I note that thecolors attribute does not appear in the API docs. So perhaps I am not supposed to use it and should docmap(i) instead? I find the attribute quite intuitive though.

This seems a bit confusing to me. Colors won't work at all on linear segmented colormaps, right?

Ah OK, better go back tocmap(i) then.

In my local build,get_cmap andrc_context failed to make a link and I have no idea why.

I found this a little confusing because you don't use it.

Don't use what?resampled is used on line 22.

This seems a bit confusing to me. Colors won't work at all on linear segmented colormaps, right?

Sorry to dribble comments, but I found "Alternatively" poor guidance here, as I immediately thought it would be "alternative" was of getting the colors, whereas its an alternative way of passing the colors toplot.

Maybe something like:

I would have used

colors = mpl.colormaps['plasma'](np.linspace(0, 1, n_lines))for i, color in enumerate(colors):    ax.plot([0, i], color=color)

in particular

• mpl.colormaps.get_cmap('plasma') is somewhat unconventional. The ColormapRegistry supports item access for colormap names. That feels more natural to me for just looking up a colormap on the registry.get_cmap() is similar but has additional functionality like Colormap-passthrough and default-lookup forNone. I would either usempl.colormaps['plasma'] or just use the pyplot functionplt.get_cmap('plasma') to save theimport mpl (technically you could also doplt.colormaps['plasma'] because we re-export the registry in pyplot).
• I didn't even know that one can index colormaps by integer, and I find it a bit harder to understand. First, one has to know the integer-indexing (which feels half an implementation detail to me because I regard the colormaps as conceptually continuous). Second, looking up the color is now split beween creating the resampledcmap (l.24) and later looking up the color (l.29). They only make sense together but are quite separted. The vectorized approach of mapping an array of floats to colors is more explicit. Note also that you can easily sample parts of the colormap in the vectorized lookup, e.g.mpl.colormaps['RdYlGn'](np.linspace(0.5, 1, n_lines)) to sample only the upper half of a diverging colormap.

Thanks@timhoffm. It seems you and@jklymak are in agreement about the integer indexing being “suprising” so we should probably change it!

I did start withplt.get_cmap but I thought we were generally supposed to avoidpyplot for most things.

The qualitative colormaps areListedColormaps and obtaining all their colors can be done easier via:

colors = mpl.colormaps['Dark2'].colors

Note that this is a significantly different task than selecting N equidistant colors from a continuous map. I'm not clear on the focus we want to set here:#27671 was primaily "I have N lines and want differnt colors", which is the first part here. Mycomment on a context example was the idea one should mention in the context of the above task that you can make a context so that you don't have to carry the colors explicitly. That's a different direction than focussing on various aspects of getting color values. Both ways are possbile, but we should not mix them.

Hmmm, I started by using thecolors attribute for both examples (plasma is also aListedColorMap), but changed it in response to one of@jklymak’s comments.

My thinking for using a qualitative colormap: if someone is changing the color cycle in rcParams then they are using it across several plots which might need different numbers of colors, so getting a specific number of colors out of a continuous map would make less sense there. This is in the context where the user was also asking for an increase in the default cycle.