id stringlengths 3 8 | content stringlengths 100 981k |
|---|---|
11524648 | import mxnet as mx
from mxnet import gluon
from mxnet import nd
from mxnet.gluon.loss import Loss, _reshape_like
import horovod.mxnet as hvd
def _as_list(arr):
"""Make sure input is a list of mxnet NDArray"""
if not isinstance(arr, (list, tuple)):
return [arr]
return arr
class SSDMultiBoxLoss(Lo... |
11524649 | import numpy as np
a = np.arange(3)
print(a)
# [0 1 2]
b = np.arange(3)
print(b)
# [0 1 2]
c = np.arange(1, 4)
print(c)
# [1 2 3]
print(np.all(a == b))
# True
print(np.all(a == c))
# False
print(np.array_equal(a, b))
# True
print(np.array_equal(a, c))
# False
print(np.array_equiv(a, b))
# True
print(np.array_e... |
11524658 | import pylearn2
import pylearn2.datasets as ds
import pickle
import numpy as np
train_sets = []
valid_sets = []
test_sets = []
for i in range(5):
with open("../../data/pylearn2/train_car_{:02d}.pkl".format(i),'r') as f:
train_sets.append(pickle.load(f))
with open("../../data/pylearn2/valid_ca... |
11524706 | import sys
import os
import argparse
import winreg
import pathlib
from IPython import embed
from traitlets.config import get_config
import winsandbox
def read_args():
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--interactive", default=False, action="store_true",
hel... |
11524721 | from django.apps import AppConfig
class HealthCenterConfig(AppConfig):
name = 'applications.health_center'
|
11524731 | from setuptools import setup
import os
setup(
name='FlaskSearch',
version='0.1',
url='https://github.com/dhamaniasad/Flask-Search',
license='BSD',
author='<NAME>',
author_email='<EMAIL>',
description='Powerful search functionality for Flask apps via ElasticSearch',
py_modules=['flask_se... |
11524748 | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from numpy import sqrt, exp, array
from inference.mcmc import HamiltonianChain
"""
# Hamiltonian sampling example
Hamiltonian Monte-Carlo (HMC) is a MCMC algorithm which is able to
efficiently sample from complex PDFs which present difficulty for... |
11524749 | from math import sqrt, floor
from collections import Counter
def center_of_geometry(Coordinates):
'''
Give me x, y, z coordinates, get back the cooresponding center of geometry
'''
return [sum(Coordinate)/len(Coordinate) for Coordinate in zip(*Coordinates)]
def distance(Coordinates):
'''
Eu... |
11524768 | import traceback
import math
import numpy as np
import litenn as nn
import litenn.core as nc
from litenn.core import CLKernelHelper as ph
def conv2DTranspose (input_t, kernel_t, stride=2, dilation=1, padding='same'):
"""
conv2DTranspose operator.
input_t Tensor shape must be
... |
11524780 | import tensorflow as tf
import edward as ed
from edward.models import Categorical, Normal, Bernoulli
from keras.utils import to_categorical
import numpy as np
############################################
### THIS INTERNAL DEFINITION FOR THE NN ####
############################################
#########################... |
11524787 | import ast
from collections import defaultdict
from typing import Any, Dict, List, Optional, Union
from flake8_plugin_utils import Visitor
from .errors import (
ImplicitReturn,
ImplicitReturnValue,
UnnecessaryAssign,
UnnecessaryReturnNone,
)
from .utils import is_false, is_none
NameToLines = Dict[str... |
11524789 | from falcon_unzip.dedup_h_tigs import main
import sys
if __name__ == "__main__":
main(sys.argv)
|
11524792 | import asyncio
from threading import Thread
class Pool:
def __init__(self):
self.loop = asyncio.get_event_loop()
self.loop_runner = Thread(target=self.loop.run_forever)
self.loop_runner.daemon = True
self.loop_runner.start()
def run(self, coro):
