Merge branch 'main' into tdc_2

src/spikeinterface/core/base.py

@@ -45,8 +45,12 @@ def __init__(self, main_ids: Sequence) -> None:
         self._kwargs = {}
 
         # 'main_ids' will either be channel_ids or units_ids
-        # They is used for properties
+        # They are used for properties
         self._main_ids = np.array(main_ids)
+        if len(self._main_ids) > 0:
+            assert (
+                self._main_ids.dtype.kind in "uiSU"
+            ), f"Main IDs can only be integers (signed/unsigned) or strings, not {self._main_ids.dtype}"
 
         # dict at object level
         self._annotations = {}
@@ -984,7 +988,7 @@ def _load_extractor_from_dict(dic) -> BaseExtractor:
     class_name = None
 
     if "kwargs" not in dic:
-        raise Exception(f"This dict cannot be load into extractor {dic}")
+        raise Exception(f"This dict cannot be loaded into extractor {dic}")
 
     # Create new kwargs to avoid modifying the original dict["kwargs"]
     new_kwargs = dict()
@@ -1005,7 +1009,7 @@ def _load_extractor_from_dict(dic) -> BaseExtractor:
     assert extractor_class is not None and class_name is not None, "Could not load spikeinterface class"
     if not _check_same_version(class_name, dic["version"]):
         warnings.warn(
-            f"Versions are not the same. This might lead compatibility errors. "
+            f"Versions are not the same. This might lead to compatibility errors. "
             f"Using {class_name.split('.')[0]}=={dic['version']} is recommended"
         )
 

src/spikeinterface/core/baserecording.py

@@ -305,7 +305,8 @@ def get_traces(
 
             if not self.has_scaled():
                 raise ValueError(
-                    "This recording do not support return_scaled=True (need gain_to_uV and offset_" "to_uV properties)"
+                    "This recording does not support return_scaled=True (need gain_to_uV and offset_"
+                    "to_uV properties)"
                 )
             else:
                 gains = self.get_property("gain_to_uV")
@@ -416,8 +417,8 @@ def set_times(self, times, segment_index=None, with_warning=True):
         if with_warning:
             warn(
                 "Setting times with Recording.set_times() is not recommended because "
-                "times are not always propagated to across preprocessing"
-                "Use use this carefully!"
+                "times are not always propagated across preprocessing"
+                "Use this carefully!"
             )
 
     def sample_index_to_time(self, sample_ind, segment_index=None):

src/spikeinterface/core/baserecordingsnippets.py

@@ -1,4 +1,4 @@
-from typing import List
+from __future__ import annotations
 from pathlib import Path
 
 import numpy as np
@@ -19,7 +19,7 @@ class BaseRecordingSnippets(BaseExtractor):
 
     has_default_locations = False
 
-    def __init__(self, sampling_frequency: float, channel_ids: List, dtype):
+    def __init__(self, sampling_frequency: float, channel_ids: list[str, int], dtype: np.dtype):
         BaseExtractor.__init__(self, channel_ids)
         self._sampling_frequency = sampling_frequency
         self._dtype = np.dtype(dtype)

src/spikeinterface/core/basesnippets.py

@@ -1,10 +1,8 @@
 from typing import List, Union
-from pathlib import Path
 from .base import BaseSegment
 from .baserecordingsnippets import BaseRecordingSnippets
 import numpy as np
 from warnings import warn
-from probeinterface import Probe, ProbeGroup, write_probeinterface, read_probeinterface, select_axes
 
 # snippets segments?
 

src/spikeinterface/core/basesorting.py

@@ -170,7 +170,7 @@ def register_recording(self, recording, check_spike_frames=True):
         if check_spike_frames:
             if has_exceeding_spikes(recording, self):
                 warnings.warn(
-                    "Some spikes are exceeding the recording's duration! "
+                    "Some spikes exceed the recording's duration! "
                     "Removing these excess spikes with `spikeinterface.curation.remove_excess_spikes()` "
                     "Might be necessary for further postprocessing."
                 )

src/spikeinterface/core/binaryrecordingextractor.py

@@ -91,7 +91,7 @@ def __init__(
             file_path_list = [Path(file_paths)]
 
         if t_starts is not None:
-            assert len(t_starts) == len(file_path_list), "t_starts must be a list of same size than file_paths"
+            assert len(t_starts) == len(file_path_list), "t_starts must be a list of the same size as file_paths"
             t_starts = [float(t_start) for t_start in t_starts]
 
         dtype = np.dtype(dtype)

src/spikeinterface/core/channelsaggregationrecording.py

@@ -104,11 +104,11 @@ def __init__(self, channel_map, parent_segments):
         times_kargs0 = parent_segment0.get_times_kwargs()
         if times_kargs0["time_vector"] is None:
             for ps in parent_segments:
-                assert ps.get_times_kwargs()["time_vector"] is None, "All segment should not have times set"
+                assert ps.get_times_kwargs()["time_vector"] is None, "All segments should not have times set"
         else:
             for ps in parent_segments:
                 assert ps.get_times_kwargs()["t_start"] == times_kargs0["t_start"], (
-                    "All segment should have the same " "t_start"
+                    "All segments should have the same " "t_start"
                 )
 
