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e474fbafcc2a622702fc14ec787cca84d3abda75 | RensselaerRocketSociety/Watney | lambda_function.py | [
"MIT"
] | Python | construct_json | <not_specific> | def construct_json(**kwargs):
'''
Given keyword arguments, construct a JSON string based on that.
Example:
construct_json(text='\'Hello\', "world"')
should return
{
"text": "'Hello', \"world\""
}
'''
return json.dumps(kwargs) |
Given keyword arguments, construct a JSON string based on that.
Example:
construct_json(text='\'Hello\', "world"')
should return
{
"text": "'Hello', \"world\""
}
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} | import json
def construct_json(**kwargs):
return json.dumps(kwargs) | 338 | 430 |
ae708334d5527ab235a4b9ecea38ba47d0f4ffd5 | samuelcolvin/em2 | em2/contacts.py | [
"MIT"
] | Python | add_contacts | null | async def add_contacts(conns: Connections, conv_id: int, actor_id: int):
"""
Every participant in the conversation should be a contact of an actor in that conversation, they might have been in
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"""
await conns.main.execute(
"""
insert into contacts (owner, profile_user)
(
select $2, user_id from participants
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)
on conflict (owner, profile_user) do nothing
""",
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bab7ec19615bc5a7884c732979ec203f8a185bc0 | stevommmm/nagios_nfs_verbose | check_nfs_verbose.py | [
"MIT"
] | Python | diff_stats | null | def diff_stats(old_ms, new_ms):
'''Diff each op-stat between our new mountstats and historical version
Alert if we have any major_timeouts
The only ops we really care about are:
- Mountstats.GETATTR.major_timeouts
- Mountstats.ACCESS.major_timeouts
'''
for attr in [x for x in new_ms.__slots__ if x.isupper()]:
if hasattr(new_ms, attr) and hasattr(old_ms, attr):
if getattr(new_ms, attr).major_timeouts > getattr(old_ms, attr).major_timeouts:
yield "%s [%d]" % (attr, getattr(new_ms, attr).major_timeouts - getattr(old_ms, attr).major_timeouts) | Diff each op-stat between our new mountstats and historical version
Alert if we have any major_timeouts
The only ops we really care about are:
- Mountstats.GETATTR.major_timeouts
- Mountstats.ACCESS.major_timeouts
| Diff each op-stat between our new mountstats and historical version
Alert if we have any major_timeouts
The only ops we really care about are:
Mountstats.GETATTR.major_timeouts
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1abd83880dcf3885c701b5afd39b49cd36ad6127 | grandrew/taskjuggler-python | taskjuggler_python/juggler.py | [
"MIT"
] | Python | to_tj3time | <not_specific> | def to_tj3time(dt):
"""
Convert python date or datetime object to TJ3 format
"""
return dt.isoformat().replace("T", "-").split(".")[0].split("+")[0] |
Convert python date or datetime object to TJ3 format
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3b87723ece85756a641fa386f493eb7727864168 | gabrielecker/ru-telegram-bot | bot/commands.py | [
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] | Python | start | <not_specific> | def start():
"""
This static method returns the message for users '/start' call in chats
"""
return 'Bot para remover a necessidade de acessar o site do RU para v\
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8ff48738243bdfb16ec4037c361008ed1a2c54d8 | colindean/peru | peru/compat.py | [
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] | Python | makedirs | null | def makedirs(path):
'''os.makedirs() has an exist_ok param, but it still throws errors when the
path exists with non-default permissions. This isn't fixed until 3.4.
Pathlib won't be getting an exist_ok param until 3.5.'''
path = str(path) # compatibility with pathlib
# Use isdir to avoid silently returning if the path exists but isn't a dir.
if not os.path.isdir(path):
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d02c086f8040179c55c86b486266b8e1378012fd | shawncal/ELL | tools/importers/CNTK/lib/cntk_layers.py | [
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] | Python | convert_cntk_layers_to_ell_layers | <not_specific> | def convert_cntk_layers_to_ell_layers(layersToConvert):
"""Walks a list of CNTK layers and returns a list of ELL Layer objects"""
ellLayers = []
for layerObject in layersToConvert:
layerObject.process(ellLayers)
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b373c169c971a78f866caac5a106962b1741b069 | pastelmind/d2animdata | src/d2animdata.py | [
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"""Sorts a list of Records in-place by COF name in ascending order.
:param records: List of `Record`s to sort in-place.
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} | def _sort_records_by_cof_name(records: List[Record]) -> None:
records.sort(key=lambda record: record.cof_name) | 346 | 593 |
e4fbee9407ddf21cc5c4ef3fba3f03068e6af1ea | mrubio-chavarria/project_2 | models.py | [
"MIT"
] | Python | dilation_formula | <not_specific> | def dilation_formula(layer_index, dilation_base):
"""
DESCRIPTION:
A formula that tells you the dilation based on the base and the layer
index.
:param dilation_base: [int] the base to compute the dilation, when no
dilation, base = 1.
:param layer_index: [int] the level of the layer.
"""
return (dilation_base ** layer_index) |
DESCRIPTION:
A formula that tells you the dilation based on the base and the layer
index.
:param dilation_base: [int] the base to compute the dilation, when no
dilation, base = 1.
:param layer_index: [int] the level of the layer.
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f0e523a9e94cad4f5f11a8a9a09af89cf1202dd1 | JuPeg/outputformat | outputformat/tools.py | [
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] | Python | hsv2rgb | <not_specific> | def hsv2rgb(hsv_tuple):
"""Converts HSV floats to 8-bit RGB."""
h = hsv_tuple[0]
s = hsv_tuple[1]
v = hsv_tuple[2]
rgb_tuple = tuple(int(round(i * 255)) for i in colorsys.hsv_to_rgb(h, s, v))
return rgb_tuple | Converts HSV floats to 8-bit RGB. | Converts HSV floats to 8-bit RGB. | [
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v = hsv_tuple[2]
rgb_tuple = tuple(int(round(i * 255)) for i in colorsys.hsv_to_rgb(h, s, v))
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def hsv2rgb(hsv_tuple):
h = hsv_tuple[0]
s = hsv_tuple[1]
v = hsv_tuple[2]
rgb_tuple = tuple(int(round(i * 255)) for i in colorsys.hsv_to_rgb(h, s, v))
return rgb_tuple | 348 | 28 |
08bb71ecdbbc421a9bca351f0905347ee2bbcd8b | kprussing/scons-contrib | sconscontrib/SCons/Tool/ocaml/__init__.py | [
"MIT"
] | Python | norm_suffix | <not_specific> | def norm_suffix(f, suffix):
"""
Add a suffix if not present
"""
p = str(f)
e = p[-len(suffix) :]
if e != suffix:
p = p + suffix
return p |
Add a suffix if not present
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fb043209fefff91fb8dbe9e3d9ec3bc700f8226a | DataONEorg/d1_python | gmn/src/d1_gmn/tests/gmn_direct.py | [
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] | Python | _get_resp_dict | <not_specific> | def _get_resp_dict(response):
"""Log return status of a django.http.response.HttpResponse and arrange the response
into a dict of items generally more convenient to work with from tests."""
body_str = (
"".join(response.streaming_content) if response.streaming else response.content
)
is_ok = response.status_code in (200,)
if not is_ok:
logging.warning(
'Request returned unexpected status code. status_code={} body="{}"'.format(
response.status_code, body_str
)
)
else:
logging.info("Request successful. status_code={}".format(response.status_code))
return {
"is_ok": is_ok,
"status_code_int": response.status_code,
"header_dict": dict(list(response.items())),
"body_str": body_str,
"response": response,
} | Log return status of a django.http.response.HttpResponse and arrange the response
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} | import logging
def _get_resp_dict(response):
body_str = (
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)
is_ok = response.status_code in (200,)
if not is_ok:
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response.status_code, body_str
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)
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logging.info("Request successful. status_code={}".format(response.status_code))
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"body_str": body_str,
"response": response,
} | 350 | 943 |
9622b29548aa7a6b8fca77fd81ff19b9db19f806 | mjuarezm/trees | code/forests.py | [
"Apache-2.0"
] | Python | split_data | <not_specific> | def split_data(data, label=0, length=50):
'Take a large text and divide it into chunks'
strings = [data[i:i+length] for i in range(0, len(data) - length, length)]
random.shuffle(strings)
strings = [(s, label) for s in strings]
test = strings[:len(strings) * 10 / 100]
training = strings[len(strings) * 10 / 100:]
return test, training | Take a large text and divide it into chunks | Take a large text and divide it into chunks | [
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strings = [data[i:i+length] for i in range(0, len(data) - length, length)]
random.shuffle(strings)
strings = [(s, label) for s in strings]
test = strings[:len(strings) * 10 / 100]
training = strings[len(strings) * 10 / 100:]
return test, training | [
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def split_data(data, label=0, length=50):
strings = [data[i:i+length] for i in range(0, len(data) - length, length)]
random.shuffle(strings)
strings = [(s, label) for s in strings]
test = strings[:len(strings) * 10 / 100]
training = strings[len(strings) * 10 / 100:]
return test, training | 351 | 191 |
7deac36b545ab9f95fe9266637491cb7c3902e26 | korpling/graphANNIS-python | graphannis/util.py | [
"Apache-2.0"
] | Python | node_name_from_match | <not_specific> | def node_name_from_match(match):
""" Takes a match identifier (which includes the matched annotation name) and returns the node name. This can take a single string or a list of strings as argument.
>>> m = node_name_from_match("tiger::cat::topcorpus/subcorpus/doc1#n2")
>>> m == "topcorpus/subcorpus/doc1#n2"
True
"""
if isinstance(match, str):
elements = re.split('::', match, 3)
if len(elements) == 3:
return elements[2]
elif len(elements) == 2:
return elements[1]
else:
return elements[0]
elif isinstance(match, list):
result = []
for m in match:
result.append(node_name_from_match(m))
return result
else:
return None | Takes a match identifier (which includes the matched annotation name) and returns the node name. This can take a single string or a list of strings as argument.
>>> m = node_name_from_match("tiger::cat::topcorpus/subcorpus/doc1#n2")
>>> m == "topcorpus/subcorpus/doc1#n2"
True
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"docstring_tokens": [],
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"is_optional": null
}
],
"outlier_params": [],
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def node_name_from_match(match):
if isinstance(match, str):
elements = re.split('::', match, 3)
if len(elements) == 3:
return elements[2]
elif len(elements) == 2:
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else:
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elif isinstance(match, list):
result = []
for m in match:
result.append(node_name_from_match(m))
return result
else:
return None | 352 | 152 |
fd91fb09110983df9589a3d20175483d1abec5b4 | matt-gardner/openreview-expertise | expertise/utils/utils.py | [
"MIT"
] | Python | content_to_text | <not_specific> | def content_to_text(content, fields=['title', 'abstract', 'fulltext']):
'''
Given a dict "content", such as the content field in an OpenReview record,
return a string that is the concatenation of the fields in "fields".
Example:
>>> sample_note_content = {
... 'title': 'My Title',
... 'abstract': 'This is the abstract of my paper.'
... }
>>> content_to_text(sample_note_content)
'My Title This is the abstract of my paper.'
'''
return ' '.join([content.get(field, '') for field in fields]) |
Given a dict "content", such as the content field in an OpenReview record,
return a string that is the concatenation of the fields in "fields".
Example:
>>> sample_note_content = {
... 'title': 'My Title',
... 'abstract': 'This is the abstract of my paper.'
... }
>>> content_to_text(sample_note_content)
'My Title This is the abstract of my paper.'
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"'''\n Given a dict \"content\", such as the content field in an OpenReview record,\n return a string that is the concatenation of the fields in \"fields\".\n\n Example:\n\n >>> sample_note_content = {\n ... 'title': 'My Title',\n ... 'abstract': 'This is the abstract of my paper.'\n ... }\n >>> content_to_text(sample_note_content)\n 'My Title This is the abstract of my paper.'\n\n '''"
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}
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} | def content_to_text(content, fields=['title', 'abstract', 'fulltext']):
return ' '.join([content.get(field, '') for field in fields]) | 353 | 48 |
fd499cacf2af731d0ff922245e1ed7e85e649cee | fashandge/deja | src/experiment/utilities.py | [
"MIT"
] | Python | tokenize_string | <not_specific> | def tokenize_string(string, vocabulary):
'''tokenize a string that is a concatenation of words
without spaces into a list of words
'''
words = []
length = len(string)
start = 0
while start < length:
found = False
for stop in range(length, start, -1):
word = string[start:stop]
if word in vocabulary:
words.append(word)
found = True
start = stop
break
if not found:
words.append(string[start:])
break
return words | tokenize a string that is a concatenation of words
without spaces into a list of words
| tokenize a string that is a concatenation of words
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words = []
length = len(string)
start = 0
while start < length:
found = False
for stop in range(length, start, -1):
word = string[start:stop]
if word in vocabulary:
words.append(word)
found = True
start = stop
break
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words.append(string[start:])
break
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],
"outlier_params": [],
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} | def tokenize_string(string, vocabulary):
words = []
length = len(string)
start = 0
while start < length:
found = False
for stop in range(length, start, -1):
word = string[start:stop]
if word in vocabulary:
words.append(word)
found = True
start = stop
break
if not found:
words.append(string[start:])
break
return words | 354 | 670 |
caa69d2503ec743b0a3c1008aaffd141c95446d7 | Einhard6176/FlightDelayPredictor | EDA/functions.py | [
"MIT"
] | Python | calculateSpeed | <not_specific> | def calculateSpeed(df):
'''
Returns column with speed values in MPH
'''
df['speed'] = df.distance/df.air_time*60
return df |
Returns column with speed values in MPH
| Returns column with speed values in MPH | [
"Returns",
"column",
"with",
"speed",
"values",
"in",
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] | def calculateSpeed(df):
df['speed'] = df.distance/df.air_time*60
return df | [
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} | def calculateSpeed(df):
df['speed'] = df.distance/df.air_time*60
return df | 355 | 247 |
f384f77e865bcd0ade438da8857f1126259d5b78 | Nitin-Mane/openvino | model-optimizer/mo/utils/versions_checker.py | [
"Apache-2.0"
] | Python | check_python_version | <not_specific> | def check_python_version():
"""
Checks python version to be greater or equal than 3.4
:return: exit code (1 - error, None - successful)
"""
if sys.version_info < (3, 4):
print('Python version should be of version 3.4 or newer')
return 1 |
Checks python version to be greater or equal than 3.4
:return: exit code (1 - error, None - successful)
| Checks python version to be greater or equal than 3.4 | [
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if sys.version_info < (3, 4):
print('Python version should be of version 3.4 or newer')
return 1 | [
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"outlier_params": [],
"others": []
} | import sys
def check_python_version():
if sys.version_info < (3, 4):
print('Python version should be of version 3.4 or newer')
return 1 | 356 | 728 |
63c38991678744f9ec00a90b685e88933077614c | Numerics88/n88tools | tests/regression/config_regression.py | [
"MIT"
] | Python | run_call | <not_specific> | def run_call(command):
'''Returns true if call succeeds, false otherwise'''
try:
subprocess.check_output(command)
except subprocess.CalledProcessError as e:
return False
except OSError as e:
return False
return True | Returns true if call succeeds, false otherwise | Returns true if call succeeds, false otherwise | [
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] | def run_call(command):
try:
subprocess.check_output(command)
except subprocess.CalledProcessError as e:
return False
except OSError as e:
return False
return True | [
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} | import subprocess
def run_call(command):
try:
subprocess.check_output(command)
except subprocess.CalledProcessError as e:
return False
except OSError as e:
return False
return True | 357 | 911 |
f5a8db0af479cf3cfe5bb9c2328760b223ec4711 | JordanDekker/ContainR | app/core/preprocessing/parser.py | [
"MIT"
] | Python | handle_zip | <not_specific> | def handle_zip(fw_file, rv_file, uuid):
"""Unzip forward and reverse FASTQ files if zipped.
