content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_hdf_filepaths(hdf_dir):
"""Get a list of downloaded HDF files which is be used for iterating through hdf file conversion."""
print "Building list of downloaded HDF files..."
hdf_filename_list = []
hdf_filepath_list = []
for dir in hdf_dir:
for dir_path, subdir, files in os.walk(dir):... | a64aa83d06b218a0faf3372e09c2ac337ce106aa | 14,306 |
def mask_depth_image(depth_image, min_depth, max_depth):
""" mask out-of-range pixel to zero """
ret, depth_image = cv2.threshold(
depth_image, min_depth, 100000, cv2.THRESH_TOZERO)
ret, depth_image = cv2.threshold(
depth_image, max_depth, 100000, cv2.THRESH_TOZERO_INV)
depth_image = np.... | 39fde62083666a9bb4a546c29aa736a23724e25f | 14,307 |
def prompt_for_password(prompt=None):
"""Fake prompt function that just returns a constant string"""
return 'promptpass' | 49499970c7698b08f38078c557637907edef3223 | 14,309 |
def heading(start, end):
"""
Find how to get from the point on a planet specified as a tuple start
to a point specified in the tuple end
"""
start = ( radians(start[0]), radians(start[1]))
end = ( radians(end[0]), radians(end[1]))
delta_lon = end[1] - start[1]
delta_lat = log(tan(pi/4 +... | 97bd69fc308bdf1a484901ad25b415def20f25ee | 14,310 |
def get_frame_list(video, jump_size = 6, **kwargs):
"""
Returns list of frame numbers including first and last frame.
"""
frame_numbers =\
[frame_number for frame_number in range(0, video.frame_count, jump_size)]
last_frame_number = video.frame_count - 1;
if frame_numbers[-1] != last_... | 786de04b4edf224045216de226ac61fdd42b0d7b | 14,311 |
def build_xlsx_response(wb, title="report"):
""" Take a workbook and return a xlsx file response """
title = generate_filename(title, '.xlsx')
myfile = BytesIO()
myfile.write(save_virtual_workbook(wb))
response = HttpResponse(
myfile.getvalue(),
content_type='application/vnd.openxmlf... | c9990c818b45fc68646ff28d39ea0b914e362db1 | 14,312 |
def top_k(*args, **kwargs):
""" See https://www.tensorflow.org/api_docs/python/tf/nn/top_k .
"""
return tensorflow.nn.top_k(*args, **kwargs) | 0b0a9f250a466f439301d840af2213f6b2758656 | 14,313 |
def determine_d_atoms_without_connectivity(zmat, coords, a_atoms, n):
"""
A helper function to determine d_atoms without connectivity information.
Args:
zmat (dict): The zmat.
coords (list, tuple): Just the 'coords' part of the xyz dict.
a_atoms (list): The determined a_atoms.
... | 8b2a60f6092e24b9bc6f71e4ad8753e06c2e6373 | 14,314 |
def all_of_them():
"""
Return page with all products with given name from API.
"""
if 'username' in session:
return render_template('productsearch.html', username=escape(session['username']), vars=lyst)
else:
return "Your are not logged in" | cee22ba57e108edaedc9330c21caa9cb60968aa5 | 14,315 |
def is_blank(line):
"""Determines if a selected line consists entirely of whitespace."""
return whitespace_re.match(line) is not None | 1120d7a70ce5c08eb5f179cd3d2a258af5cd3bc2 | 14,316 |
import re
import collections
import urllib
def _gff_line_map(line, params):
"""Map part of Map-Reduce; parses a line of GFF into a dictionary.
Given an input line from a GFF file, this:
- decides if the file passes our filtering limits
- if so:
- breaks it into component elements
- de... | 555ca7d4ce455563e7230d4b85f4f4404fa839bc | 14,317 |
def smoothEvolve(problem, orig_point, first_ref, second_ref):
"""Evolves using RVEA with abrupt change of reference vectors."""
pop = Population(problem, assign_type="empty", plotting=False)
try:
pop.evolve(slowRVEA, {"generations_per_iteration": 200, "iterations": 15})
except IndexError:
... | fd7f7e82e8b029597affd63ec04da3d40c049c98 | 14,318 |
def combine_color_channels(discrete_rgb_images):
"""
Combine discrete r,g,b images to RGB iamges.
