content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def policy_compare(sen_a, sen_b, voting_dict):
"""
Input: last names of sen_a and sen_b, and a voting dictionary mapping senator
names to lists representing their voting records.
Output: the dot-product (as a number) representing the degree of similarity
between two senators' voting p... | d90c3c584f27979ca41bd8bff939da47a8656601 | 3,626,000 |
def _py2java(sc, obj):
""" Convert Python object into Java """
if isinstance(obj, RDD):
obj = _to_java_object_rdd(obj)
elif isinstance(obj, DataFrame):
obj = obj._jdf
elif isinstance(obj, SparkContext):
obj = obj._jsc
elif isinstance(obj, (list, tuple)):
obj = ListCon... | 4889a4ce782ee00172ee29c7ff2ac98f3fa15d8d | 3,626,001 |
def docopt_attr(doc, argv=None, help=True, version=None, options_first=False):
"""docopt with options in attributes rather than dictionary elements
args['--verbose'] => args.verbose
args['<file>'] => arg.file
All else remains the same. Attributes are single values or lists
... | 9e7d28032f5634e4a5eaa8226ca33dba2827ecbd | 3,626,002 |
import os
from pathlib import Path
def get_notebooks_run_in__jobs_update():
"""
"""
#| - get_notebooks_run_in__jobs_update
#| - Read file lines
# Jobs update method in bash_methods
path_i = os.path.join(
os.environ["PROJ_irox_oer"],
"scripts/bash_methods.sh")
with open(pat... | e66f16fc00c44b1c80651ad75e20e2765ac577cc | 3,626,003 |
def must_be_known(in_limit, out_limit):
"""
Logical combinatino of limits enforcing a known state
The logic determines that we know that the device is fully inserted or
removed, alerting the MPS if the device is stuck in an unknown state or
broken
Parameters
----------
in_limit : ``boo... | 241b66b359643d069aa4066965879bb6ac76f2ae | 3,626,004 |
def delete_item(category_id, item_id):
""" Deletes Item
:param category_id:
:param item_id:
:return: render_template
"""
if 'username' not in login_session:
return redirect('/login')
category = session.query(Category).filter_by(id=category_id).one()
item = session.query(Item).f... | 13df75535eb7dde90764baa6540caa0b18ba0c64 | 3,626,005 |
import urllib
from bs4 import BeautifulSoup
import regex
def download_floras():
"""Get the floras from the main page."""
url = SITE
path = FAMILY_DIR / 'home_page.html'
urllib.request.urlretrieve(url, path)
with open(path) as in_file:
page = in_file.read()
floras = {}
soup = Bea... | 665d5afdbd5caf2ba2a61de915a4c97228433027 | 3,626,006 |
def scale(x):
"""Scales values to [-1, 1].
**Parameters**
:x: array-like, shape = arbitrary; unscaled data
**Returns**
:x_scaled: array-like, shape = x.shape; scaled data
"""
minimum = x.min()
return 2.0 * (x - minimum) / (x.max() - minimum) - 1.0 | e5c40a4a840a1fa178a2902378a51bfefc83b368 | 3,626,007 |
def group_data_by_columns(datasets, columns):
"""
:param datasets: [CxNxSxF]
:param columns: F
:return: CxNxFxS
"""
new_dataset = []
for i in range(len(datasets)):
datalist = []
for row in range(len(datasets[i][0][0])):
row_data = []
for column_idx in ... | 8a73c959501f422c26fe358c05ddc6a574123009 | 3,626,008 |
def create_er_html(relations):
"""This function create entity-relationship html.
Args:
relations (list): List of (:class:`FieldPath` :class:`FieldPath`)
Returns:
Html str.
A way might be used is
>>> print create_structure_ers_from_relations([(FieldPath('db', 'ac', 'id'), FieldPath... | bb2392b811226c849dc9a08a596a337ea855a28d | 3,626,009 |
def plotlimit(ul, alpha=0.05, CLs=True, ax=None):
"""
plot pvalue scan for different values of a parameter of interest (observed, expected and +/- sigma bands)
Args:
ul: UpperLimit instance
alpha (float, default=0.05): significance level
CLs (bool, optional): if `True` uses pvalues ... | f1ac8459ee417dd93c219073fbc2815e7e9a5362 | 3,626,010 |
from typing import BinaryIO
import asyncio
import os
async def spawn_carla(
cuda_device: int, carla_world_port: int, log_file: BinaryIO
) -> asyncio.subprocess.Process:
"""Spawns CARLA simulator in the background. Returns the process handle."""
environ = os.environ.copy()
environ["DISPLAY"] = ""
... | 002e10f0c45de149084b7033423d23753a0f1570 | 3,626,011 |
def rand_dm(N, density=0.75, pure=False, dims=None):
"""Creates a random NxN density matrix.
Parameters
----------
N : int, ndarray, list
If int, then shape of output operator. If list/ndarray then eigenvalues
of generated density matrix.
density : float
Density between [0,1... | ef91b913e4eb62f2bd7b80c27f2deabd62756c7d | 3,626,012 |
def GetStaticPipelineOptions(options_list):
"""
Takes the dictionary loaded from the yaml configuration file and returns it
in a form consistent with the others in GenerateAllPipelineOptions: a list of
(pipeline_option_name, pipeline_option_value) tuples.
The options in the options_list are a dict:
Key i... | d49effbdeb687ec62a2e2296330f66521023944c | 3,626,013 |
import os
import hashlib
def gen_signed_cert(domain, ca_crt="ca.crt", ca_key="ca.key", key_path="cert.key"):
"""
This function takes a domain name as a parameter and then creates a certificate and key with the
domain name(replacing dots by underscores), finally signing the certificate using specified CA a... | 27fdb06c08309274df8e7464b27883030e0b1bea | 3,626,014 |
import os
def _nb_dir_file_profiler(path, _f, report=False):
"""Get the profile for a single file on a specified path."""
f = os.path.join(path, _f)
if f.endswith('.ipynb'):
if report:
print(f'Profiling {f}')
return process_notebook_file(f)
return pd.DataFrame() | 090fd5532fb2ae31705f572e33d65fbeeae6c012 | 3,626,015 |
def ExtractNLargestBlobsn(binaryImage, numberToExtract=1):
"""Extract N largest blobs from binary image.
Arguments:
binaryImage: boolean numpy array one or several contours.
numberToExtract: number of blobs to extract (integer).
Returns:
binaryImage: boolean numpy are containing on... | 84c9643d2ed9007b346b20be3fef8bfd6103d687 | 3,626,016 |
import json
import collections
def awx_manage_check_license_data_datasource(broker):
"""
This datasource provides the not-sensitive information collected
from ``/usr/bin/awx-manage check_license --data``.
Typical content of ``/usr/bin/awx-manage check_license --data`` file is::
{"contact_ema... | 329a8de2848772b35e3a52e069912fd17a3e73ca | 3,626,017 |
def create_addresses(account_id):
"""
Create an Address on an Account
This endpoint will add an address to an account
"""
app.logger.info("Request to add an address to an account")
check_content_type("application/json")
account = Account.find_or_404(account_id)
address = Address()
a... | 07fa9df9708bf9d5fddd050cb65c3effcf2ca1b3 | 3,626,018 |
import torch
def anderson(
f, x0, m=5, max_iter=50, tol=1e-4, stop_mode='rel', lam=1e-4, beta=1.0, **kwargs
):
"""
Anderson acceleration for fixed point iteration.
Args:
f (`Callable` or `nn.Module`):
Function to be minimized.
x0 (`torch.Tensor`):
A batch of ve... | e731635b9e557f8a53f423842a956d0e33874290 | 3,626,019 |
def flip_axis(x, axis):
"""flip tensor中的对应轴
# Args
x: nd array
axis: int, axis of x
"""
x = np.asarray(x)
x = np.flip(x, axis=axis)
return x | d0085a6b1d1d3db7bd60fd953b640a380b8f70d4 | 3,626,020 |
from datetime import datetime
def beginning_of_day_utc(day_offset: int) -> datetime:
"""Return Local Midnight time of today +/- day_offset days in UTC time."""
return _apply_day_offset(
datetime.now().replace(hour=0, minute=0, second=0, microsecond=0), day_offset
).astimezone(timezone.utc) | e46a88edaff5619b6cf0d85cf3bfe8882f3ee1ca | 3,626,021 |
def fit_austourists_with_R_params(model, results_R, set_state=False):
"""
Fit the model with params as found by R's forecast package
"""
params = get_params_from_R(results_R)
with model.fix_params(dict(zip(model.param_names, params))):
fit = model.fit(disp=False)
if set_state:
s... | 4135e702ef27ec0c975d611852a0924db2493e6c | 3,626,022 |
from typing import Callable
from typing import Optional
from typing import Any
from typing import Dict
async def execute(
schema: "GraphQLSchema",
document: "DocumentNode",
response_builder: Callable,
root_value: Optional[Any],
context: Optional[Any],
variables: Optional[Dict[str, Any]],
o... | 13d63f943901d5895d4b0dd7e2f0590c7595d1c4 | 3,626,023 |
def validate_ip_addr(addr, version):
"""
Validates that an IP address is valid. Returns true if valid, false if
not. Version can be "4", "6", None for "IPv4", "IPv6", or "either"
respectively.
