content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def get_model_kind(model):
"""Returns the "kind" of the given model.
NOTE: A model's kind is usually, but not always, the same as a model's class
name. Specifically, the kind is different when a model overwrites the
_get_kind() class method. Although Oppia never does this, the Apache Beam
framework... | 58465fd8d9a7893aeb046b5e05e713a912ff4a2f | 3,637,039 |
def get_Zvalence_from_pseudo(pseudo):
"""
Extract the number of valence electrons from a pseudo
"""
with open(pseudo.get_file_abs_path(),'r') as f:
lines=f.readlines()
for line in lines:
if 'valence' in line:
try:
return int(float(... | aade59ef7d9d7d517c19f95d237993433f21ed7a | 3,637,040 |
import ruptures as rpt
def detect_data_shifts(time_series,
filtering=True, use_default_models=True,
method=None, cost=None, penalty=40):
"""
Detect data shifts in the time series, and return list of dates where these
data shifts occur.
Parameters
----... | d924d36a53f965b76943f1a466d3b88649cbe0ef | 3,637,041 |
def read_rds(filepath):
"""Read an RDS-format matrix into a Pandas dataframe.
Location can be data, scratch, or results.
Index is populated from first column"""
raw_df = pyreadr.read_r(filepath)[None]
if raw_df.isnull().values.any():
raise ValueError("NaN's were found in the data matrix.")
... | c4b171638883fc2c3b32397e79a413a9441567f0 | 3,637,042 |
def history():
"""Show history of transactions."""
# Read Transactions database for desired elements
transactions = db.execute("SELECT symbol, share, price, method, timestamp FROM Transactions WHERE id = :uid", uid = session["user_id"])
# Convert prices to 2 decimal places
for transaction in tr... | 5eac4a49c473467db851fe2ea6e58b29cc1a9bfe | 3,637,043 |
from typing import get_args
def generate_args(job_name, common, cloud_provider, image, k8s_version,
test_suite, job):
"""Returns a list of args fetched from the given fields."""
args = []
args.extend(get_args(job_name, common))
args.extend(get_args(job_name, cloud_provider))
args... | 7f53dcf66269b0d14f9fad1c1079cf1716529f09 | 3,637,045 |
import math
def isPrime(n):
"""
check is Prime,for positive integer.
使用试除法
"""
if n <= 1:
return False
if n == 2:
return True
i = 2
thres = math.ceil(math.sqrt(n))
while i <= thres:
if n % i == 0:
return False
i += 1
return True | 458775fbd324dc976c91a035898b3122e6bc1109 | 3,637,046 |
from typing import Tuple
import torch
def reconstruction_loss(loss_type: str,
in_dim: Tuple[int],
x: torch.Tensor,
x_reconstr: torch.Tensor,
logits: bool = True,
) -> torch.Tensor:
"""
Compu... | 30dbd75eddbc7f2d0994f867e2f9492b24f707b1 | 3,637,047 |
def NOR(*variables):
"""NOR.
Return the boolean expression for the OR of the variables. Equivalent to
``NOT(OR(*variables))``.
Parameters
----------
*variables : arguments.
``variables`` can be of arbitrary length. Each variable can be a
hashable object, which is the label of t... | e3b9d5eb3c167ac04de66609828583bb5eeb7004 | 3,637,048 |
import io
import time
def timing_run(args, shell: bool = False, stdin=None, stdout=None, stderr=None,
environ=None, cwd=None, resources=None, identification=None, shuffle=False) -> RunResult:
"""
Create an timing process with stream
:param args: arguments for execution
:param shell: use... | 936d9611769dc5e04381131cd7bf18be73580bb3 | 3,637,049 |
def alterMethods(cls):
"""
Alter Monte methods on behalf of AutoHelp.
Return the signatures of the altered methods.
