Source code for gammapy.modeling.models.core

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import collections.abc
import copy
import logging
from os.path import split
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.table import Table
import matplotlib.pyplot as plt
import yaml
from gammapy.maps import Map, RegionGeom
from gammapy.modeling import Covariance, Parameter, Parameters
from gammapy.modeling.covariance import copy_covariance
from gammapy.utils.scripts import make_name, make_path

__all__ = ["Model", "Models", "DatasetModels", "ModelBase"]


log = logging.getLogger(__name__)


def _set_link(shared_register, model):
    for param in model.parameters:
        name = param.name
        link_label = param._link_label_io
        if link_label is not None:
            if link_label in shared_register:
                new_param = shared_register[link_label]
                setattr(model, name, new_param)
            else:
                shared_register[link_label] = param
    return shared_register


def _get_model_class_from_dict(data):
    """get a model class from a dict"""
    from . import (
        MODEL_REGISTRY,
        SPATIAL_MODEL_REGISTRY,
        SPECTRAL_MODEL_REGISTRY,
        TEMPORAL_MODEL_REGISTRY,
    )

    if "type" in data:
        cls = MODEL_REGISTRY.get_cls(data["type"])
    elif "spatial" in data:
        cls = SPATIAL_MODEL_REGISTRY.get_cls(data["spatial"]["type"])
    elif "spectral" in data:
        cls = SPECTRAL_MODEL_REGISTRY.get_cls(data["spectral"]["type"])
    elif "temporal" in data:
        cls = TEMPORAL_MODEL_REGISTRY.get_cls(data["temporal"]["type"])
    return cls


def _build_parameters_from_dict(data, default_parameters):
    """Build Parameters object from input dict and default  parameter values."""
    par_data = []

    input_names = [_["name"] for _ in data]

    for par in default_parameters:
        par_dict = par.to_dict()
        try:
            index = input_names.index(par_dict["name"])
            par_dict.update(data[index])
        except ValueError:
            log.warning(
                f"Parameter '{par_dict['name']}' not defined in YAML file."
                f" Using default value: {par_dict['value']} {par_dict['unit']}"
            )
        par_data.append(par_dict)

    return Parameters.from_dict(par_data)


class ModelBase:
    """Model base class."""

    _type = None

    def __init__(self, **kwargs):
        # Copy default parameters from the class to the instance
        default_parameters = self.default_parameters.copy()

        for par in default_parameters:
            value = kwargs.get(par.name, par)

            if not isinstance(value, Parameter):
                par.quantity = u.Quantity(value)
            else:
                par = value

            setattr(self, par.name, par)

        self._covariance = Covariance(self.parameters)

    def __getattribute__(self, name):
        value = object.__getattribute__(self, name)

        if isinstance(value, Parameter):
            return value.__get__(self, None)

        return value

    @property
    def type(self):
        return self._type

    def __init_subclass__(cls, **kwargs):
        # Add parameters list on the model sub-class (not instances)
        cls.default_parameters = Parameters(
            [_ for _ in cls.__dict__.values() if isinstance(_, Parameter)]
        )

    @classmethod
    def from_parameters(cls, parameters, **kwargs):
        """Create model from parameter list

        Parameters
        ----------
        parameters : `Parameters`
            Parameters for init

        Returns
        -------
        model : `Model`
            Model instance
        """
        for par in parameters:
            kwargs[par.name] = par
        return cls(**kwargs)

    def _check_covariance(self):
        if not self.parameters == self._covariance.parameters:
            self._covariance = Covariance(self.parameters)

    @property
    def covariance(self):
        self._check_covariance()
        for par in self.parameters:
            pars = Parameters([par])
            error = np.nan_to_num(par.error**2, nan=1)
            covar = Covariance(pars, data=[[error]])
            self._covariance.set_subcovariance(covar)

        return self._covariance

    @covariance.setter
    def covariance(self, covariance):
        self._check_covariance()
        self._covariance.data = covariance

        for par in self.parameters:
            pars = Parameters([par])
            variance = self._covariance.get_subcovariance(pars)
            par.error = np.sqrt(variance)

