On Thu, May 27, 2010 at 5:44 PM, Eric Firing <efir...@hawaii.edu> wrote:
> You can't do this via any manipulation of the colormap, or any fancier
> colormap specification--instead, you have to manipulate the data value. For
> example, you could identify the "over" values in your data, and then use 2-D
> interpolation to replace them with the values you want.
>
> Basemap includes a 2-D interpolation routine:
>
> from mpl_toolkits.basemap import interp
>
Thanks!
>>
>> This is partially achieved with "white" (and I suppose I could pick
>> "grey" or "black"), but I think it might be nicer if it were a pure
>> mixture, rather than a mixture of the surrounding colors and the
>> "over" color.
>>
>> The script is attached below. Sorry it is a bit long, but I needed a
>> discrete colormap. Can we get cmap_discrete() into matplotlib?
>
> No, because it doesn't make much sense, given the mpl paradigm in which a
> colormap and a norm work together. If you want 4 colors, make a colormap
> with 4 colors, and use a norm that maps your data to those 4 colors.
>
> For example:
>
> cm4 = get_cmap('jet', 4)
> cm4a = mpl.colors.ListedColormap(get_cmap('jet', 256)([20, 70, 150, 200]))
>
> You can select any discrete set of colors you want using ListedColormap.
>
> Then you can use the default Normalize, or a custom BoundaryNorm, to map
> data ranges to the colors. You just don't need a lookup table with 1024
> entries to specify 4 colors--it doesn't gain you anything.
>
Wonderful. Definitely makes the cookbook entry seem unnecessary
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