Args:
fw_file: Original (zipped) forward FASTQ file.
rv_file: Original (zipped) reverse FASTQ file.
uuid: Unique identifier.
Returns:
fw_file: Unzipped forward files.
rv_file: Unzipped reverse files.
"""
if fw_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(fw_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/fw_file/")
for root, dirs, files in os.walk("data/"+uuid+"/fw_file/"):
for file in files:
fw_file = os.path.join(root, file)
if rv_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(rv_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/rv_file/")
for root, dirs, files in os.walk("data/"+uuid+"/rv_file/"):
for file in files:
rv_file = os.path.join(root, file)
return fw_file, rv_file | Unzip forward and reverse FASTQ files if zipped.
Args:
fw_file: Original (zipped) forward FASTQ file.
rv_file: Original (zipped) reverse FASTQ file.
uuid: Unique identifier.
Returns:
fw_file: Unzipped forward files.
rv_file: Unzipped reverse files.
| Unzip forward and reverse FASTQ files if zipped. | [
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if fw_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(fw_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/fw_file/")
for root, dirs, files in os.walk("data/"+uuid+"/fw_file/"):
for file in files:
fw_file = os.path.join(root, file)
if rv_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(rv_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/rv_file/")
for root, dirs, files in os.walk("data/"+uuid+"/rv_file/"):
for file in files:
rv_file = os.path.join(root, file)
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import os
def handle_zip(fw_file, rv_file, uuid):
if fw_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(fw_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/fw_file/")
for root, dirs, files in os.walk("data/"+uuid+"/fw_file/"):
for file in files:
fw_file = os.path.join(root, file)
if rv_file.rsplit('.', 1)[1] in ['zip', 'gz']:
with zipfile.ZipFile(rv_file, 'r') as fw_zip:
fw_zip.extractall("data/"+uuid+"/rv_file/")
for root, dirs, files in os.walk("data/"+uuid+"/rv_file/"):
for file in files:
rv_file = os.path.join(root, file)
return fw_file, rv_file | 358 | 64 |
155a57823471fe4255531cb1046c6a5c99f9da50 | sebhaner/OpenSfM | opensfm/matching.py | [
"BSD-2-Clause"
] | Python | _non_static_matches | <not_specific> | def _non_static_matches(p1: np.ndarray, p2: np.ndarray, matches):
"""Remove matches with same position in both images.
That should remove matches on that are likely belong to rig occluders,
watermarks or dust, but not discard entirely static images.
"""
threshold = 0.001
res = []
for match in matches:
d = p1[match[0]] - p2[match[1]]
if d[0] ** 2 + d[1] ** 2 >= threshold ** 2:
res.append(match)
static_ratio_threshold = 0.85
static_ratio_removed = 1 - len(res) / max(len(matches), 1)
if static_ratio_removed > static_ratio_threshold:
return matches
else:
return res | Remove matches with same position in both images.
That should remove matches on that are likely belong to rig occluders,
watermarks or dust, but not discard entirely static images.
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That should remove matches on that are likely belong to rig occluders,
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threshold = 0.001
res = []
for match in matches:
d = p1[match[0]] - p2[match[1]]
if d[0] ** 2 + d[1] ** 2 >= threshold ** 2:
res.append(match)
static_ratio_threshold = 0.85
static_ratio_removed = 1 - len(res) / max(len(matches), 1)
if static_ratio_removed > static_ratio_threshold:
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"outlier_params": [],
"others": []
} | def _non_static_matches(p1: np.ndarray, p2: np.ndarray, matches):
threshold = 0.001
res = []
for match in matches:
d = p1[match[0]] - p2[match[1]]
if d[0] ** 2 + d[1] ** 2 >= threshold ** 2:
res.append(match)
static_ratio_threshold = 0.85
static_ratio_removed = 1 - len(res) / max(len(matches), 1)
if static_ratio_removed > static_ratio_threshold:
return matches
else:
return res | 359 | 301 |
893cba8594f8231c0cf0ba968433627093cc9630 | rezoo/chainer_computational_cost | chainer_computational_cost/cost_calculators/array.py | [
"MIT"
] | Python | calc_get_item | <not_specific> | def calc_get_item(func, in_data, **kwargs):
"""[GetItem](https://docs.chainer.org/en/v4.3.0/reference/generated/chainer.functions.get_item.html)
Extract part of an array. This operation is zero FLOPs.
Most of tensor libraries have view feature, which doesn't actually create
a new array unless necessary, but this is not considered in
chainer-computational-cost.
Memory read runs only for the necessary elements, so both memory
read and write are same as the size of output tensor.
| Item | Value |
|:-------|:------|
| FLOPs | $$ 0 $$ |
| mread | $$ \| y \| $$ |
| mwrite | $$ \| y \| $$ |
| params | `slices`: list of slices, a slice is an int or a tuple with 3 elements |
""" # NOQA
x, = in_data
y = x[func.slices]
slices = [(s.start, s.stop, s.step) if type(s) is slice else s
for s in func.slices]
return (0, y.size, y.size, {'slices': slices}) | [GetItem](https://docs.chainer.org/en/v4.3.0/reference/generated/chainer.functions.get_item.html)
Extract part of an array. This operation is zero FLOPs.
Most of tensor libraries have view feature, which doesn't actually create
a new array unless necessary, but this is not considered in
chainer-computational-cost.
Memory read runs only for the necessary elements, so both memory
read and write are same as the size of output tensor.
| Item | Value |
|:-------|:------|
| FLOPs | $$ 0 $$ |
| mread | $$ \| y \| $$ |
| mwrite | $$ \| y \| $$ |
| params | `slices`: list of slices, a slice is an int or a tuple with 3 elements |
| [GetItem]
Extract part of an array. This operation is zero FLOPs.
Most of tensor libraries have view feature, which doesn't actually create
a new array unless necessary, but this is not considered in
chainer-computational-cost.
Memory read runs only for the necessary elements, so both memory
read and write are same as the size of output tensor.
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x, = in_data
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slices = [(s.start, s.stop, s.step) if type(s) is slice else s
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] | [
{
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} | def calc_get_item(func, in_data, **kwargs):
x, = in_data
y = x[func.slices]
slices = [(s.start, s.stop, s.step) if type(s) is slice else s
for s in func.slices]
return (0, y.size, y.size, {'slices': slices}) | 360 | 703 |
f9ff1381dca4e32553f27282b35a5217f8cdbdbb | MaksimFelchuck/ci-py-lib | lib-utils/git_scm.py | [
"MIT"
] | Python | download_repository | <not_specific> | def download_repository(repository_url, repository_dir, ref):
'''Download git repository and checkout to specified ref
Args:
repository_url: Long string repository url
repository_dir: short string folder name
ref_name: branch name or commit hash (by default = master)
Returns:
0: on success
-1: if fail
'''
if subprocess.run(['git', 'clone', repository_url, repository_dir],
check=True).returncode != 0: return 1
os.chdir(repository_dir)
if subprocess.run(['git', 'checkout', ref],
check=True).returncode != 0: return 1
return 0 | Download git repository and checkout to specified ref
Args:
repository_url: Long string repository url
repository_dir: short string folder name
ref_name: branch name or commit hash (by default = master)
Returns:
0: on success
-1: if fail
| Download git repository and checkout to specified ref | [
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] | def download_repository(repository_url, repository_dir, ref):
if subprocess.run(['git', 'clone', repository_url, repository_dir],
check=True).returncode != 0: return 1
os.chdir(repository_dir)
if subprocess.run(['git', 'checkout', ref],
check=True).returncode != 0: return 1
return 0 | [
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],
"default": null,
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},
{
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],
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],
"default": null,
"is_optional": null
}
],
"others": []
} | import subprocess
import os
def download_repository(repository_url, repository_dir, ref):
if subprocess.run(['git', 'clone', repository_url, repository_dir],
check=True).returncode != 0: return 1
os.chdir(repository_dir)
if subprocess.run(['git', 'checkout', ref],
check=True).returncode != 0: return 1
return 0 | 361 | 493 |
fbed579aab02c28a20e132336ce36720f6ecbbb8 | dravec-ky/mayaCityGen | src/mayaCityGen.py | [
"MIT"
] | Python | dotproduct | <not_specific> | def dotproduct(a,b):
"""Returns the dot product of two 2D vectors starting at origin.
a - [float,float], b - [float,float]
return - (float)
"""
u = (a[0]*b[0]) + (a[1]*b[1])
return u | Returns the dot product of two 2D vectors starting at origin.
a - [float,float], b - [float,float]
return - (float)
| Returns the dot product of two 2D vectors starting at origin.
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return u | 363 | 800 |
260fb63b9ef8adf236229ae1d945a09c946b6159 | jramcast/rasa_core | rasa_core/channels/channel.py | [
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] | Python | button_to_string | <not_specific> | def button_to_string(button, idx=0):
"""Create a string representation of a button."""
return "{idx}: {title} ({val})".format(
idx=idx + 1,
title=button.get('title', ''),
val=button.get('payload', '')) | Create a string representation of a button. | Create a string representation of a button. | [
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idx=idx + 1,
title=button.get('title', ''),
val=button.get('payload', '')) | [
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val=button.get('payload', '')) | 364 | 873 |
a0a1df43a44f24446d84c85f55fcc9ad37bfc21e | mriverrose/SEC10KTables | edgar_functions.py | [
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] | Python | make_timestamp | <not_specific> | def make_timestamp():
"""Create a timestamp to insert into each dataframe."""
now = datetime.now()
dtString = now.strftime("%Y-%m-%d %H:%M:%S")
return dtString | Create a timestamp to insert into each dataframe. | Create a timestamp to insert into each dataframe. | [
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def make_timestamp():
now = datetime.now()
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return dtString | 365 | 576 |
02d04ba9eaa445e144a50299432cab0037c63538 | opencinemac/vtc-py | vtc/_framerate.py | [
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] | Python | _infer_ntsc | bool | def _infer_ntsc(value: fractions.Fraction, ntsc: Optional[bool]) -> bool:
"""
_infer_ntsc looks at the parsed fraction value and infers if it should be considered
NTSC.
"""
# If no explicit ntsc value was set, assume that values with a denominator of
# 1001 are ntsc.
if ntsc is None:
if value.denominator == 1001:
ntsc = True
else:
ntsc = False
else:
ntsc = ntsc
return ntsc |
_infer_ntsc looks at the parsed fraction value and infers if it should be considered
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ntsc = False
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return ntsc | 366 | 455 |
c187f01c5b1dce2fe09491ae6d2c8a50655b76fb | chrisjsewell/pyScss | scss/selector.py | [
"MIT"
] | Python | _weave_conflicting_selectors | null | def _weave_conflicting_selectors(prefixes, a, b, suffix=()):
"""Part of the selector merge algorithm above. Not useful on its own. Pay
no attention to the man behind the curtain.
"""
# OK, what this actually does: given a list of selector chains, two
# "conflicting" selector chains, and an optional suffix, return a new list
# of chains like this:
# prefix[0] + a + b + suffix,
# prefix[0] + b + a + suffix,
# prefix[1] + a + b + suffix,
# ...
# In other words, this just appends a new chain to each of a list of given
# chains, except that the new chain might be the superposition of two
# other incompatible chains.
both = a and b
for prefix in prefixes:
yield prefix + a + b + suffix
if both:
# Only use both orderings if there's an actual conflict!
yield prefix + b + a + suffix | Part of the selector merge algorithm above. Not useful on its own. Pay
no attention to the man behind the curtain.
| Part of the selector merge algorithm above. Not useful on its own. Pay
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] | [
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{
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},
{
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"returns": [],
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{
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"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def _weave_conflicting_selectors(prefixes, a, b, suffix=()):
both = a and b
for prefix in prefixes:
yield prefix + a + b + suffix
if both:
yield prefix + b + a + suffix | 367 | 13 |
3dd10c787113fc5d06677637a837feca948424c7 | allaparthi/monorail | appengine/monorail/framework/csv_helpers.py | [
"BSD-3-Clause"
] | Python | EscapeCSV | <not_specific> | def EscapeCSV(s):
"""Return a version of string S that is safe as part of a CSV file."""
if s is None:
return ''
if isinstance(s, types.StringTypes):
s = s.strip().replace('"', '""')
# Prefix any formula cells because some spreadsheets have built-in
# formila functions that can actually have side-effects on the user's
# computer.
if s.startswith(('=', '-', '+', '@')):
s = "'" + s
return s | Return a version of string S that is safe as part of a CSV file. | Return a version of string S that is safe as part of a CSV file. | [
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if s is None:
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if isinstance(s, types.StringTypes):
s = s.strip().replace('"', '""')
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"identifier": "s",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import types
def EscapeCSV(s):
if s is None:
return ''
if isinstance(s, types.StringTypes):
s = s.strip().replace('"', '""')
if s.startswith(('=', '-', '+', '@')):
s = "'" + s
return s | 368 | 295 |
f9643c2275cb136bbd34c6ab242540173a2cf270 | nkrishnaswami/dspl | tools/dspltools/packages/dspllib/validation/xml_validation.py | [
"BSD-3-Clause"
] | Python | GetErrorLineNumber | <not_specific> | def GetErrorLineNumber(error_string):
"""Parse out the line number from a minixsv error message.
Args:
error_string: String returned by minixsv exception
Returns:
Integer line number on which error was detected
"""
line_match = re.search(': line ([0-9]+)', error_string)
return int(line_match.group(1)) | Parse out the line number from a minixsv error message.
Args:
error_string: String returned by minixsv exception
Returns:
Integer line number on which error was detected
| Parse out the line number from a minixsv error message. | [
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] | def GetErrorLineNumber(error_string):
line_match = re.search(': line ([0-9]+)', error_string)
return int(line_match.group(1)) | [
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],
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} | import re
def GetErrorLineNumber(error_string):
line_match = re.search(': line ([0-9]+)', error_string)
return int(line_match.group(1)) | 369 | 154 |
407a74fa3e877fc676b731e2da712a0ee09eb36f | Harish-developments/jspy | jspy.py | [
"MIT"
] | Python | __py_if_condition | <not_specific> | def __py_if_condition(statements,lineno):
"""returns a valid python if statement"""
if_condition = statements[statements.find('(')+1:statements.find(')')]
return statements[:statements.find('if')]\
+f"if {if_condition}:\n".replace('&&',' and ').replace('||',' or ').replace('!',' not ').replace('===','==').replace('{','').replace('/','//') | returns a valid python if statement | returns a valid python if statement | [
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] | def __py_if_condition(statements,lineno):
if_condition = statements[statements.find('(')+1:statements.find(')')]
return statements[:statements.find('if')]\
+f"if {if_condition}:\n".replace('&&',' and ').replace('||',' or ').replace('!',' not ').replace('===','==').replace('{','').replace('/','//') | [
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],
"outlier_params": [],
"others": []
} | def __py_if_condition(statements,lineno):
if_condition = statements[statements.find('(')+1:statements.find(')')]
return statements[:statements.find('if')]\
+f"if {if_condition}:\n".replace('&&',' and ').replace('||',' or ').replace('!',' not ').replace('===','==').replace('{','').replace('/','//') | 370 | 406 |
1b3186c99a60818dc9d24b438538877520aa1347 | Z2PackDev/bands-inspect | tests/conftest.py | [
"Apache-2.0"
] | Python | compare_data | <not_specific> | def compare_data(request, test_name, scope="session"): # pylint: disable=unused-argument,redefined-outer-name
"""Returns a function which either saves some data to a file or (if that file exists already) compares it to pre-existing data using a given comparison function."""
def inner(compare_fct, data, tag=None):
full_name = test_name + (tag or '')
# get rid of json-specific quirks
# store as string because I cannot add the decoder to the pytest cache
data_str = json.dumps(data)
data = json.loads(data_str)
val = json.loads(request.config.cache.get(full_name, 'null'))
if val is None:
request.config.cache.set(full_name, data_str)
raise ValueError('Reference data does not exist.')
