content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def PNT2Tidal_Tv14(XA,chiA=0,chiB=0,AqmA=0,AqmB=0,alpha2PNT=0):
""" TaylorT2 2PN Quadrupolar Tidal Coefficient, v^14 Timing Term.
XA = mass fraction of object
chiA = aligned spin-orbit component of object
chiB = aligned spin-orbit component of companion object
AqmA = dimensionless spin-induced quadrupol... | 82eae87495785a5d0cce4d3f0ae5b6654395a42c | 3,597,662 |
def calculate_variance(centers_of_intervals, quantities_of_velocities_per_interval, size, mean) :
"""" Обчислює дисперсію """
variance = 0
for i in range(0, centers_of_intervals.size) :
normalized_value = centers_of_intervals[i] - mean
variance += (normalized_value*normalized_value) * quantities_of_veloc... | d5fece546bc96ce41374b1d5b4fdc6041fd62f0a | 3,597,663 |
import random
def noisify(val, eps):
"""
Add a Gaussian White noise to function value
"""
val_noisy = (1 + random.gauss(mu=0,sigma=eps)) * (1-val)
return val_noisy | 40059eb7f87aa1b7b7096a1c84c7edef09cef4e1 | 3,597,664 |
import json
def json_response(obj):
"""Returns a json response from a json serializable python object"""
return Response(
response=json.dumps(obj, indent=4, cls=AirflowJsonEncoder), status=200, mimetype="application/json"
) | 92fcff50fbdab37fb3b2c8ebbd9e22e8d2c265b6 | 3,597,666 |
def inceptionv4(**kwargs):
"""
InceptionV4 model from 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
https://arxiv.org/abs/1602.07261.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx ... | 13e96e4cb7a02369b488e241004b2e4f7469b0ff | 3,597,667 |
def flatten_list(l):
"""Flatten list of lists"""
return [item for sublist in l for item in sublist] | a22bd5ee79aba47c058436886b435bca47ab1db1 | 3,597,668 |
def evaluate(tokenizer, nlp, valid_data, labels):
"""Evaluate model performance on a test dataset."""
texts, cats = zip(*valid_data)
golds = []
# Use the model's ops module
# to make sure this is compatible with GPU (cupy array)
# or without (numpy array)
scores = np.zeros((len(cats), len(l... | 199277feff079fb6c16cc9ec2fe72d21113c30b3 | 3,597,669 |
def simple_line_plot(x, y, x_title: str, y_title: str, error=None) -> go.Figure:
"""Generates a simple line plot
Args:
x: x-axis data
y: y-axis data
x_title: x-axis title
y_title: y-axis title
error: Optional error vector. If given, continuous
error bar will ... | b31751b3c1c1e7adaff0af842a4d0899ed81c81f | 3,597,670 |
def create_container(id_ref, project_id=None, external_project_id=None):
"""Generate a Container entity instance."""
container = models.Container()
container.id = id_ref
container.name = 'test name'
container.type = 'rsa'
container_secret = models.ContainerSecret()
container_secret.container... | 030fd789befe3fd76cd8338d50aad3e90c5c23a8 | 3,597,671 |
def str_to_int(string):
"""Convert a string to a integer."""
string = str_to_str(string)
try:
return int(string)
except ValueError:
raise errors.ConfigurationError(f'{string!r} is not an integer') | b9e538c8640ec746ce5fd457569b08927c359095 | 3,597,672 |
def is_xml(maybe_xml):
"""Determine whether a given parameter is XML.
Args:
maybe_xml (str): An string that may or may not be valid XML.
Returns:
bool: A boolean indicating whether the given Dataset is valid XML.
"""
error_log = _check_is_xml(maybe_xml)
return not error_log.c... | df6607c44ae92ac56df0c1ede837c4a5e50f2626 | 3,597,673 |
def __get_pod_service_list(pod_items):
"""
Returns a set of pod service_account names from the pod_list parameter
:param pod_items: the list of pod_items from which to extract the name
:return: set of pod names
"""
out_names = set()
for pod_item in pod_items:
if pod_item.spec.service... | da09b2bac32feade9ddbe822e64dacc3676cab88 | 3,597,675 |
def valuelist(items, field=None, enumeration=False):
"""
Common template tag to show a list of values in detail pages.
:param field: Use this attribute on each item instead of their unicode representation
:param enumeration: Show enumerations, useful to match those shown by ``mapentity/leaflet.enumerat... | 077a54536ca0e7956998cf3d37bc1c9edecfc7b9 | 3,597,676 |
def create_model_uses( ns_,
model_name = "Uses",
include_used_as = False,
used_as = "tax",
schema_list = ["prj","dmt","dmf","dsi","dsr","usr","rec"],
):
"""
Uses model
"""
uses_dict = {}
for schema in schema_list :
doc_uses = {
... | 902cbd87de468f69db125bb8ffaa636b41c0f3fb | 3,597,677 |
def list_checker(token, q):
"""
List checker
"""
try:
tainted = False
if type(q) is list:
try:
track(q, False)
for i in q:
token_mapper(i)
except Exception as e:
common.logger.error("Problem with list parsing in list_checker method of findMethods.py: " + str(e))
else:
try:
if is_... | 0f7b66c37e6c58a805cb14dd3b3ed37e7d7d4e71 | 3,597,679 |
def decode_rlp(data: str) -> list:
"""
Decode an rlp/b2b message to a list
Args:
the encoded prefixed sting
Returns:
the raw data contained in the rlp message
"""
rlp_enc = decode(data)
return rlp.decode(rlp_enc) | 5cb1cb1e2d6098f5ac1b835465c2627ac7ae9d39 | 3,597,680 |
def get_box_size(box):
"""Get box size"""
x0, y0, x1, y1 = box
sx = abs(x1 - x0) + 1
sy = abs(y1 - y0) + 1
return (sx, sy) | cefa6c8950687f0b4d244ebf9080ab29e358a7b2 | 3,597,681 |
def norm_uri(uri):
"""スキーマのURIを正規化する。
"""
if not uri.endswith('.xsd') and uri.startswith('http://disclosure.edinet-fsa.go.jp/taxonomy/'):
v = uri.split('/')
name_space = v[4]
yyyymmdd = v[5]
name_cor = v[6]
# uri : http://disclosure.edinet-fsa.go.jp/taxonomy/jppfs/2... | 2c099f56a563dceca29a35fbc375e634d040ffb3 | 3,597,682 |
from astropy.table import Table
def state_intervals(times, vals):
"""
Determine contiguous intervals during which the ``vals`` is unchanged.
Returns an Astropy Table with a row for each interval. Columns are:
* datestart: date of interval start
* datestop: date of interval stop
* duration: ... | 6770f6bb9358cbeb1efe23a4707a45376021a32d | 3,597,683 |
import math
def tfidf_async(sg):
"""DOESN'T WORK!!!!"""
all_genres = sg.all_genres
df = pd.DataFrame(columns=["token"]+all_genres)
global_vocab = set()
for genre in all_genres:
# do this async
gvoc = sg.models[genre].vocab.counts
for word in gvoc:
global_vocab.a... | e6f2b40a17ed7d88cffbb600f164c5b3884a496e | 3,597,685 |
from intake.container import container_map
import requests
import msgpack
def open_remote(url, entry, container, user_parameters, description, http_args):
"""Create either local direct data source or remote streamed source"""
if url.startswith('intake://'):
url = url[len('intake://'):]
payload = d... | b36d4cb14b0ed06970ed90b45e1bf222285488b3 | 3,597,686 |
def get_carousel_state(state=None, action_args=None):
"""Obtain carousel state from dictionary of States.
Args:
state: The original state to convert into new format.
action_args: Action arguments.
Returns:
new_state: Return state in a new format.
"""
new_state = {"focus": N... | 3faa57312e6a9fc3d8cb13c760e208866b781ef8 | 3,597,687 |
import scipy
def martinis_flux_pulse(length: float,
theta_i: float, theta_f: float,
lambda_2: float, lambda_3: float=0, lambda_4: float=0,
sampling_rate: float =2.4e9):
"""
Returns the pulse specified by Martinis and Geller as θ(t) specif... | 8e1ef2657565b6859bef34a81fa938ae9080303e | 3,597,688 |
import inspect
def parse_maker_spec(maker):
"""Return MakerSpec of `maker`."""
sig = inspect.signature(maker)
input_specs = []
for parameter in sig.parameters.values():
if parameter.annotation is sig.empty:
# We should probably not err out here because maker could be
... | 421bc9dcf655712b841d60b2d221b913ff6856fe | 3,597,689 |
def validate_hash(hash_name, hash_value):
"""
Verifies if the given hash value matches the hash algorithm.
:param hash_name: Name of the hash algorithm.
:type hash_name: str
:param hash_value: String with the hash value to check.
:type hash_value: str
:returns: True if the algorithm and v... | 800022e536ffa7394a24c8a28ac6069d53cf2053 | 3,597,690 |
import urllib
def should_add_headers(url):
"""
Should Typhos add the pre-configured headers when navigating to ``url``?
