content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import os
def _transform_build_config(config, config_files, whitelabel):
"""Transforms Config instance into target platform JSON schema.
Args:
config: Config namedtuple
config_files: Map to look up the generated config files.
whitelabel: Whether the config is for a whitelabel design
Returns:
U... | d0da1f26c194a9c2371f714ea745ca124812a88c | 46,700 |
from datetime import datetime
import binascii
def is_valid(token):
"""Checks if the token is valid.
Args:
token (str): Token
Returns:
Boolean: True or false
"""
if len(token) < 64:
return False
now = datetime.now().timestamp()
time = token[22:][:20]
decode_tim... | ea1b28d2a04f3d72a0388c41feeb2bd764a78239 | 46,701 |
def shared_dataset(filename):
"""Returns the training-, validation- and test-datasets as shared variables"""
train_set, valid_set, test_set = load_dataset(filename)
train_x, train_y = shared_variables(train_set, 'Training')
valid_x, valid_y = shared_variables(valid_set, 'Validation')
test_x, test_y... | 71ff6a10845720f0eb2bb6f7f9035574cb52970d | 46,702 |
def _stats_print(good_noise, good_gains, good_fit, test_chans):
"""Generate a simple set of statistics for the test
and print them to screen.
"""
print("\nFilter statistics:")
good_chans = [1] * len(test_chans)
Nact = len(test_chans) # Number of active channels
if good_noise is not None:
... | 31044ffd6966a0e7d910bcafa92616de36ce4e81 | 46,703 |
def text():
"""make text files"""
return build('text', 'The text files are in {}.') | b066b813722be90c69eae68972cf33475c5f0023 | 46,704 |
import requests
def upload2ipfs(file_path: str) -> str:
"""Upload to ipfs using local port.
IPFS node must be running locally. Run:
$ ipfs daemon
Args:
nft_path (str): path to metadata file
"""
with open(file_path, "rb") as f:
nft_binary = f.read()
ipfs_endpoint = "http://... | 4fdb53d4d6d61b1784673be6d84cb7ba41fc84a5 | 46,705 |
def rotateImage(imgdata, org_corners):
"""
"""
rotation = calculateRotation(org_corners.ul, org_corners.ur)
rot_img = skimage.transform.rotate(imgdata, rotation, resize = True, preserve_range = True)
return rot_img | c4649b964a99345b62b4b53184efe5757639f0e2 | 46,706 |
def mean_absolute_scaled_error(y_true: ArrayLike, y_pred: ArrayLike) -> float:
"""Compute the mean absolute scaled error (MASE).
MASE is computed by comparing mean absolute error of the predictions to
the mean absolute error obtained from a lag-1 forecast.
Args:
y_true: the actual values.
... | 7931d81f952e692f1b14f578ee93182b3289074d | 46,707 |
def GetCloudBasePath():
"""Returns the folder within the Makani bucket where all databases live."""
return 'gs://gcp-public-data-makani-deps/deps/turbsim_databases' | 40091d491fdc3960cc5aa08e0ca58ae0cf2009aa | 46,708 |
import json
def create_entity(station: WeatherStationTuple) -> dict:
""" Conversion from input data to desired properties and types """
entity = Entity()
entity.provider = 'bom'
entity.country = 'Australia'
entity.country_code = 'AU'
entity.state = station.state
entity.site = station.site... | 2e6398490afdee43a5d8d43c58634ffdff7bc3dd | 46,709 |
def line(char='-', length=48):
"""Generates a string of characters with a certain length"""
return ''.join([char for _ in range(length)]) | 32de8abb95ab7e73912e2b37f0996361ed181c5b | 46,710 |
import collections
def dict_update(d, u):
"""Recursive update dictionary"""
for k, v in u.items():
if isinstance(v, collections.Mapping):
d[k] = dict_update(d.get(k, {}), v)
else:
d[k] = v
return d | bf49b0e4a8f35595a2258aaadcad231d8b9325da | 46,711 |
def _handle_tracking(request, response):
"""Handles the share tracking logic so that users get their number of shares increased
when someone downloads Firefox mobile via their user page."""
if not _has_parent_cookie(request):
parent_username = _add_share_from_cookies(request)
# Set a 'par... | f491e44253face9148aaf4f4e09cb122c4b495b6 | 46,712 |
def prompt_yn(msg, default=None):
"""
Prompts the user for a y/n response.
default param should be either 'y' or 'n'
Returns True if 'Y' and False if 'N'
"""
if default is True:
default = 'y'
elif default is False:
default = 'n'
if default == 'y' or default is True:
... | f5df7d5bf32153dd1a0e34b26997aabd683066f8 | 46,713 |
def noise_for_example(x, extent=1000, intensity=0.1, rng=None, **kwargs):
"""
Add noise to x. Just for demonstration.
