content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_angle_direction(v1, v2, axis):
"""
Return the direction of rotation to get v1 onto v2 about the provided axis.
"""
axes_dict = {'x':0, 'y':1, 'z':2, 'X':0, 'Y':1, 'Z':2}
if axis in axes_dict:
axis = axes_dict[axis]
v1, v2 = np.atleast_2d(v1, v2) # vectors will be (1,3) or (N,3... | 3dad8268f3437f629c970c892e30df59b1b5c30b | 51,356 |
def _estimate_log_gaussian_prob(X, means, precisions_chol, covariance_type):
"""Estimate the log Gaussian probability.
Parameters
----------
X : array-like of shape (n_samples, n_features)
means : array-like of shape (n_components, n_features)
precisions_chol : array-like
Cholesky dec... | 1c5d5d4e16687b362ee01612fa9ab0c04efde85e | 51,358 |
def _build_draw_line(append):
"""Specialize a line plotting kernel for a given append/axis combination"""
@ngjit
def draw_line(x0i, y0i, x1i, y1i, i, plot_start, clipped, *aggs_and_cols):
"""Draw a line using Bresenham's algorithm
This method plots a line segment with integer coordinates on... | acd77b3259fc4c9efe9afbc6312f11737c14509d | 51,359 |
def zeros(shape):
"""
:param shape: tuple with the shape of the wanted output (filters_amount, depth, height, width)
:return: array (it's shape=param shape) with initialized values using 'zeros' initializer
"""
return np.zeros(shape=shape) | 034f61b27e23b829c8c8828f9d2e5fffad3e7055 | 51,361 |
def addr_generator(start_ip, port, count):
"""Generator a list of (ip, port).
"""
def tostr(ip):
return '.'.join([str(_) for _ in ip])
ip = [int(_) for _ in start_ip.split('.')]
addr_list = [(tostr(ip), port)]
for i in range(count-1):
ip[-1] += 1
addr_list.append((tostr(i... | 100288629f35d9108e0b364266242da0110dd8f7 | 51,362 |
def filter_threshold(delta_f, threshold_value):
""" Return indices of the data whose values are above the acceptable level """
return delta_f >= threshold_value | a0c34eba86a0fb4539174d75f2cde19994aaa8cf | 51,363 |
def handler(event, context) -> utils.Response:
"""lambda entry point."""
return handlers.SnsEventHandler(
name="twitter",
event=utils.LambdaEvent(event),
context=utils.LambdaContext(context),
action=post_status,
).response | 67b6b16b978bc229e44929d8fc0f1b18d86834d5 | 51,364 |
from functools import reduce
def vector_sum(vectors):
"""一系列向量相加"""
return reduce(vector_add, vectors) | a4ffe3cf55c1b9c75ea811e2d77a66fceeb256d8 | 51,365 |
import random
import time
def retry(func, *args, **kwargs):
"""Repeats a function until it completes successfully or fails too often.
Args:
func:
The function call to repeat.
args:
The arguments which are passed to the function.
kwargs:
Key-word arg... | cf6f7d3e434b54cf178b2867f7565f338e985968 | 51,366 |
def build_model_columns():
"""
:param data = df_preprocess_noNA
Builds a set of wide and deep feature columns.
"""
# Continuous variable columns
speed = tf.feature_column.numeric_column('speed')
inv_speed = tf.feature_column.numeric_column('inv_speed')
ais_rem_time = tf.feature_column.numeric_column('ai... | 5f5df227c12948dd1e8cee34af485db42a7a23d4 | 51,368 |
def test_custom_timeout(monkeypatch, fake_response, aqhttp):
"""Timeout should override from the aqhttp.post method."""
class FakeRequest:
def __init__(self, status_code=200, url="myfakeurl.com", method="post"):
self.status_code = status_code
self.url = url
self.meth... | 6b03e8f6124557119ef8b30191ab6d35f3d3e002 | 51,369 |
def create_tags(dataframe, periods):
"""
Función para clasificar los registros de cierre como
subidas/bajadas fuertes, subidas/bajada, o matiene de
un día con respecto a otro.
Parámetros:
----------
df pd.DataFrame:
DataFrame que se quiere clasificar.
periods int:
... | 2431b8d52fac840bbebf55bd33aa18327f0b0a01 | 51,370 |
def encrypt(data, key):
"""RC4"""
S = list(range(256))
j = 0
out = []
for i in range(256):
j = (j + S[i] + key.encode()[i % len(key)]) % 256
S[i], S[j] = S[j], S[i]
i = j = 0
for char in data:
i = (i + 1) % 256
j = (j + S[i]) % 256
S[i], S[j] = S[j]... | 6c2ced51fc2ad443ffbfee1435092b3c60207bdb | 51,371 |
from re import A
def crop_by_range(anim: Animation[A], start: float, finish: float) -> Animation[A]:
"""
Render a range of an animation.
