content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def extract_character_pairs(letter_case, reverse_letter_case):
"""
Extract character pairs. Check that two unicode value are also a mapping value of each other.
:param letter_case: case mappings dictionary which contains the conversions.
:param reverse_letter_case: Comparable case mapping table which c... | 29e5415afc4e4a3bff5cd74c1fa14f78cf715384 | 3,653,364 |
def after_timestep(simulation, is_steady, force_steady=False):
"""
Move u -> up, up -> upp and prepare for the next time step
"""
# Stopping criteria for steady state simulations
vel_diff = None
if is_steady:
vel_diff = 0
for d in range(simulation.ndim):
u_new = simul... | 7aa3436ba8bcc4ec395ba6f030b83e6fc3cb4bf3 | 3,653,365 |
def get_summary_indices(df, on='NOSC'):
""" Get the summary stats for the indices: median, mean, std, weighted mean and weighted std """
samples = get_list_samples(df)
samples.append(on)
t = df[samples]
t = t.melt(id_vars=[on], var_name='SampleID', value_name='NormIntensity')
t = t[t['NormI... | 1c430a9ad377e3d550e292b381af072d4adc78f0 | 3,653,366 |
def view_evidence(evidence_id: int):
"""View a single Evidence model."""
evidence = manager.get_evidence_by_id_or_404(evidence_id)
return render_template(
'evidence/evidence.html',
evidence=evidence,
manager=manager,
) | 8a51a3c6279a1501c26fb2de09c4450660546bf3 | 3,653,367 |
def rigidBlades(blds, hub=None, r_O=[0,0,0]):
""" return a rigid body for the three blades
All bodies should be in a similar frame
"""
blades = blds[0].toRigidBody()
for B in blds[1:]:
B_rigid = B.toRigidBody()
blades = blades.combine(B_rigid, r_O=r_O)
blades.name='blades'
re... | 89b48ba43f748fa4b2db7ee768eabe9e79e9a453 | 3,653,369 |
def mea_slow(posterior_matrix, shortest_ref_per_event, return_all=False):
"""Computes the maximum expected accuracy alignment along a reference with given events and probabilities.
Computes a very slow but thorough search through the matrix
:param posterior_matrix: matrix of posterior probabilities with r... | 4b7165a0145d2e1ad2d0550910e03de5a775733c | 3,653,370 |
import trace
def predict(cart_tree, feature_set, data_set):
"""Predict the quality."""
feature_dict = {}
for index, feature in enumerate(feature_set):
feature_dict[feature] = index
results = []
for element in data_set:
# Append a tuple.
results.append((trace(cart_tree, feat... | c7f50557202c4320194ecc5264059c1701e0de73 | 3,653,371 |
def test_incorporate_getitem_through_switch(tag):
""" test_incorporate_getitem_through_switch """
fns = FnDict()
scalar_gt = Primitive('scalar_gt')
@fns
def before(x, y):
def f1(x, y):
return x, y
def f2(x, y):
return y, x
return tuple_getitem(
... | df128faf55c48ba698340d06b3c232ebc0140511 | 3,653,372 |
def response_json(status, message, response):
"""
Helper method that converts the given data in json format
:param success: status of the APIs either true or false
:param data: data returned by the APIs
:param message: user-friendly message
:return: json response
"""
data = {
"status": status,
"message": me... | 9c7e30e81c5412998bc8523b0e45a353c82b5a41 | 3,653,373 |
from . import conf
def settings(request):
"""
"""
conf = dict(vars(conf))
# conf.update(ThemeSite.objects.get_theme_conf(request=request, fail=False))
data = request.session.get('cms_bs3_theme_conf', {})
conf.update(data)
return {'bs3_conf': conf} | 1230171ce1263083aabbd0fb79928c9236af31a9 | 3,653,374 |
def NDVI(R, NIR):
""" Compute the NDVI
INPUT : R (np.array) -> the Red band images as a numpy array of float
NIR (np.array) -> the Near Infrared images as a numpy array of float
OUTPUT : NDVI (np.array) -> the NDVI
"""
NDVI = (NIR - R) / (NIR + R + 1e-12)
return NDVI | aa1789c80720c09aa464b3ae67da7de821e2ba97 | 3,653,375 |
from typing import Union
from typing import Optional
from datetime import datetime
def get_nearby_stations_by_number(
latitude: float,
longitude: float,
num_stations_nearby: int,
parameter: Union[Parameter, str],
time_resolution: Union[TimeResolution, str],
period_type: Union[PeriodType, str],... | e53896ea4644bcce6351671ec950fe8165a2cb12 | 3,653,376 |
import scipy
def get_state(tau, i=None, h=None, delta=None, state_0=None, a_matrix=None):
"""
Compute the magnetization state.
r(τ) = e^(Aτ)r(0) eq (11) at[1]
"""
if a_matrix is not None:
# get state from a known A matrix
# A matrix can be shared and it takes time to build
... | a4ae277d41b64c9caf49758d62767030db0b244b | 3,653,377 |
import aacgmv2._aacgmv2 as c_aacgmv2
import logging
def convert_latlon_arr(in_lat, in_lon, height, dtime, code="G2A"):
"""Converts between geomagnetic coordinates and AACGM coordinates.
