content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import OrderedDict
def make_info(params, htext):
"""
Make a custom special argument: Whether to display recipe information
:param params: ParamDict, Parameter Dictionary of constants
:type params: ParamDict
:return: an ordered dictionary with argument parameters
:rtype: OrderedDi... | f40528817d17bdbb9921df11c70f21074eb71258 | 3,601,499 |
import warnings
def _root_amplitude_brentq(counts, bkg, model, root_fn=_f_cash_root):
"""Fit amplitude by finding roots using Brent algorithm.
See Appendix A Stewart (2009).
Parameters
----------
counts : `~numpy.ndarray`
Slice of count map.
bkg : `~numpy.ndarray`
Slice of ba... | dad242e3d17bca0bacb3a98bd8cc79ee3e7c4ed6 | 3,601,500 |
def get_valid_keypoint_mask_for_class(keypoint_coordinates,
class_id,
class_onehot,
class_weights=None,
keypoint_indices=None):
"""Mask keypoints by their class ids a... | b50008ce56a7885a2ae2ff188cd3ef6bb7166fa0 | 3,601,501 |
def identify_target_ligand(ligand_residues):
"""Attempts to guess the target ligand"""
# If there is only one target ligand then that must be the target
# even if there are multiple instances. That could be the case if
# the compound is peptidic for example.
if len(ligand_residues) == 1:
ret... | 50e3559b2f42aa1a60ca5270279cc2a37325f3cb | 3,601,502 |
def cue_favourite(speaker, action, args, soco_function, use_local_speaker_list):
"""Shortcut to mute, play favourite, stop favourite, and unmute.
Preserve the mute state
"""
if not speaker.is_coordinator:
error_report("Action '{}' can only be applied to a coordinator".format(action))
ret... | 793568f70783bde890ac331244902b768c3561ad | 3,601,503 |
def vocab_marking_definition(instance):
"""Ensure that the `definition_type` property of `marking-definition`
objects is one of the values in the STIX 2.0 specification.
"""
if (instance['type'] == 'marking-definition' and
'definition_type' in instance and not
instance['definitio... | 68beab22bee4d76255e8acacaf7f4fe469bc6882 | 3,601,504 |
def _header_path(header_file, module_map_file, workspace_relative):
"""Returns the path to a header file to be written in the module map.
Args:
header_file: A `File` representing the header whose path should be
returned.
module_map_file: A `File` representing the module map being wr... | 1339e5dbbecd8773fcbe4e760cae8037c553186e | 3,601,505 |
import requests
def pega_conselho(assunto=False):
""" A função acessa a API e retorna um conselho
Args:
assunto (bool, optional): Se tiver uma palavra como
argumento será retornado um conselho que tenha o assunto
relacionado com o argumento passado. Por defaults o
argumento é ... | 1b1c26c6b13fd287c0ec66412cc9372fadf38f45 | 3,601,506 |
def Recherche(tree, word):
"""Recherche le mot word dans l'arbre tree et indique s'il est dans l'arbre."""
return tree.contains(word) | f17d4024b0e5e7542fec0a502f853fdb63a21c95 | 3,601,507 |
def cov(x: to.Tensor, data_along_rows: bool = False):
"""
Compute the covariance matrix given data.
.. note::
Only real valued matrices are supported
:param x: matrix containing multiple observations of multiple variables
:param data_along_rows: if `True` the variables are stacked along th... | 3a848ebf8d97dfdfcbf77c025846b240c33b38cc | 3,601,508 |
def blrPredict(W, data):
"""
blrObjFunction predicts the label of data given the data and parameter W
of Logistic Regression
Input:
W: the matrix of weight of size (D + 1) x 10. Each column is the weight
vector of a Logistic Regression classifier.
X: the data matrix... | 220f1951157aec1d37b2bff7dccd9ff2dfbeb4d7 | 3,601,509 |
def preprocess_input(frames, augment=True):
"""Preprocesses raw frames and optionally performs data augmentation."""
preprocessing_ranges = {
preprocess_sequence.IMAGE_TO_FLOAT: (),
preprocess_sequence.RESIZE: {
'new_size': [CONFIG.IMAGE_SIZE, CONFIG.IMAGE_SIZE],
},
preprocess_seq... | 021c22924e4ff3883d6000f23fd2880fe410b438 | 3,601,511 |
import torch
def draw_parabolic_binary_spline(x0, y0, x1, y1, x2, y2,
imsize=224, width=1, tstep=0.01):
"""Non-differentiable way to draw a spline with no fuzz
:param x0: int, x coordinate of point 0
:param y0: int, y coordinate of point 0
:param x1: int, x coordinat... | b3dc1ee1223b0ecea96e94f1b39655fea485c412 | 3,601,512 |
def get_reader_extensions():
"""
Returns a list of extensions for which readers are available.
