % xy
'567721.149 351747.558 728569.133 4297989.112 4353489.644 4292106.305'
>>> lons, lats = p2(x2,y2,inverse=True)
>>> xy = lons+lats
>>> '%8.3f %8.3f %8.3f %5.3f %5.3f %5.3f' % xy
' -92.220 -94.720 -90.370 38.830 39.320 38.750'
>>> # test datum shifting, installation of extra datum grid files.
>>> p1 = Proj(proj='latlong',datum='WGS84')
>>> x1 = -111.5; y1 = 45.25919444444
>>> p2 = Proj(proj="utm",zone=10,datum='NAD27')
>>> x2, y2 = transform(p1, p2, x1, y1)
>>> "%s %s" % (str(x2)[:9],str(y2)[:9])
'1402285.9 5076292.4'
"""
# check that p1 and p2 are from the Proj class
if not isinstance(p1, Proj):
raise TypeError("p1 must be a Proj class")
if not isinstance(p2, Proj):
raise TypeError("p2 must be a Proj class")
# process inputs, making copies that support buffer API.
inx, xisfloat, xislist, xistuple = _copytobuffer(x)
iny, yisfloat, yislist, yistuple = _copytobuffer(y)
if z is not None:
inz, zisfloat, zislist, zistuple = _copytobuffer(z)
else:
inz = None
# call pj_transform. inx,iny,inz buffers modified in place.
_proj._transform(p1,p2,inx,iny,inz,radians)
# if inputs were lists, tuples or floats, convert back.
outx = _convertback(xisfloat,xislist,xistuple,inx)
outy = _convertback(yisfloat,yislist,xistuple,iny)
if inz is not None:
outz = _convertback(zisfloat,zislist,zistuple,inz)
return outx, outy, outz
else:
return outx, outy | [
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||
jobovy/galpy | 8e6a230bbe24ce16938db10053f92eb17fe4bb52 | galpy/potential/planarPotential.py | python | planarPotential.Rforce | (self,R,phi=0.,t=0.) | return self._Rforce_nodecorator(R,phi=phi,t=t) | NAME:
Rforce
PURPOSE:
evaluate the radial force
INPUT:
R - Cylindrical radius (can be Quantity)
phi= azimuth (optional; can be Quantity)
t= time (optional; can be Quantity)
OUTPUT:
F_R(R,(\phi,t)))
HISTORY:
2010-07-13 - Written - Bovy (NYU) | NAME: | [
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] | def Rforce(self,R,phi=0.,t=0.):
"""
NAME:
Rforce
PURPOSE:
evaluate the radial force
INPUT:
R - Cylindrical radius (can be Quantity)
phi= azimuth (optional; can be Quantity)
t= time (optional; can be Quantity)
OUTPUT:
F_R(R,(\phi,t)))
HISTORY:
2010-07-13 - Written - Bovy (NYU)
"""
return self._Rforce_nodecorator(R,phi=phi,t=t) | [
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openstack/tacker | a60993fc3b2d4fc0e93ab13a874fe3c314fe48de | tacker/wsgi.py | python | DefaultMethodController.options | (self, request, **kwargs) | Return a response that includes the 'Allow' header.
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headers = [('Allow', kwargs.get('allowed_methods'))]
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||
sharppy/SHARPpy | 19175269ab11fe06c917b5d10376862a4716e1db | sharppy/sharptab/params.py | python | convective_temp | (prof, **kwargs) | return tmpc | Computes the convective temperature, assuming no change in the moisture
profile. Parcels are iteratively lifted until only mincinh is left as a
cap. The first guess is the observed surface temperature.
Parameters
----------
prof : profile object
Profile Object
mincinh : parcel object (optional; default -1)
Amount of CINH left at CI
pres : number (optional)
Pressure of parcel to lift (hPa)
tmpc : number (optional)
Temperature of parcel to lift (C)
dwpc : number (optional)
Dew Point of parcel to lift (C)
Returns
-------
Convective Temperature (C) : number | Computes the convective temperature, assuming no change in the moisture
profile. Parcels are iteratively lifted until only mincinh is left as a
cap. The first guess is the observed surface temperature.
Parameters
----------
prof : profile object
Profile Object
mincinh : parcel object (optional; default -1)
Amount of CINH left at CI
pres : number (optional)
Pressure of parcel to lift (hPa)
tmpc : number (optional)
Temperature of parcel to lift (C)
dwpc : number (optional)
Dew Point of parcel to lift (C)
Returns
-------
Convective Temperature (C) : number | [
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'''
Computes the convective temperature, assuming no change in the moisture
profile. Parcels are iteratively lifted until only mincinh is left as a
cap. The first guess is the observed surface temperature.
Parameters
----------
prof : profile object
Profile Object
mincinh : parcel object (optional; default -1)
Amount of CINH left at CI
pres : number (optional)
Pressure of parcel to lift (hPa)
tmpc : number (optional)
Temperature of parcel to lift (C)
dwpc : number (optional)
Dew Point of parcel to lift (C)
Returns
-------
Convective Temperature (C) : number
'''
mincinh = kwargs.get('mincinh', 0.)
mmr = mean_mixratio(prof)
pres = kwargs.get('pres', prof.pres[prof.sfc])
tmpc = kwargs.get('tmpc', prof.tmpc[prof.sfc])
dwpc = kwargs.get('dwpc', thermo.temp_at_mixrat(mmr, pres))
# Do a quick search to fine whether to continue. If you need to heat
# up more than 25C, don't compute.
pcl = cape(prof, flag=5, pres=pres, tmpc=tmpc+25., dwpc=dwpc, trunc=True)
if pcl.bplus == 0. or not utils.QC(pcl.bminus) or pcl.bminus < mincinh: return ma.masked
excess = dwpc - tmpc
if excess > 0: tmpc = tmpc + excess + 4.
pcl = cape(prof, flag=5, pres=pres, tmpc=tmpc, dwpc=dwpc, trunc=True)
if pcl.bplus == 0. or not utils.QC(pcl.bminus): pcl.bminus = ma.masked
while not utils.QC(pcl.bminus) or pcl.bminus < mincinh:
if pcl.bminus < -100: tmpc += 2.
else: tmpc += 0.5
pcl = cape(prof, flag=5, pres=pres, tmpc=tmpc, dwpc=dwpc, trunc=True)
if pcl.bplus == 0.: pcl.bminus = ma.masked
return tmpc | [
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|
sklearn-theano/sklearn-theano | 3eba566d8b624885b75759de47e52f903c015e40 | sklearn_theano/externals/google/protobuf/message.py | python | Message.IsInitialized | (self) | Checks if the message is initialized.
Returns:
The method returns True if the message is initialized (i.e. all of its
required fields are set). | Checks if the message is initialized. | [
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] | def IsInitialized(self):
"""Checks if the message is initialized.
Returns:
The method returns True if the message is initialized (i.e. all of its
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"""
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||
globaleaks/GlobaLeaks | 4624ca937728adb8e21c4733a8aecec6a41cb3db | backend/globaleaks/handlers/admin/submission_statuses.py | python | db_update_substatus_model_from_request | (model_obj, request, language) | Populates the model from the request, as well as setting default values
:param model_obj: The object model
:param request: The request data
:param language: The language of the request | Populates the model from the request, as well as setting default values | [
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"""
Populates the model from the request, as well as setting default values
:param model_obj: The object model
:param request: The request data
:param language: The language of the request
"""
fill_localized_keys(request, models.SubmissionSubStatus.localized_keys, language)
model_obj.update(request) | [
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||
LowinLi/fushare | 5aa02159e2c3a5e47eab5149c7f14d5c5b5e3c17 | fushare/requests_fun.py | python | urllib_request_link | (url,encoding='utf-8') | 爬取网站内容,如网站链接失败,可重复爬取20次
Parameters
------
url: 网站 string
encoding: 编码类型 string:’utf-8‘、’gbk‘等
Return
-------
r: 爬取返回内容 response: | 爬取网站内容,如网站链接失败,可重复爬取20次
Parameters
------
url: 网站 string
encoding: 编码类型 string:’utf-8‘、’gbk‘等
Return
-------
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"""
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Parameters
------
url: 网站 string
encoding: 编码类型 string:’utf-8‘、’gbk‘等
Return
-------
r: 爬取返回内容 response:
"""
i=0
while True:
try:
texts = urllib.request.urlopen(url).readlines()
return texts
except:
i+=1
print('第%s次链接失败最多20次' %str(i))
time.sleep(5)
if i>20:
return None | [
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||
agronholm/pythonfutures | acba785c8a5c331edce05dfa760540b8ea8a81f1 | concurrent/futures/_base.py | python | Future.exception | (self, timeout=None) | return self.exception_info(timeout)[0] | Return the exception raised by the call that the future represents.
