content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def rand_population_uniform(m, a, b):
"""
Creates a starting population of size `m` using a uniform random
distribution, where `a` is the lower bound, and `b` is the upper bound
for the design variables.
"""
a = np.array(a)
b = np.array(b)
d = len(a)
return [list(np.multiply(a + np.r... | 576c2d77591356615347f3b119660b673e0f5453 | 59,058 |
from typing import Union
from typing import Tuple
def prepare_data(
data: np.ndarray,
lookback_steps: int,
num_inputs: int = None,
num_outputs: int = None,
input_steps: int = 1,
forecast_step: int = 0,
forecast_len: int = 1,
known_future_inputs: bool = F... | 390f045a0bd9c423d261277028cfa2228abe00fe | 59,059 |
def najnowsze(request):
"""
List the newest offers
"""
oferty = OfertyEst.objects.filter(status=0).select_related()
oferty = oferty.order_by('-data', '-id')[:10]
paginator = Paginator(oferty, 10)
page = request.GET.get('page')
try:
oferty = paginator.page(page)
except Page... | b3f4b671344a423f27e0d7b32808993495756558 | 59,061 |
import base64
import zlib
def decompress_data(content_data):
"""
Handles the actual decompression of Base64 data.
Args:
content_data: String of Base64 content
Returns:
content_data: Decompressed content of ASCII printable
"""
decoded = base64.b64decode(content_data)
# IO... | fd93747ec3bf7759e385eba7397c6b9ad22ccd7f | 59,062 |
def get_module_class(module_name, hetero=False):
"""Retrieve the module classes from the module name."""
# List all the modules and their string name in this dictionary
module_dict = {
'pointnet': [PointNet, PointNetHetero], # pointnet
'mlpdecoder': [MlpDecoder, MlpDecoderHetero], # mlpdeco... | 4a0c593086de6325aee8042d292dd8b862ce6875 | 59,063 |
def generate_election_partial_key_challenge(
backup: ElectionPartialKeyBackup, polynomial: ElectionPolynomial,
) -> ElectionPartialKeyChallenge:
"""
Generate challenge to a previous verification of a partial key backup
:param backup: Election partial key backup in question
:param poynomial: Polynomi... | 4069ccb1ed90b39804a5c5fa78563f8d8b44681e | 59,065 |
from datetime import datetime
import json
def bplist_write_bplist_to_json(bplist_file_path, to_file_path):
"""
Reads a bplist file and writes it to a json file.
Args:
bplist_file_path (str):
to_file_path (str):
"""
def _convert_datetime(o):
if isinstance(o, datetime):
... | e8fd57ea660a5ed0313e7d429736c6e13c362af0 | 59,066 |
import numpy
def stEnergyEntropy(frame, n_short_blocks=10):
"""Computes entropy of energy"""
Eol = numpy.sum(frame ** 2) # total frame energy
L = len(frame)
sub_win_len = int(numpy.floor(L / n_short_blocks))
if L != sub_win_len * n_short_blocks:
frame = frame[0:sub_win_len * n_short... | aad28aef8871246aa8c369fa786b0669e69da546 | 59,067 |
def patch_url(options):
"""Returns a Review Board or a GitHub URL for a patch."""
if options['review_id']:
# Reviewboard REST API expects '/' at the end of the URL.
return '{base}/{review}/diff/raw/'.format(
base=REVIEWBOARD_REVIEW_URL,
review=options['review_id'])
el... | fbd1b37e087738bf1b895ee0dd1e3c51d48b7037 | 59,068 |
def _gaussian_log_cdf(x, mu, sigma):
"""Log CDF of a normal distribution."""
if not isinstance(x, chainer.Variable):
x = chainer.Variable(x)
return _log_ndtr((x - mu) / sigma) | dc82b95cb61c17ae40a565d26279dd3d6c1987cf | 59,070 |
from typing import Dict
import yaml
def load_yaml(filepath: str) -> Dict:
"""
Read content from yaml file.
:param filepath: str path to yaml file.
:return: dict YAML content
"""
with open(filepath, "r") as f:
try:
return yaml.safe_load(f)
except yaml.YAMLError as ... | 37dfcc6fa463174059414fe93efe891389be2f22 | 59,071 |
import csv
import logging
def load_patterns(file_path):
"""
loads the patterns from csv file
"""
patternsDict = {}
try:
with open(file_path, mode='r') as csvfile:
reader = csv.reader(csvfile)
patternsDict = {rows[0]: (rows[1], rows[2]) for rows in reader}
except... | 330472f0507ffc4e9d967ef3027e362e47e0d4d2 | 59,072 |
def calculate_AUC(x, y):
"""
Simple function to calculate AUC of an ROC curve, given
a set of points (x,y)
Parameters
----------
x: list
The x coordinates of the points of the ROC curve.
y: list
The y coordinates of the points of the ROC curve.
