content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
import uuid
from datetime import datetime
def new_issue(
session,
repo,
title,
content,
user,
issue_id=None,
issue_uid=None,
private=False,
related_prs=[],
status=None,
close_status=None,
notify=True,
date_created=None,
milestone=None,
priority=None,
ass... | cf8c05819ca5dc48cfa826cb1f3f9d2995bee722 | 38,200 |
import argparse
def options():
"""Parse command line options.
Args:
Returns:
argparse object
Raises:
"""
parser = argparse.ArgumentParser(description="Share Clowder Datasets by associating them with a Clowder Space.",
formatter_class=argparse.A... | 6f22870c115ec2442e0f935228cca89bd9be21bc | 38,201 |
import sys
def memory_usage(obj):
"""Returns the memory usage in a human readable format."""
def get_size(obj, seen=None):
"""Recursively finds size of objects"""
size = sys.getsizeof(obj)
if seen is None:
seen = set()
obj_id = id(obj)
if obj_id in seen:
... | a3dd735797c875e03f1800ae9857da5005cc9800 | 38,202 |
import os
def read(path_to_image):
"""
Read MCCD image from file.
Parameters
----------
path_to_image : str
Path to MCCD image to read
Returns
-------
(image, metadata, mccdheader) : tuple
Returns tuple containing the ndarray of the image, experimental
metada... | 0aa1052eb9a2221c054ee003b4a53afb203d5c19 | 38,203 |
def fit_circle_to_data(in_data, verbose=False):
"""
Wrapper around _fit_circle_impl that takes care of unwrapping shapes and so on...
Returns
-------
"""
slope, intercept, r_value, p_value, std_err = linregress(in_data[0], in_data[1])
if verbose:
print("R-square of linear fit : {}"... | 223b349c37e6d04f8a3b7823ed70dd335d48d19a | 38,204 |
def dilated_components(output, dil_param, cc_thresh):
"""
Performs a version of connected components with dilation
Expands the voxels over threshold by dil_param in 2D
Runs connected components on the dilated mask
Removes the voxels which weren't originally above threshold
"""
if dil_param... | d5e9444db2fcbafcad7cf7e3b5a14e2e5f0de7ee | 38,205 |
def get_model(sess, image_shape=(80, 160, 3), gf_dim=64, df_dim=64,
batch_size=64, name="autoencoder", gpu=0):
"""
Compiles and outputs models and functions for training and running models.
"""
K.set_session(sess)
checkpoint_dir = './outputs/results_' + name
with tf.variable_scope(... | eaca2291266b865a69fc617a5b09479d1fdad134 | 38,206 |
from typing import Tuple
def define_model() -> Tuple[Model, Model]:
"""Defines the architecture of the model."""
i = Input([None, None, 3], dtype=tf.uint8)
x = tf.cast(i, tf.float32)
x = preprocess_input(x)
# base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=(192, 192, 3... | 60d5e5da86d1379360a1de7fa6877578e0f39fe5 | 38,207 |
import os
def get_pid(pidfile, default=None):
"""
Get the PID (Process ID) by trying to access an PID file.
Args:
pidfile (str): The path of the pid file.
default (int, optional): What to return if file does not exist.
Returns:
pid (str): The process id or `default`.
"""... | 5f6c94e38bf7e1c788a5861a79455b31696c4259 | 38,208 |
def get_case_forms(case_id):
"""
Get all forms that have submitted against a case (including archived and deleted forms)
wrapped by the appropriate form type.
"""
form_ids = get_case_xform_ids(case_id)
return [fetch_and_wrap_form(id) for id in form_ids] | 1d7afb1c08ec69a8f8f9d57456ecdd4c7dff08f2 | 38,209 |
def get_generic_data():
"""
Get generic variables.
Parameters
----------
gendata: str
path to generic data directory.
Returns
-------
dict: the generic variables.
"""
gen_data = {
'instance_display_name': _get_display_name(),
'auth': _get_authenticat... | ad862aa030eb255a89a0b56204918ec2474bf3a1 | 38,210 |
import torch
def generate_embeddings_of_one_image(model, dataloader, repeats=50):
"""Generates representations for all images in the dataloader with
the given model
"""
embeddings = []
filenames = []
with torch.no_grad():
for _ in range(repeats):
for (img1, img2), _, _ in ... | 00099996cae6449c9e138fbbd4334a9317098fb8 | 38,211 |
import requests
def get_status_code(url, opts):
"""
Open connection to the given url and check status code.
