AlonSaguy commited on about 22 hours ago

Commit

2434397

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1 Parent(s): 94711f6

Publish 2026_W44_unit

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Files changed (19) hide show

README.md +7 -7
autoencoder.pt +2 -2
checksums.json +26 -26
config.json +1 -1
context_transform.joblib +1 -1
context_weights_subtypes.pt +1 -1
context_weights_types.pt +1 -1
ephysatlas_model.json +9 -9
example/expected_latents.npy +1 -1
example/expected_predictions.parquet +1 -1
hierarchy_summary.json +61 -61
members.npz +2 -2
preprocessing/unit_stats.npz +2 -2
results/summary.json +14 -14
shared_latent_scaler.joblib +1 -1
split.json +544 -398
subtypes_gmm.joblib +1 -1
training_summary.json +1026 -1121
types_gmm.joblib +2 -2

README.md CHANGED Viewed

@@ -9,12 +9,12 @@ tags:
   - international-brain-lab
 ---
-# Ephys Atlas unit-level interpolation model (2026_W42)
 Predicts **which kinds of neurons you expect to record at a brain position**, and their expected
 waveform phenotype. Trained by the
 [International Brain Laboratory](https://www.internationalbrainlab.com/) on spike-sorted units of
-the Ephys Atlas release `2026_W41` (56497 training units).
 The model has four stages:
@@ -42,7 +42,7 @@ The model has four stages:
 import pandas as pd
 from ephysatlas import load_pretrained
-model = load_pretrained("int-brain-lab/ea-encoder-unit", revision="2026_W42")
 df = pd.read_parquet("example/positions_sample.parquet")   # positions (x, y, z)
 out = model.predict(df)                                     # one pred_<feature> column each
 types = model.mixture_weights(df, level="type")             # putative cell-type composition
@@ -64,7 +64,7 @@ as in `config.json`), then assigned to a putative cell type and sub-type with `m
 - `members.npz` -- the phenotype features of the training units and their sub-type posteriors
   (the readout; part of the model, like the stored points of any nearest-neighbour model).
 - `agea_vol_pca.npy`, `merfish_vol_pca.npy` -- the molecular and cellular context volumes, the same frozen
-  volumes as the channel-level model `int-brain-lab/ea-encoder-channel` of release `2026_W41`.
 - `split.json`, `config.json`, `preprocessing/unit_stats.npz`, `results/` -- the probe split, the
   training configuration, the training-data statistics and held-out evaluation summaries.
@@ -76,12 +76,12 @@ is only needed to retrain the model or to reproduce the atlas-wide analyses.
 ## Reproducibility
-**Pin the revision.** `revision="2026_W42"` is an immutable tag; omitting it resolves to `main`,
 which tracks the currently recommended model. `ephysatlas_model.json` records the training-time
 environment and random seed; `model.selftest()` verifies your install reproduces the shipped
 output (no IBL account needed).
 ## Citation
-Please cite the International Brain Laboratory Ephys Atlas. Model id `2026_W42_unit`, vintage
-`2026_W42`.

   - international-brain-lab
 ---
+# Ephys Atlas unit-level interpolation model (2026_W44)
 Predicts **which kinds of neurons you expect to record at a brain position**, and their expected
 waveform phenotype. Trained by the
 [International Brain Laboratory](https://www.internationalbrainlab.com/) on spike-sorted units of
+the Ephys Atlas release `2026_W41` (64106 training units).
 The model has four stages:
 import pandas as pd
 from ephysatlas import load_pretrained
+model = load_pretrained("int-brain-lab/ea-encoder-unit", revision="2026_W44")
 df = pd.read_parquet("example/positions_sample.parquet")   # positions (x, y, z)
 out = model.predict(df)                                     # one pred_<feature> column each
 types = model.mixture_weights(df, level="type")             # putative cell-type composition
 - `members.npz` -- the phenotype features of the training units and their sub-type posteriors
   (the readout; part of the model, like the stored points of any nearest-neighbour model).
 - `agea_vol_pca.npy`, `merfish_vol_pca.npy` -- the molecular and cellular context volumes, the same frozen
+  volumes as the channel-level model `C:/Users/bodic/ephys-atlas/results/releases/ea-encoder-channel/2026_W44_encoder` of release `2026_W41`.
 - `split.json`, `config.json`, `preprocessing/unit_stats.npz`, `results/` -- the probe split, the
   training configuration, the training-data statistics and held-out evaluation summaries.
 ## Reproducibility
+**Pin the revision.** `revision="2026_W44"` is an immutable tag; omitting it resolves to `main`,
 which tracks the currently recommended model. `ephysatlas_model.json` records the training-time
 environment and random seed; `model.selftest()` verifies your install reproduces the shipped
 output (no IBL account needed).
 ## Citation
+Please cite the International Brain Laboratory Ephys Atlas. Model id `2026_W44_unit`, vintage
+`2026_W44`.

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@@ -8,42 +8,42 @@
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@@ -73,23 +73,23 @@
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@@ -98,18 +98,18 @@
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   ]
 }

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 }

config.json CHANGED Viewed

@@ -2,7 +2,7 @@
   "seed": 0,
   "project": "ibl_neuropixel_brainwide_01_2026_W41",
   "vintage": "2026_W41",
-  "channel_model": "int-brain-lab/ea-encoder-channel",
   "force_reprepare_data": false,
   "prepare_data_if_missing": true,
   "use_acg": true,

   "seed": 0,
   "project": "ibl_neuropixel_brainwide_01_2026_W41",
   "vintage": "2026_W41",
+  "channel_model": "C:/Users/bodic/ephys-atlas/results/releases/ea-encoder-channel/2026_W44_encoder",
   "force_reprepare_data": false,
   "prepare_data_if_missing": true,
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ephysatlas_model.json CHANGED Viewed

@@ -1,16 +1,16 @@
 {
-  "model_id": "2026_W42_unit",
   "task": "unit-encoding",
   "model_class": "UnitAutoencoder",
-  "vintage": "2026_W42",
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@@ -97,7 +97,7 @@
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       "n_gene_pcs": 50,
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-      "source_model": "int-brain-lab/ea-encoder-channel",
       "data_vintage": "2026_W41"
     },
     "readout": {

 {
+  "model_id": "2026_W44_unit",
   "task": "unit-encoding",
   "model_class": "UnitAutoencoder",
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       "conditions": "the sub-type weights gamma_k(x) gamma_j|k(x) only; the type and sub-type means and covariances are global",
+      "source_model": "C:/Users/bodic/ephys-atlas/results/releases/ea-encoder-channel/2026_W44_encoder",
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 }

   "split_unit": "probe_insertion_pid",
   "policy": "Validation/test insertions follow the channel-level model's frozen split for the same vintage; unit-only insertions absent from it are assigned to train.",
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