content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from sentry.plugins import plugins
def should_process(data):
"""Quick check if processing is needed at all."""
for plugin in plugins.all(version=2):
processors = safe_execute(
plugin.get_event_preprocessors, data=data, _with_transaction=False
)
if processors:
r... | 8e6f013d54ac1e3a0b77f8969a3700c45efdc673 | 23,428 |
from typing import Tuple
from typing import List
import gzip
def load_fasta_file(input_file: str) -> Tuple[str, List]:
"""
Load a fasta file into a list of SeqRecords.
:param input_file: The path to the input fasta file.
:returns: A tuple of the sequence type ('protein' or 'dna'), and the list of Seq... | 8e62e7d7002d74da7a43315785f5ce663b5ba366 | 23,429 |
from typing import get_args
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT3 ...')
train_ds, valid_ds, test_ds = build_train_val... | 06f9532c6d60a3c3858dc08a43070b8aa4d19691 | 23,430 |
import requests
def get(username, start):
"""
Second level function to pull up to 50 reviews.
start - review number to start from
"""
r = requests.get(
'{}/user/beers/?start={}&&ba={}&order=dateD&view=R'.format(
BASE_URL, start, username
)
)
beers = []
pq = ... | 7aaccda46954b629bad37e0a77f834e5b3f40c27 | 23,431 |
def isInContinent(country_name: str, continent: str):
"""Permet de vérifier si le pays est dans un continent
Paramètres
----------
country_name : str
Le nom du pays
continent : str
Le code du continent (alpha2)
Retours
-------
is_in_continent : int
entier binair... | 5a78e181ace8574baa00eeadd21e7ecea8529f6c | 23,432 |
def encoder_decoder_archi(inputs, is_train):
"""
Input is assumed to be a 4-D Tensor, with [batch_size, phrase_len, 1, features]
"""
encoder_layers = []
encoded = inputs
encoder_layers.append(encoded)
for i in range(config.encoder_layers):
encoded = encoder_conv_block(encoded, i,... | 6b75ce8a31375173e01ccd7d33078c76aff6d2b8 | 23,433 |
def build_dict_conforming_to_schema(schema, **kwargs):
"""
Given a schema object (for example, TIMESTAMP_SCHEMA from this module) and
a set of keyword arguments, create a dictionary that conforms to the given
schema, using the keyword arguments to define the elements of the new dict.
Checks the result to mak... | 8971b7c6e1df8fd16a1b0e0946c9f21a3c601512 | 23,434 |
def drop_non_channels(overlaps_df, filename):
""" Return the overlap dataframe with all channels dropped
and index reset. Save the df as a csv with the filename
passed this function. """
df = overlaps_df
channels_df_dict = {}
for column in df.columns:
# For eac... | 0cfa7f1ec86328179612c46c6b5f4b787984a7fa | 23,435 |
def _REOM(y,t,pot,l2):
"""
NAME:
_REOM
PURPOSE:
implements the EOM, i.e., the right-hand side of the differential
equation
INPUT:
y - current phase-space position
t - current time
pot - (list of) Potential instance(s)
l2 - angular momentum squared
OU... | 427393c1eeb89214603dc8363a9b39084e9030d4 | 23,437 |
def optimize_inst(module, inst):
"""Simplify one instruction"""
for operand in inst.operands:
if isinstance(operand, ir.Id):
if operand.inst.op_name not in ir.CONSTANT_INSTRUCTIONS:
return inst
if inst.op_name == 'OpCompositeConstruct':
inst = optimize_OpComposit... | 1de61b914bdac4076be4ffb27823ad9384504814 | 23,438 |
def table_3_3(M, lambd_nos, lambd_cil):
"""
Функция для вывода Су для оживальной ГЧ
arguments: число Маха, относительное удлинение носка и цилиндрической части
return: Значение Су ГЧ
"""
cy1iz_alf_0 = [0.0350, 0.0350, 0.0350, 0.0350, 0.0362, 0.0375, 0.0380, 0.0378,
0.0374... | d0d4b2e1fa65f3e8ad2cd39bee1d0d4878293090 | 23,439 |
def ms_to_timestamp(ms):
"""Convert ms to 'HH:MM:SS,mmm'"""
# XXX throw on overflow/underflow?
if ms < 0: ms = 0
if ms > MAX_REPRESENTABLE_TIME: ms = MAX_REPRESENTABLE_TIME
h, m, s, ms = ms_to_times(ms)
return "%02d:%02d:%02d,%03d" % (h, m, s, ms) | 514773d94f4e3b78594bed4f232f34bcd2956f4d | 23,440 |
import torch
def _lovasz_softmax_flat(y_pred, y_true, classes="present"):
"""
Multi-class Lovasz-Softmax loss
y_pred: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
y_true: [P] Tensor, ground truth y_true (between 0 and C - 1)
classes: 'all' for all, 'present' for ... | 9cdbab2873e198750079e560a559b1f4eb8f256c | 23,441 |
def quantum_state_encoding_circuit(bits):
"""根据`bits`构建并返回量子态编码线路."""
