DiscreteNorm does not support set_under/set_over colormap extremes

[Description of the bug or feature.] Error when using boudary normalization for colormap.

Steps to reproduce

A “Minimal, Complete and Verifiable Example” will make it much easier for maintainers to help you.

import matplotlib.pyplot as plt
import proplot as pplt
import numpy as np
import cmaps
import matplotlib.colors as colors
import matplotlib.ticker as ticker


state = np.random.RandomState(51423)
data = (state.rand(20, 20))*50
data[5:15,5:15] = 0.002
data[10:20,10:20] = 0.6
print(data.max(),data.min())

levels = [0.1,0.5,1.,2.,3.,4.,5.,6.,8.,10.,20.,40]
norm = colors.BoundaryNorm(boundaries=np.array(levels), ncolors=len(levels)-1)

# figure with proplot
fig, ax = pplt.subplots()
m0 = ax.contourf(data, levels=levels, extend='both', cmap=cmaps.cmorph,
colorbar="r",colorbar_kw={'ticks':levels})
ax.format(suptitle='plot with proplot')
fig.savefig('color_proplot.png')

# figure with matplotlib
fig, ax = plt.subplots()
m1 = ax.contourf(data, levels=levels, norm=norm, extend='both',cmap=cmaps.cmorph[1:])
m1.cmap.set_under(cmaps.cmorph.colors[0])
cb = plt.colorbar(m1, ax=ax, ticks=levels, extendfrac='auto', 
                     aspect=18, shrink=.95)

cb.ax.minorticks_off()

ax.set_title("plot with matplotlib")
fig.savefig('color_matplotlib.png')

# figure with proplot and boudary normalization
fig, ax = pplt.subplots()
m0 = ax.contourf(data, levels=levels, norm=norm, extend='both', cmap=cmaps.cmorph[1:],
colorbar="r",colorbar_kw={'ticks':levels})
m0.cmap.set_under(cmaps.cmorph.colors[0])

ax.format(suptitle='plot with proplot1')
fig.savefig('color_proplot1.png')

Expected behavior: [What you expected to happen]

Actual behavior: [What actually happened]

When using default segmented normalizaton in proplot:

And when using boudary normailiztion of matplotlib:

Equivalent steps in matplotlib

See the code above.

Proplot version

0.6.1 Paste the result of import proplot; print(proplot.version) here.

[lacstorm]

@lukelbd Hi, luke. It’s ok now. Thanks for developing such an effective package!

[lukelbd]

Sorry I couldn’t get to this sooner, but I finally figured it out (see https://github.com/lukelbd/proplot/commit/f23ff70d54ffa668212e65c1d80ede3a310f5518). This was actually two separate bugs:

• plot.Colormap() automatically translates native matplotlib colormaps to proplot’s special colormap subclasses plot.colors.LinearSegmentedColormap and plot.colors.ListedColormap. However when doing this, proplot failed to copy the “extreme” color values.
• plot.DiscreteNorm() failed to account for the possibility that there is a discrete transition between the minimum/maximum colors “within” the colormap range (colormap coordinates 0 and 1) and the out of bounds colors (coordianates 0 - eps and 1 + eps). Now it takes this into account.

Here’s your example on master (can be installed with pip install git+https://github.com/lukelbd/proplot):

import matplotlib.pyplot as plt
import proplot as pplt
import numpy as np
import matplotlib.colors as colors
import matplotlib.ticker as ticker


state = np.random.RandomState(51423)
data = (state.rand(20, 20))*50
data[5:15,5:15] = 0.002
data[10:20,10:20] = 0.6
#print(data.max(),data.min())

# define colormap
cmorph = [[255.,255,255],
   [0,225,225],
   [0,165,185],
   [0,185, 0],
   [0 ,255 , 45],
 [255, 255 ,  0],
 [225 ,205 ,  0],
 [160, 120 ,  0],
 [160 , 40 ,  0],
 [185 ,  0 ,  0],
 [255  , 0 ,155],
 [185  , 0 ,255],
 [125 ,  0, 255]]

cmorph = np.array(cmorph) / 255.0
cmap0 = colors.ListedColormap(cmorph[1:-1])
cmap0.set_under(cmorph[0])
cmap0.set_over(cmorph[-1])
levels = [0.1,0.5,1.,2.,3.,4.,5.,6.,8.,10.,20.,40]

# Figure with proplot
fig, ax = pplt.subplots()
m0 = ax.contourf(
  data, levels=levels, extend='both', cmap=cmap0,
  colorbar='b', colorbar_kw={'ticks':levels}
)

ax.format(suptitle='plot with proplot')