content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import glob
import os
import fnmatch
def parse_directory(path, rgb_prefix='img_', flow_x_prefix='flow_x_', flow_y_prefix='flow_y_'):
"""
Parse directories holding extracted frames from standard benchmarks
"""
print('parse frames under folder {}'.format(path))
frame_folders = glob.glob(os.path.join... | 5459cbb64686718d6275c1349e2062d5ed277023 | 20,200 |
def create_effect(
effect_id: CardEffect.EffectId = CardEffect.EffectId.DMG,
target: CardLevelEffects.Target = CardLevelEffects.Target.OPPONENT,
power: int = 10,
range_: float = 5
) -> Effect:
"""
Creates effect with given data, or creates default effect dealing dmg to opponent i... | 9055250c4ab7db3700b2393c16f54cfef3566747 | 20,201 |
def part_two(stream: Stream, violation: int) -> int:
"""Find the sum of min & max in the sequence that sums to `violation`."""
for start in range(len(stream) - 1):
for end in range(start + 2, len(stream) + 1):
seq = stream[start:end]
seq_sum = sum(seq)
if seq_sum == v... | a1481af0183f1e42642a9137242d57fe75738770 | 20,202 |
def getDoubleArray(plug):
"""
Gets the float array from the supplied plug.
:type plug: om.MPlug
:rtype: om.MDoubleArray
"""
return om.MFnDoubleArrayData(plug.asMObject()).array() | 6e93113d1968a56cb3b12c500be04c554f79c165 | 20,203 |
import numpy
def get_fb(file_name):
"""#{{{
load feature file and transform to dict
return:
dict
key_list_feat
"""
ff = open(file_name, 'r')
fb = []
delta = []
fb_matrix = numpy.zeros([1, 24])
delta_matrix = numpy.zeros([1, 24])
fbanks = {}
deltas = {}
... | 85589f74f47a58f0ba36a438b3340ff0858737e4 | 20,204 |
def make_train_input_fn(
feature_spec, labels, file_pattern, batch_size, shuffle=True):
"""Makes an input_fn for training."""
return _make_train_or_eval_input_fn(
feature_spec,
labels,
file_pattern,
batch_size,
tf.estimator.ModeKeys.TRAIN,
shuffle) | 1f8d481d5fff1913f392a4286c445af757f849bd | 20,205 |
def _find(xs, predicate):
"""Locate an item in a list based on a predicate function.
Args:
xs (list) : List of data
predicate (function) : Function taking a data item and returning bool
Returns:
(object|None) : The first list item that predicate returns True for or None
"""
... | 94d8dd47e54e1887f67c5f5354d05dc0c294ae52 | 20,206 |
from typing import OrderedDict
def remove_dataparallel_prefix(state_dict):
"""Removes dataparallel prefix of layer names in a checkpoint state dictionary."""
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] if k[:7] == "module." else k
new_state_dict[name] = v
... | 28fe85b262f8d4bdefa40d34839787ba1a8ef094 | 20,207 |
def user_upload_widget(node, on_complete=''):
"""Returns a Valum Uploader widget that uploads files based on the user's
home directory.
:param node: storage type (public or private) and path indicator, e.g.
"public:foo/bar" to have the uploaded file go in
MEDIA_ROOT/$USERNAME/foo/bar.
... | 77a797aaabaf7d3d0f9923e3931e938e568952e0 | 20,208 |
def run_lsa(model, lsa_options):
"""Implements local sensitivity analysis using LSI, RSI, and parameter subset reduction.
Parameters
----------
model : Model
Object of class Model holding run information.
options : Options
Object of class Options holding run settings.
... | 2a8376f3e287dbeefa8f13321804ef6052357bf5 | 20,209 |
def condense_simple_conv3x3(in_channels,
out_channels,
groups):
"""
3x3 version of the CondenseNet specific simple convolution block.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Num... | 061d0df67fdcf5f3c56f227ca7e53d0fef3e2db2 | 20,210 |
def read_data_from(file_: str) -> list:
"""Read bitmasks and values from file."""
return open(file_, "r").read().splitlines() | ec1bd526d46ee94452df23f92448e60af4d6865c | 20,211 |
def version_for(plugin):
# (Plugin) -> Optional[str]
"""Determine the version of a plugin by its module.
:param plugin:
The loaded plugin
:type plugin:
Plugin
:returns:
version string for the module
:rtype:
str
"""
module_name = plugin.plugin.__module__
... | 6d7c4ccc868d11d28c92d1264d52488e22d7f5e5 | 20,212 |
import json
def choose_organization():
"""Allow user to input organization id.
