content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def register_multi_sampler(name):
"""
A decorator with a parameter.
This decorator returns a function which the class is passed.
"""
name = name.lower()
def _register(sampler):
if name in _registered_multi_sampler:
raise ValueError("Name {} already chosen, choose a different ... | 109f4c715e3fc5bc10679d6754d51563d1040f65 | 3,625,000 |
from datetime import datetime
def overview():
"""Shows an overview page"""
# get a list of clients for the side bar
clients = redshift.get_sponsored_client_list(cache)
# get a list of all possible locales and countries
locales = redshift.get_all_locales(cache)
countries = redshift.get_all_co... | ebef91b226a573b1f59f05aa9285f4e8461cf275 | 3,625,001 |
import logging
def get_attention_logger(file_path):
""" Make python logger """
logger = logging.getLogger("attention")
log_format = '%(message)s'
formatter = logging.Formatter(log_format)
file_handler = logging.FileHandler(file_path)
file_handler.setFormatter(formatter)
logger.addHandler(... | ee7767e86066a7f07fb251613367119aba2e59f0 | 3,625,002 |
import time
def _acquire_lease_memcache(cache_arn, correlation_id, steps, retries, timeout=LEASE_DATA.LEASE_TIMEOUT):
"""
Acquires a lease from memcache.
# https://www.quora.com/What-is-the-best-way-to-implement-a-mutex-on-top-of-memcached
# http://martin.kleppmann.com/2016/02/08/how-to-do-distribute... | 2dfbcd1d0c18699594e942f7b2c1470a9de446fa | 3,625,003 |
from typing import List
def transformer_tok2vec_v2(
name: str,
get_spans,
tokenizer_config: dict,
transformer_config: dict,
pooling: Model[Ragged, Floats2d],
grad_factor: float = 1.0,
) -> Model[List[Doc], List[Floats2d]]:
"""Use a transformer as a "Tok2Vec" layer directly. This does not a... | bb51b8e00cce5b92ffd1bf1e3f9c5f2d5a87cc69 | 3,625,004 |
def piece_together_fourth(Dp, Wp):
"""
Take the skew and symmetric parts of a algorithmic tangent and piece them back together
"""
sym_id = 0.5*(np.einsum('ik,jl', np.eye(3), np.eye(3)) + np.einsum('jk,il', np.eye(3), np.eye(3)))
skew_id = 0.5*(np.einsum('ik,jl', np.eye(3), np.eye(3)) - np.einsum('jk,il', n... | fc386d688f0e571b3b184621885be100b5cd2306 | 3,625,005 |
def to_ragged_seq_set(data):
"""Convert dataset from mapping/array of sequences
to lists of mappings of sequences."""
# data is a dict
if is_mapping(data):
new_data = {}
for name, datum in data.items():
if not is_sequence_set(datum):
# all sequences must at le... | 5b8cce9846d861c564a23a77fe8a7ae03e3cdf8a | 3,625,006 |
def get_atoms_list(mmtf_dict):
"""Creates a list of atom dictionaries from a .mmtf dictionary by zipping
together some of its fields.
:param dict mmtf_dict: the .mmtf dictionary to read.
:rtype: ``list``"""
return [{
"x": x, "y": y, "z": z, "alt_loc": a or None, "bvalue": b, "occupancy": o,
... | 3b5f29362c077585ebc659b8d8a9ff5d60908ead | 3,625,007 |
import traceback
def register_augmentation(name):
"""Registers an augmentation.
This decorator allows vertview to instantiate an augmentation
from a configuration file. To use it, apply this decorator to an
AugmentationBase2D subclass, like this:
.. code-block:: python
@register_augmentation(... | 601ea1df05997ba46687d4e8e828be1254c3b29d | 3,625,008 |
def get_os_fingerprint(data):
"""
Get the most accurate OS fingerprint for the given Data object, if any.
:param data: Data object to query.
:type data: Data
:returns: Most accurate OS fingerprint.
If no fingerprint is found, returns None.
:rtype: OSFingerprint | None
"""
# Ge... | baca5b40cff177d8534f68d2334698852937caf2 | 3,625,009 |
def ring(symbols, domain, order=lex):
"""Construct a polynomial ring returning ``(ring, x_1, ..., x_n)``.
Parameters
==========
symbols : str, Symbol/Expr or sequence of str, Symbol/Expr (non-empty)
domain : :class:`~diofant.domains.domain.Domain` or coercible
order : :class:`~diofant.polys.po... | d26eaf26d2b4c87c5b6e70d87dbf0b4bae80aace | 3,625,010 |
def trimesh_swap_edge(mesh, u, v, allow_boundary=True):
"""Replace an edge of the mesh by an edge connecting the opposite
vertices of the adjacent faces.
