$ apt install dkms
TODO
Download the source code.
class A: | |
pass | |
class B: | |
def __get__(self, instance, cls=None): | |
raise AttributeError() | |
class C: | |
a = A() | |
b = B() |
import getpass | |
import os | |
import pathlib | |
import sys | |
import sh | |
BASE_DIR = os.path.abspath(os.path.join(__file__, '..')) | |
OPENCV_VERSION = '4.5.2' |
from dataclasses import asdict, fields | |
from typing import Any, Dict | |
class DataClassBase: | |
@classmethod | |
def fromdict(cls, d: Dict[str, Any]): | |
"""Return a dataclass from a dict which may include unexpected keys.""" | |
class_fields = {f.name for f in fields(cls)} | |
return cls(**{k: v for k, v in d.items() if k in class_fields}) |
# -*- coding: utf-8 -*- | |
# ref. https://qiita.com/k-jimon/items/f02fae75e853a9c02127 | |
from collections import deque, defaultdict, Counter | |
from itertools import chain, islice, takewhile | |
import MeCab | |
import os | |
import random | |
import re |
// The original idea comes from a post by Alex Krush. | |
// https://medium.com/@akrush95/global-cached-state-in-react-using-hooks-context-and-local-storage-166eacf8ab46 | |
import React, { useCallback, useEffect, useReducer, useRef } from 'react'; | |
export const createCachedContext = ({ storageKey, defaultValue }) => { | |
const initializer = (initialState) => { | |
const localState = localStorage.getItem(storageKey); | |
if (localState) { | |
try { |
# -*- coding: utf-8 -*- | |
import FreeCAD | |
import importCSG | |
import importDXF | |
def csg_to_dxf(src, dst): | |
doc = importCSG.open(src) | |
importDXF.export([doc.TopologicalSortedObjects[0]], dst) # assumes it has single root object | |
FreeCAD.closeDocument(doc.Name) |
#!/usr/bin/env python | |
import argparse | |
import chainer | |
import chainer.functions as F | |
import chainer.links as L | |
from chainer import training | |
from chainer.training import extensions | |
import numpy as np |
>>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b') | |
array([[[0, 4], | |
[2, 6]], | |
[[1, 5], | |
[3, 7]]], dtype=int8) | |
>>> ctypes.string_at(np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').ctypes.data, 8) # .data.tobytes() doesn't work properly | |
b'\x00\x04\x02\x06\x01\x05\x03\x07' | |
>>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').__array_interface__ | |
{'data': (23426096, False), 'strides': None, 'descr': [('', '|i1')], 'typestr': '|i1', 'shape': (2, 2, 2), 'version': 3} | |
>>> np.array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]], dtype='b').flags |
$ python xp_nested_array.py --src-xp numpy --dst-xp numpy --shape "(3, 224, 224)" --batch-size 10
Shape: (3, 224, 224)
Batch size: 10
Running numpy.array(<List[numpy.ndarray]>) in 10000 times...
3.857709832955152