assert compare_fct(val, data)
return inner | Returns a function which either saves some data to a file or (if that file exists already) compares it to pre-existing data using a given comparison function. | Returns a function which either saves some data to a file or (if that file exists already) compares it to pre-existing data using a given comparison function. | [
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data = json.loads(data_str)
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"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import json
def compare_data(request, test_name, scope="session"):
def inner(compare_fct, data, tag=None):
full_name = test_name + (tag or '')
data_str = json.dumps(data)
data = json.loads(data_str)
val = json.loads(request.config.cache.get(full_name, 'null'))
if val is None:
request.config.cache.set(full_name, data_str)
raise ValueError('Reference data does not exist.')
assert compare_fct(val, data)
return inner | 371 | 480 |
98433d154c009b04a48f5d9aabb7107f5e08386a | tang88888888/naiveproxy | src/tools/grit/grit/util.py | [
"BSD-3-Clause"
] | Python | _GetCommonBaseDirectory | <not_specific> | def _GetCommonBaseDirectory(*args):
"""Returns the common prefix directory for the given paths
Args:
The list of paths (at least one of which should be a directory)
"""
prefix = os.path.commonprefix(args)
# prefix is a character-by-character prefix (i.e. it does not end
# on a directory bound, so this code fixes that)
# if the prefix ends with the separator, then it is prefect.
if len(prefix) > 0 and prefix[-1] == os.path.sep:
return prefix
# We need to loop through all paths or else we can get
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# is "c:\a" which is a directory and looks good with
# respect to the first directory but it is clear that
# isn't a common directory when the second path is
# examined.
for path in args:
assert len(path) >= len(prefix)
# If the prefix the same length as the path,
# then the prefix must be a directory (since one
# of the arguements should be a directory).
if path == prefix:
continue
# if the character after the prefix in the path
# is the separator, then the prefix appears to be a
# valid a directory as well for the given path
if path[len(prefix)] == os.path.sep:
continue
# Otherwise, the prefix is not a directory, so it needs
# to be shortened to be one
index_sep = prefix.rfind(os.path.sep)
# The use "index_sep + 1" because it includes the final sep
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prefix = prefix[:index_sep + 1]
# At this point we backed up to a directory bound which is
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# the paths.
break
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Args:
The list of paths (at least one of which should be a directory)
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prefix = os.path.commonprefix(args)
if len(prefix) > 0 and prefix[-1] == os.path.sep:
return prefix
for path in args:
assert len(path) >= len(prefix)
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index_sep = prefix.rfind(os.path.sep)
prefix = prefix[:index_sep + 1]
break
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"# the paths."
] | [] | {
"returns": [],
"raises": [],
"params": [],
"outlier_params": [],
"others": []
} | import os
def _GetCommonBaseDirectory(*args):
prefix = os.path.commonprefix(args)
if len(prefix) > 0 and prefix[-1] == os.path.sep:
return prefix
for path in args:
assert len(path) >= len(prefix)
if path == prefix:
continue
if path[len(prefix)] == os.path.sep:
continue
index_sep = prefix.rfind(os.path.sep)
prefix = prefix[:index_sep + 1]
break
return prefix | 372 | 509 |
a9cbf5a752099be378dd9c6e203e37bb366f5a97 | jitterjuice/towerlib | towerlib/entities/core.py | [
"MIT"
] | Python | validate_json | <not_specific> | def validate_json(value):
"""Validates that the provided value is a valid json."""
try:
json.loads(value)
return True
except ValueError:
return False | Validates that the provided value is a valid json. | Validates that the provided value is a valid json. | [
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{
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"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import json
def validate_json(value):
try:
json.loads(value)
return True
except ValueError:
return False | 374 | 179 |
169bdf3fb18db94df4c2311d9c6ec1ab1568d895 | shad7/tvdbapi_client | tvdbapi_client/options.py | [
"MIT"
] | Python | _make_opt_list | <not_specific> | def _make_opt_list(opts, group):
"""Generate a list of tuple containing group, options
:param opts: option lists associated with a group
:type opts: list
:param group: name of an option group
:type group: str
:return: a list of (group_name, opts) tuples
:rtype: list
"""
import copy
import itertools
_opts = [(group, list(itertools.chain(*opts)))]
return [(g, copy.deepcopy(o)) for g, o in _opts] | Generate a list of tuple containing group, options
:param opts: option lists associated with a group
:type opts: list
:param group: name of an option group
:type group: str
:return: a list of (group_name, opts) tuples
:rtype: list
| Generate a list of tuple containing group, options | [
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import copy
import itertools
_opts = [(group, list(itertools.chain(*opts)))]
return [(g, copy.deepcopy(o)) for g, o in _opts] | [
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import itertools
def _make_opt_list(opts, group):
import copy
import itertools
_opts = [(group, list(itertools.chain(*opts)))]
return [(g, copy.deepcopy(o)) for g, o in _opts] | 376 | 191 |
493f44505676a6b3afab2088fcf53b369d3ae4ac | onealbao/LDax | dax/XnatUtils.py | [
"MIT"
] | Python | extract_exp | <not_specific> | def extract_exp(expression, full_regex=False):
"""Extract the experession with or without full_regex.
:param expression: string to filter
:param full_regex: using full regex
:return: regex Object from re package
"""
if not full_regex:
exp = fnmatch.translate(expression)
return re.compile(exp) | Extract the experession with or without full_regex.
:param expression: string to filter
:param full_regex: using full regex
:return: regex Object from re package
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if not full_regex:
exp = fnmatch.translate(expression)
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} | import fnmatch
import re
def extract_exp(expression, full_regex=False):
if not full_regex:
exp = fnmatch.translate(expression)
return re.compile(exp) | 377 | 251 |
aca95e0e54e5ca1a68e0599cbacc7db0b39e9970 | NURDspace/jsonbot | jsb/plugs/common/topic.py | [
"MIT"
] | Python | checktopicmode | <not_specific> | def checktopicmode(bot, ievent):
""" callback for change in channel topic mode """
chan = ievent.channel
mode = ievent.chan.data.mode
if mode and 't' in mode:
if chan not in bot.state['opchan']:
ievent.reply("i'm not op on %s" % chan)
return 0
return 1 | callback for change in channel topic mode | callback for change in channel topic mode | [
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chan = ievent.channel
mode = ievent.chan.data.mode
if mode and 't' in mode:
if chan not in bot.state['opchan']:
ievent.reply("i'm not op on %s" % chan)
return 0
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if chan not in bot.state['opchan']:
ievent.reply("i'm not op on %s" % chan)
return 0
return 1 | 378 | 606 |
1ce74aa4b4c1f68d80f10d1537b4ca5114a8a559 | DavidNemeskey/bert | my_run_pretraining.py | [
"Apache-2.0"
] | Python | read_command_file | <not_specific> | def read_command_file(cmd_file):
"""
Reads the command line file and returns the command, as well as the output
directory.
"""
with open(cmd_file) as inf:
cmd = inf.readline().strip()
output_dir = re.search(r'--output_dir\s*=\s*([^ ]+)', cmd).group(1).rstrip('/')
return cmd, output_dir |
Reads the command line file and returns the command, as well as the output
directory.
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with open(cmd_file) as inf:
cmd = inf.readline().strip()
output_dir = re.search(r'--output_dir\s*=\s*([^ ]+)', cmd).group(1).rstrip('/')
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],
"outlier_params": [],
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} | import re
def read_command_file(cmd_file):
with open(cmd_file) as inf:
cmd = inf.readline().strip()
output_dir = re.search(r'--output_dir\s*=\s*([^ ]+)', cmd).group(1).rstrip('/')
return cmd, output_dir | 379 | 345 |
be2c86febfcc236b7423366664c39cc848358e09 | wotchin/openGauss-server | src/manager/om/script/local/LocalCollect.py | [
"MulanPSL-1.0"
] | Python | appendCommand | null | def appendCommand(cmds, newCommand, dataFileName):
"""
function: make up the commands into the array
input : cmds, newCommand, dataFileName
output: NA
"""
# Execute the command and output to the specified file
cmds.append("echo '\n************************************\n* " \
"%s \n" \
"************************************' >> %s 2>&1" % \
(newCommand, dataFileName))
cmds.append("%s >> %s 2>&1" % (newCommand, dataFileName)) |
function: make up the commands into the array
input : cmds, newCommand, dataFileName
output: NA
| make up the commands into the array
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output: NA | [
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cmds.append("echo '\n************************************\n* " \
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cmds.append("%s >> %s 2>&1" % (newCommand, dataFileName)) | 380 | 705 |
a842aaebf1e559ea74544dd1e8ed592c82211f11 | stefanpf/py2gc | py2gc/parsers.py | [
"MIT"
] | Python | parse_path | <not_specific> | def parse_path(path):
'''
Currently just returns the given path if it exists,
or the default if it doesn't.
'''
print('Parsing path...')
parsed_path = str(path)
if os.path.isdir(path):
return parsed_path
else:
print('''Cannot parse path to credentials. Will try to get credentials
from default path $home/.py2gc/.''') |
Currently just returns the given path if it exists,
or the default if it doesn't.
| Currently just returns the given path if it exists,
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print('Parsing path...')
parsed_path = str(path)
if os.path.isdir(path):
return parsed_path
else:
print('''Cannot parse path to credentials. Will try to get credentials
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],
"outlier_params": [],
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parsed_path = str(path)
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print('''Cannot parse path to credentials. Will try to get credentials
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384a5ad37bf2d4d4d4760fff465e584c6818f342 | nwthomas/code-challenges | src/daily-coding-problem/medium/synonyms/synonyms.py | [
"MIT"
] | Python | are_deeply_linked_synonyms | <not_specific> | def are_deeply_linked_synonyms(reference_dict, first_word, second_word):
"""Takes in two words with a reference dictionary and iteratively checks if the words (with any in between) are synonyms"""
if first_word not in reference_dict or second_word not in reference_dict:
return False
tracker = {}
stack = [word for word in reference_dict[first_word]]
while len(stack) > 0:
current_word = stack.pop()
if current_word in tracker:
continue
else:
tracker[current_word] = True
current_word_synonyms = reference_dict[current_word]
for word in current_word_synonyms:
if word == second_word:
return True
elif word not in tracker:
stack.append(word)
return False | Takes in two words with a reference dictionary and iteratively checks if the words (with any in between) are synonyms | Takes in two words with a reference dictionary and iteratively checks if the words (with any in between) are synonyms | [
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if first_word not in reference_dict or second_word not in reference_dict:
return False
tracker = {}
stack = [word for word in reference_dict[first_word]]
while len(stack) > 0:
current_word = stack.pop()
if current_word in tracker:
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else:
tracker[current_word] = True
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],
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} | def are_deeply_linked_synonyms(reference_dict, first_word, second_word):
if first_word not in reference_dict or second_word not in reference_dict:
return False
tracker = {}
stack = [word for word in reference_dict[first_word]]
while len(stack) > 0:
current_word = stack.pop()
if current_word in tracker:
continue
else:
tracker[current_word] = True
current_word_synonyms = reference_dict[current_word]
for word in current_word_synonyms:
if word == second_word:
return True
elif word not in tracker:
stack.append(word)
return False | 382 | 771 |
989cc6d9b0528fe28c3ee840fd81ed62515687b7 | aguai/docook | lib/mistletoe/mistletoe/block_token.py | [
"MIT"
] | Python | parse_align | <not_specific> | def parse_align(column):
"""
Helper function; returns align option from cell content.
Returns:
None if align = left;
0 if align = center;
1 if align = right.
"""
if column[:4] == ':---' and column[-4:] == '---:':
return 0
if column[-4:] == '---:':
return 1
return None |
Helper function; returns align option from cell content.
Returns:
None if align = left;
0 if align = center;
1 if align = right.
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return None | 383 | 883 |
b27e441c5c2a5d36dd0abee478f6ed4e6e8b3ffe | HtutLynn/Knowledge_Distillation_Pytorch | customs.py | [
"MIT"
] | Python | compute_param_count | <not_specific> | def compute_param_count(model):
"""
Compute the total number of trainable parameters in the model
:param model: Neural Network model, constructed in pytorch framework
:return param_count: Total number of trainable parameters in a network
"""
param_count = 0
for param in model.parameters():
if param.requires_grad:
param_count += param.numel()
return param_count |
Compute the total number of trainable parameters in the model
:param model: Neural Network model, constructed in pytorch framework
:return param_count: Total number of trainable parameters in a network
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param_count = 0
for param in model.parameters():
if param.requires_grad:
param_count += param.numel()
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param_count = 0
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return param_count | 384 | 216 |
4cd44412f948c71a3866de82c3b210354cbe1358 | rreddykeap/properties-translator | src/utils/proptrans.py | [
"MIT"
] | Python | need_translation | <not_specific> | def need_translation(file_name, src):
"""
Checks if a translation is needed for the file with the specified name.
:param file_name: name of the file
:param src: source language
:return: True if file needs to be translated else False
"""
file_name = os.path.splitext(file_name)[0]
if file_name.endswith('_' + src) or src == 'auto':
return True
return False |
Checks if a translation is needed for the file with the specified name.
:param file_name: name of the file
:param src: source language
:return: True if file needs to be translated else False
| Checks if a translation is needed for the file with the specified name. | [
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file_name = os.path.splitext(file_name)[0]
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def need_translation(file_name, src):
file_name = os.path.splitext(file_name)[0]
if file_name.endswith('_' + src) or src == 'auto':
return True
return False | 385 | 976 |
720a9e8387460cd47cb65d8dc57cec71f5f88371 | team-oss/dspg20oss | ossPy/singleFuncs/eraseFromColumn.py | [
"MIT"
] | Python | eraseFromColumn | <not_specific> | def eraseFromColumn(inputColumn,eraseList):
"""iteratively delete regex query matches from input list
Keyword arguments:
inputColumn -- a column from a pandas dataframe, this will be the set of
target words/entries that deletions will be made from
eraseList -- a column containing strings (regex expressions) which will be
deleted from the inputColumn, in an iterative fashion
"""
import pandas as pd
import re
for index, row in eraseList.iterrows():
curReplaceVal=row[0]
currentRegexExpression=curReplaceVal + '(?i)'
inputColumn.replace(regex=currentRegexExpression, value='', inplace=True)
return inputColumn | iteratively delete regex query matches from input list
Keyword arguments:
inputColumn -- a column from a pandas dataframe, this will be the set of
target words/entries that deletions will be made from
eraseList -- a column containing strings (regex expressions) which will be
deleted from the inputColumn, in an iterative fashion
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eraseList -- a column containing strings (regex expressions) which will be
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54c39e64b7db3f58f24d80fc776b582c2c72cf83 | elifesciences/builder | src/utils.py | [
"MIT"
] | Python | walk_nested_struct | <not_specific> | def walk_nested_struct(val, fn):
"walks a potentially nested structure, calling `fn` on each value it encounters"
if isinstance(val, dict):
return {key: walk_nested_struct(i, fn) for key, i in val.items()}
if isinstance(val, list):
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return fn(val) | 388 | 133 |
49790c95d8368c7bff99417246b3459cb82ef6cb | Sagyam/Major-Project-Backend | server/majorProject/helper/linear_solver.py | [
"MIT"
] | Python | isOverDetermined | <not_specific> | def isOverDetermined(equations):
'''
Checks if the equations does not contain z or Z.
'''
for eq in equations:
if 'z' in eq or 'Z' in eq:
return False
return True |
Checks if the equations does not contain z or Z.
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return True | 389 | 526 |
2eeec80c46a420e53df2b8428c292dd5eff0e5d7 | jite21/python-list | LinkedList.py | [
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'''
static method to traverse graphs, BFS and DFS, based on object passed in q
if q is stack then DFS
if q in queus then BFS
Using duck-typing here.
'''
visited = set()
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visit = 0
q.add(visit)
visited.add(visit)
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s += str(visit) + ' '
q.remove()
for i in graph.getedge(visit):
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q.add(i.value)
return s |
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q.remove()
for i in graph.getedge(visit):
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return s | 392 | 152 |
741de537d52f53a939824d47908b040042cfdd50 | CharlesGovea/Alice | alice_yacc.py | [
"MIT"
] | Python | p_unary | null | def p_unary(p):
'''
unary : postfix
| MINUS neuralgic_opr postfix neuralgic_unary
'''
if len(p) > 2:
p[0] = p[3]
else:
p[0] = p[1] |
unary : postfix
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| unary : postfix
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0fd076f9f5a0d229c98336e9b019948f8a94113c | isms/circlemud-world-parser | src/utils.py | [
"MIT"
] | Python | split_on_vnums | null | def split_on_vnums(file_text):
"""
function specifically to split the file on lines in the form
of a vnum (e.g. the entire line is something like '#1234'). this is
important because lines within entries can (and do) start with '#'
"""
split_re = r"""^\#(\d+)"""
split_pattern = re.compile(split_re, re.MULTILINE)
pieces = iter(split_pattern.split(file_text))
next(pieces) # burn the next one, we won't use it
while pieces:
vnum = next(pieces)
text = next(pieces)
yield ''.join((vnum, text)) |
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pieces = iter(split_pattern.split(file_text))
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def split_on_vnums(file_text):
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yield ''.join((vnum, text)) | 394 | 324 |
59098807a5888f1d7bb73a9e4a6475e2d323c67c | DamienIrving/ocean-analysis | modules/general_io.py | [
"MIT"
] | Python | check_depth_coordinate | <not_specific> | def check_depth_coordinate(cube):
"""Check (and fix if needed) the depth coordinate"""
aux_coords = [coord.name() for coord in cube.aux_coords]
if 'depth' in aux_coords:
cube.remove_coord('depth')
for coord in cube.dim_coords:
name = coord.name()
var_name = coord.var_name
if (name == 'lev') or (var_name == 'lev') or (var_name == 'olevel'):
coord.standard_name = 'depth'
coord.var_name = 'lev'
return cube | Check (and fix if needed) the depth coordinate | Check (and fix if needed) the depth coordinate | [
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if 'depth' in aux_coords:
cube.remove_coord('depth')
for coord in cube.dim_coords:
name = coord.name()
var_name = coord.var_name
if (name == 'lev') or (var_name == 'lev') or (var_name == 'olevel'):
coord.standard_name = 'depth'
coord.var_name = 'lev'
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return cube | 395 | 22 |
538444484c45d02ecf10efd7ba24a03f51965a03 | joanvaquer/SDV | sdv/metadata/table.py | [
"MIT"
] | Python | from_dict | <not_specific> | def from_dict(cls, metadata_dict):
"""Load a Table from a metadata dict.
Args:
metadata_dict (dict):
Dict metadata to load.
"""
instance = cls()
instance._fields_metadata = copy.deepcopy(metadata_dict['fields'])
instance._constraints = copy.deepcopy(metadata_dict.get('constraints', []))
instance._model_kwargs = copy.deepcopy(metadata_dict.get('model_kwargs'))
return instance | Load a Table from a metadata dict.
Args:
metadata_dict (dict):
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instance._fields_metadata = copy.deepcopy(metadata_dict['fields'])
instance._constraints = copy.deepcopy(metadata_dict.get('constraints', []))
instance._model_kwargs = copy.deepcopy(metadata_dict.get('model_kwargs'))
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return instance | 397 | 852 |
faa92c25daa3a05e4b8b1b2bd51019f2e620817c | eleijonmarck/spot-checking-ml | spot.py | [
"MIT"
] | Python | define_models | <not_specific> | def define_models():
"""
Defines and intantiates the models we want to
evaluate
{name : pipeline-object}
:return: dict - scikit learn pipelines
"""
models = {}
return models |
Defines and intantiates the models we want to
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:return: dict - scikit learn pipelines
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models = {}
return models | 398 | 625 |
6909ef8333e5b8a3c50ab3ab4530d304b7bfeed6 | artorious/python3_dojo | derivative.py | [
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""" (function) -> float
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4759402af82769811290c7daa73c1c19ebca474c | xieck13/nilmtk-dl | disaggregate/cnn_rnn.py | [
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] | Python | lr_schedule | <not_specific> | def lr_schedule(epoch):
"""Learning Rate Schedule
Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
Called automatically every epoch as part of callbacks during training.
# Arguments
epoch (int): The number of epochs
# Returns
lr (float32): learning rate
"""
lr = 1e-5
if epoch > 30:
lr *= 0.5e-3
elif epoch > 20:
lr *= 1e-3
elif epoch > 10:
lr *= 1e-2
elif epoch > 5:
lr *= 1e-1
print('Learning rate: ', lr)
return lr | Learning Rate Schedule
Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
Called automatically every epoch as part of callbacks during training.
# Arguments
epoch (int): The number of epochs
# Returns
lr (float32): learning rate
| Learning Rate Schedule
Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
Called automatically every epoch as part of callbacks during training.
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epoch (int): The number of epochs
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161aec5a3c1979936bd9271422d57a028c0b8171 | quantifiedcode-bot/Haul | haul/finders/pipeline/html.py | [
"MIT"
] | Python | a_href_finder | <not_specific> | def a_href_finder(pipeline_index,
soup,
finder_image_urls=[],
*args, **kwargs):
"""
Find image URL in <a>'s href attribute
"""
now_finder_image_urls = []
for a in soup.find_all('a'):
href = a.get('href', None)
if href:
href = str(href)
if filter(href.lower().endswith, ('.jpg', '.jpeg', '.gif', '.png')):
if (href not in finder_image_urls) and \
(href not in now_finder_image_urls):
now_finder_image_urls.append(href)
output = {}
output['finder_image_urls'] = finder_image_urls + now_finder_image_urls
return output |
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for a in soup.find_all('a'):
href = a.get('href', None)
if href:
href = str(href)
if filter(href.lower().endswith, ('.jpg', '.jpeg', '.gif', '.png')):
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now_finder_image_urls.append(href)
output = {}
output['finder_image_urls'] = finder_image_urls + now_finder_image_urls
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"\"\"\"\n Find image URL in <a>'s href attribute\n \"\"\""
] | [
{
"param": "pipeline_index",
"type": null
},
{
"param": "soup",
"type": null
},
{
"param": "finder_image_urls",
"type": null
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "pipeline_index",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "soup",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "finder_image_urls",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def a_href_finder(pipeline_index,
soup,
finder_image_urls=[],
*args, **kwargs):
now_finder_image_urls = []
for a in soup.find_all('a'):
href = a.get('href', None)
if href:
href = str(href)
if filter(href.lower().endswith, ('.jpg', '.jpeg', '.gif', '.png')):
if (href not in finder_image_urls) and \
(href not in now_finder_image_urls):
now_finder_image_urls.append(href)
output = {}
output['finder_image_urls'] = finder_image_urls + now_finder_image_urls
return output | 402 | 834 |
7de0bb394baa69322bb525c799ca3fd33e6cf6c1 | MaastrichtU-IDS/translator-openpredict | openpredict/openpredict_model.py | [
"MIT"
] | Python | multimetric_score | <not_specific> | def multimetric_score(estimator, X_test, y_test, scorers):
"""Return a dict of score for multimetric scoring
:param estimator: Estimator
:param X_test: X test
:param y_test: Y test
:param scorers: Dict of scorers
:return: Multimetric scores
"""
scores = {}
for name, scorer in scorers.items():
if y_test is None:
score = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
if hasattr(score, 'item'):
try:
# e.g. unwrap memmapped scalars
score = score.item()
except ValueError:
# non-scalar?
pass
scores[name] = score
if not isinstance(score, numbers.Number):
raise ValueError("scoring must return a number, got %s (%s) "
"instead. (scorer=%s)"
% (str(score), type(score), name))
return scores | Return a dict of score for multimetric scoring
:param estimator: Estimator
:param X_test: X test
:param y_test: Y test
:param scorers: Dict of scorers
:return: Multimetric scores
| Return a dict of score for multimetric scoring | [
"Return",
"a",
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"of",
"score",
"for",
"multimetric",
"scoring"
] | def multimetric_score(estimator, X_test, y_test, scorers):
scores = {}
for name, scorer in scorers.items():
if y_test is None:
score = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
if hasattr(score, 'item'):
try:
score = score.item()
except ValueError:
pass
scores[name] = score
if not isinstance(score, numbers.Number):
raise ValueError("scoring must return a number, got %s (%s) "
"instead. (scorer=%s)"
% (str(score), type(score), name))
return scores | [
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"# e.g. unwrap memmapped scalars",
"# non-scalar?"
] | [
{
"param": "estimator",
"type": null
},
{
"param": "X_test",
"type": null
},
{
"param": "y_test",
"type": null
},
{
"param": "scorers",
"type": null
}
] | {
"returns": [
{
"docstring": null,
"docstring_tokens": [
"None"
],
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}
],
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"params": [
{
"identifier": "estimator",
"type": null,
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],
"default": null,
"is_optional": null
},
{
"identifier": "X_test",
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"None"
],
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"is_optional": null
},
{
"identifier": "y_test",
"type": null,
"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": null
},
{
"identifier": "scorers",
"type": null,
"docstring": "Dict of scorers",
"docstring_tokens": [
"Dict",
"of",
"scorers"
],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import numbers
def multimetric_score(estimator, X_test, y_test, scorers):
scores = {}
for name, scorer in scorers.items():
if y_test is None:
score = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
if hasattr(score, 'item'):
try:
score = score.item()
except ValueError:
pass
scores[name] = score
if not isinstance(score, numbers.Number):
raise ValueError("scoring must return a number, got %s (%s) "
"instead. (scorer=%s)"
% (str(score), type(score), name))
return scores | 403 | 27 |
a0d5dd86967aba05281a89fc87e312fe977be123 | bgporter/wastebook | post.py | [
"MIT"
] | Python | ExtractTags | <not_specific> | def ExtractTags(txt):
''' given input text, look for #tag instances in it, and return a list
of those tags without the leading '#'. If there are no tags, we return
an empty list.
Tags must start and end with an alphanumeric, and can contain dashes
and underscores.
Before returning the list, we:
- remove dupes
- sort the tags alphabetically
'''
TAG_PATTERN = re.compile(r'(?:^|\s)#([a-zA-Z0-9][a-zA-Z0-9_-]*[a-zA-Z0-9])')
tagList = TAG_PATTERN.findall(txt)
return sorted(list(set([t.lower() for t in tagList]))) | given input text, look for #tag instances in it, and return a list
of those tags without the leading '#'. If there are no tags, we return
an empty list.
Tags must start and end with an alphanumeric, and can contain dashes
and underscores.
Before returning the list, we:
- remove dupes
- sort the tags alphabetically
| given input text, look for #tag instances in it, and return a list
of those tags without the leading '#'. If there are no tags, we return
an empty list.
Tags must start and end with an alphanumeric, and can contain dashes
and underscores.
Before returning the list, we:
remove dupes
sort the tags alphabetically | [
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TAG_PATTERN = re.compile(r'(?:^|\s)#([a-zA-Z0-9][a-zA-Z0-9_-]*[a-zA-Z0-9])')
tagList = TAG_PATTERN.findall(txt)
return sorted(list(set([t.lower() for t in tagList]))) | [
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"''' given input text, look for #tag instances in it, and return a list \n of those tags without the leading '#'. If there are no tags, we return\n an empty list.\n\n Tags must start and end with an alphanumeric, and can contain dashes \n and underscores.\n\n Before returning the list, we:\n - remove dupes\n - sort the tags alphabetically\n '''"
] | [
{
"param": "txt",
"type": null
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "txt",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import re
def ExtractTags(txt):
TAG_PATTERN = re.compile(r'(?:^|\s)#([a-zA-Z0-9][a-zA-Z0-9_-]*[a-zA-Z0-9])')
tagList = TAG_PATTERN.findall(txt)
return sorted(list(set([t.lower() for t in tagList]))) | 405 | 69 |
b7a5ab1a84072daedacf8cab436642c6f23f935d | SirAnthony/chinoka | mqtt/graphite/config.py | [
"MIT"
] | Python | usage | null | def usage(prog):
"""
Print command line help.
Takes an application name to display.
"""
# Won't strip indentation.
logging.info("""
Usage: %s [options]
Options:
-v, --version show program's version number and exit
-h, --help show this help message and exit
-c, --config <file> use different config file
-m, --map <file> use custom mapping file
""", prog) |
Print command line help.
Takes an application name to display.
| Print command line help.
Takes an application name to display. | [
"Print",
"command",
"line",
"help",
".",
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"an",
"application",
"name",
"to",
"display",
"."
] | def usage(prog):
logging.info("""
Usage: %s [options]
Options:
-v, --version show program's version number and exit
-h, --help show this help message and exit
-c, --config <file> use different config file
-m, --map <file> use custom mapping file
""", prog) | [
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] | {
"returns": [],
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"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import logging
def usage(prog):
logging.info("""
Usage: %s [options]
Options:
-v, --version show program's version number and exit
-h, --help show this help message and exit
-c, --config <file> use different config file
-m, --map <file> use custom mapping file
""", prog) | 406 | 147 |
8bff04856a0c4af3c0d1c76418267a396433f7bf | matichorvat/hsst | hsst/ruleapplication/mapping_rules.py | [
"MIT"
] | Python | find_mapping_coverages | <not_specific> | def find_mapping_coverages(mapping_node_list, reference_coverage, coverages):
"""
Find mapping coverages by searching coverages for the ones with 1 in exactly the places mapping requires it to be and nowhere else.
e.g. mapping is in node_id 2, reference coverage is [0,1,2,3], mapping coverages are [0,0,1,X0], [0,0,1,0] but not [0,1,1,0] or [0,1,X0,0]
:param mapping_node_list: List of node ids corresponding to a single mapping
:param reference_coverage: List of node ids in order which coverages are constructed
:param coverages: List of coverages obtained by running rule application
:return: List of coverages corresponding to the give mapping
"""
# mapping node positions in reference
mapping_node_positions = [reference_coverage.index(node_id) for node_id in mapping_node_list]
mapping_coverages = []
# Search rule application coverages that have 1 in exactly those coverages and nowhere else
for coverage in coverages:
if not all([True if coverage[index] == '1' else False for index in mapping_node_positions]):
continue
if sum(1 for node in coverage if node == '1') != len(mapping_node_positions):
continue
mapping_coverages.append(coverage)
return mapping_coverages |
Find mapping coverages by searching coverages for the ones with 1 in exactly the places mapping requires it to be and nowhere else.
e.g. mapping is in node_id 2, reference coverage is [0,1,2,3], mapping coverages are [0,0,1,X0], [0,0,1,0] but not [0,1,1,0] or [0,1,X0,0]
:param mapping_node_list: List of node ids corresponding to a single mapping
:param reference_coverage: List of node ids in order which coverages are constructed
:param coverages: List of coverages obtained by running rule application
:return: List of coverages corresponding to the give mapping
| Find mapping coverages by searching coverages for the ones with 1 in exactly the places mapping requires it to be and nowhere else. | [
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] | def find_mapping_coverages(mapping_node_list, reference_coverage, coverages):
mapping_node_positions = [reference_coverage.index(node_id) for node_id in mapping_node_list]
mapping_coverages = []
for coverage in coverages:
if not all([True if coverage[index] == '1' else False for index in mapping_node_positions]):
continue
if sum(1 for node in coverage if node == '1') != len(mapping_node_positions):
continue
mapping_coverages.append(coverage)
return mapping_coverages | [
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}
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}
],
"outlier_params": [],
"others": []
} | def find_mapping_coverages(mapping_node_list, reference_coverage, coverages):
mapping_node_positions = [reference_coverage.index(node_id) for node_id in mapping_node_list]
mapping_coverages = []
for coverage in coverages:
if not all([True if coverage[index] == '1' else False for index in mapping_node_positions]):
continue
if sum(1 for node in coverage if node == '1') != len(mapping_node_positions):
continue
mapping_coverages.append(coverage)
return mapping_coverages | 407 | 720 |
129b9ade7601af9dc5557dc9ef55b7cb41e9518f | boothlinux/poker-takehome | functions.py | [
"MIT"
] | Python | is_pair | <not_specific> | def is_pair(hand):
"""
This functions takes the hand (list) and returns true if it has one pair
"""
pair_count = 0
for card in hand:
if hand.count(card) is 2:
pair_count = pair_count + 1
if pair_count == 2:
return True |
This functions takes the hand (list) and returns true if it has one pair
| This functions takes the hand (list) and returns true if it has one pair | [
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pair_count = 0
for card in hand:
if hand.count(card) is 2:
pair_count = pair_count + 1
if pair_count == 2:
return True | [
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] | [
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] | {
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"type": null,
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"docstring_tokens": [],
"default": null,
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}
],
"outlier_params": [],
"others": []
} | def is_pair(hand):
pair_count = 0
for card in hand:
if hand.count(card) is 2:
pair_count = pair_count + 1
if pair_count == 2:
return True | 408 | 166 |
3f99a5c3f145e85f0bbbf4e883b6b1f8a3251dc5 | 6tudent/pyemf | pyemf.py | [
"BSD-3-Clause-No-Nuclear-Warranty"
] | Python | _round4 | <not_specific> | def _round4(num):
"""Round to the nearest multiple of 4 greater than or equal to the
given number. EMF records are required to be aligned to 4 byte
boundaries."""
return ((num+3)//4)*4 | Round to the nearest multiple of 4 greater than or equal to the
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} | def _round4(num):
return ((num+3)//4)*4 | 409 | 816 |
2d7949fa807eb841d79798368397c19a968060ee | zipated/src | third_party/catapult/third_party/gsutil/gslib/commands/perfdiag.py | [
"BSD-3-Clause"
] | Python | _GenerateFileData | null | def _GenerateFileData(fp, file_size=0, random_ratio=100,
max_unique_random_bytes=5242883):
"""Writes data into a file like object.
