id stringlengths 3 8 | content stringlengths 100 981k |
|---|---|
11423916 | import argparse
import asyncio
import contextlib
import logging
import random
import aiosip
from util import Registration
sip_config = {
'srv_host': '127.0.0.1',
'srv_port': 6000,
'realm': 'XXXXXX',
'user': None,
'pwd': '<PASSWORD>',
'local_host': '127.0.0.1',
'local_port': random.randint... |
11423917 | from .exception_hook import ExceptionHook
from typing import Iterator, List, TypeVar, Iterable, Dict
import random
from itertools import zip_longest, islice
A = TypeVar('A')
def lazy_groups_of(iterator: Iterator[A], group_size: int) -> Iterator[List[A]]:
"""
Takes an iterator and batches the invididual instanc... |
11423919 | import json
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import ImageOps
from torchvision import datasets, transforms
import activations
import models
import visualize
AFS = list(activations.__class_dict__.keys())
MODELS = list(m... |
11423927 | extensions = dict(
set_required_params="""
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, y)
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
"""
)
doc = dict(
preamble="""
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the co... |
11423977 | import requests
from e2etests.config import url
def test_version(record_property):
version_obj = requests.get(url + '/version').json()
record_property('version', version_obj)
assert 'python' in version_obj
assert 'entityservice' in version_obj
assert 'anonlink' in version_obj
def test_status(r... |
11423979 | import os
from kivy.animation import Animation
from kivy.clock import Clock
from kivy.core.window import Window
from kivy.lang import Builder
from kivy.metrics import dp
from kivy.properties import NumericProperty
from kivymd.uix.floatlayout import MDFloatLayout
Window.size = (800, 600)
Window.minimum_width, Window.m... |
11423994 | import platform
from pkg_resources import get_distribution
from sunpy.extern.distro import linux_distribution
from sunpy.util.sysinfo import find_dependencies
__all__ = ['system_info']
def system_info():
"""
Display information about your system for submitting bug reports.
"""
base_reqs = get_distr... |
11423998 | import numpy as np
class linear(object):
def __init__(self, basis, params=None, bias=None):
self.basis = basis
self.nbasis = basis.nbasis
self._init_params = params
self.bias = bias
self.params = params
if params is None:
self.params = np.zeros(self.nba... |
11424036 | def check_size(x, dim):
if not len(x)==dim:
raise Exception('The data should be a two-dimensional array')
else:
return
def print_result(result):
msg = ''
if result == 1:
print()
msg = 'Result is conclusive: B variant is winner!'
elif result == -1:
print()
... |
11424040 | import unittest
import numpy as np
from goldilocks import Goldilocks
from goldilocks.strategies import BaseStrategy, NucleotideCounterStrategy
################################################################################
# NOTE Tests following do not test the correctness of regions located by
# Goldilocks bu... |
11424043 | from scrapy.spiders import Spider
from scrapy.http import Request
from scrapy.selector import Selector
from crawler.items import ProxyIPItem
class XiciSpider(Spider):
name = "xici"
allowed_domains = ["xicidaili.com"]
start_urls = [
"http://www.xicidaili.com/nn",
"http://www.xicidaili.com/nn... |
11424101 | import os
import re
from powerline.bindings.vim import buffer_name
NERD_TREE_RE = re.compile(b'NERD_tree_\\d+')
def nerdtree(matcher_info):
name = buffer_name(matcher_info)
return name and NERD_TREE_RE.match(os.path.basename(name))
|
11424132 | import time
from typing import Callable, Tuple, List, Optional, Any
from kubernetes.client.rest import ApiException
# time to sleep between retries
SLEEP_TIME = 2
# no timeout (loop forever)
INFINITY = -1
def _current_milliseconds() -> int:
return int(round(time.time() * 1000))
def wait(
fn: Callable,
... |
11424143 | import torch
import numpy as np
def net_param_num(model):
num=0
for i in model.parameters():
v=1
for j in i.shape:
v*=j
num+=v
return num
def multi_class_accuracy(target, preds_score):
top1=0.
