content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_user_data(user):
""" Extrats user data to be save in the session """
extract_attrs = current_app.config.get('SESION_USER_FIELDS', [])
user_data = {}
for attr in extract_attrs:
user_data[attr] = getattr(user, attr, None)
return user_data | fdaff20825669a04a1973af400287119b28af5e0 | 50,200 |
import scipy
def pv_optimize_orientation(
objective,
dfsun, info, tznode, elevation,
solpos, dni_et, airmass,
systemtype,
yearsun, resolutionsun,
dflmp=None, yearlmp=None, resolutionlmp=None, tzlmp=None,
pricecutoff=None,
max_angle=60, backtrack=True, gcr=1./3.,
dcac=1.3,
l... | 1a22c07efb113daa4e2f75484bcc7c194522c98a | 50,201 |
def sanitize_metric_name(name):
"""Sanitize a metric name by removing double dots.
:param name: Metric name
:return: Sanitized metric name
"""
if name is None:
return None
return ".".join(_components_from_name(name)) | 6fa8e856d06b8ec374594a20b2376911fc428e6f | 50,202 |
import argparse
def get_parser():
"""Args Description"""
# current_year = datetime.datetime.today().year
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--data-dir", type=str, help="baseball data dire... | 6063b667be373f823e481914ed0736dc86abcbb9 | 50,203 |
from typing import Union
def multinomial_mode(
distribution_or_probs: Union[tfd.Distribution, jnp.DeviceArray]
) -> jnp.DeviceArray:
"""Calculates the (one-hot) mode of a multinomial distribution.
Args:
distribution_or_probs:
`tfp.distributions.Distribution` | List[tensors].
If the former... | 233a19bdae03dd68650a322e7960e450fb241c53 | 50,204 |
import io
def get_image(key):
"""Get image by key (index) from images preloaded when starting the viewer by preload_images().
"""
key=int(key)
img = images[key]
img_buffer = io.BytesIO(img)
return send_file(img_buffer,
attachment_filename=str(key)+'.jpeg',
mimetype='i... | 9bf5897d5c40afc20375cabd39aa40d982af754c | 50,205 |
from re import T
import re
def pql_PY(state: State, code_expr: T.string, code_setup: T.string.as_nullable() = objects.null):
"""Evaluate the given Python expression and convert the result to a Preql object
Parameters:
code_expr: The Python expression to evaluate
code_setup: Setup code to prep... | 0c642e7aa0e44d7e575c02765dcf8569fd5fa0ed | 50,206 |
import json
def load_statics():
"""Reload all static content from their files"""
global questions
global static
for file in ["admin", "end", "index", "question"]:
with open("templates/" + file + ".html") as f:
static[file] = f.read()
for file in ["client"]:
with open("... | 5423930733860fcaca19a1f7eaeb93b553a28645 | 50,207 |
import os
def set_env_variables():
"""
Set different variables if the environment is running on a local machine
or in the sandbox.
:return: a tuple with a string and a boolean
"""
if 'SERVER_SOFTWARE' not in os.environ \
or os.environ['SERVER_SOFTWARE'].startswith('Development'):
... | 33dee8f2367eea6d84d0228ce5e37a675fc45082 | 50,208 |
def farey_alg(n, largest_divisor=100):
"""
This started simple, but then i figured it could be really close and
then come farther away again, so i changed it to a much more complicated
one.
It can still gives a closing range, but it now gives the best
approximate it has come across, not the las... | 30dd6815908aa9d05cb4387909e5aac37aa7bc4a | 50,209 |
def clean(data, columns, inplace=False) -> pd.DataFrame:
"""
Cleans a dataframe
:param data: the dataframe to clean
:param columns: a single column name or list of column names to clean
:param inplace: if True, changes are made to the specified column; otherwise, a 'cleaned'
column is appended t... | 27ccc2e2fe8ba1369b0f274d904424e6761f78d3 | 50,210 |
def vector_add(v, w):
"""向量加法"""
return [v_i + w_i for v_i, w_i in zip(v, w)] | 0030a6b83bc998167a1f25c6242bd657d030a937 | 50,211 |
def load_data(
data_list,
batch_categories=None,
profile='RNA',
join='inner',
batch_key='batch',
batch_name='batch',
min_features=600,
min_cells=3,
n_top_features=2000,
batch_size=64,
chunk_size=CHUNK_SIZE,
log=None,... | a67f54f2906d54ba71362879514afe83e521d718 | 50,212 |
def fast_p_val(A, B, axis=0, metric=np.mean, numResamples=10000):
"""Return the p value that metric(A) and metric(B) differ along an axis.
