content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def shib_ds_context(context):
"""
Creates a dictionary with information:
* ServiceProvider login handler
* IdPs from cookie
"""
shib_ds = get_context(context.request)
return shib_ds | 952b844bce2fc85777f4ebd4ba8c963dc7f10d3a | 51,943 |
import warnings
def __getattr__(name): # noqa: N807
"""Get attribute."""
deprecated = __deprecated__.get(name)
if deprecated:
warnings.warn(
"'{}' is deprecated. Use '{}' instead.".format(name, deprecated[0]),
category=DeprecationWarning,
stacklevel=(3 if PY37... | 629e1981c0a000e358fcb9d8f55799c5e2302307 | 51,944 |
async def hub_pod(kube_client, kube_ns):
"""Create and return a pod running jupyterhub"""
return await create_hub_pod(kube_client, kube_ns) | b6dbb291a988a351857aa7e41b6e53324d8ecd55 | 51,945 |
import hashlib
def _hash_file(fpath, chunk_size=65535):
"""
Calculates the md5 hash of a file
:param fpath: path to the file being validated
:param chunk_size: Bytes to read at a time (leave unless large files)
:return: The file hash
"""
hasher = hashlib.md5()
with open(fpath, 'rb') ... | 9f17f13f487f95620ad705a6f2b23d57407e640e | 51,947 |
def GetIcon(name, module=None):
"""
wx.Icon GetIcon(iconName, module=None)
Get an icon from a file, possibly prepending the module's path
"""
name=modPath(name, module)
return wx.Icon(name + ".ico") | 1dec574112375ff7ec6d3a0ac4f438a3135e37dd | 51,948 |
def midpoint(start, end):
"""Find the mid-point between two points."""
x0, y0 = start
x1, y1 = end
return (x0+x1)/2, (y0+y1)/2 | dab5a66b8af759998a295d64ba3711b6e99e4c27 | 51,949 |
def scatterplot(custom_x, custom_y, xlabel=None, ylabel=None, **kwargs):
"""
Custom x-y plotting using VESIcal's built-in plot() function, built on Matplotlib's plot and scatter functions.
Parameters
----------
custom_x: list
List of groups of x-values to plot as points or lines
custom_y: list
List of group... | ca543f9c0e834a9caf353b17005e3056a9d75a3d | 51,950 |
def presale_crowdsale_miletstoned(chain, presale_fund_collector, uncapped_flatprice, presale_milestone_pricing, team_multisig):
"""ICO associated with the presale where funds will be moved to a presale.
We set a special milestone pricing that allows us to control the pricing for the presale participants.
"... | 2ae798f0e28944625fa7724218ab52132a385a20 | 51,951 |
def dict_populate_loop(pattern):
"""
Args:
pattern: array of tokens that represents the tokens.
Returns:
updated_dic: a dictionary that contains all the keywords from docstrings.
"""
updated_dic = {}
for token in pattern:
if isinstance(token, docopt_util.Branch):
... | 81f1d38eba5d1e65721c15d6f9178c8568440c83 | 51,952 |
def visualize_farm(
plane, nr_points, size_x, size_y, title="", ax=None, vmax=False):
"""
Function to plot flow-field around a wind farm.
Args:
plane (2d numpy array): Flow field of wind farm
nr_points (list or np array): List of nr of points in x and y
size_x (int): Size o... | 9c7dfa1bc211b31d5dcdd3b882b5c848a3e8c7a0 | 51,953 |
from typing import Tuple
def oov_rate(
targets: jnp.ndarray,
oov_values: Tuple[int, ...],
mask_values: Tuple[int, ...] = ()) -> MeanMetric:
"""Computes proportion of non masked tokens that are out of vocabulary.
Args:
targets: Target values of shape [batch_size, sequence_length, ...].
oov_val... | 33c35004dfbfab02b4c95d9c73b0c8d447828a19 | 51,954 |
import pathlib
def hash_to_path(kind: str, rep: str) -> pathlib.Path:
"""Derive path from content rep hash and create any folder required."""
if kind not in KINDS.values():
raise ValueError(f'unknown {kind=} for {rep=}')
folder = DB_OUT_ROOT / kind / rep[:2]
folder.mkdir(parents=True, exist_ok... | cbe7c25a46c42711d8a77fcc034f5c1191a692fc | 51,957 |
def mirror_edges(img, psf=3):
"""Given a 2D image, boundaries are padded by mirroring so that
the dimensions of the image are multiples for the width of the
point spread function.
Args:
img (np.array): Image to mirror edges.
psf (int): Size of point spread function in pixels.
