content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
from typing import Dict
from typing import Optional
from typing import Tuple
from typing import Union
def match_format(
node: FormatNode,
data: str,
*,
variables: Dict[VarName, VarDecl],
values: Optional[Dict[VarName, Dict[Tuple[int, ...], Union[int, float, str]]]] = None,
) -> Dict[VarName, Dict[... | 4d7cf10e7238f201c684e74f2f85d82ed9c52db3 | 70,300 |
def get_discussions(book_object):
"""
获得本书所有的讨论
:param book_object: Book模型实例
:return: 本书所有讨论的列表
"""
return book_object.discussions.order_by('-pub_date').all() | 842a158a18e1e7a31579f409997d56a452f1cf61 | 70,301 |
def bagging_classifier(*args, **kwargs):
"""
same as in bagging_from_scratch()
"""
return BaggingClassifier(*args, **kwargs) | ac7965cc456c3a5f0722962ab779a5710dfe2b2c | 70,302 |
def get_tool_path(folder, name, check=True, suffix=None):
"""Get tool path, returns pathlib.Path"""
bin_dir = find_kit_dir('.bin')
if not suffix:
suffix = 'exe'
name_arch = f'{name}{ARCH}'
# "Search"
tool_path = bin_dir.joinpath(f'{folder}/{name_arch}.{suffix}')
if not (tool_path.exists() or name_arc... | 37f398dfe0567db1d7b1125f1f7987bc76404db4 | 70,303 |
import google
def load_tables(dataset, tablespecs):
"""Construct a dictionary of BigQuery tables given the input tablespec.
Args:
dataset: bigquery.Dataset
tablespecs: list of strings of "NAME:DAYS", e.g. ["day:1"]
Returns:
client, {name: (bigquery.Table, incremental table name)}
... | 9320488a9d0bdd3e15de4311d14c29cd1f295279 | 70,304 |
def get_last_shifts(user, count=5):
"""
Returns the last number of shifts of user
:param user: User object
:param count: Number of shifts to return. Default is 5
:return: Shift objects or None
"""
finished_shifts = Shift.objects.filter(employee=user, finished__isnull=False)[
:count
... | 703998e841b3cf021219717e06409d01decb2317 | 70,305 |
def time_version(propagator, model, prop_kwargs=None, model_kwargs=None):
"""Measure the runtime of the specified version using the specified model.
Args:
propagator: The propagator object to use
model: The model (such as one from test_scalar) to use
Returns:
Runtime in seconds
... | ec51e21c9392056352199b9f42db6c2211ff3f0a | 70,306 |
import os
import re
import time
def get_version(package):
"""
Return package version as listed in `__version__` in `__init__.py`.
"""
init_py = open(os.path.join(package, '__init__.py')).read()
mth = re.search("__version__\s?=\s?['\"]([^'\"]+)['\"]", init_py)
if mth:
return mth.group(1... | 776afb3f41e18f2034bbd914b44018795ca13db3 | 70,307 |
def gen_service(cluster, bundle=None, prototype=None) -> models.ClusterObject:
"""Generate service of specified cluster and prototype"""
if not prototype:
bundle = bundle or gen_bundle()
prototype = gen_prototype(bundle, 'service')
return models.ClusterObject.objects.create(
cluster=... | 5b39a0d169f0a3d49c11d4ce9624212e0ee176b3 | 70,308 |
import logging
def _get_log_level(log_level_string):
"""return the logging log leve.
Validates log_level_string is a valid value"""
log_level = ''
if log_level_string == 'DEBUG':
log_level = logging.DEBUG
elif log_level_string == 'INFO':
log_level = logging.INFO
elif log_level_... | e834600b0c07f5c48e82c03aa64ee373de65ff4f | 70,309 |
def compute_kepler_orbit( a, e, inc_0, omega, Omega_0, M_0, t,
delta_n=0, inc_dot=0, Omega_dot=0, C_uc=0, C_us=0,
C_ic=0, C_is=0, C_rc=0, C_rs=0,
ref_sys='ECEF',
GM=defaults.EARTH_GM(),
ome... | 51477f90f3c17223f4382aaf2e5b48d8079642fc | 70,310 |
import logging
def phoneme_is_ok(path_mono_align_lab, path_mono_score_lab):
"""
音素数と音素記号が一致するかチェックする。
"""
mono_align_label = up.label.load(path_mono_align_lab)
mono_score_label = up.label.load(path_mono_score_lab)
# 全音素記号が一致したらTrueを返す
for mono_align_phoneme, mono_score_phoneme in zip(mono_... | 4a65ef10aa15db9548a24cca2c7bebde57598d5d | 70,311 |
def batchnorm_forward(x, gamma, beta, bn_param):
"""
Forward pass for batch normalization.
