lihuanshuai

import asyncio
import inspect


async def test_coro():
    await asyncio.sleep(1)
    print('coro')
    return 1

lihuanshuai

from __future__ import print_function

def get_mil(d, v0, ds):
    t = d * 1.0 / v0
    mil = (5 * t * t - ds) * 1000 / d
    return int(mil * 10 + 0.5) / 10.0

cols = [600, 700, 800, 900, 1000, 1100, 1200]
header = [''] + cols 
print(*header, sep='\t')

lihuanshuai

from collections import OrderedDict

class LRUCache(object):

    def __init__(self, size=5):
        self.size = size
        self.cache = OrderedDict()

    def get(self, key):
        if key in self.cache:

lihuanshuai

import re

regex = re.compile(r'\\(\d+)'

def repl(m):
    return r'\x' + hex(int(m.group(1), 8)).replace('0x', r'').zfill(2)

lines = vscode.editor.lines
lines = [line.decode() for line in lines]
lines = [regex.sub(repl, line) for line in lines]

lihuanshuai

function get_close_nodes_for_duplicate_onetab_items() {
    let closeNodes = [];
    let cache = {};
    let selector = '#contentAreaDiv > div > div:nth-child(2) > div > div:nth-child(2)';
    let nodes = Array.from(document.querySelectorAll(selector));
    for (let node of nodes) {
        let linkNode = node.querySelector('a.clickable');
        let closeNode = node.querySelector('img:nth-child(3)');
        let url = linkNode.getAttribute('href');
        if(cache[url]) {

lihuanshuai

document.querySelectorAll('.torrent-details tr').forEach(function (node) {
    var name = node.querySelector('.col-name').textContent;
    if (name.match(/^_____padding_file_/)) {
        console.log(name);
        node.querySelector('.deselect-file').click();
    }
})

lihuanshuai

document.querySelectorAll('.torrent-details tr').forEach(function (node) {
    var name = node.querySelector('.col-name').textContent;
    if (name.match(/^_____padding_file_/)) {
        console.log(name);
        node.querySelector('.deselect-file').click();
    }
})

lihuanshuai

from itertools import tee, islice as slice, chain, izip as zip

def previous_and_next(iterable, fill_value=None):
    prevs, items, nexts = tee(iterable, 3)
    prevs = chain([fill_value], prevs)
    nexts = chain(slice(nexts, 1, None), [fill_value])
    return zip(prevs, items, nexts)

lihuanshuai

import tables as tb
from numpy import array
from scipy import sparse


def store_sparse_mat(path, name, m, separator='__'):
    if (m.__class__ not in [sparse.csr.csr_matrix, sparse.csc.csc_matrix]):
        raise TypeError("This code only works for csr/csc matrices")
    with tb.openFile(path, 'a') as f:
        for par in ('data', 'indices', 'indptr', 'shape'):

lihuanshuai

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