Retur... | 1d9d85bf32a3a5cf560c4add866a053057fb5b72 | 51,960 |
def _srg_rhs_old(s, potential, kinetic, potential_weight, weights, flow,
verbose):
"""Old implementation of SRG flow equation.
New implementation is more efficient and reflects actual form better.
Keeping this for documentation and as reference for analytic evaluation of
flow equation ... | c4ee5fdafa7a11c95d3e5d0e3c83058b862a3ba9 | 51,961 |
def compute_taproot_address(pubkey, scripts):
"""Compute the address for a taproot output with given inner key and scripts."""
tap = taproot_construct(pubkey, scripts)
assert tap.scriptPubKey[0] == 0x51
assert tap.scriptPubKey[1] == 0x20
return encode_segwit_address("bcrt", 1, tap.scriptPubKey[2:]) | 709a83f131a7de4bbdaad790a96561074d7cfdae | 51,962 |
def perplexity_w_prior(probabilites, smi, d_prior, remove_end=True):
"""
Compute the perplexity of a SMILES given a list
of probabilities.
probabilites: (list) of probabilities
smi: (str) a SMILES
d_prior: (dict) with keys: SMILES characters,
and value the prior probability to sample
the... | cfb33fa8f031c0c323cb62ca7c1639c97a2c903f | 51,963 |
def plot_model_timing(title, data_sizes, fit_scores, predict_scores, ylim=None):
"""
Generate a simple plot of the given model timing data
Parameters
----------
title : string
Title for the chart.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues pl... | 3e59c3e6ee354568ae37366ded944c2646acf152 | 51,964 |
def _get_int64(data, position, dummy0, dummy1, dummy2):
"""Decode a BSON int64 to bson.int64.Int64."""
end = position + 8
return Int64(_UNPACK_LONG(data[position:end])[0]), end | 7f2d8f1939fc20848a1323aaa29210dbeaa39451 | 51,965 |
import click
def set_pin(ctx, pin, new_pin, u2f):
"""
Set or change the PIN code.
The FIDO2 PIN must be at least 4 characters long, and supports any type
of alphanumeric characters.
On YubiKey FIPS, a PIN can be set for FIDO U2F. That PIN must be at least
6 characters long.
"""
cont... | 50a4a64fa22c2b8d416bf65d1bd0e7eb5fb226c1 | 51,966 |
def verify_proof(db_contract, proof, received):
"""This queries the DB to retrieve the heartbeat, state and challenge for
the contract id, and then checks the given proof. Returns true if the
proof is valid. Can also return false if the contract is expired or if
an ip address change has been rejected
... | c0b31202425c8f0ee600e056353386f17cfbacb4 | 51,967 |
import signal
def low_pass_butter4(freqs, tod, fc):
"""4th-order low-pass filter with f3db at fc (Hz).
"""
b, a = signal.butter(4, 2*np.pi*fc, 'lowpass', analog=True)
return np.abs(signal.freqs(b, a, 2*np.pi*freqs)[1]) | df7bc610fd57c4af287bee310175e84868123ab7 | 51,968 |
def get_functions(funcset=None):
""" Return a dict of variables """
c0 = funcset in _DFUNC.keys()
if c0 is not True:
lstr = [
"\t- '{}': {}".format(k0, sorted(v0.keys()))
for k0, v0 in _DFUNC.items()
]
msg = (
"Arg varset must be a valid key to an ... | 5313ffd9768952acd16f9da503679ff537541827 | 51,969 |
import re
def replace_pair(pair, content):
"""
Uses regex to locate a pair.
First element of the tuple is the original error token.
Second element of the tuple is the replacement token.
"""
return re.sub(pair[0], ' {} '.format(pair[1]), content) | 022633d417393959a94629117a1e664709770571 | 51,971 |
def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, hvd=None, manual_fp16=False, use_fp16=False, num_accumulation_steps=1,
optimizer_type="adam", allreduce_post_accumulation=False):
"""Creates an optimizer training op."""
global_step = tf.train.get_or_create_global_step()
... | 9a1b889cf6346fc5220c7b7c63dc20363b841765 | 51,972 |
def load_positions_from_timepoints_file(path):
"""Load a text file of timepoint pseudo-paths into a list of Positions.
The file is expected to be lines of pseudo-paths of the form:
/path/to/experiment_root/position_name/timepoint_name
where:
/path/to/experiment_root/position_name
is a valid pat... | 0e39b7fd2159e2b664c237920fc378a44d7c9240 | 51,974 |
def trace_condition_function(condition: ConditionCheckerType) -> ConditionCheckerType:
"""Wrap a condition function to enable basic tracing."""
