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RasaHQ/rasa | 54823b68c1297849ba7ae841a4246193cd1223a1 | rasa/core/featurizers/precomputation.py | python | MessageContainerForCoreFeaturization.derive_messages_from_domain_and_add | (self, domain: Domain) | Adds all lookup table entries that can be derived from the domain.
That is, all action names, action texts, and intents defined in the domain
will be turned into a (separate) messages and added to this lookup table.
Args:
domain: the domain from which we extract the substates | Adds all lookup table entries that can be derived from the domain. | [
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] | def derive_messages_from_domain_and_add(self, domain: Domain) -> None:
"""Adds all lookup table entries that can be derived from the domain.
That is, all action names, action texts, and intents defined in the domain
will be turned into a (separate) messages and added to this lookup table.
Args:
domain: the domain from which we extract the substates
"""
if (
domain.action_texts
and domain.action_names_or_texts[-len(domain.action_texts) :]
!= domain.action_texts
):
raise NotImplementedError(
"We assumed that domain's `action_names_or_texts` start with a list of "
"all action names, followed by the action texts. "
"Please update the code to grab the action_name and action_texts from "
"the domain correctly."
)
action_texts = domain.action_texts
action_names = domain.action_names_or_texts[
slice(0, -len(domain.action_texts) if domain.action_texts else None)
]
for key_attribute, actions in [
(ACTION_NAME, action_names),
(ACTION_TEXT, action_texts),
]:
for action in actions:
self.add(Message({key_attribute: action}))
for intent in domain.intent_properties.keys():
self.add(Message({INTENT: intent})) | [
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horazont/aioxmpp | c701e6399c90a6bb9bec0349018a03bd7b644cde | aioxmpp/xso/model.py | python | ChildList.from_events | (self, instance, ev_args, ctx) | return obj | Like :meth:`.Child.from_events`, but instead of replacing the attribute
value, the new object is appended to the list. | Like :meth:`.Child.from_events`, but instead of replacing the attribute
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"""
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value, the new object is appended to the list.
"""
obj = yield from self._process(instance, ev_args, ctx)
self.__get__(instance, type(instance)).append(obj)
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securityclippy/elasticintel | aa08d3e9f5ab1c000128e95161139ce97ff0e334 | ingest_feed_lambda/pandas/plotting/_misc.py | python | bootstrap_plot | (series, fig=None, size=50, samples=500, **kwds) | return fig | Bootstrap plot.
Parameters:
-----------
series: Time series
fig: matplotlib figure object, optional
size: number of data points to consider during each sampling
samples: number of times the bootstrap procedure is performed
kwds: optional keyword arguments for plotting commands, must be accepted
by both hist and plot
Returns:
--------
fig: matplotlib figure | Bootstrap plot. | [
"Bootstrap",
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] | def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""Bootstrap plot.
Parameters:
-----------
series: Time series
fig: matplotlib figure object, optional
size: number of data points to consider during each sampling
samples: number of times the bootstrap procedure is performed
kwds: optional keyword arguments for plotting commands, must be accepted
by both hist and plot
Returns:
--------
fig: matplotlib figure
"""
import random
import matplotlib.pyplot as plt
# random.sample(ndarray, int) fails on python 3.3, sigh
data = list(series.values)
samplings = [random.sample(data, size) for _ in range(samples)]
means = np.array([np.mean(sampling) for sampling in samplings])
medians = np.array([np.median(sampling) for sampling in samplings])
midranges = np.array([(min(sampling) + max(sampling)) * 0.5
for sampling in samplings])
if fig is None:
fig = plt.figure()
x = lrange(samples)
axes = []
ax1 = fig.add_subplot(2, 3, 1)
ax1.set_xlabel("Sample")
axes.append(ax1)
ax1.plot(x, means, **kwds)
ax2 = fig.add_subplot(2, 3, 2)
ax2.set_xlabel("Sample")
axes.append(ax2)
ax2.plot(x, medians, **kwds)
ax3 = fig.add_subplot(2, 3, 3)
ax3.set_xlabel("Sample")
axes.append(ax3)
ax3.plot(x, midranges, **kwds)
ax4 = fig.add_subplot(2, 3, 4)
ax4.set_xlabel("Mean")
axes.append(ax4)
ax4.hist(means, **kwds)
ax5 = fig.add_subplot(2, 3, 5)
ax5.set_xlabel("Median")
axes.append(ax5)
ax5.hist(medians, **kwds)
ax6 = fig.add_subplot(2, 3, 6)
ax6.set_xlabel("Midrange")
axes.append(ax6)
ax6.hist(midranges, **kwds)
for axis in axes:
plt.setp(axis.get_xticklabels(), fontsize=8)
plt.setp(axis.get_yticklabels(), fontsize=8)
return fig | [
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joxeankoret/pyew | 8eb3e49a9bf57c0787fa79ecae0671129ef3f2e8 | vtrace/__init__.py | python | Trace.release | (self) | Release resources for this tracer. This API should be called
once you are done with the trace. | Release resources for this tracer. This API should be called
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'''
Release resources for this tracer. This API should be called
once you are done with the trace.
'''
if not self._released:
self._released = True
if self.attached:
self.detach()
self._cleanupResources() | [
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mesalock-linux/mesapy | ed546d59a21b36feb93e2309d5c6b75aa0ad95c9 | lib-python/2.7/BaseHTTPServer.py | python | BaseHTTPRequestHandler.address_string | (self) | return socket.getfqdn(host) | Return the client address formatted for logging.
This version looks up the full hostname using gethostbyaddr(),
and tries to find a name that contains at least one dot. | Return the client address formatted for logging. | [
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] | def address_string(self):
"""Return the client address formatted for logging.
This version looks up the full hostname using gethostbyaddr(),
and tries to find a name that contains at least one dot.
"""
host, port = self.client_address[:2]
return socket.getfqdn(host) | [
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xjsender/haoide | 717dd706db1169bfc41e818ac6fc6cd9a0aef12d | util.py | python | list2csv | (file_path, records, NOT_INCLUDED_COLUMNS=["urls", "attributes"]) | convert simple dict in list to csv
Arguments:
* records -- [{"1": 1}, {"2": 2}] | convert simple dict in list to csv | [
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"""convert simple dict in list to csv
Arguments:
* records -- [{"1": 1}, {"2": 2}]
"""
# If records size is 0, just return
if len(records) == 0: return "No Elements"
headers = [k.encode('utf-8') for k in records[0] if k not in NOT_INCLUDED_COLUMNS]
with open(file_path, "wb") as fp:
fp.write(b",".join(headers) + b"\n")
for record in records:
values = []
for k in headers:
strk = str(k, encoding="utf-8")
if strk not in record:
values.append(b"")
else:
values.append(('"%s"' % none_value(record[strk])).encode("utf-8"))
fp.write(b",".join(values) + b"\n") | [
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kivy/kivy-designer | 20343184a28c2851faf0c1ab451d0286d147a441 | designer/components/playground_size_selector.py | python | PlaygroundSizeSelector.on_playground | (self, *_) | Create a
:class:
`~designer.components.playground_size_selector.PlaygroundSizeView`
for the current playground. | Create a
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`~designer.components.playground_size_selector.PlaygroundSizeView`
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] | def on_playground(self, *_):
'''Create a
:class:
`~designer.components.playground_size_selector.PlaygroundSizeView`
for the current playground.
'''
self.view = PlaygroundSizeView(selected_size=self.playground.size)
self.view.bind(selected_size=self._update_playground)
self.view.bind(selected_size_name=self.setter('text'))
self.text = self.view.selected_size_name | [
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/util/templatetags/djblets_email.py | python | condense | (context, nodelist, max_newlines=3) | return text | Condenses a block of text.
This will ensure that there are never more than the given number of
consecutive newlines. It's particularly useful when formatting plain text
output, to avoid issues with template tags adding unwanted newlines. | Condenses a block of text. | [
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] | def condense(context, nodelist, max_newlines=3):
"""Condenses a block of text.
This will ensure that there are never more than the given number of
consecutive newlines. It's particularly useful when formatting plain text
output, to avoid issues with template tags adding unwanted newlines.
"""
text = nodelist.render(context).strip()
text = re.sub(r'\n{%d,}' % (max_newlines + 1), '\n' * max_newlines, text)
return text | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/op4/op4.py | python | OP4._read_real_sparse_ascii_new | (self, op4, iline, nrows, ncols, line_size, line,
dtype, is_big_mat) | return A, iline | Reads a sparse real ASCII matrix | Reads a sparse real ASCII matrix | [
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"real",
"ASCII",
"matrix"
] | def _read_real_sparse_ascii_new(self, op4, iline, nrows, ncols, line_size, line,
dtype, is_big_mat):
"""Reads a sparse real ASCII matrix"""
self.log.debug('_read_real_sparse_ascii')
rows = []
cols = []
entries = []
nloops = 0
was_broken = False
while 1:
if nloops > 0 and not was_broken:
line = op4.readline().rstrip()
iline += 1
was_broken = False
icol, irow, nwords = line.split()
icol = int(icol)
if icol > ncols:
break
irow = int(irow)
nwords = int(nwords)
# This loop condition is overly complicated, but the first time
# it will always execute.
# Later if there is a sparse continuation line marker of
# 1 (very large) integer, there will be no scientific notation value.
# There also may be another sparse marker with 2 values. These are not large.
# The scientific check prevents you from getting stuck in an infinite
# loop b/c no lines are read if there was one float value.
# The check for 1 (or 2) integers is to prevent the check for 3 integers
# which starts a new column. We only want to continue a column.
run_loop = True
sline = line.strip().split()
# next sparse entry
jrow1 = len(rows)
while (len(sline) == 1 or len(sline) == 2) and 'E' not in line or run_loop:
if is_big_mat:
irow, iline = self._get_irow_big_ascii(op4, iline, line, sline, irow)
else:
irow, iline = self._get_irow_small_ascii(op4, iline, line, sline, irow)
run_loop = False
#is_done_reading_row = False
while nwords:
line = op4.readline().rstrip()
iline += 1
nwords_in_line = line.count('E')
if nwords_in_line == 0:
was_broken = True
break
irows = list(range(irow, irow + nwords_in_line))
n = 0
for unused_i in range(nwords_in_line):
word = line[n:n + line_size]
entries.append(word)
n += line_size
rows.extend(irows)
#icols = [icol] * nwords_in_line
#cols.extend(icols)
irow += nwords_in_line
#assert len(rows) == len(cols), 'rows=%s\ncols=%s' % (rows, cols)
#assert len(rows) == len(entries)
nwords -= nwords_in_line
sline = line.strip().split()
nloops += 1
jrow2 = len(rows)
icols = [icol] * (jrow2 - jrow1)
cols.append(icols)
op4.readline()
iline += 1
#if rows == []: # NULL matrix
#raise NotImplementedError()
cols = np.hstack(cols)
rows = array(rows, dtype='int32') - 1
cols = array(cols, dtype='int32') - 1
A = coo_matrix((entries, (rows, cols)), shape=(nrows, ncols), dtype=dtype)
#print("type = %s %s" % (type(A),type(A.toarray())))
#A = A.toarray()
return A, iline | [
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pyside/PySide | bcf6058e9d0e8513f58a0f1062b428ea7eb03f0c | doc/inheritance_diagram.py | python | InheritanceGraph._class_info | (self, classes, show_builtins, parts) | return all_classes.values() | Return name and bases for all classes that are ancestors of
*classes*.
