perf: drop use of awkward in yield uncertainty calculation

[ekauffma]

ak.sum is much more expensive than anticipated, so this changes the matrix operations in yield_stdev to use numpy instead, as per suggestion by @alexander-held

partially addresses #409, follows initial improvements done in #315

[codecov]

Codecov Report

Patch coverage: 100.00% and no project coverage change.

Comparison is base (5064e38) 100.00% compared to head (ab4ceb5) 100.00%.

Additional details and impacted files

@@            Coverage Diff            @@
##            master      #408   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           22        22           
  Lines         2069      2072    +3     
  Branches       334       337    +3     
=========================================
+ Hits          2069      2072    +3     

Impacted Files	Coverage Δ
src/cabinetry/model_utils.py	100.00% <100.00%> (ø)

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[agoose77]

I had some perf suggestions after this popped up in my notifications:

Pre-convert the parameters, and use np.ufunc.at to avoid a temporary

# calculate the model distribution for every parameter varied up and down
# within the respective uncertainties
# ensure float for correct addition
float_parameters = parameters.astype(float)
for i_par in range(model.config.npars):
    # central parameter values, but one parameter varied within uncertainties
    up_pars = float_parameters.copy()
    np.add.at(up_pars, i_par, uncertainty)
    down_pars = float_parameters.copy()
    np.subtract.at(down_pars, i_par, uncertainty)

Pre-allocate stacked arrays and use in-place assignment (unsure of shapes here, so it's pseudo-code)

up_comb_next = np.empty(...)
up_comb_next[...] = up_comb
np.sum(up_comb, axis=0, out=up_comb_next[...])

Pre-allocate the up_variants and down_variations arrays, and as#-place

up_variations = np.empty(..., dtype=...)

for i_par in range(model.config.npars):
    ...
    up_variations[i] = up_yields

It might also be possible to do the above without the

up_yields = np.concatenate((up_comb, up_yields_channel_sum), axis=1)

step, i.e. directly assign the parts. I'm not sure.

[alexander-held]

Thanks a lot for the additional suggestions @agoose77! I moved them to #415. They look great, I would prefer that we address them in a separate PR to keep the changes a bit more atomic.

[alexander-held]

Looks all good to me, thanks a lot for getting this ready!

* drop use of awkward in yield uncertainty calculations
* reduced memory footprint and performance improvements for yield uncertainty calculations