:return: the list of extensions
:rtype: list
"""
result = list(get_flow_readers().keys())
result.sort()
return result | 99351c22b098b771389de80863b117907c0bd257 | 3,601,513 |
def to_feature_vector(img):
""" Converts the given image to a feature vector containing -1s (white pixels) and 1s (black pixels). """
return np.array([1 if p == 0 else -1 for p in list(img.getdata())]) | bd3d58af1e62e77148d8f8abfe5db4f4e7aabb09 | 3,601,514 |
def safe_value(name, value):
"""
Only show up to logger_settings['reveal_sensitive_prefix'] characters
from a sensitive header.
:param name: Header name
:param value: Header value
:return: Safe header value
"""
if name.lower() in LOGGER_SENSITIVE_HEADERS:
prefix_length = logger_... | b34a98d1bd161ae1f20b2028212ad6a8e8176f6f | 3,601,515 |
from pathlib import Path
async def dep_version_id(
version_id: str = Path(..., title="Version ID", **EntityID.META)
) -> str:
"""Validate and return a version id specified in an endpoint path."""
return version_id | 00c73a068ad8988d394a9bcf78c61395d6fdd98e | 3,601,516 |
import http
def movedTo(request, url, encoding):
"""
Permanently redirect C{request} to C{url}.
@param request: The L{twisted.web.server.Reqeuest} to redirect.
@param url: The new URL to which to redirect the request.
@type url: L{bytes}
@param encoding: The encoding of the response as a by... | 037d6ac418ec097242244461a2bb6f7835ac71f5 | 3,601,517 |
def fix_IE_for_attach(request, response):
"""
This function will prevent Django from serving a Content-Disposition header
while expecting the browser to cache it (only when the browser is IE). This
leads to IE not allowing the client to download.
"""
useragent = request.META.get('HTTP_USER_AGENT... | b8fd12ba12a2f7df918a3e0fa7c64094c0bd016d | 3,601,518 |
async def stocks(request: Request, ticker, period="1d", interval="1d"):
"""
Get ticker finance information
:param request:
:param ticker:
:param period: 1d, 5d , 7d, 1mo
:param interval: 1m, 2m, 5m, 15m, 30m, 60m, 1d, 5d, 1wk, 1mo
:return: result
"""
logger.info(F"NewApiRequest: /st... | dbf7b6147a451355970691cc69e415e6eb1bfa1d | 3,601,520 |
def Mean_Reciprocal_Rank(RGB):
""".
RGB: Relevance scores (list or numpy) - first element is the first rank
"""
Non_Zeros = (np.asarray(RGB).nonzero()[0])
if Non_Zeros.size == 0:
return 0.
else:
return 1. / (Non_Zeros[0] + 1) | 7576b0ac2561c3eb1265ed3ed286b2ec86ead6af | 3,601,521 |
import struct
def UnpackInt(inp_stream, bitlen, huff):
"""
Reads an int from an input BitBucket and returns it.
'bitlen' is between 1 and 32 (inclusive), and represents the number of bits
to be read and interpreted as the int.
'huff' is unused.
"""
raw_in_stream = inp_stream.GetBits(bitlen)[0]
rshif... | d77e25085720519d880f009390af59f7844e9f54 | 3,601,522 |
def to_dict(arr):
"""
Convert a list to a dict with keys drawn from '0', '1', '2', ...
Examples
--------
>>> to_dict([2, 3, 4]) # doctest: +SKIP
{'0': 2, '1': 3, '2': 4}
"""
return dict(zip(map(str, range(len(arr))), arr)) | a51c0cbb477b4569a67fda3088a200194bb8bf67 | 3,601,523 |
def downsample2(img, f):
""" Downsample image by a factor of 2
Filter with Gaussian filter then take every other row/column
Args:
img: image to downsample
f: 2d filter kernel
Returns:
downsampled image as (H, W) np.array
"""
#
# You code here
#
img_filtered =... | 1532293f53fcebd79f97cdbe8afc311334424a9f | 3,601,524 |
def is_power_of_two(a):
"""Return whether the argument, cast to int, is a power of 2."""
a = int(a)
# Bit manipulations. A power of 2 has a bit represetenation like 0...010...0.
# For such a number subtracting 1 from it turns it into 0...001...1, so ANDing
# a-1 and a should yield 0.
return a > 0 and ((a - ... | d7b0d90df8eb4287a6f56e8256aa6c40b9b46441 | 3,601,525 |
import collections
def possible_parameter(nb, jsonable_parameter=True, end_cell_index=None):
"""
Find the possible parameters from a jupyter notebook (python3 only).