:param discrete_rgb_images:
:return:
"""
color_imgs = []
for r, g, b in zip(*discrete_rgb_images):
# pca output is float64, positive and negative. normalize the images to [0, 255] rgb
... | 246653a698c997faffade25405b1dfafb8236510 | 14,319 |
def decohere_earlier_link(tA, tB, wA, wB, T_coh):
"""Applies decoherence to the earlier generated of the two links.
Parameters
----------
tA : float
Waiting time of one of the links.
wA : float
Corresponding fidelity
tB : float
Waiting time of the other link.
wB : fl... | 0ec19f50d1673f69211ac7af21ab942612fe8a67 | 14,320 |
import torch
def train(
network: RNN,
data: np.ndarray,
epochs: int = 10,
_n_seqs: int = 10,
_n_steps: int = 50,
lr: int = 0.001,
clip: int = 5,
val_frac: int = 0.2,
cuda: bool = True,
print_every: int = 10,
):
"""Train RNN."""
network.train()
opt = torch.optim.Adam... | 97c642c0b2849cb530121d914c586bc6ee76a26b | 14,321 |
def simple_lunar_phase(jd):
"""
This just does a quick-and-dirty estimate of the Moon's phase given the date.
"""
lunations = (jd - 2451550.1) / LUNAR_PERIOD
percent = lunations - int(lunations)
phase_angle = percent * 360.
delta_t = phase_angle * LUNAR_PERIOD / 360.
moon_day = int(delt... | 539d407241a0390b140fad4b67e2173ff1dee66c | 14,322 |
def fetch_traj(data, sample_index, colum_index):
""" Returns the state sequence. It also deletes the middle index, which is
the transition point from history to future.
"""
# data shape: [sample_index, time, feature]
traj = np.delete(data[sample_index, :, colum_index:colum_index+1], history_len-... | da373d3890f2c89754e36f78b78fb582b429109d | 14,323 |
from datetime import datetime
def validate_date(period: str, start: bool = False) -> pd.Timestamp:
"""Validate the format of date passed as a string.
:param period: Date in string. If None, date of today is assigned.
:type period: str
:param start: Whether argument passed is a starting date or an end... | 99c76ee2e23beaff92a8ad67bf38b26c5f33f4bd | 14,324 |
import copy
def rec_module_mic(echograms, mic_specs):
"""
Apply microphone directivity gains to a set of given echograms.
Parameters
----------
echograms : ndarray, dtype = Echogram
Target echograms. Dimension = (nSrc, nRec)
mic_specs : ndarray
Microphone directions and direct... | 91abe86095cab7ccb7d3f2f001892df4a809106b | 14,325 |
def is_string_like(obj): # from John Hunter, types-free version
"""Check if obj is string."""
try:
obj + ''
except (TypeError, ValueError):
return False
return True | cb7682f91009794011c7c663f98e539d8543c8fd | 14,326 |
def divide():
"""Handles division, returns a string of the answer"""
a = int(request.args["a"])
b = int(request.args["b"])
quotient = str(int(operations.div(a, b)))
return quotient | 50c48f9802b3f11e3322b88a45068cad7e354637 | 14,328 |
def ax2cu(ax):
"""Axis angle pair to cubochoric vector."""
return Rotation.ho2cu(Rotation.ax2ho(ax)) | 6f1c6e181d1e25a2bf28605f01d66fa6a8ffcd45 | 14,329 |
def define_genom_loc(current_loc, pstart, p_center, pend, hit_start, hit_end, hit_strand, ovl_range):
""" [Local] Returns location label to be given to the annotated peak, if upstream/downstream or overlapping one edge of feature."""
all_pos = ["start", "end"]
closest_pos, dmin = distance_to_peak_center(
... | 87f89a1d392935a008b7997930bf16dda96cf36b | 14,331 |
import torch
def actp(Gij, X0, jacobian=False):
""" action on point cloud """
X1 = Gij[:,:,None,None] * X0
if jacobian:
X, Y, Z, d = X1.unbind(dim=-1)
o = torch.zeros_like(d)
B, N, H, W = d.shape
if isinstance(Gij, SE3):
Ja = torch.stack([
... | 87f85a713b72de65064224086d9c4715f704d800 | 14,333 |
def isPalindrome(s):
"""Assumes s is a str
Returns True if s is a palindrome; False otherwise.
