07_设置坐标轴刻度、设置刻度标签（Setting Tick Labels）、调整刻度标签 (Adjusting the ticklabels)

傷城~ 2022-11-17 03:56 251阅读 0赞

7.设置刻度  
7.1.设置坐标轴刻度  
7.2.设置刻度标签（Setting Tick Labels）  
7.3.调整刻度标签 (Adjusting the ticklabels)

# 7.设置刻度 #

## 7.1.设置坐标轴刻度 ##

到目前为止，在所有例子中Matplotlib都自动负责了确定轴上间距点的任务。

例如，可以看到前面一个例子中的X轴编号为 -6. -4, -2, 0, 2, 4, 6, 而Y轴编号 -1.0，0, 1.0, 2.0, 3.0

使用xticks或yticks可获取或设置当前的刻度位置和标签。

import numpy as np
    import matplotlib.pyplot as plt
    # get the current axes, creating them if necessary:
    ax = plt.gca()
    locs, labels = plt.xticks()
    print(locs, labels)
    locs, labels = plt.xticks()
    print(locs, labels)
    locs, labels = plt.yticks()
    print(locs, labels)

输出结果：

[0.  0.2 0.4 0.6 0.8 1. ] [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')]
    [0.  0.2 0.4 0.6 0.8 1. ] [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]

plt.xticks(np.arange(10))
    locs, labels = plt.xticks()
    print(locs, labels)
    ''' 输出结果： [0 1 2 3 4 5 6 7 8 9] [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')] '''

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]

现在，设置xticks的位置和标签：

# set the locations and labels of the xticks
    plt.xticks(np.arange(4),('Berlin', 'London', 'Paris', 'Toronto'))
    plt.show()

输出：

([<matplotlib.axis.XTick at 0x17822581a48>,
      <matplotlib.axis.XTick at 0x1782257dec8>,
      <matplotlib.axis.XTick at 0x1782257da88>,
      <matplotlib.axis.XTick at 0x178225a8248>],
     <a list of 4 Text xticklabel objects>)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]

回到之前的三角函数示例。下面使用Pi的因子而非整数值作为X轴的刻度：

import numpy as np
    import matplotlib.pyplot as plt
    X = np.linspace(-2 * np.pi, 2 * np.pi, 70, endpoint=True)
    F1 = np.sin(X**2)
    F2 = X * np.sin(X)
    # get the current axes, creating them if necessary:
    ax = plt.gca()
    # making the top and right spine invisible:
    ax.spines['top'].set_color('none')
    ax.spines['right'].set_color('none')
    # moving bottom spine up to y=0 position:
    ax.xaxis.set_ticks_position('bottom')
    ax.spines['bottom'].set_position(('data',0))
    # moving left spine to the right to position x == 0:
    ax.yaxis.set_ticks_position('left')
    ax.spines['left'].set_position(('data',0))
    plt.xticks( [-6.28, -3.14, 3.14, 6.28])
    plt.yticks([-3, -1, 0, +1, 3])
    plt.plot(X, F1)
    plt.plot(X, F2)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 3]

## 7.2.设置刻度标签（Setting Tick Labels） ##

现在用自定义标记重新定义xticks。 我们将再次使用xticks方法来实现此目的。

但是这次将用两个参数调用xticks:

第一个是之前使用的相同列表，即x轴上的位置，来得到刻度。

第二个参数是具有与之相应LaTex刻度标记的相同大小的列表，即想要用文本而不是值。LaTex表示法必须是一个原始字符串（raw string）来抑制Python的转义机制，因为LaTex表示法大量使用并依赖于反斜杠。

import numpy as np
    import matplotlib.pyplot as plt
    X = np.linspace(-2 * np.pi, 2 * np.pi, 70, endpoint=True)
    F1 = X * np.sin(X)
    ax = plt.gca()
    ax.spines['top'].set_color('none')
    ax.spines['right'].set_color('none')
    ax.xaxis.set_ticks_position('bottom')
    ax.spines['bottom'].set_position(('data',0))
    ax.yaxis.set_ticks_position('left')
    ax.spines['left'].set_position(('data',0))
    # labelling the X ticks:
    plt.xticks( [-6.28, -3.14, 3.14, 6.28],
            [r'$-2\pi$', r'$-\pi$', r'$+\pi$', r'$+2\pi$'])
    plt.yticks([-3, -1, 0, +1, 3])
    plt.plot(X, F1)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 4]

## 7.3.调整刻度标签 (Adjusting the ticklabels) ##

我们希望增加ticklabels的易读性。 我们将增加字体大小，并将在半透明背景上渲染它们。

print(ax.get_xticklabels())
    ''' 输入结果： [Text(-6.28, 0, '$-2\\pi$'), Text(-3.14, 0, '$-\\pi$'), Text(3.14, 0, '$+\\pi$'), Text(6.28, 0, '$+2\\pi$')] '''
    
    for xtick in ax.get_xticklabels():
        print(xtick)
    ''' 输出结果： Text(-6.28, 0, '$-2\\pi$') Text(-3.14, 0, '$-\\pi$') Text(3.14, 0, '$+\\pi$') Text(6.28, 0, '$+2\\pi$') '''
    
    labels = [xtick.get_text() for xtick in ax.get_xticklabels()]
    print(labels)
    ''' 输出结果： ['$-2\\pi$', '$-\\pi$', '$+\\pi$', '$+2\\pi$'] '''

让我们增加字体大小并使字体半透明：

import numpy as np
    import matplotlib.pyplot as plt
    
    X = np.linspace(-2 * np.pi, 2 * np.pi, 170, endpoint=True)
    F1 = np.sin(X ** 3 / 2)
    ax = plt.gca()
    ax.spines['top'].set_color('none')
    ax.spines['right'].set_color('none')
    ax.xaxis.set_ticks_position('bottom')
    ax.spines['bottom'].set_position(('data', 0))
    ax.yaxis.set_ticks_position('left')
    ax.spines['left'].set_position(('data', 0))
    plt.xticks([-6.28, -3.14, 3.14, 6.28],
               [r'$-2\pi$', r'$-\pi$', r'$+\pi$', r'$+2\pi$'])
    plt.yticks([-3, -1, 0, +1, 3])
    for xtick in ax.get_xticklabels():
        # 设置x轴上字体大小
        xtick.set_fontsize(18)
        # 设置x轴标签的颜色
        xtick.set_bbox(dict(facecolor='red', edgecolor='None', alpha=0.7))
    for ytick in ax.get_yticklabels():
        # 设置y轴上字体大小
        ytick.set_fontsize(14)
        # 设置y轴上标签的颜色
        ytick.set_bbox(dict(facecolor='green', edgecolor='red', alpha=0.7))
    
    plt.plot(X, F1, label="$sin(x)$")
    plt.legend(loc='lower left')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 5]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70]: /images/20221022/035843dc560d4d01b6294106cef6c18b.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 1]: /images/20221022/903e188d37744282bd01c3aec8ee94e6.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 2]: /images/20221022/8eab5c247d6f4c0a9e82dacfca367e50.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 3]: /images/20221022/704428c598a84ea288256830fe7f1969.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 4]: /images/20221022/70bdabce0acd44448acbbf782b5ea996.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70 5]: /images/20221022/8aff002d3acb4e768ed16b5e139ca2cc.png