*parts* gives the number of dotted name parts that is removed from the
displayed node names. | Return name and bases for all classes that are ancestors of
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"""Return name and bases for all classes that are ancestors of
*classes*.
*parts* gives the number of dotted name parts that is removed from the
displayed node names.
"""
all_classes = {}
builtins = __builtins__.values()
def recurse(cls):
if not show_builtins and cls in builtins:
return
nodename = self.class_name(cls, parts)
fullname = self.class_name(cls, 0)
baselist = []
all_classes[cls] = (nodename, fullname, baselist)
for base in cls.__bases__:
if not show_builtins and base in builtins:
continue
if base.__name__ == "Object" and base.__module__ == "Shiboken":
continue
baselist.append(self.class_name(base, parts))
if base not in all_classes:
recurse(base)
for cls in classes:
recurse(cls)
return all_classes.values() | [
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SanPen/GridCal | d3f4566d2d72c11c7e910c9d162538ef0e60df31 | src/GridCal/Engine/IO/plexos.py | python | PlexosElement.__init__ | (self, name) | Generic element constructor
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"""
Generic element constructor
:param name: Name of the element
"""
self.name = name | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/util/versions.py | python | BaseVersion.__lt__ | (self, other) | return self.tuple() < other.tuple() | [] | def __lt__(self, other):
if not hasattr(other, "tuple"):
raise ValueError("Can't compare %r with %r" % (self, other))
return self.tuple() < other.tuple() | [
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geekori/pyqt5 | 49b2538fa5afe43b3b2bdadaa0560d0d6ef4924c | src/table_tree/ColumnSort.py | python | ColumnSort.order | (self) | [] | def order(self):
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self.orderType = Qt.AscendingOrder
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self.orderType = Qt.DescendingOrder
self.tableWidget.sortItems(2,self.orderType) | [
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/team.py | python | GroupMembersRemoveError.is_group_not_in_team | (self) | return self._tag == 'group_not_in_team' | Check if the union tag is ``group_not_in_team``.
:rtype: bool | Check if the union tag is ``group_not_in_team``. | [
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] | def is_group_not_in_team(self):
"""
Check if the union tag is ``group_not_in_team``.
:rtype: bool
"""
return self._tag == 'group_not_in_team' | [
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salabim/salabim | e0de846b042daf2dc71aaf43d8adc6486b57f376 | salabim.py | python | Environment.is_dark | (self, colorspec) | Arguments
---------
colorspec : colorspec
color to check
Returns
-------
: bool
True, if the colorspec is dark (rather black than white) |n|
False, if the colorspec is light (rather white than black |n|
if colorspec has alpha=0 (total transparent), the background_color will be tested | Arguments
---------
colorspec : colorspec
color to check | [
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"""
Arguments
---------
colorspec : colorspec
color to check
Returns
-------
: bool
True, if the colorspec is dark (rather black than white) |n|
False, if the colorspec is light (rather white than black |n|
if colorspec has alpha=0 (total transparent), the background_color will be tested
"""
rgba = self.colorspec_to_tuple(colorspec)
if rgba[3] == 0:
return self.is_dark(self.colorspec_to_tuple(("bg", 255)))
luma = ((0.299 * rgba[0]) + (0.587 * rgba[1]) + (0.114 * rgba[2])) / 255
if luma > 0.5:
return False
else:
return True | [
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/python27/1.0/lib/noarch/OpenSSL/SSL.py | python | Context.add_client_ca | (self, certificate_authority) | Add the CA certificate to the list of preferred signers for this
context.
The list of certificate authorities will be sent to the client when the
server requests a client certificate.
:param certificate_authority: certificate authority's X509 certificate.
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"""
Add the CA certificate to the list of preferred signers for this
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The list of certificate authorities will be sent to the client when the
server requests a client certificate.
:param certificate_authority: certificate authority's X509 certificate.
:return: None
"""
if not isinstance(certificate_authority, X509):
raise TypeError("certificate_authority must be an X509 instance")
add_result = _lib.SSL_CTX_add_client_CA(
self._context, certificate_authority._x509)
if not add_result:
# TODO: This is untested.
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fkie/multimaster_fkie | 3d23df29d25d71a75c66bbd3cc6e9cbb255724d8 | fkie_node_manager/src/fkie_node_manager/main_window.py | python | MainWindow.on_start_robot_clicked | (self) | Tries to start the master_discovery node on the machine requested by a dialog. | Tries to start the master_discovery node on the machine requested by a dialog. | [
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'''
Tries to start the master_discovery node on the machine requested by a dialog.
'''
# get the history list
user_list = [self.userComboBox.itemText(i) for i in reversed(range(self.userComboBox.count()))]
user_list.insert(0, 'last used')
params_optional = {'Discovery type': {':type': 'string', ':value': ['master_discovery', 'zeroconf']},
'ROS Master Name': {':type': 'string', ':value': 'autodetect'},
'ROS Master URI': {':type': 'string', ':value': 'ROS_MASTER_URI'},
'Robot hosts': {':type': 'string', ':value': ''},
'Username': {':type': 'string', ':value': user_list},
'MCast Group': {':type': 'string', ':value': '226.0.0.0'},
'Heartbeat [Hz]': {':type': 'float', ':value': 0.5}
}
params = {'Host': {':type': 'string', ':value': 'localhost'},
'Network(0..99)': {':type': 'int', ':value': str(self.mcast_port - 11511)},
'Start sync': {':type': 'bool', ':value': nm.settings().start_sync_with_discovery},
'Start daemon': {':type': 'bool', ':value': nm.settings().start_daemon_with_discovery},
'Optional Parameter': params_optional
}
dia = ParameterDialog(params, sidebar_var='Host', store_geometry="start_robot_dialog")
dia.setFilterVisible(False)
dia.setWindowTitle('Start discovery')
dia.setFocusField('Host')
if dia.exec_():
try:
params = dia.getKeywords(only_changed=False)
hostnames = params['Host'] if isinstance(params['Host'], list) else [params['Host']]
port = params['Network(0..99)']
start_sync = params['Start sync']
start_daemon = params['Start daemon']
discovery_type = params['Optional Parameter']['Discovery type']
mastername = 'autodetect'
masteruri = 'ROS_MASTER_URI'
if len(hostnames) < 2:
mastername = params['Optional Parameter']['ROS Master Name']
masteruri = params['Optional Parameter']['ROS Master URI']
robot_hosts = params['Optional Parameter']['Robot hosts']
username = params['Optional Parameter']['Username']
mcast_group = params['Optional Parameter']['MCast Group']
heartbeat_hz = params['Optional Parameter']['Heartbeat [Hz]']
if robot_hosts:
robot_hosts = robot_hosts.replace(' ', ',')
robot_hosts = robot_hosts.replace(',,', ',')
robot_hosts = robot_hosts.replace('[', '')
robot_hosts = robot_hosts.replace(']', '')
for hostname in hostnames:
try:
args = []
if port is not None and port and int(port) < 100 and int(port) >= 0:
args.append('_mcast_port:=%s' % (11511 + int(port)))
else:
args.append('_mcast_port:=%s' % (11511))
if not mastername == 'autodetect':
args.append('_name:=%s' % (mastername))
args.append('_mcast_group:=%s' % mcast_group)
args.append('_robot_hosts:=[%s]' % robot_hosts)
args.append('_heartbeat_hz:=%s' % heartbeat_hz)
# TODO: remove the name parameter from the ROS parameter server
usr = username
if username == 'last used':
usr = nm.settings().host_user(hostname)
else:
nm.settings().set_host_user(hostname, usr)
muri = None if masteruri == 'ROS_MASTER_URI' else utf8(masteruri)
# stop if master_discovery already running
self._append_stop_for('/%s' % utf8(discovery_type), hostname, muri, self._progress_queue)
self._progress_queue_sync.start()
self._progress_queue.add2queue(utf8(uuid.uuid4()),
'start discovering on %s' % hostname,
nm.starter().runNodeWithoutConfig,
{'host': utf8(hostname),
'package': 'fkie_master_discovery',
'binary': utf8(discovery_type),
'name': utf8(discovery_type),
'args': args,
'masteruri': muri,
'use_nmd': False,
'auto_pw_request': False,
'user': usr
})
# start the master sync with default settings
if start_sync:
if nm.is_local(hostname):
default_sync_args = ["_interface_url:='.'",
'_sync_topics_on_demand:=False',
'_ignore_hosts:=[]', '_sync_hosts:=[]',
'_ignore_nodes:=[]', '_sync_nodes:=[]',
'_ignore_topics:=[]', '_sync_topics:=[]',
'_ignore_services:=[]', '_sync_services:=[]',
'_sync_remote_nodes:=False']
self._append_stop_for('/master_sync', hostname, muri, self._progress_queue_sync)
self._progress_queue_sync.start()
self._progress_queue.add2queue(utf8(uuid.uuid4()),
'start sync on %s' % hostname,
nm.starter().runNodeWithoutConfig,
{'host': utf8(hostname),
'package': 'fkie_master_sync',
'binary': 'master_sync',
'name': 'master_sync',
'args': default_sync_args,
'masteruri': muri,
'use_nmd': False,
'auto_pw_request': False,
'user': usr
})
else:
if hostname not in self._syncs_to_start:
self._syncs_to_start.append(hostname)
if start_daemon:
# local daemon will be started anyway
if not nm.is_local(hostname):
if hostname not in self._daemons_to_start:
self._daemons_to_start.append(hostname)
except (Exception, nm.StartException) as e:
import traceback
print(traceback.format_exc(1))
rospy.logwarn("Error while start master_discovery for %s: %s" % (utf8(hostname), utf8(e)))
MessageBox.warning(self, "Start error",
'Error while start master_discovery',
utf8(e))
self._progress_queue.start()
except Exception as e:
MessageBox.warning(self, "Start error",
'Error while parse parameter',
utf8(e)) | [
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rossant/galry | 6201fa32fb5c9ef3cea700cc22caf52fb69ebe31 | galry/glrenderer.py | python | ShaderManager.get_attribute_location | (self, name) | return gl.glGetAttribLocation(self.program, name) | Return the location of an attribute after the shaders have compiled. | Return the location of an attribute after the shaders have compiled. | [
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"""Return the location of an attribute after the shaders have compiled."""
return gl.glGetAttribLocation(self.program, name) | [
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matrix-org/synapse | 8e57584a5859a9002759963eb546d523d2498a01 | synapse/storage/database.py | python | DatabasePool.simple_search_list | (
self,
table: str,
term: Optional[str],
col: str,
retcols: Collection[str],
desc="simple_search_list",
) | return await self.runInteraction(
desc,
self.simple_search_list_txn,
table,
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col,
retcols,
db_autocommit=True,
) | Executes a SELECT query on the named table, which may return zero or
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table: the table name
term: term for searching the table matched to a column.
col: column to query term should be matched to
retcols: the names of the columns to return
Returns:
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table: str,
term: Optional[str],
col: str,
retcols: Collection[str],
desc="simple_search_list",
) -> Optional[List[Dict[str, Any]]]:
"""Executes a SELECT query on the named table, which may return zero or
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Args:
table: the table name
term: term for searching the table matched to a column.
col: column to query term should be matched to
retcols: the names of the columns to return
Returns:
A list of dictionaries or None.
