id stringlengths 3 8 | content stringlengths 100 981k |
|---|---|
3327358 | commands = [
'base64',
'basename',
'false',
'sha1sum',
'sha224sum',
'sha256sum',
'sha384sum',
'sha512sum',
'tee',
'true',
'whoami',
]
|
3327360 | import sys
import writeGraph
import re
reHits = re.compile('T(.) (.*?) sort=(.*?): ([0-9]+) hits in ([.0-9]+) msec')
reHeapUsagePart = re.compile(r'^ ([a-z ]+) \[.*?\]: ([0-9.]+) (.B)$')
def extractSearchStats(searchLog, byQuerySort={}):
heapBytes = None
heapBytesByPart = {}
byThread = {}
with open(sear... |
3327362 | from functools import partial
from .get_import_root import get_import_root
from .panel_items import panel_items
from .debug import debug
def list_imports_command(view, import_root, entry_modules, typescript_paths=[]):
(items, matches) = panel_items(entry_modules=entry_modules, import_root=import_root)
def ... |
3327416 | import torch
import torch.nn as nn
from frechetmean.utils import d2arcosh, darcosh
def grad_var(X, y, w, K):
"""
Args
----
X (tensor): point of shape [..., points, dim]
y (tensor): mean point of shape [..., dim]
w (tensor): weight tensor of shape [..., points]
K (float): c... |
3327429 | import numpy as np
import pandas as pd
from time import time
from datetime import datetime as dt
from ..database import instance as db
def f(start, end, key):
t = time()
q = "index >= '%s' and index <= '%s'" % (str(start), str(end))
print(q)
res=st.select(key, where=q, iterator=True)
for x in res... |
3327449 | desc = """Animations of black hole spin evolution for precessing binary black holes.
Example usage:
python precession_tracking_updown.py -r pn_instability/Case_002
Where Case_002 contains Horizons.h5.
"""
import numpy as np
import matplotlib.pyplot as P
import argparse
import h5py
from scipy.interpolate import Uni... |
3327491 | import numpy as np
import ast
import scipy
import matplotlib.pyplot as plt
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.models import Model
import random
from PIL import Image
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
def get_ResNet():
# define ResNet50 model
model =... |
3327515 | import torch
from torch import nn
def conv3x3(in_channels, out_channels, stride=1, groups=1, dilation=1):
"""3x3 conv with padding"""
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=dilation, groups=groups,
bias=False, dilation=dilation)
def conv1x1(in... |
3327566 | import chex
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import pax
import pytest
# def test_batchnorm_train():
# bn = pax.BatchNorm(
# 3, True, True, 0.9, reduced_axes=[0, 1], param_shape=[1, 1, 3]
# )
# bn = pax.enable_train_mode(bn)
# x = jnp.ones((1, 10, 3))
# ... |
3327572 | import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
n_personas_obs = 2
edad_maxima_obs = 40
edad_final = np.arange(0, 500)
n_edades = len(edad_final)
n_intentos_por_edad = 10000
lista_edades_final = []
for i in range(n_edades):
n_in = 0
for j in range(n_intentos_p... |
3327575 | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import time
import math
import time
from . import predictions
class QuickTrainer():
def __init__(self):
self.metrics = {
'loss': {
'trn':[],
... |
3327659 | from selenium import webdriver
def lambda_handler(event, context):
options = webdriver.ChromeOptions()
options.add_argument("--headless")
options.add_argument("--disable-gpu")
options.add_argument("--window-size=1280x1696")
options.add_argument("--disable-application-cache")
options.add_argume... |
3327689 | from tkinter import *
""" Specifes CLEAR'S user input parameters. CLEAR sets the input parameters as global variables
whose values are NOT changed in any other module (these are CLEAR's only global variables).
