content stringlengths 35 762k | sha1 stringlengths 40 40 | id int64 0 3.66M |
|---|---|---|
def run_garage(env, seed, log_dir):
"""
Create garage model and training.
Replace the ppo with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trail.
:param log_dir: Log dir path.
:return:
"""
ext.set_seed(seed)
with tf.Grap... | a1a1f2adadab26460b0035c02d663af031db62cc | 62,835 |
def modified_helmholtz_adjoint_double_layer_singular(
test_points, trial_points, test_normal, trial_normal, kernel_parameters
):
"""Modified Helmholtz adjoint double layer for singular kernels."""
npoints = trial_points.shape[1]
dtype = trial_points.dtype
diff = _np.zeros((3, npoints), dtype=dtype)
... | afe0c8c8e9f2a6096fb4928000ae46dcc2c326cd | 62,836 |
import time
import shutil
def curate_dataset(sourcedataset, destinationdataset, pathdataset, newdatasetname,\
manifeststatus, jsonpath, jsondescription):
"""
Associated with 'Generate' button in the 'Generate dataset' section of SODA interface
Checks validity of files / paths / folders and then ge... | e4427d48d3bdb81d384d3d2673f1a88f817b377c | 62,837 |
def adp_to_cartesian(alpha, delta, parallax):
"""
Convert coordinates from (alpha, delta, parallax) format into the (x, y, z)
format.
:param azimuth: [rad]
:param altitude: [rad]
:param parallax: [mas]
:return: [parsec](x, y, z) array in parsecs.
"""
parallax = parallax / const.rad... | bc3dac955d57458fbcb3f737617850730e6862c7 | 62,838 |
def render_hunspell_word_error(
data,
fields=["filename", "word", "line_number", "word_line_index"],
sep=":",
):
"""Renders a mispelled word data dictionary.
This function allows a convenient way to render each mispelled word data
dictionary as a string, that could be useful to print in the con... | 9bbefd0b998abe25d0a977adfe69ef51185cde37 | 62,839 |
def atan2(y, x):
""" My implementation of atan2 in tensorflow. Returns in -pi .. pi."""
tan = tf.atan(y / (x + 1e-8)) # this returns in -pi/2 .. pi/2
one_map = tf.ones_like(tan)
# correct quadrant error
correction = tf.where(tf.less(x + 1e-8, 0.0), 3.141592653589793*one_map, 0.0*one_map)
tan... | 6874de9fa697102d7af47af241e73ee322ce54c7 | 62,840 |
def ElasticConstants(atoms, symmetry, minimize=False, **kwargs):
"""Calculate the anisotropic elastic constants of a system.
The atoms must be a periodic system with an orthorhombic cell,
and the lattice constant(s) should be the equilibrium value(s).
If not, the function will crash, unless calle... | ba0b70e21209c9eb1691ec8b2af247a86d755759 | 62,841 |
from typing import List
import torch
def sample_subgraph(graph_data: List[GraphInput], n_nodes=None,
max_graph_size=None, do_sample=True, permuted_nodes=None):
"""
@param do_sample: if False, return original graph.
@param graph_data: a list of N graphs, [anchor, ...], which share the ... | dca3d0af8b3f6256e7bfcf5581f9d51011731784 | 62,842 |
import re
def tokenize(document):
"""
A tokenizer for the Python Textmining Package that works for unicode strings
and doesn't replace non-ASCII characters (such as 'åäö') with spaces.
"""
document = document.lower()
document = re.sub(u'[^\w]|[0-9]', u' ', document, flags=re.UNICODE)
retur... | 1d4a1e8ed05796df3ac797a0a08b82dba5f640e6 | 62,843 |
def update_context_path_comp_service_objective_function_name_name_by_id(uuid, value_name, name): # noqa: E501
"""Update name by ID
Update operation of resource: name # noqa: E501
:param uuid: ID of uuid
:type uuid: str
:param value_name: ID of value_name
:type value_name: str
:param name:... | 921bc1f4859de1ec74b4ee84f51974c2a790639d | 62,844 |
def ldns_resolver_print(*args):
"""LDNS buffer."""
return _ldns.ldns_resolver_print(*args) | bd4aa9bceaf5d20c5a3d8da980641b952a3f2332 | 62,845 |
import logging
def show_metadata_probe(pad, info, user_data):
"""Buffer probe to show metadata in a buffer"""
gst_buffer = info.get_buffer()
if gst_buffer:
logging.info("show_metadata_probe: %20s:%-8s: pts=%23s, dts=%23s, duration=%23s, size=%8d" % (
pad.get_parent_element().name,
... | f10c06f2dc61e545bd408dc48d18bf43b2459ccd | 62,846 |
def _get_dataset_iterator(snli_data, batch_size):
"""Get a data iterator for a split of SNLI data.