"""
return await self.runInteraction(
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term,
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db_autocommit=True,
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pyocd/pyOCD | 7d164d99e816c4ef6c99f257014543188a0ca5a6 | pyocd/trace/swv.py | python | SWVReader.__init__ | (self, session, core_number=0, lock=None) | ! @brief Constructor.
@param self
@param session The Session instance.
@param core_number The number of the core being traced. Default is core 0. | ! | [
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] | def __init__(self, session, core_number=0, lock=None):
"""! @brief Constructor.
@param self
@param session The Session instance.
@param core_number The number of the core being traced. Default is core 0.
"""
super(SWVReader, self).__init__()
self.name = "SWVReader"
self.daemon = True
self._session = session
self._core_number = core_number
self._shutdown_event = threading.Event()
self._swo_clock = 0
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | chap19/monitor/monitor/monitor/db/sqlalchemy/models.py | python | MonitorBase.update | (self, values) | Make the model object behave like a dict. | Make the model object behave like a dict. | [
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pyocd/pyOCD | 7d164d99e816c4ef6c99f257014543188a0ca5a6 | pyocd/flash/flash.py | python | Flash.get_page_info | (self, addr) | return info | !
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] | def get_page_info(self, addr):
"""!
@brief Get info about the page that contains this address.
"""
assert self.region is not None
if not self.region.contains_address(addr):
return None
info = PageInfo(
program_weight=self.region.program_page_weight,
size=self.region.page_size,
base_addr=align_down(addr, self.region.page_size)
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1040003585/WebScrapingWithPython | a770fa5b03894076c8c9539b1ffff34424ffc016 | ResourceCode/wswp-places-c573d29efa3a/web2py/gluon/contrib/memdb.py | python | Set.select | (self, *fields, **attributes) | Always returns a Rows object, even if it may be empty | Always returns a Rows object, even if it may be empty | [
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return self._select_except()
else:
raise SyntaxError('select arguments not supported') | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/io/cp2k/outputs.py | python | Cp2kOutput.is_metal | (self) | return False | Was a band gap found? i.e. is it a metal | Was a band gap found? i.e. is it a metal | [
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tflearn/tflearn | db5176773299b67a2a75c5889fb2aba7fd0fea8a | tflearn/estimators/cluster/kmeans.py | python | KMeansBase.all_distances_ | (self) | return self.session.run(self._all_scores) | Distances of each point to each cluster center. | Distances of each point to each cluster center. | [
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""" Distances of each point to each cluster center. """
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Pyomo/pyomo | dbd4faee151084f343b893cc2b0c04cf2b76fd92 | pyomo/core/base/block.py | python | PseudoMap.values | (self) | Generator returning the components defined on the Block | Generator returning the components defined on the Block | [
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Generator returning the components defined on the Block
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# If there is no ctype, then we will just iterate over
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else:
walker = (obj for obj, idx in self._block._decl_order
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else:
# The user specified a desired ctype; we will leverage
# the _ctypewalker generator to walk the underlying linked
# list and just return the desired objects (again, in
# decl order)
if _active is None:
walker = (obj for obj in self._ctypewalker())
else:
walker = (obj for obj in self._ctypewalker()
if obj.active == _active)
# If the user wants this sorted by name, then there is
# nothing we can do to save memory: we must create the whole
# list (so we can sort it) and then iterate over the sorted
# temporary list
if self._sorted:
return (obj for obj in sorted(walker, key=lambda _x: _x.local_name))
else:
return walker | [
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stevezheng23/xlnet_extension_tf | 3efa272c4dcd126a0d3c79fb912b8cffae5ee572 | run_nlu.py | python | NluProcessor.get_train_examples | (self) | return example_list | Gets a collection of `InputExample`s for the train set. | Gets a collection of `InputExample`s for the train set. | [
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data_path = os.path.join(self.data_dir, "train-{0}".format(self.task_name), "train-{0}.json".format(self.task_name))
data_list = self._read_json(data_path)
example_list = self._get_example(data_list)
return example_list | [
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mysql/mysql-connector-python | c5460bcbb0dff8e4e48bf4af7a971c89bf486d85 | lib/mysqlx/statement.py | python | Statement.deallocate_prepare_execute | (self) | return self._deallocate_prepare_execute | bool: `True` to deallocate + prepare + execute statement. | bool: `True` to deallocate + prepare + execute statement. | [
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] | def deallocate_prepare_execute(self):
"""bool: `True` to deallocate + prepare + execute statement.
"""
return self._deallocate_prepare_execute | [
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SigmaHQ/sigma | 6f7d28b52a6468b2430e8d7dfefb79dc01e2f1af | tools/sigma/backends/devo.py | python | DevoBackend.generateANDNode | (self, node) | [] | def generateANDNode(self, node):
generated = []
for val in node:
if self.requireFTS(val):
generated.append(self.generateFTS(val))
else:
generated.append(self.generateNode(val))
filtered = [g for g in generated if g is not None]
if filtered:
return self.andToken.join(filtered)
else:
return None | [
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cltk/cltk | 1a8c2f5ef72389e2579dfce1fa5af8e59ebc9ec1 | src/cltk/lemmatize/processes.py | python | LatinLemmatizationProcess.algorithm | (self) | return LatinBackoffLemmatizer() | [] | def algorithm(self):
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libvips/pyvips | a6d622957743f6f015a51e6b80fdbeccc85b44ae | doc/conf.py | python | setup | (app) | [] | def setup(app):
app.connect('autodoc-skip-member', skip_deprecated)
try:
from sphinx.ext.autosummary import Autosummary
from sphinx.ext.autosummary import get_documenter
from docutils.parsers.rst import directives
from sphinx.util.inspect import safe_getattr
class AutoAutoSummary(Autosummary):
option_spec = {
'methods': directives.unchanged,
'attributes': directives.unchanged
}
required_arguments = 1
@staticmethod
def get_members(obj, typ, include_public=None):
if not include_public:
include_public = []
items = []
for name in dir(obj):
try:
documenter = get_documenter(safe_getattr(obj, name),
obj)
except AttributeError:
continue
if documenter.objtype == typ:
items.append(name)
public = [x for x in items
if x in include_public or not x.startswith('_')]
return public, items
def run(self):
clazz = str(self.arguments[0])
try:
(module_name, class_name) = clazz.rsplit('.', 1)
m = __import__(module_name, globals(), locals(),
[class_name])
c = getattr(m, class_name)
if 'methods' in self.options:
_, methods = self.get_members(c,
'method', ['__init__'])
self.content = ["~%s.%s" % (clazz, method)
for method in methods
if not method.startswith('_')]
if 'attributes' in self.options:
_, attribs = self.get_members(c, 'attribute')
self.content = ["~%s.%s" % (clazz, attrib)
for attrib in attribs
if not attrib.startswith('_')]
finally:
return super(AutoAutoSummary, self).run()
app.add_directive('autoautosummary', AutoAutoSummary)
except BaseException as e:
raise e | [
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miyuchina/mistletoe | 8220f3568d566a033518fc3a2d71935112431be0 | contrib/toc_renderer.py | python | TOCRenderer.toc | (self) | return items[0] | Returns table of contents as a block_token.List instance. | Returns table of contents as a block_token.List instance. | [
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"""
Returns table of contents as a block_token.List instance.
"""
from mistletoe.block_token import List
def get_indent(level):
if self.omit_title:
level -= 1
return ' ' * 4 * (level - 1)
def build_list_item(heading):
level, content = heading
template = '{indent}- {content}\n'
return template.format(indent=get_indent(level), content=content)
lines = [build_list_item(heading) for heading in self._headings]
items = block_token.tokenize(lines)
return items[0] | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/_abcoll.py | python | ItemsView.__contains__ | (self, item) | [] | def __contains__(self, item):
key, value = item
try:
v = self._mapping[key]
except KeyError:
return False
else:
return v == value | [
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plone/guillotina | 57ad54988f797a93630e424fd4b6a75fa26410af | guillotina/db/interfaces.py | python | ITransaction.len | (oid: str) | Get size of children for object | Get size of children for object | [
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Get size of children for object
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] | https://github.com/plone/guillotina/blob/57ad54988f797a93630e424fd4b6a75fa26410af/guillotina/db/interfaces.py#L93-L96 | ||
Zulko/easyAI | a5cbd0b600ebbeadc3730df9e7a211d7643cff8b | easyAI/games/TicTacToe-Flask.py | python | TicTacToe.unmake_move | (self, move) | [] | def unmake_move(self, move): # optional method (speeds up the AI)
self.board[int(move) - 1] = 0 | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/utils/dummy_pt_objects.py | python | RagPreTrainedModel.from_pretrained | (cls, *args, **kwargs) | [] | def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"]) | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/qtconsole-4.1.0-py3.3.egg/qtconsole/console_widget.py | python | ConsoleWidget.can_copy | (self) | return self._control.textCursor().hasSelection() | Returns whether text can be copied to the clipboard. | Returns whether text can be copied to the clipboard. | [
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""" Returns whether text can be copied to the clipboard.