Punctuation marks, blanks, and capitalization are ignored."""
def toChars(s):
s = s.lower()
letters = ''
for c in s:
if c in 'abcdefghijklmnopqrstuvwxyz':
letters =... | 496f5fa92088a5ecb1b99be811e68501513ee3a4 | 14,334 |
import logging
def compute_irs(ground_truth_data,
representation_function,
random_state,
diff_quantile=0.99,
num_train=gin.REQUIRED,
batch_size=gin.REQUIRED):
"""Computes the Interventional Robustness Score.
Args:
ground_truth_da... | 0ff37699367946fa099f4a5a48cbc66d89af8a29 | 14,336 |
def Grab_Pareto_Min_Max(ref_set_array, objective_values, num_objs, num_dec_vars, objectives_names=[],
create_txt_file='No'):
"""
Purposes: Identifies the operating policies producing the best and worst performance in each objective.
Gets called automatically by processing_reference... | 882ba260576550d2c9de20e950594d5369410187 | 14,337 |
def edist(x, y):
""" Compute the Euclidean distance between two samples x, y \in R^d."""
try:
dist = np.sqrt(np.sum((x-y)**2))
except ValueError:
print 'Dimensionality of samples must match!'
else:
return dist | e0b0e586b49bf3e19eafa2cbf271fb3b1ddc2b99 | 14,338 |
def allsec_preorder(h):
"""
Alternative to using h.allsec(). This returns all sections in order from
the root. Traverses the topology each neuron in "pre-order"
"""
#Iterate over all sections, find roots
roots = root_sections(h)
# Build list of all sections
sec_list = []
for r in ro... | 0407bd37c3f975ba91a51b3058d6c619813f2526 | 14,340 |
def get_func_bytes(*args):
"""get_func_bytes(func_t pfn) -> int"""
return _idaapi.get_func_bytes(*args) | 56000602c9d1d98fb1b76c809cce6c3458a3f426 | 14,341 |
def balanced_accuracy(y_true, y_score):
"""Compute accuracy using one-hot representaitons."""
if isinstance(y_true, list) and isinstance(y_score, list):
# Online scenario
if y_true[0].ndim == 2 and y_score[0].ndim == 2:
# Flatten to single (very long prediction)
y_true = ... | 9745769ac047863b902f0e74f17680f9ccee5a53 | 14,342 |
import json
from dateutil import tz
from datetime import datetime
async def get_weather(weather):
""" For .weather command, gets the current weather of a city. """
if not OWM_API:
await weather.reply(
f"`{JAVES_NNAME}:` **Get an API key from** https://openweathermap.org/ `first.`")
... | b93c21eadefa2b4504708ae2663bfdcbf0555668 | 14,343 |
def read_table(source, columns=None, nthreads=1, metadata=None,
use_pandas_metadata=False):
"""
Read a Table from Parquet format
Parameters
----------
source: str or pyarrow.io.NativeFile
Location of Parquet dataset. If a string passed, can be a single file
name or di... | 0d0e4990e88c39920e380cca315332f7c36f6ee8 | 14,344 |
def _export_cert_from_task_keystore(
task, keystore_path, alias, password=KEYSTORE_PASS):
"""
Retrieves certificate from the keystore with given alias by executing
a keytool in context of running container and loads the certificate to
memory.
Args:
task (str): Task id of container t... | 8ae8a0e1d46f121597d70aff35a26ce15c448399 | 14,346 |
def eta_expand(
path: qlast.Path,
stype: s_types.Type,
*,
ctx: context.ContextLevel,
) -> qlast.Expr:
"""η-expansion of an AST path"""
if not ALWAYS_EXPAND and not stype.contains_object(ctx.env.schema):
# This isn't strictly right from a "fully η expanding" perspective,
# but for... | 294e9b0e2aa158dc4e8d57917031986f824fd55d | 14,347 |
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
import timeit
def NBAccuracy(features_train, labels_train, features_test, labels_test):
""" compute the accuracy of your Naive Bayes classifier """
# create classifier
clf = GaussianNB()
# fit the classifier on the... | c4c6d5a37341a811023bb0040616ebeb1c44be13 | 14,348 |
import json
def verify(body): # noqa: E501
"""verify
Verifies user with given user id. # noqa: E501
:param body: User id that is required for verification.
