content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def onehot_to_smiles(array, indices_token: dict, start_char: str, end_char: str):
""" Convert one-hot encoded array of SMILES back to a SMILES string
Args:
array: (np.array) one-hot encoded numpy array with shape (mol_length, vocab_length)
indices_token: (dict) the location of each token in the... | 1cda48a4c7c5a08610c5152e0e3879a8b681d23a | 78,316 |
def find_basic_blocks(pp_lst, start_addr, jmp_addrs):
"""
@brief Determines which parts are basic blocks
@param pp_lst List of PseudoInstructions push/pop represtentation
@param start_addr Start address of obfuscated function
@param jmp_addrs List of Tuples:(jump address, address of jump instruction... | 977b20bf057347cf70f083a7fd3a5c28c84df92e | 78,317 |
def get_clean_old_parties():
"""Gets and cleans data about political majorities as of August 2020."""
op = get_old_parties()
op["clean_name"] = op["Authority"].apply(clean_names)
op["old_majority"] = [string.upper() for string in op["Control"]]
op = op.drop(columns=["Authority", "Control"]).reset_in... | 882d6bbe748fe0420229e5444e96e1c7e1872fc7 | 78,318 |
import itertools
def get_graphviz(saf):
"""
Create a pygraphviz graph from the SAF dependencies
"""
g = AGraph(directed=True, strict=False)
nodeset = set(itertools.chain.from_iterable((t['child'], t['parent'])
for t in saf.saf['dependencies']))
for n in so... | 24d962d3d3ca91369ff586e5945ce0f445cb6448 | 78,320 |
def simlex_analysis(word_vectors):
"""
This method computes the Spearman's rho correlation (with p-value) of the supplied word vectors.
The method also prints the gold standard SimLex-999 ranking to results/simlex_ranking.txt,
and the ranking produced using the counter-fitted vectors to results/counter_ranking.tx... | 879e22305997434b0d4eddfa504c8daa2e29016d | 78,321 |
from typing import Literal
def make_altr_graph (uri,links,tokens,labels,content_type):
""" make a serialisation of the altR model for W3C list_profiles using content type requested """
gr = Graph()
nsgr = NamespaceManager(gr)
nsgr.bind("altr", ALTRNS)
nsgr.bind("dct", DCT)
id = URIRef(uri)
... | aa96b4738168f4a5a5d768e4d49d082bdce53fb5 | 78,322 |
def compute_climate_statistics(gdir, tmin='1985', tmax='2015', lapse_rate=0.0065):
"""Computes monthly average temperature and precipitation
during the climate period defined by tmin and tmax.
Temperatures are reduced to the terminus elevation assuming
a constant lapse rate.
Parameters
... | 9e71c8b96712a7235969cb9cc44d677c58337367 | 78,323 |
def check_grid(grid):
"""Determines if any rows or columns violate the sudoku condition.
This checks for strings (letters) in the layout, because those
represent the symbols. A grid with just numbers is valid because
those integers represent shape placement, not symbols, so it can't be
falsy.
A... | 53a9a7f502900a48ff1c340262e5f8ef62367c05 | 78,324 |
def load_data(database_filepath):
"""
load sqlite datasets and seperate the features and targets.
Input: filepath name
Output:
X: fetures used to be learned contain message.
Y: targets to be classfied. contain all categories of messages.
"""
engine = create_engine('sqlite:///' + databas... | a5a4e805196d53cf2e4d37fbc725877b7fb12ea0 | 78,325 |
def latestFileUri():
"""
This will only work if you've just taken a picture. The state
will include the attribute latestFileUri. You need this to
transfer the file from the camera to your computer or phone.
"""
try:
state_data = state()["state"]
except:
return False
lat... | a6b41a8037a3e78e3246162e55298ae61514fb72 | 78,326 |
from operator import rshift
def bounding_tile(*bbox, **kwds):
"""Get the smallest tile containing a geographic bounding box
NB: when the bbox spans lines of lng 0 or lat 0, the bounding tile
will be Tile(x=0, y=0, z=0).
