content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_tworay_path_loss(range_m, freq_hz, tx_ht_m, rx_ht_m, include_atm_loss=True, atmosphere=None):
"""
Computes the two-ray path loss according to
L = 10*log10(R^4/(h_t^2*h_r^2))
This model is generally deemed appropriate for low altitude transmitters and receivers, with a flat Earth model.... | 343d3b3e2df86f209d2b175e06f36ff695178818 | 36,100 |
def check_bots_database():
"""Check for elements in bot's database.
Returns:
int: Size of list if there is any elements
None: If table is empty
"""
db_status = database.get_data("confDB", False,
"SELECT bot_name FROM tokens")
return None if not db_s... | 7f7302d9f6edb4bfd42cc053f81875de728d11f6 | 36,101 |
from datetime import datetime
def get_features_for_commit_people(git_commit):
"""Retrieves the people associated with a git commit
Arg:
git_commit(dict): a commit message parsed into a dictionary
Return:
(tuple): relevant people and type extracted from the commit
"""
curr_time = datetime.datetime.... | 2f1ba38bb1f190ead2c1f4974f023feaeb55bca2 | 36,102 |
from bs4 import BeautifulSoup
def get_url_source(url_name):
"""
If you already know the url_name or if you have run through the get_noaa_forecast_url(), then you can
send in the url here. Get the source information for the url and place the information into a BeautifulSoup
object, so that we can do an... | f7ab186d2d9f68020286317f28a169ad296d39fd | 36,103 |
import torch
def norm(x):
""" Normalize (z-norm) from [0,1] range to [-1,1] """
out = (x - 0.5) * 2.0
if isinstance(x, torch.Tensor):
return out.clamp(-1, 1)
elif isinstance(x, np.ndarray):
return np.clip(out, -1, 1)
else:
raise TypeError(
"Got unexpected objec... | 43dba4ec5c7e44daccef4231db5667319d73cc88 | 36,104 |
def gen_gp_loss(gp):
"""Generate an internal objective, `dlik_dh * H`, for a given GP layer.
"""
def loss(_, H):
dlik_dh_times_H = H * K.gather(gp.dlik_dh, gp.batch_ids[:gp.batch_sz])
return K.sum(dlik_dh_times_H, axis=1, keepdims=True)
return loss | e3fe0f3634c1309acce9cc65978e028da2fa05ff | 36,105 |
from typing import Tuple
from typing import Optional
def set_market(asset_form: NewAssetForm) -> Tuple[Optional[Market], Optional[str]]:
"""Set a market for the to-be-created asset.
Return the market (if available) and an error message."""
market = None
market_error = None
if int(asset_form.marke... | 9d597acc1b12cbc95fea929939292a0f7641f550 | 36,106 |
def _evaluateMockedResponse(table, cmd_dict,
success=True, is_save=True):
"""
Evaluates if the response from a mock is as expected.
:param Table table:
:param dict cmd_dict:
:param bool success: value of success
:param bool is_save: value of is_save
"""
response, returned_is_save = \
table.p... | 20b5a3ff800e012cffc65fd0876656ced9e45f89 | 36,107 |
def plot_resource_utilization(filename, subplt, ylim=None, pattern=None):
"""
:param filename: resource_log file
:param subplt: pyplot axes object
:param ylim: y-axes range, defaults to [0,1.05]
:param pattern: only plot resources whose name matches the pattern (in regular expression),
... | e82b902fccc77d3d7850ade72adcbdd9fb9910df | 36,108 |
import sys
def projection_simplex_sort(v, z=1):
"""
Bounds control field to agent's magnetic field budget.
...
Parameters
----------
v : numpy.array
Control field allocation of the agent.
z : float
Magnetic field budget (default 1.0)
"""
n_features = v.shape[... | 042d3ce5851c31c8d5483e25d5ca2343809cebea | 36,109 |
import collections
def _get_synthetic_digits_data():
"""Returns a dictionary suitable for `tf.data.Dataset.from_tensor_slices`.
Returns:
A dictionary that matches the structure of the data produced by
`tff.simulation.datasets.emnist.load_data`, with keys (in lexicographic
order) `label` and `pixels`.... | c770d08c9a92c7aa958037a45baf2fde306c2bc2 | 36,110 |
def all_subclasses(cls):
"""Returns recursively-generated list of all children classes inheriting from given `cls`."""
return set(cls.__subclasses__()).union([s for c in cls.__subclasses__() for s in all_subclasses(c)]) | fa9c08bd37d9bee504a404d20f5e94e8e3b2bab1 | 36,111 |
from typing import Tuple
def get_sample_input01() -> Tuple[int, np.ndarray]:
""" A single input vector sample of the "Necessary" category. """
label = 0
feature_vector = np.array([[0., 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
... | 2bab4949027b015d645a5a965867227453d29448 | 36,112 |
def calc_pose_instances(
pose_heatmaps,
pose_localmax,
pose_embed_maps,
min_embed_sep_between,
max_embed_sep_within,
max_inst_dist):
"""
Given the input parameters for a single image/frame, return a list
of PoseInstance objects
Parameters:
pose_h... | 2bd30e997e4cfe55153954bfeadbae263e24ca9a | 36,113 |
def cap_rating(flight_phase, short_period_cap, short_period_damping):
""" Give a rating of the short period mode flight quality level: Level1 Level2 or Level3 and 4
according to MIL-STD-1797A
Args:
flight_phase (string) : 'A', 'B' or 'C'
short_period_cap (float): Control Anticipation ... | 2ecebb417f32320213a916ca1366f36dd473fc62 | 36,114 |
def set_immd(*args):
"""
set_immd(ea) -> bool
Set 'has immediate operand' flag. Returns true if the 'FF_IMMD' bit
was not set and now is set
@param ea (C++: ea_t)
"""
return _ida_bytes.set_immd(*args) | 9521a509cfed727a0e48b6ac72704bbbc5218087 | 36,115 |
from slycot import sb01bd
def place_varga(A, B, p, dtime=False, alpha=None):
"""Place closed loop eigenvalues
K = place_varga(A, B, p, dtime=False, alpha=None)
Required Parameters
----------
A : 2D array_like
Dynamics matrix
B : 2D array_like
Input matrix
p : 1D array_like... | fcfcbca4e7320e39143deb6462fd98c335566b26 | 36,116 |
def kToc(k, nu = 5.0, xF = 0.2, xS = 0.8, xG = 0.0 ):
"""
kToc will convert a k-eigenvalue to a c-eigenvalue given the cross sections
for the problem.
"""
xA = xG + xF
xT = xA + xS
return (nu*xF/k + xS)/xT | a3e310884f3a260b0bc5069f3799784d81b2c93f | 36,117 |
import os
import json
def _build_parameters(statistics, gcs_data_dir):
"""Generate model params based on the stats generated from the data-set."""
# Do binning on sequence lengths
bins = np.arange(0, 1700, 100)
# Choose maximum seq length based on number which covers 50% of seq lengths
threshold ... | 4ea79d35203f55e89bd10b9c6ef1b2bb26e52edc | 36,118 |
def analysis(sliceno, prepare_res, params):
""" reading complete file, writing to this slice only"""
separator, filename, _, labels, dw = prepare_res
if options.hashlabel:
hash_ix = labels.index(options.hashlabel)
else:
hash_ix = -1
copied_lines = 0
n_labels = len(labels)
res_num = ffi.new('uint64_t [2]'... | e14d3440a24404f8487d9ecf7ac50a7aa5291f3c | 36,119 |
def construct_generic_B(n_states, n_control):
"""
Generates a fully controllable transition likelihood array, where each
action (control state) corresponds to a move to the n-th state from any
other state, for each control factor
"""
num_factors = len(n_states)
if num_factors == 1:
... | e09b6abd0b72637b914e0c94b57b04aed42c3554 | 36,120 |
from typing import Union
from typing import TextIO
from typing import Optional
import click
def parse(
in_data: Union[str, PathLike, TextIO],
input_linesep: Optional[str] = None,
max_size: int = _MAX_SIZE,
) -> dict:
"""Parse a VEP plain text statistics file into a dictionary.
:param in_data: Inp... | 171914b54aadd2cddef672951351c4a987110a9e | 36,121 |
def analysis_to_subword_dicts(ana):
"""
Returns a list of list of dicts. Each list element is an analysis. For each
analysis, there is a list of subwords. Each dict contains an Omorfi
analysis
"""
return map(pairs_to_dict, chunk_subwords(analysis_to_pairs(ana))) | 933a73c0c4787bf63cecefbedfd230a3d99906f9 | 36,122 |
from datetime import datetime
def create_directory_content_output(share_name: str, raw_response: dict, directory_path: str = "") -> dict:
"""
Create XSOAR context output for list directory command.
Args:
share_name (str): Share name.
raw_response (dict): Request raw response.
direc... | a23b3ce74f4274c2ee59823a3f9a976943b94a5a | 36,123 |
def circle_area(r):
"""Calculates the area of a trapezium using the formula:
area = \u03C0(radius squared)
Parameters
----------
r: int or float
The radius in the equation.
Returns
-------
Float
\u03C0 * (r**2)
Raises
------
ValueError
If r::
... | e8d3c8fd556a90851757a4edf6091d44452f59cc | 36,124 |
def split_to_batches(graphs, labels, size):
"""
split the same size graphs array to batches of specified size
last batch is in size num_of_graphs_this_size % size
:param graphs: array of arrays of same size graphs
:param labels: the corresponding labels of the graphs
:param size: batch size
... | 7e93c7eec3f4d5c94e41e44356c496148b78162c | 36,125 |
from matplotlib import ticker
from matplotlib.pyplot import gca
def showLines(*args, **kwargs):
"""
Show 1-D data using :func:`~matplotlib.axes.Axes.plot`.
:arg x: (optional) x coordinates. *x* can be an 1-D array or a 2-D matrix of
column vectors.
:type x: :class:`~numpy.ndarray`
... | e21bcc8fd563c632ecc2da76ebdd7af3a0cefc3c | 36,126 |
import json
def build_response(status, message):
# type: (DownloadStatus, str) -> str
"""
function to build a response as json string
"""
return json.dumps({'status': status, 'message': message}) | 078db81165a43e2bf68ddde13b4ca566507ef3ab | 36,127 |
import json
def loads_json(fid, **kwargs):
""" Loads a JSON file and returns it as a dict.
:param path: String or another object that is accepted by json.loads
:param kwargs: See ``json.dump()``.
:return: Content of the JSON file.
"""
assert isinstance(fid, str), fid
return json.loads(fi... | ccdb62568982f6a0862dd5c8167b2e1d177137ea | 36,128 |
import argparse
def cli_parse_args(args=None, namespace=None):
"""
CLI (Command Line Interface) parsing based on ``ArgumentParser.parse_args``
from the ``argparse`` module.
"""
# CLI interface description
parser = argparse.ArgumentParser(
description=__doc__,
epilog="by Danilo J. S. Bellini",
... | 8c76ec6cb3a398048c9878a3d996eed329f67ac3 | 36,129 |
def get_rad_list(H_MR, H_OR):
"""主たる居室、その他の居室という単位で設定された放熱機器を暖房区画ごとの配列に変換
Args:
H_MR(dict): 暖房機器の仕様
H_OR(dict): 暖房機器の仕様
Returns:
list: 放熱機器の暖房区画ごとの配列
"""
# 暖房区画i=1-5に対応した放熱器のリストを作成
rad_list = [None, None, None, None, None]
# 放熱系の種類
rad_types = get_rad_type_list()
... | 0645c6f5af6cbbd2568e664272cea3a0576fa3bf | 36,130 |
def GetArrayElement(tag, array_id, idx):
"""
Get value of array element.
@param tag: Tag of array, specifies one of two array types: AR_LONG, AR_STR
@param array_id: The array ID.
@param idx: Index of an element.
@return: Value of the specified array element. Note that
this functi... | 5d2c957a0f2c64d25325db55d748b0f041d34eaa | 36,131 |
import subprocess
def shutdown_pi():
"""
Shuts down the Pi system
"""
subprocess.Popen(["sudo", "shutdown", "-h", "now"])
return True | b0f80afecd4682529daed2a14497c07c36598c64 | 36,132 |
def init_lars_optimizer(current_epoch, params):
"""Initialize the LARS Optimizer."""
learning_rate = poly_rate_schedule(current_epoch, params)
optimizer = tf.contrib.opt.LARSOptimizer(
learning_rate,
momentum=params['momentum'],
weight_decay=params['weight_decay'],
skip_list=['batch_norma... | 6eba9c650c1a14b3df9a1e46d52014cb62898124 | 36,133 |
import time
import random
def suffix():
"""Generate a random string of length 4"""
alph = "abcdefghijklmnopqrstuvwxyz"
return "-".join([time.strftime("%Y-%m-%d-%H%M%S"), "".join(random.sample(alph, 4))]) | f0e6c053ffb38e09d5ed4516f07fb1533b5dd80d | 36,134 |
def augment_data(spectrogram: np.ndarray) -> list:
"""
Augment the given data.
:param spectrogram: Data to augment
:return: Augmented data
"""
result = [
mask_spectrogram(spectrogram, n_freq_masks=1, n_time_masks=0),
# mask_spectrogram(spectrogram, n_freq_masks=3, n_time_masks=0)... | a56bd5e7baaa45f6ccb53b27b73e50baecf6e3ee | 36,135 |
import datasets
import torch
def load_train(root_path, dir, batch_size, phase):
"""
Load data for train set
"""
transform_dict = {
'src': transforms.Compose(
[transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
... | 372571a5cf75c878782fa96c73860bfe04631972 | 36,136 |
def reduceModel(model, atoms, select):
"""Return reduced NMA model. Reduces a :class:`.NMA` model to a subset of
*atoms* matching *select*. This function behaves differently depending on
the type of the *model* argument. For :class:`.ANM` and :class:`.GNM` or
other :class:`.NMA` models, force constan... | 6bb5ed82638f50e311879ec623fbe027a6297258 | 36,137 |
def executable(s):
"""Attempts to use the "ex" entry in LS_COLORS, falling back to green"""
return __ls_color(s, 'ex', term.green) | 0a6ae7f999a7fa23391db68eddfc4cd67f89df2c | 36,138 |
def put_target_state():
"""SDP target State.
Sets the target state
"""
sdp_state = SDPState()
errval, errdict = _check_status(sdp_state)
if errval == "error":
LOG.debug(errdict['reason'])
rdict = dict(
current_state="unknown",
last_updated="unknown",
... | b810987edad9f0cb644fdb69974ca91d6a8cc01b | 36,139 |
def _skew_circulant_multiply(c, x, f_method='std'):
"""Multiply a skew-circulant matrix by a vector.
Runs in O(n log n) time.
See algorithm S7 in Sukhoy & Stoytchev 2019 (full reference in README).
Args:
c (np.ndarray): first column of skew-circulant matrix G
x (np.ndarray... | de20516ce427385d7462cd031cfb2001f0f3b640 | 36,140 |
def _apply_temp_conv(var, var_unit, dest_unit):
"""Return the variable converted to different units using a temperature
conversion algorithm.
Args:
var (:class:`xarray.DataArray` or :class:`numpy.ndarray`): A
variable.
var_unit (:obj:`str`): The variable's current units.
... | 6df9d02b1d74cd8554d4ad8b135986636f3e2c95 | 36,141 |
from typing import List
import os
def read_all_labels(fp_folder: str = "./rs19_val/jsons/rs19_val") -> List[dict]:
"""Loads the data of all JSON files from a provided folder and returns the contents in a list.
:param fp_folder: Filepath to the folder containing JSON files.
:dtype fp_folder: str
"""
... | aa8b0267afa66696b9ff10f2a19f4ea505ecacf9 | 36,142 |
import requests
def epidemic_history() -> pd.DataFrame:
"""
该接口最好用代理速度比较快
https://github.com/canghailan/Wuhan-2019-nCoV
2019-12-01开始
:return:
:rtype: pandas.DataFrame
"""
url = "https://raw.githubusercontent.com/canghailan/Wuhan-2019-nCoV/master/Wuhan-2019-nCoV.json"
r = requests.g... | c16e375c0e3e288667afebcbc8fa3f54dc486dcc | 36,143 |
def main(*, data, d):
"""entrypoint function for this component
Usage example:
>>> main(
... data = pd.Series(
... [1.2, 7.2, 2.8, 4.8, 10.8],
... index = [2, 3, 5, 6, 9]
... ),
... d = 2
... )["interpolation"]
2 1.2
4 5.0
6 4.8
8... | e026badb020177538c34689300cb9df7783df0aa | 36,144 |
def derivative ( fun , x , h = 0 , I = 2 , err = False , args = () , kwargs = {} ) :
"""Calculate the first derivative for the function
# @code
# >>> fun = lambda x : x*x
# >>> print derivative ( fun , x = 1 )
- see R. De Levie, ``An improved numerical approximation for the first derivative'... | f6dc64f483e9512775b078e8cd602dc9dc66cc53 | 36,145 |
def get_statevector(result) -> np.ndarray:
"""
Creates a statevector out of a simulation result.
Used when the simulator does not give a statevector.
Parameters
----------
result:
the result from the simulation.
Returns
-------
ndarray:
the statevector correspondin... | 6dab1bb772e54f084dcf0b9ce141f8dea08812e0 | 36,146 |
def weighting(distance):
"""Weighting function for pyresample."""
weight = 1 / distance**2
return weight | 2af699d6daef7a3d375fe80ee79f7414047921c8 | 36,147 |
import os
def absolute_path(path):
"""
Returns the absolute path for the given path and normalizes the
path.
:param path:
Path for which the absolute normalized path will be found.
:returns:
Absolute normalized path.
"""
return os.path.abspath(os.path.normpath(path)) | 39a8af97f223828ae521b3570116e43990e15e66 | 36,148 |
import math
def weighted(*values):
"""
Calculates the uncertainty weighted average of the provided values,
where each value is a ValueUncertainty instance. For mathematical
formulation of the weighted average see "An Introduction to Error
Analysis, 2nd Edition" by John R. Taylor, Chapter 7.2.
... | b51158e5f34d010902bbdcb6a98e94971c549c15 | 36,149 |
def average_precision_at_k(targets, ranked_predictions, k=None):
"""Computes AP@k given targets and ranked predictions."""
if k:
ranked_predictions = ranked_predictions[:k]
score = 0.0
hits = 0.0
for i, pred in enumerate(ranked_predictions):
if pred in targets and pred not in ranked_... | 57e21a1ca8b8f7fccc0b5c59dbfc799d8618fc93 | 36,150 |
def on_intent(intent_request, session):
"""
Called when the user specifies an intent for this skill.
Main logic point to determine what action to take based on users
request.
"""
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
#... | 23ed09f7c6f26a155e9a550b31fe538a33075107 | 36,151 |
import time
def check_index_by_doc(es_alias, db, doc_id, interval=10):
"""
Given a doc, update it in couch (meaningless save that updates rev)
and check to make sure that ES will eventually see it after some arbitrary delay
"""
target_rev = None
try:
couch_doc = db.open_doc(doc_id if d... | 3df71f7795859f8ca30f79858c659ce692cf7523 | 36,152 |
def formatEpisodeNumbers(episodenumbers):
"""Format episode number(s) into string, using configured values
"""
if len(episodenumbers) == 1:
epno = Config['episode_single'] % episodenumbers[0]
else:
epno = Config['episode_separator'].join(
Config['episode_single'] % x for x in... | d33da0c2da5213dfc7214f4c11d18b5c3bdd814c | 36,153 |
def _PureShape(shape):
"""Make sure shape does not contain int tensors by calling int()."""
return [int(x) for x in shape] | 1cebacd516cbf223833342ecffb6f8d9fe22ff2d | 36,154 |
from typing import cast
def sum(pda: pdarray) -> np.float64:
"""
Return the sum of all elements in the array.
Parameters
----------
pda : pdarray
Values for which to calculate the sum
Returns
-------
np.float64
The sum of all elements in the array
Raises
----... | 9a28da173746280b6fd28ad0a6c942ee5e7a7463 | 36,155 |
import textwrap
def add_ui_containerized_action(sub_parser: ArgumentParser) -> ArgumentParser:
"""Populates the sub parser with the container launch kernel arguments."""
sub_parser.add_argument(
'-d', '--directory',
dest='notebooks_directory',
default='.',
help=textwrap.dedent(... | ba21b9a990bf290db86a330faed7433a0bd4da4b | 36,156 |
from typing import Sequence
def _tf_cleanup_all(
triples_groups: Sequence[MappedTriples],
*,
random_state: TorchRandomHint = None,
) -> Sequence[MappedTriples]:
"""Cleanup a list of triples array with respect to the first array."""
reference, *others = triples_groups
rv = []
for other in o... | 7b76533b8860d17f072b54645fc11f02d1bc3ffd | 36,157 |
import copy
def apply_pdbfixer(input_file_path, output_file_path, directives):
"""
Apply PDBFixer to make changes to the specified molecule.
Single mutants are supported in the form "T315I"
Double mutants are supported in the form "L858R/T790M"
The string "WT" still pushes the molecule through P... | 3ef9a13e9d9842e8837651f197fb20b86bf86ceb | 36,158 |
def char_to_number(value: str, required_type: str) -> str:
"""Converts the string representation of a number (int or float) to a number
Args:
value (str): String representation of a number
required_type (str): Output type desired (bigint or double)
Raises:
Exception: The conversion... | cb58f24100f961d4f260473cee4fa82621eb5887 | 36,159 |
def get_PHM08Data(save=False):
"""
Function is to load PHM 2008 challenge dataset
"""
if save == False:
return np.load("./PHM08/processed_data/phm_training_data.npy"), np.load("./PHM08/processed_data/phm_testing_data.npy"), np.load(
"./PHM08/processed_data/phm_original_testing_data... | 0f963eb988ac8e9d8ead2b47f9ff13dd82449389 | 36,160 |
def _validate_index_string(index_string: str) -> bool:
"""
Handle validation of index string from the argument.
a string should be in format 't{%d}' or 'r{%d}' or 'd{%d}'
Parameters:
index_string: the string we want to validate
Returns:
a boolean indicating whether the index st... | aa6d7d0aba2a0378d2e1c836b78b0ec943cc1bdb | 36,161 |
import subprocess
import socket
import time
def _restart_debugging(interactive=True):
"""
Args:
interactive:
Returns:
"""
global tb_pid
#Kill existing TB
proc = subprocess.Popen(["kill", str(tb_pid)])
proc.wait()
debugger_socket = socket.socket(socket.AF_INET, socket.S... | 0b1accc7eece03d4d5e3f3157ba849bcbbb74b5f | 36,162 |
def get_r_GU(r_HPU):
"""1時間平均のガスユニットの暖房出力分担率 (15)
Args:
r_HPU(ndarray): 1時間平均のヒートポンプユニット暖房出力分担率 (-)
Returns:
ndarray: 1時間平均のガスユニットの暖房出力分担率
"""
return 1 - r_HPU | dc4c7a913ff7402a10d9aad08bab4f3646cff904 | 36,163 |
def onset_by_rain(date, df, window=5, rain_threshold=5):
"""
Finds true storm onset by finding the first date around the landfall that rain exceeds a threshold
Args:
date: the date to look around
df: df with a date and rain column
window: number of days around date to find max (tota... | 87e4d1f35114974a004c5b923aea05ed835cf9a7 | 36,164 |
def get_most_read(key):
"""
Gets the most read news from a given page (national or international)
:param key: the key of the source page (e.g: g1, localDF, WP, etc.)
:return: a list with the most read news from the page
"""
# Check if the News Source is national or internacional
if key in n... | 89998185adaeb4e129440ab5dd90b89f823dab6b | 36,165 |
import math
def trapezoid_list(area, rotate_and_mirror=True):
"""Returns a list of possible trapezoids (various height/width ratios,
various orientations) with approximately the requested area.
We support two kinds of trapezoids:
w1 w2
|----\ /--\
h | \ ... | 44a4d678e8f1850d74e2a2087461ea68129de755 | 36,166 |
import os
from datetime import datetime
import asyncio
async def generate_tarot_card(
id_: int,
resources: BaseTarotResource,
direction: int = 1,
*,
need_desc: bool = True,
need_upright: bool = True,
need_reversed: bool = True,
width: int = 1024) -> Resu... | 0c699388b161e557a3b93df59d47832360ede612 | 36,167 |
import os
def work_in(dir_ext):
"""Execute a function in a different directory"""
def func_decorator(func):
@wraps(func)
def wrapped_function(*args, **kwargs):
here = os.getcwd()
dir_path = os.path.join(here, dir_ext)
if not os.path.isdir(dir_path):
... | fda5ecdea5dc0a75869c2bc65f726abb00904d9a | 36,168 |
import os
def login(request):
""" Log user in using Github OAuth"""
# Create keys if not yet there!
if not request.session.get('github_token'):
request.session['github_token'] = None # To keep API token
request.session['github_info'] = None # To keep user infor (e.g. name, avatar url)
... | 30a5acbaf6bdb3c746cbc49ca4ea03f1a75eefb7 | 36,169 |
def counter(iterable):
""" Return a dict of counts for items in iterable. """
counts = defaultdict(int)
for item in iterable:
counts[item] += 1
return counts | ace03795b5204fdad77860d99965889c1dec4435 | 36,170 |
def meets_criteria(num):
"""boolean test for meeting the password criteria.
+ Two adjacent digits are the same (like 22 in 122345).
+ Going from left to right, the digits never decrease; they only ever
increase or stay the same (like 111123 or 135679)
Arg:
num: number to be tested
... | 9001f2012cb3572c52967307924bdf4f0e37e614 | 36,171 |
def return_loop(
onset_loc,
envelope,
function_time_thresh,
hist_threshold,
hist_time_samples,
nperseg=512,
):
""" This function is used by the calculate_onsets method.
This looks backwards in time from the attack time and attempts to find the exact onset point by
identifying the p... | 50c2a3afbc4b1b2a753a7f43ae30c7601ad49c1e | 36,172 |
from numpy import matrix
def hostAppMat2numpy(hostmat):
"""
This will return a matrice in a numpy.array format
This function can also be used when theire is LeftHand / RightHand
conflict.
ie a numpy matrice is a regular 4x4 matrice (3x3rot+trans)
@type hostmat: hostObject Matrice
@p... | f28ff26826edbac6f41e1882d6aa250c639d613b | 36,173 |
def integrate_entropy(data, scaling):
"""Integrates entropy data with scaling factor along last axis
Args:
data (np.ndarray): Entropy data
scaling (float): scaling factor from dT, amplitude, dx
Returns:
np.ndarray: Integrated entropy units of Kb with same shape as original array
... | 0a92f4c1da3966cc12a581f943c96bb79a1c8e4e | 36,174 |
import os
def parse_old_arhive_file_name(file_name):
"""Retrieves information from the given old archive file name and returns it in the following form:
function, dimension, instance
:param file_name: old archive file name in form f[f1]-[f2]_i[i1]-[i2]_[d]D.txt
"""
split = os.path.basename(f... | 7ffe05e80ff7f5b60b648056bb7a37fa5de5e38e | 36,175 |
import urllib
def removelink(inp, nick='', db=None):
""".removelink <search terms> -- Remove a bad link from the database. e.g., .removelink shaq"""
if nick in administrators:
result = remove_link(db, urllib.quote_plus(inp))
if result is True:
return "Removed " + inp + " successfu... | 1b2581651373fb16f6f8c24daebd4e832dc87307 | 36,176 |
def embed(sagas):
"""
The data is embedded into numerical data (vectors to represent characters) that we can use to train our RNN.
:param sagas: Training data
:return: Tensor containing the input data in embeded form, array of unique character in the input.
"""
# All unique characters in our sa... | a85fa1bc5b3ff09640a832d8ba13cd03590cec92 | 36,177 |
def create_data(step: 'projects.ProjectStep') -> STEP_DATA:
"""
Creates the data object that stores the step information in the notebook
results JavaScript file.
:param step:
Project step for which to create the data
:return:
Step data tuple containing scaffold data structure for th... | 56a55bf01ce52b69973f1054eee9b32427aed826 | 36,178 |
def byte_string_dtypes(endianness="?", min_len=0, max_len=16):
# type: (str, int, int) -> st.SearchStrategy[np.dtype]
"""Return a strategy for generating bytestring dtypes, of various lengths
and byteorder."""
order_check("len", 0, min_len, max_len)
return dtype_factory("S", list(range(min_len, max_... | 4a8de45377b2c15671049762b5f1f4de10bc8692 | 36,179 |
def get_company():
"""
Gets company for contact entered
"""
company = input("Please input a company\n")
while True:
if company == "":
print("Please input a company please")
else:
break
return company | 324d49384ab80aad3c8c09a3707ad9fd01929166 | 36,180 |
def set(request):
"""Set new CTL value to a encoder/decoder"""
def inner(func, obj, value):
result_code = func(obj, request, value)
if result_code is not opuslib.api.constants.OK:
raise opuslib.exceptions.OpusError(result_code)
return inner | 695caab9b6ac1b63b1cd22c4ad534dd60e768c5d | 36,181 |
import subprocess
import os
def getInstalledPackageVersion(pkgid):
"""
Checks a package id against the receipts to
determine if a package is already installed.
Returns the version string of the installed pkg
if it exists, or an empty string if it does not
"""
# First check (Leopard and la... | 8517615ee865feb1e7b48ce3d1a0cb6c418bd0ad | 36,182 |
def read_mapping(mapf, verbose=False):
"""
Read the mapping from metagenomes to nodes in the tree
:param mapf: the mapping file
:param verbose: more output
:return:
"""
mapping = {}
with open(mapf, 'r') as f:
for l in f:
p = l.strip().split("\t")
if p[1] ... | d6852efb1a3514e1ef4bdf545624e5ee38b07a69 | 36,183 |
def tfr_gen(func, op_defs):
"""Parse a function and emit the TFR functions."""
mlir_code, _ = TfrGen(op_defs).transform(func, None)
assert tfr.verify(mlir_code), 'mlir code not verified: {}'.format(mlir_code)
return mlir_code | 7bb43e936aa4f9dd4ed0527a7d2a533b4172b7b3 | 36,184 |
from typing import Tuple
import torch
def preprocess_image(image:'np.ndarray', input_shape:Tuple=(79,95,79), normalized=True) -> 'torch.Tensor':
"""Resize, normalize between 0-1 and convert to uint8
Args:
image('np.ndarray'):
input_shape(Tuple, optional): (Default value = (79,95,79))
norma... | c8d9f7c0d504f28fd45a1fa6fe6d43ff176f2d59 | 36,185 |
import inspect
import textwrap
def convert_to_static(dyfunc):
"""
Converts dygraph function into static function.
