matplotlib:用于多个箱线图的inset轴

I have a few boxplots in matplotlib that I want to zoom in on a particular y-range ([0,0.1]) using inset axes. It is not clear to me from the example in the documentation how I should do this for multiple boxplots on the same figure. I was trying to modify the code provided this example, but there was too much unnecessary complexity. My code is pretty simple:

我在matplotlib中有一些箱线图，我想使用inset坐标轴放大特定的y范围([0,0.1])。从文档中的示例来看，我不清楚如何对同一图上的多个箱线图进行此操作。我试图修改这个示例提供的代码，但是有太多不必要的复杂性。我的代码非常简单:

# dataToPlot is a list of lists, containing some data. 
plt.figure()
plt.boxplot(dataToPlot)
plt.savefig( 'image.jpeg', bbox_inches=0)

How do I add inset axes and zoom in on the first boxplot of the two? How can I do it for both?

如何添加内嵌坐标轴并放大它们的第一个箱线图?我怎么能做到这两点呢?

EDIT: I tried the code below, but here's what I got:

编辑:我试过下面的代码，但我得到的是:

What went wrong?

到底是哪里出了错?

# what's the meaning of these two parameters?
fig = plt.figure(1, [5,4])
# what does 111 mean?
ax = fig.add_subplot(111)
ax.boxplot(data)
# ax.set_xlim(0,21)  # done automatically based on the no. of samples, right?
# ax.set_ylim(0,300) # done automatically based on max value in my samples, right?
# Create the zoomed axes
axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6, location = 1 (upper right)
axins.boxplot(data)
# sub region of the original image
#here I am selecting the first boxplot by choosing appropriate values for x1 and x2 
# on the y-axis, I'm selecting the range which I want to zoom in, right?
x1, x2, y1, y2 = 0.9, 1.1, 0.0, 0.01
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
# even though it's false, I still see all numbers on both axes, how do I remove them?
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
# what are fc and ec here? where do loc1 and loc2 come from?
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.savefig( 'img.jpeg', bbox_inches=0)

1 个解决方案

#1

The loc determines the location of the zoomed axis, 1 for upper right, 2 for upper left and so on. I modified the example code slightly to generate multiple zoomed axis.

loc决定了缩放轴的位置，1表示右上，2表示左上等等。我稍微修改了示例代码以生成多个缩放轴。

import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset

import numpy as np

def get_demo_image():
    from matplotlib.cbook import get_sample_data
    import numpy as np
    f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
    z = np.load(f)
    # z is a numpy array of 15x15
    return z, (-3,4,-4,3)


fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)

# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z

# extent = [-3, 4, -4, 3]
ax.imshow(Z2, extent=extent, interpolation="nearest",
          origin="lower")

axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6
axins.imshow(Z2, extent=extent, interpolation="nearest",
             origin="lower")

# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)

axins1 = zoomed_inset_axes(ax, 8, loc=2) # zoom = 6
axins1.imshow(Z2, extent=extent, interpolation="nearest",
             origin="lower")

# sub region of the original image
x1, x2, y1, y2 = -1.2, -0.9, -2.2, -1.9
axins1.set_xlim(x1, x2)
axins1.set_ylim(y1, y2)

plt.xticks(visible=False)
plt.yticks(visible=False)

# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
mark_inset(ax, axins1, loc1=2, loc2=4, fc="none", ec="0.5")

plt.draw()
plt.show()

Edit1:

Edit1:

Similarly, you can also add zoomed axis in boxplot. Here is an example

同样，也可以在boxplot中添加缩放轴。这是一个例子

from pylab import *
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset

# fake up some data
spread= rand(50) * 100 
center = ones(25) * 50
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
data =concatenate((spread, center, flier_high, flier_low), 0)

# fake up some more data
spread= rand(50) * 100
center = ones(25) * 40
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
d2 = concatenate( (spread, center, flier_high, flier_low), 0 )
data.shape = (-1, 1)
d2.shape = (-1, 1)
data = [data, d2, d2[::2,0]]

# multiple box plots on one figure
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
ax.boxplot(data)
ax.set_xlim(0.5,5)
ax.set_ylim(0,300)

# Create the zoomed axes
axins = zoomed_inset_axes(ax, 3, loc=1) # zoom = 3, location = 1 (upper right)
axins.boxplot(data)

# sub region of the original image
x1, x2, y1, y2 = 0.9, 1.1, 125, 175
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)

# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")

show() 

Edit2

Edit2

In case the distribution is heterogeneous, i.e., most values are small with few very large values, the above zooming procedure might not work, as it will zoom both the x as well as y axis. In that case it is better to change the scale of y-axis to log.

如果分布是异构的，即。大多数值都很小，且值非常大，上面的缩放过程可能无法工作，因为它将缩放x和y轴。在这种情况下，最好将y轴的刻度改为log。

from pylab import *

# fake up some data
spread= rand(50) * 1
center = ones(25) * .5
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
data =concatenate((spread, center, flier_high, flier_low), 0)

# fake up some more data
spread= rand(50) * 1
center = ones(25) * .4
flier_high = rand(10) * 100 + 100
flier_low = rand(10) * -100
d2 = concatenate( (spread, center, flier_high, flier_low), 0 )
data.shape = (-1, 1)
d2.shape = (-1, 1)
data = [data, d2, d2[::2,0]]

# multiple box plots on one figure
fig = plt.figure(1, [5,4]) # Figure Size
ax = fig.add_subplot(111)  # Only 1 subplot 
ax.boxplot(data)
ax.set_xlim(0.5,5)
ax.set_ylim(.1,300)
ax.set_yscale('log')

show()