content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def align_data(data):
"""Given dict with lists, creates aligned strings
Args:
data: (dict) data["x"] = ["I", "love", "you"]
(dict) data["y"] = ["O", "O", "O"]
Returns:
data_aligned: (dict) data_align["x"] = "I love you"
data_align["y"] = "O O O "
"""
spac... | 6be0d9854cf73b4c44a91b2bbe6b4ac1bb76157e | 35,900 |
def average_surface_distance(mflo, mref):
""" average on points so not reliable if the sampling is unhomogeneous """
pd = polydata_distance(mflo, mref, do_signed=False)
xv = pd.GetPointData().GetArray("Distance")
xn = nps.vtk_to_numpy(xv)
return xn.mean() | 42e17183d58f05b1c4810f8d995b57e5b24a7bf8 | 35,901 |
def CleanUserUrl(user_url: str) -> str:
"""清理 user_url，去除其中的空格和无用参数
"""
user_url = user_url.strip()
return user_url.split("?")[0] | 7c5c2cf5879d4ddfdbd1a60a679a747f162ebe35 | 35,902 |
def get_aqi_pb_24h(pb_24h: float) -> (int, str, str):
"""
Calculates Pb (24h) India AQI
:param pb_24h: Pb average (24h), ppm
:return: Pb India AQI, Effect message, Caution message
"""
cp = __round_down(pb_24h * 1000, 3)
return __get_aqi_general_formula_texts(cp, IN_PB_24H, IN_AQI_EFFECTS, I... | dbd8beadaf57c5a14b1f91854cf004f877389ec4 | 35,903 |
from typing import Union
def check_integrity(hdpgroup: list,
verbose: bool = False,
enforce: bool = False) -> Union[list, bool]:
"""Check integrity (comprare checksums) non-proposital data corruption
Args:
hdpgroup (list): [description]
verbose (bool, o... | 24a572fb9ec7bf19830f8edb3f505d61216ce053 | 35,904 |
def id_to_ec2_snap_id(snapshot_id):
"""Get or create an ec2 volume ID (vol-[base 16 number]) from uuid."""
if uuidutils.is_uuid_like(snapshot_id):
ctxt = context.get_admin_context()
int_id = get_int_id_from_snapshot_uuid(ctxt, snapshot_id)
return id_to_ec2_id(int_id, 'snap-%08x')
els... | d13a1a4c608c0baf8727a2b5c1d928b8722f71f7 | 35,905 |
def delete_nonestimator_parameters(parameters):
"""Delete non-estimator parameters.
Delete all parameters in a parameter dictionary that are not used for the
actual estimator.
"""
if 'Number' in parameters.keys():
del parameters['Number']
if 'UsePCA' in parameters.keys():
del p... | d84984d182a5945167b8e7880e493aa8fad832b7 | 35,906 |
from pathlib import Path
def get_gene_sequence(gene_name: Path) -> str:
"""Okay, I don't understand how this is suppose to work.
\f
Parameters
----------
gene_name :
Return
------
seq : `str`
"""
try:
with open(gene_name, "r") as f:
seq = f.read()
... | 1ec4b9a2945b3e14dc87ce440734799c87560906 | 35,907 |
def get_service(credentials):
"""Get the service object corresponding to GMail."""
http = credentials.authorize(httplib2.Http())
service = discovery.build('gmail', 'v1', http=http)
user = service.users().getProfile(userId="me").execute()
print("Authenticated user: {0}".format(user["emailAddress"]))
... | f59eaeb46a3f922855c9adc96a10ef2797ce8cbc | 35,908 |
from typing import Optional
from typing import Sequence
def get_private_application_packages(display_name: Optional[str] = None,
filters: Optional[Sequence[pulumi.InputType['GetPrivateApplicationPackagesFilterArgs']]] = None,
package_types: Opt... | 3dc97aa7d3fc27cc2cf12a797d7e7d364cffe210 | 35,909 |
def load_data():
"""
:return: Data frame
"""
# load data
engine = create_engine('sqlite:///data/disaster_response.db')
df = pd.read_sql_table('disaster_response', engine)
return df | 231a0233d65717d4b171223d1870e2ba7c2822c4 | 35,910 |
def start_child_span(
operation_name: str, tracer=None, parent=None, span_tag=None
):
"""
Start a new span as a child of parent_span. If parent_span is None,
start a new root span.