:type body: dict | bytes
:rtype: UserVerificationResponse
"""
if connexion.request.is_json:
body = VerifyUser.from_dict(co... | 76b2ad631dffbf59b1c2ffeb12069983d0540b51 | 14,349 |
def load_interface(interface_name, data):
"""
Load an interface
:param interface_name: a string representing the name of the interface
:param data: a dictionary of arguments to be used for initializing the interface
:return: an Interface object of the appropriate type
"""
if interface_name n... | e57e0710d5ad5080b2da1f734581d1b205aedc77 | 14,350 |
def get_full_word(*args):
"""get_full_word(ea_t ea) -> ulonglong"""
return _idaapi.get_full_word(*args) | 8eb45586888fc836146441bb00bc3c3096ea2c5e | 14,352 |
def full_setup(battery_chemistry):
"""This function gets the baseline vehicle and creates modifications for different
configurations, as well as the mission and analyses to go with those configurations."""
# Collect baseline vehicle data and changes when using different configuration settings
vehicle ... | 9335521ad5a238ab0a566c80d1443cc5c052a37a | 14,353 |
def sph_yn_exact(n, z):
"""Return the value of y_n computed using the exact formula.
The expression used is http://dlmf.nist.gov/10.49.E4 .
"""
zm = mpmathify(z)
s1 = sum((-1)**k*_a(2*k, n)/zm**(2*k+1) for k in xrange(0, int(n/2) + 1))
s2 = sum((-1)**k*_a(2*k+1, n)/zm**(2*k+2) for k in xrange(... | 490c31a3311fb922f5d33337aaeb70f167b25772 | 14,354 |
def f_bis(n1 : float, n2 : float, n3 : float) -> str:
""" ... cf ci-dessus ...
"""
if n1 < n2:
if n2 < n3:
return 'cas 1'
elif n1 < n3:
return 'cas 2'
else:
return 'cas 5'
elif n1 < n3:
return 'cas 3'
elif n2 < n3:
return 'c... | e46c147a5baef02878700e546b11b7ae44b8909a | 14,355 |
def calc_B_effective(*B_phasors):
"""It calculates the effective value of the magnetic induction field B
(microTesla) in a given point, considering the magnetic induction of
all the cables provided.
Firstly, the function computes the resulting real and imaginary parts
of the x and y magnetic induc... | a24ae9330018f9dc24ebbba66ccc4bc4bd79a7cb | 14,356 |
def elision_count(l):
"""Returns the number of elisions in a given line
Args:
l (a bs4 <line>): The line
Returns:
(int): The number of elisions
"""
return sum([(1 if _has_elision(w) else 0) for w in l("word")]) | c031765089a030328d68718577872a6d2f70b88d | 14,357 |
def get_final_df(model, data):
"""
This function takes the `model` and `data` dict to
construct a final dataframe that includes the features along
with true and predicted prices of the testing dataset
"""
# if predicted future price is higher than the current,
# then calculate the true futur... | e28f19c5072693872bf17a89cf39cc6afa74517b | 14,358 |
def read_fragment_groups(input_string,natoms,num_channels):
""" read in the fragment groups for each channel
"""
inp_line = _get_integer_line(input_string,'FragmentGroups',natoms)
assert inp_line is not None
out=' '.join(inp_line)
return out | 81ae922add4d0c1680bbeb5223ad85a265ac3040 | 14,359 |
def pad_tile_on_edge(tile, tile_row, tile_col, tile_size, ROI):
""" add the padding to the tile on the edges. If the tile's center is
outside of ROI, move it back to the edge
Args:
tile: tile value
tile_row: row number of the tile relative to its ROI
tile_col: col number of the tile relative to its... | bf829ead79f6347423dae8f4c534352d648a3845 | 14,361 |
def calc_kfold_score(model, df, y, n_splits=3, shuffle=True):
"""
Calculate crossvalidation score for the given model and data. Uses sklearn's KFold with shuffle=True.
:param model: an instance of sklearn-model
:param df: the dataframe with training data
:param y: dependent value
:param n_... | 09e646d40b245be543c5183b8c964fe8ee4f699f | 14,362 |
def obter_forca (unidade):
"""Esta funcao devolve a forca de ataque da unidade dada como argumento"""
return unidade[2] | 34fe4acac8e0e3f1964faf8e4b26fa31148cf2a6 | 14,363 |
from typing import Dict
def get_default_configuration(cookiecutter_json: CookiecutterJson) -> Dict[str, str]:
"""
Get the default values for the cookiecutter configuration.
"""
default_options = dict()
for key, value in cookiecutter_json.items():
if isinstance(value, str) and "{{" not in v... | 8a8e3a9b80d3440f1e6031498c210b7b5577aa03 | 14,364 |
def random_nodes_generator(num_nodes, seed=20):
"""
:param int num_nodes: An Integer denoting the number of nodes
:param int seed: (Optional) Integer specifying the seed for controlled randomization.