`start` and `finish` should be floats from 0 to 1.
"""
if not (0 <= start < finish <= 1):
raise ValueError(
"Invalid range ({}, {}). Expected... | 40b90adecb659aad60b14387ec2978be67e36c0b | 51,372 |
def hist_match(cdf_org, cdf_tar):
"""
Build the lookup table described in step 3.
Arguments:
-----------
cdf_org: dict
The cumulative distribution function of orginal image.
cdf_tar: dict
The cumulative distribution function of target image.
Returns:
-----------
l... | a5a428aeed6bafcbb9d2d39c6068a2bd1717179b | 51,373 |
def run_one_step(dataframe, start_centers):
"""
Performs one iteration of K-Means.
This function takes a dataframe with dense feature vectors, a set of centroids, and returns
a new set of centroids along with the total distance of points to centroids.
This function calculates for each point the cl... | b3d3012b167ae8484ddfadd029d60442d12d9292 | 51,374 |
def report_options(func):
"""Common report options."""
func = all_option(func)
func = count_option(func)
func = length_option(func)
func = end_option(func)
func = start_option(func)
func = exclude_option(func)
func = include_option(func)
return func | 76506f9644695fb4dbd24d23ec5f868dd2ee3886 | 51,375 |
import random
def train_test_split_adjacenty(total_size, test_ratio, test_indexes):
"""
Parameters
----------
A: scipy.sparse.spmatrix
connected sparse unweighted adjacency matrix
test_indexes: list
tuple with (x, y, val) for each idx in test datasets
Returns
-------
t... | 9b826a234da30f253c0d433123a16f912843d658 | 51,376 |
import torch
def _chunk_slice(
t: torch.Tensor,
flat_start: int,
flat_end: int,
no_batch_dims: int,
):
"""
Equivalent to
t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end]
but without the need for the reshape call, which can be
memory-int... | f77f8ea76fbc382eeb8a512f0944b809087e3266 | 51,377 |
def dimshuffle(arr, pattern):
"""
Based on theano's dimshuffle function.
Permutes the axes of an array and inserts new singleton axes.
e.g. if x has shape (2,3), then dimshuffle(x, [1,0,'x']) has shape (3,2,1)
note that dimshuffle can not drop dimensions.
inputs
------
pattern: list... | 3134f33ac002171d18775b66d04577ad06103d72 | 51,378 |
import pytz
def utc_to_local(utc_dt, local_tz):
"""Accepts a datetime object in UTC time, and returns the same time, in the timezone that was also passed"""
local_dt = utc_dt.replace(tzinfo=pytz.utc).astimezone(local_tz)
return local_tz.normalize(local_dt) | 15ade0c4d1b732b4fd9ef5c2f7de0eb32a3c6936 | 51,379 |
def within_limits(cave: ArrayLike, coord: tuple[int, int]) -> bool:
"""Check whether a given coordinate is within the limits of the cave."""
return (
(0 <= coord[0])
& (coord[0] <= cave.shape[1] - 1)
& (0 <= coord[1])
& (coord[1] <= cave.shape[0] - 1)
) | f3ed4a65c5c1289a7bdc1707b7c7320e8c4e2231 | 51,380 |
def unpack_from(fmt, buf, offset=0):
"""
Unpack the buffer, containing packed C structure data, according to
fmt starting at offset. Requires len(buffer[offset:]) >= calcsize(fmt).
See struct.__doc__ for more on format strings.
"""
try:
o = _cache[fmt]
except KeyError:
o = _c... | a7f020cd7564f92d0c4d8f67a979e000a7c18724 | 51,383 |
def load_single_mat(mat_file_floder,n_clip=1,dataset="Avenue",vis=True):
"""
:param mat_file: mat file path
:return: anomaly boundary [num ,2]
"""
filename = '%s/%d_label.mat' % (mat_file_floder, n_clip)
data=sio.loadmat(filename)
volLabel=data["volLabel"]
n_bin = np.array([np.sum(vol... | 021dd4c8a0267b49bef187bbb57f6abe35b7d285 | 51,384 |
def _ScatterAddNdimShape(unused_op):
"""Shape function for ScatterAddNdim Op."""
return [] | 7c59fb40e177fea1bf1cd3970c364f4583fc37f9 | 51,385 |
def sanitize_name(original_name: str):
"""Sanitize name for output into compiler artifacts
Parameters
----------
original_name : str
Original name to sanitize
"""
return _backend.SanitizeName(original_name) | 6bc6ab6037c0925c07f5b59821a4356a673aa8c9 | 51,386 |
def parse(sql, category=None):
"""
Returns data structure for SQL statement.