Parameters
------------
in_lat : (np.ndarray or list or float)
Input latitude in degrees N (code specifies ... | d9efc4d58925ef9cd63e7c800258b99c91e14f7a | 3,653,379 |
import re
def getPredictedAnchor(title: str) -> str:
"""Return predicted anchor for given title, usually first letter."""
title = title.lower()
if title.startswith('npj '):
return 'npj series'
title = re.sub(r'^(the|a|an|der|die|das|den|dem|le|la|les|el|il)\s+', '',
title)
... | 972eaa495078bc3929967a052f031c50d439fbdc | 3,653,381 |
from typing import Optional
from typing import Mapping
def get_contact_flow(contact_flow_id: Optional[str] = None,
instance_id: Optional[str] = None,
name: Optional[str] = None,
tags: Optional[Mapping[str, str]] = None,
type: Optional... | ed57d1b17c19f66c38e67613e49653b11c13f699 | 3,653,382 |
def jensen_alpha_beta(risk_returns ,benchmark_returns,Rebalancement_frequency):
"""
Compute the Beta and alpha of the investment under the CAPM
Parameters
----------
risk_returns : np.ndarray
benchmark_returns : np.ndarray
Rebalancement_frequency : np.float64
... | ac9d1cf638e2ce67219ed16dbbffc652ff47c541 | 3,653,383 |
def cycles_run() -> int:
"""Number of cycles run so far"""
return lib.m68k_cycles_run() | 145dc9a154a0ec4c2e46fecdeb7106134307cf10 | 3,653,384 |
def loop_and_return_fabric(lines):
"""
loops lines like:
#1196 @ 349,741: 17x17
"""
fabric = {}
for line in lines:
[x, y, x_length, y_length] = parse_line(line)
i_x, i_y = 0, 0
while i_y < y_length:
i_x = 0
while i_x < x_length:
thi... | d5fd18c5b90c0e6576767a77c954b3546cbaef1a | 3,653,385 |
def get_sample(id):
"""Returns sample possessing id."""
for sample in samples_global:
if sample.id == id:
return sample
raise Exception(f'sample "{id}" could not be found') | 524305fe77ef5cc03ba51af3eb61301b697b9c1f | 3,653,386 |
def transcriptIterator(transcriptsBedStream, transcriptDetailsBedStream):
""" Iterates over the transcripts detailed in the two streams, producing
Transcript objects. Streams are any iterator that returns bedlines or empty
strings.
"""
transcriptsAnnotations = {}
for tokens in tokenizeBedStream(transcriptDe... | 2be2bbca915667be89220d92c42b8a8dce905cc4 | 3,653,387 |
import re
def convert_check_filter(tok):
"""Convert an input string into a filter function.
The filter function accepts a qualified python identifier string
and returns a bool.
The input can be a regexp or a simple string. A simple string must
match a component of the qualified name exactly. A r... | 9d1aaa9a5007371e4f33ce3b4fbc86edd15875c6 | 3,653,388 |
def region_stats(x, r_start, r_end):
"""
Generate basic stats on each region. Return a dict for easy insertion into a DataFrame.
"""
stats = Munch()
stats["start"] = r_start
stats["end"] = r_end
stats["l"] = r_end - r_start
stats["min"] = np.min(x[r_start:r_end])
stats["max"] = np.ma... | cb52f6320952be13f9715cb2259b32996bdbb0da | 3,653,389 |
def _sql_type(ptype):
"""Convert python type to SQL type"""
if "Union" in ptype.__class__.__name__:
assert len(ptype.__args__) == 2, "Cannot create sql column with more than one type."
assert type(None) in ptype.__args__, "Cannot create sql column with more than one type."
return f"{pty... | 331734ce050ca261d2d78876ebd78540a088597b | 3,653,392 |
def rescale_data(data: np.ndarray,
option: str,
args: t.Optional[t.Dict[str, t.Any]] = None) -> np.ndarray:
"""Rescale numeric fitted data accordingly to user select option.
