Publish 2026_W39_unit

README.md

@@ -25,7 +25,7 @@ The model has three stages:
    components read as putative cell types. Component shapes are global; the **mixture weights
    depend on the molecular context** (MERFISH + AGEA PCA volumes) at the unit's position;
 3. a distance-weighted **k=20 nearest-neighbour projection** onto training units maps latents
-   back to 11 interpretable waveform features: `depolarisation_slope`, `recovery_slope`, `recovery_time_secs`, `repolarisation_slope`, `tip_time_secs`, `tip_val`, `through_time_secs`, `trough_val`, `peak_time_secs`, `peak_val`, `polarity`.
+   back to 10 interpretable waveform features: `depolarisation_slope`, `recovery_slope`, `repolarisation_slope`, `spatial_spread_um`, `tip_val`, `spike_width_secs`, `predepolarisation_width_secs`, `spike_amplitude`, `peak_to_trough_ratio_log`, `polarity`.
 
 > **The input is a position.** `predict` takes `x, y, z` (IBL/Allen frame, metres) and returns the
 > expected phenotype there: the local component weights times each component's expected features.

agea_vol_pca.npy

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component_feature_expectations.npz

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conditioning_summary.json

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   "context_definition": "50 MERFISH PCs + 50 AGEA PCs; TRAIN-only StandardScaler; mixture weights gamma only"

context_transform.joblib

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context_weight_model_bundle.pt

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ephysatlas_model.json

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@@ -63,17 +64,16 @@
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example/expected_predictions.parquet

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knn_bank.npz

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merfish_vol_pca.npy

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results/summary.json

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