Getter Docstrings

CHANGELOG.md

@@ -12,6 +12,7 @@
 
 ## Improvements
 * Simple writing no longer uses a context manager [PR #1180](https://github.com/catalystneuro/neuroconv/pull/1180)
+* Added Returns section to all getter docstrings [PR #1185](https://github.com/catalystneuro/neuroconv/pull/1185)
 
 
 # v0.6.7 (January 20, 2025)

src/neuroconv/basedatainterface.py

@@ -72,12 +72,26 @@ def __init__(self, verbose: bool = False, **source_data):
         self.source_data = source_data
 
     def get_metadata_schema(self) -> dict:
-        """Retrieve JSON schema for metadata."""
+        """
+        Retrieve JSON schema for metadata.
+
+        Returns
+        -------
+        dict
+            The JSON schema defining the metadata structure.
+        """
         metadata_schema = load_dict_from_file(Path(__file__).parent / "schemas" / "base_metadata_schema.json")
         return metadata_schema
 
     def get_metadata(self) -> DeepDict:
-        """Child DataInterface classes should override this to match their metadata."""
+        """
+        Child DataInterface classes should override this to match their metadata.
+
+        Returns
+        -------
+        DeepDict
+            The metadata dictionary containing basic NWBFile metadata.
+        """
         metadata = DeepDict()
         metadata["NWBFile"]["session_description"] = ""
         metadata["NWBFile"]["identifier"] = str(uuid.uuid4())
@@ -105,7 +119,14 @@ def validate_metadata(self, metadata: dict, append_mode: bool = False) -> None:
         validate(instance=decoded_metadata, schema=metdata_schema)
 
     def get_conversion_options_schema(self) -> dict:
-        """Infer the JSON schema for the conversion options from the method signature (annotation typing)."""
+        """
+        Infer the JSON schema for the conversion options from the method signature (annotation typing).
+
+        Returns
+        -------
+        dict
+            The JSON schema for the conversion options.
+        """
         return get_json_schema_from_method_signature(self.add_to_nwbfile, exclude=["nwbfile", "metadata"])
 
     def create_nwbfile(self, metadata: Optional[dict] = None, **conversion_options) -> NWBFile:
@@ -249,7 +270,7 @@ def get_default_backend_configuration(
 
         Returns
         -------
-        backend_configuration : HDF5BackendConfiguration or ZarrBackendConfiguration
+        Union[HDF5BackendConfiguration, ZarrBackendConfiguration]
             The default configuration for the specified backend type.
         """
         return get_default_backend_configuration(nwbfile=nwbfile, backend=backend)

src/neuroconv/baseextractorinterface.py

@@ -20,6 +20,14 @@ class BaseExtractorInterface(BaseTemporalAlignmentInterface, ABC):
 
     @classmethod
     def get_extractor(cls):
+        """
+        Get the extractor class for this interface.
+
+        Returns
+        -------
+        type
+            The extractor class that will be used to read the data.
+        """
         if cls.Extractor is not None:
             return cls.Extractor
         extractor_module = get_package(package_name=cls.ExtractorModuleName)

src/neuroconv/datainterfaces/behavior/video/video_utils.py

@@ -19,6 +19,8 @@ def get_video_timestamps(file_path: FilePath, max_frames: Optional[int] = None,
         The path to a multimedia video file
     max_frames : Optional[int], optional
         If provided, extract the timestamps of the video only up to max_frames.
+    display_progress : bool, default: True
+        Whether to display a progress bar during timestamp extraction.
 
     Returns
     -------
@@ -45,7 +47,24 @@ def __init__(self, file_path: FilePath):
         self._video_open_msg = "The video file is not open!"
 
     def get_video_timestamps(self, max_frames: Optional[int] = None, display_progress: bool = True):
-        """Return numpy array of the timestamps(s) for a video file."""
+        """
+        Return numpy array of the timestamps(s) for a video file.
+
+        Parameters
+        ----------
+        max_frames : Optional[int], optional
+            If provided, extract the timestamps of the video only up to max_frames.
+        display_progress : bool, default: True
+            Whether to display a progress bar during timestamp extraction.
+
+        Returns
+        -------
+        numpy.ndarray
+            Array of timestamps in seconds, representing the time from the start
+            of the video for each frame. Timestamps are extracted from the video's
+            metadata using cv2.CAP_PROP_POS_MSEC and converted from milliseconds
+            to seconds.
+        """
         cv2 = get_package(package_name="cv2", installation_instructions="pip install opencv-python-headless")
 
