nwo
stringlengths 5
106
| sha
stringlengths 40
40
| path
stringlengths 4
174
| language
stringclasses 1
value | identifier
stringlengths 1
140
| parameters
stringlengths 0
87.7k
| argument_list
stringclasses 1
value | return_statement
stringlengths 0
426k
| docstring
stringlengths 0
64.3k
| docstring_summary
stringlengths 0
26.3k
| docstring_tokens
list | function
stringlengths 18
4.83M
| function_tokens
list | url
stringlengths 83
304
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
openvinotoolkit/open_model_zoo
|
5a4232f6c35b4916eb6e8750141f4e45761237ef
|
demos/speech_recognition_deepspeech_demo/python/asr_utils/audio_features.py
|
python
|
AudioFeaturesSeqPipelineStage._process_blocks
|
(self, audio, finish=False)
|
audio (numpy.ndarray), this buffer is guaranteed to contain data for 1 or more blocks
(audio.shape[0]>=self._block_len+self._context_len)
|
audio (numpy.ndarray), this buffer is guaranteed to contain data for 1 or more blocks
(audio.shape[0]>=self._block_len+self._context_len)
|
[
"audio",
"(",
"numpy",
".",
"ndarray",
")",
"this",
"buffer",
"is",
"guaranteed",
"to",
"contain",
"data",
"for",
"1",
"or",
"more",
"blocks",
"(",
"audio",
".",
"shape",
"[",
"0",
"]",
">",
"=",
"self",
".",
"_block_len",
"+",
"self",
".",
"_context_len",
")"
] |
def _process_blocks(self, audio, finish=False):
"""
audio (numpy.ndarray), this buffer is guaranteed to contain data for 1 or more blocks
(audio.shape[0]>=self._block_len+self._context_len)
"""
# Cut the buffer to the end of the last frame
audio_len = audio.shape[0]
processable_len = audio_len - (audio_len - self._context_len) % self._block_len
buffer_skip_len = processable_len - self._context_len
audio = audio[:processable_len]
# Convert audio data type to float32 if needed
if np.issubdtype(audio.dtype, np.uint8):
audio = audio/np.float32(128) - 1 # normalize to -1 to 1, uint8 to float32
elif np.issubdtype(audio.dtype, np.integer):
audio = audio/np.float32(32768) # normalize to -1 to 1, int16 to float32
melspectrum = samples_to_melspectrum(
audio, # samples
self.p['model_sampling_rate'], # sampling_rate
self._context_len + self._block_len, # window_size
self._block_len, # stride
n_mels = self.p['mel_num'],
fmin = self.p['mel_fmin'],
fmax = self.p['mel_fmax'],
)
if self.p['num_mfcc_dct_coefs'] is not None:
mfcc_features = melspectrum_to_mfcc(melspectrum, self.p['num_mfcc_dct_coefs'])
return [mfcc_features], buffer_skip_len
else:
return [melspectrum], buffer_skip_len
|
[
"def",
"_process_blocks",
"(",
"self",
",",
"audio",
",",
"finish",
"=",
"False",
")",
":",
"# Cut the buffer to the end of the last frame",
"audio_len",
"=",
"audio",
".",
"shape",
"[",
"0",
"]",
"processable_len",
"=",
"audio_len",
"-",
"(",
"audio_len",
"-",
"self",
".",
"_context_len",
")",
"%",
"self",
".",
"_block_len",
"buffer_skip_len",
"=",
"processable_len",
"-",
"self",
".",
"_context_len",
"audio",
"=",
"audio",
"[",
":",
"processable_len",
"]",
"# Convert audio data type to float32 if needed",
"if",
"np",
".",
"issubdtype",
"(",
"audio",
".",
"dtype",
",",
"np",
".",
"uint8",
")",
":",
"audio",
"=",
"audio",
"/",
"np",
".",
"float32",
"(",
"128",
")",
"-",
"1",
"# normalize to -1 to 1, uint8 to float32",
"elif",
"np",
".",
"issubdtype",
"(",
"audio",
".",
"dtype",
",",
"np",
".",
"integer",
")",
":",
"audio",
"=",
"audio",
"/",
"np",
".",
"float32",
"(",
"32768",
")",
"# normalize to -1 to 1, int16 to float32",
"melspectrum",
"=",
"samples_to_melspectrum",
"(",
"audio",
",",
"# samples",
"self",
".",
"p",
"[",
"'model_sampling_rate'",
"]",
",",
"# sampling_rate",
"self",
".",
"_context_len",
"+",
"self",
".",
"_block_len",
",",
"# window_size",
"self",
".",
"_block_len",
",",
"# stride",
"n_mels",
"=",
"self",
".",
"p",
"[",
"'mel_num'",
"]",
",",
"fmin",
"=",
"self",
".",
"p",
"[",
"'mel_fmin'",
"]",
",",
"fmax",
"=",
"self",
".",
"p",
"[",
"'mel_fmax'",
"]",
",",
")",
"if",
"self",
".",
"p",
"[",
"'num_mfcc_dct_coefs'",
"]",
"is",
"not",
"None",
":",
"mfcc_features",
"=",
"melspectrum_to_mfcc",
"(",
"melspectrum",
",",
"self",
".",
"p",
"[",
"'num_mfcc_dct_coefs'",
"]",
")",
"return",
"[",
"mfcc_features",
"]",
",",
"buffer_skip_len",
"else",
":",
"return",
"[",
"melspectrum",
"]",
",",
"buffer_skip_len"
] |
https://github.com/openvinotoolkit/open_model_zoo/blob/5a4232f6c35b4916eb6e8750141f4e45761237ef/demos/speech_recognition_deepspeech_demo/python/asr_utils/audio_features.py#L45-L75
|
||
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/vpc/v20170312/models.py
|
python
|
TemplateLimit.__init__
|
(self)
|
r"""
:param AddressTemplateMemberLimit: 参数模板IP地址成员配额。
:type AddressTemplateMemberLimit: int
:param AddressTemplateGroupMemberLimit: 参数模板IP地址组成员配额。
:type AddressTemplateGroupMemberLimit: int
:param ServiceTemplateMemberLimit: 参数模板I协议端口成员配额。
:type ServiceTemplateMemberLimit: int
:param ServiceTemplateGroupMemberLimit: 参数模板协议端口组成员配额。
:type ServiceTemplateGroupMemberLimit: int
|
r"""
:param AddressTemplateMemberLimit: 参数模板IP地址成员配额。
:type AddressTemplateMemberLimit: int
:param AddressTemplateGroupMemberLimit: 参数模板IP地址组成员配额。
:type AddressTemplateGroupMemberLimit: int
:param ServiceTemplateMemberLimit: 参数模板I协议端口成员配额。
:type ServiceTemplateMemberLimit: int
:param ServiceTemplateGroupMemberLimit: 参数模板协议端口组成员配额。
:type ServiceTemplateGroupMemberLimit: int
|
[
"r",
":",
"param",
"AddressTemplateMemberLimit",
":",
"参数模板IP地址成员配额。",
":",
"type",
"AddressTemplateMemberLimit",
":",
"int",
":",
"param",
"AddressTemplateGroupMemberLimit",
":",
"参数模板IP地址组成员配额。",
":",
"type",
"AddressTemplateGroupMemberLimit",
":",
"int",
":",
"param",
"ServiceTemplateMemberLimit",
":",
"参数模板I协议端口成员配额。",
":",
"type",
"ServiceTemplateMemberLimit",
":",
"int",
":",
"param",
"ServiceTemplateGroupMemberLimit",
":",
"参数模板协议端口组成员配额。",
":",
"type",
"ServiceTemplateGroupMemberLimit",
":",
"int"
] |
def __init__(self):
r"""
:param AddressTemplateMemberLimit: 参数模板IP地址成员配额。
:type AddressTemplateMemberLimit: int
:param AddressTemplateGroupMemberLimit: 参数模板IP地址组成员配额。
:type AddressTemplateGroupMemberLimit: int
:param ServiceTemplateMemberLimit: 参数模板I协议端口成员配额。
:type ServiceTemplateMemberLimit: int
:param ServiceTemplateGroupMemberLimit: 参数模板协议端口组成员配额。
:type ServiceTemplateGroupMemberLimit: int
"""
self.AddressTemplateMemberLimit = None
self.AddressTemplateGroupMemberLimit = None
self.ServiceTemplateMemberLimit = None
self.ServiceTemplateGroupMemberLimit = None
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"self",
".",
"AddressTemplateMemberLimit",
"=",
"None",
"self",
".",
"AddressTemplateGroupMemberLimit",
"=",
"None",
"self",
".",
"ServiceTemplateMemberLimit",
"=",
"None",
"self",
".",
"ServiceTemplateGroupMemberLimit",
"=",
"None"
] |
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/vpc/v20170312/models.py#L19116-L19130
|
||
tensorflow/mesh
|
57ed4018e6a173952501b074daabad32b6449f3d
|
mesh_tensorflow/auto_mtf/graph_interface.py
|
python
|
GraphInterface.get_tensor_num_entries
|
(self, tensor_name, partial_layout=None,
mesh_dimension_to_size=None)
|
return num_entries
|
The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
this case, the number of entries on a single device is returned.
Args:
tensor_name: a string, name of a tensor in the graph.
partial_layout: an optional {string: string}, from MTF dimension name to
mesh dimension name.
mesh_dimension_to_size: an optional {string: int}, from mesh dimension
name to size.
Returns:
an integer
|
The number of entries in a tensor.
|
[
"The",
"number",
"of",
"entries",
"in",
"a",
"tensor",
"."
] |
def get_tensor_num_entries(self, tensor_name, partial_layout=None,
mesh_dimension_to_size=None):
"""The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
this case, the number of entries on a single device is returned.
Args:
tensor_name: a string, name of a tensor in the graph.
partial_layout: an optional {string: string}, from MTF dimension name to
mesh dimension name.
mesh_dimension_to_size: an optional {string: int}, from mesh dimension
name to size.
Returns:
an integer
"""
shape = self.get_tensor_shape(tensor_name)
# We don't have to worry about divisiblity issues because Mesh TensorFlow
# only allows evenly divisible assignments.
num_entries = 1
for dim in shape.dims:
num_entries = num_entries * dim.value
if not partial_layout:
return num_entries
for mtf_dimension_name in self.get_tensor_mtf_dimension_names(tensor_name):
if mtf_dimension_name not in partial_layout:
continue
mesh_dimension_name = partial_layout[mtf_dimension_name]
mesh_dimension_size = mesh_dimension_to_size[mesh_dimension_name]
num_entries = int(math.ceil(num_entries / mesh_dimension_size))
return num_entries
|
[
"def",
"get_tensor_num_entries",
"(",
"self",
",",
"tensor_name",
",",
"partial_layout",
"=",
"None",
",",
"mesh_dimension_to_size",
"=",
"None",
")",
":",
"shape",
"=",
"self",
".",
"get_tensor_shape",
"(",
"tensor_name",
")",
"# We don't have to worry about divisiblity issues because Mesh TensorFlow",
"# only allows evenly divisible assignments.",
"num_entries",
"=",
"1",
"for",
"dim",
"in",
"shape",
".",
"dims",
":",
"num_entries",
"=",
"num_entries",
"*",
"dim",
".",
"value",
"if",
"not",
"partial_layout",
":",
"return",
"num_entries",
"for",
"mtf_dimension_name",
"in",
"self",
".",
"get_tensor_mtf_dimension_names",
"(",
"tensor_name",
")",
":",
"if",
"mtf_dimension_name",
"not",
"in",
"partial_layout",
":",
"continue",
"mesh_dimension_name",
"=",
"partial_layout",
"[",
"mtf_dimension_name",
"]",
"mesh_dimension_size",
"=",
"mesh_dimension_to_size",
"[",
"mesh_dimension_name",
"]",
"num_entries",
"=",
"int",
"(",
"math",
".",
"ceil",
"(",
"num_entries",
"/",
"mesh_dimension_size",
")",
")",
"return",
"num_entries"
] |
https://github.com/tensorflow/mesh/blob/57ed4018e6a173952501b074daabad32b6449f3d/mesh_tensorflow/auto_mtf/graph_interface.py#L153-L187
|
|
pinterest/mysql_utils
|
7ab237699b85de8b503b09f36e0309ac807689fe
|
retirement_queue.py
|
python
|
get_protected_hosts
|
(return_type='tuple')
|
Get data on all protected hosts
Args:
return_type - Options are:
'set'- return a set of protected hosts
'tuple' - returns all data regarding protected hosts
Returns:
A tuple which may be empty, with entries similar to:
({'protecting_user': 'rwultsch', 'reason': 'because', 'hostname': 'sharddb-14-4'},
{'protecting_user': 'rwultsch', 'reason': 'because reasons', 'hostname': 'sharddb-14-5'})
|
Get data on all protected hosts
|
[
"Get",
"data",
"on",
"all",
"protected",
"hosts"
] |
def get_protected_hosts(return_type='tuple'):
""" Get data on all protected hosts
Args:
return_type - Options are:
'set'- return a set of protected hosts
'tuple' - returns all data regarding protected hosts
Returns:
A tuple which may be empty, with entries similar to:
({'protecting_user': 'rwultsch', 'reason': 'because', 'hostname': 'sharddb-14-4'},
{'protecting_user': 'rwultsch', 'reason': 'because reasons', 'hostname': 'sharddb-14-5'})
"""
if return_type != 'tuple' and return_type != 'set':
raise Exception('Unsupported return_type '
'{return_type}'.format(return_type=return_type))
reporting_conn = mysql_lib.get_mysqlops_connections()
cursor = reporting_conn.cursor()
sql = "SELECT * FROM mysqlops.retirement_protection"
cursor.execute(sql)
results = cursor.fetchall()
if return_type == 'tuple':
return results
elif return_type == 'set':
results_set = set()
for entry in results:
results_set.add(entry['hostname'])
return results_set
|
[
"def",
"get_protected_hosts",
"(",
"return_type",
"=",
"'tuple'",
")",
":",
"if",
"return_type",
"!=",
"'tuple'",
"and",
"return_type",
"!=",
"'set'",
":",
"raise",
"Exception",
"(",
"'Unsupported return_type '",
"'{return_type}'",
".",
"format",
"(",
"return_type",
"=",
"return_type",
")",
")",
"reporting_conn",
"=",
"mysql_lib",
".",
"get_mysqlops_connections",
"(",
")",
"cursor",
"=",
"reporting_conn",
".",
"cursor",
"(",
")",
"sql",
"=",
"\"SELECT * FROM mysqlops.retirement_protection\"",
"cursor",
".",
"execute",
"(",
"sql",
")",
"results",
"=",
"cursor",
".",
"fetchall",
"(",
")",
"if",
"return_type",
"==",
"'tuple'",
":",
"return",
"results",
"elif",
"return_type",
"==",
"'set'",
":",
"results_set",
"=",
"set",
"(",
")",
"for",
"entry",
"in",
"results",
":",
"results_set",
".",
"add",
"(",
"entry",
"[",
"'hostname'",
"]",
")",
"return",
"results_set"
] |
https://github.com/pinterest/mysql_utils/blob/7ab237699b85de8b503b09f36e0309ac807689fe/retirement_queue.py#L422-L452
|
||
googleapis/python-ndb
|
e780c81cde1016651afbfcad8180d9912722cf1b
|
google/cloud/ndb/_cache.py
|
python
|
_GlobalCacheDeleteBatch.add
|
(self, key)
|
return future
|
Add a key to delete from the cache.
Arguments:
key (bytes): The key to delete.
Returns:
tasklets.Future: Eventual result will be ``None``.
|
Add a key to delete from the cache.
|
[
"Add",
"a",
"key",
"to",
"delete",
"from",
"the",
"cache",
"."
] |
def add(self, key):
"""Add a key to delete from the cache.
Arguments:
key (bytes): The key to delete.
Returns:
tasklets.Future: Eventual result will be ``None``.
"""
future = tasklets.Future(info=self.future_info(key))
self.keys.append(key)
self.futures.append(future)
return future
|
[
"def",
"add",
"(",
"self",
",",
"key",
")",
":",
"future",
"=",
"tasklets",
".",
"Future",
"(",
"info",
"=",
"self",
".",
"future_info",
"(",
"key",
")",
")",
"self",
".",
"keys",
".",
"append",
"(",
"key",
")",
"self",
".",
"futures",
".",
"append",
"(",
"future",
")",
"return",
"future"
] |
https://github.com/googleapis/python-ndb/blob/e780c81cde1016651afbfcad8180d9912722cf1b/google/cloud/ndb/_cache.py#L467-L479
|
|
duterscmy/ccks2019-ckbqa-4th-codes
|
19784e5ffc801b0647d27286b4984e0d24087a47
|
src/mention_extractor.py
|
python
|
MentionExtractor.extract_mentions
|
(self,question)
|
return entity_mention
|
返回字典，实体mentions
|
返回字典，实体mentions
|
[
"返回字典，实体mentions"
] |
def extract_mentions(self,question):
'''
返回字典，实体mentions
'''
entity_mention = {}
#使用jieba粗糙分词的方式得到候选mention
mentions = []
tokens = jieba.lcut(question)
for t in tokens:
if t in self.segment_dic:
mentions.append(t)
#使用序列标注模型来抽取候选 mention
x1, x2 = self.tokenizer.encode(first=question,max_len = self.max_seq_len)
x1,x2= np.array([x1]),np.array([x2])
predict_y = self.ner_model.predict([x1,x2],batch_size=32).tolist()[0]#(len,1)
predict_y = [1 if each[0]>0.5 else 0 for each in predict_y]
mentions_bert = self.restore_entity_from_labels(predict_y,question)
#判断是否属于mention_dic
mentions = mentions + mentions_bert
for token in mentions:
entity_mention[token] = token
return entity_mention
|
[
"def",
"extract_mentions",
"(",
"self",
",",
"question",
")",
":",
"entity_mention",
"=",
"{",
"}",
"#使用jieba粗糙分词的方式得到候选mention",
"mentions",
"=",
"[",
"]",
"tokens",
"=",
"jieba",
".",
"lcut",
"(",
"question",
")",
"for",
"t",
"in",
"tokens",
":",
"if",
"t",
"in",
"self",
".",
"segment_dic",
":",
"mentions",
".",
"append",
"(",
"t",
")",
"#使用序列标注模型来抽取候选 mention",
"x1",
",",
"x2",
"=",
"self",
".",
"tokenizer",
".",
"encode",
"(",
"first",
"=",
"question",
",",
"max_len",
"=",
"self",
".",
"max_seq_len",
")",
"x1",
",",
"x2",
"=",
"np",
".",
"array",
"(",
"[",
"x1",
"]",
")",
",",
"np",
".",
"array",
"(",
"[",
"x2",
"]",
")",
"predict_y",
"=",
"self",
".",
"ner_model",
".",
"predict",
"(",
"[",
"x1",
",",
"x2",
"]",
",",
"batch_size",
"=",
"32",
")",
".",
"tolist",
"(",
")",
"[",
"0",
"]",
"#(len,1)",
"predict_y",
"=",
"[",
"1",
"if",
"each",
"[",
"0",
"]",
">",
"0.5",
"else",
"0",
"for",
"each",
"in",
"predict_y",
"]",
"mentions_bert",
"=",
"self",
".",
"restore_entity_from_labels",
"(",
"predict_y",
",",
"question",
")",
"#判断是否属于mention_dic",
"mentions",
"=",
"mentions",
"+",
"mentions_bert",
"for",
"token",
"in",
"mentions",
":",
"entity_mention",
"[",
"token",
"]",
"=",
"token",
"return",
"entity_mention"
] |
https://github.com/duterscmy/ccks2019-ckbqa-4th-codes/blob/19784e5ffc801b0647d27286b4984e0d24087a47/src/mention_extractor.py#L42-L67
|
|
facebookresearch/ParlAI
|
e4d59c30eef44f1f67105961b82a83fd28d7d78b
|
parlai/utils/bpe.py
|
python
|
Gpt2BpeHelper.save
|
(self, dir_name: str, file_name: str)
|
Save appropriate files.
:param dir_name:
directory to save.
:param file_name:
file to save.
|
Save appropriate files.
|
[
"Save",
"appropriate",
"files",
"."
] |
def save(self, dir_name: str, file_name: str):
"""
Save appropriate files.
:param dir_name:
directory to save.
:param file_name:
file to save.
"""
out_json_path = os.path.join(dir_name, file_name + "-vocab.json")
out_merge_path = os.path.join(dir_name, file_name + "-merges.txt")
# Possibly bad assumption: if the destination file already exists,
# we don't need to copy it over again.
if not PathManager.exists(out_json_path):
logging.info(f"Copying {self.json_path} to {out_json_path}")
PathManager.copy(self.json_path, out_json_path)
if not PathManager.exists(out_merge_path):
logging.info(f"Copying {self.merge_path} to {out_merge_path}")
PathManager.copy(self.merge_path, out_merge_path)
|
[
"def",
"save",
"(",
"self",
",",
"dir_name",
":",
"str",
",",
"file_name",
":",
"str",
")",
":",
"out_json_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"dir_name",
",",
"file_name",
"+",
"\"-vocab.json\"",
")",
"out_merge_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"dir_name",
",",
"file_name",
"+",
"\"-merges.txt\"",
")",
"# Possibly bad assumption: if the destination file already exists,",
"# we don't need to copy it over again.",
"if",
"not",
"PathManager",
".",
"exists",
"(",
"out_json_path",
")",
":",
"logging",
".",
"info",
"(",
"f\"Copying {self.json_path} to {out_json_path}\"",
")",
"PathManager",
".",
"copy",
"(",
"self",
".",
"json_path",
",",
"out_json_path",
")",
"if",
"not",
"PathManager",
".",
"exists",
"(",
"out_merge_path",
")",
":",
"logging",
".",
"info",
"(",
"f\"Copying {self.merge_path} to {out_merge_path}\"",
")",
"PathManager",
".",
"copy",
"(",
"self",
".",
"merge_path",
",",
"out_merge_path",
")"
] |
https://github.com/facebookresearch/ParlAI/blob/e4d59c30eef44f1f67105961b82a83fd28d7d78b/parlai/utils/bpe.py#L759-L777
|
||
kuri65536/python-for-android
|
26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891
|
python-modules/twisted/twisted/trial/reporter.py
|
python
|
Reporter._printErrors
|
(self)
|
Print all of the non-success results to the stream in full.
|
Print all of the non-success results to the stream in full.
|
[
"Print",
"all",
"of",
"the",
"non",
"-",
"success",
"results",
"to",
"the",
"stream",
"in",
"full",
"."
] |
def _printErrors(self):
"""
Print all of the non-success results to the stream in full.
"""
self._write('\n')
self._printResults('[SKIPPED]', self.skips, lambda x : '%s\n' % x)
self._printResults('[TODO]', self.expectedFailures,
self._printExpectedFailure)
self._printResults('[FAIL]', self.failures,
self._formatFailureTraceback)
self._printResults('[ERROR]', self.errors,
self._formatFailureTraceback)
self._printResults('[SUCCESS!?!]', self.unexpectedSuccesses,
self._printUnexpectedSuccess)
|
[
"def",
"_printErrors",
"(",
"self",
")",
":",
"self",
".",
"_write",
"(",
"'\\n'",
")",
"self",
".",
"_printResults",
"(",
"'[SKIPPED]'",
",",
"self",
".",
"skips",
",",
"lambda",
"x",
":",
"'%s\\n'",
"%",
"x",
")",
"self",
".",
"_printResults",
"(",
"'[TODO]'",
",",
"self",
".",
"expectedFailures",
",",
"self",
".",
"_printExpectedFailure",
")",
"self",
".",
"_printResults",
"(",
"'[FAIL]'",
",",
"self",
".",
"failures",
",",
"self",
".",
"_formatFailureTraceback",
")",
"self",
".",
"_printResults",
"(",
"'[ERROR]'",
",",
"self",
".",
"errors",
",",
"self",
".",
"_formatFailureTraceback",
")",
"self",
".",
"_printResults",
"(",
"'[SUCCESS!?!]'",
",",
"self",
".",
"unexpectedSuccesses",
",",
"self",
".",
"_printUnexpectedSuccess",
")"
] |
https://github.com/kuri65536/python-for-android/blob/26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891/python-modules/twisted/twisted/trial/reporter.py#L598-L611
|
||
mesalock-linux/mesapy
|
ed546d59a21b36feb93e2309d5c6b75aa0ad95c9
|
lib-python/2.7/mutex.py
|
python
|
mutex.lock
|
(self, function, argument)
|
Lock a mutex, call the function with supplied argument
when it is acquired. If the mutex is already locked, place
function and argument in the queue.
|
Lock a mutex, call the function with supplied argument
when it is acquired. If the mutex is already locked, place
function and argument in the queue.
|
[
"Lock",
"a",
"mutex",
"call",
"the",
"function",
"with",
"supplied",
"argument",
"when",
"it",
"is",
"acquired",
".",
"If",
"the",
"mutex",
"is",
"already",
"locked",
"place",
"function",
"and",
"argument",
"in",
"the",
"queue",
"."
] |
def lock(self, function, argument):
"""Lock a mutex, call the function with supplied argument
when it is acquired. If the mutex is already locked, place
function and argument in the queue."""
if self.testandset():
function(argument)
else:
self.queue.append((function, argument))
|
[
"def",
"lock",
"(",
"self",
",",
"function",
",",
"argument",
")",
":",
"if",
"self",
".",
"testandset",
"(",
")",
":",
"function",
"(",
"argument",
")",
"else",
":",
"self",
".",
"queue",
".",
"append",
"(",
"(",
"function",
",",
"argument",
")",
")"
] |
https://github.com/mesalock-linux/mesapy/blob/ed546d59a21b36feb93e2309d5c6b75aa0ad95c9/lib-python/2.7/mutex.py#L39-L46
|
||
carla-simulator/scenario_runner
|
f4d00d88eda4212a1e119515c96281a4be5c234e
|
srunner/scenarios/object_crash_vehicle.py
|
python
|
StationaryObjectCrossing._create_test_criteria
|
(self)
|
return criteria
|
A list of all test criteria will be created that is later used
in parallel behavior tree.
|
A list of all test criteria will be created that is later used
in parallel behavior tree.
|
[
"A",
"list",
"of",
"all",
"test",
"criteria",
"will",
"be",
"created",
"that",
"is",
"later",
"used",
"in",
"parallel",
"behavior",
"tree",
"."
] |
def _create_test_criteria(self):
"""
A list of all test criteria will be created that is later used
in parallel behavior tree.
"""
criteria = []
collision_criterion = CollisionTest(self.ego_vehicles[0])
criteria.append(collision_criterion)
return criteria
|
[
"def",
"_create_test_criteria",
"(",
"self",
")",
":",
"criteria",
"=",
"[",
"]",
"collision_criterion",
"=",
"CollisionTest",
"(",
"self",
".",
"ego_vehicles",
"[",
"0",
"]",
")",
"criteria",
".",
"append",
"(",
"collision_criterion",
")",
"return",
"criteria"
] |
https://github.com/carla-simulator/scenario_runner/blob/f4d00d88eda4212a1e119515c96281a4be5c234e/srunner/scenarios/object_crash_vehicle.py#L109-L119
|
|
edgewall/trac
|
beb3e4eaf1e0a456d801a50a8614ecab06de29fc
|
trac/util/html.py
|
python
|
TracHTMLSanitizer.sanitize
|
(self, html)
|
return Markup(transform.out.getvalue())
|
Transforms the incoming HTML by removing anything's that deemed
unsafe.
:param html: the input HTML
:type: str
:return: the sanitized content
:rtype: Markup
|
Transforms the incoming HTML by removing anything's that deemed
unsafe.
|
[
"Transforms",
"the",
"incoming",
"HTML",
"by",
"removing",
"anything",
"s",
"that",
"deemed",
"unsafe",
"."
] |
def sanitize(self, html):
"""Transforms the incoming HTML by removing anything's that deemed
unsafe.
:param html: the input HTML
:type: str
:return: the sanitized content
:rtype: Markup
"""
transform = HTMLSanitization(self, io.StringIO())
transform.feed(html)
transform.close()
return Markup(transform.out.getvalue())
|
[
"def",
"sanitize",
"(",
"self",
",",
"html",
")",
":",
"transform",
"=",
"HTMLSanitization",
"(",
"self",
",",
"io",
".",
"StringIO",
"(",
")",
")",
"transform",
".",
"feed",
"(",
"html",
")",
"transform",
".",
"close",
"(",
")",
"return",
"Markup",
"(",
"transform",
".",
"out",
".",
"getvalue",
"(",
")",
")"
] |
https://github.com/edgewall/trac/blob/beb3e4eaf1e0a456d801a50a8614ecab06de29fc/trac/util/html.py#L608-L621
|
|
a312863063/seeprettyface-generator-yellow
|
c75b95b56c40036d00b35f3e140cc4a45598e077
|
dnnlib/tflib/network.py
|
python
|
Network._init_graph
|
(self)
|
[] |
def _init_graph(self) -> None:
# Collect inputs.
self.input_names = []
for param in inspect.signature(self._build_func).parameters.values():
if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty:
self.input_names.append(param.name)
self.num_inputs = len(self.input_names)
assert self.num_inputs >= 1
# Choose name and scope.
if self.name is None:
self.name = self._build_func_name
assert re.match("^[A-Za-z0-9_.\\-]*$", self.name)
with tf.name_scope(None):
self.scope = tf.get_default_graph().unique_name(self.name, mark_as_used=True)
# Finalize build func kwargs.
build_kwargs = dict(self.static_kwargs)
build_kwargs["is_template_graph"] = True
build_kwargs["components"] = self.components
# Build template graph.
with tfutil.absolute_variable_scope(self.scope, reuse=tf.AUTO_REUSE), tfutil.absolute_name_scope(self.scope): # ignore surrounding scopes
assert tf.get_variable_scope().name == self.scope
assert tf.get_default_graph().get_name_scope() == self.scope
with tf.control_dependencies(None): # ignore surrounding control dependencies
self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names]
out_expr = self._build_func(*self.input_templates, **build_kwargs)
# Collect outputs.
assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple)
self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr)
self.num_outputs = len(self.output_templates)
assert self.num_outputs >= 1
assert all(tfutil.is_tf_expression(t) for t in self.output_templates)
# Perform sanity checks.
if any(t.shape.ndims is None for t in self.input_templates):
raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.")
if any(t.shape.ndims is None for t in self.output_templates):
raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.")
if any(not isinstance(comp, Network) for comp in self.components.values()):
raise ValueError("Components of a Network must be Networks themselves.")
if len(self.components) != len(set(comp.name for comp in self.components.values())):
raise ValueError("Components of a Network must have unique names.")
# List inputs and outputs.
self.input_shapes = [tfutil.shape_to_list(t.shape) for t in self.input_templates]
self.output_shapes = [tfutil.shape_to_list(t.shape) for t in self.output_templates]
self.input_shape = self.input_shapes[0]
self.output_shape = self.output_shapes[0]
self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates]
# List variables.
self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/"))
self.vars = OrderedDict(self.own_vars)
self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items())
self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable)
self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items())
|
[
"def",
"_init_graph",
"(",
"self",
")",
"->",
"None",
":",
"# Collect inputs.",
"self",
".",
"input_names",
"=",
"[",
"]",
"for",
"param",
"in",
"inspect",
".",
"signature",
"(",
"self",
".",
"_build_func",
")",
".",
"parameters",
".",
"values",
"(",
")",
":",
"if",
"param",
".",
"kind",
"==",
"param",
".",
"POSITIONAL_OR_KEYWORD",
"and",
"param",
".",
"default",
"is",
"param",
".",
"empty",
":",
"self",
".",
"input_names",
".",
"append",
"(",
"param",
".",
"name",
")",
"self",
".",
"num_inputs",
"=",
"len",
"(",
"self",
".",
"input_names",
")",
"assert",
"self",
".",
"num_inputs",
">=",
"1",
"# Choose name and scope.",
"if",
"self",
".",
"name",
"is",
"None",
":",
"self",
".",
"name",
"=",
"self",
".",
"_build_func_name",
"assert",
"re",
".",
"match",
"(",
"\"^[A-Za-z0-9_.\\\\-]*$\"",
",",
"self",
".",
"name",
")",
"with",
"tf",
".",
"name_scope",
"(",
"None",
")",
":",
"self",
".",
"scope",
"=",
"tf",
".",
"get_default_graph",
"(",
")",
".",
"unique_name",
"(",
"self",
".",
"name",
",",
"mark_as_used",
"=",
"True",
")",
"# Finalize build func kwargs.",
"build_kwargs",
"=",
"dict",
"(",
"self",
".",
"static_kwargs",
")",
"build_kwargs",
"[",
"\"is_template_graph\"",
"]",
"=",
"True",
"build_kwargs",
"[",
"\"components\"",
"]",
"=",
"self",
".",
"components",
"# Build template graph.",
"with",
"tfutil",
".",
"absolute_variable_scope",
"(",
"self",
".",
"scope",
",",
"reuse",
"=",
"tf",
".",
"AUTO_REUSE",
")",
",",
"tfutil",
".",
"absolute_name_scope",
"(",
"self",
".",
"scope",
")",
":",
"# ignore surrounding scopes",
"assert",
"tf",
".",
"get_variable_scope",
"(",
")",
".",
"name",
"==",
"self",
".",
"scope",
"assert",
"tf",
".",
"get_default_graph",
"(",
")",
".",
"get_name_scope",
"(",
")",
"==",
"self",
".",
"scope",
"with",
"tf",
".",
"control_dependencies",
"(",
"None",
")",
":",
"# ignore surrounding control dependencies",
"self",
".",
"input_templates",
"=",
"[",
"tf",
".",
"placeholder",
"(",
"tf",
".",
"float32",
",",
"name",
"=",
"name",
")",
"for",
"name",
"in",
"self",
".",
"input_names",
"]",
"out_expr",
"=",
"self",
".",
"_build_func",
"(",
"*",
"self",
".",
"input_templates",
",",
"*",
"*",
"build_kwargs",
")",
"# Collect outputs.",
"assert",
"tfutil",
".",
"is_tf_expression",
"(",
"out_expr",
")",
"or",
"isinstance",
"(",
"out_expr",
",",
"tuple",
")",
"self",
".",
"output_templates",
"=",
"[",
"out_expr",
"]",
"if",
"tfutil",
".",
"is_tf_expression",
"(",
"out_expr",
")",
"else",
"list",
"(",
"out_expr",
")",
"self",
".",
"num_outputs",
"=",
"len",
"(",
"self",
".",
"output_templates",
")",
"assert",
"self",
".",
"num_outputs",
">=",
"1",
"assert",
"all",
"(",
"tfutil",
".",
"is_tf_expression",
"(",
"t",
")",
"for",
"t",
"in",
"self",
".",
"output_templates",
")",
"# Perform sanity checks.",
"if",
"any",
"(",
"t",
".",
"shape",
".",
"ndims",
"is",
"None",
"for",
"t",
"in",
"self",
".",
"input_templates",
")",
":",
"raise",
"ValueError",
"(",
"\"Network input shapes not defined. Please call x.set_shape() for each input.\"",
")",
"if",
"any",
"(",
"t",
".",
"shape",
".",
"ndims",
"is",
"None",
"for",
"t",
"in",
"self",
".",
"output_templates",
")",
":",
"raise",
"ValueError",
"(",
"\"Network output shapes not defined. Please call x.set_shape() where applicable.\"",
")",
"if",
"any",
"(",
"not",
"isinstance",
"(",
"comp",
",",
"Network",
")",
"for",
"comp",
"in",
"self",
".",
"components",
".",
"values",
"(",
")",
")",
":",
"raise",
"ValueError",
"(",
"\"Components of a Network must be Networks themselves.\"",
")",
"if",
"len",
"(",
"self",
".",
"components",
")",
"!=",
"len",
"(",
"set",
"(",
"comp",
".",
"name",
"for",
"comp",
"in",
"self",
".",
"components",
".",
"values",
"(",
")",
")",
")",
":",
"raise",
"ValueError",
"(",
"\"Components of a Network must have unique names.\"",
")",
"# List inputs and outputs.",
"self",
".",
"input_shapes",
"=",
"[",
"tfutil",
".",
"shape_to_list",
"(",
"t",
".",
"shape",
")",
"for",
"t",
"in",
"self",
".",
"input_templates",
"]",
"self",
".",
"output_shapes",
"=",
"[",
"tfutil",
".",
"shape_to_list",
"(",
"t",
".",
"shape",
")",
"for",
"t",
"in",
"self",
".",
"output_templates",
"]",
"self",
".",
"input_shape",
"=",
"self",
".",
"input_shapes",
"[",
"0",
"]",
"self",
".",
"output_shape",
"=",
"self",
".",
"output_shapes",
"[",
"0",
"]",
"self",
".",
"output_names",
"=",
"[",
"t",
".",
"name",
".",
"split",
"(",
"\"/\"",
")",
"[",
"-",
"1",
"]",
".",
"split",
"(",
"\":\"",
")",
"[",
"0",
"]",
"for",
"t",
"in",
"self",
".",
"output_templates",
"]",
"# List variables.",
"self",
".",
"own_vars",
"=",
"OrderedDict",
"(",
"(",
"var",
".",
"name",
"[",
"len",
"(",
"self",
".",
"scope",
")",
"+",
"1",
":",
"]",
".",
"split",
"(",
"\":\"",
")",
"[",
"0",
"]",
",",
"var",
")",
"for",
"var",
"in",
"tf",
".",
"global_variables",
"(",
"self",
".",
"scope",
"+",
"\"/\"",
")",
")",
"self",
".",
"vars",
"=",
"OrderedDict",
"(",
"self",
".",
"own_vars",
")",
"self",
".",
"vars",
".",
"update",
"(",
"(",
"comp",
".",
"name",
"+",
"\"/\"",
"+",
"name",
",",
"var",
")",
"for",
"comp",
"in",
"self",
".",
"components",
".",
"values",
"(",
")",
"for",
"name",
",",
"var",
"in",
"comp",
".",
"vars",
".",
"items",
"(",
")",
")",
"self",
".",
"trainables",
"=",
"OrderedDict",
"(",
"(",
"name",
",",
"var",
")",
"for",
"name",
",",
"var",
"in",
"self",
".",
"vars",
".",
"items",
"(",
")",
"if",
"var",
".",
"trainable",
")",
"self",
".",
"var_global_to_local",
"=",
"OrderedDict",
"(",
"(",
"var",
".",
"name",
".",
"split",
"(",
"\":\"",
")",
"[",
"0",
"]",
",",
"name",
")",
"for",
"name",
",",
"var",
"in",
"self",
".",
"vars",
".",
"items",
"(",
")",
")"
] |
https://github.com/a312863063/seeprettyface-generator-yellow/blob/c75b95b56c40036d00b35f3e140cc4a45598e077/dnnlib/tflib/network.py#L126-L186
|
||||
WerWolv/EdiZon_CheatsConfigsAndScripts
|
d16d36c7509c01dca770f402babd83ff2e9ae6e7
|
Scripts/lib/python3.5/lzma.py
|
python
|
LZMAFile.writable
|
(self)
|
return self._mode == _MODE_WRITE
|
Return whether the file was opened for writing.
|
Return whether the file was opened for writing.
|
[
"Return",
"whether",
"the",
"file",
"was",
"opened",
"for",
"writing",
"."
