Matplotlib ValueError on LogNorm plots

Matplotlib log10-normalized plots are enabled with plotting options

pcolormesh(dat, norm=matplotlib.colors.LogNorm(), vmin=max(dat.min(), LOGMIN))

This option also works for appropriate 2-D plots from pandas.DataFrame.plot() and xarray.DataArray.plot().

Log(0) bounds error

Explicit plot option vmin=0 or implicit (from data with a minimum of zero) in a log-norm pcolormesh() plot will cause errors like

ValueError: Data has no positive values, and therefore can not be log-scaled.


ZeroDivisionError: float division by zero


Choose a minimum plot value LOGMIN appropriate for plotting the data.

import numpy as np
from matplotlib.figure import Figure
from matplotlib.colors import LogNorm

LOGMIN = 0.1  # arbitrary lower bound, as appropriate for log-scaled data display

dat = np.random.rayleigh(1., (50,50))

dat[0,0] = 0.  # forcing the ValueError to occur with LogNorm

fg = Figure(figsize=(12,5))
ax = fg.subplots(1,2)
ax[0].pcolormesh(dat, norm=LogNorm(), vmin=max(dat.min(), LOGMIN))
# vmin= : this averts ValueError by having non-zero cdata minimum.




Matlab / GNU Octave

The equivalent code in Matlab / GNU Octave does not give an error.

dat = raylrnd(1., [50,50]);

dat(1,1) = 0;