content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def d2tf(ndp, days):
"""
Wrapper for ERFA function ``eraD2tf``.
Parameters
----------
ndp : int array
days : double array
Returns
-------
sign : char array
ihmsf : int array
Notes
-----
The ERFA documentation is below.
- - - - - - - -
e r a D 2 t f
- ... | 5577e48bf50304cbda71947e673e06ab09d18ea7 | 3,629,000 |
def libxl_init_members(ty, nesting = 0):
"""Returns a list of members of ty which require a separate init"""
if isinstance(ty, idl.Aggregate):
return [f for f in ty.fields if not f.const and isinstance(f.type,idl.KeyedUnion)]
else:
return [] | fae1eb0e3962ee59df83209ba605473f893cb2ea | 3,629,001 |
import os
from sys import path
import imp
def find_commands(cache=True):
"""
Scans the script directory and extracts all commands.
"""
global _command_cache
if _command_cache is not None and cache:
return _command_cache
files = os.walk(command_dir)
commands = {}
for (dirpath, ... | 046daf38a6b61efb67c82decf851bb5769ffa8be | 3,629,002 |
from datetime import datetime
import os
import fnmatch
def snr_and_sounding(radar, soundings_dir=None, refl_field_name='DBZ'):
"""
Compute the signal-to-noise ratio as well as interpolating the radiosounding
temperature on to the radar grid. The function looks for the radiosoundings
that happened at t... | 506f7d0c7a5be778915b1c5d22b5ad3064619400 | 3,629,003 |
from operator import concat
def make_weekly_data(con, ticker_id, begin_date, today):
"""
INPUTS:
con (mysql) - pymysql database connection
ticker_id (int) - Ticker id number from symbols table
begin_date (str) - Last price date in series. Iso8601 standard format
today (str) - T... | 8cb9f4baca363df8f081ccf8a6eae5dc90ecd5d3 | 3,629,004 |
import zmq
def kill_service(ctrl_addr):
"""kill the LLH service running at `ctrl_addr`"""
with zmq.Context.instance().socket(zmq.REQ) as sock:
sock.setsockopt(zmq.LINGER, 0)
sock.setsockopt(zmq.RCVTIMEO, 1000)
sock.connect(ctrl_addr)
sock.send_string("die")
return soc... | 8ffdb8fda4e8ed6c9b82a70fa70adbe6fcdc7f29 | 3,629,005 |
def dmp_grounds(c, n, u):
"""
Return a list of multivariate constants.
Examples
========
>>> from sympy.polys.domains import ZZ
>>> from sympy.polys.densebasic import dmp_grounds
>>> dmp_grounds(ZZ(4), 3, 2)
[[[[4]]], [[[4]]], [[[4]]]]
>>> dmp_grounds(ZZ(4), 3, -1)
[4, 4, 4]
... | 32f2e60bce921f525336fd8288c4736ee7677129 | 3,629,006 |
def has_attrs(inst, *args):
"""
checks if the instance has all attributes
as specified in *args and if they not falsy
:param inst: obj instance
:param args: attribute names of the object
"""
for a in args:
try:
if not getattr(inst, a, None)
return False
... | 297911bd61824cf171946afa26014ffcd0ee6be1 | 3,629,007 |
def get_related_offers(order):
"""
Search related offers to order from parameter
:param order: client order
:return: string with related offers to order from parameter or empty string
"""
related_offers = ""
if OrdersOffers.objects.filter(order=order).count() > 0:
order_offers = Orde... | d55c3d58b4e88161fbeaa3226d4d9ee636b53de4 | 3,629,008 |
import torch
def undo_imagenet_preprocess(image):
""" Undo imagenet preprocessing
Input:
- image (pytorch tensor): image after imagenet preprocessing in CPU, shape = (3, 224, 224)
Output:
- undo_image (pytorch tensor): pixel values in [0, 1]
"""
mean = torch.Tensor([0.485, 0.456, 0.406]).v... | 57d4cfc365c4e6c2dcfd37c8a2c500465daa421a | 3,629,009 |
def ob_mol_from_file(fname, ftype="xyz", add_hydrogen=True):
"""
Import a molecule from a file using OpenBabel
fname: the path string to the file to be opened
ftype: the file format
add_hydrogen: whether or not to insert hydrogens automatically
openbabel does not always add ... | 6dbbff4dc176637274af53799143e3bc862d403a | 3,629,010 |
def collapse(html):
"""Remove any indentation and newlines from the html."""