Args:
data (:obj:`np.ndarray`): data to rescale.
option (:obj:`str`): rescaling strate... | 5f885233c262fb2d766417e64f783f807212355e | 3,653,393 |
def extract_labels(text, spacy_model):
"""Extract entities using libratom.
Returns: core.Label list
"""
try:
document = spacy_model(text)
except ValueError:
logger.exception(f"spaCy error")
raise
labels = set()
for entity in document.ents:
label, _ = Label.o... | 782fdcb4bdd817b55a38c5efe03db676f0e00eed | 3,653,394 |
from typing import Callable
import click
def variant_option(command: Callable[..., None]) -> Callable[..., None]:
"""
An option decorator for a DC/OS variant.
"""
function = click.option(
'--variant',
type=click.Choice(['auto', 'oss', 'enterprise']),
default='auto',
hel... | 4c89dc15b46c9d147445ef458b721c7ce835cbe7 | 3,653,395 |
def GetSegByName(name):
"""
@return Address of the first byte in the Segment
with the provided name, or BADADDR
"""
for Segment in ida.Segments():
if ida.SegName(Segment) == name:
return Segment
return ida.BADADDR | 4b0353da187735095805b5a80bb0e23a2ce6491b | 3,653,396 |
def sample_points_from_plateaus(all_plateaus, mode, stack_size=10, n_samples=1):
"""
Samples points from each plateau in each video
:param all_plateaus: dictionary containing all plateaus, keys are plateaus's ids, values are the plateau objects
:param mode: either `flow` or `rgb`
:param stack_size:... | 1dd12721acc9b126d244902016e939792b220d1e | 3,653,397 |
def mobile_user_meeting_list(request):
"""
返回用户会议列表
:param request:
:return:
"""
dbs = request.dbsession
user_id = request.POST.get('user_id', '')
start_date = request.POST.get('start_date', '')
end_date = request.POST.get('end_date', '')
error_msg = ''
if not user_id:
... | 55e9a61a755ef957f4b6bf504b3efe721b13cfd7 | 3,653,398 |
import ctypes
def get_current_thread_cpu_time():
"""
<Purpose>
Gets the total CPU time for the currently executing thread.
<Exceptions>
An AssertionError will be raised if the underlying system call fails.
<Returns>
A floating amount of time in seconds.
"""
# Get the current thread handle
... | 6d83314e8ceee0336b6c0ed7f71fa49e89b24ca8 | 3,653,399 |
def get_data_upload_id(jwt: str) -> str:
"""Function to get a temporary upload ID from
DAFNI data upload API
Args:
jwt (str): Users JWT
Returns:
str: Temporary Upload ID
"""
url = f"{DATA_UPLOAD_API_URL}/nid/upload/"
data = {"cancelToken": {"promise": {}}}
return dafn... | dcce05a8efda1c90e6a78a19757f57deffd0c247 | 3,653,400 |
def StationMagnitudeContribution_TypeInfo():
"""StationMagnitudeContribution_TypeInfo() -> RTTI"""
return _DataModel.StationMagnitudeContribution_TypeInfo() | d9af45a3bbe993de37c351b5791ba8b87aeeedc9 | 3,653,401 |
def _get_operations(rescale=0.003921, normalize_weight=0.48):
"""Get operations."""
operation_0 = {
'tensor_op_module': 'minddata.transforms.c_transforms',
'tensor_op_name': 'RandomCrop',
'weight': [32, 32, 4, 4, 4, 4],
'padding_mode': "constant",
'pad_if_needed': False,
... | a3bab4147f1a2020fb87853fc30bede277f0f4bd | 3,653,402 |
def itm_command(
ticker: str = None,
):
"""Options ITM"""
# Check for argument
if ticker is None:
raise Exception("Stock ticker is required")
dates = yfinance_model.option_expirations(ticker)
if not dates:
raise Exception("Stock ticker is invalid")
current_price = yfinanc... | b2230ee5f8c520523f7ce844372a4f26d14fe53d | 3,653,403 |
def create_nx_suite(seed=0, rng=None):
"""
returns a dict of graphs generated by networkx for testing,
designed to be used in a pytest fixture
"""
if rng is None:
rng = np.random.RandomState(seed)
out_graphs = {}
for N in [1, 2, 4, 8, 16, 32, 64, 128]:
for dtype in [np... | eb64688850d48b755dc526ed3d64876d04ba3914 | 3,653,404 |
def _nearest_neighbor_features_per_object_in_chunks(
reference_embeddings_flat, query_embeddings_flat, reference_labels_flat,
ref_obj_ids, k_nearest_neighbors, n_chunks):
"""Calculates the nearest neighbor features per object in chunks to save mem.
Uses chunking to bound the memory use.
