content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Optional
from typing import Any
import time
import json
def success(message: Optional[str] = None, data: Any = None, status: int = 200, code: int = 20000) -> Response:
"""
函数体处理成功，请求流程正常结束
"""
if data is None:
data = dict()
res = {
'code': code,
'message'... | 21ed8a5748fdba20da28e92f9a0a2290166bdb75 | 3,599,186 |
import time
def dehydrate_time(value):
""" Dehydrator for `time` values.
:param value:
:type value: Time
:return:
"""
if isinstance(value, Time):
nanoseconds = int(value.ticks * 1000000000)
elif isinstance(value, time):
nanoseconds = (3600000000000 * value.hour + 600000000... | ba3528b7364a775ddcad7acebdb771f8aee666f4 | 3,599,187 |
def make_df_range(df, start, end):
"""
Given a dataframe, a starting date and an ending date (both datetime objects), return a data frame of the value counts for song ids
"""
songs = pd.DataFrame(columns=["ID","Timestamp"])
for i in range(len(df)):
timestamp = df.iloc[i,1]
parsed = p... | ea3dadc4f18e8cfdff6d7eac53760d3d420e99c0 | 3,599,188 |
def get_duration(self):
"""
Returns time taken for particles to exit the system following release
(duration of trajectory).
The duration (ns) is given as the time elapsed from the release of a
particle to it's exiting from the sytem and is returned for all
trajectories as an ndarray.
Param... | 3b9cbeac456c592d7ab66d522fce545539505344 | 3,599,189 |
def softmax(x):
"""Compute softmax values for each sets of scores in x."""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum() | 2d47067fc6fb94a823e562eb192b926314bbf963 | 3,599,191 |
import struct
def make_primitive_serializer(format):
"""Create a serializer for a primitive type from a struct format string.
The given `format` is a format string as defined in the `struct` module
and is expected to include a single primitive type.
See: http://docs.python.org/library/struct.html
... | d7db31a67d3faf8d773f0317b9f8684b6f981426 | 3,599,192 |
import datasets
def evaluate(dataset: object, predictions: object, output_folder: object, ovthresh: object) -> object:
"""evaluate dataset using different methods based on dataset type.
Args:
dataset: Dataset object
predictions(list[BoxList]): each item in the list represents the
p... | d20048ffb80bd541e227d292478a19e1fd6c6dee | 3,599,193 |
from ralph.attachments.models import Attachment, AttachmentItem
import mimetypes
from typing import Iterable
def add_attachment_from_disk(objs, local_path_to_file, owner, description=''):
"""Create attachment from absolute file path.
Function create and returns attachment object with file from local path.
... | d7ff0be7716d90ca62528d5f9a734ae8b0eb38a1 | 3,599,194 |
def estimate_poses_given_rot(factors: gtsam.BetweenFactorPose3s,
rotations: gtsam.Values,
d: int = 3):
""" Estimate Poses from measurements, given rotations. From SfmProblem in shonan.
Arguments:
factors -- data structure with many BetweenFactor... | 1542b9e1f1d82384fa3e44e5f477cee489b259ab | 3,599,195 |
def _weights(x, dx=1, orig=0, ravel=True, labels=None):
"""
Compute linear interpolation weights for projection array `x`
and regularly spaced detector pixels separated by `dx` and
starting at `orig`.
"""
if ravel:
x = np.ravel(x)
floor_x = np.floor((x - orig) / dx).astype(np.int32)
... | 10c4250a7b185ce5730e1f24258810c2b099ede4 | 3,599,196 |
def hazel_library(package_name):
"""Returns the label of the haskell_library rule for the given package."""
return "@{}//:lib".format(hazel_workspace(package_name)) | 1c67b4b3ad5468c76394e9e0b68bc91ab6bfa61d | 3,599,197 |
import re
def regex_from_segments(segments, requires=None, escape_segment=True):
"""Converts a list of segment tuple pairs into a regular expression string.
Args:
segments (list): The segment tuple pairs to convert.
requires (dict): Key/value pairs to be used in each segment.
