Add the option for multiple views loading - take2 (#346)

* moving to new branch

* final test fix

* Update movement/io/load_poses.py

Co-authored-by: Niko Sirmpilatze <[email protected]>

* doc change

* fixed doc

---------

Co-authored-by: Niko Sirmpilatze <[email protected]>

Author: vigji

docs/source/getting_started/movement_dataset.md

@@ -16,7 +16,6 @@ To discuss the specifics of both types of `movement` datasets, it is useful to c
 To learn more about `xarray` data structures in general, see the relevant
 [documentation](xarray:user-guide/data-structures.html).
 
-
 ## Dataset structure
 
 ```{figure} ../_static/dataset_structure.png
@@ -135,6 +134,20 @@ In both cases, appropriate **coordinates** are assigned to each **dimension**.
 - `space` is labelled with either `x`, `y` (2D) or `x`, `y`, `z` (3D). Note that bounding boxes datasets are restricted to 2D space.
 - `time` is labelled in seconds if `fps` is provided, otherwise the **coordinates** are expressed in frames (ascending 0-indexed integers).
 
+:::{dropdown} Additional dimensions
+:color: info
+:icon: info
+The above **dimensions** and **coordinates** are created
+by default when loading a `movement` dataset from a single
+file containing pose or bounding boxes tracks.
+
+In some cases, you may encounter or create datasets with extra
+**dimensions**. For example, the
+{func}`movement.io.load_poses.from_multiview_files()` function
+creates an additional `views` **dimension**,
+with the **coordinates** being the names given to each camera view.
+:::
+
 ### Data variables
 The data variables in a `movement` dataset are the arrays that hold the actual data, as {class}`xarray.DataArray` objects.
 

movement/io/load_poses.py

@@ -351,6 +351,41 @@ def from_dlc_file(
     )
 
 
+def from_multiview_files(
+    file_path_dict: dict[str, Path | str],
+    source_software: Literal["DeepLabCut", "SLEAP", "LightningPose"],
+    fps: float | None = None,
+) -> xr.Dataset:
+    """Load and merge pose tracking data from multiple views (cameras).
+
+    Parameters
+    ----------
+    file_path_dict : dict[str, Union[Path, str]]
+        A dict whose keys are the view names and values are the paths to load.
+    source_software : {'LightningPose', 'SLEAP', 'DeepLabCut'}
+        The source software of the file.
+    fps : float, optional
+        The number of frames per second in the video. If None (default),
+        the `time` coordinates will be in frame numbers.
+
+    Returns
+    -------
+    xarray.Dataset
+        ``movement`` dataset containing the pose tracks, confidence scores,
+        and associated metadata, with an additional ``views`` dimension.
+
+    """
+    views_list = list(file_path_dict.keys())
+    new_coord_views = xr.DataArray(views_list, dims="view")
+
+    dataset_list = [
+        from_file(f, source_software=source_software, fps=fps)
+        for f in file_path_dict.values()
+    ]
+
+    return xr.concat(dataset_list, dim=new_coord_views)
+
+
 def _ds_from_lp_or_dlc_file(
     file_path: Path | str,
     source_software: Literal["LightningPose", "DeepLabCut"],

tests/test_unit/test_load_poses.py

@@ -300,3 +300,20 @@ def test_from_numpy_valid(
             source_software=source_software,
         )
         self.assert_dataset(ds, expected_source_software=source_software)
+
+    def test_from_multiview_files(self):
+        """Test that the from_file() function delegates to the correct
+        loader function according to the source_software.
+        """
+        view_names = ["view_0", "view_1"]
+        file_path_dict = {
+            view: DATA_PATHS.get("DLC_single-wasp.predictions.h5")
+            for view in view_names
+        }
+        multi_view_ds = load_poses.from_multiview_files(
+            file_path_dict, source_software="DeepLabCut"
+        )
+
+        assert isinstance(multi_view_ds, xr.Dataset)
+        assert "view" in multi_view_ds.dims
+        assert multi_view_ds.view.values.tolist() == view_names