Args:
timeout: The number of seconds to wait for the exception if the
future isn't done. If None, then there is no limit on the wait
time.
Returns:
The exception raised by the call that the future represents or None
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Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
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"""Return the exception raised by the call that the future represents.
Args:
timeout: The number of seconds to wait for the exception if the
future isn't done. If None, then there is no limit on the wait
time.
Returns:
The exception raised by the call that the future represents or None
if the call completed without raising.
Raises:
CancelledError: If the future was cancelled.
TimeoutError: If the future didn't finish executing before the given
timeout.
"""
return self.exception_info(timeout)[0] | [
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|
deepchem/deepchem | 054eb4b2b082e3df8e1a8e77f36a52137ae6e375 | deepchem/trans/transformers.py | python | CoulombFitTransformer.normalize | (self, X: np.ndarray) | return (X - self.mean) / self.std | Normalize features.
Parameters
----------
X: np.ndarray
Features
Returns
-------
X: np.ndarray
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] | def normalize(self, X: np.ndarray) -> np.ndarray:
"""Normalize features.
Parameters
----------
X: np.ndarray
Features
Returns
-------
X: np.ndarray
Normalized features
"""
return (X - self.mean) / self.std | [
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|
CLUEbenchmark/CLUEPretrainedModels | b384fd41665a8261f9c689c940cf750b3bc21fce | baselines/models/bert/run_squad.py | python | create_model | (bert_config, is_training, input_ids, input_mask, segment_ids,
use_one_hot_embeddings) | return (start_logits, end_logits) | Creates a classification model. | Creates a classification model. | [
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] | def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
final_hidden = model.get_sequence_output()
final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
batch_size = final_hidden_shape[0]
seq_length = final_hidden_shape[1]
hidden_size = final_hidden_shape[2]
output_weights = tf.get_variable(
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initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
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final_hidden_matrix = tf.reshape(final_hidden,
[batch_size * seq_length, hidden_size])
logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [batch_size, seq_length, 2])
logits = tf.transpose(logits, [2, 0, 1])
unstacked_logits = tf.unstack(logits, axis=0)
(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
return (start_logits, end_logits) | [
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|
filerock/FileRock-Client | 37214f701666e76e723595f8f9ed238a42f6eb06 | filerockclient/ui/wxGui/MainWindow.py | python | MainWindow.OnUpdateClientInfo | (self, event) | event.infos contains:
username: string
client_id: number
client_hostname: string
client_platform: string
client_version: string
basis: string
last_commit_timestamp: number or None
used_space: number or None
user_quota: number or None | event.infos contains:
username: string
client_id: number
client_hostname: string
client_platform: string
client_version: string
basis: string
last_commit_timestamp: number or None
used_space: number or None
user_quota: number or None | [
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'''
event.infos contains:
username: string
client_id: number
client_hostname: string
client_platform: string
client_version: string
basis: string
last_commit_timestamp: number or None
used_space: number or None
user_quota: number or None
'''
client_id = event.infos['client_id']
hostname = event.infos['client_hostname']
username = event.infos["username"]
self.panel_1.version_ctrl.SetValue(event.infos['client_version'], True)
if client_id is not None:
self.panel_1.client_ctrl.SetValue(client_id, True)
if hostname is not None:
self.panel_1.host_ctrl.SetValue(hostname, True)
if username is not None:
self.panel_1.user_ctrl.SetValue(username, True)
self.OnUpdateSessionInfo(event)
self.Layout()
self.panel_1.Layout()
self.Fit() | [
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||
richshaw2015/oh-my-rss | 68b9284e0acaf44ea389d675b71949177f9f3256 | web/views/views_html.py | python | get_site_update_view | (request) | return HttpResponseNotFound("No Feeds Subscribed") | 获取更新的全局站点视图,游客 100 个,登陆用户 200 个站点 | 获取更新的全局站点视图,游客 100 个,登陆用户 200 个站点 | [
"获取更新的全局站点视图,游客",
"100",
"个,登陆用户",
"200",
"个站点"
] | def get_site_update_view(request):
"""
获取更新的全局站点视图,游客 100 个,登陆用户 200 个站点
"""
sub_feeds = json.loads(request.POST.get('sub_feeds') or '[]')
unsub_feeds = json.loads(request.POST.get('unsub_feeds') or '[]')
page_size = int(request.POST.get('page_size', 10))
page = int(request.POST.get('page', 1))
onlyunread = request.POST.get('onlyunread', 'no') == 'yes'
user = get_login_user(request)
if user is None:
my_feeds = get_visitor_subscribe_feeds(tuple(sub_feeds), tuple(unsub_feeds))
else:
my_feeds = get_user_subscribe_feeds(user.oauth_id, user_level=user.level)
# 过滤有内容更新的
if user and onlyunread:
my_feeds = get_user_unread_sites(user.oauth_id, my_feeds)
my_feeds = sorted(my_feeds, key=lambda t: get_site_last_id(t), reverse=True)
if my_feeds:
# 分页处理
try:
paginator_obj = Paginator(my_feeds, page_size)
except:
logger.warning(f"分页参数错误:`{page}`{page_size}`{sub_feeds}`{unsub_feeds}")
return HttpResponseNotFound("Page Number Error")
pg = paginator_obj.page(page)
num_pages = paginator_obj.num_pages
sites = Site.objects.filter(pk__in=pg.object_list, status='active').order_by('-star')[:50]
for site in sites:
recent_articles = get_recent_site_articles(site.pk)
site.update_count = len(recent_articles)
site.update_ids = json.dumps(list(recent_articles))
site.update_time = get_site_last_id(site.pk)
if user:
site.unread_count = get_user_unread_count(user.oauth_id, recent_articles)
context = dict()
context['pg'] = pg
context['sites'] = sites
context['num_pages'] = num_pages
context['user'] = user
return render(request, 'left/site_view.html', context=context)
return HttpResponseNotFound("No Feeds Subscribed") | [
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|
uclanlp/visualbert | 2a9e8bd58a20af9ed32c0cb5d3a293f5bf5df019 | visualbert/utils/pytorch_misc.py | python | find_latest_checkpoint_step | (serialization_dir, epoch_to_load = None) | return model_path, training_state_path | Return the location of the latest model and training state files.
If there isn't a valid checkpoint then return None. | Return the location of the latest model and training state files.
If there isn't a valid checkpoint then return None. | [
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] | def find_latest_checkpoint_step(serialization_dir, epoch_to_load = None):
"""
Return the location of the latest model and training state files.
If there isn't a valid checkpoint then return None.
"""
have_checkpoint = (serialization_dir is not None and
any("model_step_" in x for x in os.listdir(serialization_dir)))
if not have_checkpoint:
return None
serialization_files = os.listdir(serialization_dir)
model_checkpoints = [x for x in serialization_files if "model_step_" in x]
# Get the last checkpoint file. Epochs are specified as either an
# int (for end of epoch files) or with epoch and timestamp for
# within epoch checkpoints, e.g. 5.2018-02-02-15-33-42
info = [(x, int(x.split('_')[2]), int(x.split('_')[4].split('.')[0])) for x in model_checkpoints]
max_epoch = -1
max_step = -1
max_index = -1
for index, i in enumerate(info):
if i[2] > max_epoch:
max_epoch = i[2]
max_step = i[1]
max_index = index
elif i[2] == max_epoch:
if i[1] > max_step:
max_step = i[1]
max_index = index
model_path = os.path.join(serialization_dir,
"model_step_{}_epoch_{}.th".format(max_step, max_epoch))
training_state_path = os.path.join(serialization_dir,
"training_step_{}_epoch_{}.th".format(max_step, max_epoch))
return model_path, training_state_path | [
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|
celery/kombu | 853b13f1d018ebfe7ad2d064a3111cac9fcf5383 | kombu/transport/qpid.py | python | Channel.queue_delete | (self, queue, if_unused=False, if_empty=False, **kwargs) | Delete a queue by name.
Delete a queue specified by name. Using the if_unused keyword
argument, the delete can only occur if there are 0 consumers bound
to it. Using the if_empty keyword argument, the delete can only
occur if there are 0 messages in the queue.
:param queue: The name of the queue to be deleted.
:type queue: str
:keyword if_unused: If True, delete only if the queue has 0
consumers. If False, delete a queue even with consumers bound
to it.
