content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def unique():
"""Return unique identification number."""
global uniqueLock
global counter
with uniqueLock:
counter = counter + 1
return counter | 12ac0e8f9ec5d4f8d6a41066f2325ef57d593d26 | 3,652,800 |
def pointCoordsDP2LP(dpX, dpY, dptZero, lPix = 1.0):
"""Convert device coordinates into logical coordinates
dpX - x device coordinate
dpY - y device coordinate
dptZero - device coordinates of logical 0,0 point
lPix - zoom value, number of logical points inside one device point (... | 2494b5d95756aab33434969fe2b02917a4529ef9 | 3,652,801 |
def geocode_input(api_key, input, geolocator):
"""
Use parallel processing to process inputted addresses as geocode
Parameters:
api_key (string): Google API key
input (string): user inputted addresses
geolocator: object from Google Maps API that generate geocode of address
Returns:... | b7c31ccc1364364a704602438e263b107de9046c | 3,652,802 |
def satContact(sat_R, gs_R):
"""
Determines if satellite is within sight of a Ground Station
Parameters
----------
sat_R : numpy matrix [3, 1]
- Input radius vector in Inertial System ([[X], [Y], [Y]])
gs_R : numpy matrix [3, 1]
- Input radius vector in Inertial System ([[X], [Y... | 6fb6d5fc9121ddb0627f276a13446891f1da7542 | 3,652,803 |
def determine_visible_field_names(hard_coded_keys, filter_string,
ref_genome):
"""Determine which fields to show, combining hard-coded keys and
the keys in the filter string.
"""
fields_from_filter_string = extract_filter_keys(filter_string, ref_genome)
return list(set(hard_coded_keys) | set... | 2d885e7caa183916691def8abf685a6560f55309 | 3,652,804 |
def get_data_day(data: pd.DataFrame):
"""Get weekday/weekend designation value from data.
:param pandas.DataFrame data: the data to get day of week from.
:return: (*numpy.array*) -- indicates weekend or weekday for every day.
"""
return np.array(data["If Weekend"]) | 3e4654cf3ad3c2f0e213563e0dac3b21c7fb847c | 3,652,805 |
import sys
def bisection(f, a, b, power, iter_guess="yes"):
"""Given f(x) in [`a`,`b`] find x within tolerance, `tol`.
Root-finding method: f(x) = 0.
Parameters
----------
f : expression
Input function.
a : float
Left-hand bound of interval.
b : float
Right-hand ... | eaaa1a28201fceaae39ced5edeb9e819a0c76ae1 | 3,652,806 |
def make_pretty(image, white_level=50):
"""Rescale and clip an astronomical image to make features more obvious.
This rescaling massively improves the sensitivity of alignment by
removing background and decreases the impact of hot pixels and cosmic
rays by introducing a white clipping level that should... | c6d95a76db8aee7a8e2ca2bbc881094577e547ca | 3,652,807 |
import requests
import json
import click
def get_examples_version(idaes_version: str):
"""Given the specified 'idaes-pse' repository release version,
identify the matching 'examples-pse' repository release version.
Args:
idaes_version: IDAES version, e.g. "1.5.0" or "1.5.0.dev0+e1bbb[...]"
R... | 6f9ee4d6cf9e9c542065d77ae6b7dcc41848247c | 3,652,808 |
def hash(data: bytes) -> bytes:
"""
Compute the hash of the input data using the default algorithm
Args:
data(bytes): the data to hash
Returns:
the hash of the input data
"""
return _blake2b_digest(data) | 62dec8f0e05b668dd486deb87bd3cc64a0cd5d08 | 3,652,809 |
import torch
def compute_cd_small_batch(gt, output,batch_size=50):
"""
compute cd in case n_pcd is large
"""
n_pcd = gt.shape[0]
dist = []
for i in range(0, n_pcd, batch_size):
last_idx = min(i+batch_size,n_pcd)
dist1, dist2 , _, _ = distChamfer(gt[i:last_idx], output[i:last_id... | b7e1b22ab63624afd154a3228314a954304a3941 | 3,652,810 |
def find_sub_supra(axon, stimulus, eqdiff, sub_value=0, sup_value=0.1e-3):
"""
'find_sub_supra' computes boundary values for the bisection method (used to identify the threeshold)
Parameters
----------
axon (AxonModel): axon model
stimulus (StimulusModel): stimulus model
eqdiff (function): ... | 6efe62ac2d00d946422b1e0f915714cb9bd4dc50 | 3,652,811 |
def constantly(x):
"""constantly: returns the function const(x)"""
@wraps(const)
def wrapper(*args, **kwargs):
return x
return wrapper | 7fdc78248f6279b96a2d45edaa2f76abe7d60d54 | 3,652,812 |
