在python中使用matplotlib制作自定义色彩映射表

概述我有一个用matplotlib显示的图像.   该图像由以下代码生成： import numpy as npimport matplotlib.pyplot as pltfrom matplotlib import cmlabels = ['Name1', 'Name2', 'Name3', 'Name4', 'Name5', 'Name6']data = np.array( [[ 我有一个用matplotlib显示的图像.

 

该图像由以下代码生成：

import numpy as npimport matplotlib.pyplot as pltfrom matplotlib import cmlabels = ['name1','name2','name3','name4','name5','name6']data = np.array( [[ 0.000,0.120,0.043,0.094,0.037,0.045],[ 0.120,0.000,0.108,0.107,0.105,0.108],[ 0.043,0.083,0.042],[ 0.094,0.089],[ 0.037,2.440],[ 0.045,0.042,0.089,2.440,0.000]])mask =  np.tri(data.shape[0],k=-1)data = np.ma.array(data,mask=mask) # Mask out the lower triangle of data.fig,ax = plt.subplots(sharex=True)im = ax.pcolor(data,edgecolors='black',linewidths=0.3)# Formatfig = plt.gcf()fig.set_size_inches(10,10)ax.set_yticks(np.arange(data.shape[0]) + 0.5,minor=False)ax.set_xticks(np.arange(data.shape[1]) + 0.5,minor=False)# Turn off the frame.ax.set_frame_on(False)ax.set_aspect('equal')  # Ensure heatmap cells are square.# Want a more natural,table-like display.ax.invert_yaxis()ax.yaxis.tick_right()ax.xaxis.tick_top()ax.set_xticklabels(labels,minor=False)ax.set_yticklabels(labels,minor=False)# Rotate the upper labels.plt.xticks(rotation=90)ax.grID(False)ax = plt.gca()for t in ax.xaxis.get_major_ticks():    t.tick1On = False    t.tick2On = Falsefor t in ax.yaxis.get_major_ticks():    t.tick1On = False    t.tick2On = Falsefig.colorbar(im)fig.savefig('out.png',transparent=False,bBox_inches='tight',pad_inches=0)

我想应用自定义色图,以便值：

> 0-1之间是蓝色和白色的线性渐变
> 1-3之间
白色和红色的线性渐变.

任何帮助将不胜感激.

解决方法 这样做的方法不止一种.在您的情况下,最简单的方法是使用linearSegmentedcolormap.from_List并指定颜色的相对位置以及颜色名称. (如果你有均匀间隔的变化,你可以跳过元组,只做from_List(‘我的cmap’,[‘blue’,’white’,’red’]).)然后你需要指定一个手动min和最大数据(vmin和vmax kwargs到imshow / pcolor / etc).

举个例子：

import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.colors import linearSegmentedcolormapdata = np.array(             [[ 0.000,0.000]])mask = np.tri(data.shape[0],k=-1)data = np.ma.masked_where(mask,data)vmax = 3.0cmap = linearSegmentedcolormap.from_List('mycmap',[(0 / vmax,'blue'),(1 / vmax,'white'),(3 / vmax,'red')]                                        )fig,ax = plt.subplots()im = ax.pcolor(data,cmap=cmap,vmin=0,vmax=vmax,edgecolors='black')cbar = fig.colorbar(im)cbar.set_ticks(range(4)) # Integer colorbar tick locationsax.set(frame_on=False,aspect=1,xticks=[],yticks=[])ax.invert_yaxis()plt.show()

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