Args:
fp: A file like object to write the data to.
file_size: The amount of data to write to the file.
random_ratio: The percentage of randomly generated data to write. This can
be any number between 0 and 100 (inclusive), with 0 producing uniform
data, and 100 producing random data.
max_unique_random_bytes: The maximum number of bytes to generate
pseudo-randomly before beginning to repeat
bytes. The default was chosen as the next prime
larger than 5 MiB.
"""
# Normalize the ratio.
random_ratio /= 100.0
random_bytes = os.urandom(min(file_size, max_unique_random_bytes))
total_bytes_written = 0
while total_bytes_written < file_size:
num_bytes = min(max_unique_random_bytes, file_size - total_bytes_written)
# Calculate the amount of random and sequential data to write and
# resolve rounding errors by adjusting the random bytes to write.
num_bytes_seq = int(num_bytes * (1 - random_ratio))
num_bytes_random = num_bytes - num_bytes_seq
fp.write(random_bytes[:num_bytes_random])
fp.write(b'x' * num_bytes_seq)
total_bytes_written += num_bytes | Writes data into a file like object.
Args:
fp: A file like object to write the data to.
file_size: The amount of data to write to the file.
random_ratio: The percentage of randomly generated data to write. This can
be any number between 0 and 100 (inclusive), with 0 producing uniform
data, and 100 producing random data.
max_unique_random_bytes: The maximum number of bytes to generate
pseudo-randomly before beginning to repeat
bytes. The default was chosen as the next prime
larger than 5 MiB.
| Writes data into a file like object. | [
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] | def _GenerateFileData(fp, file_size=0, random_ratio=100,
max_unique_random_bytes=5242883):
random_ratio /= 100.0
random_bytes = os.urandom(min(file_size, max_unique_random_bytes))
total_bytes_written = 0
while total_bytes_written < file_size:
num_bytes = min(max_unique_random_bytes, file_size - total_bytes_written)
num_bytes_seq = int(num_bytes * (1 - random_ratio))
num_bytes_random = num_bytes - num_bytes_seq
fp.write(random_bytes[:num_bytes_random])
fp.write(b'x' * num_bytes_seq)
total_bytes_written += num_bytes | [
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"default": null,
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],
"outlier_params": [],
"others": []
} | import os
def _GenerateFileData(fp, file_size=0, random_ratio=100,
max_unique_random_bytes=5242883):
random_ratio /= 100.0
random_bytes = os.urandom(min(file_size, max_unique_random_bytes))
total_bytes_written = 0
while total_bytes_written < file_size:
num_bytes = min(max_unique_random_bytes, file_size - total_bytes_written)
num_bytes_seq = int(num_bytes * (1 - random_ratio))
num_bytes_random = num_bytes - num_bytes_seq
fp.write(random_bytes[:num_bytes_random])
fp.write(b'x' * num_bytes_seq)
total_bytes_written += num_bytes | 410 | 82 |
fc66633f2455e1a12c1c67d1419b9b97b698a313 | dsysoev/fun-with-tensorflow | timeseries/smoothing.py | [
"MIT"
] | Python | weighted_average | <not_specific> | def weighted_average(series, weights):
""" calculate list with weighted moving average values
weights - list of weigths for averaging
"""
weights_sum = sum(weights)
# normalize the weights and reverse it
weigths_norm = [weight / weights_sum for weight in reversed(weights)]
num_weights = len(weights)
# set NaN for the first undefined values
lst = [float('NaN')] * (num_weights - 1)
for pos in range(num_weights, len(series) + 1):
# calculate value for current time frame
value = sum([v * w for v, w in zip(series[pos-num_weights:pos], weigths_norm)])
lst.append(value)
return lst | calculate list with weighted moving average values
weights - list of weigths for averaging
| calculate list with weighted moving average values
weights - list of weigths for averaging | [
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weights_sum = sum(weights)
weigths_norm = [weight / weights_sum for weight in reversed(weights)]
num_weights = len(weights)
lst = [float('NaN')] * (num_weights - 1)
for pos in range(num_weights, len(series) + 1):
value = sum([v * w for v, w in zip(series[pos-num_weights:pos], weigths_norm)])
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weigths_norm = [weight / weights_sum for weight in reversed(weights)]
num_weights = len(weights)
lst = [float('NaN')] * (num_weights - 1)
for pos in range(num_weights, len(series) + 1):
value = sum([v * w for v, w in zip(series[pos-num_weights:pos], weigths_norm)])
lst.append(value)
return lst | 411 | 659 |
a4fb7ed0f5b98337029e0b4c7e10490b1c2606a8 | dunnkers/client | wandb/vendor/prompt_toolkit/layout/utils.py | [
"MIT"
] | Python | split_lines | null | def split_lines(tokenlist):
"""
Take a single list of (Token, text) tuples and yield one such list for each
line. Just like str.split, this will yield at least one item.
:param tokenlist: List of (token, text) or (token, text, mouse_handler)
tuples.
"""
line = []
for item in tokenlist:
# For (token, text) tuples.
if len(item) == 2:
token, string = item
parts = string.split('\n')
for part in parts[:-1]:
if part:
line.append((token, part))
yield line
line = []
line.append((token, parts[-1]))
# Note that parts[-1] can be empty, and that's fine. It happens
# in the case of [(Token.SetCursorPosition, '')].
# For (token, text, mouse_handler) tuples.
# I know, partly copy/paste, but understandable and more efficient
# than many tests.
else:
token, string, mouse_handler = item
parts = string.split('\n')
for part in parts[:-1]:
if part:
line.append((token, part, mouse_handler))
yield line
line = []
line.append((token, parts[-1], mouse_handler))
# Always yield the last line, even when this is an empty line. This ensures
# that when `tokenlist` ends with a newline character, an additional empty
# line is yielded. (Otherwise, there's no way to differentiate between the
# cases where `tokenlist` does and doesn't end with a newline.)
yield line |
Take a single list of (Token, text) tuples and yield one such list for each
line. Just like str.split, this will yield at least one item.
:param tokenlist: List of (token, text) or (token, text, mouse_handler)
tuples.
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parts = string.split('\n')
for part in parts[:-1]:
if part:
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yield line | 412 | 600 |
df427d58ef84be05535375800c3a58bfd5474c17 | spiperac/bctf | apps/administration/utils.py | [
"MIT"
] | Python | check_docker | <not_specific> | def check_docker():
"""
Checks if docker is installed.
"""
return True if shutil.which('docker') else False |
Checks if docker is installed.
| Checks if docker is installed. | [
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] | def check_docker():
return True if shutil.which('docker') else False | [
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] | [] | {
"returns": [],
"raises": [],
"params": [],
"outlier_params": [],
"others": []
} | import shutil
def check_docker():
return True if shutil.which('docker') else False | 413 | 293 |
425a6e622b676a6ec90bb1362fc863d5bc040308 | snassef/aqueduct | civis/imports/socrata_helpers.py | [
"Apache-2.0"
] | Python | Merge | <not_specific> | def Merge(dict1, dict2):
"""
Appends two dicts and returns a dict.
"""
res = {**dict1, **dict2}
return res |
Appends two dicts and returns a dict.
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26b86de77121bf1b8c931518ae5f4a380a40c1bd | python-packages/vanity_app | src/vanity_app/vanity_app/utils.py | [
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"""
Convenience method used by rjones' PyPI OAuth1 code
"""
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for v in v:
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e9122b3509c67221f7c1e287a8008aedaef96198 | stgeke/pyJac-v2 | pyjac/utils.py | [
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"""
Convenience method that converts an IntEnum/Enum to string
Parameters
----------
enum: Enum
The enum to convert
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-------
name: str
The stringified enum
"""
enum = str(enum)
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5f32aef4f913fa9d09c2e9d898570d0776a576c9 | jlmartinnc/PythonStdioGames | src/gamesbyexample/parkingvalet.py | [
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"""Return True if the 'A' car has reached the right edge."""
# The puzzle is solved when the 'A' car reaches the right edge.
for y in range(board['height']):
if board[(board['width'] - 1, y)] == 'A':
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691bbca4c37aa6dfa95dd54a2aa3d519c36bfc9b | klen/euler | 003/solution.py | [
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63325c1c505f9b52dbc8ec019d46dd1fcce92f02 | Kushmaniar/clover | clover_simulation/src/clover_simulation/world.py | [
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4092e8f3a5362f34410668538d095bf2fb41be83 | ahstewart/OASIS | OasisPy/initialize.py | [
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cb56ca1b5612ab68ab8fa1c8f65ce9a97e9a3d63 | dbmi-bgm/cgap-portal | src/encoded/inheritance_mode.py | [
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""" Collects and summarizes Compound het caller results """
inheritance_modes_set = set()
inheritance_modes = []
if cmphet is not None:
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impact = entry.get('comhet_impact_gene')
s = 'Compound Het (%s/%s)' % (phase, impact.lower())
inheritance_modes_set.add(s)
inheritance_modes = list(inheritance_modes_set)
inheritance_modes.sort()
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"\"\"\" Collects and summarizes Compound het caller results \"\"\""
] | [
{
"param": "cmphet",
"type": null
}
] | {
"returns": [],
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"params": [
{
"identifier": "cmphet",
"type": null,
"docstring": null,
"docstring_tokens": [],
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}
],
"outlier_params": [],
"others": []
} | def compute_cmphet_inheritance_modes(cmphet):
inheritance_modes_set = set()
inheritance_modes = []
if cmphet is not None:
for entry in cmphet:
phase = entry.get('comhet_phase')
impact = entry.get('comhet_impact_gene')
s = 'Compound Het (%s/%s)' % (phase, impact.lower())
inheritance_modes_set.add(s)
inheritance_modes = list(inheritance_modes_set)
inheritance_modes.sort()
return inheritance_modes | 423 | 557 |
230ebf4fbb1c3e6e0bbdadb5d6727cf5b8f11f93 | suomesta/ken3 | checker/errortest/errortest.py | [
"MIT"
] | Python | filenames | <not_specific> | def filenames(targets, exclude):
"""Pick cpp files from current directory.
param[in] targets: appointed targets file names.
if None, pick all files with extension '.cpp'
should be string-list or None
param[in] exclude: appointed file names to be excluded.
should be string-list or None
return targer file names in string-list
"""
if targets:
names = list(targets)
else:
names = [i for i in glob.glob('*.cpp')]
names += [i for i in glob.glob('*/*.cpp')]
if exclude:
names = [i for i in names if i not in exclude]
return sorted(names) | Pick cpp files from current directory.
param[in] targets: appointed targets file names.
if None, pick all files with extension '.cpp'
should be string-list or None
param[in] exclude: appointed file names to be excluded.
should be string-list or None
return targer file names in string-list
| Pick cpp files from current directory.
param[in] targets: appointed targets file names.
if None, pick all files with extension '.cpp'
should be string-list or None
param[in] exclude: appointed file names to be excluded.
should be string-list or None
return targer file names in string-list | [
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names = [i for i in glob.glob('*.cpp')]
names += [i for i in glob.glob('*/*.cpp')]
if exclude:
names = [i for i in names if i not in exclude]
return sorted(names) | 424 | 978 |
11d85f36dfd1258ff9f241077c986ef780555a53 | pyantonio-coder/crypto | cryptology/tools.py | [
"BSD-3-Clause"
] | Python | playfair_encrypt_col_pair | <not_specific> | def playfair_encrypt_col_pair(grid, pair):
"""if two letters are in the same col,
this is the method to decrypt
Args:
grid ([list]): [list of the playfair grid]
pair ([str]): [pair of strings in form e.g "AB"]
Returns:
[first_decrpyted_letter,
second_decrpyted_letter]: [Returns encrypted letters as a tuple]
"""
pos_in_list_1=grid.index(pair[0])
pos_in_list_2=grid.index(pair[1])
if pos_in_list_1 >19:
first_encrypted_letter=grid[(pos_in_list_1-20)]
else:
first_encrypted_letter=grid[(pos_in_list_1+5)]
if pos_in_list_2 >19:
second_encrypted_letter=grid[(pos_in_list_2-20)]
else:
second_encrypted_letter=grid[(pos_in_list_2+5)]
return first_encrypted_letter,second_encrypted_letter | if two letters are in the same col,
this is the method to decrypt
Args:
grid ([list]): [list of the playfair grid]
pair ([str]): [pair of strings in form e.g "AB"]
Returns:
[first_decrpyted_letter,
second_decrpyted_letter]: [Returns encrypted letters as a tuple]
| if two letters are in the same col,
this is the method to decrypt | [
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pos_in_list_1=grid.index(pair[0])
pos_in_list_2=grid.index(pair[1])
if pos_in_list_1 >19:
first_encrypted_letter=grid[(pos_in_list_1-20)]
else:
first_encrypted_letter=grid[(pos_in_list_1+5)]
if pos_in_list_2 >19:
second_encrypted_letter=grid[(pos_in_list_2-20)]
else:
second_encrypted_letter=grid[(pos_in_list_2+5)]
return first_encrypted_letter,second_encrypted_letter | [
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pos_in_list_1=grid.index(pair[0])
pos_in_list_2=grid.index(pair[1])
if pos_in_list_1 >19:
first_encrypted_letter=grid[(pos_in_list_1-20)]
else:
first_encrypted_letter=grid[(pos_in_list_1+5)]
if pos_in_list_2 >19:
second_encrypted_letter=grid[(pos_in_list_2-20)]
else:
second_encrypted_letter=grid[(pos_in_list_2+5)]
return first_encrypted_letter,second_encrypted_letter | 425 | 96 |
450b4eba7a889b24a6d040fea2e53b2828858542 | stan-v/wsn-simulation | node.py | [
"MIT"
] | Python | calc_toa_ble | <not_specific> | def calc_toa_ble(PL=10, SF=0):
'''
BLE has a protocol which sends 14 bytes of overhead along with the actual
payload. The information is modulated on a 1 MBit/s speed, which gives a
Time on air of 1 microsecond per bit or 8 microsecond per byte/octet.
