Dipolarbackgroundmodel func

[HKaras]

This adds a new function called dipolarbackgroundmodel which has the aim of simplifying how dipolar background models are evaluated and propagated for multi-pathway experiments.

In the past a long convulated line had to added:

Bfcn = lambda lam1,lam5,reftime1,reftime5,conc: results.P_scale*(1-lam1-lam5)*dl.bg_hom3d(t-reftime1,conc,lam1)*dl.bg_hom3d(t-reftime5,conc,lam5)
Bfit = results.evaluate(Bfcn)
Bci = results.propagate(Bfcn).ci(95)

This how now been replaced with:

Bfcn = dl.dipolarbackgroundmodel(experimentInfo)
Bfit = results.P_scale*results.evaluate(Bfcn,t)
Bci = results.P_scale*results.propagate(Bfcn,t).ci(95)

Limitations

• The model constant t must be specified in evaluate, as FitResult.evaluate does not automatically identify constants.
• As before the linear scale parameter needs to be applied.