The possible parameters are obtained by parsing the abstract syntax tree of
the python code generated from the jupyter notebook.
F... | f655b4cc24db37052c95194393a27cf61193f581 | 3,601,526 |
from typing import Callable
from functools import reduce
def clean(text: str, *cleaners: Callable[[str], str]) -> str:
"""Cleans the given text using the provided cleaning functions.
Arguments:
text:
The text string to be cleaned.
cleaners:
The simple cleaner functions... | 67a53637bca0b19b49bd157ccc520d8dc053a12f | 3,601,527 |
import itertools
def _stitch(sequences, th=1):
""" trying to connect two different sequences """
if len(sequences) <= 1:
return sequences
if th == 0:
return [sum(sequences, [])]
for i, j in itertools.combinations(range(len(sequences)), r=2):
s1, s2 = sequences[i], sequences[j... | bc7131c8415532b85f981974aa0e0abf4c4a060b | 3,601,528 |
def process_parallel(binary, input_file, procs, logger):
"""
Performs parallel apbs runs of the input file
"""
logger.message("Splitting the input file into %d separate files using the inputgen utility\n\n" % procs)
# Get the base name, and split the input file using inputgen's split_input
bas... | 4072b495dcde3c04c0c02ef41a7313f27b1fad3d | 3,601,529 |
def strip_parens(text):
""" strips parenthesis from a string (works with nested parentheses)
note that this method will leave the extra spaces behind, but this will not affect tokenization
:param text: original string
:return: text stripped of parenthetical words
"""
left_parens = []
... | 1976636150bc6705e39ab3929e1214f0b0c09644 | 3,601,530 |
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer):
"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
print("Writing... | 8a59723ec1cc82a03096616bc79e968e1f5a1fe3 | 3,601,531 |
import nltk
def easy_convert_sentence_to_values(sentence_array, input_size):
"""
---------------------------------------------------------------------
DESCRIPTION
Converts the array of sentences to an array of word value arrays. If
necessary, they might be padded.
-----------------------------... | f743f923fedb4434e208ac2971004fc062dd8fad | 3,601,532 |
def get_table_delete_stmt(cls):
"""Returns SQL DROP table statement for the given
cls class using specified Column fields"""
table_name = get_table_name(cls)
exec_stmt = [f"DROP TABLE {table_name}"]
return ''.join(exec_stmt) | 1509e873d1f98ea165333a13ff4b7048415d56be | 3,601,534 |
def get_sensor_parameters(sensor, set="adjustable"):
"""
Get a set of the CSM parameters for a CSM sensor
Parameters
----------
sensor : CSM sensor
The CSM sensor model
set : str
The CSM parameter set to get. Either valid, adjustable, or non_adjustable
Returns
--... | 2c0f7c59c620a3a482013a0570bc5e083fc25315 | 3,601,535 |
def adaptive_avg_pool1d(input, output_size):
"""Applies a 1D adaptive average pooling over an input signal composed of several input planes.
See :mod:`oneflow.nn.AdaptiveAvgPool1d`
Args:
input: input tensor
output_size: the target output size (single integer)
"""
return AdaptiveAvg... | dbc15c7e55d31cff416c9e60cf947f3406a196f9 | 3,601,536 |
import numpy
def less_equal(self, other):
""" Equivalent to the <= operator.
"""
return _PropOpB(self, other, numpy.less_equal, numpy.uint8) | 5e2c7192c81ea5142c6bbe67106f925d8e69d9cf | 3,601,537 |
def variable_name_to_title( variable_name, latex_flag=True ):
"""
Translates a variable name into a title suitable for inclusion in Matplotlib
title strings. Variable names are assumed to be lowercased as found in IWP
datasets and titles may include LaTeX markers for mathematical typesetting.
Unkno... | 3f9d56a13f4aacb2ec6e9cf15ad1d2ff1c4288bc | 3,601,538 |
import json
def workerById():
"""
Accède à un ouvrier par son identifiant
Args:
request.form doit contenir :
- num : l'identifiant de l'ouvrier en BDD
Returns:
code 1, data : data contient l'ouvrier si on le trouve
code -1, message : erreur lors de... | f0a38572f384a3194857ea242b93b000527b7428 | 3,601,539 |
def survival_cross_validation(row, phenotype_df, duration_col = 'T', event_col = 'E', other_cols = []):
"""
duration_col: survival time
event_col: whether an event (death or other) has ocured or not. 0 for no, 1 for yes
other_cols: other variables to consider in the regression
"""
phenotype_df =... | cd7664c5d091c770f0cab7d1952d03b048b8fb81 | 3,601,540 |
def scrub(value, scrub_text=_keep_whitespace, scrub_number=_scrub_number):
"""
REMOVE/REPLACE VALUES THAT CAN NOT BE JSON-IZED
"""
return _scrub(value, set(), [], scrub_text=scrub_text, scrub_number=scrub_number) | d72bbbd1bbfa3a75f3ccd2e3ee023874b82ecad8 | 3,601,541 |
def GetToolchainTupleForBoard(board, buildroot=constants.SOURCE_ROOT):
"""Gets a tuple for the default and non-default toolchains for a board.
