content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def app_validate_batch(app_name_or_id, alias=None, input_params={}, always_retry=True, **kwargs):
"""
Invokes the /app-xxxx/validateBatch API method.
For more info, see: https://documentation.dnanexus.com/developer/api/running-analyses/apps#api-method-app-xxxx-yyyy-validatebatch
"""
fully_qualified... | 07ea5f856749e578d66f0138acf86d4a9f46ae7c | 47,400 |
import collections
def build_dataset(words, vocabulary_size=50000):
"""Returns:
data:
list of the same length as words, with each word replaced by a unique
numeric ID.
count:
counters for the vocabulary_size most common words in 'words'.
dictionary:
maps word->ID
r... | 149f72d18a1304fe4414a95ed1491a7e6bd6c4de | 47,401 |
import shutil
import os
def build_wheel(wheel_directory, config_settings, metadata_directory=None):
"""Invoke the mandatory build_wheel hook.
If a wheel was already built in the
prepare_metadata_for_build_wheel fallback, this
will copy it rather than rebuilding the wheel.
"""
prebuilt_... | 683633757d26f7b6d6c0015992d747995d360da0 | 47,402 |
import os
def check_folder(folder):
"""
Test if folder exists and is absolute
"""
if os.path.isdir(folder):
if os.path.isabs(folder):
return True
else:
raise ValueError("The path to the folder must be absolute")
else:
raise OSError("Can't find the pa... | e2d606ab5bb68e104c8896da753d2e76d6ac7697 | 47,403 |
def used_voltage_params():
"""
Returns a list of qdac voltage parameters for the used channels
"""
station = qc.Station.default
qdac = station['qdac']
chans = sorted(used_channels())
voltages = [qdac.channels[ii - 1] for ii in chans]
return voltages | 4fde688214c98ca92a9f6b640e484ac713c5f7b8 | 47,404 |
def get_config_files():
"""Get the config file for dataduct
Note:
The order of precedence is:
1. /etc/dataduct.cfg
2. ~/.dataduct/dataduct.cfg
3. DATADUCT_CONFIG_PATH environment variable, if it exists
Returns:
A list of file paths of dataduct config file locations,... | f043cbed27d3b49f50cb2c235931fdbdc49b5d27 | 47,405 |
from typing import Dict
from datetime import datetime
def make_epoch_log(seconds: float,
metric_data: Dict[str, AverageValueMeter],
epoch: int) -> str:
"""Create the log message basen on input parameters.
Args:
seconds (float): Time spent on train and valid epoch... | 23e3931133944ce2ec588180f4898a3fef4f32d2 | 47,406 |
def _error_matches_criteria(error, criteria):
"""
Check if an error matches a set of criteria.
Args:
error:
The error to check.
criteria:
A list of key value pairs to check for in the error.
Returns:
A boolean indicating if the provided error matches the... | 8f52f7288fdefa496084b4faf689ed269360050a | 47,407 |
import torch
def eval(device, model, datas, criterion):
"""Eval the model"""
losses = 0
model.eval()
with torch.no_grad():
for data, target in datas:
output = model(data.to(device)).flatten()
losses += criterion(output.flatten(), target.to(device)).item()
return los... | bf9d71640922e3c3a9d9bcd0fc83bc37f6c2da7d | 47,408 |
def find_first_link(content: str) -> Link:
"""Metin içerisindeki ilk bağlantıyı bulur
Arguments:
content {str} -- Metin
Returns:
Link -- Bulunan bağlantı objesi
Examles:
>>> find_first_link('[name1](path1) [name2](path2)')
Link(name='name1', path='path1')
"""
r... | 7b6b437b8ff407b0244b9c5f067b1d524e9eed9c | 47,409 |
import asyncio
def create_rfxtrx_tcp_dsmr_reader(host, port, dsmr_version,
telegram_callback, loop=None,
keep_alive_interval=None):
"""Creates a DSMR asyncio protocol coroutine using a RFXtrx TCP connection."""
if not loop:
loop = asy... | 18254fb10a422a9c63255a64832ad457dbf43651 | 47,410 |
from lifelines import KaplanMeierFitter
def find_mean_differential_survival(outcomes, interventions):
"""
Given outcomes and interventions, find the maximum restricted mean survival time
"""
treated_km = KaplanMeierFitter().fit(outcomes['uncensored time treated'].values, np.ones(len(outcomes)).astype... | 9d0fca74ec4ee8a8fbdd59e7a08f67e1ccd49f9e | 47,411 |
import os
def get_engine(onnx_file_path, engine_file_path=""):
"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""
def build_engine():
"""Takes an ONNX file and creates a TensorRT engine to run inference with"""
with trt.Builder(TRT_LOG... | d4648a6f2b5bbe6b1b39be06f36300884f64987e | 47,412 |
def check_if_git_is_installed():
"""Check if GIT is installed by calling 'git --version'."""