Avoids sharing tokens with unrelated sites that the user may click.
Parameters
----------
url : str
The URL in question
Returns
-------
True if headers... | a8376b6e4ff544d51edd102517f7a24739384ae6 | 3,597,691 |
def index(request):
"""View function for home page of site."""
# Generate counts of some of the main objects
num_postcards = Postcard.objects.all().count()
# Number of Approved postcards
num_postcards_approved = Postcard.objects.filter(approved=True).count()
context = {
... | 4a98bee7e469c2f5ce468ffb2628f1f1190c3c02 | 3,597,692 |
import scipy
def compute_depth(mask,N):
"""
compute the depth picture
"""
im_h, im_w = mask.shape
N = np.reshape(N, (im_h, im_w, 3))
# =================get the non-zero index of mask=================
obj_h, obj_w = np.where(mask != 0)
no_pix = np.size(obj_h) #37244
full2obj = np.z... | 428a9f18f253308c5ed1bff5170594a0e03bf4d7 | 3,597,693 |
import array
import warnings
def save_list_as_array(list_obj, grp_target, compress_float=False):
"""
Works for both lists and tuples. Returns True if the saving was successful.
"""
N = len(list_obj)
if N > 0:
type0 = type(list_obj[0])
if type0 in (BasicNumTypes + (str, ndarray)):... | 8040da38671f1db0348182128683388f8a8cbee5 | 3,597,694 |
import re
def get_rotations(sequence):
"""
Given a cube sequence,
find all 24 rotations of it.
Need to fix this so it doesn't
necessarily expect the U-first case.
"""
cubes = ["UBFLRD",
"UFBRLD",
"ULRFBD",
"URLBFD",
"DFBLRU",
... | 47061c29c0692f44213603d6826c53852c0de8eb | 3,597,698 |
from io import open
def get_serial():
"""Get serial number of Raspberry Pi."""
serial = None
with open('/proc/cpuinfo', 'r') as fh:
for line in fh.readlines():
if 'Serial' in line[0:6]:
serial = line[10:26]
if not serial:
raise IOError('Serial not found,... | e865288aa620cc214804e4320e54f9c3be364d78 | 3,597,699 |
from datetime import datetime
def unix_epoch_to_datetime(ux_epoch):
"""Convert number of seconds since 1970-01-01 to `datetime.datetime`
object.
"""
return datetime.utcfromtimestamp(ux_epoch) | 13edceec1631a2a3db06dad215380f609693f441 | 3,597,701 |
import torch
def rf2_ex(FpFmZ, alpha):
""" Propagate EPG states through an RF excitation of
alpha (radians) along the y direction, i.e. phase of pi/2.
in Pytorch
INPUT:
FpFmZ = 3xN vector of F+, F- and Z states.
alpha = RF pulse flip angle in radians
OUTPUT:
FpFmZ = Updat... | 926d3c7a9b5db418f63fbec30cf7a15b55e58885 | 3,597,703 |
def get_token():
"""request to get token for session"""
return {"token": encode_auth_token()}, 200 | afb407bbc4e2ff0a14664bdf03afa6a6ced74a6d | 3,597,705 |
import torch
def point_edt2(point: torch.Tensor, grid: torch.Tensor) -> torch.Tensor:
"""Batched version of a Squared Euclidean Distance Transform for a D-dimensional point.
Args:
point: torch.Tensor of size [batch, *, D]. Each element is interpreted as a D-dimensional point (e.g. [row, col]
or... | b186ccccd94d8b709974efd3b2ec5817c698f77b | 3,597,706 |
def mergeuniques(xs, ys):
""" merge lists xs and ys. Return a sorted result """
result = []
xi = 0
yi = 0
xs.sort() # sort clause unneeded if lists already sorted (as in instructions), but added for generalising
ys.sort()
while True:
if xi >= len(xs): # If xs list is finish... | da02def34b5d347b43f26fbe1da6679f2e0db07a | 3,597,707 |
def get_ip_input():
"""Get IP address input."""
ip_addr = request.args.get("ip")
return ip_addr | 89a4706b98d2abedd99af6d278b4fa6ef2b5d0e4 | 3,597,708 |
def construct_coordinate_grid(model, coordinates, N=5):
"""Given n coordinates get the coordinate range and generate a
coordinate grid of combinations using cartesian product.