Args:
board: board name in question (e.g. 'daisy').
buildroot: path to buildroot.
Returns:
The tuples of toolchain targets ordered default, non-default for t... | ae2fb39b7d2ef00375f07af777cdd697dece5ecb | 3,601,542 |
def show_rule_by_num(table_name, chain_name, rule_num):
"""
Handler for rule request URL
:param table_name: Name of table to look in
:param chain_name: Name of chain to look in
:param rule_num: Index of rule to return
:returns: Flask Response with rule in JSON format
"""
if table_na... | 750ac4e9428d6925a0b604a8dbb569641d105326 | 3,601,544 |
import random
def welzl_circle(point_set):
"""
Creer le cercle minimum de l'ensemble de point passé en paramètre en utilisant l'algorithme de welzl
params :
- point_set = L'ensemble de points à évaluer
return -> Circle = Le cercle minimum de l'ensemble de points
"""
# On transforme ... | 3284906bf30f3a4d1ce68a6bff6f4c10343d3d7c | 3,601,545 |
def parse_paragraphs(m, desc):
"""Parse Markdown-formatted paragraphs into a list of HTML paragraphs."""
def strip(s):
start = s.find('>') + 1
end = len(s) - s[::-1].find('<') - 1
return s[start:end]
return [strip(parse_markdown(s)) for s in m.splitlines()] | 3c22b6505dc22a596d1ff8ab724a90681375d55a | 3,601,546 |
import logging
def get_filewriters(basename, subset, num_shards):
"""Opens and returns list of tfrecord writers."""
fh = []
logging.info('Opening %s.%s.*.tfrecord for writing.', basename, subset)
for i in range(num_shards):
fh.append(tf.python_io.TFRecordWriter(
basename + '.%s.%s.tfrecord' % (sub... | 5fc9d53a5f726876ab7a550d79009fd99571b105 | 3,601,548 |
def mean_dice(results,
gt_seg_maps,
num_classes,
ignore_index,
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False):
"""Calculate Mean Dice (mDice)
Args:
results (list[ndarray]): List of prediction segmentation ma... | 0fdcb2b5d65d03b51992aa18320d2529950c3809 | 3,601,550 |
def fix_filter_query(filter):
"""
fix filter query from user
Args:
filter: filter from users
Returns:
dict
"""
if filter:
try:
filter = {
_.split("=")[0]: _.split("=")[1] for _ in list(
set(
str(filt... | 11dce2c8471205118277bb396ec298de7fb7bf67 | 3,601,551 |
def index():
"""
Home page
:return:
"""
_jobs_collections = mongo.db.jobs # get the jobs collections from mongo db
_users_collections = mongo.db.users # get the users collections from mongo db
# coming post from user page =========================
... | a7354bf96250aeac1fb3ec423300a5de988efca2 | 3,601,552 |
def rectangle_area(length, width):
"""
Calculates the area of a rectangle.
:param length: The length of the rectangle.
:param width: The width of the rectangle.
:return: The area of the rectangle.
"""
return length * width | 0cbe453fbd4c3c6a061f520d57f303dae55fdc25 | 3,601,553 |
def linear_assignment(cost_matrix, thresh):
""" Hungarian algorithm: pair new detections with previous tracklets
return
matches: [idx of tracked_stracks, idx of detections]
u_track: [index of undefined track]
u_detection: [index of undefined detection]
"""
if cost_mat... | 4bfea019ff033e2433a765c1295662b2340f0642 | 3,601,554 |
def inspect_bursts(xc_r, starts, ends, ang_velocities, min_dist_between_start_end=50):
"""
An entire time series should only have as many starts as it has ang_velocities
Args:
xc_r (array): cross correlation between signal and rect
starts (array): list of segment start locations
... | bbb2bd82740ef80f5028cf24d1b2c5226b621443 | 3,601,556 |
def has_level_five(line):
"""Check if we need to explode"""
count = 0
for c in line:
if c == '[':
count += 1
if count > 4:
return True
elif c == ']':
count -= 1
return False | 6d2e9d4aed708a9d71a9f01f90519f804e1fd3d9 | 3,601,557 |
def cal_merit_lda(spectra: np.ndarray,
wavenumbers: np.ndarray,
replicate: np.ndarray,
label: np.ndarray):
"""
Benchmark of replicate EMSC correction based on LDA classification
:param spectra: ndarray of shape [n_samples, n_channels]
:param wavenumb... | 22dbd195952dbce25dc2f3f10a13b10d716d48f2 | 3,601,558 |
def combinecrinfo(crinfo1, crinfo2):
"""
Combine two crinfos. First used is crinfo1, second used is crinfo2.