] |
def writable(self):
"""Return whether the file was opened for writing."""
self._check_not_closed()
return self._mode == _MODE_WRITE
|
[
"def",
"writable",
"(",
"self",
")",
":",
"self",
".",
"_check_not_closed",
"(",
")",
"return",
"self",
".",
"_mode",
"==",
"_MODE_WRITE"
] |
https://github.com/WerWolv/EdiZon_CheatsConfigsAndScripts/blob/d16d36c7509c01dca770f402babd83ff2e9ae6e7/Scripts/lib/python3.5/lzma.py#L175-L178
|
|
xuanzebi/BERT-CH-NER
|
24bf95411e831281ffc6c13f3ea9382870a17d64
|
bert-master/extract_features.py
|
python
|
input_fn_builder
|
(features, seq_length)
|
return input_fn
|
Creates an `input_fn` closure to be passed to TPUEstimator.
|
Creates an `input_fn` closure to be passed to TPUEstimator.
|
[
"Creates",
"an",
"input_fn",
"closure",
"to",
"be",
"passed",
"to",
"TPUEstimator",
"."
] |
def input_fn_builder(features, seq_length):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
all_unique_ids = []
all_input_ids = []
all_input_mask = []
all_input_type_ids = []
for feature in features:
all_unique_ids.append(feature.unique_id)
all_input_ids.append(feature.input_ids)
all_input_mask.append(feature.input_mask)
all_input_type_ids.append(feature.input_type_ids)
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
num_examples = len(features)
# This is for demo purposes and does NOT scale to large data sets. We do
# not use Dataset.from_generator() because that uses tf.py_func which is
# not TPU compatible. The right way to load data is with TFRecordReader.
d = tf.data.Dataset.from_tensor_slices({
"unique_ids":
tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
"input_ids":
tf.constant(
all_input_ids, shape=[num_examples, seq_length],
dtype=tf.int32),
"input_mask":
tf.constant(
all_input_mask,
shape=[num_examples, seq_length],
dtype=tf.int32),
"input_type_ids":
tf.constant(
all_input_type_ids,
shape=[num_examples, seq_length],
dtype=tf.int32),
})
d = d.batch(batch_size=batch_size, drop_remainder=False)
return d
return input_fn
|
[
"def",
"input_fn_builder",
"(",
"features",
",",
"seq_length",
")",
":",
"all_unique_ids",
"=",
"[",
"]",
"all_input_ids",
"=",
"[",
"]",
"all_input_mask",
"=",
"[",
"]",
"all_input_type_ids",
"=",
"[",
"]",
"for",
"feature",
"in",
"features",
":",
"all_unique_ids",
".",
"append",
"(",
"feature",
".",
"unique_id",
")",
"all_input_ids",
".",
"append",
"(",
"feature",
".",
"input_ids",
")",
"all_input_mask",
".",
"append",
"(",
"feature",
".",
"input_mask",
")",
"all_input_type_ids",
".",
"append",
"(",
"feature",
".",
"input_type_ids",
")",
"def",
"input_fn",
"(",
"params",
")",
":",
"\"\"\"The actual input function.\"\"\"",
"batch_size",
"=",
"params",
"[",
"\"batch_size\"",
"]",
"num_examples",
"=",
"len",
"(",
"features",
")",
"# This is for demo purposes and does NOT scale to large data sets. We do",
"# not use Dataset.from_generator() because that uses tf.py_func which is",
"# not TPU compatible. The right way to load data is with TFRecordReader.",
"d",
"=",
"tf",
".",
"data",
".",
"Dataset",
".",
"from_tensor_slices",
"(",
"{",
"\"unique_ids\"",
":",
"tf",
".",
"constant",
"(",
"all_unique_ids",
",",
"shape",
"=",
"[",
"num_examples",
"]",
",",
"dtype",
"=",
"tf",
".",
"int32",
")",
",",
"\"input_ids\"",
":",
"tf",
".",
"constant",
"(",
"all_input_ids",
",",
"shape",
"=",
"[",
"num_examples",
",",
"seq_length",
"]",
",",
"dtype",
"=",
"tf",
".",
"int32",
")",
",",
"\"input_mask\"",
":",
"tf",
".",
"constant",
"(",
"all_input_mask",
",",
"shape",
"=",
"[",
"num_examples",
",",
"seq_length",
"]",
",",
"dtype",
"=",
"tf",
".",
"int32",
")",
",",
"\"input_type_ids\"",
":",
"tf",
".",
"constant",
"(",
"all_input_type_ids",
",",
"shape",
"=",
"[",
"num_examples",
",",
"seq_length",
"]",
",",
"dtype",
"=",
"tf",
".",
"int32",
")",
",",
"}",
")",
"d",
"=",
"d",
".",
"batch",
"(",
"batch_size",
"=",
"batch_size",
",",
"drop_remainder",
"=",
"False",
")",
"return",
"d",
"return",
"input_fn"
] |
https://github.com/xuanzebi/BERT-CH-NER/blob/24bf95411e831281ffc6c13f3ea9382870a17d64/bert-master/extract_features.py#L100-L145
|
|
Nuitka/Nuitka
|
39262276993757fa4e299f497654065600453fc9
|
nuitka/build/inline_copy/lib/scons-3.1.2/SCons/Node/FS.py
|
python
|
Dir.glob
|
(self, pathname, ondisk=True, source=False, strings=False, exclude=None)
|
return sorted(result, key=lambda a: str(a))
|
Returns a list of Nodes (or strings) matching a specified
pathname pattern.
Pathname patterns follow UNIX shell semantics: * matches
any-length strings of any characters, ? matches any character,
and [] can enclose lists or ranges of characters. Matches do
not span directory separators.
The matches take into account Repositories, returning local
Nodes if a corresponding entry exists in a Repository (either
an in-memory Node or something on disk).
By defafult, the glob() function matches entries that exist
on-disk, in addition to in-memory Nodes. Setting the "ondisk"
argument to False (or some other non-true value) causes the glob()
function to only match in-memory Nodes. The default behavior is
to return both the on-disk and in-memory Nodes.
The "source" argument, when true, specifies that corresponding
source Nodes must be returned if you're globbing in a build
directory (initialized with VariantDir()). The default behavior
is to return Nodes local to the VariantDir().
The "strings" argument, when true, returns the matches as strings,
not Nodes. The strings are path names relative to this directory.
The "exclude" argument, if not None, must be a pattern or a list
of patterns following the same UNIX shell semantics.
Elements matching a least one pattern of this list will be excluded
from the result.
The underlying algorithm is adapted from the glob.glob() function
in the Python library (but heavily modified), and uses fnmatch()
under the covers.
|
Returns a list of Nodes (or strings) matching a specified
pathname pattern.
|
[
"Returns",
"a",
"list",
"of",
"Nodes",
"(",
"or",
"strings",
")",
"matching",
"a",
"specified",
"pathname",
"pattern",
"."
] |
def glob(self, pathname, ondisk=True, source=False, strings=False, exclude=None):
"""
Returns a list of Nodes (or strings) matching a specified
pathname pattern.
Pathname patterns follow UNIX shell semantics: * matches
any-length strings of any characters, ? matches any character,
and [] can enclose lists or ranges of characters. Matches do
not span directory separators.
The matches take into account Repositories, returning local
Nodes if a corresponding entry exists in a Repository (either
an in-memory Node or something on disk).
By defafult, the glob() function matches entries that exist
on-disk, in addition to in-memory Nodes. Setting the "ondisk"
argument to False (or some other non-true value) causes the glob()
function to only match in-memory Nodes. The default behavior is
to return both the on-disk and in-memory Nodes.
The "source" argument, when true, specifies that corresponding
source Nodes must be returned if you're globbing in a build
directory (initialized with VariantDir()). The default behavior
is to return Nodes local to the VariantDir().
The "strings" argument, when true, returns the matches as strings,
not Nodes. The strings are path names relative to this directory.
The "exclude" argument, if not None, must be a pattern or a list
of patterns following the same UNIX shell semantics.
Elements matching a least one pattern of this list will be excluded
from the result.
The underlying algorithm is adapted from the glob.glob() function
in the Python library (but heavily modified), and uses fnmatch()
under the covers.
"""
dirname, basename = os.path.split(pathname)
if not dirname:
result = self._glob1(basename, ondisk, source, strings)
else:
if has_glob_magic(dirname):
list = self.glob(dirname, ondisk, source, False, exclude)
else:
list = [self.Dir(dirname, create=True)]
result = []
for dir in list:
r = dir._glob1(basename, ondisk, source, strings)
if strings:
r = [os.path.join(str(dir), x) for x in r]
result.extend(r)
if exclude:
excludes = []
excludeList = SCons.Util.flatten(exclude)
for x in excludeList:
r = self.glob(x, ondisk, source, strings)
excludes.extend(r)
result = [x for x in result if not any(fnmatch.fnmatch(str(x), str(e)) for e in SCons.Util.flatten(excludes))]
return sorted(result, key=lambda a: str(a))
|
[
"def",
"glob",
"(",
"self",
",",
"pathname",
",",
"ondisk",
"=",
"True",
",",
"source",
"=",
"False",
",",
"strings",
"=",
"False",
",",
"exclude",
"=",
"None",
")",
":",
"dirname",
",",
"basename",
"=",
"os",
".",
"path",
".",
"split",
"(",
"pathname",
")",
"if",
"not",
"dirname",
":",
"result",
"=",
"self",
".",
"_glob1",
"(",
"basename",
",",
"ondisk",
",",
"source",
",",
"strings",
")",
"else",
":",
"if",
"has_glob_magic",
"(",
"dirname",
")",
":",
"list",
"=",
"self",
".",
"glob",
"(",
"dirname",
",",
"ondisk",
",",
"source",
",",
"False",
",",
"exclude",
")",
"else",
":",
"list",
"=",
"[",
"self",
".",
"Dir",
"(",
"dirname",
",",
"create",
"=",
"True",
")",
"]",
"result",
"=",
"[",
"]",
"for",
"dir",
"in",
"list",
":",
"r",
"=",
"dir",
".",
"_glob1",
"(",
"basename",
",",
"ondisk",
",",
"source",
",",
"strings",
")",
"if",
"strings",
":",
"r",
"=",
"[",
"os",
".",
"path",
".",
"join",
"(",
"str",
"(",
"dir",
")",
",",
"x",
")",
"for",
"x",
"in",
"r",
"]",
"result",
".",
"extend",
"(",
"r",
")",
"if",
"exclude",
":",
"excludes",
"=",
"[",
"]",
"excludeList",
"=",
"SCons",
".",
"Util",
".",
"flatten",
"(",
"exclude",
")",
"for",
"x",
"in",
"excludeList",
":",
"r",
"=",
"self",
".",
"glob",
"(",
"x",
",",
"ondisk",
",",
"source",
",",
"strings",
")",
"excludes",
".",
"extend",
"(",
"r",
")",
"result",
"=",
"[",
"x",
"for",
"x",
"in",
"result",
"if",
"not",
"any",
"(",
"fnmatch",
".",
"fnmatch",
"(",
"str",
"(",
"x",
")",
",",
"str",
"(",
"e",
")",
")",
"for",
"e",
"in",
"SCons",
".",
"Util",
".",
"flatten",
"(",
"excludes",
")",
")",
"]",
"return",
"sorted",
"(",
"result",
",",
"key",
"=",
"lambda",
"a",
":",
"str",
"(",
"a",
")",
")"
] |
https://github.com/Nuitka/Nuitka/blob/39262276993757fa4e299f497654065600453fc9/nuitka/build/inline_copy/lib/scons-3.1.2/SCons/Node/FS.py#L2131-L2189
|
|
biopython/biopython
|
2dd97e71762af7b046d7f7f8a4f1e38db6b06c86
|
Bio/Align/__init__.py
|
python
|
Alignment.__getitem__
|
(self, key)
|
Return self[key].
Indices of the form
self[:, :]
return a copy of the Alignment object;
self[:, i:]
self[:, :j]
self[:, i:j]
self[:, iterable] (where iterable returns integers)
return a new Alignment object spanning the selected columns;
self[k, i]
self[k, i:]
self[k, :j]
self[k, i:j]
self[k, iterable] (where iterable returns integers)
self[k] (equivalent to self[k, :])
return a string with the aligned sequence (including gaps) for the
selected columns, where k = 0 represents the target and k = 1
represents the query sequence; and
self[:, i]
returns a string with the selected column in the alignment.
>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> alignments = aligner.align("ACCGGTTT", "ACGGGTT")
>>> alignment = alignments[0]
>>> print(alignment)
ACCGG-TTT
||-||-||-
AC-GGGTT-
<BLANKLINE>
>>> alignment[0, :]
'ACCGG-TTT'
>>> alignment[1, :]
'AC-GGGTT-'
>>> alignment[0]
'ACCGG-TTT'
>>> alignment[1]
'AC-GGGTT-'
>>> alignment[0, 1:-2]
'CCGG-T'
>>> alignment[1, 1:-2]
'C-GGGT'
>>> alignment[0, (1, 5, 2)]
'C-C'
>>> alignment[1, ::2]
'A-GT-'
>>> alignment[1, range(0, 9, 2)]
'A-GT-'
>>> alignment[:, 0]
'AA'
>>> alignment[:, 5]
'-G'
>>> alignment[:, 1:] # doctest:+ELLIPSIS
<Bio.Align.Alignment object (2 rows x 8 columns) at 0x...>
>>> print(alignment[:, 1:])
ACCGG-TTT
|-||-||-
AC-GGGTT-
<BLANKLINE>
>>> print(alignment[:, 2:])
ACCGG-TTT
-||-||-
AC-GGGTT-
<BLANKLINE>
>>> print(alignment[:, 3:])
ACCGG-TTT
||-||-
ACGGGTT-
<BLANKLINE>
>>> print(alignment[:, 3:-1])
ACCGG-TTT
||-||
ACGGGTT
<BLANKLINE>
>>> print(alignment[:, ::2])
ACGTT
|-||-
A-GT-
<BLANKLINE>
>>> print(alignment[:, range(1, 9, 2)])
CG-T
||-|
CGGT
<BLANKLINE>
>>> print(alignment[:, (2, 7, 3)])
CTG
-||
-TG
<BLANKLINE>
|
Return self[key].
|
[
"Return",
"self",
"[",
"key",
"]",
"."
] |
def __getitem__(self, key):
"""Return self[key].
Indices of the form
self[:, :]
return a copy of the Alignment object;
self[:, i:]
self[:, :j]
self[:, i:j]
self[:, iterable] (where iterable returns integers)
return a new Alignment object spanning the selected columns;
self[k, i]
self[k, i:]
self[k, :j]
self[k, i:j]
self[k, iterable] (where iterable returns integers)
self[k] (equivalent to self[k, :])
return a string with the aligned sequence (including gaps) for the
selected columns, where k = 0 represents the target and k = 1
represents the query sequence; and
self[:, i]
returns a string with the selected column in the alignment.
>>> from Bio.Align import PairwiseAligner
>>> aligner = PairwiseAligner()
>>> alignments = aligner.align("ACCGGTTT", "ACGGGTT")
>>> alignment = alignments[0]
>>> print(alignment)
ACCGG-TTT
||-||-||-
AC-GGGTT-
<BLANKLINE>
>>> alignment[0, :]
'ACCGG-TTT'
>>> alignment[1, :]
'AC-GGGTT-'
>>> alignment[0]
'ACCGG-TTT'
>>> alignment[1]
'AC-GGGTT-'
>>> alignment[0, 1:-2]
'CCGG-T'
>>> alignment[1, 1:-2]
'C-GGGT'
>>> alignment[0, (1, 5, 2)]
'C-C'
>>> alignment[1, ::2]
'A-GT-'
>>> alignment[1, range(0, 9, 2)]
'A-GT-'
>>> alignment[:, 0]
'AA'
>>> alignment[:, 5]
'-G'
>>> alignment[:, 1:] # doctest:+ELLIPSIS
<Bio.Align.Alignment object (2 rows x 8 columns) at 0x...>
>>> print(alignment[:, 1:])
ACCGG-TTT
|-||-||-
AC-GGGTT-
<BLANKLINE>
>>> print(alignment[:, 2:])
ACCGG-TTT
-||-||-
AC-GGGTT-
<BLANKLINE>
>>> print(alignment[:, 3:])
ACCGG-TTT
||-||-
ACGGGTT-
<BLANKLINE>
>>> print(alignment[:, 3:-1])
ACCGG-TTT
||-||
ACGGGTT
<BLANKLINE>
>>> print(alignment[:, ::2])
ACGTT
|-||-
A-GT-
<BLANKLINE>
>>> print(alignment[:, range(1, 9, 2)])
CG-T
||-|
CGGT
<BLANKLINE>
>>> print(alignment[:, (2, 7, 3)])
CTG
-||
-TG
<BLANKLINE>
"""
import numpy
if isinstance(key, slice):
if key.indices(len(self)) == (0, 2, 1):
sequences = self.sequences
coordinates = self.coordinates.copy()
alignment = Alignment(sequences, coordinates)
alignment.score = self.score
return alignment
raise NotImplementedError
sequences = list(self.sequences)
coordinates = self.coordinates.copy()
for i, sequence in enumerate(sequences):
if coordinates[i, 0] > coordinates[i, -1]: # mapped to reverse strand
n = len(sequences[i])
coordinates[i, :] = n - coordinates[i, :]
sequences[i] = reverse_complement(sequences[i], inplace=False)
if isinstance(key, int):
n, m = self.shape
row = key
if row < 0:
row += n
if row < 0 or row >= n:
raise IndexError("row index %d is out of bounds (%d rows)" % (row, n))
sequence = sequences[row]
try:
sequence = sequence.seq # SeqRecord confusion
except AttributeError:
pass
line = ""
steps = numpy.diff(coordinates, 1)
gaps = steps[row] == 0 # seriously, flake8??
steps = steps.max(0)
i = coordinates[row, 0]
for step, gap in zip(steps, gaps):
if gap:
line += "-" * step
else:
j = i + step
line += str(sequence[i:j])
i = j
# line may be a str or a Seq at this point.
return str(line)
if isinstance(key, tuple):
try:
row, col = key
except ValueError:
raise ValueError("only tuples of length 2 can be alignment indices")
if isinstance(row, int):
n, m = self.shape
if row < 0:
row += n
if row < 0 or row >= n:
raise IndexError(
"row index %d is out of bounds (%d rows)" % (row, n)
)
if isinstance(col, int):
start_index = col
if start_index < 0:
start_index += m
if start_index < 0 or start_index >= m:
raise IndexError(
"column index %d is out of bounds (%d columns)" % (col, m)
)
steps = numpy.diff(coordinates, 1)
indices = steps.max(0).cumsum()
steps = steps[row]
index = indices.searchsorted(start_index, side="right")
if steps[index]:
offset = start_index - indices[index]
indices = coordinates[row, 0] + steps.cumsum()
i = indices[index] + offset
line = sequences[row][i : i + 1]
else:
line = "-"
return line
if isinstance(col, slice):
sequence = sequences[row]
try:
sequence = sequence.seq # SeqRecord confusion
except AttributeError:
pass
sequence = str(sequence)
start_index, stop_index, step = col.indices(m)
if start_index < stop_index and step == 1:
steps = numpy.diff(coordinates, 1)
gaps = steps[row] == 0 # come on flake8, this is ugly
sequence_indices = coordinates[row, 0] + steps[row, :].cumsum()
steps = steps.max(0)
indices = steps.cumsum()
i = indices.searchsorted(start_index, side="right")
j = i + indices[i:].searchsorted(stop_index, side="right")
if i == j:
length = stop_index - start_index
if gaps[i]:
line = "-" * length
else:
offset = start_index - indices[i]
start = sequence_indices[i] + offset
stop = start + length
line = sequence[start:stop]
else:
length = indices[i] - start_index
stop = sequence_indices[i]
if gaps[i]:
line = "-" * length
else:
start = stop - length
line = sequence[start:stop]
i += 1
while i < j:
step = steps[i]
if gaps[i]:
line += "-" * step
else:
start = stop
stop = start + step
line += sequence[start:stop]
i += 1
length = stop_index - indices[j - 1]
if length > 0:
if gaps[j]:
line += "-" * length
else:
start = stop
stop = start + length
line += sequence[start:stop]
return line
# make an iterable if step != 1
col = range(start_index, stop_index, step)
# try if we can use col as an iterable
line = self[row]
try:
line = "".join(line[index] for index in col)
except IndexError:
raise
except Exception:
raise TypeError(
"second index must be an integer, slice, or iterable of integers"
) from None
else:
return line
if isinstance(row, slice):
n, m = self.shape
if isinstance(col, int):
if col < 0:
col += m
if col < 0 or col >= m:
raise IndexError(
"column index %d is out of bounds (%d columns)" % (col, m)
)
starts = numpy.full(n, sys.maxsize)
for ends in coordinates.transpose():
step = max(ends - starts)
if step < 0:
index = 0
else:
index += step
if col < index:
break
starts = ends
else:
raise IndexError("column index %d is out of bounds" % col)
offset = index - col
line = ""
start, stop, step = row.indices(n)
for i in range(start, stop, step):
s = starts[i]
e = ends[i]
if s == e:
line += "-"
else:
sequence = sequences[i]
line += sequence[e - offset]
return line
if row.indices(n) != (0, n, 1):
raise NotImplementedError
if isinstance(col, slice):
start_index, stop_index, step = col.indices(m)
if start_index < stop_index and step == 1:
steps = numpy.diff(coordinates, 1)
indices = steps.max(0).cumsum()
i = indices.searchsorted(start_index, side="right")
j = i + indices[i:].searchsorted(stop_index, side="left") + 1
offset = steps[:, i] - indices[i] + start_index
coordinates[:, i] += offset * (steps[:, i] > 0)
offset = indices[j - 1] - stop_index
coordinates[:, j] -= offset * (steps[:, j - 1] > 0)
coordinates = coordinates[:, i : j + 1]
for i, sequence in enumerate(sequences):
if self.coordinates[i, 0] > self.coordinates[i, -1]:
# mapped to reverse strand
n = len(sequence)
coordinates[i, :] = n - coordinates[i, :]
sequences = self.sequences
alignment = Alignment(sequences, coordinates)
if numpy.array_equal(coordinates, self.coordinates):
try:
alignment.score = self.score
except AttributeError:
pass
try:
column_annotations = self.column_annotations
except AttributeError:
pass
else:
alignment.column_annotations = {}
for key, value in column_annotations.items():
value = value[start_index:stop_index]
try:
value = value.copy()
except AttributeError:
# immutable tuples like str, tuple
pass
alignment.column_annotations[key] = value
return alignment
# make an iterable if step != 1
col = range(start_index, stop_index, step)
# try if we can use col as an iterable
indices = tuple(col)
try:
lines = [self[i, indices] for i in range(n)]
except IndexError:
raise
except Exception:
raise TypeError(
"second index must be an integer, slice, or iterable of integers"
) from None
else:
sequences = [line.replace("-", "") for line in lines]
coordinates = self.infer_coordinates(lines)
alignment = Alignment(sequences, coordinates)
try:
column_annotations = self.column_annotations
except AttributeError:
pass
else:
alignment.column_annotations = {}
for key, value in column_annotations.items():
value_generator = (value[index] for index in indices)
if isinstance(value, str):
value = "".join(value_generator)
else:
value = value.__class__(value_generator)
alignment.column_annotations[key] = value
return alignment
raise TypeError("first index must be an integer or slice")
raise TypeError("alignment indices must be integers, slices, or tuples")
|
[
"def",
"__getitem__",
"(",
"self",
",",
"key",
")",
":",
"import",
"numpy",
"if",
"isinstance",
"(",
"key",
",",
"slice",
")",
":",
"if",
"key",
".",
"indices",
"(",
"len",
"(",
"self",
")",
")",
"==",
"(",
"0",
",",
"2",
",",
"1",
")",
":",
"sequences",
"=",
"self",
".",
"sequences",
"coordinates",
"=",
"self",
".",
"coordinates",
".",
"copy",
"(",
")",
"alignment",
"=",
"Alignment",
"(",
"sequences",
",",
"coordinates",
")",
"alignment",
".",
"score",
"=",
"self",
".",
"score",
"return",
"alignment",
"raise",
"NotImplementedError",
"sequences",
"=",
"list",
"(",
"self",
".",
"sequences",
")",
"coordinates",
"=",
"self",
".",
"coordinates",
".",
"copy",
"(",
")",
"for",
"i",
",",
"sequence",
"in",
"enumerate",
"(",
"sequences",
")",
":",
"if",
"coordinates",
"[",
"i",
",",
"0",
"]",
">",
"coordinates",
"[",
"i",
",",
"-",
"1",
"]",
":",
"# mapped to reverse strand",
"n",
"=",
"len",
"(",
"sequences",
"[",
"i",
"]",
")",
"coordinates",
"[",
"i",
",",
":",
"]",
"=",
"n",
"-",
"coordinates",
"[",
"i",
",",
":",
"]",
"sequences",
"[",
"i",
"]",
"=",
"reverse_complement",
"(",
"sequences",
"[",
"i",
"]",
",",
"inplace",
"=",
"False",
")",
"if",
"isinstance",
"(",
"key",
",",
"int",
")",
":",
"n",
",",
"m",
"=",
"self",
".",
"shape",
"row",
"=",
"key",
"if",
"row",
"<",
"0",
":",
"row",
"+=",
"n",
"if",
"row",
"<",
"0",
"or",
"row",
">=",
"n",
":",
"raise",
"IndexError",
"(",
"\"row index %d is out of bounds (%d rows)\"",
"%",
"(",
"row",
",",
"n",
")",
")",
"sequence",
"=",
"sequences",
"[",
"row",
"]",
"try",
":",
"sequence",
"=",
"sequence",
".",
"seq",
"# SeqRecord confusion",
"except",
"AttributeError",
":",
"pass",
"line",
"=",
"\"\"",
"steps",
"=",
"numpy",
".",
"diff",
"(",
"coordinates",
",",
"1",
")",
"gaps",
"=",
"steps",
"[",
"row",
"]",
"==",
"0",
"# seriously, flake8??",
"steps",
"=",
"steps",
".",
"max",
"(",
"0",
")",
"i",
"=",
"coordinates",
"[",
"row",
",",
"0",
"]",
"for",
"step",
",",
"gap",
"in",
"zip",
"(",
"steps",
",",
"gaps",
")",
":",
"if",
"gap",
":",
"line",
"+=",
"\"-\"",
"*",
"step",
"else",
":",
"j",
"=",
"i",
"+",
"step",
"line",
"+=",
"str",
"(",
"sequence",
"[",
"i",
":",
"j",
"]",
")",
"i",
"=",
"j",
"# line may be a str or a Seq at this point.",
"return",
"str",
"(",
"line",
")",
"if",
"isinstance",
"(",
"key",
",",
"tuple",
")",
":",
"try",
":",
"row",
",",
"col",
"=",
"key",
"except",
"ValueError",
":",
"raise",
"ValueError",
"(",
"\"only tuples of length 2 can be alignment indices\"",
")",
"if",
"isinstance",
"(",
"row",
",",
"int",
")",
":",
"n",
",",
"m",
"=",
"self",
".",
"shape",
"if",
"row",
"<",
"0",
":",
"row",
"+=",
"n",
"if",
"row",
"<",
"0",
"or",
"row",
">=",
"n",
":",
"raise",
"IndexError",
"(",
"\"row index %d is out of bounds (%d rows)\"",
"%",
"(",
"row",
",",
"n",
")",
")",
"if",
"isinstance",
"(",
"col",
",",
"int",
")",
":",
"start_index",
"=",
"col",
"if",
"start_index",
"<",
"0",
":",
"start_index",
"+=",
"m",
"if",
"start_index",
"<",
"0",
"or",
"start_index",
">=",
"m",
":",
"raise",
"IndexError",
"(",
"\"column index %d is out of bounds (%d columns)\"",
"%",
"(",
"col",
",",
"m",
")",
")",
"steps",
"=",
"numpy",
".",
"diff",
"(",
"coordinates",
",",
"1",
")",
"indices",
"=",
"steps",
".",
"max",
"(",
"0",
")",
".",
"cumsum",
"(",
")",
"steps",
"=",
"steps",
"[",
"row",
"]",
"index",
"=",
"indices",
".",
"searchsorted",
"(",
"start_index",
",",
"side",
"=",
"\"right\"",
")",
"if",
"steps",
"[",
"index",
"]",
":",
"offset",
"=",
"start_index",
"-",
"indices",
"[",
"index",
"]",
"indices",
"=",
"coordinates",
"[",
"row",
",",
"0",
"]",
"+",
"steps",
".",
"cumsum",
"(",
")",
"i",
"=",
"indices",
"[",
"index",
"]",
"+",
"offset",
"line",
"=",
"sequences",
"[",
"row",
"]",
"[",
"i",
":",
"i",
"+",
"1",
"]",
"else",
":",
"line",
"=",
"\"-\"",
"return",
"line",
"if",
"isinstance",
"(",
"col",
",",
"slice",
")",
":",
"sequence",
"=",
"sequences",
"[",
"row",
"]",
"try",
":",
"sequence",
"=",
"sequence",
".",
"seq",
"# SeqRecord confusion",
"except",
"AttributeError",
":",
"pass",
"sequence",
"=",
"str",
"(",
"sequence",
")",
"start_index",
",",
"stop_index",
",",
"step",
"=",
"col",
".",
"indices",
"(",
"m",
")",
"if",
"start_index",
"<",
"stop_index",
"and",
"step",
"==",
"1",
":",
"steps",
"=",
"numpy",
".",
"diff",
"(",
"coordinates",
",",
"1",
")",
"gaps",
"=",
"steps",
"[",
"row",
"]",
"==",
"0",
"# come on flake8, this is ugly",
"sequence_indices",
"=",
"coordinates",
"[",
"row",
",",
"0",
"]",
"+",
"steps",
"[",
"row",
",",
":",
"]",
".",
"cumsum",
"(",
")",
"steps",
"=",
"steps",
".",
"max",
"(",
"0",
")",
"indices",
"=",
"steps",
".",
"cumsum",
"(",
")",
"i",
"=",
"indices",
".",
"searchsorted",
"(",
"start_index",
",",
"side",
"=",
"\"right\"",
")",
"j",
"=",
"i",
"+",
"indices",
"[",
"i",
":",
"]",
".",
"searchsorted",
"(",
"stop_index",
",",
"side",
"=",
"\"right\"",
")",
"if",
"i",
"==",
"j",
":",
"length",
"=",
"stop_index",
"-",
"start_index",
"if",
"gaps",
"[",
"i",
"]",
":",
"line",
"=",
"\"-\"",
"*",
"length",
"else",
":",
"offset",
"=",
"start_index",
"-",
"indices",
"[",
"i",
"]",
"start",
"=",
"sequence_indices",
"[",
"i",
"]",
"+",
"offset",
"stop",
"=",
"start",
"+",
"length",
"line",
"=",
"sequence",
"[",
"start",
":",
"stop",
"]",
"else",
":",
"length",
"=",
"indices",
"[",
"i",
"]",
"-",
"start_index",
"stop",
"=",
"sequence_indices",
"[",
"i",
"]",
"if",
"gaps",
"[",
"i",
"]",
":",
"line",
"=",
"\"-\"",
"*",
"length",
"else",
":",
"start",
"=",
"stop",
"-",
"length",
"line",
"=",
"sequence",
"[",
"start",
":",
"stop",
"]",
"i",
"+=",
"1",
"while",
"i",
"<",
"j",
":",
"step",
"=",
"steps",
"[",
"i",
"]",
"if",
"gaps",
"[",
"i",
"]",
":",
"line",
"+=",
"\"-\"",
"*",
"step",
"else",
":",
"start",
"=",
"stop",
"stop",
"=",
"start",
"+",
"step",
"line",
"+=",
"sequence",
"[",
"start",
":",
"stop",
"]",
"i",
"+=",
"1",
"length",
"=",
"stop_index",
"-",
"indices",
"[",
"j",
"-",
"1",
"]",
"if",
"length",
">",
"0",
":",
"if",
"gaps",
"[",
"j",
"]",
":",
"line",
"+=",
"\"-\"",
"*",
"length",
"else",
":",
"start",
"=",
"stop",
"stop",
"=",
"start",
"+",
"length",
"line",
"+=",
"sequence",
"[",
"start",
":",
"stop",
"]",
"return",
"line",
"# make an iterable if step != 1",
"col",
"=",
"range",
"(",
"start_index",
",",
"stop_index",
",",
"step",
")",
"# try if we can use col as an iterable",
"line",
"=",
"self",
"[",
"row",
"]",
"try",
":",
"line",
"=",
"\"\"",
".",
"join",
"(",
"line",
"[",
"index",
"]",
"for",
"index",
"in",
"col",
")",
"except",
"IndexError",
":",
"raise",
"except",
"Exception",
":",
"raise",
"TypeError",
"(",
"\"second index must be an integer, slice, or iterable of integers\"",
")",
"from",
"None",
"else",
":",
"return",
"line",
"if",
"isinstance",
"(",
"row",
",",
"slice",
")",
":",
"n",
",",
"m",
"=",
"self",
".",
"shape",
"if",
"isinstance",
"(",
"col",
",",
"int",
")",
":",
"if",
"col",
"<",
"0",
":",
"col",
"+=",
"m",
"if",
"col",
"<",
"0",
"or",
"col",
">=",
"m",
":",
"raise",
"IndexError",
"(",
"\"column index %d is out of bounds (%d columns)\"",
"%",
"(",
"col",
",",
"m",
")",
")",
"starts",
"=",
"numpy",
".",
"full",
"(",
"n",
",",
"sys",
".",
"maxsize",
")",
"for",
"ends",
"in",
"coordinates",
".",
"transpose",
"(",
")",
":",
"step",
"=",
"max",
"(",
"ends",
"-",
"starts",
")",
"if",
"step",
"<",
"0",
":",
"index",
"=",
"0",
"else",
":",
"index",
"+=",
"step",
"if",
"col",
"<",
"index",
":",
"break",
"starts",
"=",
"ends",
"else",
":",
"raise",
"IndexError",
"(",
"\"column index %d is out of bounds\"",
"%",
"col",
")",
"offset",
"=",
"index",
"-",
"col",
"line",
"=",
"\"\"",
"start",
",",
"stop",
",",
"step",
"=",
"row",
".",
"indices",
"(",
"n",
")",
"for",
"i",
"in",
"range",
"(",
"start",
",",
"stop",
",",
"step",
")",
":",
"s",
"=",
"starts",
"[",
"i",
"]",
"e",
"=",
"ends",
"[",
"i",
"]",
"if",
"s",
"==",
"e",
":",
"line",
"+=",
"\"-\"",
"else",
":",
"sequence",
"=",
"sequences",
"[",
"i",
"]",
"line",
"+=",
"sequence",
"[",
"e",
"-",
"offset",
"]",
"return",
"line",
"if",
"row",
".",
"indices",
"(",
"n",
")",
"!=",
"(",
"0",
",",
"n",
",",
"1",
")",
":",
"raise",
"NotImplementedError",
"if",
"isinstance",
"(",
"col",
",",
"slice",
")",
":",
"start_index",
",",
"stop_index",
",",
"step",
"=",
"col",
".",
"indices",
"(",
"m",
")",
"if",
"start_index",
"<",
"stop_index",
"and",
"step",
"==",
"1",
":",
"steps",
"=",
"numpy",
".",
"diff",
"(",
"coordinates",
",",
"1",
")",
"indices",
"=",
"steps",
".",
"max",
"(",
"0",
")",
".",
"cumsum",
"(",
")",
"i",
"=",
"indices",
".",
"searchsorted",
"(",
"start_index",
",",
"side",
"=",
"\"right\"",
")",
"j",
"=",
"i",
"+",
"indices",
"[",
"i",
":",
"]",
".",
"searchsorted",
"(",
"stop_index",
",",
"side",
"=",
"\"left\"",
")",
"+",
"1",
"offset",
"=",
"steps",
"[",
":",
",",
"i",
"]",
"-",
"indices",
"[",
"i",
"]",
"+",
"start_index",
"coordinates",
"[",
":",
",",
"i",
"]",
"+=",
"offset",
"*",
"(",
"steps",
"[",
":",
",",
"i",
"]",
">",
"0",
")",
"offset",
"=",
"indices",
"[",
"j",
"-",
"1",
"]",
"-",
"stop_index",
"coordinates",
"[",
":",
",",
"j",
"]",
"-=",
"offset",
"*",
"(",
"steps",
"[",
":",
",",
"j",
"-",
"1",
"]",
">",
"0",
")",
"coordinates",
"=",
"coordinates",
"[",
":",
",",
"i",
":",
"j",
"+",
"1",
"]",
"for",
"i",
",",
"sequence",
"in",
"enumerate",
"(",
"sequences",
")",
":",
"if",
"self",
".",
"coordinates",
"[",
"i",
",",
"0",
"]",
">",
"self",
".",
"coordinates",
"[",
"i",
",",
"-",
"1",
"]",
":",
"# mapped to reverse strand",
"n",
"=",
"len",
"(",
"sequence",
")",
"coordinates",
"[",
"i",
",",
":",
"]",
"=",
"n",
"-",
"coordinates",
"[",
"i",
",",
":",
"]",
"sequences",
"=",
"self",
".",
"sequences",
"alignment",
"=",
"Alignment",
"(",
"sequences",
",",
"coordinates",
")",
"if",
"numpy",
".",
"array_equal",
"(",
"coordinates",
",",
"self",
".",
"coordinates",
")",
":",
"try",
":",
"alignment",
".",
"score",
"=",
"self",
".",
"score",
"except",
"AttributeError",
":",
"pass",
"try",
":",
"column_annotations",
"=",
"self",
".",
"column_annotations",
"except",
"AttributeError",
":",
"pass",
"else",
":",
"alignment",
".",
"column_annotations",
"=",
"{",
"}",
"for",
"key",
",",
"value",
"in",
"column_annotations",
".",
"items",
"(",
")",
":",
"value",
"=",
"value",
"[",
"start_index",
":",
"stop_index",
"]",
"try",
":",
"value",
"=",
"value",
".",
"copy",
"(",
")",
"except",
"AttributeError",
":",
"# immutable tuples like str, tuple",
"pass",
"alignment",
".",
"column_annotations",
"[",
"key",
"]",
"=",
"value",
"return",
"alignment",
"# make an iterable if step != 1",
"col",
"=",
"range",
"(",
"start_index",
",",
"stop_index",
",",
"step",
")",
"# try if we can use col as an iterable",
"indices",
"=",
"tuple",
"(",
"col",
")",
"try",
":",
"lines",
"=",
"[",
"self",
"[",
"i",
",",
"indices",
"]",
"for",
"i",
"in",
"range",
"(",
"n",
")",
"]",
"except",
"IndexError",
":",
"raise",
"except",
"Exception",
":",
"raise",
"TypeError",
"(",
"\"second index must be an integer, slice, or iterable of integers\"",
")",
"from",
"None",
"else",
":",
"sequences",
"=",
"[",
"line",
".",
"replace",
"(",
"\"-\"",
",",
"\"\"",
")",
"for",
"line",
"in",
"lines",
"]",
"coordinates",
"=",
"self",
".",
"infer_coordinates",
"(",
"lines",
")",
"alignment",
"=",
"Alignment",
"(",
"sequences",
",",
"coordinates",
")",
"try",
":",
"column_annotations",
"=",
"self",
".",
"column_annotations",
"except",
"AttributeError",
":",
"pass",
"else",
":",
"alignment",
".",
"column_annotations",
"=",
"{",
"}",
"for",
"key",
",",
"value",
"in",
"column_annotations",
".",
"items",
"(",
")",
":",
"value_generator",
"=",
"(",
"value",
"[",
"index",
"]",
"for",
"index",
"in",
"indices",
")",
"if",
"isinstance",
"(",
"value",
",",
"str",
")",
":",
"value",
"=",
"\"\"",
".",
"join",
"(",
"value_generator",
")",
"else",
":",
"value",
"=",
"value",
".",
"__class__",
"(",
"value_generator",
")",
"alignment",
".",
"column_annotations",
"[",
"key",
"]",
"=",
"value",
"return",
"alignment",
"raise",
"TypeError",
"(",
"\"first index must be an integer or slice\"",
")",
"raise",
"TypeError",
"(",
"\"alignment indices must be integers, slices, or tuples\"",
")"
] |
https://github.com/biopython/biopython/blob/2dd97e71762af7b046d7f7f8a4f1e38db6b06c86/Bio/Align/__init__.py#L1245-L1592
|
||
networkx/networkx
|
1620568e36702b1cfeaf1c0277b167b6cb93e48d
|
networkx/algorithms/isomorphism/ismags.py
|
python
|
ISMAGS._find_node_edge_color
|
(graph, node_colors, edge_colors)
|
return node_edge_colors
|
For every node in graph, come up with a color that combines 1) the
color of the node, and 2) the number of edges of a color to each type
of node.