Parameters
----------
bbox : sequence of float
west, south, east... | a5e9f2b4e492b35d60d0679408da6cfaebd62386 | 78,327 |
from typing import List
def linspace(start: float, stop: float, n: int) -> List[float]:
"""Returns a List of linearly spaced numbers."""
if n <= 0:
return []
elif n == 1:
return [start]
else:
step = (stop - start)/(n - 1)
return [start + step*i for i in range(n)] | 0ba6eb029f96cb662c48c8c8b997a73e2f5a9bc9 | 78,328 |
def volcoords_from_polar_irregular(sitecoords, elevs, azimuths, ranges, proj=None):
"""Create Cartesian coordinates for polar volumes with irregular \
sweep specifications
Parameters
----------
sitecoords : tuple
sequence of three floats indicating the radar position
(longitude in d... | 1578f7d1b3f8acc3f7a4601b1bdaf77a84dec8c2 | 78,329 |
def build_model(arch, hidden_units):
"""
Builds a neural network from a pretrained architecture with a
variable number of hidden_units
Parameters
arch - a shorhand string for an architecture
hidden_units - the number of hidden units to include in the classifier
Returns
model - a nn.Mo... | 6526fe83ebe71bf16220575f9b13dfa740995843 | 78,330 |
def ntail(l, u):
"""
% samples a column vector of length=length(l)=length(u)
% from the standard multivariate normal distribution,
% truncated over the region [l,u], where l>0 and
% l and u are column vectors;
% uses acceptance-rejection from Rayleigh distr.
% similar to Marsaglia (1964);
... | 4f7442993e8142b1b157343ff2b60a6add287c83 | 78,331 |
def insert_into_statement(db_name, table_name, query_text, column_names=None):
"""
Create a vanilla INSERT statement for the given query.
Optionally derive the column names from the query directly.
>>> print(insert_into_statement(
... "my_db",
... "my_table ' uea$3}",
... "SELEC... | e8b8a389e830bec99a9a1754dd02436b26c5dc28 | 78,332 |
from operator import concat
def prototypical_clustering_learn_layer(nclasses,
x_train,
y_train,
x_test,
phi,
num_clust... | 1616f1442b5f4cdd07a96842700f0cc218a79ff6 | 78,333 |
import torch
def _grad_spherical_harmonics_l0(xyz):
"""Compute the nabla of l=0 Spherical Harmonics
Args:
xyz : array (Nbatch,Nelec,Nrbf,Ndim) x,y,z, of (Point - Center)
Returns
\nabla * Y00 = 0
"""
return torch.zeros_like(xyz) | 1afdec897c80ff75e0d30b98f2187616102e5ab8 | 78,334 |
def get_critic_network(train_env, fc_layer_params=(256, 256)):
"""Critic network to give estimates of Q(s, a)."""
observation_spec, action_spec, time_step_spec = (
spec_utils.get_tensor_specs(train_env))
# Input: state and action
# Output: Q value
return critic_network.CriticNetwork(
... | bd4eb56c8eb7595474e64b901a6137b6f31083d1 | 78,335 |
def aut(results, metric=None):
"""Compute the AUT with respect to the given metric.
Note that for results spanning a _single_ time period, AUT = 0 as this is
not considered a time-aware evaluation.
Args:
results: The set of time-aware results to operate over.
metric: The metric to oper... | 14d2b2f59cc3d67aa16e72fb4785f71ec0c2b8d8 | 78,336 |
def frequency_analysis(text, m, p = None):
""" Calcola la frequenza(occorrenze) delle lettere o serie di lettere in una stringa
:param text: stringa di testo
:param m: parametro per divisione stringa in m_grams
:param p: se p = 'plot' stampo il diagramma
:return: un dizionario con le keys = m_grams , values = freq... | 79e777da374fe538a285b79c19d60a38dcc67873 | 78,337 |
def crossmatch_distance(pos, match):
"""Returns distance in arcsec between two sets of coordinates."""
c1 = SkyCoord(pos[0]*u.deg, pos[1]*u.deg, frame='icrs')
c2 = SkyCoord(match[0]*u.deg, match[1]*u.deg, frame='icrs')
return c1.separation(c2).to(u.arcsec) | bf51099585c1e56b5c0f708660d973cf940abdbe | 78,338 |
def define_period():
"""Define the default periods for the fake data functions
Parameters
----------
None
Returns
-------
period : dict
Dictionary of periods to use in test instruments
"""
period = {'lt': 5820, # 97 minutes
'lon': 6240, # 104 minutes
... | 974fc41760e68a0d4ef553d82f212f8b3449f552 | 78,340 |
def correlation_with_indices(*, index_filename, pc_df):
"""Correlate principal component with SV of magnetic index read from file.
SV of indices AE, Dst, PC, Em must be precalculated and written to file.
Index files are only read here, not calculated. The SV for the magnetic
indices should be the same ... | 5fdf50cf55a44ba6749b44479e35b14dcd5e5179 | 78,341 |
def ibm_cos_resource_to_json(ibm_cos_resource: IBMCOSResource):
"""
The function converts an object of IBMCOSResource to a json string.
:param ibm_cos_resource: A IBMCOSResource object.
