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common_interval.py

IntervalList

Bases: SpyglassIngestion, Manual

Source code in src/spyglass/common/common_interval.py 
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@schema
class IntervalList(SpyglassIngestion, dj.Manual):
    definition = """
    # Time intervals used for analysis
    -> Session
    interval_list_name: varchar(170)  # descriptive name of this interval list
    ---
    valid_times: longblob  # numpy array with start/end times for each interval
    pipeline = "": varchar(64)  # type of interval list
    """

    # See #630, #664. Excessive key length.
    _source_nwb_object_name = "epochs"

    @property
    def _source_nwb_object_type(self):
        return pynwb.epoch.TimeIntervals

    @property
    def table_key_to_obj_attr(self):
        return {
            "self": {
                "interval_list_name": self.interval_name_from_tags,
                "valid_times": self.interval_from_start_stop_time,
            }
        }

    @staticmethod
    def interval_name_from_tags(epoch_row):
        """Extract interval name from tags attribute.

        This function handles both:
        1. NWB objects (pynwb.epoch.TimeIntervals rows) with .tags attribute
        2. Pandas namedtuples from DataFrame.itertuples()

        SpyglassIngestion converts table-like NWB objects to DataFrames and
        iterates using .itertuples(), which produces namedtuples.
        """
        tags = getattr(epoch_row, "tags", None)

        # For namedtuples from itertuples(), the index is stored as 'Index'
        if hasattr(epoch_row, "Index"):
            name = epoch_row.Index
        else:
            name = getattr(epoch_row, "name", None)

        # Handle formats: list, tuple, numpy array, single value, or None
        if isinstance(tags, (list, tuple, np.ndarray)):
            return tags[0] if len(tags) > 0 else f"interval_{name}"
        elif tags:  # Single value (string or other scalar)
            return tags
        else:
            return f"interval_{name}"

    @staticmethod
    def interval_from_start_stop_time(epoch_row):
        """Extract start and stop times from epoch row.

        This function handles both:
        1. NWB objects with .start_time and .stop_time attributes
        2. Pandas namedtuples from DataFrame.itertuples() (new ingestion pattern)

        Returns
        -------
        np.ndarray
            Array of shape (1, 2) containing [start_time, stop_time]
        """
        start_time = getattr(epoch_row, "start_time", None)
        stop_time = getattr(epoch_row, "stop_time", None)
        return np.asarray([[start_time, stop_time]])

    def fetch_interval(self):
        """Fetch interval list object for a given key."""
        if not len(self) == 1:
            raise ValueError(f"Expected one row, got {len(self)}")
        return Interval(self.fetch1())

    def plot_intervals(
        self, start_time: float = 0, return_fig: bool = False
    ) -> Optional[plt.Figure]:
        """
        Plot all intervals in the given IntervalList table.

        Parameters
        ----------
        start_time : float, optional
            The reference time (in seconds) for the interval comparison plot.
            For example, the first timepoint of a session. Defaults to 0.
        return_fig : bool, optional
            If True, return the matplotlib Figure object. Defaults to False.

        Returns
        -------
        fig : matplotlib.figure.Figure or None
            The matplotlib Figure object if `return_fig` is True, otherwise None.

        Raises
        ------
        ValueError
            If more than one unique `nwb_file_name` is found in the IntervalList.
            The intended use is to compare intervals within a single NWB file.
        UserWarning
            If more than 100 intervals are being plotted.
        """
        interval_lists_df = pd.DataFrame(self)

        if len(interval_lists_df["nwb_file_name"].unique()) > 1:
            raise ValueError(
                ">1 nwb_file_name found in IntervalList. "
                + "the intended use of plot_intervals is to compare intervals "
                + "within a single nwb_file_name."
            )

        interval_list_names = interval_lists_df["interval_list_name"].values

        n_compare = len(interval_list_names)

        if n_compare > 100:
            warnings.warn(
                f"plot_intervals is plotting {n_compare} intervals. "
                + "if this is unintended, please pass in a smaller IntervalList.",
                UserWarning,
            )

        # plot broken bar horizontals
        fig, ax = plt.subplots(figsize=(20, 2 / 3 * n_compare))

        # get n colors
        cmap = plt.get_cmap("turbo", n_compare)
        custom_palette = [mpl.colors.rgb2hex(cmap(i)) for i in range(cmap.N)]

        def convert_intervals_to_range(intervals, start_time):
            return [
                ((i[0] - start_time) / 60, (i[1] - i[0]) / 60)
                for i in intervals
            ]  # return time in minutes

        all_intervals = interval_lists_df["valid_times"].values
        for i, (intervals, color) in enumerate(
            zip(all_intervals, custom_palette)
        ):
            if getattr(intervals, "shape", None) == (2,):
                intervals = [intervals]
            int_range = convert_intervals_to_range(intervals, start_time)
            ax.broken_barh(
                int_range, (10 * (i + 1), 6), facecolors=color, alpha=0.7
            )

        ax.set_ylim(5, 10 * (n_compare + 1) + 5)
        ax.set_xlabel("time from start (min)", fontsize=16)
        ax.set_yticks(
            np.arange(n_compare) * 10 + 15,
            labels=interval_list_names,
            fontsize=16,
        )
        ax.set_xticks(ax.get_xticks())
        ax.set_xticklabels(ax.get_xticklabels())
        ax.tick_params(axis="x", labelsize=16)

        if return_fig:
            return fig

    def plot_epoch_pos_raw_intervals(self, figsize=(20, 5), return_fig=False):
        """Plot an epoch's position, raw data, and valid times intervals."""
        interval_list = pd.DataFrame(self)
        fig, ax = plt.subplots(figsize=(30, 3))

        raw_data_valid_times = interval_list.loc[
            interval_list.interval_list_name == "raw data valid times"
        ].valid_times
        interval_y = 1

        for interval in np.asarray(raw_data_valid_times)[0]:
            ax.plot(interval, [interval_y, interval_y])
            ax.scatter(interval, [interval_y, interval_y], alpha=0.8, zorder=2)

        epoch_valid_times = (
            interval_list.set_index("interval_list_name")
            .filter(regex=r"^[0-9]", axis=0)
            .valid_times
        )
        interval_y = 2
        for epoch, valid_times in zip(
            epoch_valid_times.index, epoch_valid_times
        ):
            for interval in valid_times:
                ax.plot(interval, [interval_y, interval_y])
                ax.scatter(
                    interval, [interval_y, interval_y], alpha=0.8, zorder=2
                )
                ax.text(
                    interval[0] + np.diff(interval)[0] / 2,
                    interval_y,
                    epoch,
                    ha="center",
                    va="bottom",
                )

        pos_valid_times = (
            interval_list.set_index("interval_list_name")
            .filter(regex=r"^pos \d+ valid times$", axis=0)
            .valid_times
        ).sort_index(key=lambda index: [int(name.split()[1]) for name in index])
        interval_y = 0
        for epoch, valid_times in zip(pos_valid_times.index, pos_valid_times):
            for interval in valid_times:
                ax.plot(interval, [interval_y, interval_y])
                ax.scatter(
                    interval, [interval_y, interval_y], alpha=0.8, zorder=2
                )
                ax.text(
                    interval[0] + np.diff(interval)[0] / 2,
                    interval_y,
                    epoch.replace(" valid times", ""),
                    ha="center",
                    va="bottom",
                )

        ax.set_ylim((-0.25, 2.25))
        ax.set_yticks(np.arange(3))
        ax.set_yticklabels(["pos valid times", "raw data valid times", "epoch"])
        ax.set_xlabel("Time [s]")
        ax.grid(True)
        if return_fig:
            return fig

    def insert(self, *args, **kwargs):
        """Insert with cautious insert by default."""
        self.cautious_insert(*args, **kwargs)

    def super_insert(self, *args, **kwargs):
        """Insert without cautious insert."""
        super().insert(*args, **kwargs)

    def cautious_insert(self, inserts, update=False, **kwargs):
        """On existing primary key, check secondary key and update if needed.

        `replace=True` will attempt to delete/replace the existing entry. When
        the row has a foreign key constraint, this will fail. This method will
        check if an update is needed.