:param url: URL of the website to be checked
:return: (availibility, success, HTTP code)
"""
availibility, success, code = (False, False, None)
timeout_duration_seconds = get_opt(op... | 5799152a6e040d273b8720572330abf5a1ff17fc | 38,212 |
def get_default_kubernetes_metadata_config(
) -> metadata_store_pb2.ConnectionConfig:
"""Returns the default metadata connection config for a kubernetes cluster.
Returns:
A config proto that will be serialized as JSON and passed to the running
container so the TFX component driver is able to communicate wi... | ae6fe0a73bd9453f81223b2602a32f95d7fea31c | 38,213 |
def camb_fisher_derivs(p):
"""
Get derivatives (as a function of ell) for the full set of CAMB Fisher
parameters. Includes TT, EE, TE derivatives.
"""
# Get derivatives for various parameters
# {n_s, w0, wa, w_b, omega_k, w_cdm, h}
d_ns = camb_deriv("scalar_spectral_index__1___", 0.004, p)
... | 37b4a5d7b193c8f63085dcd611cf61f672772d0f | 38,214 |
def compute_hard_volumes(labels, voxel_volume=1., label_list=None, skip_background=True):
"""Compute hard volumes in a label map.
:param labels: a label map
:param voxel_volume: (optional) volume of voxel. Default is 1 (i.e. returned volumes are voxel counts).
:param label_list: (optional) list of label... | c107e1ef2b69de4dca2564fbf2f35b6ff15e8277 | 38,215 |
def pairwise(iterable, pairs=2):
"""
a generator to return n consecutive values from an iterable, e.g.:
pairs = 2
s -> (s0,s1), (s1,s2), (s2, s3), ...
pairs = 3
s -> (s0, s1, s2), (s1, s2, s3), (s2, s3, s4), ...
adapted from https://docs.python.org/3.7/library/itertools.ht... | afa62818854ea7bf175efded7307aa1f74435711 | 38,216 |
def logical_intervals(vals, x=None):
"""
Determine contiguous intervals during which ``vals`` is True.
Returns an Astropy Table with a row for each interval. Columns are:
* idx_start: index of interval start
* idx_stop: index of interval stop
* x_start: x value at idx_start (if ``x`` is supplie... | 2c692b412004096c0ad6314ddcc7bf705abb1598 | 38,217 |
def coords2uv(coords, width, height):
"""
Image coordinates (xy) to uv
"""
middleX = width / 2 + 0.5
middleY = height / 2 + 0.5
uv = np.hstack([
(coords[:, [0]] - middleX) / width * 2 * np.pi,
-(coords[:, [1]] - middleY) / height * np.pi])
return uv | 4264bca17713835da8ce7ce3063d00796c5a9c0d | 38,218 |
def _compute_teleport_distribution_from_ratings(user_rating, all_movies):
"""
returns the teleporting distribution as explained in the homework
if a movie M has been rated, its probability is: RATE_M / SUM_OF_ALL_RATINGS
else, its probability is: 0
:param user_rating: a dict of (movie_id, rating)
:param all_movie... | 7a88cf8a69c9fafc70e14d9337f0af25829bfb20 | 38,219 |
def extract_subdomain(url):
"""Uses teldextract to retrieve the subdomain of the url"""
return tldextract.extract(url).subdomain | 368fe66d84c6e4b2380b60089be99b6d8a8fe205 | 38,220 |
from typing import Optional
from typing import Any
def check_empty(value: Optional[Any]):
"""Validate whether the value provided is of class `Empty`."""
if isinstance(value, Empty):
return True
return False | e9cb5ff4685599bfd6f680a72931017f220fc0cd | 38,221 |
def get_env_config(env_name=None, config_file=None):
"""Fetch an environment name and config object from the config.json file.
If the name is None and there's only one environment, we'll select the
first, otherwise we'll die to avoid ambiguity.
:param config_file: a `file`-like object that contains the... | e2188cd912ca78274fd111bdc2951c267f5de575 | 38,222 |
import ntpath
def path_base_and_leaf(path):
"""
Splits path to a base part and a file or directory name, as in the following example:
path: '/a/b'; base: '/a'; leaf: 'b'
"""
head, tail = ntpath.split(path)
if not tail: # in case there is trailing slash at the end of path
return {'base... | 956daa06f87cc60c8e304fa129fb86e49c4776ce | 38,223 |
import torch
def mutate_input(model, input_list, input_of_interest=0, output_extract_fn=lambda x: x, mutate_val=0,
return_diff=True, max_batch_size=128, **kwargs):
""" Switches each value in input_list[input_of_interest].size(1) to mutate_val
one-by-one and collects output_extract_fn(mode... | c7f845ba279d97c9f8ac0e4caa0e794bd69ef543 | 38,224 |
def UnsignedShortCast(value):
"""Explicitly cast a value to type 'usigned short'."""
return UnsignedCast(And(value, ConstInt(0xFFFF))) | 8f7e3780df17b23054d38843792e503447c17ce8 | 38,225 |
def Normalize_C3V(vec):
"""Scales the argument by its Euclidean length, assuming its a 3d vector
(indexable object of length 3)."""
length = Hypot_C3V(vec);
vec[0] /= length
vec[1] /= length
vec[2] /= length
return None; | d5ba177c1350728a63e4225f3f04e28fd68458fb | 38,226 |
import sympy
def extract_coefficients(equation: sympy.Expr,
local_map: dict,
global_coords: list) -> tuple:
"""
Args:
equation: The equation in local coordinates.
local_map: The mapping from local coordinates to the index of a
glob... | 39905209860464d17c7bb73e96f9b62ef78cb2bc | 38,227 |
from typing import TextIO
from typing import Dict
import csv
import re
import unicodedata
def read_variables(fh: TextIO) -> Dict[str, str]:
"""Read variables from file"""
reader = csv.reader(fh, delimiter=',')
headers = filter_map(normalize, next(reader))
assert all(f in headers
for f... | b188fde84862b11223c3fd49c067067985192e6d | 38,228 |
def _is_dunder(name):
"""Returns True if a __dunder__ name, False otherwise."""
return (name[:2] == name[-2:] == '__' and
name[2:3] != '_' and
name[-3:-2] != '_' and
len(name) > 4) | 507ce31674c8e6307ac3f76230c88886b6f1a79c | 38,229 |
def get_oidc_fxa_setting(attr):
"""Helper method to return the appropriate setting for Firefox Accounts authentication."""
FXA_CONFIGURATION = {
'OIDC_OP_TOKEN_ENDPOINT': settings.FXA_OP_TOKEN_ENDPOINT,
'OIDC_OP_AUTHORIZATION_ENDPOINT': settings.FXA_OP_AUTHORIZATION_ENDPOINT,
'OIDC_OP_US... | 87d59ff0bbd5cdf08b5832e830ac18acff5eebb6 | 38,230 |
import io
def detect_nb_doctype(path):
"""Dectect what sort of document we are dealing with."""
# This is inefficient because we end up opening the file twice?
with io.open(fp, encoding="utf-8") as f:
txt = f.read()
typ = jupytext.formats.divine_format(txt)
return typ | c43b9a1944b7ac53508cf890f469206af612d776 | 38,231 |
import tqdm
def from_cell_to_heatmap(slide, trans, cell_table, filter_out="LBP", level=7, n_comp=2):
"""
Parameters
----------
slide : wsi object,
openslide object from which we extract.
trans : function,
infers the new coordinates of a given point. It is or:
- the... | 918e63262ec9ec5af1b7c7f506e3e171c011a8e8 | 38,232 |
def get_genome_subsequence(gseq=None, start=None, end=None):
"""
The letters of nucleotides in the reference are shown in upper case.
"""
subseq = gseq[start-1:end]
return subseq.upper() | 6bcdacad1e5714b6e5d04434adc7c36b292e9d11 | 38,233 |
def mvee(atoms, tol = 0.00001):
"""
Find the minimum volume ellipse around a set of atom objects.
Return A, c where the equation for the ellipse given in "center form" is
(x-c).T * A * (x-c) = 1
[U Q V] = svd(A);
where r = 1/sqrt(Q)
V is rotation matrix
U is ???
"""
points_asar... | 60d2b5d03a0c0ce185133b33d1daac09e8dcd0c4 | 38,234 |
async def list_credentials(hub, ctx, name, resource_group, **kwargs):
"""
.. versionadded:: 3.0.0
Lists the login credentials for the specified container registry.
:param name: The name of the container registry.
:param resource_group: The name of the resource group to which the container registr... | 5ca069fe456dd7366220c095da431b6b0a1661da | 38,235 |
from collections import defaultdict
def analyze_hash_miss(hashchain, htask, hcomp, taskname, skipcache):
"""
Function that analyzes and gives out a printout of
possible hass miss reasons. The importance of a
candidate is calculated as Ic = Nm/Nc where:
- Ic is an imporance coefficient;
... | 7a1728c7c47f400c9164acb409afe738271c40a9 | 38,236 |
def set_cv_folds(dataset_name, k):
"""
Split data into different folds. The resulting split names have the
form cv_<k>-<i>, where i is the current folds used for testing.
param dataset_name: name of dataset without .csv
param k: number of folds
"""
return sd.cross_validation_folds(dataset_name, k, return_... | 248f52c1f6d8adfa0f743f940ebbf800ff0bfd54 | 38,237 |
def fetch_test_names(app, host_app, release, enabled_tests_only=True):
"""Determine the list of (TestCase, testMethod) for the app.