try:
git_version = check_output(["git", "--version"]).decode('utf-8')
if git_version.startswith("git version"):
return True
raise Exception('Git not installed')
except:
re... | 6702e1302c361954f557fec468b2d89e78c19fb1 | 47,413 |
def try_replace_with_core_lstm(op):
"""
Inputs:
op (Operation): op.op_type must be 'tf_lstm_block_cell' or `tf_lstm_block`
Returns:
True if op can be represented by mb.lstm op in SSA. False otherwise
"""
if op.op_type == "tf_lstm_block_cell":
batch = op.x.shape[0]
else: # tf_... | 35a1071c71e4c2e4500799d96295c7b9eee672cb | 47,414 |
import re
def pad_punctuation_w_space(text: str) -> str:
"""Pad punctuation marks with space for separate tokenization."""
result = re.sub(r'([:;"*.,!?()/\=-])', r" \1 ", text)
result = re.sub(r"[^a-zA-Z]", " ", result)
result = re.sub(r"\s{2,}", " ", result)
# code for removing single characters
... | 8bdb82865d5e127e32d483f83246f4ad1b96b0be | 47,415 |
from typing import Type
def get_fastx_flag_extractor(fmt: FastxFormats) -> Type[ABCFlagExtractor]:
"""Retrieves appropriate flag extractor class."""
if FastxFormats.FASTA == fmt:
return FastaFlagExtractor
elif FastxFormats.FASTQ == fmt:
return FastqFlagExtractor
else:
return AB... | 009698de417857cd9254527e74f6e7ac12eabad8 | 47,416 |
def mu_post(xs, xs_train, ys_train, kernel, hparams):
"""
Posterior mean conditioned on xs.
Note: a numerical jitter term of 1e-9 is added to avoid nans when inverting
"""
# TODO: use cholesky decomposition for inversion
cov_train = kernels.cov_map(kernel, hparams, xs_train, xs_train) \
... | 847329a0a421bb77f749c9280e5137b4e5ccf836 | 47,417 |
def tensors2classlist(tensor, seq_lens):
"""
Converts a 3d tensor (max(seq_len), batch_size, output_dim=1) to a 2d class list (list[batch_size * list[seq_len]])
Arguments:
tensor (torch.tensor) : 3d padded tensor of different sequence lengths of
shape (max(seq_lens), batch_size, output_... | 52de31050a32ce54b2733f4c4dd348044e3da259 | 47,418 |
def skippable_exons(exons):
""" Determine which exon(s) can be skipped
For each exon (except the first and second, which cannot be skipped), we
want to find the minimum number of exons which together have a size that
can be divided by 3.
>>> list(skippable_exons([30]))
[]
>>> list(skippable... | f96ec0da6d72191d252cfe0ba5cdbeb21bc4388c | 47,419 |
from typing import Callable
from typing import Any
def not_pf(predicate: Callable[[Any], bool]):
"""
Negates the predicate
* **predicate**: predicate to be tested
* **return**: a predicate that is the negation of the passed predicate
>>> p = not_pf(true_p)
>>> p(1)
False
>>> p = not_... | 50d3993c4a83e5794a63134b65c732d1aa0ca1fa | 47,420 |
def _ShouldSkip(commit_check, modified_lines, line, rule, test_class=False):
"""Returns whether an error on a given line should be skipped.
Args:
commit_check: Whether Checkstyle is being run on a specific commit.
modified_lines: A list of lines that has been modified.
line: The line that has a rule vi... | 7f7cd6410f6c8357d1cd465b11445cf49bd500b5 | 47,421 |
def monte_carlo(cycles, precision, concunique, bottom_temp_est, dp, por,
por_fit, seddepths, sedtimes, temp_d, bottom_temp, z,
advection, leg, site, solute_db, ds, por_error, conc_fit,
runtime_errors, line_fit):
"""
Monte Carlo simulation of flux_model output to f... | d7a435fd982f525b74ee05d377e9ee8a1f7eecda | 47,422 |
from typing import List
def getViewsAlias()->List[str]:
"""获取所有views.py的别名"""
obj = getEnvXmlObj()
return obj.get_childnode_lists('alias/file[name=views]') | 3dc5a38e44f8eef707beb9361fcbca8b16476b52 | 47,423 |
from typing import Optional
def get_logs(experiment_name: Optional[str] = None, save: bool = False) -> pd.DataFrame:
"""
Returns a table of experiment logs. Only works when ``log_experiment``
is True when initializing the ``setup`` function.
