common_filter.py

FirFilterParameters

Bases: SpyglassMixin, Manual

Source code in src/spyglass/common/common_filter.py

@schema
class FirFilterParameters(SpyglassMixin, dj.Manual):
    definition = """
    filter_name: varchar(80)           # descriptive name of this filter
    filter_sampling_rate: int          # sampling rate for this filter
    ---
    filter_type: enum("lowpass", "highpass", "bandpass")
    filter_low_stop = 0: float     # lowest freq for stop band for low filt
    filter_low_pass = 0: float     # lowest freq for pass band of low filt
    filter_high_pass = 0: float    # highest freq for pass band for high filt
    filter_high_stop = 0: float    # highest freq for stop band of high filt
    filter_comments: varchar(2000) # comments about the filter
    filter_band_edges: blob        # numpy array of filter bands
                                   # redundant with individual parameters
    filter_coeff: longblob         # numpy array of filter coefficients
    """

    def add_filter(
        self,
        filter_name: str,
        fs: float,
        filter_type: str,
        band_edges: list,
        comments: str = "",
    ) -> None:
        """Add filter to the Filter table.

        Parameters
        ----------
        filter_name: str
            The name of the filter.
        fs: float
            The filter sampling rate.
        filter_type: str
            The type of the filter ('lowpass', 'highpass', or 'bandpass').
        band_edges: List[float]
            The band edges for the filter.
        comments: str, optional)
            Additional comments for the filter. Default "".

        Returns
        -------
        None
            Returns None if there is an error in the filter type or band
            frequencies.

        Raises
        ------
        Exception:
            Raises an exception if an unexpected filter type is encountered.
        """
        VALID_FILTERS = {"lowpass": 2, "highpass": 2, "bandpass": 4}
        FILTER_ERR = "Error in Filter.add_filter: "
        FILTER_N_ERR = FILTER_ERR + "filter {} requires {} band_frequencies."

        # add an FIR bandpass filter of the specified type.
        # band_edges should be as follows:
        #   low pass : [high_pass high_stop]
        #   high pass: [low stop low pass]
        #   band pass: [low_stop low_pass high_pass high_stop].
        if filter_type not in VALID_FILTERS:
            logger.error(
                FILTER_ERR
                + f"{filter_type} not valid type: {VALID_FILTERS.keys()}"
            )
            return None

        if not len(band_edges) == VALID_FILTERS[filter_type]:
            logger.error(
                FILTER_N_ERR.format(filter_name, VALID_FILTERS[filter_type])
            )
            return None

        gsp = _import_ghostipy()
        TRANS_SPLINE = 2  # transition spline will be quadratic

        if filter_type != "bandpass":
            transition_width = band_edges[1] - band_edges[0]

        else:
            # transition width is mean of left and right transition regions
            transition_width = (
                (band_edges[1] - band_edges[0])
                + (band_edges[3] - band_edges[2])
            ) / 2.0

        numtaps = gsp.estimate_taps(fs, transition_width)
        filterdict = {
            "filter_type": filter_type,
            "filter_name": filter_name,
            "filter_sampling_rate": fs,
            "filter_comments": comments,
            "filter_low_stop": 0,
            "filter_low_pass": 0,
            "filter_high_pass": 0,
            "filter_high_stop": 0,
            "filter_band_edges": np.asarray(band_edges),
        }

        # set the desired frequency response
        if filter_type == "lowpass":
            desired = [1, 0]
            pass_stop_dict = {
                "filter_high_pass": band_edges[0],
                "filter_high_stop": band_edges[1],
            }
        elif filter_type == "highpass":
            desired = [0, 1]
            pass_stop_dict = {
                "filter_low_stop": band_edges[0],
                "filter_low_pass": band_edges[1],
            }
        else:
            desired = [0, 1, 1, 0]
            pass_stop_dict = {
                "filter_low_stop": band_edges[0],
                "filter_low_pass": band_edges[1],
                "filter_high_pass": band_edges[2],
                "filter_high_stop": band_edges[3],
            }

