content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def compact_axis_angle_from_matrix(R):
"""Compute compact axis-angle from rotation matrix.
This operation is called logarithmic map. Note that there are two possible
solutions for the rotation axis when the angle is 180 degrees (pi).
We usually assume active rotations.
Parameters
----------
... | a7493a5ed1c622b9cbec6e9f0771e62f7f4712e2 | 3,656,600 |
def _generate_IPRange(Range):
"""
IP range to CIDR and IPNetwork type
Args:
Range: IP range
Returns:
an array with CIDRs
"""
if len(Range.rsplit('.')) == 7 and '-' in Range and '/' not in Range:
if len(Range.rsplit('-')) == 2:
start_ip, stop_ip = Range.rspli... | d86f8db8e87313b12f35669ee25cc3f3d229c631 | 3,656,601 |
def is_dict_homogeneous(data):
"""Returns True for homogeneous, False for heterogeneous.
An empty dict is homogeneous.
ndarray behaves like collection for this purpose.
"""
if len(data) == 0:
return True
k0, v0 = next(iter(data.items()))
ktype0 = type(k0)
vtype0 = type(v0)
i... | 921e66639cd6a8584e99e14852158594b1001ef9 | 3,656,602 |
from typing import Union
from typing import Callable
from re import T
from typing import Generator
from typing import Any
def translate(item: Union[Callable[P, T], Request]) -> Union[Generator[Any, Any, None], Callable[P, T]]:
"""Override current language with one from language header or 'lang' parameter.
Can... | 5042cc77efb1477444f8f9611055fb3e183cf3d3 | 3,656,603 |
def get_all(isamAppliance, check_mode=False, force=False, ignore_error=False):
"""
Retrieving the current runtime template files directory contents
"""
return isamAppliance.invoke_get("Retrieving the current runtime template files directory contents",
"/mga/template_f... | 9ff291b63471b57b110885c35939c8afe3d2f0d8 | 3,656,604 |
from sys import path
def build_path(dirpath, outputfile):
"""
Build function
"""
#some checks
if not path.exists(dirpath):
print("Path does not exist!")
return 1
if not path.isdir(dirpath):
print("Path is not folder")
return 1
#for now SQLite
t... | 01492296925a259873e327d8eae938710fe78f20 | 3,656,605 |
import os
def _fix_importname(mname):
"""
:param mname:
"""
mname = os.path.normpath(mname)
mname = mname.replace(".", "")
mname = mname.replace("-", "")
mname = mname.replace("_", "")
mname = mname.replace(os.path.sep, "")
mname = mname.replace(os.path.pathsep, "")
return mn... | 22f8ab56800a593502822a612c3f642e8cec22ea | 3,656,606 |
def main(args, out, err):
""" This wraps GURepair's real main function so
that we can handle exceptions and trigger our own exit
commands.
This is the entry point that should be used if you want
to use this file as a module rather than as a script.
"""
cleanUpHandler = BatchCaller(args.ve... | c506306a93804ab60c1a6805e9c53a0fd9dd7cfd | 3,656,607 |
import requests
import os
import io
def getSymbolData(symbol, sDate=(2000,1,1), adjust=False, verbose=True, dumpDest=None):
"""
get data from Yahoo finance and return pandas dataframe
Parameters
-----------
symbol : str
Yahoo finanance symbol
sDate : tuple , default (2000,1,1)
... | 24e06e34e4f5a4705d59a2ffb32c767bbc25adcf | 3,656,608 |
def generateLouvainCluster(edgeList):
"""
Louvain Clustering using igraph
"""
Gtmp = nx.Graph()
Gtmp.add_weighted_edges_from(edgeList)
W = nx.adjacency_matrix(Gtmp)
W = W.todense()
graph = Graph.Weighted_Adjacency(
W.tolist(), mode=ADJ_UNDIRECTED, attr="weight", loops=False) # ig... | c171474bdd81456cbbc488b0a8cb826f881419ec | 3,656,609 |
def exprvars(name, *dims):
"""Return a multi-dimensional array of expression variables.
The *name* argument is passed directly to the
:func:`pyeda.boolalg.expr.exprvar` function,
and may be either a ``str`` or tuple of ``str``.
The variadic *dims* input is a sequence of dimension specs.
A dime... | 6b65872029de938d37c9e968f696587e2a03ff8c | 3,656,610 |
def cell_segmenter(im, thresh='otsu', radius=20.0, image_mode='phase',
area_bounds=(0,1e7), ecc_bounds=(0, 1)):
"""
This function segments a given image via thresholding and returns
a labeled segmentation mask.
