content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_url(query_str: str, base_url: str, page: int = 1) -> str:
"""Create query URL for Google CSE API search.
Args:
query_str: Search term to use.
base_url: Site URL.
page: Result page to start at.
Returns:
Completed URL.
"""
return (
_API_URL
+ "... | 151c47a1680356a9c6744d774bbec70ba0d9cc3c | 3,620,300 |
def build_full_traversal():
"""
Builds a traversal spec that will recurse through all objects .. or at
least I think it does. additions welcome.
See com.vmware.apputils.vim25.ServiceUtil.buildFullTraversal in the java
API. Extended by Sebastian Tello's examples from pysphere to reach networks
a... | 03f039945d4d5d34462e8f6dc9266a730300b837 | 3,620,301 |
def tonum(s):
"""Converts a string representation of a decimal, hexadecimal, or binary number to a number value or None."""
if type(s) in (int, float):
return s
if s is None:
return 0
base = 10
if isinstance(s, str):
if "x" in s:
base = 16
elif "b" in s:... | 4878a5619fcd96897f5bec6167bb8f0b4539588a | 3,620,302 |
from pathlib import Path
def mock_derived_metadata_derivation(
path: Path, parameters: any_pb2.Any) -> symfs_pb2.Metadata:
"""Mock custom derived metadata function.
The metadata this function generates will be simply a copy of the parameters,
which is assumed to be everchanging.symfs.ext.TestMessage, with ... | 9a2a3bd79765e2b8abdb0af15990a66234744ef3 | 3,620,303 |
import typing
def has_permissions(*permissions: typing.Tuple[str]):
"""
Checks if the user has the permissions.
"""
def decorator(func: typing.Awaitable):
func.has_permissions = permissions
return func
return decorator | faf5cdc4eb63bfb8925bed8fde7d7afbe29501a5 | 3,620,304 |
from typing import Optional
from typing import Type
from typing import MutableMapping
from typing import Any
def fromtree(
tree: etree.Element,
use_builtin_types: Optional[bool] = None,
dict_type: Type[MutableMapping[str, Any]] = dict,
) -> Any:
"""Convert an XML tree to a plist structure.
Args:
... | 67861407353fbf745a547854ad76971a3ec217d3 | 3,620,305 |
def patch_with_user_globals(data, skip_noexist=True):
"""
Patch the paths.json data structure with the user's globals in place.
:param data: the paths.json data structure
:param skip_noexist: skip applying global data if it doesn't exist
otherwise raise an error for a non-existant file
:ret... | 785bddc58f6bb2d0394e79b9a11cdbf2e90957fd | 3,620,306 |
import sys
def crop_frames(args, data):
""" Indexing is done this way
##################
# 1 # 2 # 3 #
# 4 # 5 # 6 #
#################
"""
if args.num_features % (args.v_crop_scale * args.h_crop_scale) != 0:
print("Incorrect Crop Scaling selected. Please ensure original d... | 15f95c5840782e6eb0aebf3a95f2e11e77749fff | 3,620,307 |
def getdeleted(ref):
"""
Return the deleted positions (p=0 or p=1) from a report file.
"""
return [int(i) for i in fopen(ref+'.rep')[7]] | 72144fdd5f6ada178b303107c6c8d966aad28115 | 3,620,308 |
import os
def recursive_glob(rootdir=".", suffix=""):
"""Performs recursive glob with given suffix and rootdir
:param rootdir is the root directory
:param suffix is the suffix to be searched
"""
return [
os.path.join(looproot, filename)
for looproot, _, filenames in os.walk... | cb4eec634cee15fdd2109828f1ec5574f58614b9 | 3,620,309 |
def generate_permutations(ls):
"""
Question 16.3: Generate all permutations of a list
"""
if len(ls) <= 1:
return [ls]
result = []
for perm in generate_permutations(ls[1:]):
for idx in xrange(len(ls)):
result.append(perm[:idx] + ls[0:1] + perm[idx:])
return resu... | f0d622b599d8b79e4c290f00a807afb9ac32cf55 | 3,620,310 |
import glob
import logging
def get_filename(work_dir, product_id):
"""Retrieve the Landsat metadata filename to use
The file may have issues, so call the fix function to remove those issues.
"""
try:
parser = lambda x: 'old' not in x and not x.startswith('lnd')
filename = filter(parse... | a6b04e6954671b9904541f79bffa3c807afbcf00 | 3,620,311 |
from typing import Callable
from typing import Iterable
from typing import Any
async def async_setup_platform(
hass: HomeAssistantType,
config: ConfigType,
async_add_entities: Callable[[Iterable[Entity]], Any],
discovery_info=None,
):
"""Set up the sensor platform"""
return False | 912aedbc1cc844a6b18c93c3e566324173a4ba10 | 3,620,312 |
def l2_regularization(W, reg_strength):
"""
Computes L2 regularization loss on weights and its gradient
Arguments:
W, np array - weights
reg_strength - float value
Returns:
loss, single value - l2 regularization loss
gradient, np.array same shape as W - gradient of weight by l2... | d43af7204a7de8d1b1d922d186a235e7889681dc | 3,620,313 |
from china_stock_data_api import netease
def financial_info(index):
""" get the company financial info for index
:param index: stock index
:return: data frame containing financial data by season
"""
return netease.NeteaseStockInfo.latest(index) | 14a1f85ea8ed70db11d25a0a7f5e93c991dc8969 | 3,620,314 |
import sys
def handle_response(response, display=False):
"""
Dispatch response based on scripting response or event.
"""
def handle_script_response(response, display=False):
if display:
sys.stderr.write(
u'Scripting request submitted with request id: {}\n'.form... | a254ffc5084d8b0d2eaef255bbd122a5a10ec8f2 | 3,620,315 |
import pathlib
import glob
def presto_fft(eventfile,segment_length,demod,PI1,PI2,t1,t2):
"""
Obtain the FFT files that were generated from PRESTO
eventfile - path to the event file. Will extract ObsID from this for the NICER files.
segment_length - length of the segments
demod - whether we're dea... | 48d1579dd72575214dd757750964d4eb0b24926f | 3,620,316 |
import socket
def hostname():
"""
Check the system's hostname
:rtype: str
:return: system hostname
"""
return socket.gethostname().split('.')[0] | 438ccd56367565e0cf0c9e8783bc73cb4507e83b | 3,620,317 |
def run_optimizers(args):
"""Optimize each of an iterable of PySource objects."""
sources = tuple(load_path(args.source))
for source in sources:
utils.execute(args, source.node)
return sources | 4e43a1addbf3693eb29282ed0e0c85a4d6440976 | 3,620,318 |
import csv
def import_data(file):
"""
Imports instruction data from a .csv file, at the specified filepath
:param file: file
File object, to be read by the CSV reader
:return: list
A list of dictionaries, per row in the file, with the keys specified in the headers list
below
... | 472f47c2e2111d5ce9a9d20e1ac447a504941aa2 | 3,620,319 |
def recipe_footpod_symmetry(
df_footpods, df_music, df_phone_activity, df_sessions, sections=None
):
"""
Recipe for extracting statistical symmetry information per song for
valid bouts of running of the real-time footpod data.
Parameters
----------
df_footpods : pandas.DataFrame
Dat... | b2112eea9df824678b1405b39f3825e1fea9b697 | 3,620,320 |
import os
import json
def get_authed_registries():
"""Reads the local Docker client config for the current user
and returns all registries to which the user may be logged in.
This is intended to be run client-side, not by the daemon."""
result = set()
if not os.path.exists(constants.DOCKER_CONFIG_... | 2091ff4beed7bf3f4dcf3083d8ba3610e965aa18 | 3,620,321 |
import re
def filter_data(data_frame):
"""
Process string value
:param data_frame: raw data in data frame type
:return: data frame
"""
data_frame[data_frame.columns[len(data_frame.columns) - 2]] = \
data_frame[data_frame.columns[len(data_frame.columns) - 2]].apply(lambda x: int("".join... | 75e3a40c0c1d06db7da71e8b6ddb0001c5933634 | 3,620,322 |
from datetime import datetime
def convert_timezone(date_str, tz_from, tz_to="UTC", fmt=None):
""" get timezone as tz_offset """
tz_offset = datetime_to_timezone(
datetime.now(), tz=tz_from).strftime('%z')
tz_offset = tz_offset[:3] + ':' + tz_offset[3:]
date = parse_date(str(date_str) + tz_off... | 35aa52eee969dd3532b50c3a3679c24f0cf4f04e | 3,620,323 |
from typing import Tuple
from typing import List
from typing import Any
def _extract_bracket_params(meta_type: str) -> Tuple[str, List[Any]]:
"""
Gets parameters from the string representation of the type
Args:
meta_type (str): The string name of the metadata type
Returns:
Tuple[str,... | 38fc3872c18bb788a54d50b09a36cf3f5925550e | 3,620,324 |
def make_classification_df(
seed: int,
n_samples: int = 10_000,
n_features: int = 25,
n_informative: int = 10,
n_redundant: int = 10,
n_repeated: int = 5,
class_sep: float = 0.2,
flip_y: float = 0.1,
target_col: str = "target",
) -> pd.DataFrame:
"""Source of default values: http... | 31ea883341dbd2900ed7bd8402a796bad490ebde | 3,620,325 |
import hashlib
def create_view_ID(img):
"""
Generates 12-digit ID of image to make sure each image has a unique identifier. When saving and reading
image features, two images with the same filename will have unique identifiers.