Arguments:
extent -- number of points to add noise at
intensity -- scale parameter of half-normal distribution; samples
from this distribution will be added at each nois... | 55a76ea075f6f36d874293ee8aacbd1419fd1e32 | 46,714 |
def get_spectra(st, event, inventory, synthetic_picks,
calc_displacement=False,
S_win_len=.1, P_or_S='P'):
""" Calculate the fft at each channel in the stream that has an arrival
in event.arrivals
:param st: uquake.core.stream.Stream
:type st: uquake.core.stream... | 2c25911efcc03657178980f643265823d7bcf25d | 46,715 |
from typing import List
import os
import shutil
import threading
async def upload_file(request: Request, file: List[UploadFile] = File(...)):
"""
Uploads temporary file which is loadied in to the cdQA model as the data
"""
query = await request.form()
user = request.headers["authorization"]
#c... | 357d33e2c238841935671512c0cb826e9b244f00 | 46,716 |
def datasets_loader(conf, train=True, dev=True, test=True):
"""
simple wrapper for unified interface of datasets loaders
"""
fmt = conf.pop("fmt")
if fmt == "kaldi_ra":
return kaldi_ra_datasets_loader(conf, train, dev, test)
else:
raise ValueError("dataset format %s not supported... | d82f097e00cffc31ee53ac1a57635a1fcc871990 | 46,717 |
import json
def remove_existing_datasets(viable_datasets, connection):
"""Remove the viable datasets that are already existing datasets."""
for dataset_type in viable_datasets:
for container, datasets in viable_datasets[dataset_type].items():
actually_viable_datasets = []
for ... | 889a87d144c795b1bfe40aaad92ca1b36c64b3b3 | 46,718 |
import logging
def group_by_size(input_tensors, bytes_per_pack):
"""Groups `input_tensors` into chunks of `bytes_per_pack`.
The method preserves the original order of `input_tensors`. The grouping is
best effort, each pack could have more or less bytes than `bytes_per_pack`.
It only groups values with known ... | 9ab5805898678b1541f116e5ef5ae1b9a1c42791 | 46,719 |
def np_emd_loss(y_true, y_pred, reduction_axis=None, num_bins=327, **kwargs):
"""Earth Mover Distance between two waveforms
Parameters
----------
y_true : np.ndarray
A tensorflow tensor defining the true waveform
shape: [batch_size, num_bins]
y_pred : np.ndarray
A tensorflow... | d5c70b35d466d8c62efb31c91cef87da5eebd076 | 46,720 |
def adapters(text):
"""
Parse lines of text into a list of adapters (represented by their joltage),
supplemented by the outlet (0) and your device (maximum + 3).
"""
adapters = list(sorted(map(int, text.splitlines())))
adapters = [0] + adapters + [max(adapters) + 3]
return adapters | cb5aa44963506e8d0ea6aa0aeb89d094bfbb0bc8 | 46,721 |
def assign_bonds_to_groups(tors, group):
"""
|
**Description:** Make a group for each torsion bond
and keep track of how many members
Finally it returns the biggest group.
**Input:**
- Tors: atoms with torsions
- Group: Atoms grouped by proximity
**Output:**
- output: li... | 8147c016efe435f46b587bc86e6fab713375bb70 | 46,722 |
def _build_generator_map():
"""Build a map of sdtype to data generator.
Output:
dict:
A mapping of sdtype (str) to a list of data
generators (rdt.tests.datasets.BaseDatasetGenerator).
"""
generators = defaultdict(list)
for generator in BaseDatasetGenerator.get_subcl... | 4660f8b84f1a048942e519d188b17bac10903b1f | 46,723 |
import glob
import tqdm
import os
def read_file(path, sep):
"""
ファイルの読み込みを行う。一度に、複数ファイルの読み込みも可能。
相対パスと絶対パス、どちらでも可能。
Input
------
path : 読み込みたいファイルへのパス
sep : 読み込みたいファイルの区切り文字の指定
Output
------
df_array : 読み込んだdfのリスト
path_array : globで取得した、ファイルパスのリスト
Raises
-----... | 0a892d55e5fdb6f99a603a6624d8d24bc7246822 | 46,724 |
import re
def get_ignore_unknown_patterns():
"""
Fetch the value of the setting that tells us what unknown overrides we
should ignore in reports. The regular expressions from the settings file
(if any) are compiled in the returned list.
