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naftaliharris/tauthon | 5587ceec329b75f7caf6d65a036db61ac1bae214 | Lib/lib-tk/Tkinter.py | python | Wm.wm_overrideredirect | (self, boolean=None) | return self._getboolean(self.tk.call(
'wm', 'overrideredirect', self._w, boolean)) | Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given. | Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given. | [
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] | def wm_overrideredirect(self, boolean=None):
"""Instruct the window manager to ignore this widget
if BOOLEAN is given with 1. Return the current value if None
is given."""
return self._getboolean(self.tk.call(
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pik-copan/pyunicorn | b18316fc08ef34b434a1a4d69dfe3e57e24435ee | pyunicorn/core/spatial_network.py | python | SpatialNetwork.max_link_distance | (self) | return maximum_link_distance | Return maximum angular geodesic link distances.
.. note::
Does not use directionality information.
**Example:**
>>> SpatialNetwork.SmallTestNetwork().max_link_distance()
array([27.95085, 16.77051, 11.18034, 16.77051, 22.36068, 27.95085],
dtype=float32)
:rtype: 1D Numpy array [index]
:return: the maximum link distance sequence. | Return maximum angular geodesic link distances. | [
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] | def max_link_distance(self):
"""
Return maximum angular geodesic link distances.
.. note::
Does not use directionality information.
**Example:**
>>> SpatialNetwork.SmallTestNetwork().max_link_distance()
array([27.95085, 16.77051, 11.18034, 16.77051, 22.36068, 27.95085],
dtype=float32)
:rtype: 1D Numpy array [index]
:return: the maximum link distance sequence.
"""
if self.silence_level <= 1:
print("Calculating maximum link distance...")
A = self.undirected_adjacency().A
D = self.grid.distance()
maximum_link_distance = (D * A).max(axis=1)
return maximum_link_distance | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/difflib.py | python | Differ._qformat | (self, aline, bline, atags, btags) | r"""
Format "?" output and deal with leading tabs.
Example:
>>> d = Differ()
>>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
... ' ^ ^ ^ ', ' ^ ^ ^ ')
>>> for line in results: print repr(line)
...
'- \tabcDefghiJkl\n'
'? \t ^ ^ ^\n'
'+ \tabcdefGhijkl\n'
'? \t ^ ^ ^\n' | r"""
Format "?" output and deal with leading tabs. | [
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] | def _qformat(self, aline, bline, atags, btags):
r"""
Format "?" output and deal with leading tabs.
Example:
>>> d = Differ()
>>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
... ' ^ ^ ^ ', ' ^ ^ ^ ')
>>> for line in results: print repr(line)
...
'- \tabcDefghiJkl\n'
'? \t ^ ^ ^\n'
'+ \tabcdefGhijkl\n'
'? \t ^ ^ ^\n'
"""
# Can hurt, but will probably help most of the time.
common = min(_count_leading(aline, "\t"),
_count_leading(bline, "\t"))
common = min(common, _count_leading(atags[:common], " "))
common = min(common, _count_leading(btags[:common], " "))
atags = atags[common:].rstrip()
btags = btags[common:].rstrip()
yield "- " + aline
if atags:
yield "? %s%s\n" % ("\t" * common, atags)
yield "+ " + bline
if btags:
yield "? %s%s\n" % ("\t" * common, btags) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pygments/lexers/clean.py | python | CleanLexer.store_indent | (lexer, match, ctx) | [] | def store_indent(lexer, match, ctx):
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brianwrf/hackUtils | 168123350d93b040fa0c437c9d59faf8fa65d8e6 | bs4/element.py | python | Tag.string | (self) | return child.string | Convenience property to get the single string within this tag.
:Return: If this tag has a single string child, return value
is that string. If this tag has no children, or more than one
child, return value is None. If this tag has one child tag,
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"""Convenience property to get the single string within this tag.
:Return: If this tag has a single string child, return value
is that string. If this tag has no children, or more than one
child, return value is None. If this tag has one child tag,
return value is the 'string' attribute of the child tag,
recursively.
"""
if len(self.contents) != 1:
return None
child = self.contents[0]
if isinstance(child, NavigableString):
return child
return child.string | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/permutation.py | python | Permutations_nk.cardinality | (self) | return ZZ.zero() | EXAMPLES::
sage: Permutations(3,0).cardinality()
1
sage: Permutations(3,1).cardinality()
3
sage: Permutations(3,2).cardinality()
6
sage: Permutations(3,3).cardinality()
6
sage: Permutations(3,4).cardinality()
0 | EXAMPLES:: | [
"EXAMPLES",
"::"
] | def cardinality(self) -> Integer:
"""
EXAMPLES::
sage: Permutations(3,0).cardinality()
1
sage: Permutations(3,1).cardinality()
3
sage: Permutations(3,2).cardinality()
6
sage: Permutations(3,3).cardinality()
6
sage: Permutations(3,4).cardinality()
0
"""
if 0 <= self._k <= self.n:
return factorial(self.n) // factorial(self.n - self._k)
return ZZ.zero() | [
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tdamdouni/Pythonista | 3e082d53b6b9b501a3c8cf3251a8ad4c8be9c2ad | markdown/markdown2pdf/reportlab/graphics/widgetbase.py | python | TwoFaces.draw | (self) | return shapes.Group(self.faceOne, self.faceTwo) | Just return a group | Just return a group | [
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"""Just return a group"""
return shapes.Group(self.faceOne, self.faceTwo) | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1_probe.py | python | V1Probe.success_threshold | (self, success_threshold) | Sets the success_threshold of this V1Probe.
Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness and startup. Minimum value is 1. # noqa: E501
:param success_threshold: The success_threshold of this V1Probe. # noqa: E501
:type: int | Sets the success_threshold of this V1Probe. | [
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] | def success_threshold(self, success_threshold):
"""Sets the success_threshold of this V1Probe.
Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness and startup. Minimum value is 1. # noqa: E501
:param success_threshold: The success_threshold of this V1Probe. # noqa: E501
:type: int
"""
self._success_threshold = success_threshold | [
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tomerfiliba/plumbum | 20cdda5e8bbd9f83d64b154f6b4fcd28216c63e1 | plumbum/path/base.py | python | Path.__div__ | (self, other) | return self.join(other) | Joins two paths | Joins two paths | [
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"""Joins two paths"""
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Abjad/abjad | d0646dfbe83db3dc5ab268f76a0950712b87b7fd | abjad/typedcollections.py | python | TypedFrozenset.__sub__ | (self, argument) | return result | Subtracts ``argument`` from typed frozen set.
Returns new typed frozen set. | Subtracts ``argument`` from typed frozen set. | [
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] | def __sub__(self, argument):
"""
Subtracts ``argument`` from typed frozen set.
Returns new typed frozen set.
"""
argument = type(self)(argument)
result = self._collection.__sub__(argument._collection)
result = type(self)(result)
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/nntplib.py | python | NNTP.artcmd | (self, line, file=None) | return resp, nr, id, list | Internal: process a HEAD, BODY or ARTICLE command. | Internal: process a HEAD, BODY or ARTICLE command. | [
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] | def artcmd(self, line, file=None):
"""Internal: process a HEAD, BODY or ARTICLE command."""
resp, list = self.longcmd(line, file)
resp, nr, id = self.statparse(resp)
return resp, nr, id, list | [
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TengXiaoDai/DistributedCrawling | f5c2439e6ce68dd9b49bde084d76473ff9ed4963 | Lib/site-packages/pip/_vendor/packaging/specifiers.py | python | BaseSpecifier.__eq__ | (self, other) | Returns a boolean representing whether or not the two Specifier like
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Returns a boolean representing whether or not the two Specifier like
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lutris/lutris | 66675a4d5537f6b2a2ba2b6df0b3cdf8924c823a | lutris/runners/wine.py | python | wine.run_winetricks | (self, *args) | Run winetricks in the current context | Run winetricks in the current context | [
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"current",
"context"
] | def run_winetricks(self, *args):
"""Run winetricks in the current context"""
self.prelaunch()
winetricks(
"", prefix=self.prefix_path, wine_path=self.get_executable(), config=self, env=self.get_env(os_env=True)
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/cwp/v20180228/models.py | python | BaselineEventLevelInfo.__init__ | (self) | r"""
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注意：此字段可能返回 null，表示取不到有效值。
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:param EventCount: 漏洞数量
注意：此字段可能返回 null，表示取不到有效值。
:type EventCount: int | r"""
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注意：此字段可能返回 null，表示取不到有效值。
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NVIDIA/OpenSeq2Seq | 8681d381ed404fde516e2c1b823de5a213c59aba | scripts/calibrate_model.py | python | calibrate | (source, target) | return mean_start_shift, mean_end_shift | This function calculates the mean start and end shift
needed for your model to get word to speech alignments | This function calculates the mean start and end shift
needed for your model to get word to speech alignments | [
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] | def calibrate(source, target):
"""This function calculates the mean start and end shift
needed for your model to get word to speech alignments
"""
print("calibrating {}".format(source))
start_shift = []
end_shift = []
dump = pickle.load(open(source, "rb"))
results = dump["logits"]
vocab = dump["vocab"]
step_size = dump["step_size"]
blank_idx = len(vocab)
with open(target, "r") as read_file:
target = json.load(read_file)
for wave_file in results:
transcript, start, end = ctc_greedy_decoder(results[wave_file], vocab,
step_size, blank_idx, 0, 0)
words = transcript.split(" ")
k = 0
print(words)
alignments = []
for new_word in words:
alignments.append({"word": new_word, "start": start[k], "end": end[k]})
k += 1
if len(target[wave_file]["words"]) == len(words):
for i, new_word in enumerate(target[wave_file]["words"]):
if new_word["case"] == "success" and \
new_word["alignedWord"] == alignments[i]["word"]:
start_shift.append(new_word["start"] - alignments[i]["start"])
end_shift.append(new_word["end"] - alignments[i]["end"])
mean_start_shift = np.mean(start_shift)
mean_end_shift = np.mean(end_shift)
return mean_start_shift, mean_end_shift | [
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"s... | https://github.com/NVIDIA/OpenSeq2Seq/blob/8681d381ed404fde516e2c1b823de5a213c59aba/scripts/calibrate_model.py#L83-L116 | |
knownsec/VxPwn | 6555f49675f0317d4a48568a89d0ec4332658402 | sulley/sulley/sessions.py | python | session.fuzz | (self, this_node=None, path=[]) | Call this routine to get the ball rolling. No arguments are necessary as they are both utilized internally
during the recursive traversal of the session graph.
@type this_node: request (node)
@param this_node: (Optional, def=None) Current node that is being fuzzed.
@type path: List
@param path: (Optional, def=[]) Nodes along the path to the current one being fuzzed. | Call this routine to get the ball rolling. No arguments are necessary as they are both utilized internally
during the recursive traversal of the session graph. | [
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"internally",
"during",
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"recursive",
"traversal",
"of",
"the",
"session",
"graph",
"."
] | def fuzz (self, this_node=None, path=[]):
'''
Call this routine to get the ball rolling. No arguments are necessary as they are both utilized internally
during the recursive traversal of the session graph.
@type this_node: request (node)
@param this_node: (Optional, def=None) Current node that is being fuzzed.
@type path: List
@param path: (Optional, def=[]) Nodes along the path to the current one being fuzzed.