Args:
app: (string) path to app
host_app: (string) path to host app. None or "NO_PATH" for EG1.
release: (bool) whether this is a release build.
enab... | 1bd592d69bf72350eea259154296030079791a37 | 38,238 |
def augment_data(X_train, Y_train):
"""
Augments the data 8-fold by 90 degree rotations and flipping.
Parameters
----------
X_train : array(float)
Array of source images.
Y_train : float
Array of label images.
Returns
-------
X_train_aug : array(float)
Augmen... | 8167f19d13f44ed83e645da7fa65463ee68ed09a | 38,239 |
import json
def load_config(run_name):
""" Load configuration json """
with open(get_config_loc(run_name)) as data_file:
data = json.load(data_file)
return data | 1545f1387c0ec2588d61cd1ab201451618a11333 | 38,240 |
import collections
import os
def generate_graphs(data_frames, hardware_info, steps, outdir,
verbose=False):
"""Generate all graphs for a bcbio run."""
_setup_matplotlib()
# Hash of hosts containing (data, hardware, steps) tuple
collectl_info = collections.defaultdict(dict)
for... | 495fbf26f59a91528586c748ddb32ab167743978 | 38,241 |
def get_nmdc_jsonschema_string() -> str:
"""Retruns the nmdc.schema.json file as a string.
Returns
-------
str
A string containing the contents of nmdc.schema.json file.
"""
nmdc_json = get_nmdc_jsonschema_bytes()
return nmdc_json.decode("utf-8") | 340b7e0e4eac708366c247b3496b094955e7ce09 | 38,242 |
def stackmean(array):
"""Cacluate the mean of a stack
This function calculates the mean of a stack of images (or any array).
It ignores values that are np.NAN and does not include them in the mean
calculation. It assumes an array of shape (.. i, j, x, y) where x and y
are the size of the returned a... | 132f012a00c2a49eadab54f1cfb4cff3f5192dc4 | 38,243 |
from pathlib import Path
import logging
def nfs_remap(path, depth, autofs=None):
"""Remap filesystems that are exported over NFS to their path on GPFS.
Eg:
radonc-ljungman-dataden.dataden.arc-ts.umich.edu:/gpfs/locker0/ces/dataden/g/radonc-ljungman-dataden /nfs/dataden/radonc-ljungman-dataden
umms-bl... | f35c0c591474b40770bd1e860058ff29038d72b8 | 38,244 |
def cvSetReal3D(*args):
"""cvSetReal3D(CvArr arr, int idx0, int idx1, int idx2, double value)"""
return _cv.cvSetReal3D(*args) | 2a86cb6448d289b81e297a12bc784e7a049c0525 | 38,245 |
def api_settings(_request):
"""Repond with user prefs in JSON."""
account = models.Account.current_user_account
return {
'xsrf_token': account.get_xsrf_token(),
'email': account.email,
'nickname': account.nickname,
'deprecated_ui': account.deprecated_ui,
'default_context': account.default_cont... | 1245b253e975aba825d9199b1265457c653cb7f9 | 38,246 |
import argparse
def get_config(parse=True, **optional_kwargs):
"""
Get configurations as attributes of class
1. Parse configurations with argparse.
2. Create Config class initialized with parsed kwargs.
3. Return Config class.
"""
parser = argparse.ArgumentParser(formatter_class=argparse.A... | a566934f30b01c20e9dbbba52a88a224f6309f41 | 38,247 |
from scipy.spatial.qhull import Delaunay
from typing import Union
def create_mesh_from_trace(linestring,
zmax: Union[float, int],
zmin: Union[float, int],
):
"""
Args:
linestring (shapely.geometry.LineString):
zm... | e2998e1cd82d2d74d36392af56dc401891a6ef79 | 38,248 |
import math
def find_nearest(array,value):
"""
Find nearest value in array
"""
idx = np.searchsorted(array, value, side="left")
if idx > 0 and (idx == len(array) or math.fabs(value - array[idx-1]) < math.fabs(value - array[idx])):
return array[idx-1]
else:
return array[idx] | a48f8f29fab539993f80af7d31ae9efef5a5d08a | 38,249 |
def handle_context_opt(ctx, param, value):
"""Handle eager CLI options to configure context.