@param category expected statement category if any, like "table"
@return ({..}, None), or (None, error)
"""
result, err = None, None
try:
result, err = Parser().parse(sql, category)
except... | 2381eef37d4102037920712bb665ca3b3dfbebd7 | 51,387 |
def store_to_flat_XML(model, vpath):
"""Store current LO document to vpath, as a ODF flat XML file"""
return store_to__by_extn(model, vpath, '.fodt') or \
store_to__by_extn(model, vpath, '.fods') or False | bdac5d13b0e57db39575fff91ce163e98e365c28 | 51,388 |
def eDFA(F: np.ndarray) -> np.ndarray:
"""
In the reference indicated below a measure of nonstationarity was added by
including a subsequent calculation of the extrema of the DFA. Denoted
:math:`dF_q^2(s)` the difference of the extrema at each segment, i.e.,
.. math::
dF_q^2(s) = \max[F_q^... | 7df59a38ad2b414efcfd0be16f67dbcf264df4c6 | 51,389 |
import time
import tqdm
import torch
from typing import OrderedDict
def train(loader, model, epoch, criterion, optimizer, threshold, class_weights=None, use_cuda=None):
"""
:param loader:
:param model:
:param epoch:
:param criterion:
:param optimizer:
:param threshold:
:param class_w... | 5cac64cc77d67ae6b4504f86c6cc101eda696105 | 51,390 |
def memoize(timeout=None, fast=False):
"""
Cache a function based on its arguments.
Args:
timeout: Time the result stays valid in the cache.
Returns:
The functions result.
"""
assert (not fast or (fast and timeout is not None)
), "You cannot set fast cache without a ... | dd290a19b675c25327668215e583b5b30f077a18 | 51,391 |
def get_appliance_bgp_neighbors(
self,
ne_id: str,
) -> dict:
"""Returns appliance BGP neighbor configuration
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - bgp
- GET
- /bgp/config/neighbor/{neId}
:param ... | f8e777f9e9fabbdb9610ce0bf5f0824b888a9e04 | 51,392 |
def pages_sub_menu(context, page, url='/'):
"""Get the root page of the given page and
render a nested list of all root's children pages.
Good for rendering a secondary menu.
:param page: the page where to start the menu from.
:param url: not used anymore.
"""
lang = context.get('lang', pag... | f718d6513da8f05683a7b85e2bebcdc359487616 | 51,393 |
def JsonToMessage(message_type, message):
"""Convert the given JSON to a message of type message_type."""
return _ProtoJsonApiTools.Get().decode_message(message_type, message) | b642c5160e6c06ff60daffedbb4d665b8005b711 | 51,394 |
import torch
def generate_angular_spectrum_kernel(shape, pixel_size, wavelength, \
numerical_aperture=None, flag_band_limited=True, \
dtype=torch.float32, device=torch.device('cuda')):
"""
Function that generates angular spectrum propa... | b07e54f087b8271ef27f88497717ed96ae3136a2 | 51,395 |
from typing import List
def getBarcodesFromSnap(fname: str) -> List[str]:
"""Read barcodes from a snap file
Attributes:
fname - a snap-format file
Return:
a dictionary contains all barcode in the snap file
"""
with h5py.File(fname, "r") as f:
barcodes: List[str] = [item.d... | 4fb9821e3184886688c795429742bf123aed49f8 | 51,396 |
def slicing_where(condition, full_input, true_branch, false_branch):
"""Split 'full_input' between 'true_branch' and 'false_branch' on 'condition'.
Args:
condition: A boolean Tensor with shape [B_1, ..., B_N].
full_input: A Tensor or nested tuple of Tensors of any dtype, each with
shape [B_1, ..., B_... | 8d1faf5c2b1b3f9ddc9e3a27529ef06de740551c | 51,397 |
def radiation_pressure_length_noise(
ff,
Pin,
finesse,
mass=2.92,
lam=1064e-9,
):
"""Radiation pressure length noise for a single optic in a cavity in units of m/rtHz.
