content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import sys
def has_newer_fw( current_fw, bundled_fw ):
"""
:param current_fw: current FW version of a device
:param bundled_fw: bundled FW version of the same device
:return: True if the bundled version is newer than the current one
"""
current_fw_digits = current_fw.split( '.' )
bundled_f... | 22daa7346981bdeed394518993dcbbb6b7835c23 | 3,644,800 |
def is_idaq(*args):
"""
is_idaq() -> bool
Returns True or False depending if IDAPython is hosted by IDAQ
"""
return _ida_kernwin.is_idaq(*args) | 5d18067b31be9c165a847815eb0bab92f89b0381 | 3,644,801 |
import requests
import yaml
def get_stats_yaml():
"""grab national stats yaml from scorecard repo"""
nat_dict = {}
try:
nat_yaml = requests.get(COLLEGE_CHOICE_NATIONAL_DATA_URL)
if nat_yaml.ok and nat_yaml.text:
nat_dict = yaml.safe_load(nat_yaml.text)
except AttributeError... | 045eeba3bfc42fa9e1821728260fd4d33e216731 | 3,644,802 |
import scipy
def signal_interpolate(x_values, y_values, desired_length, method="quadratic"):
"""Interpolate a signal.
Interpolate (fills the values between data points) a signal using different methods.
Parameters
----------
x_values : list, array or Series
The samples corresponding to t... | f3b20589591d2fed6054bbfc236894be70ddb598 | 3,644,803 |
from sys import modules
def check_module(feature):
"""
Checks if a module is available.
:param feature: The module to check for.
:returns: ``True`` if available, ``False`` otherwise.
:raises ValueError: If the module is not defined in this version of Pillow.
"""
if not (feature in modules... | c00680a135a2464cfb9a04ebae348c74d3c80271 | 3,644,804 |
def get_original(N: int = 64) -> np.ndarray:
"""radontea logo base image"""
x = np.linspace(-N / 2, N / 2, N, endpoint=False)
X = x.reshape(1, -1)
Y = x.reshape(-1, 1)
z = logo(X, Y, N)
return np.array((z) * 255, dtype=np.uint16) | 2bab08961d444f6ecfa097258872d02ae185944b | 3,644,805 |
from typing import List
def get_sql_update_by_ids(table: str, columns: List[str], ids_length: int):
"""
获取添加数据的字符串
:param table:
:param columns:
:param ids_length:
:return:
"""
# 校验数据
if not table:
raise ParamError(f"table 参数错误：table={table}")
if not columns or not isin... | ac70aa43aea4fad06ac2fd521239687040143b28 | 3,644,806 |
import os
import nibabel as nib
import numpy as np
import torch
def extract_roi(
input_img,
masks_location,
mask_pattern,
cropped_input,
roi_list,
uncrop_output,
):
"""Extracts regions of interest defined by masks
This function extracts regions of interest from preprocessed nifti image... | 434e7115032c7b4575b5fe8f046df4a6d3c49db8 | 3,644,807 |
import random
import sympy
def add_X_to_both_sides(latex_dict: dict) -> str:
"""
https://docs.sympy.org/latest/gotchas.html#double-equals-signs
https://stackoverflow.com/questions/37112738/sympy-comparing-expressions
Given a = b
add c to both sides
get a + c = b + c
>>> latex_dict = {}
... | 2ab0af9acbb09dcace00575a58afb66cebf2a07c | 3,644,808 |
def init_var_dict(init_args, var_list):
"""Init var with different methods.
"""
var_map = {}
_, max_val = init_args
for i, _ in enumerate(var_list):
key, shape, method = var_list[i]
if key not in var_map.keys():
if method in ['random', 'uniform']:
var_map[... | 05a3bece9598426010466c27ce794eb7d2aea937 | 3,644,809 |
def get_member_name(refobject):
""" return the best readable name
"""
try:
member_name = refobject.__name__
except AttributeError:
member_name = type(refobject).__name__
except Exception as error:
logger.debug('get_member_name :'+str(error))
member_name = str(refobj... | 103dfb1110ef8372e76b5ef734e842528d2b8f16 | 3,644,810 |
import os
import warnings
def _check_path(path=None):
"""
Returns the absolute path corresponding to ``path`` and creates folders.
Parameters
----------
path : None, str or list(str)
Absolute path or subfolder hierarchy that will be created and returned.