        Parameters
        ----------
        inserts : list of dict
            List of dictionaries to insert.
        update : bool, optional
            If True, update the existing entry. Defaults to False.
        **kwargs : dict
            Additional keyword arguments to pass to `insert`.
        """
        if not isinstance(inserts, (list, tuple)):  # Table.insert1 makes tuple
            inserts = [inserts]
        if not inserts:  # No data to insert
            return
        if not isinstance(inserts[0], dict):
            self.super_insert(inserts, **kwargs)  # fallback
            return

        pk = self.heading.primary_key

        def pk_match(row):
            match = self & {k: str(v) for k, v in row.items() if k in pk}
            return match.fetch(as_dict=True)[0] if match else None

        def sk_match(new, old):
            return np.array_equal(
                new["valid_times"], old["valid_times"]
            ) and new.get("pipeline", "") == old.get("pipeline", "")

        basic_inserts, need_update = [], []
        for row in inserts:
            existing = pk_match(row)
            if not existing:  # if no existing entry, insert
                basic_inserts.append(row)
            elif existing and not sk_match(row, existing):  # diff sk, update
                need_update.append(row)

        self.super_insert(basic_inserts, **kwargs)

        if update:
            for row in need_update:
                self.update1(row)
        elif need_update:
            raise ValueError(
                f"Found {len(need_update)} rows with existing names, but "
                + f"different times:{need_update}"
            )

    def cleanup(self, dry_run=True):
        """Clean up orphaned IntervalList entries."""
        orphans = self - get_child_tables(self)
        if dry_run:
            return orphans
        orphans.super_delete(warn=False)

interval_name_from_tags(epoch_row) staticmethod

Extract interval name from tags attribute.

This function handles both: 1. NWB objects (pynwb.epoch.TimeIntervals rows) with .tags attribute 2. Pandas namedtuples from DataFrame.itertuples()

SpyglassIngestion converts table-like NWB objects to DataFrames and iterates using .itertuples(), which produces namedtuples.

Source code in src/spyglass/common/common_interval.py 
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@staticmethod
def interval_name_from_tags(epoch_row):
    """Extract interval name from tags attribute.

    This function handles both:
    1. NWB objects (pynwb.epoch.TimeIntervals rows) with .tags attribute
    2. Pandas namedtuples from DataFrame.itertuples()

    SpyglassIngestion converts table-like NWB objects to DataFrames and
    iterates using .itertuples(), which produces namedtuples.
    """
    tags = getattr(epoch_row, "tags", None)

    # For namedtuples from itertuples(), the index is stored as 'Index'
    if hasattr(epoch_row, "Index"):
        name = epoch_row.Index
    else:
        name = getattr(epoch_row, "name", None)

    # Handle formats: list, tuple, numpy array, single value, or None
    if isinstance(tags, (list, tuple, np.ndarray)):
        return tags[0] if len(tags) > 0 else f"interval_{name}"
    elif tags:  # Single value (string or other scalar)
        return tags
    else:
        return f"interval_{name}"

interval_from_start_stop_time(epoch_row) staticmethod

Extract start and stop times from epoch row.

This function handles both: 1. NWB objects with .start_time and .stop_time attributes 2. Pandas namedtuples from DataFrame.itertuples() (new ingestion pattern)

Returns:

Type Description
ndarray

Array of shape (1, 2) containing [start_time, stop_time]
Source code in src/spyglass/common/common_interval.py 
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@staticmethod
def interval_from_start_stop_time(epoch_row):
    """Extract start and stop times from epoch row.

    This function handles both:
    1. NWB objects with .start_time and .stop_time attributes
    2. Pandas namedtuples from DataFrame.itertuples() (new ingestion pattern)

    Returns
    -------
    np.ndarray
        Array of shape (1, 2) containing [start_time, stop_time]
    """
    start_time = getattr(epoch_row, "start_time", None)
    stop_time = getattr(epoch_row, "stop_time", None)
    return np.asarray([[start_time, stop_time]])

fetch_interval()

Fetch interval list object for a given key.

Source code in src/spyglass/common/common_interval.py 
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def fetch_interval(self):
    """Fetch interval list object for a given key."""
    if not len(self) == 1:
        raise ValueError(f"Expected one row, got {len(self)}")
    return Interval(self.fetch1())

plot_intervals(start_time=0, return_fig=False)

Plot all intervals in the given IntervalList table.

Parameters:

Name Type Description Default
start_time float

The reference time (in seconds) for the interval comparison plot. For example, the first timepoint of a session. Defaults to 0.

 0
return_fig bool

If True, return the matplotlib Figure object. Defaults to False.

 False

Returns:

Name Type Description
fig Figure or None

The matplotlib Figure object if return_fig is True, otherwise None.

Raises:

Type Description
ValueError

If more than one unique nwb_file_name is found in the IntervalList. The intended use is to compare intervals within a single NWB file.
UserWarning

If more than 100 intervals are being plotted.
Source code in src/spyglass/common/common_interval.py 
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def plot_intervals(
    self, start_time: float = 0, return_fig: bool = False
) -> Optional[plt.Figure]:
    """
    Plot all intervals in the given IntervalList table.

    Parameters
    ----------
    start_time : float, optional
        The reference time (in seconds) for the interval comparison plot.
        For example, the first timepoint of a session. Defaults to 0.
    return_fig : bool, optional
        If True, return the matplotlib Figure object. Defaults to False.

    Returns
    -------
    fig : matplotlib.figure.Figure or None
        The matplotlib Figure object if `return_fig` is True, otherwise None.

    Raises
    ------
    ValueError
        If more than one unique `nwb_file_name` is found in the IntervalList.
        The intended use is to compare intervals within a single NWB file.
    UserWarning
        If more than 100 intervals are being plotted.
    """
    interval_lists_df = pd.DataFrame(self)

    if len(interval_lists_df["nwb_file_name"].unique()) > 1:
        raise ValueError(
            ">1 nwb_file_name found in IntervalList. "
            + "the intended use of plot_intervals is to compare intervals "
            + "within a single nwb_file_name."
        )

    interval_list_names = interval_lists_df["interval_list_name"].values

    n_compare = len(interval_list_names)

    if n_compare > 100:
        warnings.warn(
            f"plot_intervals is plotting {n_compare} intervals. "
            + "if this is unintended, please pass in a smaller IntervalList.",
            UserWarning,
        )

    # plot broken bar horizontals
    fig, ax = plt.subplots(figsize=(20, 2 / 3 * n_compare))

    # get n colors
    cmap = plt.get_cmap("turbo", n_compare)
    custom_palette = [mpl.colors.rgb2hex(cmap(i)) for i in range(cmap.N)]

    def convert_intervals_to_range(intervals, start_time):
        return [
            ((i[0] - start_time) / 60, (i[1] - i[0]) / 60)
            for i in intervals
        ]  # return time in minutes

    all_intervals = interval_lists_df["valid_times"].values
    for i, (intervals, color) in enumerate(
        zip(all_intervals, custom_palette)
    ):
        if getattr(intervals, "shape", None) == (2,):
            intervals = [intervals]
        int_range = convert_intervals_to_range(intervals, start_time)
        ax.broken_barh(
            int_range, (10 * (i + 1), 6), facecolors=color, alpha=0.7
        )

    ax.set_ylim(5, 10 * (n_compare + 1) + 5)
    ax.set_xlabel("time from start (min)", fontsize=16)
    ax.set_yticks(
        np.arange(n_compare) * 10 + 15,
        labels=interval_list_names,
        fontsize=16,
    )
    ax.set_xticks(ax.get_xticks())
    ax.set_xticklabels(ax.get_xticklabels())
    ax.tick_params(axis="x", labelsize=16)

    if return_fig:
        return fig

plot_epoch_pos_raw_intervals(figsize=(20, 5), return_fig=False)

Plot an epoch's position, raw data, and valid times intervals.

