content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def conv_variance_scaling_initializer(in_channel, out_channel, kernel_size):
"""conv init"""
fan_in = in_channel * kernel_size * kernel_size
scale = 1.0
scale /= max(1., fan_in)
stddev = (scale ** 0.5) / .87962566103423978
mu, sigma = 0, stddev
weight = truncnorm(-2, 2, loc=mu, scale=sigma).... | 925339a12e4f2e04c403ad8148145df0497da0da | 449 |
def vgg8(**kwargs):
"""VGG 8-layer model (configuration "S")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = VGG(cfg['S'], **kwargs)
return model | 61d9f2e98c68691c1ed26631220f447eef28ba11 | 450 |
def get_char_from_ascii(key_num):
"""Function that converts a character to an ascii code
Parameters
----------
ascii_code : int
Ascii code of character
Returns
-------
char : character
character converted from ascii
"""
return chr(key_num) | 79f6a5627805909a005d5921f4e9fe738fb09936 | 451 |
def start():
"""
view for data entry for optimisation
"""
form = LocationForm()
if form.validate_on_submit():
return optimise(form.data)
return flask.render_template("start.html",
title="Start", form=form) | caba5a4d20d544ed480bda4d4e4d4377880bbd40 | 453 |
def c_flag(opt, test_not=False):
""" convert a test parameter into t if true for the Fortran build system """
if test_not:
if opt: return "FALSE"
else: return "TRUE"
else:
if opt: return "TRUE"
else: return "FALSE" | cf78668ae19287822fba9946fa472187848e0084 | 455 |
def false_function():
"""Sample function to test unit testing."""
return False | 7823ac0f533c97544a8f73f73715bebb8e5b45cc | 456 |
def broker_task_send(task_uuid, request, broker_point, reply_to=None):
"""Command to publish `primitives.Request` to customer
Args:
task_uuid(str): task identification
request: Serialized request
broker_point(gromozeka.BrokerPoint):
reply_to(gromozeka.BrokerPoint):
Returns:... | 52b389982676f65547f10a2cd45ac225e6486673 | 457 |
import numpy
def process_axis_labels(datadesc, blobs, offset=0):
"""Convert the raw axis label descriptions.
Similar to LiveDataPanel._process_axis_labels, but is flexible in datadesc.
"""
CLASSIC = {'define': 'classic'}
labels = {}
titles = {}
for size, axis in zip(reversed(datadesc['shap... | d0f880c69160b2a620affe7b1cfe8c7dda12d807 | 458 |
def _to_ranks_by_group(dat, group, formula, exclude_cols=[]):
"""
Covert predictors to ranks separately for each group for use in rank Lmer. Any columns not in the model formula or in exclude_cols will not be converted to ranks. Used by models.Lmer
Args:
dat (pd.DataFrame): dataframe of data
... | 6cff465b0a1877d6594953dda75913dfb36a67ad | 459 |
def list_scans():
"""
:return: A JSON containing a list of:
- Scan resource URL (eg. /scans/1)
- Scan target
- Scan status
"""
data = []
for scan_id, scan_info in SCANS.iteritems():
if scan_info is None:
continue
target_urls = scan_info.target_u... | 60d5eb5c33c09ac6e35ffae2c10b6aca566c6027 | 461 |
def factor_list(f, *gens, **args):
"""
Compute a list of irreducible factors of ``f``.
**Examples**
>>> from sympy import factor_list
>>> from sympy.abc import x, y
>>> factor_list(2*x**5 + 2*x**4*y + 4*x**3 + 4*x**2*y + 2*x + 2*y)
(2, [(x + y, 1), (1 + x**2, 2)])
"""
return _gen... | c13e503a631d3bfc5ead05dc8de8cc5243614241 | 462 |
import pickle
import torch
def all_gather(data):
"""
Run all_gather on arbitrary picklable data (not necessarily tensors)
Args:
data: any picklable object
Returns:
list[data]: list of data gathered from each rank
"""
world_size = dist.get_world_size()
if world_size == 1:
... | 9e89ed2f299f5de8dec55d5529478177d45c21fa | 463 |
import torch
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=()):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an i... | 2ddeda15b84bca0b83a7b5b516c83f991cec44c7 | 464 |
def remove_from_group(group_name, nodes=None, nodes_by_col='SUID', edges=None, edges_by_col='SUID', network=None,
base_url=DEFAULT_BASE_URL):
"""Remove the specified nodes and edges from the specified group.
