如何使matplotlib中的tight_layout（）用于插入绘图？

Question

我在用matplotlib.pyplot用几个小插画出一个情节。这是我最终想要的：一个2x2数组的“主要”情节。每个曲线图中都有两条曲线，每个曲线使用不同的y轴。另外，我想在每一个小插画。在

到目前为止，我已经得到了第一部分，使用以下示例代码：

import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import time
import sys

#Simplest working example of tight_layout and plots problem

def two_scales(ax1, time, data1, data2, c1, c2, xlabel, y1label, y2label):

    ax2 = ax1.twinx()

    ax1.plot(time, data1, color=c1)
    ax1.set_xlabel(xlabel)
    ax1.set_ylabel(y1label)

    ax2.plot(time, data2, color=c2)
    ax2.set_ylabel(y2label)
    return ax1, ax2

# Change color of each axis
def color_y_axis(ax, color):
    """Color your axes."""
    for t in ax.get_yticklabels():
        t.set_color(color)
    return None

def insetPlots():

    t = np.arange(0.01, 10.0, 0.01)


    #Figure stuff
    fig, baseAxes = plt.subplots(2,2,figsize=(10, 6))
    baseAxesFlattened = baseAxes.flatten()


    for i, dat in enumerate(baseAxesFlattened):

        s1 = np.exp((i+1)*t)
        s2 = .3*np.sin((i+1)*.2 * np.pi * t)

        #Plotting them together
        tempAx1, tempAx2 = two_scales(baseAxesFlattened[i], t, s1, s2, 'b', 'r','heyheyhey','yayaya','woopwoop')

        #Changing the color of the axes
        color_y_axis(tempAx1, 'b')
        color_y_axis(tempAx2, 'r')


    plt.tight_layout()
    #plt.figure(figsize=(6, 8))
    picname="/mypath/testtesttest.png"
    plt.savefig(picname)

insetPlots()

这就产生了，目前为止还不错：

现在我要添加插图。我可以很容易地做到：

^{pr2}$

请注意，这里我注释掉了tight_layout（）。这会产生这样的结果，在我想要的位置有插入图：

因此，这使插入图处于正确的位置，但由于tight_layout（）已不存在，主绘图的轴标签是重叠的。如果我有tight_layout（）（因此，与上面的代码完全相同，但该行未加注释），我得到：

主要情节的轴不再重叠，但插图现在处于错误的位置。我运行代码时也会收到以下警告：

/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:2022: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  warnings.warn("This figure includes Axes that are not compatible "

我怎样才能使它们都工作呢？我怀疑我做了一些简单的错误，比如把插图放错了地方。在

编辑：我已经找到了一个解决办法，但这很难看，我希望不是“恰当”的方法。我怀疑tight_layout（）正在移动对象，因此在tight_layout（）之后，插入图的位置（取决于主要图块的位置）相对于主要图块的位置变得混乱。所以我解决了这个问题，绘制了主要的图，做了紧凑的布局，然后添加了插入的：

import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import time
import sys

#Simplest working example of tight_layout and plots problem

def two_scales(ax1, time, data1, data2, c1, c2, xlabel, y1label, y2label):

    ax2 = ax1.twinx()

    ax1.plot(time, data1, color=c1)
    ax1.set_xlabel(xlabel)
    ax1.set_ylabel(y1label)

    ax2.plot(time, data2, color=c2)
    ax2.set_ylabel(y2label)
    return ax1, ax2

# Change color of each axis
def color_y_axis(ax, color):
    """Color your axes."""
    for t in ax.get_yticklabels():
        t.set_color(color)
    return None

def insetPlots():

    t = np.arange(0.01, 10.0, 0.01)


    #Figure stuff
    fig, baseAxes = plt.subplots(2,2,figsize=(10, 6))
    baseAxesFlattened = baseAxes.flatten()

    majorAxes = []

    for i, dat in enumerate(baseAxesFlattened):

        s1 = np.exp((i+1)*t)
        s2 = .3*np.sin((i+1)*.2 * np.pi * t)