When the setting is a boolean, the result is either an empty... | 5437159c9bddbc80585e31156d0740108df2dcca | 46,725 |
from typing import Any
import os
import pickle
def main(xyz_file) -> Any:
"""
Execute main code for function by reading an xyz file and returning persistence diagram
Arg: xyz_file is the name of an xyz structure (which is stored in 'xyz_structures')
Return: persistence diagram from Delaunay triangul... | 41cf54df7520fc7173fbd66038cd7a3e40d3015d | 46,726 |
def test_dal_method_middleware():
"""
Verify that when calling a method on a service off the DAL,
the method middlewares run, in the correct order and run
before the method itself is called.
We deliberately do not use the DalMethodResolverMiddleware here,
because we are testing middleware call... | 71f43002001b5c4bb2b43f72ac29ab4bae9e6159 | 46,727 |
def chain_functions(fun_first: tp.Callable[..., tp.Union[tp.Tuple[tp.Tuple, tp.Dict],
tp.Dict, tp.Tuple]]) -> tp.Callable:
"""
A decorator to chain function calls.
This function is expected to return:
* a 2-tuple [tp.Tuple, tp.Dict] - args and k... | e78f42d30fb788751703f1b56bafd485b27c1a0e | 46,728 |
from typing import Union
import types
from typing import List
from typing import Tuple
from typing import Set
def find_variables(
expr: Union[types.Constraint, List[types.Constraint], Tuple[types.Constraint]]
) -> Set[core.Symbol]:
"""Find variables in constraints."""
if not isinstance(expr, (list, tuple)):
... | d9b737260b1c2b5d25f6718530ed74245c76abfd | 46,729 |
def _number_of_neighbors(i, j, height, width, state):
"""
Counts the number of LIVE neighbors of the cell in position (i, j).
:param i: Row of the cell.
:param j: Column of the cell.
:param height: Height of the input state.
:param width: Width of the input state.
:param state: A game state... | cb88e81fb7124ef6c97de75cfecef5b11767da14 | 46,730 |
def _format_cpu_memory(container_group):
"""Format CPU and memory. """
containers = container_group.get('containers')
if containers is not None and containers:
total_cpu = 0
total_memory = 0
for container in containers:
resources = container.get('resources')
i... | 1e1db12afd45a6d0634a6d9f701f3c3bb7bc9570 | 46,731 |
def create_category(name):
"""
Create a new category
:param name:
:return object:
"""
category = Category(name=name)
session.add(category)
session.commit()
return category | 512092a2b3c3c9fd4a912f99dbfe35a8645d228e | 46,732 |
def logpost_negb(state,params):
"""
Compute log-posterior density values; this function assumes
the likelihood is a product of negative-binomial distributions
Parameters
----------
state: python list or numpy array
model parameters
params: dictionary
detailed settings for... | 0b88b2f39f70d7cd07f8cfbd9a8434f56ba306ad | 46,733 |
import os
def GetBinhostCache(options):
"""Get and optionally clear the binhost cache."""
cache_dir = os.path.join(path_util.FindCacheDir(),
'cros_install_debug_syms-v' + CACHE_VERSION)
if options.clearcache:
osutils.RmDir(cache_dir, ignore_missing=True)
binhost_cache = None
... | 1305cd893e3f809ee32b9e01dab3d7057e0eb0ff | 46,734 |
import functools
def matcher(callable_or_names, names=None):
"""
Implicitly registers it with function's __name__, "be_a_dog":
@matcher
def be_a_dog(x):
return type(x) == dog
Explicitly registers it with names "be_a_dog" and "be_canine"
@matcher("be_a_dog, be_canine")
def whatev... | 6e79149c45ba32576d9fa7cde373d14279c401e9 | 46,735 |
def update_tree(
tree: EmptyTree, oid: str, current_user: User = Depends(get_current_active_user)
):
"""
Update tree DB entry
"""
print(tree)
try:
selected_tree = TreeDB.objects.get(id=oid)
selected_tree.species = tree.species if tree.species else selected_tree.species
s... | 49ac41202d2b6471574a55cacfcf746b646437b3 | 46,736 |
def get_queue_jobs(queue_name):
"""Get the jobs by status of a Queue.