Returns
-------
... | ba890f3348c9e5aa7b20e643b6369bdc16d7fe5c | 59,073 |
from typing import List
def get_personnel_list(
personnel_file: str = CONFIG['input_personnel_list_path'],
) -> List[str]:
"""Get Personnel list
Args:
personnel_file (str): Path to '\n' delimited text file containing
list of personnel.
Returns:
int: Number of rows
"""
... | 54259fd73542478eb9808bb8af9023e819cde56b | 59,075 |
import requests
def sf_put(path='', token=None, data=None):
"""
Sends a PUTS to PetSafe SmartFeed API.
Example: sf_put(path='feeders', token=user_token, data=my_data)
:param path: the path on the API
:param token: the account's token
:param data: the PUT data
:return: the request respons... | 9ae31119cd2422314939760c8fda1b6a18ca05d2 | 59,076 |
def read_params_Coulomb(fname='in.params.Coulomb'):
"""
Read in.params.Coulomb. But only for fixed or fixed_bvs charges.
"""
with open(fname,'r') as f:
lines = f.readlines()
mode = None
chgtype = None
chgs = {}
coul = {}
bvs = {}
for line in lines:
if line[0] in ... | a870d1a7b504fdbbc87f09db758bfd11b770be63 | 59,078 |
def get_programs_by_type_slug(site, program_type_slug):
"""
Keyword Arguments:
site (Site): The corresponding Site object to fetch programs for.
program_type_slug (string): The type slug that matching programs must have.
Returns:
A list of programs for the given site with the given ... | 7a3b697718f081a1d55c74e70de401829dfd42c6 | 59,080 |
def timeshifted_data_2d(data, shift, padding_value='zero'):
"""
Same as timeshifted_data_1d but with a 2D array as input. In other words, performs shifting of multiple data
vectors (e.g. multiple voxel timeseries).
Inputs
data: 2D numpy array, with shape (n, v), where n is the length of a... | e877933870d19eff3d1f3697a42b6c2e36fd600a | 59,081 |
def dice_from_confusion_matrix(cm: ndarray) -> ndarray:
"""
Computes Dice scores from a confusion matrix
Parameters
----------
cm
N x N Input confusion matrix
Returns
-------
1d ndarray containing Dice scores for all N classes
"""
if cm.ndim != 2 or cm.shape[0] != cm.s... | 6c2b256166fb9eb54d02bbcc013eeafd5e49255f | 59,083 |
def find_attribute_helpers(attr_type, name, custom_formatter):
"""
Tries to format following the OIDs info and format_helper specification.
Search for attribute oid, then attribute name (can be multiple), then attribute syntax
Precedence is:
1. attribute name
2. attribute oid(from schema)
3.... | d98c08bf40275131f24395566a62d1ac5e80e2ca | 59,085 |
def getValidationHandlers(template, cohort_start_year, val_feature_start, val_feature_end, cohort_name, engine):
"""
Queries database for active handlers within the given train/test dates.
:param template: string, SQL template from config file
:param cohort_start_year: starting year for cohort
:para... | ad4b4171d6d4f2bdb25decd0dd8da02a77786f84 | 59,086 |
def integrate_dopri5(dydt, t, y0, *args):
"""
Integrate an ODE system with the Runge-Kutte Dormand Prince
method (with step-size control).
Parameters
----------
dydt : function
A function returning the momenta of the ODE system.
t : numpy.ndarray of float
Array of time point... | b9db428a04bb8d03c294fa2cf9562aeeda265e87 | 59,087 |
import json
def mac_entry_info_get(client, vlan_name, port, peer_mac_addr):
"""based on the input information,found any mac entry is matched.
:returns: True if one entry is matched
"""
status = False
# show ethernet-switching table interface xxx
command = 'exec cli show ethernet-switching tabl... | d8e01b4aa60741c355d80125d87b0a1ae02ee6f6 | 59,088 |
from typing import Optional
from typing import Dict
from typing import Any
def render_packager_template(template_name: str, values: Optional[Dict[str, Any]] = None) -> str:
"""
Helper function to read template from a file and render it
:param template_name: name of a rest packager template
:param val... | ae778e7c82616fd85e0cb1c0b71a27e5d74f11bf | 59,089 |
def read_datafiles():
"""Returns lines from files in current directory.
