content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def signup() -> Response | str | tuple[dict[str, str | int], int]:
"""Sign up"""
# Bypass if user is logged in
if current_user.is_authenticated:
return redirect(url_for("home"))
# Process user data
try:
# Return template if request.method is GET
assert request.method != "GE... | 9496c11a9015b69c7cf2f89d19a45f93405f5dfe | 3,640,981 |
import copy
def evaluate_all_configs(hparams, agent_model_dir):
"""Evaluate the agent with multiple eval configurations."""
def make_eval_hparams(hparams, policy_to_action, max_num_noops):
hparams = copy.copy(hparams)
hparams.add_hparam("num_agents", hparams.eval_num_agents)
hparams.add_hparam("policy... | 107fb692adec1ce7dbb580c30e3e7b0402874054 | 3,640,982 |
from typing import Tuple
def shape(a: Matrix) -> Tuple[int, int]:
""" Returns the num of rows and columns of A """
num_rows = len(a)
num_cols = len(a[0]) if a else 0 # number of elements so columns in first element if it exists
return num_rows, num_cols | 24ef9045e86b6027f76ddf57bf5eba44553798c5 | 3,640,983 |
def solve(A, b):
"""
:param A: Matrix R x C
:param b: Vector R
:return: Vector C 'x' solving Ax=b
>>> M = Mat(({'a', 'b', 'c', 'd'}, {'A', 'B', 'C', 'D'}), { \
('a', 'A'): one, ('a', 'B'): one, ('a', 'D'): one, \
('b', 'A'): one, ('b', 'D'): one, \
('c', 'A'): one, ('c', 'B')... | b25a762ee8f8229d1bc573c828a7151770d3240c | 3,640,984 |
def token_groups(self):
"""The groups the Token owner is a member of."""
return self.created_by.groups | 9db411660db1def09b8dc52db800ca4c09a38cce | 3,640,985 |
import requests
def get_html_content_in_text(url):
"""
Grab all the content in webpage url and return it's content in text.
Arguments:
url -- a webpage url string.
Returns:
r.text -- the content of webpage in text.
"""
r = requests.get(url)
return r.text | fd8ddc992f34c186051ca8985ffb110c50004970 | 3,640,986 |
def subscribe():
"""Subscribe new message"""
webhook_url = request.form.get("webhook_url")
header_key = request.form.get("header_key")
header_value = request.form.get("header_value")
g.driver.subscribe_new_messages(NewMessageObserver(webhook_url, header_key, header_value))
return jsonify({"succe... | 97a47fb298bbc0bac3e333037210556525dd837f | 3,640,987 |
def SegAlign(ea, alignment):
"""
Change alignment of the segment
@param ea: any address in the segment
@param alignment: new alignment of the segment (one of the sa... constants)
@return: success (boolean)
"""
return SetSegmentAttr(ea, SEGATTR_ALIGN, alignment) | c0c380e194fbed43b87be81108eecf864809c447 | 3,640,988 |
import requests
def create_bitlink(logger, headers='', long_url='google.com'):
"""
Функция создает короткие ссылки из длинных
:param logger: logger object
:param headers: Generic Access Token сформированнный на сайте
:param long_url: Ссылка которую надо укоротить
:return: созданная короткая сс... | be5c7882a8577c8d406412c790f3d5fbcbd11019 | 3,640,989 |
import numpy
def compute_neq(count_mat):
"""
Compute the Neq for each residue from an occurence matrix.
Parameters
----------
count_mat : numpy array
an occurence matrix returned by `count_matrix`.
Returns
-------
neq_array : numpy array
a 1D array containing the neq ... | e3d738eb1c8ed58a3c4d4a4efc5323930d03be1f | 3,640,990 |
import optparse
def _GetOptionsParser():
"""Get the options parser."""
parser = optparse.OptionParser(__doc__)
parser.add_option('-i',
'--input',
dest='inputs',
action='append',
default=[],
help='One or more inp... | e1ec0530357ad3bebbac80c86b9d9b1010e6688c | 3,640,991 |
def spikalize_img(experiment, image, label):
"""
Transform image to spikes. Spike with poisson distributed rate proportional to pixel brightness.
:param experiment:
:param image:
:param label:
:return:
"""
image_shape = np.append(np.array(experiment.timesteps), np.array(image.shape))
... | 7159334b40c3841977a0772ac25c71a934d268ac | 3,640,992 |
def update_security_schemes(spec, security, login_headers, security_schemes,
unauthorized_schema):
"""Patch OpenAPI spec to include security schemas.