Args:
snli_data: A `data.SnliData` object.
batch_size: The desired batch size.
Returns:
A dataset iterator.
"""
with tf.device("/device:CPU:0"):
# Some tf.data ops, such as ShuffleDataset, are ... | 5574e3d9eecbfbe4e3db6390b0ca9bb10e5ac813 | 62,847 |
import signal
def coherence(original_values, frames=[0]):
"""Calculate coherence and an idea ribo-seq signal
Parameters
----------
values : array like
List of values
Returns
-------
periodicity : float
Periodicity score calculated as
coher... | 0656b7c5a43feb9c6264a24177754afb9e56de0a | 62,848 |
def distance_matrix(coords: list) -> np.ndarray:
"""Return distance matrix from coords"""
return np.triu(spt.distance.cdist(coords, coords, "euclidean")) | 050061e9624148a83e056511535094a5345f57e6 | 62,849 |
def get_domain_alerts_command(client: MsClient, args: dict):
"""Retrieves a collection of Alerts related to a given domain address.
Returns:
(str, dict, dict). Human readable, context, raw response
"""
headers = ['ID', 'Title', 'Description', 'IncidentID', 'Severity', 'Status', 'Classification'... | 4d6a8fbaf929e3c55849d67e08abf5b88dd91a5c | 62,850 |
import torch
def load_model(config, from_checkpoint=False):
"""
Wrapper around initialize_model. Loads model with parameters in config
and loads weights if from_checkpoint is set to True.
Args:
config (coconet.core.Configuration object)
from_checkpoint (bool): whether to load a pre-tr... | 3bfae38e940f8158eddae0a0922a45054cb2b901 | 62,851 |
from create_gitrepo import create_gitrepo
import random
import tqdm
from pathlib import Path
import re
import json
from datetime import datetime
def make_gt_line_pairs(ctx, fpaths, outputfolder, psm, lang, ext,
autocontrast, level, padval, padprc,
regex, min_len, max_len,... | 86082484b630a3e3e52bb4f657bb354612054a91 | 62,852 |
def postprocess_metainfo( opts, doxyindex, result ):
"""finalizes a metainfo by adding some generic fields"""
result['base_name'] = result['base'].decl_string.lstrip( '::' )
assert result['base'] and result['type'] and result['category']
result['gcc'] = create_decl( result['base'], doxyindex, opts )
... | 8382e41a6cda86904364b9010e94f3ed9fd2837a | 62,854 |
def int2lehex(value, width):
"""
Convert an unsigned integer to a little endian ASCII hex string.
Args:
value (int): value
width (int): byte width
Returns:
string: ASCII hex string
"""
return value.to_bytes(width, byteorder='little').hex() | 1ce9bb9447236c36bb906560c65ffd8e058c5aa4 | 62,855 |
def get_df_from_list(audio_files):
"""
Will return a data frame
filled with the features from audio files from the list
"""
frames = [get_features_from_file(f) for f in audio_files]
return pd.concat(frames, ignore_index=True) | e68f80d7f556a746f4232c031d6d917fb30f95bb | 62,856 |
def simple_env():
"""Start up a super simple OneVsOne config."""
env = envs.v0.Pomme
game_type = constants.GameType.OneVsOne
env_entry_point = 'pommerman.envs.v0:Pomme'
env_id = 'Simple-v0'
env_kwargs = {
'game_type': game_type,
'board_size': constants.BOARD_SIZE_SIMPLE,
... | ddfd7b03a95abb85ca23ddc332dc62aa7968a89f | 62,858 |
def batchnorm_forward(x, gamma, beta, bn_param):
"""
使用使用类似动量衰减的运行时平均，计算总体均值与方差 例如:
running_mean = momentum * running_mean + (1 - momentum) * sample_mean
running_var = momentum * running_var + (1 - momentum) * sample_var
Input:
- x: 数据(N, D)
- gamma: 缩放参数 (D,)
- beta: 平移参数 (D,)
... | 32d662f5a40f35c8d97d3823d25ca13fc6f5916f | 62,859 |
def nearest_cluster_center(point, cluster_centers):
"""Distance and index of the closest cluster center"""
min_index = point.group
min_dist = FLOAT_MAX
for i, cc in enumerate(cluster_centers):
d = sqr_distance_2D(cc, point)
if min_dist > d:
min_dist = d
min_inde... | ccbebac91402e1bb0d3e5f4a8dd42028f858201b | 62,860 |
from typing import Callable
from typing import Tuple
import time
import warnings
def lanczos_algorithm(mvp_fn: Callable[[tf.Tensor], tf.Tensor],
dim: int,
order: int,
random_seed: int = 0,
only_gpu: bool = True) -> Tuple[tf.Tensor... | 859c395531ff3edf3f2a8495a52eb03c288379b3 | 62,861 |
def calculate_active_senders(api, all_messages, top_n=20, template=None):
"""
Return a top_n long df group of number of messages and average length.