Returns:
str: Access target id
"""
target_id = None
while not target_id:
orgs = None
return_code, out, err = utils.run_command([
'gcloud', 'organizations', 'list', '--format=json'])
... | fa46edc07e45eaa53bb6a52a6f0e7992a836fad7 | 20,213 |
def start_server(function):
"""
Decorator.
Tries to call function, if it fails, try to (re)start inotify server.
Raise QueryFailed if something went wrong
"""
def decorated_function(self, *args):
result = None
try:
return function(self, *args)
except (OSError,... | e6de74cacb703172ff25c30c8a1bd54937f47d7b | 20,214 |
def get_subscriber_groups(publication_id, subscription_id='', full_uri=False):
"""This function identifies the subscriber groups for one or more subscriptions within a publication.
.. versionchanged:: 3.1.0
Refactored the function to be more efficient.
:param publication_id: The ID of the publicati... | e7dd2a052992109a2673dcdbbac388ee0babf7ec | 20,215 |
def get_salutation_from_title(title):
"""
Described here: https://github.com/VNG-Realisatie/Haal-Centraal-BRP-bevragen/blob/v1.0.0/features/aanhef.feature#L4-L38
"""
if title in [BARON, HERTOG, JONKHEER, MARKIES, RIDDER]:
return HOOGWELGEBOREN_HEER
if title in [BARONES, HERTOGIN, JONKVROUW,... | afbafcf7c2ec2a77b44d2e9aad5930f83d5cc10c | 20,216 |
def hourOfDayNy(dateTime):
"""
Returns an int value of the hour of the day for a DBDateTime in the New York time zone.
The hour is on a 24 hour clock (0 - 23).
:param dateTime: (io.deephaven.db.tables.utils.DBDateTime) - The DBDateTime for which to find the hour of the day.
:return: (int) A Qu... | eac5db0723bf44162d50a787c56244d5bcb094d9 | 20,217 |
def _extract_action_num_and_node_id(m):
"""Helper method: Extract *action_num* and *node_id* from the given regex
match. Convert *action_num* to a 0-indexed integer."""
return dict(
action_num=(int(m.group('action_num')) - 1),
node_id=m.group('node_id'),
) | f1e5f0b81d6d82856b7c00d67270048e0e4caf38 | 20,218 |
import re
def get_uid_cidx(img_name):
"""
:param img_name: format output_path / f'{uid} cam{cidx} rgb.png'
"""
img_name = img_name.split("/")[-1]
assert img_name[-8:] == " rgb.png"
img_name = img_name[:-8]
m = re.search(r'\d+$', img_name)
assert not m is None
cidx = int(m.group())... | 29363f4fc686fa972c2249e5e1db1a333625be36 | 20,219 |
def parse_color(hex_color):
"""Parse color values"""
cval = int(hex_color, 16)
x = lambda b: ((cval >> b) & 0xff) / 255.0
return {k: x(v) for k, v in dict(r=16, g=8, b=0).iteritems()} | 70ca92f7696dd5193730326de141ad30c039f7c6 | 20,220 |
def apply_4x4(RT, XYZ):
"""
RT: B x 4 x 4
XYZ: B x N x 3
"""
#RT = RT.to(XYZ.device)
B, N, _ = list(XYZ.shape)
ones = np.ones([B, N, 1])
XYZ1 = np.concatenate([XYZ, ones], 2)
XYZ1_t = np.transpose(XYZ1, 1, 2)
# this is B x 4 x N
XYZ2_t = np.matmul(RT, XYZ1_t)
XYZ2 = np.tr... | b2b2e76a79dbbdf2bc0039fb073a0a6209c9f82d | 20,221 |
def smoothmax(value1, value2, hardness):
"""
A smooth maximum between two functions. Also referred to as the logsumexp() function.
Useful because it's differentiable and preserves convexity!
Great writeup by John D Cook here:
https://www.johndcook.com/soft_maximum.pdf
:param value1: Value of... | 60ef9d14b6867aaa205c186309d0c9f53e4edb21 | 20,222 |
import os
def base_app(instance_path):
"""Flask application fixture."""
app_ = Flask('testapp', instance_path=instance_path)
app_.config.update(
SECRET_KEY='SECRET_KEY',
SQLALCHEMY_DATABASE_URI=os.environ.get(
'SQLALCHEMY_DATABASE_URI', 'sqlite:///test.db'),
SQLALCHEMY_... | e4391a35fc92b228b3a1ae425478a9a777f85594 | 20,223 |
def udf_con(udf_backend):
"""
Instance of Client, already connected to the db (if applies).