Args:
spec: OpenAPI spec dictionary
Returns:
Patched spec
"""
# login_headers = {'Set-Cookie':
# ... | 1ecb5cc3a121fc151794e4e24cd4aca4bc07ce46 | 3,640,993 |
def get_geckodriver_url(version):
"""
Generates the download URL for current platform , architecture and the given version.
Supports Linux, MacOS and Windows.
:param version: the version of geckodriver
:return: Download URL for geckodriver
"""
platform, architecture = get_platform_architectu... | 9d71728c551c67e86a61c3b870728bc70cad48ba | 3,640,994 |
def get_graph_size(depth: int):
"""returns how many nodes are in fully-equipped with nodes graph of the given depth"""
size = 1
cur_size = 1
ln = len(expand_sizes)
for i in range(min(ln, depth)):
cur_size *= expand_sizes[i]
size += cur_size
if ln < depth:
size += cur_siz... | 00f2a2ce714550785e99c0d193f47df05cc30b68 | 3,640,995 |
def inherits_from(obj, parent):
"""
Takes an object and tries to determine if it inherits at *any*
distance from parent.
Args:
obj (any): Object to analyze. This may be either an instance
or a class.
parent (any): Can be either instance, class or python path to class.
R... | 9d7e0665b4e4fe2a3f7c136436a2502c8b72527c | 3,640,996 |
from typing import Optional
def load_df_from_googlesheet(
url_string: str,
skiprows: Optional[int] = 0,
skipfooter: Optional[int] = 0,
) -> pd.DataFrame:
"""Load a Pandas DataFrame from a google sheet.
Given a file object, try to read the content as a CSV file and transform
into a data frame.... | adfcff1968eccfa44640b5e9a7e3143703284dfb | 3,640,997 |
def decomp(bits, dummies=default_dummies, width=default_width):
"""Translate 0s and 1s to dummies[0] and dummies[1]."""
words = (dummies[i] for i in bits)
unwrapped = ' '.join(words)
return wrap_source(unwrapped, width=width) | a6540cc90412b9e72b62c57fe6828b45ad5df593 | 3,640,998 |
def get_word_node_attrs(word: Word) -> WordNodeAttrs:
"""Create the graph's node attribute for a `Word`.
Build an attribute dict with the word's features. Note that we're using the
term `Word` instead of `Token` to be closer to the implementation of these
data structures in stanza. From stanza's docume... | 143a00206cfa8d98419f2d0e21e1013ea6dd02ab | 3,640,999 |
def numpy_bbox_to_image(image, bbox_list, labels=None, scores=None, class_name=[], config=None):
""" Numpy function used to display the bbox (target or prediction)
"""
assert(image.dtype == np.float32 and image.dtype == np.float32 and len(image.shape) == 3)
if config is not None and config.normalized_m... | c9070cb917e376357363f99e9d951d84e3274684 | 3,641,000 |
def AsyncSleep(delay, name=None):
"""Pause for `delay` seconds (which need not be an integer).
This is an asynchronous (non-blocking) version of a sleep op. It includes
any time spent being blocked by another thread in `delay`. If it is blocked
for a fraction of the time specified by `delay`, it only calls `sl... | c6ccb12aa7e27a28591ac5282b0e78baa68df9df | 3,641,002 |
def palide(string, length, ellipsis="...", pad=" ", position=1.0, left=False):
"""
A combination of `elide` and `pad`.
"""
return globals()["pad"](
elide(string, length, ellipsis=ellipsis, position=position),
length, pad=pad, left=left) | be14ecb386ef7d49a6c85514bb5bee93d482be3d | 3,641,003 |
def get_email_adderess(email_addr):
""" Return dict from opalstack for given email address, or None """
mails = get_request("mail/list/")
for record in mails['mails']:
if record['address'] == email_addr:
return get_request("mail/read/{}".format(record['id']))
return None | f31ae883b40da8b9ddf743744a3611dd0968e787 | 3,641,004 |
def GK3toUTM(ea, no=None, zone=32):
"""Transform Gauss-Krueger zone 3 into UTM (for backward compatibility)."""
return GKtoUTM(ea, no, zone, gkzone=3) | aeab5433c8a676f862c9271f62a574bff2f74444 | 3,641,005 |
from typing import Optional
def get_all_predictions(
model: nn.Module,
dataloader: DataLoader,
device: _Device,
threshold_prob: Optional[float] = None,
decouple_fn: Optional[_DecoupleFnTest] = None,
) -> _TestResult:
"""
Make predictions on entire dataset and return raw outputs
and opt... | e2d04376a935a1acd1d2e0645209cb865997669e | 3,641,006 |
def plot_perf_stats(returns, factor_returns):
"""
Create box plot of some performance metrics of the strategy.
The width of the box whiskers is determined by a bootstrap.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanat... | f948c29df18bc93d08fd6ad1b366db310b4bf8c2 | 3,641,007 |
def create(user):
"""
This function creates a new user in the database
based on the passed-in user data.
:param user: User to create in database
:return: 201 on success, 400 on bad postal code, 406 on user exists
"""
username = user.get("username", None)
postalcode = user.get("post... | 2b3a64211fd66c7fbe93f51aecbceab6675f5b99 | 3,641,008 |
def show_profile(uid):
"""
Return serializable users data
:param uid:
:return String: (JSON)
"""
user = get_user_by_id(uid)
return jsonify(user.serialize) | cb8bc7934575b99b6098a911c4dc7ad9fb1b7a48 | 3,641,009 |
def degree_correlation(coeffs_1, coeffs_2):
"""
Correlation per spherical harmonic degree between two models 1 and 2.
Parameters
----------
coeffs_1, coeffs_2 : ndarray, shape (N,)
Two sets of coefficients of equal length `N`.
Returns
-------
C_n : ndarray, shape (nmax,)
... | 10dea06b6e1f9a1c4202f3478523fe7bdcc8ab6e | 3,641,010 |
import cdr_cleaner.args_parser as parser
def parse_args():
"""
Add file_path to the default cdr_cleaner.args_parser argument list
:return: an expanded argument list object
"""
help_text = 'path to csv file (with header row) containing pids whose observation records are to be removed'
addition... | f5f89a55799ceee801b06a51e902cdd252068e50 | 3,641,011 |
def name(model):
"""A repeatable way to get the formatted model name."""
return model.__name__.replace('_', '').lower() | 3d9ca275bfbfff6d734f49a47459761c559d906e | 3,641,012 |
from typing import List
from typing import Optional
def render_fields(
fields: List[Field], instance_name: Optional[str] = None
) -> List[str]:
"""Renders fields to string.
Arguments:
fields:
The fields to render.
instance_name:
The name of model instance for which... | ba75827eac0ccea3e68259e27274feea17121cb2 | 3,641,013 |
def get_primary_tasks_for_service(service_arn):
"""Get the task ARN of the primary service"""
response = ecs.describe_services(cluster=cluster, services=[service_arn])
for deployment in response['services'][0]['deployments']:
if deployment['status'] == 'PRIMARY':
return get_tasks_for_tas... | 113e3ab3a20646d20caf08a6e1dfc4d546d2d950 | 3,641,014 |
def load_data(csv_file):
"""
@type csv_file: string
@param csv_file: path to csv file
Loads data from specified csv file
@rtype: pandas.DataFrame
@return: DataFrame from csv file without Month column
"""
return pd.read_csv(csv_file).drop('Month', 1) | 5a458c104d763e431f0faf63a98bf4a59fd7902c | 3,641,016 |
def nearest(array,value):
"""
Find the index of the array that is close to value
Args:
array (array): array to be tested
value (float): value to be tested
Returns:
int: index
"""
return (np.abs(array-value)).argmin() | 5197d0cae968557b519d1fa4025d2b834d7065c5 | 3,641,017 |
def fine_tune_model(trainX: np.ndarray, trainy: np.ndarray, cv: int = 5) -> SVC:
"""Receives training set and run a grid search to find the best
hyperparameters. It returns the best model, already trained.
Args:
trainX (np.ndarray): train array containg embedding images.
