Use per-pixel std to scale goodness of fit

This actually makes little difference
Calculate image of systematic error of nebular fit

Uses a technique of Monte Carlo fuzzing of the model parameters around
the best-fit values.
Improve estimate of systematic errors from nebular model fits

* Use bottleneck.nanstd to calculate stdev of fuzzed model stack
* Make sure that we respect predefined min/max for model parameters

I'm not sure why NaNs were creeping into the results, but one of those
two fixes seem to have sorted it out.
Use full covariance array for estimating systematic errors