circuit = cirq.Circuit()
circuit.append(cirq.H.on_each(bits))
return circuit | 75734a349187af7ac32683d5faf6aec331f25713 | 23,442 |
from datetime import datetime
def parse_mov_date(date_str):
"""converts string to date"""
try:
return datetime.datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S%z")
except (TypeError, ValueError):
pass
return None | 6d4f1ad566f3e3914eeed7f9c29d914f1ced96df | 23,443 |
def get_settable_attr(attr):
"""
If attr is not settable, navigate upp in the connection hierarchy until we find the settable attribute.
For example, in RigSqueeze, the ikFk state attribute will be redirected to the root ctrl.
Note that in some case the attribute might have been piped in an utility node... | aca71e6e7f9e1312beaf1c4dcba897073ae3b3ea | 23,444 |
def adds(repo, subset, x):
"""Changesets that add a file matching pattern.
The pattern without explicit kind like ``glob:`` is expected to be
relative to the current directory and match against a file or a
directory.
"""
# i18n: "adds" is a keyword
pat = getstring(x, _(b"adds requires a pat... | 6d9d1879c77f64bb68d43483cc2d3095328fd26f | 23,445 |
def data_context_topology_context_topologyuuid_linklink_uuid_available_capacity_bandwidth_profile_committed_information_rate_get(uuid, link_uuid): # noqa: E501
"""data_context_topology_context_topologyuuid_linklink_uuid_available_capacity_bandwidth_profile_committed_information_rate_get
returns tapi.common.Ca... | b44e48aa0fff6b01da22576fc73352deba812636 | 23,446 |
from typing import List
from typing import Dict
from operator import and_
def update_mlwh_with_cog_uk_ids(samples: List[Dict[str, str]]) -> None:
"""Update the MLWH to write the COG UK barcode for each sample.
Arguments:
samples {List[Dict[str, str]]} -- list of samples to be updated
"""
if l... | b4d6dfaec4bb40a59cbdfef619f7f4542e55e2a9 | 23,450 |
def make_09f9():
"""倉庫インベントリーフッタ"""
return "" | 91d21aeb58fc004865db91846d73f978f48f9be4 | 23,451 |
def get_last_successful_hour_or_start_hour():
"""Get the last hour that ran successfully or the start hour."""
last_hour = crash_stats.get_last_successful_hour()
if last_hour:
return last_hour
return get_start_hour() | 86518100bafe3296d63a8ac3612de1fa2c2ed8d4 | 23,452 |
import copy
from datetime import datetime
def encode_jwt(payload, secret):
"""
Return ``payload`` as a JWT encoded with ``secret``.
Return a JWT whose payload is ``payload`` and that is signed using
``secret``.
:arg payload: the payload to encode
:type payload: dict
:arg secret: the secr... | 497d5180e8956a737ad6edbd1113d73aeb915e80 | 23,453 |
def make_model():
"""
Loads pretrained torchvision model and redefines fc layer for car classification
"""
# uses about 1 GiB of GPU memory
model = models.vgg19(pretrained = True)
#model = models.resnet50(pretrained = True)
in_feat_num = model.classifier[3].in_features
mid_feat_num = int... | cd189f4b4d4dcadf6dd686aad08e2e494e0c2200 | 23,454 |
def empty_call_false(*args, **kwargs) -> bool:
"""
Do nothing and return False
"""
return False | 3b3964c859a47698f0000e1b26963953980fad51 | 23,455 |
def cookie_is_encoded(data):
""" Tests whether or not a cookie is encoded / HMAC signed
-> #bool True if encoded
..
from vital.security import cookie_is_encoded
cookie_is_encoded(
"!YuOoKwDp8GhrwwojdjTxSCj1c2Z+7yz7r6cC7E3hBWo=?IkhlbGxvLCB3b3JsZC4i")
... | baf2a05b516a23cacca4985944974112019abfda | 23,456 |
import torch
def l2_normalize(x: torch.Tensor, eps: float = 1e-12) -> torch.Tensor:
"""Normalizes the input tensor using L2-norm.
