# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
import astropy.units as u
from astropy.io import fits
from astropy.table import Table
from regions import CircleSkyRegion
from gammapy.data import GTI
from gammapy.irf import EDispKernelMap, EDispMap, PSFKernel, PSFMap
from gammapy.maps import Map, MapAxis
from gammapy.modeling.models import DatasetModels, FoVBackgroundModel
from gammapy.stats import (
    CashCountsStatistic,
    WStatCountsStatistic,
    cash,
    cash_sum_cython,
    get_wstat_mu_bkg,
    wstat,
)
from gammapy.utils.fits import HDULocation, LazyFitsData
from gammapy.utils.random import get_random_state
from gammapy.utils.scripts import make_name, make_path
from gammapy.utils.table import hstack_columns
from .core import Dataset
from .evaluator import MapEvaluator
from .utils import get_axes

__all__ = ["MapDataset", "MapDatasetOnOff", "create_map_dataset_geoms"]

log = logging.getLogger(__name__)

RAD_MAX = 0.66
RAD_AXIS_DEFAULT = MapAxis.from_bounds(
    0, RAD_MAX, nbin=66, node_type="edges", name="rad", unit="deg"
)
MIGRA_AXIS_DEFAULT = MapAxis.from_bounds(
    0.2, 5, nbin=48, node_type="edges", name="migra"
)

BINSZ_IRF_DEFAULT = 0.2

EVALUATION_MODE = "local"
USE_NPRED_CACHE = True

def create_map_dataset_geoms(
    geom,
    energy_axis_true=None,
    migra_axis=None,
    rad_axis=None,
    binsz_irf=None,
):
    """Create map geometries for a `MapDataset`

    Parameters
    ----------
    geom : `~gammapy.maps.WcsGeom`
        Reference target geometry in reco energy, used for counts and background maps
    energy_axis_true : `~gammapy.maps.MapAxis`
        True energy axis used for IRF maps
    migra_axis : `~gammapy.maps.MapAxis`
        If set, this provides the migration axis for the energy dispersion map.
        If not set, an EDispKernelMap is produced instead. Default is None
    rad_axis : `~gammapy.maps.MapAxis`
        Rad axis for the psf map
    binsz_irf : float
        IRF Map pixel size in degrees.

    Returns
    -------
    geoms : dict
        Dict with map geometries.
    """
    rad_axis = rad_axis or RAD_AXIS_DEFAULT

    if energy_axis_true is not None:
        energy_axis_true.assert_name("energy_true")
    else:
        energy_axis_true = geom.axes["energy"].copy(name="energy_true")

    binsz_irf = binsz_irf or BINSZ_IRF_DEFAULT
    geom_image = geom.to_image()
    geom_exposure = geom_image.to_cube([energy_axis_true])
    geom_irf = geom_image.to_binsz(binsz=binsz_irf)
    geom_psf = geom_irf.to_cube([rad_axis, energy_axis_true])

    if migra_axis:
        geom_edisp = geom_irf.to_cube([migra_axis, energy_axis_true])
    else:
        geom_edisp = geom_irf.to_cube([geom.axes["energy"], energy_axis_true])

    return {
        "geom": geom,
        "geom_exposure": geom_exposure,
        "geom_psf": geom_psf,
        "geom_edisp": geom_edisp,
    }

class MapDataset(Dataset):
    """
    Bundle together binned counts, background, IRFs, models and compute a likelihood.
     Uses Cash statistics by default.

    Parameters
    ----------
    models : `~gammapy.modeling.models.Models`
        Source sky models.
    counts : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
        Counts cube
    exposure : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
        Exposure cube
    background : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
        Background cube
    mask_fit : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
        Mask to apply to the likelihood for fitting.
    psf : `~gammapy.irf.PSFMap` or `~gammapy.utils.fits.HDULocation`
        PSF kernel
    edisp : `~gammapy.irf.EDispKernel` or `~gammapy.irf.EDispMap` or `~gammapy.utils.fits.HDULocation`
        Energy dispersion kernel
    mask_safe : `~gammapy.maps.WcsNDMap` or `~gammapy.utils.fits.HDULocation`
        Mask defining the safe data range.
    gti : `~gammapy.data.GTI`
        GTI of the observation or union of GTI if it is a stacked observation
    meta_table : `~astropy.table.Table`
        Table listing information on observations used to create the dataset.
        One line per observation for stacked datasets.

    If an `HDULocation` is passed the map is loaded lazily. This means the
    map data is only loaded in memory as the corresponding data attribute
    on the MapDataset is accessed. If it was accessed once it is cached for
    the next time.

    Examples
    --------
    Example of loading a dataset

    >>> from gammapy.datasets import MapDataset
    >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz", name="cta_dataset")
    >>> print(dataset)
    MapDataset
    ----------
    <BLANKLINE>
      Name                            : cta_dataset
    <BLANKLINE>
      Total counts                    : 104317
      Total background counts         : 91507.70
      Total excess counts             : 12809.30
    <BLANKLINE>
      Predicted counts                : 91507.69
      Predicted background counts     : 91507.70
      Predicted excess counts         : nan
    <BLANKLINE>
      Exposure min                    : 6.28e+07 m2 s
      Exposure max                    : 1.90e+10 m2 s
    <BLANKLINE>
      Number of total bins            : 768000
      Number of fit bins              : 691680
    <BLANKLINE>
      Fit statistic type              : cash
      Fit statistic value (-2 log(L)) : nan
    <BLANKLINE>
      Number of models                : 0
      Number of parameters            : 0
      Number of free parameters       : 0

    See Also
    --------
    MapDatasetOnOff, SpectrumDataset, FluxPointsDataset
    """

    stat_type = "cash"
    tag = "MapDataset"
    counts = LazyFitsData(cache=True)
    exposure = LazyFitsData(cache=True)
    edisp = LazyFitsData(cache=True)
    background = LazyFitsData(cache=True)
    psf = LazyFitsData(cache=True)
    mask_fit = LazyFitsData(cache=True)
    mask_safe = LazyFitsData(cache=True)

    _lazy_data_members = [
        "counts",
        "exposure",
        "edisp",
        "psf",
        "mask_fit",
        "mask_safe",
        "background",
    ]

    def __init__(
        self,
        models=None,
        counts=None,
        exposure=None,
        background=None,
        psf=None,
        edisp=None,
        mask_safe=None,
        mask_fit=None,
        gti=None,
        meta_table=None,
        name=None,
    ):
        self._name = make_name(name)
        self._evaluators = {}

        self.counts = counts
        self.exposure = exposure
        self.background = background
        self._background_cached = None
        self._background_parameters_cached = None

        self.mask_fit = mask_fit

        if psf and not isinstance(psf, (PSFMap, HDULocation)):
            raise ValueError(
                f"'psf' must be a 'PSFMap' or `HDULocation` object, got {type(psf)}"
            )

        self.psf = psf

        if edisp and not isinstance(edisp, (EDispMap, EDispKernelMap, HDULocation)):
            raise ValueError(
                f"'edisp' must be a 'EDispMap', `EDispKernelMap` or 'HDULocation' object, got {type(edisp)}"
            )

        self.edisp = edisp
        self.mask_safe = mask_safe
        self.gti = gti
        self.models = models
        self.meta_table = meta_table

    # TODO: keep or remove?
    @property
    def background_model(self):
        try:
            return self.models[f"{self.name}-bkg"]
        except (ValueError, TypeError):
            pass

    def __str__(self):
        str_ = f"{self.__class__.__name__}\n"
        str_ += "-" * len(self.__class__.__name__) + "\n"
        str_ += "\n"
        str_ += "\t{:32}: {{name}} \n\n".format("Name")
        str_ += "\t{:32}: {{counts:.0f}} \n".format("Total counts")
        str_ += "\t{:32}: {{background:.2f}}\n".format("Total background counts")
        str_ += "\t{:32}: {{excess:.2f}}\n\n".format("Total excess counts")

        str_ += "\t{:32}: {{npred:.2f}}\n".format("Predicted counts")
        str_ += "\t{:32}: {{npred_background:.2f}}\n".format(
            "Predicted background counts"
        )
        str_ += "\t{:32}: {{npred_signal:.2f}}\n\n".format("Predicted excess counts")

        str_ += "\t{:32}: {{exposure_min:.2e}}\n".format("Exposure min")
        str_ += "\t{:32}: {{exposure_max:.2e}}\n\n".format("Exposure max")

        str_ += "\t{:32}: {{n_bins}} \n".format("Number of total bins")
        str_ += "\t{:32}: {{n_fit_bins}} \n\n".format("Number of fit bins")

        # likelihood section
        str_ += "\t{:32}: {{stat_type}}\n".format("Fit statistic type")
        str_ += "\t{:32}: {{stat_sum:.2f}}\n\n".format(
            "Fit statistic value (-2 log(L))"
        )

        info = self.info_dict()
        str_ = str_.format(**info)

        # model section
        n_models, n_pars, n_free_pars = 0, 0, 0
        if self.models is not None:
            n_models = len(self.models)
            n_pars = len(self.models.parameters)
            n_free_pars = len(self.models.parameters.free_parameters)

        str_ += "\t{:32}: {} \n".format("Number of models", n_models)
        str_ += "\t{:32}: {}\n".format("Number of parameters", n_pars)
        str_ += "\t{:32}: {}\n\n".format("Number of free parameters", n_free_pars)

        if self.models is not None:
            str_ += "\t" + "\n\t".join(str(self.models).split("\n")[2:])

        return str_.expandtabs(tabsize=2)

    @property
    def geoms(self):
        """Map geometries

        Returns
        -------
        geoms : dict
            Dict of map geometries involved in the dataset.
        """
        geoms = {}

        geoms["geom"] = self._geom

        if self.exposure:
            geoms["geom_exposure"] = self.exposure.geom

        if self.psf:
            geoms["geom_psf"] = self.psf.psf_map.geom

        if self.edisp:
            geoms["geom_edisp"] = self.edisp.edisp_map.geom

        return geoms

    @property
    def models(self):
        """Models set on the dataset (`~gammapy.modeling.models.Models`)."""
        return self._models

    @property
    def excess(self):
        """Observed excess: counts-background"""
        return self.counts - self.background

