lfp_band.py

LFPBandSelection

Bases: SpyglassMixin, Manual

The user's selection of LFP data to be filtered in a given frequency band.

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

@schema
class LFPBandSelection(SpyglassMixin, dj.Manual):
    """The user's selection of LFP data to be filtered in a given frequency band."""

    definition = """
    -> LFPOutput.proj(lfp_merge_id='merge_id')                            # the LFP data to be filtered
    -> FirFilterParameters                                                # the filter to use for the data
    -> IntervalList.proj(target_interval_list_name='interval_list_name')  # the original set of times to be filtered
    lfp_band_sampling_rate: int                                           # the sampling rate for this band
    ---
    min_interval_len = 1.0: float  # the minimum length of a valid interval to filter
    """

    class LFPBandElectrode(SpyglassMixin, dj.Part):
        definition = """
        -> LFPBandSelection # the LFP band selection
        -> LFPElectrodeGroup.LFPElectrode  # the LFP electrode to be filtered
        reference_elect_id = -1: int  # the reference electrode to use; -1 for no reference
        """

    def set_lfp_band_electrodes(
        self,
        nwb_file_name: str,
        lfp_merge_id: int,
        electrode_list: list[int],
        filter_name: str,
        interval_list_name: str,
        reference_electrode_list: list[int],
        lfp_band_sampling_rate: int,
    ):
        """Sets the electrodes to be filtered for a given LFP

        Parameters
        ----------
        nwb_file_name: str
            The name of the NWB file containing the LFP data
        lfp_merge_id: int
            The uuid of the LFP data to be filtered
        electrode_list: list
            A list of the electrodes to be filtered
        filter_name: str
            The name of the filter to be used
        interval_list_name: str
            The name of the interval list to be used
        reference_electrode_list: list
            A list of the reference electrodes to be used
        lfp_band_sampling_rate: int
        """
        # Error checks on parameters
        # electrode_list

        lfp_key = {"merge_id": lfp_merge_id}
        lfp_part_table = LFPOutput.merge_get_part(lfp_key)

        query = LFPElectrodeGroup().LFPElectrode() & lfp_key
        available_electrodes = query.fetch("electrode_id")
        if not np.all(np.isin(electrode_list, available_electrodes)):
            raise ValueError(
                "All elements in electrode_list must be valid electrode_ids in"
                + " the LFPElectodeGroup table: "
                + f"{electrode_list} not in {available_electrodes}"
            )
        # sampling rate
        lfp_sampling_rate = LFPOutput.merge_get_parent(lfp_key).fetch1(
            "lfp_sampling_rate"
        )
        decimation = lfp_sampling_rate // lfp_band_sampling_rate
        # filter
        filter_query = FirFilterParameters() & {
            "filter_name": filter_name,
            "filter_sampling_rate": lfp_sampling_rate,
        }
        if not filter_query:
            raise ValueError(
                f"Filter {filter_name}, sampling rate {lfp_sampling_rate} is "
                + "not in the FirFilterParameters table"
            )
        # interval_list
        interval_query = IntervalList() & {
            "nwb_file_name": nwb_file_name,
            "interval_name": interval_list_name,
        }
        if not interval_query:
            raise ValueError(
                f"interval list {interval_list_name} is not in the IntervalList"
                " table; the list must be added before this function is called"
            )
        # reference_electrode_list
        if len(reference_electrode_list) != 1 and len(
            reference_electrode_list
        ) != len(electrode_list):
            raise ValueError(
                "reference_electrode_list must contain either 1 or "
                + "len(electrode_list) elements"
            )
        # add a -1 element to the list to allow for the no reference option
        available_electrodes = np.append(available_electrodes, [-1])
        if not np.all(np.isin(reference_electrode_list, available_electrodes)):
            raise ValueError(
                "All elements in reference_electrode_list must be valid "
                "electrode_ids in the LFPSelection table"
            )