         timestamps = []
@@ -65,13 +84,27 @@ def get_video_timestamps(self, max_frames: Optional[int] = None, display_progres
         return np.array(timestamps) / 1000
 
     def get_video_fps(self):
-        """Return the internal frames per second (fps) for a video file."""
+        """
+        Return the internal frames per second (fps) for a video file.
+
+        Returns
+        -------
+        float
+            The frames per second of the video.
+        """
         assert self.isOpened(), self._video_open_msg
         prop = self.get_cv_attribute("CAP_PROP_FPS")
         return self.vc.get(prop)
 
     def get_frame_shape(self) -> Tuple:
-        """Return the shape of frames from a video file."""
+        """
+        Return the shape of frames from a video file.
+
+        Returns
+        -------
+        Tuple
+            The shape of the video frames (height, width, channels).
+        """
         frame = self.get_video_frame(0)
         if frame is not None:
             return frame.shape
@@ -91,6 +124,14 @@ def frame_count(self, val: int):
         self._frame_count = val
 
     def get_video_frame_count(self):
+        """
+        Get the total number of frames in the video.
+
+        Returns
+        -------
+        int
+            The total number of frames in the video.
+        """
         return self.frame_count
 
     def _video_frame_count(self):
@@ -101,6 +142,19 @@ def _video_frame_count(self):
 
     @staticmethod
     def get_cv_attribute(attribute_name: str):
+        """
+        Get an OpenCV attribute by name.
+
+        Parameters
+        ----------
+        attribute_name : str
+            The name of the OpenCV attribute to get.
+
+        Returns
+        -------
+        Any
+            The OpenCV attribute value.
+        """
         cv2 = get_package(package_name="cv2", installation_instructions="pip install opencv-python-headless")
 
         if int(cv2.__version__.split(".")[0]) < 3:  # pragma: no cover
@@ -122,7 +176,19 @@ def current_frame(self, frame_number: int):
             raise ValueError(f"Could not set frame number (received {frame_number}).")
 
     def get_video_frame(self, frame_number: int):
-        """Return the specific frame from a video as an RGB colorspace."""
+        """
+        Return the specific frame from a video as an RGB colorspace.
+
+        Parameters
+        ----------
+        frame_number : int
+            The index of the frame to retrieve.
+
+        Returns
+        -------
+        numpy.ndarray
+            The video frame in RGB colorspace with shape (height, width, 3).
+        """
         assert self.isOpened(), self._video_open_msg
         assert frame_number < self.get_video_frame_count(), "frame number is greater than length of video"
         initial_frame_number = self.current_frame
@@ -132,7 +198,14 @@ def get_video_frame(self, frame_number: int):
         return np.flip(frame, 2)  # np.flip to re-order color channels to RGB
 
     def get_video_frame_dtype(self):
-        """Return the dtype for frame in a video file."""
+        """
+        Return the dtype for frame in a video file.
+
+        Returns
+        -------
+        numpy.dtype
+            The data type of the video frames.
+        """
         frame = self.get_video_frame(0)
         if frame is not None:
             return frame.dtype

src/neuroconv/datainterfaces/behavior/video/videodatainterface.py

@@ -135,7 +135,7 @@ def get_timing_type(self) -> Literal["starting_time and rate", "timestamps"]:
 
         Returns
         -------
-        timing_type : 'starting_time and rate' or 'timestamps'
+        Literal["starting_time and rate", "timestamps"]
             The type of timing that has been set explicitly according to alignment.
 
             If only timestamps have been set, then only those will be used.

src/neuroconv/datainterfaces/ecephys/axona/axona_utils.py

@@ -20,7 +20,7 @@ def get_eeg_sampling_frequency(file_path: FilePath) -> float:
     Returns
     -------
     float
-        Sampling frequency
+        The sampling frequency in Hz extracted from the file header's sample_rate field.
     """
     Fs_entry = parse_generic_header(file_path, ["sample_rate"])
     Fs = float(Fs_entry.get("sample_rate").split(" ")[0])
@@ -76,8 +76,9 @@ def get_all_file_paths(file_path: FilePath) -> list:
 
     Returns
     -------
-    path_list : list
-        List of file_paths
+    list
+        List of file paths for all .eeg or .egf files found in the same directory
+        as the input file path.
     """
 
     suffix = Path(file_path).suffix[0:4]
@@ -183,8 +184,9 @@ def get_header_bstring(file: FilePath) -> bytes:
 
     Returns
     -------
-    str
-        header byte content
+    bytes
+        The header content as bytes, including everything from the start of the file
+        up to and including the 'data_start' marker.
     """
     header = b""
     with open(file, "rb") as f:
@@ -365,14 +367,18 @@ def get_position_object(file_path: FilePath) -> Position:
     be preferred to read position data from the `.bin` file to ensure
     samples are locked to ecephys time courses.
 