Example
-------
>>> from pycaret.datasets import ... | 6f1b55864361098ac9d5c8bd843d5405d59765ac | 47,424 |
def filter_genes(centroids):
"""returns genes that have std > 0"""
return centroids.index[(centroids.std(axis=1) != 0).tolist()] | fcfbd18b6d657d6758feb324642c4118b80aecfd | 47,425 |
def multivariate_t_logpdf(x, m, S, df=np.inf):
"""calculate log pdf for each value
Parameters
----------
x : array_like, shape=(n_samples, n_features)
m : array_like, shape=(n_features,)
S : array_like, shape=(n_features, n_features)
covariance matrix
df : int or float
de... | 8a53d603b6e91fbc2e74e5f444d024b38af38d23 | 47,426 |
def get_iou_score(class_weights=1., smooth=SMOOTH, per_image=True, threshold=None):
"""Change default parameters of IoU/Jaccard score
Args:
class_weights: 1. or list of class weights, len(weights) = C
smooth: value to avoid division by zero
per_image: if ``True``, metric is calculated a... | 03123ab38dad5ff1d8efaa44a0b4fd00cd3d47dd | 47,427 |
from .ctwrapper import IVIVisaLibrary
def _get_default_wrapper() -> str:
"""Return an available default VISA wrapper as a string ('ivi' or 'py').
Use IVI if the binary is found, else try to use pyvisa-py.
'ni' VISA wrapper is NOT used since version > 1.10.0
and will be removed in 1.12
Raises
... | 95527140a935996fb2453835395f5a0199de9320 | 47,428 |
from typing import List
from typing import Tuple
def make_flfacts_mach_sweep(alt: float, machs: List[float], eas_limit: float=1000.,
alt_units: str='m',
velocity_units: str='m/s',
density_units: str='kg/m^3',
... | fd31ad68da7f5a3167457370e4e78ecda66235fd | 47,429 |
def create_rnn_numpy_batches(
array, batch_size=500, timesteps=TIMESTEPS, features=1, array_type="X"
):
"""Transform a numpy array, so that it can be fed into an RNN.
RNNs require all batches to be the exact same length. This function
removes excess elements from the array and so ensures all batches ar... | 5ba4d1c23fb1e9e2040e075a02d78f872545ef05 | 47,430 |
def nll_loss(input,
label,
weight=None,
ignore_index=-100,
reduction='mean',
name=None):
"""
This api returns negative log likelihood.
See more detail in :ref:`api_nn_loss_NLLLoss` .
Parameters:
input (Tensor): Input tensor, the ... | 2b22c63a2c3847bf259ff579782df0316ae7665d | 47,431 |
def image_to_string(
image,
lang=None,
config='',
nice=0,
output_type=Output.STRING,
timeout=0,
):
"""
Returns the result of a Tesseract OCR run on the provided image to string
"""
args = [image, 'txt', lang, config, nice, timeout]
return {
Output.BYTES: lambda: run_... | d2ccb74f2cc9dceb3036e234af4cc541f82255bf | 47,432 |
def organic_pdf_to_img(pdf_file, pdf_dim=None):
"""
Converts pdf file into image
:param pdf_file: path to the pdf file
:return: wand image object
"""
if not pdf_dim:
pdf_dim = get_pdf_dim(pdf_file)
page_width, page_height = pdf_dim
print('read pdf {}'.format(pdf_file))
# img ... | 3c355edc017d19662ed11647ba9f176272daa851 | 47,433 |
import os
def correct_bias(in_file, out_file, image_type=sitk.sitkFloat64):
"""
Corrects the bias using ANTs N4BiasFieldCorrection. If this fails, will then attempt to correct bias using SimpleITK
:param in_file: input file path
:param out_file: output file path
:return: file path to the bias corr... | b7ff6023688a4ebd0d6a966a474fba139c188954 | 47,434 |
import io
import zipfile
import os
def download_contest(request, contest_id):
"""Download all submissions of the contest as zip file."""
contest = get_object_or_404(Contest, pk=contest_id)
buffer = io.BytesIO()
zip_archive = zipfile.ZipFile(buffer, mode='w')
for theme in Theme.objects.filter(con... | c2ea1f00d53604268c1a2d9f9c60777ac9f5f919 | 47,435 |
from typing import Dict
from typing import List
def list_keys(bucket: str, prefix: str, suffix: str, delta_to: Dict[str, str] = None) -> List[str]:
"""
Lists all the keys belonging to a give key prefix in object storage
:param bucket: The object storage bucket
:param prefix: The key prefix
:param ... | 9db7dfa842d721a6285b87a27448e5cdbfe88fee | 47,436 |
def info_panel_factory(db):
"""
Returns:
The factory class used to generate info panels for testing.
"""
return InfoPanelFactory | e43aa05f28d1ebead281f6caa5faf46a1b2b8b27 | 47,437 |
import numpy
def brier_score(survival_train, survival_test, estimate, times):
"""Estimate the time-dependent Brier score for right censored data.
The time-dependent Brier score is the mean squared error at time point :math:`t`:
.. math::
\\mathrm{BS}^c(t) = \\frac{1}{n} \\sum_{i=1}^n I(y_i \\le... | 02c4568451b054838b203ed037677f015a9a10b6 | 47,438 |
from operator import mul
def dot(A, B):
"""
Dot product between two arrays.
A -> n_dim = 1
B -> n_dim = 2
"""
arr = []
for i in range(len(B)):
if isinstance(A, dict):
val = sum([v * B[i][k] for k, v in A.items()])
else:
val = sum(map(mul, A, B[i]))
... | 9ea609f78e27eb3046507db3e366531090b26d6d | 47,439 |
def get_invocation_command(toolset, tool, user_provided_command = [],
additional_paths = [], path_last = False):
""" Same as get_invocation_command_nodefault, except that if no tool is found,
returns either the user-provided-command, if present, or the 'tool' parameter.
"""
... | 7fd76a04468de1764e234183f758640de00e6141 | 47,440 |
def decoding_layer(dec_input, encoder_state,
target_sequence_length, max_target_sequence_length,
rnn_size,
num_layers, target_vocab_to_int, target_vocab_size,
batch_size, keep_prob, decoding_embedding_size):
"""
Create decoding layer
... | 2d832771359c7af2443629f488b03aa386e0cbe5 | 47,441 |
def create_interaction(principal_id, **kw):
"""
Create a new interaction for the given principal ID, make it the
:func:`current interaction
<zope.security.management.newInteraction>`, and return the
:class:`Principal` object.
"""
principal = Principal(principal_id, **kw)
participation = ... | ef7c5eb7045e3504bdd00cc9420c1a0402c2bcce | 47,442 |
from datetime import datetime
import uuid
def python_type(type_description):
"""Return object representing the Python type.
Args:
type_description (str): Arc-style type description/code.
Returns:
Python object representing the type.
"""
instance = {
"date": datetime.datet... | 007367c9b7852c0d24c9bfddb8bf710afcd3f89f | 47,443 |
def openedx_extract_transform_factory(get_config):
"""
Factory for generating OpenEdx extract and transform functions based on the configuration
Args:
get_config (callable): callable to get configuration for the openedx backend
Returns:
OpenEdxExtractTransform: the generated extract an... | 73c1231fdcd6208f29af00d0a38aaafba59468e2 | 47,444 |
def _mint(challenge, bits):
"""Answer a 'generalized hashcash' challenge'
Hashcash requires stamps of form 'ver:bits:date:res:ext:rand:counter'
This internal function accepts a generalized prefix 'challenge',
and returns only a suffix that produces the requested SHA leading zeros.
NOTE: Number of ... | 691631769f09413b8257a92c7831ab8953bbcb6b | 47,445 |
def product(A, B, p_name):
""" Computes the product automaton of two DFAs.
Args
----
A, B : variables referencing a DFA.
p_name : name of the product automaton.
Returns
-------
An DFA which is a product automaton of input DFA or A and B with name "p_name".
Testable Code
... | 3dd06d21f3659c2c17c270a729d9a1fa751902a5 | 47,446 |
def load_data(dataset_str):
"""
Loads input data from gcn/data directory
ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;
ind.dataset_str.al... | 4611a50d4d7fde4659b766ce13d1363e35fcdc8c | 47,447 |
from typing import OrderedDict
def dense_video_sampling(videos, annotations=None, bckg_label=201, t_res=16,
t_stride=16, drop_video=True):
"""Sample clips to extract C3D.