NOT_RPYTHON
"""
atoms = []
imports = set()
def nextName(nameIndex=[0]):
name = "_%d" % nameIndex[0]
nameIndex[0] += 1
return name
execNames... | 9c1dcbda1a96196bdde3f31563d53f8c2be6eeb1 | 3,637,050 |
from typing import List
from typing import Union
def make_multiclouds(docs: List[Union[dict, object, str, tuple]],
opts: dict = None,
ncols: int = 3,
title: str = None,
labels: List[str] = None,
show: bool = True,
... | 9c1f6363d1cc6cd0e20591c1ab54b1761414d29c | 3,637,051 |
def action_prop(param, val=1):
"""A param that performs an action"""
def fdo(self):
self.setter(param, val)
return fdo | 6a4f6e7e178e62755113d6b93a59534675dfa2dd | 3,637,052 |
def find_or_create(find, create):
"""Given a find and a create function, create a resource if it doesn't exist"""
result = find()
return result if result else create() | ffe608bf2da1b83d662b93266f4309976424300f | 3,637,053 |
import math
def Gsigma(sigma):
"""Pickle a gaussian function G(x) for given sigma"""
def G(x):
return (math.e ** (-(x**2)/(2*sigma**2)))/(2 * math.pi* sigma**2)**0.5
return G | 77eac3ca8b6ced0063074527b83c50e8681f980d | 3,637,054 |
from indico.modules.events.contributions.ical import generate_contribution_component
def session_to_ical(session, detailed=False):
"""Serialize a session into an iCal.
:param session: The session to serialize
:param detailed: If True, iCal will include the session's contributions
"""
calendar = i... | 9f0cb5a5ce6f31c6690b71948fbe6e8eeb2f7080 | 3,637,055 |
def _normalize_hosts(hosts):
"""
Helper function to transform hosts argument to
:class:`~elasticsearch.Elasticsearch` to a list of dicts.
"""
# if hosts are empty, just defer to defaults down the line
if hosts is None:
return [{}]
# passed in just one string
if isinstance(hosts,... | ef3a6cfadd6a297f31afdfec4b8a77a0f88cd08f | 3,637,056 |
def data(self: Client) -> DataProxy:
"""Delegates to a
:py:class:`mcipc.rcon.je.commands.data.DataProxy`
"""
return DataProxy(self, 'data') | 072806ad6f27e8bd645bd04cf34619946a83bf06 | 3,637,057 |
def proximal_policy_optimization_loss(advantage, old_prediction, loss_clipping=0.2, entropy_loss=5e-3):
"""
https://github.com/LuEE-C/PPO-Keras/blob/master/Main.py
# Only implemented clipping for the surrogate loss, paper said it was best
:param advantage:
:param old_prediction:
:param loss_clip... | ca7e1a602a6da6236fbd85facb373fa623fc62d5 | 3,637,058 |
import re
def tokenize_string(string):
"""Split a string up into analyzable characters.
Returns a list of individual characters that can
then be matched with the regex patterns.
Note that all accent characters can be found with
the range: \u0300-\u036F. Thus, strings are split
by [an... | f3757e190f99d3430dee17ca51ea6a6d7fa70ff9 | 3,637,059 |
def compute_final_metrics(source_waveforms, separated_waveforms, mixture_waveform):
"""Permutation-invariant SI-SNR, powers, and under/equal/over-separation."""
perm_inv_loss = wrap(lambda tar, est: -signal_to_noise_ratio_gain_invariant(est, tar))
_, separated_waveforms = perm_inv_loss(source_waveforms,sepa... | 3e7a6a52b8a26c4a4fa7fec9de17559617e4d467 | 3,637,060 |
import numpy
import pandas
def gen_sdc_pandas_series_rolling_impl(pop, put, get_result=result_or_nan,
init_result=numpy.nan):
"""Generate series rolling methods implementations based on pop/put funcs"""
def impl(self):
win = self._window
minp = self._min_... | 8fb25c10e862d21af75b244053ac96075c1efa19 | 3,637,061 |
def gen_random_colors(num_groups, colors=None):
"""
Generates random colors.
Parameters
----------
num_groups : int
The number of groups for which colors should be generated.
colors : list : optional (contains strs)
Hex based colors that should be appended if no... | 462835c6bacd5024ac20bab960d2c2e9d95e4dab | 3,637,062 |
def build_graph(sorted_sequence):
"""
Each node points to a list of the nodes that are reacheable from it.
"""
elements = set(sorted_sequence)
graph = defaultdict(lambda : [])
for element in sorted_sequence:
for i in [1, 2, 3]:
if element + i in elements:
grap... | a14d2278909df459856e23c7073d551b354f258d | 3,637,065 |
from numpy import std
def _findCentralBond(mol, distmat):
""" Helper function to identify the atoms of the most central bond.
Arguments:
- mol: the molecule of interest
- distmat: distance matrix of the molecule
Return: atom indices of the two most central atoms (in order)
"""
# ge... | bbaca8c48bf8c5e1a5d2ffa317448f05235c834e | 3,637,066 |
def transform(data, transformer):
"""This hook defines how DataRobot will use the trained object from fit() to transform new data.
DataRobot runs this hook when the task is used for scoring inside a blueprint.
As an output, this hook is expected to return the transformed data.
The input parameters are p... | b52577c0b2a3f3edb1297dcf9c567f9845f04bd5 | 3,637,067 |
import asyncio
import base64
async def sign_params(params, certificate_file, private_key_file):
"""
Signs params adding client_secret key, containing signature based on `scope`, `timestamp`, `client_id` and `state`
keys values.
:param dict params: requests parameters
:param str certificate_file: p... | be9980e5fb0b60da8a21c77b4ac7c9795560b557 | 3,637,068 |
def sum_of_fourth_powers(matrix):
"""
:param matrix: (numpy.ndarray) A numpy array.
:return: The fourth power of the four-norm of the matrix. In other words,
the sum of the fourth power of all of its entries.
"""
squared_entries = matrix * matrix
return np.sum(squared_entries * squared_e... | 51039a259594205a88b223b1e3d8387e05581c0f | 3,637,069 |
from typing import Dict
def key_in_direction(start: Key, direction: str, keypad: Keypad) -> Key:
"""
Return the value of the key in the given direction.
"""
row = next(r for r in keypad if start in r)
x_pos = row.index(start)
col = [c[x_pos] for c in keypad]
y_pos = col.index(start)
d... | c0a8909517ec1de29325d0acc18e0c8968bda3b5 | 3,637,070 |
def vectorize_args(nums):
"""
Decorator for vectorization of arguments of a function.
The positions of the arguments are given in the tuple nums.
See numpy.vectorize.
"""
def wrap(func):
@wraps(func)
def wrapped(*args, ** kwargs):
args = list(args)
for i,... | cd9b13bdcd26f1c74a2eaa18396ebfb11ed02446 | 3,637,071 |
def parse_lambda_config(x):
"""
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
# to 0 during the first 1000 iterations
... | d85980c2efd46284de8e939f42ef4f5dd49dfd73 | 3,637,072 |
def format_cols(colname, direction='in'):
"""Formats columns beween human-readable and pandorable
Keyword arguments:
real -- the real part (default 0.0)
imag -- the imaginary part (default 0.0)
"""
if imag == 0.0 and real == 0.0:
return complex_zero
...
if direction == 'in':
... | a61dbedb2e08c4de03c719c4daff10de41e19304 | 3,637,073 |
def convert_decimal_to_binary(number):
"""
Parameters
----------
number: int
Returns
-------
out: str
>>> convert_decimal_to_binary(10)
'1010'
"""
return bin(number)[2:] | 01a9be2e70c87091adc1d85759075668da9270f2 | 3,637,074 |
from typing import Optional
import pathlib
import tarfile
def fetch_tgz(
dataname: str,
urlname: str,
subfolder: Optional[str] = None,
data_home: Optional[str] = None,
) -> pathlib.Path:
"""Fetch tgz dataset.
Fetch a tgz file from a given url, unzips and stores it in a given
directory.
... | 00c4f91a657e37767a43b3af0766b5b407144617 | 3,637,075 |
def choisir_action():
"""Choisir action de cryptage ou de décryptage
Entree : -
Sortie: True pour cryptage, False pour décryptage"""
action_est_crypter = True
action = input("Quelle est l'action, crypter ou décrypter ? \n<Entrée> pour crypter, autre touche pour decrypter, ou <Crtl> + Z ou X pour arréter.\n")
... | c0bceb748afb1fc32b865136c4a477f06a6412b2 | 3,637,076 |
def σ(u, p, μ):
"""Stress tensor of isotropic Newtonian fluid.
σ = 2 μ (symm ∇)(u) - p I
This method returns a UFL expression the whole stress tensor. If you want
to plot, extract and interpolate or project what you need. For example,
to plot the von Mises stress::
from dolfin import ... | 03f61ea7c128503ee930714107a8f7a007641cee | 3,637,077 |
async def cycle(command: Command, switches: PowerSwitch, name: str, portnum: int):
"""cycle power to an Outlet"""
command.info(text=f"Cycle port {name}...")
for switch in switches:
current_status = await switch.statusAsJson(name, portnum)
if current_status:
break
# print(... | 7b5a17eaeecb4d8f1072f014de716bb1bb95dc97 | 3,637,078 |
def zk_delete_working_node(zk_client, server):
"""删除服务节点"""
node_path, root_path = get_path_to_current_working_node(server)
zk_client.ensure_path(root_path)
result = zk_client.delete(node_path, ephemeral=True)
return result | 45effe39d8cd5eb22742c6eed19984ae40b0e192 | 3,637,079 |
import torch
def construct_filters_from_2d(matrix, filter_starts, decomp_level):
"""
construct the filters in the proper shape for the DWT inverse forward step
Parameters
----------
matrix
filter_starts
decomp_level
Returns
-------
"""
exp = filter_starts[0]
low = ma... | 10411e774dc654586cd9b88b40e405b695a12919 | 3,637,080 |
def minpoly(firstterms):
"""
Return the minimal polynomial having at most degree n of of the
linearly recurrent sequence whose first 2n terms are given.