:return: A dictionary containing the coordinates.
:rtype: dict
"""
np.random.seed(seed)
... | 5fa08ccc2cd3a4c34962a29ca9a7566ca5e64592 | 14,365 |
def OpenDocumentTextMaster():
""" Creates a text master document """
doc = OpenDocument('application/vnd.oasis.opendocument.text-master')
doc.text = Text()
doc.body.addElement(doc.text)
return doc | 6813f527ced0f1cd89e0824ac10aeb78d06799a4 | 14,366 |
def get_visible_desktops():
"""
Returns a list of visible desktops.
The first desktop is on Xinerama screen 0, the second is on Xinerama
screen 1, etc.
:return: A list of visible desktops.
:rtype: util.PropertyCookie (CARDINAL[]/32)
"""
return util.PropertyCookie(util.ge... | 9fba922a5c83ed4635391f6ed5319fcfe4fd424d | 14,367 |
def spherical_noise(
gridData=None, order_max=8, kind="complex", spherical_harmonic_bases=None
):
"""Returns order-limited random weights on a spherical surface.
Parameters
----------
gridData : io.SphericalGrid
SphericalGrid containing azimuth and colatitude
order_max : int, optional
... | 82ef238ef0d72100435306267bf8248f82b70fd8 | 14,369 |
def rollback_command():
"""Command to perform a rollback fo the repo."""
return Command().command(_rollback_command).require_clean().require_migration().with_database() | abfed20ff8cfa08af084fc8c956ebb8d312c985f | 14,370 |
import numpy
def circumcenter(vertices):
"""
Compute the circumcenter of a triangle (the center of the circle which passes through all the vertices of the
triangle).
:param vertices: The triangle vertices (3 by n matrix with the vertices as rows (where n is the dimension of the
space)).
:... | 314e7650b6fec6c82880541de32c1378e720c8c8 | 14,371 |
def get_ROC_curve_naive(values, classes):
""" Naive implementation of a ROC curve generator that iterates over a number of thresholds.
"""
# get number of positives and negatives:
n_values = len(values);
totalP = len(np.where(classes > 0)[0]);
totalN = n_values - totalP;
min_v... | 5950c207746c39c4787b3a472c83fbca5599d654 | 14,373 |
import re
def worker(path, opt):
"""Worker for each process.
Args:
path (str): Image path.
opt (dict): Configuration dict. It contains:
crop_size (int): Crop size.
step (int): Step for overlapped sliding window.
thresh_size (int): Threshold size. Patches wh... | 68b43995cb147bedad7b94c6f4960cef2646ed87 | 14,374 |
def RobotNet(images,dropout):
"""
Build the model for Robot where it will be used as RobotNet.
Args:
images: 4-D tensor with shape [batch_size, height, width, channals].
dropout: A Python float. The probability that each element is kept.
Returns:
Output tensor with the computed ... | 9bab69a9a0a01436c9b4225ec882231dc561c204 | 14,375 |
def cvReleaseMemStorage(*args):
"""cvReleaseMemStorage(PyObject obj)"""
return _cv.cvReleaseMemStorage(*args) | a11985f756672ab7b7c5ed58336daad1a975c0d2 | 14,376 |
import typing
def GetCommitsInOrder(
repo: git.Repo,
head_ref: str = "HEAD",
tail_ref: typing.Optional[str] = None) -> typing.List[git.Commit]:
"""Get a list of all commits, in chronological order from old to new.
Args:
repo: The repo to list the commits of.
head_ref: The starting point for i... | 57db975d44af80a5c6a8e251842182f6a0f572af | 14,378 |
def return_sw_checked(softwareversion, osversion):
"""
Check software existence, return boolean.
:param softwareversion: Software release version.
:type softwareversion: str
:param osversion: OS version.