    @models.setter
    def models(self, models):
        """Models setter"""
        self._evaluators = {}

        if models is not None:
            models = DatasetModels(models)
            models = models.select(datasets_names=self.name)

            for model in models:
                if not isinstance(model, FoVBackgroundModel):
                    evaluator = MapEvaluator(
                        model=model,
                        evaluation_mode=EVALUATION_MODE,
                        gti=self.gti,
                        use_cache=USE_NPRED_CACHE,
                    )
                    self._evaluators[model.name] = evaluator

        self._models = models

    @property
    def evaluators(self):
        """Model evaluators"""
        return self._evaluators

    @property
    def _geom(self):
        """Main analysis geometry"""
        if self.counts is not None:
            return self.counts.geom
        elif self.background is not None:
            return self.background.geom
        elif self.mask_safe is not None:
            return self.mask_safe.geom
        elif self.mask_fit is not None:
            return self.mask_fit.geom
        else:
            raise ValueError(
                "Either 'counts', 'background', 'mask_fit'"
                " or 'mask_safe' must be defined."
            )

    @property
    def data_shape(self):
        """Shape of the counts or background data (tuple)"""
        return self._geom.data_shape

    def _energy_range(self, mask_map=None):
        """Compute the energy range maps with or without the fit mask."""
        geom = self._geom
        energy = geom.axes["energy"].edges
        e_i = geom.axes.index_data("energy")
        geom = geom.drop("energy")

        if mask_map is not None:
            mask = mask_map.data
            if mask.any():
                idx = mask.argmax(e_i)
                energy_min = energy.value[idx]
                mask_nan = ~mask.any(e_i)
                energy_min[mask_nan] = np.nan

                mask = np.flip(mask, e_i)
                idx = mask.argmax(e_i)
                energy_max = energy.value[::-1][idx]
                energy_max[mask_nan] = np.nan
            else:
                energy_min = np.full(geom.data_shape, np.nan)
                energy_max = energy_min.copy()
        else:
            data_shape = geom.data_shape
            energy_min = np.full(data_shape, energy.value[0])
            energy_max = np.full(data_shape, energy.value[-1])

        map_min = Map.from_geom(geom, data=energy_min, unit=energy.unit)
        map_max = Map.from_geom(geom, data=energy_max, unit=energy.unit)
        return map_min, map_max

    @property
    def energy_range(self):
        """Energy range maps defined by the full mask (mask_safe and mask_fit)."""
        return self._energy_range(self.mask)

    @property
    def energy_range_safe(self):
        """Energy range maps defined by the mask_safe only."""
        return self._energy_range(self.mask_safe)

    @property
    def energy_range_fit(self):
        """Energy range maps defined by the mask_fit only."""
        return self._energy_range(self.mask_fit)

    def npred(self):
        """Total predicted source and background counts

        Returns
        -------
        npred : `Map`
            Total predicted counts
        """
        npred_total = self.npred_signal()

        if self.background:
            npred_total += self.npred_background()
        npred_total.data[npred_total.data < 0.0] = 0
        return npred_total

    def npred_background(self):
        """Predicted background counts

        The predicted background counts depend on the parameters
        of the `FoVBackgroundModel` defined in the dataset.

        Returns
        -------
        npred_background : `Map`
            Predicted counts from the background.
        """
        background = self.background
        if self.background_model and background:
            if self._background_parameters_changed:
                values = self.background_model.evaluate_geom(geom=self.background.geom)
                if self._background_cached is None:
                    self._background_cached = background * values
                else:
                    self._background_cached.data = background.data * values.value
                    self._background_cached.unit = background.unit
            return self._background_cached
        else:
            return background

        return background

    def _background_parameters_changed(self):
        values = self.background_model.parameters.value
        # TODO: possibly allow for a tolerance here?
        changed = ~np.all(self._background_parameters_cached == values)
        if changed:
            self._background_parameters_cached = values
        return changed

    def npred_signal(self, model_name=None):
        """Model predicted signal counts.

        If a model name is passed, predicted counts from that component are returned.
        Else, the total signal counts are returned.

        Parameters
        ----------
        model_name: str
            Name of  SkyModel for which to compute the npred for.
            If none, the sum of all components (minus the background model)
            is returned

        Returns
        -------
        npred_sig: `gammapy.maps.Map`
            Map of the predicted signal counts
        """
        npred_total = Map.from_geom(self._geom, dtype=float)

        evaluators = self.evaluators
        if model_name is not None:
            evaluators = {model_name: self.evaluators[model_name]}

        for evaluator in evaluators.values():
            if evaluator.needs_update:
                evaluator.update(
                    self.exposure,
                    self.psf,
                    self.edisp,
                    self._geom,
                    self.mask_image,
                )

            if evaluator.contributes:
                npred = evaluator.compute_npred()
                npred_total.stack(npred)

        return npred_total

    @classmethod
    def from_geoms(
        cls,
        geom,
        geom_exposure=None,
        geom_psf=None,
        geom_edisp=None,
        reference_time="2000-01-01",
        name=None,
        **kwargs,
    ):
        """
        Create a MapDataset object with zero filled maps according to the specified geometries

        Parameters
        ----------
        geom : `Geom`
            geometry for the counts and background maps
        geom_exposure : `Geom`
            geometry for the exposure map
        geom_psf : `Geom`
            geometry for the psf map
        geom_edisp : `Geom`
            geometry for the energy dispersion kernel map.
            If geom_edisp has a migra axis, this will create an EDispMap instead.
        reference_time : `~astropy.time.Time`
            the reference time to use in GTI definition
        name : str
            Name of the returned dataset.

        Returns
        -------
        dataset : `MapDataset` or `SpectrumDataset`
            A dataset containing zero filled maps
        """
        name = make_name(name)
        kwargs = kwargs.copy()
        kwargs["name"] = name
        kwargs["counts"] = Map.from_geom(geom, unit="")
        kwargs["background"] = Map.from_geom(geom, unit="")

        if geom_exposure:
            kwargs["exposure"] = Map.from_geom(geom_exposure, unit="m2 s")

        if geom_edisp:
            if "energy" in geom_edisp.axes.names:
                kwargs["edisp"] = EDispKernelMap.from_geom(geom_edisp)
            else:
                kwargs["edisp"] = EDispMap.from_geom(geom_edisp)

        if geom_psf:
            kwargs["psf"] = PSFMap.from_geom(geom_psf)

        kwargs.setdefault(
            "gti", GTI.create([] * u.s, [] * u.s, reference_time=reference_time)
        )
        kwargs["mask_safe"] = Map.from_geom(geom, unit="", dtype=bool)
        return cls(**kwargs)

    @classmethod
    def create(
        cls,
        geom,
        energy_axis_true=None,
        migra_axis=None,
        rad_axis=None,
        binsz_irf=None,
        reference_time="2000-01-01",
        name=None,
        meta_table=None,
        **kwargs,
    ):
        """Create a MapDataset object with zero filled maps.

        Parameters
        ----------
        geom : `~gammapy.maps.WcsGeom`
            Reference target geometry in reco energy, used for counts and background maps
        energy_axis_true : `~gammapy.maps.MapAxis`
            True energy axis used for IRF maps
        migra_axis : `~gammapy.maps.MapAxis`
            If set, this provides the migration axis for the energy dispersion map.
            If not set, an EDispKernelMap is produced instead. Default is None
        rad_axis : `~gammapy.maps.MapAxis`
            Rad axis for the psf map
        binsz_irf : float
            IRF Map pixel size in degrees.
        reference_time : `~astropy.time.Time`
            the reference time to use in GTI definition
        name : str
            Name of the returned dataset.
        meta_table : `~astropy.table.Table`
            Table listing information on observations used to create the dataset.
            One line per observation for stacked datasets.

        Returns
        -------
        empty_maps : `MapDataset`
            A MapDataset containing zero filled maps

        Examples
        --------

        >>> from gammapy.datasets import MapDataset
        >>> from gammapy.maps import WcsGeom, MapAxis

        >>> energy_axis = MapAxis.from_energy_bounds(1.0, 10.0, 4, unit="TeV")
        >>> energy_axis_true = MapAxis.from_energy_bounds( 0.5, 20, 10, unit="TeV", name="energy_true")
        >>> geom = WcsGeom.create(skydir=(83.633, 22.014), binsz=0.02, width=(2, 2), frame="icrs", proj="CAR", axes=[energy_axis])

        >>> empty = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true, name="empty")
        """

        geoms = create_map_dataset_geoms(
            geom=geom,
            energy_axis_true=energy_axis_true,
            rad_axis=rad_axis,
            migra_axis=migra_axis,
            binsz_irf=binsz_irf,
        )

        kwargs.update(geoms)
        return cls.from_geoms(reference_time=reference_time, name=name, **kwargs)

    @property
    def mask_safe_image(self):
        """Reduced mask safe"""
        if self.mask_safe is None:
            return None
        return self.mask_safe.reduce_over_axes(func=np.logical_or)

    @property
    def mask_fit_image(self):
        """Reduced mask fit"""
        if self.mask_fit is None:
            return None
        return self.mask_fit.reduce_over_axes(func=np.logical_or)

    @property
    def mask_image(self):
        """Reduced mask"""
        if self.mask is None:
            mask = Map.from_geom(self._geom.to_image(), dtype=bool)
            mask.data |= True
            return mask

        return self.mask.reduce_over_axes(func=np.logical_or)

    @property
    def mask_safe_psf(self):
        """Mask safe for psf maps"""
        if self.mask_safe is None or self.psf is None:
            return None

        geom = self.psf.psf_map.geom.squash("energy_true").squash("rad")
        mask_safe_psf = self.mask_safe_image.interp_to_geom(geom.to_image())
        return mask_safe_psf.to_cube(geom.axes)

    @property
    def mask_safe_edisp(self):
        """Mask safe for edisp maps"""
        if self.mask_safe is None or self.edisp is None:
            return None

        if self.mask_safe.geom.is_region:
            return self.mask_safe

        geom = self.edisp.edisp_map.geom.squash("energy_true")

        if "migra" in geom.axes.names:
            geom = geom.squash("migra")
            mask_safe_edisp = self.mask_safe_image.interp_to_geom(geom.to_image())
            return mask_safe_edisp.to_cube(geom.axes)

        return self.mask_safe.interp_to_geom(geom)

    def to_masked(self, name=None, nan_to_num=True):
        """Return masked dataset

        Parameters
        ----------
        name : str
            Name of the masked dataset.