If None, os.getcwd() is us... | 2a1b18ac39cfd2573432911cd1aa6dfa5a740709 | 3,644,811 |
import warnings
def _eval_bernstein_1d(x, fvals, method="binom"):
"""Evaluate 1-dimensional bernstein polynomial given grid of values.
experimental, comparing methods
Parameters
----------
x : array_like
Values at which to evaluate the Bernstein polynomial.
fvals : ndarray
Gr... | 5561d4099bd07b0fc75dcbf47c53f5ff589e2d9d | 3,644,812 |
def exp_bar(self, user, size=20):
"""\
Returns a string visualizing the current exp of the user as a bar.
"""
bar_length = user.exp * size // exp_next_lvl(user.lvl)
space_length = size - bar_length
bar = '#' * bar_length + '.' * space_length
return '[' + bar + ']' | 575d475d602d0fdd4ded9eb2a139484c5d78887e | 3,644,813 |
def linear(input_, output_size, scope=None, stddev=0.02, with_w=False):
"""Define lienar activation function used for fc layer.
Args:
input_: An input tensor for activation function.
output_dim: A output tensor size after passing through linearity.
scope: variable scope, if None... | 8a5a4b06598d9c3c799c4a82d07a9d3d11962f23 | 3,644,814 |
from pathlib import Path
import json
import hashlib
import math
def generate_patches(patch_cache_location,
axis,
image_input_channels,
brain_mask_channel,
classification_mask,
patch_size,
k_fo... | d8dc0d1312acff05bfdbc56192ee3c7caeb65c86 | 3,644,815 |
def _parse_locals_to_data_packet(locals_dict):
"""
Takes the locals object (i.e. function inputs as a dict), maps keys from.
TODO retire this function, its pretty hacky
:param locals_dict:
:return: parsed locals object
"""
if 'self' in locals_dict:
locals_dict.pop('self')
if 'kwa... | 1d7c6e3bcc3ee86d42717690d3739cc624279bb6 | 3,644,816 |
from typing import Union
from typing import Sequence
from typing import List
def query_user_joins(user_group: Union[User, Sequence[User], None]) \
-> List[JoinRecord]:
"""
:param user_group: User or user group as an iterable of users.
:return:
"""
# Input validation
user_list = [user_g... | 5481e4512b7b28b0832f9fec00ef0cf4e7cfd5de | 3,644,817 |
import os
def is_running(process):
"""Returns True if the requested process looks like it's still running"""
if not process[0]:
return False # The process doesn't exist
if process[1]:
return process[1].poll() == None
try:
# check if the process is active by sending a dummy sig... | 7dc002da5bbd87c5d8d8745fd49e6723478186c4 | 3,644,818 |
def rec_test(test_type: str):
"""
Rec test decorator
"""
def decorator(f):
@wraps(f)
def w(*args, **kwargs):
return f(*args, **kwargs)
# add attributes to f
w.is_test = True
w.test_type = test_type
try:
w.test_desc = f.__doc__.lstr... | 94eca60bd4d3f96fd3346da5bcc2b70c3a167ace | 3,644,819 |
def display_convw(w, s, r, c, fig, vmax=None, vmin=None, dataset='mnist', title='conv_filters'):
"""
w2 = np.zeros(w.shape)
d = w.shape[1]/3
print w.shape
for i in range(w.shape[0]):
for j in range(w.shape[1]/3):
w2[i, j] = w[i, 3*j]
w2[i, j + d] = w[i, 3*j+1]
w2[i, j + 2*d] = w[i, 3*j+... | 87742ea0831f731e800385134379ce1b786b834f | 3,644,820 |
def get_optional_list(all_tasks=ALL_TASKS, grade=-1, *keys) -> list:
"""获取可选的任务列表
:param keys: 缩小范围的关键字，不定长，定位第一级有一个键，要定位到第二级就应该有两个键
:param all_tasks: dict，两级, 所有的任务
:param grade: 字典层级 第0层即为最外层，依次向内层嵌套，默认值-1层获取所有最内层的汇总列表
:return:
"""
optional_list = []
# 按照指定层级获取相应的可选任务列表
if grade ... | ee54e65e724520d8ed9e3d994811c26ed2205add | 3,644,821 |
def process_genotypes(filepath, snp_maf, snp_list=None, **kwargs):
"""
Process genotype file.
:param filepath:
:param snp_maf:
:param snp_list: get specified snp if provided
:param bool genotype_label: True if first column is the label of specimen, default False
:param bool skip_none_rs: Tr... | 501aa7b648d970b21dff1a4bd98102680e5ea774 | 3,644,822 |
import sys
import subprocess
def check_output(*cmd):
"""Log and run the command, raising on errors, return output"""
print >>sys.stderr, 'Run:', cmd
return subprocess.check_output(cmd) | e7108876e45a59a80785b9be696c71f1b4a5fe1e | 3,644,823 |
def table_exists(conn, table_name, schema=False):
"""Checks if a table exists.
Parameters
----------
conn
A Psycopg2 connection.
table_name : str
The table name.
schema : str
The schema to which the table belongs.
"""
cur = conn.cursor()
table_exists_sql =... | c9b698afbe795a6a73ddfb87b2725c3c4205f35e | 3,644,824 |
import re
def _dict_from_dir(previous_run_path):
"""
build dictionary that maps training set durations to a list of
training subset csv paths, ordered by replicate number
factored out as helper function so we can test this works correctly
Parameters
----------
previous_run_path : str, Pa... | 32d49b6ec6a8472a3864fc95cc52502a63038cdc | 3,644,825 |
def aggregate_pixel(arr,x_step,y_step):
"""Aggregation code for a single pixel"""
# Set x/y to zero to mimic the setting in a loop
# Assumes x_step and y_step in an array-type of length 2
x = 0
y = 0
# initialize sum variable
s = 0.0
# sum center pixels
left = int(ceil(x_step[x]))... | d9cdad36c7eeff3581310d13bedce204e7431560 | 3,644,826 |
def simplify_datatype(config):
""" Converts ndarray to list, useful for saving config as a yaml file """
for k, v in config.items():
if isinstance(v, dict):
config[k] = simplify_datatype(v)
elif isinstance(v, tuple):
config[k] = list(v)
elif isinstance(v, np.ndarr... | f3e8ae76e04479ed9b1b5fbd450edec20342e5a9 | 3,644,827 |
def _strict_random_crop_image(image,
boxes,
labels,
is_crowd,
difficult,
masks=None,
sem_seg=None,
min_object_... | 749107213a8bf34d2b159d38657a9c63af6699c3 | 3,644,828 |
def aggregate_by_player_id(statistics, playerid, fields):
"""
Inputs:
statistics - List of batting statistics dictionaries
playerid - Player ID field name
fields - List of fields to aggregate
Output:
Returns a nested dictionary whose keys are player IDs and whose values
a... | c137fc8820f8898ebc63c54de03be5b919fed97a | 3,644,829 |
import pickle
def loadStatesFromFile(filename):
"""Loads a list of states from a file."""
try:
with open(filename, 'rb') as inputfile:
result = pickle.load(inputfile)
except:
result = []
return result | cc2f64a977ff030ec6af94d3601c094e14f5b584 | 3,644,830 |
import tkinter
def get_configuration_item(configuration_file, item, default_values):
"""Return configuration value on file for item or builtin default.
configuration_file Name of configuration file.
item Item in configuation file whose value is required.
default_values dict of... | c077989d2d90468a80b27f32a68b827fbdb49b92 | 3,644,831 |
import os
import logging
def tflite_stream_state_external_model_accuracy(
flags,
folder,
tflite_model_name='stream_state_external.tflite',
accuracy_name='tflite_stream_state_external_model_accuracy.txt',
reset_state=False):
"""Compute accuracy of streamable model with external state using TFLite... | d0209ef72ea6f29f5410b2c493f5286685b88e53 | 3,644,832 |
def sexa2deg(ra, dec):
"""Convert sexagesimal to degree; taken from ryan's code"""
ra = coordinates.Angle(ra, units.hour).degree
dec = coordinates.Angle(dec, units.degree).degree
return ra, dec | 3a016b1163c6ceda403cfe5c8d24467d1646c7aa | 3,644,833 |
from theano import compile, shared
import theano.tensor
from theano.tensor import as_tensor_variable, TensorType
def verify_grad(fun, pt, n_tests=2, rng=None, eps=None,
out_type=None, abs_tol=None,
rel_tol=None, mode=None, cast_to_output_type=False):
"""Test a gradient by Finite Di... | 9fe5d2a8605b29d97f40d6830efeca6542a98603 | 3,644,834 |
import os
def get_filenames():
""" get file names given path """
files = []
for file in os.listdir(cwd):
if file.endswith(".vcf"):
fullPath = cwd + file
files.append(fullPath)
return files | 4f18e104f21e284e603d88fc96f2407932908356 | 3,644,835 |
import re
def is_mismatch_before_n_flank_of_read(md, n):
"""
Returns True if there is a mismatch before the first n nucleotides
of a read, or if there is a mismatch before the last n nucleotides
of a read.
:param md: string
:param n: int
:return is_mismatch: boolean
"""
is_mismatc... | 1e41c67e29687d93855ed212e2d9f683ef8a88d7 | 3,644,836 |
from typing import Dict
def get_county() -> Dict:
"""Main method for populating county data"""
api = SocrataApi('https://data.marincounty.org/')
notes = ('This data only accounts for Marin residents and does not '
'include inmates at San Quentin State Prison. '
'The tests timeser... | 62fd267141e3cdcb3f5b81b78be2aafb1322335b | 3,644,837 |
from typing import List
import logging
def optimize_player_strategy(
player_cards: List[int], opponent_cards: List[int], payoff_matrix: Matrix
) -> Strategy:
"""
Get the optimal strategy for the player, by solving
a simple linear program based on payoff matrix.
"""
lp = mip.Model("player_strat... | 49e04138daea3c78f117e2372e54419384c70810 | 3,644,838 |
import traceback
def address_book(request):
"""
This Endpoint is for getting contact
details of all people at a time.
We will paginate this for 10 items at a time.
"""
try:
paginator = PageNumberPagination()
paginator.page_size = 10
persons = Person.objects.all()
... | 88ec5613a7433128a2d06665319a6e3fd83f870f | 3,644,839 |
def decrement_items (inventory, items):
"""
:param inventory: dict - inventory dictionary.
:param items: list - list of items to decrement from the inventory.
:return: dict - updated inventory dictionary with items decremented.
"""
return add_or_decrement_items (inventory, items, 'minus') | 253339e3a8f9ff49e69372dc99d8b8f626a3b98b | 3,644,840 |
def global_ave_pool(x):
"""Global Average pooling of convolutional layers over the spatioal dimensions.
Results in 2D tensor with dimension: (batch_size, number of channels) """
return th.mean(x, dim=[2, 3]) | 3f681e39041762ee2ca8bc52c542952eebd9b97c | 3,644,841 |
import joblib
def train_models(models, train_data, target, logger, dask_client=None, randomized_search=False, scoring_metric=None):
"""Trains a set of models on the given training data/labels
:param models: a dictionary of models which need to be trained
:param train_data: a dataframe containing all pos... | 619565639eb4e59d5b639e3f687b43002c716800 | 3,644,842 |
import operator
def get_output(interpreter, top_k=1, score_threshold=0.0):
"""Returns no more than top_k classes with score >= score_threshold."""
scores = output_tensor(interpreter)
classes = [
Class(i, scores[i])
for i in np.argpartition(scores, -top_k)[-top_k:]
if scores[i] >= score_thresho... | 69c4e956cee796384fa74d12338f3fb2cc90ba31 | 3,644,843 |
def bag_of_words_features(data, binary=False):
"""Return features using bag of words"""
vectorizer = CountVectorizer(
ngram_range=(1, 3), min_df=3, stop_words="english", binary=binary
)
return vectorizer.fit_transform(data["joined_lemmas"]) | 55ed963df31c2db79eaab58b585ad264a257c241 | 3,644,844 |
import time
def duration(func):
"""
计时装饰器
"""
def wrapper(*args, **kwargs):
print('2')
start = time.time()
f = func(*args, **kwargs)
print(str("扫描完成， 用时 ") + str(int(time.time()-start)) + "秒！")
return f
return wrapper | c55a941574a92cbe70c9b265eaa39563b91ab45a | 3,644,845 |
def enumerate_assignments(max_context_number):
"""
enumerate all possible assignments of contexts to clusters for a fixed
number of contexts. Has the hard assumption that the first context belongs
to cluster #1, to remove redundant assignments that differ in labeling.
:param max_context_number:... | 881723e2ca6a663821979a9029e03bb4f35195dc | 3,644,846 |
def KL_monte_carlo(z, mean, sigma=None, log_sigma=None):
"""Computes the KL divergence at a point, given by z.
Implemented based on https://www.tensorflow.org/tutorials/generative/cvae
This is the part "log(p(z)) - log(q(z|x)) where z is sampled from
q(z|x).
Parameters
----------
z : (B, N... | 6d509607b3d4d6c248544330af06f2ef92fc3739 | 3,644,847 |
def get_order_discrete(p, x, x_val, n_full=None):
""" Calculate the order of the discrete features according to the alt/null ratio
Args:
p ((n,) ndarray): The p-values.
x ((n,) ndarray): The covaraites. The data is assumed to have been preprocessed.
x_val ((n_val,) ndarray): All possible... | de8f05d7a882c2917e618bf315a45969f55dbd16 | 3,644,848 |
def _read_txt(file_path: str) -> str:
"""
Read specified file path's text.
Parameters
----------
file_path : str
Target file path to read.
Returns
-------
txt : str
Read txt.
"""
with open(file_path) as f:
txt: str = f.read()
return txt | 5f0657ee223ca9f8d96bb612e35304a405d2339e | 3,644,849 |
import os
def init_statick():
"""Fixture to initialize a Statick instance."""
args = Args("Statick tool")
return Statick(args.get_user_paths(["--user-paths", os.path.dirname(__file__)])) | 11c7c4a0ddfc0dcb0d4838aaabb6f130ecc6b11d | 3,644,850 |
def dedupe(entries):
"""
Uses fuzzy matching to remove duplicate entries.
"""
return thefuzz.process.dedupe(entries, THRESHOLD, fuzz.token_set_ratio) | d5d56f2acc25a107b5f78eefc4adc71676712f98 | 3,644,851 |
import binascii
def generate_openssl_rsa_refkey(key_pub_raw, # pylint: disable=too-many-locals, too-many-branches, too-many-arguments, too-many-statements
keyid_int, refkey_file,
key_size, encode_format="", password="nxp",
... | ca3acdcf4fe615378f2f7088d015a7acbc58b7ff | 3,644,852 |
import select
async def fetch_ongoing_alerts(
requester=Security(get_current_access, scopes=[AccessType.admin, AccessType.user]),
session=Depends(get_session)
):
"""
Retrieves the list of ongoing alerts and their information
"""
if await is_admin_access(requester.id):
query = (
... | 721deaac7cca5f6589417f07d66a83111a062134 | 3,644,853 |
def breweryBeers(id):
"""Finds the beers that belong to the brewery with the id provided
id: string
return: json object list or empty json list
"""
try:
# [:-1:] this is because the id has a - added to the end to indicate
# that it is for this method, removes the last charact... | f2d8824ad49ffeeec68077cb5e0ed143f4603d4e | 3,644,854 |
def min_max_date(rdb, patient):
""" Returns min and max date for selected patient """
sql = """SELECT min_date,max_date FROM patient WHERE "Name"='{}'""".format(patient)
try:
df = pd.read_sql(sql, rdb)
min_date, max_date = df['min_date'].iloc[0].date(), df['max_date'].iloc[0].date()
ex... | 7f08f42bd7dd9742bef300f5f7009807e47b7f23 | 3,644,855 |
def integrate(f, a, b, N, method):
"""
@param f: function to integrate
@param a: initial point
@param b: end point
@param N: number of intervals for precision
@param method: trapeze, rectangle, Simpson, Gauss2
@return: integral from a to b of f(x)
"""
h = (b-a)/(N)
if method == "... | e716733160fd46943de3518e573215b3cf058113 | 3,644,856 |
def sum_naturals(n):
"""Sum the first N natural numbers.
>>> sum_naturals(5)
15
"""
total, k = 0, 1
while k <= n:
total, k = total + k, k + 1
return total | 0ef1ff7e8f0f2df522c73d6d4affc890ba4ad2fa | 3,644,857 |
def load_data(data_map,config,log):
"""Collect data locally and write to CSV.
:param data_map: transform DataFrame map
:param config: configurations
:param log: logger object
:return: None
"""
for key,df in data_map.items():
(df
.coalesce(1)
.write
.csv(f'{co... | 2b690c4f5970df7f9e98ce22970ce3eb892f15bc | 3,644,858 |
import yaml
import os
import time
import torch
def get_config(config_file, exp_dir=None, is_test=False):
""" Construct and snapshot hyper parameters """
# config = edict(yaml.load(open(config_file, 'r'), Loader=yaml.FullLoader))
config = edict(yaml.load(open(config_file, 'r'), Loader=yaml.FullLoader))
# crea... | 69d57ecf8538e1ca89124b148b068ec58098e046 | 3,644,859 |
import logging
def _filter_credential_warning(record) -> bool:
"""Rewrite out credential not found message."""
if (
not record.name.startswith("azure.identity")
or record.levelno != logging.WARNING
):
return True
message = record.getMessage()
if ".get_token" in message:
... | bc9d2a96ccadfbdb297af86bbdf0f80ab8d2dafa | 3,644,860 |
import importlib
def import_module_from_path(mod_name, mod_path):
"""Import module with name `mod_name` from file path `mod_path`"""
spec = importlib.util.spec_from_file_location(mod_name, mod_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
return mod | 18891db514b4f1e41bce6de69f5b66fbf51d06e5 | 3,644,861 |
def preprocessing(text, checkpoint_dir, minocc):
"""
This time, we cannot leave the file as it is. We have to modify it first.