Tkinter is used to provide some checks on the user's inputs. The file
'Input Parameters for CLEAR.pdf' ... |
3327695 | def chromosoneCheck(sperm):
""" chromosome_check == PEP8 (forced mixedCase by Codewars)
Spelling mistake courtesy of Codewars as well
"""
return "Congratulations! You're going to have a {}.".format(
'son' if 'Y' in sperm else 'daughter'
)
|
3327710 | import numpy as np
import torch
import torch.nn as nn
class StringsModel(nn.Module):
def forward(self, x, y):
return {"out": np.array([f + " " + s for f, s in zip(x, y)])}
def get_model(_):
return StringsModel()
|
3327759 | from __future__ import absolute_import
import __builtin__
import re
from textwrap import dedent
from assemblyline.common.charset import translate_str
from assemblyline.al.common.heuristics import Heuristic
from assemblyline.al.common.result import Result, ResultSection, SCORE
from assemblyline.al.common.result import... |
3327775 | import torch.nn as nn
from torch_fidelity.helpers import vassert
class SampleSimilarityBase(nn.Module):
def __init__(self, name):
"""
Base class for samples similarity measures that can be used in :func:`calculate_metrics`.
Args:
name (str): Unique name of the subclassed sam... |
3327787 | import unittest
from util import roslaunch_to_dot, Color, ErrorMsg
class TestNodesSameName(unittest.TestCase):
IdenticalNodeName = "a_node"
def testNodeSameName(self):
launchFile = "examples/fake_package/launch/identical_nodes.launch"
status, output, graph = roslaunch_to_dot(launchFile)
... |
3327798 | from .main import Voice
from .phonemes import (PhonemeList, Phoneme, FrenchPhonemes,
BritishEnglishPhonemes, GreekPhonemes, ArabicPhonemes,
SpanishPhonemes, GermanPhonemes, ItalianPhonemes,
PortuguesePhonemes, AmericanEnglishPhonemes)
|
3327837 | import setuptools
with open("README.md", "r") as rm:
long_description = rm.read()
with open("requirements.txt") as r:
requirements = r.readlines()
setuptools.setup(
name="canano",
version="0.0.3",
author="<NAME>",
author_email="<EMAIL>",
description="Python library for Canano Raspberry Pi... |
3327841 | import base64
from typing import Dict, List, Optional, Union
import mmh3
from pydantic import BaseModel
from common_osint_model.models import ShodanDataHandler, CensysDataHandler, BinaryEdgeDataHandler, Logger
from common_osint_model.utils import hash_all
class HTTPComponentContentFavicon(BaseModel, ShodanDataHandl... |
3327854 | from __future__ import absolute_import, division, print_function
from telnyx.api_resources.abstract import UpdateableAPIResource
class InboundChannel(UpdateableAPIResource):
OBJECT_NAME = "inbound_channels"
def instance_url(self):
return "/v2/phone_numbers/inbound_channels"
@classmethod
def... |
3327883 | import numpy as np
import torch
import torch.optim as optim
import wandb
from gym import Wrapper
from gym_maze.envs.maze_env import MazeEnvSample5x5
from config import config
from embedding_model import EmbeddingModel, compute_intrinsic_reward
from memory import Memory, LocalBuffer
from model import R2D2
def get_act... |
3327926 | class WriteOutEnum:
RDKIT_NAME = "_Name"
INDEX_STRING = "index_string"
COMPOUND_NAME = "compound_name"
# REINVENT-compatible JSON write-out
JSON_RESULTS = "results"
JSON_NAMES = "names"
JSON_NA = ""
JSON_VALUES = "values"
JSON_VALUES_KEY = "values_key"
SDF = "sdf"
PDB = "p... |
3327942 | import sys
import numpy as np
import fn_tensors as fnt
from copy import deepcopy
from joblib import Parallel, delayed
L = 10 #sys.argv[1]
datroot = 'dataRandom' + str(L)
ntrials = 40
graph, num_bonds = fnt.randomtn(L,0.8)
#graph, num_bonds = fnt.squaretn(L)
bdims = [10 for i in range(num_bonds)]
np.save(datroot+'/gr... |
3327987 | from typing import Iterator, Tuple, TypeVar
T = TypeVar("T")
def last_flag(iterator: Iterator[T]) -> Iterator[Tuple[bool, T]]:
items = list(iterator)
for i, item in enumerate(items):
yield i == len(items) - 1, item
def first_flag(iterator: Iterator[T]) -> Iterator[Tuple[bool, T]]:
items = list(... |
3327988 | from selenium.webdriver.remote.webdriver import WebDriver, By
from selenium.webdriver.remote.webelement import WebElement
from selenium.webdriver.support.wait import WebDriverWait
from selenium.common.exceptions import StaleElementReferenceException
from pylenium import utils
def inject(driver: WebDriver, version="3.... |
3327993 | import scrapy
class betScore(scrapy.Spider):
name = "scores"
def start_requests(self):
urls = ["https://www.academiadasapostas.com"]
for url in urls:
yield scrapy.Request(url=url, callback=self.parse)
def parse(self, response):
fixtures = response.xpath('//div[contains(@id, "fh_main_tab")]')
for game ... |
3328016 | from collections import namedtuple
Regression = namedtuple("Regression", ["wave", "hscale", "period", "phase", "expected"])
Regression.__new__.__defaults__ = ("", 1, 1, 0, [])
basic = [
Regression(wave="P", expected=['Pclk', 'nclk']),
Regression(wave="P", hscale=2, expected=['Pclk', '111', 'nclk', '000']),
... |
3328097 | from schema_registry.client import utils
def test_avro_version_does_not_exists(client, avro_country_schema):
assert client.check_version("test-avro-schema-version", avro_country_schema) is None
def test_avro_get_versions(client, avro_country_schema):
subject = "test-avro-schema-version"
client.register(... |
3328112 | import sys
PY3 = sys.version_info[0] == 3
if PY3:
def b(s):
if not isinstance(s, bytes):
return s.encode('utf-8')
return s
def u(s):
if not isinstance(s, str):
return s.decode('utf-8')
return s
native = u
else:
def b(s):
if not isinstan... |
3328149 | from .base import BaseEngine
from django.conf import settings
from feder.virus_scan.models import Request
import requests
class AttachmentScannerEngine(BaseEngine):
name = "Attachmentscanner"
def __init__(self):
self.key = settings.ATTACHMENTSCANNER_API_KEY
self.url = settings.ATTACHMENTSCANN... |
3328178 | from HTMLParser import HTMLParser
from Emoticons import Emoticons
from Acronyms import Acronyms
from nltk.stem.porter import *
import sys
class TweetPreProcessing:
# Replace Html character codes, replace emoticons with sentiment and replace acronym with words
def preProcessing(self,text):
