Allow run_sorter to accept dicts

doc/how_to/process_by_channel_group.rst

@@ -99,7 +99,8 @@ to any preprocessing function.
     referenced_recording = spre.common_reference(filtered_recording)
     good_channels_recording = spre.detect_and_remove_bad_channels(filtered_recording)
 
-We can then aggregate the recordings back together using the ``aggregate_channels`` function
+We can then aggregate the recordings back together using the ``aggregate_channels`` function.
+Note that we do not need to do this to sort the data (see :ref:`sorting a recording by channel group`)
 
 .. code-block:: python
 
@@ -141,16 +142,38 @@ Sorting a Recording by Channel Group
 We can also sort a recording for each channel group separately. It is not necessary to preprocess
 a recording by channel group in order to sort by channel group.
 
-There are two ways to sort a recording by channel group. First, we can split the preprocessed
-recording (or, if it was already split during preprocessing as above, skip the :py:func:`~aggregate_channels` step
-directly use the :py:func:`~split_recording_dict`).
+There are two ways to sort a recording by channel group. First, we can simply pass the output from
+our preprocessing-by-group method above. Second, for more control, we can loop over the recordings
+ourselves.
 
-**Option 1: Manual splitting**
+**Option 1 : Automatic splitting**
 
-In this example, similar to above we loop over all preprocessed recordings that
+Simply pass the split recording to the `run_sorter` function, as if it was a non-split recording.
+This will return a dict of sortings, with the keys corresponding to the groups.
+
+.. code-block:: python
+
+    split_recording = raw_recording.split_by("group")
+
+    # do preprocessing if needed
+    pp_recording = spre.bandpass_filter(split_recording)
+
+     dict_of_sortings = run_sorter(
+        sorter_name='kilosort2',
+        recording=pp_recording,
+        working_folder='working_path'
+    )
+
+
+**Option 2: Manual splitting**
+
+In this example, we loop over all preprocessed recordings that
 are grouped by channel, and apply the sorting separately. We store the
 sorting objects in a dictionary for later use.
 
+You might do this if you want extra control e.g. to apply bespoke steps
+to different groups.
+
 .. code-block:: python
 
     split_preprocessed_recording = preprocessed_recording.split_by("group")
@@ -163,16 +186,3 @@ sorting objects in a dictionary for later use.
             output_folder=f"folder_KS2_group{group}"
             )
         sortings[group] = sorting
-
-**Option 2 : Automatic splitting**
-
-Alternatively, SpikeInterface provides a convenience function to sort the recording by property:
-
-.. code-block:: python
-
-     aggregate_sorting = run_sorter_by_property(
-        sorter_name='kilosort2',
-        recording=preprocessed_recording,
-        grouping_property='group',
-        working_folder='working_path'
-    )

doc/modules/sorters.rst

@@ -339,8 +339,8 @@ Running spike sorting by group is indeed a very common need.
 A :py:class:`~spikeinterface.core.BaseRecording` object has the ability to split itself into a dictionary of
 sub-recordings given a certain property (see :py:meth:`~spikeinterface.core.BaseRecording.split_by`).
 So it is easy to loop over this dictionary and sequentially run spike sorting on these sub-recordings.
-SpikeInterface also provides a high-level function to automate the process of splitting the
-recording and then aggregating the results with the :py:func:`~spikeinterface.sorters.run_sorter_by_property` function.
+The :py:func:`~spikeinterface.sorters.run_sorter` method can also accept the dictionary which is returned
+by :py:meth:`~spikeinterface.core.BaseRecording.split_by` and will return a dictionary of sortings.
 
 In this example, we create a 16-channel recording with 4 tetrodes:
 
@@ -368,7 +368,19 @@ In this example, we create a 16-channel recording with 4 tetrodes:
     # >>> [0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3]
 
 
-**Option 1: Manual splitting**
+**Option 1 : Automatic splitting**
+
+.. code-block:: python
+
+    # here the result is a dict of sortings
+    dict_of_sortings = run_sorter(
+      sorter_name='kilosort2',
+      recording=recording_4_tetrodes,
+      working_folder='working_path'
+    )
+
+
+**Option 2: Manual splitting**
 
 .. code-block:: python
 
@@ -383,15 +395,6 @@ In this example, we create a 16-channel recording with 4 tetrodes:
         sorting = run_sorter(sorter_name='kilosort2', recording=recording, output_folder=f"folder_KS2_group{group}")
         sortings[group] = sorting
 