Parameters
----------
mesh : :class:`compas.datastructures.Mesh`
Instance of mesh.
u : int
The key of one of the vertices of ... | 43f150bb52b1f4a9180f6c40a9d420fbe5e67e30 | 3,625,011 |
def perc_col_nans(col):
"""
Returns the percent of NaNs of a specific column in the dataset.
Parameters:
col (pandas Series): Column in the dataset
Returns:
col_perc_nans (float): Percent of NaNs in col
"""
col_perc_nans = count_col_nans(col) / len(col)
return... | 66db913b1299a280e867ff05bd8e438c25c03861 | 3,625,012 |
import os
import contextlib
import shutil
def set_up_directory(day):
"""Make a new directory for working on an advent of code problem
Args:
day: int
day of the month to work on
Returns:
new_dir: str
path to the directory for that day
"""
this_dir = os.path... | ba76d4abe7ab40517adcfd54af66b8946df31886 | 3,625,013 |
def get_string(key):
"""
Get localized string. First, try language as set in config. Then, try English locale. Else - raise an exception.
:param key: string name
:return: localized string
"""
lang = strings.get(Config.BOT_LANGUAGE)
if not lang:
if not strings.get("en"):
... | 90089fbe8bbbdfc0f1aa6560197489cbfb76f41a | 3,625,014 |
def prompt_vial_range():
"""Prompt user for first and last vial of run"""
first_ic_vial = int(input('First IC vial? '))
last_ic_vial = int(input('Last IC vial? '))
return first_ic_vial, last_ic_vial | bf5cf536c0aae3808bf16dd6d87a9d83bfbdab04 | 3,625,015 |
import logging
def precision_k_curve(y_true, y_pred, max_k=0):
"""
Calculate precision@k for various values of k.
y_true indicates the products that the user actually interacted with. it must be a 'multilabel-indicator'.
If max_k is zero, precision@k is calculated for all values of k from 1 to numbe... | 74d13ef014afeca12e27fa40bec37d4fd81e556b | 3,625,016 |
from collections import defaultdict
def getFollowupPhotometry(candidate, djangoRawObject = None, conn = None):
"""Use the query followupPhotometryQuery but organise the data for plotting"""
# We need to create a dictionary of filters which contain arrays of the data.
# It's messy to do this from scratch,... | d4f496360cdfde82f4dc81bb199374606929137f | 3,625,017 |
def generate_random_map(
size=6, p=0.7, num_pseudo_rewards=0, only_optimal_pseudo_rewards=True
):
"""Generates a random valid map (one that has a path from start to goal)
:param size: size of each side of the grid
:param p: probability that a tile is empty
:param num_pseudo_rewards: number of tiles ... | b50ce1575e0b8715b0ac4f271f3cf741e8a22b6f | 3,625,018 |
def approx_count_distinct(col, rsd=None):
"""Returns a new :class:`Column` for approximate distinct count of ``col``.
>>> df.agg(approx_count_distinct(df.age).alias('c')).collect()
[Row(c=2)]
"""
sc = SparkContext._active_spark_context
if rsd is None:
jc = sc._jvm.functions.approx_count... | 7e7736db6c1ecf7d7b2476fd13f5698498aa3cff | 3,625,019 |
def VLBAGetSumCC(image, err, CCver=1,
logfile='', check=False, debug=False):
"""
Sum fluxes in a CC table
Sums the flux densities in a CC Table on an image
Returns sum
Returns with err set on error
* image = Image with CC table
* err = Python Obit Error/mes... | c7bc0d84d895db2b4e2521311cb9b2c6f6d6a613 | 3,625,020 |
def private_mean(x, epsilon, sum_sensitivity):
"""
Computes a private mean with privacy budget epsilon using the given
sensitivity.
:param x: Vector over which the mean will be computed.
:param epsilon: The privacy budget.
:param sum_sensitivity: The sensitivity of the sum calculation.
:ret... | 3b400c17e1fa9b899d1b322291459724cd6cece8 | 3,625,021 |
from vtk import vtkImageData
def make_child_dataset(dataobject):
"""Creates a child dataset with the same size as the reference_dataset.
"""
new_child = vtkImageData()
new_child.CopyStructure(dataobject)
input_spacing = dataobject.GetSpacing()
# For a reconstruction we copy the X spacing from ... | a7e460f21564559f4731df76c978eac3a951dbe7 | 3,625,022 |
def calc_residuals(Amat,Bvec,coeffs,perconf=False):
"""Calculate residuals and other performance metrics"""
residuals = []
matmul = Bvec - Amat*coeffs
residuals = matmul*627.51
r2 = 1-sum(residuals**2)/sum((Bvec*627.51)**2)
rel_MAE = sum(abs(residuals))/sum(abs(Bvec*627.51))
if perconf:
... | fbeb72688f4e58dcca85b425be2ef0460a3cfcac | 3,625,023 |
def bucket_by_sequence_length(data_reader,
example_length_fn,
bucket_boundaries,
bucket_batch_sizes,
trainer_nums,
trainer_id):
"""Bucket entries in dataset by lengt... | 4adfe4f0780b4e97934b6715ab9227e3647a2592 | 3,625,024 |
from typing import Any
from typing import Dict
def get_overridable_parameters(config: Any) -> Dict[str, param.Parameter]:
"""
Get properties that are not constant, readonly or private (eg: prefixed with an underscore).