The eager options are evaluated directly during parsing phase, and can
affect other options parsing (like required/not).
The only side effect of consuming these options are setting attributes
of the global... | 846008bac7a8ed67fea0e31da9a8ed69223b2ccc | 38,250 |
from typing import OrderedDict
def make_minimal_feature_set(feature_definitions, features):
"""Reduce feature_definitions to the minimum necessary features
Parameters
----------
feature_definitions : list of dict
Feature definitions from the elasticsearch LTR plugin
features : list of str... | 3759acdba258c54cec2669cc7a32beecdbe0c04a | 38,251 |
def nonroster_player_attr_by_id(player_id, attribute):
"""Returns the attribute of a non-roster player via the NHL People API.
Args:
player_id (str): Player unique identifier (IDXXXXXXX)
attribute (str): Attribute from roster dictionary.
Returns:
string: Attribute of the person req... | 016a06bb59249b18537fc9f467e7e1a050d959a3 | 38,252 |
import torch
def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
"""Merge augmented detection bboxes and scores.
Args:
aug_bboxes (list[Tensor]): shape (n, 4*#class)
aug_scores (list[Tensor] or None): shape (n, #class)
img_shapes (list[Tensor]): shape (3, ).
... | b887f9f279664823104f435d63bd0fc2a49b3dd4 | 38,253 |
import os
def local_helper_exists(production=False):
"""This function checks to see if a helper file is present in the ``local/`` directory.
.. versionadded:: 4.1.0
:param production: Defines whether or not the helper file is associated with a Production environment
:type production: bool, None
... | cb300e8a488f8001e2f22d36185f9054ab7b3c30 | 38,254 |
def is_provided_and_is_date_less_than_a_year_ago(value):
"""Returns True if the date value is provided and within the last year."""
if not value:
return False
return _is_provided_and_is_date_in_the_past(
value,
) and (
value > date.today() - relativedelta(years=1)
) | 28e87fac14161e9c1772288a58e13747786cc906 | 38,255 |
def load_data(train_data, valid_data, test_data, user_review, item_review, user_rid, item_rid, stopwords):
"""
Loads and preprocessed data for the MR dataset.
Returns input vectors, labels, vocabulary, and inverse vocabulary.
"""
# Load and preprocess data
u_text, i_text, u_text_old, i_text_old,... | e02cce3f208046e805851aced54ee74e22e9de0c | 38,256 |
def filter_records(records, key, operator, test_value):
"""Removes all records from a list of records, which do not
fulfill the specified criteria.
"""
filtered_records = []
for record in records:
# @shoeffner: missing , and :
if isinstance(record[key] int)
test_value = i... | c2bac026ba3d048898bb147263b937ce81db37bd | 38,257 |
from typing import FrozenSet
def _numeric_range_is_disjoint(
bin_ranges: FrozenSet[Range],
numeric_accuracy: float
) -> bool:
"""
A method that checks if the numeric range bins are disjoint and don't contain gaps.
:param bin_ranges: the set of ranges we want to check
:param numeric_ac... | e291451d612332c2ceb0e615a239448613250c95 | 38,258 |
import torch
def large_state_termination(state, action, next_state=None):
"""Termination condition for environment."""
if not isinstance(state, torch.Tensor):
state = torch.tensor(state)
if not isinstance(action, torch.Tensor):
action = torch.tensor(action)
done = torch.any(torch.abs(... | 99491952d0f57a24de112c686d3a13436c7b3a50 | 38,259 |
def get_filtered_properties_df(prefix: str, prop: str, **kwargs) -> pd.DataFrame:
"""Extract a single property for each term."""
path = prefix_directory_join(prefix, 'cache', 'properties', f"{prop}.tsv")
@cached_df(path=path, dtype=str)
def _df_getter() -> pd.DataFrame:
obo = get(prefix, **kwar... | ea8866733f7476bd5d1eae0510cbda4a08352eb7 | 38,260 |
def to_1d(arg, raw=False):
"""Reshape argument to one dimension.
If `raw` is `True`, returns NumPy array.
If 2-dim, will collapse along axis 1 (i.e., DataFrame with one column to Series)."""
if raw or not checks.is_array(arg):
arg = np.asarray(arg)
if arg.ndim == 2:
if arg.shape[1]... | 02973e0fe29a9b5b54f34ac82f322c1ecc572d12 | 38,261 |
import cloudpickle
import pickle
def _load_model_from_local_file(path, serialization_format):
"""Load a scikit-learn model saved as an MLflow artifact on the local file system.
