On Wed, Dec 8, 2010 at 9:15 PM, Timothy W. Hilton <hil...@meteo.psu.edu>wrote:
> Hello,
>
> I'm trying to teach myself to create custom colormaps to highlight
> certain aspects of a dataset I am working with. The script below
> produces two plots -- the first shows a 4x4 array foo of random floats
> between 0.0 and 1.0, and the second shows the same array, but normalized
> such that [foo.min(), foo.max()] is mapped to [0.0, 1.0].
>
> As I understand it, I am plotting two slightly different datasets using
> the same colormap, yet the two colorbars are different -- note the
> value at the transition from grayscale to red.
>
> I am not sure whether the colors are being assigned to slightly
> different data values in the two plots, or if the problem is in plotting
> the colorbar. I'd appreciate any help!
>
> Thanks,
> Tim
>
>
Hello Tim,
Nice to see more students at PSU meteo jumping in on matplotlib (I am a
graduate from there).
When you call pcolor() without specifying the vmin and the vmax values,
pcolor() will automatically normalize your input data to the range of 0.0 to
1.0 for the purposes of coloring, but the ticks on the colorbar will be
based on the original input values. When you normalized the data yourself,
the colorbar for that plot will use the given input values for deciding the
tick values, since it could not know what the original data had.
Note that both pcolors are producing identically colored plots, it is just
the colorbar that shows a different color<->value relationship. Now, to
illustrate this point further, let's pick a colormap that changes rapidly,
such as 'flag'. Change the line:
mycm1 = mpl.colors.LinearSegmentedColormap('mycm1', mycmdata1)
to
mycm1 = 'flag'
And then run the script. You will see the plots are still identical. Now,
change the first pcolor call from
plt.pcolor(foo, cmap=mycm1)
to
plt.pcolor(foo, cmap=mycm1, vmin=0.0, vmax=1.0)
and run the script. The plots are different! But the colorbars are the
same! This is because the data going into the two pcolors calls are
different and so all of this behavior is as expected.
I hope this clears things up for you, and I hope you continue to enjoy using
matplotlib!
Ben Root
P.S. - Tell Dr. Young I said "Hi!"
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