Inputs:
-------
ff: float
audio frequency in Hz
Pin: float
cavity input power in watts
fines... | 1be70f39ac1739acf430ec9980a3ddbd645ff8aa | 51,398 |
def snic_distance_mod(pos_i, pos_j, col_i, col_j, si, mi):
"""
Computes the SNIC pixel distance
None of the components can be negative -> omitting the root does not change item order
:param pos_i: position of pixel i
:param pos_j: position of pixel j
:param col_i: color of pixel i
:param col... | 0d083e9f28bd36f0fc38e149371ad033b4bf54e5 | 51,399 |
import inspect
def get_gradient_clip_fn(hparams=None):
"""Creates a gradient clipping function based on the hyperparameters.
See the :attr:`gradient_clip` field in
:meth:`~texar.core.optimization.default_optimization_hparams` for all
hyperparameters and default values.
The gradient clipping func... | 88dedd060763b529eafe4982a4f3abd9d27b0455 | 51,400 |
def find_delimiter_in(value):
"""Find a good delimiter to split the value by"""
for d in [';', ':', ',']:
if d in value:
return d
return ';' | 4a60fbe6294fff048645a6fbc397ec96fd748d67 | 51,401 |
from nzmath.prime import generator_eratosthenes as generator_eratosthenes
def powerDetection(n, largest_exp=False):
"""
param positive integer n
param boolean largest_exp
return integer x, k s.t. n == x ** k
(2 <= k if exist else x, k == n, 1)
if largest_exp is true then return l... | 5e5a4e374957660b60a5bb51b7b780801ce8e570 | 51,402 |
def get_parser_from_config():
"""
Uses the 'parser' field in the config file to find and return the parser
:return:
"""
parser_instance = config.read_config_option("parser")
return parsers.parser_factory(parser_instance) | a83a8ba2e2b88c29276400b3fb405227950027ec | 51,403 |
def _SeqPaddingLength(inputs_nmap):
"""Returns the lengths of paddings at the beginning and end of the sequence.
Args:
inputs_nmap: A `.NestedMap` of tensors that may have 'padding'
Every tensor's 0-th dim has the same size.
Returns:
padding length at the beginning, padding length at th... | 9ceeccca913dd8ed0738db88073a7bc8c791480e | 51,404 |
def get_ROT(ch,pe,te,ze,verbose=True):
"""
calculate ROT and depol ratio
"""
args = [ch, pe, ze, te, MISSING, verbose]
ROT, depol_ratio, rc = VLIDORT_POLAR_.rot_calc(*args)
return ROT,depol_ratio | 54b6173db6d676f6eaeb45b3980b3af74a6ffa0d | 51,405 |
def getFailsafeQueue():
"""
Method returns failsafe queue
"""
global failsafeq
return failsafeq | 43ee8f9090e6cc719946176b2673a93553be8aae | 51,406 |
def wait_for_read(socks, timeout=None):
"""Waits for reading to be available from a list of sockets or optionally a single socket if passed in.
Returns:
A list of sockets that can be read from immediately.
"""
return _wait_for_io_events(socks, EVENT_READ, timeout) | ba254b3fe46c104d90252d212e79587f2a5fd38a | 51,407 |
def group_frame(frame_partitions, num_new_parts, key_columns):
"""Group frame to prepare for the join
"""
return [
local_shuffle(part, num_new_parts, key_columns) for part in frame_partitions
] | 116b32cd69655c6f88dd6835af3d939a83d24074 | 51,410 |
def read(**kwds):
"""
Read all data.
"""
title = kwds.get("title")
text = kwds.get("text")
tags = kwds.get("tags")
offset = kwds.get("offset")
limit = kwds.get("limit")
query = Article.query
if title:
query = query.filter(Article.title.contains(title))
if text:
... | d277e43384d3487941cf5ed7e16ccde5f02a27f9 | 51,412 |
from datetime import datetime
def project_with_max_gross_value_added():
"""Test fixture returns an investment project with the max gross value."""
gva_multiplier = GVAMultiplierFactory(
multiplier=Decimal('9.999999'),
financial_year=1980,
)
with mock.patch(
'datahub.investment... | a0ec8d6a700f54c6e56f33ab3750e51dcd4a2283 | 51,415 |
def Adaptor3d_HSurfaceTool_VResolution(*args):
"""
:param S:
:type S: Handle_Adaptor3d_HSurface &
:param R3d:
:type R3d: float
:rtype: float
"""
return _Adaptor3d.Adaptor3d_HSurfaceTool_VResolution(*args) | 3d26f8bca3ff02ac58d607c43f885d75e878e183 | 51,416 |
def enhance_contrast(im, cmap='pink', display_contrast=1):
"""Enhance contrast of the input image."""