"""
field = ring.getRing(firstterms[0])
r_0 = uniutil.polynomial({len(firstterms):field.one}, field)
r_1 = uniutil.polynomial(enumerate(rev... | 8cad899aa40859884b4cdbe01b0734de84782804 | 3,637,081 |
def scale_gradient(tensor, scale):
"""Scales the gradient for the backward pass."""
return tf.add(tensor * scale ,tf.stop_gradient(tensor) * (1 - scale)) | e3ea3a7baf06ebab5de0510ea13260e89b9397ca | 3,637,082 |
import ast
import random
def t_rename_local_variables(the_ast, all_sites=False):
"""
Local variables get replaced by holes.
"""
changed = False
candidates = []
for node in ast.walk(the_ast):
if isinstance(node, ast.Name) and isinstance(node.ctx, ast.Store):
if node.id not i... | 8faeea81faac55d5d45b897776cd87cb508404a5 | 3,637,084 |
from typing import List
def get_scale(notes: List[str]) -> int:
"""Convert a list of notes to a scale constant.
# Args
- *notes*: list of notes in the scale. This should be a list of string
where each string is a note ABC notation. Sharps should be
represented with a pound sign preceding... | 91cbcc7bfa05df52adf741b85f78beeabf819966 | 3,637,085 |
import math
def slurm_format_bytes_ceil(n):
""" Format bytes as text.
SLURM expects KiB, MiB or Gib, but names it KB, MB, GB. SLURM does not handle Bytes, only starts at KB.
>>> slurm_format_bytes_ceil(1)
'1K'
>>> slurm_format_bytes_ceil(1234)
'2K'
>>> slurm_format_bytes_ceil(12345678)
... | ce48c778b9605105ed9b66a55d27796fb90499cc | 3,637,086 |
def factory_payment_account(corp_number: str = 'CP0001234', corp_type_code: str = 'CP',
payment_system_code: str = 'PAYBC'):
"""Factory."""
return PaymentAccount(
corp_number=corp_number,
corp_type_code=corp_type_code,
payment_system_code=payment_system_code,
... | 896fe2ac0162455c4da97bd629d0e3f2d9b2a1e2 | 3,637,087 |
def foo():
"""多参数函数的传参书写格式, 和类实例化的格式"""
ret = foo_long(a=1, b=2, c=3, d=4,
e=5, f=6, g=7, h=8)
# 类实例化,传多个参数的格式
object_ = ClassName(
a=1, b=2, c=3, d=4,
e=5, f=6, g=7, h=8
)
return ret | 4571ef723cab1601acfa01eb0765eaf8002df2e0 | 3,637,089 |
def posture_seq(directory,postures,sampling_fraction):
"""posture_seq grabs samples locomotion files from a directory and
converts them to strings of posture_sequences
Input:
directory = the directory containing locomotion files
postures = the mat file or numpy array of template postur... | 7e1554f85dfc68b293c9db5a5db3aa5bd6414bff | 3,637,090 |
from ._finite_differences import _window1d, _lincomb
import torch
def membrane_diag(voxel_size=1, bound='dct2', dim=None, weights=None):
"""Diagonal of the membrane regulariser.
If no weight map is provided, the diagonal of the membrane regulariser
is a scaled identity with scale `2 * alpha`, where
`... | 3329c43aa5ae025a14660e1ddd4c1f658740e1d4 | 3,637,091 |
def get_groups(parsed, store, conf):
"""
Return groups based on argument provided
:param Namespace parsed: arguments parsed
:param store: Otter scaling group collection
:param dict conf: config
:return: Deferred fired with list of {"tenantId": .., "groupId": ..} dict
"""
log = mock_log... | 0441863984173236b09b50987c6f22838679a497 | 3,637,093 |
import json
def get_content_details(site_code, release_uuid, content_type, content_key):
""" get_content_details """
publisher_api = PublisherAPI()
content_release = None
try:
if release_uuid:
# get ContentRelease
content_release = WSSPContentRelease.objects.get(
... | f71a4e4584474e24cfb6d25aad2465538575cbdf | 3,637,094 |
import scipy
def _czt(x, M=None, W=None, A=1.0):
"""Calculate CZT (Stripped down to the basics)."""