:param path: Local filesystem path to the MLflow Model saved with the ``sklearn`` flavor
:param serialization_format: The format... | d183a1d92e683507611d827469f14344b6f78b8b | 38,262 |
def sum_sq_diff(img_0, img_1, u, x, y, x_len, y_len):
"""
Returns the summed square difference between two image patches, using even
weighting across the patch.
Parameters :
img_0, img_1 : two images being compared
u : displacement vector between patches
x, y ... | 85e2f7095d766948058e6ce3ed0b0632026217cc | 38,263 |
import re
import zipfile
def instance_name_from_zip(path):
"""Determines the instance filename within a SEC EDGAR zip archive."""
re_instance_name = re.compile(r'.+-\d{8}\.xml')
for name in zipfile.ZipFile(path).namelist():
if re_instance_name.fullmatch(name):
return name
raise Run... | 59b2154d433e500e9b0cdf39ee70d4c058da1d06 | 38,264 |
def delete_load_blanacer(ilb_name):
"""
Delete a deployed Internal Load Balancer
:param ilb_name:
:return: None or str
"""
response = describe_load_balancers(ilb_name)
for i in response['LoadBalancerDescriptions']:
if 'LoadBalancerName' in i:
elb.delete_load_balancer(Loa... | 32448835674a60766c395917301659ae42041f6e | 38,265 |
import argparse
def get_arguments():
"""Obtains command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--inputs',
required=True,
nargs='+',
metavar='INPUT',
help='read Q2 inputs from Feather files %(metavar)ss')
parser.add_argumen... | 118e76d633bf5ea37b85a53dcd5ba07cf7e46e04 | 38,266 |
from scipy.stats import pearsonr
def crosscorrelation(a, b):
"""
Calculates a normalized cross-correlation between two vectors.
Returns the Pearson correlation coefficient.
"""
return (pearsonr(a, b))[0] | 155f46ec9ed7e71b7fc26afaf3fd470ebca58705 | 38,267 |
def hypothesis_test_v1(unknown_agent_actions, known_agent_dists,
update_times, num_actions, num_samples=50,
score_funcs_list = [z1_score_function, z2_score_function, z3_score_function],
weight_value=(1.0/3)):
"""
Calculate p-values for various timesteps of an i... | 9e5fc69a8e638fac7e094c57def4c70ad454af0b | 38,268 |
def area_hexagon(r):
"""Return the area of a regular hexagon with side length R."""
return r * r * 3 * sqrt(3) / 2 | aacb70f57bf3b7e202a085b9ce5cdd694688468a | 38,269 |
def index2state(Is, n, Juv, Jdv=None):
"""
Returns state with a given index
Parameters
----------
Is : ndarray
list of indices
n : int
number of sites
Juv : ndarray
Lin table of spin-up states
Jdv : ndarray
Lin table for spin-down states
"""
Nu = ... | dba20dad94332d8bcea64c7ed1f43d96e692ed31 | 38,270 |
import re
def headers_ok(meta):
"""check that headers are 'name' or end with c/d/ll"""
meta_fh = open(meta)
headers = meta_fh.readline().rstrip().split('\t')
return headers[0] == 'name' and \
all(map(lambda s: re.search(r'\.(c|d|ll)$', s), headers[1:])) | 408975c795de8bf22529cf917ca881ca98ede4f9 | 38,271 |
import os
import errno
def rasterizeSSURGOFeatures(config, outputDir, featureFilename, featureLayername, featureAttrList, \
getResolutionFromRasterFileNamed=None, rasterResolutionX=None, rasterResolutionY=None):
""" Create raster maps, in GeoTIFF format, for SSURGO attributes associate... | 27ecefc55dea2b6a91b224d99793ee75fb081071 | 38,272 |
import urllib
def get_fontawesome_panel_express() -> str:
"""Converts the official css file at FONTAWESOME_CSS_URL into it's panel
representation
Returns:
str -- [description]
"""
with urllib.request.urlopen(FONTAWESOME_CSS_URL) as file:
fontawesome_css = file.read().decode("utf-... | e71acbe7dcef7a8f57a511c5945ebdba7fabd979 | 38,273 |
def search_index_for_variable(index,parent_tag,variable_expression,resolve=False):
"""
:param str parent_tag: tag created of colon-separated identifiers, e.g. "mymodule" or "mymodule:mysubroutine".