- replace "\n" by " \n " -> newline is a word
- insert space between punctuation and last word of sentence
- create vocab, but only for those words that occur more than once... | f3dd597ac144d1c52ca2a65852ef59f2cee63d8b | 3,644,862 |
def dwave_chimera_graph(
m,
n=None,
t=4,
draw_inter_weight=draw_inter_weight,
draw_intra_weight=draw_intra_weight,
draw_other_weight=draw_inter_weight,
seed=0,
):
"""
Generate DWave Chimera graph as described in [1] using dwave_networkx.
Parameters
----------
m: int
... | cec6232d1f3413b6cedd74d909e8d9fa03d9b43f | 3,644,863 |
def extract_first_value_in_quotes(line, quote_mark):
"""
Extracts first value in quotes (single or double) from a string.
Line is left-stripped from whitespaces before extraction.
:param line: string
:param quote_mark: type of quotation mark: ' or "
:return: Dict: 'value': extracted value;
... | 4f614cbbb3a1a04ece0b4da63ea18afb32c1c86b | 3,644,864 |
def dynamic(graph):
"""Returns shortest tour using dynamic programming approach.
The idea is to store lengths of smaller sub-paths and re-use them
to compute larger sub-paths.
"""
adjacency_M = graph.adjacency_matrix()
tour = _dynamic(adjacency_M, start_node=0)
return tour | 06d1adcadc6456aa29a7c0d176329f9d1569bf58 | 3,644,865 |
import yaml
def read_login_file():
"""
Parse the credentials file into username and password.
Returns
-------
dict
"""
with open('.robinhood_login', 'r') as login_file:
credentials = yaml.safe_load(login_file)
return credentials | 16ef8a74c9523ac0809e80995069c3bbc0e8f8c0 | 3,644,866 |
def flatten(ls):
"""
Flatten list of list
"""
return list(chain.from_iterable(ls)) | afab4515644ce340a73f5a5cf9f97e59fa8c4d7e | 3,644,867 |
def gaussian_kernel(size, size_y=None):
""" Gaussian kernel.
"""
size = int(size)
if not size_y:
size_y = size
else:
size_y = int(size_y)
x, y = np.mgrid[-size:size+1, -size_y:size_y+1]
g = np.exp(-(x**2/float(size)+y**2/float(size_y)))
fwhm = size
fwhm_aper = photut... | 6752c4fc9355507d3b411515b8c687dc02b81d2b | 3,644,868 |
from typing import Any
def parse_property_value(prop_tag: int, raw_values: list, mem_id: int = 0) -> Any:
"""
Parse property raw values
:param prop_tag: The property tag, see 'PropertyTag' enum
:param raw_values: The property values
:param mem_id: External memory ID (default: 0)
"""
if pr... | fc8d54a3f8b8ca762acdc5f6123749236e4eaeb3 | 3,644,869 |
from typing import Optional
from typing import Iterator
from typing import List
from typing import Tuple
def scan_stanzas_string(
s: str,
*,
separator_regex: Optional[RgxType] = None,
skip_leading_newlines: bool = False,
) -> Iterator[List[Tuple[str, str]]]:
"""
.. versionadded:: 0.4.0
Sc... | f68694ce344b738f23b689b74d92f7ab4c20b237 | 3,644,870 |
def format_dependency(dependency: str) -> str:
"""Format the dependency for the table."""
return "[coverage]" if dependency == "coverage" else f"[{dependency}]" | 981a38074dbfb1f332cc49bce2c6d408aad3e9e2 | 3,644,871 |
def _addSuffixToFilename(suffix, fname):
"""Add suffix to filename, whilst preserving original extension, eg:
'file.ext1.ext2' + '_suffix' -> 'file_suffix.ext1.ext2'
"""
head = op.split(fname)[0]
fname, ext = _splitExts(fname)
return op.join(head, fname + suffix + ext) | 2fc0a16f6f8b8be1f27fd7ff32673ed79f84fccb | 3,644,872 |
import re
def parse_into_tree(abbr, doc_type = 'html'):
"""
Преобразует аббревиатуру в дерево элементов
@param abbr: Аббревиатура
@type abbr: str
@param doc_type: Тип документа (xsl, html)
@type doc_type: str
@return: Tag
"""
root = Tag('', 1, doc_type)
parent = root
last = None
token = re.compile(r'([\+>... | 8bb0ecaa9b2a2e9ce41882b8f140442f28f3c922 | 3,644,873 |
import os
import csv
def map_pao1_genes(gene_list):
"""Takes a list of PAO1 genes and returns the corresponding PA14 names."""
pa14_pao1_mapping = dict()
mapping_path = os.path.join(os.getcwd(), 'data', 'ortholuge_pa14_to_pao1_20190708.tsv')
with open(mapping_path) as mapping:
reader = csv.rea... | 675cf26d259bee1f6ff148f1a4ad2a71b8253ef5 | 3,644,874 |
def banner():
"""Verify banner in HTML file match expected."""
def match(path, expected_url=None, expected_base=None):
"""Assert equals and return file contents.
