Brian Larsen wrote:
Hello all,
several of my colleagues and I are 99% sure we are making the change from IDL
to python-matplotlib. I have just one issue that I am trying to work out
that I need to solve. We are so far really impressed and looking forward to
the change.
I have seen discussion on this list about imagesc and imshow but none of them
have quite answered the questions. In IDL we spent way too much time writing
an clone that is still not full featured:
http://people.bu.edu/balarsen/IDLdoc/imagesc.html
I have data of probability distributions which have an X and Y array
associated with the axes of the 2-d distribution (image). What I don't see
how to do in any easy fashion is plot this data in a imshow() manner with the
axes correct (which are unevenly distributed and need to be plotted on a log
axes).
This can be done with contourf(X,Y,Z) but this has a few issues:
- I dont see how to do a log axes on a contour
- contour is the wrong plot as the inherent smoothing that a contour does is
highly undesirable.
Using matlab imagesc one can easily make plots similar to:
http://img269.yfrog.com/i/2dprob.png/
Imagine taking the above plot and make the pixels different sizes so that
each pixel has identical counting statistics. Now assume that one wanted
the Y-axis to be plotted in log.
Anyone have any thoughts or toy examples?
I am not sure I understand exactly what you want to do, but it sounds
like pcolormesh would do it. e.g. with ipython -pylab:
ax = gca()
ax.set_yscale('log')
x = np.arange(10)**1.5
y = np.arange(20)**1.8
z = x[1:] * y[1:, np.newaxis]
pcolormesh(x, y, z)
axis('tight')
Note that with x and y, which can be 1-D or 2-D, you are specifying the
grid boundaries, not the pixel centers.
Eric
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