Returns:
... | 2753db6616bd7ac7d66c0fe84585f400a696d794 | 3,599,198 |
async def update_user(
id: str,
user: UserUpdate = Body(...),
current_user: User = Depends(services.get_current_user),
):
"""Endpoint for updating user."""
user = services.update_user(id=id, user=user)
if user:
return JSONResponse(status_code=200,
... | 8ab6500292b950ed760b1ba58a5f7ac0bceb1b5e | 3,599,199 |
def findOnePath(startPoint, endPoint, accessMap):
"""
@ Parameter:
startPoint (int, int)
the coordinate of starting point
endPoint (int, int)
the coordinate of ending point
accessMap [str]
the string list map that indicates whether a place is
... | 75fd5df6f586595421dc42e6c40c6e3f700bed82 | 3,599,200 |
def index():
"""Show portfolio of stocks"""
# Define variables that describe the users portfolio using SQL queries
cash = db.execute("SELECT cash FROM users WHERE id = ?", session["user_id"])[0]["cash"]
stocks = db.execute(
"SELECT stock AS symbol, SUM(CASE WHEN transaction_type='buy' THEN shar... | 669d308d45177868b671596d77f29896462cd867 | 3,599,201 |
def president(request):
""" Renders the President page and all relevant information """
return render(request, 'president.html', {'semester_picker': SelectSemester()}) | d3ac37ca46f77c1c4202184d0f03e9a9696d15dd | 3,599,202 |
from datetime import datetime
def start_of_month(dt, d_years=0, d_months=0):
"""
Given a date, return a date first day of the month.
@param dt: The date to base the return value upon.
@param d_years: Specify a delta in years to apply to date.
@param d_months: Specify a delta in months to appl... | 90976e7df97b8581622df77528540487181cbb8f | 3,599,203 |
def period_circ(p0,p1,pb,asini,t0,t,pepoch):
"""
Calculate apparent pulse period, given los velocity.
Inputs:
p0 - pulsar spin period (s)
p1 - pulsar period derivative (s/s)
pb - orbital period (s)
asini - projected semi-major axis (m)
t0 - epoch of periapsis (s)
t - measurement epo... | d72120f293b6009b1bb165ab847ccef2d7e56e56 | 3,599,204 |
def negate(func):
"""
Creates a function that negates the result of the predicate `func`. The `func` function is
executed with the arguments of the created function.
Args:
func (callable): Function to negate execute.
Returns:
Negate: Function wrapped in a :class:`Negate` context.
... | 9dc4ad29cf8fc447a6e5dc2143b75c9f6f87f73e | 3,599,205 |
def send_message():
"""Send message"""
first_name = request.form.get("first_name")
last_name = request.form.get("last_name")
email = request.form.get("email")
# message_type = request.form.get("message_type")
message = request.form.get("message")
print(first_name, last_name, email, message,... | ea10861c787964dfba5d77d3f1031671eb7f1f84 | 3,599,206 |
def create_hypergraph(labels):
"""Create the incidence matrix of labels' hypergraph.
Parameter
----------
labels: Labels generated by multiple clustering algorithms such as K-Means.
Return
-------
H: Incidence matrix of labels' hypergraph.
"""
H = []
for label in labels:
... | b325cba36941b5ba262dfe9671d8fb3d33822eb2 | 3,599,207 |
def _graph_from_voronoi(vor, geometry):
"""Return networkx.Graph from Voronoi diagram within geometry."""
graph = nx.Graph()
for x, y, dist in _yield_ridge_vertices(vor, geometry, dist=True):
graph.add_nodes_from([x, y])
graph.add_edge(x, y, weight=dist)
return graph | 9bd2f949eb355dbb9061ec58e38c394c0d1fd974 | 3,599,211 |
def data_transforms():
"""get transform of dataset"""
if FLAGS.data_transforms in [
'imagenet1k_basic', 'imagenet1k_inception', 'imagenet1k_mobile']:
if FLAGS.data_transforms == 'imagenet1k_inception':
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
crop_scal... | a485a7deccd94e65b6fd16a1c6a4f7bd0f6eddc3 | 3,599,212 |
def frames(string):
"""Takes a comma separate list of frames/frame ranges, and returns a set containing those frames
Example: "2,3,4-7,10" => set([2,3,4,5,6,7,10])
"""
def decode_frames(string):
print('s', string)
if '-' in string:
a = string.split('-')
start, en... | a00a80bba4ab369a9682db6b62dad88911ecc711 | 3,599,213 |
def isdisc(node):
"""Test whether a particular node has a discontinuous yield.
i.e., test whether its yield contains two or more non-adjacent strings.
Nodes can be continuous even if some of their children are
discontinuous."""
if not isinstance(node, Tree):
return False
elif isinstance(node, ImmutableTree):
... | 5afc9645e5744706db210a5f805c23091e2ac3af | 3,599,214 |
def key_exists(dictionary, *keys):
"""Check if key exist in a nested dictionary.