|
For every node in graph, come up with a color that combines 1) the
color of the node, and 2) the number of edges of a color to each type
of node.
|
[
"For",
"every",
"node",
"in",
"graph",
"come",
"up",
"with",
"a",
"color",
"that",
"combines",
"1",
")",
"the",
"color",
"of",
"the",
"node",
"and",
"2",
")",
"the",
"number",
"of",
"edges",
"of",
"a",
"color",
"to",
"each",
"type",
"of",
"node",
"."
] |
def _find_node_edge_color(graph, node_colors, edge_colors):
"""
For every node in graph, come up with a color that combines 1) the
color of the node, and 2) the number of edges of a color to each type
of node.
"""
counts = defaultdict(lambda: defaultdict(int))
for node1, node2 in graph.edges:
if (node1, node2) in edge_colors:
# FIXME directed graphs
ecolor = edge_colors[node1, node2]
else:
ecolor = edge_colors[node2, node1]
# Count per node how many edges it has of what color to nodes of
# what color
counts[node1][ecolor, node_colors[node2]] += 1
counts[node2][ecolor, node_colors[node1]] += 1
node_edge_colors = dict()
for node in graph.nodes:
node_edge_colors[node] = node_colors[node], set(counts[node].items())
return node_edge_colors
|
[
"def",
"_find_node_edge_color",
"(",
"graph",
",",
"node_colors",
",",
"edge_colors",
")",
":",
"counts",
"=",
"defaultdict",
"(",
"lambda",
":",
"defaultdict",
"(",
"int",
")",
")",
"for",
"node1",
",",
"node2",
"in",
"graph",
".",
"edges",
":",
"if",
"(",
"node1",
",",
"node2",
")",
"in",
"edge_colors",
":",
"# FIXME directed graphs",
"ecolor",
"=",
"edge_colors",
"[",
"node1",
",",
"node2",
"]",
"else",
":",
"ecolor",
"=",
"edge_colors",
"[",
"node2",
",",
"node1",
"]",
"# Count per node how many edges it has of what color to nodes of",
"# what color",
"counts",
"[",
"node1",
"]",
"[",
"ecolor",
",",
"node_colors",
"[",
"node2",
"]",
"]",
"+=",
"1",
"counts",
"[",
"node2",
"]",
"[",
"ecolor",
",",
"node_colors",
"[",
"node1",
"]",
"]",
"+=",
"1",
"node_edge_colors",
"=",
"dict",
"(",
")",
"for",
"node",
"in",
"graph",
".",
"nodes",
":",
"node_edge_colors",
"[",
"node",
"]",
"=",
"node_colors",
"[",
"node",
"]",
",",
"set",
"(",
"counts",
"[",
"node",
"]",
".",
"items",
"(",
")",
")",
"return",
"node_edge_colors"
] |
https://github.com/networkx/networkx/blob/1620568e36702b1cfeaf1c0277b167b6cb93e48d/networkx/algorithms/isomorphism/ismags.py#L688-L710
|
|
quantumlib/Cirq
|
89f88b01d69222d3f1ec14d649b7b3a85ed9211f
|
cirq-google/cirq_google/engine/engine_client.py
|
python
|
EngineClient.remove_job_labels
|
(
self, project_id: str, program_id: str, job_id: str, label_keys: List[str]
)
|
return job
|
Removes labels with given keys from the labels of a previously
created quantum job.
Args:
project_id: A project_id of the parent Google Cloud Project.
program_id: Unique ID of the program within the parent project.
job_id: Unique ID of the job within the parent program.
label_keys: Label keys to remove from the existing job labels.
Returns:
The updated quantum job.
|
Removes labels with given keys from the labels of a previously
created quantum job.
|
[
"Removes",
"labels",
"with",
"given",
"keys",
"from",
"the",
"labels",
"of",
"a",
"previously",
"created",
"quantum",
"job",
"."
] |
def remove_job_labels(
self, project_id: str, program_id: str, job_id: str, label_keys: List[str]
) -> qtypes.QuantumJob:
"""Removes labels with given keys from the labels of a previously
created quantum job.
Args:
project_id: A project_id of the parent Google Cloud Project.
program_id: Unique ID of the program within the parent project.
job_id: Unique ID of the job within the parent program.
label_keys: Label keys to remove from the existing job labels.
Returns:
The updated quantum job.
"""
job = self.get_job(project_id, program_id, job_id, False)
old_labels = job.labels
new_labels = dict(old_labels)
for key in label_keys:
new_labels.pop(key, None)
if new_labels != old_labels:
fingerprint = job.label_fingerprint
return self._set_job_labels(project_id, program_id, job_id, new_labels, fingerprint)
return job
|
[
"def",
"remove_job_labels",
"(",
"self",
",",
"project_id",
":",
"str",
",",
"program_id",
":",
"str",
",",
"job_id",
":",
"str",
",",
"label_keys",
":",
"List",
"[",
"str",
"]",
")",
"->",
"qtypes",
".",
"QuantumJob",
":",
"job",
"=",
"self",
".",
"get_job",
"(",
"project_id",
",",
"program_id",
",",
"job_id",
",",
"False",
")",
"old_labels",
"=",
"job",
".",
"labels",
"new_labels",
"=",
"dict",
"(",
"old_labels",
")",
"for",
"key",
"in",
"label_keys",
":",
"new_labels",
".",
"pop",
"(",
"key",
",",
"None",
")",
"if",
"new_labels",
"!=",
"old_labels",
":",
"fingerprint",
"=",
"job",
".",
"label_fingerprint",
"return",
"self",
".",
"_set_job_labels",
"(",
"project_id",
",",
"program_id",
",",
"job_id",
",",
"new_labels",
",",
"fingerprint",
")",
"return",
"job"
] |
https://github.com/quantumlib/Cirq/blob/89f88b01d69222d3f1ec14d649b7b3a85ed9211f/cirq-google/cirq_google/engine/engine_client.py#L536-L559
|
|
numenta/nupic
|
b9ebedaf54f49a33de22d8d44dff7c765cdb5548
|
external/linux32/lib/python2.6/site-packages/pkg_resources.py
|
python
|
Environment.obtain
|
(self, requirement, installer=None)
|
Obtain a distribution matching `requirement` (e.g. via download)
Obtain a distro that matches requirement (e.g. via download). In the
base ``Environment`` class, this routine just returns
``installer(requirement)``, unless `installer` is None, in which case
None is returned instead. This method is a hook that allows subclasses
to attempt other ways of obtaining a distribution before falling back
to the `installer` argument.
|
Obtain a distribution matching `requirement` (e.g. via download)
|
[
"Obtain",
"a",
"distribution",
"matching",
"requirement",
"(",
"e",
".",
"g",
".",
"via",
"download",
")"
] |
def obtain(self, requirement, installer=None):
"""Obtain a distribution matching `requirement` (e.g. via download)
Obtain a distro that matches requirement (e.g. via download). In the
base ``Environment`` class, this routine just returns
``installer(requirement)``, unless `installer` is None, in which case
None is returned instead. This method is a hook that allows subclasses
to attempt other ways of obtaining a distribution before falling back
to the `installer` argument."""
if installer is not None:
return installer(requirement)
|
[
"def",
"obtain",
"(",
"self",
",",
"requirement",
",",
"installer",
"=",
"None",
")",
":",
"if",
"installer",
"is",
"not",
"None",
":",
"return",
"installer",
"(",
"requirement",
")"
] |
https://github.com/numenta/nupic/blob/b9ebedaf54f49a33de22d8d44dff7c765cdb5548/external/linux32/lib/python2.6/site-packages/pkg_resources.py#L760-L770
|
||
mozilla/treeherder
|
228750c5774cfbe7d395e10e2c84665d9122496d
|
treeherder/services/pulse/exchange.py
|
python
|
get_exchange
|
(connection, name, create=False)
|
return bound_exchange
|
Get a Kombu Exchange object using the passed in name.
Can create an Exchange but this is typically not wanted in production-like
environments and only useful for testing.
|
Get a Kombu Exchange object using the passed in name.
|
[
"Get",
"a",
"Kombu",
"Exchange",
"object",
"using",
"the",
"passed",
"in",
"name",
"."
] |
def get_exchange(connection, name, create=False):
"""
Get a Kombu Exchange object using the passed in name.
Can create an Exchange but this is typically not wanted in production-like
environments and only useful for testing.
"""
exchange = Exchange(name, type="topic", passive=not create)
# bind the exchange to our connection so operations can be performed on it
bound_exchange = exchange(connection)
# ensure the exchange exists. Throw an error if it was created with
# passive=True and it doesn't exist.
bound_exchange.declare()
return bound_exchange
|
[
"def",
"get_exchange",
"(",
"connection",
",",
"name",
",",
"create",
"=",
"False",
")",
":",
"exchange",
"=",
"Exchange",
"(",
"name",
",",
"type",
"=",
"\"topic\"",
",",
"passive",
"=",
"not",
"create",
")",
"# bind the exchange to our connection so operations can be performed on it",
"bound_exchange",
"=",
"exchange",
"(",
"connection",
")",
"# ensure the exchange exists. Throw an error if it was created with",
"# passive=True and it doesn't exist.",
"bound_exchange",
".",
"declare",
"(",
")",
"return",
"bound_exchange"
] |
https://github.com/mozilla/treeherder/blob/228750c5774cfbe7d395e10e2c84665d9122496d/treeherder/services/pulse/exchange.py#L4-L20
|
|
KhronosGroup/glTF-Blender-Exporter
|
dd7a3dbd8f43a79d572e7c45f4215f770bb92a37
|
scripts/addons/io_scene_gltf2/gltf2_get.py
|
python
|
get_light_index
|
(glTF, name)
|
return -1
|
Return the light index in the glTF array.
|
Return the light index in the glTF array.
|
[
"Return",
"the",
"light",
"index",
"in",
"the",
"glTF",
"array",
"."
] |
def get_light_index(glTF, name):
"""
Return the light index in the glTF array.
"""
if glTF.get('extensions') is None:
return -1
extensions = glTF['extensions']
if extensions.get('KHR_lights') is None:
return -1
khr_lights = extensions['KHR_lights']
if khr_lights.get('lights') is None:
return -1
lights = khr_lights['lights']
index = 0
for light in lights:
if light['name'] == name:
return index
index += 1
return -1
|
[
"def",
"get_light_index",
"(",
"glTF",
",",
"name",
")",
":",
"if",
"glTF",
".",
"get",
"(",
"'extensions'",
")",
"is",
"None",
":",
"return",
"-",
"1",
"extensions",
"=",
"glTF",
"[",
"'extensions'",
"]",
"if",
"extensions",
".",
"get",
"(",
"'KHR_lights'",
")",
"is",
"None",
":",
"return",
"-",
"1",
"khr_lights",
"=",
"extensions",
"[",
"'KHR_lights'",
"]",
"if",
"khr_lights",
".",
"get",
"(",
"'lights'",
")",
"is",
"None",
":",
"return",
"-",
"1",
"lights",
"=",
"khr_lights",
"[",
"'lights'",
"]",
"index",
"=",
"0",
"for",
"light",
"in",
"lights",
":",
"if",
"light",
"[",
"'name'",
"]",
"==",
"name",
":",
"return",
"index",
"index",
"+=",
"1",
"return",
"-",
"1"
] |
https://github.com/KhronosGroup/glTF-Blender-Exporter/blob/dd7a3dbd8f43a79d572e7c45f4215f770bb92a37/scripts/addons/io_scene_gltf2/gltf2_get.py#L287-L314
|
|
holoviz/holoviews
|
cc6b27f01710402fdfee2aeef1507425ca78c91f
|
holoviews/core/spaces.py
|
python
|
HoloMap.overlay
|
(self, dimensions=None, **kwargs)
|
Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
along the dimension(s).
Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
NdOverlay object(s) with supplied dimensions
|
Group by supplied dimension(s) and overlay each group
|
[
"Group",
"by",
"supplied",
"dimension",
"(",
"s",
")",
"and",
"overlay",
"each",
"group"
] |
def overlay(self, dimensions=None, **kwargs):
"""Group by supplied dimension(s) and overlay each group
Groups data by supplied dimension(s) overlaying the groups
along the dimension(s).
Args:
dimensions: Dimension(s) of dimensions to group by
Returns:
NdOverlay object(s) with supplied dimensions
"""
dimensions = self._valid_dimensions(dimensions)
if len(dimensions) == self.ndims:
with item_check(False):
return NdOverlay(self, **kwargs).reindex(dimensions)
else:
dims = [d for d in self.kdims if d not in dimensions]
return self.groupby(dims, group_type=NdOverlay, **kwargs)
|
[
"def",
"overlay",
"(",
"self",
",",
"dimensions",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"dimensions",
"=",
"self",
".",
"_valid_dimensions",
"(",
"dimensions",
")",
"if",
"len",
"(",
"dimensions",
")",
"==",
"self",
".",
"ndims",
":",
"with",
"item_check",
"(",
"False",
")",
":",
"return",
"NdOverlay",
"(",
"self",
",",
"*",
"*",
"kwargs",
")",
".",
"reindex",
"(",
"dimensions",
")",
"else",
":",
"dims",
"=",
"[",
"d",
"for",
"d",
"in",
"self",
".",
"kdims",
"if",
"d",
"not",
"in",
"dimensions",
"]",
"return",
"self",
".",
"groupby",
"(",
"dims",
",",
"group_type",
"=",
"NdOverlay",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/holoviz/holoviews/blob/cc6b27f01710402fdfee2aeef1507425ca78c91f/holoviews/core/spaces.py#L47-L65
|
||
ruotianluo/Image_Captioning_AI_Challenger
|
ad230614efb8b964b4ced49a3622f4585d4de1cc
|
scripts/prepro_ngrams.py
|
python
|
cook_refs
|
(refs, n=4)
|
return [precook(ref, n) for ref in refs]
|
Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (list of dict)
|
Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (list of dict)
|
[
"Takes",
"a",
"list",
"of",
"reference",
"sentences",
"for",
"a",
"single",
"segment",
"and",
"returns",
"an",
"object",
"that",
"encapsulates",
"everything",
"that",
"BLEU",
"needs",
"to",
"know",
"about",
"them",
".",
":",
"param",
"refs",
":",
"list",
"of",
"string",
":",
"reference",
"sentences",
"for",
"some",
"image",
":",
"param",
"n",
":",
"int",
":",
"number",
"of",
"ngrams",
"for",
"which",
"(",
"ngram",
")",
"representation",
"is",
"calculated",
":",
"return",
":",
"result",
"(",
"list",
"of",
"dict",
")"
] |
def cook_refs(refs, n=4): ## lhuang: oracle will call with "average"
'''Takes a list of reference sentences for a single segment
and returns an object that encapsulates everything that BLEU
needs to know about them.
:param refs: list of string : reference sentences for some image
:param n: int : number of ngrams for which (ngram) representation is calculated
:return: result (list of dict)
'''
return [precook(ref, n) for ref in refs]
|
[
"def",
"cook_refs",
"(",
"refs",
",",
"n",
"=",
"4",
")",
":",
"## lhuang: oracle will call with \"average\"",
"return",
"[",
"precook",
"(",
"ref",
",",
"n",
")",
"for",
"ref",
"in",
"refs",
"]"
] |
https://github.com/ruotianluo/Image_Captioning_AI_Challenger/blob/ad230614efb8b964b4ced49a3622f4585d4de1cc/scripts/prepro_ngrams.py#L49-L57
|
|
mila-iqia/myia
|
56774a39579b4ec4123f44843ad4ca688acc859b
|
myia/monomorphize.py
|
python
|
_MonoRemapper.__init__
|
(
self,
graphs,
inlines,
manager,
relation,
graph_relation,
clone_constants,
engine,
graph_repl,
fv_function,
)
|
Initialize the _MonoRemapper.
|
Initialize the _MonoRemapper.
|
[
"Initialize",
"the",
"_MonoRemapper",
"."
] |
def __init__(
self,
graphs,
inlines,
manager,
relation,
graph_relation,
clone_constants,
engine,
graph_repl,
fv_function,
):
"""Initialize the _MonoRemapper."""
super().__init__(
graphs=graphs,
inlines=inlines,
manager=manager,
relation=relation,
graph_repl=graph_repl,
graph_relation=graph_relation,
clone_constants=clone_constants,
set_abstract=False,
)
self.engine = engine
self.fv_function = fv_function
|
[
"def",
"__init__",
"(",
"self",
",",
"graphs",
",",
"inlines",
",",
"manager",
",",
"relation",
",",
"graph_relation",
",",
"clone_constants",
",",
"engine",
",",
"graph_repl",
",",
"fv_function",
",",
")",
":",
"super",
"(",
")",
".",
"__init__",
"(",
"graphs",
"=",
"graphs",
",",
"inlines",
"=",
"inlines",
",",
"manager",
"=",
"manager",
",",
"relation",
"=",
"relation",
",",
"graph_repl",
"=",
"graph_repl",
",",
"graph_relation",
"=",
"graph_relation",
",",
"clone_constants",
"=",
"clone_constants",
",",
"set_abstract",
"=",
"False",
",",
")",
"self",
".",
"engine",
"=",
"engine",
"self",
".",
"fv_function",
"=",
"fv_function"
] |
https://github.com/mila-iqia/myia/blob/56774a39579b4ec4123f44843ad4ca688acc859b/myia/monomorphize.py#L739-L763
|
||
Tautulli/Tautulli
|
2410eb33805aaac4bd1c5dad0f71e4f15afaf742
|
lib/pyparsing/core.py
|
python
|
ParseExpression.ignore_whitespace
|
(self, recursive=True)
|
return self
|
Extends ``ignore_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on
all contained expressions.
|
Extends ``ignore_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on
all contained expressions.
|
[
"Extends",
"ignore_whitespace",
"defined",
"in",
"base",
"class",
"and",
"also",
"invokes",
"leave_whitespace",
"on",
"all",
"contained",
"expressions",
"."
] |
def ignore_whitespace(self, recursive=True):
"""
Extends ``ignore_whitespace`` defined in base class, and also invokes ``leave_whitespace`` on
all contained expressions.
"""
super().ignore_whitespace(recursive)
if recursive:
self.exprs = [e.copy() for e in self.exprs]
for e in self.exprs:
e.ignore_whitespace(recursive)
return self
|
[
"def",
"ignore_whitespace",
"(",
"self",
",",
"recursive",
"=",
"True",
")",
":",
"super",
"(",
")",
".",
"ignore_whitespace",
"(",
"recursive",
")",
"if",
"recursive",
":",
"self",
".",
"exprs",
"=",
"[",
"e",
".",
"copy",
"(",
")",
"for",
"e",
"in",
"self",
".",
"exprs",
"]",
"for",
"e",
"in",
"self",
".",
"exprs",
":",
"e",
".",
"ignore_whitespace",
"(",
"recursive",
")",
"return",
"self"
] |
https://github.com/Tautulli/Tautulli/blob/2410eb33805aaac4bd1c5dad0f71e4f15afaf742/lib/pyparsing/core.py#L3619-L3629
|
|
garnaat/kappa
|
a06a0047cc51f6a210beec4028a5fbe8ebcd5c53
|
kappa/restapi.py
|
python
|
RestApi.api_exists
|
(self)
|
return self._get_api()
|
[] |
def api_exists(self):
return self._get_api()
|
[
"def",
"api_exists",
"(",
"self",
")",
":",
"return",
"self",
".",
"_get_api",
"(",
")"
] |
https://github.com/garnaat/kappa/blob/a06a0047cc51f6a210beec4028a5fbe8ebcd5c53/kappa/restapi.py#L197-L198
|
|||
tobegit3hub/deep_image_model
|
8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e
|
java_predict_client/src/main/proto/tensorflow/python/ops/gradients_impl.py
|
python
|
_MultiDeviceAddN
|
(tensor_list)
|
return math_ops.add_n(summands)
|
Adds tensors from potentially multiple devices.
|
Adds tensors from potentially multiple devices.
|
[
"Adds",
"tensors",
"from",
"potentially",
"multiple",
"devices",
"."
] |
def _MultiDeviceAddN(tensor_list):
"""Adds tensors from potentially multiple devices."""
# Basic function structure comes from control_flow_ops.group().
# Sort tensors according to their devices.
tensors_on_device = collections.defaultdict(lambda: [])
for tensor in tensor_list:
tensors_on_device[tensor.device].append(tensor)
# For each device, add the tensors on that device first.
# Then gather the partial sums from multiple devices.
# TODO(sjhwang): Create hierarchical aggregation tree as pbar's suggestion.
# E.g., aggregate per GPU, then per task, and so on.
summands = []
def DeviceKey(dev):
return "" if dev is None else dev
for dev in sorted(six.iterkeys(tensors_on_device), key=DeviceKey):
tensors = tensors_on_device[dev]
with ops.colocate_with(tensors[0].op, ignore_existing=True):
summands.append(math_ops.add_n(tensors))
return math_ops.add_n(summands)
|
[
"def",
"_MultiDeviceAddN",
"(",
"tensor_list",
")",
":",
"# Basic function structure comes from control_flow_ops.group().",
"# Sort tensors according to their devices.",
"tensors_on_device",
"=",
"collections",
".",
"defaultdict",
"(",
"lambda",
":",
"[",
"]",
")",
"for",
"tensor",
"in",
"tensor_list",
":",
"tensors_on_device",
"[",
"tensor",
".",
"device",
"]",
".",
"append",
"(",
"tensor",
")",
"# For each device, add the tensors on that device first.",
"# Then gather the partial sums from multiple devices.",
"# TODO(sjhwang): Create hierarchical aggregation tree as pbar's suggestion.",
"# E.g., aggregate per GPU, then per task, and so on.",
"summands",
"=",
"[",
"]",
"def",
"DeviceKey",
"(",
"dev",
")",
":",
"return",
"\"\"",
"if",
"dev",
"is",
"None",
"else",
"dev",
"for",
"dev",
"in",
"sorted",
"(",
"six",
".",
"iterkeys",
"(",
"tensors_on_device",
")",
",",
"key",
"=",
"DeviceKey",
")",
":",
"tensors",
"=",
"tensors_on_device",
"[",
"dev",
"]",
"with",
"ops",
".",
"colocate_with",
"(",
"tensors",
"[",
"0",
"]",
".",
"op",
",",
"ignore_existing",
"=",
"True",
")",
":",
"summands",
".",
"append",
"(",
"math_ops",
".",
"add_n",
"(",
"tensors",
")",
")",
"return",
"math_ops",
".",
"add_n",
"(",
"summands",
")"
] |
https://github.com/tobegit3hub/deep_image_model/blob/8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e/java_predict_client/src/main/proto/tensorflow/python/ops/gradients_impl.py#L638-L660
|
|
wikimedia/pywikibot
|
81a01ffaec7271bf5b4b170f85a80388420a4e78
|
scripts/welcome.py
|
python
|
WelcomeBot.define_sign
|
(self, force=False)
|
return self._random_signature
|
Setup signature.
|
Setup signature.
|
[
"Setup",
"signature",
"."
] |
def define_sign(self, force=False) -> List[str]:
"""Setup signature."""
if hasattr(self, '_random_signature') and not force:
return self._random_signature
sign_text = ''
creg = re.compile(r'^\* ?(.*?)$', re.M)
if not globalvar.sign_file_name:
sign_page_name = i18n.translate(self.site, random_sign)
if not sign_page_name:
self.show_status(Msg.WARN)
pywikibot.output(
"{} doesn't allow random signature, force disable."
.format(self.site))
globalvar.random_sign = False
return []
sign_page = pywikibot.Page(self.site, sign_page_name)
if sign_page.exists():
pywikibot.output('Loading signature list...')
sign_text = sign_page.get()
else:
pywikibot.output('The signature list page does not exist, '
'random signature will be disabled.')
globalvar.random_sign = False
else:
try:
f = codecs.open(
pywikibot.config.datafilepath(globalvar.sign_file_name),
'r',
encoding=config.console_encoding)
except LookupError:
f = codecs.open(pywikibot.config.datafilepath(
globalvar.sign_file_name), 'r', encoding='utf-8')
except IOError:
pywikibot.error('No fileName!')
raise FilenameNotSet('No signature filename specified.')
sign_text = f.read()
f.close()
self._random_signature = creg.findall(sign_text)
return self._random_signature
|
[
"def",
"define_sign",
"(",
"self",
",",
"force",
"=",
"False",
")",
"->",
"List",
"[",
"str",
"]",
":",
"if",
"hasattr",
"(",
"self",
",",
"'_random_signature'",
")",
"and",
"not",
"force",
":",
"return",
"self",
".",
"_random_signature",
"sign_text",
"=",
"''",
"creg",
"=",
"re",
".",
"compile",
"(",
"r'^\\* ?(.*?)$'",
",",
"re",
".",
"M",
")",
"if",
"not",
"globalvar",
".",
"sign_file_name",
":",
"sign_page_name",
"=",
"i18n",
".",
"translate",
"(",
"self",
".",
"site",
",",
"random_sign",
")",
"if",
"not",
"sign_page_name",
":",
"self",
".",
"show_status",
"(",
"Msg",
".",
"WARN",
")",
"pywikibot",
".",
"output",
"(",
"\"{} doesn't allow random signature, force disable.\"",
".",
"format",
"(",
"self",
".",
"site",
")",
")",
"globalvar",
".",
"random_sign",
"=",
"False",
"return",
"[",
"]",
"sign_page",
"=",
"pywikibot",
".",
"Page",
"(",
"self",
".",
"site",
",",
"sign_page_name",
")",
"if",
"sign_page",
".",
"exists",
"(",
")",
":",
"pywikibot",
".",
"output",
"(",
"'Loading signature list...'",
")",
"sign_text",
"=",
"sign_page",
".",
"get",
"(",
")",
"else",
":",
"pywikibot",
".",
"output",
"(",
"'The signature list page does not exist, '",
"'random signature will be disabled.'",
")",
"globalvar",
".",
"random_sign",
"=",
"False",
"else",
":",
"try",
":",
"f",
"=",
"codecs",
".",
"open",
"(",
"pywikibot",
".",
"config",
".",
"datafilepath",
"(",
"globalvar",
".",
"sign_file_name",
")",
",",
"'r'",
",",
"encoding",
"=",
"config",
".",
"console_encoding",
")",
"except",
"LookupError",
":",
"f",
"=",
"codecs",
".",
"open",
"(",
"pywikibot",
".",
"config",
".",
"datafilepath",
"(",
"globalvar",
".",
"sign_file_name",
")",
",",
"'r'",
",",
"encoding",
"=",
"'utf-8'",
")",
"except",
"IOError",
":",
"pywikibot",
".",
"error",
"(",
"'No fileName!'",
")",
"raise",
"FilenameNotSet",
"(",
"'No signature filename specified.'",
")",
"sign_text",
"=",
"f",
".",
"read",
"(",
")",
"f",
".",
"close",
"(",
")",
"self",
".",
"_random_signature",
"=",
"creg",
".",
"findall",
"(",
"sign_text",
")",
"return",
"self",
".",
"_random_signature"
] |
https://github.com/wikimedia/pywikibot/blob/81a01ffaec7271bf5b4b170f85a80388420a4e78/scripts/welcome.py#L727-L768
|
|
twke18/Adaptive_Affinity_Fields
|
8488aa6ad16022ab4b89fb8626386997559cb951
|
pyscripts/train/train_affinity_mgpu.py
|
python
|
save
|
(saver, sess, logdir, step)
|
Save the trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
logdir: path to the snapshots directory.
step: current training step.
|
Save the trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
logdir: path to the snapshots directory.
step: current training step.
|
[
"Save",
"the",
"trained",
"weights",
".",
"Args",
":",
"saver",
":",
"TensorFlow",
"Saver",
"object",
".",
"sess",
":",
"TensorFlow",
"session",
".",
"logdir",
":",
"path",
"to",
"the",
"snapshots",
"directory",
".",
"step",
":",
"current",
"training",
"step",
"."
] |
def save(saver, sess, logdir, step):
"""Save the trained weights.
Args:
saver: TensorFlow Saver object.
sess: TensorFlow session.
logdir: path to the snapshots directory.
step: current training step.