"""
return self._control.textCursor().hasSelection() | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/library/oc_adm_manage_node.py | python | Utils.filter_versions | (stdout) | return version_dict | filter the oc version output | filter the oc version output | [
"filter",
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"oc",
"version",
"output"
] | def filter_versions(stdout):
''' filter the oc version output '''
version_dict = {}
version_search = ['oc', 'openshift', 'kubernetes']
for line in stdout.strip().split('\n'):
for term in version_search:
if not line:
continue
if line.startswith(term):
version_dict[term] = line.split()[-1]
# horrible hack to get openshift version in Openshift 3.2
# By default "oc version in 3.2 does not return an "openshift" version
if "openshift" not in version_dict:
version_dict["openshift"] = version_dict["oc"]
return version_dict | [
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HCIILAB/DeRPN | 21e6738ee1f7d3f159ee48d435c543e773f8ce99 | lib/datasets/imdb.py | python | imdb.create_roidb_from_box_list | (self, box_list, gt_roidb) | return roidb | [] | def create_roidb_from_box_list(self, box_list, gt_roidb):
assert len(box_list) == self.num_images, \
'Number of boxes must match number of ground-truth images'
roidb = []
for i in xrange(self.num_images):
boxes = box_list[i]
num_boxes = boxes.shape[0]
overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32)
if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0:
gt_boxes = gt_roidb[i]['boxes']
gt_classes = gt_roidb[i]['gt_classes']
gt_overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
argmaxes = gt_overlaps.argmax(axis=1)
maxes = gt_overlaps.max(axis=1)
I = np.where(maxes > 0)[0]
overlaps[I, gt_classes[argmaxes[I]]] = maxes[I]
overlaps = scipy.sparse.csr_matrix(overlaps)
roidb.append({
'boxes' : boxes,
'gt_classes' : np.zeros((num_boxes,), dtype=np.int32),
'gt_overlaps' : overlaps,
'flipped' : False,
'seg_areas' : np.zeros((num_boxes,), dtype=np.float32),
})
return roidb | [
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lunixbochs/ActualVim | 1f555ce719e49d6584f0e35e9f0db2f216b98fa5 | lib/neovim/msgpack_rpc/msgpack_stream.py | python | MsgpackStream.stop | (self) | Stop the event loop. | Stop the event loop. | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_webhook_conversion.py | python | V1WebhookConversion.__ne__ | (self, other) | return self.to_dict() != other.to_dict() | Returns true if both objects are not equal | Returns true if both objects are not equal | [
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] | def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, V1WebhookConversion):
return True
return self.to_dict() != other.to_dict() | [
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MasoniteFramework/masonite | faa448377916e9e0f618ea6bdc82330fa6604efc | src/masonite/helpers/urls.py | python | UrlsHelper.asset | (self, alias, filename) | return join(location, filename) | Generates a fully qualified URL for the given asset using the given disk
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asset("local", "avatar.jpg") (take first pat)
asset("s3.private", "doc.pdf") (when multiple paths are specified for the disk) | Generates a fully qualified URL for the given asset using the given disk
Example:
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asset("s3.private", "doc.pdf") (when multiple paths are specified for the disk)
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disks = config("filesystem.disks")
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filename = filename.lstrip("/")
if "." in alias:
alias = alias.split(".")
location = disks[alias[0]]["path"][alias[1]]
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location = disks[alias]["path"]
# take first path if no path specified
if isinstance(location, dict):
location = list(location.values())[0]
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iotvideo/v20201215/iotvideo_client.py | python | IotvideoClient.DescribeModelDefinition | (self, request) | 查询产品配置的数据模板信息
:param request: Request instance for DescribeModelDefinition.
:type request: :class:`tencentcloud.iotvideo.v20201215.models.DescribeModelDefinitionRequest`
:rtype: :class:`tencentcloud.iotvideo.v20201215.models.DescribeModelDefinitionResponse` | 查询产品配置的数据模板信息 | [
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"""查询产品配置的数据模板信息
:param request: Request instance for DescribeModelDefinition.
:type request: :class:`tencentcloud.iotvideo.v20201215.models.DescribeModelDefinitionRequest`
:rtype: :class:`tencentcloud.iotvideo.v20201215.models.DescribeModelDefinitionResponse`
"""
try:
params = request._serialize()
body = self.call("DescribeModelDefinition", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.DescribeModelDefinitionResponse()
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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"... | https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/iotvideo/v20201215/iotvideo_client.py#L1429-L1454 | ||
aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/kubernetes/client/apis/storage_v1_api.py | python | StorageV1Api.delete_collection_storage_class_with_http_info | (self, **kwargs) | return self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1Status',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats) | delete collection of StorageClass
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_collection_storage_class_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str pretty: If 'true', then the output is pretty printed.
:param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything.
:param bool include_uninitialized: If true, partially initialized resources are included in the response.
:param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything.
:param str resource_version: When specified with a watch call, shows changes that occur after that particular version of a resource. Defaults to changes from the beginning of history. When specified for list: - if unset, then the result is returned from remote storage based on quorum-read flag; - if it's 0, then we simply return what we currently have in cache, no guarantee; - if set to non zero, then the result is at least as fresh as given rv.
:param int timeout_seconds: Timeout for the list/watch call.
:param bool watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion.
:return: V1Status
If the method is called asynchronously,
returns the request thread. | delete collection of StorageClass
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_collection_storage_class_with_http_info(callback=callback_function) | [
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"""
delete collection of StorageClass
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_collection_storage_class_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str pretty: If 'true', then the output is pretty printed.
:param str field_selector: A selector to restrict the list of returned objects by their fields. Defaults to everything.
:param bool include_uninitialized: If true, partially initialized resources are included in the response.
:param str label_selector: A selector to restrict the list of returned objects by their labels. Defaults to everything.
:param str resource_version: When specified with a watch call, shows changes that occur after that particular version of a resource. Defaults to changes from the beginning of history. When specified for list: - if unset, then the result is returned from remote storage based on quorum-read flag; - if it's 0, then we simply return what we currently have in cache, no guarantee; - if set to non zero, then the result is at least as fresh as given rv.
:param int timeout_seconds: Timeout for the list/watch call.
:param bool watch: Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion.
:return: V1Status
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['pretty', 'field_selector', 'include_uninitialized', 'label_selector', 'resource_version', 'timeout_seconds', 'watch']
all_params.append('callback')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_collection_storage_class" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
resource_path = '/apis/storage.k8s.io/v1/storageclasses'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'pretty' in params:
query_params['pretty'] = params['pretty']
if 'field_selector' in params:
query_params['fieldSelector'] = params['field_selector']
if 'include_uninitialized' in params:
query_params['includeUninitialized'] = params['include_uninitialized']
if 'label_selector' in params:
query_params['labelSelector'] = params['label_selector']
if 'resource_version' in params:
query_params['resourceVersion'] = params['resource_version']
if 'timeout_seconds' in params:
query_params['timeoutSeconds'] = params['timeout_seconds']
if 'watch' in params:
query_params['watch'] = params['watch']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'application/yaml', 'application/vnd.kubernetes.protobuf'])
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['*/*'])
# Authentication setting
auth_settings = ['BearerToken']
return self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='V1Status',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats) | [
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SciTools/iris | a12d0b15bab3377b23a148e891270b13a0419c38 | lib/iris/coord_systems.py | python | GeogCS.__init__ | (
self,
semi_major_axis=None,
semi_minor_axis=None,
inverse_flattening=None,
longitude_of_prime_meridian=None,
) | Creates a new GeogCS.
Kwargs:
* semi_major_axis, semi_minor_axis:
Axes of ellipsoid, in metres. At least one must be given
(see note below).
* inverse_flattening:
Can be omitted if both axes given (see note below).
Defaults to 0.0 .
* longitude_of_prime_meridian:
Specifies the prime meridian on the ellipsoid, in degrees.
Defaults to 0.0 .
If just semi_major_axis is set, with no semi_minor_axis or
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radius.
If just two of semi_major_axis, semi_minor_axis, and
inverse_flattening are given the missing element is calculated from the
formula:
:math:`flattening = (major - minor) / major`
Currently, Iris will not allow over-specification (all three ellipsoid
parameters).
Examples::
cs = GeogCS(6371229)
pp_cs = GeogCS(iris.fileformats.pp.EARTH_RADIUS)
airy1830 = GeogCS(semi_major_axis=6377563.396,
semi_minor_axis=6356256.909)
airy1830 = GeogCS(semi_major_axis=6377563.396,
inverse_flattening=299.3249646)
custom_cs = GeogCS(6400000, 6300000) | Creates a new GeogCS. | [
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"."
] | def __init__(
self,
semi_major_axis=None,
semi_minor_axis=None,
inverse_flattening=None,
longitude_of_prime_meridian=None,
):
"""
Creates a new GeogCS.
Kwargs:
* semi_major_axis, semi_minor_axis:
Axes of ellipsoid, in metres. At least one must be given
(see note below).
* inverse_flattening:
Can be omitted if both axes given (see note below).
Defaults to 0.0 .
* longitude_of_prime_meridian:
Specifies the prime meridian on the ellipsoid, in degrees.
Defaults to 0.0 .
If just semi_major_axis is set, with no semi_minor_axis or
inverse_flattening, then a perfect sphere is created from the given
radius.
If just two of semi_major_axis, semi_minor_axis, and
inverse_flattening are given the missing element is calculated from the
formula:
:math:`flattening = (major - minor) / major`
Currently, Iris will not allow over-specification (all three ellipsoid
parameters).
Examples::
cs = GeogCS(6371229)
pp_cs = GeogCS(iris.fileformats.pp.EARTH_RADIUS)
airy1830 = GeogCS(semi_major_axis=6377563.396,
semi_minor_axis=6356256.909)
airy1830 = GeogCS(semi_major_axis=6377563.396,
inverse_flattening=299.3249646)
custom_cs = GeogCS(6400000, 6300000)
"""
# No ellipsoid specified? (0 0 0)
if (
(semi_major_axis is None)
and (semi_minor_axis is None)
and (inverse_flattening is None)
):
raise ValueError("No ellipsoid specified")
# Ellipsoid over-specified? (1 1 1)
if (
(semi_major_axis is not None)
and (semi_minor_axis is not None)
and (inverse_flattening is not None)
):
raise ValueError("Ellipsoid is overspecified")
# Perfect sphere (semi_major_axis only)? (1 0 0)
elif semi_major_axis is not None and (
semi_minor_axis is None and not inverse_flattening
):
semi_minor_axis = semi_major_axis
inverse_flattening = 0.0
# Calculate semi_major_axis? (0 1 1)
elif semi_major_axis is None and (
semi_minor_axis is not None and inverse_flattening is not None
):
semi_major_axis = -semi_minor_axis / (
(1.0 - inverse_flattening) / inverse_flattening
)
# Calculate semi_minor_axis? (1 0 1)
elif semi_minor_axis is None and (
semi_major_axis is not None and inverse_flattening is not None
):
semi_minor_axis = semi_major_axis - (
(1.0 / inverse_flattening) * semi_major_axis
)
# Calculate inverse_flattening? (1 1 0)
elif inverse_flattening is None and (
semi_major_axis is not None and semi_minor_axis is not None
):
if semi_major_axis == semi_minor_axis:
inverse_flattening = 0.0
else:
inverse_flattening = 1.0 / (
(semi_major_axis - semi_minor_axis) / semi_major_axis
)
# We didn't get enough to specify an ellipse.
else:
raise ValueError("Insufficient ellipsoid specification")
#: Major radius of the ellipsoid in metres.
