content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def _hist2d_add(list_results: list):
"""
Quick helper function that we can submit to dask cluster to sum the results of running hist2d_numba_seq on multiple
chunks of data.
Parameters
----------
list_results
list, list of numpy ndarray histograms that we want to sum to get global result... | e00efe2716e83b26354c0f6ca3c339a470921592 | 3,625,455 |
def dwnld_worker(qtbot, mocker, workdir):
"""A fixture for the WeatherDataGapfiller."""
dwnld_worker = RawDataDownloader()
return dwnld_worker | c3f0d6b57d8a39c7e90e202106a6708d662f1031 | 3,625,456 |
import re
def camel_case_to_title_case(camel_case_string):
"""
Turn Camel Case string into Title Case string in which first characters of
all the words are capitalized.
:param camel_case_string: Camel Case string
:return: Title Case string
"""
if not isinstance(camel_case_string, str):
... | bcc40753a8672355519741f5aec64c262431d582 | 3,625,457 |
def add_subplot_axes(ax, rect):
"""
Plotting utility
"""
fig = plt.gcf()
box = ax.get_position()
width = box.width
height = box.height
inax_position = ax.transAxes.transform(rect[0:2])
transFigure = fig.transFigure.inverted()
infig_position = transFigure.transform(inax_position)
... | c91126c81e1608e35ef3a7aa48ae9ea9da4ea684 | 3,625,458 |
def starfind(data, snr, background, noise, fwhm, mask=None, box_size=35,
sharp_limit=(0.2, 1.0), round_limit=(-1.0, 1.0),
logger=logger):
"""Find stars using daofind AND sexfind."""
# First, we identify the sources with sepfind (fwhm independent)
sources = sepfind(data, snr, backgr... | 1f9792f75a9a0d877993260118d7ca95480a85ba | 3,625,459 |
import torch
def displace(a, delta):
"""
fns = {
-1: lambda x: -x,
0: lambda x: 1 - np.abs(x),
1: lambda x: x,
}"""
delta_x, delta_y = delta[:, :, :, 0], delta[:, :, :, 1]
delta_x = delta_x.unsqueeze(3)
delta_y = delta_y.unsqueeze(3)
# BatchSize x Height X Width x 3
x_multipliers = torch.relu(torch.cat(... | cf8819193d331c7edcb1a90989bf0fb2e3896370 | 3,625,460 |
def inferType(fname):
"""Return the type of the given X5 file - either ``'linear'`` or
``'nonlinear'``.
:arg fname: Name of a X5 file
:returns: ``'linear'`` or ``'nonlinear'``
"""
with h5py.File(fname, 'r') as f:
ftype = f.attrs.get('Type')
if ftype not in ('linear', 'nonlin... | 7be135f126dc20da70c6fb0f9e2d5f2f5ebc0a1c | 3,625,461 |
def pmr_corr(vlos, r, d):
"""
Correction on radial proper motion due to apparent contraction/expansion
of the cluster.
Parameters
----------
vlos : float
Line of sight velocity, in km/s.
r : array_like, float
Projected radius, in degrees.
d : float
Cluster distan... | c9136c5ae33e89d6f57b936b92c23001fd30ccfa | 3,625,462 |
def get_min_corner(sparse_voxel):
"""
Voxel should either be a schematic, a list of ((x, y, z), (block_id, ?)) objects
or a list of coordinates.
Returns the minimum corner of the bounding box around the voxel.
"""
if len(sparse_voxel) == 0:
return [0, 0, 0]
# A schematic
if len(... | 371b25b795a1a2ffeb0fc3724f01f1a329f917ae | 3,625,463 |
def ExtractListsFromVertices(vertexProp, g):
"""
Method to extract the lists at each vertex of a vertex property,
vertexProp, belonging to a graph, g, and to return a list of lists,
where each sub-list is a list of values from each vertex
:param vertexProp:
:param g:
:return:
... | fd10e9285f9382511a526468e41e8a516a6f50bd | 3,625,464 |
def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
"... | 651532a7337322171f8fcf8874048a5b840505cf | 3,625,465 |
def _traverse_dirtable(rsrc, off, rtype):
"""Recursively traverse the dirtable, returning all data entries under the given type id."""
# resource directory header
resdir = _IMAGE_RESOURCE_DIRECTORY.from_bytes(rsrc, off)
number = resdir.NumberOfNamedEntries + resdir.NumberOfIdEntries
# followed by re... | 51de90b056fae726faea0732f3fcd09ba8ec1db8 | 3,625,467 |
def i18n_enabled():
"""
Return the projects i18n setting
"""
return getattr(settings, "USE_I18N", False) | 35931fcfc6fbfd19508024c8fa3405d82bd59dfb | 3,625,468 |
def delete(request, testplan_id, rule_id):
"""
Delete test plan based on testplan_id.