Args:
x: Tensor to be normalized.
eps: Small value to avoid division by zero.
Returns:
Normalized tensor.
"""
return x / (torch.norm(x, p=2, ... | 22273bbbda7bece511d31d517790bfa14427d76f | 23,457 |
from re import S
def ssq_cwt(x, wavelet='gmw', scales='log-piecewise', nv=None, fs=None, t=None,
ssq_freqs=None, padtype='reflect', squeezing='sum', maprange='peak',
difftype='trig', difforder=None, gamma=None, vectorized=True,
preserve_transform=None, astensor=True, order=0, patie... | 2776e85dde171b1c47fdce028bf9c845298b3a93 | 23,458 |
import torch
def predict_image_classification(model: nn.Module, input_: torch.Tensor):
"""
Predict using an image classification model.
Args:
model (`nn.Module`):
Pytorch model.
input_ (`Tensor`):
Input image tensor.
Returns:
(`tuple`)
Predic... | 2343d4db9b93910337e0e55b9935783714710330 | 23,459 |
def _id_to_box(id_, dim):
"""Convert id to box ID"""
row = id_ // (dim ** 3)
col = (id_ % (dim ** 2)) // dim
return row * dim + col | 8e6c4779872fff5cdc5a6ca6b4143a1519d8aaf2 | 23,460 |
import string
def _load_hex(instream):
"""Load font from a .hex file."""
global_comment = []
glyphs = []
comment = []
for line in instream:
line = line.rstrip('\r\n')
if ':' in line:
# parse code line
key, value = line.rsplit(':', 1)
value = valu... | 6e5980e53ee598d813f10bdbcb775e8d47102fa8 | 23,461 |
def make_small_graph(graph_description, create_using=None):
"""
Return the small graph described by graph_description.
graph_description is a list of the form [ltype,name,n,xlist]
Here ltype is one of "adjacencylist" or "edgelist",
name is the name of the graph and n the number of nodes.
This ... | deb1cf0d08bba91a538c7d2c47c1d89e2c2a28da | 23,462 |
def get_masksize(mask, labelnum = None):
"""
Compute mask size in surface space
Parameters:
----------
mask: label image (mask)
labelnum: mask's label number, use for group analysis
Return:
--------
masksize: mask size of each roi
Example:
--------
>>> masksize = g... | c8ccd82d9887f923e3d2581f97dd2a8f016cc182 | 23,463 |
def _context_py2rpmversion(context):
"""get a python PEP0440 compatible version and translate it to an RPM
version"""
# the context needs a variable set via {% set upstream_version = 'ver' %}
_context_check_variable(context, CONTEXT_VAR_UPSTREAM_VERSION,
'py2rpmversion')
... | 3f9110dff377a6c819e6b87ab5fd9c81a7532694 | 23,464 |
def check_and_format_address(address):
"""
check address
"""
try:
formatted_address = to_checksum_address(address)
return formatted_address
except Exception as e:
raise ArgumentsError("invalid address {}, reason: {}"
.format(address, e)) | 1b0c88aede34386d1ccd5facd1bdbd4724538ab7 | 23,465 |
from typing import Optional
def get_cache_name(cache_type: str, tag: Optional[str] = None) -> str:
"""
Get the canonical cache name (e.g., "tmp.cache.mem.tag") for a type of
cache.
:param cache_type: type of a cache
:param tag: optional unique tag of the cache, empty by default
:return: name ... | ff933829314dd1794406ca4282eaf4efdf860b39 | 23,466 |
def _aves2_cfg():
""" Read aipctl config
"""
config = ConfigObj()
# The result is a merge of all the files as they appear in the list
f_list = cfg_files()
if not f_list:
print("error: configuration file not found")
exit(1)
for f in cfg_files():
_cfg = ConfigObj(f, e... | 527f1e94d5ec2c5cd13aa1a886d4c56914828f4d | 23,467 |
def estimate_responsivity(mis_MU, norm_MU):
"""from the estimated base intensities, we return onlu users which have zero base intensity for misinformation
and greater than zero base intensity for normal content. """
no_bad_intentions_ids = []
for id in range(len(mis_MU)):
if mis_MU[id] == 0 and... | 4d944478694f1be1474eea963fad284079d5fe57 | 23,469 |
from typing import Union
from typing import Any
from datetime import datetime
def parse_field_constraint(
x: Union[str, int, float, bool, list],
constraint: str,
type: str = "string",
**field: Any,
) -> Union[str, int, float, bool, list, datetime.datetime, ConstraintTypeError]:
"""
Parse field... | 531e33a1bc79e8a232032ebe9d340f829a3f513c | 23,470 |
def compute_ab_cycles(c_cycles, linear_combinations, g, tretkoff_graph):
"""
Returns the a- and b-cycles of the Riemann surface given the
intermediate 'c-cycles' and linear combinations matrix.