self.semi_major_axis = float(semi_major_axis)
#: Minor radius of the ellipsoid in metres.
self.semi_minor_axis = float(semi_minor_axis)
#: :math:`1/f` where :math:`f = (a-b)/a`.
self.inverse_flattening = float(inverse_flattening)
#: Describes 'zero' on the ellipsoid in degrees.
self.longitude_of_prime_meridian = _arg_default(
longitude_of_prime_meridian, 0
) | [
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mypaint/mypaint | 90b36dbc7b8bd2f323383f7edf608a5e0a3a1a33 | gui/colors/bases.py | python | CachedBgWidgetMixin.paint_foreground_cb | (self, cr, w, h) | Paints the foreground over the background | Paints the foreground over the background | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/plugins/leoOPML.py | python | PutToOPML.tnodeListAttributes | (self, p) | return s | Put the tnodeList attribute of p.v | Put the tnodeList attribute of p.v | [
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# Remember: entries in the tnodeList correspond to @+node sentinels, _not_ to tnodes!
if not hasattr(p.v, 'tnodeList') or not p.v.tnodeList:
return None
# Assign fileIndices.
for v in p.v.tnodeList:
try: # Will fail for None or any pre 4.1 file index.
theId, time, n = p.v.fileIndex
except Exception:
g.trace("assigning gnx for ", p.v)
gnx = g.app.nodeIndices.getNewIndex(p.v)
p.v.setFileIndex(gnx) # Don't convert to string until the actual write.
s = ','.join([g.app.nodeIndices.tupleToString(v.fileIndex) for v in p.v.tnodeList])
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log2timeline/plaso | fe2e316b8c76a0141760c0f2f181d84acb83abc2 | plaso/single_process/extraction_engine.py | python | SingleProcessEngine._ProcessSources | (self, source_configurations, parser_mediator) | Processes the sources.
Args:
source_configurations (list[SourceConfigurationArtifact]): configurations
of the sources to process.
parser_mediator (ParserMediator): parser mediator. | Processes the sources. | [
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] | def _ProcessSources(self, source_configurations, parser_mediator):
"""Processes the sources.
Args:
source_configurations (list[SourceConfigurationArtifact]): configurations
of the sources to process.
parser_mediator (ParserMediator): parser mediator.
"""
if self._processing_profiler:
self._processing_profiler.StartTiming('process_sources')
self._status = definitions.STATUS_INDICATOR_COLLECTING
self._current_display_name = ''
self._number_of_consumed_sources = 0
find_specs = None
if self.collection_filters_helper:
find_specs = (
self.collection_filters_helper.included_file_system_find_specs)
source_path_specs = [
configuration.path_spec for configuration in source_configurations]
path_spec_generator = self._path_spec_extractor.ExtractPathSpecs(
source_path_specs, find_specs=find_specs, recurse_file_system=False,
resolver_context=self._resolver_context)
for path_spec in path_spec_generator:
if self._abort:
break
self._status = definitions.STATUS_INDICATOR_COLLECTING
self._current_display_name = parser_mediator.GetDisplayNameForPathSpec(
path_spec)
# TODO: determine if event sources should be DataStream or FileEntry
# or both.
event_source = event_sources.FileEntryEventSource(path_spec=path_spec)
parser_mediator.ProduceEventSource(event_source)
self._status = definitions.STATUS_INDICATOR_RUNNING
if self._processing_profiler:
self._processing_profiler.StartTiming('get_event_source')
event_source = self._storage_writer.GetFirstWrittenEventSource()
if self._processing_profiler:
self._processing_profiler.StopTiming('get_event_source')
while event_source:
if self._abort:
break
self._ProcessPathSpec(
self._extraction_worker, parser_mediator, event_source.path_spec)
self._number_of_consumed_sources += 1
if self._processing_profiler:
self._processing_profiler.StartTiming('get_event_source')
event_source = self._storage_writer.GetNextWrittenEventSource()
if self._processing_profiler:
self._processing_profiler.StopTiming('get_event_source')
if self._abort:
self._status = definitions.STATUS_INDICATOR_ABORTED
else:
self._status = definitions.STATUS_INDICATOR_COMPLETED
if self._processing_profiler:
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natea/django-deployer | 5ce7d972db2f8500ec53ad89e7eb312d3360d074 | django_deployer/helpers.py | python | _join | (*args) | return os.path.join(*args) | Convenience wrapper around os.path.join to make the rest of our
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Convenience wrapper around os.path.join to make the rest of our
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pwnieexpress/pwn_plug_sources | 1a23324f5dc2c3de20f9c810269b6a29b2758cad | src/wifitap/scapy.py | python | Dot11PacketList.toEthernet | (self) | return PacketList(r2,name="Ether from %s"%self.listname) | [] | def toEthernet(self):
data = map(lambda x:x.getlayer(Dot11), filter(lambda x : x.haslayer(Dot11) and x.type == 2, self.res))
r2 = []
for p in data:
q = p.copy()
q.unwep()
r2.append(Ether()/q.payload.payload.payload) #Dot11/LLC/SNAP/IP
return PacketList(r2,name="Ether from %s"%self.listname) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.5/django/contrib/gis/db/models/sql/compiler.py | python | GeoSQLCompiler.get_field_select | (self, field, alias=None, column=None) | return field_sel | Returns the SELECT SQL string for the given field. Figures out
if any custom selection SQL is needed for the column The `alias`
keyword may be used to manually specify the database table where
the column exists, if not in the model associated with this
`GeoQuery`. Similarly, `column` may be used to specify the exact
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sel_fmt = self.get_select_format(field)
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field_sel = sel_fmt % self.query.custom_select[field]
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field_sel = sel_fmt % self._field_column(field, alias, column)
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jina-ai/jina | c77a492fcd5adba0fc3de5347bea83dd4e7d8087 | docarray/simple/map.py | python | NamedScoreMap.__contains__ | (self, key: str) | return key in self._pb_body | [] | def __contains__(self, key: str):
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PaddlePaddle/PARL | 5fb7a5d2b5d0f0dac57fdf4acb9e79485c7efa96 | parl/utils/utils.py | python | kill_process | (regex_pattern) | kill process whose execution commnad is matched by regex pattern
Args:
regex_pattern(string): regex pattern used to filter the process to be killed
NOTE:
In windows, we will replace sep `/` with `\\\\` | kill process whose execution commnad is matched by regex pattern | [
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] | def kill_process(regex_pattern):
"""kill process whose execution commnad is matched by regex pattern
Args:
regex_pattern(string): regex pattern used to filter the process to be killed
NOTE:
In windows, we will replace sep `/` with `\\\\`
"""
if _IS_WINDOWS:
regex_pattern = regex_pattern.replace('/', '\\\\')
command = r'''for /F "skip=2 tokens=2 delims=," %a in ('wmic process where "commandline like '%{}%'" get processid^,status /format:csv') do taskkill /F /T /pid %a'''.format(
regex_pattern)
with os.popen(command) as p:
p.read()
else:
command = "ps aux | grep {} | awk '{{print $2}}' | xargs kill -9".format(
regex_pattern)
subprocess.call([command], shell=True) | [
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joxeankoret/diaphora | dcb5a25ac9fe23a285b657e5389cf770de7ac928 | diaphora.py | python | CBinDiff.compare_graphs_pass | (self, bblocks1, bblocks2, colours1, colours2, is_second = False) | return colours1, colours2 | [] | def compare_graphs_pass(self, bblocks1, bblocks2, colours1, colours2, is_second = False):
dones1 = set()
dones2 = set()
# Now compare each basic block from the first function to all the
# basic blocks in the 2nd function
for key1 in bblocks1:
if key1 in dones1:
continue
for key2 in bblocks2:
if key2 in dones2:
continue
# Same number of instructions?
if len(bblocks1[key1]) == len(bblocks2[key2]):
mod = False
partial = True
i = 0
for ins1 in bblocks1[key1]:
ins2 = bblocks2[key2][i]
# Same mnemonic? The change can be only partial
if ins1[1] != ins2[1]:
partial = False
# Try to compare the assembly after doing some cleaning
cmp_asm1 = self.get_cmp_asm(ins1[2])
cmp_asm2 = self.get_cmp_asm(ins2[2])
if cmp_asm1 != cmp_asm2:
mod = True
if not partial:
continue
i += 1
if not mod:
# Perfect match, we discovered a basic block equal in both
# functions
colours1[key1] = 0xffffff
colours2[key2] = 0xffffff
dones1.add(key1)
dones2.add(key2)
break
elif not is_second and partial:
# Partial match, we discovered a basic block with the same
# mnemonics but something changed
#
# NOTE:
# Do not add the partial matches to the dones lists, as we
# can have complete matches after a partial match!
colours1[key1] = 0xCCffff
colours2[key2] = 0xCCffff
break
return colours1, colours2 | [
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andycasey/ads | 7939be2650bfee85be13cd46cfa2e037381da7fc | ads/search.py | python | Article.bibtex | (self) | return ExportQuery(bibcodes=self.bibcode, format="bibtex").execute() | Return a BiBTeX entry for the current article. | Return a BiBTeX entry for the current article. | [
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"""Return a BiBTeX entry for the current article."""
warnings.warn("bibtex should be queried with ads.ExportQuery(); You will "
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return ExportQuery(bibcodes=self.bibcode, format="bibtex").execute() | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/pandas-0.17.0-py3.3-win-amd64.egg/pandas/core/series.py | python | Series.unstack | (self, level=-1) | return unstack(self, level) | Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame.
The level involved will automatically get sorted.
Parameters
----------
level : int, string, or list of these, default last level
Level(s) to unstack, can pass level name
Examples
--------
>>> s
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one b 2.
two a 3.
two b 4.
>>> s.unstack(level=-1)
a b
one 1. 2.
two 3. 4.
>>> s.unstack(level=0)
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a 1. 2.
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Returns
-------
unstacked : DataFrame | Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame.
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"""
Unstack, a.k.a. pivot, Series with MultiIndex to produce DataFrame.
The level involved will automatically get sorted.
Parameters
----------
level : int, string, or list of these, default last level
Level(s) to unstack, can pass level name
Examples
--------
>>> s
one a 1.
one b 2.
two a 3.
two b 4.
>>> s.unstack(level=-1)
a b
one 1. 2.
two 3. 4.
>>> s.unstack(level=0)
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a 1. 2.