# Unpack arguments
N = len(x)
if M is None:
M = N
if W is None:
W = np.exp(-2j * np.pi / M)
A = np.complex128(A)
W = np.complex128(W)
# CZT algorithm
k = np.arange(... | a0852eacd8d4e35e0c6e96cc59e8692d9d806c5d | 3,637,095 |
from typing import Any
from typing import Optional
def build_obs_act_forward_fc(
n_out: int,
depth: int,
hidden: int,
act_layer: Any,
last_layer: Optional[Any] = None,
) -> hk.Transformed:
"""Build a simple fully-connected forward step that takes an observation & an action.
Args:
... | 0d330910730ccf80213852aa7cd08950f09e6300 | 3,637,097 |
def update_nested(key, d, other):
"""Update *d[key]* with the *other* dictionary preserving data.
If *d* doesn't contain the *key*, it is updated with *{key: other}*.
If *d* contains the *key*, *d[key]* is inserted into *other[key]*
(so that it is not overriden).
If *other* contains *key* (and poss... | efbbfd576652710c92939581c48e32edce1a956e | 3,637,098 |
def quicksort(arr, low, high):
""" Quicksort function uses the partition helper function.
"""
if low < high:
pi = partition(arr, low, high)
quicksort(arr, low, pi-1)
quicksort(arr, pi+1, high)
return arr | aa51f8536f47f8529c2bda74ea96138062d939e7 | 3,637,099 |
def make_word_dict():
"""read 'words.txt ' and create word list from it
"""
word_dict = dict()
fin = open('words.txt')
for line in fin:
word = line.strip()
word_dict[word] = ''
return word_dict | a4213cf5ff246200c7a55a6d1525d6fd6067e31f | 3,637,100 |
def voidobject(key_position: int, offset: int) -> HitObject:
"""
引数から判定のないヒットオブジェクト(シングルノーツのみ)のHitObjectクラスを生成します
引数
----
key_position : int
-> キーポジション、1から入れる場合はkey_assetから参照したものを入れてください
offset : int
-> (配置する)オフセット値
戻り値
------
HitObject
-> 空ノーツのHitObjectクラス
"""
return HitObject(key_position, max_off... | d7d47204bfb09592811fa85c4aa71e3e80bfa7bc | 3,637,101 |
def mock_user_save():
"""Функция-пустышка для эмуляции исключения во время записи пользователя."""
def user_save(*args, **kwargs):
raise IntegrityError
return user_save | 144ad41b9b9a2d477d622b6c2284c36514581ea1 | 3,637,102 |
def index():
"""首页"""
banners = Banner.query_used()
page = request.args.get("page", 1, type=int) # 指定的页码
per_page = current_app.config["MYZONE_ARTICLE_PER_PAGE"] # 每页的文章数
pagination = Article.query_order_by_createtime(page, per_page=per_page) # 创建分页器对象
articles = pagination.items # 从分页器中获取查询... | ba3f6a558e4edb60025ef01832bb5ff5a1fb7f7a | 3,637,103 |
def create_temporal_vis(ldf, col):
"""
Creates and populates Vis objects for different timescales in the provided temporal column.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
col : str
Name of temporal column.
Returns
----... | 9a52600c1aac10a76b85b63c2879341dcc14b415 | 3,637,104 |
def num_neighbours(skel) -> np.ndarray:
"""Computes the number of neighbours of each skeleton pixel.
Parameters
----------
skel : (H, W) array_like
Input skeleton image.
Returns
-------
(H, W) array_like
Array containing the numbers of neighbours at each skeleton pixel and ... | aad9f1de0f192777ebc41e603cd6ac47aa3cd49f | 3,637,106 |
def FakeSubject(n=300, conc=0.1, num_reads=400, prevalences=None):
"""Makes a fake Subject.