:param img: image to create an ID for.
:return viewID: a unique viewId... | 2b429a5c504386e486542ac9f7a4fa663a08c996 | 3,620,326 |
import os
def readList(flist, list_type='array',sep='\s+'):
"""
Function to read a key files and performs checks for required columns parameters
Parameters:
flist: str or pandas dataframe
A path to the key file or DataFrame itself
list_type: str
"array" for array list, "event" for earthquake list, "coda"... | a2705888fb7bea5ff87abf9ca1185fe9ee78048b | 3,620,327 |
import hashlib
def calc_local_file_md5_sum(path):
"""
Calculate and return the MD5 checksum of a local file
Arguments:
path(str): The path to the file
Returns:
str: The MD5 checksum
"""
with open(path, "rb") as file_to_hash:
file_as_bytes = file_to_hash.read()
re... | 78020e86a6d9a6939de6b9050d34fca0d482aab4 | 3,620,328 |
import json
def news_keywords() -> str:
"""[Fetches the keywords used to gather news articles]
Returns:
str: [A string of the keywords searched for by the news API]
"""
with open("config.json", encoding="UTF-8") as config:
config_data = json.load(config)
news_kw = config_data[... | b0830625f58de9089130a3d571588ab558b2a3db | 3,620,329 |
from typing import Any
def default_weapon_type_option(
description: str = "Restrict the weapon type. Default: All types", required: bool = False
) -> Any:
"""
Decorator that replaces @slash_option()
Call with `@default_weapon_type_option()`
"""
def wrapper(func):
return slash_option(... | 5e5fe8e05e39ec7329264b4828b4047ea06b368a | 3,620,330 |
from datetime import datetime
async def get_patrol_id(request: Request):
""" 获取当前order id，已经加 1 的了 """
_date_str = datetime.now().strftime('%Y%m')
patrol_id = await request.app['redis'].get(f'it:patrolID:{_date_str}')
if patrol_id is None:
async with request.app['mysql'].acquire() as conn:
... | fcc0137e1af3ba2a22dc6d54b459af05db4c0e8b | 3,620,331 |
from typing import Union
from typing import List
def parse(
response_text: str, *, batch: bool, validate_against_schema: bool = True
) -> Union[JSONRPCResponse, List[JSONRPCResponse]]:
"""
Parses response text, returning JSONRPCResponse objects.
Args:
response_text: JSON-RPC response string.
... | ff0d2c5a4852255588db28068685f92dd6c4c66d | 3,620,332 |
import os
def install_client(request):
"""Displays an agreement that the user must agree to before they can download the MDM Client installer"""
context = {}
if request.method == 'POST':
form = ClientForm(request.POST)
if form.is_valid():
installer = os.path.join(settings.MEDIA... | 7de1ed614ed1ffa0ec9701c85e27616db07deba5 | 3,620,333 |
def get_ping_ip():
# TODO: convert return to json object
"""
Get ips for network testing.
@return: HTML containing a list of IPs
"""
config = request.form['config']
peers = g.cur.execute("SELECT id, name, allowed_ip, endpoint FROM " + config).fetchall()
html = ""
for i in peers:
... | f0428d485972a01a861de2820f1cb08a11b2d06e | 3,620,334 |
def compute_warped_image_multiNC(
I0, phi, spacing, spline_order, zero_boundary=False, use_01_input=True
):
"""Warps image.
:param I0: image to warp, image size BxCxXxYxZ
:param phi: map for the warping, size BxdimxXxYxZ
:param spacing: image spacing [dx,dy,dz]
:return: returns the warped image ... | 1f2fffb1ed09fe2b40061dfc5728ed5b672e8828 | 3,620,335 |
from datetime import datetime
def get_day(days: int = 0):
"""
返回的日期格式 %Y-%m-%d
:param days: 距离今天的天数
:return: 格式 %Y-%m-%d
"""
return (datetime.datetime.now() + datetime.timedelta(days=days)).strftime("%Y-%m-%d") | 9626098ad53489d8fa9c1d5364e7a471003c48d6 | 3,620,336 |
def emit_compare(field_name, value, session, model):
"""Emit a comparison operation comparing the value of ``field_name`` on ``model`` to ``value``."""
property = getattr(model, field_name)
return property == value | a9ad880951f87f488b12c4ce7c38c5e0e463a798 | 3,620,337 |
def line_loglog(x, m, n):
"""a straight line in loglog-space"""
return x ** m * np.e ** n | 119c6b1d4e758279c10c4667839ea83742556492 | 3,620,338 |
def plot_statistical_uncertainty(response_matrix, filename=None, **kwargs):
"""Plot the maximum sqrt(statistical variance) of each truth bin.