During training the sample mean and (uncorrected) sample variance
are computed from minibatch statistics and used to normalize the
incoming data. During training we also keep an exponentially
decaying run... | 04a55f6bd07f15927800ac82a97442bb81950d16 | 70,312 |
from typing import Type
from re import T
from typing import Union
def try_enum(cls: Type[T], val, default: D = None) -> Union[T, D]:
"""Attempt to convert a value into their enum value.
Parameters
----------
cls: :class:`Enum`
The enum to convert to.
val:
The value to try to conve... | 4a5f25dbc2d71655388252aab40f71e332eeebdd | 70,313 |
def SimInspiralNRWaveformGetRotationAnglesFromH5File(h5File,inclination,orb_phase):
"""
Compute the angles necessary to rotate from the intrinsic NR source frame
into the LAL frame. See DCC-T1600045 for details.
Implementation copied from
https://github.com/lscsoft/lalsuite/blob/d6e071988b46ec4497a... | 2551248ee7578cda998e47d4fb0c717b7f1fab5a | 70,314 |
def char_cnn_model(x, y):
"""Character level convolutional neural network model to predict classes."""
y = tf.one_hot(y, 15, 1, 0)
byte_list = tf.reshape(learn.ops.one_hot_matrix(x, 256),
[-1, MAX_DOCUMENT_LENGTH, 256, 1])
with tf.variable_scope('CNN_Layer1'):
# Apply Convolution fi... | 3254a81f4030a4c661ccee40437a7da3f4275e80 | 70,315 |
import os
def query_all_libraries(repository_root_path, packages):
"""
Given a list of (BUILD.pom) packages, walks the paths up to find the root
library directories, and returns those (without duplicates).
"""
lib_roots = set()
for package in packages:
while len(package) > 0 and packag... | 382bb5d197ca6cf661a2646bc71c8fa87ee97fd6 | 70,316 |
def convert(root: 'TreeNode') -> 'TreeNode':
"""
解法1：遍历两次。
"""
if not root:
return None
inorder = []
def dfs(node):
if node:
dfs(node.left)
inorder.append(node)
dfs(node.right)
dfs(root)
for i, n in enumerate(inorder[:-1]):
n.r... | a24d29d89459ec6be783fbc8d6f455665df7df68 | 70,317 |
import torch
def batch_shuffle_ddp(x): # pragma: no-cover
"""
Batch shuffle, for making use of BatchNorm.
*** Only support DistributedDataParallel (DDP) model. ***
"""
# gather from all gpus
batch_size_this = x.shape[0]
x_gather = concat_all_gather(x)
batch_size_all = x_gather.shape[0... | 6e49dbd4532054672d770d1f5825b13ce058a85d | 70,318 |
def hostconfigurator(request, hosts):
"""Returns a configurator page with graphs on it"""
stat, obj = parse()
service_list = real_meta(hosts)
return configurator(request, stat, obj, graphs=service_list) | 272889a5e6cb9f37e0880853d0a13906fc186d9d | 70,319 |
def process_args(*args, **kwargs):
"""returns a pair (artifact name, arg invocation)"""
positional_kwargs = []
named_kwargs = []
for name in kwargs:
if name.startswith('_'):
positional_kwargs.append(name)
else:
named_kwargs.append(name)
positional_kwargs.sort()
named_kwargs.sort(... | a3ee66317b8309c7f49e543ece46bdb4024c5002 | 70,320 |
def create_graph(mdl, feature_names=None, cmap=None):
"""
Display a graph of the decision tree.
Args:
mdl (obj): Model used for prediction.
feature_names (list): Names of the features.
cmap (obj): Colormap.
Returns:
graph (obj): Graphviz graph.
Example usage:
... | 8e9131a2811e9eb32340fbd06d381df7e3d74f88 | 70,321 |
import json
import html
def build_alerts_elements(images_url_live_alerts, live_alerts, map_style):
"""
This function is used in the main.py file to create alerts-related elements such as the alert button (banner)
or the alert markers on the map.
It takes as arguments:
- 'images_url_live_alerts':... | 666c16029b73839107d0e9280416164a549ca565 | 70,322 |
import inspect
def list_formatter(cls):
"""
This returns a function to map a given list of dictionaries
to a list of class instances via a _from_dict class method.