Parameters
----------
videos : pandas.DataFrame
Table with info about videos in dataset i.e. uniq... | 7bb9d4ae4de3868e63b5ad2afc308c026f98dd28 | 47,448 |
import os
def dem_quality_check(gdir):
"""Run a simple quality check on the rgitopo DEMs
Parameters
----------
gdir : GlacierDirectory
the glacier directory
Returns
-------
a dict of DEMSOURCE:frac pairs, where frac is the percentage of
valid DEM grid points on the glacier.
... | 4ed19aac77dc8c48663ccbb6636172b9e9ced945 | 47,449 |
def IsImage(a):
"""Return if the object is of Image type.
Args:
a: object whose type needs to be checked
Returns:
True if |a| is of Image type, False otherwise
"""
return isinstance(a, Image) | fe43e6ef7d25d0f7e470507540d10a1e935beacb | 47,450 |
def upload_to_object_store(
client,
bucket,
full_path_to_filename,
object_name=None,
content_type="binary/octet-stream",
):
"""Uploads the specified file to the object store
Parameters:
client: str, the boto3 client object
bucket: str, target bucket location
full_pat... | 1652a69341664e8cfa5b5e32787808f9022004e8 | 47,451 |
def set_autocommit(autocommit, using=None):
"""Set the autocommit status of the connection."""
return get_connection(using).set_autocommit(autocommit) | 792cb38ec8f31b3390489742dceef67bc52dc70f | 47,452 |
import copy
def copy_attributes_from_object(in_config, in_object, attr_list):
"""Clones a object and copies attributes from an object to it."""
config = copy.deepcopy(in_config)
for item in attr_list:
if hasattr(in_object, item):
value = getattr(in_object, item)
else:
raise ValueError('attr_... | 6751c947ef77c51909632a366d09fa35cc82f859 | 47,453 |
def get_frame_size(*args):
"""get_frame_size(func_t pfn) -> asize_t"""
return _idaapi.get_frame_size(*args) | a5e08be1463e808107e84fce1f1c049b7815a424 | 47,454 |
from operator import index
def z_decode(p):
"""
调用方式,
while p:
v,p=z_decode(p) #v：值 p：bytes(每次z_decode计算偏移量)
params = v
递归地取字符串p,每次取一些到v,剩下的更新为p，继续迭代
decode php param from string to python 根据php serialize
p: str
"""
#print(p)
if p[0]=='N': #NULL 0x4e-'N'
return None,p[2:]
elif p[0]=='b':... | 1038dcf8a356e53574fb6eab6a9842bec78a5db6 | 47,455 |
def scaling_aligned_one_dim_cascade(
time,
x,
y,
z,
track_azimuth,
track_zenith,
):
"""Cascade with topology defined by a cascade at `SCALING_CASCADE_ENERGY`,
and changing energy only modifies number of photons produced"""
return aligned_one_dim_cascade(
time=time,
x=... | 81d6e23a6c3bd7308a32c24311db47a063df7e81 | 47,456 |
def GetParsedDeps(deps_file):
"""Returns the full parsed DEPS file dictionary, and merged deps.
Arguments:
deps_file: Path to the .DEPS.git file.
Returns:
An (x,y) tuple. x is a dictionary containing the contents of the DEPS file,
and y is a dictionary containing the result of merging unix and comm... | cfa7c4dd3134b131063e4cf288be730fd93b0c19 | 47,457 |
def _parse_option(line):
"""
Parses option line.
Returns (name, value).
Raises ValueError on invalid syntax or unknown option.
"""
match = _OPTION_REGEX.match(line)
if not match:
raise ValueError('Invalid syntax')
for name, type_ in _OPTIONS:
if name == match.group(1):
... | bf8f7e71c2a5d0ed61fdbecb8f8ac033e8771c43 | 47,458 |
def convert(from_unit, to_unit, *args, **kwargs):
"""
Convert the value from one unit to another
:param from_unit: Source unit
:param to_unit: Target unit
:param args: Additional parameters (values)
:param kwargs: Additional parameters (additional values like for conversion watts to ohms)
:r... | 233c16fda7943ae15093a8e455f6634bd75bbd4a | 47,459 |
def hex_validator(length=0):
"""
Returns a function to be used as a model validator for a hex-encoded
CharField. This is useful for secret keys of all kinds::
def key_validator(value):
return hex_validator(20)(value)
key = models.CharField(max_length=40, validators=[key_validat... | 6ab272ab19f3b801ec612309ab41c845154a1eae | 47,460 |
def bytes_feature(value):
"""Create a multi-valued bytes feature from a single value."""