:type ibm_cos_resource: ibmpairs.external.ibm.IBMCOSResource
:rtype: ... | 290fa58e8aac96bb45fe370ebd4ea956e5b36099 | 78,342 |
from unittest.mock import Mock
from click.testing import CliRunner
import click
def runner(mock_fancy_print: Mock, mock_requests_get: Mock) -> CliRunner:
"""Fixture for running command-line interface."""
return click.testing.CliRunner().invoke(
console.main, "--iterations 1 --time_to_sleep 0"
) | 54eae3c364570d1af52c1b953a47b0899d6e4a13 | 78,343 |
def _parse_example(serialized, parse_function):
"""Decode the given feature."""
ds = tf.data.Dataset.from_tensors(serialized)
ds = ds.map(parse_function)
if tf.executing_eagerly():
return next(iter(ds))
else:
return tf.compat.v1.data.make_one_shot_iterator(ds).get_next() | 68b85145b0bdf6e40fad481eaefff5f972b750ec | 78,344 |
def ftvhunter_parse(driver: webdriver.Firefox) -> tuple[list[str], int, str]:
"""Read the html for ftvhunter.com"""
# Parses the html of the site
soup = soupify(driver)
image_list = soup.find("ul", class_="list-justified2").find_all("a")
images = [image.get("href") for image in image_list]
num_f... | b7fa854ea1e57f814c5d526f4eca1e04a15e10b6 | 78,345 |
def run(request, run_id):
"""Details of single run page"""
run = ModelRun.objects.get(pk=run_id)
redis_conn = django_rq.get_connection()
job = Job.fetch(run.job_id, redis_conn)
run.job_metadata = job.meta
metrics = run.metrics.order_by("name").values("name").distinct()
return render(requ... | a6985ae21c66e2eddaa018ed7a584dbbfd0c4ce2 | 78,347 |
def count_substring_regex(array, pattern, *, ignore_case=False):
"""
Count the non-overlapping matches of regex *pattern* in each value
of a string array.
Parameters
----------
array : pyarrow.Array or pyarrow.ChunkedArray
pattern : str
pattern to search for exact matches
ignore... | 8cd0fb3c29250f158853631fbac0dd07b4b38302 | 78,348 |
from htk.utils.emails import find_company_emails_for_name
import re
def findemail(event, **kwargs):
"""Find email event handler for Slack webhook events
"""
text = kwargs.get('text')
command = kwargs.get('command')
args = kwargs.get('args')
if command == 'findemail':
if args:
... | bf5decb0251100a280d4375a7fb11dd227df3110 | 78,350 |
def getABsC(_quad, _edge, _vertex, regularPoints):
"""
:param:_quad: Quad
:param:_edge: Edge
:param:_vertex: Vertex
:param:regularPoints: numpy.ndarray
:return: A, B1, B2, C
:rtype: numpy.ndarray([int, int, int, int])
"""
assert isinstance(regularPoints, np.ndarray)
assert isin... | ced424cb247600d77a958bdecf7b207d4cb0d6d2 | 78,351 |
import torch
def compute_m(z: torch.sparse, c: torch.sparse, max_K: int):
"""
This function computes the m_i = sum{n_ij}.
:param z: the sparse matrix recoding z_ij. node_number X node_number. Each node has at least one edge required.
:param c: the sparse matrix recoding c_ij. node_number X node_numbe... | 36bdd0e7c9ff062508c79244f0a44fb2694d6853 | 78,352 |
import functools
def randomize(table, ntrials=100):
"""
Parameters
----------
table : :class:`pandas.DataFrame`
Table of coupling strengths
ntrials : int, optional
Number of trials for eigenvalues
Returns
-------
Array of eigenvalues
"""
_, Ntime = table.s... | f4ac751979f688b74cdf85ed6d78ab4e23aa08ff | 78,353 |
from re import A
from re import T
def site():
""" RESTful CRUD controller """
# Load the Models
s3mgr.load("event_event")
# Parse the Request
r = s3mgr.parse_request()
link = request.vars.get("link", None)
# Pre-Process
if r.id and link:
# Go back to the facility list of th... | 1c0ec0307f8c40f6a7444c3a5201e1ad82d69017 | 78,354 |
def nemeth_smoother(particles, log_weights, statistics,
additive_statistic_func, kernel,
lambduh = 0.95, **kwargs):
""" Algorithm 2 of Nemeth et al. (2015)
Calculate E[SUM[h(x_t, x_{t+1})] | Y]
Args:
particles (ndarray): N by n, latent states (x_t)
log_weights (ndarray): N,... | 7ff0714c121a38b5b1c2f919727ad3162a7dcdd5 | 78,355 |
def _find_curv_local_minima(curv, peak_curv_value, filtered_maxima, start, dir, rel_thre = 0.45, minima_dir_thre = -0.25):
""" Find a negative curvature value that is a local minima or a filtered local maxima with respect to density value.
dir represents the direction of search, choosing from '+' or '-'.