Parameters
----------
A : array_like
Array containing numbers of first group.
B : array_like
Array containing numbers of second group.
... | 91a2655538e8d8cfa7ee05f9b6a2487972976c23 | 50,213 |
def api_categorize():
"""
the API will receive a POST request containing a JSON with the labels title and tags,
with the data provided it'll serve a category that best fit the data
"""
model = CategoryCLF()
all_predicted = list()
if not request.is_json:
r = {"message": "Input is not... | 0df3b39d757cb00ca583006c8aedd019db367052 | 50,214 |
def is_calldef_pointer(type):
"""returns True, if type represents pointer to free/member function, False otherwise"""
if not is_pointer(type):
return False
nake_type = remove_alias( type )
nake_type = remove_const( nake_type )
nake_type = remove_volatile( nake_type )
return isinstance( n... | 089f255d0497b2949641b13be735bc94f98379f7 | 50,215 |
def collate_amplifyers():
"""Grab the RT and QT columns then concat assign post type and hashtag
Returns:
amplifyers: A dataframe with a filtered columns bearing the fields with the
network
"""
select_columns = ['hit_sentence','influencer','post_type... | 142f4eb219832b5cc66119487586a84b39e6420c | 50,216 |
import time
def _get_valid_stan_args(base_args=None):
"""Fill in default values for arguments not provided in `base_args`.
RStan does this in C++ in stan_args.hpp in the stan_args constructor.
It seems easier to deal with here in Python.
"""
args = base_args.copy() if base_args is not None else ... | a0bdeba03dca4c5e0f8162ca7c1010a7aaf8fa65 | 50,217 |
from re import X
def cancel_env_set_get(resources, node, equiv):
"""Simplify combinations of env_get/setitem.
* get(set(env, k1, v), k2, dflt) =>
* v when k1 == k2
* get(env, k2, dflt) when k1 != k2
"""
key1 = equiv[C1]
key2 = equiv[C2]
if key1.value == key2.v... | 8799830d9c085b72208ba52978f1779970470df2 | 50,218 |
from typing import Iterable
def get_donchian(quotes: Iterable[Quote], lookback_periods: int = 20):
"""Get Donchian Channels calculated.
Donchian Channels, also called Price Channels, are derived from highest High and lowest Low values over a lookback window.
Parameters:
`quotes` : Iterable[Quote... | ae9f8df115ac719ce475b35f33b098d84e1ab33e | 50,219 |
def getLigandCodeFromSdf ( sdfFileName ):
"""
Funkcja sluzy do pobierania kodow ligandow z pliku .sdf
Wejscie:
sdfFileName - nazwa pliku sdf
Wyjscie:
ligandCodes - lista znalezionych kodow ligandow
"""
sdfFile= open(sdfFileName, 'r' )
line = sdfFile.readline()
ligandC... | 9b25f91b754448f6fab4ce11e0f816cbf5406dea | 50,220 |
def pav(y):
"""
PAV uses the pair adjacent violators method to produce a monotonic
smoothing of y
translated from matlab by Sean Collins (2006) as part of the EMAP toolbox
Author : Alexandre Gramfort
license : BSD
"""
y = np.asarray(y)
assert y.ndim == 1
n_samples = len(y)
v... | 64b4e4bff18c5d7bdf34556cad2fae75ac01d1b0 | 50,221 |
def get_segment_library_list(instrument, detector, filt,
library_path, pupil='CLEAR'):
"""Given an instrument and filter name along with the path of
the PSF library, find the appropriate 18 segment PSF library files.
Parameters
-----------
instrument : str
Name ... | a16d49548a66ab598ae2e1ffe4784cd1cd027e80 | 50,222 |
def resize(mat, width, height):
"""
Resize the input image to width x height
:param mat: input image
:param width: new width
:param height: new height
:return: resized image
"""
return cv2.resize(mat, (width, height)) | a521aa2c1839a67989708590840e3fd3afe72009 | 50,223 |
import os
from re import DEBUG
def read_write_star(file, mics_to_remove, line_range, star_column_num, output_fname) :
""" Open a .star file and read it line-by-line. Evaluate each line with conditional functions.