        #Plotting them together
        tempAx1, tempAx2 = two_scales(baseAxesFlattened[i], t, s1, s2, 'b', 'r','heyheyhey','yayaya','woopwoop')

        majorAxes.append(tempAx1)

        #Changing the color of the axes
        color_y_axis(tempAx1, 'b')
        color_y_axis(tempAx2, 'r')


    plt.tight_layout()

    for i, dat in enumerate(baseAxesFlattened):

        tempAx1 = majorAxes[i]
        pos = tempAx1.get_position()
        #print(pos)
        posString = str(pos)
        x0Ind, y0Ind, x1Ind, y1Ind = posString.find('x0'),posString.find('y0'),posString.find('x1'),posString.find('y1')
        #print(x0Ind, y0Ind, x1Ind, y1Ind)
        x0, y0, x1, y1 = float(posString[x0Ind+3:y0Ind-2]), float(posString[y0Ind+3:x1Ind-2]), float(posString[x1Ind+3:y1Ind-2]), float(posString[y1Ind+3:-1])
        #print(x0, y0, x1, y1)

        mainPlotW = x1 - x0
        mainPlotH = y1 - y0

        w, h = 0.3*mainPlotW, 0.25*mainPlotH
        left, bottom, width, height = [x0 + .15*mainPlotW, y0 + .7*mainPlotH, w, h]
        insetAx = fig.add_axes([left, bottom, width, height])


        #insetAx.plot(range(6)[::-1], color='green')
        s3 = np.sin(.2 * np.pi * t/(i+1))
        insetAx.plot(t,s3, color='green')


    #plt.tight_layout()
    #plt.figure(figsize=(6, 8))
    picname="/mypath/testtesttest.png"
    plt.savefig(picname)

insetPlots()

有没有更干净的方法？在

Answer 1

我建议使用^{}来定位插入。这简化了很多事情，不需要用任何东西来增加绘图大小。在

然后，您可以选择在创建inset之前或之后调用fig.tight_layout()，生成的绘图不会改变（尽管在之后调用它会发出警告，在这种情况下可以忽略）。在

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
import numpy as np

def two_scales(ax1, time, data1, data2, c1, c2, xlabel, y1label, y2label):

    ax2 = ax1.twinx()

    ax1.plot(time, data1, color=c1)
    ax1.set_xlabel(xlabel)
    ax1.set_ylabel(y1label)

    ax2.plot(time, data2, color=c2)
    ax2.set_ylabel(y2label)
    return ax1, ax2

# Change color of each axis
def color_y_axis(ax, color):
    """Color your axes."""
    for t in ax.get_yticklabels():
        t.set_color(color)
    return None

def insetPlots():

    t = np.arange(0.01, 10.0, 0.01)
    #Figure stuff
    fig, baseAxes = plt.subplots(2,2,figsize=(10, 6))
    baseAxesFlattened = baseAxes.flatten()

    for i, ax in enumerate(baseAxesFlattened):

        s1 = np.exp((i+1)*t)
        s2 = .3*np.sin((i+1)*.2 * np.pi * t)
        #Plotting them together
        tempAx1, tempAx2 = two_scales(ax, t, s1, s2, 'b', 'r',
                                      'heyheyhey','yayaya','woopwoop')
        #Changing the color of the axes
        color_y_axis(tempAx1, 'b')
        color_y_axis(tempAx2, 'r')

    fig.tight_layout()

    for i, ax in enumerate(baseAxesFlattened):

        insetAx = fig.add_axes([0, 0, 1, 1], label="{}".format(i))
        ip = InsetPosition(ax, [.15, 0.7, 0.3, 0.25]) #posx, posy, width, height
        insetAx.set_axes_locator(ip)

        insetAx.plot(range(6)[::-1], color='green')
        s3 = np.sin(.2 * np.pi * t/(i+1))
        insetAx.plot(t,s3, color='green')

    # putting tight_layout here will produce a warning, 
    # yet the resulting plot is the same
    # fig.tight_layout() 


insetPlots()
plt.show()