-    Parameters:
+    Parameters
     ----------
-    file_path (Path or Str):
+    file_path : Path or str
         Full file_path of Axona file with any extension.
 
-    Returns:
+    Returns
     -------
-    position: pynwb.behavior.Position
+    pynwb.behavior.Position
+        Position object containing multiple SpatialSeries, one for each position
+        channel (time, X, Y, x, y, PX, px, px_total, unused). Each series contains
+        timestamps and corresponding position data. The timestamps are in milliseconds
+        and are aligned to the start of raw acquisition if reading from a .bin file.
     """
     position = Position()
 

src/neuroconv/datainterfaces/ecephys/baserecordingextractorinterface.py

@@ -48,7 +48,15 @@ def __init__(self, verbose: bool = False, es_key: str = "ElectricalSeries", **so
         self._number_of_segments = self.recording_extractor.get_num_segments()
 
     def get_metadata_schema(self) -> dict:
-        """Compile metadata schema for the RecordingExtractor."""
+        """
+        Compile metadata schema for the RecordingExtractor.
+
+        Returns
+        -------
+        dict
+            The metadata schema dictionary containing definitions for Device, ElectrodeGroup,
+            Electrodes, and optionally ElectricalSeries.
+        """
         metadata_schema = super().get_metadata_schema()
         metadata_schema["properties"]["Ecephys"] = get_base_schema(tag="Ecephys")
         metadata_schema["properties"]["Ecephys"]["required"] = ["Device", "ElectrodeGroup"]
@@ -86,6 +94,15 @@ def get_metadata_schema(self) -> dict:
         return metadata_schema
 
     def get_metadata(self) -> DeepDict:
+        """
+        Get metadata for the recording extractor.
+
+        Returns
+        -------
+        DeepDict
+            Dictionary containing metadata including device information, electrode groups,
+            and electrical series configuration.
+        """
         metadata = super().get_metadata()
 
         from ...tools.spikeinterface.spikeinterface import _get_group_name
@@ -250,8 +267,8 @@ def has_probe(self) -> bool:
 
         Returns
         -------
-        has_probe : bool
-            True if the recording extractor has probe information.
+        bool
+            True if the recording extractor has probe information, False otherwise.
         """
         return self.recording_extractor.has_probe()
 
@@ -274,6 +291,12 @@ def subset_recording(self, stub_test: bool = False):
         Parameters
         ----------
         stub_test : bool, default: False
+            If True, only a subset of frames will be included.
+
+        Returns
+        -------
+        spikeinterface.core.BaseRecording
+            The subsetted recording extractor.
         """
         from spikeinterface.core.segmentutils import AppendSegmentRecording
 

src/neuroconv/datainterfaces/ecephys/basesortingextractorinterface.py

@@ -26,7 +26,15 @@ def __init__(self, verbose: bool = False, **source_data):
         self._number_of_segments = self.sorting_extractor.get_num_segments()
 
     def get_metadata_schema(self) -> dict:
-        """Compile metadata schema for the RecordingExtractor."""
+        """
+        Compile metadata schema for the RecordingExtractor.
+
+        Returns
+        -------
+        dict
+            The metadata schema dictionary containing definitions for Device, ElectrodeGroup,
+            Electrodes, and UnitProperties.
+        """
 
         # Initiate Ecephys metadata
         metadata_schema = super().get_metadata_schema()
@@ -85,6 +93,20 @@ def get_original_timestamps(self) -> np.ndarray:
         )
 
     def get_timestamps(self) -> Union[np.ndarray, list[np.ndarray]]:
+        """
+        Get the timestamps for the sorting data.
+
+        Returns
+        -------
+        numpy.ndarray or list of numpy.ndarray
+            The timestamps for each spike in the sorting data. If there are multiple segments,
+            returns a list of timestamp arrays.
+
+        Raises
+        ------
+        NotImplementedError
+            If no recording is attached to the sorting extractor.
+        """
         if not self.sorting_extractor.has_recording():
             raise NotImplementedError(
                 "In order to align timestamps for a SortingInterface, it must have a recording "