@ft.wraps(condition)
def wrapper(hass: HomeAssistant, variables: TemplateVarsType = None) -> bool:
"""Trace condition."""
with trace_condition(vari... | 629f6d2a17dd48167da2ce4a04747d35b47d0ee2 | 51,976 |
from datetime import datetime
import requests
def log_me_in(base_url, username=None):
"""
:param username:
:returns cookies
:param base_url:
:return:
"""
add_url = "/geo/init"
if username is None:
user_data = {"userName": "python " + str(datetime.now())}
else:
user_... | ed6e767548a61f0c7086db5fe45b12ac75df5498 | 51,977 |
from niworkflows.interfaces.reportlets.registration import (
SimpleBeforeAfterRPT as SimpleBeforeAfter,
)
from niworkflows.interfaces.reportlets.masks import ROIsPlot
from smriprep.interfaces.templateflow import TemplateFlowSelect
from smriprep.workflows.outputs import (
_fmt, _empty_report, _rpt_ma... | df39d46731213a8313b5eaa1701f302df3d14a94 | 51,978 |
from datetime import datetime
def extract_sitename_date(directory_path,
sitename_location,
datetime_location):
"""Extract sitename and datetime from directory path name.
Parameters
-----------
directory_path : string
A path to the directory ... | 86a2085ba68b234585ef9855da64fa1fdd5459ce | 51,979 |
from typing import List
from typing import Optional
def make_blanks(spans: List[Span], length: Optional[int] = None, fill: str = TokenFunction.OUTSIDE) -> List[str]:
"""Create a list of outside tags that we can populate with tags generated from spans.
Args:
spans: The list of spans that will eventual... | 36e6b225ff55da66c9192c87e6697bf75af842ad | 51,980 |
from datetime import datetime
def month_holidays(year, month):
"""
その月の祝日日、祝日名を返します。
"""
date = datetime.date(year, month, 1)
output = []
while date.month == month:
name = is_holiday_name(date)
if name is not None:
output.append((date, name))
date = date +... | f74bd3f6600b9c82140808ba5061e20a46619b33 | 51,982 |
def collection_test(in_dim1, in_dim2, channels, out_dim):
"""
Return a small collection of models and their names
"""
names = []
for a in range(1, 3):
for b in range(3):
n1 = a * 8
n2 = b * 8
m_str = str(n1)
if n... | d46033bc26acf02a1faacc8b37a314d2b0e58ea2 | 51,983 |
def favourites_add(request):
"""
Function to add recipe to favourites.
This function is used via AJAX.
"""
user = request.user
title = request.POST.get('title')
recipe_id = request.POST.get('recipe_id')
image_url = request.POST.get('image_url')
favourite, created = Recipe.objects.get... | 315773153f1135b7ef677a1c84d3371e9114cb5a | 51,984 |
def read_chain_header(line):
"""
Convenience wrapper to parse chain header information
:param line:
:return:
"""
_, score, tName, tSize, tStrand, tStart, tEnd, qName, qSize, qStrand, qStart, qEnd, chainid = line.strip().split()
return Chain(tName, int(tSize), tStrand, int(tStart), int(tEnd),... | f2d43234d26d61d90995a0cb8ab741734fbf1c4b | 51,985 |
async def get_host_by_slug(host_slug: constr(strip_whitespace=True)):
"""Retrieve a Host object, based on Host slug string, containing:
Host ID, name, slug string, and gender."""
try:
host = Host(database_connection=_database_connection)
host_info = host.retrieve_by_slug(host_slug)
i... | d05319f9e8224584b54210d0a78ee7649d881cca | 51,986 |
def cleanup_text(line: str) -> str:
"""
Args:
line: text line of the opinion
Returns:
text line where not alphabetical characters removed
"""
corrections = {""": "", "&": ""}
line_lower = line.lower()
for wrong, cor in corrections.items():
line_lower = lin... | 3414fb429ea76aabcfc06ae57aca9c2cadfcc96f | 51,988 |
def send_mail(to_mail, mail_subject, mail_body):
"""
Function to send a mail using Sendgrid API
Parameters:
arg1 (string): The recipient's email address.
arg2 (string): The subject of the mail.
arg3 (string): The body of the mail.