"""
return udf_backend.connection | 7e95460b4e6808cc148406dfbdb8e952ebb4739a | 20,224 |
from typing import Union
from pathlib import Path
from typing import Optional
def resolve_config(*, config: Union[Path, str]) -> Optional[Path]:
"""Resolves a config to an absolute Path."""
path = config if isinstance(config, Path) else Path(config)
# Is it absolute, or relative to the CWD?
if path.e... | 4290e46cda385fd60c164af712d28e5e7ad22c83 | 20,225 |
def get_default_config() -> DefaultConfig:
"""
Get the default config.
Returns:
A dict with the default config.
"""
images = assets.get_images()
return {
"static_url": "/static",
"favicon_ico": images.favicon_ico.name,
"favicon_png": images.favicon_png.name,
... | 72685f6bb2a45e03f42d96c82cd0436a826fed68 | 20,226 |
import string
import re
def normalize_elt(elt, alphanum=True):
"""
Normalize string by removing newlines, punctuation, spaces,
and optionally filtering for alphanumeric chars
Args:
elt (string):
string to normalize
alphanum (bool, optional, default True):
if Tr... | 79aad0a7425270b8708598fe5429ecd7c46bffde | 20,227 |
from typing import Tuple
from typing import Optional
def check_importability(code: str, func_name: str) -> Tuple[bool, Optional[Exception]]:
"""Very simple check just to see whether the code is at least importable"""
try:
import_func_from_code(
code,
func_name,
rais... | b7823344bf2ab7055882fb366b0903fcab1a5366 | 20,228 |
import torch
def compute_q(u, v, omega, k_hat, m_hat, N=100, map_est=False):
"""
Inputs:
u, v - (B,L*2)
omega - (L,n)
k_hat, m_hat - (B,J)
"""
B, L = u.size()[0], int(u.size()[1]/2)
unique_omega, inverse_idx = torch.unique(omega, dim=0, return_inverse=True) # (J,n), (L)
c, s = util... | d8ab2b71a9c149ed3c8e25daa86954c8d24ce79e | 20,229 |
def stationarity(sequence):
"""
Compute the stationarity of a sequence.
A stationary transition is one whose source and destination symbols
are the same. The stationarity measures the percentage of transitions
to the same location.
Parameters
----------
sequence : list
A list of ... | 39f96d4a07a83ef2c46033dcac9cfaa343747b2f | 20,230 |
def build_nmt_model(Vs, Vt, demb=128, h=128, drop_p=0.5, tied=True, mask=True, attn=True, l2_ratio=1e-4,
training=None, rnn_fn='lstm'):
"""
Builds the target machine translation model.
:param demb: Embedding dimension.
:param h: Number of hidden units.
:param drop_p: Dropout per... | ff3991dab1b4d6e8e5556f064356e1cce1320e78 | 20,231 |
from typing import List
import json
import requests
def get_available_tf_versions(include_prerelease: bool = False) -> List[str]:
"""Return available Terraform versions."""
tf_releases = json.loads(
requests.get("https://releases.hashicorp.com/index.json").text
)["terraform"]
tf_versions = sor... | 4be8820bb7cc2b5e5a649b8690fdef3d376a80ed | 20,232 |
def relay_tagged(c, x, tag):
"""Implementation of tagged for Relay."""
assert tag.is_constant(int)
rtag = get_union_ctr(tag.value, None)
return rtag(c.ref(x)) | b9d97051b3bfe13194a4123ec044e79d53c7587f | 20,233 |
def get_vf(Xf, Nf):
"""
compute the 1-spectrogram of the projection of a frequency band of the mix at 1 frequency on some directions
:param Xf: T x I complex STFT of mix at a given f
:param Nf: Mp x Md x I projection matrix
:return: Vf: Mp x Ml x Nt magnitude spectrogram of projection... | 921bebfb4129f9ae7b0b2d5878c20eb957328c6c | 20,234 |
def gen_data_code(stream, bits=ic.core_opts.data_bits):
# type: (ic.Stream, int) -> dict
"""
Create a similarity preserving ISCC Data-Code with the latest standard algorithm.
:param Stream stream: Input data stream.
:param int bits: Bit-length of ISCC Data-Code (default 64).
