partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | random_bytes | Returns n random bytes generated with given seed
---
tags:
- Dynamic data
parameters:
- in: path
name: n
type: int
produces:
- application/octet-stream
responses:
200:
description: Bytes. | httpbin/core.py | def random_bytes(n):
"""Returns n random bytes generated with given seed
---
tags:
- Dynamic data
parameters:
- in: path
name: n
type: int
produces:
- application/octet-stream
responses:
200:
description: Bytes.
"""
n = min(n, 100 * 1024) ... | def random_bytes(n):
"""Returns n random bytes generated with given seed
---
tags:
- Dynamic data
parameters:
- in: path
name: n
type: int
produces:
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responses:
200:
description: Bytes.
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train | stream_random_bytes | Streams n random bytes generated with given seed, at given chunk size per packet.
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description: Bytes. | httpbin/core.py | def stream_random_bytes(n):
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tags:
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type: int
produces:
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train | range_request | Streams n random bytes generated with given seed, at given chunk size per packet.
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description: Bytes. | httpbin/core.py | def range_request(numbytes):
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tags:
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name: numbytes
type: int
produces:
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train | link_page | Generate a page containing n links to other pages which do the same.
---
tags:
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parameters:
- in: path
name: n
type: int
- in: path
name: offset
type: int
produces:
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responses:
200:
description: HTML links... | httpbin/core.py | def link_page(n, offset):
"""Generate a page containing n links to other pages which do the same.
---
tags:
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parameters:
- in: path
name: n
type: int
- in: path
name: offset
type: int
produces:
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responses:
200... | def link_page(n, offset):
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---
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train | image | Returns a simple image of the type suggest by the Accept header.
---
tags:
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produces:
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- image/svg+xml
- image/jpeg
- image/png
- image/*
responses:
200:
description: An image. | httpbin/core.py | def image():
"""Returns a simple image of the type suggest by the Accept header.
---
tags:
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produces:
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- image/svg+xml
- image/jpeg
- image/png
- image/*
responses:
200:
description: An image.
"""
headers = get_headers()
... | def image():
"""Returns a simple image of the type suggest by the Accept header.
---
tags:
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produces:
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- image/jpeg
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train | weighted_choice | Returns a value from choices chosen by weighted random selection
choices should be a list of (value, weight) tuples.
eg. weighted_choice([('val1', 5), ('val2', 0.3), ('val3', 1)]) | httpbin/utils.py | def weighted_choice(choices):
"""Returns a value from choices chosen by weighted random selection
choices should be a list of (value, weight) tuples.
eg. weighted_choice([('val1', 5), ('val2', 0.3), ('val3', 1)])
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train | x_runtime | X-Runtime Flask Response Decorator. | httpbin/filters.py | def x_runtime(f, *args, **kwargs):
"""X-Runtime Flask Response Decorator."""
_t0 = now()
r = f(*args, **kwargs)
_t1 = now()
r.headers['X-Runtime'] = '{0}s'.format(Decimal(str(_t1 - _t0)))
return r | def x_runtime(f, *args, **kwargs):
"""X-Runtime Flask Response Decorator."""
_t0 = now()
r = f(*args, **kwargs)
_t1 = now()
r.headers['X-Runtime'] = '{0}s'.format(Decimal(str(_t1 - _t0)))
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train | gzip | GZip Flask Response Decorator. | httpbin/filters.py | def gzip(f, *args, **kwargs):
"""GZip Flask Response Decorator."""
data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
else:
content = data
gzip_buffer = BytesIO()
gzip_file = gzip2.GzipFile(
mode='wb',
compresslevel=4,
fileobj=... | def gzip(f, *args, **kwargs):
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data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
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content = data
gzip_buffer = BytesIO()
gzip_file = gzip2.GzipFile(
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train | deflate | Deflate Flask Response Decorator. | httpbin/filters.py | def deflate(f, *args, **kwargs):
"""Deflate Flask Response Decorator."""
data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
else:
content = data
deflater = zlib.compressobj()
deflated_data = deflater.compress(content)
deflated_data += deflater.flu... | def deflate(f, *args, **kwargs):
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data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
else:
content = data
deflater = zlib.compressobj()
deflated_data = deflater.compress(content)
deflated_data += deflater.flu... | [
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train | brotli | Brotli Flask Response Decorator | httpbin/filters.py | def brotli(f, *args, **kwargs):
"""Brotli Flask Response Decorator"""
data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
else:
content = data
deflated_data = _brotli.compress(content)
if isinstance(data, Response):
data.data = deflated_data
... | def brotli(f, *args, **kwargs):
"""Brotli Flask Response Decorator"""
data = f(*args, **kwargs)
if isinstance(data, Response):
content = data.data
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deflated_data = _brotli.compress(content)
if isinstance(data, Response):
data.data = deflated_data
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train | get_version | Parses the version out of libjsonnet.h | setup.py | def get_version():
"""
Parses the version out of libjsonnet.h
"""
with open(os.path.join(DIR, 'include/libjsonnet.h')) as f:
for line in f:
if '#define' in line and 'LIB_JSONNET_VERSION' in line:
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... | def get_version():
"""
Parses the version out of libjsonnet.h
"""
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if '#define' in line and 'LIB_JSONNET_VERSION' in line:
v_code = line.partition('LIB_JSONNET_VERSION')[2].strip('\n "')
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train | handle_fractal | Get fractal coordinates from query string, call mandelbrot to generate image.