return asyncio.run_coro... |
11524826 | import sys
assert sys.version_info >= (3, 5)
import os
import pathlib
import json
import open3d as o3d
if __name__ == '__main__':
base_folder = "/cluster/project/infk/courses/3d_vision_21/group_14/1_data"
model_base_folder = os.path.join(base_folder, "ShapeNetCore.v2")
pcd_base_folder = os.path.joi... |
11524857 | import torch
def to_batch(state, action, reward, next_state, done, device):
state = torch.FloatTensor(state).unsqueeze(0).to(device)
action = torch.FloatTensor([action]).view(1, -1).to(device)
reward = torch.FloatTensor([reward]).unsqueeze(0).to(device)
next_state = torch.FloatTensor(next_state).unsqu... |
11524917 | import sys
import subprocess
from xml.dom import minidom
import json
class TestResultParser:
def parse_file(self, filename):
return self.parse(open(filename).read())
def parse(self, xmltext):
doc = minidom.parseString(xmltext)
root = doc.documentElement
test_suites = [self.proc... |
11524927 | import argparse
import sys
import csv
import os
parser = argparse.ArgumentParser()
parser.add_argument('--primer-snp-bed', type=str, required=True)
parser.add_argument('--amplicon-depth-tsv', type=str, required=True)
parser.add_argument('--sample-name', type=str, default="none")
args, files = parser.parse_known_args()... |
11524940 | from osr2mp4.ImageProcess import imageproc
from osr2mp4.ImageProcess.PrepareFrames.YImage import YImage
scoreboard = "menu-button-background"
def prepare_scoreboard(scale, settings):
"""
:param scale: float
:return: [PIL.Image]
"""
img = YImage(scoreboard, settings, scale).img
img = img.crop((int(img.size[0] ... |
11524983 | import os
SERVICE_VERSIONS = (
("service-less-equal", "2.2", "3.0"),
("service-greater-equal", "1.5", "2.2"),
("service-equal", "2.2", '2.2'),
('service-less', '2.3', '2.4'),
("service-greater", '1', '2'),
)
CLUSTER_VERSIONS = (
("cluster-less-equal", "1.6", "2.0"),
("cluster-greater-equal... |
11524992 | from __future__ import absolute_import
import os
import deepthought.spearmint.wrapper as spearmint_wrapper
# Write a function like this called 'main'
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #:', str(job_id);
print params;
print os.environ['PYTHONPATH']... |
11525026 | import os
import sqlalchemy
import datetime
from sqlalchemy import Column, VARCHAR, Integer, String, DateTime, ForeignKey, Boolean
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, relationship
from sqlalchemy.sql import exists
from dot... |
11525034 | import pytest
@pytest.mark.parametrize('query, expected_terms', [
# query expected
('the one', ['the one', 'the', 'one', '1']),
('to be or not to be', ['to be', 'be or', 'or not', 'not to', 'to', 'be', 'or']),
('html', ['html', 'Hypertext Markup Language'])
... |
11525036 | import tensorflow as tf
from data_utils import Vocabulary, batch_generator
from model import LSTMModel
import os
import codecs
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('checkpoint_path', 'checkpoint/base', 'model path')
tf.flags.DEFINE_integer('batch_size', 100, 'number of seqs in one batch')
tf.flags.DEFINE_int... |
11525100 | import numpy as np
from scipy.stats import norm
from scipy.special import gammaln
def two_tailed_ztest(success1, success2, total1, total2):
"""
Two-tailed z score for proportions
Parameters
-------
success1 : int
the number of success in `total1` trials/observations
success2 : int
... |
11525102 | import numpy as np
import cv2
import scipy.misc
def normalization(img):
# rescale input img within [-1,1]
return img / 127.5 - 1
def inverse_normalization(img):
# rescale output img within [0,1], then saving by 'scipy.misc.imsave'
return (img + 1.) / 2.