This plots will contain the minimum, maximum, and median marginalization of
these maximum numbers.
Parameters
----------
response_matrix : ResponseMatr... | 7b86e929a4d620d3302dce67bfb546f4b05f7c42 | 3,620,339 |
import numpy as np
import scipy.linalg as la
def milcshake_a ( dt, bond, r_old, r, v ):
"""First part of velocity Verlet algorithm with constraints."""
# This subroutine iteratively adjusts the positions stored in the array r
# and the velocities stored in the array v, to satisfy the bond constraints
... | 9184b7861383710d84cbe6176e4cca1c206d29c0 | 3,620,340 |
def parse_LDI_ins(tokens):
"""Attempts to parse a LDI instruction."""
failure = None
assert len(tokens) > 0
token1 = tokens[0]
op = token1.text
if op.upper() != 'LDI':
return failure
statement = Obj()
statement.type = 'STATEMENT'
statement.statement_type = 'INSTRUCTION'
s... | 92c6102ccaea4b5fda025c7e7ac10cf0549b56d3 | 3,620,341 |
def mad(a, axis=None):
"""
Compute *Median Absolute Deviation* of an array along given axis.
"""
# Median along given axis, but *keeping* the reduced axis so that result can still broadcast against a.
med = np.nanmedian(a, axis=axis, keepdims=True)
mad = np.nanmedian(np.absolute(a - med), axis=a... | 7a439c970bab9696d4a1b50725afd6636f437843 | 3,620,342 |
import torch
def torch_image_to_numpy(image: torch.Tensor):
"""
We've created a function `torch_image_to_numpy` to help you out.
This function transforms an torch tensor with shape (batch size, num channels, height, width) to
(batch size, height, width, num channels) numpy array
"""
... | 0ce27349d40a8063137a79351fa7a696cd8c6615 | 3,620,343 |
import os
def has_data(file_path):
"""
Check if a file has any data in it.
`Args:`
file_path: str
The file path.
`Returns:`
boolean
``True`` if data in the file and ``False`` if not.
"""
if os.stat(file_path).st_size == 0:
return False
els... | d135bbca9281f1f258b845506c40c95f01a80e48 | 3,620,344 |
def unsigned32(i):
"""cast signed 32 bit integer to an unsigned integer"""
return i & 0xFFFFFFFF | 1d4e06406d3ee7ce7d8f5cefd28955f135059917 | 3,620,345 |
import json
def get_snippet_edit_code(request, snippet_id):
"""
Returns a HTTPResponse that renders the admin of a smartsnippet.
:param request: Request needed to create the rendering context
:param snippet_id: id of the smartsnippet model
:param request.POST.config: dictionary containing existi... | 5eaf24488ef8d3ffa8f46356d840fa4b63cf99b3 | 3,620,346 |
def get_user_legs(df, user_id, use_multiprocessing=True) -> pd.DataFrame:
"""
Builds the legs DataFrame for the given user.
Args:
df (pandas.DataFrame): waypoints DataFrame
user_id (str): ID of the user whose legs are to be created
use_multiprocessing (bool, optional): Specifie... | 6d21192236ad72578e4d9c443f9fdaab12be254a | 3,620,347 |
from typing import List
from typing import Tuple
from typing import Dict
import torch
from typing import OrderedDict
def generate_input(
seq_len: int, batch_size: int, input_names: List[str], device: str = "cuda"
) -> Tuple[Dict[str, torch.Tensor], Dict[str, np.ndarray]]:
"""
Generate dummy inputs.
:p... | d084e1a982ee2ecd60b0e986ead75bc5273ccbb1 | 3,620,348 |
def get_task_or_404(challenge_slug, task_identifier):
"""Return a task based on its challenge and task identifier"""
t = Task.query.filter(
Task.challenge_slug == challenge_slug).filter(
Task.identifier == task_identifier).first()
if not t:
abort(404)
return t | 1765069d18604ba00a4efd1fd18a645ccc666fd6 | 3,620,349 |
def BuildToken(request, execution_time):
"""Build an ACLToken from the request."""
token = access_control.ACLToken(
username=request.user,
reason=request.REQ.get("reason", ""),
process="GRRAdminUI",
expiry=rdfvalue.RDFDatetime().Now() + execution_time)
for field in ["REMOTE_ADDR", "HTTP_X... | 164244ca93710c45d0eb8b19b1eb0c934ac89361 | 3,620,350 |
import re
from datetime import datetime
def parse_logfile_event_marker(line_str):
"""
Parse a logfile line as an event marker.
