content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def calc_diarization_error(pred, label, label_delay=0):
"""
Calculates diarization error stats for reporting.
Args:
pred (ndarray): (T,C)-shaped pre-activation values
label (ndarray): (T,C)-shaped labels in {0,1}
label_delay: if label_delay == 5:
pred: 0 1 2 3 4 | 5 6 ... 99 10... | 8149d872ccf25998ae4f352cfd78504a150ad83c | 51,500 |
def lineage_proof_for_coin_spend(coin_spend: CoinSpend) -> Program:
"""Take a coin solution, return a lineage proof for their child to use in spends"""
coin = coin_spend.coin
parent_name = coin.parent_coin_info
amount = coin.amount
inner_puzzle_hash = None
if coin.puzzle_hash == LAUNCHER_PUZZLE... | 15dd1febbec472a6a59ed72722745f67b7961d6f | 51,501 |
import sqlalchemy.orm
def literalquery(statement):
"""NOTE: This is entirely insecure. DO NOT execute the resulting strings."""
if isinstance(statement, sqlalchemy.orm.Query):
statement = statement.statement
return statement.compile(
dialect=LiteralDialect(),
compile_kwargs={'liter... | a0a4b63c235e790015940f6bc449b8ee1ab692f4 | 51,502 |
def filter_nb(nb):
"""Returns the parts of nb tagged for export and a list of question metadata."""
new_cells = []
for i, cell in enumerate(nb['cells']):
if is_question_cell(cell):
src = str(cell["source"])
assert len(nb['cells']) > i + 1, 'A response cell must follow questio... | 111214b1f1f8e408ffea7ea9a9baceee20000e99 | 51,503 |
def status_embed(ctx, member: Member) -> Embed:
"""
Construct status embed for certain member.
Status will have info such as member device, online status, activity, roles etc.
:param ctx: context variable to get the member
:param member: member to get data from
:return: discord.Embed
"""
... | 572edc0be0326c397e25b146a9de5e1a0fd6099d | 51,504 |
import os
import re
def get_fw_version():
"""
Try to get version of the framework. First try pkg_resources.require, if that fails
read from setup.py
:return: Version as str
"""
version = 'unknown'
try:
pkg = require(get_fw_name())[0]
except DistributionNotFound:
# Icete... | 10d6f407b49e16dbde292afea46db16860a1cbb6 | 51,505 |
def sequentially_resolve_permutation(
blocks: tf.Tensor,
window: tf.Tensor,
loss_fn: LossType = _mag_stft_mse_loss) -> tf.Tensor:
"""Resolves permutation between overlapping blocks.
Args:
blocks: Waveform in blocks (blocks, sources, samples)
window: Window function used to obtain the blocks.
... | 48305c140e8698600a8292fb880ac3f74fcb2387 | 51,506 |
import os
import re
def read_SLR_geocenter(geocenter_file, RADIUS=None, HEADER=0,
COLUMNS=['time','X','Y','Z','X_sigma','Y_sigma','Z_sigma']):
"""
Reads monthly geocenter files from satellite laser ranging
Arguments
---------
geocenter_file: Satellite Laser Ranging file
Keyword arguments... | f67dc3cb666349a0cc96ab0d2bebb89101c16e74 | 51,507 |
async def invalid_content_length_and_chunking(request: HttpRequest) -> HttpResponse:
"""A response handler which sends it's body in chunks with an invalid content length"""
headers = [
(b'content-type', b'text/plain'),
(b'content-length',
str(int(len(request.info['text']) / 2)).encode('... | f65b7f793201bacbe810ed2a8aab20b045b04f70 | 51,508 |
import os
import json
def get_activity(profile_id):
""" only API_INSTANCE that will be serve /a/.* """
if os.environ['API_INSTANCE'] in request.url_root:
http = credentials.authorize(httplib2.Http(memcache))
service_http = discovery.build("plus", "v1", http=http)
activities = service_h... | fde96861abe2d8b6d7b5e473978e1a0c04d55f23 | 51,509 |
def linear_increasing_velocity(model_param_set, **kwargs):
""" Friendly wrapper for instantiating the linear-increased-velocity medium model. """
# Setup the defaults
model_config = dict(z_delta=None,
min_ppw_at_freq=(6,10.0), # 6ppw at 10hz
y_length=None, y_... | 1000cbdff9cdf086946da5eddbe6e6a420aedf6a | 51,510 |
from typing import OrderedDict
import math
def determine_fit_quality(imglist, filtered_table, catalogs_remaining, print_fit_parameters=True):
"""Determine the quality of the fit to the data
Parameters
----------
imglist : list
output of interpret_fits. Contains sourcelist tables, newly comput... | 930767957621fc537d8e6bd833f0de18685fcb9e | 51,511 |
def fetch_movies_for(category_id):
"""
Fetches movies for the selected category
Updates the placeholder text for the Movies column
:param category_id:
:return: index.html
"""
categories = Category.query.order_by(Category.name).all()
selected_category = Category.query.filter_by(
c... | 8c9d902d22fffa6bc2712873eb8e8041648376c6 | 51,512 |
def interp1d(x, y, target):
"""
return 1d linear interpolation
"""
return np.interp(target, x, y) | 7546d37b1a083f4e5197c3717335ee5316d229d0 | 51,513 |
def get_vm_ip(vmName, poolName):
"""Get IP address for a VM on a NIMBUS cluster
'vmName' is required. For example, vmName='pgu-repTest-01'
"""
command = 'NIMBUS=%s /mts/git/bin/nimbus-ctl ip %s' % (poolName, vmName)
output = run_cmd_on_pdc(command)
print 'Got IP address on %s for %s: %s' % (pool... | 6277668ab0b933f005753309968dd9613a36b055 | 51,514 |
def fin_diff():
"""Pytest fixture for basic FiniteDifference class with centered method."""
