More advanced PDE support in dynestyx - #309
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EnKF and now supports sparse observation matrices (PF already did this). EKF and KF will raise a warning if a sparse matrix is passed and this is likely to break under the hood. Added a couple tests to verify that computation is the same for both sparse and dense matrices.
KF will now raise an error. EKF will raise a warning, indicating that the computation is supported but likely inefficient.
Added tests to check warnings/error messages as well verification that computation is identical.
Brings in sparse-matrix support for EnKF/EKF filters (observation handling, cuthbert/cd_dynamax discrete filters) from BasisResearch/dynestyx@enkf_sparse_support. No file overlap with pde_v2's own changes, so the merge applied cleanly.
Fist tutorial illustrating: 1. random sensor placements + interpolation 2. use of sparse observation matrices 3. gp prior
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Added a tutorial and some utilities for filtering with PDEs: