On Mon, Nov 8, 2010 at 6:55 PM, Matthew Brett <matthew.br...@gmail.com> wrote:
> First - thank you - it makes my heart very glad to be able to do this:
>
> .. plot::
> :include-source:
>
> import matplotlib.pyplot as plt
> plt.plot(range(10))
> plt.show()
>
> Here's my question. This is already a huge step forward for me, but
> the full monty would be to be able to do:
>
> .. testcode::
>
> import some_module
> res = some_module.use_it('a string')
>
> .. plot::
> :include-source:
>
> plt.imshow(res)
>
> and so on. I mean, the ability to keep the code context across the
> page, both in the ..plot: and ..testcode:: and even >>> directives, so
> I can build up my tutorial examples on top of the previous results.
> That step would make it the perfect tool for the tutorials that I have
> ready to port - and I am sure - many others.
>
> Is that already possible? If not, how easy would it be? It if isn't
> easy, can y'all give me some pointers as to how to get there?This is a useful feature I've wanted myself. I just contributed a change to the plot directive in svn to support this using two new options :context: and :nofigs:, and updated the sampledoc tutorial. The relevant bit from the tutorial is in the link below: http://matplotlib.sourceforge.net/sampledoc/extensions.html#inserting-matplotlib-plots Also, we have a really useful ipython directive that is stateful by default, and includes many options for suppressing input blocks, doctesting on output blocks, saving figures, and more. It is included in the ipython src tree. My original proposal is at http://matplotlib.sourceforge.net/ipymode/_build/html/proposal.html, which I've implemented with minor changes. A real world working example from some lecture notes I prepared recently is attached as convolution.rst, and some notes are below. I need to get this added to the sampledoc tutorial.... ================= Ipython Directive ================= 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 starting at ``1``. The inputs will be fed to an embedded ipython interpreter and the outputs from that interpreter will be inserted as well. .. ipython:: In [136]: x = 2 In [137]: x**3 Out[137]: 8 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 ``In [3]:`` in the rendered docs. .. ipython:: In [138]: z = x*3 # x is recalled from previous block In [139]: z Out[139]: 6 In [140]: print z --------> print(z) 6 In [141]: 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 The embedded interpeter 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 embbedded 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 be .. ipython:: In [1]: x = 'hello world' # this will raise an error if the ipython output is different @doctest In [2]: x.upper() Out[2]: 'HELLO WORLD' # some readline features cannot be supported, so we allow # "verbatim" blocks, which are dumped in verbatim except prompts # are continuously numbered @verbatim In [3]: x.st<TAB> x.startswith x.strip Multi-line input is supported. .. ipython:: In [130]: 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 [131]: print url.split('&') --------> print(url.split('&')) ['http://ichart.finance.yahoo.com/table.csv?s=CROX', 'd=9', 'e=22', 'f=2009', 'g=d', 'a=1', 'b=8', 'c=2006', 'ignore=.csv'] In [60]: import urllib 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 ruin 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 .. ipython:: In [133]: import numpy.random @suppress In [134]: numpy.random.seed(2358) @doctest In [135]: np.random.rand(10,2) Out[135]: 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]]) Another demonstration of multi-line input and output .. ipython:: In [106]: print x --------> print(x) jdh In [109]: 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 maptlotlib 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 interpeter and subsequent line numbers will be incremented to reflect the inputs:: .. ipython:: :suppress: In [144]: from pylab import * In [145]: ion() .. ipython:: :suppress: In [144]: from pylab import * In [145]: ion() Likewise, you can set ``:doctest:`` or ``:verbatim:`` to apply these settings to the entire block. You can create one or more pyplot plots and insert them with the `...@savefig`` decorator. .. ipython:: @savefig plot_simple.png width=4in In [151]: plot([1,2,3]); # use a semicolon to suppress the output @savefig hist_simple.png width=4in In [151]: hist(np.random.randn(10000), 100); In a subsequent session, we can update the current figure with some text, and then resave .. ipython:: In [151]: ylabel('number') In [152]: title('normal distribution') @savefig hist_with_text.png In [153]: grid(True)
convolution.rst
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