content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def askfont():
"""
Opens a :class:`FontChooser` toplevel to allow the user to select a font
:return: font tuple (family_name, size, \*options), :class:`~font.Font` object
"""
chooser = FontChooser()
chooser.wait_window()
return chooser.font | 8bce830a24d92be38c23ba09b6754f2e6cc6d161 | 4,100 |
def load_data(train_file, test_file):
"""
The method reads train and test data from their dataset files.
Then, it splits train data into features and labels.
Parameters
----------
train_file: directory of the file in which train data set is located
test_file: directory of the file in which ... | d830f4bcd3efe467a23cab0dfa4a3cdb4694559e | 4,101 |
import os
def split_dataset_random(data_path, test_size=0.2):
"""Split the dataset in two sets train and test with a repartition given by test_size
data_path is a string
test_size is a float between 0 and 1"""
#Initialise list for the names of the files
sample_name = []
#Get the file of the da... | 8c24f4a0dda3b3da8c637b624655dd98c6f9ff0a | 4,102 |
def guide(batch_X, batch_y=None, num_obs_total=None):
"""Defines the probabilistic guide for z (variational approximation to posterior): q(z) ~ p(z|x)
"""
# we are interested in the posterior of w and intercept
# since this is a fairly simple model, we just initialize them according
# to our prior b... | 889f3224424496a4f001d81b046e1279ba0efe77 | 4,103 |
import os
def downloads_dir():
"""
:returns string: default downloads directory path.
"""
return os.path.expanduser('~') + "/Downloads/" | f8c328a3176a664387059ebf6af567d018bcd57e | 4,104 |
def get_reddit_tables():
"""Returns 12 reddit tables corresponding to 2016"""
reddit_2016_tables = []
temp = '`fh-bigquery.reddit_posts.2016_{}`'
for i in range(1, 10):
reddit_2016_tables.append(temp.format('0' + str(i)))
for i in range(10, 13):
reddit_2016_tables.append(temp.format(... | e590ab35becbe46aa220257f6629e54f720b3a13 | 4,105 |
def first_empty():
"""Return the lowest numbered workspace that is empty."""
workspaces = sorted(get_workspace_numbers(get_workspaces().keys()))
for i in range(len(workspaces)):
if workspaces[i] != i + 1:
return str(i + 1)
return str(len(workspaces) + 1) | f9c9f868570bbcc15a28097930d304b308ddf452 | 4,106 |
import argparse
def get_args():
"""get command-line arguments"""
parser = argparse.ArgumentParser(
description='Demonstrate affect on SVM of removing a support vector' ,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'-o',
'--outfile',
... | ab279b344be0c8b632eafe9e1f86a6638792eb8f | 4,107 |
def get_local_tzone():
"""Get the current time zone on the local host"""
if localtime().tm_isdst:
if altzone < 0:
tzone = '+' + \
str(int(float(altzone) / 60 // 60)).rjust(2,
'0') + \
str(int(float(
... | dec1d9f9c5ecf937779de55a33397436841913bc | 4,108 |
def subscribers_tables_merge(tablename1: Tablename, tablename2: Tablename, csv_path=csvpath, verbose=True):
"""
Сводит таблицы, полученные загрузчиком, в одну. Может принимать pandas.DataFrame или имя группы, в этом
случае группа должна быть в списке групп, а соответствующий файл - в <csv_path>
"""
... | 56c5c80b57aa4103f1836f8b9a5ca7bbb67e25bc | 4,109 |
def get_current_offset(session):
"""
For backfilling only, this function works with the init container to look up
it's job_id so it can line that up with it's consumer group and offest so that
we can backfill up to a given point and then kill the worker afterwards.
"""
if settings.JOB_ID is None... | 97d0b0485005a709a047582667f56a79f636388d | 4,110 |
def get_params(p1, p2, L):
"""
Return the curve parameters 'a', 'p', 'q' as well as the integration
constant 'c', given the input parameters.