Exits with error message if it encounters:
* file that has an invalid name
* file that is not UTF8-encoded
* file that has blank lines."""
visiblefiles_list = get_visible_filenames()
all_datalines = []
for datafile ... | 2a30029a453069c53add23c5d3f55799a95e638b | 59,090 |
def _calculate_normals(rr, tris):
"""Efficiently compute vertex normals for triangulated surface"""
# ensure highest precision for our summation/vectorization "trick"
rr = rr.astype(np.float64)
# first, compute triangle normals
r1 = rr[tris[:, 0], :]
r2 = rr[tris[:, 1], :]
r3 = rr[tris[:, 2]... | 30b06bf23cf3322f59d4feb1dad56fb23e544ada | 59,092 |
def _GetMaxDup(data):
"""Find the maximum i inside ABCD{i}.
data should be a [[[Key]]], where Key is a string that may look like
ABCD{i}.
"""
max_dup = 0
for label in data:
for run in label:
for key in run:
match = _DUP_KEY_REGEX.match(key)
if match:
max_dup = max(max_du... | c865d1ebb862da5be799154d67f06a4fd2812c58 | 59,093 |
def parse_workflows(bactopia_path, include_merlin=False, build_all=False):
"""Parse Bactopia's workflows.conf to get modules per-workflow"""
workflows = {}
available_workflows = []
nf_config, stderr = execute(f'nextflow config -flat {bactopia_path}/main.nf', capture=True)
for line in nf_config.split... | 38eb03793a76431b959fbe76e9cfd217309fc926 | 59,095 |
def price_attractiveness_on_afv():
"""
Real Name: b'Price Attractiveness on AFV'
Original Eqn: b'lagged effect of price on attractiveness on AFV*Reference price Attractiveness on AFV'
Units: b'Dmnl'
Limits: (None, None)
Type: component
b''
"""
return lagged_effect_of_price_on_attrac... | c81be938d55e6e4f2be0c4dea790512e46e551a3 | 59,096 |
def write_climate_file(filename, datadir, arrs, time, changes=None):
"""Write climate file
Parameters
----------
filename : str
template file name
datadir : Path
directory to write to
arrs : list of 1D arrays
data to write to columns
time : 1D array of datetime.datet... | b00e7510d5eb7bc6f70fbc9771720c4171dcdf3c | 59,099 |
def merge_dict(o, keys_to_uppercase=False):
"""Utility function to assist in merging python dictionary objects.
It uses python way which is tricky to do so, by separating keys and values of dictionary objection into separate data structure
"""
print("log: {}".format(log))
# log.info("-------")
K = []
V = ... | b2ae52a034972b771efce793ccd9bfde9af67f42 | 59,100 |
def get_optimal_betas_df(lambda_vec, epsilon_vec, lambda_arrays_list, model_string='G1'):
"""Return a dataframe containing the optimal values for beta."""
running_list = []
model_repeated = np.repeat(model_string, len(epsilon_vec))
for i, lambda_array in enumerate(lambda_arrays_list):
lambda_rep... | 57c8e92937964a0b52dcb5b4110c16d325153d53 | 59,101 |
from multiprocessing import Pool
def multiprocessing_write_abstracts_for_gensim():
"""
Takes an abstract and id number and saves the abstract text to a folder and the int values to a folder
:param tuple_in: tuple of form (abstract, int_tokens)
:return: True on success, False on error out
"""
... | daca6b9aa890dc36938d754d964784462ac28011 | 59,104 |
def re2_full_match(input, pattern): # pylint: disable=redefined-builtin
"""Extract regex groups
Args:
dataset: A `tf.string` tensor
pattern: A pattern string.
"""
return core_ops.re2_full_match(input, pattern) | 60ea0c474dee26d4a03c4089c7d68ccd3816fa37 | 59,105 |
def resnet101(pretrained=True, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
# if pretrained:
# model.load_state_dict(model_zoo.load_url(
# mod... | d7011d753169b5ab5f2cc3a4a6018eae0134451b | 59,106 |
def get_formatted_message(message):
"""Gets a log-friendly format of the given message."""
if message.edited_timestamp:
edited = ' (edited {})'.format(message.edited_timestamp)
else:
edited = ''
if message.attachments:
urls = [attachment['url'] for attachment in message.attachmen... | f41d0da41751f7ded28b230d68c38415820ae7f1 | 59,107 |
def generate_graph(data, course_to_id):
"""
Method to read the JSON and build the directed graph of courses (= DAG of course IDs)
The graph is represented in an adjacency list format
:return: A graph in adjacency list format, with vertices as course IDs and directed edges implying prerequisite relation
:rtype: L... | 0a6fe305e39bd812e000a1d22d2374636a7900b2 | 59,109 |
def vpt(*args, **kwargs):
""" Compute the valid prediction time for a set of parameters
Parameters:
-----------
system (rescomp.optimizer.System): the system to get data from
pred_type: Predict continuation of training trajectory or predict evolution
of a random initial ... | 7bf5fe47672fc06c4fa872bc53f847e5d2433687 | 59,112 |
import select
def recipebook():
"""
Recipe book provides the user with a select menu containing all their recipes.