If prevalences is provided, n and conc are ignored.
n: number of species
conc: concentration parameter
num_reads: number of reads
prevalences: numpy array of prevalences (overrides n and conc)
"... | 91230288344c55cd4417175560ec7b3e714d9f98 | 3,637,107 |
from datetime import datetime
import pytz
def build_results_candidate_people():
"""
Return DataFrame containing results, candidates, and people joined
"""
people = pd.read_csv('data/people.csv')
candidates = pd.read_csv('data/candidates.csv')
results = pd.read_csv('data/results.csv')
res... | 5e330b026b3546e728f9a06df33eaf8fc429775c | 3,637,108 |
def div(lhs: Value, rhs: Value) -> Value:
""" Divides `lhs` by `rhs`. """
return lhs.run() // rhs.run() | 73cb05b536c94e56331054e92e7d9fb84f75fdb5 | 3,637,109 |
def get_seat_total_per_area(party_id: PartyID) -> dict[AreaID, int]:
"""Return the number of seats per area for that party."""
area_ids_and_seat_counts = db.session \
.query(
DbArea.id,
db.func.count(DbSeat.id)
) \
.filter_by(party_id=party_id) \
.outerjoi... | 35aced1f8e149a06f54ed43f41b80f796608316b | 3,637,110 |
def toCamelCase(string: str):
"""
Converts a string to camel case
Parameters
----------
string: str
The string to convert
"""
string = str(string)
if string.isupper():
return string
split = string.split("_") # split by underscore
final_split = []
for... | 5197ad3353f2e88ccf1dfca62aeae59260e016e7 | 3,637,111 |
def aggregate_testsuite(testsuite):
""" Compute aggregate results for a single test suite (ElemTree node)
:param testsuite: ElemTree XML node for a testsuite
:return: AggregateResult
"""
if testsuite is None:
return None
tests = int(testsuite.attrib.get('tests') or 0)
failures = int... | 3b7ff5b353e0f6efffed673e1dcb463f00a0e708 | 3,637,112 |
def rowwidth(view, row):
"""Returns the number of characters of ``row`` in ``view``.
"""
return view.rowcol(view.line(view.text_point(row, 0)).end())[1] | f8db1bf6e3d512d1a2bd5eeb059af93e8ac3bc5f | 3,637,113 |
import json
def dry_query(event, *args):
"""Handles running a dry query
Args:
url: dry_query?page&page_length&review_id
body:
search: search dict <wrapper/input_format.py>
Returns:
{
<wrapper/output_format.py>
}
"""
# try:
body = json.l... | 0c69da353d958e9628e31dce68fe6bcafd482f2c | 3,637,115 |
def fixed_prior_to_measurements(coords, priors):
"""
Convert the fixed exchange and met conc priors to measurements.
"""
fixed_exchange = get_name_ordered_overlap(coords, "reaction_ind", ["exchange", "fixed_x_names"])
fixed_met_conc = get_name_ordered_overlap(coords, "metabolite_ind", ["metabolite",... | 3dab3eddb5f785dd04bba4caddbc631a0cdfd187 | 3,637,116 |
def get_batch_size():
"""Returns the batch size tensor."""
return get_global_variable(GraphKeys.BATCH_SIZE) | 4b030738c78fa5a06d27a2aee62f15ff3e6be347 | 3,637,117 |
from altdataset import CSVDataset
def get_dataloader(config: ExperimentConfig, tfms: Tuple[List, List] = None):
""" get the dataloaders for training/validation """
if config.dim > 1:
# get data augmentation if not defined
train_tfms, valid_tfms = get_data_augmentation(config) if tfms is None e... | d314a0bf6f7c9707ce46127e06bc8c22183246f1 | 3,637,118 |
def retournerTas(x,numéro):
"""
retournerTas(x,numéro) retourne la partie du tas x qui commence à
l'indice numéro
"""
tasDuBas = x[:numéro]
tasDuHaut = x[numéro:]
tasDuHaut.reverse()
result = tasDuBas + tasDuHaut
# print(result)
return result | 579798cf5fe8bec02109bfd46c5a945faee1a42c | 3,637,119 |
def nback(n, k, length):
"""Random n-back targets given n, number of digits k and sequence length"""
Xi = random_state.randint(k, size=length)
yi = np.zeros(length, dtype=int)
for t in range(n, length):
yi[t] = (Xi[t - n] == Xi[t])
return Xi, yi | 37ec70fdc60104fc5a99c6ba13923a2e3d56f0a4 | 3,637,121 |
def makeStateVector(sys, start_time=0):
"""
Constructs the initial state vector recursively.
Parameters
----------
sys: inherits from control.InputOutputSystem
start_time: float
Returns
-------
list
"""
x_lst = []
if "InterconnectedSystem" in str(type(sys)):
for... | e184d476c9ba94d88ee462c95987cabc31e459d0 | 3,637,122 |
def make_random_tensors(spec_structure, batch_size = 2):
"""Create random inputs for tensor_spec (for unit testing).
