matplotlib中的色条colormap 及色带colorbar

谁践踏了优雅 2023-07-02 05:21 13阅读 0赞

# colormap #

关键只有三个语句，

*  初始化

jet = cm = plt.get_cmap('Reds')
    cNorm = colors.Normalize(vmin=0, vmax=5)
    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)

将色条设置范围`vmin - vmax`, 并设置色条系列,也可以使用`Reds,Blues`更多参考以下链接及图片  
其中 colormap 可参考链接 https://www.matplotlib.org.cn/tutorials/colors/colormaps.html

*  使用：

colorVal = scalarMap.to_rgba(i)

# color bar #

参考链接： https://matplotlib.org/2.0.2/examples/api/colorbar\_only.html

*  新建轴域 `ax = fig.add_axes([left, bottom, width, height])`
 *  明确 `cmap, norm`
 *  新建colorbar

`cb = matplotlib.colorbar.ColorbarBase(ax, cmap=None, norm=None, alpha=None, values=None, boundaries=None, orientation=‘vertical’, ticklocation=‘auto’, extend=‘neither’, spacing=‘uniform’, ticks=None, format=None, drawedges=False, filled=True, extendfrac=None, extendrect=False, label=’’)`

%matplotlib notebook
    import matplotlib
    from matplotlib import pyplot as plt
    
    #%matplotlib tk
    from matplotlib import rcdefaults
    rcdefaults()
    import numpy as np
    import pandas as pd

jet = cm = plt.get_cmap('Reds') 
    #这里可以选择色条方案
    #e.g. Greys, 'Blues'
    cNorm = matplotlib.colors.Normalize(vmin=0, vmax=5)
    scalarMap = matplotlib.cm.ScalarMappable(norm=cNorm, cmap=jet)
    print(scalarMap.get_clim()) 
    
    fig = plt.figure(figsize=(8, 3))
    #ax = fig.add_subplot(111)
    # 这里为设置并排效果 不采用add_subplot, 而是 add_axes, 其中关于 这二者的区别详见 matplotlib基础教程
    ax = fig.add_axes([0.0, 0.0, 0.60, 0.8])
    #------绘图---------------------
    x = np.arange(10, 15, 1)
    y = np.arange(10, 15, 1)
    
    for i in range(5):
        colorVal = scalarMap.to_rgba(i)
        print('value',i,'color value', colorVal)
        ax.plot(x, y + i, c = colorVal )  
        
    #----设置color bar----------
    ax_bar = fig.add_axes([0.6, 0.0, 0.05, 0.8])
    bounds = [0,1,2,3,4,5]
    # to use 'extend', you must specify two extra boundaries: boundaries=[-1] + bounds + [6]
    # extend = 'both',
    cb1 = matplotlib.colorbar.ColorbarBase(ax_bar, cmap=jet,
                                    norm=cNorm,
                                    orientation='vertical',
                                    boundaries=[-1] + bounds + [6],
                                    extend = 'both',
                                    ticks = bounds,
                                    spacing = 'uniform')
    cb1.set_label('Color bar',fontsize = 8)
    
    plt.show()

(0.0, 5.0)
    value 0 color value (1.0, 0.9607843137254902, 0.9411764705882353, 1.0)
    value 1 color value (0.9913725490196079, 0.7913725490196079, 0.7082352941176471, 1.0)
    value 2 color value (0.9874509803921568, 0.5411764705882353, 0.41568627450980394, 1.0)
    value 3 color value (0.9466666666666667, 0.26823529411764707, 0.19607843137254902, 1.0)
    value 4 color value (0.7364705882352941, 0.08, 0.10117647058823528, 1.0)
    
    
    C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\pyplot.py:522: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
      max_open_warning, RuntimeWarning)
    
    
    
    <IPython.core.display.Javascript object>

![Image 1][]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODEwMjkxMg_size_16_color_FFFFFF_t_70]

[Image 1]: 
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODEwMjkxMg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200126163807434.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODEwMjkxMg==,size_16,color_FFFFFF,t_70