        # create 1d array for coefficients
        filterdict.update(
            {
                **pass_stop_dict,
                "filter_coeff": np.array(
                    gsp.firdesign(
                        numtaps, band_edges, desired, fs=fs, p=TRANS_SPLINE
                    ),
                    ndmin=1,
                ),
            }
        )

        self.insert1(filterdict, skip_duplicates=True)

    def _filter_restrict(self, filter_name, fs):
        return (
            self & {"filter_name": filter_name} & {"filter_sampling_rate": fs}
        ).fetch1()

    def plot_magnitude(self, filter_name, fs, return_fig=False):
        filter_dict = self._filter_restrict(filter_name, fs)
        plt.figure()
        w, h = signal.freqz(filter_dict["filter_coeff"], worN=65536)
        magnitude = 20 * np.log10(np.abs(h))
        plt.plot(w / np.pi * fs / 2, magnitude)
        plt.xlabel("Frequency (Hz)")
        plt.ylabel("Magnitude")
        plt.title("Frequency Response")
        plt.xlim(0, np.max(filter_dict["filter_band_edges"] * 2))
        plt.ylim(np.min(magnitude), -1 * np.min(magnitude) * 0.1)
        plt.grid(True)
        if return_fig:
            return plt.gcf()

    def plot_fir_filter(self, filter_name, fs, return_fig=False):
        filter_dict = self._filter_restrict(filter_name, fs)
        plt.figure()
        plt.clf()
        plt.plot(filter_dict["filter_coeff"], "k")
        plt.xlabel("Coefficient")
        plt.ylabel("Magnitude")
        plt.title("Filter Taps")
        plt.grid(True)
        if return_fig:
            return plt.gcf()

    def filter_delay(self, filter_name, fs):
        return self.calc_filter_delay(
            self._filter_restrict(filter_name, fs)["filter_coeff"]
        )

    def _time_bound_check(self, start, stop, all, nsamples):
        timestamp_warn = "Interval time warning: "
        if start < all[0]:
            warnings.warn(
                timestamp_warn
                + "start time smaller than first timestamp, "
                + f"substituting first: {start} < {all[0]}"
            )
            start = all[0]

        if stop > all[-1]:
            warnings.warn(
                timestamp_warn
                + "stop time larger than last timestamp, "
                + f"substituting last: {stop} < {all[-1]}"
            )
            stop = all[-1]

        frm, to = np.searchsorted(all, (start, stop))
        to = min(to, nsamples)
        return frm, to

    def filter_data_nwb(
        self,
        analysis_file_abs_path: str,
        eseries: pynwb.ecephys.ElectricalSeries,
        filter_coeff: np.ndarray,
        valid_times: np.ndarray,
        electrode_ids: list,
        decimation: int,
        description: str = "filtered data",
        data_type: Union[None, str] = None,
    ):
        """
        Filter data from an NWB electrical series using the ghostipy package,
        and save the result as a new electrical series in the analysis NWB file.

        Parameters
        ----------
        analysis_file_abs_path : str
            Full path to the analysis NWB file.
        eseries : pynwb.ecephys.ElectricalSeries
            Electrical series with data to be filtered.
        filter_coeff : np.ndarray
            Array with filter coefficients for FIR filter.
        valid_times : np.ndarray
            Array with start and stop times of intervals to be filtered.
        electrode_ids : list
            List of electrode IDs to filter.
        decimation : int
            Decimation factor.
        description : str
            Description of the filtered data.
        data_type : Union[None, str]
            Type of data (e.g., "LFP").