# Html char... |
3328194 | from django.contrib import admin
# Register your models here.
from .models import Profile, Address, SMSVerification, DeactivateUser
admin.site.register(Profile)
admin.site.register(Address)
admin.site.register(SMSVerification)
admin.site.register(DeactivateUser) |
3328196 | from redisolar.dao.redis.base import RedisDaoBase
from redisolar.dao.redis.capacity_report import CapacityReportDaoRedis
from redisolar.dao.redis.feed import FeedDaoRedis
from redisolar.dao.redis.fixed_rate_limiter import FixedRateLimiter
from redisolar.dao.redis.meter_reading import MeterReadingDaoRedis
from redisolar... |
3328241 | import torch
import torch.nn.functional as F
import torch.optim as optim
from os.path import join
import os
from bayes_nn.data_loader import Dataloader
from torch.autograd import Variable
from bayes_nn.util.util import to_tensor
from bayes_nn.model.model_definition import Net
from bayes_nn import conf
# Set up defau... |
3328275 | import random
from pydub import AudioSegment
from pydub.playback import play
import glob
import operator
import naming
import filezart
import copy
import math
from filezart import instrument
#from multiprocessing import Pool
import threading
def notenames():
return ("C", "Db", "D", "Eb", "E", "F", "G... |
3328276 | import riff
import struct
import sys
from mpeg4 import is_iframe
class Stream(object):
def __init__(self, num, stream_type):
self.num = int(num)
self.type = stream_type
self.chunks = []
def add_frame(self, chunk):
self.chunks.append(chunk)
def __getitem__(self, index):
return self.chunks._... |
3328288 | from splitterkit import readwave, writewave, split, merge, combine
src = 'res/money.wav'
dest = 'res/output-'
# extract data from wav file
data = readwave(src)
# split file into equal 1-second intervals
splitted = split(data)
# save each 1-second interval to output as individual files
ex1 = writewave(dest + '1-', s... |
3328289 | from .sugartex_pandoc_filter import main, kiwi_hack
kiwi_hack()
if __name__ == '__main__':
main()
|
3328304 | import sys
import time
import re
import os
import configparser
from datetime import datetime, date
from influxdb import InfluxDBClient
from pyunifi import controller
# Logging Related
import logging
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s',
stream=sys.stderr, level=logg... |
3328310 | import pandas as pd
import numpy as np
import os
import re
import sys
from os.path import join, expanduser
import json
import warnings
warnings.filterwarnings(action='ignore')
def load_json(path):
"""
Load json files
Parameters
----------
path: str
Filepath of json file
"""
w... |
3328320 | from __future__ import annotations
from di.container import Container
from di.dependant import Dependant
from di.executors import SyncExecutor
class Test:
def __call__(self: Test) -> Test:
return self
def test_postponed_evaluation_solving():
container = Container()
with container.enter_scope(No... |
3328332 | from django.contrib import admin
from .models import ChocoSoftware, InstalledSoftware
class ChocoAdmin(admin.ModelAdmin):
readonly_fields = ("added",)
admin.site.register(ChocoSoftware, ChocoAdmin)
admin.site.register(InstalledSoftware)
|
3328343 | import csv
import json
import os.path
from jinja2 import Environment, FileSystemLoader, select_autoescape
from weasyprint import HTML
from . import config
class BaseCertificator:
def __init__(self, destination_path='.', template_path=None, template_filename='template.html',
filename_format='cer... |
3328374 | from __future__ import division
from builtins import object
import numpy as np
from sporco import interp
class TestSet01(object):
def test_01(self):
x = np.arange(0, 11).astype(np.float32)
m0 = 2.0
c0 = 1.0
y0 = m0 * x + c0
y = y0.copy()
y[4] += 2.0
A = n... |
3328399 | import pandas as pd
# Set display options for pandas for easier printing
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
rapm = pd.read_csv('data/rapm_adjusted.csv')
abs_player_change = []
def calculate_change(player):
