Adds FittingProblem arg for model rebuild on evaluation, DFN examples and data-generation scripts

[BradyPlanden]

Description

Completes the following:

• Creates initial synthetic data generation examples
  • Aligns our examples to better represent true PyBOP usage
  • Allows us to filter the model.predict output for discontinuities that occur from multi-step experiments (this currently causes large issues with the interpolated IDAKLU)
• Adds DFN identification examples

Issue reference

Fixes #662

Review

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.21%. Comparing base (f4cc252) to head (df79ec2).
Report is 30 commits behind head on develop.

Additional details and impacted files

@@           Coverage Diff            @@
##           develop     #663   +/-   ##
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  Coverage    99.21%   99.21%           
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  Files           67       67           
  Lines         4834     4842    +8     
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+ Hits          4796     4804    +8     
  Misses          38       38           

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[martinjrobins]

thanks @BradyPlanden, looks good to me :)

[NicolaCourtier]

Thanks for these additions @BradyPlanden!

It seems to me that the safe option would be to set build_on_evaluation to True by default. But does this flag control rebuilding for geometric parameters or just rebuilding for an initial state?

[BradyPlanden]

Thanks for the review/comments @martinjrobins @NicolaCourtier!

I've removed the extra build_on_evaluation argument and replaced it with the model.set_initial_state method in the examples. For the time being, I'd agree that this is more consistent with how we expect users to interact with the model and problem classes; however, I think we should reconsider allowing the management of the models' initial_state from both classes in the future, as it can be confusing to users how they differ, and when to use one versus the other. Anyway, I'll cross-post this to a separate issue for us to discuss!