:param dictionary: Nested dicionary
:type dictionary: dict
:param keys: Keys to test agains the dictionary
:type keys: args
:return: ``True`` if at least one key is in the dicionary
:rtype: bool
:raise KeyE... | 5bf414987e3bf25083cecdcff0af5c201561b15a | 3,599,215 |
def intrusion_sets(filters=None):
"""Retrieve all Intrusion Set objects.
Args:
filters (list, optional): A list of additional filters to apply to
the query.
"""
filter_list = FilterSet(filters)
filter_list.add(Filter('type', '=', 'intrusion-set'))
return query(filter_list) | 2c985aad3e8a8ffdef52acbc1e9a83e73e717f9d | 3,599,216 |
def five_zero_zero(exception):
"""
Usage::
The 500 page for this flask application.
"""
mail(exception)
return render_template("error-handler/500.html") | 86733f4672af164e5078e26f4538a13a6e936fd9 | 3,599,217 |
async def create_follow_record_for_user(
request_body: schemas.FollowsCreateRequestBody,
bg_tasks: BackgroundTasks,
db: Session = Depends(get_db),
current_user: schemas.User = Depends(get_current_user)
):
"""
The POST method for this endpoint will create a follow relationship between two users.
... | 0886352471d1bef36c4585552b3a37104d3328b1 | 3,599,218 |
def extendClusterSet(coi,
adata=None,
G="MetaNeighborUS_metacluster_graph",
max_neighbor_distance=2):
"""Extend cluster set to nearest neighbor on cluster graph
Note that the graph is directed, i.e. the neighbors are retrieved
by following the ... | 7cdad3853d8da98719e1393d0f0b36494f3c92c9 | 3,599,219 |
from typing import Tuple
def log_normal_distribution(
radius_g: float, sigma_g: float, n_bins: int
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Function for returning a log-normal size distribution. See Eq. 9
in Ackerman & Marley (2001).
Parameters
----------
radius_g : float
... | ef0a623fa3496e7ac042d4355a55c5d9e2730d39 | 3,599,220 |
def dumps(structure):
"""
Returns structure encoded as a single string, encoded as XML
"""
buffer = []
env = { 'envelope': structure }
__descend(buffer, 0, env)
return "".join(buffer) | 8cc8d49f033e4807ae56ef32c8c8e953eee015a8 | 3,599,222 |
def get_creation_hook_name(job_name):
"""Return the name of the creation hook for the given job (as a string) or None"""
return settings.JOBS[job_name].get(CREATION_HOOK_KEY) | 76d545eb3ead317efd401828d52c972725b5eade | 3,599,224 |
def brute_force_cow_transport(cows,limit=10):
"""
Finds the allocation of cows that minimizes the number of spaceship trips
via brute force. The brute force algorithm should follow the following method:
1. Enumerate all possible ways that the cows can be divided into separate trips
Use the gi... | 31a443ba74d778568a1e6c0cd22fef5974a1369c | 3,599,225 |
def process_windshear(procstatus, dscfg, radar_list=None):
"""
Estimates the wind shear from the wind velocity
Parameters
----------
procstatus : int
Processing status: 0 initializing, 1 processing volume,
2 post-processing
dscfg : dictionary of dictionaries
data set con... | 11f5c60b5d66f95cbf75aac4c34465e80d588ef0 | 3,599,226 |
from uncertainties import unumpy
def power_law_I_with_err(
f_val=1, f_err=0, g_val=g_DEFAULT, g_err=0, e=1, e1=1, e2=E_INF
):
"""Wrapper for f so the user doesn't have to know about
the uncertainties module"""
f = unumpy.uarray(f_val, f_err)
g = unumpy.uarray(g_val, g_err)
_I = power_law_inte... | 5d95f6dcee9ee0734b73c81646357d66ea486e81 | 3,599,227 |
import importlib
def class_from_module_path(module_path):
"""Given the module name and path of a class, tries to retrieve the class.
The loaded class can be used to instantiate new objects. """
# load the module, will raise ImportError if module cannot be loaded
if "." in module_path:
module... | e1b4935efd165c38c6394274c030c52eca5e241d | 3,599,229 |
import logging
async def load_gene(ensembl, gene_id):
""" sort out all the necessary sequences and positions for a gene
Args:
ensembl: EnsemblRequest object to request data from ensembl
gene_id: HGNC symbol for gene
Returns:
Gene object, containing info for each trans... | a9f1a8c2afaa5f71f599f25f1ac0afb5f1af0644 | 3,599,230 |
import copy
def mask_secret_parameters(parameters, secret_parameters):
"""
Introspect the parameters dict and return a new dict with masked secret
parameters.
:param parameters: Parameters to process.
