content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def lzo_stream(*, length: int = 4096):
"""
Compress a string of null bytes, the length being defined by the
argument to this function.
"""
compressor = Popen(["lzop", "-c"], stdin=PIPE, stdout=PIPE, stderr=PIPE)
stdout, stderr = compressor.communicate(input=b"\x00" * length)
if stderr:
... | a44d8b37384ad29d19c35b8f28ddbcbc3f6308e3 | 48,200 |
async def buy(client, event, item: (ShopItem.item_choices(), "Buy cat items with your Neko coins (NC).")):
"""Buy cat items with your Neko coins (NC)."""
neko_coins = await get_coins_helper(event.user.id)
selected_item: ShopItem = ShopItem[item]
new_balance = neko_coins - selected_item.price
if new_... | d2d05f08f49cf873f4ef48dd98a10c1db593b061 | 48,201 |
import htcondor
def config_val(attr):
"""Query HTCondor for the value of a configuration variable using the python
bindings if available, condor_config_val otherwise
"""
try:
# Necessary for checking config between different flavors of HTCondor
htcondor.reload_config()
try:
... | c9bff2321b615939c4a4dcffe01a19718782d882 | 48,202 |
def like(lhs: str,
pattern: str,
wildcard: str,
singlechar: str,
escapechar: str,
not_: bool = False,
) -> 'elasticsearch_dsl.query.Query':
""" Create a filter to filter elements according to a string attribute using
wildcard expressions.
:param... | 31c23a2a255f8d39bef0ba9e9c2f33e04c5e94d1 | 48,203 |
def bug_to_response(bug, detailed=True):
"""Convert a Bug entity to a response object."""
response = osv.vulnerability_to_dict(bug.to_vulnerability())
response.update({
'isFixed': bug.is_fixed,
'invalid': bug.status == osv.BugStatus.INVALID
})
if detailed:
add_links(response)
add_source_i... | b250339d65db15a777f86f81ed44b4cb42e8b8fe | 48,204 |
import sys
from sys import path
def get_current_path() -> str:
"""Get current path of script/executable"""
application_path = ""
if getattr(sys, "frozen", False):
application_path = path.dirname(sys.executable)
elif __file__:
application_path = path.dirname(__file__)
return appl... | e582f638d4f95ab57cb21dc154c11a94ec78488e | 48,205 |
import os
import json
def get_pmc(uid, metadata_df, directory='data/cord-19/'):
"""
In:
uid [str]: cord-uid of required file
metadata_df: DataFrame containing metadata for file
Returns:
json of required file"""
uid_df = metadata_df[metadata_df.cord_uid == uid]
pmc = uid_df.il... | da8d0825272493dceb0ce26d98410f9a5481acf6 | 48,206 |
import ipaddress
def parse_cidr(value):
"""Process cidr ranges."""
klass = IPv4Network
if '/' not in value:
klass = ipaddress.ip_address
try:
v = klass(str(value))
except (ipaddress.AddressValueError, ValueError):
v = None
return v | 4bf2fbbf39558421b397be3d85719cb85bfecf46 | 48,207 |
def lab_mean_std(im_input, mask_out=None):
"""Compute the mean and standard deviation of the intensities.
... of each channel of the given RGB image in LAB color space.
The outputs of this function is for reinhard normalization.
Parameters
----------
im_input : array_like
An RGB image
... | 89a8f13882d4aabb41310182e52cad85dab2f6d8 | 48,208 |
import requests
def sel(host, args, session):
"""
prints out the bmc alerts
@param host: string, the hostname or IP address of the bmc
@param args: contains additional arguments used by the sel sub command
@param session: the active session to use
@param ar... | 3fb583d7f8ddbade281b5975b9f9a7dd21b0caef | 48,209 |
import errno
import csv
def read_order_metrics(csvfile, required=False):
"""
Read oredred metrics.
Routine to read in ordered list of metrics csv file.
Not really csv but easily read in by csv package. This is a line by line
ordered list of the metrics that will be plotted on a NAC plot. It shou... | c2ec2329459aad5c57b3feaa900aaf34e9927f8f | 48,210 |
from django.contrib.auth.views import redirect_to_login
def request_passes_test(test_func, login_url=None,
redirect_field_name=REDIRECT_FIELD_NAME):
"""
Decorator for views that checks that the request passes the given test,
redirecting to the log-in page if necessary. The test sho... | e7559e94dcb60dc436b931b27c9af641558650a8 | 48,211 |
def generate_dataset_code(id): # noqa: E501
"""generate_dataset_code
Generate sample code to use dataset in a pipeline # noqa: E501
:param id:
:type id: str
:rtype: ApiGenerateCodeResponse
"""
return util.invoke_controller_impl() | 2acb572bf55a7edcd52bce682814e179f28e6bb5 | 48,212 |
import resource
def GetManagedRelationalDbClass(cloud):
"""Get the ManagedRelationalDb class corresponding to 'cloud'.