Returns:
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"""Get fractal coordinates from query string, call mandelbrot to generate image.
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train | preprocess | Return a copy of the config with ${-} handled. | case_studies/micromanage/micromanage.py | def preprocess(config):
"""Return a copy of the config with ${-} handled."""
def aux(ctx, service_name, service):
compiler, _ = get_compiler(config, service)
r2 = compiler.preprocess(ctx, service_name, service)
ctx = ctx + [service_name]
for child_name, child in compiler.childre... | def preprocess(config):
"""Return a copy of the config with ${-} handled."""
def aux(ctx, service_name, service):
compiler, _ = get_compiler(config, service)
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train | get_build_artefacts | Create all required build artefacts, modify config to refer to them. | case_studies/micromanage/micromanage.py | def get_build_artefacts(config):
"""Create all required build artefacts, modify config to refer to them."""
def aux(ctx, service_name, service):
compiler, environment = get_compiler(config, service)
new_service, service_barts = compiler.getBuildArtefacts(environment, ctx, service)
ctx =... | def get_build_artefacts(config):
"""Create all required build artefacts, modify config to refer to them."""
def aux(ctx, service_name, service):
compiler, environment = get_compiler(config, service)
new_service, service_barts = compiler.getBuildArtefacts(environment, ctx, service)
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train | KeyedVectors.load_word2vec_format | Load the input-hidden weight matrix from the original C word2vec-tool format.
Note that the information stored in the file is incomplete (the binary tree is missing),
so while you can query for word similarity etc., you cannot continue training
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`binary` is a... | synonyms/word2vec.py | def load_word2vec_format(
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datatype=REAL):
"""
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train | KeyedVectors.word_vec | Accept a single word as input.
Returns the word's representations in vector space, as a 1D numpy array.
If `use_norm` is True, returns the normalized word vector.
Example::
>>> trained_model['office']
array([ -1.40128313e-02, ...]) | synonyms/word2vec.py | def word_vec(self, word, use_norm=False):
"""
Accept a single word as input.
Returns the word's representations in vector space, as a 1D numpy array.
If `use_norm` is True, returns the normalized word vector.
Example::
>>> trained_model['office']
array([ -1.40... | def word_vec(self, word, use_norm=False):
"""
Accept a single word as input.
Returns the word's representations in vector space, as a 1D numpy array.
If `use_norm` is True, returns the normalized word vector.
Example::
>>> trained_model['office']
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train | KeyedVectors.neighbours | Get nearest words with KDTree, ranking by cosine distance | synonyms/word2vec.py | def neighbours(self, word, size = 10):
"""
Get nearest words with KDTree, ranking by cosine distance
"""
word = word.strip()
v = self.word_vec(word)
[distances], [points] = self.kdt.query(array([v]), k = size, return_distance = True)
assert len(distances) == len(p... | def neighbours(self, word, size = 10):
"""
Get nearest words with KDTree, ranking by cosine distance
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v = self.word_vec(word)
[distances], [points] = self.kdt.query(array([v]), k = size, return_distance = True)
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train | get_random_state | Turn seed into a np.random.RandomState instance.
Method originally from maciejkula/glove-python, and written by @joshloyal. | synonyms/utils.py | def get_random_state(seed):
"""
Turn seed into a np.random.RandomState instance.
Method originally from maciejkula/glove-python, and written by @joshloyal.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
... | def get_random_state(seed):
"""
Turn seed into a np.random.RandomState instance.
Method originally from maciejkula/glove-python, and written by @joshloyal.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
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train | file_or_filename | Return a file-like object ready to be read from the beginning. `input` is either
a filename (gz/bz2 also supported) or a file-like object supporting seek. | synonyms/utils.py | def file_or_filename(input):
"""
Return a file-like object ready to be read from the beginning. `input` is either
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"""
if isinstance(input, string_types):
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Return a file-like object ready to be read from the beginning. `input` is either
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train | deaccent | Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal postou bily prasek' | synonyms/utils.py | def deaccent(text):
"""
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
u'Sef chomutovskych komunistu dostal post... | def deaccent(text):
"""
Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
Return input string with accents removed, as unicode.
>>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
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train | copytree_hardlink | Recursively copy a directory ala shutils.copytree, but hardlink files
instead of copying. Available on UNIX systems only. | synonyms/utils.py | def copytree_hardlink(source, dest):
"""
Recursively copy a directory ala shutils.copytree, but hardlink files
instead of copying. Available on UNIX systems only.