        Returns
        -------
        dataset : `MapDataset` or `SpectrumDataset`
            Masked dataset
        """
        dataset = self.__class__.from_geoms(**self.geoms, name=name)
        dataset.stack(self, nan_to_num=nan_to_num)
        return dataset

    def stack(self, other, nan_to_num=True):
        r"""Stack another dataset in place. The original dataset is modified.

        Safe mask is applied to compute the stacked counts data. Counts outside
        each dataset safe mask are lost.

        The stacking of 2 datasets is implemented as follows. Here, :math:`k`
        denotes a bin in reconstructed energy and :math:`j = {1,2}` is the dataset number

        The ``mask_safe`` of each dataset is defined as:

        .. math::

            \epsilon_{jk} =\left\{\begin{array}{cl} 1, &
            \mbox{if bin k is inside the thresholds}\\ 0, &
            \mbox{otherwise} \end{array}\right.

        Then the total ``counts`` and model background ``bkg`` are computed according to:

        .. math::

            \overline{\mathrm{n_{on}}}_k =  \mathrm{n_{on}}_{1k} \cdot \epsilon_{1k} +
             \mathrm{n_{on}}_{2k} \cdot \epsilon_{2k}

            \overline{bkg}_k = bkg_{1k} \cdot \epsilon_{1k} +
             bkg_{2k} \cdot \epsilon_{2k}

        The stacked ``safe_mask`` is then:

        .. math::

            \overline{\epsilon_k} = \epsilon_{1k} OR \epsilon_{2k}

        Parameters
        ----------
        other: `~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
            Map dataset to be stacked with this one. If other is an on-off
            dataset alpha * counts_off is used as a background model.
        nan_to_num: bool
            Non-finite values are replaced by zero if True (default).

        """
        if self.counts and other.counts:
            self.counts.stack(
                other.counts, weights=other.mask_safe, nan_to_num=nan_to_num
            )

        if self.exposure and other.exposure:
            self.exposure.stack(
                other.exposure, weights=other.mask_safe_image, nan_to_num=nan_to_num
            )
            # TODO: check whether this can be improved e.g. handling this in GTI

            if "livetime" in other.exposure.meta and np.any(other.mask_safe_image):
                if "livetime" in self.exposure.meta:
                    self.exposure.meta["livetime"] += other.exposure.meta["livetime"]
                else:
                    self.exposure.meta["livetime"] = other.exposure.meta[
                        "livetime"
                    ].copy()

        if self.stat_type == "cash":
            if self.background and other.background:
                background = self.npred_background() * self.mask_safe
                background.stack(
                    other.npred_background(),
                    weights=other.mask_safe,
                    nan_to_num=nan_to_num,
                )
                self.background = background

        if self.psf and other.psf:
            self.psf.stack(other.psf, weights=other.mask_safe_psf)

        if self.edisp and other.edisp:
            self.edisp.stack(other.edisp, weights=other.mask_safe_edisp)

        if self.mask_safe and other.mask_safe:
            self.mask_safe.stack(other.mask_safe)

        if self.gti and other.gti:
            self.gti.stack(other.gti)
            self.gti = self.gti.union()

        if self.meta_table and other.meta_table:
            self.meta_table = hstack_columns(self.meta_table, other.meta_table)
        elif other.meta_table:
            self.meta_table = other.meta_table.copy()

    def stat_array(self):
        """Likelihood per bin given the current model parameters"""
        return cash(n_on=self.counts.data, mu_on=self.npred().data)

    def residuals(self, method="diff", **kwargs):
        """Compute residuals map.

        Parameters
        ----------
        method: {"diff", "diff/model", "diff/sqrt(model)"}
            Method used to compute the residuals. Available options are:
                - "diff" (default): data - model
                - "diff/model": (data - model) / model
                - "diff/sqrt(model)": (data - model) / sqrt(model)
        **kwargs : dict
            Keyword arguments forwarded to `Map.smooth()`

        Returns
        -------
        residuals : `gammapy.maps.Map`
            Residual map.
        """
        npred, counts = self.npred(), self.counts.copy()

        if self.mask:
            npred = npred * self.mask
            counts = counts * self.mask

        if kwargs:
            kwargs.setdefault("mode", "constant")
            kwargs.setdefault("width", "0.1 deg")
            kwargs.setdefault("kernel", "gauss")
            with np.errstate(invalid="ignore", divide="ignore"):
                npred = npred.smooth(**kwargs)
                counts = counts.smooth(**kwargs)
                if self.mask:
                    mask = self.mask.smooth(**kwargs)
                    npred /= mask
                    counts /= mask

        residuals = self._compute_residuals(counts, npred, method=method)

        if self.mask:
            residuals.data[~self.mask.data] = np.nan

        return residuals

    def plot_residuals_spatial(
        self,
        ax=None,
        method="diff",
        smooth_kernel="gauss",
        smooth_radius="0.1 deg",
        **kwargs,
    ):
        """Plot spatial residuals.

        The normalization used for the residuals computation can be controlled
        using the method parameter.

        Parameters
        ----------
        ax : `~astropy.visualization.wcsaxes.WCSAxes`
            Axes to plot on.
        method : {"diff", "diff/model", "diff/sqrt(model)"}
            Normalization used to compute the residuals, see `MapDataset.residuals`.
        smooth_kernel : {"gauss", "box"}
            Kernel shape.
        smooth_radius: `~astropy.units.Quantity`, str or float
            Smoothing width given as quantity or float. If a float is given, it
            is interpreted as smoothing width in pixels.
        **kwargs : dict
            Keyword arguments passed to `~matplotlib.axes.Axes.imshow`.

        Returns
        -------
        ax : `~astropy.visualization.wcsaxes.WCSAxes`
            WCSAxes object.

        Example
        -------
        >>> from gammapy.datasets import MapDataset
        >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
        >>> kwargs = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
        >>> dataset.plot_residuals_spatial(method="diff/sqrt(model)", **kwargs) # doctest: +SKIP
        """
        counts, npred = self.counts.copy(), self.npred()

        if counts.geom.is_region:
            raise ValueError("Cannot plot spatial residuals for RegionNDMap")

        if self.mask is not None:
            counts *= self.mask
            npred *= self.mask

        counts_spatial = counts.sum_over_axes().smooth(
            width=smooth_radius, kernel=smooth_kernel
        )
        npred_spatial = npred.sum_over_axes().smooth(
            width=smooth_radius, kernel=smooth_kernel
        )
        residuals = self._compute_residuals(counts_spatial, npred_spatial, method)

        if self.mask_safe is not None:
            mask = self.mask_safe.reduce_over_axes(func=np.logical_or, keepdims=True)
            residuals.data[~mask.data] = np.nan

        kwargs.setdefault("add_cbar", True)
        kwargs.setdefault("cmap", "coolwarm")
        kwargs.setdefault("vmin", -5)
        kwargs.setdefault("vmax", 5)
        ax = residuals.plot(ax, **kwargs)
        return ax

    def plot_residuals_spectral(self, ax=None, method="diff", region=None, **kwargs):
        """Plot spectral residuals.

        The residuals are extracted from the provided region, and the normalization
        used for its computation can be controlled using the method parameter.

        Parameters
        ----------
        ax : `~matplotlib.axes.Axes`
            Axes to plot on.
        method : {"diff", "diff/sqrt(model)"}
            Normalization used to compute the residuals, see `SpectrumDataset.residuals`.
        region: `~regions.SkyRegion` (required)
            Target sky region.
        **kwargs : dict
            Keyword arguments passed to `~matplotlib.axes.Axes.errorbar`.

        Returns
        -------
        ax : `~matplotlib.axes.Axes`
            Axes object.

        Examples
        --------
        >>> from gammapy.datasets import MapDataset
        >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
        >>> kwargs = {"markerfacecolor": "blue", "markersize":8, "marker":'s', } #plot big blue squares
        >>> dataset.plot_residuals_spectral(method="diff/sqrt(model)", **kwargs) # doctest: +SKIP

        """
        counts, npred = self.counts.copy(), self.npred()

        if self.mask is None:
            mask = self.counts.copy()
            mask.data = 1
        else:
            mask = self.mask
        counts *= mask
        npred *= mask

        counts_spec = counts.get_spectrum(region)
        npred_spec = npred.get_spectrum(region)
        residuals = self._compute_residuals(counts_spec, npred_spec, method)

        if method == "diff":
            if self.stat_type == "wstat":
                counts_off = (self.counts_off * mask).get_spectrum(region)

                with np.errstate(invalid="ignore"):
                    alpha = (self.background * mask).get_spectrum(region) / counts_off

                mu_sig = (self.npred_signal() * mask).get_spectrum(region)
                stat = WStatCountsStatistic(
                    n_on=counts_spec,
                    n_off=counts_off,
                    alpha=alpha,
                    mu_sig=mu_sig,
                )
            elif self.stat_type == "cash":
                stat = CashCountsStatistic(counts_spec.data, npred_spec.data)
            yerr = stat.error
        elif method == "diff/sqrt(model)":
            yerr = np.ones_like(residuals.data)
        else:
            raise ValueError(
                'Invalid method, choose between "diff" and "diff/sqrt(model)"'
            )

        kwargs.setdefault("color", kwargs.pop("c", "black"))
        ax = residuals.plot(ax, yerr=yerr, **kwargs)
        ax.axhline(0, color=kwargs["color"], lw=0.5)

        label = self._residuals_labels[method]
        ax.set_ylabel(f"Residuals ({label})")
        ax.set_yscale("linear")
        ymin = 1.05 * np.nanmin(residuals.data - yerr)
        ymax = 1.05 * np.nanmax(residuals.data + yerr)
        ax.set_ylim(ymin, ymax)
        return ax

    def plot_residuals(
        self,
        ax_spatial=None,
        ax_spectral=None,
        kwargs_spatial=None,
        kwargs_spectral=None,
    ):
        """Plot spatial and spectral residuals in two panels.