Args:
group_name (str): Specifies the name used to identify the group
... | 0f7ae3b161aa1b189be14973ddaa7a7a4fef4bbf | 465 |
def filter_bank_2high(t, Nj, Nj_1, ac=2.0, bc=2.0):
"""
computes the filter bank for control points N_j, Nj_1 given the variable t
:param t: data points on the real line R arranged in numpy array
:param Nj: control point, Nj > Nj_1, integer
:param Nj_1: control point, Nj > Nj_1, integer
:param ... | a197cbd99ea4d2ce6fcf9c277cff3e634b539049 | 466 |
def as_public():
"""Return requests session without authentication"""
return BaseUrlSession() | d55cc3616c6910e88d99083cf4e530987c1d8d6c | 468 |
def transform_real_2_sim(real_position):
"""
Transforms a position from the 'real' coordinate system to the 'sim' coordinate system.
:param real_position: dictionary with 'x', 'y' and 'z' keys to floating point values
:return: position in sim space as dictionary with 'x', 'y' and 'z' keys to floating po... | 29b83be1f6f4e49f777e085db651e4f31d47c2e0 | 469 |
import torch
def generate_tgt_mask(sz):
"""Generate a square mask for the sequence. The masked positions
are filled with float('-inf'). Unmasked positions are filled with
float(0.0).
This function is a slight modification of the version in the PyTorch
repository.
Parameters
----------
... | 3fce5eb1cb852ca162fda58407c2cf81c1bdc849 | 470 |
def SceneAddPipeline(builder, pipeline):
"""This method is deprecated. Please switch to AddPipeline."""
return AddPipeline(builder, pipeline) | f220a53ad13923b1f00d208f59e575926e5b7fa2 | 471 |
def SynthesizeUserId(email):
"""Return a synthetic user ID from an email address.
Note that this is not the same user ID found in the production system.
Args:
email: An email address.
Returns:
A string userid derived from the email address.
"""
user_id_digest = _MD5_FUNC(email.lower()).digest()
... | fb3f81e37decaa941857ac575a5fd034f92a2324 | 472 |
import torch
def compute_jacobian(fn, x0: torch.Tensor, bs: int):
"""
Computes the Jacobian matrix of the given function at x0, using vector-Jacobian products
"""
input_shape = x0.shape
assert len(input_shape) == 3
dim = x0.numel()
eye = torch.eye(dim, dtype=x0.dtype, device=x0.device)
... | c184fd03abea440e27bdd27bb1105778e7bde4b6 | 474 |
import math
def pixel_distance(A, B):
"""
In 9th grade I sat in geometry class wondering "when then hell am I
ever going to use this?"...today is that day.
Return the distance between two pixels
"""
(col_A, row_A) = A
(col_B, row_B) = B
return math.sqrt(math.pow(col_B - col_A, 2) + m... | 64853c44400428c8040ae47d1cc2cca17aed0a5f | 475 |
def word_ngrams(s, n=3, token_fn=tokens.on_whitespace):
"""
Word-level n-grams in a string
By default, whitespace is assumed to be a word boundary.
>>> ng.word_ngrams('This is not a test!')
[('This', 'is', 'not'), ('is', 'not', 'a'), ('not', 'a', 'test!')]
If the sequence'... | 8be360785e38b8f427c509d63f5ecba3b6b2c020 | 476 |
def phosites_detail(text):
"""
create detail view output of phosphosites by accession.
:param text: string of phos group ID
:return: template
"""
results = browse_queries.browse_detail(text,'Phosphosite')
table = browse_queries.phos_kin_query(text)
# pass tables, results and style indic... | 5f1b67fadda3eb1dfe86e7e996e65197ff1eca3a | 477 |
def convert_to_np_arrays(X):
"""
Converts the input arrays to dense numpy arrays to allow the methods to work properly
"""
try:
X = X.todense()
except:
pass
X = np.array(X)
if len(X.shape) > 2:
X = reduce_shape(X)
return X | 68fdf6fd87df160e96acec5abb8af310886fccc2 | 478 |
def reduce_arr(arr):
"""
Return which elements on which axis are unique
Args:
arr (np.ndarray) : input array which to reduce to unique value
Returns:
reduced array(np.ndarray) : array with reduced data.
data_axis (list) : the axises that have changing data.