'''
# if no node is specified, then we start from the root node and initialize the session.
if not this_node:
# we can't fuzz if we don't have at least one target and one request.
if not self.targets:
raise sex.SullyRuntimeError("NO TARGETS SPECIFIED IN SESSION")
if not self.edges_from(self.root.id):
raise sex.SullyRuntimeError("NO REQUESTS SPECIFIED IN SESSION")
this_node = self.root
try: self.server_init()
except: return
# TODO: complete parallel fuzzing, will likely have to thread out each target
target = self.targets[0]
# step through every edge from the current node.
for edge in self.edges_from(this_node.id):
# the destination node is the one actually being fuzzed.
self.fuzz_node = self.nodes[edge.dst]
num_mutations = self.fuzz_node.num_mutations()
# keep track of the path as we fuzz through it, don't count the root node.
# we keep track of edges as opposed to nodes because if there is more then one path through a set of
# given nodes we don't want any ambiguity.
path.append(edge)
current_path = " -> ".join([self.nodes[e.src].name for e in path[1:]])
current_path += " -> %s" % self.fuzz_node.name
self.logger.info("current fuzz path: %s" % current_path)
self.logger.info("fuzzed %d of %d total cases" % (self.total_mutant_index, self.total_num_mutations))
done_with_fuzz_node = False
crash_count = 0
# loop through all possible mutations of the fuzz node.
while not done_with_fuzz_node:
# if we need to pause, do so.
self.pause()
# if we have exhausted the mutations of the fuzz node, break out of the while(1).
# note: when mutate() returns False, the node has been reverted to the default (valid) state.
if not self.fuzz_node.mutate():
self.logger.error("all possible mutations for current fuzz node exhausted")
done_with_fuzz_node = True
continue
# make a record in the session that a mutation was made.
self.total_mutant_index += 1
# if we've hit the restart interval, restart the target.
if self.restart_interval and self.total_mutant_index % self.restart_interval == 0:
self.logger.error("restart interval of %d reached" % self.restart_interval)
self.restart_target(target)
# exception error handling routine, print log message and restart target.
def error_handler (e, msg, target, sock=None):
if sock:
sock.close()
msg += "\nException caught: %s" % repr(e)
msg += "\nRestarting target and trying again"
self.logger.critical(msg)
self.restart_target(target)
# if we don't need to skip the current test case.
if self.total_mutant_index > self.skip:
self.logger.info("fuzzing %d of %d" % (self.fuzz_node.mutant_index, num_mutations))
# attempt to complete a fuzz transmission. keep trying until we are successful, whenever a failure
# occurs, restart the target.
while 1:
# instruct the debugger/sniffer that we are about to send a new fuzz.
if target.procmon:
try:
target.procmon.pre_send(self.total_mutant_index)
except Exception, e:
error_handler(e, "failed on procmon.pre_send()", target)
continue
if target.netmon:
try:
target.netmon.pre_send(self.total_mutant_index)
except Exception, e:
error_handler(e, "failed on netmon.pre_send()", target)
continue
try:
# establish a connection to the target.
sock = socket.socket(socket.AF_INET, self.proto)
except Exception, e:
error_handler(e, "failed creating socket", target)
continue
if self.bind:
try:
sock.bind(self.bind)
except Exception, e:
error_handler(e, "failed binding on socket", target, sock)
continue
try:
sock.settimeout(self.timeout)
# Connect is needed only for TCP stream
if self.proto == socket.SOCK_STREAM:
sock.connect((target.host, target.port))
except Exception, e:
error_handler(e, "failed connecting on socket", target, sock)
continue
# if SSL is requested, then enable it.
if self.ssl:
try:
ssl = socket.ssl(sock)
sock = httplib.FakeSocket(sock, ssl)
except Exception, e:
error_handler(e, "failed ssl setup", target, sock)
continue
# if the user registered a pre-send function, pass it the sock and let it do the deed.
try:
self.pre_send(sock)
except Exception, e:
error_handler(e, "pre_send() failed", target, sock)
continue
# send out valid requests for each node in the current path up to the node we are fuzzing.
try:
for e in path[:-1]:
node = self.nodes[e.dst]
self.transmit(sock, node, e, target)
except Exception, e:
error_handler(e, "failed transmitting a node up the path", target, sock)
continue
# now send the current node we are fuzzing.
try:
self.transmit(sock, self.fuzz_node, edge, target)
except Exception, e:
error_handler(e, "failed transmitting fuzz node", target, sock)
continue
# if we reach this point the send was successful for break out of the while(1).
break
# if the user registered a post-send function, pass it the sock and let it do the deed.
# we do this outside the try/except loop because if our fuzz causes a crash then the post_send()
# will likely fail and we don't want to sit in an endless loop.
try:
self.post_send(sock)
except Exception, e:
error_handler(e, "post_send() failed", target, sock)
# done with the socket.
sock.close()
# delay in between test cases.
self.logger.info("sleeping for %f seconds" % self.sleep_time)
time.sleep(self.sleep_time)
# poll the PED-RPC endpoints (netmon, procmon etc...) for the target.
self.poll_pedrpc(target)
# serialize the current session state to disk.
self.export_file()
# recursively fuzz the remainder of the nodes in the session graph.
self.fuzz(self.fuzz_node, path)
# finished with the last node on the path, pop it off the path stack.
if path:
path.pop()
# loop to keep the main thread running and be able to receive signals
if self.signal_module:
# wait for a signal only if fuzzing is finished (this function is recursive)
# if fuzzing is not finished, web interface thread will catch it
if self.total_mutant_index == self.total_num_mutations:
import signal
try:
while True:
signal.pause()
except AttributeError:
# signal.pause() is missing for Windows; wait 1ms and loop instead
while True:
time.sleep(0.001) | [
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slimkrazy/python-google-places | 1ddb7aa35983e845ede912f8cc415b115ef4d8be | googleplaces/__init__.py | python | _validate_response | (url, response) | Validates that the response from Google was successful. | Validates that the response from Google was successful. | [
"Validates",
"that",
"the",
"response",
"from",
"Google",
"was",
"successful",
"."
] | def _validate_response(url, response):
"""Validates that the response from Google was successful."""
if response['status'] not in [GooglePlaces.RESPONSE_STATUS_OK,
GooglePlaces.RESPONSE_STATUS_ZERO_RESULTS]:
error_detail = ('Request to URL %s failed with response code: %s' %
(url, response['status']))
raise GooglePlacesError(error_detail) | [
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gxcuizy/Python | 72167d12439a615a8fd4b935eae1fb6516ed4e69 | 英雄联盟皮肤爬图/get_lol_skin.py | python | GetLolSkin.download_skin | (self, skin_id, skin_name) | 下载皮肤图片 | 下载皮肤图片 | [
"下载皮肤图片"
] | def download_skin(self, skin_id, skin_name):
"""下载皮肤图片"""
# 下载图片
img_url = self.skin_url + skin_id + '.jpg'
request = requests.get(img_url)
if request.status_code == 200:
print('downloading……%s' % skin_name)
img_path = os.path.join(self.skin_folder, skin_name)
with open(img_path, 'wb') as img:
img.write(request.content)
else:
print('img error!') | [
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sa7mon/S3Scanner | dd81646a3c3691d09a54883a853cd57f61d61c85 | S3Scanner/S3Bucket.py | python | S3Bucket.get_human_readable_permissions | (self) | return f"AuthUsers: [{', '.join(authUsersPermissions)}], AllUsers: [{', '.join(allUsersPermissions)}]" | Returns a human-readable string of allowed permissions for this bucket
i.e. "AuthUsers: [Read | WriteACP], AllUsers: [FullControl]"
:return: str: Human-readable permissions | Returns a human-readable string of allowed permissions for this bucket
i.e. "AuthUsers: [Read | WriteACP], AllUsers: [FullControl]" | [
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"|",
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"AllUsers",
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"[",
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"]"
] | def get_human_readable_permissions(self):
"""
Returns a human-readable string of allowed permissions for this bucket
i.e. "AuthUsers: [Read | WriteACP], AllUsers: [FullControl]"
:return: str: Human-readable permissions
"""
# Add AuthUsers permissions
authUsersPermissions = []
if self.AuthUsersFullControl == Permission.ALLOWED:
authUsersPermissions.append("FullControl")
else:
if self.AuthUsersRead == Permission.ALLOWED:
authUsersPermissions.append("Read")
if self.AuthUsersWrite == Permission.ALLOWED:
authUsersPermissions.append("Write")
if self.AuthUsersReadACP == Permission.ALLOWED:
authUsersPermissions.append("ReadACP")
if self.AuthUsersWriteACP == Permission.ALLOWED:
authUsersPermissions.append("WriteACP")
# Add AllUsers permissions
allUsersPermissions = []
if self.AllUsersFullControl == Permission.ALLOWED:
allUsersPermissions.append("FullControl")
else:
if self.AllUsersRead == Permission.ALLOWED:
allUsersPermissions.append("Read")
if self.AllUsersWrite == Permission.ALLOWED:
allUsersPermissions.append("Write")
if self.AllUsersReadACP == Permission.ALLOWED:
allUsersPermissions.append("ReadACP")
if self.AllUsersWriteACP == Permission.ALLOWED:
allUsersPermissions.append("WriteACP")
return f"AuthUsers: [{', '.join(authUsersPermissions)}], AllUsers: [{', '.join(allUsersPermissions)}]" | [
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yoda-pa/yoda | e6b4325737b877488af4d1bf0b86eb1d98b88aed | modules/life.py | python | is_in_params | (params, query, article) | return query in article_filter | Get file path for today's tasks entry file
:param params:
:param query:
:param article:
:return: | Get file path for today's tasks entry file | [
"Get",
"file",
"path",
"for",
"today",
"s",
"tasks",
"entry",
"file"
] | def is_in_params(params, query, article):
"""
Get file path for today's tasks entry file
:param params:
:param query:
:param article:
:return:
"""
query = query.lower()
article_filter = article[params]
if type(article_filter) is list:
article_filter = [item.lower() for item in article_filter]
else:
article_filter = article_filter.lower()
return query in article_filter | [
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brycedrennan/eulerian-magnification | 8f7612f00efead03c154a173bed64fdc9f7f4112 | eulerian_magnification/base.py | python | combine_pyramid_and_save | (g_video, orig_video, enlarge_multiple, fps, save_filename='media/output.avi') | Combine a gaussian video representation with the original and save to file | Combine a gaussian video representation with the original and save to file | [
"Combine",
"a",
"gaussian",
"video",
"representation",
"with",
"the",
"original",
"and",
"save",
"to",
"file"
] | def combine_pyramid_and_save(g_video, orig_video, enlarge_multiple, fps, save_filename='media/output.avi'):
"""Combine a gaussian video representation with the original and save to file"""
width, height = get_frame_dimensions(orig_video[0])
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
print("Outputting to %s" % save_filename)
writer = cv2.VideoWriter(save_filename, fourcc, fps, (width, height), 1)
for x in range(0, g_video.shape[0]):
img = np.ndarray(shape=g_video[x].shape, dtype='float')
img[:] = g_video[x]
for i in range(enlarge_multiple):
img = cv2.pyrUp(img)
img[:height, :width] = img[:height, :width] + orig_video[x]
res = cv2.convertScaleAbs(img[:height, :width])
writer.write(res) | [
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timothyb0912/pylogit | cffc9c523b5368966ef2481c7dc30f0a5d296de8 | src/pylogit/base_multinomial_cm_v2.py | python | MNDC_Model.get_mappings_for_fit | (self, dense=False) | return create_long_form_mappings(self.data,
self.obs_id_col,
self.alt_id_col,
choice_col=self.choice_col,
nest_spec=self.nest_spec,
mix_id_col=self.mixing_id_col,
dense=dense) | Parameters
----------
dense : bool, optional.
Dictates if sparse matrices will be returned or dense numpy arrays.
Returns
-------
mapping_dict : OrderedDict.
Keys will be `["rows_to_obs", "rows_to_alts", "chosen_row_to_obs",
"rows_to_nests"]`. The value for `rows_to_obs` will map the rows of
the `long_form` to the unique observations (on the columns) in
their order of appearance. The value for `rows_to_alts` will map
the rows of the `long_form` to the unique alternatives which are
possible in the dataset (on the columns), in sorted order--not
order of appearance. The value for `chosen_row_to_obs`, if not
None, will map the rows of the `long_form` that contain the chosen
alternatives to the specific observations those rows are associated
with (denoted by the columns). The value of `rows_to_nests`, if not
None, will map the rows of the `long_form` to the nest (denoted by
the column) that contains the row's alternative. If `dense==True`,
the returned values will be dense numpy arrays. Otherwise, the
returned values will be scipy sparse arrays. | Parameters
----------
dense : bool, optional.
Dictates if sparse matrices will be returned or dense numpy arrays. | [
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"or",
"dense",
"numpy",
"arrays",
"."