... | 7912854e417cf7534ac064e5f8765e73bcec9661 | 78,356 |
def obtain_header_fields(first_line, sep='\t'):
"""
Obtain a dictionary: "field_name" => "position"
"""
fields_dict = {}
header_fields = first_line.strip().split(sep)
for x in xrange(0, len(header_fields)):
fields_dict[header_fields[x].lower()] = x
return fields_dict | 8b399918ff56e784eba549fe2f83db1cbaf728b4 | 78,357 |
from typing import Iterable
def generate_bias_volts(bias, steps=None, polarity=1, check_monotonic=True):
"""
Create and return a np.array of bias voltages.
If step is None and bias is a number, np.linspace is used and creates *bias* voltages.
If *bias* is already an iterable of voltages, checks are pe... | 603f5f3fa56c21418642de20b0cc8a22c0dd12c2 | 78,358 |
import warnings
def _set_arrayorder(obj, arrayorder='C', sep=None):
""" Set the memory order of all np.ndarrays in a tofu object """
msg = "Arg arrayorder must be in ['C','F']"
assert arrayorder in ['C','F'], msg
d = obj.to_dict(strip=-1, sep=sep)
account = {'Success':[], 'Failed':[]}
for k, ... | a6eb9e61600a93c376c84aee7565bb40b85587f9 | 78,359 |
import typing
def GetOptPasses(
cflags: typing.Optional[typing.List[str]] = None,
language: typing.Optional[str] = "c",
stubfile: typing.Optional[str] = "int main() {}",
) -> typing.List[OptPassRunInvocation]:
"""Get the list of passes run by opt.
Args:
cflags: The cflags passed to clang. Defaults to -... | bd9a419a7915fd20e198b5ae7f54ebcfd5ea8b8b | 78,360 |
def traj_passes_pt(traj, pt, threshold=1e-10):
"""
Returns true if trajectory crosses a point within
radius 1e-10.
"""
return np.amin(np.abs(traj- pt)) < threshold | df28a7832c5c81560ba2919ab09257873e86cf41 | 78,362 |
import warnings
def pmap(function, *arguments, **kwargs):
"""
Parallelized version of map. It calls function on arguments using numprocesses threads.
Also try the numexpr package or numba, which are easy to use and may give you better
performance.
Parameters
----------
function : functio... | 035af858a0b2cbd400c1b0ffe73d4a4a1ed3934a | 78,363 |
def ellipsis_after(text, length):
""" Truncates text and adds ellipses at the end. Does not truncate words in the middle. """
if not text or len(text) <= length:
return text
else:
return text[:length].rsplit(' ', 1)[0]+u"\u2026" | 9b15c5e8f63caec0a7327ae1ce872bac932208ab | 78,365 |
def popcount(arr):
"""
Provides a population count implementation.
The population count is the number of one bits.
Based on GCC's __builtin_popcount().
The implementation is written in C.
Parameters
==========
arr : numpy array
Must have dtype of uint8, uint16, uint32
o... | bd0ecbf1c95b51a400d6d86f66d7f8d296994620 | 78,366 |
def order_columns(dataset, col_names, feature_to_predict):
"""
Method is responsible of putting the 'feature_to_predict' name as the first column of the dataset
and sorts the other columns names.
Parameters
----------
dataset
col_names = column names to be sorted
feature_to_predict = fea... | 05cb77502defa2648c6cc9279cf130d07ffd9bb7 | 78,367 |
import re
def _pathname_to_category(pathName):
"""This match the path name againt the set of regular expressions
defining broad categories of files: Sockets, libraries, temporary files...
These categories are not technical but based on application best practices,
rules of thumbs etc..."""
for rgxT... | 8423d8ccdd30053f0fbddddf07150a65f5f06f94 | 78,368 |
import csv
def read_csv(inp, header=True):
"""
Reads a csv file.
:param inp: input file
:param header: inp contains a header
:return:
:titles: titles of the columns
:xs: x-values (1- or 2-dim np.array)
:ys: y-values (1- or 2-dim np.array, matches x)
"""
titles = No... | a4137246f228478014252d0dd73fe1185ea509d4 | 78,369 |
def precision_at_k(data, k):
"""Returns a float.