"""
groupbys = {group: am.groupby("usender") for group, am in all_messages.items()}
active_senders = {group: pd.DataFrame(groupby.count()['body'].sort_... | 64de84679c26b1b396a8335db65b9d50374ef98c | 62,862 |
def detect() -> tp.List[Device]:
"""
Calls all detectors and returns a list of available devices.
"""
devices = []
SmlReader.detect(devices)
PlainReader.detect(devices)
Bme280Reader.detect(devices)
return devices | 696813b833c9a28f4a1862de27327f7d6ac66cdc | 62,863 |
from datetime import datetime
from typing import Optional
def update_datetime(
dt: datetime.datetime,
date: Optional[datetime.date] = None,
time: Optional[datetime.time] = None,
date_delta: Optional[datetime.timedelta] = None,
time_delta: Optional[datetime.timedelta] = None,
) -> datetime.datetime... | f78e61232e6a886f669c210da0a8e051594acd87 | 62,864 |
def load_lr_data(data_path, sep='\t'):
"""
load data with features
:param data_path:
:return:
"""
data_x = []
data_y = []
with open(data_path, encoding='utf-8') as f:
for line in f:
parts = line.strip().split(sep)
if len(parts) < 2:
... | ec10ec910d250b6de17b45a4d3f38b485eca61ad | 62,865 |
def patient_eval_before_2015(patient_eval_date, patient_phen):
"""Gets an updated dictionary of patient phenotype, with patients before 2015 with no negative \
values (cf paper for explanation of possible bias)
Parameters: patient_eval_date (dict): dict with patient as key, evaluation date as value
... | e9f3c9456dac3b178f3746eb7a923148a1b81763 | 62,867 |
from typing import List
def describeMSCollectionTime(trap_metrics: mzqc.QualityMetric, ms_level: int) -> List[mzqc.QualityMetric]:
"""
describeMSCollectionTime calculates the descriptive statistics metrics for ion collection times of spectra from a given level.
From the proto-metrics on ion collection for ... | 08904a388ae7b9f3521dc1ab6bdc1766f9241b3f | 62,868 |
def parse_file(infile, LOGFC_threshold=1.6, FDR_threshold=0.05):
"""to do"""
count = 0
gene_to_logFC = defaultdict(str)
f_in = open(infile, "r")
for line in f_in:
if test_line(line):
data = line.split()
gene = data[0].strip()
logfc = data[3].strip()
... | 01f9abe719da8c0db0170bfa949543085a4e35ef | 62,869 |
def post_message_log(body: hug.types.json, response: 'hug.Response') -> hug.types.json:
"""Creates a new message log from the given data."""
try:
log = create_message_log(body, db)
except ValidationError:
response.status = HTTP_422
else:
return {'log_id': log.id} | 857f4b4917ee101eae0a333d0754f93d46849f58 | 62,870 |
def skewness(data_list):
""" Skewness of the data
Inputs:
data_list - a list of floats
Outputs:
val - The skewness of the data
"""
sigma = std(data_list)
mu = mean(data_list)
n = count(data_list)
if n == 0 or sigma == 0:
return 'NaN'
skew = sum... | c187828fcdf54406dcb8b0117bdeecbbf30e3b5f | 62,871 |
import platform
def getAllContexts():
"""Returns all available contexts.
>>> allContext = getAllContexts()
"""
contexts = []
for contextType in contextTypes:
contexts.append(getPlatformContext(contextType=contextType))
if platform == 'darwin':
for contextType in contextTypesO... | d93d5173d9f00628092c38ddda59e8a342084ab6 | 62,872 |
def muted_help(ctx):
""" Returns help for muted players embed
"""
embed = Embed(colour=Color.blurple())
embed.add_field(name='You are currently muted!',
value='`=escape` See how long you are muted for, give back permissions if no longer muted',
inline=False)
r... | 84fe8f5855f2de9afe3d2140d590865fe8435095 | 62,873 |
import socket
import struct
def ip4_from_int(ip):
"""Convert :py:class:`int` to IPv4 string
:param ip: int representing an IPv4
:type ip: int
:return: IP in dot-decimal notation
:rtype: str
"""
return socket.inet_ntoa(struct.pack(">L", ip)) | 94091bd650cf15bb216e82072478e73180c0027c | 62,874 |
def keras_prepare_labels(model, labels):
""" Preformat labels to be processed by the Keras model
- mainly handle the case of categorical_crossentropy
"""
loss = None
if hasattr(model, 'loss_functions') and model.loss_functions is not None and len(model.loss_functions) > 0:
# As in test mod... | 22e6afe1370848c1db9be2f8b803da2b1fee281c | 62,875 |
def nameToClass(name):
"""Converts a class name to a class. Returns None if not an ee class.
Args:
name: The class name.
Returns:
The named class.
"""
return _registered_classes.get(name) | 00688c60ba236e39d9d4d538e154b01cc4c59ca2 | 62,876 |
import torch
def simulator(param, n=100):
"""
Simulate a given parameter combination.
Parameters
----------
param : vector or 1D array
Parameters to simulate (\theta).
n : integer
Time series length
"""
m = len(param)
g = np.random.normal(0, 1, n)
gy = np.rando... | 18c330b0890541dca27fdd1f81fd1e9f7b35c14b | 62,877 |
def overlap(start_1, end_1, start_2, end_2):
"""Return the `range` covered by two sets of coordinates.
The coordinates should be supplied inclusive, that is, the end coordinates
are included in the region. The `range` returned will be exclusive, in
keeping with the correct usage of that type.
Para... | 0bf76a98feaf94fffa2a13eb74f2a16e4fafe350 | 62,878 |
def fill_card(template: Template,
row: Row,
card_index: int,
card_copy_index: int,
definitions: dict) -> (str, TemplateRenderData):
""" Return the contents of a card using the specified template. """
# attempt to fill all fields discovered in the template... | e8cf14fd24e3f6c4c5909e7c2879df97146b7a65 | 62,879 |
import inspect
def _get_all_prior_frames():
"""
Gets the calling stack formatted as a string seperated by periods, e.g.:
__main__.bar.baz
"""
frame = inspect.currentframe()
out = [] #container for output
while True:
frame = frame.f_back
name = _filter_frame(frame)
i... | 84473e7ab6b26fa5a848936d3aae4eebef7a3191 | 62,880 |
import asyncio
from typing import Optional
def _begin_validation(
session: UpdateSession,
config: config.Config,
loop: asyncio.AbstractEventLoop,
downloaded_update_path: str,
actions: update_actions.UpdateActionsInterface,
) -> "asyncio.futures.Future[Optional[str]]":
"""Start the validation p... | 0aaca2bd393dd795a19befa608f8214bccd0709f | 62,881 |
def csc_zero_cols(csc, cols_to_zero):
"""Set rows given by cols_to_zero in a sparse csc matrix to zero.
NOTE: Inplace operation! Does not return a copy of sparse matrix."""
rows, cols = csc.shape
mask = np.ones((cols,), dtype=np.bool)
mask[cols_to_zero] = False
nnz_per_row = np.diff(csc.indptr)
... | ff3b9868d26c7b773d7073afa723f29aca00033f | 62,882 |
def tracer_injection(logger, log_method, event_dict):
"""Inject tracer information into custom structlog log"""
correlation_log_context = tracer.get_log_correlation_context()
# add ids and configs to structlog event dictionary
event_dict["dd"] = correlation_log_context
return event_dict | b0f29e1db7c9f8422b9a82f2635f146413079ad9 | 62,885 |
def view_public_cluster(name):
"""
- List Clusters Registered on SLATE
"""
if request.method == 'GET':
# Check if cluster exists
if cluster_exists(name):
print("Found cluster: {}".format(name))
# cluster_info = get_cluster_info(name)
# print("Response ... | 8e952c19acf3e897253a01351765e61aade7c724 | 62,886 |
def get_best_beam(beam_model, sess, inp, target, batch_size, beam_size,
bucket, history, p, test_mode=False):
"""Run beam_model, score beams, and return the best as target and in input."""
_, output_logits, _, _ = beam_model.step(
sess, inp, target, None, beam_size=FLAGS.beam_size)
new_tar... | 09ffe12279ec06333725832a77a8331c5d7e60d1 | 62,887 |
def total_anchors(object_boxes, object_class_name=None, class_name_set=None):
"""
对所有待检测目标进行聚类，求出聚类中心，并绘制散点图
:param object_boxes: 所有物体的boxes
:param object_class_name: 对应的boxes的物体类别的名称
:param class_name_set: 物体类别名称的集合
:return 聚类中心
"""
# 开始聚类
cluster_machine = KMeans(object_boxes, k=5,... | 0c1d0e33e7f4bbecd1c196f199e26c10b97ea8a9 | 62,888 |
def async_run_job(jsondata, program, env):
""" Run job asynchronously.
:param jsondata: a dictionary that will be sent as request body.
:param program: a dictionary that defined request meta and parameter.
:param env: a dictionary that contained environments.
:return: AsyncResult, a celery task.
... | 346f059deb367070aaed106b72b5acb01070c178 | 62,890 |
import torch
def count_examples(loader: data.DataLoader):
"""Count the number of examples in a dataloader."""
count = 0
for batch in loader:
unpacked = batch
while not torch.is_tensor(unpacked):
unpacked = unpacked[0]
count += unpacked.size(0)
return count | 93e8d9e7e3e74e94a5fdc81fbe5666f583802882 | 62,891 |
def issubset(a, b):
"""Determines if a exists in b
Args:
a: sequence a
b: sequence b
Returns:
bool: True or False
"""
return set(a).issubset(set(b)) | 39e3c974cb2f3bc3ecfe17589292646f7a1a3383 | 62,892 |
def compare_pdf_file(path1, path2):
"""
Compare content of 2 pdf files by converting pages to png image files
:param path1: path of the pdf file to compare
:param path2: path of the pdf file to compare
:return: difference of image files
"""
img1 = Image.open(path1)
img2 = Image.open(pat... | b89cedf19d805d4ce1113e3d4543a38239cbd444 | 62,893 |
def bot() -> Bot:
"""
Creates a valid bot instance, with a token from 'TOKEN' environment variable if exists,
or the default one from PUBLIC_TEST_BOT_PARAMETERS otherwise.
:return: a bot instance
"""
return Bot(_get_bot_parameter('token')) | a17f5daeeadda5f78107d9dcfa5f6fcaac7d47e9 | 62,895 |
def checkins_to_string(count: int, checkins: list):
"""Takes a list of checkins and returns a string"""
# checkin_text = ("**checkin** - **beerID** - **beer (caps)**\n\t**brewery** - **badges** - **when**\n")
checkin_text = ""
for num, checkin in zip(range(count), checkins):
checkin_text += ("{!... | 4bde62bd6a6c0b194e306080413ff686d29bd8c5 | 62,896 |
def check_from_dict(method):
"""A wrapper that wrap a parameter checker to the original function."""
@wraps(method)
def new_method(self, *args, **kwargs):
word_dict, = (list(args) + [None])[:1]
if "word_dict" in kwargs:
word_dict = kwargs.get("word_dict")
if not isinstan... | 79158edde23d2e2b8b812d6dd117ab91351b44fd | 62,897 |
from src.bot.common import bot
def safe(msg=None):
"""Call the decorated function and log any exception thrown but do not interrupt the program.
"""
def deco_safe(f):
@wraps(f)
def f_safe(*args, **kwargs):
try:
return f(*args, **kwargs)
except Except... | 20864f671617ba32677be884f13fb202c1b1fef5 | 62,898 |
def _get_eigen_vector(dot_matrix, variance_thresh):
"""
Get eigen values and eigen vector from matrix which explain % variance_thresh of total variance
:param dot_matrix: (np.array): matrix for which eigen values/vectors should be computed
:param variance_thresh: (float): % of overall variance whic... | 1e98aa9e6c1c1a5716d4c6b3f85a399de73a19f4 | 62,899 |
def cache_except_staff(decorator):
""" Returns decorated view if user is not staff. Un-decorated otherwise """
def _decorator(view):
decorated_view = decorator(view)
def _view(request, *args, **kwargs):
if request.user.is_staff:
# view without @cache
... | 83bf8f76e4b09cb651fd3fda9745a94fd65d80d8 | 62,900 |
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments """
total = data.sum()
X, Y = np.indices(data.shape)
x = (X*data).sum()/total
y = (Y*data).sum()/total
col = data[:, int(y)]
width_x = np.sqrt(np.abs... | 3bcce628e8d5548327a787540b9839d2a6ba31a8 | 62,901 |
import math
def Conv2D(x, filters, kernel_size=(3, 3), strides=(1, 1), padding='same', dilations=(1, 1), use_bias=True,
kernel_initializer=None, bias_initializer=tf.zeros_initializer(), trainable=True, name='Conv2D'):
"""Convolution 2D layer."""
if kernel_initializer is None:
kernel_initial... | 676a337ad27cf319441bcbd361e5207730a88018 | 62,903 |
from datetime import datetime
import pytz
import json
import requests
import traceback
def rule_pseudo_request(request, rule_type_id):
"""
[メソッド概要]
テストリクエスト実行時のリクエストを処理する
"""
logger.logic_log('LOSI00001', 'None', request=request)
err_flg = 1
msg = ''
now = datetime.datetime.now(pytz... | d955e4df3be7344d93f3ccec9c1cb13f462cb6c1 | 62,904 |
def sigmoid(z):
"""
Sigmoid function
:param z: input of the sigmoid function
:return: sigmoid calculation
"""
return 1 / (1 + np.exp(-z)) | 835a2682f80cccefec884730b02adb9989ad4699 | 62,905 |
import random
def random_noisy_bipartite_graph(n, p, seed):
"""Generates a base bipartite graph with evently split nodes.