def read_one_img(img_dir):
img = cv2.imr... |
11525130 | from django.db import models
from .product import Product
class TestCase(models.Model):
PRIORITY_CHOICES = ((0, 'Urgent'), (1, 'High'), (2, 'Medium'), (3, 'Low'))
name = models.CharField(max_length=200)
full_name = models.CharField(max_length=400, default='')
keyword = models.CharField(max_length=10... |
11525140 | import base64
import secrets
import string
import struct
from itertools import islice, cycle
from encoders.Encoder import Encoder
from engine.component.CallComponent import CallComponent
from engine.component.CodeComponent import CodeComponent
from engine.modules.EncoderModule import EncoderModule
from enums.Language ... |
11525250 | from udpwkpf import WuClass, Device
import sys
import mraa
from twisted.protocols import basic
from twisted.internet import reactor, protocol
PORT = 2222
if __name__ == "__main__":
class Number(WuClass): #"Number" WuClass has been defined in ~/wukong-darjeeling/wukong/ComponentDefinitions/WuKongStandardLibrary.x... |
11525259 | from unittest import TestCase
from DivideTwoIntegers import DivideTwoIntegers
class TestDivideTwoIntegers(TestCase):
def test_divide(self):
d = DivideTwoIntegers()
self.assertTrue(d.divide(1, 1) == 1)
self.assertTrue(d.divide(0, 1) == 0)
self.assertTrue(d.divide(-1, -1) == 1)
... |
11525264 | DEBUG = True
SECRET_KEY = "unguessable"
INSTALLED_APPS = [
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.sites",
"rest_framework.authtoken",
"redis_pubsub",
"testapp"
]
DATABASES = {
"default": {
... |
11525269 | import Cell
class GridWorld:
def __init__(self, width, height, obstacles):
self.width = width
self.height = height
self.cells = [[Cell.Cell(i, j) for j in range(width)] for i in range(height)]
self.obstacles = obstacles
for obstacle in self.obstacles:
self.cells[obstacle[0]][obstacle[1]].obsta... |
11525282 | from models import (cluster)
generator_dict = {'standard': cluster.G}
discriminator_dict = {'standard': cluster.D}
|
11525289 | from .model import Model
import numpy as np
import time
def softmax(x):
r"""Compute softmax values for each sets of scores in $x$.
Args:
x (numpy.ndarray): Input vector to compute softmax
Returns:
numpy.ndarray: softmax(x)
"""
e_x = np.exp(x - np.max(x))
return e_x / e_x.sum(... |
11525290 | try:
import ldap
except ImportError:
pass
import logging
from django.conf import settings
from django.contrib.auth.backends import ModelBackend
from django.contrib.auth.models import Group, User
from django.core.exceptions import PermissionDenied
from django_auth_adfs.backend import AdfsAuthCodeBackend
from ... |
11525295 | import numpy as np
from scipy.special import hyp2f1, gammaln
def get_r2(iv, dv, stack_intercept=True):
""" Regress dv onto iv and return r-squared.
Parameters
----------
iv : numpy array
Array of shape N (samples) x K (features)
dv : numpy array
Array of shape N (samples) x 1
... |
11525310 | import warnings
from typing import Any
from prefect.executors import LocalExecutor as _LocalExecutor
class LocalExecutor(_LocalExecutor):
def __new__(cls, *args: Any, **kwargs: Any) -> "LocalExecutor":
warnings.warn(
"prefect.engine.executors.LocalExecutor has been moved to "
"`pr... |
11525312 | import os
import sys
import time
import pytest
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
from helpers.cluster import ClickHouseCluster
cluster = ClickHouseCluster(__file__)
node = cluster.add_instance('node', stay_alive=T... |
11525326 | import dexy.reporters.nodegraph.d3
import dexy.reporters.nodegraph.text
import dexy.reporters.nodegraph.graphviz
|
11525338 | from django.db import models
from django.contrib.contenttypes import generic
from django.contrib.contenttypes.models import ContentType
from django.contrib.auth.models import User
class SimpleText(models.Model):
"""A Testing app"""
firstname = models.CharField(blank=True, max_length=255)
lastname = models.... |
11525361 | from flask_restplus import Resource, reqparse
from flask_jwt import jwt_required
from models.item import ItemModel
class Item(Resource):
# Adding parser as part of the class
parser = reqparse.RequestParser()
parser.add_argument('price',
type = float,
required = True,
help = "Price is required!" )... |
11525368 | import keras.layers as KL
import keras.backend as K
import tensorflow as tf
from lib.nets.resnet_backbone import identity_block as bottleneck
from keras.utils import conv_utils
from keras.engine import InputSpec
import numpy as np
class UpsampleBilinear(KL.Layer):
def call(self, inputs, **kwargs):
source, ... |
11525385 | import FWCore.ParameterSet.Config as cms
pythiaUESettingsBlock = cms.PSet(
pythiaUESettings = cms.vstring(
'MSTU(21)=1 ! Check on possible errors during program execution',
'MSTJ(22)=2 ! Decay those unstable particles',
'PARJ(71)=10 . ! for which ctau 10 mm',
'MSTP(33)=0 ! no K factors in har... |
11525409 | try:
import tensorflow as tf
except ImportError:
tf = None
class NegBinOutput(tf.keras.layers.Layer):
"""Negative binomial output layer"""
def __init__(
self,
original_dim=None,
name='neg_bin_output',
**kwargs
):
super().__init__(name=name,... |
11525429 | from django.db import models
from data_refinery_common.models.computational_result import ComputationalResult
from data_refinery_common.models.sample import Sample
class SampleResultAssociation(models.Model):
sample = models.ForeignKey(Sample, blank=False, null=False, on_delete=models.CASCADE)
result = mode... |
11525447 | from django.contrib.auth import get_user_model
from django.core.management.base import BaseCommand
class Command(BaseCommand):
help = """Print the list of users."""