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Returns
-------
unstacked : DataFrame
"""
from pandas.core.reshape import unstack
return unstack(self, level) | [
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log2timeline/dfvfs | 4ca7bf06b15cdc000297a7122a065f0ca71de544 | dfvfs/vfs/cpio_file_entry.py | python | CPIOFileEntry.name | (self) | return self._file_system.BasenamePath(self._cpio_archive_file_entry.path) | str: name of the file entry, which does not include the full path. | str: name of the file entry, which does not include the full path. | [
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# Note that the root file entry is virtual and has no
# cpio_archive_file_entry.
if self._cpio_archive_file_entry is None:
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return self._file_system.BasenamePath(self._cpio_archive_file_entry.path) | [
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google-research/motion_imitation | d0e7b963c5a301984352d25a3ee0820266fa4218 | motion_imitation/envs/sensors/environment_sensors.py | python | LastActionSensor.on_reset | (self, env) | From the callback, the sensor remembers the environment.
Args:
env: the environment who invokes this callback function. | From the callback, the sensor remembers the environment. | [
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] | def on_reset(self, env):
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Args:
env: the environment who invokes this callback function.
"""
self._env = env | [
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vanheeringen-lab/genomepy | 4c10e69b6886cf52381caf6498395391834a675b | genomepy/providers/base.py | python | BaseProvider.head_annotation | (self, name: str, genomes_dir=None, n: int = 5, **kwargs) | Download the first n lines of the annotation.
The first line of the GTF is printed for review
(of the gene_name field, for instance).
Parameters
----------
name : str
genome name
genomes_dir : str, optional
genomes directory to install the annotation in.
n : int, optional
download the annotation for n genes. | Download the first n lines of the annotation. | [
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] | def head_annotation(self, name: str, genomes_dir=None, n: int = 5, **kwargs):
"""
Download the first n lines of the annotation.
The first line of the GTF is printed for review
(of the gene_name field, for instance).
Parameters
----------
name : str
genome name
genomes_dir : str, optional
genomes directory to install the annotation in.
n : int, optional
download the annotation for n genes.
"""
name = self._check_name(name)
link = self.get_annotation_download_link(name, **kwargs)
localname = f"{name}_head"
genomes_dir = get_genomes_dir(genomes_dir, check_exist=False)
fpath = os.path.join(genomes_dir, localname, f"{localname}.annotation.gtf")
download_annotation(genomes_dir, link, localname, n=n)
logger.info(self.name)
m = 0
with open(fpath) as f:
for line in f:
line = line.strip()
if line and line[0] != "#":
print(line)
m += 1
if m == n:
break | [
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FSecureLABS/Jandroid | e31d0dab58a2bfd6ed8e0a387172b8bd7c893436 | libs/platform-tools/platform-tools_linux/systrace/catapult/devil/devil/android/tools/system_app.py | python | _TemporarilyInstallApp | (device, apk) | A context manager that installs an app while in scope. | A context manager that installs an app while in scope. | [
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] | def _TemporarilyInstallApp(device, apk):
"""A context manager that installs an app while in scope."""
device.Install(apk, reinstall=True)
try:
yield
finally:
device.Uninstall(apk_helper.GetPackageName(apk)) | [
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blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/abstract/mixer/trackgroup.py | python | AbstractTrackGroup.filters | (self) | return None | Returns a list of stringified names of enabled filters on the
TrackGroup.
Returns:
list: A list of the string names of enabled filters. | Returns a list of stringified names of enabled filters on the
TrackGroup. | [
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"""Returns a list of stringified names of enabled filters on the
TrackGroup.
Returns:
list: A list of the string names of enabled filters.
"""
return None | [
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ym2011/POC-EXP | 206b22d3a6b2a172359678df33bbc5b2ad04b6c3 | windows/SYNfulKnock/scapy-2.3.1/scapy/contrib/gsm_um.py | python | rrInitialisationRequest | () | return packet | RR Initialisation Request Section 9.1.28.a | RR Initialisation Request Section 9.1.28.a | [
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"Initialisation",
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"Section",
"9",
".",
"1",
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] | def rrInitialisationRequest():
"""RR Initialisation Request Section 9.1.28.a"""
a = TpPd(pd=0x6)
b = MessageType(mesType=0x3c) # 00111100
c = CiphKeySeqNrAndMacModeAndChannelCodingRequest()
e = MobileStationClassmark2()
f = Tlli()
g = ChannelRequestDescription()
h = GprsMeasurementResults()
packet = a / b / c / e / f / g / h
return packet | [
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astropy/photutils | 3caa48e4e4d139976ed7457dc41583fb2c56ba20 | photutils/segmentation/catalog.py | python | SourceCatalog.cxx | (self) | return ((np.cos(self.orientation) / self.semimajor_sigma)**2
+ (np.sin(self.orientation) / self.semiminor_sigma)**2) | r"""
`SourceExtractor`_'s CXX ellipse parameter in units of
pixel**(-2).
The ellipse is defined as
.. math::
cxx (x - \bar{x})^2 + cxy (x - \bar{x}) (y - \bar{y}) +
cyy (y - \bar{y})^2 = R^2
where :math:`R` is a parameter which scales the ellipse (in
units of the axes lengths). `SourceExtractor`_ reports that the
isophotal limit of a source is well represented by :math:`R
\approx 3`. | r"""
`SourceExtractor`_'s CXX ellipse parameter in units of
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r"""
`SourceExtractor`_'s CXX ellipse parameter in units of
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"""
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makerbot/ReplicatorG | d6f2b07785a5a5f1e172fb87cb4303b17c575d5d | skein_engines/skeinforge-35/fabmetheus_utilities/geometry/geometry_tools/path_elements/cubic.py | python | getCubicPathByBeginEnd | (begin, controlPoints, end, xmlElement) | return svg_reader.getCubicPoints(begin, controlPoints, end, lineation.getNumberOfBezierPoints(begin, end, xmlElement)) | Get the cubic path by begin and end. | Get the cubic path by begin and end. | [
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uqfoundation/klepto | a2b941fa2053ccbad731180d015bd39d3ee18c27 | klepto/rounding.py | python | simple_round_factory | (tol) | return simple_round | helper function for simple_round (a factory for simple_round functions) | helper function for simple_round (a factory for simple_round functions) | [
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def simple_round(*args, **kwds):
argstype = type(args)
_args = list(args)
_kwds = kwds.copy()
for i,j in enumerate(args):
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for i,j in kwds.items():
if isinstance(j, float): _kwds[i] = round(j, tol)
return argstype(_args), _kwds
return simple_round | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/celery/platforms.py | python | check_privileges | (accept_content) | [] | def check_privileges(accept_content):
uid = os.getuid() if hasattr(os, 'getuid') else 65535
gid = os.getgid() if hasattr(os, 'getgid') else 65535
euid = os.geteuid() if hasattr(os, 'geteuid') else 65535
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try:
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finally:
os._exit(1)
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/urllib3/util/selectors.py | python | BaseSelector._key_from_fd | (self, fd) | Return the key associated to a given file descriptor
Return None if it is not found. | Return the key associated to a given file descriptor
Return None if it is not found. | [
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] | def _key_from_fd(self, fd):
""" Return the key associated to a given file descriptor
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try:
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pyrocko/pyrocko | b6baefb7540fb7fce6ed9b856ec0c413961a4320 | src/plot/dynamic_rupture.py | python | _save_grid | (lats, lons, data, filename) | Save lat-lon gridded data as gmt .grd file.
:param lats:
Grid latitude coordinates in [deg].
:type lats:
iterable
:param lons:
Grid longitude coordinates in [deg].
:type lons:
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:param data:
Grid data of any kind.
:type data:
:py:class:`~numpy.ndarray`, ``(n_lons, n_lats)``
:param filename:
Filename of the written grid file.
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str | Save lat-lon gridded data as gmt .grd file. | [
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'''
Save lat-lon gridded data as gmt .grd file.
:param lats:
Grid latitude coordinates in [deg].
:type lats:
iterable
:param lons:
Grid longitude coordinates in [deg].
:type lons:
iterable
:param data:
Grid data of any kind.
:type data:
:py:class:`~numpy.ndarray`, ``(n_lons, n_lats)``
:param filename:
Filename of the written grid file.
:type filename:
str
'''
gmtpy.savegrd(lons, lats, data, filename=filename, naming='lonlat') | [
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romanz/trezor-agent | 23f8ef09a5b8eda2187b6883487d7ce0d9bd0b5e | libagent/age/client.py | python | create_identity | (user_id) | return result | Create AGE identity for hardware device. | Create AGE identity for hardware device. | [
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] | def create_identity(user_id):
"""Create AGE identity for hardware device."""
result = interface.Identity(identity_str='age://', curve_name="ed25519")
result.identity_dict['host'] = user_id
return result | [
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sripathikrishnan/redis-rdb-tools | 548b11ec3c81a603f5b321228d07a61a0b940159 | rdbtools/memprofiler.py | python | MemoryCallback.start_stream | (self, key, listpacks_count, expiry, info) | [] | def start_stream(self, key, listpacks_count, expiry, info):
self._key_expiry = expiry
self._current_encoding = info['encoding']
self._current_size = self.top_level_object_overhead(key, expiry)
self._current_size += self.sizeof_pointer()*2 + 8 + 16 # stream struct
self._current_size += self.sizeof_pointer() + 8*2 # rax struct
self._listpacks_count = listpacks_count | [
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BEEmod/BEE2.4 | 02767f3cf476581789425ab308ca1bea978f6a74 | src/editoritems_props.py | python | UnknownProp.__getstate__ | (self) | return (self.name, self.default, self.index, self.allow_user_default) | All the properties have the same attributes. | All the properties have the same attributes. | [
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] | def __getstate__(self):
"""All the properties have the same attributes."""
return (self.name, self.default, self.index, self.allow_user_default) | [
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houtianze/bypy | 10fd0f18378174a775a05a366cc20ba6609f96c6 | bypy/bypy.py | python | ByPy.__listrecycle_act | (self, r, args) | return const.ENoError | [] | def __listrecycle_act(self, r, args):
print_pcs_list(r.json())
return const.ENoError | [
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aiidateam/aiida-core | c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2 | aiida/transports/transport.py | python | Transport.copy | (self, remotesource, remotedestination, dereference=False, recursive=True) | Copy a file or a directory from remote source to remote destination
(On the same remote machine)
:param str remotesource: path of the remote source directory / file
:param str remotedestination: path of the remote destination directory / file
:param dereference: if True copy the contents of any symlinks found, otherwise copy the symlinks themselves
:type dereference: bool
:param recursive: if True copy directories recursively, otherwise only copy the specified file(s)
:type recursive: bool
:raises: IOError, if one of src or dst does not exist | Copy a file or a directory from remote source to remote destination
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] | def copy(self, remotesource, remotedestination, dereference=False, recursive=True):
"""
Copy a file or a directory from remote source to remote destination
(On the same remote machine)
:param str remotesource: path of the remote source directory / file
:param str remotedestination: path of the remote destination directory / file
:param dereference: if True copy the contents of any symlinks found, otherwise copy the symlinks themselves
:type dereference: bool
:param recursive: if True copy directories recursively, otherwise only copy the specified file(s)
:type recursive: bool
:raises: IOError, if one of src or dst does not exist
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dr-prodigy/python-holidays | 57dd2ed2b8659c76f776f10207456e5231c82099 | holidays/holiday_base.py | python | HolidayBase.pop_named | (self, name: str) | return to_pop | Remove (no longer treat at as holiday) all dates matching the
provided holiday name. The match will be made case insensitively and
partial matches will be removed. If default is not given and date is
not a holiday, a KeyError is raised. | Remove (no longer treat at as holiday) all dates matching the
provided holiday name. The match will be made case insensitively and
partial matches will be removed. If default is not given and date is
not a holiday, a KeyError is raised. | [
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"""Remove (no longer treat at as holiday) all dates matching the
provided holiday name. The match will be made case insensitively and
partial matches will be removed. If default is not given and date is
not a holiday, a KeyError is raised."""
to_pop = self.get_named(name)
if not to_pop:
raise KeyError(name)
for key in to_pop:
self.pop(key)
return to_pop | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/distlib/database.py | python | InstalledDistribution.__repr__ | (self) | return '<InstalledDistribution %r %s at %r>' % (
self.name, self.version, self.path) | [] | def __repr__(self):
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bleakie/MaskInsightface | 94511404eaa7912945fa087e6445a3608c46aaea | src/align/detect_face.py | python | Network.feed | (self, *args) | return self | Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers. | Set the input(s) for the next operation by replacing the terminal nodes.