im = ensure_positive(im)
color_img = ind2cmap(im, cmap)
color_img = color_img * 255 * display_contrast
color_img[color_img > 255] = 255
color_img = np.uint8(color_img)
return color_img | 5a150d658f89bf09a6238cdaa01a6b9d986a3555 | 51,418 |
def drift_stability_ind(
missing_recs_drift, drift_tab, missing_recs_stability, stability_tab
):
"""
This function helps to produce the drift & stability indicator for further processing. Ideally a data with both drift & stability should produce a list of [1,1]
Parameters
----------
missing_recs... | 6964f0b258d0c531e242b47ccc864b54c72d4dd0 | 51,420 |
import time
def sample_function(integer1: int, integer2: int):
"""
This function takes as input two integers (integer1 and integer2) and returns
integer1 raised to the power of integer2
Parameters:
-----------
integer1 (int) a number greater than or equal to zero
... | 2e3ff92a398adda8cc558c8a20f939614b0e0b94 | 51,421 |
def get_dfs(path):
"""
Grabs the different data splits and converts them into dataframes.
Expects format from Code Search Net Challenge.
"""
dfs = []
for split in ["train", "valid", "test"]:
files = sorted((path/split).glob("**/*.gz"))
df = jsonl_list_to_dataframe(files, ... | 6d293f2a4562c132675bab091d8f5d0b314e939d | 51,422 |
def _get_entropy(data):
""" helper function that calculates the entropy of the input array """
H = 0
for value in data:
if value > 0:
H -= value * np.log2(value)
return H | 0cf33bc39964571c9fb52ccef694e41bf91b003d | 51,424 |
def make_doc():
""" Only used for sphinx documentation to add the prefix """
doc_app = Flask(__name__)
doc_app.register_blueprint(bp, url_prefix='/tmp_dids')
return doc_app | 045b16f10aae4b55fd9c11f8309b6092f03b1bb0 | 51,425 |
def tf_box_4c_to_box_3d(boxes_4c, ground_plane):
"""Vectorized box_4c to box_3d conversion
Args:
boxes_4c: Tensor of boxes_4c (N, 10)
ground_plane: Tensor of ground plane coefficients (4,)
Returns:
Tensor of boxes_3d (N, 7)
"""
format_checker.check_box_4c_format(boxes_4c)
... | f8ac4d71577510e58ed3b00255657fb4fe89be21 | 51,426 |
def timed(function):
"""
Log how long a function took
:param function: Function to time
:return: Result of the function
"""
@wraps(function)
def wrapper(*args, **kwargs):
start = timer()
result = function(*args, **kwargs)
print("{} took {} ms".format(function.__name__... | f2932a6acd13730eb2d450c24dc1bde8910a0096 | 51,427 |
def tfmatrix2tf(sys):
"""Convert a matrix of transfer functions to a MIMO transfer function.
Parameters
----------
sys: list of (list of control.xferfcn.TransferFunction)
The transfer function matrix representing a MIMO system. sys[i][j]
is the transfer function from the i+1... | 6e0ee5224162a0784e3fc30fde6ecb78ca5f4191 | 51,428 |
def active_genes_and_tf(active):
"""
From differentially expressed genes, return the genes and transcription
factors.
"""
active = list(active)
t_factors = list({gene.regulatory_product for gene in active if\
isinstance(gene.regulatory_product, TranscriptionFactor)})
return t_fac... | 74a6393294f3f69666235ffae1e7d1a97b200566 | 51,429 |
def get_cmap(name: str) -> np.ndarray:
"""Retrieve the given colormap and return RGB values as a uint8 NumPy array of shape (255, 3)"""
name = name.lower()
if name not in AVAILABLE_CMAPS:
raise ValueError(f'Unknown colormap {name}, must be one of {AVAILABLE_CMAPS}')
with _get_cmap_data(name) a... | 527c51d3c0cae517637509f9907c5d37215df3e8 | 51,430 |
def _addvFuncNumba(
mNrmNext,
mNrmGridNext,
vNvrsNext,
vNvrsPNext,
MPCminNvrsNext,
hNrmNext,
CRRA,
PermShkVals_temp,
PermGroFac,
DiscFacEff,
ShkPrbs_temp,
EndOfPrdvP,
aNrmNow,
BoroCnstNat,
mNrmGrid,
cFuncNow,
mNrmMinNow,
MPCmaxEff,
MPCminNow,
)... | 1736d5451cf573067f19f7cab63490cb0cf6731e | 51,431 |
def rename_get_variable(mapping):
"""
Args:
mapping(dict): an old -> new mapping for variable basename. e.g. {'kernel': 'W'}
Returns:
A context where the variables are renamed.