         BaseRecordingSegment.__init__(self, **times_kargs0)

src/spikeinterface/core/channelslice.py

@@ -35,7 +35,7 @@ def __init__(self, parent_recording, channel_ids=None, renamed_channel_ids=None)
         ), "ChannelSliceRecording: renamed channel_ids must be the same size"
         assert (
             self._channel_ids.size == np.unique(self._channel_ids).size
-        ), "ChannelSliceRecording : channel_ids not unique"
+        ), "ChannelSliceRecording : channel_ids are not unique"
 
         sampling_frequency = parent_recording.get_sampling_frequency()
 
@@ -123,7 +123,7 @@ def __init__(self, parent_snippets, channel_ids=None, renamed_channel_ids=None):
         ), "ChannelSliceSnippets: renamed channel_ids must be the same size"
         assert (
             self._channel_ids.size == np.unique(self._channel_ids).size
-        ), "ChannelSliceSnippets : channel_ids not unique"
+        ), "ChannelSliceSnippets : channel_ids are not unique"
 
         sampling_frequency = parent_snippets.get_sampling_frequency()
 

src/spikeinterface/core/frameslicerecording.py

@@ -27,7 +27,7 @@ class FrameSliceRecording(BaseRecording):
     def __init__(self, parent_recording, start_frame=None, end_frame=None):
         channel_ids = parent_recording.get_channel_ids()
 
-        assert parent_recording.get_num_segments() == 1, "FrameSliceRecording work only with one segment"
+        assert parent_recording.get_num_segments() == 1, "FrameSliceRecording only works with one segment"
 
         parent_size = parent_recording.get_num_samples(0)
         if start_frame is None:

src/spikeinterface/core/frameslicesorting.py

@@ -36,7 +36,7 @@ class FrameSliceSorting(BaseSorting):
     def __init__(self, parent_sorting, start_frame=None, end_frame=None, check_spike_frames=True):
         unit_ids = parent_sorting.get_unit_ids()
 
-        assert parent_sorting.get_num_segments() == 1, "FrameSliceSorting work only with one segment"
+        assert parent_sorting.get_num_segments() == 1, "FrameSliceSorting only works with one segment"
 
         if start_frame is None:
             start_frame = 0
@@ -49,10 +49,10 @@ def __init__(self, parent_sorting, start_frame=None, end_frame=None, check_spike
                 end_frame = parent_n_samples
             assert (
                 end_frame <= parent_n_samples
-            ), "`end_frame` should be smaller than the sortings total number of samples."
+            ), "`end_frame` should be smaller than the sortings' total number of samples."
             assert (
                 start_frame <= parent_n_samples
-            ), "`start_frame` should be smaller than the sortings total number of samples."
+            ), "`start_frame` should be smaller than the sortings' total number of samples."
             if check_spike_frames and has_exceeding_spikes(parent_sorting._recording, parent_sorting):
                 raise ValueError(
                     "The sorting object has spikes exceeding the recording duration. You have to remove those spikes "
@@ -67,7 +67,7 @@ def __init__(self, parent_sorting, start_frame=None, end_frame=None, check_spike
                 end_frame = max_spike_time + 1
 
         assert start_frame < end_frame, (
-            "`start_frame` should be greater than `end_frame`. "
+            "`start_frame` should be less than `end_frame`. "
             "This may be due to start_frame >= max_spike_time, if the end frame "
             "was not specified explicitly."
         )

src/spikeinterface/core/generate.py

@@ -1101,11 +1101,11 @@ def __init__(
             # handle also upsampling and jitter
             upsample_factor = templates.shape[3]
         elif templates.ndim == 5:
-            # handle also dirft
+            # handle also drift
             raise NotImplementedError("Drift will be implented soon...")
             # upsample_factor = templates.shape[3]
         else:
-            raise ValueError("templates have wring dim should 3 or 4")
+            raise ValueError("templates have wrong dim should 3 or 4")
 
         if upsample_factor is not None:
             assert upsample_vector is not None

src/spikeinterface/core/npysnippetsextractor.py

@@ -27,6 +27,9 @@ def __init__(
         num_segments = len(file_paths)
         data = np.load(file_paths[0], mmap_mode="r")
 
