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spikesorting_sorting.py

SpikeSorterParameters

Bases: SpyglassMixin, Manual

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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@schema
class SpikeSorterParameters(SpyglassMixin, dj.Manual):
    definition = """
    sorter: varchar(32)
    sorter_params_name: varchar(64)
    ---
    sorter_params: blob
    """

    # NOTE: See #630, #664. Excessive key length.

    def insert_default(self):
        """Default params from spike sorters available via spikeinterface"""
        sorters = sis.available_sorters()
        for sorter in sorters:
            sorter_params = sis.get_default_sorter_params(sorter)
            self.insert1(
                [sorter, "default", sorter_params], skip_duplicates=True
            )

        # Insert Frank lab defaults
        # Hippocampus tetrode default
        sorter = "mountainsort4"
        sorter_params_name = "franklab_tetrode_hippocampus_30KHz"
        sorter_params = {
            "detect_sign": -1,
            "adjacency_radius": 100,
            "freq_min": 600,
            "freq_max": 6000,
            "filter": False,
            "whiten": True,
            "num_workers": 1,
            "clip_size": 40,
            "detect_threshold": 3,
            "detect_interval": 10,
        }
        self.insert1(
            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
        )

        # Cortical probe default
        sorter = "mountainsort4"
        sorter_params_name = "franklab_probe_ctx_30KHz"
        sorter_params = {
            "detect_sign": -1,
            "adjacency_radius": 100,
            "freq_min": 300,
            "freq_max": 6000,
            "filter": False,
            "whiten": True,
            "num_workers": 1,
            "clip_size": 40,
            "detect_threshold": 3,
            "detect_interval": 10,
        }
        self.insert1(
            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
        )

        # clusterless defaults
        sorter = "clusterless_thresholder"
        sorter_params_name = "default_clusterless"
        sorter_params = dict(
            detect_threshold=100.0,  # uV
            # Locally exclusive means one unit per spike detected
            method="locally_exclusive",
            peak_sign="neg",
            exclude_sweep_ms=0.1,
            local_radius_um=100,
            # noise levels needs to be 1.0 so the units are in uV and not MAD
            noise_levels=np.asarray([1.0]),
            random_chunk_kwargs={},
            # output needs to be set to sorting for the rest of the pipeline
            outputs="sorting",
        )
        self.insert1(
            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
        )

insert_default()

Default params from spike sorters available via spikeinterface

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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def insert_default(self):
    """Default params from spike sorters available via spikeinterface"""
    sorters = sis.available_sorters()
    for sorter in sorters:
        sorter_params = sis.get_default_sorter_params(sorter)
        self.insert1(
            [sorter, "default", sorter_params], skip_duplicates=True
        )

    # Insert Frank lab defaults
    # Hippocampus tetrode default
    sorter = "mountainsort4"
    sorter_params_name = "franklab_tetrode_hippocampus_30KHz"
    sorter_params = {
        "detect_sign": -1,
        "adjacency_radius": 100,
        "freq_min": 600,
        "freq_max": 6000,
        "filter": False,
        "whiten": True,
        "num_workers": 1,
        "clip_size": 40,
        "detect_threshold": 3,
        "detect_interval": 10,
    }
    self.insert1(
        [sorter, sorter_params_name, sorter_params], skip_duplicates=True
    )

    # Cortical probe default
    sorter = "mountainsort4"
    sorter_params_name = "franklab_probe_ctx_30KHz"
    sorter_params = {
        "detect_sign": -1,
        "adjacency_radius": 100,
        "freq_min": 300,
        "freq_max": 6000,
        "filter": False,
        "whiten": True,
        "num_workers": 1,
        "clip_size": 40,
        "detect_threshold": 3,
        "detect_interval": 10,
    }
    self.insert1(
        [sorter, sorter_params_name, sorter_params], skip_duplicates=True
    )

