Output the location, width and signal in the spots for the benchmark
data
Add mean to the plot
Output more benchmark logging information on the expected noise in the
image.
Ensure the stack is initialised in the ImageSample object.

Add 5% to the plotted points so they can all be seen.
Check for a range of zero and use 0.05 as a minimum
Added deviations estimate to the MLE fitted parameters. This is derived
from the Fisher Information matrix and assumes the fit has achieved the
Cramer-Roa lower bounds.

Note that the deviations from the least squares estimators are lower
than the deviations from the Fisher matrix. However the Apache LVM
fitter and the custom NonLinearFit class produce the same output so this
is presumed correct, it is just that the fitters have a smaller
confidence interval on the fitted parameters than the Cramer-Roa lower
bounds.