def ToBaseBand(xc, f_offset, fs):
"""
Parametros:
xc: Señal a mandar a banda base
f_offset: Frecuencia que esta corrido
fs: Frecuencia de muestreo
"""
if PLOT:
PlotSpectrum(xc, "xc", "xc_offset_spectrum.pdf", fs)
# Se lo vuelve a banda base, multiplicando por una exponencial con fase f_offset / fs
x_b... | 0389c3a25b3268b04be8c47cebaf1bbb6b863235 | 3,652,813 |
def hvp(
f: DynamicJaxFunction,
x: TracerOrArray,
v: TracerOrArray,
) -> TracerOrArray:
"""Hessian-vector product function"""
return jax.grad(lambda y: jnp.vdot(jax.grad(f)(y), v))(x) | 585ca7a5c749b6d393ae04e1e89f21f87c6f0269 | 3,652,814 |
def concat_all_gather(tensor):
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
return hvd.allgather(tensor.contiguous()) | 97b2a3e43cf36adda6c517264f3307deb4d98ed6 | 3,652,815 |
from typing import List
import os
def find_files(
path: str,
skip_folders: tuple,
skip_files: tuple,
extensions: tuple = (".py",),
) -> List[str]:
"""Find recursively all files in path.
Parameters
----------
path : str
Path to a folder to find files in.
skip_folders : tupl... | c3513c68cb246052f4ad677d1dc0116d253eae1a | 3,652,816 |
def get_min_area_rect(points):
"""
【得到点集的最小面积外接矩形】
:param points: 轮廓点集，n*1*2的ndarray
:return: 最小面积外接矩形的四个端点，4*1*2的ndarray
"""
rect = cv2.minAreaRect(points) # 最小面积外接矩形
box = cv2.boxPoints(rect) # 得到矩形的四个端点
box = np.int0(box)
box = box[:, np.newaxis, :] # 从4*2转化为4*1*2
return bo... | 59b801e77d03d3f81227c645a55b2c56f2ce5959 | 3,652,817 |
def vector_to_cyclic_matrix(vec):
"""vec is the first column of the cyclic matrix"""
n = len(vec)
if vec.is_sparse():
matrix_dict = dict((((x+y)%n, y), True) for x in vec.dict() for y in xrange(n))
return matrix(GF(2), n, n, matrix_dict)
vec_list = vec.list()
matrix_lists = [vec_list... | 79fdb28f1b254de4700e1e163b95b4bdbf579294 | 3,652,818 |
def cfn_resource_helper():
""" A helper method for the custom cloudformation resource """
# Custom logic goes here. This might include side effects or
# Producing a a return value used elsewhere in your code.
logger.info("cfn_resource_helper logic")
return True | 865216f77f09681e36e8b8409a8673c8dbcdffa0 | 3,652,819 |
def get_ts_code_and_list_date(engine):
"""查询ts_code"""
return pd.read_sql('select ts_code,list_date from stock_basic', engine) | 4bd31cbadfdb92a70983d53c74426b0727ad4d0b | 3,652,820 |
def nested_cv_ridge(
X, y, test_index, n_bins=4, n_folds=3,
alphas = 10**np.linspace(-20, 20, 81),
npcs=[10, 20, 40, 80, 160, 320, None],
train_index=None,
):
"""
Predict the scores of the testing subjects based on data from the training subjects using ridge regression. Hyper... | 47d5d8821b796031298a194aaf1781dc4df68a2f | 3,652,821 |
def absolute_time(time_delta, meta):
"""Convert a MET into human readable date and time.
Parameters
----------
time_delta : `~astropy.time.TimeDelta`
time in seconds after the MET reference
meta : dict
dictionary with the keywords ``MJDREFI`` and ``MJDREFF``
Returns
-------... | dd6c02be87840022e88769d3d70e67ce50f24d64 | 3,652,822 |
from controllers.main import main
from controllers.user import user
def create_app(object_name, env="prod"):
"""
Arguments:
object_name: the python path of the config object,
e.g. webapp.settings.ProdConfig
env: The name of the current environment, e.g. prod or dev
""... | a2760a759f3afebf8e09c498398712fb26d44de8 | 3,652,823 |
from datetime import datetime
def yyyydoy_to_date(yyyydoy):
"""
Convert a string in the form of either 'yyyydoy' or 'yyyy.doy' to a
datetime.date object, where yyyy is the 4 character year number and doy
is the 3 character day of year
:param yyyydoy: string with date in the form 'yyyy.doy' or 'yyy... | b289419c14321afc37ea05501307e36203191fec | 3,652,824 |
from typing import Optional
def create_selection():
""" Create a selection expression """
operation = Forward()
nested = Group(Suppress("(") + operation + Suppress(")")).setResultsName("nested")
select_expr = Forward()
functions = select_functions(select_expr)
maybe_nested = functions | nested... | 38a3eaef51d0559e796ce7b6bef6127a771a395d | 3,652,825 |
def move_nodes(source_scene, dest_scene):
"""
Moves scene nodes from the source scene to the destination scene.