"""
dbc = db_model.connect()
try:
testplan = dbc.testplan.find_one({"_id": ObjectId(testplan_id)})
except InvalidId:
return HttpResponseNotFound("testplan '%s' not found" % testplan_id)
if t... | 473d87f8ea5f586e8e1c6803d3bc10bdcb3e5f49 | 3,625,470 |
def _third_order(B3, Y_res, C3, R, n3, m3, lambdax):
"""Compute third order sensitivities."""
Y_ijk = np.zeros((R, n3)) # Initialize 1st order contributions
T3 = np.zeros((m3, R, n3)) # Initialize T(emporary) matrix - 1st
# First order individual estimation
for j in range(n3):
# Re... | ebc33133cdb8fa5006c47621b1f867230cc9d73d | 3,625,471 |
from typing import Optional
def _query_statistics(
total_queries_name: str = TOTAL_QUERIES_NAME,
total_documents_name: str = TOTAL_DOCUMENTS_NAME,
min_documents_name: str = MIN_DOCUMENTS_NAME,
max_documents_name: str = MAX_DOCUMENTS_NAME,
eval_config: Optional[config_pb2.EvalConfig] = None,
mo... | 4472dd35220a2788005f366b46a5275dcc3b207d | 3,625,473 |
def get_scrapyd(client):
"""
get scrapyd of client
:param client: client
:return: scrapyd
"""
if not client.auth:
return ScrapydAPI(scrapyd_url(client.ip, client.port))
return ScrapydAPI(scrapyd_url(client.ip, client.port), auth=(client.username, client.password)) | 40c0fd8afb4b499aaf05264f66d0a6558109f5c2 | 3,625,474 |
def byteListToU32leList(data):
"""Convert a list of bytes to a list of 32-bit integers (little endian)"""
res = []
for i in range(len(data) / 4):
res.append(data[i * 4 + 0] |
data[i * 4 + 1] << 8 |
data[i * 4 + 2] << 16 |
data[i * 4 + 3] << 24... | a06cdb918070837728c5f6100ca237bab4a4773f | 3,625,475 |
def normalize_fr(fr):
"""Normalize an input flavor combination to a flavor ratio.
Parameters
----------
fr : list, length = 3
flavor combination
Returns
----------
numpy ndarray flavor ratio
Examples
----------
>>> from fr import normalize_fr
>>> print(normalize_fr... | b290c0e03b862306d995c5426b833d701b4093d1 | 3,625,477 |
def max_row_by_row(arrays,NaN=False):
"""Perform row by row min"""
if NaN:
rowmax=[max_w_nan(arr) for arr in arrays]
else:
rowmax=[max(arr) for arr in arrays]
return rowmax | 5897efc74f473d1d2d40f8479df11ce0f08c607d | 3,625,479 |
def is_palindrome_letters_only(str):
"""
Confirm Palindrome, even when string contains non-alphabet letters and ignore capitalization.
casefold() method, which was introduced in Python 3.3, could be used instead of this older method,
which converts to lower().
"""
i = 0
j = hi = len(str) - ... | 5f95add0ecf3fbe31af2b9cd0e27aac36b2c3985 | 3,625,481 |
def extract_std_bandwidth(spectrogram, doppler_bins, render = False, render_time = None, idstr = None):
""" Extracts the mean time between peaks in the spectrogram.
"""
spectrogram, doppler_bins = clean_spectrogram(spectrogram, doppler_bins)
bandwidth = get_bandwidth(spectrogram, doppler_bins)
std_b... | 1a0c83786825fb54e6c3c285becdd6adbf9e29a8 | 3,625,482 |
import json
def Schedule(name, config, scheduled_by, executor_requirements, priority=0):
"""Adds a new Task with given name, config, user and requirements."""
webhook = json.loads(config)['task'].get('webhook', None)
task = Task(parent=MakeParentKey(),
name=name,
config=config,
... | 73924c5a590a4b9791673f16f52a0a19f03c743f | 3,625,483 |
import time
import logging
def createResponseBody(lines, context, client, lang='en'):
"""Parse the **lines** from an incoming email request and determine how to
respond.
:param list lines: The list of lines from the original request sent by the
client.
:type context: class:`bridgedb.email.ser... | 663bdf2b90a471b4b65db26de7d8165f6a6ec2f0 | 3,625,484 |
from typing import Any
def validate_delta(delta: Any) -> float:
"""Make sure that delta is in a reasonable range
Args:
delta (Any): Delta hyperparameter
Raises:
ValueError: Delta must be in [0,1].
Returns:
float: delta
"""
if (delta > 1) | (delta < 0):
raise ... | 1fd3084c047724a14df753e792b8500a103a34c0 | 3,625,485 |
def deparagraph(element: Element, doc: Doc) -> Element:
"""Panflute filter function that converts content wrapped in a Para to
Plain.