        # make a list of all the references
        ref_list = np.zeros((len(electrode_list),))
        ref_list[:] = reference_electrode_list

        key = dict(
            nwb_file_name=nwb_file_name,
            lfp_merge_id=lfp_merge_id,
            filter_name=filter_name,
            filter_sampling_rate=lfp_sampling_rate,
            target_interval_list_name=interval_list_name,
            lfp_band_sampling_rate=lfp_sampling_rate // decimation,
        )
        # insert an entry into the main LFPBandSelectionTable
        self.insert1(key, skip_duplicates=True)

        key["lfp_electrode_group_name"] = lfp_part_table.fetch1(
            "lfp_electrode_group_name"
        )
        # iterate through all of the new elements and add them
        for e, r in zip(electrode_list, ref_list):
            elect_key = (
                LFPElectrodeGroup.LFPElectrode
                & {
                    "nwb_file_name": nwb_file_name,
                    "lfp_electrode_group_name": key["lfp_electrode_group_name"],
                    "electrode_id": e,
                }
            ).fetch1("KEY")
            for item in elect_key:
                key[item] = elect_key[item]
            query = Electrode & {
                "nwb_file_name": nwb_file_name,
                "electrode_id": e,
            }
            key["reference_elect_id"] = r
            self.LFPBandElectrode().insert1(key, skip_duplicates=True)

set_lfp_band_electrodes(nwb_file_name, lfp_merge_id, electrode_list, filter_name, interval_list_name, reference_electrode_list, lfp_band_sampling_rate)

Sets the electrodes to be filtered for a given LFP

Parameters:

Name	Type	Description	Default
nwb_file_name	str	The name of the NWB file containing the LFP data	required
lfp_merge_id	int	The uuid of the LFP data to be filtered	required
electrode_list	list[int]	A list of the electrodes to be filtered	required
filter_name	str	The name of the filter to be used	required
interval_list_name	str	The name of the interval list to be used	required
reference_electrode_list	list[int]	A list of the reference electrodes to be used	required
lfp_band_sampling_rate	int		required

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def set_lfp_band_electrodes(
    self,
    nwb_file_name: str,
    lfp_merge_id: int,
    electrode_list: list[int],
    filter_name: str,
    interval_list_name: str,
    reference_electrode_list: list[int],
    lfp_band_sampling_rate: int,
):
    """Sets the electrodes to be filtered for a given LFP

    Parameters
    ----------
    nwb_file_name: str
        The name of the NWB file containing the LFP data
    lfp_merge_id: int
        The uuid of the LFP data to be filtered
    electrode_list: list
        A list of the electrodes to be filtered
    filter_name: str
        The name of the filter to be used
    interval_list_name: str
        The name of the interval list to be used
    reference_electrode_list: list
        A list of the reference electrodes to be used
    lfp_band_sampling_rate: int
    """
    # Error checks on parameters
    # electrode_list

    lfp_key = {"merge_id": lfp_merge_id}
    lfp_part_table = LFPOutput.merge_get_part(lfp_key)

    query = LFPElectrodeGroup().LFPElectrode() & lfp_key
    available_electrodes = query.fetch("electrode_id")
    if not np.all(np.isin(electrode_list, available_electrodes)):
        raise ValueError(
            "All elements in electrode_list must be valid electrode_ids in"
            + " the LFPElectodeGroup table: "
            + f"{electrode_list} not in {available_electrodes}"
        )
    # sampling rate
    lfp_sampling_rate = LFPOutput.merge_get_parent(lfp_key).fetch1(
        "lfp_sampling_rate"
    )
    decimation = lfp_sampling_rate // lfp_band_sampling_rate
    # filter
    filter_query = FirFilterParameters() & {
        "filter_name": filter_name,
        "filter_sampling_rate": lfp_sampling_rate,
    }
    if not filter_query:
        raise ValueError(
            f"Filter {filter_name}, sampling rate {lfp_sampling_rate} is "
            + "not in the FirFilterParameters table"
        )
    # interval_list
    interval_query = IntervalList() & {
        "nwb_file_name": nwb_file_name,
        "interval_name": interval_list_name,
    }
    if not interval_query:
        raise ValueError(
            f"interval list {interval_list_name} is not in the IntervalList"
            " table; the list must be added before this function is called"
        )
    # reference_electrode_list
    if len(reference_electrode_list) != 1 and len(
        reference_electrode_list
    ) != len(electrode_list):
        raise ValueError(
            "reference_electrode_list must contain either 1 or "
            + "len(electrode_list) elements"
        )
    # add a -1 element to the list to allow for the no reference option
    available_electrodes = np.append(available_electrodes, [-1])
    if not np.all(np.isin(reference_electrode_list, available_electrodes)):
        raise ValueError(
            "All elements in reference_electrode_list must be valid "
            "electrode_ids in the LFPSelection table"
        )