"""
hv = p2 - p1
m = p1 + p2
def f_bind(a): return f(a, *hv, L)
def fprime_bind(a): return fprime(a, hv[0])
# Newton-Raphson algorithm to fin... | eae7d942b4272b3addc6c3f3912abc564e93f339 | 4,111 |
def _unpack(f):
"""to unpack arguments"""
def decorated(input):
if not isinstance(input, tuple):
input = (input,)
return f(*input)
return decorated | 245d425b45d9d7ef90239b791d6d65bcbd0433d5 | 4,112 |
from .ops.classes import WriteRichOp
from typing import Iterable
from functools import reduce
def chain_rich(iterable: Iterable['WriteRichOp']) -> 'WriteRichOp':
"""Take an `iterable` of `WriteRich` segments and combine them to produce a single WriteRich operation."""
return reduce(WriteRichOp.then, iterable... | fa75ab929fb01b9c68e58938aa04aebddc26f245 | 4,113 |
def sum_plot_chi2_curve(bin_num, sum_bin, r_mpc, ax=None, cov_type='bt', label=None,
xlabel=True, ylabel=True, show_bin=True, ref_sig=None):
"""Plot the chi2 curve."""
if ax is None:
fig = plt.figure(figsize=(6, 6))
fig.subplots_adjust(
left=0.165, bottom=0.13... | 6f0b7adf2daa98ecac9ff722eab9f6b748ef188b | 4,114 |
import dfim
import dfim.util
def compute_importance(model, sequences, tasks,
score_type='gradient_input',
find_scores_layer_idx=0,
target_layer_idx=-2,
reference_gc=0.46,
reference_shuffle_type=None,
... | a7ebe928f4e3b50d5c8735d438d28c034d5dfeb9 | 4,115 |
from typing import Iterable
def test_check_non_existing() -> None:
"""Test a check on a non-existing column."""
class Schema(pa.SchemaModel):
a: Series[int]
@pa.check("nope")
@classmethod
def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:
return seri... | 473b0e1c4b4c785970bdc648e4290426524882c7 | 4,116 |
import requests
def fetch_url(url):
"""Fetches the specified URL.
:param url: The URL to fetch
:type url: string
:returns: The response object
"""
return requests.get(url) | 26198dbc4f7af306e7a09c86b59a7da1a4802241 | 4,117 |
def _nw_score_(s1, s2, insert=lambda c: -2,
delete=lambda c: -2,
substitute=lambda c1, c2: 2 if c1 == c2 else -1):
"""Compute Needleman Wunsch score for aligning two strings.
This algorithm basically performs the same operations as Needleman Wunsch
alignment, but is made more ... | 009c9eb4afec828adde53bddfd2a8b4d2a952c24 | 4,118 |
import pickle
import torch
import re
import warnings
from typing import Counter
from typing import OrderedDict
def load_gisaid_data(
*,
device="cpu",
min_region_size=50,
include={},
exclude={},
end_day=None,
columns_filename="results/usher.columns.pkl",
features_filename="results/usher... | eaa9c5b3735f291706ea783272b3372ad9e7937c | 4,119 |
def get_symbol_size(sym):
"""Get the size of a symbol"""
return sym["st_size"] | b2d39afe39542e7a4e1b4fed60acfc83e6a58677 | 4,120 |
import argparse
import functools
import os
def parse_args(argv):
"""Parse and validate command line flags"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--base-image',
type=functools.partial(
validation_utils.validate_arg_regex, flag_regex=IMAGE_REGEX),
def... | afc558b05d247d1096c4c276edc4a74a34f93827 | 4,121 |
def to_unnamed_recursive(sexpr, scheme):
"""Convert all named column references to unnamed column references."""
def convert(n):
if isinstance(n, NamedAttributeRef):
n = toUnnamed(n, scheme)
n.apply(convert)
return n
return convert(sexpr) | ffb58acb1cfbef654c5c936880961b8cc982ec01 | 4,122 |
def login_process():
"""Process login."""
email_address = request.form.get("email")
password = request.form.get("password")
user = User.query.filter_by(email_address=email_address).first()
if not user:
flash("Please try again!")
return redirect('/')
if user.password != passwor... | afee068b653e5f759329658e3614b0ce7ee2d405 | 4,123 |
def get_doc_translations(doctype, name):
"""
Returns a dict custom tailored for the document.
- Translations with the following contexts are handled:
- doctype:name:docfield
- doctype:name
- doctype:docfield (Select fields only)
- 'Select' docfields will have a values dict which will have
trans... | e7fd896de3162452a77ab989670e61b01e8e35a2 | 4,124 |
from datetime import datetime
def fetch_newer_version(
installed_version=scancode_version,
new_version_url='https://pypi.org/pypi/scancode-toolkit/json',
force=False,
):
"""
Return a version string if there is an updated version of scancode-toolkit
newer than the installed version and availabl... | efd0e92219efd8fb54064200acbdc3e512071f37 | 4,125 |
def app(request):
"""
Default view for Person Authority App
"""
return direct_to_template(request,
'person_authority/app.html',
{'app':APP}) | 9e75c9cf381c69b19bfdf08c74b2e0dc2106822b | 4,126 |
def is_xbar(top, name):
"""Check if the given name is crossbar
"""
xbars = list(filter(lambda node: node["name"] == name, top["xbar"]))
if len(xbars) == 0:
return False, None
if len(xbars) > 1:
log.error("Matching crossbar {} is more than one.".format(name))
raise SystemExit... | 435b84a30f3f749f07d0cc6dfdd5e7f0c5343c4f | 4,127 |
def index():
""" Root URL response """
return "Reminder: return some useful information in json format about the service here", status.HTTP_200_OK | d8128c8ba8976238c1d68376eaa64a77d09ce525 | 4,128 |
def backproject(depth, K):
"""Backproject a depth map to a cloud map
depth: depth
----
organized cloud map: (H,W,3)
"""
H, W = depth.shape
X, Y = np.meshgrid(np.asarray(range(W)) - K[0, 2], np.asarray(range(H)) - K[1, 2])
return np.stack((X * depth / K[0, 0], Y * depth / K[1, 1], depth)... | 5433fd408932f48c238cad7e5e8d7b14ee7b00de | 4,129 |
from pathlib import Path
def get_parent_dir(os_path: str) -> str:
"""
Get the parent directory.
"""
return str(Path(os_path).parents[1]) | 3a6e518119e39bfbdb9381bc570ac772b88b1334 | 4,130 |
import os
import ctypes
def test_is_admin():
"""Returns True if the program is ran as administrator.
Returns False if not ran as administrator.
"""
try:
is_admin = (os.getuid() == 0)
except AttributeError:
is_admin = ctypes.windll.shell32.IsUserAnAdmin() != 0
if is_admin == 1:
... | 6ace6a49a40ded8df9dd065bfd2d8f8359850b68 | 4,131 |
def parse_work_url(work_url):
"""Extract work id from work url
Args:
work_url (str): work url
Returns:
str: bdrc work id
"""
work_id = ""
if work_url:
work_url_parts = work_url.split("/")
work_id = work_url_parts[-1]
return work_id | 1e7f5e222a2f6c7d01cbcb7df556adf6dd33f7cf | 4,132 |
def room():
"""Create a Room instance for all tests to share."""
return Room({"x": 4, "y": 4, "z": 4}, savable=False) | f031faa1bf654ff32868b678f79c2af040926b44 | 4,133 |
import re
def searchLiteralLocation(a_string, patterns):
"""assumes a_string is a string, being searched in
assumes patterns is a list of strings, to be search for in a_string
returns a list of re span object, representing the found literal if it exists,
else returns an empty list"""
results = []
... | 0f751bae801eaee594216688551919ed61784187 | 4,134 |
def UIOSelector_Highlight(inUIOSelector):
"""
Highlight (draw outline) the element (in app) by the UIO selector.
:param inUIOSelector: UIOSelector - List of items, which contains condition attributes
:return:
"""
# Check the bitness
lSafeOtherProcess = UIOSelector_SafeOtherGet_Process(inUI... | 9ab5930396aa9813f09d858c4bb94adc2170f312 | 4,135 |
import torch
def completeMessage_BERT(mod, tok, ind, max_length=50):
"""
Sentence Completion of the secret text from BERT
"""
tokens_tensor = torch.tensor([ind])
outInd = mod.generate(tokens_tensor, max_length=50)
outText=tok.decode(outInd[0].tolist())
newText=outText[len(tok.decode(ind)):]
newText=n... | c2a47bbe90a9e5d222af0bbe5959c82d2ebd2cd3 | 4,136 |
import copy
import json
def _select_train_and_seat_type(train_names, seat_types, query_trains):
"""
选择订票车次、席别
:param train_names 预定的车次列表
:param seat_types 预定席别列表
:param query_trains 查询到火车车次列表
:return select_train, select_seat_type
"""
def _select_trains(query_trains, train_names=None):... | 519cba93eca3a676734f05d196a7f125917da88a | 4,137 |
def load_real_tcs():
""" Load real timecourses after djICA preprocessing """
try:
return sio.loadmat(REAL_TC_DIR)['Shat'][0]
except KeyError:
try:
return sio.loadmat(REAL_TC_DIR)['Shat_'][0]
except KeyError:
print("Incorrect key")
pass | 68b148e6fc6088d8ef9f90daf25e07609010d9fe | 4,138 |
def create_fsaverage_forward(epoch, **kwargs):
"""
A forward model is an estimation of the potential or field distribution for a known source
and for a known model of the head. Returns EEG forward operator with a downloaded template
MRI (fsaverage).
Parameters:
epoch: mne.epochs.Epochs
... | 34d72211babf23e41927ebe7df13c58bf6876e4d | 4,139 |
import os
def make_file(path):
"""
Factory function for File strategies
:param str path: A local relative path or s3://, file:// protocol urls
:return:
"""
try:
if not is_valid_url(path):
return LocalFile(os.path.abspath(path))
url_obj = urlparse(path)
if... | 29163e04c676e81a8f01456e29529b219e9ad2a8 | 4,140 |
def midi_to_hz(notes):
"""Hello Part 6! You should add documentation to this function.
"""
return 440.0 * (2.0 ** ((np.asanyarray(notes) - 69.0) / 12.0)) | 7215126d25ff969a8aa187c7f49216ec7743a9e9 | 4,141 |
def bond_stereo_parities(chi, one_indexed=False):
""" Parse the bond stereo parities from the stereochemistry layers.
:param chi: ChI string
:type chi: str
:param one_indexed: Return indices in one-indexing?
:type one_indexed: bool
:returns: A dictionary mapping bond keys on... | 02729db6888899a91e69a25dae81c06777b89182 | 4,142 |
def filter_camera_angle(places):
"""Filter camera angles for KiTTI Datasets"""
bool_in = np.logical_and((places[:, 1] < places[:, 0] - 0.27), (-places[:, 1] < places[:, 0] - 0.27))
# bool_in = np.logical_and((places[:, 1] < places[:, 0]), (-places[:, 1] < places[:, 0]))
return places[bool_in] | 417fccfbb240c5defc36b4ce465fe14333922b94 | 4,143 |
def neural_log_literal_function(identifier):
"""
A decorator for NeuralLog literal functions.
:param identifier: the identifier of the function
:type identifier: str
:return: the decorated function
:rtype: function
"""
return lambda x: registry(x, identifier, literal_functions) | 84651d58b7da677ee213d1ff4667dc3be702f243 | 4,144 |
def get_factors(n: int) -> list:
"""Returns the factors of a given integer.
"""
return [i for i in range(1, n+1) if n % i == 0] | c15a0e30e58597daf439facd3900c214831687f2 | 4,145 |
def fetch_tables():
""" Used by the frontend, returns a JSON list of all the tables including metadata. """
return jsonify([
{
"tab": "animeTables",
"name": "Anime",
"tables": [
{
"id": "englishAnimeSites",
"titl... | 5c07e7bc9f3366bc72e21dd5468edf57b6c448b3 | 4,146 |
def base_positive_warps():
"""
Get warp functions associated with domain (0,inf), scale 1.0
Warp function is defined as f(x) = log(exp(x)-1)
Returns
-------
Callable[torch.Tensor,torch.Tensor],
Callable[torch.Tensor,torch.Tensor],
Callable[torch.Tensor,torch.Tensor]
Function... | 389db769f55f7542a45e6acbbccf5760dc7b8c26 | 4,147 |
import re
import json
from datetime import datetime
def dev_work_create():
"""
Create work order.
:return:
"""
db_ins = current_user.dbs
audits = User.query.filter(User.role == 'audit')
form = WorkForm()
if form.validate_on_submit():
sql_content = form.sql_content.data
... | b11f840bbc6428696afabe7f2fe00b5d0b6ad7d1 | 4,148 |
def blur(x, mean=0.0, stddev=1.0):
"""
Resize to smaller size (AREA) and then resize to original size (BILINEAR)
"""
size = tf.shape(x)[:2]
downsample_factor = 1 + tf.math.abs(tf.random.normal([], mean=mean, stddev=stddev))
small_size = tf.to_int32(tf.to_float(size)/downsample_factor)
x = tf.image.resize_... | b0101a4b820beb84c627bef048bbafeb1d11cdea | 4,149 |
def improve(update, close, guess=1, max_updates=100):
"""Iteratively improve guess with update until close(guess) is true or
max_updates have been applied."""
k = 0
while not close(guess) and k < max_updates:
guess = update(guess)
k = k + 1
return guess | 3475c07a3e9a674661d90e116bfb91fa12344d63 | 4,150 |
def images_to_sequence(tensor):
"""Convert a batch of images into a batch of sequences.
Args:
tensor: a (num_images, height, width, depth) tensor
Returns:
(width, num_images*height, depth) sequence tensor
"""
num_image_batches, height, width, depth = _shape(tensor)
transposed = tf.transpose(tenso... | cc89ce931239b5335d5788bc6e9007e5186648bf | 4,151 |
from typing import Any
import logging
def transform_regions(regions: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""
Transform aggregated region data for map
regions -- aggregated data from region pipeline
"""
records = []
for record in regions:
if "latitude" in record["_id"].keys(... | 599e58e3bd66159114d0dbf27b339c47134c29c3 | 4,152 |
def _file_to_import_exists(storage_client: storage.client.Client,
bucket_name: str, filename: str) -> bool:
"""Helper function that returns whether the given GCS file exists or not."""
storage_bucket = storage_client.get_bucket(bucket_name)
return storage.Blob(
bucket=storage_buc... | cb051aba0d5e787e85dbc0283aa439e3c17e819c | 4,153 |
import sys
def run(args, options):
""" Compile a file and output a Program object.
If options.merge_opens is set to True, will attempt to merge any
parallelisable open instructions. """
prog = Program(args, options)
VARS['program'] = prog
if options.binary:
VARS['sint'] = GC_... | bdaf9327a94c38b9e6ba3e8206ca6ea3664b1073 | 4,154 |
from typing import Optional
from typing import List
from typing import Tuple
def get_relative_poses(
num_frames: int,
frames: np.ndarray,
selected_track_id: Optional[int],
agents: List[np.ndarray],
agent_from_world: np.ndarray,
current_agent_yaw: float,
) -> Tuple[np.ndarray, np.ndarray, np.nd... | e1dad983e5070310ce239615c98f85d8b09b9c45 | 4,155 |
import numpy
def read_mat_cplx_bin(fname):
"""
Reads a .bin file containing floating-point values (complex) saved by Koala
Parameters
----------
fname : string
Path to the file
Returns
-------
buffer : ndarray
An array containing the complex floating-point values read... | f2761f4cd7031dc16cb2f9903fd431bc7b4212d8 | 4,156 |
def DeleteDataBundle(**kwargs):
"""
Deletes a Data Bundle by ID.
:param kwargs:
:return:
"""
data_bundle_id = kwargs['data_bundle_id']
del data_bundles[data_bundle_id]
return(kwargs, 200) | 88ded979e45beebe885eeb1890ce66ae367b1fd6 | 4,157 |
def determineactions(repo, deficiencies, sourcereqs, destreqs):
"""Determine upgrade actions that will be performed.
Given a list of improvements as returned by ``finddeficiencies`` and
``findoptimizations``, determine the list of upgrade actions that
will be performed.
The role of this function i... | 0ec771565560607e839ce87a65426e01d0276f36 | 4,158 |
def filter_ccfs(ccfs, sc_thresh, min_ccf):
"""
Remove noisy ccfs from irrelevant experiments
:param ccfs: 2d array
:param sc_thresh: int
number of sign changes expected
:param min_ccf: float
cutoff value for a ccf to be above the noise threshold
:return:
"""
if sc_thresh ... | 06941eaea7bc5dc25f261669532c66ac37cbb9ab | 4,159 |
def market_data(symbol, expirationDate, strike, optionType, info=None):
"""Gets option market data from information. Takes time to load pages."""
assert all(isinstance(i, str) for i in [symbol, expirationDate, strike, optionType])
return robin_stocks.options.get_option_market_data(symbol, expirationDate, strike, opt... | 153d15af1030be22fa6c97b8d68fdf2049ebc416 | 4,160 |
def get_documents_embeddings (y, embedder, column):
"""
Given a Dataframe containing study_id and a text column, return a numpy array of embeddings
The idea of this function is to prevent to embed two times the same text (for computation efficiency)
Parameters:
-----------
... | 9a748ef8b276d68a61d78c6fa567a40aae4fc222 | 4,161 |
def index(request):
"""view fonction de la page d'accueil
Render templates de la page d'accueil
"""
return render(request, "t_myapp/index.html") | b3cf3be5d3c2a286d5705281e35042ad19d0a050 | 4,162 |
def cidr_mask_to_subnet_mask(mask_num):
"""
掩码位数转换为点分掩码
:param mask_num: 掩码位数, 如 16
:return: 十进制点分ipv4地址
"""
return convert_to_ipv4(cidr_mask_to_ip_int(mask_num), stype='int') | 83556c856f68e82824fa1f3a34b4d629361081af | 4,163 |
def correlate(A,B,
rows=None,columns=None, mode_row='zero', mode_column='zero'):
"""Correlate A and B.
Input:
------
A,B : array
Input data.
columns : int
Do correlation at columns 0..columns, defaults to the number of columns in A.
rows : int
Do correlatio... | 88bfec52c318aaf119a6fac5cff731855f0a0d81 | 4,164 |
def getChrLenList(chrLenDict, c):
""" Given a chromosome length dictionary keyed on chromosome names and
a chromosome name (c) this returns a list of all the runtimes for a given
chromosome across all Step names.
"""
l = []
if c not in chrLenDict:
return l
for n in chrLenDict[c]:
... | aedf613484262ac5bd31baf384ade2eb35f3e1eb | 4,165 |
import argparse
def build_arg_parser():
"""
Build an argument parser using argparse. Use it when python version is 2.7 or later.
"""
parser = argparse.ArgumentParser(description="Smatch table calculator -- arguments")
parser.add_argument("--fl", type=argparse.FileType('r'), help='AMR ID list file... | 199332d5c6c6811ba4c11437c0cad8387bb8dd60 | 4,166 |
from typing import Optional
def query_sessions(user_id: Optional[int]) -> TList[Session]:
"""
Return all user's sessions
:param user_id: current user ID (None if user auth is disabled)
:return: list of session objects
"""
adb = get_data_file_db(user_id)
return [Session(db_session) for db... | c7449c7805f1ba0c425140603952215b67e3ce0e | 4,167 |
import torch
import math
def positionalencoding3d(d_model, dx, dy, dz):
"""
:param d_model: dimension of the model
:param height: height of the positions
:param width: width of the positions
:return: d_model*height*width position matrix
"""
# if d_model % 6 != 0:
# raise ValueError("... | 178dc3b86e3be0c9e799f5f0c658808f541f1eca | 4,168 |
def make_headers(context: TraceContext) -> Headers:
"""Creates dict with zipkin headers from supplied trace context.
"""
headers = {
TRACE_ID_HEADER: context.trace_id,
SPAN_ID_HEADER: context.span_id,
FLAGS_HEADER: '0',
SAMPLED_ID_HEADER: '1' if context.sampled else '0',
... | 474e3a57af1bda99585f7d140fbd0bb1d9bd18b2 | 4,169 |
def shiftRightUnsigned(col, numBits):
"""Unsigned shift the given value numBits right.
>>> df = spark.createDataFrame([(-42,)], ['a'])
>>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()
[Row(r=9223372036854775787)]
"""
sc = SparkContext._active_spark_context
jc = sc._jvm.functio... | 342d08644c56c2cce5e02f0d3d0ddd9df0b2f173 | 4,170 |
def scalar_sub(x: Number, y: Number) -> Number:
"""Implement `scalar_sub`."""
_assert_scalar(x, y)
return x - y | 74c9d44eaaabb1bfeea012b4ec1503e37d7c9f8b | 4,171 |
def predict_attack(h1,h2,h3,h4,h5,h6,h7,h8,h9,h10,h11,h12,h13):
"""
Parameters:
-name:h1
in:query
type:number
required=True
-name:h5
in:query
type:number
required:True
-name:h4
in:query
type:number
required:True
-name:h8
... | 907f6b52c3b1c24a409b8b7ebc157412bd67777d | 4,172 |
def _check_varrlist_integrity(vlist):
"""Return true if shapes and datatypes are the same"""
shape = vlist[0].data.shape
datatype = vlist[0].data.dtype
for v in vlist:
if v.data.shape != shape:
raise(Exception("Data shapes don't match"))
if v.data.dtype != datatype:
... | 1b6fedd1222757c0bc92490be85d8030ee877842 | 4,173 |
def subclassfactory(fact_method):
"""fact_method takes the same args as init and returns the subclass appropriate to those args
that subclass may in turn override the same factory method and choose amoung it's subclasses.
If this factory method isn't overridden in the subclass an object of that class is ini... | eb0b8227276ed7499d21d9998ec08fb830d89642 | 4,174 |
def simulate_var1(x_tnow, b, mu, sigma2, m_, *, j_=1000, nu=10**9,
init_value=True):
"""For details, see here.
Parameters
----------
x_tnow : array, shape(n_, )
b : array, shape(n_,n_)
mu : array, shape(n_, )
sigma2 : array, shape(n_,n_)
m_ : int
... | 66bf82052e933e14d16e82738d36a4c96b51ca43 | 4,175 |
from typing import Optional
def is_drom(insee_city: Optional[str] = None, insee_region: Optional[str] = None) -> bool:
"""
Est-ce que le code INSEE de la ville ou de la région correspond à un DROM ?
Args:
insee_city: Code INSEE de la ville
insee_region: Code INSEE de la région
Return... | 7a33516eb31c5ff7800eb6dc663d76d5e2c445cb | 4,176 |
import math
def pack_rows(rows, bitdepth):
"""Yield packed rows that are a byte array.
Each byte is packed with the values from several pixels.
"""
assert bitdepth < 8
assert 8 % bitdepth == 0
# samples per byte
spb = int(8 / bitdepth)
def make_byte(block):
"""Take a block o... | e0b8a4701adf1757a558475e2ea5830a3d53ab2a | 4,177 |
def reset_user_pwd(username: str) -> int:
"""
:param username: 用户名
:return: 结果代码: 1: 成功, 0: 失败
"""
return update_user_info(username=username, args={
'password': '12345678'
}) | a9703bb82913b47e9b59ba36cd9257323cbfeec2 | 4,178 |
def location_engineering(df: pd.DataFrame) -> pd.DataFrame:
"""Call the `location_dict()` function to get the location dictionary and the
`location_dataframe()` one to add the location dictionary info to the DataFrame.
Parameters
----------
df :
The dataframe to work with.
Returns
... | cca3e1724da08ffcb895aa9fc323ebaf380760e4 | 4,179 |
import re
def extract_energyxtb(logfile=None):
"""
Extracts xtb energies from xtb logfile using regex matching.
Args:
logfile (str): Specifies logfile to pull energy from
Returns:
energy (list[float]): List of floats containing the energy in each step
"""
re_energy = re.comp... | 075f9d48d3bcc9f6bd12aa791cc4d0444299dd74 | 4,180 |
import os
def GetPID():
"""Returns the PID of the shell."""
return os.getppid() | 28e56a9d0c1c6c1d005c58f5c9fffeb3857d8877 | 4,181 |
def make_transaction_frame(transactions):
"""
Formats a transaction DataFrame.
Parameters
----------
transactions : pd.DataFrame
Contains improperly formatted transactional data.
Returns
-------
df : pd.DataFrame
Daily transaction volume and dollar ammount.
- S... | ab8feafb1a441fddf574ebd12a7720a7c4d7398b | 4,182 |
def find_or_create_role(name, desc):
""" Find existing role or create new role """
role = Role.query.filter(Role.name == name).first()
if not role:
role = Role(name=name, desc=desc)
return role
return role | 414b960488d55ea6c2cc41121132f06f0d677abd | 4,183 |
import os
def enumerate_shapefile_fields(shapefile_uri):
"""Enumerate all the fielfd in a shapefile.
Inputs:
-shapefile_uri: uri to the shapefile which fields have to be
enumerated
Returns a nested list of the field names in the order they are stored
in the layer,... | 1a1a128daa991854629894b7e23e90253761a1c8 | 4,184 |
def parse_nrrdvector(inp):
"""Parse a vector from a nrrd header, return a list."""
assert inp[0] == '(', "Vector should be enclosed by parenthesis."
assert inp[-1] == ')', "Vector should be enclosed by parenthesis."
return [_to_reproducible_float(x) for x in inp[1:-1].split(',')] | 3e3c793d3ee53198c4cdb01832062be4f0c02876 | 4,185 |
def _estimate_gaussian_covariances_spherical(resp, X, nk, means, reg_covar):
"""Estimate the spherical variance values.
Parameters
----------
responsibilities : array-like of shape (n_samples, n_components)
X : array-like of shape (n_samples, n_features)
nk : array-like of shape (n_components... | 6f08d04528f5e515d5ae75d4dc47753cc4cebc7b | 4,186 |
import os
def parsed_codebook_importer(codebook):
"""
Import the parsed CPS codebook
Parameters:
codebook (str): the filename of the parsed codebook
Returns:
dataframe
"""
path_finder('codebooks')
skip = row_skipper(codebook)
codebook = pd.read_csv(codebook, sep='\t',... | abb0b261ab894f6ea5111be07eaa00d256bfa3c9 | 4,187 |
def get_html(url):
"""Returns html content of the url. Retries until successful without overloading the server."""