Parameters
----------
im : 2d-array
Image to be segmente... | f9a8fa3c29cbb213ed67c3df93106a81f53ae985 | 3,656,611 |
from datetime import datetime
def generate_report(start_date, end_date):
"""Generate the text report"""
pgconn = get_dbconn('isuag', user='nobody')
days = (end_date - start_date).days + 1
totalobs = days * 24 * 17
df = read_sql("""
SELECT station, count(*) from sm_hourly WHERE valid >= %s
... | f71b5ab58922b9018abc1868661f88c268de8f94 | 3,656,612 |
import pathlib
def whole(eventfile,par_list,tbin_size,mode,ps_type,oversampling,xlims,vlines):
"""
Plot the entire power spectrum without any cuts to the data.
eventfile - path to the event file. Will extract ObsID from this for the NICER files.
par_list - A list of parameters we'd like to extract fr... | 77b51cc8774bdb1b670e2a6b56a9cd65213f70de | 3,656,613 |
def handle_postback():
"""Handles a postback."""
# we need to set an Access-Control-Allow-Origin for use with the test AJAX postback sender
# in normal operations this is NOT needed
response.set_header('Access-Control-Allow-Origin', '*')
args = request.json
loan_id = args['request_token']
m... | 59683921b7a21f50c2905c47c33036fd75ce54f4 | 3,656,614 |
def get_bb_bev_from_obs(dict_obs, pixor_size=128):
"""Input dict_obs with (B,H,W,C), return (B,H,W,3)"""
vh_clas = tf.squeeze(dict_obs['vh_clas'], axis=-1) # (B,H,W,1)
# vh_clas = tf.gather(vh_clas, 0, axis=-1) # (B,H,W)
vh_regr = dict_obs['vh_regr'] # (B,H,W,6)
decoded_reg = decode_reg(vh_regr, pixor_size... | d286ec0c3132c2dcb931cb941fd247810c0ce1cf | 3,656,615 |
def get_hard_edges(obj):
"""
:param str obj:
:returns: all hard edges from the given mesh in a flat list
:rtype: list of str
"""
return [obj + '.e[' + str(i) + ']'
for i, edgeInfo in enumerate(cmds.polyInfo(obj + '.e[*]', ev=True))
if edgeInfo.endswith('Hard\n')] | 67de22469a38e55e88d21f1853280138795a04cb | 3,656,616 |
def make_system(l=70):
"""
Making and finalizing a kwant.builder object describing the system
graph of a closed, one-dimensional wire with l number of sites.
"""
sys = kwant.Builder()
lat = kwant.lattice.chain()
sys[(lat(x) for x in range(l))] = onsite
sys[lat.neighbors()] = hopping
return sys.finalized() | fa3d25933fd086519569cbb24ff77bf3c86c1303 | 3,656,617 |
from typing import Type
from pathlib import Path
from typing import Dict
def _gen_test_methods_for_rule(
rule: Type[CstLintRule],
fixture_dir: Path,
rules_package: str
) -> TestCasePrecursor:
"""Aggregates all of the cases inside a single CstLintRule's VALID and INVALID attributes
and maps them to altered nam... | 5a12d84bdcff039179ef9b9f1105e6beecccbf05 | 3,656,618 |
def evaluate_score_batch(
predicted_classes=[], # list, len(num_classes), str(code)
predicted_labels=[], # shape (num_examples, num_classes), T/F for each code
predicted_probabilities=[], # shape (num_examples, num_classes), prob. [0-1] for each code
raw_ground_truth_labels=[], # list(('dx1', 'dx2')... | 314f94433704cc2986df9082a749caaf52738f08 | 3,656,619 |
def pair(data, color=None, tooltip=None, mark='point', width=150, height=150):
"""
Create pairwise scatter plots of all column combinations.
In contrast to many other pairplot tools,
this function creates a single scatter plot per column pair,
and no distribution plots along the diagonal.
Para... | ed712972e503795bbfaeac6844a225444d946018 | 3,656,620 |
import tqdm
def gauss_kernel(model_cell, x, y, z, sigma=1):
"""
Convolute aligned pixels given coordinates `x`, `y` and values `z` with a gaussian kernel to form the final image.
Parameters
----------
model_cell : :class:`~colicoords.cell.Cell`
Model cell defining output shape.
x : :c... | 0ff61121fbf330e3e15862b82b0929ae3b8748f9 | 3,656,621 |
def get_configs_from_multiple_files():
"""Reads training configuration from multiple config files.
