Python 3.9 GitHub tests

.github/workflows/ci.yml

@@ -38,7 +38,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: "3.12"
+        python-version: "3.9"
 
     - name: Install dependencies
       run: |
@@ -54,15 +54,12 @@ jobs:
 
   # Tests with pytest
   tests:
-    timeout-minutes: 15
+    timeout-minutes: 40
     strategy:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest", "windows-latest", "macos-14"]
-        python: ["3.8", "3.12"]
-        exclude:
-          - os: "macos-14"
-            python: "3.8"
+        python: ["3.9"]
 
     name: Tests (${{ matrix.os }}, Python ${{ matrix.python }})
     runs-on: ${{ matrix.os }}

README.md

@@ -41,6 +41,11 @@ labels = sio.load_file("predictions.slp")
 sio.save_file(labels, "predictions.nwb")
 # Or:
 # labels.save("predictions.nwb")
+
+# Save to an NWB file and convert SLEAP training data to NWB training data:
+frame_inds = [i for i in range(20)]
+sio.save_file(labels, "predictions.nwb", as_training=True, frame_inds=frame_inds)
+# This will save the first 20 frames of the video as individual images
 ```
 
 ### Convert labels to raw arrays

pyproject.toml

@@ -24,7 +24,7 @@ dependencies = [
     "attrs",
     "h5py>=3.8.0",
     "pynwb",
-    "ndx-pose",
+    "ndx-pose @ git+https://github.com/rly/ndx-pose@a847ad4be75e60ef9e413b8cbfc99c616fc9fd05",
     "pandas",
     "simplejson",
     "imageio",

sleap_io/io/main.py

@@ -6,6 +6,8 @@
 from typing import Optional, Union
 from pathlib import Path
 
+from pynwb import NWBHDF5IO
+
 
 def load_slp(filename: str) -> Labels:
     """Load a SLEAP dataset.
@@ -59,21 +61,45 @@
     return nwb.read_nwb(filename)
 
 
-def save_nwb(labels: Labels, filename: str, append: bool = True):
+def save_nwb(
+    labels: Labels,
+    filename: str,
+    as_training: bool = False,
+    append: bool = True,
+    frame_inds: Optional[list[int]] = None,
+    frame_path: Optional[str] = None,
+):
     """Save a SLEAP dataset to NWB format.
 
     Args:
         labels: A SLEAP `Labels` object (see `load_slp`).
         filename: Path to NWB file to save to. Must end in `.nwb`.
+        as_training: If `True`, save the dataset as a training dataset.
         append: If `True` (the default), append to existing NWB file. File will be
             created if it does not exist.
+        frame_inds: Optional list of frame indices to save. If None, all frames
+            will be saved.
+        frame_path: The path to save the frames. If None, the path is the video
+            filename without the extension.
 
-    See also: nwb.write_nwb, nwb.append_nwb
+    See also: nwb.write_nwb, nwb.append_nwb, nwb.append_nwb_training
     """
     if append and Path(filename).exists():
-        nwb.append_nwb(labels, filename)
+        nwb.append_nwb(
+            labels,
+            filename,
+            as_training=as_training,
+            frame_inds=frame_inds,
+            frame_path=frame_path,
+        )
     else:
-        nwb.write_nwb(labels, filename)
+        nwb.write_nwb(
+            labels,
+            filename,
+            as_training=as_training,
+            frame_inds=frame_inds,
+            frame_path=frame_path,
+        )
 
 
 def load_labelstudio(
@@ -190,6 +216,8 @@
         return load_jabs(filename, **kwargs)
     elif format == "video":
         return load_video(filename, **kwargs)
+    else:
+        raise ValueError(f"Unknown format '{format}' for filename: '{filename}'.")
 
 
 def save_file(
@@ -219,8 +247,10 @@
 
     if format == "slp":
         save_slp(labels, filename, **kwargs)
-    elif format == "nwb":
-        save_nwb(labels, filename, **kwargs)
+    elif format in ("nwb", "nwb_predictions"):
+        save_nwb(labels, filename, False)
+    elif format == "nwb_training":
+        save_nwb(labels, filename, True, frame_inds=kwargs.pop("frame_inds", None))
     elif format == "labelstudio":
         save_labelstudio(labels, filename, **kwargs)
     elif format == "jabs":