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":