Introduce inconsistent fit closure test data

[comane]

Reimplements #1682

This pull request introduces a new class, InconsistentCommonData, to handle inconsistencies within closure tests and includes several related changes across multiple files. The most important changes include the addition of the new class, updates to the configuration parsing, and new filtering methods for handling inconsistent closure data.

New Class and Methods:

• validphys2/src/validphys/closuretest/inconsistent_closuretest/inconsistent_ct.py: Added InconsistentCommonData class with methods to introduce inconsistencies in closure tests, including systematic_errors property, select_systype_table_indices, rescale_systematics, process_commondata, and export_uncertainties.

Configuration Updates:

• validphys2/src/validphys/config.py: Added parse_inconsistent_data_settings method to parse inconsistent data settings from the YAML file.
• validphys2/src/validphys/config.py: Updated produce_filter_data to include inconsistent_fakedata parameter for filtering inconsistent closure data. [1] [2]

Filtering Methods:

• validphys2/src/validphys/filters.py: Added filter_inconsistent_closure_data_by_experiment and _filter_inconsistent_closure_data methods to handle filtering of inconsistent closure data. [1] [2]

Testing:

• validphys2/src/validphys/tests/test_inconsistent_ct.py: Added unit tests for InconsistentCommonData class methods, including tests for the getter and setter of systematic_errors, select_systype_table_indices, rescale_systematics, process_commondata, and export_uncertainties.

Benchmarking of Code

• Test against old code to see whether we reproduce it, eg the same Ratio bias variance results.

Test 0.0

Test that an inconsistent ct fit (using the inconsistent closure test filter) with lambda = 1.0 gives the same results as a consistent closure test:

https://vp.nnpdf.science/VIdN_z0ETx6Ph0szzKLY0g==

Further tests..

This is the type of agreement found between old and new branch for a DIS only inconsistent closure test with lambda = 0
https://vp.nnpdf.science/sH-C_csbTGCPCdEGFXj_1w==

I also used parallel_models: true for the new fit. Is this compatible with the type of agreement that is found between standard runned fits and parallel_models run fits? (@scarlehoff)

[scarlehoff]

This is the type of agreement found between old and new branch for a DIS only inconsistent closure test with lambda = 0
https://vp.nnpdf.science/sH-C_csbTGCPCdEGFXj_1w==

I also used parallel_models: true for the new fit. Is this compatible with the type of agreement that is found between standard runned fits and parallel_models run fits? (@scarlehoff)

I would need to see chi2 / distances / etc. By running in parallel the seeding might happen differently at some stage but the initialization (if everything goes well) should be equivalent.

By eye they look too different to me, but if all metrics are very close it could well be a random fluctuation.

[RoyStegeman]

Looking at the plots I share @scarlehoff's impression, but indeed a full compare report including chi2s and distances would be needed to say it more conclusively.

From Stefano's comments during the PC I have the impression that the paper is about to be finished, so I think this PR deserves some priority. It would be very good to have this merged before the paper is on arxiv, because otherwise I'm afraid it may never happen.