"""
try:
ip = netaddr.IPAddress(addr, version=version)
return True
except (netaddr.... | 938de99ed978887619463c55ce6128c1eabee0ba | 3,626,024 |
from datetime import datetime
def create_localized_datetime(*args, timezone='UTC', **kwargs):
""" Creates an aware time in the given timezone.
The intuitive way of doing this will give you the wrong answer:
https://stackoverflow.com/questions/24856643/unexpected-results-converting-timezones-in-python... | 0750455d8203ebf01699876c31bdd6993989f394 | 3,626,025 |
def resnet18(resnet_cls, **kwargs):
"""Construct a ResNet-18 model."""
return resnet_cls(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs) | b66cece9f691b252d67b7547dd3c077ea3eefaf0 | 3,626,026 |
import logging
import re
def beautify_declaration_markup(markup : str) -> str:
"""Format our function and class declarations in NOMNOML to be a consistent size"""
# We do not want to break before separators or before the end of a word
# A 'word' in this case may include a trailing colon
# Also catch ... | ae1124acce7b2a1df775c97423067c95dc1a93ae | 3,626,027 |
import os
from datetime import datetime
def calibration_run(param_set_dirpath: str) -> str:
"""
Allows a user to select what model run they want, given an app
Returns the directory name selected.
"""
# Read model runs from filesystem
model_run_dirs = os.listdir(param_set_dirpath)
# Parse... | d8ab9c4c43f7e5c786ea6a8e002496908c27cacb | 3,626,028 |
def post(host, path, data):
"""Sends POST request using HttpClient and the data from GUI form.
:param host: host ip addr
:param path: resource endpoint path
:param data: data to be sent as body of the POST request
:return: HTTP response body
"""
cover = open(data, 'rb').read()
print('Sen... | 82a11497e8d2052eea32efa946616042fb1cb997 | 3,626,029 |
import pytest
from typing import Optional
def round_trip_pathlib(writer, reader, path: Optional[str] = None):
"""
Write an object to file specified by a pathlib.Path and read it back
Parameters
----------
writer : callable bound to pandas object
IO writing function (e.g. DataFrame.to_csv ... | 742201b375f5e85a7adc485e60ca95d36e4c37e1 | 3,626,030 |
def conv_junc_to_exon():
""" Converting sorted junctions to exons here """
def overlap(start, end, start2, end2):
return not (start > end2 or end < start2)
cons_exons = []
overlaps = 0
prev_astart, prev_aend = juncs[0][-2], juncs[0][-1]
prev_jstart, prev_jend = juncs[0][2], juncs[0][3]
... | 1e98de11fa14cd0271abd6e1937e4f8bd7eb203e | 3,626,031 |
import json
from datetime import datetime
def check_temperature (device, root_dir):
"""Check the temperature status and generates files with the tests result.
Required EOS command: show system environment temperature | json
Test failure conditions: A sensor test fails if a sensor HW status is not OK or i... | 14951c63080e5d8d97eb5544b1d306d4cca39728 | 3,626,032 |
def create_folder_hierarchy(item, user, folder):
"""
Create a folder hierarchy that matches the original if the original is
under a project folder.
:param item: the item that will be moved or copied.
:param user: the user that will own the created folders.
:param folder: the destination project... | b5a7f0069fbdcd28ace8787c52963a6ae199f5e8 | 3,626,033 |
def compute_eval_metrics(gt_mask, pred_mask):
"""
Evaluate a mask w.r.t a GT mask
:param gt_mask: m x n grid of 0s and 1s
:param pred_mask: m x n grid of 0s and 1s
:return:
"""
assert (gt_mask.size == pred_mask.size)
tp = float(np.sum(np.logical_and(pred_mask == 1, gt_mask == 1)))
fp... | bce074a18835fde4532b24b8568d6b7c382bdfac | 3,626,034 |
import pickle
def load_current_test_data(collection_name="current_test_data"):
"""loads the current test data and converts it back to normal
Args:
collection_name (str, optional): name of the collection. Defaults to "current_test_data".
Returns:
List of DataFrames: List of the current te... | f929d399b75ea6ab5d2723add3eefb4b8d50c743 | 3,626,035 |
def convolutional_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
"""A block that has a conv layer at shortcut.
