id stringlengths 3 8 | content stringlengths 100 981k |
|---|---|
183547 | import time
from ..ExcelDataUtil.xlsxDataGetter import XlsxDataGetter
from ..ExcelDataUtil.xlsxDataWriter import XlsxDataWriter
from ..Util.dateUtil import timestamp2datetime
from ..Variables.Status import Status
class RealNotes(object):
def __init__(self, code, strategy, **kwargs):
self.file_name = code ... |
183550 | import csv
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
import scipy.signal
import matplotlib
plt.style.use('dark_background')
# with open('csv/run_PPO_summary-tag-Info_cumulative_reward.csv', newline='') as csvfile:
with open('csv/run_PPO_summary-tag-Info_episode_... |
183551 | from rapidfuzz import process, fuzz
from .load_dicts import FORMULA_URI_DICT, SMILES_URI_DICT, NAME_URI_DICT, \
FORMULA_KEYS, NAME_KEYS, SMILES_KEYS, ATTRIBUTE_URI_DICT, \
ATTRIBUTE_KEYS, CLASS_URI_DICT, CLASS_KEYS, process_species, process_species_reversed
def find_nearest_match(entity_value, entity_type):
... |
183562 | from parameterized import parameterized
from integration.helpers.base_test import BaseTest
class TestIntrinsicFunctionsSupport(BaseTest):
# test code definition uri object and serverless function properties support
@parameterized.expand(
[
"combination/intrinsics_code_definition_uri",
... |
183580 | import os
import config
import basehandler
import models
import accounts
import urllib
import admin
from google.appengine.api import users
from google.appengine.ext import webapp
from google.appengine.ext.webapp import template
from google.appengine.ext import db
class AdjustScore(basehandler.BaseHandler):
def ge... |
183651 | import pandas as pd
from report import reporting as rep
from checks import NaptanCheck
# %%
class MultiRoadName(NaptanCheck):
"""[summary] A collection of methods to check that the roads names contain
the correct types and collection of words.
Args:
NaptanCheck ([type]): [description]
Retu... |
183657 | import asyncio
import json
from threading import Timer
import httpx
import pytest
from ariadne.asgi import GQL_CONNECTION_INIT, GQL_START
from websockets import connect
my_storage = {}
@pytest.fixture
def storage():
return my_storage
@pytest.mark.asyncio
async def test_create_user(host, credentials, storage):... |
183662 | def stockmax(p):
ind_max = p.index(max(p)) #find the max price
inv = sum(p[:ind_max]) #split the array before and after max price
pf = len(p[:ind_max])*p[ind_max] - inv #buy all stocks before max price
if len(p[ind_max+1:]) > 0:
pf += stockmax(p[ind_max+1:]) #then sell them at max price
ret... |
183688 | import csv
import numpy as np
import cv2
def resize_and_crop(image, img_size):
""" Resize an image to the given img_size by first rescaling it
and then applying a central crop to fit the given dimension. """
source_size = np.array(image.shape[:2], dtype=float)
target_size = np.array(img_size, dtyp... |
183741 | import warnings
import os.path as osp
import tensorflow as tf
import numpy as np
import time
from tflearn import is_training
from in_out import create_dir
from general_utils import iterate_in_chunks
from latent_3d_points.neural_net import Neural_Net, MODEL_SAVER_ID
try:
from latent_3d_points.structural_loss... |
183789 | class BaseContest(object):
'''BaseContest is an abstract class for contest-specific modifications to
Marathoner.