"""
def custom_getter(getter, name, *args, **kwargs):
splits = name.split('/')
basename = s... | 17aded74fb06d7aeaba7ecedccb07622775d5d96 | 51,432 |
import functools
def with_color(color_code: str):
"""Coloring decorator
Arguments:
color_code {str} -- e.g.: '\033[91m'
"""
def wrapper(func):
@functools.wraps(func)
def inner(args):
result = func(f'{color_code}{args}\033[0m')
return result
retu... | 3f5ecd79b3d4579ba4348b2492eaaa3688201907 | 51,433 |
def compute_accuracy(idxR,idx,k):
"""
# ===============================
#----- INPUT
# idxR : real labels
# idx : estimated labels
# k : number of class
#----- OUTPUT
# ACC_glob : global accuracy
# tab_acc : accuracy per class
# ===============================
"""
... | bf70ac41be460cd7623ccf8dcc85073c7bac69a6 | 51,434 |
def tri_lexique_lemme(corpus,lex_5g,lex_4g,lex_3g,lex_2g,dict_lexique):
"""
dans un corpus, retrouve les mwes d'un lexique donné en passant par les lemmes
entrée :
- corpus .conllu (lu par read_file())
- lexiques de mwe ordonnées par tailles de mwe
- dico {'mwe' : 'POS associée'}
sortie :
- dico {phrase : [lis... | 814d07ef0a8517c7161aa0038d7c2b3cca44f298 | 51,435 |
def count_ones(value: bytes) -> int:
"""Count the number of 1's in the binary string."""
return bin(value).count("1") | 0752a267b977df5a6ff11160deb6ef2d14712d92 | 51,437 |
def test_kernel_shap_with_a1a_sparse_nonzero_background():
""" Check with a sparse non zero background matrix.
"""
np.set_printoptions(threshold=100000)
np.random.seed(0)
X, y = shap.datasets.a1a() # pylint: disable=unbalanced-tuple-unpacking
x_train, x_test, y_train, _ = sklearn.model_selectio... | 6eb54eee11efc99f983c37d55e6439344a6a0f5f | 51,438 |
def is_lyrics_content_ok(title, text):
"""Compare lyrics text to expected lyrics for given title"""
setexpected = set(LYRICS_TEXTS[lyrics.slugify(title)].split())
settext = set(text.split())
setinter = setexpected.intersection(settext)
# consider lyrics ok if they share 50% or more with the referen... | 330f60eab27ea754d383cd98d9671926e0801bff | 51,439 |
def ratio_enum(anchor, ratios):
"""Enumerate a set of anchors for each aspect ratio wrt an anchor
"""
base_w, base_h, x_ctr, y_ctr = genwhctrs(anchor)
size = base_w * base_h # 16 * 16
size_ratios = size / ratios # (2*16**2, 1*16**2, 0.5*16**2)
a_w = np.round(np.sqrt(size_ratios))
a_h = np.round(a_w * ra... | 16871ffa286ef0091ffb9074b143e87474e26c57 | 51,440 |
import shlex
from typing import Sequence
def escape_value(value: OptionValueType) -> OptionValueType:
"""Escapes an option value based on its type."""
if isinstance(value, str):
return shlex.quote(value)
elif isinstance(value, Sequence):
return [shlex.quote(v) for v in value]
else:
... | 20636c3239edc5cf8c328a5df68478cf0366e979 | 51,441 |
def over(funcs):
"""Creates a function that invokes all functions in `funcs` with the
arguments it receives and returns their results.
Args:
funcs (list): List of functions to be invoked.
Returns:
function: Returns the new pass-thru function.
Example:
>>> func = over([max... | 6cd1a966366ee372c18dd35d71adf91028a04b1c | 51,442 |
import logging
def get_logger():
"""retrieves logger ("singleton").
Returns
-------
logging.Logger
logger for this module
"""
global module_name
try:
get_logger.logger
except AttributeError:
get_logger.logger = logging.getLogger(module_name)
return get_... | ba83ed2db59f879ccaf8b04f8a0a5ee2bb7e4d5e | 51,443 |
def load_labels_map(labels_map_path):
"""Loads the labels map from the given path.
The labels mmap must be in the following plain text format::
1:label1
2:label2
3:label3
...