Arguments:
input_tensor: input tensor
kernel_size: default 3, the kernel size of middle conv layer at main path
filters: list of integers, the filters... | 7fafe41621aa155b8c9cb6dbccb92a09b780bc4b | 3,626,036 |
def check_type_data(data, data_type=np.ndarray, dim=2):
""" Some basic type checking on data """
# Code assumes that we have a matrix, so force it for single samples
if len(data.shape)==1:
data = data.reshape((data.size,1))
if type(data) != data_type:
raise TypeError('data is n... | 0eb9b2d958ce7b142133ecc68022b77783a418c3 | 3,626,037 |
from malaya_speech.utils import describe_availability
def available_model():
"""
List available speaker change deep models.
"""
return describe_availability(
_availability,
text='last accuracy during training session before early stopping.',
) | bbcea119f0a888720928c6c583f6652e65946d73 | 3,626,038 |
def is_date_field(field, field_schema):
""" Helper method that determines if field_schema is """
return determine_if_is_date_field(field, field_schema) | d8ffcfedcfeb1a2ca8d636be2e462522cca40acd | 3,626,039 |
def generate_script_pick_and_place_block(tcp, frames, ur_ip, ur_port, velocity = 0.05, radius = 0, vacuum_on=2, vacuum_off=5):
"""Generate multiple linear movements and Airpick on/off commands.
Parameters
----------
tcp : sequence of float
Tool center point in a form of list.
tcp = [x, ... | bfe81dd48691c44b7e42a6feaa71a766bcadc088 | 3,626,040 |
import pyranges as pr
import warnings
import os
import subprocess
import shutil
from packaging.version import parse as parse_version
from Bio import SeqIO
from Bio.SeqIO.FastaIO import SimpleFastaParser
from Bio.Seq import Seq
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.SeqRecord import SeqRecord
... | 8e1ec157b6b5616d5b30ae0d2c489d09f255a068 | 3,626,041 |
def get_ds003_downsampled(data_dir=None, url=None, resume=True, verbose=1):
"""Download and load the BIDS-fied ds003_downsampled
:param str data_dir: path of the data directory. Used to force data storage
in a non-standard location.
:param str url: download URL of the dataset. Overwrite the defaul... | d55870e661abc040bad993303c8ee60614e58371 | 3,626,042 |
def open_clean_bands(band_path,
valid_range=None,):
"""Open/mask single landsat band using a valid reflectance value range.
Parameters
-----------
band_path : string
A path to the array to be opened
valid_range : tuple (optional)
A tuple of min and max values of... | e91a8b358558d5e2f7781525a7a637e4a2b74c75 | 3,626,043 |
from datetime import datetime
from typing import Tuple
def calculate_new_case_data_by_region(
region_timeseries: OneRegionTimeseriesDataset,
t0: datetime,
include_testing_correction: bool = False,
testing_correction_smoothing_tau: float = 5,
) -> Tuple[np.array, np.array]:
"""
Calculate new ca... | 3ed0829dacbc3809834b7550ea1dc12cb18a6806 | 3,626,044 |
def _field_object_metadata(field_object):
"""Return mapping of field metadata key to value.
Args:
field_object (arcpy.Field): ArcPy field object.
Returns:
dict.
"""
meta = {"object": field_object}
key_attribute_name = {
"alias_name": "aliasName",
"base_name": "b... | 1e653737f1dee41c7ce786735368e55f4908b116 | 3,626,045 |
import os
def _cohn_kanade(datadir, im_shape, na_val=-1):
"""Creates dataset (pair of X and y) from Cohn-Kanade
image data (CK+)"""
images = []
labels = []
for name in os.listdir(os.path.join(datadir, 'faces')):
impath = os.path.join(datadir, 'faces', name)
labelpath = os.path.join... | 7b84aeb41dafd7b852f17b506e08fb9b121d0ecf | 3,626,046 |
def svn_utf_initialize2(*args):
"""svn_utf_initialize2(svn_boolean_t assume_native_utf8, apr_pool_t pool)"""
return _core.svn_utf_initialize2(*args) | da0c3296fa6477e3a9d20669fdfd61ed5207a76d | 3,626,047 |
def init_console(parser):
"""Initialises the console"""
font = pygame.font.SysFont("Courier", 12)
text = Text(font, size=(200, 40), position=(0, 0))
error_text = init_error_message(parser)
return Console(parser, text, error_text) | 8f387f83fca2fbe25283dc62c3c6ed50ec24ebd3 | 3,626,048 |
import sys
def _xinf_ND(xdot,x0,args=(),xddot=None,xtol=1.49012e-8):
"""Private function for wrapping the fsolving for x_infinity
for a variable x in N dimensions"""
try:
result = fsolve(xdot,x0,args,fprime=xddot,xtol=xtol,full_output=1)
except (ValueError, TypeError, OverflowError):
x... | ae23fdb8baa832fb181ba523fdf43aa596dbe6a2 | 3,626,049 |
def read_targets(targets):
"""Reads generic key-value pairs from input files"""
results = {}
for target, regexer in regexer_for_targets(targets):
with open(target) as fh:
results.update(extract_keypairs(fh.readlines(), regexer))
_LOG.debug("found the following key-value pairs in sour... | 7b0689252f81328f5430acc59ad3f9c32878aafa | 3,626,050 |
import logging
async def refresh_pool_ledger(handle: int) -> None:
"""
Refreshes a local copy of a pool ledger and updates pool nodes connections.
:param handle: pool handle returned by indy_open_pool_ledger
:return: Error code
"""
logger = logging.getLogger(__name__)
logger.debug("refre... | d6c3d53d406f9ca37b063dfd3d65a19d798a4f60 | 3,626,051 |
import _datetime
def seconds_function(context, string=None):
"""
The date:seconds function returns the number of seconds specified by the
argument string. If no argument is given, then the current local
date/time, as returned by date:date-time is used as a default argument.
Implements version 1.
... | 8d9d1d5d6cd9d5261746ca14257bf8c2e0dc9886 | 3,626,052 |
def mean_of_cluster(list_of_points):
"""Calculates the center of the list of points
"""
number_of_points = float(len(list_of_points))
vector_total = [float(0), float(0)]
for point in list_of_points:
for index, component in enumerate(point):
vector_total[index] += component
re... | b94b5ea40fb08253bcade35282692bbb58b85e98 | 3,626,053 |
def symlog(values, threshold):
"""
Convert values to log with linear threshold near zero
"""
return np.sign(values) * np.log10(1 + np.abs(values) / threshold) | f0bcd06326eedc7a2dd65b239b2ad81499ae2d68 | 3,626,054 |
def cluster_by_best_antecedent(document, predictions, threshold=0.5):
"""
Clusters the document's mentions by matching each with its best antecedent
with a score above the 0.5 threshold.
@arg predictions Mention-pair predictions.
@arg threshold The classification threshold, above this value mention... | a4c3db4ae799047340c2cfcfa4f21d7414797a86 | 3,626,055 |
def max_pooling(x, pool_h, pool_w, stride):
"""Max pooling."""
validator.check_integer("stride", stride, 0, Rel.GT, None)
num, channel, height, width = x.shape
out_h = (height - pool_h)//stride + 1
out_w = (width - pool_w)//stride + 1
col = im2col(x, pool_h, pool_w, stride)
col = col.reshap... | c427c2ecd555ce48d73aa989ccfd4595d84ef36d | 3,626,056 |
import os
from functools import reduce
def multiple_process(distribute_list, partition_func, task_func, n_jobs, reduce_func, parameters):
"""
Args:
distribute_list(list): The "data" list to be partitioned, such as a list of files which will be
distributed among different tasks and each t... | a070e510b071b5be6ccdf906e5e17ed693251d42 | 3,626,057 |
def get_unique_pairs(pairs, return_indices=False) -> np.array:
"""Extract unique pairs."""
# idx: Indices in triples of unique pairs
_, idx = np.unique(pairs, return_index=True, axis=0)
sorted_indices = np.sort(idx)
# uniquoe pairs where original order of triples is preserved
unique_pairs = pai... | 3f8c6408d9a6f871e7f89278448dcaa1b5028c12 | 3,626,058 |
def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, vocab, world_size=None):
"""Train/eval Data preparation function."""
label_dtype = 'int32' if task.class_labels else 'float32'
truncate_length = max_len - 3 if task.is_pair else max_len - 2
trans = partial(convert_examples_to_feat... | 8ee375a58d8a827f3ab09658bf3d3927696c303b | 3,626,059 |
def revSequence(channels, n_block):
"""Make a sequence of multiple reversible block
Arguments:
channels {[int]} -- [number of channels fixed]
n_block {[int]} -- [Number of blocks]
Returns:
[nn.Module] -- [The reversible sequence]
"""
sequence = []
for i in range(n_block):
sequence.append(revBlock(chan... | cd145bb5901b2e389a5fa772e3e08abba44b227d | 3,626,060 |
def calculate_angle(v1, v2):
"""
Calculate the angle ([0, Pi]) between two vectors according to:
p = u * v = |u||v|cos(a)
Parameters
----------
v1 : arr
v2 : arr
Returns
-------
angle : float
The angle ([0, Pi]) between these two given vectors
"""
product = np... | 9c50fe95f15ff2a6dc41d9c30f792e8aee27d831 | 3,626,061 |
def range_overlap(a_min, a_max, b_min, b_max):
"""
Neither range is completely greater than the other
"""
return (a_min <= b_max) and (b_min <= a_max) | c05d8b0799f62300760ad69704a5091c3830ad26 | 3,626,062 |
def flip_errors(data):
"""Flip sign for lower boundary responses.
:Arguments:
data : numpy.recarray
Input array with at least one column named 'RT' and one named 'response'
:Returns:
data : numpy.recarray
Input array with RTs sign flipped wher... | 2ae325534658c055ff4d0cb841de696875a46aa6 | 3,626,063 |
def predict(patches, DEBUG):
"""
predict zebra crossing for every patches 1 is zc 0 is background
"""
#print(len(patches))
labels = np.zeros(len(patches))
index = 0
for Amplitude, theta in patches:
mask = (Amplitude>25).astype(np.float32)
h, b = np.histogram(theta[mask.astype(np.... | 3bc52da0c4e6e44549ab7fb8ee7598d362f1f5e1 | 3,626,064 |
from datetime import datetime
import ipaddress
import socket
from operator import or_
def is_clone(nickname, hostmask, withdate=False):
"""
Checks whether a nickname is considered a clone by the bot.
:param withdate: Whether to return a tuple containing both matches and the last timestamp of connection
... | 8d7ee5d6a39e8fdb356f61d7e3890a9f34e3b4ca | 3,626,065 |
import logging
import copy
def test_online_reads_checkpoint():
"""Test that online analysis reads the checkpoint correctly in all cases"""
current_log_level = logger.level
logger.setLevel(logging.ERROR) # Temporarily suppress some of the logging output
raw_template_script = get_template_script()
... | 0a89f8bccbb7f278c7d236834518fc45b28236bb | 3,626,066 |
def preview(df,preview_rows,preview_max_cols):
""" Returns a preview of a dataframe, which contains both header
rows and tail rows.
"""
assert type(df) is pd.DataFrame
if preview_rows <= 0:
preview_rows = 1
initial_max_cols = pd.get_option('display.max_columns')
pd.set_option('displa... | 10a6ee5c59de16cf9ff11bcb739afa8cdc8bf462 | 3,626,067 |
import json
def read_json(json_file_path: str) -> dict:
"""Takes a JSON file and returns a dictionary"""
with open(json_file_path, "r") as fp:
data = json.load(fp)
return data | 07cb6c606de83b2b51ddcbf64f7eb45d6907f973 | 3,626,068 |
def _log10_cumulative_shmf(logmp, y0, m, xc, x0, kc, dy):
"""Differentiable kernel of the cumulative subhalo mass function."""
y = y0 + m * (logmp - x0)
return _jax_sigmoid(logmp, xc, kc, y, y - dy) | cb82fb8d6d0cffbfe6553c9e11b4ff006ab24583 | 3,626,069 |
def internal_token_encoder() -> TokenEncoder[InternalToken]:
"""Return InternalToken encoder with correct secret embedded."""
return TokenEncoder(
schema=InternalToken,
secret=INTERNAL_TOKEN_SECRET,
) | e9239e81dfa0f385f02387886a52399d2093fd94 | 3,626,070 |
import os
def _get_user_guide_directory():
"""Returns absolute path to docs/ directory"""
docsdir = os.path.join("docs", "user_guide")
return os.path.abspath(docsdir) | 147294fe005f7d6756aeb4c3579bddf795668087 | 3,626,071 |
import os
import math
import pickle
def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
"""
Trains a k-nearest neighbors classifier for face recognition.
:param train_dir: directory that contains a sub-directory for each known person, with its name.
