python - 在 matplotlib 中的正方形绘图区域上绘制对数线性图

Question

我想在 matplotlib 的方形绘图区域上绘制一个具有对数 y 轴和线性 x 轴的图。我可以在正方形上绘制线性-线性图和对数-对数图，但我使用的方法 Axes.set_aspect(...) 并未针对对数-线性图实现。有好的解决方法吗？

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正方形上的线性线性图:

from pylab import *
x = linspace(1,10,1000)
y = sin(x)**2+0.5
plot (x,y)
ax = gca()
data_aspect = ax.get_data_ratio()
ax.set_aspect(1./data_aspect)
show()

---

正方形上的对数对数图:

from pylab import *
x = linspace(1,10,1000)
y = sin(x)**2+0.5
plot (x,y)
ax = gca()
ax.set_yscale("log")
ax.set_xscale("log")
xmin,xmax = ax.get_xbound()
ymin,ymax = ax.get_ybound()
data_aspect = (log(ymax)-log(ymin))/(log(xmax)-log(xmin))
ax.set_aspect(1./data_aspect)
show()

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但是当我用对数线性图尝试这个时，我没有得到方形区域，而是一个警告

from pylab import *
x = linspace(1,10,1000)
y = sin(x)**2+0.5
plot (x,y)
ax = gca()
ax.set_yscale("log")
xmin,xmax = ax.get_xbound()
ymin,ymax = ax.get_ybound()
data_aspect = (log(ymax)-log(ymin))/(xmax-xmin)
ax.set_aspect(1./data_aspect)
show()

发出警告:

axes.py:1173: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log

---

尽管缺少 Axes.set_aspect 的支持，是否有实现方形对数线性图的好方法？

Answer 1

好吧，有一种解决方法。实际轴区域(绘图所在的区域，不包括外部刻度 &c)可以调整为您想要的任何大小。

您可以使用 ax.set_position 设置绘图的相对(相对于图形)大小和位置。为了在您的案例中使用它，我们需要一些数学知识:

from pylab import *

x = linspace(1,10,1000)
y = sin(x)**2+0.5
plot (x,y)
ax = gca()
ax.set_yscale("log")

# now get the figure size in real coordinates:
fig  = gcf()
fwidth = fig.get_figwidth()
fheight = fig.get_figheight()

# get the axis size and position in relative coordinates
# this gives a BBox object
bb = ax.get_position()

# calculate them into real world coordinates
axwidth = fwidth * (bb.x1 - bb.x0)
axheight = fheight * (bb.y1 - bb.y0)

# if the axis is wider than tall, then it has to be narrowe
if axwidth > axheight:
    # calculate the narrowing relative to the figure
    narrow_by = (axwidth - axheight) / fwidth
    # move bounding box edges inwards the same amount to give the correct width
    bb.x0 += narrow_by / 2
    bb.x1 -= narrow_by / 2
# else if the axis is taller than wide, make it vertically smaller
# works the same as above
elif axheight > axwidth:
    shrink_by = (axheight - axwidth) / fheight
    bb.y0 += shrink_by / 2
    bb.y1 -= shrink_by / 2

ax.set_position(bb)

show()

一个轻微的文体评论是通常不使用import pylab。传说是这样的:

import matplotlib.pyplot as plt

pylab 作为 numpy 和 matplotlib 导入的奇特混合体，旨在使交互式 IPython 使用更轻松。 (我也用它。)