        Returns
        -------
        tuple
            The NWB object ID of the filtered data and a list containing the
            first and last timestamps.
        """
        # Note: type -> data_type to avoid conflict with builtin type
        # All existing refs to this func use positional args, so no need to
        # adjust elsewhere, but low probability of issues with custom scripts

        MEM_USE_LIMIT = 0.9  # % of RAM use permitted

        gsp = _import_ghostipy()

        data_on_disk = eseries.data
        timestamps_on_disk = eseries.timestamps

        n_samples = len(timestamps_on_disk)
        time_axis = 0 if data_on_disk.shape[0] == n_samples else 1
        electrode_axis = 1 - time_axis

        n_electrodes = data_on_disk.shape[electrode_axis]
        input_dim_restrictions = [None] * len(data_on_disk.shape)

        # Get input dimension restrictions
        input_dim_restrictions[electrode_axis] = np.s_[
            get_electrode_indices(eseries, electrode_ids)
        ]

        indices = []
        output_shape_list = [0] * len(data_on_disk.shape)
        output_shape_list[electrode_axis] = len(electrode_ids)
        data_dtype = data_on_disk[0][0].dtype

        filter_delay = self.calc_filter_delay(filter_coeff)

        output_offsets = [0]

        for a_start, a_stop in valid_times:
            frm, to = self._time_bound_check(
                a_start, a_stop, timestamps_on_disk, n_samples
            )

            indices.append((frm, to))

            shape, _ = gsp.filter_data_fir(
                data_on_disk,
                filter_coeff,
                axis=time_axis,
                input_index_bounds=[frm, to - 1],
                output_index_bounds=[filter_delay, filter_delay + to - frm],
                describe_dims=True,
                ds=decimation,
                input_dim_restrictions=input_dim_restrictions,
            )
            output_offsets.append(output_offsets[-1] + shape[time_axis])
            output_shape_list[time_axis] += shape[time_axis]

        # Create dynamic table region and electrode series, write/close file
        with pynwb.NWBHDF5IO(
            path=analysis_file_abs_path, mode="a", load_namespaces=True
        ) as io:
            nwbf = io.read()

            # get the indices of the electrodes in the electrode table
            elect_ind = get_electrode_indices(nwbf, electrode_ids)

            electrode_table_region = nwbf.create_electrode_table_region(
                elect_ind, "filtered electrode table"
            )
            es = pynwb.ecephys.ElectricalSeries(
                name="filtered data",
                data=np.empty(tuple(output_shape_list), dtype=data_dtype),
                electrodes=electrode_table_region,
                timestamps=np.empty(output_shape_list[time_axis]),
                description=description,
            )
            if data_type == "LFP":
                ecephys_module = nwbf.create_processing_module(
                    name="ecephys", description=description
                )
                ecephys_module.add(pynwb.ecephys.LFP(electrical_series=es))
            else:
                nwbf.add_scratch(es)

            io.write(nwbf)

        # Reload NWB file to get h5py objects for data/timestamps
        # NOTE: CBroz - why io context within io context? Unindenting
        with pynwb.NWBHDF5IO(
            path=analysis_file_abs_path, mode="a", load_namespaces=True
        ) as io:
            nwbf = io.read()
            es = nwbf.objects[es.object_id]
            filtered_data = es.data
            new_timestamps = es.timestamps
            indices = np.array(indices, ndmin=2)
            # Filter and write the output dataset
            ts_offset = 0

            logger.info("Filtering data")
            for ii, (start, stop) in enumerate(indices):
                # Calc size of timestamps + data, check if < 90% of RAM
                interval_samples = stop - start
                req_mem = interval_samples * (
                    timestamps_on_disk[0].itemsize
                    + n_electrodes * data_on_disk[0][0].itemsize
                )
                if req_mem < MEM_USE_LIMIT * psutil.virtual_memory().available:
                    logger.info(f"Interval {ii}: loading data into memory")
                    timestamps = np.asarray(
                        timestamps_on_disk[start:stop],
                        dtype=timestamps_on_disk[0].dtype,
                    )
                    if time_axis == 0:
                        data = np.asarray(
                            data_on_disk[start:stop, :], dtype=data_dtype
                        )
                    else:
                        data = np.asarray(
                            data_on_disk[:, start:stop], dtype=data_dtype
                        )
                    extracted_ts = timestamps[0::decimation]
                    new_timestamps[
                        ts_offset : ts_offset + len(extracted_ts)
                    ] = extracted_ts
                    ts_offset += len(extracted_ts)
                    input_index_bounds = [0, interval_samples - 1]

                else:
                    logger.info(f"Interval {ii}: leaving data on disk")
                    data = data_on_disk
                    timestamps = timestamps_on_disk
                    extracted_ts = timestamps[start:stop:decimation]
                    new_timestamps[
                        ts_offset : ts_offset + len(extracted_ts)
                    ] = extracted_ts
                    ts_offset += len(extracted_ts)
                    input_index_bounds = [start, stop]