"""
copy2 = shutil.copy2
try:
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shutil.copytree(source, dest)
finally:
shutil.copy2 ... | def copytree_hardlink(source, dest):
"""
Recursively copy a directory ala shutils.copytree, but hardlink files
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"""
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train | tokenize | Iteratively yield tokens as unicode strings, removing accent marks
and optionally lowercasing the unidoce string by assigning True
to one of the parameters, lowercase, to_lower, or lower.
Input text may be either unicode or utf8-encoded byte string.
The tokens on output are maximal contiguous sequence... | synonyms/utils.py | def tokenize(
text,
lowercase=False,
deacc=False,
encoding='utf8',
errors="strict",
to_lower=False,
lower=False):
"""
Iteratively yield tokens as unicode strings, removing accent marks
and optionally lowercasing the unidoce string by assigning True
... | def tokenize(
text,
lowercase=False,
deacc=False,
encoding='utf8',
errors="strict",
to_lower=False,
lower=False):
"""
Iteratively yield tokens as unicode strings, removing accent marks
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train | simple_preprocess | Convert a document into a list of tokens.
This lowercases, tokenizes, de-accents (optional). -- the output are final
tokens = unicode strings, that won't be processed any further. | synonyms/utils.py | def simple_preprocess(doc, deacc=False, min_len=2, max_len=15):
"""
Convert a document into a list of tokens.
This lowercases, tokenizes, de-accents (optional). -- the output are final
tokens = unicode strings, that won't be processed any further.
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tokens = [
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Convert a document into a list of tokens.
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tokens = unicode strings, that won't be processed any further.
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train | any2utf8 | Convert a string (unicode or bytestring in `encoding`), to bytestring in utf8. | synonyms/utils.py | def any2utf8(text, errors='strict', encoding='utf8'):
"""Convert a string (unicode or bytestring in `encoding`), to bytestring in utf8."""
if isinstance(text, unicode):
return text.encode('utf8')
# do bytestring -> unicode -> utf8 full circle, to ensure valid utf8
return unicode(text, encoding, ... | def any2utf8(text, errors='strict', encoding='utf8'):
"""Convert a string (unicode or bytestring in `encoding`), to bytestring in utf8."""
if isinstance(text, unicode):
return text.encode('utf8')
# do bytestring -> unicode -> utf8 full circle, to ensure valid utf8
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train | any2unicode | Convert a string (bytestring in `encoding` or unicode), to unicode. | synonyms/utils.py | def any2unicode(text, encoding='utf8', errors='strict'):
"""Convert a string (bytestring in `encoding` or unicode), to unicode."""
if isinstance(text, unicode):
return text
return unicode(text, encoding, errors=errors) | def any2unicode(text, encoding='utf8', errors='strict'):
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train | is_digit | Check if an object is Number | synonyms/utils.py | def is_digit(obj):
'''
Check if an object is Number
'''
return isinstance(obj, (numbers.Integral, numbers.Complex, numbers.Real)) | def is_digit(obj):
'''
Check if an object is Number
'''
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train | is_zh | return True if ch is Chinese character.
full-width puncts/latins are not counted in. | synonyms/utils.py | def is_zh(ch):
"""return True if ch is Chinese character.
full-width puncts/latins are not counted in.
"""
x = ord(ch)
# CJK Radicals Supplement and Kangxi radicals
if 0x2e80 <= x <= 0x2fef:
return True
# CJK Unified Ideographs Extension A
elif 0x3400 <= x <= 0x4dbf:
retu... | def is_zh(ch):
"""return True if ch is Chinese character.
full-width puncts/latins are not counted in.
"""
x = ord(ch)
# CJK Radicals Supplement and Kangxi radicals
if 0x2e80 <= x <= 0x2fef:
return True
# CJK Unified Ideographs Extension A
elif 0x3400 <= x <= 0x4dbf:
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train | _load_stopwords | load stop words | synonyms/synonyms.py | def _load_stopwords(file_path):
'''
load stop words
'''
global _stopwords
if sys.version_info[0] < 3:
words = open(file_path, 'r')
else:
words = open(file_path, 'r', encoding='utf-8')
stopwords = words.readlines()
for w in stopwords:
_stopwords.add(any2unicode(w).... | def _load_stopwords(file_path):
'''
load stop words
'''
global _stopwords
if sys.version_info[0] < 3:
words = open(file_path, 'r')
else:
words = open(file_path, 'r', encoding='utf-8')
stopwords = words.readlines()
for w in stopwords:
_stopwords.add(any2unicode(w).... | [
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train | _segment_words | segment words with jieba | synonyms/synonyms.py | def _segment_words(sen):
'''
segment words with jieba
'''
words, tags = [], []
m = _tokenizer.cut(sen, HMM=True) # HMM更好的识别新词
for x in m:
words.append(x.word)
tags.append(x.flag)
return words, tags | def _segment_words(sen):
'''
segment words with jieba
'''
words, tags = [], []
m = _tokenizer.cut(sen, HMM=True) # HMM更好的识别新词
for x in m:
words.append(x.word)
tags.append(x.flag)
return words, tags | [
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train | _load_w2v | load word2vec model | synonyms/synonyms.py | def _load_w2v(model_file=_f_model, binary=True):
'''
load word2vec model
'''
if not os.path.exists(model_file):
print("os.path : ", os.path)
raise Exception("Model file [%s] does not exist." % model_file)
return KeyedVectors.load_word2vec_format(
model_file, binary=binary, un... | def _load_w2v(model_file=_f_model, binary=True):
'''
load word2vec model
'''
if not os.path.exists(model_file):
print("os.path : ", os.path)
raise Exception("Model file [%s] does not exist." % model_file)
return KeyedVectors.load_word2vec_format(
model_file, binary=binary, un... | [
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train | _get_wv | get word2vec data by sentence
sentence is segmented string. | synonyms/synonyms.py | def _get_wv(sentence, ignore=False):
'''
get word2vec data by sentence
sentence is segmented string.