"""
model_name = 'model.ckpt'
checkpoint_path = os.path.join(logdir, model_name)
if not os.path.exists(logdir):
os.makedirs(logdir)
saver.save(sess, checkpoint_path, global_step=step)
print('The checkpoint has been created.')
|
[
"def",
"save",
"(",
"saver",
",",
"sess",
",",
"logdir",
",",
"step",
")",
":",
"model_name",
"=",
"'model.ckpt'",
"checkpoint_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"logdir",
",",
"model_name",
")",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"logdir",
")",
":",
"os",
".",
"makedirs",
"(",
"logdir",
")",
"saver",
".",
"save",
"(",
"sess",
",",
"checkpoint_path",
",",
"global_step",
"=",
"step",
")",
"print",
"(",
"'The checkpoint has been created.'",
")"
] |
https://github.com/twke18/Adaptive_Affinity_Fields/blob/8488aa6ad16022ab4b89fb8626386997559cb951/pyscripts/train/train_affinity_mgpu.py#L89-L104
|
||
fzlee/alipay
|
0f8eab30fea7adb43284182cc6bcac08f51b2e08
|
alipay/__init__.py
|
python
|
DCAliPay.load_alipay_public_key_string
|
(self)
|
return OpenSSL.crypto.dump_publickey(
OpenSSL.crypto.FILETYPE_PEM, cert.get_pubkey()
).decode("utf-8")
|
[] |
def load_alipay_public_key_string(self):
cert = OpenSSL.crypto.load_certificate(
OpenSSL.crypto.FILETYPE_PEM, self._alipay_public_key_cert_string
)
return OpenSSL.crypto.dump_publickey(
OpenSSL.crypto.FILETYPE_PEM, cert.get_pubkey()
).decode("utf-8")
|
[
"def",
"load_alipay_public_key_string",
"(",
"self",
")",
":",
"cert",
"=",
"OpenSSL",
".",
"crypto",
".",
"load_certificate",
"(",
"OpenSSL",
".",
"crypto",
".",
"FILETYPE_PEM",
",",
"self",
".",
"_alipay_public_key_cert_string",
")",
"return",
"OpenSSL",
".",
"crypto",
".",
"dump_publickey",
"(",
"OpenSSL",
".",
"crypto",
".",
"FILETYPE_PEM",
",",
"cert",
".",
"get_pubkey",
"(",
")",
")",
".",
"decode",
"(",
"\"utf-8\"",
")"
] |
https://github.com/fzlee/alipay/blob/0f8eab30fea7adb43284182cc6bcac08f51b2e08/alipay/__init__.py#L691-L697
|
|||
home-assistant/core
|
265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1
|
homeassistant/components/acmeda/sensor.py
|
python
|
AcmedaBattery.name
|
(self)
|
return f"{super().name} Battery"
|
Return the name of roller.
|
Return the name of roller.
|
[
"Return",
"the",
"name",
"of",
"roller",
"."
] |
def name(self):
"""Return the name of roller."""
return f"{super().name} Battery"
|
[
"def",
"name",
"(",
"self",
")",
":",
"return",
"f\"{super().name} Battery\""
] |
https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/acmeda/sensor.py#L48-L50
|
|
andresriancho/w3af
|
cd22e5252243a87aaa6d0ddea47cf58dacfe00a9
|
w3af/plugins/attack/db/sqlmap/lib/utils/hash.py
|
python
|
vbulletin_passwd
|
(password, salt, **kwargs)
|
return "%s:%s" % (md5("%s%s" % (md5(password).hexdigest(), salt)).hexdigest(), salt)
|
Reference: https://stackoverflow.com/a/2202810
>>> vbulletin_passwd(password='testpass', salt='salt')
'85c4d8ea77ebef2236fb7e9d24ba9482:salt'
|
Reference: https://stackoverflow.com/a/2202810
|
[
"Reference",
":",
"https",
":",
"//",
"stackoverflow",
".",
"com",
"/",
"a",
"/",
"2202810"
] |
def vbulletin_passwd(password, salt, **kwargs):
"""
Reference: https://stackoverflow.com/a/2202810
>>> vbulletin_passwd(password='testpass', salt='salt')
'85c4d8ea77ebef2236fb7e9d24ba9482:salt'
"""
return "%s:%s" % (md5("%s%s" % (md5(password).hexdigest(), salt)).hexdigest(), salt)
|
[
"def",
"vbulletin_passwd",
"(",
"password",
",",
"salt",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"\"%s:%s\"",
"%",
"(",
"md5",
"(",
"\"%s%s\"",
"%",
"(",
"md5",
"(",
"password",
")",
".",
"hexdigest",
"(",
")",
",",
"salt",
")",
")",
".",
"hexdigest",
"(",
")",
",",
"salt",
")"
] |
https://github.com/andresriancho/w3af/blob/cd22e5252243a87aaa6d0ddea47cf58dacfe00a9/w3af/plugins/attack/db/sqlmap/lib/utils/hash.py#L449-L457
|
|
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/federatedml/util/sample_weight.py
|
python
|
SampleWeight.transform_weighted_instance
|
(self, data_instances, weight_loc)
|
return SampleWeight.assign_sample_weight(data_instances, self.class_weight_dict, weight_loc, self.normalize)
|
[] |
def transform_weighted_instance(self, data_instances, weight_loc):
if self.class_weight and self.class_weight == 'balanced':
self.class_weight_dict = SampleWeight.get_class_weight(data_instances)
else:
if self.class_weight_dict is None:
self.class_weight_dict = self.class_weight
return SampleWeight.assign_sample_weight(data_instances, self.class_weight_dict, weight_loc, self.normalize)
|
[
"def",
"transform_weighted_instance",
"(",
"self",
",",
"data_instances",
",",
"weight_loc",
")",
":",
"if",
"self",
".",
"class_weight",
"and",
"self",
".",
"class_weight",
"==",
"'balanced'",
":",
"self",
".",
"class_weight_dict",
"=",
"SampleWeight",
".",
"get_class_weight",
"(",
"data_instances",
")",
"else",
":",
"if",
"self",
".",
"class_weight_dict",
"is",
"None",
":",
"self",
".",
"class_weight_dict",
"=",
"self",
".",
"class_weight",
"return",
"SampleWeight",
".",
"assign_sample_weight",
"(",
"data_instances",
",",
"self",
".",
"class_weight_dict",
",",
"weight_loc",
",",
"self",
".",
"normalize",
")"
] |
https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/federatedml/util/sample_weight.py#L102-L108
|
|||
MichiganCOG/ViP
|
74776f2575bd5339ba39c784bbda4f04cc859add
|
train.py
|
python
|
train
|
(**args)
|
Evaluate selected model
Args:
rerun (Int): Integer indicating number of repetitions for the select experiment
seed (Int): Integer indicating set seed for random state
save_dir (String): Top level directory to generate results folder
model (String): Name of selected model
dataset (String): Name of selected dataset
exp (String): Name of experiment
debug (Int): Debug state to avoid saving variables
load_type (String): Keyword indicator to evaluate the testing or validation set
pretrained (Int/String): Int/String indicating loading of random, pretrained or saved weights
opt (String): Int/String indicating loading of random, pretrained or saved weights
lr (Float): Learning rate
momentum (Float): Momentum in optimizer
weight_decay (Float): Weight_decay value
final_shape ([Int, Int]): Shape of data when passed into network
Return:
None
|
Evaluate selected model
Args:
rerun (Int): Integer indicating number of repetitions for the select experiment
seed (Int): Integer indicating set seed for random state
save_dir (String): Top level directory to generate results folder
model (String): Name of selected model
dataset (String): Name of selected dataset
exp (String): Name of experiment
debug (Int): Debug state to avoid saving variables
load_type (String): Keyword indicator to evaluate the testing or validation set
pretrained (Int/String): Int/String indicating loading of random, pretrained or saved weights
opt (String): Int/String indicating loading of random, pretrained or saved weights
lr (Float): Learning rate
momentum (Float): Momentum in optimizer
weight_decay (Float): Weight_decay value
final_shape ([Int, Int]): Shape of data when passed into network
Return:
None
|
[
"Evaluate",
"selected",
"model",
"Args",
":",
"rerun",
"(",
"Int",
")",
":",
"Integer",
"indicating",
"number",
"of",
"repetitions",
"for",
"the",
"select",
"experiment",
"seed",
"(",
"Int",
")",
":",
"Integer",
"indicating",
"set",
"seed",
"for",
"random",
"state",
"save_dir",
"(",
"String",
")",
":",
"Top",
"level",
"directory",
"to",
"generate",
"results",
"folder",
"model",
"(",
"String",
")",
":",
"Name",
"of",
"selected",
"model",
"dataset",
"(",
"String",
")",
":",
"Name",
"of",
"selected",
"dataset",
"exp",
"(",
"String",
")",
":",
"Name",
"of",
"experiment",
"debug",
"(",
"Int",
")",
":",
"Debug",
"state",
"to",
"avoid",
"saving",
"variables",
"load_type",
"(",
"String",
")",
":",
"Keyword",
"indicator",
"to",
"evaluate",
"the",
"testing",
"or",
"validation",
"set",
"pretrained",
"(",
"Int",
"/",
"String",
")",
":",
"Int",
"/",
"String",
"indicating",
"loading",
"of",
"random",
"pretrained",
"or",
"saved",
"weights",
"opt",
"(",
"String",
")",
":",
"Int",
"/",
"String",
"indicating",
"loading",
"of",
"random",
"pretrained",
"or",
"saved",
"weights",
"lr",
"(",
"Float",
")",
":",
"Learning",
"rate",
"momentum",
"(",
"Float",
")",
":",
"Momentum",
"in",
"optimizer",
"weight_decay",
"(",
"Float",
")",
":",
"Weight_decay",
"value",
"final_shape",
"(",
"[",
"Int",
"Int",
"]",
")",
":",
"Shape",
"of",
"data",
"when",
"passed",
"into",
"network",
"Return",
":",
"None"
] |
def train(**args):
"""
Evaluate selected model
Args:
rerun (Int): Integer indicating number of repetitions for the select experiment
seed (Int): Integer indicating set seed for random state
save_dir (String): Top level directory to generate results folder
model (String): Name of selected model
dataset (String): Name of selected dataset
exp (String): Name of experiment
debug (Int): Debug state to avoid saving variables
load_type (String): Keyword indicator to evaluate the testing or validation set
pretrained (Int/String): Int/String indicating loading of random, pretrained or saved weights
opt (String): Int/String indicating loading of random, pretrained or saved weights
lr (Float): Learning rate
momentum (Float): Momentum in optimizer
weight_decay (Float): Weight_decay value
final_shape ([Int, Int]): Shape of data when passed into network
Return:
None
"""
print("\n############################################################################\n")
print("Experimental Setup: ", args)
print("\n############################################################################\n")
for total_iteration in range(args['rerun']):
# Generate Results Directory
d = datetime.datetime.today()
date = d.strftime('%Y%m%d-%H%M%S')
result_dir = os.path.join(args['save_dir'], args['model'], '_'.join((args['dataset'],args['exp'],date)))
log_dir = os.path.join(result_dir, 'logs')
save_dir = os.path.join(result_dir, 'checkpoints')
if not args['debug']:
os.makedirs(result_dir, exist_ok=True)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_dir, exist_ok=True)
# Save copy of config file
with open(os.path.join(result_dir, 'config.yaml'),'w') as outfile:
yaml.dump(args, outfile, default_flow_style=False)
# Tensorboard Element
writer = SummaryWriter(log_dir)
# Check if GPU is available (CUDA)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load Network
model = create_model_object(**args).to(device)
# Load Data
loader = data_loader(model_obj=model, **args)
if args['load_type'] == 'train':
train_loader = loader['train']
valid_loader = loader['train'] # Run accuracy on train data if only `train` selected
elif args['load_type'] == 'train_val':
train_loader = loader['train']
valid_loader = loader['valid']
else:
sys.exit('Invalid environment selection for training, exiting')
# END IF
# Training Setup
params = [p for p in model.parameters() if p.requires_grad]
if args['opt'] == 'sgd':
optimizer = optim.SGD(params, lr=args['lr'], momentum=args['momentum'], weight_decay=args['weight_decay'])
elif args['opt'] == 'adam':
optimizer = optim.Adam(params, lr=args['lr'], weight_decay=args['weight_decay'])
else:
sys.exit('Unsupported optimizer selected. Exiting')
# END IF
scheduler = MultiStepLR(optimizer, milestones=args['milestones'], gamma=args['gamma'])
if isinstance(args['pretrained'], str):
ckpt = load_checkpoint(args['pretrained'])
model.load_state_dict(ckpt)
if args['resume']:
start_epoch = load_checkpoint(args['pretrained'], key_name='epoch') + 1
optimizer.load_state_dict(load_checkpoint(args['pretrained'], key_name='optimizer'))
scheduler.step(epoch=start_epoch)
else:
start_epoch = 0
# END IF
else:
start_epoch = 0
# END IF
model_loss = Losses(device=device, **args)
best_val_acc = 0.0
############################################################################################################################################################################
# Start: Training Loop
for epoch in range(start_epoch, args['epoch']):
running_loss = 0.0
print('Epoch: ', epoch)
# Setup Model To Train
model.train()
# Start: Epoch
for step, data in enumerate(train_loader):
if step% args['pseudo_batch_loop'] == 0:
loss = 0.0
running_batch = 0
optimizer.zero_grad()
# END IF
x_input = data['data']
annotations = data['annots']
if isinstance(x_input, torch.Tensor):
mini_batch_size = x_input.shape[0]
outputs = model(x_input.to(device))
assert args['final_shape']==list(x_input.size()[-2:]), "Input to model does not match final_shape argument"
else: #Model takes several inputs in forward function
mini_batch_size = x_input[0].shape[0] #Assuming the first element contains the true data input
for i, item in enumerate(x_input):
if isinstance(item, torch.Tensor):
x_input[i] = item.to(device)
outputs = model(*x_input)
loss = model_loss.loss(outputs, annotations)
loss = loss * mini_batch_size
loss.backward()
running_loss += loss.item()
running_batch += mini_batch_size
if np.isnan(running_loss):
import pdb; pdb.set_trace()
# END IF
if not args['debug']:
# Add Learning Rate Element
for param_group in optimizer.param_groups:
writer.add_scalar(args['dataset']+'/'+args['model']+'/learning_rate', param_group['lr'], epoch*len(train_loader) + step)
# END FOR
# Add Loss Element
writer.add_scalar(args['dataset']+'/'+args['model']+'/minibatch_loss', loss.item()/mini_batch_size, epoch*len(train_loader) + step)
# END IF
if ((epoch*len(train_loader) + step+1) % 100 == 0):
print('Epoch: {}/{}, step: {}/{} | train loss: {:.4f}'.format(epoch, args['epoch'], step+1, len(train_loader), running_loss/float(step+1)/mini_batch_size))
# END IF
if (epoch * len(train_loader) + (step+1)) % args['pseudo_batch_loop'] == 0 and step > 0:
# Apply large mini-batch normalization
for param in model.parameters():
if param.requires_grad:
param.grad *= 1./float(running_batch)
# END FOR
# Apply gradient clipping
if ("grad_max_norm" in args) and float(args['grad_max_norm'] > 0):
nn.utils.clip_grad_norm_(model.parameters(),float(args['grad_max_norm']))
optimizer.step()
running_batch = 0
# END IF
# END FOR: Epoch
scheduler.step(epoch=epoch)
print('Schedulers lr: %f', scheduler.get_lr()[0])
if not args['debug']:
# Save Current Model
save_path = os.path.join(save_dir, args['dataset']+'_epoch'+str(epoch)+'.pkl')
save_checkpoint(epoch, step, model, optimizer, save_path)
# END IF: Debug
## START FOR: Validation Accuracy
running_acc = []
running_acc = valid(valid_loader, running_acc, model, device)
if not args['debug']:
writer.add_scalar(args['dataset']+'/'+args['model']+'/validation_accuracy', 100.*running_acc[-1], epoch*len(train_loader) + step)
print('Accuracy of the network on the validation set: %f %%\n' % (100.*running_acc[-1]))
# Save Best Validation Accuracy Model Separately
if best_val_acc < running_acc[-1]:
best_val_acc = running_acc[-1]
if not args['debug']:
# Save Current Model
save_path = os.path.join(save_dir, args['dataset']+'_best_model.pkl')
save_checkpoint(epoch, step, model, optimizer, save_path)
# END IF
# END IF
# END FOR: Training Loop
############################################################################################################################################################################
if not args['debug']:
# Close Tensorboard Element
writer.close()
|
[
"def",
"train",
"(",
"*",
"*",
"args",
")",
":",
"print",
"(",
"\"\\n############################################################################\\n\"",
")",
"print",
"(",
"\"Experimental Setup: \"",
",",
"args",
")",
"print",
"(",
"\"\\n############################################################################\\n\"",
")",
"for",
"total_iteration",
"in",
"range",
"(",
"args",
"[",
"'rerun'",
"]",
")",
":",
"# Generate Results Directory",
"d",
"=",
"datetime",
".",
"datetime",
".",
"today",
"(",
")",
"date",
"=",
"d",
".",
"strftime",
"(",
"'%Y%m%d-%H%M%S'",
")",
"result_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"args",
"[",
"'save_dir'",
"]",
",",
"args",
"[",
"'model'",
"]",
",",
"'_'",
".",
"join",
"(",
"(",
"args",
"[",
"'dataset'",
"]",
",",
"args",
"[",
"'exp'",
"]",
",",
"date",
")",
")",
")",
"log_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"result_dir",
",",
"'logs'",
")",
"save_dir",
"=",
"os",
".",
"path",
".",
"join",
"(",
"result_dir",
",",
"'checkpoints'",
")",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"os",
".",
"makedirs",
"(",
"result_dir",
",",
"exist_ok",
"=",
"True",
")",
"os",
".",
"makedirs",
"(",
"log_dir",
",",
"exist_ok",
"=",
"True",
")",
"os",
".",
"makedirs",
"(",
"save_dir",
",",
"exist_ok",
"=",
"True",
")",
"# Save copy of config file",
"with",
"open",
"(",
"os",
".",
"path",
".",
"join",
"(",
"result_dir",
",",
"'config.yaml'",
")",
",",
"'w'",
")",
"as",
"outfile",
":",
"yaml",
".",
"dump",
"(",
"args",
",",
"outfile",
",",
"default_flow_style",
"=",
"False",
")",
"# Tensorboard Element",
"writer",
"=",
"SummaryWriter",
"(",
"log_dir",
")",
"# Check if GPU is available (CUDA)",
"device",
"=",
"torch",
".",
"device",
"(",
"\"cuda:0\"",
"if",
"torch",
".",
"cuda",
".",
"is_available",
"(",
")",
"else",
"\"cpu\"",
")",
"# Load Network",
"model",
"=",
"create_model_object",
"(",
"*",
"*",
"args",
")",
".",
"to",
"(",
"device",
")",
"# Load Data",
"loader",
"=",
"data_loader",
"(",
"model_obj",
"=",
"model",
",",
"*",
"*",
"args",
")",
"if",
"args",
"[",
"'load_type'",
"]",
"==",
"'train'",
":",
"train_loader",
"=",
"loader",
"[",
"'train'",
"]",
"valid_loader",
"=",
"loader",
"[",
"'train'",
"]",
"# Run accuracy on train data if only `train` selected",
"elif",
"args",
"[",
"'load_type'",
"]",
"==",
"'train_val'",
":",
"train_loader",
"=",
"loader",
"[",
"'train'",
"]",
"valid_loader",
"=",
"loader",
"[",
"'valid'",
"]",
"else",
":",
"sys",
".",
"exit",
"(",
"'Invalid environment selection for training, exiting'",
")",
"# END IF",
"# Training Setup",
"params",
"=",
"[",
"p",
"for",
"p",
"in",
"model",
".",
"parameters",
"(",
")",
"if",
"p",
".",
"requires_grad",
"]",
"if",
"args",
"[",
"'opt'",
"]",
"==",
"'sgd'",
":",
"optimizer",
"=",
"optim",
".",
"SGD",
"(",
"params",
",",
"lr",
"=",
"args",
"[",
"'lr'",
"]",
",",
"momentum",
"=",
"args",
"[",
"'momentum'",
"]",
",",
"weight_decay",
"=",
"args",
"[",
"'weight_decay'",
"]",
")",
"elif",
"args",
"[",
"'opt'",
"]",
"==",
"'adam'",
":",
"optimizer",
"=",
"optim",
".",
"Adam",
"(",
"params",
",",
"lr",
"=",
"args",
"[",
"'lr'",
"]",
",",
"weight_decay",
"=",
"args",
"[",
"'weight_decay'",
"]",
")",
"else",
":",
"sys",
".",
"exit",
"(",
"'Unsupported optimizer selected. Exiting'",
")",
"# END IF",
"scheduler",
"=",
"MultiStepLR",
"(",
"optimizer",
",",
"milestones",
"=",
"args",
"[",
"'milestones'",
"]",
",",
"gamma",
"=",
"args",
"[",
"'gamma'",
"]",
")",
"if",
"isinstance",
"(",
"args",
"[",
"'pretrained'",
"]",
",",
"str",
")",
":",
"ckpt",
"=",
"load_checkpoint",
"(",
"args",
"[",
"'pretrained'",
"]",
")",
"model",
".",
"load_state_dict",
"(",
"ckpt",
")",
"if",
"args",
"[",
"'resume'",
"]",
":",
"start_epoch",
"=",
"load_checkpoint",
"(",
"args",
"[",
"'pretrained'",
"]",
",",
"key_name",
"=",
"'epoch'",
")",
"+",
"1",
"optimizer",
".",
"load_state_dict",
"(",
"load_checkpoint",
"(",
"args",
"[",
"'pretrained'",
"]",
",",
"key_name",
"=",
"'optimizer'",
")",
")",
"scheduler",
".",
"step",
"(",
"epoch",
"=",
"start_epoch",
")",
"else",
":",
"start_epoch",
"=",
"0",
"# END IF ",
"else",
":",
"start_epoch",
"=",
"0",
"# END IF",
"model_loss",
"=",
"Losses",
"(",
"device",
"=",
"device",
",",
"*",
"*",
"args",
")",
"best_val_acc",
"=",
"0.0",
"############################################################################################################################################################################",
"# Start: Training Loop",
"for",
"epoch",
"in",
"range",
"(",
"start_epoch",
",",
"args",
"[",
"'epoch'",
"]",
")",
":",
"running_loss",
"=",
"0.0",
"print",
"(",
"'Epoch: '",
",",
"epoch",
")",
"# Setup Model To Train ",
"model",
".",
"train",
"(",
")",
"# Start: Epoch",
"for",
"step",
",",
"data",
"in",
"enumerate",
"(",
"train_loader",
")",
":",
"if",
"step",
"%",
"args",
"[",
"'pseudo_batch_loop'",
"]",
"==",
"0",
":",
"loss",
"=",
"0.0",
"running_batch",
"=",
"0",
"optimizer",
".",
"zero_grad",
"(",
")",
"# END IF",
"x_input",
"=",
"data",
"[",
"'data'",
"]",
"annotations",
"=",
"data",
"[",
"'annots'",
"]",
"if",
"isinstance",
"(",
"x_input",
",",
"torch",
".",
"Tensor",
")",
":",
"mini_batch_size",
"=",
"x_input",
".",
"shape",
"[",
"0",
"]",
"outputs",
"=",
"model",
"(",
"x_input",
".",
"to",
"(",
"device",
")",
")",
"assert",
"args",
"[",
"'final_shape'",
"]",
"==",
"list",
"(",
"x_input",
".",
"size",
"(",
")",
"[",
"-",
"2",
":",
"]",
")",
",",
"\"Input to model does not match final_shape argument\"",
"else",
":",
"#Model takes several inputs in forward function ",
"mini_batch_size",
"=",
"x_input",
"[",
"0",
"]",
".",
"shape",
"[",
"0",
"]",
"#Assuming the first element contains the true data input ",
"for",
"i",
",",
"item",
"in",
"enumerate",
"(",
"x_input",
")",
":",
"if",
"isinstance",
"(",
"item",
",",
"torch",
".",
"Tensor",
")",
":",
"x_input",
"[",
"i",
"]",
"=",
"item",
".",
"to",
"(",
"device",
")",
"outputs",
"=",
"model",
"(",
"*",
"x_input",
")",
"loss",
"=",
"model_loss",
".",
"loss",
"(",
"outputs",
",",
"annotations",
")",
"loss",
"=",
"loss",
"*",
"mini_batch_size",
"loss",
".",
"backward",
"(",
")",
"running_loss",
"+=",
"loss",
".",
"item",
"(",
")",
"running_batch",
"+=",
"mini_batch_size",
"if",
"np",
".",
"isnan",
"(",
"running_loss",
")",
":",
"import",
"pdb",
"pdb",
".",
"set_trace",
"(",
")",
"# END IF",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"# Add Learning Rate Element",
"for",
"param_group",
"in",
"optimizer",
".",
"param_groups",
":",
"writer",
".",
"add_scalar",
"(",
"args",
"[",
"'dataset'",
"]",
"+",
"'/'",
"+",
"args",
"[",
"'model'",
"]",
"+",
"'/learning_rate'",
",",
"param_group",
"[",
"'lr'",
"]",
",",
"epoch",
"*",
"len",
"(",
"train_loader",
")",
"+",
"step",
")",
"# END FOR",
"# Add Loss Element",
"writer",
".",
"add_scalar",
"(",
"args",
"[",
"'dataset'",
"]",
"+",
"'/'",
"+",
"args",
"[",
"'model'",
"]",
"+",
"'/minibatch_loss'",
",",
"loss",
".",
"item",
"(",
")",
"/",
"mini_batch_size",
",",
"epoch",
"*",
"len",
"(",
"train_loader",
")",
"+",
"step",
")",
"# END IF",
"if",
"(",
"(",
"epoch",
"*",
"len",
"(",
"train_loader",
")",
"+",
"step",
"+",
"1",
")",
"%",
"100",
"==",
"0",
")",
":",
"print",
"(",
"'Epoch: {}/{}, step: {}/{} | train loss: {:.4f}'",
".",
"format",
"(",
"epoch",
",",
"args",
"[",
"'epoch'",
"]",
",",
"step",
"+",
"1",
",",
"len",
"(",
"train_loader",
")",
",",
"running_loss",
"/",
"float",
"(",
"step",
"+",
"1",
")",
"/",
"mini_batch_size",
")",
")",
"# END IF",
"if",
"(",
"epoch",
"*",
"len",
"(",
"train_loader",
")",
"+",
"(",
"step",
"+",
"1",
")",
")",
"%",
"args",
"[",
"'pseudo_batch_loop'",
"]",
"==",
"0",
"and",
"step",
">",
"0",
":",
"# Apply large mini-batch normalization",
"for",
"param",
"in",
"model",
".",
"parameters",
"(",
")",
":",
"if",
"param",
".",
"requires_grad",
":",
"param",
".",
"grad",
"*=",
"1.",
"/",
"float",
"(",
"running_batch",
")",
"# END FOR",
"# Apply gradient clipping",
"if",
"(",
"\"grad_max_norm\"",
"in",
"args",
")",
"and",
"float",
"(",
"args",
"[",
"'grad_max_norm'",
"]",
">",
"0",
")",
":",
"nn",
".",
"utils",
".",
"clip_grad_norm_",
"(",
"model",
".",
"parameters",
"(",
")",
",",
"float",
"(",
"args",
"[",
"'grad_max_norm'",
"]",
")",
")",
"optimizer",
".",
"step",
"(",
")",
"running_batch",
"=",
"0",
"# END IF",
"# END FOR: Epoch",
"scheduler",
".",
"step",
"(",
"epoch",
"=",
"epoch",
")",
"print",
"(",
"'Schedulers lr: %f'",
",",
"scheduler",
".",
"get_lr",
"(",
")",
"[",
"0",
"]",
")",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"# Save Current Model",
"save_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"save_dir",
",",
"args",
"[",
"'dataset'",
"]",
"+",
"'_epoch'",
"+",
"str",
"(",
"epoch",
")",
"+",
"'.pkl'",
")",
"save_checkpoint",
"(",
"epoch",
",",
"step",
",",
"model",
",",
"optimizer",
",",
"save_path",
")",
"# END IF: Debug",
"## START FOR: Validation Accuracy",
"running_acc",
"=",
"[",
"]",
"running_acc",
"=",
"valid",
"(",
"valid_loader",
",",
"running_acc",
",",
"model",
",",
"device",
")",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"writer",
".",
"add_scalar",
"(",
"args",
"[",
"'dataset'",
"]",
"+",
"'/'",
"+",
"args",
"[",
"'model'",
"]",
"+",
"'/validation_accuracy'",
",",
"100.",
"*",
"running_acc",
"[",
"-",
"1",
"]",
",",
"epoch",
"*",
"len",
"(",
"train_loader",
")",
"+",
"step",
")",
"print",
"(",
"'Accuracy of the network on the validation set: %f %%\\n'",
"%",
"(",
"100.",
"*",
"running_acc",
"[",
"-",
"1",
"]",
")",
")",
"# Save Best Validation Accuracy Model Separately",
"if",
"best_val_acc",
"<",
"running_acc",
"[",
"-",
"1",
"]",
":",
"best_val_acc",
"=",
"running_acc",
"[",
"-",
"1",
"]",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"# Save Current Model",
"save_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"save_dir",
",",
"args",
"[",
"'dataset'",
"]",
"+",
"'_best_model.pkl'",
")",
"save_checkpoint",
"(",
"epoch",
",",
"step",
",",
"model",
",",
"optimizer",
",",
"save_path",
")",
"# END IF",
"# END IF",
"# END FOR: Training Loop",
"############################################################################################################################################################################",
"if",
"not",
"args",
"[",
"'debug'",
"]",
":",
"# Close Tensorboard Element",
"writer",
".",
"close",
"(",
")"
] |
https://github.com/MichiganCOG/ViP/blob/74776f2575bd5339ba39c784bbda4f04cc859add/train.py#L21-L252
|
||
telegraphic/hickle
|
a5aac2db4e236d90990e3387c5822b466e8e84fa
|
hickle/loaders/load_numpy.py
|
python
|
create_np_scalar_dataset
|
(py_obj, h_group, name, **kwargs)
|
return d,()
|
dumps an numpy.dtype object to h5py file
Parameters
----------
py_obj (numpy.scalar):
python object to dump; should be a numpy scalar, e.g. numpy.float16(1)
h_group (h5.File.group):
group to dump data into.
name (str):
the name of the resulting dataset
kwargs (dict):
keyword arguments to be passed to create_dataset function
Returns
-------
tuple containing h5py.Dataset and empty list of subitems
|
dumps an numpy.dtype object to h5py file
|
[
"dumps",
"an",
"numpy",
".",
"dtype",
"object",
"to",
"h5py",
"file"
] |
def create_np_scalar_dataset(py_obj, h_group, name, **kwargs):
""" dumps an numpy.dtype object to h5py file
Parameters
----------
py_obj (numpy.scalar):
python object to dump; should be a numpy scalar, e.g. numpy.float16(1)
h_group (h5.File.group):
group to dump data into.
name (str):
the name of the resulting dataset
kwargs (dict):
keyword arguments to be passed to create_dataset function
Returns
-------
tuple containing h5py.Dataset and empty list of subitems
"""
d = h_group.create_dataset(name, data=py_obj, **no_compression(kwargs))
d.attrs["np_dtype"] = py_obj.dtype.str.encode("ascii")
return d,()
|
[
"def",
"create_np_scalar_dataset",
"(",
"py_obj",
",",
"h_group",
",",
"name",
",",
"*",
"*",
"kwargs",
")",
":",
"d",
"=",
"h_group",
".",
"create_dataset",
"(",
"name",
",",
"data",
"=",
"py_obj",
",",
"*",
"*",
"no_compression",
"(",
"kwargs",
")",
")",
"d",
".",
"attrs",
"[",
"\"np_dtype\"",
"]",
"=",
"py_obj",
".",
"dtype",
".",
"str",
".",
"encode",
"(",
"\"ascii\"",
")",
"return",
"d",
",",
"(",
")"
] |
https://github.com/telegraphic/hickle/blob/a5aac2db4e236d90990e3387c5822b466e8e84fa/hickle/loaders/load_numpy.py#L21-L46
|
|
avalonstrel/GatedConvolution_pytorch
|
0a49013a70e77cc484ab45a5da535c2ac003b252
|
data/base_dataset.py
|
python
|
NoriBaseDataset.__len__
|
(self)
|
return len(self.nori_list)
|
[] |
def __len__(self):
return len(self.nori_list)
|
[
"def",
"__len__",
"(",
"self",
")",
":",
"return",
"len",
"(",
"self",
".",
"nori_list",
")"
] |
https://github.com/avalonstrel/GatedConvolution_pytorch/blob/0a49013a70e77cc484ab45a5da535c2ac003b252/data/base_dataset.py#L70-L71
|
|||
tobegit3hub/deep_image_model
|
8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e
|
java_predict_client/src/main/proto/tensorflow/contrib/distributions/python/ops/normal.py
|
python
|
_kl_normal_normal
|
(n_a, n_b, name=None)
|
Calculate the batched KL divergence KL(n_a || n_b) with n_a and n_b Normal.
Args:
n_a: instance of a Normal distribution object.
n_b: instance of a Normal distribution object.
name: (optional) Name to use for created operations.
default is "kl_normal_normal".
Returns:
Batchwise KL(n_a || n_b)
|
Calculate the batched KL divergence KL(n_a || n_b) with n_a and n_b Normal.
|
[
"Calculate",
"the",
"batched",
"KL",
"divergence",
"KL",
"(",
"n_a",
"||",
"n_b",
")",
"with",
"n_a",
"and",
"n_b",
"Normal",
"."
] |
def _kl_normal_normal(n_a, n_b, name=None):
"""Calculate the batched KL divergence KL(n_a || n_b) with n_a and n_b Normal.
Args:
n_a: instance of a Normal distribution object.
n_b: instance of a Normal distribution object.
name: (optional) Name to use for created operations.
default is "kl_normal_normal".