return ''.join([line.strip() for line in html.split('\n')]).strip() | a5a55691f2f51401dbd8b933562266cbed90c63d | 3,629,011 |
import torch
def multiclass_cross_entropy(phat, y, N_classes, weights, EPS = 1e-30) :
""" Per instance weighted cross entropy loss
(negative log-likelihood)
"""
y = F.one_hot(y, N_classes)
# Protection
loss = - y*torch.log(phat + EPS) * weights
loss = loss.sum() / y.shape[0]
ret... | 782e230c49314e8426a8a78cc78709929dadcf67 | 3,629,012 |
def get_arc_polygon(resolution,size=[1,1],arc=[0,1]):
"""resolution is the quantity of polygon points
horizontal and vertical size are requested
arc give the starting and ending angles"""
polygon=[]
for increment in range(resolution+1):
inc= arc[1]*(increment/resolution)
angl=(arc[0]+inc)*pi*2
polygon.append... | 7688bd90c093fb3db4a822af8e4ddd317d55cd62 | 3,629,013 |
def load(filename):
"""
Loads data line by line from a .wbp file, initiates a WellPlan object
and populates it with data.
Parameters
----------
filename: string
The location and filename of the .wbp file to load.
Returns
-------
A welleng.exchange.wbp.WellPlan o... | d0606514ffe89e9e3f09b76c8becd65db94e0bd9 | 3,629,014 |
def get_input_fn(data_dir, is_training, num_epochs, batch_size, shuffle, normalize=True):
"""
This will return input_fn from which batches of data can be obtained.
Parameters
----------
data_dir: str
Path to where the mnist data resides
is_training: bool
Whether to read the trai... | 4f41ff8939df638749efaf4a29106cc363a6737e | 3,629,015 |
from pyngrok import ngrok
from jupyter_dash import JupyterDash
from dash import Dash
def getDashApp(title:str, notebook:bool, usetunneling:bool, host:str, port:int, mode: str, theme, folder):
"""
Creates a dash or jupyter dash app, returns the app and a function to run it
:param title: Passed to dash app... | dfc5c8196f9a86a6efaa79fdb938189b10f1b1aa | 3,629,016 |
def get_pagination_request_params():
"""
Pagination request params for a @doc decorator in API view.
"""
return {
"page": "Page",
"per_page": "Items per page",
} | 8ceb2f8ead3d9285017b595671f02817d098bc40 | 3,629,017 |
def access_bit(data, num):
""" from bytes array to bits by num position
"""
base = int(num // 8)
shift = 7 - int(num % 8)
return (data[base] & (1 << shift)) >> shift | fed874d0d7703c9e697da86c5a5832d20b46ebe5 | 3,629,018 |
def _any_isclose(left, right):
"""Short circuit any isclose for ndarray."""
return _any(np.isclose, left, right) | 8732c8db0b3e574a220c534ae7336acd89dbe753 | 3,629,019 |
def push(src, dest):
"""
Push object from host to target
:param src: string path to source object on host
:param dest: string destination path on target
:return: result of _exec_command() execution
"""
adb_full_cmd = [v.ADB_COMMAND_PREFIX, v.ADB_COMMAND_PUSH, src, dest]
return _exec_comm... | 926964ac7aa8b6c9e83c2049128bee138c5157ba | 3,629,020 |
def merge_set_if_true(set_1, set_2):
"""
Merges two sets if True
:return: New Set
"""
if set_1 and set_2:
return set_1.from_merge(set_1, set_2)
elif set_1 and not set_2:
return set_1
elif set_2 and not set_1:
return set_2
else:
return None | 833e6925ef2b3f70160238cdc32516be2482082d | 3,629,021 |
import pytz
def localtime(utc_dt, tz_str):
"""
Convert utc datetime to local timezone datetime
:param utc_dt: datetime, utc
:param tz_str: str, pytz e.g. 'US/Eastern'
:return: datetime, in timezone of tz
"""
tz = pytz.timezone(tz_str)
local_dt = tz.normalize(utc_dt.astimezone(tz))
... | f48844c72895813fdcd3913cfe7de0e6f6d0ac3c | 3,629,022 |
from typing import List
import torch
def _flatten_tensor_optim_state(
state_name: str,
pos_dim_tensors: List[torch.Tensor],
unflat_param_names: List[str],
unflat_param_shapes: List[torch.Size],
flat_param: FlatParameter,
) -> torch.Tensor:
"""
Flattens the positive-dimension tensor optimiz... | 1b8ebbbe99cc5d0ce6f48ef9f321c8ea4fd0b7ee | 3,629,023 |
def variance ( func , xmin = None , xmax = None , err = False ) :
"""Get the variance for the distribution using
>>> fun = ...