trainy (np.ndarray... | d493a0b0023f57116858878163b81463c1a7166e | 3,641,018 |
from datetime import datetime
import numpy
def get_empty_array_year(year=datetime.now().year, start_end=True, variable_list=['TEST', ], variable_list_dtype=None, record_interval='HH'):
"""
Allocates and returns new empty record array for given year using list of dtypes
(or variable labels as 8byte floats ... | d703ddc41233125b2b426c0423b0a3dcb85f73a0 | 3,641,019 |
def validate_geojson(data):
"""
Validate geojson
"""
if not (isinstance(data, dict)):
return False
if not isinstance(data.get('features'), list):
return False
gj = geojson.FeatureCollection([geojson.Feature(f) for f in data['features']])
return gj.is_valid | c48dfb76ff6d0255299f3913a644edff679b1a1a | 3,641,020 |
from pathlib import Path
import time
def run_wps(conn, config_wpsprocess, **kwargs):
"""
primary function to orchestrate running the wps job from submission to download (if required)
Parameters:
-----------
conn: dict,
Connection parameters
Example: conn = {'domain': '... | d629939aaa32399a52a2f8ed1b0c8b5e94206f29 | 3,641,021 |
from chat.models import Chat
from ct.models import Role
def get_redirect_url(user):
"""
Analyse user and redirect:
Instructor:
onboarding is disabled - to /ctms/
onboarding is enabled and not achieved needed percent - to /ctms/onboarding/
onboarding is enabled and... | 28faf82e8b22eb3602ba70e00a97a97ae50d93a1 | 3,641,022 |
def _ds_to_arrraylist(
ds, bands, time_dim, x_dim, y_dim, percentile_stretch, image_proc_func=None
):
"""
Converts an xarray dataset to a list of numpy arrays for plt.imshow plotting
"""
# Compute percents
p_low, p_high = ds[bands].to_array().quantile(percentile_stretch).values
array_list ... | 85b05277cf874a32eb006045437eee8ae02ce0ef | 3,641,023 |
import six
import binascii
def derive_key(secret, salt, iterations=1000, keylen=32):
"""
Computes a derived cryptographic key from a password according to PBKDF2.
.. seealso:: http://en.wikipedia.org/wiki/PBKDF2
:param secret: The secret.
:type secret: bytes or unicode
:param salt: The salt ... | 04adaf71f3f9cf94e602029a242fedd037a40187 | 3,641,024 |
import torch
def calc_params_l2_norm(model: torch.nn.Module, bf16: bool):
"""Calculate l2 norm of parameters """
# args = get_args()
if not isinstance(model, list):
model = [model]
# Remove duplicate params.
params_data = []
for model_ in model:
for param in model_.parameters()... | 399fc47296ac1ba3398dd5be834358f5ef50c9a4 | 3,641,026 |
def gradient_descent(f,init_val_dict, learning_rate=0.001, max_iter=1000, stop_stepsize=1e-6,return_history=False):
"""
Gradient Descent finding minimum for a
single expression
INPUTS
=======
f: expression
init_val_dict:dictionary containing initial value of variables
learning_rat... | ba9edd1b41b7ac8e2e1d14b0a2958b7fe07bcf2a | 3,641,027 |
from typing import Dict
from typing import Any
def gcp_iam_organization_role_permission_remove_command(client: Client, args: Dict[str, Any]) -> CommandResults:
"""
Remove permissions from custom organization role.
Args:
client (Client): GCP API client.
args (dict): Command arguments from X... | 42f56361d36b4fa0fbdea09031923235a0a3eb47 | 3,641,028 |
import socket
def get_hostname(ipv) -> str:
"""
Get hostname from IPv4 and IPv6.
:param ipv: ip address
:return: hostname
"""
return socket.gethostbyaddr(ipv)[0] | e7d660dc3c5e30def646e56fa628099e997145be | 3,641,029 |
import torch
def load_model():
"""
Load CLIP model into memory.
Will download the model from the internet if it's not found in `WAGTAIL_CLIP_DOWNLOAD_PATH`.
"""
device = torch.device("cpu")
model, preprocess = clip.load("ViT-B/32", device, download_root=DOWNLOAD_PATH)
return model, device,... | deebe0a5d5edb82b34d386771367ab65aaf6cb4b | 3,641,030 |
from traceback import format_exception
def exception_response(ex: Exception):
"""Generate JSON payload from ApiException or Exception object."""
if not ex:
app.logger.error("Function received argument: None!")
return __make_response(
500,
{
"error" : "... | d3b8d58d3214d3543cea5135a46455fc824b78d7 | 3,641,031 |
def check(pack, inst):
"""
A function to check if an instruction is present in the packet
Input:
- pack: The packet to be checked
- inst: The instruction
Output:
Returns True if the instruction is present in the packet else Fase
"""
inst_key = getPacketKey(inst[0])
for key ... | 905fb2061b2fd5c129cdb0903ef84184c55844af | 3,641,032 |
def get_score(true, predicted):
"""Returns F1 per instance"""
numerator = len(set(predicted.tolist()).intersection(set(true.tolist())))
p = numerator / float(len(predicted))
r = numerator / float(len(true))
if r == 0.:
return 0.
return 2 * p * r / float(p + r) | 115a4847e3d991f47415554401df25d72d74bb2f | 3,641,034 |
def check_ratio_argv(_argv):
"""Return bool, check optional argument if images are searched by same ratio"""
# [-1] To avoid checking 3 places at one, this argument is always last
return bool(_argv[-2] in ARGV["search by ratio"] and _argv[-1] in ARGV["search by ratio"]) | 23a677af4042fcc3616a25378af4e7721971de56 | 3,641,035 |
def binary_erosion(input, structure = None, iterations = 1, mask = None,
output = None, border_value = 0, origin = 0, brute_force = False):
"""Multi-dimensional binary erosion with the given structure.