Input:
- c_cycles
- linear_combinations: output of the Frobenius transform of the
"""
linco... | 645d569ee06cb87161b12158603b1b6dcfb92077 | 23,471 |
import pickle
import pathlib
def pmlb_multiclass_classification_dataset_names():
"""Returns list of multiclass classification datasets in PMLB."""
try:
name = pickle.load(open(".pmlb/mcdn.pkl", "rb"))
except FileNotFoundError:
pathlib.Path(".pmlb").mkdir(parents=True, exist_ok=True)
... | d3030441c119de0c96c9d83df026b7f922fe21e6 | 23,472 |
from losses.loss_functions import BalancedCrossEntropyLoss
from losses.loss_functions import SoftMaxwithLoss
from losses.loss_functions import NormalsLoss
from losses.loss_functions import BalancedCrossEntropyLoss
from losses.loss_functions import DepthLoss
def get_loss(p, task=None):
""" Return loss function for... | 6284d2e40fc8aa220c153307fc7199a47549d15d | 23,473 |
def compute_embeddings(image):
"""A mock function for a call to a deep learning model or a web service."""
del image # this is just a mock and doesn't do anything with the input
return 42 | 31536d4a2371140e962aadb63b8645685328b3df | 23,474 |
def text_to_string(filename):
"""Read a text file and return a string."""
with open(filename) as infile:
return infile.read() | dbd79e78c84c3374c0252544086885b909ae9bd9 | 23,476 |
def lgsvlToScenicElevation(pos):
"""Convert LGSVL positions to Scenic elevations."""
return pos.y | d90f7509285b08c791eac56c1a119f91120cf556 | 23,477 |
import jinja2
def render_to_string(backend, filename, context):
# type: (str, str, Dict) -> str
"""
Render a template using the specified context
:param backend: The backend for which the template is rendered
:param filename: The template name
:param context: The data to use when rendering the... | c645a9867acdb50236a5604144a104cb38e841f9 | 23,478 |
def customfield_by_name(self, name):
"""
Get the value of a customfield by name
"""
# Get all fields from Jira. This is expensive, so only do it once
if not hasattr(self, '_fields'):
response = self._session.get(
self._base_url.format(
server=self._options['server... | 35f7ee1e88029201086fc75bbc280beb386cca44 | 23,479 |
from pathlib import Path
def download_images(imgs):
"""Save any images on page to local directory"""
had_download_issue = False
for img in imgs:
image_url = 'https://projecteuler.net/{}'.format(img.get('src'))
logger.info(f'downloading image {image_url}')
image_name = Path(image_ur... | 7d39dff40797a698215a589f9ff65f3df4a85e9f | 23,480 |
def admin_order_pdf(request, order_id):
"""
1. Get data (and templates for displaying data)
2. Set type (cuz you'll need to download it, right?)
3. Using the module (configuring stuff, e.g. the CSS :P)
"""
order = get_object_or_404(Order, id=order_id)
html = render_to_string... | c4cf5a38743f573ef8dfa704cfe2d12bb47a679c | 23,481 |
import traceback
def delete_container(request, container):
""" Deletes a container """
storage_url = request.session.get('storage_url', '')
#meta_storage_url = request.session.get('meta_storage_url', '')
auth_token = request.session.get('auth_token', '')
#meta_auth_token = request.session.get('me... | ce205d6112239905707064f0357b6c19fe3bd688 | 23,482 |
def dense_encoder(X, params):
"""Dense model encoder subgraph that produces latent matrix.
Given data matrix tensor X and dictionary of parameters, process through dense
model encoder subgraph and return encoder latent vector for each example in
batch.
Args:
X: tf.float64 matrix tensor of input data.