'''
global _vectors
vectors = []
for y in sentence:
y_ = any2unicode(y).strip()
if y_ not in _stopwords:
syns = nearby(y_)[0]
# print("sentence %s word... | def _get_wv(sentence, ignore=False):
'''
get word2vec data by sentence
sentence is segmented string.
'''
global _vectors
vectors = []
for y in sentence:
y_ = any2unicode(y).strip()
if y_ not in _stopwords:
syns = nearby(y_)[0]
# print("sentence %s word... | [
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train | _levenshtein_distance | Return the Levenshtein distance between two strings.
Based on:
http://rosettacode.org/wiki/Levenshtein_distance#Python | synonyms/synonyms.py | def _levenshtein_distance(sentence1, sentence2):
'''
Return the Levenshtein distance between two strings.
Based on:
http://rosettacode.org/wiki/Levenshtein_distance#Python
'''
first = any2utf8(sentence1).decode('utf-8', 'ignore')
second = any2utf8(sentence2).decode('utf-8', 'ignore')
... | def _levenshtein_distance(sentence1, sentence2):
'''
Return the Levenshtein distance between two strings.
Based on:
http://rosettacode.org/wiki/Levenshtein_distance#Python
'''
first = any2utf8(sentence1).decode('utf-8', 'ignore')
second = any2utf8(sentence2).decode('utf-8', 'ignore')
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train | _nearby_levenshtein_distance | 使用空间距离近的词汇优化编辑距离计算 | synonyms/synonyms.py | def _nearby_levenshtein_distance(s1, s2):
'''
使用空间距离近的词汇优化编辑距离计算
'''
s1_len, s2_len = len(s1), len(s2)
maxlen = s1_len
if s1_len == s2_len:
first, second = sorted([s1, s2])
elif s1_len < s2_len:
first = s1
second = s2
maxlen = s2_len
else:
first = ... | def _nearby_levenshtein_distance(s1, s2):
'''
使用空间距离近的词汇优化编辑距离计算
'''
s1_len, s2_len = len(s1), len(s2)
maxlen = s1_len
if s1_len == s2_len:
first, second = sorted([s1, s2])
elif s1_len < s2_len:
first = s1
second = s2
maxlen = s2_len
else:
first = ... | [
"使用空间距离近的词汇优化编辑距离计算"
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train | _similarity_distance | compute similarity with distance measurement | synonyms/synonyms.py | def _similarity_distance(s1, s2, ignore):
'''
compute similarity with distance measurement
'''
g = 0.0
try:
g_ = cosine(_flat_sum_array(_get_wv(s1, ignore)), _flat_sum_array(_get_wv(s2, ignore)))
if is_digit(g_): g = g_
except: pass
u = _nearby_levenshtein_distance(s1, s2)
... | def _similarity_distance(s1, s2, ignore):
'''
compute similarity with distance measurement
'''
g = 0.0
try:
g_ = cosine(_flat_sum_array(_get_wv(s1, ignore)), _flat_sum_array(_get_wv(s2, ignore)))
if is_digit(g_): g = g_
except: pass
u = _nearby_levenshtein_distance(s1, s2)
... | [
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train | nearby | Nearby word | synonyms/synonyms.py | def nearby(word):
'''
Nearby word
'''
w = any2unicode(word)
# read from cache
if w in _cache_nearby: return _cache_nearby[w]
words, scores = [], []
try:
for x in _vectors.neighbours(w):
words.append(x[0])
scores.append(x[1])
except: pass # ignore key ... | def nearby(word):
'''
Nearby word
'''
w = any2unicode(word)
# read from cache
if w in _cache_nearby: return _cache_nearby[w]
words, scores = [], []
try:
for x in _vectors.neighbours(w):
words.append(x[0])
scores.append(x[1])
except: pass # ignore key ... | [
"Nearby",
"word"
] | huyingxi/Synonyms | python | https://github.com/huyingxi/Synonyms/blob/fe7450d51d9ad825fdba86b9377da9dc76ae26a4/synonyms/synonyms.py#L290-L306 | [
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train | compare | compare similarity
s1 : sentence1
s2 : sentence2
seg : True : The original sentences need jieba.cut
Flase : The original sentences have been cut.
ignore: True: ignore OOV words
False: get vector randomly for OOV words | synonyms/synonyms.py | def compare(s1, s2, seg=True, ignore=False, stopwords=False):
'''
compare similarity
s1 : sentence1
s2 : sentence2
seg : True : The original sentences need jieba.cut
Flase : The original sentences have been cut.
ignore: True: ignore OOV words
False: get vector randomly for ... | def compare(s1, s2, seg=True, ignore=False, stopwords=False):
'''
compare similarity
s1 : sentence1
s2 : sentence2
seg : True : The original sentences need jieba.cut
Flase : The original sentences have been cut.
ignore: True: ignore OOV words
False: get vector randomly for ... | [
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train | Metric.add_sample | Add a sample to the metric.
