python colormap(颜色映射)

转载（有增减，后面有举例）：http://blog.csdn.net/guduruyu/article/details/60868501

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import numpy as np  
import matplotlib.pyplot as plt  
  
# Have colormaps separated into categories:  
# http://matplotlib.org/examples/color/colormaps_reference.html  
  
cmaps = [('Perceptually Uniform Sequential',  
                            ['viridis', 'inferno', 'plasma', 'magma']),  
         ('Sequential',     ['Blues', 'BuGn', 'BuPu',  
                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',  
                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',  
                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),  
         ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',  
                             'copper', 'gist_heat', 'gray', 'hot',  
                             'pink', 'spring', 'summer', 'winter']),  
         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',  
                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',  
                             'seismic']),  
         ('Qualitative',    ['Accent', 'Dark2', 'Paired', 'Pastel1',  
                             'Pastel2', 'Set1', 'Set2', 'Set3']),  
         ('Miscellaneous',  ['gist_earth', 'terrain', 'ocean', 'gist_stern',  
                             'brg', 'CMRmap', 'cubehelix',  
                             'gnuplot', 'gnuplot2', 'gist_ncar',  
                             'nipy_spectral', 'jet', 'rainbow',  
                             'gist_rainbow', 'hsv', 'flag', 'prism'])]  
  
  
nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)  
gradient = np.linspace(0, 1, 256)  
gradient = np.vstack((gradient, gradient))  
  
  
def plot_color_gradients(cmap_category, cmap_list):  
    fig, axes = plt.subplots(nrows=nrows)  
    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)  
    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)  
  
    for ax, name in zip(axes, cmap_list):  
        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))  
        pos = list(ax.get_position().bounds)  
        x_text = pos[0] - 0.01  
        y_text = pos[1] + pos[3]/2.  
        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)  
  
    # Turn off *all* ticks & spines, not just the ones with colormaps.  
    for ax in axes:  
        ax.set_axis_off()  
  
for cmap_category, cmap_list in cmaps:  
    plot_color_gradients(cmap_category, cmap_list)  
  
plt.show() 

举例1：

横坐标是从1到1000的数，纵坐标是横坐标的平方，要求使用颜色映射为：gist_rainbow(即最后一张图的倒数第四个颜色映射)

import matplotlib.pyplot as plt

x = list(range(1,1001))
y = [i**2 for i in x]#使用列表解析

plt.scatter(x,y,c=y,cmap=plt.cm.gist_rainbow,s=20)#cm即colormap，c=y表示颜色随y变化
plt.xlabel('number',fontsize=10)
plt.ylabel('number square',fontsize=10)
plt.title('figure 1',fontsize =20)
plt.axis([0,1100,0,1100000])
plt.show()

代码中cm即colormap，c=y表示颜色随y变化（第一图），也可以使用c=x，使得颜色随x映射（第二图），变化不大
图如下:

举例2

画一个2维无关高斯分布

这里实际关于颜色映射的就只有ax.plot_surface(X,Y,F_x_y,cmap='jet')这一行代码，jet是上面给的渐变色中的一个。

import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.axisartist as axisartist
from mpl_toolkits.mplot3d import Axes3D #画三维图不可少
from matplotlib import cm  #cm 是colormap的简写

#定义坐标轴函数
def setup_axes(fig, rect):
    ax = axisartist.Subplot(fig, rect)
    fig.add_axes(ax)

    ax.set_ylim(-.2, 1.2)
    #自定义刻度
#    ax.set_yticks([-10, 0,9])
    ax.set_xlim(-10,10)
    ax.axis[:].set_visible(False)

	#第2条线，即y轴,经过x=0的点
    ax.axis["y"] = ax.new_floating_axis(1, 0)
    ax.axis["y"].set_axisline_style("-|>", size=1.5)
#    第一条线，x轴，经过y=0的点
    ax.axis["x"] = ax.new_floating_axis(0, 0)
    ax.axis["x"].set_axisline_style("-|>", size=1.5)

    return(ax)
# 1_dimension gaussian function
def gaussian(x,mu,sigma):
    f_x = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power(x-mu, 2.)/(2*np.power(sigma,2.)))
    return(f_x)

# 2_dimension gaussian function
def gaussian_2(x,y,mu_x,mu_y,sigma_x,sigma_y):
    f_x_y = 1/(sigma_x*sigma_y*(np.sqrt(2*np.pi))**2)*np.exp(-np.power\
              (x-mu_x, 2.)/(2*np.power(sigma_x,2.))-np.power(y-mu_y, 2.)/\
              (2*np.power(sigma_y,2.)))
    return(f_x_y)

#在已经定义好的画布上加入高斯函数
x_values = np.linspace(-5,5,2000)
y_values = np.linspace(-5,5,2000)
X,Y = np.meshgrid(x_values,y_values)
mu_x,mu_y,sigma_x,sigma_y = 0,0,0.8,0.8
F_x_y = gaussian_2(X,Y,mu_x,mu_y,sigma_x,sigma_y)
#显示三维图
fig = plt.figure()
ax = plt.gca(projection='3d')
ax.plot_surface(X,Y,F_x_y,cmap='jet')
# 显示等高线图
#ax.contour3D(X,Y,F_x_y,50,cmap='jet')
#plt.show()