Use this filter with pandoc as::
pandoc [..] --filter=lander-deparagraph
Only lone paragraphs are affected. Para elements with siblings (like a
second Para... | aff8bdb03baa8427f4026c22a125d286a1fb323b | 3,625,486 |
def get_residual_loss(query_images, encoded_images, params):
"""Gets residual loss between query and encoded images.
Args:
query_images: tensor, query image input for predictions.
encoded_images: tensor, image from generator from encoder's logits.
params: dict, user passed parameters.
... | 2d3614a6f1b0d99f7b7a8907d0a90d1b1ce69cbc | 3,625,488 |
def directed_connected_components(digr):
""" Returns a list of strongly connected components
in a directed graph using Kosaraju's two pass algorithm """
if not digr.DIRECTED:
raise Exception("%s is not a directed graph" % digr)
finishing_times = DFS_loop(digr.get_transpose())
# use finishing... | 4e0b69aa6d7feeccafc94354210568c21ecbc775 | 3,625,489 |
from typing import Any
async def leave_group_by_id(id: str, number: str) -> Any:
"""
leave a group by id
"""
cmd = ["-u", quote(number), "quitGroup", "-g", quote(id)]
await run_signal_cli_command(cmd)
return id | c0fc42a00a67e50c53a5f1608516a066ec2d3141 | 3,625,490 |
from datetime import datetime
def capacitytoactivity(trade, outPutFile, input_data):
"""
builds the CapacityToActivityUnit (Region, Technology, CapacitytoActivityUnit)
-------------
Arguments
trade, outPutFile, input_data
outPutFile: is a string containing the OSeMOSYS parameters file
... | 88560b00505c31e8f438206e4ff6da03362df634 | 3,625,491 |
import numpy as np
def molpos_1Dbin(data,bins,diameter):
"""Creates a 1D histogram from X,Y location data of a single tracked molecule over time
Args:
data (pandas dataframe): time series 2D location data of a tracked molecule
bins (int): # of rectangular bins to discretize cell with (bin... | fbc05b9c14d82c19f62ae30ec1d05b7e314b57e8 | 3,625,492 |
import test
def suite():
""" A test suite for the ITU-P Recommendations. Recommendations tested:
* ITU-P R-676-9
* ITU-P R-676-11
* ITU-P R-618-12
* ITU-P R-618-13
* ITU-P R-453-12
* ITU-P R-837-6
* ITU-P R-837-7
* ITU-P R-838-3
* ITU-P R-839-4
* ITU-P R-840-4
* ITU-P R... | c1b87ef1781945e0b7ffb3577959176ea28b4469 | 3,625,493 |
def build_authenticate_header(realm=''):
"""Optional WWW-Authenticate header (401 error)"""
return {'WWW-Authenticate': 'OAuth realm="%s"' % realm} | 74c2e4e4188b608120f241aadb20d8480ac84008 | 3,625,494 |
def add_ops(op_classes):
"""
Decorator to add default implementation of ops.
"""
def f(cls):
for op_attr_name, op_class in op_classes.items():
ops = getattr(cls, f"{op_attr_name}_ops")
ops_map = getattr(cls, f"{op_attr_name}_op_nodes_map")
for op in ops:
... | c2721444a5b211d2d7f3a78c61df3e68e143f3f4 | 3,625,495 |
def clear_history_fixture_test():
"""define a function that will run each time you pass it to a test, it is called a fixture"""
return Calculations.clear_history() | 6baac016824cef4d710366f376e4ea3ff2d74990 | 3,625,496 |
def get_bool_mask_from_ivar(ivar):
"""
Return mask determined by pixels that are nonzero in all ivar maps.
Parameters
----------
ivar : (..., nsplit, 1, ny, nx) ndmap
Inverse variance maps for N splits.
Returns
-------
mask : (ny, nx) bool enmap
Mask, True in ob... | 8776e4089dcb612cbe32e58f3a9d654c0a29e938 | 3,625,497 |
from typing import Optional
def check_if_function_can_be_run(
ctx: typer.Context, param: typer.CallbackParam, value: str
) -> Optional[str]:
"""Callback that validates if a function can be run"""
if not is_function_built(value):
raise typer.BadParameter(
f"Function - '{value}' is not a... | d6cff1f76c41acc7b7a1732536c7b50e2cd217ef | 3,625,498 |
import math
def random_rotate(X, y, rotation_range=0):
"""Randomly rotate centroids and appearances.
Args:
X (dict): Dictionary of feature data.
rotation_range (int): Maximum rotation range in degrees.
Returns:
dict: Rotated ``X`` data.