:param operation_name: operation name
:param tracer: Tracer or None (defaults to opentracing.tracer)
:param par... | e5465d45800560e601fde1d513c204d5ca1284de | 35,911 |
def demslv08old():
"""Nonlinear complementarity problem methods
Solve nonlinear complementarity problem on R^2 using semismooth and minmax methods
"""
''' function to be solved'''
def f(z):
x, y = z
fval = np.array([200 * x * (y - x ** 2) + 1 - x,
100 * (x ... | 4bf4b37ca8dbf7503443292996cfaf2a1ef4d368 | 35,912 |
from re import A
import math
def from_mercator(x, y):
"""Convert x,y coordinate from Spherical Mercator to lon, lat
Ported from mercantile.
Parameters
----------
x : float
y : float
Returns
-------
(longitude, latitude)
"""
return (x * R2D / A, ((math.pi * 0.5) - 2.0 * ... | 09a6a5ae85290fc230398de5ee0cfa28e8893366 | 35,913 |
def GetMaxIndex(tree):
"""get maximum node number."""
return tree.id | f443a9006765dface834aa9120c3ab38cd1d4369 | 35,914 |
def audit_name_starts_with(prefixes):
"""
Given a list of prefixes, returns a function that takes a folder and prints the folder path if the folder name does not start with one of the prefixes.
"""
def action(folder):
if any(folder.name.startswith(prefix) for prefix in prefixes) == False:
... | 886af28239aa989bed15d8f5a4462dd94cd1363b | 35,915 |
def error_handler(error):
"""
Handle errors in views.
"""
return render('error.html'), 500 | 307846d8445f03ac8acb48585ccab5ffdbab972e | 35,916 |
def save_work(work_id):
"""Save a work"""
if not auth.session_user().can_manage_works:
return Response('User not logged in or not authorized to manage works.',
401)
if int(work_id) == -1:
# New work
work = Work()
else:
# Existing work
work = Work.f... | 296ed0eff93ea64b4541b4a84c62f8be673b6e89 | 35,917 |
def get_feedback_expertise_levels(): # noqa: E501
"""Request a list of allowable expertise levels
# noqa: E501
:rtype: ExpertiseLevels
"""
rtxFeedback = RTXFeedback()
return rtxFeedback.getExpertiseLevels() | fc76ea2daf885159f4461ae694b7b68e203befdc | 35,918 |
def get_coord_limits(coord):
"""get cooordinate limits"""
lower_limit = float('.'.join([str(coord).split('.')[0], str(coord).split('.')[1][:2]]))
if lower_limit > 0:
upper_limit = lower_limit + 0.01
else:
tmp = lower_limit - 0.01
upper_limit = lower_limit
lower_limit = t... | 803c0804e34a97d46a9555b4566be72949f55e8d | 35,919 |
import torch
def decode(loc, priors, use_yolo_regressors:bool=False):
"""
Decode predicted bbox coordinates using the same scheme
employed by Yolov2: https://arxiv.org/pdf/1612.08242.pdf
b_x = (sigmoid(pred_x) - .5) / conv_w + prior_x
b_y = (sigmoid(pred_y) - .5) / conv_h + prior_y
... | 8158b29c7557f1bde0e7873a6eace78d039c6d5d | 35,920 |
def read_relative_file(filename):
"""Returns contents of the given file, whose path is supposed relative
to this module."""
with open(join(dirname(abspath(__file__)), filename)) as f:
return f.read() | b20d61e4ddc049c4beeacf106b04d30e3d0cc966 | 35,921 |
import torch
import tqdm
def get_activations(data_loader, model, device=None, batch_size=32, resize=False, n_samples=None):
"""Computes the activation of the given images
Args:
imgs: Torch dataset of (3xHxW) numpy images normalized in the
range [-1, 1]
cuda: whether or not to run on GPU
... | 2755bd34f05c3fb72f4c1da83bdb25d3df2f7662 | 35,922 |
from pathlib import Path
def write(
# Basic setup
input_path,
# preset_nickname=None,
stream_name="",
stream_description="",
output_directory=None,
output_mode="video",
stream_name_file_output=False,
max_cpu_cores=0,
# Stream configurat... | f9b7335b5a4fe95d9f53631d78f2b1c9708d5eea | 35,923 |
def shell_escape(string):
"""
Escape double quotes, backticks and dollar signs in given ``string``.
