content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def _SquaredDifferenceGrad(op, grad):
"""Returns the gradient for (x-y)^2."""
x = op.inputs[0]
y = op.inputs[1]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
# pylint: disable=protected-access
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
# .op works with ... | 474d506e51b72971e3532d701365f1f18d3ac8ee | 55,435 |
def keep_only_this_derivation(name_of_set_to_make,connections_list_of_dics):
"""
"""
new_connection_list_of_dics=[]
# print(name_of_set_to_make)
for connection_dic in connections_list_of_dics:
# print(connection_dic["derivation name"])
if (connection_dic["derivation name"]==name_of_set_to_make):
... | 4ec8a010055d6f3740c489184f8b123bcd8780ee | 55,436 |
def decompose_z_matrix(z_matrix, fixed):
"""Decompose the z-matrix into blocks to allow parallel (batched) reconstruction
of cartesian coordinates starting from the fixed atoms.
Parameters
----------
z_matrix : np.ndarray
Z-matrix definition for the internal coordinate transform.
Ea... | 0c0c8ce05f4735101308885eb156e60f87f44cb6 | 55,437 |
from typing import Tuple
from typing import List
from typing import cast
def search(database: str, expression: str) -> 'Tuple[int, List[int]]':
"""
Прямой поиск по словарю
:param database: имя базы данных
:param expression: поисковое выражение
:return: пара "код возврата-найденные MFN"
"""
... | 0619e289a3e6904686aff7b13f91047c49600c2e | 55,439 |
def make_ngmlr_index_cmd(in_fa):
"""return cmd string which generates ngmlr indices"""
assert is_fasta(in_fa)
return 'ngmlr -x pacbio --no-progress -t 1 -R 0.01 -r %s -q /dev/null 1>/dev/null' % in_fa | f00162068f1d21f0190107aa3ba4f3f16e779a03 | 55,441 |
import typing
def get_group_member_ids(group_id: int) -> typing.List[int]:
"""
Returns a list of the user IDs of all members of the group with the given
group ID.
:param group_id: the ID of an existing group
:return: the member ID list
:raise errors.GroupDoesNotExistError: when no group with ... | 027d00b349c54ac6d5eb5e4a943a9e0acd43e61e | 55,442 |
def tone3_to_normal(tone3, v_to_u=False):
"""将 :py:attr:`~pypinyin.Style.TONE3` 风格的拼音转换为
:py:attr:`~pypinyin.Style.NORMAL` 风格的拼音
:param tone3: :py:attr:`~pypinyin.Style.TONE3` 风格的拼音
:param v_to_u: 是否使用 ``ü`` 代替原来的 ``v``，
当为 False 时结果中将使用 ``v`` 表示 ``ü``
:return: :py:attr:`~pypinyi... | 3cd3db860d008adbabec0d3931bd0e63c4b41ec1 | 55,443 |
def check_purchase(paykey):
"""
When a purchase is complete checks paypal that the purchase has gone
through.
"""
with statsd.timer('paypal.payment.details'):
try:
response = _call(settings.PAYPAL_PAY_URL + 'PaymentDetails',
{'payKey': paykey})
... | 0d0ac7c6dc79eaf5e3ec60c7542adc2529e62840 | 55,444 |
def deblend(net, images, normalised = False):
"""
Deblend the image using the network
parameters:
net: neural network used to do the deblending
images: array of images. It can contain only one image
normalised: boolean to indicate if images need to be normalised
"""
if normal... | a9dd536826dfb90f17be7c8783f54f08111b693d | 55,445 |
from typing import Union
from typing import Tuple
from typing import Optional
def roll(
x: Union[ivy.Array, ivy.NativeArray],
shift: Union[int, Tuple[int, ...]],
axis: Optional[Union[int, Tuple[int, ...]]] = None,
*,
out: Optional[ivy.Array] = None,
) -> ivy.Array:
"""Rolls array elements alon... | 2c6dacce51fc0924ad9fda8dfb0d311a2617c5f9 | 55,446 |
def assessors_detail(request):
"""Managerial Detail View on the status, progress and returned review for
each Expert Assessor."""
return render(request, 'review/assessors_detail.html', {}) | db5e2214f468faac612d4a62b76b5b0a7cfd864d | 55,447 |
def convert_plf_to_transition_points(plf):
"""
identify mid points of intervals and return them as transition points
:param plf:
:return:
"""
plf_transition_points = plf.transition_points
mid_points = []
for index, point in enumerate(plf_transition_points):
if point < LARGE_NUMBE... | 8b20f084074584795f97d4c2645a3f31fcd633aa | 55,448 |
def make_task(func):
"""make decorated function a task-creator"""
func.create_doit_tasks = func
return func | 4a7d23765aa47f4c87723efc080913ff0c126205 | 55,449 |
import numpy
def CalculateAllSumSquareCharge(mol):
"""
#################################################################
The sum of square charges on all atoms
-->Qass
Usage:
result=CalculateAllSumSquareCharge(mol)
Input: mol is a molecule object.