    @property
    def parameters(self):
        """Parameters (`~gammapy.modeling.Parameters`)"""
        return Parameters(
            [getattr(self, name) for name in self.default_parameters.names]
        )

    @copy_covariance
    def copy(self, **kwargs):
        """A deep copy."""
        return copy.deepcopy(self)

    def to_dict(self, full_output=False):
        """Create dict for YAML serialisation"""
        tag = self.tag[0] if isinstance(self.tag, list) else self.tag
        params = self.parameters.to_dict()

        if not full_output:
            for par, par_default in zip(params, self.default_parameters):
                init = par_default.to_dict()
                for item in [
                    "min",
                    "max",
                    "error",
                    "interp",
                    "scale_method",
                    "is_norm",
                ]:
                    default = init[item]

                    if par[item] == default or (
                        np.isnan(par[item]) and np.isnan(default)
                    ):
                        del par[item]

                if par["frozen"] == init["frozen"]:
                    del par["frozen"]

                if init["unit"] == "":
                    del par["unit"]

        data = {"type": tag, "parameters": params}

        if self.type is None:
            return data
        else:
            return {self.type: data}

    @classmethod
    def from_dict(cls, data):
        kwargs = {}

        key0 = next(iter(data))

        if key0 in ["spatial", "temporal", "spectral"]:
            data = data[key0]

        if data["type"] not in cls.tag:
            raise ValueError(
                f"Invalid model type {data['type']} for class {cls.__name__}"
            )

        parameters = _build_parameters_from_dict(
            data["parameters"], cls.default_parameters
        )

        # TODO: this is a special case for spatial models, maybe better move to
        #  `SpatialModel` base class
        if "frame" in data:
            kwargs["frame"] = data["frame"]

        return cls.from_parameters(parameters, **kwargs)

    def __str__(self):
        string = f"{self.__class__.__name__}\n"
        if len(self.parameters) > 0:
            string += f"\n{self.parameters.to_table()}"
        return string

    @property
    def frozen(self):
        """Frozen status of a model, True if all parameters are frozen"""
        return np.all([p.frozen for p in self.parameters])

    def freeze(self):
        """Freeze all parameters"""
        self.parameters.freeze_all()

    def unfreeze(self):
        """Restore parameters frozen status to default"""
        for p, default in zip(self.parameters, self.default_parameters):
            p.frozen = default.frozen

    def reassign(self, datasets_names, new_datasets_names):
        """Reassign a model from one dataset to another

        Parameters
        ----------
        datasets_names : str or list
            Name of the datasets where the model is currently defined
        new_datasets_names : str or list
            Name of the datasets where the model should be defined instead.
            If multiple names are given the two list must have the save length,
            as the reassignment is element-wise.

        Returns
        -------
        model : `Model`
            Reassigned model.

        """
        model = self.copy(name=self.name)

        if not isinstance(datasets_names, list):
            datasets_names = [datasets_names]

        if not isinstance(new_datasets_names, list):
            new_datasets_names = [new_datasets_names]

        if isinstance(model.datasets_names, str):
            model.datasets_names = [model.datasets_names]

        if getattr(model, "datasets_names", None):
            for name, name_new in zip(datasets_names, new_datasets_names):
                model.datasets_names = [
                    _.replace(name, name_new) for _ in model.datasets_names
                ]

        return model


class Model:
    """Model class that contains only methods to create a model listed in the registries."""

    @staticmethod
    def create(tag, model_type=None, *args, **kwargs):
        """Create a model instance.