Reads the training config from the following files:
model_config: Read from --model_config_path
train_config: Read from --train_config_path
input_config: Read from --input_config_path
Returns:
mode... | 4f561235568667a6fe71d77c23769ea8878ebe20 | 3,656,622 |
def line_to_numbers(line: str) -> t.List[int]:
"""Split a spreadsneet line into a list of numbers.
raises:
ValueError
"""
return list(map(int, line.split())) | fce9af5e1c213fd91f0edf8d7fa5877f15374908 | 3,656,623 |
def bits_to_amps(bits):
"""helper function to convert raw data from usb device to amps"""
return bits*BITS_TO_AMPS_SLOPE + BITS_TO_AMPS_Y_INTERCEPT | 5653582987b6a7924c11f037badc1a61541c6ca2 | 3,656,624 |
import re
def fields_to_dict(fields):
""" FIXME:
https://www.debuggex.com/r/24QPqzm5EsR0e2bt
https://www.debuggex.com/r/0SjmBL55ySna0kFF
https://www.debuggex.com/r/Vh9qvHkCV4ZquS14
"""
result = {}
if not fields or len(fields.strip()) == 0:
return result
# look_beh... | 2e7ebc2e9277ef693498d04c731f309e17fd4501 | 3,656,625 |
import os
import glob
def get_font_paths(fonts_dir):
"""
Load font path recursively from a folder
:param fonts_dir: folder contains ttf、otf or ttc format font
:return: path of all fonts
"""
print('Load fonts from %s' % os.path.abspath(fonts_dir))
fonts = glob.glob(fonts_dir + '/**/*', recu... | bf6368f90023fd59d64d358e6dac919627feb9ab | 3,656,626 |
def time_difference(t_early, t_later):
"""
Compute the time difference between t_early and t_later
Parameters:
t_early: np.datetime64, list or pandas series.
t_later: np.datetime64, list or pandas series.
"""
if type(t_early) == list:
t1 = np.array(t_early)
elif type(t_early) ==... | 0d4e6bac3aed2e5a2848c4289dadc92120a4f7a1 | 3,656,627 |
def conv2d_block(input_tensor, n_filters, kernel_size=3, batchnorm=True):
""" Convolutional block with two convolutions followed by batch normalisation (if True) and with ReLU activations.
input_tensor: A tensor. Input tensor on which the convolutional block acts.
n_filters: An integer. Number of filters in... | 8bb435ed1e091fff26d49290a8ca6d0c9c12ec67 | 3,656,628 |
def check_credentials(username):
"""
Function that check if a Credentials exists with that username and return true or false
"""
return Credentials.if_credential_exist(username) | 8515bbc39afd003fc193cbb80c97f5f718657fa6 | 3,656,629 |
def rpc_category_to_super_category(category_id, num_classes):
"""Map category to super-category id
Args:
category_id: list of category ids, 1-based
num_classes: 1, 17, 200
Returns:
super-category id, 0-based
"""
cat_id = -1
assert num_classes in RPC_SUPPORT_CATEGORIES, \
... | 8056aea308f66a65a4135a6fc7f061873d990624 | 3,656,630 |
def setup_integration():
"""Set up a test resource."""
print('Setting up a test integration for an API')
return Integration(name='myapi',
base_url='https://jsonplaceholder.typicode.com') | d2720db6ae520e21edc555ad0c899652c6584406 | 3,656,631 |
def secondsToHMS(intervalInSeconds):
"""converts time in seconds to a string representing time in hours, minutes, and seconds
:param intervalInSeconds: a time measured in seconds
:returns: time in HH:MM:SS format
"""
interval = [0, 0, intervalInSeconds]
interval[0] = (inter... | b38d4b886eaabd1361c162b6b7f55e11493dfb60 | 3,656,632 |
import itertools
def build_rdn(coords, r, **kwargs):
"""
Reconstruct edges between nodes by radial distance neighbors (rdn) method.
An edge is drawn between each node and the nodes closer
than a threshold distance (within a radius).
Parameters
----------
coords : ndarray
Coordina... | 83f2d68fbb854e2ef25e03f5d58d6c96c02c0127 | 3,656,633 |
def find_layer(model, type, order=0):
"""
Given a model, find the Nth layer of the specified type.
:param model: the model that will be searched
:param type: the lowercase type, as it is automatically saved by keras in the layer's name (e.g. conv2d, dense)
:param order: 0 by default (the first matc... | 6d4e08c181900774b9e5666a11df9767f68a10ca | 3,656,634 |
def _interpretable(model):
# type: (Union[str, h2o.model.ModelBase]) -> bool
"""
Returns True if model_id is easily interpretable.
:param model: model or a string containing a model_id
:returns: bool
"""
return _get_algorithm(model) in ["glm", "gam", "rulefit"] | 4ae73e5b7ed98b61b56920985128212e3051c789 | 3,656,635 |
def apply_pb_correction(obs,
pb_sensitivity_curve,
cutoff_radius):
"""
Updates the primary beam response maps for cleaned images in an ObsInfo object.
Args:
obs (ObsInfo): Observation to generate maps for.
pb_sensitivity_curve: Primary beam se... | 02ee2913ce781f4a02e85910c69cfe5b534e62f4 | 3,656,636 |
def makeLoadParams(args):
"""
Create load parameters for start load request out of command line arguments.
Args:
args (dict): Parsed command line arguments.
"""
load_params = {'target': {},
'format': {'date_time': {},
'boolean': {}},
... | f1c0e9297775305c36acbb950bfc05e785bde87c | 3,656,637 |
from hash import HashTable
def empty_hash():
"""Initialize empty hash table."""
test_hash = HashTable()
return test_hash | 02700169c89427af4d2db123e110ec383d9332eb | 3,656,638 |
def denoise_sim(image, std, denoiser):
"""Simulate denoising problem
Args:
image (torch.Tensor): image tensor with shape (C, H, W).
std (float): standard deviation of additive Gaussian noise
on the scale [0., 1.].
denoiser: a denoiser instance (as in algorithms.denoiser).
... | 216944b26c3ca0e04b8b5801766321fe60ee7e02 | 3,656,639 |
def _find_weektime(datetime, time_type='min'):
"""
Finds the minutes/seconds aways from midnight between Sunday and Monday.
Parameters
----------
datetime : datetime
The date and time that needs to be converted.
time_type : 'min' or 'sec'
States whether the time difference shoul... | 2ed28166d239dabdc9f8811812e472810b10c7d7 | 3,656,640 |
from typing import List
from typing import Tuple
def linear_to_image_array(pixels:List[List[int]], size:Tuple[int,int]) -> np.ndarray:
"""\
Converts a linear array ( shape=(width*height, channels) ) into an array
usable by PIL ( shape=(height, width, channels) )."""
a = np.array(pixels, dtype=np.uint8)
sp... | 431170c71a3d6464be5dd5b9d248b2866ba3ac6a | 3,656,641 |
def stop_processes(hosts, pattern, verbose=True, timeout=60):
"""Stop the processes on each hosts that match the pattern.
Args:
hosts (list): hosts on which to stop the processes
pattern (str): regular expression used to find process names to stop
verbose (bool, optional): display comma... | 898a358b5e61952d72be15eecb10b00ce8bd2efd | 3,656,642 |
def field_as_table_row(field):
"""Prints a newforms field as a table row.
This function actually does very little, simply passing the supplied
form field instance in a simple context used by the _field_as_table_row.html
template (which is actually doing all of the work).
See soc/templates/soc/templatetags/_... | 74d120e2a46ae8465832d98ddf02848b5b2cc936 | 3,656,643 |
def get_samples(select_samples: list, avail_samples: list) -> list:
"""Get while checking the validity of the requested samples
:param select_samples: The selected samples
:param avail_samples: The list of all available samples based on the range
:return: The selected samples, verified
"""
# S... | e1c0c98697d2c504d315064cbdfbad379165d317 | 3,656,644 |
def createMemoLayer(type="", crs=4326, name="", fields={"id":"integer"}, index="no"):
"""
Créer une couche en mémoire en fonction des paramètres
:param type (string): c'est le type de geometrie "point", "linestring",
"polygon", "multipoint","multilinestring","multipolygon"
:par... | 713823d9b59b7c4ccf7bdd938a720d385629e02f | 3,656,645 |
import json
def load_templates(package):
"""
Returns a dictionary {name: template} for the given instrument.
Templates are defined as JSON objects, with stored in a file named
"<instrument>.<name>.json". All templates for an instrument should
be stored in a templates subdirectory, made into a pa... | 6213eb6e8b7be0bb7057da49d02fe495d7db6660 | 3,656,646 |
def get_count_matrix(args):
"""首先获取数据库中全部文档的id，然后遍历id获取文档内容，再逐文档
进行分词，生成计数矩阵。"""
global DOC2IDX
with DocDB(args.db_path) as doc_db:
doc_ids = doc_db.get_doc_ids()
DOC2IDX = {doc_id: i for i, doc_id in enumerate(doc_ids)}
row, col, data = [], [], []
_count = partial(count, args)
... | 6279666c6dfdf66dba13edfe57e55525de15d894 | 3,656,647 |
def communication_round(model, clients, train_data, train_labels, train_people, val_data, val_labels, val_people,
val_all_labels, local_epochs, weights_accountant, individual_validation, local_operation):
"""
One round of communication between a 'server' and the 'clients'. Each client 'd... | f8a8ef93845e09394cea6a2f6077a0ae2dfaed18 | 3,656,648 |
import collections
def _find_stop_area_mode(query_result, ref):
""" Finds the mode of references for each stop area.