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'''Set the input(s) for the next operation by replacing the terminal nodes.
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'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, string_types):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self | [
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python-excel/xlrd | 0c4e80b3d48dfe2250ac4e514c8231a742fee221 | xlrd/book.py | python | Book.unload_sheet | (self, sheet_name_or_index) | :param sheet_name_or_index: Name or index of sheet to be unloaded.
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"""
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.. versionadded:: 0.7.1
"""
if isinstance(sheet_name_or_index, int):
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sheetx = self._sheet_names.index(sheet_name_or_index)
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raise XLRDError('No sheet named <%r>' % sheet_name_or_index)
self._sheet_list[sheetx] = None | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/sklearn/cross_validation.py | python | BaseShuffleSplit._iter_indices | (self) | Generate (train, test) indices | Generate (train, test) indices | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/xmlrpc/client.py | python | Unmarshaller.close | (self) | return tuple(self._stack) | [] | def close(self):
# return response tuple and target method
if self._type is None or self._marks:
raise ResponseError()
if self._type == "fault":
raise Fault(**self._stack[0])
return tuple(self._stack) | [
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HymanLiuTS/flaskTs | 286648286976e85d9b9a5873632331efcafe0b21 | flasky/lib/python2.7/site-packages/setuptools/dist.py | python | Distribution.get_command_class | (self, command) | Pluggable version of get_command_class() | Pluggable version of get_command_class() | [
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"""Pluggable version of get_command_class()"""
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return self.cmdclass[command]
for ep in pkg_resources.iter_entry_points('distutils.commands', command):
ep.require(installer=self.fetch_build_egg)
self.cmdclass[command] = cmdclass = ep.load()
return cmdclass
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return _Distribution.get_command_class(self, command) | [
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wimleers/fileconveyor | 4b2e21d9fde39152b28b41781013f6c0cff02c06 | fileconveyor/fsmonitor_inotify.py | python | FSMonitorInotify.__process_pathscanner_updates | (self, update_list, callback) | [] | def __process_pathscanner_updates(self, update_list, callback):
self.lock.acquire()
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del update_list[:] # Clear the list with updates.
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Fortran-FOSS-Programmers/ford | e93b27188c763a019713f64e2270ae98c8f226e1 | ford/utils.py | python | split_path | (path) | return retlist | Splits the argument into its constituent directories and returns them as
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"""
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"""
def recurse_path(path, retlist):
if len(retlist) > 100:
fullpath = os.path.join(
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print("Directory '{}' contains too many levels".format(fullpath))
exit(1)
head, tail = os.path.split(path)
if len(tail) > 0:
retlist.insert(0, tail)
recurse_path(head, retlist)
elif len(head) > 1:
recurse_path(head, retlist)
else:
return
retlist = []
path = os.path.realpath(os.path.normpath(path))
drive, path = os.path.splitdrive(path)
if len(drive) > 0:
retlist.append(drive)
recurse_path(path, retlist)
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | twistedcaldav/directory/addressbook.py | python | DirectoryAddressBookHomeTypeProvisioningResource.listChildren | (self) | [] | def listChildren(self):
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... | https://github.com/apple/ccs-calendarserver/blob/13c706b985fb728b9aab42dc0fef85aae21921c3/twistedcaldav/directory/addressbook.py#L192-L205 | ||||
jiasenlu/NeuralBabyTalk | c351ae6ca59380eb4cf38384d3e66233b4d9e1b1 | tools/pycider/pyciderevalcap/ciderD/ciderD_scorer.py | python | cook_refs | (refs, n=4) | return [precook(ref, n) for ref in refs] | Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (list of dict) | Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (list of dict) | [
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'''Takes a list of reference sentences for a single segment
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needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
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'''
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lonePatient/BERT-NER-Pytorch | bc9276185539c59bbd3d310ea808fea2b618fedf | processors/utils_ner.py | python | DataProcessor._read_text | (self,input_file) | return lines | [] | def _read_text(self,input_file):
lines = []
with open(input_file,'r') as f:
words = []
labels = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
lines.append({"words":words,"labels":labels})
words = []
labels = []
else:
splits = line.split(" ")
words.append(splits[0])
if len(splits) > 1:
labels.append(splits[-1].replace("\n", ""))
else:
# Examples could have no label for mode = "test"
labels.append("O")
if words:
lines.append({"words":words,"labels":labels})
return lines | [
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rhinstaller/anaconda | 63edc8680f1b05cbfe11bef28703acba808c5174 | pyanaconda/core/configuration/system.py | python | SystemSection.can_initialize_system_clock | (self) | return self._is_boot_iso or self._is_live_os or self._is_booted_os | Can we initialize the System Clock?
FIXME: This is a temporary workaround. | Can we initialize the System Clock? | [
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"""Can we initialize the System Clock?
FIXME: This is a temporary workaround.
"""
return self._is_boot_iso or self._is_live_os or self._is_booted_os | [
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google/clusterfuzz | f358af24f414daa17a3649b143e71ea71871ef59 | src/appengine/handlers/performance_report/performance_analyzer.py | python | LibFuzzerPerformanceAnalyzer.analyzer_timeout | (self, stats) | return 0.0 | Check if there is a libFuzzer timeout in the stats.
Returns a number in range [0.0, 1.0]. | Check if there is a libFuzzer timeout in the stats.
Returns a number in range [0.0, 1.0]. | [
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"""Check if there is a libFuzzer timeout in the stats.
Returns a number in range [0.0, 1.0]."""
if stats['timeout_count']:
return 1.0
return 0.0 | [
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NeuromorphicProcessorProject/snn_toolbox | a85ada7b5d060500703285ef8a68f06ea1ffda65 | snntoolbox/parsing/utils.py | python | AbstractModelParser.parse_concatenate | (self, layer, attributes) | Parse a concatenation layer.
Parameters
----------
layer:
Layer.
attributes: dict
The layer attributes as key-value pairs in a dict. | Parse a concatenation layer. | [
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"""Parse a concatenation layer.
Parameters
----------
layer:
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attributes: dict
The layer attributes as key-value pairs in a dict.
"""
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_internal/commands/wheel.py | python | WheelCommand.__init__ | (self, *args, **kw) | [] | def __init__(self, *args, **kw):
super(WheelCommand, self).__init__(*args, **kw)
cmd_opts = self.cmd_opts
cmd_opts.add_option(
'-w', '--wheel-dir',
dest='wheel_dir',
metavar='dir',
default=os.curdir,
help=("Build wheels into <dir>, where the default is the "
"current working directory."),
)
cmd_opts.add_option(cmdoptions.no_binary())
cmd_opts.add_option(cmdoptions.only_binary())
cmd_opts.add_option(cmdoptions.prefer_binary())
cmd_opts.add_option(
'--build-option',
dest='build_options',
metavar='options',
action='append',
help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
)
cmd_opts.add_option(cmdoptions.no_build_isolation())
cmd_opts.add_option(cmdoptions.constraints())
cmd_opts.add_option(cmdoptions.editable())
cmd_opts.add_option(cmdoptions.requirements())
cmd_opts.add_option(cmdoptions.src())
cmd_opts.add_option(cmdoptions.ignore_requires_python())
cmd_opts.add_option(cmdoptions.no_deps())
cmd_opts.add_option(cmdoptions.build_dir())
cmd_opts.add_option(cmdoptions.progress_bar())
cmd_opts.add_option(
'--global-option',
dest='global_options',
action='append',
metavar='options',
help="Extra global options to be supplied to the setup.py "
"call before the 'bdist_wheel' command.")
cmd_opts.add_option(
'--pre',
action='store_true',
default=False,
help=("Include pre-release and development versions. By default, "
"pip only finds stable versions."),
)
cmd_opts.add_option(cmdoptions.no_clean())
cmd_opts.add_option(cmdoptions.require_hashes())
index_opts = cmdoptions.make_option_group(
cmdoptions.index_group,
self.parser,
)
self.parser.insert_option_group(0, index_opts)
self.parser.insert_option_group(0, cmd_opts) | [
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obspy/obspy | 0ee5a0d2db293c8d5d4c3b1f148a6c5a85fea55f | obspy/clients/seishub/client.py | python | _BaseRESTClient.get_xml_resource | (self, resource_name, **kwargs) | return self.client._objectify(url, **kwargs) | Gets a XML resource.
:type resource_name: str
:param resource_name: Name of the resource.
:return: Resource as :class:`lxml.objectify.ObjectifiedElement` | Gets a XML resource. | [
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"""
Gets a XML resource.
:type resource_name: str
:param resource_name: Name of the resource.