] | def get_mappings_for_fit(self, dense=False):
"""
Parameters
----------
dense : bool, optional.
Dictates if sparse matrices will be returned or dense numpy arrays.
Returns
-------
mapping_dict : OrderedDict.
Keys will be `["rows_to_obs", "rows_to_alts", "chosen_row_to_obs",
"rows_to_nests"]`. The value for `rows_to_obs` will map the rows of
the `long_form` to the unique observations (on the columns) in
their order of appearance. The value for `rows_to_alts` will map
the rows of the `long_form` to the unique alternatives which are
possible in the dataset (on the columns), in sorted order--not
order of appearance. The value for `chosen_row_to_obs`, if not
None, will map the rows of the `long_form` that contain the chosen
alternatives to the specific observations those rows are associated
with (denoted by the columns). The value of `rows_to_nests`, if not
None, will map the rows of the `long_form` to the nest (denoted by
the column) that contains the row's alternative. If `dense==True`,
the returned values will be dense numpy arrays. Otherwise, the
returned values will be scipy sparse arrays.
"""
return create_long_form_mappings(self.data,
self.obs_id_col,
self.alt_id_col,
choice_col=self.choice_col,
nest_spec=self.nest_spec,
mix_id_col=self.mixing_id_col,
dense=dense) | [
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lmb-freiburg/demon | bf871aa796342bc12693ef584ab49eb58bcb96b3 | python/depthmotionnet/dataset_tools/helpers.py | python | safe_crop_image | (image, box, fill_value) | crops an image and adds a border if necessary
image: PIL.Image
box: 4 tuple
(x0,y0,x1,y1) tuple
fill_value: color value, scalar or tuple
Returns the cropped image | crops an image and adds a border if necessary
image: PIL.Image | [
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"image",
"and",
"adds",
"a",
"border",
"if",
"necessary",
"image",
":",
"PIL",
".",
"Image"
] | def safe_crop_image(image, box, fill_value):
"""crops an image and adds a border if necessary
image: PIL.Image
box: 4 tuple
(x0,y0,x1,y1) tuple
fill_value: color value, scalar or tuple
Returns the cropped image
"""
x0, y0, x1, y1 = box
if x0 >=0 and y0 >= 0 and x1 < image.width and y1 < image.height:
return image.crop(box)
else:
crop_width = x1-x0
crop_height = y1-y0
tmp = Image.new(image.mode, (crop_width, crop_height), fill_value)
safe_box = (
max(0,min(x0,image.width-1)),
max(0,min(y0,image.height-1)),
max(0,min(x1,image.width)),
max(0,min(y1,image.height)),
)
img_crop = image.crop(safe_box)
x = -x0 if x0 < 0 else 0
y = -y0 if y0 < 0 else 0
tmp.paste(image, (x,y))
return tmp | [
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polaris-gslb/polaris-gslb | 73c0b121f802bc6a3221a6d41f97d27e40f7f651 | polaris_health/protocols/tcp.py | python | TCPSocket.recv | (self) | return received | Read response ONCE from connected self._sock
up to RECV_BUFF_SIZE bytes
returns:
bytes() received | Read response ONCE from connected self._sock
up to RECV_BUFF_SIZE bytes
returns:
bytes() received | [
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"returns",
":",
"bytes",
"()",
"received"
] | def recv(self):
"""Read response ONCE from connected self._sock
up to RECV_BUFF_SIZE bytes
returns:
bytes() received
"""
start_time = time.monotonic()
try:
received = self._sock.recv(RECV_BUFF_SIZE)
except OSError as e:
self._sock.close()
raise ProtocolError('{} {} during socket.recv()'
.format(e.__class__.__name__, e))
self._decrease_timeout(time.monotonic() - start_time)
return received | [
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yiranran/Audio-driven-TalkingFace-HeadPose | d062a00a46a5d0ebb4bf66751e7a9af92ee418e8 | render-to-video/util/get_data.py | python | GetData._get_options | (r) | return options | [] | def _get_options(r):
soup = BeautifulSoup(r.text, 'lxml')
options = [h.text for h in soup.find_all('a', href=True)
if h.text.endswith(('.zip', 'tar.gz'))]
return options | [
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timkpaine/pyEX | 254acd2b0cf7cb7183100106f4ecc11d1860c46a | pyEX/premium/fraudfactors/__init__.py | python | nonTimelyFilingsFraudFactorsDF | (symbol="", **kwargs) | return _baseDF(
id="PREMIUM_FRAUD_FACTORS_NON_TIMELY_FILINGS", symbol=symbol, **kwargs
) | The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the inability to file annual/quarterly disclosures on time is a red-flag and thus a valuable signal for algorithmic strategies and fundamental investing alike.
Data available from 1994 with coverage of about 18,000 equities
https://iexcloud.io/docs/api/#non-timely-filings
Args:
symbol (str): symbol to use
Supports all kwargs from `pyEX.timeseries.timeSeries` | The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the inability to file annual/quarterly disclosures on time is a red-flag and thus a valuable signal for algorithmic strategies and fundamental investing alike.
Data available from 1994 with coverage of about 18,000 equities
https://iexcloud.io/docs/api/#non-timely-filings | [
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"""The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the inability to file annual/quarterly disclosures on time is a red-flag and thus a valuable signal for algorithmic strategies and fundamental investing alike.
Data available from 1994 with coverage of about 18,000 equities
https://iexcloud.io/docs/api/#non-timely-filings
Args:
symbol (str): symbol to use
Supports all kwargs from `pyEX.timeseries.timeSeries`
"""
return _baseDF(
id="PREMIUM_FRAUD_FACTORS_NON_TIMELY_FILINGS", symbol=symbol, **kwargs
) | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/cloud/clouds/clc.py | python | list_nodes_full | (call=None, for_output=True) | return servers | Return a list of the VMs that are on the provider | Return a list of the VMs that are on the provider | [
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] | def list_nodes_full(call=None, for_output=True):
"""
Return a list of the VMs that are on the provider
"""
if call == "action":
raise SaltCloudSystemExit(
"The list_nodes_full function must be called with -f or --function."
)
creds = get_creds()
clc.v1.SetCredentials(creds["token"], creds["token_pass"])
servers_raw = clc.v1.Server.GetServers(location=None)
servers_raw = salt.utils.json.dumps(servers_raw)
servers = salt.utils.json.loads(servers_raw)
return servers | [
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Nekmo/amazon-dash | ac2b2f98282ec08036e1671fe937dfda381a911f | amazon_dash/management.py | python | create_logger | (name, level=logging.INFO) | Create a Logger and set handler and formatter
:param name: logger name
:param level: logging level
:return: None | Create a Logger and set handler and formatter | [
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"""Create a Logger and set handler and formatter
:param name: logger name
:param level: logging level
:return: None
"""
# create logger
logger = logging.getLogger(name)
logger.setLevel(level)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(level)
# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)-7s - %(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch) | [
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Xilinx/finn | d1cc9cf94f1c33354cc169c5a6517314d0e94e3b | src/finn/transformation/fpgadataflow/insert_iodma.py | python | InsertIODMA.__init__ | (self, max_intfwidth=32) | [] | def __init__(self, max_intfwidth=32):
super().__init__()
assert (
2 ** math.log2(max_intfwidth) == max_intfwidth
), "max_intfwidth must be a power of 2"
self.max_intfwidth = max_intfwidth | [
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/pymongo/auth.py | python | _authenticate_gssapi | (username, sock_info, cmd_func) | Authenticate using GSSAPI. | Authenticate using GSSAPI. | [
"Authenticate",
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"."
] | def _authenticate_gssapi(username, sock_info, cmd_func):
"""Authenticate using GSSAPI.
"""
try:
# Starting here and continuing through the while loop below - establish
# the security context. See RFC 4752, Section 3.1, first paragraph.
result, ctx = kerberos.authGSSClientInit('mongodb@' + sock_info.host,
kerberos.GSS_C_MUTUAL_FLAG)
if result != kerberos.AUTH_GSS_COMPLETE:
raise OperationFailure('Kerberos context failed to initialize.')
try:
# pykerberos uses a weird mix of exceptions and return values
# to indicate errors.
# 0 == continue, 1 == complete, -1 == error
# Only authGSSClientStep can return 0.
if kerberos.authGSSClientStep(ctx, '') != 0:
raise OperationFailure('Unknown kerberos '
'failure in step function.')
# Start a SASL conversation with mongod/s
# Note: pykerberos deals with base64 encoded byte strings.
# Since mongo accepts base64 strings as the payload we don't
# have to use bson.binary.Binary.
payload = kerberos.authGSSClientResponse(ctx)
cmd = SON([('saslStart', 1),
('mechanism', 'GSSAPI'),
('payload', payload),
('autoAuthorize', 1)])
response, _ = cmd_func(sock_info, '$external', cmd)
# Limit how many times we loop to catch protocol / library issues
for _ in xrange(10):
result = kerberos.authGSSClientStep(ctx,
str(response['payload']))
if result == -1:
raise OperationFailure('Unknown kerberos '
'failure in step function.')
payload = kerberos.authGSSClientResponse(ctx) or ''
cmd = SON([('saslContinue', 1),
('conversationId', response['conversationId']),
('payload', payload)])
response, _ = cmd_func(sock_info, '$external', cmd)
if result == kerberos.AUTH_GSS_COMPLETE:
break
else:
raise OperationFailure('Kerberos '
'authentication failed to complete.')
# Once the security context is established actually authenticate.
# See RFC 4752, Section 3.1, last two paragraphs.
if kerberos.authGSSClientUnwrap(ctx,
str(response['payload'])) != 1:
raise OperationFailure('Unknown kerberos '
'failure during GSS_Unwrap step.')
if kerberos.authGSSClientWrap(ctx,
kerberos.authGSSClientResponse(ctx),
username) != 1:
raise OperationFailure('Unknown kerberos '
'failure during GSS_Wrap step.')
payload = kerberos.authGSSClientResponse(ctx)
cmd = SON([('saslContinue', 1),
('conversationId', response['conversationId']),
('payload', payload)])
response, _ = cmd_func(sock_info, '$external', cmd)
finally:
kerberos.authGSSClientClean(ctx)
except kerberos.KrbError, exc:
raise OperationFailure(str(exc)) | [
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/在线机器平台/阿里云/tensorflow_mnist.py | python | read_image_batch | (file_queue, batch_size) | return image_batch, one_hot_labels | [] | def read_image_batch(file_queue, batch_size):
img, label = read_image(file_queue)
capacity = 3 * batch_size
image_batch, label_batch = tf.train.batch([img, label], batch_size=batch_size, capacity=capacity, num_threads=10)
one_hot_labels = tf.to_float(tf.one_hot(label_batch, 10, 1, 0))
return image_batch, one_hot_labels | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/kombu/utils/div.py | python | emergency_dump_state | (state, open_file=open, dump=None, stderr=None) | return persist | Dump message state to stdout or file. | Dump message state to stdout or file. | [
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"file",
"."
] | def emergency_dump_state(state, open_file=open, dump=None, stderr=None):
"""Dump message state to stdout or file."""
from pprint import pformat
from tempfile import mktemp
stderr = sys.stderr if stderr is None else stderr
if dump is None:
import pickle
dump = pickle.dump
persist = mktemp()
print('EMERGENCY DUMP STATE TO FILE -> {0} <-'.format(persist), # noqa
file=stderr)
fh = open_file(persist, 'w')
try:
try:
dump(state, fh, protocol=0)
except Exception as exc:
print( # noqa
'Cannot pickle state: {0!r}. Fallback to pformat.'.format(exc),
file=stderr,
)
fh.write(default_encode(pformat(state)))
finally:
fh.flush()
fh.close()
return persist | [
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/Uptycs/Integrations/Uptycs/Uptycs.py | python | uptycs_get_alert_rules | () | return restcall(http_method, api_call) | return list of alert rules | return list of alert rules | [
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"rules"
] | def uptycs_get_alert_rules():
"""
return list of alert rules
"""
http_method = 'get'
api_call = "/alertRules"
limit = demisto.args().get('limit')
if limit != -1 and limit is not None:
api_call = ("%s?limit=%s" % (api_call, limit))
return restcall(http_method, api_call) | [
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ChintanTrivedi/DeepGamingAI_FIFA | b79a5b670756056b7b6aedd2c5c84fd5f59d569e | utils/shape_utils.py | python | pad_tensor | (t, length) | return padded_t | Pads the input tensor with 0s along the first dimension up to the length.