Args:
cloud: name of cloud to get the class for
"""
return resource.GetResourceClass(BaseManagedRelationalDb, CLOUD=cloud) | 562b17daab2c9b96548eecdb3154b59fe4c62b4a | 48,213 |
import os
def main():
""" Main route """
# Query logs
query_logs = []
# Update params with user's settings
params = get_params()
# Get query
default_user_query = "What do we know about Chloroquine to treat covid-19?"
try:
user_query = str(
request.form["user_quer... | 6698e46a080ea7e87ae0ce061223315de9d27422 | 48,214 |
import re
def normalize_summary(summary):
"""Return normalized docstring summary."""
# Remove newlines
summary = re.sub(r'\s*\n\s*', ' ', summary.rstrip())
# Add period at end of sentence
if (
summary and
(summary[-1].isalnum() or summary[-1] in ['"', "'"]) and
(not summar... | 002e72668e87d668c2d6df678092ac57fc2b1d37 | 48,215 |
import torch
def compute_metrics(data , pred_transforms):
"""
Compute metrics required in the paper
"""
def square_distance(src, dst):
return torch.sum((src[:, :, None, :] - dst[:, None, :, :]) ** 2, dim=-1)
with torch.no_grad():
pred_transforms = pred_transforms
gt_transf... | 5c27279448542994560ecb0ee5d3b47b1fe079be | 48,216 |
def get_datetime_timedelta_conversion(datetime_unit, timedelta_unit):
"""
Compute a possible conversion for combining *datetime_unit* and
*timedelta_unit* (presumably for adding or subtracting).
Return (result unit, integer datetime multiplier, integer timedelta
multiplier). RuntimeError is raised i... | 02ab9ecb182ed1cfcec4138cb9ac5f576411e8a9 | 48,217 |
def create_workflow_to_resample_baw_files(name="ResampleBAWOutputs"):
"""
This function...
:param name:
:return:
"""
workflow = Workflow(name)
inputs_to_resample = ["t1_file", "t2_file", "hncma_file", "abc_file"]
other_inputs = ["reference_file", "acpc_transform"]
label_maps = ["hnc... | 73146fee1f8acb6d31e3b804efde78e57ec6a617 | 48,218 |
def absorb(expression):
"""
A AND (A OR B) -> A
A OR (A AND B) -> A
A AND (NOT A OR B) -> A AND B
A OR (NOT A AND B) -> A OR B
"""
if isinstance(expression, exp.And):
return _absorb(expression, exp.Or)
if isinstance(expression, exp.Or):
return _absorb(expression, exp.And)... | fa6814aea142651f46fd0311df18130cf6d39653 | 48,219 |
def gen_data(shape_matrix, shape_diagonal, dtype):
"""generate valid data to test"""
input_matrix = random_gaussian(shape_matrix, miu=10, sigma=0.3).astype(dtype)
input_diagonal = random_gaussian(shape_diagonal, miu=5, sigma=0.3).astype(dtype)
# make shape_diagonal can support broadcast
if shape_mat... | 69f97f0372a964d923bf584a8da6998b357ef03f | 48,220 |
def pe_7():
"""Return the 10,001st prime number."""
primes = list(lpe.prime_sieve(2_000_00))
primes.sort()
return f'The 10,001st prime number is {primes[10_000]:,}.' | 9b60be4d0e3d3f2502fee94bce5462e9bb3e93a4 | 48,221 |
def minimal_stat_test(agonists, antagonists, stat_test, start, stop, threshold=0.05, cache=None):
"""
Inputs a list of agonists and a list of antagonists and finds the most significant residues. We do not return
the p_value but only the residue ids.
.. note::
RMSF calculations are cached to a... | 82b71dfedd2e8c9def77e969515aaa0827aee516 | 48,222 |
import torch
def variance(values):
"""
Variance function.
"""
mean_value = mean(values)
var = 0.0
for value in values:
var = var + torch.sum(torch.sqrt(torch.pow(value-mean_value,2))).item()/len(values)
return var | 38be900320427475b30c40d9364649243b4c9752 | 48,223 |
import subprocess
def task_lint():
"""Check linting"""
def run(args):
args = args or []
subprocess.run(
['flake8', 'ruly', 'test', 'setup.py', 'dodo.py', *args])
return {'actions': [run], 'pos_arg': 'args'} | f152eecd3fa21e62502ebf0223046ce75807e098 | 48,224 |
def update_link():
"""
This is a route for ALL NODES. When a new node is inserted in
the RING (via the '/bootstrap/node/join' route), then the neighbors of that
node must update their links, so that they point at that new node.