:type source_scene: fbx.FbxScene
:type dest_scene: fbx.FbxScene
"""
source_scene_root = source_scene.GetRootNode() # type: fbx.FbxNode
dest_scene_root = dest_scene.GetRootNode()... | 26a413736ab5fee46182f05247fe989d66358f19 | 3,652,826 |
def extract_values(*args):
"""
Wrapper around `extract_value`; iteratively applies that method to all items
in a list. If only one item was passed in, then we return that one item's
value; if multiple items were passed in, we return a list of the corresponding
item values.
"""
processed = [... | 2906ca3aa42bfb47b231fd23b2a69a816399c255 | 3,652,827 |
def predefined_split(dataset):
"""Uses ``dataset`` for validiation in :class:`.NeuralNet`.
Examples
--------
>>> valid_ds = skorch.dataset.Dataset(X, y)
>>> net = NeuralNet(..., train_split=predefined_split(valid_ds))
Parameters
----------
dataset: torch Dataset
Validiation data... | 4f4f775e41b07efba3425bc2243d9766b41f5bc1 | 3,652,828 |
import os
import re
def writeBremDecay( # Might want a config later
lhe,
mAp,
eps,
zlims,
seed,
outdir,
outname,
nevents=10_000
):
""... | c6ab2695ce8d4984acc9f1e50898089ab4f7aaf1 | 3,652,829 |
from typing import Union
def bgr_to_rgba(image: Tensor, alpha_val: Union[float, Tensor]) -> Tensor:
"""Convert an image from BGR to RGBA.
Args:
image (Tensor[B, 3, H, W]):
BGR Image to be converted to RGBA.
alpha_val (float, Tensor[B, 1, H, W]):
A float number or tenso... | 654cb3df7432d799b2a391bf5cfa19a15a26b1fa | 3,652,830 |
def d_matrix_1d(n, r, v):
"""Initializes the differentiation matrices on the interval.
Args:
n: The order of the polynomial.
r: The nodal points.
v: The Vandemonde matrix.
Returns:
The gradient matrix D.
"""
vr = grad_vandermonde_1d(n, r)
return np.linalg.lstsq(v.T, vr.... | a8d1df34726ea1ac6ef7b49209c45374cb2bed04 | 3,652,831 |
import functools
def compile_replace(pattern, repl, flags=0):
"""Construct a method that can be used as a replace method for sub, subn, etc."""
call = None
if pattern is not None and isinstance(pattern, RE_TYPE):
if isinstance(repl, (compat.string_type, compat.binary_type)):
repl = Re... | eb753edeb9c212a28968eaf9c070aeeec8678d49 | 3,652,832 |
import six
def python_2_unicode_compatible(klass):
"""
From Django
A decorator that defines __unicode__ and __str__ methods under Python 2.
Under Python 3 it does nothing.
To support Python 2 and 3 with a single code base, define a __str__ method
returning text and apply this decorator to th... | 18c290d649e0299c72f85209c4db6a7a4b716300 | 3,652,833 |
import re
import logging
def ParseNewPingMsg(msg):
"""Attempt to parse the message for a ping (in the new format). Return the request and response strings
(json-ified dict) if parsing succeeded. Return None otherwise.
"""
parsed = re.match(kNewPingMsgRe, msg)
if not parsed:
return None
try:
return... | 6bca164892ea13b598af75d468580a7d4bd04d4c | 3,652,834 |
from faker import Faker
def parse_main_dict():
"""Parses dict to get the lists of
countries, cities, and fakers. Fakers allow generation of region specific fake data.