:return: ISCC Data-... | ede11d67f305b57a2734cc5898e22102c0db07bb | 20,235 |
import os
def file_get_size_in_bytes(path: str) -> int:
"""Return the size of the file in bytes."""
return int(os.stat(path).st_size) | e9692259d2f5cb8f536cb8c6a0ae53e0b7c6efd1 | 20,236 |
def model_fn_builder(
bert_config,
init_checkpoint,
layer_indexes,
use_tpu,
use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstim... | 7a57c1557f4643c738a22b0baf4dbdce0fa06a3a | 20,237 |
def git2pep440(ver_str):
"""
Converts a git description to a PEP440 conforming string
:param ver_str: git version description
:return: PEP440 version description
"""
dash_count = ver_str.count('-')
if dash_count == 0:
return ver_str
elif dash_count == 1:
return ver_str.s... | 7c4a5185305627c22118722b73b2facfa830875a | 20,238 |
def rejoin(hyphenated, line):
"""Add hyphenated word part to line start, dehyphenating when required."""
first_part, hyphen = split_hyphen(hyphenated)
second_part, rest = split_first_token(line)
if is_same_vowel(first_part[-1], second_part[0]):
# same vowel before and after hyphen
keep_... | ced2bd2b791660e45741997e17ba3bdc0adfad6f | 20,239 |
from typing import Union
import zmq
def msg_bytes(msg: Union[bytes, bytearray, zmq.Frame]) -> Union[bytes, bytearray]:
"""Return message frame as bytes.
"""
return msg.bytes if isinstance(msg, zmq.Frame) else msg | 166865f5d51526cf70c767fc90e3d7b474501fb0 | 20,240 |
import math
def iucr_string(values):
"""Convert a central value (average) and its s.u. into an IUCr compliant number representation.
:param values: pair of central value (average) and s.u.
:type values: tuple((float, float))
:return: IUCr compliant representation
:rtype: str
"""
if values... | c6c6602aa0ba481ed5467ed62df59a16f26a8091 | 20,241 |
def augment_signals(ds, augment_configs):
"""
Apply all augmentation methods specified in 'augment_config' and return a dataset where all elements are drawn randomly from the augmented and unaugmented datasets.
"""
augmented_datasets = []
for conf in augment_configs:
aug_kwargs = {k: v for k... | 5c07fcaefc277a4190336995440d1380f4263409 | 20,242 |
def sse_pack(d):
"""For sending sse to client. Formats a dictionary into correct form for SSE"""
buf = ''
for k in ['retry','id','event','data']:
if k in d.keys():
buf += '{}: {}\n'.format(k, d[k])
return buf + '\n' | a497c7ab919115d59d49f25abfdb9d88b0963af3 | 20,243 |
def proj_beta_model(r2d_kpc, n0, r_c, beta):
"""
Compute a projected beta model:
P(R) = \int n_e dl at given R
Parameters
----------
- r2d_kpc: array of projected radius at which to compute integration
- n0 : normalization
- r_c : core radius parameter
- beta : slope of the profile... | 1d581d73da98833ca0df33df9de19b7cd71d7164 | 20,244 |
def read_raw_datafile(filename):
"""
Read and format the weather data from one csv file downloaded from the
climate.weather.gc.ca website.
"""
dataset = pd.read_csv(filename, dtype='str')
valid_columns = [
'Date/Time', 'Year', 'Month', 'Day', 'Max Temp (°C)', 'Min Temp (°C)',
'Me... | 08d56daa375e3a05d0bca9041c8e973ec80ebe11 | 20,245 |
def zeros(shape, backend=TensorFunctions):
"""
Produce a zero tensor of size `shape`.
Args:
shape (tuple): shape of tensor
backend (:class:`Backend`): tensor backend
Returns:
:class:`Tensor` : new tensor
"""
return Tensor.make([0] * int(operators.prod(shape)), shape, back... | 1c3115613850819ece6f5a86142e7feb532930e7 | 20,246 |
def formatLabels(labels, total_time, time):
"""Format labels into vector where each value represents a window of
time seconds"""
time_threshold = 1
num_windows = total_time // time
Y = np.zeros(num_windows)
for label in labels:
start = label['start']
duration = label['duration']
... | 714bfda3c8a997abb9389f74261bcc9aa2fb768b | 20,247 |
import re
def line(line_def, **kwargs):
"""Highlights a character in the line"""
def replace(s):
return "(%s)" % ansi.aformat(s.group()[1:], attrs=["bold", ])
return ansi.aformat(
re.sub('@.?', replace, line_def),
**kwargs) | 755dee2147f606e57825314642dd35cc713cb9ff | 20,248 |
from datetime import datetime
def ceil_datetime_at_minute_interval(timestamp, minute):
"""
From http://stackoverflow.com/questions/13071384/python-ceil-a-datetime-to-next-quarter-of-an-hour
:param timestamp:
:type timestamp: datetime.datetime
:param minute:
:type minute: int
:return:
... | 7e9522ac8eebdab531d13d842a5018f3813ab86c | 20,249 |
import requests
import json
def make_response(
activated=True,
expires_in=0,
auto_activation_supported=True,
oauth_server=None,
DATA=None,
):
"""
Helper for making ActivationRequirementsResponses with known fields
"""
DATA = DATA or []
data = {
"activated": activated,
... | eaf971695752dfd3d0e4b414d3085c3d60349534 | 20,250 |
def post_create_manager_config(
api_client,
id,
log_files=None,
configuration_files=None,
ports=None,
process_manager=None,
executables=None,
**kwargs
): # noqa: E501
"""post_create_manager_config # noqa: E501
Create a new plugin manager configuration. If no params are provide... | c3f27767a6b94604390b683b4169d7ecc54e945f | 20,251 |
import re
import os
def get_long_desc() -> str:
""" read long description and adjust master with version for badges or links
only for release versions (x.y.z)
"""
get_version()
release = re.compile(r'(\d+\.){0,2}\d+$')
with open(os.path.join(wd, 'README.md')) as fd:
if _version == '0.0... | 6a2366a7555d654897271a43cf680e7555cc03a1 | 20,252 |
def get_current_user():
"""Load current user or use anon user."""
return auth.User(
uuid=None,
login='anon',
password='',
name='anon',
visiblity=None,
language=None,
last_seen=None,
) | 2074b73d970e0549726b887e67b9c7b36db6e463 | 20,253 |
def validate(number, alphabet='0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ*'):
"""Check whether the check digit is valid."""
try:
valid = checksum(number, alphabet) == 1
except Exception: # noqa: B902
raise InvalidFormat()
if not valid:
raise InvalidChecksum()
return number | 1d521eaa369c6436d8c8cfc93889d769ca15c193 | 20,254 |
from typing import Union
def pct_chg(x: Union[np.ndarray, pd.Series]) -> np.ndarray:
"""Percentage change between the current and a prior element.
Args:
x: A numpy.ndarray or pandas.Series object
Returns:
A numpy.ndarray with the results
"""
x = x.astype("float64")
if isins... | 253a8223c58fa9d89d50c554f54051724961c3bb | 20,255 |
def calc_gram_matrix(input_mat):
"""
Paper directly mentions about calculating Gram matrix:
G_{ij}^l = \sum_k F_{ij}^l F_{jk}^l
i and j stand for filter position and k stands for position in each filters.
If matrix A is composed of vectors, a1, a2, a3, etc,
e.g. A = [a1, a2, a3, ...] note... | 386db7deec87127f8c2872e091283fb555611f04 | 20,256 |
def parse_annotations_with_food_part_template(annotations, premise):
""" """
annotations_aggregated = []
annotations_reported = []
rows_grouped_by_premises = annotations[annotations["premise"] == premise]
for hypothesis in rows_grouped_by_premises["hypothesis"].unique():
rows_grouped_by_hy... | 1bf17aa605b9ec581ec1379ed0ae7f7340461a19 | 20,257 |
def filter_dictionary(dictionary, filter_func):
"""
returns the first element of `dictionary` where the element's key pass the filter_func.
filter_func can be either a callable or a value.
- if callable filtering is checked with `test(element_value)`
- if value filtering is checked ... | f5fa77a51241323845eb9a59adc9df7f662f287b | 20,258 |
def restart_workflow(workflow_id, clear_data=False, delete_files=False):
"""Restart a workflow with the latest spec.