"""
ndim = l... | d207876b820c7d7b30f8af6c302181620b00bf25 | 480 |
import torch
def nll_lorentzian(preds, target, gamma):
"""
Isotropic lorentzian loss function
:param preds: prediction values from NN of size [batch, particles, timesteps, (x,y,v_x,v_y)]
:param target: target data of size [batch, particles, timesteps, (x,y,v_x,v_y)]
:param gamma: The tensor for t... | 24d6ea2c4b40bc0f8c27eebf0e402261d865836e | 481 |
from pathlib import Path
def get_archive():
"""Ensure that the archive file exists and return its path.
This is a function so the path can be made configurable in the future.
Returns:
:obj:`str`: The full local path to the archive file.
"""
filename = '/config/archive.txt'
archfile =... | 78abc493d7f256ebf53ec2cfeb9ab4f1d42b5c02 | 482 |
from typing import Sequence
from typing import Callable
from typing import List
def _filter_unique_configs(
configs: Sequence[ProblemConfig],
filter_fn: Callable[[ProblemConfig], bool] = lambda _: True,
) -> List[ProblemConfig]: # pytype: disable=annotation-type-mismatch
"""Filters a list of problem_config... | 98481fa9991726f3ba4253fb132f7f7e3cb2a420 | 483 |
def convert_units(str):
""" Convert some string with binary prefix to int bytes"""
unit = ''.join(ele for ele in str if not ele.isdigit()).strip().lower()
return int(''.join(ele for ele in str if ele.isdigit()))*{
"b": 1,
"B": 1,
"k": 2**10,
"kb": 2**10,
"m": 2**20,
... | a9de044090bfd4311a27dbbf373361e7d88a1e06 | 484 |
def match_piecewise(candidates: set, symbol: str, sep: str='::') -> set:
"""
Match the requested symbol reverse piecewise (split on ``::``) against the candidates.
This allows you to under-specify the base namespace so that ``"MyClass"`` can match ``my_namespace::MyClass``
Args:
candidates: set... | 1c6d7240365ef22f753aa4195cfb5e879fc453e0 | 485 |
def is_kube_version_supported(kube_version, min_version=None, max_version=None):
"""Check if the k8s version is supported by the application.
:param kube_version: the running or target k8s version
:param min_version (optional): minimum k8s version supported by the app
:param max_version (optional): max... | f08a5e5eb9ac9928e2e08ddddd6d30db90e8c868 | 486 |
def chebi(name=None, identifier=None):
"""Build a ChEBI abundance node.
:rtype: Abundance
"""
return Abundance(namespace='CHEBI', name=name, identifier=identifier) | bbe8cf217a545f2818d7957b01d7bbaf0a2cc6d2 | 487 |
def get_group(request):
"""returns all the groups in database
"""
group_id = request.matchdict.get('id', -1)
group = Group.query.filter_by(id=group_id).first()
return [
{
'id': group.id,
'name': group.name,
'thumbnail_full_path':
group.thumbna... | 6d53d8969ecebb882c6626b128a72f24dafa6997 | 488 |
def create_histogram(path_to_image, target_path=''):
"""
creates a histogram of a given image and either shows or saves a plot
Args:
path_to_image: path to the image
target_path: if given, saves a plot, otherwise (if empty) shows the plot
Returns:
the histogram plot
"""
... | cb68b5f8bd55120d4f720020b092af02b727a6ba | 489 |
def task_6_list_all_supplier_countries(cur) -> list:
"""
List all supplier countries
Args:
cur: psycopg cursor
Returns: 29 records
"""
cur.execute("""SELECT country FROM suppliers""")
return cur.fetchall() | a3d8af1eb2948ebc01e408265d20b0055f1a0504 | 490 |
def _energy_to_length_factor(e_unit, l_unit):
"""
Convert the units of Planck's constant and speed of light
:param e_unit:
:type e_unit: str
:param l_unit:
:type l_unit: str
:return: c,h
"""
dest_h_u = ug.parse_units('%s s' % e_unit)
dest_c_u = ug.parse_units('%s/s' % l_unit)
... | 23e1dbfac7265ff1df4cf62e3609f91d5e327a35 | 491 |
def kev_to_wavelength(kev):
"""Calculate the wavelength from kev"""
lamda = 12.3984 / kev #keV to Angstrom
return lamda | cfb3126e56bc0890dd8cf2caa50a240b380dad56 | 492 |
def _convert_rde_to_1_0_format(rde_data: dict) -> dict:
"""Convert defined entity to RDE 1.0.