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) | f3b7ec25baca436fae7851fecbcd29787e6cc639 | 47,461 |
import os
import torch
def setup_model(args):
"""Setup model and optimizer."""
model = get_model(args)
# if args.deepspeed:
# print_rank_0("DeepSpeed is enabled.")
#
# model, _, _, _ = deepspeed.initialize(
# model=model,
# model_parameters=model.parameters(),... | 664531b31fab4cc33be68145ee09e363b938a62f | 47,462 |
def mkmatrix(m, n):
"""
Return a random invalid correlation matrix of order `m+n` with
a positive definite top left block of order `m`.
"""
while True:
A = randcorr(m)
#B = randcorr(n)
B = np.identity(n)
Y = np.matrix(np.random.randn(m, n) / (m+n)**1.2)
M0 = ... | a8d45f0b99adc08b43ca3a5f4c740edaab1e95de | 47,463 |
def remove_last_range(some_list):
"""
Returns a given list with its last range removed.
list -> list
"""
return some_list[:-1] | ea2063c901d3aaf67caad97f1760f6fb6afb31c1 | 47,464 |
import torch
def lazy_bind(concrete_type, unbound_method):
"""
Returns a function that lazily binds `unbound_method` to a provided
Module IValue, then invokes the method. We do this so that any Python
shenanigans that will poison type sharing are impossible at compile
time.
"""
def lazy_bi... | 2d483782772bd1d4dc8b81dd621bc029d5876c38 | 47,465 |
import six
def FigureToSummary(name, fig):
"""Create tf.Summary proto from matplotlib.figure.Figure.
Args:
name: Summary name.
fig: A matplotlib figure object.
Returns:
A `tf.Summary` proto containing the figure rendered to an image.
"""
canvas = backend_agg.FigureCanvasAgg(fig)
fig.canvas.d... | 6182c127bd05003ff40a324240dfd5e8ffb3c679 | 47,466 |
from typing import Optional
from typing import Dict
from typing import Any
from typing import cast
def require_dict(value: Optional[Dict[Any, Any]], key_type: Any=None, value_type: Any=None,
allow_none: bool=False) -> Any:
"""Make sure a value is a Dict[key_type, value_type].
Used when deali... | 31611fa58de5a09b4bf9833cf22be1e151df00df | 47,467 |
def reconstructTimestamp(currentTimestamp, lsbTimestamp):
"""
Reconstructs a timestamp from a partial timestamp, that only has the least significant bytes.
:param currentTimestamp: Current time, obtained with time.time().
:param lsbTimestamp: Partial (least significant) timestamp, as a uint16.
:retu... | df8836b3e849051fe62b8d2105fd46f9c1211bc1 | 47,468 |
def get_date_print_format(date_filter):
"""
Utility for returning the date format for a given date filter in human readable format
@param date filter : the given date filter
@return the date format for a given filter
"""
vals = {
'day': '[YYYY-MM-DD]',
'hour': '[YYYY-MM-DD : HH]'
}
return vals[date_filter] | a7b15f944905c44c6bff1ed65514fdb0133d150b | 47,469 |
import os
import json
def loadTempTranscript(tempFileName):
"""
Load a temp transcript file by name
"""
# todo lot of copy/paste from above
if os.path.exists(tempFileName):
with open(tempFileName, 'rb') as tempFile:
try:
return [m for line in tempFile.readlines(... | 4368d3dee4aa98ffe5e8a556daf12c325c7d8a7f | 47,470 |
def pre_submission(*args, **kwargs):
"""
Perform a presubmit of a list of local files. This is the first
stage for a batch submit of files.
Variables:
None
Arguments:
None
Data Block (REQUIRED):
{
"1": # File ID
{"sh... | fa68404d833145a3a44fe1a0dd1be09fd1b3cbb9 | 47,471 |
from typing import List
def _historical_user_data_for_decisions(user: User, days: int) -> List:
"""
Return public data regarding the clicked Statements for a certain user
:param days: The number of days ending with today for which the data shall be procured.
:param user: The user for which the data s... | 6536c474fa90a388cf2d599c5e147017dfe62427 | 47,472 |
import os
import pickle
def OptimiseGuestPositionAtWindow(xnDataset,hostData,hostCrystal,arGuestAtomFracPositions,lsGuestAtomChemSymbols,sHostGuestName,sRootOutputDir,bReloadWindowState):
"""Optimise guest molecule position at the window position using global optimiser.
[xnDataset]: XML node, config 'dataset' node ... | 65c8a3455389b4569dafaebd1d807cd86e3387d0 | 47,473 |
def collapse_locations(obj_list, keyname):
"""
Given a CustomQuerySet object, filter/aggregate it down
so we just have one item per country or city.
keyname is 'country' or 'city'.
