Non-linear color map with Matplotlib

Question

I have data that I would like to build using contourf / tricontourf using a non-linear color scheme.

I found a script (see below) that provides a good solution for the color palette, as long as the levels are between 0 and a positive number.

However, my data is negative (levels between -50 and 0). Unfortunately, the level setting in my case does not work at all (see Figure, subrecord 3). So what should I consider? Anyone have any suggestions for me or maybe even run into the same problem?

I am very grateful for your help.

from pylab import *
from numpy import *
from matplotlib.colors import LinearSegmentedColormap

class nlcmap(LinearSegmentedColormap):
    """A nonlinear colormap"""

    name = 'nlcmap'

    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.monochrome = self.cmap.monochrome
        self.levels = asarray(levels, dtype='float64')
        self._x = self.levels/ self.levels.max()
        self.levmax = self.levels.max()
        self.levmin = self.levels.min()
        self._y = linspace(self.levmin, self.levmax, len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = interp(xi, self._x, self._y)
        return self.cmap(yi/self.levmax, alpha)

if __name__ == '__main__':

    y, x = mgrid[0.0:3.0:100j, 0.0:5.0:100j]
    H = 50.0 * exp( -(x**2 + y**2) / 4.0 )
    levels = [0, 1, 2, 3, 6, 9, 20, 50]

    H1 = -50.0 * exp( -(x**2 + y**2) / 4.0 )
    levels1 = [-50, -20, -9, -6, -3, -2, -1, 0]

    cmap_lin = cm.jet
    cmap_nonlin = nlcmap(cmap_lin, levels)
    cmap_lin1 = cm.jet
    cmap_nonlin1 = nlcmap(cmap_lin1, levels1)

    subplot(4,1,1)
    contourf(x, y, H, levels, cmap=cmap_nonlin)
    colorbar()
    subplot(4,1,2)
    contourf(x, y, H, levels, cmap=cmap_lin)
    colorbar()
    subplot(4,1,3)
    contourf(x, y, H1, levels1, cmap=cmap_nonlin1)
    colorbar()
    subplot(4,1,4)
    contourf(x, y, H1, levels1, cmap=cmap_lin1)
    colorbar()

    plt.show()  

Answer 1

, levels1 = [-50, -20, -9, -6, -3, -2, -1, 0], , self._x = self.levels/ self.levels.max(). , , pcolor contourf, 0 1 . , , , . , :

class nlcmap(LinearSegmentedColormap):
    """A nonlinear colormap"""

    name = 'nlcmap'

    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.monochrome = self.cmap.monochrome
        self.levels = asarray(levels, dtype='float64')
        self._x = self.levels-self.levels.min()
        self._x/= self._x.max()
        self._y = linspace(0, 1, len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = interp(xi, self._x, self._y)
        return self.cmap(yi, alpha)

Answer 2

, [0, 1], max, max, ... :

from pylab import *
from numpy import *
from matplotlib.colors import LinearSegmentedColormap

class nlcmap(LinearSegmentedColormap):
    """A nonlinear colormap"""

    name = 'nlcmap'

    def __init__(self, cmap, levels):
        self.cmap = cmap
        self.monochrome = self.cmap.monochrome
        self.levels = asarray(levels, dtype='float64')
        self.levmax = self.levels.max()
        self.levmin = self.levels.min()
        self._x = (self.levels - self.levmin) / (self.levmax - self.levmin)
        self._y = linspace(0, 1, len(self.levels))

    def __call__(self, xi, alpha=1.0, **kw):
        yi = interp(xi, self._x, self._y)
        return self.cmap(yi, alpha)

if __name__ == '__main__':

    y, x = mgrid[0.0:3.0:100j, 0.0:5.0:100j]
    H = 50.0 * exp( -(x**2 + y**2) / 4.0 )
    levels = [0, 1, 2, 3, 6, 9, 20, 50]

    H1 = -50.0 * exp( -(x**2 + y**2) / 4.0 )
    levels1 = [-50, -20, -9, -6, -3, -2, -1, 0]

    cmap_lin = cm.jet
    cmap_nonlin = nlcmap(cmap_lin, levels)
    cmap_lin1 = cm.jet
    cmap_nonlin1 = nlcmap(cmap_lin1, levels1)

    subplot(4,1,1)
    contourf(x, y, H, levels, cmap=cmap_nonlin)
    colorbar()
    subplot(4,1,2)
    contourf(x, y, H, levels, cmap=cmap_lin)
    colorbar()
    subplot(4,1,3)
    contourf(x, y, H1, levels1, cmap=cmap_nonlin1)
    colorbar()
    subplot(4,1,4)
    contourf(x, y, H1, levels1, cmap=cmap_lin1)
    colorbar()

    plt.show()  

Answer 3

, [0, 1].

:

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

class PiecewiseNorm(Normalize):
    def __init__(self, levels, clip=False):
        # the input levels
        self._levels = np.sort(levels)
        # corresponding normalized values between 0 and 1
        self._normed = np.linspace(0, 1, len(levels))
        Normalize.__init__(self, None, None, clip)

    def __call__(self, value, clip=None):
        # linearly interpolate to get the normalized value
        return np.ma.masked_array(np.interp(value, self._levels, self._normed))

:

y, x = np.mgrid[0.0:3.0:100j, 0.0:5.0:100j]
H = 50.0 * np.exp( -(x**2 + y**2) / 4.0 )
levels = [0, 1, 2, 3, 6, 9, 20, 50]

H1 = -50.0 * np.exp( -(x**2 + y**2) / 4.0 )
levels1 = [-50, -20, -9, -6, -3, -2, -1, 0]

fig, ax = plt.subplots(2, 2, gridspec_kw={'width_ratios':(20, 1), 'wspace':0.05})

im0 = ax[0, 0].contourf(x, y, H, levels, cmap='jet', norm=PiecewiseNorm(levels))
cb0 = fig.colorbar(im0, cax=ax[0, 1])

im1 = ax[1, 0].contourf(x, y, H1, levels1, cmap='jet', norm=PiecewiseNorm(levels1))
cb1 = fig.colorbar(im1, cax=ax[1, 1])

plt.show()

, .