When they select one and press the "let's cook!" button,
a screen appears displaying the desired recipe.
"""
# If user arrives via GET (that is, if they've clicked on "Book o' Re... | a9c0fd2cd802977dd4cf4b61b8f419d5e00f19a4 | 59,113 |
def _build_external_session(conn_params):
"""
new a sqlalchemy database session.
"""
conn_info = '{conn_type}://{username}:{password}@{host}:{port}/{schema}'
conn_info = conn_info.format(**conn_params)
global _session_class
if conn_info not in _session_class:
engine = create_engine(c... | 45b9efb84fd0ec00937815e8b33ad482e51f11ca | 59,117 |
def stats(token="", version="stable", filter="", format="json"):
"""https://iexcloud.io/docs/api/#stats-intraday
Args:
token (str): Access token
version (str): API version
filter (str): filters: https://iexcloud.io/docs/api/#filter-results
format (str): return format, defaults t... | c523444e0f7f3484664fc78dd648365b4d80e693 | 59,118 |
from typing import Optional
import math
def round_for_theta(
v: float,
theta: Optional[float]
) -> float:
"""
Round a value based on the precision of theta.
:param v: Value.
:param theta: Theta.
:return: Rounded value.
"""
if theta is None:
return v
else:
... | ef6d18df588df8a80be9dc6c9b7f9104e3109d69 | 59,119 |
def _resolve_name(name, package, level):
"""Return the absolute name of the module to be imported."""
if not hasattr(package, 'rindex'):
raise ValueError("'package' not set to a string")
dot = len(package)
for x in range(level, 1, -1):
try:
dot = package.rindex('.', 0, dot)
... | 61841fd4d854a59978e82fa5708332b91d8e7680 | 59,120 |
import torch
def calculate_contrastive_empowerment(
discriminator,
obs,
next_obs,
latents,
num_prior_samples=512,
distribution_type='uniform',
split_group=4096*32,
obs_mean=None,
obs_std=None,
return_diagnostics=False,
prior=None,... | 71f3819054072f1141e63da1b95bc894ede76099 | 59,122 |
def sql_column_old(key, sqltype, lst, sqlval_func=lambda x: x, fileval_func=lambda x: x):
"""
Examples
--------
>>> _vals = [25,50,75]
>>> vals = sql_column('ecutfwc', 'float', _vals,
... fileval_func=lambda x: 'ecutfwc=%s'%x)
>>> for v in vals:
... print(v.key, v.s... | 0262f7ebf7d63dbe391507fe8156885815c04730 | 59,125 |
from typing import List
def split_badge_list(badges: str, separator: str) -> List[str]:
"""
Splits a string of badges into a list, removing all empty badges.
"""
if badges is None:
return []
return [badge for badge in badges.split(separator) if badge] | 6dc53d45cc8390422e5a511e39475ae969bf37c9 | 59,126 |
def delete_firewall(name):
"""Delete a firewall with the given name"""
log("Deleting firewall '" + name + "'. This may take a while...", prefix=True)
return run('compute firewall-rules delete ' + name + ' -q') | f91c76c6358d9fadd79630b1cfe126d4f6f90e07 | 59,127 |
from subroutines import permutations_m_to_n_str
def euler_problem_43():
"""
The number, 1406357289, is a 0 to 9 pandigital number because it is made up of each of the digits 0 to 9 in some order, but it also has a rather interesting sub-string divisibility property.
Let d1 be the 1st digit, d2 be the 2nd ... | c81d14e3e773e5c0b3c2e4e6f6eebc01b9a5a186 | 59,128 |
def indexify(expr):
"""
Given a SymPy expression, return a new SymPy expression in which all
AbstractFunction objects have been converted into Indexed objects.
