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    Python 作图实现坐标轴截断(打断)的效果

    主题:利用python画图实现坐标轴截断或打断

    关键词:python, plot, matplotlib, break axes

    方法一:

    首先介绍一种简单快速的方法——调用包 brokenaxes。

    详细请点击参考

    import matplotlib.pyplot as plt
    from brokenaxes import brokenaxes
    import numpy as np
    fig = plt.figure(figsize=(5,2))
    bax = brokenaxes(xlims=((0, .1), (.4, .7)), ylims=((-1, .7), (.79, 1)), hspace=.05, despine=False)
    x = np.linspace(0, 1, 100)
    bax.plot(x, np.sin(10 * x), label='sin')
    bax.plot(x, np.cos(10 * x), label='cos')
    bax.legend(loc=3)
    bax.set_xlabel('time')
    bax.set_ylabel('value')
    

    效果如下:

    方法二:

    拼接法,该种方法代码更繁琐,但更有可能满足个性化的需求。

    请点击参考链接

    """
    Broken axis example, where the y-axis will have a portion cut out.
    """
    import matplotlib.pyplot as plt
    import numpy as np
    # 30 points between [0, 0.2) originally made using np.random.rand(30)*.2
    pts = np.array([
        0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195, 0.039, 0.161, 0.018,
        0.143, 0.056, 0.125, 0.096, 0.094, 0.051, 0.043, 0.021, 0.138, 0.075,
        0.109, 0.195, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 0.061, 0.008])
    # Now let's make two outlier points which are far away from everything.
    pts[[3, 14]] += .8
    # If we were to simply plot pts, we'd lose most of the interesting
    # details due to the outliers. So let's 'break' or 'cut-out' the y-axis
    # into two portions - use the top (ax) for the outliers, and the bottom
    # (ax2) for the details of the majority of our data
    f, (ax, ax2) = plt.subplots(2, 1, sharex=True)
    # plot the same data on both axes
    ax.plot(pts)
    ax2.plot(pts)
    # zoom-in / limit the view to different portions of the data
    ax.set_ylim(.78, 1.)  # outliers only
    ax2.set_ylim(0, .22)  # most of the data
    # hide the spines between ax and ax2
    ax.spines['bottom'].set_visible(False)
    ax2.spines['top'].set_visible(False)
    ax.xaxis.tick_top()
    ax.tick_params(labeltop='off')  # don't put tick labels at the top
    ax2.xaxis.tick_bottom()
    # This looks pretty good, and was fairly painless, but you can get that
    # cut-out diagonal lines look with just a bit more work. The important
    # thing to know here is that in axes coordinates, which are always
    # between 0-1, spine endpoints are at these locations (0,0), (0,1),
    # (1,0), and (1,1).  Thus, we just need to put the diagonals in the
    # appropriate corners of each of our axes, and so long as we use the
    # right transform and disable clipping.
    d = .015  # how big to make the diagonal lines in axes coordinates
    # arguments to pass to plot, just so we don't keep repeating them
    kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
    ax.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal
    ax.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal
    kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes
    ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal
    ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal
    # What's cool about this is that now if we vary the distance between
    # ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
    # the diagonal lines will move accordingly, and stay right at the tips
    # of the spines they are 'breaking'
    plt.show()
    

    效果如下:

    补充:python绘制折线图--纵坐标y轴截断

    看代码吧~

    # -*- coding: utf-8 -*-
    """
    Created on Wed Dec  4 21:50:38 2019
    @author: muli
    """
    import matplotlib.pyplot as plt
    from pylab import *                 
    mpl.rcParams['font.sans-serif'] = ['SimHei'] #支持中文
     
    names = ["1","2","3","4","5"]  # 刻度值命名
    x = [1,2,3,4,5]    # 横坐标
    y3= [2,3,1,4,5]    # 纵坐标
    y4= [4,6,8,5,9]    # 纵坐标
    y5=[24,27,22,26,28]     # 纵坐标
    f, (ax3, ax) = plt.subplots(2, 1, sharex=False)  # 绘制两个子图
    plt.subplots_adjust(wspace=0,hspace=0.08) # 设置 子图间距
    ax.plot(x, y3, color='red', marker='o', linestyle='solid',label=u'1')   # 绘制折线
    ax.plot(x, y4, color='g', marker='o', linestyle='solid',label=u'2')  # 绘制折线
    plt.xticks(x, names, rotation=45) # 刻度值
    ax3.xaxis.set_major_locator(plt.NullLocator()) # 删除坐标轴的刻度显示
    ax3.plot(x, y5, color='blue', marker='o', linestyle='solid',label=u'3')  # 绘制折线
    ax3.plot(x, y3, color='red', marker='o', linestyle='solid',label=u'1') # 起图例作用
    ax3.plot(x, y4, color='g', marker='o', linestyle='solid',label=u'2') # 起图例作用
    ax3.set_ylim(21, 30) # 设置纵坐标范围
    ax.set_ylim(0, 10)  # 设置纵坐标范围
    ax3.grid(axis='both',linestyle='-.') # 打开网格线
    ax.grid(axis='y',linestyle='-.')   # 打开网格线
    ax3.legend() # 让图例生效
    plt.xlabel(u"λ") #X轴标签
    plt.ylabel("mAP") #Y轴标签
    ax.spines['top'].set_visible(False)    # 边框控制
    ax.spines['bottom'].set_visible(True) # 边框控制
    ax.spines['right'].set_visible(False)  # 边框控制
    ax3.spines['top'].set_visible(False)   # 边框控制
    ax3.spines['bottom'].set_visible(False) # 边框控制
    ax3.spines['right'].set_visible(False)  # 边框控制
    ax.tick_params(labeltop='off')  
    # 绘制断层线
    d = 0.01  # 断层线的大小
    kwargs = dict(transform=ax3.transAxes, color='k', clip_on=False)
    ax3.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal
    kwargs.update(transform=ax.transAxes, color='k')  # switch to the bottom axes
    ax.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal
    plt.show()
    

    结果如图所示:

    以上为个人经验,希望能给大家一个参考,也希望大家多多支持脚本之家。如有错误或未考虑完全的地方,望不吝赐教。

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