An output array can optionally be provided. The origin parameter
controls the placement of th... | 06de7142d7eca9ca3f5712f76318215950f4c710 | 3,641,036 |
def chord_to_freq_ratios(chord):
"""Return the frequency ratios of the pitches in <chord>
Args:
chord (tuple of ints): see <get_consonance_score>.
Returns:
list of ints:
"""
numerators = [JI_NUMS[i] for i in chord]
denoms = [JI_DENOMS[i] for i in chord]
denominator = get_lc... | 4811a6b69e6fd646adf5dc7e7a31a23be8fa6708 | 3,641,037 |
import torch
def proto_factor_cosine(local_proto, global_proto):
"""
[C, D]: D is 64 or 4
"""
# factor = 1
norm_local = torch.norm(local_proto, dim=-1, keepdim=False)
norm_global = torch.norm(global_proto, dim=-1, keepdim=False) # [C]
factor_refined = torch.sum(local_proto*global_proto, di... | 6e9f7540ec1339efe3961b103633f5175cb38c49 | 3,641,038 |
def urlparse(d, keys=None):
"""Returns a copy of the given dictionary with url values parsed."""
d = d.copy()
if keys is None:
keys = d.keys()
for key in keys:
d[key] = _urlparse(d[key])
return d | 91cd40ef294443431a772ec14ef4aa54dab34ea8 | 3,641,039 |
import numpy
import math
def doFDR(pvalues,
vlambda=numpy.arange(0,0.95,0.05),
pi0_method="smoother",
fdr_level=None,
robust=False,
smooth_df = 3,
smooth_log_pi0 = False):
"""modeled after code taken from http://genomics.princeton.edu/storeylab/qvalu... | 17919d989ca07b4fb87930141cef3ce392b66ad4 | 3,641,040 |
def sequence(ini, end, step=1):
""" Create a sequence from ini to end by step. Similar to
ee.List.sequence, but if end != last item then adds the end to the end
of the resuting list
"""
end = ee.Number(end)
if step == 0:
step = 1
amplitude = end.subtract(ini)
mod = ee.Number(ampl... | cca23fd00ddf1237a95a53b7f6a3f1bc264f84da | 3,641,041 |
def kBET_single(
matrix,
batch,
k0=10,
knn=None,
verbose=False
):
"""
params:
matrix: expression matrix (at the moment: a PCA matrix, so do.pca is set to FALSE
batch: series or list of batch assignemnts
returns:
kBET observed rejection rate
... | 42ecfb9dee65806a25e92764ea7c1ef54316be02 | 3,641,042 |
from typing import Tuple
import numpy
def get_eye_center_position(face: Face) -> Tuple[numpy.int64, numpy.int64]:
"""Get the center position between the eyes of the given face.
Args:
face (:class:`~.types.Face`):
The face to extract the center position from.
Returns:
Tuple[:d... | 562dca1971996e8d7497d146aa7ccefcb3ce8006 | 3,641,043 |
def esc_quotes(strng):
""" Return the input string with single and double quotes escaped out.
"""
return strng.replace('"', '\\"').replace("'", "\\'") | 25956257e06901d4f59088dd2c17ddd5ea620407 | 3,641,044 |
def gen_fake_game_data():
"""Creates an example Game object"""
game = Game(
gameday_id='2014/04/04/atlmlb-wasmlb-1',
venue='Nationals Park',
start_time=parser.parse('2014-04-04T13:05:00-0400'),
game_data_directory='/components/game/mlb/year_2014/month_04/day_04/gid_2014_04_04_atl... | 415c85eb0ba4e03c135ab56791177ebb634ea5e3 | 3,641,045 |
def unsafe_load_all(stream):
"""
Parse all YAML documents in a stream
and produce corresponding Python objects.
Resolve all tags, even those known to be
unsafe on untrusted input.
"""
return load_all(stream, UnsafeLoader) | f6307614e776b221ec22b063680f34f5e2ddf789 | 3,641,046 |
def jaccard(set1, set2):
"""
computes the jaccard coefficient between two sets
@param set1: first set
@param set2: second set
@return: the jaccard coefficient
"""
if len(set1) == 0 or len(set2) == 0:
return 0
inter = len(set1.intersection(set2))
return inter / (len(set1) + le... | 9a99c6c5251bdb7cb10f6d3088ac6ac52bb02a55 | 3,641,049 |
def to_numeric(arg):
"""
Converts a string either to int or to float.