"""
mapper = {}
for i in retrieve_functions(expr):
try:
if i.is_AbstractFunction:
mapper[i] = i.inde... | 27b4a00dfde7eee13fbb9be92060853e55feed72 | 59,129 |
def trepeat(elem, n):
"""
>>> trepeat(1, 2)
(1, 1)
"""
return tuple(repeat(elem, n)) | e908b9e2634bfff882ae8d88fadcfbbb95cfb0bb | 59,130 |
def parse_table(file_name, sep, header=False, excel=False):
"""Opens standard delimited files and outputs a nested list."""
with open_plus(file_name) as infile:
line = infile.readline()
# First figure out if this table came from excel and has \r for line breaks
if line.count('\r') > 0:
... | 378432271ac11a733a7d78e58671ef52652320bf | 59,131 |
def merge_dicts(*dicts):
"""
Merges dicts in reversed order.
:param dicts: dicts to be merged
:return: merged dict
"""
result = {}
for d in reversed(dicts):
if d:
result.update(d)
return result | 2076a23f8cb5d92fc4fa93c3ea4e872eec5202b6 | 59,132 |
def chol_reindex_params(params_sdcorr):
"""
Creates the params Df with Cholesky factors and the right indices for
respy. This transformation holds only true for the parametrization
in KW94 Dataset 1.
Thus, this function simply changes SD-Corr indices to cholesky indices.
Without the slicing and ... | 3dc6d59edd051ec9fab4e64b531b8298c04b4be2 | 59,133 |
def preprocess_images_from_directory(train_dir_path, test_dir_path
, label_mode, img_size, seed=45,
batch_size=32, val_split=0.2, ):
"""
preprocess images using the training and testing directories only, a separate validiation dataset is ... | 41814554c544858c15af0fbfadb4e077bc20ae62 | 59,134 |
def get_model_name(name, batch_size, learning_rate, epoch):
""" Generate a name for the model consisting of all the hyperparameter values
Args:
config: Configuration object containing the hyperparameters
Returns:
path: A string with the hyperparameter name and value concatenated
"""
... | 6f2d8ae3d9ae03eb9cf30b538196b7cb08cbdbe2 | 59,135 |
import torch
def soft_weighted_medoid_k_neighborhood(
A: torch_sparse.SparseTensor,
x: torch.Tensor,
k: int = 32,
temperature: float = 1.0,
with_weight_correction: bool = True,
threshold_for_dense_if_cpu: int = 5_000,
**kwargs
) -> torch.Tensor:
"""Soft Weighted Medoid in the top `k` n... | f0a5e799d45c5338d2f423a74fc0c9369d9eecfb | 59,136 |
def compute_area_signed(pr) -> float:
"""Return the signed area enclosed by a ring using the linear time
algorithm at http://www.cgafaq.info/wiki/Polygon_Area. A value >= 0
indicates a counter-clockwise oriented ring."""
xs, ys = map(list, zip(*pr))
xs.append(xs[1])
ys.append(ys[1])
return s... | 9c0e3a8daf5a1fb8a3bd25b28aaa7b941df7b29b | 59,137 |
def get_teams_of_coach(cursor, coach):
"""gets all the teams a coach has
Args:
cursor: the cursor
coach: the name of the coach
Returns:
a list of all the teams a coach has
"""
cursor.execute(
"Select team_name from TeamsCoaches where coach_name = '{}'".format(coach... | 3d189a20ee23a2ace4b16bb66aca134f65635efb | 59,139 |
def get_timeline(api, screen_name=None, user_id=None):
"""
Get tweets of a selected user.
"""
count = constants.MaxCount.TIMELINE
tweet_mode = constants.TweetMode.EXTENDED
cursor = tweepy.Cursor(
api.user_timeline,
screen_name=screen_name,
user_id=user_id,
count=... | c75d58bf9fb2e013fa36cc8cfc96d258d145d7d6 | 59,140 |
import random
def seq_generator():
"""
Generate each part of sequence.
:return: sequence part as str
"""
return random.choice(["T", "H"]) | 4714194dea06611b269a7157ac6f0f0cb7c1415c | 59,141 |
def svn_ra_get_lock(*args):
"""
svn_ra_get_lock(svn_ra_session_t session, svn_lock_t lock, char path,
apr_pool_t pool) -> svn_error_t
"""
return apply(_ra.svn_ra_get_lock, args) | 0dd4185a9aef9f6ac7c8e249a428ad1db0a47222 | 59,142 |
def check_if_image_file(file: FileStorage) -> bool:
"""
Checks, if file is image file based on ending of file. Currently supported: png, gif, jpg/ jpeg
:param file: File from request body
:return: Boolean, if file is an image file or not
"""
return any([file.filename.endswith(image_format) for i... | 697f16908c8d8f6d16eb04e1b3e4c89a4422ea13 | 59,143 |
import re
import html
def convert_html_escapes_to_xml(html_text):
"""Avoid XML parsing errors by converting HTML escape codes to XML."""
