如何修改离散线性分段颜色图

我是气象学家，经常使用例如“蓝-白色-红色”的颜色图绘制温度场距平值。为了使图表更具可读性，我使用在互联网上“找到”的函数对颜色图进行一定数量的级别（箱）离散化（但我并不完全理解它）：

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
import matplotlib.colors as cols
from numpy.random import randn

def cmap_discretize(cmap, N):
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., N+1)
    cdict = {}
    for ki,key in enumerate(('red','green','blue')):
        cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)


cmap_disc= cmap_discretize(cm.RdBu_r,12)


fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)

cax = ax.pcolor(data, cmap=cmap_disc)
plt.colorbar(cax)

plt.show()

这将导致以下结果：

[图片]

现在，我想将最中间的两个片段（即接近 0 的两个片段）设置为白色，因为我不想显示非常小的偏差。
我的目标是最终得到类似于此的结果：

[图片]

我真的很难以理解这些 LinearSegmentedColormap 如何能够相应地进行修改。有人可以帮助我解决这个问题吗？

2、解决方案

方法 1：

第一个回答者提供了以下解决方案：

def cmap_discretize(cmap, N):
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., N+1)
    cdict = {}
    for ki,key in enumerate(('red','green','blue')):
        cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
    # "white out" the bands closest to the middle
    num_middle_bands = 2 - (N % 2)
    middle_band_start_idx = (N - num_middle_bands) // 2
    for middle_band_idx in range(middle_band_start_idx,
                                 middle_band_start_idx + num_middle_bands):
        for key in cdict.keys():
            old = cdict[key][middle_band_idx]
            cdict[key][middle_band_idx] = old[:2] + (1.,)
            old = cdict[key][middle_band_idx + 1]
            cdict[key][middle_band_idx + 1] = old[:1] + (1.,) + old[2:]
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

方法 2：

第二个回答者提供了以下解决方案：

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
import matplotlib.colors as cols
from numpy.random import randn

def cmap_double_discretize(cmap_bottom, cmap_top, N, split=.5):
    """
    Generates a descritized color map using two existing color maps

    Parameters
    ----------
    cmap_bottom : cmap
        The bottom cmap

    cmap_top : cmap
        The top cmap

    N : int
       The number of bins in each color map

    split : float, optional
       Where to join the maps, must be in [0, 1]
    """
    # sanity check
    assert split < 1 and split > 0
    # set up the data structure
    cdict = {lab: [] for lab in ('red','green','blue')}
    # do this in a fancy loop to a) save typing, b) make it easy to
    # retrofit to do arbitrary splits
    for cmap, ends in zip((cmap_bottom, cmap_top), ((0, split), (split, 1))):

        # run over the _whole_ range for each color map
        colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
        # map the color
        colors_rgba = cmap(colors_i)
        # get the values 
        indices = np.linspace(ends[0], ends[1], N+1, endpoint=True)

        for ki,key in enumerate(('red','green','blue')):
            cdict[key].extend((indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1))
            #    print cdict
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

red_cdict = {'red': [(0, 0, 1),
                     (1, 1, 0)],
            'blue': [(0, 0, 0),
                     (1, 1, 0)],
            'green': [(0, 0, 0),
                     (1, 1, 0)]}

blue_cdict = {'blue': [(0, 0, 1),
                       (1, 1, 0),],
            'red': [(0, 0, 1),
                    (1, 0, 0)],
            'green': [(0, 0, 1),
                     (1, 0, 0)]}
red_cmap = cols.LinearSegmentedColormap('red', red_cdict, 1024)
blue_cmap = cols.LinearSegmentedColormap('blue', blue_cdict, 1024)

test_cmap = cmap_double_discretize(red_cmap, blue_cmap, 6)
# these don't actually go to white!
# test_cmap = cmap_double_discretize(cm.get_cmap('Reds_r'), cm.get_cmap('Blues'), 6)


fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)

cax = ax.pcolor(data, cmap=test_cmap)
plt.colorbar(cax)

plt.show()

你也可以很容易地修改它来跨越两个以上的颜色图。