Parameters
----------
model: opensim.Model
coordinates: list of string
N: int (default=5)
the number of ... | d6e54b5b570ef6687f8dc3e6236b3109db183e89 | 3,597,710 |
def real(x):
"""
A generic real-part operator with backend selector
"""
backend = getBackend(x)
if backend == 'numpy':
return np.real(x)
elif backend == 'arrayfire':
return arrayfire.arith.real(x)
elif backend == 'scalar':
return np.real(x)
elif backend == 'list':... | bfc48e5a212e5197e9a0dfce1b27490221b1d34c | 3,597,711 |
import scipy
def mcnemar_midp(b, c):
"""Computes McNemar's test using the "mid-p" variant.
This is based closely on:
M.W. Fagerland, S. Lydersen, P. Laake. 2013. The McNemar test for
binary matched-pairs data: Mid-p and asymptotic are better than exact
conditional. BMC Medical Research Methodology 13... | 1a92ae0a0c27ae3c0a2707b10c75c2fedd210ddd | 3,597,712 |
def image_other_2_numpy(input_image, return_all=False):
""" Loads image data and returns a numpy array. There
will likely be many parameters to specify because
of the strange quantization issues seemingly inherent
in loading images.
"""
output_array = imread(input_image)
i... | 8b32b77b22f7d123e946941394127e7a5bc796c5 | 3,597,714 |
def search_choice():
"""
accepts input strings exit/help or string within range of options.
calls validation function on user input.
calls functions relative to the user's choice.
"""
while True:
user_input = input(
Style.BRIGHT +
Fore.BLUE +
"\nType y... | 9211a94ae230241eb76e6a0004b04b34189aaf86 | 3,597,715 |
from typing import Callable
def auth_required(route: Callable[P, R]) -> Callable[P, R]:
"""
Protect the given route with an authentication
check. Wrapping the route with this decorator ensures
that the current user will be authenticated before
the route call.
:param route: The route to protec... | a5255bbd63e2bf36698fa73bb8de4571d9e7d08d | 3,597,716 |
def op_transform(ops, affine_matrix):
"""
x, y, z -> x+1/2, y+1/2, z
0, 1/2, z -> 1/2, 0, z
Args:
ops: SymmOp object
permutation: list, e.g. [0, 1, 2]
Returns:
the new SymmOp object
"""
matrix2 = affine_matrix.dot(ops.affine_matrix)
return SymmOp(matrix2) | 8313a9d07f05eea873d939be98227431feb7832c | 3,597,717 |
def bottom_right(bbox=full_frame()):
"""Returns a bounding box covering the bottom right quadrant of ``bbox``."""
return right_half(bottom_half(bbox)) | 014a1c5ccd5c5efcccf25d2c8b29385b5eab188c | 3,597,718 |
def Sword():
"""
Creates a sword
"""
item = (ItemBuilder()
.with_name('sword')
.with_damage(2, 'piercing')
.with_damage(2, 'slashing')
.build())
return item | 5959d040213b2228906bf2855c00f09ecc0bc062 | 3,597,719 |
import yaml
def _read_conf(conf):
"""
Convert a YAML configuration into a dictionary
:param conf: The configuration filename
:return: A dictionary
"""
with open(conf, 'r') as stream:
try:
d = yaml.load(stream)
return d
except yaml.YAMLError as exc:
... | e44211e6423f3e5bb77df01fcaeec0d3653a227b | 3,597,720 |
from functools import reduce
def merge_dicts(*dicts):
"""
Merges dictionaries recursively. Accepts also `None` and returns always a (possibly empty) dictionary
"""
# if len(dicts) == 1: return dicts[0]
return reduce(lambda a, nd: merge_two_dicts(a, nd if nd else {}), dicts, {}) | 52f22c6900e4bc6fda3dcd05ca16ccbf4521e6a3 | 3,597,722 |
import queue
def bfs(G, start):
""" A simple breadth-first search algorithm implemented using native queues. """
seen = set()
q = queue.Queue()
# we don't care about threading so don't ask Queue to block execution
q.put_nowait(start)
while not q.empty():
# get the waiting node, again ... | a15f6ef42f8873b108c2bd48c462175c77fdeee8 | 3,597,723 |
import re
def content_type(response, **patterns):
"""Return name for response's content-type based on regular expression matches."""
