Merge branch 'main' into set_verbose_to_false

CHANGELOG.md

@@ -6,13 +6,17 @@
 * Interfaces and converters now have `verbose=False` by default [PR #1153](https://github.com/catalystneuro/neuroconv/pull/1153)
 * Completely removed compression settings from most places [PR #1126](https://github.com/catalystneuro/neuroconv/pull/1126)
 * Soft deprecation for `file_path` as an argument of  `SpikeGLXNIDQInterface` and `SpikeGLXRecordingInterface` [PR #1155](https://github.com/catalystneuro/neuroconv/pull/1155)
+* `starting_time` in RecordingInterfaces has given a soft deprecation in favor of time alignment methods [PR #1158](https://github.com/catalystneuro/neuroconv/pull/1158)
+
 
 ## Bug Fixes
 * datetime objects now can be validated as conversion options [#1139](https://github.com/catalystneuro/neuroconv/pull/1126)
 * Make `NWBMetaDataEncoder` public again [PR #1142](https://github.com/catalystneuro/neuroconv/pull/1142)
 * Fix a bug where data in `DeepLabCutInterface` failed to write when `ndx-pose` was not imported. [#1144](https://github.com/catalystneuro/neuroconv/pull/1144)
 * `SpikeGLXConverterPipe` converter now accepts multi-probe structures with multi-trigger and does not assume a specific folder structure [#1150](https://github.com/catalystneuro/neuroconv/pull/1150)
 * `SpikeGLXNIDQInterface` is no longer written as an ElectricalSeries [#1152](https://github.com/catalystneuro/neuroconv/pull/1152)
+* Fix a bug on ecephys interfaces where extra electrode group and devices were written if the property of the "group_name" was set in the recording extractor [#1164](https://github.com/catalystneuro/neuroconv/pull/1164)
+
 
 ## Features
 * Propagate the `unit_electrode_indices` argument from the spikeinterface tools to `BaseSortingExtractorInterface`. This allows users to map units to the electrode table when adding sorting data [PR #1124](https://github.com/catalystneuro/neuroconv/pull/1124)
@@ -28,7 +32,8 @@
 * Use mixing tests for ecephy's mocks [PR #1136](https://github.com/catalystneuro/neuroconv/pull/1136)
 * Use pytest format for dandi tests to avoid window permission error on teardown [PR #1151](https://github.com/catalystneuro/neuroconv/pull/1151)
 * Added many docstrings for public functions [PR #1063](https://github.com/catalystneuro/neuroconv/pull/1063)
-
+* Clean up with warnings and deprecations in the testing framework [PR #1158](https://github.com/catalystneuro/neuroconv/pull/1158)
+* Enhance the typing of the signature on the `NWBConverter` by adding zarr as a literal option on the backend and backend configuration [PR #1160](https://github.com/catalystneuro/neuroconv/pull/1160)
 
 # v0.6.5 (November 1, 2024)
 

docs/conversion_examples_gallery/fiberphotometry/tdt_fp.rst

@@ -207,15 +207,13 @@ Convert TDT Fiber Photometry data to NWB using
     >>> LOCAL_PATH = Path(".") # Path to neuroconv
     >>> editable_metadata_path = LOCAL_PATH / "tests" / "test_on_data" / "ophys" / "fiber_photometry_metadata.yaml"
 
-    >>> interface = TDTFiberPhotometryInterface(folder_path=folder_path, verbose=True)
-    Source data is valid!
+    >>> interface = TDTFiberPhotometryInterface(folder_path=folder_path, verbose=False)
     >>> metadata = interface.get_metadata()
     >>> metadata["NWBFile"]["session_start_time"] = datetime.now(tz=ZoneInfo("US/Pacific"))
     >>> editable_metadata = load_dict_from_file(editable_metadata_path)
     >>> metadata = dict_deep_update(metadata, editable_metadata)
 
     >>> # Choose a path for saving the nwb file and run the conversion
-    >>> nwbfile_path = LOCAL_PATH / "example_tdtfp.nwb"
+    >>> nwbfile_path =  f"{path_to_save_nwbfile}"
     >>> # t1 and t2 are optional arguments to specify the start and end times for the conversion
     >>> interface.run_conversion(nwbfile_path=nwbfile_path, metadata=metadata, t1=0.0, t2=1.0)
-    NWB file saved at example_tdtfp.nwb!