'''
amount_of_bytes = 14 + PL
T = amount_of_bytes*8/10**6
return T |
BLE has a protocol which sends 14 bytes of overhead along with the actual
payload. The information is modulated on a 1 MBit/s speed, which gives a
Time on air of 1 microsecond per bit or 8 microsecond per byte/octet.
| BLE has a protocol which sends 14 bytes of overhead along with the actual
payload. The information is modulated on a 1 MBit/s speed, which gives a
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} | def calc_toa_ble(PL=10, SF=0):
amount_of_bytes = 14 + PL
T = amount_of_bytes*8/10**6
return T | 426 | 209 |
4b4f1d434b98c446c7e6a755868ba6c092294fa1 | igor-93/ml_utils | timeseries/stock_indicators.py | [
"BSD-2-Clause"
] | Python | macd | <not_specific> | def macd(df, ewa_short, ewa_long, ewa_signal, price_col="adj_close"):
"""Moving Average Convergence Divergence
Parameters:
-----------
df : DataFrame
Input dataframe.
ewa_short : int
Exponentially weighted average time-window for a short time-span.
A common choice for the short time-window is 12 intervals.
ewa_long : int
Exponentially weighted average time-window for a longer time-span.
A common choice for the long time-window is 26 intervals.
ewa_signal : int
Time-window for the EWA of the difference between long and short
averages.
price_col : str
Column name in `df` used for defining the current indicator (e.g. "open",
"close", etc.)
Returns:
--------
macd_ts : Series
Moving average convergence-divergence indicator for the time series.
"""
ewa_short = int(ewa_short)
ewa_long = int(ewa_long)
ewa_signal = int(ewa_signal)
ewa12 = df[price_col].ewm(span=ewa_short).mean()
ewa26 = df[price_col].ewm(span=ewa_long).mean()
macd_ts = ewa12 - ewa26
signal_line = macd_ts.ewm(span=ewa_signal).mean()
return macd_ts - signal_line, 'stationary' | Moving Average Convergence Divergence
Parameters:
-----------
df : DataFrame
Input dataframe.
ewa_short : int
Exponentially weighted average time-window for a short time-span.
A common choice for the short time-window is 12 intervals.
ewa_long : int
Exponentially weighted average time-window for a longer time-span.
A common choice for the long time-window is 26 intervals.
ewa_signal : int
Time-window for the EWA of the difference between long and short
averages.
price_col : str
Column name in `df` used for defining the current indicator (e.g. "open",
"close", etc.)
Returns:
--------
macd_ts : Series
Moving average convergence-divergence indicator for the time series.
| Moving Average Convergence Divergence
Parameters.
df : DataFrame
Input dataframe.
ewa_short : int
Exponentially weighted average time-window for a short time-span.
A common choice for the short time-window is 12 intervals.
ewa_long : int
Exponentially weighted average time-window for a longer time-span.
A common choice for the long time-window is 26 intervals.
ewa_signal : int
Time-window for the EWA of the difference between long and short
averages.
price_col : str
Column name in `df` used for defining the current indicator
Returns.
macd_ts : Series
Moving average convergence-divergence indicator for the time series. | [
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ewa_short = int(ewa_short)
ewa_long = int(ewa_long)
ewa_signal = int(ewa_signal)
ewa12 = df[price_col].ewm(span=ewa_short).mean()
ewa26 = df[price_col].ewm(span=ewa_long).mean()
macd_ts = ewa12 - ewa26
signal_line = macd_ts.ewm(span=ewa_signal).mean()
return macd_ts - signal_line, 'stationary' | [
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ewa_short = int(ewa_short)
ewa_long = int(ewa_long)
ewa_signal = int(ewa_signal)
ewa12 = df[price_col].ewm(span=ewa_short).mean()
ewa26 = df[price_col].ewm(span=ewa_long).mean()
macd_ts = ewa12 - ewa26
signal_line = macd_ts.ewm(span=ewa_signal).mean()
return macd_ts - signal_line, 'stationary' | 427 | 861 |
387bab85d398c1b63a768cddc905268628528d4c | kkkkv/tgnms | optimizer_service/optimizer_service/optimizations/graph.py | [
"MIT"
] | Python | remove_low_uptime_links | None | def remove_low_uptime_links(
graph: nx.Graph, active_links: Dict, link_uptime_threshold: float
) -> None:
"""Removes links with an average uptime below 'link_uptime_threshold' from 'graph'."""
for edge in graph.edges:
link_attributes = graph.get_edge_data(*edge)
link_name = link_attributes.get("name")
uptime = active_links.get(link_name, 0)
if uptime < link_uptime_threshold:
logging.debug(
f"Removing {link_name}: Uptime ({uptime}) is less than {link_uptime_threshold}."
)
graph.remove_edge(*edge) | Removes links with an average uptime below 'link_uptime_threshold' from 'graph'. | Removes links with an average uptime below 'link_uptime_threshold' from 'graph'. | [
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] | def remove_low_uptime_links(
graph: nx.Graph, active_links: Dict, link_uptime_threshold: float
) -> None:
for edge in graph.edges:
link_attributes = graph.get_edge_data(*edge)
link_name = link_attributes.get("name")
uptime = active_links.get(link_name, 0)
if uptime < link_uptime_threshold:
logging.debug(
f"Removing {link_name}: Uptime ({uptime}) is less than {link_uptime_threshold}."
)
graph.remove_edge(*edge) | [
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"\"\"\"Removes links with an average uptime below 'link_uptime_threshold' from 'graph'.\"\"\""
] | [
{
"param": "graph",
"type": "nx.Graph"
},
{
"param": "active_links",
"type": "Dict"
},
{
"param": "link_uptime_threshold",
"type": "float"
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "graph",
"type": "nx.Graph",
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "active_links",
"type": "Dict",
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "link_uptime_threshold",
"type": "float",
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import logging
def remove_low_uptime_links(
graph: nx.Graph, active_links: Dict, link_uptime_threshold: float
) -> None:
for edge in graph.edges:
link_attributes = graph.get_edge_data(*edge)
link_name = link_attributes.get("name")
uptime = active_links.get(link_name, 0)
if uptime < link_uptime_threshold:
logging.debug(
f"Removing {link_name}: Uptime ({uptime}) is less than {link_uptime_threshold}."
)
graph.remove_edge(*edge) | 428 | 418 |
8db2a3b3e21c3ff2b0194c67a24acc363a88470d | UpyExplorer/upy-error | upy_error/base.py | [
"MIT"
] | Python | format_exception | <not_specific> | def format_exception(error):
"""Return the exception in readable form
Args:
error (class): Exception
Returns:
str: Traceback Error
"""
exception_list = traceback.format_stack()
exception_list = exception_list[:-2]
exception_list.extend(traceback.format_tb(sys.exc_info()[2]))
exception_list.extend(traceback.format_exception_only(sys.exc_info()[0], sys.exc_info()[1]))
exception_str = "Traceback (most recent call last):\n"
exception_str += "".join(exception_list)
exception_str = exception_str[:-1]
return exception_str | Return the exception in readable form
Args:
error (class): Exception
Returns:
str: Traceback Error
| Return the exception in readable form | [
"Return",
"the",
"exception",
"in",
"readable",
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] | def format_exception(error):
exception_list = traceback.format_stack()
exception_list = exception_list[:-2]
exception_list.extend(traceback.format_tb(sys.exc_info()[2]))
exception_list.extend(traceback.format_exception_only(sys.exc_info()[0], sys.exc_info()[1]))
exception_str = "Traceback (most recent call last):\n"
exception_str += "".join(exception_list)
exception_str = exception_str[:-1]
return exception_str | [
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"\"\"\"Return the exception in readable form\n Args:\n error (class): Exception\n Returns:\n str: Traceback Error\n \"\"\""
] | [
{
"param": "error",
"type": null
}
] | {
"returns": [
{
"docstring": null,
"docstring_tokens": [
"None"
],
"type": "str"
}
],
"raises": [],
"params": [
{
"identifier": "error",
"type": null,
"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": false
}
],
"outlier_params": [],
"others": []
} | import sys
import traceback
def format_exception(error):
exception_list = traceback.format_stack()
exception_list = exception_list[:-2]
exception_list.extend(traceback.format_tb(sys.exc_info()[2]))
exception_list.extend(traceback.format_exception_only(sys.exc_info()[0], sys.exc_info()[1]))
exception_str = "Traceback (most recent call last):\n"
exception_str += "".join(exception_list)
exception_str = exception_str[:-1]
return exception_str | 429 | 809 |
e16597e3131fe21f99a4452f8c411c7ab0737c6a | inomuh/indoor_localization | src/indoor_localization/positioning_node.py | [
"Apache-2.0"
] | Python | add_noise | <not_specific> | def add_noise(denominator):
"""
This function adds some noie to the denominator to prevent the ZeroDivisionError
while calculating the pre_tag_z, the pre_tag_y and the pre_tag_x values.
"""
if (denominator >= -0.01) and (denominator <= 0):
denominator -= random.uniform(0, 0.1)
elif (denominator >= 0) and (denominator <= 0.01):
denominator += random.uniform(0, 0.1)
return denominator |
This function adds some noie to the denominator to prevent the ZeroDivisionError
while calculating the pre_tag_z, the pre_tag_y and the pre_tag_x values.
| This function adds some noie to the denominator to prevent the ZeroDivisionError
while calculating the pre_tag_z, the pre_tag_y and the pre_tag_x values. | [
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if (denominator >= -0.01) and (denominator <= 0):
denominator -= random.uniform(0, 0.1)
elif (denominator >= 0) and (denominator <= 0.01):
denominator += random.uniform(0, 0.1)
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] | [
{
"param": "denominator",
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}
] | {
"returns": [],
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{
"identifier": "denominator",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import random
def add_noise(denominator):
if (denominator >= -0.01) and (denominator <= 0):
denominator -= random.uniform(0, 0.1)
elif (denominator >= 0) and (denominator <= 0.01):
denominator += random.uniform(0, 0.1)
return denominator | 430 | 432 |
a49702faea3f8a66804bbbb4b2090eff2ca9f885 | Edinburgh-Genome-Foundry/BandWitch | bandwitch/DigestionProblem/SeparatingDigestionProblem.py | [
"MIT"
] | Python | _score_to_color | <not_specific> | def _score_to_color(score, maxi=0.1):
"""Transform a similarity score to a green/red color.
Parameters
----------
score
Value between 0 (perfect similarity, green) and 1 (red).
maxi
Value of the score above which everything appears completely red.
Below this value the color goes progressively from red to green in 0.
"""
return (
max(0, min(1, score / maxi)),
min(1, max(0, 1 - score / maxi)),
0,
0.5,
) | Transform a similarity score to a green/red color.
Parameters
----------
score
Value between 0 (perfect similarity, green) and 1 (red).
maxi
Value of the score above which everything appears completely red.
Below this value the color goes progressively from red to green in 0.
| Transform a similarity score to a green/red color.
Parameters
score
Value between 0 (perfect similarity, green) and 1 (red).
maxi
Value of the score above which everything appears completely red.
Below this value the color goes progressively from red to green in 0. | [
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"docstring_tokens": [],
"default": null,
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}
],
"outlier_params": [],
"others": []
} | def _score_to_color(score, maxi=0.1):
return (
max(0, min(1, score / maxi)),
min(1, max(0, 1 - score / maxi)),
0,
0.5,
) | 431 | 220 |
86ab0518fd4c0743ea0af1dd4809bdce6edd0e4b | valentinogeron/RAMP | RAMP/ramp.py | [
"BSD-2-Clause"
] | Python | comma_seperated_to_list | <not_specific> | def comma_seperated_to_list(ctx, param, value):
"""
Converts a comma seperated string into a list.
"""
if value is None:
return []
else:
items = value.split(',')
return list(set(items)) |
Converts a comma seperated string into a list.
| Converts a comma seperated string into a list. | [
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] | def comma_seperated_to_list(ctx, param, value):
if value is None:
return []
else:
items = value.split(',')
return list(set(items)) | [
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if value is None:
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items = value.split(',')
return list(set(items)) | 432 | 188 |
1e61993ae8e335287792a99de45ad4497c8703f1 | jfirebaugh/rules_rust | rust/private/utils.bzl | [
"Apache-2.0"
] | Python | _replace_all | <not_specific> | def _replace_all(string, substitutions):
"""Replaces occurrences of the given patterns in `string`.
There are a few reasons this looks complicated:
* The substitutions are performed with some priority, i.e. patterns that are
listed first in `substitutions` are higher priority than patterns that are
listed later.
* We also take pains to avoid doing replacements that overlap with each
other, since overlaps invalidate pattern matches.
* To avoid hairy offset invalidation, we apply the substitutions
right-to-left.
* To avoid the "_quote" -> "_quotequote_" rule introducing new pattern
matches later in the string during decoding, we take the leftmost
replacement, in cases of overlap. (Note that no rule can induce new
pattern matches *earlier* in the string.) (E.g. "_quotedot_" encodes to
"_quotequote_dot_". Note that "_quotequote_" and "_dot_" both occur in
this string, and overlap.).
Args:
string (string): the string in which the replacements should be performed.
substitutions: the list of patterns and replacements to apply.
Returns:
A string with the appropriate substitutions performed.
"""
# Find the highest-priority pattern matches for each string index, going
# left-to-right and skipping indices that are already involved in a
# pattern match.
plan = {}
matched_indices_set = {}
for pattern_start in range(len(string)):
if pattern_start in matched_indices_set:
continue
for (pattern, replacement) in substitutions:
if not string.startswith(pattern, pattern_start):
continue
length = len(pattern)
plan[pattern_start] = (length, replacement)
matched_indices_set.update([(pattern_start + i, True) for i in range(length)])
break
# Execute the replacement plan, working from right to left.
for pattern_start in sorted(plan.keys(), reverse = True):
length, replacement = plan[pattern_start]
after_pattern = pattern_start + length
string = string[:pattern_start] + replacement + string[after_pattern:]
return string | Replaces occurrences of the given patterns in `string`.
There are a few reasons this looks complicated:
* The substitutions are performed with some priority, i.e. patterns that are
listed first in `substitutions` are higher priority than patterns that are
listed later.
* We also take pains to avoid doing replacements that overlap with each
other, since overlaps invalidate pattern matches.
* To avoid hairy offset invalidation, we apply the substitutions
right-to-left.
* To avoid the "_quote" -> "_quotequote_" rule introducing new pattern
matches later in the string during decoding, we take the leftmost
replacement, in cases of overlap. (Note that no rule can induce new
pattern matches *earlier* in the string.) (E.g. "_quotedot_" encodes to
"_quotequote_dot_". Note that "_quotequote_" and "_dot_" both occur in
this string, and overlap.).
Args:
string (string): the string in which the replacements should be performed.
substitutions: the list of patterns and replacements to apply.
Returns:
A string with the appropriate substitutions performed.
| Replaces occurrences of the given patterns in `string`.
There are a few reasons this looks complicated:
The substitutions are performed with some priority, i.e. patterns that are
listed first in `substitutions` are higher priority than patterns that are
listed later.
We also take pains to avoid doing replacements that overlap with each
other, since overlaps invalidate pattern matches.
To avoid hairy offset invalidation, we apply the substitutions
right-to-left.