:param DefEntity rde_data: Defined entity dictionary
:return: converted defined entity
:rtype: dict
"""
new_rde = common_models.DefEntity(**rde_data)
new_native_entity: AbstractNativeEntity = rde_u... | 931bc93c7326a4640892a3876885fcc19430bbe1 | 493 |
def additive_symbols(tokens, base_url):
"""``additive-symbols`` descriptor validation."""
results = []
for part in split_on_comma(tokens):
result = pad(remove_whitespace(part), base_url)
if result is None:
return
if results and results[-1][0] <= result[0]:
ret... | 346eae19c5d4d936d0ad7f2cdba2191943cc7bca | 494 |
def _index_list(key_or_list, direction=None):
"""Helper to generate a list of (key, direction) pairs.
Takes such a list, or a single key, or a single key and direction.
"""
if direction is not None:
return [(key_or_list, direction)]
else:
if isinstance(key_or_list, string_type):
... | e32ddb70a10d52e1f2595cac9cb99c0381b9a3e4 | 496 |
def CalculateOSNames(os_name, os_variants):
"""Calculates all the names an OS can be called, according to its variants.
@type os_name: string
@param os_name: base name of the os
@type os_variants: list or None
@param os_variants: list of supported variants
@rtype: list
@return: list of valid names
"""... | 5689ed7da55cec929045e95344c60e7a06af711d | 498 |
def c4x(c: Circuit, c0: int, c1: int, c2: int, c3: int, t: int) -> Circuit:
"""A macro of 4-controlled X gate"""
return c.h[t].c4z(c0, c1, c2, c3, t).h[t] | 89b5d790a70448a1d46452554ab234e113e63c59 | 500 |
def pad(data, pad_id):
""" Pad all lists in data to the same length. """
width = max(len(d) for d in data)
return [d + [pad_id] * (width - len(d)) for d in data] | a0951f4332879600d25c061cf1c553126d6df8d2 | 501 |
from netharn import util
def draw_boxes_on_image(img, boxes, color='blue', thickness=1,
box_format=None):
"""
Example:
>>> from netharn import util
>>> img = np.zeros((10, 10, 3), dtype=np.uint8)
>>> color = 'blue'
>>> thickness = 1
>>> boxes = ... | 3c4a3b547d39bac940ea9f6999a98f2db62f938b | 502 |
def _select_random_features(feature_list, amount):
"""Selects a given amount of random features from the feature list"""
set_size = len(feature_list) -1
random_features = []
for i in range(amount):
while(True):
random_feature = feature_list[randint(0, set_size)]
if(random... | e281bfa75e153aa195119f84777f41db9d5e806c | 503 |
def matrixop_inp_matr():
"""
Функция возвращает матрицу, введённую пользователем с клавиатуры.
Returns
-------
a : [[float, float, ...],
[float, float, ...],
...]
Матрица, введенная пользователем
"""
while True:
try:
m = int(input('Сколько буде... | c373af0c7493ff32f919d903644b2031cc51162c | 504 |
def dropannotation(annotation_list):
"""
Drop out the annotation contained in annotation_list
"""
target = ""
for c in annotation_list:
if not c == "#":
target += c
else:
return target
return target | 9f4a695eaf80f79dce943f2f91926d9c823483b6 | 506 |
def do_associate_latest_edit(parser, token):
"""
AssociateLatestEdit
"""
try:
tag, node = token.split_contents()
except ValueError:
raise template.TemplateSyntaxError, "%r tag requires one argument" % token.contents.split()[0]
return AssociateLatestEdit(node) | 75cf36f1cccd2191636f3cb603503c6655ae0c67 | 507 |
def open_monitoring_db(dbhost, dbuser, dbpass, database):
"""
Open MySQL monitoring DB
"""
try:
conn = MySQLdb.connect(host=dbhost, user=dbuser,
passwd=dbpass, db=database)
except MySQLdb.Error, err:
print "Error %d: %s" % (err.args[0], err.args[1])
... | db33430d20c7c72c7428d85f161d1c186404dc05 | 508 |
def matdiff(matrix1,matrix2,figsize=None,cmap=None):
"""
display the difference between two real matrices, alongside this plot this difference
on a log- colour scale (if diff!=0)
"""
if not figsize:
figsize = defaults['figsize']
if not cmap:
cmap = defaults['cmap']
_matdiff = matrix1-matrix2
f, (ax1, ax2)... | fb11354f7388e461ac49bcac942a9b6a2b5528d4 | 509 |
def _tokens_by_class_of(tokens):
"""Generates lookup table of tokens in each class."""
out = defaultdict(set)
for token, token_classes in tokens.items():
for token_class in token_classes:
out[token_class].add(token)
return out | c335582785e4c8a5b82232849ccee579f5ab068f | 510 |
def load_mnist_dataset(shape=(-1, 784), path='data'):
"""Load the original mnist.