"""
prev_or_next = request.form['prev_or_next']
if prev_or_next... | 71d5d2da5d7b61f65868f3b7c65f7133ba7ac8ff | 48,225 |
from typing import Optional
from typing import Sequence
from typing import Tuple
def instances_to_boxes_np(
seg: np.ndarray,
dim: int = None,
instances: Optional[Sequence[int]] = None,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Convert instance segmentation to bounding boxes (not batched)
Ar... | c0a4aace311ac5b07a1168f94b89d3de6fa5f049 | 48,226 |
import argparse
def parseargs(description: Text) -> argparse.ArgumentParser:
""" Parse arguments """
parser = argparse.ArgumentParser(
allow_abbrev=False,
description=description
)
parser.add_argument("--server", help="Mattermost Server")
parser.add_argument("--user", help="Matter... | 95c127bf73360afe7227f01ca9591864f1ccd614 | 48,227 |
import hashlib
def md5_key(string):
"""
Use this to generate filenae keys
"""
m = hashlib.md5()
m.update(string.encode('utf-8'))
return m.hexdigest() | ffa2d26933b5a18f43d2c8ed696e880a38039ece | 48,228 |
def run_all_gluon_nn_loss_operations_benchmarks(ctx, inputs):
"""Helper to run all Gluon Loss Layer benchmarks. Just runs the benchmarks with default input values.
This is just a utility to run benchmarks with all default input values.
:return: list[dict], list of dictionary of benchmark results. Each item... | 7d3e1c6835292051277d35c3d13865985c257a31 | 48,229 |
def name_to_hash(name: str) -> int:
"""
given a name, generate a unique-ish number.
cannot simply use hash(), since that is different each
time we re-run the process...
"""
hash_v = sum([ord(c) for c in name])
print('hash_v', hash_v)
return hash_v | e707d401911d7ca41b019e73d1afcd0c66fe045e | 48,230 |
def attack_targets_by_country(country):
"""Returns the targets list with the corresponding number of attacks in descending order of the given country."""
cur = get_db().execute('SELECT targtype1_txt, num_attacks FROM (SELECT targtype1_txt, COUNT(targtype1_txt) num_attacks FROM Attacks WHERE iso_code="{}" GROUP ... | ded89ef143ad1db373fa495edc1f9a05859721dd | 48,231 |
def calculate_average(list_of_nums):
"""Calculates the average of a list of numbers."""
average = calculate_sum(list_of_nums) / len(list_of_nums)
return average | 122fc06e55f5932c088712777691179047cce3eb | 48,232 |
def query_introspection() -> str:
"""Retrieve available queries."""
return """query {
__type(name: "Query") {
kind
name
fields {
name
description
args {
name
description
defaultValue
}
... | f01c4a79517b60a5c130805a673665b9bfae858e | 48,233 |
def re_exp_matching_backward(s, p):
"""
:type s: str for match
:type p: pattern str
:rtype: match or not
"""
def is_match(chr_for_match, match_pattern):
return match_pattern == '.' or match_pattern == chr_for_match
def match_core(str, pattern):
if pattern < 0:
re... | 03fb3bb85123435779b46086b1c2ef1705b686f3 | 48,234 |
def _batched_table_lookup(tbl, row, col):
"""Mapped 2D table lookup.
Args:
tbl: a `Tensor` of shape `[r, s, t]`.
row: a `Tensor` of dtype `int32` with shape `[r]` and values in
the range `[0, s - 1]`.
col: a `Tensor` of dtype `int32` with shape `[r]` and values in
the range `[0, t - 1]`.
... | 048100b750a91afe2f6809950a0c97d2cc482fce | 48,235 |
def avg_pool2d(inputs,
kernel_size,
scope,
stride=[2, 2],
padding='VALID'):
""" 2D avg pooling.