============================================
PARAMETERS:
====================... | 3ca2d18271b21c4d5e03f8d9abb2877cd9932384 | 50,224 |
def stockout_box_pack_api(order_id):
"""
包裹信息, 有明细行
post req:
{
lines: [
{sku, qty_pack}
]
}
"""
box = None
if request.method == 'POST':
order = Stockout.query.t_query.filter_by(id=order_id).with_for_update().first()
lines ... | 063c89afccfb74512232af6c6069201a411c9be8 | 50,225 |
def create_model(google_colab, n_features):
"""Creates Keras model"""
LSTM_ = CuDNNLSTM if google_colab else LSTM
inputs = Input(shape=(None, n_features))
x = Conv1D(
filters=32,
kernel_size=16,
padding="same",
kernel_initializer="he_uniform",
)(inputs)
x = Batc... | 801a490beccb3f8032f192acb0dcb88b95e2ee6f | 50,226 |
def get_hash_command(client: Client, args: dict) -> CommandResults:
"""
Get hash reputation.
Removed hash classification since SentinelOne has deprecated it - Breaking BC.
"""
hash_ = args.get('hash')
type_ = get_hash_type(hash_)
if type_ == 'Unknown':
raise DemistoException('Enter a... | d14b910353b2c6621fc9fab4e38c3e9c026f1efe | 50,227 |
from typing import Optional
import random
def map_color(color: Optional[str]) -> Color:
"""Maps color onto Nozbe color"""
colors = list(list(Color.allowed_values.values())[0].values())
colors.remove("null")
return Color(color if color in colors else random.choice(colors)) | ec7c6d4b83daa07223e7aca38dd6887098aedbc9 | 50,228 |
def transform_labels_into_names(labels, entity_idx_to_name):
"""
Trasform a list of number labels to the related names of characters
:param labels:
:param entity_idx_to_name:
:return: list of names labels
"""
names_labels = []
for label in labels:
if label < len(entity_idx_to_nam... | 87ad913737eda52ee7cf808cf1a930e1a81b02c6 | 50,229 |
from typing import Dict
from typing import Any
def validate_alert_report_type_arguments(args: Dict[str, Any], params: Dict[str, Any]) -> Dict[str, Any]:
"""
Validates the arguments required for alert details report type from input arguments of reports command.
Will raise ValueError if inappropriate input ... | b57df54c93b09ed73607a0de8863fb7d836f93d4 | 50,230 |
def laplacian(csgraph, normed=False, return_diag=False, use_out_degree=False,
*, copy=True):
"""
Return the Laplacian matrix of a directed graph.
Parameters
----------
csgraph : array_like or sparse matrix, 2 dimensions
compressed-sparse graph, with shape (N, N).
normed : ... | 895b742e7a7b9f0640085a9cd6b958901bb67354 | 50,231 |
def individual_amalgamate(subitem, past_subitem, create_subitem, ys1):
""" amalgamate the individual items. Recurse when needed. """
try:
if subitem['name'] != past_subitem['name']:
pass
else:
create_subitem['children'].append(subitem['children'][0])
past_subi... | f066b11f5d2995b26b704ae4bc90db3631dd0ec5 | 50,232 |
import warnings
def interpolated_acf(times, fluxes, cadences=None):
"""
Calculate the autocorrelation function after interpolating over
missing times and fluxes.
Parameters
----------
times : numpy.ndarray
Incomplete but otherwise uniformly sampled times
fluxes : numpy.ndarray
... | c2fdcdf7705e5eb4dc36b32c94511a5333041485 | 50,233 |
def _transformer(
emb_dim=512,
num_heads=8,
num_layers=6,
qkv_dim=512,
mlp_dim=2048,
dropout_rate=None,
attention_dropout_rate=None,
nonlinearity='gelu',
):
"""Transformer config."""
configs = {
'models.build_transformer_config.emb_dim': emb_dim,
'models.build_transformer... | 547f585d68a798324ef47e0c5093ff89956e9e9a | 50,234 |
def get_vpc_list():
"""获取所有的vpc信息"""
s = get_aws_session(**settings.get("aws_key"))
clients = s.client("ec2")
resp_dict = clients.describe_vpcs()
vpc_list = resp_dict.get("Vpcs")
return vpc_list | 5e866331704d6ae81c3b37e616d9ba5e08a4b6bf | 50,235 |
import os
def get_package_path(repodir, packagename):
""" Return the path to an individual package file. """
return os.path.join(repodir, PACKAGESDIR, packagename) | 011fcc9232bf51cc736ff49db071475c1d4a74a8 | 50,236 |
import random
def make_bank_act_tp_data(ctif_tp):
"""
生成银行账号种类
:param ctif_tp: 主体类型
:return: 账号类型
"""
if ctif_tp == "1":
act_tp = random.choice(["02", "03"])
elif ctif_tp == "2":
act_tp = random.choice(["01", "03"])
else:
raise TypeError("ctif_tp={}类型错误！".format... | 90e394b44dd6802b5c9a7773edb3b9baa32b0eef | 50,237 |
def v70_from_params_asdict(v70_from_params):
"""Converts a sparse `v70_from_params` array to a dict."""