Internal-only, do not use. | prometheus_client/metrics_core.py | def add_sample(self, name, labels, value, timestamp=None, exemplar=None):
"""Add a sample to the metric.
Internal-only, do not use."""
self.samples.append(Sample(name, labels, value, timestamp, exemplar)) | def add_sample(self, name, labels, value, timestamp=None, exemplar=None):
"""Add a sample to the metric.
Internal-only, do not use."""
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train | CounterMetricFamily.add_metric | Add a metric to the metric family.
Args:
labels: A list of label values
value: The value of the metric
created: Optional unix timestamp the child was created at. | prometheus_client/metrics_core.py | def add_metric(self, labels, value, created=None, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
value: The value of the metric
created: Optional unix timestamp the child was created at.
"""
self.samples.append(S... | def add_metric(self, labels, value, created=None, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
value: The value of the metric
created: Optional unix timestamp the child was created at.
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self.samples.append(S... | [
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train | SummaryMetricFamily.add_metric | Add a metric to the metric family.
Args:
labels: A list of label values
count_value: The count value of the metric.
sum_value: The sum value of the metric. | prometheus_client/metrics_core.py | def add_metric(self, labels, count_value, sum_value, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
count_value: The count value of the metric.
sum_value: The sum value of the metric.
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self.samples.append(Samp... | def add_metric(self, labels, count_value, sum_value, timestamp=None):
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Args:
labels: A list of label values
count_value: The count value of the metric.
sum_value: The sum value of the metric.
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train | HistogramMetricFamily.add_metric | Add a metric to the metric family.
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labels: A list of label values
buckets: A list of lists.
Each inner list can be a pair of bucket name and value,
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Args:
labels: A list of label values
buckets: A list of lists.
Each inner list can be a pair of bucket name and value,
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labels: A list of label values
buckets: A list of lists.
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train | GaugeHistogramMetricFamily.add_metric | Add a metric to the metric family.
Args:
labels: A list of label values
buckets: A list of pairs of bucket names and values.
The buckets must be sorted, and +Inf present.
gsum_value: The sum value of the metric. | prometheus_client/metrics_core.py | def add_metric(self, labels, buckets, gsum_value, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
buckets: A list of pairs of bucket names and values.
The buckets must be sorted, and +Inf present.
gsum_value: The su... | def add_metric(self, labels, buckets, gsum_value, timestamp=None):
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labels: A list of label values
buckets: A list of pairs of bucket names and values.
The buckets must be sorted, and +Inf present.
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train | InfoMetricFamily.add_metric | Add a metric to the metric family.
Args:
labels: A list of label values
value: A dict of labels | prometheus_client/metrics_core.py | def add_metric(self, labels, value, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
value: A dict of labels
"""
self.samples.append(Sample(
self.name + '_info',
dict(dict(zip(self._labelnames, labels... | def add_metric(self, labels, value, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
value: A dict of labels
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train | StateSetMetricFamily.add_metric | Add a metric to the metric family.
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labels: A list of label values
value: A dict of string state names to booleans | prometheus_client/metrics_core.py | def add_metric(self, labels, value, timestamp=None):
"""Add a metric to the metric family.
Args:
labels: A list of label values
value: A dict of string state names to booleans
"""
labels = tuple(labels)
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"""Add a metric to the metric family.
Args:
labels: A list of label values
value: A dict of string state names to booleans
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labels = tuple(labels)
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train | make_wsgi_app | Create a WSGI app which serves the metrics from a registry. | prometheus_client/exposition.py | def make_wsgi_app(registry=REGISTRY):
"""Create a WSGI app which serves the metrics from a registry."""
def prometheus_app(environ, start_response):
params = parse_qs(environ.get('QUERY_STRING', ''))
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train | start_wsgi_server | Starts a WSGI server for prometheus metrics as a daemon thread. | prometheus_client/exposition.py | def start_wsgi_server(port, addr='', registry=REGISTRY):
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httpd = make_server(addr, port, app, handler_class=_SilentHandler)
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t.daemon = True
t.start... | def start_wsgi_server(port, addr='', registry=REGISTRY):
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train | start_http_server | Starts an HTTP server for prometheus metrics as a daemon thread | prometheus_client/exposition.py | def start_http_server(port, addr='', registry=REGISTRY):
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CustomMetricsHandler = MetricsHandler.factory(registry)
httpd = _ThreadingSimpleServer((addr, port), CustomMetricsHandler)
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train | write_to_textfile | Write metrics to the given path.