Args:
inputs: no_dropout-D tensor BxHxWxC
kernel_size: a list of 128 ints
stride: a list of 128 ints
Returns:
Variable tensor
... | fe8c7825832b9b13f0f363036d82d0198ff913f3 | 48,236 |
import scipy
def repressilator():
"""Replaces the plot of the protein-only repressilator. Replaces
Python code:
def repressilator_rhs(x, t, beta, n):
'''
Returns 3-array of (dx_1/dt, dx_2/dt, dx_3/dt)
'''
x_1, x_2, x_3 = x
return np.array(
[
... | f4446493d3552fc6f1006f65cccaea194bc56dd9 | 48,237 |
def date_list(start, end):
"""
:param start: year start; format: 2017, int
:param end: year end; format: 2019, int
:return: a list include all the month
"""
assert int(start / 1000) == 0 or type(start) is int, 'start error'
assert int(end / 1000) == 0 or type(end) is int, 'end error'
... | 4af977d47e611013ead4dd6538e7bdcbb87bf5be | 48,238 |
def get_y_axis_max(x, chr=None, start=0, end=0, fix_end=True):
"""
Parameters
----------
x : list
A list of bigwig files
chr : str
The name of chromosome
start : int
The start position
end : int
The end position, 0 indicate t... | 4154ff99805321801c43bb2bdafaf8de983da066 | 48,239 |
def index():
""" Module's Home Page """
response.view = "mad/index.html"
module_name = deployment_settings.modules[module].name_nice
response.title = module_name
return dict(module_name=module_name) | 2e18da3d02bcae6b34f920dc0bb1a800e4d541c5 | 48,240 |
def reflect_coef(ip):
"""
Computes the reflection coefficient for a plane incident P-wave.
Parameters
----------
ip : array
P-impedance.
Returns
-------
rc : array
The reflection coefficient
"""
rc=(ip[1:]-ip[:-1])/(ip[1:]+ip[:-1])
rc=np.append(rc,rc[-1]... | 9ff8c8e103bca6b9cc79663daca8610adfdc954a | 48,241 |
import torch
def subsequent_mask(size: int) -> Tensor:
"""
Mask out subsequent positions (to prevent attending to future positions)
Transformer helper function.
:param size: size of mask (2nd and 3rd dim)
:return: Tensor with 0s and 1s of shape (1, size, size)
"""
mask = np.triu(np.ones((... | 7f4b11b7b62441991ae095b9a15b0111ad6e90e1 | 48,242 |
def list_reuters_codes(codes=None, report_types=None, statement_types=None):
"""
List available Chart of Account (COA) codes from the Reuters financials database
and/or indicator codes from the Reuters estimates/actuals database
Note: you must collect Reuters financials into the database before you can... | 50854e075022aaaa31bb54fe4c034ac66904cf18 | 48,243 |
def waveletdec(s, wavelet, lvl, coord):
"""
Decompose .
Args:
s (list): list of numbers representing time-series signal
wavelet (string): name of the wavelet; ex: 'sym2', 'bior1.3', etc.
lvl (int): level of decomposition; length of coeffs will be lvl+1
coord (str): genomic coordi... | 916cb3f11f176b4ef8c97cea9c3fbae8e6939ea1 | 48,244 |
def concatenate_datasets(current_images, current_labels, prev_images, prev_labels):
"""
Concatnates current dataset with the previous one. This will be used for
adding important samples from the previous datasets
Args:
current_images Images of current dataset
current_labels Lab... | b7183adf59bc0be95995db7764a826dec4926a18 | 48,245 |
def dot(v: Vector, w: Vector) -> float:
"""Returns v_1 * w_1 + ... + v_n * w_n"""
assert len(v) == len(w), "vectors must be same length"
return sum(v_i * w_i for v_i, w_i in zip(v, w)) | da0744bc6cd838f6bcbb06479bed0bcdcbbb2836 | 48,246 |
def ww_sim(word, mat, topn=10):
"""Calculate topn most similar words to word"""
index = tok2index[word]
if isinstance(mat, sparse.csr_matrix):
v1 = mat.getrow(index)
else:
v1 = mat[index:index+1, :]
sims = cosine_similarity(mat, v1).flatten()
sindexs = np.argsort(-sims)
sim_w... | aee7528805ad069f921441c85cbfde655a7a92a9 | 48,247 |
import re
def count_characters(text, whites=False):
"""
Get character count of a text
Args:
whites: If True, whitespaces are not counted
"""
if whites:
return len(text)
else:
return len(re.sub(r"\s", "", text)) | e4db9e873e800282cf7f2398272a8b4546fe171e | 48,248 |
def get_posixtime_from_uuid(uuid1):
"""Convert the uuid1 timestamp to a standard posix timestamp
"""
assert uuid1.version == 1, ValueError('only applies to type 1')
t = uuid1.time
t = t - 0x01b21dd213814000
t = t / 1e7
return t | 1e7751026aae6d0534403707d89fee8e99984137 | 48,249 |
import re
def remove_html(raw_text):
"""
Remove html tags
"""
text = str(raw_text)
cleaner = re.compile('<.*?>')
text = re.sub(cleaner, '', text)
return text | 397b49c052e055a71876d9883ab259f871b5015e | 48,250 |
def process_special_event(events_list: str) -> tuple:
"""Gets word list from parse events and turns into list"""
in_key_dicts = [
x in shift_key_codes or x in modifier_codes or x in codes for x in events_list
]
final_keys = []
if False not in in_key_dicts:
for word in events_list:
... | 89f9f976cf3f44383c8f0c4ef11574b5b9a957f9 | 48,251 |
def pull_words(words_file, word_length):
"""Compile set of words, converted to lower case and matching length of
start and end words.
Args:
words_file: str, name of the file containing all words
word_length: int, length of the start/end words
Returns:
words_set: set, all possible... | cbecb29bd93177cb14a208e7e3a7bcee14f7c010 | 48,252 |
def format_participants_packet(data: PacketParticipantsData, index: int, race: Race,
lap: int):
"""
"""
formatted_data = []
for key, value in data.to_dict().items():
if key == 'm_header':
continue
elif key.endswith('_participants'):
... | 133a42779443ae4ad84891d56137bb97d0c11c3f | 48,253 |
def classify_cells_majority(data, burnt_samples, table, cell_type_name2idx):
"""
This function is an extension of "classify_cells". It extends to the case when you need to ensemble a list of MP trees by majority.
INPUT:
data: N*D np.array
burnt_samples: A list of MP trees
table: a da... | 4d932a4602a573196298b2c61fd350c7fa1b9585 | 48,254 |
def vertices_vector_to_matrix(vertices):
"""vertices_vector_to_matrix(vertices) -> List[List[float]]
PyPRT outputs the GeneratedModel vertex coordinates as a list. The list
contains the x, y, z coordinates of all the vertices. This function converts the
vertex list into a list of N vertex coordinates ... | 0d03a60f32ed722d089500840e1a2a2e645c20b4 | 48,255 |
import torch
def random_well_conditioned_matrix(*shape, dtype, device, mean=1.0, sigma=0.001):
"""
Returns a random rectangular matrix (batch of matrices)
with singular values sampled from a Gaussian with
mean `mean` and standard deviation `sigma`.
The smaller the `sigma`, the better conditioned
... | bd2d7e232ffcd2848b836e9187d32a00339477de | 48,256 |
def get_index_str(n, i):
"""
To convert an int 'i' to a string.
Parameters
----------
n : int
Order to put 0 if necessary.
i : int
The number to convert.
Returns
-------
res : str
The number as a string.
Examples
--------
```python
getI... | e7b3561a49b447d1edec22da8cc86d2a702ec039 | 48,257 |
def print_red(text):
""""
Prints a sentence in the color red.
:param: text
:return: Fore.RED + Style.BRIGHT + text + Style.NORMAL + Fore.WHITE
"""
return Fore.RED + Style.BRIGHT + text + Style.NORMAL + Fore.WHITE | 87d805cd2c499d95d51da76d6b5ef2b6571679b1 | 48,258 |
def extract_metrics(postprocessors):
"""
Extract performance metrics from multiple runs.
:param postprocessors:
:param data_projection:
:return:
"""
n_runs = len(postprocessors[0])
n_estimators = len(postprocessors)
n_clusters = postprocessors[0][0].nclusters
x_vals = np.arange(... | 55548be2d5ed83dda94252877143cbdd9ffd71f8 | 48,259 |
import os
import io
def load_notebook(filename):
"""load a notebook object from a filename"""
if not os.path.exists(filename) and not filename.endswith(".ipynb"):
filename = filename + ".ipynb"
with io.open(filename) as f:
return nbf.read(f, as_version=4) | 8de24bac73429ccbadb9e69a9f4af00cf8d485c6 | 48,260 |
import pandas
def concat_gdf(*args):
"""
Concatenates an arbitrary number of GeoDataFrames
"""
return geopandas.GeoDataFrame(pandas.concat([*args], ignore_index=True)) | 4790545c2b3b2589e8f505574d0c35b64b2d0ebb | 48,261 |
def match_stops_in_model(stops):
"""
Matches a list of bus stops with stops present in the model.
The bus_station_ids provides a handling method for bus stations.
Input all the ATCO codes associated with bus station stops into the array
and ensure the Bus Terminal in Aimsun has the EID 'Bus Station'.