"""
message = Mail(
from_email=current_app.con... | c8f2a1796bbb0584ae4370af8af2d7eb7c6b5bbd | 51,989 |
from run4it.api.scripts import script_token_registry_purge as script_func
def token_cleanup():
"""Removes revoked and expired tokens from database"""
return script_func('token_cleanup') | ec2876a20cca14e271b7036ec35b7f64af8e6021 | 51,991 |
import requests
import json
def get_vlans(ip, port, username, password) -> list:
"""Gets device components /restconf/data/openconfig-platform:components"""
data = []
uri = f"https://{ip}:{port}/restconf/data/Cisco-IOS-XE-vlan-oper:vlans"
response = requests.get(uri, headers=headers, verify=False, au... | 72130eac7fb103956e97b9e8dd09e54034f9075e | 51,992 |
import time
import random
import traceback
def _run_work_order_fn(zap_id, work_order_i):
"""
Wrap the function to handle args, kwargs, capture exceptions, and re-seed RNG.
Note: This may run in the sub-process or thread and therefore should not use stdio.
"""
start_time = time.time()
try:
... | 38c2b705fc8a38d04d18670e746c0edb550c6612 | 51,993 |
def head(path, lines=10):
"""
Get the first lines of a file.
Args:
path (str): Path to the file to read.
lines (int): Number of lines to read.
Returns:
:obj:`list` of :obj:`str`: The lines found.
"""
with open(path) as f:
return list(islice(f, lines)) | 829c7c910798e967cea0d40aef43ed21ee4ff421 | 51,994 |
def ntg_collate(data):
"""Creates mini-batch tensors from the list of tuples (src_seq, trg_seq)."""
# separate source and target sequences
src_sent,tgt_sent = list(zip(*data))
# t2i
_inputs = [batch_sentences(src_sent),
batch_sentences(tgt_sent),
]
return _inputs | ea2a9900bdc3e1cdc5dc3889a8c6bfb8d8f232db | 51,995 |
def FnutoFlambda(Fnu, wavelength):
"""
Converts F_nu to F_lambda.
:param Fnu: Fnu [ers/s/cm**2/Hz]
:type Fnu: float or ndarray
:param wavelength: wavelength [AA]
:type wavelength: float or ndarray
:return: Flambda [ergs/s/cm**2/AA]
"""
return Fnu / wavelength / wavelength * C | 78b1775aa391c5cc9441684f109203be0af41ce9 | 51,996 |
import torch
def PSNR(gt, img, drange):
"""
Computes the highest PSNR by affine rescaling of x,
such that the mean squared error to gt is minimal.
:param gt: ground truth
:param img: image
:param drange: data range
:return: PSNR
"""
img = normalize_minmse(img, gt)
mse = torch.... | 69296c1b563371cf4dfc5b8a59bff50f922088b5 | 51,997 |
def get_postgres_connection_url() -> str:
""" Собирает ссылку для подключения к БД и возращает её.
"""
db = POSTGRES_DB
host = POSTGRES_HOST
port = POSTGRES_PORT
user = POSTGRES_USER
password = POSTGRES_PASSWORD
if not all([db, host, port, user, password]):
raise DatabaseSetting... | a637f5a108ec7b1a0f696a41d457d284b2a28eae | 51,998 |
def check_if_string_in_file(file_name, string_to_search): #string checker
""" Check if any line in the file contains given string """
# Open the file in read only mode
with open(file_name, 'r') as read_obj:
# Read all lines in the file one by one
for line in read_obj:
# For each ... | 9cc958279cbfab75273ef122603df378ffa55038 | 51,999 |
import torch
from typing import Tuple
def count_per_domain_statistics(
domain_idxs: torch.Tensor
) -> Tuple[np.ndarray, np.ndarray]:
"""
Args:
- domain_idxs: Pytorch tensor of shape (N,) showing assignment
of each example to each particular domain
Returns:
-... | 4e900e1f611e942a07efc17481462e025bfa4e10 | 52,000 |
def handler500(request):
"""
This is a Django handler function for the 500 server error page. Because a server error can imply an serious issue
such as corrupted database, a nice render of the 500 page may be impossible. So the server first attemps to nicely
render the page with all the dynamic element,... | 5e818aa9ab9d3ebaef2d843e8b32cfd3da90941c | 52,001 |
def featurenet_backbone(input_tensor=None, input_shape=None,
n_filters=32, **kwargs):
"""Construct the deepcell backbone with five convolutional units
Args:
input_tensor (tensor): Input tensor to specify input size
n_filters (int): Defaults to 32. Number of filters for
... | 653646aa594c174ea3f0050401dbca176315fc49 | 52,002 |
from typing import Optional
def call_somewhat_forgivingly(
func, args, kwargs, enforce_sig: Optional[SignatureAble] = None
):
"""Call function on given args and kwargs, but with controllable argument leniency.
