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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/site-packages/cerealizer/__init__.py | python | Handler.undump_data | (self, obj, dumper, s) | Handler.undump_data(obj, dumper, s)
Reads the data for OBJ, from DUMPER and file S.
If you override undump_data, you should use DUMPER.undump_ref(S) to
read a reference or a basic type (=a string, an int,...). | Handler.undump_data(obj, dumper, s) | [
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Reads the data for OBJ, from DUMPER and file S.
If you override undump_data, you should use DUMPER.undump_ref(S) to
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tuckerbalch/QSTK | 4981506c37227a72404229d5e1e0887f797a5d57 | epydoc-3.0.1/epydoc/gui.py | python | _version | () | Display the version information, and exit.
@rtype: C{None} | Display the version information, and exit. | [
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"""
Display the version information, and exit.
@rtype: C{None}
"""
import epydoc
print "Epydoc version %s" % epydoc.__version__
sys.exit(0) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/django-extensions-1.8.0/django_extensions/management/commands/sqldiff.py | python | PostgresqlSQLDiff.get_constraints | (self, cursor, table_name, introspection) | return constraints | backport of django's introspection.get_constraints(...) | backport of django's introspection.get_constraints(...) | [
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""" backport of django's introspection.get_constraints(...) """
constraints = {}
# Loop over the key table, collecting things as constraints
# This will get PKs, FKs, and uniques, but not CHECK
cursor.execute("""
SELECT
kc.constraint_name,
kc.column_name,
c.constraint_type,
array(SELECT table_name::text || '.' || column_name::text FROM information_schema.constraint_column_usage WHERE constraint_name = kc.constraint_name)
FROM information_schema.key_column_usage AS kc
JOIN information_schema.table_constraints AS c ON
kc.table_schema = c.table_schema AND
kc.table_name = c.table_name AND
kc.constraint_name = c.constraint_name
WHERE
kc.table_schema = %s AND
kc.table_name = %s
""", ["public", table_name])
for constraint, column, kind, used_cols in cursor.fetchall():
# If we're the first column, make the record
if constraint not in constraints:
constraints[constraint] = {
"columns": [],
"primary_key": kind.lower() == "primary key",
"unique": kind.lower() in ["primary key", "unique"],
"foreign_key": tuple(used_cols[0].split(".", 1)) if kind.lower() == "foreign key" else None,
"check": False,
"index": False,
}
# Record the details
constraints[constraint]['columns'].append(column)
# Now get CHECK constraint columns
cursor.execute("""
SELECT kc.constraint_name, kc.column_name
FROM information_schema.constraint_column_usage AS kc
JOIN information_schema.table_constraints AS c ON
kc.table_schema = c.table_schema AND
kc.table_name = c.table_name AND
kc.constraint_name = c.constraint_name
WHERE
c.constraint_type = 'CHECK' AND
kc.table_schema = %s AND
kc.table_name = %s
""", ["public", table_name])
for constraint, column in cursor.fetchall():
# If we're the first column, make the record
if constraint not in constraints:
constraints[constraint] = {
"columns": [],
"primary_key": False,
"unique": False,
"foreign_key": None,
"check": True,
"index": False,
}
# Record the details
constraints[constraint]['columns'].append(column)
# Now get indexes
cursor.execute("""
SELECT
c2.relname,
ARRAY(
SELECT (SELECT attname FROM pg_catalog.pg_attribute WHERE attnum = i AND attrelid = c.oid)
FROM unnest(idx.indkey) i
),
idx.indisunique,
idx.indisprimary
FROM pg_catalog.pg_class c, pg_catalog.pg_class c2,
pg_catalog.pg_index idx
WHERE c.oid = idx.indrelid
AND idx.indexrelid = c2.oid
AND c.relname = %s
""", [table_name])
for index, columns, unique, primary in cursor.fetchall():
if index not in constraints:
constraints[index] = {
"columns": list(columns),
"primary_key": primary,
"unique": unique,
"foreign_key": None,
"check": False,
"index": True,
}
return constraints | [
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log2timeline/plaso | fe2e316b8c76a0141760c0f2f181d84acb83abc2 | plaso/multi_process/analysis_engine.py | python | AnalysisMultiProcessEngine.AnalyzeEvents | (
self, session, knowledge_base_object, storage_writer, data_location,
analysis_plugins, processing_configuration, event_filter=None,
event_filter_expression=None, status_update_callback=None,
storage_file_path=None) | return self._processing_status | Analyzes events in a Plaso storage.
Args:
session (Session): session in which the events are analyzed.
knowledge_base_object (KnowledgeBase): contains information from
the source data needed for processing.
storage_writer (StorageWriter): storage writer.
data_location (str): path to the location that data files should
be loaded from.
analysis_plugins (dict[str, AnalysisPlugin]): analysis plugins that
should be run and their names.
processing_configuration (ProcessingConfiguration): processing
configuration.
event_filter (Optional[EventObjectFilter]): event filter.
event_filter_expression (Optional[str]): event filter expression.
status_update_callback (Optional[function]): callback function for status
updates.
storage_file_path (Optional[str]): path to the session storage file.
Returns:
ProcessingStatus: processing status.
Raises:
KeyboardInterrupt: if a keyboard interrupt was raised.
ValueError: if analysis plugins are missing. | Analyzes events in a Plaso storage. | [
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] | def AnalyzeEvents(
self, session, knowledge_base_object, storage_writer, data_location,
analysis_plugins, processing_configuration, event_filter=None,
event_filter_expression=None, status_update_callback=None,
storage_file_path=None):
"""Analyzes events in a Plaso storage.
Args:
session (Session): session in which the events are analyzed.
knowledge_base_object (KnowledgeBase): contains information from
the source data needed for processing.
storage_writer (StorageWriter): storage writer.
data_location (str): path to the location that data files should
be loaded from.
analysis_plugins (dict[str, AnalysisPlugin]): analysis plugins that
should be run and their names.
processing_configuration (ProcessingConfiguration): processing
configuration.
event_filter (Optional[EventObjectFilter]): event filter.
event_filter_expression (Optional[str]): event filter expression.
status_update_callback (Optional[function]): callback function for status
updates.
storage_file_path (Optional[str]): path to the session storage file.
Returns:
ProcessingStatus: processing status.
Raises:
KeyboardInterrupt: if a keyboard interrupt was raised.
ValueError: if analysis plugins are missing.
"""
if not analysis_plugins:
raise ValueError('Missing analysis plugins')
abort_kill = False
keyboard_interrupt = False
queue_full = False
self._analysis_plugins = {}
self._data_location = data_location
self._event_filter_expression = event_filter_expression
self._events_status = processing_status.EventsStatus()
self._knowledge_base = knowledge_base_object
self._processing_configuration = processing_configuration
self._session = session
self._status_update_callback = status_update_callback
self._storage_file_path = storage_file_path
stored_event_labels_counter = {}
if storage_writer.HasAttributeContainers('event_label_count'):
stored_event_labels_counter = {
event_label_count.label: event_label_count
for event_label_count in storage_writer.GetAttributeContainers(
'event_label_count')}
self._event_labels_counter = collections.Counter()
if storage_writer.HasAttributeContainers('parser_count'):
parsers_counter = {
parser_count.name: parser_count.number_of_events
for parser_count in storage_writer.GetAttributeContainers(
'parser_count')}
total_number_of_events = parsers_counter['total']
else:
total_number_of_events = 0
for stored_session in storage_writer.GetSessions():
total_number_of_events += stored_session.parsers_counter['total']
self._events_status.total_number_of_events = total_number_of_events
# Set up the storage writer before the analysis processes.
self._StartTaskStorage(definitions.STORAGE_FORMAT_SQLITE)
self._StartAnalysisProcesses(analysis_plugins)
self._StartProfiling(self._processing_configuration.profiling)
# Start the status update thread after open of the storage writer
# so we don't have to clean up the thread if the open fails.
self._StartStatusUpdateThread()
try:
self._AnalyzeEvents(
storage_writer, analysis_plugins, event_filter=event_filter)
for key, value in self._event_labels_counter.items():
event_label_count = stored_event_labels_counter.get(key, None)
if event_label_count:
event_label_count.number_of_events += value
storage_writer.UpdateAttributeContainer(event_label_count)
else:
event_label_count = counts.EventLabelCount(
label=key, number_of_events=value)
storage_writer.AddAttributeContainer(event_label_count)
self._status = definitions.STATUS_INDICATOR_FINALIZING
except errors.QueueFull:
queue_full = True
self._abort = True
except KeyboardInterrupt:
keyboard_interrupt = True
self._abort = True
finally:
self._processing_status.aborted = self._abort
session.aborted = self._abort
# Stop the status update thread after close of the storage writer
# so we include the storage sync to disk in the status updates.
self._StopStatusUpdateThread()
self._StopProfiling()
# Update the status view one last time before the analysis processses are
# stopped.
self._UpdateStatus()
if queue_full:
# TODO: handle abort on queue full more elegant.
abort_kill = True
else:
try:
self._StopAnalysisProcesses(abort=self._abort)
except KeyboardInterrupt:
keyboard_interrupt = True
abort_kill = True
if abort_kill:
self._AbortKill()
# The abort can leave the main process unresponsive
# due to incorrectly finalized IPC.
self._KillProcess(os.getpid())
try:
self._StopTaskStorage(
definitions.STORAGE_FORMAT_SQLITE, session.identifier,
abort=self._abort)
except (IOError, OSError) as exception:
logger.error('Unable to stop task storage with error: {0!s}'.format(
exception))
if self._abort:
logger.debug('Analysis aborted.')
else:
logger.debug('Analysis completed.')
# Update the status view one last time.
self._UpdateStatus()
# Reset values.
self._analysis_plugins = {}
self._data_location = None
self._event_filter_expression = None
self._knowledge_base = None
self._processing_configuration = None
self._session = None
self._status_update_callback = None
self._storage_file_path = None
if keyboard_interrupt:
raise KeyboardInterrupt
return self._processing_status | [
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plotly/plotly.py | cfad7862594b35965c0e000813bd7805e8494a5b | packages/python/plotly/plotly/graph_objs/layout/geo/_center.py | python | Center.lat | (self) | return self["lat"] | Sets the latitude of the map's center. For all projection
types, the map's latitude center lies at the middle of the
latitude range by default.
The 'lat' property is a number and may be specified as:
- An int or float
Returns
-------
int|float | Sets the latitude of the map's center. For all projection
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The 'lat' property is a number and may be specified as:
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The 'lat' property is a number and may be specified as:
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Calysto/calysto_scheme | 15bf81987870bcae1264e5a0a06feb9a8ee12b8b | calysto_scheme/src/Scheme.py | python | caaddr | (lyst) | return lyst.cdr.cdr.car.car | [] | def caaddr(lyst):
return lyst.cdr.cdr.car.car | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/musicbrainzngs/musicbrainz.py | python | browse_works | (artist=None, includes=[], limit=None, offset=None) | return _browse_impl("work", includes, limit, offset, params) | Get all works linked to an artist
*Available includes*: {includes} | Get all works linked to an artist | [
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"""Get all works linked to an artist
*Available includes*: {includes}"""
params = {"artist": artist}
return _browse_impl("work", includes, limit, offset, params) | [
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nopernik/mpDNS | b17dc39e7068406df82cb3431b3042e74e520cf9 | circuits/core/pollers.py | python | Poll._process | (self, fileno, event) | [] | def _process(self, fileno, event):
if fileno not in self._map:
return
fd = self._map[fileno]
if fd == self._ctrl_recv:
self._read_ctrl()
return
if event & self._disconnected_flag and not (event & select.POLLIN):
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self._poller.unregister(fileno)
super(Poll, self).discard(fd)
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else:
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self.fire(_read(fd), self.getTarget(fd))
if event & select.POLLOUT:
self.fire(_write(fd), self.getTarget(fd))
except Exception as e:
self.fire(_error(fd, e), self.getTarget(fd))
self.fire(_disconnect(fd), self.getTarget(fd))
self._poller.unregister(fileno)
super(Poll, self).discard(fd)
del self._map[fileno] | [
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taoyds/spider | 7036cf422b1da08a907acbdc062e598bb0761bf7 | baselines/seq2seq_attention_copy/seq2seq/data/sequence_example_decoder.py | python | TFSEquenceExampleDecoder.decode | (self, serialized_example, items=None) | return outputs | Decodes the given serialized TF-example.
Args:
serialized_example: a serialized TF-example tensor.
items: the list of items to decode. These must be a subset of the item
keys in self._items_to_handlers. If `items` is left as None, then all
of the items in self._items_to_handlers are decoded.
Returns:
the decoded items, a list of tensor. | Decodes the given serialized TF-example.
Args:
serialized_example: a serialized TF-example tensor.
items: the list of items to decode. These must be a subset of the item
keys in self._items_to_handlers. If `items` is left as None, then all
of the items in self._items_to_handlers are decoded.
Returns:
the decoded items, a list of tensor. | [
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"""Decodes the given serialized TF-example.
Args:
serialized_example: a serialized TF-example tensor.
items: the list of items to decode. These must be a subset of the item
keys in self._items_to_handlers. If `items` is left as None, then all
of the items in self._items_to_handlers are decoded.
Returns:
the decoded items, a list of tensor.