(V... | 17ffe96bd12b1b80b9a977b8b103b0758a6e7687 | 3,626,072 |
def index(request):
"""
Serve view for home page
"""
return render(request, "index.html") | ddcafaf5312f7c811f4aacbb3fcb6285e9b6ab22 | 3,626,073 |
import glob
import os
def get_env(pathname=None, *, profile_dir=None, prefix=None):
"""Read the BASH file and extract the variables. Currently this is
done with pattern matching. Another way would be to run the BASH
script as a subshell and then do a printenv and actually capture the
variables
:param pathname... | 4b772415276796926d0466966e6867f7063c7cee | 3,626,074 |
from typing import Tuple
def _get_property_types(layer: Layer) -> Tuple[str, ...]:
"""Given a GDAL Layer, return the non-geometry field types."""
layer_definition = layer.GetLayerDefn()
type_codes = tuple(
layer_definition.GetFieldDefn(index).GetType()
for index in range(layer_definition.G... | 54377e5fb50b7c6953a3cf863bbaa8bf3831b0e5 | 3,626,075 |
def TInt_GetKiloStr(*args):
"""
TInt_GetKiloStr(int const & Val) -> TStr
Parameters:
Val: int const &
"""
return _snap.TInt_GetKiloStr(*args) | b2a0582548d86dcf3eb9e3b489776116ae9d68bc | 3,626,076 |
def linear(x, n_units, scope=None, stddev=0.02,
activation=lambda x: x):
"""Fully-connected network.
Parameters
----------
x : Tensor
Input tensor to the network.
n_units : int
Number of units to connect to.
scope : str, optional
Variable scope to use.
stdd... | e0b2a70f6480dae16e384ceab6aabfc21daaa5ca | 3,626,077 |
def RadialSymmetryFunction(R, rc, rs, e):
"""Calculates radial symmetry function.
B = batch_size, N = max_num_atoms, M = max_num_neighbors, d = num_filters
Parameters
----------
R: tf.Tensor of shape (B, N, M)
Distance matrix.
rc: float
Interaction cutoff [Angstrom].
rs: float
Gaussian dista... | 7f6dc67d6f7c1d490d116528c14ca90f2b732d8d | 3,626,078 |
def plot_curve(axis, params, train_column, valid_column, linewidth = 2, train_linestyle = "b-", valid_linestyle = "g-"):
"""
Plots a pair of validation and training curves on a single plot.
"""
model_history = np.load(Paths(params).train_history_path + ".npz")
train_values = model_history[train_colu... | 2efcf1a780091ae2ce2025555aeb270d20dc07e9 | 3,626,079 |
from typing import Optional
from typing import Dict
def set_magmoms(
atoms: Atoms,
elemental_mags_dict: Optional[Dict] = None,
copy_magmoms: bool = True,
mag_default: Optional[float] = 1.0,
mag_cutoff: float = 0.05,
) -> Atoms:
"""
Sets the initial magnetic moments in the Atoms object.
... | 80aee80bb737963247dfdd8ff2e223dbe3c9b971 | 3,626,080 |
def round_list(x, digits=6):
"""helper for approximate tests, round a list"""
if isinstance(x, csr_matrix):
x = sparse_to_dense(x)
return [round(_, digits) for _ in list(x)] | ff61b1266bf6bfc5618aed13af125a64333d1457 | 3,626,081 |
def iter_to_table(value):
"""Convert raw API responses to response tables."""
if isinstance(value, list):
return _format_list(value)
if isinstance(value, dict):
return _format_dict(value)
return value | a4d12f677e425330368218f050d2a83d9d459a4f | 3,626,082 |
def get_last_line(fn):
"""Returns the last line of a file
Args:
fn (str): File name of the file to read from
"""
with open(fn, 'r') as fin:
for line in fin:
pass
return line | 40867816657af6350aa400ab17d60b816566d5c5 | 3,626,083 |
def _gen_tinynet(variant_cfg, channel_multiplier=1.0, depth_multiplier=1.0, depth_trunc='round', pretrained=False, **kwargs):
"""Creates a TinyNet model.
"""
arch_def = [
['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'],
['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'... | 4836e4fbacb36af927b8ad6b81084cbd769111d3 | 3,626,084 |
from typing import OrderedDict
def full_sdssmatch(img1,img2,inst,gmaglim=19):
"""
This function requires two stacked images, one each filter that will be used
in solving the color equations. The purpose of this function is to first
collect all of the SDSS sources in a given field using the
``odi.s... | b8640db90998eda702709dff33130a84301891d6 | 3,626,085 |
def calcula_menor_caminho(nome, origem, destino):
"""Retorna o menor caminho entre os dois pontos."""
mapa = Mapa()
rotas = Rota.objects.filter(nome=nome)
for rota in rotas:
mapa.add_ponto(rota.origem)
mapa.add_rota(rota.origem,
rota.destino,
... | 44000464ec156c5a0abc5e93c91564921aa827ba | 3,626,086 |
import argparse
def init():
"""
The root entrypoint for the ``wa_cli`` is ``wa``. This the first command you need to access the CLI. All subsequent subcommands succeed ``wa``.
