content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
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 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 |
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 |
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 |
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 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 |
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 |
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 |
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 |
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 |
def add_forth_coord(points):
"""forth coordinate is const = 1"""
return np.hstack((points, np.ones((len(points), 1)))) | 09bfcd59bafd7764b78568d6496ec895dd923e31 | 3,620,386 |
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 |
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 |
def player_team(replay_json):
""" Returns a list of names of the players on the replay recorder's team """
own_team = get_own_team(replay_json)
return [v['name'] for v in replay_json['first']['vehicles'].values() if v['team'] == own_team] | e1510fae3b5f68f22f70ac440fc509ed51c28994 | 3,620,400 |
def get_image_writer_set():
"""Return the set of installed image writers.
The set is returned as a dictionary where names of the image writers are
the keys, and the image writer class objects are the values.
"""
return ImageWriterLoader().loader.get_object_set() | 3d703fe96513d2b142efee71d68038df28011aee | 3,620,401 |
import torch
from typing import Tuple
def convert_to_distributed_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, str]:
"""
For some backends, such as NCCL, communication only works if the
tensor is on the GPU. This helper function converts to the correct
device and returns the tensor + original de... | 71eb98868aa89bbfdea4019eff0b93256cb82025 | 3,620,404 |
def update_global_variable():
"""
修改全集变量(global关键字)
:return:
"""
global count
count = 10
return count | 69c91bce24fc77dc731e05178849528e59bae4ba | 3,620,405 |
def cancelReserve(request):
"""
알림톡/친구톡 전송요청시 발급받은 접수번호(receiptNum)로 예약전송건을 취소합니다.
- 예약취소는 예약전송시간 10분전까지만 가능합니다.
- https://docs.popbill.com/kakao/python/api#CancelReserve
"""
try:
# 팝빌회원 사업자번호
CorpNum = settings.testCorpNum
# 예약 알림톡/친구톡 전송 접수번호
receiptNum = "0180... | 63960abac51f2121a56e38b48de737c1c9c604b9 | 3,620,406 |
def _staircase_ecdf(p, data, complementary=False, q_axis="x", line_kwargs={}):
"""
Create a plot of an ECDF.
Parameters
----------
p : bokeh.plotting.Figure instance, or None (default)
If None, create a new figure. Otherwise, populate the existing
figure `p`.
data : array_like
... | 19f8945698cfb6138c67ce025df3bcffb6463f46 | 3,620,407 |
def GenerateConfig(context):
"""Generates the route config"""
resources = [
{
'name': context.env["name"],
'type': 'pubsub.v1.topic',
'properties': {
'topic': context.env["name"]
},
'accessControl': {
'gcpIamPolicy': MergeCallingServiceAccountWithAdminPermissionsIntoBindings(context.env, c... | 53f7e44ae4f2514826d673572932f91767dd0efd | 3,620,408 |
def get_programs(x_gw_ims_org_id, authorization, x_api_key): # noqa: E501
"""Lists Programs
Returns all programs that the requesting user has access to # noqa: E501
:param x_gw_ims_org_id: IMS organization ID that the request is being made under.
:type x_gw_ims_org_id: str
:param authorization: B... | b0907272c5539aec88d2e761a271cd08b61d7b75 | 3,620,409 |
def get_non_lib(functions):
"""
Get all non-library functions
@param functions: List of db_DataTypes.dbFunction objects
@return: a subset list of db_DataTypes.dbFunction objects that are not library functions.
"""
return [f for f in functions if not f.is_lib_func] | 7f536ff98d647ba5e497b8550bc2497ef45e814b | 3,620,410 |
def _check_duplicates(data, name):
"""Checks if `data` has duplicates.
Parameters
----------
data : pd.core.series.Series
name : str
Name of the column (extracted from geopandas.GeoDataFrame) to check duplicates.
Returns
-------
bool : True if no duplicates in data.
"""
... | 429ce8d092b3a39fc44eeca91d593db22fe7364d | 3,620,411 |
def path_surroundings(md, path, *,
radius_pix=130,
maxwidth_pix=1000,
maxheight_pix=800,
maxdist_pix=500,
path_color=(255, 100, 0),
shorten_by_rotating=True):
"""Create a generator of ... | a07a66d673f407f37e582d578196bb5053597aa1 | 3,620,412 |
def _has_textframe(obj):
""" Check if placeholder has TextFrame """
return hasattr(obj, 'TextFrame') and hasattr(obj.TextFrame, 'TextRange') | 087e6df38e55d99637e8e7ea998257f323d6d141 | 3,620,413 |
def get_mapping(combinable_list):
"""Determine the mapping from acceptance_id to the register id that can be used
to index into an array.
