Proposal to implement a sorting_analyzer.merge_units() syntax

doc/modules/core.rst

@@ -377,6 +377,20 @@ backends without writing to disk. So, you can compute an extension *in-memory* w
 you have decided on your desired parameters you can either use :code:`compute` with :code:`save=True` or use :code:`save_as`
 to write everything out to disk.
 
+
+Finally, the :code:`SortingAnalyzer` object can be used directly to curate a spike sorting output by selecting/removing units
+and merging unit groups.
+
+.. code-block:: python
+
+    sorting_analyzer_select = sorting_analyzer.select_units(unit_ids=[0, 1, 2, 3])
+    sorting_analyzer_remove = sorting_analyzer.remove_units(remove_unit_ids=[0])
+    sorting_analyzer_merge = sorting_analyzer.merge_units([0, 1], [2, 3])
+
+All computed extensions will be automatically propagated or merged when curating. Please refer to the
+:ref:`modules/curation` documentation for more information.
+
+
 Event
 -----
 

src/spikeinterface/core/analyzer_extension_core.py

@@ -9,6 +9,8 @@
   * ComputeNoiseLevels which is very convenient to have
 """
 
+import warnings
+
 import numpy as np
 
 from .sortinganalyzer import AnalyzerExtension, register_result_extension
@@ -76,6 +78,20 @@ def _select_extension_data(self, unit_ids):
         new_data["random_spikes_indices"] = np.flatnonzero(selected_mask[keep_spike_mask])
         return new_data
 
+    def _merge_extension_data(
+        self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
+    ):
+        new_data = dict()
+        random_spikes_indices = self.data["random_spikes_indices"]
+        if keep_mask is None:
+            new_data["random_spikes_indices"] = random_spikes_indices.copy()
+        else:
+            spikes = self.sorting_analyzer.sorting.to_spike_vector()
+            selected_mask = np.zeros(spikes.size, dtype=bool)
+            selected_mask[random_spikes_indices] = True
+            new_data["random_spikes_indices"] = np.flatnonzero(selected_mask[keep_mask])
+        return new_data
+
     def _get_data(self):
         return self.data["random_spikes_indices"]
 
@@ -224,18 +240,66 @@ def _select_extension_data(self, unit_ids):
 
         return new_data
 
-    def get_waveforms_one_unit(
-        self,
-        unit_id,
-        force_dense: bool = False,
+    def _merge_extension_data(
+        self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
     ):
+        new_data = dict()
+
+        waveforms = self.data["waveforms"]
+        some_spikes = self.sorting_analyzer.get_extension("random_spikes").get_random_spikes()
+        if keep_mask is not None:
+            spike_indices = self.sorting_analyzer.get_extension("random_spikes").get_data()
+            valid = keep_mask[spike_indices]
+            some_spikes = some_spikes[valid]
+            waveforms = waveforms[valid]
+        else:
+            waveforms = waveforms.copy()
+
+        old_sparsity = self.sorting_analyzer.sparsity
+        if old_sparsity is not None:
+            # we need a realignement inside each group because we take the channel intersection sparsity
+            for group_ids in merge_unit_groups:
+                group_indices = self.sorting_analyzer.sorting.ids_to_indices(group_ids)
+                group_sparsity_mask = old_sparsity.mask[group_indices, :]
+                group_selection = []
+                for unit_id in group_ids:
+                    unit_index = self.sorting_analyzer.sorting.id_to_index(unit_id)
+                    selection = np.flatnonzero(some_spikes["unit_index"] == unit_index)
+                    group_selection.append(selection)
+                _inplace_sparse_realign_waveforms(waveforms, group_selection, group_sparsity_mask)
+
+            old_num_chans = int(np.max(np.sum(old_sparsity.mask, axis=1)))
+            new_num_chans = int(np.max(np.sum(new_sorting_analyzer.sparsity.mask, axis=1)))
+            if new_num_chans < old_num_chans:
+                waveforms = waveforms[:, :, :new_num_chans]
+
+        return dict(waveforms=waveforms)
+
+    def get_waveforms_one_unit(self, unit_id, force_dense: bool = False):
+        """
+        Returns the waveforms of a unit id.
+
+        Parameters
+        ----------
+        unit_id : int or str
+            The unit id to return waveforms for
+        force_dense : bool, default: False
+            If True, and SortingAnalyzer must be sparse then only waveforms on sparse channels are returned.
+
+        Returns
+        -------
+        waveforms: np.array
+            The waveforms (num_waveforms, num_samples, num_channels).
+            In case sparsity is used, only the waveforms on sparse channels are returned.
+        """
         sorting = self.sorting_analyzer.sorting
         unit_index = sorting.id_to_index(unit_id)
-        # spikes = sorting.to_spike_vector()
-        # some_spikes = spikes[self.sorting_analyzer.random_spikes_indices]
+
+        waveforms = self.data["waveforms"]
         some_spikes = self.sorting_analyzer.get_extension("random_spikes").get_random_spikes()
+
         spike_mask = some_spikes["unit_index"] == unit_index
-        wfs = self.data["waveforms"][spike_mask, :, :]
+        wfs = waveforms[spike_mask, :, :]
 