For example::
>>> _shell_escape('abc$')
'abc\\\\$'
>>> _shell_escape('"')
'\\\\"'
"""
for char in ('"', '$', '`'):
string = string.replace(char, '\\{}'.format... | 03fcec5cdd99685e821fea11a69a234f3123fd9b | 35,924 |
def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number):
"""
:param start: start coordinate
:param goal: goal coordinate
:param top_vertex: top right vertex coordinate of boundary
:param bottom_vertex: bottom left vertex coordinate of boundary
:param obs_number: number of ... | b0203b782e7655184c60d3a9e6c277ea880a2184 | 35,925 |
import logging
import time
def build_uncertain_table(args, scores, timestamp_list, image_path_list):
"""phase 3: build table from detection prediction"""
logging.info('phase 3 start.')
start = time.time()
uncertain_scores = build_uncertain_table_fast(scores)
save_uncertain_table(timestamp_list, u... | 43edf61e10718dc6156060851fed3173ab47159f | 35,926 |
import glob
import os
def get_current_phase(path):
"""Returns the current phase of the current iteration"""
files = glob.glob(path + "/phase_*.sh")
phases = [0]
for file in files:
file = os.path.basename(file)
phase = file.split(".")[0] # -> phase_x
phase_num = int(phase.split... | 4f8f74fa4d71551c27f7757c28990a7b00fea55b | 35,927 |
def mvg_logpdf_fixedcov(x, mean, inv_cov):
"""
Log-pdf of the multivariate Gaussian where the determinant and inverse of the covariance matrix are precomputed
and fixed.
Note that this neglects the additive constant: -0.5 * (len(x) * log(2 * pi) + log_det_cov), because it is
irrelevant when comparin... | 3b2d256d58f9dce655d8aae9e04f3f4f87030981 | 35,928 |
def exec_flat_python_func(func, *args, **kwargs):
"""Execute a flat python function (defined with def funcname(args):...)"""
# Prepare a small piece of python code which calls the requested function
# To do this we need to prepare two things - a set of variables we can use to pass
# the values of argume... | 9c494daec2172fe59d65e625cc3a1b98656de5df | 35,929 |
def robust_scale(df):
"""Return copy of `df` scaled by (df - df.median()) / MAD(df) where MAD is a function returning the median absolute deviation."""
median_subtracted = df - df.median()
mad = median_subtracted.abs().median()
return median_subtracted/mad | ba9ce747612c99997d890930e7ac7c582ba1af70 | 35,930 |
def generate_context_menu_mainmenu(menu_id):
"""Generate context menu items for a listitem"""
items = []
if menu_id == 'myList':
items.append(_ctx_item('force_update_mylist', None))
return items | d1cd169ec71a33c7bccf50b611d61b533ebcca54 | 35,931 |
def unroll_edges(domain, xgrid):
"""If necessary, "unroll" intervals that cross boundary of periodic domain.
"""
xA, xB = domain
assert all(np.diff(xgrid) >= 0)
assert xA < xB
assert xA <= xgrid[0]
assert xgrid[-1] <= xB
if xgrid[0] == xA and xgrid[-1] == xB:
return xgrid
... | 274c446f60bb953e2b864bed06e23bb0153fac94 | 35,932 |
def create_correct_bias_pipe(params={}, name="correct_bias_pipe"):
"""
Description: Correct bias using T1 and T2 images
Same as bash_regis.T1xT2BiasFieldCorrection
Params:
- smooth (see `MathsCommand <https://nipype.readthedocs.io/en/0.12.1/\
interfaces/generated/nipype.interfaces.... | ec0be858a900f1b38ea6c61d9bcbcb8ac8535b85 | 35,933 |
def infer_gaps_in_tree(df_seq, tree, id_col='id', sequence_col='sequence'):
"""Adds a character matrix to DendroPy tree and infers gaps using
Fitch's algorithm.
Infer gaps in sequences at ancestral nodes.
"""
taxa = tree.taxon_namespace
# Get alignment as fasta
alignment = df_seq.phylo.to_... | ccd71b7b10977441a25a7d753f5a228287ed8d28 | 35,934 |
def _sklearn_booster_to_model(booster: GradientBoostingClassifier):
"""
Load a scikit-learn gradient boosting classifier as a Model instance. A multiclass booster gets turned into a one-vs-all representation inside the JSON.
.
Parameters
----------
booster : sklearn.ensemble.Grad... | 0c1b0cb1eb396db707d22a1bb509f3592647ddb2 | 35,935 |
def encode_utf8_with_error_log(arg):
"""Return byte string encoded with UTF-8, but log and replace on error.
The text is encoded, but if that fails, an error is logged, and the
offending characters are replaced with "?".
Parameters
----------
arg : str
Text to be encoded.
Returns
... | 9a2ebc69f00220cfba92e0561b526ee3b5fd23d2 | 35,936 |
def gearys_c(adata, vals):
"""
Compute Geary's C statistics for an AnnData.
Adopted from https://github.com/ivirshup/scanpy/blob/metrics/scanpy/metrics/_gearys_c.py
:math:`C=\\frac{(N - 1)\\sum_{i,j} w_{i,j} (x_i - x_j)^2}{2W \\sum_i (x_i - \\bar{x})^2}`
Parameters
----------
... | ea5441619f0c893242b6d851781251b93a63b05d | 35,937 |
def eq_kinematic_src():
"""
Factory associated with EqKinSrc.
"""
return EqKinSrc() | be15e4169f90c28323dd881f67b325182547ad43 | 35,938 |
def generate_materials_string(materials, mtlfilename, basename):
"""Generate final materials string.
"""
if not materials:
materials = { 'default': 0 }
mtl = create_materials(materials, mtlfilename, basename)
return generate_materials(mtl, materials) | e417a3a07576b61dd6b247251834e2c04ba0a947 | 35,939 |
def _mktyperef(obj):
"""Return a typeref dictionary. Used for references.
>>> from jsonpickle import tags
>>> _mktyperef(AssertionError)[tags.TYPE].rsplit('.', 1)[0]
'exceptions'
>>> _mktyperef(AssertionError)[tags.TYPE].rsplit('.', 1)[-1]
'AssertionError'
"""
return {tags.TYPE: '%s.%... | 0c89d3771c3531773e13475487b4466231e067e5 | 35,940 |
from typing import Tuple
def colorize(img: np.ndarray, color: Tuple) -> np.ndarray:
"""colorize a single-channel (alpha) image into a 4-channel RGBA image"""
# ensure color to RGBA
if len(color) == 3:
color = (color[0], color[1], color[2], 255)
# created result image filled with solid "color... | 88e79e7adc7785f1391db94c9a295b2a55943c7c | 35,941 |
def prepare_template_stream(stream, base_url):
"""Prepares the stream to be stored in the DB"""
document_tree = _get_document_tree(stream)
_make_links_absolute(document_tree, base_url)
return _serialize_stream(document_tree) | 1072573ee0856725351a015f104e20604ef3b9c3 | 35,942 |
def _jsarr(x):
"""Return a string that would work for a javascript array"""
return "[" + ", ".join(['"{}"'.format(i) for i in x]) + "]" | 9c9b6df65bf4c01fa1c321445bbb7c86c6d28c5a | 35,943 |
import torch
def class_channels_to_rgb(input_batch, output_batch, label_batch):
""" Converts multichannel tensor to RGB image -- i.e. model output to final mask. """
# colors = get_color_encoding_CamVid()
colors = get_color_encoding_Elements()
rgb_batch_size = list(output_batch.size())
rgb_batch_... | 4aef60d85fbe1a6ed87a50409f36918d9cf7c717 | 35,944 |
def PyApp_SetMacPreferencesMenuItemId(*args, **kwargs):
"""PyApp_SetMacPreferencesMenuItemId(long val)"""
return _core_.PyApp_SetMacPreferencesMenuItemId(*args, **kwargs) | 7704e544439e27f362fe703027af7d393e1ab647 | 35,945 |
def isnum(value):
"""
Check if a value is a type of number (decimal or integer)
value:
The value to check
"""
try:
return bool(isinstance(value, (float, int)))
except BaseException:
return False | 6116d0d4c61f5f3afefe311b06d9b23b03ab9bfc | 35,946 |
import httpx
import json
async def _async_json_object(api_call):
"""async function to make a request to the wiki api and return a json object with article information
Args:
api_call (text): link to the api call
Returns:
dict: json content from the api call
"""
asy... | a047bbf16af314bd96c5d12c03ff8c2d0a16d17b | 35,947 |
def yices_model_set_bv_int64(model, var, val):
"""Assign an integer value to a bitvector uninterpreted term.
"""
return libyices.yices_model_set_bv_int64(model, var, val) | 5575adec31c297ff685884283bce7d1d9c47e386 | 35,948 |
def generate_histograms(
num_users: int,
counts_iid_param: float,
avg_count: float,
ref_distribution: np.ndarray,
hist_iid_param: float,
rng=np.random.default_rng()) -> np.ndarray:
"""Generate histograms with different total counts and distributions.
Args:
num_users: An integer indicati... | 0e3c00a908cfe139dd273326d63a2c18cd0b1294 | 35,949 |
import sys
import array
def editable_str(initial_str):# -> array
"""Exactly the same as array.array except that it switches types based on Python Version:
Python 2: character, one byte
Python 3: unicode, two to four bytes"""
array_type = 'u'
if sys.version_info < (3, 0):
array_type = 'c'
... | e901b1a9dffd7570448421f56390c4bdb778f139 | 35,950 |
import requests
def neo_create_bucket(**kwargs):
"""Create a bucket with headers.
:param auth: Tuple, consists of auth object and endpoint string
:param acl: Input for canned ACL, defaults to "private"
:param policy_id: String represent `x-gmt-policyid` or determines how data in the bucket will be di... | 52d5c6f3c49731c7fdfa95e85cdfc06c96a84f0a | 35,951 |
def babi_handler(data_dir, task_number):
"""
Handle for bAbI task.
Args:
data_dir (string) : Path to bAbI data directory.
task_number (int) : The task ID from the bAbI dataset (1-20).
Returns:
BABI : Handler for bAbI task.
"""
task = task_list[task_number - 1]
retur... | 12472b8b2430bf7d04e51e41f1b34124583935aa | 35,952 |
def _full_ner(text_analyzer):
"""
Run complete NER.
This includes extraction of different entity types and geotagging.
:param class text_analyzer: the text_analyzer of nlp_components
:return dict: json with persons, geotagged locations and metadata,
readalbe by the viewer
"""
named... | e182e4d4bbc61ce9d7eca52b1a490d8705a81808 | 35,953 |
def frequency_impulse_response(magnitudes: tf.Tensor,
window_size: int = 0) -> tf.Tensor:
"""Get windowed impulse responses using the frequency sampling method.
Follows the approach in:
https://ccrma.stanford.edu/~jos/sasp/Windowing_Desired_Impulse_Response.html
Args:
magnit... | 9e307778511b7fb4b79caab338b016a1f7e91120 | 35,954 |
def parse_station_list_to_json(filepath_or_buffer) -> str:
""" Return JSON-formatted data """
return _parse_station_list(filepath_or_buffer).to_json(orient="records") | 32d3e6e122433692a1dd16bd3750cda528c70596 | 35,955 |
from typing import List
from typing import Callable
from typing import Set
import numpy
def get_minimal_intactness_ls_centralities(
nodes: List[Node], definitions: Definitions,
get_ill_behaved_weight: Callable[[Set[Node]], float],
get_mu: Callable[[numpy.array], float]
) -> numpy.array... | ac2d01a8d5ba1842379044c77398c93292e8a4f9 | 35,956 |
def inverse_sigmoid_numpy(x):
"""
.. todo::
WRITEME
"""
return np.log(x / (1. - x)) | 17332beacc0a6b097dfa82c8f150981eda77a722 | 35,957 |
def join_kwargs(**kwargs) -> str:
"""
Joins keyword arguments and their values in parenthesis.
Example: key1{value1}_key2{value2}
"""
return "_".join(key + "{" + value + "}" for key, value in kwargs.items()) | 3054573ec51676bb8d93e2fcabd4cb5097e4b897 | 35,958 |
def zipped_lambda_function():
"""Return a simple test lambda function, zipped."""
func_str = """
def lambda_handler(event, context):
print("testing")
return event
"""
zip_output = BytesIO()
with ZipFile(zip_output, "w", ZIP_DEFLATED) as zip_file:
zip_file.writestr("lambda_function.py", f... | 38f2862c4e9401a32866ec54d5db420265bda2a1 | 35,959 |
def byte_builtin():
"""byte: Immutable bytes array."""
return bytes("\xd0\xd2NUT", "utf-8").decode() | 62e0d556d20ece651adb0a13b11d07670e0ea4f6 | 35,960 |
import torch
def predict_interaction(model, n0, n1, tensors, use_cuda):
"""
Predict whether a list of protein pairs will interact.
:param model: Model to be trained
:type model: dscript.models.interaction.ModelInteraction
:param n0: First protein names
:type n0: list[str]
:param n1: Secon... | 2c7ffecbd4435c7a5df620f895e9f3c299684494 | 35,961 |
def hessian(f, varlist, constraints=[]):
"""Compute Hessian matrix for a function f wrt parameters in varlist
which may be given as a sequence or a row/column vector. A list of
constraints may optionally be given.
