Publish 2026_W39_encoder

LICENSE

@@ -1,6 +1,6 @@
-Creative Commons Attribution 4.0 International (CC-BY-4.0)
-
-You are free to share and adapt this material for any purpose, including commercially,
-provided you give appropriate credit.
-
-Full licence text: https://creativecommons.org/licenses/by/4.0/legalcode
+Creative Commons Attribution 4.0 International (CC-BY-4.0)
+
+You are free to share and adapt this material for any purpose, including commercially,
+provided you give appropriate credit.
+
+Full licence text: https://creativecommons.org/licenses/by/4.0/legalcode

README.md

@@ -1,103 +1,114 @@
----
-license: cc-by-4.0
-library_name: pytorch
-tags:
-  - neuroscience
-  - electrophysiology
-  - neuropixels
-  - spatial-encoding
-  - international-brain-lab
----
-
-# Ephys Atlas spatial encoder (2026_W32)
-
-Predicts the **electrophysiological feature vector expected at a channel's position** -- from
-that position plus anatomical context and the recorded features of nearby channels on other
-insertions. Trained by the
-[International Brain Laboratory](https://www.internationalbrainlab.com/) on the Ephys Atlas
-feature release `2026_W32`.
-
-> **The input is a position, not features.** This is the inverse of the region classifier: that
-> model takes features and returns a region; this one takes a channel's `x, y, z` (Allen frame,
-> metres) and returns the 41 ephys features it would expect there. Feeding it the
-> feature columns instead of coordinates is the most common mistake -- `predict` raises and names
-> the coordinates it needs.
-
-## What ships in this repo, and why all of it is needed
-
-A published encoder is not the weights alone. `predict` cannot run without every one of:
-
-- `spatial_encoder.pt` -- the trained network.
-- the context PCA volumes (`agea_vol_pca.npy`, `merfish_vol_pca.npy`) -- the anatomical context
-  it samples at each position.
-- `neighbor_bank.npz` -- the **neighbour bank**: the position, standardised feature vector and
-  insertion id of every training channel. At inference the model gathers the nearest recorded
-  channels from this bank and attends to them, so the bank *is* part of the model, the way the
-  stored points are part of a k-nearest-neighbours model. It cannot be reconstructed from the
-  weights, which is why it is shipped here rather than recomputed on your machine.
-
-> **First run downloads the Allen volume.** Building the context sampler constructs an
-> `AllenAtlas`, which fetches the Allen CCF volume from `download.alleninstitute.org` (public, no
-> account, a few hundred MB) the first time it runs on a machine, then caches it.
-
-## Quickstart
-
-```python
-import pandas as pd
-from ephysatlas import load_pretrained
-
-model = load_pretrained("int-brain-lab/ea-encoder-channel", revision="2026_W32")
-df = pd.read_parquet("example/features_sample.parquet")   # channel positions (x, y, z)
-out = model.predict(df)                                    # one pred_<feature> column each
-print(out.head())
-```
-
-`load_pretrained` is the entry point for every ephysatlas model, whatever its family -- it reads
-`ephysatlas_model.json` and returns the right wrapper. Use it rather than importing a concrete
-class, so your code keeps working as the package evolves.
-
-`predict` returns one row per input channel, indexed identically to the input, with a
-`pred_<feature>` column per entry in `outputs.columns`. The `pred_` prefix keeps `df.join(out)`
-from colliding with the ground-truth feature columns of the same names.
-
-## Inputs
-
-- Indexed by `(pid, channel)`, one row per recording channel.
-- The coordinate columns `x, y, z` named in `ephysatlas_model.json` under `inputs.columns`, in
-  the Allen atlas frame, **in metres**. The feature columns are *not* read -- they are the output.
-
-## Neighbourhood selection
-
-For each query position the model attends to recorded channels within **500.0 µm**, taking
-the **nearest 8** (`selection: nearest`). This is deterministic: two calls on the same
-input return identical predictions.
-
-> Training used a *random* subset of the in-radius neighbours; this published `predict` takes the
-> nearest instead. The two agree exactly whenever a position has at most 8 neighbours in
-> radius, and differ only in dense regions where the training-time subset would itself have varied
-> run to run. Deterministic output is the right contract for a published model.
-
-## Limitations
-
-- Trained on IBL Neuropixels 1.0 recordings in mouse. Transfer to NP2, other species or other
-  rigs is untested.
-- Coverage follows IBL brain-wide-map targeting; predictions far from any recorded channel fall
-  back on anatomical context alone and are correspondingly weaker.
-
-## Reproducibility
-
-**Pin the revision.** `revision="2026_W32"` is an immutable tag. Omitting `revision` resolves to
-`main`, which tracks whichever model is currently recommended and *will* change when a new feature
-vintage is published -- fine for a first look, not for anything you publish or re-run.
-
-`ephysatlas_model.json` records the training-time `environment` and `random_seed`. Verify your
-install reproduces the shipped output:
-
-```python
-model.selftest()
-```
-
-## Citation
-
-Please cite the International Brain Laboratory Ephys Atlas. Model id `2026_W32_encoder`,
-feature vintage `2026_W32`.
+---
+license: cc-by-4.0
+library_name: pytorch
