Merge branch 'main' into add_docstirng_for_generate_unit_locations

Author: h-mayorquin

.github/scripts/determine_testing_environment.py

@@ -31,6 +31,7 @@
 sortingcomponents_changed = False
 generation_changed = False
 stream_extractors_changed = False
+github_actions_changed = False
 
 
 for changed_file in changed_files_in_the_pull_request_paths:
@@ -78,9 +79,12 @@
             sorters_internal_changed = True
         else:
             sorters_changed = True
+    elif ".github" in changed_file.parts:
+        if "workflows" in changed_file.parts:
+            github_actions_changed = True
 
 
-run_everything = core_changed or pyproject_toml_changed or neobaseextractor_changed
+run_everything = core_changed or pyproject_toml_changed or neobaseextractor_changed or github_actions_changed
 run_generation_tests = run_everything or generation_changed
 run_extractor_tests = run_everything or extractors_changed or plexon2_changed
 run_preprocessing_tests = run_everything or preprocessing_changed
@@ -96,7 +100,7 @@
 run_sorters_test = run_everything or sorters_changed
 run_internal_sorters_test = run_everything or run_sortingcomponents_tests or sorters_internal_changed
 
-run_streaming_extractors_test = stream_extractors_changed
+run_streaming_extractors_test = stream_extractors_changed or github_actions_changed
 
 install_plexon_dependencies = plexon2_changed
 

.github/workflows/all-tests.yml

@@ -12,6 +12,7 @@ on:
 env:
   KACHERY_CLOUD_CLIENT_ID: ${{ secrets.KACHERY_CLOUD_CLIENT_ID }}
   KACHERY_CLOUD_PRIVATE_KEY: ${{ secrets.KACHERY_CLOUD_PRIVATE_KEY }}
+  KACHERY_ZONE: ${{ secrets.KACHERY_ZONE }}
 
 concurrency:  # Cancel previous workflows on the same pull request
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -25,7 +26,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ["3.9", "3.12"]  # Lower and higher versions we support
-        os: [macos-13, windows-latest, ubuntu-latest]
+        os: [macos-latest, windows-latest, ubuntu-latest]
     steps:
       - uses: actions/checkout@v4
       - name: Setup Python ${{ matrix.python-version }}

.github/workflows/full-test-with-codecov.yml

@@ -8,6 +8,7 @@ on:
 env:
   KACHERY_CLOUD_CLIENT_ID: ${{ secrets.KACHERY_CLOUD_CLIENT_ID }}
   KACHERY_CLOUD_PRIVATE_KEY: ${{ secrets.KACHERY_CLOUD_PRIVATE_KEY }}
+  KACHERY_ZONE: ${{ secrets.KACHERY_ZONE }}
 
 jobs:
   full-tests-with-codecov:

src/spikeinterface/core/baserecording.py

@@ -608,11 +608,11 @@ def _save(self, format="binary", verbose: bool = False, **save_kwargs):
             probegroup = self.get_probegroup()
             cached.set_probegroup(probegroup)
 
-        time_vectors = self._get_time_vectors()
-        if time_vectors is not None:
-            for segment_index, time_vector in enumerate(time_vectors):
-                if time_vector is not None:
-                    cached.set_times(time_vector, segment_index=segment_index)
+        for segment_index in range(self.get_num_segments()):
+            if self.has_time_vector(segment_index):
+                # the use of get_times is preferred since timestamps are converted to array
+                time_vector = self.get_times(segment_index=segment_index)
+                cached.set_times(time_vector, segment_index=segment_index)
 
         return cached
 
@@ -746,6 +746,30 @@ def _select_segments(self, segment_indices):
 
         return SelectSegmentRecording(self, segment_indices=segment_indices)
 
+    def get_channel_locations(
+        self,
+        channel_ids: list | np.ndarray | tuple | None = None,
+        axes: "xy" | "yz" | "xz" | "xyz" = "xy",
+    ) -> np.ndarray:
+        """
+        Get the physical locations of specified channels.
+
+        Parameters
+        ----------
+        channel_ids : array-like, optional
+            The IDs of the channels for which to retrieve locations. If None, retrieves locations
+            for all available channels. Default is None.
+        axes : "xy" | "yz" | "xz" | "xyz", default: "xy"
+            The spatial axes to return, specified as a string (e.g., "xy", "xyz"). Default is "xy".
+
+        Returns
+        -------
+        np.ndarray
+            A 2D or 3D array of shape (n_channels, n_dimensions) containing the locations of the channels.
+            The number of dimensions depends on the `axes` argument (e.g., 2 for "xy", 3 for "xyz").
+        """
+        return super().get_channel_locations(channel_ids=channel_ids, axes=axes)
+
     def is_binary_compatible(self) -> bool:
         """
         Checks if the recording is "binary" compatible.