This is important, because e.g. {"!==": [{"+": "0"}, 0.0]}
"""
if isinstance(arg, str):
if '.' in arg:
return float(arg)
else:
return int(arg)
return arg | e82746e1c5c84b57e59086030ff7b1e93c89a8ec | 3,641,053 |
def get_kde_polyfit_estimator(samples, N=100000, bandwidth=200, maxlength=150000, points=500, degree=50):
"""多項式近似したバージョンを返すやつ 一応両方かえす"""
f = get_kde_estimator(samples, N, bandwidth)
x = np.linspace(1, maxlength, points)
z = np.polyfit(x, f(x), degree)
return (lambda x: np.where(x<=maxlength, np.pol... | 72801a8be25826ef42ab700e5cae742ed59fcea4 | 3,641,054 |
def read_tracker(file_name):
"""
"""
with open(file_name, "r") as f:
return int(f.readline()) | 9d1f43b8f833b5ca86c247760ae79e18f33aa019 | 3,641,055 |
def MixR2VaporPress(qv, p):
"""Return Vapor Pressure given Mixing Ratio and Pressure
INPUTS
qv (kg kg^-1) Water vapor mixing ratio`
p (Pa) Ambient pressure
RETURNS
e (Pa) Water vapor pressure
"""
return qv * p / (Epsilon + qv) | 71d7ee564d07292ef4831bf39e54f51071b7d7dd | 3,641,056 |
def sigmoid_derivative(dA, cache):
"""
Implement the backward propagation for a single SIGMOID unit.
Arguments:
dA -- post-activation gradient, of any shape
cache -- 'Z' where we store for computing backward propagation efficiently
Returns:
dZ -- Gradient of the cost with respect to Z
... | 6b45e722bc48b4cf60170de267c235d0943d022c | 3,641,057 |
from typing import Counter
def part2(lines, rounds=100):
"""
>>> data = load_example(__file__, '24')
>>> part2(data, 0)
10
>>> part2(data, 1)
15
>>> part2(data, 2)
12
>>> part2(data, 3)
25
>>> part2(data, 4)
14
>>> part2(data, 5)
23
>>> part2(data, 6)
28... | ff33f249628b1dec33f7ea886f5423012914fc4c | 3,641,058 |
def binary_search(
items,
target_key,
target_key_hi=None,
key=None,
lo=None,
hi=None,
target=Target.any,
):
"""
Search for a target key using binary search and return (found?,
index / range).
The returned index / range is as follows according to t... | c0539322c0a2dd9cd3b53fa65bf6ecf28840546e | 3,641,059 |
def func_real_dirty_gauss(dirty_beam):
"""Returns a parameteric model for the map of a point source,
consisting of the interpolated dirty beam along the y-axis
and a sinusoid with gaussian envelope along the x-axis.
This function is a wrapper that defines the interpolated
dirty beam.
Parameter... | 2a0da2176f67a9cd9dd31af48b987f7f3d9d8342 | 3,641,060 |
def shortest_path(graph, a_node, b_node):
""" code by Eryk Kopczynski """
front = deque()
front.append(a_node)
came_from = {a_node: [a_node]}
while front:
cp = front.popleft()
for np in graph.neighbors(cp):
if np not in came_from:
front.append(np)
came_from[np] = [came_from[cp], ... | 6c2f04282eac52fcad7c4b15fa23753373940d6b | 3,641,061 |
def rank_compute(prediction, att_plt, key, byte):
"""
- prediction : predictions of the NN
- att_plt : plaintext of the attack traces
- key : Key used during encryption
- byte : byte to attack
"""
(nb_trs, nb_hyp) = prediction.shape
idx_min = nb_trs
min_rk = 255
... | a21a5fbf5db64cab1fe87ed37c4a9770f5ccd9f8 | 3,641,062 |
def purelin(n):
"""
Linear
"""
return n | 493c4ae481702194fe32eec44e589e5d15614b99 | 3,641,063 |
def arrayDimension(inputArray):
"""Returns the dimension of a list-formatted array.
The dimension of the array is defined as the number of nested lists.
"""
return len(arraySize(inputArray)) | 4d98b76ce6f5aeae9a8171918a78c31ef75dd33e | 3,641,064 |
from datetime import datetime
def process_data(records, root) -> bool:
"""Creates the xml file that will be imported in pure."""
for record in records:
item_metadata = record["metadata"]
# If the rdm record has a uuid means that it was imported from pure - REVIEW
if "uuid" in item_met... | 3e78792c2f147cb56ac502d783fb2a1e3346be53 | 3,641,065 |
def hopcroft(G, S):
"""Hopcroft's algorthm for computing state equivalence.