-**Option 2 : Automatic splitting**
-
-.. code-block:: python
-
-    # here the result is one sorting that aggregates all sub sorting objects
-    aggregate_sorting = run_sorter_by_property(sorter_name='kilosort2', recording=recording_4_tetrodes,
-                                               grouping_property='group',
-                                               working_folder='working_path')
-
 
 Handling multi-segment recordings
 ---------------------------------

src/spikeinterface/core/loading.py

@@ -196,6 +196,12 @@ def _guess_object_from_local_folder(folder):
         with open(folder / "spikeinterface_info.json", "r") as f:
             spikeinterface_info = json.load(f)
             return _guess_object_from_dict(spikeinterface_info)
+    elif (
+        (folder / "sorter_output").is_dir()
+        and (folder / "spikeinterface_params.json").is_file()
+        and (folder / "spikeinterface_log.json").is_file()
+    ):
+        return "SorterOutput"
     elif (folder / "waveforms").is_dir():
         # before the SortingAnlazer, it was WaveformExtractor (v<0.101)
         return "WaveformExtractor"
@@ -212,13 +218,20 @@ def _guess_object_from_local_folder(folder):
         return "Recording|Sorting"
 
 
-def _load_object_from_folder(folder, object_type, **kwargs):
+def _load_object_from_folder(folder, object_type: str, **kwargs):
+
     if object_type == "SortingAnalyzer":
         from .sortinganalyzer import load_sorting_analyzer
 
         analyzer = load_sorting_analyzer(folder, **kwargs)
         return analyzer
 
+    elif object_type == "SorterOutput":
+        from spikeinterface.sorters import read_sorter_folder
+
+        sorting = read_sorter_folder(folder)
+        return sorting
+
     elif object_type == "Motion":
         from spikeinterface.core.motion import Motion
 
@@ -244,6 +257,16 @@ def _load_object_from_folder(folder, object_type, **kwargs):
                     si_file = f
         return BaseExtractor.load(si_file, base_folder=folder)
 
+    elif object_type.startswith("Group"):
+
+        sub_object_type = object_type.split("[")[1].split("]")[0]
+        with open(folder / "spikeinterface_info.json", "r") as f:
+            spikeinterface_info = json.load(f)
+        group_keys = spikeinterface_info.get("dict_keys")
+
+        group_of_objects = {key: _load_object_from_folder(folder / str(key), sub_object_type) for key in group_keys}
+        return group_of_objects
+
 
 def _guess_object_from_zarr(zarr_folder):
     # here it can be a zarr folder for Recording|Sorting|SortingAnalyzer|Template

src/spikeinterface/sorters/runsorter.py

@@ -70,7 +70,7 @@
     ----------
     sorter_name : str
         The sorter name
-    recording : RecordingExtractor
+    recording : RecordingExtractor | dict of RecordingExtractor
         The recording extractor to be spike sorted
     folder : str or Path
         Path to output folder
@@ -100,16 +100,12 @@
     **sorter_params : keyword args
         Spike sorter specific arguments (they can be retrieved with `get_default_sorter_params(sorter_name_or_class)`)
 
-    Returns
-    -------
-    BaseSorting | None
-        The spike sorted data (it `with_output` is True) or None (if `with_output` is False)
     """
 
 
 def run_sorter(
     sorter_name: str,
-    recording: BaseRecording,
+    recording: BaseRecording | dict,
     folder: Optional[str] = None,
     remove_existing_folder: bool = False,
     delete_output_folder: bool = False,
@@ -124,8 +120,11 @@ def run_sorter(
 ):
     """
     Generic function to run a sorter via function approach.
-
     {}
+    Returns
+    -------
+    BaseSorting | dict of BaseSorting | None
+        The spike sorted data (it `with_output` is True) or None (if `with_output` is False)
 
     Examples
     --------
@@ -151,6 +150,20 @@ def run_sorter(
         **sorter_params,
     )
 
+    if isinstance(recording, dict):
+
+        all_kwargs = common_kwargs
+        all_kwargs.update(
+            dict(
+                docker_image=docker_image,
+                singularity_image=singularity_image,
+                delete_container_files=delete_container_files,
+            )
+        )
+
+        dict_of_sorters = _run_sorter_by_dict(recording, **all_kwargs)
+        return dict_of_sorters
+
     if docker_image or singularity_image:
         common_kwargs.update(dict(delete_container_files=delete_container_files))
         if docker_image:
@@ -201,6 +214,56 @@ def run_sorter(
 run_sorter.__doc__ = run_sorter.__doc__.format(_common_param_doc)
 