"""
context, sequence = tf.parse_single_sequence_example(
serialized_example, self._context_keys_to_features,
self._sequence_keys_to_features)
# Merge context and sequence features
example = {}
example.update(context)
example.update(sequence)
all_features = {}
all_features.update(self._context_keys_to_features)
all_features.update(self._sequence_keys_to_features)
# Reshape non-sparse elements just once:
for k, value in all_features.items():
if isinstance(value, tf.FixedLenFeature):
example[k] = tf.reshape(example[k], value.shape)
if not items:
items = self._items_to_handlers.keys()
outputs = []
for item in items:
handler = self._items_to_handlers[item]
keys_to_tensors = {key: example[key] for key in handler.keys}
outputs.append(handler.tensors_to_item(keys_to_tensors))
return outputs | [
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google/ml-fairness-gym | 5b1cd336b844059aa4e4426b54d1f0e6b8c4c7e9 | agents/recommenders/utils.py | python | accumulate_rewards | (rewards, gamma) | return np.array(acc[::-1]) | Computes the discounted reward for the entire episode. | Computes the discounted reward for the entire episode. | [
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"""Computes the discounted reward for the entire episode."""
reversed_rewards = rewards[::-1] # list reversal
acc = list(itertools.accumulate(reversed_rewards, lambda x, y: x*gamma + y))
return np.array(acc[::-1]) | [
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adewes/blitzdb | 36191579be14fbc2d7a47ede099bcdf31297a9fa | blitzdb/backends/mongo/queryset.py | python | QuerySet.limit | (self, *args, **kwargs) | return self | [] | def limit(self, *args, **kwargs):
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return self | [
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log2timeline/plaso | fe2e316b8c76a0141760c0f2f181d84acb83abc2 | plaso/parsers/fseventsd.py | python | FseventsdParser._GetParentModificationTime | (self, gzip_file_entry) | return parent_file_entry.modification_time | Retrieves the modification time of the file entry's parent file.
Note that this retrieves the time from the file entry of the parent of the
gzip file entry's path spec, which is different from trying to retrieve it
from the gzip file entry's parent file entry.
It would be preferable to retrieve the modification time from the metadata
in the gzip file itself, but it appears to not be set when the file is
written by fseventsd.
Args:
gzip_file_entry (dfvfs.FileEntry): file entry of the gzip file containing
the fseventsd data.
Returns:
dfdatetime.DateTimeValues: parent modification time, or None if not
available. | Retrieves the modification time of the file entry's parent file. | [
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"""Retrieves the modification time of the file entry's parent file.
Note that this retrieves the time from the file entry of the parent of the
gzip file entry's path spec, which is different from trying to retrieve it
from the gzip file entry's parent file entry.
It would be preferable to retrieve the modification time from the metadata
in the gzip file itself, but it appears to not be set when the file is
written by fseventsd.
Args:
gzip_file_entry (dfvfs.FileEntry): file entry of the gzip file containing
the fseventsd data.
Returns:
dfdatetime.DateTimeValues: parent modification time, or None if not
available.
"""
parent_file_entry = path_spec_resolver.Resolver.OpenFileEntry(
gzip_file_entry.path_spec.parent)
if not parent_file_entry:
return None
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python3-alpha/python-libs/gdata/analytics/data.py | python | AnalyticsLink.parent | (cls) | return '%s#parent' % GA_NS[1:-3] | Parent target_kind | Parent target_kind | [
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mbusb/multibootusb | fa89b28f27891a9ce8d6e2a5737baa2e6ee83dfd | scripts/imager.py | python | Imager.__init__ | (self) | [] | def __init__(self):
QtWidgets.QMainWindow.__init__(self)
self.ui = Ui_MainWindow()
self.ui.setupUi(self) | [
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OpenRCE/paimei | d78f574129f9aade35af03ced08765ea2a15288c | console/modules/_PAIMEIpstalker/PIDAModulesListCtrl.py | python | PIDAModulesListCtrl.on_right_down | (self, event) | Grab the x/y coordinates when the right mouse button is clicked. | Grab the x/y coordinates when the right mouse button is clicked. | [
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'''
Grab the x/y coordinates when the right mouse button is clicked.
'''
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self.y = event.GetY()
item, flags = self.HitTest((self.x, self.y))
if flags & wx.LIST_HITTEST_ONITEM:
self.Select(item)
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self.x = None
self.y = None | [
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sqlmapproject/sqlmap | 3b07b70864624dff4c29dcaa8a61c78e7f9189f7 | lib/core/session.py | python | setOs | () | Example of kb.bannerFp dictionary:
{
'sp': set(['Service Pack 4']),
'dbmsVersion': '8.00.194',
'dbmsServicePack': '0',
'distrib': set(['2000']),
'dbmsRelease': '2000',
'type': set(['Windows'])
} | Example of kb.bannerFp dictionary: | [
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"""
Example of kb.bannerFp dictionary:
{
'sp': set(['Service Pack 4']),
'dbmsVersion': '8.00.194',
'dbmsServicePack': '0',
'distrib': set(['2000']),
'dbmsRelease': '2000',
'type': set(['Windows'])
}
"""
infoMsg = ""
if not kb.bannerFp:
return
if "type" in kb.bannerFp:
Backend.setOs(Format.humanize(kb.bannerFp["type"]))
infoMsg = "the back-end DBMS operating system is %s" % Backend.getOs()
if "distrib" in kb.bannerFp:
kb.osVersion = Format.humanize(kb.bannerFp["distrib"])
infoMsg += " %s" % kb.osVersion
if "sp" in kb.bannerFp:
kb.osSP = int(Format.humanize(kb.bannerFp["sp"]).replace("Service Pack ", ""))
elif "sp" not in kb.bannerFp and Backend.isOs(OS.WINDOWS):
kb.osSP = 0
if Backend.getOs() and kb.osVersion and kb.osSP:
infoMsg += " Service Pack %d" % kb.osSP
if infoMsg:
logger.info(infoMsg)
hashDBWrite(HASHDB_KEYS.OS, Backend.getOs()) | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/com.oracle.graal.python.cext/setup.py | python | Bzip2Depedency.build | (self, extracted_dir=None) | return lib_src_folder | [] | def build(self, extracted_dir=None):
if not extracted_dir:
extracted_dir = self.download()
lib_src_folder = os.path.join(extracted_dir, self.package_name + "-" + self.version)
logger.info("Building dependency %s in %s using Makefile %s" % (self.package_name, lib_src_folder, self.makefile))
# On Darwin, we need to use -install_name for the native linker
if darwin_native:
makefile_path = os.path.join(lib_src_folder, self.makefile)
with open(makefile_path, "r") as f:
content = f.read()
content = content.replace("-Wl,-soname -Wl,%s" % self.install_name, "-Wl,-install_name -Wl,@rpath/%s" % self.install_name)
with open(makefile_path, "w") as f:
f.write(content)
parallel_arg = "-j" + str(min(4, os.cpu_count())) if threaded else ""
system("make -C '%s' %s -f '%s' CC='%s'" % (lib_src_folder, parallel_arg, self.makefile, get_config_var("CC")), msg="Could not build libbz2")
return lib_src_folder | [
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tflearn/tflearn | db5176773299b67a2a75c5889fb2aba7fd0fea8a | tflearn/initializations.py | python | truncated_normal | (shape=None, mean=0.0, stddev=0.02, dtype=tf.float32,
seed=None) | Truncated Normal.
Initialization with random values from a normal truncated distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Arguments:
shape: List of `int`. A shape to initialize a Tensor (optional).
mean: Same as `dtype`. The mean of the truncated normal distribution.
stddev: Same as `dtype`. The standard deviation of the truncated
normal distribution.
dtype: The tensor data type.
seed: `int`. Used to create a random seed for the distribution.
Returns:
The Initializer, or an initialized `Tensor` if shape is specified. | Truncated Normal. | [
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] | def truncated_normal(shape=None, mean=0.0, stddev=0.02, dtype=tf.float32,
seed=None):
""" Truncated Normal.
Initialization with random values from a normal truncated distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Arguments:
shape: List of `int`. A shape to initialize a Tensor (optional).
mean: Same as `dtype`. The mean of the truncated normal distribution.
stddev: Same as `dtype`. The standard deviation of the truncated
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dtype: The tensor data type.
seed: `int`. Used to create a random seed for the distribution.
Returns:
The Initializer, or an initialized `Tensor` if shape is specified.
"""
if shape:
return tf.truncated_normal(shape=shape, mean=mean, stddev=stddev,
seed=seed, dtype=dtype)
else:
return tf.truncated_normal_initializer(mean=mean, stddev=stddev,
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AstroPrint/AstroBox | e7e3b8a7d33ea85fcb6b2696869c0d719ceb8b75 | src/ext/makerbot_driver/FileReader/FileReader.py | python | FileReader.GetNextCommand | (self) | return cmd | Assuming the file pointer is at a command, gets the next command number
If ReadBytes raises an InsufficientDataError (indicating no more information
if available in the file), we throw an EndOfFileError
@return int The command number | Assuming the file pointer is at a command, gets the next command number
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"""Assuming the file pointer is at a command, gets the next command number
If ReadBytes raises an InsufficientDataError (indicating no more information
if available in the file), we throw an EndOfFileError
@return int The command number
"""
try:
cmd = ord(self.ReadBytes(1))
except makerbot_driver.FileReader.InsufficientDataError:
raise makerbot_driver.FileReader.EndOfFileError
# TODO: Break the tool action commands out of here
if (not cmd in makerbot_driver.slave_action_command_dict.values()) and \
(not cmd in makerbot_driver.host_action_command_dict.values()):
self._log.debug('{"event":"bad_read_command", "command":%s}', cmd)
raise makerbot_driver.FileReader.BadCommandError(cmd)
return cmd | [
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ninja-ide/ninja-ide | 87d91131bd19fdc3dcfd91eb97ad1e41c49c60c0 | ninja_ide/dependencies/notimportchecker.py | python | Checker.get_not_imports_on_file | (self, stmt, path=None) | Get imports that dont exist on the file
Parameters
----------
stmt: dict -> dict of not imports in the file
{'sys':{'lineno': 1, 'mod_name': 'sys'}}
if stmt == -1 (see parse_file) return None
path: string -> default None. path is the basename
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"""Get imports that dont exist on the file
Parameters
----------
stmt: dict -> dict of not imports in the file
{'sys':{'lineno': 1, 'mod_name': 'sys'}}
if stmt == -1 (see parse_file) return None
path: string -> default None. path is the basename
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"""
if (stmt == -1):
self._import_error_list = {}
return None
if path is None:
path = self._path
workspace = os.getcwd()
dn = os.path.dirname(path)
if dn == '': # if path file is in the current folder
os.chdir(workspace)
else:
os.chdir(dn) # if path is complete
for key, value in stmt.items():
for mod_name in value['mod_name']:
try:
if key == mod_name:
exec('import {}'.format(key))
else:
exec('from {} import {}'.format(key, mod_name))
except RuntimeError as e:
pass
except ImportError as e:
self._import_error_list.setdefault(key,
{'mod_name': mod_name,
'lineno':
value['lineno']})
os.chdir(workspace)
if len(self._import_error_list) == 0:
return None
else:
return self._import_error_list | [
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wakatime/komodo-wakatime | 8923c04ded9fa64d7374fadd8cde3a6fadfe053d | components/wakatime/packages/urllib3/packages/ordered_dict.py | python | OrderedDict.update | (*args, **kwds) | od.update(E, **F) -> None. Update od from dict/iterable E and F.
If E is a dict instance, does: for k in E: od[k] = E[k]
If E has a .keys() method, does: for k in E.keys(): od[k] = E[k]
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VirtueSecurity/aws-extender | d123b7e1a845847709ba3a481f11996bddc68a1c | BappModules/docutils/parsers/rst/states.py | python | Body.directive | (self, match, **option_presets) | Returns a 2-tuple: list of nodes, and a "blank finish" boolean. | Returns a 2-tuple: list of nodes, and a "blank finish" boolean. | [
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directive_class, messages = directives.directive(
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self.parent += messages
if directive_class:
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Dvlv/Tkinter-By-Example | 30721f15f7bea41489a7c46819beb5282781e563 | Code/Chapter6-1.py | python | Editor.destroy_autocomplete_menu | (self, event=None) | [] | def destroy_autocomplete_menu(self, event=None):
try:
self.complete_menu.destroy()
self.main_text.unbind("<Down>")
self.main_text.focus_force()
except AttributeError:
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gen/proxy/proxybase.py | python | ProxyDbBase.get_note_from_handle | (self, handle) | return self.gfilter(self.include_note,
self.db.get_note_from_handle(handle)) | Finds a Note in the database from the passed gramps handle.
If no such Note exists, None is returned. | Finds a Note in the database from the passed gramps handle.
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return self.gfilter(self.include_note,
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ThaWeatherman/scrapers | d82e2b707bbb2b450176dbf85842aeccb6e812f9 | craigslist/free_stuff_requests.py | python | get_args | () | return parser.parse_args() | [] | def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('-w', type=str, default='http://baltimore.craigslist.org', help='your local craigslist website, including HTTP schema')
return parser.parse_args() | [
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GoogleChrome/chromium-dashboard | 7ef9184080fc4cdf11efbe29dba27f95f07a20d9 | internals/search.py | python | _get_referenced_features | (approvals, reverse=False) | return features | Retrieve the features being approved, withuot duplicates. | Retrieve the features being approved, withuot duplicates. | [
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"""Retrieve the features being approved, withuot duplicates."""
logging.info('approvals is %r', [(a.feature_id, a.state) for a in approvals])
feature_ids = []
seen = set()
for appr in approvals:
if appr.feature_id not in seen:
seen.add(appr.feature_id)
feature_ids.append(appr.feature_id)
features = models.Feature.get_by_ids(feature_ids)
return features | [
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polakowo/vectorbt | 6638735c131655760474d72b9f045d1dbdbd8fe9 | vectorbt/generic/nb.py | python | pct_change_nb | (a: tp.Array2d, n: int = 1) | return out | 2-dim version of `pct_change_1d_nb`. | 2-dim version of `pct_change_1d_nb`. | [
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"""2-dim version of `pct_change_1d_nb`."""
out = np.empty_like(a, dtype=np.float_)
for col in range(a.shape[1]):
out[:, col] = pct_change_1d_nb(a[:, col], n=n)
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titu1994/keras-non-local-nets | 334b0331d230383bf36328a1cd8cd409246bcafd | non_local.py | python | non_local_block | (ip, intermediate_dim=None, compression=2,
mode='embedded', add_residual=True) | return y | Adds a Non-Local block for self attention to the input tensor.