        Calls `~MapDataset.plot_residuals_spatial` and `~MapDataset.plot_residuals_spectral`.
        The spectral residuals are extracted from the provided region, and the
        normalization used for its computation can be controlled using the method
        parameter. The region outline is overlaid on the residuals map. If no region is passed,
        the residuals are computed for the entire map

        Parameters
        ----------
        ax_spatial : `~astropy.visualization.wcsaxes.WCSAxes`
            Axes to plot spatial residuals on.
        ax_spectral : `~matplotlib.axes.Axes`
            Axes to plot spectral residuals on.
        kwargs_spatial : dict
            Keyword arguments passed to `~MapDataset.plot_residuals_spatial`.
        kwargs_spectral : dict
            Keyword arguments passed to `~MapDataset.plot_residuals_spectral`.
            The region should be passed as a dictionary key

        Returns
        -------
        ax_spatial, ax_spectral : `~astropy.visualization.wcsaxes.WCSAxes`, `~matplotlib.axes.Axes`
            Spatial and spectral residuals plots.

        Examples
        --------
        >>> from regions import CircleSkyRegion
        >>> from astropy.coordinates import SkyCoord
        >>> import astropy.units as u
        >>> from gammapy.datasets import MapDataset
        >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
        >>> reg = CircleSkyRegion(SkyCoord(0,0, unit="deg", frame="galactic"), radius=1.0*u.deg)
        >>> kwargs_spatial = {"cmap": "RdBu_r", "vmin":-5, "vmax":5, "add_cbar": True}
        >>> kwargs_spectral = {"region":reg, "markerfacecolor": "blue", "markersize":8, "marker":'s'}
        >>> dataset.plot_residuals(kwargs_spatial=kwargs_spatial, kwargs_spectral=kwargs_spectral) # doctest: +SKIP
        """
        ax_spatial, ax_spectral = get_axes(
            ax_spatial,
            ax_spectral,
            12,
            4,
            [1, 2, 1],
            [1, 2, 2],
            {"projection": self._geom.to_image().wcs},
        )
        kwargs_spatial = kwargs_spatial or {}
        kwargs_spectral = kwargs_spectral or {}

        self.plot_residuals_spatial(ax_spatial, **kwargs_spatial)
        self.plot_residuals_spectral(ax_spectral, **kwargs_spectral)

        # Overlay spectral extraction region on the spatial residuals
        region = kwargs_spectral.get("region")
        if region is not None:
            pix_region = region.to_pixel(self._geom.to_image().wcs)
            pix_region.plot(ax=ax_spatial)

        return ax_spatial, ax_spectral

    def stat_sum(self):
        """Total likelihood given the current model parameters."""
        counts, npred = self.counts.data.astype(float), self.npred().data

        if self.mask is not None:
            return cash_sum_cython(counts[self.mask.data], npred[self.mask.data])
        else:
            return cash_sum_cython(counts.ravel(), npred.ravel())

    def fake(self, random_state="random-seed"):
        """Simulate fake counts for the current model and reduced IRFs.

        This method overwrites the counts defined on the dataset object.

        Parameters
        ----------
        random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
                Defines random number generator initialisation.
                Passed to `~gammapy.utils.random.get_random_state`.
        """
        random_state = get_random_state(random_state)
        npred = self.npred()
        data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
        npred.data = random_state.poisson(data)
        self.counts = npred

    def to_hdulist(self):
        """Convert map dataset to list of HDUs.

        Returns
        -------
        hdulist : `~astropy.io.fits.HDUList`
            Map dataset list of HDUs.
        """
        # TODO: what todo about the model and background model parameters?
        exclude_primary = slice(1, None)

        hdu_primary = fits.PrimaryHDU()
        hdulist = fits.HDUList([hdu_primary])
        if self.counts is not None:
            hdulist += self.counts.to_hdulist(hdu="counts")[exclude_primary]

        if self.exposure is not None:
            hdulist += self.exposure.to_hdulist(hdu="exposure")[exclude_primary]

        if self.background is not None:
            hdulist += self.background.to_hdulist(hdu="background")[exclude_primary]

        if self.edisp is not None:
            hdulist += self.edisp.to_hdulist()[exclude_primary]

        if self.psf is not None:
            hdulist += self.psf.to_hdulist()[exclude_primary]

        if self.mask_safe is not None:
            hdulist += self.mask_safe.to_hdulist(hdu="mask_safe")[exclude_primary]

        if self.mask_fit is not None:
            hdulist += self.mask_fit.to_hdulist(hdu="mask_fit")[exclude_primary]

        if self.gti is not None:
            hdulist.append(fits.BinTableHDU(self.gti.table, name="GTI"))

        return hdulist

    @classmethod
    def from_hdulist(cls, hdulist, name=None, lazy=False, format="gadf"):
        """Create map dataset from list of HDUs.

        Parameters
        ----------
        hdulist : `~astropy.io.fits.HDUList`
            List of HDUs.
        name : str
            Name of the new dataset.
        format : {"gadf"}
            Format the hdulist is given in.

        Returns
        -------
        dataset : `MapDataset`
            Map dataset.
        """
        name = make_name(name)
        kwargs = {"name": name}

        if "COUNTS" in hdulist:
            kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts", format=format)

        if "EXPOSURE" in hdulist:
            exposure = Map.from_hdulist(hdulist, hdu="exposure", format=format)
            if exposure.geom.axes[0].name == "energy":
                exposure.geom.axes[0].name = "energy_true"
            kwargs["exposure"] = exposure

        if "BACKGROUND" in hdulist:
            kwargs["background"] = Map.from_hdulist(
                hdulist, hdu="background", format=format
            )

        if "EDISP" in hdulist:
            edisp_map = Map.from_hdulist(hdulist, hdu="edisp", format=format)

            try:
                exposure_map = Map.from_hdulist(
                    hdulist, hdu="edisp_exposure", format=format
                )
            except KeyError:
                exposure_map = None

            if edisp_map.geom.axes[0].name == "energy":
                kwargs["edisp"] = EDispKernelMap(edisp_map, exposure_map)
            else:
                kwargs["edisp"] = EDispMap(edisp_map, exposure_map)

        if "PSF" in hdulist:
            psf_map = Map.from_hdulist(hdulist, hdu="psf", format=format)
            try:
                exposure_map = Map.from_hdulist(
                    hdulist, hdu="psf_exposure", format=format
                )
            except KeyError:
                exposure_map = None
            kwargs["psf"] = PSFMap(psf_map, exposure_map)

        if "MASK_SAFE" in hdulist:
            mask_safe = Map.from_hdulist(hdulist, hdu="mask_safe", format=format)
            mask_safe.data = mask_safe.data.astype(bool)
            kwargs["mask_safe"] = mask_safe

        if "MASK_FIT" in hdulist:
            mask_fit = Map.from_hdulist(hdulist, hdu="mask_fit", format=format)
            mask_fit.data = mask_fit.data.astype(bool)
            kwargs["mask_fit"] = mask_fit

        if "GTI" in hdulist:
            gti = GTI(Table.read(hdulist, hdu="GTI"))
            kwargs["gti"] = gti

        return cls(**kwargs)

    def write(self, filename, overwrite=False):
        """Write map dataset to file.

        Parameters
        ----------
        filename : str
            Filename to write to.
        overwrite : bool
            Overwrite file if it exists.
        """
        self.to_hdulist().writeto(str(make_path(filename)), overwrite=overwrite)

    @classmethod
    def _read_lazy(cls, name, filename, cache, format=format):
        kwargs = {"name": name}
        try:
            kwargs["gti"] = GTI.read(filename)
        except KeyError:
            pass

        path = make_path(filename)
        for hdu_name in ["counts", "exposure", "mask_fit", "mask_safe", "background"]:
            kwargs[hdu_name] = HDULocation(
                hdu_class="map",
                file_dir=path.parent,
                file_name=path.name,
                hdu_name=hdu_name.upper(),
                cache=cache,
                format=format,
            )

        kwargs["edisp"] = HDULocation(
            hdu_class="edisp_kernel_map",
            file_dir=path.parent,
            file_name=path.name,
            hdu_name="EDISP",
            cache=cache,
            format=format,
        )

        kwargs["psf"] = HDULocation(
            hdu_class="psf_map",
            file_dir=path.parent,
            file_name=path.name,
            hdu_name="PSF",
            cache=cache,
            format=format,
        )

        return cls(**kwargs)

    @classmethod
    def read(cls, filename, name=None, lazy=False, cache=True, format="gadf"):
        """Read map dataset from file.

        Parameters
        ----------
        filename : str
            Filename to read from.
        name : str
            Name of the new dataset.
        lazy : bool
            Whether to lazy load data into memory
        cache : bool
            Whether to cache the data after loading.
        format : {"gadf"}
            Format of the dataset file.

        Returns
        -------
        dataset : `MapDataset`
            Map dataset.
        """
        name = make_name(name)

        if lazy:
            return cls._read_lazy(
                name=name, filename=filename, cache=cache, format=format
            )
        else:
            with fits.open(str(make_path(filename)), memmap=False) as hdulist:
                return cls.from_hdulist(hdulist, name=name, format=format)

    @classmethod
    def from_dict(cls, data, lazy=False, cache=True):
        """Create from dicts and models list generated from YAML serialization."""
        filename = make_path(data["filename"])
        dataset = cls.read(filename, name=data["name"], lazy=lazy, cache=cache)
        return dataset

    def info_dict(self, in_safe_data_range=True):
        """Info dict with summary statistics, summed over energy

        Parameters
        ----------
        in_safe_data_range : bool
            Whether to sum only in the safe energy range

        Returns
        -------
        info_dict : dict
            Dictionary with summary info.
        """
        info = {}
        info["name"] = self.name

        if self.mask_safe and in_safe_data_range:
            mask = self.mask_safe.data.astype(bool)
        else:
            mask = slice(None)

        counts = 0
        if self.counts:
            counts = self.counts.data[mask].sum()

        info["counts"] = int(counts)

        background = np.nan
        if self.background:
            background = self.background.data[mask].sum()

        info["background"] = float(background)

        info["excess"] = counts - background
        info["sqrt_ts"] = CashCountsStatistic(counts, background).sqrt_ts

        npred = np.nan
        if self.models or not np.isnan(background):
            npred = self.npred().data[mask].sum()

        info["npred"] = float(npred)

        npred_background = np.nan
        if self.background:
            npred_background = self.npred_background().data[mask].sum()

        info["npred_background"] = float(npred_background)

        npred_signal = np.nan
        if self.models:
            npred_signal = self.npred_signal().data[mask].sum()

        info["npred_signal"] = float(npred_signal)

        exposure_min = np.nan * u.Unit("cm s")
        exposure_max = np.nan * u.Unit("cm s")
        livetime = np.nan * u.s

        if self.exposure is not None:
            mask_exposure = self.exposure.data > 0

            if self.mask_safe is not None:
                mask_spatial = self.mask_safe.reduce_over_axes(func=np.logical_or).data
                mask_exposure = mask_exposure & mask_spatial[np.newaxis, :, :]

            if not mask_exposure.any():
                mask_exposure = slice(None)

            exposure_min = np.min(self.exposure.quantity[mask_exposure])
            exposure_max = np.max(self.exposure.quantity[mask_exposure])
            livetime = self.exposure.meta.get("livetime", np.nan * u.s).copy()

        info["exposure_min"] = exposure_min.item()
        info["exposure_max"] = exposure_max.item()
        info["livetime"] = livetime

        ontime = u.Quantity(np.nan, "s")
        if self.gti:
            ontime = self.gti.time_sum

        info["ontime"] = ontime

        info["counts_rate"] = info["counts"] / info["livetime"]
        info["background_rate"] = info["background"] / info["livetime"]
        info["excess_rate"] = info["excess"] / info["livetime"]

        # data section
        n_bins = 0
        if self.counts is not None:
            n_bins = self.counts.data.size
        info["n_bins"] = int(n_bins)

        n_fit_bins = 0
        if self.mask is not None:
            n_fit_bins = np.sum(self.mask.data)

        info["n_fit_bins"] = int(n_fit_bins)
        info["stat_type"] = self.stat_type

        stat_sum = np.nan
        if self.counts is not None and self.models is not None:
            stat_sum = self.stat_sum()

        info["stat_sum"] = float(stat_sum)

        return info

    def to_spectrum_dataset(self, on_region, containment_correction=False, name=None):
        """Return a ~gammapy.datasets.SpectrumDataset from on_region.

        Counts and background are summed in the on_region. Exposure is taken
        from the average exposure.

        The energy dispersion kernel is obtained at the on_region center.
        Only regions with centers are supported.

        The model is not exported to the ~gammapy.datasets.SpectrumDataset.
        It must be set after the dataset extraction.

        Parameters
        ----------
        on_region : `~regions.SkyRegion`
            the input ON region on which to extract the spectrum
        containment_correction : bool
            Apply containment correction for point sources and circular on regions
        name : str
            Name of the new dataset.

        Returns
        -------
        dataset : `~gammapy.datasets.SpectrumDataset`
            the resulting reduced dataset
        """
        from .spectrum import SpectrumDataset

        dataset = self.to_region_map_dataset(region=on_region, name=name)

        if containment_correction:
            if not isinstance(on_region, CircleSkyRegion):
                raise TypeError(
                    "Containment correction is only supported for" " `CircleSkyRegion`."
                )
            elif self.psf is None or isinstance(self.psf, PSFKernel):
                raise ValueError("No PSFMap set. Containment correction impossible")
            else:
                geom = dataset.exposure.geom
                energy_true = geom.axes["energy_true"].center
                containment = self.psf.containment(
                    position=on_region.center,
                    energy_true=energy_true,
                    rad=on_region.radius,
                )
                dataset.exposure.quantity *= containment.reshape(geom.data_shape)

        kwargs = {"name": name}

        for key in [
            "counts",
            "edisp",
            "mask_safe",
            "mask_fit",
            "exposure",
            "gti",
            "meta_table",
        ]:
            kwargs[key] = getattr(dataset, key)

        if self.stat_type == "cash":
            kwargs["background"] = dataset.background

        return SpectrumDataset(**kwargs)

    def to_region_map_dataset(self, region, name=None):
        """Integrate the map dataset in a given region.

        Counts and background of the dataset are integrated in the given region,
        taking the safe mask into accounts. The exposure is averaged in the
        region again taking the safe mask into account. The PSF and energy
        dispersion kernel are taken at the center of the region.

        Parameters
        ----------
        region : `~regions.SkyRegion`
            Region from which to extract the spectrum
        name : str
            Name of the new dataset.

        Returns
        -------
        dataset : `~gammapy.datasets.MapDataset`
            the resulting reduced dataset
        """
        name = make_name(name)
        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}

        if self.mask_safe:
            kwargs["mask_safe"] = self.mask_safe.to_region_nd_map(region, func=np.any)

        if self.mask_fit:
            kwargs["mask_fit"] = self.mask_fit.to_region_nd_map(region, func=np.any)

        if self.counts:
            kwargs["counts"] = self.counts.to_region_nd_map(
                region, np.sum, weights=self.mask_safe
            )

        if self.stat_type == "cash" and self.background:
            kwargs["background"] = self.background.to_region_nd_map(
                region, func=np.sum, weights=self.mask_safe
            )

        if self.exposure:
            kwargs["exposure"] = self.exposure.to_region_nd_map(region, func=np.mean)

        region = region.center if region else None

        # TODO: Compute average psf in region
        if self.psf:
            kwargs["psf"] = self.psf.to_region_nd_map(region)

        # TODO: Compute average edisp in region
        if self.edisp is not None:
            kwargs["edisp"] = self.edisp.to_region_nd_map(region)

        return self.__class__(**kwargs)

    def cutout(self, position, width, mode="trim", name=None):
        """Cutout map dataset.

        Parameters
        ----------
        position : `~astropy.coordinates.SkyCoord`
            Center position of the cutout region.
        width : tuple of `~astropy.coordinates.Angle`
            Angular sizes of the region in (lon, lat) in that specific order.
            If only one value is passed, a square region is extracted.
        mode : {'trim', 'partial', 'strict'}
            Mode option for Cutout2D, for details see `~astropy.nddata.utils.Cutout2D`.
        name : str
            Name of the new dataset.

        Returns
        -------
        cutout : `MapDataset`
            Cutout map dataset.
        """
        name = make_name(name)
        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}
        cutout_kwargs = {"position": position, "width": width, "mode": mode}

        if self.counts is not None:
            kwargs["counts"] = self.counts.cutout(**cutout_kwargs)

        if self.exposure is not None:
            kwargs["exposure"] = self.exposure.cutout(**cutout_kwargs)

        if self.background is not None and self.stat_type == "cash":
            kwargs["background"] = self.background.cutout(**cutout_kwargs)

        if self.edisp is not None:
            kwargs["edisp"] = self.edisp.cutout(**cutout_kwargs)

        if self.psf is not None:
            kwargs["psf"] = self.psf.cutout(**cutout_kwargs)

        if self.mask_safe is not None:
            kwargs["mask_safe"] = self.mask_safe.cutout(**cutout_kwargs)

        if self.mask_fit is not None:
            kwargs["mask_fit"] = self.mask_fit.cutout(**cutout_kwargs)

        return self.__class__(**kwargs)

    def downsample(self, factor, axis_name=None, name=None):
        """Downsample map dataset.

        The PSFMap and EDispKernelMap are not downsampled, except if
        a corresponding axis is given.

        Parameters
        ----------
        factor : int
            Downsampling factor.
        axis_name : str
            Which non-spatial axis to downsample. By default only spatial axes are downsampled.
        name : str
            Name of the downsampled dataset.

        Returns
        -------
        dataset : `MapDataset` or `SpectrumDataset`
            Downsampled map dataset.
        """
        name = make_name(name)

        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}

        if self.counts is not None:
            kwargs["counts"] = self.counts.downsample(
                factor=factor,
                preserve_counts=True,
                axis_name=axis_name,
                weights=self.mask_safe,
            )

        if self.exposure is not None:
            if axis_name is None:
                kwargs["exposure"] = self.exposure.downsample(
                    factor=factor, preserve_counts=False, axis_name=None
                )
            else:
                kwargs["exposure"] = self.exposure.copy()

        if self.background is not None and self.stat_type == "cash":
            kwargs["background"] = self.background.downsample(
                factor=factor, axis_name=axis_name, weights=self.mask_safe
            )

        if self.edisp is not None:
            if axis_name is not None:
                kwargs["edisp"] = self.edisp.downsample(
                    factor=factor, axis_name=axis_name, weights=self.mask_safe_edisp
                )
            else:
                kwargs["edisp"] = self.edisp.copy()

        if self.psf is not None:
            kwargs["psf"] = self.psf.copy()

        if self.mask_safe is not None:
            kwargs["mask_safe"] = self.mask_safe.downsample(
                factor=factor, preserve_counts=False, axis_name=axis_name
            )

        if self.mask_fit is not None:
            kwargs["mask_fit"] = self.mask_fit.downsample(
                factor=factor, preserve_counts=False, axis_name=axis_name
            )

        return self.__class__(**kwargs)

    def pad(self, pad_width, mode="constant", name=None):
        """Pad the spatial dimensions of the dataset.

        The padding only applies to counts, masks, background and exposure.

        Counts, background and masks are padded with zeros, exposure is padded with edge value.

        Parameters
        ----------
        pad_width : {sequence, array_like, int}
            Number of pixels padded to the edges of each axis.
        name : str
            Name of the padded dataset.

        Returns
        -------
        dataset : `MapDataset`
            Padded map dataset.

        """
        name = make_name(name)
        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}

        if self.counts is not None:
            kwargs["counts"] = self.counts.pad(pad_width=pad_width, mode=mode)

        if self.exposure is not None:
            kwargs["exposure"] = self.exposure.pad(pad_width=pad_width, mode=mode)

        if self.background is not None:
            kwargs["background"] = self.background.pad(pad_width=pad_width, mode=mode)

        if self.edisp is not None:
            kwargs["edisp"] = self.edisp.copy()

        if self.psf is not None:
            kwargs["psf"] = self.psf.copy()

        if self.mask_safe is not None:
            kwargs["mask_safe"] = self.mask_safe.pad(pad_width=pad_width, mode=mode)

        if self.mask_fit is not None:
            kwargs["mask_fit"] = self.mask_fit.pad(pad_width=pad_width, mode=mode)

        return self.__class__(**kwargs)

    def slice_by_idx(self, slices, name=None):
        """Slice sub dataset.

        The slicing only applies to the maps that define the corresponding 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.
        name : str
            Name of the sliced dataset.