Outpu... | 7a115bee67a2b3ecff53a4b220fe8119e7adc6b7 | 55,450 |
def ifelse(condition, then_expression, else_expression):
"""Controls if else logic flow inside a computational graph """
if condition:
output = then_expression
else:
output = else_expression
return output | 69190f0f9c51cd97db97d64203f51ae66370e3ff | 55,455 |
def mat4_scale(factor, origin=None, direction=None):
"""Return matrix to scale by factor around origin in direction.
Use factor -1 for point symmetry.
>>> v = (np.random.rand(4, 5) - 0.5) * 20
>>> v[3] = 1
>>> S = scale_matrix(-1.234)
>>> np.allclose(np.dot(S, v)[:3], -1.234*v[:3])
True
... | c3151f37bbcba584505d1c5a090ba4d5fa38942a | 55,456 |
from .settings import has_channels
def create_schema():
"""
Root schema object that graphene is pointed at.
It inherits its queries from each of the specific type mixins.
"""
query_mixins = []
for fn in hooks.get_hooks("register_schema_query"):
fn(query_mixins)
# ensure graphene... | b506155bcecc39d8b651ce365a8bc3c4af7d3804 | 55,457 |
from model import CaptchaModel
from kaptcha_model import CaptchaModel
def predict(captcha, model_dir='./model/model-latest.pkl', use_gpu=True, mode='captcha'):
"""
:param captcha:
:param model_dir:
:param use_gpu:
:param mode:
:return:
"""
gpu_available = torch.cuda.is_available()
... | 74d9f3310c659c2347e71c04b535ef77594a2fb3 | 55,459 |
import logging
def map_significance(filename, ap_radius=2, Npix=100, data_hdu=0):
"""
Make a full significance map of the data in 'filename'.
:param filename: A string giving the path to a fits file to analyze
:param ap_radius: The aperture radius to use. Default = 2 pixels
:param Npix: The numb... | b519b54a9c0f8d5399d4efeaae295cef1ab909a8 | 55,460 |
def instrLeakAddr():
"""Build the Leak Kernel Address instruction.
Args:
(none)
Return Value:
string containing the serialized instruction
"""
return addHeader(SCOUT_INST_LEAK_ADDR, b'') | 6f4fbcc3254e7f0f28445178e2b9e1d46199166e | 55,461 |
def create_media_types(
tator_api: tator.api,
project: int) -> list:
""" Create the media types
"""
spec = {
"name": "Image",
"description": "Image type",
"dtype": "image",
"attribute_types": [
{
"name": "Trip ID",
"dtype": "stri... | 552411718da7d651dcd089606a4b7495d915cf02 | 55,462 |
def entradas():
"""faz as perguntas sobre o contato e retorna os dados"""
contato = {}
nome = input("qual o nome do contato: ")
contato['nome'] = nome
contato["numero"] = input("qual o telefone/celular: ")
contato["endereco"] = input("qual o endereço: ")
contato["email"] = input("qual o emai... | dbcb82d91facf4c324db6cff07d2080ca3330c6a | 55,463 |
def sigmoid(x):
"""Compute the sigmoid function.
We don't bother with clipping the input value because IEEE floating
point behaves reasonably with this function even for infinities."""
return 1.0/(1.0+np.exp(-x)) | df95d9cadde13ddbcadcf3bb55d6b250fd48c219 | 55,464 |
import numpy
def get_N_colors(N, cmap_name='Dark2'):
""" given N and a colormap, get N evenly spaced colors"""
try:
color_map = plt.get_cmap(cmap_name)
return [color_map(c) for c in numpy.linspace(0, 1, N)]
except ValueError:
# try reversing the colormap name
color_map = pl... | 220ef94d832064519015c39b8fa2ad2b57e73607 | 55,465 |
def hopf_bifurcation_modified(beta):
"""Return a systems of equations relating to the hopf bifurcation"""
return lambda t, X : [
beta*X[0]-X[1]+X[0]*(X[0]**2+X[1]**2)-X[0]*(X[0]**2+X[1]**2)**2,
X[0]+beta*X[1]+X[1]*(X[0]**2+X[1]**2)-X[1]*(X[0]**2+X[1]**2)**2,
] | 42003966274c0cda99630f1b86d746c965caa149 | 55,466 |
def Sparsity_unit(*args):
"""
Create the sparsity pattern for a unit vector of length n and a nonzero on
unit(int n, int el) -> Sparsity
position el.