Adds a bipatite edge with probability p.
Add every edge with probability 1/10.
Parameters
----------
n : int
Number of nodes
p : double
Probability of ... | e724a8e6af04a422dea77c9bc80113a00a63d0a6 | 62,906 |
def random_explorer_player(bot, state):
""" Least visited random player. Will prefer moving to a position it’s never seen before. """
if not bot.turn in state:
# initialize bot
state[bot.turn] = { 'visited': [] }
if bot.position in state[bot.turn]['visited']:
state[bot.turn]['visit... | 95b05d749b9fa7994ddb0b1e6adae5ef5c3b25ea | 62,907 |
def get_latest():
"""Get 3 latest transactions."""
return render_template(
'input.html.j2',
today=date.today().strftime('%Y-%m-%d'),
suppliers=get_suppliers(),
categories=get_categories(),
transactions=get_transactions(3, True)
) | 5801d2d5e9d9a38937a38b66b8ef2153e7cb7a8c | 62,908 |
import json
def is_master():
"""
Returns true if executed on the AWS EMR master node
:return:
"""
with open('/mnt/var/lib/info/instance.json', 'r') as f:
data = f.read()
return json.loads(data)['isMaster'] | e18cba58143cb3bd0a990324ae6bd6013cf47739 | 62,909 |
def f(x,y):
"""dy/dx = f(x,y) = 3x^2y"""
Y = 3.*x**2. * y
return Y | 4e18d6411be1c1445ea1aa4d9836b8bf5e26e3d6 | 62,910 |
import warnings
def encoder(encoder_type='Default',
input_tensor=None,
encoder_weights=None,
encoder_freeze=False,
num_blocks=5,
encoder_filters=[32, 64, 128, 256, 512]):
"""
Instantiates the encoder architecture for the DoubleU-Net segmentation mode... | 855fdf6edfb4b779cdb8b66d4e091519580752e2 | 62,911 |
def products_delete(id):
"""Exclui item selecionado"""
#Valida acesso
if int(session.get('user_role')) == 2:
flash("Acesso não permitido")
return redirect(url_for("index"))
message = "Cadastro excluído com successo."
if Activity.query.filter_by(id_product=id).count() > 0:
... | aedc4616acb3808469b3c5390cc4c48962419321 | 62,912 |
def central_moment(dist, n):
"""
Computes the `n`th central moment of a distribution.
Parameters
----------
dist : Distribution
The distribution to take the moment of.
n : int
Which moment to take.
Returns
-------
moments : ndarray
The `n`th central moment o... | 85cae07cd61662f33b4f85babad4ecc7eb666e19 | 62,914 |
def get_face_points(gray_frame, face):
"""
"""
land_mark = predictor(gray_frame, face)
face_points = []
for n in range(68):
x, y = land_mark.part(n).x, land_mark.part(n).y
face_points.append([x,y])
return face_points | 3bc2d47ea9c89362decfd1855b4db95afbc35c8e | 62,915 |
def anim_classic_walk(transition, projection, start_pd, n_steps=10, show_equil=False, frame_option='pairsmean'):
"""
calculate the evolution of random walk and prepare dataframe df of the evolutionvof probability distribution
the probability distribution at start is given as input start_pd. i
in ea... | 17358b078e04e324db59b1f144efc811a1f2e704 | 62,916 |
import logging
import base64
import hashlib
def check_password(ssha_ldap_salted_password, password) -> bool:
"""Check the OpenLDAP tagged digest against the given password."""
# See https://www.openldap.org/doc/admin24/security.html#SSHA%20password%20storage%20scheme
# We should (also) support SHA512 as S... | 003972ff32df6a6b55ada07b512154ee7b99d957 | 62,918 |
def input_fn(request_body, request_content_type):
"""
The SageMaker XGBoost model server receives the request data body and the content type,
and invokes the `input_fn`.
Return a DMatrix (an object that can be passed to predict_fn).
"""
if request_content_type == "text/libsvm":
return xg... | b408fc27ba3291da236848f6d81514863bb4e8d7 | 62,919 |
import re
def generate_c_header_finders():
""" Generate the header finders for c style files.