... | 1dfe2b876cb32b5d8b89e70e451a732730762a14 | 23,483 |
def __asset_inventory_espanol(asset):
""" Renombra los encabezados del inventario de bases de datos de Datos \
Abiertos Colombia a términos en español.
:param asset: (pandas.DataFrame) - Tabla de inventario del portal de datos\
abiertos Colombia (https://www.datos.gov.co).
:return: base de ... | dfb508cec458ecb63c371849d84cb3b3d79335ba | 23,484 |
def end_of_sign_found(token: str, preceding_token: str):
"""
This function receives a token and its preceding token and returns whether that token ends an Akkadian sign.
"""
if not preceding_token:
return False
if '-' in token or '.' in token:
return True
if not preceding_token.e... | 30024ddad31c3149d1d2363842b085d2923c1387 | 23,485 |
from typing import Optional
from typing import Dict
import datasets
def get_loaders(
dataset: str, batch_size: int, num_workers: Optional[int]
) -> Dict[str, DataLoader]:
"""Init loaders based on parsed parametrs.
Args:
dataset: dataset for the experiment
batch_size: batch size for loader... | 2340d05f69057bcb034a8ec4ad5515055d0bde71 | 23,488 |
def pipeline():
""" Creates a pipeline configured to use a given model with a specified configuration.
Notes
-----
Pipeline can be executed only if its config contains the following parameters:
model_class : TFModel
Architecture of model. List of available models is defined at 'AVAILABLE_M... | f8fbbe3898b58b1b1621d742e4acdf80f17ba11c | 23,489 |
import copy
def get_screen_point_array(width: float, height: float):
"""Get screen points(corners) in pixels from normalized points_in_square
:param width: screen width
:param height: screen height
:return:
"""
points = copy.deepcopy(points_in_square)
for i in range(len(points_in_square))... | 34d88ddb1a24e4e3ebc81f0c7e99530548ed8a8b | 23,490 |
def get_spacing_matrix(size, spacing, offset):
"""Returns a sparse matrix LinOp that spaces out an expression.
Parameters
----------
size : tuple
(rows in matrix, columns in matrix)
spacing : int
The number of rows between each non-zero.
offset : int
The number of zero r... | 5871385bcdcb9ce538fe1e4525c947c2cfa582c9 | 23,491 |
def next_power2(x):
"""
:param x: an integer number
:return: the power of 2 which is the larger than x but the smallest possible
>>> result = next_power2(5)
>>> np.testing.assert_equal(result, 8)
"""
return 2 ** np.ceil(np.log2(x)).astype(int) | 379c2170d0dbd25ee01a47eb0765f4dfd143efbb | 23,492 |
def category_induced_page():
"""Form to compute the Category induced."""
return render_template('category-induced.html') | 176af8bbbb67afce78c11483f66b3d5ac15f6d76 | 23,493 |
import array
from operator import concat
def zext(value, n):
"""Extend `value` by `n` zeros"""
assert (isinstance(value, (UInt, SInt, Bits)) or
(isinstance(value, Array) and issubclass(value.T, Digital)))
if not is_int(n) or n < 0:
raise TypeError(f"Expected non-negative integer, got '... | dfd666446f1b93ebdeeb94b932d8de7b243f6a4e | 23,494 |
import math
def _distance(point0, point1, point2, seg_len):
"""Compute distance between point0 and segment [point1, point2]. Based on Mark McClure's
PolylineEncoder.js."""
if (point1[0] == point2[0]) and (point1[1] == point2[1]):
out = _dist(point0, point2)
else:
uuu = ((point0[0] - po... | 1927a5fe46dcb0245031b395aade67ec01270930 | 23,495 |
def delete_node(
graph: xpb2.GraphProto,
node_name: str = "",
**kwargs):
""" Add node appends a node to graph g and returns the extended graph
Prints a message and returns False if fails.
Args:
graph: A graph, onnx.onnx_ml_pb2.GraphProto.
node_name: Name of the node... | 620e325a0ea9da7cd83e897fee49fb6ef9183da4 | 23,496 |
from PIL import Image
def image_to_term256(pil_image):
"""Convert image to a string that resembles it when printed on a terminal
Needs a PIL image as input and a 256-color xterm for output.
"""
result = []
im = pil_image.convert('RGBA')
try:
except ImportError:
im.thumbnail((80, 8... | 482f6c868adf5f302d88898abeff426d9ed000e7 | 23,497 |
def false_discovery(alpha,beta,rho):
"""The false discovery rate.