html_entities = set(
re.findall(r'&(?!quot|lt|gt|amp|apos)[a-zA-Z]{1,30};', html_text)
)
for entity in html_entities:
html_text = html_text.replace(e... | 0ada386b69c8249db7cf1b356051e167a1b8dbdf | 59,144 |
def column(matrix, col):
"""Returns a column from a matrix given the (0-indexed) column number."""
res = []
for r in range(len(matrix)):
res.append(matrix[r][col])
return res | f5214b5a41bf265f6892fdfd9422061b02f61c63 | 59,146 |
import re
def parse_scenario_gtest_name(name):
"""Parse scenario name to form acceptable by gtest"""
# strip
name = name.strip()
# space and tab to _
name = name.replace(" ", "_").replace("\t", "_")
# remove dots. commas
name = re.sub('[^a-zA-Z_]+', '', name)
# make lower
name = na... | faca8406a6b033536c3f9e0da21d9dc818fd2a7e | 59,147 |
def comma_ai(width, height, channels):
""" Get the model, this is a slight modification of comma.ai model
"""
# image size should be (45,160) with 3 color channels
ch, row, col = channels, height, width
model = Sequential()
model.add(Lambda(lambda x: x / 255. - 0.5,
input_s... | 9e50e5a705a1697fbb96fe0431d0485ee23b0581 | 59,148 |
def data_loader(dataroot, trainset, valset, batch_size, shuffle, num_workers):
"""dataset Loader.
Args:
trainset: training set
valset: validation set
batch size
shuffle ratio
num_workers: number of workers in DataLoader
Returns:
trainloader (torch.utils.data.... | c4797cbf6fa0322b607113196b7f4c2240c9dfed | 59,149 |
def assign_inband_ip(module):
"""
Method to assign in-band ips to switches.
:param module: The Ansible module to fetch input parameters.
:return: String describing if in-band ip got assigned or not.
"""
global CHANGED_FLAG
inband_ip = module.params['pn_inband_ip']
leaf_list = module.para... | d21da3a1d74648f1ea8bf49ffaaafcf8bbb55d4b | 59,150 |
def one_hot_matrix(labels, C):
"""
Creates a matrix where the i-th row corresponds to the ith class number and the jth column
corresponds to the jth training example. So if example j had a label i. Then entry (i,j)
will be 1.
Arguments:
l... | 4d8c95dab95d398f6ea6639b6b566036f293f92c | 59,151 |
def check_stl(filename, threshold):
"""
Parse the (poorly-formatted) STL file and check that no face has an area
below a certain threshold (in angstroms squared).
"""
# Load stl
vertex_data = []
with open(filename) as fp:
for line in fp:
if line.split(" ")[0] == " vertex... | 8d8a97efa109e0e501eadb5c4ef61bb17f4a956e | 59,152 |
import json
def load_json(filepath):
"""Load a json file.
Args:
filepath (str): A path to an input json file.
Returns:
dict: A dictionary data loaded from a json file.
"""
with open(filepath, 'r') as f:
ret = json.load(f)
return ret | c64fec0c861d223e28001051906e32d3aa9ffc7a | 59,153 |
import json
from datetime import datetime
from dateutil import tz
def puzzle_view(request, puzzle_id):
"""
A view to handle answer submissions via POST, handle response update requests via AJAX, and
render the basic per-puzzle pages.
"""
puzzle = get_object_or_404(Puzzle, puzzle_id__iexact=puzzle_... | 8a8fe907d176769102be636b6281ccfe67948b4a | 59,154 |
def linear_unbin(arr):
"""
Convert a categorical array to value.
See Also
--------
linear_bin
"""
if not len(arr) == 15:
raise ValueError('Illegal array length, must be 15')
b = np.argmax(arr)
a = b * (2 / 14) - 1
return a | ef4154d4f32b8e14f19beba848d54a848ed04e2b | 59,155 |
def student_detail_url(student_id):
"""return url for the student detail"""
return reverse('user:student-detail', args=[student_id]) | e0ce19282b9e5bc596965fe2fa1d888cbc75d377 | 59,156 |
def get_orders_from_gunboat_openings(power_name):
""" Samples openings orders from the gunboat openings
:param power_name: The name of the power we are playing
:return: A list of opening orders for that power
"""
gunboat_openings = {'AUSTRIA': {('A VIE - GAL', 'F TRI - ALB', 'A BUD - SER'): ... | 142c4e3871e3e26746f6ccfbe018662e394ccb43 | 59,157 |
def is_insensitive():
"""Check if user name should be treated case-insensitive."""
case_spec = getattr(settings, 'HTTP_BASIC_AUTH_CASE', "y")
if isinstance(case_spec, str):
return case_spec[:1].lower() in ("0", "f", "n")
return not bool(case_spec) | ff210f4bec2df61792ba3777d2ff33744ceaf720 | 59,158 |
def get_images_paths(path):
"""
Get sorted images paths.