                # filter the data
                gsp.filter_data_fir(
                    data,
                    filter_coeff,
                    axis=time_axis,
                    input_index_bounds=input_index_bounds,
                    output_index_bounds=[
                        filter_delay,
                        filter_delay + stop - start,
                    ],
                    ds=decimation,
                    input_dim_restrictions=input_dim_restrictions,
                    outarray=filtered_data,
                    output_offset=output_offsets[ii],
                )

            start_end = [new_timestamps[0], new_timestamps[-1]]

            io.write(nwbf)

        return es.object_id, start_end

    def filter_data(
        self,
        timestamps,
        data,
        filter_coeff,
        valid_times,
        electrodes,
        decimation,
    ):
        """
        Parameters
        ----------
        timestamps: numpy array
            List of timestamps for data
        data:
            original data array
        filter_coeff: numpy array
            Filter coefficients for FIR filter
        valid_times: 2D numpy array
            Start and stop times of intervals to be filtered
        electrodes: list
            Electrodes to filter
        decimation:
            decimation factor

        Return
        ------
        filtered_data, timestamps
        """

        gsp = _import_ghostipy()

        n_dim = len(data.shape)
        n_samples = len(timestamps)
        time_axis = 0 if data.shape[0] == n_samples else 1
        electrode_axis = 1 - time_axis
        input_dim_restrictions = [None] * n_dim
        input_dim_restrictions[electrode_axis] = np.s_[electrodes]

        indices = []
        output_shape_list = [0] * n_dim
        output_shape_list[electrode_axis] = len(electrodes)
        output_offsets = [0]

        filter_delay = self.calc_filter_delay(filter_coeff)
        for a_start, a_stop in valid_times:
            frm, to = self._time_bound_check(
                a_start, a_stop, timestamps, n_samples
            )
            if np.isclose(frm, to, rtol=0, atol=1e-8):
                continue
            indices.append((frm, to))

            shape, _ = gsp.filter_data_fir(
                data,
                filter_coeff,
                axis=time_axis,
                input_index_bounds=[frm, to],
                output_index_bounds=[filter_delay, filter_delay + to - frm],
                describe_dims=True,
                ds=decimation,
                input_dim_restrictions=input_dim_restrictions,
            )
            output_offsets.append(output_offsets[-1] + shape[time_axis])
            output_shape_list[time_axis] += shape[time_axis]

        # create the dataset and the timestamps array
        filtered_data = np.empty(tuple(output_shape_list), dtype=data.dtype)

        new_timestamps = np.empty(
            (output_shape_list[time_axis],), timestamps.dtype
        )

        indices = np.array(indices, ndmin=2)

        # Filter  the output dataset
        ts_offset = 0

        for ii, (start, stop) in enumerate(indices):
            extracted_ts = timestamps[start:stop:decimation]

            new_timestamps[
                ts_offset : ts_offset + len(extracted_ts)
            ] = extracted_ts
            ts_offset += len(extracted_ts)

            # finally ready to filter data!
            gsp.filter_data_fir(
                data,
                filter_coeff,
                axis=time_axis,
                input_index_bounds=[start, stop],
                output_index_bounds=[filter_delay, filter_delay + stop - start],
                ds=decimation,
                input_dim_restrictions=input_dim_restrictions,
                outarray=filtered_data,
                output_offset=output_offsets[ii],
            )

        return filtered_data, new_timestamps

    def calc_filter_delay(self, filter_coeff):
        """
        Parameters
        ----------
        filter_coeff: numpy array

        Return
        ------
        filter delay: int
        """
        return (len(filter_coeff) - 1) // 2

    def create_standard_filters(self):
        """Add standard filters to the Filter table

        Includes 0-400 Hz low pass for continuous raw data -> LFP
        """
        self.add_filter(
            "LFP 0-400 Hz",
            20000,
            "lowpass",
            [400, 425],
            "standard LFP filter for 20 KHz data",
        )
        self.add_filter(
            "LFP 0-400 Hz",
            30000,
            "lowpass",
            [400, 425],
            "standard LFP filter for 30 KHz data",
        )

add_filter(filter_name, fs, filter_type, band_edges, comments='')

Add filter to the Filter table.