def handle(self, *args, **options):
# Print out users.
User = get_user_model()
for x in User.objects.all():
p... |
11525458 | from __future__ import annotations
from textwrap import dedent
from typing import Callable
import pytest
from pytest_mock import MockerFixture
from tomlkit.toml_document import TOMLDocument
from pyproject_fmt.formatter import format_pyproject
from pyproject_fmt.formatter.config import Config
from tests import Fmt
... |
11525474 | import numpy as np
full_data_dir = '../cifar100/cifar-100-python/train'
vali_dir = '../cifar100/cifar-100-python/test'
DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
IMG_WIDTH = 32
IMG_HEIGHT = 32
IMG_DEPTH = 3
NUM_CLASS = 100
NUM_TRAIN_BATCH = 1
EPOCH_SIZE = 50000 * NUM_TRAIN_BATCH
def _read... |
11525480 | import json
class BaseModel(object):
def to_dict(self):
return self.schema.dump(self).data
@classmethod
def from_dict(cls, dct):
return cls.schema.load(dct).data
|
11525494 | import datetime
import logging
from django.core.management.base import BaseCommand
from main.models import Localization
logger = logging.getLogger(__name__)
class Command(BaseCommand):
help = 'Deletes any localizations marked for deletion with null project, type, version, or media.'
def add_arguments(self, ... |
11525520 | from runboat.github import CommitInfo
from runboat.k8s import DeploymentMode, _render_kubefiles, make_deployment_vars
from runboat.settings import BuildSettings
EXPECTED = """\
resources:
- pvc.yaml
- deployment.yaml
- service.yaml
- ingress.yaml
namespace: runboat-builds
namePrefix: "build-name-"
commonLab... |
11525532 | import os
import sys
import logging
from pyomo.environ import *
from pyomo.opt import TerminationCondition
import numpy as np
import pandas as pd
class CALVIN():
def __init__(self, linksfile, ic=None, log_name="calvin"):
"""
Initialize CALVIN model object.
:param linksfile: (string) CSV file containin... |
11525542 | from flask import render_template
from flask import request
from flask import Blueprint
from flask import flash
from flask import redirect
from flask import url_for
from flask import jsonify
from flaskapp.config import ExampleData
from flaskapp.utils import gmaps_tool as tls
main = Blueprint("main", __name__)
@ma... |
11525545 | import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../pytorch'))
import numpy as np
import argparse
import h5py
import math
import time
import logging
import yaml
import pickle
import matplotlib.pyplot as plt
from sklearn import metrics
import torch
import torch.nn as nn
import torch.nn.functional as F... |
11525553 | import pytest
from dbt.tests.util import run_dbt
from tests.functional.graph_selection.fixtures import SelectionFixtures
def run_schema_and_assert(project, include, exclude, expected_tests):
# deps must run before seed
run_dbt(["deps"])
run_dbt(["seed"])
results = run_dbt(["run", "--exclude", "never... |
11525592 | from amaranth_boards.upduino_v2 import *
from amaranth_boards.upduino_v2 import __all__
import warnings
warnings.warn("instead of nmigen_boards.upduino_v2, use amaranth_boards.upduino_v2",
DeprecationWarning, stacklevel=2)
|
11525596 | import string
import random
from time import sleep
from decimal import Decimal
from mock import patch, MagicMock
from bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException
from django.test import TransactionTestCase
from cc.models import Wallet, Address, Currency, Operation, Transaction, WithdrawTransaction
f... |
11525598 | import numpy as np
import scipy.stats
import subprocess
import os
import warnings
from genome_integration import simulate_mr
from genome_integration import utils