Args:
queue_name (str): The RQ Queue name
Returns:
dict: Number of jobs by job status
Raises:
redis.exceptions.RedisError: On Redis connection errors
"""
queue = Queue(queue_name)
return {
... | a5ba497545955ad5bb42406cf289d69f05b1101a | 46,737 |
def update_lengths(lengths, eoses, idx):
"""Update the length of a generated tensor based on the first EOS found.
This is useful for a decoding situation where tokens after an EOS
can be something other than EOS. This also makes sure that a second
generated EOS doesn't effect the lengths.
:param l... | bad4dce46e498da09400673abacebdba8db5ddae | 46,738 |
def delete_widget_config(id_):
"""Deletes a widget
:param id: ID of the widget to delete
:type id: str
:rtype: tuple
"""
widget_config = WidgetConfig.query.get(id_)
if not widget_config:
return "Not Found", 404
else:
session.delete(widget_config)
session.commit(... | 74e9e1568cbbcfe825dc936699682db270aa0125 | 46,739 |
def detail_view(request):
"""
Directs user to the detail template
"""
try:
symbol = request.matchdict['symbol']
except KeyError:
return HTTPNotFound()
try:
query = request.dbsession(Account)
stock_detail = query.filter(Account.username == request.authenticated_us... | aa45381bce120007a6fd957b0d3c433ec0fa3303 | 46,740 |
def is_right_censored(lc, frange):
""" Returns true if the light curve is cutoff on the right. """
return len(lc['t0'])-1 in frange | de2e81605db2dc2a5f073d8400e2e8ee1b46f199 | 46,741 |
def neural_net_learning_rate_input():
"""
Return a Tensor for the learning rate
"""
return tf.placeholder(tf.float64, name="learning_rate") | 3d7e21cafd617b69aafa439c238f9d16c0eb28b4 | 46,742 |
def decode_csr(b64der):
"""
Decode JOSE Base-64 DER-encoded CSR.
:param str b64der: The encoded CSR.
:rtype: `cryptography.x509.CertificateSigningRequest`
:return: The decoded CSR.
"""
try:
return x509.load_der_x509_csr(
jose.decode_b64jose(b64der), default_backend())
... | 0eb7dd2acd8cd8a210969e0ea1d2e99ecca07a97 | 46,743 |
def retrieve_span(task, task_id):
"""Helper to retrieve an active `Span` stored in a `Task`
instance
"""
weak_dict = getattr(task, CTX_KEY, None)
if weak_dict is None:
return
else:
return weak_dict.get(task_id) | 74d003e9b2a019a8b0b1527952dabcb8a21f6e2b | 46,744 |
def check_data(func):
"""Decorator function for checking possible exceptions during extraction.
Args:
func (obj): function used in try-except block
Except:
(str) : in case of exception assigns '-' for the missing data.
"""
def inner(line):
try:
return func(line)... | b9dad9ff8adbee9f8307c4c61fc2d5e1918092e2 | 46,745 |
def fscale(ns, si=1, one_sided=False):
"""
numpy.fft.fftfreq returns Nyquist as a negative frequency so we propose this instead
:param ns: number of samples
:param si: sampling interval in seconds
:param one_sided: if True, returns only positive frequencies
:return: fscale: numpy vector contain... | f6cf820c184651396c3fc61a366391c9d03f086b | 46,746 |
def get_features_from_files(
data_dir,
features_ext='.proc.c3d-avg.npy',
test_split=[],
classes=None,
max_elements=13320):
"""
Creates and executes mPyPl pipe to load feature vectors from serialized files and returns a preprocessed
data stream that can be further used with respect to... | f53f0781a6108e8e31ba75613c007368b095fdfb | 46,747 |
import json
import hashlib
def crypto_hash(*args):
"""
Return a SHA-256 hash of the given arguments
"""
stringified_args = sorted(map(lambda x: json.dumps(x), args))
joined_data = "".join(stringified_args)
return hashlib.sha256(joined_data.encode("utf-8")).hexdigest() | 3c4329a6e99826a15e52e2921040045697ce5aa0 | 46,748 |
def vectorized(csm, e, h, r0, rm, kj):
""" Uses Numpys fast array operations, distributed via the mkl-package.
Those oparations are already optimized and use all available physical cores.
"""
nFreqs = csm.shape[0]
nGridPoints = r0.shape[0]
beamformOutput = np.zeros((nFreqs, nGridPoints), np.comp... | 15c2fb6a9f7f8551314d0a0e9c219308dc7b2741 | 46,749 |
def is_empty_line(line: str) -> bool:
"""Checks whether a line is empty."""
return line.strip("\n").strip("\t").strip() == "" | ad58cc78e5f25353419682343c34c21e2679304d | 46,750 |
def to_host_list(value):
"""Space separated list of FQDNs."""
return value.split() | 85740e6e90096d5711022a7ae18b919673899b36 | 46,751 |
import click
def drop_prediction_column(data_path, group_name, column_name, yes=True):
"""
Deletes prediction columns in a h5py file if the columns exist.
Including 'mean' and 'std' columns.
"""
n_del = 0
with h5py.File(data_path, "r+") as f:
if group_name not in f.keys():
... | 9ac7e06ddcf71af87b383ecefe5f3aac1a0e2159 | 46,752 |
def create_indep_noise_weight_matrix(Nbm, Knoise, gamma, g, w):
"""create a weight matrix for Nbm * Knoise sources projecting to Nbm
targets with a fixed indegree of Knoise. no shared inputs are
allowed, hence each target receives uncorrelated input of the
sources are uncorrelated.
"""
Nnoise ... | 4760fcf51d9418243df7ecb9027d81b77dd85a00 | 46,753 |
import torch
def tensordot(x, y, axis=2):
""" Compute tensor dot product along specified axes.
axes : int or (2,) array_like
integer_like If an int N, sum over the last N axes of a and the first N axes
of b in order. The sizes of the corresponding axes must match.
(2,) array_like Or, a list of axes t... | c833140019247602643166e3527cd8c506efd794 | 46,754 |
def _is_tarfile(filename):
"""Returns true if 'filename' is TAR file."""
return (filename.endswith(".tar") or filename.endswith(".tar.gz") or
filename.endswith(".tgz")) | 761b776e0e8078ddd4bee694e0a9d853dd2e31fd | 46,755 |
def deleteAnonMsgs(key, dbn='anon', env=None):
"""
Deletes messages at key uid
Parameters:
key is anon uid
dbn is name str of named sub database, Default is 'anon'
env is main LMDB database environment
If env is not provided then use global gDbEnv
"""
global gDbE... | 054645e2267f6add970b5b44ad13825e8a739f3b | 46,756 |
import pydantic
import json
def configured_model():
"""
Fixture for configured model - this was how aliasing was achieved for the first schema,
but since nearly every object will need aliasing methods, the models now inherit from
AbstractModel - this fixture is for testing that AbstractModel behaves t... | 7f06c86a78c445a1f68343623a8ab7ef305ffcd6 | 46,757 |
def get_rho(w):
"""
:param w: 対象の値
最小位から数えて最初の1ビットの位置(1-indexed)
"""
return libc.ffs(w) | 6b05211eb86eff1a1473e1f42681063ef8878cc0 | 46,758 |
import tqdm
import torch
def data_collection(env: gym.Env,
num_episodes: int = 10,
epsilon: float = 0.2,
use_gt_states: bool = False,
use_tqdm: bool = False):
""" Collect demonstration dataset """
train_inputs = []
train_actio... | 7d8967934526bb1ef56e4097a3da9f9c099402e7 | 46,759 |
from typing import Optional
import asyncio
import contextlib
from typing import cast
def get_running_loop() -> Optional[asyncio.AbstractEventLoop]:
"""Check if an event loop is already running."""
with contextlib.suppress(RuntimeError):
if hasattr(asyncio, "get_running_loop"):
return cast(... | 48750bc03be5d8cd17da20a3ca01d02149d471f5 | 46,760 |
def distance_diff_catl(ra, dist, gap):
"""
Computes the necessary distance between catalogues
Parameters
-----------
ra: float
1st distance
dist: float
2nd distance
Returns
-----------
dist_diff: float
amount of distance necessary between mocks
"""
... | 2a523d1c9c132dc8fcb65bd8d633bf24fcf46f42 | 46,761 |
def calculate_degree(network):
"""Calculates the degree of the nodes from the from and to ids. It
is not wise to call this method after removing nodes or edges
without first resetting the ids
Args:
network (class): A network composed of nodes (points in space) and edges (lines)
Returns:
... | 11e1d0ad1d02f1e32c89688a368e359c278c1581 | 46,762 |
def parse_token(filehandle, token):
"""Iterates through filehandle until token found. If value found after
token, returns it."""
for line in filehandle:
line = line.strip()
if line.startswith(token):
if len(line) > len(token):
return line.rsplit('\t', 1)[1]
... | 9f65ec378b33903250173aaa3f97cd058de13d2b | 46,763 |
import sys
import random
def send_transaction(orderers, tran_req, tx_context):
"""Send a transaction to the chain's orderer service (one or more
orderer endpoints) for consensus and committing to the ledger.
This call is asynchronous and the successful transaction commit is
notified via a BLOCK or CH... | 3a932847ec38361be94e1716f07fcf868ddfe9fb | 46,764 |
def logsumexp(matrix, dim=None):
"""
Compute log(sum(exp(matrix), dim)) in a numerically stable way.
:param matrix: input ndarray
:type matrix: ndarray
:param dim: integer indicating which dimension to sum along
:type dim: int
:return: numerically stable equivalent of np.log(np.sum(np.e... | 2fefc6e4a88d5dfa006d8e587ee18845960689c9 | 46,765 |
import os
import csv
def import_book(book_path):
"""
Read book at (book_path)
"""
book = dict(name=os.path.basename(os.path.splitext(book_path)[0]))
with open(book_path, 'r') as f:
book['content'] = f.read()
region_file = to_region_file(book_path)
if os.path.exists(region_file):
... | 1d1bfd5b10ab523eea4b9fe1aea4e39920973e95 | 46,766 |
def _run_adb(adbcommand):
"""Run the adb binary."""
cmd = [ADB]
cmd.extend(adbcommand.split())
return process.check_output(cmd) | 72e718f77e0d23055dcc9ba3e83ad547bfac26f2 | 46,767 |
import os
def get_hooks(additional_dirs=None):
""" Helper function to find and load all hooks. """
log.debug("get_hooks()")
if additional_dirs is None:
additional_dirs = []
hooks = annex.Annex(BaseHermesHook, [
os.path.join(BUILTIN_PLUGIN_DIR, "hooks"),
"/etc/hermes/plugins/hoo... | 5fee059ed4df510005c0f0df58a4e310f40964ce | 46,768 |
def _label_constraint(key, value):
"""
Returns a label constraint for host limit 1
:param key: The key fo the label
:param value: The value of the label
"""
return task_pb2.Constraint(
type=1,
labelConstraint=task_pb2.LabelConstraint(
kind=1,
condition=2,
... | 6c18e9a852b0bd924cf3ac70669acb7cc8676e57 | 46,769 |
def trim(infile, outfile):
"""Trim video with ffmpeg. Returns tuple of filename and dict of trim times"""
if not pyask.yes_no("Does the video need to be trimmed?", default="yes"):
return (infile, {"start": 0, "end": None})
start, end = get_trim_times()
run(
[
"ffmpeg",
... | a21e8515cbf5e03b5e68493279a021bf0bd41c12 | 46,770 |
def u3u2(u3):
"""
feet&inch to inch
"""
f = u3[0]
if f == '': f = 0
else: f = int(f)
i = u3[1]
if i == '': i = 0
else: i = round(convert_to_float(u3[1]), 2)
u2 = f*12 + i
return str(u2) | 271bec37281a89e4add68828e437ae909d8cbbd6 | 46,771 |
import os
def is_csv(path):
"""Is a file a CSV file extension?"""
(filename, ext) = os.path.splitext(path)
return ext.lower() in ['.csv', '.CSV'] | d9ccb799a432f9695ac64c87b3f5eda54748f5df | 46,772 |
from pydantic_factories import ModelFactory
def create(policy_backbone: ModelFactory, value_backbone: ModelFactory):
""" Vel creation function """
return DeterministicPolicyModelFactory(
policy_backbone=policy_backbone, value_backbone=value_backbone
) | 05970ef98baec267a33629c0eb13da1dd807db12 | 46,773 |
def main(args: Namespace) -> bool:
"""
The Function to process the data at the given path using the given transformation rules
and store them at given destination path. if any file fails at transformation level,
it will be stored in error path
Args:
args (Namespace): Argument Namespace con... | 96a3098f9df10b58fdc55b894ff3901d80ab7eeb | 46,774 |
def rising_edge(signal):
"""
FUNCTION: rising_edge(a str)
a: signal name
Rising edge wait function.
"""
global sim_time_queue
if (sim_time > 0):
signal_cur_value = 0
signal_prev_value = 0
... | 61d3f654f908b4e50fbbb64f23629a6d08bb08b5 | 46,775 |
def alt(*args: Pat) -> Pat:
"""Matches one of the given choices.