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The path must end in .prom for the textfile collector to process it. | prometheus_client/exposition.py | def write_to_textfile(path, registry):
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This is intended for use with the Node exporter textfile collector.
The path must end in .prom for the textfile collector to process it."""
tmppath = '%s.%s.%s' % (path, os.getpid(), threading.current_thread().ident)
with op... | def write_to_textfile(path, registry):
"""Write metrics to the given path.
This is intended for use with the Node exporter textfile collector.
The path must end in .prom for the textfile collector to process it."""
tmppath = '%s.%s.%s' % (path, os.getpid(), threading.current_thread().ident)
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train | default_handler | Default handler that implements HTTP/HTTPS connections.
Used by the push_to_gateway functions. Can be re-used by other handlers. | prometheus_client/exposition.py | def default_handler(url, method, timeout, headers, data):
"""Default handler that implements HTTP/HTTPS connections.
Used by the push_to_gateway functions. Can be re-used by other handlers."""
def handle():
request = Request(url, data=data)
request.get_method = lambda: method
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"""Default handler that implements HTTP/HTTPS connections.
Used by the push_to_gateway functions. Can be re-used by other handlers."""
def handle():
request = Request(url, data=data)
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train | basic_auth_handler | Handler that implements HTTP/HTTPS connections with Basic Auth.
Sets auth headers using supplied 'username' and 'password', if set.
Used by the push_to_gateway functions. Can be re-used by other handlers. | prometheus_client/exposition.py | def basic_auth_handler(url, method, timeout, headers, data, username=None, password=None):
"""Handler that implements HTTP/HTTPS connections with Basic Auth.
Sets auth headers using supplied 'username' and 'password', if set.
Used by the push_to_gateway functions. Can be re-used by other handlers."""
... | def basic_auth_handler(url, method, timeout, headers, data, username=None, password=None):
"""Handler that implements HTTP/HTTPS connections with Basic Auth.
Sets auth headers using supplied 'username' and 'password', if set.
Used by the push_to_gateway functions. Can be re-used by other handlers."""
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train | push_to_gateway | Push metrics to the given pushgateway.
`gateway` the url for your push gateway. Either of the form
'http://pushgateway.local', or 'pushgateway.local'.
Scheme defaults to 'http' if none is provided
`job` is the job label to be attached to all pushed metrics
`registry` is an insta... | prometheus_client/exposition.py | def push_to_gateway(
gateway, job, registry, grouping_key=None, timeout=30,
handler=default_handler):
"""Push metrics to the given pushgateway.
`gateway` the url for your push gateway. Either of the form
'http://pushgateway.local', or 'pushgateway.local'.
Scheme defa... | def push_to_gateway(
gateway, job, registry, grouping_key=None, timeout=30,
handler=default_handler):
"""Push metrics to the given pushgateway.
`gateway` the url for your push gateway. Either of the form
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train | delete_from_gateway | Delete metrics from the given pushgateway.
`gateway` the url for your push gateway. Either of the form
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Scheme defaults to 'http' if none is provided
`job` is the job label to be attached to all pushed metrics
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`gateway` the url for your push gateway. Either of the form
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Scheme defaults to 'h... | def delete_from_gateway(
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train | instance_ip_grouping_key | Grouping key with instance set to the IP Address of this host. | prometheus_client/exposition.py | def instance_ip_grouping_key():
"""Grouping key with instance set to the IP Address of this host."""
with closing(socket.socket(socket.AF_INET, socket.SOCK_DGRAM)) as s:
s.connect(('localhost', 0))
return {'instance': s.getsockname()[0]} | def instance_ip_grouping_key():
"""Grouping key with instance set to the IP Address of this host."""
with closing(socket.socket(socket.AF_INET, socket.SOCK_DGRAM)) as s:
s.connect(('localhost', 0))
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train | MetricsHandler.factory | Returns a dynamic MetricsHandler class tied
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"""Returns a dynamic MetricsHandler class tied
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"""
# This implementation relies on MetricsHandler.registry
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# As we have unicode_literals, we need to create a str()
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train | text_fd_to_metric_families | Parse Prometheus text format from a file descriptor.
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Yields Metric's. | prometheus_client/openmetrics/parser.py | def text_fd_to_metric_families(fd):
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This is a laxer parser than the main Go parser,
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eof = F... | def text_fd_to_metric_families(fd):
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train | CollectorRegistry.register | Add a collector to the registry. | prometheus_client/registry.py | def register(self, collector):
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with self._lock:
names = self._get_names(collector)
duplicates = set(self._names_to_collectors).intersection(names)
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train | CollectorRegistry.unregister | Remove a collector from the registry. | prometheus_client/registry.py | def unregister(self, collector):
"""Remove a collector from the registry."""
with self._lock:
for name in self._collector_to_names[collector]:
del self._names_to_collectors[name]
del self._collector_to_names[collector] | def unregister(self, collector):
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train | CollectorRegistry._get_names | Get names of timeseries the collector produces. | prometheus_client/registry.py | def _get_names(self, collector):
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desc_func = None
# If there's a describe function, use it.
try:
desc_func = collector.describe
except AttributeError:
pass
# Otherwise, if auto describe is enabl... | def _get_names(self, collector):
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train | CollectorRegistry.collect | Yields metrics from the collectors in the registry. | prometheus_client/registry.py | def collect(self):
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train | CollectorRegistry.restricted_registry | Returns object that only collects some metrics.