Source code in src/spyglass/common/common_interval.py 
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def plot_epoch_pos_raw_intervals(self, figsize=(20, 5), return_fig=False):
    """Plot an epoch's position, raw data, and valid times intervals."""
    interval_list = pd.DataFrame(self)
    fig, ax = plt.subplots(figsize=(30, 3))

    raw_data_valid_times = interval_list.loc[
        interval_list.interval_list_name == "raw data valid times"
    ].valid_times
    interval_y = 1

    for interval in np.asarray(raw_data_valid_times)[0]:
        ax.plot(interval, [interval_y, interval_y])
        ax.scatter(interval, [interval_y, interval_y], alpha=0.8, zorder=2)

    epoch_valid_times = (
        interval_list.set_index("interval_list_name")
        .filter(regex=r"^[0-9]", axis=0)
        .valid_times
    )
    interval_y = 2
    for epoch, valid_times in zip(
        epoch_valid_times.index, epoch_valid_times
    ):
        for interval in valid_times:
            ax.plot(interval, [interval_y, interval_y])
            ax.scatter(
                interval, [interval_y, interval_y], alpha=0.8, zorder=2
            )
            ax.text(
                interval[0] + np.diff(interval)[0] / 2,
                interval_y,
                epoch,
                ha="center",
                va="bottom",
            )

    pos_valid_times = (
        interval_list.set_index("interval_list_name")
        .filter(regex=r"^pos \d+ valid times$", axis=0)
        .valid_times
    ).sort_index(key=lambda index: [int(name.split()[1]) for name in index])
    interval_y = 0
    for epoch, valid_times in zip(pos_valid_times.index, pos_valid_times):
        for interval in valid_times:
            ax.plot(interval, [interval_y, interval_y])
            ax.scatter(
                interval, [interval_y, interval_y], alpha=0.8, zorder=2
            )
            ax.text(
                interval[0] + np.diff(interval)[0] / 2,
                interval_y,
                epoch.replace(" valid times", ""),
                ha="center",
                va="bottom",
            )

    ax.set_ylim((-0.25, 2.25))
    ax.set_yticks(np.arange(3))
    ax.set_yticklabels(["pos valid times", "raw data valid times", "epoch"])
    ax.set_xlabel("Time [s]")
    ax.grid(True)
    if return_fig:
        return fig

insert(*args, **kwargs)

Insert with cautious insert by default.

Source code in src/spyglass/common/common_interval.py 
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def insert(self, *args, **kwargs):
    """Insert with cautious insert by default."""
    self.cautious_insert(*args, **kwargs)

super_insert(*args, **kwargs)

Insert without cautious insert.

Source code in src/spyglass/common/common_interval.py 
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def super_insert(self, *args, **kwargs):
    """Insert without cautious insert."""
    super().insert(*args, **kwargs)

cautious_insert(inserts, update=False, **kwargs)

On existing primary key, check secondary key and update if needed.

replace=True will attempt to delete/replace the existing entry. When the row has a foreign key constraint, this will fail. This method will check if an update is needed.

Parameters:

Name Type Description Default
inserts list of dict

List of dictionaries to insert.

 required
update bool

If True, update the existing entry. Defaults to False.

 False
**kwargs dict

Additional keyword arguments to pass to insert.

 {}
Source code in src/spyglass/common/common_interval.py 
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def cautious_insert(self, inserts, update=False, **kwargs):
    """On existing primary key, check secondary key and update if needed.

    `replace=True` will attempt to delete/replace the existing entry. When
    the row has a foreign key constraint, this will fail. This method will
    check if an update is needed.

    Parameters
    ----------
    inserts : list of dict
        List of dictionaries to insert.
    update : bool, optional
        If True, update the existing entry. Defaults to False.
    **kwargs : dict
        Additional keyword arguments to pass to `insert`.
    """
    if not isinstance(inserts, (list, tuple)):  # Table.insert1 makes tuple
        inserts = [inserts]
    if not inserts:  # No data to insert
        return
    if not isinstance(inserts[0], dict):
        self.super_insert(inserts, **kwargs)  # fallback
        return

    pk = self.heading.primary_key

    def pk_match(row):
        match = self & {k: str(v) for k, v in row.items() if k in pk}
        return match.fetch(as_dict=True)[0] if match else None

    def sk_match(new, old):
        return np.array_equal(
            new["valid_times"], old["valid_times"]
        ) and new.get("pipeline", "") == old.get("pipeline", "")

    basic_inserts, need_update = [], []
    for row in inserts:
        existing = pk_match(row)
        if not existing:  # if no existing entry, insert
            basic_inserts.append(row)
        elif existing and not sk_match(row, existing):  # diff sk, update
            need_update.append(row)

    self.super_insert(basic_inserts, **kwargs)

    if update:
        for row in need_update:
            self.update1(row)
    elif need_update:
        raise ValueError(
            f"Found {len(need_update)} rows with existing names, but "
            + f"different times:{need_update}"
        )

cleanup(dry_run=True)

Clean up orphaned IntervalList entries.

Source code in src/spyglass/common/common_interval.py 
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def cleanup(self, dry_run=True):
    """Clean up orphaned IntervalList entries."""
    orphans = self - get_child_tables(self)
    if dry_run:
        return orphans
    orphans.super_delete(warn=False)

Interval

Class to handle interval lists

NOTE: methods with -prefix are for internal use comparing two interval lists to allow for inverting non-communicative methods (i.e., AxB!=BxA). External equivalents (without -prefix) runs these methods on self vs other.

Source code in src/spyglass/common/common_interval.py 
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class Interval:
    """Class to handle interval lists

    NOTE: methods with _-prefix are for internal use comparing two interval
    lists to allow for inverting non-communicative methods (i.e., AxB!=BxA).
    External equivalents (without _-prefix) runs these methods on self vs other.
    """

    # TODO: Class currently isn't aware of whether intervals are indices or
    # timestamps. This should be fixed in the future.

    def __init__(
        self,
        interval_list: IntervalLike = None,
        name=None,
        from_inds=False,
        no_overlap=False,
        no_duplicates=True,
        warn=not test_mode,  # warn by default, unless running pytests
        **kwargs,
    ) -> None:
        """Initialize the Intervals class with a list of intervals.

        Parameters
        ----------
        interval_list : dict or np.ndarray
            A key of IntervalList table, a numpy array of intervals, or a list
            of indices (integers).
        name : str, optional
            Name of the interval list. If not provided, can be taken from
            kwargs["interval_list_name"].
        from_inds : bool, optional
            If True, treat the interval_list as a list of indices and
            convert to intervals. Defaults to False.
        no_overlap : bool, optional
            If True, consolidate overlapping intervals. Defaults to False.
        no_duplicates : bool, optional
            If True, remove duplicate intervals. Defaults to True.
        kwargs : dict, optional
            Additional keyword arguments to pass to the class, including
            "valid_times" and "interval_list_name" for times and name.
        """

        self.kwargs = dict(  # Returned objects will set this behavior
            kwargs,
            no_overlap=no_overlap,
            no_duplicates=no_duplicates,
            warn=False,  # child instances do not warn
        )

        self.key = dict()  # let _extract decide if this is a key
        self.name = name or kwargs.get("interval_list_name")
        self.pipeline = kwargs.get("pipeline")
        self.nwb_file_name = kwargs.get("nwb_file_name")
        self.times = kwargs.get("valid_times") or interval_list
        self.times = self._extract(self.times, from_inds=from_inds)

        if warn or no_duplicates:
            dupes_removed = np.unique(self.times, axis=0)
        if no_duplicates:
            self.times = dupes_removed
        if warn and not np.array_equal(self.times, dupes_removed):
            logger.warning(
                "Found duplicate(s). Use no_duplicates flag to remove or not: "
                f"{self.to_str(self.times)} vs {self.to_str(dupes_removed)}"
            )

        if warn or no_overlap:  # otherwise recursive _consolidate call
            overlap_removed = self._consolidate(self.times)
        if no_overlap:
            self.times = overlap_removed
        if warn and not np.array_equal(self.times, overlap_removed):
            logger.warning(
                "Found overlap(s). Use no_overlap flag to consolidate or not: "
                f"{self.to_str(self.times)} vs {self.to_str(overlap_removed)}"
            )