:param config: The model configuration
:return: A dictionary of parameter names an... | 9dbe89416d4dd0f232f3f714fc652bcb18ec80cc | 3,625,025 |
def compute_score(sent, sents):
"""Computes the average score of sent vs the other sentences (the result of
sent vs itself isn't counted because it's 1, and that's above
UPPER_BOUND)"""
if not len(sent):
return 0
return sum(compare_sents_bounded(sent, sent1) for sent1 in sents) / float(
... | ba3e0a16551daba922e87de6698f8e95494b01cd | 3,625,026 |
def computeCentroids(X, idx, K):
"""
returns the new centroids by
computing the means of the data points assigned to each centroid. It is
given a dataset X where each row is a single data point, a vector
idx of centroid assignments (i.e. each entry in range [1..K]) for each
example, and K, the n... | d21a5acd3ba481a9864a016dcfe79b629147f788 | 3,625,027 |
def async_track_time_interval_backoff(hass, action, intervals) -> CALLBACK_TYPE:
"""Add a listener that fires repetitively at every timedelta interval."""
if not iscoroutinefunction:
_LOGGER.error("Action needs to be a coroutine and return True/False")
return
if not isinstance(intervals, (l... | be7d3619b9ddc8336251fb50196a74ab9f58c36b | 3,625,028 |
import shlex
def grr_grep(line):
"""Greps for a given content of a specified file.
Args:
line: A string representing arguments passed to the magic command.
Returns:
A list of buffer references to the matched content.
Raises:
NoClientSelectedError: Client is not selected to perform this operatio... | 28c771f12d162e667009241cdba2f2e485736599 | 3,625,029 |
def convertMCFOSTdataToJy(data, wavelength, spatialUnit = 'arcsec', spatialResolution = None):
"""Convert data in MCFOST units into Jansky/pixel or Jansky/arcsec^2:
Input: data: 2D array, MCFOST-generated data.
wavelength: float, wavelength of the data to be converted in micron.
spatial... | 20e493989513e4e08e9211f7a6f7d606817aaa44 | 3,625,030 |
def defaults(dict=None):
"""
Adds a set of default values to the settings registry. These can and will be updated
by any settings modules in effect, such as the Settings Manager.
If dict is None, it'll return the current defaults.
"""
if dict:
_defaults.update(dict)
else:
re... | 8b7423d0fdbbd522a01e04cd77e6aca863951e9a | 3,625,031 |
import os
from .magics import register_magics
from .hierarchymagic import load_ipython_extension as load_hierarchy
def init(path=None, ipython=None):
"""Initiate noWorkflow extension.
Load D3, IPython magics, and connect to database
Keyword Arguments:
path -- database path (default=current directory... | 5b9692b70d98797f84bf1a5a9d64aa165965207e | 3,625,032 |
def send_email(email_subject, recipient, message, config = None):
"""Send an email using SendGrid."""
try:
config = current_app.config
except:
config = config
sender = sendgrid.SendGridClient(config['SENDGRID_API_KEY'])
email = sendgrid.Mail()
email.set_subject(email_subjec... | 18c20e086ddacc719898f2ad3e3a03f5c560d0fb | 3,625,033 |
import sys
from io import StringIO
def redirect_stdout(func):
"""temporarily redirect stdout to new Unicode output stream"""
@wraps(func)
def wrapper(*args, **kwargs):
original_stdout = sys.stdout
out = StringIO()
try:
sys.stdout = out
return func(out, *args... | 585f490b0e0b8c9b365cf0e0f24e13f6346f59ff | 3,625,034 |
import wsgiref
def simulate_request(app, method='GET', path='/', query_string=None,
headers=None, content_type=None, body=None, json=None,
file_wrapper=None, wsgierrors=None, params=None,
params_csv=False, protocol='http', host=helpers.DEFAULT_HOST,
... | 15950c9b880ac791454bd64becf176aec0ab4d99 | 3,625,035 |
def rcnn_encode(bboxes, targets):
"""
:param bboxes: (N, 4) bounding boxes of [x0, y0, x1, y1]
:param targets: (N, 4) target ground truth boxes of [x0, y0, x1, y1]
:return: deltas
"""
bw = bboxes[:, 2] - bboxes[:, 0] + 1.0
bh = bboxes[:, 3] - bboxes[:, 1] + 1.0
bx = bboxes[:, 0] + 0.5 *... | 919395705dd20e6872fac21ab8abf52a04ff8167 | 3,625,036 |
import argparse
import sys
def argparse_setup():
"""Initialize the argument parser.