>>> alt() # will never match
alt()
>>> alt('a')
'a'
>>> alt(alt('b', 'c'), 'd')
alt('b', 'c', 'd')
"""
choices = []
for a in args:
if isinstance(a, Alt):
choices.extend(a.choices)
else... | 88f8490cb800d80315ad81aac7b9a62f6055fe56 | 46,776 |
def batch_get_value(ops):
"""Returns the value of more than one tensor variable.
# Arguments
ops: list of ops to run.
# Returns
A list of Numpy arrays.
"""
if ops:
return get_session().run(ops)
else:
return [] | ef3018db0f3640c13910cc9d1b7af848499d4e51 | 46,777 |
def calculate_interest_years(starting_amount, requested_amount, interest_rate, stipend_rate):
"""
If I want X in the bank, how long will it take?
:param starting_amount: The amount of money the bank has to start with.
:type starting_amount: double
:param requested_amount: The amount requested in the... | 9af2d7917d4b7184d09467a1a996bdb86adcbd21 | 46,778 |
def short_information(title, index=0):
"""
Takes in track information and returns everything as a short formatted String.
Args:
title (str): track title string
index (str): optional track number string
Returns:
A short formatted string of all track information.
"""
if ... | 6754af1f2327eb5d9f37f4d25aa4f808d4793553 | 46,779 |
def _reduction_a_cell_do(ip, p, filters, net_type, cell_num, total_num_cells, total_training_steps, do_p=0.3, block_id=None):
"""Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
Args:
ip: Input tensor `x`
p: Input tensor `p`
filters: Number of output filters
block_id: S... | 017d5b05951c8c60667ff9dd0a77baf29e3b9c55 | 46,780 |
from typing import List
from typing import Dict
from typing import Callable
def post_processor(provider: str = None, parser: str = None,
provider_parser_list: List[Dict] = None) -> Callable:
"""
Decorator that register function for post processing. You can insert provider and parser or
... | a0a4a27d405c130547c0c83908133e4a882804e6 | 46,781 |
def AddAttachedCertificatesFlagsToParser(parser):
"""Adds flags describing certificate update without resource args."""
is_clear_certificates = base.Argument(
'--clear-certificates',
help='Removes all certificates from the entry',
action='store_true')
group = base.ArgumentGroup(
help='Arg... | 5b5cbea6f5b18db1ea4b04bd45c1693e8c78d2f0 | 46,782 |
def vmaddrelu(x, y, z):
"""
calculate relu(x * z + y), only support float16, float32.
Args:
x (tvm.tensor.Tensor): input.
y (tvm.tensor.Tensor): input.
z (tvm.tensor.Tensor): input.
Returns:
tvm.tensor.Tensor, relu(X * Z + Y).
"""
return multiple_elewise_op(x,... | c1f10e19208ba43c60f98832a99f4aad20792202 | 46,783 |
def setup(hass, config):
"""Set up the keyboard_remote."""
config = config.get(DOMAIN)
keyboard_remote = KeyboardRemote(hass, config)
def _start_keyboard_remote(_event):
keyboard_remote.run()
def _stop_keyboard_remote(_event):
keyboard_remote.stop()
hass.bus.listen_once(EVENT... | 2b519b4a7ce979e3c74e9f299f4b8d54bc6dd7d2 | 46,784 |
import six
def _write_metadata(output_fp, metadata, metadata_args):
""" Write the metadata to a file pointer.
Parameters
----------
output_fp : file like
the file pointer to write to
metadata : dict
the metadata to write
metadata_args : MetadataArgs
the metadata args
... | d42df9bad4462ad0095f1ae62e19f6a23f89c055 | 46,785 |
def _compute_taylor(data_input):
"""Algorithm: atanh(x) = x + x^3/3 + x^5/5 + x^7/7"""
taylor_para = [0, 1.0, 0, 1/3.0, 0, 1.0/5, 0, 1.0/7]
# x^2
data_mul_2 = topi.multiply(data_input, data_input)
# 1/5 + x^2/7
data_mul_2_7 = topi.multiply(data_mul_2, tvm.const(taylor_para[7], "float32"))
... | a4e96d647ddc8531c8ba1a4c1d878922881a13e1 | 46,786 |
def infer_feature_types(data, feature_types=None):
"""Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns.