%param str variable_expression% a simple identifier such as 'a' or 'A_d' or a more complicated tag representin... | 1b427243dbb4b8c9a14a2ef60e76aa51b56ad470 | 38,274 |
import re
def has_number(name):
"""判断名name内是否出现了数字（包括中文的数字）"""
if bool(re.search(r'\d',name)):
return True
num_str = ['一','二','三','四','五','六','七','八','九','十']
for s in num_str:
if s in name:
return True
return False | 56dec9664e945d852cbfee4791f386aaab15f215 | 38,275 |
import re
def validate_entity_arn(entity_arn):
"""Validate entity ARN"""
# account_number = SESSION.client('sts').get_caller_identity()["Account"]
# Roles are valid: arn:aws:iam::842337631775:role/1S-Admins
# arn:aws:sts::281782457076:assumed-role/1S-Admins/alex
# Users are inval... | 601e2d7ef8f214b4b9882b599eb4fbd74edcbb76 | 38,276 |
import random
def seed_student_proposal(request, i):
"""Returns the properties of a new student proposal.
"""
ensureUser()
org = Organization.get_by_key_name('google/gsoc2009/org_%d' % i)
mentor = Mentor.get_by_key_name('google/gsoc2009/org_%d/mentor' % i)
user = User.get_by_key_name('user_%d' % i)
stu... | 22aca19e5b901a236e9a49a3b47c284496ec8f56 | 38,277 |
def get_data_f2(x, a, b, c, d, noise):
"""
return function y = a*exp(-(x-b/c)^2) + d + noise
"""
y = a * np.exp(-(
(x - b) / c)**2) + d + noise * np.random.rand(x.shape[0], x.shape[1])
return y | 58cc76971b21d970ad08d55c61168849e3ed9f2f | 38,278 |
def subpixel_contours(da,
z_values=[0.0],
crs=None,
affine=None,
attribute_df=None,
output_path=None,
min_vertices=2,
dim='time',
errors='ignore... | c9dedeb7f3f911dc32f940936aed7e9f3f349018 | 38,279 |
def collect_hosts(hosts):
"""Collect a set of hosts and an optional chroot from a string."""
host_ports, chroot = hosts.partition("/")[::2]
chroot = "/" + chroot if chroot else None
result = []
for host_port in host_ports.split(","):
# put all complexity of dealing with
# IPv4 & IPv... | 6749f893337505d66b7eab12c9699934b90afea7 | 38,280 |
def process_frame(img, frame, face_cascade):
"""
Determine whether the current frame contains the faces of people from our database
"""
global ready_to_detect_identity
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# Loop through all the f... | a05d9939e2c0383c513fda791af9029b821bb060 | 38,281 |
def GetMtdDevForNameOrNone(partname):
"""Find the mtd# for a named partition.
In /proc/mtd we have:
dev: size erasesize name
mtd0: 00200000 00010000 "cfe"
mtd1: 00200000 00010000 "reserve0"
mtd2: 10000000 00100000 "kernel0"
mtd3: 10000000 00100000 "kernel1"
Args:
partname: the partition to ... | 9bc0be382c428935c0ee53576fd2a90cb103867d | 38,282 |
def PIoN2SolNTableConvert (inUV, outSNVer, NITable, pos, err):
"""
Evaluate Ionospheric model table at pos and convert to SN table
Returns resultant SN table
* inUV = UV data for output SN table.
Control parameters on inUV info member:
========== ================ =================... | b817f9383ffd617f0d25c131b0f3fbe1a812c1d4 | 38,283 |
def hasp(
revisions: pd.DataFrame,
window: int = 12,
) -> pd.DataFrame:
"""Apply the half window splice revision extension method.
Parameters
----------
revisions : pandas DataFrame
A DataFrame of the rolling revisions. The values only need to be
correct for the size of the ... | cfaddfa509fb1e596caf19fd298154fd47c7f3a5 | 38,284 |
import numpy
def get_vignette_mask(image, percent_vignetted=5):
"""Convert a well-exposed image (ideally a brightfield image with ~uniform
intensity) into a mask delimiting the image region from the dark,
vignetted borders of the image.
percent_vignetted: percent (0-100) of pixels estimated to be in ... | ac41f9b7f80b76ac948a4e7815dd84296063469a | 38,285 |
def cohort_to_int(year, season, base=16):
"""cohort_to_int(year, season[, base])
Converts cohort tuple to a unique sequential ID.
Positional arguments:
year (int) - 2-digit year
season (int) - season ID
Keyword arguments:
base (int) - base year to treat as 0
Returns:
... | 1f1981eb6c43ab6f77abf6d04ba3b92d9053953d | 38,286 |
import re
import time
def delete_project(project_ids: list) -> bool:
"""
Deletes project, groups, tasks and results from Firebase and Postgres.