"""
def find_header_start(line):
match = re.search("/\*\*", line)
if match:
return True
else:
return False
def find_header_end(line):
match = re.search... | fa094e7be0e3b9b89b8fdc3f32d729f024f62c7e | 62,920 |
import copy
def pred_twostage(ReasonerObj, args):
"""Based on gt labels in original implementation by Zeng et al.:
https://github.com/andyzeng/arc-robot-vision/image-matching/evaluateTwoStage.m"
but here no novel vs known prediction are made, simply both K-net and N-net preds are returned
"""
... | e3bae64db3e100d8e85de166c047ca7c41461cef | 62,922 |
def hxlm_factum_to_object(factum) -> dict:
"""Factum to an object"""
# return dict(factum) # this does not transpile well...
return factum | 54cba326d6874768f2893f97277a691e7e77a2d7 | 62,923 |
def parse_example(example_proto):
"""Parse a serialized SequenceExample read from a TFRecord.
Args:
example_proto: str, a single binary serialized SequenceExample proto.
Returns:
length: 1-D Tensor of length 1
label: 1-D Tensor of length 1
words: 1-D Tensor of variable length... | 55b6af7f58fd3151faff79e7833decaddc6de96e | 62,924 |
from typing import Tuple
def _compute_chroma(rgb: np.ndarray,
axis: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Compute the chroma for an RGB triple"""
big_m = np.max(rgb, axis=axis, keepdims=True)
little_m = np.min(rgb, axis=axis, keepdims=True)
chroma = big_m - little_m... | 0b8d185c8bf969dfdf489e2c56e1fd5c0f2bb2fa | 62,925 |
def pareto(costs: np.ndarray):
"""
Find the pareto-optimal points
:param costs: (n_points, m_cost_values) array
:return: (n_points, 1) indicator if point is on pareto front or not.
"""
assert type(costs) == np.ndarray
assert costs.ndim == 2
# first assume all points are pareto optimal
... | 9062696d4ce69115aa745a7affae24a578d05f74 | 62,926 |
def pop(obj, key=0, *args, **kwargs):
"""Pop an element from a mutable collection.
Parameters
----------
obj : dict or list
Collection
key : str or int
Key or index
default : optional
Default value. Raise error if not provided.
Returns
-------
elem
P... | 71222b35f52a1ee118a352596caefebe9b7070fa | 62,928 |
def _ValidateAgentVersion(agent_type, version):
"""Validates agent version format.
This validation happens after the arg parsing stage. At this point, we can
assume that the field is a valid string.
Args:
agent_type: str. The type of agent to be installed. Allowed values:
* "logging"
* "metric... | d8cf8f8584d2d8b8c4e3ce8aa07ba9c0ae4a75fd | 62,929 |
def calculate_bridge_score(bid : Bid, tricks : int, vul : bool,joueur : str) -> FinalContract :
"""Knowing the contract and the number or tricks, return the bridge score (absolute negative = EW scores)"""
PRIME_PARTIELLE = 50
PRIME_MANCHE = 300
PRIME_PETIT_CHELEM = 500
PRIME_GRAND_CHELEM = 1000
... | dd231dfd749fefaa29ffdda8e1c31b9f1d739f5b | 62,931 |
def validate_notebook_defaults(value):
"""Returns the parameter, if it's a dict, otherwise raises an exception.
:param value: Object to validate.
:raises TypeError: On wrong type.
"""
if isinstance(value, dict):
return value
raise TypeError(validation_error_message(
... | bcef39d7e7c7986d3baa6e85e55c2d18ce36c4e7 | 62,932 |
from typing import List
def classify_from_patchlist(patchlist: List,
pyparams: dict,
modeldef_path: str,
modelweighs_path: str,
scorelayer: str = 'score',
startlayer: str = 'conv... | 180386632f9972db2ec920d3a1dd76f5c1345631 | 62,934 |
def route_index():
"""Display an overview of the exporters capabilities."""
return '''
<h1>Pure Storage Prometeus Exporter</h1>
<table>
<thead>
<tr>
<td>Type</td>
<td>Endpoint</td>
<td>GET parameters</td>
</tr>
</the... | a0d0d505a73f2239f1b0fe30358d52d0dd5be5fe | 62,935 |
def create_model(params, training, custom_pos_enc=False, attn_weights=False):
"""
Args:
params: dict, hyperparameter dictionary
training: bool, whether model is called in training mode or not
custom_pos_enc, bool, whether a custom positional encoding is provided as additional input
... | 8d58c4e24f1fe38af09c408a847121f7a04d52a6 | 62,936 |
import itertools
def calc_feature_values(db_file, identifiers, feature, window=0):
"""
Calculate the given feature for a list of identifiers.
Args:
db_file: The sqlite3 DB file.
identifiers: List of identifiers.
feature: Feature as class instance.
window: Window size.