:type parameters: ``dict``
:param secret_parameters: List of parameter names which are secr... | 380045cfe738e3831dd7fe29633d9a8f5fa91306 | 3,599,231 |
def matched_allocation(allocation_str_list, **kwargs):
"""Get allocation declaration"""
out = shared_orchestrator.check_add_allocation(allocation_str_list, **kwargs)
return out | f0c794b5bbf344a71f9f16400a11a5cc5fbcea59 | 3,599,232 |
import tqdm
import torch
def eval_epoch(model, data_loader, loss, model_device, loss_device):
"""
Calculates the total dice score and classwise dice score for full volume based evaluations. The loss function is
designed to be an torch.nn.Module object
Args:
model (torch.nn.Module): The mo... | 1b80f9e3c539cbfed51d842185ff65d489d4051a | 3,599,233 |
def get_kernel_devel_rpms():
"""
Get all installed kernel-devel packages ordered by release number (ascending).
"""
rpms = next(api.consume(InstalledRedHatSignedRPM), InstalledRedHatSignedRPM())
return sorted([pkg for pkg in rpms.items if pkg.name == 'kernel-devel'], key=get_kernel_rpm_release) | 19e92fbf10fc0986dbc0453eaa7a88304109b2f1 | 3,599,235 |
def share_get_all_by_share_server(context, share_server_id, filters=None,
sort_key=None, sort_dir=None):
"""Returns all shares with given share server ID."""
return IMPL.share_get_all_by_share_server(
context, share_server_id, filters=filters, sort_key=sort_key,
... | 3c0bbc4b0ca12f98527fb2dfc5e4cf61d6ba6cef | 3,599,237 |
def dictfetchall(cursor):
"""Returns all rows from a cursor as a dict"""
desc = cursor.description
return [dict(zip([col[0] for col in desc], row)) for row in cursor.fetchall()] | 72dfab4ad6de6359777d248252fc5e711edc86ad | 3,599,238 |
def dateAsRFC3339Text(date: Date) -> str:
"""
Convert a :class:`Date` into an RFC 3339 formatted date string.
:param date: A date to convert.
:return: An RFC 3339 formatted date string corresponding to :obj:`date`.
"""
return date.isoformat() | 0b1641e5a100d6fd28de6ca0a7e73602f2dedb8c | 3,599,239 |
def place(vertices_resources, nets, machine, constraints,
random=default_random):
"""A random placer.
This algorithm performs uniform-random placement of vertices (completely
ignoring connectivty) and thus in the general case is likely to produce
very poor quality placements. It exists primar... | 877a75df9732f31b363a0284e29b60851f54dbb8 | 3,599,241 |
def fillin_for_test(template_name, rendered_filename='ta3.yml', **kwargs):
"""Run for DM template winter eval as in
https://datadrivendiscovery.org/wiki/pages/viewpage.action?pageId=11276800
"""
global CURRENT_DIR
info_dict = dict()
# Add or overwrite any arguments
#
additional_info = ... | f83ae9feb84ecaf422256852d7183554e31f33fd | 3,599,242 |
def get_archive_formats():
"""Returns a list of supported formats for archiving and unarchiving.
Each element of the returned sequence is a tuple (name, description)
"""
formats = [(name, registry[2]) for name, registry in
_ARCHIVE_FORMATS.items()]
formats.sort()
return formats | e795c931c9a9061301516134468f5cc970908c79 | 3,599,243 |
def add(
name, address, root_password, cluster, wait=True,
deploy_hosted_engine=False, **kwargs
):
"""
Add new host
Args:
name (str): Host name
address (str): Host FQDN or IP
root_password (str): Host root password
cluster (str): Host cluster name
wait (bool)... | 0b67dc60ab5fa90d92005e3f26e8bb4bebe0f4e4 | 3,599,244 |
import json
def monitor(plugin):
"""Monitors channels of this node."""
reply = {}
reply['num_connected'] = 0
reply['num_channels'] = 0
reply['format-hint'] = 'simple'
peers = plugin.rpc.listpeers()
info = plugin.rpc.getinfo()
nid = info["id"]
chans = {}
states = {}
for p in... | 22a3dd3f161f2c5adc91d7d869981bab0ae73450 | 3,599,245 |
def plotvol2(dim, ax=None, equal=False):
"""
Create 2D plot area
:param ax: axes of initializer, defaults to new subplot
:type ax: AxesSubplot, optional
:param equal: set aspect ratio to 1:1, default False
:type equal: bool
:return: initialized axes
:rtype: AxesSubplot
Initialize a... | ce7f4b911a7569e4579a161987dbd10359d914d0 | 3,599,246 |
def create_annual_projections_pipeline(**kwargs):
"""
Combine together all the annual projection sources into a single annual projection
This only works for the current year, which is assumed
Returns:
Pipeline: this pipeline gets all the annual projections from various sources and combines them... | 1e235f131c576a96279036210088745a536245c0 | 3,599,247 |
def get_thick_line(point1, point2, length_spacing=1, line_thickness=0, width_spacing=1):
"""Construct a list of points representing a discrete line. If `line_thickness` > 0 then
the line is composed of multiple parallel line to each other with a width equal to
`line_thickness`.