>>> v = variance( fun , xmin = 10 , xmax = 50 )
"""
##
## get the functions from ostap.stats.moments
actor = lambda x1,x2 : Variance ( x1 , x2 , err )
## use... | da2978abca2ecaa2754564d3bc7f5a82915211ac | 3,629,024 |
def get_mysql_entitySets(username, databaseName):
""" View all the enity sets in the databaseName """
password = get_password(username)
try:
cnx = connectSQLServerDB(username, password, username + "_" + databaseName)
mycursor = cnx.cursor()
sql = "USE " + username + "_" + databaseNam... | 3eac962c936422258a2740e5ef429a72a440da92 | 3,629,025 |
def check_dbconnect_success(sess, system):
"""
測試資料庫是否成功連上(若連上且查詢成功代表資料庫存在)
Args:
sess: database connect session
system: 使用之系統名稱
Returns:
[0]: status(狀態，True/False)
[1]: err_msg(返回訊息)
"""
try:
if not sess.execute("select 1 as is_alive"): raise Exception
... | 28a4bfc0ac1a71ba9d7d13f62cea1f4bb9cec385 | 3,629,026 |
def split(filename, size=10.):
"""split the figure into color bands"""
arr, aspect = _load_array(filename)
fig = Figure(figsize=(size, size*aspect))
cmaps = ['Reds_r', 'Greens_r', 'Blues_r', 'gray_r']
for i, band in enumerate(arr):
ax = fig.add_axes([(i % 2) * .5, (1 - i // 2) * .5, .5, .5... | 4e64ed73c2054aef1f729674f13c059010466c0a | 3,629,027 |
from sys import argv
def main():
""" Main body """
# validate the input
if len(argv) != 2:
print("usage: python gen_rand_sys.py <size of grid>")
return 1
else:
# get size from input
size = int(argv[1])
grid = gen_rand_sys(size)
print(grid) | c93361f08c6f7ea1bd0f57bb8c129fcf0f717cd9 | 3,629,028 |
def _compute_common_args(mapping):
"""Compute the list of arguments for dialog common options.
Compute a list of the command-line arguments to pass to dialog
from a keyword arguments dictionary for options listed as "common
options" in the manual page for dialog. These are the options
that are not ... | 3e3dff995864d64452e8ef091ec949b281899455 | 3,629,029 |
def is_url(url):
"""URL書式チェック"""
return url.startswith("https://") or url.startswith("http://") | bc8f59d2e96e0a625317e86216b9b93077bbf8e2 | 3,629,030 |
import re
def _preclean(Q):
"""
Clean before annotation.
"""
Q = re.sub('#([0-9])', r'# \1', Q)
Q = Q.replace('€', ' €').replace('\'', ' ').replace(',', '').replace('?', '').replace('\"', '').replace('(s)', '').replace(' ', ' ').replace(u'\xa0', u' ')
return Q.lower() | 115828037b884108b9e3324337874c6b23dc066c | 3,629,031 |
import aiohttp
async def job_info(request):
"""Get job info."""
conn_manager = request.app[common.KEY_CONN_MANAGER]
conn_uid = request.match_info[ROUTE_VARIABLE_CONNECTION_UID]
job_uid = request.match_info[ROUTE_VARIABLE_JOB_UID]
connection = conn_manager.connection(conn_uid)
info = await conn... | 29ad0a6fb10d1ce0b982743443889d0771940d7d | 3,629,032 |
from typing import Dict
from typing import Callable
def load_dataset_map() -> Dict[str, Callable]:
"""
Get a map of datasets.
Returns:
Dict[str, Callable]: Key: Dataset name, Value: loader function which returns (X, y).
"""
dss = {
"iris-2d": load_iris_2d,
"wine-2d": load_... | 9ecb35ba59ef1c15f0d0d609d269c2c8a54aed3b | 3,629,033 |
def m2fs_pixel_flat(flatfname, fiberconfig, Npixcut):
"""
Use the flat to find pixel variations.
DON'T USE THIS. It doesn't work.
"""
R, eR, header = read_fits_two(flatfname)
#shape = R.shape
#X, Y = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing="ij")
tracefn = m2fs... | 07a82fe106f38e9da70c02d1195e596606c9d835 | 3,629,034 |
def HTTP405(environ, start_response):
"""
HTTP 405 Response
"""
start_response('405 METHOD NOT ALLOWED', [('Content-Type', 'text/plain')])
return [''] | f07522ac904ec5ab1367ef42eb5afe8a2f0d1fce | 3,629,035 |
from operator import concat
import torch
import io
import time
def train_one_batch(batch, generator, optimizer, reward_obj, opt, global_step, tb_writer, lagrangian_params=None, cost_objs=[]):
#src, src_lens, src_mask, src_oov, oov_lists, src_str_list, trg_sent_2d_list, trg, trg_oov, trg_lens, trg_mask, _ = batch
... | d8f1df07641584860022def119f8f49db3e6e5dc | 3,629,036 |
import sys
import os
def getProgramName():
"""Get the name of the currently running program."""