Parameters:

Name	Type	Description	Default
filter_name	str	The name of the filter.	required
fs	float	The filter sampling rate.	required
filter_type	str	The type of the filter ('lowpass', 'highpass', or 'bandpass').	required
band_edges	list	The band edges for the filter.	required
comments	str	Additional comments for the filter. Default "".	''

Returns:

Type	Description
None	Returns None if there is an error in the filter type or band frequencies.

Raises:

Type	Description
Exception:	Raises an exception if an unexpected filter type is encountered.

Source code in src/spyglass/common/common_filter.py

def add_filter(
    self,
    filter_name: str,
    fs: float,
    filter_type: str,
    band_edges: list,
    comments: str = "",
) -> None:
    """Add filter to the Filter table.

    Parameters
    ----------
    filter_name: str
        The name of the filter.
    fs: float
        The filter sampling rate.
    filter_type: str
        The type of the filter ('lowpass', 'highpass', or 'bandpass').
    band_edges: List[float]
        The band edges for the filter.
    comments: str, optional)
        Additional comments for the filter. Default "".

    Returns
    -------
    None
        Returns None if there is an error in the filter type or band
        frequencies.

    Raises
    ------
    Exception:
        Raises an exception if an unexpected filter type is encountered.
    """
    VALID_FILTERS = {"lowpass": 2, "highpass": 2, "bandpass": 4}
    FILTER_ERR = "Error in Filter.add_filter: "
    FILTER_N_ERR = FILTER_ERR + "filter {} requires {} band_frequencies."

    # add an FIR bandpass filter of the specified type.
    # band_edges should be as follows:
    #   low pass : [high_pass high_stop]
    #   high pass: [low stop low pass]
    #   band pass: [low_stop low_pass high_pass high_stop].
    if filter_type not in VALID_FILTERS:
        logger.error(
            FILTER_ERR
            + f"{filter_type} not valid type: {VALID_FILTERS.keys()}"
        )
        return None

    if not len(band_edges) == VALID_FILTERS[filter_type]:
        logger.error(
            FILTER_N_ERR.format(filter_name, VALID_FILTERS[filter_type])
        )
        return None

    gsp = _import_ghostipy()
    TRANS_SPLINE = 2  # transition spline will be quadratic

    if filter_type != "bandpass":
        transition_width = band_edges[1] - band_edges[0]

    else:
        # transition width is mean of left and right transition regions
        transition_width = (
            (band_edges[1] - band_edges[0])
            + (band_edges[3] - band_edges[2])
        ) / 2.0

    numtaps = gsp.estimate_taps(fs, transition_width)
    filterdict = {
        "filter_type": filter_type,
        "filter_name": filter_name,
        "filter_sampling_rate": fs,
        "filter_comments": comments,
        "filter_low_stop": 0,
        "filter_low_pass": 0,
        "filter_high_pass": 0,
        "filter_high_stop": 0,
        "filter_band_edges": np.asarray(band_edges),
    }

    # set the desired frequency response
    if filter_type == "lowpass":
        desired = [1, 0]
        pass_stop_dict = {
            "filter_high_pass": band_edges[0],
            "filter_high_stop": band_edges[1],
        }
    elif filter_type == "highpass":
        desired = [0, 1]
        pass_stop_dict = {
            "filter_low_stop": band_edges[0],
            "filter_low_pass": band_edges[1],
        }
    else:
        desired = [0, 1, 1, 0]
        pass_stop_dict = {
            "filter_low_stop": band_edges[0],
            "filter_low_pass": band_edges[1],
            "filter_high_pass": band_edges[2],
            "filter_high_stop": band_edges[3],
        }