    # make a list of all the references
    ref_list = np.zeros((len(electrode_list),))
    ref_list[:] = reference_electrode_list

    key = dict(
        nwb_file_name=nwb_file_name,
        lfp_merge_id=lfp_merge_id,
        filter_name=filter_name,
        filter_sampling_rate=lfp_sampling_rate,
        target_interval_list_name=interval_list_name,
        lfp_band_sampling_rate=lfp_sampling_rate // decimation,
    )
    # insert an entry into the main LFPBandSelectionTable
    self.insert1(key, skip_duplicates=True)

    key["lfp_electrode_group_name"] = lfp_part_table.fetch1(
        "lfp_electrode_group_name"
    )
    # iterate through all of the new elements and add them
    for e, r in zip(electrode_list, ref_list):
        elect_key = (
            LFPElectrodeGroup.LFPElectrode
            & {
                "nwb_file_name": nwb_file_name,
                "lfp_electrode_group_name": key["lfp_electrode_group_name"],
                "electrode_id": e,
            }
        ).fetch1("KEY")
        for item in elect_key:
            key[item] = elect_key[item]
        query = Electrode & {
            "nwb_file_name": nwb_file_name,
            "electrode_id": e,
        }
        key["reference_elect_id"] = r
        self.LFPBandElectrode().insert1(key, skip_duplicates=True)

LFPBandV1

Bases: SpyglassMixin, Computed

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

@schema
class LFPBandV1(SpyglassMixin, dj.Computed):
    definition = """
    -> LFPBandSelection              # the LFP band selection
    ---
    -> AnalysisNwbfile               # the name of the nwb file with the lfp data
    -> IntervalList                  # the final interval list of valid times for the data
    lfp_band_object_id: varchar(40)  # the NWB object ID for loading this object from the file
    """

    def make(self, key):
        """Populate LFPBandV1"""
        # create the analysis nwb file to store the results.
        lfp_band_file_name = AnalysisNwbfile().create(  # logged
            key["nwb_file_name"]
        )
        # get the NWB object with the lfp data;
        # FIX: change to fetch with additional infrastructure
        lfp_key = {"merge_id": key["lfp_merge_id"]}
        lfp_object = (LFPOutput & lfp_key).fetch_nwb()[0]["lfp"]

        # get the electrodes to be filtered and their references
        lfp_band_elect_id, lfp_band_ref_id = (
            LFPBandSelection().LFPBandElectrode() & key
        ).fetch("electrode_id", "reference_elect_id")

        # sort the electrodes to make sure they are in ascending order
        lfp_band_elect_id = np.asarray(lfp_band_elect_id)
        lfp_band_ref_id = np.asarray(lfp_band_ref_id)
        lfp_sort_order = np.argsort(lfp_band_elect_id)
        lfp_band_elect_id = lfp_band_elect_id[lfp_sort_order]
        lfp_band_ref_id = lfp_band_ref_id[lfp_sort_order]

        lfp_sampling_rate, lfp_interval_list = LFPOutput.merge_get_parent(
            lfp_key
        ).fetch1("lfp_sampling_rate", "interval_list_name")
        interval_list_name, lfp_band_sampling_rate = (
            LFPBandSelection() & key
        ).fetch1("target_interval_list_name", "lfp_band_sampling_rate")
        valid_times = (
            IntervalList()
            & {
                "nwb_file_name": key["nwb_file_name"],
                "interval_list_name": interval_list_name,
            }
        ).fetch1("valid_times")
        # the valid_times for this interval may be slightly beyond the valid
        # times for the lfp itself, so we have to intersect the two lists
        lfp_valid_times = (
            IntervalList()
            & {
                "nwb_file_name": key["nwb_file_name"],
                "interval_list_name": lfp_interval_list,
            }
        ).fetch1("valid_times")
        min_length = (LFPBandSelection & key).fetch1("min_interval_len")
        lfp_band_valid_times = interval_list_intersect(
            valid_times, lfp_valid_times, min_length=min_length
        )