"""
appearances = X['appearances']
... | ad7b1bd47e4fd5e1cdda6bd6b192df67475ade0f | 3,625,499 |
def decorator_with_default_params(real_decorator, args, kwargs, default_args=None, default_kwargs=None):
"""
This function makes it easy to build a parametrized decorator, having a default value.
Construct your decorator like this:
>>> def decorator(*d_args, **d_kwargs): # d_args with d like decorator
... | 63e38bbdaea3483250db7281908a0053e072e866 | 3,625,500 |
import json
import copy
def get_args(request, required_args):
"""
Helper function to get arguments for an HTTP request
Currently takes args from the top level keys of a json object or
www-form-urlencoded for backwards compatability.
Returns a tuple (error, args) where if error is non-null,
the... | b44e058590945211ca410005a5be2405b4756ca4 | 3,625,501 |
def compute_ndcg_ps_parity_check(scores, labels, topk=10):
"""
adapter for pps ndcg calculating util
:param scores: raw scores
:param labels: actual labels, ordered numerically
:param topk: default is 10
:return: ndcg score as float
"""
return compute_ndcg_ps_parity_check_original_api(li... | de49eea462b7562c11fa1bfe5445578a3a739d32 | 3,625,502 |
import re
def create_table(request):
"""
创建餐桌
---
serializer: table.serializers.TableCreateSerializer
omit_serializer: false
responseMessages:
- code: 201
message: Created
- code: 400
message: Bad Request
- code: 401
message: Not authentic... | 25e6a62f26a26014c6f1b972628ed369f7cd14d5 | 3,625,504 |
def transl(tx,ty=None,tz=None):
"""Returns a translation matrix (M)
:param float tx: translation along the X axis
:param float ty: translation along the Y axis
:param float tz: translation along the Z axis
"""
if ty is None:
xx = tx[0]
yy = tx[1]
zz = tx[2]
else:
... | 7d79b5db7a64966738171fbcecc4e25f63804cf3 | 3,625,506 |
def find_most_sim(mesh, doc_id, top_n=10):
"""Find documents most similar to the given document."""
doc = mesh.doc_cache[doc_id]
results = []
doc_conc = list(map(lambda x: x[1], mesh.graph.out_edges(doc_id)))
for other_doc in mesh.doc_cache.values():
if other_doc._.id != doc_id:
... | 2eef0091bebb7dd8f4aa1a186020aafb6eefca7c | 3,625,508 |
def split_hosts_list(hosts_list, split_type, log_file=None):
"""
Return a list of multiple hosts list for the safe deployment.
:param hosts_list list: Dictionnaries instances infos(id and private IP).
:param split_type: string: The way to split the hosts list(1by1-1/3-25%-50%).
... | e30951f809700246ef142e206ca3a7a3cc89ce90 | 3,625,509 |
def load_panel_from_excel(excelfile):
"""Load a pandas Panel object by reading it from an Excel spreadsheet.
:param excelfile:
Path to Excel file.
:returns:
pandas Panel object.
"""
paneldict = {}
xl = pd.ExcelFile(excelfile)
for sheet in xl.sheet_names:
frame = pd.read_... | e4b2c7a63ed726997fca67fe0521cfe891ff1146 | 3,625,510 |
import tqdm
import requests
def tweets_request(tweets_ids):
"""
Make a request to Tweeter API
"""
df_lst = []
for batch in tqdm(tweets_ids):
url = "https://api.twitter.com/2/tweets?ids={}&&tweet.fields=created_at,entities,geo,id,public_metrics,text&user.fields=description,entities,id,... | a022504710096393403f6bf806fe2f12d20008be | 3,625,512 |
def prefix_to_netmask(in_prefix):
"""
Converts a prefix into a netmask
:param in_prefix: Cidr prefix n <= 32
:return: Netmask value
"""
prefix = int(in_prefix)
if prefix > 32 or prefix <= 0:
return None
return IPAddress(inet_ntoa(pack(">I", (0xffffffff << (32 - prefix)) & 0xffff... | 387d25ac1d568ebaa285556b5b8e99f964b3ccf9 | 3,625,513 |
import itertools
def compute_class_correspondence(predicted_domain, true_domain, verbose=True):
"""Compute the best match of two lists of labels among all permutations."""
def mismatches(domain1, domain2):
return (domain1 != domain2).sum()
def associate(domain, current_ids, new_ids):
new_... | 48e71dd115a150a26ece9ec4879b37f3a6699dce | 3,625,514 |
from typing import Union
from typing import Optional
from typing import Iterable
def l2norm(
mdata: Union[MuData, AnnData],
mod: Optional[Union[Iterable[str], str]] = None,
rep: Optional[Union[Iterable[str], str]] = None,
n_pcs: Optional[Union[Iterable[int], int]] = 0,
copy: bool = False,
) -> Opt... | 5a9130e48c2067acf7b366ff18fd5256473acb25 | 3,625,515 |
def topsis(df, weights, impacts):
"""
Validates dataframe, impacts, weights and calculates performance score.