The indexes are irrelevant to this function, they can be in any order and
can start from zero,... | 8ff1e41b87fedffa053981299c48488add754ff9 | 51,444 |
def get_user(email):
"""
Return a user object by email lookup
"""
model = get_user_model()
return model.objects.get(email=email) | 3d85bdac487c06c33cf754976be87bb9873b335f | 51,445 |
def generate_pruned_random_no_correlations_dataset(n_token, n_docs, n_subjects, min_samples):
"""Generate random data without any subject correlations, but with minimum samples per subject."""
dataset = generate_random_dataset(n_token, n_docs, n_subjects)
return filter_subjects_with_insufficient_samples(dat... | ae31b487da75f7bc4c079aa3981cd41b10c9e1ac | 51,446 |
async def m_aggregate(table, pipeline, **kwargs):
""" aggregate """
db, kwargs = get_motor(**kwargs)
plan = await db.command(SON([("aggregate", table), ("pipeline", pipeline)]))
return plan | 08e1969a22250cba648a2880a207f8699e0a6bb6 | 51,447 |
from typing import List
def neurons_values(lstm_layer: md.ml.nn.layers.LSTMLayer, data: List[float], ppty_name: str) -> List[np.ndarray]:
"""
Get the activation values for a LSTM layer
Args:
lstm_layer: LSTM layer
data: data to process
ppty_name: the type of values to get
Returns: List where each position ... | 780fcf16698385e71f3426274f4f34782ec2a005 | 51,448 |
import math
def bbox_to_integer_coords(t, l, b, r, image_h, image_w):
"""
t, l, b, r:
float
Bbox coordinates in a space where image takes [0; 1] x [0; 1].
image_h, image_w:
int
return: t, l, b, r
int
Bbox coordinates in given ima... | 90fb198e2d6cd170a2e7a2b648f15554a9997389 | 51,450 |
def load_config(args):
"""Tries to load the configuration file or throws ExitWithError."""
config = ConfigManager(DEFAULT_CONFIG)
config_dir = home_dir.get()
try:
if args.config_file is not None:
filename_override = os.path.abspath(args.config_file)
else:
filename... | 4f8070d8d5373571d44d6c72a9c5e04f7b33497e | 51,451 |
import numpy as np
def circular_layout(G, scale=1, center=None, dim=2):
# dim=2 only
"""Position nodes on a circle.
Parameters
----------
G : NetworkX graph or list of nodes
A position will be assigned to every node in G.
scale : number (default: 1)
Scale factor for positions... | eb679719704fa260381b26b558d505deeb09a8a0 | 51,452 |
def sdp_sub(f, g, u, O, K):
"""Subtract distributed polynomials in `K[X]`. """
h = dict(f)
for monom, c in g:
if h.has_key(monom):
coeff = h[monom] - c
if not coeff:
del h[monom]
else:
h[monom] = coeff
else:
h[... | 2c82175cca3f209327ac48a8ef436300231d3337 | 51,453 |
def create_meeting():
"""
При GET запросе возвращает страницу для создания встречи.
При POST запросе, в случае успешной валидации сохраняет
новую встречу в БД.
"""
title = 'Создание встречи'
meeting_form = MeetingForm(request.form)
with db_session() as session:
meet_data = GameMe... | 473ac76693589b5f46d846ea05d0e623e991b85b | 51,454 |
def correlation_ratio(col1: ndarray, col2: ndarray) -> float:
"""Returns the correlation ratio for col1 and col2.
Used for categorical - numerical variable pairs.
Args:
col1 (ndarray): A categorical column with no null values
col2 (ndarray): A numerical column with no null values"""
uni... | 016ff428c1895bf4cb5380baac95fbba9ec76c74 | 51,455 |
def read_txt_tensor(filePath):
""" read tensor data from text file.
Parameters
----------
filePath : str
the path of file
Returns
-------
tensor
three-way numpy array
"""
tensor = pd.read_csv(filePath, header=0, names= ["v1", "v2", "v3", "v4"])
rowIndices ... | 15f65958d9937f00c532dbc383bac5b2cb4ef990 | 51,456 |
from typing import Tuple
from typing import Any
def train_sequence_model(data: Tuple[Any, Any], output_dir: str, embedding_dim: int = 200, dropout_rate: float = 0.2,
filters: int = 3, kernel_size: int = 3, pool_size: int = 3, num_classes: int = 3,
learning_rate: float... | da3769bfa017e68ec9e7bca2c40a4692c1f6a028 | 51,457 |
def get_pose3_vector(num_poses: int) -> Pose3Vector:
"""Generate camera poses for use in triangulation tests"""
# Looking along X-axis, 1 meter above ground plane (x-y)
upright = Rot3.Ypr(-np.pi / 2, 0.0, -np.pi / 2)
pose1 = Pose3(upright, Point3(0, 0, 1))
# create second camera 1 meter to the rig... | 196c33faec30cb74b59a2302397ac41866bc0d3a | 51,459 |
def from_datestring(datestring):
"""Parse an arbitrary datestring and return a datetime object using
dateutils' parser.