Also generates total number of agents
"""
Faker.seed(seed) # required to generate reproducible data
countries = main_dict.key... | 7cf9870c86c40bb2d1565479d6789d9cd7114024 | 3,652,835 |
import json
def format_payload(svalue):
"""formats mqtt payload"""
data = {"idx": IDX, "nvalue": 0, "svalue": svalue}
return json.dumps(data) | 1cbee0d5169acde802be176cc47a25c2db1c2f62 | 3,652,836 |
def load_auth_client():
"""Create an AuthClient for the portal
No credentials are used if the server is not production
Returns
-------
globus_sdk.ConfidentialAppAuthClient
Client used to perform GlobusAuth actions
"""
_prod = True
if _prod:
app = globus_sdk.Confidenti... | 8e16303fa80e775d94e669d96db24a9f7a63e0b6 | 3,652,837 |
def DCGAN_discriminator(img_dim, nb_patch, bn_mode, model_name="DCGAN_discriminator", use_mbd=True):
"""
Discriminator model of the DCGAN
args : img_dim (tuple of int) num_chan, height, width
pretr_weights_file (str) file holding pre trained weights
returns : model (keras NN) the Neural Net... | 7aeabfffcc15a10c2eb2c81c795cbc4ff70a890b | 3,652,838 |
def common_stat_style():
"""
The common style for info statistics.
Should be used in a dash component className.
Returns:
(str): The style to be used in className.
"""
return "has-margin-right-10 has-margin-left-10 has-text-centered has-text-weight-bold" | 899381fc56e28ecd042e19507f6bc51ceeca3ef0 | 3,652,839 |
def TourType_LB_rule(M, t):
"""
Lower bound on tour type
:param M: Model
:param t: tour type
:return: Constraint rule
"""
return sum(M.TourType[i, t] for (i, s) in M.okTourType if s == t) >= M.tt_lb[t] | 0495e2d01c7d5d02e8bc85374ec1d05a8fdcbd91 | 3,652,840 |
import json
def build_auto_dicts(jsonfile):
"""Build auto dictionaries from json"""
dicts = {}
with open(jsonfile, "r") as jsondata:
data = json.load(jsondata)
for dicti in data:
partialstr = data[dicti]["partial"]
partial = bool(partialstr == "True")
dictlist = data[... | 50978acc9696647746e2065144fda8537d0c6dba | 3,652,841 |
def log_gammainv_pdf(x, a, b):
"""
log density of the inverse gamma distribution with shape a and scale b,
at point x, using Stirling's approximation for a > 100
"""
return a * np.log(b) - sp.gammaln(a) - (a + 1) * np.log(x) - b / x | 27bc239770e94cb68a27291abd01050f9780c4fb | 3,652,842 |
from pathlib import Path
def read_basin() -> gpd.GeoDataFrame:
"""Read the basin shapefile."""
basin = gpd.read_file(Path(ROOT, "HCDN_nhru_final_671.shp"))
basin = basin.to_crs("epsg:4326")
basin["hru_id"] = basin.hru_id.astype(str).str.zfill(8)
return basin.set_index("hru_id").geometry | 9d590d478b71bdd2a857ab8f0864144ac598cc58 | 3,652,843 |
from typing import Callable
from typing import Tuple
def cross_validate(estimator: BaseEstimator, X: np.ndarray, y: np.ndarray,
scoring: Callable[[np.ndarray, np.ndarray, ...], float], cv: int = 5) -> Tuple[float, float]:
"""
Evaluate metric by cross-validation for given estimator
Para... | c127b1cf68d011e76fdbf813673bf1d84a7520bb | 3,652,844 |
def unpack_request(environ, content_length=0):
"""
Unpacks a get or post request query string.
:param environ: whiskey application environment.
:return: A dictionary with parameters.
"""
data = None
if environ["REQUEST_METHOD"] == "GET":
data = unpack_get(environ)
elif environ["R... | 02280666d6e4aee3ec1465cca17d7118a72b072b | 3,652,845 |
def GetMembership(name, release_track=None):
"""Gets a Membership resource from the GKE Hub API.
Args:
name: the full resource name of the membership to get, e.g.,
projects/foo/locations/global/memberships/name.
release_track: the release_track used in the gcloud command,
or None if it is not a... | b2232faec0a2302ec554a8658cdf0a44f9374861 | 3,652,846 |
def receive_messages(queue, max_number, wait_time):
"""
Receive a batch of messages in a single request from an SQS queue.
Usage is shown in usage_demo at the end of this module.
:param queue: The queue from which to receive messages.
:param max_number: The maximum number of messages to receive. T... | dd422eb96ddb41513bcf248cf2dc3761a9b56191 | 3,652,847 |
def get_snmp_community(device, find_filter=None):
"""Retrieves snmp community settings for a given device
Args:
device (Device): This is the device object of an NX-API enabled device
using the Device class
community (str): optional arg to filter out this specific community
Retu... | ae36269133fcc482c30bd29f58e44d3d1e10dcd1 | 3,652,848 |
def get_header_size(tif):
"""
Gets the header size of a GeoTIFF file in bytes.