"""
return _casadi.Sparsity_unit(*args) | 12a1dc895f915918ef100575d4cdff10a95e054b | 55,468 |
import warnings
def exponential(start=None, end=None, startt=None, endt=None, dt=None, t=None,
extend="constant"):
"""
``exponential`` is deprecated. Use ``ease_in(... method='exponential')``
instead.
"""
warnings.warn("exponential is deprecated. Use "
"ease_in(... method='ex... | f0b52c1fccf6714598967e079d72b2b2c7142a5e | 55,469 |
from typing import Optional
from typing import Union
from typing import List
def list_contacts(connection: 'Connection', to_dictionary: bool = False,
limit: Optional[int] = None, **filters) -> Union[List['Contact'], List[dict]]:
"""Get all contacts as list of Contact objects or dictionaries.
... | be14351740d882f8e73a637921c4a5c94a6920d3 | 55,470 |
def show_asm_context(occurrence: adsk.fusion.Occurrence, prefix: str, msg_list) -> None:
"""Walks up assembly tree for selected item
Args:
occurrence: input occurrence then recursively sm context
prefix: continue building up parent contexts as you recursively move up the tree
"""
app = ... | 385456a773908dce1ea3788e257149f883c7e1e8 | 55,471 |
def pass_exchange(request):
"""Display the list of sites one can exchange a voucher for a pass at."""
state = request.GET.get('state')
if state:
sites = get_active_pass_exchange_sites(state)
form = PassSiteStateForm(initial={'state': state})
state_name = STATES[state]
else:
... | e16aa1ecd01b4e67a0b4e5b033d5dd71285a8304 | 55,472 |
def cotes_combine_all_sites(*matches, freebet=False):
"""
Calcule les cotes combinées de matches dont on connait les cotes sur plusieurs
bookmakers
"""
sites = set(matches[0]["odds"].keys())
for match in matches:
sites = sites.intersection(match["odds"].keys())
combine_dict = {"date"... | b17c019e19d839c51c070bb99d63edb695685317 | 55,473 |
import re
def find_title(text):
"""
This function extracts and returns title case words found in a string variable.
:param text: a string variable
:return: a list of characters
"""
return re.findall(r'\b[A-Z][a-z]+', text) | 9b599f6e0d708bce041ac7764f514457bd384a4f | 55,474 |
from pathlib import Path
def read_image_scipy2(input_filename: Path) -> np.array: # type: ignore
"""
Read an image file with scipy and return a numpy array.
:param input_filename: Source image file path.
:return: numpy array of shape (H, W), (H, W, 3).
"""
numpy_array = imageio.imread(input_... | 54d3aa7d8a3043e5a79e2668be1e971b543669d9 | 55,475 |
def get_rule_by_id(neutron, sec_grp, rule_id):
"""
Deletes a security group object from OpenStack
:param neutron: the client
:param sec_grp: the SNAPS SecurityGroup domain object
:param rule_id: the rule's ID
:param sec_grp: a SNAPS SecurityGroupRule domain object
"""
rules = neutron.lis... | 00afc40d003279cbd347678db0b56b4be8222568 | 55,476 |
def parse_image_names(data):
"""
Returns list of the Images names grouped by type.
@parameter{data,dict} dictionary with keys: @val{images_private}, @val{images_public}, @val{images_group}
@returns{list} Images names.
"""
emty_category_counter = 1
categories = []
categories.append([_(... | 562d70988ce264bbadec571979c70cb6a123f98c | 55,477 |
import torch
import random
from pathlib import Path
from datetime import datetime
import socket
import logging
def init_train(args, cfg, dataset):
""" Training initialization.
Including Data generator, model, optimizer initialization.
"""
'''Cuda'''
args.cuda = not args.no_cuda and torch.cuda.is... | 5da29b76803cacb8435d85fb202388efca2fb512 | 55,478 |
def select_distinct_best_records(records: list[Record], n_records: int) -> list[Record]:
"""
Select records related to highest scores (without duplicates).
:param records:
original records
:param n_records:
number of unique records to select
:return:
best records
"""
... | ae37db4ce36cb3f04f9c2912719b3dcfcbb97caf | 55,479 |
from typing import OrderedDict
def list_ports():
"""Return an alphabetically-sorted list of ports."""
return OrderedDict(sorted(
(port.device, {
'name': port.name,
'device': port.device,
'description': port.description,
'hwid': port.hwid,
'vi... | a3f92dfe35e7d21cc9b9b2ce443da75970b2ee9c | 55,481 |
def LE(ccs_sample, energy_sample):
"""Finds the lowest energy conformer of a sample.