:type if_unused: bool
:keyword if_empty: If True, only delete the queue if it is empty. If
False, delete the queue if it is empty or not.
:type if_empty: bool | Delete a queue by name. | [
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] | def queue_delete(self, queue, if_unused=False, if_empty=False, **kwargs):
"""Delete a queue by name.
Delete a queue specified by name. Using the if_unused keyword
argument, the delete can only occur if there are 0 consumers bound
to it. Using the if_empty keyword argument, the delete can only
occur if there are 0 messages in the queue.
:param queue: The name of the queue to be deleted.
:type queue: str
:keyword if_unused: If True, delete only if the queue has 0
consumers. If False, delete a queue even with consumers bound
to it.
:type if_unused: bool
:keyword if_empty: If True, only delete the queue if it is empty. If
False, delete the queue if it is empty or not.
:type if_empty: bool
"""
if self._has_queue(queue):
if if_empty and self._size(queue):
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queue_obj = self._broker.getQueue(queue)
consumer_count = queue_obj.getAttributes()['consumerCount']
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self._delete(queue) | [
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||
FSecureLABS/Jandroid | e31d0dab58a2bfd6ed8e0a387172b8bd7c893436 | libs/platform-tools/platform-tools_darwin/systrace/catapult/devil/devil/utils/lazy/weak_constant.py | python | WeakConstant.read | (self) | return self._val | Get the object, creating it if necessary. | Get the object, creating it if necessary. | [
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"""Get the object, creating it if necessary."""
if self._initialized.is_set():
return self._val
with self._lock:
if not self._initialized.is_set():
# We initialize the value on a separate thread to protect
# from holding self._lock indefinitely in the event that
# self._initializer hangs.
initializer_thread = reraiser_thread.ReraiserThread(
self._initializer)
initializer_thread.start()
timeout_retry.WaitFor(
lambda: initializer_thread.join(1) or not initializer_thread.isAlive(),
wait_period=0)
self._val = initializer_thread.GetReturnValue()
self._initialized.set()
return self._val | [
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007gzs/dingtalk-sdk | 7979da2e259fdbc571728cae2425a04dbc65850a | dingtalk/client/api/taobao.py | python | TbDMP.taobao_dmp_analysis_coverage | (
self,
select_tag_option_set_d_t_o
) | return self._top_request(
"taobao.dmp.analysis.coverage",
{
"select_tag_option_set_d_t_o": select_tag_option_set_d_t_o
}
) | 获取标签组合覆盖人数
文档地址:https://open-doc.dingtalk.com/docs/api.htm?apiId=29408
:param select_tag_option_set_d_t_o: 标签组合规则 | 获取标签组合覆盖人数
文档地址:https://open-doc.dingtalk.com/docs/api.htm?apiId=29408 | [
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self,
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):
"""
获取标签组合覆盖人数
文档地址:https://open-doc.dingtalk.com/docs/api.htm?apiId=29408
:param select_tag_option_set_d_t_o: 标签组合规则
"""
return self._top_request(
"taobao.dmp.analysis.coverage",
{
"select_tag_option_set_d_t_o": select_tag_option_set_d_t_o
}
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|
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pip/_vendor/distro.py | python | LinuxDistribution.minor_version | (self, best=False) | return self.version_parts(best)[1] | Return the minor version number of the current distribution.
For details, see :func:`distro.minor_version`. | Return the minor version number of the current distribution. | [
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"distribution",
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] | def minor_version(self, best=False):
"""
Return the minor version number of the current distribution.
For details, see :func:`distro.minor_version`.
"""
return self.version_parts(best)[1] | [
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|
weldr/lorax | d692ce366287ae468c52bc8becde2fef113661a3 | src/pylorax/monitor.py | python | LogRequestHandler.iserror | (self, line) | Check a line to see if it contains an error indicating installation failure
:param str line: log line to check for failure
If the line contains IGNORED it will be skipped. | Check a line to see if it contains an error indicating installation failure | [
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"""
Check a line to see if it contains an error indicating installation failure
:param str line: log line to check for failure
If the line contains IGNORED it will be skipped.
"""
if "IGNORED" in line:
return
for t in self.simple_tests:
if t in line:
self.server.log_error = True
self.server.error_line = line
return
for t in self.re_tests:
if re.search(t, line):
self.server.log_error = True
self.server.error_line = line
return | [
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||
lightforever/mlcomp | c78fdb77ec9c4ec8ff11beea50b90cab20903ad9 | mlcomp/db/providers/dag.py | python | DagProvider.remove_all | (self, ids: List[int]) | [] | def remove_all(self, ids: List[int]):
self.query(Dag).filter(Dag.id.in_(ids)).delete(
synchronize_session=False)
self.commit() | [
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||||
linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/tarfile.py | python | TarInfo._create_gnu_long_header | (cls, name, type, encoding, errors) | return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
cls._create_payload(name) | Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
for name. | Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
for name. | [
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] | def _create_gnu_long_header(cls, name, type, encoding, errors):
"""Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
for name.
"""
name = name.encode(encoding, errors) + NUL
info = {}
info["name"] = "././@LongLink"
info["type"] = type
info["size"] = len(name)
info["magic"] = GNU_MAGIC
# create extended header + name blocks.
return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
cls._create_payload(name) | [
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cortex-lab/phy | 9a330b9437a3d0b40a37a201d147224e6e7fb462 | plugins/matplotlib_view.py | python | FeatureDensityView.__init__ | (self, features=None) | features is a function (cluster_id => Bunch(data, ...)) where data is a 3D array. | features is a function (cluster_id => Bunch(data, ...)) where data is a 3D array. | [
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"""features is a function (cluster_id => Bunch(data, ...)) where data is a 3D array."""
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self.features = features | [
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||
IntelAI/models | 1d7a53ccfad3e6f0e7378c9e3c8840895d63df8c | models/language_translation/tensorflow/transformer_mlperf/inference/int8/transformer/data_download.py | python | download_from_url | (path, url) | Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file | Download content from a url. | [
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] | def download_from_url(path, url):
"""Download content from a url.
Args:
path: string directory where file will be downloaded
url: string url
Returns:
Full path to downloaded file
"""
filename = url.split("/")[-1]
found_file = find_file(path, filename, max_depth=0)
if found_file is None:
filename = os.path.join(path, filename)
tf.compat.v1.logging.info("Downloading from %s to %s." % (url, filename))
inprogress_filepath = filename + ".incomplete"
inprogress_filepath, _ = urllib.request.urlretrieve(
url, inprogress_filepath, reporthook=download_report_hook)
# Print newline to clear the carriage return from the download progress.
print()
tf.io.gfile.rename(inprogress_filepath, filename)
return filename
else:
tf.compat.v1.logging.info("Already downloaded: %s (at %s)." % (url, found_file))
return found_file | [
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||
riptideio/pymodbus | c5772b35ae3f29d1947f3ab453d8d00df846459f | pymodbus/repl/client/helper.py | python | Result.__init__ | (self, result) | :param result: Response of a modbus command. | :param result: Response of a modbus command. | [
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"""
:param result: Response of a modbus command.
"""
if isinstance(result, dict): # Modbus response
self.function_code = result.pop('function_code', None)
self.data = dict(result)
else:
self.data = result | [
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/winobject/process.py | python | WinProcess.low_read_memory | (self, addr, buffer_addr, size) | return winproxy.ReadProcessMemory(self.handle, addr, lpBuffer=buffer_addr, nSize=size) | [] | def low_read_memory(self, addr, buffer_addr, size):
if windows.current_process.bitness == 32 and self.bitness == 64:
# OptionalExport can be None (see winproxy.py)
if not winproxy.is_implemented(winproxy.NtWow64ReadVirtualMemory64):
raise ValueError("NtWow64ReadVirtualMemory64 non available in ntdll: cannot read into 64bits processus")
return winproxy.NtWow64ReadVirtualMemory64(self.handle, addr, buffer_addr, size)
#if self.is_wow_64 and addr > 0xffffffff:
# return winproxy.NtWow64ReadVirtualMemory64(self.handle, addr, buffer_addr, size)
return winproxy.ReadProcessMemory(self.handle, addr, lpBuffer=buffer_addr, nSize=size) | [
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|||
landlab/landlab | a5dd80b8ebfd03d1ba87ef6c4368c409485f222c | landlab/components/lake_fill/lake_fill_barnes.py | python | LakeMapperBarnes._track_original_surface | (self) | return orig_surf | This helper method ensures that if flow is to be redircted, the
_redirect_flowdirs() method can still get access to this information
when it needs it. The idea here is that the operation is essentially
free when surface and fill_surface were different to start with, which
should make us faster.