    # create 1d array for coefficients
    filterdict.update(
        {
            **pass_stop_dict,
            "filter_coeff": np.array(
                gsp.firdesign(
                    numtaps, band_edges, desired, fs=fs, p=TRANS_SPLINE
                ),
                ndmin=1,
            ),
        }
    )

    self.insert1(filterdict, skip_duplicates=True)

filter_data_nwb(analysis_file_abs_path, eseries, filter_coeff, valid_times, electrode_ids, decimation, description='filtered data', data_type=None)

Filter data from an NWB electrical series using the ghostipy package, and save the result as a new electrical series in the analysis NWB file.

Parameters:

Name	Type	Description	Default
analysis_file_abs_path	str	Full path to the analysis NWB file.	required
eseries	ElectricalSeries	Electrical series with data to be filtered.	required
filter_coeff	ndarray	Array with filter coefficients for FIR filter.	required
valid_times	ndarray	Array with start and stop times of intervals to be filtered.	required
electrode_ids	list	List of electrode IDs to filter.	required
decimation	int	Decimation factor.	required
description	str	Description of the filtered data.	'filtered data'
data_type	Union[None, str]	Type of data (e.g., "LFP").	None

Returns:

Type	Description
tuple	The NWB object ID of the filtered data and a list containing the first and last timestamps.

Source code in src/spyglass/common/common_filter.py

def filter_data_nwb(
    self,
    analysis_file_abs_path: str,
    eseries: pynwb.ecephys.ElectricalSeries,
    filter_coeff: np.ndarray,
    valid_times: np.ndarray,
    electrode_ids: list,
    decimation: int,
    description: str = "filtered data",
    data_type: Union[None, str] = None,
):
    """
    Filter data from an NWB electrical series using the ghostipy package,
    and save the result as a new electrical series in the analysis NWB file.

    Parameters
    ----------
    analysis_file_abs_path : str
        Full path to the analysis NWB file.
    eseries : pynwb.ecephys.ElectricalSeries
        Electrical series with data to be filtered.
    filter_coeff : np.ndarray
        Array with filter coefficients for FIR filter.
    valid_times : np.ndarray
        Array with start and stop times of intervals to be filtered.
    electrode_ids : list
        List of electrode IDs to filter.
    decimation : int
        Decimation factor.
    description : str
        Description of the filtered data.
    data_type : Union[None, str]
        Type of data (e.g., "LFP").

    Returns
    -------
    tuple
        The NWB object ID of the filtered data and a list containing the
        first and last timestamps.
    """
    # Note: type -> data_type to avoid conflict with builtin type
    # All existing refs to this func use positional args, so no need to
    # adjust elsewhere, but low probability of issues with custom scripts

    MEM_USE_LIMIT = 0.9  # % of RAM use permitted

    gsp = _import_ghostipy()

    data_on_disk = eseries.data
    timestamps_on_disk = eseries.timestamps

    n_samples = len(timestamps_on_disk)
    time_axis = 0 if data_on_disk.shape[0] == n_samples else 1
    electrode_axis = 1 - time_axis

    n_electrodes = data_on_disk.shape[electrode_axis]
    input_dim_restrictions = [None] * len(data_on_disk.shape)

    # Get input dimension restrictions
    input_dim_restrictions[electrode_axis] = np.s_[
        get_electrode_indices(eseries, electrode_ids)
    ]

    indices = []
    output_shape_list = [0] * len(data_on_disk.shape)
    output_shape_list[electrode_axis] = len(electrode_ids)
    data_dtype = data_on_disk[0][0].dtype

    filter_delay = self.calc_filter_delay(filter_coeff)

    output_offsets = [0]

    for a_start, a_stop in valid_times:
        frm, to = self._time_bound_check(
            a_start, a_stop, timestamps_on_disk, n_samples
        )

        indices.append((frm, to))

        shape, _ = gsp.filter_data_fir(
            data_on_disk,
            filter_coeff,
            axis=time_axis,
            input_index_bounds=[frm, to - 1],
            output_index_bounds=[filter_delay, filter_delay + to - frm],
            describe_dims=True,
            ds=decimation,
            input_dim_restrictions=input_dim_restrictions,
        )
        output_offsets.append(output_offsets[-1] + shape[time_axis])
        output_shape_list[time_axis] += shape[time_axis]