dict_v70_from_params = {}
for i in range(v70_from_params.shape[0]):
for j in range(v70_from_params.shape[1]):
v = v70_from_params[i, j]
if v:
dict_v70_from_params[(i, j)] = str(v)
retu... | 355ba80c131d08b6aedf20680545b4b31e07832e | 50,238 |
def open_r(filename):
"""Open a file for reading with encoding utf-8 in text mode."""
return open(filename, 'r', encoding='utf-8') | 08086625a9c05738a3536001a158eff3b0718ddf | 50,239 |
def get_current_kernel_release():
"""
Get the release of the current kernel as a string.
"""
return api.current_actor().configuration.kernel.split('-')[1] | 98353b45b458a45414823c1927597eaa4fbb43eb | 50,240 |
import random
def genetic_algorithm_optimizer(starting_path, cost_func, new_path_func, pop_size, generations):
"""Selects best path from set of coordinates by randomly joining two sets of coordinates
Arguments:
starting_path -- List of coordinates, e.g. [(0,0), (1,1)]
cost_func -- Optimization me... | c9dcc1517f41e9a22e070a3cd670a4c249611b72 | 50,241 |
def get_a_mock_request_packet_and_raw():
"""Returns a tuple of mock (IncomingPacket, REQID, RegisterResponse message)"""
reqid = REQID.generate()
message = mock_protobuf.get_mock_register_response()
pkt = convert_to_incoming_packet(reqid, message)
return pkt, reqid, message | b0096a6567ce3f5f7b7bd7f472dfc14800ad2414 | 50,242 |
import os
def _settingFileList():
"""
Get list of setting files from settings folder.
"""
ret = []
files = [f for f in os.listdir(os.path.dirname(__file__))
if os.path.isfile(os.path.join(os.path.dirname(__file__), f))]
for f in files:
if f.endswith(Global.settingFileExten... | e7862584edab8896d261ef514966294933379ff1 | 50,243 |
import torch
import pickle
def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors).
Args:
data: any picklable object
group: a torch process group. By default, will use a group which
contains all ranks on gloo backend.
Returns:
... | b68f27b5b9f5edf94e2f959ae35a4b3955052d2f | 50,244 |
def random_transform(random_state=np.random.RandomState(0)):
"""Generate random transform.
Each component of the translation will be sampled from
:math:`\mathcal{N}(\mu=0, \sigma=1)`.
Parameters
----------
random_state : np.random.RandomState, optional (default: random seed 0)
Random n... | 844a76f790e10befdd29ebc013d9166b3b3b9d4c | 50,245 |
import warnings
def normalize_frequency_locations(samples, Kmax=None):
"""
This function normalize the samples locations between [-0.5; 0.5[ for
the non-cartesian case
Parameters:
-----------
samples: np.ndarray
Unnormalized samples
Kmax: float
Maximum Frequency of the sam... | 4520ef6bb9a9e13be30dc9a2141e4f7987d71c41 | 50,246 |
def max_value(uncert_val):
"""Maximum confidence interval for a ufloat quantity."""
return uncert_val.nominal_value + uncert_val.std_dev | e9a3b8541e8456d370945e9fcc4d80d0a49b6d0b | 50,247 |
from config import SWAP12
from config import CUT_INTERP
from config import EXTRA_MDET_CONFIG
from config import DO_METACAL_MOF
from config import DO_METACAL_TRUEDETECT
from config import DO_METACAL_SEP
from config import METACAL_GAUSS_FIT
from config import SHEAR_MEAS_CONFIG
from config import DO_END2END_SIM
def get_... | 885fc49f8849d39316aba6d784dcb49cc903f652 | 50,248 |
import json
def load_labels_schema():
"""Loads the label json schema file
:return: json schema
"""
json_schema = {}
with open(label_schema_json, "r") as schema_file:
json_schema = json.load(schema_file)
return json_schema | 3120e71d0dd04972c822b78ff91bb1076864b34e | 50,249 |
import argparse
def run(argv=None):
"""The main function which creates the pipeline and runs it."""
parser = argparse.ArgumentParser()
# Here we add some specific command line arguments we expect. Specifically
# we have the input file to load and the output table to write to.
parser.add_argument... | 0c40b7310979d929ecb316f787149e4510894349 | 50,250 |
def _parabolic_interpolation(y_frames):
"""Piecewise parabolic interpolation for yin and pyin.
Parameters
----------
y_frames : np.ndarray [shape=(frame_length, n_frames)]
framed audio time series.