from genome_integration.association import GeneticAssociation
def read_assocs_from_plink_qassoc(assoc_file):
assocs = {}
with open(assoc_file, "r") ... |
11525607 | import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
input_img = Input(shape=(28, 28, 1)) # adapt this if using `channels_first` image data format
x = Conv2D(16, (3, 3), activat... |
11525629 | from bayesnet.sampler.hmc import hmc
from bayesnet.sampler.metropolis import metropolis
__all__ = [
"hmc",
"metropolis"
]
|
11525649 | from google.appengine.ext import ndb
from werkzeug.test import Client
from backend.api.handlers.helpers.model_properties import simple_team_properties
from backend.common.consts.auth_type import AuthType
from backend.common.models.api_auth_access import ApiAuthAccess
from backend.common.models.event_team import EventT... |
11525667 | import visdom
import pickle
import os
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
class FeatureVisualizer(object):
def __init__(self, data_root, feature_root, env):
self.data_root = data_root
self.feature_root = feature_root
self.feature_data = pi... |
11525668 | from glob import glob
from PyQt5.QtGui import QIcon
from PyQt5.QtWidgets import QVBoxLayout, QDialog, QLabel, QWidget, QHBoxLayout, QSizePolicy, QApplication
from bauh import __version__, __app_name__, ROOT_DIR
from bauh.context import generate_i18n
from bauh.view.util import resource
PROJECT_URL = 'https://github.c... |
11525709 | from __future__ import annotations
from typing import List, Iterator, Type, Tuple, Union, TYPE_CHECKING
import ast
from ..action import default_actions, actions
if TYPE_CHECKING:
from ..models import (
SchemaActionModel,
MorphActionModel,
CategoryActionModel,
ColumnModel,
C... |
11525717 | from unittest import TestCase
import numpy as np
import nibabel as nib
from unet3d.utils.resample import resample
from unet3d.utils.augment import scale_affine, generate_permutation_keys, permute_data
class TestAugmentation(TestCase):
def setUp(self):
self.shape = (4, 4, 4)
self.affine = np.dia... |
11525729 | import numpy as np
from skimage.measure import compare_ssim as ssim
import imageio
import os
def calculate_ssim_l1_given_paths(paths):
file_list = os.listdir(paths[0])
ssim_value = 0
l1_value = 0
for f in file_list:
# assert(i[0] == i[1])
fake = load_img(paths[0] + f)
real = lo... |
11525767 | import re
import numpy as np
import pandas as pd
def read_file(path):
with open(path, 'r') as f:
content = f.read()
return content
def tosec(t):
return round(float(re.findall(r'.*(?=s)', t)[0]), 3)
def minsec2sec(t):
m = re.findall(r'\d+(?=min)', t)[0]
_s = re.findall(r'\d+(?=s)', t)
... |
11525773 | import ntpath
from datetime import datetime as dt
import os
import pandas as pd
import numpy as np
import math
import sqlite3
# clean the original raw data by storing only the columns that we need, and removing the rest.
def clean(from_path, to_path, columns):
def convert_date(date):
if date == '':
... |
11525796 | import logging
logging.warning('ramiro_analysis is deprecated, use composite analysis instead')
from pycqed.analysis.composite_analysis import *
|
11525801 | import os
import unittest
import chapter
test_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'test_files')
class ChapterTests(unittest.TestCase):
def setUp(self):
self.factory = chapter.ChapterFactory()
## def test_create_chapter_from_url(self):
## c = self.fac... |
11525804 | from .helpers import *
import numpy as np
import json
import copy
import scipy.interpolate as interp
import matplotlib.pyplot as plt
class Airfoil:
"""A class defining an airfoil.
Parameters
----------
name : str
Name of the airfoil.