Returns:
Batchwise KL(n_a || n_b)
"""
with ops.name_scope(name, "kl_normal_normal", [n_a.mu, n_b.mu]):
one = constant_op.constant(1, dtype=n_a.dtype)
two = constant_op.constant(2, dtype=n_a.dtype)
half = constant_op.constant(0.5, dtype=n_a.dtype)
s_a_squared = math_ops.square(n_a.sigma)
s_b_squared = math_ops.square(n_b.sigma)
ratio = s_a_squared / s_b_squared
return (math_ops.square(n_a.mu - n_b.mu) / (two * s_b_squared) +
half * (ratio - one - math_ops.log(ratio)))
|
[
"def",
"_kl_normal_normal",
"(",
"n_a",
",",
"n_b",
",",
"name",
"=",
"None",
")",
":",
"with",
"ops",
".",
"name_scope",
"(",
"name",
",",
"\"kl_normal_normal\"",
",",
"[",
"n_a",
".",
"mu",
",",
"n_b",
".",
"mu",
"]",
")",
":",
"one",
"=",
"constant_op",
".",
"constant",
"(",
"1",
",",
"dtype",
"=",
"n_a",
".",
"dtype",
")",
"two",
"=",
"constant_op",
".",
"constant",
"(",
"2",
",",
"dtype",
"=",
"n_a",
".",
"dtype",
")",
"half",
"=",
"constant_op",
".",
"constant",
"(",
"0.5",
",",
"dtype",
"=",
"n_a",
".",
"dtype",
")",
"s_a_squared",
"=",
"math_ops",
".",
"square",
"(",
"n_a",
".",
"sigma",
")",
"s_b_squared",
"=",
"math_ops",
".",
"square",
"(",
"n_b",
".",
"sigma",
")",
"ratio",
"=",
"s_a_squared",
"/",
"s_b_squared",
"return",
"(",
"math_ops",
".",
"square",
"(",
"n_a",
".",
"mu",
"-",
"n_b",
".",
"mu",
")",
"/",
"(",
"two",
"*",
"s_b_squared",
")",
"+",
"half",
"*",
"(",
"ratio",
"-",
"one",
"-",
"math_ops",
".",
"log",
"(",
"ratio",
")",
")",
")"
] |
https://github.com/tobegit3hub/deep_image_model/blob/8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e/java_predict_client/src/main/proto/tensorflow/contrib/distributions/python/ops/normal.py#L229-L249
|
||
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/vm/v20201229/models.py
|
python
|
AudioResultDetailMoanResult.__init__
|
(self)
|
r"""
:param Label: 该字段用于返回检测结果需要检测的内容类型，此处固定为**Moan**（呻吟）以调用呻吟检测功能。
注意：此字段可能返回 null，表示取不到有效值。
:type Label: str
:param Score: 该字段用于返回呻吟检测的置信度，取值范围：0（**置信度最低**）-100（**置信度最高**），越高代表音频越有可能属于呻吟内容。
:type Score: int
:param StartTime: 该字段用于返回对应呻吟标签的片段在音频文件内的开始时间，单位为毫秒。
:type StartTime: float
:param EndTime: 该字段用于返回对应呻吟标签的片段在音频文件内的结束时间，单位为毫秒。
:type EndTime: float
:param SubLabelCode: *内测中，敬请期待*
:type SubLabelCode: str
:param SubLabel: 该字段用于返回当前标签（Lable）下的二级标签。
注意：此字段可能返回 null，表示取不到有效值。
:type SubLabel: str
|
r"""
:param Label: 该字段用于返回检测结果需要检测的内容类型，此处固定为**Moan**（呻吟）以调用呻吟检测功能。
注意：此字段可能返回 null，表示取不到有效值。
:type Label: str
:param Score: 该字段用于返回呻吟检测的置信度，取值范围：0（**置信度最低**）-100（**置信度最高**），越高代表音频越有可能属于呻吟内容。
:type Score: int
:param StartTime: 该字段用于返回对应呻吟标签的片段在音频文件内的开始时间，单位为毫秒。
:type StartTime: float
:param EndTime: 该字段用于返回对应呻吟标签的片段在音频文件内的结束时间，单位为毫秒。
:type EndTime: float
:param SubLabelCode: *内测中，敬请期待*
:type SubLabelCode: str
:param SubLabel: 该字段用于返回当前标签（Lable）下的二级标签。
注意：此字段可能返回 null，表示取不到有效值。
:type SubLabel: str
|
[
"r",
":",
"param",
"Label",
":",
"该字段用于返回检测结果需要检测的内容类型，此处固定为",
"**",
"Moan",
"**",
"（呻吟）以调用呻吟检测功能。",
"注意：此字段可能返回",
"null，表示取不到有效值。",
":",
"type",
"Label",
":",
"str",
":",
"param",
"Score",
":",
"该字段用于返回呻吟检测的置信度，取值范围：0（",
"**",
"置信度最低",
"**",
"）",
"-",
"100（",
"**",
"置信度最高",
"**",
"），越高代表音频越有可能属于呻吟内容。",
":",
"type",
"Score",
":",
"int",
":",
"param",
"StartTime",
":",
"该字段用于返回对应呻吟标签的片段在音频文件内的开始时间，单位为毫秒。",
":",
"type",
"StartTime",
":",
"float",
":",
"param",
"EndTime",
":",
"该字段用于返回对应呻吟标签的片段在音频文件内的结束时间，单位为毫秒。",
":",
"type",
"EndTime",
":",
"float",
":",
"param",
"SubLabelCode",
":",
"*",
"内测中，敬请期待",
"*",
":",
"type",
"SubLabelCode",
":",
"str",
":",
"param",
"SubLabel",
":",
"该字段用于返回当前标签（Lable）下的二级标签。",
"注意：此字段可能返回",
"null，表示取不到有效值。",
":",
"type",
"SubLabel",
":",
"str"
] |
def __init__(self):
r"""
:param Label: 该字段用于返回检测结果需要检测的内容类型，此处固定为**Moan**（呻吟）以调用呻吟检测功能。
注意：此字段可能返回 null，表示取不到有效值。
:type Label: str
:param Score: 该字段用于返回呻吟检测的置信度，取值范围：0（**置信度最低**）-100（**置信度最高**），越高代表音频越有可能属于呻吟内容。
:type Score: int
:param StartTime: 该字段用于返回对应呻吟标签的片段在音频文件内的开始时间，单位为毫秒。
:type StartTime: float
:param EndTime: 该字段用于返回对应呻吟标签的片段在音频文件内的结束时间，单位为毫秒。
:type EndTime: float
:param SubLabelCode: *内测中，敬请期待*
:type SubLabelCode: str
:param SubLabel: 该字段用于返回当前标签（Lable）下的二级标签。
注意：此字段可能返回 null，表示取不到有效值。
:type SubLabel: str
"""
self.Label = None
self.Score = None
self.StartTime = None
self.EndTime = None
self.SubLabelCode = None
self.SubLabel = None
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"self",
".",
"Label",
"=",
"None",
"self",
".",
"Score",
"=",
"None",
"self",
".",
"StartTime",
"=",
"None",
"self",
".",
"EndTime",
"=",
"None",
"self",
".",
"SubLabelCode",
"=",
"None",
"self",
".",
"SubLabel",
"=",
"None"
] |
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/vm/v20201229/models.py#L167-L189
|
||
City-Bureau/city-scrapers
|
b295d0aa612e3979a9fccab7c5f55ecea9ed074c
|
city_scrapers/spiders/cook_emergency_telephone.py
|
python
|
CookEmergencyTelephoneSpider.parse
|
(self, response)
|
`parse` should always `yield` Meeting items.
Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping
needs.
|
`parse` should always `yield` Meeting items.
|
[
"parse",
"should",
"always",
"yield",
"Meeting",
"items",
"."
] |
def parse(self, response):
"""
`parse` should always `yield` Meeting items.
Change the `_parse_title`, `_parse_start`, etc methods to fit your scraping
needs.
"""
for link in response.css(".sliding_box a"):
link_text = link.css("*::text").get()
if "Minutes" in link_text:
self.docs_link = link.attrib["href"]
elif "Agenda" in link_text:
self.agenda_link = link.attrib["href"]
if not (self.docs_link == "" and self.agenda_link == ""):
yield scrapy.Request(
response.urljoin(self.schedule_pdf_link),
callback=self._parse_schedule,
dont_filter=True,
)
else:
raise ValueError("Required links not found")
|
[
"def",
"parse",
"(",
"self",
",",
"response",
")",
":",
"for",
"link",
"in",
"response",
".",
"css",
"(",
"\".sliding_box a\"",
")",
":",
"link_text",
"=",
"link",
".",
"css",
"(",
"\"*::text\"",
")",
".",
"get",
"(",
")",
"if",
"\"Minutes\"",
"in",
"link_text",
":",
"self",
".",
"docs_link",
"=",
"link",
".",
"attrib",
"[",
"\"href\"",
"]",
"elif",
"\"Agenda\"",
"in",
"link_text",
":",
"self",
".",
"agenda_link",
"=",
"link",
".",
"attrib",
"[",
"\"href\"",
"]",
"if",
"not",
"(",
"self",
".",
"docs_link",
"==",
"\"\"",
"and",
"self",
".",
"agenda_link",
"==",
"\"\"",
")",
":",
"yield",
"scrapy",
".",
"Request",
"(",
"response",
".",
"urljoin",
"(",
"self",
".",
"schedule_pdf_link",
")",
",",
"callback",
"=",
"self",
".",
"_parse_schedule",
",",
"dont_filter",
"=",
"True",
",",
")",
"else",
":",
"raise",
"ValueError",
"(",
"\"Required links not found\"",
")"
] |
https://github.com/City-Bureau/city-scrapers/blob/b295d0aa612e3979a9fccab7c5f55ecea9ed074c/city_scrapers/spiders/cook_emergency_telephone.py#L30-L53
|
||
openshift/openshift-tools
|
1188778e728a6e4781acf728123e5b356380fe6f
|
openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_utils/library/yedit.py
|
python
|
Yedit.write
|
(self)
|
return (True, self.yaml_dict)
|
write to file
|
write to file
|
[
"write",
"to",
"file"
] |
def write(self):
''' write to file '''
if not self.filename:
raise YeditException('Please specify a filename.')
if self.backup and self.file_exists():
shutil.copy(self.filename, '{}{}'.format(self.filename, self.backup_ext))
# Try to set format attributes if supported
try:
self.yaml_dict.fa.set_block_style()
except AttributeError:
pass
# Try to use RoundTripDumper if supported.
if self.content_type == 'yaml':
try:
Yedit._write(self.filename, yaml.dump(self.yaml_dict, Dumper=yaml.RoundTripDumper))
except AttributeError:
Yedit._write(self.filename, yaml.safe_dump(self.yaml_dict, default_flow_style=False))
elif self.content_type == 'json':
Yedit._write(self.filename, json.dumps(self.yaml_dict, indent=4, sort_keys=True))
else:
raise YeditException('Unsupported content_type: {}.'.format(self.content_type) +
'Please specify a content_type of yaml or json.')
return (True, self.yaml_dict)
|
[
"def",
"write",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"filename",
":",
"raise",
"YeditException",
"(",
"'Please specify a filename.'",
")",
"if",
"self",
".",
"backup",
"and",
"self",
".",
"file_exists",
"(",
")",
":",
"shutil",
".",
"copy",
"(",
"self",
".",
"filename",
",",
"'{}{}'",
".",
"format",
"(",
"self",
".",
"filename",
",",
"self",
".",
"backup_ext",
")",
")",
"# Try to set format attributes if supported",
"try",
":",
"self",
".",
"yaml_dict",
".",
"fa",
".",
"set_block_style",
"(",
")",
"except",
"AttributeError",
":",
"pass",
"# Try to use RoundTripDumper if supported.",
"if",
"self",
".",
"content_type",
"==",
"'yaml'",
":",
"try",
":",
"Yedit",
".",
"_write",
"(",
"self",
".",
"filename",
",",
"yaml",
".",
"dump",
"(",
"self",
".",
"yaml_dict",
",",
"Dumper",
"=",
"yaml",
".",
"RoundTripDumper",
")",
")",
"except",
"AttributeError",
":",
"Yedit",
".",
"_write",
"(",
"self",
".",
"filename",
",",
"yaml",
".",
"safe_dump",
"(",
"self",
".",
"yaml_dict",
",",
"default_flow_style",
"=",
"False",
")",
")",
"elif",
"self",
".",
"content_type",
"==",
"'json'",
":",
"Yedit",
".",
"_write",
"(",
"self",
".",
"filename",
",",
"json",
".",
"dumps",
"(",
"self",
".",
"yaml_dict",
",",
"indent",
"=",
"4",
",",
"sort_keys",
"=",
"True",
")",
")",
"else",
":",
"raise",
"YeditException",
"(",
"'Unsupported content_type: {}.'",
".",
"format",
"(",
"self",
".",
"content_type",
")",
"+",
"'Please specify a content_type of yaml or json.'",
")",
"return",
"(",
"True",
",",
"self",
".",
"yaml_dict",
")"
] |
https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_utils/library/yedit.py#L434-L460
|
|
DigitalSlideArchive/HistomicsTK
|
db2ceb4831bec0efa557cf5b18078ae790253de5
|
histomicstk/annotations_and_masks/annotation_database_parser.py
|
python
|
dump_annotations_locally
|
(
gc, folderid, local, save_json=True,
save_sqlite=False, dbcon=None,
callback=None, callback_kwargs=None)
|
Dump annotations of folder and subfolders locally recursively.
This reproduces this tiered structure locally and (possibly) dumps
annotations there. Adapted from Lee A.D. Cooper
Parameters
-----------
gc : girder_client.GirderClient
authenticated girder client instance
folderid : str
girder id of source (base) folder
local : str
local path to dump annotations
save_json : bool
whether to dump annotations as json file
save_sqlite : bool
whether to save the backup into an sqlite database
dbcon : sqlalchemy.create_engine.connect() object
IGNORE THIS PARAMETER!! This is used internally.
callback : function
function to call that CAN accept AT LEAST the following params
- item: girder response with item information
- annotations: loaded annotations
- local: local directory
- monitorPrefix: string
- dbcon: sqlalchemy.create_engine.connect() object
You can just add kwargs at the end of your callback definition
for simplicity.
callback_kwargs : dict
kwargs to pass along to callback. DO NOT pass any of the parameters
item, annotations, local, monitorPrefix, or dbcon as these will be
internally passed. Just include any specific paremeters for the
callback. See parse_annotations_to_local_tables() above for
an example of a callback and the unir test of this function.
|
Dump annotations of folder and subfolders locally recursively.
|
[
"Dump",
"annotations",
"of",
"folder",
"and",
"subfolders",
"locally",
"recursively",
"."
] |
def dump_annotations_locally(
gc, folderid, local, save_json=True,
save_sqlite=False, dbcon=None,
callback=None, callback_kwargs=None):
"""Dump annotations of folder and subfolders locally recursively.
This reproduces this tiered structure locally and (possibly) dumps
annotations there. Adapted from Lee A.D. Cooper
Parameters
-----------
gc : girder_client.GirderClient
authenticated girder client instance
folderid : str
girder id of source (base) folder
local : str
local path to dump annotations
save_json : bool
whether to dump annotations as json file
save_sqlite : bool
whether to save the backup into an sqlite database
dbcon : sqlalchemy.create_engine.connect() object
IGNORE THIS PARAMETER!! This is used internally.
callback : function
function to call that CAN accept AT LEAST the following params
- item: girder response with item information
- annotations: loaded annotations
- local: local directory
- monitorPrefix: string
- dbcon: sqlalchemy.create_engine.connect() object
You can just add kwargs at the end of your callback definition
for simplicity.
callback_kwargs : dict
kwargs to pass along to callback. DO NOT pass any of the parameters
item, annotations, local, monitorPrefix, or dbcon as these will be
internally passed. Just include any specific paremeters for the
callback. See parse_annotations_to_local_tables() above for
an example of a callback and the unir test of this function.
"""
callback_kwargs = callback_kwargs or {}
assert(save_json or save_sqlite), "must save results somehow!"
monitor = os.path.basename(local)
# get folder info
folder_info = gc.get("folder/%s" % folderid)
folder_info['folder_path'] = get_absolute_girder_folderpath(
gc=gc, folder_info=folder_info)
# connect to sqlite database -- only first stack does this
if save_sqlite and (dbcon is None):
db_path = os.path.join(local, folder_info['name'] + ".sqlite")
sql_engine = create_engine('sqlite:///' + db_path, echo=False)
dbcon = sql_engine.connect()
# save folder information json
if save_json:
print("%s: save folder info" % monitor)
savepath = os.path.join(local, folder_info['name'] + '.json')
with open(savepath, 'w') as fout:
json.dump(folder_info, fout)
# save folder info to sqlite
if save_sqlite:
_add_folder_to_sqlite(dbcon, folder_info)
# pull annotations for each slide in folder
workflow_runner = Workflow_runner(
slide_iterator=Slide_iterator(
gc, source_folder_id=folderid,
keep_slides=None,
),
workflow=dump_annotations_workflow,
workflow_kwargs={
'gc': gc,
'local': local,
'save_json': save_json,
'save_sqlite': save_sqlite,
'dbcon': dbcon,
'callback': callback,
'callback_kwargs': callback_kwargs,
},
monitorPrefix=monitor)
workflow_runner.run()
# for each subfolder, create a new folder locally and call self
for folder in gc.listFolder(parentId=folderid):
# create folder in local
new_folder = os.path.join(local, folder['name'])
os.mkdir(new_folder)
# call self with same prameters
dump_annotations_locally(
gc=gc, folderid=folder['_id'], local=new_folder,
save_json=save_json, save_sqlite=save_sqlite, dbcon=dbcon,
callback=callback, callback_kwargs=callback_kwargs)
|
[
"def",
"dump_annotations_locally",
"(",
"gc",
",",
"folderid",
",",
"local",
",",
"save_json",
"=",
"True",
",",
"save_sqlite",
"=",
"False",
",",
"dbcon",
"=",
"None",
",",
"callback",
"=",
"None",
",",
"callback_kwargs",
"=",
"None",
")",
":",
"callback_kwargs",
"=",
"callback_kwargs",
"or",
"{",
"}",
"assert",
"(",
"save_json",
"or",
"save_sqlite",
")",
",",
"\"must save results somehow!\"",
"monitor",
"=",
"os",
".",
"path",
".",
"basename",
"(",
"local",
")",
"# get folder info",
"folder_info",
"=",
"gc",
".",
"get",
"(",
"\"folder/%s\"",
"%",
"folderid",
")",
"folder_info",
"[",
"'folder_path'",
"]",
"=",
"get_absolute_girder_folderpath",
"(",
"gc",
"=",
"gc",
",",
"folder_info",
"=",
"folder_info",
")",
"# connect to sqlite database -- only first stack does this",
"if",
"save_sqlite",
"and",
"(",
"dbcon",
"is",
"None",
")",
":",
"db_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"local",
",",
"folder_info",
"[",
"'name'",
"]",
"+",
"\".sqlite\"",
")",
"sql_engine",
"=",
"create_engine",
"(",
"'sqlite:///'",
"+",
"db_path",
",",
"echo",
"=",
"False",
")",
"dbcon",
"=",
"sql_engine",
".",
"connect",
"(",
")",
"# save folder information json",
"if",
"save_json",
":",
"print",
"(",
"\"%s: save folder info\"",
"%",
"monitor",
")",
"savepath",
"=",
"os",
".",
"path",
".",
"join",
"(",
"local",
",",
"folder_info",
"[",
"'name'",
"]",
"+",
"'.json'",
")",
"with",
"open",
"(",
"savepath",
",",
"'w'",
")",
"as",
"fout",
":",
"json",
".",
"dump",
"(",
"folder_info",
",",
"fout",
")",
"# save folder info to sqlite",
"if",
"save_sqlite",
":",
"_add_folder_to_sqlite",
"(",
"dbcon",
",",
"folder_info",
")",
"# pull annotations for each slide in folder",
"workflow_runner",
"=",
"Workflow_runner",
"(",
"slide_iterator",
"=",
"Slide_iterator",
"(",
"gc",
",",
"source_folder_id",
"=",
"folderid",
",",
"keep_slides",
"=",
"None",
",",
")",
",",
"workflow",
"=",
"dump_annotations_workflow",
",",
"workflow_kwargs",
"=",
"{",
"'gc'",
":",
"gc",
",",
"'local'",
":",
"local",
",",
"'save_json'",
":",
"save_json",
",",
"'save_sqlite'",
":",
"save_sqlite",
",",
"'dbcon'",
":",
"dbcon",
",",
"'callback'",
":",
"callback",
",",
"'callback_kwargs'",
":",
"callback_kwargs",
",",
"}",
",",
"monitorPrefix",
"=",
"monitor",
")",
"workflow_runner",
".",
"run",
"(",
")",
"# for each subfolder, create a new folder locally and call self",
"for",
"folder",
"in",
"gc",
".",
"listFolder",
"(",
"parentId",
"=",
"folderid",
")",
":",
"# create folder in local",
"new_folder",
"=",
"os",
".",
"path",
".",
"join",
"(",
"local",
",",
"folder",
"[",
"'name'",
"]",
")",
"os",
".",
"mkdir",
"(",
"new_folder",
")",
"# call self with same prameters",
"dump_annotations_locally",
"(",
"gc",
"=",
"gc",
",",
"folderid",
"=",
"folder",
"[",
"'_id'",
"]",
",",
"local",
"=",
"new_folder",
",",
"save_json",
"=",
"save_json",
",",
"save_sqlite",
"=",
"save_sqlite",
",",
"dbcon",
"=",
"dbcon",
",",
"callback",
"=",
"callback",
",",
"callback_kwargs",
"=",
"callback_kwargs",
")"
] |
https://github.com/DigitalSlideArchive/HistomicsTK/blob/db2ceb4831bec0efa557cf5b18078ae790253de5/histomicstk/annotations_and_masks/annotation_database_parser.py#L275-L379
|
||
makerbot/ReplicatorG
|
d6f2b07785a5a5f1e172fb87cb4303b17c575d5d
|
skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/craft_plugins/dwindle.py
|
python
|
DwindleSkein.addThread
|
(self)
|
Add the thread sections to the gcode.
|
Add the thread sections to the gcode.
|
[
"Add",
"the",
"thread",
"sections",
"to",
"the",
"gcode",
"."
] |
def addThread(self):
'Add the thread sections to the gcode.'
if len(self.threadSections) == 0:
return
area = self.area
dwindlePortion = 0.0
endRateMultiplier = self.repository.endRateMultiplier.value
halfOverSteps = self.halfOverSteps
oneOverSteps = self.oneOverSteps
currentPentUpVolume = self.repository.pentUpVolume.value * self.oldFlowRate / self.operatingFlowRate
slowdownFlowRateMultiplier = 1.0 - (currentPentUpVolume / self.repository.slowdownVolume.value)
operatingFeedRateMinute = self.operatingFeedRateMinute
slowdownVolume = self.repository.slowdownVolume.value
for threadSectionIndex in xrange(len(self.threadSections) - 1, -1, -1):
threadSection = self.threadSections[threadSectionIndex]
dwindlePortion = threadSection.getDwindlePortion(area, dwindlePortion, operatingFeedRateMinute, self.operatingFlowRate, slowdownVolume)
for threadSection in self.threadSections:
threadSection.addGcodeThreadSection(self.distanceFeedRate, endRateMultiplier, halfOverSteps, oneOverSteps, slowdownFlowRateMultiplier)
self.distanceFeedRate.addFlowRateLine(self.oldFlowRate)
self.threadSections = []
|
[
"def",
"addThread",
"(",
"self",
")",
":",
"if",
"len",
"(",
"self",
".",
"threadSections",
")",
"==",
"0",
":",
"return",
"area",
"=",
"self",
".",
"area",
"dwindlePortion",
"=",
"0.0",
"endRateMultiplier",
"=",
"self",
".",
"repository",
".",
"endRateMultiplier",
".",
"value",
"halfOverSteps",
"=",
"self",
".",
"halfOverSteps",
"oneOverSteps",
"=",
"self",
".",
"oneOverSteps",
"currentPentUpVolume",
"=",
"self",
".",
"repository",
".",
"pentUpVolume",
".",
"value",
"*",
"self",
".",
"oldFlowRate",
"/",
"self",
".",
"operatingFlowRate",
"slowdownFlowRateMultiplier",
"=",
"1.0",
"-",
"(",
"currentPentUpVolume",
"/",
"self",
".",
"repository",
".",
"slowdownVolume",
".",
"value",
")",
"operatingFeedRateMinute",
"=",
"self",
".",
"operatingFeedRateMinute",
"slowdownVolume",
"=",
"self",
".",
"repository",
".",
"slowdownVolume",
".",
"value",
"for",
"threadSectionIndex",
"in",
"xrange",
"(",
"len",
"(",
"self",
".",
"threadSections",
")",
"-",
"1",
",",
"-",
"1",
",",
"-",
"1",
")",
":",
"threadSection",
"=",
"self",
".",
"threadSections",
"[",
"threadSectionIndex",
"]",
"dwindlePortion",
"=",
"threadSection",
".",
"getDwindlePortion",
"(",
"area",
",",
"dwindlePortion",
",",
"operatingFeedRateMinute",
",",
"self",
".",
"operatingFlowRate",
",",
"slowdownVolume",
")",
"for",
"threadSection",
"in",
"self",
".",
"threadSections",
":",
"threadSection",
".",
"addGcodeThreadSection",
"(",
"self",
".",
"distanceFeedRate",
",",
"endRateMultiplier",
",",
"halfOverSteps",
",",
"oneOverSteps",
",",
"slowdownFlowRateMultiplier",
")",
"self",
".",
"distanceFeedRate",
".",
"addFlowRateLine",
"(",
"self",
".",
"oldFlowRate",
")",
"self",
".",
"threadSections",
"=",
"[",
"]"
] |
https://github.com/makerbot/ReplicatorG/blob/d6f2b07785a5a5f1e172fb87cb4303b17c575d5d/skein_engines/skeinforge-50/skeinforge_application/skeinforge_plugins/craft_plugins/dwindle.py#L129-L148
|
||
linkchecker/linkchecker
|
d1078ed8480e5cfc4264d0dbf026b45b45aede4d
|
linkcheck/dummy.py
|
python
|
Dummy.__getitem__
|
(self, key)
|
return self
|
Return self
|
Return self
|
[
"Return",
"self"
] |
def __getitem__(self, key):
"""Return self"""
return self
|
[
"def",
"__getitem__",
"(",
"self",
",",
"key",
")",
":",
"return",
"self"
] |
https://github.com/linkchecker/linkchecker/blob/d1078ed8480e5cfc4264d0dbf026b45b45aede4d/linkcheck/dummy.py#L60-L62
|
|
plotly/plotly.py
|
cfad7862594b35965c0e000813bd7805e8494a5b
|
packages/python/plotly/plotly/graph_objs/funnel/_textfont.py
|
python
|
Textfont.color
|
(self)
|
return self["color"]
|
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
|
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
|
[
"The",
"color",
"property",
"is",
"a",
"color",
"and",
"may",
"be",
"specified",
"as",
":",
"-",
"A",
"hex",
"string",
"(",
"e",
".",
"g",
".",
"#ff0000",
")",
"-",
"An",
"rgb",
"/",
"rgba",
"string",
"(",
"e",
".",
"g",
".",
"rgb",
"(",
"255",
"0",
"0",
")",
")",
"-",
"An",
"hsl",
"/",
"hsla",
"string",
"(",
"e",
".",
"g",
".",
"hsl",
"(",
"0",
"100%",
"50%",
")",
")",
"-",
"An",
"hsv",
"/",
"hsva",
"string",
"(",
"e",
".",
"g",
".",
"hsv",
"(",
"0",
"100%",
"100%",
")",
")",
"-",
"A",
"named",
"CSS",
"color",
":",
"aliceblue",
"antiquewhite",
"aqua",
"aquamarine",
"azure",
"beige",
"bisque",
"black",
"blanchedalmond",
"blue",
"blueviolet",
"brown",
"burlywood",
"cadetblue",
"chartreuse",
"chocolate",
"coral",
"cornflowerblue",
"cornsilk",
"crimson",
"cyan",
"darkblue",
"darkcyan",
"darkgoldenrod",
"darkgray",
"darkgrey",
"darkgreen",
"darkkhaki",
"darkmagenta",
"darkolivegreen",
"darkorange",
"darkorchid",
"darkred",
"darksalmon",
"darkseagreen",
"darkslateblue",
"darkslategray",
"darkslategrey",
"darkturquoise",
"darkviolet",
"deeppink",
"deepskyblue",
"dimgray",
"dimgrey",
"dodgerblue",
"firebrick",
"floralwhite",
"forestgreen",
"fuchsia",
"gainsboro",
"ghostwhite",
"gold",
"goldenrod",
"gray",
"grey",
"green",
"greenyellow",
"honeydew",
"hotpink",
"indianred",
"indigo",
"ivory",
"khaki",
"lavender",
"lavenderblush",
"lawngreen",
"lemonchiffon",
"lightblue",
"lightcoral",
"lightcyan",
"lightgoldenrodyellow",
"lightgray",
"lightgrey",
"lightgreen",
"lightpink",
"lightsalmon",
"lightseagreen",
"lightskyblue",
"lightslategray",
"lightslategrey",
"lightsteelblue",
"lightyellow",
"lime",
"limegreen",
"linen",
"magenta",
"maroon",
"mediumaquamarine",
"mediumblue",
"mediumorchid",
"mediumpurple",
"mediumseagreen",
"mediumslateblue",
"mediumspringgreen",
"mediumturquoise",
"mediumvioletred",
"midnightblue",
"mintcream",
"mistyrose",
"moccasin",
"navajowhite",
"navy",
"oldlace",
"olive",
"olivedrab",
"orange",
"orangered",
"orchid",
"palegoldenrod",
"palegreen",
"paleturquoise",
"palevioletred",
"papayawhip",
"peachpuff",
"peru",
"pink",
"plum",
"powderblue",
"purple",
"red",
"rosybrown",
"royalblue",
"rebeccapurple",
"saddlebrown",
"salmon",
"sandybrown",
"seagreen",
"seashell",
"sienna",
"silver",
"skyblue",
"slateblue",
"slategray",
"slategrey",
"snow",
"springgreen",
"steelblue",
"tan",
"teal",
"thistle",
"tomato",
"turquoise",
"violet",
"wheat",
"white",
"whitesmoke",
"yellow",
"yellowgreen",
"-",
"A",
"list",
"or",
"array",
"of",
"any",
"of",
"the",
"above"
] |
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
- A list or array of any of the above
Returns
-------
str|numpy.ndarray
"""
return self["color"]
|
[
"def",
"color",
"(",
"self",
")",
":",
"return",
"self",
"[",
"\"color\"",
"]"
] |
https://github.com/plotly/plotly.py/blob/cfad7862594b35965c0e000813bd7805e8494a5b/packages/python/plotly/plotly/graph_objs/funnel/_textfont.py#L16-L65
|
|
quantumlib/Cirq
|
89f88b01d69222d3f1ec14d649b7b3a85ed9211f
|
cirq-ionq/cirq_ionq/ionq_client.py
|
python
|
_IonQClient.list_jobs
|
(
self, status: Optional[str] = None, limit: int = 100, batch_size: int = 1000
)
|
return self._list('jobs', params, 'jobs', limit, batch_size)
|
Lists jobs from the IonQ API.
Args:
status: If not None, filter to jobs with this status.
limit: The maximum number of jobs to return.
batch_size: The size of the batches requested per http GET call.
Returns:
A list of the json bodies of the job dicts.
Raises:
IonQException: If the API call fails.
|
Lists jobs from the IonQ API.
|
[
"Lists",
"jobs",
"from",
"the",
"IonQ",
"API",
"."
] |
def list_jobs(
self, status: Optional[str] = None, limit: int = 100, batch_size: int = 1000
) -> List[Dict[str, Any]]:
"""Lists jobs from the IonQ API.
Args:
status: If not None, filter to jobs with this status.
limit: The maximum number of jobs to return.
batch_size: The size of the batches requested per http GET call.
Returns:
A list of the json bodies of the job dicts.
Raises:
IonQException: If the API call fails.
"""
params = {}
if status:
params['status'] = status
return self._list('jobs', params, 'jobs', limit, batch_size)
|
[
"def",
"list_jobs",
"(",
"self",
",",
"status",
":",
"Optional",
"[",
"str",
"]",
"=",
"None",
",",
"limit",
":",
"int",
"=",
"100",
",",
"batch_size",
":",
"int",
"=",
"1000",
")",
"->",
"List",
"[",
"Dict",
"[",
"str",
",",
"Any",
"]",
"]",
":",
"params",
"=",
"{",
"}",
"if",
"status",
":",
"params",
"[",
"'status'",
"]",
"=",
"status",
"return",
"self",
".",
"_list",
"(",
"'jobs'",
",",
"params",
",",
"'jobs'",
",",
"limit",
",",
"batch_size",
")"
] |
https://github.com/quantumlib/Cirq/blob/89f88b01d69222d3f1ec14d649b7b3a85ed9211f/cirq-ionq/cirq_ionq/ionq_client.py#L157-L176
|
|
phantomcyber/playbooks
|
9e850ecc44cb98c5dde53784744213a1ed5799bd
|
zscaler_hunt_and_block_url.py
|
python
|
exec_short_description
|
(action=None, success=None, container=None, results=None, handle=None, filtered_artifacts=None, filtered_results=None, custom_function=None, **kwargs)
|
return
|
[] |
def exec_short_description(action=None, success=None, container=None, results=None, handle=None, filtered_artifacts=None, filtered_results=None, custom_function=None, **kwargs):
phantom.debug('exec_short_description() called')
template = """Please Investigate Executive Workstation - {0}"""
# parameter list for template variable replacement
parameters = [
"filtered-data:filter_2:condition_1:run_query_1:action_result.data.*.client_ip",
]
phantom.format(container=container, template=template, parameters=parameters, name="exec_short_description")
exec_long_description(container=container)
return
|
[
"def",
"exec_short_description",
"(",
"action",
"=",
"None",
",",
"success",
"=",
"None",
",",
"container",
"=",
"None",
",",
"results",
"=",
"None",
",",
"handle",
"=",
"None",
",",
"filtered_artifacts",
"=",
"None",
",",
"filtered_results",
"=",
"None",
",",
"custom_function",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"phantom",
".",
"debug",
"(",
"'exec_short_description() called'",
")",
"template",
"=",
"\"\"\"Please Investigate Executive Workstation - {0}\"\"\"",
"# parameter list for template variable replacement",
"parameters",
"=",
"[",
"\"filtered-data:filter_2:condition_1:run_query_1:action_result.data.*.client_ip\"",
",",
"]",
"phantom",
".",
"format",
"(",
"container",
"=",
"container",
",",
"template",
"=",
"template",
",",
"parameters",
"=",
"parameters",
",",
"name",
"=",
"\"exec_short_description\"",
")",
"exec_long_description",
"(",
"container",
"=",
"container",
")",
"return"
] |
https://github.com/phantomcyber/playbooks/blob/9e850ecc44cb98c5dde53784744213a1ed5799bd/zscaler_hunt_and_block_url.py#L412-L426
|
|||
hasegaw/IkaLog
|
bd476da541fcc296f792d4db76a6b9174c4777ad
|
ikalog/engine.py
|
python
|
IkaEngine.disble_profile
|
(self)
|
[] |
def disble_profile(self):
self._enable_profile = False
|
[
"def",
"disble_profile",
"(",
"self",
")",
":",
"self",
".",
"_enable_profile",
"=",
"False"
] |
https://github.com/hasegaw/IkaLog/blob/bd476da541fcc296f792d4db76a6b9174c4777ad/ikalog/engine.py#L51-L52
|
||||
huggingface/transformers
|
623b4f7c63f60cce917677ee704d6c93ee960b4b
|
src/transformers/utils/dummy_tf_objects.py
|
python
|
TFDistilBertForSequenceClassification.call
|
(self, *args, **kwargs)
|
[] |
def call(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
[
"def",
"call",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"requires_backends",
"(",
"self",
",",
"[",
"\"tf\"",
"]",
")"
] |
https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/utils/dummy_tf_objects.py#L1098-L1099
|
||||
josiah-wolf-oberholtzer/supriya
|
5ca725a6b97edfbe016a75666d420ecfdf49592f
|
supriya/intervals/Interval.py
|
python
|
Interval.intersects
|
(self, expr: Union["Interval", float])
|
return self.start_offset <= expr < self.stop_offset
|
True when interval intersects ``expr``, another interval or offset:
::
>>> from supriya.intervals import Interval
>>> interval_1 = Interval(0, 10)
>>> interval_2 = Interval(5, 15)
>>> interval_3 = Interval(10, 15)
>>> interval_4 = Interval(15, 25)
::
>>> interval_1.intersects(interval_1)
True
>>> interval_1.intersects(interval_2)
True
>>> interval_1.intersects(interval_3)
False
>>> interval_1.intersects(interval_4)
False
::
>>> for offset in [-5, 0, 5, 10, 15]:
... print(offset, interval_1.intersects(offset))
...
-5 False
0 True
5 True
10 False
15 False
|
True when interval intersects ``expr``, another interval or offset:
|
[
"True",
"when",
"interval",
"intersects",
"expr",
"another",
"interval",
"or",
"offset",
":"
] |
def intersects(self, expr: Union["Interval", float]) -> bool:
"""
True when interval intersects ``expr``, another interval or offset:
::
>>> from supriya.intervals import Interval
>>> interval_1 = Interval(0, 10)
>>> interval_2 = Interval(5, 15)
>>> interval_3 = Interval(10, 15)
>>> interval_4 = Interval(15, 25)
::
>>> interval_1.intersects(interval_1)
True
>>> interval_1.intersects(interval_2)
True
>>> interval_1.intersects(interval_3)
False
>>> interval_1.intersects(interval_4)
False
::
>>> for offset in [-5, 0, 5, 10, 15]:
... print(offset, interval_1.intersects(offset))
...
-5 False
0 True
5 True
10 False
15 False
"""
if isinstance(expr, Interval):
return (
expr.start_offset <= self.start_offset
and self.start_offset < expr.stop_offset
) or (
self.start_offset <= expr.start_offset
and expr.start_offset < self.stop_offset
)
return self.start_offset <= expr < self.stop_offset
|
[
"def",
"intersects",
"(",
"self",
",",
"expr",
":",
"Union",
"[",
"\"Interval\"",
",",
"float",
"]",
")",
"->",
"bool",
":",
"if",
"isinstance",
"(",
"expr",
",",
"Interval",
")",
":",
"return",
"(",
"expr",
".",
"start_offset",
"<=",
"self",
".",
"start_offset",
"and",
"self",
".",
"start_offset",
"<",
"expr",
".",
"stop_offset",
")",
"or",
"(",
"self",
".",
"start_offset",
"<=",
"expr",
".",
"start_offset",
"and",
"expr",
".",
"start_offset",
"<",
"self",
".",
"stop_offset",
")",
"return",
"self",
".",
"start_offset",
"<=",
"expr",
"<",
"self",
".",
"stop_offset"
] |
https://github.com/josiah-wolf-oberholtzer/supriya/blob/5ca725a6b97edfbe016a75666d420ecfdf49592f/supriya/intervals/Interval.py#L488-L531
|
|
fkie/multimaster_fkie
|
3d23df29d25d71a75c66bbd3cc6e9cbb255724d8
|
fkie_node_manager_daemon/src/fkie_node_manager_daemon/url.py
|
python
|
equal_uri
|
(url1, url2)
|
return url1.rstrip(os.path.sep) == url2.rstrip(os.path.sep)
|
Removes to string after remove last slash character.
|
Removes to string after remove last slash character.
|
[
"Removes",
"to",
"string",
"after",
"remove",
"last",
"slash",
"character",
"."