Returns
-------
parabolic_shifts : np.ndarray [shape=(frame_length, n_frames)]
... | 0d629c9027d59a6e4360a55bad0ac550cf13a20e | 50,251 |
import logging
def create_api():
"""
Create API with auth info provided in keys.txt
Return and log relevant errors.
"""
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(ACCESS_KEY, ACCESS_SECRET)
api = tweepy.API(
auth, wait_on_rate_limit=True, wait_o... | ad2778841d5976abcc3ca3f7c40887592c6a53a5 | 50,252 |
def exp(h, Omega):
"""
:param h: steglengde
:param Omega: matrise med vinkelhastigheter
:return: exp(h*Omega) etter definisjonen i likning (21) i oppgaven
"""
I = np.identity(3, dtype=np.double)
omega = np.sqrt(Omega[2, 1] ** 2 + Omega[0, 2] ** 2 + Omega[1, 0] ** 1)
return (
I
... | c274fc6aa97566b7816b159438464aa6f07ca4c5 | 50,253 |
import torch
def compute_mae(vec1, vec2):
"""
vec1, vec2 is torch.Tensor
"""
vec1 = vec1.reshape(-1, vec1.shape[-1])
vec2 = vec2.reshape(-1, vec2.shape[-1])
if vec2.shape[-1] == 2 and vec1.shape[-1] == 3:
vec1 = vec1[..., :2] / torch.norm(vec1[..., :2], dim=-1, keepdim=True)
if v... | 14b023666d726004b05f57efa874d2cbe6b81db7 | 50,254 |
from rest_framework_simplejwt.settings import api_settings as jwt_settings
from rest_framework_simplejwt.views import TokenRefreshView
def get_refresh_view():
""" Returns a Token Refresh CBV without a circular import """
class RefreshViewWithCookieSupport(TokenRefreshView):
serializer_class = CookieT... | 82c702d96b5dd670c0723db812b4bdfc075eb6a7 | 50,255 |
def prepend_protocol(url: str) -> str:
"""Prefix a URL with a protocol schema if not present
Args:
url (str)
Returns:
str
"""
if '://' not in url:
url = 'https://' + url
return url | 856961526207510c630fe503dce77bdcfc58d0cc | 50,256 |
def jackknife_errors(data_input, weights_input, bins, num_sub_samps):
"""Returns the jackknife resampling errors for the estimation of histogram
bar height, for the provided weighted data and bin edges.
Parameters
----------
data : numpy.ndarray
Input first-passage time data.
weights: n... | bdb5d5da0e6c303ead396eaca03db7a00f72ab0b | 50,257 |
import os
def test_nov0512_as_planned():
"""NOV0512 the way it really is. This is how old loads with no characteristics will
process when run through the checker."""
ok, lines, sc = run_nov0512(with_characteristics=False)
# hopper.pcad now uses the default ODB_SI_ALIGN if not supplied,
# so we ge... | c88a435ee116d00b15a4328ab46ff96e6353dc29 | 50,258 |
def NL3P_sinc(A, to, BW, E1, k2, cDiss, nu, R, dt, startprint, simultime, fo1, k_m1, zb, printstep = 1, Q1=156.7, Q2=300, Q3=450):
"""This function performs sinc excitation simulation of the cantilever at the tip"""
"""This is designed for the NL3P model described in: “Theory of single-impact atomic force spect... | b1c46a1cb2f96ce68f4beb92c2325e0e1d3e853f | 50,259 |
def get_dataset(dataset_id, client_email, private_key_path):
"""Shortcut method to establish a connection to a particular dataset in the Cloud Datastore.
You'll generally use this as the first call to working with the API:
>>> from gcloud import datastore
>>> dataset = datastore.get_dataset('dataset-id', emai... | eb23b6565afb8e882e705ecd809d6b929e9710bb | 50,260 |
def authenticate(inner_function):
""":param inner_function: any python function that accepts a user object
Wrap any python function and check the current session to see if
the user has logged in. If login, it will call the inner_function
with the logged in user object.