progName = sys.argv[0].strip()
if progName.startswith('./'):
progName = progName[2:]
if progName.endswith('.py'):
progName = progName[:-3]
# Only return the name of the program not the ... | 486c454756dea87b7b422fb00e80e1346183d9d2 | 3,629,037 |
from datetime import datetime
from typing import Iterable
def get_all_trips(*, date_from: datetime, date_to: datetime, departure_station: BusStation, arrival_station: BusStation) \
-> Iterable[Trip]:
"""
[Step1] filter trips with departure time between `date_form` and `date_to`
[Step2] filter trip... | be58f578740bde35b45435d1f82a4d5e82b4dc6d | 3,629,038 |
from typing import Callable
from typing import Optional
from datetime import datetime
import httpx
import time
def get_coinbase_api_response(
get_api_url: Callable,
base_url: str,
timestamp_from: Optional[datetime] = None,
pagination_id: Optional[str] = None,
retry=30,
):
"""Get Coinbase API r... | bc34ab3980f782403321d65214f0dad27e92e800 | 3,629,039 |
def intersect(A, B, C, D):
""" Finds the intersection of two lines represented by four points.
@parameter A: point #1, belongs to line #1
@parameter B: point #2, belongs to line #1
@parameter C: point #3, belongs to line #2
@parameter D: point #4, belongs to line #2
@returns: None if lines are... | 54bb9fc4c826de00d144120edea55463699214ac | 3,629,040 |
def detect(inputs, anchors, n_classes, img_size, scope='detection'):
"""Detect layer
"""
with tf.name_scope(scope, 'detection',[inputs]):
n_anchors = len(anchors)
bbox_attrs = 5+n_classes
predictions = inputs
grid_size = predictions.get_shape().as_list()[1:3]
n_dims = grid_size[0] * grid_size... | 4751db9980137cf3f5acaee9a900653d5f31e90e | 3,629,041 |
def get_scenario_data():
"""Return sample scenario_data
"""
return [
{
'population_count': 100,
'county': 'oxford',
'season': 'cold_month',
'timestep': 2017
},
{
'population_count': 150,
'county': 'oxford',
... | b66ba716e6bd33e1a0ff80735acb64041663ed99 | 3,629,042 |
def user_not_found(error):
"""Custom error handler.
More info: http://flask.pocoo.org/docs/1.0/patterns/apierrors/#registering-an-error-handler
"""
response = jsonify(error.to_dict())
response.status_code = error.status_code
return response | 3b63876308d616d4d206b3eaa4742a77490f4c50 | 3,629,043 |
from typing import Dict
from typing import Any
def get_do_pass_with_amendments_by_committee(
biennium: str, agency: str, committee_name: str
) -> Dict[str, Any]:
"""See: http://wslwebservices.leg.wa.gov/committeeactionservice.asmx?op=GetDoPassWithAmendmentsByCommittee"""
argdict: Dict[str, Any] = dict(bie... | f27622c2840b3375181f42e373ad96c569d2edc6 | 3,629,044 |
def petrosian_fd(x):
"""Petrosian fractal dimension.
Parameters
----------
x : list or np.array
One dimensional time series
Returns
-------
pfd : float
Petrosian fractal dimension
Notes
-----
The Petrosian algorithm can be used to provide a fast computation of
... | 775d0e27d305d0111d20a001282d369f78f7d48e | 3,629,045 |
def lemma(word):
"""
Transforms a given word to its lemmatized form, checking a lemma dictionary. If the word is
not included in the dictionary, the same word is returned.
:param word: string containing a single word
:type: string
:return: the word's lemma
:type: string
"""
return le... | 04f434d7a86ceeadc16a2d2a4eb329f7b814e61a | 3,629,046 |
def is_style_file(filename):
"""Return True if the filename looks like a style file."""
return STYLE_FILE_PATTERN.match(filename) is not None | 18a85b21d898b27e65d8debcda408507ba5ca5d9 | 3,629,047 |
def add_subnet():
"""add subnet.
Must fields: ['subnet']
Optional fields: ['name']
"""
data = _get_request_data()
return utils.make_json_response(
200,
network_api.add_subnet(user=current_user, **data)
) | 82db191d2b678cbf873ac80878138708a10371c7 | 3,629,048 |
def V_beta(gamma, pi):
""" Defining function for the expected utility for insured agent,
where we know the coverage ratio gamma and x is drawn from beta
distribution.
Args:
pi(float): insurance premium
gamma(float): coverage ratio
Returns:
Expected utility for agent.