"""
if dateutil_available:
return parser.parse(datestring)
else:
raise RuntimeError('from_datestring requires the python-dateutil library') | 98efc647c609c6cf34474ecdb18c985551975089 | 51,461 |
def clean_word(word):
"""Clean the word.
Parameters
----------
word : str
Returns
-------
str
"""
new_word = (word
.replace('(', '')
.replace(')', '')
.replace(':', '')
.replace(';', '')
.replace('+', '... | 5d776bd9ad6bcb7824e56bafb15d6fec0fae6024 | 51,462 |
from emonitor.extensions import scheduler
from emonitor.lib.osm.loaddata import parseOsmData
def getAdminData(self, **params):
"""
Deliver admin content of module maps (ajax)
:return: rendered template as string or json dict
"""
if request.args.get('action') == 'checkpath':
if os.path.exi... | cda30d64a5294b30716cfa35598efcf29bbb969d | 51,464 |
def get_attrib_recursive(element, *attribs):
"""Find the first attribute in attribs in element or its closest ancestor
that has any of the attributes in attribs.
Usage examples:
get_attrib_recursive(el, "fallback-langs")
get_attrib_recursive(el, "xml:lang", "lang")
Args:
elemen... | d04ba71a280bd1697cc61b79af21df46023473d2 | 51,465 |
def _antisymmetricity_score(v_1: float, v_2: float, coef_lambda: float):
"""
degree of the anti-symmetry between two values.
z = exp(-\lambda*d(v_1,v_2))
d(v_1,v_2) = |v_1 + v_2|
:param v_1: a value
:param v_2: another value
:param lambda: exponential decay parameter.
:return: z
"""
... | 8e8912cae7b009a27303a33a79c30898da7ac88f | 51,466 |
def find_extra_inferred_properties(spec_dict: dict) -> list:
"""Finds if there are any inferred properties which are used.
Args:
spec_dict: Dict obj containing configurations for the import.
Returns:
List of properties that appear in inferredSpec but are not part of 'pvs' section.
"""
re... | 3be2950a227cfca8d0ab4c4413322f8aa8b22cc0 | 51,467 |
import time
def signup(driver, Users, ISAAC_WEB, WAIT_DUR, **kwargs):
"""Test whether users can sign up to Isaac using an email address.
- 'driver' should be a Selenium WebDriver.
- 'Users' must be a TestUsers object.
- 'ISAAC_WEB' is the string URL of the Isaac website to be tested.
... | 1f9cfd19c48984c93cf721033e0218fbb1463153 | 51,468 |
import torch
def collate(data, tokenizer, block_size, device):
""" Collate formats the data passed to the data loader.
In particular we tokenize the data batch after batch to avoid keeping them
all in memory. We output the data as a namedtuple to fit the original BertAbs's
API.
"""
data = [x f... | bb5b04bf69b33f033570910020dc4985f93670e4 | 51,469 |
def _read_json(nc):
"""
This is easily the laziest piece of code in this library. In the future, I want
to modify this to be a simple CFG parser to identify valid JSON, but in the meantime,
performance wasn't my priority, and this gets the job done.
"""
json_str = ''
last_char = ''
whil... | 11f5e7259d106044f5fd0d33711c367b92dba96c | 51,470 |
def zeroDistanceByFit(analysis, axis='y', useTrap='pm'):
"""
Find the trap distance at which the beads touch and use this as an offset to calculate the extension.
Args:
analysis (:class:`.TweezersAnalysis`): analysis file to check
axis (str): axis on which to do the correction, either 'y' (... | 89c7906a166dc3d66e58f74da65b3b1df697300f | 51,471 |
def log_file_path():
"""
Provides the path where tor's log file resides, if one exists.
:returns: **str** with the absolute path of tor log file, or **None**
if one doesn't exist
"""
controller = tor_controller()
for log_entry in controller.get_conf('Log', [], True):
entry_comp = lo... | b485955858b55c14f88a5349be46204ec85eebaa | 51,472 |
def topics(request):
"""显示所有的主题"""
topics = Topic.objects.order_by('date_added')
context = {'topics': topics}
return render(request, 'learning_logs/topics.html', context) | b31f6094d8a138e681414c7518dd746c0bcf924f | 51,473 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.