There is a corner case in which a passed in class might be a
primative like a string. This also handles that case by skipping
the... | a54f4c37a13343701133780dedc2dc3979cabde2 | 70,323 |
def get_edge_to_unique_attr(edgelist, src_col, dst_col):
"""Get a map from edge indexes to unique attribute indexes.
Parameters
----------
edgelist : DataFrame
Each row of the dataframe should contain the source, destination, and attributes of an edge.
src_col : str
Column name for ... | 8d9c0a4871b9be6ba3f67eb6efc1cdbd711d18e8 | 70,324 |
def make_matrix_horizontal(l):
""" Get the matrix with dimension 1*n needed for n*n masks, from an 1D list """
return np.array(l).reshape(1, -1) | 345c1e075f6411f987b4edb34eb8bb1a92fe6612 | 70,325 |
def update_validator_registry(state: BeaconState, config: Eth2Config) -> BeaconState:
"""
Update validator registry.
"""
current_epoch = state.current_epoch(config.SLOTS_PER_EPOCH)
# The active validators
active_validator_indices = get_active_validator_indices(state.validator_registry, current_e... | 934d378f5d299e98bed7a608fb3b1376cdaaf154 | 70,326 |
def membership_in_stream(in_stream, out_stream, membership_object):
"""
Parameters
----------
in_stream: Stream
The input stream of the agent.
Each element of the input stream is a pair:
(function_name, string) where function_name is
a string which is one of 'a... | 8d145ef195da13f4e2078a661277b71deeecaf23 | 70,327 |
def _object_format(o):
""" Object arrays containing lists should be printed unambiguously """
if type(o) is list:
fmt = 'list({!r})'
else:
fmt = '{!r}'
return fmt.format(o) | c71c0713d5b44c4733bc934d6026431e919db252 | 70,328 |
import copy
def tensor_matrix_mult(X, V, n=None, Transpose=False, use_gpu=True):
"""
tensor_matrix_mult: Tensor times matrix.
Parameters
----------
X: Tensor with N modes
V: 2D array
n: mode of tensor X to which to multiply the matrix by
Transpose: Should we ro... | 22a76d58909cb2b1213a3c2a5bb42d784bf7758a | 70,329 |
import traceback
def handle_error(error):
"""Error handler when a routed function raises unhandled error"""
print(traceback.format_exc())
return 'Internal Server Error', 500 | d93d3c4bbabb578b28ade961bb46793187a5ee06 | 70,330 |
def cifar100_rnn_gate_74(pretrained=False, **kwargs):
"""SkipNet-74 with Recurrent Gate"""
model = ResNetRecurrentGateSP(BasicBlock, [12, 12, 12], num_classes=100, **kwargs)
return model | 2f4d40f804237972364bcd322ce3a6c6d8261ea0 | 70,331 |
def appliance_status_json():
"""
Appliance Status JSON call
:return:
"""
return jsonify(verify_appliance_status()) | 93a117a20f602e05b34bdf8df70e9c0784422708 | 70,332 |
import os
def plot_recall(out_fold):
"""
plot barplot recovery complexes for every condition
"""
csfont = {"fontname": "sans-serif"}
fig, ax = plt.subplots(figsize=(6, 6), facecolor="white")
repo = os.path.join(out_fold, "ComplexReport.txt")
repo = pd.read_csv(repo, sep="\t")
repo = re... | 964733761fbd476eece59bcad6c7e55acda4df91 | 70,333 |
import numpy
def equalization(img):
"""
Função que equaliza o histograma
:param img: a imagem a aplicar a equalização
:return: O histograma equalizado
"""
h,r = numpy.histogram(img, bins=256, range=(0, 256))
p = h/float(dim[0] * dim[1])
pa = numpy.cumsum(p)
pa_norm = pa*255
equ... | 02d55f2dd848848062d340fccb5a8aede4ee6c9f | 70,334 |
from typing import Optional
def filter_by_chart(
chart_filter_pattern: Optional[FilterPattern], chart_name: str
) -> bool:
"""
Return True if the schema needs to be filtered, False otherwise
Include takes precedence over exclude
:param chart_filter_pattern: Model defining chart filtering logic
... | 22e24e91eb85bc4cdcc1bdb638eea9c008bcde6e | 70,335 |
def get_agencies_dict(agencies_file):
"""
Pulls agencies list into a dictionary for use in abbreviations pipeline.
Args:
agencies: file of the agency documents. currently set to agencies.csv
Returns:
dictionary of all agencies
"""
df = pd.read_csv(agencies_file)
aliases = {... | eb80f30f570913959b63c0bc8a215d9d7942b19e | 70,336 |
def compute_batch_indices(batch_size, k):
""" Computes the i'th coordinate that contains the batch index for gathers.
Batch pos is a tensor like [[0,0,0,0,],[1,1,1,1],..]. It says which
batch the beam item is in. This will create the i of the i,j coordinate
needed for the gather.
Args:
bat... | d342ced62fc776b78ff940be55c05497639d5897 | 70,337 |
def _set_conditions(condition_list):
"""Returns an argument for config_settings that sets specific options.
Args:
condition_list: a list of three booleans
Returns:
a dictionary parameter for config_settings such that ":conditionN" is True
iff condition_list[N + 1] is True
"""
... | a1d4443a8f638754d8ece8692ab702f80670c09a | 70,338 |
def getAvgEvalScores_ofTrainRuns(nr_runs,X_train,y_train,ID_train,label_dict, scores_list, print_flag):
"""
Get the average scores of the specified score list from a number of runs.
Args:
nr_runs (int): number of runs.
X_train (dataframe of features, once removed the columns of label an... | 6ceeb648463a88cf0cc671c65049ab82196830b1 | 70,339 |
import os
def pretty_path(input_path):
""" return path string replacing '~' for home directory """
home_path = os.path.expanduser('~')
cwd_path = os.getcwd()
output_path = input_path.replace(home_path, '~').replace(cwd_path, './')
return output_path | b10bc85e4fba7ac7c73b53bc2bf5c8a61573e39f | 70,340 |
import colorsys
def scale_lightness(rgb, scale_l):
"""
Scales the lightness of a color. Takes in a color defined in RGB, converts to HLS, lightens
by a factor, and then converts back to RGB.
"""
# converts rgb to hls
h, l, s = colorsys.rgb_to_hls(*rgb)
# manipulates h, l, s values and retu... | 2fee635f26419cfe8abc21edb0092a8c916df6ef | 70,341 |
def get_hw_type_from_client(hw_client) -> HWType:
"""
Return hardware wallet type (HWType) based on reference to a hw client.
"""
if hw_client:
t = type(hw_client).__name__
if t.lower().find('trezor') >= 0:
return HWType.trezor
elif t.lower().find('keepkey') >= 0:
... | 02946f1284705bc06e89874f240b0dbfb93b5f55 | 70,342 |
def kmeans(X, n_features, batch_size, n_iterations, verbose=False):
"""
Finds kmeans for X.
Parameters:
X: 2D matrix with shape: (n_samples, dimensions)
config:
n_features: number of centers (k). NOTE: the final
k can be less than n_features since this code
... | 29a0c8f378c22f77dc699e3b87ceffa0b4391779 | 70,343 |
def fit_linreg_robust(x, y, mask=None, intercept=False, r2=True, est_method='rlm'):
"""Apply robust linear regression of y w.r.t x.
Arguments
---------
x: :class:`~numpy.ndarray` or sparse `csr_matrix`
A vector of independent variables.
y: :class:`~numpy.ndarray` or sparse `csr_matrix`
... | 6b6372a2aa0a7239b7e3598c8972f25fea62260b | 70,344 |
def emg_process(emg, sampling_rate=1000, emg_names=None, envelope_freqs=[10, 400], envelope_lfreq=4, activation_treshold="default", activation_n_above=0.25, activation_n_below=1):
"""
Automated processing of EMG signal.
Parameters
----------
emg : list, array or DataFrame
EMG signal array.... | bd95e1a8708bce5fda3ca7c5274363faa2bcea36 | 70,345 |
def peek(src: TextIOBase, n: int = 1) -> str:
"""Read up to `n` bytes from `src` without advancing the offset"""
with peeking(src):
return src.read(n) | 5975dc8c94c262e700281120c81eb936adf677d6 | 70,346 |
def generate_key(**kwargs):
"""
Generates a new RSA private key.
:param size:
:param key_gen:
:param kwargs:
:return:
"""
key_gen = kwargs.get("key_gen", default_key_gen)
return key_gen(**kwargs) | ae8355e4a3dfc62af24c947b3526b0892ea36d03 | 70,347 |
import torch
def dummy_compute() -> torch.Tensor:
"""
returns a predefined size random Tensor
"""
return torch.rand(100, 100) | 9b1c05359c9b57573bd2771124e878ccee7eae87 | 70,348 |
import typing
def validator(*kinds: typing.List[str], collect=True,
raise_on_invalid_kind=False) \
-> typing.Callable:
"""
decorator to generate validator functions.
Expects LintError exceptions on invalid configurations.