Args:
spec_structure: A dict, (named)tuple, list or a hierarchy thereof filled by
TensorSpecs(subclasses).
batch_size: If None, we will have a flexible shape (None,) + shape. If <= 0
... | dd2569def0863b1e9722de9c6175e680353ccf56 | 3,637,123 |
def simulate(robot, task, opt_seed, thread_count, episode_count=1):
"""Run trajectory optimization for the robot on the given task, and return the
resulting input sequence and result."""
robot_init_pos, has_self_collision = presimulate(robot)
if has_self_collision:
return None, None ... | 13c069282636e7b4215654d958621ed418bc40a8 | 3,637,124 |
import time
def config_worker():
"""
Enable worker functionality for AIO system.
:return: True if worker-config-complete is executed
"""
if utils.get_system_type() == si_const.TIS_AIO_BUILD:
console_log("Applying worker manifests for {}. "
"Node will reboot on completio... | 4ab82a2988a70ec9fe2f2ab6aa45099b7237b07a | 3,637,125 |
def convert_dict_to_df(dict_data: dict):
"""
This method is used to convert dictionary data to pandas data frame
:param dict_data:
:return:
"""
# create df using dict
dict_data_df = pd.DataFrame.from_dict([dict_data])
# return the converted df
return dict_data_df | 550e33b0b3bacbdfb3abeb8019296be2c647000e | 3,637,126 |
def sec2msec(sec):
"""Convert `sec` to milliseconds."""
return int(sec * 1000) | f1b3c0bf60ab56615ed93f295e7716e56c6a1117 | 3,637,127 |
import aiohttp
async def _request(session:aiohttp.ClientSession, url:str, headers:dict[str,str]) -> str:
"""
获取单一url的愿望单页面
"""
async with session.get(url=url, headers=headers, proxy=PROXY) as resp:
try:
text = await resp.text()
except Exception as err:
text = ""... | f891736d4598adc0005c096e12ab43d41544ab36 | 3,637,128 |
def get_pretrained_i2v(name, model_dir=MODEL_DIR):
"""
Parameters
----------
name
model_dir
Returns
-------
i2v model: I2V
"""
if name not in MODELS:
raise KeyError(
"Unknown model name %s, use one of the provided models: %s" % (name, ", ".join(MODELS.keys(... | 75657f039763ae73219eae900061a426ed2b11fd | 3,637,129 |
def object_get_HostChilds(obj):
"""Return List of Objects that have set Host(s) to this object."""
# source:
# FreeCAD/src/Mod/Arch/ArchComponent.py
# https://github.com/FreeCAD/FreeCAD/blob/master/src/Mod/Arch/ArchComponent.py#L1109
# def getHosts(self,obj)
hosts = []
for link in obj.InLis... | dccba2ef151207ebaa42728ee1395e1b0ec48e7d | 3,637,130 |
import torch
def collate_fn(batch):
"""
Collate function for combining Hdf5Dataset returns
:param batch: list
List of items in a batch
:return: tuple
Tuple of items to return
"""
# batch is a list of items
numEntries = [];
allTensors = [];
allLabels = [];
for... | b49ec88b4de844787d24140f5ef99ad9a573c6e3 | 3,637,131 |
def test_psf_estimation(psf_data, true_psf_file, kernel=None, metric='mean'):
"""Test PSF Estimation
This method tests the quality of the estimated PSFs
Parameters
----------
psf_data : np.ndarray
Estimated PSFs, 3D array
true_psf_file : str
True PSFs file name
kernel : int... | 10feef6a483cfa6345561dcf5d1717a466a78c7d | 3,637,132 |
def EulerBack(V_m0,n_0,m_0,h_0,T,opcion,t1,t2,t3,t4,I1,I2,h_res=0.01):
"""
:param V_m0: Potencial de membrana inicial
:param n_0: Probabilidad inicial de n
:param m_0: Probabilidad inicial de m
:param h_0: Probabilidad inicial de h
:param T: Temperatura indicada por el usuario
:param opcion:... | 33660894f80d3060206da3ddbb96d40b8453fc72 | 3,637,133 |
def wiggle(shape, scope, offset, seed=0):
"""Shift points/contours/paths by a random amount."""
if shape is None: return None
functions = { "points": wiggle_points,
"contours": wiggle_contours,
"paths": wiggle_paths}
fn = functions.get(scope)
if fn is None: retu... | 0cd587646013810ca512de5d327c2fdc24b110f5 | 3,637,134 |
def parseAndDisplay(line, indentLevel):
"""Indents lines."""
if line.startswith("starting "):
printArgumentLine(indentLevel, line)
indentLevel += 1
elif line.startswith("ending "):
indentLevel -= 1
printArgumentLine(indentLevel, line)
else:
printLine(indentLevel, ... | 14c9ebe27140aa77f5f7980e1da2bec30e7ccf8b | 3,637,135 |
def insert_question(question):
"""
Insert a particular question
@param: question - JSON object containing question data to be inserted
"""
return db.questions.insert_one(question) | f4d22a137a1e7d9fbe43a1e03414d551cceb27c9 | 3,637,136 |
def sequence_vectorize(train_texts, val_texts):
"""Vectorizes texts as sequence vectors.