:return: Resource as :class:`lxml.objectify.ObjectifiedElement`
"""
url = '/xml/' + self.package + '/' + self.resourcetype + '/' + \
resource_name
return self.client._objectify(url, **kwargs) | [
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taigaio/taiga-ncurses | 65312098f2d167762e0dbd1c16019754ab64d068 | taiga_ncurses/api/client.py | python | TaigaClient.update_issue | (self, id, data_dict={}, params={}) | return self._patch(url, data_dict, params) | [] | def update_issue(self, id, data_dict={}, params={}):
url = urljoin(self._host, self.URLS.get("issue").format(id))
return self._patch(url, data_dict, params) | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/helpers/template.py | python | timestamp_custom | (value, date_format=DATE_STR_FORMAT, local=True, default=_SENTINEL) | Filter to convert given timestamp to format. | Filter to convert given timestamp to format. | [
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] | def timestamp_custom(value, date_format=DATE_STR_FORMAT, local=True, default=_SENTINEL):
"""Filter to convert given timestamp to format."""
try:
date = dt_util.utc_from_timestamp(value)
if local:
date = dt_util.as_local(date)
return date.strftime(date_format)
except (ValueError, TypeError):
# If timestamp can't be converted
if default is _SENTINEL:
warn_no_default("timestamp_custom", value, value)
return value
return default | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/cws/v20180312/models.py | python | DeleteMonitorsRequest.__init__ | (self) | r"""
:param MonitorIds: 监控任务ID列表
:type MonitorIds: list of int non-negative | r"""
:param MonitorIds: 监控任务ID列表
:type MonitorIds: list of int non-negative | [
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r"""
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self.MonitorIds = None | [
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/objc/_convenience_nsset.py | python | nsset_symmetric_difference | (self, other) | return result | [] | def nsset_symmetric_difference(self, other):
result = NSMutableSet()
if not hasattr(other, '__contains__'):
other = list(other)
for item in iter(self):
if item not in other:
result.add(item)
for item in other:
if item not in self:
result.add(item)
return result | [
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khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment2/q2_NER.py | python | NERModel.add_training_op | (self, loss) | return train_op | Sets up the training Ops.
Creates an optimizer and applies the gradients to all trainable variables.
The Op returned by this function is what must be passed to the
`sess.run()` call to cause the model to train. See
https://www.tensorflow.org/versions/r0.7/api_docs/python/train.html#Optimizer
for more information.
Hint: Use tf.train.AdamOptimizer for this model.
Calling optimizer.minimize() will return a train_op object.
Args:
loss: Loss tensor, from cross_entropy_loss.
Returns:
train_op: The Op for training. | Sets up the training Ops. | [
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] | def add_training_op(self, loss):
"""Sets up the training Ops.
Creates an optimizer and applies the gradients to all trainable variables.
The Op returned by this function is what must be passed to the
`sess.run()` call to cause the model to train. See
https://www.tensorflow.org/versions/r0.7/api_docs/python/train.html#Optimizer
for more information.
Hint: Use tf.train.AdamOptimizer for this model.
Calling optimizer.minimize() will return a train_op object.
Args:
loss: Loss tensor, from cross_entropy_loss.
Returns:
train_op: The Op for training.
"""
### YOUR CODE HERE
train_op = tf.train.AdamOptimizer(self.config.lr).minimize(loss)
### END YOUR CODE
return train_op | [
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nanotube/supybot-bitcoin-marketmonitor | 8cdcd6055b716fa9dbdba8f2311c44f836ba55ef | RatingSystem/plugin.py | python | RatingSystem._getrating | (self, nick) | return data | Get cumulative rating for user. For use from other plugins. | Get cumulative rating for user. For use from other plugins. | [
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"""Get cumulative rating for user. For use from other plugins."""
data = self.db.get(nick)
if len(data) == 0:
return None
data = data[0]
return data | [
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richardaecn/class-balanced-loss | 1d7857208a2abc03d84e35a9d5383af8225d4b4d | tpu/models/official/mobilenet/mobilenet.py | python | model_fn | (features, labels, mode, params) | return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=loss, train_op=train_op, eval_metrics=eval_metrics) | Mobilenet v1 model using Estimator API. | Mobilenet v1 model using Estimator API. | [
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] | def model_fn(features, labels, mode, params):
"""Mobilenet v1 model using Estimator API."""
num_classes = FLAGS.num_classes
training_active = (mode == tf.estimator.ModeKeys.TRAIN)
eval_active = (mode == tf.estimator.ModeKeys.EVAL)
features = tensor_transform_fn(features, params['input_perm'])
if FLAGS.clear_update_collections:
# updates_collections must be set to None in order to use fused batchnorm
with arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
logits, end_points = mobilenet_v1.mobilenet_v1(
features,
num_classes,
is_training=training_active,
depth_multiplier=FLAGS.depth_multiplier)
else:
with arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
logits, end_points = mobilenet_v1.mobilenet_v1(
features,
num_classes,
is_training=training_active,
depth_multiplier=FLAGS.depth_multiplier)
predictions = {
'classes': tf.argmax(input=logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
if mode == tf.estimator.ModeKeys.EVAL and FLAGS.display_tensors and (
not FLAGS.use_tpu):
with tf.control_dependencies([
tf.Print(
predictions['classes'], [predictions['classes']],
summarize=FLAGS.eval_batch_size,
message='prediction: ')
]):
labels = tf.Print(
labels, [labels], summarize=FLAGS.eval_batch_size, message='label: ')
one_hot_labels = tf.one_hot(labels, FLAGS.num_classes, dtype=tf.int32)
tf.losses.softmax_cross_entropy(
onehot_labels=one_hot_labels,
logits=logits,
weights=1.0,
label_smoothing=0.1)
loss = tf.losses.get_total_loss(add_regularization_losses=True)
initial_learning_rate = FLAGS.learning_rate * FLAGS.train_batch_size / 256
final_learning_rate = 0.0001 * initial_learning_rate
train_op = None
if training_active:
batches_per_epoch = _NUM_TRAIN_IMAGES // FLAGS.train_batch_size
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.exponential_decay(
learning_rate=initial_learning_rate,
global_step=global_step,
decay_steps=FLAGS.learning_rate_decay_epochs * batches_per_epoch,
decay_rate=FLAGS.learning_rate_decay,
staircase=True)
# Set a minimum boundary for the learning rate.
learning_rate = tf.maximum(
learning_rate, final_learning_rate, name='learning_rate')
if FLAGS.optimizer == 'sgd':
tf.logging.info('Using SGD optimizer')
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate)
elif FLAGS.optimizer == 'momentum':
tf.logging.info('Using Momentum optimizer')
optimizer = tf.train.MomentumOptimizer(
learning_rate=learning_rate, momentum=0.9)
elif FLAGS.optimizer == 'RMS':
tf.logging.info('Using RMS optimizer')
optimizer = tf.train.RMSPropOptimizer(
learning_rate,
RMSPROP_DECAY,
momentum=RMSPROP_MOMENTUM,
epsilon=RMSPROP_EPSILON)
else:
tf.logging.fatal('Unknown optimizer:', FLAGS.optimizer)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step=global_step)
if FLAGS.moving_average:
ema = tf.train.ExponentialMovingAverage(
decay=MOVING_AVERAGE_DECAY, num_updates=global_step)
variables_to_average = (tf.trainable_variables() +
tf.moving_average_variables())
with tf.control_dependencies([train_op]), tf.name_scope('moving_average'):
train_op = ema.apply(variables_to_average)
eval_metrics = None
if eval_active:
def metric_fn(labels, predictions):
accuracy = tf.metrics.accuracy(labels, tf.argmax(
input=predictions, axis=1))
return {'accuracy': accuracy}
if FLAGS.use_logits:
eval_predictions = logits
else:
eval_predictions = end_points['Predictions']
eval_metrics = (metric_fn, [labels, eval_predictions])
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=loss, train_op=train_op, eval_metrics=eval_metrics) | [
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... | https://github.com/richardaecn/class-balanced-loss/blob/1d7857208a2abc03d84e35a9d5383af8225d4b4d/tpu/models/official/mobilenet/mobilenet.py#L418-L536 | |
nneonneo/eqgrp-free-file | 49e457d7ac870d5e00f5a247b94476cd5643f3ba | Firewall/EXPLOITS/EXBA/scapy/utils6.py | python | in6_ctop | (addr) | return inet_ntop(socket.AF_INET6, "".join(res)) | Convert an IPv6 address in Compact Representation Notation
(RFC 1924) to printable representation ;-)
Returns None on error. | Convert an IPv6 address in Compact Representation Notation
(RFC 1924) to printable representation ;-)
Returns None on error. | [
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] | def in6_ctop(addr):
"""
Convert an IPv6 address in Compact Representation Notation
(RFC 1924) to printable representation ;-)
Returns None on error.
"""
if len(addr) != 20 or not reduce(lambda x,y: x and y,
map(lambda x: x in _rfc1924map, addr)):
return None
i = 0
for c in addr:
j = _rfc1924map.index(c)
i = 85*i + j
res = []
for j in range(4):
res.append(struct.pack("!I", i%2**32))
i = i/(2**32)
res.reverse()
return inet_ntop(socket.AF_INET6, "".join(res)) | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | tensorflow_dl_models/research/skip_thoughts/skip_thoughts/vocabulary_expansion.py | python | _load_skip_thoughts_embeddings | (checkpoint_path) | return word_embedding | Loads the embedding matrix from a skip-thoughts model checkpoint.
Args:
checkpoint_path: Model checkpoint file or directory containing a checkpoint
file.
Returns:
word_embedding: A numpy array of shape [vocab_size, embedding_dim].
Raises:
ValueError: If no checkpoint file matches checkpoint_path. | Loads the embedding matrix from a skip-thoughts model checkpoint. | [
"Loads",
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"embedding",
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"from",
"a",
"skip",
"-",
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"model",
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] | def _load_skip_thoughts_embeddings(checkpoint_path):
"""Loads the embedding matrix from a skip-thoughts model checkpoint.
Args:
checkpoint_path: Model checkpoint file or directory containing a checkpoint
file.
Returns:
word_embedding: A numpy array of shape [vocab_size, embedding_dim].
Raises:
ValueError: If no checkpoint file matches checkpoint_path.
"""
if tf.gfile.IsDirectory(checkpoint_path):
checkpoint_file = tf.train.latest_checkpoint(checkpoint_path)
if not checkpoint_file:
raise ValueError("No checkpoint file found in %s" % checkpoint_path)
else:
checkpoint_file = checkpoint_path
tf.logging.info("Loading skip-thoughts embedding matrix from %s",
checkpoint_file)
reader = tf.train.NewCheckpointReader(checkpoint_file)
word_embedding = reader.get_tensor("word_embedding")
tf.logging.info("Loaded skip-thoughts embedding matrix of shape %s",
word_embedding.shape)
return word_embedding | [
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KhronosGroup/OpenXR-SDK-Source | 76756e2e7849b15466d29bee7d80cada92865550 | specification/scripts/validitygenerator.py | python | ValidityOutputGenerator.makeExternalTypeName | (self, name) | return self.conventions.makeExternalTypeName(name) | Prepend the appropriate format macro for an external type like uint32_t to a type name. | Prepend the appropriate format macro for an external type like uint32_t to a type name. | [
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] | def makeExternalTypeName(self, name):
"""Prepend the appropriate format macro for an external type like uint32_t to a type name."""
# delegate to conventions
return self.conventions.makeExternalTypeName(name) | [
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] | https://github.com/KhronosGroup/OpenXR-SDK-Source/blob/76756e2e7849b15466d29bee7d80cada92865550/specification/scripts/validitygenerator.py#L320-L323 |
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