top3=0.
top5=0.
top10=0.
preds_score = preds_score.detach().cpu().numpy()
target = target.cpu().numpy()
... |
11424172 | import logging
import grpc
from client import Client
from common import IntegrationTestConfig
from util import load_test_config
from pool import Pool
from workflow import Workflow
from google.protobuf import json_format
from peloton_client.pbgen.peloton.api.v0 import peloton_pb2 as peloton
from peloton_client.pbgen.... |
11424178 | from dataclasses import dataclass
from typing import List, Union
from nlp_gym.envs.common.observation import BaseObservation, BaseObservationFeaturizer
from abc import abstractmethod
import torch
import copy
class ObservationFeaturizer(BaseObservationFeaturizer):
@abstractmethod
def init_on_reset(self, input... |
11424204 | from firetail.lib import db
from firetail.utils import make_embed
import time
import datetime
import asyncio
import json
class Notifications:
def __init__(self, bot):
self.bot = bot
self.session = bot.session
self.config = bot.config
self.logger = bot.logger
self.loop = asy... |
11424229 | from __future__ import absolute_import, division, print_function
from scitbx.array_family import flex
import libtbx.load_env
import sys
from six.moves import range
def extend_sys_path():
sys.path.insert(0,
libtbx.env.under_build("scitbx/array_family/boost_python"))
def exercise_std_vector_conversions(verbose=0)... |
11424232 | import tacoma as tc
ec = tc.flockwork_P_varying_rates([],10,[0.4,0.8], 600,[ (0, 1.), (300,2.) ], 600,seed=25456)
print(ec.edges_out[:5])
print(ec.edges_in[:5])
import numpy as np
ec = tc.flockwork_P_varying_rates_for_each_node([],
10,
... |
11424270 | import random
from copy import copy
import torch
import torch.nn as nn
import torch.nn.functional as func
from .connection import (
AbstractStructure, ConnectionStructure,
SubgraphStructure, ConstantStructureMixin,
ConstantStructure, ScatterStructure, MessageMode
)
def to_graph(batched_tensor):
squashed_tens... |
11424284 | from typing import Any, List, NamedTuple, Optional, Set, Tuple, cast
import EoEfunc as eoe
import vapoursynth as vs
from havsfunc import LSFmod
from lvsfunc.kernels import Bicubic
from lvsfunc.types import Range
from lvsfunc.util import replace_ranges
from vardefunc.aa import Eedi3SR, Nnedi3SS, upscaled_sraa
from vard... |
11424300 | from utils.metrics import displacement_error, final_displacement_error, cal_l2_losses, cal_fde, cal_ade, \
l2_loss, miss_rate, linear_velocity_acceleration_1D
from utils.absolute import relative_to_abs
from utils.adj_matrix import compute_adjs_distsim, compute_adjs_knnsim, compute_adjs
from utils.losses import l2_e... |
11424311 | import json
import logging
logging.basicConfig(level=logging.INFO, format='')
class Logger:
def __init__(self):
self.entries = {}
def add_entry(self, entry):
self.entries[len(self.entries) + 1] = entry
def __str__(self):
return json.dumps(self.entries, sort_keys=True, indent=4)
|
11424353 | import pytest
import responses
from box import Box, BoxList
@pytest.fixture(name="pubse_vips")
def fixture_pubse_vips():
return {
"zscaler.net": {
"continent : apac": {
"city :_auckland": [
{
"range": "172.16.31.10/24",
... |
11424358 | from typing import Callable