'''
def __init__(self, project):
self.project = project
self.maximize = project.maximize
def extract_score(self, visualizer_stdout, solution_stderr):
'''Extract r... |
183838 | from mstrio.connection import get_connection
from mstrio.server.job_monitor import (Job, JobStatus, JobType, kill_all_jobs, kill_jobs,
list_jobs, ObjectType, PUName)
from mstrio.server.project import Project
from mstrio.users_and_groups.user import User
# connect to environment ... |
183847 | import os
import parser
import unittest
class TestParser(unittest.TestCase):
# Tests for parsers for the months from July 2019 to November 2020
def test_active_members_jan_2020(self):
self.maxDiff = None
expected = {
"reg": "3725",
"name": "<NAME>",
"role"... |
183849 | from .module import PytorchModel, TrainingInterface
from .scheduler import ConstantScheduler, TeacherForcingScheduler, \
OptimizerScheduler, ParameterScheduler
from .manager import LogPathManager, DataLoaders, SummaryWriters
from .example import MinExponentialLR
|
183881 | from BlindMIUtil import *
from dataLoader import *
import tensorflow as tf
from tensorflow.keras.models import load_model
from sklearn import svm
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
tf.config.experimental.set_memory_growth(tf.config.experimental.list_physical_devices('GPU')[0], True)
DATA_NAME... |
183883 | from math import *
import random
import timeit
import matplotlib.pyplot as plt
n, omega, N = 12, 2400, 1024
result = ''
result22 = ''
def show_graphic(num: int, signal: list, title: str):
plt.plot(list(range(0, num)), signal)
plt.grid(True)
plt.title(title)
plt.show()
def random_signal():
all_... |
183884 | import csv
import json
import sys
from pathlib import Path
from typing import Dict, List
from simcore_service_webserver.projects.projects_db import _convert_to_schema_names
SEPARATOR = ","
current_file = Path(sys.argv[0] if __name__ == "__main__" else __file__).resolve()
current_dir = current_file.parent
def load_... |
183891 | def flatten(myList):
newList = []
for item in myList:
if type(item) == list:
newList.extend(flatten(item))
else:
newList.append(item)
return newList
def main():
myList1 = [1,2,3,[1,2],5,[3,4,5,6,7]]
print(flatten(myList1))
myList2 = [1,[2,[3,[4,[5,[6,[7,[... |
183928 | import conx as cx
import numpy as np
from keras.datasets import mnist
from keras.utils import (to_categorical, get_file)
description = """
Original source: http://yann.lecun.com/exdb/mnist/
The MNIST dataset contains 70,000 images of handwritten digits (zero
to nine) that have been size-normalized and centered in a s... |
183942 | import hashlib
import json
import random
import re
import time
import requests
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.75 Safari/537.36",
"Referer": "http://fanyi.youdao.com/"
}
def test01():
"""36kr.com的内容是存放在script标签中... |
183971 | import bspump.unittest
import bspump.common
class TestNullSink(bspump.unittest.ProcessorTestCase):
def test_null(self):
events = [
(None, "Don't let this out!"),
]
self.set_up_processor(bspump.common.NullSink)
output = self.execute(
events
)
self.assertEqual(
[event for context, event in out... |
184003 | from pupa.scrape import Scraper
from pupa.scrape import Event
import datetime as dt
import lxml.html
import re
CAL_PAGE = ("http://www.cityoftemecula.org/Temecula/Visitors/Calendar.htm")
class TemeculaEventScraper(Scraper):
def lxmlize(self, url):
entry = self.urlopen(url)
page = lxml.html.fro... |
184035 | import datetime
from django.utils import timezone
from django.test import TestCase
from hknweb.events.tests.models.utils import ModelFactory
class EventModelTests(TestCase):
def setUp(self):
user = ModelFactory.create_user()
event_type = ModelFactory.create_event_type()
event_name = "cu... |
184062 | from __future__ import print_function
from orphics import maps,io,cosmology,symcoupling as sc,stats,lensing
from enlib import enmap,bench
import numpy as np
import os,sys
cache = True
hdv = False
deg = 5
px = 1.5
shape,wcs = maps.rect_geometry(width_deg = deg,px_res_arcmin=px)
mc = sc.LensingModeCoupling(shape,wcs)
... |
184089 | from ._line import Line
from ._colorbar import ColorBar
from plotly.graph_objs.histogram.marker import colorbar
|
184095 | from typing import Callable, Any, List
import numpy as np
import math
import autograd
from .dataset import Dataset
class DataLoader(object):
"""
Dataloader class
"""
def __init__(self, dataset: Dataset, batch_size: int = 1, shuffle: bool = True,
collate_fn: Callable[[List], Any] = ... |
184116 | from quest.admin import admin_site
from .models import Goal, Task, TaskStatus
admin_site.register(Goal)
admin_site.register(Task)
admin_site.register(TaskStatus)
|
184196 | from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import (PointGenerator, multi_apply, multiclass_nms_kp,
point_target_kp)
from mmdet.ops import DeformConv
from ..builder import build_loss
from ..registry impo... |
184256 | import wandb
import numpy as np
import pandas as pd
api = wandb.Api()
wandb_entity = os.environ['WANDB_ENTITY']
# Project is specified by <entity/project-name>
runs = api.runs(f"{wandb_entity}/invalid_action_masking")
analysis = True
summary_list = []
config_list = []
name_list = []
for run in runs:
# run.sum... |
184271 | from .base import *
DEBUG = True
if DEBUG is True:
from .dev import *
else:
from .production import *
# 站点名称
SITE_NAME = 'manage'
# 后台首页
SITE_PAGE = '/%s/service/article/' % SITE_NAME
|
184278 | from CoreFoundation import (
CFPreferencesCopyValue,
kCFPreferencesAnyHost,
kCFPreferencesAnyUser,
)
factoid = "updates_app_autoupdate"
def fact():
"""Returns the status of automatic updates to MAS apps"""
status = "disabled"
pref = CFPreferencesCopyValue(
"AutoUpdate",
"/Libr... |
184310 | from homeassistant.const import (
ENERGY_KILO_WATT_HOUR,
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
DEVICE_CLASS_POWER,
DEVICE_CLASS_TEMPERATURE,
TEMP_CELSIUS,
POWER_WATT,
)
from homeassistant.components.sensor import (
STATE_CLASS_MEASUREMENT,
)
from homeassistant.components.integration.sen... |
184353 | from __future__ import print_function
from fenics import *
from mshr import *
import numpy as np
from scipy import integrate
set_log_level(LogLevel.INFO)
T = 500.0 # final time
num_steps = 1000 # number of time steps
dt = T / num_steps # time step size
mu = 16 # dynamic viscosity
rho = 1 ... |
184363 | import re as RE
import operator
from logging import info, getLogger
getLogger().setLevel(1)
def conv(input,output):
info("reading input file "+input)
fd=open(input)
line=reduce(operator.add,fd.readlines())
fd.close()
info("pruning trailing comments, labels, declarations and indentation")
... |
184365 | import numba
import numpy as np
NP_COMPLEX = np.complex128
NUMBA_COMPLEX = numba.complex128
NP_FLOAT = np.float64
NUMBA_FLOAT = numba.float64
|
184370 | from .algorithms import OnlineNNClassifier, OnlineNNRuLSIF
from .rulsif import RuLSIF
from .dataset import generate_dataset
from .viz import display
__all__ = [
'OnlineNNClassifier', 'OnlineNNRuLSIF', 'RuLSIF', 'generate_dataset', 'display'
]
|
184401 | from fastai.callbacks.mixup import *
from fastai.tabular import *
class TabMixUpCallback(LearnerCallback):
"Callback that creates the mixed-up input and target."
def __init__(self, learn:Learner, alpha:float=0.3, stack_x:bool=False, stack_y:bool=True):
super().__init__(learn)
self.alpha,self.st... |
184442 | import numpy as np
import matplotlib.pyplot as plt
import torch
from collections import namedtuple
Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward', 'done'))
class DeepRLTrainer:
NB_EPISODES = 3000
SAVE_EVERY = 500
INFO_EVERY = 50
SOFT_MAX = False
DEVICE = 'cpu'... |
184495 | DATABASE_ENGINE = 'sqlite3'
SECRET_KEY = 'abcd123'
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': './example.db',
}
}
INSTALLED_APPS = (
'django_bouncy',
)
BOUNCY_TOPIC_ARN = ['arn:aws:sns:us-east-1:250214102493:Demo_App_Unsubscribes']
TEST_RUNNER = 'django_n... |
184503 | from robox import Options, Robox
with Robox() as robox:
page = robox.open("https://httpbin.org/forms/post")
form = page.get_form()
form.fill_in("custname", value="foo")
form.check("topping", values=["Onion"])
form.choose("size", option="Medium")
form.fill_in("comments", value="all good in the h... |
184540 | import os
import asyncio
import json
from pyppeteer import launch
network = []
javascript = []
async def intercept_network_response(response):
network.append(str(response.status) + response.url)
async def intercept_console(response):
javascript.append(response.text)
async def collect_msgs_and_screenshot(... |
184606 | import asyncio
import json
import time
import unittest
import requests
import websockets
from BaseTest import BaseTest, BASE_URL, BASE_WS_URL_CUSTOM, BASE_WS_URL_JSONRPC, BaseAsyncTest
class IntentionsHttpTestCase(BaseTest):
@classmethod
def setUpClass(cls):
BaseTest.setUpClass()
def setUp(self)... |
184615 | from functools import partial
import numpy as np
import torch
from torch import nn
from counterfactualms.arch.layers import Conv2d, ConvTranspose2d
class HierarchicalEncoder(nn.Module):
def __init__(self, num_convolutions=3, filters=(16,32,64,128,256), latent_dim=100,
input_size=(1,128,128), us... |
184638 | import lief
def lief_from_raw(bytes):
"""Create a lief binary object from raw bytes"""
b_list = list(bytes)
lief_binary = lief.parse(raw=b_list)