By default, the function will only pick from args and kwargs what matches it's
signature, ig... | c2c32cd0b1e8cedc3bcdf030e81d90777a303a54 | 52,003 |
from typing import List
def _get_list_of_layers(
input_dim: int, hidden_dim: int, output_dim: int, num_layers: int
) -> List[nn.Module]:
"""Utility function to get a list of layers. This assumes all the hidden
layers are using the same dimensionality.
Args:
input_dim (int): input dimension.
... | 9d30258aa06c39e76ddb1b9d96fb699d85b48d22 | 52,005 |
import scipy
import numpy
def LagrangeMultiplierMethod(H, f):
"""
LEXMIN Solve the multi-objective minimization problem:
min {E1(x), E2(x), ... , Ek(x)}
x
where
Ei = 0.5 * x.T * H[i] * x + x.T * f[i]
and Ei is deemed "more important" than Ei+1 (lexicographical ordering):
... | 6a557dc153e3b291eb8b2c9f1c85e3baf1eff091 | 52,006 |
def rescale_to_11bits(im_float):
"""Rescale a float image in [0, 1] to integers in [0, 2047].
This operation makes rank filtering much faster.
Parameters
----------
im_float : array of float in [0, 1]
The float image. The range and type are *not* checked prior to
conversion!
R... | ec15a3599b03184ee520f61e29f40bb0cbb24581 | 52,007 |
import re
def get_data(session_id, n_intervals):
"""Query Database and prepare data as DataFrame.
Parameters
----------
session_id : str
unique session id hex used for caching.
interval : int
update interval from counter used for updating data.
Returns
-------
df : pa... | c84253c0e6838b68454e228395f6a7bca578d282 | 52,008 |
def multi_dict_unpacking(lst):
"""
Receive a list of dictionaries, join them into one big dictionary
"""
result = {}
for d in lst:
for key, val in d.items():
result[key] = val
return result | 530947224d682cffb809e83f308a97a108002080 | 52,009 |
import math
def dist(a: list, b: list) -> float:
"""Calculate the distancie between 2 points
Args:
a (list): point a
b (list): point b
Returns:
float: Distance between a and b
"""
return math.sqrt((a[0]-b[0])**2+(a[1]-b[1])**2) | 7ad6d2f36c0bccee106c7b54a4abebd246c4a3fa | 52,010 |
def is_array_like(item):
"""Check if item is an array-like object."""
return isinstance(item, (list, set, tuple, np.ndarray)) | 7176330220ca903a38d28710053aee6c4edf9df5 | 52,011 |
async def verifyGroupPath(h5path):
""" create any groups along the path that doesn't exist
"""
#print("current task: ", asyncio.Task.current_task())
client = globals["client"]
domain = globals["domain"]
h5path_cache = globals["h5path_cache"]
params = {"host": domain}
parent_group = h5pat... | 5c5c5bc4984b1e58e7008142aa5a0d42ba280921 | 52,012 |
def coord(x, y, integer=False):
"""
Convert world coordinates to pixel coordinates. Setting 'integer' to
True will return integer coordinates.
"""
xs = (DRAW_OFFSET[0] + DRAW_SCALE*x)
ys = (DRAW_OFFSET[1] - DRAW_SCALE*y)
if integer:
return int(round(xs)), int(round(ys))
else:
... | 7962dcf2dcbf16dfaaa7fd553ce99530f096106a | 52,015 |
def handle_match(match, url):
"""Should there be a match, format a message and send a notification."""
if match:
message = "Match found at {}:\n{}".format(url, match)
return __send_notification(url, message)
else:
return 'Missing match at: {}'.format(url) | bd019baa184df41a209b19549074b38127ba29b6 | 52,016 |
import requests
import io
def call_model(model_type="mosaic", file_path="tests/flowers.jpg"):
"""helper function"""
model_endpoint = 'http://localhost:5000/model/predict?model=' + model_type
with open(file_path, 'rb') as file:
file_form = {'image': (file_path, file, 'image/jpeg')}
r = re... | 47a5038de6f56c4ae6c099803b8cffc9482ed9f4 | 52,017 |
from bs4 import BeautifulSoup
def get_soup(url):
"""Gets BS object from the given URL"""
# - Get Data
html = get_data(url)
# - Get Soup
soup = BeautifulSoup(html, 'html.parser')
if soup is None:
raise Exception("No data from URL")
return soup | 00b0ef5379e470b5603da54e93aa0d878132d41a | 52,018 |
def node_wise_2_sample(latent, node_ind):
"""Get dcorr t stat for a single node"""
node_latent_pop1 = np.squeeze(latent[:n_graphs, node_ind, :])
node_latent_pop2 = np.squeeze(latent[n_graphs:, node_ind, :])
t_stat = compute_t_stat(node_latent_pop1, node_latent_pop2)
return t_stat | a1939f474d7407c92aea0eb4b0a6fdb30cf60045 | 52,019 |
def create(sender, recipients=None, cc=None, bcc=None, subject='', message='',
html_message='', scheduled_time=None, headers=None,
template=None, priority=None, commit=True,
backend=''):
"""
Creates an email from supplied keyword arguments. If template is
specified, email su... | 4bfe19949c0c07b34d9a5443c5aee8afb785dcca | 52,021 |
from typing import Dict
from typing import Union
from typing import Set
def get_fields_eligible_for_pagination(
schema_info: QueryPlanningSchemaInfo,
types: Dict[VertexPath, Union[GraphQLObjectType, GraphQLInterfaceType]],
single_field_filters: Dict[PropertyPath, Set[FilterInfo]],
fold_scope_roots: Di... | a9b606bb5f44fcb8ae3cfdbe2678f359e83e3a45 | 52,022 |
import re
def _parse_commit_position(commit_message):
"""Parses a commit message for the commit position.