    def to_str(self, array: np.ndarray, remove_breaks=True) -> str:
        """Convert the interval list to a string."""
        kwarg = dict(separator=", ") if remove_breaks else dict()
        link = " " if remove_breaks else "\n\t"
        if isinstance(array, Iterable):
            return link.join(
                [np.array2string(i, **kwarg) for i in np.array(array)]
            )
        return str(array)

    def __repr__(self) -> str:
        return f"Interval({self.to_str(self.times, remove_breaks=False)})"

    def __len__(self) -> int:
        return len(self.times)

    def __getitem__(self, item: Union[int, slice, tuple]) -> np.ndarray:
        """Get item from the interval list."""
        if isinstance(item, (slice, int, tuple)):
            return self.times[item]
        else:
            raise ValueError(
                f"Unrecognized item type: {type(item)}. Must be int, slice, or tuple."
            )

    def __iter__(self) -> iter:
        """Iterate over the intervals in the interval list."""
        for interval in self.times:
            yield interval

    def __hash__(self) -> int:  # required for dict dunder
        """Hash the interval list for use in sets or dictionaries."""
        times = self.times
        if hasattr(times, "tolist"):
            times = times.tolist()
        if not isinstance(times, Iterable):
            times = [times]
        return hash(f"{self.name}{tuple(times)}")

    def set_key(self, **kwargs: dict) -> None:
        """Set the key for the interval list."""
        self.key = kwargs
        self.nwb_file_name = kwargs.get("nwb_file_name") or kwargs.get("nwb")
        self.name = kwargs.get("interval_list_name") or kwargs.get("name")
        self.pipeline = kwargs.get("pipeline")

    @property
    def as_dict(self) -> dict:
        """Convert the interval list to a dictionary."""
        ret = {
            "nwb_file_name": self.nwb_file_name,
            "interval_list_name": self.name,
            "valid_times": self.times,
            "pipeline": self.pipeline,
        }
        return {k: v for k, v in ret.items() if v is not None}

    @property
    def primary_key(self):
        if not (self.name and self.nwb_file_name):
            raise ValueError("Both name and file name required for primary key")
        return {
            "interval_list_name": self.name,
            "nwb_file_name": self.nwb_file_name,
        }

    def __eq__(self, other: IntervalLike) -> bool:
        """Check if two interval lists are equal."""
        return np.array_equal(self.times, self._extract(other))

    def _extract(
        self, interval_list: IntervalLike, from_inds: bool = False
    ) -> np.ndarray:
        times = None

        # extract times from interval_list based on type
        if from_inds:
            times = self.from_inds(interval_list)
        elif hasattr(interval_list, "times"):
            times = interval_list.times
        elif isinstance(interval_list, dict):
            times = self._import_from_table(interval_list)
        elif isinstance(
            interval_list, (np.generic, np.ndarray, list, int, float, tuple)
        ):
            times = interval_list
        elif interval_list is None:
            return np.array([])

        # validate times format
        if times is None:
            raise TypeError(
                f"Unrecognized interval_list type: {type(interval_list)}"
            )

        times = self._expand_1d(np.asarray(times))
        if len(times) and not np.all(np.diff(times, axis=1) >= 0):
            raise ValueError(
                "All intervals must be in the form [start, stop] with start <= stop."
            )
        if len(times) and times.shape[1] != 2:
            raise ValueError(
                f"Intervals must have shape (N, 2). Got shape {times.shape}."
            )

        return np.asarray(times)

    @staticmethod
    def from_inds(list_frames) -> List[List[int]]:
        """Converts a list of indices to a list of intervals.

        To use, initialize the Interval class with from_inds=True.

        e.g. [2,3,4,6,7,8,9,10] -> [[2,4],[6,10]]

        Parameters
        ----------
        list_frames : array_like of int
        """
        list_frames = np.unique(list_frames)
        interval_list = []
        for key, group in itertools.groupby(
            enumerate(list_frames), lambda t: t[1] - t[0]
        ):
            group = list(group)
            interval_list.append([group[0][1], group[-1][1]])
        return np.asarray(interval_list)

    @staticmethod
    def _by_length(
        x, min_length: Optional[float] = 0.0, max_length: Optional[float] = 1e10
    ) -> List[float]:
        """Select intervals of certain lengths from an interval list."""
        min_length = 0 if min_length is None else min_length
        max_length = 1e10 if max_length is None else max_length
        lengths = np.ravel(np.diff(x))
        return x[np.logical_and(lengths > min_length, lengths < max_length)]

    def by_length(
        self,
        min_length: Optional[float] = 0.0,
        max_length: Optional[float] = 1e10,
    ) -> T:
        """Select intervals of certain lengths from an interval list.

        Parameters
        ----------
        min_length : float, optional
            Minimum interval length in seconds. Defaults to 0.0.
        max_length : float, optional
            Maximum interval length in seconds. Defaults to 1e10.
        """
        return Interval(
            self._by_length(self.times, min_length, max_length), **self.kwargs
        )

    def contains(
        self,
        timestamps: np.ndarray,
        as_indices: Optional[bool] = False,
        padding: Optional[int] = None,
    ) -> T:
        """Find timestamps that are contained in an interval list.

        Parameters
        ----------
        timestamps : array_like
        as_indices : bool, optional
            If True, return indices of timestamps in the interval list.
            If False, return the timestamps themselves. Defaults to False.
        padding : int, optional
            If True, pad the first and last timestamps by val. Defaults to None.
        """
        ind = []
        for interval in self.times:
            ind += np.ravel(
                np.argwhere(
                    np.logical_and(
                        timestamps >= interval[0], timestamps <= interval[1]
                    )
                )
            ).tolist()
        ret = np.asarray(ind) if as_indices else timestamps[ind]

        if padding:
            if ret[0] > 0:
                ret[0] -= padding
            if ret[-1] != len(timestamps) - padding:
                ret[-1] += padding

        return ret

    def excludes(
        self, timestamps: np.ndarray, as_indices: Optional[bool] = False
    ) -> T:
        """Find timestamps that are not contained in an interval list.

        Parameters
        ----------
        timestamps : array_like
        """
        contained_times = self.contains(timestamps, as_indices=as_indices)
        if as_indices:
            timestamps = np.arange(len(timestamps))
        return np.setdiff1d(timestamps, contained_times)

    @staticmethod
    def _expand_1d(interval_list: np.ndarray) -> np.ndarray:
        """Expand a 1D interval list to 2D."""
        if interval_list.ndim == 1 and interval_list.size > 0:
            return np.expand_dims(interval_list, 0)
        return interval_list

    def _union_consolidate(self, interval_list: IntervalLike) -> T:
        kwargs = dict(self.kwargs, no_overlap=False)
        return Interval(reduce(self._union_concat, interval_list), **kwargs)

    def union_consolidate(self) -> T:
        return Interval(self._union_consolidate(self.times), **self.kwargs)

    def _consolidate(self, interval_list: IntervalLike) -> T:
        """Consolidate overlapping intervals in an interval list."""
        if not isinstance(interval_list, np.ndarray):
            interval_list = np.asarray(interval_list)
        if interval_list.ndim == 1:
            return self._expand_1d(interval_list)

        interval_list = interval_list[np.argsort(interval_list[:, 0])]
        interval_list = self._union_consolidate(interval_list).times

        # reduce may convert to 1D, so check and expand
        return self._expand_1d(interval_list)

    def consolidate(self) -> T:
        return Interval(self._consolidate(self.times), **self.kwargs)

    @staticmethod
    def _set_intersect(
        interval1: IntervalLike, interval2: IntervalLike
    ) -> Union[np.ndarray, None]:
        """Takes the (set-theoretic) intersection of two intervals"""
        start = max(interval1[0], interval2[0])
        end = min(interval1[1], interval2[1])
        return np.array([start, end]) if end > start else None

    def _intersect(
        self,
        interval1: IntervalLike,
        interval2: IntervalLike,
        min_length: Optional[float] = 0,
    ) -> T:
        """Finds the intersection of two interval lists."""

        # Consolidate interval lists to disjoint by sorting & applying union
        interval_list1 = self._consolidate(interval1)
        interval_list2 = self._consolidate(interval2)

        # then do pairwise comparison and collect intersections
        intersection = [
            self._set_intersect(i2, i1)
            for i2 in interval_list2
            for i1 in interval_list1
            if self._set_intersect(i1, i2) is not None
        ]

        # if no intersection, then return an empty list
        if not intersection:
            return []

        intersection = np.asarray(intersection)
        intersection = intersection[np.argsort(intersection[:, 0])]

        return self._by_length(intersection, min_length=min_length)

    def intersect(
        self, other: IntervalLike, min_length: Optional[float] = 0
    ) -> T:
        """Finds the intersections between this and another interval list.

        Each interval is (start time, stop time)

        Parameters
        ----------
        other : Union[Interval, np.array, list, dict]
            Interval list to intersect with self.
        min_length : float, optional.
            Minimum length of intervals to include, default 0

        Returns
        -------
        interval_list: np.array, (N,2)
        """
        return Interval(
            self._intersect(self.times, self._extract(other), min_length),
            **self.kwargs,
        )

    def _union_concat(
        self, interval1: IntervalLike, interval2: IntervalLike
    ) -> np.ndarray:
        """Compare last interval of interval list to given interval.

        If overlapping, take union. If not, concatenate interval to list.
        Recursively called with `reduce`.
        """

        interval1 = self._expand_1d(interval1)
        interval2 = self._expand_1d(interval2)

        end1, start1 = interval1[-1], interval2[0]
        intersect = self._set_intersect(end1, start1)
        set_union = self._expand_1d(
            np.array([end1, start1])
            if intersect is None
            else np.array([min(end1[0], start1[0]), max(end1[1], start1[1])])
        )

        return np.concatenate((interval1[:-1], set_union), axis=0)

    def union_adjacent_index(self, other: IntervalLike) -> T:
        """Union index-adjacent intervals. If not adjacent, just concatenate.

        e.g. [a,b] and [b+1, c] is converted to [a,c]

        Parameters
        ----------
        other : Union[Interval, np.ndarray]
            Interval list to union with self.
        """
        interval1 = np.atleast_2d(self.times)
        interval2 = np.atleast_2d(self._extract(other))

        if not (
            interval1[-1][1] + 1 == interval2[0][0]
            or interval2[0][1] + 1 == interval1[-1][0]
        ):
            return Interval(
                np.concatenate((interval1, interval2), axis=0), **self.kwargs
            )

        x = np.array(
            [
                [
                    np.min([interval1[-1][0], interval2[0][0]]),
                    np.max([interval1[-1][1], interval2[0][1]]),
                ]
            ]
        )
        return Interval(
            np.concatenate((interval1[:-1], x), axis=0), **self.kwargs
        )

    def union_adjacent_consolidate(self) -> T:
        times = [self.times] if len(self.times) > 1 else self.times
        return Interval(
            self._expand_1d(reduce(self.union_adjacent_index, times)),
            **self.kwargs,
        )

    def union(
        self,
        other: IntervalLike,
        min_length: Optional[float] = 0.0,
        max_length: Optional[float] = 1e10,
    ) -> T:
        """Finds the union (all times in one or both) for two interval lists

        Parameters
        ----------
        other : Union[Interval, np.ndarray]
            Interval list to union with self.
        min_length : float, optional
            Minimum length of interval for inclusion in output, default 0.0
        max_length : float, optional
            Maximum length of interval for inclusion in output, default 1e10

        Returns
        -------
        np.ndarray
            Array of intervals [start, stop]
        """
        interval_list1 = self.times
        interval_list2 = self._extract(other)

        def _parallel_union(interval_list):
            """Create a parallel list where 1 is start and -1 the end"""
            interval_list = np.ravel(interval_list)
            interval_list_start_end = np.ones(interval_list.shape)
            interval_list_start_end[1::2] = -1
            return interval_list, interval_list_start_end

        il1, il1_start_end = _parallel_union(interval_list1)
        il2, il2_start_end = _parallel_union(interval_list2)

        # Concatenate the two lists so we can resort the intervals and apply the
        # same sorting to the start-end arrays
        combined_intervals = np.concatenate((il1, il2))
        if len(combined_intervals) == 0:
            return Interval(np.array([]), **self.kwargs)
        ss = np.concatenate((il1_start_end, il2_start_end))
        sort_ind = np.lexsort((-1 * ss, combined_intervals))
        combined_intervals = combined_intervals[sort_ind]
        ss_cumsum = np.cumsum(ss[sort_ind])
        if np.any(ss_cumsum < 0):
            raise ValueError(
                "Negative cumulative sum found in union. "
                + "This indicates an error in the interval lists, "
                + "such as an end time before a start time. "
                + "Please check the input interval lists for validity."
            )

        # a switch of cumulative sum from 0 to 1 indicates the beginning of a
        # joint interval; a cumulative sum of 0 indicates the end
        cumsum_flip = np.logical_and(
            ss_cumsum[1:] == 1,
            ss_cumsum[:-1] == 0,
        )
        union_starts = np.ravel(np.array(np.where(cumsum_flip)[0] + 1))
        union_starts = (
            np.insert(union_starts, 0, 0)
            if ss[sort_ind][0] == 1
            else union_starts
        )
        union_stops = np.ravel(np.array(np.where(ss_cumsum == 0)))
        if union_starts.size != union_stops.size:
            raise ValueError(
                "Mismatched number of union starts and stops. "
                + "This indicates an error in the interval lists, "
                + "such as an end time before a start time. "
                + "Please check the input interval lists for validity."
            )
        union = [
            [combined_intervals[start], combined_intervals[stop]]
            for start, stop in zip(union_starts, union_stops)
        ]

        return Interval(np.asarray(union), **self.kwargs)

    def censor(self, timestamps: Union[np.ndarray, List[int]]) -> T:
        """Returns new interval list that starts/ends at first/last timestamp

        Parameters
        ----------
        timestamps : numpy array or list

        Returns
        -------
        interval_list (numpy array of intervals [start, stop])
        """
        interval_list = self.times
        # check that all timestamps are in the interval list
        if len(self.contains(timestamps, as_indices=True)) != len(timestamps):
            raise ValueError("Interval_list must contain all timestamps")

        timestamps_interval = np.asarray([[timestamps[0], timestamps[-1]]])
        return Interval(
            self._intersect(interval_list, timestamps_interval), **self.kwargs
        )

    def subtract(
        self,
        other: IntervalLike,
        reverse: Optional[bool] = False,
        min_length: Optional[float] = None,
        max_length: Optional[float] = None,
    ) -> T:
        """Finds intervals in self that are not in other.

        Works on indices:
            intervals1 = [(0, 5), (8, 10)]
            intervals2 = [(1, 2), (3, 4), (6, 9)]
            result = [(0, 1), (4, 5), (9, 10)]

        Parameters
        ----------
        other : Union[Interval, np.ndarray]
        reverse : bool, optional
            If True, reverse the order of the intervals. Defaults to False.
        min_length : float, optional
            Minimum interval length in seconds. If None, no filtering applied.
        max_length : float, optional
            Maximum interval length in seconds. If None, no filtering applied.