dic = {"Grid": {"N": 10, "r_min": 0, "r_max": 10},
"Clock": {"start_time": 0,
"end_time": 10,
"num_steps": 100},
"Tools": {},
"PhysicsModules": {... | 4697c837559dc733eefa589a7299df519b14f78a | 51,515 |
import collections
def scope_symbols(dfg):
""" Returns all symbols used in scopes within the given DFG, separated
into (iteration variables, symbols used in subsets). """
iteration_variables = collections.OrderedDict()
subset_symbols = collections.OrderedDict()
for n in dfg.nodes():
if... | 49ee517f65ffa05d4a9a8f3bc927d34b9d465d74 | 51,516 |
import subprocess
def query():
"""
Query the attached TEMPer device via temper-query command and parse the
result.
"""
p = subprocess.run(['/usr/local/bin/temper_query'], stdout=subprocess.PIPE)
p.check_returncode()
temp = float(p.stdout.decode(encoding='ascii').strip())
return temp | c1b00f79b78b73f46f65fa7f4a98695e4501710e | 51,517 |
import string
def solve(moves, nr=16):
"""Return program order after doing all the moves.
:moves: dance moves, separated by commas
:nr: number of programs
:returns: program order
>>> solve('s1,x3/4,pe/b', nr=5)
'baedc'
"""
pgms = list(string.ascii_lowercase[:nr])
for move in mo... | 92fed6e01bd09a60d8b9b81ac63bef7045b9ccd6 | 51,518 |
def values():
"""Get the full current set of B3 values.
:return: A dict containing the keys "X-B3-TraceId", "X-B3-ParentSpanId", "X-B3-SpanId", "X-B3-Sampled" and
"X-B3-Flags" for the current span or subspan. NB some of the values are likely be None, but
all keys will be present.
"""
result = {}... | 2d6324a319a13b93a0fa019f8d0b7de51d6bf977 | 51,519 |
def sort_flavors(flavors):
"""
Sorting flavors, flavors with low resources first
:param flavors: flavor list in JSON
:return: sorted flavor list in JSON
"""
return sorted(flavors, key=compare_flavors) | 98b02e72f69e7c2b764eb6707ac92ac5685ad10c | 51,520 |
def convert_file_encoding(filename: str, target_encoding: str):
"""Convert the text encoding of the file to the desired one. The file may already be in the target encoding, in which case nothing is changed.
Parameters
----------
1. filename : str
- The full path and name of the file in ques... | 135dc12b61e3d3da908451e3b445c9dbe23a4691 | 51,521 |
def test_decorator(f):
"""Decorator that does nothing"""
return f | b60b815e336a3f1ca3f12712a2d1d207a5fe110c | 51,522 |
def get_audio(file_path: str) -> mutagen.File:
"""Get audio object from file"""
return mutagen.File(file_path) | 0d32e6b95687361590990ad6bcecb177fef18771 | 51,523 |
def find_machine_id(agents, host):
"""
:param agents: Array of mesos agents properties (machine_id + additional infos)
:type: list of dict
:param host: Host to find. Can be ip, hostname of agent ID
:type: string
:returns: a machine_id
:rtype: dict
"""
for agent in agents:
# d... | 706bb16d02aec1fe6844f3ed49b13e6fe54a08a6 | 51,524 |
def get_unit_values(ds_inds, ds_vals, dim_names=None, all_dim_names=None, is_spec=None, verbose=False):
"""
Gets the unit arrays of values that describe the spectroscopic dimensions
Parameters
----------
ds_inds : h5py.Dataset or numpy.ndarray
Spectroscopic or Position Indices dataset
d... | 4a3ac92c49fb0c0469fd33a5a285313e59f6fce3 | 51,525 |
def get_connection_string(include_password=True):
"""
Return the connection string based on the configuration specified in the
`merlin.yaml` config file.