    # clusterless defaults
    sorter = "clusterless_thresholder"
    sorter_params_name = "default_clusterless"
    sorter_params = dict(
        detect_threshold=100.0,  # uV
        # Locally exclusive means one unit per spike detected
        method="locally_exclusive",
        peak_sign="neg",
        exclude_sweep_ms=0.1,
        local_radius_um=100,
        # noise levels needs to be 1.0 so the units are in uV and not MAD
        noise_levels=np.asarray([1.0]),
        random_chunk_kwargs={},
        # output needs to be set to sorting for the rest of the pipeline
        outputs="sorting",
    )
    self.insert1(
        [sorter, sorter_params_name, sorter_params], skip_duplicates=True
    )

SpikeSorting

Bases: SpyglassMixin, Computed

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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@schema
class SpikeSorting(SpyglassMixin, dj.Computed):
    definition = """
    -> SpikeSortingSelection
    ---
    sorting_path: varchar(1000)
    time_of_sort: int   # in Unix time, to the nearest second
    """

    def make(self, key: dict):
        """Runs spike sorting on the data and parameters specified by the
        SpikeSortingSelection table and inserts a new entry to SpikeSorting table.

        Specifically,
        1. Loads saved recording and runs the sort on it with spikeinterface
        2. Saves the sorting with spikeinterface
        3. Creates an analysis NWB file and saves the sorting there
           (this is redundant with 2; will change in the future)

        """
        # CBroz: does this not work w/o arg? as .populate() ?
        recording_path = (SpikeSortingRecording & key).fetch1("recording_path")
        recording = si.load_extractor(recording_path)

        # first, get the timestamps
        timestamps = SpikeSortingRecording._get_recording_timestamps(recording)
        _ = recording.get_sampling_frequency()
        # then concatenate the recordings
        # Note: the timestamps are lost upon concatenation,
        # i.e. concat_recording.get_times() doesn't return true timestamps anymore.
        # but concat_recording.recoring_list[i].get_times() will return correct
        # timestamps for ith recording.
        if recording.get_num_segments() > 1 and isinstance(
            recording, si.AppendSegmentRecording
        ):
            recording = si.concatenate_recordings(recording.recording_list)
        elif recording.get_num_segments() > 1 and isinstance(
            recording, si.BinaryRecordingExtractor
        ):
            recording = si.concatenate_recordings([recording])

        # load artifact intervals
        artifact_times = (
            ArtifactRemovedIntervalList
            & {
                "artifact_removed_interval_list_name": key[
                    "artifact_removed_interval_list_name"
                ]
            }
        ).fetch1("artifact_times")
        if len(artifact_times):
            if artifact_times.ndim == 1:
                artifact_times = np.expand_dims(artifact_times, 0)

            # convert artifact intervals to indices
            list_triggers = []
            for interval in artifact_times:
                list_triggers.append(
                    np.arange(
                        np.searchsorted(timestamps, interval[0]),
                        np.searchsorted(timestamps, interval[1]),
                    )
                )
            list_triggers = [list(np.concatenate(list_triggers))]
            recording = sip.remove_artifacts(
                recording=recording,
                list_triggers=list_triggers,
                ms_before=None,
                ms_after=None,
                mode="zeros",
            )

        logger.info(f"Running spike sorting on {key}...")
        sorter, sorter_params = (SpikeSorterParameters & key).fetch1(
            "sorter", "sorter_params"
        )

        sorter_temp_dir = tempfile.TemporaryDirectory(dir=temp_dir)
        # add tempdir option for mountainsort
        sorter_params["tempdir"] = sorter_temp_dir.name

        if sorter == "clusterless_thresholder":
            # need to remove tempdir and whiten from sorter_params
            sorter_params.pop("tempdir", None)
            sorter_params.pop("whiten", None)
            sorter_params.pop("outputs", None)
            if "local_radius_um" in sorter_params:
                sorter_params["radius_um"] = sorter_params.pop(
                    "local_radius_um"
                )  # correct existing parameter sets for spikeinterface>=0.99.1

            # Detect peaks for clusterless decoding
            detected_spikes = detect_peaks(recording, **sorter_params)
            sorting = si.NumpySorting.from_times_labels(
                times_list=detected_spikes["sample_index"],
                labels_list=np.zeros(len(detected_spikes), dtype=np.int32),
                sampling_frequency=recording.get_sampling_frequency(),
            )
        else:
            if "whiten" in sorter_params.keys():
                if sorter_params["whiten"]:
                    sorter_params["whiten"] = False  # set whiten to False
            # whiten recording separately; make sure dtype is float32
            # to avoid downstream error with svd
            recording = sip.whiten(recording, dtype="float32")
            sorting = sis.run_sorter(
                sorter,
                recording,
                output_folder=sorter_temp_dir.name,
                remove_existing_folder=True,
                delete_output_folder=True,
                **sorter_params,
            )
        key["time_of_sort"] = int(time.time())

        logger.info("Saving sorting results...")

        sorting_folder = Path(sorting_dir)

        sorting_name = self._get_sorting_name(key)
        key["sorting_path"] = str(sorting_folder / Path(sorting_name))
        if os.path.exists(key["sorting_path"]):
            shutil.rmtree(key["sorting_path"])
        sorting = sorting.save(folder=key["sorting_path"])
        self.insert1(key)

    def fetch_nwb(self, *attrs, **kwargs):
        """Placeholder to override mixin method"""
        raise NotImplementedError

    def nightly_cleanup(self):
        """Clean up spike sorting directories that are not in the SpikeSorting table.
        This should be run after AnalysisNwbFile().nightly_cleanup()
        """
        # get a list of the files in the spike sorting storage directory
        dir_names = next(os.walk(sorting_dir))[1]
        # now retrieve a list of the currently used analysis nwb files
        analysis_file_names = self.fetch("analysis_file_name")
        for dir in dir_names:
            if dir not in analysis_file_names:
                full_path = str(Path(sorting_dir) / dir)
                logger.info(f"removing {full_path}")
                shutil.rmtree(str(Path(sorting_dir) / dir))

    @staticmethod
    def _get_sorting_name(key):
        recording_name = SpikeSortingRecording._get_recording_name(key)
        sorting_name = (
            recording_name + "_" + str(uuid.uuid4())[0:8] + "_spikesorting"
        )
        return sorting_name

    # TODO: write a function to import sorting done outside of dj

    def _import_sorting(self, key):
        raise NotImplementedError

make(key)

Runs spike sorting on the data and parameters specified by the SpikeSortingSelection table and inserts a new entry to SpikeSorting table.

Specifically, 1. Loads saved recording and runs the sort on it with spikeinterface 2. Saves the sorting with spikeinterface 3. Creates an analysis NWB file and saves the sorting there (this is redundant with 2; will change in the future)

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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def make(self, key: dict):
    """Runs spike sorting on the data and parameters specified by the
    SpikeSortingSelection table and inserts a new entry to SpikeSorting table.

    Specifically,
    1. Loads saved recording and runs the sort on it with spikeinterface
    2. Saves the sorting with spikeinterface
    3. Creates an analysis NWB file and saves the sorting there
       (this is redundant with 2; will change in the future)

    """
    # CBroz: does this not work w/o arg? as .populate() ?
    recording_path = (SpikeSortingRecording & key).fetch1("recording_path")
    recording = si.load_extractor(recording_path)

    # first, get the timestamps
    timestamps = SpikeSortingRecording._get_recording_timestamps(recording)
    _ = recording.get_sampling_frequency()
    # then concatenate the recordings
    # Note: the timestamps are lost upon concatenation,
    # i.e. concat_recording.get_times() doesn't return true timestamps anymore.
    # but concat_recording.recoring_list[i].get_times() will return correct
    # timestamps for ith recording.
    if recording.get_num_segments() > 1 and isinstance(
        recording, si.AppendSegmentRecording
    ):
        recording = si.concatenate_recordings(recording.recording_list)
    elif recording.get_num_segments() > 1 and isinstance(
        recording, si.BinaryRecordingExtractor
    ):
        recording = si.concatenate_recordings([recording])

    # load artifact intervals
    artifact_times = (
        ArtifactRemovedIntervalList
        & {
            "artifact_removed_interval_list_name": key[
                "artifact_removed_interval_list_name"
            ]
        }
    ).fetch1("artifact_times")
    if len(artifact_times):
        if artifact_times.ndim == 1:
            artifact_times = np.expand_dims(artifact_times, 0)

        # convert artifact intervals to indices
        list_triggers = []
        for interval in artifact_times:
            list_triggers.append(
                np.arange(
                    np.searchsorted(timestamps, interval[0]),
                    np.searchsorted(timestamps, interval[1]),
                )
            )
        list_triggers = [list(np.concatenate(list_triggers))]
        recording = sip.remove_artifacts(
            recording=recording,
            list_triggers=list_triggers,
            ms_before=None,
            ms_after=None,
            mode="zeros",
        )

    logger.info(f"Running spike sorting on {key}...")
    sorter, sorter_params = (SpikeSorterParameters & key).fetch1(
        "sorter", "sorter_params"
    )

    sorter_temp_dir = tempfile.TemporaryDirectory(dir=temp_dir)
    # add tempdir option for mountainsort
    sorter_params["tempdir"] = sorter_temp_dir.name

    if sorter == "clusterless_thresholder":
        # need to remove tempdir and whiten from sorter_params
        sorter_params.pop("tempdir", None)
        sorter_params.pop("whiten", None)
        sorter_params.pop("outputs", None)
        if "local_radius_um" in sorter_params:
            sorter_params["radius_um"] = sorter_params.pop(
                "local_radius_um"
            )  # correct existing parameter sets for spikeinterface>=0.99.1

        # Detect peaks for clusterless decoding
        detected_spikes = detect_peaks(recording, **sorter_params)
        sorting = si.NumpySorting.from_times_labels(
            times_list=detected_spikes["sample_index"],
            labels_list=np.zeros(len(detected_spikes), dtype=np.int32),
            sampling_frequency=recording.get_sampling_frequency(),
        )
    else:
        if "whiten" in sorter_params.keys():
            if sorter_params["whiten"]:
                sorter_params["whiten"] = False  # set whiten to False
        # whiten recording separately; make sure dtype is float32
        # to avoid downstream error with svd
        recording = sip.whiten(recording, dtype="float32")
        sorting = sis.run_sorter(
            sorter,
            recording,
            output_folder=sorter_temp_dir.name,
            remove_existing_folder=True,
            delete_output_folder=True,
            **sorter_params,
        )
    key["time_of_sort"] = int(time.time())

    logger.info("Saving sorting results...")

    sorting_folder = Path(sorting_dir)

    sorting_name = self._get_sorting_name(key)
    key["sorting_path"] = str(sorting_folder / Path(sorting_name))
    if os.path.exists(key["sorting_path"]):
        shutil.rmtree(key["sorting_path"])
    sorting = sorting.save(folder=key["sorting_path"])
    self.insert1(key)

fetch_nwb(*attrs, **kwargs)

Placeholder to override mixin method

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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def fetch_nwb(self, *attrs, **kwargs):
    """Placeholder to override mixin method"""
    raise NotImplementedError

nightly_cleanup()

Clean up spike sorting directories that are not in the SpikeSorting table. This should be run after AnalysisNwbFile().nightly_cleanup()

Source code in src/spyglass/spikesorting/v0/spikesorting_sorting.py 
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def nightly_cleanup(self):
    """Clean up spike sorting directories that are not in the SpikeSorting table.
    This should be run after AnalysisNwbFile().nightly_cleanup()
    """
    # get a list of the files in the spike sorting storage directory
    dir_names = next(os.walk(sorting_dir))[1]
    # now retrieve a list of the currently used analysis nwb files
    analysis_file_names = self.fetch("analysis_file_name")
    for dir in dir_names:
        if dir not in analysis_file_names:
            full_path = str(Path(sorting_dir) / dir)
            logger.info(f"removing {full_path}")
            shutil.rmtree(str(Path(sorting_dir) / dir))