"""
# Main entrypoint and initialize the cmd method
# set_defaults specifies a method that is called if that parser is use... | e423482375874f082718b8b672e754e9cd3c1f78 | 3,626,087 |
import logging
import warnings
import os
def configure_logging():
"""
Initialization of the logging system for the framework
:return:
"""
global LOG
debug = ConfigHelper.get(CFS_GENERAL, "debug")
if debug.lower() == "true":
file_level = logging.DEBUG
else:
file_level = ... | fa832c4716df2afb00cd2dae39e282c69ea9e63e | 3,626,088 |
import re
def HasServices(proto_path):
"""Does a .proto file have any service definitions?
Args:
proto_path: path to .proto.
Returns:
True iff there are service definitions in the .proto at proto_path.
"""
with open(proto_path, 'r', encoding='utf8') as f:
for line in f:
if re.match(SERVI... | e8393a0ec23dece420d7074df122adf52fe142f6 | 3,626,089 |
import posixpath
import requests
def _remote_file_size(url=None, file_name=None, pn_dir=None):
"""
Get the remote file size in bytes.
Parameters
----------
url : str, optional
The full url of the file. Use this option to explicitly
state the full url.
file_name : str, optional... | 62d478f3620dd5532e9ee5657946afbce1d233b2 | 3,626,090 |
import random
import tqdm
import torch
def estimate_compression(model, data, nsamples, context, batch_size, verbose=False):
"""
Estimates the compression by sampling random subsequences instead of predicting all characters.
NB: This doesn't work for GPT-2 style models with super-character tokenization, s... | 700e13925d7781f383c3470cd273be1d83acdab5 | 3,626,091 |
def OptionalLibraryDefines():
"""
Work out what optional libraries have been asked for,
and return the appropriate #define names, as a list.
"""
# Todo #2367 take out adaptivity, and replace with warning/error?
possible_flags = {'cvode': 'CHASTE_CVODE', 'vtk': 'CHASTE_VTK', 'adaptivity': 'CHASTE... | 5448a616e449a9eab0929d99a29113645e56a3dc | 3,626,092 |
def filter_by_distance(points, mindist=4):
"""Evaluate the distance between each pair os points in @points
and return just the ones with distance gt @mindist
Args:
points(set of tuples): set of positions
mindist(int): minimum distance
Returns:
set: set of points with a minimum distance be... | 1fe33da983fee9bab2fcde533191d93180cdb01e | 3,626,093 |
def read_point_cloud_log(path: str, row_size: int, double_precision: bool = True) -> np.ndarray:
"""Reads a .pcl file and containing x, y, z values specifying a point cloud."""
with open(path, 'rb') as f:
data_type = np.double if double_precision else np.single
data = np.fromfile(f, data_type)
... | a63eb568a73f803bbb194fbd67f8759fe0b21c73 | 3,626,094 |
def HLRBRep_CurveTool_Parabola(*args):
"""
:param C:
:type C: Standard_Address
:rtype: gp_Parab2d
"""
return _HLRBRep.HLRBRep_CurveTool_Parabola(*args) | 557fd2b10fd86db72d443fcd1f534a2496d232bc | 3,626,095 |
import uuid
def unique_variable_name():
"""Creates a unique variable name. Useful when attempting to introduce
a new token to see if it can fix specific cases of SyntaxError."""
name = uuid.uuid4()
return "_%s" % name.hex | d4b54a8ab76fa8bddd6fe62a735f1dd886e9e62a | 3,626,096 |
def impersonated_session_status(request):
"""
Adds variable to all contexts
:param request:
:return bool:
"""
return {"is_impersonated_session": is_impersonated_session(request)} | 2499946653a2cb411fbe7e2b389f72c08fcec92d | 3,626,097 |
def random_dates(start, end, size):
"""
Generate random dates within range between start and end.
Adapted from: https://stackoverflow.com/a/50668285
"""
# Unix timestamp is in nanoseconds by default, so divide it by
# 24*60*60*10**9 to convert to days.
divide_by = 24 * 60 * 60 * 10**9
st... | 589b974b262d41f5903362fd62bfc506ed8ff40d | 3,626,098 |
def cleaner_unicode(string):
"""
Objective :
This method is used to clean the special characters from the report string and
place ascii characters in place of them
"""
if string is not None:
return string.encode('ascii', errors='backslashreplace')
else:
return string | f4e2c4b9fa7f4a644e409a5d429531a34bc1c6c2 | 3,626,099 |
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