Examples
========
>>> from sympy import Function, hessian, pprint
>>> from s... | 7033abc3342d3de4efb91a3a461b8c09632bc395 | 35,962 |
def extract_column_names(row_list):
"""
Extract names of columns from row list obtained from table csv. The first row contains all row names
:param row_list: List of all rows in csv used for table creation
:return: List of names present in table csv
"""
return row_list[0] | 2adef82a7f583c262922ad28aa0de47b8b9b5e51 | 35,963 |
def index_to_point(index, origin, spacing):
"""Transform voxel indices to image data point coordinates."""
x = origin[0] + index[0] * spacing[0]
y = origin[1] + index[1] * spacing[1]
z = origin[2] + index[2] * spacing[2]
return (x, y, z) | 072f1ad5d1adc1e81d4771725475f6a07f32f3ce | 35,964 |
def MakeEmptyTable(in_table=[[]], row_count=0, column_count=0):
"""
1 Read in *in_table*
2 Create an empty table
of '' values,
which has with the same number of rows and columns as the table,
(where columns based on the first row).
3 If the user has specified *row_count* and/or ... | 55b32c2914cb8e2194999e1b8ba2211373ef1bcb | 35,965 |
import torch
def choose_pseudo_gt(boxes, cls_prob, im_labels):
"""Get proposals with highest score.
inputs are all variables"""
num_images, num_classes = im_labels.size()
boxes = boxes[:,1:]
assert num_images == 1, 'batch size shoud be equal to 1'
im_labels_tmp = im_labels[0, :]
gt_b... | 7565a9a72052c839cad5cf12033fdf144bfcb4b0 | 35,966 |
import argparse
import os
import tqdm
def create_submission(args: argparse.Namespace, cfg: CfgNode) -> str:
"""inferece models and save prediction
Args:
args (argparse.Namespace): argparse namespace
cfg (CfgNode): cfg for parameters
Returns:
str: path to the saved prediction
... | 77ea2bca6620cee102dc7d50151073b494d994bf | 35,967 |
def transform_covariance_matrix(transformation, histogram):
"""
Compute the covariance matrix of a new histogram given by:
new_histogram = transformation * histogram
"""
A = transformation
V = compute_numpy_covariance_matrix(histogram)
return A * V * A.T | f75918f83fdc8b06ab6c4a2b973a02549cb286d5 | 35,968 |
import math, random
def split_list(data, splits={'train': 8, 'test': 2}, shuffle=True, seed=0):
"""
Split the list according to a given ratio
Args:
data (list): a list of data to split
splits (dict): a dictionary specifying the ratio of splits
shuffle (bool): shuffle the list befo... | d9b25512e666a03ec2b589850c47a45231b279a0 | 35,969 |
def decoder_gen(
original_input: tuple,
decoder_config: dict
):
"""
Create the architecture for the VAE decoder
"""
decoder_inputs = keras.layers.Input(shape=[decoder_config["latent_dim"]])
print("decoder_inputs", decoder_inputs._keras_shape)
# Reshape input to be an image
#for ori... | fdcc683d48c795fba7e25c4ce47bf71a9459b6e4 | 35,970 |
from typing import List
from typing import DefaultDict
from typing import Set
def get_ents_by_label(
data: List[Example], case_sensitive: bool = False
) -> DefaultDict[str, List[str]]:
"""Get a dictionary of unique text spans by label for your data
# TODO: Ok so this needs to return more than just a set ... | ed5b93ed577417fffab823a1a44bad3a7aa506e7 | 35,971 |
def root():
"""
root - returns 200 OK
"""
return Response("It's alive!", status=200) | 5fa735a9cc2242928165d4e7c58e7ac576973044 | 35,972 |
def testplugin(module):
"""returns True if module has nessesary parameters"""
logger.write( module.title )
return True | c2e66b38735e43e16f351fde5d59a43d71a0b734 | 35,973 |
import requests
def put(*args, **kwargs):
"""Sends an HTTP PUT Request.
:param args: URL argument on the first index(args[1]).
:param kwargs: Optional arguments that ``requests.put`` takes.
:returns: :class:`Response` object or `None` if an error occurred.
:rtype: :class:`requests.Response` or `... | dc2e01aa6f6c293264b0435eeca9adf17a95820d | 35,974 |
async def save_transform(data):
"""
Calculate the transormation matrix that calibrates the gantry to the deck
:param data: Information obtained from a POST request.
The content type is application/json.