"""
# Get AST from dygraph function
raw_code = inspect.getsource(dyfunc)
code = textwrap.dedent(raw_code)
root = gast.parse(code)
# Transform AST
dygraph_to_static = D... | 00c2e2126832ab36a43d7537fa147cb2a91a6592 | 36,186 |
from datetime import datetime
def add_minutes(sourcedate, min_, debug=False):
"""
Incremental increase of datetime by given minutes
"""
sourcedate += datetime.timedelta(minutes=float(min_))
return sourcedate | f146837b640327f20c81f5ca95531fc684cf817f | 36,187 |
def poly_kernel(X, Y=None, gamma=1.0, coef0=0.0, degree=3):
"""
Compute the poly kernel between X and Y:
K(x, y) = (scale*dot(x, y^T) + shift)**degree
for each pair of rows x in X and y in Y.
Parameters
----------
X : ndarray of shape (n_samples_X, n_features)
Y : ndarray of shap... | c47ebba04bf50e474c0eff94b777110ead0a4f61 | 36,188 |
def filter_process(session, file, action, wkt, **kwargs):
"""Wrapper function for the filtering process.
Arguments:
session (dict): Dictionary with session information.
file (str): The full path of the source file.
action (str): The filtering operation.
wkt (str): Well-Known Tex... | 68a2e8be4df13996d9a45ee5138076faa2378539 | 36,189 |
def help_text():
"""
Retreive human-readable description of all added craftable items
:return: description of all added craftable items
:rtype: str
"""
ret = []
for name in craftables:
items, item = craftables[name]
item_names = utils.list_to_english([str(x) for x in items])... | ce8ffb7ed681ad875242317b8ae426ca13ffb9dc | 36,190 |
def _load_image(path):
"""
Reads image image from the given path and returns an numpy array.
"""
image = np.load(path)
assert image.dtype == np.uint8
assert image.shape == (64, 64, 3)
return image | 0950925b484dbe75bb578df781c6ee09c1e80634 | 36,191 |
def nb(model, ref):
"""
Normalzied bias.
model = nuemrical solution of shape M by N
ref = analytical solution or observation of shape M by N
returns nb
"""
e = model - ref
mean_e = nanmean(e)
mean_abs_ref = nanmean(abs(ref))
nb = mean_e / mean_abs_ref
return nb | d021af55091f161e6621222d3be9ed5f85541cc7 | 36,192 |
def annotate_heatmap(im, data=None, valfmt="{x:.2f}", textcolors=["black", "white"], threshold=None, **textkw):
"""
A function to annotate a heatmap.
Parameters
----------
im
The AxesImage to be labeled.
data
Data used to annotate. If None, the image's data is used. Optional.
... | 9b75485cf6b9f33b1feccbdd552fe7c68378fb40 | 36,193 |
def sigmoid(x):
"""
Sigmoid activation function
@param x: input array
@return:
out: output array
"""
return 1 / (1 + np.exp(-x)) | a1b23861287b50c7f78537e026b2da9bd2b664bd | 36,194 |
def _flagword(obj):
"""Internal use only"""
#create the flags word for an objective
_f = B_DISC #everything's discoverable
if obj.neg:
_f |= B_NEG
if obj.synch:
_f |= B_SYNCH
if obj.dry:
_f |= B_DRY
return _f | eb34deececffdbd8a69c600d437d4cd0965b49dd | 36,195 |
def create_model(conditioning_dim, flags):
"""Define model, and initialize classifiers."""
model = VAEBasic(flags.height, flags.width, latent_dim=flags.latent_dim, conditioning_dim=conditioning_dim,
start_filters=flags.start_filters, name='VAE')
return model | 8903edadfafbb60183d740855fb6f1733ff4d0f3 | 36,196 |
def palette(name):
"""Palette decorator"""
def wrapper(cls):
palette_providers[name] = cls
return cls
return wrapper | 27e8dffd4eb9eeb7df2e31812f5c2f6e64c98ea0 | 36,197 |
import copy
def remove_one_path(path_graph, dest=None, preserv_cov=False):
"""
Removes one path from the path set in a way that
the vertices covered by the set of paths does not
decrease.
"""
complete = False
ix = 0
if dest is None:
dest = max(list(path_graph.keys())) + 1
w... | e8bf21429937e36190c530a1fd69a2a83007ca9b | 36,198 |
import types
import inspect
def spec_builtin(obj):
""" Describe a builtin function """
# Built-in functions cannot be inspected by
# inspect.getargspec. We have to try and parse
# the __doc__ attribute of the function.
docstr = obj.__doc__
args = ''
if not docstr:
if isinstance(obj,types.... | b892bc8268e7394e87de63e00154698084fba302 | 36,199 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.