        filter_name, filter_sampling_rate, lfp_band_sampling_rate = (
            LFPBandSelection() & key
        ).fetch1(
            "filter_name", "filter_sampling_rate", "lfp_band_sampling_rate"
        )

        decimation = int(lfp_sampling_rate) // lfp_band_sampling_rate

        # load in the timestamps
        timestamps = np.asarray(lfp_object.timestamps)
        # get the indices of the first timestamp and the last timestamp that
        # are within the valid times
        included_indices = interval_list_contains_ind(
            lfp_band_valid_times, timestamps
        )
        # pad the indices by 1 on each side to avoid message in filter_data
        if included_indices[0] > 0:
            included_indices[0] -= 1
        if included_indices[-1] != len(timestamps) - 1:
            included_indices[-1] += 1

        timestamps = timestamps[included_indices[0] : included_indices[-1]]

        # load all the data to speed filtering
        lfp_data = np.asarray(
            lfp_object.data[included_indices[0] : included_indices[-1], :],
            dtype=type(lfp_object.data[0][0]),
        )

        # get the indices of the electrodes to be filtered and the references
        lfp_band_elect_index = get_electrode_indices(
            lfp_object, lfp_band_elect_id
        )
        lfp_band_ref_index = get_electrode_indices(lfp_object, lfp_band_ref_id)

        # subtract off the references for the selected channels
        lfp_data_original = lfp_data.copy()
        for index, elect_index in enumerate(lfp_band_elect_index):
            if lfp_band_ref_id[index] != -1:
                lfp_data[:, elect_index] = (
                    lfp_data_original[:, elect_index]
                    - lfp_data_original[:, lfp_band_ref_index[index]]
                )

        # get the LFP filter that matches the raw data
        filter = (
            FirFilterParameters()
            & {"filter_name": filter_name}
            & {"filter_sampling_rate": filter_sampling_rate}
        ).fetch(as_dict=True)

        filter_coeff = filter[0]["filter_coeff"]
        if len(filter_coeff) == 0:
            logger.error(
                "LFPBand: no filter found with data "
                + f"sampling rate of {lfp_band_sampling_rate}"
            )
            return None

        lfp_band_file_abspath = AnalysisNwbfile().get_abs_path(
            lfp_band_file_name
        )
        # filter the data and write to an the nwb file
        filtered_data, new_timestamps = FirFilterParameters().filter_data(
            timestamps,
            lfp_data,
            filter_coeff,
            lfp_band_valid_times,
            lfp_band_elect_index,
            decimation,
        )

        # now that the LFP is filtered, we create an electrical series for it
        # and add it to the file
        with pynwb.NWBHDF5IO(
            path=lfp_band_file_abspath, mode="a", load_namespaces=True
        ) as io:
            nwbf = io.read()

            # get the indices of the electrodes in the electrode table of the
            # file to get the right values
            elect_index = get_electrode_indices(nwbf, lfp_band_elect_id)
            electrode_table_region = nwbf.create_electrode_table_region(
                elect_index, "filtered electrode table"
            )
            eseries_name = "filtered data"
            # TODO: use datatype of data
            es = pynwb.ecephys.ElectricalSeries(
                name=eseries_name,
                data=filtered_data,
                electrodes=electrode_table_region,
                timestamps=new_timestamps,
            )
            lfp = pynwb.ecephys.LFP(electrical_series=es)
            ecephys_module = nwbf.create_processing_module(
                name="ecephys",
                description=f"LFP data processed with {filter_name}",
            )
            ecephys_module.add(lfp)
            io.write(nwbf)
            lfp_band_object_id = es.object_id
        #
        # add the file to the AnalysisNwbfile table
        AnalysisNwbfile().add(key["nwb_file_name"], lfp_band_file_name)
        key["analysis_file_name"] = lfp_band_file_name
        key["lfp_band_object_id"] = lfp_band_object_id

