content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import json
def deliver_hybrid():
"""
Endpoint for submissions intended for dap and legacy systems. POST request requires the submission JSON to be
uploaded as "submission", the zipped transformed artifact as "transformed", and the filename passed in the
query parameters.
"""
logger.info('Proc... | 87bb05f376c1791668bd5e160cc5940377363f64 | 3,644,125 |
def midi_to_chroma(pitch):
"""Given a midi pitch (e.g. 60 == C), returns its corresponding
chroma class value. A == 0, A# == 1, ..., G# == 11 """
return ((pitch % 12) + 3) % 12 | 25ef72f78269c3f494ca7431f1291891ddea594a | 3,644,127 |
import re
def _snippet_items(snippet):
"""Return all markdown items in the snippet text.
For this we expect it the snippet to contain *nothing* but a markdown list.
We do not support "indented" list style, only one item per linebreak.
Raises SyntaxError if snippet not in proper format (e.g. contains... | bdeb5b5c5e97ef3a8082b7131d46990de02a59af | 3,644,128 |
def get_collection(*args, **kwargs):
""" Returns event collection schema
:param event_collection: string, the event collection from which schema is to be returned,
if left blank will return schema for all collections
"""
_initialize_client_from_environment()
return _client.get_collection(*args,... | 95698a5c750b2d40caad0f0ddfe9e17a8354be03 | 3,644,129 |
def get_tf_generator(data_source: extr.PymiaDatasource):
"""Returns a generator that wraps :class:`.PymiaDatasource` for the tensorflow data handling.
The returned generator can be used with `tf.data.Dataset.from_generator
<https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator>`_ in or... | 2b786b111c2e2b17c3ee2887f93aff02de63f369 | 3,644,130 |
def is_mechanical_ventilation_heat_recovery_active(bpr, tsd, t):
"""
Control of activity of heat exchanger of mechanical ventilation system
Author: Gabriel Happle
Date: APR 2017
:param bpr: Building Properties
:type bpr: BuildingPropertiesRow
:param tsd: Time series data of buildin... | 626e24da9f0676be27e15a4422676034a94e1702 | 3,644,131 |
import aiohttp
async def fetch_user(user_id):
"""
Asynchronous function which performs an API call to retrieve a user from their ID
"""
session = aiohttp.ClientSession()
res = await session.get(url=str(f'{MAIN_URL}/api/user/{user_id}'),
headers=headers)
await sessio... | 725c4f7f89efc242948799c48541a25a2bd17d8c | 3,644,132 |
from typing import List
import requests
from bs4 import BeautifulSoup
def category(category: str) -> List[str]:
"""Get list of emojis in the given category"""
emoji_url = f"https://emojipedia.org/{category}"
page = requests.get(emoji_url)
soup = BeautifulSoup(page.content, 'lxml')
symbols: List... | 61eaff867e9d9c75582f31435a6c22f3b92fd85a | 3,644,133 |
from typing import Optional
def calc_cumulative_bin_metrics(
labels: np.ndarray,
probability_predictions: np.ndarray,
number_bins: int = 10,
decimal_points: Optional[int] = 4) -> pd.DataFrame:
"""Calculates performance metrics for cumulative bins of the predictions.
Args:
labels: An array of ... | c3574c8e74d5c6fd649ea4258b9a8518811210f6 | 3,644,134 |
def rootbeta_cdf(x, alpha, beta_, a, b, bounds=(), root=2.):
"""
Calculates the cumulative density function of the log-beta distribution, i.e.::
F(z; a, b) = I_z(a, b)
where ``z=(ln(x)-ln(a))/(ln(b)-ln(a))`` and ``I_z(a, b)`` is the regularized incomplete beta function.
Parameters
-------... | e0b951c177f288bc89536494485904e1839af7de | 3,644,135 |
def get_scores(treatment, outcome, prediction, p, scoring_range=(0,1), plot_type='all'):
"""Calculate AUC scoring metrics.
Parameters
----------
treatment : array-like
outcome : array-like
prediction : array-like
p : array-like
Treatment policy (probability of treatment for each row... | c59cc98e08cfff6b01eff5c3ff4f74973ababf34 | 3,644,136 |
def get_arima_nemo_pipeline():
""" Function return complex pipeline with the following structure
arima \
linear
nemo |
"""
node_arima = PrimaryNode('arima')
node_nemo = PrimaryNode('exog_ts')
node_final = SecondaryNode('linear', nodes_from=[node_arima, node_nemo])
... | 1ae171d29624ecc615f213f343c4a88c733d3554 | 3,644,137 |
from typing import Counter
import math
def conditional_entropy(x,
y,
nan_strategy=REPLACE,
nan_replace_value=DEFAULT_REPLACE_VALUE):
"""
Calculates the conditional entropy of x given y: S(x|y)
Wikipedia: https://en.wikipedia.org/wiki/... | c0a9c943efdd4da1ad2f248ef7eaa2e4b1b7be06 | 3,644,138 |
def peaks_in_time(dat, troughs=False):
"""Find indices of peaks or troughs in data.
Parameters
----------
dat : ndarray (dtype='float')
vector with the data
troughs : bool
if True, will return indices of troughs instead of peaks
Returns
-------
nadarray of i... | acafee26ac6bc236aa68f48fbea5953020faa471 | 3,644,139 |
def read_submod_def(line):
"""Attempt to read SUBMODULE definition line"""
submod_match = SUBMOD_REGEX.match(line)
if submod_match is None:
return None
else:
parent_name = None
name = None
trailing_line = line[submod_match.end(0):].split('!')[0]
trailing_line = tr... | 27ed8d88fdb8fd112b072f50dba00bad783eb9f3 | 3,644,140 |
def predict(model, images, labels=None):
"""Predict.
Parameters
----------
model : tf.keras.Model
Model used to predict labels.
images : List(np.ndarray)
Images to classify.
labels : List(str)
Labels to return.
"""
if type(images) == list:
i... | a6c2261e7fea262fb1372f870ba3096a9faf2a68 | 3,644,141 |
import codecs
import re
def process_span_file(doc, filename):
"""Reads event annotation from filename, and add to doc
:type filename: str
:type doc: nlplingo.text.text_theory.Document
<Event type="CloseAccount">
CloseAccount 0 230
anchor 181 187
CloseAccount/Source 165 170
CloseAccou... | e2ae8f32947a6c99dfba69b0da06adcfffa3fc3c | 3,644,142 |
from typing import Tuple
def mask_frame_around_position(
frame: np.ndarray,
position: Tuple[float, float],
radius: float = 5,
) -> np.ndarray:
"""
Create a circular mask with the given ``radius`` at the given
position and set the frame outside this mask to zero. This is
sometimes required ... | cf616a0193cf9150821ed00c8e20c61a88b64d9e | 3,644,143 |
import numpy as np
def apogeeid_digit(arr):
"""
NAME:
apogeeid_digit
PURPOSE:
Extract digits from apogeeid because its too painful to deal with APOGEE ID in h5py
INPUT:
arr (ndarray): apogee_id
OUTPUT:
apogee_id with digits only (ndarray)
HISTORY:
2017-O... | 48e21ab69c9f733dbf7b612994bfed35b8980424 | 3,644,144 |
def transform_user_weekly_artist_chart(chart):
"""Converts lastfm api weekly artist chart data into neo4j friendly
weekly artist chart data
Args:
chart (dict): lastfm api weekly artist chart
Returns:
list - neo4j friendly artist data
"""
chart = chart['weeklyartistchart']
a... | 1034211f6c21774044d767aeb7861b6aa80b4023 | 3,644,145 |
def plotter(fdict):
""" Go """
pgconn = get_dbconn('isuag')
ctx = get_autoplot_context(fdict, get_description())
threshold = 50
threshold_c = temperature(threshold, 'F').value('C')
hours1 = ctx['hours1']
hours2 = ctx['hours2']
station = ctx['station']
oldstation = XREF[station]
... | f8a412065700ab111f5bf846938721aa397803b3 | 3,644,146 |
def config_namespace(config_file=None, auto_find=False,
verify=True, **cfg_options):
"""
Return configuration options as a Namespace.
.. code:: python
reusables.config_namespace(os.path.join("test", "data",
"test_config.ini"))
... | c3293fa36e32d2ebea610a88a6e29ba47906ab7b | 3,644,147 |
import pandas
import numpy
import tqdm
import torch
def extract_peaks(peaks, sequences, signals, controls=None, chroms=None,
in_window=2114, out_window=1000, max_jitter=128, min_counts=None,
max_counts=None, verbose=False):
"""Extract sequences and signals at coordinates from a peak file.
This function will tak... | f3a3696f2e31b7b91384df50dd0374c2e4e46443 | 3,644,148 |
def map_feature(value, f_type):
""" Builds the Tensorflow feature for the given feature information """
if f_type == np.dtype('object'):
return bytes_feature(value)
elif f_type == np.dtype('int'):
return int64_feature(value)
elif f_type == np.dtype('float'):
return float64_featur... | 26416b27737542c8ac6100168775f47b271206a3 | 3,644,150 |
def is_text_area(input):
"""
Template tag to check if input is file
:param input: Input field
:return: True if is file, False if not
"""
return input.field.widget.__class__.__name__ == "Textarea" | 4657a93809e123aaa27ee0a202b33e0383ac23cc | 3,644,151 |
def print_album_list(album_list):
"""Print album list and return the album name choice.
If return is all then all photos on page will be download."""
for i in range(len(album_list)):
print("{}. {} ({} photo(s))".format(
i + 1, album_list[i]['name'], album_list[i]['count']))
choice... | 2a3c4fde9fc56da179ea43c88f966735fc5c7beb | 3,644,152 |
import struct
def read_bool(data):
"""
Read 1 byte of data as `bool` type.
Parameters
----------
data : io.BufferedReader
File open to read in binary mode
Returns
-------
bool
True or False
"""
s_type = "=%s" % get_type("bool")
return struct.unpack(s_type,... | 9302a3f4831143c44b0a67cfe0f146463e8ba27e | 3,644,156 |
def sectorize(position):
""" Returns a tuple representing the sector for the given `position`.
Parameters
----------
position : tuple of len 3
Returns
-------
sector : tuple of len 3
"""
x, y, z = normalize(position)
x, y, z = x // GameSettings.SECTOR_SIZE, y // GameSettings.S... | 689fc3ee350e5493d037df290c5df05d50621b7e | 3,644,157 |
import random
def add_random_phase_shift(hkl, phases, fshifts=None):
"""
Introduce a random phase shift, at most one unit cell length along each axis.
Parameters
----------
hkl : numpy.ndarray, shape (n_refls, 3)
Miller indices
phases : numpy.ndarray, shape (n_refls,)
phas... | 7739d99b58bec80283a5e49fc2e6eaa6161286ae | 3,644,158 |
import itertools
import re
def parse_cluster_file(filename):
"""
Parse the output of the CD-HIT clustering and return a dictionnary of clusters.
In order to parse the list of cluster and sequences, we have to parse the CD-HIT
output file. Following solution is adapted from a small wrapper script
... | d50eaeb926be3a7b8d1139c82142e4a1b595c1a0 | 3,644,162 |
def app(par=None):
"""
Return the Miniweb object instance.
:param par: Dictionary with configuration parameters. (optional parameter)
:return: Miniweb object instance.
"""
return Miniweb.get_instance(par) | 3d2b0d1a9fd87e9e5c26ea9a141e40fbe342b764 | 3,644,163 |
def openTopics():
"""Opens topics file
:return: list of topics
"""
topicsFile = 'topics'
with open(topicsFile) as f:
topics = f.read().split()
return topics | e6d43ff6717122532a71355b71134d6f78f9db85 | 3,644,164 |
from django.forms.boundfield import BoundField
from django.utils.inspect import func_supports_parameter, func_accepts_kwargs
def fix_behaviour_widget_render_forced_renderer(utils):
"""
Restore the behaviour where the "renderer" parameter of Widget.render() may not be supported by subclasses.
"""
orig... | 7d55ecc18fae91af221b806448fa30203fdd9cd4 | 3,644,165 |
from typing import List
def split_blocks(blocks:List[Block], ncells_per_block:int,direction:Direction=None):
"""Split blocks is used to divide an array of blocks based on number of cells per block. This code maintains the greatest common denominator of the parent block. Number of cells per block is simply an esti... | e7ebf6189b3f140b006d74846c4979058023784a | 3,644,166 |
def get_entry_details(db_path, entry_id):
"""Get all information about an entry in database.
Args:
db_path: path to database file
entry_id: string
Return:
out: dictionary
"""
s = connect_database(db_path)
# find entry
try:
sim = s.query(Main).filter(Main.... | 7a4023fa32a0e41cf3440bcd8fd2140ce88b8c33 | 3,644,167 |
import bisect
def pose_interp(poses, timestamps_in, timestamps_out, r_interp='slerp'):
"""
:param poses: N x 7, (t,q)
:param timestamps: (N,)
:param t: (K,)
:return: (K,)
"""
# assert t_interp in ['linear', 'spline']
assert r_interp in ['slerp', 'squad']
assert len(pos... | cc8e49b6bab918c6887e37973d09469fcddc298d | 3,644,168 |
from datetime import datetime
def checklist_saved_action(report_id):
"""
View saved report
"""
report = Report.query.filter_by(id=report_id).first()
return render_template(
'checklist_saved.html',
uid=str(report.id),
save_date=datetime.now(),
report=report,
... | 302bc174ffe0ed7d3180b2a59c5212b3a38e7eaf | 3,644,169 |
def trilinear_memory_efficient(a, b, d, use_activation=False):
"""W1a + W2b + aW3b."""
n = tf.shape(a)[0]
len_a = tf.shape(a)[1]
len_b = tf.shape(b)[1]
w1 = tf.get_variable('w1', shape=[d, 1], dtype=tf.float32)
w2 = tf.get_variable('w2', shape=[d, 1], dtype=tf.float32)
w3 = tf.get_variable('w3', shape=[... | d6ed8cc216019987674b86ef36377a6af45a6702 | 3,644,170 |
def private_questions_get_unique_code(assignment_id: str):
"""
Get all questions for the given assignment.
:param assignment_id:
:return:
"""
# Try to find assignment
assignment: Assignment = Assignment.query.filter(
Assignment.id == assignment_id
).first()
# Verify that t... | 1c94404168ac659e9ee3c45b3ecf7c2c398d1cca | 3,644,171 |
def make_ngram(tokenised_corpus, n_gram=2, threshold=10):
"""Extract bigrams from tokenised corpus
Args:
tokenised_corpus (list): List of tokenised corpus
n_gram (int): maximum length of n-grams. Defaults to 2 (bigrams)
threshold (int): min number of n-gram occurrences before inclusion
... | 8897456e9da4cd3c0f1c3f055b43e7d27c7261d8 | 3,644,172 |
def bw_estimate(samples):
"""Computes Abraham's bandwidth heuristic."""
sigma = np.std(samples)
cand = ((4 * sigma**5.0) / (3.0 * len(samples)))**(1.0 / 5.0)
if cand < 1e-7:
return 1.0
return cand | 44629f9e774d07f7c55a5a77dcb7b06ae38a964b | 3,644,173 |
def process_coins():
"""calculate the amount of money paid based on the coins entered"""
number_of_quarters = int(input("How many quarters? "))
number_of_dimes = int(input("How many dimes? "))
number_of_nickels = int(input("How many nickels? "))
number_of_pennies = int(input("How many pennies? "))
... | 6a26ad161720554079a76f6bdadbbf9555d6b82d | 3,644,174 |
def getLastSegyTraceHeader(SH,THN='cdp',data='none', bheadSize = 3600, endian='>'): # added by A Squelch
"""
getLastSegyTraceHeader(SH,TraceHeaderName)
"""
bps=getBytePerSample(SH)
if (data=='none'):
data = open(SH["filename"]).read()
#... | 19de6339bcc3ec63b0e33007f51fa50ddb619449 | 3,644,175 |
def get_data_url(data_type):
"""Gets the latest url from the kff's github data repo for the given data type
data_type: string value representing which url to get from the github api; must be either 'pct_total' or 'pct_share'
"""
data_types_to_strings = {
'pct_total': 'Percent of Total Populatio... | f92520243ee7f952ff69c7c62c315225982a24fe | 3,644,176 |
def kl(p, q):
"""Kullback-Leibler divergence D(P || Q) for discrete distributions
Parameters
----------
p, q : array-like, dtype=float, shape=n
Discrete probability distributions.
"""
p = np.asarray(p, dtype=np.float)
q = np.asarray(q, dtype=np.float)
return np.sum(np.where(p != 0, ... | 06b6283ea83a729f9c374dabbe1c1a94a8ed8480 | 3,644,177 |
import torch
def get_loaders(opt):
""" Make dataloaders for train and validation sets
"""
# train loader
opt.mean = get_mean(opt.norm_value, dataset=opt.mean_dataset)
# opt.std = get_std()
if opt.no_mean_norm and not opt.std_norm:
norm_method = transforms.Normalize([0, 0, 0], [1, 1, 1])
elif not opt.std_norm... | d7a166a477c535a60846e05598dd19bbe84062be | 3,644,178 |
def trapezoidal(f, a, b, n):
"""Trapezoidal integration via iteration."""
h = (b-a)/float(n)
I = f(a) + f(b)
for k in xrange(1, n, 1):
x = a + k*h
I += 2*f(x)
I *= h/2
return I | f2887a3b0d1732f322dca52d0d869c1063e08c22 | 3,644,179 |
def writetree(tree, sent, key, fmt, comment=None, morphology=None,
sentid=False):
"""Convert a tree to a string representation in the given treebank format.
:param tree: should have indices as terminals
:param sent: contains the words corresponding to the indices in ``tree``
:param key: an identifier for this tr... | cf8181596a4882ae18a8adcd0411e1c4e2ee8a33 | 3,644,180 |
import struct
def xor_string(hash1, hash2, hash_size):
"""Encrypt/Decrypt function used for password encryption in
authentication, using a simple XOR.
Args:
hash1 (str): The first hash.
hash2 (str): The second hash.
Returns:
str: A string with the xor applied.
"""
xor... | 4efc263a0ff9fb05b0ee7cb7b7b3fdd4c8c0c2ec | 3,644,181 |
def create_secret_key(string):
"""
:param string: A string that will be returned as a md5 hash/hexdigest.
:return: the hexdigest (hash) of the string.
"""
h = md5()
h.update(string.encode('utf-8'))
return h.hexdigest() | eb31e149684074b18fdbc1989ecfc14f21756dea | 3,644,182 |
import base64
def decode_password(base64_string: str) -> str:
"""
Decode a base64 encoded string.
Args:
base64_string: str
The base64 encoded string.
Returns:
str
The decoded string.
"""
base64_bytes = base64_string.encode("ascii")
sample_st... | 0f04617c239fbc740a9b4c9c2d1ae867a52e0c74 | 3,644,183 |
def _generate_overpass_api(endpoint=None):
""" Create and initialise the Overpass API object.
Passing the endpoint argument will override the default
endpoint URL.
"""
# Create API object with default settings
api = overpass.API()
# Change endpoint if desired
if endpoint is not None:
... | 9b8016035e87428286f68622e9a6129bcf818c4a | 3,644,184 |
def to_pascal_case(value):
"""
Converts the value string to PascalCase.
:param value: The value that needs to be converted.
:type value: str
:return: The value in PascalCase.
:rtype: str
"""
return "".join(character for character in value.title() if not character.isspace()) | 138ab9ddf7ca814b50bf8ff0618de03b236535c7 | 3,644,185 |
from typing import Iterable
from typing import Any
from typing import List
def drop(n: int, it: Iterable[Any]) -> List[Any]:
"""
Return a list of N elements drop from the iterable object
Args:
n: Number to drop from the top
it: Iterable object
Examples:
>>> fpsm.drop(3, [1, 2... | 0732bd560f0da0a43f65ee3b5ed46fd3a05e26f5 | 3,644,186 |
def generate_classification_style_dataset(classification='multiclass'):
"""
Dummy data to test models
"""
x_data = np.array([
[1,1,1,0,0,0],
[1,0,1,0,0,0],
[1,1,1,0,0,0],
[0,0,1,1,1,0],
[0,0,1,1,0,0],
[0,0,1,1,1,0]])
if classification=='multiclass':
y_data = np.array([
[1, 0, 0],
[1, 0, 0],
... | 77a65bb3445216a9a21aa30a7c7201983328efce | 3,644,187 |
def getSupportedDatatypes():
"""
Gets the datatypes that are supported by the framework
Returns:
a list of strings of supported datatypes
"""
return router.getSupportedDatatypes() | 635612975c271bdbe22b622787a2d7f823277baa | 3,644,189 |
def run_stacking(named_data, subjects_data, cv=10, alphas=None,
train_sizes=None, n_jobs=None):
"""Run stacking.
Parameters
----------
named_data : list(tuple(str, pandas.DataFrame))
List of tuples (name, data) with name and corresponding features
to be used for predict... | 75b97509097652fdccc444cfd3731ce68b49e992 | 3,644,190 |
def add_random_shadow(img, w_low=0.6, w_high=0.85):
"""
Overlays supplied image with a random shadow poligon
The weight range (i.e. darkness) of the shadow can be configured via the interval [w_low, w_high)
"""
cols, rows = (img.shape[0], img.shape[1])
top_y = np.random.random_sample() * rows
... | 3b520312941ffc4b125ce0a777aeb76fecd6b263 | 3,644,191 |
def csv_args(value):
"""Parse a CSV string into a Python list of strings.
Used in command line parsing."""
return map(str, value.split(",")) | b2596180054f835bfe70e3f900caa5b56a7856a6 | 3,644,192 |
def get_tokens():
"""
Returns a tuple of tokens in the format {{site/property}} that will be used to build the dictionary passed into execute
"""
return (HAWQMASTER_PORT, HAWQSTANDBY_ADDRESS) | 4664feb568a3a5599b9da64594d09a034e9aaebb | 3,644,193 |
def projl1_epigraph(center):
"""
Project center=proxq.true_center onto the l1 epigraph. The bound term is
center[0], the coef term is center[1:]
The l1 epigraph is the collection of points $(u,v): \|v\|_1 \leq u$
np.fabs(coef).sum() <= bound.
"""
norm = center[0]
coef = center[1:]
... | d7b8c70f45853eef61322fdb9583c8279780982f | 3,644,194 |
import requests
from datetime import datetime
def crypto_command(text):
""" <ticker> -- Returns current value of a cryptocurrency """
try:
encoded = quote_plus(text)
request = requests.get(API_URL.format(encoded))
request.raise_for_status()
except (requests.exceptions.HTTPError, re... | 0b0757a8b657791204d74b8536be3b6cb5af2ff5 | 3,644,195 |
import torch
def byol_loss_multi_views_func(p: torch.Tensor, z: torch.Tensor,p1: torch.Tensor, z1: torch.Tensor, simplified: bool = True) -> torch.Tensor:
"""Computes BYOL's loss given batch of predicted features p and projected momentum features z.
Args:
p, p1 (torch.Tensor): NxD Tensor containing p... | 705cbe9e62fa1e58da0a1f4087e6090d7b8002b8 | 3,644,196 |
def a_test_model(n_classes=2):
"""
recover model and test data from disk, and test the model
"""
images_test, labels_test, data_num_test = load_test_data_full()
model = load_model(BASE_PATH + 'models/Inception_hemorrhage_model.hdf5')
adam_optimizer = keras.optimizers.Adam(
lr=0.0001,
... | d060f79a149d7659d74ffac316f71d7ef7b63368 | 3,644,197 |
def generate_synchronous_trajectory(initial_state):
"""
Simulate the network starting from a given initial state in the synchronous strategy
:param initial_state: initial state of the network
:return: a trajectory in matrix from, where each row denotes a state
"""
trajectory = [initial_state]
... | 85f452f7665028e29085296820f67cf2e5cdb8bf | 3,644,198 |
import inspect
from textwrap import dedent
import ast
def arg_names(level=2):
"""Try to determine names of the variables given as arguments to the caller
of the caller. This works only for trivial function invocations. Otherwise
either results may be corrupted or exception will be raised.
level: 0 is... | ce5b26747404442bfd017827435e9515c60aace0 | 3,644,199 |
import jinja2
def render_template(path, ctx):
"""Render a Jinja2 template"""
with path.open() as f:
content = f.read()
tmpl = jinja2.Template(content)
return html_minify(tmpl.render(**ctx)) | 0eb4b2a73a645283998260cdadbab37da32d6784 | 3,644,200 |
def reverse( sequence ):
"""Return the reverse of any sequence
"""
return sequence[::-1] | f08ae428844347e52d8dbf1cd8ad07cfbf4ef597 | 3,644,202 |
def createOutputBuffer(file, encoding):
"""Create a libxml2 output buffer from a Python file """
ret = libxml2mod.xmlCreateOutputBuffer(file, encoding)
if ret is None:raise treeError('xmlCreateOutputBuffer() failed')
return outputBuffer(_obj=ret) | 28ece9b710362d710ff6df25f426d91a0b318ebf | 3,644,203 |
def wait_for_proof(node, proofid_hex, timeout=60, expect_orphan=None):
"""
Wait for the proof to be known by the node. If expect_orphan is set, the
proof should match the orphan state, otherwise it's a don't care parameter.
"""
def proof_found():
try:
wait_for_proof.is_orphan = n... | f8f390424fe084bf8bf62bf1d16ac780d5c5df69 | 3,644,205 |
def check(verbose=1):
"""
Runs a couple of functions to check the module is working.
:param verbose: 0 to hide the standout output
:return: list of dictionaries, result of each test
"""
return [] | 4ecf144fc64a165b5b0f9766b76eb6b703eba130 | 3,644,206 |
def cylinder_sideways():
"""
sideways cylinder for poster
"""
call_separator('cylinder sidweays')
T1 = .1
#gs = gridspec.GridSpec(nrows=2,ncols=3,wspace=-.1,hspace=.5)
fig = plt.figure(figsize=(5,4))
ax11 = fig.add_subplot(111,projection='3d')
#ax12 = fig.add_subplot(... | 98c0ed70c11ffe619d28623a5c5f4c4e2be40889 | 3,644,207 |
def get_generic_or_msg(intent, result):
""" The master method. This method takes in the
intent and the result dict structure
and calls the proper interface method. """
return Msg_Fn_Dict[intent](result) | 00853e2e74892a6d01ba1c6986e72f6436c88a92 | 3,644,208 |
def s3_example_tile(gtiff_s3):
"""Example tile for fixture."""
return (5, 15, 32) | a4b7e35fc6f7bf51a551ac8cb18003c23ff35a01 | 3,644,209 |
def execute_list_of_commands(command_list):
"""
INPUT:
- ``command_list`` -- a list of strings or pairs
OUTPUT:
For each entry in command_list, we attempt to run the command.
If it is a string, we call ``os.system()``. If it is a pair [f, v],
we call f(v).
If the environment variable... | 79247f8dc15cc790b6f1811e3cb79de47c514bc4 | 3,644,210 |
import requests
def get_transceiver_diagnostics(baseurl, cookie_header, transceiver):
"""
Get the diagnostics of a given transceivers in the switch
:param baseurl: imported baseurl variable
:param cookie_header: Parse cookie resulting from successful loginOS.login_os(baseurl)
:param transceiver: d... | c2863b54b03ae3bdcf779fbd18a50e2bcdb2edd7 | 3,644,211 |
def mask_valid_boxes(boxes, return_mask=False):
"""
:param boxes: (cx, cy, w, h,*_)
:return: mask
"""
w = boxes[:,2]
h = boxes[:,3]
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
mask = (w > 2) & (h > 2) & (ar < 30)
if return_mask:
return mask
else:
return... | 3a3c00f934dabce78ee8a28f0ece2105d79f9f3f | 3,644,213 |
import tokenize
def import_buffer_to_hst(buf):
"""Import content from buf and return an Hy AST."""
return tokenize(buf + "\n") | 4571bac8987911bf9b9a277590be6204be6120ab | 3,644,214 |
def preprocess_input(x, **kwargs):
"""Preprocesses a numpy array encoding a batch of images.
# Arguments
x: a 4D numpy array consists of RGB values within [0, 255].
# Returns
Preprocessed array.
"""
return imagenet_utils.preprocess_input(x, mode='tf', **kwargs) | ca81dff57f51184042899849dff6623d32e475c0 | 3,644,217 |
def build_gauss_kernel(sigma_x, sigma_y, angle):
"""
Build the rotated anisotropic gaussian filter kernel
Parameters
----------
sigma_x : numpy.float64
sigma in x-direction
sigma_y: numpy.float64
sigma in y-direction
angle: int
angle in degrees of the needle holder ... | 14dd4143ad94bcdfa3298b4acf9b2d4c2bd0b7e6 | 3,644,218 |
def kwargs_to_flags(**kwargs):
"""Convert `kwargs` to flags to pass on to CLI."""
flag_strings = []
for (key, val) in kwargs.items():
if isinstance(val, bool):
if val:
flag_strings.append(f"--{key}")
else:
flag_strings.append(f"--{key}={val}")
retu... | aa672fe26c81e7aaf8a6e7c38354d1649495b8df | 3,644,219 |
def extractBananas(item):
"""
Parser for 'Bananas'
"""
badwords = [
'iya na kao manga chapters',
]
if any([bad in item['tags'] for bad in badwords]):
return None
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or 'preview' in item['title'].lower():
r... | f06167a0d379ec3b1921bb7ad8146b0bca9fd8aa | 3,644,220 |
def get_template_parameters_s3(template_key, s3_resource):
"""
Checks for existance of parameters object in S3 against supported suffixes and returns parameters file key if found
Args:
template_key: S3 key for template file. omit bucket.
s3_resource: a boto3 s3 resource
Returns:
filename of paramete... | 3b68dc9c1fa8636bd0d066780aab43a6e55ecf2f | 3,644,222 |
def cell_from_system(sdict):
"""
Function to obtain cell from namelist SYSTEM read from PW input.
Args:
sdict (dict): Dictinary generated from namelist SYSTEM of PW input.
Returns:
ndarray with shape (3,3):
Cell is 3x3 matrix with entries::
[[a_x b_x c_x]
... | fbd6e034f738f42be45d7e5304892a9e69a8493b | 3,644,223 |
def A12_6_3_2(FAxial, eta, Pp, Pu, Muey , Muez, Muay, Muaz,
Ppls, Mby, Mbz, GammaRPa, GammaRPb):
"""
A.12.6.3.2 Interaction equation approach
where :
Pu is the applied axial force in a member due to factored actions,
determined in an analysis that includes Pu effects (see A.12.4);
... | 7a36ec489681100f99563f9c336df1306363851d | 3,644,224 |
def gain_deploy_data():
"""
@api {get} /v1/deploy/new_data 获取当前deploy_id 的信息
@apiName deployNew_data
@apiGroup Deploy
@apiDescription 获取当前deploy_id 的信息
@apiParam {int} project_id 项目id
@apiParam {int} flow_id 流程id
@apiParam {int} deploy_id 部署id
@apiParamExample {json} Request-Example:... | 9dc5e5faa53235ac6c5d8f0d37a2989b15ead477 | 3,644,225 |
def topk_mask(score, k):
"""Efficient implementation of topk_mask for TPUs.
This is a more efficient implementation of the following snippet with support
for higher rank tensors. It has the limitation that it only supports float32
as element type. The mask only contains k elements even if other elements
have... | 0a33dc6d5b9c621ab3fbd86c54c9ec90ac00f21f | 3,644,226 |
import calendar
def valueSearch(stat_type,op,value,**kwargs):
"""Quick function to designate a value, and the days or months where the
attribute of interest exceeded, equalled, or was less than the passed
value
valueSearch("attribute","operator",value,**{sortmonth=False})
* "attribute" must ... | 94b55a362d179f6acce705b002eb99f330a5427b | 3,644,228 |
import requests
def get_gnid(rec):
"""
Use geonames API (slow and quota limit for free accounts)
"""
if not any("http://www.geonames.org" in s for s in rec.get("sameAs")) and rec["geo"].get("latitude") and rec["geo"].get("longitude"):
changed = False
r = requests.get("http://api.geonam... | ab9d5e50e45217e3742f1d1ca7f58326ed3bf6f6 | 3,644,229 |
def allowed_file(filename):
""" Is file extension allowed for upload"""
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS | 3d0a3a15eecf8f6b0d76b52935a14628f1655328 | 3,644,231 |
import re
def parse_tsv(filename, name_dict):
"""
"""
output_matrix = []
with open(filename, 'rU') as handle:
curr_protein = []
for line in handle:
if line[0] == "#" or line[0] == "-" or len(line.strip('\n')) < 1:
continue
if re.match("Protein",... | 12aa31ab3ff033ecc514518700c22ea467f01ef6 | 3,644,232 |
def get_orlist(site=DEFAULT_SITE, namespace="0|6|10|14|100|828", redirects="nonredirects"):
"""Get list of oldreviewed pages."""
request = Request(site=site,
action="query",
list="oldreviewedpages",
ornamespace=namespace,
or... | 8253b2ac8ea72690086fa7864e5ca4ffcc33de50 | 3,644,233 |
def meshVolume(verts, norm, tri):
"""Compute the Volume of a mesh specified by vertices, their normals, and
indices of triangular faces
"""
# TEST
zeronorms = []
for i, n in enumerate(norm):
#if n == [0., 0., 0.] or n == (0., 0., 0.):
if n[0] == 0 and n[1] == 0 and n[2] == 0:
#print "norma... | 018818ab558b64b9699250bf6f45f0a1c47f92c8 | 3,644,234 |
def _groupby_clause(uuid=None, owner=None, human_name=None, processing_name=None):
"""
Build the groupby clause. Simply detect which fields are set, and group by those.
Args:
uuid:
owner:
human_name:
processing_name:
Returns:
(str): "field, ..., field"
"""... | 21546efa19e841661ed3a7ad8a84cf9a9a76d416 | 3,644,235 |
def _coeff_mod_wfe_drift(self, wfe_drift, key='wfe_drift'):
"""
Modify PSF polynomial coefficients as a function of WFE drift.
"""
# Modify PSF coefficients based on WFE drift
if wfe_drift==0:
cf_mod = 0 # Don't modify coefficients
elif (self._psf_coeff_mod[key] is None):
_log.w... | 345d07a8850ec702d42f5c527fae0311f50a69b1 | 3,644,236 |
def get_transformed_webhook_payload(gh_payload, default_branch=None, lookup_user=None):
""" Returns the GitHub webhook JSON payload transformed into our own payload
format. If the gh_payload is not valid, returns None.
"""
try:
validate(gh_payload, GITHUB_WEBHOOK_PAYLOAD_SCHEMA)
except Exception as ex... | 26e645219b816405521ddb6033a0a44c2ab7bba5 | 3,644,237 |
def get_retweeted_tweet(tweet):
"""
Get the retweeted Tweet and return it as a dictionary
If the Tweet is not a Retweet, return None
Args:
tweet (Tweet or dict): A Tweet object or a dictionary
Returns:
dict: A dictionary representing the retweeted status
or None if there is... | f852d45deadb1622687d097f2c724bdaef72ccc9 | 3,644,238 |
def listminus(c1, c2):
"""Return a list of all elements of C1 that are not in C2."""
s2 = {}
for delta in c2:
s2[delta] = 1
c = []
for delta in c1:
if not s2.has_key(delta):
c.append(delta)
return c | 829c347343d6a305fef2ad2f71539d7267b5a973 | 3,644,239 |
import random
import torch
def distribute_quantity_skew(batch_size, grouped_data, distributed_dataset, groupings, p=0.5, scalar=1.5):
"""
Adds quantity skew to the data distribution. If p=0. or scalar=1., no skew is applied and the data are divided
evenly among the workers in each label group.
:pa... | b4ebd1d6058550d2e32cedd62a56b50441d93b4c | 3,644,240 |
def get_dtype(names, array_dtype=DEFAULT_FLOAT_DTYPE):
"""
Get a list of tuples containing the dtypes for the structured array
Parameters
----------
names : list of str
Names of parameters
array_dtype : optional
dtype to use
Returns
-------
list of tuple
Dty... | 9f29dae78b3839429f13b8513293e9ce4c240e2f | 3,644,243 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.