"""
def add(f: typing.Callable):
@wraps(f)
... | baee56ab9d7ed2aa9f538567e75d11f189d9c708 | 70,349 |
def mlp_net_input(h, w, b):
"""
Return the network input z as a function of h, w, and b.
:param h: the input from the previous layer.
:param w: the weights for this layer.
:param b: the biases for this layer.
:return: the linear network activations for this layer.
"""
result = np.dot(h, ... | 21c035ea5c8704d27bd53e22cd253113cd602e61 | 70,350 |
import json
async def get_schedules_handler(
request: Request,
ip_address: str,
phone_id: str,
device_id: str,
device_password: str,
) -> HTTPResponse:
"""Use for handling requests to /switcher/get_schedules.
Args:
request: ``sanic``'s request object.
ip_address: the local ip ... | 14e54ec0872689a7730e6c9b26de4bf100423ea4 | 70,351 |
def extractPrefix(x, prefix_length, as_string):
"""
Returns an IPNetwork object of the ipaddress module created using
ip address `x` given as string with a prefix length of `prefix_length`
x (String): ip address
prefix_length (int): number of bits to use as a prefix
as_string (boolean) :
... | 4fa68867ebebac28b2fd69fd7b33146be81e2fa3 | 70,352 |
import os
def get_fallback_executable(command_name='vmrun'):
"""Get a fallback executable for a command line tool."""
if 'PATH' in os.environ:
LOGGER.debug("os.environ['PATH']:%s", os.environ['PATH'])
for path in os.environ['PATH'].split(os.pathsep):
vmrun = os.path.join(path, comm... | c421aa3b1c1e492f42e57dcef05b40182fcfe3d1 | 70,353 |
def vel_distance(v1, v2, theta):
"""
Velocity distance. The Equation (2) of Mould et al. (2000).
Parameters
----------
v1 : float
The velocity of the first object, units: km/s.
v2 : float
The velocity of the second object, units: km/s.
theta : float
The angular dist... | f34354d6f8ef6c731c329524db9fbcefe4bb1ba9 | 70,354 |
def for_list_lang(context):
"""Вывод списка языков"""
return settings.LANGUAGES | 081a0ee93ce0901827407abeb711f6763dc67eb7 | 70,355 |
from typing import Optional
from typing import List
import logging
import itertools
def _process_table_identifiers(
pdf: DataFrame, dimension_combinations: Optional[List[List[str]]] = None, max_combination_length: int = 5
) -> List[List[str]]:
"""
Dask wrapper around extracting identifiers from a single s... | 7d7d81723310be77808109f236fc595705b0464c | 70,356 |
def raster2array(rasters,band_no=1):
"""
Arguments:
rast A gdal Raster object
band_no band numerical order
Example :
raster = gdal.Open(rasterfn)
raster2array(raster,1)
"""
bands = rasters.RasterCount
if band_no>0 and band_no <=bands:
band = rasters.Get... | 76bdcffbf22a0936fe24c29eba975f21edd7cd41 | 70,357 |
def early_stopping(cost, opt_cost, threshold, patience, count):
"""
Determines if you should stop gradient descent. Early stopping should
occur when the validation cost of the network has not decreased relative
to the optimal validation cost by more than the threshold over a specific
patience count
... | 5baea9f867e8ca8326270f250327494a5c47af46 | 70,358 |
def search():
"""search for a specific item"""
searchword = request.args.get('search', '')
json = request.args.get('json', '')
if not json:
# if the user didn't provide a search word return error message.
if not searchword:
return render_template("mes.html", error="must prov... | adeff4717d9884a6c903aa7aeb10c0f22e40df06 | 70,359 |
def diameter(domain):
"""
Returns the diameter of a domain.
:param domain: a domain
:type domain: `escript.Domain`
:rtype: ``float``
"""
return sqrt(sum( [ v**2 for v in boundingBoxEdgeLengths(domain) ] )) | 8fe13ecf19f96ea624f0a2d20f165033593bc3ad | 70,360 |
import asyncio
def chain_execution(commands):
"""
Executes given commands sequential. If one commands fails all other commands fail
too.
"""
result = []
error = False
for command in commands:
try:
cmd = Command(
command["method"],
uuid=c... | d06be2a3f9395343be5787d248e95e1996b9ec9a | 70,361 |
import os
def _wait():
"""Replacement for perl wait() call"""
global CHILD_ERROR
try:
(pid, stat) = os.wait()
CHILD_ERROR = stat
return pid
except Exception:
return -1 | e3b5e41ebea00f8c57827e3455db667118271fe9 | 70,362 |
import random
import sys
def next_positive_random(modulus):
"""Calculates a positive random number in the range(0, modulus - 1).