Args:
t: the input tensor, assuming the rank is at least 1.
length: a tensor of shape [1] or an integer, indicating the first dimension
of the input tensor t after padding, assuming length <= t.shape[0].
Returns:
padded_t: the padded tensor, whose first dimension is length. If the length
is an integer, the first dimension of padded_t is set to length
statically. | Pads the input tensor with 0s along the first dimension up to the length. | [
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] | def pad_tensor(t, length):
"""Pads the input tensor with 0s along the first dimension up to the length.
Args:
t: the input tensor, assuming the rank is at least 1.
length: a tensor of shape [1] or an integer, indicating the first dimension
of the input tensor t after padding, assuming length <= t.shape[0].
Returns:
padded_t: the padded tensor, whose first dimension is length. If the length
is an integer, the first dimension of padded_t is set to length
statically.
"""
t_rank = tf.rank(t)
t_shape = tf.shape(t)
t_d0 = t_shape[0]
pad_d0 = tf.expand_dims(length - t_d0, 0)
pad_shape = tf.cond(
tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
lambda: tf.expand_dims(length - t_d0, 0))
padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
if not _is_tensor(length):
padded_t = _set_dim_0(padded_t, length)
return padded_t | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/distlib/util.py | python | _get_external_data | (url) | return result | [] | def _get_external_data(url):
result = {}
try:
# urlopen might fail if it runs into redirections,
# because of Python issue #13696. Fixed in locators
# using a custom redirect handler.
resp = urlopen(url)
headers = resp.info()
ct = headers.get('Content-Type')
if not ct.startswith('application/json'):
logger.debug('Unexpected response for JSON request: %s', ct)
else:
reader = codecs.getreader('utf-8')(resp)
#data = reader.read().decode('utf-8')
#result = json.loads(data)
result = json.load(reader)
except Exception as e:
logger.exception('Failed to get external data for %s: %s', url, e)
return result | [
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jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/venv/lib/python3.7/site.py | python | check_enableusersite | () | return True | Check if user site directory is safe for inclusion
The function tests for the command line flag (including environment var),
process uid/gid equal to effective uid/gid.
None: Disabled for security reasons
False: Disabled by user (command line option)
True: Safe and enabled | Check if user site directory is safe for inclusion | [
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] | def check_enableusersite():
"""Check if user site directory is safe for inclusion
The function tests for the command line flag (including environment var),
process uid/gid equal to effective uid/gid.
None: Disabled for security reasons
False: Disabled by user (command line option)
True: Safe and enabled
"""
if hasattr(sys, "flags") and getattr(sys.flags, "no_user_site", False):
return False
if hasattr(os, "getuid") and hasattr(os, "geteuid"):
# check process uid == effective uid
if os.geteuid() != os.getuid():
return None
if hasattr(os, "getgid") and hasattr(os, "getegid"):
# check process gid == effective gid
if os.getegid() != os.getgid():
return None
return True | [
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zestedesavoir/zds-site | 2ba922223c859984a413cc6c108a8aa4023b113e | zds/utils/models.py | python | Comment.update_content | (self, text, on_error=None) | Updates the content of this comment.
This method will render the new comment to HTML, store the rendered
version, and store data to later analyze pings and spam.
This method updates fields, but does not save the instance.
:param text: The new comment content.
:param on_error: A callable called if zmd returns an error, provided
with a single argument: a list of user-friendly errors.
See render_markdown. | Updates the content of this comment. | [
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"."
] | def update_content(self, text, on_error=None):
"""
Updates the content of this comment.
This method will render the new comment to HTML, store the rendered
version, and store data to later analyze pings and spam.
This method updates fields, but does not save the instance.
:param text: The new comment content.
:param on_error: A callable called if zmd returns an error, provided
with a single argument: a list of user-friendly errors.
See render_markdown.
"""
# This attribute will be used by `_save_check_spam`, called after save (but not saved into the database).
# We only update it if it does not already exist, so if this method is called multiple times, the oldest
# version (i.e. the one currently in the database) is used for comparison. `_save_check_spam` will delete
# the attribute, so if we re-save the same instance, this will be re-set.
if not hasattr(self, "old_text"):
self.old_text = self.text
_, old_metadata, _ = render_markdown(self.text)
html, new_metadata, _ = render_markdown(text, on_error=on_error)
# These attributes will be used by `_save_compute_pings` to create notifications if needed.
# For the same reason as `old_text`, we only update `old_metadata` if not already set.
if not hasattr(self, "old_metadata"):
self.old_metadata = old_metadata
self.new_metadata = new_metadata
self.text = text
self.text_html = html | [
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NVIDIA/NeMo | 5b0c0b4dec12d87d3cd960846de4105309ce938e | nemo/collections/common/parts/utils.py | python | _compute_softmax | (scores) | return probs | Compute softmax probability over raw logits. | Compute softmax probability over raw logits. | [
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] | def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs | [
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hack12306/12306-booking | 726c830c2e771d5cd8e09772e7646e480dddb086 | booking/query.py | python | query_left_tickets | (train_date, from_station, to_station, seat_types, train_names=None) | return result | 信息查询-剩余车票
:param train_date
:param from_station
:param to_station
:param seat_types
:param train_names
:return JSON 对象 | 信息查询-剩余车票
:param train_date
:param from_station
:param to_station
:param seat_types
:param train_names
:return JSON 对象 | [
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":",
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":",
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"JSON",
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] | def query_left_tickets(train_date, from_station, to_station, seat_types, train_names=None):
"""
信息查询-剩余车票
:param train_date
:param from_station
:param to_station
:param seat_types
:param train_names
:return JSON 对象
"""
date_pattern = re.compile(r'^\d{4}-\d{2}-\d{2}$')
assert date_pattern.match(train_date), 'Invalid train_date param. %s' % train_date
assert isinstance(seat_types, list), u'Invalid seat_types param. %s' % seat_types
assert frozenset(seat_types) <= frozenset(dict(SEAT_TYPE_CODE_MAP).keys()
), u'Invalid seat_types param. %s' % seat_types
train_info = {}
trains = TrainInfoQueryAPI().info_query_left_tickets(train_date, from_station, to_station)
train_info, select_seat_type = _select_train_and_seat_type(train_names, seat_types, trains)
if not train_info or not select_seat_type:
raise exceptions.BookingTrainNoLeftTicket('无票')
_logger.debug('select train info. %s' % json.dumps(train_info, ensure_ascii=False))
result = {
'train_date': train_date,
'from_station': train_info['from_station'],
'to_station': train_info['to_station'],
'seat_type': select_seat_type,
'seat_type_code': dict(SEAT_TYPE_CODE_MAP)[select_seat_type],
'departure_time': train_info['departure_time'],
'arrival_time': train_info['arrival_time'],
'secret': train_info['secret'],
'train_name': train_info['train_name'],
'duration': train_info['duration'],
'train_num': train_info['train_num']
}
return result | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/tornado-6.0.1-py3.7-macosx-12.1-iPad6,7.egg/tornado/routing.py | python | RuleRouter.add_rules | (self, rules: _RuleList) | Appends new rules to the router.
:arg rules: a list of Rule instances (or tuples of arguments, which are
passed to Rule constructor). | Appends new rules to the router. | [
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] | def add_rules(self, rules: _RuleList) -> None:
"""Appends new rules to the router.
:arg rules: a list of Rule instances (or tuples of arguments, which are
passed to Rule constructor).
"""
for rule in rules:
if isinstance(rule, (tuple, list)):
assert len(rule) in (2, 3, 4)
if isinstance(rule[0], basestring_type):
rule = Rule(PathMatches(rule[0]), *rule[1:])
else:
rule = Rule(*rule)
self.rules.append(self.process_rule(rule)) | [
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zhausong/zabbix-book | 94ec20171d1ad3f690b2f2ee1f480e0bb1b763dd | tools/screen_create/zabbix_api.py | python | checkauth | (fn) | return ret | Decorator to check authentication of the decorated method | Decorator to check authentication of the decorated method | [
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"authentication",
"of",
"the",
"decorated",
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] | def checkauth(fn):
""" Decorator to check authentication of the decorated method """
def ret(self, *args):
self.__checkauth__()
return fn(self, args)
return ret | [
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bilelmoussaoui/Hardcode-Tray | 8b264ca2386cabe595b027933a9c80c41adc1ea4 | HardcodeTray/modules/applications/helpers/extract.py | python | ExtractApplication.extract | (self, icon_path) | Extract binary file. | Extract binary file. | [
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] | def extract(self, icon_path):
"""Extract binary file.""" | [
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asteroid-team/asteroid | fae2f7d1d4eb83da741818a5c375267fe8d98847 | egs/avspeech/looking-to-listen/model.py | python | fast_icRM | (Y, crm, K=10, C=0.1) | return S | fast iCRM.
Args:
Y (torch.Tensor): mixed/noised stft.
crm (torch.Tensor): DNN output of compressed crm.
K (torch.Tensor): parameter to control the compression.
C (torch.Tensor): parameter to control the compression.
Returns:
S (torch.Tensor): clean stft. | fast iCRM. | [
"fast",
"iCRM",
"."
] | def fast_icRM(Y, crm, K=10, C=0.1):
"""fast iCRM.
Args:
Y (torch.Tensor): mixed/noised stft.
crm (torch.Tensor): DNN output of compressed crm.
K (torch.Tensor): parameter to control the compression.
C (torch.Tensor): parameter to control the compression.
Returns:
S (torch.Tensor): clean stft.
"""
M = cRM_tanh_recover(crm, K, C)
S = torch.zeros(M.shape)
S[:, 0, ...] = (M[:, 0, ...] * Y[:, 0, ...]) - (M[:, 1, ...] * Y[:, 1, ...])
S[:, 1, ...] = (M[:, 0, ...] * Y[:, 1, ...]) + (M[:, 1, ...] * Y[:, 0, ...])
return S | [
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researchmm/TracKit | 510e240faf71b4a225d6f18bcf49b3eebf8b436e | lib/utils/utils.py | python | remove_prefix | (state_dict, prefix) | return {f(key): value for key, value in state_dict.items()} | Old style model is stored with all names of parameters share common prefix 'module.' | Old style model is stored with all names of parameters share common prefix 'module.' | [
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"."
] | def remove_prefix(state_dict, prefix):
'''
Old style model is stored with all names of parameters share common prefix 'module.'
'''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()} | [
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ljean/modbus-tk | 1159c71794071ae67f73f86fa14dd71c989b4859 | modbus_tk/modbus_rtu.py | python | RtuMaster._do_open | (self) | Open the given serial port if not already opened | Open the given serial port if not already opened | [
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"""Open the given serial port if not already opened"""
if not self._serial.is_open:
call_hooks("modbus_rtu.RtuMaster.before_open", (self, ))
self._serial.open() | [
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m-labs/artiq | eaa1505c947c7987cdbd31c24056823c740e84e0 | artiq/coredevice/phaser.py | python | Phaser.dac_write | (self, addr, data) | Write 16 bit to a DAC register.
:param addr: Register address
:param data: Register data to write | Write 16 bit to a DAC register. | [
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"to",
"a",
"DAC",
"register",
"."
] | def dac_write(self, addr, data):
"""Write 16 bit to a DAC register.