... | bf21816d5c80f34e8bc8c8883de14b739520a0da | 3,629,049 |
def counted(fn):
"""
count number of times a subroutine is called
:param fn:
:return:
"""
def wrapper(*args, **kwargs):
wrapper.called+= 1
return fn(*args, **kwargs)
wrapper.called= 0
wrapper.__name__= fn.__name__
return wrapper | 5c1ad20af39ed745718726045fa9f23938d7e479 | 3,629,050 |
def removeneg(im, key=0):
"""
remove NAN and INF in an image
"""
im2 = np.copy(im)
arr = im2 < 0
im2[arr] = key
return im2 | 572aa73d2f50f48b7940e476ba4cd885f93151d4 | 3,629,051 |
def mape(df, w):
"""Mean absolute percent error
Parameters
----------
df: Cross-validation results dataframe.
w: Aggregation window size.
Returns
-------
Dataframe with columns horizon and mape.
"""
ape = np.abs((df['y'] - df['yhat']) / df['y'])
if w < 0:
return pd.... | c42c1fd32d71f0f2a2c2224fc88e1f9ecc467c39 | 3,629,052 |
def p3p(pts_2d, pts_3d, K, q_ref=None, allow_imag_roots=False):
"""An implementation of the ... problem from "A Stable Algebraic Camera
Pose Estimation for Minimal Configurations of 2D/3D Point and Line Correspondences",
from Zhou et al. at ACCV 2018.
3 points
pts_2d - pixels in 2d. Each pixel is ... | a042a9adb9f4c8d705fe8fdc54c4e100c083db50 | 3,629,053 |
def status() -> tuple:
"""Health check endpoint."""
return xmlify('<status>ok</status>'), HTTP_200_OK | 0479c7f3a18b30680f8878c3f439ff9c8a18538b | 3,629,054 |
from typing import Dict
from typing import Any
from typing import Sequence
def nested_keys(nested_dict: Dict[Text, Any],
delimiter: Text = '/',
prefix: Text = '') -> Sequence[Text]:
"""Returns a flattend list of nested key strings of a nested dict.
Args:
nested_dict: Nested di... | 7403321634986897e3ab9b974ba91dc81a2b0941 | 3,629,055 |
import os
def add_it(workbench, file_list, labels):
"""Add the given file_list to workbench as samples, also add them as nodes.
Args:
workbench: Instance of Workbench Client.
file_list: list of files.
labels: labels for the nodes.
Returns:
A list of md5s.
"""
md5... | 88e85b8bcc2fdbf6fdac64b9b4e84d82e7ea3185 | 3,629,056 |
def has_id(sxpr, id):
"""Test if an s-expression has a given id.
"""
return attribute(sxpr, 'id') == id | a7e7ce73c8c99af003dfff9954b351bb0e02cd41 | 3,629,057 |
def gf_gcdex(f, g, p, K):
"""Extended Euclidean Algorithm in `GF(p)[x]`.
Given polynomials `f` and `g` in `GF(p)[x]`, computes polynomials
`s`, `t` and `h`, such that `h = gcd(f, g)` and `s*f + t*g = h`. The
typical application of EEA is solving polynomial diophantine equations.
Consid... | e40ceccf34173d4b6349ea2c77147e2a81ff09e4 | 3,629,058 |
from datetime import datetime
def _filetime_from_timestamp(timestamp):
""" See filetimes.py for details """
# Timezones are hard, sorry
moment = datetime.fromtimestamp(timestamp)
delta_from_utc = moment - datetime.utcfromtimestamp(timestamp)
return dt_to_filetime(moment, delta_from_utc) | 81122e093c78004392e3eee7819c52bc62d8d60b | 3,629,059 |
from typing import Callable
from typing import Optional
from typing import Union
from typing import Type
from typing import Sequence
from typing import Any
from typing import get_type_hints
def optimize(
func: Callable[[np.ndarray], float],
x: ArrayLike,
trials: int = 3,
iterations: Optional[int] = 15... | ccef3d67669e0420e88b119d9c180fb35a0a98f8 | 3,629,060 |
import types
def _create_reconstruction(
n_cameras: int=0,
n_shots_cam=None,
n_pano_shots_cam=None,
n_points: int=0,
dist_to_shots: bool=False,
dist_to_pano_shots: bool=False,
):
"""Creates a reconstruction with n_cameras random cameras and
shots, where n_shots_cam is a dictionary, con... | c381724cbaf7da1e368744ce874d4a9702b0033e | 3,629,061 |
from typing import Set
from typing import Tuple
from typing import cast
from typing import List
from typing import Dict
from typing import Any
def _validate_dialogue_section(
protocol_specification: ProtocolSpecification, performatives_set: Set[str]
) -> Tuple[bool, str]:
"""
Evaluate whether the dialogue... | a9155544cae98723bb629d40ae24275c483c807a | 3,629,062 |
def _hsic_naive(x, y, scale=False, sigma_x=None, sigma_y=None,
kernel='gaussian', dof=0):
"""
Naive (slow) implementation of HSIC (Hilbert-Schmidt Independence
Criterion). This function is only used to assert correct results of the
faster method ``hsic``.