Parameters
----------
modulus : int
Modulus of the operation.
Returns
-------
int
New positive random integer.
"""
rn = random.randint... | 63b0493d5eecb960eea0f2d33f2a17ea7ed0d49b | 70,363 |
import json
def export_roast():
"""Export a roast log."""
c = mongo.db[app.config['HISTORY_COLLECTION']]
roast_id = request.args.get('id')
roast_id = paranoid_clean(roast_id)
item = c.find_one({'_id': ObjectId(roast_id)}, {'_id': 0})
if not item:
return jsonify({'success': False, 'mess... | deb25421561c6e66782c351ccfa4f6b82f94e898 | 70,364 |
def tagset_mapping(source, target):
"""
Retrieve the mapping dictionary between tagsets.
>>> tagset_mapping('ru-rnc', 'universal') == {'!': '.', 'A': 'ADJ', 'C': 'CONJ', 'AD': 'ADV',\
'NN': 'NOUN', 'VG': 'VERB', 'COMP': 'CONJ', 'NC': 'NUM', 'VP': 'VERB', 'P': 'ADP',\
'IJ': 'X', 'V':... | fc1acd0d33dc201b1d692e84c1469d1899488f53 | 70,365 |
def beqs(screen, asof=None, typ='PRIVATE', group='General', **kwargs) -> pd.DataFrame:
"""
Bloomberg equity screening
Args:
screen: screen name
asof: as of date
typ: GLOBAL/B (Bloomberg) or PRIVATE/C (Custom, default)
group: group name if screen is organized into groups
... | 48db4985943383d62bcabe201206aecf7f2afc88 | 70,366 |
def generate_launch_description():
"""Run demo nodes via launch."""
return LaunchDescription([
launch_ros.actions.Node(
package='pub_sub_tests',
executable='talker',
output='screen',
remappings=[('chatter', 'my_chatter')]),
]) | 578f9763da52cb8991f07c9e2f6a865069a170c3 | 70,367 |
def get_features(contour):
"""Get the three features that identify a corner marker"""
try:
return (cv2.contourArea(contour),
cv2.arcLength(contour, True),
cv2.contourArea(get_bounding_rect(contour)))
except ZeroDivisionError:
#return 4*[np.inf]
return ... | 54bfa67d8bc59b145ef93bc3ee29b00665e4c8ce | 70,368 |
from alpa.pipeline_parallel.stage_construction import get_submesh_choices
import os
import pickle
import ray
import time
def profile_all(device_cluster, cluster_key, max_comm_size_intra_node,
max_comm_size_inter_node, max_fail_retry, cache_filename,
dot_range=(0, 64)):
"""Profile c... | c5cc52cbd7c5164762e3f1b776446d17ccd342d3 | 70,369 |
import re
def _MatchStaticFile(handler, path):
"""Match a path to a regular expression and return a StaticPage.
Args:
handler: A handler dictionary with a static_files field.
path: The path portion of the url.
Returns:
A StaticPage object or None.
"""
pattern = handler['url']
if not pattern.... | 2dca79df358d0c51710f8d559d7d23139b8a378b | 70,370 |
def apply_non_max_suppression(boxes, scores, iou_thresh=.45, top_k=200):
"""Apply non maximum suppression.
# Arguments
boxes: Numpy array, box coordinates of shape (num_boxes, 4)
where each columns corresponds to x_min, y_min, x_max, y_max
scores: Numpy array, of scores given for ea... | 5651577e32a7dbb23400e354ff6527fbdb785a7c | 70,371 |
def get_default_configuration(invocation_id, config_id=None):
"""
Returns a new Configuration with fields initialized to defaults.
Args:
invocation_id (str): The id of the invocation for the configuration.
config_id (str): The id of the configuration.
Returns:
A new Configurati... | d5a82349bc5031869e919b2bee859b11a022362b | 70,372 |
def cosh(input, name=None):
"""Element-wise hyperbolic cosine of a tensor."""
if is_tensor(input, 'tf'):
return tf.math.cosh(input, name=name)
else:
return np.cosh(input) | aec39ba69b6c3836114338564fdde3b149e2b86b | 70,373 |
def blockwise(
func,
output,
output_indices,
*arrind_pairs,
numblocks=None,
concatenate=None,
new_axes=None,
dependencies=(),
**kwargs,
):
"""Create a Blockwise symbolic mutable mapping
This is like the ``make_blockwise_graph`` function, but rather than construct a
dict,... | 0433678321afbff5d999185a98de6bdfb2a6f59f | 70,374 |
import os
def scan_videos_from_txt(file_list_path, extensions=()):
"""Get existing files from the file list .txt file."""
files = _read_lines(file_list_path)
files = [x for x in files if os.path.isfile(x)]
if len(extensions) > 0:
files = filter_extensions(files, extensions)
return files | ae2fc140528e58a7b6e03ce59ea1ede1b0c902fa | 70,375 |
def extract_biofuel_shares_from_IAM(
model, fp, IAM_region, years, allocate_all_synfuel=False
):
"""
This function extracts biofuel shares from a IAM file provided.