If a column's type is not specified, it will be inferred by Woodwork.
Arguments:
data (pd.DataFrame): Input data to convert to a Woodw... | f584d5d6d8c4034a547af0e75d46a4ed9edd377d | 46,787 |
def apply_token_replay(log, net, initial_marking, final_marking, parameters=None):
"""
Calculates all metrics based on token-based replay and returns a unified dictionary
Parameters
-----------
log
Trace log
net
Petri net
initial_marking
Initial marking
final_mar... | 3712011039921d88bfd0026fd824dd84148353cd | 46,788 |
def add_css_file_extension(name):
"""
Appends the CSS file extension to a string.
:return: name with '.css' append at the end append at the end
:rType: string
"""
return '%s.css' % name | fbe4569e4660cc4145bac36a5ea88ae87ec4c319 | 46,789 |
import collections
import numpy
def get_data_hlsp_everest(obsid):
"""
Given a EVEREST observation ID, returns the lightcurve data.
:param obsid: The EVEREST observation ID to retrieve the data from.
:type obsid: str
:returns: JSON -- The lightcurve data for this observation ID.
Error codes... | 1a3ff1f4421fc1b6ca0ccdd83aa69c6c684b83f3 | 46,790 |
def try_protection(var, target, attacker, attacker_role, reason):
"""Attempt to protect the player, and return a list of messages or None."""
prots = []
for protector, entries in PROTECTIONS.get(target, {}).items():
for scope, protector_role in entries:
if attacker_role in scope:
... | 2e226135a222a887ce39bda8a61c146fd7dfeade | 46,791 |
def _get_position(a, n):
""" returns position of substring :n: as "start", "end" or "middle" """
position = a.index(n)
if position == 0:
return ("start", position)
elif position+len(n) == len(a):
return ("end", position)
else:
return ("middle", position) | f7a18c540542f117df822c18396e3e554d1eba45 | 46,792 |
import six
import uuid
def data_factory(value, encoding='UTF-8'):
"""Wrap a Python type in the equivalent C AMQP type.
If the Python type has already been wrapped in a ~uamqp.types.AMQPType
object - then this will be used to select the appropriate C type.
- bool => c_uamqp.BoolValue
- int => c_uam... | 47021c6afb22bee803c36ba1b4eb3ab4b8a3687c | 46,793 |
def AllTokens():
"""Retrieves all descendants of pygments.token.Token."""
def Traverse(token):
for tok in token.subtypes:
yield tok
for sub_token in Traverse(tok):
yield sub_token
return sorted(Traverse(pygments.token.Token)) | 86686f691a3023477ce9c1892641de1d4a6e509a | 46,794 |
def previous(values, elements, scope=None, strict=True):
"""Return closest previous (index, elem) of values withing scope.
Assumption:
values and elements are sorted
"""
# Init iterator on elements
elem_indexes = enumerate(elements)
index, elem = next(elem_indexes)
try:
nin... | 36b83dc2665539a3a9cb8b50419cd15410a8969c | 46,795 |
from typing import Dict
from typing import Any
import toml
def _load_prefs() -> Dict[str, Any]:
"""Load user preferences from TOML and return as a dict
:return: dict containing user preferences
"""
try:
return dict(toml.load(PREFERENCES))
except (IOError, toml.TomlDecodeError):
l... | 369296b7292b02cec01503462ed5bf6a54c949c9 | 46,796 |
def slice(from_index, to_index, list_or_string):
"""Returns the elements of the given list or string (or object with a slice
method) from fromIndex (inclusive) to toIndex (exclusive).
Dispatches to the slice method of the third argument, if present"""
return list_or_string[from_index:to_index] | 130692bad6f7de87a07786afe0ea3d6d30902ba7 | 46,797 |
def make_app():
"""Create page handlers and create a Tornado app."""
handlers = create_route_handlers(config.pages)
settings = {
"static_path": config.static_path,
"template_path": config.template_path,
"debug": True
}
return tornado.web.Application(handlers, **settings) | 895697f04161c3c73e57d55cdd652a30107a8337 | 46,798 |
def accuracy(output, target):
"""Computes the precision@k for the specified values of k"""
# batch_size = target.size(0) * target.size(1) * target.size(2)
_, pred = output.max(1)
pred = pred.view(1, -1)
target = target.view(1, -1)
correct = pred.eq(target)
# correct = correct[target != 255]
... | 4f6f25311f1579d8d638a684d40bd80d99387eae | 46,799 |
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