The false discovery rate is the probability that an observed edge is
incorrectly identified, namely that is doesn't exist in the 'true' network.
This is one measure of how reliable the results are.
Parameters
----------
... | 849c236157070c5d1becfec3e4e5f46a63d232d2 | 23,498 |
def add_default_legend(axes, subplots, traces):
"""
Add legend to the axes of the plot. This is needed to be done using matplotlib shapes
rather than the build in matplotlib legend because otherwise the animation will add
a legend at each time step rather than just once.
Parameters
----------
... | d352c1d90dac882f687be426d63dea35dca4ba46 | 23,499 |
def split_data(n_samps, percent_test):
"""
:param n_samps: number of data samples
:param percent_test: percent of data to hold out
:return: two sets of indices corresponding to training and validation data
"""
# generate and randomly shuffle
idx = np.arange(n_samps)
np.random.shuffle(id... | 68d63d28b2aaab2697f2aab70fc7341a9a31811d | 23,501 |
def compute_totals(songs, limit_n, save_file=None):
"""
Return array of shape (4, 3, 35) representing counts for
each group of each context type of each label
"""
totals = np.zeros((4, 3, 35), dtype='int32')
i = 0
for song_path, beatmap_ids in songs:
print('song {}'.format(i))
... | d8e845912d6e1b5e0fab864e8a19cdc08500b4c5 | 23,502 |
def _initialize_arrays(initial_values,
num_steps):
"""Construct a structure of `TraceArray`s from initial values."""
trace_arrays = tf.nest.map_structure(
lambda t: tf.TensorArray( # pylint: disable=g-long-lambda
dtype=t.dtype,
size=num_steps, # Initial size.
... | f63e13f35aade7979b4090964c593c2d222e94bd | 23,503 |
def blend(image1, image2, factor):
"""Blend image1 and image2 using 'factor'.
Factor can be above 0.0. A value of 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than... | 5012d34ab9974e88bfc7dae4683521313fd37cd0 | 23,504 |
def start_of_next_clk_period(time: float, clk_period: float):
"""
:return: start time of next clk period
"""
return (start_clk(time, clk_period) + 1) * clk_period | d59dafc3a8fdec9d199dcf379eefce52267ea4c1 | 23,506 |
import re
def eval_formula(formula, assignment):
""" Evaluates a formula represented as a string.
**Attention**: Be extremely careful about what to pass to this function.
All parameters are plugged into the formula and evaluated using `eval()`
which executes arbitrary python code.
Parameters
... | c1f344fc0049e20e86feb2428a46d51f9eee5898 | 23,507 |
def soil_temperature(jth: int, states: States, weather: Weather): # j = 1,2,..,5
"""
Equation 2.4 / 8.4
cap_soil_j * soil_j_t = sensible_heat_flux_soil_j_minus_soil_j - sensible_heat_flux_soil_j_soil_j_plus
0 is Floor, 6 is SoOut
"""
h_soil_j_minus = Coefficients.Floor.floor_thickness if jth =... | ddd3e50b30dc1240d5f6c6200aea710beba6b498 | 23,508 |
def clean_user_data(model_fields):
"""
Transforms the user data loaded from
LDAP into a form suitable for creating a user.
"""
# Create an unusable password for the user.
model_fields["password"] = make_password(None)
return model_fields | 9b9f968c4a775527dac36597ecadee476549dc7d | 23,509 |
import json
def case_structure_generator(path):
"""Create test cases from reference data files."""