Args:
commit_message: The commit message as a string.
Returns:
An int if there is a commit position, or None otherwise."""
match = re.search(
'^Cr-Commit-Position: [a-z/]... | 7f4d93f8fc88a18276570f2d33ff2fbb75673025 | 52,023 |
from typing import Any
def convert_simple_type(value: Any, json_definition: dict) -> Any:
"""
:param value: Can be of any JSON type except list or dict.
:param json_definition: OpenAPI definition of the value (format used to parse it).
"""
if isinstance(value, str):
field_format = json_def... | 0d83275efa3cc2320d21ed8fb848ee3b2931aa91 | 52,024 |
import torch
def block_diag(m):
"""
Make a block diagonal matrix along dim=-3
EXAMPLE:
block_diag(torch.ones(4,3,2))
should give a 12 x 8 matrix with blocks of 3 x 2 ones.
Prepend batch dimensions if needed.
You can also give a list of matrices.
:type m: torch.Tensor, list
:rtype: ... | c814118d08b34a168e0e9a1ade047c3be26ae5e6 | 52,025 |
def _twoByteStringToNum(bytestring, numberOfDecimals=0, signed=False):
"""Convert a two-byte string to a numerical value, possibly scaling it.
Args:
* bytestring (str): A string of length 2.
* numberOfDecimals (int): The number of decimals. Defaults to 0.
* signed (bol): Whether large p... | 246bff7ec138778718ac8aa47c6348e240a5051b | 52,026 |
def smape(y_true, y_pred):
"""
Calculates the symmetric mean absolute percentage error of the predicted
values to the true values. `y_true`, `y_pred` should be of the same
type and shape.
The SMAPE is calculated as:
2 * mean(|y_pred - y_true| / (|y_pred| + |y_true|))
Therefore, it is ... | ea109400eebf0768b485e434cef992f58e9e3b22 | 52,027 |
def _api_rss_now(name, output, kwargs):
""" API: accepts output """
# Run RSS scan async, because it can take a long time
scheduler.force_rss()
return report(output) | 72e74532fcd2cd4b5c41f9273ce6ebd00c5b566a | 52,028 |
def get_middle_opt(string: str) -> str:
"""Get middle value of a string (efficient).
Examples:
>>> assert get_middle_opt('middle') == 'dd'
"""
return (
string[int(len(string) // 2) - 1 : int(len(string) // 2) + 1]
if not len(string) % 2
else string[int(len(string) // 2)]... | 1636f39e22bacc4f571de876dd71add690e940e6 | 52,029 |
import csv
def vmess_IO(class_):
"""
获取可用订阅链接并刷新存储池
class_: ssr ; v2ray
"""
def refresh_log(dataFlow):
with open(SYS_AIRPORT_INFO_PATH, 'w', encoding='utf-8', newline='') as fp:
writer = csv.writer(fp)
writer.writerows(dataFlow)
try:
with open(SYS_AIRP... | 6e99847f5b3b1ff09b74a1e669a9bb18ee0e5bfa | 52,030 |
from typing import Optional
def get_anonymous_current_user(locale: Optional[str]) -> CurrentUser:
"""Return an anonymous current user object."""
return CurrentUser(
id=ANONYMOUS_USER_ID,
screen_name=None,
suspended=False,
deleted=False,
locale=locale,
avatar_url... | 3c503154f986cfd420751ce331943a8d5854b60a | 52,031 |
def cut_sites_with_region(df_sites, df_region):
"""Find peak interval the crosslinks belong to."""