+tags:
+  - neuroscience
+  - electrophysiology
+  - neuropixels
+  - spatial-encoding
+  - international-brain-lab
+---
+
+# Ephys Atlas spatial encoder (2026_W39)
+
+Predicts the **electrophysiological feature vector expected at a channel's position** -- from
+that position plus anatomical context and the recorded features of nearby channels on other
+insertions. Trained by the
+[International Brain Laboratory](https://www.internationalbrainlab.com/) on the Ephys Atlas
+feature release `2026_W39`.
+
+> **The input is a position, not features.** This is the inverse of the region classifier: that
+> model takes features and returns a region; this one takes a channel's `x, y, z` (Allen frame,
+> metres) and returns the 43 ephys features it would expect there. Feeding it the
+> feature columns instead of coordinates is the most common mistake -- `predict` raises and names
+> the coordinates it needs.
+
+## What ships in this repo, and why all of it is needed
+
+A published encoder is not the weights alone. `predict` cannot run without every one of:
+
+- `spatial_encoder.pt` -- the trained network.
+- the context PCA volumes (`agea_vol_pca.npy`, `merfish_vol_pca.npy`) -- the anatomical context
+  it samples at each position.
+- `neighbor_bank.npz` -- the **neighbour bank**: the position, standardised feature vector and
+  insertion id of every training channel. At inference the model gathers the nearest recorded
+  channels from this bank and attends to them, so the bank *is* part of the model, the way the
+  stored points are part of a k-nearest-neighbours model. It cannot be reconstructed from the
+  weights, which is why it is shipped here rather than recomputed on your machine.
+
+Also published, for users who preprocess new recordings or evaluate the model:
+
+- `preprocessing/channel_stats.npz` -- the training-data statistics: per-feature clipping
+  thresholds (0.5th/99.5th percentiles of the training channels) and the feature and context
+  standardisation.
+- `split.json` -- the probe split (train / validation / test insertions).
+- `results/` -- held-out evaluation summaries (per-feature test R²; confidence-model validation).
+- `confidence_model.pt` -- a per-channel alignment-confidence model scoring how consistent a
+  probe's recorded features are with this encoder's predictions at its reported positions; load it
+  with `model.load_confidence_model()`.
+
+> **First run downloads the Allen volume.** Building the context sampler constructs an
+> `AllenAtlas`, which fetches the Allen CCF volume from `download.alleninstitute.org` (public, no
+> account, a few hundred MB) the first time it runs on a machine, then caches it.
+
+## Quickstart
+
+```python
+import pandas as pd
+from ephysatlas import load_pretrained
+
+model = load_pretrained("int-brain-lab/ea-encoder-channel", revision="2026_W39")
+df = pd.read_parquet("example/features_sample.parquet")   # channel positions (x, y, z)
+out = model.predict(df)                                    # one pred_<feature> column each
+print(out.head())
+```
+
+`load_pretrained` is the entry point for every ephysatlas model, whatever its family -- it reads
+`ephysatlas_model.json` and returns the right wrapper. Use it rather than importing a concrete
+class, so your code keeps working as the package evolves.
+
+`predict` returns one row per input channel, indexed identically to the input, with a
+`pred_<feature>` column per entry in `outputs.columns`. The `pred_` prefix keeps `df.join(out)`
+from colliding with the ground-truth feature columns of the same names.
+
+## Inputs
+
+- Indexed by `(pid, channel)`, one row per recording channel.
+- The coordinate columns `x, y, z` named in `ephysatlas_model.json` under `inputs.columns`, in
+  the Allen atlas frame, **in metres**. The feature columns are *not* read -- they are the output.
+
+## Neighbourhood selection
+
+For each query position the model attends to recorded channels within **500.0 µm**, taking
+the **nearest 8** (`selection: nearest`). This is deterministic: two calls on the same
+input return identical predictions.
+
+> Training used a *random* subset of the in-radius neighbours; this published `predict` takes the
+> nearest instead. The two agree exactly whenever a position has at most 8 neighbours in
+> radius, and differ only in dense regions where the training-time subset would itself have varied
+> run to run. Deterministic output is the right contract for a published model.
+
+## Limitations
+
+- Trained on IBL Neuropixels 1.0 recordings in mouse. Transfer to NP2, other species or other
+  rigs is untested.
+- Coverage follows IBL brain-wide-map targeting; predictions far from any recorded channel fall
+  back on anatomical context alone and are correspondingly weaker.
+
+## Reproducibility
+
+**Pin the revision.** `revision="2026_W39"` is an immutable tag. Omitting `revision` resolves to
+`main`, which tracks whichever model is currently recommended and *will* change when a new feature
+vintage is published -- fine for a first look, not for anything you publish or re-run.
+
+`ephysatlas_model.json` records the training-time `environment` and `random_seed`. Verify your
+install reproduces the shipped output:
+
+```python
+model.selftest()
+```
+
+## Citation
+
+Please cite the International Brain Laboratory Ephys Atlas. Model id `2026_W39_encoder`,
+feature vintage `2026_W39`.

