gammapy.maps.axes.TimeMapAxis.from_time_bounds() - Code Metrics - Inspection of "Merge pull request #3647 from adonath/time_axis_fr..." - gammapy/gammapy - Measure and Improve Code Quality continuously with Scrutinizer

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gammapy.maps.axes.TimeMapAxis.from_time_bounds() A

↳ Parent: gammapy.maps.axes
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Code Lines	10
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cc	1
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nop	6
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rs	9.9
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import inspect
from collections.abc import Sequence
import numpy as np
import scipy
import astropy.units as u
from astropy.io import fits
from astropy.table import Column, Table, hstack
from astropy.time import Time
from astropy.utils import lazyproperty
from gammapy.utils.interpolation import interpolation_scale
from gammapy.utils.time import time_ref_from_dict, time_ref_to_dict
from .utils import INVALID_INDEX, edges_from_lo_hi

__all__ = ["MapAxes", "MapAxis", "TimeMapAxis", "LabelMapAxis"]


def flat_if_equal(array):
    if array.ndim == 2 and np.all(array == array[0]):
        return array[0]
    else:
        return array


def coord_to_pix(edges, coord, interp="lin"):
    """Convert axis to pixel coordinates for given interpolation scheme."""
    scale = interpolation_scale(interp)

    interp_fn = scipy.interpolate.interp1d(
        scale(edges), np.arange(len(edges), dtype=float), fill_value="extrapolate"
    )

    return interp_fn(scale(coord))


def pix_to_coord(edges, pix, interp="lin"):
    """Convert pixel to grid coordinates for given interpolation scheme."""
    scale = interpolation_scale(interp)

    interp_fn = scipy.interpolate.interp1d(
        np.arange(len(edges), dtype=float), scale(edges), fill_value="extrapolate"
    )

    return scale.inverse(interp_fn(pix))


class MapAxis:
    """Class representing an axis of a map.

    Provides methods for
    transforming to/from axis and pixel coordinates.  An axis is
    defined by a sequence of node values that lie at the center of
    each bin.  The pixel coordinate at each node is equal to its index
    in the node array (0, 1, ..).  Bin edges are offset by 0.5 in
    pixel coordinates from the nodes such that the lower/upper edge of
    the first bin is (-0.5,0.5).

    Parameters
    ----------
    nodes : `~numpy.ndarray` or `~astropy.units.Quantity`
        Array of node values.  These will be interpreted as either bin
        edges or centers according to ``node_type``.
    interp : str
        Interpolation method used to transform between axis and pixel
        coordinates.  Valid options are 'log', 'lin', and 'sqrt'.
    name : str
        Axis name
    node_type : str
        Flag indicating whether coordinate nodes correspond to pixel
        edges (node_type = 'edge') or pixel centers (node_type =
        'center').  'center' should be used where the map values are
        defined at a specific coordinate (e.g. differential
        quantities). 'edge' should be used where map values are
        defined by an integral over coordinate intervals (e.g. a
        counts histogram).
    unit : str
        String specifying the data units.
    """

    # TODO: Cache an interpolation object?
    def __init__(self, nodes, interp="lin", name="", node_type="edges", unit=""):
        self._name = name

        if len(nodes) != len(np.unique(nodes)):
            raise ValueError("MapAxis: node values must be unique")

        if ~(np.all(nodes == np.sort(nodes)) or np.all(nodes[::-1] == np.sort(nodes))):
            raise ValueError("MapAxis: node values must be sorted")

        if len(nodes) == 1 and node_type == "center":
            raise ValueError("Single bins can only be used with node-type 'edges'")

        if isinstance(nodes, u.Quantity):
            unit = nodes.unit if nodes.unit is not None else ""
            nodes = nodes.value
        else:
            nodes = np.array(nodes)

        self._unit = u.Unit(unit)
        self._nodes = nodes.astype(float)
        self._node_type = node_type
        self._interp = interp

        if (self._nodes < 0).any() and interp != "lin":
            raise ValueError(
                f"Interpolation scaling {interp!r} only support for positive node values."
            )

        # Set pixel coordinate of first node
        if node_type == "edges":
            self._pix_offset = -0.5
            nbin = len(nodes) - 1
        elif node_type == "center":
            self._pix_offset = 0.0
            nbin = len(nodes)
        else:
            raise ValueError(f"Invalid node type: {node_type!r}")

        self._nbin = nbin

    def assert_name(self, required_name):
        """Assert axis name if a specific one is required.

        Parameters
        ----------
        required_name : str
            Required
        """
        if self.name != required_name:
            raise ValueError(
                "Unexpected axis name,"
                f' expected "{required_name}", got: "{self.name}"'
            )

    def is_aligned(self, other, atol=2e-2):
        """Check if other map axis is aligned.

        Two axes are aligned if their center coordinate values map to integers
        on the other axes as well and if the interpolation modes are equivalent.

        Parameters
        ----------
        other : `MapAxis`
            Other map axis.
        atol : float
            Absolute numerical tolerance for the comparison measured in bins.

        Returns
        -------
        aligned : bool
            Whether the axes are aligned
        """
        pix = self.coord_to_pix(other.center)
        pix_other = other.coord_to_pix(self.center)
        pix_all = np.append(pix, pix_other)
        aligned = np.allclose(np.round(pix_all) - pix_all, 0, atol=atol)
        return aligned and self.interp == other.interp

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented

        # TODO: implement an allclose method for MapAxis and call it here
        if self.edges.shape != other.edges.shape:
            return False
        if not self.unit.is_equivalent(other.unit):
            return False
        return (
            np.allclose(
                self.edges.to(other.unit).value, other.edges.value, atol=1e-6, rtol=1e-6
            )
            and self._node_type == other._node_type
            and self._interp == other._interp
            and self.name.upper() == other.name.upper()
        )

    def __ne__(self, other):
        return not self.__eq__(other)

    def __hash__(self):
        return id(self)

    @property
    def is_energy_axis(self):
        return self.name in ["energy", "energy_true"]

    @property
    def interp(self):
        """Interpolation scale of the axis."""
        return self._interp

    @property
    def name(self):
        """Name of the axis."""
        return self._name

    @name.setter
    def name(self, value):
        """Name of the axis."""
        self._name = value

    @lazyproperty
    def edges(self):
        """Return array of bin edges."""
        pix = np.arange(self.nbin + 1, dtype=float) - 0.5
        return u.Quantity(self.pix_to_coord(pix), self._unit, copy=False)

    @property
    def edges_min(self):
        """Return array of bin edges max values."""
        return self.edges[:-1]

    @property
    def edges_max(self):
        """Return array of bin edges min values."""
        return self.edges[1:]

    @property
    def bounds(self):
        """Bounds of the axis (~astropy.units.Quantity)"""
        idx = [0, -1]
        if self.node_type == "edges":
            return self.edges[idx]
        else:
            return self.center[idx]

    @property
    def as_plot_xerr(self):
        """Return tuple of xerr to be used with plt.errorbar()"""
        return (
            self.center - self.edges_min,
            self.edges_max - self.center,
        )

    @property
    def as_plot_labels(self):
        """Return list of axis plot labels"""
        if self.node_type == "edges":
            labels = [
                f"{val_min:.2e} - {val_max:.2e}"
                for val_min, val_max in self.iter_by_edges
            ]
        else:
            labels = [f"{val:.2e}" for val in self.center]

        return labels

    @property
    def as_plot_edges(self):
        """Plot edges"""
        return self.edges

    @property
    def as_plot_center(self):
        """Plot center"""
        return self.center

    @property
    def as_plot_scale(self):
        """Plot axis scale"""
        mpl_scale = {"lin": "linear", "sqrt": "linear", "log": "log"}

        return mpl_scale[self.interp]

    def format_plot_xaxis(self, ax):
        """Format plot axis

        Parameters
        ----------
        ax : `~matplotlib.pyplot.Axis`
            Plot axis to format

        Returns
        -------
        ax : `~matplotlib.pyplot.Axis`
            Formatted plot axis
        """
        ax.set_xscale(self.as_plot_scale)

        xlabel = self.name.capitalize() + f" [{ax.xaxis.units}]"
        ax.set_xlabel(xlabel)
        ax.set_xlim(self.bounds)
        return ax

    def format_plot_yaxis(self, ax):
        """Format plot axis

        Parameters
        ----------
        ax : `~matplotlib.pyplot.Axis`
            Plot axis to format

        Returns
        -------
        ax : `~matplotlib.pyplot.Axis`
            Formatted plot axis
        """
        ax.set_yscale(self.as_plot_scale)

        ylabel = self.name.capitalize() + f" [{ax.yaxis.units}]"
        ax.set_ylabel(ylabel)
        ax.set_ylim(self.bounds)
        return ax

    @property
    def iter_by_edges(self):
        """Iterate by intervals defined by the edges"""
        for value_min, value_max in zip(self.edges[:-1], self.edges[1:]):
            yield (value_min, value_max)

    @lazyproperty
    def center(self):
        """Return array of bin centers."""
        pix = np.arange(self.nbin, dtype=float)
        return u.Quantity(self.pix_to_coord(pix), self._unit, copy=False)

    @lazyproperty
    def bin_width(self):
        """Array of bin widths."""
        return np.diff(self.edges)

    @property
    def nbin(self):
        """Return number of bins."""
        return self._nbin

    @property
    def nbin_per_decade(self):
        """Return number of bins."""
        if self.interp != "log":
            raise ValueError("Bins per decade can only be computed for log-spaced axes")

        if self.node_type == "edges":
            values = self.edges
        else:
            values = self.center

        ndecades = np.log10(values.max() / values.min())
        return (self._nbin / ndecades).value

    @property
    def node_type(self):
        """Return node type ('center' or 'edge')."""
        return self._node_type

    @property
    def unit(self):
        """Return coordinate axis unit."""
        return self._unit

    @classmethod
    def from_bounds(cls, lo_bnd, hi_bnd, nbin, **kwargs):
        """Generate an axis object from a lower/upper bound and number of bins.

        If node_type = 'edge' then bounds correspond to the
        lower and upper bound of the first and last bin.  If node_type
        = 'center' then bounds correspond to the centers of the first
        and last bin.

        Parameters
        ----------
        lo_bnd : float
            Lower bound of first axis bin.
        hi_bnd : float
            Upper bound of last axis bin.
        nbin : int
            Number of bins.
        interp : {'lin', 'log', 'sqrt'}
            Interpolation method used to transform between axis and pixel
            coordinates.  Default: 'lin'.
        """
        nbin = int(nbin)
        interp = kwargs.setdefault("interp", "lin")
        node_type = kwargs.setdefault("node_type", "edges")

        if node_type == "edges":
            nnode = nbin + 1
        elif node_type == "center":
            nnode = nbin
        else:
            raise ValueError(f"Invalid node type: {node_type!r}")

        if interp == "lin":
            nodes = np.linspace(lo_bnd, hi_bnd, nnode)
        elif interp == "log":
            nodes = np.exp(np.linspace(np.log(lo_bnd), np.log(hi_bnd), nnode))
        elif interp == "sqrt":
            nodes = np.linspace(lo_bnd ** 0.5, hi_bnd ** 0.5, nnode) ** 2.0
        else:
            raise ValueError(f"Invalid interp: {interp}")

        return cls(nodes, **kwargs)

    @classmethod
    def from_energy_edges(cls, energy_edges, unit=None, name=None, interp="log"):
        """Make an energy axis from adjacent edges.

        Parameters
        ----------
        energy_edges : `~astropy.units.Quantity`, float
            Energy edges
        unit : `~astropy.units.Unit`
            Energy unit
        name : str
            Name of the energy axis, either 'energy' or 'energy_true'
        interp: str
            interpolation mode. Default is 'log'.

        Returns
        -------
        axis : `MapAxis`
            Axis with name "energy" and interp "log".
        """
        energy_edges = u.Quantity(energy_edges, unit)

        if not energy_edges.unit.is_equivalent("TeV"):
            raise ValueError(
                f"Please provide a valid energy unit, got {energy_edges.unit} instead."
            )

        if name is None:
            name = "energy"

        if name not in ["energy", "energy_true"]:
            raise ValueError("Energy axis can only be named 'energy' or 'energy_true'")

        return cls.from_edges(energy_edges, unit=unit, interp=interp, name=name)

    @classmethod
    def from_energy_bounds(
        cls,
        energy_min,
        energy_max,
        nbin,
        unit=None,
        per_decade=False,
        name=None,
        node_type="edges",
    ):
        """Make an energy axis.

        Used frequently also to make energy grids, by making
        the axis, and then using ``axis.center`` or ``axis.edges``.

        Parameters
        ----------
        energy_min, energy_max : `~astropy.units.Quantity`, float
            Energy range
        nbin : int
            Number of bins
        unit : `~astropy.units.Unit`
            Energy unit
        per_decade : bool
            Whether `nbin` is given per decade.
        name : str
            Name of the energy axis, either 'energy' or 'energy_true'

        Returns
        -------
        axis : `MapAxis`
            Axis with name "energy" and interp "log".
        """
        energy_min = u.Quantity(energy_min, unit)
        energy_max = u.Quantity(energy_max, unit)

        if unit is None:
            unit = energy_max.unit
            energy_min = energy_min.to(unit)

        if not energy_max.unit.is_equivalent("TeV"):
            raise ValueError(
                f"Please provide a valid energy unit, got {energy_max.unit} instead."
            )

        if per_decade:
            nbin = np.ceil(np.log10(energy_max / energy_min).value * nbin)

        if name is None:
            name = "energy"

        if name not in ["energy", "energy_true"]:
            raise ValueError("Energy axis can only be named 'energy' or 'energy_true'")

        return cls.from_bounds(
            energy_min.value,
            energy_max.value,
            nbin=nbin,
            unit=unit,
            interp="log",
            name=name,
            node_type=node_type,
        )

    @classmethod
    def from_nodes(cls, nodes, **kwargs):
        """Generate an axis object from a sequence of nodes (bin centers).

        This will create a sequence of bins with edges half-way
        between the node values.  This method should be used to
        construct an axis where the bin center should lie at a
        specific value (e.g. a map of a continuous function).

        Parameters
        ----------
        nodes : `~numpy.ndarray`
            Axis nodes (bin center).
        interp : {'lin', 'log', 'sqrt'}
            Interpolation method used to transform between axis and pixel
            coordinates.  Default: 'lin'.
        """
        if len(nodes) < 1:
            raise ValueError("Nodes array must have at least one element.")

        return cls(nodes, node_type="center", **kwargs)

    @classmethod
    def from_edges(cls, edges, **kwargs):
        """Generate an axis object from a sequence of bin edges.

        This method should be used to construct an axis where the bin
        edges should lie at specific values (e.g. a histogram).  The
        number of bins will be one less than the number of edges.

        Parameters
        ----------
        edges : `~numpy.ndarray`
            Axis bin edges.
        interp : {'lin', 'log', 'sqrt'}
            Interpolation method used to transform between axis and pixel
            coordinates.  Default: 'lin'.
        """
        if len(edges) < 2:
            raise ValueError("Edges array must have at least two elements.")

        return cls(edges, node_type="edges", **kwargs)

    def append(self, axis):
        """Append another map axis to this axis

        Name, interp type and node type must agree between the axes. If the node
        type is "edges", the edges must be contiguous and non-overlapping.

        Parameters
        ----------
        axis : `MapAxis`
            Axis to append.

        Returns
        -------
        axis : `MapAxis`
            Appended axis
        """
        if self.node_type != axis.node_type:
            raise ValueError(
                f"Node type must agree, got {self.node_type} and {axis.node_type}"
            )

        if self.name != axis.name:
            raise ValueError(f"Names must agree, got {self.name} and {axis.name} ")

        if self.interp != axis.interp:
            raise ValueError(
                f"Interp type must agree, got {self.interp} and {axis.interp}"
            )

        if self.node_type == "edges":
            edges = np.append(self.edges, axis.edges[1:])
            return self.from_edges(edges=edges, interp=self.interp, name=self.name)
        else:
            nodes = np.append(self.center, axis.center)
            return self.from_nodes(nodes=nodes, interp=self.interp, name=self.name)

    def pad(self, pad_width):
        """Pad axis by a given number of pixels

        Parameters
        ----------
        pad_width : int or tuple of int
            A single int pads in both direction of the axis, a tuple specifies,
            which number of bins to pad at the low and high edge of the axis.

        Returns
        -------
        axis : `MapAxis`
            Padded axis
        """
        if isinstance(pad_width, tuple):
            pad_low, pad_high = pad_width
        else:
            pad_low, pad_high = pad_width, pad_width

        if self.node_type == "edges":
            pix = np.arange(-pad_low, self.nbin + pad_high + 1) - 0.5
            edges = self.pix_to_coord(pix)
            return self.from_edges(edges=edges, interp=self.interp, name=self.name)
        else:
            pix = np.arange(-pad_low, self.nbin + pad_high)
            nodes = self.pix_to_coord(pix)
            return self.from_nodes(nodes=nodes, interp=self.interp, name=self.name)

    @classmethod
    def from_stack(cls, axes):
        """Create a map axis by merging a list of other map axes.

        If the node type is "edges" the bin edges in the provided axes must be
        contiguous and non-overlapping.

        Parameters
        ----------
        axes : list of `MapAxis`
            List of map axis to merge.

        Returns
        -------
        axis : `MapAxis`
            Merged axis
        """
        ax_stacked = axes[0]

        for ax in axes[1:]:
            ax_stacked = ax_stacked.append(ax)

        return ax_stacked

    def pix_to_coord(self, pix):
        """Transform from pixel to axis coordinates.

        Parameters
        ----------
        pix : `~numpy.ndarray`
            Array of pixel coordinate values.

        Returns
        -------
        coord : `~numpy.ndarray`
            Array of axis coordinate values.
        """
        pix = pix - self._pix_offset
        values = pix_to_coord(self._nodes, pix, interp=self._interp)
        return u.Quantity(values, unit=self.unit, copy=False)

    def pix_to_idx(self, pix, clip=False):

        """Convert pix to idx

        Parameters
        ----------
        pix : `~numpy.ndarray`
            Pixel coordinates.
        clip : bool
            Choose whether to clip indices to the valid range of the
            axis.  If false then indices for coordinates outside
            the axi range will be set -1.

        Returns
        -------
        idx : `~numpy.ndarray`
            Pixel indices.
        """
        if clip:
            idx = np.clip(pix, 0, self.nbin - 1)
        else:
            condition = (pix < 0) | (pix >= self.nbin)
            idx = np.where(condition, -1, pix)

        return idx

    def coord_to_pix(self, coord):
        """Transform from axis to pixel coordinates.

        Parameters
        ----------
        coord : `~numpy.ndarray`
            Array of axis coordinate values.

        Returns
        -------
        pix : `~numpy.ndarray`
            Array of pixel coordinate values.
        """
        coord = u.Quantity(coord, self.unit, copy=False).value
        pix = coord_to_pix(self._nodes, coord, interp=self._interp)
        return np.array(pix + self._pix_offset, ndmin=1)

    def coord_to_idx(self, coord, clip=False):
        """Transform from axis coordinate to bin index.

        Parameters
        ----------
        coord : `~numpy.ndarray`
            Array of axis coordinate values.
        clip : bool
            Choose whether to clip the index to the valid range of the
            axis.  If false then indices for values outside the axis
            range will be set -1.

        Returns
        -------
        idx : `~numpy.ndarray`
            Array of bin indices.
        """
        coord = u.Quantity(coord, self.unit, copy=False, ndmin=1).value
        edges = self.edges.value
        idx = np.digitize(coord, edges) - 1

        if clip:
            idx = np.clip(idx, 0, self.nbin - 1)
        else:
            with np.errstate(invalid="ignore"):
                idx[coord > edges[-1]] = INVALID_INDEX.int

        idx[~np.isfinite(coord)] = INVALID_INDEX.int

        return idx

    def slice(self, idx):
        """Create a new axis object by extracting a slice from this axis.

        Parameters
        ----------
        idx : slice
            Slice object selecting a subselection of the axis.

        Returns
        -------
        axis : `~MapAxis`
            Sliced axis object.
        """
        center = self.center[idx].value
        idx = self.coord_to_idx(center)
        # For edge nodes we need to keep N+1 nodes
        if self._node_type == "edges":
            idx = tuple(list(idx) + [1 + idx[-1]])

        nodes = self._nodes[(idx,)]
        return MapAxis(
            nodes,
            interp=self._interp,
            name=self._name,
            node_type=self._node_type,
            unit=self._unit,
        )

    def squash(self):
        """Create a new axis object by squashing the axis into one bin.

        Returns
        -------
        axis : `~MapAxis`
            Sliced axis object.
        """
        # TODO: Decide on handling node_type=center
        # See https://github.com/gammapy/gammapy/issues/1952
        return MapAxis.from_bounds(
            lo_bnd=self.edges[0].value,
            hi_bnd=self.edges[-1].value,
            nbin=1,
            interp=self._interp,
            name=self._name,
            unit=self._unit,
        )

    def __repr__(self):
        str_ = self.__class__.__name__
        str_ += "\n\n"
        fmt = "\t{:<10s} : {:<10s}\n"
        str_ += fmt.format("name", self.name)
        str_ += fmt.format("unit", "{!r}".format(str(self.unit)))
        str_ += fmt.format("nbins", str(self.nbin))
        str_ += fmt.format("node type", self.node_type)
        vals = self.edges if self.node_type == "edges" else self.center
        str_ += fmt.format(f"{self.node_type} min", "{:.1e}".format(vals.min()))
        str_ += fmt.format(f"{self.node_type} max", "{:.1e}".format(vals.max()))
        str_ += fmt.format("interp", self._interp)
        return str_

    def _init_copy(self, **kwargs):

        """Init map axis instance by copying missing init arguments from self."""
        argnames = inspect.getfullargspec(self.__init__).args
        argnames.remove("self")

        for arg in argnames:
            value = getattr(self, "_" + arg)
            kwargs.setdefault(arg, copy.deepcopy(value))

        return self.__class__(**kwargs)

    def copy(self, **kwargs):
        """Copy `MapAxis` instance and overwrite given attributes.

        Parameters
        ----------
        **kwargs : dict
            Keyword arguments to overwrite in the map axis constructor.

        Returns
        -------
        copy : `MapAxis`
            Copied map axis.
        """
        return self._init_copy(**kwargs)

    def round(self, coord, clip=False):
        """Round coord to nearest axis edge.

        Parameters
        ----------
        coord : `~astropy.units.Quantity`
            Coordinates
        clip : bool
            Choose whether to clip indices to the valid range of the axis.

        Returns
        -------
        coord : `~astropy.units.Quantity`
            Rounded coordinates
        """
        edges_pix = self.coord_to_pix(coord)

        if clip:
            edges_pix = np.clip(edges_pix, -0.5, self.nbin - 0.5)

        edges_idx = np.round(edges_pix + 0.5) - 0.5
        return self.pix_to_coord(edges_idx)

    def group_table(self, edges):
        """Compute bin groups table for the map axis, given coarser bin edges.

        Parameters
        ----------
        edges : `~astropy.units.Quantity`
            Group bin edges.

        Returns
        -------
        groups : `~astropy.table.Table`
            Map axis group table.
        """
        # TODO: try to simplify this code
        if self.node_type != "edges":
            raise ValueError("Only edge based map axis can be grouped")

        edges_pix = self.coord_to_pix(edges)
        edges_pix = np.clip(edges_pix, -0.5, self.nbin - 0.5)
        edges_idx = np.round(edges_pix + 0.5) - 0.5
        edges_idx = np.unique(edges_idx)
        edges_ref = self.pix_to_coord(edges_idx)

        groups = Table()
        groups[f"{self.name}_min"] = edges_ref[:-1]
        groups[f"{self.name}_max"] = edges_ref[1:]

        groups["idx_min"] = (edges_idx[:-1] + 0.5).astype(int)
        groups["idx_max"] = (edges_idx[1:] - 0.5).astype(int)

        if len(groups) == 0:
            raise ValueError("No overlap between reference and target edges.")

        groups["bin_type"] = "normal   "

        edge_idx_start, edge_ref_start = edges_idx[0], edges_ref[0]
        if edge_idx_start > 0:
            underflow = {
                "bin_type": "underflow",
                "idx_min": 0,
                "idx_max": edge_idx_start,
                f"{self.name}_min": self.pix_to_coord(-0.5),
                f"{self.name}_max": edge_ref_start,
            }
            groups.insert_row(0, vals=underflow)

        edge_idx_end, edge_ref_end = edges_idx[-1], edges_ref[-1]

        if edge_idx_end < (self.nbin - 0.5):
            overflow = {
                "bin_type": "overflow",
                "idx_min": edge_idx_end + 1,
                "idx_max": self.nbin - 1,
                f"{self.name}_min": edge_ref_end,
                f"{self.name}_max": self.pix_to_coord(self.nbin - 0.5),
            }
            groups.add_row(vals=overflow)

        group_idx = Column(np.arange(len(groups)))
        groups.add_column(group_idx, name="group_idx", index=0)
        return groups

    def upsample(self, factor):
        """Upsample map axis by a given factor.

        When up-sampling for each node specified in the axis, the corresponding
        number of sub-nodes are introduced and preserving the initial nodes. For
        node type "edges" this results in nbin * factor new bins. For node type
        "center" this results in (nbin - 1) * factor + 1 new bins.

        Parameters
        ----------
        factor : int
            Upsampling factor.

        Returns
        -------
        axis : `MapAxis`
            Usampled map axis.

        """
        if self.node_type == "edges":
            pix = self.coord_to_pix(self.edges)
            nbin = int(self.nbin * factor) + 1
            pix_new = np.linspace(pix.min(), pix.max(), nbin)
            edges = self.pix_to_coord(pix_new)
            return self.from_edges(edges, name=self.name, interp=self.interp)
        else:
            pix = self.coord_to_pix(self.center)
            nbin = int((self.nbin - 1) * factor) + 1
            pix_new = np.linspace(pix.min(), pix.max(), nbin)
            nodes = self.pix_to_coord(pix_new)
            return self.from_nodes(nodes, name=self.name, interp=self.interp)

    def downsample(self, factor):
        """Downsample map axis by a given factor.

        When down-sampling each n-th (given by the factor) bin is selected from
        the axis while preserving the axis limits. For node type "edges" this
        requires nbin to be dividable by the factor, for node type "center" this
        requires nbin - 1 to be dividable by the factor.

        Parameters
        ----------
        factor : int
            Downsampling factor.


        Returns
        -------
        axis : `MapAxis`
            Downsampled map axis.
        """
        if self.node_type == "edges":
            nbin = self.nbin / factor

            if np.mod(nbin, 1) > 0:
                raise ValueError(
                    f"Number of {self.name} bins is not divisible by {factor}"
                )

            edges = self.edges[::factor]
            return self.from_edges(edges, name=self.name, interp=self.interp)
        else:
            nbin = (self.nbin - 1) / factor

            if np.mod(nbin, 1) > 0:
                raise ValueError(
                    f"Number of {self.name} bins - 1 is not divisible by {factor}"
                )

            nodes = self.center[::factor]
            return self.from_nodes(nodes, name=self.name, interp=self.interp)

    def to_header(self, format="ogip", idx=0):
        """Create FITS header

        Parameters
        ----------
        format : {"ogip"}
            Format specification
        idx : int
            Column index of the axis.

        Returns
        -------
        header : `~astropy.io.fits.Header`
            Header to extend.
        """
        header = fits.Header()

        if format in ["ogip", "ogip-sherpa"]:
            header["EXTNAME"] = "EBOUNDS", "Name of this binary table extension"
            header["TELESCOP"] = "DUMMY", "Mission/satellite name"
            header["INSTRUME"] = "DUMMY", "Instrument/detector"
            header["FILTER"] = "None", "Filter information"
            header["CHANTYPE"] = "PHA", "Type of channels (PHA, PI etc)"
            header["DETCHANS"] = self.nbin, "Total number of detector PHA channels"
            header["HDUCLASS"] = "OGIP", "Organisation devising file format"
            header["HDUCLAS1"] = "RESPONSE", "File relates to response of instrument"
            header["HDUCLAS2"] = "EBOUNDS", "This is an EBOUNDS extension"
            header["HDUVERS"] = "1.2.0", "Version of file format"
        elif format in ["gadf", "fgst-ccube", "fgst-template"]:
            key = f"AXCOLS{idx}"
            name = self.name.upper()

            if self.name == "energy" and self.node_type == "edges":
                header[key] = "E_MIN,E_MAX"
            elif self.name == "energy" and self.node_type == "center":
                header[key] = "ENERGY"
            elif self.node_type == "edges":
                header[key] = f"{name}_MIN,{name}_MAX"
            elif self.node_type == "center":
                header[key] = name
            else:
                raise ValueError(f"Invalid node type {self.node_type!r}")

            key_interp = f"INTERP{idx}"
            header[key_interp] = self.interp

        else:
            raise ValueError(f"Unknown format {format}")

        return header

    def to_table(self, format="ogip"):
        """Convert `~astropy.units.Quantity` to OGIP ``EBOUNDS`` extension.

        See https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#tth_sEc3.2

        The 'ogip-sherpa' format is equivalent to 'ogip' but uses keV energy units.

        Parameters
        ----------
        format : {"ogip", "ogip-sherpa", "gadf-dl3", "gtpsf"}
            Format specification

        Returns
        -------
        table : `~astropy.table.Table`
            Table HDU
        """
        table = Table()
        edges = self.edges

        if format in ["ogip", "ogip-sherpa"]:
            self.assert_name("energy")

            if format == "ogip-sherpa":
                edges = edges.to("keV")

            table["CHANNEL"] = np.arange(self.nbin, dtype=np.int16)
            table["E_MIN"] = edges[:-1]
            table["E_MAX"] = edges[1:]
        elif format in ["ogip-arf", "ogip-arf-sherpa"]:
            self.assert_name("energy_true")

            if format == "ogip-arf-sherpa":
                edges = edges.to("keV")

            table["ENERG_LO"] = edges[:-1]
            table["ENERG_HI"] = edges[1:]
        elif format == "gadf-sed":
            if self.is_energy_axis:
                table["e_ref"] = self.center
                table["e_min"] = self.edges_min
                table["e_max"] = self.edges_max
        elif format == "gadf-dl3":
            from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION

            if self.name == "energy":
                column_prefix = "ENERG"
            else:
                for column_prefix, spec in IRF_DL3_AXES_SPECIFICATION.items():
                    if spec["name"] == self.name:
                        break

            if self.node_type == "edges":
                edges_hi, edges_lo = edges[:-1], edges[1:]
            else:
                edges_hi, edges_lo = self.center, self.center

            table[f"{column_prefix}_LO"] = edges_hi[np.newaxis]
            table[f"{column_prefix}_HI"] = edges_lo[np.newaxis]
        elif format == "gtpsf":
            if self.name == "energy_true":
                table["Energy"] = self.center.to("MeV")
            elif self.name == "rad":
                table["Theta"] = self.center.to("deg")
            else:
                raise ValueError(
                    "Can only convert true energy or rad axis to"
                    f"'gtpsf' format, got {self.name}"
                )
        else:
            raise ValueError(f"{format} is not a valid format")

        return table

    def to_table_hdu(self, format="ogip"):
        """Convert `~astropy.units.Quantity` to OGIP ``EBOUNDS`` extension.

        See https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#tth_sEc3.2

        The 'ogip-sherpa' format is equivalent to 'ogip' but uses keV energy units.

        Parameters
        ----------
        format : {"ogip", "ogip-sherpa", "gtpsf"}
            Format specification

        Returns
        -------
        hdu : `~astropy.io.fits.BinTableHDU`
            Table HDU
        """
        table = self.to_table(format=format)

        if format == "gtpsf":
            name = "THETA"
        else:
            name = None

        hdu = fits.BinTableHDU(table, name=name)

        if format in ["ogip", "ogip-sherpa"]:
            hdu.header.update(self.to_header(format=format))

        return hdu

    @classmethod
    def from_table(cls, table, format="ogip", idx=0, column_prefix=""):
        """Instantiate MapAxis from table HDU

        Parameters
        ----------
        table : `~astropy.table.Table`
            Table
        format : {"ogip", "ogip-arf", "fgst-ccube", "fgst-template", "gadf", "gadf-dl3"}
            Format specification
        idx : int
            Column index of the axis.
        column_prefix : str
            Column name prefix of the axis, used for creating the axis.

        Returns
        -------
        axis : `MapAxis`
            Map Axis
        """
        if format in ["ogip", "fgst-ccube"]:
            energy_min = table["E_MIN"].quantity
            energy_max = table["E_MAX"].quantity
            energy_edges = (
                np.append(energy_min.value, energy_max.value[-1]) * energy_min.unit
            )
            axis = cls.from_edges(energy_edges, name="energy", interp="log")

        elif format == "ogip-arf":
            energy_min = table["ENERG_LO"].quantity
            energy_max = table["ENERG_HI"].quantity
            energy_edges = (
                np.append(energy_min.value, energy_max.value[-1]) * energy_min.unit
            )
            axis = cls.from_edges(energy_edges, name="energy_true", interp="log")

        elif format in ["fgst-template", "fgst-bexpcube"]:
            allowed_names = ["Energy", "ENERGY", "energy"]
            for colname in table.colnames:
                if colname in allowed_names:
                    tag = colname
                    break

            nodes = table[tag].data

            axis = cls.from_nodes(
                nodes=nodes, name="energy_true", unit="MeV", interp="log"
            )

        elif format == "gadf":
            axcols = table.meta.get("AXCOLS{}".format(idx + 1))
            colnames = axcols.split(",")
            node_type = "edges" if len(colnames) == 2 else "center"

            # TODO: check why this extra case is needed
            if colnames[0] == "E_MIN":
                name = "energy"
            else:
                name = colnames[0].replace("_MIN", "").lower()
                # this is need for backward compatibility
                if name == "theta":
                    name = "rad"

            interp = table.meta.get("INTERP{}".format(idx + 1), "lin")

            if node_type == "center":
                nodes = np.unique(table[colnames[0]].quantity)
            else:
                edges_min = np.unique(table[colnames[0]].quantity)
                edges_max = np.unique(table[colnames[1]].quantity)
                nodes = edges_from_lo_hi(edges_min, edges_max)

            axis = MapAxis(nodes=nodes, node_type=node_type, interp=interp, name=name)

        elif format == "gadf-dl3":
            from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION

            spec = IRF_DL3_AXES_SPECIFICATION[column_prefix]
            name, interp = spec["name"], spec["interp"]

            # background models are stored in reconstructed energy
            hduclass = table.meta.get("HDUCLAS2")
            if hduclass in {"BKG", "RAD_MAX"} and column_prefix == "ENERG":
                name = "energy"

            edges_lo = table[f"{column_prefix}_LO"].quantity[0]
            edges_hi = table[f"{column_prefix}_HI"].quantity[0]

            if np.allclose(edges_hi, edges_lo):
                axis = MapAxis.from_nodes(edges_hi, interp=interp, name=name)
            else:
                edges = edges_from_lo_hi(edges_lo, edges_hi)
                axis = MapAxis.from_edges(edges, interp=interp, name=name)
        elif format == "gtpsf":
            try:
                energy = table["Energy"].data * u.MeV
                axis = MapAxis.from_nodes(energy, name="energy_true", interp="log")
            except KeyError:
                rad = table["Theta"].data * u.deg
                axis = MapAxis.from_nodes(rad, name="rad")
        elif format == "gadf-sed-energy":
            if "e_min" in table.colnames and "e_max" in table.colnames:
                e_min = flat_if_equal(table["e_min"].quantity)
                e_max = flat_if_equal(table["e_max"].quantity)
                edges = edges_from_lo_hi(e_min, e_max)
                axis = MapAxis.from_energy_edges(edges)
            elif "e_ref" in table.colnames:
                e_ref = flat_if_equal(table["e_ref"].quantity)
                axis = MapAxis.from_nodes(e_ref, name="energy", interp="log")
            else:
                raise ValueError(
                    "Either 'e_ref', 'e_min' or 'e_max' column " "names are required"
                )
        elif format == "gadf-sed-norm":
            # TODO: guess interp here
            nodes = flat_if_equal(table["norm_scan"][0])
            axis = MapAxis.from_nodes(nodes, name="norm")
        elif format == "gadf-sed-counts":
            if "datasets" in table.colnames:
                labels = np.unique(table["datasets"])
                axis = LabelMapAxis(labels=labels, name="dataset")
            else:
                shape = table["counts"].shape
                edges = np.arange(shape[-1] + 1) - 0.5
                axis = MapAxis.from_edges(edges, name="dataset")
        else:
            raise ValueError(f"Format '{format}' not supported")

        return axis

    @classmethod
    def from_table_hdu(cls, hdu, format="ogip", idx=0):
        """Instantiate MapAxis from table HDU

        Parameters
        ----------
        hdu : `~astropy.io.fits.BinTableHDU`
            Table HDU
        format : {"ogip", "ogip-arf", "fgst-ccube", "fgst-template"}
            Format specification
        idx : int
            Column index of the axis.

        Returns
        -------
        axis : `MapAxis`
            Map Axis
        """
        table = Table.read(hdu)
        return cls.from_table(table, format=format, idx=idx)


class MapAxes(Sequence):
    """MapAxis container class.

    Parameters
    ----------
    axes : list of `MapAxis`
        List of map axis objects.
    """

    def __init__(self, axes, n_spatial_axes=None):
        unique_names = []

        for ax in axes:
            if ax.name in unique_names:
                raise (
                    ValueError(f"Axis names must be unique, got: '{ax.name}' twice.")
                )
            unique_names.append(ax.name)

        self._axes = axes
        self._n_spatial_axes = n_spatial_axes

    @property
    def primary_axis(self):
        """Primary extra axis, defined as the one longest

        Returns
        -------
        axis : `MapAxis`
            Map axis
        """
        # get longest axis
        idx = np.argmax(self.shape)
        return self[int(idx)]

    @property
    def is_flat(self):
        """Whether axes is flat"""
        return np.all(self.shape == 1)

    @property
    def is_unidimensional(self):
        """Whether axes is unidimensional"""
        value = (np.array(self.shape) > 1).sum()
        return value == 1

    @property
    def reverse(self):
        """Reverse axes order"""
        return MapAxes(self[::-1])

    @property
    def iter_with_reshape(self):
        """Iterate by shape"""
        for idx, axis in enumerate(self):
            # Extract values for each axis, default: nodes
            shape = [1] * len(self)
            shape[idx] = -1
            if self._n_spatial_axes:
                shape = (
                    shape[::-1]
                    + [
                        1,
                    ]
                    * self._n_spatial_axes
                )
            yield tuple(shape), axis

    def get_coord(self, mode="center", axis_name=None):
        """Get axes coordinates

        Parameters
        ----------
        mode : {"center", "edges"}
            Coordinate center or edges
        axis_name : str
            Axis name for which mode='edges' applies

        Returns
        -------
        coords : dict of `~astropy.units.Quanity`
            Map coordinates
        """
        coords = {}

        for shape, axis in self.iter_with_reshape:
            if mode == "edges" and axis.name == axis_name:
                coord = axis.edges
            else:
                coord = axis.center
            coords[axis.name] = coord.reshape(shape)

        return coords

    def bin_volume(self):
        """Bin axes volume

        Returns
        -------
        bin_volume : `~astropy.units.Quantity`
            Bin volume
        """
        bin_volume = np.array(1)

        for shape, axis in self.iter_with_reshape:
            bin_volume = bin_volume * axis.bin_width.reshape(shape)

        return bin_volume

    @property
    def shape(self):
        """Shape of the axes"""
        return tuple([ax.nbin for ax in self])

    @property
    def names(self):
        """Names of the axes"""
        return [ax.name for ax in self]

    def index(self, axis_name):
        """Get index in list"""
        return self.names.index(axis_name)

    def index_data(self, axis_name):
        """Get data index of the axes

        Parameters
        ----------
        axis_name : str
            Name of the axis.

        Returns
        -------
        idx : int
            Data index
        """
        idx = self.names.index(axis_name)
        return len(self) - idx - 1

    def __len__(self):
        return len(self._axes)

    def __add__(self, other):
        return self.__class__(list(self) + list(other))

    def upsample(self, factor, axis_name):
        """Upsample axis by a given factor

        Parameters
        ----------
        factor : int
            Upsampling factor.
        axis_name : str
            Axis to upsample.

        Returns
        -------
        axes : `MapAxes`
            Map axes
        """
        axes = []

        for ax in self:
            if ax.name == axis_name:
                ax = ax.upsample(factor=factor)

            axes.append(ax.copy())

        return self.__class__(axes=axes)

    def replace(self, axis):
        """Replace a given axis

        Parameters
        ----------
        axis : `MapAxis`
            Map axis

        Returns
        -------
        axes : MapAxes
            Map axe
        """
        axes = []

        for ax in self:
            if ax.name == axis.name:
                ax = axis

            axes.append(ax)

        return self.__class__(axes=axes)

    def resample(self, axis):
        """Resample axis binning.

        This method groups the existing bins into a new binning.

        Parameters
        ----------
        axis : `MapAxis`
            New map axis.

        Returns
        -------
        axes : `MapAxes`
            Axes object with resampled axis.
        """
        axis_self = self[axis.name]
        groups = axis_self.group_table(axis.edges)

        # Keep only normal bins
        groups = groups[groups["bin_type"] == "normal   "]

        edges = edges_from_lo_hi(
            groups[axis.name + "_min"].quantity,
            groups[axis.name + "_max"].quantity,
        )

        axis_resampled = MapAxis.from_edges(
            edges=edges, interp=axis.interp, name=axis.name
        )

        axes = []
        for ax in self:
            if ax.name == axis.name:
                axes.append(axis_resampled)
            else:
                axes.append(ax.copy())

        return self.__class__(axes=axes)

    def downsample(self, factor, axis_name):
        """Downsample axis by a given factor

        Parameters
        ----------
        factor : int
            Upsampling factor.
        axis_name : str
            Axis to upsample.

        Returns
        -------
        axes : `MapAxes`
            Map axes

        """
        axes = []

        for ax in self:
            if ax.name == axis_name:
                ax = ax.downsample(factor=factor)

            axes.append(ax.copy())

        return self.__class__(axes=axes)

    def squash(self, axis_name):
        """Squash axis.

        Parameters
        ----------
        axis_name : str
            Axis to squash.

        Returns
        -------
        axes : `MapAxes`
            Axes with squashed axis.
        """
        axes = []

        for ax in self:
            if ax.name == axis_name:
                ax = ax.squash()
            axes.append(ax.copy())

        return self.__class__(axes=axes)

    def pad(self, axis_name, pad_width):
        """Pad axes

        Parameters
        ----------
        axis_name : str
            Name of the axis to pad.
        pad_width : int or tuple of int
            Pad width

        Returns
        -------
        axes : `MapAxes`
            Axes with squashed axis.

        """
        axes = []

        for ax in self:
            if ax.name == axis_name:
                ax = ax.pad(pad_width=pad_width)
            axes.append(ax)

        return self.__class__(axes=axes)

    def drop(self, axis_name):
        """Drop an axis.

        Parameters
        ----------
        axis_name : str
            Name of the axis to remove.

        Returns
        -------
        axes : `MapAxes`
            Axes with squashed axis.
        """
        axes = []
        for ax in self:
            if ax.name == axis_name:
                continue
            axes.append(ax.copy())

        return self.__class__(axes=axes)

    def __getitem__(self, idx):
        if isinstance(idx, (int, slice)):
            return self._axes[idx]
        elif isinstance(idx, str):
            for ax in self._axes:
                if ax.name == idx:
                    return ax
            raise KeyError(f"No axes: {idx!r}")
        elif isinstance(idx, list):
            axes = []
            for name in idx:
                axes.append(self[name])

            return self.__class__(axes=axes)
        else:
            raise TypeError(f"Invalid type: {type(idx)!r}")

    def coord_to_idx(self, coord, clip=True):
        """Transform from axis to pixel indices.

        Parameters
        ----------
        coord : dict of `~numpy.ndarray` or `MapCoord`
            Array of axis coordinate values.

        Returns
        -------
        pix : tuple of `~numpy.ndarray`
            Array of pixel indices values.
        """
        return tuple([ax.coord_to_idx(coord[ax.name], clip=clip) for ax in self])

    def coord_to_pix(self, coord):
        """Transform from axis to pixel coordinates.

        Parameters
        ----------
        coord : dict of `~numpy.ndarray`
            Array of axis coordinate values.

        Returns
        -------
        pix : tuple of `~numpy.ndarray`
            Array of pixel coordinate values.
        """
        return tuple([ax.coord_to_pix(coord[ax.name]) for ax in self])

    def pix_to_coord(self, pix):
        """Convert pixel coordinates to map coordinates.

        Parameters
        ----------
        pix : tuple
            Tuple of pixel coordinates.

        Returns
        -------
        coords : tuple
            Tuple of map coordinates.
        """
        return tuple([ax.pix_to_coord(p) for ax, p in zip(self, pix)])

    def pix_to_idx(self, pix, clip=False):
        """Convert pix to idx

        Parameters
        ----------
        pix : tuple of `~numpy.ndarray`
            Pixel coordinates.
        clip : bool
            Choose whether to clip indices to the valid range of the
            axis.  If false then indices for coordinates outside
            the axi range will be set -1.

        Returns
        -------
        idx : tuple `~numpy.ndarray`
            Pixel indices.
        """
        idx = []

        for pix_array, ax in zip(pix, self):
            idx.append(ax.pix_to_idx(pix_array, clip=clip))

        return tuple(idx)

    def slice_by_idx(self, slices):
        """Create a new geometry by slicing the non-spatial axes.

        Parameters
        ----------
        slices : dict
            Dict of axes names and integers or `slice` object pairs. Contains one
            element for each non-spatial dimension. For integer indexing the
            corresponding axes is dropped from the map. Axes not specified in the
            dict are kept unchanged.

        Returns
        -------
        geom : `~Geom`
            Sliced geometry.
        """
        axes = []
        for ax in self:
            ax_slice = slices.get(ax.name, slice(None))

            # in the case where isinstance(ax_slice, int) the axes is dropped
            if isinstance(ax_slice, slice):
                ax_sliced = ax.slice(ax_slice)
                axes.append(ax_sliced.copy())

        return self.__class__(axes=axes)

    def to_header(self, format="gadf"):
        """Convert axes to FITS header

        Parameters
        ----------
        format : {"gadf"}
            Header format

        Returns
        -------
        header : `~astropy.io.fits.Header`
            FITS header.
        """
        header = fits.Header()

        for idx, ax in enumerate(self, start=1):
            header_ax = ax.to_header(format=format, idx=idx)
            header.update(header_ax)

        return header

    def to_table(self, format="gadf"):
        """Convert axes to table

        Parameters
        ----------
        format : {"gadf", "gadf-dl3", "fgst-ccube", "fgst-template", "ogip", "ogip-sherpa", "ogip-arf", "ogip-arf-sherpa"}
            Format to use.

        Returns
        -------
        table : `~astropy.table.Table`
            Table with axis data
        """
        if format == "gadf-dl3":
            tables = []

            for ax in self:
                tables.append(ax.to_table(format=format))

            table = hstack(tables)
        elif format in ["gadf", "fgst-ccube", "fgst-template"]:
            table = Table()
            table["CHANNEL"] = np.arange(np.prod(self.shape))

            axes_ctr = np.meshgrid(*[ax.center for ax in self])
            axes_min = np.meshgrid(*[ax.edges_min for ax in self])
            axes_max = np.meshgrid(*[ax.edges_max for ax in self])

            for idx, ax in enumerate(self):
                name = ax.name.upper()

                if name == "ENERGY":
                    colnames = ["ENERGY", "E_MIN", "E_MAX"]
                else:
                    colnames = [name, name + "_MIN", name + "_MAX"]

                for colname, v in zip(colnames, [axes_ctr, axes_min, axes_max]):
                    # do not store edges for label axis
                    if ax.node_type == "label" and colname != name:
                        continue

                    table[colname] = np.ravel(v[idx])

                if isinstance(ax, TimeMapAxis):
                    ref_dict = time_ref_to_dict(ax.reference_time)
                    table.meta.update(ref_dict)

        elif format in ["ogip", "ogip-sherpa", "ogip", "ogip-arf"]:
            energy_axis = self["energy"]
            table = energy_axis.to_table(format=format)
        else:
            raise ValueError(f"Unsupported format: '{format}'")

        return table

    def to_table_hdu(self, format="gadf", hdu_bands=None):
        """Make FITS table columns for map axes.

        Parameters
        ----------
        format : {"gadf", "fgst-ccube", "fgst-template"}
            Format to use.
        hdu_bands : str
            Name of the bands HDU to use.

        Returns
        -------
        hdu : `~astropy.io.fits.BinTableHDU`
            Bin table HDU.
        """
        # FIXME: Check whether convention is compatible with
        #  dimensionality of geometry and simplify!!!

        if format in ["fgst-ccube", "ogip", "ogip-sherpa"]:
            hdu_bands = "EBOUNDS"
        elif format == "fgst-template":
            hdu_bands = "ENERGIES"
        elif format == "gadf" or format is None:
            if hdu_bands is None:
                hdu_bands = "BANDS"
        else:
            raise ValueError(f"Unknown format {format}")

        table = self.to_table(format=format)
        header = self.to_header(format=format)
        return fits.BinTableHDU(table, name=hdu_bands, header=header)

    @classmethod
    def from_table_hdu(cls, hdu, format="gadf"):
        """Create MapAxes from BinTableHDU

        Parameters
        ----------
        hdu : `~astropy.io.fits.BinTableHDU`
            Bin table HDU


        Returns
        -------
        axes : `MapAxes`
            Map axes object
        """
        if hdu is None:
            return cls([])

        table = Table.read(hdu)
        return cls.from_table(table, format=format)

    @classmethod
    def from_table(cls, table, format="gadf"):
        """Create MapAxes from BinTableHDU

        Parameters
        ----------
        table : `~astropy.table.Table`
            Bin table HDU
        format : {"gadf", "gadf-dl3", "fgst-ccube", "fgst-template", "fgst-bexcube", "ogip-arf"}
            Format to use.

        Returns
        -------
        axes : `MapAxes`
            Map axes object
        """
        from gammapy.irf.io import IRF_DL3_AXES_SPECIFICATION

        axes = []

        # Formats that support only one energy axis
        if format in [
            "fgst-ccube",
            "fgst-template",
            "fgst-bexpcube",
            "ogip",
            "ogip-arf",
        ]:
            axes.append(MapAxis.from_table(table, format=format))
        elif format == "gadf":
            # This limits the max number of axes to 5
            for idx in range(5):
                axcols = table.meta.get("AXCOLS{}".format(idx + 1))
                if axcols is None:
                    break

                # TODO: what is good way to check whether it is a given axis type?
                try:
                    axis = LabelMapAxis.from_table(table, format=format, idx=idx)
                except (KeyError, TypeError):
                    try:
                        axis = TimeMapAxis.from_table(table, format=format, idx=idx)
                    except (KeyError, ValueError):
                        axis = MapAxis.from_table(table, format=format, idx=idx)

                axes.append(axis)
        elif format == "gadf-dl3":
            for column_prefix in IRF_DL3_AXES_SPECIFICATION:
                try:
                    axis = MapAxis.from_table(
                        table, format=format, column_prefix=column_prefix
                    )
                except KeyError:
                    continue

                axes.append(axis)
        elif format == "gadf-sed":
            for axis_format in ["gadf-sed-norm", "gadf-sed-energy", "gadf-sed-counts"]:
                try:
                    axis = MapAxis.from_table(table=table, format=axis_format)
                except KeyError:
                    continue
                axes.append(axis)
        elif format == "lightcurve":
            axes.extend(cls.from_table(table=table, format="gadf-sed"))
            axes.append(TimeMapAxis.from_table(table, format="lightcurve"))
        else:
            raise ValueError(f"Unsupported format: '{format}'")

        return cls(axes)

    @classmethod
    def from_default(cls, axes, n_spatial_axes=None):
        """Make a sequence of `~MapAxis` objects."""
        if axes is None:
            return cls([])

        axes_out = []
        for idx, ax in enumerate(axes):
            if isinstance(ax, np.ndarray):
                ax = MapAxis(ax)

            if ax.name == "":
                ax.name = f"axis{idx}"

            axes_out.append(ax)

        return cls(axes_out, n_spatial_axes=n_spatial_axes)

    def assert_names(self, required_names):
        """Assert required axis names and order

        Parameters
        ----------
        required_names : list of str
            Required
        """
        message = (
            "Incorrect axis order or names. Expected axis "
            f"order: {required_names}, got: {self.names}."
        )

        if not len(self) == len(required_names):
            raise ValueError(message)

        try:
            for ax, required_name in zip(self, required_names):
                ax.assert_name(required_name)

        except ValueError:
            raise ValueError(message)

    @property
    def center_coord(self):
        """Center coordinates"""
        return tuple([ax.pix_to_coord((float(ax.nbin) - 1.0) / 2.0) for ax in self])


class TimeMapAxis:
    """Class representing a time axis.

    Provides methods for transforming to/from axis and pixel coordinates.
    A time axis can represent non-contiguous sequences of non-overlapping time intervals.

    Time intervals must be provided in increasing order.

    Parameters
    ----------
    edges_min : `~astropy.units.Quantity`
        Array of edge time values. This the time delta w.r.t. to the reference time.
    edges_max : `~astropy.units.Quantity`
        Array of edge time values. This the time delta w.r.t. to the reference time.
    reference_time : `~astropy.time.Time`
        Reference time to use.
    name : str
        Axis name
    interp : str
        Interpolation method used to transform between axis and pixel
        coordinates.  For now only 'lin' is supported.
    """

    node_type = "intervals"
    time_format = "iso"

    def __init__(self, edges_min, edges_max, reference_time, name="time", interp="lin"):
        self._name = name

        edges_min = u.Quantity(edges_min, ndmin=1)
        edges_max = u.Quantity(edges_max, ndmin=1)

        if not edges_min.unit.is_equivalent("s"):
            raise ValueError(
                f"Time edges min must have a valid time unit, got {edges_min.unit}"
            )

        if not edges_max.unit.is_equivalent("s"):
            raise ValueError(
                f"Time edges max must have a valid time unit, got {edges_max.unit}"
            )

        if not edges_min.shape == edges_max.shape:
            raise ValueError(
                "Edges min and edges max must have the same shape,"
                f" got {edges_min.shape} and {edges_max.shape}."
            )

        if not np.all(edges_max > edges_min):
            raise ValueError("Edges max must all be larger than edge min")

        if not np.all(edges_min == np.sort(edges_min)):
            raise ValueError("Time edges min values must be sorted")

        if not np.all(edges_max == np.sort(edges_max)):
            raise ValueError("Time edges max values must be sorted")

        if interp != "lin":
            raise NotImplementedError(
                f"Non-linear scaling scheme are not supported yet, got {interp}"
            )

        self._edges_min = edges_min
        self._edges_max = edges_max
        self._reference_time = Time(reference_time)
        self._pix_offset = -0.5
        self._interp = interp

        delta = edges_min[1:] - edges_max[:-1]
        if np.any(delta < 0 * u.s):
            raise ValueError("Time intervals must not overlap.")

    @property
    def is_contiguous(self):
        """Whether the axis is contiguous"""
        return np.all(self.edges_min[1:] == self.edges_max[:-1])

    def to_contiguous(self):
        """Make the time axis contiguous

        Returns
        -------
        axis : `TimeMapAxis`
            Contiguous time axis
        """
        edges = np.unique(np.stack([self.edges_min, self.edges_max]))
        return self.__class__(
            edges_min=edges[:-1],
            edges_max=edges[1:],
            reference_time=self.reference_time,
            name=self.name,
            interp=self.interp,
        )

    @property
    def unit(self):
        """Axes unit"""
        return self.edges_max.unit

    @property
    def interp(self):
        """Interp"""
        return self._interp

    @property
    def reference_time(self):
        """Return reference time used for the axis."""
        return self._reference_time

    @property
    def name(self):
        """Return axis name."""
        return self._name

    @property
    def nbin(self):
        """Return number of bins in the axis."""
        return len(self.edges_min.flatten())

    @property
    def edges_min(self):
        """Return array of bin edges max values."""
        return self._edges_min

    @property
    def edges_max(self):
        """Return array of bin edges min values."""
        return self._edges_max

    @property
    def edges(self):
        """Return array of bin edges values."""
        if not self.is_contiguous:
            raise ValueError("Time axis is not contiguous")

        return edges_from_lo_hi(self.edges_min, self.edges_max)

    @property
    def time_min(self):
        """Return axis lower edges as Time objects."""
        return self._edges_min + self.reference_time

    @property
    def time_max(self):
        """Return axis upper edges as Time objects."""
        return self._edges_max + self.reference_time

    @property
    def time_delta(self):
        """Return axis time bin width (`~astropy.time.TimeDelta`)."""
        return self.time_max - self.time_min

    @property
    def time_mid(self):
        """Return time bin center (`~astropy.time.Time`)."""
        return self.time_min + 0.5 * self.time_delta

    @property
    def time_edges(self):
        """Time edges"""
        return self.reference_time + self.edges

    @property
    def as_plot_xerr(self):
        """Plot x error"""
        xn, xp = self.time_mid - self.time_min, self.time_max - self.time_mid

        if self.time_format == "iso":
            x_errn = xn.to_datetime()
            x_errp = xp.to_datetime()
        elif self.time_format == "mjd":
            x_errn = xn.to("day")
            x_errp = xp.to("day")
        else:
            raise ValueError(f"Invalid time_format: {self.time_format}")

        return x_errn, x_errp

    @property
    def as_plot_labels(self):
        """Plot labels"""
        labels = []

        for t_min, t_max in self.iter_by_edges:
            label = f"{getattr(t_min, self.time_format)} - {getattr(t_max, self.time_format)}"
            labels.append(label)

        return labels

    @property
    def as_plot_edges(self):
        """Plot edges"""
        if self.time_format == "iso":
            edges = self.time_edges.to_datetime()
        elif self.time_format == "mjd":
            edges = self.time_edges.mjd * u.day
        else:
            raise ValueError(f"Invalid time_format: {self.time_format}")

        return edges

    @property
    def as_plot_center(self):
        """Plot center"""
        if self.time_format == "iso":
            center = self.time_mid.datetime
        elif self.time_format == "mjd":
            center = self.time_mid.mjd * u.day

        return center


    def format_plot_xaxis(self, ax):
        """Format plot axis

        Parameters
        ----------
        ax : `~matplotlib.pyplot.Axis`
            Plot axis to format

        Returns
        -------
        ax : `~matplotlib.pyplot.Axis`
            Formatted plot axis
        """
        import matplotlib.pyplot as plt
        from matplotlib.dates import DateFormatter

        xlabel = self.name.capitalize() + f" [{self.time_format}]"

        ax.set_xlabel(xlabel)

        if self.time_format == "iso":
            ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d %H:%M:%S"))
            plt.setp(
                ax.xaxis.get_majorticklabels(),
                rotation=30,
                ha="right",
                rotation_mode="anchor",
            )

        return ax

    def assert_name(self, required_name):
        """Assert axis name if a specific one is required.

        Parameters
        ----------
        required_name : str
            Required
        """
        if self.name != required_name:
            raise ValueError(
                "Unexpected axis name,"
                f' expected "{required_name}", got: "{self.name}"'
            )

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented

        if self._edges_min.shape != other._edges_min.shape:
            return False

        # This will test equality at microsec level.
        delta_min = self.time_min - other.time_min
        delta_max = self.time_max - other.time_max

        return (
            np.allclose(delta_min.to_value("s"), 0.0, atol=1e-6)
            and np.allclose(delta_max.to_value("s"), 0.0, atol=1e-6)
            and self._interp == other._interp
            and self.name.upper() == other.name.upper()
        )

    def __ne__(self, other):
        return not self.__eq__(other)

    def __hash__(self):
        return id(self)

    def is_aligned(self, other, atol=2e-2):
        raise NotImplementedError

    @property
    def iter_by_edges(self):
        """Iterate by intervals defined by the edges"""
        for time_min, time_max in zip(self.time_min, self.time_max):
            yield (time_min, time_max)

    def coord_to_idx(self, coord, **kwargs):
        """Transform from axis time coordinate to bin index.

        Indices of time values falling outside time bins will be
        set to -1.

        Parameters
        ----------
        coord : `~astropy.time.Time` or `~astropy.units.Quantity`
            Array of axis coordinate values. The quantity is assumed
            to be relative to the reference time.

        Returns
        -------
        idx : `~numpy.ndarray`
            Array of bin indices.
        """
        if isinstance(coord, u.Quantity):
            coord = self.reference_time + coord

        time = Time(coord[..., np.newaxis])
        delta_plus = (time - self.time_min).value > 0.0
        delta_minus = (time - self.time_max).value <= 0.0
        mask = np.logical_and(delta_plus, delta_minus)

        idx = np.asanyarray(np.argmax(mask, axis=-1))
        idx[~np.any(mask, axis=-1)] = INVALID_INDEX.int
        return idx

    def coord_to_pix(self, coord, **kwargs):
        """Transform from time to coordinate to pixel position.

        Pixels of time values falling outside time bins will be
        set to -1.

        Parameters
        ----------
        coord : `~astropy.time.Time`
            Array of axis coordinate values.

        Returns
        -------
        pix : `~numpy.ndarray`
            Array of pixel positions.
        """
        if isinstance(coord, u.Quantity):
            coord = self.reference_time + coord

        idx = np.atleast_1d(self.coord_to_idx(coord))

        valid_pix = idx != INVALID_INDEX.int
        pix = np.atleast_1d(idx).astype("float")

        # TODO: is there the equivalent of np.atleast1d for astropy.time.Time?
        if coord.shape == ():
            coord = coord.reshape((1,))

        relative_time = coord[valid_pix] - self.reference_time

        scale = interpolation_scale(self._interp)
        valid_idx = idx[valid_pix]
        s_min = scale(self._edges_min[valid_idx])
        s_max = scale(self._edges_max[valid_idx])
        s_coord = scale(relative_time.to(self._edges_min.unit))

        pix[valid_pix] += (s_coord - s_min) / (s_max - s_min)
        pix[~valid_pix] = INVALID_INDEX.float
        return pix - 0.5

    @staticmethod
    def pix_to_idx(pix, clip=False):
        return pix

    @property
    def center(self):
        """Return `~astropy.units.Quantity` at interval centers."""
        return self.edges_min + 0.5 * self.bin_width

    @property
    def bin_width(self):
        """Return time interval width (`~astropy.units.Quantity`)."""
        return self.time_delta.to("h")

    def __repr__(self):
        str_ = self.__class__.__name__ + "\n"
        str_ += "-" * len(self.__class__.__name__) + "\n\n"
        fmt = "\t{:<14s} : {:<10s}\n"
        str_ += fmt.format("name", self.name)
        str_ += fmt.format("nbins", str(self.nbin))
        str_ += fmt.format("reference time", self.reference_time.iso)
        str_ += fmt.format("scale", self.reference_time.scale)
        str_ += fmt.format("time min.", self.time_min.min().iso)
        str_ += fmt.format("time max.", self.time_max.max().iso)
        str_ += fmt.format("total time", np.sum(self.bin_width))
        return str_.expandtabs(tabsize=2)

    def upsample(self):
        raise NotImplementedError

    def downsample(self):
        raise NotImplementedError

    def _init_copy(self, **kwargs):

        """Init map axis instance by copying missing init arguments from self."""
        argnames = inspect.getfullargspec(self.__init__).args
        argnames.remove("self")

        for arg in argnames:
            value = getattr(self, "_" + arg)
            kwargs.setdefault(arg, copy.deepcopy(value))

        return self.__class__(**kwargs)

    def copy(self, **kwargs):
        """Copy `MapAxis` instance and overwrite given attributes.

        Parameters
        ----------
        **kwargs : dict
            Keyword arguments to overwrite in the map axis constructor.

        Returns
        -------
        copy : `MapAxis`
            Copied map axis.
        """
        return self._init_copy(**kwargs)

    def slice(self, idx):
        """Create a new axis object by extracting a slice from this axis.

        Parameters
        ----------
        idx : slice
            Slice object selecting a subselection of the axis.

        Returns
        -------
        axis : `~TimeMapAxis`
            Sliced axis object.
        """
        return TimeMapAxis(
            self._edges_min[idx].copy(),
            self._edges_max[idx].copy(),
            self.reference_time,
            interp=self._interp,
            name=self.name,
        )

    def squash(self):
        """Create a new axis object by squashing the axis into one bin.

        Returns
        -------
        axis : `~MapAxis`
            Sliced axis object.
        """
        return TimeMapAxis(
            self._edges_min[0],
            self._edges_max[-1],
            self.reference_time,
            interp=self._interp,
            name=self._name,
        )

    # TODO: if we are to allow log or sqrt bins the reference time should always
    #  be strictly lower than all times
    #  Should we define a mechanism to ensure this is always correct?
    @classmethod
    def from_time_edges(cls, time_min, time_max, unit="d", interp="lin", name="time"):
        """Create TimeMapAxis from the time interval edges defined as `~astropy.time.Time`.

        The reference time is defined as the lower edge of the first interval.

        Parameters
        ----------
        time_min : `~astropy.time.Time`
            Array of lower edge times.
        time_max : `~astropy.time.Time`
            Array of lower edge times.
        unit : `~astropy.units.Unit` or str
            The unit to convert the edges to. Default is 'd' (day).
        interp : str
            Interpolation method used to transform between axis and pixel
            coordinates.  Valid options are 'log', 'lin', and 'sqrt'.
        name : str
            Axis name

        Returns
        -------
        axis : `TimeMapAxis`
            Time map axis.
        """
        unit = u.Unit(unit)
        reference_time = time_min[0]
        edges_min = time_min - reference_time
        edges_max = time_max - reference_time

        return cls(
            edges_min.to(unit),
            edges_max.to(unit),
            reference_time,
            interp=interp,
            name=name,
        )

    # TODO: how configurable should that be? column names?
    @classmethod
    def from_table(cls, table, format="gadf", idx=0):
        """Create time map axis from table

        Parameters
        ----------
        table : `~astropy.table.Table`
            Bin table HDU
        format : {"gadf", "fermi-fgl", "lightcurve"}
            Format to use.

        Returns
        -------
        axis : `TimeMapAxis`
            Time map axis.
        """
        if format == "gadf":
            axcols = table.meta.get("AXCOLS{}".format(idx + 1))
            colnames = axcols.split(",")
            name = colnames[0].replace("_MIN", "").lower()
            reference_time = time_ref_from_dict(table.meta)
            edges_min = np.unique(table[colnames[0]].quantity)
            edges_max = np.unique(table[colnames[1]].quantity)
        elif format == "fermi-fgl":
            meta = table.meta.copy()
            meta["MJDREFF"] = str(meta["MJDREFF"]).replace("D-4", "e-4")
            reference_time = time_ref_from_dict(meta=meta)
            name = "time"
            edges_min = table["Hist_Start"][:-1]
            edges_max = table["Hist_Start"][1:]
        elif format == "lightcurve":
            # TODO: is this a good format? It just supports mjd...
            name = "time"
            scale = table.meta.get("TIMESYS", "utc")
            time_min = Time(table["time_min"].data, format="mjd", scale=scale)
            time_max = Time(table["time_max"].data, format="mjd", scale=scale)
            reference_time = Time("2001-01-01T00:00:00")
            reference_time.format = "mjd"
            edges_min = (time_min - reference_time).to("s")
            edges_max = (time_max - reference_time).to("s")
        else:
            raise ValueError(f"Not a supported format: {format}")

        return cls(
            edges_min=edges_min,
            edges_max=edges_max,
            reference_time=reference_time,
            name=name,
        )

    @classmethod
    def from_gti(cls, gti, name="time"):
        """Create a time axis from an input GTI.

        Parameters
        ----------
        gti : `GTI`
            GTI table
        name : str
            Axis name

        Returns
        -------
        axis : `TimeMapAxis`
            Time map axis.

        """
        tmin = gti.time_start - gti.time_ref
        tmax = gti.time_stop - gti.time_ref

        return cls(
            edges_min=tmin.to("s"),
            edges_max=tmax.to("s"),
            reference_time=gti.time_ref,
            name=name,
        )

    @classmethod
    def from_time_bounds(cls, time_min, time_max, nbin, unit="d", name="time"):
        """Create linearily spaced time axis from bounds

        Parameters
        ----------
        time_min : `~astropy.time.Time`
            Lower bound
        time_max : `~astropy.time.Time`
            Upper bound
        nbin : int
            Number of bins
        name : str
            Name of the axis.
        """
        delta = time_max - time_min
        time_edges = time_min + delta * np.linspace(0, 1, nbin + 1)
        return cls.from_time_edges(
            time_min=time_edges[:-1],
            time_max=time_edges[1:],
            interp="lin",
            unit=unit,
            name=name
        )

    def to_header(self, format="gadf", idx=0):
        """Create FITS header

        Parameters
        ----------
        format : {"ogip"}
            Format specification
        idx : int
            Column index of the axis.

        Returns
        -------
        header : `~astropy.io.fits.Header`
            Header to extend.
        """
        header = fits.Header()

        if format == "gadf":
            key = f"AXCOLS{idx}"
            name = self.name.upper()
            header[key] = f"{name}_MIN,{name}_MAX"
            key_interp = f"INTERP{idx}"
            header[key_interp] = self.interp

            ref_dict = time_ref_to_dict(self.reference_time)
            header.update(ref_dict)
        else:
            raise ValueError(f"Unknown format {format}")

        return header


class LabelMapAxis:
    """Map axis using labels

    Parameters
    ----------
    labels : list of str
        Labels to be used for the axis nodes.
    name : str
        Name of the axis.

    """

    node_type = "label"

    def __init__(self, labels, name=""):
        unique_labels = set(labels)

        if not len(unique_labels) == len(labels):
            raise ValueError("Node labels must be unique")

        self._labels = np.array(labels)
        self._name = name

    @property
    def unit(self):
        """Unit"""
        return u.Unit("")

    @property
    def name(self):
        """Name of the axis"""
        return self._name

    def assert_name(self, required_name):
        """Assert axis name if a specific one is required.

        Parameters
        ----------
        required_name : str
            Required
        """
        if self.name != required_name:
            raise ValueError(
                "Unexpected axis name,"
                f' expected "{required_name}", got: "{self.name}"'
            )

    @property
    def nbin(self):
        """Number of bins"""
        return len(self._labels)

    def pix_to_coord(self, pix):
        """Transform from pixel to axis coordinates.

        Parameters
        ----------
        pix : `~numpy.ndarray`
            Array of pixel coordinate values.

        Returns
        -------
        coord : `~numpy.ndarray`
            Array of axis coordinate values.
        """
        idx = np.round(pix).astype(int)
        return self._labels[idx]

    def coord_to_idx(self, coord, **kwargs):
        """Transform labels to indices

        If the label is not present an error is raised.

        Parameters
        ----------
        coord : `~astropy.time.Time`
            Array of axis coordinate values.

        Returns
        -------
        idx : `~numpy.ndarray`
            Array of bin indices.
        """
        coord = np.array(coord)[..., np.newaxis]
        is_equal = coord == self._labels

        if not np.all(np.any(is_equal, axis=-1)):
            label = coord[~np.any(is_equal, axis=-1)]
            raise ValueError(f"Not a valid label: {label}")

        return np.argmax(is_equal, axis=-1)

    def coord_to_pix(self, coord):
        """Transform from axis labels to pixel coordinates.

        Parameters
        ----------
        coord : `~numpy.ndarray`
            Array of axis label values.

        Returns
        -------
        pix : `~numpy.ndarray`
            Array of pixel coordinate values.
        """
        return self.coord_to_idx(coord).astype("float")

    def pix_to_idx(self, pix, clip=False):

        """Convert pix to idx

        Parameters
        ----------
        pix : tuple of `~numpy.ndarray`
            Pixel coordinates.
        clip : bool
            Choose whether to clip indices to the valid range of the
            axis.  If false then indices for coordinates outside
            the axi range will be set -1.

        Returns
        -------
        idx : tuple `~numpy.ndarray`
            Pixel indices.
        """
        if clip:
            idx = np.clip(pix, 0, self.nbin - 1)
        else:
            condition = (pix < 0) | (pix >= self.nbin)
            idx = np.where(condition, -1, pix)

        return idx

    @property
    def center(self):
        """Center of the label axis"""
        return self._labels

    @property
    def edges(self):
        """Edges of the label axis"""
        raise ValueError("A LabelMapAxis does not define edges")

    @property
    def edges_min(self):
        """Edges of the label axis"""
        return self._labels

    @property
    def edges_max(self):
        """Edges of the label axis"""
        return self._labels

    @property
    def bin_width(self):
        """Bin width is unity"""
        return np.ones(self.nbin)

    @property
    def as_plot_xerr(self):
        """Plot labels"""
        return 0.5 * np.ones(self.nbin)

    @property
    def as_plot_labels(self):
        """Plot labels"""
        return self._labels.tolist()

    @property
    def as_plot_center(self):
        """Plot labels"""
        return np.arange(self.nbin)

    @property
    def as_plot_edges(self):
        """Plot labels"""
        return np.arange(self.nbin + 1) - 0.5

    def format_plot_xaxis(self, ax):
        """Format plot axis.

        Parameters
        ----------
        ax : `~matplotlib.pyplot.Axis`
            Plot axis to format.

        Returns
        -------
        ax : `~matplotlib.pyplot.Axis`
            Formatted plot axis.
        """
        ax.set_xticks(self.as_plot_center)
        ax.set_xticklabels(
            self.as_plot_labels,
            rotation=30,
            ha="right",
            rotation_mode="anchor",
        )
        return ax

    def to_header(self, format="gadf", idx=0):
        """Create FITS header

        Parameters
        ----------
        format : {"ogip"}
            Format specification
        idx : int
            Column index of the axis.

        Returns
        -------
        header : `~astropy.io.fits.Header`
            Header to extend.
        """
        header = fits.Header()

        if format == "gadf":
            key = f"AXCOLS{idx}"
            header[key] = self.name.upper()
        else:
            raise ValueError(f"Unknown format {format}")

        return header

    # TODO: how configurable should that be? column names?
    @classmethod
    def from_table(cls, table, format="gadf", idx=0):
        """Create time map axis from table

        Parameters
        ----------
        table : `~astropy.table.Table`
            Bin table HDU
        format : {"gadf"}
            Format to use.

        Returns
        -------
        axis : `TimeMapAxis`
            Time map axis.
        """
        if format == "gadf":
            colname = table.meta.get("AXCOLS{}".format(idx + 1))
            column = table[colname]
            if not np.issubdtype(column.dtype, np.str_):
                raise TypeError(f"Not a valid dtype for label axis: '{column.dtype}'")
            labels = np.unique(column.data)
        else:
            raise ValueError(f"Not a supported format: {format}")

        return cls(labels=labels, name=colname.lower())

    def __repr__(self):
        str_ = self.__class__.__name__ + "\n"
        str_ += "-" * len(self.__class__.__name__) + "\n\n"
        fmt = "\t{:<10s} : {:<10s}\n"
        str_ += fmt.format("name", self.name)
        str_ += fmt.format("nbins", str(self.nbin))
        str_ += fmt.format("node type", self.node_type)
        str_ += fmt.format("labels", "{0}".format(list(self._labels)))
        return str_.expandtabs(tabsize=2)

    def __eq__(self, other):
        if not isinstance(other, self.__class__):
            return NotImplemented

        name_equal = self.name.upper() == other.name.upper()
        labels_equal = np.all(self.center == other.center)
        return name_equal & labels_equal

    def __ne__(self, other):
        return not self.__eq__(other)

    # TODO: could create sub-labels here using dashes like "label-1-a", etc.
    def upsample(self, *args, **kwargs):
        """Upsample axis"""
        raise NotImplementedError("Upsampling a LabelMapAxis is not supported")

    # TODO: could merge labels here like "label-1-label2", etc.
    def downsample(self, *args, **kwargs):
        """Downsample axis"""
        raise NotImplementedError("Downsampling a LabelMapAxis is not supported")

    # TODO: could merge labels here like "label-1-label2", etc.
    def resample(self, *args, **kwargs):
        """Resample axis"""
        raise NotImplementedError("Resampling a LabelMapAxis is not supported")

    # TODO: could create new labels here like "label-10-a"
    def pad(self, *args, **kwargs):
        """Resample axis"""
        raise NotImplementedError("Padding a LabelMapAxis is not supported")

    def copy(self):
        """Copy axis"""
        return copy.deepcopy(self)

    def slice(self, idx):
        """Create a new axis object by extracting a slice from this axis.

        Parameters
        ----------
        idx : slice
            Slice object selecting a subselection of the axis.

        Returns
        -------
        axis : `~LabelMapAxis`
            Sliced axis object.
        """
        return self.__class__(
            labels=self._labels[idx],
            name=self.name,
        )

gammapy / gammapy

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