         if self.sorting_analyzer.sparsity is not None:
             chan_inds = self.sorting_analyzer.sparsity.unit_id_to_channel_indices[unit_id]
@@ -252,6 +316,22 @@ def _get_data(self):
         return self.data["waveforms"]
 
 
+def _inplace_sparse_realign_waveforms(waveforms, group_selection, group_sparsity_mask):
+    # this is used by "waveforms" extension but also "pca"
+
+    # common mask is intersection
+    common_mask = np.all(group_sparsity_mask, axis=0)
+
+    for i in range(len(group_selection)):
+        chan_mask = group_sparsity_mask[i, :]
+        sel = group_selection[i]
+        wfs = waveforms[sel, :, :][:, :, : np.sum(chan_mask)]
+        keep_mask = common_mask[chan_mask]
+        wfs = wfs[:, :, keep_mask]
+        waveforms[:, :, : wfs.shape[2]][sel, :, :] = wfs
+        waveforms[:, :, wfs.shape[2] :][sel, :, :] = 0.0
+
+
 compute_waveforms = ComputeWaveforms.function_factory()
 register_result_extension(ComputeWaveforms)
 
@@ -298,16 +378,13 @@ def _set_params(self, ms_before: float = 1.0, ms_after: float = 2.0, operators=N
 
         waveforms_extension = self.sorting_analyzer.get_extension("waveforms")
         if waveforms_extension is not None:
-            nbefore = waveforms_extension.nbefore
-            nafter = waveforms_extension.nafter
-        else:
-            nbefore = int(ms_before * self.sorting_analyzer.sampling_frequency / 1000.0)
-            nafter = int(ms_after * self.sorting_analyzer.sampling_frequency / 1000.0)
+            ms_before = waveforms_extension.params["ms_before"]
+            ms_after = waveforms_extension.params["ms_after"]
 
         params = dict(
             operators=operators,
-            nbefore=nbefore,
-            nafter=nafter,
+            ms_before=ms_before,
+            ms_after=ms_after,
         )
         return params
 
@@ -316,6 +393,7 @@ def _run(self, verbose=False, **job_kwargs):
 
         if self.sorting_analyzer.has_extension("waveforms"):
             self._compute_and_append_from_waveforms(self.params["operators"])
+
         else:
             for operator in self.params["operators"]:
                 if operator not in ("average", "std"):
@@ -380,7 +458,6 @@ def _compute_and_append_from_waveforms(self, operators):
             "random_spikes"
         ), "compute templates requires the random_spikes extension. You can run sorting_analyzer.get_random_spikes()"
         some_spikes = self.sorting_analyzer.get_extension("random_spikes").get_random_spikes()
-
         for unit_index, unit_id in enumerate(unit_ids):
             spike_mask = some_spikes["unit_index"] == unit_index
             wfs = waveforms[spike_mask, :, :]
@@ -410,11 +487,33 @@ def _compute_and_append_from_waveforms(self, operators):
 
     @property
     def nbefore(self):
-        return self.params["nbefore"]
+        if "ms_before" not in self.params:
+            # compatibility february 2024 > july 2024
+            self.params["ms_before"] = self.params["nbefore"] * 1000.0 / self.sorting_analyzer.sampling_frequency
+            warnings.warn(
+                "The 'nbefore' parameter is deprecated and it's been replaced by 'ms_before' in the params."
+                "You can save the sorting_analyzer to update the params.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        nbefore = int(self.params["ms_before"] * self.sorting_analyzer.sampling_frequency / 1000.0)
+        return nbefore
 
     @property
     def nafter(self):
-        return self.params["nafter"]
+        if "ms_after" not in self.params:
+            # compatibility february 2024 > july 2024
+            warnings.warn(
+                "The 'nafter' parameter is deprecated and it's been replaced by 'ms_after' in the params."
+                "You can save the sorting_analyzer to update the params.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            self.params["ms_after"] = self.params["nafter"] * 1000.0 / self.sorting_analyzer.sampling_frequency
+
+        nafter = int(self.params["ms_after"] * self.sorting_analyzer.sampling_frequency / 1000.0)
+        return nafter
 
     def _select_extension_data(self, unit_ids):
         keep_unit_indices = np.flatnonzero(np.isin(self.sorting_analyzer.unit_ids, unit_ids))
@@ -425,12 +524,43 @@ def _select_extension_data(self, unit_ids):
 
         return new_data
 
+    def _merge_extension_data(
+        self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
+    ):
+
+        all_new_units = new_sorting_analyzer.unit_ids
+        new_data = dict()
+        counts = self.sorting_analyzer.sorting.count_num_spikes_per_unit()
+        for key, arr in self.data.items():
+            new_data[key] = np.zeros((len(all_new_units), arr.shape[1], arr.shape[2]), dtype=arr.dtype)
+            for unit_index, unit_id in enumerate(all_new_units):
+                if unit_id not in new_unit_ids:
+                    keep_unit_index = self.sorting_analyzer.sorting.id_to_index(unit_id)
+                    new_data[key][unit_index] = arr[keep_unit_index, :, :]
+                else:
+                    merge_group = merge_unit_groups[list(new_unit_ids).index(unit_id)]
+                    keep_unit_indices = self.sorting_analyzer.sorting.ids_to_indices(merge_group)
+                    # We do a weighted sum of the templates
+                    weights = np.zeros(len(merge_group), dtype=np.float32)
+                    for count, merge_unit_id in enumerate(merge_group):
+                        weights[count] = counts[merge_unit_id]
+                    weights /= weights.sum()
+                    new_data[key][unit_index] = (arr[keep_unit_indices, :, :] * weights[:, np.newaxis, np.newaxis]).sum(
+                        0
+                    )
+                    if new_sorting_analyzer.sparsity is not None:
+                        chan_ids = new_sorting_analyzer.sparsity.unit_id_to_channel_indices[unit_id]
+                        mask = ~np.isin(np.arange(arr.shape[2]), chan_ids)
+                        new_data[key][unit_index][:, mask] = 0
+
+        return new_data
+
     def _get_data(self, operator="average", percentile=None, outputs="numpy"):
         if operator != "percentile":
             key = operator
         else:
             assert percentile is not None, "You must provide percentile=..."
-            key = f"pencentile_{percentile}"
+            key = f"percentile_{percentile}"
 
         templates_array = self.data[key]
 
@@ -582,6 +712,12 @@ def _select_extension_data(self, unit_ids):
         # this do not depend on units
         return self.data
 
+    def _merge_extension_data(
+        self, merge_unit_groups, new_unit_ids, new_sorting_analyzer, keep_mask=None, verbose=False, **job_kwargs
+    ):
+        # this do not depend on units
+        return self.data.copy()
+
     def _run(self, verbose=False):
         self.data["noise_levels"] = get_noise_levels(
             self.sorting_analyzer.recording, return_scaled=self.sorting_analyzer.return_scaled, **self.params

src/spikeinterface/core/generate.py

@@ -973,6 +973,8 @@ def inject_some_split_units(sorting, split_ids: list, num_split=2, output_ids=Fa
     -------
     sorting_with_split : NumpySorting
         A sorting with split units.
+    other_ids : dict
+        The dictionary with the split unit_ids. Returned only if output_ids is True.
     """
     unit_ids = sorting.unit_ids
     assert unit_ids.dtype.kind == "i"