The code used in this function and its helper function `_get_block_offset` were extracted from the following
source:
https://github.com/OSGeo/gdal/blob/master/swig/python/gdal-utils/osgeo_utils/samples/validate_cloud... | f7d41b9f6140e2d555c8de7e857612c692ebea16 | 3,652,849 |
def format_x_ticks_as_dates(plot):
"""Formats x ticks YYYY-MM-DD and removes the default 'Date' label.
Args:
plot: matplotlib.AxesSubplot object.
"""
plot.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y-%m-%d'))
plot.get_xaxis().get_label().set_visible(False)
return plot | 00838b40582c9205e3ba6f87192852af37a88e7a | 3,652,850 |
def operations():
"""Gets the base class for the operations class.
We have to use the configured base back-end's operations class for
this.
"""
return base_backend_instance().ops.__class__ | 845d50884e58491539fb9ebfcf0da62e5cad66d4 | 3,652,851 |
import mimetypes
def office_convert_get_page(request, repo_id, commit_id, path, filename):
"""Valid static file path inclueds:
- index.html for spreadsheets and index_html_xxx.png for images embedded in spreadsheets
- 77e168722458356507a1f373714aa9b575491f09.pdf
"""
if not HAS_OFFICE_CONVERTER:
... | 48a3c5716b833e639a10c0366829185a1ce623aa | 3,652,852 |
def tensorize_data(
uvdata,
corr_inds,
ants_map,
polarization,
time,
data_scale_factor=1.0,
weights=None,
nsamples_in_weights=False,
dtype=np.float32,
):
"""Convert data in uvdata object to a tensor
Parameters
----------
uvdata: UVData object
UVData object co... | 0a780bb022854c83341ed13c0a7ad0346bb43016 | 3,652,853 |
import torch
def _normalize_rows(t, softmax=False):
"""
Normalizes the rows of a tensor either using
a softmax or just plain division by row sums
Args:
t (:obj:`batch_like`)
Returns:
Normalized version of t where rows sum to 1
"""
if not softmax:
# EPSILON hack av... | 3ffcedbaf279ead72414256290d2b88078aff468 | 3,652,854 |
def calculate_baselines(baselines: pd.DataFrame) -> dict:
"""
Read a file that contains multiple runs of the same pair. The format of the
file must be:
workload id, workload argument, run number, tFC, tVM
This function calculates the average over all runs of each unique pair of
workload id and... | 69cd0473fc21366e57d20ee39fceb704001aba1b | 3,652,855 |
def pick_ind(x, minmax):
""" Return indices between minmax[0] and minmax[1].
Args:
x : Input vector
minmax : Minimum and maximum values
Returns:
indices
"""
return (x >= minmax[0]) & (x <= minmax[1]) | 915a1003589b880d4edf5771a23518d2d4224094 | 3,652,856 |
def read_files(file_prefix,start=0,end=100,nfmt=3,pixel_map=None):
"""
read files that have a numerical suffix
"""
images = []
format = '%' + str(nfmt) + '.' + str(nfmt) + 'd'
for j in range(start,end+1):
ext = format % j
file = file_prefix + '_' + ext + '.tif'
arr = r... | 95d283f04b8ef6652da290396bb4649deedff665 | 3,652,857 |
def describing_function(
F, A, num_points=100, zero_check=True, try_method=True):
"""Numerical compute the describing function of a nonlinear function
The describing function of a nonlinearity is given by magnitude and phase
of the first harmonic of the function when evaluated along a sinusoidal
... | 4e9b779ba30f2588262e2ecff7a993d210533b59 | 3,652,858 |
from typing import List
def _read_point(asset: str, *args, **kwargs) -> List:
"""Read pixel value at a point from an asset"""
with COGReader(asset) as cog:
return cog.point(*args, **kwargs) | 246c98d55fd27465bc2c6f737cac342ccf9d52d8 | 3,652,859 |
def get_unquoted_text(token):
"""
:param token: Token
:return: String
"""
if isinstance(token, UnquotedText):
return token.value()
else:
raise exceptions.BugOrBroken(
"tried to get unquoted text from " + token) | 0fabfb504f725a84a75cada6e5d04a9aeda9a406 | 3,652,860 |
import torch
def image2tensor(image: np.ndarray, range_norm: bool, half: bool) -> torch.Tensor:
"""Convert ``PIL.Image`` to Tensor.
Args:
image (np.ndarray): The image data read by ``PIL.Image``
range_norm (bool): Scale [0, 1] data to between [-1, 1]
half (bool): Whether to convert to... | 86ab04d599ac9b1bfe2e90d0b719ea47dc8f7671 | 3,652,861 |
def panda_four_load_branch():
"""
This function creates a simple six bus system with four radial low voltage nodes connected to \
a medium valtage slack bus. At every low voltage node the same load is connected.