print(player)
base = player['RAPM'].values[0]
player... |
3328427 | import cv2 as cv
import numpy as np
img = cv.imread("dahai.jpg")
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 灰度图均衡化
equ = cv.equalizeHist(gray)
# 水平拼接原图和均衡图
result1 = np.hstack((gray, equ))
cv.imwrite('grey_equ.png', result1)
# 彩色图像均衡化,需要分解通道 对每一个通道均衡化
(b, g, r) = cv.split(img)
bH = cv.equalizeHist(b)
gH = cv.equal... |
3328445 | from .default import add_model
from .default import add_variational
from .default import add_optim
from .default import add_logging
from .default import args_to_string
from .default import add_proximity
|
3328463 | from setuptools import setup, find_packages
with open('requirements.txt') as f:
required = f.read().splitlines()
setup(
name='pyicloudreminders',
version='0.1.0',
url='https://github.com/coumbole/pyicloudreminders',
description=(
'PyiCloudReminders is a module (a fork of pyicloud) which'... |
3328499 | import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-o', type=str, help='output file')
|
3328563 | class D:
def __init__(self, x, y, z=None):
self.x = x
self.y = y
self.z = z if z is not None else 0
def __str__(self):
return f"D({self.x}, {self.y}, {self.z})"
if __name__ == "__main__":
d = D(1, 2)
print(d)
|
3328566 | from dataclasses import dataclass, field
from datetime import datetime
from typing import Union
import flatbuffers
import numpy as np
import reinforcement_learning.messages.flatbuff.v2.CbEvent as _CbEvent
import reinforcement_learning.messages.flatbuff.v2.Event as _Event
import reinforcement_learning.messages.flatbuf... |
3328569 | import os
import bpy
from tests import utils
class TestLevel(utils.XRayTestCase):
def test_default(self):
prefs = utils.get_preferences()
prefs.gamemtl_file = os.path.join(self.relpath(), 'gamemtl.xr')
# Import
bpy.ops.xray_import.level(filepath=os.path.join(
self.re... |
3328582 | from optimus.engines.base.dataframe.rows import DataFrameBaseRows
from optimus.engines.base.pandas.rows import PandasBaseRows
from optimus.engines.base.rows import BaseRows
class Rows(DataFrameBaseRows, PandasBaseRows, BaseRows):
pass
|
3328585 | from typing import List
from sqlalchemy.orm import Session
from crud.base import CrudBase
from crud.crud_config import crud_isp
from models.vps import Vps
class CrudVps(CrudBase):
def get_vps_list(self, db_session: Session) -> List:
return db_session.query(self.model).all()
@classmethod
def get... |
3328594 | import re
from django.template.loader import get_template
from django.template import Context
from moocng.videos.download import process_video
from .base import MediaContentHandlerBase
class YoutubeMediaContentHandler(MediaContentHandlerBase):
def get_iframe_template(self, content_id, **kwargs):
templa... |
3328627 | from omrdatasettools.image_generators.MeasureVisualizer import MeasureVisualizer
visualizer = MeasureVisualizer(False, False, True)
visualizer.draw_bounding_boxes_for_all_images_in_directory(r"E:\Stave Detection\deep_scores_temp",
r"E:\Stave Detection\deep_scores_temp\annotati... |
3328632 | import time
from lithops.multiprocessing import Pool, Barrier, current_process
# from multiprocessing import Pool, Barrier, current_process
def f():
print('waiting...')
barrier.wait()
pid = current_process().pid
msg = 'process: {} - timestamp: {}'.format(pid, time.time())
return msg
if __name__... |
3328639 | from django.contrib import admin
from .models import ArtistReview
# Register your models here.
# Register your models here.
@admin.register(ArtistReview)
class ArtistReview(admin.ModelAdmin):
list_display = ('message','user_reviewing', 'user_reviewer', 'created_at','updated_at','id',)
pass |
3328677 | import numpy as np
from PIL import Image, ImageFilter
from pathlib import Path
import torch
from torch.utils.data import Dataset
from .util import Wrapper
class TransformData(Wrapper, Dataset):
"""
Transform a dataset by registering a transform for every input and the
target. Skip transformation by sett... |
3328762 | from sympy import Tuple, S, I, Plane