        Returns
        -------
        Interval
            The resulting intervals after the difference operation.
            If indices, List[Tuple[int, int]].
        """
        intervals1 = self.times if not reverse else self._extract(other)
        intervals2 = self._extract(other) if not reverse else self.times

        result = []
        i, j = 0, 0
        while i < len(intervals1):
            start1, end1 = intervals1[i]
            while j < len(intervals2) and intervals2[j][1] <= start1:
                j += 1  # Skip intervals2 that end before start1
            current_start = start1
            while j < len(intervals2) and intervals2[j][0] < end1:
                start2, end2 = intervals2[j]
                # Add the non-overlap before the current interval in intervals2
                if current_start < start2:
                    result.append((current_start, min(start2, end1)))
                # Update the current start to exclude the overlapping part
                current_start = max(current_start, end2)
                j += 1  # next interval in intervals2
            if current_start < end1:  # if non-overlap btwn last part and end1
                result.append((current_start, end1))
            i += 1  # next interval in intervals1

        if min_length is not None or max_length is not None:
            result = self._by_length(
                np.asarray(result), min_length=min_length, max_length=max_length
            )

        return Interval(result, **self.kwargs)

    def add_removal_window(
        self, removal_window_ms: int, timestamps: np.ndarray
    ) -> T:
        """Add half of a removal window to start/end of each interval"""
        half_win = removal_window_ms / 1000 * 0.5
        new_interval = np.zeros((len(self.times), 2), dtype=np.float64)
        # for each interval, add half removal window to start and end
        for idx, interval in enumerate(self.times):
            start, end = interval[0], interval[1]
            new_interval[idx] = [
                timestamps[start] - half_win,
                np.minimum(timestamps[end] + half_win, timestamps[-1]),
            ]
        # NOTE: np.min and if len cond were in
        # spikesorting_artifact, but not lfp_artifact
        return (
            self._union_consolidate(new_interval)
            if len(new_interval) > 1
            else Interval(new_interval, **self.kwargs)
        )

    def to_indices(
        self, timestamps: np.ndarray, as_interval: bool = False
    ) -> Union[List[List[int]], T]:
        """Convert intervals to indices in the given timestamps.

        Parameters
        ----------
        timestamps : array_like
            The timestamps to convert the intervals to indices in.
        as_interval : bool, optional
            If True, return as an Interval object. Defaults to False.

        Returns
        -------
        np.ndarray
            The indices of the intervals in the given timestamps.
        """
        ret = np.searchsorted(timestamps, self.times.ravel()).reshape(-1, 2)
        if as_interval:
            if len(ret) == 0:
                return Interval([], **self.kwargs)
            if not hasattr(ret[0], "__len__"):
                # handle list of ints case from v0.spikesorting_artifact
                ret = [ret]
            ret = Interval(ret, **self.kwargs)
        return ret

    def to_seconds(self, timestamps: np.ndarray) -> List[Tuple[float]]:
        """Convert intervals to seconds in the given timestamps.

        Parameters
        ----------
        timestamps : array_like
            The timestamps to convert the intervals to seconds in.

        Returns
        -------
        np.ndarray
            The seconds of the intervals in the given timestamps.
        """
        return [(timestamps[i[0]], timestamps[i[1]]) for i in self.times]

    # ---------------------------- Requiring Table ----------------------------

    def _import_from_table(
        self, interval_key: dict, return_table: Optional[bool] = False
    ) -> Union[dj.expression.QueryExpression, np.ndarray]:
        self.key = interval_key
        query = IntervalList & {
            k: v
            for k, v in interval_key.items()
            if k in IntervalList.primary_key
        }

        if return_table:
            return query
        count = len(query)
        if count != 1:  # Should be union of existing intervals?
            raise ValueError(
                f"Found {count} interval entries found for {interval_key}"
            )
        self.name, self.pipeline, self.nwb_file_name, times = query.fetch1(
            "interval_list_name", "pipeline", "nwb_file_name", "valid_times"
        )
        return times

__init__(interval_list=None, name=None, from_inds=False, no_overlap=False, no_duplicates=True, warn=not test_mode, **kwargs)

Initialize the Intervals class with a list of intervals.

Parameters:

Name Type Description Default
interval_list dict or ndarray

A key of IntervalList table, a numpy array of intervals, or a list of indices (integers).

 None
name str

Name of the interval list. If not provided, can be taken from kwargs["interval_list_name"].

 None
from_inds bool

If True, treat the interval_list as a list of indices and convert to intervals. Defaults to False.

 False
no_overlap bool

If True, consolidate overlapping intervals. Defaults to False.

 False
no_duplicates bool

If True, remove duplicate intervals. Defaults to True.

 True
kwargs dict

Additional keyword arguments to pass to the class, including "valid_times" and "interval_list_name" for times and name.

 {}
Source code in src/spyglass/common/common_interval.py 
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def __init__(
    self,
    interval_list: IntervalLike = None,
    name=None,
    from_inds=False,
    no_overlap=False,
    no_duplicates=True,
    warn=not test_mode,  # warn by default, unless running pytests
    **kwargs,
) -> None:
    """Initialize the Intervals class with a list of intervals.

    Parameters
    ----------
    interval_list : dict or np.ndarray
        A key of IntervalList table, a numpy array of intervals, or a list
        of indices (integers).
    name : str, optional
        Name of the interval list. If not provided, can be taken from
        kwargs["interval_list_name"].
    from_inds : bool, optional
        If True, treat the interval_list as a list of indices and
        convert to intervals. Defaults to False.
    no_overlap : bool, optional
        If True, consolidate overlapping intervals. Defaults to False.
    no_duplicates : bool, optional
        If True, remove duplicate intervals. Defaults to True.
    kwargs : dict, optional
        Additional keyword arguments to pass to the class, including
        "valid_times" and "interval_list_name" for times and name.
    """

    self.kwargs = dict(  # Returned objects will set this behavior
        kwargs,
        no_overlap=no_overlap,
        no_duplicates=no_duplicates,
        warn=False,  # child instances do not warn
    )

    self.key = dict()  # let _extract decide if this is a key
    self.name = name or kwargs.get("interval_list_name")
    self.pipeline = kwargs.get("pipeline")
    self.nwb_file_name = kwargs.get("nwb_file_name")
    self.times = kwargs.get("valid_times") or interval_list
    self.times = self._extract(self.times, from_inds=from_inds)

    if warn or no_duplicates:
        dupes_removed = np.unique(self.times, axis=0)
    if no_duplicates:
        self.times = dupes_removed
    if warn and not np.array_equal(self.times, dupes_removed):
        logger.warning(
            "Found duplicate(s). Use no_duplicates flag to remove or not: "
            f"{self.to_str(self.times)} vs {self.to_str(dupes_removed)}"
        )

    if warn or no_overlap:  # otherwise recursive _consolidate call
        overlap_removed = self._consolidate(self.times)
    if no_overlap:
        self.times = overlap_removed
    if warn and not np.array_equal(self.times, overlap_removed):
        logger.warning(
            "Found overlap(s). Use no_overlap flag to consolidate or not: "
            f"{self.to_str(self.times)} vs {self.to_str(overlap_removed)}"
        )

to_str(array, remove_breaks=True)

Convert the interval list to a string.

Source code in src/spyglass/common/common_interval.py 
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def to_str(self, array: np.ndarray, remove_breaks=True) -> str:
    """Convert the interval list to a string."""
    kwarg = dict(separator=", ") if remove_breaks else dict()
    link = " " if remove_breaks else "\n\t"
    if isinstance(array, Iterable):
        return link.join(
            [np.array2string(i, **kwarg) for i in np.array(array)]
        )
    return str(array)

__getitem__(item)

Get item from the interval list.

Source code in src/spyglass/common/common_interval.py 
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def __getitem__(self, item: Union[int, slice, tuple]) -> np.ndarray:
    """Get item from the interval list."""
    if isinstance(item, (slice, int, tuple)):
        return self.times[item]
    else:
        raise ValueError(
            f"Unrecognized item type: {type(item)}. Must be int, slice, or tuple."
        )

__iter__()

Iterate over the intervals in the interval list.

Source code in src/spyglass/common/common_interval.py 
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def __iter__(self) -> iter:
    """Iterate over the intervals in the interval list."""
    for interval in self.times:
        yield interval

__hash__()

Hash the interval list for use in sets or dictionaries.

Source code in src/spyglass/common/common_interval.py 
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def __hash__(self) -> int:  # required for dict dunder
    """Hash the interval list for use in sets or dictionaries."""
    times = self.times
    if hasattr(times, "tolist"):
        times = times.tolist()
    if not isinstance(times, Iterable):
        times = [times]
    return hash(f"{self.name}{tuple(times)}")

set_key(**kwargs)

Set the key for the interval list.

Source code in src/spyglass/common/common_interval.py 
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def set_key(self, **kwargs: dict) -> None:
    """Set the key for the interval list."""
    self.key = kwargs
    self.nwb_file_name = kwargs.get("nwb_file_name") or kwargs.get("nwb")
    self.name = kwargs.get("interval_list_name") or kwargs.get("name")
    self.pipeline = kwargs.get("pipeline")

as_dict property

Convert the interval list to a dictionary.

