load spike sorting: expose collection field

Author: oliche

brainbox/io/one.py

@@ -388,7 +388,7 @@ def load_channel_locations(eid, probe=None, one=None, aligned=False, brain_atlas
 
 
 def load_spike_sorting_fast(eid, one=None, probe=None, dataset_types=None, spike_sorter=None, revision=None,
-                            brain_regions=None, nested=True):
+                            brain_regions=None, nested=True, collection=None):
     """
     From an eid, loads spikes and clusters for all probes
     The following set of dataset types are loaded:
@@ -403,12 +403,14 @@ def load_spike_sorting_fast(eid, one=None, probe=None, dataset_types=None, spike
     :param probe: name of probe to load in, if not given all probes for session will be loaded
     :param dataset_types: additional spikes/clusters objects to add to the standard default list
     :param spike_sorter: name of the spike sorting you want to load (None for default)
+    :param collection: name of the spike sorting collection to load - exclusive with spike sorter name ex: "alf/probe00"
     :param return_channels: (bool) defaults to False otherwise tries and load channels from disk
     :param brain_regions: ibllib.atlas.regions.BrainRegions object - will label acronyms if provided
     :param nested: if a single probe is required, do not output a dictionary with the probe name as key
     :return: spikes, clusters (dict of bunch, 1 bunch per probe)
     """
-    collection = _collection_filter_from_args(probe, spike_sorter)
+    if collection is None:
+        collection = _collection_filter_from_args(probe, spike_sorter)
     _logger.debug(f"load spike sorting with collection filter {collection}")
     kwargs = dict(eid=eid, one=one, collection=collection, revision=revision, dataset_types=dataset_types,
                   brain_regions=brain_regions)