from sympy.core.relational import Relational
from sympy.logic.boolalg import Boolean
from sympy.matrices.dense import DenseMatrix
from sympy.vector import Vector
from sympy.geometry.entity import GeometryEntity
from spb.series import (
LineOver1DRangeSeries,
Parametric2DLine... |
3328764 | import json
import os
from common.helpers.dictionaries import keys_omit
from django.core.management.base import BaseCommand
def upsert_testimonial(testimonial_json, priority):
from civictechprojects.models import Testimonial
testimonial = Testimonial.objects.filter(name=testimonial_json['name']).first()
i... |
3328847 | import torch
import torch.nn as nn
import torch.optim as optim
from backpack import backpack
from backpack.extensions import BatchGrad
from lib.BaseAlg import BaseAlg, get_device, Network, obs_data_all
import numpy as np
from backpack import backpack, extend
class NeuralTSUserStruct:
def __init__(self, feature, f... |
3328854 | import pytest
from danger_python.exceptions import DangerfileException, SystemConfigurationException
from danger_python.shell import (
build_danger_command,
execute_dangerfile,
resolve_danger_path,
)
@pytest.mark.parametrize("danger_js_path", ["/usr/bin/fake-danger-js \n \r"])
@pytest.mark.usefixtures("r... |
3328867 | import math
from math import pi
import numpy as np
from gym.spaces import Box, Discrete
from ray.rllib.utils import merge_dicts
from ray.rllib.env.multi_agent_env import MultiAgentEnv
def direction_to_coord(direction):
"""takes x \in [0,1), returns 2d coords on unit circle"""
return tuple([math.cos(2*pi*direc... |
3328918 | import tensorflow as tf
import copy
from tensorflow.contrib.rnn import BasicRNNCell
from icecaps.estimators.rnn_estimator import RNNEstimator
from icecaps.estimators.convolutional_estimator import ConvolutionalEstimator
class NGramCell(BasicRNNCell):
def __init__(self, hparams):
super().__init__(hparam... |
3328919 | import re
from ..base import Base
class Regex(Base):
_description = 'matching {}'
def _check(self, value):
return bool(re.search(self.value, value))
|
3328936 | from django.db import models
from thenewboston.models.confirmation_service import ConfirmationService
from v1.validators.models.validator import Validator
class ValidatorConfirmationService(ConfirmationService):
validator = models.ForeignKey(Validator, on_delete=models.CASCADE)
class Meta:
default_r... |
3328960 | import os
import re
import json
import time
import numpy as np
import pandas as pd
from plotnine import *
# Config
PATH = os.getcwd()
path_n = re.split(pattern=r"/|\\", string=PATH)[1:]
if os.name == "posix":
path_n = "/" + os.path.join(*path_n)
else:
drive = PATH[0:3]
path_n = drive + os.path.join(*path_n... |
3328991 | from easycli import Root
from .documentary import DocumentaryLauncher
from .mockupserver import MockupServer
class BDDRESTCommand(Root):
__help__ = 'bddrest'
__completion__ = True
__arguments__ = [
DocumentaryLauncher,
MockupServer,
]
def main():
BDDRESTCommand().main()
|
3329008 | import txaio
txaio.use_twisted()
from autobahn.xbr._schema import FbsSchema
for filename in [
'/tmp/test/bfbs/climate.bfbs',
'/tmp/test/bfbs/network.bfbs',
'/tmp/test/bfbs/location.bfbs']:
schema = FbsSchema.load(filename)
print(schema)
|
3329061 | from typing import Dict, Tuple, List, Any
import array
import concurrent.futures
import datetime
import logging
import numpy
import pymongo
import pytz
import zlib
from . import DataProvider
from wx_explore.common import tracing
from wx_explore.common.models import (
Projection,
SourceField,
DataPointSet,... |
3329072 | from .. import *
from bfg9000.arguments.windows import *
class TestWindowsArgParse(TestCase):
def test_empty(self):
parser = ArgumentParser()
self.assertEqual(parser.parse_known([]), ({}, []))
self.assertEqual(parser.parse_known(['extra']), ({}, ['extra']))
self.assertEqual(parser... |
3329081 | import gym, yumi_gym
import pybullet as p
import h5py
import numpy as np
class h5Parser(object):
def __init__(self, h5_file):