    # Create dynamic table region and electrode series, write/close file
    with pynwb.NWBHDF5IO(
        path=analysis_file_abs_path, mode="a", load_namespaces=True
    ) as io:
        nwbf = io.read()

        # get the indices of the electrodes in the electrode table
        elect_ind = get_electrode_indices(nwbf, electrode_ids)

        electrode_table_region = nwbf.create_electrode_table_region(
            elect_ind, "filtered electrode table"
        )
        es = pynwb.ecephys.ElectricalSeries(
            name="filtered data",
            data=np.empty(tuple(output_shape_list), dtype=data_dtype),
            electrodes=electrode_table_region,
            timestamps=np.empty(output_shape_list[time_axis]),
            description=description,
        )
        if data_type == "LFP":
            ecephys_module = nwbf.create_processing_module(
                name="ecephys", description=description
            )
            ecephys_module.add(pynwb.ecephys.LFP(electrical_series=es))
        else:
            nwbf.add_scratch(es)

        io.write(nwbf)

    # Reload NWB file to get h5py objects for data/timestamps
    # NOTE: CBroz - why io context within io context? Unindenting
    with pynwb.NWBHDF5IO(
        path=analysis_file_abs_path, mode="a", load_namespaces=True
    ) as io:
        nwbf = io.read()
        es = nwbf.objects[es.object_id]
        filtered_data = es.data
        new_timestamps = es.timestamps
        indices = np.array(indices, ndmin=2)
        # Filter and write the output dataset
        ts_offset = 0

        logger.info("Filtering data")
        for ii, (start, stop) in enumerate(indices):
            # Calc size of timestamps + data, check if < 90% of RAM
            interval_samples = stop - start
            req_mem = interval_samples * (
                timestamps_on_disk[0].itemsize
                + n_electrodes * data_on_disk[0][0].itemsize
            )
            if req_mem < MEM_USE_LIMIT * psutil.virtual_memory().available:
                logger.info(f"Interval {ii}: loading data into memory")
                timestamps = np.asarray(
                    timestamps_on_disk[start:stop],
                    dtype=timestamps_on_disk[0].dtype,
                )
                if time_axis == 0:
                    data = np.asarray(
                        data_on_disk[start:stop, :], dtype=data_dtype
                    )
                else:
                    data = np.asarray(
                        data_on_disk[:, start:stop], dtype=data_dtype
                    )
                extracted_ts = timestamps[0::decimation]
                new_timestamps[
                    ts_offset : ts_offset + len(extracted_ts)
                ] = extracted_ts
                ts_offset += len(extracted_ts)
                input_index_bounds = [0, interval_samples - 1]

            else:
                logger.info(f"Interval {ii}: leaving data on disk")
                data = data_on_disk
                timestamps = timestamps_on_disk
                extracted_ts = timestamps[start:stop:decimation]
                new_timestamps[
                    ts_offset : ts_offset + len(extracted_ts)
                ] = extracted_ts
                ts_offset += len(extracted_ts)
                input_index_bounds = [start, stop]

            # filter the data
            gsp.filter_data_fir(
                data,
                filter_coeff,
                axis=time_axis,
                input_index_bounds=input_index_bounds,
                output_index_bounds=[
                    filter_delay,
                    filter_delay + stop - start,
                ],
                ds=decimation,
                input_dim_restrictions=input_dim_restrictions,
                outarray=filtered_data,
                output_offset=output_offsets[ii],
            )

        start_end = [new_timestamps[0], new_timestamps[-1]]

        io.write(nwbf)

    return es.object_id, start_end

filter_data(timestamps, data, filter_coeff, valid_times, electrodes, decimation)

Parameters:

Name	Type	Description	Default
timestamps		List of timestamps for data	required
data		original data array	required
filter_coeff		Filter coefficients for FIR filter	required
valid_times		Start and stop times of intervals to be filtered	required
electrodes		Electrodes to filter	required
decimation		decimation factor	required

