Update optim.log to store cost

[NicolaCourtier]

Description

Update the log attribute of the optimisation object to store the cost at each iteration as well as the parameter values. Use these cost values in the convergence plot instead of recomputing them.

Issue reference

Fixes #165

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 97.47%. Comparing base (aa38b95) to head (0bd0315).
Report is 733 commits behind head on develop.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop     #335      +/-   ##
===========================================
+ Coverage    97.33%   97.47%   +0.13%     
===========================================
  Files           42       42              
  Lines         2404     2418      +14     
===========================================
+ Hits          2340     2357      +17     
+ Misses          64       61       -3     

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[NicolaCourtier]

Merge after #334

[BradyPlanden]

Thanks Nicola! One area that needs a bit of attention, otherwise a nice addition :)

[BradyPlanden]

Is the aim of this codeblock to append the locations the optimiser visited to the cost landscapes? At the moment, it breaks the gradient plots below it due to cost function being overwritten as a np.array, and as such isn't used in the plot2d object.

I added the below to line 127 of unit/test_plots.py to catch the error.

# Plot gradient cost landscape
pybop.plot2d(optim, gradient=True, steps=5)

[NicolaCourtier]

Great catch! Yes, it should have been costs not cost... and thanks for the test.

From further testing, I've seen that the extra points (and nans) can modify the contour plot in unexpected ways. So I have implemented interpolation to avoid the nans and made this feature optional with the switch use_optim_log.

[BradyPlanden]

LGTM, thanks @NicolaCourtier!