RETURN:
**net** - Returns the required four load system
EXAMPLE:
i... | dd5bc45a75943f0c078ab3bde9aa94b4bafc804f | 3,652,862 |
def word_flipper(our_string):
"""
Flip the individual words in a sentence
Args:
our_string(string): Strings to have individual words flip
Returns:
string: String with words flipped
"""
word_list = our_string.split(" ")
for idx in range(len(word_list)):
word_list[idx]... | fd484079407342925fc13583fb1fbee9ee472b14 | 3,652,863 |
import json
import base64
def load_json(ctx, param, value):
"""Decode and load json for click option."""
value = value[1:]
return json.loads(base64.standard_b64decode(value).decode()) | 99236d6fcde6c69a4bdadad4c6f3487d88fb7ce0 | 3,652,864 |
def hyperparam_search(model_config, train, test):
"""Perform hyperparameter search using Bayesian optimization on a given model and
dataset.
Args:
model_config (dict): the model and the parameter ranges to search in. Format:
{
"name": str,
"model": sklearn.base.BaseE... | 8f496a2c4494545ffdba2a5f63512ff45da4bb03 | 3,652,865 |
def profile_tags(profile):
"""
Get the tags from a given security profile.
"""
# TODO: This is going to be a no-op now, so consider removing it.
return profile.id.split('_') | 3d3cda3d67e9574f31a7fea4aee714cca39af5db | 3,652,866 |
def _sawtooth_wave_samples(freq, rate, amp, num):
"""
Generates a set of audio samples taken at the given sampling rate
representing a sawtooth wave oscillating at the given frequency with
the given amplitude lasting for the given duration.
:param float freq The frequency of oscillation of the sa... | 4691fb94e1709c5dc1a1dcb8ed02795d0b3cfe40 | 3,652,867 |
from keras.models import Model
from keras.layers import Conv2D, SpatialDropout2D
from keras.layers import UpSampling2D, Reshape, concatenate
from keras.applications.resnet50 import ResNet50
def ResNet_UNet_Dropout(dim=512, num_classes=6, dropout=0.5, final_activation=True):
"""
Returns a ResNet50 Nework with ... | 6d99cbb9f5986a87e79653b03cc91ca652ca2d2d | 3,652,868 |
import sqlite3
def _parse_accounts_ce(database, uid, result_path):
"""Parse accounts_ce.db.
Args:
database (SQLite3): target SQLite3 database.
uid (str): user id.
result_path (str): result path.
"""
cursor = database.cursor()
try:
cursor.execute(query)
except s... | 05538c21342f854d8465a415c32f5e2ea4f3f14d | 3,652,869 |
from flask import current_app
def resolve_grant_endpoint(doi_grant_code):
"""Resolve the OpenAIRE grant."""
# jsonresolver will evaluate current_app on import if outside of function.
pid_value = '10.13039/{0}'.format(doi_grant_code)
try:
_, record = Resolver(pid_type='grant', object_type='rec'... | e3217aeda5e6dec935c3ccb96e1164be66083e4f | 3,652,870 |
from typing import Union
from pathlib import Path
def from_tiff(path: Union[Path, str]) -> OME:
"""Generate OME metadata object from OME-TIFF path.
This will use the first ImageDescription tag found in the TIFF header.
Parameters
----------
path : Union[Path, str]
Path to OME TIFF.
... | 98ed750bba4b6aeaa791cc9041cf394e43fc50f9 | 3,652,871 |
def increase_structure_depth(previous_architecture, added_block, problem_type):
"""Returns new structure given the old one and the added block.
Increases the depth of the neural network by adding `added_block`.
For the case of cnns, if the block is convolutional, it will add it before
the flattening operation.... | 3735ca2c66a1a5856fb7fac69b6e02daf25868d2 | 3,652,872 |
def create_table_string(data, highlight=(True, False, False, False), table_class='wikitable', style=''):
"""
Takes a list and returns a wikitable.
@param data: The list that is converted to a wikitable.