"""
broker = CONFIG.broker.name
config_path = CONFIG.celery.certs
if broker not in BROKERS:
raise ValueError(f"Error: {broker} is... | 6b97a1cf84447c6dcf503c190d80ea331e3c13f0 | 51,526 |
import torch
import tqdm
def train(train_path, learning_rate, latent_dim,
batch_size=128, device='cpu', num_iters=10000, **kwargs):
"""
Train a 64x64 DCGAN network.
"""
dis = DCGANDiscriminator().to(device)
gen = DCGANGenerator(nz=latent_dim).to(device)
image_size = kwargs.get('im... | fa9bea82d633541f020f0720d1d373838fa87d46 | 51,527 |
from typing import Dict
def _create_Particles_dict() -> Dict[str, dict]:
"""
Create a dictionary of dictionaries that contains physical
information for particles and antiparticles that are not elements or
ions.
The keys of the top-level dictionary are the standard particle
symbols. The values... | 5f737b79ac18d4332be1b88d948aa1212c8d595d | 51,528 |
def load_from_secretsmanager(environment=None):
"""
Load the secrets into a dictionary, skip if no environment available
"""
if environment is None:
try:
environment = environ["MICROCOSM_ENVIRONMENT"]
except KeyError:
# noop
return load_from_dict(dict... | 9880d7478d44d0794e86cd8ce235d544edfc08b0 | 51,529 |
def _dfs_out(atom):
"""Traverse an Atom's outgoing neighborhood iteratively with a depth-first search."""
atoms = [atom]
stack = list(atom.out)
while stack:
atom = stack.pop(0)
atoms.append(atom)
stack[0:0] = atom.out
return atoms | 099258d3d9a51782cacf274e6a3dea6dcd5e6464 | 51,530 |
def _read_csv_with_fallback_encoding(filepath, dtypes=None):
"""read a CSV to a pandas DataFrame using default utf-8 encoding,
but try alternate Windows-compatible cp1252 if unicode fails
"""
try:
logger.info('Reading CSV file %s' % filepath)
df = pd.read_csv(filepath, comment='#', dty... | 1e09a47b90b9e2a3cd0466eb6cf56d00e4824c43 | 51,531 |
def cnn_model(features, labels, mode):
"""CNN model function.
Arguments:
features -- Batch features from input function
labels -- Batch labels from input function
mode -- train, eval, predict, instance of tf.estimator.Modekeys
Returns:
The estimator spec depending on the chosen mode
"""
... | a16bc923075bc957cc64adbef435fe0b58e0bc07 | 51,532 |
def create_classification_dataset(n_samples, n_features, n_informative, n_redundant, n_repeated,
n_clusters_per_class, weights, n_classes, random_state=None):
"""
Creates a binary classifier dataset
:param n_samples: number of observations
:param n_features: number of ... | f245fa4dda1c3f73840b7c01cd932d4779a547d5 | 51,533 |
def median(data, interval=34):
"""Calculate the median and ``interval`` size quartile uncertainties.
Parameters
----------
data: numpy.nparray
Needs to be an 1D array.
interval: int
The percentile (to one side) you want for uncertainties.
Returns
-------
numpy.nparray... | 0019e7b1671c5be451fddbaa8556039faba889ac | 51,534 |
def npgettext(context, singular, plural, num, **variables):
"""Translates `singular` and `plural` and returns the appropriate string
based on `number` and `context`.
"""
return _translate('npgettext', context, singular, plural, num, **variables) | 95cbb18ddb253104a82616bcf8765f5bf748e0b5 | 51,535 |
def load_data(in_path,
out_path,
in_vocab,
out_vocab,
max_input_len=56,
max_output_len=66):
"""Loads the data from the given path and preprocesses the data
by converting the tokens into ID's using the vocab dictionary.