src/spikeinterface/core/baserecordingsnippets.py

@@ -349,7 +349,7 @@ def set_channel_locations(self, locations, channel_ids=None):
             raise ValueError("set_channel_locations(..) destroys the probe description, prefer _set_probes(..)")
         self.set_property("location", locations, ids=channel_ids)
 
-    def get_channel_locations(self, channel_ids=None, axes: str = "xy"):
+    def get_channel_locations(self, channel_ids=None, axes: str = "xy") -> np.ndarray:
         if channel_ids is None:
             channel_ids = self.get_channel_ids()
         channel_indices = self.ids_to_indices(channel_ids)

src/spikeinterface/core/job_tools.py

@@ -136,11 +136,8 @@ def divide_segment_into_chunks(num_frames, chunk_size):
     else:
         n = num_frames // chunk_size
 
-        frame_starts = np.arange(n) * chunk_size
-        frame_stops = frame_starts + chunk_size
-
-        frame_starts = frame_starts.tolist()
-        frame_stops = frame_stops.tolist()
+        frame_starts = [i * chunk_size for i in range(n)]
+        frame_stops = [frame_start + chunk_size for frame_start in frame_starts]
 
         if (num_frames % chunk_size) > 0:
             frame_starts.append(n * chunk_size)

src/spikeinterface/core/sortinganalyzer.py

@@ -232,6 +232,8 @@ def __repr__(self) -> str:
             txt += " - sparse"
         if self.has_recording():
             txt += " - has recording"
+        if self.has_temporary_recording():
+            txt += " - has temporary recording"
         ext_txt = f"Loaded {len(self.extensions)} extensions: " + ", ".join(self.extensions.keys())
         txt += "\n" + ext_txt
         return txt
@@ -350,7 +352,7 @@ def create_memory(cls, sorting, recording, sparsity, return_scaled, rec_attribut
     def create_binary_folder(cls, folder, sorting, recording, sparsity, return_scaled, rec_attributes):
         # used by create and save_as
 
-        assert recording is not None, "To create a SortingAnalyzer you need recording not None"
+        assert recording is not None, "To create a SortingAnalyzer you need to specify the recording"
 
         folder = Path(folder)
         if folder.is_dir():
@@ -1221,7 +1223,7 @@ def compute(self, input, save=True, extension_params=None, verbose=False, **kwar
                     extensions[ext_name] = ext_params
             self.compute_several_extensions(extensions=extensions, save=save, verbose=verbose, **job_kwargs)
         else:
-            raise ValueError("SortingAnalyzer.compute() need str, dict or list")
+            raise ValueError("SortingAnalyzer.compute() needs a str, dict or list")
 
     def compute_one_extension(self, extension_name, save=True, verbose=False, **kwargs) -> "AnalyzerExtension":
         """
@@ -1355,7 +1357,9 @@ def compute_several_extensions(self, extensions, save=True, verbose=False, **job
 
             for extension_name, extension_params in extensions_with_pipeline.items():
                 extension_class = get_extension_class(extension_name)
-                assert self.has_recording(), f"Extension {extension_name} need the recording"
+                assert (
+                    self.has_recording() or self.has_temporary_recording()
+                ), f"Extension {extension_name} requires the recording"
 
                 for variable_name in extension_class.nodepipeline_variables:
                     result_routage.append((extension_name, variable_name))
@@ -1603,17 +1607,17 @@ def _sort_extensions_by_dependency(extensions):
 def _get_children_dependencies(extension_name):
     """
     Extension classes have a `depend_on` attribute to declare on which class they
-    depend. For instance "templates" depend on "waveforms". "waveforms depends on "random_spikes".
+    depend on. For instance "templates" depends on "waveforms". "waveforms" depends on "random_spikes".
 
-    This function is making the reverse way : get all children that depend of a
+    This function is going the opposite way: it finds all children that depend on a
     particular extension.
 
-    This is recursive so this includes : children and so grand children and great grand children
+    The implementation is recursive so that the output includes children, grand children, great grand children, etc.
 
-    This function is usefull for deleting on recompute.
-    For instance recompute the "waveforms" need to delete "template"
-    This make sens if "ms_before" is change in "waveforms" because the template also depends
-    on this parameters.
+    This function is useful for deleting existing extensions on recompute.
+    For instance, recomputing the "waveforms" needs to delete the "templates", since the latter depends on the former.
+    For this particular example, if we change the "ms_before" parameter of the "waveforms", also the "templates" will
+    require recomputation as this parameter is inherited.
     """
     names = []
     children = _extension_children[extension_name]

src/spikeinterface/core/waveforms_extractor_backwards_compatibility.py

@@ -536,6 +536,7 @@ def _read_old_waveforms_extractor_binary(folder, sorting):
         ext = ComputeRandomSpikes(sorting_analyzer)
         ext.params = dict()
         ext.data = dict(random_spikes_indices=random_spikes_indices)
+        ext.run_info = None
         sorting_analyzer.extensions["random_spikes"] = ext
 
         ext = ComputeWaveforms(sorting_analyzer)
@@ -545,6 +546,7 @@ def _read_old_waveforms_extractor_binary(folder, sorting):
             dtype=params["dtype"],
         )
         ext.data["waveforms"] = waveforms
+        ext.run_info = None
         sorting_analyzer.extensions["waveforms"] = ext
 