Outputs a dataframe with "Performance Score" and "Rank" column appended.
"""
validate_data(df, weights, impacts)
ops_df = df.iloc[:,1:]
## normalized matrix
ops_df1 = ops_df ** 2
demoninat... | b8ecf2ee4e8f14a00a9a9a4408cb76f8d1bb1d3c | 3,625,516 |
def mergeGC_genecategories(GC_content_df, gene_categories):
"""merged GC content df with gene categories"""
# read in gene categories
gene_cats = pd.read_csv(gene_categories, sep="\t", header=None)
gene_cats.columns = ["AGI", "gene_type"]
# merge to limit to genes of interest
GC_content_categori... | a9b5f67d0fce5106a2c426309b896df8b2e139e1 | 3,625,517 |
def noise_lut_range(atracks, xtracks, noiseLuts):
"""
Parameters
----------
atracks: np.ndarray
1D array of atracks. lut is defined at each atrack
xtracks: list of np.ndarray
arrays of xtracks. list length is same as xtracks. each array define xtracks where lut is defined
noiseL... | 23e977eb83de87f1b24d892efc271e823b86ca05 | 3,625,518 |
async def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):
"""Set up this integration using UI."""
if hass.data.get(DOMAIN) is None:
hass.data.setdefault(DOMAIN, {})
_LOGGER.info(STARTUP_MESSAGE)
username = entry.data.get(CONF_USERNAME)
password = entry.data.get(CONF_PASSWORD... | 1d5e99281fdfa642c637fc8d85be86f11456c8b3 | 3,625,519 |
def view_shape(shape, view):
"""Return the shape of a view of an array
:param shape: Tuple describing shape of the array
:param view: View object -- a valid index into a numpy array, or None
Returns equivalent of np.zeros(shape)[view].shape
"""
if view is None:
return shape
shp = t... | 7b9d5321d84e5ca1f90b0531ca2d0837a90e9153 | 3,625,520 |
def get_requester_ip(request):
""" Get the IP address from a request """
x_forwarded_for = request.META.get('HTTP_X_FORWARDED_FOR')
if x_forwarded_for:
ip_addr = x_forwarded_for.split(',')[0]
else:
ip_addr = request.META.get('REMOTE_ADDR')
return ip_addr | f7cde3745fdd40947a1c63068802b0d79584d21d | 3,625,521 |
from typing import Dict
def _get_info_source(context) -> Dict:
"""Returns the current information source"""
return context.transaction_data[TransactionLoops.INFORMATION_SOURCE][-1] | d3062d91c4456ed94002acaf92481bdd501a84ad | 3,625,523 |
from datetime import datetime
from typing import List
def history_snapshot(
order_book_id: str,
bar_count: int,
dt: datetime.datetime,
fields: List[str]=None,
skip_suspended: bool=True,
include_now: bool=False,
adjust_type: str="none",
adjust_orig:datetime = None,
) -> pd.DataFrame:
... | 98b30d3607add30ae8e08e394dc2b7321beb14e9 | 3,625,524 |
def determine_header_length(trf_contents: bytes) -> int:
"""Returns the header length of a TRF file
Determined through brute force reverse engineering only. Not based
upon official documentation.
Parameters
----------
trf_contents : bytes
Returns
-------
header_length : int
... | e8aa5110691e877c34f208af5bd508f0f5ec4760 | 3,625,525 |
import xml
def _parse_define(define: xml.etree.ElementTree.Element) -> str:
"""Parse <define> manifest stanza.
Schema:
<define name="EXAMPLE" value="1"/>
<define name="OTHER"/>
Args:
define: XML Element for <define>.
Returns:
str with a value NAME=VALUE or NAME.
... | 00f366d7dc969e32431301cce094144e9398e471 | 3,625,527 |
import re
def _join_lines(source):
"""Remove Fortran line continuations"""
return re.sub(r'[\t ]*&[\t ]*[\r\n]+[\t ]*&[\t ]*', ' ', source) | 9caa3b6f470a96a7b473f3cce12f57d3787e91dd | 3,625,528 |
import decimal
from datetime import datetime
def fromjson(datatype, value):
"""A generic converter from json base types to python datatype.
"""
if value is None:
return None
if isinstance(datatype, atypes.ArrayType):
return fromjson_array(datatype, value)
if isinstance(datatype,... | fcbb08948f1f4cf97850c7190d860c4788177858 | 3,625,529 |
def format(t):
"""
function to format the stopwatch time to A:BC.D
Returns: the formatted six character string
"""
A = t/600
t = t - A * 600
# this round function isn't needed when t is an integer
CD = round(t/10.0, 1)
B = ""
if CD < 10.0:
B = "0"
r... | ad6dc72f0d6b090cf4d94d27e1f0560c58a9f42d | 3,625,531 |
def var(array, axis=None, controller=None):
"""Returns the variance of all values along a particular axis (dimension).