"""
crinfo1 = fix_crinfo(crinfo1)
crinfo2 = fix_crinfo(crinfo2)
crinfo = [
[crinfo1[0][0] + crinfo2[0][0], crinfo1[0][0] + crinfo2[0][1]],
[crinfo1[1][0] + crinfo2[1][0],... | e4066909c446d7c0de8d88959eb8880fef9f928c | 3,601,560 |
def threshold_array(arr, threshold=2e-4):
"""
Thresholds an array, returning a binary array
Parameters
---------
arr : NumpyArray
Contains the data to threshold
Returns
---------
NumpyArray
Returns arr with binary values, depending of the threshold
"""
return (ar... | 254692de4f82dbf6c3e684b10324cb73c436ff28 | 3,601,561 |
def df_large():
"""Create a large random dataframe for testing."""
# Sample the discrete sets of values (just so they are not np.arange).
N = 1000
n_date = 100
n_name = 100
n_country = 100
n_team = 100
n_z = 4
DATE_set = np.random.choice(N, n_date, replace=False)
NAME_set = np.ra... | 1322b7fcfe54670fcb876727264cd1dc47522914 | 3,601,563 |
def get_canonical_tag():
""" Uses the Config object's CANONICAL_TAG attribute to get a tag object;
that tag object is the blog's canonical tag. """
canonical_tag_record = app.config['MDB'].tags.find_one(
{'name': app.config['CANONICAL_TAG_NAME']}
)
if canonical_tag_record is None:
... | 8f4d8697fb3f1b1f602335e4947367924abfd2e8 | 3,601,565 |
import functools
def python_wrapper(func):
"""Epsagon's general python wrapper."""
@functools.wraps(func)
def _python_wrapper(*args, **kwargs):
epsagon.trace.tracer.prepare()
return wrap_python_function(func, args, kwargs)
return _python_wrapper | edf19d6b94be85b339a0e06cdd79b0333259b149 | 3,601,566 |
def snip1d_quad(y, w=4, numiter=2):
"""Return SNIP-estimated baseline-background for given spectrum y.
Adds a quadratic kernel convolution in parallel with the linear kernel."""
convolve1d = ndimage.convolve1d
kernels = []
for p in range(w, 1, -2):
N = p * 2 + 1
# linear kernel
... | 884d82614c7bca6f01dce9e6d559e3ca708819b1 | 3,601,567 |
def LONG_LINE(data):
"""
Long Line Candle
:param pd.DataFrame data: pandas DataFrame with open, high, low, close data
:return pd.Series: with indicator data calculation results
"""
fn = Function('CDLLONGLINE')
return fn(data) | b7a7494f7fdf27acebbb943524156d5079b2a2e8 | 3,601,568 |
def __binary_search(arr, f, g):
"""
Perform the binary search in order to find the minimum feasible value of ``c`` inside ``arr``.
:param arr: The array on which to perform the binary search.
:param f: Function to be applied to ``g`` and ``arr[mid]`` (``check_th7`` or ``feas``).
:param g: A Network... | 415528fd46a33d1efe8d736717ac8b041d34c5d7 | 3,601,569 |
from typing import List
from typing import Union
def keypress_prompt(accept: List[Union[str, int]]) -> Union[str, int]:
"""Infinitely waits until an acceptable key is pressed
Parameters:
accept (list): a list with allowed keys
Returns:
result (str, int): The pressed key
"""
while... | 8b7990515d896f7107cf7b41dc3b37dec3c3eda0 | 3,601,570 |
def get_onpolicy_dataset(load_dir, env_name, tabular_obs, max_trajectory_length,
alpha, seed):
"""Get on-policy dataset."""