input_dict : dict
Dictionary describing the ai... |
11525811 | from typing import Callable, List
from pip._internal.req.req_install import InstallRequirement
from pip._internal.req.req_set import RequirementSet
InstallRequirementProvider = Callable[[str, InstallRequirement], InstallRequirement]
class BaseResolver:
def resolve(
self, root_reqs: List[Instal... |
11525823 | from typing import Any, Dict, Optional
from aiogram import Bot
from aiogram.dispatcher.filters.state import State
from aiogram.types import Chat, User
from .protocols import DialogRegistryProto, BaseDialogManager
from ..context.events import (
Data, Action, DialogStartEvent, DialogSwitchEvent, DialogUpdateEvent, ... |
11525834 | from typing import Optional, List
import torch
import torch.nn.functional as F
import torch.distributed as dist
from torch import Tensor
from torch.nn.parameter import Parameter
from . import _reduction as _Reduction
from ..__init__ import _PARALLEL_DIM
from ..__init__ import set_attribute
from ..distributed import... |
11525837 | import tkinter as tk
import requests
from bs4 import BeautifulSoup
url = 'https://weather.com/en-IN/weather/today/l/32355ced66b7ce3ab7ccafb0a4f45f12e7c915bcf8454f712efa57474ba8d6c8'
frameWindow = tk.Tk()
frameWindow.title("Weather")
frameWindow.config(bg = 'white')
def getWeather():
page = requests.get(url)
... |
11525842 | from __future__ import absolute_import, print_function, unicode_literals
import sys
from collections.abc import Callable
is_ironpython = "IronPython" in sys.version
def is_callable(x):
return isinstance(x, Callable)
def execfile(fname, glob, loc=None):
loc = loc if (loc is not None) else glob
exec(
... |
11525855 | from __future__ import annotations
from typing import Any, Dict, Optional
from boa3.constants import GAS_SCRIPT
from boa3.model.method import Method
from boa3.model.property import Property
from boa3.model.type.classes.classarraytype import ClassArrayType
from boa3.model.variable import Variable
class GasClass(Clas... |
11525876 | import hoverxref
import setuptools
with open('README.rst', 'r') as fh:
long_description = fh.read()
setuptools.setup(
name='sphinx-hoverxref',
version=hoverxref.version,
author='<NAME>',
author_email='<EMAIL>',
description='Sphinx extension to embed content in a tooltip on xref hover',
ur... |
11525881 | import functools
import re
import parsimonious
from parsimonious import (
expressions,
)
from eth_abi.exceptions import (
ABITypeError,
ParseError,
)
grammar = parsimonious.Grammar(r"""
type = tuple_type / basic_type
tuple_type = components arrlist?
components = non_zero_tuple / zero_tuple
non_zero_tup... |
11525902 | from ramda import *
from ramda.private.asserts import *
def remove_test():
assert_equal(remove(2, 3, [1, 2, 3, 4, 5, 6, 7, 8]), [1, 2, 6, 7, 8])
|
11525942 | from typing import NamedTuple, List, Dict
from ..asserts.asserts import FieldValidator
class Production(NamedTuple):
id: str
children: List[str]
@classmethod
def from_json(cls, d: dict) -> 'Production':
v = FieldValidator(cls, d)
return Production(
id=v.get('id', int),
... |
11525959 | from random import randint
class Product(object):
def __init__(self, name, price = 10, weight = 20, flammability = 0.5):
self.name = name
self.price = price
self.weight = weight
self.flammability = flammability
self.identifier = randint(10e5, 10e6-1)
def stealability(se... |
11525991 | del_items(0x8013CAB4)
SetType(0x8013CAB4, "struct Creds CreditsTitle[6]")
del_items(0x8013CC5C)
SetType(0x8013CC5C, "struct Creds CreditsSubTitle[28]")
del_items(0x8013D0F8)
SetType(0x8013D0F8, "struct Creds CreditsText[35]")
del_items(0x8013D210)
SetType(0x8013D210, "int CreditsTable[224]")
del_items(0x8013E420)
SetTy... |
11525993 | import numpy as np
import cv2
from lanetracker.window import Window
from lanetracker.line import Line
from lanetracker.gradients import get_edges
from lanetracker.perspective import flatten_perspective
class LaneTracker(object):
"""
Tracks the lane in a series of consecutive frames.
"""
def __init__(... |
11526013 | import gym
from gym.wrappers import TimeLimit
from rlkit.envs.wrappers import CustomInfoEnv, NormalizedBoxEnv
def make_env(name):
env = gym.make(name)
# Remove TimeLimit Wrapper
if isinstance(env, TimeLimit):
env = env.unwrapped
env = CustomInfoEnv(env)
env = NormalizedBoxEnv(... |
11526062 | from functools import partial
from textwrap import dedent
from unittest import skip, expectedFailure # noqa: F401
import sublime
from SublimeLinter.lint import (
backend,
Linter,
linter as linter_module,
util,
)
from unittesting import DeferrableTestCase
from SublimeLinter.tests.parameterized import ... |
11526118 | from .dataloader import *
from .dataset import *
from .openml_download import *
from .mxutils import *
|
11526127 | from unification import var
from kanren import run, membero
from kanren.arith import lt, gt, lte, gte, add, sub, mul, mod, div
x = var('x')
y = var('y')
def results(g):
return list(g({}))
def test_lt():
assert results(lt(1, 2))
assert not results(lt(2, 1))
assert not results(lt(2, 2))
def test_g... |
11526136 | import wx
import re
import platform
import datetime
import Utils
reNonTimeChars = re.compile('[^0-9:.]')