... | af96f1ebcb71d5cefd25ace7c8251ad1c0f6d944 | 62,937 |
def new_ipc_message(draft=None):
"""Basic ipc message we can send to the bots
Parameters
----------
draft: Optional[int]
0, or 1, 0 to disable ML drafting and fallback to hardcoded hero selection.
"""
base = {
'uid': 0,
TEAM_RADIANT: {
0: dict(),
... | b0ff0dfdd9d4c45d618a0f5748538b8b5bb340f5 | 62,938 |
def parse_rp_inventory_defaults(inventory_defaults):
"""Parse and validate config option: parse_rp_inventory_defaults.
Cast the dict values to the proper numerical types.
Input in the config:
resource_provider_inventory_defaults = allocation_ratio:1.0,min_unit:1
Input here:
{
... | e09dc1aa8f02de3046a4348cda25b3b8cdcfd8de | 62,939 |
def get_compiler(compiler, **compiler_attrs):
"""get and customize a compiler"""
if compiler is None or isinstance(compiler, str):
cc = ccompiler.new_compiler(compiler=compiler)
# customize_compiler(cc)
if cc.compiler_type == 'mingw32':
customize_mingw(cc)
else:
c... | 61815a3bccb9b8d98e3b748c59a3249629d3974a | 62,940 |
def read_ascii(file):
"""
Function to read in photometric data from
original '_phot.dat' style sedbys file.
- file is a pathlib.Path object
"""
wvlen, band, mag, emag, fmag, unit, beam, odate, ref = [],[],[],[],[],[],[],[],[]
with open(file, 'r') as f_in:
for line in f_in:
... | 1baee61f90321a6006bcd58e5e5de4ff08e96b26 | 62,941 |
import sqlite3
def get_test_database() -> sqlite3.Connection:
"""get_test_database for unit testing the model classes"""
db_conn = sqlite3.connect(
":memory:",
detect_types=sqlite3.PARSE_DECLTYPES
)
with open("ticket/schema.sql") as schema_file:
db_conn.executescript(s... | aeff27686a7fc4efd66597a5e2264056de9cee8a | 62,942 |
import re
def normalize_text(text):
"""
Strips formating spaces/tabs, carriage returns and trailing whitespace.
"""
text = re.sub(r"[ \t]+", " ", text)
text = re.sub(r"\r", "", text)
# Remove whitespace in the middle of text.
text = re.sub(r"[ \t]+\n", "\n", text)
# Remove whitespace ... | aec04cc84aa91e16ca0f8ac18530813a6de3c187 | 62,943 |
def convert_tractor_e1e2(e1, e2):
"""Convert Tractor epsilon1, epsilon2 values to ellipticity and position angle.
Taken from tractor.ellipses.EllipseE
"""
e = np.hypot(e1, e2)
ba = (1 - e) / (1 + e)
#e = (ba + 1) / (ba - 1)
phi = -np.rad2deg(np.arctan2(e2, e1) / 2)
#angle = np.deg2rad... | 56cd5c4e6705165f263fbed385c03694b41c1c99 | 62,944 |
def app_plan(service, custom_app_plan, request):
"""
Creates an app plan with a limit on hits
"""
metric = service.metrics.list()[0]
plan = custom_app_plan(
rawobj.ApplicationPlan(blame(request, "app")), service)
plan.limits(metric).create({
"metric_id": metric["id"], "period": ... | bc8cad2f682bb0ec47ba65cf2cfb4f3e279cb7c2 | 62,945 |
from typing import Sequence
from typing import Callable
from typing import Union
def predmix_upper_cs(
x: Sequence[float],
v: Sequence[float],
lambdas_fn: Callable[[Sequence[float]], Sequence[float]],
psi_fn: Callable[[Sequence[float]], Sequence[float]],
alpha: float = 0.05,
running_intersecti... | 1a3559ef109640fcf4bf9a2b5de9afb6e8e0fd41 | 62,946 |
def image_to_rgb(img):
"""
Convert *img* (an :class:`~PIL.Image.Image`) to an :class:`~numpy.ndarray`
in RGB format (structured floating-point type with 3 values each between 0
and 1).
"""
return rgb888_to_rgb(image_to_rgb888(img)) | d8c87a3733145e061b18c3333e9ca64767174d19 | 62,947 |
import copy
import numpy
def sbs_with_forward_steps(
training_table, validation_table, testing_table, feature_names,
target_name, estimator_object, cost_function=_cross_entropy_function,
num_forward_steps=DEFAULT_NUM_FORWARD_STEPS_FOR_SBS,
num_backward_steps=DEFAULT_NUM_BACKWARD_STEPS_... | dbee37b87c61bf078217e64e73755a3057af3cbd | 62,948 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.