Given an array of m tuples and n components:
* Default is to return the variance of all values in an array.
* axis=0: Return the variance values of all components and return a one
... | 696ab35d107cf758fe0fac2241a00b3167353418 | 3,625,532 |
import requests
def get_kalliope_bijlage(path, session):
"""
Perform the API-call to get a poststuk-uit bijlage.
:param path: url of the api endpoint that we want to fetch
:param session: a Kalliope session, as returned by open_kalliope_api_session()
:returns: buffer with bijlage
"""
r = ... | b2a126b8e33bab50b1e2aa122645d7a45d6dfea9 | 3,625,533 |
def validate_dice_seed(dice, min_length):
"""
Validates dice data (i.e. ensures all digits are between 1 and 6).
returns => <boolean>
dice: <string> representing list of dice rolls (e.g. "5261435236...")
"""
if len(dice) < min_length:
print("Error: You must provide at least {0} dice rol... | e63922476e66825c159ba94db5eb9e65ae906a40 | 3,625,534 |
import re
def make_vocab_from_docs(docs):
"""
Make a dictionary that contains all words from the docs. The order of words is arbitrary.
docs: iterable of documents
"""
vocab_words=set()
for doc in docs:
doc=doc.lower()
doc=re.sub(r'-',' ',doc)
doc=re.sub(r' +',' ',doc) ... | 6869bae9cbfe1b17a105feb0f3c5a06afa1ec8a0 | 3,625,535 |
def filter_by_book_style(bibtexs, book_style):
"""Returns bibtex objects of the selected book type.
Args:
bibtexs (list of core.models.Bibtex): queryset of Bibtex.
book_style (str): book style key (e.g. JOUNRAL)
Returns:
list of Bibtex objectsxs
"""
return [bib for bib in ... | ca3b46772930a6f6e28b6fc0ee4d175ee8d69c3c | 3,625,536 |
def _rmse(a, b, weights, axis):
"""
Root Mean Squared Error.
Parameters
----------
a : ndarray
Input array.
b : ndarray
Input array.
axis : int
The axis to apply the rmse along.
weights : ndarray
Input array.
Returns
-------
res : ndarray
... | 77e6296a08d9c8210cb3e1f05d2fc0788a77b2a0 | 3,625,538 |
from typing import Counter
def cdf_function_5(cd: ChemicalDiagram):
"""exclusion: all M hydroxide"""
env_dict = cd.get_env_dict()
def neighboring_hydrogen_count(node):
return max(
Counter(env_dict[node]["nb_elements"])["H"],
Counter(env_dict[node]["nb_elements"])["D"],
... | 59e394369d5ec5d9427c79a5a15c01030886fd57 | 3,625,540 |
def _ns_tag(ns_id, tag):
"""Return a namespace/tag item. The ns_id is translated to a full name
space via the NS module variable.
:param ns_id: The name space ID. Translated to a namespace via the module
variable NS
:type ns_id: str
:param tag: The tag
:type str: str
"""
return... | b5cf6d00c3c4a8afe373e12e7761ba9eba9287aa | 3,625,542 |
def half_size(A, amount=50, interp='bicubic', mode=None):
""" nearest, bilinear, bicubic, cubic """
return misc.imresize(A, amount, interp, mode) | 97cca97676fd56a90330a877fca3e6b45d347541 | 3,625,543 |
def load_labels(lamost_ids, filename='lamost_labels_all_dates.csv'):
""" Extracts training labels from file.
Assumes that first row is # then label names, first col is # then
filenames, remaining values are floats and user wants all the labels.
"""
print("Loading reference labels from file %s" %fi... | 13123d919dd1d2166fe8ce39fcf8727582324c4c | 3,625,544 |
def _create_alb(
stack, name: str, vpc: ec2.Vpc, target_group: elbv2.ApplicationTargetGroup
) -> elbv2.ApplicationListener:
"""Create Application Load Balancer for integration to the service's API"""
sg = ec2.SecurityGroup(stack, f'{name}-http-public-sg', vpc=vpc)
sg.add_ingress_rule(ec2.Peer.any_ipv4()... | 208f03b313acb799690800cb3f613e3eed5d2bfc | 3,625,545 |
import json
def load_mock_response(file_name: str) -> dict:
"""
Load one of the mock responses to be used for assertion.
Args:
file_name (str): Name of the mock response JSON file to return.