__eq__(other)

Check if two interval lists are equal.

Source code in src/spyglass/common/common_interval.py 
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def __eq__(self, other: IntervalLike) -> bool:
    """Check if two interval lists are equal."""
    return np.array_equal(self.times, self._extract(other))

from_inds(list_frames) staticmethod

Converts a list of indices to a list of intervals.

To use, initialize the Interval class with from_inds=True.

e.g. [2,3,4,6,7,8,9,10] -> [[2,4],[6,10]]

Parameters:

Name Type Description Default
list_frames array_like of int
required
Source code in src/spyglass/common/common_interval.py 
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@staticmethod
def from_inds(list_frames) -> List[List[int]]:
    """Converts a list of indices to a list of intervals.

    To use, initialize the Interval class with from_inds=True.

    e.g. [2,3,4,6,7,8,9,10] -> [[2,4],[6,10]]

    Parameters
    ----------
    list_frames : array_like of int
    """
    list_frames = np.unique(list_frames)
    interval_list = []
    for key, group in itertools.groupby(
        enumerate(list_frames), lambda t: t[1] - t[0]
    ):
        group = list(group)
        interval_list.append([group[0][1], group[-1][1]])
    return np.asarray(interval_list)

by_length(min_length=0.0, max_length=10000000000.0)

Select intervals of certain lengths from an interval list.

Parameters:

Name Type Description Default
min_length float

Minimum interval length in seconds. Defaults to 0.0.

 0.0
max_length float

Maximum interval length in seconds. Defaults to 1e10.

 10000000000.0
Source code in src/spyglass/common/common_interval.py 
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def by_length(
    self,
    min_length: Optional[float] = 0.0,
    max_length: Optional[float] = 1e10,
) -> T:
    """Select intervals of certain lengths from an interval list.

    Parameters
    ----------
    min_length : float, optional
        Minimum interval length in seconds. Defaults to 0.0.
    max_length : float, optional
        Maximum interval length in seconds. Defaults to 1e10.
    """
    return Interval(
        self._by_length(self.times, min_length, max_length), **self.kwargs
    )

contains(timestamps, as_indices=False, padding=None)

Find timestamps that are contained in an interval list.

Parameters:

Name Type Description Default
timestamps array_like
required
as_indices bool

If True, return indices of timestamps in the interval list. If False, return the timestamps themselves. Defaults to False.

 False
padding int

If True, pad the first and last timestamps by val. Defaults to None.

 None
Source code in src/spyglass/common/common_interval.py 
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def contains(
    self,
    timestamps: np.ndarray,
    as_indices: Optional[bool] = False,
    padding: Optional[int] = None,
) -> T:
    """Find timestamps that are contained in an interval list.

    Parameters
    ----------
    timestamps : array_like
    as_indices : bool, optional
        If True, return indices of timestamps in the interval list.
        If False, return the timestamps themselves. Defaults to False.
    padding : int, optional
        If True, pad the first and last timestamps by val. Defaults to None.
    """
    ind = []
    for interval in self.times:
        ind += np.ravel(
            np.argwhere(
                np.logical_and(
                    timestamps >= interval[0], timestamps <= interval[1]
                )
            )
        ).tolist()
    ret = np.asarray(ind) if as_indices else timestamps[ind]

    if padding:
        if ret[0] > 0:
            ret[0] -= padding
        if ret[-1] != len(timestamps) - padding:
            ret[-1] += padding

    return ret

excludes(timestamps, as_indices=False)

Find timestamps that are not contained in an interval list.

Parameters:

Name Type Description Default
timestamps array_like
required
Source code in src/spyglass/common/common_interval.py 
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def excludes(
    self, timestamps: np.ndarray, as_indices: Optional[bool] = False
) -> T:
    """Find timestamps that are not contained in an interval list.

    Parameters
    ----------
    timestamps : array_like
    """
    contained_times = self.contains(timestamps, as_indices=as_indices)
    if as_indices:
        timestamps = np.arange(len(timestamps))
    return np.setdiff1d(timestamps, contained_times)

intersect(other, min_length=0)

Finds the intersections between this and another interval list.

Each interval is (start time, stop time)

Parameters:

Name Type Description Default
other Union[Interval, array, list, dict]

Interval list to intersect with self.

 required
min_length float, optional.

Minimum length of intervals to include, default 0

 0

Returns:

Name Type Description
interval_list (array, (N, 2))
Source code in src/spyglass/common/common_interval.py 
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def intersect(
    self, other: IntervalLike, min_length: Optional[float] = 0
) -> T:
    """Finds the intersections between this and another interval list.

    Each interval is (start time, stop time)

    Parameters
    ----------
    other : Union[Interval, np.array, list, dict]
        Interval list to intersect with self.
    min_length : float, optional.
        Minimum length of intervals to include, default 0

    Returns
    -------
    interval_list: np.array, (N,2)
    """
    return Interval(
        self._intersect(self.times, self._extract(other), min_length),
        **self.kwargs,
    )

union_adjacent_index(other)

Union index-adjacent intervals. If not adjacent, just concatenate.

e.g. [a,b] and [b+1, c] is converted to [a,c]

Parameters:

Name Type Description Default
other Union[Interval, ndarray]

Interval list to union with self.

 required
Source code in src/spyglass/common/common_interval.py 
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def union_adjacent_index(self, other: IntervalLike) -> T:
    """Union index-adjacent intervals. If not adjacent, just concatenate.

    e.g. [a,b] and [b+1, c] is converted to [a,c]

    Parameters
    ----------
    other : Union[Interval, np.ndarray]
        Interval list to union with self.
    """
    interval1 = np.atleast_2d(self.times)
    interval2 = np.atleast_2d(self._extract(other))

    if not (
        interval1[-1][1] + 1 == interval2[0][0]
        or interval2[0][1] + 1 == interval1[-1][0]
    ):
        return Interval(
            np.concatenate((interval1, interval2), axis=0), **self.kwargs
        )

    x = np.array(
        [
            [
                np.min([interval1[-1][0], interval2[0][0]]),
                np.max([interval1[-1][1], interval2[0][1]]),
            ]
        ]
    )
    return Interval(
        np.concatenate((interval1[:-1], x), axis=0), **self.kwargs
    )

union(other, min_length=0.0, max_length=10000000000.0)

Finds the union (all times in one or both) for two interval lists

Parameters:

Name Type Description Default
other Union[Interval, ndarray]

Interval list to union with self.

 required
min_length float

Minimum length of interval for inclusion in output, default 0.0

 0.0
max_length float

Maximum length of interval for inclusion in output, default 1e10

 10000000000.0

Returns:

Type Description
ndarray

Array of intervals [start, stop]
Source code in src/spyglass/common/common_interval.py 
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def union(
    self,
    other: IntervalLike,
    min_length: Optional[float] = 0.0,
    max_length: Optional[float] = 1e10,
) -> T:
    """Finds the union (all times in one or both) for two interval lists

    Parameters
    ----------
    other : Union[Interval, np.ndarray]
        Interval list to union with self.
    min_length : float, optional
        Minimum length of interval for inclusion in output, default 0.0
    max_length : float, optional
        Maximum length of interval for inclusion in output, default 1e10

    Returns
    -------
    np.ndarray
        Array of intervals [start, stop]
    """
    interval_list1 = self.times
    interval_list2 = self._extract(other)

    def _parallel_union(interval_list):
        """Create a parallel list where 1 is start and -1 the end"""
        interval_list = np.ravel(interval_list)
        interval_list_start_end = np.ones(interval_list.shape)
        interval_list_start_end[1::2] = -1
        return interval_list, interval_list_start_end

    il1, il1_start_end = _parallel_union(interval_list1)
    il2, il2_start_end = _parallel_union(interval_list2)