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ephysatlas_model.json

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-{
-  "model_id": "2026_W32_encoder",
-  "task": "spatial-encoding",
-  "model_class": "NeighborInpaintingModel",
-  "vintage": "2026_W32",
-  "training": {
-    "random_seed": 0,
-    "training_size": 536,
-    "validation_size": 76,
-    "testing_size": 153
-  },
-  "environment": {
-    "python": "3.11.14",
-    "xgboost": "3.2.0",
-    "scikit_learn": "1.8.0",
-    "numpy": "2.4.6",
-    "pandas": "3.0.5",
-    "torch": "2.14.0",
-    "ephysatlas": "0.7.0"
-  },
-  "method": "transformer",
-  "granularity": "channel",
-  "artifacts": {
-    "weights": "spatial_encoder.pt",
-    "context": [
-      "agea_vol_pca.npy",
-      "merfish_vol_pca.npy"
-    ],
-    "confidence": "confidence_model.pt",
-    "neighbor_bank": "neighbor_bank.npz"
-  },
-  "inputs": {
-    "table": "channels.pqt",
-    "index": [
-      "pid",
-      "channel"
-    ],
-    "columns": [
-      "x",
-      "y",
-      "z"
-    ]
-  },
-  "outputs": {
-    "kind": "continuous",
-    "columns": [
-      "rms_lf",
-      "psd_lfp",
-      "psd_alpha",
-      "psd_beta",
-      "psd_gamma",
-      "psd_delta",
-      "psd_theta",
-      "psd_lfp_csd_diff1",
-      "psd_alpha_csd_diff1",
-      "psd_beta_csd_diff1",
-      "psd_gamma_csd_diff1",
-      "psd_delta_csd_diff1",
-      "psd_theta_csd_diff1",
-      "rms_lf_csd_diff1",
-      "psd_residual_lfp",
-      "psd_residual_alpha",
-      "psd_residual_beta",
-      "psd_residual_gamma",
-      "psd_residual_delta",
-      "psd_residual_theta",
-      "decay_fit_error",
-      "decay_fit_r_squared",
-      "decay_n_peaks",
-      "aperiodic_exponent",
-      "aperiodic_offset",
-      "cor_ratio",
-      "rms_ap",
-      "alpha_mean",
-      "alpha_std",
-      "spike_count",
-      "tip_time_secs",
-      "recovery_time_secs",
-      "peak_time_secs",
-      "trough_time_secs",
-      "trough_val",
-      "tip_val",
-      "peak_val",
-      "recovery_slope",
-      "depolarisation_slope",
-      "repolarisation_slope",
-      "polarity"
-    ],
-    "feature_order_sha256": "ebc1285d9121753c4adf0f96ff456df3c0afd1ac4124ffa3556907283489efaa"
-  },
-  "config": {
-    "architecture": {
-      "f_ctx": 100,
-      "f_ephys": 41,
-      "f_out": 41,
-      "d_model": 128,
-      "nhead": 8,
-      "depth": 2,
-      "drop": 0.15
-    },
-    "neighbourhood": {
-      "radius_um": 500.0,
-      "m_max": 8,
-      "selection": "nearest",
-      "allow_same_probe": false
-    },
-    "context": {
-      "n_cell_pcs": 50,
-      "n_gene_pcs": 50,
-      "voxel_um": 200
-    }
-  }
-}
+{
+  "model_id": "2026_W39_encoder",
+  "task": "spatial-encoding",
+  "model_class": "NeighborInpaintingModel",
+  "vintage": "2026_W39",
+  "training": {
+    "random_seed": 0,
+    "training_size": 568,
+    "validation_size": 81,
+    "testing_size": 162,
+    "best_epoch": 3,
+    "mean_test_r2": 0.39225515723228455
+  },
+  "environment": {
+    "python": "3.11.9",
+    "xgboost": "3.2.0",
+    "scikit_learn": "1.8.0",
+    "numpy": "2.3.5",
+    "pandas": "3.0.5",
+    "torch": "2.7.1+cu118",
+    "ephysatlas": "0.9.0"
+  },
+  "method": "transformer",
+  "granularity": "channel",
+  "artifacts": {
+    "weights": "spatial_encoder.pt",
+    "context": [
+      "agea_vol_pca.npy",
+      "merfish_vol_pca.npy"
+    ],
+    "confidence": "confidence_model.pt",
+    "neighbor_bank": "neighbor_bank.npz",
+    "stats": "preprocessing/channel_stats.npz",
+    "split": "split.json",
+    "results": [
+      "results/confidence_validation.json",
+      "results/test_r2.json"
+    ]
+  },
+  "inputs": {
+    "table": "channels.pqt",
+    "index": [
+      "pid",
+      "channel"
+    ],
+    "columns": [
+      "x",
+      "y",