"""
with open(f'{file_name}', mode='r', encoding='utf-8') as json_file:
return json.loads(j... | 8385f435bf666e5c5a4636c0f93cf90436f6aff9 | 3,625,546 |
def fixedvals_from_searchspaces(params):
"""Converts any search space hyperparams in params dict into fixed default values."""
if any(isinstance(params[hyperparam], Space) for hyperparam in params):
logger.warning("Attempting to fit model without HPO, but search space is provided. fit() will only consid... | a2a2712efd55cab49f9288d800ad15b56580c479 | 3,625,547 |
import torch
def attributions(scores: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
"""Error analysis for antecedent scoring
## Inputs
- `scores`: `(batch_size, max_antecedent_number)`-shaped float tensor.
The first dimension might be padded with `-float("-inf")` or
approximations... | 6ce0c443d6e344bfc84565f8f901984904216194 | 3,625,549 |
def get_recommendation_and_prediction_from_text(input_text, num_feats=10):
"""
플래스크 앱에 출력할 점수와 추천을 구합니다.
:param input_text: 입력 문자열
:param num_feats: 추천으로 제시한 특성 개수
:return: 추천과 현재 점수
"""
global MODEL
feats = get_features_from_input_text(input_text)
pos_score = MODEL.predict_proba([f... | f5998c9025beabf13e32f41bee7ff43236f58cc6 | 3,625,550 |
def Ct_a(a, F=None, method='Glauert', ac=None):
"""
High thrust corrections of the form: Ct = Ct(a)
see a_Ct
"""
if F is None:
F=np.ones(a.shape)
if method=='Glauert':
Ct = 4*a*F*(1-a)
if ac is None:
ac = 1/3
Ic = a>ac
Ct[Ic] =4*a[... | 9a503e2566e78e149cbc6225edf01841c56a6a7d | 3,625,551 |
def authors_to_string(*authors):
"""
>>> author1 = {'first': 'S.', 'last': 'Miyamoto'}
>>> author2 = {'first': 'K.', 'last': 'Kondo'}
>>> author3 = {'first': 'H.', 'last': 'Tanaka'}
>>> authors_to_string(author1)
'S. Miyamoto'
>>> authors_to_string(author1, author2)
'S. Miyamoto and K. K... | 2e6f63954dd6ad9c031eecbc42a39423196baba5 | 3,625,552 |
def square_quad_f(x, a, matrix):
"""
Compute the square of the quadratic function.
Parameters
----------
x : 1-D array
Point in which the square of the quadratic is to be evaluated.
minimizer : 1-D array
Minimizer of the square of the quadratic function.
matrix : 2-D... | 1ff7aafd5d4259bfccd110d4f2880071b8fad18a | 3,625,554 |
def calc_dist_matrix(chain_one, chain_two) :
"""Returns a matrix of C-alpha distances between two chains"""
''' an example
chain_one = chain
chain_two = chain'''
answer = np.zeros((len(chain_one), len(chain_two)), np.float)
for row, residue_one in enumerate(chain_one) :
for col, residue... | 88e12fdc9511c445a998c66d82a2aa3a51545c08 | 3,625,555 |
def compare_sequence(old, new):
"""Compare two Seq or DBSeq objects."""
assert len(old) == len(new), "%i vs %i" % (len(old), len(new))
assert str(old) == str(new), "%s vs %s" % (old, new)
if isinstance(old, UnknownSeq):
assert isinstance(new, UnknownSeq)
else:
assert not isinstance(... | bee02d5c4af5ea7faf7b30dcfca2f1ee1174c47a | 3,625,556 |
def _get_slope(x, y):
"""
Retrun the slope of x and y data, using scipy.signal.linregress
"""
slope = linregress(x, y)
return slope | 14882f8cd604c0360c3f75c6c0c065a5344c9855 | 3,625,558 |
def get_spans(tokens, tags):
"""Convert tags to textspans."""
spans = tags_to_spans(tags)
text_spans = [
x[0] + ": " + " ".join([tokens[i]
for i in range(x[1][0], x[1][1] + 1)])
for x in spans
]
if not text_spans:
text_spans = ["None"]
return text_spans | fa23e1164e6753652a123412879760529a675626 | 3,625,559 |
def applyPCA(data_points,pcaComponents):
"""Apply PCA to a list of values
Parameters
----------
data_points : numpy.ndarray
Array of type numpy.ndarray.
pcaComponents : int
Number of components to be used in PCA analysis.
Returns
Array of t... | 65b2d4e9c29dfc8e4c7d44c83ed761c69c55127e | 3,625,560 |
import requests
import json
def delete_account(Id, token: str):
"""Deletes the account resource."""
headers = Header(token)
URL = "https://api.mail.tm/accounts/"+Id
response = requests.delete(url=URL, headers=headers.header)
if response.status_code == 204:
return response.status_code
... | 9d173aca8968af511059734d9adb2e959527bfe8 | 3,625,561 |
import json
def load_quran_obj_from_json(input_json_path):
"""
Loads the Json object containing Qur'anic data from the specific path.