The query results must have 3 columns: primary key, foreign key
reference and number of stop points within each area matching that
reference, in that order.
:param... | e4677638b272e67d2ae21ee97f71f1f1700fd072 | 3,656,649 |
def get_all_funds_ranking(fund_type: str = 'all',
start_date: str = '-1y',
end_date: str = arrow.now(),
sort: str = 'desc',
subopts: str = '',
available: str = 1):
"""Get all funds ranki... | 55dd84c8f8830d6c60411de858a9aec1f14a30be | 3,656,650 |
from typing import List
from typing import Any
from re import T
def _conform_list(li: List[Any]) -> List[T]:
"""
Ensures that every element in *li* can conform to one type
:param li: list to conform
:return: conformed list
"""
conform_type = li[0].__class__
for i in li:
if isinstan... | 29131a9f5979318e0fc50408b67938ffbd56fa5a | 3,656,651 |
def _255_to_tanh(x):
"""
range [0, 255] to range [-1, 1]
:param x:
:return:
"""
return (x - 127.5) / 127.5 | a60a67ee489093292fc58136a8f01387482fb162 | 3,656,652 |
import os
import re
import sys
def read_content(filename):
"""Read content and metadata from file into a dictionary."""
# Read file content.
text = fread(filename)
# Read metadata and save it in a dictionary.
date_slug = os.path.basename(filename).split('.')[0]
match = re.search('^(?:(\\d\\d\... | 5337830593959e0bf29b4d369789120a265badfc | 3,656,653 |
import torch
def train_one_epoch(train_loader, model, criterion, optimizer, epoch, opt, num_train_samples, no_acc_eval=False):
""" model training
:param train_loader: train dataset loader
:param model: model
:param criterion: loss criterion
:param optimizer:
:param epoch: ... | 5b5efd1292322090abcb795fc633638f478f0afa | 3,656,654 |
import datetime
def Write(Variable, f):
"""Function to Convert None Strings to Strings and Format to write to file with ,"""
if isinstance(Variable, str) == False:
if isinstance(Variable, datetime.datetime) == True:
return f.write(f"{Variable.strftime('%Y-%m-%d')},")
else:
... | 9963c4117c7cc3f19d91331ed6c36e5733cffb56 | 3,656,655 |
def graphs_infos():
"""
Build and return a JSON file containing some information on all the graphs.
The json file is built with the following format:
[
For each graph in the database :
{
'graph_id': the id of the graph,
'name': the name of the graph,
'iso'... | ab6fee49188ad422e1e3a5e2763510ae791a840b | 3,656,656 |
def collect_compare(left, right):
"""
returns a tuple of four lists describing the file paths that have
been (in order) added, removed, altered, or left the same
"""
return collect_compare_into(left, right, [], [], [], []) | 2a29d7b896fb037a8784e7c82794d9b67eb2924a | 3,656,657 |
def _get_smallest_vectors(supercell, primitive, symprec):
"""
shortest_vectors:
Shortest vectors from an atom in primitive cell to an atom in
supercell in the fractional coordinates. If an atom in supercell
is on the border centered at an atom in primitive and there are
multiple vectors... | 352d4e7ba9552fa4fe5abdb9eb45c4555dff603d | 3,656,658 |
def root():
"""Root endpoint that only checks if the server is running."""
return 'Server is running...' | ea9ecd1c736e9379795f361462ed54f464a4008b | 3,656,659 |
def clone_model(model, **new_values):
"""Clones the entity, adding or overriding constructor attributes.
The cloned entity will have exactly the same property values as the
original entity, except where overridden. By default, it will have no
parent entity or key name, unless supplied.
Args:
... | ed668632c8917ad685b86fb5c71146be7c9b3b96 | 3,656,660 |
def learn_laterals(frcs, bu_msg, perturb_factor, use_adjaceny_graph=False):
"""Given the sparse representation of each training example,
learn perturbation laterals. See train_image for parameters and returns.
"""
if use_adjaceny_graph:
graph = make_adjacency_graph(frcs, bu_msg)
graph = ... | 68333bca0fc3231470268ece6478b372767a6648 | 3,656,661 |
def get_info(ingest_ldd_src_dir):
"""Get LDD version and namespace id."""