        # finally, censor the valid times to account for the downsampling if
        # this is the first time we've downsampled these data
        key["interval_list_name"] = (
            interval_list_name
            + " lfp band "
            + str(lfp_band_sampling_rate)
            + "Hz"
        )
        tmp_valid_times = (
            IntervalList
            & {
                "nwb_file_name": key["nwb_file_name"],
                "interval_list_name": key["interval_list_name"],
            }
        ).fetch("valid_times")
        if len(tmp_valid_times) == 0:
            lfp_band_valid_times = interval_list_censor(
                lfp_band_valid_times, new_timestamps
            )
            # add an interval list for the LFP valid times
            IntervalList.insert1(
                {
                    "nwb_file_name": key["nwb_file_name"],
                    "interval_list_name": key["interval_list_name"],
                    "valid_times": lfp_band_valid_times,
                    "pipeline": "lfp band",
                }
            )
        else:
            lfp_band_valid_times = interval_list_censor(
                lfp_band_valid_times, new_timestamps
            )
            # check that the valid times are the same
            assert np.isclose(
                tmp_valid_times[0], lfp_band_valid_times
            ).all(), (
                "previously saved lfp band times do not match current times"
            )

        AnalysisNwbfile().log(key, table=self.full_table_name)
        self.insert1(key)

    def fetch1_dataframe(self, *attrs, **kwargs):
        """Fetches the filtered data as a dataframe"""
        filtered_nwb = self.fetch_nwb()[0]
        return pd.DataFrame(
            filtered_nwb["lfp_band"].data,
            index=pd.Index(filtered_nwb["lfp_band"].timestamps, name="time"),
        )

    def compute_analytic_signal(self, electrode_list: list[int], **kwargs):
        """Computes the hilbert transform of a given LFPBand signal

        Uses scipy.signal.hilbert to compute the hilbert transform

        Parameters
        ----------
        electrode_list: list[int]
            A list of the electrodes to compute the hilbert transform of

        Returns
        -------
        analytic_signal_df: pd.DataFrame
            DataFrame containing hilbert transform of signal

        Raises
        ------
        ValueError
            If items in electrode_list are invalid for the dataset
        """

        filtered_band = self.fetch_nwb()[0]["lfp_band"]
        electrode_index = np.isin(
            filtered_band.electrodes.data[:], electrode_list
        )
        if len(electrode_list) != np.sum(electrode_index):
            raise ValueError(
                "Some of the electrodes specified in electrode_list are missing"
                + " in the current LFPBand table."
            )
        analytic_signal_df = pd.DataFrame(
            hilbert(filtered_band.data[:, electrode_index], axis=0),
            index=pd.Index(filtered_band.timestamps, name="time"),
            columns=[f"electrode {e}" for e in electrode_list],
        )
        return analytic_signal_df

    def compute_signal_phase(
        self, electrode_list: list[int] = None, **kwargs
    ) -> pd.DataFrame:
        """Computes phase of LFPBand signals using the hilbert transform

        Parameters
        ----------
        electrode_list : list[int], optional
            A list of the electrodes to compute the phase of, by default None

        Returns
        -------
        signal_phase_df : pd.DataFrame
            DataFrame containing the phase of the signals
        """
        if electrode_list is None:
            electrode_list = []

        analytic_signal_df = self.compute_analytic_signal(
            electrode_list, **kwargs
        )

        return pd.DataFrame(
            np.angle(analytic_signal_df) + np.pi,
            columns=analytic_signal_df.columns,
            index=analytic_signal_df.index,
        )

    def compute_signal_power(
        self, electrode_list: list[int] = None, **kwargs
    ) -> pd.DataFrame:
        """Computes power LFPBand signals using the hilbert transform