Input tensor can be or rank 3 (temporal), 4 (spatial) or 5 (spatio-temporal).
Arguments:
ip: input tensor
intermediate_dim: The dimension of the intermediate representation. Can be
`None` or a positive integer greater than 0. If `None`, computes the
intermediate dimension as half of the input channel dimension.
compression: None or positive integer. Compresses the intermediate
representation during the dot products to reduce memory consumption.
Default is set to 2, which states halve the time/space/spatio-time
dimension for the intermediate step. Set to 1 to prevent computation
compression. None or 1 causes no reduction.
mode: Mode of operation. Can be one of `embedded`, `gaussian`, `dot` or
`concatenate`.
add_residual: Boolean value to decide if the residual connection should be
added or not. Default is True for ResNets, and False for Self Attention.
Returns:
a tensor of same shape as input | Adds a Non-Local block for self attention to the input tensor.
Input tensor can be or rank 3 (temporal), 4 (spatial) or 5 (spatio-temporal). | [
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mode='embedded', add_residual=True):
"""
Adds a Non-Local block for self attention to the input tensor.
Input tensor can be or rank 3 (temporal), 4 (spatial) or 5 (spatio-temporal).
Arguments:
ip: input tensor
intermediate_dim: The dimension of the intermediate representation. Can be
`None` or a positive integer greater than 0. If `None`, computes the
intermediate dimension as half of the input channel dimension.
compression: None or positive integer. Compresses the intermediate
representation during the dot products to reduce memory consumption.
Default is set to 2, which states halve the time/space/spatio-time
dimension for the intermediate step. Set to 1 to prevent computation
compression. None or 1 causes no reduction.
mode: Mode of operation. Can be one of `embedded`, `gaussian`, `dot` or
`concatenate`.
add_residual: Boolean value to decide if the residual connection should be
added or not. Default is True for ResNets, and False for Self Attention.
Returns:
a tensor of same shape as input
"""
channel_dim = 1 if K.image_data_format() == 'channels_first' else -1
ip_shape = K.int_shape(ip)
if mode not in ['gaussian', 'embedded', 'dot', 'concatenate']:
raise ValueError('`mode` must be one of `gaussian`, `embedded`, `dot` or `concatenate`')
if compression is None:
compression = 1
dim1, dim2, dim3 = None, None, None
# check rank and calculate the input shape
if len(ip_shape) == 3: # temporal / time series data
rank = 3
batchsize, dim1, channels = ip_shape
elif len(ip_shape) == 4: # spatial / image data
rank = 4
if channel_dim == 1:
batchsize, channels, dim1, dim2 = ip_shape
else:
batchsize, dim1, dim2, channels = ip_shape
elif len(ip_shape) == 5: # spatio-temporal / Video or Voxel data
rank = 5
if channel_dim == 1:
batchsize, channels, dim1, dim2, dim3 = ip_shape
else:
batchsize, dim1, dim2, dim3, channels = ip_shape
else:
raise ValueError('Input dimension has to be either 3 (temporal), 4 (spatial) or 5 (spatio-temporal)')
# verify correct intermediate dimension specified
if intermediate_dim is None:
intermediate_dim = channels // 2
if intermediate_dim < 1:
intermediate_dim = 1
else:
intermediate_dim = int(intermediate_dim)
if intermediate_dim < 1:
raise ValueError('`intermediate_dim` must be either `None` or positive integer greater than 1.')
if mode == 'gaussian': # Gaussian instantiation
x1 = Reshape((-1, channels))(ip) # xi
x2 = Reshape((-1, channels))(ip) # xj
f = dot([x1, x2], axes=2)
f = Activation('softmax')(f)
elif mode == 'dot': # Dot instantiation
# theta path
theta = _convND(ip, rank, intermediate_dim)
theta = Reshape((-1, intermediate_dim))(theta)
# phi path
phi = _convND(ip, rank, intermediate_dim)
phi = Reshape((-1, intermediate_dim))(phi)
f = dot([theta, phi], axes=2)
size = K.int_shape(f)
# scale the values to make it size invariant
f = Lambda(lambda z: (1. / float(size[-1])) * z)(f)
elif mode == 'concatenate': # Concatenation instantiation
raise NotImplementedError('Concatenate model has not been implemented yet')
else: # Embedded Gaussian instantiation
# theta path
theta = _convND(ip, rank, intermediate_dim)
theta = Reshape((-1, intermediate_dim))(theta)
# phi path
phi = _convND(ip, rank, intermediate_dim)
phi = Reshape((-1, intermediate_dim))(phi)
if compression > 1:
# shielded computation
phi = MaxPool1D(compression)(phi)
f = dot([theta, phi], axes=2)
f = Activation('softmax')(f)
# g path
g = _convND(ip, rank, intermediate_dim)
g = Reshape((-1, intermediate_dim))(g)
if compression > 1 and mode == 'embedded':
# shielded computation
g = MaxPool1D(compression)(g)
# compute output path
y = dot([f, g], axes=[2, 1])
# reshape to input tensor format
if rank == 3:
y = Reshape((dim1, intermediate_dim))(y)
elif rank == 4:
if channel_dim == -1:
y = Reshape((dim1, dim2, intermediate_dim))(y)
else:
y = Reshape((intermediate_dim, dim1, dim2))(y)
else:
if channel_dim == -1:
y = Reshape((dim1, dim2, dim3, intermediate_dim))(y)
else:
y = Reshape((intermediate_dim, dim1, dim2, dim3))(y)
# project filters
y = _convND(y, rank, channels)
# residual connection
if add_residual:
y = add([ip, y])
return y | [
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Scalsol/mega.pytorch | a6aa6e0537b82d70da94228100a51e6a53d98f82 | mega_core/modeling/roi_heads/mask_head/inference.py | python | expand_boxes | (boxes, scale) | return boxes_exp | [] | def expand_boxes(boxes, scale):
w_half = (boxes[:, 2] - boxes[:, 0]) * .5
h_half = (boxes[:, 3] - boxes[:, 1]) * .5
x_c = (boxes[:, 2] + boxes[:, 0]) * .5
y_c = (boxes[:, 3] + boxes[:, 1]) * .5
w_half *= scale
h_half *= scale
boxes_exp = torch.zeros_like(boxes)
boxes_exp[:, 0] = x_c - w_half
boxes_exp[:, 2] = x_c + w_half
boxes_exp[:, 1] = y_c - h_half
boxes_exp[:, 3] = y_c + h_half
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jython/jython3 | def4f8ec47cb7a9c799ea4c745f12badf92c5769 | lib-python/3.5.1/imp.py | python | get_magic | () | return util.MAGIC_NUMBER | **DEPRECATED**
Return the magic number for .pyc files. | **DEPRECATED** | [
"**",
"DEPRECATED",
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] | def get_magic():
"""**DEPRECATED**
Return the magic number for .pyc files.
"""
return util.MAGIC_NUMBER | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/requests/cookies.py | python | MockRequest.get_new_headers | (self) | return self._new_headers | [] | def get_new_headers(self):
return self._new_headers | [
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Yubico/yubikey-manager | 32914673d1d0004aba820e614ac9a9a640b4d196 | ykman/pcsc/__init__.py | python | ScardYubiKeyDevice.__init__ | (self, reader) | [] | def __init__(self, reader):
# Base transport on reader name: NFC readers will have a different name
if YK_READER_NAME in reader.name.lower():
transport = TRANSPORT.USB
else:
transport = TRANSPORT.NFC
super(ScardYubiKeyDevice, self).__init__(
transport, reader.name, _pid_from_name(reader.name)
)
self.reader = reader | [
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tuckerbalch/QSTK | 4981506c37227a72404229d5e1e0887f797a5d57 | epydoc-3.0.1/epydoc/docwriter/dotgraph.py | python | DotGraphEdge.to_dotfile | (self) | return 'node%d -> node%d%s' % (self.start.id, self.end.id, attribs) | Return the dot commands that should be used to render this edge. | Return the dot commands that should be used to render this edge. | [
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] | def to_dotfile(self):
"""
Return the dot commands that should be used to render this edge.
"""
# Set head & tail ports, if the nodes have preferred ports.
attribs = self._attribs.copy()
if (self.start.port is not None and 'headport' not in attribs):
attribs['headport'] = self.start.port
if (self.end.port is not None and 'tailport' not in attribs):
attribs['tailport'] = self.end.port
# Convert attribs to a string
attribs = ','.join(['%s="%s"' % (k,v) for (k,v) in attribs.items()
if v is not None])
if attribs: attribs = ' [%s]' % attribs
# Return the dotfile edge.
return 'node%d -> node%d%s' % (self.start.id, self.end.id, attribs) | [
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conda/conda-build | 2a19925f2b2ca188f80ffa625cd783e7d403793f | conda_build/environ.py | python | Environment.package_specs | (self) | return specs | List all package specs in the environment. | List all package specs in the environment. | [
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] | def package_specs(self):
"""
List all package specs in the environment.
"""
self._read_package_json()
json_objs = self._packages.values()
specs = []
for i in json_objs:
p, v, b = i['name'], i['version'], i['build']
specs.append(f'{p} {v} {b}')
return specs | [
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JetBrains/python-skeletons | 95ad24b666e475998e5d1cc02ed53a2188036167 | os/path.py | python | expanduser | (path) | return path | On Unix and Windows, return the argument with an initial component of ~
or ~user replaced by that user's home directory.
:type path: bytes | unicode | os.PathLike
:rtype: bytes | unicode | On Unix and Windows, return the argument with an initial component of ~
or ~user replaced by that user's home directory. | [
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"""On Unix and Windows, return the argument with an initial component of ~
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:type path: bytes | unicode | os.PathLike
:rtype: bytes | unicode
"""
return path | [
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mnot/thor | 8a6f14e15bd4e59e2348e6308e935a401e39d4a6 | thor/http/common.py | python | header_dict | (
hdr_tuples: RawHeaderListType, omit: List[bytes] = None
) | return out | Given a list of header tuples, return a dictionary keyed upon the
lower-cased header names.
If omit is defined, each header listed (by lower-cased name) will not be
returned in the dictionary. | Given a list of header tuples, return a dictionary keyed upon the
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hdr_tuples: RawHeaderListType, omit: List[bytes] = None
) -> Dict[bytes, List[bytes]]:
"""
Given a list of header tuples, return a dictionary keyed upon the
lower-cased header names.
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"""
out = defaultdict(list) # type: Dict[bytes, List[bytes]]
for (n, v) in hdr_tuples:
n = n.lower()
if n in (omit or []):
continue
out[n].extend([i.strip() for i in v.split(b",")])
return out | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/pkg_resource.py | python | pack_sources | (sources, normalize=True) | return ret | Accepts list of dicts (or a string representing a list of dicts) and packs
the key/value pairs into a single dict.
``'[{"foo": "salt://foo.rpm"}, {"bar": "salt://bar.rpm"}]'`` would become
``{"foo": "salt://foo.rpm", "bar": "salt://bar.rpm"}``
normalize : True
Normalize the package name by removing the architecture, if the
architecture of the package is different from the architecture of the
operating system. The ability to disable this behavior is useful for
poorly-created packages which include the architecture as an actual
part of the name, such as kernel modules which match a specific kernel
version.
.. versionadded:: 2015.8.0
CLI Example:
.. code-block:: bash
salt '*' pkg_resource.pack_sources '[{"foo": "salt://foo.rpm"}, {"bar": "salt://bar.rpm"}]' | Accepts list of dicts (or a string representing a list of dicts) and packs
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"""
Accepts list of dicts (or a string representing a list of dicts) and packs
the key/value pairs into a single dict.
``'[{"foo": "salt://foo.rpm"}, {"bar": "salt://bar.rpm"}]'`` would become
``{"foo": "salt://foo.rpm", "bar": "salt://bar.rpm"}``
normalize : True
Normalize the package name by removing the architecture, if the
architecture of the package is different from the architecture of the
operating system. The ability to disable this behavior is useful for
poorly-created packages which include the architecture as an actual
part of the name, such as kernel modules which match a specific kernel
version.
.. versionadded:: 2015.8.0
CLI Example:
.. code-block:: bash
salt '*' pkg_resource.pack_sources '[{"foo": "salt://foo.rpm"}, {"bar": "salt://bar.rpm"}]'
"""
if normalize and "pkg.normalize_name" in __salt__:
_normalize_name = __salt__["pkg.normalize_name"]
else:
_normalize_name = lambda pkgname: pkgname
if isinstance(sources, str):
try:
sources = salt.utils.yaml.safe_load(sources)
except salt.utils.yaml.parser.ParserError as err:
log.error(err)
return {}
ret = {}
for source in sources:
if (not isinstance(source, dict)) or len(source) != 1:
log.error("Invalid input: %s", pprint.pformat(sources))
log.error("Input must be a list of 1-element dicts")
return {}
else:
key = next(iter(source))
ret[_normalize_name(key)] = source[key]
return ret | [
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TengXiaoDai/DistributedCrawling | f5c2439e6ce68dd9b49bde084d76473ff9ed4963 | Lib/site-packages/pip/wheel.py | python | check_compatibility | (version, name) | Raises errors or warns if called with an incompatible Wheel-Version.