Clear data allows user to restart the workflow without previous data."""
workflow_model: WorkflowModel = session.query(WorkflowModel).filter_by(id=workflow_id).first()
WorkflowProcesso... | 70c5df4b31559830c94d01cfb86e9a68070f63b1 | 20,259 |
def spike_lmax(S, Q):
"""Maximum spike given a perturbation"""
S2 = S * S
return ((1.0 / Q) + S2) * (1 + (1.0 / S2)) | ba845e3255e4d3eb5116d279a209f4424062603b | 20,260 |
def get_engine():
"""Returns the db engine."""
if not hasattr(g, 'sqlite_engine'):
g.sqlite_engine = create_engine('sqlite:///' + app.config['DATABASE'], echo=True)
return g.sqlite_engine | 579d7110cd27787095c6ede2f25841cbac5e8ca0 | 20,261 |
def is_on_curve(point):
"""Returns True if the given point lies on the elliptic curve."""
if point is None:
# None represents the point at infinity.
return True
x, y = point
return (y * y - x * x * x - curve.a * x - curve.b) % curve.p == 0 | 563d567c7dadc9d23bf6467c4fe30c697b0fd9fa | 20,262 |
from typing import Dict
from typing import List
from typing import Tuple
def find_closest_point(
odlc: Dict[str, float],
boundary_points: List[Dict[str, float]],
obstacles: List[Dict[str, float]],
) -> Tuple[Dict[str, float], List[float]]:
"""Finds the closest safe point to the ODLC while staying with... | c07f2a1922f5083d9155da12a7273208ae440cf5 | 20,263 |
from typing import Tuple
from typing import Dict
from typing import List
def _get_band_edge_indices(
band_structure: BandStructure,
tol: float = 0.005,
) -> Tuple[Dict[Spin, List[int]], Dict[Spin, List[int]]]:
"""
Get indices of degenerate band edge states, within a tolerance.
Parameters
----... | bd0ed67b879c627c77e35c519dbfcf86890523e1 | 20,264 |
import logging
import os
def make_val_dataloader(data_config, data_path, task=None, data_strct=None):
""" Return a data loader for a validation set """
if not "val_data" in data_config or data_config["val_data"] is None:
print_rank("Validation data list is not set", loglevel=logging.DEBUG)
re... | cbcb53fc3a9f8a27b2c371df693b5abe059d3f37 | 20,265 |
def _get_urls():
"""Stores the URLs for histology file downloads.
Returns
-------
dict
Dictionary with template names as keys and urls to the files as values.
"""
return {
"fsaverage": "https://box.bic.mni.mcgill.ca/s/znBp7Emls0mMW1a/download",
"fsaverage5": "https://box... | 697cf29b3caaeda079014fd342fbe7ad4c650d30 | 20,266 |
import struct
def guid_bytes_to_string(stream):
"""
Read a byte stream to parse as GUID
:ivar bytes stream: GUID in raw mode
:returns: GUID as a string
:rtype: str
"""
Data1 = struct.unpack("<I", stream[0:4])[0]
Data2 = struct.unpack("<H", stream[4:6])[0]
Data3 = struct.unpack("<H"... | 23f013b48806d1d2d4b4bec4ab4a5fcf6fc2e6b0 | 20,267 |
def thaiword_to_time(text: str, padding: bool = True) -> str:
"""
Convert Thai time in words into time (H:M).
:param str text: Thai time in words
:param bool padding: Zero padding the hour if True
:return: time string
:rtype: str
:Example:
thaiword_to_time"บ่ายโมงครึ่ง")
... | 235874bf252908eeba96f17b2ccc4d7ab32b90ce | 20,268 |
import torch
import os
import time
def _build_index_mappings(name, data_prefix, documents, sizes,
num_samples, seq_length, seed):
"""Build doc-idx, sample-idx, and shuffle-idx.
doc-idx: is an array (ordered) of documents to be used in training.
sample-idx: is the start document i... | 396299fffff56b6edcb1be075561dd4a20f1f326 | 20,269 |
import random
def get_grains(ng,gdmin,angrange0,angrange1,two_dim):
"""
Get specified number of grains with conditions of minimum distance and angle range.
"""
dang = (angrange1-angrange0) /180.0 *np.pi /ng
grains= []
ig = 0
dmin = 1e+30
while True:
if ig >= ng: break
p... | 591858e16353d5363767c7816d59f64b890929f8 | 20,270 |
from typing import List
def two_loops(N: List[int]) -> List[int]:
"""Semi-dynamic programming approach using O(2n):
- Calculate the product of all items before item i
- Calculate the product of all items after item i
- For each item i, multiply the products for before and after i
L[i] = N[i-1] *... | 3620aa19833b2e967b2c295fa53ba39bf3b6b70d | 20,271 |
import os
import subprocess
def make_slurm_queue(dirmain, print_level=0):
"""get queue list from slurm """
# Check slurm
list_ids = []
list_scripts = []
usr = os.environ.get('USER')
proc = subprocess.run(['squeue', "-u", usr, "-O", "jobid:.50,name:.150,stdout:.200"], capture_output=True)
a... | 5ce9da0a90720175d0730a6c8ca099e3f54e3667 | 20,272 |
from typing import Union
from pathlib import Path
from typing import Optional
import re
def read_renku_version_from_dockerfile(path: Union[Path, str]) -> Optional[str]:
"""Read RENKU_VERSION from the content of path if a valid version is available."""