Additionally, token... | 5a9d842ed654f6b20409306d874213e7c0d81a18 | 51,536 |
def is_compatible(subreddit, index_type, status):
"""
Checks if the submission exists and belongs
to the required type of subreddit
"""
if status == 'Exists':
if index_type == 'AV':
keys = list(subreddits[index_type].keys())
for key in keys:
if s... | 11ce4f3af63a5610f857e8da104ebbb5a9ae3844 | 51,537 |
def makePoint(x, y):
""" create a point or a dict """
if type(x) == float:
return Point(x, y)
else:
return { 'x': x, 'y': y } | 72e3af9e307c9f8f45fa5ab0317748a7181143a3 | 51,538 |
import json
def search_page():
"""Page Search DICOM Instances"""
def get_unique_series_ids(instances):
"""Retrieve the list of series ID related to the instances"""
result = []
for instance in instances:
series_id = instance[1]
if not series_id in result:
result.append(series_id... | 55c222d28dbd36ee28049126dad3ed63b546ccea | 51,539 |
def convert_confusion_matrix_to_MCM(conf_matrix):
"""
Converts a confusion matrix into the MCM format for precision/recall/fscore/
support computation by sklearn. The format is as specified by sklearn below:
In multilabel confusion matrix :math:`MCM`, the count of true negatives
is :math:`MCM_{:,0,0... | 78017974a3d99c506f93e0767df0042bce1d3e91 | 51,540 |
def setup(hass, config):
"""Setup the Hello State component. """
_LOGGER.info("The 'hello state' component is ready!")
# Get the text from the configuration. Use DEFAULT_TEXT if no name is provided.
text = config[DOMAIN].get(CONF_TEXT, DEFAULT_TEXT)
# States are in the format DOMAIN.OBJECT_ID
... | 5ddc9259ad752a04ca9ed443bc99d810fa28a001 | 51,541 |
def render_report(jobs_with_error):
"""Build a text report for the jobs with errors
"""
output = []
for job in jobs_with_error:
errors_count = job.info.get('errors_count', 0)
close_reason = job.info.get('close_reason')
job_id = job.info["id"].split('/')
url = 'https://ap... | 42c0ef405b3b684830433aa5ddd38d4283c7472c | 51,542 |
def in_range(target, bounds):
"""
Check whether target integer x lies within the closed interval [a,b]
where bounds (a,b) are given as a tuple of integers.
Returns boolean value of the expression a <= x <= b
"""
lower, upper = bounds
return lower <= target <= upper | ac9dee9092388d150611ab5e1a4a800b72cf8f83 | 51,543 |
def get_default_settings():
"""Return a dict of default values."""
default_settings = {
'debug': False,
'parse_lines': False,
'output_file': False,
'output_mqtt': False,
'frequency': 433748300,
'binary': "/usr/bin/rtl_433",
'file_path': "/tmp/433sensors",
... | 6039d4172427be540a9455b1a72bf04b74e0499e | 51,544 |
def score_sent(text, sent_data, normalize=False):
"""
Evaluate the data
"""
test_sent = next(iter(sent_data.values()))
sents = np.zeros_like(test_sent).astype(np.float).reshape(-1)
tokens = word_tokenize(text.lower())
for token in tokens:
try:
sent = np.array(sent_data[t... | c1c85685a2b9231e58a11e3db9e3d96200726eff | 51,545 |
def is_master_process(rank=0):
"""Check if master process or not.."""
if not dist.is_initialized():
return True
if rank == dist.get_rank():
return True
return False | 077423bd97175ce271a5edd2e5734e47ed5de4a1 | 51,546 |
import zmq
import gc
import time
def test_zmq_with_poller(count):
"""single thread zmq with poller"""
print(".", end="", flush=True)
ctx = zmq.Context()
router = ctx.socket(zmq.ROUTER)
router.bind("tcp://127.0.0.1:*")
address = router.getsockopt(zmq.LAST_ENDPOINT).rstrip(b"\0")
dealer = ct... | b6ac176b684d306b8e33480ecdd1cf3f17b1a1be | 51,547 |
def _parse_path_segment(acc, segment):
"""
Parse a single path segment.
If the segment is an identifier or wildcard, it's constraint is also
derived.
:param _ParsedRoutePath acc: Accumulated parse result.
:param unicode segment: Path segment.
:rtype: _ParsedRoutePath
"""
match = _t... | 03d7d53a9bee78df083b1bc077366f0403699947 | 51,548 |
def valid_chrom():
""" Valid chromosomes should be 1 - 22, X, Y, and MT.
Validity is not checked or enforced """
return '1' | 050baa71f61eaa1f8160953aacfe3f1cd0193899 | 51,549 |
def annotateTree(bT, fn):
"""
annotate a tree in an external array using the given function
"""
l = [None]*bT.traversalID.midEnd
def fn2(bT):
l[bT.traversalID.mid] = fn(bT)
if bT.internal:
fn2(bT.left)
fn2(bT.right)
fn2(bT)
return l | c254a1258e0bc4b0bbbe17c9a2830b955f6a7a55 | 51,550 |
import scipy
def plotRadiants(pickle_trajs, plot_type='geocentric', ra_cent=None, dec_cent=None, radius=1, plt_handle=None,
label=None, plot_stddev=True, **kwargs):
""" Plots geocentric radiants of the given pickle files.
Arguments:
pickle_trajs: [list] A list of trajectory objects loaded from ... | bbbccf1ff5c68fbaf39aff39929ab83762d223c4 | 51,551 |
def read_o01(fn):
"""Read a formatted output file of the ENVIRO program and extract the electric and magnetic fields across the model's transect, returning them in a DataFrame
args:
fn - str, path to the target file, must have a ".o01" extension
returns:
df - DataFrame of fields across the t... | 470a83064f56abb5d295782f55a1fd0bce60dc00 | 51,552 |
def median_squared_percentage_error(
y_true,
y_pred,
horizon_weight=None,
multioutput="uniform_average",
square_root=False,
symmetric=True,
**kwargs,
):
"""Median squared percentage error (MdSPE) or square root version.
If `square_root` is False then calculates MdSPE and if `square... | a9e12d9ae34e5d7fb08512e840b581290b436ef0 | 51,553 |
import traceback
def error_response(ex):
"""
Handles any errors raised in the execution of the backend
Builds a JSON representation of the error messages, to be handled by the client
Complies with the Swagger definnition of an error response
"""
if 'code' in ex:
code = ex.code
elif... | ed6a492e34a3cd6b00b6e751ccfa58152922a993 | 51,554 |
def simulate_from_psd(S_func, m=2000, dt=1, ymean=0, sigma=0.2, size=1, seed=None, **args):
"""
Simulate light curve given input times, model PSD, and ymean
S_func: model PSD function S(omega) [note omega = 2 pi f]
m: number of bins [output will have length 2(m - 1)]
dt: equal spacing in time
... | c4118d1e31e3f9b70a8518576a161b4fa18c3277 | 51,555 |
def eta_error_no_initial(ratio, k1, k2, k3, p1, p2, p3, sratio, sp1, sp2, sp3):
"""
:param ratio:
:param k1:
:param k2:
:param k3:
:param p1:
:param p2:
:param p3:
:param sratio:
:param sp1:
:param sp2:
:param sp3:
:return:
"""
aux0 = ((((-((1. +ratio) ** -2.... | f360c462be605c83f797905036e1dc153919efef | 51,556 |
def beta_gen_lasso(p):
"""
Generate the linear model coefficient in constrained lasso case.
@param p int: dimension.
@return np.array(p,1): the coefficient.
"""
cardi = 0.005
return np.array([0]*int(p-int(cardi*p)) + [1]*int(cardi*p)) | 7618a84bc983596aa17749f56cb79be895281597 | 51,557 |
from typing import Optional
from typing import Dict
import logging
def extract_bibliographic_data(node: ET.Element) -> Optional[Dict]:
"""
Find bibliographic data like title, authors and editors
"""
data = dict()
title = node.find(f"{TEI}titleStmt")
if not title is None:
extract_title(... | 44b9418da114243ab4767fe388b115bf4f29d885 | 51,558 |
from typing import Optional
def whiten(
strain,
dt,
phase_shift=0,
time_shift=0,
interp_psd: Optional[np.ndarray] = None,
psd: Optional[np.ndarray] = None,
):
"""Whitens strain data given the psd and sample rate, also applying a phase
shift and time shift.
Args:
strain (nd... | 6da702f6f849466a8ef30bddb8fef8007b99854c | 51,559 |
def separator(node, label=None):
"""Create a visual separator for the channel box using a dummy attribute.
This create a maya enum attribute at the last position of the channel box.
The name section will be left empty, and the enum section will be filled
with the value specified in the ``label`` param... | 91459855e98580873a6b743bfdd0f32f9c1934b1 | 51,560 |
from typing import Tuple
from typing import Optional
def find_operating_point(
x: np.ndarray, y: np.ndarray, z: np.ndarray, required_x: float
) -> Tuple[float, Optional[float], Optional[float]]:
"""
Find the highest y (and corresponding z) with x at least `required_x`.