ct = response.headers.get('content-type', '')
matches = (name for name, pattern in patterns.items() if re.match(pattern, ct))
return next(matches, '') | c1071b2feae41bd049a26542d89414de54c06d8e | 3,597,724 |
import gzip
import csv
def load_precomp_pairs_single_cohort(pairs_tsv_in):
"""
Load precomputed HPO pairs for a single cohorts, if optioned
Returns a dict of sample counts keyed on sorted HPO term pairs
"""
counts = {}
if pairs_tsv_in.endswith('.gz'):
fin = gzip.open(pairs_tsv_in, 'r... | 315628206445e58e9147a96cea0009d4b72b57ba | 3,597,725 |
import re
def convert_ip_address(ip_address: str):
"""
This function converts a ipv4 address in standard string format to a HEX representation
:param ip_address: string with IPv4 address in format '192.168.0.1'
:return: HEX representation of IPv4 address (string)
"""
if re.search('^((25[0-5]|... | 1f50e6e2cb34325d07680a58090b58a1e00b745e | 3,597,726 |
import base64
def base32_decode(encoded_bytes: bytes) -> str:
"""
Decodes a given bytes-like object to a string, returning a string
>>> base32_decode(b'JBSWY3DPEBLW64TMMQQQ====')
'Hello World!'
>>> base32_decode(b'GEZDGNBVGY======')
'123456'
>>> base32_decode(b'ONXW2ZJANRXW4ZZAMNXW24DMMV4C... | 215a1aacd815fe11b93cf7dc6105abdff3492ab2 | 3,597,727 |
import re
def replaceThreeOrMore(word):
"""
look for 3 or more repetitions of letters and replace with this letter itself only once
"""
pattern = re.compile(r"(.)\1{3,}", re.DOTALL)
return pattern.sub(r"\1", word) | c052a082d74873da1a4c64dc035aeff429b29efd | 3,597,728 |
def distance(p1: Point, p2: Point) -> float:
"""Calculate the distance between two points."""
d = ((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2) ** 0.5
if isinstance(d, complex):
return d.real
else:
return d | a797f4fa89157a3b8c9800df5f3db0c1af293583 | 3,597,730 |
def getSensor(deviceName, pinNum):
"""
Function to get a specific sensor
:param deviceName: The name of the device the sensor is connected to
:type deviceName: String
:param pinNum: The pin that the sensor is connected to (Note that in the case of mulitple pins, us... | 0b69e073cdc66743f87b391d9d9eb383d26c8730 | 3,597,731 |
def compute_modules(Z_scores, min_gene_threshold=10, fdr_threshold=None, z_threshold=None, core_only=False):
"""
Assigns modules from the gene pair-wise Z-scores
Parameters
----------
Z_scores: pandas.DataFrame
local correlations between genes
min_gene_threshold: int, optional
m... | 175e9cede54174cff4d84d93af819c7f396aff7a | 3,597,732 |
def abre_arquivo(caminho):
"""Abre arquivos"""
with open(caminho, 'r') as arquivo:
return arquivo.read() | f09d970eb4f142e9576e22fdf72984656b7b14b9 | 3,597,733 |
from re import X
def player(board):
"""
Returns player who has the next turn on a board.
"""
# Stores all the possible values of the board in variables
total_places = sum(1 for i in board for j in i)
empty_places = sum(1 for i in board for j in i if j == None)
x_moves = sum(1 for i in boa... | dcdab52bde69c519d2082d962117b8ff7ea4184f | 3,597,734 |
def get_b32_config():
"""Returns the ViT-B/32 configuration."""
config = get_b16_config()
config.name = 'ViT-B_32'
config.patches.size = (32, 32)
return config | 78fd7469c4f162df4a6bdaa1f172339ea369a359 | 3,597,735 |
def luhn_validation(credit_card_number: str) -> bool:
"""
Function to luhn algorithm validation for a given credit card number.
>>> luhn_validation('4111111111111111')
True
>>> luhn_validation('36111111111111')
True
>>> luhn_validation('41111111111111')
False
"""
cc_number = cred... | 4ed1cd788b54819ae4caead22bf34975a774dace | 3,597,736 |
def _rescale_score_by_abs(score: float, max_score: float,
min_score: float) -> float:
"""
Normalizes an attribution score to the range [0., 1.], where a score
score of 0. is mapped to 0.5.
:param score: An attribution score
:param max_score: The maximum possible attributio... | a9ab337cc47f2d62de33c267bfe19c9522190db4 | 3,597,737 |
import itertools
def tokenize_text_to_word_id(stringList, bert_preprocess):
"""
:param stringList:
:param bert_preprocess:
:return:
"""
tok = bert_preprocess.tokenize(tf.constant(stringList))
tok_list = tok.to_list()
tok_list_flatten = [list(itertools.chain.from_iterable(_i)) for _i ... | 39b837b752a90a0c80f612df82a70d4b01b34093 | 3,597,738 |
def citations_per_author(df, author_db):
"""
!!!! TAKES A LONG TIME --> PARALLELIZATION OR JUST DON'T INTEGRATE?