docs/conversion_examples_gallery/sorting/blackrock.rst

@@ -19,7 +19,7 @@ Convert Blackrock sorting data to NWB using
     >>>
     >>> file_path = f"{ECEPHY_DATA_PATH}/blackrock/FileSpec2.3001.nev"
     >>> # Change the file_path to the location of the file in your system
-    >>> interface = BlackrockSortingInterface(file_path=file_path, verbose=False)
+    >>> interface = BlackrockSortingInterface(file_path=file_path, sampling_frequency=30000.0, verbose=False)
     >>>
     >>> # Extract what metadata we can from the source files
     >>> metadata = interface.get_metadata()

docs/conversion_examples_gallery/sorting/neuralynx.rst

@@ -20,7 +20,8 @@ Convert Neuralynx data to NWB using
     >>>
     >>> folder_path = f"{ECEPHY_DATA_PATH}/neuralynx/Cheetah_v5.5.1/original_data"
     >>> # Change the folder_path to the location of the data in your system
-    >>> interface = NeuralynxSortingInterface(folder_path=folder_path, verbose=False)
+    >>> # The stream is optional but is used to specify the sampling frequency of the data
+    >>> interface = NeuralynxSortingInterface(folder_path=folder_path, verbose=False, stream_id="0")
     >>>
     >>> metadata = interface.get_metadata()
     >>> session_start_time = datetime(2020, 1, 1, 12, 30, 0, tzinfo=ZoneInfo("US/Pacific")).isoformat()

src/neuroconv/datainterfaces/ecephys/baserecordingextractorinterface.py

@@ -88,7 +88,9 @@ def get_metadata_schema(self) -> dict:
     def get_metadata(self) -> DeepDict:
         metadata = super().get_metadata()
 
-        channel_groups_array = self.recording_extractor.get_channel_groups()
+        from ...tools.spikeinterface.spikeinterface import _get_group_name
+
+        channel_groups_array = _get_group_name(recording=self.recording_extractor)
         unique_channel_groups = set(channel_groups_array) if channel_groups_array is not None else ["ElectrodeGroup"]
         electrode_metadata = [
             dict(name=str(group_id), description="no description", location="unknown", device="DeviceEcephys")

src/neuroconv/datainterfaces/ecephys/blackrock/blackrockdatainterface.py

@@ -98,8 +98,8 @@ def __init__(self, file_path: FilePath, sampling_frequency: Optional[float] = No
             The file path to the ``.nev`` data
         sampling_frequency: float, optional
             The sampling frequency for the sorting extractor. When the signal data is available (.ncs) those files will be
-        used to extract the frequency automatically. Otherwise, the sampling frequency needs to be specified for
-        this extractor to be initialized.
+            used to extract the frequency automatically. Otherwise, the sampling frequency needs to be specified for
+            this extractor to be initialized.
         verbose : bool, default: False
             Enables verbosity
         """

src/neuroconv/datainterfaces/ecephys/neuralynx/neuralynxdatainterface.py

@@ -112,7 +112,14 @@ class NeuralynxSortingInterface(BaseSortingExtractorInterface):
     associated_suffixes = (".nse", ".ntt", ".nse", ".nev")
     info = "Interface for Neuralynx sorting data."
 
-    def __init__(self, folder_path: DirectoryPath, sampling_frequency: Optional[float] = None, verbose: bool = False):
+
+    def __init__(
+        self,
+        folder_path: DirectoryPath,
+        sampling_frequency: Optional[float] = None,
+        verbose: bool = False,
+        stream_id: Optional[str] = None,
+    ):
         """_summary_
 
         Parameters
@@ -123,9 +130,14 @@ def __init__(self, folder_path: DirectoryPath, sampling_frequency: Optional[floa
             If a specific sampling_frequency is desired it can be set with this argument.
         verbose : bool, default: False
             Enables verbosity
+        stream_id: str, optional
+            Used by Spikeinterface and neo to calculate the t_start, if not provided and the stream is unique
+            it will be chosen automatically
         """
 
-        super().__init__(folder_path=folder_path, sampling_frequency=sampling_frequency, verbose=verbose)
+        super().__init__(
+            folder_path=folder_path, sampling_frequency=sampling_frequency, stream_id=stream_id, verbose=verbose
+        )
 
 
 def extract_neo_header_metadata(neo_reader) -> dict:

src/neuroconv/nwbconverter.py

@@ -204,11 +204,8 @@ def run_conversion(
         nwbfile: Optional[NWBFile] = None,
         metadata: Optional[dict] = None,
         overwrite: bool = False,
-        # TODO: when all H5DataIO prewraps are gone, introduce Zarr safely
-        # backend: Union[Literal["hdf5", "zarr"]],
-        # backend_configuration: Optional[Union[HDF5BackendConfiguration, ZarrBackendConfiguration]] = None,
-        backend: Optional[Literal["hdf5"]] = None,
-        backend_configuration: Optional[HDF5BackendConfiguration] = None,
+        backend: Optional[Literal["hdf5", "zarr"]] = None,
+        backend_configuration: Optional[Union[HDF5BackendConfiguration, ZarrBackendConfiguration]] = None,
         conversion_options: Optional[dict] = None,
     ) -> None:
         """
@@ -226,11 +223,11 @@ def run_conversion(
         overwrite : bool, default: False
             Whether to overwrite the NWBFile if one exists at the nwbfile_path.
             The default is False (append mode).
-        backend : "hdf5", optional
+        backend : {"hdf5", "zarr"}, optional
             The type of backend to use when writing the file.
             If a `backend_configuration` is not specified, the default type will be "hdf5".
             If a `backend_configuration` is specified, then the type will be auto-detected.
-        backend_configuration : HDF5BackendConfiguration, optional
+        backend_configuration : HDF5BackendConfiguration or ZarrBackendConfiguration, optional
             The configuration model to use when configuring the datasets for this backend.
             To customize, call the `.get_default_backend_configuration(...)` method, modify the returned
             BackendConfiguration object, and pass that instead.