from OpenGL.GL import *
from robot.common import logger
from .exceptions import UniformArraySizeError, UniformTypeError, UniformSizeError
import robot.visual.opengl.decorators as decorators
GL_TYPE_UNIFORM_FN = {
GL_INT: decorators.primative(glUniform1iv, int),
GL_FLOAT: ... |
11424372 | from cipher_description import CipherDescription
# State
# 0 4 8 12
# 1 5 9 13
# 2 6 10 14
# 3 7 11 15
sbox = [0xB, 0xF, 3, 2, 0xA, 0xC, 9, 1, 6, 7, 8, 0, 0xE, 5, 0xD, 4]
invsbox = [11, 7, 3, 2, 15, 13, 8, 9, 10, 6, 4, 0, 5, 14, 12, 1]
shuffle = [0, 5, 10, 15, 4, 9, 14, 3, 8, 13, 2, 7, 12, 1, 6, 11]
shufflei = ra... |
11424376 | class Solution:
def minCostClimbingStairs(self, cost: List[int]) -> int:
prev, curr = cost[0], cost[1]
for i in range(2, len(cost)): prev, curr = curr, cost[i] + min(prev, curr)
return min(prev, curr)
|
11424407 | import requests
import time
import asyncio
import aiohttp
from timeit import default_timer
# 第六節第一小節例子
async def func1():
print("func1")
await asyncio.sleep(1)
print("func1 end")
async def func2():
print("func2")
await asyncio.sleep(1)
print("func2 end")
async def gatherwait():
task = [... |
11424468 | import subprocess
import unittest
import os
import re
from unittest import TestCase
url = 'https://front'
cores = os.cpu_count()
def _bytes_2_lines(output: bytes):
lines = []
for each_line in output.split(b'\n'):
line = each_line.decode('utf-8').strip()
print(line)
lines.append(line)
return lines
... |
11424474 | import pandas as pd
from pymatgen.ext.matproj import MPRester
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import matplotlib.gridspec as gridspec
filename = r'C:\Users\taylo\Google Drive\teaching\5050 Materials Informatics\apikey.txt'
def get_file_contents(filename):
try:
... |
11424480 | import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from torch.nn import GRU
def truncate_param(param, value, eps=1e-6):
param_copy = param.clone()
mean = torch.mean(param)
param_copy[torch.abs(param_copy) >= value] =\
torch.rand_like(param_copy[torch.abs(param_co... |
11424482 | from flask_restful import Resource
from flask import request
from db import db
from schemas.staff_tenants import StaffTenantSchema
from models.staff_tenant_link import StaffTenantLink
from utils.authorizations import admin_required
class StaffTenants(Resource):
@admin_required
def patch(self):
data = ... |
11424547 | import torch.nn as nn
import torch
import torch.nn.functional as F
from avalanche.models.simple_mlp import SimpleMLP
class DQNModel(nn.Module):
def __init__(self):
super().__init__()
def forward(x: torch.Tensor, task_label=None):
raise NotImplementedError()
@torch.no_grad()
def get_... |
11424580 | import pytest
import tensorflow as tf
from tensorflow.python.keras import layers, models
from barrage import model
def simple_net(dense_dim=5, input_dim=4, output_dim=3, **params):
net = models.Sequential()
net.add(layers.Input(shape=(input_dim,), name="input"))
net.add(layers.Dense(dense_dim, activatio... |
11424581 | from sklearn import svm, tree
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from typing import Dict, Any
import os
import shutil
import yaml
import numpy as np
import sys
sys.path.append(os.path.abspath(".."))