Args:
point1: 1D a... | 4dd909d044041b2152e88aad630b32e24222bc53 | 3,599,248 |
def run_interpolator(interpolator, filename, visualize, dump):
"""Testing an interpolation method."""
grid = load_data()
lon = np.arange(-180, 180, 1 / 3.0) + 1 / 3.0
lat = np.arange(-90, 90, 1 / 3.0) + 1 / 3.0
time = 898500 + 3
x, y, t = np.meshgrid(lon, lat, time, indexing="ij")
z0 = core.... | 05ddf7bb71ab181453ed8ec59b74000b55b9b2ab | 3,599,249 |
def bar_plot(df, colormap, horizontal=False):
"""
"""
# Get the color palette
# colors = [colormap(np.arange(len(df)))]
colors = get_colors(df, colormap, axis=0)
width=0.8
if horizontal:
ax = df.plot.barh(label='index', width=width, color=colors)
else:
ax = df.plot.bar(la... | ec81c0501a648a6ddd69585440183b9021a5c677 | 3,599,250 |
def threshold_tukey(signal, threshold):
"""
Calculate threshold by Tukey method.
Parameters
----------
signal : numpy array
1D signal for threshold determination.
threshold : float
Number of interquartile interval above the 75th percentile.
Returns
-------
ths_value... | 8b2987e7f99ad5538e44d32a7977242348b645b8 | 3,599,251 |
from django.db import models
import types
def check_model_signals(app_configs=None, **kwargs):
"""
Ensure lazily referenced model signals senders are installed.
"""
# Avoid circular import
errors = []
for name in dir(models.signals):
obj = getattr(models.signals, name)
if isin... | 1e4da612f4a9b481f1bd44b938cf80ece895b55f | 3,599,252 |
import tqdm
def img_resize(imgs,
img_shape,
img_mode='L',
resize_filter=None,
verbose=True):
"""Resize images"""
resized_imgs = []
if verbose:
iterator = tqdm(imgs, ncols=100, unit=' images')
else:
iterator = imgs
for img in iterator:
... | 711e458edd8758ac9a7e9bc8cc1c37c529e3ab51 | 3,599,253 |
def create_sqlalchemy_model(
table_name: str,
Schema: mm.Schema,
segmentation_source: str = None,
table_metadata: dict = None,
with_crud_columns: bool = False,
) -> DeclarativeMeta:
"""Create a SQLAlchemy model from supplied
marshmallow schema.
Parameters
----------
table_name :... | dd3ce35302b95e91cbea55616df2ca0886566a7b | 3,599,255 |
def write_answers(references, hypotheses, rev_vocab, dir_name, id_end):
"""Writes text answers in files.
One file for the reference, one file for each hypotheses
Args:
references: list of list (one reference)
hypotheses: list of list of list (multiple hypotheses)
hypoth... | e9e6b10322926bd81f3b18a7fdcee12e360525ac | 3,599,256 |
import re
def bcftoolsversion(bcftoolsbin):
"""
:param bcftoolsbin: bwa bin path
:return: string, version of bcftools
"""
bcftoolscmd = [bcftoolsbin]
bcftoolsrun = Popen(bcftoolscmd, stdout=PIPE, stderr=PIPE)
pat = re.compile('Version')
version = 'None'
for i in bcftoolsrun.s... | 6a59d04b2fe78ad42c45f294ffb9c101e515d68a | 3,599,257 |
import yaml
def load_urls():
"""Loads the URLs yaml file and returns a yaml object / dictionary..
Args:
None
Returns:
A yaml (dictionary) object.
"""
with open(URLS_PATH) as ymlfile:
urls = yaml.load(ymlfile, Loader=yaml.FullLoader)
return urls | 758f4455887ea09686a00d5519a5287605cfbf0a | 3,599,258 |
def has_dynamic_registration_perm(user_level, obj, ctnr, action):
"""Permissions for static registrations."""
return {
'cyder_admin': True,
'ctnr_admin': True,
'ctnr_user': True,
'ctnr_guest': action == ACTION_VIEW,
'cyder_guest': action == ACTION_VIEW,
}.get(user_lev... | 30169b515d7d5b782e3971e6a5060c349cf445dd | 3,599,259 |
import re
import ast
def package_meta():
"""Read __init__.py for global package metadata.