@type data: List (Nested)
@param highlight: Tuple of rows and columns that should be highlighte... | f586fac681e1b4f06ad5e2a1cc451d9250fae929 | 3,652,873 |
from pathlib import Path
import os
def path_to_dnd(path: Path) -> str:
"""Converts a `Path` into an acceptable value for `tkinterdnd2.`"""
# tkinterdnd2 will only accept fs paths with forward slashes, even on Windows.
wants_sep = '/'
if os.path.sep == wants_sep:
return str(path)
else:
... | 61c4f88b944551f16f1baf127ddc3ccc5018267a | 3,652,874 |
def registry_dispatcher_document(self, code, collection):
"""
This task receive a list of codes that should be queued for DOI registry
"""
return _registry_dispatcher_document(code, collection, skip_deposited=False) | 530b2d183e6e50dc475ac9ec258fc13bea76aa8d | 3,652,875 |
from typing import Collection
import requests
def get_reddit_oauth_scopes(
scopes: Collection[str] | None = None,
) -> dict[str, dict[str, str]]:
"""Get metadata on the OAUTH scopes offered by the Reddit API."""
# Set up the request for scopes
scopes_endpoint = "/api/v1/scopes"
scopes_endpoint_url... | 0a55facfd07af259c1229aa30417b516b268602b | 3,652,876 |
def beta_reader(direc):
"""
Function to read in beta values for each tag
"""
path = direc
H_beta = np.loadtxt('%s/Beta Values/h_beta_final2.txt' % path)
Si_beta = np.loadtxt('%s/Beta Values/si_beta_final2.txt' % path)
He_emi_beta = np.loadtxt('%s/Beta Values/he_emi_beta_final2.txt' % path)
... | ab8aef0acd6a9cd86301d5cc99e45511cf193a10 | 3,652,877 |
import os
def boto3_s3_upload(s3, dst, file):
"""Upload Item to s3.
:param s3: -- Sqlalchemy session object.
:param dst: -- str. Location to storage ???
:param file: -- ???. File object.
Return Type: Bool
"""
s3.Object(settings.config_type['AWS_BUCKET'], file).put(Body=open(os.path.join(... | 487d25cad72225ddde8ee91f8b12e1696c3163a0 | 3,652,878 |
def get_logging_format():
"""return the format string for the logger"""
formt = "[%(asctime)s] %(levelname)s:%(message)s"
return formt | 3380cdd34f1a44cf15b9c55d2c05d3ecb81116cb | 3,652,879 |
def plot_hydrogen_balance(results):
""" Plot the hydrogen balance over time """
n_axes = results["times"].shape[0]
fig = plt.figure(figsize=(6.0, 5.5))
fig.suptitle('Hydrogen production and utilization over the year', fontsize=fontsize+1, fontweight='normal', color='k')
axes = fig.subplots(n_axes)
... | e352b1885b53ec9f5fc41f32f67afc5f86cae647 | 3,652,880 |
def ref_dw(fc, fmod):
"""Give the reference value for roughness by linear interpolation from the data
given in "Psychoacoustical roughness:implementation of an optimized model"
by Daniel and Weber in 1997
Parameters
----------
fc: integer
carrier frequency
fmod: integer
modu... | adf7a67c7b9d4448074f6ccd5fbf8e62c52b113d | 3,652,881 |
from typing import Optional
def points_2d_inside_image(
width: int,
height: int,
camera_model: str,
points_2d: np.ndarray,
points_3d: Optional[np.ndarray] = None,
) -> np.ndarray:
"""Returns the indices for an array of 2D image points that are inside the image canvas.
Args:
width:... | 95d235e475555c184e95b1e30c3cac686fe3e65f | 3,652,882 |
import torch
def list2tensors(some_list):
"""
:math:``
Description:
Implemented:
[True/False]
Args:
(:):
(:):
Default:
Shape:
- Input: list
- Output: list of tensors
Examples::
"""
t_list=[]
for i in some_list... | 35efe7c13c8c4f75266eceb912e8afccd25408cf | 3,652,883 |
def interpret_input(inputs):
""" convert input entries to usable dictionaries """
for key, value in inputs.items(): # interpret each line's worth of entries
if key in ['v0', 'y0', 'angle']: # for variables, intepret distributions
converted = interpret_distribution(key,... | 5a68f8e551ae3e31e107ab5a6a9aacc2db358263 | 3,652,884 |
def time(prompt=None, output_hour_clock=24, milli_seconds=False, fill_0s=True, allow_na=False):
"""
Repeatedly ask the user to input hours, minutes and seconds until they input valid values and return this in a defined format
:param prompt: Message to display to the user before asking them for inputs. Defa... | 82c0d8fae1f82e3f19b6af220ada5fadcea63bb3 | 3,652,885 |
def byol_a_url(ckpt, refresh=False, *args, **kwargs):
"""
The model from URL
ckpt (str): URL
"""
return byol_a_local(_urls_to_filepaths(ckpt, refresh=refresh), *args, **kwargs) | c9a8ce31ae5b6b59832d8ae9bb4e05d697f96cc9 | 3,652,886 |
def bellman_ford(g, start):
"""
Given an directed graph with possibly negative edge weights and with n vertices and m edges as well
as its vertex s, compute the length of shortest paths from s to all other vertices of the graph.
