matplotlib.axes.Axes.semilogy#

Axes.semilogy(*args, **kwargs)[source]#

Make a plot with log scaling on the y axis.

Call signatures:

semilogy([x], y, [fmt], data=None, **kwargs)
semilogy([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

This is just a thin wrapper around plot which additionally changes the y-axis to log scaling. All of the concepts and parameters of plot can be used here as well.

The additional parameters base, subs, and nonpositive control the y-axis properties. They are just forwarded to Axes.set_yscale.

Parameters:
basefloat, default: 10

Base of the y logarithm.

subsarray-like, optional

The location of the minor yticks. If None, reasonable locations are automatically chosen depending on the number of decades in the plot. See Axes.set_yscale for details.

nonpositive{'mask', 'clip'}, default: 'mask'

Non-positive values in y can be masked as invalid, or clipped to a very small positive number.

**kwargs

All parameters supported by plot.

Returns:
list of Line2D

Objects representing the plotted data.

Examples using matplotlib.axes.Axes.semilogy#

Log Demo

Log Demo

Log Demo
SkewT-logP diagram: using transforms and custom projections

SkewT-logP diagram: using transforms and custom projections

SkewT-logP diagram: using transforms and custom projections