        Returns
        -------
        dataset : `MapDataset` or `SpectrumDataset`
            Sliced dataset

        Example
        -------
        >>> from gammapy.datasets import MapDataset
        >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
        >>> slices = {"energy": slice(0, 3)} #to get the first 3 energy slices
        >>> sliced = dataset.slice_by_idx(slices)
        >>> print(sliced.geoms["geom"])
        WcsGeom
	        axes       : ['lon', 'lat', 'energy']
	        shape      : (320, 240, 3)
	        ndim       : 3
	        frame      : galactic
	        projection : CAR
	        center     : 0.0 deg, 0.0 deg
	        width      : 8.0 deg x 6.0 deg
	        wcs ref    : 0.0 deg, 0.0 deg
        """
        name = make_name(name)
        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}

        if self.counts is not None:
            kwargs["counts"] = self.counts.slice_by_idx(slices=slices)

        if self.exposure is not None:
            kwargs["exposure"] = self.exposure.slice_by_idx(slices=slices)

        if self.background is not None and self.stat_type == "cash":
            kwargs["background"] = self.background.slice_by_idx(slices=slices)

        if self.edisp is not None:
            kwargs["edisp"] = self.edisp.slice_by_idx(slices=slices)

        if self.psf is not None:
            kwargs["psf"] = self.psf.slice_by_idx(slices=slices)

        if self.mask_safe is not None:
            kwargs["mask_safe"] = self.mask_safe.slice_by_idx(slices=slices)

        if self.mask_fit is not None:
            kwargs["mask_fit"] = self.mask_fit.slice_by_idx(slices=slices)

        return self.__class__(**kwargs)

    def slice_by_energy(self, energy_min=None, energy_max=None, name=None):
        """Select and slice datasets in energy range

        Parameters
        ----------
        energy_min, energy_max : `~astropy.units.Quantity`
            Energy bounds to compute the flux point for.
        name : str
            Name of the sliced dataset.

        Returns
        -------
        dataset : `MapDataset`
            Sliced Dataset

        Example
        -------
        >>> from gammapy.datasets import MapDataset
        >>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
        >>> sliced = dataset.slice_by_energy(energy_min="1 TeV", energy_max="5 TeV")
        >>> sliced.data_shape
        (3, 240, 320)
        """
        name = make_name(name)

        energy_axis = self._geom.axes["energy"]

        if energy_min is None:
            energy_min = energy_axis.bounds[0]

        if energy_max is None:
            energy_max = energy_axis.bounds[1]

        energy_min, energy_max = u.Quantity(energy_min), u.Quantity(energy_max)

        group = energy_axis.group_table(edges=[energy_min, energy_max])

        is_normal = group["bin_type"] == "normal   "
        group = group[is_normal]

        slices = {
            "energy": slice(int(group["idx_min"][0]), int(group["idx_max"][0]) + 1)
        }

        return self.slice_by_idx(slices, name=name)

    def reset_data_cache(self):
        """Reset data cache to free memory space"""
        for name in self._lazy_data_members:
            if self.__dict__.pop(name, False):
                log.info(f"Clearing {name} cache for dataset {self.name}")

    def resample_energy_axis(self, energy_axis, name=None):
        """Resample MapDataset over new reco energy axis.

        Counts are summed taking into account safe mask.

        Parameters
        ----------
        energy_axis : `~gammapy.maps.MapAxis`
            New reconstructed energy axis.
        name: str
            Name of the new dataset.

        Returns
        -------
        dataset: `MapDataset` or `SpectrumDataset`
            Resampled dataset.
        """
        name = make_name(name)
        kwargs = {"gti": self.gti, "name": name, "meta_table": self.meta_table}

        if self.exposure:
            kwargs["exposure"] = self.exposure

        if self.psf:
            kwargs["psf"] = self.psf

        if self.mask_safe is not None:
            kwargs["mask_safe"] = self.mask_safe.resample_axis(
                axis=energy_axis, ufunc=np.logical_or
            )

        if self.mask_fit is not None:
            kwargs["mask_fit"] = self.mask_fit.resample_axis(
                axis=energy_axis, ufunc=np.logical_or
            )

        if self.counts is not None:
            kwargs["counts"] = self.counts.resample_axis(
                axis=energy_axis, weights=self.mask_safe
            )

        if self.background is not None and self.stat_type == "cash":
            kwargs["background"] = self.background.resample_axis(
                axis=energy_axis, weights=self.mask_safe
            )

        # Mask_safe or mask_irf??
        if isinstance(self.edisp, EDispKernelMap):
            kwargs["edisp"] = self.edisp.resample_energy_axis(
                energy_axis=energy_axis, weights=self.mask_safe_edisp
            )
        else:  # None or EDispMap
            kwargs["edisp"] = self.edisp

        return self.__class__(**kwargs)

    def to_image(self, name=None):
        """Create images by summing over the reconstructed energy axis.

        Parameters
        ----------
        name : str
            Name of the new dataset.

        Returns
        -------
        dataset : `MapDataset` or `SpectrumDataset`
            Dataset integrated over non-spatial axes.
        """
        energy_axis = self._geom.axes["energy"].squash()
        return self.resample_energy_axis(energy_axis=energy_axis, name=name)

    def peek(self, figsize=(12, 10)):
        """Quick-look summary plots.

        Parameters
        ----------
        fig : `~matplotlib.figure.Figure`
            Figure to add AxesSubplot on.

        Returns
        -------
        ax1, ax2, ax3 : `~matplotlib.axes.AxesSubplot`
            Counts, excess and exposure.
        """

        def plot_mask(ax, mask, **kwargs):
            if mask is not None:
                mask.plot_mask(ax=ax, **kwargs)

        import matplotlib.pyplot as plt

        fig, axes = plt.subplots(
            ncols=2,
            nrows=2,
            subplot_kw={"projection": self._geom.wcs},
            figsize=figsize,
            gridspec_kw={"hspace": 0.1, "wspace": 0.1},
        )

        axes = axes.flat
        axes[0].set_title("Counts")
        self.counts.sum_over_axes().plot(ax=axes[0], add_cbar=True)
        plot_mask(ax=axes[0], mask=self.mask_fit_image, alpha=0.2)
        plot_mask(ax=axes[0], mask=self.mask_safe_image, hatches=["///"], colors="w")

        axes[1].set_title("Excess counts")
        self.excess.sum_over_axes().plot(ax=axes[1], add_cbar=True)
        plot_mask(ax=axes[1], mask=self.mask_fit_image, alpha=0.2)
        plot_mask(ax=axes[1], mask=self.mask_safe_image, hatches=["///"], colors="w")

        axes[2].set_title("Exposure")
        self.exposure.sum_over_axes().plot(ax=axes[2], add_cbar=True)
        plot_mask(ax=axes[2], mask=self.mask_safe_image, hatches=["///"], colors="w")

        axes[3].set_title("Background")
        self.background.sum_over_axes().plot(ax=axes[3], add_cbar=True)
        plot_mask(ax=axes[3], mask=self.mask_fit_image, alpha=0.2)
        plot_mask(ax=axes[3], mask=self.mask_safe_image, hatches=["///"], colors="w")

class MapDatasetOnOff(MapDataset):
    """Map dataset for on-off likelihood fitting. Uses wstat statistics.

    Parameters
    ----------
    models : `~gammapy.modeling.models.Models`
        Source sky models.
    counts : `~gammapy.maps.WcsNDMap`
        Counts cube
    counts_off : `~gammapy.maps.WcsNDMap`
        Ring-convolved counts cube
    acceptance : `~gammapy.maps.WcsNDMap`
        Acceptance from the IRFs
    acceptance_off : `~gammapy.maps.WcsNDMap`
        Acceptance off
    exposure : `~gammapy.maps.WcsNDMap`
        Exposure cube
    mask_fit : `~gammapy.maps.WcsNDMap`
        Mask to apply to the likelihood for fitting.
    psf : `~gammapy.irf.PSFKernel`
        PSF kernel
    edisp : `~gammapy.irf.EDispKernel`
        Energy dispersion
    mask_safe : `~gammapy.maps.WcsNDMap`
        Mask defining the safe data range.
    gti : `~gammapy.data.GTI`
        GTI of the observation or union of GTI if it is a stacked observation
    meta_table : `~astropy.table.Table`
        Table listing information on observations used to create the dataset.
        One line per observation for stacked datasets.
    name : str
        Name of the dataset.

    See Also
    --------
    MapDataset, SpectrumDataset, FluxPointsDataset

    """

    stat_type = "wstat"
    tag = "MapDatasetOnOff"

    def __init__(
        self,
        models=None,
        counts=None,
        counts_off=None,
        acceptance=None,
        acceptance_off=None,
        exposure=None,
        mask_fit=None,
        psf=None,
        edisp=None,
        name=None,
        mask_safe=None,
        gti=None,
        meta_table=None,
    ):
        self._name = make_name(name)
        self._evaluators = {}

        self.counts = counts
        self.counts_off = counts_off
        self.exposure = exposure
        self.acceptance = acceptance
        self.acceptance_off = acceptance_off
        self.gti = gti
        self.mask_fit = mask_fit
        self.psf = psf
        self.edisp = edisp
        self.models = models
        self.mask_safe = mask_safe
        self.meta_table = meta_table

    def __str__(self):
        str_ = super().__str__()

        counts_off = np.nan
        if self.counts_off is not None:
            counts_off = np.sum(self.counts_off.data)
        str_ += "\t{:32}: {:.0f} \n".format("Total counts_off", counts_off)

        acceptance = np.nan
        if self.acceptance is not None:
            acceptance = np.sum(self.acceptance.data)
        str_ += "\t{:32}: {:.0f} \n".format("Acceptance", acceptance)

        acceptance_off = np.nan
        if self.acceptance_off is not None:
            acceptance_off = np.sum(self.acceptance_off.data)
        str_ += "\t{:32}: {:.0f} \n".format("Acceptance off", acceptance_off)

        return str_.expandtabs(tabsize=2)

    @property
    def _geom(self):
        """Main analysis geometry"""
        if self.counts is not None:
            return self.counts.geom
        elif self.counts_off is not None:
            return self.counts_off.geom
        elif self.acceptance is not None:
            return self.acceptance.geom
        elif self.acceptance_off is not None:
            return self.acceptance_off.geom
        else:
            raise ValueError(
                "Either 'counts', 'counts_off', 'acceptance' or 'acceptance_of' must be defined."
            )

    @property
    def alpha(self):
        """Exposure ratio between signal and background regions

        See :ref:`wstat`

        Returns
        -------
        alpha : `Map`
            Alpha map
        """
        with np.errstate(invalid="ignore", divide="ignore"):
            alpha = self.acceptance / self.acceptance_off

        alpha.data = np.nan_to_num(alpha.data)
        return alpha

    def npred_background(self):
        """Predicted background counts (mu_bkg) estimated from the marginalized likelihood estimate.