Parameters
----------
G : fully deterministic graph
S : iterable
one half of the initial (bi)partition
Returns
-------
Partition
"""
sigma = alphabet(G)
partition = Partition(list(G))
p1... | a841dc0a77bb82a27937b20a7335c399ff9b53f5 | 3,641,066 |
import logging
def TVRegDiff(data, itern, alph, u0=None, scale='small', ep=1e-6, dx=None,
plotflag=_has_matplotlib, diagflag=True, precondflag=True,
diffkernel='abs', cgtol=1e-4, cgmaxit=100):
"""
Estimate derivatives from noisy data based using the Total
Variation Regularized ... | b4497f4ac6d09f5240f551f6d25077d2e7624af2 | 3,641,067 |
def replaceidlcode(lines,mjd,day=None):
"""
Replace IDL code in lines (array of strings) with the results of code
execution. This is a small helper function for translate_idl_mjd5_script().
"""
# day
# psfid=day+138
# domeid=day+134
if day is not None:
ind,nind = dln.where( (l... | fd8a2bc9a374c36e7973cfc01d38582e25ce9438 | 3,641,068 |
import torch
import tqdm
def test(
model: nn.Module,
classes: dict,
data_loader: torch.utils.data.DataLoader,
criterion: nn.Module,
# scheduler: nn.Module,
epoch: int,
num_iteration: int,
use_cuda: bool,
tensorboard_writer: torch.utils.tensorboard.SummaryWriter,
name_step: str,... | 306b43730554492d1d541be1a8c8d4c202b932f4 | 3,641,069 |
import torch
def get_grad_spherical_harmonics(xyz, l, m):
"""Compute the gradient of the Real Spherical Harmonics of the AO.
Args:
xyz : array (Nbatch,Nelec,Nrbf,Ndim) x,y,z, distance component of each
point from each RBF center
l : array(Nrbf) l quantum number
m : array(... | a01b529f98276e41fa3ed8c9934db770979d8702 | 3,641,070 |
from typing import List
from typing import Union
import re
def references_from_string(string: str) -> List[
Union[InputReference, TaskReference, ItemReference]
]:
"""Generate a reference object from a reference string
Arguments:
string {str} -- A reference string (eg: `{{inputs.exampl... | 4e28d082e7fc638470b2f4753e6283d9630be073 | 3,641,072 |
def arrange_images(normalized_posters, blur_factor, blur_radius):
"""
Arranges images to create a collage.
Arguments:
norm_time_posters: tuple(float, PIL.Image)
Normalized instances of time and area for time and posters respectively.
blur_factor:
Number of times to a... | e3d4dc90c8ff4ec435061a3507423cd8c5f7c6d4 | 3,641,073 |
def make_text(text, position=(0, 0, 0), height=1):
"""
Return a text object at the specified location with a given height
"""
sm = SpriteMaterial(map=TextTexture(string=text, color='white', size=100, squareTexture=False))
return Sprite(material=sm, position = position, scaleToTexture=True, scale=[1,... | 19daad7ae7f93ce1a4f06596fe2799c8e9701b72 | 3,641,074 |
import copy
def get_features(user_features, documents, ARGS, BOW = False, Conversational = False, User = False, SNAPSHOT_LEN = False, Questions = False, COMMENT_LEN = True):
"""
Generates Features:
Type of Features:
- BOW: bag of words features
- Conversational: features extracted... | b200c9783661db13bfecb76eee18f39bc67301b6 | 3,641,075 |
import time
import traceback
def incomeStat(headers):
"""
收益统计
:param headers:
:return:
"""
time.sleep(0.3)
url = f'https://kd.youth.cn/wap/user/balance?{headers["Referer"].split("?")[1]}'
try:
response = requests_session().get(url=url, headers=headers, timeout=50).json()
print('收益统计')
pri... | 8c023314835ab46b354ae0d793d6de3694711a65 | 3,641,076 |
def t_matrix(phi, theta, psi, sequence):
""" Return t_matrix to convert angle rate to angular velocity"""
if sequence == 'ZYX':
t_m = np.array([[1, np.sin(phi)*np.tan(theta), np.cos(phi)*np.tan(theta)],\
[0, np.cos(phi), -np.sin(phi)],\
[0, np.sin(phi)/np.cos(... | 65c5595cc286a442777651f888a6e3fee032ba21 | 3,641,077 |
def point_in_fence(x, y, points):
"""
计算点是否在围栏内
:param x: 经度
:param y: 纬度
:param points: 格式[[lon1,lat1],[lon2,lat2]……]
:return:
"""
count = 0
x1, y1 = points[0]
x1_part = (y1 > y) or ((x1 - x > 0) and (y1 == y)) # x1在哪一部分中
points.append((x1, y1))
for point in points[1:]... | bb25f399eadf818fbafdeee6c8adbb1254a579f7 | 3,641,078 |
def parse_prior(composition, alphabet, weight=None):
"""Parse a description of the expected monomer distribution of a sequence.
