在matplotlib轴上设置科学限制后调整指数文本

时间：2015-07-20 12:56:52

标签： python matplotlib

目前，如果我将matplotlib y轴刻度标签设置为科学模式，它会在1e-5

形式的y轴顶部给出一个指数

我想将其调整为阅读r'$\mathregular{10^{-5}}$'，以便打印出来。

这是我的示例代码：

# Create a figure and axis
fig, ax = plt.subplots()

# Plot 100 random points 
# the y values of which are very small
ax.scatter(np.random.rand(100), np.random.rand(100)/100000.0)

# Set the y limits appropriately
ax.set_ylim(0, 1/100000.0)

# Change the y ticklabel format to scientific format
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))

# Get the offset value
offset = ax.yaxis.get_offset_text()

# Print it out
print '1st offset printout: {}'.format(offset)

# Run plt.tight_layout()
plt.tight_layout()

# Print out offset again - you can see the value now!
print '2nd offset printout: {}'.format(offset)

# Change it to latex format
offset.set_text(r'$\mathregular{10^{-5}}$')

# Print it out
print '3rd offset printout: {}'.format(offset)

# Add some text to the middle of the figure just to 
# check that it isn't the latex format that's the problem
ax.text(0.5, 0.5/100000.0, r'$\mathregular{10^{-2}}$')

# And show the figure
plt.show()

我的输出如下：

1st offset printout: Text(0,0.5,u'')
2nd offset printout: Text(0,636.933,u'1e\u22125')
3rd offset printout: Text(0,636.933,u'$\\mathregular{10^{-5}}$')

enter image description here

您可以找到代码和输出图here。

有两个奇怪的地方：一个是我不能覆盖y轴顶部的1e-5（这是目标），另一个是我必须按顺序运行plt.tight_layout()甚至将unicode值视为偏移量。

谁能告诉我哪里出错了？

谢谢

编辑：原始问题没有明确表示我想自动检测当前由ticklabel_format计算的指数。因此，不应将设置字符串传递给偏移文本，而应自动检测该值并相应地调整乳胶字符串。

5 个答案:

答案 0 :(得分：3)

在@ edsmith的基础上回答一个可能的工作，我喜欢的是获取偏移文本，将其转换为乳胶字符串，关闭偏移量并在该字符串中添加轴顶部。

def format_exponent(ax, axis='y'):

    # Change the ticklabel format to scientific format
    ax.ticklabel_format(axis=axis, style='sci', scilimits=(-2, 2))

    # Get the appropriate axis
    if axis == 'y':
        ax_axis = ax.yaxis
        x_pos = 0.0
        y_pos = 1.0
        horizontalalignment='left'
        verticalalignment='bottom'
    else:
        ax_axis = ax.xaxis
        x_pos = 1.0
        y_pos = -0.05
        horizontalalignment='right'
        verticalalignment='top'

    # Run plt.tight_layout() because otherwise the offset text doesn't update
    plt.tight_layout()
    ##### THIS IS A BUG 
    ##### Well, at least it's sub-optimal because you might not
    ##### want to use tight_layout(). If anyone has a better way of 
    ##### ensuring the offset text is updated appropriately
    ##### please comment!

    # Get the offset value
    offset = ax_axis.get_offset_text().get_text()

    if len(offset) > 0:
        # Get that exponent value and change it into latex format
        minus_sign = u'\u2212'
        expo = np.float(offset.replace(minus_sign, '-').split('e')[-1])
        offset_text = r'x$\mathregular{10^{%d}}$' %expo

        # Turn off the offset text that's calculated automatically
        ax_axis.offsetText.set_visible(False)

        # Add in a text box at the top of the y axis
        ax.text(x_pos, y_pos, offset_text, transform=ax.transAxes,
               horizontalalignment=horizontalalignment,
               verticalalignment=verticalalignment)
    return ax

请注意，您应该能够通过调用pos = ax_axis.get_offset_text().get_position()来使用偏移文本的位置，但这些值不是以轴为单位的（它们可能是像素单位 - 感谢@EdSmith - 因此不是很有用）。因此，我只是根据我们正在查看的轴设置x_pos和y_pos值。

我还写了一个小函数来自动检测适当的x和y限制（尽管我知道matplotlib有很多花哨的方法）。

def get_min_max(x, pad=0.05):
    '''
    Find min and max values such that
    all the data lies within 90% of
    of the axis range
    '''
    r = np.max(x) - np.min(x)
    x_min = np.min(x) - pad * r
    x_max = np.max(x) + pad * r
    return x_min, x_max

所以，要从问题中更新我的例子（略微改变以使两个轴都需要指数）：

import matplotlib.pylab as plt
import numpy as np

# Create a figure and axis
fig, ax = plt.subplots()

# Plot 100 random points that are very small
x = np.random.rand(100)/100000.0
y = np.random.rand(100)/100000.0
ax.scatter(x, y)

# Set the x and y limits
x_min, x_max = get_min_max(x)
ax.set_xlim(x_min, x_max)
y_min, y_max = get_min_max(y)    
ax.set_ylim(y_min, y_max)

# Format the exponents nicely
ax = format_exponent(ax, axis='x')
ax = format_exponent(ax, axis='y')

# And show the figure
plt.show()

enter image description here

有一个显示代码输出的ipython笔记本的要点here。

我希望有所帮助！

答案 1 :(得分：2)

你得到offset并设置文本值，但似乎没有办法将其实际应用到轴...即使调用ax.yaxis.offsetText.set_text(offset)也不会更新显示的偏移量。围绕它去除偏移文本并用轴标签上的括号替换

ax.yaxis.offsetText.set_visible(False)
ax.set_ylabel("datalabel " +  r'$\left(\mathregular{10^{-5}}\right)$')

或者用手动文本框替换它，作为一个最小的例子，

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

# Create a figure and axis
fig, ax = plt.subplots()
mpl.rc('text', usetex = True)

# Plot 100 random points 
# the y values of which are very small
large = 100000.0
x = np.random.rand(100)
y = np.random.rand(100)/large

ax.scatter(x,y)

# Set the y limits appropriately
ax.set_ylim(0, 1/large)

# Change the y ticklabel format to scientific format
ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))

#print(ax.yaxis.offsetText.get_position())
ax.yaxis.offsetText.set_visible(False)
ax.text(-0.21, 1.01/large, r'$\mathregular{10^{-2}}$')

# And show the figure
plt.show()

我知道这不太理想，但可能是偏移文字无法手动更改或只能与数值一致...

答案 2 :(得分：2)

似乎plt.ticklabel_format无效。但是，如果您自己定义ScalarFormatter 和将科学记数法的限制设置为格式化程序，则可以使用mathtext格式自动获取偏移量，如下所示：

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker

x = np.linspace(3,5)
y = np.sin(np.linspace(0,6*np.pi))*1e5

plt.plot(x,y)

mf = matplotlib.ticker.ScalarFormatter(useMathText=True)
mf.set_powerlimits((-2,2))
plt.gca().yaxis.set_major_formatter(mf)

plt.show()

enter image description here

答案 3 :(得分：0)

在代码中添加两行

import matplotlib.ticker as ptick
ax.yaxis.set_major_formatter(ptick.ScalarFormatter(useMathText=True))

答案 4 :(得分：0)

在@KirstieJane's solution的基础上，我发现了一种更好的方法，它也不需要调用tight_layout也可以工作，尤其是对于使用constrained_layout的图形也可以工作。设置文本仅需调用ax.get_tightbbox(renderer)。这是MWE：

import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.tight_layout import get_renderer
from matplotlib.backends.backend_qt5agg import \
    FigureCanvasQTAgg as FigureCanvas
# from matplotlib.transforms import Bbox
# from mpl_toolkits.axes_grid1 import make_axes_locatable

from PyQt5.QtWidgets import QDialog, QApplication, QGridLayout


class MainWindow(QDialog):
    def __init__(self):
        super().__init__()
        fig, ax = plt.subplots(constrained_layout=True)
        canvas = FigureCanvas(fig)
        lay = QGridLayout(self)
        lay.addWidget(canvas)
        self.setLayout(lay)

        image = np.random.uniform(10000000, 100000000, (100, 100))
        image_artist = ax.imshow(image)
        colorbar = fig.colorbar(image_artist)
        colorbar.ax.ticklabel_format()
        renderer = get_renderer(fig)
        colorbar.ax.get_tightbbox(renderer)
        colorbar.ax.yaxis.offsetText.set_visible(False)
        offset_text = colorbar.ax.yaxis.get_offset_text()
        exp = offset_text.get_text().split('e')[1].replace('+', '')
        colorbar.ax.set_ylabel(rf'Parameter [U${{\times}}10^{{{exp}}}$]')

        canvas.draw_idle()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    GUI = MainWindow()
    GUI.show()
    sys.exit(app.exec_())

有关该解决方案的详细讨论，请参见我的回答here。

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