:type osversion: str
"""
if softwareversion is None:
serv = bbconstants.SE... | 9f7efb06150468e553ac6a066b2c7750fd233d4c | 14,380 |
from re import A
import copy
def apply(
f: tp.Callable[..., None],
obj: A,
*rest: A,
inplace: bool = False,
_top_inplace: tp.Optional[bool] = None,
_top_level: bool = True,
) -> A:
"""
Applies a function to all `to.Tree`s in a Pytree. Works very similar to `jax.tree_map`,
but its v... | 9a4d29546eb47ba4838fccb58f78495411c99e1c | 14,381 |
import requests
def imageSearch(query, top=10):
"""Returns the decoded json response content
:param query: query for search
:param top: number of search result
"""
# set search url
query = '%27' + parse.quote_plus(query) + '%27'
# web result only base url
base_url = 'https://api.datam... | 4c731b0ead57e5ec4ab290c9afc67d6066cce093 | 14,382 |
def pack_inputs(inputs):
"""Pack a list of `inputs` tensors to a tuple.
Args:
inputs: a list of tensors.
Returns:
a tuple of tensors. if any input is None, replace it with a special constant
tensor.
"""
inputs = tf.nest.flatten(inputs)
outputs = []
for x in inputs:
if x is None... | 2801929a1109cd3c416d8b3229399a0a9b73a38f | 14,383 |
def entries_as_dict(month_index):
"""Convert index xml list to list of dictionaries."""
# Search path
findentrylist = etree.ETXPath("//section[@id='month-index']/ul/li")
# Extract data
entries_xml = findentrylist(month_index)
entries = [to_entry_dict(entry_index_xml)
for entry_ind... | 2fba6699457ca9726d4ce93b480e722bb6c8223d | 14,384 |
def resnet50():
"""Constructs a ResNet-50 model.
"""
return Bottleneck, [3, 4, 6, 3] | 197dc833c966146226721e56315d3f12d9c13398 | 14,385 |
def build_service_job_mapping(client, configured_jobs):
"""
:param client: A Chronos client used for getting the list of running jobs
:param configured_jobs: A list of jobs configured in Paasta, i.e. jobs we
expect to be able to find
:returns: A dict of {(service, instance): last_chronos_job}
... | 58cdf0a7f7561d1383f8f4c4e4cdd0f46ccfad0c | 14,386 |
def _validate_labels(labels, lon=True):
"""
Convert labels argument to length-4 boolean array.
"""
if labels is None:
return [None] * 4
which = 'lon' if lon else 'lat'
if isinstance(labels, str):
labels = (labels,)
array = np.atleast_1d(labels).tolist()
if all(isinstance(... | 6b4f4870692b3c89f2c51f920892852eeecc418d | 14,387 |
def celsius_to_fahrenheit(temperature_C):
""" converts C -> F """
return temperature_C * 9.0 / 5.0 + 32.0 | 47c789c560c5b7d035252418bd7fb0819b7631a4 | 14,388 |
import re
from datetime import datetime
def _parse_date_time(date):
"""Parse time string.
This matches 17:29:43.
Args:
date (str): the date string to be parsed.
Returns:
A tuple of the format (date_time, nsec), where date_time is a
datetime.time object and nsec is 0.
Ra... | 2d7f4b067d1215623c2e8e4217c98591a1794481 | 14,389 |
def parsed_user(request, institute_obj):
"""Return user info"""
user_info = {
'email': '[email protected]',
'name': 'John Doe',
'location': 'here',
'institutes': [institute_obj['internal_id']],
'roles': ['admin']
}
return user_info | 773363dc41f599abe27a5913434f88d1a20c131d | 14,390 |
def lastFromUT1(ut1, longitude):
"""Convert from universal time (MJD)
to local apparent sidereal time (deg).
Inputs:
- ut1 UT1 MJD
- longitude longitude east (deg)
Returns:
- last local apparent sideral time (deg)
History:
2002-08-05 ROwen First version, loosely base... | 0ba587125c0c422349acf7bb9753b09956fd9bed | 14,391 |
import itertools
def strip_translations_header(translations: str) -> str:
"""
Strip header from translations generated by ``xgettext``.
Header consists of multiple lines separated from the body by an empty line.
"""
return "\n".join(itertools.dropwhile(len, translations.splitlines())) | b96c964502724008306d627d785224be08bddb86 | 14,393 |
import random
def sample_targets_and_primes(targets, primes, n_rounds,
already_sampled_targets=None, already_sampled_primes=None):
"""
Sample targets `targets` and primes `primes` for `n_rounds` number of rounds. Omit already sampled targets
or primes which can be passed as s... | 6b73cdadf3016f1dc248deb2625eae1dd620553b | 14,394 |
def getsign(num):
"""input the raw num string, return a tuple (sign_num, num_abs).