:param addr: Register address
:param data: Register data to write
"""
div = 34 # 100 ns min period
t_xfer = self.core.seconds_to_mu((8 + 1)*div*4*ns)
self.spi_cfg(select=PHASER_SEL_DAC, div=div, end=0)
self.spi_write(addr)
delay_mu(t_xfer)
self.spi_write(data >> 8)
delay_mu(t_xfer)
self.spi_cfg(select=PHASER_SEL_DAC, div=div, end=1)
self.spi_write(data)
delay_mu(t_xfer) | [
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quantumlib/Cirq | 89f88b01d69222d3f1ec14d649b7b3a85ed9211f | cirq-google/cirq_google/engine/abstract_job.py | python | AbstractJob.results | (self) | Returns the job results, blocking until the job is complete. | Returns the job results, blocking until the job is complete. | [
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] | def results(self) -> Sequence[cirq.Result]:
"""Returns the job results, blocking until the job is complete.""" | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift_tools/monitoring/generic_metric_sender.py | python | GenericMetricSender.add_heartbeat | (self, add_heartbeat, host=None) | empty implementation overridden by derived classes | empty implementation overridden by derived classes | [
"empty",
"implementation",
"overridden",
"by",
"derived",
"classes"
] | def add_heartbeat(self, add_heartbeat, host=None):
""" empty implementation overridden by derived classes """
pass | [
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nodejs/node-gyp | a2f298870692022302fa27a1d42363c4a72df407 | gyp/pylib/gyp/generator/android.py | python | AndroidMkWriter.WriteTarget | (
self, spec, configs, deps, link_deps, part_of_all, write_alias_target
) | Write Makefile code to produce the final target of the gyp spec.
spec, configs: input from gyp.
deps, link_deps: dependency lists; see ComputeDeps()
part_of_all: flag indicating this target is part of 'all'
write_alias_target: flag indicating whether to create short aliases for this
target | Write Makefile code to produce the final target of the gyp spec. | [
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] | def WriteTarget(
self, spec, configs, deps, link_deps, part_of_all, write_alias_target
):
"""Write Makefile code to produce the final target of the gyp spec.
spec, configs: input from gyp.
deps, link_deps: dependency lists; see ComputeDeps()
part_of_all: flag indicating this target is part of 'all'
write_alias_target: flag indicating whether to create short aliases for this
target
"""
self.WriteLn("### Rules for final target.")
if self.type != "none":
self.WriteTargetFlags(spec, configs, link_deps)
settings = spec.get("aosp_build_settings", {})
if settings:
self.WriteLn("### Set directly by aosp_build_settings.")
for k, v in settings.items():
if isinstance(v, list):
self.WriteList(v, k)
else:
self.WriteLn(f"{k} := {make.QuoteIfNecessary(v)}")
self.WriteLn("")
# Add to the set of targets which represent the gyp 'all' target. We use the
# name 'gyp_all_modules' as the Android build system doesn't allow the use
# of the Make target 'all' and because 'all_modules' is the equivalent of
# the Make target 'all' on Android.
if part_of_all and write_alias_target:
self.WriteLn('# Add target alias to "gyp_all_modules" target.')
self.WriteLn(".PHONY: gyp_all_modules")
self.WriteLn("gyp_all_modules: %s" % self.android_module)
self.WriteLn("")
# Add an alias from the gyp target name to the Android module name. This
# simplifies manual builds of the target, and is required by the test
# framework.
if self.target != self.android_module and write_alias_target:
self.WriteLn("# Alias gyp target name.")
self.WriteLn(".PHONY: %s" % self.target)
self.WriteLn(f"{self.target}: {self.android_module}")
self.WriteLn("")
# Add the command to trigger build of the target type depending
# on the toolset. Ex: BUILD_STATIC_LIBRARY vs. BUILD_HOST_STATIC_LIBRARY
# NOTE: This has to come last!
modifier = ""
if self.toolset == "host":
modifier = "HOST_"
if self.type == "static_library":
self.WriteLn("include $(BUILD_%sSTATIC_LIBRARY)" % modifier)
elif self.type == "shared_library":
self.WriteLn("LOCAL_PRELINK_MODULE := false")
self.WriteLn("include $(BUILD_%sSHARED_LIBRARY)" % modifier)
elif self.type == "executable":
self.WriteLn("LOCAL_CXX_STL := libc++_static")
# Executables are for build and test purposes only, so they're installed
# to a directory that doesn't get included in the system image.
self.WriteLn("LOCAL_MODULE_PATH := $(gyp_shared_intermediate_dir)")
self.WriteLn("include $(BUILD_%sEXECUTABLE)" % modifier)
else:
self.WriteLn("LOCAL_MODULE_PATH := $(PRODUCT_OUT)/gyp_stamp")
self.WriteLn("LOCAL_UNINSTALLABLE_MODULE := true")
if self.toolset == "target":
self.WriteLn("LOCAL_2ND_ARCH_VAR_PREFIX := $(GYP_VAR_PREFIX)")
else:
self.WriteLn("LOCAL_2ND_ARCH_VAR_PREFIX := $(GYP_HOST_VAR_PREFIX)")
self.WriteLn()
self.WriteLn("include $(BUILD_SYSTEM)/base_rules.mk")
self.WriteLn()
self.WriteLn("$(LOCAL_BUILT_MODULE): $(LOCAL_ADDITIONAL_DEPENDENCIES)")
self.WriteLn('\t$(hide) echo "Gyp timestamp: $@"')
self.WriteLn("\t$(hide) mkdir -p $(dir $@)")
self.WriteLn("\t$(hide) touch $@")
self.WriteLn()
self.WriteLn("LOCAL_2ND_ARCH_VAR_PREFIX :=") | [
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Azure/batch-shipyard | d6da749f9cd678037bd520bc074e40066ea35b56 | convoy/settings.py | python | get_gluster_on_compute_volume | () | return _GLUSTER_ON_COMPUTE_VOLUME | Get gluster on compute volume mount suffix
:rtype: str
:return: gluster on compute volume mount | Get gluster on compute volume mount suffix
:rtype: str
:return: gluster on compute volume mount | [
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"volume",
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] | def get_gluster_on_compute_volume():
# type: (None) -> str
"""Get gluster on compute volume mount suffix
:rtype: str
:return: gluster on compute volume mount
"""
return _GLUSTER_ON_COMPUTE_VOLUME | [
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biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | BioSQL/BioSeqDatabase.py | python | Adaptor.autocommit | (self, y=True) | return self.dbutils.autocommit(self.conn, y) | Set the autocommit mode. True values enable; False value disable. | Set the autocommit mode. True values enable; False value disable. | [
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nuxeo/FunkLoad | 8a3a44c20398098d03197baeef27a4177858df1b | src/funkload/ReportRenderRst.py | python | RenderRst.renderDefinitions | (self) | Render field definition. | Render field definition. | [
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"""Render field definition."""
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self.append(LI + ' Request: a single GET/POST/redirect/XML-RPC request.')
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self.append(LI + ' STPS: Successful tests per second.')
self.append(LI + ' SPPS: Successful pages per second.')
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trailofbits/manticore | b050fdf0939f6c63f503cdf87ec0ab159dd41159 | manticore/native/cpu/x86.py | python | X86Cpu.SETNBE | (cpu, dest) | Sets byte if not below or equal.
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carla-simulator/scenario_runner | f4d00d88eda4212a1e119515c96281a4be5c234e | srunner/tools/openscenario_parser.py | python | ParameterRef.is_parameter | (self) | return self._is_matching(pattern=r"[$][A-Za-z_][\w]*") | Returns: True when text is a parameter | Returns: True when text is a parameter | [
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sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/contrib/gis/gdal/raster/band.py | python | GDALBand.nodata_value | (self, value) | Set the nodata value for this band. | Set the nodata value for this band. | [
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"""
Set the nodata value for this band.
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if value is None:
if not capi.delete_band_nodata_value:
raise ValueError('GDAL >= 2.1 required to delete nodata values.')
capi.delete_band_nodata_value(self._ptr)
elif not isinstance(value, (int, float)):
raise ValueError('Nodata value must be numeric or None.')
else:
capi.set_band_nodata_value(self._ptr, value)
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awslabs/aws-security-automation | d659c1752c5b7af59acad7c2bc540cd210040cdb | EC2 Auto Clean Room Forensics/Lambda-Functions/generateSupportTicket.py | python | lambda_handler | (event, context) | return event | [] | def lambda_handler(event, context):
# TODO Implement ITSM Connectivity to log an incident in ITSM system
# Python Sample to connect to ServiceNow
#Need to install requests package for python
# #easy_install requests
# import requests
#
# # Set the request parameters
# url = 'https://instance.service-now.com/api/now/table/incident'
#
# # Eg. User name="admin", Password="admin" for this code sample.
# user = 'admin'
# pwd = 'admin'
#
# # Set proper headers
# headers = {"Content-Type":"application/xml","Accept":"application/xml"}
#
# # Do the HTTP request
# response = requests.post(url, auth=(user, pwd), headers=headers ,data="<request><entry><short_description>Unable to connect to office wifi</short_description><assignment_group>287ebd7da9fe198100f92cc8d1d2154e</assignment_group><urgency>2</urgency><impact>2</impact></entry></request>")
#
# # Check for HTTP codes other than 200
# if response.status_code != 200:
# print('Status:', response.status_code, 'Headers:', response.headers, 'Error Response:',response.json())
# exit()
#
# # Decode the JSON response into a dictionary and use the data
# data = response.json()
# print(data)
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/bs4/builder/__init__.py | python | TreeBuilderRegistry.lookup | (self, *features) | return None | [] | def lookup(self, *features):
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# There are no builders at all.
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# They didn't ask for any features. Give them the most
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features = list(features)
features.reverse()
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candidate_set = None
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feature = features.pop()
we_have_the_feature = self.builders_for_feature.get(feature, [])
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/utils/dummy_pt_objects.py | python | MegatronBertModel.forward | (self, *args, **kwargs) | [] | def forward(self, *args, **kwargs):
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/controllers/core_helpers/status.py | python | CoreStatus.get_grep_eta | (self) | return self.calculate_eta(self.get_grep_input_speed(),
self.get_grep_output_speed(),
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adjustment=adjustment) | [] | def get_grep_eta(self):
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return self.calculate_eta(self.get_grep_input_speed(),
self.get_grep_output_speed(),
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Cue/scales | 0aced26eb050ceb98ee9d5d6cdca8db448666986 | src/greplin/scales/samplestats.py | python | UniformSample.__init__ | (self) | Create an empty sample. | Create an empty sample. | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/markdown/markdown/extensions/footnotes.py | python | FootnoteExtension.makeFootnoteId | (self, id) | Return footnote link id. | Return footnote link id. | [
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""" Return footnote link id. """
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enthought/traitsui | b7c38c7a47bf6ae7971f9ddab70c8a358647dd25 | traitsui/table_column.py | python | TableColumn.get_edit_height | (self, object) | return self.edit_height | Returns the height of the column cell's row while it is being
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Runbook/runbook | 7b68622f75ef09f654046f0394540025f3ee7445 | src/actions/actions/rackspace-reboot/__init__.py | python | action | (**kwargs) | This method is called to action a reaction | This method is called to action a reaction | [
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jdata = kwargs['jdata']
logger = kwargs['logger']
run = True
# Check for Trigger
if redata['trigger'] > jdata['failcount']:
run = False
# Check for lastrun
checktime = time.time() - float(redata['lastrun'])
if checktime < redata['frequency']:
run = False
if redata['data']['call_on'] not in jdata['check']['status']:
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if run:
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | txdav/common/datastore/podding/migration/home_sync.py | python | CrossPodHomeSync._remoteHome | (self, txn) | Create a synthetic external home object that maps to the actual remote home. | Create a synthetic external home object that maps to the actual remote home. | [
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resourceID = yield txn.store().conduit.send_home_resource_id(txn, self.record, migrating=True)
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if self.disabledRemote:
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/cloud/clouds/linode.py | python | LinodeAPI.create | (self, vm_) | create implementation | create implementation | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/modform_hecketriangle/hecke_triangle_groups.py | python | HeckeTriangleGroup.U | (self) | return self.T() * self.S() | r"""
Return an alternative generator of ``self`` instead of ``T``.
``U`` stabilizes ``rho`` and has order ``2*self.n()``.
If ``n=infinity`` then ``U`` is parabolic and has infinite order,
it then fixes the cusp ``[-1]``.