tf_env, tf_policy = env_policies.get_env_and_policy(
load_dir, env_name, alpha, env_seed=seed, tabular_obs=tabular_obs)
dataset = tf_agents_onpolicy_dataset.T... | 89fab13cab99231f611f9191f98e949da1e9abda | 3,601,571 |
import re
from bs4 import BeautifulSoup
import requests
def sourceforge(url: str) -> str:
""" SourceForge direct links generator """
try:
link = re.findall(r"\bhttps?://.*sourceforge\.net\S+", url)[0]
except IndexError:
reply = "`No SourceForge links found`\n"
return reply
file... | 1a8b1795b286b8e85194d99de1ba8368dce00ebd | 3,601,572 |
def get_accounts(account_id):
"""
Retrieve a single Account
This endpoint will return an Account based on it's id
"""
app.logger.info("Request for Account with id: %s", account_id)
account = Account.find(account_id)
if not account:
abort(status.HTTP_404_NOT_FOUND, f"Account with id ... | a6e6209d31881d018409acc75701faa5566616f9 | 3,601,573 |
def summarize_tree(tree):
"""Summarize a pytree."""
def _describe(x):
if (hasattr(x, "shape") and x.shape and not isinstance(x, jax.core.Tracer)):
flat = np.asarray(x).reshape([-1])
if flat.shape[0] == 0:
return f"{np.dtype(x.dtype).name}{x.shape}"
info = f"{np.dtype(x.dtype).name}{x.... | ec44481ea97db6a7673027b15aee85e0073ff7c9 | 3,601,574 |
def stratified_sample(df, N=1000, return_indices=False):
"""
Build a stratified sample from a dataset. Maps NAs in partially
labeled records to -1.
:df: DataFrame containing file paths (in a "filepath" column) and
labels in other columns
:N: number of samples
:return_indices: whethe... | 42d622e4df15771c403c4f3bf4aa197b15b51265 | 3,601,575 |
def slot_tokenize(tokenizer):
"""Tokenization and aligning labels for slot tagging task"""
def tokenize(examples):
# remove punct symbol in utterance, since the tokenizer not consider punct as the sub word
examples["utterances"] = [
remove_punct(text) for text in examples["utteranc... | 17355312679f20119219144c2bee51405add3d7a | 3,601,576 |
from typing import Sequence
import copy
def createAnnulusMesh3d(nodes, mesh, nextNodeIdentifier, nextElementIdentifier, startPointsx, startPointsd1,
startPointsd2, startPointsd3, startNodeId, startDerivativesMap, endPointsx, endPointsd1,
endPointsd2, endPointsd3, endNod... | ca86728e6782d495a849cf5f82d63ce7e42089ba | 3,601,577 |
def create_role(keystone, name):
"""
Creates an OpenStack role
:param keystone: the keystone client
:param name: the role name
:return: a SNAPS-OO Role domain object
"""
role = keystone.roles.create(name)
logger.info('Created role with name - %s', role.name)
return Role(name=role.nam... | 7388d6db89aa018ff9efff2bb9ae4f47cf2243cf | 3,601,578 |
import random
def owo_convertor(text):
""" Its your OwO Convertor """
# replacing 'L', 'i', 'R' and 'r' with 'W' and 'w' respectively
text = text.replace('L', 'W').replace('l', 'w')
text = text.replace('R', 'W').replace('r', 'w')
# deal with ending
text = end(text, '!', '! {}'.format(random.choice(e... | 3b873b4c061e193318c6de1e847e3c0a08c0a674 | 3,601,579 |
def _current_window_for_event(event):
"""
Return the `Window` for the currently focussed Buffer.
"""
return event.app.layout.current_window | 4b9859c7bf7fc4b072362d2d5b9e896022769587 | 3,601,580 |
import re
def clean_xml(xml):
"""Clean the given XML string of namespace definition, namespace
prefixes and syntactical but otherwise meaningless differences.
Parameters
----------
xml : str
String representation of XML document.
Returns
-------
str
String representat... | 3f566975ab512ccc22824c45e7ef04fc861a5c03 | 3,601,582 |
def preproc_data(df, NA_fill=None, add_tags=False, tag_meta=None,
verbose=False):
"""
* NA_fill = float, 'mean' or 'median'
* If add_tags==True, must provide tag_meta
"""
df_out = df.copy()
# add feature tags
if add_tags:
df_out = gen_tag_features(df_out, tag_meta,... | 0d5be319d5811ea2760766f55321d03407f06574 | 3,601,583 |
def purestr(x):
""" A string that follows obj = type(obj)(*obj.args) exactly """
if not isinstance(x, Basic):
return str(x)
if type(x) in slotClasses:
args = [getattr(x, slot) for slot in x.__slots__]
elif type(x) in sort_classes:
args = sorted(x.args, key=default_sort_key)
e... | 0fa0fd1f8774ef9151c06cdc89c4e88f260b0e48 | 3,601,584 |
from textwrap import dedent
def compareDocReplacer(doc):
"""Make replacements for comparison docstrings."""
if not doc:
return ""
doc = dedent(doc)
for magic, replace in COMPARE_DOC_MAPPING.items():
lookF = '{' + magic + '}'
if lookF in doc:
doc = doc.replace(lookF,... | 6f75ccf9e79168df18869c93d4b2e7060410ac0a | 3,601,585 |
import torch
def _make_orthogonal(A):
""" Assume that A is a tall matrix.