Input format for data is list of tuples (article_id, prediction, label).
The P@k for given prediction scores for an article is the percentage
fake sentences among the top-k.
This function returns the mean P@k of the given articles.
"""
... | 653c9e5d257b14859125e036b76e8139b8b24f83 | 78,370 |
import math
def modify_hough_results(hough_results: list, img_size: tuple) -> list:
"""
cv2.HoughLinesの結果を整形する．
:param hough_results: ハフ変換結果のリスト
:param img_size: ライン検出に使った画像のサイズ(height, width)
:return: 各線の情報を整形したリスト
"""
n_lines, _, _ = hough_results.shape
height, width = img_size
l... | 0b01f287da014a275646d707fb8ef674e5ea8da1 | 78,372 |
def fake_stereo_geometry(gra, ntries=5, max_dist_err=0.5):
""" generate a fake stereo geometry
"""
return _fake_stereo_geometry(
gra, ntries=ntries, max_dist_err=max_dist_err) | f55e0ca4222a6a58d18b861febef13c1271acfac | 78,373 |
def memoize_dropdown(func):
"""Checks if the dropdown list has already been created.
If it did, it returns the cached list. otherwise, it creates one.
"""
dropdown_cache = []
def wrapper():
if not dropdown_cache:
for city in func():
dropdown_cache.append((city, ... | 728e105d4e45c9504790bd68253c0d37f122cff5 | 78,374 |
def load_data(data_dir, train_img_ids, val_img_ids, test_img_ids, context_size, batch_size, num_workers=4, max_bg_boxes=-1):
"""
Args:
data_dir: directory which contains x.png Image and corresponding x.pkl BBox coordinates file
train_img_ids: list of img_names to consider in train split
... | 92825f23e8878cc876f9b8f00ef896b5cf004b38 | 78,375 |
def data_summary(df):
"""
Return summary sof the dataframe e.g. Missing values, unique values, first and last values of the dataset
:param
df: pandas dataframe
:return:
summary: dataframe that contains summary of df
"""
print(f"Dataset Shape: {df.shape}")
summary = pd.DataF... | 871793791c05efdd289e5e0108bfed74b64f28ed | 78,376 |
def plist_url_path(plist):
"""Returns a constructed URL."""
result = None
result = '{}/{}/{}'.format(config.AUDIOCONTENT_URL,
config.LP10_MS3_CONTENT,
plist)
return result | f3d772618ebbde1b6e7ef75ce0381f5fbcfc532e | 78,377 |
def modulo(x, y):
"""Divides the first parameter by the second and returns the remainder. Note
that this function preserves the JavaScript-style behavior for modulo. For
mathematical modulo see mathMod"""
return x % y | efea1409596564a393cae7b46afe17141326552b | 78,378 |
def transform_by_pose(point: np.ndarray, pose: np.ndarray) -> np.ndarray:
"""Transforms a list of 3D points by a pose.
Args:
point: array containing (x, y, z) coords or numpy array of shape (3, N).
pose: pose for the transformation.
Returns:
array containing (x, y, z) points of shape (3, N).
"""
... | 907dff888153df4cf9a0b4ef51849143bd8722f7 | 78,379 |
def render_sidemenu(menu_id, active_item=None, **kwargs):
"""Render a sidemenu with sections/items.
:param menu_id: The identifier of the menu.
:param active_item: The name of the currently-active menu item.
:param kwargs: Additional arguments passed to the menu signals.
"""
items = build_menu_... | 8098d496a55deff0f0a3adb8bbfa97aebd3c6770 | 78,380 |
import socket
def get_host_name():
"""Returns the name of the host."""
return socket.gethostname() | feddd2a5bf674ff956b280d02649f5872b61991b | 78,381 |
def get_prng(seed=None):
"""Turn seed into a PRNG instance
Parameters
----------
seed : {None, int, object}
If seed is None, return a randomly seeded instance of SHA256.
If seed is an int, return a new SHA256 instance seeded with seed.
If seed is already a PRNG instance, return ... | 97b7dffbb704354f56868964199eedd4eac0a158 | 78,382 |
def sortArrayByParity2(A):
"""
:type A: List[int]
:rtype: List[int]
"""
i, j = 0, len(A) - 1
while i < j:
if A[i] % 2 == 1 and A[j] % 2 == 0:
A[i], A[j] = A[j], A[i]
if A[i] % 2 == 0:
i+=1
if A[j] % 2 == 1:
j-=1
return A | 048786203eab589fa74a78ddfc2bc05a104a8f4e | 78,383 |
import pickle
def predict_which_user(user1_handle, user2_handle, tweet_text, cache=None):
"""
Determines which user is more likely to tweet a given phrase.
Parameters
----------
user1_handle : str
First user's Twitter handle.
user2_handle : str
Second user's Twitter handle... | b9acebeae0e1fac7c7a7357ad1099703ffe44480 | 78,384 |
import typing
def merge_reports(reports: typing.Iterable[dict]) -> dict:
"""Merge the size reports in reports.
:param reports: The reports to merge.
:return: the merged report.
"""
final_report = dict()
for report in reports:
for k, v in report.items():
final_report[k] = f... | 7045d0e7f8047678a5bdb90c885717bc917cb46a | 78,385 |
def uncover_dash(guess, answer, dashed_word):
"""
:param guess: str, the character that user type and has been converted to upper case
:param answer: str, the answer word
:param dashed_word: str, the present dashed_word
:return: str, the new_dashed_word with all correct guess been shown in dashed_wo... | 30e2134e607ef24071cbb50e43723ae0d5fc88a9 | 78,386 |
def rotation2quaternion(rot):
"""
Calculate quaternion when rotation matrix is provided
Reference: https://gist.github.com/shubh-agrawal/76754b9bfb0f4143819dbd146d15d4c8
Reference: http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/
:param rot: numpy matrix with sh... | 0b0c6ce7b1fd4dd5da506fbc68e38a4443f0a64e | 78,387 |
def get_meter_value(poll):
"""Return the actual value for the meter element."""
users_voted = float(get_users_voted(poll))
voters_group = float(get_voters_per_group(poll))
try:
val = (users_voted/voters_group)*100
return int(val)
except ZeroDivisionError:
return 0 | 69b275fb1c8cc24e2d67273a5a000a909c4d7775 | 78,388 |
def filter_bandstop(
trace: Trace, sampling_frequency: int, low: int, high: int
) -> Trace:
"""
Apply a bandstop digital filter (Butterworth) to `trace`, given `sampling_frequency`, with the stopband from `low` to `high`.
:param trace:
:param sampling_frequency:
:param low:
:param high:
... | 77f64a994d248cf920de3a36707b4e81e57f64c9 | 78,389 |
def make_password_message(model, password):
"""Creates email message for password."""
return NEW_PASSWORD_MESSAGE.format(
name=model.full_name,
password=password
) | 8e3dab8be2e1a4013387a884fd27c9a15ab83e37 | 78,390 |
import math
def angle_3p(p0, p1, p2):
"""get the angle between three 3D points, p0 is the intersection point"""
u, v = p1-p0, p2-p0
costheta = u.dot(v) / math.sqrt(u.dot(u) * v.dot(v))
return math.degrees(math.acos(costheta)) | 3a5ac0c4d0cfa8ae97e979175292fe416cc683b8 | 78,391 |
def create_uNet(args, num_output_final):
"""Create network in barrista.
Create a network in barrista similar to a combination of U-Net and
the one in the learning data-driven reflectance priors paper.
"""
print("Using u-net")
layers = []
filler = design.PROTODETAIL.FillerParameter()
fi... | 64d952eb8b88710d3a795e278831ddde16bd72f0 | 78,392 |
import socket
import logging
def get_ip():
"""
Request to the external host sending.
Returns an external IP-address of a current host.
"""
sock = socket(AF_INET, SOCK_DGRAM)
sock.settimeout(TIMEOUT)
try:
# Any arbitrary existing host and opened
# for TCP-requests port of t... | 309145e80685a96277864f9ef360ecbaace1acf1 | 78,393 |
def strictly_increasing(L):
"""严格单调递增"""
# None as 0
return all(x < y for x, y in zip(L[:-1], L[1:])) | 0e3d8a8f8032f0ea97b2097280b7960bbd0e43a0 | 78,394 |
import hmac
import hashlib
def mailgun_signature(timestamp, token, webhook_signing_key):
"""Generates a Mailgun webhook signature"""
# https://documentation.mailgun.com/en/latest/user_manual.html#securing-webhooks
return hmac.new(
key=webhook_signing_key.encode('ascii'),
msg='{timestamp}{t... | 64d549e53a7c8f6842d176caa501335a0bc1ca61 | 78,395 |
import copy
def create_annotation_data(save_data):
"""Returns the final annotation JSON which is a cleane dup version of the save JSON"""
#annotation_data = save_data
annotation_data = {}
annotation_data["image"] = copy.deepcopy(save_data["image"])
annotation_data["grid_cells"] = copy.deepcopy(sav... | ee8ceb51cac98b7ac795c9bfefd525d23168d6d1 | 78,396 |
def _x_like(prototype, dtype, shape, constructor, fill_value=None):
"""
Returns a tensor with the same shape and type as prototype,
using constructor.
