contourf_logScale.py

import numpy as np
from matplotlib.colors import LinearSegmentedColormap


class nlcmap(LinearSegmentedColormap):
    """
    nlcmap - a nonlinear cmap from specified levels

    Copyright (c) 2006-2007, Robert Hetland <hetland@tamu.edu>
    Release under MIT license.

    Some hacks added 2012 noted in code (@MRR)
    """
    def __init__(self, cmap, levels):
        if isinstance(cmap, str):
            cmap = plt.get_cmap(cmap)
        self.cmap = cmap
        self.N = cmap.N
        self.monochrome = self.cmap.monochrome
        self.levels = np.asarray(levels, dtype='float64')
        self._x = self.levels / self.levels.max()
        self._y = np.linspace(0.0, 1.0, len(self.levels))

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


def logcenter(center, x=None):
    # Authors: Jean-Remi King, <jeanremi.king@gmail.com>
    #          Clément Levrard <clement.levrard@gmail.com>
    #
    # License: BSD (3-clause)
    """
    Creates a logarithmic scale centered around center, and bounded between
    [0., 1.] such that:
        f(0, center) = 0
        f(1, center) = 1
        f(center, center) = .5

    Parameters
    ----------
        x : float | np.array | None
            If float or np.array, 0. < x < 1.
            If None, set to np.linspace(0., 1., 256).
            Defaults to None.
        center : float
            0. < center < 1.
    Returns
    -------
        y : float | np.array
    """

    from numpy import exp, log
    if x is None:
        x = np.linspace(0., 1., 256)
    if center >= 1. or center <= 0.:
        raise ValueError('center must be between 0 and 1')
    if center == .5:
        y = x
    else:
        n = 1. / center
        y = (exp(2 * log(n - 1) * x) - 1) / (n * (n - 2))
    if center > .5:
        y = 1. - y
    return y


# Example
import matplotlib.pyplot as plt
from pylab import mgrid

yy, xx = mgrid[0.0:1.0:100j, 0.0:1.0:100j]

H = (xx + yy) / 2.
# Define color levels with 4 categories
n_classes_list = range(2, 10, 2)
fig, axes = plt.subplots(1, len(n_classes_list))
for n_classes, ax in zip(n_classes_list, axes):
    levels = logcenter(1. / n_classes)
    cs = ax.contourf(xx, yy, H, levels, cmap=nlcmap('RdBu_r', levels),
                     aspect='equal')
    c = plt.colorbar(cs,ax=ax)
    c.set_ticks([0., 1. / n_classes, 1.])
    c.set_clim([0., 1.])
    ax.set_xticks([])
    ax.set_yticks([])
plt.show()