Pip should refuse to install a Wheel-Version that's a major series
ahead of what it's compatible with (e.g 2.0 > 1.1); and warn when
installing a version only minor version ahead (e.g 1.2 > 1.1).
version: a 2-tuple representing a Wheel-Version (Major, Minor)
name: name of wheel or package to raise exception about
:raises UnsupportedWheel: when an incompatible Wheel-Version is given | Raises errors or warns if called with an incompatible Wheel-Version. | [
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"""
Raises errors or warns if called with an incompatible Wheel-Version.
Pip should refuse to install a Wheel-Version that's a major series
ahead of what it's compatible with (e.g 2.0 > 1.1); and warn when
installing a version only minor version ahead (e.g 1.2 > 1.1).
version: a 2-tuple representing a Wheel-Version (Major, Minor)
name: name of wheel or package to raise exception about
:raises UnsupportedWheel: when an incompatible Wheel-Version is given
"""
if not version:
raise UnsupportedWheel(
"%s is in an unsupported or invalid wheel" % name
)
if version[0] > VERSION_COMPATIBLE[0]:
raise UnsupportedWheel(
"%s's Wheel-Version (%s) is not compatible with this version "
"of pip" % (name, '.'.join(map(str, version)))
)
elif version > VERSION_COMPATIBLE:
logger.warning(
'Installing from a newer Wheel-Version (%s)',
'.'.join(map(str, version)),
) | [
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pdfminer/pdfminer.six | 10f6fb40c258c86fd04d86bade20f69fb07faabd | pdfminer/_saslprep.py | python | saslprep | (data: str, prohibit_unassigned_code_points: bool = True) | return data | An implementation of RFC4013 SASLprep.
:param data:
The string to SASLprep.
:param prohibit_unassigned_code_points:
RFC 3454 and RFCs for various SASL mechanisms distinguish between
`queries` (unassigned code points allowed) and
`stored strings` (unassigned code points prohibited). Defaults
to ``True`` (unassigned code points are prohibited).
:return: The SASLprep'ed version of `data`. | An implementation of RFC4013 SASLprep.
:param data:
The string to SASLprep.
:param prohibit_unassigned_code_points:
RFC 3454 and RFCs for various SASL mechanisms distinguish between
`queries` (unassigned code points allowed) and
`stored strings` (unassigned code points prohibited). Defaults
to ``True`` (unassigned code points are prohibited).
:return: The SASLprep'ed version of `data`. | [
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"""An implementation of RFC4013 SASLprep.
:param data:
The string to SASLprep.
:param prohibit_unassigned_code_points:
RFC 3454 and RFCs for various SASL mechanisms distinguish between
`queries` (unassigned code points allowed) and
`stored strings` (unassigned code points prohibited). Defaults
to ``True`` (unassigned code points are prohibited).
:return: The SASLprep'ed version of `data`.
"""
if prohibit_unassigned_code_points:
prohibited = _PROHIBITED + (stringprep.in_table_a1,)
else:
prohibited = _PROHIBITED
# RFC3454 section 2, step 1 - Map
# RFC4013 section 2.1 mappings
# Map Non-ASCII space characters to SPACE (U+0020). Map
# commonly mapped to nothing characters to, well, nothing.
in_table_c12 = stringprep.in_table_c12
in_table_b1 = stringprep.in_table_b1
data = "".join(
["\u0020" if in_table_c12(elt) else elt
for elt in data if not in_table_b1(elt)])
# RFC3454 section 2, step 2 - Normalize
# RFC4013 section 2.2 normalization
data = unicodedata.ucd_3_2_0.normalize('NFKC', data)
in_table_d1 = stringprep.in_table_d1
if in_table_d1(data[0]):
if not in_table_d1(data[-1]):
# RFC3454, Section 6, #3. If a string contains any
# RandALCat character, the first and last characters
# MUST be RandALCat characters.
raise ValueError("SASLprep: failed bidirectional check")
# RFC3454, Section 6, #2. If a string contains any RandALCat
# character, it MUST NOT contain any LCat character.
prohibited = prohibited + (stringprep.in_table_d2,)
else:
# RFC3454, Section 6, #3. Following the logic of #3, if
# the first character is not a RandALCat, no other character
# can be either.
prohibited = prohibited + (in_table_d1,)
# RFC3454 section 2, step 3 and 4 - Prohibit and check bidi
for char in data:
if any(in_table(char) for in_table in prohibited):
raise ValueError(
"SASLprep: failed prohibited character check")
return data | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/dateutil/tz/_common.py | python | _tzinfo.fromutc | (self, dt) | return enfold(dt_wall, fold=_fold) | Given a timezone-aware datetime in a given timezone, calculates a
timezone-aware datetime in a new timezone.
Since this is the one time that we *know* we have an unambiguous
datetime object, we take this opportunity to determine whether the
datetime is ambiguous and in a "fold" state (e.g. if it's the first
occurance, chronologically, of the ambiguous datetime).
:param dt:
A timezone-aware :class:`datetime.datetime` object. | Given a timezone-aware datetime in a given timezone, calculates a
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] | def fromutc(self, dt):
"""
Given a timezone-aware datetime in a given timezone, calculates a
timezone-aware datetime in a new timezone.
Since this is the one time that we *know* we have an unambiguous
datetime object, we take this opportunity to determine whether the
datetime is ambiguous and in a "fold" state (e.g. if it's the first
occurance, chronologically, of the ambiguous datetime).
:param dt:
A timezone-aware :class:`datetime.datetime` object.
"""
dt_wall = self._fromutc(dt)
# Calculate the fold status given the two datetimes.
_fold = self._fold_status(dt, dt_wall)
# Set the default fold value for ambiguous dates
return enfold(dt_wall, fold=_fold) | [
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LiuRoy/zhihu_spider | 8eb925d294689ad9ab13c1532fb7ba8c556b5ea7 | zhihu/zhihu/pipelines.py | python | ZhihuPipeline._process_relation | (self, item) | 存储人际拓扑关系 | 存储人际拓扑关系 | [
"存储人际拓扑关系"
] | def _process_relation(self, item):
"""
存储人际拓扑关系
"""
collection = self.db['relation']
data = collection.find_one({
'zhihu_id': item['zhihu_id'],
'user_type': item['user_type']})
if not data:
self.db['relation'].insert(dict(item))
else:
origin_list = data['user_list']
new_list = item['user_list']
data['user_list'] = list(set(origin_list) | set(new_list))
collection.update({'zhihu_id': item['zhihu_id'],
'user_type': item['user_type']}, data) | [
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Tencent/bk-bcs-saas | 2b437bf2f5fd5ce2078f7787c3a12df609f7679d | bcs-app/backend/utils/local.py | python | Local.get_http_request_id | (self) | return new_request_id() | 从接入层获取request_id，或者生成一个新的request_id | 从接入层获取request_id，或者生成一个新的request_id | [
"从接入层获取request_id，或者生成一个新的request_id"
] | def get_http_request_id(self):
"""从接入层获取request_id，或者生成一个新的request_id"""
# 在从header中获取
request_id = self.request.META.get(settings.REQUEST_ID_HEADER, '')
if request_id:
return request_id
# 最后主动生成一个
return new_request_id() | [
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log2timeline/plaso | fe2e316b8c76a0141760c0f2f181d84acb83abc2 | plaso/analysis/chrome_extension.py | python | ChromeExtensionPlugin._GetPathSegmentSeparator | (self, path) | return '/' | Given a path give back the path separator as a best guess.
Args:
path (str): path.
Returns:
str: path segment separator. | Given a path give back the path separator as a best guess. | [
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"""Given a path give back the path separator as a best guess.
Args:
path (str): path.
Returns:
str: path segment separator.
"""
if path[0] in ('\\', '/'):
return path[0]
if path[1:].startswith(':\\'):
return '\\'
backward_slash_count = path.count('\\')
forward_slash_count = path.count('/')
if backward_slash_count > forward_slash_count:
return '\\'
return '/' | [
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h5py/h5py | aa31f03bef99e5807d1d6381e36233325d944279 | h5py/_hl/files.py | python | register_driver | (name, set_fapl) | Register a custom driver.
Parameters
----------
name : str
The name of the driver.
set_fapl : callable[PropFAID, **kwargs] -> NoneType
The function to set the fapl to use your custom driver. | Register a custom driver. | [
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"."
] | def register_driver(name, set_fapl):
"""Register a custom driver.
Parameters
----------
name : str
The name of the driver.
set_fapl : callable[PropFAID, **kwargs] -> NoneType
The function to set the fapl to use your custom driver.
"""
_drivers[name] = set_fapl | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/encodings/cp720.py | python | IncrementalEncoder.encode | (self, input, final=False) | return codecs.charmap_encode(input,self.errors,encoding_table)[0] | [] | def encode(self, input, final=False):
return codecs.charmap_encode(input,self.errors,encoding_table)[0] | [
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mjwestcott/Goodrich | dc2516591bd28488516c0337a62e64248debe47c | ch08/linked_binary_tree.py | python | LinkedBinaryTree._delete | (self, p) | return node._element | Delete the node at Position p, and replace it with its child, if any.
Return the element that had been stored at Position p.
Raise ValueError if Position p is invalid or p has two children. | Delete the node at Position p, and replace it with its child, if any. | [
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] | def _delete(self, p):
"""Delete the node at Position p, and replace it with its child, if any.
Return the element that had been stored at Position p.
Raise ValueError if Position p is invalid or p has two children.
"""
node = self._validate(p)
if self.num_children(p) == 2:
raise ValueError('Position has two children')
child = node._left if node._left else node._right # might be None
if child is not None:
child._parent = node._parent # child's grandparent becomes parent
if node is self._root:
self._root = child # child becomes root
else:
parent = node._parent
if node is parent._left:
parent._left = child
else:
parent._right = child
self._size -= 1
node._parent = node # convention for deprecated node
return node._element | [
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researchmm/tasn | 5dba8ccc096cedc63913730eeea14a9647911129 | tasn-mxnet/3rdparty/tvm/python/tvm/autotvm/database.py | python | Database.load | (self, inp, get_all=False) | Load a result based on an input's string key
Parameters
----------
inp: MeasureInput
to be translated into key for RedisDB
get_all: bool, optional
Whether the latest result (or all matching results) should be returned
Returns
-------
rec: MeasureResult if previously saved, otherwise None | Load a result based on an input's string key | [
"Load",
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] | def load(self, inp, get_all=False):
"""
Load a result based on an input's string key
Parameters
----------
inp: MeasureInput
to be translated into key for RedisDB
get_all: bool, optional
Whether the latest result (or all matching results) should be returned
Returns
-------
rec: MeasureResult if previously saved, otherwise None
"""
raise NotImplementedError() | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/shutil.py | python | _unpack_zipfile | (filename, extract_dir) | Unpack zip `filename` to `extract_dir` | Unpack zip `filename` to `extract_dir` | [
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"zip",
"filename",
"to",
"extract_dir"
] | def _unpack_zipfile(filename, extract_dir):
"""Unpack zip `filename` to `extract_dir`
"""
try:
import zipfile
except ImportError:
raise ReadError('zlib not supported, cannot unpack this archive.')
if not zipfile.is_zipfile(filename):
raise ReadError("%s is not a zip file" % filename)
zip = zipfile.ZipFile(filename)
try:
for info in zip.infolist():
name = info.filename
# don't extract absolute paths or ones with .. in them
if name.startswith('/') or '..' in name:
continue
target = os.path.join(extract_dir, *name.split('/'))
if not target:
continue
_ensure_directory(target)
if not name.endswith('/'):
# file
data = zip.read(info.filename)
f = open(target, 'wb')
try:
f.write(data)
finally:
f.close()
del data
finally:
zip.close() | [
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simonw/sqlite-utils | e0c476bc380744680c8b7675c24fb0e9f5ec6dcd | sqlite_utils/db.py | python | Database.vacuum | (self) | Run a SQLite ``VACUUM`` against the database. | Run a SQLite ``VACUUM`` against the database. | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twisted/twisted/spread/pb.py | python | RemoteReference.callRemote | (self, _name, *args, **kw) | return self.broker._sendMessage('',self.perspective, self.luid,
_name, args, kw) | Asynchronously invoke a remote method.
@type _name: C{string}
@param _name: the name of the remote method to invoke
@param args: arguments to serialize for the remote function
@param kw: keyword arguments to serialize for the remote function.
@rtype: L{twisted.internet.defer.Deferred}
@returns: a Deferred which will be fired when the result of
this remote call is received. | Asynchronously invoke a remote method. | [
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"invoke",
"a",
"remote",
"method",
"."
] | def callRemote(self, _name, *args, **kw):
"""Asynchronously invoke a remote method.
@type _name: C{string}
@param _name: the name of the remote method to invoke
@param args: arguments to serialize for the remote function
@param kw: keyword arguments to serialize for the remote function.
@rtype: L{twisted.internet.defer.Deferred}
@returns: a Deferred which will be fired when the result of
this remote call is received.
"""
# note that we use '_name' instead of 'name' so the user can call
# remote methods with 'name' as a keyword parameter, like this:
# ref.callRemote("getPeopleNamed", count=12, name="Bob")
return self.broker._sendMessage('',self.perspective, self.luid,
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_internal/metadata/pkg_resources.py | python | Distribution.metadata | (self) | return feed_parser.close() | :raises NoneMetadataError: if the distribution reports `has_metadata()`
True but `get_metadata()` returns None. | :raises NoneMetadataError: if the distribution reports `has_metadata()`
True but `get_metadata()` returns None. | [
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"""
:raises NoneMetadataError: if the distribution reports `has_metadata()`
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"""
if isinstance(self._dist, pkg_resources.DistInfoDistribution):
metadata_name = "METADATA"
else:
metadata_name = "PKG-INFO"
try:
metadata = self.read_text(metadata_name)
except FileNotFoundError:
if self.location:
displaying_path = display_path(self.location)
else:
displaying_path = repr(self.location)
logger.warning("No metadata found in %s", displaying_path)
metadata = ""
feed_parser = email.parser.FeedParser()
feed_parser.feed(metadata)
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/attack/db/sqlmap/thirdparty/odict/odict.py | python | Keys.__call__ | (self) | return self._main._keys() | Pretend to be the keys method. | Pretend to be the keys method. | [
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kennethreitz-archive/requests3 | 69eb662703b40db58fdc6c095d0fe130c56649bb | requests3/__init__.py | python | _check_cryptography | (cryptography_version: str) | [] | def _check_cryptography(cryptography_version: str) -> None:
# cryptography < 1.3.4
try:
cryptography_version = list(
map(int, cryptography_version.split("."))
) # type: ignore
except ValueError:
return
if cryptography_version < [1, 3, 4]:
warning = "Old version of cryptography ({}) may cause slowdown.".format(
cryptography_version
)
warnings.warn(warning, RequestsDependencyWarning) | [
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yahoo/lopq | 0f17655b901e6dfabe5c2aa62b4c8e492f34b05a | scripts/query_runtime.py | python | subquantizer_flops | (D, M, clusters=256) | return (M / 2) * (D / M) * clusters * 2 | Given the dimension of the data, the number of subquantizers and the
subquantizer vocabulary size, compute the number of flops to compute
a projected query's LOPQ distance for a single half of the query. | Given the dimension of the data, the number of subquantizers and the
subquantizer vocabulary size, compute the number of flops to compute
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"""
Given the dimension of the data, the number of subquantizers and the
subquantizer vocabulary size, compute the number of flops to compute
a projected query's LOPQ distance for a single half of the query.
"""
# (subquants per half) * (dims per subquant) * (cluster per subquant) * (flops per squared distance)
return (M / 2) * (D / M) * clusters * 2 | [
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HCIILAB/DeRPN | 21e6738ee1f7d3f159ee48d435c543e773f8ce99 | tools/train_svms.py | python | SVMTrainer.train | (self) | [] | def train(self):
# Initialize SVMs using
# a. w_i = fc8_w_i - fc8_w_0
# b. b_i = fc8_b_i - fc8_b_0
# c. Install SVMs into net
self.initialize_net()
# Pass over roidb to count num positives for each class
# a. Pre-allocate arrays for positive feature vectors
# Pass over roidb, computing features for positives only
self.get_pos_examples()
# Pass over roidb
# a. Compute cls_score with forward pass
# b. For each class
# i. Select hard negatives
# ii. Add them to cache
# c. For each class
# i. If SVM retrain criteria met, update SVM
# ii. Install new SVM into net
self.train_with_hard_negatives()
# One final SVM retraining for each class
# Install SVMs into net
for j in xrange(1, self.imdb.num_classes):
new_w_b = self.trainers[j].append_neg_and_retrain(force=True)
self.update_net(j, new_w_b[0], new_w_b[1]) | [
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qiucheng025/zao- | 3a5edf3607b3a523f95746bc69b688090c76d89a | lib/face_filter.py | python | FaceFilter.get_filter_encodings | (self) | Return filter face encodings from Keras VGG Face | Return filter face encodings from Keras VGG Face | [
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""" Return filter face encodings from Keras VGG Face """
for filename, face in self.filters.items():
logger.debug("Getting encodings for: '%s'", filename)
encodings = self.vgg_face.predict(face["face"])
logger.debug("Filter Filename: %s, encoding shape: %s", filename, encodings.shape)
face["encoding"] = encodings
del face["face"] | [
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flennerhag/mlens | 6cbc11354b5f9500a33d9cefb700a1bba9d3199a | mlens/index/subsemble.py | python | SubsetIndex._gen_indices | (self) | Create generator for subsample.
Generate indices of training set and test set for
- each partition
- each fold in the partition
Note that the test index return is *global*, i.e. it contains the
test indices of that fold across partitions. See Examples for
further details. | Create generator for subsample. | [
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] | def _gen_indices(self):
"""Create generator for subsample.
Generate indices of training set and test set for
- each partition
- each fold in the partition
Note that the test index return is *global*, i.e. it contains the
test indices of that fold across partitions. See Examples for
further details.
"""
partitions = self.partitions
n_samples = self.n_samples
folds = self.folds
T = self._build_test_sets()
# For each partition, for each fold, get the global test fold
# from T and index the partition samples not in T as train set
p_len = partition(n_samples, partitions)
p_last = 0
for p_size in p_len:
p_start, p_stop = p_last, p_last + p_size
t_len = partition(p_stop - p_start, folds)
t_last = p_start
for i, t_size in enumerate(t_len):
t_start, t_stop = t_last, t_last + t_size
# Get global test set indices
tei = T[i]
# Construct train set
tri_start_below, tri_stop_below = p_start, t_start
tri_start_above, tri_stop_above = t_stop, p_stop
tri = prune_train(tri_start_below, tri_stop_below,
tri_start_above, tri_stop_above)
yield tri, tei
t_last += t_size
p_last += p_size | [
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dipy/dipy | be956a529465b28085f8fc435a756947ddee1c89 | dipy/io/stateful_tractogram.py | python | StatefulTractogram._vox_to_rasmm | (self) | Unsafe function to transform streamlines | Unsafe function to transform streamlines | [
"Unsafe",
"function",
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"transform",
"streamlines"
] | def _vox_to_rasmm(self):
""" Unsafe function to transform streamlines """
if self._space == Space.VOX:
if self._tractogram.streamlines._data.size > 0:
self._tractogram.apply_affine(self._affine)
self._space = Space.RASMM
logger.debug('Moved streamlines from vox to rasmm.')
else:
logger.warning('Wrong initial space for this function.')
return | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/uptimerobot/__init__.py | python | UptimeRobotDataUpdateCoordinator.__init__ | (
self,
hass: HomeAssistant,
config_entry_id: str,
dev_reg: DeviceRegistry,
api: UptimeRobot,
) | Initialize coordinator. | Initialize coordinator. | [
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] | def __init__(
self,
hass: HomeAssistant,
config_entry_id: str,
dev_reg: DeviceRegistry,
api: UptimeRobot,
) -> None:
"""Initialize coordinator."""
super().__init__(
hass,
LOGGER,
name=DOMAIN,
update_method=self._async_update_data,
update_interval=COORDINATOR_UPDATE_INTERVAL,
)
self._config_entry_id = config_entry_id
self._device_registry = dev_reg
self._api = api | [
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graphcore/examples | 46d2b7687b829778369fc6328170a7b14761e5c6 | applications/tensorflow/cnns/inference/models/tf_layers.py | python | crop | (input_tensor: tf.Tensor, cropping: Tuple[Tuple[int, int], Tuple[int, int]]) | return input_tensor[:, cropping[0][0]:rows - cropping[0][1], cropping[1][0]:cols - cropping[1][1], :] | Crop input along width and height dimensions, assumes channels_last.
Args:
input_tensor: Input to be cropped.
cropping: Start and stop index along height and width.
Returns: Cropped tensor. | Crop input along width and height dimensions, assumes channels_last. | [
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] | def crop(input_tensor: tf.Tensor, cropping: Tuple[Tuple[int, int], Tuple[int, int]]):
"""Crop input along width and height dimensions, assumes channels_last.
Args:
input_tensor: Input to be cropped.
cropping: Start and stop index along height and width.
Returns: Cropped tensor.
"""
_, rows, cols, _ = input_tensor.get_shape().as_list()
return input_tensor[:, cropping[0][0]:rows - cropping[0][1], cropping[1][0]:cols - cropping[1][1], :] | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/distutils/command/sdist.py | python | sdist.add_defaults | (self) | Add all the default files to self.filelist:
- README or README.txt
- setup.py
- test/test*.py
- all pure Python modules mentioned in setup script
- all files pointed by package_data (build_py)
- all files defined in data_files.
- all files defined as scripts.
- all C sources listed as part of extensions or C libraries
in the setup script (doesn't catch C headers!)
Warns if (README or README.txt) or setup.py are missing; everything
else is optional. | Add all the default files to self.filelist:
- README or README.txt
- setup.py
- test/test*.py
- all pure Python modules mentioned in setup script
- all files pointed by package_data (build_py)
- all files defined in data_files.
- all files defined as scripts.
- all C sources listed as part of extensions or C libraries
in the setup script (doesn't catch C headers!)
Warns if (README or README.txt) or setup.py are missing; everything
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"""Add all the default files to self.filelist:
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- all pure Python modules mentioned in setup script
- all files pointed by package_data (build_py)
- all files defined in data_files.
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- all C sources listed as part of extensions or C libraries
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Warns if (README or README.txt) or setup.py are missing; everything
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"""
standards = [('README', 'README.txt'), self.distribution.script_name]
for fn in standards:
if isinstance(fn, tuple):
alts = fn
got_it = 0
for fn in alts:
if os.path.exists(fn):
got_it = 1
self.filelist.append(fn)
break
if not got_it:
self.warn("standard file not found: should have one of " +
string.join(alts, ', '))
else:
if os.path.exists(fn):
self.filelist.append(fn)
else:
self.warn("standard file '%s' not found" % fn)
optional = ['test/test*.py', 'setup.cfg']
for pattern in optional:
files = filter(os.path.isfile, glob(pattern))
if files:
self.filelist.extend(files)
# build_py is used to get:
# - python modules
# - files defined in package_data
build_py = self.get_finalized_command('build_py')
# getting python files
if self.distribution.has_pure_modules():
self.filelist.extend(build_py.get_source_files())
# getting package_data files
# (computed in build_py.data_files by build_py.finalize_options)
for pkg, src_dir, build_dir, filenames in build_py.data_files:
for filename in filenames:
self.filelist.append(os.path.join(src_dir, filename))
# getting distribution.data_files
if self.distribution.has_data_files():
for item in self.distribution.data_files:
if isinstance(item, str): # plain file
item = convert_path(item)
if os.path.isfile(item):
self.filelist.append(item)
else: # a (dirname, filenames) tuple
dirname, filenames = item
for f in filenames:
f = convert_path(f)
if os.path.isfile(f):
self.filelist.append(f)
if self.distribution.has_ext_modules():
build_ext = self.get_finalized_command('build_ext')
self.filelist.extend(build_ext.get_source_files())
if self.distribution.has_c_libraries():
build_clib = self.get_finalized_command('build_clib')
self.filelist.extend(build_clib.get_source_files())
if self.distribution.has_scripts():
build_scripts = self.get_finalized_command('build_scripts')
self.filelist.extend(build_scripts.get_source_files()) | [
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IlyaSkriblovsky/txredisapi | c89a49829d1c48baa10d03c41d39c261c02ad6e1 | txredisapi.py | python | BaseRedisProtocol.lindex | (self, key, index) | return self.execute_command("LINDEX", key, index) | Return the element at index position from the List at key | Return the element at index position from the List at key | [
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"""
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"""
return self.execute_command("LINDEX", key, index) | [
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Abjad/abjad | d0646dfbe83db3dc5ab268f76a0950712b87b7fd | abjad/indicators/StartPianoPedal.py | python | StartPianoPedal.tweaks | (self) | return self._tweaks | r"""
Gets tweaks
.. container:: example
REGRESSION. Tweaks survive copy:
>>> import copy
>>> start_piano_pedal = abjad.StartPianoPedal()
>>> abjad.tweak(start_piano_pedal).color = "#blue"
>>> string = abjad.storage(start_piano_pedal)
>>> print(string)
abjad.StartPianoPedal(
tweaks=TweakInterface(('_literal', None), ('color', '#blue')),
)
>>> start_piano_pedal_2 = copy.copy(start_piano_pedal)
>>> string = abjad.storage(start_piano_pedal_2)
>>> print(string)
abjad.StartPianoPedal(
tweaks=TweakInterface(('_literal', None), ('color', '#blue')),
) | r"""
Gets tweaks | [
"r",
"Gets",
"tweaks"
] | def tweaks(self) -> typing.Optional[_overrides.TweakInterface]:
r"""
Gets tweaks
.. container:: example
REGRESSION. Tweaks survive copy:
>>> import copy
>>> start_piano_pedal = abjad.StartPianoPedal()
>>> abjad.tweak(start_piano_pedal).color = "#blue"
>>> string = abjad.storage(start_piano_pedal)
>>> print(string)
abjad.StartPianoPedal(
tweaks=TweakInterface(('_literal', None), ('color', '#blue')),
)
>>> start_piano_pedal_2 = copy.copy(start_piano_pedal)
>>> string = abjad.storage(start_piano_pedal_2)
>>> print(string)
abjad.StartPianoPedal(
tweaks=TweakInterface(('_literal', None), ('color', '#blue')),
)
"""
return self._tweaks | [
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barseghyanartur/django-fobi | a998feae007d7fe3637429a80e42952ec7cda79f | src/fobi/contrib/plugins/form_elements/content/content_text/base.py | python | ContentTextPlugin.get_rendered_text | (self) | return rendered_text | Get rendered text. | Get rendered text. | [
"Get",
"rendered",
"text",
"."