"""
_ = _check_input_for_asarray(prototype)
dtype_out = dtype
shape_out = shape
if not dtype_out:
dtype_out = _get_dtype(prototyp... | 2e0eb07b02012ab4d9694a7b4485a7eee7ac3226 | 78,397 |
def pad_ranges(inranges,padding,chr_ranges=None):
"""Add the specfied amount onto the edges the transcripts
:param inranges: List of genomic ranges in Bed o GenomicRange format.
:param padding: how much to add on
:param chr_ranges: looks like the list of ranges within which to pad
:type inranges: GenomicRang... | 7733bf975e7d4aaa4d548f231fd81cad37ca2229 | 78,398 |
def get_interface_ip(interface):
"""
Get an interface IP address
"""
print()
task_start(f"Finding IP address of {interface} using ifconfig")
ip_address = run_command_shell(
f'ifconfig {interface} | grep \'inet\' | awk \'$1 == "inet" {{ print $2 }}\'').rstrip()
if ip_address:
... | 0dc02bcc8eb7d74d56625cf3eca847f57842b7af | 78,399 |
def _parse_data_row(row, columns, counters, **options):
"""Parse table data row.
If a cell has multiple tags within it then each will be seperated
by `sep` character.
Parameters
----------
row : BeautifulSoup Tag object
A <tr> tag from the html, with data in at least one cell.
... | 500c9634b8110575d3c7800a0a1f2616fa08ac03 | 78,400 |
def cal_det_fpfn(data,
categories,
iou_thr=0.5,
score_thr=-1,
category_to_show=None):
"""Calculate fp and fn for detection.
Args:
data (dict): Annotation of current image.
categories (list): Categories of whole dataset.
... | d9b37acc29dd0d2714ee2c65eb42e409c2636aa2 | 78,401 |
import aiohttp
import ssl
import certifi
def create_tcp_connector(*args, **kwargs) -> aiohttp.TCPConnector:
"""
Creates TCP connector with resonable defaults.
For details about available parameters refer to
`aiohttp.TCPConnector <https://docs.aiohttp.org/en/stable/client_reference.html#tcpconnector>`_... | 7ad31deb002487bbc40ce561fd9dd2d0060cace5 | 78,402 |
def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
"""Helper function for random generators."""
if isinstance(params[0], NDArray):
for i in params[1:]:
assert isinstance(i, NDArray), \
"Distribution parameters must all have the same type, but got " \... | 205bf061768de0b3c000f178a63cf675e1844283 | 78,403 |
def get_brain(data_store, project_id, brain_id):
"""Get cached brain or read brain from data_store.
Args:
data_store: data_store.DataStore to read from if the brain is not cached.
project_id: Project ID associated with the requested brain.
brain_id: Brain ID associated with the requested brain.
Retu... | 06cc32691a381e90d2ef0de4dc3342ed826a42c0 | 78,404 |
def get_sample_answers(exploration_id, exploration_version, state_name):
"""Fetches a list of sample answers that were submitted to the specified
exploration state (at the given version of the exploration).
Args:
exploration_id: str. The exploration ID.
exploration_version: int. The version... | 6806561150bd86a42dd9191badc7234aa751e22d | 78,405 |
def clear_all_messages(request):
"""
Clears all message from the cache
"""
clear_outbox()
return JsonResponse({"status": "success"}) | 04712124426ea17145cba606f96ca6808d55eff6 | 78,406 |
def generate_triggers() -> list:
"""Generate triggers list."""
result = []
for s in WEATHER_STATES_CONVERSION.keys():
for d in [True, False]:
result.append(map_state(src=s, is_day=d, mapping=WEATHER_STATES_CONVERSION))
return list(set(result)) | b646155cdab880d626b9eb4ef7e90cbd069a329b | 78,407 |
def encode(request_data):
"""
with tempfile.NamedTemporaryFile(mode="wb", suffix=".jpg") as f:
#f.write(request_data)
#img = cv2.imread(f.name)
#img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
"""
img = np.array(Image.open(BytesIO(request_data)))
images = [img]
molded_imag... | a9dcd96c94935c6994838c6615a76becef5fd5fb | 78,408 |
def _format_message(key, *args):
"""
Format the specified message key with the specified arguments.