return lief_binary
|
184687 | graphite_url = 'http://graphite.intra.douban.com'
graphite_index_url = graphite_url + '/metrics/index.json'
metrics_file = 'metrics.json'
diamond_cache = 'diamond.cache'
debug = False
listen_host = '0.0.0.0'
listen_port = 8808
try:
from local_config import *
except:
pass
|
184688 | import torch
import torch.nn as nn
# model
class net_le(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
return torch.le(inputs[0], inputs[1])
_model_ = net_le()
# dummy input for onnx generation
_dummy_ = [torch.randn(1, 2, 3, 3), torch.randn(1, 2, 3, 3)]
|
184705 | from pepnet.encoder import Encoder
from nose.tools import eq_
import numpy as np
def test_encoder_index_lists():
encoder = Encoder()
S_idx = encoder.index_dict["S"]
A_idx = encoder.index_dict["A"]
index_lists = encoder.encode_index_lists(["SSS", "AAA", "SAS"])
eq_(index_lists, [
[S_idx, S_i... |
184724 | def multiplication_table(row, col):
return [range(a, a * col + 1, a) for a in xrange(1, row + 1)]
|
184728 | from _tutorial import *
explorerhat = None
name = ''
horse = '''
>>\.
/_ )`.
/ _)`^)`. _.---. _
(_,' \ `^-)"" `.\\
| | \\
\ / |
/ \ /.___.'\ (\ (_
< ,"|| \ |`. \`-'
\\\\ () )| )/
hjw |_>|> /_] //
/_] ... |
184752 | from unittest import TestCase
from irlib.progress import Progress
class TestProgress(TestCase):
def setUp(self):
self.p = Progress(n=1002, percent=10)
def test_progress_counter(self):
total = 0
for i in range(0,1002):
total += self.p.show(message='Testing progress:', sile... |
184795 | import os
from PIL import Image
class SlideCrack(object):
def __init__(self, gap_img, bg):
self.gap_img = gap_img
self.bg = bg
@staticmethod
def pixel_is_equal(image1, image2, x, y):
"""
判断两张图片的像素是否相等,不想等即为缺口位置
:param image1:
:param image2:
... |
184810 | import logging
import os
import csv
import codecs
from decimal import Decimal as D
import nose
from . import pygrowup
from six.moves import zip
class WHOResult(object):
def __init__(self, indicator, values):
self.indicator = indicator
columns = 'id,region,GENDER,agemons,WEIGHT,_HEIGHT,measure,oe... |
184862 | from ocdskit.cli.__main__ import main
from tests import assert_streaming
def test_command(capsys, monkeypatch):
assert_streaming(capsys, monkeypatch, main, ['split-record-packages', '1'],
['realdata/record-package_package.json'], ['realdata/record-package_split.json'])
|
184863 | import math
import sys
import time
import numpy as np
from fj_refactored import fisher_jenks, fj_generate_sample
def testfull():
"""
Tests the fully enumerated Fisher-Jenks implementation
"""
cores = [1,2,4,16,32]
classes = [5,6,7]
data_sizes = [500, 1000, 2500, 5000, 7500, 10000, 12500, 15000... |
184877 | import numpy as np
from imread import ijrois
from . import file_path
def test_rois_smoke():
rois = ijrois.read_roi_zip(file_path('rois.zip'))
assert len(rois) == 4
r = ijrois.read_roi(open(file_path('0186-0099.roi'), 'rb'))
assert any([np.array_equal(ri, r) for ri in rois])
|
184881 | import pandas as pd
import sqlalchemy
from constants import DB_FOLDER, SYMBOL
import matplotlib.pyplot as plt
def create_engine(symbol):
engine = sqlalchemy.create_engine(f"sqlite:///{DB_FOLDER}/{symbol}-stream.db")
return engine
def fetch_dataframe(symbol, engine):
try:
return pd.read_sql(symbo... |
184909 | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils as nn_utils
import torch.backends.cudnn as cudnn
from torch.nn import SyncBatchNorm
import torch.optim.lr_scheduler as lr_scheduler
from torch.nn.parallel import DistributedDataParallel
import uti... |
184922 | import numpy as np
import torch.nn as nn
from networks.ResidualBlocks import ResidualBlock1dTransposeConv
def make_res_block_decoder_feature_generator(channels_in, channels_out, a_val=2.0, b_val=0.3):
upsample = None;
if channels_in != channels_out:
upsample = nn.Sequential(nn.ConvTranspose1d(channe... |
184930 | import os
from typing import NamedTuple
from unittest.mock import Mock, sentinel
import pytest
import requests
import requests_mock
import vcr
from apiclient import APIClient
from apiclient.request_formatters import BaseRequestFormatter
from apiclient.response_handlers import BaseResponseHandler
BASE_DIR = os.path.a... |
184952 | from __future__ import print_function, division
#
import sys,os
quspin_path = os.path.join(os.getcwd(),"../../")
sys.path.insert(0,quspin_path)
#
from quspin.operators import hamiltonian, exp_op # Hamiltonians, operators and exp_op
from quspin.basis import spin_basis_1d # Hilbert space spin basis
import numpy as np # g... |
185013 | import re
import string
DEFAULT_TOKENIZER_DELIMITER = ' '
def remove_all_whitespace(str):
"""
Strips all whitespace from a given string.
:return: new string without whitespaces, will return the original string if it is empty or None
"""
if str:
return re.sub(r'\s+', '', str)
el... |
185045 | import unittest
import sympy
from means.approximation.mea.eq_central_moments import eq_central_moments
from means.core import Moment
from means.util.sympyhelpers import to_sympy_matrix, assert_sympy_expressions_equal
class CentralMomentsTestCase(unittest.TestCase):
def test_centralmoments_using_p53model(self):
... |
185059 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import tensorflow as tf
from ccgnet import experiment as exp
from ccgnet.finetune import *
from ccgnet import layers
from ccgnet.layers import *
import numpy as np
import time
import random
from sklearn.metrics import bal... |
185064 | import pytest
import sqlalchemy as sa
from postgresql_audit import (
add_column,
alter_column,
change_column_name,
remove_column,
rename_table
)
from .utils import last_activity
@pytest.mark.usefixtures('activity_cls', 'table_creator')
class TestRenameTable(object):
def test_only_updates_giv... |
185137 | from office365.sharepoint.changes.change import Change
class ChangeWeb(Change):
@property
def web_id(self):
return self.properties.get("WebId", None)
|
185169 | from pathlib import Path
path = Path().absolute()
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_DATABASE_URI = "sqlite:///" + str(path) + "/Database/database.db"
SECRET_KEY = "e9515dfe457bfe64c1c30d73e161de0f76f6b03f"
|
185242 | from .decorator import log_on_start, log_on_error, log_on_end, log_exception
__all__ = ["log_on_start", "log_on_error", "log_on_end", "log_exception"]
|
185259 | import os
from substance.logs import *
from substance import (Command, Box, EngineProfile)
from substance.exceptions import (InvalidOptionError)
class Create(Command):
def getUsage(self):
return "substance engine init [options] [ENGINE NAME]"
def getHelpTitle(self):
return "Create a new engi... |
185280 | from examples.paper.initialize import *
# user settings
settings = {
#
# audit settings
'data_name': 'credit',
'method_name': 'logreg',
'normalize_data': True,
'force_rational_actions': False,
#
# script flags
'audit_recourse': True,
'plot_audits': True,
'print_flag': True,
... |
185293 | import unittest
import urllib.parse
from ingenico.connect.sdk.defaultimpl.authorization_type import AuthorizationType
from ingenico.connect.sdk.defaultimpl.default_authenticator import DefaultAuthenticator
from ingenico.connect.sdk.request_header import RequestHeader
class DefaultAuthenticatorTest(unittest.TestCase)... |
185294 | import numpy as np
from scipy.fft import dct, idct
import math
def idct_basis_2d(len_basis, num_basis):
'''
Generate basic 2D DCT basis for dictionary learning
Inputs:
len_basis: length of the flattened atom, e.g. 36 for 6x6 basis
num_basis: number of the atoms. usually it is o... |
185325 | from typing import List
class Solution:
def nextGreatestLetter(self, letters: List[str], target: str) -> str:
left, right = 0, len(letters) - 1
while left <= right:
middle = left + (right - left) // 2
if letters[middle] <= target: left = middle + 1
else: right =... |
185344 | import torch
EPSILON = 1E-10
def xyxy_to_xywh(boxes_xyxy):
assert torch.all(boxes_xyxy[..., 0] < boxes_xyxy[..., 2])
assert torch.all(boxes_xyxy[..., 1] < boxes_xyxy[..., 3])
return torch.cat([
(boxes_xyxy[..., [0]] + boxes_xyxy[..., [2]]) / 2.,
(boxes_xyxy[..., [1]] + boxes_xyxy[..., [3]... |
185386 | l = [[True] * 4 for _ in range(4)]
def s(r, c):
if r == 3 and c == 3:
return 1
elif max(r, c) > 3 or min(r, c) < 0:
return 0
elif not l[r][c]:
return 0
else:
l[r][c] = False
cnt = s(r + 1, c) + s(r - 1, c) + s(r, c + 1) + s(r, c - 1)
l[r][c] = True
... |
185414 | from datetime import datetime
from unittest.mock import Mock
import pytest
from toucan_connectors.google_sheets_2.google_sheets_2_connector import GoogleSheets2Connector
from toucan_connectors.http_api.http_api_connector import HttpAPIConnector
from toucan_connectors.json_wrapper import JsonWrapper
from toucan_connec... |
185416 | import logging
from typing import Optional
class LazyLogger:
log_level: int = logging.WARNING
log_format: str = "%(name)s: %(levelname)-8s %(message)s"
logger_name: str = "dynamoquery"
_lazy_logger: Optional[logging.Logger]
@property
def _logger(self) -> logging.Logger:
if self._lazy_... |
185431 | from django.db import models
import datetime
#---------------- Drive Profile Logic Function ------------------------#
class Category(models.Model):
name = models.CharField(max_length=300)
def __str__(self):
return self.name
class SubCategory(models.Model):
name = models.CharField(max_length=300)
... |
185438 | from __future__ import print_function
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.io as sio
import scipy.sparse as sp
import scipy.sparse.linalg as slinalg
import scipy.linalg as linalg
from scipy.sparse.linalg.eigen.arpack import eigsh
from sklearn.feature_extraction.text import TfidfTr... |
185452 | import logging
from django.core.management.base import BaseCommand
import stats.models
import openkamer.dossier
import openkamer.parliament
import openkamer.kamervraag
logger = logging.getLogger(__name__)
class Command(BaseCommand):
MAX_TRIES = 3
def add_arguments(self, parser):
# Named (optional... |
185471 | import os
import numpy as np
from PIL import Image
from fnmatch import fnmatch
def remove_low_resolution_images(path, min_resolution=20):
"""
A function that removes all the single pixel images
Parameters
-------------------------
path: str
Path to the folder that we would like to clear o... |
185501 | import os
import unittest
from tempfile import mkstemp
from clips import Environment, Symbol, InstanceName
from clips import CLIPSError, ClassDefaultMode, LoggingRouter
DEFCLASSES = [
"""
(defclass AbstractClass (is-a USER)
(role abstract))
""",
"""(defclass InheritClass (is-a AbstractClass))""... |
185511 | def test_home_retorna_status_code_200(client):
response = client.get("/")
assert response.status_code == 200
def test_home_retorna_texto_ola(client):
response = client.get("/")
assert response.text == "ola"
def test_echo_retorna_status_code_200(client):
response = client.get("/echo")
assert ... |
185512 | import numpy as np
import torch
from pytorchrl.distributions.base import Distribution
from pytorchrl.misc.tensor_utils import constant
class DiagonalGaussian(Distribution):
"""
Instead of a distribution, rather a collection of distribution.
"""
def __init__(self, means, log_stds):
"""
... |
185535 | from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
def create_model():
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size... |
185539 | from xgboost_ray.tests.utils import create_parquet
def main():
create_parquet(
"example.parquet",
num_rows=1_000_000,
num_partitions=100,
num_features=8,
num_classes=2)
if __name__ == "__main__":
main()
|
185569 | from motionAE.src.models import lstmCVAE2
from torch.distributions.kl import kl_divergence
from torch.distributions.normal import Normal
import torch
from motionAE.src.motionCVAETrainer import motionCVAETrainer
import numpy as np
class motionCVAE2Trainer(motionCVAETrainer):
def load_param(self, arg_parser, **kw... |
185578 | import ui
def hide_action(sender):
s = sender.superview
s['view1'].hidden = True
s2 = s['view2']
def a():
s2.transform=ui.Transform.scale(1.0, 1.33).concat(ui.Transform.translation(0,-100))
ui.animate(a, 1.0)
def reveal_action(sender):
s = sender.superview
s2 = s['view2']
... |
185593 | import torch
import torch.nn as nn
class contrastive_loss(nn.Module):
def __init__(self, tau=1, normalize=False):
super(contrastive_loss, self).__init__()
self.tau = tau
self.normalize = normalize
def forward(self, xi, xj):
x = torch.cat((xi, xj), dim=0)
is_cuda = x.... |
185645 | from inspect import getmembers
from fastapi import FastAPI
from tortoise.contrib.starlette import register_tortoise
from app.config import tortoise_config
from app.utils.api.router import TypedAPIRouter
def init(app: FastAPI):
"""
Init routers and etc.
:return:
"""
init_routers(app)
init_db(... |
185657 | from EventEngine.DyEvent import *
class DyStockStrategyState:
running = 'sRunning'
monitoring = 'sMonitoring'
backTesting = 'sBackTesting'
def __init__(self, *states):
self._state = None
self.add(*states)
@property
def state(self):
if self._state is None:
... |
185658 | import os
import sys
import random
import datetime
import time
import shutil
import numpy as np
import pandas as pd
import scipy.io
import scipy.signal
import math
from skimage.measure import compare_ssim as sk_ssim
import torch
from torch import nn
class ProgressMeter(object):
def __init__(sel... |
185691 | import argparse
import pyBigWig
def extract_regions(bwfile, bedfile, chsizefile, bwoutfile):
bw = pyBigWig.open(bwfile)
bed_el = []
intervals = []
for line in open(bedfile):
cols = line.strip().split()
intervals.append(bw.intervals(cols[0], int(cols[1]), int(cols[2])))
bed_el.... |
185695 | from common import *
import datetime
import argparse
import time
here = os.path.abspath(os.path.dirname(__file__))
app_dir = os.path.join(here, '../dgl/multi_gpu')
"""
if log_dir is not None, it will only parse logs
"""
def motivation_test(log_folder=None):
tic = time.time()
if log_folder:
mock... |
185696 | from .translated_object import TranslatedObject
from .base_translator import BaseTranslator
BASE_HEADERS: dict = {
"User-Agent": "GoogleTranslate/6.6.1.RC09.302039986 (Linux; U; Android 9; Redmi Note 8)",
}
|
185698 | import io
import pathlib
import pytest
from mopidy.m3u import translator
from mopidy.m3u.translator import path_to_uri
from mopidy.models import Playlist, Ref, Track
def loads(s, basedir):
return translator.load_items(io.StringIO(s), basedir)
def dumps(items):
fp = io.StringIO()
translator.dump_items(... |
185734 | import random
import unittest
from podman import PodmanClient
from podman.errors import NotFound
from podman.tests.integration import base
class VolumesIntegrationTest(base.IntegrationTest):
def setUp(self):
super().setUp()
self.client = PodmanClient(base_url=self.socket_uri)
self.addCle... |
185747 | import unittest
from test.robotTestUtil import RobotTestUtil
class MyTestCase(unittest.TestCase):
def test_pose(self):
robot = RobotTestUtil.make_fake_dash()
packet = {}
packet['2002'] = {
'x' : 1.2,
'y' : 3.4,
'degree': 5.6,
}
... |
185753 | from PIL import Image
import numpy as np
import cv2
PAPER_EXT = {".gloria_chx": "gloria_chx_open_image"}
def gloria_chx_open_image(img):
def _resize_img(img, scale):
"""
Args:
img - image as numpy array (cv2)
scale - desired output image-size as scale x scale
Retur... |
185760 | import copy
from dataclasses import dataclass, field
import functools
from typing import Optional, Tuple
from pycparser import c_ast as ca
from .compiler import Compiler
from .randomizer import Randomizer
from .scorer import Scorer
from .perm.perm import EvalState
from .perm.ast import apply_ast_perms
from .helpers i... |
185763 | import pickle
import re
from unidecode import unidecode
from collections import defaultdict
from math import log
from opentapioca.readers.dumpreader import WikidataDumpReader
separator_re = re.compile(r'[,\-_/:;!?)]? [,\-_/:;!?(]?')
def tokenize(phrase):
"""
Split a text into lists of words
"""
words... |
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