To wrap a function, we can p... | f45b7608052eb082131f3ac31721c5c08519473f | 50,261 |
def Ptoa(P, m1, m2): # input not in SI
"""
Calculates orbital radius from period
:param P: period [yr]
:param m1: mass of primary [Msol]
:param m2: mass of secondary [Mjup]
:return: semi-major axis [AU]
"""
# a^3/P^2 = (G / 4 pi pi) (m1 + m2)
c = G / (4. * np.pi * np.pi)
mu = (m... | 46ced6e6521cbc242c0fa4ad349779eabd67b418 | 50,262 |
import time
import os
import platform
def buildinfo(lenv, build_type):
"""
Generate a buildinfo object
"""
build_date = time.strftime ("%Y-%m-%d")
build_time = time.strftime ("%H:%M:%S")
build_rev = os.popen('svnversion').read()[:-1] # remove \n
if build_rev == '':
build_rev = '-U... | ec7066e318b2ffe3054fab0975868ae6cbc43ae2 | 50,263 |
def nlopt_bobyqa(
criterion_and_derivative,
x,
lower_bounds,
upper_bounds,
*,
convergence_relative_params_tolerance=CONVERGENCE_RELATIVE_PARAMS_TOLERANCE,
convergence_absolute_params_tolerance=CONVERGENCE_ABSOLUTE_PARAMS_TOLERANCE,
convergence_relative_criterion_tolerance=CONVERGENCE_REL... | e85a29f68067cf9120ed4b9789769d4a64379356 | 50,264 |
def templated_name_logger(template):
"""Returns a classmethod that calculates logger names by first applying the class name to `template`.
For instance:
class Foo(core.Configuration):
NAME = 'foo'
get_logger_name = helpers.templated_name_logger('my.%s')
class Bar(Foo):... | 9ac506c1b676d3d9283af6b1c5eda7a0ce22580e | 50,265 |
from datetime import datetime
def search_vehicle(alarm_day_count, plate_num):
"""
获取需要报警的车辆信息
:parameter: alarm_day_count 提前报警天数
:parameter: plate_num 车牌号
:return: List[
(客户名称，客户性别，身份证号，电话，
车牌号，车辆型号，车辆登记日期，公里数，过户次数
贷款产品，贷款期次，贷款年限，贷款金额，贷款提报日期，贷款通过日期，放款日期，
承保公司，险种，保险... | 37ce0e80cddf8770165b8721e0ba2e000a35828f | 50,266 |
def readBinaryWatch(self, num): # ! 44ms，进行双重循环，并统计1的数量，没什么太多的技巧性
"""
:type num: int
:rtype: List[str]
"""
return ['%d:%02d' % (h, m)
for h in range(12) for m in range(60)
if (bin(h) + bin(m)).count('1') == num] | 1e14be488a54f4746b39771c81e4dd50bce1a9b3 | 50,267 |
def tdb_minus_tt(jd_tdb, fraction_tdb=0.0):
"""Computes how far TDB is in advance of TT, given TDB.
Given that the two time scales never diverge by more than 2ms, TT
can also be given as the argument to perform the conversion in the
other direction.
"""
t = (jd_tdb - T0 + fraction_tdb) / 36525... | 066c8a112bb11377b80afdf6b34572d6426303d2 | 50,268 |
def login_not_required(f):
""" Decorate routes to not require login. """
@wraps(f)
def decorated_function(*args, **kwargs):
if session.get("user_id"):
return redirect("/home")
return f(*args, **kwargs)
return decorated_function | 93ae33003eaf719b0ab5dfdde5b038b8e5aea0b4 | 50,269 |
def build_function(type_, params, variable):
"""
Create object function matching type and parameters given.
Parameters
----------
type_ : str
'name' attribute of one of the classes of this module.
params : dict
Dict mapping parameter names with their values.
variable : str
... | 88d93db2a15b13608d295ef17c7528a5c2bb406b | 50,270 |
def test_empty_block():
"""Test an empty program
"""
@mb.program(input_specs=[mb.TensorSpec(shape=(2, 4))])
def prog(x0):
return x0
block = prog.functions["main"]
if len(block.operations) != 0:
raise AssertionError
if len(block.inputs) != 1:
raise AssertionError
... | 83b61f860d61e62f1410e6b0faa8f7db521843ed | 50,271 |
def download_blow(load=True): # pragma: no cover
"""Download blow dataset.
Parameters
----------
load : bool, optional
Load the dataset after downloading it when ``True``. Set this
to ``False`` and only the filename will be returned.
Returns
-------
pyvista.UnstructuredGr... | 64ae542ee110a1efaa55752d86c99153bd87a0dc | 50,272 |
def batch_retrieve_for_processing(ftp_as_object):
""" Used for mapping an s3_retrieve function. """
# Convert the ftp object to a dict so we can use __getattr__
ftp = ftp_as_object.as_dict()
data_type = file_path_to_data_type(ftp['s3_file_path'])
# Create a dictionary to populate and retur... | 50b06d7bd88296c8e22bfcab4c14ad58c7f917e1 | 50,273 |
def register_map(**kwargs):
"""Register an ObjectMapper to use for a Container class type
If mapper_cls is not specified, returns a decorator for registering an ObjectMapper class
as the mapper for container_cls. If mapper_cls specified, register the class as the mapper for container_cls
"""
contain... | 5cd90411e84059bd76643790cd48a6374e3bcbd7 | 50,274 |
def textilize(s):
"""Remove markup from html"""
s = s.replace("<p>", " ").replace(" ", " ")
return _re_html.sub("", s) | 51eff59c5194d5fcd90c9956d8ff923cb4875cd5 | 50,275 |
import posixpath
def safe_join(base, *paths):
"""
A version of django.utils._os.safe_join for S3 paths.
