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Eac/mst matching - #27
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Introduces MSTWcsMatch and MSTShearMatch, which replace the footprint/pixelization-based initial clustering with a minimum-spanning-tree equivalent: sources within mst_cut arcseconds (via a chain of neighbours) are placed in the same cluster using scipy connected_components on a sparse near-neighbour graph. This makes initial cluster assignment robust against pixel scale choices while preserving the existing hierarchical confusion-resolution pipeline. New files: - src/hpmcm/mst_utils.py -- mst_group_sources() - src/hpmcm/mst_cell.py -- MSTCellMixin, MSTCellData, MSTShearCellData - src/hpmcm/mst_match.py -- MSTWcsMatch, MSTShearMatch - tests/test_mst_match.py -- 4 tests including a synthetic grouping check Generated with AI Co-Authored-By: SLAC AI
Drop-in replacement for RunMatching.ipynb using MSTShearMatch and MSTWcsMatch. Adds mst_cut_shear and mst_cut_wcs tuneable parameters (in arcseconds) and writes outputs to *_mst_* paths to avoid clobbering standard-matcher results. Generated with AI Co-Authored-By: SLAC AI
mstProcessCluster, mstProcessObject, and mstSplitObject replace the heirarchical* pixel-zoom functions for MST cells. Splitting now re-runs mst_group_sources with mst_cut halved at each recursion level, keeping the entire pipeline free of pixelization choices. Generated with AI Co-Authored-By: SLAC AI
Draw MST links between sources instead of a pixel counts-map background. showMSTCluster colours nodes by catalog ID; showMSTObjects colours intra-object edges solid by object and inter-object (cut) edges dashed grey. Generated with AI Co-Authored-By: SLAC AI
Counterpart to VizMatches.ipynb that loads MST match outputs and displays clusters and objects with showMSTCluster / showMSTObjects instead of the pixel counts-map based showCluster. Generated with AI Co-Authored-By: SLAC AI
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