Parameters:
line_str (str): a line from the locust log for a load test with markers
enabled.
Returns:
dict: dict object with the following keys: ... | 0746e23926c3f4262d163c6e10d0ae8177e36ece | 3,620,351 |
import logging
import torch
def preprocess_pipeline(image):
"""preprocess_pipeline processes the image data.
args:
image: a numpy array in shape of (224, 224, 3)
returns:
image: the processed image numpy array
"""
assert np.array_equal(image.shape, (224, 224, 3)), \
"... | e5ae77b0a0f8c18825b546a4ec4135eb8385bcc1 | 3,620,352 |
import tkinter as tk
from tkinter import filedialog
import os
def ask_path(folder_flag=True, multiple_files_flag=False):
"""Makes a tkinter dialog for choosing the folder if folder_flag=True
or file(s) otherwise. For multiple files the multiple_files_flag should
be True.
"""
# This method is almos... | 675f36f34a97a3445d35d1e7a45ee71e60a7ea48 | 3,620,353 |
def compute_gad_point_indices_mp(args: tuple) -> dict:
"""
Computes geometric anomaly detection (GAD) Procedure 1 from [1], for data point
indices, taking in args for multiprocessing purposes.
Parameters
----------
args : tuple
Multiprocessing argument tuple:
data_points : n... | c0cc874af561f15b0b8234a5279f54d4df9afdee | 3,620,354 |
def has_and_not_none(obj, name):
"""
Returns True iff obj has attribute name and obj.name is not None
"""
return hasattr(obj, name) and (getattr(obj, name) is not None) | 0d68a9b01d56ba056768d06a88c68b4bd5bbd4d2 | 3,620,355 |
from typing import OrderedDict
import attr
def _dump(config_instance, dict_type=OrderedDict):
""" Dumps an instance from ``instance`` to a dictionary type mapping.
:param object instance: The instance to serialized to a dictionary
:param object dict_type: Some dictionary type, defaults to ``OrderedDict``... | d8b1242d4ab47b518dfd4523e83c1d67f5e87537 | 3,620,356 |
def eval_jac_g(x, out):
"""Values of the jacobian of g"""
assert len(x) == nvar
out[()] = [
x[1] * x[2] * x[3],
x[0] * x[2] * x[3],
x[0] * x[1] * x[3],
x[0] * x[1] * x[2],
2.0 * x[0],
2.0 * x[1],
2.0 * x[2],
2.0 * x[3],
]
return out | a50f3e8621ad5a54f69d2d154464e6ba8e818337 | 3,620,357 |
import torch
from typing import Optional
import os
def pytorch_load_save_functions(
state_dict_objects: dict,
mutable_state: Optional[dict] = None,
fname: str = "checkpoint.json",
):
"""
Provides default `load_model_fn`, `save_model_fn` functions for standard
PyTorch models (arguments to `resu... | ee232e386daffbdcd29ef08511fc36683d6adbca | 3,620,358 |
import re
def trivial_tokenize_urdu(s):
"""
A trivial tokenizer which just tokenizes on the punctuation boundaries. This also includes punctuations for the Urdu script.
These punctuations characters were identified from the Unicode database for Arabic script by looking for punctuation symbols.
return... | a631328849afb1fbee74788a85169874f1a895ef | 3,620,359 |
def fpath_to_link(repo_id, path, is_dir=False):
"""Translate file path of a repo to its view link"""
if is_dir:
url = reverse("repo", args=[repo_id])
else:
url = reverse("repo_view_file", args=[repo_id])
href = url + '?p=/%s' % urllib2.quote(path.encode('utf-8'))
return '<a href="%... | bd4763c12983763480631714befbb771459a6b1e | 3,620,360 |
def _qname_matches(tag, namespace, qname):
"""Logic determines if a QName matches the desired local tag and namespace.
This is used in XmlElement.get_elements and XmlElement.get_attributes to
find matches in the element's members (among all expected-and-unexpected
elements-and-attributes).
Args:
... | 66aa9272fd6e4a6e281d39f03dd63acabad0bbe7 | 3,620,361 |
def NodeArrangementHexToTet(C):
"""Node ordering for conversion of a hexahedron into 6 tetrahedra
refer to: [Julien Dompierre et.al. "How to Subdivide Pyramids, Prisms and Hexahedra into Tetrahedra",
8th International Meshing Roundtable, Lake Tahoe, California, 10-13 October 1999.] for only p=1... | 919773449d323e6d7b4c632938d89cb6d25440d3 | 3,620,362 |
def datetogmt(str):
""" Convert date string to gmt time. """
date_tuple = datetotuple(str)
return mkgmtime(date_tuple) | 5fa593d94926c787c700e5f9aff73d3b5dc5d2e2 | 3,620,363 |
def parse_color(color):
""" Parses color into a vtk friendly rgb list """
if color is None:
color = rcParams['color']
if isinstance(color, str):
return vtki.string_to_rgb(color)
elif len(color) == 3:
return color
else:
raise Exception("""
Invalid color input
M... | c06eec3de5d50e2e3902a66e469f6df96e679971 | 3,620,364 |
def skewness(iterable, sample=False):
""" Returns the degree of asymmetry of the given list of values:
> 0.0 => relatively few values are higher than mean(list),
< 0.0 => relatively few values are lower than mean(list),
= 0.0 => evenly distributed on both sides of the mean (= normal distribu... | 3cee6e2eaf7095d92229d6744deb775e7d5b64a0 | 3,620,365 |
def create_plant(user_id, plant_name, plant_type, photo_url, germinate_date,
directsow, transplant_date, growing_medium, location,
environment, lighting, schedule):
""" For example:
>>> create_plant(1, "Nadine", "Aloe", "/static/img/aloe.jpg", "11/11/2020",
"direct s... | 517ff885f551ec898a52138a3179ca2c6830af52 | 3,620,366 |
def writeDaily(config, daily, data_binding, table_type):
"""
Writes a Daily object or list of Daily objects to the specified data_binding and table. table_type must be 'obs',
'verif', 'climo', 'hourly_forecast', or 'daily_forecast', or something defined in the schema of data_binding as
%(stid)_%(table_t... | 445c0e645fc0c94d5347401648fc4d0131a47cae | 3,620,367 |
import json
def get_line_extents_from_json(json_data, font_file_name):
"""Find the vertical extents of a line based on HarfBuzz JSON output."""
max_height = None
min_height = None
for glyph_position in json.loads(json_data):
glyph_id = glyph_position['g']
glyph_ymin, glyph_ymax = get_g... | 241e715e1393ab2de9f592178ccc9e7f1f516b2f | 3,620,368 |
def findSpikes(
file_name,
sweep_IB_concatenated,
prominence_min = None,
prominence_max = None,
wlen_ms = 10,
sampling_rate_khz = 25
):
"""
`findSpikes` uses scipy's `find_peaks` on the concatenated sweeps to detect peaks in the data and obtain their prominences. It then plots the di... | 52586d3e55dbc8fbf5cd2a97afb612e1ce8fd3b3 | 3,620,369 |
def promptForFlirtFiles(parent, overlay, overlayList, displayCtx, save=False):
"""Displays a dialog prompting the user to select a FLIRT
transformation matrix file and associated reference image for
the given overlay.
:arg parent: The :mod:`wx` parent object.
:arg overlay: The overlay to l... | b17834d762c236b4b6cabfcae779fbd4d4899b29 | 3,620,370 |
def coord_c2g(map_):
"""Rotate a map from celestial co-ordinates into galactic co-ordinates.
This will operate on a series of maps (provided the Healpix index is last).
Parameters
----------
map : np.ndarray[..., npix]
Healpix map.
Returns
-------
rotmap : np.ndarray
T... | 4850fb1be975bc2acf6020aa878381f40e1c29ba | 3,620,371 |
def tourism_per_year_by_country(country):
"""Returns the number of arrivals (tourism) per year of the given country."""
cur = get_db().execute('SELECT Year, Value FROM Indicators WHERE CountryCode="{}" AND IndicatorCode="ST.INT.ARVL"'.format(country))
tourism = cur.fetchall()
cur.close()
return json... | e8515f8d31de021b53c568ee01bb29aeda188e8e | 3,620,372 |
from typing import Optional
from typing import Union
import subprocess
def run_local(command: str, str_output: Optional[bool] = False) -> Union[int, str]:
"""Runs a command locally and captures output."""
print(command)
if str_output:
process = subprocess.Popen(
command,
s... | fc56e2106a4f4104c30ec2f4a3252b8e43e8bdf3 | 3,620,373 |
def get_hst_to_jwst_coefficient_order(polynomial_degree):
"""Return array of indices that convert an aeeay of HST coefficients to JWST ordering.