Automatically download MNIST dataset and return the training, validation and test set with 50000, 10000 and 10000 digit images respectively.
Parameters
----------
shape : tuple
The shape of digit images (the ... | 856a758864ef85b4a5bea742dd90a49e997ad9b7 | 511 |
def EntryToSlaveName(entry):
"""Produces slave name from the slaves config dict."""
name = entry.get('slavename') or entry.get('hostname')
if 'subdir' in entry:
return '%s#%s' % (name, entry['subdir'])
return name | 258e68c683592c21ea8111f21ba3ab648ddb8c57 | 513 |
def is_symmetric_re(root: TreeNode) -> bool:
"""Check if a binary tree is a mirror of itself (symmetric around its center)."""
if not root:
return False
def is_mirror(t1, t2):
if not t1 and not t2:
return True
if not t1 or not t2:
return False
return... | b2d0450a881e0a1748575baa8d7c6ae1224fb3c0 | 515 |
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:
"""Set up paperless from a config entry."""
hass.config_entries.async_setup_platforms(entry, PLATFORMS)
return True | 8793312f379c510e3420e785eba6ce4db3f098c7 | 516 |
def azimuthal_average(image, center=None, stddev=True, binsize=0.5, interpnan=False):
"""
Modified based on https://github.com/keflavich/image_tools/blob/master/image_tools/radialprofile.py
Calculate the azimuthally averaged radial profile.
Parameters:
imgae (numpy ndarray): 2-D image
... | 3ebadf5fa93cc93e6a5b14327392a2fdecb5d266 | 517 |
import re
def find_assign(data, varname):
"""Finds a substring that looks like an assignment.
:param data: Source to search in.
:param varname: Name of the variable for which an assignment should be
found.
"""
ASSIGN_RE = re.compile(BASE_ASSIGN_PATTERN.format(varname))
if... | 99e1d1436307dd278fbef8b7e52c4d2eedd6d657 | 518 |
import requests
def remove(token: str, server: str="http://localhost:8080/remove", params: dict=None) -> int:
"""
Removes the data associated with the token.
:param token: the token to download the data for
:type token: str
:param server: the URL of the server to upload to
:type server: str
... | a74f2c5f84ae064a909df717917f4589b59eacb5 | 519 |
import requests
def get_pending_surveys_batch_number(batch_no):
"""
Gets batch number for the shared survey
:param batch_no: Shared survey batch number
:type batch_no: str
:raises ApiError: Raised when party returns api error
:return: list share surveys
"""
bound_logger = logger.bind(... | ee31c28c393e29b6cd628aefc38d2ca948c7cdaf | 520 |
def make_sign_initializer(random_sign_init):
"""Random sign intitializer for HyperBatchEnsemble layers."""
if random_sign_init > 0:
return ed.initializers.RandomSign(random_sign_init)
else:
return tf.keras.initializers.RandomNormal(
mean=1.0, stddev=-random_sign_init) | e8ea2653ef9c7c5ba447921d7def990e29a7c9b2 | 522 |
def _parallel_predict_proba(ensemble, X, idx, results):
"""
Compute predictions of SCM estimators
"""
for k in idx:
res = ensemble.estimators[k].predict(X[:, ensemble.estim_features[k]])
results = results + res
return results | b0a2d5c59318506202c9331597ab2a11eacb7a32 | 523 |
def compute_FP_TP_Probs(Ycorr, Xcorr, Probs, is_tumor, evaluation_mask, Isolated_Tumor_Cells, level):
"""Generates true positive and false positive stats for the analyzed image
Args:
Probs: list of the Probabilities of the detected lesions
Xcorr: list of X-coordinates of the lesions
... | 434abefeba56201d20799b1f00783bc77dcbf2c0 | 524 |
def make_sentences(text, src):
"""
Builds a list of dictionaries, one for each sentence resulting from
the sentence parser. The dictionary schema is
{"src": src, "label": 0, "sentence": sent}
Substitutions are made for the identified tokens.