Parameters
... | a5490b8e23776843bbd12d1f42bbf9725595e842 | 48,262 |
def sigma_at_error_rate_with_good_examples(
model, sess, x, y, desired_error_rate,
gaussian_samples_at_sigma_1, num_examples_wanted, distance_scale,
initial_guess=0.1, tol=0.001, sample_batch_size=10):
"""The scale at which Gaussian noise produces the provided error rate.
Args:
model: A `Model`; th... | a98a32206469fe35f3fdb663451eba497cb95f95 | 48,263 |
def get_all_matching_models(cars=cars, grep='trail'):
"""return a list of all models containing the case insensitive
'grep' string which defaults to 'trail' for this exercise,
sort the resulting sequence alphabetically"""
matches = []
for mfg, modellist in cars.items():
for model in mo... | 0d854fc3e934c3657cf27c3c9bddd2c895997776 | 48,264 |
from scipy import interpolate
def psresp(t, y, dy, slopes, dt, df, percentile, oversampling=10, number_simulations=100):
"""
Compute power spectral density of a light curve assuming an unbroken power law with the PSRESP method.
The artificial light curves are generated using the algorithm by Timmer and K... | d06b53c50f897be908d3b48378c556de2b9718ab | 48,265 |
def gaussian_mixture(x: np.ndarray, params: np.ndarray
) -> np.ndarray: # pragma: no cover
"""
Mixture of gaussian curves.
Parameters
----------
x : np.array
params: np.ndarray
parameter for each curve the shape of the array is n_curves by
3. Each row has ... | 9a272c2f4394bff7f2741d8f77d8393f838dad86 | 48,266 |
import argparse
def get_parser() -> argparse.ArgumentParser:
"""Create and return the argparser for undiscord flask/cheroot server"""
parser = argparse.ArgumentParser(
description="Start the UnDiscord flask/cheroot server",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
gro... | 5f158f7fef854b67420573de0c1c671bd05259e4 | 48,267 |
def text(body: t.Any, status: int = 200, content_type: str = 'text/plain', headers: Headers = None) -> Response:
"""
Response object that contains prepared plain text
:param body: dict object
:param status: http status
:param content_type: response content type (default text/plain)
:param header... | 54cd50e0fc58c745204566c008ee750690b3a36b | 48,268 |
def risk_estimate(est, gt, loss_func='zero-one-loss'):
""" est: cause x effect (indirect) boolean matrix"""
if loss_func == 'zero-one-loss':
return np.logical_xor(est.astype('bool'), gt.astype('bool')).sum()
else:
raise Exception('Specify correct loss function') | 449aa52a6554ca411de71bc1c1b2022654928610 | 48,269 |
def mf_session(mf_engine):
"""Define a default fixture in for the session, in case the user defines only `mf_engine`.
"""
Session = sessionmaker(mf_engine)
return Session() | e19458933e171614e0abc4d50363260ea7b4db31 | 48,270 |
def mafiaAlgorithm(transactions, min_support_count):
""" Extract the MFIs (Maximal Frequent Itemsets) from transactions with min support count using MAFIA Algorithm
Parameters
----------
transactions : list of sets
The list of transactions
min_support_count : int
The minimum support count threshold
Returns... | b2870ed93330df86f81d4b1c5c174027b63ffca3 | 48,271 |
import numpy
from typing import Counter
def train_test_apart_stratify(df, group, test_size=0.25, train_size=None,
stratify=None, force=False, random_state=None,
fLOG=None):
"""
This split is for a specific case where data is linked
in one way. Le... | a5f82a39bc0a037785df0cb6de3d25006c47e8d9 | 48,272 |
def get_ipsec_udp_key_status(
self,
) -> dict:
"""Get IPSEC UDP key status for all appliances
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ikeless
- GET
- /ikeless/seedStatus
:return: Returns dictionary ike... | db5ac6fee37574987a023183f8416d40234ac4e4 | 48,273 |
def compute_loss_sparse(logits, indices, weights=None):
"""Cross-entropy loss when only 1 label per example."""
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=indices, logits=logits)
if weights is not None:
row_sums = tf.squeeze(batch_gather(weights, tf.expand_dims(indices, 1)), 1)
... | 1bb2de7c4cd6e8838e31ae5fd8f162ee86194ff7 | 48,274 |
def convert_poly(p, zoom, im_height):
"""Move a polygon to the correct zoom level and referential"""
polygon = affine_transform(p, [powdiv(1, zoom), 0, 0, powdiv(1, zoom), 0, 0])
return affine_transform(polygon, [1, 0, 0, -1, 0, im_height]) | 3ec27e81f1c432ede8520f7f7918536d357e6c61 | 48,275 |
import time
def get_volume_from_pod(volume_name, namespace, experiment_id):
"""
Get volume content zipped on base64.