Create an array of citations (one number per paper) for every author in a df.
Used later on to compute the author h-index.
Input:
- df:
- author_db:
Output:
- ... | ded7a3210e1eb63e81658800d0215e8d6ff6c229 | 3,597,739 |
def _create_curve_building_tensors(float_leg_start_times,
float_leg_end_times,
float_leg_daycount_fractions,
fixed_leg_end_times,
fixed_leg_cashflows,
... | 458e4f0c2d615da7b6ee7039d5df0ecb783c081f | 3,597,740 |
def star(num_nodes: int, directed: bool = False) -> NDArray:
"""
Returns a star graph.
Parameters
----------
num_nodes : int
The number of nodes
directed : bool, optional
If set to True, will return a directed graph, by default False
Returns
-------
NDArray
... | 57a4bbf91786099f4375aa65181e46843f9ef693 | 3,597,741 |
from typing import List
import torch
def bootstrap(
full_a_scores: List[float],
full_b_scores: List[float],
num_samples: int = 10_000,
test_size: int = 1000,
is_pairwise: bool = True,
batch_size: int = 10_000,
) -> float:
"""
Uses bootstrap method to perform the... | dc891074a500ee8f8749625e08f3edc7c9acd0f7 | 3,597,742 |
import re
def get_installation_cell(notebook, libs):
"""Return a cell for installing the additional libs"""
lib_dict = dict(split_config_str(libs, 2))
lib1_re = re.compile('from ([_\w\d]+) import')
lib2_re = re.compile('import ([_\w\d]+)')
find_libs = []
for cell in notebook.cells:
if ... | ce7eb9a5713d186156c05a693ce83f7175c1b7b5 | 3,597,743 |
def store_cloudconvert_credentials( clientID, clientTOKEN ):
"""
stores the CloudConvert_ API V2 credentials into the SQLite3_ configuration database.
:param str clientID: the CloudConvert_ API V2 client ID. This is the *name* on the CloudConvert_ dashboard's API V2 key name. For example, for myself it is ... | b54a2a234e5b5585552b23fc16d78dffdeacff54 | 3,597,744 |
def __get_leading_zeros(fl):
"""Returns the number of leading zeros in a float decimal."""
if fl > 1.0:
return 0
else:
fl_splitted = str(fl).split(".")[-1]
N_unstripped = len(fl_splitted)
N_left_stripped = len(fl_splitted.lstrip("0"))
return N_unstripped - N_left_stri... | c009a9dcf7f2c57baee3043acf9ec416679fab67 | 3,597,745 |
import yaml
def load(path):
"""
:param path: config path
:returns dict of {name -> loaded config file} from all liminal.y[a]ml files under given path
"""
if cached_files.get(path):
return cached_files[path]
config_entities = {}
for file_data in find_config_files(path):
w... | 12c31adf4520b5c62a4ed1a71a33c39c01225d5b | 3,597,746 |
def kraus_tensor(klist, n):
"""
Recursive function that produces every (n-fold) tensor product of a list of kraus operators.
Parameters
----------
klist: list:
a list of numpy.ndarrays, to tensor together
n: int:
the number of terms that must ultimately be tensored together into ... | 2e8f6d2a24ec67936ccdd5b2e1464a3e972525ea | 3,597,747 |
def twist_dict(nested):
""" Make count dictionary with {cellbarcode : {taxonomyID : transcriptcount}} """
newdict = {}
for ckey, tdict in nested.items():
for tkey, kvalue in tdict.items():
if ckey in newdict:
if kvalue in newdict[ckey]:
newdict[ckey][k... | 81eba31fb7e5b6024b0d20a0d5e70c0f711d6b4d | 3,597,748 |
import requests
from bs4 import BeautifulSoup
def queryAEC(postcode, extrapage):
"""
Queries the AEC url and returns soup. If extrapage is empty
then we pass the soup to findFollowups before returning.
"""
url = "https://electorate.aec.gov.au/LocalitySearchResults.aspx?"
url += "filter={0}&fil... | 514cb9a471ab7636249eb7745c155bb23521e3ba | 3,597,749 |
from typing import Optional
def lru_cache_df(maxsize: Optional[int], typed: bool = False):
"""Least-recently-used cache decorator for pandas Dataframes.
Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can
save time when an expensive or I/O bound f... | 9e05bc2ba1f8c108f313ed9355ca5f34e595fdef | 3,597,750 |
def df_to_dictionaries(df, change_names={}, include_index=True):
"""Returns a list of dictionaries, one dictionary for each row in the
DataFrame 'df'. The keys of the dictionary match the DataFrame column names,
except for any substitututions provided in the 'change_names' dictionary;
{'old_name': 'ne... | f02c97318dda4da6bb082d3a78898584e27d353c | 3,597,751 |
import warnings
def _import_module(module_name, warn=True, prefix='_py_', ignore='_'):
"""Try import all public attributes from module into global namespace.
Existing attributes with name clashes are renamed with prefix.
Attributes starting with underscore are ignored by default.
Return True on succ... | b1e4b9cf741c34aebe2e22e77ba9fc10f20d1682 | 3,597,753 |
def _s(word, seq, suffix='s'):
"""Adds a suffix to ``word`` if some sequence has anything other than
exactly one element.
word : str
The string to add the suffix to.
seq : sequence
The sequence to check the length of.
suffix : str, optional.
The suffix to add to ``word``
... | 7ae7a7ac50b6b6ee92718877d242569d7339fd0e | 3,597,754 |
def tri_cube(xx, idx):
"""
The tri-cube kernel estimated for xx values at indices idx (zero
elsewhere)
Parameters
----------
xx: float array
Values of the function on which the kernel is computed. Typically,
these are Euclidean distances from some point x0 (see do_kernel)
... | 976d29b7202ee202057d666288ea23ae1ee4a86d | 3,597,755 |
def createParaDomain2D(*args, **kwargs):
"""API change here .. use createParaMeshPLC instead."""
print("createParaDomain2D: API change: use createParaMeshPLC instead")
return createParaMeshPLC(*args, **kwargs) | 05d64eace6e80f129e0e4c4aafa0f98b4288caa8 | 3,597,756 |
from typing import Optional
from typing import List
def send_slack_invite(email: str, *, channels: Optional[List[str]] = None,
resend: bool = True):
"""
Send a slack invitation to the provided email
:param email: Email to send the invitation to
:param channels: Channels for the u... | 6bede9b586c1d813f9fc4c016fca145df18b9454 | 3,597,757 |
def read_mat_file_body_identification(path=None, channels_img=None,labels_name=None,dim=3,read_img=True,read_label=True,read_info=True):
"""Especially for network body identification
Read mat file which contains image data and label data.
Return images data and labels data with shape(x,y.z, channels)
... | 43a61e232a3960110e8aa4c1beb92286ab0c7f87 | 3,597,758 |
def save_results_hdfs_directory(request, query_history_id):
"""
Save the results of a query to an HDFS directory.
Rerun the query.
"""
response = {'status': 0, 'message': ''}
query_history = authorized_get_query_history(request, query_history_id, must_exist=True)
server_id, state = _get_query_handle_and... | 8d10ea684a1647743a3728fcfe4042ab64081298 | 3,597,759 |
import json
def jsonPP(obj):
"""
Get a pretty stringified JSON
"""
str = json.dumps(obj, indent=4, separators=(',', ': '), sort_keys=True)
return str | 5840f63d5579f84667294cddd0887d4233d8c7f7 | 3,597,760 |
def list_rows(layout_id):
"""
Show the page that lists the row definitions for a specified layout and is the entry point for editing them
:param layout_id: ID for the layout for which to show the row definitions
:return: The HTML for the row definition listing page
"""
layout = get_layout(layou... | 428a0cb72f0474f90b2b6d37775c0b484a485a7b | 3,597,761 |
def cpp_enum_to_int_impl(item):
"""Returns a C++-function for casting an enum value to an integer.
"""
cc = cpp.Code()
cc += f'int to_int({item.cpp_enum.name} v){{'
cc += 'return static_cast<int>(v);'
cc += '}'
cc += ''
return cc | 5458c64bc12497687fa06797e91807c24ec44b14 | 3,597,762 |
def label2Addr(label, labels):
""" Return int address associated with label or None.
label is a string, either digits or symbolic.
Array labels has labels for all addresses in mem.