 
+def _run_sorter_by_dict(dict_of_recordings: dict, folder: str | Path | None = None, **run_sorter_params):
+    """
+    Applies `run_sorter` to each recording in a dict of recordings and saves
+    the results.
+    {}
+    Returns
+    -------
+    dict
+        Dictionary of `BaseSorting`s, with the same keys as the input dict of `BaseRecording`s.
+    """
+
+    sorter_name = run_sorter_params["sorter_name"]
+    remove_existing_folder = run_sorter_params["remove_existing_folder"]
+
+    if folder is None:
+        folder = Path(sorter_name + "_output")
+
+    folder = Path(folder)
+    folder.mkdir(exist_ok=remove_existing_folder)
+
+    # If we know how the recording was split, save this in the info file
+    first_recording = next(iter(dict_of_recordings.values()))
+    split_by_property = first_recording.get_annotation("split_by_property")
+    if split_by_property is None:
+        split_by_property = "Unknown"
+
+    info_file = folder / "spikeinterface_info.json"
+    info = dict(
+        version=spikeinterface.__version__,
+        dev_mode=spikeinterface.DEV_MODE,
+        object="Group[SorterOutput]",
+        dict_keys=list(dict_of_recordings.keys()),
+    )
+    with open(info_file, mode="w") as f:
+        json.dump(check_json(info), f, indent=4)
+
+    sorter_dict = {}
+    for group_key, recording in dict_of_recordings.items():
+
+        if "recording" in run_sorter_params:
+            run_sorter_params.pop("recording")
+
+        sorter_dict[group_key] = run_sorter(recording=recording, folder=folder / f"{group_key}", **run_sorter_params)
+
+    return sorter_dict
+
+
+_run_sorter_by_dict.__doc__ = _run_sorter_by_dict.__doc__.format(_common_param_doc)
+
+
 def run_sorter_local(
     sorter_name,
     recording,

src/spikeinterface/sorters/tests/test_runsorter.py

@@ -4,8 +4,10 @@
 from pathlib import Path
 import shutil
 from packaging.version import parse
+import json
+import numpy as np
 
-from spikeinterface import generate_ground_truth_recording
+from spikeinterface import generate_ground_truth_recording, load
 from spikeinterface.sorters import run_sorter
 
 ON_GITHUB = bool(os.getenv("GITHUB_ACTIONS"))
@@ -45,6 +47,54 @@ def test_run_sorter_local(generate_recording, create_cache_folder):
     print(sorting)
 
 
+def test_run_sorter_dict(generate_recording, create_cache_folder):
+    recording = generate_recording
+    cache_folder = create_cache_folder
+
+    recording = recording.time_slice(start_time=0, end_time=3)
+
+    recording.set_property(key="split_property", values=[4, 4, "g", "g", 4, 4, 4, "g"])
+    dict_of_recordings = recording.split_by("split_property")
+
+    sorter_params = {"detection": {"detect_threshold": 4.9}}
+
+    output_folder = cache_folder / "sorting_tdc_local_dict"
+
+    dict_of_sortings = run_sorter(
+        "simple",
+        dict_of_recordings,
+        output_folder=output_folder,
+        remove_existing_folder=True,
+        delete_output_folder=False,
+        verbose=True,
+        raise_error=True,
+        **sorter_params,
+    )
+
+    assert set(list(dict_of_sortings.keys())) == set(["g", "4"])
+    assert (output_folder / "g").is_dir()
+    assert (output_folder / "4").is_dir()
+
+    assert dict_of_sortings["g"]._recording.get_num_channels() == 3
+    assert dict_of_sortings["4"]._recording.get_num_channels() == 5
+
+    info_filepath = output_folder / "spikeinterface_info.json"
+    assert info_filepath.is_file()
+
+    with open(info_filepath) as f:
+        spikeinterface_info = json.load(f)
+
+    si_info_keys = spikeinterface_info.keys()
+    for key in ["version", "dev_mode", "object"]:
+        assert key in si_info_keys
+
+    loaded_sortings = load(output_folder)
+    assert loaded_sortings.keys() == dict_of_sortings.keys()
+    for key, sorting in loaded_sortings.items():
+        assert np.all(sorting.unit_ids == dict_of_sortings[key].unit_ids)
+        assert np.all(sorting.to_spike_vector() == dict_of_sortings[key].to_spike_vector())
+
+
 @pytest.mark.skipif(ON_GITHUB, reason="Docker tests don't run on github: test locally")
 def test_run_sorter_docker(generate_recording, create_cache_folder):
     recording = generate_recording