        See :ref:`wstat`

        Returns
        -------
        npred_background : `Map`
            Predicted background counts
        """
        mu_bkg = self.alpha.data * get_wstat_mu_bkg(
            n_on=self.counts.data,
            n_off=self.counts_off.data,
            alpha=self.alpha.data,
            mu_sig=self.npred_signal().data,
        )
        mu_bkg = np.nan_to_num(mu_bkg)
        return Map.from_geom(geom=self._geom, data=mu_bkg)

    def npred_off(self):
        """Predicted counts in the off region; mu_bkg/alpha

        See :ref:`wstat`

        Returns
        -------
        npred_off : `Map`
            Predicted off counts
        """
        return self.npred_background() / self.alpha

    @property
    def background(self):
        """Computed as alpha * n_off

        See :ref:`wstat`

        Returns
        -------
        background : `Map`
            Background map
        """
        return self.alpha * self.counts_off

    def stat_array(self):
        """Likelihood per bin given the current model parameters"""
        mu_sig = self.npred_signal().data
        on_stat_ = wstat(
            n_on=self.counts.data,
            n_off=self.counts_off.data,
            alpha=list(self.alpha.data),
            mu_sig=mu_sig,
        )
        return np.nan_to_num(on_stat_)

    @classmethod
    def from_geoms(
        cls,
        geom,
        geom_exposure,
        geom_psf=None,
        geom_edisp=None,
        reference_time="2000-01-01",
        name=None,
        **kwargs,
    ):
        """Create a MapDatasetOnOff object  switch zero filled maps according to the specified geometries

        Parameters
        ----------
        geom : `gammapy.maps.WcsGeom`
            geometry for the counts, counts_off, acceptance and acceptance_off maps
        geom_exposure : `gammapy.maps.WcsGeom`
            geometry for the exposure map
        geom_psf : `gammapy.maps.WcsGeom`
            geometry for the psf map
        geom_edisp : `gammapy.maps.WcsGeom`
            geometry for the energy dispersion kernel map.
            If geom_edisp has a migra axis, this will create an EDispMap instead.
        reference_time : `~astropy.time.Time`
            the reference time to use in GTI definition
        name : str
            Name of the returned dataset.

        Returns
        -------
        empty_maps : `MapDatasetOnOff`
            A MapDatasetOnOff containing zero filled maps
        """
        #  TODO: it seems the super() pattern does not work here?
        dataset = MapDataset.from_geoms(
            geom=geom,
            geom_exposure=geom_exposure,
            geom_psf=geom_psf,
            geom_edisp=geom_edisp,
            name=name,
            reference_time=reference_time,
            **kwargs,
        )

        off_maps = {}

        for key in ["counts_off", "acceptance", "acceptance_off"]:
            off_maps[key] = Map.from_geom(geom, unit="")

        return cls.from_map_dataset(dataset, name=name, **off_maps)

    @classmethod
    def from_map_dataset(
        cls, dataset, acceptance, acceptance_off, counts_off=None, name=None
    ):
        """Create on off dataset from a map dataset.

        Parameters
        ----------
        dataset : `MapDataset`
            Spectrum dataset defining counts, edisp, aeff, livetime etc.
        acceptance : `Map`
            Relative background efficiency in the on region.
        acceptance_off : `Map`
            Relative background efficiency in the off region.
        counts_off : `Map`
            Off counts map . If the dataset provides a background model,
            and no off counts are defined. The off counts are deferred from
            counts_off / alpha.
        name : str
            Name of the returned dataset.

        Returns
        -------
        dataset : `MapDatasetOnOff`
            Map dataset on off.

        """
        if counts_off is None and dataset.background is not None:
            alpha = acceptance / acceptance_off
            counts_off = dataset.npred_background() / alpha

        if np.isscalar(acceptance):
            acceptance = Map.from_geom(dataset._geom, data=acceptance)

        if np.isscalar(acceptance_off):
            acceptance_off = Map.from_geom(dataset._geom, data=acceptance_off)

        return cls(
            models=dataset.models,
            counts=dataset.counts,
            exposure=dataset.exposure,
            counts_off=counts_off,
            edisp=dataset.edisp,
            psf=dataset.psf,
            mask_safe=dataset.mask_safe,
            mask_fit=dataset.mask_fit,
            acceptance=acceptance,
            acceptance_off=acceptance_off,
            gti=dataset.gti,
            name=name,
            meta_table=dataset.meta_table,
        )

    def to_map_dataset(self, name=None):
        """Convert a MapDatasetOnOff to  MapDataset
        The background model template is taken as alpha * counts_off

        Parameters
        ----------
        name: str
            Name of the new dataset

        Returns
        -------
        dataset: `MapDataset`
            Map dataset with cash statistics
        """
        name = make_name(name)

        return MapDataset(
            counts=self.counts,
            exposure=self.exposure,
            psf=self.psf,
            edisp=self.edisp,
            name=name,
            gti=self.gti,
            mask_fit=self.mask_fit,
            mask_safe=self.mask_safe,
            background=self.counts_off * self.alpha,
            meta_table=self.meta_table,
        )

    @property
    def _is_stackable(self):
        """Check if the Dataset contains enough information to be stacked"""
        incomplete = (
            self.acceptance_off is None
            or self.acceptance is None
            or self.counts_off is None
        )
        unmasked = np.any(self.mask_safe.data)
        if incomplete and unmasked:
            return False
        else:
            return True

    def stack(self, other, nan_to_num=True):
        r"""Stack another dataset in place.

        The ``acceptance`` of the stacked dataset is normalized to 1,
        and the stacked ``acceptance_off`` is scaled so that:

        .. math::
            \alpha_\text{stacked} =
            \frac{1}{a_\text{off}} =
            \frac{\alpha_1\text{OFF}_1 + \alpha_2\text{OFF}_2}{\text{OFF}_1 + OFF_2}

        Parameters
        ----------
        other : `MapDatasetOnOff`
            Other dataset
        nan_to_num: bool
            Non-finite values are replaced by zero if True (default).
        """
        if not isinstance(other, MapDatasetOnOff):
            raise TypeError("Incompatible types for MapDatasetOnOff stacking")

        if not self._is_stackable or not other._is_stackable:
            raise ValueError("Cannot stack incomplete MapDatsetOnOff.")

        geom = self.counts.geom
        total_off = Map.from_geom(geom)
        total_alpha = Map.from_geom(geom)

        if self.counts_off:
            total_off.stack(
                self.counts_off, weights=self.mask_safe, nan_to_num=nan_to_num
            )
            total_alpha.stack(
                self.alpha * self.counts_off,
                weights=self.mask_safe,
                nan_to_num=nan_to_num,
            )
        if other.counts_off:
            total_off.stack(
                other.counts_off, weights=other.mask_safe, nan_to_num=nan_to_num
            )
            total_alpha.stack(
                other.alpha * other.counts_off,
                weights=other.mask_safe,
                nan_to_num=nan_to_num,
            )

        with np.errstate(divide="ignore", invalid="ignore"):
            acceptance_off = total_off / total_alpha
            average_alpha = total_alpha.data.sum() / total_off.data.sum()

        # For the bins where the stacked OFF counts equal 0, the alpha value is performed by weighting on the total
        # OFF counts of each run
        is_zero = total_off.data == 0
        acceptance_off.data[is_zero] = 1 / average_alpha

        self.acceptance.data[...] = 1
        self.acceptance_off = acceptance_off

        self.counts_off = total_off

        super().stack(other, nan_to_num=nan_to_num)

    def stat_sum(self):
        """Total likelihood given the current model parameters."""
        return Dataset.stat_sum(self)

    def fake(self, npred_background, random_state="random-seed"):
        """Simulate fake counts (on and off) for the current model and reduced IRFs.

        This method overwrites the counts defined on the dataset object.

        Parameters
        ----------
        random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
                Defines random number generator initialisation.
                Passed to `~gammapy.utils.random.get_random_state`.
        """
        random_state = get_random_state(random_state)
        npred = self.npred_signal()
        data = np.nan_to_num(npred.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0)
        npred.data = random_state.poisson(data)

        npred_bkg = random_state.poisson(npred_background.data)

        self.counts = npred + npred_bkg

        npred_off = npred_background / self.alpha
        data_off = np.nan_to_num(
            npred_off.data, copy=True, nan=0.0, posinf=0.0, neginf=0.0
        )
        npred_off.data = random_state.poisson(data_off)
        self.counts_off = npred_off

    def to_hdulist(self):
        """Convert map dataset to list of HDUs.

        Returns
        -------
        hdulist : `~astropy.io.fits.HDUList`
            Map dataset list of HDUs.
        """
        hdulist = super().to_hdulist()
        exclude_primary = slice(1, None)

        del hdulist["BACKGROUND"]
        del hdulist["BACKGROUND_BANDS"]

        if self.counts_off is not None:
            hdulist += self.counts_off.to_hdulist(hdu="counts_off")[exclude_primary]

        if self.acceptance is not None:
            hdulist += self.acceptance.to_hdulist(hdu="acceptance")[exclude_primary]

        if self.acceptance_off is not None:
            hdulist += self.acceptance_off.to_hdulist(hdu="acceptance_off")[
                exclude_primary
            ]

        return hdulist

    @classmethod
    def from_hdulist(cls, hdulist, name=None, format="gadf"):
        """Create map dataset from list of HDUs.

        Parameters
        ----------
        hdulist : `~astropy.io.fits.HDUList`
            List of HDUs.
        name : str
            Name of the new dataset.
        format : {"gadf"}
            Format the hdulist is given in.