To avoid the "_quote" -> "_quotequote_" rule introducing new pattern
matches later in the string during decoding, we take the leftmost
replacement, in cases of overlap. (Note that no rule can induce new
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plan = {}
matched_indices_set = {}
for pattern_start in range(len(string)):
if pattern_start in matched_indices_set:
continue
for (pattern, replacement) in substitutions:
if not string.startswith(pattern, pattern_start):
continue
length = len(pattern)
plan[pattern_start] = (length, replacement)
matched_indices_set.update([(pattern_start + i, True) for i in range(length)])
break
for pattern_start in sorted(plan.keys(), reverse = True):
length, replacement = plan[pattern_start]
after_pattern = pattern_start + length
string = string[:pattern_start] + replacement + string[after_pattern:]
return string | [
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"default": null,
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],
"outlier_params": [],
"others": []
} | def _replace_all(string, substitutions):
plan = {}
matched_indices_set = {}
for pattern_start in range(len(string)):
if pattern_start in matched_indices_set:
continue
for (pattern, replacement) in substitutions:
if not string.startswith(pattern, pattern_start):
continue
length = len(pattern)
plan[pattern_start] = (length, replacement)
matched_indices_set.update([(pattern_start + i, True) for i in range(length)])
break
for pattern_start in sorted(plan.keys(), reverse = True):
length, replacement = plan[pattern_start]
after_pattern = pattern_start + length
string = string[:pattern_start] + replacement + string[after_pattern:]
return string | 433 | 158 |
8e77f12a9d529c15d0b0df96ddb782b424bed341 | noahjgreen295/feature_engine | feature_engine/dataframe_checks.py | [
"BSD-3-Clause"
] | Python | _check_X_matches_training_df | None | def _check_X_matches_training_df(X: pd.DataFrame, reference: int) -> None:
"""
Checks that DataFrame to transform has the same number of columns that the
DataFrame used with the fit() method.
Parameters
----------
X : Pandas DataFrame
The df to be checked
reference : int
The number of columns in the dataframe that was used with the fit() method.
Raises
------
ValueError
If the number of columns does not match.
Returns
-------
None
"""
if X.shape[1] != reference:
raise ValueError(
"The number of columns in this dataset is different from the one used to "
"fit this transformer (when using the fit() method)."
)
return None |
Checks that DataFrame to transform has the same number of columns that the
DataFrame used with the fit() method.
Parameters
----------
X : Pandas DataFrame
The df to be checked
reference : int
The number of columns in the dataframe that was used with the fit() method.
Raises
------
ValueError
If the number of columns does not match.
Returns
-------
None
| Checks that DataFrame to transform has the same number of columns that the
DataFrame used with the fit() method.
Parameters
X : Pandas DataFrame
The df to be checked
reference : int
The number of columns in the dataframe that was used with the fit() method.
Raises
ValueError
If the number of columns does not match.
Returns
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if X.shape[1] != reference:
raise ValueError(
"The number of columns in this dataset is different from the one used to "
"fit this transformer (when using the fit() method)."
)
return None | [
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],
"outlier_params": [],
"others": []
} | def _check_X_matches_training_df(X: pd.DataFrame, reference: int) -> None:
if X.shape[1] != reference:
raise ValueError(
"The number of columns in this dataset is different from the one used to "
"fit this transformer (when using the fit() method)."
)
return None | 434 | 685 |
a1cea4c505c018427cfc66b5f3abf5dda1ff6556 | quantmatt/nitrade-trading-software | Build/Release/python_scripts/data_builder.py | [
"Apache-2.0"
] | Python | add_additional_features | <not_specific> | def add_additional_features(asset_data, features, save_path=None, verbose=True):
""" Can add in more features to the data if already processed, saves
calculating all features again
Args:
asset_data ((str, pd.DataFrame) or (str, pd.DataFrame)[]): a single
or list of tuples (asset name, bar data) containing the bar data
features (str[]): a string list of features to calculate
save_path (str): Path to save the bar data with features. A
placeholder {ASSET} that will be substituted with the asset name
verbose (bool): True if progress printing to console is desired
Returns:
(str, pd.DataFrame): a single tuple of (asset name, dataframe) of the
bar data if a single asset was passed or an array of (asset name, dataframes)
if an array of assets was passed
"""
import pandas as pd
#if a single dataframe is passed just put it in a single item list
if type(asset_data) == tuple:
asset_data = [asset_data]
elif type(asset_data) != list:
raise ValueError('asset_data must be a (str, pandas.DataFrame) or a list of (str, pandas.Dataframe).')
feature_bars = []
for asset, data in asset_data:
#add a new feature - this line is temporary and can be change to
#whatever calcualting is required
data["feature"] = 0
feature_bars.append((asset, data))
if save_path is not None:
if verbose: print("Saving {0}...".format(asset))
save_location = save_path.replace("{ASSET}", asset)
data.to_csv(save_location)
#if there was just one dataFrame just return a single dataframe, otherwise
#return the list of dataframes, one for each asset
if len(feature_bars) == 1:
return feature_bars[0]
else:
return feature_bars | Can add in more features to the data if already processed, saves
calculating all features again
Args:
asset_data ((str, pd.DataFrame) or (str, pd.DataFrame)[]): a single
or list of tuples (asset name, bar data) containing the bar data
features (str[]): a string list of features to calculate
save_path (str): Path to save the bar data with features. A
placeholder {ASSET} that will be substituted with the asset name
verbose (bool): True if progress printing to console is desired
Returns:
(str, pd.DataFrame): a single tuple of (asset name, dataframe) of the
bar data if a single asset was passed or an array of (asset name, dataframes)
if an array of assets was passed
| Can add in more features to the data if already processed, saves
calculating all features again | [
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] | def add_additional_features(asset_data, features, save_path=None, verbose=True):
import pandas as pd
if type(asset_data) == tuple:
asset_data = [asset_data]
elif type(asset_data) != list:
raise ValueError('asset_data must be a (str, pandas.DataFrame) or a list of (str, pandas.Dataframe).')
feature_bars = []
for asset, data in asset_data:
data["feature"] = 0
feature_bars.append((asset, data))
if save_path is not None:
if verbose: print("Saving {0}...".format(asset))
save_location = save_path.replace("{ASSET}", asset)
data.to_csv(save_location)
if len(feature_bars) == 1:
return feature_bars[0]
else:
return feature_bars | [
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"#if a single dataframe is passed just put it in a single item list",
"#add a new feature - this line is temporary and can be change to ",
"#whatever calcualting is required",
"#if there was just one dataFrame just return a single dataframe, otherwise ",
"#return the list of dataframes, one for each asset"
] | [
{
"param": "asset_data",
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},
{
"param": "features",
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},
{
"param": "save_path",
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},
{
"param": "verbose",
"type": null
}
] | {
"returns": [
{
"docstring": null,
"docstring_tokens": [
"None"
],
"type": null
}
],
"raises": [],
"params": [
{
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"default": null,
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}
],
"outlier_params": [],
"others": []
} | def add_additional_features(asset_data, features, save_path=None, verbose=True):
import pandas as pd
if type(asset_data) == tuple:
asset_data = [asset_data]
elif type(asset_data) != list:
raise ValueError('asset_data must be a (str, pandas.DataFrame) or a list of (str, pandas.Dataframe).')
feature_bars = []
for asset, data in asset_data:
data["feature"] = 0
feature_bars.append((asset, data))
if save_path is not None:
if verbose: print("Saving {0}...".format(asset))
save_location = save_path.replace("{ASSET}", asset)
data.to_csv(save_location)
if len(feature_bars) == 1:
return feature_bars[0]
else:
return feature_bars | 435 | 539 |
34f3be64ca9c7626da8bd021c650d2a479fb9102 | dsambugaro/TSP_genetic_algorithm | Bilbo.py | [
"MIT"
] | Python | alternativeCrossover | <not_specific> | def alternativeCrossover(parent_1, parent_2):
'''
Returns a child created with an alternative crossover
'''
child = list(parent_1)
cut = random.randrange(0,len(parent_1)-1)
for i in range(cut, len(child)):
if parent_1[i] not in parent_2[cut:] and parent_2[i] not in child[:cut]:
child[child.index(parent_2[i])] = child[i]
child[i] = parent_2[i]
return child |
Returns a child created with an alternative crossover
| Returns a child created with an alternative crossover | [
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"an",
"alternative",
"crossover"
] | def alternativeCrossover(parent_1, parent_2):
child = list(parent_1)
cut = random.randrange(0,len(parent_1)-1)
for i in range(cut, len(child)):
if parent_1[i] not in parent_2[cut:] and parent_2[i] not in child[:cut]:
child[child.index(parent_2[i])] = child[i]
child[i] = parent_2[i]
return child | [
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] | [
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{
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}
] | {
"returns": [],
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"params": [
{
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"docstring": null,
"docstring_tokens": [],
"default": null,
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},
{
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"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import random
def alternativeCrossover(parent_1, parent_2):
child = list(parent_1)
cut = random.randrange(0,len(parent_1)-1)
for i in range(cut, len(child)):
if parent_1[i] not in parent_2[cut:] and parent_2[i] not in child[:cut]:
child[child.index(parent_2[i])] = child[i]
child[i] = parent_2[i]
return child | 436 | 364 |
e00685998ae336089328842d142228c5a311f3d3 | jbn/vaquero | vaquero/util.py | [
"MIT"
] | Python | nth | <not_specific> | def nth(items, i):
"""
Get the nth item from an iterable.
Warning: It consumes from a generator.
:param items: an iterable
:return: the first in the iterable
"""
for j, item in enumerate(items):
if j == i:
return item |
Get the nth item from an iterable.
Warning: It consumes from a generator.
:param items: an iterable
:return: the first in the iterable
| Get the nth item from an iterable.
Warning: It consumes from a generator. | [
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for j, item in enumerate(items):
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"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def nth(items, i):
for j, item in enumerate(items):
if j == i:
return item | 437 | 526 |
8fd3ee6e325022722b10964e06763d64cc2b34fe | blumt/GerryChain | gerrychain/graph/graph.py | [
"BSD-3-Clause"
] | Python | convert_geometries_to_geojson | null | def convert_geometries_to_geojson(data):
"""Convert geometry attributes in a NetworkX adjacency data object
to GeoJSON, so that they can be serialized. Mutates the ``data`` object.
Does nothing if no geometry attributes are found.
:param data: an adjacency data object (returned by
:func:`networkx.readwrite.json_graph.adjacency_data`)
"""
for node in data["nodes"]:
for key in node:
# having a ``__geo_interface__``` property identifies the object
# as being a ``shapely`` geometry object
if hasattr(node[key], "__geo_interface__"):
# The ``__geo_interface__`` property is essentially GeoJSON.
# This is what :func:`geopandas.GeoSeries.to_json` uses under
# the hood.
node[key] = node[key].__geo_interface__ | Convert geometry attributes in a NetworkX adjacency data object
to GeoJSON, so that they can be serialized. Mutates the ``data`` object.
Does nothing if no geometry attributes are found.
:param data: an adjacency data object (returned by
:func:`networkx.readwrite.json_graph.adjacency_data`)
| Convert geometry attributes in a NetworkX adjacency data object
to GeoJSON, so that they can be serialized. Mutates the ``data`` object.
Does nothing if no geometry attributes are found. | [
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for node in data["nodes"]:
for key in node:
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node[key] = node[key].__geo_interface__ | [
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{
"param": "data",
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}
] | {
"returns": [],
"raises": [],
"params": [
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"identifier": "data",
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"docstring": "an adjacency data object (returned by\n:func:`networkx.readwrite.json_graph.adjacency_data`)",
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],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def convert_geometries_to_geojson(data):
for node in data["nodes"]:
for key in node:
if hasattr(node[key], "__geo_interface__"):
node[key] = node[key].__geo_interface__ | 438 | 143 |
b1e83a1ec6fc57ffe4228cf1246a9a1dda9b2f1f | purplediane/shortbread | tests/test_shortbread.py | [
"MIT"
] | Python | check_four_doz_plain | null | def check_four_doz_plain(result):
"""Helper function for output of four dozen cookie recipe."""
assert 'Shortbread Cookies - makes approx. 4 doz.' in result.output
assert '4 cups flour' in result.output
assert '1 cup sugar' in result.output
assert '2 cups salted butter' in result.output
assert '1 teaspoon vanilla (optional)' in result.output | Helper function for output of four dozen cookie recipe. | Helper function for output of four dozen cookie recipe. | [
"Helper",
"function",
"for",
"output",
"of",
"four",
"dozen",
"cookie",
"recipe",
"."
] | def check_four_doz_plain(result):
assert 'Shortbread Cookies - makes approx. 4 doz.' in result.output
assert '4 cups flour' in result.output
assert '1 cup sugar' in result.output
assert '2 cups salted butter' in result.output
assert '1 teaspoon vanilla (optional)' in result.output | [
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],
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"others": []
} | def check_four_doz_plain(result):
assert 'Shortbread Cookies - makes approx. 4 doz.' in result.output
assert '4 cups flour' in result.output
assert '1 cup sugar' in result.output
assert '2 cups salted butter' in result.output
assert '1 teaspoon vanilla (optional)' in result.output | 439 | 724 |
d1e6681cd5231d3a2b5e2a239a8f8e87280593d3 | Sephta/time-series-modeling | transformation_tree/tree.py | [
"MIT"
] | Python | load_tree | <not_specific> | def load_tree(file: str):
"""Loads tree as python object from specified file path"""
with open(file, 'rb') as handle:
tree = pickle.load(handle)
return tree | Loads tree as python object from specified file path | Loads tree as python object from specified file path | [
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with open(file, 'rb') as handle:
tree = pickle.load(handle)
return tree | [
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"\"\"\"Loads tree as python object from specified file path\"\"\""
] | [
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"returns": [],
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"params": [
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"type": "str",
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import pickle
def load_tree(file: str):
with open(file, 'rb') as handle:
tree = pickle.load(handle)
return tree | 440 | 515 |
35bd00dff5214f7993e5de8418e39979b321422a | PointKernel/TiMemory | scripts/type-generator/generate-native-extern.py | [
"MIT"
] | Python | generate_extern | <not_specific> | def generate_extern(component, key, alias, suffix, specify_comp=True):
"""
This function generates a type trait label for C++
"""
if specify_comp:
return "TIMEMORY_{}_EXTERN_{}({}, {})".format(
key.upper(), suffix.upper(), alias, "::tim::component::{}".format(component))
else:
return "TIMEMORY_{}_EXTERN_{}({})".format(
key.upper(), suffix.upper(), alias) |
This function generates a type trait label for C++
| This function generates a type trait label for C | [
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if specify_comp:
return "TIMEMORY_{}_EXTERN_{}({}, {})".format(
key.upper(), suffix.upper(), alias, "::tim::component::{}".format(component))
else:
return "TIMEMORY_{}_EXTERN_{}({})".format(
key.upper(), suffix.upper(), alias) | [
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],
"outlier_params": [],
"others": []
} | def generate_extern(component, key, alias, suffix, specify_comp=True):
if specify_comp:
return "TIMEMORY_{}_EXTERN_{}({}, {})".format(
key.upper(), suffix.upper(), alias, "::tim::component::{}".format(component))
else:
return "TIMEMORY_{}_EXTERN_{}({})".format(
key.upper(), suffix.upper(), alias) | 441 | 228 |
bea0dbc56a41b696f25dbcad67a27f8b96a4cd67 | biocad/leadoptmap | contribute/graph.py | [
"BSD-3-Clause"
] | Python | cutoff_graph | <not_specific> | def cutoff_graph( g, simi_cutoff ) :
"""
Generates a new graph by cutting off the similarity scores.
@type g: C{networkx.Graph}
@param g: Original graph
@type simi_cutoff: C{float}
@param simi_cutoff: Similarity cutoff
@return: A new graph
"""
g = copy.deepcopy( g )
edges_to_be_deleted = []
for e in g.edges() :
if (g[e[0]][e[1]]["similarity"] < simi_cutoff) :
edges_to_be_deleted.append( e )
g.remove_edges_from( edges_to_be_deleted )
return g |
Generates a new graph by cutting off the similarity scores.