Returns
-------
x, y, z... | be1489ea8cfa85d0c0a15c0a355fae5c3eac4b0c | 51,561 |
import json
import requests
def save_request(url, output_file):
"""
Attempts to read from file. If there's no file, then it will
save the contents of a url in json/html
"""
# check for cached version
try:
with open(output_file) as fp:
data = fp.read()
try:
... | 79d20b101b0fe811ef9129a701c6f5487e2c8a84 | 51,562 |
def load_dependency_files(path):
"""
Recursively (if necessary) gather all the manifests referenced by the starting_path and return as list
:param starting_path:
:return:
"""
f = load_dependency_file(path)
files = [f]
# recursively call
if f.dparser:
for file in f.dparser.re... | 9af8fde21a65b5ce7e0288abcd8d2fd41d3a9a45 | 51,563 |
def accuracy(pred, target, topk):
"""accuracy = top-k correct"""
correct_k = topk_correct(pred, target, topk)
accuracy_list = [(x / pred.size(0)) * 100.0 for x in correct_k]
return accuracy_list | 258b42c760552952adff3a9b7b197badc717d0cf | 51,564 |
import doctest
def load_tests(loader, tests, ignore):
"""Run doctests and file-based doctests."""
tests.addTests(doctest.DocTestSuite(server))
return tests | cf2fbb713885393167782dce783b4292044107ca | 51,565 |
def get_avg_heart_rate(envelope=None, sampling_rate=1000.):
"""Compute average heart rate from the signal's homomorphic envelope.
Follows the approach described by Schmidt et al. [Schimdt10]_, with
code adapted from David Springer [Springer16]_.
Parameters
----------
envelope : ar... | 8f007908c40170835b4963f0ee6aea7f9343990c | 51,566 |
from random import random
def busqueda_local(solucion_inicial, evaluacion, obtener_vecinos,
T_max, T_min, reduccion):
"""
Simulated Annealing.
"""
solucion_mejor = solucion_actual = solucion_inicial
evaluacion_mejor = evaluacion_actual = evaluacion(solucion_actual)
sol... | f21a03657161d75ca513b6f0f56cc3d9a0c5f27b | 51,567 |
from typing import Dict
from typing import Any
def _get_aggregate_funcs(
df: DataFrame,
aggregates: Dict[str, Dict[str, Any]],
) -> Dict[str, NamedAgg]:
"""
Converts a set of aggregate config objects into functions that pandas can use as
aggregators. Currently only numpy aggregators are supported.... | 5f3730c4be4c285154096a943b6b2c68b3c05305 | 51,568 |
from scipy.spatial.distance import pdist, squareform
from typing import Optional
def distance_weights(
X: "npt.ArrayLike",
y: "npt.ArrayLike",
grouping: "Optional[npt.ArrayLike]" = None,
) -> np.ndarray:
""" Compute weights based on Manhattan distance of the X-values.
This function ignores inform... | 0c581b3674466963603748c7c28fa66c9d83e431 | 51,569 |
def flatten_array(grid):
"""
Takes a multi-dimensional array and returns a 1 dimensional array with the
same contents.
"""
grid = [grid[i][j] for i in range(len(grid)) for j in range(len(grid[i]))]
while type(grid[0]) is list:
grid = flatten_array(grid)
return grid | 4c0361cf8e63d7608b4213ddd8f8a4c498282dcf | 51,570 |
from typing import Callable
from typing import Tuple
def plot_2d(*plotters: Callable) -> Tuple:
"""Plot multiple spatial objects in 2D."""
fig, ax = plt.subplots()
for plotter in plotters:
plotter(ax)
return fig, ax | 7eb169f19ba83e5ebb9d98e9ead7ef3cd0041af0 | 51,571 |
import torch
def cosine_sim(x1, x2, dim=1, eps=1e-8):
"""Returns cosine similarity between x1 and x2, computed along dim."""
x1 = torch.tensor(x1)
x2 = torch.tensor(x2)
w12 = torch.sum(x1 * x2, dim)
w1 = torch.norm(x1, 2, dim)
w2 = torch.norm(x2, 2, dim)
return (w12 / (w1 * w2).clamp(min=... | a4992b3f3a4a483c96a5b18bbc3402df70a8b44d | 51,572 |
import os
import pathlib
def find_files(path, exts=None):
"""
查找路径下的文件，返回指定类型的文件列表
:param:
* path: (string) 查找路径
* exts: (list) 文件类型列表，默认为空
:return:
* files_list: (list) 文件列表
举例如下::
print('--- find_files demo ---')
path1 = '/root/fishbase_issue'
... | 342a6c4d79dca23676502a116be2b242aeb9d086 | 51,573 |
def cut_descendants(D, nodes, page_tree):
"""Given the distance matrix D, a set of nodes and a PageTree
perform a multicut of the complete graph of nodes separating
the nodes that are descendant/ascendants of each other according to the
PageTree"""
index = {node: i for i, node in enumerate(nodes)}
... | 4cbf96bfde518fcd8d4f76fd0a002bf672568140 | 51,574 |
import math
def rotate_around_point(xy, radians, origin=(0, 0)):
"""Rotate a point around a given point.
I call this the "high performance" version since we're caching some
values that are needed >1 time. It's less readable than the previous
function but it's faster.
"""
x, y = xy
off... | f4d2c5639b8ac8378ad1f7dafeaa6563350fe92a | 51,575 |
from typing import Dict
import re
import os
def map_ids_to_bounding_boxes(
inkscape: str,
xml: etree.Element,
) -> Dict[str, BoundingBox]:
# noinspection SpellCheckingInspection
"""
Query an svg file for bounding-box dimensions
:param inkscape: path to an inkscape executable on your local fil... | 15a42905fcc98088e957d2351b30a64c9c634a09 | 51,576 |
def create_ensemble(datasets, mf_flag=False):
"""Create an xarray datset of ensemble of climate simulation from a list of netcdf files. Input data is
concatenated along a newly created data dimension ('realization')
Returns a xarray dataset object containing input data from the list of netcdf files concate... | 45ae20e7d3dffc5b67efd99df2432baa57c89dcb | 51,577 |
def isabs(s):
"""Test whether a path is absolute. """
return s.startswith('/') | c8d33faabd7a1ec0d5fc4902e2d271057037111c | 51,578 |
import pandas
import os
def load_csv_as_dataframe(gcs_bucket, filename, dtype=None, chunksize=None,
parse_dates=False, thousands=None):
"""Loads csv data from the provided gcs_bucket and filename to a DataFrame.
Expects the data to be in csv format, with the first row as the colum... | c40ea4c1ca66c857b8ce93da5ec4f755b773dae7 | 51,579 |
import psutil
def check_memory():
"""Return True when less than 0.5gb RAM available"""
mu = psutil.virtual_memory()
# memory in GB
GB_mem_free = mu.available/(2**30)
stats["check_memory_wrap"] = (f"\n Total RAM available(in GB): {mu.total/(2**30):.2f}\n\
Available RAM(in GB): {GB_mem_free:.2f}")
if GB... | 2fc3703cfe62c61d4b556b959466a4f16d560bbb | 51,580 |
def read_csv(fname, log = True, since_midnight = True):
"""Generates a single Peak instance from a file or list of files
Arguments
---------
fname: string or list of strings
log: bool.
If the actual or the log of the amplitude is given. If log is True the value in the file will be 10**amp
... | 75ea31a66154e57e194290780a4f1512b9c86af2 | 51,581 |
def aer2geodetic(az, el, srange, lat0, lon0, h0, ell=None, deg=True):
# type: (float, float, float, float, float, float, Ellipsoid, bool) -> tuple
"""
gives geodetic coordinates of a point with az, el, range
from an observer at lat0, lon0, h0
Parameters
----------
az : float
azimut... | c84d331a50aaaaca795e8814b79a540926f18d0b | 51,582 |
async def play_game_human(client, player_id, realtime, game_time_limit):
"""Allow humans to play"""
while True:
state = await client.observation()
if client.game_result:
return client.game_result[player_id]
if game_time_limit and (state.observation.observation.game_loop * 0.7... | 1428a097f3c90fb07cce9c1865d351b90572912b | 51,583 |
import binascii
def random_secret_exponent(curve_order):
""" Generates a random secret exponent. """
random_256bit_hex_string = binascii.hexlify(get_entropy(32))
random_256bit_int = int(random_256bit_hex_string, 16)
int_secret_exponent = fit_number_in_range(random_256bit_int, 1, curve_order)
retu... | 42f95df6164647d6bdc376e91debba9bc5a2812f | 51,584 |
import os
def get_tile_num(fname, key='tile'):
""" Given 'key' extract 'num' from 'key_num' in string. """
l = os.path.splitext(fname)[0].split('_') # fname -> list
i = l.index(key)
return int(l[i+1]) | f0bd167bef38834609cda377aa7f28b06be79cac | 51,585 |
def astype(value, types=None):
"""Return argument as one of types if possible."""
if value[0] in '\'"':
return value[1:-1]
if types is None:
types = int, float, str
for typ in types:
try:
return typ(value)
except (ValueError, TypeError, UnicodeEncodeError):
... | 47d066d9d4bb5b0b96216cc722c6896d6fdcf1a4 | 51,586 |
from functools import reduce
import operator
def adjlist_to_metis(adjlist, nodew=None, nodesz=None):
"""
Rudimentary adjacency list converter.
Primarily of use if you don't have or don't want to use NetworkX.
:param adjlist: A list of tuples. Each list element represents a node or vertex
in the... | 1b4854904ce40f2d03e76db78763639044bc167a | 51,587 |
def _minmax(*args):
""" Return the min and max of the input arguments """
min_ = min(*args)
max_ = max(*args)
return(min_, max_) | 9985ebbffd3ee0b03dc751a3c90db00e922ab489 | 51,588 |
def find_variable(text, start):
""" Return `(name, var_start, var_end)` or None.
The variable may be of two forms: `$XYZ` or `{$XYZ}`. The latter is to
be used when otherwise the variable name would be followed by a name
character, which would make it ambiguous.
"""
result = None
i = text.... | 35f8d0a1ead0abb54746399321e406fcb9d67b0b | 51,589 |
def get_payday_leap_years(
payday,
frequency='biweekly',
count=5,
starting_year=date.today().year
):
"""Get the next n payday leap years.
Return a list of the next n payday leap years, where n is specified
by `count`.
Args:
payday (date): A payday from the specified pay calenda... | 87808c0ee596005c2dd90db30b2cd168cf7ebf63 | 51,590 |
import json
def output_message(message):
"""This function will output an error message formatted in JSON to display on the StatusBoard app"""
output = json.dumps({"error": message}, indent=4)
return output | 06f9ed6350cfc9e721cfb1e24b9a695bff6d9ffe | 51,591 |
def xslt(req, *opts):
"""
Transform the working document using an XSLT file.
:param req: The request
:param opts: Options (unused)
:return: the transformation result
Apply an XSLT stylesheet to the working document. The xslt pipe takes a set of keyword arguments. The only required
argument is 'stylesheet' which i... | f224399ab7968bdf21930206959d59b66445611d | 51,592 |
def _time_distributed_dense(x, w, b=None, dropout=None,
input_dim=None, output_dim=None,
timesteps=None, training=None):
"""Apply `y . w + b` for every temporal slice y of x.
# Arguments
x: input tensor.
w: weight matrix.
b: optiona... | 8d81621ef720c9eed044f7d58c55030bdd9eea30 | 51,593 |
from typing import Callable
import collections
def build_scheduled_client_work(
model_fn: Callable[[], model_lib.Model],
learning_rate_fn: Callable[[int], float],
optimizer_fn: Callable[[float], TFFOrKerasOptimizer],
metrics_aggregator: Callable[[
model_lib.MetricFinalizersType, computation_ty... | a4e2c0b635ffab3cd781a8584aff0811b1d2bd4d | 51,594 |
from typing import Set
from typing import Iterator
def filter_by_letterset(letters: Set[str], words_set: Iterator[str]) \
-> Iterator[str]:
"""
Filters the words in the dictionary by the letter-set. So, the set
{'h', 'o', 'w'} will match 'how' and 'who' but not 'whom'
Args:
letters: Th... | a072c80c3bddf0f7ff06437c635f46f4b2e43c97 | 51,595 |
import copy
def evaluate_confusion_matrix(y_true, y_pred, labels):
"""
Calculates a Confusion for each each of for y_true / y_pred and returns a formatted dictionary with the results.
:param ndarray y_true: The (potentially multiple) observed targets
:param ndarray y_pred: The (potentially multiple) ... | fa83931c085b147dd6c432bb45ba0a1eb773f0e7 | 51,596 |
from typing import Callable
from typing import Optional
from pydantic import BaseModel # noqa: E0611
def get_redirects(func_name: str, attr: str,
redirect_from: Callable) -> Optional[BaseModel]:
"""Get an attribute :code:`attr` from function :code:`func_name` from context of
:code:`redirect... | 25a4bfc1e6872bcff45cb6b67b3cfde3e8e075bb | 51,597 |
import msgpack
def send_msgpack(socket, obj, flags=0, protocol=-1):
"""pickle an object, and zip the pickle before sending it"""
p = msgpack.packb(obj, use_bin_type=True)
return socket.send(p, flags=flags) | e80dee26986c4975b46e4527f8425e25cecf0621 | 51,598 |
import glob
import os
def decompile_apk_dir(apps_dir, out_dir=None):
"""depr"""
apk_ls = glob(os.path.join(apps_dir, '*.apk'))
assert all(apk.endswith('.apk') for apk in apk_ls)
# count = 0
app_dir_ls = []
for apk_fn in apk_ls:
try:
app_dir, package = prep_dir_apk(apps_dir... | eced2c85775b586bf3ccc4bb8e48e4aeb4e4a643 | 51,599 |
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