Compute the Q factor s.t. A = QR (A may be complex) and diag(R) is real and non-negative
"""
X, tau = torch.geqrf(A)
Q = torch.linalg.householder_product(X, tau)
# The diagonal of X is the diagonal of R (which is always ... | ad2cfaa0c041062c8628bc02faedcfdbc550f4bb | 3,601,586 |
def prepare_train_test_data():
"""
Read csv file, convert it to
machine learning representation,
split features and labels, and
split into training and testing data.
"""
# Reaed data from csv file
data = pd.read_csv(f'{config.data_directory}/{config.data_filename}')
# Split into ... | 9999a62c9299ba3af8753a3e84ee9d7d82a3b5ed | 3,601,587 |
from traceback import extract_stack
from rootpy import _ROOTPY_SOURCE_PATH
def rp_module_level_in_stack():
"""
Returns true if we're during a rootpy import
"""
modlevel_files = [filename for filename, _, func, _ in extract_stack()
if func == "<module>"]
return any(path.start... | 02e67bbbdc8ae087a132596405cc11807a489cca | 3,601,588 |
def run_unthrottled(task):
"""
Determines whether a task needs to run unthrottled. Tasks that don't specify a target throughput are only considered
as unthrottled if no explicit schedule or one of the built-in schedules is specified. Custom schedules might need
more flexible approaches (e.g. because the... | b7523e623f4f5109aec4f75827d7f13be376722a | 3,601,590 |
def login_required(f):
"""Decorate routes to require login.
http://flask.pocoo.org/docs/1.0/patterns/viewdecorators/"""
@wraps(f)
def decorated_function(*args, **kwargs):
if session.get("user_id") is None:
return redirect("/login")
return f(*args, **kwargs)
return decorat... | fb7c095e1bdd30e19483dae0d620274960d608ee | 3,601,592 |
def reduce_mem_usage(df: pd.DataFrame, verbose: bool = True) -> pd.DataFrame:
"""
Function to reduce memory usage of numerical columns.
Args:
df (pd.DataFrame): Dataframe to reduce memory usage.
verbose (bool, optional): If show how the memory usage decreased or not. Defaults to True.
... | bbe1938258cee169d77577f993f18a7a7028445f | 3,601,593 |
def do_stats(covering, covered, maxD, bin_width=1,
fignum=1, num_panels=4, s_frac=0.5,
nhd_size_thresh=None, nhd_max_plot_size=None, minD=None,
D_estimate_method="absolute", weight_by_size=False,
force_xaxis_limits=False, radius_yaxis_limits=None,
save=''... | a09085b65fdd4be6ca976d8aa34b0e15861c1849 | 3,601,594 |
def getVersion(includeSubrelease=False):
"""Returns wxWidgets version as a tuple: (major,minor,release)."""
wxVersion = None
major = None
minor = None
release = None
subrelease = None
if wxVersion == None:
f = open(VERSION_FILE, 'rt')
lines = f.readlines()
f.close()
... | 2db9bd7a13d65325ba3bcbf1fe9ad9e1e319cb90 | 3,601,595 |
def join(name, args, arg_separator=None):
"""
Returns name( argument_1, argument_2, ..., argument_n ).
"""
return __THE_PARSER.join(name, args, arg_separator) | ef5a7cfbeba59defa3b2569402ec2ceef63ab938 | 3,601,596 |
from typing import Tuple
def masked_accuracy_fn(
targets: jnp.ndarray,
preds: jnp.ndarray,
mask_values: Tuple[int, ...] = (),) -> MeanMetric:
"""Computes accuracy after discounting masked values.
Args:
targets: Target values of shape [batch_size, ...] in range [0, num_classes).
preds: Unnorma... | be8f8e8897c312454f0a7f778feef4f6a86c393b | 3,601,597 |
def plot(Si_df, ax=None):
"""Create bar chart of results.