self.data = h5py.File(h5_file, "r")
def parse(self, group_name):
# @return:x np.ndarray with shape of 4 * T * 7
# @return:Q np.ndarray with shap... |
3329101 | import abc
import os
import torch
from tensorboardX import SummaryWriter
from sklearn.metrics import confusion_matrix
from imbalanceddl.utils.metrics import shot_acc
import numpy as np
class BaseTrainer(metaclass=abc.ABCMeta):
"""Base trainer for Deep Imbalanced Learning
A trainer that will be learning with ... |
3329110 | import json
import os
import re
import sqlite3
from chilin2.modules.config.helpers import JinjaTemplateCommand, template_dump, r_exec, json_load, underline_to_space, count_in_million, decimal_to_latex_percent, json_dump
from samflow.command import PythonCommand
from samflow.command import ShellCommand
from samflow.wor... |
3329111 | import os
from os import listdir
from os.path import join
import numpy as np
from PIL import Image
from bbox_tr import *
in_dir = r'D:\ImageSamples\DOTA v1.5\train\images\images'
out_dir = r'D:\ImageSamples\DOTA v1.5\train\images\cwh'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for i... |
3329116 | import pytest
import pylas
from pylas import PointFormat
from pylastests.test_common import extra_bytes_laz
@pytest.mark.skipif(
len(pylas.LazBackend.detect_available()) == 0, reason="No Laz Backend installed"
)
def test_extra_dims_not_equal():
"""Test to confirm that two point format with same id but
no... |
3329119 | import pandas as pd
import pytest
from oam.score.isolation_path import IsolationPath
from oam.search.simple_combination import SimpleCombination
from oam.visualization import visualize_oam_results, zscore_heatmap
@pytest.fixture
def dataframe():
df = pd.read_csv("datasets/df_outliers.csv")
df = df.sort_value... |
3329150 | from launchers.parsers import build_parser
from launchers.decentralized import DecentralizedLauncher
def parse_args():
parser = build_parser(auction=True, transformation='subpolicy')
args = parser.parse_args()
args.hrl = True
return args
if __name__ == '__main__':
launcher = DecentralizedLauncher(... |
3329189 | import numpy as np
from numpy.testing import assert_allclose
import pytest
from .. import SphericalDust, IsotropicDust
from ...util.functions import random_id, B_nu
def test_missing_properties(tmpdir):
d = SphericalDust()
with pytest.raises(Exception) as e:
d.write(tmpdir.join(random_id()).strpath)
... |
3329213 | from heapq import heappush, heappop
class Solution:
def kSmallestPairs(self, nums1: List[int], nums2: List[int], k: int) -> List[List[int]]:
if not nums1 or not nums2:
return []
heap = [(nums1[0] + nums2[0], 0, 0)]
seen = set((0, 0))
result = []
... |
3329217 | import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from torch.nn.modules.utils import _single, _pair, _triple
from torchmeta.modules.module import MetaModule
class MetaConv1d(nn.Conv1d, MetaModule):
__doc__ = nn.Conv1d.__doc__
def forward(self, input, params=None):
... |
3329220 | from keras.layers.core import Activation, Dropout
from keras.layers.convolutional import Conv2D
from keras.layers.merge import Concatenate
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras.models import Model
from ..encoder import build_encoder, scope_table
from ..u... |
3329237 | from django import forms
import voxel_globe.meta.models as models
class FilterNumberObservationsForm(forms.Form):
voxel_world = forms.ModelChoiceField(label="Voxel World",
queryset=models.VoxelWorld.objects.all().order_by('name'))
number_means = forms.FloatField(min_value=0,
label="Mean Multiplier", r... |
3329251 | from collections import OrderedDict
from indy_common.types import ClientGetAuthRuleOperation, AuthRuleValueField
from plenum.common.messages.fields import ConstantField, LimitedLengthStringField, ChooseField
EXPECTED_ORDERED_FIELDS = OrderedDict([
("type", ConstantField),
("auth_action", ChooseField),
("a... |
3329254 | import visdom
import os.path
class VisdomInstance(object):
"""
This class is a singleton for getting an instance of Visdom client.