Return

filtered_data, timestamps

Source code in src/spyglass/common/common_filter.py

def filter_data(
    self,
    timestamps,
    data,
    filter_coeff,
    valid_times,
    electrodes,
    decimation,
):
    """
    Parameters
    ----------
    timestamps: numpy array
        List of timestamps for data
    data:
        original data array
    filter_coeff: numpy array
        Filter coefficients for FIR filter
    valid_times: 2D numpy array
        Start and stop times of intervals to be filtered
    electrodes: list
        Electrodes to filter
    decimation:
        decimation factor

    Return
    ------
    filtered_data, timestamps
    """

    gsp = _import_ghostipy()

    n_dim = len(data.shape)
    n_samples = len(timestamps)
    time_axis = 0 if data.shape[0] == n_samples else 1
    electrode_axis = 1 - time_axis
    input_dim_restrictions = [None] * n_dim
    input_dim_restrictions[electrode_axis] = np.s_[electrodes]

    indices = []
    output_shape_list = [0] * n_dim
    output_shape_list[electrode_axis] = len(electrodes)
    output_offsets = [0]

    filter_delay = self.calc_filter_delay(filter_coeff)
    for a_start, a_stop in valid_times:
        frm, to = self._time_bound_check(
            a_start, a_stop, timestamps, n_samples
        )
        if np.isclose(frm, to, rtol=0, atol=1e-8):
            continue
        indices.append((frm, to))

        shape, _ = gsp.filter_data_fir(
            data,
            filter_coeff,
            axis=time_axis,
            input_index_bounds=[frm, to],
            output_index_bounds=[filter_delay, filter_delay + to - frm],
            describe_dims=True,
            ds=decimation,
            input_dim_restrictions=input_dim_restrictions,
        )
        output_offsets.append(output_offsets[-1] + shape[time_axis])
        output_shape_list[time_axis] += shape[time_axis]

    # create the dataset and the timestamps array
    filtered_data = np.empty(tuple(output_shape_list), dtype=data.dtype)

    new_timestamps = np.empty(
        (output_shape_list[time_axis],), timestamps.dtype
    )

    indices = np.array(indices, ndmin=2)

    # Filter  the output dataset
    ts_offset = 0

    for ii, (start, stop) in enumerate(indices):
        extracted_ts = timestamps[start:stop:decimation]

        new_timestamps[
            ts_offset : ts_offset + len(extracted_ts)
        ] = extracted_ts
        ts_offset += len(extracted_ts)

        # finally ready to filter data!
        gsp.filter_data_fir(
            data,
            filter_coeff,
            axis=time_axis,
            input_index_bounds=[start, stop],
            output_index_bounds=[filter_delay, filter_delay + stop - start],
            ds=decimation,
            input_dim_restrictions=input_dim_restrictions,
            outarray=filtered_data,
            output_offset=output_offsets[ii],
        )

    return filtered_data, new_timestamps

calc_filter_delay(filter_coeff)

Parameters:

Name	Type	Description	Default
filter_coeff			required

Return

filter delay: int

Source code in src/spyglass/common/common_filter.py

def calc_filter_delay(self, filter_coeff):
    """
    Parameters
    ----------
    filter_coeff: numpy array

    Return
    ------
    filter delay: int
    """
    return (len(filter_coeff) - 1) // 2

create_standard_filters()

Add standard filters to the Filter table

Includes 0-400 Hz low pass for continuous raw data -> LFP

Source code in src/spyglass/common/common_filter.py

def create_standard_filters(self):
    """Add standard filters to the Filter table

    Includes 0-400 Hz low pass for continuous raw data -> LFP
    """
    self.add_filter(
        "LFP 0-400 Hz",
        20000,
        "lowpass",
        [400, 425],
        "standard LFP filter for 20 KHz data",
    )
    self.add_filter(
        "LFP 0-400 Hz",
        30000,
        "lowpass",
        [400, 425],
        "standard LFP filter for 30 KHz data",
    )