Args:
path (str): Images directory.
Returns:
list: Sorted images paths.
"""
return _get_sorted_paths(path, "jpg") | d923b290cb986a554facc5de058ed48ef8980d67 | 59,159 |
def split_msg(msg, newline_type):
"""Split a HTTP message and return the head and body in a list."""
double_newline = msg.find(newline_type + newline_type)
if not bool(~double_newline):
raise errors.NewlineError('Missing double newline!')
return SplitMessage(
head=msg[:double_newline],
... | 0f3d37692dfa4d363d40468b6a166ec9cc51e06c | 59,162 |
def render_state_history_pca_3d(
hidden_history,
lw=0.1,
alpha=0.1,
color="k",
_show=True,
actors=None,
mark_start=False,
start_color="k",
mark_end=False,
end_color="k",
axes=0,
color_by_trial=False,
pca=None,
):
"""
Fits a PCA to high dim hidden state history... | 9b5962758fec40bbd4c5fb736f0205716f1b9a25 | 59,163 |
import re
def branch_config_map(option):
"""Return {branch: <|option| value>} for all branches."""
try:
reg = re.compile(r'^branch\.(.*)\.%s$' % option)
lines = get_config_regexp(reg.pattern)
return {reg.match(k).group(1): v for k, v in (l.split() for l in lines)}
except subprocess2.CalledProcessErr... | 5417055f37c5dfd6fa87b51db1c7661a66ded9d1 | 59,164 |
def plot_bars(info, metric, data, regnam):
"""Add legend and save plot to either png or pdf.
Parameters
----------
info : dict
compilation of plot properties
metric : str
plot type [area, fraction or bias]
data : list
list of floats for plotting
regnam : list
... | 038aa61ff4a1f2016730d02ef25b8632d51a13ae | 59,165 |
def row_sums(
x,
na_rm=False,
# dims=1,
# weights = None,
# freq = None,
# n = None
):
"""Calculate sum of a data frame by row
Args:
x: The data frame
na_rm: Specifies how to handle missing values in `x`.
Returns:
The sums by row.
"""
_check_all_nume... | 0598208eb8c4ff52a4a6fc6eed73eb6063479b95 | 59,166 |
import math
from numpy import zeros, sum
def rs1_score(sequence):
"""
Generates a binary matrix for DNA/RNA sequence, where each column is a possible base
and each row is a position along the sequence. Matrix column order is A, T/U, C, G
"""
# Import Libraries
# Function
"""
Scoring al... | 00736fc2002eebeb2d1fec4a0999c96ae5ff580d | 59,167 |
def _count_atoms(expr, **kwargs):
"""Counts the occurrences of atoms in SymPy expression "expr".
Internal function; implementation for count_atoms(). Used also as-is by analyze().
Number atoms are ignored by default. See kwargs.
Return value:
dictionary: atom -> (#occurrences, topmost_level_... | fb52938fd842ae84b742193ffe9f5b0c7be6fbe7 | 59,169 |
def compute_tpm(input_counts):
"""
Default TPM normalization
"""
tpm = input_counts.copy()
sc.pp.normalize_per_cell(tpm, counts_per_cell_after=1e6)
return(tpm) | b8254905f826238e6a6222cab267ab52eeea777c | 59,170 |
def listdata():
"""
List the available datasets by querying the DB and
returning metadata about the available user uploaded data.