        Returns
        -------
        dataset : `MapDatasetOnOff`
            Map dataset.
        """
        kwargs = {}
        kwargs["name"] = name

        if "COUNTS" in hdulist:
            kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts", format=format)

        if "COUNTS_OFF" in hdulist:
            kwargs["counts_off"] = Map.from_hdulist(
                hdulist, hdu="counts_off", format=format
            )

        if "ACCEPTANCE" in hdulist:
            kwargs["acceptance"] = Map.from_hdulist(
                hdulist, hdu="acceptance", format=format
            )

        if "ACCEPTANCE_OFF" in hdulist:
            kwargs["acceptance_off"] = Map.from_hdulist(
                hdulist, hdu="acceptance_off", format=format
            )

        if "EXPOSURE" in hdulist:
            kwargs["exposure"] = Map.from_hdulist(
                hdulist, hdu="exposure", format=format
            )

        if "EDISP" in hdulist:
            edisp_map = Map.from_hdulist(hdulist, hdu="edisp", format=format)

            try:
                exposure_map = Map.from_hdulist(
                    hdulist, hdu="edisp_exposure", format=format
                )
            except KeyError:
                exposure_map = None

            if edisp_map.geom.axes[0].name == "energy":
                kwargs["edisp"] = EDispKernelMap(edisp_map, exposure_map)
            else:
                kwargs["edisp"] = EDispMap(edisp_map, exposure_map)

        if "PSF" in hdulist:
            psf_map = Map.from_hdulist(hdulist, hdu="psf", format=format)
            try:
                exposure_map = Map.from_hdulist(
                    hdulist, hdu="psf_exposure", format=format
                )
            except KeyError:
                exposure_map = None
            kwargs["psf"] = PSFMap(psf_map, exposure_map)

        if "MASK_SAFE" in hdulist:
            mask_safe = Map.from_hdulist(hdulist, hdu="mask_safe", format=format)
            kwargs["mask_safe"] = mask_safe

        if "MASK_FIT" in hdulist:
            mask_fit = Map.from_hdulist(hdulist, hdu="mask_fit", format=format)
            kwargs["mask_fit"] = mask_fit

        if "GTI" in hdulist:
            gti = GTI(Table.read(hdulist, hdu="GTI"))
            kwargs["gti"] = gti
        return cls(**kwargs)

    def info_dict(self, in_safe_data_range=True):
        """Basic info dict with summary statistics

        If a region is passed, then a spectrum dataset is
        extracted, and the corresponding info returned.

        Parameters
        ----------
        in_safe_data_range : bool
            Whether to sum only in the safe energy range

        Returns
        -------
        info_dict : dict
            Dictionary with summary info.
        """
        # TODO: remove code duplication with SpectrumDatasetOnOff
        info = super().info_dict(in_safe_data_range)

        if self.mask_safe and in_safe_data_range:
            mask = self.mask_safe.data.astype(bool)
        else:
            mask = slice(None)

        counts_off = 0
        if self.counts_off is not None:
            counts_off = self.counts_off.data[mask].sum()

        info["counts_off"] = int(counts_off)

        acceptance = 1
        if self.acceptance:
            # TODO: handle energy dependent a_on / a_off
            acceptance = self.acceptance.data[mask].sum()

        info["acceptance"] = float(acceptance)

        acceptance_off = np.nan
        if self.acceptance_off:
            acceptance_off = acceptance * counts_off / info["background"]

        info["acceptance_off"] = float(acceptance_off)

        alpha = np.nan
        if self.acceptance_off and self.acceptance:
            alpha = np.mean(self.alpha.data[mask])

        info["alpha"] = float(alpha)

        info["sqrt_ts"] = WStatCountsStatistic(
            info["counts"],
            info["counts_off"],
            acceptance / acceptance_off,
        ).sqrt_ts
        info["stat_sum"] = self.stat_sum()
        return info

    def to_spectrum_dataset(self, on_region, containment_correction=False, name=None):
        """Return a ~gammapy.datasets.SpectrumDatasetOnOff from on_region.

        Counts and OFF counts are summed in the on_region.

        Acceptance is the average of all acceptances while acceptance OFF
        is taken such that number of excess is preserved in the on_region.

        Effective area is taken from the average exposure divided by the livetime.
        Here we assume it is the sum of the GTIs.

        The energy dispersion kernel is obtained at the on_region center.
        Only regions with centers are supported.

        The model is not exported to the ~gammapy.dataset.SpectrumDataset.
        It must be set after the dataset extraction.

        Parameters
        ----------
        on_region : `~regions.SkyRegion`
            the input ON region on which to extract the spectrum
        containment_correction : bool
            Apply containment correction for point sources and circular on regions
        name : str
            Name of the new dataset.

        Returns
        -------
        dataset : `~gammapy.datasets.SpectrumDatasetOnOff`
            the resulting reduced dataset
        """
        from .spectrum import SpectrumDatasetOnOff

        dataset = super().to_spectrum_dataset(
            on_region=on_region,
            containment_correction=containment_correction,
            name=name,
        )

        kwargs = {"name": name}

        if self.counts_off is not None:
            kwargs["counts_off"] = self.counts_off.get_spectrum(
                on_region, np.sum, weights=self.mask_safe
            )

        if self.acceptance is not None:
            kwargs["acceptance"] = self.acceptance.get_spectrum(
                on_region, np.mean, weights=self.mask_safe
            )
            norm = self.background.get_spectrum(
                on_region, np.sum, weights=self.mask_safe
            )
            acceptance_off = kwargs["acceptance"] * kwargs["counts_off"] / norm
            np.nan_to_num(acceptance_off.data, copy=False)
            kwargs["acceptance_off"] = acceptance_off

        return SpectrumDatasetOnOff.from_spectrum_dataset(dataset=dataset, **kwargs)

    def cutout(self, position, width, mode="trim", name=None):
        """Cutout map dataset.

        Parameters
        ----------
        position : `~astropy.coordinates.SkyCoord`
            Center position of the cutout region.
        width : tuple of `~astropy.coordinates.Angle`
            Angular sizes of the region in (lon, lat) in that specific order.
            If only one value is passed, a square region is extracted.
        mode : {'trim', 'partial', 'strict'}
            Mode option for Cutout2D, for details see `~astropy.nddata.utils.Cutout2D`.
        name : str
            Name of the new dataset.

        Returns
        -------
        cutout : `MapDatasetOnOff`
            Cutout map dataset.
        """
        cutout_kwargs = {
            "position": position,
            "width": width,
            "mode": mode,
            "name": name,
        }

        cutout_dataset = super().cutout(**cutout_kwargs)

        del cutout_kwargs["name"]

        if self.counts_off is not None:
            cutout_dataset.counts_off = self.counts_off.cutout(**cutout_kwargs)

        if self.acceptance is not None:
            cutout_dataset.acceptance = self.acceptance.cutout(**cutout_kwargs)

        if self.acceptance_off is not None:
            cutout_dataset.acceptance_off = self.acceptance_off.cutout(**cutout_kwargs)

        return cutout_dataset

    def downsample(self, factor, axis_name=None, name=None):
        """Downsample map dataset.

        The PSFMap and EDispKernelMap are not downsampled, except if
        a corresponding axis is given.

        Parameters
        ----------
        factor : int
            Downsampling factor.
        axis_name : str
            Which non-spatial axis to downsample. By default only spatial axes are downsampled.
        name : str
            Name of the downsampled dataset.

        Returns
        -------
        dataset : `MapDatasetOnOff`
            Downsampled map dataset.
        """

        dataset = super().downsample(factor, axis_name, name)

        counts_off = None
        if self.counts_off is not None:
            counts_off = self.counts_off.downsample(
                factor=factor,
                preserve_counts=True,
                axis_name=axis_name,
                weights=self.mask_safe,
            )

        acceptance, acceptance_off = None, None
        if self.acceptance_off is not None:
            acceptance = self.acceptance.downsample(
                factor=factor, preserve_counts=False, axis_name=axis_name
            )
            factor = self.background.downsample(
                factor=factor,
                preserve_counts=True,
                axis_name=axis_name,
                weights=self.mask_safe,
            )
            acceptance_off = acceptance * counts_off / factor

        return self.__class__.from_map_dataset(
            dataset,
            acceptance=acceptance,
            acceptance_off=acceptance_off,
            counts_off=counts_off,
        )

    def pad(self):
        raise NotImplementedError

    def slice_by_idx(self, slices, name=None):
        """Slice sub dataset.

        The slicing only applies to the maps that define the corresponding 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.
        name : str
            Name of the sliced dataset.

        Returns
        -------
        map_out : `Map`
            Sliced map object.
        """
        kwargs = {"name": name}
        dataset = super().slice_by_idx(slices, name)

        if self.counts_off is not None:
            kwargs["counts_off"] = self.counts_off.slice_by_idx(slices=slices)

        if self.acceptance is not None:
            kwargs["acceptance"] = self.acceptance.slice_by_idx(slices=slices)

        if self.acceptance_off is not None:
            kwargs["acceptance_off"] = self.acceptance_off.slice_by_idx(slices=slices)

        return self.from_map_dataset(dataset, **kwargs)

    def resample_energy_axis(self, energy_axis, name=None):
        """Resample MapDatasetOnOff over reconstructed energy edges.

        Counts are summed taking into account safe mask.

        Parameters
        ----------
        energy_axis : `~gammapy.maps.MapAxis`
            New reco energy axis.
        name: str
            Name of the new dataset.

        Returns
        -------
        dataset: `SpectrumDataset`
            Resampled spectrum dataset .
        """
        dataset = super().resample_energy_axis(energy_axis, name)

        counts_off = None
        if self.counts_off is not None:
            counts_off = self.counts_off
            counts_off = counts_off.resample_axis(
                axis=energy_axis, weights=self.mask_safe
            )

        acceptance = 1
        acceptance_off = None
        if self.acceptance is not None:
            acceptance = self.acceptance
            acceptance = acceptance.resample_axis(
                axis=energy_axis, weights=self.mask_safe
            )

            norm_factor = self.background.resample_axis(
                axis=energy_axis, weights=self.mask_safe
            )

            acceptance_off = acceptance * counts_off / norm_factor

        return self.__class__.from_map_dataset(
            dataset,
            acceptance=acceptance,
            acceptance_off=acceptance_off,
            counts_off=counts_off,
            name=name,
        )

import numpy,import astropy,from astropy