with open(str(path), 'r') as in_f:
case_data = json.load(in_f)
system_dict = case_data['namelists']['SYSTEM']
ibrav = system_dict['ibrav']
ins = {'ibrav': ibrav, 'cell': case_data['cell']}
... | 1c7249c207032ed623bbfe274ed117283cd6ef4d | 23,510 |
from typing import Optional
from typing import Type
from typing import Dict
from typing import List
from typing import Any
def load_ascii(file: 'BinaryFile', # pylint: disable=unused-argument,keyword-arg-before-vararg
parser: 'Optional[Type[ASCIIParser]]' = None,
type_hook: 'Optional[Di... | fa7f0ba4a98dc295fb23651373a6489dad5c205e | 23,511 |
def differences_dict(input_dict):
"""Create a dictionary of combinations of readers to create bar graphs"""
# Getting the combinations of the formats
for each_case in input_dict.keys():
comb = combinations(input_dict[each_case].keys(), 2)
x = list(comb)
comp_values = {}
comp_... | a15ef7bab8a9abaf556e1ce97a4c695b50d5b460 | 23,512 |
import psutil
def available_memory():
"""
Returns total system wide available memory in bytes
"""
return psutil.virtual_memory().available | 5071312f64aa37e1d777c8f20009fa38137381a4 | 23,513 |
from typing import List
from typing import Set
import numpy
def get_hypergraph_incidence_matrix(node_list: List[Node],
hyperedge_list: List[Set[Node]]
) -> numpy.array:
"""Get the incidence matrix of a hypergraph"""
node_to_index = {node:... | 706bdd53a1fefec3ee3f77fa79248361ffff0351 | 23,515 |
from re import X
def fformat(last_data, last_records):
"""
@param last_data: dictionary(node_name => node's data segment)
@param last_records: dictionary(node_name => timestamp, node when
last transmitted)
@return: html
"""
nodelist = last_data... | 28da148b43c616652872cabc7815cba51dafd16c | 23,516 |
import math
def ceil(base):
"""Get the ceil of a number"""
return math.ceil(float(base)) | ebe78a5eb8fa47e6cfba48327ebb1bdc469b970d | 23,517 |
def train(
dir,
input_s3_dir,
output_s3_dir,
hyperparams_file,
ec2_type,
volume_size,
time_out,
docker_tag,
aws_role,
external_id,
base_job_name,
job_name,
use_spot_instances=False,
metric_names=None,
... | 7cd92363bcde8dc86989a8932236cd4b2961b0e3 | 23,519 |
def wikitext_page(d, e, title, fmt='wikitext'):
"""Create infobox with stats about a single page from a category.
Create infobox with stats about a single page from a category. Currently only supports formatting as wikitext.
Only returns the string of the text, does not save any files or modify other data ... | 1288a1fea5bc54ef6243089eea0578cc43ec311e | 23,520 |
from typing import Tuple
def _quadratic(
self: qp.utils.Minimize[Vector],
direction: Vector,
step_size_test: float,
state: qp.utils.MinimizeState[Vector],
) -> Tuple[float, float, bool]:
"""Take a quadratic step calculated from an energy-only test step.
Adjusts step size to back off if energy ... | 49b2e75d0ae39e968e7288690dea7d42c423a2df | 23,521 |
def create_image(ds: "Dataset", data_element: "DataElement") -> "gdcm.Image":
"""Return a ``gdcm.Image``.
Parameters
----------
ds : dataset.Dataset
The :class:`~pydicom.dataset.Dataset` containing the Image
Pixel module.
data_element : gdcm.DataElement
The ``gdcm.DataElemen... | 95f96b0f666903529811fbf3aaeb71305dfcb1bc | 23,522 |
def linear_interpolate_by_datetime(datetime_axis, y_axis, datetime_new_axis,
enable_warning=True):
"""A datetime-version that takes datetime object list as x_axis
"""
numeric_datetime_axis = [
totimestamp(a_datetime) for a_datetime in datetime_axis
]
numer... | 515eb1e389b711ff3add707abe91bf577b38192d | 23,523 |
def calculate_index(
target_ts: pd.Timestamp, timestamps: pd.DatetimeIndex
) -> pd.Timestamp:
"""
Return the first index value after the target timestamp if the exact timestamp is not available
"""
# noinspection PyUnresolvedReferences
target_beyond_available = (target_ts > timestamps).all()
... | db1ad3130b3763115cb88e8798618d9632996bd7 | 23,524 |
from typing import OrderedDict
import pydoc
def walk_through_package(package):
"""
Get the documentation for each of the modules in the package:
Args:
package: An imported python package.
Returns:
output: A dictionary with documentation strings for each module.
"""
output = ... | 9ad0e9d935a812608fb42af788c1ae6746b78684 | 23,525 |
import gzip
def extract_images_2(f):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
Args:
f: A file object that can be passed into a gzip reader.
Returns:
data: A 4D unit8 numpy array [index, y, x, depth].
Raises:
ValueError: If the bytestream does not start with 2051.
"... | eb3b44051c6cc3721a82641346c233d9d4bfe1da | 23,526 |
from datetime import datetime
def slope_finder(station):
""" This function computes the slope of a least-squares fit of polynomial
of degree p to water level data and return that is it positive or negative"""
try:
dt = 2
dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.... | 035a78a4e54b94945837e97c6dce53bc36770956 | 23,527 |
def get_attr_counts(datas, attr):
"""
不同属性值的数量.