:param fp: file path to IAM file
:type fp: str
:param IAM_region: IAM region for which to extract the biofuel shares
:type IAM... | 372412f6e2355cf8cc9c0c7ab19dac4b234d7668 | 70,376 |
import os
def check_diagnostics_figure():
""" Simple check if .png figures were created. """
result = True
fig = './tests/lakes/results/lakes_test_20190401_20190402/diagnostics_lakes_test.png'
if not os.path.isfile(os.path.abspath(fig)):
result = False
return result | a626103d3d1886862eed5572948f318bb7f9bb57 | 70,377 |
def blendLayers(
background: Image.Image,
foreground: Image.Image,
blendType: BlendType | tuple[str, ...],
opacity: float = 1.0,
) -> Image.Image:
"""Blend layers using numpy array.
Args:
background (Image.Image): background layer
foreground (Image.Image): foreground layer (must be same size as background)
... | df352c3d9bff6a7972a98984b8e032408bb5ac22 | 70,378 |
import importlib
def class_from_class_path(class_path):
"""
Get a python class instance given the python "class path".
EG:
# >>> class_from_class_path("decimal.Decimal")
# <class 'decimal.Decimal'>
Args:
class_path - str - dot-delimited python path followed by class name.
Returns:
class... | 5584c827da786dec73b85026289a0580214d022d | 70,379 |
def save_all_animations():
"""Save all animation actions in Blender file
"""
animations = []
if len(bpy.data.armatures) == 0:
return animations
# get armature, assume single armature
armature = bpy.data.armatures[0]
try:
obj_armature = bpy.data.objects[armature.name]
... | 0e1d68459a69e1fe1e40b1aee921abf5c755442c | 70,380 |
def is_if_then(tokens):
""":note: we assume single-line if have been
transformed in preprocessing step."""
return tokens[0:1+1] == ["if","("] | aa1a946ed94299e689534ca98dcf1d0fa9bdca32 | 70,381 |
import multiprocessing
import os
import subprocess
import glob
import traceback
def multipart_s3_file_transfer(bucket, s3_key_name, tarball, mb_size, use_rr=True):
""" file transfer above 5GB to s3 """
cores = multiprocessing.cpu_count()
def split_file(in_file, mb_size, split_num=5):
prefix = os.... | d47902e0bedefa7ebf1640098ac07daf60fc8315 | 70,382 |
import os
from datetime import datetime
def batlogger2guano(fname):
"""Convert an Elekon BatLogger .WAV with sidecar .XML to GUANO metadata"""
xmlfname = os.path.splitext(fname)[0] + '.xml'
if not os.path.exists(xmlfname):
raise ValueError('Unable to find XML metadata file for %s' % fname)
g =... | 16b93b1efa4576815c5336920a572d601bd6bfa1 | 70,383 |
def get_implline(cursor: Cursor) -> int:
"""
finds the first line of implementation source code for class method pointed by the cursor
parameter.
Args:
cursor: libclang cursor pointing to the target mock class definition.
Returns:
an integer, the line number of the first line of the correspondi... | ad1fe32e6dba22a42ee2da0295e95490288e5daf | 70,384 |
def inverse_dissim(X):
"""
:param X: int or np.array
:return:
"""
X = np.asarray(X)
X = zscore(X)
X = minmax(X, 0, 10)
return 1./(1+X) | 87737ba4d425bb91d58060ef4aa535010099d002 | 70,385 |
import yaml
def yaml_load(file):
"""
Load single-document YAML and return document
"""
with open(file, "r") as f:
return yaml.safe_load(f) | 76402fd742f5aaf1776751f44b3583a62c1f3581 | 70,386 |
import time
def is_available(workshift_profile, shift):
"""
Check whether a specified user is able to do a specified workshift.