"""
sign_num = ''
if num.startswith('±'):
sign_num = plus_minus
num_abs = num.lstrip('±+-')
if not islegal(num_abs):
return sign_num, ''
else:
try:
temp = float(... | 46fc5a7ac8e366479c40e2ccc1a33f57a736b343 | 14,395 |
from typing import Mapping
from typing import Union
def _convert_actions_to_commands(
subvol: Subvol,
build_appliance: Subvol,
action_to_names_or_rpms: Mapping[RpmAction, Union[str, _LocalRpm]],
) -> Mapping[YumDnfCommand, Union[str, _LocalRpm]]:
"""
Go through the list of RPMs to install and chan... | 519d09754b18bcabef405f3d39a959b1296d3c6c | 14,396 |
def fit_DBscan (image_X,
eps,
eps_grain_boundary,
min_sample,
min_sample_grain_boundary,
filter_boundary,
remove_large_clusters,
remove_small_clusters,
binarize_bdr_coord,
b... | b32218e6d45dc766d749af9247ee1d343236fef0 | 14,397 |
def get_today_timestring():
"""Docen."""
return pd.Timestamp.today().strftime('%Y-%m-%d') | f749a5a63f7c55053918eb3f95bb56c2325f4362 | 14,398 |
from pathlib import Path
import json
def load_json(filepath):
"""
Load a json file
:param filepath: path to json file
"""
fp = Path(filepath)
if not fp.exists():
raise ValueError("Unrecognized file path: {}".format(filepath))
with open(filepath) as f:
data = json.load(f)
... | 657509a50961f7b9c83536a8973884eef5bbed5e | 14,399 |
import requests
import random
def handle(req):
"""handle a request to the function
Args:
req (str): request body
"""
r = requests.get("http://api.open-notify.org/astros.json")
result = r.json()
index = random.randint(0, len(result["people"]) - 1)
name = result["people"][index]["nam... | 7d951443bc5b6f3db86602d635a8c9ce84b703fb | 14,400 |
import hashlib
def get_file_hashsum(file_name: str):
"""Generate a SHA-256 hashsum of the given file."""
hash_sha256 = hashlib.sha256()
with open(file_name, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_sha256.update(chunk)
return hash_sha256.hexdigest() | d515b6b7b743396240ada8d888b8bfbc4c316373 | 14,401 |
def generate_primes(n):
"""Generates a list of prime numbers up to `n`
"""
global PRIMES
k = PRIMES[-1] + 2
while k <= n:
primes_so_far = PRIMES[:]
divisible = False
for p in primes_so_far:
if k % p == 0:
divisible = True
break
... | 4ea29f8dc8dad11bf6c71fcb1dcc6b75b91bece5 | 14,402 |
def Convex(loss, L2_reg):
"""
loss: src_number loss
[loss_1, loss_2, ... loss_src_number]
"""
src_number = len(loss)
lam = cp.Variable(src_number)
prob = cp.Problem(
cp.Minimize(lam @ loss + L2_reg * cp.norm(lam, 2)), [cp.sum(lam) == 1, lam >= 0]
)
# prob.solve()
prob.so... | f2a6ecc464e2f87684d1537775816d22dc30d837 | 14,403 |
from limitlessled.pipeline import Pipeline
def state(new_state):
"""State decorator.
Specify True (turn on) or False (turn off).
"""
def decorator(function):
"""Decorator function."""
# pylint: disable=no-member,protected-access
def wrapper(self, **kwargs):
"""Wrap... | 156b7bbad0a943af6bb4280e4fdb1dde2b6e320a | 14,404 |
def rotTransMatrixNOAD(axis, s, c, t):
"""
build a rotate * translate matrix - MUCH faster for derivatives
since we know there are a ton of zeros and can act accordingly
:param axis: x y or z as a character
:param s: sin of theta
:param c: cos of theta
:param t: translation (a 3 tuple)
:... | f6e8d6474ba90e3a253229124e0571d67025c818 | 14,405 |
def angular_diameter_distance(z, cosmo=None):
""" Angular diameter distance in Mpc at a given redshift.
This gives the proper (sometimes called 'physical') transverse
distance corresponding to an angle of 1 radian for an object at
redshift `z`.
Parameters
----------
z : array_like
In... | 694f9fcaa6ce2585315f63e69351ac47589e248c | 14,406 |
def main(stdin):
"""
Take sorted standard in from Hadoop and return lines.
Value is just a place holder.