@type g: C{networkx.Graph}
@param g: Original graph
@type simi_cutoff: C{float}
@param simi_cutoff: Similarity cutoff
@return: A new graph
| Generates a new graph by cutting off the similarity scores. | [
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] | def cutoff_graph( g, simi_cutoff ) :
g = copy.deepcopy( g )
edges_to_be_deleted = []
for e in g.edges() :
if (g[e[0]][e[1]]["similarity"] < simi_cutoff) :
edges_to_be_deleted.append( e )
g.remove_edges_from( edges_to_be_deleted )
return g | [
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"docstring_tokens": [
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"default": null,
"is_optional": false
}
],
"outlier_params": [],
"others": []
} | import copy
def cutoff_graph( g, simi_cutoff ) :
g = copy.deepcopy( g )
edges_to_be_deleted = []
for e in g.edges() :
if (g[e[0]][e[1]]["similarity"] < simi_cutoff) :
edges_to_be_deleted.append( e )
g.remove_edges_from( edges_to_be_deleted )
return g | 442 | 934 |
856e7f70af58fb3968d4c8a1f54500fdfc4f1731 | stackdump/factom-hackathon | bitwrap_io/config.py | [
"MIT"
] | Python | options | <not_specific> | def options(opts=None):
""" return options as dict from env vars """
if opts is None:
opts = {}
def _opt(optkey, key, default):
if optkey not in opts:
opts[optkey] = os.environ.get(key, default)
_opt('pg-host', 'POSTGRES_HOST', '127.0.0.1')
_opt('pg-username', 'POSTGRES_USER', 'bitwrap')
_opt('pg-database', 'POSTGRES_DB', 'bitwrap')
_opt('pg-password', 'POSTGRES_PASS', 'bitwrap')
_opt('listen-port', 'LISTEN_PORT', '8080')
_opt('listen-ip', 'LISTEN_IP', '0.0.0.0')
return opts | return options as dict from env vars | return options as dict from env vars | [
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] | def options(opts=None):
if opts is None:
opts = {}
def _opt(optkey, key, default):
if optkey not in opts:
opts[optkey] = os.environ.get(key, default)
_opt('pg-host', 'POSTGRES_HOST', '127.0.0.1')
_opt('pg-username', 'POSTGRES_USER', 'bitwrap')
_opt('pg-database', 'POSTGRES_DB', 'bitwrap')
_opt('pg-password', 'POSTGRES_PASS', 'bitwrap')
_opt('listen-port', 'LISTEN_PORT', '8080')
_opt('listen-ip', 'LISTEN_IP', '0.0.0.0')
return opts | [
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"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import os
def options(opts=None):
if opts is None:
opts = {}
def _opt(optkey, key, default):
if optkey not in opts:
opts[optkey] = os.environ.get(key, default)
_opt('pg-host', 'POSTGRES_HOST', '127.0.0.1')
_opt('pg-username', 'POSTGRES_USER', 'bitwrap')
_opt('pg-database', 'POSTGRES_DB', 'bitwrap')
_opt('pg-password', 'POSTGRES_PASS', 'bitwrap')
_opt('listen-port', 'LISTEN_PORT', '8080')
_opt('listen-ip', 'LISTEN_IP', '0.0.0.0')
return opts | 443 | 679 |
0f6ed79790a112be9c4f21a9605d914a0fa8aafd | InnovativeInventor/retworkx | retworkx/__init__.py | [
"Apache-2.0"
] | Python | is_subgraph_isomorphic | null | def is_subgraph_isomorphic(
first,
second,
node_matcher=None,
edge_matcher=None,
id_order=False,
induced=True,
call_limit=None,
):
"""Determine if 2 graphs are subgraph isomorphic
This checks if 2 graphs are subgraph isomorphic both structurally and also
comparing the node and edge data using the provided matcher functions.
The matcher functions take in 2 data objects and will compare them.
Since there is an ambiguity in the term 'subgraph', do note that we check
for an node-induced subgraph if argument `induced` is set to `True`. If it is
set to `False`, we check for a non induced subgraph, meaning the second graph
can have fewer edges than the subgraph of the first. By default it's `True`. A
simple example that checks if they're just equal would be::
graph_a = retworkx.PyGraph()
graph_b = retworkx.PyGraph()
retworkx.is_subgraph_isomorphic(graph_a, graph_b,
lambda x, y: x == y)
:param first: The first graph to compare. Can either be a
:class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.
:param second: The second graph to compare. Can either be a
:class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.
It should be the same type as the first graph.
:param callable node_matcher: A python callable object that takes 2
positional one for each node data object. If the return of this
function evaluates to True then the nodes passed to it are viewed
as matching.
:param callable edge_matcher: A python callable object that takes 2
positional one for each edge data object. If the return of this
function evaluates to True then the edges passed to it are viewed
as matching.
:param bool id_order: If set to ``True`` this function will match the nodes
in order specified by their ids. Otherwise it will default to a heuristic
matching order based on [VF2]_ paper.
:param bool induced: If set to ``True`` this function will check the existence
of a node-induced subgraph of first isomorphic to second graph.
Default: ``True``.
:param int call_limit: An optional bound on the number of states that VF2
algorithm visits while searching for a solution. If it exceeds this limit,
the algorithm will stop and return ``False``.
:returns: ``True`` if there is a subgraph of `first` isomorphic to `second`
, ``False`` if there is not.
:rtype: bool
"""
raise TypeError("Invalid Input Type %s for graph" % type(first)) | Determine if 2 graphs are subgraph isomorphic
This checks if 2 graphs are subgraph isomorphic both structurally and also
comparing the node and edge data using the provided matcher functions.
The matcher functions take in 2 data objects and will compare them.
Since there is an ambiguity in the term 'subgraph', do note that we check
for an node-induced subgraph if argument `induced` is set to `True`. If it is
set to `False`, we check for a non induced subgraph, meaning the second graph
can have fewer edges than the subgraph of the first. By default it's `True`. A
simple example that checks if they're just equal would be::
graph_a = retworkx.PyGraph()
graph_b = retworkx.PyGraph()
retworkx.is_subgraph_isomorphic(graph_a, graph_b,
lambda x, y: x == y)
:param first: The first graph to compare. Can either be a
:class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.
:param second: The second graph to compare. Can either be a
:class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.
It should be the same type as the first graph.
:param callable node_matcher: A python callable object that takes 2
positional one for each node data object. If the return of this
function evaluates to True then the nodes passed to it are viewed
as matching.
:param callable edge_matcher: A python callable object that takes 2
positional one for each edge data object. If the return of this
function evaluates to True then the edges passed to it are viewed
as matching.
:param bool id_order: If set to ``True`` this function will match the nodes
in order specified by their ids. Otherwise it will default to a heuristic
matching order based on [VF2]_ paper.
:param bool induced: If set to ``True`` this function will check the existence
of a node-induced subgraph of first isomorphic to second graph.
Default: ``True``.
:param int call_limit: An optional bound on the number of states that VF2
algorithm visits while searching for a solution. If it exceeds this limit,
the algorithm will stop and return ``False``.
:returns: ``True`` if there is a subgraph of `first` isomorphic to `second`
, ``False`` if there is not.
:rtype: bool
| Determine if 2 graphs are subgraph isomorphic
This checks if 2 graphs are subgraph isomorphic both structurally and also
comparing the node and edge data using the provided matcher functions.
The matcher functions take in 2 data objects and will compare them.
Since there is an ambiguity in the term 'subgraph', do note that we check
for an node-induced subgraph if argument `induced` is set to `True`. If it is
set to `False`, we check for a non induced subgraph, meaning the second graph
can have fewer edges than the subgraph of the first. By default it's `True`. A
simple example that checks if they're just equal would be:.
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first,
second,
node_matcher=None,
edge_matcher=None,
id_order=False,
induced=True,
call_limit=None,
):
raise TypeError("Invalid Input Type %s for graph" % type(first)) | [
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"\"\"\"Determine if 2 graphs are subgraph isomorphic\n\n This checks if 2 graphs are subgraph isomorphic both structurally and also\n comparing the node and edge data using the provided matcher functions.\n The matcher functions take in 2 data objects and will compare them.\n Since there is an ambiguity in the term 'subgraph', do note that we check\n for an node-induced subgraph if argument `induced` is set to `True`. If it is\n set to `False`, we check for a non induced subgraph, meaning the second graph\n can have fewer edges than the subgraph of the first. By default it's `True`. A\n simple example that checks if they're just equal would be::\n\n graph_a = retworkx.PyGraph()\n graph_b = retworkx.PyGraph()\n retworkx.is_subgraph_isomorphic(graph_a, graph_b,\n lambda x, y: x == y)\n\n\n :param first: The first graph to compare. Can either be a\n :class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.\n :param second: The second graph to compare. Can either be a\n :class:`~retworkx.PyGraph` or :class:`~retworkx.PyDiGraph`.\n It should be the same type as the first graph.\n :param callable node_matcher: A python callable object that takes 2\n positional one for each node data object. If the return of this\n function evaluates to True then the nodes passed to it are viewed\n as matching.\n :param callable edge_matcher: A python callable object that takes 2\n positional one for each edge data object. If the return of this\n function evaluates to True then the edges passed to it are viewed\n as matching.\n :param bool id_order: If set to ``True`` this function will match the nodes\n in order specified by their ids. Otherwise it will default to a heuristic\n matching order based on [VF2]_ paper.\n :param bool induced: If set to ``True`` this function will check the existence\n of a node-induced subgraph of first isomorphic to second graph.\n Default: ``True``.\n :param int call_limit: An optional bound on the number of states that VF2\n algorithm visits while searching for a solution. If it exceeds this limit,\n the algorithm will stop and return ``False``.\n\n :returns: ``True`` if there is a subgraph of `first` isomorphic to `second`\n , ``False`` if there is not.\n :rtype: bool\n \"\"\""
] | [
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} | def is_subgraph_isomorphic(
first,
second,
node_matcher=None,
edge_matcher=None,
id_order=False,
induced=True,
call_limit=None,
):
raise TypeError("Invalid Input Type %s for graph" % type(first)) | 445 | 544 |
1ad35fc8373760c197d3bd8dba0fcb0a89fdd040 | scuc/GoPro-Concat-Automation | gopro_concat.py | [
"MIT"
] | Python | print_intro | <not_specific> | def print_intro():
'''
Get the user input for starting values (Source, Output, Downcovert)
and begin the concat process.
'''
open_msg = f"\n\
================================================================\n \
GoPro Automation Script, version 0.2 \n \
This script will take a collection of chaptered GoPro video files \n \
and stitch them together with the option to also downconvert them \n \
to a 10Mbit file. \n \
================================================================\n"
print(open_msg)
source_path = str(input("Source path: "))
output_path = str(input("Output path: "))
down_convert = str(input("Downconvert the GoPro files to 10Mbit [Yes/No]: "))
while True:
if down_convert.lower() == 'yes':
down_convert = True
break
elif down_convert.lower() == 'no':
down_convert = False
break
else:
print(f"{down_convert} is not a valid choice.")
down_convert = str(input("Please select Yes or No (Y/N): "))
continue
return [source_path, output_path, down_convert] |
Get the user input for starting values (Source, Output, Downcovert)
and begin the concat process.
| Get the user input for starting values (Source, Output, Downcovert)
and begin the concat process. | [
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] | def print_intro():
open_msg = f"\n\
================================================================\n \
GoPro Automation Script, version 0.2 \n \
This script will take a collection of chaptered GoPro video files \n \
and stitch them together with the option to also downconvert them \n \
to a 10Mbit file. \n \
================================================================\n"
print(open_msg)
source_path = str(input("Source path: "))
output_path = str(input("Output path: "))
down_convert = str(input("Downconvert the GoPro files to 10Mbit [Yes/No]: "))
while True:
if down_convert.lower() == 'yes':
down_convert = True
break
elif down_convert.lower() == 'no':
down_convert = False
break
else:
print(f"{down_convert} is not a valid choice.")
down_convert = str(input("Please select Yes or No (Y/N): "))
continue
return [source_path, output_path, down_convert] | [
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] | [] | {
"returns": [],
"raises": [],
"params": [],
"outlier_params": [],
"others": []
} | def print_intro():
open_msg = f"\n\
================================================================\n \
GoPro Automation Script, version 0.2 \n \
This script will take a collection of chaptered GoPro video files \n \
and stitch them together with the option to also downconvert them \n \
to a 10Mbit file. \n \
================================================================\n"
print(open_msg)
source_path = str(input("Source path: "))
output_path = str(input("Output path: "))
down_convert = str(input("Downconvert the GoPro files to 10Mbit [Yes/No]: "))
while True:
if down_convert.lower() == 'yes':
down_convert = True
break
elif down_convert.lower() == 'no':
down_convert = False
break
else:
print(f"{down_convert} is not a valid choice.")
down_convert = str(input("Please select Yes or No (Y/N): "))
continue
return [source_path, output_path, down_convert] | 446 | 489 |
7bf3f7e197f0f0a4e6481eb4681c63649883ecdc | conan25216/pypi_mal | malware/dark-magic-0.1.2/dark_magic/__main__.py | [
"Unlicense"
] | Python | check_valid_lp | <not_specific> | def check_valid_lp(var_signs, A, directions, bvals, max_or_min, cvals, verbose):
'''
Checks if an LP is valid. By valid we mean that the number constraints
and variables match in all four files.
'''
num_constraints, num_vars = A.shape
if len(var_signs) != num_vars:
return False, 'Number of variables in A and x file do not match.'
if len(directions) != num_constraints or len(bvals) != num_constraints:
return False, 'Number of constraints in A and b do not match.'
if len(cvals) != num_vars:
return False, 'Number of variables in A and c file do not match.'
return True, '' |
Checks if an LP is valid. By valid we mean that the number constraints
and variables match in all four files.
| Checks if an LP is valid. By valid we mean that the number constraints
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num_constraints, num_vars = A.shape
if len(var_signs) != num_vars:
return False, 'Number of variables in A and x file do not match.'
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if len(cvals) != num_vars:
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{
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{
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{
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}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "var_signs",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "A",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "directions",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "bvals",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "max_or_min",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "cvals",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "verbose",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def check_valid_lp(var_signs, A, directions, bvals, max_or_min, cvals, verbose):
num_constraints, num_vars = A.shape
if len(var_signs) != num_vars:
return False, 'Number of variables in A and x file do not match.'
if len(directions) != num_constraints or len(bvals) != num_constraints:
return False, 'Number of constraints in A and b do not match.'
if len(cvals) != num_vars:
return False, 'Number of variables in A and c file do not match.'
return True, '' | 447 | 744 |
53692e5e806f1fd4536ddd48dcc9d09a09c52ba5 | amyhemmeter/baseline | python/baseline/utils.py | [
"Apache-2.0"
] | Python | convert_iobes_to_bio | <not_specific> | def convert_iobes_to_bio(seq):
"""Convert a sequence of IOBES tags to BIO tags
:param seq: `List[str]` The list of IOBES tags.
:returns: `List[str]` The list of BIO tags.
"""
# Use re over `.replace` to make sure it only acts on the beginning of the token.
return list(map(lambda x: re.sub(r'^S-', 'B-', re.sub(r'^E-', 'I-', x)), seq)) | Convert a sequence of IOBES tags to BIO tags
:param seq: `List[str]` The list of IOBES tags.
:returns: `List[str]` The list of BIO tags.
| Convert a sequence of IOBES tags to BIO tags | [
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"BIO",
"tags"
] | def convert_iobes_to_bio(seq):
return list(map(lambda x: re.sub(r'^S-', 'B-', re.sub(r'^E-', 'I-', x)), seq)) | [
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] | [
"\"\"\"Convert a sequence of IOBES tags to BIO tags\n\n :param seq: `List[str]` The list of IOBES tags.\n\n :returns: `List[str]` The list of BIO tags.\n \"\"\"",
"# Use re over `.replace` to make sure it only acts on the beginning of the token."
] | [
{
"param": "seq",
"type": null
}
] | {
"returns": [
{
"docstring": "`List[str]` The list of BIO tags.",
"docstring_tokens": [
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],
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],
"default": null,
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}
],
"outlier_params": [],
"others": []
} | import re
def convert_iobes_to_bio(seq):
return list(map(lambda x: re.sub(r'^S-', 'B-', re.sub(r'^E-', 'I-', x)), seq)) | 448 | 735 |
Subsets and Splits