matplotlib中的自定义颜色图 [英] Custom colourmaps in matplotlib

问题描述

我正在尝试在 matplotlib 中制作我自己的颜色图，但我似乎无法让它正常工作，它输出的颜色不是我期望的颜色我已经尝试过其他类似问题的答案，但是无济于事，我似乎无法正确设置我的cdict元组.

我有一个强度从0到1的图表.我希望零强度的RBG为(0,99,136)，0.5强度的RBG为(159,161,97)，而1强度的RBG为(170，43,74)

这是我目前正在尝试的，但我误解了如何使用它.

从matplotlib.colors中的

 导入LinearSegmentedColormapcdict1 = {'blue':((0.0，0.0，0.0)，(0.5, 159/255, 159/255),(1.0, 170/255, 170/255)),'绿色': ((0.0, 99/255, 99/255),(0.5，161/255，161/255)，(1.0, 43/255, 43/255)),'红色':((0.0，136/255，136/255)，(0.5, 97/255, 97/255),(1.0, 74/255, 74/255))}cm = LinearSegmentedColormap('cm', cdict1)

我正在实施它

  plt.subplot(1、2、1)matplotlib.rcParams.update({'font.size':24})plt.imshow(abs(store(0))** 2，插值='无'，范围= [-MaxMin，MaxMin，0，N * dt]，长宽比= 7，cmap = cm，vmin = 0，vmax =1)plt.xlabel(r'$x$')plt.ylabel(r'$ t $')'cbar = plt.colorbar()'plt.subplot(1, 2, 2)plt.imshow(abs(store(4 * x * x))** 2，插值='none'，范围= [-MaxMin，MaxMin，0，N * dt]，长宽比= 8，cmap = cm，vmin =0, vmax=1)plt.xlabel(r'$x$')plt.ylabel(r'$ t $')cbar = plt.colorbar()cbar.set_label(r'$|\psi(x,t)|^{2}$', fontsize=24)plt.show()

这是我目前得到的输出

I'm trying to make my own colourmap in matplotlib but I can't seem to get it to work correctly, the colours it is outputting are not the ones I was expecting I've tried the other similar question's answers but to no avail, I can't seem to set my cdict tuples right.

I have a graph that has intensity going from 0 to 1. I want the zero intensity to have an RBG of (0,99,136), 0.5 intensity to have (159,161,97) and the 1 intensity to have (170,43,74)

This is what I am currently trying but I thing I have misunderstood how to use it.

from matplotlib.colors import LinearSegmentedColormap

cdict1 = {'blue':   ((0.0, 0.0, 0.0),
                    (0.5, 159/255, 159/255),
                    (1.0, 170/255, 170/255)),

         'green': ((0.0, 99/255, 99/255),
                   (0.5, 161/255, 161/255),
                   (1.0, 43/255, 43/255)),

         'red':  ((0.0, 136/255, 136/255),
                   (0.5, 97/255, 97/255),
                   (1.0, 74/255, 74/255))
         }

cm = LinearSegmentedColormap('cm', cdict1)

I'm implementing it in as so

plt.subplot(1, 2, 1)
matplotlib.rcParams.update({'font.size': 24})
plt.imshow(abs(store(0))**2, interpolation='none', extent=[-MaxMin, MaxMin, 0, N*dt], aspect=7, cmap=cm, vmin=0, vmax=1)
plt.xlabel(r'$x$')
plt.ylabel(r'$t$')
'cbar = plt.colorbar()'
plt.subplot(1, 2, 2)
plt.imshow(abs(store(4*x*x))**2, interpolation='none', extent=[-MaxMin, MaxMin, 0, N*dt], aspect=8, cmap=cm, vmin=0, vmax=1)
plt.xlabel(r'$x$')
plt.ylabel(r'$t$')
cbar = plt.colorbar()
cbar.set_label(r'$|\psi(x,t)|^{2}$', fontsize=24)
plt.show()

This is the output I currently get

Colourmap I get currently

And this is what I want to achieve Colourmap I want

解决方案

I think what you want can be achieved much easier than using this complicated dictionary.

Just create a 2D array which has in the first row the RGB values from the first color, in the second those from the middle color, and in the last row the values from the final color.
Then use the matplotlib.colors.LinearSegmentedColormap.from_list function to obtain a colormap.

import numpy as np
import matplotlib.colors

colors = np.array([(0,99,136), (159,161,97), (170,43,74)])/255.
cm = matplotlib.colors.LinearSegmentedColormap.from_list('cm', colors)

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