content stringlengths 35 416k | sha1 stringlengths 40 40 | id int64 0 710k |
|---|---|---|
import json
def parse_line(header, line):
"""Parse one line of data from the message file.
Each line is expected to contain chunk key - comma - tile key (CSV style).
Args:
header (dict): Data to join with contents of line to construct a full message.
line (string): Contents of the line.
... | 452dd80f84a35f6e3532330155bade7f424c102a | 9,179 |
def filequote(text):
"""Transform text to file name."""
trans = str.maketrans(' /()', '____')
return text.translate(trans) | dd6237fe6c66f60c00c8a569636adf17d45d66cc | 9,180 |
def characteristic(text, ontology=None):
"""
Making a ENA Biosamples characteristic
"""
if ontology:
return [{"text": text, "ontologyTerms": [ontology]}]
else:
return [{"text": text}] | e7f175a1ef8137b4c0e19a28a5d74055d9363c66 | 9,181 |
def _strip_trailing_ffs(binary_table):
"""
Strip all FFs down to the last 32 bytes (terminating entry)
"""
while binary_table.endswith("\xFF"*64):
binary_table = binary_table[0:len(binary_table)-32]
return binary_table | 43c14297da709f78316e460180c6c4515650f34d | 9,182 |
import struct
def byte_to_float(b1, b2, b3, b4):
"""
A function to get a 32 bit float from 4 bytes read in order [b1, b2, b3, b4]
:param b1: first byte
:param b2: second byte
:param b3: third byte
:param b4: fourth byte
:return: the byte array from b1, b2, b3, b4 unpacked as a float using ... | 962480d1b9d2c50e3196b5480e9c62bf696a8f0d | 9,184 |
import re
def get_used_by_from_comments(lines: "list[str]") -> "tuple[int, list[str]]":
"""Read the module-used-by block comment from a module file.
Args:
lines (list[str]): The content of the module file as a list of strings.
Returns:
tuple[int, list[str]]: The integer indicates the las... | 6ac30266524373d0de7cf7bb9ad9fd8dcd1933a2 | 9,185 |
from pathlib import Path
def construct_target_path(participant_name, model_name, roi):
"""Construct path to save results to."""
project_root = Path(__file__).parents[1]
return project_root / "results" / participant_name / f"model_{model_name}"\
/ f"roi_{roi}" | 072681647a3362563829c25d4890aa13425cff2c | 9,186 |
import codecs
def txidFromBroadcast (hexStr):
"""Extracts the hex txid from a broadcast in hex."""
# The prevout txid is the first part of the broadcast data
# in serialised form. But we need to reverse the bytes.
hexRev = hexStr[:64]
bytesRev = codecs.decode (hexRev, "hex")
return bytesRev[::-1].hex ... | 96690f4fdef5f0cff857188045696e427914b887 | 9,187 |
def filter_properties(person, PERSON_PROPERTIES):
"""
Extract specific properties of the given person into a new dictionary.
Parameters:
person (dict): the dictionary containing properties of a person.
PERSON_PROPERTIES (tupl): a tuple containing the characteristics of a person
Returns... | 2a3ec4ab32c5d99d475ebffaefe0d8c40ce137af | 9,190 |
def get_chain_hash(contract, s, u_i, s_i, a, b, bytes_30, dyn_bytes, bar_uint, arr) -> bytes:
"""Uses the contract to create and hash a Foo struct with the given parameters."""
result = contract.functions.hashFooStructFromParams(s, u_i, s_i, a, b, bytes_30, dyn_bytes, bar_uint, arr).call()
return result | 2faeb03eff5ee1a4e564a50f8bff78fb99cdd169 | 9,191 |
import math
def f1(myList):
"""Solves x = Sqrt((120-y)/8)"""
return math.sqrt((120-myList[1])/8)
#return 120-8*myList[0]**2 | 5d84417d52ec3b667862a3750f6d46f74964a586 | 9,193 |
def _filter_by_variance(frame, threshold=0.005):
"""Removes from frame any columns with a relative variance
beneath the given threshold.
"""
# first, for each column X, compute relative variance as
# var((X-min(X))/(max(X)-min(X)))
numerators = frame.subtract(frame.min(axis='index'), axis='colum... | f4225fe41adab6f5f1a0189f9eaeae071109a5ee | 9,194 |
def is_not_csv_file(filename):
"""Retun an indication if the file entered is the clusterserviceversion (csv) file """
return not filename.endswith('clusterserviceversion.yaml') | 3afcecfee95f300b9a5e6128f33a58dcdfc2c443 | 9,196 |
import torch
def load_snapshot(model_path):
"""
Load snapshot
:param model_path: path to snapshot
:type model_path: str
:return: built state
:rtype: dict
"""
state = torch.load(model_path)
return state | bdeba078302b8c8c6ac39f156877ef58e91341ec | 9,198 |
def create_input_list(pdb_list_fname):
"""
create a list of tuples (pdb_id, chain) from a text file
"""
pdb_list = []
with open(pdb_list_fname, 'r') as f:
for record in f.read().splitlines():
pdb_id, chain = record[:-1], record[-1]
# check PDB ID and chain are valid
... | d02588ec1d2ff55454782b337ac15cf9e6f67a80 | 9,200 |
def _query_worrying_level(time_elapsed, state):
"""
Gives a "worriness" level to a query
For instance, long times waiting for something to happen is bad
Very long times sending is bad too
Return a value between 0 and 1 rating the "worrying level"
See http://dev.mysql.com/doc/refman/5.7/en/genera... | 383e36c75d68a9e975d48efc6c68deeee446c987 | 9,201 |
def dict_to_casl_rules(rules: dict):
"""
Given a dict where the keys are the subject and the values are the actions, return
a list of dicts ready to be serialized as JSON
:return:
"""
perms = []
for key, actions in rules.items():
perms.append({
'subject': key,
... | 5d0f3dfd610a1cd7deb7f09a668e291997419b2a | 9,202 |
def bbox_hflip(bboxes, img_width):
"""horizontal flip the bboxes
^
.............
. . .
. . .
. . .
. . .
.............
^
Args:
bbox (ndarray): bbox ndarray [box_nums, 4]
flip_code (int, optional): [description]. Defaults... | 00e45f69a517ccb15623afb813fc05ad1c7c7eee | 9,203 |
import argparse
def create_arg_parser():
""""
Creates and returns the ArgumentParser object.
"""
parser = argparse.ArgumentParser(description='Parses git log output object for change-log generation.')
parser.add_argument('-b', '--branch', default='dev',
help='current git bran... | 167bcf54b079583b10e4ef72ff2c1a6e82ded6bc | 9,204 |
def _pathjoin(a, b):
"""
POSIX-like path join for Globus Transfer paths
As with _normpath above, this is meant to behave correctly even on Windows systems
"""
if not b: # given "" as a file path
return a
elif b.startswith("/"): # a path starting with / is absolute
return b
... | 20079d97be4e07499a9b0dfa80458a7e151826c3 | 9,205 |
def probability(vector, x, t):
"""
Finds the probability of vector[x] in t occurnences
If x is not in vector then the probability is .001/t
@param {Vector} vector
{int} x
{float} t
@return {float}
"""
t = t*1.0
return vector[x] / t or 0.001 / t | bb6c731a157104a653669730be0569f555402167 | 9,206 |
def getNodeDictVlans(nodesInfo, hostname, switchName):
"""Get Node dictionary."""
if not nodesInfo:
return None, {}
for _, nodeDict in list(nodesInfo['nodes'].items()):
if nodeDict['hostname'] == hostname:
for intf, intfDict in list(nodeDict['NetInfo'].items()):
p... | 1113f0eb1829c9e84791ed151ce05c7165168b10 | 9,207 |
def rob(nums):
"""
You are a professional robber planning to rob houses along a street. Each house has a certain amount of money stashed, the only constraint stopping you from robbing each of them is that adjacent houses have security system connected and it will automatically contact the police if two adjacent... | 9bfb631b2781bbf95fa299a6474e0b1fe36ac19b | 9,209 |
def tamiz1(m):
"""Algoritmo clásico para el tamiz de Eratóstenes"""
l, n = [i for i in range(2, m+1)], 2
while n:
for i in l[l.index(n)+1:]:
if i % n == 0:
l.remove(i)
if l.index(n) +1 < len(l):
n = l[l.index(n) + 1]
else:
return l | 3063e5007360cfbbda53e10b84e7ca141473a552 | 9,210 |
import numpy
import warnings
def getMetrics(sector, symbols):
"""Returns a 2xN numpy.Array of metrics for the given symbols from the
given sector.
"""
metrics = [ # hard-coded for now, could easily be parameterized
"Price Performance (52 Weeks)",
"Standard Deviation (1 Yr Annualized... | df00d21031056bb2faf3ad9d420c308cdf075b5a | 9,213 |
def single_text_phrase(context, slug=None, language=None):
"""
for using this template tag you must
enable one of the text_phrase context_processors.
this templatetag will return the first text phrase object,
if there is more then one object.
if you want single text phrase in special language se... | 7e9b5a28cbf1ae0215e201e3af0f22631aad9ac2 | 9,215 |
import os
def _find_files(root_dir, should_include):
"""
Return a list of paths to all modules below the given directory.
Arguments:
should_include: a function that accepts a file path and returns True or False.
"""
paths = [] # Return value.
is_module = lambda path: path.endswith("... | 0f572880279a28914ad99f7635c0f573fa01044a | 9,216 |
def linear_scale(input, in_low, in_high, out_low, out_high):
""" (number, number, number, number, number) -> float
Linear scaling. Scales old_value in the range (in_high - in-low) to a value
in the range (out_high - out_low). Returns the result.
>>> linear_scale(0.5, 0.0, 1.0, 0, 127)
63.5
""... | caeab8e992caca2dba96f48b0eb617fd361bb9eb | 9,218 |
import os
def check_folder(folder):
"""
Test if folder exists and is absolute
"""
if os.path.isdir(folder):
if os.path.isabs(folder):
return True
else:
raise ValueError("The path to the folder must be absolute")
else:
raise OSError("Can't find the pa... | e2d606ab5bb68e104c8896da753d2e76d6ac7697 | 9,219 |
def _error_matches_criteria(error, criteria):
"""
Check if an error matches a set of criteria.
Args:
error:
The error to check.
criteria:
A list of key value pairs to check for in the error.
Returns:
A boolean indicating if the provided error matches the... | 8f52f7288fdefa496084b4faf689ed269360050a | 9,220 |
import torch
def eval(device, model, datas, criterion):
"""Eval the model"""
losses = 0
model.eval()
with torch.no_grad():
for data, target in datas:
output = model(data.to(device)).flatten()
losses += criterion(output.flatten(), target.to(device)).item()
return los... | bf9d71640922e3c3a9d9bcd0fc83bc37f6c2da7d | 9,221 |
import re
def pad_punctuation_w_space(text: str) -> str:
"""Pad punctuation marks with space for separate tokenization."""
result = re.sub(r'([:;"*.,!?()/\=-])', r" \1 ", text)
result = re.sub(r"[^a-zA-Z]", " ", result)
result = re.sub(r"\s{2,}", " ", result)
# code for removing single characters
... | 8bdb82865d5e127e32d483f83246f4ad1b96b0be | 9,222 |
def tensors2classlist(tensor, seq_lens):
"""
Converts a 3d tensor (max(seq_len), batch_size, output_dim=1) to a 2d class list (list[batch_size * list[seq_len]])
Arguments:
tensor (torch.tensor) : 3d padded tensor of different sequence lengths of
shape (max(seq_lens), batch_size, output_... | 52de31050a32ce54b2733f4c4dd348044e3da259 | 9,223 |
def skippable_exons(exons):
""" Determine which exon(s) can be skipped
For each exon (except the first and second, which cannot be skipped), we
want to find the minimum number of exons which together have a size that
can be divided by 3.
>>> list(skippable_exons([30]))
[]
>>> list(skippable... | f96ec0da6d72191d252cfe0ba5cdbeb21bc4388c | 9,224 |
from typing import Callable
from typing import Any
def not_pf(predicate: Callable[[Any], bool]):
"""
Negates the predicate
* **predicate**: predicate to be tested
* **return**: a predicate that is the negation of the passed predicate
>>> p = not_pf(true_p)
>>> p(1)
False
>>> p = not_... | 50d3993c4a83e5794a63134b65c732d1aa0ca1fa | 9,225 |
def filter_genes(centroids):
"""returns genes that have std > 0"""
return centroids.index[(centroids.std(axis=1) != 0).tolist()] | fcfbd18b6d657d6758feb324642c4118b80aecfd | 9,226 |
from operator import mul
def dot(A, B):
"""
Dot product between two arrays.
A -> n_dim = 1
B -> n_dim = 2
"""
arr = []
for i in range(len(B)):
if isinstance(A, dict):
val = sum([v * B[i][k] for k, v in A.items()])
else:
val = sum(map(mul, A, B[i]))
... | 9ea609f78e27eb3046507db3e366531090b26d6d | 9,227 |
def remove_last_range(some_list):
"""
Returns a given list with its last range removed.
list -> list
"""
return some_list[:-1] | ea2063c901d3aaf67caad97f1760f6fb6afb31c1 | 9,228 |
def filter_by_indices(good_indices, vals):
""" 从分段算法得到的下标集合中得到 对应的轨迹点集合
:param good_indices: 下标集合
:param vals: 原始点数据（未分段） 集合
:return: 分段后的点集合
"""
vals_iter = iter(vals)
good_indices_iter = iter(good_indices)
out_vals = []
num_vals = 0
for i in good_indices_iter:
if i != ... | c38dd76a90452cdbe96c92c8850752f56cc9882f | 9,233 |
import numpy as np
import os
def gen_index_noddi(in_bval, b0_index):
"""
This is a function to generate the index file for FSL eddy
:param in_bval:
:param b0_index:
:return:
"""
out_file = os.path.abspath('index.txt')
bvals = np.loadtxt(in_bval)
vols = len(bvals)
index_list = [... | 84ac37def63d1714030d797930e3de958b8ff6a4 | 9,234 |
def speed_control(target, current, Kp=1.0):
"""
Proportional control for the speed.
:param target: target speed (m/s)
:param current: current speed (m/s)
:param Kp: speed proportional gain
:return: controller output (m/ss)
"""
return Kp * (target - current) | ce01369dc9445f65249a82cfb7882223ded38f36 | 9,235 |
import os
def get_outpath(filename, outdir):
"""Get output filepath.
:filename: name of music file
:outdir: path of output directory
:returns: path of converted music file
"""
outname = '{}.mp3'.format(os.path.splitext(filename)[0])
outpath = os.path.join(outdir, outname)
return outp... | 048c1ce65c21a0a561f928eb42882eaa60ee8b1a | 9,237 |
def is_parsed_result_successful(parsed_result):
"""Returns True if a parsed result is successful"""
return parsed_result['ResponseMetadata']['HTTPStatusCode'] < 300 | 717f8aa88b814405a5a008e9706338fd0f91a7ff | 9,239 |
def PopularTagsPerLang(df, lang, top_k = 10):
"""
Function:
Get top k tags with largest number of fanworks by media and in selected language.
Input:
- df: pandas.DataFrame.
- lang: list[str], languages to include.
- top_k: int, number of top tags to include.
Output:
- df_top: pandas.... | c5e0d4e459924292880b3eaee770412083ea59e7 | 9,241 |
def r_min_KimKim(T_sat, sigma, h_fg, rho, deltaT_sub):
""" minimum droplet radius """
r_min = 2*T_sat*sigma / (h_fg * rho * deltaT_sub)
return r_min | c2e9a7e0741f6d663a73ff04eb32939732c34f36 | 9,242 |
import re
import zipfile
def northwind(table_name):
"""
Yield a stream of "records" as dictionaries, with certain adjustments.
So it turns out my source of NorthWind data has a bizarre nonstandard format:
Embedded commas are those followed by whitespace!
The usual csv module doesn't handle that by default and ... | 303fa89d19e12c17ab14cc0591a4182ce28f489c | 9,244 |
def sub_field(k, v):
"""Return a nested dictionary with field keys k and value v."""
res = {}
field_d = res
fields = k.split('.')
for f in fields[:-1]:
field_d[f] = {}
field_d = field_d[f]
field_d[fields[-1]] = v
return res | 193869fdfaca84172c71ca935f5fdb312682b19e | 9,247 |
import torch
def get_prediction(model, batch, device):
"""Get predicted labels for given input batch and model."""
images = torch.tensor(batch, dtype=torch.float).to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
return predicted | e8bb4257dc19f26fa206e26fa844ec9717974e52 | 9,250 |
def list_split(l, indices):
"""Split list at given indices.
Closed lists have the same first and last elements.
If the list is closed, splitting wraps around if the first or last index is not in the indices to split.
Parameters
----------
l : list
A list.
indices : list
... | a882842f6d51eeda010017dbdd2bfa722ebb363d | 9,252 |
def no_op(ctx, node, name, args):
"""Skip node."""
return None | 1fede015a843657f3959bb8da4c2216a8674e60c | 9,253 |
import gzip
import os
def hook_compressed_text(filename, mode, encoding='utf8'):
"""
#lines are byte strings and not text string if we use gzip.open by default.
"""
ext = os.path.splitext(filename)[1]
if ext == '.gz':
return gzip.open(filename, mode + 't', encoding=encoding)
#elif ext... | 7c3b76e4d33cb400020e677554f0d9584fa799b1 | 9,254 |
import warnings
import functools
def deprecated(func):
""" Decorator to be used to mark functions as deprecated.
It will result in a warning being emitted when the function is used.
Usage::
@other_decorators_must_be_upper
@deprecated
def some_old_function(x,y):
return... | ef4ca24b5da4a4df2b3c2a11f2e6b71791233a85 | 9,255 |
import argparse
def setup_cli(args, cfg):
""" Configure command-line arguements """
description ="""
Benign_domains outputs a list of preceived benign domains. This is
intended to help gather data for ML training sets and generate white
lists. The core set of domains are provided by majestic mill... | 3bdd81fa9526ce06bf56bf847e04def67b9ce72e | 9,256 |
import subprocess
def get_course_ids():
"""
Get a list of course ids that is necessary for the rest of the
functions to work.
"""
global course_ids
dump_course_ids = subprocess.Popen(['/edx/bin/python.edxapp',
'/edx/app/edxapp/edx-platform/manage.py',
... | 9017db92c197a756646e916ceab7ebacd481f453 | 9,258 |
import math
def divisors(n: int) -> list[int]:
"""Get the proper divisors of a number n"""
limit = int(math.sqrt(n)) + 1
proper_divisors = {1}
for i in range(2, limit):
if n % i == 0:
proper_divisors.add(n // i)
proper_divisors.add(i)
return list(proper_divisors) | 0a71ecccbda802d3a3575f024073fac575355ffa | 9,260 |
def get_lsb_num(num):
"""docstring"""
cnt = 0
while not (num >> cnt) & 1:
cnt += 1
#
return cnt + 1 | 6abf34d4831b80310dbf57bf08b7fce0b6c0a73d | 9,261 |
def foreign_key(
table_name: str, schema: str, parent_name: str, parent_schema: str
) -> str:
"""Return column names (child and parent) of the foreign key."""
return f"""
SELECT
att2.attname as child_column,
att.attname as parent_column
FROM
(SELECT
unnest(con1.co... | e1c7221fd308ee44f7b09718e66028351262334a | 9,262 |
from pathlib import Path
def available_models():
"""Check for available neural network models.
This function returns a list of all neural network models saved.
If None is available, it returns a message informing there's no models previously
saved.
Returns
-------
dirs : list
Lis... | f3bbfa56ea0eaa2e06467e479cdefd18d02e8021 | 9,263 |
import math
def compute_dimension(bounds, pixel_resolution: tuple):
"""
:param bounds:
:param pixel_resolution: width and height of pixels in the units of its coordinate reference system extracted from
transformation of image
:return:
"""
output_width = int(math.ceil((bounds[2] - bounds[0... | 83d3c133a8471d41d69cad4fb00d529e36634731 | 9,265 |
def get_num_adain_params(model):
"""
input:
- model: nn.module
output:
- num_adain_params: int
"""
# return the number of AdaIN parameters needed by the model
num_adain_params = 0
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm1d":
num_... | 1ba52ef9284415dfad1cb0d6808447a71614e318 | 9,267 |
def GetSweepParamList(core):
"""
スイープパラメータ値リストの生成
[in] core PDIコアデータ
戻り値 -> スイープパラメータ値リスト
"""
if not core or not core.pd:
return None
spl = []
for p in core.pd.plist:
if not p.disable and p.calcCaseNum() > 1:
spl.append(p)
continue # end of for(p)
... | 9f50c4475ae5414681e15f1f43e7900575a8d500 | 9,270 |
import os
import re
def format_file_path(filepath):
"""Formats a path as absolute and with the correct platform separator."""
try:
filepath = os.path.realpath(os.path.abspath(filepath))
filepath = re.sub(r'[/\\]', os.path.sep, filepath)
except: # pragma: nocover
pass
return f... | 858a5f7b3126233165e25cc5d54e227287cf68b6 | 9,272 |
import numpy
def rand_index(l):
"""
Return an index of the list with the probability given in the list.
Example: prob_index([0.5,0.25,0.25]) should return 0 50% of the time, 1 25%
of the time and 2 25% of the time.
"""
r = numpy.random.uniform(0., sum(l))
s = l[0]
for i,p in enum... | 2dc37eb292034284053b4d91c42bcfcada8376cd | 9,273 |
def get_smaller_channel(channel, channel_range):
"""
get channels which is smaller than inputs
:param channel:input channel
:param channel_range:list,channel range
:return:list,channels which is larger than inputs
"""
return list(filter(lambda x: x < channel, channel_range)) | 4b9f862756663f12d8f9a8208239ca16fc88042b | 9,275 |
def flatten(game_data):
"""Flatten the game data into a vector.
Parameters
----------
game_data : ndarray
An ndarray of shape (# games, 2, 11, 52). 2 teams, 10 players + 1 team aggregate
statistic, and 52 features.
Returns
-------
flattened_data : ndarray
An ndarray... | f71316e87d54fdefde52074e1ef58c87cbabb212 | 9,276 |
import socket
def get_host_ip():
# https://www.cnblogs.com/z-x-y/p/9529930.html
"""查询本机ip地址
用于快速获取主机的IP地址
Returns:
一个字符串，表示IP地址
例如：
127.0.0.1
"""
s = None
try:
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(('8.8.8.8', 80))
... | 4f1d4a1d709c467a70a3a91ad985ff0954e6c492 | 9,277 |
import json
def load_json(filepath):
"""Return parsed json file as dictionary."""
with open(filepath) as json_file:
try:
json_data = json.load(json_file)
except json.JSONDecodeError:
return None
return json_data | daa1d93aaf0602c0e4771e78b5f36ac3d04e4891 | 9,278 |
def lighten(color, scale=1.0):
"""
Lighten a color.
- color is a tuple (r, g, b, a)
- scale can be any number, if < 1, color will be darken
"""
return tuple(map(
lambda x: int(min(max(x * scale, 0), 255)),
color[:3]
)) + color[3:] | 4c520c00ca3509b3e09090b7d72790db2a80f63c | 9,279 |
def AB2Jy(ABmag):
"""Convert AB magnitudes to Jansky"""
return 10.**(-0.4*(ABmag+48.60))/1e-23 | a55b70df44f56461d935c8e5aa8aff50df26a982 | 9,280 |
def all_valid(formsets):
"""Validate every formset and return True if all are valid."""
# List comprehension ensures is_valid() is called for all formsets.
return all([formset.is_valid() for formset in formsets]) | 3cffd9879143e4879794e86bbb65e49f4f2fd975 | 9,281 |
def horizontal_link_count(shape):
"""Number of horizontal links."""
assert len(shape) == 2
return shape[0] * (shape[1] - 1) | e4d997cd668a75410e3fb208e7a200cbba3fb6bf | 9,282 |
def build_err_payload(aggregator, import_):
"""
Builds a JSON error response to return as a WS client notification.
"""
# flatten errors & warnings into a single list to send to the UI. Each ImportErrorSummary
# may optionally contain multiple related errors grouped by subcategory
errs = []
... | 1fae5be0308ea5086ac7a3a62be96779162bb2cd | 9,283 |
def m(x0: float, x1: float, y0: float, y1: float) -> float:
"""
Simple gradient function.
Parameters
----------
x0 : float
x co-ordinate at time 0.
x1 : float
x co-ordinate at time 1.
y0 : float
y co-ordinate at time 0.
y1 : float
y co-ordinate at time... | d138dfedd1e381a575ff6f5108b8841470febbd7 | 9,284 |
def _refresh(fn):
"""Decorator to refresh the attributes of this object from the cluster"""
def wrapper(self, *args, **kwargs):
self.Refresh()
fn(self, *args, **kwargs)
self.Refresh()
return wrapper | 5db0f25fc3042aec25f1af8f5a65edf8436cacdc | 9,287 |
import time
def getDateRange(dt):
""" 获取时间戳范围
参数
--------------
dt: str
url请求中传递过来的date参数
返回值
--------------
turple
返回一对 (起始时间戳,终止时间戳)
"""
if dt == 'weekend':
startDT = "2017-03-25 00:00:00"
endDT = "2017-03-27 00:00:00"
elif dt == 'weekday':
... | 1265df8cb736a8ac13515220b68296c7517771ad | 9,288 |
import random
def generate_random_color():
"""
Generate random color.
"""
r = random.random()
g = random.random()
b = random.random()
return (r, g, b) | 11416e6714a08bfbea8c6939774d1f0e54664ac4 | 9,289 |
import copy
def tiwary_mmvt_model(tmpdir_factory, tiwary_mmvt_model_persistent):
"""
Create a copy of the model that is not persistent. But this at least
doesn't require us to generate an entirely new model
"""
tiwary_mmvt_model = copy.deepcopy(tiwary_mmvt_model_persistent)
return tiwary_mmvt_... | 381dd449c8cbcfbdf43ed9a3e8b71fff09f9a2c9 | 9,290 |
def _get_disposable_app_filename(clientInfo):
"""
Get name of file used to store creds.
"""
return clientInfo.get('file', clientInfo['name'] + '.client_data.json') | 7d6a67443cd8815ddfde3f69aae450d59f59a437 | 9,291 |
def _timestamp_from_record_tuple(record):
"""Extract timestamp from HBase tuple record
"""
return record[0]['timestamp'] | de0ff6f12e14093a236cab651d4baae2299d2124 | 9,292 |
import subprocess
def make_lint_report(nb_fpath):
"""Run the tutorial linter on a notebook and capture the output."""
cmdline = ["python", "ci/lint_tutorial.py", nb_fpath]
res = subprocess.run(cmdline, capture_output=True)
return res.stdout.decode() | efa21f6ed52affee862300ae18ed4b639ac9e29a | 9,293 |
def get_valid_ip_address(ls):
"""
Question 7.10: Compute all valid IP addresses
from decimal string, given that
"""
ips = []
# loop over first ip packet
first_idx = 1
while first_idx < 4 and first_idx < len(ls):
second_idx = 1
while second_idx < 4 and \
fi... | eb8b8fc574f489ab260c1a854c4fb1302ca170f8 | 9,294 |
import csv
def process_csv(csv_path, image='image_id_', stage='image_stage_', fake='image_fake_', answer='image_answer_'):
"""
Process a csv file to a list of Result objects
:param csv_path:
:param image:
:param stage:
:param fake:
:param answer:
:return:
"""
results = []
w... | f363521cac13e80d92b9398e284ed6c9b7cf3e54 | 9,295 |
def move_odict_item(odict, key, newpos):
"""
References:
http://stackoverflow.com/questions/22663966/changing-order-of-ordered-dictionary-in-python
CommandLine:
python -m utool.util_dict --exec-move_odict_item
Example:
>>> # ENABLE_DOCTEST
>>> from utool.util_dict impor... | 2bf86d8a5da8b474b8d487a0247424cb360c0d35 | 9,297 |
def document_search_keys():
"""A list of Regulations.gov document search keys."""
return ['documents', 'totalNumRecords'] | c0969bde192a249c087590332d5deeff19ee06eb | 9,298 |
import pandas as pd
def describe(col, data):
"""
return basic statistical descriptions
"""
d = {}
# number of observations
d['Nobs'] = [data[col].count()]
# mean
d['Mean'] = [data[col].mean()]
# std
d['Std.'] = [data[col].std()]
# mad
d['Mad'] = [data[col].mad()]
# ... | 300310365fe67a5b76747474c77d0a674299fe49 | 9,299 |
def computeIoUs(preds, truths):
"""
Compute intersection over union for the predicted masks vs ground-truth masks. @preds and @truths must have the same length and both are iterables of numpy matrices of same dimensions.
"""
# List to collect IoU for each pair
IoUs = []
# Iterate over the collections and comput... | 208606710c07878bccf8cae0f3b95ce65cb4180a | 9,300 |
import re
def remove_special_char(in_seq):
"""
Function is responsible for normalize strings to defined format (UPPERCASE with '_' replacing any special character)
:param in_seq: list of strings
:return: list of strings
"""
_sub = re.sub(" {1,5}", "_", in_seq.strip()).lower()
_chars = ['*', '\\', '&', '/', '+'... | 425f8a7fcd6a2df7db667063564f419536ae68d9 | 9,301 |
import numpy
def position_from_msg(tf_msg, fmt='xyz'):
"""Extract position from geomety_msg/TransformStamed message."""
return numpy.array([getattr(tf_msg.transform.translation, d) for d in fmt]) | f74b5aa4fe9e9e462e6eedc4dafa22c7ba2be1e8 | 9,302 |
import json
def format_navigation_links(additional_languages, default_lang, messages, strip_indexes=False):
"""Return the string to configure NAVIGATION_LINKS."""
f = u"""\
{0}: (
("{1}/archive.html", "{2[Archive]}"),
("{1}/categories/{3}", "{2[Tags]}"),
("{1}/rss.xml", "{2[RSS fee... | 81882137af3e80ba24a3de797ebb9f30e6d5a877 | 9,303 |
def fixture_spring_metadata(
first_read, second_read, spring_tmp_path, checksum_first_read, checksum_second_read
):
"""Return metada information"""
metadata = [
{
"path": str(first_read.absolute()),
"file": "first_read",
"checksum": checksum_first_read,
... | f6e9964b811fd1ce4e873f7a5f57d392ebb3fe98 | 9,304 |
import re
import os
def reformat_comment(comment: str, add_tab=False):
"""
:param comment:
:param add_tab
:return:
"""
comment = re.sub(r'\s+', ' ', comment)
sentence_len = 0
tmp = []
for word in comment.split(' '):
if sentence_len >= 70:
sentence_len = len(w... | 2e14cb1866c5f098ee40e7301551d5e145214389 | 9,306 |
def fizz_buzz(num):
"""
return 'Fizz', 'Buzz', 'FizzBuzz', or the argument it receives,
all depending on the argument of the function,
a number that is divisible by, 3, 5, or both 3 and 5, respectively.
"""
if not isinstance(num, int):
raise TypeError("Expected integer as... | 8b741800f80ebe631f6821a865c9080c33eb4e27 | 9,309 |
def dict_to_cvode_stats_file(file_dict: dict, log_path: str) -> bool:
"""
Turns a dictionary into a delphin cvode stats file.
:param file_dict: Dictionary holding the information for the cvode stats file
:param log_path: Path to were the cvode stats file should be written
:return: True
"""
... | 4b6d92ad610c47eed5b2e593980a74f617ed44f4 | 9,310 |
import re
def isNumber(test):
"""
Test if the string is a valid number
Return the converted number or None if string is not a number.
"""
try:
test = str(test)
if re.search('\.',test):
try:
return float(test)
except:
return N... | 93f3afd1c3e8cefc64b1ff738e3f8336a1b8ffd6 | 9,311 |
def unskew_S1(S1, M, N):
"""
Unskew the sensivity indice
(Jean-Yves Tissot, Clémentine Prieur (2012) "Bias correction for the
estimation of sensitivity indices based on random balance designs.",
Reliability Engineering and System Safety, Elsevier, 107, 205-213.
doi:10.1016/j.ress.2012.06.010)
... | c82dfb842ff61781a45d132acd66f88ab018690c | 9,312 |
def split_list(l, break_pts):
"""returns list l split up into sublists at break point indices"""
l_0 = len(l)
sl = []
# Return a list containing the input list if no breakpoints indices selected
if len(break_pts) == 0:
return [l]
# Else splits the list and return a list of sub lists. A... | 940fe3425e9708e1852fd4930cb5af3e96076b1f | 9,313 |
import sys
def HaveGoodGUI():
"""Returns true if we currently have a good gui available.
"""
return "pywin.framework.startup" in sys.modules | e57c7063959024baaa84bd545fa6650516ba11e8 | 9,315 |
def has_func(obj, fun):
"""check if a class has specified function: https://stackoverflow.com/a/5268474
Args:
obj: the class to check
fun: specified function to check
Returns:
A bool to indicate if obj has funtion "fun"
"""
check_fun = getattr(obj, fun, None)
return call... | 3284c1a30c3b74c93c1c34c102632beb99bf5576 | 9,318 |
import torch
def detr_load():
"""
Loads the detr model using resnet50
Returns: the detr model pretrained on COCO dataset
"""
model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)
model.eval()
return model | 71e20ac9f29ff7211ecb36514758145d929636fc | 9,319 |
import math
def distance(p0, p1):
"""calculate distance between two joint/3D-tuple in the XZ plane (2D)"""
return math.sqrt((p0[0] - p1[0])**2 + (p0[2] - p1[2])**2) | 02e1a1488c32f465f2a1817adb8dfbdb4ea26431 | 9,322 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.