Use better contour defaults with logarithmic norms

[Illviljan]

Contour and contourf does some strange things when norm is changed from the defaults. Using a logarithmic norm is not uncommon.
It seems better to let matplotlib handle most of the default values.

• Maybe closes Logscale color normalization in contourf #10551 ?
• Tests added
• User visible changes (including notable bug fixes) are documented in whats-new.rst

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

import xarray as xr


# %%
N = 100
x = np.linspace(-3, 3, N)
y = np.linspace(-2, 2, N)

X, Y = np.meshgrid(x, y)

Z1 = np.exp(-(X**2) - Y**2)
Z2 = np.exp(-((X * 10) ** 2) - (Y * 10) ** 2)

Z = Z1 + 50 * Z2

vmin = Z.min()
vmax = Z.max()
norm = LogNorm(vmin=vmin, vmax=vmax)


# %% xarray
da_z = xr.DataArray(data=(Z), coords=dict(x=("x", x), y=("y", y)))

fig, axs = plt.subplots(2, 3, layout="constrained")

for i, plotfunc in enumerate(("pcolormesh", "contourf", "contour")):
    p = getattr(da_z.plot, plotfunc)
    p(x="x", y="y", ax=axs[0, i], vmin=vmin, vmax=vmax, add_colorbar=True)
    axs[0, i].set(title=f"{plotfunc} linear")
    p(x="x", y="y", ax=axs[1, i], norm=norm, add_colorbar=True)
    axs[1, i].set(title=f"{plotfunc} log")

PR:

Main: