Modify how spatial cache is created in StdMatrixKDEEnabledLLH

[sathanas31]

TL;DR Improve runtime for gamma-independent KDE cases used, by not evaluating the splines for all gamma points when creating spatial cache

Main changes

• Introduce optional gamma-dependency in signal_spatial of NorthernTracksKDE

  1. If 3D KDE, the spline is already created at given gamma, so no gamma-dependency when evaluating the spline => signal_spatial with gamma = None
  2. If 4D KDE, the spline evaluation is gamma-dependent. Added the option of specifying the gamma as "spatial_pdf_index" in spatial_pdf_dict, so this case is also gamma-independent and signal_spatial is called with gamma = None. If gamma not specified by user, then signal_spatial is gamma-dependent

• Apply changes in StdMatrixKDEEnabledLLH

  1. signal_pdf method has now optional gamma-dependence,
  2. this affects how coincident events are selected (mask for signal pdf above 1e-21)
  3. in the gamma-independent cases, SoB-only sparse matrix is created outside joint_SoB so we avoid having the same spline evaluation for all the gamma support points.

Miscellaneous Change the scale in ResultsHandler in order to avoid error when using "fixed_scale"

Testing For some (unknown) reason, the full dataset passed in the llh is randomized even though the configuration is the same: no Poission injection, same seed, same catalog & dataset, 1 local trial for fixed scale. If dataset is saved and used for all testing trials then results are identical for main and branch

[mlincett]

Hi, I am not so familiar with the KDE support components of flarestack so feel free to move on without my approval, but I will try to take a look nevertheless in the upcoming days.

About unwanted randomisations, my guess is that somewhere we use a RNG that is not appropriately set up with the provided seed. If I remember correctly, we still use a lot of np.random.* calls instead of creating an instance of default_rng(), which would be the recommended approach.

[JannisNe]

Could you check why mypy and black are still failing, @sathanas31 ?