"""
idx = np.argmin(energy_sample)
return ccs_sample[idx] | 2b0b649086632fb2a4127185a600631eaa9a6129 | 55,482 |
from typing import Dict
def get(id_: int, type_: str, api_name: str, session: scoped_session, recursive: bool = False) -> Dict[str, str]:
"""Retrieve an Instance with given ID from the database [GET]."""
object_template = {
"@type": "",
} # type: Dict[str, Any]
try:
rdf_class = session... | 23790cdd15279331feeb5da94340ca09c412e5c4 | 55,483 |
from typing import List
from typing import Tuple
def sentence_from_tokens(
tokens: List[str], pos_list: List[str]
) -> Tuple[str, List[Tuple[int, int]]]:
"""
Apply some heuristics to create a sensible text text from the tokens.
Unfortunately the original whitespace information is not available.
"... | df212e0324efb4135f7674ea7d598d73fea7d2cb | 55,486 |
import logging
def read_labels(label_file):
"""
Reads a labels.csv file into memory and converts columns to their
appropriate dtype.
Parameters
----------
label_file: str
Path to labels.csv
Returns
-------
data: list of OrderedDict
A list of the rows of the csv, w... | 82f65eed0395295bb8aaa6fd3c4294204e52905e | 55,487 |
def euler2dcm2(angles, sequence='321'):
"""euler rotation sequence utilizing predefined matrix
:param a1st: angle for first rotation axis (rad)
:param a2nd: angle for second rotation axis (rad)
:param a3rd: angle for third rotation axis (rad)
:param sequence: euler sequence; default='321'
:retur... | 8428aa98da4f3e3aea11b8244cac36267ac2045e | 55,488 |
def condition_ndm_nfe(data, nr, nc, upper, lower, NDM, NFE,
p, kk, dlimit, average, normalize, ndm, nfe):
"""
fcndm = cndm(data, 0.1, 0.1, 1) for test
Construct the CSN for sepecified cells
Parameters:
`cells` Construct the CSNs for all cells, set ... | 69eba8829462fed03554db9589f438538326430f | 55,489 |
def generator_executor(func):
"""
Decorator for running generator function through GeneratorThread.
"""
def wrapper(*args, **kwargs):
with GeneratorExecutor() as executor:
return executor.submit(func, *args, **kwargs)
return wrapper
# class BaseTask(object):
# pass
... | cfb6cdc57db1841491b2fc296eaa18b6f579b941 | 55,492 |
def check_hdr(hdr):
"""
:param hdr:
Header from a NIRPS image to be cheked against a number of QCs
We'll need to add more checks with time.
:return:
"""
keys_to_check = ['OBJECT']
output = ''
for key in keys_to_check:
if key not in hdr:
output+='missing:'+... | 9626f472d5194217a7d6f794e6192aea7112154b | 55,494 |
def load_script(filename):
"""
Loads a python script as a module.
This function is provided to allow applications to load a Python module by its file name.
:param string filename:
Name of the python file to be loaded as a module.
:return:
A |Python|_ module loaded from the specifi... | 954c42d53164963bfa02d3d8abd305b973f602fd | 55,495 |
def has_prefix(sub_s):
"""
:param sub_s: (str) A substring that is constructed by neighboring letters on a 4x4 square grid
:return: (bool) If there is any words with prefix stored in sub_s
"""
for s in dic:
if s.startswith(sub_s):
return True
return False | c73940feb94158377cafeaf6f2f6414fc9829969 | 55,496 |
def get_partition_disk(ns, partition):
"""
Return a device on which is located the given partition.
:type partition: LMIInstance/CIM_GenericDiskPartition or string
:param partition: Partition to examine.
:rtype: LMIInstance/CIM_StorageExtent.
"""
partition = common.str2device(ns, partition)... | 01e3b25d1f0fc3c8802f3ab3ef07286d247764da | 55,497 |
def checkParts(coordsList):
"""
Builds the part status dictionaries for each plane part and other part (bear, cloud and colours)
Then generates the list of blocks to be placed (newCoordsList).
Also calls calculateFlightDistance() to generate the flight score.
"""
global planePartStatus, otherPar... | 2e7504cb8870c5a46d6be53ec70077ba2b637b4f | 55,498 |
import warnings
def _insul_diamond_dense(mat, window=10, ignore_diags=2, norm_by_median=True):
"""
Calculates the insulation score of a Hi-C interaction matrix.
Parameters
----------
mat : numpy.array
A dense square matrix of Hi-C interaction frequencies.
May contain nans, e.g. in... | 493785d9430e39d9708f5bc65a13495bbcfb78ad | 55,499 |
def is_gate_this_standard_unitary(gate_unitary, standard_gate_name):
"""
Whether a unitary is, up to a phase, the standard gate specified by the name `standard_gate_name`.
The correspondence between the standard names and unitaries is w.r.t the
internally-used gatenames (see internal_gate_unitaries()).... | 2fa182c31172e498103491e2da0029bb0bcbbd6f | 55,500 |
def topodic_supported_languages():
"""
Show supported languages.
This will show the user which languages are currently supported.
And this path operation will:
* return a dictionary of supported languages along with their ISO2 language codes
"""
return settings.LANGUAGE_DICT | dcdc366f2e795e64897f65350b7d8bb9eca483b0 | 55,501 |
def find_values(result):
"""
extract values from resultset
returns
<dict>[instance] = <tuple>values
"""
values = {}
for metric_series in result.value:
instance = metric_series.id.instance
values[instance] = metric_series.value
return values | 50e2206eb46ff3594658ff7f87461dc778dedd3e | 55,502 |
def process_results(results):
"""
Gets user choice that needs to be played
:param results: stores the results which we got from get_items
:return: user choice
"""
if len(results) >= 1:
temp_data = {}
count = 1
print("Here are the top {} results for your query, which one w... | 23d3087ddf39ab6fa94072fabaa2bcde68add2a9 | 55,503 |
def get_fun_inter_sequence_diagram(fun_inter_str, **kwargs):
"""
Check and get all "show sequence Fun_inter" strings, find Fun_inter obj and then get/filter
needed lists for plantuml_adapter.
Args:
fun_inter_str: functional interface name/alias from input cell
**kwargs: whole lists
... | aa1e1eec5c63e1115530407fc1544d06807dc7db | 55,504 |
def sia_thickness_via_optim(slope, width, flux, shape='rectangular',
glen_a=None, fs=None, t_lambda=None):
"""Compute the thickness numerically instead of analytically.
It's the only way that works for trapezoid shapes.
Parameters
----------
slope : -np.gradient(hgt, dx... | 4ed34ecac2a1ffcfaa287a268a88eccb396365e8 | 55,505 |
def generate_alpha(scores, length):
""" Generates a scoring vector with given length from a list of scores. """
assert isinstance(scores, list)
len_scores = len(scores)
if len_scores > length:
return np.array(scores)[:length]
return np.pad(scores, (0, length-len_scores), 'constant', constan... | 1d31ccc730468fff434640d91ff38467f616372d | 55,506 |
def speech_to_text(file):
"""
Applies SpeechRecognition to the audio files
"""
recognizer = sr.Recognizer()
a = ''
with sr.AudioFile(file) as source:
#recognizer.adjust_for_ambient_noise(source) # adjust for noisy audio
audio = recognizer.record(source)
try:
... | d26597e88d84b7b1b0e886dbec251ef8b8c4a0a5 | 55,508 |
def create_socket(app):
"""Create a socketio object"""
# We don't want to monkey patch the naive python functions
# which destuct some of the PyPPL functionalities
# We might be swtiching to gevent or eventlet if PyPPL is
# switching to greenlet.
namespace = RootNamespace('/')
socketio = Soc... | 8c6a1872dd43a4a4e83720b2ca1a56c104c93cc9 | 55,509 |
import numpy
def svdown(omega, wvno, d, a, b, rho):
"""
Find the values of the Haskell vectors for Rayleigh-wave.
Values are calculated at each layer boundaries from top layer downward.
"""
mmax = len(d)
aa0 = numpy.zeros(4, dtype=numpy.float64)
vv = numpy.zeros((mmax, 4), dtype=numpy.f... | 11cd910813324b9f5f2de3147f53d598975849f4 | 55,510 |
def load_train_valid_test_data(DisNet_dataDict_save_path):
"""
load train valid test data from npy file
:param DisNet_dataDict_save_path: npy file which contains training data
:return:
"""
data_dict = np.load(DisNet_dataDict_save_path).item()
X_train_distance = data_dict["X_train_distance"]
... | 6024ee6629eb938738e8b7b571c01621646818c5 | 55,511 |
import re
def pem_as_string(cert):
"""
Only return False if the certificate is a file path. Otherwise it
is a file object or raw string and will need to be fed to the
file open context.
"""
if hasattr(cert, 'read'): # File object - return as is
return cert
cert = cert.encode('utf-8... | c4ada5957115fce9a31775d33e1787482fab8c9f | 55,512 |
def bit_perm_2_mat(a):
"""transforms a permutation into a bit matrix.