+      "z"
+    ]
+  },
+  "outputs": {
+    "kind": "continuous",
+    "columns": [
+      "rms_lf",
+      "psd_delta",
+      "psd_theta",
+      "psd_alpha",
+      "psd_beta",
+      "psd_gamma",
+      "psd_lfp",
+      "rms_lf_csd_diff1",
+      "psd_delta_csd_diff1",
+      "psd_theta_csd_diff1",
+      "psd_alpha_csd_diff1",
+      "psd_beta_csd_diff1",
+      "psd_gamma_csd_diff1",
+      "psd_lfp_csd_diff1",
+      "psd_residual_delta",
+      "psd_residual_theta",
+      "psd_residual_alpha",
+      "psd_residual_beta",
+      "psd_residual_gamma",
+      "psd_residual_lfp",
+      "aperiodic_offset",
+      "aperiodic_exponent",
+      "decay_fit_error",
+      "decay_fit_r_squared",
+      "decay_n_peaks",
+      "cor_ratio",
+      "rms_ap",
+      "alpha_mean",
+      "alpha_std",
+      "spike_count",
+      "depolarisation_slope",
+      "polarity",
+      "recovery_slope",
+      "repolarisation_slope",
+      "slowness_s_per_m",
+      "slowness_s_per_m_std",
+      "spatial_spread_um",
+      "spatial_spread_um_std",
+      "tip_val",
+      "spike_width_secs",
+      "predepolarisation_width_secs",
+      "spike_amplitude",
+      "peak_to_trough_ratio_log"
+    ],
+    "feature_order_sha256": "53611665ad0aac7e0ce34a6bba7df17bc005cc414c9eb6ccb06dc9e70de3d578"
+  },
+  "config": {
+    "architecture": {
+      "f_ctx": 100,
+      "f_ephys": 43,
+      "f_out": 43,
+      "d_model": 128,
+      "nhead": 8,
+      "depth": 2,
+      "drop": 0.15
+    },
+    "neighbourhood": {
+      "radius_um": 500.0,
+      "m_max": 8,
+      "selection": "nearest",
+      "allow_same_probe": false
+    },
+    "context": {
+      "n_cell_pcs": 50,
+      "n_gene_pcs": 50,
+      "voxel_um": 200
+    },
+    "preprocessing": {
+      "clip_percentiles": [
+        0.5,
+        99.5
+      ],
+      "clipping": "per feature, thresholds from the training channels",
+      "standardisation": "clipped training channels (features); all non-recorded atlas voxels (context)",
+      "stats_file": "preprocessing/channel_stats.npz",
+      "hemisphere": "x mirrored to the left hemisphere (x = -|x|)"
+    },
+    "confidence": {
+      "architecture": {
+        "f_ctx": 100,
+        "f_e": 43,
+        "d_model": 64,
+        "nhead": 4,
+        "depth": 2,
+        "mlp_ratio": 2.0,
+        "drop": 0.1
+      },
+      "training": {
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+          "psd_theta_csd_diff1",
+          "psd_alpha_csd_diff1",
+          "psd_beta_csd_diff1",
+          "psd_gamma_csd_diff1",
+          "psd_lfp_csd_diff1",
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+          "psd_residual_theta",
+          "psd_residual_alpha",
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+        "ap": [
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+        "waveform": [
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+}

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meta.yaml

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-MODEL_CLASS: NeighborInpaintingModel
-VINTAGE: 2026_W26
-FEATURES:
-- rms_lf
-- psd_lfp
-- psd_alpha
-- psd_beta
-- psd_gamma
-- psd_delta
-- psd_theta
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-- alpha_std
-- spike_count
-- tip_time_secs
-- recovery_time_secs
-- peak_time_secs
-- trough_time_secs
-- trough_val
-- tip_val
-- peak_val
-- recovery_slope
-- depolarisation_slope
-- repolarisation_slope
-- polarity
-F_CTX: 100
-D_MODEL: 128
-NHEAD: 8
-DEPTH: 2
-DROP: 0.15
-RADIUS_UM: 500.0
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results/confidence_validation.json

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+}

results/test_r2.json

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+{
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