Args:
text (str): text to process
... | 5da57a55f76a3d4d29f1d0ed681b8597e958b9d0 | 525 |
def read_test_case(file_path):
"""
reads one test case from file.
returns contents of test case
Parameters
----------
file_path : str
the path of the test case file to read.
Returns
-------
list
a list of contents of the test case.
"""
file = open(file_path... | 6a87ff979d0b1ccf838ebef56401a48760711541 | 526 |
import torch
def accuracy4batch(model, testloader, criterion):
"""save a model checkpoint
INPUT:
model: pytorch nn model.
testloader: DataLoader. test data set
criterion: criterion. loss criterion
device: torch.device. device on which model/data is based
OUTPUT:
accuracy: float in ... | 2005984b94f17bf601034953bbea3dca6542143d | 528 |
def pick_an_experiment(i):
"""
Input: {
(repo_uoa) - experiment repository name (defaults to hackathon_local_repo, but can be overridden by '*')
(extra_tags) - extra tags to filter
}
Output: {
return - return code = ... | 97671967ab153173280a142143fb4b07892c92fa | 530 |
import uuid
import pathlib
def run(args, image: str) -> str:
"""
Run docker image and mount user-provided folder with C++ files.
Parameters
----------
args : dict-like
User provided arguments parsed by argparse.ArgumentParser instance.
image : str
Name of image from which cont... | eba0ab0e93543410ae4a2375ec80a9015168d303 | 531 |
def get_top_design_list(oProject):
"""
Returns a list of the names of the top-level designs.
Parameters
----------
oProject : pywin32 COMObject
The HFSS project in which the operation will be performed.
designname : str
Name of the design to insert.
Returns
-------
... | 6610cd68a90e20fd916a2ec13b54f37a75c31050 | 532 |
from typing import Union
from typing import Dict
from typing import Any
def chi01(param_name: Union[str, None], yval: float, **kwargs) -> Dict[str, Any]:
"""Plot defaults for sweep_plotting.chi01"""
kwargs["xlabel"] = kwargs.get("xlabel") or recast_name(param_name)
kwargs["ylabel"] = kwargs.get("ylabel") ... | 58e5d09152062a9307526bd953ff91832ef80321 | 533 |
def cstring(*args, **kwargs):
"""Return a colored string.
Parameters
----------
args : iterable of str
bold : bool
color : str, {'HEADER', 'LIGHTBLUE', 'LIGHTGREEN', 'WARNING', 'FAIL',
'ENDC', 'BOLD', 'UNDERLINE' 'BLACK', 'RED', 'GREEN',
'YELLOW', 'BLUE', 'MA... | 69bc81f0e9267743297bec25b2fccc8f2da2c89d | 534 |
from . import routes # Import routes
def create_app():
"""Construct the core application."""
app = Flask(__name__, instance_relative_config=False)
app.config.from_object('config.Config')
db.init_app(app)
admin.init_app(app)
basic_auth.init_app(app)
with app.app_context():
db.cre... | 28bfb75626a5be05aef21404956f6ef7adf8f80a | 535 |
def bytes_index(x: bytes, sub: bytes, start: int, end: int) -> int:
"""Where is the first location of a subsequence within a given slice of a bytes object?
Compiling bytes.index compiles this function, when sub is a bytes object.
This function is only intended to be executed in this compiled form.
Arg... | 3a78e029a96d27fdf5b5cdea397b21f57ca939d9 | 536 |
def uniform(minimum, maximum, shape=[]):
"""uniform(minimum, maximum, shape=[]) returns array of given shape of random reals
in given range"""
if shape == []:
shape = None
return mt.uniform(minimum, maximum, shape) | 882cd915cb7dfec0e1b6857f99ecefe876ae21b1 | 538 |
def unquote_to_bytes(urlencoded_string):
"""Replace %xx escapes by their single-character equivalent,
using the “iso-8859-1” encoding to decode all 8-bit values.
"""
return bytes(
unquote(urlencoded_string, encoding='iso-8859-1'),
encoding='iso-8859-1'
) | 60216d170381e356520c283f308add08754c987d | 539 |
def qualifiedName(item):
"""Return the full name of an item, including any projects that it's in.
If the item does not have a name, return ``None``.