from constants import PREDICTION_TYPE, MODEL_RUNTIME, DATA_TYPE
impo... |
11424593 | from ..helpers import IFPTestCase
from intficpy.thing_base import Thing
from intficpy.things import (
Surface,
Container,
)
class TestPlayerGetOn(IFPTestCase):
def setUp(self):
super().setUp()
self.surface = Surface(self.game, "bench")
self.start_room.addThing(self.surface)
d... |
11424629 | import esphome.codegen as cg
import esphome.config_validation as cv
from esphome.const import CONF_ID, CONF_MODEL
from esphome.components import esp32_ble
from esphome.core import CORE
from esphome.components.esp32 import add_idf_sdkconfig_option
AUTO_LOAD = ["esp32_ble"]
CODEOWNERS = ["@jesserockz"]
CONFLICTS_WITH = ... |
11424635 | import socket, os
import tempfile
import subprocess
# Global variables
HostAddress = "localhost"
HostPort = 48879
Protocol = "tcp"
# Helper function to check if a number is valid
def isNumber(givenObject):
try:
int(givenObject)
return True
except:
return False
def getSocket():
glo... |
11424655 | import torch
import torch.nn as nn
from zerovl.utils import Registry, ENV
LOSS = Registry('loss')
def build_loss(name, param):
if hasattr(nn, name):
if name == 'CrossEntropyLoss' and 'weight' in param:
param['weight'] = torch.tensor(param['weight'], dtype=torch.float32)
if name == 'C... |
11424683 | from .__version__ import __version__
from ._middleware import MessagePackMiddleware
__all__ = ["__version__", "MessagePackMiddleware"]
|
11424728 | import sys
import numpy as np
from matplotlib import pylab as plt
from sklearn.decomposition import PCA
from data.dataimport import import_data
from encoders.baseencoder import AbstractEncoder
if __name__ == '__main__':
if len(sys.argv) != 3:
print("Usage <encoderPkl> <dataset.json.gz>")
sys.exit... |
11424773 | class Solution(object):
def readBinaryWatch(self, num):
"""
:type num: int
:rtype: List[str]
"""
result = []
for h in range(12):
for m in range(60):
if bin(h).count("1") + bin(m).count("1") == num:
result.append('%d:%02d... |
11424777 | import certificate
import py.test
import datetime
import time
def disabled_test_make_certificate():
"""Generate a certificate"""
c = certificate.CertificatePrinter(title="Test Certificate")
c.add_params({"@@NAME@@":"A very precise data set",
"@@DATE@@":datetime.datetime.now().isoformat()[... |
11424793 | from rest_framework.renderers import JSONRenderer
class CustomJSONRenderer(JSONRenderer):
def render(self, data, accepted_media_type=None, renderer_context=None):
response_data = {'message': '', 'errors': [], 'data': data, 'status': 'success'}
# getattr(renderer_context.get('view').get_serialize... |
11424812 | from dexy.filter import Filter
from tests.utils import runfilter
from nose.exc import SkipTest
from nose.tools import raises
from dexy.utils import s
from dexy.utils import split_path
from dexy.utils import iter_paths
def test_iter_path():
full_path = "/foo/bar/baz"
expected_paths = {
0 : '/',
... |
11424868 | import os
import pytsk3
import sys
import pyewf
class EWFImgInfo(pytsk3.Img_Info):
"""EWF Image Format helper class"""
def __init__(self, ewf_handle):
self._ewf_handle = ewf_handle
super(EWFImgInfo, self).__init__(url="", type=pytsk3.TSK_IMG_TYPE_EXTERNAL)
def close(self):
self._ew... |
11424882 | from django.db import models
class Course(models.Model):
id = models.BigIntegerField(primary_key=True)
name = models.CharField(max_length=50, verbose_name="课程名")
desc = models.CharField(max_length=300, verbose_name=u"课程描述")
degree = models.CharField(
choices=(("primary", '初级'), ("middle", "中级"... |
11424905 | import numpy as np
import matplotlib.pyplot as plt
import auralib as aura
from numpy.fft import fftfreq, fft, ifft, fftshift, ifftshift
from scipy.interpolate import interp1d
import scipy as sp
def get_traces_for_matching_filter(basefile, monfile, step):
buf1 = aura.segy.Segy(basefile)
buf2 = aura.segy.Segy(m... |
11424938 | import numpy as np
import cv2
import os
from PIL import Image
def resize(img, new_size=(48,48)):
return img.resize(new_size)
def convert2grayscale(img):
return img.convert('LA')
def create_dilation(img, kernel_size=2, iterations=2):
kernel = np.ones((kernel_size, kernel_size), np.uint8) *0.5
dilati... |
11424951 | import pytest
from polog.handlers.file.rotation.parser import Parser
from polog.handlers.file.file_dependency_wrapper import FileDependencyWrapper
class RulesElectorMock:
def __init__(self, file):
pass
def choose(self, source):
return source
def test_parse_single_rule_with_mock(filename_for_... |
11424971 | import os
import cv2
import numpy as np
from scipy.special import softmax
from Operators.DummyAlgorithmWithModel import DummyAlgorithmWithModel
from Utils.GeometryUtils import force_convert_image_to_bgr, resize_with_height, pad_image_with_specific_base
from Utils.InferenceHelpers import TritonInferenceHelper
class C... |
11424990 | import sys
import logging
import pluggy
from pkg_resources import iter_entry_points, DistributionNotFound
from .models import PluginRegistry
from .utils import parse_pkg_metadata
logger = logging.getLogger(__name__)
class FlaskshopPluginManager(pluggy.PluginManager):
def __init__(self, project_name, implprefi... |
11425003 | from office365.onenote.operations.onenote_operation_error import OnenoteOperationError
from office365.onenote.operations.operation import Operation
class OnenoteOperation(Operation):
"""The status of certain long-running OneNote operations."""