:param py.path.local path: Path to file to read.
:param str expected_url: Expected URL in <a href="" /> link.
:param ... | 54777fe767075561cbb20c3e7ab88ca209fa8c87 | 3,644,875 |
import tqdm
import operator
def rerank(x2ys, x2cnt, x2xs, width, n_trans):
"""Re-rank word translations by computing CPE scores.
See paper for details about the CPE method."""
x2ys_cpe = dict()
for x, ys in tqdm(x2ys.items()):
cntx = x2cnt[x]
y_scores = []
for y, cnty in sorte... | 57d9c5012341acf89e92ffd6df29688af5d6965f | 3,644,876 |
def ParallelTempering(num_sweeps=10000, num_replicas=10,
max_iter=None, max_time=None, convergence=3):
"""Parallel tempering workflow generator.
Args:
num_sweeps (int, optional):
Number of sweeps in the fixed temperature sampling.
num_replicas (int, optional):... | 48b62b2814f67b66823fc1c35024eaab6cde7591 | 3,644,877 |
def get_document_info(file):
"""
Scrape document information using ChemDataExtractor Scrapers
:param file: file path to target article
:type file: str
:return: list of dicts containing the document information
"""
if file.endswith('.html'):
file_type = 'html'
elif file.endswith(... | 5d5697ce9a7916920c938a3cff17fdeda8b5f81b | 3,644,878 |
def qlog(q):
"""
Compute logarithm of a unit quaternion (unit norm is important here).
Let q = [a, qv], where a is the scalar part and qv is the vector part.
qv = sin(phi/2)*nv, where nv is a unit vector. Then
ln(q) = ln(||q||) + qv / ||qv|| * arccos(a / ||q||)
Therefore for a unit quaternion, t... | 80e01568cc5fe2ab2c7d11bdd642906374992985 | 3,644,879 |
from datetime import datetime
def trx():
"""Response from ADN about current transaction APPROVED/DECLINED and showing Receipt of transaction"""
trx = web.trxs[-1]
trx.shoppingCartUuid = request.args.get('shoppingCartUuid', default = "", type = str)
trx.mediaType = request.args.get('mediaType', default... | 4ffa01c2d6682a6320870ac158f564c37aa5a32e | 3,644,880 |
def get_counts_by_domain(df):
"""
Parameters:
df (pandas.Dataframe) - form of `get_counts_df` output
Returns:
pandas.Dataframe
"""
columns = ['study', 'study_label', 'domain_code', 'domain_label']
df2 = df.groupby(columns, as_index=False)[["count", "subjects"]].max()
retur... | 544aaa734858209c36c84d87bb6beb05761a5194 | 3,644,881 |
def batch_cosine_similarity(x1, x2):
""" https://en.wikipedia.org/wiki/Cosine_similarity """
mul = np.multiply(x1, x2)
s = np.sum(mul, axis=1)
return s | 6ed5e4ca426cc61d25dd272f92ba9220186bfd8e | 3,644,882 |
def plot(ax, x, y):
"""Plot """
return ax._plot(x, y) | 90cc2616d21e3c1239524437f653f85602c1984b | 3,644,883 |
def concatenatePDFs(filelist, pdfname, pdftk='pdftk', gs='gs', cleanup=False,
quiet=False):
"""
Takes a list or a string list of PDF filenames (space-delimited), and an
output name, and concatenates them.
It first tries pdftk (better quality), and if that fails, it tries
ghostscr... | 3e138e84db9650af3afbbab4d904dc3a4cb581c9 | 3,644,884 |
def get_module_offset(
process_id: int,
process_name: str
) -> Address:
"""Returns an Adress with the base offset of the process.
Args:
process_id (int): PID
process_name (str): Name of the process. Case does not matter.
Returns:
Address: Adress with the base offset of the ... | 09e0775213e4a32f1ea786ad9d1184e7f4dbd7cf | 3,644,885 |
from typing import Sequence
def sequence_to_header(sequence: Sequence[Bytes]) -> Header:
"""
Build a Header object from a sequence of bytes. The sequence should be
containing exactly 15 byte sequences.