"""
if label.isdigit():
return int(label)
if label in labels:
return labels.index(label) | 27b1fe8edce3aa4ee33f2e7ff25843fa958da620 | 3,597,763 |
def package_res_results(popt, pcov, res_number=None, flux_ramp_current_ua=None, parent_file=None, verbose=False):
"""
Do some parsing of the outputs of the fitting routine, wrap_simple_res_gain_slope_complex()
Optionally prints to the console a human-readable blurb
Inputs:
- (popt, pcov) = (list of ... | 8b4d65616b276a05f91603449ab98d69ab0adf4c | 3,597,764 |
def get_analysis_result_with_data():
"""Return an analysis result with one anlysis module for groups or samples."""
analysis_result = AnalysisResultMeta().save()
analysis_result.set_module_status(MODULE_NAME, 'S')
result_wrapper = getattr(analysis_result, MODULE_NAME).fetch()
result_wrapper.data = c... | b75151c3a40abddf798ac87555ed2cee5d33a308 | 3,597,765 |
import re
def _get_ocsp_uri(cert, executable=_DEFAULT_OPENSSL_EXECUTABLE_NAME, openssl_version=_DEFAULT_OPENSSL_VERSION):
"""Retrieve the OCSP URL from a certificate. Must be given as path on disk."""
_ = openssl_version
cert_data = _run_openssl(['x509', '-in', cert, '-text'], executable=executable).decod... | c8b4b08086de280bb9c79200f09be96cebbe0372 | 3,597,767 |
def deep_transducer(
model: str = 'conformer', quantized: bool = False, **kwargs
):
"""
Load Encoder-Transducer ASR model.
Parameters
----------
model : str, optional (default='conformer')
Model architecture supported. Allowed values:
* ``'small-conformer'`` - SMALL size Google... | 70711302edd0bb7e8e143193f04c8be7d0e5b040 | 3,597,769 |
def get_vocabs(datasets):
"""Build vocabulary from an iterable of datasets objects
Args:
datasets: a list of dataset objects
Returns:
a set of all the words in the dataset
"""
print("Building vocab...")
vocab_intents = set()
vocab_words = set()
vocab_tags = set()
f... | f1f284fd58e8139d4ad5d67551bf642a94556fba | 3,597,771 |
def jobs_page(request):
""" Renders the jobs page on the admin side
URL: /admin/jobs
:param request:
:return:
"""
template = get_template('admin_jobs.html')
return HttpResponse(template.render({}, request)) | b084eae1a31adec69da169484c8f492320cd9ad8 | 3,597,773 |
import types
from typing import Optional
from typing import Collection
from typing import Dict
import operator
def to_bag_of_terms(
doclike: types.DocLike,
*,
by: str = "lemma_", # Literal["lemma_", "lemma", "lower_", "lower", "orth_", "orth"]
weighting: str = "count", # Literal["count", "freq", "bi... | 5238b5700687e0463c6f7b8bb91dfe3364d92f7a | 3,597,774 |
def fixDict(json_data) -> dict:
"""Converts JSON data created by hass back into the dict we expect
Specifically, the string keys will become numeric if possible"""
correctedDict = {}
for key, value in json_data.items():
if isinstance(value, list):
value = [fixDict(item) if isinst... | acd2feb646fab5df2f77180097faa3cbf01eaeff | 3,597,776 |
def get_model_cls(model_name):
""""Selects Vivit model type."""
if model_name == 'vivit_multilabel_classification':
return ViViTMultilabelClassificationModel
elif model_name == 'vivit_classification':
return ViViTClassificationModel
elif model_name == 'vivit_multihead_classification':
return ViViTMu... | 8ffc99e8f03b560fec529e1f4ce5afa91ba8f4da | 3,597,778 |
def mean_squared_error(y_true, y_pred, **args):
"""Mean squared error based on the `sklearn.metrics.mean_squared_error`_ function.
More details here : `Mean squared error`_
.. _sklearn.metrics.mean_squared_error: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#skl... | 01a85494099cc767ace8f3c1258e416602c3f0a3 | 3,597,779 |
def _rd_rm_ ( rdir , name , cycle=';*') :
"""Delete an object from ROOT file/directory
>>> rfile.rm( 'h1')
>>> rfile.rm('mydir/h1')
"""
##
try :
return _rd_delitem_ ( rdir , name , cycle )
except KeyError :
return | ceb2e2d0257effc7afb0a367b31d7c76d6f8afc9 | 3,597,781 |
import datasets
def fetch_aal_vascular_atlas(target_affine=np.diag((5, 5, 5))):
""" Fetch the AAL brain atlas given its resolution.
Parameters
----------
target_affine : np.array, (default=np.diag((5, 5, 5))), affine matrix for
the produced Nifti images
Return
------
mask_full_br... | 9ccd18207bd0e2c54957ca0c73375bfbb0c4faea | 3,597,783 |
def parse_bool_param(field):
"""Converts a url param to a boolean value"""
return field.lower() == "true" if field else False | 53530b60768be4f2fb9f4a7a62d6b49e4c079eeb | 3,597,784 |
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