Representation of permutations and bit matrices see comment
on module.
"""
if isinteger(a): return a
return [1 << x for x in a] | d58ba0ebc38690ad23b7972b634f29674419faaa | 55,513 |
def box_refinement(box, gt_box):
"""Compute refinement needed to transform box to gt_box.
box and gt_box are [N, (center_x, center_y, center_z, l, w, h)]
"""
box = tf.cast(box, tf.float32)
gt_box = tf.cast(gt_box, tf.float32)
dz = (gt_box[:,2] - box[:,2]) / (box[:,5]+1e-8)
dy = (gt_box[:,1]... | 146b261033cba10df109eeb5956aa637c566932f | 55,514 |
def initializer(name, storage_socket, meta, molecule):
"""Initializes a service from a API call
Parameters
----------
name : str
Description
storage_socket : StorageSocket
A StorageSocket to the currently active database
meta : dict
A JSON blob with the required keys for... | a3c6a49f70062f66ebce08807bea376ab3f69dc6 | 55,515 |
def preprocess_image(x, mode='caffe'):
""" Preprocess an image by subtracting the ImageNet mean.
Args
x: np.array of shape (None, None, 3) or (3, None, None).
mode: One of "caffe" or "tf".
- caffe: will zero-center each color channel with
respect to the ImageNet data... | 4b17cd2db37aa037d1e773823ee796899d7be2f2 | 55,516 |
def slice_score_path(filepath, exp_str):
""" Highest-level selection function; creates a selection from a MusicXML filepath and EMA expression string.
:param filepath: A filepath to a MusicXML score.
:type filepath: str
:param exp_str: A string describing an EMA selection.
:type exp_str: str
:r... | ec3705434ba529c671e968a127d8e0a140eb21ef | 55,517 |
def format_secret(secret):
"""
Format secret to compatible decrypt string
Args:
secret (string): KMS secret hash
Returns:
formatted ef resolvable KMS decrypt string
Raises:
None
"""
return "{{aws:kms:decrypt,%s}}" % secret | 274a0686db07621d657ebc29eda21ec18c1d0afa | 55,518 |
import types
def arrangeDictbySecondaryKey(data, labels, sep='__'):
"""Re-arrange a dict of dicts by each records secondary keys"""
newdata = {}
if not type(data) is types.DictType:
return data
key1 = data.keys()[0]
if type(data[key1]) is types.DictType:
fields = data[key1].keys()... | 47381c90ccb624015ad0bb936b01e0eec33aa19a | 55,519 |
def mmr_load_params(param_config):
"""
Loads parameters for the MMR algorithm from YAML configuration
Parameters
----------
param_config: dict
YAML config dict containing parameters for the MMR algorithm
Returns
-------
params: dict
a dictionary of loaded parameters for... | ce726a5ae22ef53b393ec36915072a6405770df6 | 55,520 |
from typing import Sequence
def compute_success(
ground_truth: pd.DataFrame,
prediction: pd.DataFrame,
iou_thresholds: Sequence[float] = np.linspace(0.5, 0.95, 10),
mode: str = "take_max_stiou",
) -> np.ndarray:
"""Compute success %, i.e., the % of cases where there is sufficient overlap
betwe... | ee5fb6956faff7f223fd1494996e912221a9cfbc | 55,522 |
from datetime import datetime
def get_patched_is_natively_supported(mpl):
"""Returns a patched version of
`matplotlib.units._is_natively_supported`"""
mpl_native = mpl.units._is_natively_supported
def is_natively_supported(x, *args, **kwargs):
# returns false if x is a timedelta
# cal... | 5b03616ffc0a65af83f359ba11f696c93127ee20 | 55,523 |
def get_projected_coords(c, ref_c):
"""
Parameters
----------
c : `astropy.coordinates.SkyCoord`
The coordinates that you want to project.
ref_c : `astropy.coordinates.SkyCoord`
The reference coordinates, e.g., could be of the progenitor like in
the case of Pal 5.
Return... | 45caebd1b04d8bcd8fe9245b0be4a31ec28fa148 | 55,524 |
def to_jraph(neighbor: NeighborList, mask: Array = None) -> jraph.GraphsTuple:
"""Convert a sparse neighbor list to a `jraph.GraphsTuple`.
As in jraph, padding here is accomplished by adding a ficticious graph with a
single node.