EXAMPLES::
sage: from sage.modular.modform_hecketriangle.hecke_triangle_groups import HeckeTriangleGroup
sage: HeckeTriangleGroup(3).U()
[ 1 -1]
[ 1 0]
sage: HeckeTriangleGroup(3).U()^3 == -HeckeTriangleGroup(3).I()
True
sage: HeckeTriangleGroup(3).U()^6 == HeckeTriangleGroup(3).I()
True
sage: HeckeTriangleGroup(10).U()
[lam -1]
[ 1 0]
sage: HeckeTriangleGroup(10).U()^10 == -HeckeTriangleGroup(10).I()
True
sage: HeckeTriangleGroup(10).U()^20 == HeckeTriangleGroup(10).I()
True
sage: HeckeTriangleGroup(10).U().parent()
Hecke triangle group for n = 10 | r"""
Return an alternative generator of ``self`` instead of ``T``.
``U`` stabilizes ``rho`` and has order ``2*self.n()``. | [
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EXAMPLES::
sage: from sage.modular.modform_hecketriangle.hecke_triangle_groups import HeckeTriangleGroup
sage: HeckeTriangleGroup(3).U()
[ 1 -1]
[ 1 0]
sage: HeckeTriangleGroup(3).U()^3 == -HeckeTriangleGroup(3).I()
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sage: HeckeTriangleGroup(10).U()
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sage: HeckeTriangleGroup(10).U()^10 == -HeckeTriangleGroup(10).I()
True
sage: HeckeTriangleGroup(10).U()^20 == HeckeTriangleGroup(10).I()
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sage: HeckeTriangleGroup(10).U().parent()
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return self.T() * self.S() | [
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raiden-network/raiden | 76c68b426a6f81f173b9a2c09bd88a610502c38b | raiden/transfer/mediated_transfer/initiator.py | python | handle_block | (
initiator_state: InitiatorTransferState,
state_change: Block,
channel_state: NettingChannelState,
pseudo_random_generator: random.Random,
) | Checks if the lock has expired, and if it has sends a remove expired
lock and emits the failing events. | Checks if the lock has expired, and if it has sends a remove expired
lock and emits the failing events. | [
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] | def handle_block(
initiator_state: InitiatorTransferState,
state_change: Block,
channel_state: NettingChannelState,
pseudo_random_generator: random.Random,
) -> TransitionResult[Optional[InitiatorTransferState]]:
"""Checks if the lock has expired, and if it has sends a remove expired
lock and emits the failing events.
"""
secrethash = initiator_state.transfer.lock.secrethash
locked_lock = channel_state.our_state.secrethashes_to_lockedlocks.get(secrethash)
if not locked_lock:
if channel_state.partner_state.secrethashes_to_lockedlocks.get(secrethash):
return TransitionResult(initiator_state, [])
else:
# if lock is not in our or our partner's locked locks then the
# task can go
return TransitionResult(None, [])
lock_expiration_threshold = BlockExpiration(
locked_lock.expiration + DEFAULT_WAIT_BEFORE_LOCK_REMOVAL
)
lock_has_expired = channel.is_lock_expired(
end_state=channel_state.our_state,
lock=locked_lock,
block_number=state_change.block_number,
lock_expiration_threshold=lock_expiration_threshold,
)
events: List[Event] = []
if lock_has_expired and initiator_state.transfer_state != "transfer_expired":
is_channel_open = channel.get_status(channel_state) == ChannelState.STATE_OPENED
if is_channel_open:
recipient_address = channel_state.partner_state.address
recipient_metadata = get_address_metadata(recipient_address, [initiator_state.route])
expired_lock_events = channel.send_lock_expired(
channel_state=channel_state,
locked_lock=locked_lock,
pseudo_random_generator=pseudo_random_generator,
recipient_metadata=recipient_metadata,
)
events.extend(expired_lock_events)
if initiator_state.received_secret_request:
reason = "lock expired, despite receiving secret request"
else:
reason = "lock expired"
transfer_description = initiator_state.transfer_description
payment_identifier = transfer_description.payment_identifier
# TODO: When we introduce multiple transfers per payment this needs to be
# reconsidered. As we would want to try other routes once a route
# has failed, and a transfer failing does not mean the entire payment
# would have to fail.
# Related issue: https://github.com/raiden-network/raiden/issues/2329
payment_failed = EventPaymentSentFailed(
token_network_registry_address=transfer_description.token_network_registry_address,
token_network_address=transfer_description.token_network_address,
identifier=payment_identifier,
target=transfer_description.target,
reason=reason,
)
route_failed = EventRouteFailed(
secrethash=secrethash,
route=initiator_state.route.route,
token_network_address=transfer_description.token_network_address,
)
unlock_failed = EventUnlockFailed(
identifier=payment_identifier,
secrethash=initiator_state.transfer_description.secrethash,
reason=reason,
)
lock_exists = channel.lock_exists_in_either_channel_side(
channel_state=channel_state, secrethash=secrethash
)
initiator_state.transfer_state = "transfer_expired"
return TransitionResult(
# If the lock is either in our state or partner state we keep the
# task around to wait for the LockExpired messages to sync.
# Check https://github.com/raiden-network/raiden/issues/3183
initiator_state if lock_exists else None,
events + [payment_failed, route_failed, unlock_failed],
)
else:
return TransitionResult(initiator_state, events) | [
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panchunguang/ccks_baidu_entity_link | f6eb5298620b460f2f1222a3b182d15c938c4ea2 | code/ER_ner_bert_crf.py | python | train | () | NER模型训练，9折交叉验证，分贝用loss和f1保存模型，共18个
:return: | NER模型训练，9折交叉验证，分贝用loss和f1保存模型，共18个
:return: | [
"NER模型训练，9折交叉验证，分贝用loss和f1保存模型，共18个",
":",
"return",
":"
] | def train():
'''
NER模型训练，9折交叉验证，分贝用loss和f1保存模型，共18个
:return:
'''
train = get_input('data/input_train_ner.pkl')
kfold = KFold(n_splits=9, shuffle=False)
for i, (tra_index, val_index) in enumerate(kfold.split(train[0])):
K.clear_session()
input_train = [tra[tra_index] for tra in train]
input_val = [tra[val_index] for tra in train]
filepath_loss = "model/NER_bert_crf_loss.h5_"+str(i)
filepath_f1 = "model/NER_bert_crf_f1.h5_"+str(i)
evaluate = Evaluate((input_val[:-1], input_val[-1]), filepath_f1)
checkpoint = ModelCheckpoint(filepath_loss, monitor='val_loss', verbose=2, save_best_only=True, mode='min')
earlystopping = EarlyStopping(monitor='val_loss', min_delta=0.000001, patience=2, verbose=2, mode='auto')
lrate = LearningRateScheduler(step_decay,verbose=2)
callbacks = [checkpoint,evaluate,lrate,earlystopping]
model = bert_model()
print(model.summary())
logging.debug(str(i)*30)
model = model.fit(input_train[:-1], input_train[-1], batch_size=arg.batch_size, epochs=arg.num_epochs,
validation_data=(input_val[:-1], input_val[-1]), verbose=1, callbacks=callbacks)
logging.debug(arg.__str__())
logging.debug(model.history)
logging.debug(np.min(model.history['val_loss'])) | [
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cheind/tf-matplotlib | c6904d3d2d306d9a479c24fbcb1f674a57dafd0e | tfmpl/create.py | python | create_figure | (*fig_args, **fig_kwargs) | return fig | Create a single figure.
Args and Kwargs are passed to `matplotlib.figure.Figure`.
This routine is provided in order to avoid usage of pyplot which
is stateful and not thread safe. As drawing routines in tf-matplotlib
are called from py-funcs in their respective thread, avoid usage
of pyplot where possible. | Create a single figure. | [
"Create",
"a",
"single",
"figure",
"."
] | def create_figure(*fig_args, **fig_kwargs):
'''Create a single figure.
Args and Kwargs are passed to `matplotlib.figure.Figure`.
This routine is provided in order to avoid usage of pyplot which
is stateful and not thread safe. As drawing routines in tf-matplotlib
are called from py-funcs in their respective thread, avoid usage
of pyplot where possible.
'''
fig = Figure(*fig_args, **fig_kwargs)
# Attach canvas
FigureCanvas(fig)
return fig | [
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Tencent/GAutomator | 0ac9f849d1ca2c59760a91c5c94d3db375a380cd | GAutomatorIos/ga2/device/iOS/wda/__init__.py | python | Alert.click | (self, button_name) | return self.http.post('/alert/accept', data={"name": button_name}) | Args:
- button_name: the name of the button | Args:
- button_name: the name of the button | [
"Args",
":",
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":",
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] | def click(self, button_name):
"""
Args:
- button_name: the name of the button
"""
# Actually, It has no difference POST to accept or dismiss
return self.http.post('/alert/accept', data={"name": button_name}) | [
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santhoshkolloju/Abstractive-Summarization-With-Transfer-Learning | 97ff2ae3ba9f2d478e174444c4e0f5349f28c319 | texar_repo/texar/data/data/mono_text_data.py | python | MonoTextData.embedding_init_value | (self) | return self._embedding.word_vecs | The `Tensor` containing the embedding value loaded from file.
`None` if embedding is not specified. | The `Tensor` containing the embedding value loaded from file.
`None` if embedding is not specified. | [
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] | def embedding_init_value(self):
"""The `Tensor` containing the embedding value loaded from file.
`None` if embedding is not specified.
"""
if self._embedding is None:
return None
return self._embedding.word_vecs | [
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achillesrasquinha/pipupgrade | b78e06cbdc5bf4dde8e9568423f9341a91cd44e1 | src/pipupgrade/commands/util/__init__.py | python | group_commands | (group, commands) | return group | Add command-paths to a click.Group | Add command-paths to a click.Group | [
"Add",
"command",
"-",
"paths",
"to",
"a",
"click",
".",
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] | def group_commands(group, commands):
"""
Add command-paths to a click.Group
"""
commands = sequencify(commands, type_ = tuple)
for command in commands:
head, tail = command.rsplit(".", 1)
tails = ("", tail, "command")
for i, tail in enumerate(tails):
try:
path = "%s.%s" % (command, tail)
command = import_handler(path)
break
except:
if i == len(tails) - 1:
raise
group.add_command(command)
return group | [
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astropy/astroquery | 11c9c83fa8e5f948822f8f73c854ec4b72043016 | astroquery/esa/jwst/core.py | python | JwstClass.__get_proposal_id_condition | (self, *, value=None) | return condition | [] | def __get_proposal_id_condition(self, *, value=None):
condition = ""
if(value is not None):
if(not isinstance(value, str)):
raise ValueError("proposal_id must be string")
else:
condition = " AND proposal_id ILIKE '%"+value+"%' "
return condition | [
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... | https://github.com/astropy/astroquery/blob/11c9c83fa8e5f948822f8f73c854ec4b72043016/astroquery/esa/jwst/core.py#L1186-L1194 | |||
mcfletch/pyopengl | 02d11dad9ff18e50db10e975c4756e17bf198464 | OpenGL/GL/EXT/EGL_image_storage.py | python | glInitEglImageStorageEXT | () | return extensions.hasGLExtension( _EXTENSION_NAME ) | Return boolean indicating whether this extension is available | Return boolean indicating whether this extension is available | [
"Return",
"boolean",
"indicating",
"whether",
"this",
"extension",
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] | def glInitEglImageStorageEXT():
'''Return boolean indicating whether this extension is available'''
from OpenGL import extensions
return extensions.hasGLExtension( _EXTENSION_NAME ) | [
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jamesls/python-keepassx | cf3c8f33b17b8eb6beaa1a8dd83ce1921dcde975 | keepassx/db.py | python | Header.encryption_type | (self) | [] | def encryption_type(self):
for name, value in self.ENCRYPTION_TYPES[1:]:
if value & self.flags:
return name | [
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matousc89/padasip | b44c2815000dd4d1b855c49e469072e919df15cd | padasip/ann/mlp.py | python | Layer.__init__ | (self, n_layer, n_input, activation_f, mu) | [] | def __init__(self, n_layer, n_input, activation_f, mu):
sigma = n_input**(-0.5)
if mu == "auto":
self.mu = sigma
else:
self.mu = mu
self.n_input = n_input
self.w = np.random.normal(0, sigma, (n_layer, n_input+1))
self.x = np.ones(n_input+1)
self.y = np.zeros(n_input+1)
self.f = activation_f | [
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cleverhans-lab/cleverhans | e5d00e537ce7ad6119ed5a8db1f0e9736d1f6e1d | cleverhans/experimental/certification/utils.py | python | tf_lanczos_smallest_eigval | (
vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.float32,
) | return smallest_eigval, smallest_eigvec | Computes smallest eigenvector and eigenvalue using Lanczos in pure TF.