:param datas:
:type datas: list[BaseDataSample]
:param attr:
:type attr: str
:return:
"""
results = {}
for data in datas:
value = data.get_value(attr)
if isinstance(value, list):
for v in value:
... | bea8e6e1c99efe1ad18894831006f0e218517c74 | 23,528 |
def split(string: str, separator: str = " ") -> list:
"""
Will split the string up into all the values separated by the separator (defaults to spaces)
>>> split("apple#banana#cherry#orange",separator='#')
['apple', 'banana', 'cherry', 'orange']
>>> split("Hello there")
['Hello', 'there... | 73e01d7ff9111d949f31f37b36c3b0656d06e340 | 23,529 |
import ast
def _find_class(name: str, target: ast.Module) -> t.Tuple[int, ast.ClassDef]:
"""Returns tuple containing index of classdef in the module and the ast.ClassDef object"""
for idx, definition in enumerate(target.body):
if isinstance(definition, ast.ClassDef) and definition.name == name:
... | ad67d36772ef9541edb72a9d56f3553dc9eaffd2 | 23,530 |
def tidy_osx_command_line_tools_command(client: TidyClient, **kwargs) -> DemistoResult:
""" Install OSX command line tools
Args:
client: Tidy client object.
**kwargs: command kwargs.
Returns:
DemistoResults: Demisto structured response.
"""
runner: Runner = client.osx_comma... | 0045848f0cef054dfab24a7698e4b3432843f747 | 23,532 |
def nav_entries(context):
"""
Renders dynamic nav bar entries from nav_registry for the provided user.
"""
context['nav_registry'] = nav_registry
return context | 8af1917c04a9cbd17895c0fab0239d6fd7c009d2 | 23,533 |
from typing import Any
def get_largest_component(graph: ig.Graph, **kwds: Any) -> ig.Graph:
"""Get largest component of a graph.
``**kwds`` are passed to :py:meth:`igraph.Graph.components`.
"""
vids = None
for component in graph.components(**kwds):
if vids is None or len(component) > len(... | 24f04905c767f02a03b5a6fbf4ae0ba0b1f49269 | 23,534 |
def hiring_contests():
"""Gets all the hiring challenges from all the availbale platforms"""
contests_data = get_contests_data()
active_contests = contests_data["active"]
upcoming_contests = contests_data["pending"]
get_challenge_name = lambda x : x.lower().split()
hiring_challenges = [contest for contest i... | 91566f0117fc5bc38db7bc930d5e4c7bd1bd2992 | 23,535 |
import torch
def _find_quantized_op_num(model, white_list, op_count=0):
"""This is a helper function for `_fallback_quantizable_ops_recursively`
Args:
model (object): input model
white_list (list): list of quantizable op types in pytorch
op_count (int, optional): count the quantizable... | c51b06e476ff4804d5bdfca5a187717536a0418f | 23,536 |
from unittest.mock import patch
def test_process_bulk_queue_errors(app, queue):
"""Test error handling during indexing."""
with app.app_context():
# Create a test record
r1 = Record.create({
'title': 'invalid', 'reffail': {'$ref': '#/invalid'}})
r2 = Record.create({
... | 83c4609eb62d65fb7d53117906a0d6f128fe7b30 | 23,539 |
def list_to_string(the_list):
"""Converts list into one string."""
strings_of_list_items = [str(i) + ", " for i in the_list]
the_string = "".join(strings_of_list_items)
return the_string | f580dd8646526e64bb50297608e8ad8e338d9197 | 23,540 |
from typing import Optional
def get_rate_plan(apiproduct_id: Optional[str] = None,
organization_id: Optional[str] = None,
rateplan_id: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRatePlanResult:
"""
Gets the details of... | c439d2b991174b2fa4137d0b88f04af0ba4a22b9 | 23,542 |
def detail_blotter(backtest, positions, holdings, mode='simplified'):
"""
分品种获取详细交易状况，合并市场数据、交易情况和账户变动
参数：
backtest, positions, holdings为回测引擎返回的变量
mode: 'simplified'则市场行情数据只保留'close'列
（DataFrame的字典）
返回：
字典，键为symbol，值为DataFrame格式
示例：
blotter = detail_blotter(backtest, positions, ... | 9a2d8168cfc9ee979be847c6dac783a70503503c | 23,543 |
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