Args:
refere... | e9b7af295ddfab56f70748e42fb7b06f6192a3ac | 3,653,405 |
import heapq
def heap_pop(heap):
"""
Wrapper around heapq's heappop method to support updating priorities of
items in the queue.
Main difference here is that we toss out any queue entries that have been
updated since insertion.
"""
while len(heap) > 0:
pri_board_tup = heapq.heappo... | c640fd178a399332fc10ccbb55085cb08d118865 | 3,653,406 |
def _variant_po_to_dict(tokens) -> CentralDogma:
"""Convert a PyParsing data dictionary to a central dogma abundance (i.e., Protein, RNA, miRNA, Gene).
:type tokens: ParseResult
"""
dsl = FUNC_TO_DSL.get(tokens[FUNCTION])
if dsl is None:
raise ValueError('invalid tokens: {}'.format(tokens))... | 28e989087a91accf793eaaada2e65a71ee145c32 | 3,653,407 |
def project(name, param):
"""a tilemill project description, including a basic countries-of-the-world layer."""
return {
"bounds": [-180, -85.05112877980659, 180, 85.05112877980659],
"center": [0, 0, 2],
"format": "png",
"interactivity": False,
"minzoom": 0,
"maxz... | 9609c523cccc99168bbc0e7dbf10fe8624d399c2 | 3,653,408 |
def get_deepest():
"""Return tokens with largest liquidities.
Returns:
str: HTML-formatted message.
"""
url = config.URLS['deepest']
api_params = {'limit': 5,
'orderBy': 'usdLiquidity',
'direction': 'desc',
'key': POOLS_KEY
... | c944f20f65dd68716b4f436b02ec5e373c04848f | 3,653,409 |
def _grompp_str(op_name, gro_name, checkpoint_file=None):
"""Helper function, returns grompp command string for operation."""
mdp_file = signac.get_project().fn('mdp_files/{op}.mdp'.format(op=op_name))
cmd = '{gmx} grompp -f {mdp_file} -c {gro_file} {checkpoint} -o {op}.tpr -p'.format(
gmx=gmx_exec,... | 9201bd49fd09ce9faa268d8ea4d33482cea5d7ad | 3,653,410 |
def get_role_with_name(role_name: str) -> Role:
"""Get role with given name."""
role = Role.query.filter(Role.name == role_name).one()
return role | e20858ef1bcbb54d2c1d09ba9d3a54bf15dfa658 | 3,653,412 |
def namespace_store_factory(
request, cld_mgr, mcg_obj_session, cloud_uls_factory_session, pvc_factory_session
):
"""
Create a NamespaceStore factory.
Calling this fixture lets the user create namespace stores.
Args:
request (object): Pytest built-in fixture
cld_mgr (CloudManager): ... | cc2d090d8dc0f12d89331ada54e4054e117e544d | 3,653,413 |
def get_user(request, username):
"""
Gets a user's information.
return:
{
status: HTTP status,
name: string,
gender: string,
marital_status: string,
first_name: string
}
"""
data = get_user_info(username)
if data:
... | 9820b441718629780ff72ab00776fc2d4c95a63f | 3,653,414 |
def find_changes(d_before, d_after):
"""
Returns a dictionary of changes in the format:
{
<system id>: {
<changed key>: <Change type>,
...
},
...
}
The changes should describe the differences between d_before and d_after.
... | 02e5eea5ac1264c593d542a8f745a8d3571d5fac | 3,653,415 |
def check_vector_inbetween(v1, v2, point):
""" Checks if point lies inbetween two vectors v1, v2. Returns boolean. """
if (np.dot(np.cross(v1, point), np.cross(v1, v2))) >= 0 and (np.dot(np.cross(v2, point), np.cross(v2, v1))) >= 0:
return True
else:
return False | 6eeaa4a9e37ea345c3399c103dadbc45c306887c | 3,653,416 |
def accuracy(y_preds, y_test):
"""
Function to calculate the accuracy of algorithm
:param y_preds: predictions for test data
:param y_test: actual labels for test data
:return: accuracy in percentage
"""
return np.sum(np.where(y_preds == y_test, 1, 0)) * 100 / len(y_test) | f41522663ae9a35e976f4d848f14e42ef0993fd9 | 3,653,417 |
def get_all(factory='official', **kwargs):
"""Construct and return an list of Class `Event`.
hookを呼び出す.
Args:
factory: `Event` の取得用マネージャ 今のところ,京大公式HP用のみ.
EventFactoryMixin classを継承したクラスか 'official' に対応
date (:obj:`datetime`, optional): 欲しいイベントのdatetime.
`month` , `y... | bc1fabe37fc8065ff5394259607caf32c6345b41 | 3,653,418 |
def nudupl(f):
"""Square(f) following Cohen, Alg. 5.4.8.
"""
L = int(((abs(f.discriminant))/4)**(1/4))
a, b, c = f[0], f[1], f[2]
# Step 1 Euclidean step
d1, u, v = extended_euclid_xgcd(b, a)
A = a//d1
B = b//d1
C = (-c*u) % A
C1 = A-C
if C1 < C:
C = -C1
# Step ... | 7f5a16c0fc2611a5f9dc0ebde00e3587c188f944 | 3,653,420 |
def remove_schema(name):
"""Removes a configuration schema from the database"""
schema = controller.ConfigurationSchema()
schema.remove(name)
return 0 | e45b415ea6eb57402790b04bbf1fa63749242a77 | 3,653,421 |
def get_unassigned_independent_hyperparameters(outputs):
"""Going backward from the outputs provided, gets all the independent
hyperparameters that are not set yet.
Setting an hyperparameter may lead to the creation of additional hyperparameters,
which will be most likely not set. Such behavior happens... | 6f28f3fcbaf4a875c3a42cdb4fc9ad715c99a093 | 3,653,422 |
def get_theta_benchmark_matrix(theta_type, theta_value, benchmarks, morpher=None):
"""Calculates vector A such that dsigma(theta) = A * dsigma_benchmarks"""
if theta_type == "benchmark":
n_benchmarks = len(benchmarks)
index = list(benchmarks).index(theta_value)
theta_matrix = np.zeros(n... | aa84006b7a69faa8803ecea20ffd24e35f178185 | 3,653,423 |
def reorder_points(point_list):
"""
Reorder points of quadrangle.
(top-left, top-right, bottom right, bottom left).
:param point_list: List of point. Point is (x, y).
:return: Reorder points.
"""
# Find the first point which x is minimum.
ordered_point_list = sorted(point_list, key=lambd... | 8ef3466616ecf003750cce7e1125d913d258cf15 | 3,653,425 |
import torch
def ppg_acoustics_collate(batch):
"""Zero-pad the PPG and acoustic sequences in a mini-batch.
Also creates the stop token mini-batch.
Args:
batch: An array with B elements, each is a tuple (PPG, acoustic).
Consider this is the return value of [val for val in dataset], where
... | 1357a8a9fa901a9be4f79ea13fd5ae7c3810bbeb | 3,653,426 |
def problem004():
"""
Find the largest palindrome made from the product of two 3-digit numbers.
"""
return largest_palindrome_from_product_of_two_n_digit_numbers(3) | 516c98d75ac2b4e286e58ea940d49c1d2bcd2dc7 | 3,653,427 |
import torch
def residual_l1_max(reconstruction: Tensor, original: Tensor) -> Tensor:
"""Construct l1 difference between original and reconstruction.
Note: Only positive values in the residual are considered, i.e. values below zero are clamped.
That means only cases where bright pixels which are brighter... | 8649b1947845c0e3f9e57c0ec2e68d7bed94be5d | 3,653,428 |
def build_url(path):
"""
Construct an absolute url by appending a path to a domain.
"""
return 'http://%s%s' % (DOMAIN, path) | fa9df465607082993571ca71c576d7b250f6cc76 | 3,653,429 |
def get_registration_url(request, event_id):
"""
Compute the absolute URL to create a booking on a given event
@param request: An HttpRequest used to discover the FQDN and path
@param event_id: the ID of the event to register to
"""
registration_url_rel = reverse(booking_create, kwargs={"event_i... | d8b344c6574a120d365a718934f5dc0e78173a6f | 3,653,430 |
def create_no_args_decorator(decorator_function,
function_for_metadata=None,
):
"""
Utility method to create a decorator that has no arguments at all and is implemented by `decorator_function`, in
implementation-first mode or usage-first mode.
T... | 7067974d7bd15c238968f78aa0057086458940bf | 3,653,431 |
import torch
def compute_batch_jacobian(input, output, retain_graph=False):
"""
Compute the Jacobian matrix of a batch of outputs with respect to
some input (normally, the activations of a hidden layer).
Returned Jacobian has dimensions Batch x SizeOutput x SizeInput
Args:
input (list or t... | c18f596a3500f2f82e2b4716e6f9892a01fb31c7 | 3,653,433 |
def is_associative(value):
"""Checks if `value` is an associative object meaning that it can be
accessed via an index or key
Args:
value (mixed): Value to check.
Returns:
bool: Whether `value` is associative.
