Proplot ignores non-none vmin and vmax values on normalizer instances

[Jhsmit]

Description

I'm trying to clip colors in a scatter plot using norm, but the keyword argument seems to be ignored, unless I pass a DiscreteNorm

Steps to reproduce

import proplot as pplt
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import numpy as np

print(pplt.__version__)
import matplotlib
print(matplotlib.__version__)

x = np.arange(10)
y = np.random.rand(10)
c = x**2

fig, ax = pplt.subplots()
ax.scatter(x, y, c=c, cmap='viridis', norm=pplt.Norm('linear', 0., 1.))
ax.format(title='proplot')
pplt.show()

fig, ax = pplt.subplots()
ax.scatter(x, y, c=c, cmap='viridis', norm=pplt.DiscreteNorm(np.linspace(0, 1, 6)))
ax.format(title='proplot discrete')
pplt.show()

fig, ax = plt.subplots()
ax.scatter(x, y, c=c, cmap='viridis', norm=Normalize(0, 1))
ax.set_title("matplotlib")
pplt.show()

Proplot:

Proplot with discrete norm:

Matplotlib:

Proplot version

matplotlib: 3.4.3 proplot: 0.9.5

[lukelbd]

Thanks for the report. For now, you can also accomplish clipping by passing vmin=0 and vmax=1 instead of a normalizer (this is not possible in matplotlib, but proplot tries to standardize arguments across different commands, so e.g. any command that accepts cmap also accepts colormap-related keywords like vmin, vmax, levels, etc.):

import proplot as pplt
import numpy as np

x = np.arange(10)
y = np.random.rand(10)
c = x**2

fig, ax = pplt.subplots()
ax.scatter(x, y, c=c, cmap='viridis', vmin=0, vmax=1)
ax.format(title='proplot')

I see that in matplotlib, if a normalizer is passed with explicitly-set vmin and vmax values (i.e., not the default None values), then matplotlib will not set them automatically. However, proplot always overrides the vmin and vmax values. I'll change this so that normalizer vmin and vmax are respected.

[Jhsmit]

FYI the behavior is the same for pcolor / pcolormesh, also the passed normalizer is modified:

arr = np.random.rand(20, 40)*1000
xe = np.linspace(0, 1, num=40, endpoint=True)
ye = np.linspace(0, 1, num=20, endpoint=True)

fig, ax = pplt.subplots()
norm = pplt.Norm("linear", vmin=0, vmax=1)
print(norm.vmin, norm.vmax)
ax.pcolor(xe, ye, arr, cmap="viridis", norm=norm)
print(norm.vmin, norm.vmax)

prints 0.0 1.0 0.0 1000.0