Valid compositions:
* None or 'none'
No composition sepecified
* 'auto' or 'automatic'
Use the typical average distribution
for proteins and an equipr... | 50795a21231138bb3576a50a3791d9136264754e | 3,641,079 |
def get_queues(prefix=None):
"""
Gets a list of SQS queues. When a prefix is specified, only queues with names
that start with the prefix are returned.
:param prefix: The prefix used to restrict the list of returned queues.
:return: A list of Queue objects.
"""
if prefix:
queue_iter... | c459cad66561d887abdcc40157fea09481e267c7 | 3,641,080 |
def _genNodesNormal(numNodes=None, center=None, standardDeviation=None):
"""
Generate randomized node using Normal distribution within a bounding area
Parameters
----------
numNodes: int
Required, number of nodes to be generated
centerLat: float, Required
Latitude of the center point
centerLon: float, Requi... | 62d7f44056621786a1b796bfe85df4eac0ec9574 | 3,641,081 |
def util_test_normalize(mean, std, op_type):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
if op_type == "cpp":
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize(mean, std)
# Generate dataset... | 43fe33a124a8d52252738697eccd4775edb6e4b8 | 3,641,082 |
def calc_sub_from_constant(func, in_data, **kwargs):
"""[SubFromConstant](https://docs.chainer.org/en/v4.3.0/reference/generated/chainer.functions.add.html)
See the documentation for [AddConstant](#addconstant)
"""
return _calc(func, in_data, **kwargs) | d40fcf668c2dc8e0c7d889d2cf145de208cc0ae6 | 3,641,083 |
import torch
def mask_distance_matrix(dmat, weight_bins=weight_bins):
"""
Answer: yep, a larger weight is assigned to a pair of residues forming a contact.
I assigned 20.5, 5.4, 1 to the distance 0-8, 8-15, and >15, respectively, for residue pairs (i, j) where |i-j| >=24.
These numbers were derived fr... | 4d8ca64834b6de9e4dd0077278cb4a687f7cf33e | 3,641,084 |
def get_output():
"""Return the set output setting."""
return _output | 4332661d333d4ca2c364761b35bb2d9ed0b9d302 | 3,641,085 |
def cash_grouped_nb(target_shape, cash_flow_grouped, group_lens, init_cash_grouped):
"""Get cash series per group."""
check_group_lens(group_lens, target_shape[1])
out = np.empty_like(cash_flow_grouped)
from_col = 0
for group in range(len(group_lens)):
to_col = from_col + group_lens[group]
... | 3a7978493503cbae4fc867d8ca864193d913f33f | 3,641,086 |
def agreement():
"""Input for Accepting license
"""
form = LicenseForm()
if form.validate_on_submit():
gluu_settings.db.set("ACCEPT_GLUU_LICENSE", "Y" if form.accept_gluu_license.data else "N")
return redirect(url_for(wizard_steps.next_step()))
with open("./LICENSE", "r") as f:
... | e213d9ce33014b03fd97fdd3991eb72c52e3e9e7 | 3,641,087 |
import torch
def rotmat2quat(mat: torch.Tensor) -> torch.Tensor:
"""Converts rotation matrix to quaternion.
This uses the algorithm found on
https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion
, and follows the code from ceres-solver
https://github.com/ceres-solver/ceres-solver/blob/master/in... | 470d2890eb5c07dff1fdc3de7d347fc86dd3fd1e | 3,641,088 |
def equalize(image):
"""
Equalize the image histogram. This function applies a non-linear
mapping to the input image, in order to create a uniform
distribution of grayscale values in the output image.
Args:
image (PIL image): Image to be equalized
Returns:
image (PIL image), Eq... | 5283609b316452da5aa9969e999dcdeb4de26b2b | 3,641,089 |
def validate_output(value):
"""Validate "output" parameter."""
if value is not None:
if isinstance(value, str):
value = value.split(",")
# filter out empty names
value = list(filter(None, value))
return value | f00773674868ebde741f64b47fdc3372ad6a1e7d | 3,641,090 |
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