Example:
>>> is_associative([])
True
>>> is_ass... | 5d2a9e0e69ad793a98657dc13b26f79900f29294 | 3,653,434 |
def join_audio(audio1, audio2):
"""
>>> join_audio(([1], [4]), ([2, 3], [5, 6]))
([1, 2, 3], [4, 5, 6])
"""
(left1, right1) = audio1
(left2, right2) = audio2
left = left1 + left2
right = right1 + right2
audio = (left, right)
return audio | 23348b746469d362fd66371d61142b4227814ff3 | 3,653,435 |
def csi_from_sr_and_pod(success_ratio_array, pod_array):
"""Computes CSI (critical success index) from success ratio and POD.
POD = probability of detection
:param success_ratio_array: np array (any shape) of success ratios.
:param pod_array: np array (same shape) of POD values.
:return: csi_array: ... | 84952fe6f7c8bd780c64c53183342ab0d8f3f90f | 3,653,436 |
def compute_secondary_observables(data):
"""Computes secondary observables and extends matrix of observables.
Argument
--------
data -- structured array
must contains following fields: length, width, fluo, area, time
Returns
-------
out -- structured array
new fields are ad... | 7141d16a579e4b629e25fee3a33c9a844a08e48f | 3,653,438 |
def get_account_number(arn):
"""
Extract the account number from an arn.
:param arn: IAM SSL arn
:return: account number associated with ARN
"""
return arn.split(":")[4] | 3d0fe552691ae98cf0dc70bc2055297f01a5d800 | 3,653,439 |
def get_hashtags(tweet):
"""return hashtags from a given tweet
Args:
tweet (object): an object representing a tweet
Returns:
list: list of hastags in a tweet
"""
entities = tweet.get('entities', {})
hashtags = entities.get('hashtags', [])
return [get_text(tag) for tag in ha... | ef222d64294c62d27e86a4c8520bb197701ed1af | 3,653,440 |
def get_appliance_ospf_neighbors_state(
self,
ne_id: str,
) -> dict:
"""Get appliance OSPF neighbors state
.. list-table::
:header-rows: 1
* - Swagger Section
- Method
- Endpoint
* - ospf
- GET
- /ospf/state/neighbors/{neId}
:param n... | 25a985ccf8b00ee3f27ea43d2a8371eef2443963 | 3,653,442 |
def info(obj):
"""Return info on shape and dtype of a numpy array or TensorFlow tensor."""
if obj is None:
return 'None.'
elif isinstance(obj, list):
if obj:
return 'List of %d... %s' % (len(obj), info(obj[0]))
else:
return 'Empty list.'
elif isinstance(obj, tuple):
if obj:
ret... | 64ffab112b0f0397541f8661a861a958c8ccf26e | 3,653,444 |
def first_index_k_zeros_left(qstr, k, P):
"""
For a binary string qstr, return the first index of q with k (mod P) zeros to the left.
Return: index in [0, qstr.length]
"""
num_zeros_left = 0
for j in range(qstr.length+1):
if (num_zeros_left - k) % P == 0:
return j
if ... | 62e505290fb32b43860deae3477dec718028e7af | 3,653,445 |
def transform_points(points, transf_matrix):
"""
Transform (3,N) or (4,N) points using transformation matrix.
"""
if points.shape[0] not in [3, 4]:
raise Exception("Points input should be (3,N) or (4,N) shape, received {}".format(points.shape))
return transf_matrix.dot(np.vstack((points[:3, ... | f478dfdfe41c694ada251deca33820336001d61e | 3,653,446 |
def get_lat_long(zip):
"""
This function takes a zip code and looks up the latitude and longitude using
the uszipcode package. Documentation: https://pypi.python.org/pypi/uszipcode
"""
search = ZipcodeSearchEngine()
zip_data = search.by_zipcode(zip)
lat = zip_data['Latitude']
long =... | 4fa8dc583bba9a6068db58ab86c2cab5f310edc4 | 3,653,447 |
def propose_perturbation_requests(current_input, task_idx, perturbations):
"""Wraps requests for perturbations of one task in a EvaluationRequest PB.
Generates one request for each perturbation, given by adding the perturbation
to current_input.
Args:
current_input: the current policy weights
task_idx... | 279da36eb633005c8f8ee79e66b71b3bdf8783f3 | 3,653,448 |
import google
def id_token_call_credentials(credentials):
"""Constructs `grpc.CallCredentials` using
`google.auth.Credentials.id_token`.
Args:
credentials (google.auth.credentials.Credentials): The credentials to use.
Returns:
grpc.CallCredentials: The call credentials.
"""
reque... | 433bb494d9f8de529a891f529a42f89af0b5ef77 | 3,653,449 |
import time
def test_analyze(request,hash,db_name):
"""
Get features of a sequence, using the sequence's sha-1 hash as the
identifier.