This assumes that the coefficient orders are as follows
HST: 1, y, x, y^2, xy, x^2, y^3, xy^2, x^2y, x^3 ... (according to Cox's
/grp/hst/OTA/alignment/Fo... | ceec3c2a7202ba1250f11e0d2ce3e01dc771de0f | 3,620,374 |
from typing import List
def get_categories(title: str) -> List[str]:
"""
Gets the categories of the wikipedia page `title` from
https://en.wikipedia.org/w/api.php?action=query&format=json&titles={title}&prop=categories
Returns empty list for invalid title with no redirects
"""
params = {
... | 644eb7b110ff7e7684b8cc4dff5ca957c754941f | 3,620,375 |
def get_chombo_box_extent(box, space_dim):
"""
Parse box extents from Chombo HDF5 files into low and high limits
Parameters
----------
box : List
Chombo HDF5 format box limits,
e.g. [x_lo, y_lo, x_hi, y_hi] = [0,0,1,1]
space_dim : int
Number of spatial dimensions
... | d72b409a96c8a1936f456d87a341fba22ee9f97e | 3,620,376 |
def _fast_rcnn_box_loss(box_outputs, box_targets, class_targets, normalizer=1.0,
delta=1.):
"""Computes box regression loss."""
# delta is typically around the mean value of regression target.
# for instances, the regression targets of 512x512 input with 6 anchors on
# P2-P6 pyramid is a... | e408ab6068ee56af3a96be41ae894abea1a0e439 | 3,620,377 |
def calc_gcn_norm(edge_index, num_nodes, edge_weight=None):
"""
calculate GCN Normalization.
Parameters
----------
edge_index:
edge index
num_nodes:
number of nodes of graph
edge_weight:
edge weights of graph
Returns
-------
1-dim Tensor
"... | da1f74b1a0ad907320a6426354228d4da97df7aa | 3,620,378 |
def get_raw_pdb_filename_from_interim_filename(interim_filename, raw_pdb_dir):
"""Get raw pdb filename from interim filename."""
pdb_name = interim_filename
slash_tokens = pdb_name.split('/')
slash_dot_tokens = slash_tokens[-1].split(".")
raw_pdb_filename = raw_pdb_dir + '/' + slash_tokens[-2] + '/'... | 084239659220ea65ae57a006c8ce28df73b2fd5e | 3,620,379 |
import re
import os
def txt_to_csv(dfile):
"""Converting the textfile to another textfile where elements are seperated with commas
Function meant for false detection module only
"""
with open("resultsdata_mod_.txt", "a") as f:
f.write("_id,time_ratio,intensity_ratio,bandwidth_1,bandwidth_2,er... | 689128ca8f9b951e76aedb83f6e1a57925d895e4 | 3,620,380 |
def mae(y_true, y_pred):
"""Mean absolute error"""
assert y_true.shape == y_pred.shape, f"{y_true.shape} != {y_pred.shape}"
return np.mean(np.abs(y_true - y_pred), axis=0) | 6c2a230b6a109af097cdc4fd44dadc87a6de9f5b | 3,620,381 |
def class_tree_graph(bases, linker, context=None, **options):
"""
Return a `DotGraph` that graphically displays the class
hierarchy for the given classes. Options:
- exclude
- dir: LR|RL|BT requests a left-to-right, right-to-left, or
bottom-to- top, drawing. (corresponds to the dot op... | 9782086a8928cdebc4f619d5bdf742b53bd8a502 | 3,620,382 |
import logging
import sympy
import operator
def generate_parameters(phase_models, datasets, ref_state, excess_model, ridge_alpha=None, aicc_penalty_factor=None, dbf=None):
"""Generate parameters from given phase models and datasets
Parameters
----------
phase_models : dict
Dictionary of compo... | 16099955dbae2dc7333b0f3e9e53d9769f9c76ad | 3,620,383 |
def cg_optimize(th,floss,fgradloss,metric_length, substeps,damping,cg_iters=10, fmetric=None,
num_diff_eps=1e-4,with_projection=False, use_scipy=False, fancy_damping=0,do_linesearch=True, min_lm = 0.0):
"""
Use CG to take one or more truncated newton steps, where a line search is used to
enforce improv... | 2320e3847ec88fa1d88859d610b902b99daa662e | 3,620,384 |
import signal
import re
import sys
def create_ipython_shortcuts(shell):
"""Set up the prompt_toolkit keyboard shortcuts for IPython"""
kb = KeyBindings()
insert_mode = vi_insert_mode | emacs_insert_mode
if getattr(shell, 'handle_return', None):
return_handler = shell.handle_return(shell)
... | 558e1c3d244b947d3e33970bdc26679b61cabe70 | 3,620,385 |
def add_forth_coord(points):
"""forth coordinate is const = 1"""
return np.hstack((points, np.ones((len(points), 1)))) | 09bfcd59bafd7764b78568d6496ec895dd923e31 | 3,620,386 |
import os
def tf_tol():
"""Numerical tolerance for equality tests."""
return float(os.environ.get("TF_TOL", TF_TOL)) | 7de67b0839131caac72af785cfe2a41c07905633 | 3,620,387 |
def _read_reaction_gpr_from_sbml(reaction, mass_notes, f_replace):
"""Read the GPR information from SBMLDocument and return as a string.