:param key: the message key
:return: the formatted text message
"""
return ExceptionHelper.getMessage(key, list(args)) | 7e32e6b09298b6bae8669915ea333b5fce7879fe | 78,409 |
def generate_wpe_labels(labels):
"""
:param probs: [batch, seq_len, n_words]
:param labels: [batch, seq_len]
:return: out: labels with ignored position being padding_idx. [batch, 1, seq_len]
"""
# [batch, 1, seq_len]
out = labels.unsqueeze(1)
return out | 6a32a0bd29ca20b543a1b052c0284a6e0d3127f0 | 78,410 |
def get_input_month(model_year_dti: pd.DatetimeIndex) -> np.ndarray(int):
"""Determine month of each day
:param pd.DatetimeIndex model_year_dti: a DatetimeIndex encompassing the model year.
:return: (*np.ndarray*) -- stores the month the day is in for each day of the year.
"""
return model_year_dti... | 6553e6fc2b3905ce967283970691623890bdf666 | 78,411 |
def make_conv_fatality_rate(infection_fatality_rate, total_days):
"""Computes the probability of death on day `i` given infection on day `j`."""
p_fatal_all_countries = daily_fatality_probability(
infection_fatality_rate, total_days)
# Use the probability of death d days after infection in each count... | 502e442964ba2767fc4ef48bef067fc338ed2474 | 78,412 |
def find_parser(path_or_stream, forced_type=None, is_path_=False):
"""
Find out config parser object appropriate to load from a file of given path
or file/file-like object.
:param path_or_stream: Configuration file path or file / file-like object
:param forced_type: Forced configuration parser type... | 8e639929c04eddc33389f98ffb4cd20d4402fb1d | 78,413 |
from typing import Optional
async def get_last_update_time(region: Region, config: Config) -> Optional[Arrow]:
"""Get time the price data was updated last.
:returns None: If file not found.
:returns arrow.Arrow: Last update time.
"""
file_dir = config.paths.price_data_dir
file_name = defaults... | 85b03399352b5196901ae071f69865c0ce0be27a | 78,414 |
async def test_execute_autoruns(mocker):
"""
Test execute_autoruns function
"""
state = {
'queue': []
}
def get_autoruns(self, file_type): # pylint: disable=unused-argument
return [
('1', '1', None),
('2', '2', None),
('3', '3', None),
... | 7f6569eb2d5dc96b45f55c0ff0e75e9d5dbe43e4 | 78,415 |
import random
def select(dataset, size, digits_to_keep, stratified_sampling=False):
"""Select randomly specific elements given by digits_to_keep."""
len_dataset = len(dataset)
indices = list(range(len_dataset))
random_select_indices = []
random.shuffle(indices)
if stratified_sampling:
... | 9b69f09bbb5ba717d3600a097a40dbef5255770a | 78,416 |
from typing import Union
from typing import Sequence
def launch(args:'Union[Sequence[str], str]',
**kwargs)->Popen:
"""launch process"""
if isinstance(args, str):
proc = Popen(args, stdout=PIPE, shell=True, **kwargs)
else:
proc = Popen(list(args), stdout=PIPE, shell=False, **kw... | c0f634558459036424b32a0ac4de578a2784c010 | 78,417 |
def generic_tabs(context, object_list=[], tab_id='tab', title_key='title',
content_key='content', *args, **kwargs):
"""
Crea un componente <Tabs> con el QuerySet en object_list
El `title` pasa a ser el título del tab, y el `content` es el contenido
"""
_object_list = []
for obj in ob... | 32060dbea81713bb737edc19d5780db90ba1f655 | 78,419 |
def cci(df, high, low, close, cci, n, c=0.015):
"""
The CCI is designed to detect beginning and ending market trends. The range
of 100 to -100 is the normal trading range. CCI values outside of this range
indicate overbought or oversold conditions. You can also look for price divergence
in the CCI. ... | d564a4f22106702ac39d5d5de9ed17eb96623237 | 78,420 |
def _spreadsheet_by_url(url, api):
"""[summary]
Args:
url ([type]): [description]
api (obj): gspread object
Returns:
[type]: [description]
"""
return api.open_by_url(url) | c7c5ab72c7d88cc7ff8d13a50e6838da6a6fbdeb | 78,421 |
def condicion_del_producto():
""" se pide la condicion enviada por parametro junto el request"""
c = request.args.get('condicion')
""" se filtra segun la condicion """
productos = op.filtrar_por_condicion(db, c)
""" se obtienen el resto de los datos necesarios para renderizar """
categorias = ... | 09938a0ed7b1d1ec14203a517e93ecdffa73223a | 78,423 |
def FL(f):
"""get fully-parenthesized string representation of formula"""
return '(' + f.to_str('spot', full_parens=True) + ')' | 5d35b96ed7089294d85ca31ed24c8824a243e7ee | 78,424 |
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