Automatically apply diverging colormaps and add `robust=True` option

Is there any way to have an automatic scaling of the colorbar when 0 is present, like the default behavior of Cartopy that center the colorbar on 0 and automatically use a diverging colormap?

And in addition, is that possible to use the option robust=True of Cartopy? When I try to use it, it tells me that it is not recognized.

[lukelbd]

These features are finally on master. Below is an example of the robust=True feature. You can also pass a number e.g. robust=90 to select colormap limits based on the middle 90 percentile range, or pass a tuple e.g. robust=(0, 90) to use that particular percentile range. As in xarray, the default percentile range implied by robust=True is 96. The keyword choice is maybe a bit clunky here since robust can now accept these other inputs but I’d rather stay consistent with xarray.

import proplot as pplt
import numpy as np
fig, axs = pplt.subplots(ncols=2)
N = 10
data = np.random.rand(N, N)
data.flat[0] = 100.0
for i, ax in enumerate(axs):
    ax.pcolor(data, robust=bool(i))
    ax.format(title='Robust ' + ('on' if i else 'off'))

And here’s an example of the automatic diverging colormap selection. A diverging colormap is picked as follows:

• If discrete=True and levels are being used, then levels must have more than one each positive and negative values.
• If discrete=False and only vmin and vmax are being used, then vmin and vmax must have the opposite sign.

I’ve also added a whole system for selecting default “sequential”, “diverging”, “qualitative”, and “cyclic” colormaps with corresponding rc settings and keyword arguments that can be passed to plot commands (e.g., rc['cmap.cyclic'] = cmap or to change the default cyclic colormap and ax.pcolor(..., cyclic=True) to select the default cyclic colormap). Details are in the changelog on master (about to put these in a big release).

import proplot as pplt
import numpy as np
fig, axs = pplt.subplots(ncols=2)
N = 10
data = np.random.rand(N, N) * 10
for i, ax in enumerate(axs):
    ax.pcolor(data - i * 5, colorbar='b')
    ax.format(title='Diverging ' + ('on' if i else 'off'))

[lukelbd]

Btw proplot has a plot.arange(...) function that’s great for generating lists of ticks, colorbar levels, etc. Behavior is identical to np.arange except it is endpoint inclusive. So your example would be plot.arange(-45, 45, 5) rather than np.arange(-45, 50, 5) which IMO is more intuitive.