+        if channel_ids is None:
+            channel_ids = np.arange(data["snippet"].shape[2])
+
         BaseSnippets.__init__(
             self,
             sampling_frequency,
@@ -84,7 +87,7 @@ def write_snippets(snippets, file_paths, dtype=None):
             arr = np.empty(n, dtype=snippets_t, order="F")
             arr["frame"] = snippets.get_frames(segment_index=i)
             arr["snippet"] = snippets.get_snippets(segment_index=i).astype(dtype, copy=False)
-
+            file_paths[i].parent.mkdir(parents=True, exist_ok=True)
             np.save(file_paths[i], arr)
 
 

src/spikeinterface/core/sparsity.py

@@ -102,7 +102,11 @@ def __init__(self, mask, unit_ids, channel_ids):
 
         self.num_channels = self.channel_ids.size
         self.num_units = self.unit_ids.size
-        self.max_num_active_channels = self.mask.sum(axis=1).max()
+        if self.mask.shape[0]:
+            self.max_num_active_channels = self.mask.sum(axis=1).max()
+        else:
+            # empty sorting without units
+            self.max_num_active_channels = 0
 
     def __repr__(self):
         density = np.mean(self.mask)

src/spikeinterface/core/template_tools.py

@@ -1,21 +1,24 @@
+from __future__ import annotations
 import numpy as np
 import warnings
 
 from .sparsity import compute_sparsity, _sparsity_doc
 from .recording_tools import get_channel_distances, get_noise_levels
 
 
-def get_template_amplitudes(waveform_extractor, peak_sign: str = "neg", mode: str = "extremum"):
+def get_template_amplitudes(
+    waveform_extractor, peak_sign: "neg" | "pos" | "both" = "neg", mode: "extremum" | "at_index" = "extremum"
+):
     """
     Get amplitude per channel for each unit.
 
     Parameters
     ----------
     waveform_extractor: WaveformExtractor
         The waveform extractor
-    peak_sign: str
-        Sign of the template to compute best channels ('neg', 'pos', 'both')
-    mode: str
+    peak_sign: "neg" | "pos" | "both", default: "neg"
+        Sign of the template to compute best channels
+    mode: "extremum" | "at_index", default: "extremum"
         'extremum':  max or min
         'at_index': take value at spike index
 
@@ -24,8 +27,8 @@ def get_template_amplitudes(waveform_extractor, peak_sign: str = "neg", mode: st
     peak_values: dict
         Dictionary with unit ids as keys and template amplitudes as values
     """
-    assert peak_sign in ("both", "neg", "pos")
-    assert mode in ("extremum", "at_index")
+    assert peak_sign in ("both", "neg", "pos"), "'peak_sign' must be 'both', 'neg', or 'pos'"
+    assert mode in ("extremum", "at_index"), "'mode' must be 'extremum' or 'at_index'"
     unit_ids = waveform_extractor.sorting.unit_ids
 
     before = waveform_extractor.nbefore
@@ -57,7 +60,10 @@ def get_template_amplitudes(waveform_extractor, peak_sign: str = "neg", mode: st
 
 
 def get_template_extremum_channel(
-    waveform_extractor, peak_sign: str = "neg", mode: str = "extremum", outputs: str = "id"
+    waveform_extractor,
+    peak_sign: "neg" | "pos" | "both" = "neg",
+    mode: "extremum" | "at_index" = "extremum",
+    outputs: "id" | "index" = "id",
 ):
     """
     Compute the channel with the extremum peak for each unit.
@@ -66,12 +72,12 @@ def get_template_extremum_channel(
     ----------
     waveform_extractor: WaveformExtractor
         The waveform extractor
-    peak_sign: str
-        Sign of the template to compute best channels ('neg', 'pos', 'both')
-    mode: str
+    peak_sign: "neg" | "pos" | "both", default: "neg"
+        Sign of the template to compute best channels
+    mode: "extremum" | "at_index", default: "extremum"
         'extremum':  max or min
         'at_index': take value at spike index
-    outputs: str
+    outputs: "id" | "index", default: "id"
         * 'id': channel id
         * 'index': channel index
 
@@ -159,7 +165,7 @@ def get_template_channel_sparsity(
 get_template_channel_sparsity.__doc__ = get_template_channel_sparsity.__doc__.format(_sparsity_doc)
 