path = Path(path)
if not path.exists():
return... | 9b9c343db6ca0604e04c90cb6a51be9bba3e0b1c | 20,273 |
def zero_mean(framed):
"""Calculate zero-mean of frames"""
mean = np.mean(framed, axis=1)
framed = framed - mean[np.newaxis, :].T
return framed | f970522327b019cfddc42a5764f9854eaf681378 | 20,274 |
def now():
"""
返回当前时间
"""
return timezone.now() | 86dee41549eeef546f5447ed657eb849c36239cc | 20,275 |
import os
def calc_thickness_of_wing(XFOILdirectory, chordArray2):
"""
calculation wing thickness list
"""
# open airfoil data
data = io_fpa.open2read(os.path.join("data", XFOILdirectory, "foil.dat"))
# make airfoil list
xlist = [float(i.split()[0]) for i in data[1:]]
ylist = [float(i... | 67d9b5fa03b9dec00739a3e4e4fffa82a539848c | 20,276 |
def rgb_to_hex(r, g, b):
"""Turn an RGB float tuple into a hex code.
Args:
r (float): R value
g (float): G value
b (float): B value
Returns:
str: A hex code (no #)
"""
r_int = round((r + 1.0) / 2 * 255)
g_int = round((g + 1.0) / 2 * 255)
b_int = round((b + 1... | a5181c475c798bbd03020d81da10d8fbf86cc396 | 20,277 |
def calc_R(x,y, xc, yc):
"""
calculate the distance of each 2D points from the center (xc, yc)
"""
return np.sqrt((x-xc)**2 + (y-yc)**2) | 7a10251f3048a3d7c07f6fd886225b841e19a1a2 | 20,278 |
def get_followers_list(user_url, driver, followers=True):
"""
Returns a list of users who follow or are followed by a user.
Parameters
----------
user_url: string
driver: selenium.webdriver
followers: bool
If True, gets users who are followers of this user.
If False, gets us... | fbb592c29b66b41b51d67a938659177ead9a13c6 | 20,279 |
from typing import Optional
def policy_to_dict(player_policy,
game,
all_states=None,
state_to_information_state=None,
player_id: Optional = None):
"""Converts a Policy instance into a tabular policy represented as a dict.
This is com... | c5e048d7886dac6b36197c0ee1593f8602972fa5 | 20,280 |
import time
def compressed_gw(Dist1,Dist2,p1,p2,node_subset1,node_subset2, verbose = False, return_dense = True):
"""
In:
Dist1, Dist2 --- distance matrices of size nxn and mxm
p1,p2 --- probability vectors of length n and m
node_subset1, node_subset2 --- subsets of point indices. This version o... | 7008e62138e2f98238fdba6bf698559690c83972 | 20,281 |
import sys
def get_stats(service: googleapiclient.discovery, videos_list: list):
"""Get duration, views and live status of YouTube video with their ID
:param service: a YouTube service build with 'googleapiclient.discovery'
:param videos_list: list of YouTube video IDs
:return items: playlist items (v... | 52ec8ccfe10609f175c3ffdd480c9292ad306e90 | 20,282 |
import sys
def NotecardExceptionInfo(exception):
"""Construct a formatted Exception string.
Args:
exception (Exception): An exception object.
Returns:
string: a summary of the exception with line number and details.
