I wrote:
>> How can I decrease the size of the actual graph so that the
>> labels are displayed?
>>
> The answer seems to be to use the following after drawing
> the graph: ax = gca() ax.set_position([0.2,0.2,0.6,0.6])
> This was taken from the mailing list discussion on
> GnuPlot's 'set size ratio' command -
> (http://sourceforge.net/mailarchive/forum.php?thread_id=5562487&forum_id=33405)
> Is this the correct approach?
Yep, that's it -- this is also discussed here
http://matplotlib.sf.net/faq.html#TEXTOVERLAP , which also gives
an alternative suggestion.
> PS One thing that I am having trouble getting my head
> around fully is how best to handle the coding, i.e. I'd
> prefer to use the class library approach as I like it's
> clean, well structured nature, but a number of techniques,
> such as the above, are written/illustrated using the
> Pylab/Matlab commands which I find difficult to translate
> into the class library code. What is the best approach to
> getting up the learning curve? Are there any problems with
> mixing the two approaches in the one code base?
It's a common complaint, so don't feel along. Have you seen
examples/pythonic_matplotlib.py -- there is some header documentation
there that offers some pointers. That is an example using the pylab
interface in a more OO way. For pure OO w/o the pylab interface at
all, there is a new example in CVS which I'll put here
#!/usr/bin/env python
"""
A pure OO (look Ma, no pylab!) example using the agg backend
"""
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.plot([1,2,3])
ax.set_title('hi mom')
ax.grid(True)
ax.set_xlabel('time')
ax.set_ylabel('volts')
canvas.print_figure('test')
My advice, don't be afraid to open up matplotlib/pylab.py to see how
the pylab interface forwards its calls to the OO layer. I appreciate
that "read the source" is not very comforting, but that, the examples
I pointed you too above, the all-too-short Chapter 7 of the user's
guide, the examples/embedding* demos, and the mailing lists, which are
regularly read by many developers, are what we've got right now.
I always encourage new users starting on the path to matplotlib OO API
enlightenment to make notes and write a tutorial as you go. It would
be a useful addition to the documentation.
JDH