 
-def get_template_extremum_channel_peak_shift(waveform_extractor, peak_sign: str = "neg"):
+def get_template_extremum_channel_peak_shift(waveform_extractor, peak_sign: "neg" | "pos" | "both" = "neg"):
     """
     In some situations spike sorters could return a spike index with a small shift related to the waveform peak.
     This function estimates and return these alignment shifts for the mean template.
@@ -169,8 +175,8 @@ def get_template_extremum_channel_peak_shift(waveform_extractor, peak_sign: str
     ----------
     waveform_extractor: WaveformExtractor
         The waveform extractor
-    peak_sign: str
-        Sign of the template to compute best channels ('neg', 'pos', 'both')
+    peak_sign: "neg" | "pos" | "both", default: "neg"
+        Sign of the template to compute best channels
 
     Returns
     -------
@@ -203,17 +209,19 @@ def get_template_extremum_channel_peak_shift(waveform_extractor, peak_sign: str
     return shifts
 
 
-def get_template_extremum_amplitude(waveform_extractor, peak_sign: str = "neg", mode: str = "at_index"):
+def get_template_extremum_amplitude(
+    waveform_extractor, peak_sign: "neg" | "pos" | "both" = "neg", mode: "extremum" | "at_index" = "at_index"
+):
     """
     Computes amplitudes on the best channel.
 
     Parameters
     ----------
     waveform_extractor: WaveformExtractor
         The waveform extractor
-    peak_sign: str
-        Sign of the template to compute best channels ('neg', 'pos', 'both')
-    mode: str
+    peak_sign:  "neg" | "pos" | "both"
+        Sign of the template to compute best channels
+    mode: "extremum" | "at_index", default: "at_index"
         Where the amplitude is computed
         'extremum':  max or min
         'at_index': take value at spike index
@@ -223,8 +231,8 @@ def get_template_extremum_amplitude(waveform_extractor, peak_sign: str = "neg",
     amplitudes: dict
         Dictionary with unit ids as keys and amplitudes as values
     """
-    assert peak_sign in ("both", "neg", "pos")
-    assert mode in ("extremum", "at_index")
+    assert peak_sign in ("both", "neg", "pos"), "'peak_sign' must be  'neg' or 'pos' or 'both'"
+    assert mode in ("extremum", "at_index"), "'mode' must be 'extremum' or 'at_index'"
     unit_ids = waveform_extractor.sorting.unit_ids
 
     before = waveform_extractor.nbefore

src/spikeinterface/core/tests/test_waveform_extractor.py

@@ -559,3 +559,4 @@ def test_non_json_object():
     test_recordingless()
     # test_compute_sparsity()
     # test_non_json_object()
+    test_empty_sorting()

src/spikeinterface/core/unitsaggregationsorting.py

@@ -95,7 +95,7 @@ def __init__(self, sorting_list, renamed_unit_ids=None):
                 try:
                     property_dict[prop_name] = np.concatenate((property_dict[prop_name], values))
                 except Exception as e:
-                    print(f"Skipping property '{prop_name}' for shape inconsistency")
+                    print(f"Skipping property '{prop_name}' due to shape inconsistency")
                     del property_dict[prop_name]
                     break
         for prop_name, prop_values in property_dict.items():

src/spikeinterface/core/waveform_extractor.py

@@ -1457,13 +1457,13 @@ def extract_waveforms(
     folder=None,
     mode="folder",
     precompute_template=("average",),
-    ms_before=3.0,
-    ms_after=4.0,
+    ms_before=1.0,
+    ms_after=2.0,
     max_spikes_per_unit=500,
     overwrite=False,
     return_scaled=True,
     dtype=None,
-    sparse=False,
+    sparse=True,
     sparsity=None,
     num_spikes_for_sparsity=100,
     allow_unfiltered=False,
@@ -1507,7 +1507,7 @@ def extract_waveforms(
         If True and recording has gain_to_uV/offset_to_uV properties, waveforms are converted to uV.
     dtype: dtype or None
         Dtype of the output waveforms. If None, the recording dtype is maintained.
-    sparse: bool (default False)
+    sparse: bool, default: True
         If True, before extracting all waveforms the `precompute_sparsity()` function is run using
         a few spikes to get an estimate of dense templates to create a ChannelSparsity object.
         Then, the waveforms will be sparse at extraction time, which saves a lot of memory.
@@ -1726,6 +1726,7 @@ def precompute_sparsity(
             max_spikes_per_unit=num_spikes_for_sparsity,
             return_scaled=False,
             allow_unfiltered=allow_unfiltered,
+            sparse=False,
             **job_kwargs,
         )
         local_sparsity = compute_sparsity(local_we, **sparse_kwargs)