Args:
neighbor: A neighbor list that we will convert to the jraph format. Mu... | cf9c3016684d02ac7a69c4e454a2698868627f10 | 55,526 |
async def search(request: Request, args: SearchJSON):
"""Handles search query"""
args = args.dict()
if not is_valid_payload(args):
return Response(content="Invalid data", status_code=400)
offers = await parse_offers(**args, parsers=config.PARSERS, semaphore=app.semaphore)
return app.templat... | b9773d4891d2fcea6dbcb8a74176ee55c139b7e7 | 55,527 |
def standardize_colorfulness(img, c=DEFAULT_COLOR_STD_C, h=0):
"""Give image constant colorfulness and hue
Image is converted to cylindrical CAM-16UCS assigned a constant
hue and colorfulness, and then coverted back to RGB.
Parameters
----------
img : ndarray
Image to be processed
... | ce49cf179454454b2e79705a72453c45bb7a2d78 | 55,528 |
def randomTerminals(PCSFInputObj, seed, excludeT):
"""
Selects nodes with a similar degree distribution to the original terminals, and assigns the
prizes to them.
INPUT: a PCSFInput object
seed - number to give to the random number generator
RETURNS: a new PCSFInput object with a new lis... | c1050dbf8b5a110c1b557442aec05bb6c25b6c63 | 55,529 |
from typing import Tuple
from typing import List
def opr_test(
cases,
func,
mode=("eager", "static", "dynamic_shape"),
compare_fn=_default_compare_fn,
ref_fn=None,
**kwargs
):
"""
mode: the list of test mode which are eager, static and dynamic_shape
will test all the cases if... | fb305714f48db0e22f0c46f673982e5c00531253 | 55,530 |
import torch
def le(a, b, square=False, **kwargs):
"""
Encodes the loss function for "a <= b". If square is false d = |a - b| is
used, else d = (a - b)^2.
"""
if square:
return torch.clamp((a - b).sign() * (a - b) * (a - b), min=0)
else:
return torch.clamp(a - b, min=0) | 02c8a2f5255255f754be6335a7ed8c55a1910192 | 55,531 |
def split_list(l, sizes):
"""
Split a list into several chunks, each chunk with a size in `sizes`.
"""
chunks = []
offset = 0
for size in sizes:
chunks.append(l[offset:offset + size])
offset += size
return chunks | cac573660717e9b1782810ef948f510c07f530c1 | 55,532 |
def randomize_box(bit_array, targets, epsilon):
"""
The object detection unary encoding method.
"""
assert isinstance(bit_array, tensor)
img = symmetric_tensor_encoding(bit_array, 1)
label = symmetric_tensor_encoding(bit_array, epsilon)
targets_new = targets.clone()
targets_new = targets... | dbef8e3b08a0a5fd684a4c7eaeff9e39142fb72c | 55,533 |
def make_tensor_list_set_item_tests(options):
"""Make a set of tests to do TensorListSetItem."""
test_parameters = [
{
"element_dtype": [tf.float32, tf.int32],
"num_elements": [4, 5, 6],
"element_shape": [[], [5], [3, 3]],
"index": [0, 1, 2, 3],
},
]
def bui... | ffac8496c96434da912a00514053983cd2dbb0e5 | 55,536 |
import random
def get_fixed_samples(a_list, num=20000):
"""
固定数量的样本
"""
if num <= 0:
return a_list
a_n = len(a_list)
n_piece = num // a_n + 1
x_list = a_list * n_piece
random.seed(47)
random.shuffle(x_list)
x_list = x_list[:num]
return x_list | fe7c22329001ea0cde155a6077c28aeaffeb0a65 | 55,537 |
def SI_prefix_and_scale_factor(val, unit=None):
"""
Takes in a value and unit and if applicable returns the proper scale factor
and SI prefix.
If the unit is None, no scaling is done.
If the unit is "SI_PREFIX_ONLY", the value is scaled and an SI prefix is applied
without a base unit.
Para... | 045e844be712f03b5fe65826cc103f34da8de072 | 55,538 |
def amp_ppm(moms):
"""
Amplitude for k^+(k) -> pi^+(p1) + pi^+(p2) + pi^-(p3)
Parameters
----------
moms : ndarray[dim=2]
moms[0] = pi
moms[1] = pi
moms[2] = pi
"""
p1 = moms[0]
p2 = moms[1]
p3 = moms[2]
k = p1 + p2 + p3
s0 = (1.0 / 3.0) * (mk ** 2 ... | dd4a89a3916e5ef418501d8c97fb3bd463071458 | 55,539 |
def template_json(data):
"""Returns json as a str
Normally json is rendered as bytes, but putting bytes in a template that
is itself encoded to bytes causes problems when trying to re-read the
json from the template (with javascript).
:arg data: the data to jsonify.