Also drop the 'privacy_country' or 'privacy_city' field.
On input, we might have:
country privacy_coun... | 75586dc7fbd180ff6db031a0a706ea2221ced387 | 47,474 |
def updateHand(hand, word):
"""
Assumes that 'hand' has all the letters in word.
In other words, this assumes that however many times
a letter appears in 'word', 'hand' has at least as
many of that letter in it.
Updates the hand: uses up the letters in the given word
and returns the new ha... | 767aec56bec10900a3ccd67e2772355baf7886d0 | 47,475 |
def is_sciobj_valid_for_create():
"""When RESOURCE_MAP_CREATE == 'reserve', objects that are created and that are also
aggregated in one or more resource maps can only be created by a DataONE subject
that has write or changePermission on the resource map."""
# TODO
return True | 2a57bb1295791d4f67652c3fb2477da2d9733462 | 47,476 |
def filter_by_indices(good_indices, vals):
""" 从分段算法得到的下标集合中得到 对应的轨迹点集合
:param good_indices: 下标集合
:param vals: 原始点数据（未分段） 集合
:return: 分段后的点集合
"""
vals_iter = iter(vals)
good_indices_iter = iter(good_indices)
out_vals = []
num_vals = 0
for i in good_indices_iter:
if i != ... | c38dd76a90452cdbe96c92c8850752f56cc9882f | 47,477 |
import logging
def match_func(cor, exc, tolerance):
"""
Check if coordinate matches expected coordinate within a given tolerance.
cor - coordinate
exc - expected coordinate
tolerance - error rate
float coordinate elements will be checked based on this value
list/tu... | b2ee7b41e2d56cee14c2078ef51f0900b1f4b617 | 47,478 |
def transform_tabular_data(xml_input):
"""
Converts table data (xml) from BambooHR into a dictionary with employee
id as key and a list of dictionaries.
Each field is a dict with the id as the key and inner text as the value
e.g.
<table>
<row id="321" employeeId="123">
... | 025831e3192a9a7ce6b8130b76e4e1bf827a1744 | 47,479 |
import numpy as np
import os
def gen_index_noddi(in_bval, b0_index):
"""
This is a function to generate the index file for FSL eddy
:param in_bval:
:param b0_index:
:return:
"""
out_file = os.path.abspath('index.txt')
bvals = np.loadtxt(in_bval)
vols = len(bvals)
index_list = [... | 84ac37def63d1714030d797930e3de958b8ff6a4 | 47,480 |
def create_workflow(name=None, namespace=None, bucket=None, **kwargs): # pylint: disable=too-many-statements
"""Create workflow returns an Argo workflow to test kfctl upgrades.
Args:
name: Name to give to the workflow. This can also be used to name things
associated with the workflow.
"""
builder = B... | 62e967e6e767fbdbfd3aec1151dc1213fe95212d | 47,481 |
def create_annotation_model(table_name: str,
annotation_columns: dict,
with_crud_columns: bool=True):
""" Create an declarative sqlalchemy annotation model.
Parameters
----------
table_name : str
Specified table_name.
annotation_colum... | c33af504cc921537474574a8f30948b772498ce4 | 47,482 |
def speed_control(target, current, Kp=1.0):
"""
Proportional control for the speed.
:param target: target speed (m/s)
:param current: current speed (m/s)
:param Kp: speed proportional gain
:return: controller output (m/ss)
"""
return Kp * (target - current) | ce01369dc9445f65249a82cfb7882223ded38f36 | 47,483 |
def value_to_name(klass):
"""
Generate a function to convert an IntEnum value to its name.
:param type klass: the class defining the IntEnum
:returns: a function to convert a single number to a name
:rtype: int -> str
"""
def the_func(num, terse_unknown=False):
"""
Convert ... | 69e45e9e07388dacc27d5cc5b4d3822d6d716d3d | 47,484 |
def np_ortho(shape, random_state):
""" Builds a theano variable filled with orthonormal random values """
g = random_state.randn(*shape)
o_g = linalg.svd(g)[0]
return o_g.astype(theano.config.floatX) | f5762934c902cd6118aaec6a43f0856c0992702d | 47,485 |
def force_func(biorbd_model: biorbd.Model, use_excitation: bool = False):
"""
Define the casadi function that compute the muscle force.
Parameters
----------
biorbd_model : biorbd.Model
Model of the system.
use_excitation : bool
If True, use the excitation of the muscles.