src/neuroconv/tools/spikeinterface/spikeinterface.py

@@ -862,6 +862,16 @@ def add_electrical_series_to_nwbfile(
     whenever possible.
     """
 
+    if starting_time is not None:
+        warnings.warn(
+            "The 'starting_time' parameter is deprecated and will be removed in June 2025. "
+            "Use the time alignment methods or set the recording times directlyfor modifying the starting time or timestamps "
+            "of the data if needed: "
+            "https://neuroconv.readthedocs.io/en/main/user_guide/temporal_alignment.html",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
     assert write_as in [
         "raw",
         "processed",

src/neuroconv/tools/testing/data_interface_mixins.py

@@ -1,4 +1,3 @@
-import inspect
 import json
 import tempfile
 from abc import abstractmethod
@@ -407,7 +406,7 @@ def check_read_nwb(self, nwbfile_path: str):
             # Spikeinterface behavior is to load the electrode table channel_name property as a channel_id
             self.nwb_recording = NwbRecordingExtractor(
                 file_path=nwbfile_path,
-                electrical_series_name=electrical_series_name,
+                electrical_series_path=f"acquisition/{electrical_series_name}",
                 use_pynwb=True,
             )
 
@@ -439,7 +438,7 @@ def check_read_nwb(self, nwbfile_path: str):
                         assert_array_equal(
                             recording.get_property(property_name), self.nwb_recording.get_property(property_name)
                         )
-                if recording.has_scaled_traces() and self.nwb_recording.has_scaled_traces():
+                if recording.has_scaleable_traces() and self.nwb_recording.has_scaleable_traces():
                     check_recordings_equal(RX1=recording, RX2=self.nwb_recording, return_scaled=True)
 
             # Compare channel groups
@@ -625,29 +624,22 @@ def check_read_nwb(self, nwbfile_path: str):
 
         # NWBSortingExtractor on spikeinterface does not yet support loading data written from multiple segment.
         if sorting.get_num_segments() == 1:
-            # TODO after 0.100 release remove this if
-            signature = inspect.signature(NwbSortingExtractor)
-            if "t_start" in signature.parameters:
-                nwb_sorting = NwbSortingExtractor(file_path=nwbfile_path, sampling_frequency=sf, t_start=0.0)
-            else:
-                nwb_sorting = NwbSortingExtractor(file_path=nwbfile_path, sampling_frequency=sf)
+            nwb_sorting = NwbSortingExtractor(file_path=nwbfile_path, sampling_frequency=sf, t_start=0.0)
+
             # In the NWBSortingExtractor, since unit_names could be not unique,
             # table "ids" are loaded as unit_ids. Here we rename the original sorting accordingly
             if "unit_name" in sorting.get_property_keys():
                 renamed_unit_ids = sorting.get_property("unit_name")
-                # sorting_renamed = sorting.rename_units(new_unit_ids=renamed_unit_ids)  #TODO after 0.100 release use this
-                sorting_renamed = sorting.select_units(unit_ids=sorting.unit_ids, renamed_unit_ids=renamed_unit_ids)
+                sorting_renamed = sorting.rename_units(new_unit_ids=renamed_unit_ids)
 
             else:
                 nwb_has_ids_as_strings = all(isinstance(id, str) for id in nwb_sorting.unit_ids)
                 if nwb_has_ids_as_strings:
-                    renamed_unit_ids = sorting.get_unit_ids()
-                    renamed_unit_ids = [str(id) for id in renamed_unit_ids]
+                    renamed_unit_ids = [str(id) for id in sorting.get_unit_ids()]
                 else:
                     renamed_unit_ids = np.arange(len(sorting.unit_ids))
 
-                # sorting_renamed = sorting.rename_units(new_unit_ids=sorting.unit_ids) #TODO after 0.100 release use this
-                sorting_renamed = sorting.select_units(unit_ids=sorting.unit_ids, renamed_unit_ids=renamed_unit_ids)
+                sorting_renamed = sorting.rename_units(new_unit_ids=renamed_unit_ids)
             check_sortings_equal(SX1=sorting_renamed, SX2=nwb_sorting)
 
     def check_interface_set_aligned_segment_timestamps(self):

src/neuroconv/tools/testing/mock_interfaces.py

@@ -271,6 +271,10 @@ def __init__(
             verbose=verbose,
         )
 
+        # Sorting extractor to have string unit ids until is changed in SpikeInterface
+        string_unit_ids = [str(id) for id in self.sorting_extractor.unit_ids]
+        self.sorting_extractor = self.sorting_extractor.rename_units(new_unit_ids=string_unit_ids)
+
     def get_metadata(self) -> dict:
         metadata = super().get_metadata()
         session_start_time = datetime.now().astimezone()