> From: Nikolaus Rath [mailto:nikol...@rath.org]
> Sent: Tuesday, November 02, 2010 21:38
>
> In [16]: matplotlib.__version__
> Out[16]: '1.0.0'
>
> I attached the result of fig.savefig(). Let's see if it makes
> it through
> the list.
The bug in question was fixed at revision 8652, after 1.0.0 was released. The
distinction between the x and y axes in your case is because the y axis is
inverted.
You can work around the bug by avoiding the Axes.set_xticks() and set_yticks()
methods and instead setting the tick locator and formatter [1] for each axis.
(That's what set_xticks and set_yticks do, in addition to other conveniences
wherein the bug lies.) This would look something like:
import matplotlib.ticker as mticker
# (Plot here.)
tick_locs = 2 * np.arange(len(modes)) + 0.5
tick_labels = ['%d/%d' % (x[1], x[0]) for x in modes]
for axis in (ax.xaxis, ax.yaxis):
axis.set_major_locator(mticker.FixedLocator(tick_locs))
axis.set_major_formatter(mticker.FixedFormatter(tick_labels))
Separately, because you're ticking on the element boundaries instead of
centers, you might consider passing a custom extent to matshow, as in
N = 5
res = np.diag(np.arange(2 * N))
modes = [ (x+1, 0) for x in range(N) ]
cs = ax.matshow(res, extent=[-0.5, N - 0.5, N - 0.5, -0.5])
Then you can simply tick on the integers, using
tick_locs = np.arange(N)
I hope that helps.
[1] http://matplotlib.sourceforge.net/api/ticker_api.html
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