R... | 23866a48b0501d86612e85835ec177730d89d47c | 47,486 |
import os
import json
def geolocalize_map(request):
"""
Args:
request (flask.Request): HTTP request object
JSON example: {"uri": "https://i.stack.imgur.com/WiDpa.jpg"}
"""
request_json = request.get_json()
if request.args and 'uri' in request.args:
uri = request.args.get('u... | 9923f0b7e2e8b96ad401403b1953653b72cb0fb9 | 47,487 |
def ReLU(x, alpha):
""" Wrapper for using a ReLU activation function
TODO: Implement an option for choosing between relu, leaky relu and prelu.
"""
# return tf.nn.relu(x)
# return leakyReLU(x, 0.001)
# return parametricReLU(x, alpha)
return tf.nn.elu(x)
# return tf.tanh(x) | 3b66a945a38b17225718168690328cae45aad17a | 47,488 |
def _is_valid_case(obj):
"""Returns True if ``obj`` is a valid test case
Criteria for being a valid test case:
- Is a class and a subclass of :class:`WebDriverTestCase
<webdriver_test_tools.testcase.webdriver.WebDriverTestCase>`
- Is not :class:`WebDriverTestCase
<webdriver... | 09d7d07af46e24354f81256430b21e0cb6462fce | 47,489 |
def cajeroExist():
"""
Verifica la existencia de un registro en CajaCajero
:return:
"""
try:
movimientoid = CajaCajero.objects.latest('id_movimiento')
except CajaCajero.DoesNotExist:
movimientoid = None
pass
return movimientoid | 74ff2c0a6c8fedeb8b1e2b819e07f397dc4bb775 | 47,490 |
def get_reaktor():
"""Returns the reaktor instance from the app globals."""
if not hasattr(g, '_reaktor'):
g._reaktor = Reaktor(**current_app.config['REAKTOR_CONFIG'])
return g._reaktor | eeaefb7f7eb2498a0bac8d4cbc3e3399fe69fc78 | 47,491 |
import os
def get_outpath(filename, outdir):
"""Get output filepath.
:filename: name of music file
:outdir: path of output directory
:returns: path of converted music file
"""
outname = '{}.mp3'.format(os.path.splitext(filename)[0])
outpath = os.path.join(outdir, outname)
return outp... | 048c1ce65c21a0a561f928eb42882eaa60ee8b1a | 47,492 |
def _Bezier3Seg(p1, p2, c1, c2, gs):
"""Return a 'B' segment, transforming coordinates.
Args:
p1: (float, float) - start point
p2: (float, float) - end point
c1: (float, float) - first control point
c2: (float, float) - second control point
gs: _SState - used to transform coordina... | f4d661bdcf0e9b294d4c428c54ed1c5d682a1ed7 | 47,493 |
import torch
def gather(
v: torch.Tensor,
split_in: qp.utils.TaskDivision,
comm: qp.MPI.Comm,
dim: int,
) -> torch.Tensor:
"""Return the contents of v, changed from split based on split_in on
communicator comm and dimension dim, to not-split i.e. fully available
on all processes."""
# ... | 8c54dc42e7fae4bc75ad151737f5b40fd0d96aed | 47,494 |
def match_nans(x, y):
"""Performs pairwise matching of nans between ``x`` and ``y``.
Args:
x, y (ndarray or xarray object): Array-like objects to pairwise match nans over.
Returns:
x, y (ndarray or xarray object): If either ``x`` or ``y`` has missing data,
adds nans in the same... | 8bbeba78210d9344c4b8faa57454462e7b761ad8 | 47,495 |
def index():
"""User's homepage after logging or signing in"""
# Fetch posts of user and whoever the user follows.
posts = Posts.query.all()
return render_template("index.html", posts=posts) | 74a4f72231fd3ccb8e3b03aa5f9ed37250c75e7c | 47,496 |
def calculator_post():
"""index_get"""
return CalculatorController.post() | 413b31a61c85139415c7a69fc7788e9121c57d99 | 47,497 |
import numpy
import random
def randomPairs(n_records, sample_size):
"""
Return random combinations of indices for a square matrix of size n
records. For a discussion of how this works see
http://stackoverflow.com/a/14839010/98080
"""
n = int(n_records * (n_records - 1) / 2)
if sample_siz... | 378634b99a83c8f18c9c137737c32e6d12816ae7 | 47,498 |
def is_parsed_result_successful(parsed_result):
"""Returns True if a parsed result is successful"""
return parsed_result['ResponseMetadata']['HTTPStatusCode'] < 300 | 717f8aa88b814405a5a008e9706338fd0f91a7ff | 47,499 |
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