This function computes smallest eigenvector and eigenvalue of the matrix
which is implicitly specified by `vector_prod_fn`.
`vector_prod_fn` is a function which takes `x` and returns a product of matrix
in consideration and `x`.
Computation is done using Lanczos algorithm, see
https://en.wikipedia.org/wiki/Lanczos_algorithm#The_algorithm
Args:
vector_prod_fn: function which takes a vector as an input and returns
matrix vector product.
matrix_dim: dimentionality of the matrix.
initial_vector: guess vector to start the algorithm with
num_iter: user-defined number of iterations for the algorithm
max_iter: maximum number of iterations.
collapse_tol: tolerance to determine collapse of the Krylov subspace
dtype: type of data
Returns:
tuple of (eigenvalue, eigenvector) of smallest eigenvalue and corresponding
eigenvector. | Computes smallest eigenvector and eigenvalue using Lanczos in pure TF. | [
"Computes",
"smallest",
"eigenvector",
"and",
"eigenvalue",
"using",
"Lanczos",
"in",
"pure",
"TF",
"."
] | def tf_lanczos_smallest_eigval(
vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.float32,
):
"""Computes smallest eigenvector and eigenvalue using Lanczos in pure TF.
This function computes smallest eigenvector and eigenvalue of the matrix
which is implicitly specified by `vector_prod_fn`.
`vector_prod_fn` is a function which takes `x` and returns a product of matrix
in consideration and `x`.
Computation is done using Lanczos algorithm, see
https://en.wikipedia.org/wiki/Lanczos_algorithm#The_algorithm
Args:
vector_prod_fn: function which takes a vector as an input and returns
matrix vector product.
matrix_dim: dimentionality of the matrix.
initial_vector: guess vector to start the algorithm with
num_iter: user-defined number of iterations for the algorithm
max_iter: maximum number of iterations.
collapse_tol: tolerance to determine collapse of the Krylov subspace
dtype: type of data
Returns:
tuple of (eigenvalue, eigenvector) of smallest eigenvalue and corresponding
eigenvector.
"""
# alpha will store diagonal elements
alpha = tf.TensorArray(dtype, size=1, dynamic_size=True, element_shape=())
# beta will store off diagonal elements
beta = tf.TensorArray(dtype, size=0, dynamic_size=True, element_shape=())
# q will store Krylov space basis
q_vectors = tf.TensorArray(
dtype, size=1, dynamic_size=True, element_shape=(matrix_dim, 1)
)
# If start vector is all zeros, make it a random normal vector and run for max_iter
if tf.norm(initial_vector) < collapse_tol:
initial_vector = tf.random_normal(shape=(matrix_dim, 1), dtype=dtype)
num_iter = max_iter
w = initial_vector / tf.norm(initial_vector)
# Iteration 0 of Lanczos
q_vectors = q_vectors.write(0, w)
w_ = vector_prod_fn(w)
cur_alpha = tf.reduce_sum(w_ * w)
alpha = alpha.write(0, cur_alpha)
w_ = w_ - tf.scalar_mul(cur_alpha, w)
w_prev = w
w = w_
# Subsequent iterations of Lanczos
for i in tf.range(1, num_iter):
cur_beta = tf.norm(w)
if cur_beta < collapse_tol:
# return early if Krylov subspace collapsed
break
# cur_beta is larger than collapse_tol,
# so division will return finite result.
w = w / cur_beta
w_ = vector_prod_fn(w)
cur_alpha = tf.reduce_sum(w_ * w)
q_vectors = q_vectors.write(i, w)
alpha = alpha.write(i, cur_alpha)
beta = beta.write(i - 1, cur_beta)
w_ = w_ - tf.scalar_mul(cur_alpha, w) - tf.scalar_mul(cur_beta, w_prev)
w_prev = w
w = w_
alpha = alpha.stack()
beta = beta.stack()
q_vectors = tf.reshape(q_vectors.stack(), (-1, matrix_dim))
offdiag_submatrix = tf.linalg.diag(beta)
tridiag_matrix = (
tf.linalg.diag(alpha)
+ tf.pad(offdiag_submatrix, [[0, 1], [1, 0]])
+ tf.pad(offdiag_submatrix, [[1, 0], [0, 1]])
)
eigvals, eigvecs = tf.linalg.eigh(tridiag_matrix)
smallest_eigval = eigvals[0]
smallest_eigvec = tf.matmul(tf.reshape(eigvecs[:, 0], (1, -1)), q_vectors)
smallest_eigvec = smallest_eigvec / tf.norm(smallest_eigvec)
smallest_eigvec = tf.reshape(smallest_eigvec, (matrix_dim, 1))
return smallest_eigval, smallest_eigvec | [
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OpenZWave/python-openzwave | 8be4c070294348f3fc268bc1d7ad2c535f352f5a | src-api/openzwave/controller.py | python | ZWaveController.begin_command_has_node_failed | (self, node_id) | return self._network.manager.beginControllerCommand(self.home_id, \
self.CMD_HASNODEFAILED, self.zwcallback, nodeId=node_id) | Check whether a node is in the controller's failed nodes list.
:param node_id: Used only with the ReplaceFailedNode command, to specify the node that is going to be replaced.
:type node_id: int
:return: True if the command was accepted and has started.
:rtype: bool | Check whether a node is in the controller's failed nodes list. | [
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] | def begin_command_has_node_failed(self, node_id):
"""
Check whether a node is in the controller's failed nodes list.
:param node_id: Used only with the ReplaceFailedNode command, to specify the node that is going to be replaced.
:type node_id: int
:return: True if the command was accepted and has started.
:rtype: bool
"""
return self._network.manager.beginControllerCommand(self.home_id, \
self.CMD_HASNODEFAILED, self.zwcallback, nodeId=node_id) | [
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mit-han-lab/once-for-all | 4f6fce3652ee4553ea811d38f32f90ac8b1bc378 | ofa/tutorial/evolution_finder.py | python | EvolutionFinder.crossover_sample | (self, sample1, sample2) | [] | def crossover_sample(self, sample1, sample2):
constraint = self.efficiency_constraint
while True:
new_sample = copy.deepcopy(sample1)
for key in new_sample.keys():
if not isinstance(new_sample[key], list):
continue
for i in range(len(new_sample[key])):
new_sample[key][i] = random.choice([sample1[key][i], sample2[key][i]])
efficiency = self.efficiency_predictor.predict_efficiency(new_sample)
if efficiency <= constraint:
return new_sample, efficiency | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/musicbrainzngs/musicbrainz.py | python | browse_artists | (recording=None, release=None, release_group=None,
work=None, includes=[], limit=None, offset=None) | return _browse_impl("artist", includes, limit, offset, params) | Get all artists linked to a recording, a release or a release group.
You need to give one MusicBrainz ID.
*Available includes*: {includes} | Get all artists linked to a recording, a release or a release group.
You need to give one MusicBrainz ID. | [
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work=None, includes=[], limit=None, offset=None):
"""Get all artists linked to a recording, a release or a release group.
You need to give one MusicBrainz ID.
*Available includes*: {includes}"""
params = {"recording": recording,
"release": release,
"release-group": release_group,
"work": work}
return _browse_impl("artist", includes, limit, offset, params) | [
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arskom/spyne | 88b8e278335f03c7e615b913d6dabc2b8141730e | spyne/util/six.py | python | add_metaclass | (metaclass) | return wrapper | Class decorator for creating a class with a metaclass. | Class decorator for creating a class with a metaclass. | [
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"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
slots = orig_vars.get('__slots__')
if slots is not None:
if isinstance(slots, str):
slots = [slots]
for slots_var in slots:
orig_vars.pop(slots_var)
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
if hasattr(cls, '__qualname__'):
orig_vars['__qualname__'] = cls.__qualname__
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper | [
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tensorflow/lingvo | ce10019243d954c3c3ebe739f7589b5eebfdf907 | lingvo/jax/schedules.py | python | BaseSchedule.value | (self, count: JTensor) | Returns the value of schedule at step 'count'.
Args:
count: a scalar uint32 array.
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"""Returns the value of schedule at step 'count'.
Args:
count: a scalar uint32 array.
Returns:
A float32 value of the schedule at step 'count' as a scalar array.
"""
raise NotImplementedError() | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/partition_tuple.py | python | RegularPartitionTuples_size.__iter__ | (self) | r"""
Iterate through the class of `\ell`-regular partition tuples
of a fixed size.
EXAMPLES::
sage: PartitionTuples(size=4, regular=2)[:10]
[([4]),
([3, 1]),
([4], []),
([3, 1], []),
([3], [1]),
([2, 1], [1]),
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([1], [3]),
([1], [2, 1]),
([], [4])] | r"""
Iterate through the class of `\ell`-regular partition tuples
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r"""
Iterate through the class of `\ell`-regular partition tuples
of a fixed size.
EXAMPLES::
sage: PartitionTuples(size=4, regular=2)[:10]
[([4]),
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"""
for level in PositiveIntegers():
for mu in RegularPartitionTuples_level_size(level, self._size, self._ell):
yield self.element_class(self, list(mu)) | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | HierarchicalRL/hrl_rewards.py | python | reward_user_deepmoji | (conversations) | return rewards | Allocates reward based on deepmoji sentiment of user response | Allocates reward based on deepmoji sentiment of user response | [
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"""Allocates reward based on deepmoji sentiment of user response"""
# Init deepmoji just once
if 'botmoji' not in globals():
print('Loading deepmoji')
global botmoji
botmoji = Botmoji()
num_convs = len(conversations)
episode_len = (len(conversations[0]) - 1) // 2
# Flattened user responses
user_responses = [resp for conv in conversations for resp in conv[2::2]]
# Run deepmoji
reward_multiplier = _get_reward_multiplier()
user_emojis = botmoji.encode_multiple(user_responses)
rewards = np.dot(user_emojis, reward_multiplier)
for i, resp in enumerate(user_responses):
if '<unk>' in user_responses[i]:
rewards[i] = -0.5
rewards = rewards.reshape(num_convs, episode_len)
return rewards | [
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pysmt/pysmt | ade4dc2a825727615033a96d31c71e9f53ce4764 | pysmt/typing.py | python | _IntType.__init__ | (self) | [] | def __init__(self):
decl = _TypeDecl("Int", 0)
PySMTType.__init__(self, decl=decl, args=None) | [
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pyGrowler/Growler | 5492466d8828115bb04c665917d6aeb4f4323f44 | growler/routing.py | python | Router.subrouters | (self) | Generator of sub-routers (middleware inheriting from Router)
contained within this router. | Generator of sub-routers (middleware inheriting from Router)
contained within this router. | [
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] | def subrouters(self):
"""
Generator of sub-routers (middleware inheriting from Router)
contained within this router.
"""
yield from filter(lambda mw: isinstance(mw.func, Router), self.mw_list) | [
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renemarc/home-assistant-config | 775d60ad436cd0f432d2260e503b530920041165 | custom_components/hacs/repositories/repository.py | python | HacsRepository.config_flow | (self) | return False | Return bool if integration has config_flow. | Return bool if integration has config_flow. | [
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] | def config_flow(self):
"""Return bool if integration has config_flow."""
if self.integration_manifest:
if self.data.full_name == "hacs/integration":
return False
return self.integration_manifest.get("config_flow", False)
return False | [
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theelous3/asks | 5c58e2ad3ff8158bf6673475bfb6ee0f6817b2aa | asks/request_object.py | python | RequestProcessor._redirect | (self, response_obj) | return response_obj | Calls the _check_redirect method of the supplied response object
in order to determine if the http status code indicates a redirect.