It also replays all the logs at the loading time.
:class:`~digideep.pipeline.session.Session` is responsible for initializing the log_file
and replaying the old lo... |
3329262 | from torch._C import device
import torch.nn as nn
import torch
from matchmaker.losses.soft_crossentropy import SoftCrossEntropy
class QA_StartEndCrossEntropy(nn.Module):
def __init__(self):
super(QA_StartEndCrossEntropy, self).__init__()
self.loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
... |
3329263 | from __future__ import print_function
import sys
import argparse
import os
from collections import namedtuple
from statsmodels.stats.proportion import proportion_confint
# Appending current working directory to sys.path
# So that user can run randomtester from the directory where sut.py is located
current_working_dir... |
3329290 | def giving():
i01.moveHead(44,82)
i01.moveArm("left",15,55,68,10)
i01.moveArm("right",13,40,74,13)
i01.moveHand("left",61,0,14,0,0,180)
i01.moveHand("right",0,24,24,19,21,25)
i01.moveTorso(90,90,90) |
3329308 | from .utils import pkrTestCase
class TestBaseDriver(pkrTestCase):
PKR = "pkr"
pkr_folder = "base_driver"
kard_env = "dev"
kard_driver = "base"
kard_extra = {"flag": "flag_value"}
def test_base_driver_values(self):
self.kard_extra["src_path"] = self.src_path
self.generate_kard(... |
3329328 | import unittest
from .exceptions import InvalidHeader
from .headers import *
class HeadersTests(unittest.TestCase):
def test_parse_extension_list(self):
for header, parsed in [
# Synthetic examples
(
'foo',
[('foo', [])],
),
... |
3329344 | import tensorflow as tf
_PAD = "_PAD"
_GO = "_GO"
_EOS = "_EOS"
_UNK = "_UNK"
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
def create_vocab_tables_with_input(vocab_file, vocab_size):
vocab_placeholder = tf.placeholder(tf.int64, [None, None])
#string_to_index_table = tf.... |
3329373 | import inspect
import os
import sys
def get_home():
"""
Returns the path to the user directory.
"""
return os.path.expanduser("~")
def get_parameters():
"""
Returns a list of parameters (without filename).
"""
return sys.argv[1:]
def get_script_dir(follow_symlinks=True):
"""
... |
3329385 | import netomaton as ntm
from netomaton import TuringMachine, TapeCentricTuringMachine
if __name__ == "__main__":
# A Turing machine with 7 states for the head, and 7 states for each cell in the tape, for the language
# L = {a^nb^nc^n | n > 0}. If the evolution of the automaton settles on the head state 'q6',... |
3329419 | from __future__ import absolute_import
from hashlib import md5
from flask import request
from pytest import fixture, raises, mark
from huskar_api import settings
from huskar_api.app import create_app
from huskar_api.api.utils import (
api_response, with_etag, deliver_email_safe, with_cache_control)
from huskar_a... |
3329430 | import numpy as np
import sympy
from ..api import utils
def as_dimension(value):
if isinstance(value, Dimension):
return value
elif isinstance(value, (int, np.int64)):
return Dimension(int(value))
elif isinstance(value, (str, sympy.Symbol, sympy.Expr)):
if isinstance(value, str):
... |
3329480 | from imagepy.core.engine import Free
class HelloWorld(Free):
title = 'Hello World'
def run(self, para=None):
self.app.alert('Hello World, I am ImagePy!')
class WhoAreYou(Free):
title = 'Who Are You'
para = {'name':'', 'age':0}
view = [(str, 'name', 'name', 'please'),
(int, 'age', (0,120), 0, 'age', 'years ... |
3329481 | import telnetlib
import time
from pprint import pprint
import socket
import re
import yaml
class CiscoTelnet:
def __init__(
self, host, username, password, enable_pass, encoding="utf-8", timeout=10
):
self.host = host
self.username = username
self.password = password
s... |
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