Examples
--------
>>> from landlab import RasterModelGrid
>>> from landlab.components import LakeMapperBarnes, FlowAccumulator
>>> mg = RasterModelGrid((5, 6), xy_spacing=2.)
>>> z = mg.add_zeros("topographic__elevation", at="node", dtype=float)
>>> z_new = mg.add_zeros("topographic__fill", at="node", dtype=float)
>>> fa = FlowAccumulator(mg)
>>> lmb = LakeMapperBarnes(mg, method='D8',
... surface='topographic__elevation',
... fill_surface='topographic__fill',
... redirect_flow_steepest_descent=False,
... track_lakes=False)
>>> orig_surf = lmb._track_original_surface()
>>> z is orig_surf
True
>>> lmb = LakeMapperBarnes(mg, method='D8',
... surface='topographic__elevation',
... fill_surface='topographic__elevation',
... redirect_flow_steepest_descent=False,
... track_lakes=False)
>>> orig_surf = lmb._track_original_surface()
>>> z is orig_surf
False | This helper method ensures that if flow is to be redircted, the
_redirect_flowdirs() method can still get access to this information
when it needs it. The idea here is that the operation is essentially
free when surface and fill_surface were different to start with, which
should make us faster. | [
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"faster",
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] | def _track_original_surface(self):
"""This helper method ensures that if flow is to be redircted, the
_redirect_flowdirs() method can still get access to this information
when it needs it. The idea here is that the operation is essentially
free when surface and fill_surface were different to start with, which
should make us faster.
Examples
--------
>>> from landlab import RasterModelGrid
>>> from landlab.components import LakeMapperBarnes, FlowAccumulator
>>> mg = RasterModelGrid((5, 6), xy_spacing=2.)
>>> z = mg.add_zeros("topographic__elevation", at="node", dtype=float)
>>> z_new = mg.add_zeros("topographic__fill", at="node", dtype=float)
>>> fa = FlowAccumulator(mg)
>>> lmb = LakeMapperBarnes(mg, method='D8',
... surface='topographic__elevation',
... fill_surface='topographic__fill',
... redirect_flow_steepest_descent=False,
... track_lakes=False)
>>> orig_surf = lmb._track_original_surface()
>>> z is orig_surf
True
>>> lmb = LakeMapperBarnes(mg, method='D8',
... surface='topographic__elevation',
... fill_surface='topographic__elevation',
... redirect_flow_steepest_descent=False,
... track_lakes=False)
>>> orig_surf = lmb._track_original_surface()
>>> z is orig_surf
False
"""
if self._inplace:
orig_surf = self._surface.copy()
else:
orig_surf = self._surface
return orig_surf | [
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|
jupyter/jupyter_core | 66e16cada28a15c1e0dd14e00f48c5bb7578db09 | jupyter_core/troubleshoot.py | python | main | () | print out useful info | print out useful info | [
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"info"
] | def main():
"""
print out useful info
"""
#pylint: disable=superfluous-parens
# args = get_args()
environment_data = get_data()
print('$PATH:')
for directory in environment_data['path'].split(os.pathsep):
print('\t' + directory)
print('\n' + 'sys.path:')
for directory in environment_data['sys_path']:
print('\t' + directory)
print('\n' + 'sys.executable:')
print('\t' + environment_data['sys_exe'])
print('\n' + 'sys.version:')
if '\n' in environment_data['sys_version']:
for data in environment_data['sys_version'].split('\n'):
print('\t' + data)
else:
print('\t' + environment_data['sys_version'])
print('\n' + 'platform.platform():')
print('\t' + environment_data['platform'])
if environment_data['which']:
print('\n' + 'which -a jupyter:')
for line in environment_data['which'].split('\n'):
print('\t' + line)
if environment_data['where']:
print('\n' + 'where jupyter:')
for line in environment_data['where'].split('\n'):
print('\t' + line)
if environment_data['pip']:
print('\n' + 'pip list:')
for package in environment_data['pip'].split('\n'):
print('\t' + package)
if environment_data['conda']:
print('\n' + 'conda list:')
for package in environment_data['conda'].split('\n'):
print('\t' + package)
if environment_data['conda-env']:
print('\n' + 'conda env:')
for package in environment_data['conda-env'].split('\n'):
print('\t' + package) | [
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||
coursera/dataduct | 83aea17c1b1abd376270bc8fd4a180ce09181cc5 | examples/resources/scripts/s3_profiler.py | python | recurse_directory | (directory_path) | return result | Recursively walk directories and output basic stats on files
Args:
directory_path(str): Path to the directory which is read
Returns:
result(list of tuples): (filename, count of lines in file, size of file) | Recursively walk directories and output basic stats on files | [
"Recursively",
"walk",
"directories",
"and",
"output",
"basic",
"stats",
"on",
"files"
] | def recurse_directory(directory_path):
"""Recursively walk directories and output basic stats on files
Args:
directory_path(str): Path to the directory which is read
Returns:
result(list of tuples): (filename, count of lines in file, size of file)
"""
result = []
for root, _, files in walk(directory_path):
for f in files:
filename = join(root, f)
result.append((
filename,
run_command(['wc', '-l', filename]).split(' ').pop(0),
str(stat(filename).st_size),
))
return result | [
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|
yt-project/yt | dc7b24f9b266703db4c843e329c6c8644d47b824 | yt/frontends/gadget_fof/data_structures.py | python | GadgetFOFHaloParticleIndex._get_halo_values | (self, ptype, identifiers, fields, f=None) | return data | Get field values for halos. IDs are likely to be
sequential (or at least monotonic), but not necessarily
all within the same file.
This does not do much to minimize file i/o, but with
halos randomly distributed across files, there's not
much more we can do. | Get field values for halos. IDs are likely to be
sequential (or at least monotonic), but not necessarily
all within the same file. | [
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"."
] | def _get_halo_values(self, ptype, identifiers, fields, f=None):
"""
Get field values for halos. IDs are likely to be
sequential (or at least monotonic), but not necessarily
all within the same file.
This does not do much to minimize file i/o, but with
halos randomly distributed across files, there's not
much more we can do.
"""
# if a file is already open, don't open it again
filename = None if f is None else f.filename
data = defaultdict(lambda: np.empty(identifiers.size))
i_scalars = self._get_halo_file_indices(ptype, identifiers)
for i_scalar in np.unique(i_scalars):
target = i_scalars == i_scalar
scalar_indices = identifiers - self._halo_index_start[ptype][i_scalar]
# only open file if it's not already open
my_f = (
f
if self.data_files[i_scalar].filename == filename
else h5py.File(self.data_files[i_scalar].filename, mode="r")
)
for field in fields:
data[field][target] = my_f[os.path.join(ptype, field)][()][
scalar_indices[target]
]
if self.data_files[i_scalar].filename != filename:
my_f.close()
return data | [
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] | https://github.com/yt-project/yt/blob/dc7b24f9b266703db4c843e329c6c8644d47b824/yt/frontends/gadget_fof/data_structures.py#L386-L421 |
|
ganeti/ganeti | d340a9ddd12f501bef57da421b5f9b969a4ba905 | lib/client/gnt_cluster.py | python | _ReadIntentToUpgrade | () | return (contents[0], contents[1]) | Read the file documenting the intent to upgrade the cluster.
@rtype: (string, string) or (None, None)
@return: (old version, version to upgrade to), if the file exists,
and (None, None) otherwise. | Read the file documenting the intent to upgrade the cluster. | [
"Read",
"the",
"file",
"documenting",
"the",
"intent",
"to",
"upgrade",
"the",
"cluster",
"."
] | def _ReadIntentToUpgrade():
"""Read the file documenting the intent to upgrade the cluster.
@rtype: (string, string) or (None, None)
@return: (old version, version to upgrade to), if the file exists,
and (None, None) otherwise.