df_p = df_sites[df_sites['strand'] == '+'].copy()
df_m = df_sites[df_sites['strand'] == '-'].copy()
df_region_p = df_region[df_region['strand'] == '+'].copy()
df_region_m = df_region[df_region['stran... | df40f8fc522e9be65d146b30a17c587899749957 | 52,032 |
def check_inplane_vector(vector, normal):
"""
Determines if a vector is in plane with the current element
:param vector: Test vector
:param normal: Normal of element
:return:
"""
checkdot = np.dot(normal, vector)
err = 1e-9
if -err < checkdot < err:
return True
else:
... | e535dd30d6f03b1ca9048958e769ec02b70e0ba2 | 52,033 |
def parse_temp_data(json_graph):
"""Parse the temp data"""
temp_list = list(filter(lambda x: '100kHz' in x['label'], json_graph['graph_kind_1008']))
if len(temp_list) == 0:
return None
return clean_data(temp_list[0]['data']) | dff2ee90c7312a713815860668551619dceaba97 | 52,034 |
from typing import List
def brl_out(offset: int, data: List[int]) -> bytes:
"""send data to braille display
@param offset: Must be positive.
"""
d2 = len(data)+7
ret = bytearray([
STX,
ord(b'S')
])
ret.extend(offset.to_bytes(2, "big", signed=False))
ret.extend(d2.to_bytes(2, "big", signed=False))
ret.ex... | c761dd0eadd4a59264c1dcca6d2dfed6323f0078 | 52,035 |
def degree_as_dm7(degree):
"""
Given a degree, return the equivalent dm7. Does not perform range validation.
Performs integer conversion of the result.
"""
return int(round(_DM7_PER_DEGREE * degree)) | 1bdd862e32942175ba06f54987d89646e43dae4d | 52,036 |
def duration(duration):
"""Filter that converts a duration in seconds to something like 01:54:01
"""
if duration is None:
return ''
duration = int(duration)
seconds = duration % 60
minutes = (duration // 60) % 60
hours = (duration // 60) // 60
s = '%02d' % (seconds)
m = '%0... | 7cd89654a84c2e3e41d96cb1b13688833ee54387 | 52,037 |
def test_log_dtypes(dtypes, capsys, test_df):
"""Test logging of dtypes can be switched on and off"""
@log_step(dtypes=dtypes)
def do_nothing(df, *args, **kwargs):
return df
test_df.pipe(do_nothing)
captured = capsys.readouterr()
assert ("dtypes=" in captured.out) == dtypes
if d... | 735b1479304dcc15bff10a95c37af9417da79ce7 | 52,038 |
def lookup_embedding_func(
id_tensors, max_ids, embedding_dim,
):
"""
Args:
id_layers: dict, the key is a string and
the value is tf.keras.Input.
standardized_tensor:
input_tensors: dict, the key is a string and the value
is a tensor outputed by the transform ... | 2666832656bf9d98a933b068215c3076328f3a3d | 52,039 |
import types
def name(function):
"""
Retrieve a pretty name for the function
:param function: function to get name from
:return: pretty name
"""
if isinstance(function, types.FunctionType):
return function.__name__
else:
return str(function) | 4002d7a945c3b3e3e4d957c0b10ff202f2cf0246 | 52,040 |
def loading_effects_decorator(func):
"""
Decorator for creating an loading cursor.
>>> @loading_effects_decorator
>>> def do_lengthy_process():
>>> # DO something
>>> pass
Parameters
----------
func: function
Returns
-------
new_function: object
"""
d... | 28c495d0c5b31e124173184ccb2d00dd90986376 | 52,041 |
import requests
def get_response_json(url: str):
"""
Function get response json dictionary from url.
:param url: url address to get response json
:type url: str
:return: dictionary data
:rtype: json
:usage:
function calling
.. code:: python
get_response_json... | dd209b1dba7f4320cd91addb1e49fa98bab0ae2b | 52,042 |
def JobPostingDetails(request, pk):
"""
Retrieve, update or delete a job posting.