@property
def error(self):
"""The error returned by t... |
11425009 | import pickle
import hashids
from django.test import TestCase
from django.utils.encoding import force_str
from hashid_field import Hashid
class HashidTests(TestCase):
def test_integer(self):
h = Hashid(5)
self.assertIsInstance(h, Hashid)
self.assertEqual(h.id, 5)
self.assertEqual... |
11425021 | import click
from .model import *
from .console import *
from ..helpers import check_console_input_config
@click.group("registry", help="Docker registry actions")
@click.pass_context
def registry(ctx, **kwargs):
pass
@registry.command(help="get a summary from remote registry")
@click.pass_context
@click.argum... |
11425066 | from .configure import create_data_set_properties
from ..experiment_1.generate import run
from graph_io.classes.dataset_name import DatasetName
def gen_run(n):
DATASET_NAME = DatasetName('review_hidden_real_'+str(n))
return DATASET_NAME, lambda client: run(client, create_data_set_properties(DATASET_NAME, n))
runne... |
11425090 | import gevent
from textwrap import dedent
from jumpscale.loader import j
from jumpscale.sals.chatflows.chatflows import chatflow_step
from jumpscale.packages.vdc_dashboard.sals.solutions_chatflow import SolutionsChatflowDeploy, POD_INITIALIZING_TIMEOUT
from jumpscale.packages.vdc_dashboard.sals.vdc_dashboard_sals impor... |
11425101 | import os
import warnings
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
warnings.filterwarnings("ignore")
matplotlib.use("Agg")
colors = {
"Car": "b",
"Tram": "r",
"Cyclist": "g",
"Van": "c",
"Truck": "m",
"Pedestrian": "y",
... |
11425116 | import json
import os
from vp import geom_tools
import dataset
from print_progress import print_progress
IMAGE_EXTENSION = 'jpg'
# Pixel dimensions of the images in the dataset.
IMAGE_DIMS = (1920, 1080)
def load_dataset(dataset_path, show_progress_bar=True):
"""Loads the Toulouse vanishing point dataset.
... |
11425153 | import chainer
def concat_variable(gx, g_input):
"""concatenate the inputs to a tuple of variable
Inputs:
None
~chainer.Variable
tuple of variable
Outputs:
None: When both of gx and g_input is None
Variable: When one is None, and the other is variable
tupl... |
11425191 | import logging
import wx
def filename_repr(filenames):
if filenames:
if isinstance(filenames, str):
filenames = [filenames]
