content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def duration(start_time, end_time=None):
"""Get a timedelta between end_time and start_time, where end_time defaults
to now().
WARNING: mixing tz-aware and naive datetimes in start_time and end_time
will cause an error.
"""
if not start_time:
return None
last_time = end_time if end_... | 89febebf342225525bf7543342b884f130e7b3f2 | 28,086 |
def get_commands(cfg, clargs, *, what, **kwargs):
"""
Delegates the creation of commands lists to appropriate functions based on `what` parameter.
Parameters
----------
cfg: dict
Configuration dictionary.
clargs: Namespace
Command line arguments.
cmds: iter(tuple(str))
what: str... | 360410064a24d547729722c4f5843d78af9444c8 | 28,087 |
def heappush(heap, item):
"""
>>> heappush([4, 4, 8, 9, 4, 12, 9, 11, 13], 7)
[4, 4, 8, 9, 4, 12, 9, 11, 13, 7]
>>> heappush([4, 4, 8, 9, 4, 12, 9, 11, 13, 7], 10)
[4, 4, 8, 9, 4, 12, 9, 11, 13, 7, 10]
>>> heappush([4, 4, 8, 9, 4, 12, 9, 11, 13, 7, 10], 5)
[4, 4, 5, 9, 4, 8, 9, 11, 13, 7, ... | 99e6814828e42da8a14f4d0873e62af920a800b8 | 28,088 |
def dx(scalar_field):
"""
Computes first derivative of a 1D scalar field
:param scalar_field:
:return:
"""
first_derivative = np.zeros((scalar_field.size - 1))
for i_scalar in range(scalar_field.size - 1):
i_next_scalar = i_scalar + 1
first_derivative[i_scalar] = scalar_fiel... | b0af862210a2a395dcdfdab2e921f2c305a536d2 | 28,089 |
def get_genetic_profiles(study_id, profile_filter=None):
"""Return all the genetic profiles (data sets) for a given study.
Genetic profiles are different types of data for a given study. For
instance the study 'cellline_ccle_broad' has profiles such as
'cellline_ccle_broad_mutations' for mutations, 'ce... | b409a1511112cafab0330a23684b3e255fa0a60c | 28,091 |
import string
def cipher(sentence, n_rotate):
"""
Cipher string with Caesar algorithm ( Anything else than letters stays the same. )
:param sentence: String containing sentence/sentences/word/words.
:param n_rotate: number to translate letters
:return: string with ciphered words
"""
upper ... | e0606949f254971431faf7899bd254f4792176d4 | 28,092 |
from typing import Union
from typing import List
def _assert_in_fc(
r: RestClient, uuids: Union[str, List[str]], all_keys: bool = False
) -> StrDict:
"""Also return data."""
if isinstance(uuids, str):
uuids = [uuids]
if all_keys:
data = r.request_seq('GET', '/api/files', {'all-keys': ... | 908f12309a93abc472e05598abbb0e5ee29cc798 | 28,093 |
def power_law(uref, h, href, shear):
"""
Extrapolate wind speed (or other) according to power law.
NOTE: see https://en.wikipedia.org/wiki/Wind_profile_power_law
:param uref: wind speed at reference height (same units as extrapolated wind speed, u)
:param h: height of extrapolated wind speed (same ... | cb5d002dfeed022af694060bfe9e516191835742 | 28,094 |
def binary_weight_convolution(inp, outmaps, kernel,
pad=None, stride=None, dilation=None, group=1,
w_init=None, wb_init=None, b_init=None,
base_axis=1, fix_parameters=False, rng=None,
with_bias=True):... | 3cae56fee85ba0c7679e9de7fd2743c9ce252d1a | 28,096 |
def dataset2Xy(dataset):
"""Convert a dataset (pd.DataFrame) to X, y and output_dim
where X is the features, y is the labels (one-hot vectors),
and output_dim is the number of labels overall.
Args:
dataset: A pandas dataframe that is composed of features
columns and th... | ef07873db639a7a9c34b149959acd419d4a0b9d3 | 28,097 |
def pairwise_list(a_list):
"""
list转换为成对list
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
:param a_list: list
:return: 成对list
"""
if len(a_list) % 2 != 0:
raise Exception("pairwise_list error!")
r_list = []
for i in range(0, len(a_list) - 1, 2):
r_list.append([a_list[i], a... | 5142fb2e00c931ab57fc9028eb9b6df5a98c0342 | 28,099 |
import math
def BaseWaveguideOFF(args,state,options)->GeomGroup:
"""
Offset the waveguide (jumps left or right of waveguide)
Parameters
----------
args : list
1 argument: offset (in um), positive means on left of waveguide direction.
state : dict
Current state.
options : d... | 235004836ea149b02c7dd12dde400861a6739954 | 28,100 |
def read_csv_profiles(file):
"""Read csv file and parse dates."""
return pd.read_csv(file, parse_dates=["valid"], index_col="valid") | ce45c4200e8cc71daaec4dcb91e5bdd9199709c6 | 28,102 |
def uCSIsMathematicalAlphanumericSymbols(code):
"""Check whether the character is part of
MathematicalAlphanumericSymbols UCS Block """
ret = libxml2mod.xmlUCSIsMathematicalAlphanumericSymbols(code)
return ret | 2812f04afe4d977636a1c7c41dac712f1d63e184 | 28,103 |
import time
def builtin_localtime(t):
"""
Convert an epoch time to a [yr mon day hr min sec wd yd dst] list
structure in the local timezone, or vice-versa.
"""
if isinstance(t, BReal):
tv = [BInt(xv) for xv in time.localtime(t.value)]
return BList(tv)
elif isinstance(t, BList):
tvv = [x.value for x in t.v... | ebc900d3b82f5223489e3fedb761302713e27fba | 28,104 |
def avg_gate_infidelity(A, B, mxBasis):
""" Returns the average gate infidelity between A and B, where B is the "target" operation."""
return _tools.average_gate_infidelity(A, B, mxBasis) | 282b930fbaf1822ec52b937c854491ed8512ae89 | 28,105 |
from bs4 import BeautifulSoup
def html_to_plain_text(html_str):
"""
Takes an HTML string and returns text with HTML tags removed and line breaks replaced with spaces.
Shamelessly copied from open-discussions.
Args:
html_str (str): A string containing HTML tags
Returns:
str: Plain ... | d681ffdf2878f13673d66dadf964dcb2d0d06644 | 28,106 |
from datetime import datetime
def read_hotfilm_from_lvm(filename, dt=1e-3):
"""Reads 2-channel hotfilm data from a Labview text file."""
times = []
ch1 = []
ch2 = []
data = [line.rstrip() for line in open(filename).readlines()]
line = data[0].split(',')[1:]
t = [int(float(n)) for n in line... | e0dadac656120173e5833e6eb36498943613e8f5 | 28,107 |
import string
def GetCategoryWrapper(func_name):
"""Return a C preprocessor token to test in order to wrap code.
This handles extensions.
Example: GetTestWrapper("glActiveTextureARB") = "CR_multitexture"
Example: GetTestWrapper("glBegin") = ""
"""
cat = Category(func_name)
if (cat == "1.0" or
cat == "1.1" or... | eba3d524b3bcebfe96d253fa9348e3927ae9c737 | 28,110 |
from typing import Dict
def get_capability_text(src_capability: SourceCapability) -> str:
"""
Returns markdown format cell text for a capability, hyperlinked to capability feature page if known
"""
capability_docs_mapping: Dict[SourceCapability, str] = {
SourceCapability.DELETION_DETECTION: ".... | df66582caa42828b5c0099c3399612973cfaab7c | 28,111 |
import re
def camel_case_split(identifier):
"""CamelCase split"""
matches = re.finditer(
".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)",
identifier)
return [m.group(0) for m in matches] | bfaf845a0fa6eae46a4a8c96f91aee8c755255f5 | 28,112 |
def init_formation_goal_msg():
"""Create formation goal msg
Swarm goal is represented by a green sphere and arrow.
Returns:
[type]: [description]
"""
msg = Marker()
msg.header.frame_id = "world"
msg.header.stamp = rospy.Time()
msg.ns = "swarm"
msg.id = 2
msg.type = 2 ... | 359e5422908b94693968fb73c7ad5e367d715e42 | 28,113 |
def allow_pay_as_you_go(**kwargs):
"""Allow Pay As You Go
Set pay as you go account settings to ``allow=True``
Reference: https://iexcloud.io/docs/api/#pay-as-you-go
Data Weighting: ``Free``
.. warning:: This endpoint is only available using IEX Cloud. See
:ref:`Migrating` for m... | f2435da3b6d8e8265bf19249fbe38c8734f06d1b | 28,115 |
def _process_user_data_for_order(checkout: Checkout):
"""Fetch, process and return shipping data from checkout."""
shipping_address = checkout.shipping_address
if checkout.user:
store_user_address(checkout.user, shipping_address, AddressType.SHIPPING)
if (
shipping_address
... | e6ab0d248469fadb9233dc45d563ec2c185da8e5 | 28,116 |
def catastrophic_energy(rpb,rho,Vi):
"""
Return the catastrofic energy in c.g.s.
Based in Stewart and Leinhardt(2009)
rpb: parental radius in km
rho: mean density of the system in cgs
Vi: impact velocity in km/s
"""
# qs, qg, fi, mi: constants of material
if rho < 4.0:
qs, q... | 4b7472df40fa7e9dc2622854805055e28d2789f4 | 28,117 |
def hide_toolbar(notebook):
"""
Finds the display toolbar tag and hides it
"""
if 'celltoolbar' in notebook['metadata']:
del(notebook['metadata']['celltoolbar'])
return notebook | 09d594f26f2ad8ee5af2563332ff77136713a7ef | 28,118 |
def draft_github_comment(template, result):
"""
Use a template to draft a GitHub comment
:template: (str) the name of the template file
:result: (ContentError) the error to display in the comment
"""
# start with template
with open(template, 'r') as f:
contents = f.read()
# replace variables in template wi... | 42842b7af06da8a54c0647e5aac079132e82de5a | 28,119 |
def get_domains(sequence,
disorder,
disorder_threshold=0.42,
minimum_IDR_size=12,
minimum_folded_domain=50,
gap_closure=10,
override_folded_domain_minsize=False):
"""
Parameters
-------------
sequence : str
... | 5d7f17e0cf2b54c2da927b4f2689dddb182dfb4a | 28,120 |
def get_topic(topic_id):
"""
Endpunkt `/topic/<topic_id>`.
Der Response enthält die JSON-Datei des Thema.
"""
try:
file_path = queries.get_topic_file(topic_id)
if file_path is None:
err = flask.jsonify({"err_msg": "Unknown topic"})
return err, 400
r... | 2fee779cb7c0937441bf11470da31759a8704c0e | 28,121 |
def get_epaipm_file(filename, read_file_args, data_dir):
"""Reads in files to create dataframes.
No need to use ExcelFile objects with the IPM files because each file
is only a single sheet.
Args:
filename (str): ['single_transmission', 'joint_transmission']
read_file_args (dict): dict... | 0f19187bfc926abc926a5251fe52e48c93f9863c | 28,122 |
import requests
def futures_rule(trade_date: str = "20200712") -> pd.DataFrame:
"""
国泰君安期货-交易日历数据表
https://www.gtjaqh.com/pc/calendar.html
:return: 交易日历数据
:rtype: pandas.DataFrame
"""
url = "https://www.gtjaqh.com/fn/128"
params = {"base_date": f"{trade_date}"}
r = requests.post(ur... | b15c538b11d73d22706786cc40fe9b480e647a63 | 28,123 |
import scipy.sparse.linalg
def conjgrad_scipy(A, Y, sigma, tol=1e-4):
"""Solve the least-squares system using Scipy's conjugate gradient."""
Y, m, n, d, matrix_in = _format_system(A, Y)
damp = m * sigma**2
calcAA = lambda x: np.dot(A.T, np.dot(A, x)) + damp * x
G = scipy.sparse.linalg.LinearOpera... | d25c147223df15e1934e62c59f166d50d11c3bbe | 28,124 |
def get_total_revenue(data: list) -> float:
"""
data: any list with product_id at [0] and revenue at [1]
returns: total revenue for data
"""
revenue = get_revenue(data)
total = 0
for r in revenue:
total += r[1]
return float(total) | 0387a8aff8ff8163c284b395f1bce779ec6126c4 | 28,125 |
def plot_tttdprc(data_frame):
"""
Plot Change Total Travel Time % vs distance
"""
figtt, axtt = plot_var(
data_frame=data_frame,
x_var="distance",
y_var="totTT %",
label_var="mpr",
pivot="flow",
x_label="Distance [m]",
y_label=r"Change in Tota... | e7fb39a208bfba0e27968d728cd9980adff7c2ba | 28,126 |
import apyfal.host.alibaba as alibaba
from apyfal.host.alibaba import AlibabaCSP
import apyfal.exceptions as exc
from aliyunsdkcore import client
from aliyunsdkcore.acs_exception.exceptions import ServerException
import json
def test_alibibaclass_request():
"""AlibabaHost._request"""
# Mocks some variables
... | 480b2408b55a3975bb317a3e0710753a6b256ed4 | 28,127 |
import pathlib
def write_pdf_file(
fpath: pathlib.Path,
puzzle: Puzzle,
key: Key,
level: int,
) -> pathlib.Path:
"""Write a PDF file of the current puzzle to `path`.
Args:
fpath (pathlib.Path): Path to write the CSV to.
puzzle (Puzzle): Current Word Search puzzle.
key ... | 8a41134437c8a8989021164e92c17fe3d64284be | 28,128 |
import csv
def get_zip_rate_areas(file_path):
"""
reads zips.csv file and returns the content as a dictionary
Args:
file_path: the path to zips.csv file
Returns:
a dictionary mapping each zip code into a set of rate areas
"""
zip_rate_areas = dict()
with open(file_pat... | d8405bc466e7bbe949fded4360d4f184024653d2 | 28,130 |
def lucas_mod(n, mod):
"""
Compute n-th element of Fibonacci sequence modulo mod.
"""
P, Q = 1, -1
x, y = 0, 1 # U_n, U_{n+1}, n=0
for b in bin(n)[2:]:
x, y = ((y - P * x) * x + x * y) % mod, (-Q * x * x + y * y) % mod # double
if b == "1":
x, y = y, (-Q * x + P * y... | 319e74c13c370becc255bbab9d304aa220e69a0d | 28,131 |
def compute_demand_balancing(demand_sector, energy_service, energy_carrier):
"""aggregates the demand over balancing area and balancing time
"""
cols = get_group_cols_for_ec(energy_carrier)
return compute_demand_with_coarseness(demand_sector,
energy_service,
... | e70d9f42929ca88478f09ac0e5d5423457108de7 | 28,132 |
def get_file_encoding(filename):
"""
Get the file encoding for the file with the given filename
"""
with open(filename, 'rb') as fp:
# The encoding is usually specified on the second line
txt = fp.read().splitlines()[1]
txt = txt.decode('utf-8')
if 'encoding' in txt:
... | c91a8f71429ed5f6eccd7379c66b4ee4c2d73989 | 28,133 |
def alias(name):
"""Make property given by name be known under a different name"""
def get(self): return getattr(self,name)
def set(self,value): setattr(self,name,value)
return property(get,set) | 37539fbb2d413a4964fec09f9c1b40fed203dc34 | 28,134 |
def is_feature_class(symbol):
"""Check whether the symbols imported from a module correspond to
classes defining a feature. We assume that the symbol in question
defines a feature if it inherits from the FeatureDefinition class.
:param symbol: An imported symbol to check
:type symbol: object
:r... | f7556869105e3a20c05e05560baf56759d08f374 | 28,136 |
def overall_sentiment(text):
"""Function to calculate the overall sentiment using NLTK's vader library.
param text: The input text blob being entered by user
"""
sid = SentimentIntensityAnalyzer()
ss = sid.polarity_scores(text)
for _ in sorted(ss):
if ss["compound"] >= 0.15:
... | faf5ba826b6affbb9ee7f972fa55098b681a15ee | 28,139 |
def new_category(blog_id, username, password, category_struct):
"""
wp.newCategory(blog_id, username, password, category)
=> category_id
"""
authenticate(username, password, 'zinnia.add_category')
category_dict = {'title': category_struct['name'],
'description': category_str... | 9de1684fca80b7bf1a0190be1f239e2b4cae7428 | 28,140 |
def scatt(coords, id, plot_col='w', bckg_col='k', z_dim=0, **kwargs):
"""
2D scatter plot
Parameters
----------
coords : np.array
unit : str
id : str
plot_col : str, default = 'w'
bckg_col : str, default = 'k'
z_dim : int, default = 0
axes : AxesSubplot, optional
Return... | a59d176e1032c745b9b066fe9dc306c7ecae04a9 | 28,141 |
import re
def keep_text_characters(text):
"""
INPUT:
text - Python str object - the raw text
OUTPUT:
text- Python str object - the text after removing all non-text characters
"""
filtered_tokens = []
tokens = tokenize_text(text)
for token in tokens:
if re.search('... | e0da2f6517c1aa2b0f34349583147323a4c17ef7 | 28,142 |
def global_access(key, val):
"""function test"""
local = 1
MY_DICT[key] = val
for i in val:
if i:
del MY_DICT[i]
continue
else:
break
else:
return local | b48b8e86f266abe79f64d665ea73055e133f04ce | 28,143 |
def generate_auxiliar_basis(
sett: Settings, auxiliar_basis: str, quality: str) -> Settings:
"""Generate the `auxiliar_basis` for all the atoms in the `sett`.
Use the`quality` of the auxiliar basis provided by the user.
"""
quality_to_number = {"low": 0, "medium": 1,
"g... | 178e9f5eee8d192252ad48f6df19dfab670d9f4c | 28,144 |
import pathlib
def extract_user_inputs(job: Job):
"""Extract the various combinations of user inputs for a job."""
# variables to be used while looping through the runs
all_inputs = {}
job_id = job.id
results_folder = pathlib.Path('data', job_id).resolve()
# loop through the runs and find all ... | a0bcd3db9ff90c30b6981c9f76c54531da0e2d49 | 28,145 |
def __process_args(args):
"""
Process the command-line arguments and prompt the user for any missing information
:param args: the command-line arguments list
:raises CLAException: if an error occurs while validating and processing the command-line arguments
"""
cla_util = CommandLineArgUtil(_pro... | 8806a4050a1cbcfa549584da4832a1c6e7f7faa6 | 28,146 |
from typing import Any
def float_converter(value: Any) -> float:
"""Validator that ensures value is a float."""
if isinstance(value, bool):
raise ValueError()
if isinstance(value, float):
return value
elif isinstance(value, int):
return float(value)
else:
raise Valu... | c46832a017c0d83017e75fa58090a030bc5091c2 | 28,147 |
from operator import add
def generate_row(world_x,
world_rpy=[0.0, 0.0, 0.0],
algae_obj_dir=None,
algae_texture_dir=None):
"""Generate a row of algaes using the given world transform"""
components = []
buoy_z = 0
rope_y = 0
rope_z = buoy_z + 2
... | c8ee8afab4d3ca4ebf386fcd0795aad9c7768eef | 28,149 |
def split_chunk_for_display(raw_bytes):
"""
Given some raw bytes, return a display string
Only show the beginning and end of largish (2x CONTENT_CHUNK_SIZE) arrays.
:param raw_bytes:
:return: display string
"""
CONTENT_CHUNK_SIZE = 50 # Content repeats after chunks this big - used by echo c... | de53bf679552c52f5075aedf9156cdb3b3fc69f7 | 28,150 |
def get_cvi(nodes):
"""
Returns a dictionary whose keys correspond to samples, and whose values are sets of tuples, where the tuples represnts
the two characters this sample caused incompatability
:param nodes:
A list of target nodes, where each node is in the form 'Ch1|Ch2|....|Chn'
:return:
A dictionary w... | aeae708f8e7cd2bc1a3863dd4e77b00be8c2fc4a | 28,151 |
import torch
def warp(x, flo):
"""
warp an image/tensor (im2) back to im1, according to the optical flow
x: [B, C, H, W] (im2)
flo: [B, 2, H, W] flow
"""
B, C, H, W = x.size()
# mesh grid
xx = torch.arange(0, W).view(1,-1).repeat(H,1)
yy = torch.arange(0, H).view(-1,1).repeat(1,W)... | 9f5ab5338f18cf249962454deff996a556f7b2ea | 28,153 |
import logging
async def search(username):
"""
Do Maigret search on a chosen username
:return:
- list of telegram messages
- list of dicts with found results data
"""
try:
results = await maigret_search(username)
except Exception as e:
logging.er... | b0cbebdea838c0a0a422af4ece86c8c0af6a7aff | 28,154 |
def train(frame, columns, mean_centered=True, k=None):
"""
Creates a PcaModel by training on the given frame
Parameters
----------
:param frame: (Frame) A frame of training data.
:param columns: (str or list[str]) Names of columns containing the observations for training.
:param mean_cente... | 057261f8a1fd7d2a02f094d35fb28a5084f5ab47 | 28,155 |
def validate_spin(value):
"""
Validate the value of the spin
"""
if not isinstance(value, int):
raise ValidationError('spin must be integer')
if value < -1 or value > 1:
raise ValidationError('spin must be among -1, 1 or 0 (undefined)')
return value | d651d8ace39e92d324c823ab88a5b0e1a6637760 | 28,156 |
def _get_basis_functions(basis, deriv):
"""
Returns a list of interpolation function for the interpolation
definition and derivatives specified by the user. Also returns the
number of dimensions as defined in the basis parameter.
"""
# List of basis functions
bsfn_list = {
'L1': [L1,... | 1116989fd5d3af74bb0c499e930f98013b3b1b9a | 28,157 |
from typing import List
import logging
def get_all_loggers() -> List:
"""Return list of all registered loggers."""
logger_dict = logging.root.manager.loggerDict # type: ignore
loggers = [logging.getLogger(name) for name in logger_dict]
return loggers | 97059a78925ff669a841644b186e39ccd366472d | 28,158 |
from typing import cast
def argmax(input, axis=None, dtype='int64', name=None):
"""Index of the maximum value along an axis.
Parameters
----------
input : tensor_like
Input tensor.
axis : () tensor_like, default=None
Axis along which to extract the maximum index.
If None, ... | 2c04eaafadd91b19bbd3342ece5ad0a6cefa3cc0 | 28,159 |
import string
def __clean(contents):
""" remove comments from the list 'contents', and remove
unnecessary whitespace """
answer = []
for line in contents:
cleaned = string.strip(line)
if len(cleaned)>0:
if cleaned[0] != "#":
answer.append(cleaned)
return answer | e7c6a1f6a7b065fbc4da9e32f37f9e3b152848b4 | 28,160 |
import inspect
def _get_extract_params():
"""
Utility function for extracting the parameters from a Prophet model.
The following attributes are not considered for parameter extraction for the reasons listed:
The changepoints attribute is being removed due to the fact that it is a utility NumPy array
... | 00b9c6526be2cc78766426386d443f3500e8668c | 28,161 |
from typing import Mapping
def deep_update(target, source):
"""Update a nested dictionary with another nested dictionary."""
for key, value in source.items():
if isinstance(value, Mapping):
target[key] = deep_update(target.get(key, {}), value)
else:
target[key] = value
... | 01ca285ed907850b17490290c6eb92f931ed537a | 28,162 |
from typing import Callable
def distmat(func: Callable, x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""distance matrix"""
return jax.vmap(lambda x1: jax.vmap(lambda y1: func(x1, y1))(y))(x) | 0c09289e795f9f90af1a3d0363eefb82eebe6822 | 28,164 |
def feature_length(attribute_name_var):
""" Return number of a attribute values """
attribute = attrDictionary[attribute_name_var]
attribute_index, attribute_values = attribute
return len(attribute_values) | 4aa5816d38dac02cc528d6d5ee528fcaf5e39b4a | 28,165 |
def iso2_to_country_name(country_2_code):
"""Convert country code to country name.
"""
if country_2_code not in COUNTRY_ALPHA2_TO_COUNTRY_NAME:
print(country_2_code, 'NOT FOUND in iso2 to country_name')
raise KeyError
return COUNTRY_ALPHA2_TO_COUNTRY_NAME[country_2_code] | 4c3e5f46a8c7aac6851a1d304c89e298fcc31a71 | 28,166 |
from typing import List
from typing import Dict
def get_related_objects(api_key: str, obj: MISPObject, rel: str, disable_output: bool = False) -> List[Dict]:
"""Gets related objects from VT."""
if obj.name == "file":
vt_id = obj.get_attributes_by_relation("sha256")[0].value
else:
print_err... | 5c0c36febc2f5c79c98496b835dbb2e8e5b564e6 | 28,167 |
def _take_along_axis(arr, indices, axis):
"""Implements a simplified version of np.take_along_axis if numpy
version < 1.15"""
if np_version >= parse_version('1.15'):
return np.take_along_axis(arr=arr, indices=indices, axis=axis)
else:
if axis is None:
arr = arr.flatten()
... | b7eb8c49503e6364694af3becadf7faba5814f68 | 28,168 |
def moveeffect_022(score: int, move: Move, user: Pokemon, target: Pokemon, battle: AbstractBattle) -> int:
"""
Move Effect Name: Increases user evasion by one stage
"""
if move.category == MoveCategory.STATUS:
if user.boosts.get("evasion", 0) == 6:
score -= 90
else:
... | bde07c2101132d44e78bb9877689d7ffc36058e7 | 28,169 |
def chan_freq(header, fine_channel, tdwidth, ref_frame):
"""
Args:
header:
fine_channel:
tdwidth:
ref_frame:
Returns:
"""
fftlen = header['NAXIS1']
chan_index = fine_channel - (tdwidth-fftlen)/2
chanfreq = header['FCNTR'] + (chan_index - fftlen/2)*header['DELTA... | b7cb67d2d7b21a475fdaddee47ebbd6d112125f8 | 28,170 |
import torch
from typing import Any
from typing import Dict
def extract_logger_info(
model_a: torch.nn.Module,
model_b: torch.nn.Module,
model_name_to_use_for_layer_names: str,
) -> Any:
"""
Extracts intermediate activations from model_a and model_b.
"""
model_name_a, results_a = _extract... | 73872f1a3083373a8767ca60668479e24b0aa240 | 28,171 |
def make_masked_msa(protein, config, replace_fraction):
"""Create data for BERT on raw MSA."""
# Add a random amino acid uniformly
random_aa = tf.constant([0.05] * 20 + [0., 0.], dtype=tf.float32)
categorical_probs = (
config.uniform_prob * random_aa +
config.profile_prob * protein['hhblits_profile... | 47d3f40c27c6a6722ac7a8cab21844decdd21a0f | 28,172 |
from winguhub.settings import HTTP_SERVER_ROOT
def get_httpserver_root():
""" Construct wingufile httpserver address and port.
Returns:
Constructed httpserver root.
"""
assert HTTP_SERVER_ROOT is not None, "SERVICE_URL is not set in ccnet.conf."
return HTTP_SERVER_ROOT | 5327f05fdbdbd062435c90d51cb8ee1667199e9f | 28,173 |
def csys_torusv1_to_xyz_jacobian(v: np.ndarray, rho_0: float, rho_1: float, aspect: float = 1) -> np.ndarray:
"""
Compute Jacobian matrix of the toroidal coordinate system with respect to the Cartesian coordinate system.
:param v: Toroidal coordinates (r, psi, phi)
:param rho_0: Torus Radius (major)
... | e402a0fbc6f8f33ccc556ab9eeaeee323f3fde99 | 28,174 |
def normalize_answer(s):
"""
Lower text and remove punctuation, articles and extra whitespace.
"""
def remove_articles(text):
return re_art.sub(' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
return re_punc.sub(' ', text) # c... | 1146af739b33992aae551a0b1ab5059c0d645608 | 28,175 |
import typing
def intensity(pixel: typing.Tuple[int, int, int]) -> int:
"""Sort by the intensity of a pixel, i.e. the sum of all the RGB values."""
return pixel[0] + pixel[1] + pixel[2] | 5ba060d409a2f0df148cdc50684b80f920cf314f | 28,176 |
def load_statistics(
input_path):
"""Loads data statistics proto from file.
Args:
input_path: Data statistics file path.
Returns:
A DatasetFeatureStatisticsList proto.
"""
serialized_stats = tf.python_io.tf_record_iterator(input_path).next()
result = statistics_pb2.DatasetFeatureStatisticsList... | f7c094e79e7f6c8654aa703cd75da62164f9d421 | 28,177 |
def plot_grid(data):
""" Plot a grid of heatmaps showing performance for each tile pairing. """
def _draw_heatmap(*args, **kwargs):
""" Draw a heatmap showing flops for (ch_vect x feat_vect). """
data = kwargs.pop('data')
reshaped = pd.pivot_table(
data,
index='c... | 834472a852b98916f9a9507bc56dde5bb71c55ec | 28,178 |
from datetime import datetime
from typing import Optional
def get_national_holiday(day: datetime.date) -> Optional[Holiday]:
"""
For a given date, return the national holiday object if the particular date is a Czech national holiday;
return None otherwise
"""
for holiday in Holidays(year=day.year)... | 8cc2b83a16e6a07fc8e5f1c8cc95bec66bf8ffe5 | 28,179 |
def robust_topological_sort(graph):
"""
First identify strongly connected components, then perform a
topological sort on these components.
graph should be a dictionary mapping node names to lists of
successor nodes.
"""
components = strongly_connected_components(graph)
node_component ... | 3ecb7dbeea4f4b9f2d205472ee2c1a59465b09ea | 28,180 |
from typing import List
def similar_consonants_to(phonet_1: UnmarkableConsonant) -> List[Phonet]:
"""
a list of vowels that are similar to a specified consonant.
"""
vocal_folds: List[VocalFolds] = similar_in_voice(phonet_1.vocal_folds)
place: List[Place] = similar_in_place(phonet_1.place)
manner: List[Manner] ... | 168603fff454bb6ba9cb29a051ec674547720344 | 28,181 |
def error_handler(error):
"""Render errors as JSON"""
if not all(hasattr(error, attr) for attr in ("code", "name", "description")):
LOGGER.exception("An error occured: %r", error)
error = InternalServerError()
resp = {
"code": error.code,
"name": error.name,
"descript... | 4fd4834254e78503817e621cafc14e8d477b3bad | 28,182 |
def get_encoded_len_value(typename, package_specs):
"""
Get the encoded size of a given type on the wire
"""
if typename in PRIMITIVE_TYPES:
return PRIMITIVE_SIZES[typename]
encoded_len = 0
for package in package_specs:
for msg in package.definitions:
if get_v4_typen... | 0d11c409fb760a317b456d0397876a97cf43a593 | 28,183 |
def parsestream(stream, encoding=None):
"""Parses sql statements from file-like object.
:param stream: A file-like object.
:param encoding: The encoding of the stream contents (optional).
:returns: A generator of :class:`~sqlparse.sql.Statement` instances.
"""
stack = engine.FilterStack()
s... | 90c0cb061f1cdd370b5c31b8326fc80be16f310d | 28,184 |
import torch
def apply_hardmask_with_map(input, attention, mask):
""" Apply any number of attention masks over the input.
input: [batch, num_objs, visual_features] = [b, o ,v]
attention: [batch, num_objs, glimpses] = [b, o ,g]
return: masked_input, masked_weight
"""
b, o, v = inpu... | 3dd40399e42a02770351cd772feb89ed7b2dd16b | 28,185 |
def _make_merge_query(increment_table_id, target_table_id):
"""merge incremental_load to existing target_table"""
client = bigquery.Client()
#try ge target table columns for merge, if not possible then it means it does not exist so just copy instead
try:
table = client.get_table(target_table_id)... | f42f895268e5417c904c2d2c40c7f4d75306c8d2 | 28,186 |
def delete_alert(id):
"""
delete an alert and associated thread from the database
"""
alert = Alert.query.get(id)
if alert is None:
return {'message': 'Alert ' + str(id) + ' does not exist'}, 404
# delete associated thread
delete_thread(id)
# delete alert
db.session.delete(al... | 414e448d9b0b256342d13baea0e316cbddf007aa | 28,187 |
def data_file_read_calltree(filename):
"""
Extracts the calltree of a fuzzer from a .data file.
This is for C/C++ files
"""
read_tree = False
function_call_depths = []
tmp_function_depths = {
'depth' : -2,
'function_calls' : []
}
with open(fil... | 10d876a8aa585a767f1939434edc018e5c44404d | 28,188 |
def bidirected(DAG):
"""Return a graph with an opposing edge added for every edge in DAG."""
return skeleton(DAG).to_directed() | 8ca8d79f9d5fb6d66543b91004e83854b5b9d57e | 28,189 |
def get_census_data(independent_var, dependent_var):
"""
Returns a dict containing the data for the requested variables in a table format
:param census_vars: the chosen variable
:return:
"""
# Adjust the names of the variables for querying
if dependent_var == PAYPEREMP:
dependent_va... | 9dd13284ab28aefa93d5caa8940208211eeb5bbc | 28,191 |
def generate_chirp_exp(dur, freq_start, freq_end, Fs=22050):
"""Generation chirp with exponential frequency increase
Notebook: C1/C1S3_Dynamics.ipynb
Args:
dur (float): Length (seconds) of the signal
freq_start (float): Start frequency of the chirp
freq_end (float): End frequency o... | c0c9b7b374fbef62e73fab3bb97caaca11388a15 | 28,192 |
def get_geoId(row: str, geo_map: dict) -> str:
"""
Dataframe utility function to map DC geoId based on latitude, longitude
"""
loc = f"{str(row['Latitude'])},{str(row['Longitude'])}"
try:
return ', '.join(geo_map[loc])
except:
print(f"{loc} -- does not exist in the map") | a06edaaf6b7c848f4580bdd78e5514b65c23bf16 | 28,193 |
import shutil
def update_cm_working_file(source_file, target):
"""Copies source file to target and stages in git. Returns True on success."""
print('<<Copy {} to {}>>'.format(source_file, target))
try:
shutil.copyfile(source_file, target)
except OSError as ex:
print('Exception copying ... | 2ec65b077a9c1954071f4daf4082c4eabc245252 | 28,195 |
def farthest(pts, xlims, ylims, n):
""" Find the 'n' points that lie farthest from the points given
in the region bounded by 'xlims' and 'ylims'.
'pts' is an array of points.
'xlims' and 'ylims are tuples storing the maximum and minimum
values to consider along the x and y axes."""
# There are a... | 708d8ac66da7bc478c9df64be70f0fccc5b5f28d | 28,196 |
def vis_ss_barplot(m, s, sample, hyperparams, stats=None, t_on=None, t_off=None, with_ss=True, with_params=True, voltage_trace=None,
test_idx=None, case=None, title=None, date_today=None, counter=0, save_fig=False, legend_offset=0.0,
axss=None, axmemparams=None, axsynparams=N... | ad374ff232505579f9a557d26553e9e860147d29 | 28,197 |
import torch
def delta_to_binary_mask(delta, k, hop_size, sample_rate, return_indeces=False):
"""returns a binary mask indicating the k segments with the largest norm / magnitude """
delta_factorization = TimeFrequencyTorchFactorization(delta,
frequenc... | fb8eb2234565a8247df24588a77a9c6d3fe302d2 | 28,199 |
def get_patron_pid(record):
"""Get patron_pid from existing users."""
user = get_user_by_legacy_id(record["id_crcBORROWER"])
if not user:
# user was deleted, fallback to the AnonymousUser
anonym = current_app.config["ILS_PATRON_ANONYMOUS_CLASS"]()
patron_pid = str(anonym.id)
else... | e2ba9102052c0ce84f7fc04fdc5be97a6050634c | 28,200 |
import PySide.QtGui as QtGui
import PyQt5.QtWidgets as QtWidgets
def get_QGroupBox():
"""QGroupBox getter."""
try:
return QtGui.QGroupBox
except ImportError:
return QtWidgets.QGroupBox | 9c0333c9c8a4fafd1c4f4f5739546aaa19a6e321 | 28,201 |
import collections
def update_dict(orig_dict: dict, new_dict: collections.abc.Mapping) -> dict:
"""Updates exisitng dictionary with values from a new dictionory.
This function mimics the dict.update() function. However, it works for
nested dictionories as well.
Ref: https://stackoverflow.com/questio... | e176f4cf293be67aece6e9811fe75de679bb1e94 | 28,202 |
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