"""
try:
job_posting = JobPosting.objects.get(pk=pk)
except JobPosting.DoesNotExist:
return HttpResponse(status=500)
if request.method == "GET":
serializer = JobPostingSerializer(job_postin... | 1f1cc4be467fd64cf87bbab01fcae68a19905969 | 52,043 |
import ast
import re
def strip_typehints(source):
"""Strip the type hints from a function"""
if black:
source = black.format_str(source, line_length=82)
# parse the source code into an AST
parsed_source = ast.parse(source)
# remove all type annotations, function return type definitions
... | 46df36fcffd38b6657df041fb366f001c410ece6 | 52,045 |
def upconv(x, channels, kernel=3, stride=2, pad=1, use_bias=True, sn=False, scope='upconv_0'):
"""
upsampling + conv
"""
with tf.variable_scope(scope):
x = up_sample(x, scale_factor=stride)
x = conv(x, channels=channels, kernel=kernel, stride=1, pad=1, use_bias=use_bias, sn=sn, scope=sco... | 6e3e13e6143757afb549a42632a877b9cb7089a9 | 52,046 |
import copy
def create_temporary_copy(directory_path: str, target_directory_prefix: str = "") -> str:
"""Creates a copy of a directory in a temporary directory and returns its path"""
copy_path = get_temporary_directory_path(target_directory_prefix)
copy(directory_path, copy_path)
return copy_path | b835724145ee64cc52d9193cc73b2b25eb33ec2a | 52,047 |
import pickle
def dumps_content(content):
"""
pickle 序列化响应对象
:param content: 响应对象
:return: 序列化内容
"""
return pickle.dumps(content) | f88159b9d016a6e39744e7af09a76705c9f4e76f | 52,048 |
def hide_solution(key, tpl_dict = latex_doc.tpl_dict):
"""Hide solutions by default"""
return """
{%- if cell.metadata.solution_first or cell.metadata.solution2_first or cell.solution_first or cell.solution2_first -%}
{# exercise #}
{%- elif cell.metadata.solution == 'hidden' or cell.metadata... | 836e6bb8009d5626b08ef339145758f7f3ce4214 | 52,050 |
def midpoint(f, a, b, N):
"""Zusammengesetzte Mittelpunktsregel in 1d.
Input:
f : Funktion f(x) welche integiert werden soll.
a, b : untere/obere Grenze des Integrals.
N : Anzahl Teilintervalle in der zusammengesetzten Regel.
"""
x, h = np.linspace(a, b, N+1, retstep=True)... | 0c07c51a8f9c917c1b22ebfebb8c8904fa4dccba | 52,051 |
def content_disposition_filename(filename):
"""
Sanitize a file name to be used in the Content-Disposition HTTP
header.
Even if the standard is quite permissive in terms of
characters, there are a lot of edge cases that are not supported by
different browsers.
See http://greenbytes.de/tech... | dd60cddee1ccb08344e41a2ffbd4bb68b599d5da | 52,053 |
from datetime import datetime
def fmt_datetime(object_: datetime) -> str:
"""Parse datetime into pydantic's JSON encoded datetime string"""
return object_.astimezone(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | 2e39b9d6a262aaa0297f50af8c1fbf9069ac5f4b | 52,055 |
import json
def get_table_list(transaction_executor):
"""
Connect to a given ledger using default settings.
"""
statement = "SELECT name FROM information_schema.user_tables"
cursor = transaction_executor.execute_statement(statement)
try:
table_list = {}
i = 0
for r... | c4d50798c0b6340ebe064e087664cfaca5f28a49 | 52,058 |
def _apply_conv_mode(ret, s1, s2, mode, axes):
"""Calculate the convolution result shape based on the `mode` argument.
Returns the result sliced to the correct size for the given mode.
Parameters
----------
ret : array
The result array, with the appropriate shape for the 'full' mode.
s... | 873f5529da5e88a52278fb5c90537c7751b50845 | 52,059 |
def format_time(ogtime):
"""Increase readability of given catalog times."""
return pd.Timestamp(' '.join(ogtime.split('T'))[:-1]) | 93207ba2b09c3f25c15f5aa2fcf8030950f530f3 | 52,061 |
import time
def train(loss_fn):
"""使用损失函数loss_fn训练回归"""
print("Training;loss function:" + loss_fn.__name__)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
def loss_for_example(x, y):
return loss_fn(y, prediction(x))
# `grad_fn(x_i, y_i)` returns (1) the value of `loss... | 20de6a0bf6245efc4351e0339b942fbf80e0b9b2 | 52,062 |
def estimator(data):
"""
Takes input data and returns it in a
specified format.
"""
# get days
days = timeEstimateDays(data["periodType"], data["timeToElapse"])
# Challenge 1
# Currently infected estimates
currentlyInfectedImpact = int(data["reportedCases"] * 10)
curren... | 1b7e6bd6772aae1b2b1d76fec083ec601515f7f8 | 52,063 |
def run(method):
"""Run method."""
switcher = {
"construct": lambda: construct(),
"execute": lambda: execute()
}
return switcher.get(method)() | 602cf4765144bbb838fab3f8733cf953216a6d06 | 52,064 |
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