Parameters
----------
volume_name: str
namespace: str
experiment_id: str
Returns
-------
str
Volume content in base64.
"""
load_kub... | 2453e11543eaa3303ed00647ff72c770c4a57628 | 48,276 |
def PyOS_double_to_string(space, val, format_code, precision, flags, ptype):
"""Convert a double val to a string using supplied
format_code, precision, and flags.
format_code must be one of 'e', 'E', 'f', 'F',
'g', 'G' or 'r'. For 'r', the supplied precision
must be 0 and is ignored. The 'r' form... | 8d10031e7331a569a27919b25528363f693ca028 | 48,277 |
import typing
def describe_services(
ecs, cluster: str, services: typing.Set[str]
) -> typing.List[typing.Dict[str, typing.Any]]:
"""Wrap `ECS.Client.describe_services` to allow more then 10 services in
one call.
"""
result: typing.List[typing.Dict[str, typing.Any]] = []
services_list = list(... | f585610480aa7c657974b6f3163888fe7e9b6a32 | 48,278 |
from datetime import datetime
async def Custom(title:str, text:str) -> nextcord.Embed:
"""
Success Embed
-------------
Input: `Text`
Output: `nextcord.Embed` object
"""
# Start constructing the embed
embed = nextcord.Embed(
title = f"[ ■ ] {title}",
descriptio... | 0dcb35ff1e3005fd487716ae94f05bba0ddcf49e | 48,279 |
from typing import Counter
def _get_article_nb_links_and_scores(url, links_dict=None, score_counter=None, base_score=1, current_deep=1, max_deep=1, top_n=10):
"""Function to accumulate article neighborhood scores recursively."""
article, _ = get_or_create_article_by_url(url)
# if article == None:
... | 8082d41ff106a03c48b6344be629440364db990b | 48,280 |
import warnings
def handle_deprecated_data_source(data_collection, data_source, default=None):
""" Joins parameters used to specify a data collection. In case data_source is given it raises a warning. In case
both are given it raises an error. In case neither are given but there is a default collection it rai... | 95067948dd49973895fd66fb8e00ae0ee239e57c | 48,281 |
import re
def extract_page_nr(some_string):
""" extracts the page number from a string like `Seite 21`
:param some_string: e.g. `Seite 21`
:type some_string: str
:return: The page number e.g. `21`
:rtype: str
"""
page_nr = re.findall(r'\d+', some_string)
if len(page_nr) > 0:
... | 6d39314de89c8f4bf4d931f2dc329fe394a10091 | 48,282 |
def is_notification_center_valid(notification_center):
""" Given notification_center determine if it is valid or not.
Args:
notification_center: Instance of notification_center.NotificationCenter
Returns:
Boolean denoting instance is valid or not.
"""
return isinstance(notification_center, Notifica... | 30df9ea9d5bea4a048ec0a590c6dfd78bb79d8ce | 48,283 |
def get_codec(module):
"""Creates and returns the codec defined in the given module path
(ex: ``"myapp.mypackage.mymodule"``). The argument can also be an alias to
a built-in codec, such as ``"json"``, ``"json_zlib"`` or ``"pickle"``.
"""
if module in CODECS:
# The "_codec" suffix is to avoi... | 975344688d64cc8efe226a493aff2391e41626d2 | 48,284 |
def zeros(shape, dtype=hl.tfloat64):
"""Creates a hail :class:`.NDArrayNumericExpression` full of zeros.
Examples
--------
Create a 5 by 7 NDArray of type `tfloat64` zeros.
>>> hl._nd.zeros((5, 7))
It is possible to specify a type other than `tfloat64` with the `dtype` argumen... | 91291485a857fb851ead8a3474aa6b8fb321e197 | 48,285 |
from datetime import datetime
from operator import and_
def get_expenses_by_year(session, user_id, year):
"""
Function to get expenses by year
:param session: current db session
:param user_id: user id
:param year: year
:return: returns list of expenses
"""
date_begin = datetime(year=y... | d9e31f911f47995a459762ae750160b322949430 | 48,286 |
from operator import gt
def get_graph_tool_from_adjacency(adjacency, directed=None):
"""Get graph-tool graph from adjacency matrix."""
idx = np.nonzero(np.triu(adjacency.todense(),1))
weights = adjacency[idx]
if isinstance(weights, np.matrix):
weights = weights.A1
g = gt.Graph(directed=dir... | 420e7a77c42d81f1ddafdc760395f666109c027a | 48,287 |
def embed_batch(X, Y, mask):
"""Embed a square matrix x with y where mask is true.
Args:
x <list<np.array>>: set of to be embedded matrices
y <list<np.array>>: set of elements that are embedded into elements of X.
Same size as X.
mask <np.array<bool>>: marks where to embed.... | 3ba7956b4e0994567859981fe0c8c379ca24acb4 | 48,288 |
def ttgrange(*args, **kwargs):
"""
A shortcut for `tqdm.contrib.telegram.tqdm(xrange(*args), **kwargs)`.
On Python3+, `range` is used instead of `xrange`.
"""
return tqdm_telegram(_range(*args), **kwargs) | 9d10f380e6d267c189ab5292867cdd69a0e7cce4 | 48,289 |
def decode_lookup(key, dataset, description):
"""Convert a reference to a description to be used in data files"""
if key in dataset:
return dataset[key]
else:
decoded = input("Please enter {desc} for {key}: ".format(desc=description, key=key))
dataset[key] = decoded
return de... | 4df44c411ef4d1ffe76e489611c4a65888b0a3cd | 48,290 |
def comp_conv2d(conv2d, X):
"""
# 定义一个函数来计算卷积层，它初始化卷积层权重，并对输入和输出做相应的升维和降维
(主要是增删批量大小和通道数两个维度的信息)
"""
conv2d.initialize()
X = X.reshape((1, 1) + X.shape) # (1, 1)代表批量大小和通道数（“多输入通道和多输出通道”一节将介绍）均为1
Y = conv2d(X)
return Y.reshape(Y.shape[2:]) | 1fa8bce8a7efc2f53ba0146b1dbf607140bdbef0 | 48,291 |
def get_axes():
"""It returns the value set for the option 'axes' of the plot."""
return h.axes | 3aecd917da341160bb4b15b9760436717332ccfe | 48,292 |
def create_explicit_child_condition(parentage_tuple_list):
"""
This states for a parent node, what its explicit children are.
"""
def explicit_child_condition(G):
return all(
[sorted(G.out_edges(y[0])) == sorted([(y[0],x) for x in y[1]])
for y in parentage_tuple_list])... | 81860f24e7538feb84e9205dc233d2bf7d1dd1b3 | 48,293 |
from typing import List
def similar_in_backness(backness_1: UnmarkableBackness) -> List[Backness]:
"""
If the value is a wildcard value, return
all possible backness values, otherwise
return the single corresponding backness value.
"""
if isinstance(backness_1, MarkedBackness):
return backness_1.backness
ret... | 0096ba9f8a2f4d0e5a851330da383caa77155bb6 | 48,294 |
def ajax_delete_comment():
""" Deletes a comment from the recipe document """
response = {
"success" : False,
"flash" : {"message" : "Deletion Failed!", "category" : "error"},
"response" : None
}
if "comment" in request.json and "recipe" in request.json:
index = int(requ... | 952c5405fd977e91b7cbbb8035dd393123e432ca | 48,295 |
def line_job():
"""
每条线路的计划分配情况
:return: "data": [
{
"choice_plan": "5e71dd0d3ae156497e114364",
"device_id": "7WIZya2wsIKGuitNpHyIWjCq",
"id": "5e6ee2ddc1094a4d94ed0264",
"limit": 100.0,
"line": 1,
"line_name": "线路一",
... | 54c95021add6c706a70ffc60fb9889598ceec054 | 48,296 |
import random
def generate_random_GenericWindRoseVT():
"""
Generate a random GenericWindRoseVT object
Parameters
----------
N/A
Returns
-------
wind_rose GenericWindRoseVT
A wind rose variable tree
"""
weibull_array = np.array([[ 0.00000000e+00, 3.59673... | 42cd08447618b7710dd6e9c3a744feb9b8745cdb | 48,297 |
def CMYK_to_CMY(CMYK):
"""
Converts from *CMYK* colourspace to *CMY* colourspace.
Parameters
----------
CMYK : array_like, (4,)
*CMYK* colourspace matrix.
Returns
-------
ndarray, (3,)
*CMY* matrix.
Notes
-----
- Input *CMYK* colourspace matrix is in doma... | 51f64dae0ed43439f958cfb7210a65ba91e4613b | 48,298 |
def intDictToStringDict(dictionary):
"""
Converts dictionary keys into strings.
:param dictionary:
:return:
"""
result = {}
for k in dictionary:
result[str(k)] = dictionary[k]
return result | 65e519f04433a5dfcb4d7ace9bad91d8e06db4e5 | 48,299 |
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