Joins one or more path components to the base path component
intelligently. Returns a normalized version of the final path.
The final path must be located inside of the base path component
... | ecb8c2b155ef2872ca443e4fae093ba3e7055e35 | 50,276 |
import configparser
def miniterm(owf_instance=None, *args):
"""
Run a serial console session (using miniterm from serial package).
:param args: Varargs command options.
:param owf_instance: Octowire framework instance (self).
:return: Nothing.
"""
if len(args) < 1:
config = None
... | 52079d07b5af8c8bf73021bf71c1f3acd49937bb | 50,277 |
import os
import pickle
def create_dataset(data_dir,
train_mode=True,
epochs=1,
batch_size=4096,
is_tf_dataset=True,
line_per_sample=4096,
rank_size=None,
rank_id=None):
"""
cre... | 332296ef66fab2f755210a12d70b8e22c22591bc | 50,278 |
import requests
def _request_user_ids():
"""Get dataframe of user emails, gids, names."""
params = {
'workspace': WORKSPACE_ID,
'opt_fields': 'email,name'
}
endpoint = 'teams/{}/users/'.format(TEAM_ID)
url = 'https://app.asana.com/api/1.0/{}'.format(endpoint)
r = requests.g... | 1f5c0cd29785ca25b8beb8654901ad11fc503712 | 50,279 |
def _squash_context(*args):
"""
Unwraps ``RequiresContext`` values, merges them into tuple, and wraps back.
.. code:: python
>>> from returns.context import RequiresContext
>>> from returns.converters import squash_context
>>> assert squash_context(
... RequiresContext.... | cc25dcd2df3de351ce58c95514e9a5c3ddffadd1 | 50,280 |
import six
def decode_message(buf, message_type=None, config=None):
"""Decode a message to a Python dictionary.
Returns tuple of (values, types)
"""
if config is None:
config = blackboxprotobuf.lib.config.default
if isinstance(buf, bytearray):
buf = bytes(buf)
buf = six.ensur... | 459ffafda7848f2342feb7865a4759f8f4a429cb | 50,281 |
def ca_all(request):
"""Lists all files readable by the current user"""
method = request.method
if method == 'POST':
keys = ['c', 'st', 'l', 'o', 'ou', 'cn', 'email']
values = {}
for key in keys:
if request.data.get(key):
values[key] = request.data.get(ke... | 625c2ddbd97e17efa94ac43d5cfe793bcbad892a | 50,282 |
def supervised_disagreement(labels, logits_1, logits_2):
""" Supervised disagreement """
labels = tf.cast(labels, tf.int32)
preds_1 = tf.argmax(logits_1, axis=-1, output_type=tf.int32)
preds_2 = tf.argmax(logits_2, axis=-1, output_type=tf.int32)
par1 = tf.reduce_mean(tf.cast(tf.math.logical_and(preds_1 == lab... | e632a0bde59a3e669c8abb4d733f57e2fec4de49 | 50,283 |
import unittest
import inspect
def sort_tests(tests) -> unittest.TestSuite:
"""Sort supplied test suites such that MemoryTestCases are at the end.
`lsst.utils.tests.MemoryTestCase` tests should always run after any other
tests in the module.
Parameters
----------
tests : sequence
Seq... | 8dee0b3be96874b2f2020bb489aeb9716ca6f151 | 50,284 |
def chunk_by_image(boxes):
"""
turn a flat list of boxes into a hierarchy of:
image
category
[boxes]
:param boxes: list of box detections
:return: dictionary of boxes chunked by image/category
"""
chunks = {}
for b in boxes:
if b['image_id'] not in chunks:
... | d6eaf46214a97853407112a9d0a3c47a132fb3c4 | 50,285 |
from typing import List
from typing import Dict
def get_all_secret_registry_events(
chain: BlockChainService,
secret_registry_address: Address,
events: List[str] = ALL_EVENTS,
from_block: BlockSpecification = 0,
to_block: BlockSpecification = 'latest',
) -> List[Dict]:
""" ... | d865c852237db1566e937200b08c0602228a74ff | 50,286 |
def test_label_fiber_array_align_ports():
"""Test that adds the correct label for measurements."""