    # Concatenate the two lists so we can resort the intervals and apply the
    # same sorting to the start-end arrays
    combined_intervals = np.concatenate((il1, il2))
    if len(combined_intervals) == 0:
        return Interval(np.array([]), **self.kwargs)
    ss = np.concatenate((il1_start_end, il2_start_end))
    sort_ind = np.lexsort((-1 * ss, combined_intervals))
    combined_intervals = combined_intervals[sort_ind]
    ss_cumsum = np.cumsum(ss[sort_ind])
    if np.any(ss_cumsum < 0):
        raise ValueError(
            "Negative cumulative sum found in union. "
            + "This indicates an error in the interval lists, "
            + "such as an end time before a start time. "
            + "Please check the input interval lists for validity."
        )

    # a switch of cumulative sum from 0 to 1 indicates the beginning of a
    # joint interval; a cumulative sum of 0 indicates the end
    cumsum_flip = np.logical_and(
        ss_cumsum[1:] == 1,
        ss_cumsum[:-1] == 0,
    )
    union_starts = np.ravel(np.array(np.where(cumsum_flip)[0] + 1))
    union_starts = (
        np.insert(union_starts, 0, 0)
        if ss[sort_ind][0] == 1
        else union_starts
    )
    union_stops = np.ravel(np.array(np.where(ss_cumsum == 0)))
    if union_starts.size != union_stops.size:
        raise ValueError(
            "Mismatched number of union starts and stops. "
            + "This indicates an error in the interval lists, "
            + "such as an end time before a start time. "
            + "Please check the input interval lists for validity."
        )
    union = [
        [combined_intervals[start], combined_intervals[stop]]
        for start, stop in zip(union_starts, union_stops)
    ]

    return Interval(np.asarray(union), **self.kwargs)

censor(timestamps)

Returns new interval list that starts/ends at first/last timestamp

Parameters:

Name Type Description Default
timestamps numpy array or list
required

Returns:

Type Description
interval_list (numpy array of intervals [start, stop])
Source code in src/spyglass/common/common_interval.py 
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def censor(self, timestamps: Union[np.ndarray, List[int]]) -> T:
    """Returns new interval list that starts/ends at first/last timestamp

    Parameters
    ----------
    timestamps : numpy array or list

    Returns
    -------
    interval_list (numpy array of intervals [start, stop])
    """
    interval_list = self.times
    # check that all timestamps are in the interval list
    if len(self.contains(timestamps, as_indices=True)) != len(timestamps):
        raise ValueError("Interval_list must contain all timestamps")

    timestamps_interval = np.asarray([[timestamps[0], timestamps[-1]]])
    return Interval(
        self._intersect(interval_list, timestamps_interval), **self.kwargs
    )

subtract(other, reverse=False, min_length=None, max_length=None)

Finds intervals in self that are not in other.

Works on indices: intervals1 = [(0, 5), (8, 10)] intervals2 = [(1, 2), (3, 4), (6, 9)] result = [(0, 1), (4, 5), (9, 10)]

Parameters:

Name Type Description Default
other Union[Interval, ndarray]
required
reverse bool

If True, reverse the order of the intervals. Defaults to False.

 False
min_length float

Minimum interval length in seconds. If None, no filtering applied.

 None
max_length float

Maximum interval length in seconds. If None, no filtering applied.

 None

Returns:

Type Description
Interval

The resulting intervals after the difference operation. If indices, List[Tuple[int, int]].
Source code in src/spyglass/common/common_interval.py 
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def subtract(
    self,
    other: IntervalLike,
    reverse: Optional[bool] = False,
    min_length: Optional[float] = None,
    max_length: Optional[float] = None,
) -> T:
    """Finds intervals in self that are not in other.

    Works on indices:
        intervals1 = [(0, 5), (8, 10)]
        intervals2 = [(1, 2), (3, 4), (6, 9)]
        result = [(0, 1), (4, 5), (9, 10)]

    Parameters
    ----------
    other : Union[Interval, np.ndarray]
    reverse : bool, optional
        If True, reverse the order of the intervals. Defaults to False.
    min_length : float, optional
        Minimum interval length in seconds. If None, no filtering applied.
    max_length : float, optional
        Maximum interval length in seconds. If None, no filtering applied.

    Returns
    -------
    Interval
        The resulting intervals after the difference operation.
        If indices, List[Tuple[int, int]].
    """
    intervals1 = self.times if not reverse else self._extract(other)
    intervals2 = self._extract(other) if not reverse else self.times

    result = []
    i, j = 0, 0
    while i < len(intervals1):
        start1, end1 = intervals1[i]
        while j < len(intervals2) and intervals2[j][1] <= start1:
            j += 1  # Skip intervals2 that end before start1
        current_start = start1
        while j < len(intervals2) and intervals2[j][0] < end1:
            start2, end2 = intervals2[j]
            # Add the non-overlap before the current interval in intervals2
            if current_start < start2:
                result.append((current_start, min(start2, end1)))
            # Update the current start to exclude the overlapping part
            current_start = max(current_start, end2)
            j += 1  # next interval in intervals2
        if current_start < end1:  # if non-overlap btwn last part and end1
            result.append((current_start, end1))
        i += 1  # next interval in intervals1

    if min_length is not None or max_length is not None:
        result = self._by_length(
            np.asarray(result), min_length=min_length, max_length=max_length
        )

    return Interval(result, **self.kwargs)

add_removal_window(removal_window_ms, timestamps)

Add half of a removal window to start/end of each interval

Source code in src/spyglass/common/common_interval.py 
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def add_removal_window(
    self, removal_window_ms: int, timestamps: np.ndarray
) -> T:
    """Add half of a removal window to start/end of each interval"""
    half_win = removal_window_ms / 1000 * 0.5
    new_interval = np.zeros((len(self.times), 2), dtype=np.float64)
    # for each interval, add half removal window to start and end
    for idx, interval in enumerate(self.times):
        start, end = interval[0], interval[1]
        new_interval[idx] = [
            timestamps[start] - half_win,
            np.minimum(timestamps[end] + half_win, timestamps[-1]),
        ]
    # NOTE: np.min and if len cond were in
    # spikesorting_artifact, but not lfp_artifact
    return (
        self._union_consolidate(new_interval)
        if len(new_interval) > 1
        else Interval(new_interval, **self.kwargs)
    )

to_indices(timestamps, as_interval=False)

Convert intervals to indices in the given timestamps.

Parameters:

Name Type Description Default
timestamps array_like

The timestamps to convert the intervals to indices in.

 required
as_interval bool

If True, return as an Interval object. Defaults to False.

 False

Returns:

Type Description
ndarray

The indices of the intervals in the given timestamps.
Source code in src/spyglass/common/common_interval.py 
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def to_indices(
    self, timestamps: np.ndarray, as_interval: bool = False
) -> Union[List[List[int]], T]:
    """Convert intervals to indices in the given timestamps.

    Parameters
    ----------
    timestamps : array_like
        The timestamps to convert the intervals to indices in.
    as_interval : bool, optional
        If True, return as an Interval object. Defaults to False.

    Returns
    -------
    np.ndarray
        The indices of the intervals in the given timestamps.
    """
    ret = np.searchsorted(timestamps, self.times.ravel()).reshape(-1, 2)
    if as_interval:
        if len(ret) == 0:
            return Interval([], **self.kwargs)
        if not hasattr(ret[0], "__len__"):
            # handle list of ints case from v0.spikesorting_artifact
            ret = [ret]
        ret = Interval(ret, **self.kwargs)
    return ret

to_seconds(timestamps)

Convert intervals to seconds in the given timestamps.

Parameters:

Name Type Description Default
timestamps array_like

The timestamps to convert the intervals to seconds in.

 required

Returns:

Type Description
ndarray

The seconds of the intervals in the given timestamps.
Source code in src/spyglass/common/common_interval.py 
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def to_seconds(self, timestamps: np.ndarray) -> List[Tuple[float]]:
    """Convert intervals to seconds in the given timestamps.

    Parameters
    ----------
    timestamps : array_like
        The timestamps to convert the intervals to seconds in.

    Returns
    -------
    np.ndarray
        The seconds of the intervals in the given timestamps.
    """
    return [(timestamps[i[0]], timestamps[i[1]]) for i in self.times]