1 text = 1 sequence vector with fixed length.
# Arguments
train_texts: list, training text strings.
val_texts: list, validation text strings.
# Returns
x_train, x_val, word_index: vectoriz... | f32c40ca2f8bc6d2c78f8093ccf94fee192b87c8 | 3,637,137 |
def parse_preferences(file, preferences):
"""Parse preferences to the dictionary."""
for line in open(file, "r").readlines():
# all lower case
line = line.lower()
# ignore comment lines
if line[0] == "!" or line[0] == "#" or not line.split():
continue
key ... | 09c0251cd34cfbb6c9342eccd697a08259c744c6 | 3,637,138 |
def func_hex2str(*args):
"""字符串 -> Hex"""
return func_hex2byte(*args).decode('utf-8') | 732f333cd942ecd8bee4ac4b974f0301e0c69baf | 3,637,139 |
import collections
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n")
vocab[token] = ind... | 801833664a67e5d6e62dfb5379cabeb1b1b5058c | 3,637,141 |
from typing import List
def triage(routes: List[Route]) -> Route:
"""
This function will be used to determine which route to use
"""
eva = {}
for i, route in enumerate(routes):
stored_route: StoredRoute = route.pop("stored_route")
reg_path = stored_route["path"]
segments = ... | 625b143c3284526b71d21a7c0113e892df92ed3a | 3,637,142 |
def upsert_object(data, cursor=None):
"""
Upsert an object in the repository.
"""
cursor = check_cursor(cursor)
data = _set_object_defaults(data, cursor)
cursor.execute('''
INSERT INTO objects (pid_id, namespace, state, owner, label, versioned,
log, created,... | de0de4a48bf4f1d846938e174bb5a5300dd49083 | 3,637,143 |
import torch
def sparsity_line(M,tol=1.0e-3,device='cpu'):
"""Get the line sparsity(%) of M
Attributes:
M: Tensor - the matrix.
tol: Scalar,optional - the threshold to select zeros.
device: device, cpu or gpu
Returns:
spacity: Scalar (%)- the spacity of the matr... | b8675a768c8686571d1f7709d89e3abeb5b56a80 | 3,637,144 |
def geospace(lat0, lon0, length, dx, strike):
""" returns a series of points in geographic coordinates"""
pts_a = []
npts = length // dx + 1
for idx in range(npts):
# convert to lat, lon
new = convert_local_idx_to_geo(idx, lat0, lon0, length, dx, strike)
pts_a.append(new)
ret... | 78a380b59768cf83eca8edba5f1e21a0b6b61636 | 3,637,145 |
def linearOutcomePrediction(zs, params_pred, scope=None):
"""
English:
Model for predictions outcomes from latent representations Z,
zs = batch of z-vectors (encoder-states, matrix)
Japanese:
このモデルにおける、潜在表現Zから得られる出力の予測です。
zs = ベクトル z のバッチ(袋)です。 (encoder の状態であり、行列です)
(恐らく、[z_0, z_1, z_2, ... | 3e92fe0c0d16d8565066216c1da96b6fdbeb8dc9 | 3,637,146 |
from datetime import datetime
import collections
def _check_flag_value(flag_value):
"""
Search for a given flag in a given blockette for the current record.
This is a utility function for set_flags_in_fixed_headers and is not
designed to be called by someone else.
This function checks for valid ... | 2e4da676ad7abf95aa157aaca5aae80975b893e2 | 3,637,147 |
def logout():
""" Logout a user """
session.pop('user_id', None)
session.pop('player_id', None)
return redirect(url_for('index')) | d7d375e28a3e432c42b845cccf0adecb37cf46e1 | 3,637,148 |
def get_available_gpus():
"""Returns a list of available GPU devices names. """
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == "GPU"] | 9c62204fa1bdc8ad22fd56ecad14bde895a08ec6 | 3,637,149 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.