"""
db = blat.models.Feature_Database.objects.get(name=db_name)
sequence = blat.models.Sequence.objects.get(db=db,hash=hash)
ts = int(time.mktime(sequence.... | 173ebb356167558cb64a35265caa39e828a43bae | 3,653,450 |
from typing import List
def _collect_scaling_groups(owner: str) -> List:
"""Collect autoscaling groups that contain key `ES_role` and belong to the specified owner"""
client = boto3.client("autoscaling")
print("Collecting scaling groups")
resp = client.describe_auto_scaling_groups()
assert "NextT... | f1f75e6158450aaef834a910f8c36bb8812b1ede | 3,653,451 |
def cross_entropy_loss(logits, labels, label_smoothing=0., dtype=jnp.float32):
"""Compute cross entropy for logits and labels w/ label smoothing
Args:
logits: [batch, length, num_classes] float array.
labels: categorical labels [batch, length] int array.
label_smoothing: label smoothing ... | 7b6ce3145bc85433e54cef0ac85570eeb0fe7230 | 3,653,452 |
import torch
def set_optimizer(name, model, learning_rate):
"""
Specify which optimizer to use during training.
Initialize a torch.optim optimizer for the given model based on the specified name and learning rate.
Parameters
----------
name : string or None, default = 'adam'
The name... | 5c1a5e836176b90506ca6344c01ce6828b43d917 | 3,653,453 |
import httplib
import urllib
def get_http_url(server_path, get_path):
"""
Вариант с использованием httplib напрямую; ничем не лучше urllib2
server_path = "example.com"
get_path = "/some_path"
"""
# urllib - более высокого уровня библиотека, которая в случае http использует
# httplib;
... | d759609b1c48af28e678fa75bd9ff102f7eaafae | 3,653,454 |
def not_found_view(request):
"""Not Found view.
"""
model = request.context
return render_main_template(model, request, contenttile='not_found') | 0fe250d09f8fc007ffb07f848e59e779da9aefb0 | 3,653,455 |
import torch
def top_filtering(
logits, top_k=0, top_p=0.0, threshold=-float("Inf"), filter_value=-float("Inf")
):
""" Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k: <=0: n... | 7b230fc959e0078f1cfc5b2f2f991c79e0f4fd86 | 3,653,456 |
def get_physical_type(obj):
"""
Return the physical type that corresponds to a unit (or another
physical type representation).
Parameters
----------
obj : quantity-like or `~astropy.units.PhysicalType`-like
An object that (implicitly or explicitly) has a corresponding
physical t... | 03d28bdb9a507939e52bc0021dae3c539b4954a5 | 3,653,457 |
def reverse(list):
"""Returns a new list or string with the elements or characters in reverse
order"""
if isinstance(list, str):
return "".join(reversed(list))
return _list(reversed(list)) | ba74d9e4e54782114f534fb4c888c681ab708b67 | 3,653,458 |
from typing import Dict
def PubMedDiabetes(
directed: bool = False,
preprocess: bool = True,
load_nodes: bool = True,
verbose: int = 2,
cache: bool = True,
cache_path: str = "graphs/linqs",
version: str = "latest",
**additional_graph_kwargs: Dict
) -> Graph:
"""Return new instance ... | ddac0cfb8a525c42fe5a8d6c1a70677ab57451e0 | 3,653,459 |
def find_neighbor_indices(atoms, probe, k):
"""
Returns list of indices of atoms within probe distance to atom k.
"""
neighbor_indices = []
atom_k = atoms[k]
radius = atom_k.radius + probe + probe
indices = list(range(k))
indices = indices + list(range(k+1, len(atoms)))
for i in ind... | 05c3218357d660d6b66c3d614bfcb0d78431d32e | 3,653,460 |
def genDir(EAs):
"""
Generate the projection direction given the euler angles. Since the image
is in the x-y plane, the projection direction is given by R(EA)*z where
z = (0,0,1)
"""
dir_vec = np.array([rotmat3D_EA(*EA)[:, 2] for EA in EAs])
return dir_vec | 0753fad9638ca8b0ac4e899ad103dc08266a208b | 3,653,461 |
def plainica(x, reducedim=0.99, backend=None, random_state=None):
""" Source decomposition with ICA.
Apply ICA to the data x, with optional PCA dimensionality reduction.