Returns dictionary with vertex as key.
- If vertex not present in th... | dd09de61d26a6ee988e549c5a0f8aafdf54b78ab | 3,652,887 |
import locale
from datetime import datetime
def _read_date(settings_file):
"""Get the data from the settings.xml file
Parameters
----------
settings_file : Path
path to settings.xml inside open-ephys folder
Returns
-------
datetime
start time of the recordings
Notes
... | 2f762bd7e190323acc44e5408c5f0977069d8828 | 3,652,888 |
def conv_res_step(x, hparams, padding, mask):
"""One step of convolutions and mid-residual."""
k = (hparams.kernel_height, hparams.kernel_width)
k2 = (hparams.large_kernel_size, 1)
dilations_and_kernels1 = [((1, 1), k), ((1, 1), k)]
dilations_and_kernels2 = [((1, 1), k2), ((4, 4), k2)]
with tf.variable_scop... | e0d2728f4991112a0dbd504121048f8670a4406b | 3,652,889 |
import six
from typing import Any
def _get_kind_name(item):
"""Returns the kind name in CollectionDef.
Args:
item: A data item.
Returns:
The string representation of the kind in CollectionDef.
"""
if isinstance(item, (six.string_types, six.binary_type)):
kind = "bytes_list"
elif isinstance(i... | 094298763f9bf1e3e7a421c19e08016f2138b7d7 | 3,652,890 |
def Froude_number(v, h, g=9.80665):
"""
Calculate the Froude Number of the river, channel or duct flow,
to check subcritical flow assumption (if Fr <1).
Parameters
------------
v : int/float
Average velocity [m/s].
h : int/float
Mean hydrolic depth float [m].
g : in... | 754225397baa6a27ae58adc63f09bba5287f18e9 | 3,652,891 |
from typing import Callable
from typing import Any
def handle_error(
func: Callable[[Command | list[Command]], Any]
) -> Callable[[str], Any]:
"""Handle tradfri api call error."""
@wraps(func)
async def wrapper(command: Command | list[Command]) -> None:
"""Decorate api call."""
try:
... | 1604f8ae224a9fb565f81ae70d74c24e68e60b9e | 3,652,892 |
def write(ser, command, log):
"""Write command to serial port, append what you write to log."""
ser.write(command)
summary = " I wrote: " + repr(command)
log += summary + "\n"
print summary
return log | 769e345d90121d4bf2d8cc23c128c2a588cba37c | 3,652,893 |
def anscombe(x):
"""Compute Anscombe transform."""
return 2 * np.sqrt(x + 3 / 8) | 9a47318733568892c4695db2cf153e59e78bb8d7 | 3,652,894 |
def max_accuracy(c1, c2):
"""
Relabel the predicted labels *in order* to
achieve the best accuracy, and return that
score and the best labelling
Parameters
----------
c1 : np.array
numpy array with label of predicted cluster
c2 : np.array
numpy array with label of true ... | 7ec438b500463859c27ea94d315312b88f5954f1 | 3,652,895 |
def create_sphere():
"""Create and return a single sphere of radius 5."""
sphere = rt.sphere()
sphere.radius = 5
return sphere | a8d5e2e8c0ec7d00f75c4007214d21aa0d2b64ad | 3,652,896 |
def calc_entropy(data):
"""
Calculate the entropy of a dataset.
Input:
- data: any dataset where the last column holds the labels.
Returns the entropy of the dataset.
"""
entropy = 0.0
###########################################################################
# TODO: Implement... | 418054f9a36b100daf788814e8549bc818e2e27a | 3,652,897 |
import time
def get_input(prompt=None):
"""Sets the prompt and waits for input.
:type prompt: None | list[Text] | str
"""
if not isinstance(prompt, type(None)):
if type(prompt) == str:
text_list = [Text(prompt, color=prompt_color,
new_line=True)]
... | bbcd5bbd7f97bff8d213d13afe22ae9111849e10 | 3,652,898 |
def alpha_liq(Nu, lyambda_feed, d_inner):
"""
Calculates the coefficent of heat transfer(alpha) from liquid to wall of pipe.
Parameters
----------
Nu : float
The Nusselt criterion, [dimensionless]
lyambda_feed : float
The thermal conductivity of feed, [W / (m * degreec celcium)]
... | 13d0371248c106fb0f12d26335381675d7484000 | 3,652,899 |
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