] | def get_rendered_text(self):
"""Get rendered text."""
rendered_text = "<p>{0}</p>".format(smart_str(self.data.text))
return rendered_text | [
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luispedro/jug | e967c6388ca69c78698e1522b9535d647a2c5b22 | jug/hooks/exit_checks.py | python | exit_after_n_tasks | (n) | Exit after a specific number of tasks have been executed
Parameters
----------
n : int
Number of tasks to execute | Exit after a specific number of tasks have been executed | [
"Exit",
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"number",
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"have",
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"executed"
] | def exit_after_n_tasks(n):
'''Exit after a specific number of tasks have been executed
Parameters
----------
n : int
Number of tasks to execute
'''
from jug import hooks
# In newer Python, we could use nonlocal, but this is a work around
# (http://stackoverflow.com/questions/9603278/is-there-something-like-nonlocal-in-python-3/9603491#9603491)
executed = [0]
def exit_after(_t):
executed[0] += 1
if executed[0] >= n:
exit(0)
hooks.register_hook('execute.task-executed1', exit_after) | [
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lsbardel/python-stdnet | 78db5320bdedc3f28c5e4f38cda13a4469e35db7 | stdnet/odm/fields.py | python | JSONField.set_get_value | (self, instance, value) | [] | def set_get_value(self, instance, value):
# Optimisation, avoid to call serialise since it is the same
# as to_python
value = self.to_python(value)
setattr(instance, self.attname, value)
if self.as_string:
# dump as a string
return self.serialise(value)
else:
# unwind as a dictionary
value = dict(dict_flat_generator(value,
attname=self.attname,
dumps=self.serialise,
error=FieldValueError))
# If the dictionary is empty we modify so that
# an update is possible.
if not value:
value = {self.attname: self.serialise(None)}
elif value.get(self.attname, None) is None:
# TODO Better implementation of this is a ack!
# set the root value to an empty string to distinguish
# from None.
value[self.attname] = self.serialise('')
return value | [
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nerdvegas/rez | d392c65bf63b4bca8106f938cec49144ba54e770 | src/rez/rex.py | python | RexExecutor.compile_code | (cls, code, filename=None, exec_namespace=None) | return pyc | Compile and possibly execute rex code.
Args:
code (str or SourceCode): The python code to compile.
filename (str): File to associate with the code, will default to
'<string>'.
exec_namespace (dict): Namespace to execute the code in. If None,
the code is not executed.
Returns:
Compiled code object. | Compile and possibly execute rex code. | [
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] | def compile_code(cls, code, filename=None, exec_namespace=None):
"""Compile and possibly execute rex code.
Args:
code (str or SourceCode): The python code to compile.
filename (str): File to associate with the code, will default to
'<string>'.
exec_namespace (dict): Namespace to execute the code in. If None,
the code is not executed.
Returns:
Compiled code object.
"""
if filename is None:
if isinstance(code, SourceCode):
filename = code.sourcename
else:
filename = "<string>"
# compile
try:
if isinstance(code, SourceCode):
pyc = code.compiled
else:
pyc = compile(code, filename, 'exec')
except SourceCodeError as e:
reraise(e, RexError)
except:
stack = traceback.format_exc()
raise RexError("Failed to compile %s:\n\n%s" % (filename, stack))
exc_type = Exception if config.catch_rex_errors else _NeverError
# execute
if exec_namespace is not None:
try:
if isinstance(code, SourceCode):
code.exec_(globals_=exec_namespace)
else:
exec(pyc, exec_namespace)
except RexError:
raise
except SourceCodeError as e:
reraise(e, RexError)
except exc_type:
stack = traceback.format_exc()
raise RexError("Failed to exec %s:\n\n%s" % (filename, stack))
return pyc | [
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seopbo/nlp_classification | 21ea6e3f5737e7074bdd8dd190e5f5172f86f6bf | Efficient_Character-level_Document_Classification_by_Combining_Convolution_and_Recurrent_Layers/model/data.py | python | batchify | (data: List[Tuple[torch.Tensor, torch.Tensor]]) | return indices, labels | custom collate_fn for DataLoader
Args:
data (list): list of torch.Tensors
Returns:
data (tuple): tuple of torch.Tensors | custom collate_fn for DataLoader | [
"custom",
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] | def batchify(data: List[Tuple[torch.Tensor, torch.Tensor]]) -> Tuple[torch.Tensor, torch.Tensor]:
"""custom collate_fn for DataLoader
Args:
data (list): list of torch.Tensors
Returns:
data (tuple): tuple of torch.Tensors
"""
indices, labels = zip(*data)
indices = pad_sequence(indices, batch_first=True, padding_value=1)
labels = torch.stack(labels, 0)
return indices, labels | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.4/django/views/csrf.py | python | csrf_failure | (request, reason="") | return HttpResponseForbidden(t.render(c), mimetype='text/html') | Default view used when request fails CSRF protection | Default view used when request fails CSRF protection | [
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] | def csrf_failure(request, reason=""):
"""
Default view used when request fails CSRF protection
"""
from django.middleware.csrf import REASON_NO_REFERER
t = Template(CSRF_FAILURE_TEMPLATE)
c = Context({'DEBUG': settings.DEBUG,
'reason': reason,
'no_referer': reason == REASON_NO_REFERER
})
return HttpResponseForbidden(t.render(c), mimetype='text/html') | [
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cjdrake/pyeda | 554ee53aa678f4b61bcd7e07ba2c74ddc749d665 | pyeda/parsing/dimacs.py | python | parse_cnf | (s, varname='x') | return ast | Parse an input string in DIMACS CNF format,
and return an expression abstract syntax tree.
Parameters
----------
s : str
String containing a DIMACS CNF.
varname : str, optional
The variable name used for creating literals.
Defaults to 'x'.
Returns
-------
An ast tuple, defined recursively:
ast := ('var', names, indices)
| ('not', ast)
| ('or', ast, ...)
| ('and', ast, ...)
names := (name, ...)
indices := (index, ...) | Parse an input string in DIMACS CNF format,
and return an expression abstract syntax tree. | [
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] | def parse_cnf(s, varname='x'):
"""
Parse an input string in DIMACS CNF format,
and return an expression abstract syntax tree.
Parameters
----------
s : str
String containing a DIMACS CNF.
varname : str, optional
The variable name used for creating literals.
Defaults to 'x'.
Returns
-------
An ast tuple, defined recursively:
ast := ('var', names, indices)
| ('not', ast)
| ('or', ast, ...)
| ('and', ast, ...)
names := (name, ...)
indices := (index, ...)
"""
lexer = iter(CNFLexer(s))
try:
ast = _cnf(lexer, varname)
except lex.RunError as exc:
fstr = ("{0.args[0]}: "
"(line: {0.lineno}, offset: {0.offset}, text: {0.text})")
raise Error(fstr.format(exc))
# Check for end of buffer
_expect_token(lexer, {EndToken})
return ast | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/urllib/request.py | python | HTTPPasswordMgr.reduce_uri | (self, uri, default_port=True) | return authority, path | Accept authority or URI and extract only the authority and path. | Accept authority or URI and extract only the authority and path. | [
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] | def reduce_uri(self, uri, default_port=True):
"""Accept authority or URI and extract only the authority and path."""
# note HTTP URLs do not have a userinfo component
parts = urlsplit(uri)
if parts[1]:
# URI
scheme = parts[0]
authority = parts[1]
path = parts[2] or '/'
else:
# host or host:port
scheme = None
authority = uri
path = '/'
host, port = splitport(authority)
if default_port and port is None and scheme is not None:
dport = {"http": 80,
"https": 443,
}.get(scheme)
if dport is not None:
authority = "%s:%d" % (host, dport)
return authority, path | [
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NVlabs/noise2noise | 7355519e7bfc49e0606cca8867748e736431244b | dnnlib/tflib/autosummary.py | python | autosummary | (name: str, value: TfExpressionEx) | Create a new autosummary. | Create a new autosummary. | [
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"."
] | def autosummary(name: str, value: TfExpressionEx) -> TfExpressionEx:
"""Create a new autosummary."""
tfutil.assert_tf_initialized()
name_id = name.replace("/", "_")
if tfutil.is_tf_expression(value):
with tf.name_scope("summary_" + name_id), tf.device(value.device):
update_op = _create_var(name, value)
with tf.control_dependencies([update_op]):
return tf.identity(value)
else: # python scalar or numpy array
if name not in _immediate:
with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
update_value = tf.placeholder(_dtype)
update_op = _create_var(name, update_value)
_immediate[name] = update_op, update_value
update_op, update_value = _immediate[name]
tfutil.run(update_op, {update_value: value})
return value | [
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awesto/django-shop | 13d9a77aff7eede74a5f363c1d540e005d88dbcd | shop/templatetags/shop_tags.py | python | from_iso8601 | (value) | [] | def from_iso8601(value):
try:
return datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%fZ")
except ValueError:
return datetime.strptime(value, "%Y-%m-%dT%H:%M:%SZ") | [
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uqfoundation/mystic | 154e6302d1f2f94e8f13e88ecc5f24241cc28ac7 | mystic/math/legacydata.py | python | lipschitzcone.contains | (self, point) | return abs(y - G) <= self.distance(point) | return True if a given point is within the cone | return True if a given point is within the cone | [
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""" return True if a given point is within the cone """
L = self.slopes
if any([i == NULLSLOPE for i in L]): return True
x = point.position
Z = self.vertex.position
if all([x[i] == Z[i] for i in range(len(x))]): return True
y = point.value
G = self.vertex.value
return abs(y - G) <= self.distance(point) | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/distlib/database.py | python | _Cache.add | (self, dist) | Add a distribution to the cache.
:param dist: The distribution to add. | Add a distribution to the cache.
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"""
Add a distribution to the cache.
:param dist: The distribution to add.
"""
if dist.path not in self.path:
self.path[dist.path] = dist
self.name.setdefault(dist.key, []).append(dist) | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/tbm/v20180129/tbm_client.py | python | TbmClient.DescribeBrandMediaReport | (self, request) | 监测品牌关键词出现在媒体网站（新闻媒体、网络门户、政府网站、微信公众号、天天快报等）发布资讯标题和正文中的报道数。按天输出结果。
:param request: Request instance for DescribeBrandMediaReport.
:type request: :class:`tencentcloud.tbm.v20180129.models.DescribeBrandMediaReportRequest`
:rtype: :class:`tencentcloud.tbm.v20180129.models.DescribeBrandMediaReportResponse` | 监测品牌关键词出现在媒体网站（新闻媒体、网络门户、政府网站、微信公众号、天天快报等）发布资讯标题和正文中的报道数。按天输出结果。 | [
"监测品牌关键词出现在媒体网站（新闻媒体、网络门户、政府网站、微信公众号、天天快报等）发布资讯标题和正文中的报道数。按天输出结果。"
] | def DescribeBrandMediaReport(self, request):
"""监测品牌关键词出现在媒体网站（新闻媒体、网络门户、政府网站、微信公众号、天天快报等）发布资讯标题和正文中的报道数。按天输出结果。
:param request: Request instance for DescribeBrandMediaReport.
:type request: :class:`tencentcloud.tbm.v20180129.models.DescribeBrandMediaReportRequest`
:rtype: :class:`tencentcloud.tbm.v20180129.models.DescribeBrandMediaReportResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeBrandMediaReport", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeBrandMediaReportResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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stevearc/flywheel | ac6eea314f6d88b593cf809336d8723df0b78f6f | flywheel/models.py | python | Model.cached_ | (self, name, default=None) | return field.get_cached_value(self) | Get the cached (server) value of a field | Get the cached (server) value of a field | [
"Get",
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"cached",
"(",
"server",
")",
"value",
"of",
"a",
"field"
] | def cached_(self, name, default=None):
""" Get the cached (server) value of a field """
if not self.persisted_:
return default
if name in self.__cache__:
return self.__cache__[name]
field = self.meta_.fields.get(name)
# Need this redirection for Composite fields
return field.get_cached_value(self) | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/pip-7.1.2-py3.3.egg/pip/download.py | python | get_file_content | (url, comes_from=None, session=None) | return url, content | Gets the content of a file; it may be a filename, file: URL, or
http: URL. Returns (location, content). Content is unicode. | Gets the content of a file; it may be a filename, file: URL, or
http: URL. Returns (location, content). Content is unicode. | [
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] | def get_file_content(url, comes_from=None, session=None):
"""Gets the content of a file; it may be a filename, file: URL, or
http: URL. Returns (location, content). Content is unicode."""
if session is None:
raise TypeError(
"get_file_content() missing 1 required keyword argument: 'session'"
)
match = _scheme_re.search(url)
if match:
scheme = match.group(1).lower()
if (scheme == 'file' and comes_from and
comes_from.startswith('http')):
raise InstallationError(
'Requirements file %s references URL %s, which is local'
% (comes_from, url))
if scheme == 'file':
path = url.split(':', 1)[1]
path = path.replace('\\', '/')
match = _url_slash_drive_re.match(path)
if match:
path = match.group(1) + ':' + path.split('|', 1)[1]
path = urllib_parse.unquote(path)
if path.startswith('/'):
path = '/' + path.lstrip('/')
url = path
else:
# FIXME: catch some errors
resp = session.get(url)
resp.raise_for_status()
if six.PY3:
return resp.url, resp.text
else:
return resp.url, resp.content
try:
with open(url) as f:
content = f.read()
except IOError as exc:
raise InstallationError(
'Could not open requirements file: %s' % str(exc)
)
return url, content | [
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exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xlgui/widgets/dialogs.py | python | error | (parent, message=None, markup=None) | Shows an error dialog | Shows an error dialog | [
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"an",
"error",
"dialog"
] | def error(parent, message=None, markup=None):
"""
Shows an error dialog
"""
if message is markup is None:
raise ValueError("message or markup must be specified")
dialog = Gtk.MessageDialog(
buttons=Gtk.ButtonsType.CLOSE,
message_type=Gtk.MessageType.ERROR,
modal=True,
transient_for=parent,
)
if markup is None:
dialog.props.text = message
else:
dialog.set_markup(markup)
dialog.run()
dialog.destroy() | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/video/v1/composition_hook.py | python | CompositionHookInstance.trim | (self) | return self._properties['trim'] | :returns: Whether intervals with no media are clipped
:rtype: bool | :returns: Whether intervals with no media are clipped
:rtype: bool | [
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"""
:returns: Whether intervals with no media are clipped
:rtype: bool
"""
return self._properties['trim'] | [
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plasma-umass/scalene | e5d1c4d8cac49d0caa43e1a2c3898a3720071913 | scalene/leak_analysis.py | python | zlog | (x: float) | Redefine log so that if x is <= 0, log x is 0. | Redefine log so that if x is <= 0, log x is 0. | [
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] | def zlog(x: float) -> float:
"""Redefine log so that if x is <= 0, log x is 0."""
if x <= 0:
return 0
else:
return math.log(x) | [
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vispy/vispy | 26256fdc2574259dd227022fbce0767cae4e244b | vispy/gloo/glir.py | python | GlirProgram._get_active_attributes_and_uniforms | (self) | return set([v[0] for v in attributes] + [v[0] for v in uniforms]) | Retrieve active attributes and uniforms to be able to check that
all uniforms/attributes are set by the user.