Parameters:
None
Return:
None
"""
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
# Subparser for implementation listing
info_parser = subparsers.add_parser("i... | 10dbea13f782e9fd19371537984001d300a289ee | 3,625,037 |
def is_alloc_promotable(alloc_inst: ir.Alloc):
""" Check if alloc value is only used by load and store operations. """
assert isinstance(alloc_inst, ir.Alloc)
if len(alloc_inst.used_by) != 1:
return False
addr_inst = list(alloc_inst.used_by)[0]
if not isinstance(addr_inst, ir.AddressOf):
... | ac81c42fba5b5c618b5a083e7b3dbf2cc624e2f5 | 3,625,038 |
def mera_ansatz_parameters(num_qubits, depth, value):
"""Returns a Parameters object for the MERA Tensor network ansatz.
Args:
num_qubits : int
Number of qubits in the parameterized circuit.
depth : int [must equal log2(num_qubits)]
Number of "hyperlayers" in MERA netwo... | 8e491189527a95686c99a10da1527ec321c48036 | 3,625,039 |
def gaussianfilter(N, alpha, Ts, Fs):
"""
Generates a gaussian filter (FIR) impulse response.
Parameters
----------
N : int
Length of the filter in samples.
alpha: float
Roll off factor (Valid values are [0, 1]).
Ts : float
Symbol period in seconds.
... | 8f3ce2f3aa155dcf3163f6a252b98aa506137013 | 3,625,040 |
def minimal_copy(ds: Dataset) -> Dataset:
"""Make reduced copy with only the attributes needed for a QueryResult"""
res = Dataset()
for attr in chain.from_iterable(chain(req_elems.values(), opt_elems.values())):
val = getattr(ds, attr, None)
if val is not None:
setattr(res, attr,... | f0df04aa6e1a5447b7e6caf0a489cc89a7de9423 | 3,625,041 |
import numpy as np
import io
import torch
def read_txt_embeddings(logger, path):
"""
Reload pretrained embeddings from a text file.
"""
word2id = {}
vectors = []
# load pretrained embeddings
# _emb_dim_file = params.emb_dim
_emb_dim_file = 0
with io.open(path, 'r', encoding='utf-8', newline='\n', e... | 1d7cc0845488bb3bd37eef4ca0cdeff67787f6e8 | 3,625,042 |
def corrected_chance(clustering1, clustering2, measure='jaccard_index',
random_model='perm', norm_type='sum', n_samples=100):
"""
This function calculates the adjusted Similarity for one of six random
models.
.. note:: Clustering 2 is considered the gold-standard clustering for one... | 86505ed8eeb38decb551fa77cbec00b8ba9d16f1 | 3,625,043 |
def update_account():
"""Update account settings."""
form = AccountSettingsForm(request.form)
if form.validate():
if 'user_id' not in request.form:
return jsonify({'success': False,
'error': 'ID not found in edit!'})
edit_id = paranoid_clean(request.fo... | 247d60dbf333f7ed162b091c485e9bffe8418062 | 3,625,044 |
def make_weighted_loss(loss_fn, weight=1.0):
""" Adapts the given loss function by multiplying by a given constant.
Parameters
----------
loss_fn: a function to create the loss
weight: the value by which to weigh the loss.
Returns
-------
fn: The adapted loss
"""
def fn(*args, ... | bfe235e012cc134b98b5d995935327b11bba8651 | 3,625,045 |
def create_differences_matrix(rows, cols):
"""
Creates the central differences matrix A for an n by m shaped grid
"""
n = rows*cols
M = np.zeros((n,n))
for r in range(rows):
for c in range(cols):
i = r*cols + c
# Two inner diagonals
if c > 0: M[i-1,i] ... | ea199ed8fb3f33e4e059d5748d347a46dc78d7d7 | 3,625,046 |
def isvalid_corr(corrmat):
"""
Check if
1. corrmat is symmetric
2. off-diagonal values in [-1, 1]
3. diagonal values = 1
4. the matrix is positive semidefinite.
@param corrmat: numpy nxn ndarray
@return: CorrDiagnostics object ---> evaluates to True if corrmat is valid and False otherwi... | 6f2509daaf9901d3b35841bda08dd062d2b1caa1 | 3,625,047 |
def multi_JMI(X, y, is_disc, cost_vec, cost_param_vec,
num_features_to_select = None, random_seed = 123, num_cores = 1):
""" Cost-based JMI feature ranking with multiple penalization parameters.
Function to obtain the rankings associated to different cost parameters,
with the filter featu... | 8e0f641d89b07548fde555225ff9c2dad094e484 | 3,625,048 |
def arcsin(x):
"""
Compute the inverse sine of x.
Return the "principal value" (for a description of this, see
`numpy.arcsin`) of the inverse sine of `x`. For real `x` such that
`abs(x) <= 1`, this is a real number in the closed interval
:math:`[-\\pi/2, \\pi/2]`. Otherwise, the complex princi... | 6692bb51b5347ab77d2d12969a8fc98ccc43ac62 | 3,625,049 |
def run_queued(fn, params, processes=1, queued_params=None, logging_level=None):
"""
Same as run_function, but the function should be such that it accepts a
parameter queue and reads its inputs from there; params still contains
options for the function.
If logging_level is not None, a QueueHandler ... | bef4ae414bbe8787ef6c606c5391a49832aa0e90 | 3,625,050 |
import signal
def ExtractFrequencyTime(data, bands):
"""Computes the spectrogram of data, and the takes the sum of frequencies inside the band range.
"""
bands_results = {'all_spec':[], 'alpha':[], 'beta1':[], 'beta2':[], 'beta3':[]}
f, t, Sxx = signal.spectrogram(data,window = 'hamming', fs = 500,n... | 1ebf9e9a39d34d6a1657c6a2c440996e1ba88302 | 3,625,051 |
from typing import Any
from typing import List
def ensure_list(data: Any) -> List:
"""Ensure input is a list.
Parameters
----------
data: object
Returns
-------
list
"""
if data is None:
return []
if not isinstance(data, (list, tuple)):
return [data]
ret... | 58016feaf49d63255944fa615e7e2e1dac6dcc76 | 3,625,052 |
def decode_logistic_mixture(
targets, means, log_scales, logit_probs_softmax, # CDF
input_string):
"""
NOTE: This function uses either the CUDA or CPU backend, depending on the device of the input tensors.
NOTE: targets, means, log_scales, logit_probs_softmax must all be on the same device ... | 66ad78e22a538c788729bdbd1bb6a74a88d24be3 | 3,625,053 |
def _match_up_provided_args_with_field_defs(field_defs, args, kwargs):
"""
Returns a list of provided values, aligned with the field definitions. Missing values are represented using the NVP
(No Value Provided) token (as None can be a valid provided value).
"""
values = [NVP] * len(field_defs)
... | 440b218e3bde552a10890304278ef9604d8f988b | 3,625,054 |
def var_recode(TEDS_A_Imputed, user_target, TEDS_Af):
"""Recodes categorical variables present in the data.
Returns
A recoded data frame."""
# Identify the variable types
col_list = list(TEDS_A_Imputed.columns)
col_list_cat = [s for s in col_list if s != 'CASEID' and s != 'ADMY... | cc656e9a6840af4d95a6a114073459d815c533dc | 3,625,055 |
def get_cannonical(group):
"""
For each variant, that is unique combination of #CHROM, POS, REF, ALT, it keeps CANONICAL transcript annotations.
If more than consequence type is annotated, it keeps the most damaging or severe either if there is more than one
entry (row) per variant or either is coma-sep... | 31417a30fbfb7800dfdd46352ea9f9e5763be3e3 | 3,625,056 |
def get(blob_key):
"""Gets a `BlobInfo` record from blobstore.
Does the same as `BlobInfo.get`.
Args:
blob_key: The `BlobKey` of the record you want to retrieve.
Returns:
A `BlobInfo` instance that is associated with the provided key or a list
of `BlobInfo` instances if a list of keys was pro... | 03c3fe9da40c2986ccce19b62e99c07abe49cbd0 | 3,625,057 |
from typing import List
from typing import Dict
def go_struct_variables(variables: List[Dict]) -> str:
"""Generate Go code containing struct field definitions."""
lines = []
for var in variables:
if var["description"]:
description = (
var["description"]
... | 58f50207e33105752f8904046e281ae2288a3cdf | 3,625,058 |
def init_split(df, featname, init_bins=100):
"""
对df下的featname特征进行分割, 最后返回中间的分割点刻度
为了保证所有值都有对应的区间, 取两个值之间的中值作为分割刻度
注意这里的分割方式不是等频等常用的方法, 仅仅是简单地找出分割点再进行融合最终进行分割
注意, 分出的箱刻度与是否闭区间无关, 这个点取决于用户,这个函数仅考虑分箱的个数
同时, 分箱多余的部分会进入最后一个箱, 如101个分100箱, 则最后一个箱有两个样本
Parameters:
----------
df: dataframe,... | 172f48bb019fd4cf4941b2ff0fefcb24709fe7a4 | 3,625,059 |
import os
def read_image(point_file):
"""Read the corresponding image."""
head, tail = os.path.split(point_file)
image_file = tail.split('.')[-2]
img_jpg = os.path.join(head, image_file + ".jpg")
img_png = os.path.join(head, image_file + ".png")
if os.path.exists(img_jpg):
img = cv2.im... | aa59c3618699e9ff93603cf72ffb58ce422a0824 | 3,625,060 |
def dist_of_entity_k(adjacency_dict: dict, levels_dict: dict, c_k: dict, c_notk: dict) -> int:
"""
Performs the final iterations of the algorithm proposed in the Alternative Geographic Spine document to find
the "off spine entity distance" (OSED), which is the number of geounits that must be added or subtra... | 787466d3de923fa495d93794823c6b0d51c22f5b | 3,625,061 |
import tkinter
def repr_content_and_tags(text_widget: tkinter.Text) -> str:
"""Represent the content, indices and the tag starts and ends as a text table."""
tbl = prettytable.PrettyTable()
tbl.field_names = ["Index", "Char", "Tag starts", "Tag ends"]
for index, character, tag_starts, tag_ends in en... | 534c1642d77f0229322a1065b237c65e6b0ccf5c | 3,625,062 |
from typing import Callable
from typing import Union
from typing import Iterable
def build_dataset_reduce_fn(
simulation_flag: bool = True
) -> Callable[[_ReduceFnCallable, Union[tf.data.Dataset, Iterable]], tf.Tensor]: # pylint: disable=g-bare-generic
# TODO(b/162683412): remove `Iterable` after pytype fix.
... | 7000bc59ed40c4bdc3af1aed6fcaec86236e0456 | 3,625,063 |
def isotropic_twirl_state(X, d):
"""
Applies the twirling channel
X -> ∫ (U ⊗ conj(U))*X*(U ⊗ conj(U)).H dU
to the input operator X acting on two d-dimensional systems.
For d=2, this is equivalent to
X -> (1/24)*sum_i (c_i ⊗ conj(c_i))*X*(c_i ⊗ conj(c_i)).H
where the unitaries c... | 5ff32bb3b4c6b2671e9acb8d7963683bcc3c3261 | 3,625,064 |
from functools import reduce
def flatten_index(builder, index, const_shape):
"""Converts N-dimensional index into 1-dimensional one.
index is of a form ``(i0, i1, ... iN)``, where *i* is ValueRefs
holding individual dimension indices.
First dimension is considered to be variable. Given array shape
... | 2864701c5e3d0263d82fd585ad9d77c7c584ea2b | 3,625,065 |
def init_conds(MdiscI, P):
"""
Function to convert a disc mass from solar masses to grams and an initial spin
period in milliseconds into an angular frequency.
:param MdiscI: disc mass - solar masses
:param P: initial spin period - milliseconds
:return: an array containing the disc mass in grams and th... | 612411a3fad7f41309858f20e9aefad16904e16e | 3,625,066 |
from bs4 import BeautifulSoup
def fetch_feed() -> list[RenjiData]:
"""
Get news feed from renji.com
"""
logger.info("Start fetching info from renji.com ...")
res = urlopen("https://www.renji.com/default.php?mod=article&fid=38")
logger.info("Info fetched. Parsing ...")
soup = BeautifulSoup(... | 14cc274b17de03266626166592ae65e88bd54286 | 3,625,067 |
from typing import Optional
def get_me_ssh_key(key_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetMeSSHKeyResult:
"""
Use this data source to retrieve information about an SSH key.
## Example Usage
```python
import pulumi
import pul... | fe957f701869608e818092e22f08dcb0e6d8b27d | 3,625,068 |
import numpy
def gower_distance_numpy(point1, point2, max_range):
"""!
@brief Calculate Gower distance between two vectors using numpy.
@param[in] point1 (array_like): The first vector.
@param[in] point2 (array_like): The second vector.
@param[in] max_range (array_like): Max range in each data di... | b97c40cd62654172b0d502e35bbef8ce9e9175c5 | 3,625,069 |
def is_fix_only_distro(distro_name: str) -> bool:
"""
Does the given distro's security feed/db support vulnerability records before a fix is available?
:param distro_name:
:return: bool
"""
return distro_name in FIX_ONLY_DISTROS | 35eaf42b7111a3ddcec11dd351a7d00378607f86 | 3,625,070 |
def step_f(z: np.ndarray, scale: float, loc: float) -> np.ndarray:
"""
:param z: z-dimension
:param scale: width of step function, always 0
:param loc: position of step
:return: positioned step function
"""
new_z = z - loc
f = np.ones_like(new_z) * 0.5
f[new_z <= -scale] = 0
... | f94d0f18aa0805f6e0739927f88517c9e70a802a | 3,625,071 |
def build_SVM(X_train, y_train, kernel_):
"""
The function builds SVM
"""
scaler = StandardScaler()
X_train_std = scaler.fit_transform(X_train)
""" for i in range(1, 5):
svc = SVC(kernel=kernel_, random_state=0, gamma=i)
model = svc.fit(X_train_std, y_train.values.ravel())
... | a3ced31185a224b79366d44e3b2ce02f334f7ed6 | 3,625,072 |
def status(args: tuple[str]) -> list[res.Response]:
"""Batch `git status` command."""
return _call_git_use_case("status", args) | 855160c530f606535956b5decf766b32abdbeb4d | 3,625,073 |
def get_worker(project, object_id=None, global_id=None, user_id=None):
""" Gets the identified worker. Exactly one form of identification should be provided.