# if 'wxMSW' in wx.PlatformInfo:
# does windows require separate handling for to backslash?
# on os-x, if the path contains a bac... |
11425193 | import numpy as np
from typing import List, Optional, Tuple, Union
from app import crud
from app.core.config import settings
from app.worker_utils.metrics import spherical_mean
import faiss
import logging
class KNN:
def __init__(self, db):
embeddings = crud.embedding.get_active_embeddings_by_embedding_mo... |
11425196 | class Pista():
def __init__ (self, nombre: str, favorita: bool, duracion: int, artista: str):
#la duracion esta contemplada en segundos
self.nombre = nombre
self.favorita = favorita
self.duracion = duracion
self.artista = artista
def get_informacion(self):
return ... |
11425210 | import math
import warnings
import chainer
import chainer.functions as F
import chainer.links as L
from tgan2.models.bn.categorical_conditional_batch_normalization \
import CategoricalConditionalBatchNormalization
try:
from chainermn.links import MultiNodeBatchNormalization
except Exception:
warnings.war... |
11425221 | import zipfile
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
BGCOLOR = '#AAAACC'
background = ListedColormap([BGCOLOR])
mario = ListedColormap([BGCOLOR, 'red', 'orange', 'brown'])
fire_mario = ListedColormap([BGCOLOR, 'white', 'orange', 'red'])
NUL = 252
class ... |
11425233 | from puq import *
import numpy as np
def model_0(x, y):
return x*x + 0.75 * y*y + 2*y + x*y - 7
def run():
# create some "experimental" data
real_x = NormalPDF(5, .2)
real_y = NormalPDF(3.4, .25)
sigma = 0.5
num_samples = 5
x_data = np.linspace(*real_x.range, num=num_samples)
y_data =... |
11425252 | import torch
import random
import numpy as np
from pre_processing import *
import torch.nn as nn
from random import randint
from PIL import Image, ImageSequence
import glob
from torch.utils.data.dataset import Dataset
BATCH_SIZE = 2
IN_SIZE = 1024
OUT_SIZE = 1024
TRAIN_VALID_RATIO = 0.8
# train_index = random.sample(r... |
11425266 | from tir import Webapp
import unittest
class CTBA161(unittest.TestCase):
@classmethod
def setUpClass(inst):
inst.oHelper = Webapp()
inst.oHelper.Setup("SIGACTB", "01/01/2019", "T1", "D MG 01", "34")
inst.oHelper.Program("CTBA161")
#########################################
# Inclus... |
11425267 | import h5py as h5
import numpy as np
import pandas as pd
DEFAULT_COMPRESSION = 'gzip'
DEFAULT_COMPRESSION_VALUE = 8 # 0 - 9
class HDFDataset(h5.File):
"""
Read / Write an HDF5 dataset using h5py. If HDF5 is compiled with
parallel support, this class will support parallel I/O of all supported
types... |
11425277 | from django.contrib import admin
from grandchallenge.products.models import Company, Product, ProjectAirFiles
admin.site.register(Company)
admin.site.register(Product)
admin.site.register(ProjectAirFiles)
|
11425328 | import pytest
import packerlicious.builder as builder
class TestAliCloudBuilder(object):
def test_required_fields_missing(self):
b = builder.AliCloud()
with pytest.raises(ValueError) as excinfo:
b.to_dict()
assert 'required' in str(excinfo.value)
|
11425338 | import torch.nn as nn
from torchvision import models
from lib.rpn_util import *
import torch.nn.functional as F
import torch
def dilate_layer(layer, val):
layer.dilation = val
layer.padding = val
class LocalConv2d(nn.Module):
def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1, padding=0... |
11425341 | import unittest
from collections import defaultdict
from tasky.stats import DictWrapper
class TestDictWrapper(unittest.TestCase):
def test_dict(self):
d = DictWrapper({})
self.assertFalse(d)
self.assertEqual(len(d), 0)
self.assertNotIn('foo', d)
d['foo'] = 'bar'
... |
11425345 | import numpy as np
import pandas as pd
from scipy import sparse
# Errors
class RootCellError(Exception):
def __init__(self, message):
self.message = message
class NeighborsError(Exception):
def __init__(self, message):
self.message = message
# Diffusion
def diffusion_conn(adata, min_k=50,... |
11425411 | from distutils.core import setup, Extension
from Cython.Build import cythonize
import numpy
import runpy
cfg = runpy.run_path('../.config.py')
setup(
name="OpenephysTridesclousPyPlugin",
include_dirs=[numpy.get_include(), cfg['PYTHON_PLUGIN_SRC_DIR']],
ext_modules=cythonize(
Ex... |
11425435 | import sys
import pdb
import pysam
import time
import re
import scipy as sp
import h5py
import cPickle
import os
def parse_options(argv):
"""Parses options from the command line """
from optparse import OptionParser, OptionGroup
parser = OptionParser()
required = OptionGroup(parser, 'REQUIRED')