        Parameters
        ----------
        electrode_list : list[int], optional
            A list of the electrodes to compute the power of, by default None

        Returns
        -------
        signal_power_df : pd.DataFrame
            DataFrame containing the power of the signals
        """
        if electrode_list is None:
            electrode_list = []

        analytic_signal_df = self.compute_analytic_signal(
            electrode_list, **kwargs
        )

        return pd.DataFrame(
            np.abs(analytic_signal_df) ** 2,
            columns=analytic_signal_df.columns,
            index=analytic_signal_df.index,
        )

make(key)

Populate LFPBandV1

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def make(self, key):
    """Populate LFPBandV1"""
    # create the analysis nwb file to store the results.
    lfp_band_file_name = AnalysisNwbfile().create(  # logged
        key["nwb_file_name"]
    )
    # get the NWB object with the lfp data;
    # FIX: change to fetch with additional infrastructure
    lfp_key = {"merge_id": key["lfp_merge_id"]}
    lfp_object = (LFPOutput & lfp_key).fetch_nwb()[0]["lfp"]

    # get the electrodes to be filtered and their references
    lfp_band_elect_id, lfp_band_ref_id = (
        LFPBandSelection().LFPBandElectrode() & key
    ).fetch("electrode_id", "reference_elect_id")

    # sort the electrodes to make sure they are in ascending order
    lfp_band_elect_id = np.asarray(lfp_band_elect_id)
    lfp_band_ref_id = np.asarray(lfp_band_ref_id)
    lfp_sort_order = np.argsort(lfp_band_elect_id)
    lfp_band_elect_id = lfp_band_elect_id[lfp_sort_order]
    lfp_band_ref_id = lfp_band_ref_id[lfp_sort_order]

    lfp_sampling_rate, lfp_interval_list = LFPOutput.merge_get_parent(
        lfp_key
    ).fetch1("lfp_sampling_rate", "interval_list_name")
    interval_list_name, lfp_band_sampling_rate = (
        LFPBandSelection() & key
    ).fetch1("target_interval_list_name", "lfp_band_sampling_rate")
    valid_times = (
        IntervalList()
        & {
            "nwb_file_name": key["nwb_file_name"],
            "interval_list_name": interval_list_name,
        }
    ).fetch1("valid_times")
    # the valid_times for this interval may be slightly beyond the valid
    # times for the lfp itself, so we have to intersect the two lists
    lfp_valid_times = (
        IntervalList()
        & {
            "nwb_file_name": key["nwb_file_name"],
            "interval_list_name": lfp_interval_list,
        }
    ).fetch1("valid_times")
    min_length = (LFPBandSelection & key).fetch1("min_interval_len")
    lfp_band_valid_times = interval_list_intersect(
        valid_times, lfp_valid_times, min_length=min_length
    )

    filter_name, filter_sampling_rate, lfp_band_sampling_rate = (
        LFPBandSelection() & key
    ).fetch1(
        "filter_name", "filter_sampling_rate", "lfp_band_sampling_rate"
    )

    decimation = int(lfp_sampling_rate) // lfp_band_sampling_rate

    # load in the timestamps
    timestamps = np.asarray(lfp_object.timestamps)
    # get the indices of the first timestamp and the last timestamp that
    # are within the valid times
    included_indices = interval_list_contains_ind(
        lfp_band_valid_times, timestamps
    )
    # pad the indices by 1 on each side to avoid message in filter_data
    if included_indices[0] > 0:
        included_indices[0] -= 1
    if included_indices[-1] != len(timestamps) - 1:
        included_indices[-1] += 1

    timestamps = timestamps[included_indices[0] : included_indices[-1]]

    # load all the data to speed filtering
    lfp_data = np.asarray(
        lfp_object.data[included_indices[0] : included_indices[-1], :],
        dtype=type(lfp_object.data[0][0]),
    )

    # get the indices of the electrodes to be filtered and the references
    lfp_band_elect_index = get_electrode_indices(
        lfp_object, lfp_band_elect_id
    )
    lfp_band_ref_index = get_electrode_indices(lfp_object, lfp_band_ref_id)