:param project:
:param object_id: The worker's OBJECTID.
:param global_id: The worker's GlobalID.
:param user_id: The worker'... | bff455591f48c7a45f387364c13b1d6e70c5196d | 3,625,074 |
import xml
import re
def xml_to_string(elem, pretty=False):
"""
Returns a string from an xml tree.
"""
try:
if elem is not None:
if PY2:
xml_str = ElementTree.tostring(elem, encoding='utf-8')
else:
xml_str = ElementTree.tostring(elem, enc... | 55d385a55d8cc998470b19286460267d24ddd26a | 3,625,075 |
from typing import Type
from typing import Optional
from typing import List
def build_config(
config_file: str,
task_cls: Type[GeneralizedRCNNTask],
opts: Optional[List[str]] = None,
) -> CfgNode:
"""Build config node from config file
Args:
config_file: Path to a D2go config file
o... | 5387bf56461c3ad1c71bf25592abd5c77ab4b43c | 3,625,076 |
def get_windowed_mean_expression(loom,
list_of_gene_windows,
patient_column='Patient_ID',
patient=0,
cell_type_column=None,
cell_type=None,
... | 4ce876d00d5f3320c535b43ccf4f1ec478ac6180 | 3,625,077 |
def parse_trajectory_file(fname):
"""
This routine reads a file of particle trajectory data generated by the ptm simulation.
Data from ptm is output in formatted ascii, with the time history of a particle trajectory
given by a 8-column array with an unknown number of rows. The data from each particle is... | 3a18f05222e44f1d4f439cc513926b5209da4c72 | 3,625,078 |
def get_learner(config_args, train_loader, val_loader, test_loader, start_epoch, device):
"""
Return a new instance of model
"""
# Available models
learners_factory = {
"default": DefaultLearner,
"selfconfid": SelfConfidLearner,
"oodconfid": OODConfidLearner,
}
... | 71e566ac47f78598d9ea3e9a79bb0f3c5f9e0276 | 3,625,079 |
def localHomologyMultiproc(
k, cplx, numProcs, localSimplices=None, iterate=True, rankOnly=False
):
"""Compute local homology relative to the star over a list of simplices in parallel"""
if localSimplices is None:
localSimplices = []
for ki in range(k + 1):
localSimplices += ksi... | de3998fc755b8c002e960bf0cfd5b28ec9150426 | 3,625,080 |
def text_repr(diffs):
"""
Helper function to dump xml elements into plain string form for the sake of comparison.
It also get rid of all attributes in xml elements if any.
"""
return {change_type:[etree.tostring(elem.attrib.clear() or elem).decode('utf-8') for elem in elems]
for change_t... | 9e9e2b6b4fff642bb82278642b9c6ea02d0519b3 | 3,625,081 |
def get_default_variables(defaults, source_id, model):
"""
Returns list of strings, representing selection default variable IDs
Keyword Parameters:
defaults -- List of strings, representing requested default query
names
source_id -- String, representing API ID of the selection source
... | 260bd63b3c2366f4bf04edc0f0ebe6a78600cbd6 | 3,625,082 |
import json
def search_nlucell():
"""Search NluCell 搜索问答节点
Get：返回所有问答节点
POST：返回所有包含搜索关键词的问答节点
"""
data = {
'skb': database.skb,
'result': []
}
state = {
'success' : 0,
'message' : "请先选择知识库再搜索节点"
}
if not database.skb:
return json.dumps(state)... | 7a398ce5dddf9e6f2f5fe82b475eef7eba32b2cb | 3,625,083 |
def generate_gate_swap_mat() -> np.ndarray:
"""Return the Hilbert-Schmidt representation matrix for a SWAP gate with respect to the orthonormal Hermitian matrix basis with the normalized identity matrix as the 0th element.
The result is a 16 times 16 real matrix.
Parameters
----------
Returns
... | df86f8ef3c2e4321695a87822a063ebee5c4f27c | 3,625,084 |
import subprocess
def disableRemoveIPCLog(cmd):
"""
function : disable remove IPCLog
input : cmd
output : NA
"""
(status, output) = subprocess.getstatusoutput(cmd)
if status != 0:
g_logger.debug("Failed to disbale RemoveIPC. Commands"
" for disbale RemoveIPC... | 9b61ac7ea453c179d389174f9c49393838101d7e | 3,625,085 |
def can_register(extra, password_meta):
"""
:param extra: {str: any?}, additional fields that the user will fill out when registering such as gender and birth.
