不幸的是，matplotlib没有提供Matlab的demcmap的功能。

实际上，python basemap包中可能有一些内置特性，我不知道这些特性。在

所以，坚持matplotlib板上的选项，我们可以子类^{}来构建一个以colormap中间的点为中心的颜色规范化。此技术可以在StackOverflow上的another question中找到，并根据具体需要进行调整，即设置sealevel(可能最好选择为0)和颜色映射col_val(范围在0和1之间)中的值，该值应与该sealevel对应。在地形图的情况下，0.22，对应于一个turqoise颜色，可能是一个不错的选择。

然后可以将Normalize实例作为imshow的参数。在图片的第一行下面可以看到结果图。在

由于海平面周围的平滑过渡，0周围的值显示为杂色，因此很难区分陆地和海洋。

因此，我们可以稍微改变一下地形图，去掉那些颜色，这样海岸线就更清晰可见了。这是通过地图的combining two parts来完成的，范围从0到0.17，从0.25到1，并因此切断了它的一部分。在import numpy as np

import matplotlib.pyplot as plt

import matplotlib.colors

class FixPointNormalize(matplotlib.colors.Normalize):

"""

Inspired by https://stackoverflow.com/questions/20144529/shifted-colorbar-matplotlib

Subclassing Normalize to obtain a colormap with a fixpoint

somewhere in the middle of the colormap.

This may be useful for a `terrain` map, to set the "sea level"

to a color in the blue/turquise range.

"""

def __init__(self, vmin=None, vmax=None, sealevel=0, col_val = 0.21875, clip=False):

# sealevel is the fix point of the colormap (in data units)

self.sealevel = sealevel

# col_val is the color value in the range [0,1] that should represent the sealevel.

self.col_val = col_val

matplotlib.colors.Normalize.__init__(self, vmin, vmax, clip)

def __call__(self, value, clip=None):

x, y = [self.vmin, self.sealevel, self.vmax], [0, self.col_val, 1]

return np.ma.masked_array(np.interp(value, x, y))

# Combine the lower and upper range of the terrain colormap with a gap in the middle

# to let the coastline appear more prominently.

# inspired by https://stackoverflow.com/questions/31051488/combining-two-matplotlib-colormaps

colors_undersea = plt.cm.terrain(np.linspace(0, 0.17, 56))

colors_land = plt.cm.terrain(np.linspace(0.25, 1, 200))

# combine them and build a new colormap

colors = np.vstack((colors_undersea, colors_land))

cut_terrain_map = matplotlib.colors.LinearSegmentedColormap.from_list('cut_terrain', colors)

# invent some data (height in meters relative to sea level)

data = np.linspace(-1000,2400,15**2).reshape((15,15))

# plot example data

fig, ax = plt.subplots(nrows = 2, ncols=3, figsize=(11,6) )

plt.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.92, hspace = 0.28, wspace = 0.15)

plt.figtext(.5, 0.95, "Using 'terrain' and FixedPointNormalize", ha="center", size=14)

norm = FixPointNormalize(sealevel=0, vmax=3400)

im = ax[0,0].imshow(data+1000, norm=norm, cmap=plt.cm.terrain)

fig.colorbar(im, ax=ax[0,0])

norm2 = FixPointNormalize(sealevel=0, vmax=3400)

im2 = ax[0,1].imshow(data, norm=norm2, cmap=plt.cm.terrain)

fig.colorbar(im2, ax=ax[0,1])

norm3 = FixPointNormalize(sealevel=0, vmax=0)

im3 = ax[0,2].imshow(data-2400.1, norm=norm3, cmap=plt.cm.terrain)

fig.colorbar(im3, ax=ax[0,2])

plt.figtext(.5, 0.46, "Using custom cut map and FixedPointNormalize (adding hard edge between land and sea)", ha="center", size=14)

norm4 = FixPointNormalize(sealevel=0, vmax=3400)

im4 = ax[1,0].imshow(data+1000, norm=norm4, cmap=cut_terrain_map)

fig.colorbar(im4, ax=ax[1,0])

norm5 = FixPointNormalize(sealevel=0, vmax=3400)

im5 = ax[1,1].imshow(data, norm=norm5, cmap=cut_terrain_map)

cbar = fig.colorbar(im5, ax=ax[1,1])

norm6 = FixPointNormalize(sealevel=0, vmax=0)

im6 = ax[1,2].imshow(data-2400.1, norm=norm6, cmap=cut_terrain_map)

fig.colorbar(im6, ax=ax[1,2])

for i, name in enumerate(["land only", "coast line", "sea only"]):

for j in range(2):

ax[j,i].text(0.96,0.96,name, ha="right", va="top", transform=ax[j,i].transAxes, color="w" )

plt.show()