Do this without importing the package.
"""
_version_re = re.compile(r'__version__\s+=\s+(.*)')
_url_re = re.compile(r'__url__\s+=\s+(.*)')
_license_re = re.compile(r'__license__\s+=\s+(.*)')
with ope... | 70a4f8c2ad820f5817d059f8ddbf6fc844d4a88a | 3,599,260 |
def _fraser_correction(
pd,
fraserflux,
yesterday,
afterHope,
NonFraser,
fraserratio,
otherratio,
runoff,
area,
):
"""For the Fraser Basin only, replace basic values with the new
climatology after Hope and the observed values for Hope.
Note, we are changing runoff and ens... | 5609c011d7cf95d07953d7ca2b19736a2c687c09 | 3,599,261 |
import select
import logging
def list_sprint():
"""
List ALl Sprint
:return:
"""
sprints = list()
for item in select(s for s in Sprint if s.status in ['active', 'disable']).order_by(Sprint.name):
logging.info('Get sprint %s[%s] info' % (item.uuid, item.name))
sprints.append({
... | 76406d18ec1e6b91d1aa0cf44514e79bca9fe253 | 3,599,262 |
def saveper():
"""
Real Name: SAVEPER
Original Eqn: TIME STEP
Units: Month
Limits: (0.0, None)
Type: component
The frequency with which output is stored.
"""
return time_step() | 21fca12744791334fd0f2604c51e64c9099315d9 | 3,599,264 |
import re
import string
import time
def main(question: str):
""" takes in question from user, then trasnslates an answer """
print("Started Program")
thisData = Data(API_KEY, PROJECT_TOKEN)
ASK_PATTERNS = {
# ASK FOR HELP
re.compile("[\w\s]+ help [\w\s]+ "): lambda: thisData.getHelp(... | 4622659f728b482ce3a713fcca7efe9938a870d0 | 3,599,265 |
def get_ocr_json_url_for_an_image(first_three_digits,
second_three_digits,
third_three_digits,
fourth_three_digits,
image_name):
"""
Get the URL of a JSON file given a barcode ... | 44c4d70971f24beee622ff2e11bef0474001aaa3 | 3,599,266 |
def download_yt(url):
"""
Download youtube video by url and save to video folder
"""
youtube = pytube.YouTube(url)
video = youtube.streams.get_highest_resolution()
path = video.download(app.config['VIDEO_FOLDER'])
return path | 196fbe0e099887bfa88c53a888fc0b90cb7bf742 | 3,599,267 |
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up the sensors from a ConfigEntry."""
try:
api = await real_time_api(
entry.data[CONF_IP_ADDRESS],
entry.data[CONF_PORT],
entry.data[CONF_PASSWORD],
)
await api.get_d... | b72c73d7acc421cda830b009e5c9281018f3cbe0 | 3,599,268 |
def toWKB(g):
"""Use :func:`writeWKB`"""
return writeWKB(g) | 81f0f5642f03a07bbe7f8bca67ced786f7295fed | 3,599,269 |
def cumulate_productivity(df, window=5):
"""Compute productivity over current and past 4 years."""
return (df.pivot(index="scopus_id", columns="t", values="SJR")
.fillna(0).rolling(window, min_periods=1, axis=1).sum()
.reset_index().melt(id_vars="scopus_id", value_name="SJR")) | 2330b39408136b4a0609f02c1174498c857085cb | 3,599,270 |
def tupleRemoveByIndex(initTuple, indexList):
"""
Remove the elements of given indices from a tuple.
Parameters
----------
initTuple : tuple of any
The given tuple from which we will remove elements.
indexList : list of ints
The indices of elements that we want to remove.
R... | 17009fc3616afa586467f43b1e7910a17c89ed0d | 3,599,271 |
def is_verb(word):
"""
Проверяет является ли переданное слово глаголом
@param word: слово
@return: True если слово является глаголом, иначе False
"""
if not word:
return False
pos_info = pos_tag([word])
return pos_info[0][1] == 'VB' | a37b9f2f9fea11dc12a7beb9add307b4ec0212c0 | 3,599,273 |
def parse_mot_record(serialized_example):
"""Parse serialized example of TfRecord and extract dictionary of all the information
"""
features = tf.io.parse_single_example(
serialized_example,
features={
'video_name': tf.io.FixedLenFeature([], tf.string),
'height': tf.i... | 2b52b6ae22546be75cfe7299207a7a5261831902 | 3,599,275 |
def set_hybridization_and_saturate(mol):
""" Assign bond orders, saturate with hydrogens, and assign formal charges
Specifically, this is a convenience function that runs:
``mdt.guess_bond_orders``, ``mdt.add_hydrogen``, and ``mdt.assign_formal_charges``
Note:
This does NOT add missing residue... | abb3dbbade675bf62ba640a56c20183ee30397b8 | 3,599,277 |
def executors_benchmark(fn):
"""Generates different local executors for basic benchmarks."""
