neuromaancer

// ==UserScript==
// @name         Overleaf Editor Custom VIM Keybindings
// @namespace    http://tampermonkey.net/
// @version      0.1
// @match        https://www.overleaf.com/project/*
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(function() {
    'use strict';

neuromaancer

"""
@Created Date: Friday February 4th 2022
@Author: Alafate Abulimiti
@Company: INRIA
@Lab: CoML/Articulab
@School: PSL/ENS
@Description: Save the frequent useful functions
--------------
@HISTORY:
Date						 By				            Comments

neuromaancer

# Verify conda is installed, check version number
conda info

# Update conda to the current version
conda update conda

# conda install PACKAGENAME 
Install a package included in Anaconda

neuromaancer

import os

libs = {
    "pillow",
    "sklearn",
    "numpy",
    "pandas",
    "jupyterlab",
    "scikit-learn",
    "torch torchvision",

neuromaancer

一行 Python 实现树

使用 Python 内置的 defaultdict，我们可以很容易的定义一个树形数据结构：

def tree(): return defaultdict(tree)

就是这样！

neuromaancer

# add row
scores = list()
for row in df.itemrows():
  df.loc[len(df)] = scores

def get_data(data, features):
    """
    Data is an array each row being a tuple with possibily several features.
    Keep only those features whose indices appear in the 'features' vector
    """

neuromaancer

# 合并两个dict 同keys， values 相加
def get(compe_list, text_list):
    span_score = calcul_span_score.calcul_span_score(text_list, compe_list)
    fre_score = calcul_fre_score.calcul_fre_score(text_list, compe_list)
    span_score_counter = Counter(span_score)
    fre_score_counter = Counter(fre_score)
    scores_dict = dict(span_score_counter + fre_score_counter)
    return scores_dict

neuromaancer

# docs is 2 dims
vocabs = set(itertools.chain.from_iterable(docs))
return vocabs

neuromaancer

# 这是装饰器函数，参数 func 是被装饰的函数
def logger(func):
    def wrapper(*args, **kw):
        print('主人，我准备开始执行：{} 函数了:'.format(func.__name__))

        # 真正执行的是这行。
        func(*args, **kw)

        print('主人，我执行完啦。')
    return wrapper

neuromaancer

# 列表对象（list）是一个查询效率高于更新操作的数据结构，比如删除一个元素和插入一个元素时执行效率就非常低，因为还要对剩下的元素进行移动
names = ['raymond', 'rachel', 'matthew', 'roger',
         'betty', 'melissa', 'judith', 'charlie']
names.pop(0)
names.insert(0, 'mark')

## pythonic
from collections import deque
names = deque(['raymond', 'rachel', 'matthew', 'roger',
               'betty', 'melissa', 'judith', 'charlie'])