def secsToValue( secs, allow_none, display_seconds, display_milliseconds ):
if secs is None and allow_none:
return secs
v = Utils.formatTime(
secs,
highPrecision=True, extraPrecision=True,
forceHours... |
11526142 | from machine import Pin
from time import sleep_ms, sleep_us
ser = Pin(5, Pin.OUT)
ser.value(0)
rclock = Pin(6, Pin.OUT)
rclock.value(0)
srclock = Pin(7, Pin.OUT)
srclock.value(0)
def srclock_pulse():
srclock.value(1)
sleep_us(10)
srclock.value(0)
sleep_us(10)
def rclock_pulse():
rclock.value(1)... |
11526181 | from casadi import *
x = SX.sym('x')
u = SX.sym('u')
xu = vertcat(x, u)
rhs = x - u
discrete_model = Function('discrete_model', [x, u], [rhs, jacobian(rhs, xu)])
discrete_model.generate('discrete_model.c', {'with_header': True})
discrete_model_cost = Function('discrete_model_cost', [x, u], [xu, jacobian(xu, xu)])
d... |
11526218 | import random
from torch.utils.data.sampler import Sampler
def _batchify(l, batch_size):
for i in range(0, len(l), batch_size):
yield l[i:i + batch_size]
def _flatten(l):
return [item for sublist in l for item in sublist]
class SubsetSequentialSampler(Sampler):
"""
Samples elements sequen... |
11526232 | from flask import Flask, render_template, send_file, make_response, url_for, Response,request,redirect
from flask_restful import reqparse, abort, Api, Resource
import pickle
import numpy as np
import werkzeug
from Predictor import *
app = Flask(__name__)
api = Api(app)
predictor = Predictor()
parser = reqparse.Request... |
11526238 | import json
from ..exceptions import SparkPostAPIException as RequestsSparkPostAPIException
class SparkPostAPIException(RequestsSparkPostAPIException):
def __init__(self, response, *args, **kwargs):
errors = None
# noinspection PyBroadException
try:
data = json.loads(response.... |
11526241 | import unittest
from smsframework import Gateway
from smsframework.providers import NullProvider
from smsframework import OutgoingMessage, IncomingMessage, MessageStatus
class GatewayTest(unittest.TestCase):
""" Test Gateway """
def setUp(self):
self.gw = Gateway()
# Providers
self.... |
11526245 | import itertools
import json
import random
import time
from ast import literal_eval as make_tuple
from multiprocessing import Process, Queue
import numpy as np
import psutil
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.gaussian_process i... |
11526250 | import unittest
import torch
from torch.autograd import Variable
from torchsample.metrics import CategoricalAccuracy
class TestMetrics(unittest.TestCase):
def test_categorical_accuracy(self):
metric = CategoricalAccuracy()
predicted = Variable(torch.eye(10))
expected = Variable(torch.Long... |
11526258 | import torch
from torch.autograd import Variable
from unittest import TestCase
from wavenet_modules import dilate
class Test_Dilation(TestCase):
def test_dilate(self):
input = Variable(torch.linspace(0, 12, steps=13).view(1, 1, 13))
dilated = dilate(input, 1)
assert dilated.size() == (1, ... |
11526259 | import requests
plugin_name = 'malshare'
config = None
def check(query):
API_KEY = config['api']
url ='https://malshare.com/api.php?api_key={}&action=details&hash={}'.format(API_KEY, query)
print(url)
req = requests.get(url)
res = {}
res['found'] = True if b'Sample not found by hash' not in re... |
11526261 | import numpy as np
import matplotlib.pyplot as plt
from moviepy.editor import *
from moviepy.video.io.bindings import mplfig_to_npimage
if __name__ == '__main__':
#Boolean to set whether to include audio or not in final product
include_audio = False
# Read in data generated from previous video a... |
11526268 | from openstatesapi.jurisdiction import make_jurisdiction
J = make_jurisdiction('ks')
J.url = 'http://kansas.gov'
|
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