Parameters
----------
... | 03ff899048ce740873cd32fc64ba4ce517e8cd5a | 3,629,063 |
from datetime import datetime
def translate(raw_path):
"""Reads official Rio de Janeiro BRT realized trips file
and converts to standarized realized trips.
TODO:
- get trip_id, maybe from GTFS?
- get departure_id and arrival_id, also from GTFS?
Parameters
----------
raw_path : str
... | 333daf9883a959b1fac53cc676405b7c629551e1 | 3,629,064 |
def get_percentage(numerator, denominator, precision = 2):
"""
Return a percentage value with the specified precision.
"""
return round(float(numerator) / float(denominator) * 100, precision) | 7104f6bf2d88f9081913ec3fbae596254cdcc878 | 3,629,065 |
def _calculate_verification_code(hash: bytes) -> int:
"""
Verification code is a 4-digit number used in mobile authentication and mobile signing linked with the hash value to be signed.
See https://github.com/SK-EID/MID#241-verification-code-calculation-algorithm
"""
return ((0xFC & hash[0]) << 5) |... | 173f9653f9914672160fb263a04fff7130ddf687 | 3,629,066 |
def add_message(user, text, can_dismiss=True):
"""Add a message to the user's message queue for a variety of purposes.
:param user: the instance of `KlaxerUser` to add a message to
:param text: the text of the message
:param can_dismiss: (optional) whether or not the message can be dismissed
:retur... | f0deff8ed230716b88ed9876a06509f04c1cbfbf | 3,629,067 |
def dataQC(json_data):
""" perform quality analysis on data """
bad_data = {}
for device in json_data.keys():
for item in json_data[device]:
if item[1] <= check_lower * abs_std[0+omit_lower]:
if device not in bad_data:
bad_data[device] = []
... | 388465f3388f871f7a1bd397c964bff63681fac4 | 3,629,068 |
from typing import Union
from typing import Tuple
from typing import List
from typing import Optional
def get_interatomic_r(atoms: Union[Tuple[str], List[str]],
expand: Optional[float] = None) -> float:
"""
Calculates bond length between two elements
Args:
atoms (list or tup... | befa7950d0cd52ba26576c9c5c12589f71604577 | 3,629,069 |
def annualized_return_nb(returns, ann_factor):
"""2-dim version of `annualized_return_1d_nb`."""
result = np.empty(returns.shape[1], dtype=np.float_)
for col in range(returns.shape[1]):
result[col] = annualized_return_1d_nb(returns[:, col], ann_factor)
return result | 8e7c3ae47a81b7a700b5714544aabd0d9105f88c | 3,629,070 |
import resource
def canon_ref(did: str, ref: str, delimiter: str = None, did_type: str = None):
"""
Given a reference in a DID document, return it in its canonical form of a URI.
Args:
did: DID acting as the identifier of the DID document
ref: reference to canonicalize, either a DID or a ... | 5a0fa42a1a28597ec4863ace221c272ad5114076 | 3,629,071 |
def cars_produced_this_year() -> dict:
"""Get number of cars produced this year."""
return get_metric_of(label='cars_produced_this_year') | 96f25b773eaaaaf74bdb9d660830d1ddcff821f5 | 3,629,072 |
import argparse
def parse_script_args():
"""
"""
parser = argparse.ArgumentParser(description="Delete images from filesystem.")
parser.add_argument('--reg_ip', type=str, required=True,
help='Registry host address e.g. 1.2.3.4')
parser.add_argument('images', type=str, nargs=... | fc77674d45f22febcb93b6bb0317cb5a0ef18e0f | 3,629,073 |
def merge_authentication_authorities(managed_auth_authority, user_record):
"""Merge two authentication_authority values, giving precedence to the
managed_auth_authority"""
existing_auth_authority = get_attribute_for_user(
"authentication_authority", user_record)
if existing_auth_authority:
... | db5e47be422dbc27d1117040bc3632717112a461 | 3,629,074 |
def preprocess_pil_image(pil_img, color_mode='rgb', target_size=None):
"""Preprocesses the PIL image
Arguments
img: PIL Image
color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
The desired image format.
target_size: Either `None` (default to original size)
... | 83cb54157d7bd85299b6bc3149f7f14d9ed52d95 | 3,629,075 |
import torch
def _handle_coord(c, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
"""
Helper function for _handle_input.