    # subtract off the references for the selected channels
    lfp_data_original = lfp_data.copy()
    for index, elect_index in enumerate(lfp_band_elect_index):
        if lfp_band_ref_id[index] != -1:
            lfp_data[:, elect_index] = (
                lfp_data_original[:, elect_index]
                - lfp_data_original[:, lfp_band_ref_index[index]]
            )

    # get the LFP filter that matches the raw data
    filter = (
        FirFilterParameters()
        & {"filter_name": filter_name}
        & {"filter_sampling_rate": filter_sampling_rate}
    ).fetch(as_dict=True)

    filter_coeff = filter[0]["filter_coeff"]
    if len(filter_coeff) == 0:
        logger.error(
            "LFPBand: no filter found with data "
            + f"sampling rate of {lfp_band_sampling_rate}"
        )
        return None

    lfp_band_file_abspath = AnalysisNwbfile().get_abs_path(
        lfp_band_file_name
    )
    # filter the data and write to an the nwb file
    filtered_data, new_timestamps = FirFilterParameters().filter_data(
        timestamps,
        lfp_data,
        filter_coeff,
        lfp_band_valid_times,
        lfp_band_elect_index,
        decimation,
    )

    # now that the LFP is filtered, we create an electrical series for it
    # and add it to the file
    with pynwb.NWBHDF5IO(
        path=lfp_band_file_abspath, mode="a", load_namespaces=True
    ) as io:
        nwbf = io.read()

        # get the indices of the electrodes in the electrode table of the
        # file to get the right values
        elect_index = get_electrode_indices(nwbf, lfp_band_elect_id)
        electrode_table_region = nwbf.create_electrode_table_region(
            elect_index, "filtered electrode table"
        )
        eseries_name = "filtered data"
        # TODO: use datatype of data
        es = pynwb.ecephys.ElectricalSeries(
            name=eseries_name,
            data=filtered_data,
            electrodes=electrode_table_region,
            timestamps=new_timestamps,
        )
        lfp = pynwb.ecephys.LFP(electrical_series=es)
        ecephys_module = nwbf.create_processing_module(
            name="ecephys",
            description=f"LFP data processed with {filter_name}",
        )
        ecephys_module.add(lfp)
        io.write(nwbf)
        lfp_band_object_id = es.object_id
    #
    # add the file to the AnalysisNwbfile table
    AnalysisNwbfile().add(key["nwb_file_name"], lfp_band_file_name)
    key["analysis_file_name"] = lfp_band_file_name
    key["lfp_band_object_id"] = lfp_band_object_id

    # finally, censor the valid times to account for the downsampling if
    # this is the first time we've downsampled these data
    key["interval_list_name"] = (
        interval_list_name
        + " lfp band "
        + str(lfp_band_sampling_rate)
        + "Hz"
    )
    tmp_valid_times = (
        IntervalList
        & {
            "nwb_file_name": key["nwb_file_name"],
            "interval_list_name": key["interval_list_name"],
        }
    ).fetch("valid_times")
    if len(tmp_valid_times) == 0:
        lfp_band_valid_times = interval_list_censor(
            lfp_band_valid_times, new_timestamps
        )
        # add an interval list for the LFP valid times
        IntervalList.insert1(
            {
                "nwb_file_name": key["nwb_file_name"],
                "interval_list_name": key["interval_list_name"],
                "valid_times": lfp_band_valid_times,
                "pipeline": "lfp band",
            }
        )
    else:
        lfp_band_valid_times = interval_list_censor(
            lfp_band_valid_times, new_timestamps
        )
        # check that the valid times are the same
        assert np.isclose(
            tmp_valid_times[0], lfp_band_valid_times
        ).all(), (
            "previously saved lfp band times do not match current times"
        )

    AnalysisNwbfile().log(key, table=self.full_table_name)
    self.insert1(key)

fetch1_dataframe(*attrs, **kwargs)