:param password_meta: {'count': int, 'count_number': int, 'count_uppercase': int, 'count_lowercase': int,
'count_special'... | a6ba8cc6fc4a4180bbbceb9a8ae14b07d7dad809 | 3,625,086 |
def cross_entropy(output, target):
"""Calculate Cross-entropy loss."""
return F.cross_entropy(input=output, target=target) | e836dd4d0dbae597932aa3265a92776b7a032ccb | 3,625,087 |
def plate_to_genesift_sequencing_order_spreadsheet(plate, output_file,
sample_name_function,
well_filter=None,
direction='row'):
"""Generate an excel spreadsheet f... | bd317bd535bfa1e465ee0e0df6a020d299e83789 | 3,625,088 |
def hours_of_daylight(date, axis=23.44, latitude=39.87):
"""Compute the hours of daylight for the given date"""
#date = datetime.strptime(start_time, "%Y-%m-%d")
diff = date - pd.datetime(2000, 12, 21)
day = diff.total_seconds() / 24. / 3600
day %= 365.25
m = 1. - np.tan(np.radians(latitude... | 390cd02b805686a824b8e1bfa8641d1adeb01e9d | 3,625,089 |
def reject(p, xs):
"""The complement of filter.
Acts as a transducer if a transformer is given in list position. Filterable
objects include plain objects or any object that has a filter method such
as Array"""
return filter(complement(p), xs) | a2556ab9d4514dfb18a513938960211473a508a9 | 3,625,090 |
import functools
import copy
def mutable_cache(maxsize=10):
"""In-memory cache like functools.lru_cache but for any object
This is a re-implementation of functools.lru_cache. Unlike
functools.lru_cache, it works for any objects, mutable or not.
Therefore, it returns returns a copy and it is wrong if... | f8d5f76c035c9c6dda66dfc1a0787062684c6d75 | 3,625,091 |
from typing import OrderedDict
def filter_excluded_fields(fields, Meta, exclude_dump_only):
"""Filter fields that should be ignored in the OpenAPI spec
:param dict fields: A dictionary of of fields name field object pairs
:param Meta: the schema's Meta class
:param bool exclude_dump_only: whether to ... | a39a7665052cde0ba9f694696a57f2b1f6ca0603 | 3,625,092 |
def parse_xmpp_addresses(text):
"""."""
xmpp_addresses = ioc_grammars.xmpp_address.searchString(text)
return _listify(xmpp_addresses) | 36e5567e43bf48674787130a10bc77a69d9dcd54 | 3,625,093 |
def login_user():
""" This route handles user login """
user_info = request.get_json()
user_instance = UserModel.query.filter_by(email=user_info["email"]).first()
if not user_instance:
return {
"message": "Your email or password is not correct",
"status": "failed",
... | de93dc48865c75ae96a43f7cc9534ac64aba443e | 3,625,094 |
from typing import Any
def test_email(
email_to: EmailStr,
current_user: models.User = Depends(deps.get_current_active_superuser),
) -> Any:
"""
Test emails.
"""
send_test_email(email_to=email_to)
return {"msg": f"Test email sent to {email_to}"} | 13e7bcb0e96ae11c910619f4a4f161738c10ac6f | 3,625,095 |
def euclidean_distance(vects):
"""Compute Euclidean Distance between two vectors.
Euclidean distance is defined as the length of a line
segment between the two points.
d(p,q) = √ [Σ(qi – pi)^2]
Args:
vects : vectors
Returns:
euclidean distance between vects.
"""
x, y ... | f48eac6ff7adaefee700fe48f60cd614655ac704 | 3,625,096 |
def process_typedef(line):
"""处理类型定义"""
content = line.split(' ')
type_ = type_dict[content[1]]
keyword = content[2]
if '[' in keyword:
i = keyword.index('[')
keyword = keyword[:i]
else:
keyword = keyword.replace('\n', '') # 删除行末分号
keyword = keyword.replace('\r'... | e36f0f6298e2cdf7edc1d843c3fbaae7aa428a97 | 3,625,097 |
def findNearestNeighbourPixel(img, seg, i, j, segSize, fourConnected):
"""
For the (i, j) pixel, choose which of the neighbouring
pixels is the most similar, spectrally.
Returns tuple (ii, jj) of the row and column of the most
spectrally similar neighbour, which is also in a
clump of size... | e205652d7b39c922a203162f3cdc58672347b938 | 3,625,098 |
import json
def _load_json_as_list(path: str) -> list:
"""Load json file and convert it into list.
:param path: path to json file
:type path: str
:return: dict
:rtype: dict
"""
with open(path, "r") as json_data:
data = json.load(json_data)
if isinstance(data, list):
re... | d7aa301a830a826937522dc9c737bf84bf6f4f9a | 3,625,099 |
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