This documentation covers IPython versions 6.0 and higher. Beginning with version 6.0, IPython stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7.
If you are looking for an IPython version compatible with Python 2.7, please use the IPython 5.x LTS release and refer to its documentation (LTS is the long term support release).
IPython Sphinx Directive¶
The IPython Sphinx Directive is in ‘beta’ and currently under active development. Improvements to the code or documentation are welcome!
The ipython directive is a stateful ipython shell for embedding in
sphinx documents. It knows about standard ipython prompts, and
extracts the input and output lines. These prompts will be renumbered
1. The inputs will be fed to an embedded ipython
interpreter and the outputs from that interpreter will be inserted as
well. For example, code blocks like the following:
.. ipython:: In : x = 2 In : x**3 Out: 8
will be rendered as
In : x = 2 In : x**3 Out: 8
This tutorial should be read side-by-side with the Sphinx source for this document because otherwise you will see only the rendered output and not the code that generated it. Excepting the example above, we will not in general be showing the literal ReST in this document that generates the rendered output.
Persisting the Python session across IPython directive blocks¶
The state from previous sessions is stored, and standard error is trapped. At doc build time, ipython’s output and std err will be inserted, and prompts will be renumbered. So the prompt below should be renumbered in the rendered docs, and pick up where the block above left off.
In : z = x*3 # x is recalled from previous block In : z Out: 6 In : print(z) 6 In : q = z[) # this is a syntax error -- we trap ipy exceptions ------------------------------------------------------------ File "<ipython console>", line 1 q = z[) # this is a syntax error -- we trap ipy exceptions ^ SyntaxError: invalid syntax
Adding documentation tests to your IPython directive¶
The embedded interpreter supports some limited markup. For example, you can put comments in your ipython sessions, which are reported verbatim. There are some handy “pseudo-decorators” that let you doctest the output. The inputs are fed to an embedded ipython session and the outputs from the ipython session are inserted into your doc. If the output in your doc and in the ipython session don’t match on a doctest assertion, an error will occur.
In : x = 'hello world' # this will raise an error if the ipython output is different In : x.upper() Out: 'HELLO WORLD' # some readline features cannot be supported, so we allow # "verbatim" blocks, which are dumped in verbatim except prompts # are continuously numbered In : x.st<TAB> x.startswith x.strip
For more information on @doctest decorator, please refer to the end of this page in Pseudo-Decorators section.
Multi-line input is supported.
In : url = 'http://ichart.finance.yahoo.com/table.csv?s=CROX\ ....: &d=9&e=22&f=2009&g=d&a=1&br=8&c=2006&ignore=.csv' ....: In : print(url.split('&')) ['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22',
Testing directive outputs¶
The IPython Sphinx Directive makes it possible to test the outputs that you provide with your code. To do this, decorate the contents in your directive block with one of the following:
- list directives here
If an IPython doctest decorator is found, it will take these steps when your documentation is built:
1. Run the input lines in your IPython directive block against the current Python kernel (remember that the session persists across IPython directive blocks);
2. Compare the output of this with the output text that you’ve put in the IPython directive block (what comes
- If there is a difference, the directive will raise an error and your documentation build will fail.
You can do doctesting on multi-line output as well. Just be careful when using non-deterministic inputs like random numbers in the ipython directive, because your inputs are run through a live interpreter, so if you are doctesting random output you will get an error. Here we “seed” the random number generator for deterministic output, and we suppress the seed line so it doesn’t show up in the rendered output
In : import numpy.random In : numpy.random.rand(10,2) Out: array([[0.64524308, 0.59943846], [0.47102322, 0.8715456 ], [0.29370834, 0.74776844], [0.99539577, 0.1313423 ], [0.16250302, 0.21103583], [0.81626524, 0.1312433 ], [0.67338089, 0.72302393], [0.7566368 , 0.07033696], [0.22591016, 0.77731835], [0.0072729 , 0.34273127]])
For more information on @supress and @doctest decorators, please refer to the end of this file in Pseudo-Decorators section.
Another demonstration of multi-line input and output
In : print(x) jdh In : for i in range(10): ....: print(i) ....: ....: 0 1 2 3 4 5 6 7 8 9
Most of the “pseudo-decorators” can be used an options to ipython
mode. For example, to setup matplotlib pylab but suppress the output,
you can do. When using the matplotlib
use directive, it should
occur before any import of pylab. This will not show up in the
rendered docs, but the commands will be executed in the embedded
interpreter and subsequent line numbers will be incremented to reflect
.. ipython:: :suppress: In : from matplotlib.pylab import * In : ion()
Likewise, you can set
:verbatim: to apply these
settings to the entire block. For example,
In : cd mpl/examples/ /home/jdhunter/mpl/examples In : pwd Out: '/home/jdhunter/mpl/examples' In : cd mpl/examples/<TAB> mpl/examples/animation/ mpl/examples/misc/ mpl/examples/api/ mpl/examples/mplot3d/ mpl/examples/axes_grid/ mpl/examples/pylab_examples/ mpl/examples/event_handling/ mpl/examples/widgets In : cd mpl/examples/widgets/ /home/msierig/mpl/examples/widgets In : !wc * 2 12 77 README.txt 40 97 884 buttons.py 26 90 712 check_buttons.py 19 52 416 cursor.py 180 404 4882 menu.py 16 45 337 multicursor.py 36 106 916 radio_buttons.py 48 226 2082 rectangle_selector.py 43 118 1063 slider_demo.py 40 124 1088 span_selector.py 450 1274 12457 total
You can create one or more pyplot plots and insert them with the
For more information on @savefig decorator, please refer to the end of this page in Pseudo-Decorators section.
In : plot([1,2,3]); # use a semicolon to suppress the output In : hist(np.random.randn(10000), 100);
In a subsequent session, we can update the current figure with some text, and then resave
In : ylabel('number') Out: Text(38.222222222222214, 0.5, 'number') In : title('normal distribution')