Encoder testing#495
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- rtf.rs: prepare_features() builds (1,257,37) photometry tensor, (1,257) pad mask, and (1,3,63,63) CHW cutout from ZTF alert data. 30 metadata channels match ALERT_META_KEYS from training pipeline. - mtan.rs: prepare_features() filters g/r photometry, merges nearby obs, normalizes magnitudes and time, pads to 200 steps. Adds pool_embedding() to mean-pool qz0_mean into 2D vector. - ztf.rs: ZtfAlertEmbeddings struct, compute_embeddings() method, integrated into process_alerts() to store embeddings in MongoDB alongside existing classifications.
Adds an initContainer to the ZTF scheduler k8s deployment that pulls rtf_embed.onnx and mtan_embed.onnx from boom-astro/boomEncoders into an emptyDir volume mounted at /app/data/models. The main container reads these model files at startup to initialize ONNX inference.
# Conflicts: # src/enrichment/ztf.rs
…dings
- compute_embeddings: replace .lock().unwrap() with match on lock()
to gracefully handle poisoned mutex instead of panicking the worker
- compute_embeddings: return None when both RTF and mTAN fail, so
MongoDB doesn't get { rtf: null, mtan: null } written needlessly
- mtan.rs: add comment explaining last-write-wins merge matches the
Python training pipeline for numerical consistency
- k8s: remove stale NOTE about uploading ACAI/BTSBot (already done)
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