"""
result = {}
array_index = 0
while len(combinable_list) != 0:
# Allways, try to combine the largest combinable set first.
k ... | 085f28c8d1263bc2e547a5671e115d86992af231 | 3,620,414 |
import gzip
def read_sbs_from_vcf(vcf_file):
"""
Only reads the chrom, pos, ref and alts from the VCF file.
Not strict about checking the headers, and will not filter any mutations.
Lines with multiple alts will be split into one mutation for each alt.
Non-single-base-substitutions (e.g. deletions... | 3b669928e74fcf357c9586998ae908afec5f2008 | 3,620,415 |
def get_path(abs_url, **kwargs):
"""
"""
if abs_url is None:
return None
return urlparse(abs_url, **kwargs).path | caf61367db8cf289251185c967cfb28212f61cbe | 3,620,416 |
def clean_chamber_input(chamber):
""" Turns ambiguous chamber information into tuple (int, str) with chamber id and chamber name """
if type(chamber) == str:
if chamber == '1':
chamber = 1
elif chamber == '2':
chamber = 2
elif chamber == 'GA':
chamber ... | 0ad20c117fc90e523e85ef7061a548b20c68dc92 | 3,620,417 |
def search_for_letters(phrase: str = 'life, the universe, and everything', letters: str = 'forty two') -> set:
"""Display any 'letters' found in a 'phrase'."""
return set(letters).intersection(set(phrase)) | 36944391599abf971512d21819f88dee9af42f36 | 3,620,418 |
import yaml
def loadyaml(file, default={}):
"""Utility function to load from yaml file"""
try:
with open(file, "r", encoding="utf-8") as f:
t = yaml.load(f, Loader=yaml.FullLoader)
except FileNotFoundError:
t = default
return t | a6720094340b71039d2e53ec1f48493480e4e26a | 3,620,419 |
import getpass
def secret(prompt=None, empty=False, default=None):
"""Prompt a string without echoing.
Parameters
----------
prompt : str, optional
Use an alternative prompt.
empty : bool, optional
Allow an empty response.
default : float, optional
Value to return if r... | 45c0857e24421dd04909119b324fa6c942f4e640 | 3,620,422 |
def parse_station_list_to_csv(filepath_or_buffer) -> str:
""" Return CSV-formatted data """
return _parse_station_list(filepath_or_buffer).to_csv() | c231daaf5bb343f05a959aa2cdbdf85d4f3984b0 | 3,620,424 |
import urllib
def govuk_url(path):
"""
:returns: url to the GOV.UK Pay endpoint defined by `path`
:param path: path without leading `/` e.g. `payments`
"""
return urllib.parse.urljoin(settings.GOVUK_PAY_URL, path) | 0e0a179d3a6107f9a7f253b5a4d18f8ed3558dcd | 3,620,426 |
def _any_sat(bdd, u, l):
"""
Recursive part of any_sat
"""
#Base Cases
if u in [0,1]:
return l
var = _get_var_name(bdd, u)
#Arbitrarily consider lower branch
if bdd["t_table"][u][1] == 0:
l.append("%s" % var)
new_u = bdd["t_table"][u][2]
else:
l... | d8ed5287dff81bb51db711d2929e4810741e67e7 | 3,620,427 |
def map_mean_of_horizontal_active_links_to_node(grid, var_name, out=None):
"""Map the mean of active links in the x direction touching node to the
node.
map_mean_of_horizontal_active_links_to_node takes an array *at the links*
and finds the average of all horizontal (x-direction) link neighbor values
... | b6ef9d12c9f458052473161cd79feb9476b169fd | 3,620,428 |
def gmm_density_centered(x, std):
"""
Assumes dim=-1 is the component dimension and dim=-2 is feature dimension. Rest are sample dimension.
"""
if x.dim() == std.dim() - 1:
x = x.unsqueeze(-1)
elif not (x.dim() == std.dim() and x.shape[-1] == 1):
raise ValueError('Last dimension must... | b050114b27ea9163cdaee61077b972bea744cf72 | 3,620,429 |
def process_pairs_vg(alns_tuple):
"""
Finds the pairs in alignments of one read
:param alns_tuple: alignments of one read in as a tuple
:return: read_id, AS pairs, mapq, metric scores
"""
read_id, alignments, mapqs, obs_max, end = alns_tuple
return read_id, alignments, mapqs, calc_scores((re... | e1bb6cf24ba51e011b937f52586f52bc8d382cd5 | 3,620,430 |
from pathlib import Path
import logging
import yaml
def params_from_yaml(args):
"""Extract the parameters for preparation from a yaml file and return a dict"""
# Check the path exists
try:
config_file_path = Path(args.config)
assert config_file_path.exists()
except Exception:
l... | 36beadd8fa4f27471c514a963838aac216aad434 | 3,620,431 |
def get_task_monitor(node, uri):
"""Get a TaskMonitor for a node.
:param node: an Ironic node object
:param uri: the URI of a TaskMonitor
:raises: RedfishConnectionError when it fails to connect to Redfish
:raises: RedfishError when the TaskMonitor is not available in Redfish
"""
try:
... | ee69431e5c9f6711840b01150791296658920fc5 | 3,620,432 |
def get_user_ids_from_assigned_location_ids(domain, location_ids):
"""
Returns {user_id: [location_id, location_id, ...], ...}
"""
result = (
UserES()
.domain(domain)
.location(location_ids)
.non_null('assigned_location_ids')
.fields(['assigned_location_ids', '_id... | 12e50ad5befece3c64e7d8172c7cfdb4751ec6de | 3,620,433 |
def extract_sift(fn, extractor, detector):
"""提取图像特征"""
im = cv2.imread(fn, cv2.IMREAD_GRAYSCALE)
# SIFT检测器可以检测特征，而基于SIFT的提取器可以提取特征并返回它们
return extractor.compute(im, detector.detect(im))[1] | 3d9997b5f57f1fd496dbedb6a327bdfa3a59915f | 3,620,434 |
def is_operator_or_function(term):
"""
Checks if the term is a LaTeX mathematical operator or function.
Source: http://web.ift.uib.no/Teori/KURS/WRK/TeX/symALL.html
Args:
term: string to be checked.
Returns:
True if the term is a mathematical operator, False otherwise.
"""
... | b4992ffeb213979e9507cbc1b76ad1e861eef7f2 | 3,620,435 |
def getGasDensity(x=None, y=None, z=None, grid=None, ppar=None):
"""Calculates the gas density
Parameters
----------
x : ndarray
Coordinate of the cell centers in the first dimension
y : ndarray
Coordinate of the cell centers in the second dimension
y ... | 04b7a1f4b4441d06ce92b5f6b6fc09e2e614affb | 3,620,436 |
def load_embeds(text_embed, img_embed, dcca_embed):
""" Load image and sentence embeddings and create a concatenated version
:param text_embed: pickle file containing the sentence embeddings
:param img_embed: pickle file containing the image embeddings
:param dcca_embed: pickle file containing the deep... | c74da889f05126d4ac2ace30463041d5cb9e22c3 | 3,620,437 |
import re
def serialize(settings, exclude=None):
"""Return a consistent, human-readable string serialization of settings."""
if exclude is None:
exclude = []
sdict = dict(_variables(settings))
sdict = { k: v for k, v in sdict.items() if k not in exclude }
sstr = dumps(sdict, sort_keys=True... | 535da5800141d6ef5ebdbeff23579a246480a271 | 3,620,438 |
from typing import List
from typing import Union
def list_or_first(x: List[str]) -> Union[List[str], str]:
"""
Returns a list if the number of elements is
greater than 1 else returns the first element of
that list
"""
return x if len(x) > 1 else x[0] | 82e86001b35ecd6542a22fac3c5dd7f7723966d6 | 3,620,439 |
import re
def parse_version(version_str):
"""'10.6' => [10, 6]"""
return [int(s) for s in re.findall(r'(\d+)', version_str)] | 16cfcfc292eb89b6231a266c687f9dfd8caa5a8d | 3,620,440 |
def user():
"""Get user details depending on friendship.
If you are friends, sensitive data will be shown aswell.
Returns:
JSON reponse with the basic and sensitive user details.
"""
username = request.args.get('username')
if username is None or username == '':
username = auth... | 842395d3206356f369db985abcc5de719848e7de | 3,620,441 |
import requests
def display_selected_patient_info(MRI):
""" Get a patient's latest information and ECG trace image
As a very important functionality of the server, the function
sends a 'GET' request to the server, get a string that includes
all patient's latest info and ECG image b64 string. Then ... | d0a47848451801517e9125bbd7de3b40023cbd60 | 3,620,442 |
def circ(x, y, d=1):
""" Generation of a circular aperture.
Args:
| x (np.array[N,M]): x-grid, metres
| y (np.array[N,M]): y-grid, metres
| d (float): diameter in metres.
| comment (string): the symbol used to comment out lines, default value is None.
| delimiter (string... | 7ff75431b25489db40c6d54710443c2bc4b105d1 | 3,620,443 |
from typing import Optional
def labels(
adata: AnnData,
label_filepath: str = None,
index_col: int = 0,
sep: str = "\t",
copy: bool = False,
) -> Optional[AnnData]:
"""Add label transfer results into AnnData object
Parameters
----------
adata: AnnData The data object to a... | 221ab6f4ed9d4eb8a189010c45205fbffc3878ab | 3,620,444 |
def contains_common_item_2(arr1, arr2):
"""
loop through first array and create dictionary object where the keys are the items in the array
loop through the second array and check if item in second array exists in the created dictionary
"""
array1_dict = {}
for item in arr1:
array1_dict[... | 83b4eafe7904d47fd65db3fc3e5a4d598a51efea | 3,620,445 |
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