#!/usr/bin/env python
# coding: utf-8

# # IPython: beyond plain Python

# When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient.

# ## First things first: running code, getting help

# In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:
#     
# - `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).
# - `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently.

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print("Hi")


# Getting help:

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get_ipython().show_usage()


# Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes.

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import collections
get_ipython().run_line_magic('pinfo', 'collections.namedtuple')


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get_ipython().run_line_magic('pinfo2', 'collections.Counter')


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get_ipython().run_line_magic('psearch', '*int*')


# An IPython quick reference card:

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get_ipython().run_line_magic('quickref', '')


# ## Tab completion

# Tab completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type `object_name.<TAB>` to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names.

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collections.


# ## The interactive workflow: input, output, history

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2+10


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_+10


# You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):

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10+20;


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_


# The output is stored in `_N` and `Out[N]` variables:

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_10 == Out[10]


# And the last three have shorthands for convenience:

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from __future__ import print_function

print('last output:', _)
print('next one   :', __)
print('and next   :', ___)


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In[11]


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_i


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_ii


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print('last input:', _i)
print('next one  :', _ii)
print('and next  :', _iii)


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get_ipython().run_line_magic('history', '-n 1-5')


# **Exercise**
# 
# Write the last 10 lines of history to a file named `log.py`.

# ## Accessing the underlying operating system

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get_ipython().system('pwd')


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files = get_ipython().getoutput('ls')
print("My current directory's files:")
print(files)


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get_ipython().system('echo $files')


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get_ipython().system('echo {files[0].upper()}')


# Note that all this is available even in multiline blocks:

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import os
for i,f in enumerate(files):
    if f.endswith('ipynb'):
        get_ipython().system('echo {"%02d" % i} - "{os.path.splitext(f)[0]}"')
    else:
        print('--')


# ## Beyond Python: magic functions

# The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface.  They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:
#     
# - To provide an orthogonal namespace for controlling IPython itself and exposing other system-oriented functionality.
# 
# - To expose a calling mode that requires minimal verbosity and typing while working interactively.  Thus the inspiration taken from the classic Unix shell style for commands.

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get_ipython().run_line_magic('magic', '')


# Line vs cell magics:

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get_ipython().run_line_magic('timeit', 'list(range(1000))')


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get_ipython().run_cell_magic('timeit', '', 'list(range(10))\nlist(range(100))\n')


# Line magics can be used even inside code blocks:

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for i in range(1, 5):
    size = i*100
    print('size:', size, end=' ')
    get_ipython().run_line_magic('timeit', 'list(range(size))')


# Magics can do anything they want with their input, so it doesn't have to be valid Python:

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get_ipython().run_cell_magic('bash', '', 'echo "My shell is:" $SHELL\necho "My disk usage is:"\ndf -h\n')


# Another interesting cell magic: create any file you want locally from the notebook:

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get_ipython().run_cell_magic('writefile', 'test.txt', 'This is a test file!\nIt can contain anything I want...\n\nAnd more...\n')


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get_ipython().system('cat test.txt')


# Let's see what other magics are currently defined in the system:

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get_ipython().run_line_magic('lsmagic', '')


# ## Running normal Python code: execution and errors

# Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:

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# Fibonacci series:
# the sum of two elements defines the next
a, b = 0, 1
while b < 10:
    print(b)
    a, b = b, a+b


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for i in range(10):
    print(i, end=' ')
    


# And when your code produces errors, you can control how they are displayed with the `%xmode` magic:

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get_ipython().run_cell_magic('writefile', 'mod.py', '\ndef f(x):\n    return 1.0/(x-1)\n\ndef g(y):\n    return f(y+1)\n')


# Now let's call the function `g` with an argument that would produce an error:

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import mod
mod.g(0)


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get_ipython().run_line_magic('xmode', 'plain')
mod.g(0)


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get_ipython().run_line_magic('xmode', 'verbose')
mod.g(0)


# The default `%xmode` is "context", which shows additional context but not all local variables.  Let's restore that one for the rest of our session.

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get_ipython().run_line_magic('xmode', 'context')


# ## Running code in other languages with special `%%` magics

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get_ipython().run_cell_magic('perl', '', '@months = ("July", "August", "September");\nprint $months[0];\n')


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get_ipython().run_cell_magic('ruby', '', 'name = "world"\nputs "Hello #{name.capitalize}!"\n')


# ## Raw Input in the notebook

# Since 1.0 the IPython notebook web application support `raw_input` which for example allow us to invoke the `%debug` magic in the notebook:

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mod.g(0)


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get_ipython().run_line_magic('debug', '')


# Don't foget to exit your debugging session. Raw input can of course be use to ask for user input:

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enjoy = input('Are you enjoying this tutorial? ')
print('enjoy is:', enjoy)


# ## Plotting in the notebook

# This magic configures matplotlib to render its figures inline:

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get_ipython().run_line_magic('matplotlib', 'inline')


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import numpy as np
import matplotlib.pyplot as plt


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x = np.linspace(0, 2*np.pi, 300)
y = np.sin(x**2)
plt.plot(x, y)
plt.title("A little chirp")
fig = plt.gcf()  # let's keep the figure object around for later...


# ## The IPython kernel/client model

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get_ipython().run_line_magic('connect_info', '')


# We can connect automatically a Qt Console to the currently running kernel with the `%qtconsole` magic, or by typing `ipython console --existing <kernel-UUID>` in any terminal:

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get_ipython().run_line_magic('qtconsole', '')