"""
for line_num in stdin:
# Remove trailing newlines.
line_num = line_num.rstrip()
# Omit empty lines.
try:
(line, num) = line_num.rsplit('\t', 1)
... | 811e184d9425c1c76681c823b463b99ebde2c25c | 14,407 |
def _ignore_module_import_frames(file_name, name, line_number, line):
"""
Ignores import frames of extension loading.
Parameters
----------
file_name : `str`
The frame's respective file's name.
name : `str`
The frame's respective function's name.
line_number : `int`
... | 8f3e506e99b32d2945ea665367d5447ef1b05732 | 14,408 |
def get_tf_tensor_data(tensor):
"""Get data from tensor."""
assert isinstance(tensor, tensor_pb2.TensorProto)
is_raw = False
if tensor.tensor_content:
data = tensor.tensor_content
is_raw = True
elif tensor.float_val:
data = tensor.float_val
elif tensor.dcomplex_val:
... | f2d62a7ccba252d5c94cd9979eb01a0b44282d4a | 14,409 |
def coth(x):
"""
Return the hyperbolic cotangent of x.
"""
return 1.0/tanh(x) | 92d490563c8595b8c11334cd38afbf9ef389dfe8 | 14,411 |
def param_is_numeric(p):
"""
Test whether any parameter is numeric; functionally, determines if any
parameter is convertible to a float.
:param p: An input parameter
:return:
"""
try:
float(p)
return True
except ValueError:
return False | b92579ba019389cf21002b63ca6e2ebdfad7d86f | 14,412 |
def convert_graph_to_angular_abstract_graph(graph: Graph, simple_graph=True, return_tripel_edges=False) -> Graph:
"""Converts a graph into an abstract angular graph
Can be used to calculate a path tsp
Arguments:
graph {Graph} -- Graph to be converted
simple_graph {bool} -- Indicates if ... | 2f81743824549d8e19f70f1843d6449eb12e7e5d | 14,413 |
def login_to_site(url, username, password, user_tag, pass_tag):
"""
:param url:
:param username:
:param password:
:param user_tag:
:param pass_tag:
:return: :raise:
"""
browser = mechanize.Browser(factory=mechanize.RobustFactory())
browser.set_handle_robots(False)
browser.se... | 6c906e037a619031eb45bb26f01da71833fa3e41 | 14,414 |
async def test_db(
service: Service = Depends(Service)
) -> HTTPSuccess:
"""Test the API to determine if the database is connected."""
if service.test().__class__ is not None:
return { "message": "Database connected." }
else:
return {"message": "Database not connected." } | 67e4530af6959fef03f55879ee3781ebf993f11c | 14,415 |
import tqdm
def model_fit_predict():
"""
Training example was implemented according to machine-learning-mastery forum
The function takes data from the dictionary returned from splitWindows.create_windows function
https://machinelearningmastery.com/stateful-stateless-lstm-time-series-forecasting-python... | 753ca4e90034864e709809cc1bd2f30640554f28 | 14,416 |
from functools import partial
import multiprocessing as mp
import gc
def mp_variant_annotations(df_mp, df_split_cols='', df_sampleid='all',
drop_hom_ref=True, n_cores=1):
"""
Multiprocessing variant annotations
see variantAnnotations.process_variant_annotations for description ... | 8569a4eb82ff04db1bee46b00bbd9eedf8b4d094 | 14,417 |
def find_attachments(pattern, cursor):
"""Return a list of attachments that match the specified pattern.
Args:
pattern: The path to the attachment, as a SQLite pattern (to be
passed to a LIKE clause).
cursor: The Cursor object through which the SQLite queries are
sent to... | 614649f6fd5972b026b191bb1a272e270dedffe5 | 14,418 |
def generate_symmetric_matrix(n_unique_action: int, random_state: int) -> np.ndarray:
"""Generate symmetric matrix
Parameters
-----------
n_unique_action: int (>= len_list)
Number of actions.
random_state: int
Controls the random seed in sampling elements of matrix.
Returns
... | acb5d537762f2f306be8f300845dc6560c1dd121 | 14,419 |
def model_fields_map(model, fields=None, exclude=None, prefix='', prefixm='', attname=True, rename=None):
"""
На основании переданной модели, возвращает список tuple, содержащих путь в орм к этому полю,
и с каким именем оно должно войти в результат.
Обрабатываются только обычные поля, m2m и generic сюда... | 812247543e5f714e0d2ef57cf018b0741679f83e | 14,420 |
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