"""
cuid = current_user.get_id()
response = {'status':'success','links':[]}
links = response['links']
for i in getdatalist(cuid).itervalues():
response['link... | 65b1602b6dd3f5550a30d773b5f48d377b32fc55 | 59,171 |
def load_from_file(filename):
"""load from .yaml file or .sql file"""
suffix = filename.split('.')[-1]
loader = SchemaLoader()
if suffix == 'yaml':
return loader.load_from_file(filename)
if suffix == 'sql':
return loader.read_from_oracle(filename) | 1b752ded3b3f9d0d525a0cbbeb2c17e674ac5ba5 | 59,172 |
def makeTitlePage(title, subtitle1='', subtitle2='', subtitle3='', center=False):
"""
Draw a title page, return a PIL image
"""
font = ImageFont.truetype(config.titleFont, config.titleFontsize)
imgsize = [800,800]
bgcolor = (0,0,0)
fgcolor = (200,200,200)
img = Image.new("RGBA", imgsi... | 3cd888923a8b6850f3855ce7fdc2177c6281f3e1 | 59,174 |
import ctypes
def macos_api_ignore():
"""
Starting with macOS 11, the security API becomes
non-viable except on universal2 binaries.
Ref #525.
"""
try:
ctypes.CDLL(ctypes.util.find_library('Security')).SecItemAdd
return False
except Exception:
return True | 68c2a9e7f3b59cc0a2c59ba06506de28b4c3f49b | 59,175 |
def preprocess(img, img_shape, detection, margin=0.25):
"""Preprocessing"""
left = detection.left()
right = detection.right()
top = detection.top()
bottom = detection.bottom()
w, h = abs(left - right), abs(top - bottom)
face_size = max(w, h)
desired_size = 1 - 2 * margin
desired_size... | fa7de290d1cf079f6ca1fcbc0215832651e64293 | 59,176 |
def get_email_of_user(auth0_id):
"""
Get the email of a user given their auth0 ID
Parameters
----------
auth0_id : str
The auth0 ID of the user
Returns
-------
str
The email if found, otherwise 'Unable to retrieve'
Raises
------
ValueError
If the au... | f3e09d7c225888489c38c1f32574b7a9a1511fa8 | 59,177 |
def represent_str(dumper, data):
"""Allows PyYaml module to dump strings as literals """
if utils.is_multiline(data):
return dumper.represent_scalar('tag:yaml.org,2002:str', data, style='|')
return dumper.represent_scalar('tag:yaml.org,2002:str', data) | 0e40b7cad8f13c1e2a31aaf0077f7efecc81ca28 | 59,179 |
def get_cert_contents(public_key_filename):
"""
Print cert
"""
if public_key_filename.endswith('.pub'):
public_key_filename = public_key_filename[:-4]
cert_filename = public_key_filename + '-cert.pub'
with open(cert_filename, 'r') as pub_key:
cert_contents = pub_key.read()
re... | 7be010d5a8f0d86129748f3936cc3afccb641e81 | 59,180 |
def rw_simul(prob):
"""simulate RW for prob as probability of going left"""
x_pos = np.zeros((10000, 10))
for i in range(10000):
pos = 0
for j in range(10):
for _ in range(100):
pos = rw_next(pos, prob)
x_pos[i, j] = pos
x_mean = np.mean(x_pos,... | 89acfbae89ef07a0aeb841d9179a1b7de071f613 | 59,181 |
def sort_states(state, columns, reverse=True):
"""
Sort the states according to the list given by prioritize_jobs.
prioritize_jobs (or columns in this case) is list of according to
which state should be prioritized for job submission. The position
in the list indicates the prioritization. columns =... | ae8c042d87e5bd2553ceaa08f5e7c88ea7ac2909 | 59,182 |
def phase(J):
"""Return the phase."""
gamma = np.angle(J[..., 1]) - np.angle(J[..., 0])
return gamma | 40187ef149d14b7bde755ed4c76153ffd8a10887 | 59,183 |
async def vocprez_profiles(request: Request):
"""Returns a JSON list of the profiles accepted by VocPrez"""
return await profiles_func(request, "VocPrez") | 4138b121bb852b3e64f9b626a2cac03fe02f32f1 | 59,184 |
def get_thread_id() -> str:
"""Returns thread_id."""
return current_process().name | f625d7c2f1b95367123efa50291046866c6dcfd3 | 59,185 |
import numpy as np
def fearly3_kasen(td, rstar, band):
"""
This function produces the shock-heated emssion light curve. Written for the SN 2015F work based on the Kasen (2010) model [2015. M. Im].
Currently, the explosion energy is set to 10^51 erg which is appropriate for Type Ia SN.
This value may n... | a30efff373685840531e268ef596e098e24f48d9 | 59,186 |
def get_job_chart_metadata(job: Job) -> str:
"""
Get a string containing a javascript object used to draw the Gannt chart for a job's timing
:param job:
:return:
"""
job.refresh()
return job.get_chart_metadata() | 107ea87b5ed82c9745e953ee0eaf8e3284074ebd | 59,187 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.