Axes' in all plots using Matplotlib are linear by default, yscale() and xscale() method of the matplotlib. pyplot library can be used to change the y-axis or x-axis scale to logarithmic respectively.
You can use the Axes.set_yscale
method. That allows you to change the scale after the Axes
object is created. That would also allow you to build a control to let the user pick the scale if you needed to.
The relevant line to add is:
ax.set_yscale('log')
You can use 'linear'
to switch back to a linear scale. Here's what your code would look like:
import pylab
import matplotlib.pyplot as plt
a = [pow(10, i) for i in range(10)]
fig = plt.figure()
ax = fig.add_subplot(2, 1, 1)
line, = ax.plot(a, color='blue', lw=2)
ax.set_yscale('log')
pylab.show()
First of all, it's not very tidy to mix pylab
and pyplot
code. What's more, pyplot style is preferred over using pylab.
Here is a slightly cleaned up code, using only pyplot
functions:
from matplotlib import pyplot
a = [ pow(10,i) for i in range(10) ]
pyplot.subplot(2,1,1)
pyplot.plot(a, color='blue', lw=2)
pyplot.yscale('log')
pyplot.show()
The relevant function is pyplot.yscale()
. If you use the object-oriented version, replace it by the method Axes.set_yscale()
. Remember that you can also change the scale of X axis, using pyplot.xscale()
(or Axes.set_xscale()
).
Check my question What is the difference between ‘log’ and ‘symlog’? to see a few examples of the graph scales that matplotlib offers.
You simply need to use semilogy instead of plot:
from pylab import *
import matplotlib.pyplot as pyplot
a = [ pow(10,i) for i in range(10) ]
fig = pyplot.figure()
ax = fig.add_subplot(2,1,1)
line, = ax.semilogy(a, color='blue', lw=2)
show()
if you want to change the base of logarithm, just add:
plt.yscale('log',base=2)
Before Matplotlib 3.3, you would have to use basex/basey as the bases of log
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