Drawing log-linear plot on a square plot area in matplotlib

Question

I would like to draw a plot with a logarithmic y axis and a linear x axis on a square plot area in matplotlib. I can draw linear-linear as well as log-log plots on squares, but the method I use, Axes.set_aspect(...), is not implemented for log-linear plots. Is there a good workaround?

linear-linear plot on a square:

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()

Square linear-linear plot

log-log plot on a square:

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()

square log-log plot

But when I try this with a log-linear plot, I do not get the square area, but a warning

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()

yielding the warning:

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

not-square log-linear plot

Is there a good way of achieving square log-linear plots despite the lack support in Axes.set_aspect?

DrV · Accepted Answer · 2014-07-02 18:18:54Z

Well, there is a sort of a workaround. The actual axis area (the area where the plot is, not including external ticks &c) can be resized to any size you want it to have.

You may use the ax.set_position to set the relative (to the figure) size and position of the plot. In order to use it in your case we need a bit of maths:

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()

A slight stylistic comment is that import pylab is not usually used. The lore goes:

import matplotlib.pyplot as plt

pylab as an odd mixture of numpy and matplotlib imports created to make interactive IPython use easier. (I use it, too.)

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