def wrapped_fn(self):
"""Runs `fn` against different local executor stacks."""
tff.framework.set_default_executor(
tff.test.ReferenceExecutor(compiler=None))
fn(self, 'reference executor')
tff.frame... | 074834d5f141716df283565664a30e49952493f7 | 3,599,278 |
def hadamard_matrix_paleyII(n):
"""
Implements the Paley type II construction.
EXAMPLES::
sage: sage.combinat.matrices.hadamard_matrix.hadamard_matrix_paleyI(12).det()
2985984
sage: 12^6
2985984
"""
N = ZZ(n/2)
if is_prime(N-1) and (N-1)%4==1:
p = N-1
... | e057c02c46fbb46a3da05468a2d660c84f689408 | 3,599,279 |
def format_one_query(q, read_seq, read_coords, barcode_dict=None):
"""Formats output.
Parameters
----------
q : dict
A dictionary of fuzzy searching result. Key is levenshtein distance.
Value is list of matched barcodes.
read_seq : str
A DNA string (full length read).
re... | 2523ca6e15547a89e466fc4b4c9dad322fe91dbf | 3,599,281 |
def CDLEVENINGDOJISTAR(barDs, count, penetration=-4e37):
"""Evening Doji Star"""
return call_talib_with_ohlc(barDs, count, talib.CDLEVENINGDOJISTAR, penetration) | ceafd0817b72b760658242d1eae0ea0be621fed9 | 3,599,283 |
def norm_sym_dsn_dist(X, g):
"""
Returns the normalized pdf of the derivative of a symmetric shot noise process, (td/2)*dS(t)/dt, lambda = 1/2.
Input:
X: The normalized variable X = (x-<x>)/x_rms, 1d numpy array
g: shape parameter
Output:
F: The pdf of X.
"""
F = np.zeros... | 3dc8353ce347b222feb2da91b52dc5f239843160 | 3,599,285 |
from typing import Any
from typing import Optional
from typing import List
from typing import Dict
def specs_are_the_same(obj1: Any, obj2: Any, field_paths: Optional[List[str]]=None) -> bool:
"""Determine if the specifications for two Kubernetes objects are equivalent
Only the relevant parts of the two objec... | d24b7b90d040ef7f0ad4715300fb2c39483a634e | 3,599,286 |
def split_graph_feature(node_feat, edge_feat, node_index_list, edge_index_list):
"""
Split batched node and edge features (graph features) to individual lists
:param node_feat: torch.Tensor of shape [N1 + N2 + ..., D1]
:param edge_feat: torch.Tensor of shape [E1 + E2 + ..., D2]
:param node_index_lis... | 628790616dbc0712691b2d61c7ded63519c9568f | 3,599,287 |
import codecs
def read_relative_file(filename):
"""
Returns contents of the given file, whose path is supposed relative
to this module.
"""
with codecs.open(join(dirname(abspath(__file__)), filename), encoding='utf-8') as f:
return f.read() | 8c3e167247ed0a70350095b9d9eb0eced6023193 | 3,599,288 |
def skipThirdPay():
"""
无需支付基本逻辑
1.获取参数
2.校验参数
3.查询订单支付信息
4.判断支付金额与支付状态
5.返回响应
"""
# 1.获取参数: token, orders_id
token = request.json.get("token")
orders_id = request.json.get("orders_id")
# 2.校验参数
# 判断用户是否登陆
if not token:
return jsonify(code=RET.TOKENERR, ... | ef121fa87ee4af37ce658e8ff04b949fc24f8e23 | 3,599,290 |
import random
def flirt(text, conn, nick, message):
"""<user> - flirts with <user>"""
target = text.strip()
if not is_valid(target):
return "I can't flirt with that."
if is_self(conn, target):
# user is trying to make the bot attack itself!
target = nick
message('{}, {}'... | cb840e414567b3be3b2ad8318fa03167a449db35 | 3,599,291 |
def deepclustering_loss(targets, logits, usedbins, seq_length, batch_size):
"""
Compute the deep clustering loss
cost function based on Hershey et al. 2016
Args:
targets: a [batch_size x time x (feat_dim*nrS)] tensor containing the binary targets
logits: a [batch_size x time x (feat_dim*emb_dim)] tensor contai... | ff18834a32d712e691964b51201b80b77c4c406d | 3,599,292 |
def refine_with_details_criteria(flats_list, constraint):
"""
Filter a list of flats according to the criteria which require the full
details to be fetched. These include minimum number of photos and terms
that should appear in description.
.. note ::
This has to be done in a separate func... | e5f86e272ca3a73a5146efa71dc53222d5175bc8 | 3,599,294 |
def merge_spike_trains(spike_trains):
""" Merges a number of spike trains into a single spike train.
:param spike_trains: list of :class:`.SpikeTrain`
:returns: spike train with the merged spike times
"""
# concatenating and sorting with numpy is fast, it also means we can handle
# empty spike ... | d4972b008fb60ceb6fc0a51a3eba83104049bea2 | 3,599,295 |
def create_mysql_mysqldb(username, password, host, port, database, **kwargs): # pragma: no cover
"""
create an engine connected to a mysql database using mysqldb.
"""
return create_engine(
_create_mysql_mysqldb(username, password, host, port, database),
**kwargs
) | d1b599b33012c85f893665817136ae6d3c08647b | 3,599,296 |
import json
async def create(_req, gameid, jwt):
"""Create a new group for the given game"""
groupid = await KVS.create_group(jwt["uid"], gameid)
return json({"groupid": str(groupid)}) | a1c6ab446b86e911bc759854c56f84838863f552 | 3,599,297 |
def get_diagfile(results_dir):
"""Return the name of a diagnostics file in results_dir that is completed.
Useful for the functions that insert git commits into files and need a
representative diagnostic file to get the git commits from. We arbitrarily
pick the last complete directory."""
... | 09b429dea1df12aae1e2c26c15c31ebc63fbee52 | 3,599,298 |
import torch
def regression(src_bbox, loc):
"""
Apply regression, by applying the offset coefficient
Input:
srx_bbox [N, R, 4]: coordinates of bounding boxes in XYXY.REL format
loc [N, R, 4]: offset coefficient t_x, t_y, t_w, t_h in CxCyWH.REL format
Return:
dst_bbox[N, R, 4]: ... | 1d6137ecf1895fc92f63c37a510f2d0f3d68f54f | 3,599,299 |
from typing import List
from datetime import datetime
def filter_archives(
archives: List[Archive],
intervals: List[datetime.timedelta],
current_date: datetime.datetime = None,
) -> List[Archive]:
"""
Return a list of archive names which should be kept.
``archives`` is a list of ``jdsnap.Arch... | 82dd416734ba9b7811a71c0ef2661b369db4ff62 | 3,599,300 |
def get_action(state, player):
"""
Returns an action from the _ACTION_CATALOGUE, given the state.
Args:
state (connect_x.game.connect_x.ConnectXState): The state.
player (int): The player.
Returns:
int: The action.
"""
matrix = bitmaps_to_matrix(state.bitmaps)
mark ... | 03e10eeca9cf9d912d226766cc0bb3f7cf0af5a6 | 3,599,301 |
def get_path(name, scheme=_get_default_scheme(), vars=None, expand=True):
"""Returns a path corresponding to the scheme.
``scheme`` is the install scheme name.
"""
return get_paths(scheme, vars, expand)[name] | 755223fea25a7153a3441ca048ffeb0aad68123f | 3,599,302 |
def get_bot():
"""
Gets the already instantiated Telegram client
:return: Telegram client instance
:rtype: telegram.Bot
"""
if not _bot:
initialize_bot()
return _bot | 4be9e7a3bdc4ee21a2bc126f7fcefb92daee6714 | 3,599,303 |
def get_binwise_reduced_column(a_gpu, stride, a_range):
"""
description:
calls the cuda jit function calc_binwise_reduced_column and
returns the result
input:
- a_gpu -> single col nd-array
- stride -> stride value
- a_range -> min-max values (ndarray => shape(2,))
... | 2dddf4012480d5645d86e9b0c2a61c8175977ad5 | 3,599,304 |
from typing import Dict
def select_dict_from_resname(u: Universe) -> Dict[str, str]:
"""
Infer select_dict (possibly interested atom species selection) from resnames in a MDAnalysis.universe object.
The resname must be pre-assigned already.
Args:
u: The universe object to work with.
retu... | a9e844ade104303ffc9c8b1f6b5c742570353156 | 3,599,305 |
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