Parameters
----------
x : array, shape (n_trials, n_channels, n_samples) or (n_channels, n_samples)
data set
reduced... | 7ffe9ebc78220898c84459fed61fc0f32fe05e69 | 3,653,462 |
def all_equal(values: list):
"""Check that all values in given list are equal"""
return all(values[0] == v for v in values) | 8ed08f63959367f3327554adc11b1286291963d8 | 3,653,464 |
def _tester(func, *args):
"""
Tests function ``func`` on arguments and returns first positive.
>>> _tester(lambda x: x%3 == 0, 1, 2, 3, 4, 5, 6)
3
>>> _tester(lambda x: x%3 == 0, 1, 2)
None
:param func: function(arg)->boolean
:param args: other arguments
:return: something or none
... | 035c8bf68b4ff7e4fbdb7ed1b2601f04110287d8 | 3,653,465 |
from datetime import datetime
def new_revision(partno):
"""
Presents the form to add a new revision, and creates it upon POST submit
"""
_load_if_released(partno) # ensures the component exists and is released
form = RevisionForm(request.form)
if request.method == 'POST' and form.validate_on_... | 722a3860e9daeb4bd5d9339f7dcaf5245c51b5de | 3,653,466 |
def fresnel_parameter(rays, diffraction_points):
""" returns the fresnel diffraction parameter (always as a positive)
Parameters
----------
rays : [n] list of shapely LineString (3d)
diffraction_points: [n] list of Points (3d)
diffraction point which the ray is rounding
Returns
---... | cd398797161f1e9e66805cd09162359ed6e89330 | 3,653,467 |
def validate(net, val_data, ctx, eval_metric):
"""Test on validation dataset."""
eval_metric.reset()
# set nms threshold and topk constraint
net.set_nms(nms_thresh=0.45, nms_topk=400)
net.hybridize()
for batch in val_data:
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_a... | 79dcd9b0d1920952b5badd4aa9f3f234776f6e06 | 3,653,468 |
from re import S
def add_unique_geom_id(point_gdf: gpd.GeoDataFrame, log: Logger=None) -> gpd.GeoDataFrame:
"""Adds an unique identifier (string) to GeoDataFrame of points based on point locations (x/y).
"""
point_gdf[S.xy_id] = [f'{str(round(geom.x, 1))}_{str(round(geom.y, 1))}' for geom in point_gdf[S.... | 0663e24b217c2911083d68146a5d8ff25c4fd8bd | 3,653,469 |
def get_data_parallel_rank():
"""Return my rank for the data parallel group."""
return _TOPOLOGY.get_data_parallel_rank() | a1da062793f6798e2e56809b3076c811f786a82b | 3,653,470 |
import math
def entropy(data):
"""
Compute the Shannon entropy, a measure of uncertainty.
"""
if len(data) == 0:
return None
n = sum(data)
_op = lambda f: f * math.log(f)
return - sum(_op(float(i) / n) for i in data) | ebfd9a84885a95ec6e4e7b2d88a0fb69fbbfaea1 | 3,653,471 |
import torch
def alexnet(pretrained=False):
"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = AlexNet()
if pretrained:
model_path = './mod... | a42df7c926472b88501001eefd691959e6acb3ac | 3,653,473 |
import torch
def generate_random_targets(labels: Tensor, num_classes: int) -> Tensor:
"""
Generates one random target in (num_classes - 1) possibilities for each label that is different from the original
label.
Parameters
----------
labels: Tensor
Original labels. Generated targets wi... | c0740c5ddc7c1f866b4c3cb2986f45a672d22e49 | 3,653,474 |
def recall_k(sent_im_dist, im_labels, ks=(1, 5, 10)):
"""
Compute recall at given ks.
"""
im_labels = tf.cast(im_labels, tf.bool)
def retrieval_recall(dist, labels, k):
# Use negative distance to find the index of
# the smallest k elements in each row.
pred = tf.nn.top_k(-dist, k=k)[1]
# ... | 188f2cb4c3581f9c565253fbb17797a408ce3d74 | 3,653,475 |
def get_suggestion(project_slug, lang_slug, version_slug, pagename, user):
"""
| # | project | version | language | What to show |
| 1 | 0 | 0 | 0 | Error message |
| 2 | 0 | 0 | 1 | Error message (Can't happen) |
| 3 | 0 | 1 | 0 | Error messa... | 66ddf3e44f006fcd1339b0483c3219c429643353 | 3,653,476 |
def resample_data_or_seg(data, new_shape, is_seg, axis=None, order=3, do_separate_z=False, cval=0, order_z=0):
"""
separate_z=True will resample with order 0 along z
:param data:
:param new_shape:
:param is_seg:
:param axis:
:param order:
:param do_separate_z:
:param cval:
:param... | ab7aa7ab1db40ec605d7069ccf3b1bc8751c3855 | 3,653,477 |
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