Cartopy Pcolormesh With Re-normalized Colorbar

I'm trying to plot global Aerosol Optical Depths (AOD), and the values are typically around 0.2, but in some regions can reach 1.2 or more. Ideally I want to plot these high values

Solution 1:

It seems to have something to do with the masking inside the normalization class. So here is a version that is working:

classMidpointNormalize(colors.Normalize):
    def__init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
        self.midpoint = midpoint
        colors.Normalize.__init__(self, vmin, vmax, clip)

    def__call__(self, value, clip=None):
        result, is_scalar = self.process_value(value)
        (vmin,), _ = self.process_value(self.vmin)
        (vmax,), _ = self.process_value(self.vmax)
        resdat = np.asarray(result.data)
        result = np.ma.array(resdat, mask=result.mask, copy=False)
        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
        res = np.interp(result, x, y)
        result = np.ma.array(res, mask=result.mask, copy=False)
        if is_scalar:
            result = result[0]
        return result

The complete code:

import matplotlib.pyplot as plt
import matplotlib.colors as colors
import numpy as np
import cartopy.crs as ccrs

classMidpointNormalize(colors.Normalize):
    def__init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
        self.midpoint = midpoint
        colors.Normalize.__init__(self, vmin, vmax, clip)

    def__call__(self, value, clip=None):
        result, is_scalar = self.process_value(value)
        (vmin,), _ = self.process_value(self.vmin)
        (vmax,), _ = self.process_value(self.vmax)
        resdat = np.asarray(result.data)
        result = np.ma.array(resdat, mask=result.mask, copy=False)
        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
        res = np.interp(result, x, y)
        result = np.ma.array(res, mask=result.mask, copy=False)
        if is_scalar:
            result = result[0]
        return result

defsample_data(shape=(73, 145)):
    """Returns ``lons``, ``lats`` and ``data`` of some fake data."""
    nlats, nlons = shape
    lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
    lons = np.linspace(0, 2 * np.pi, nlons)
    lons, lats = np.meshgrid(lons, lats)
    wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
    mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

    lats = np.rad2deg(lats)
    lons = np.rad2deg(lons)
    data = wave + mean

    return lons, lats, data


ax = plt.axes(projection=ccrs.Mollweide())
lons, lats, data = sample_data()

norm = norm=MidpointNormalize(midpoint=0.8)
cm = ax.pcolormesh(lons, lats, data, 
            transform=ccrs.PlateCarree(),
            cmap='spectral', norm=norm )

ax.coastlines()
plt.colorbar(cm, orientation="horizontal")
ax.set_global()
plt.show()

produces