"""
if not os.path.isfile(pathutils.INTENT_TO_UPGRADE):
return (None, None)
contentstring = utils.ReadFile(pathutils.INTENT_TO_UPGRADE)
contents = utils.UnescapeAndSplit(contentstring)
if len(contents) != 3:
# file syntactically mal-formed
return (None, None)
return (contents[0], contents[1]) | [
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|
PacktPublishing/Mastering-OpenCV-4-with-Python | ea5372c6d8758ebc56ef5c775f9785d4427f81e6 | Chapter08/01-chapter-content/contours_shape_recognition.py | python | array_to_tuple | (arr) | return tuple(arr.reshape(1, -1)[0]) | Converts array to tuple | Converts array to tuple | [
"Converts",
"array",
"to",
"tuple"
] | def array_to_tuple(arr):
"""Converts array to tuple"""
return tuple(arr.reshape(1, -1)[0]) | [
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|
exodrifter/unity-python | bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d | Lib/mailbox.py | python | _create_temporary | (path) | return _create_carefully('%s.%s.%s.%s' % (path, int(time.time()),
socket.gethostname(),
os.getpid())) | Create a temp file based on path and open for reading and writing. | Create a temp file based on path and open for reading and writing. | [
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"and",
"writing",
"."
] | def _create_temporary(path):
"""Create a temp file based on path and open for reading and writing."""
return _create_carefully('%s.%s.%s.%s' % (path, int(time.time()),
socket.gethostname(),
os.getpid())) | [
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] | https://github.com/exodrifter/unity-python/blob/bef6e4e9ddfbbf1eaf7acbbb973e9aa3dd64a20d/Lib/mailbox.py#L2031-L2035 |
|
selfboot/LeetCode | 473c0c5451651140d75cbd143309c51cd8fe1cf1 | String/67_AddBinary.py | python | Solution_2.addBinary | (self, a, b) | return result | Iteratively way. | Iteratively way. | [
"Iteratively",
"way",
"."
] | def addBinary(self, a, b):
"""Iteratively way.
"""
carry_in, index = '0', 0
result = ""
while index < max(len(a), len(b)) or carry_in == '1':
num_a = a[-1 - index] if index < len(a) else '0'
num_b = b[-1 - index] if index < len(b) else '0'
val = int(num_a) + int(num_b) + int(carry_in)
result = str(val % 2) + result
carry_in = '1' if val > 1 else '0'
index += 1
return result | [
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|
pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/pandas-0.17.0-py3.3-win-amd64.egg/pandas/compat/chainmap_impl.py | python | ChainMap.parents | (self) | return self.__class__(*self.maps[1:]) | New ChainMap from maps[1:]. | New ChainMap from maps[1:]. | [
"New",
"ChainMap",
"from",
"maps",
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":",
"]",
"."
] | def parents(self): # like Django's Context.pop()
'New ChainMap from maps[1:].'
return self.__class__(*self.maps[1:]) | [
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|
mozilla/zamboni | 14b1a44658e47b9f048962fa52dbf00a3beaaf30 | mkt/reviewers/models.py | python | ReviewerScore.all_users_by_score | (cls) | return scores | Returns reviewers ordered by highest total points first. | Returns reviewers ordered by highest total points first. | [
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] | def all_users_by_score(cls):
"""
Returns reviewers ordered by highest total points first.
"""
query = cls._leaderboard_query()
scores = []
for row in query:
user_id, name, total = row
user_level = len(mkt.REVIEWED_LEVELS) - 1
for i, level in enumerate(mkt.REVIEWED_LEVELS):
if total < level['points']:
user_level = i - 1
break
# Only show level if it changes.
if user_level < 0:
level = ''
else:
level = mkt.REVIEWED_LEVELS[user_level]['name']
scores.append({
'user_id': user_id,
'name': name,
'total': int(total),
'level': level,
})
prev = None
for score in reversed(scores):
if score['level'] == prev:
score['level'] = ''
else:
prev = score['level']
return scores | [
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|
mihaip/readerisdead | 0e35cf26e88f27e0a07432182757c1ce230f6936 | third_party/web/wsgiserver/__init__.py | python | WSGIGateway_u0.get_environ | (self) | return env | Return a new environ dict targeting the given wsgi.version | Return a new environ dict targeting the given wsgi.version | [
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"targeting",
"the",
"given",
"wsgi",
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"version"
] | def get_environ(self):
"""Return a new environ dict targeting the given wsgi.version"""
req = self.req
env_10 = WSGIGateway_10.get_environ(self)
env = dict([(k.decode('ISO-8859-1'), v) for k, v in env_10.iteritems()])
env[u'wsgi.version'] = ('u', 0)
# Request-URI
env.setdefault(u'wsgi.url_encoding', u'utf-8')
try:
for key in [u"PATH_INFO", u"SCRIPT_NAME", u"QUERY_STRING"]:
env[key] = env_10[str(key)].decode(env[u'wsgi.url_encoding'])
except UnicodeDecodeError:
# Fall back to latin 1 so apps can transcode if needed.
env[u'wsgi.url_encoding'] = u'ISO-8859-1'
for key in [u"PATH_INFO", u"SCRIPT_NAME", u"QUERY_STRING"]:
env[key] = env_10[str(key)].decode(env[u'wsgi.url_encoding'])
for k, v in sorted(env.items()):
if isinstance(v, str) and k not in ('REQUEST_URI', 'wsgi.input'):
env[k] = v.decode('ISO-8859-1')
return env | [
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|
deepgram/kur | fd0c120e50815c1e5be64e5dde964dcd47234556 | kur/__main__.py | python | evaluate | (args) | Evaluates a model. | Evaluates a model. | [
"Evaluates",
"a",
"model",
"."
] | def evaluate(args):
""" Evaluates a model.
"""
spec = parse_kurfile(args.kurfile, args.engine)
func = spec.get_evaluation_function()
func(step=args.step) | [
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||
IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/calendar.py | python | LocaleHTMLCalendar.__init__ | (self, firstweekday=0, locale=None) | [] | def __init__(self, firstweekday=0, locale=None):
HTMLCalendar.__init__(self, firstweekday)
if locale is None:
locale = _locale.getdefaultlocale()
self.locale = locale | [
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||||
googleapis/python-ndb | e780c81cde1016651afbfcad8180d9912722cf1b | google/cloud/ndb/tasklets.py | python | tasklet | (wrapped) | return tasklet_wrapper | A decorator to turn a function or method into a tasklet.
Calling a tasklet will return a :class:`~Future` instance which can be used
to get the eventual return value of the tasklet.
For more information on tasklets and cooperative multitasking, see the main
documentation.
Args:
wrapped (Callable): The wrapped function. | A decorator to turn a function or method into a tasklet. | [
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"a",
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] | def tasklet(wrapped):
"""
A decorator to turn a function or method into a tasklet.
Calling a tasklet will return a :class:`~Future` instance which can be used
to get the eventual return value of the tasklet.
For more information on tasklets and cooperative multitasking, see the main
documentation.
Args:
wrapped (Callable): The wrapped function.
"""
@functools.wraps(wrapped)
def tasklet_wrapper(*args, **kwargs):
# Avoid Python 2.7 circular import
from google.cloud.ndb import context as context_module
# The normal case is that the wrapped function is a generator function
# that returns a generator when called. We also support the case that
# the user has wrapped a regular function with the tasklet decorator.
# In this case, we fail to realize an actual tasklet, but we go ahead
# and create a future object and set the result to the function's
# return value so that from the user perspective there is no problem.
# This permissive behavior is inherited from legacy NDB.
context = context_module.get_context()
try:
returned = wrapped(*args, **kwargs)
except Return as stop:
# If wrapped is a regular function and the function uses "raise
# Return(result)" pattern rather than just returning the result,
# then we'll extract the result from the StopIteration exception.
returned = _get_return_value(stop)
if isinstance(returned, types.GeneratorType):
# We have a tasklet, start it
future = _TaskletFuture(returned, context, info=wrapped.__name__)
future._advance_tasklet()
else:
# We don't have a tasklet, but we fake it anyway
future = Future(info=wrapped.__name__)
future.set_result(returned)
return future
return tasklet_wrapper | [
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|
Yelp/paasta | 6c08c04a577359509575c794b973ea84d72accf9 | paasta_tools/contrib/graceful_container_drain.py | python | _timeout | (process) | Helper function for _run. It terminates the process.
Doesn't raise OSError, if we try to terminate a non-existing
process as there can be a very small window between poll() and kill() | Helper function for _run. It terminates the process.
Doesn't raise OSError, if we try to terminate a non-existing
process as there can be a very small window between poll() and kill() | [
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] | def _timeout(process):
"""Helper function for _run. It terminates the process.