Args:
c: Python scalar, torch scalar, or 1D torch tensor
Returns:
c_vec: 1D torch tensor
"""
if not torch.is_tensor(c):
c = torch.tensor... | 129a03900e8047a9c37400568d16316601e25671 | 3,629,076 |
import time
def current_time_hhmmss():
"""
Fetches current time in GMT UTC+0
Returns:
(str): Current time in GMT UTC+0
"""
return str(time.gmtime().tm_hour) + ":" + str(time.gmtime().tm_min) + ":" + str(time.gmtime().tm_sec) | 11a2874237c3fc7d25b93d6f10da167c7d700b33 | 3,629,077 |
def equalize_adaptive_clahe(image, ntiles=8, clip_limit=0.01):
"""Return contrast limited adaptive histogram equalized image.
The return value is normalised to the range 0 to 1.
:param image: numpy array or :class:`jicimagelib.image.Image` of dtype float
:param ntiles: number of tile regions
:... | 8ecd36bc50e9fd147bee676a92a8ebb1a892c59b | 3,629,078 |
def suggest_parameters_DRE_NMNIST(trial,
list_lr, list_bs, list_opt,
list_wd, list_multLam, list_order):
""" Suggest hyperparameters.
Args:
trial: A trial object for optuna optimization.
list_lr: A list of floats. Candidates of learning rates.
list_bs: A list of ints. Candidate... | 627855f5fe8fd15d43cc7c8ca3da22b704b5907e | 3,629,079 |
def format_seconds(seconds, hide_seconds=False):
"""
Returns a human-readable string representation of the given amount
of seconds.
"""
if seconds <= 60:
return str(seconds)
output = ""
for period, period_seconds in (
('y', 31557600),
('d', 86400),
('h', 3600)... | 341ab077b9f83a91e89a4b96cb16410efab90c1c | 3,629,080 |
def _contains_atom(example, atoms, get_atoms_fn):
"""Returns True if example contains any atom in atoms."""
example_atoms = get_atoms_fn(example)
for example_atom in example_atoms:
if example_atom in atoms:
return True
return False | c9e60d956585c185f9fb62cc0d11f169e6b79f88 | 3,629,081 |
import yaml
import sys
def load_config_file(filename):
"""Load the YAML configuration file."""
try:
config = None
with open(filename, 'r') as f:
config = yaml.load(f)
print('Using configuration at {0}'.format(filename))
if not config.keys() == default_config.key... | 183ae3ad10caa0ddc4324772c32181660c162e6c | 3,629,082 |
import torch
import math
def kllossGn2(o, l: 'xtrue'):
"""KL loss for Gaussian-mixture output, 2D, precision-matrix parameters."""
dx = o[:,0::6] - l[:,0,np.newaxis]
dy = o[:,2::6] - l[:,1,np.newaxis]
# precision matrix is positive definite, so has positive diagonal terms
Fxx = o[:,1::6]**2
Fyy = o[... | 198b6b5189d72171b87e05998d326d006e6d156d | 3,629,083 |
def IngestApprovalDelta(cnxn, user_service, approval_delta, setter_id, config):
"""Ingest a protoc ApprovalDelta and create a protorpc ApprovalDelta."""
fids_by_name = {fd.field_name.lower(): fd.field_id for
fd in config.field_defs}
approver_ids_add = IngestUserRefs(
cnxn, approval_d... | 5f1851ac2cfb7f2515da9468701a2c92e9d457cd | 3,629,084 |
def get_diff_level(files):
"""Return the lowest hierarchical file parts level at which there are differences among file paths."""
for i, parts in enumerate(zip(*[f.parts for f in files])):
if len(set(parts)) > 1:
return i | c9c3f774712684c6817c8bb5b3bf9c101e1df8fa | 3,629,085 |
def get_max(list_tuples):
"""
Returns from a list a tuple which has the highest value as first element.
If empty, it returns -2's
"""
if len(list_tuples) == 0:
return (-2, -2, -2, -2)
# evaluate the max result
found = max(tup[0] for tup in list_tuples)
for result in list_tuples:... | 91c662d5865de346a1ac73025ced78a996077111 | 3,629,086 |
def prepare_observation_lst(observation_lst):
"""Prepare the observations to satisfy the input fomat of torch
[B, S, W, H, C] -> [B, S x C, W, H]
batch, stack num, width, height, channel
"""
# B, S, W, H, C
observation_lst = np.array(observation_lst, dtype=np.uint8)
observation_lst = np.move... | 28a4d190c515b4f6aed882b4a3448e49b93f6b39 | 3,629,087 |
def test_problem_builder(name: str, n_of_variables: int = None, n_of_objectives: int = None) -> MOProblem:
"""Build test problems. Currently supported: ZDT1-4, ZDT6, and DTLZ1-7.