Warnings
--------
This method is intended for internal use only.
"""
reaction_fbc = reaction.getPlugin("fbc")
if reaction_fbc:
# GPR rules
... | a9d1a91f93345c57ab91dd8f7b2eb70a2bb6e28b | 3,620,388 |
def conv_block(x, nfeat, strides=1, name=None):
"""
Specific convolutional block followed by leakyrelu for unet.
"""
ndims = len(x.get_shape()) - 2
assert ndims in (1, 2, 3), 'ndims should be one of 1, 2, or 3. found: %d' % ndims
Conv = getattr(KL, 'Conv%dD' % ndims)
convolved = Conv(nfeat,... | 90ed04ca5122158d609b0999db63665fa6d5121b | 3,620,389 |
import torch
def sample_from_discretized_mix_logistic(y, log_scale_min=None):
"""
https://github.com/fatchord/WaveRNN/blob/master/utils/distribution.py
Sample from discretized mixture of logistic distributions
Args:
y (Tensor): B x C x T
log_scale_min (float): Log scale minimum value
... | 4d864be504bc0a08f58c5332785578c4c8cc551f | 3,620,390 |
def min_dist_conformer_zma(dist_name, cnf_save_fs):
""" locators for minimum energy conformer """
cnf_locs_lst = cnf_save_fs[-1].existing()
cnf_zmas = []
for locs in cnf_locs_lst:
zma_fs = autofile.fs.zmatrix(cnf_save_fs[-1].path(locs))
cnf_zmas.append(zma_fs[-1].file.zmatrix.read([0]))
... | 6575a7ff9f1af4d971bb01580cbbf2af2f93cac9 | 3,620,391 |
def array_offset(x):
"""Get offset of array data from base data in bytes."""
if x.base is None:
return 0
base_start = x.base.__array_interface__["data"][0]
start = x.__array_interface__["data"][0]
return start - base_start | b383a91790b06ffb1b5b976b9575efe637fa47a6 | 3,620,392 |
def get_kernelf(config, context={}):
"""Get a kernel function."""
return _from_config(config, classes=classes, context=context) | ff549e6e20f48bec1e9bda9721fdd9a0afc84003 | 3,620,393 |
def solve_evolutionary_op(problem,
save_results=True, return_history=False,
step_hook=None, post_process_hook=None,
nls_status=None):
"""TODO return_history"""
step_args = problem, nls_status
time_solver = problem.get_time_s... | 80238385181b6d6d624de036ef5b65ca27aa35e1 | 3,620,394 |
import requests
def layer_ogc_request(request, layername):
"""Provide one OGC server per layer, with their own GetCapabilities.
:param layername: The layer name in Geonode.
:type layername: basestring
:return: The HTTPResponse with the response from QGIS Server.
"""
layer = get_object_or_404... | 5707e3aed6223160260b2b6313d03cb98f612362 | 3,620,395 |
import torch
def _demo_inputs_pair(img_shape=(64, 64), batch_size=1, cuda=False):
"""
Create a superset of inputs needed to run backbone.
Args:
img_shape (tuple): shape of the input image.
batch_size (int): batch size of the input batch.
cuda (bool): whether transfer input into gp... | fd0d68db73338f30de4d4dbe7939068b453981cd | 3,620,396 |
def get_song(song_name):
"""Return information about a song.
Parameters
----------
song_name : str
The song name.
Returns
-------
dict
name - The song name.
files - Dictionary with downloaded files for this song.
"""
song = cache.get(song_name)
if song ... | 40a9752f077aeaf185d1bbc7a21f6be28658f926 | 3,620,397 |
def message_create(request, slug, topic_id, template_name='groups/message_form.html'):
"""
Returns a group message form.
Templates: ``groups/message_form.html``
Context:
form
GroupMessageForm object
"""
group = get_object_or_404(Group, slug=slug)
topic = get_object_or_40... | a0ff989c73fecbe681c167637fee35d86d3e1afd | 3,620,398 |
def index():
"""
Simple Home Page
"""
module_name = deployment_settings.modules[module].name_nice
return dict(module_name=module_name) | 5628a30596688917fdc19956f16cd7d0f68754e6 | 3,620,399 |
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