] |
def equal_uri(url1, url2):
'''
Removes to string after remove last slash character.
'''
return url1.rstrip(os.path.sep) == url2.rstrip(os.path.sep)
|
[
"def",
"equal_uri",
"(",
"url1",
",",
"url2",
")",
":",
"return",
"url1",
".",
"rstrip",
"(",
"os",
".",
"path",
".",
"sep",
")",
"==",
"url2",
".",
"rstrip",
"(",
"os",
".",
"path",
".",
"sep",
")"
] |
https://github.com/fkie/multimaster_fkie/blob/3d23df29d25d71a75c66bbd3cc6e9cbb255724d8/fkie_node_manager_daemon/src/fkie_node_manager_daemon/url.py#L47-L51
|
|
jansel/opentuner
|
070c5cef6d933eb760a2f9cd5cd08c95f27aee75
|
examples/gccflags/gccflags.py
|
python
|
GccFlagsTuner.save_final_config
|
(self, configuration)
|
called at the end of tuning
|
called at the end of tuning
|
[
"called",
"at",
"the",
"end",
"of",
"tuning"
] |
def save_final_config(self, configuration):
"""called at the end of tuning"""
print("Best flags written to gccflags_final_config.{json,cmd}")
self.manipulator().save_to_file(configuration.data,
'gccflags_final_config.json')
with open('gccflags_final_config.cmd', 'w') as fd:
fd.write(self.make_command(configuration.data))
|
[
"def",
"save_final_config",
"(",
"self",
",",
"configuration",
")",
":",
"print",
"(",
"\"Best flags written to gccflags_final_config.{json,cmd}\"",
")",
"self",
".",
"manipulator",
"(",
")",
".",
"save_to_file",
"(",
"configuration",
".",
"data",
",",
"'gccflags_final_config.json'",
")",
"with",
"open",
"(",
"'gccflags_final_config.cmd'",
",",
"'w'",
")",
"as",
"fd",
":",
"fd",
".",
"write",
"(",
"self",
".",
"make_command",
"(",
"configuration",
".",
"data",
")",
")"
] |
https://github.com/jansel/opentuner/blob/070c5cef6d933eb760a2f9cd5cd08c95f27aee75/examples/gccflags/gccflags.py#L345-L351
|
||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/whoosh/codec/whoosh3.py
|
python
|
_lenfield
|
(fieldname)
|
return "_%s_len" % fieldname
|
[] |
def _lenfield(fieldname):
return "_%s_len" % fieldname
|
[
"def",
"_lenfield",
"(",
"fieldname",
")",
":",
"return",
"\"_%s_len\"",
"%",
"fieldname"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/whoosh/codec/whoosh3.py#L150-L151
|
|||
ninja-ide/ninja-ide
|
87d91131bd19fdc3dcfd91eb97ad1e41c49c60c0
|
ninja_ide/gui/explorer/tabs/tree_projects_widget.py
|
python
|
ProjectTreeColumn._add_file_to_project
|
(self, path)
|
Add the file for 'path' in the project the user choose here.
|
Add the file for 'path' in the project the user choose here.
|
[
"Add",
"the",
"file",
"for",
"path",
"in",
"the",
"project",
"the",
"user",
"choose",
"here",
"."
] |
def _add_file_to_project(self, path):
"""Add the file for 'path' in the project the user choose here."""
if self._projects_area.count() > 0:
path_project = [self.current_project]
_add_to_project = add_to_project.AddToProject(path_project, self)
_add_to_project.exec_()
if not _add_to_project.path_selected:
return
main_container = IDE.get_service('main_container')
if not main_container:
return
editorWidget = main_container.get_current_editor()
if not editorWidget.file_path:
name = QInputDialog.getText(
None,
translations.TR_ADD_FILE_TO_PROJECT,
translations.TR_FILENAME + ": ")[0]
if not name:
QMessageBox.information(
self,
translations.TR_INVALID_FILENAME,
translations.TR_INVALID_FILENAME_ENTER_A_FILENAME)
return
else:
name = file_manager.get_basename(editorWidget.file_path)
new_path = file_manager.create_path(
_add_to_project.path_selected, name)
ide_srv = IDE.get_service("ide")
old_file = ide_srv.get_or_create_nfile(path)
new_file = old_file.save(editorWidget.text(), new_path)
# FIXME: Make this file replace the original in the open tab
else:
pass
|
[
"def",
"_add_file_to_project",
"(",
"self",
",",
"path",
")",
":",
"if",
"self",
".",
"_projects_area",
".",
"count",
"(",
")",
">",
"0",
":",
"path_project",
"=",
"[",
"self",
".",
"current_project",
"]",
"_add_to_project",
"=",
"add_to_project",
".",
"AddToProject",
"(",
"path_project",
",",
"self",
")",
"_add_to_project",
".",
"exec_",
"(",
")",
"if",
"not",
"_add_to_project",
".",
"path_selected",
":",
"return",
"main_container",
"=",
"IDE",
".",
"get_service",
"(",
"'main_container'",
")",
"if",
"not",
"main_container",
":",
"return",
"editorWidget",
"=",
"main_container",
".",
"get_current_editor",
"(",
")",
"if",
"not",
"editorWidget",
".",
"file_path",
":",
"name",
"=",
"QInputDialog",
".",
"getText",
"(",
"None",
",",
"translations",
".",
"TR_ADD_FILE_TO_PROJECT",
",",
"translations",
".",
"TR_FILENAME",
"+",
"\": \"",
")",
"[",
"0",
"]",
"if",
"not",
"name",
":",
"QMessageBox",
".",
"information",
"(",
"self",
",",
"translations",
".",
"TR_INVALID_FILENAME",
",",
"translations",
".",
"TR_INVALID_FILENAME_ENTER_A_FILENAME",
")",
"return",
"else",
":",
"name",
"=",
"file_manager",
".",
"get_basename",
"(",
"editorWidget",
".",
"file_path",
")",
"new_path",
"=",
"file_manager",
".",
"create_path",
"(",
"_add_to_project",
".",
"path_selected",
",",
"name",
")",
"ide_srv",
"=",
"IDE",
".",
"get_service",
"(",
"\"ide\"",
")",
"old_file",
"=",
"ide_srv",
".",
"get_or_create_nfile",
"(",
"path",
")",
"new_file",
"=",
"old_file",
".",
"save",
"(",
"editorWidget",
".",
"text",
"(",
")",
",",
"new_path",
")",
"# FIXME: Make this file replace the original in the open tab",
"else",
":",
"pass"
] |
https://github.com/ninja-ide/ninja-ide/blob/87d91131bd19fdc3dcfd91eb97ad1e41c49c60c0/ninja_ide/gui/explorer/tabs/tree_projects_widget.py#L200-L232
|
||
FederatedAI/FATE
|
32540492623568ecd1afcb367360133616e02fa3
|
python/federatedml/statistic/data_overview.py
|
python
|
with_weight
|
(data_instances)
|
return False
|
[] |
def with_weight(data_instances):
first_entry = data_instances.first()[1]
if isinstance(first_entry, Instance) and first_entry.weight is not None:
return True
return False
|
[
"def",
"with_weight",
"(",
"data_instances",
")",
":",
"first_entry",
"=",
"data_instances",
".",
"first",
"(",
")",
"[",
"1",
"]",
"if",
"isinstance",
"(",
"first_entry",
",",
"Instance",
")",
"and",
"first_entry",
".",
"weight",
"is",
"not",
"None",
":",
"return",
"True",
"return",
"False"
] |
https://github.com/FederatedAI/FATE/blob/32540492623568ecd1afcb367360133616e02fa3/python/federatedml/statistic/data_overview.py#L180-L184
|
|||
zhl2008/awd-platform
|
0416b31abea29743387b10b3914581fbe8e7da5e
|
web_flaskbb/lib/python2.7/site-packages/urllib3/util/selectors.py
|
python
|
BaseSelector._key_from_fd
|
(self, fd)
|
Return the key associated to a given file descriptor
Return None if it is not found.
|
Return the key associated to a given file descriptor
Return None if it is not found.
|
[
"Return",
"the",
"key",
"associated",
"to",
"a",
"given",
"file",
"descriptor",
"Return",
"None",
"if",
"it",
"is",
"not",
"found",
"."
] |
def _key_from_fd(self, fd):
""" Return the key associated to a given file descriptor
Return None if it is not found. """
try:
return self._fd_to_key[fd]
except KeyError:
return None
|
[
"def",
"_key_from_fd",
"(",
"self",
",",
"fd",
")",
":",
"try",
":",
"return",
"self",
".",
"_fd_to_key",
"[",
"fd",
"]",
"except",
"KeyError",
":",
"return",
"None"
] |
https://github.com/zhl2008/awd-platform/blob/0416b31abea29743387b10b3914581fbe8e7da5e/web_flaskbb/lib/python2.7/site-packages/urllib3/util/selectors.py#L270-L276
|
||
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/full/ssl.py
|
python
|
_inet_paton
|
(ipname)
|
Try to convert an IP address to packed binary form
Supports IPv4 addresses on all platforms and IPv6 on platforms with IPv6
support.
|
Try to convert an IP address to packed binary form
|
[
"Try",
"to",
"convert",
"an",
"IP",
"address",
"to",
"packed",
"binary",
"form"
] |
def _inet_paton(ipname):
"""Try to convert an IP address to packed binary form
Supports IPv4 addresses on all platforms and IPv6 on platforms with IPv6
support.
"""
# inet_aton() also accepts strings like '1', '127.1', some also trailing
# data like '127.0.0.1 whatever'.
try:
addr = _socket.inet_aton(ipname)
except OSError:
# not an IPv4 address
pass
else:
if _socket.inet_ntoa(addr) == ipname:
# only accept injective ipnames
return addr
else:
# refuse for short IPv4 notation and additional trailing data
raise ValueError(
"{!r} is not a quad-dotted IPv4 address.".format(ipname)
)
try:
return _socket.inet_pton(_socket.AF_INET6, ipname)
except OSError:
raise ValueError("{!r} is neither an IPv4 nor an IP6 "
"address.".format(ipname))
except AttributeError:
# AF_INET6 not available
pass
raise ValueError("{!r} is not an IPv4 address.".format(ipname))
|
[
"def",
"_inet_paton",
"(",
"ipname",
")",
":",
"# inet_aton() also accepts strings like '1', '127.1', some also trailing",
"# data like '127.0.0.1 whatever'.",
"try",
":",
"addr",
"=",
"_socket",
".",
"inet_aton",
"(",
"ipname",
")",
"except",
"OSError",
":",
"# not an IPv4 address",
"pass",
"else",
":",
"if",
"_socket",
".",
"inet_ntoa",
"(",
"addr",
")",
"==",
"ipname",
":",
"# only accept injective ipnames",
"return",
"addr",
"else",
":",
"# refuse for short IPv4 notation and additional trailing data",
"raise",
"ValueError",
"(",
"\"{!r} is not a quad-dotted IPv4 address.\"",
".",
"format",
"(",
"ipname",
")",
")",
"try",
":",
"return",
"_socket",
".",
"inet_pton",
"(",
"_socket",
".",
"AF_INET6",
",",
"ipname",
")",
"except",
"OSError",
":",
"raise",
"ValueError",
"(",
"\"{!r} is neither an IPv4 nor an IP6 \"",
"\"address.\"",
".",
"format",
"(",
"ipname",
")",
")",
"except",
"AttributeError",
":",
"# AF_INET6 not available",
"pass",
"raise",
"ValueError",
"(",
"\"{!r} is not an IPv4 address.\"",
".",
"format",
"(",
"ipname",
")",
")"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/full/ssl.py#L324-L356
|
||
TencentCloud/tencentcloud-sdk-python
|
3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2
|
tencentcloud/cpdp/v20190820/models.py
|
python
|
ApplyDeclareData.__init__
|
(self)
|
r"""
:param MerchantId: 商户号
:type MerchantId: str
:param TransactionId: 第三方指令编号
:type TransactionId: str
:param Status: 受理状态
:type Status: str
:param DeclareId: 申报流水号
:type DeclareId: str
:param OriginalDeclareId: 原申报流水号
注意：此字段可能返回 null，表示取不到有效值。
:type OriginalDeclareId: str
:param PayerId: 付款人ID
:type PayerId: str
|
r"""
:param MerchantId: 商户号
:type MerchantId: str
:param TransactionId: 第三方指令编号
:type TransactionId: str
:param Status: 受理状态
:type Status: str
:param DeclareId: 申报流水号
:type DeclareId: str
:param OriginalDeclareId: 原申报流水号
注意：此字段可能返回 null，表示取不到有效值。
:type OriginalDeclareId: str
:param PayerId: 付款人ID
:type PayerId: str
|
[
"r",
":",
"param",
"MerchantId",
":",
"商户号",
":",
"type",
"MerchantId",
":",
"str",
":",
"param",
"TransactionId",
":",
"第三方指令编号",
":",
"type",
"TransactionId",
":",
"str",
":",
"param",
"Status",
":",
"受理状态",
":",
"type",
"Status",
":",
"str",
":",
"param",
"DeclareId",
":",
"申报流水号",
":",
"type",
"DeclareId",
":",
"str",
":",
"param",
"OriginalDeclareId",
":",
"原申报流水号",
"注意：此字段可能返回",
"null，表示取不到有效值。",
":",
"type",
"OriginalDeclareId",
":",
"str",
":",
"param",
"PayerId",
":",
"付款人ID",
":",
"type",
"PayerId",
":",
"str"
] |
def __init__(self):
r"""
:param MerchantId: 商户号
:type MerchantId: str
:param TransactionId: 第三方指令编号
:type TransactionId: str
:param Status: 受理状态
:type Status: str
:param DeclareId: 申报流水号
:type DeclareId: str
:param OriginalDeclareId: 原申报流水号
注意：此字段可能返回 null，表示取不到有效值。
:type OriginalDeclareId: str
:param PayerId: 付款人ID
:type PayerId: str
"""
self.MerchantId = None
self.TransactionId = None
self.Status = None
self.DeclareId = None
self.OriginalDeclareId = None
self.PayerId = None
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"self",
".",
"MerchantId",
"=",
"None",
"self",
".",
"TransactionId",
"=",
"None",
"self",
".",
"Status",
"=",
"None",
"self",
".",
"DeclareId",
"=",
"None",
"self",
".",
"OriginalDeclareId",
"=",
"None",
"self",
".",
"PayerId",
"=",
"None"
] |
https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/cpdp/v20190820/models.py#L1052-L1073
|
||
sqlalchemy/sqlalchemy
|
eb716884a4abcabae84a6aaba105568e925b7d27
|
lib/sqlalchemy/orm/attributes.py
|
python
|
History.has_changes
|
(self)
|
return bool(self.added or self.deleted)
|
Return True if this :class:`.History` has changes.
|
Return True if this :class:`.History` has changes.
|
[
"Return",
"True",
"if",
"this",
":",
"class",
":",
".",
"History",
"has",
"changes",
"."
] |
def has_changes(self):
"""Return True if this :class:`.History` has changes."""
return bool(self.added or self.deleted)
|
[
"def",
"has_changes",
"(",
"self",
")",
":",
"return",
"bool",
"(",
"self",
".",
"added",
"or",
"self",
".",
"deleted",
")"
] |
https://github.com/sqlalchemy/sqlalchemy/blob/eb716884a4abcabae84a6aaba105568e925b7d27/lib/sqlalchemy/orm/attributes.py#L1828-L1831
|
|
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/graphs/generators/random.py
|
python
|
growing_subtrees
|
(T, k)
|
return S
|
r"""
Return a list of the vertex sets of ``n`` randomly chosen subtrees of ``T``.
For a tree of order `n`, the collection contains `n` subtrees with maximum
order `k` and average order `\frac{k + 1}{2}`.
This method is part of
:meth:`~sage.graphs.generators.random.RandomChordalGraph`.
ALGORITHM:
For each subtree `T_i`, the algorithm picks a size `k_i` randomly from
`[1,k]`. Then a random node of `T` is chosen as the first node of `T_i`. In
each of the subsequent `k_i - 1` iterations, it picks a random node in the
neighborhood of `T_i` and adds it to `T_i`.
See [SHET2018]_ for more details.
INPUT:
- ``T`` -- a tree
- ``k`` -- a strictly positive integer; maximum size of a subtree
EXAMPLES::
sage: from sage.graphs.generators.random import growing_subtrees
sage: T = graphs.RandomTree(10)
sage: S = growing_subtrees(T, 5)
sage: len(S)
10
|
r"""
Return a list of the vertex sets of ``n`` randomly chosen subtrees of ``T``.
|
[
"r",
"Return",
"a",
"list",
"of",
"the",
"vertex",
"sets",
"of",
"n",
"randomly",
"chosen",
"subtrees",
"of",
"T",
"."
] |
def growing_subtrees(T, k):
r"""
Return a list of the vertex sets of ``n`` randomly chosen subtrees of ``T``.
For a tree of order `n`, the collection contains `n` subtrees with maximum
order `k` and average order `\frac{k + 1}{2}`.
This method is part of
:meth:`~sage.graphs.generators.random.RandomChordalGraph`.
ALGORITHM:
For each subtree `T_i`, the algorithm picks a size `k_i` randomly from
`[1,k]`. Then a random node of `T` is chosen as the first node of `T_i`. In
each of the subsequent `k_i - 1` iterations, it picks a random node in the
neighborhood of `T_i` and adds it to `T_i`.
See [SHET2018]_ for more details.
INPUT:
- ``T`` -- a tree
- ``k`` -- a strictly positive integer; maximum size of a subtree
EXAMPLES::
sage: from sage.graphs.generators.random import growing_subtrees
sage: T = graphs.RandomTree(10)
sage: S = growing_subtrees(T, 5)
sage: len(S)
10
"""
from sage.misc.prandom import choice
n = T.order()
S = []
for _ in range(n):
ki = randint(1, k)
if ki == n:
Vi = frozenset(T)
else:
x = T.random_vertex()
Ti = set([x])
neighbors = set(T.neighbor_iterator(x))
for j in range(ki - 1):
# Select a random neighbor z outside of Ti and add it to Ti
z = choice(tuple(neighbors))
Ti.add(z)
neighbors.update(y for y in T.neighbor_iterator(z) if y not in Ti)
Vi = frozenset(Ti)
S.append(Vi)
return S
|
[
"def",
"growing_subtrees",
"(",
"T",
",",
"k",
")",
":",
"from",
"sage",
".",
"misc",
".",
"prandom",
"import",
"choice",
"n",
"=",
"T",
".",
"order",
"(",
")",
"S",
"=",
"[",
"]",
"for",
"_",
"in",
"range",
"(",
"n",
")",
":",
"ki",
"=",
"randint",
"(",
"1",
",",
"k",
")",
"if",
"ki",
"==",
"n",
":",
"Vi",
"=",
"frozenset",
"(",
"T",
")",
"else",
":",
"x",
"=",
"T",
".",
"random_vertex",
"(",
")",
"Ti",
"=",
"set",
"(",
"[",
"x",
"]",
")",
"neighbors",
"=",
"set",
"(",
"T",
".",
"neighbor_iterator",
"(",
"x",
")",
")",
"for",
"j",
"in",
"range",
"(",
"ki",
"-",
"1",
")",
":",
"# Select a random neighbor z outside of Ti and add it to Ti",
"z",
"=",
"choice",
"(",
"tuple",
"(",
"neighbors",
")",
")",
"Ti",
".",
"add",
"(",
"z",
")",
"neighbors",
".",
"update",
"(",
"y",
"for",
"y",
"in",
"T",
".",
"neighbor_iterator",
"(",
"z",
")",
"if",
"y",
"not",
"in",
"Ti",
")",
"Vi",
"=",
"frozenset",
"(",
"Ti",
")",
"S",
".",
"append",
"(",
"Vi",
")",
"return",
"S"
] |
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/graphs/generators/random.py#L857-L909
|
|
openhatch/oh-mainline
|
ce29352a034e1223141dcc2f317030bbc3359a51
|
vendor/packages/whoosh/src/whoosh/matching.py
|
python
|
FilterMatcher.__init__
|
(self, child, ids, exclude=False, boost=1.0)
|
:param child: the child matcher.
:param ids: a set of IDs to filter by.
:param exclude: by default, only IDs from the wrapped matcher that are
**in** the set are used. If this argument is True, only IDs from
the wrapped matcher that are **not in** the set are used.
|
:param child: the child matcher.
:param ids: a set of IDs to filter by.
:param exclude: by default, only IDs from the wrapped matcher that are
**in** the set are used. If this argument is True, only IDs from
the wrapped matcher that are **not in** the set are used.
|
[
":",
"param",
"child",
":",
"the",
"child",
"matcher",
".",
":",
"param",
"ids",
":",
"a",
"set",
"of",
"IDs",
"to",
"filter",
"by",
".",
":",
"param",
"exclude",
":",
"by",
"default",
"only",
"IDs",
"from",
"the",
"wrapped",
"matcher",
"that",
"are",
"**",
"in",
"**",
"the",
"set",
"are",
"used",
".",
"If",
"this",
"argument",
"is",
"True",
"only",
"IDs",
"from",
"the",
"wrapped",
"matcher",
"that",
"are",
"**",
"not",
"in",
"**",
"the",
"set",
"are",
"used",
"."
] |
def __init__(self, child, ids, exclude=False, boost=1.0):
"""
:param child: the child matcher.
:param ids: a set of IDs to filter by.
:param exclude: by default, only IDs from the wrapped matcher that are
**in** the set are used. If this argument is True, only IDs from
the wrapped matcher that are **not in** the set are used.
"""
super(FilterMatcher, self).__init__(child)
self._ids = ids
self._exclude = exclude
self.boost = boost
self._find_next()
|
[
"def",
"__init__",
"(",
"self",
",",
"child",
",",
"ids",
",",
"exclude",
"=",
"False",
",",
"boost",
"=",
"1.0",
")",
":",
"super",
"(",
"FilterMatcher",
",",
"self",
")",
".",
"__init__",
"(",
"child",
")",
"self",
".",
"_ids",
"=",
"ids",
"self",
".",
"_exclude",
"=",
"exclude",
"self",
".",
"boost",
"=",
"boost",
"self",
".",
"_find_next",
"(",
")"
] |
https://github.com/openhatch/oh-mainline/blob/ce29352a034e1223141dcc2f317030bbc3359a51/vendor/packages/whoosh/src/whoosh/matching.py#L737-L750
|
||
libertysoft3/saidit
|
271c7d03adb369f82921d811360b00812e42da24
|
r2/r2/controllers/api.py
|
python
|
ApiController.POST_subreddit_stylesheet
|
(self, form, jquery,
stylesheet_contents = '', prevstyle='',
op='save', reason=None)
|
Update a subreddit's stylesheet.
`op` should be `save` to update the contents of the stylesheet.
|
Update a subreddit's stylesheet.
|
[
"Update",
"a",
"subreddit",
"s",
"stylesheet",
"."
] |
def POST_subreddit_stylesheet(self, form, jquery,
stylesheet_contents = '', prevstyle='',
op='save', reason=None):
"""Update a subreddit's stylesheet.
`op` should be `save` to update the contents of the stylesheet.
"""
if g.css_killswitch:
return abort(403, 'forbidden')
css_errors, parsed = c.site.parse_css(stylesheet_contents)
# The hook passes errors back by setting them on the form.
hooks.get_hook('subreddit.css.validate').call(
request=request, form=form, op=op,
stylesheet_contents=stylesheet_contents,
parsed_stylesheet=parsed,
css_errors=css_errors,
subreddit=c.site,
user=c.user
)
if css_errors:
error_items = [CssError(x).render(style='html') for x in css_errors]
form.set_text(".status", _('validation errors'))
form.set_html(".errors ul", ''.join(error_items))
form.find('.errors').show()
c.errors.add(errors.BAD_CSS, field="stylesheet_contents")
form.has_errors("stylesheet_contents", errors.BAD_CSS)
return
else:
form.find('.errors').hide()
form.set_html(".errors ul", '')
# Don't allow users in timeout to modify the stylesheet
VNotInTimeout().run(action_name="editsettings",
details_text="%s_stylesheet" % op, target=c.site)
if op == 'save' and not form.has_error():
wr = c.site.change_css(stylesheet_contents, parsed, reason=reason)
form.find('.errors').hide()
form.set_text(".status", _('saved'))
form.set_html(".errors ul", "")
jquery.apply_stylesheet(parsed)
if op == 'preview':
# try to find a link to use, otherwise give up and
# return
links = SubredditStylesheet.find_preview_links(c.site)
if links:
jquery('#preview-table').show()
# do a regular link
jquery('#preview_link_normal').html(
SubredditStylesheet.rendered_link(
links, media='off', compress=False))
# now do one with media
jquery('#preview_link_media').html(
SubredditStylesheet.rendered_link(
links, media='on', compress=False))
# do a compressed link
jquery('#preview_link_compressed').html(
SubredditStylesheet.rendered_link(
links, media='off', compress=True))
# do a stickied link
jquery('#preview_link_stickied').html(
SubredditStylesheet.rendered_link(
links, media='off', compress=False, stickied=True))
# and do a comment
comments = SubredditStylesheet.find_preview_comments(c.site)
if comments:
jquery('#preview_comment').html(
SubredditStylesheet.rendered_comment(comments))
jquery('#preview_comment_gilded').html(
SubredditStylesheet.rendered_comment(
comments, gilded=True))
|
[
"def",
"POST_subreddit_stylesheet",
"(",
"self",
",",
"form",
",",
"jquery",
",",
"stylesheet_contents",
"=",
"''",
",",
"prevstyle",
"=",
"''",
",",
"op",
"=",
"'save'",
",",
"reason",
"=",
"None",
")",
":",
"if",
"g",
".",
"css_killswitch",
":",
"return",
"abort",
"(",
"403",
",",
"'forbidden'",
")",
"css_errors",
",",
"parsed",
"=",
"c",
".",
"site",
".",
"parse_css",
"(",
"stylesheet_contents",
")",
"# The hook passes errors back by setting them on the form.",
"hooks",
".",
"get_hook",
"(",
"'subreddit.css.validate'",
")",
".",
"call",
"(",
"request",
"=",
"request",
",",
"form",
"=",
"form",
",",
"op",
"=",
"op",
",",
"stylesheet_contents",
"=",
"stylesheet_contents",
",",
"parsed_stylesheet",
"=",
"parsed",
",",
"css_errors",
"=",
"css_errors",
",",
"subreddit",
"=",
"c",
".",
"site",
",",
"user",
"=",
"c",
".",
"user",
")",
"if",
"css_errors",
":",
"error_items",
"=",
"[",
"CssError",
"(",
"x",
")",
".",
"render",
"(",
"style",
"=",
"'html'",
")",
"for",
"x",
"in",
"css_errors",
"]",
"form",
".",
"set_text",
"(",
"\".status\"",
",",
"_",
"(",
"'validation errors'",
")",
")",
"form",
".",
"set_html",
"(",
"\".errors ul\"",
",",
"''",
".",
"join",
"(",
"error_items",
")",
")",
"form",
".",
"find",
"(",
"'.errors'",
")",
".",
"show",
"(",
")",
"c",
".",
"errors",
".",
"add",
"(",
"errors",
".",
"BAD_CSS",
",",
"field",
"=",
"\"stylesheet_contents\"",
")",
"form",
".",
"has_errors",
"(",
"\"stylesheet_contents\"",
",",
"errors",
".",
"BAD_CSS",
")",
"return",
"else",
":",
"form",
".",
"find",
"(",
"'.errors'",
")",
".",
"hide",
"(",
")",
"form",
".",
"set_html",
"(",
"\".errors ul\"",
",",
"''",
")",
"# Don't allow users in timeout to modify the stylesheet",
"VNotInTimeout",
"(",
")",
".",
"run",
"(",
"action_name",
"=",
"\"editsettings\"",
",",
"details_text",
"=",
"\"%s_stylesheet\"",
"%",
"op",
",",
"target",
"=",
"c",
".",
"site",
")",
"if",
"op",
"==",
"'save'",
"and",
"not",
"form",
".",
"has_error",
"(",
")",
":",
"wr",
"=",
"c",
".",
"site",
".",
"change_css",
"(",
"stylesheet_contents",
",",
"parsed",
",",
"reason",
"=",
"reason",
")",
"form",
".",
"find",
"(",
"'.errors'",
")",
".",
"hide",
"(",
")",
"form",
".",
"set_text",
"(",
"\".status\"",
",",
"_",
"(",
"'saved'",
")",
")",
"form",
".",
"set_html",
"(",
"\".errors ul\"",
",",
"\"\"",
")",
"jquery",
".",
"apply_stylesheet",
"(",
"parsed",
")",
"if",
"op",
"==",
"'preview'",
":",
"# try to find a link to use, otherwise give up and",
"# return",
"links",
"=",
"SubredditStylesheet",
".",
"find_preview_links",
"(",
"c",
".",
"site",
")",
"if",
"links",
":",
"jquery",
"(",
"'#preview-table'",
")",
".",
"show",
"(",
")",
"# do a regular link",
"jquery",
"(",
"'#preview_link_normal'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_link",
"(",
"links",
",",
"media",
"=",
"'off'",
",",
"compress",
"=",
"False",
")",
")",
"# now do one with media",
"jquery",
"(",
"'#preview_link_media'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_link",
"(",
"links",
",",
"media",
"=",
"'on'",
",",
"compress",
"=",
"False",
")",
")",
"# do a compressed link",
"jquery",
"(",
"'#preview_link_compressed'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_link",
"(",
"links",
",",
"media",
"=",
"'off'",
",",
"compress",
"=",
"True",
")",
")",
"# do a stickied link",
"jquery",
"(",
"'#preview_link_stickied'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_link",
"(",
"links",
",",
"media",
"=",
"'off'",
",",
"compress",
"=",
"False",
",",
"stickied",
"=",
"True",
")",
")",
"# and do a comment",
"comments",
"=",
"SubredditStylesheet",
".",
"find_preview_comments",
"(",
"c",
".",
"site",
")",
"if",
"comments",
":",
"jquery",
"(",
"'#preview_comment'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_comment",
"(",
"comments",
")",
")",
"jquery",
"(",
"'#preview_comment_gilded'",
")",
".",
"html",
"(",
"SubredditStylesheet",
".",
"rendered_comment",
"(",
"comments",
",",
"gilded",
"=",
"True",
")",
")"
] |
https://github.com/libertysoft3/saidit/blob/271c7d03adb369f82921d811360b00812e42da24/r2/r2/controllers/api.py#L2417-L2498
|
||
tomplus/kubernetes_asyncio
|
f028cc793e3a2c519be6a52a49fb77ff0b014c9b
|
kubernetes_asyncio/client/models/v1_service_list.py
|
python
|
V1ServiceList.__ne__
|
(self, other)
|
return self.to_dict() != other.to_dict()
|
Returns true if both objects are not equal
|
Returns true if both objects are not equal
|
[
"Returns",
"true",
"if",
"both",
"objects",
"are",
"not",
"equal"
] |
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, V1ServiceList):
return True
return self.to_dict() != other.to_dict()
|
[
"def",
"__ne__",
"(",
"self",
",",
"other",
")",
":",
"if",
"not",
"isinstance",
"(",
"other",
",",
"V1ServiceList",
")",
":",
"return",
"True",
"return",
"self",
".",
"to_dict",
"(",
")",
"!=",
"other",
".",
"to_dict",
"(",
")"
] |
https://github.com/tomplus/kubernetes_asyncio/blob/f028cc793e3a2c519be6a52a49fb77ff0b014c9b/kubernetes_asyncio/client/models/v1_service_list.py#L200-L205
|
|
CharlesBlonde/libpurecoollink
|
a91362c57a0bc4126279c8c51c407dd713b08e10
|
libpurecoollink/dyson_360_eye.py
|
python
|
Dyson360Eye.on_message
|
(client, userdata, msg)
|
Set function Callback when message received.
|
Set function Callback when message received.
|
[
"Set",
"function",
"Callback",
"when",
"message",
"received",
"."
] |
def on_message(client, userdata, msg):
# pylint: disable=unused-argument
"""Set function Callback when message received."""
payload = msg.payload.decode("utf-8")
device_msg = None
if Dyson360EyeState.is_state_message(payload):
device_msg = Dyson360EyeState(payload)
if not userdata.device_available:
userdata.state_data_available()
userdata.state = device_msg
elif Dyson360EyeMapGlobal.is_map_global(payload):
device_msg = Dyson360EyeMapGlobal(payload)
elif Dyson360EyeTelemetryData.is_telemetry_data(payload):
device_msg = Dyson360EyeTelemetryData(payload)
elif Dyson360EyeMapGrid.is_map_grid(payload):
device_msg = Dyson360EyeMapGrid(payload)
elif Dyson360EyeMapData.is_map_data(payload):
device_msg = Dyson360EyeMapData(payload)
elif Dyson360Goodbye.is_goodbye_message(payload):
device_msg = Dyson360Goodbye(payload)
else:
_LOGGER.warning(payload)
if device_msg:
Dyson360Eye.call_callback_functions(userdata.callback_message,
device_msg)
|
[
"def",
"on_message",
"(",
"client",
",",
"userdata",
",",
"msg",
")",
":",
"# pylint: disable=unused-argument",
"payload",
"=",
"msg",
".",
"payload",
".",
"decode",
"(",
"\"utf-8\"",
")",
"device_msg",
"=",
"None",
"if",
"Dyson360EyeState",
".",
"is_state_message",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360EyeState",
"(",
"payload",
")",
"if",
"not",
"userdata",
".",
"device_available",
":",
"userdata",
".",
"state_data_available",
"(",
")",
"userdata",
".",
"state",
"=",
"device_msg",
"elif",
"Dyson360EyeMapGlobal",
".",
"is_map_global",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360EyeMapGlobal",
"(",
"payload",
")",
"elif",
"Dyson360EyeTelemetryData",
".",
"is_telemetry_data",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360EyeTelemetryData",
"(",
"payload",
")",
"elif",
"Dyson360EyeMapGrid",
".",
"is_map_grid",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360EyeMapGrid",
"(",
"payload",
")",
"elif",
"Dyson360EyeMapData",
".",
"is_map_data",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360EyeMapData",
"(",
"payload",
")",
"elif",
"Dyson360Goodbye",
".",
"is_goodbye_message",
"(",
"payload",
")",
":",
"device_msg",
"=",
"Dyson360Goodbye",
"(",
"payload",
")",
"else",
":",
"_LOGGER",
".",
"warning",
"(",
"payload",
")",
"if",
"device_msg",
":",
"Dyson360Eye",
".",
"call_callback_functions",
"(",
"userdata",
".",
"callback_message",
",",
"device_msg",
")"
] |
https://github.com/CharlesBlonde/libpurecoollink/blob/a91362c57a0bc4126279c8c51c407dd713b08e10/libpurecoollink/dyson_360_eye.py#L109-L134
|
||
wizyoung/googletranslate.popclipext
|
a3c465685a5a75213e2ec8517eb98d336984bc50
|
src/httpx/_utils.py
|
python
|
peek_filelike_length
|
(stream: typing.IO)
|
Given a file-like stream object, return its length in number of bytes
without reading it into memory.
|
Given a file-like stream object, return its length in number of bytes
without reading it into memory.
|
[
"Given",
"a",
"file",
"-",
"like",
"stream",
"object",
"return",
"its",
"length",
"in",
"number",
"of",
"bytes",
"without",
"reading",
"it",
"into",
"memory",
"."