while True:
# Retry until succesful
try:
sleep(2)
debug('Crawling %s' % url)
html = urllib2.urlopen(url).read()
return html
e... | a444151add46273c6e72ead585d04aa65e7e7734 | 4,188 |
def map_min(process):
"""
"""
param_dict = {'ignore_nodata': 'bool'}
return map_default(process, 'min', 'reduce', param_dict) | 33dcc2192fd8b979e7238c1fdbe5e9bec551dd3f | 4,189 |
def geoname_exhaustive_search(request, searchstring):
"""
List all children of a geoname filtered by a list of featurecodes
"""
if request.query_params.get('fcode'):
fcodes = [ s.upper() for s in request.query_params.get('fcode').split(',')]
else:
fcodes = []
limit = request.qu... | 5a04a158a146e7e0ad3265d89520774b65c3780a | 4,190 |
import sys
def guess_temperature_sensor():
"""
Try guessing the location of the installed temperature sensor
"""
devices = listdir(DEVICE_FOLDER)
devices = [device for device in devices if device.startswith('28-')]
if devices:
# print "Found", len(devices), "devices which maybe tempera... | d1a37d34eedb1e9a99e481ac3ffb6f5777fcdb7a | 4,191 |
def count_reads(regions_list, params):
""" Count reads from bam within regions (counts position of cutsite to prevent double-counting) """
bam_f = params.bam
read_shift = params.read_shift
bam_obj = pysam.AlignmentFile(bam_f, "rb")
log_q = params.log_q
logger = TobiasLogger("", params.verbosity, log_q) #sending... | ffd8cc6afc6c0b5b92d82292ab9d4a54ef918641 | 4,192 |
def rgbImage2grayVector(img):
""" Turns a row and column rgb image into a 1D grayscale vector """
gray = []
for row_index in range(0, len(img)):
for pixel_index, pixel in enumerate(img[row_index]):
gray.append(rgbPixel2grayscaleValue(pixel))
return gray | a93bbb2dfa29cb3d4013334226e77f6beb526a13 | 4,193 |
def compute_MSE(predicted, observed):
""" predicted is scalar and observed as array"""
if len(observed) == 0:
return 0
err = 0
for o in observed:
err += (predicted - o)**2/predicted
return err/len(observed) | e2cc326dde2ece551f78cd842d1bf44707bfb6db | 4,194 |
def log_sum(log_u):
"""Compute `log(sum(exp(log_u)))`"""
if len(log_u) == 0:
return NEG_INF
maxi = np.argmax(log_u)
max = log_u[maxi]
if max == NEG_INF:
return max
else:
exp = log_u - max
np.exp(exp, out = exp)
return np.log1p(np.sum(exp[:maxi]) + np.sum(... | f2c7917bc806dc7ec3fbbb1404725f590a82e194 | 4,195 |
import os
from datetime import datetime
def gather_basic_file_info(filename: str):
"""
Build out the basic file metadata that can be gathered from any file on the file system.
Parameters
----------
filename
full file path to a file
Returns
-------
dict
basic file attr... | cb18c5213ce7a7d4f1355e84e6f6debe2052490b | 4,196 |
def special_value_sub(lhs, rhs):
""" Subtraction between special values or between special values and
numbers """
if is_nan(lhs):
return FP_QNaN(lhs.precision)
elif is_nan(rhs):
return FP_QNaN(rhs.precision)
elif (is_plus_infty(lhs) and is_plus_infty(rhs)) or \
(is_minus... | df64cf6c306c3192ba28d08e878add7ce0f27a2c | 4,197 |
def parse_git_repo(git_repo):
"""Parse a git repository URL.
git-clone(1) lists these as examples of supported URLs:
- ssh://[user@]host.xz[:port]/path/to/repo.git/
- git://host.xz[:port]/path/to/repo.git/
- http[s]://host.xz[:port]/path/to/repo.git/
- ftp[s]://host.xz[:port]/path/to/repo.git/... | 5eddf3aa9016996fb8aa1720b506c2f86b2e9c14 | 4,198 |
def make_wavefunction_list(circuit, include_initial_wavefunction=True):
""" simulate the circuit, keeping track of the state vectors at ench step"""
wavefunctions = []
simulator = cirq.Simulator()
for i, step in enumerate(simulator.simulate_moment_steps(circuit)):
wavefunction_scrambled = step.... | af33d4a7be58ccfa7737deb289cbf5d581246e86 | 4,199 |
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