Parameters
----------
sequence :
The sequence of bytes which is supposed to form th... | b1c4040b216162777e33bbbab0f7774b8b02af91 | 3,644,886 |
def makeASdef(isd_id, as_id_tail, label, public_ip, is_core=False, is_ap=False):
""" Helper for readable ASdef declaration """
return ASdef(isd_id, _expand_as_id(as_id_tail), label, public_ip, is_core, is_ap) | 19bc51a648ac558f524f29744e1574a245e50cf2 | 3,644,887 |
from netneurotools.utils import check_fs_subjid
from netneurotools.datasets import fetch_fsaverage
from netneurotools.datasets.utils import _get_data_dir
import os
def _get_fs_subjid(subject_id, subjects_dir=None):
"""
Gets fsaverage version `subject_id`, fetching if required
Parameters
----------
... | ce4599ab875c7a33aa71cb9bc07143a04b6b2643 | 3,644,888 |
def EnableTrt(mod, params=None, trt_version=None):
"""Converts the "main" function in the module into one that can be executed using
TensorRT. If any of the operators are not supported by the TensorRT
conversion, the unmodified program will be returned instead.
Parameters
----------
mod: Module... | c3cac75de48e2c2a9af30ce427bc57d86a56dbc4 | 3,644,889 |
import cupy
def _setup_cuda_fft_resample(n_jobs, W, new_len):
"""Set up CUDA FFT resampling.
Parameters
----------
n_jobs : int | str
If n_jobs == 'cuda', the function will attempt to set up for CUDA
FFT resampling.
W : array
The filtering function to be used during resamp... | 34a949250239b5334650b89d6566b81460079591 | 3,644,890 |
def sentensize(text):
"""Break a text into sentences.
Args:
text (str): A text containing sentence(s).
Returns:
list of str: A list of sentences.
"""
return nltk.tokenize.sent_tokenize(text) | ae16aff476842c8e0fc2fa2506b68ad60dc603f0 | 3,644,891 |
def tokenize(texts, context_length=77):
"""
Returns the tokenized representation of given input string(s)
Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
context_length : int
The context length to use; all CLIP models use... | 1fe73425cb30f0f6fbce6caa740f118ee9591347 | 3,644,892 |
def _int64_feature_list(values):
"""Wrapper for inserting an int64 FeatureList into a SequenceExample proto,
e.g, sentence in list of ints
"""
return tf.train.FeatureList(feature=[_int64_feature(v) for v in values]) | edf4605c1dd9ad45d3a2508122b85213657f56cb | 3,644,893 |
def read_relative_pose(object_frame_data: dict) -> tf.Transform:
"""
Read the pose of an object relative to the camera, from the frame data.
For reasons (known only to the developer), these poses are in OpenCV convention.
So x is right, y is down, z is forward.
Scale is still 1cm, so we divide by 10... | dae13aa0a10db2133f87c399ec90113ef157a210 | 3,644,894 |
import select
def upsert_task(task_uuid: str, task: Task) -> Task:
"""Upsert a task.
It is used to create a task in the database if it does not already exists,
else it is used to update the existing one.
Args:
task_uuid:
The uuid of the task to upsert.
task:
The task data... | 7fbf296377fb1e4e59b7c9884c6191ff2b0a273b | 3,644,895 |
def shuffle_entries(x, entry_cls, config=None, value_type=sgf2n, reverse=False, perm_size=None):
""" Shuffle a list of ORAM entries.
Randomly permutes the first "perm_size" entries, leaving the rest (empty
entry padding) in the same position. """
n = len(x)
l = len(x[0])
if n & (n-1) !=... | 827506de7e572b1df1b210ccfb990db5839b5273 | 3,644,896 |
import os
import random
import logging
import sys
def file(input_file):
"""Import colorscheme from json file."""
theme_name = ".".join((input_file, "json"))
user_theme_file = os.path.join(CONF_DIR, "colorschemes", theme_name)
theme_file = os.path.join(MODULE_DIR, "colorschemes", theme_name)
util.... | 9439f44c6d71b52d800fd95f0269e46f0185a8fa | 3,644,897 |
import json
def entities(request):
"""Get entities for the specified project, locale and paths."""
try:
project = request.GET['project']
locale = request.GET['locale']
paths = json.loads(request.GET['paths'])
except MultiValueDictKeyError as e:
log.error(str(e))
ret... | 686f9298302d30e89ad0d34ed4c0c96d22fd455d | 3,644,898 |
import json
def info(request, token):
"""
Return the HireFire json data needed to scale worker dynos
"""
if not settings.HIREFIRE_TOKEN:
return HttpResponseBadRequest(
"Hirefire not configured. Set the HIREFIRE_TOKEN environment variable on the app to use Hirefire for dyno scaling... | 7164d7f19b14ef601480484d6182f4b62cc250bf | 3,644,899 |
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