The correct packet form should be as follows:
{
'token': UUID token from current sessio... | 696b92dbba0c220988129d64d14f5d1f9e2ab828 | 35,975 |
import os
def data_trending_dashboard(start=default_start, end=now):
"""Bulilds dashboard
Parameters
----------
start : time
configures start time for query and visualisation
end : time
configures end time for query and visualisation
Return
------
plot_data : list
... | 4811398f7754f473bfe0c0152c3cb85f0b6aee4b | 35,976 |
import os
def dl2_to_sensitivity(
dl2_dir,
log_from_dl1_dl2,
gamma_offset="off0.0deg",
prod_id=None,
source_env="",
wait_jobs_dl1_dl2="",
):
"""
Function to run the `script_dl2_to_sensitivity` for the gamma (and the different gamma offsets) and gamma-diffuse
particles.
Creates ... | afade682b3e4ca99ecacf6a00bbf8ae6f02e990c | 35,977 |
import unittest
def test_with_html():
"""Runs the unit tests with html."""
tests = unittest.TestLoader().discover('./app/test', pattern='test*.py')
outfile = open("./Report.html", "wb")
runner = HTMLTestRunner(
stream=outfile,
title='Unit Test Backend Report',
... | 2067907861625118e4cec54d1d02638b6b41cac2 | 35,978 |
def cpm_what_channels(folder):
"""Helper functions to print available channels given a folder with
polysomnography files"""
pg = PSGcompumedics(folder)
return pg.available_channel | d8b494ff93917769272036d2eac570838f8aca5d | 35,979 |
def all_close(goal, actual, tolerance):
"""
Convenience method for testing if a list of values are within a tolerance of
their counterparts in another list
@param: goal A list of floats, a Pose or a PoseStamped
@param: actual A list of floats, a Pose or a PoseStamped
@param: tolerance ... | a6aa910798e8ad6516274cee49bc2c2445afc5d3 | 35,980 |
def cloud_to_clusters(cloud: np.array) -> np.array:
"""Converts provided cloud into a set of clusters."""
clusters = defaultdict(lambda: [])
for vertex in cloud:
center = tuple(vertex[6:9])
if center != (0, 0, 0):
clusters[center].append(vertex)
print(' * [INFO]', len(list(c... | 1a7d5396e238b622e2dd3339990c6d876e689503 | 35,981 |
def split_remote_url(url):
"""check if the user provided a local file path or a
remote. If remote, try to strip off protocol info.
"""
remote_url = is_remote_url(url)
if not remote_url:
return url
for prefix in REMOTE_PREFIXES:
url = url.replace(prefix, '')
if '@' in ur... | e672d79a7b00f1a0c6928869efe5ca811673edaa | 35,982 |
import json
def load_json(filename, user_limit=0, badge_limit=0):
"""
Loads data form JSON
"""
with open(filename) as f:
data = json.loads(f.read())
if user_limit:
data['transactions'] = data['transactions'][:user_limit]
if badge_limit:
data['badges'] = data['badges'][:... | 79890f86dcc0090f89b7c401959e3e2dfc86828c | 35,983 |
def lon2decimal(input):
"""
Convert a Longitude into decimal angle.
"""
lng_degrees = int(input[0:3])
lng_minutes = int(input[3:5])
lng_seconds = float(input[5:-2])
lng_negative = ('W' == input[-1])
return dms2decimal(lng_degrees, lng_minutes, lng_seconds, lng_negative) | 557af79e9ef4fcd773ab28fea4efa99fbeb1ed38 | 35,984 |
def get_coordinate_color(color_image, coordinate, colors: list):
"""
Takes a color_image, coordinate (tuple), and list of color thresholds in HSV.
If the pixel is within a color threshold, return the index of the color in the list.
Coordinate should be in y, x format.
"""
try:
if coordinate == None:
return... | 3dade67def84f6fde70df3a7241e410c306a3806 | 35,985 |
def constraint_wrapper(fun, constraint):
"""
Wrap a function such that it's first argument is constrained
"""
def inner(x, *args, **kwargs):
"""the wrapped function"""
return fun(constraint(x), *args, **kwargs)
return inner | f9055fe2cd269e4586c545bfa951bdf2ba0677c1 | 35,986 |
import os
import base64
def extract_zip(base64String, extract_to):
"""
1. Decode base64String to zip
2. Extract zip to files
"""
# Decode base64String to zip
if not os.path.exists(extract_to): os.makedirs(extract_to)
zipfile_path = extract_to + "/package.zip"
with open(zipfile_path, "... | 9a53493c0c202dc0ef7cb3dd804dba92ebe7984f | 35,987 |
import collections
def parse():
"""Parse input. Return the polymer template, its pairs, and the rules."""