Fetches the filtered data as a dataframe

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def fetch1_dataframe(self, *attrs, **kwargs):
    """Fetches the filtered data as a dataframe"""
    filtered_nwb = self.fetch_nwb()[0]
    return pd.DataFrame(
        filtered_nwb["lfp_band"].data,
        index=pd.Index(filtered_nwb["lfp_band"].timestamps, name="time"),
    )

compute_analytic_signal(electrode_list, **kwargs)

Computes the hilbert transform of a given LFPBand signal

Uses scipy.signal.hilbert to compute the hilbert transform

Parameters:

Name	Type	Description	Default
electrode_list	list[int]	A list of the electrodes to compute the hilbert transform of	required

Returns:

Name	Type	Description
analytic_signal_df	DataFrame	DataFrame containing hilbert transform of signal

Raises:

Type	Description
ValueError	If items in electrode_list are invalid for the dataset

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def compute_analytic_signal(self, electrode_list: list[int], **kwargs):
    """Computes the hilbert transform of a given LFPBand signal

    Uses scipy.signal.hilbert to compute the hilbert transform

    Parameters
    ----------
    electrode_list: list[int]
        A list of the electrodes to compute the hilbert transform of

    Returns
    -------
    analytic_signal_df: pd.DataFrame
        DataFrame containing hilbert transform of signal

    Raises
    ------
    ValueError
        If items in electrode_list are invalid for the dataset
    """

    filtered_band = self.fetch_nwb()[0]["lfp_band"]
    electrode_index = np.isin(
        filtered_band.electrodes.data[:], electrode_list
    )
    if len(electrode_list) != np.sum(electrode_index):
        raise ValueError(
            "Some of the electrodes specified in electrode_list are missing"
            + " in the current LFPBand table."
        )
    analytic_signal_df = pd.DataFrame(
        hilbert(filtered_band.data[:, electrode_index], axis=0),
        index=pd.Index(filtered_band.timestamps, name="time"),
        columns=[f"electrode {e}" for e in electrode_list],
    )
    return analytic_signal_df

compute_signal_phase(electrode_list=None, **kwargs)

Computes phase of LFPBand signals using the hilbert transform

Parameters:

Name	Type	Description	Default
electrode_list	list[int]	A list of the electrodes to compute the phase of, by default None	None

Returns:

Name	Type	Description
signal_phase_df	DataFrame	DataFrame containing the phase of the signals

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def compute_signal_phase(
    self, electrode_list: list[int] = None, **kwargs
) -> pd.DataFrame:
    """Computes phase of LFPBand signals using the hilbert transform

    Parameters
    ----------
    electrode_list : list[int], optional
        A list of the electrodes to compute the phase of, by default None

    Returns
    -------
    signal_phase_df : pd.DataFrame
        DataFrame containing the phase of the signals
    """
    if electrode_list is None:
        electrode_list = []

    analytic_signal_df = self.compute_analytic_signal(
        electrode_list, **kwargs
    )

    return pd.DataFrame(
        np.angle(analytic_signal_df) + np.pi,
        columns=analytic_signal_df.columns,
        index=analytic_signal_df.index,
    )

compute_signal_power(electrode_list=None, **kwargs)

Computes power LFPBand signals using the hilbert transform

Parameters:

Name	Type	Description	Default
electrode_list	list[int]	A list of the electrodes to compute the power of, by default None	None

Returns:

Name	Type	Description
signal_power_df	DataFrame	DataFrame containing the power of the signals

Source code in src/spyglass/lfp/analysis/v1/lfp_band.py

def compute_signal_power(
    self, electrode_list: list[int] = None, **kwargs
) -> pd.DataFrame:
    """Computes power LFPBand signals using the hilbert transform

    Parameters
    ----------
    electrode_list : list[int], optional
        A list of the electrodes to compute the power of, by default None

    Returns
    -------
    signal_power_df : pd.DataFrame
        DataFrame containing the power of the signals
    """
    if electrode_list is None:
        electrode_list = []

    analytic_signal_df = self.compute_analytic_signal(
        electrode_list, **kwargs
    )

    return pd.DataFrame(
        np.abs(analytic_signal_df) ** 2,
        columns=analytic_signal_df.columns,
        index=analytic_signal_df.index,
    )