I finally found some time to do some experiments in order to understand the difference between them. Here's what I discovered:
logonly allows positive values, and lets you choose how to handle negative ones (
symlogmeans symmetrical log , and allows positive and negative values.
symlogallows to set a range around zero within the plot will be linear instead of logarithmic.
I think everything will get a lot easier to understand with graphics and examples, so let's try them:
import numpy from matplotlib import pyplot # Enable interactive mode pyplot.ion() # Draw the grid lines pyplot.grid(True) # Numbers from -50 to 50, with 0.1 as step xdomain = numpy.arange(-50,50, 0.1) # Plots a simple linear function 'f(x) = x' pyplot.plot(xdomain, xdomain) # Plots 'sin(x)' pyplot.plot(xdomain, numpy.sin(xdomain)) # 'linear' is the default mode, so this next line is redundant: pyplot.xscale('linear')
# How to treat negative values? # 'mask' will treat negative values as invalid # 'mask' is the default, so the next two lines are equivalent pyplot.xscale('log') pyplot.xscale('log', nonposx='mask')
# 'clip' will map all negative values a very small positive one pyplot.xscale('log', nonposx='clip')
# 'symlog' scaling, however, handles negative values nicely pyplot.xscale('symlog')
# And you can even set a linear range around zero pyplot.xscale('symlog', linthreshx=20)
Just for completeness, I've used the following code to save each figure:
# Default dpi is 80 pyplot.savefig('matplotlib_xscale_linear.png', dpi=50, bbox_inches='tight')
Remember you can change the figure size using:
fig = pyplot.gcf() fig.set_size_inches([4., 3.]) # Default size: [8., 6.]
(If you are unsure about me answering my own question, read this)