... |
11425450 | import datetime
import mock
from database_sanitizer.sanitizers import times
class _FakeDateTime(datetime.datetime):
@staticmethod
def now():
return datetime.datetime(2018, 1, 1, 12, 00, 00)
@mock.patch('random.randint', return_value=42005)
@mock.patch.object(datetime, 'datetime', _FakeDateTime)
de... |
11425464 | import getpass
import json
import os
from io import StringIO
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryFile
import pytest
import yaml
from Pegasus.api.errors import PegasusError
from Pegasus.api.writable import Writable, _CustomEncoder, _filter_out_nones
@pytest.fixture(scope="funct... |
11425492 | from unittest import mock
from cauldron import cli
from cauldron.cli.server import run as server_run
from cauldron.test import support
from cauldron.test.support.flask_scaffolds import FlaskResultsTest
class TestServer(FlaskResultsTest):
"""..."""
def test_execute(self):
"""Should execute the comman... |
11425494 | from interbotix_sdk.robot_manipulation import InterbotixRobot
from interbotix_descriptions import interbotix_mr_descriptions as mrd
import numpy as np
# This script makes the end-effector perform pick, pour, and place tasks
#
# To get started, open a terminal and type 'roslaunch interbotix_sdk arm_run.launch robot_nam... |
11425521 | from enum import Enum, IntEnum
class CertifiedStatus(Enum):
"""Enumeration that represents what can be passed in the certified_status
attribute of the IServer quick search command."""
ALL = 'ALL'
CERTIFIED_ONLY = 'CERTIFIED_ONLY'
NOT_CERTIFIED_ONLY = 'NOT_CERTIFIED_ONLY'
OFF = 'OFF'
class Se... |
11425550 | from typing import List, Optional
from pathlib import Path
from dataclasses import dataclass, field
from dataclasses_json import dataclass_json
from cached_property import cached_property
@dataclass_json
@dataclass
class Config(object):
"""
Attributes
----------
decay_rate:
Decay rate of the... |
11425566 | from sqlalchemy_unchained.foreign_key import _get_fk_col_args
from typing import *
from .column import Column
from .types import BigInteger
def foreign_key(*args,
fk_col: Optional[str] = None,
primary_key: bool = False,
nullable: bool = False,
ondelete:... |
11425593 | import json
import os
import sys
# https://github.com/oasis-open/cti-pattern-validator
# Tested with stix2-patterns 1.1.0, antlr4-python2-runtime 4.7.2
from stix2patterns.validator import run_validator
def validate_pattern(pb, oid, pattern):
# Additional logic could be added here
errors = run_validator(patte... |
11425603 | from video_reid_performance import compute_video_cmc_map
from update_module import get_vision_record_dist, visual_affinity_update, trajectory_distance_update, norm_data
from eval_tools import get_signal_match_cmc
from copy import deepcopy
import numpy as np
class RecurrentContextPropagationModule(object):
... |
11425623 | import math
import numpy as np
import sys
resolutionHor = 256
resolutionVert = 256
scale = 20
# fill cells for cell noise
cells0 = np.random.random_sample((scale , scale , scale , 3))
cells1 = np.random.random_sample((scale*2, scale*2, scale*2, 3))
cells2 = np.random.random_sample((scale*4, scale*4, scale*4, 3))
... |
11425627 | import torch
import torch.nn as nn
import torch.nn.functional as F
def discriminator_loss(d_real, d_fake, eps):
return -torch.mean(torch.log(d_real+eps)+torch.log(1-d_fake+eps))
def adverserial_loss(d_fake, eps):
return -torch.mean(torch.log(d_fake+eps))
class OnlineTripletLoss(nn.Module):
"""
Onl... |
11425637 | from oscar.apps.dashboard.catalogue import views as catalogue_admin_views
from .formsets import ProductAttributesFormSet
from .forms import ProductForm, CategoryForm
class ProductClassCreateView(catalogue_admin_views.ProductClassCreateView):
product_attributes_formset = ProductAttributesFormSet
class ProductCla... |
11425664 | import itertools
import unittest
from functools import partial
from itertools import cycle
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from mup.coord_check import get_coord_data
from mup.optim import MuAdam, MuSGD
from mup.shape import get_infshapes, get_shapes, make_base_shapes... |
11425705 | import datetime
from ftc.management.commands._base_scraper import CSVScraper
from ftc.models import Organisation
class Command(CSVScraper):
name = "mutuals"
allowed_domains = ["fcastoragemprprod.blob.core.windows.net", "mutuals.fca.org.uk"]
start_urls = [
"https://fcastoragemprprod.blob.core.wind... |
11425713 | import prometheus_client
class PrometheusWrapper:
""" Exposes ElastAlert metrics on a Prometheus metrics endpoint.
Wraps ElastAlerter run_rule and writeback to collect metrics. """
def __init__(self, client):
self.prometheus_port = client.prometheus_port
self.run_rule = client.run_rul... |
11425732 | import warnings
warnings.filterwarnings("ignore")
import multiprocessing
# from datetime import datetime
# from apscheduler.schedulers.background import BackgroundScheduler
from zvt.api.data_type import Region
from zvt.api.fetch import fetch_data
# from zvt.utils.time_utils import next_date
# sched = BackgroundSched... |
11425741 | from rest_framework import test as rest_test
from human_services.organizations.tests.helpers import OrganizationBuilder
from human_services.services.tests.helpers import ServiceBuilder
from common.testhelpers.random_test_values import an_integer
# pylint: disable=too-many-public-methods
class TestPagination(rest_test.... |
11425746 | import os
from torch.utils.data import DataLoader
from datasets import SpatialMNISTDataset
from survae.data import DATA_PATH
dataset_choices = {'spatial_mnist'}
def get_data(args):
assert args.dataset in dataset_choices
# Dataset
if args.dataset == 'spatial_mnist':
dataset = DataContainer(Spatia... |
11425784 | import itertools
from typing import List
import discord
from crtoolsdb.crtoolsdb import Constants
class InvalidRole(Exception):
pass
class Helper:
def __init__(self, bot):
self.bot = bot
self.constants = Constants()
@staticmethod
async def get_user_count(guild: discord.Guild, name... |
11425812 | from __future__ import print_function
'''
Preprocess audio
'''
import numpy as np
import librosa
import librosa.display
import os
def get_class_names(path="Samples/"): # class names are subdirectory names in Samples/ directory
class_names = os.listdir(path)
return class_names
def preprocess_dataset(inpath=... |
11425853 | import unittest
from doubly_linked_list import Node, DoublyLinkedList
class TestNode(unittest.TestCase):
def test_instantiation(self):
"""
Tests that a new Node has been instantiated
"""
node = Node(1)
self.assertIsInstance(node, Node)
def test_insert_before(self):
... |
11425868 | import unittest
import pexpect
import time
global config
global mtfterm
class TestWMTIME(unittest.TestCase):
def setUp(self):
mtfterm.sendline("Module wmtime registered");
mtfterm.getprompt();
#Comparison function that compares two time values.
def compareNormalTime(self, expectedVal, li... |
11425928 | def get_head(line, releases, **kwargs):
for release in releases:
if ":release:`{} ".format(release) in line:
return release
return False
|
11425979 | import tensorflow as tf
import matplotlib.pyplot as plt
HR_SIZE = 128
SCALE = 4
LR_SIZE = int(HR_SIZE / 4)
BATCH_SIZE = 8
# [====================================================]
# [================ Random Compressions ===============]
# [====================================================]
def random_compression(... |
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