# look in src directory for ingest LDD
ingest_ldd = find_primary_ingest_ldd(ingest_ldd_src_dir)
# get ingest ldd version
tree = ETree.parse(ingest_ldd[0])
root = tree.getroot()
ldd_version = root.findall(f'.//{{{PDS_N... | 92c4d6f8f18c4204d2a8483584b6f1409d9ee243 | 3,656,662 |
def generate_tfidf(corpus_df, dictionary):
"""Generates TFIDF matrix for the given corpus.
Parameters
----------
corpus_df : pd.DataFrame
The corpus dataframe.
dictionary : gensim.corpora.dictionary.Dictionary
Dictionary defining the vocabulary of the TFIDF.
Returns
-------... | 6c5cd6b569010c69b446223a099cfd745d51ce6c | 3,656,663 |
import os
import logging
import yaml
def load_config_file(file_path, fallback_file_path):
"""Load YAML format configuration file
:param file_path: The path to config file
:type file_path: `str`
:param fallback_file_path: The fallback path to config file
:type fallback_file_path: `str`
:return... | 1c756b10892f1a6cffa78dd80852049061b7521d | 3,656,664 |
from typing import Tuple
from typing import Optional
import torch
def compute_mask_indices(
shape: Tuple[int, int],
padding_mask: Optional[torch.Tensor],
mask_prob: float,
mask_length: int,
mask_type: str = "static",
mask_other: float = 0.0,
min_masks: int = 0,
... | 8ecd84ca805112312d43bd8ba3f4c0aa3918800d | 3,656,665 |
import matplotlib.pyplot as plt
import numpy as np
def _rankingmap_mpl(countrymasksnc, ranking, x, scenario=None, method='number', title='', label=''):
"""
countrymasksnc : nc.Dataset instance of countrymasks.nc
ranking: Ranking instance
method: "number" (default) or "value"
"""
if method not... | d0e7006d832408fcd77b8b0fe219a6e9faf478e5 | 3,656,666 |
from typing import Optional
from typing import List
from typing import Dict
from typing import Any
def fetch_data(
property: Property,
start_date: dt.date,
*,
end_date: Optional[dt.date] = None,
dimensions: Optional[List[Dimension]] = None,
) -> List[Dict[str, Any]]:
"""Query Google Search Con... | cb871f6e269005db9a338c4bf75949b8ba9ea04a | 3,656,667 |
import os
def fileOpenDlg(tryFilePath="",
tryFileName="",
prompt=_translate("Select file to open"),
allowed=None):
"""A simple dialogue allowing read access to the file system.
:parameters:
tryFilePath: string
default file path on which to ... | 932625c6779a5738c23ff340c448fb594d38c7ca | 3,656,668 |
def inport(port_type, disconnected_value):
"""Marks this field as an inport"""
assert port_type in port_types, \
"Got %r, expected one of %s" % (port_type, port_types)
tag = "inport:%s:%s" % (port_type, disconnected_value)
return tag | a9335d99b65a4944ef58f06b90f8978e7478ec13 | 3,656,669 |
def Align(samInHandle, fa, id, position, varId, refseq, altseq, mapq = 20):
"""
position is the left break point of the variants
And the position should be 1-base for convenience.
Because I use something like fa[id][position-1] to get bases from fa string
"""
if position < 1:
raise Val... | aa7238f421472e1be24ce290ddd5c3b6f5e2cba4 | 3,656,670 |
def _empty_aggregate(*args: npt.ArrayLike, **kwargs) -> npt.ArrayLike:
"""Return unchaged array."""
return args[0] | c7f6ebc345517b10a3b65c5ac0f0bf060cdf7634 | 3,656,671 |
def kfpartial(fun, *args, **kwargs):
""" Allows to create partial functions with arbitrary arguments/keywords """
return partial(keywords_first(fun), *args, **kwargs) | 7f7dbbdf484e36c2734e47b448f081812cb8a326 | 3,656,672 |
def power_state_update(system_id, state):
"""Report to the region about a node's power state.
:param system_id: The system ID for the node.
:param state: Typically "on", "off", or "error".
"""
client = getRegionClient()
return client(
UpdateNodePowerState,
system_id=system_id,
... | b05730fe9e45b3ee81adb7e8047b0b87e3bf7556 | 3,656,673 |
from typing import Any
def build_post307_request(*, json: Any = None, content: Any = None, **kwargs: Any) -> HttpRequest:
"""Post redirected with 307, resulting in a 200 after redirect.
See https://aka.ms/azsdk/python/protocol/quickstart for how to incorporate this request builder
into your code flow.
... | 2c26cfed95a33fe700b83d7e1fa4eb93ef312721 | 3,656,674 |
def rm_ssp_storage(ssp_wrap, lus, del_unused_images=True):
"""Remove some number of LogicalUnits from a SharedStoragePool.
The changes are flushed back to the REST server.
:param ssp_wrap: SSP EntryWrapper representing the SharedStoragePool to
modify.
:param lus: Iterable of LU ElementWrappers or ... | 0c61becd8f9e23ac269ef0546abb0857facd89de | 3,656,675 |
def urp_detail_view(request, pk):
"""Renders the URP detail page
"""
urp = get_object_or_404(URP, pk=pk)
ctx = {
'urp': urp,
}
# if user is logged in as a student, check if user has already applied
if request.user.is_authenticated:
if request.user.uapuser.is_student:
... | 15e7e86cf2e47bccda52682bdf205e43d8a03f5f | 3,656,676 |
import functools
def squeeze_excite(input_name, squeeze_factor):
"""Returns a squeeze-excite block."""
ops = []
append = functools.partial(append_op, ops)
append(op_name="se/pool0",
op_type=OpType.AVG_POOL,
input_kwargs={"window_shape": 0},
input_names=[input_name])
append(op_name... | 907acc7f31db9ab4d70f976320fdd779b66b7160 | 3,656,677 |
def get_code_v2(fl = r'C:\Users\bogdan\code_seurat\WholeGenome_MERFISH\Coordinates_code_1000region.csv'):
"""
Given a .csv file with header this returns 2 dictionaries: tad_to_PR,PR_to_tad
"""
lst = [(ln[:-1].split(',')[0].replace('__','_'),['R'+R for R in ln[:-1].split(',')[3].split('--')])
for ln... | f5a9e1bbd1f404819a700ee43cff826333ce736c | 3,656,678 |
from funcs.modeling_funcs import modeling_settings, generate_observation_ensemble
def run_source_lsq(vars, vs_list=vs_list):
"""
Script used to run_source and return the output file.
The function is called by AdaptiveLejaPCE.
"""
print('Read Parameters')
parameters = pd.read_csv('../data/Param... | e43679a0808108560714e32def9399ce45a6bd8e | 3,656,679 |
def finnegans_wake_unicode_chars():
"""Data fixture that returns a string of all unicode characters in Finnegan's Wake."""
return '¤·àáãéìóôþŒŠŸˆ–—‘’‚“”‡…‹' | 78205c9181545544a61ef1eab6c2f51d212dac13 | 3,656,680 |
from pathlib import Path
import posixpath
import os
def get_upload(upload_key: UploadPath = Path(..., description="上传文件块位置")):
"""
获取文件上传目录
:param upload_key:
:return:
"""
root_path = posixpath.abspath(UPLOAD_PATH_DICT[upload_key])
def func(folder):
path = security.safe_join(ro... | 39d0d4055f2a9933b9578b74fb14d5fa637154f0 | 3,656,681 |
def kit(): # simpler version
"""Open communication with the dev-kit once for all tests."""
return usp.Devkit() | 3001cbfeaf212e9a09e512c102eae6bffa263375 | 3,656,682 |
def givens_rotation(A):
"""Perform QR decomposition of matrix A using Givens rotation."""
(num_rows, num_cols) = np.shape(A)
# Initialize orthogonal matrix Q and upper triangular matrix R.
Q = np.identity(num_rows)
R = np.copy(A)
# Iterate over lower triangular matrix.
(rows, cols) = np.tr... | 207cadc90c7c4aab76c7422d314b5470ce17251a | 3,656,683 |
from typing import Union
from pathlib import Path
from typing import Optional
import json
def lex_from_str(
*,
in_str: Union[str, Path],
grammar: str = "standard",
ir_file: Optional[Union[str, Path]] = None,
) -> JSONDict:
"""Run grammar of choice on input string.
Parameters
----------
... | 5416bd56426012c56050a0dba2835385fa4177e5 | 3,656,684 |
def e() -> ProcessBuilder:
"""
Euler's number (e)
:return: The numerical value of Euler's number.
"""
return process('e', ) | f984b5de5a0b95109c9ec2fe5a2b30c880226b28 | 3,656,685 |
def get_or_create_anonymous_cart_from_token(token,
cart_queryset=Cart.objects.all()):
"""Returns open anonymous cart with given token or creates new.
:type cart_queryset: saleor.cart.models.CartQueryset
:type token: string
:rtype: Cart
"""
return cart... | 8ffb1f64b77c97b260502f1d4c689e3a4edc4f36 | 3,656,686 |
import os
def outcar_parser(request):
"""A fixture that loads OUTCAR."""
try:
name = request.param
except AttributeError:
# Test not parametrized
name = 'OUTCAR'
testdir = os.path.dirname(__file__)
outcarfile = testdir + '/' + name
outcar = Outcar(file_path=outcarfile)... | 5fe8b1ddb7f55e233104cec9a5be94624bc77ce9 | 3,656,687 |
from typing import Any
def accept_data(x: Any) -> Any:
"""Accept any types of data and return it as convenient type.