XXX: Doesn't include folders.
"""
names = []
# Note: assumes that the presence of a single null name in the parent tree
# means that the item... | 2a1cdc9e15897c104f63793041ed2d4fe91e383d | 540 |
def assign_columns_of_sector_levels(df_load):
"""
Add additional column capturing the sector level in the two columns
:param df_load: df with at least on sector column
:param ambiguous_sector_assignment: if there are sectors that can be
assigned to multiple sector lengths (e.g., for government or ho... | 4dd60267702f21e103cab18293975a5f62c934d2 | 541 |
def add_matrices(matrix_a, matrix_b):
"""Add two n x n matrices
"""
return [[x + y for x, y in zip(matrix_a[i], matrix_b[i])]
for i in range(len(matrix_a))] | a9f6a857892872fde584b6884e59a8b624220061 | 542 |
import scipy
def no_pretrain_inner_speech(subject):
"""This function aims at training a model without pretraining by training
only on the inner speech condition of a sigle subject
:return: metric history for every of the n k-folds
:rtype: list of dictonaries
"""
###### DATA
data, events =... | daa08a8ea88838b8e66c3fc23c2b6997bfdff490 | 543 |
from typing import Any
def build_put_big_decimal_negative_decimal_request(**kwargs: Any) -> HttpRequest:
"""Put big decimal value -99999999.99.
See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder
into your code flow.
:keyword json: The default value is -99... | e41800c8bee6a9c4874b3625ed374c9514c81fe7 | 544 |
def make_subplots(
rows=1,
cols=1,
shared_xaxes=False,
shared_yaxes=False,
start_cell="top-left",
print_grid=False,
horizontal_spacing=None,
vertical_spacing=None,
subplot_titles=None,
column_widths=None,
row_heights=None,
specs=None,
insets=None,
column_titles=No... | 1119aba8e9b9f35b18959f765e4528e2d065a5b8 | 545 |
from re import T
def op_table(name):
"""Get the symbol `name' as an int8_t[]."""
return gdb.parse_and_eval("&'" + name + "'").cast(T('int8_t').pointer()) | 8266128cb1bf59b9d71d7dabb7d002ff22e41192 | 546 |
from typing import Dict
from typing import Any
def nf_masks_to_neurof_dict(binary_masks: np.ndarray, dataset_name: str) -> Dict[str, Any]:
"""
Take as input a tensor of binary mask and produces dict format for neurofinder
Args:
binary_masks: 3d ndarray (components x dimension 1 x dimension 2)
... | daee76afc56e6e0da0939530a49f4a77b4c1d5f6 | 547 |
def get_domain_machine_command():
"""Retrieves a collection of Machines that have communicated to or from a given domain address.
Returns:
(str, dict, dict). Human readable, context, raw response
"""
headers = ['ID', 'ComputerDNSName', 'OSPlatform', 'LastIPAddress', 'LastExternalIPAddress', 'H... | 0e614d15abd3e99408d4a3d7a93ecc49dba694ad | 548 |
import copy
from functools import reduce
def flatten_dict(source_dict, name_delimiter='_', inner_name=False):
"""
flatten nest dict
Parameters
----------
source_dict : nest dict
name_delimiter : flatten name delimiter(non-use when inner_name is True)
inner_name : False, use innermost name... | fbba7666c25f5eafd47642b8308a486e25cdc6f9 | 549 |
def matrix_multiply(A, B):
""" Multiply two matrices A and B.
:param A: the right matrix
:param B: the left matrix
:return: A * B
"""
# define m and n for the matrix as well as l, the connecting dimension between A and B
m, l, n = len(A), len(A[0]), len(B[0])
# initialize an all zeros ... | 3cd551ea87d9f925654a4153106c2fe87e33fa8c | 550 |
def hard_light(image1, image2):
"""
Superimposes two videos on top of each other using the Hard Light algorithm
:rtype: :py:class:`~PIL.Image.Image`
"""
image1.load()
image2.load()
return image1._new(image1.im.chop_hard_light(image2.im)) | dcfd25a83f142c2d42c38a787569001af6f6bfc9 | 551 |
def show(*actors, **options):
"""
Create on the fly an instance of class ``Plotter`` and show the object(s) provided.