Doesn't raise OSError, if we try to terminate a non-existing
process as there can be a very small window between poll() and kill()
"""
if process.poll() is None:
try:
# sending SIGKILL to the process
process.kill()
except OSError as e:
# No such process error
# The process could have been terminated meanwhile
if e.errno != errno.ESRCH:
raise | [
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||
SmallVagetable/machine_learning_python | a9cc8074adf91567407a5cb70a4c17b64f299fbc | decision_tree/tree_id3.py | python | DTreeID3.train | (self, datasets, node) | [] | def train(self, datasets, node):
labely = datasets.columns[-1]
# 判断样本是否为同一类输出Di,如果是则返回单节点树T。标记类别为Di
if len(datasets[labely].value_counts()) == 1:
node.data = datasets[labely]
node.y = datasets[labely][0]
return
# 判断特征是否为空,如果是则返回单节点树T,标记类别为样本中输出类别D实例数最多的类别
if len(datasets.columns[:-1]) == 0:
node.data = datasets[labely]
node.y = datasets[labely].value_counts().index[0]
return
# 计算A中的各个特征(一共n个)对输出D的信息增益,选择信息增益最大的特征Ag。
gainmaxi, gainmax = self.info_gain_train(datasets, datasets.columns)
# 如果Ag的信息增益小于阈值ε,则返回单节点树T,标记类别为样本中输出类别D实例数最多的类别。
if gainmax <= self.epsilon:
node.data = datasets[labely]
node.y = datasets[labely].value_counts().index[0]
return
# 按特征Ag的不同取值Agi将对应的样本输出D分成不同的类别Di。每个类别产生一个子节点。对应特征值为Agi。返回增加了节点的数T。
vc = datasets[datasets.columns[gainmaxi]].value_counts()
for Di in vc.index:
node.label = gainmaxi
child = Node(Di)
node.append(child)
new_datasets = pd.DataFrame([list(i) for i in datasets.values if i[gainmaxi]==Di], columns=datasets.columns)
self.train(new_datasets, child) | [
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||||
linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/html5lib/_trie/datrie.py | python | Trie.__init__ | (self, data) | [] | def __init__(self, data):
chars = set()
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rhinstaller/anaconda | 63edc8680f1b05cbfe11bef28703acba808c5174 | pyanaconda/core/configuration/storage_constraints.py | python | StorageConstraints._convert_device_types | (self, value) | return set(map(DeviceType.from_name, value.split())) | Convert the given value into a set of device types. | Convert the given value into a set of device types. | [
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rougier/from-python-to-numpy | eb21651fc84d132414603e5a16d93f54ef45ec99 | code/gpudata.py | python | GPUData.stride | (self) | Item stride in the base array. | Item stride in the base array. | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/python/usage.py | python | Options.parseArgs | (self) | I am called with any leftover arguments which were not options.
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||
tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1beta1_custom_resource_definition.py | python | V1beta1CustomResourceDefinition.spec | (self, spec) | Sets the spec of this V1beta1CustomResourceDefinition.
:param spec: The spec of this V1beta1CustomResourceDefinition. # noqa: E501
:type: V1beta1CustomResourceDefinitionSpec | Sets the spec of this V1beta1CustomResourceDefinition. | [
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:param spec: The spec of this V1beta1CustomResourceDefinition. # noqa: E501
:type: V1beta1CustomResourceDefinitionSpec
"""
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raise ValueError("Invalid value for `spec`, must not be `None`") # noqa: E501
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_oa_openshift/library/oc_label.py | python | OpenShiftCLI._evacuate | (self, node=None, selector=None, pod_selector=None, dry_run=False, grace_period=None, force=False) | return self.openshift_cmd(cmd, oadm=True, output=True, output_type='raw') | perform oadm manage-node evacuate | perform oadm manage-node evacuate | [
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''' perform oadm manage-node evacuate '''
cmd = ['manage-node']
if node:
cmd.extend(node)
else:
cmd.append('--selector={}'.format(selector))
if dry_run:
cmd.append('--dry-run')
if pod_selector:
cmd.append('--pod-selector={}'.format(pod_selector))
if grace_period:
cmd.append('--grace-period={}'.format(int(grace_period)))
if force:
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/pandas/core/reshape.py | python | make_axis_dummies | (frame, axis='minor', transform=None) | return DataFrame(values, columns=items, index=frame.index) | Construct 1-0 dummy variables corresponding to designated axis
labels
Parameters
----------
frame : DataFrame
axis : {'major', 'minor'}, default 'minor'
transform : function, default None
Function to apply to axis labels first. For example, to
get "day of week" dummies in a time series regression
you might call::
make_axis_dummies(panel, axis='major',
transform=lambda d: d.weekday())
Returns
-------
dummies : DataFrame
Column names taken from chosen axis | Construct 1-0 dummy variables corresponding to designated axis
labels | [
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"""
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Parameters
----------
frame : DataFrame
axis : {'major', 'minor'}, default 'minor'
transform : function, default None
Function to apply to axis labels first. For example, to
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you might call::
make_axis_dummies(panel, axis='major',
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Returns
-------
dummies : DataFrame
Column names taken from chosen axis
"""
numbers = {'major': 0, 'minor': 1}
num = numbers.get(axis, axis)
items = frame.index.levels[num]
labels = frame.index.labels[num]
if transform is not None:
mapped_items = items.map(transform)
labels, items = _factorize_from_iterable(mapped_items.take(labels))
values = np.eye(len(items), dtype=float)
values = values.take(labels, axis=0)
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Esri/ArcREST | ab240fde2b0200f61d4a5f6df033516e53f2f416 | src/arcrest/manageorg/_content.py | python | UserItem.languages | (self) | return self._languages | gets the property value for languages | gets the property value for languages | [
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'''gets the property value for languages'''
if self._languages is None:
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return self._languages | [
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|
google/clusterfuzz | f358af24f414daa17a3649b143e71ea71871ef59 | src/clusterfuzz/_internal/datastore/data_types.py | python | Testcase.get_metadata | (self, key=None, default=None) | Get metadata for a test case. Slow on first access. | Get metadata for a test case. Slow on first access. | [
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relekang/python-semantic-release | 09af5f11a6134c8711b59a5bcd57c917c0c91b5e | semantic_release/vcs_helpers.py | python | get_current_head_hash | () | return repo.head.commit.name_rev.split(" ")[0] | Get the commit hash of the current HEAD.
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jeffknupp/sandman | 253ea4d15cbccd9f0016d66fedd7478614cc0b2f | sandman/sandman.py | python | _single_attribute_html_response | (resource, name, value) | return make_response(render_template(
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name=name, value=value)) | Return the json representation of a single attribute of a resource.
:param :class:`sandman.model.Model` resource: resource for attribute
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:param string value: string value of the attribute
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yaohungt/Gated-Spatio-Temporal-Energy-Graph | bc8f44b3d95cbfe3032bb3612daa07b4d9cd4298 | models/layers/AsyncTFCriterion.py | python | axb | (a, x, b) | return (a * xb.squeeze()).sum(1) | [] | def axb(a, x, b):
# a and b are batched vectors, X is batched matrix
# returns a^t * X * b
xb = torch.bmm(x, b[:, :, None])
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|||
wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/Snake/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/pkg_resources/__init__.py | python | Environment.__iter__ | (self) | Yield the unique project names of the available distributions | Yield the unique project names of the available distributions | [
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"""Yield the unique project names of the available distributions"""
for key in self._distmap.keys():
if self[key]:
yield key | [
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||
oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/lib2to3/fixes/fix_imports.py | python | alternates | (members) | return "(" + "|".join(map(repr, members)) + ")" | [] | def alternates(members):
return "(" + "|".join(map(repr, members)) + ")" | [
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|||
kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/flask_cache/__init__.py | python | Cache._memoize_kwargs_to_args | (self, f, *args, **kwargs) | return tuple(new_args), {} | [] | def _memoize_kwargs_to_args(self, f, *args, **kwargs):
#: Inspect the arguments to the function
#: This allows the memoization to be the same
#: whether the function was called with
#: 1, b=2 is equivilant to a=1, b=2, etc.
new_args = []
arg_num = 0
argspec = inspect.getargspec(f)
args_len = len(argspec.args)
for i in range(args_len):
if i == 0 and argspec.args[i] in ('self', 'cls'):
#: use the repr of the class instance
#: this supports instance methods for
#: the memoized functions, giving more
#: flexibility to developers
arg = repr(args[0])
arg_num += 1
elif argspec.args[i] in kwargs:
arg = kwargs[argspec.args[i]]
elif arg_num < len(args):
arg = args[arg_num]
arg_num += 1
elif abs(i-args_len) <= len(argspec.defaults):
arg = argspec.defaults[i-args_len]
arg_num += 1
else:
arg = None
arg_num += 1
#: Attempt to convert all arguments to a
#: hash/id or a representation?
