02-Ipython Magic魔术

IPython Magic 魔术

具体见个人Python图书馆：https://ds-ebooks.github.io

简介

两种magic命令：
* Line magics：%，只接受该行的参数
* Cell magics: %%，只能用在cell首行，%%接收的magic参数包括整个cell，并且每个单元格只能用一个cell

# 列出可用的魔法命令
%lsmagic

Available line magics:
%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode

Available cell magics:
%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile

Automagic is ON, % prefix IS NOT needed for line magics.

简单Magic

%matplotlib inline
import numpy as np 
import matplotlib.pyplot as plt

# %timeit计算代码执行时间
%timeit np.linalg.eigvals(np.random.rand(100,100))

100 loops, best of 3: 7.31 ms per loop

计算整个cell 代码运行时间

%%timeit a = np.random.rand(100, 100)
np.linalg.eigvals(a)

100 loops, best of 3: 6.98 ms per loop

%%capture捕获cell输出stdout/stderr

%%capture capt
from __future__ import print_function
import sys
print('Hello stdout')
print('and stderr', file=sys.stderr)

capt.stdout,capt.stderr

('Hello stdout\n', 'and stderr\n')

capt.show()

Hello stdout


and stderr

%%capture result有同样的功能

%%capture result

print([1,2,3])

result.stdout

'[1, 2, 3]\n'

result.show()

[1, 2, 3]

%%scipy cell magic

• %%writefile我就不说了，IPython具有%%scipy cell magic，它允许您在系统上的任何解释器的子进程中运行cell，例如：bash，ruby，perl，zsh，R等,甚至自己的脚本也能运行.
• 要使用它，只需将path或shell命令传递给要在%%scipy行上运行的程序，该脚本的其余部分将由该脚本运行，并捕获并显示子进程中的stdout/stderr。

%%script python
import sys
print 'hello from Python %s' % sys.version

hello from Python 2.7.13 (v2.7.13:a06454b1afa1, Dec 17 2016, 20:42:59) [MSC v.1500 32 bit (Intel)]

%%script python3
import sys
print('hello from Python: %s' % sys.version)

hello from Python: 3.6.3 (v3.6.3:2c5fed8, Oct  3 2017, 17:26:49) [MSC v.1900 32 bit (Intel)]

IPython还为一些常见的解释器创建别名，例如bash，ruby，perl等。
这些都等同于%% script

%%bash
echo "hello from $BASH"

hello from /usr/bin/bash

--out --err捕获cell魔法的输出

%%bash
echo "hi, stdout"
echo "hello, stderr" >&2

hi, stdout


hello, stderr

%%bash --out output --err error
echo "hi, stdout"
echo "hello, stderr" >&2

print(error)
print(output)

hello, stderr

hi, stdout

%matplotlib inline
import pylab as pl
pl.seed(1)
data = pl.randn(100)
pl.plot(data);

配合 %config InlineBackend.figure_format="svg"做图片输出格式的设置

%config InlineBackend.figure_format="svg"
%matplotlib inline

pl.plot(data);

调试代码的模式 %xmode Plain和原来的模式%xmode Verbose

def f1(a,b):
    return a/b
def f2(x):
    a = x
    b = x-1
    return f1(a,b)

# 精简模式
%xmode Plain

Exception reporting mode: Plain

f2(1)

Traceback (most recent call last):


  File "", line 1, in 
    f2(1)


  File "", line 6, in f2
    return f1(a,b)


  File "", line 2, in f1
    return a/b


ZeroDivisionError: integer division or modulo by zero

# 原来的模式
%xmode Verbose

Exception reporting mode: Verbose

f2(1)

ZeroDivisionErrorTraceback (most recent call last)

 in ()
----> 1 f2(1)
        global f2 = 


 in f2(x=1)
      4     a = x
      5     b = x-1
----> 6     return f1(a,b)
        global f1 = 
        a = 1
        b = 0


 in f1(a=1, b=0)
      1 def f1(a,b):
----> 2     return a/b
        a = 1
        b = 0
      3 def f2(x):
      4     a = x
      5     b = x-1


ZeroDivisionError: integer division or modulo by zero

%debug用户调试错误

使用%debug会在报错时进去调试模式,在调试模式中我们可以

• 输入变量名来获取变量的情况,
• 输入up来进入上一层查看
• 要退出输入quit即可

%timeit和%time

• %timeit故名思义就是当前行代码运行时间，就是时间的意思
• %time是程序运行到此处时的时间，就是时刻的意思

%timeit sum(map(lambda x:x**2,range(100)))

10000 loops, best of 3: 18.5 µs per loop

%time sum(map(lambda x:x**2,range(100)))

Wall time: 0 ns





328350

%%prun/%prun命令调用profile模块，对单元中的代码进行宏观上的性能剖析

%%prun
def fib(n):
    if n<2:
        return n
    return fib(n-1)+fib(n-2)
fib(20)

像这样：

         21893 function calls (3 primitive calls) in 0.009 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  21891/1    0.009    0.000    0.009    0.009 :2(fib)
        1    0.000    0.000    0.009    0.009 :2(<module>)
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

参考资料

Jupyter攻略