Other GLIR implementations may omit this. | Retrieve active attributes and uniforms to be able to check that
all uniforms/attributes are set by the user.
Other GLIR implementations may omit this. | [
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] | def _get_active_attributes_and_uniforms(self):
"""Retrieve active attributes and uniforms to be able to check that
all uniforms/attributes are set by the user.
Other GLIR implementations may omit this.
"""
# This match a name of the form "name[size]" (= array)
regex = re.compile(r"""(?P<name>\w+)\s*(\[(?P<size>\d+)\])\s*""")
# Get how many active attributes and uniforms there are
cu = gl.glGetProgramParameter(self._handle, gl.GL_ACTIVE_UNIFORMS)
ca = gl.glGetProgramParameter(self.handle, gl.GL_ACTIVE_ATTRIBUTES)
# Get info on each one
attributes = []
uniforms = []
for container, count, func in [(attributes, ca, gl.glGetActiveAttrib),
(uniforms, cu, gl.glGetActiveUniform)]:
for i in range(count):
name, size, gtype = func(self._handle, i)
m = regex.match(name) # Check if xxx[0] instead of xx
if m:
name = m.group('name')
for i in range(size):
container.append(('%s[%d]' % (name, i), gtype))
else:
container.append((name, gtype))
# return attributes, uniforms
return set([v[0] for v in attributes] + [v[0] for v in uniforms]) | [
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fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/varLib/instancer/names.py | python | _updateNameTableStyleRecords | (
varfont,
familyNameSuffix,
subFamilyName,
typoSubFamilyName,
platformID=3,
platEncID=1,
langID=0x409,
) | [] | def _updateNameTableStyleRecords(
varfont,
familyNameSuffix,
subFamilyName,
typoSubFamilyName,
platformID=3,
platEncID=1,
langID=0x409,
):
# TODO (Marc F) It may be nice to make this part a standalone
# font renamer in the future.
nametable = varfont["name"]
platform = (platformID, platEncID, langID)
currentFamilyName = nametable.getName(
NameID.TYPOGRAPHIC_FAMILY_NAME, *platform
) or nametable.getName(NameID.FAMILY_NAME, *platform)
currentStyleName = nametable.getName(
NameID.TYPOGRAPHIC_SUBFAMILY_NAME, *platform
) or nametable.getName(NameID.SUBFAMILY_NAME, *platform)
if not all([currentFamilyName, currentStyleName]):
raise ValueError(f"Missing required NameIDs 1 and 2 for platform {platform}")
currentFamilyName = currentFamilyName.toUnicode()
currentStyleName = currentStyleName.toUnicode()
nameIDs = {
NameID.FAMILY_NAME: currentFamilyName,
NameID.SUBFAMILY_NAME: subFamilyName or "Regular",
}
if typoSubFamilyName:
nameIDs[NameID.FAMILY_NAME] = f"{currentFamilyName} {familyNameSuffix}".strip()
nameIDs[NameID.TYPOGRAPHIC_FAMILY_NAME] = currentFamilyName
nameIDs[NameID.TYPOGRAPHIC_SUBFAMILY_NAME] = typoSubFamilyName
else:
# Remove previous Typographic Family and SubFamily names since they're
# no longer required
for nameID in (
NameID.TYPOGRAPHIC_FAMILY_NAME,
NameID.TYPOGRAPHIC_SUBFAMILY_NAME,
):
nametable.removeNames(nameID=nameID)
newFamilyName = (
nameIDs.get(NameID.TYPOGRAPHIC_FAMILY_NAME) or nameIDs[NameID.FAMILY_NAME]
)
newStyleName = (
nameIDs.get(NameID.TYPOGRAPHIC_SUBFAMILY_NAME) or nameIDs[NameID.SUBFAMILY_NAME]
)
nameIDs[NameID.FULL_FONT_NAME] = f"{newFamilyName} {newStyleName}"
nameIDs[NameID.POSTSCRIPT_NAME] = _updatePSNameRecord(
varfont, newFamilyName, newStyleName, platform
)
uniqueID = _updateUniqueIdNameRecord(varfont, nameIDs, platform)
if uniqueID:
nameIDs[NameID.UNIQUE_FONT_IDENTIFIER] = uniqueID
for nameID, string in nameIDs.items():
assert string, nameID
nametable.setName(string, nameID, *platform)
if "fvar" not in varfont:
nametable.removeNames(NameID.VARIATIONS_POSTSCRIPT_NAME_PREFIX) | [
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FederatedAI/FATE | 32540492623568ecd1afcb367360133616e02fa3 | python/fate_arch/storage/_table.py | python | StorageTableMeta.get_engine | (self) | return self.engine | [] | def get_engine(self):
return self.engine | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | mesos_master/datadog_checks/mesos_master/config_models/defaults.py | python | instance_kerberos_hostname | (field, value) | return get_default_field_value(field, value) | [] | def instance_kerberos_hostname(field, value):
return get_default_field_value(field, value) | [
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ldx/python-iptables | 542efdb739b4e3ef6f28274d23b506bf0027eec2 | iptc/xtables.py | python | xtables._fcheck_match_old | (self, match) | [] | def _fcheck_match_old(self, match):
# old API
if not match.final_check:
return
rv = _wrap_uintfn(match.final_check, match.mflags)
if rv:
raise XTablesError("%s.final_check() has failed" %
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/requests/utils.py | python | urldefragauth | (url) | return urlunparse((scheme, netloc, path, params, query, '')) | Given a url remove the fragment and the authentication part.
:rtype: str | Given a url remove the fragment and the authentication part. | [
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] | def urldefragauth(url):
"""
Given a url remove the fragment and the authentication part.
:rtype: str
"""
scheme, netloc, path, params, query, fragment = urlparse(url)
# see func:`prepend_scheme_if_needed`
if not netloc:
netloc, path = path, netloc
netloc = netloc.rsplit('@', 1)[-1]
return urlunparse((scheme, netloc, path, params, query, '')) | [
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dingjiansw101/AerialDetection | fbb7726bc0c6898fc00e50a418a3f5e0838b30d4 | DOTA_devkit/ImgSplit_multi_process.py | python | splitbase.__init__ | (self,
basepath,
outpath,
code = 'utf-8',
gap=512,
subsize=1024,
thresh=0.7,
choosebestpoint=True,
ext = '.png',
padding=True,
num_process=8
) | :param basepath: base path for dota data
:param outpath: output base path for dota data,
the basepath and outputpath have the similar subdirectory, 'images' and 'labelTxt'
:param code: encodeing format of txt file
:param gap: overlap between two patches
:param subsize: subsize of patch
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:param choosebestpoint: used to choose the first point for the
:param ext: ext for the image format
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:param outpath: output base path for dota data,
the basepath and outputpath have the similar subdirectory, 'images' and 'labelTxt'
:param code: encodeing format of txt file
:param gap: overlap between two patches
:param subsize: subsize of patch
:param thresh: the thresh determine whether to keep the instance if the instance is cut down in the process of split
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:param ext: ext for the image format
:param padding: if to padding the images so that all the images have the same size | [
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basepath,
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code = 'utf-8',
gap=512,
subsize=1024,
thresh=0.7,
choosebestpoint=True,
ext = '.png',
padding=True,
num_process=8
):
"""
:param basepath: base path for dota data
:param outpath: output base path for dota data,
the basepath and outputpath have the similar subdirectory, 'images' and 'labelTxt'
:param code: encodeing format of txt file
:param gap: overlap between two patches
:param subsize: subsize of patch
:param thresh: the thresh determine whether to keep the instance if the instance is cut down in the process of split
:param choosebestpoint: used to choose the first point for the
:param ext: ext for the image format
:param padding: if to padding the images so that all the images have the same size
"""
self.basepath = basepath
self.outpath = outpath
self.code = code
self.gap = gap
self.subsize = subsize
self.slide = self.subsize - self.gap
self.thresh = thresh
self.imagepath = os.path.join(self.basepath, 'images')
self.labelpath = os.path.join(self.basepath, 'labelTxt')
self.outimagepath = os.path.join(self.outpath, 'images')
self.outlabelpath = os.path.join(self.outpath, 'labelTxt')
self.choosebestpoint = choosebestpoint
self.ext = ext
self.padding = padding
self.pool = Pool(num_process)
print('padding:', padding)
# pdb.set_trace()
if not os.path.isdir(self.outpath):
os.mkdir(self.outpath)
if not os.path.isdir(self.outimagepath):
# pdb.set_trace()
os.mkdir(self.outimagepath)
if not os.path.isdir(self.outlabelpath):
os.mkdir(self.outlabelpath) | [
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asyml/texar | a23f021dae289a3d768dc099b220952111da04fd | texar/tf/core/layers.py | python | gelu | (input_tensor) | return input_tensor * cdf | Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
input_tensor: float Tensor to perform activation.
Returns:
`input_tensor` with the GELU activation applied. | Gaussian Error Linear Unit. | [
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"Linear",
"Unit",
"."
] | def gelu(input_tensor):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
input_tensor: float Tensor to perform activation.
Returns:
`input_tensor` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))
return input_tensor * cdf | [
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mozilla-services/socorro | 8bff4a90e9e3320eabe7e067adbe0e89f6a39ba7 | bin/permadelete_crash_data.py | python | cmd_permadelete | (ctx, crashidsfile) | Permanently deletes crash report data from crash storage.
The crashidsfile should be a complete path to the file with
crashids in it--one per line. This will skip lines prefixed
with a # treating them like comments. | Permanently deletes crash report data from crash storage. | [
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"data",
"from",
"crash",
"storage",
"."
] | def cmd_permadelete(ctx, crashidsfile):
"""
Permanently deletes crash report data from crash storage.
The crashidsfile should be a complete path to the file with
crashids in it--one per line. This will skip lines prefixed
with a # treating them like comments.
"""
if not os.path.exists(crashidsfile):
click.echo("File %s does not exist." % crashidsfile)
return 1
crashids = crashid_generator(crashidsfile)
for crashid in crashids:
click.echo("Working on %s" % crashid)
s3_delete(crashid)
es_delete(crashid)
click.echo("Done!") | [
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hbrobotics/ros_arduino_bridge | a960c8a88a6255d0104c92838045c06257c509d0 | ros_arduino_python/src/ros_arduino_python/arduino_driver.py | python | Arduino.reset_encoders | (self) | return self.execute_ack('r') | Reset the encoder counts to 0 | Reset the encoder counts to 0 | [
"Reset",
"the",
"encoder",
"counts",
"to",
"0"
] | def reset_encoders(self):
''' Reset the encoder counts to 0
'''
return self.execute_ack('r') | [
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"(",
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")",
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] | https://github.com/hbrobotics/ros_arduino_bridge/blob/a960c8a88a6255d0104c92838045c06257c509d0/ros_arduino_python/src/ros_arduino_python/arduino_driver.py#L277-L280 | |
TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iotexplorer/v20190423/models.py | python | GetFamilyDeviceUserListResponse.__init__ | (self) | r"""
:param UserList: 设备的用户列表
注意：此字段可能返回 null，表示取不到有效值。
:type UserList: list of DeviceUser
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | r"""
:param UserList: 设备的用户列表
注意：此字段可能返回 null，表示取不到有效值。
:type UserList: list of DeviceUser
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str | [
"r",
":",
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"设备的用户列表",
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"RequestId。",
":",
"type",
"RequestId",
":",
"str"
] | def __init__(self):
r"""
:param UserList: 设备的用户列表
注意：此字段可能返回 null，表示取不到有效值。
:type UserList: list of DeviceUser
:param RequestId: 唯一请求 ID，每次请求都会返回。定位问题时需要提供该次请求的 RequestId。
:type RequestId: str
"""
self.UserList = None
self.RequestId = None | [
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")",
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DamnWidget/anaconda | a9998fb362320f907d5ccbc6fcf5b62baca677c0 | anaconda_lib/linting/pycodestyle.py | python | normalize_paths | (value, parent=os.curdir) | return paths | Parse a comma-separated list of paths.
Return a list of absolute paths. | Parse a comma-separated list of paths. | [
"Parse",
"a",
"comma",
"-",
"separated",
"list",
"of",
"paths",
"."
] | def normalize_paths(value, parent=os.curdir):
"""Parse a comma-separated list of paths.
Return a list of absolute paths.