     # templates saved dense
@@ -559,6 +561,7 @@ def _read_old_waveforms_extractor_binary(folder, sorting):
         ext.params = dict(ms_before=params["ms_before"], ms_after=params["ms_after"], operators=list(templates.keys()))
         for mode, arr in templates.items():
             ext.data[mode] = arr
+        ext.run_info = None
         sorting_analyzer.extensions["templates"] = ext
 
     for old_name, new_name in old_extension_to_new_class_map.items():
@@ -631,6 +634,7 @@ def _read_old_waveforms_extractor_binary(folder, sorting):
         ext.set_params(**updated_params, save=False)
         if ext.need_backward_compatibility_on_load:
             ext._handle_backward_compatibility_on_load()
+        ext.run_info = None
 
         sorting_analyzer.extensions[new_name] = ext
 

src/spikeinterface/extractors/tests/test_neoextractors.py

@@ -234,7 +234,7 @@ class BlackrockSortingTest(SortingCommonTestSuite, unittest.TestCase):
     ExtractorClass = BlackrockSortingExtractor
     downloads = ["blackrock"]
     entities = [
-        "blackrock/FileSpec2.3001.nev",
+        dict(file_path=local_folder / "blackrock/FileSpec2.3001.nev", sampling_frequency=30_000.0),
         dict(file_path=local_folder / "blackrock/blackrock_2_1/l101210-001.nev", sampling_frequency=30_000.0),
     ]
 
@@ -278,8 +278,8 @@ class Spike2RecordingTest(RecordingCommonTestSuite, unittest.TestCase):
 
 
 @pytest.mark.skipif(
-    version.parse(platform.python_version()) >= version.parse("3.10"),
-    reason="Sonpy only testing with Python < 3.10!",
+    version.parse(platform.python_version()) >= version.parse("3.10") or platform.system() == "Darwin",
+    reason="Sonpy only testing with Python < 3.10 and not supported on macOS!",
 )
 class CedRecordingTest(RecordingCommonTestSuite, unittest.TestCase):
     ExtractorClass = CedRecordingExtractor
@@ -293,6 +293,7 @@ class CedRecordingTest(RecordingCommonTestSuite, unittest.TestCase):
     ]
 
 
+@pytest.mark.skipif(platform.system() == "Darwin", reason="Maxwell plugin not supported on macOS")
 class MaxwellRecordingTest(RecordingCommonTestSuite, unittest.TestCase):
     ExtractorClass = MaxwellRecordingExtractor
     downloads = ["maxwell"]

src/spikeinterface/postprocessing/principal_component.py

@@ -359,12 +359,12 @@ def run_for_all_spikes(self, file_path=None, verbose=False, **job_kwargs):
 
         job_kwargs = fix_job_kwargs(job_kwargs)
         p = self.params
-        we = self.sorting_analyzer
-        sorting = we.sorting
+        sorting_analyzer = self.sorting_analyzer
+        sorting = sorting_analyzer.sorting
         assert (
-            we.has_recording()
-        ), "To compute PCA projections for all spikes, the waveform extractor needs the recording"
-        recording = we.recording
+            sorting_analyzer.has_recording() or sorting_analyzer.has_temporary_recording()
+        ), "To compute PCA projections for all spikes, the sorting analyzer needs the recording"
+        recording = sorting_analyzer.recording
 
         # assert sorting.get_num_segments() == 1
         assert p["mode"] in ("by_channel_local", "by_channel_global")
@@ -374,8 +374,9 @@ def run_for_all_spikes(self, file_path=None, verbose=False, **job_kwargs):
 
         sparsity = self.sorting_analyzer.sparsity
         if sparsity is None:
-            sparse_channels_indices = {unit_id: np.arange(we.get_num_channels()) for unit_id in we.unit_ids}
-            max_channels_per_template = we.get_num_channels()
+            num_channels = recording.get_num_channels()
+            sparse_channels_indices = {unit_id: np.arange(num_channels) for unit_id in sorting_analyzer.unit_ids}
+            max_channels_per_template = num_channels
         else:
             sparse_channels_indices = sparsity.unit_id_to_channel_indices
             max_channels_per_template = max([chan_inds.size for chan_inds in sparse_channels_indices.values()])
@@ -449,9 +450,7 @@ def _fit_by_channel_local(self, n_jobs, progress_bar):
         return pca_models
 
     def _fit_by_channel_global(self, progress_bar):
-        # we = self.sorting_analyzer
         p = self.params
-        # unit_ids = we.unit_ids
         unit_ids = self.sorting_analyzer.unit_ids
 
         # there is one unique PCA accross channels