#: Not sure if this is necessary, since
#: using objects as keys gets tricky quickly.
# if hasattr(arg, '__class__'):
# try:
# arg = hash(arg)
# except:
# arg = repr(arg)
#: Or what about a special __cacherepr__ function
#: on an object, this allows objects to act normal
#: upon inspection, yet they can define a representation
#: that can be used to make the object unique in the
#: cache key. Given that a case comes across that
#: an object "must" be used as a cache key
# if hasattr(arg, '__cacherepr__'):
# arg = arg.__cacherepr__
new_args.append(arg)
return tuple(new_args), {} | [
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|||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/tornado-6.0.1-py3.7-macosx-12.1-iPad6,7.egg/tornado/locks.py | python | Event.set | (self) | Set the internal flag to ``True``. All waiters are awakened.
Calling `.wait` once the flag is set will not block. | Set the internal flag to ``True``. All waiters are awakened. | [
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"""Set the internal flag to ``True``. All waiters are awakened.
Calling `.wait` once the flag is set will not block.
"""
if not self._value:
self._value = True
for fut in self._waiters:
if not fut.done():
fut.set_result(None) | [
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||
descarteslabs/descarteslabs-python | ace8a1a89d58b75df1bcaa613a4b3544d7bdc4be | descarteslabs/tables/client.py | python | Tables._normalize_features | (self, obj) | :param object obj: Python object representing GeoJSON-like features.
This can be an object with __geo_interface__ method (e.g. GeoDataFrame),
a GeoJSON-like FeatureCollection mapping, or
a single GeoJSON-like Feature mapping, or
an iterable of GeoJSON-like Feature mappings
:return: Iterable of GeoJSON-like Feature mappings | :param object obj: Python object representing GeoJSON-like features.
This can be an object with __geo_interface__ method (e.g. GeoDataFrame),
a GeoJSON-like FeatureCollection mapping, or
a single GeoJSON-like Feature mapping, or
an iterable of GeoJSON-like Feature mappings | [
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"""
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a GeoJSON-like FeatureCollection mapping, or
a single GeoJSON-like Feature mapping, or
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:return: Iterable of GeoJSON-like Feature mappings
"""
if hasattr(obj, "__geo_interface__"):
features = obj.__geo_interface__["features"]
elif isinstance(obj, pd.DataFrame):
features = (
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)
elif "features" in obj:
features = obj["features"]
elif "properties" in obj and "geometry" in obj:
features = [obj]
elif isinstance(obj, Iterable):
# TODO we have to trust that the contents are GeoJSON-like features
# to avoid consuming any stateful iterators
features = obj
else:
raise BadRequestError("Could not find any GeoJSON-like features")
yield from features | [
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||
erikrose/parsimonious | 3da7e804c07d4e495873be208701b5c955247c58 | parsimonious/grammar.py | python | RuleVisitor.visit_sequence | (self, node, sequence) | return Sequence(term, *other_terms) | A parsed Sequence looks like [term node, OneOrMore node of
``another_term``s]. Flatten it out. | A parsed Sequence looks like [term node, OneOrMore node of
``another_term``s]. Flatten it out. | [
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] | def visit_sequence(self, node, sequence):
"""A parsed Sequence looks like [term node, OneOrMore node of
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term, other_terms = sequence
return Sequence(term, *other_terms) | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-0.96/django/utils/simplejson/decoder.py | python | JSONDecoder.__init__ | (self, encoding=None, object_hook=None) | ``encoding`` determines the encoding used to interpret any ``str``
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Note that currently only encodings that are a superset of ASCII work,
strings of other encodings should be passed in as ``unicode``.
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of every JSON object decoded and its return value will be used in
place of the given ``dict``. This can be used to provide custom
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objects decoded by this instance (utf-8 by default). It has no
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``encoding`` determines the encoding used to interpret any ``str``
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Note that currently only encodings that are a superset of ASCII work,
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self.encoding = encoding
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||
hubblestack/hubble | 763142474edcecdec5fd25591dc29c3536e8f969 | hubblestack/audit/misc.py | python | _check_password_fields_not_empty | (block_id, block_dict, extra_args) | return True if result == "" else result | Ensure password fields are not empty | Ensure password fields are not empty | [
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] | def _check_password_fields_not_empty(block_id, block_dict, extra_args):
"""
Ensure password fields are not empty
"""
result = _execute_shell_command(
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return True if result == "" else result | [
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|
CenterForOpenScience/osf.io | cc02691be017e61e2cd64f19b848b2f4c18dcc84 | api/base/requests.py | python | EmbeddedRequest.method | (self) | return 'GET' | Overrides method to be 'GET' | Overrides method to be 'GET' | [
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|
Crypto-toolbox/bitex | 56d46ea3db6de5219a72dad9b052fbabc921232f | bitex/api/WSS/bitfinex.py | python | BitfinexWSS.process | (self) | Processes the Client queue, and passes the data to the respective
methods.
:return: | Processes the Client queue, and passes the data to the respective
methods.
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"""
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:return:
"""
while self.running:
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if self.ping_timer:
try:
self._check_ping()
except TimeoutError:
log.exception("BitfinexWSS.ping(): TimedOut! (%ss)" %
self.ping_timer)
except (WebSocketConnectionClosedException,
ConnectionResetError):
log.exception("BitfinexWSS.ping(): Connection Error!")
self.conn = None
if not self.conn:
# The connection was killed - initiate restart
self._controller_q.put('restart')
skip_processing = False
try:
ts, data = self.receiver_q.get(timeout=0.1)
except queue.Empty:
skip_processing = True
ts = time.time()
data = None
if not skip_processing:
log.debug("Processing Data: %s", data)
if isinstance(data, list):
self.handle_data(ts, data)
else: # Not a list, hence it could be a response
try:
self.handle_response(ts, data)
except UnknownEventError:
# We don't know what event this is- Raise an
# error & log data!
log.exception("main() - UnknownEventError: %s",
data)
log.info("main() - Shutting Down due to "
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self._controller_q.put('stop')
except ConnectionResetError:
log.info("processor Thread: Connection Was reset, "
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self._controller_q.put('restart')
self._check_heartbeats(ts)
self._processor_lock.release()
else:
time.sleep(0.5) | [
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/numpy/core/fromnumeric.py | python | sometrue | (a, axis=None, out=None, keepdims=np._NoValue) | return arr.any(axis=axis, out=out, **kwargs) | Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function | Check whether some values are true. | [
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"""
Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.any(axis=axis, out=out, **kwargs) | [
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/Gui/GridEditorWidget/vsc_graphics.py | python | VscGraphicItem.remove | (self, ask=True) | Remove this object in the diagram and the API
@return: | Remove this object in the diagram and the API | [
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"""
Remove this object in the diagram and the API
@return:
"""
if ask:
ok = yes_no_question('Do you want to remove this VSC?', 'Remove VSC')
else:
ok = True
if ok:
self.diagramScene.circuit.delete_vsc_converter(self.api_object)
self.diagramScene.removeItem(self) | [
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/_pydecimal.py | python | Decimal._iseven | (self) | return self._int[-1+self._exp] in '02468' | Returns True if self is even. Assumes self is an integer. | Returns True if self is even. Assumes self is an integer. | [
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if not self or self._exp > 0:
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spyder-ide/spyder | 55da47c032dfcf519600f67f8b30eab467f965e7 | spyder/utils/qthelpers.py | python | get_origin_filename | () | return f.f_code.co_filename | Return the filename at the top of the stack | Return the filename at the top of the stack | [
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f = sys._getframe()
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f = f.f_back
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|
implus/GFocal | db48199ff91466f071519cefd068fb05359dcbf2 | tools/upgrade_model_version.py | python | convert | (in_file, out_file) | Convert keys in checkpoints.
There can be some breaking changes during the development of mmdetection,
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There can be some breaking changes during the development of mmdetection,
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to the latest one.
"""
checkpoint = torch.load(in_file)
in_state_dict = checkpoint.pop('state_dict')
out_state_dict = OrderedDict()
for key, val in in_state_dict.items():
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# cls_convs.0.weight -> cls_convs.0.conv.weight
m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key)
if m is not None:
param = m.groups()[1]
new_key = key.replace(param, 'conv.{}'.format(param))
out_state_dict[new_key] = val
continue
out_state_dict[key] = val
checkpoint['state_dict'] = out_state_dict
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/prioritizer_types_entity.py | python | PrioritizerTypesEntity.__init__ | (self, prioritizer_types=None) | PrioritizerTypesEntity - a model defined in Swagger | PrioritizerTypesEntity - a model defined in Swagger | [
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"""
PrioritizerTypesEntity - a model defined in Swagger
"""
self._prioritizer_types = None
if prioritizer_types is not None:
self.prioritizer_types = prioritizer_types | [
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||
fossasia/knittingpattern | e440429884182d6f2684b0ac051c0605ba31ae75 | knittingpattern/Loader.py | python | ContentLoader.url | (self, url, encoding="UTF-8") | return self.string(webpage_content) | load and process the content behind a url
:return: the processed result of the :paramref:`url's <url>` content
:param str url: the url to retrieve the content from
:param str encoding: the encoding of the retrieved content.
The default encoding is UTF-8. | load and process the content behind a url | [
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:return: the processed result of the :paramref:`url's <url>` content
:param str url: the url to retrieve the content from
:param str encoding: the encoding of the retrieved content.
The default encoding is UTF-8.
"""
import urllib.request
with urllib.request.urlopen(url) as file:
webpage_content = file.read()
webpage_content = webpage_content.decode(encoding)
return self.string(webpage_content) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/utils/glibc.py | python | libc_ver | () | [] | def libc_ver():
glibc_version = glibc_version_string()
if glibc_version is None:
# For non-glibc platforms, fall back on platform.libc_ver
return platform.libc_ver()
else:
return ("glibc", glibc_version) | [
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||||
etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/nexml/_nexml.py | python | DNAMatrixSeqRow.export | (self, outfile, level, namespace_='', name_='DNAMatrixSeqRow', namespacedef_='') | [] | def export(self, outfile, level, namespace_='', name_='DNAMatrixSeqRow', namespacedef_=''):
showIndent(outfile, level)
outfile.write('<%s%s%s' % (namespace_, name_, namespacedef_ and ' ' + namespacedef_ or '', ))
already_processed = []
self.exportAttributes(outfile, level, already_processed, namespace_, name_='DNAMatrixSeqRow')
if self.hasContent_():
outfile.write('>\n')
self.exportChildren(outfile, level + 1, namespace_, name_)
showIndent(outfile, level)
outfile.write('</%s%s>\n' % (namespace_, name_))
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outfile.write('/>\n') | [
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||||
LinkedInAttic/indextank-service | 880c6295ce8e7a3a55bf9b3777cc35c7680e0d7e | storefront/boto/sdb/connection.py | python | SDBConnection.query_with_attributes | (self, domain_or_name, query='', attr_names=None,
max_items=None, next_token=None) | return self.get_list('QueryWithAttributes', params, [('Item', self.item_cls)], parent=domain) | Returns a set of Attributes for item names within domain_name that match the query.
:type domain_or_name: string or :class:`boto.sdb.domain.Domain` object.
:param domain_or_name: Either the name of a domain or a Domain object
:type query: string
:param query: The SimpleDB query to be performed.
:type attr_names: list
:param attr_names: The name of the attributes to be returned.
If no attributes are specified, all attributes
will be returned.
:type max_items: int
:param max_items: The maximum number of items to return. If not
supplied, the default is None which returns all
items matching the query.
:rtype: ResultSet
:return: An iterator containing the results. | Returns a set of Attributes for item names within domain_name that match the query.
:type domain_or_name: string or :class:`boto.sdb.domain.Domain` object.
:param domain_or_name: Either the name of a domain or a Domain object | [
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"""
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:type domain_or_name: string or :class:`boto.sdb.domain.Domain` object.
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:rtype: ResultSet
:return: An iterator containing the results.
"""
warnings.warn('Query interface is deprecated', DeprecationWarning)
domain, domain_name = self.get_domain_and_name(domain_or_name)
params = {'DomainName':domain_name,
'QueryExpression' : query}
if max_items:
params['MaxNumberOfItems'] = max_items
if next_token:
params['NextToken'] = next_token
if attr_names:
self.build_list_params(params, attr_names, 'AttributeName')
return self.get_list('QueryWithAttributes', params, [('Item', self.item_cls)], parent=domain) | [
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OpenMDAO/OpenMDAO-Framework | f2e37b7de3edeaaeb2d251b375917adec059db9b | openmdao.main/src/openmdao/main/container.py | python | Container.save_to_egg | (self, name, version, py_dir=None, src_dir=None,
src_files=None, child_objs=None, dst_dir=None,
observer=None, need_requirements=True) | Save state and other files to an egg. Typically used to copy all or
part of a simulation to another user or machine. By specifying child
containers in `child_objs`, it will be possible to create instances of
just those containers from the installed egg. Child container names
should be specified relative to this container.
name: string
Name for egg; must be an alphanumeric string.
version: string
Version for egg; must be an alphanumeric string.
py_dir: string
The (root) directory for local Python files. It defaults to
the current directory.
src_dir: string
The root of all (relative) `src_files`.
src_files: list
List of paths to files to be included in the egg.
child_objs: list
List of child objects for additional entry points.
dst_dir: string
The directory to write the egg in.
observer: callable
Will be called via an :class:`EggObserver`.
need_requirements: bool
Passed to :meth:`eggsaver.save_to_egg`.
After collecting entry point information, calls
:meth:`eggsaver.save_to_egg`.
Returns ``(egg_filename, required_distributions, orphan_modules)``. | Save state and other files to an egg. Typically used to copy all or
part of a simulation to another user or machine. By specifying child
containers in `child_objs`, it will be possible to create instances of
just those containers from the installed egg. Child container names
should be specified relative to this container. | [
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] | def save_to_egg(self, name, version, py_dir=None, src_dir=None,
src_files=None, child_objs=None, dst_dir=None,
observer=None, need_requirements=True):
"""Save state and other files to an egg. Typically used to copy all or
part of a simulation to another user or machine. By specifying child
containers in `child_objs`, it will be possible to create instances of
just those containers from the installed egg. Child container names
should be specified relative to this container.
name: string
Name for egg; must be an alphanumeric string.
version: string
Version for egg; must be an alphanumeric string.
py_dir: string
The (root) directory for local Python files. It defaults to
the current directory.
src_dir: string
The root of all (relative) `src_files`.
src_files: list
List of paths to files to be included in the egg.
child_objs: list
List of child objects for additional entry points.
dst_dir: string
The directory to write the egg in.
observer: callable
Will be called via an :class:`EggObserver`.
need_requirements: bool
Passed to :meth:`eggsaver.save_to_egg`.
After collecting entry point information, calls
:meth:`eggsaver.save_to_egg`.
Returns ``(egg_filename, required_distributions, orphan_modules)``.
"""
assert name and isinstance(name, basestring)
assert version and isinstance(version, basestring)
if not version.endswith('.'):
version += '.'
now = datetime.datetime.now() # Could consider using utcnow().
tstamp = '%d.%02d.%02d.%02d.%02d' % \
(now.year, now.month, now.day, now.hour, now.minute)
version += tstamp
observer = eggobserver.EggObserver(observer, self._logger)
# Child entry point names are the pathname, starting at self.
entry_pts = [(self, name, _get_entry_group(self))]
if child_objs is not None:
root_pathname = self.get_pathname()
root_start = root_pathname.rfind('.')
root_start = root_start+1 if root_start >= 0 else 0
root_pathname += '.'
for child in child_objs:
pathname = child.get_pathname()
if not pathname.startswith(root_pathname):
msg = '%s is not a child of %s' % (pathname, root_pathname)
observer.exception(msg)
self.raise_exception(msg, RuntimeError)
entry_pts.append((child, pathname[root_start:],
_get_entry_group(child)))
parent = self.parent
self.parent = None # Don't want to save stuff above us.
try:
return eggsaver.save_to_egg(entry_pts, version, py_dir,
src_dir, src_files, dst_dir,
self._logger, observer.observer,
need_requirements)
except Exception:
self.reraise_exception(info=sys.exc_info()) # Just to get a pathname.
finally:
self.parent = parent | [
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||
replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/lib-tk/Tkinter.py | python | Misc.tk_menuBar | (self, *args) | Do not use. Needed in Tk 3.6 and earlier. | Do not use. Needed in Tk 3.6 and earlier. | [
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] | def tk_menuBar(self, *args):
"""Do not use. Needed in Tk 3.6 and earlier."""
pass # obsolete since Tk 4.0 | [
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] | https://github.com/replit-archive/empythoned/blob/977ec10ced29a3541a4973dc2b59910805695752/cpython/Lib/lib-tk/Tkinter.py#L388-L390 |