Parameters:
workshift_profile is the workshift profile for a user
shift is a weekly recurring workshift
Returns:
True if the user has enough free time... | a847e3d7e91d4272c035a9a8dd609e20a0247b8a | 70,387 |
def BuildEdgeFunction(function_ref, arch, source, function):
"""Calls Cloudbuild to build Edge Function.
Args:
function_ref: Edge function resource
arch: str, target architecture,
should be one of 'x86-64', 'armhf', or 'aarch64'
source: str, GCS URI of source provided from command line
functi... | f18c90a3db684b847bd3de2289802599e916656f | 70,388 |
def convert_event_data_timestamp(ts):
"""
Convert the default event_data timestamp format to Pandas and unixtime format.
Parameters
----------
ts : str
UTC timestamp in custom API event data format.
Returns
-------
timestamp : datetime
Pandas Timestamp object format.
... | 99f4604b4a1c5d164dfcf4567c85fcf12e149337 | 70,389 |
import tempfile
import zipfile
def unzip_csar_to_tmp(zip_src):
"""
Unzip csar package to temp path
:param zip_src:
:return:
"""
dirpath = tempfile.mkdtemp()
zip_ref = zipfile.ZipFile(zip_src, 'r')
zip_ref.extractall(dirpath)
return dirpath | b6be57f4e20c208583cc15dc9d0b997f6b41e2f1 | 70,390 |
import time
from re import L
def integrate_verlet(X, V, iparams, blist, sp):
"""
Verlet integration for Nt steps.
Save each thermo-multiple step into xyz_frames.
Mass set to 1.0. Input:
* X: (N, 3) matrix
* V: (N, 3) matrix
* iparams: (Nbt, Nbt) matrix with interaction params
* blist: ... | e9b60a294e257a87461afe8f745f61083f4a1075 | 70,391 |
import torch
def seq_collate_fn(batch):
"""
Returns several tensors. Tensor of lengths should not be sent to CUDA.
"""
idx2batch = pd.Series(np.arange(len(batch)), index = [b["idx"] for b in batch])
df = pd.concat(b["path"] for b in batch)
value_cols = [c.startswith("Value") for c in ... | 2a1fcbd3ce386f5cc9b6851359ac7ade0d0e979a | 70,392 |
def adminSummaryHomeView(request, dieName):
"""
An administrative view that displays a list of images and info about
the typed information for each for the given Die. The administrator
can see how much data has been entered for each image and click on
the image to open a new view showing more detai... | 95baa0450e730f85ab51a587c7df40691cc1a6cf | 70,393 |
def get_device_fn(device):
"""Choose device for different ops."""
OPS_ON_CPU = set(get_cpu_list())
def _device_fn(op):
# Startswith "Save" is a hack...
if op.type in OPS_ON_CPU or op.name.startswith('save'):
return '/cpu:0'
else:
# Other ops will be placed on... | fe7993d624d38f003311c4a7d1b010af5320c6f8 | 70,394 |
def handle_element(elem, args, func=None, func2=None):
"""
A helper function to handle elements from BeautifulSoup element.find()
:param elem: BeautifulSoup Tag (element)
:param args: args tuple for find method. String can be used for a single
arg, otherwise tuple
:param func: ad hoc fun... | 7f63b4ec8f762bfdf427328eeec05846504ee8d8 | 70,395 |
def add_columns(input_header, input_rows, column_names,
fill_with="NULL", in_place=True):
"""Add new columns filled with the requested value
Parameters:
input_header (list): List of column headers from input file
input_rows (list): List of lists of fields from each i... | 12979c354c0031f1ebc5f17d229a09dc59b020d3 | 70,396 |
from datetime import datetime
def date2datetime(dt):
"""
date 类型转换问 datetime类型
:param dt:
:return:
"""
return datetime(dt.year, dt.month, dt.day) | 53bf2cabe3d8a70791cb92fb12387a78d0beedd7 | 70,397 |
import re
def splitSentence(content):
"""
Given block of text, split into sentence
Output: list of sentences
"""
# Multiple space to single space, remove separators like - and _
if pd.notnull(content):
content = re.sub('\s*\t\t\t', ' ', content)
content = re.sub('--+|==+|__+', ... | 85a4eea6928f689b87a60edd2d4fe47b9f6d0a15 | 70,398 |
def save_data(outpath, header, row, data):
""" Simple shortcut
If `feather-format` is available, save data to `.feather` file
otherwise `.csv`
"""
try:
return save_data_to_R(outpath, header, row, data)
except ImportError as e:
return save_data_to_csv(outpath, header, row, data) | 17e59bad3e68cab15b40d6e229c09842b62df3fe | 70,399 |
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