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generate_latest(REGISTRY.restricted_registry(['a_timeseries']))
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train | CollectorRegistry.get_sample_value | Returns the sample value, or None if not found.
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labels = {}
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train | mark_process_dead | Do bookkeeping for when one process dies in a multi-process setup. | prometheus_client/multiprocess.py | def mark_process_dead(pid, path=None):
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train | MultiProcessCollector.merge | Merge metrics from given mmap files.
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train | getargspec | A replacement for inspect.getargspec | prometheus_client/decorator.py | def getargspec(f):
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return ArgSpec(spec.args, spec.varargs, spec.varkw, spec.defaults) | def getargspec(f):
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train | decorate | decorate(func, caller) decorates a function using a caller. | prometheus_client/decorator.py | def decorate(func, caller):
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evaldict = dict(_call_=caller, _func_=func)
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if hasattr(func, '__qualname__'):... | def decorate(func, caller):
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train | decorator | decorator(caller) converts a caller function into a decorator | prometheus_client/decorator.py | def decorator(caller, _func=None):
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train | append | Append ``a`` to the list of the virtual ancestors, unless it is already
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Append ``a`` to the list of the virtual ancestors, unless it is already
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train | text_fd_to_metric_families | Parse Prometheus text format from a file descriptor.
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name = ''
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train | mmap_key | Format a key for use in the mmap file. | prometheus_client/mmap_dict.py | def mmap_key(metric_name, name, labelnames, labelvalues):
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# ensure labels are in consistent order for identity
labels = dict(zip(labelnames, labelvalues))
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train | MmapedDict._init_value | Initialize a value. Lock must be held by caller. | prometheus_client/mmap_dict.py | def _init_value(self, key):
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train | MmapedDict._read_all_values | Yield (key, value, pos). No locking is performed. | prometheus_client/mmap_dict.py | def _read_all_values(self):
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train | MetricWrapperBase.labels | Return the child for the given labelset.
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c = Counter('my_requests_total', 'HTTP Failures', ['method', 'endpoint'])
c.labels(... | prometheus_client/metrics.py | def labels(self, *labelvalues, **labelkwargs):
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train | MetricWrapperBase.remove | Remove the given labelset from the metric. | prometheus_client/metrics.py | def remove(self, *labelvalues):
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train | Gauge.set_function | Call the provided function to return the Gauge value.
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train | Summary.observe | Observe the given amount. | prometheus_client/metrics.py | def observe(self, amount):
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train | Info.info | Set info metric. | prometheus_client/metrics.py | def info(self, val):
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train | Enum.state | Set enum metric state. | prometheus_client/metrics.py | def state(self, state):
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train | RFPDupeFilter.from_settings | Returns an instance from given settings.
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train | RFPDupeFilter.request_seen | Returns True if request was already seen.
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request : scrapy.http.Request
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train | RFPDupeFilter.log | Logs given request.
Parameters
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"""Logs given request.
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request : scrapy.http.Request
spider : scrapy.spiders.Spider
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request : scrapy.http.Request
spider : scrapy.spiders.Spider
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train | process_items | Process items from a redis queue.
Parameters
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Redis connection instance.
keys : list
List of keys to read the items from.
timeout: int
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Redis connection instance.
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Read timeout.
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List of keys to read the items from.
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url : str, optional
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url : str, optional
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train | bytes_to_str | Returns a str if a bytes object is given. | src/scrapy_redis/utils.py | def bytes_to_str(s, encoding='utf-8'):
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train | RedisMixin.setup_redis | Setup redis connection and idle signal.
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"""Setup redis connection and idle signal.
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"""
if self.server is not None:
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# We allow optional crawler argument to keep bac... | def setup_redis(self, crawler=None):
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train | RedisMixin.next_requests | Returns a request to be scheduled or none. | src/scrapy_redis/spiders.py | def next_requests(self):
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use_set = self.settings.getbool('REDIS_START_URLS_AS_SET', defaults.START_URLS_AS_SET)
fetch_one = self.server.spop if use_set else self.server.lpop
# XXX: Do we need to use a timeout here?
found = 0
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train | RedisMixin.make_request_from_data | Returns a Request instance from data coming from Redis.
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----------
data : bytes
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----------
data : bytes
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train | RedisMixin.schedule_next_requests | Schedules a request if available | src/scrapy_redis/spiders.py | def schedule_next_requests(self):
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# TODO: While there is capacity, schedule a batch of redis requests.
for req in self.next_requests():
self.crawler.engine.crawl(req, spider=self) | def schedule_next_requests(self):
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train | Base._encode_request | Encode a request object | src/scrapy_redis/queue.py | def _encode_request(self, request):
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obj = request_to_dict(request, self.spider)
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train | Base._decode_request | Decode an request previously encoded | src/scrapy_redis/queue.py | def _decode_request(self, encoded_request):
"""Decode an request previously encoded"""
obj = self.serializer.loads(encoded_request)
return request_from_dict(obj, self.spider) | def _decode_request(self, encoded_request):
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train | FifoQueue.push | Push a request | src/scrapy_redis/queue.py | def push(self, request):
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train | PriorityQueue.push | Push a request | src/scrapy_redis/queue.py | def push(self, request):
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score = -request.priority
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train | PriorityQueue.pop | Pop a request
timeout not support in this queue class | src/scrapy_redis/queue.py | def pop(self, timeout=0):
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Pop a request
timeout not support in this queue class
"""
# use atomic range/remove using multi/exec
pipe = self.server.pipeline()
pipe.multi()
pipe.zrange(self.key, 0, 0).zremrangebyrank(self.key, 0, 0)
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train | rgb2short | Find the closest xterm-256 approximation to the given RGB value.
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@returns: integer between 0 and 255, compatible with xterm.
>>> rgb2short(18, 52, 86)
23
>>> rgb2short(255, 255, 255)
231
>>> rgb2short(13, 173, 2... | powerline_shell/colortrans.py | def rgb2short(r, g, b):
""" Find the closest xterm-256 approximation to the given RGB value.
@param r,g,b: each is a number between 0-255 for the Red, Green, and Blue values
@returns: integer between 0 and 255, compatible with xterm.
>>> rgb2short(18, 52, 86)
23
>>> rgb2short(255, 255, 255)
... | def rgb2short(r, g, b):
""" Find the closest xterm-256 approximation to the given RGB value.
@param r,g,b: each is a number between 0-255 for the Red, Green, and Blue values
@returns: integer between 0 and 255, compatible with xterm.
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>>> rgb2short(255, 255, 255)
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As an example, if you want to show an icon for new files, but you only
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train | maybe_shorten_name | If the user has asked for each directory name to be shortened, will
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name. | powerline_shell/segments/cwd.py | def maybe_shorten_name(powerline, name):
"""If the user has asked for each directory name to be shortened, will
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name."""
max_size = powerline.segment_conf("cwd", "max_dir_size")
if max_size:
return name[:max_size]
r... | def maybe_shorten_name(powerline, name):
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max_size = powerline.segment_conf("cwd", "max_dir_size")
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train | get_fg_bg | Returns the foreground and background color to use for the given name. | powerline_shell/segments/cwd.py | def get_fg_bg(powerline, name, is_last_dir):
"""Returns the foreground and background color to use for the given name.
"""
if requires_special_home_display(powerline, name):
return (powerline.theme.HOME_FG, powerline.theme.HOME_BG,)
if is_last_dir:
return (powerline.theme.CWD_FG, powerl... | def get_fg_bg(powerline, name, is_last_dir):
"""Returns the foreground and background color to use for the given name.
"""
if requires_special_home_display(powerline, name):
return (powerline.theme.HOME_FG, powerline.theme.HOME_BG,)
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train | get_valid_cwd | Determine and check the current working directory for validity.
Typically, an directory arises when you checkout a different branch on git
that doesn't have this directory. When an invalid directory is found, a
warning is printed to the screen, but the directory is still returned
as-is, since this is w... | powerline_shell/__init__.py | def get_valid_cwd():
"""Determine and check the current working directory for validity.
Typically, an directory arises when you checkout a different branch on git
that doesn't have this directory. When an invalid directory is found, a
warning is printed to the screen, but the directory is still returne... | def get_valid_cwd():
"""Determine and check the current working directory for validity.
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train | validate_email | Brutally simple email address validation. Note unlike most email address validation
* raw ip address (literal) domain parts are not allowed.
* "John Doe <local_part@domain.com>" style "pretty" email addresses are processed
* the local part check is extremely basic. This raises the possibility of unicode spo... | pydantic/utils.py | def validate_email(value: str) -> Tuple[str, str]:
"""
Brutally simple email address validation. Note unlike most email address validation
* raw ip address (literal) domain parts are not allowed.
* "John Doe <local_part@domain.com>" style "pretty" email addresses are processed
* the local part check... | def validate_email(value: str) -> Tuple[str, str]:
"""
Brutally simple email address validation. Note unlike most email address validation
* raw ip address (literal) domain parts are not allowed.
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train | make_dsn | Create a DSN from from connection settings.
Stolen approximately from sqlalchemy/engine/url.py:URL. | pydantic/utils.py | def make_dsn(
*,
driver: str,
user: str = None,
password: str = None,
host: str = None,
port: str = None,
name: str = None,
query: Dict[str, Any] = None,
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"""
Create a DSN from from connection settings.
Stolen approximately from sqlalchemy/engine/url.py:URL.
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password: str = None,
host: str = None,
port: str = None,
name: str = None,
query: Dict[str, Any] = None,
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Create a DSN from from connection settings.
Stolen approximately from sqlalchemy/engine/url.py:URL.
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