"""
try:
with open(input_json_path, 'rb') as quran_json_file:
# Import json file and return Qur'an object.
return json.load(quran_json_... | 708b873bb5694472585ce05924a8262fe60de77e | 3,625,562 |
import json
import requests
def get_company_info(secret1, secret2, symbol):
"""
scrapes data (currently from alphavantage) and returns the response in json format
Arguments:
secret1: one of the API keys to scrape data
secret2: a second API key to scrape data
symbol: synonymous to... | 145230e4cefd062c14bad11cc09344354d9701f9 | 3,625,563 |
from typing import Union
def cyclic_digraph(n: int = 3, metadata: bool = False) -> Union[sparse.csr_matrix, Bunch]:
"""Cyclic graph (directed).
Parameters
----------
n : int
Number of nodes.
metadata : bool
If ``True``, return a `Bunch` object with metadata.
Returns
-----... | fae1c67831a1bc151b05ec123c5d069fb4a3da40 | 3,625,564 |
def is_wildcard_query(query):
"""
Checks if provided query selects using a * wildcard
:type query str
:rtype bool
"""
if not is_select_query(query):
return False
query = preprocess_query(query)
tokens = get_query_tokens(query)
last_token = None
for token in tokens:
... | b952850f6f5eb4fcc7155889ab7da4d6b97f7900 | 3,625,565 |
import random
def random_val(index, tune_params):
"""return a random value for a parameter"""
key = list(tune_params.keys())[index]
return random.choice(tune_params[key]) | 1cf7ba9d1a3ff651f946a8013e338d62f4fec3ab | 3,625,566 |
def read_envvar_file(name, extension):
"""
Read values from a file provided as a environment variable
``NAME_CONFIG_FILE``.
:param name: environment variable prefix to look for (without the
``_CONFIG_FILE``)
:param extension: *(unused)*
:return: a `.Configuration`, possibly `.NotConfigu... | 6717f9f442d3693878c213cbb1a6c0276d86ac3a | 3,625,567 |
def take_while(array, callback=None):
"""Creates a slice of `array` with elements taken from the beginning.
Elements are taken until the `callback` returns falsey. The
`callback` is invoked with three arguments: ``(value, index, array)``.
Args:
array (list): List to process.
callback (m... | b00f3c43c769022443d8a19af739eb7a263cca20 | 3,625,568 |
import tokenize
def build_model():
"""
Using grid search, builds the model to classify the messages
Returns:
model (): The trained model over the data
"""
# text pipeline
text_pipeline = Pipeline([
('vect', CountVectorizer(tokenizer=tokenize)),
... | d874cba5c06483963bd60d360ac5a972ca0d54f0 | 3,625,569 |
def extract_cutted_data_and_timesteps_from_given_indexes(dataframe_indexes, dict_instances, result_data_shape, result_timesteps_shape):
"""This function extracts data and labels in window format via corresponding indexes located in dataframe_indexes
Cut format is needed for train recurrent models. The techni... | a1c256f48f16be94e8b67c1d7d407513fdda6764 | 3,625,570 |
def git_version_specifier(refspec, branch, commit, tag):
"""
Return the minimal set of specifiers that the user
input reduces to, in a dict of variables for Ansible.
:param refspec: provided refspec like 'pull/1/head'
:param branch: provided branch like 'master'
:param commit: provided commit S... | 9b723b44f3bad03a74b78a10153f4d6120202fc6 | 3,625,572 |
import socket
def receive_bytes(socket: socket.socket, buffer_size: int) -> str:
"""
Receives the specified number of bytes
from the specified socket
@param socket - the socket from which to receive
@param buffer_size - the number of bytes to receive
@return - string
"""
receiver_buffe... | bb28fa45641596a02f6e325e3a62ccc51c9f9905 | 3,625,573 |
def roulette_selection(population, pop_fitness, select_n, config):
"""
Metoda selekcji ruletki
Minimalizujemy wartośc funkcji dopasowania, co jest podejściem odwrotnym do standardowego. By moc poprawnie
zastosowac algorytm selekcji ruletki wykorzystujemy odwrocone wartosci funkcji.
W celu rozproszen... | ac187093f9b22329449fffd0773b0d408089218a | 3,625,574 |
def create_additional_front_points(pt6x, pt7x, pt14x, pt9z, pt15x, pt8z, pt14z, pt9x, pt8x, pt15z):
"""Create pot surface points to create faces--Nameing them 21(L)-22(R) to not collide with current fuel vert numbers"""
# Left point
pt20x = pt6x
pt20z = pt14z
pt20y = 0
# Right point
pt21x = ... | c6e021c4ce1bf514be80b60558e6152c571da68c | 3,625,575 |
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