Args:
name (str): Name of the problem in all caps. For example: "ZDT1", "DTLZ4", etc.
n_of_variables (int, optional)... | e6917dd1edcd711ae1dd6370223b9bfbca252592 | 3,629,088 |
def get_coord_y(x, y):
"""
Function returns the y value of the coordinate
:param x: x value of coordinate
:param y: y value of coordinate
:return: y value of coordinate
"""
coord = Coordinates(x, y)
return coord.get_y() | ba844806a430130e20cfd320865b78f0af09cf25 | 3,629,089 |
import numpy
def calc_som2_flow(som2c_1, cmix, defac):
"""Calculate the C that flows from surface SOM2 to soil SOM2.
Some C flows from surface SOM2 to soil SOM2 via mixing. This flow is
controlled by the parameter cmix.
Parameters:
som2c_1 (numpy.ndarray): state variable, C in surface SOM2
... | a90089f65fa2ff8ea681d9f2c3375920e8cda52c | 3,629,090 |
def cdlseparatinglines(opn, high, low, close):
"""Separating Lines:
Bullish Separating Lines Pattern:
With just two candles – one black (or red) and one white (or green) – the Bullish Separating
Lines pattern is easy to learn and spot. To confirm its presence, seek out the following
criteria:
F... | 37d4a8e1721cc52157b944c2bf0a8db9a8439f52 | 3,629,091 |
def get_context(file_in):
"""Get genomic context from bed file"""
output_dict = {}
handle = open(file_in,'r')
header = handle.readline().rstrip('\n').split('\t')
for line in handle:
split_line = line.rstrip('\n').split('\t')
contig,pos,context = split_line[:3]
if context == '... | 90975b6eb929c546372fdce0eb449f455e9ffc18 | 3,629,092 |
def get_rating_users_total(contest_id: ContestID) -> int:
"""Return the number of unique users that have rated bungalows in
this contest.
"""
return User.query \
.join(Rating) \
.join(Contestant) \
.filter(Contestant.contest_id == contest_id) \
.distinct() \
.coun... | 25b649f32273c208ae3ad163ffd3ec1929eb1d1c | 3,629,093 |
def read_from_file(filename):
"""
Reads the set of known plaintexts from the given file.
"""
candidates = []
with open(filename) as f:
lines = f.readlines()
if len(lines) > 5:
# from first candidate,
# to the end of the file,
# counting in incre... | d7adda3d84ac02bf2d8095a42dae20b66ca4d4f0 | 3,629,094 |
def galactic_offsets_to_celestial(RA, Dec, glongoff=3, glatoff=0):
"""
Converts offsets in Galactic coordinates to celestial
The defaults were chosen by Pineda for Galactic plane survey
@param RA : FK5 right ascension in degrees
@type RA : float
@param Dec : FK5 declination in degrees
@type Dec... | 737072bc3d26b91dab93a7cbefafe732eba0b43a | 3,629,095 |
import torch
from typing import Tuple
from typing import List
def compute_regularizer_term(model: LinearNet, criterion: torch.nn.modules.loss.CrossEntropyLoss,
train_data: Tuple[torch.Tensor, torch.Tensor],
valid_data: Tuple[torch.Tensor, torch.Tensor],
... | 9216e938f611d085f3c877bb3d9aeb98aaa6565d | 3,629,096 |
def get_to_and(num_bits: int) -> np.ndarray:
"""
Overview:
Get an np.ndarray with ``num_bits`` elements, each equals to :math:`2^n` (n decreases from num_bits-1 to 0).
Used by ``batch_binary_encode`` to make bit-wise `and`.
Arguments:
- num_bits (:obj:`int`): length of the generating... | 0da0d253f8951bf54e9d4c2d9d720813fd99c538 | 3,629,097 |
def in_role_list(role_list):
"""Requires user is associated with any role in the list"""
roles = []
for role in role_list:
try:
role = Role.query.filter_by(
name=role).one()
roles.append(role)
except NoResultFound:
raise ValueError("role '{... | 82f171601b4e823fdc2ab265bd955272d033936d | 3,629,098 |
def handle_not_start(fsm_ctx):
"""
:param ctx: FSM Context
:return: False
"""
global plugin_ctx
plugin_ctx.error("Could not start this install operation because an install operation is still in progress")
return False | dc69de1e2c49ed5cda20fecbf9338081c027ff7b | 3,629,099 |
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