"""
for project_id in project_ids:
logger.info(
f"Delete project, groups, tasks and results of project: {project_id}"
)
f... | a039882efaf269ece21866eff0a0665c47d874b0 | 38,287 |
def set_data_format(array_values):
"""
Check and set the corresponding format for each value
:param list[list[str]] array_values: list of values
:return: list[list[str]]: array formatted
"""
formatted_data = []
for d in array_values:
values = []
for v in d:
# Try... | 7546f0dd5c661790d384f2f8063abed1701a3e8c | 38,288 |
def dict_of_transition_matrix(mat):
""" Convert a transition matrix (list of list or numpy array) to a dictionary mapping (state, state) to probabilities (as used by :class:`pykov.Chain`)."""
if isinstance(mat, list):
return {(i, j): mat[i][j] for i in range(len(mat)) for j in range(len(mat[i]))}
el... | b823ff496a751f4ffe305a31f1c1d019f7a25d33 | 38,289 |
def taxii2_collection_by_id(api_root: str, collection_id: str) -> Response:
"""
Defines TAXII API - Collections:
Get Collection section (5.2) `here for v.2.0
<http://docs.oasis-open.org/cti/taxii/v2.0/cs01/taxii-v2.0-cs01.html#_Toc496542736>`__
and `here for v.2.1 <https://docs.oasis-open.org/cti/ta... | f0932d93e0843747b56dc50ee7019814fa3e0a6e | 38,290 |
def argmaxn(value_list, n, order='desc'):
"""
Return the index of top n elements in the list if order is set to 'desc',
otherwise return the index of n smallest elements
:param value_list: a list containing all values
:type value_list: list, array
:param n: the number of the elements to select
... | 423c16e1c7bd83ef547a2ac6cd7e51498b18bab5 | 38,291 |
import math
def nms(args, classes, offsets, anchors):
"""Perform NMS (Algorithm 11.12.1).
Arguments:
args : User-defined configurations
classes (tensor): Predicted classes
offsets (tensor): Predicted offsets
Returns:
objects (tensor): class predictions per anchor
... | f5cfef75972a4d8416161398199185e704f88db2 | 38,292 |
def extract_features(data_frame: pd.DataFrame) -> pd.DataFrame:
"""Obtains training features from the data frame containing subreddit data.
Params:
- data_frame (pd.DataFrame): data frame containing subreddit data
Returns:
- features (pd.DataFrame): the training features returned by the data frame... | 59d2407ba758c8f2e6937d2ffb03d9648b792b37 | 38,293 |
def get_bugzilla_bug(bugzilla_url, bug_id):
"""
Read bug XML, return all fields and values in a dictionary.
"""
bug_xml = _fetch_bug_content(bugzilla_url, bug_id)
return parse_bug_fields(bug_xml) | 3ac255f5135ab639b9e7137d5d52e0c5eed61b51 | 38,294 |
def _funded_by(record, extra_data):
"""Check if publication has "Funded by SCOAP3" marking *in pdf(a) file* """
patterns = ['funded.?by.?scoap3?', ]
return __find_regexp_in_pdf(extra_data, patterns) | c3c60103f117512e6553478c828f95715f81511c | 38,295 |
def profile(request, profile_slug):
"""
Display profile details.
"""
speaker_profile = get_object_or_404(AttendeeProfile, slug=profile_slug)
if not profile_page_visible(profile=speaker_profile, for_user=request.user):
return TemplateResponse(request, "conference/profiles/profile_unavailable... | a03d10a9c9641f1fdf04810e915e8a483cb4a01b | 38,296 |
def save_file(filename, contents):
"""Save a file from the editor"""
if not filename:
return 0, 0
with open(filename, 'w') as f:
f.write(contents)
return len(contents), hash(contents) | 8e973f67a22a2e7b0836f8db25090c65238492e3 | 38,297 |
def GetDockerImageFromTagOrDigest(image_name):
"""Gets an image object given either a tag or a digest.
Args:
image_name: Either a fully qualified tag or a fully qualified digest.
Defaults to latest if no tag specified.
Returns:
Either a docker_name.Tag or a docker_name.Digest object.
"""
if no... | 7e61f8a3dde64ec5efa833fc72e909e9140f77f9 | 38,298 |
def line_label(ax, pos, label, dir='v', loc='top', xx=None, yy=None, ha=None, va=None,
line_kwargs={}, text_kwargs={}, dashes=None, rot=None):
"""Plot a vertical line, and give it a label outside the axes.
Arguments
---------
ax : `matplotlib.axes.Axes` object
Axes on which to pl... | 9612f2218ce6559f7deffc0f148c4d205b3608b1 | 38,299 |
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