] |
def peek_filelike_length(stream: typing.IO) -> int:
"""
Given a file-like stream object, return its length in number of bytes
without reading it into memory.
"""
try:
# Is it an actual file?
fd = stream.fileno()
except OSError:
# No... Maybe it's something that supports random access, like `io.BytesIO`?
try:
# Assuming so, go to end of stream to figure out its length,
# then put it back in place.
offset = stream.tell()
length = stream.seek(0, os.SEEK_END)
stream.seek(offset)
except OSError:
# Not even that? Sorry, we're doomed...
raise
else:
return length
else:
# Yup, seems to be an actual file.
return os.fstat(fd).st_size
|
[
"def",
"peek_filelike_length",
"(",
"stream",
":",
"typing",
".",
"IO",
")",
"->",
"int",
":",
"try",
":",
"# Is it an actual file?",
"fd",
"=",
"stream",
".",
"fileno",
"(",
")",
"except",
"OSError",
":",
"# No... Maybe it's something that supports random access, like `io.BytesIO`?",
"try",
":",
"# Assuming so, go to end of stream to figure out its length,",
"# then put it back in place.",
"offset",
"=",
"stream",
".",
"tell",
"(",
")",
"length",
"=",
"stream",
".",
"seek",
"(",
"0",
",",
"os",
".",
"SEEK_END",
")",
"stream",
".",
"seek",
"(",
"offset",
")",
"except",
"OSError",
":",
"# Not even that? Sorry, we're doomed...",
"raise",
"else",
":",
"return",
"length",
"else",
":",
"# Yup, seems to be an actual file.",
"return",
"os",
".",
"fstat",
"(",
"fd",
")",
".",
"st_size"
] |
https://github.com/wizyoung/googletranslate.popclipext/blob/a3c465685a5a75213e2ec8517eb98d336984bc50/src/httpx/_utils.py#L324-L347
|
||
facelessuser/ColorHelper
|
cfed17c35dbae4db49a14165ef222407c48a3014
|
ch_preview.py
|
python
|
ColorHelperListener.should_update
|
(self, view)
|
return force_update
|
Check if an update should be performed.
|
Check if an update should be performed.
|
[
"Check",
"if",
"an",
"update",
"should",
"be",
"performed",
"."
] |
def should_update(self, view):
"""Check if an update should be performed."""
force_update = False
rules = view.settings().get('color_helper.scan', None)
if rules:
last_updated = rules.get('last_updated', None)
if last_updated is None or last_updated < ch_last_updated:
force_update = True
file_name = view.file_name()
ext = os.path.splitext(file_name)[1].lower() if file_name is not None else None
old_ext = rules.get('current_ext')
if ext != old_ext:
force_update = True
syntax = os.path.splitext(view.settings().get('syntax').replace('Packages/', '', 1))[0]
old_syntax = rules.get("current_syntax")
if old_syntax is None or old_syntax != syntax:
force_update = True
else:
force_update = True
return force_update
|
[
"def",
"should_update",
"(",
"self",
",",
"view",
")",
":",
"force_update",
"=",
"False",
"rules",
"=",
"view",
".",
"settings",
"(",
")",
".",
"get",
"(",
"'color_helper.scan'",
",",
"None",
")",
"if",
"rules",
":",
"last_updated",
"=",
"rules",
".",
"get",
"(",
"'last_updated'",
",",
"None",
")",
"if",
"last_updated",
"is",
"None",
"or",
"last_updated",
"<",
"ch_last_updated",
":",
"force_update",
"=",
"True",
"file_name",
"=",
"view",
".",
"file_name",
"(",
")",
"ext",
"=",
"os",
".",
"path",
".",
"splitext",
"(",
"file_name",
")",
"[",
"1",
"]",
".",
"lower",
"(",
")",
"if",
"file_name",
"is",
"not",
"None",
"else",
"None",
"old_ext",
"=",
"rules",
".",
"get",
"(",
"'current_ext'",
")",
"if",
"ext",
"!=",
"old_ext",
":",
"force_update",
"=",
"True",
"syntax",
"=",
"os",
".",
"path",
".",
"splitext",
"(",
"view",
".",
"settings",
"(",
")",
".",
"get",
"(",
"'syntax'",
")",
".",
"replace",
"(",
"'Packages/'",
",",
"''",
",",
"1",
")",
")",
"[",
"0",
"]",
"old_syntax",
"=",
"rules",
".",
"get",
"(",
"\"current_syntax\"",
")",
"if",
"old_syntax",
"is",
"None",
"or",
"old_syntax",
"!=",
"syntax",
":",
"force_update",
"=",
"True",
"else",
":",
"force_update",
"=",
"True",
"return",
"force_update"
] |
https://github.com/facelessuser/ColorHelper/blob/cfed17c35dbae4db49a14165ef222407c48a3014/ch_preview.py#L780-L800
|
|
bruderstein/PythonScript
|
df9f7071ddf3a079e3a301b9b53a6dc78cf1208f
|
PythonLib/min/numbers.py
|
python
|
Real.__le__
|
(self, other)
|
self <= other
|
self <= other
|
[
"self",
"<",
"=",
"other"
] |
def __le__(self, other):
"""self <= other"""
raise NotImplementedError
|
[
"def",
"__le__",
"(",
"self",
",",
"other",
")",
":",
"raise",
"NotImplementedError"
] |
https://github.com/bruderstein/PythonScript/blob/df9f7071ddf3a079e3a301b9b53a6dc78cf1208f/PythonLib/min/numbers.py#L241-L243
|
||
angr/angr-management
|
60ae5fa483e74810fb3a3da8d37b00034d7fefab
|
angrmanagement/plugins/base_plugin.py
|
python
|
BasePlugin.on_workspace_initialized
|
(self, workspace: 'Workspace')
|
A handler that is called right after a workspace is initialized.
|
A handler that is called right after a workspace is initialized.
|
[
"A",
"handler",
"that",
"is",
"called",
"right",
"after",
"a",
"workspace",
"is",
"initialized",
"."
] |
def on_workspace_initialized(self, workspace: 'Workspace'):
"""
A handler that is called right after a workspace is initialized.
"""
|
[
"def",
"on_workspace_initialized",
"(",
"self",
",",
"workspace",
":",
"'Workspace'",
")",
":"
] |
https://github.com/angr/angr-management/blob/60ae5fa483e74810fb3a3da8d37b00034d7fefab/angrmanagement/plugins/base_plugin.py#L61-L64
|
||
cisco/mindmeld
|
809c36112e9ea8019fe29d54d136ca14eb4fd8db
|
mindmeld/resource_loader.py
|
python
|
Hasher._get_algorithm
|
(self)
|
return self._algorithm
|
Getter for algorithm property.
Returns:
str: the hashing algorithm
|
Getter for algorithm property.
|
[
"Getter",
"for",
"algorithm",
"property",
"."
] |
def _get_algorithm(self):
"""Getter for algorithm property.
Returns:
str: the hashing algorithm
"""
return self._algorithm
|
[
"def",
"_get_algorithm",
"(",
"self",
")",
":",
"return",
"self",
".",
"_algorithm"
] |
https://github.com/cisco/mindmeld/blob/809c36112e9ea8019fe29d54d136ca14eb4fd8db/mindmeld/resource_loader.py#L956-L962
|
|
Nuitka/Nuitka
|
39262276993757fa4e299f497654065600453fc9
|
nuitka/build/inline_copy/lib/scons-4.3.0/SCons/Taskmaster.py
|
python
|
Task.failed
|
(self)
|
Default action when a task fails: stop the build.
Note: Although this function is normally invoked on nodes in
the executing state, it might also be invoked on up-to-date
nodes when using Configure().
|
Default action when a task fails: stop the build.
|
[
"Default",
"action",
"when",
"a",
"task",
"fails",
":",
"stop",
"the",
"build",
"."
] |
def failed(self):
"""
Default action when a task fails: stop the build.
Note: Although this function is normally invoked on nodes in
the executing state, it might also be invoked on up-to-date
nodes when using Configure().
"""
self.fail_stop()
|
[
"def",
"failed",
"(",
"self",
")",
":",
"self",
".",
"fail_stop",
"(",
")"
] |
https://github.com/Nuitka/Nuitka/blob/39262276993757fa4e299f497654065600453fc9/nuitka/build/inline_copy/lib/scons-4.3.0/SCons/Taskmaster.py#L303-L311
|
||
tensorlayer/tensorlayer
|
cb4eb896dd063e650ef22533ed6fa6056a71cad5
|
tensorlayer/layers/normalization.py
|
python
|
LocalResponseNorm.forward
|
(self, inputs)
|
return outputs
|
prev_layer : :class:`Layer`
The previous layer with a 4D output shape.
|
prev_layer : :class:`Layer`
The previous layer with a 4D output shape.
|
[
"prev_layer",
":",
":",
"class",
":",
"Layer",
"The",
"previous",
"layer",
"with",
"a",
"4D",
"output",
"shape",
"."
] |
def forward(self, inputs):
"""
prev_layer : :class:`Layer`
The previous layer with a 4D output shape.
"""
outputs = tf.nn.lrn(inputs, depth_radius=self.depth_radius, bias=self.bias, alpha=self.alpha, beta=self.beta)
return outputs
|
[
"def",
"forward",
"(",
"self",
",",
"inputs",
")",
":",
"outputs",
"=",
"tf",
".",
"nn",
".",
"lrn",
"(",
"inputs",
",",
"depth_radius",
"=",
"self",
".",
"depth_radius",
",",
"bias",
"=",
"self",
".",
"bias",
",",
"alpha",
"=",
"self",
".",
"alpha",
",",
"beta",
"=",
"self",
".",
"beta",
")",
"return",
"outputs"
] |
https://github.com/tensorlayer/tensorlayer/blob/cb4eb896dd063e650ef22533ed6fa6056a71cad5/tensorlayer/layers/normalization.py#L73-L79
|
|
calmevtime/DCTNet
|
bd7c669b478e47fde230119045133d10e135de97
|
segmentation/mmdet/core/bbox/samplers/base_sampler.py
|
python
|
BaseSampler.sample
|
(self,
assign_result,
bboxes,
gt_bboxes,
gt_labels=None,
**kwargs)
|
return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
assign_result, gt_flags)
|
Sample positive and negative bboxes.
This is a simple implementation of bbox sampling given candidates,
assigning results and ground truth bboxes.
Args:
assign_result (:obj:`AssignResult`): Bbox assigning results.
bboxes (Tensor): Boxes to be sampled from.
gt_bboxes (Tensor): Ground truth bboxes.
gt_labels (Tensor, optional): Class labels of ground truth bboxes.
Returns:
:obj:`SamplingResult`: Sampling result.
|
Sample positive and negative bboxes.
|
[
"Sample",
"positive",
"and",
"negative",
"bboxes",
"."
] |
def sample(self,
assign_result,
bboxes,
gt_bboxes,
gt_labels=None,
**kwargs):
"""Sample positive and negative bboxes.
This is a simple implementation of bbox sampling given candidates,
assigning results and ground truth bboxes.
Args:
assign_result (:obj:`AssignResult`): Bbox assigning results.
bboxes (Tensor): Boxes to be sampled from.
gt_bboxes (Tensor): Ground truth bboxes.
gt_labels (Tensor, optional): Class labels of ground truth bboxes.
Returns:
:obj:`SamplingResult`: Sampling result.
"""
bboxes = bboxes[:, :4]
gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
if self.add_gt_as_proposals:
bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
assign_result.add_gt_(gt_labels)
gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
gt_flags = torch.cat([gt_ones, gt_flags])
num_expected_pos = int(self.num * self.pos_fraction)
pos_inds = self.pos_sampler._sample_pos(
assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
# We found that sampled indices have duplicated items occasionally.
# (may be a bug of PyTorch)
pos_inds = pos_inds.unique()
num_sampled_pos = pos_inds.numel()
num_expected_neg = self.num - num_sampled_pos
if self.neg_pos_ub >= 0:
_pos = max(1, num_sampled_pos)
neg_upper_bound = int(self.neg_pos_ub * _pos)
if num_expected_neg > neg_upper_bound:
num_expected_neg = neg_upper_bound
neg_inds = self.neg_sampler._sample_neg(
assign_result, num_expected_neg, bboxes=bboxes, **kwargs)
neg_inds = neg_inds.unique()
return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
assign_result, gt_flags)
|
[
"def",
"sample",
"(",
"self",
",",
"assign_result",
",",
"bboxes",
",",
"gt_bboxes",
",",
"gt_labels",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"bboxes",
"=",
"bboxes",
"[",
":",
",",
":",
"4",
"]",
"gt_flags",
"=",
"bboxes",
".",
"new_zeros",
"(",
"(",
"bboxes",
".",
"shape",
"[",
"0",
"]",
",",
")",
",",
"dtype",
"=",
"torch",
".",
"uint8",
")",
"if",
"self",
".",
"add_gt_as_proposals",
":",
"bboxes",
"=",
"torch",
".",
"cat",
"(",
"[",
"gt_bboxes",
",",
"bboxes",
"]",
",",
"dim",
"=",
"0",
")",
"assign_result",
".",
"add_gt_",
"(",
"gt_labels",
")",
"gt_ones",
"=",
"bboxes",
".",
"new_ones",
"(",
"gt_bboxes",
".",
"shape",
"[",
"0",
"]",
",",
"dtype",
"=",
"torch",
".",
"uint8",
")",
"gt_flags",
"=",
"torch",
".",
"cat",
"(",
"[",
"gt_ones",
",",
"gt_flags",
"]",
")",
"num_expected_pos",
"=",
"int",
"(",
"self",
".",
"num",
"*",
"self",
".",
"pos_fraction",
")",
"pos_inds",
"=",
"self",
".",
"pos_sampler",
".",
"_sample_pos",
"(",
"assign_result",
",",
"num_expected_pos",
",",
"bboxes",
"=",
"bboxes",
",",
"*",
"*",
"kwargs",
")",
"# We found that sampled indices have duplicated items occasionally.",
"# (may be a bug of PyTorch)",
"pos_inds",
"=",
"pos_inds",
".",
"unique",
"(",
")",
"num_sampled_pos",
"=",
"pos_inds",
".",
"numel",
"(",
")",
"num_expected_neg",
"=",
"self",
".",
"num",
"-",
"num_sampled_pos",
"if",
"self",
".",
"neg_pos_ub",
">=",
"0",
":",
"_pos",
"=",
"max",
"(",
"1",
",",
"num_sampled_pos",
")",
"neg_upper_bound",
"=",
"int",
"(",
"self",
".",
"neg_pos_ub",
"*",
"_pos",
")",
"if",
"num_expected_neg",
">",
"neg_upper_bound",
":",
"num_expected_neg",
"=",
"neg_upper_bound",
"neg_inds",
"=",
"self",
".",
"neg_sampler",
".",
"_sample_neg",
"(",
"assign_result",
",",
"num_expected_neg",
",",
"bboxes",
"=",
"bboxes",
",",
"*",
"*",
"kwargs",
")",
"neg_inds",
"=",
"neg_inds",
".",
"unique",
"(",
")",
"return",
"SamplingResult",
"(",
"pos_inds",
",",
"neg_inds",
",",
"bboxes",
",",
"gt_bboxes",
",",
"assign_result",
",",
"gt_flags",
")"
] |
https://github.com/calmevtime/DCTNet/blob/bd7c669b478e47fde230119045133d10e135de97/segmentation/mmdet/core/bbox/samplers/base_sampler.py#L31-L78
|
|
omz/PythonistaAppTemplate
|
f560f93f8876d82a21d108977f90583df08d55af
|
PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/pyplot.py
|
python
|
colormaps
|
()
|
return sorted(cm.cmap_d.keys())
|
Matplotlib provides a number of colormaps, and others can be added using
:func:`~matplotlib.cm.register_cmap`. This function documents the built-in
colormaps, and will also return a list of all registered colormaps if called.
You can set the colormap for an image, pcolor, scatter, etc,
using a keyword argument::
imshow(X, cmap=cm.hot)
or using the :func:`set_cmap` function::
imshow(X)
pyplot.set_cmap('hot')
pyplot.set_cmap('jet')
In interactive mode, :func:`set_cmap` will update the colormap post-hoc,
allowing you to see which one works best for your data.
All built-in colormaps can be reversed by appending ``_r``: For instance,
``gray_r`` is the reverse of ``gray``.
There are several common color schemes used in visualization:
Sequential schemes
for unipolar data that progresses from low to high
Diverging schemes
for bipolar data that emphasizes positive or negative deviations from a
central value
Cyclic schemes
meant for plotting values that wrap around at the
endpoints, such as phase angle, wind direction, or time of day
Qualitative schemes
for nominal data that has no inherent ordering, where color is used
only to distinguish categories
The base colormaps are derived from those of the same name provided
with Matlab:
========= =======================================================
Colormap Description
========= =======================================================
autumn sequential linearly-increasing shades of red-orange-yellow
bone sequential increasing black-white color map with
a tinge of blue, to emulate X-ray film
cool linearly-decreasing shades of cyan-magenta
copper sequential increasing shades of black-copper
flag repetitive red-white-blue-black pattern (not cyclic at
endpoints)
gray sequential linearly-increasing black-to-white
grayscale
hot sequential black-red-yellow-white, to emulate blackbody
radiation from an object at increasing temperatures
hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed
by changing the hue component in the HSV color space
jet a spectral map with dark endpoints, blue-cyan-yellow-red;
based on a fluid-jet simulation by NCSA [#]_
pink sequential increasing pastel black-pink-white, meant
for sepia tone colorization of photographs
prism repetitive red-yellow-green-blue-purple-...-green pattern
(not cyclic at endpoints)
spring linearly-increasing shades of magenta-yellow
summer sequential linearly-increasing shades of green-yellow
winter linearly-increasing shades of blue-green
========= =======================================================
For the above list only, you can also set the colormap using the
corresponding pylab shortcut interface function, similar to Matlab::
imshow(X)
hot()
jet()
The next set of palettes are from the `Yorick scientific visualisation
package <http://yorick.sourceforge.net/index.php>`_, an evolution of
the GIST package, both by David H. Munro:
============ =======================================================
Colormap Description
============ =======================================================
gist_earth mapmaker's colors from dark blue deep ocean to green
lowlands to brown highlands to white mountains
gist_heat sequential increasing black-red-orange-white, to emulate
blackbody radiation from an iron bar as it grows hotter
gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white
colormap from National Center for Atmospheric
Research [#]_
gist_rainbow runs through the colors in spectral order from red to
violet at full saturation (like *hsv* but not cyclic)
gist_stern "Stern special" color table from Interactive Data
Language software
============ =======================================================
The following colormaps are based on the `ColorBrewer
<http://colorbrewer.org>`_ color specifications and designs developed by
Cynthia Brewer:
ColorBrewer Diverging (luminance is highest at the midpoint, and
decreases towards differently-colored endpoints):
======== ===================================
Colormap Description
======== ===================================
BrBG brown, white, blue-green
PiYG pink, white, yellow-green
PRGn purple, white, green
PuOr orange, white, purple
RdBu red, white, blue
RdGy red, white, gray
RdYlBu red, yellow, blue
RdYlGn red, yellow, green
Spectral red, orange, yellow, green, blue
======== ===================================
ColorBrewer Sequential (luminance decreases monotonically):
======== ====================================
Colormap Description
======== ====================================
Blues white to dark blue
BuGn white, light blue, dark green
BuPu white, light blue, dark purple
GnBu white, light green, dark blue
Greens white to dark green
Greys white to black (not linear)
Oranges white, orange, dark brown
OrRd white, orange, dark red
PuBu white, light purple, dark blue
PuBuGn white, light purple, dark green
PuRd white, light purple, dark red
Purples white to dark purple
RdPu white, pink, dark purple
Reds white to dark red
YlGn light yellow, dark green
YlGnBu light yellow, light green, dark blue
YlOrBr light yellow, orange, dark brown
YlOrRd light yellow, orange, dark red
======== ====================================
ColorBrewer Qualitative:
(For plotting nominal data, :class:`ListedColormap` should be used,
not :class:`LinearSegmentedColormap`. Different sets of colors are
recommended for different numbers of categories. These continuous
versions of the qualitative schemes may be removed or converted in the
future.)
* Accent
* Dark2
* Paired
* Pastel1
* Pastel2
* Set1
* Set2
* Set3
Other miscellaneous schemes:
============= =======================================================
Colormap Description
============= =======================================================
afmhot sequential black-orange-yellow-white blackbody
spectrum, commonly used in atomic force microscopy
brg blue-red-green
bwr diverging blue-white-red
coolwarm diverging blue-gray-red, meant to avoid issues with 3D
shading, color blindness, and ordering of colors [#]_
CMRmap "Default colormaps on color images often reproduce to
confusing grayscale images. The proposed colormap
maintains an aesthetically pleasing color image that
automatically reproduces to a monotonic grayscale with
discrete, quantifiable saturation levels." [#]_
cubehelix Unlike most other color schemes cubehelix was designed
by D.A. Green to be monotonically increasing in terms
of perceived brightness. Also, when printed on a black
and white postscript printer, the scheme results in a
greyscale with monotonically increasing brightness.
This color scheme is named cubehelix because the r,g,b
values produced can be visualised as a squashed helix
around the diagonal in the r,g,b color cube.
gnuplot gnuplot's traditional pm3d scheme
(black-blue-red-yellow)
gnuplot2 sequential color printable as gray
(black-blue-violet-yellow-white)
ocean green-blue-white
rainbow spectral purple-blue-green-yellow-orange-red colormap
with diverging luminance
seismic diverging blue-white-red
nipy_spectral black-purple-blue-green-yellow-red-white spectrum,
originally from the Neuroimaging in Python project
terrain mapmaker's colors, blue-green-yellow-brown-white,
originally from IGOR Pro
============= =======================================================
The following colormaps are redundant and may be removed in future
versions. It's recommended to use the names in the descriptions
instead, which produce identical output:
========= =======================================================
Colormap Description
========= =======================================================
gist_gray identical to *gray*
gist_yarg identical to *gray_r*
binary identical to *gray_r*
spectral identical to *nipy_spectral* [#]_
========= =======================================================
.. rubric:: Footnotes
.. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
choice for scientific visualization by many researchers: `Rainbow Color
Map (Still) Considered Harmful
<http://www.jwave.vt.edu/%7Erkriz/Projects/create_color_table/color_07.pdf>`_
.. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
Language. See `Color Table Gallery
<http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_
.. [#] See `Diverging Color Maps for Scientific Visualization
<http://www.cs.unm.edu/~kmorel/documents/ColorMaps/>`_ by Kenneth
Moreland.
.. [#] See `A Color Map for Effective Black-and-White Rendering of
Color-Scale Images
<http://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_
by Carey Rappaport
.. [#] Changed to distinguish from ColorBrewer's *Spectral* map.
:func:`spectral` still works, but
``set_cmap('nipy_spectral')`` is recommended for clarity.
|
Matplotlib provides a number of colormaps, and others can be added using
:func:`~matplotlib.cm.register_cmap`. This function documents the built-in
colormaps, and will also return a list of all registered colormaps if called.
|
[
"Matplotlib",
"provides",
"a",
"number",
"of",
"colormaps",
"and",
"others",
"can",
"be",
"added",
"using",
":",
"func",
":",
"~matplotlib",
".",
"cm",
".",
"register_cmap",
".",
"This",
"function",
"documents",
"the",
"built",
"-",
"in",
"colormaps",
"and",
"will",
"also",
"return",
"a",
"list",
"of",
"all",
"registered",
"colormaps",
"if",
"called",
"."
] |
def colormaps():
"""
Matplotlib provides a number of colormaps, and others can be added using
:func:`~matplotlib.cm.register_cmap`. This function documents the built-in
colormaps, and will also return a list of all registered colormaps if called.
You can set the colormap for an image, pcolor, scatter, etc,
using a keyword argument::
imshow(X, cmap=cm.hot)
or using the :func:`set_cmap` function::
imshow(X)
pyplot.set_cmap('hot')
pyplot.set_cmap('jet')
In interactive mode, :func:`set_cmap` will update the colormap post-hoc,
allowing you to see which one works best for your data.
All built-in colormaps can be reversed by appending ``_r``: For instance,
``gray_r`` is the reverse of ``gray``.
There are several common color schemes used in visualization:
Sequential schemes
for unipolar data that progresses from low to high
Diverging schemes
for bipolar data that emphasizes positive or negative deviations from a
central value
Cyclic schemes
meant for plotting values that wrap around at the
endpoints, such as phase angle, wind direction, or time of day
Qualitative schemes
for nominal data that has no inherent ordering, where color is used
only to distinguish categories
The base colormaps are derived from those of the same name provided
with Matlab:
========= =======================================================
Colormap Description
========= =======================================================
autumn sequential linearly-increasing shades of red-orange-yellow
bone sequential increasing black-white color map with
a tinge of blue, to emulate X-ray film
cool linearly-decreasing shades of cyan-magenta
copper sequential increasing shades of black-copper
flag repetitive red-white-blue-black pattern (not cyclic at
endpoints)
gray sequential linearly-increasing black-to-white
grayscale
hot sequential black-red-yellow-white, to emulate blackbody
radiation from an object at increasing temperatures
hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed
by changing the hue component in the HSV color space
jet a spectral map with dark endpoints, blue-cyan-yellow-red;
based on a fluid-jet simulation by NCSA [#]_
pink sequential increasing pastel black-pink-white, meant
for sepia tone colorization of photographs
prism repetitive red-yellow-green-blue-purple-...-green pattern
(not cyclic at endpoints)
spring linearly-increasing shades of magenta-yellow
summer sequential linearly-increasing shades of green-yellow
winter linearly-increasing shades of blue-green
========= =======================================================
For the above list only, you can also set the colormap using the
corresponding pylab shortcut interface function, similar to Matlab::
imshow(X)
hot()
jet()
The next set of palettes are from the `Yorick scientific visualisation
package <http://yorick.sourceforge.net/index.php>`_, an evolution of
the GIST package, both by David H. Munro:
============ =======================================================
Colormap Description
============ =======================================================
gist_earth mapmaker's colors from dark blue deep ocean to green
lowlands to brown highlands to white mountains
gist_heat sequential increasing black-red-orange-white, to emulate
blackbody radiation from an iron bar as it grows hotter
gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white
colormap from National Center for Atmospheric
Research [#]_
gist_rainbow runs through the colors in spectral order from red to
violet at full saturation (like *hsv* but not cyclic)
gist_stern "Stern special" color table from Interactive Data
Language software
============ =======================================================
The following colormaps are based on the `ColorBrewer
<http://colorbrewer.org>`_ color specifications and designs developed by
Cynthia Brewer:
ColorBrewer Diverging (luminance is highest at the midpoint, and
decreases towards differently-colored endpoints):
======== ===================================
Colormap Description
======== ===================================
BrBG brown, white, blue-green
PiYG pink, white, yellow-green
PRGn purple, white, green
PuOr orange, white, purple
RdBu red, white, blue
RdGy red, white, gray
RdYlBu red, yellow, blue
RdYlGn red, yellow, green
Spectral red, orange, yellow, green, blue
======== ===================================
ColorBrewer Sequential (luminance decreases monotonically):
======== ====================================
Colormap Description
======== ====================================
Blues white to dark blue
BuGn white, light blue, dark green
BuPu white, light blue, dark purple
GnBu white, light green, dark blue
Greens white to dark green
Greys white to black (not linear)
Oranges white, orange, dark brown
OrRd white, orange, dark red
PuBu white, light purple, dark blue
PuBuGn white, light purple, dark green
PuRd white, light purple, dark red
Purples white to dark purple
RdPu white, pink, dark purple
Reds white to dark red
YlGn light yellow, dark green
YlGnBu light yellow, light green, dark blue
YlOrBr light yellow, orange, dark brown
YlOrRd light yellow, orange, dark red
======== ====================================
ColorBrewer Qualitative:
(For plotting nominal data, :class:`ListedColormap` should be used,
not :class:`LinearSegmentedColormap`. Different sets of colors are
recommended for different numbers of categories. These continuous
versions of the qualitative schemes may be removed or converted in the
future.)
* Accent
* Dark2
* Paired
* Pastel1
* Pastel2
* Set1
* Set2
* Set3
Other miscellaneous schemes:
============= =======================================================
Colormap Description
============= =======================================================
afmhot sequential black-orange-yellow-white blackbody
spectrum, commonly used in atomic force microscopy
brg blue-red-green
bwr diverging blue-white-red
coolwarm diverging blue-gray-red, meant to avoid issues with 3D
shading, color blindness, and ordering of colors [#]_
CMRmap "Default colormaps on color images often reproduce to
confusing grayscale images. The proposed colormap
maintains an aesthetically pleasing color image that
automatically reproduces to a monotonic grayscale with
discrete, quantifiable saturation levels." [#]_
cubehelix Unlike most other color schemes cubehelix was designed
by D.A. Green to be monotonically increasing in terms
of perceived brightness. Also, when printed on a black
and white postscript printer, the scheme results in a
greyscale with monotonically increasing brightness.
This color scheme is named cubehelix because the r,g,b
values produced can be visualised as a squashed helix
around the diagonal in the r,g,b color cube.
gnuplot gnuplot's traditional pm3d scheme
(black-blue-red-yellow)
gnuplot2 sequential color printable as gray
(black-blue-violet-yellow-white)
ocean green-blue-white
rainbow spectral purple-blue-green-yellow-orange-red colormap
with diverging luminance
seismic diverging blue-white-red
nipy_spectral black-purple-blue-green-yellow-red-white spectrum,
originally from the Neuroimaging in Python project
terrain mapmaker's colors, blue-green-yellow-brown-white,
originally from IGOR Pro
============= =======================================================
The following colormaps are redundant and may be removed in future
versions. It's recommended to use the names in the descriptions
instead, which produce identical output:
========= =======================================================
Colormap Description
========= =======================================================
gist_gray identical to *gray*
gist_yarg identical to *gray_r*
binary identical to *gray_r*
spectral identical to *nipy_spectral* [#]_
========= =======================================================
.. rubric:: Footnotes
.. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
choice for scientific visualization by many researchers: `Rainbow Color
Map (Still) Considered Harmful
<http://www.jwave.vt.edu/%7Erkriz/Projects/create_color_table/color_07.pdf>`_
.. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
Language. See `Color Table Gallery
<http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_
.. [#] See `Diverging Color Maps for Scientific Visualization
<http://www.cs.unm.edu/~kmorel/documents/ColorMaps/>`_ by Kenneth
Moreland.
.. [#] See `A Color Map for Effective Black-and-White Rendering of
Color-Scale Images
<http://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_
by Carey Rappaport
.. [#] Changed to distinguish from ColorBrewer's *Spectral* map.
:func:`spectral` still works, but
``set_cmap('nipy_spectral')`` is recommended for clarity.
"""
return sorted(cm.cmap_d.keys())
|
[
"def",
"colormaps",
"(",
")",
":",
"return",
"sorted",
"(",
"cm",
".",
"cmap_d",
".",
"keys",
"(",
")",
")"
] |
https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/pyplot.py#L1820-L2054
|
|
deepmind/dm_control
|
806a10e896e7c887635328bfa8352604ad0fedae
|
dm_control/entities/manipulators/kinova/jaco_arm.py
|
python
|
JacoArm.joints
|
(self)
|
return self._joints
|
List of joint elements belonging to the arm.
|
List of joint elements belonging to the arm.
|
[
"List",
"of",
"joint",
"elements",
"belonging",
"to",
"the",
"arm",
"."
] |
def joints(self):
"""List of joint elements belonging to the arm."""
return self._joints
|
[
"def",
"joints",
"(",
"self",
")",
":",
"return",
"self",
".",
"_joints"
] |
https://github.com/deepmind/dm_control/blob/806a10e896e7c887635328bfa8352604ad0fedae/dm_control/entities/manipulators/kinova/jaco_arm.py#L77-L79
|
|
omz/PythonistaAppTemplate
|
f560f93f8876d82a21d108977f90583df08d55af
|
PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/httplib2/__init__.py
|
python
|
Http.add_certificate
|
(self, key, cert, domain)
|
Add a key and cert that will be used
any time a request requires authentication.
|
Add a key and cert that will be used
any time a request requires authentication.
|
[
"Add",
"a",
"key",
"and",
"cert",
"that",
"will",
"be",
"used",
"any",
"time",
"a",
"request",
"requires",
"authentication",
"."
] |
def add_certificate(self, key, cert, domain):
"""Add a key and cert that will be used
any time a request requires authentication."""
self.certificates.add(key, cert, domain)
|
[
"def",
"add_certificate",
"(",
"self",
",",
"key",
",",
"cert",
",",
"domain",
")",
":",
"self",
".",
"certificates",
".",
"add",
"(",
"key",
",",
"cert",
",",
"domain",
")"
] |
https://github.com/omz/PythonistaAppTemplate/blob/f560f93f8876d82a21d108977f90583df08d55af/PythonistaAppTemplate/PythonistaKit.framework/pylib/site-packages/httplib2/__init__.py#L1237-L1240
|
||
IronLanguages/main
|
a949455434b1fda8c783289e897e78a9a0caabb5
|
External.LCA_RESTRICTED/Languages/CPython/27/Lib/logging/__init__.py
|
python
|
Filterer.filter
|
(self, record)
|
return rv
|
Determine if a record is loggable by consulting all the filters.
The default is to allow the record to be logged; any filter can veto
this and the record is then dropped. Returns a zero value if a record
is to be dropped, else non-zero.
|
Determine if a record is loggable by consulting all the filters.
|
[
"Determine",
"if",
"a",
"record",
"is",
"loggable",
"by",
"consulting",
"all",
"the",
"filters",
"."
] |
def filter(self, record):
"""
Determine if a record is loggable by consulting all the filters.
The default is to allow the record to be logged; any filter can veto
this and the record is then dropped. Returns a zero value if a record
is to be dropped, else non-zero.