Allowed input objects types are:
``str``, ``Mesh``, ``Volume``, ``Picture``, ``Assembly``
``vtkPolyData``, ``vtkActor``, ``vtkActor2D``, ``vtkImageActor``,
``vtkAssembly`` or ``... | e95c9bbd325e8e6da6ee4f33ca861496c3048bd3 | 552 |
from typing import List
def get_followup_question_list(intent: str) -> List[str]:
"""
Get all imported followup questions for this intent as a list
* `intent`: name-parameter of the yml-section with which the followup questions were imported
**Returns:** None if no followup questions are known for ... | 00581a497d4e09670edaa0d44870a2a0d7589ada | 554 |
from typing import Tuple
def event_train_test_split(
evs: np.ndarray, n_evs: int, train_split: float, random_seed: int=1
) -> Tuple[np.ndarray, np.ndarray]:
"""[summary]
Args:
n_evs (int): [description]
train_split (float): [description]
random_seed (int, optional): [descripti... | 69deb28d199fc8636ed45a90630a69753f4c1066 | 555 |
def get_cd(wcs, n=1):
"""
Get the value of the change in world coordinate per pixel across a linear axis.
Defaults to wcs.wcs.cd if present. Does not support rotated headers (e.g.,
with nonzero CDm_n where m!=n)
"""
if hasattr(wcs.wcs,'cd'):
if wcs.wcs.cd[n-1,n-1] != 0:
re... | 9b31c81a1a5e87efeb201ffef7f8f65f846fe0b7 | 556 |
def mock_clear():
"""Clear MOCK_DATA_HEAP"""
MOCK_DATA_HEAP.clear()
return "" | 96212726db3f1e29ac4da54e62df70cb89ba1f2e | 557 |
from datetime import datetime
def cls_merge_type(classification):
""" classification type이 2가지일 때 합쳐주는 함수
Parameters
----------
classification: cls
classification 리스트
Returns
-------
list of cls
변환된 classification 리스트
"""
cls_type = {'instant' if cls.get('instant_... | e6a59f45adecdc21acb81f6890ac79cfaa93b4d6 | 558 |
def duplicate_detector(gate_orders: list[tuple[str]]) -> int:
"""Detects any schematics that have an identical combination of gates."""
difference = len(gate_orders) - len(list(set(gate_orders))) # List - list with no duplicates
return difference | e439a106abc0ff21bfe9773b3185d35b5bf05aa0 | 559 |
def permutations(x):
"""Return all permutations of x"""
def fn(i):
if i == len(x): ans.append(x.copy())
for k in range(i, len(x)):
x[i], x[k] = x[k], x[i]
fn(i+1)
x[i], x[k] = x[k], x[i]
ans = []
fn(0)
return ans | 691c701e1ac17da5dabb0fc3fe607ff68ac8fcdc | 560 |
def encode(model, text, out_file=None, topic_priors=None, prior_weight=1.0):
"""
Perform text-to-image encoding.
Parameters
----------
model : :obj:`gclda.model.Model`
Model object needed for decoding.
text : :obj:`str` or :obj:`list`
Text to encode into an image.
out_file :... | 941745be7b84c3af9d7f7e62cb2d93fabc3b22c1 | 562 |
def clean_string(s: str) -> str:
"""Cleans and returns an input string
>>> clean_string(" xYz ")
'XYZ'
"""
return str(s).strip().upper() | c97281505492ded5b9167076312959c5eee41a6c | 563 |
import collections
def get_unique_region_cov_df(unique_region_dict, fuzzer_names):
"""Returns a DataFrame where the two columns are fuzzers and the number
of unique regions covered."""
fuzzers = collections.defaultdict(int)
for region in unique_region_dict:
for fuzzer in unique_region_dict[reg... | 923227fb804549252bf51cd94e65180c3f8564e8 | 564 |
def display_generation_hit_results(hit_info, hit_results):
"""Displays the results of a generation HIT
Parameters
----------
hit_info : GenerationHITInfo
HITInfo object storing information regarding the HIT
hit_results : GenerationResults
HIT results object storing the results ... | 649cf05d0ee3a87032993fd05fc120162c7f8d2b | 566 |
def XOR(v1, v2):
"""
XOR operation element by element from 2 lists
:param v1: [1, 0, 1, 0, 0, 1]
:param v2: [1, 1, 0, 0, 1, 1]
:return: [0, 1, 1, 0, 1, 0]
"""
return [a ^ b for a, b in zip(v1, v2)] | e3b94b35ccf4e1dd99cc51f32c70f96c5fe99795 | 568 |
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