"""
name = exception.__class__.__name__
return sys.platform ... | bf45535776298a8d0afd326539f9492bfb710a9c | 20,283 |
from typing import Dict
from typing import Tuple
from typing import List
def load_test_dataset(cfg: Dict) -> Tuple[Tuple[List]]:
"""Read config and load test dataset
Args:
cfg (Dict): config from config.json
Returns:
Tuple[Tuple[List]]: Test dataset
"""
X_test, y_test, test_promp... | b4306f48a927ab0036b5815c5b119480ea4452ba | 20,284 |
import shutil
def clear_caches() -> None:
""" Clear all Caches created by instagramy in current dir """
return shutil.rmtree(cache_dir, ignore_errors=True) | 262cb8117d5987b2b2c7bef6c1f9444061f527a2 | 20,285 |
import numpy
import warnings
def SingleCameraCalibration_from_xml(elem, helper=None):
""" loads a camera calibration from an Elementree XML node """
assert ET.iselement(elem)
assert elem.tag == "single_camera_calibration"
cam_id = elem.find("cam_id").text
pmat = numpy.array(numpy.mat(elem.find("ca... | ad9f8d36b82dc3dbf4b3189166e48c0b5304a389 | 20,286 |
def contrast_augm_cv2(images,fmin,fmax):
"""
this function is equivalent to the numpy version, but 2.8x faster
"""
images = np.copy(images)
contr_rnd = rand_state.uniform(low=fmin,high=fmax,size=images.shape[0])
for i in range(images.shape[0]):
fac = contr_rnd[i]
images[i] = np.a... | 7bd56e2b053e0ede33bdd62c269e66858745c642 | 20,287 |
def load_preprocess():
"""
Load the Preprocessed Training data and return them in batches of <batch_size> or less
"""
return pickle_load('preprocess.p') | 80bf8a509c972291767ade03a64e74aed11b8298 | 20,288 |
def detection_layer(config, rois, mrcnn_class, mrcnn_bbox, image_meta):
"""Takes classified proposal boxes and their bounding box deltas and
returns the final detection boxes.
Returns:
[batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
"""
# Currently only supports batchsize 1
... | 7770241c44dc050c0fb20f18b729eba388a47721 | 20,289 |
def connectedSocketDiscover():
"""
Try to discover the internal address by using a connected UDP
socket.
@return: a L{Deferred} called with the internal address.
"""
def cb(address):
protocol = DatagramProtocol()
listeningPort = reactor.listenUDP(0, protocol)
protocol.tr... | a443ef9774774fe4a31cb3f76462d35255380caa | 20,290 |
from typing import Dict
from typing import Tuple
from typing import List
from typing import Optional
def construct_source_plate_not_recognised_message(
params: Dict[str, str]
) -> Tuple[List[str], Optional[Message]]:
"""Constructs a message representing a source plate not recognised event;
otherwise retur... | 73c34f090dcdd802e0161d0f808deb03aef2c660 | 20,291 |
def odd(x):
"""True if x is odd."""
return (x & 1) | 9cd383ea01e0fed56f6df42648306cf2415f89e9 | 20,292 |
def sparse_transformer_local():
"""Set of hyperparameters for a sparse model using only local."""
hparams = common_hparams.basic_params1()
hparams.max_length = 4096
hparams.batch_size = 4096
hparams.add_hparam("max_target_length", 4096)
hparams.add_hparam("add_timing_signal", False)
hparams.add_hparam("lo... | 29c4ce03eabc311cc6526d381ff1b09e33cd9667 | 20,293 |
import scipy
def make_aperture_mask(self, snr_threshold=5, margin=4):
"""Returns an aperture photometry mask.
Parameters
----------
snr_threshold : float
Background detection threshold.
"""
# Find the pixels that are above the threshold in the median flux image
median = np.nanmed... | 26e176f68155f2d7c8756eb794083e71cd1a920c | 20,294 |
def verify_password(username, password):
"""Verify the password."""
if username in users:
return check_password_hash(users.get(username), password)
return False | a41664c6121f9f88522d69d5f2720da94fddc299 | 20,295 |
def data_mnist(one_hot=True):
"""
Preprocess MNIST dataset
"""
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0],
FLAGS.IMAGE_ROWS,
... | d160d9033acc0e84255cf43faf094d7ecd83e99c | 20,296 |
import glob
import os
def get_file_paths_by_pattern(pattern='*', folder=None):
"""Get a file path list matched given pattern.
Args:
pattern(str): a pattern to match files.
folder(str): searching folder.
Returns
(list of str): a list of matching paths.
Examples
>>> ge... | 467485dd4b162654bd73cf412c93558fa43a5b8e | 20,297 |
def pval_two(n, m, N, Z_all, tau_obs):
"""
Calculate the p-value of a two sided test.
Given a tau_obs value use absolute value to
find values more extreme than the observed tau.
Parameters
----------
n : int
the sum of all subjects in the sample group
m : int
number of ... | efc1f602d72d71a0270ef2f4c2c2ed8610832d62 | 20,298 |
def make_where_tests(options):
"""Make a set of tests to do where."""
test_parameters = [
{
"input_dtype": [tf.float32, tf.int32],
"input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]),],
"use_where_v2": [False, True],
},
{
"input_dtype": [tf.float32, tf.int32],... | fa10458af3e9043ea5779ff05cb679ef4b798d1b | 20,299 |
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