Parameters
----------
* Si_df: pd.DataFrame, of sensitivity results
Returns
----------
* ax : matplotlib axes object
Examples
----------
>>> from SALib.plotting.bar import plot as barplot
>>> from SALib.test_functio... | c82c493a8655281fd8e841ad079a0af9869059c1 | 3,601,598 |
def build_model(config):
"""
"""
if config["task"] == "enc_dec":
model = build_enc_dec_model(config)
elif config["task"] == "lm":
model = build_lm_model(config)
elif config["task"] == "classify":
model = build_classify_model(config)
elif config["task"] == "bi_lm":
... | 6837e8577f91b9607c6f45510da7031474a38b94 | 3,601,599 |
def weighted_average(gini_or_entropy_left, left_cnt, gini__or_entropy_right, right_cnt):
"""
calculate weighted average for Gini index or Entropy
:param right_cnt: count of total records on the right side of node
:param left_cnt: count of total records on left side of node
:param gini_or_entropy_le... | 7914a0164427de4e9cebf7637dda670694f8df59 | 3,601,600 |
import torch
def concat_entities(entities):
"""
Concat multiple graphs via concatenation of their entities tensors.
Parameters
----------
entities: a list of graph tuples.
Either [(v,e,c),...] or [(v,e),...] when the graph has no global attribute.
Returns v,e,c - concatenated entities... | 06d0619836d4cb8c977028ee0cdccd9075136c72 | 3,601,601 |
def format_filesize(num, suffix='B'):
"""
See: https://stackoverflow.com/questions/1094841/reusable-library-to-get-human-readable-version-of-file-size
"""
for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
... | 2647aad4f4e09d9253b34b4ec39f601a3a558717 | 3,601,602 |
def filter_out_installed_pkgs(event_out_pkgs, installed_pkgs):
"""Do not install those packages that are already installed."""
return {p for p in event_out_pkgs if (p.name, p.modulestream) not in installed_pkgs} | 16fb8f68372fb070869a744b15ddce0ed6591887 | 3,601,603 |
def maxlinedev(x, y):
"""
Find the point of maximum deviation in the contour from a line joining
two points on the contour.
Input: x, numpy array of abscissas of the contour
y, numpy array of ordinates of the contour
Output: maxdev, maximum deviation
idxmaxdev, index of the ... | 37f666f17aa56a7c50dc7a55d7be567d179c8a27 | 3,601,605 |
def vcs_org_url(orgname=None):
"""
Returns VCS endpoint for orgs in general or the specified org
"""
url = VCS_URL_BASE + "/orgs"
if orgname == None:
return url
return url + "/" + orgname | 38be1c95082bbf0d61ed9039edd01043b2a99703 | 3,601,606 |
def stats_variance_2d(data, ddof=0, axis=1):
"""Pre compiled method to get 2d variance."""
a_a, b_b = data.shape
if axis == 1:
var = np.zeros(a_a)
for i in range(a_a):
var[i] = _stats_variance_1d_2d(data[i], ddof=ddof)
return var
else:
var = np.zeros(b_b)
... | acca95077d43ae63f88fda26d4320cfbaa628b8f | 3,601,607 |
def root():
"""Root."""
return redirect(url_for('sender.create')) | d08d4a328d4832a7b48f75db761b2e49332e4efc | 3,601,608 |
def add_roi_Xconv1fc_head(model, blob_in, dim_in, spatial_scale):
"""Add a X conv + 1fc head, as a reference if not using GroupNorm"""
hidden_dim = cfg.FAST_RCNN.CONV_HEAD_DIM
roi_size = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION
roi_feat = model.RoIFeatureTransform(
blob_in,
'roi_feat',
... | 560a391a189c4415da18ab01e3670ca1eb6b69f7 | 3,601,609 |
def ensure_str(s, encoding='utf-8', errors='strict'):
"""Coerce *s* to `str`.
For Python 2:
- `unicode` -> encoded to `str`
- `str` -> `str`
For Python 3:
- `str` -> `str`
- `bytes` -> decoded to `str`
"""
if not isinstance(s, (text_type, binary_type)):
raise TypeErr... | 9f92e636351332f5cb84677fe7555186b21779f9 | 3,601,610 |
def query_database(database, username, password, hostname, query, port=3306):
"""
Input: database, username, passward, hostname: str
Output: pd.Dataframe with display all columns
"""
conn = pymysql.connect(db=database, user=username, password=password, host=hostname, port=port)
cur = conn.curs... | ba602979e4d6a1addb2cfc0be44ce9ab23e62549 | 3,601,611 |
def exposed_dbus_prop(name):
"""Exposed a property on DBus and cache locally"""
local_key = '__%s' % (name,)
def _getter(self, name):
return self.__dict__[local_key]
def _setter(self, name, value):
self.__dict__[local_key] = value
self.props_iface.Set(
self.DBUS_NAM... | 6458decc814d9a5c055a409c13ae46501f59c7cd | 3,601,614 |
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