"""
rv = 1
for f in self.filters:
if not f.filter(record):
rv = 0
break
return rv
|
[
"def",
"filter",
"(",
"self",
",",
"record",
")",
":",
"rv",
"=",
"1",
"for",
"f",
"in",
"self",
".",
"filters",
":",
"if",
"not",
"f",
".",
"filter",
"(",
"record",
")",
":",
"rv",
"=",
"0",
"break",
"return",
"rv"
] |
https://github.com/IronLanguages/main/blob/a949455434b1fda8c783289e897e78a9a0caabb5/External.LCA_RESTRICTED/Languages/CPython/27/Lib/logging/__init__.py#L594-L607
|
|
memray/seq2seq-keyphrase
|
9145c63ebdc4c3bc431f8091dc52547a46804012
|
emolga/models/covc_encdec.py
|
python
|
AutoEncoder.__init__
|
(self,
config, n_rng, rng,
mode='Evaluation')
|
[] |
def __init__(self,
config, n_rng, rng,
mode='Evaluation'):
super(RNNLM, self).__init__()
self.config = config
self.n_rng = n_rng # numpy random stream
self.rng = rng # Theano random stream
self.mode = mode
self.name = 'vae'
|
[
"def",
"__init__",
"(",
"self",
",",
"config",
",",
"n_rng",
",",
"rng",
",",
"mode",
"=",
"'Evaluation'",
")",
":",
"super",
"(",
"RNNLM",
",",
"self",
")",
".",
"__init__",
"(",
")",
"self",
".",
"config",
"=",
"config",
"self",
".",
"n_rng",
"=",
"n_rng",
"# numpy random stream",
"self",
".",
"rng",
"=",
"rng",
"# Theano random stream",
"self",
".",
"mode",
"=",
"mode",
"self",
".",
"name",
"=",
"'vae'"
] |
https://github.com/memray/seq2seq-keyphrase/blob/9145c63ebdc4c3bc431f8091dc52547a46804012/emolga/models/covc_encdec.py#L1712-L1721
|
||||
pm4py/pm4py-core
|
7807b09a088b02199cd0149d724d0e28793971bf
|
pm4py/write.py
|
python
|
write_xes
|
(log: EventLog, file_path: str)
|
Exports a XES log
Parameters
--------------
log
Event log
file_path
Destination path
Returns
-------------
void
|
Exports a XES log
|
[
"Exports",
"a",
"XES",
"log"
] |
def write_xes(log: EventLog, file_path: str) -> None:
"""
Exports a XES log
Parameters
--------------
log
Event log
file_path
Destination path
Returns
-------------
void
"""
general_checks_classical_event_log(log)
from pm4py.objects.log.exporter.xes import exporter as xes_exporter
xes_exporter.apply(log, file_path)
|
[
"def",
"write_xes",
"(",
"log",
":",
"EventLog",
",",
"file_path",
":",
"str",
")",
"->",
"None",
":",
"general_checks_classical_event_log",
"(",
"log",
")",
"from",
"pm4py",
".",
"objects",
".",
"log",
".",
"exporter",
".",
"xes",
"import",
"exporter",
"as",
"xes_exporter",
"xes_exporter",
".",
"apply",
"(",
"log",
",",
"file_path",
")"
] |
https://github.com/pm4py/pm4py-core/blob/7807b09a088b02199cd0149d724d0e28793971bf/pm4py/write.py#L29-L46
|
||
spesmilo/electrum
|
bdbd59300fbd35b01605e66145458e5f396108e8
|
electrum/lnpeer.py
|
python
|
close_and_cleanup
|
(self)
|
[] |
def close_and_cleanup(self):
# note: This method might get called multiple times!
# E.g. if you call close_and_cleanup() to cause a disconnection from the peer,
# it will get called a second time in handle_disconnect().
try:
if self.transport:
self.transport.close()
except:
pass
self.lnworker.peer_closed(self)
self.got_disconnected.set()
|
[
"def",
"close_and_cleanup",
"(",
"self",
")",
":",
"# note: This method might get called multiple times!",
"# E.g. if you call close_and_cleanup() to cause a disconnection from the peer,",
"# it will get called a second time in handle_disconnect().",
"try",
":",
"if",
"self",
".",
"transport",
":",
"self",
".",
"transport",
".",
"close",
"(",
")",
"except",
":",
"pass",
"self",
".",
"lnworker",
".",
"peer_closed",
"(",
"self",
")",
"self",
".",
"got_disconnected",
".",
"set",
"(",
")"
] |
https://github.com/spesmilo/electrum/blob/bdbd59300fbd35b01605e66145458e5f396108e8/electrum/lnpeer.py#L493-L503
|
||||
sagemath/sage
|
f9b2db94f675ff16963ccdefba4f1a3393b3fe0d
|
src/sage/rings/asymptotic/term_monoid.py
|
python
|
GenericTerm._eq_
|
(self, other)
|
return self.growth == other.growth
|
r"""
Return whether this generic term is equal to ``other``.
INPUT:
- ``other`` -- an asymptotic term.
OUTPUT:
A boolean.
.. NOTE::
This method gets called by the coercion framework, so it
can be assumed that this asymptotic term is from the
same parent as ``other``.
EXAMPLES::
sage: from sage.rings.asymptotic.growth_group import GrowthGroup
sage: from sage.rings.asymptotic.term_monoid import (GenericTermMonoid,
....: ExactTermMonoid, OTermMonoid)
sage: from sage.rings.asymptotic.term_monoid import DefaultTermMonoidFactory as TermMonoid
sage: GT = GenericTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: ET = ExactTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), ZZ)
sage: OT = OTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: g = GT.an_element(); e = ET.an_element(); o = OT.an_element()
sage: g, e, o
(Generic Term with growth x, x, O(x))
sage: e == e^2 # indirect doctest
False
sage: e == ET(x, coefficient=1) # indirect doctest
True
sage: o == OT(x^2) # indirect doctest
False
TESTS::
sage: T = GenericTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: t = T.an_element()
sage: t == t
True
::
sage: OT = OTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: t = OT.an_element(); t
O(x)
sage: t == OT(x) # indirect doctest
True
sage: t == OT(x^2) # indirect doctest
False
|
r"""
Return whether this generic term is equal to ``other``.
|
[
"r",
"Return",
"whether",
"this",
"generic",
"term",
"is",
"equal",
"to",
"other",
"."
] |
def _eq_(self, other):
r"""
Return whether this generic term is equal to ``other``.
INPUT:
- ``other`` -- an asymptotic term.
OUTPUT:
A boolean.
.. NOTE::
This method gets called by the coercion framework, so it
can be assumed that this asymptotic term is from the
same parent as ``other``.
EXAMPLES::
sage: from sage.rings.asymptotic.growth_group import GrowthGroup
sage: from sage.rings.asymptotic.term_monoid import (GenericTermMonoid,
....: ExactTermMonoid, OTermMonoid)
sage: from sage.rings.asymptotic.term_monoid import DefaultTermMonoidFactory as TermMonoid
sage: GT = GenericTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: ET = ExactTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), ZZ)
sage: OT = OTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: g = GT.an_element(); e = ET.an_element(); o = OT.an_element()
sage: g, e, o
(Generic Term with growth x, x, O(x))
sage: e == e^2 # indirect doctest
False
sage: e == ET(x, coefficient=1) # indirect doctest
True
sage: o == OT(x^2) # indirect doctest
False
TESTS::
sage: T = GenericTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: t = T.an_element()
sage: t == t
True
::
sage: OT = OTermMonoid(TermMonoid, GrowthGroup('x^ZZ'), QQ)
sage: t = OT.an_element(); t
O(x)
sage: t == OT(x) # indirect doctest
True
sage: t == OT(x^2) # indirect doctest
False
"""
return self.growth == other.growth
|
[
"def",
"_eq_",
"(",
"self",
",",
"other",
")",
":",
"return",
"self",
".",
"growth",
"==",
"other",
".",
"growth"
] |
https://github.com/sagemath/sage/blob/f9b2db94f675ff16963ccdefba4f1a3393b3fe0d/src/sage/rings/asymptotic/term_monoid.py#L1019-L1074
|
|
mesonbuild/meson
|
a22d0f9a0a787df70ce79b05d0c45de90a970048
|
mesonbuild/compilers/d.py
|
python
|
DmdLikeCompilerMixin._get_crt_args
|
(self, crt_val: str, buildtype: str)
|
[] |
def _get_crt_args(self, crt_val: str, buildtype: str) -> T.List[str]:
if not self.info.is_windows():
return []
if crt_val in self.mscrt_args:
return self.mscrt_args[crt_val]
assert crt_val in ['from_buildtype', 'static_from_buildtype']
dbg = 'mdd'
rel = 'md'
if crt_val == 'static_from_buildtype':
dbg = 'mtd'
rel = 'mt'
# Match what build type flags used to do.
if buildtype == 'plain':
return []
elif buildtype == 'debug':
return self.mscrt_args[dbg]
elif buildtype == 'debugoptimized':
return self.mscrt_args[rel]
elif buildtype == 'release':
return self.mscrt_args[rel]
elif buildtype == 'minsize':
return self.mscrt_args[rel]
else:
assert buildtype == 'custom'
raise EnvironmentException('Requested C runtime based on buildtype, but buildtype is "custom".')
|
[
"def",
"_get_crt_args",
"(",
"self",
",",
"crt_val",
":",
"str",
",",
"buildtype",
":",
"str",
")",
"->",
"T",
".",
"List",
"[",
"str",
"]",
":",
"if",
"not",
"self",
".",
"info",
".",
"is_windows",
"(",
")",
":",
"return",
"[",
"]",
"if",
"crt_val",
"in",
"self",
".",
"mscrt_args",
":",
"return",
"self",
".",
"mscrt_args",
"[",
"crt_val",
"]",
"assert",
"crt_val",
"in",
"[",
"'from_buildtype'",
",",
"'static_from_buildtype'",
"]",
"dbg",
"=",
"'mdd'",
"rel",
"=",
"'md'",
"if",
"crt_val",
"==",
"'static_from_buildtype'",
":",
"dbg",
"=",
"'mtd'",
"rel",
"=",
"'mt'",
"# Match what build type flags used to do.",
"if",
"buildtype",
"==",
"'plain'",
":",
"return",
"[",
"]",
"elif",
"buildtype",
"==",
"'debug'",
":",
"return",
"self",
".",
"mscrt_args",
"[",
"dbg",
"]",
"elif",
"buildtype",
"==",
"'debugoptimized'",
":",
"return",
"self",
".",
"mscrt_args",
"[",
"rel",
"]",
"elif",
"buildtype",
"==",
"'release'",
":",
"return",
"self",
".",
"mscrt_args",
"[",
"rel",
"]",
"elif",
"buildtype",
"==",
"'minsize'",
":",
"return",
"self",
".",
"mscrt_args",
"[",
"rel",
"]",
"else",
":",
"assert",
"buildtype",
"==",
"'custom'",
"raise",
"EnvironmentException",
"(",
"'Requested C runtime based on buildtype, but buildtype is \"custom\".'",
")"
] |
https://github.com/mesonbuild/meson/blob/a22d0f9a0a787df70ce79b05d0c45de90a970048/mesonbuild/compilers/d.py#L441-L468
|
||||
Delta-ML/delta
|
31dfebc8f20b7cb282b62f291ff25a87e403cc86
|
delta/utils/postprocess/base_postproc.py
|
python
|
PostProcABC.call
|
(self)
|
implementation func
|
implementation func
|
[
"implementation",
"func"
] |
def call(self):
''' implementation func '''
raise NotImplementedError()
|
[
"def",
"call",
"(",
"self",
")",
":",
"raise",
"NotImplementedError",
"(",
")"
] |
https://github.com/Delta-ML/delta/blob/31dfebc8f20b7cb282b62f291ff25a87e403cc86/delta/utils/postprocess/base_postproc.py#L28-L30
|
||
home-assistant/core
|
265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1
|
homeassistant/components/sisyphus/media_player.py
|
python
|
SisyphusPlayer.media_position_updated_at
|
(self)
|
return self._table.active_track_remaining_time_as_of
|
Return the last time we got a position update.
|
Return the last time we got a position update.
|
[
"Return",
"the",
"last",
"time",
"we",
"got",
"a",
"position",
"update",
"."
] |
def media_position_updated_at(self):
"""Return the last time we got a position update."""
return self._table.active_track_remaining_time_as_of
|
[
"def",
"media_position_updated_at",
"(",
"self",
")",
":",
"return",
"self",
".",
"_table",
".",
"active_track_remaining_time_as_of"
] |
https://github.com/home-assistant/core/blob/265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1/homeassistant/components/sisyphus/media_player.py#L150-L152
|
|
PowerScript/KatanaFramework
|
0f6ad90a88de865d58ec26941cb4460501e75496
|
lib/MySQLdb/connections.py
|
python
|
Connection.show_warnings
|
(self)
|
return warnings
|
Return detailed information about warnings as a
sequence of tuples of (Level, Code, Message). This
is only supported in MySQL-4.1 and up. If your server
is an earlier version, an empty sequence is returned.
|
Return detailed information about warnings as a
sequence of tuples of (Level, Code, Message). This
is only supported in MySQL-4.1 and up. If your server
is an earlier version, an empty sequence is returned.
|
[
"Return",
"detailed",
"information",
"about",
"warnings",
"as",
"a",
"sequence",
"of",
"tuples",
"of",
"(",
"Level",
"Code",
"Message",
")",
".",
"This",
"is",
"only",
"supported",
"in",
"MySQL",
"-",
"4",
".",
"1",
"and",
"up",
".",
"If",
"your",
"server",
"is",
"an",
"earlier",
"version",
"an",
"empty",
"sequence",
"is",
"returned",
"."
] |
def show_warnings(self):
"""Return detailed information about warnings as a
sequence of tuples of (Level, Code, Message). This
is only supported in MySQL-4.1 and up. If your server
is an earlier version, an empty sequence is returned."""
if self._server_version < (4,1): return ()
self.query("SHOW WARNINGS")
r = self.store_result()
warnings = r.fetch_row(0)
return warnings
|
[
"def",
"show_warnings",
"(",
"self",
")",
":",
"if",
"self",
".",
"_server_version",
"<",
"(",
"4",
",",
"1",
")",
":",
"return",
"(",
")",
"self",
".",
"query",
"(",
"\"SHOW WARNINGS\"",
")",
"r",
"=",
"self",
".",
"store_result",
"(",
")",
"warnings",
"=",
"r",
".",
"fetch_row",
"(",
"0",
")",
"return",
"warnings"
] |
https://github.com/PowerScript/KatanaFramework/blob/0f6ad90a88de865d58ec26941cb4460501e75496/lib/MySQLdb/connections.py#L311-L320
|
|
Fizzadar/pyinfra
|
ff0913d6a172966760b63fe59e55dff9ea852e0d
|
pyinfra/api/command.py
|
python
|
FileDownloadCommand.__repr__
|
(self)
|
return 'FileDownloadCommand({0}, {1})'.format(self.src, self.dest)
|
[] |
def __repr__(self):
return 'FileDownloadCommand({0}, {1})'.format(self.src, self.dest)
|
[
"def",
"__repr__",
"(",
"self",
")",
":",
"return",
"'FileDownloadCommand({0}, {1})'",
".",
"format",
"(",
"self",
".",
"src",
",",
"self",
".",
"dest",
")"
] |
https://github.com/Fizzadar/pyinfra/blob/ff0913d6a172966760b63fe59e55dff9ea852e0d/pyinfra/api/command.py#L155-L156
|
|||
oracle/graalpython
|
577e02da9755d916056184ec441c26e00b70145c
|
graalpython/lib-python/3/asyncio/trsock.py
|
python
|
TransportSocket.send
|
(self, *args, **kwargs)
|
return self._sock.send(*args, **kwargs)
|
[] |
def send(self, *args, **kwargs):
self._na('send() method')
return self._sock.send(*args, **kwargs)
|
[
"def",
"send",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"self",
".",
"_na",
"(",
"'send() method'",
")",
"return",
"self",
".",
"_sock",
".",
"send",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/oracle/graalpython/blob/577e02da9755d916056184ec441c26e00b70145c/graalpython/lib-python/3/asyncio/trsock.py#L145-L147
|
|||
PaloAltoNetworks/pan-os-python
|
30f6cd9e29d0e3c2549d46c722f6dcb507acd437
|
panos/base.py
|
python
|
PanDevice.update_connection_method
|
(self)
|
return self._xapi_private
|
Regenerate the xapi object used to connect to the device
This is only necessary if the API key, password, hostname, or other
connectivity information in this object has changed. In this case,
the xapi object used to communicate with the firewall must be regenerated
to use the new connectivity information.
The new xapi is stored in the PanDevice object and returned.
Returns:
XapiWrapper: The xapi object which is also stored in self.xapi.
|
Regenerate the xapi object used to connect to the device
|
[
"Regenerate",
"the",
"xapi",
"object",
"used",
"to",
"connect",
"to",
"the",
"device"
] |
def update_connection_method(self):
"""Regenerate the xapi object used to connect to the device
This is only necessary if the API key, password, hostname, or other
connectivity information in this object has changed. In this case,
the xapi object used to communicate with the firewall must be regenerated
to use the new connectivity information.
The new xapi is stored in the PanDevice object and returned.
Returns:
XapiWrapper: The xapi object which is also stored in self.xapi.
"""
self._xapi_private = self.generate_xapi()
return self._xapi_private
|
[
"def",
"update_connection_method",
"(",
"self",
")",
":",
"self",
".",
"_xapi_private",
"=",
"self",
".",
"generate_xapi",
"(",
")",
"return",
"self",
".",
"_xapi_private"
] |
https://github.com/PaloAltoNetworks/pan-os-python/blob/30f6cd9e29d0e3c2549d46c722f6dcb507acd437/panos/base.py#L3832-L3847
|
|
nilearn/nilearn
|
9edba4471747efacf21260bf470a346307f52706
|
nilearn/interfaces/fmriprep/load_confounds_utils.py
|
python
|
MissingConfound.__init__
|
(self, params=None, keywords=None)
|
Set missing parameters and keywords.
|
Set missing parameters and keywords.
|
[
"Set",
"missing",
"parameters",
"and",
"keywords",
"."
] |
def __init__(self, params=None, keywords=None):
"""Set missing parameters and keywords."""
self.params = params or []
self.keywords = keywords or []
|
[
"def",
"__init__",
"(",
"self",
",",
"params",
"=",
"None",
",",
"keywords",
"=",
"None",
")",
":",
"self",
".",
"params",
"=",
"params",
"or",
"[",
"]",
"self",
".",
"keywords",
"=",
"keywords",
"or",
"[",
"]"
] |
https://github.com/nilearn/nilearn/blob/9edba4471747efacf21260bf470a346307f52706/nilearn/interfaces/fmriprep/load_confounds_utils.py#L246-L249
|
||
MushroomRL/mushroom-rl
|
a0eaa2cf8001e433419234a9fc48b64170e3f61c
|
mushroom_rl/utils/parameters.py
|
python
|
Parameter._compute
|
(self, *idx, **kwargs)
|
return self._initial_value
|
Returns:
The value of the parameter in the provided index.
|
Returns:
The value of the parameter in the provided index.
|
[
"Returns",
":",
"The",
"value",
"of",
"the",
"parameter",
"in",
"the",
"provided",
"index",
"."
] |
def _compute(self, *idx, **kwargs):
"""
Returns:
The value of the parameter in the provided index.
"""
return self._initial_value
|
[
"def",
"_compute",
"(",
"self",
",",
"*",
"idx",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"_initial_value"
] |
https://github.com/MushroomRL/mushroom-rl/blob/a0eaa2cf8001e433419234a9fc48b64170e3f61c/mushroom_rl/utils/parameters.py#L83-L89
|
|
coherence-project/Coherence
|
88016204c7778bf0d3ad1ae331b4d8fd725dd2af
|
coherence/extern/youtubedl/youtubedl.py
|
python
|
YoutubeIE.report_video_info_webpage_download
|
(self, video_id)
|
Report attempt to download video info webpage.
|
Report attempt to download video info webpage.
|
[
"Report",
"attempt",
"to",
"download",
"video",
"info",
"webpage",
"."
] |
def report_video_info_webpage_download(self, video_id):
"""Report attempt to download video info webpage."""
self._downloader.to_stdout(u'[youtube] %s: Downloading video info webpage' % video_id)
|
[
"def",
"report_video_info_webpage_download",
"(",
"self",
",",
"video_id",
")",
":",
"self",
".",
"_downloader",
".",
"to_stdout",
"(",
"u'[youtube] %s: Downloading video info webpage'",
"%",
"video_id",
")"
] |
https://github.com/coherence-project/Coherence/blob/88016204c7778bf0d3ad1ae331b4d8fd725dd2af/coherence/extern/youtubedl/youtubedl.py#L559-L561
|
||
Eniac-Xie/faster-rcnn-resnet
|
aba743e8404b47fc9bcccba4920846d1068c7e3c
|
lib/roi_data_layer/layer.py
|
python
|
RoIDataLayer.reshape
|
(self, bottom, top)
|
Reshaping happens during the call to forward.
|
Reshaping happens during the call to forward.
|
[
"Reshaping",
"happens",
"during",
"the",
"call",
"to",
"forward",
"."
] |
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
|
[
"def",
"reshape",
"(",
"self",
",",
"bottom",
",",
"top",
")",
":",
"pass"
] |
https://github.com/Eniac-Xie/faster-rcnn-resnet/blob/aba743e8404b47fc9bcccba4920846d1068c7e3c/lib/roi_data_layer/layer.py#L161-L163
|
||
TheAlgorithms/Python
|
9af2eef9b3761bf51580dedfb6fa7136ca0c5c2c
|
data_structures/binary_tree/binary_tree_traversals.py
|
python
|
zigzag
|
(root: Node | None)
|
return output
|
ZigZag traverse:
Returns a list of nodes value from left to right and right to left, alternatively.
|
ZigZag traverse:
Returns a list of nodes value from left to right and right to left, alternatively.
|
[
"ZigZag",
"traverse",
":",
"Returns",
"a",
"list",
"of",
"nodes",
"value",
"from",
"left",
"to",
"right",
"and",
"right",
"to",
"left",
"alternatively",
"."
] |
def zigzag(root: Node | None) -> Sequence[Node | None] | list[Any]:
"""
ZigZag traverse:
Returns a list of nodes value from left to right and right to left, alternatively.
"""
if root is None:
return []
output: list[Sequence[Node | None]] = []
flag = 0
height_tree = height(root)
for h in range(1, height_tree + 1):
if not flag:
output.append(get_nodes_from_left_to_right(root, h))
flag = 1
else:
output.append(get_nodes_from_right_to_left(root, h))
flag = 0
return output
|
[
"def",
"zigzag",
"(",
"root",
":",
"Node",
"|",
"None",
")",
"->",
"Sequence",
"[",
"Node",
"|",
"None",
"]",
"|",
"list",
"[",
"Any",
"]",
":",
"if",
"root",
"is",
"None",
":",
"return",
"[",
"]",
"output",
":",
"list",
"[",
"Sequence",
"[",
"Node",
"|",
"None",
"]",
"]",
"=",
"[",
"]",
"flag",
"=",
"0",
"height_tree",
"=",
"height",
"(",
"root",
")",
"for",
"h",
"in",
"range",
"(",
"1",
",",
"height_tree",
"+",
"1",
")",
":",
"if",
"not",
"flag",
":",
"output",
".",
"append",
"(",
"get_nodes_from_left_to_right",
"(",
"root",
",",
"h",
")",
")",
"flag",
"=",
"1",
"else",
":",
"output",
".",
"append",
"(",
"get_nodes_from_right_to_left",
"(",
"root",
",",
"h",
")",
")",
"flag",
"=",
"0",
"return",
"output"
] |
https://github.com/TheAlgorithms/Python/blob/9af2eef9b3761bf51580dedfb6fa7136ca0c5c2c/data_structures/binary_tree/binary_tree_traversals.py#L126-L147
|
|
BigBrotherBot/big-brother-bot
|
848823c71413c86e7f1ff9584f43e08d40a7f2c0
|
b3/tools/debug/statlib/stats.py
|
python
|
azmap
|
(scores, compare, dimension=0)
|
return (scores - mns) / sstd
|
Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to
array passed to compare (e.g., [time,x,y]). Assumes collapsing over dim 0
of the compare array.
Usage: azs(scores, compare, dimension=0)
|
Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to
array passed to compare (e.g., [time,x,y]). Assumes collapsing over dim 0
of the compare array.
Usage: azs(scores, compare, dimension=0)
|
[
"Returns",
"an",
"array",
"of",
"z",
"-",
"scores",
"the",
"shape",
"of",
"scores",
"(",
"e",
".",
"g",
".",
"[",
"x",
"y",
"]",
")",
"compared",
"to",
"array",
"passed",
"to",
"compare",
"(",
"e",
".",
"g",
".",
"[",
"time",
"x",
"y",
"]",
")",
".",
"Assumes",
"collapsing",
"over",
"dim",
"0",
"of",
"the",
"compare",
"array",
".",
"Usage",
":",
"azs",
"(",
"scores",
"compare",
"dimension",
"=",
"0",
")"
] |
def azmap (scores, compare, dimension=0):
"""
Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to
array passed to compare (e.g., [time,x,y]). Assumes collapsing over dim 0
of the compare array.
Usage: azs(scores, compare, dimension=0)
"""
mns = amean(compare,dimension)
sstd = asamplestdev(compare,0)
return (scores - mns) / sstd
|
[
"def",
"azmap",
"(",
"scores",
",",
"compare",
",",
"dimension",
"=",
"0",
")",
":",
"mns",
"=",
"amean",
"(",
"compare",
",",
"dimension",
")",
"sstd",
"=",
"asamplestdev",
"(",
"compare",
",",
"0",
")",
"return",
"(",
"scores",
"-",
"mns",
")",
"/",
"sstd"
] |
https://github.com/BigBrotherBot/big-brother-bot/blob/848823c71413c86e7f1ff9584f43e08d40a7f2c0/b3/tools/debug/statlib/stats.py#L2916-L2926
|
|
ns2250225/py-mt4
|
60b6b98f80923289dc6a73542e4b31e34abc030c
|
pythonicMT4.py
|
python
|
zmq_python.get_data
|
(self, symbol, timeframe, start_bar, end_bar)
|
return price_arr
|
only start_bar and end_bar as int
|
only start_bar and end_bar as int
|
[
"only",
"start_bar",
"and",
"end_bar",
"as",
"int"
] |
def get_data(self, symbol, timeframe, start_bar, end_bar):
'''
only start_bar and end_bar as int
'''
self.data = "DATA|"+ symbol+"|"+"PERIOD_"+timeframe+"|"+str(start_bar)+"|"+str(end_bar+1)
self.remote_send(self.reqSocket, self.data)
prices= self.remote_pull(self.pullSocket)
prices_str= str(prices)
price_lst= prices_str.split(sep='|')[1:-1]
price_lst= [float(i) for i in price_lst]
price_lst= price_lst[::-1]
price_arr= np.array(price_lst)
return price_arr
|
[
"def",
"get_data",
"(",
"self",
",",
"symbol",
",",
"timeframe",
",",
"start_bar",
",",
"end_bar",
")",
":",
"self",
".",
"data",
"=",
"\"DATA|\"",
"+",
"symbol",
"+",
"\"|\"",
"+",
"\"PERIOD_\"",
"+",
"timeframe",
"+",
"\"|\"",
"+",
"str",
"(",
"start_bar",
")",
"+",
"\"|\"",
"+",
"str",
"(",
"end_bar",
"+",
"1",
")",
"self",
".",
"remote_send",
"(",
"self",
".",
"reqSocket",
",",
"self",
".",
"data",
")",
"prices",
"=",
"self",
".",
"remote_pull",
"(",
"self",
".",
"pullSocket",
")",
"prices_str",
"=",
"str",
"(",
"prices",
")",
"price_lst",
"=",
"prices_str",
".",
"split",
"(",
"sep",
"=",
"'|'",
")",
"[",
"1",
":",
"-",
"1",
"]",
"price_lst",
"=",
"[",
"float",
"(",
"i",
")",
"for",
"i",
"in",
"price_lst",
"]",
"price_lst",
"=",
"price_lst",
"[",
":",
":",
"-",
"1",
"]",
"price_arr",
"=",
"np",
".",
"array",
"(",
"price_lst",
")",
"return",
"price_arr"
] |
https://github.com/ns2250225/py-mt4/blob/60b6b98f80923289dc6a73542e4b31e34abc030c/pythonicMT4.py#L42-L54
|
|
golismero/golismero
|
7d605b937e241f51c1ca4f47b20f755eeefb9d76
|
thirdparty_libs/django/db/models/sql/query.py
|
python
|
Query.reset_refcounts
|
(self, to_counts)
|
This method will reset reference counts for aliases so that they match
the value passed in :param to_counts:.
|
This method will reset reference counts for aliases so that they match
the value passed in :param to_counts:.
|
[
"This",
"method",
"will",
"reset",
"reference",
"counts",
"for",
"aliases",
"so",
"that",
"they",
"match",
"the",
"value",
"passed",
"in",
":",
"param",
"to_counts",
":",
"."
] |
def reset_refcounts(self, to_counts):
"""
This method will reset reference counts for aliases so that they match
the value passed in :param to_counts:.
"""
for alias, cur_refcount in self.alias_refcount.copy().items():
unref_amount = cur_refcount - to_counts.get(alias, 0)
self.unref_alias(alias, unref_amount)
|
[
"def",
"reset_refcounts",
"(",
"self",
",",
"to_counts",
")",
":",
"for",
"alias",
",",
"cur_refcount",
"in",
"self",
".",
"alias_refcount",
".",
"copy",
"(",
")",
".",
"items",
"(",
")",
":",
"unref_amount",
"=",
"cur_refcount",
"-",
"to_counts",
".",
"get",
"(",
"alias",
",",
"0",
")",
"self",
".",
"unref_alias",
"(",
"alias",
",",
"unref_amount",
")"
] |
https://github.com/golismero/golismero/blob/7d605b937e241f51c1ca4f47b20f755eeefb9d76/thirdparty_libs/django/db/models/sql/query.py#L733-L740
|
||
Chaffelson/nipyapi
|
d3b186fd701ce308c2812746d98af9120955e810
|
nipyapi/nifi/models/reporting_task_dto.py
|
python
|
ReportingTaskDTO.scheduling_period
|
(self)
|
return self._scheduling_period
|
Gets the scheduling_period of this ReportingTaskDTO.
The frequency with which to schedule the reporting task. The format of the value willd epend on the valud of the schedulingStrategy.
:return: The scheduling_period of this ReportingTaskDTO.
:rtype: str
|
Gets the scheduling_period of this ReportingTaskDTO.
The frequency with which to schedule the reporting task. The format of the value willd epend on the valud of the schedulingStrategy.
|
[
"Gets",
"the",
"scheduling_period",
"of",
"this",
"ReportingTaskDTO",
".",
"The",
"frequency",
"with",
"which",
"to",
"schedule",
"the",
"reporting",
"task",
".",
"The",
"format",
"of",
"the",
"value",
"willd",
"epend",
"on",
"the",
"valud",
"of",
"the",
"schedulingStrategy",
"."
] |
def scheduling_period(self):
"""
Gets the scheduling_period of this ReportingTaskDTO.
The frequency with which to schedule the reporting task. The format of the value willd epend on the valud of the schedulingStrategy.
:return: The scheduling_period of this ReportingTaskDTO.
:rtype: str
"""
return self._scheduling_period
|
[
"def",
"scheduling_period",
"(",
"self",
")",
":",
"return",
"self",
".",
"_scheduling_period"
] |
https://github.com/Chaffelson/nipyapi/blob/d3b186fd701ce308c2812746d98af9120955e810/nipyapi/nifi/models/reporting_task_dto.py#L472-L480
|
|
RasaHQ/rasa
|
54823b68c1297849ba7ae841a4246193cd1223a1
|
rasa/shared/core/events.py
|
python
|
StoryExported.__init__
|
(
self,
path: Optional[Text] = None,
timestamp: Optional[float] = None,
metadata: Optional[Dict[Text, Any]] = None,
)
|
Creates event about story exporting.
Args:
path: Path to which story was exported to.
timestamp: When the event was created.
metadata: Additional event metadata.
|
Creates event about story exporting.
|
[
"Creates",
"event",
"about",
"story",
"exporting",
"."
] |
def __init__(
self,
path: Optional[Text] = None,
timestamp: Optional[float] = None,
metadata: Optional[Dict[Text, Any]] = None,
) -> None:
"""Creates event about story exporting.
Args:
path: Path to which story was exported to.
timestamp: When the event was created.
metadata: Additional event metadata.
"""
self.path = path
super().__init__(timestamp, metadata)
|
[
"def",
"__init__",
"(",
"self",
",",
"path",
":",
"Optional",
"[",
"Text",
"]",
"=",
"None",
",",
"timestamp",
":",
"Optional",
"[",
"float",
"]",
"=",
"None",
",",
"metadata",
":",
"Optional",
"[",
"Dict",
"[",
"Text",
",",
"Any",
"]",
"]",
"=",
"None",
",",
")",
"->",
"None",
":",
"self",
".",
"path",
"=",
"path",
"super",
"(",
")",
".",
"__init__",
"(",
"timestamp",
",",
"metadata",
")"
] |
https://github.com/RasaHQ/rasa/blob/54823b68c1297849ba7ae841a4246193cd1223a1/rasa/shared/core/events.py#L1330-L1344
|
||
holzschu/Carnets
|
44effb10ddfc6aa5c8b0687582a724ba82c6b547
|
Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/cosmology/core.py
|
python
|
wpwaCDM.wp
|
(self)
|
return self._wp
|
Dark energy equation of state at the pivot redshift zp
|
Dark energy equation of state at the pivot redshift zp
|
[
"Dark",
"energy",
"equation",
"of",
"state",
"at",
"the",
"pivot",
"redshift",
"zp"
] |
def wp(self):
""" Dark energy equation of state at the pivot redshift zp"""
return self._wp
|
[
"def",
"wp",
"(",
"self",
")",
":",
"return",
"self",
".",
"_wp"
] |
https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/cosmology/core.py#L2999-L3001
|
|
number5/cloud-init
|
19948dbaf40309355e1a2dbef116efb0ce66245c
|
cloudinit/distros/debian.py
|
python
|
read_system_locale
|
(sys_path=LOCALE_CONF_FN, keyname="LANG")
|
return sys_val
|
Read system default locale setting, if present
|
Read system default locale setting, if present
|
[
"Read",
"system",
"default",
"locale",
"setting",
"if",
"present"
] |
def read_system_locale(sys_path=LOCALE_CONF_FN, keyname="LANG"):
"""Read system default locale setting, if present"""
sys_val = ""
if not sys_path:
raise ValueError("Invalid path: %s" % sys_path)
if os.path.exists(sys_path):
locale_content = util.load_file(sys_path)
sys_defaults = util.load_shell_content(locale_content)
sys_val = sys_defaults.get(keyname, "")
return sys_val
|
[
"def",
"read_system_locale",
"(",
"sys_path",
"=",
"LOCALE_CONF_FN",
",",
"keyname",
"=",
"\"LANG\"",
")",
":",
"sys_val",
"=",
"\"\"",
"if",
"not",
"sys_path",
":",
"raise",
"ValueError",
"(",
"\"Invalid path: %s\"",
"%",
"sys_path",
")",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"sys_path",
")",
":",
"locale_content",
"=",
"util",
".",
"load_file",
"(",
"sys_path",
")",
"sys_defaults",
"=",
"util",
".",
"load_shell_content",
"(",
"locale_content",
")",
"sys_val",
"=",
"sys_defaults",
".",
"get",
"(",
"keyname",
",",
"\"\"",
")",
"return",
"sys_val"
] |
https://github.com/number5/cloud-init/blob/19948dbaf40309355e1a2dbef116efb0ce66245c/cloudinit/distros/debian.py#L348-L359
|
|
spotify/luigi
|
c3b66f4a5fa7eaa52f9a72eb6704b1049035c789
|
luigi/contrib/rdbms.py
|
python
|
CopyToTable.init_copy
|
(self, connection)
|
Override to perform custom queries.
Any code here will be formed in the same transaction as the main copy, just prior to copying data.
Example use cases include truncating the table or removing all data older than X in the database
to keep a rolling window of data available in the table.
|
Override to perform custom queries.
|
[
"Override",
"to",
"perform",
"custom",
"queries",
"."
] |
def init_copy(self, connection):
"""
Override to perform custom queries.
Any code here will be formed in the same transaction as the main copy, just prior to copying data.
Example use cases include truncating the table or removing all data older than X in the database
to keep a rolling window of data available in the table.
"""
# TODO: remove this after sufficient time so most people using the
# clear_table attribtue will have noticed it doesn't work anymore
if hasattr(self, "clear_table"):
raise Exception("The clear_table attribute has been removed. Override init_copy instead!")
if self.enable_metadata_columns:
self._add_metadata_columns(connection.cursor())
|
[
"def",
"init_copy",
"(",
"self",
",",
"connection",
")",
":",
"# TODO: remove this after sufficient time so most people using the",
"# clear_table attribtue will have noticed it doesn't work anymore",
"if",
"hasattr",
"(",
"self",
",",
"\"clear_table\"",
")",
":",
"raise",
"Exception",
"(",
"\"The clear_table attribute has been removed. Override init_copy instead!\"",
")",
"if",
"self",
".",
"enable_metadata_columns",
":",
"self",
".",
"_add_metadata_columns",
"(",
"connection",
".",
"cursor",
"(",
")",
")"
] |
https://github.com/spotify/luigi/blob/c3b66f4a5fa7eaa52f9a72eb6704b1049035c789/luigi/contrib/rdbms.py#L237-L252
|
||
dfirtrack/dfirtrack
|
d405e9e2520f3f95372883e6543fa2aa7dac0121
|
dfirtrack_main/exporter/markdown/write_report.py
|
python
|
write_headline
|
(django_report, system)
|
write headline
|
write headline
|
[
"write",
"headline"
] |
def write_headline(django_report, system):
"""write headline"""
django_report.write("# " + system.system_name + "\n")
emptyline(django_report)
|
[
"def",
"write_headline",
"(",
"django_report",
",",
"system",
")",
":",
"django_report",
".",
"write",
"(",
"\"# \"",
"+",
"system",
".",
"system_name",
"+",
"\"\\n\"",
")",
"emptyline",
"(",
"django_report",
")"
] |
https://github.com/dfirtrack/dfirtrack/blob/d405e9e2520f3f95372883e6543fa2aa7dac0121/dfirtrack_main/exporter/markdown/write_report.py#L9-L13
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.