:returns: str
"""
... | 098f0de06ad6bdffe1ab3bef92bd7e9f79ff7c48 | 55,540 |
def csrf():
"""Provide a csrf token for the registration form.
https://medium.com/@iaincollins/csrf-tokens-via-ajax-a885c7305d4a
"""
form = RegistrationForm()
return flask.jsonify({"token": form.csrf_token.current_token}) | 1a13b013a29f152c775f4020ca801d9ec3ca2381 | 55,541 |
import torch
def get_junction_loss_and_weight(model_cfg, global_w_policy):
""" Get the junction loss function and weight. """
junction_loss_cfg = model_cfg.get("junction_loss_cfg", {})
# Get the junction loss weight
w_policy = junction_loss_cfg.get("policy", global_w_policy)
if w_policy == "s... | f528f43f4ec5cb540a30bbee6e304c53b3e11b64 | 55,543 |
def scatter_geo(
data_frame=None,
lat=None,
lon=None,
locations=None,
locationmode=None,
geojson=None,
featureidkey=None,
color=None,
text=None,
symbol=None,
facet_row=None,
facet_col=None,
facet_col_wrap=0,
facet_row_spacing=None,
facet_col_spacing=None,
... | 481b6c326ace1411fe6465ea714eda42b5c5e40d | 55,544 |
import io
def bytes_image_output(f):
"""
Decorator which marks that function returns images stored as bytes objects.
:param f: function to decorate
:return: decorated function
"""
@wraps(f)
def inner(*args, **kwargs):
im = f(*args, **kwargs)
buffer = io.BytesIO()
... | 03b82f74ea00d66c4485c0605ce5bf9c3da1b473 | 55,545 |
def remove_item(commons: dict = Depends(common_parameters)):
"""
Endpoint. Removes an item from cart.
:param Depends(common_parameters) commons: args from `common_parameters` function
:return: dict with cart and message
:rtype: dict
"""
logger.info(f'Request@/remove_item/{commons["cart_id"]... | ae5453f86d157d11b429a3f68fc08cb794ef4b31 | 55,546 |
def intersect(*selects, **kwargs):
"""Return an ``INTERSECT`` of multiple selectables.
The returned object is an instance of
:class:`.CompoundSelect`.
\*selects
a list of :class:`.Select` instances.
\**kwargs
available keyword arguments are the same as those of
:func:`select`.
... | f394fa6f0ba621858bacc7c7bb3cfbc89e9e32bc | 55,547 |
import numpy
def CalculateSimpleTopoIndex(mol):
"""
#################################################################
Calculation of the logarithm of the simple topological index by Narumi,
which is defined as the product of the vertex degree.
---->Sito
Usage:
... | 9d8ebb84d491fd91cd1d9e2ebd2198dd731de0f2 | 55,548 |
def num2date(num):
"""num2date wrapper"""
return mpl.dates.num2date(num).replace(tzinfo=None) | a8b5ed7d89a67a28b053e955658240164bc46eac | 55,550 |
def replace_question_mark_with_nan(data_table: pd.DataFrame) -> pd.DataFrame:
"""
Replace question mark with NaN
:return: None
"""
return data_table.replace(Values.RAW_DATA_NULL.value, np.nan) | bd4723f03de8e770fee671273e62520bf3b4d4aa | 55,551 |
import itertools
def infer_impact_of_variables_2by2(tabnum, protocols):
"""Builds the first table of my report"""
for n, p in enumerate(protocols):
print(
"\nTable %d: Variable combinations, 2x2 for Protocol `%s`:"
% (n + tabnum, p)
)
print(60 * "-")
... | 0d6fff2d8ad52a01a9a1767d9aee152154b2e0d7 | 55,552 |
from typing import Dict
def create_security_context_xsuaa(token, service_credentials: Dict[str, str]):
"""
Creates the XSUAA Security Context by validating the received access token.
:param token: string containing the access_token
:param service_credentials: dict containing the uaa/ias credentials
... | 0e1ce70f7ce32844107b420516e3d0c18d316a92 | 55,553 |
def read_distributed_data(db, cursor, mode='promiscuous', normalize=None, load_history=False, exclude_keys=None,
format='dask', npartitions=None, spark_context=None, data_tag=None):
"""
This function should be used to read an entire dataset that is to be handled
by subsequent paral... | bdfdce3a86bf2eb195f824da426b32ca422343a3 | 55,554 |
import random
from functools import reduce
def diceroll (dice, sides):
"""
Simulate rolling d dice of s sides each
From Python Cookbook1st Edn pg 531
See the Python Cookbook entry on this
function for the non-standard use of 'reduce'
for example diceroll(5, 6) would be 5 rolls
of a 6 sided... | ad51a53a9df1df2c64e7ad7f361365ff8df219c6 | 55,555 |
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