Args:
x: Any type of data.
Returns:
Any: Accepted data.
"""
if isinstance(x, str):
return x
elif isinstance(x, list):
return x
elif i... | 9862995eafb7015fc446466e2dbb7774be39f54b | 3,656,688 |
def custom_model_template(model_type: str, target: str, result0: str, result1: str) -> str:
"""Template for feature behaviour reason generated from DICE
Returns:
str: behaviour
"""
if model_type == 'classifier':
tipo = 'category'
elif model_type == 'regressor':
tipo = 'con... | bbd43a462f6d9d65984dbd242c7fe8a5d2be5e39 | 3,656,689 |
def merge_dict_list(merged, x):
""" merge x into merged recursively.
x is either a dict or a list
"""
if type(x) is list:
return merged + x
for key in x.keys():
if key not in merged.keys():
merged[key] = x[key]
elif x[key] is not None:
merged[key... | 00685be39a0b1447c81ecd8de777ebab38aa9bfe | 3,656,690 |
def is_ref(variant, exclude_alleles=None):
"""Returns true if variant is a reference record.
Variant protos can encode sites that aren't actually mutations in the
sample. For example, the record ref='A', alt='.' indicates that there is
no mutation present (i.e., alt is the missing value).
Args:
variant:... | 2c762bbf070f375b546f0902e3567ca5542cc774 | 3,656,691 |
def gomc_sim_completed_properly(job, control_filename_str):
"""General check to see if the gomc simulation was completed properly."""
job_run_properly_bool = False
output_log_file = "out_{}.dat".format(control_filename_str)
if job.isfile(output_log_file):
# with open(f"workspace/{job.id}/{output... | 20635ba94b5176298216ad5807e6428a5fb957c2 | 3,656,692 |
from typing import Union
from typing import Optional
def rv_precision(
wavelength: Union[Quantity, ndarray],
flux: Union[Quantity, ndarray],
mask: Optional[ndarray] = None,
**kwargs,
) -> Quantity:
"""Calculate the theoretical RV precision achievable on a spectrum.
Parameters
----------
... | 91d6a741d992bd915549becd371d29b6634b92ef | 3,656,693 |
def changenonetoNone(s):
"""Convert str 'None' to Nonetype
"""
if s=='None':
return None
else:
return s | 9f6af1580d8b47d2a7852e433f7ba8bbd5c7044d | 3,656,694 |
def quaternion_2_rotation_matrix(q):
"""
四元数转化为旋转矩阵
:param q:
:return: 旋转矩阵
"""
rotation_matrix = np.array([[np.square(q[0]) + np.square(q[1]) - np.square(q[2]) - np.square(q[3]),
2 * (q[1] * q[2] - q[0] * q[3]), 2 * (q[1] * q[3] + q[0] * q[2])],
... | f2e420a1e0b6838fb2ce5f9288842e1ae39134c9 | 3,656,695 |
def sum(mat, axis, target=None):
"""
Sum the matrix along the given dimension, where 0 represents the leading
dimension and 1 represents the non-leading dimension. If a target is
not prvided, a new vector is created for storing the result.
"""
m = _eigenmat.get_leading_dimension(mat.p_mat)
n = _eigenmat.... | 426ba7b2673a52663e04d3c6f07fb2f4e001244b | 3,656,696 |
from datetime import datetime
def convert_created_time_to_datetime(datestring):
"""
Args:
datestring (str): a string object either as a date or
a unix timestamp
Returns:
a pandas datetime object
"""
if len(datestring) == 30:
return pd.to_datetime(datestring)
el... | 2559d079b5b7174d192e3a5d9178701ae7080d3b | 3,656,697 |
def identify_word_classes(tokens, word_classes):
"""
Match word classes to the token list
:param list tokens: List of tokens
:param dict word_classes: Dictionary of word lists to find and tag with the
respective dictionary key
:return: Matched word classes
:rtype: list
"""
if w... | ca7aa602d19ac196321af19c42a60df415c7d115 | 3,656,698 |
from typing import List
from typing import Tuple
def find_connecting_stops(routes) -> List[Tuple[Stop, List[Route]]]:
"""
Find all stops that connect more than one route.
Return [Stop, [Route]]
"""
stops = {}
for route in sorted(routes, key=Route.name):
for stop in route.stops():
... | 599e9e5d3fc0a6d0de84a58f1549da9423f35af3 | 3,656,699 |
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