"""
if not value:
return []
if isinstance(value, list):
return value
paths = []
for path in value.split(','):
path = path.strip()
if '/' in path:
path = os.path.abspath(os.path.join(parent, path))
paths.append(path.rstrip('/'))
return paths | [
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HunterMcGushion/hyperparameter_hunter | 28b1d48e01a993818510811b82a677e0a7a232b2 | hyperparameter_hunter/experiments.py | python | BaseExperiment._empty_output_like | (self, like: pd.DataFrame, index=None, target_column=None) | return pd.DataFrame(0, index=index, columns=target_column) | Make an empty DataFrame of the same shape and with the same index as `like`, intended for
use with output :mod:`~hyperparameter_hunter.data.data_chunks`, like descendants of
:class:`~hyperparameter_hunter.data.data_chunks.prediction_chunks.BasePredictionChunk` and
:class:`~hyperparameter_hunter.data.data_chunks.target_chunks.BaseTargetChunk`
Parameters
----------
like: pd.DataFrame
DataFrame to use as the basis for the shape and index of the returned DataFrame. `like`
has no bearing on the column names of the returned DataFrame
index: Array-like, or None, default=None
If None, defaults to `like.index`. Else, defines the index of the returned DataFrame
target_column: List[str], or None, default=None
If None, defaults to the experiment's :attr:`target_column`. Else, defines the column
names of the returned DataFrame
Returns
-------
pd.DataFrame
Zero-filled DataFrame with index of `index` or `like.index` and column names of
`target_column` or :attr:`target_column` | Make an empty DataFrame of the same shape and with the same index as `like`, intended for
use with output :mod:`~hyperparameter_hunter.data.data_chunks`, like descendants of
:class:`~hyperparameter_hunter.data.data_chunks.prediction_chunks.BasePredictionChunk` and
:class:`~hyperparameter_hunter.data.data_chunks.target_chunks.BaseTargetChunk` | [
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":",
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".",
"data",
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"like",
"descendant... | def _empty_output_like(self, like: pd.DataFrame, index=None, target_column=None):
"""Make an empty DataFrame of the same shape and with the same index as `like`, intended for
use with output :mod:`~hyperparameter_hunter.data.data_chunks`, like descendants of
:class:`~hyperparameter_hunter.data.data_chunks.prediction_chunks.BasePredictionChunk` and
:class:`~hyperparameter_hunter.data.data_chunks.target_chunks.BaseTargetChunk`
Parameters
----------
like: pd.DataFrame
DataFrame to use as the basis for the shape and index of the returned DataFrame. `like`
has no bearing on the column names of the returned DataFrame
index: Array-like, or None, default=None
If None, defaults to `like.index`. Else, defines the index of the returned DataFrame
target_column: List[str], or None, default=None
If None, defaults to the experiment's :attr:`target_column`. Else, defines the column
names of the returned DataFrame
Returns
-------
pd.DataFrame
Zero-filled DataFrame with index of `index` or `like.index` and column names of
`target_column` or :attr:`target_column`"""
index = like.index.copy() if index is None else index
target_column = self.target_column if target_column is None else target_column
return pd.DataFrame(0, index=index, columns=target_column) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/geometry/line.py | python | Ray.contains | (self, o) | return False | Is other GeometryEntity contained in this Ray? | Is other GeometryEntity contained in this Ray? | [
"Is",
"other",
"GeometryEntity",
"contained",
"in",
"this",
"Ray?"
] | def contains(self, o):
"""Is other GeometryEntity contained in this Ray?"""
if isinstance(o, Ray):
return (Point.is_collinear(self.p1, self.p2, o.p1, o.p2) and
self.xdirection == o.xdirection and
self.ydirection == o.ydirection)
elif isinstance(o, Segment):
return o.p1 in self and o.p2 in self
elif isinstance(o, Point):
if Point.is_collinear(self.p1, self.p2, o):
if self.xdirection is S.Infinity:
rv = o.x >= self.source.x
elif self.xdirection is S.NegativeInfinity:
rv = o.x <= self.source.x
elif self.ydirection is S.Infinity:
rv = o.y >= self.source.y
else:
rv = o.y <= self.source.y
if rv == True or rv == False:
return bool(rv)
raise Undecidable(
'Cannot determine if %s is in %s' % (o, self))
else:
# Points are not collinear, so the rays are not parallel
# and hence it is impossible for self to contain o
return False
# No other known entity can be contained in a Ray
return False | [
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MartinThoma/algorithms | 6199cfa3446e1056c7b4d75ca6e306e9e56fd95b | ML/nlp/reuters_mlp.py | python | main | (data_module) | Load data, train model and evaluate it. | Load data, train model and evaluate it. | [
"Load",
"data",
"train",
"model",
"and",
"evaluate",
"it",
"."
] | def main(data_module):
"""Load data, train model and evaluate it."""
data = data_module.load_data()
model = create_model(data_module.n_classes, (data['x_train'].shape[1], ))
print(model.summary())
optimizer = get_optimizer({'optimizer': {'initial_lr': 0.001}})
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=[precision, recall, f1, accuracy])
t0 = time.time()
model.fit(data['x_train'], data['y_train'],
batch_size=32,
epochs=30,
validation_data=(data['x_test'], data['y_test']),
shuffle=True,
# callbacks=callbacks
)
t1 = time.time()
# res = get_tptnfpfn(model, data)
preds = model.predict(data['x_test'])
preds[preds >= 0.5] = 1
preds[preds < 0.5] = 0
t2 = time.time()
print(("{clf_name:<30}: {acc:0.2f}% {f1:0.2f}% in {train_time:0.2f}s "
"train / {test_time:0.2f}s test")
.format(clf_name="MLP",
acc=(accuracy_score(y_true=data['y_test'], y_pred=preds) * 100),
f1=(fbeta_score(y_true=data['y_test'], y_pred=preds, beta=1, average="weighted") * 100),
train_time=(t1 - t0),
test_time=(t2 - t1))) | [
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theislab/scvelo | 1805ab4a72d3f34496f0ef246500a159f619d3a2 | scvelo/tools/rank_velocity_genes.py | python | velocity_clusters | (
data,
vkey="velocity",
match_with="clusters",
sort_by="velocity_pseudotime",
resolution=None,
min_likelihood=None,
copy=False,
) | return adata if copy else None | Computes velocity clusters via louvain on velocities.
.. code:: python
scv.tl.velocity_clusters(adata)
scv.pl.scatter(adata, color='velocity_clusters')
.. image:: https://user-images.githubusercontent.com/31883718/69625627-484dc480-1047-11ea-847f-6607a3430427.png
:width: 600px
Arguments
----------
data : :class:`~anndata.AnnData`
Annotated data matrix.
vkey: `str` (default: `'velocity'`)
Key of velocities computed in `tl.velocity`
match_with : `int`, optional (default: 100)
The number of genes that appear in the returned tables.
match_with: `str` (default: `'clusters'`)
Match the names of the velocity clusters with the names of this key (.obs).
sort_by: `str` or `None` (default: `'dpt_pseudotime'`)
Sort velocity clusters by this key (.obs).
resolution: `float` (default: 0.7)
Resolution for louvain modularity.
min_likelihood: `float` between `0` and `1` or `None` (default: `None`)
Only rank velocity of genes with a likelihood higher than min_likelihood.
copy: `bool` (default: `False`)
Return a copy instead of writing to data.
Returns
-------
velocity_clusters : `.obs`
Clusters obtained from applying louvain modularity on velocity expression. | Computes velocity clusters via louvain on velocities. | [
"Computes",
"velocity",
"clusters",
"via",
"louvain",
"on",
"velocities",
"."
] | def velocity_clusters(
data,
vkey="velocity",
match_with="clusters",
sort_by="velocity_pseudotime",
resolution=None,
min_likelihood=None,
copy=False,
):
"""Computes velocity clusters via louvain on velocities.
.. code:: python
scv.tl.velocity_clusters(adata)
scv.pl.scatter(adata, color='velocity_clusters')
.. image:: https://user-images.githubusercontent.com/31883718/69625627-484dc480-1047-11ea-847f-6607a3430427.png
:width: 600px
Arguments
----------
data : :class:`~anndata.AnnData`
Annotated data matrix.
vkey: `str` (default: `'velocity'`)
Key of velocities computed in `tl.velocity`
match_with : `int`, optional (default: 100)
The number of genes that appear in the returned tables.
match_with: `str` (default: `'clusters'`)
Match the names of the velocity clusters with the names of this key (.obs).
sort_by: `str` or `None` (default: `'dpt_pseudotime'`)
Sort velocity clusters by this key (.obs).
resolution: `float` (default: 0.7)
Resolution for louvain modularity.
min_likelihood: `float` between `0` and `1` or `None` (default: `None`)
Only rank velocity of genes with a likelihood higher than min_likelihood.
copy: `bool` (default: `False`)
Return a copy instead of writing to data.
Returns
-------
velocity_clusters : `.obs`
Clusters obtained from applying louvain modularity on velocity expression.
""" # noqa E501
adata = data.copy() if copy else data
logg.info("computing velocity clusters", r=True)
tmp_filter = ~np.isnan(adata.layers[vkey].sum(0))
if f"{vkey}_genes" in adata.var.keys():
tmp_filter &= np.array(adata.var[f"{vkey}_genes"].values, dtype=bool)
if "unspliced" in adata.layers.keys():
n_counts = (adata.layers["unspliced"] > 0).sum(0)
n_counts = n_counts.A1 if issparse(adata.layers["unspliced"]) else n_counts
min_counts = min(50, np.percentile(n_counts, 50))
tmp_filter &= np.ravel(n_counts > min_counts)
if "r2" in adata.var.keys():
r2 = adata.var.velocity_r2
min_r2 = np.percentile(r2[r2 > 0], 50)
tmp_filter &= r2 > min_r2
if "dispersions_norm" in adata.var.keys():
dispersions = adata.var.dispersions_norm
min_dispersion = np.percentile(dispersions, 20)
tmp_filter &= dispersions > min_dispersion
if "fit_likelihood" in adata.var.keys() and min_likelihood is not None:
tmp_filter &= adata.var["fit_likelihood"] > min_likelihood
from anndata import AnnData
vdata = AnnData(adata.layers[vkey][:, tmp_filter])
vdata.obs = adata.obs.copy()
vdata.var = adata.var[tmp_filter].copy()
if "highly_variable" in vdata.var.keys():
vdata.var["highly_variable"] = np.array(
vdata.var["highly_variable"], dtype=bool
)
import scanpy as sc
logg.switch_verbosity("off", module="scanpy")
sc.pp.pca(vdata, n_comps=20, svd_solver="arpack")
sc.pp.neighbors(vdata, n_pcs=20)
sc.tl.louvain(vdata, resolution=0.7 if resolution is None else resolution)
logg.switch_verbosity("on", module="scanpy")
if sort_by == "velocity_pseudotime" and sort_by not in adata.obs.keys():
velocity_pseudotime(adata, vkey=vkey)
if sort_by in vdata.obs.keys():
vc = vdata.obs["louvain"]
vc_cats = vc.cat.categories
mean_times = [np.mean(vdata.obs[sort_by][vc == cat]) for cat in vc_cats]
vdata.obs["louvain"].cat.reorder_categories(
vc_cats[np.argsort(mean_times)], inplace=True
)
if isinstance(match_with, str) and match_with in adata.obs.keys():
from .utils import most_common_in_list
vc = vdata.obs["louvain"]
cats_nums = {cat: 0 for cat in adata.obs[match_with].cat.categories}
for cat in vc.cat.categories:
cells_in_cat = np.where(vc == cat)[0]
new_cat = most_common_in_list(adata.obs[match_with][cells_in_cat])
cats_nums[new_cat] += 1
vc = vc.cat.rename_categories({cat: f"{new_cat} ({cats_nums[new_cat]})"})
vdata.obs["louvain"] = vc
else:
vdata.obs["louvain"].cat.categories = np.arange(
len(vdata.obs["louvain"].cat.categories)
)
adata.obs[f"{vkey}_clusters"] = vdata.obs["louvain"].copy()
del vdata
logg.info(" finished", time=True, end=" " if settings.verbosity > 2 else "\n")
logg.hint(
"added \n"
f" '{vkey}_clusters', "
f"clusters based on louvain modularity on velocity vector field (adata.obs)"
)
return adata if copy else None | [
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intel/virtual-storage-manager | 00706ab9701acbd0d5e04b19cc80c6b66a2973b8 | source/vsm/vsm/image/glance.py | python | GlanceImageService._is_image_available | (context, image) | return str(user_id) == str(context.user_id) | Check image availability.
This check is needed in case Nova and Glance are deployed
without authentication turned on. | Check image availability. | [
"Check",
"image",
"availability",
"."
] | def _is_image_available(context, image):
"""Check image availability.
This check is needed in case Nova and Glance are deployed
without authentication turned on.
"""
# The presence of an auth token implies this is an authenticated
# request and we need not handle the noauth use-case.
if hasattr(context, 'auth_token') and context.auth_token:
return True
if image.is_public or context.is_admin:
return True
properties = image.properties
if context.project_id and ('owner_id' in properties):
return str(properties['owner_id']) == str(context.project_id)
if context.project_id and ('project_id' in properties):
return str(properties['project_id']) == str(context.project_id)
try:
user_id = properties['user_id']
except KeyError:
return False
return str(user_id) == str(context.user_id) | [
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google/pytype | fa43edc95dd42ade6e3147d6580d63e778c9d506 | pytype/tools/analyze_project/parse_args.py | python | parse_jobs | (s) | Parse the --jobs option. | Parse the --jobs option. | [
"Parse",
"the",
"--",
"jobs",
"option",
"."
] | def parse_jobs(s):
"""Parse the --jobs option."""
if s == 'auto':
n = _auto_detect_cpus()
return n if n else 1
elif s is not None:
return int(s)
else:
return None | [
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"return",... | https://github.com/google/pytype/blob/fa43edc95dd42ade6e3147d6580d63e778c9d506/pytype/tools/analyze_project/parse_args.py#L22-L30 | ||
huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/utils/dummy_pt_objects.py | python | GPTJForCausalLM.__init__ | (self, *args, **kwargs) | [] | def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | [
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