Returns:
Response: May or may not be the result of recursive calls due
to redirects!
Notes:
If it does redirect, it calls the appropriate method with the
redirect location, returning the response object. Furthermore,
if there is a redirect, this function is recursive in a roundabout
way, storing the previous response object in `.history_objects`. | Calls the _check_redirect method of the supplied response object
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"""
Calls the _check_redirect method of the supplied response object
in order to determine if the http status code indicates a redirect.
Returns:
Response: May or may not be the result of recursive calls due
to redirects!
Notes:
If it does redirect, it calls the appropriate method with the
redirect location, returning the response object. Furthermore,
if there is a redirect, this function is recursive in a roundabout
way, storing the previous response object in `.history_objects`.
"""
redirect, force_get, location = False, None, None
if 300 <= response_obj.status_code < 400:
if response_obj.status_code == 303:
self.data, self.json, self.files = None, None, None
if response_obj.status_code in [301, 305]:
# redirect / force GET / location
redirect = True
force_get = False
else:
redirect = True
force_get = True
location = response_obj.headers["Location"]
if redirect:
allow_redirect = True
location = urljoin(self.uri, location.strip())
if self.auth is not None:
if not self.auth_off_domain:
allow_redirect = self._location_auth_protect(location)
self.uri = location
l_scheme, l_netloc, *_ = urlparse(location)
if l_scheme != self.scheme or l_netloc != self.host:
await self._get_new_sock()
# follow redirect with correct http method type
if force_get:
self.history_objects.append(response_obj)
self.method = "GET"
else:
self.history_objects.append(response_obj)
self.max_redirects -= 1
try:
if response_obj.headers["connection"].lower() == "close":
await self._get_new_sock()
except KeyError:
pass
if allow_redirect:
_, response_obj = await self.make_request()
return response_obj | [
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vlachoudis/bCNC | 67126b4894dabf6579baf47af8d0f9b7de35e6e3 | bCNC/CNCCanvas.py | python | CNCCanvas.menuZoomOut | (self, event=None) | [] | def menuZoomOut(self, event=None):
x = int(self.cget("width" ))//2
y = int(self.cget("height"))//2
self.zoomCanvas(x, y, 0.5) | [
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dragonfly/dragonfly | a579b5eadf452e23b07d4caf27b402703b0012b7 | dragonfly/opt/opt_method_evaluator.py | python | OptMethodEvaluator.run_trial_iteration | (self) | Runs each method in self.methods once and stores the results to be saved. | Runs each method in self.methods once and stores the results to be saved. | [
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""" Runs each method in self.methods once and stores the results to be saved. """
curr_iter_results = Namespace()
for data_type in self.data_to_be_saved:
setattr(curr_iter_results, data_type, self._get_new_iter_results_array())
for data_type in self.data_to_be_saved_if_available:
setattr(curr_iter_results, data_type, self._get_new_iter_results_array())
# Fetch pre-evaluation points.
self.worker_manager.reset()
prev_eval_qinfos = self._get_prev_eval_qinfos()
if prev_eval_qinfos is not None:
prev_eval_vals = [qinfo.val for qinfo in prev_eval_qinfos]
self.reporter.writeln('Using %d pre-eval points with values. eval: %s (%0.4f).'%( \
len(prev_eval_qinfos), prev_eval_vals, max(prev_eval_vals)))
else:
self.reporter.writeln('Not using any pre-eval points.')
# Will go through each method in this loop.
for meth_iter in range(self.num_methods):
curr_method = self.methods[meth_iter]
curr_meth_options = self.method_options[curr_method]
# Set prev_eval points and vals
if prev_eval_qinfos is not None:
curr_meth_options.prev_evaluations = Namespace(qinfos=prev_eval_qinfos)
else:
curr_meth_options.prev_evaluations = None
# Reset worker manager
self.worker_manager.reset()
self.reporter.writeln( \
'\nResetting worker manager: worker_manager.experiment_designer:%s'%( \
str(self.worker_manager.experiment_designer)))
# Call the method here.
self._print_method_header(curr_method)
history = self._optimise_with_method_on_func_caller(curr_method, self.func_caller, \
self.worker_manager, self.max_capital, curr_meth_options, self.reporter)
# Now save results for current method
for data_type in self.data_to_be_saved:
data = getattr(history, data_type)
data_pointer = getattr(curr_iter_results, data_type)
data_pointer[meth_iter, 0] = data
for data_type in self.data_to_be_saved_if_available:
if hasattr(history, data_type):
data = getattr(history, data_type)
else:
data = ['xx'] * len(history.query_points)
data_pointer = getattr(curr_iter_results, data_type)
data_pointer[meth_iter, 0] = data
# Print out results
comp_opt_val = history.curr_true_opt_vals[-1]
num_evals = len(history.curr_true_opt_vals)
self._print_method_result(curr_method, comp_opt_val, num_evals)
# Save results of current iteration
self.update_to_be_saved(curr_iter_results)
self.save_pickle()
self.save_results()
# Save here
self.update_to_be_saved(curr_iter_results)
self.save_pickle() | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/queue.py | python | _PySimpleQueue.put | (self, item, block=True, timeout=None) | Put the item on the queue.
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never blocks. They are provided for compatibility with the Queue class. | Put the item on the queue. | [
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'''Put the item on the queue.
The optional 'block' and 'timeout' arguments are ignored, as this method
never blocks. They are provided for compatibility with the Queue class.
'''
self._queue.append(item)
self._count.release() | [
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] | https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/full/queue.py#L272-L279 | ||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pexpect/fdpexpect.py | python | fdspawn.terminate | (self, force=False) | Deprecated and invalid. Just raises an exception. | Deprecated and invalid. Just raises an exception. | [
"Deprecated",
"and",
"invalid",
".",
"Just",
"raises",
"an",
"exception",
"."
] | def terminate (self, force=False): # pragma: no cover
'''Deprecated and invalid. Just raises an exception.'''
raise ExceptionPexpect('This method is not valid for file descriptors.') | [
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chapmanb/bcbb | dbfb52711f0bfcc1d26c5a5b53c9ff4f50dc0027 | distblast/scripts/homolog_seq_retrieval.py | python | _all_seqs | (ids, orgs, indexes) | Lazy generator of sequences from our indexes, with IDs properly set. | Lazy generator of sequences from our indexes, with IDs properly set. | [
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] | def _all_seqs(ids, orgs, indexes):
"""Lazy generator of sequences from our indexes, with IDs properly set.
"""
for i, cur_id in enumerate(ids):
if cur_id:
rec = indexes[i][cur_id]
rec.id = cur_id
rec.description = orgs[i]
yield rec | [
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tushushu/imylu | 33b0534981e59d81f225b246b608106a97af2a1f | imylu/tree/decision_tree.py | python | DecisionTree.predict | (self, data: ndarray, threshold=0.5) | return (prob >= threshold).astype(int) | Get the prediction of label.
Arguments:
data {ndarray} -- Testing data.
Keyword Arguments:
threshold {float} -- (default: {0.5})
Returns:
ndarray -- Prediction of label. | Get the prediction of label. | [
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"."
] | def predict(self, data: ndarray, threshold=0.5):
"""Get the prediction of label.
Arguments:
data {ndarray} -- Testing data.
Keyword Arguments:
threshold {float} -- (default: {0.5})
Returns:
ndarray -- Prediction of label.
"""
prob = self.predict_prob(data)
return (prob >= threshold).astype(int) | [
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kamalgill/flask-appengine-template | 11760f83faccbb0d0afe416fc58e67ecfb4643c2 | src/lib/werkzeug/_internal.py | python | _DictAccessorProperty.__init__ | (self, name, default=None, load_func=None, dump_func=None,
read_only=None, doc=None) | [] | def __init__(self, name, default=None, load_func=None, dump_func=None,
read_only=None, doc=None):
self.name = name
self.default = default
self.load_func = load_func
self.dump_func = dump_func
if read_only is not None:
self.read_only = read_only
self.__doc__ = doc | [
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Ridter/acefile | 31f90219fb560364b6f5d27f512fccfae81d04f4 | acefile.py | python | LZ77.dic_setsize | (self, dicsize) | Set the required dictionary size for the next LZ77 decompression run. | Set the required dictionary size for the next LZ77 decompression run. | [
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"."
] | def dic_setsize(self, dicsize):
"""
Set the required dictionary size for the next LZ77 decompression run.
"""
self.__dictionary.set_size(dicsize) | [
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] | https://github.com/Ridter/acefile/blob/31f90219fb560364b6f5d27f512fccfae81d04f4/acefile.py#L1515-L1519 | ||
mikecrittenden/zen-coding-gedit | 49966219b1e9b7a1d0d8b4def6a32b6c386b8041 | zencoding/stparser.py | python | _parse_abbreviations | (obj) | Parses all abbreviations inside dictionary
@param obj: dict | Parses all abbreviations inside dictionary | [
"Parses",
"all",
"abbreviations",
"inside",
"dictionary"
] | def _parse_abbreviations(obj):
"""
Parses all abbreviations inside dictionary
@param obj: dict
"""
for key, value in list(obj.items()):
key = key.strip()
if key[-1] == '+':
# this is expando, leave 'value' as is
obj[key] = _make_expando(key, value)
else:
m = re.search(re_tag, value)
if m:
obj[key] = _make_abbreviation(key, m.group(1), m.group(2), (m.group(3) == '/'))
else:
# assume it's reference to another abbreviation
obj[key] = Entry(TYPE_REFERENCE, key, value) | [
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matthiask/plata | a743b8ef3838c1c2764620b18c394afc9708c4d5 | plata/shop/views.py | python | Shop.reverse_url | (self, url_name, *args, **kwargs) | return reverse(url_name, *args, **kwargs) | Hook for customizing the reverse function | Hook for customizing the reverse function | [
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] | def reverse_url(self, url_name, *args, **kwargs):
"""
Hook for customizing the reverse function
"""
return reverse(url_name, *args, **kwargs) | [
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blurstudio/cross3d | 277968d1227de740fc87ef61005c75034420eadf | cross3d/studiomax/studiomaxscene.py | python | StudiomaxScene._createNativeLayerGroup | (self, name, nativeLayers=[]) | return '' | \remarks implements the AbstractScene._createNativeLayerGroup method to create a new layer group in this scene based on the inputed name with the given layers
\param name <str>
\return <str> nativeLayerGroup || None | \remarks implements the AbstractScene._createNativeLayerGroup method to create a new layer group in this scene based on the inputed name with the given layers
\param name <str>
\return <str> nativeLayerGroup || None | [
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"""
\remarks implements the AbstractScene._createNativeLayerGroup method to create a new layer group in this scene based on the inputed name with the given layers
\param name <str>
\return <str> nativeLayerGroup || None
"""
names = list(self.metaData().value('layerGroupNames'))
states = list(self.metaData().value('layerGroupStates'))
if (not name in names):
names.append(str(name))
states.append(True)
self.metaData().setValue('layerGroupNames', names)
self.metaData().setValue('layerGroupStates', states)
return name
return '' | [
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nipy/nipype | cd4c34d935a43812d1756482fdc4034844e485b8 | nipype/scripts/cli.py | python | crash | (crashfile, rerun, debug, ipydebug, dir) | Display Nipype crash files.
For certain crash files, one can rerun a failed node in a temp directory.
Examples:\n
nipypecli crash crashfile.pklz\n
nipypecli crash crashfile.pklz -r -i\n | Display Nipype crash files. | [
"Display",
"Nipype",
"crash",
"files",
"."
] | def crash(crashfile, rerun, debug, ipydebug, dir):
"""Display Nipype crash files.
For certain crash files, one can rerun a failed node in a temp directory.
Examples:\n
nipypecli crash crashfile.pklz\n
nipypecli crash crashfile.pklz -r -i\n
"""
from .crash_files import display_crash_file
debug = "ipython" if ipydebug else debug
if debug == "ipython":
import sys
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(
mode="Verbose", color_scheme="Linux", call_pdb=1
)
display_crash_file(crashfile, rerun, debug, dir) | [
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