with open('../data/day14.txt') as f:
polymer = f.readline().strip()
pairs = collections.defaultdict(int)
for i in range(len(polymer) - 1):
pairs[polymer[i:i+2]] += 1
... | b1e00b1176a6e60362af47e0fda9113104ed8083 | 35,988 |
def tourney_key(proto_obj):
"""Build a key from a scores_messages.Tournament protobuf object."""
return tourney_key_full(proto_obj.id_str) | 768a1e84b7ce95ec65450cb6775355b48704b335 | 35,989 |
import re
def timedelta_from_duration_string(s):
"""Convert time duration string to number of seconds."""
if re.match(r'^\d+$', s):
return timedelta(seconds=int(s))
td_args = {}
for part in s.split(','):
part = part.strip()
m = re.match(r'(\d+)\s+(year|month|day|hour|minute|se... | b3c7bd937a40057518cfb19eb2af2ff3bb070602 | 35,990 |
import functools
def wrapped(wrapped_function, additional_result_wrapper=None, self_index=0):
"""
Using these decorators will take care of unwrapping and rewrapping the target object.
Thus all following code is written as if the methods live on the wrapped object
Also perfect to adapt free functi... | 0f5c024109ecab4103d70feb5c04a84323180d30 | 35,991 |
import os
import torch
import logging
def train(model, optim, sche, db, opt, exp_id):
"""
Args:
model: the model to be trained
optim: pytorch optimizer to be used
db : prepared torch dataset object
opt: command line input from the user
exp_id: experiment id
"""
... | 2f9d5bf71ef8be25f947445daacde18ea47712ef | 35,992 |
def format_y_ticks_as_percents(plot):
"""Formats y ticks as nice-looking percents.
Args:
plot: matplotlib.AxesSubplot object.
"""
y_ticks = plot.get_yticks()
plot.set_yticklabels(get_percent_strings(y_ticks))
return plot | 5d45ede7fa51fbe9d18fd9cd2130714035b3e6a1 | 35,993 |
def can_cast(value, class_type):
"""
Check if the value can be cast to the class_type, used in the parse tcl string function for tcl expressions like
[Ada inputs 0] or [Ada alias robotblur]
Args:
value (object): The object we're attempting to cast.
class_type (class): The class we're at... | 85c415d2eaadb16a532e209110c1fd0f778cb681 | 35,994 |
def conv(
value,
filter_size,
output_dim,
dropout=0.0,
activation="linear",
padding="valid",
weight_source=None):
"""
Perform a single scale of convolution and optionally add spatial dropout.
"""
if weight_source is None:
conv_layer = Conv1... | 19b002b5059ff28da5cdf255fde8547e3597852c | 35,995 |
def insert_row(dataset_name, position, validate=False):
"""Create instance of insert row command.
Parameters
----------
dataset_name: string
Name of the dataset
position: int
Index position where row is inserted
validate: bool, optional
Validate the created command speci... | bc2fed6ce8a6a50a21bb1e8c60d067518722a628 | 35,996 |
def build(argmap, data):
"""Builds an array of arguments from the provided map and data.
The argmap must consist of a mapping of keys to argbuilder functions.
keys in the argmap are indexed into data, and if they are present, the
corresponding values are passed to the corresponding argmap function. Th... | d9c6c9eede6d6a9ae36fea77dceb3c70cbfbdbbd | 35,997 |
def get_prox_dist_points_cll(ppCll1, key=[]):
""" get prox and dist ends of the centerline assuming that origin is cranial,
right, anterior
"""
# Get prox and dist point for ppCll1
pends1 = np.array([ppCll1[0], ppCll1[-1]]) # pp centerline is in order of centerline points
if 'vLRA' in key:
... | 418f7aea0d7fba1fa3201deed02f61c0e7c4a804 | 35,998 |
def proxy_a_distance(source_X, target_X, verbose=False):
"""
Compute the Proxy-A-Distance of a source/target representation
"""
nb_source = np.shape(source_X)[0]
nb_target = np.shape(target_X)[0]
if verbose:
print('PAD on', (nb_source, nb_target), 'examples')
multiple_search = Fals... | 1bdb183717d16daad4377436376ee2d0118fc7e4 | 35,999 |
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