c = pp.c.waveguide()
assert len(c.labels) == 0
c = pp.routing.add_fiber_single(c, with_align_ports=True)
pp.show(c)
print(len(c.labels))
assert len(c.labels) == 4
l0 = c.labels[0].text
... | 30dfb9ed239ce549d621c01be9456c7fc801ed4b | 50,287 |
def run_simulation(simulation, n_steps, run_ideal=False, simdir="r"):
"""
Run a neural network model simulation computing occupancy histograms
:param simulation: The simulation to run
:param n_steps: The number of steps to simulation
:param run_ideal: If True, instead of using neural network movemen... | 0c291ec950a1c23752547dc541244f037cf0b946 | 50,288 |
def find_duplicate_uuids(images):
"""
Create error records for UUID duplicates.
There is no real way to figure out which is the correct image to keep, so
we keep the first one and mark all of the others as an error. We also
remove the images with duplicate UUIDs from the image dataframe.
"""
... | ac8b396692df921de56c0a18aa2d9f1953f4c5ca | 50,289 |
import json
def accounts_root():
"""
---
get:
summary: Get all accounts this user has access to
tags:
- Accounts
description:
Returns the list of accounts that this user (person or bot) has permission to access. The list is returned in a single... | 7e45f0a2a8ac849293508ab5604e9ce3aff045d6 | 50,290 |
from datetime import datetime
async def arrival(request: Request): # data { name, phone, remark }
""" D -> H 到达现场并开始处理 """
_oid = get_maintenance_id(request)
data = await request.json()
_time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
try:
_edit = data['name']
_phone = da... | 3d01a3a60a02ab78c2fddde34a4387f7878e2ef3 | 50,291 |
from typing import Union
from typing import Optional
def to_graph(inp: Union[Graph, str], fmt: Optional[str] = "turtle") -> Graph:
"""
Convert inp into a graph
:param inp: Graph, file name, url or text
:param fmt: expected format of inp
:return: Graph representing inp
"""
if isinstance(inp... | cd17e018a65859153de463d0187ee6e9373faa8c | 50,292 |
def file_exists(session, ds_browser, ds_path, file_name):
"""Check if the file exists on the datastore."""
client_factory = session._get_vim().client.factory
search_spec = search_datastore_spec(client_factory, file_name)
search_task = session._call_method(session._get_vim(),
... | 79090ad5095d5e2e9e37bc5d8297148d9a7ceb0c | 50,293 |
def data_source_get_all(context, regex_search=False, **kwargs):
"""Get all Data Sources filtered by **kwargs.
:param context: The context, and associated authentication, to use with
this operation
:param regex_search: If True, enable regex matching for filter
v... | f03e328ff8d79931b45a891a3fd729521ff035d0 | 50,294 |
from typing import Union
import torch
def normalize_img(img: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
"""
Normalizes an input image.
Parameters
----------
img: Image which should be normalized.
Returns
-------
img: Normalized image.
"""
if type(im... | b110e882235bad48e7eb2e9404307ff3159bd6e5 | 50,295 |
def get_horizon_endpoint(goc_db_url, path, service_type, host):
""" Retrieve the Horizon OpenStack dashboard of the given hostname """
dashboard_endpoint = ""
endpoints = get_GOCDB_endpoints(goc_db_url, path, service_type)
for item in endpoints.findall("SERVICE_ENDPOINT"):
sitename = item.find... | 9c83aaa2cf752f33f43a3577ed393262b5e44cbe | 50,296 |
def create_process_chain_entry(input_object: DataObject, formula,
operators, output_object: DataObject):
"""Create a Actinia process description.
:param input_object: The input time series object
:param output_object: The output time series or raster object
:return: A Act... | 245677913af3cefebfc0c13505e1f798bd46472a | 50,297 |
def checksync(st, resample=False):
"""Check if all traces in st are synced, if so, return basic info
"""
samprates = [trace.stats.sampling_rate for trace in st]
if np.mean(samprates) != samprates[0]:
if resample:
print('sample rates are not all equal, resampling to lowest sample rat... | 500b9df5869473a52b0837133cb3e49821fd3343 | 50,298 |
from scipy.optimize import minimize
def hill_estimator(points, counts, xmin, xmax=np.inf, discrete=False, **kwargs):
"""
Give the MLE for continuous power-law distribution exponent.
:param points: observed values, shape (n,)
:param counts: number of occurrences for `points`, shape (n,)
:param xmin... | 7fec0ee8077188958d4f5d349590de6c951937a1 | 50,299 |
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