        Examples
        --------
        >>> from gammapy.modeling.models import Model
        >>> spectral_model = Model.create(
                    "pl-2", model_type="spectral", amplitude="1e-10 cm-2 s-1", index=3
                )
        >>> type(spectral_model)
        <class 'gammapy.modeling.models.spectral.PowerLaw2SpectralModel'>
        """

        data = {"type": tag}
        if model_type is not None:
            data = {model_type: data}

        cls = _get_model_class_from_dict(data)
        return cls(*args, **kwargs)

    @staticmethod
    def from_dict(data):
        """Create a model instance from a dict"""

        cls = _get_model_class_from_dict(data)
        return cls.from_dict(data)


class DatasetModels(collections.abc.Sequence):
    """Immutable models container

    Parameters
    ----------
    models : `SkyModel`, list of `SkyModel` or `Models`
        Sky models
    """

    def __init__(self, models=None):
        if models is None:
            models = []

        if isinstance(models, (Models, DatasetModels)):
            models = models._models
        elif isinstance(models, ModelBase):
            models = [models]
        elif not isinstance(models, list):
            raise TypeError(f"Invalid type: {models!r}")

        unique_names = []
        for model in models:
            if model.name in unique_names:
                raise (ValueError("Model names must be unique"))
            unique_names.append(model.name)

        self._models = models
        self._covar_file = None
        self._covariance = Covariance(self.parameters)

    def _check_covariance(self):
        if not self.parameters == self._covariance.parameters:
            self._covariance = Covariance.from_stack(
                [model.covariance for model in self._models]
            )

    @property
    def covariance(self):
        self._check_covariance()

        for model in self._models:
            self._covariance.set_subcovariance(model.covariance)

        return self._covariance

    @covariance.setter
    def covariance(self, covariance):
        self._check_covariance()
        self._covariance.data = covariance

        for model in self._models:
            subcovar = self._covariance.get_subcovariance(model.covariance.parameters)
            model.covariance = subcovar

    @property
    def parameters(self):
        return Parameters.from_stack([_.parameters for _ in self._models])

    @property
    def parameters_unique_names(self):
        """List of unique parameter names as model_name.par_type.par_name"""
        names = []
        for model in self:
            for par in model.parameters:
                components = [model.name, par.type, par.name]
                name = ".".join(components)
                names.append(name)

        return names

    @property
    def names(self):
        return [m.name for m in self._models]

    @classmethod
    def read(cls, filename):
        """Read from YAML file."""
        yaml_str = make_path(filename).read_text()
        path, filename = split(filename)
        return cls.from_yaml(yaml_str, path=path)

    @classmethod
    def from_yaml(cls, yaml_str, path=""):
        """Create from YAML string."""
        data = yaml.safe_load(yaml_str)
        return cls.from_dict(data, path=path)

    @classmethod
    def from_dict(cls, data, path=""):
        """Create from dict."""
        from . import MODEL_REGISTRY, SkyModel

        models = []

        for component in data["components"]:
            model_cls = MODEL_REGISTRY.get_cls(component["type"])
            model = model_cls.from_dict(component)
            models.append(model)

        models = cls(models)

        if "covariance" in data:
            filename = data["covariance"]
            path = make_path(path)
            if not (path / filename).exists():
                path, filename = split(filename)

            models.read_covariance(path, filename, format="ascii.fixed_width")

        shared_register = {}
        for model in models:
            if isinstance(model, SkyModel):
                submodels = [
                    model.spectral_model,
                    model.spatial_model,
                    model.temporal_model,
                ]
                for submodel in submodels:
                    if submodel is not None:
                        shared_register = _set_link(shared_register, submodel)
            else:
                shared_register = _set_link(shared_register, model)
        return models

    def write(
        self,
        path,
        overwrite=False,
        full_output=False,
        overwrite_templates=False,
        write_covariance=True,
    ):
        """Write to YAML file.

        Parameters
        ----------
        path : `pathlib.Path` or str
            path to write files
        overwrite : bool
            overwrite YAML files
        full_output : bool
            Store full parameter output.
        overwrite_templates : bool
            overwrite templates FITS files
        write_covariance : bool
            save covariance or not
        """
        base_path, _ = split(path)
        path = make_path(path)
        base_path = make_path(base_path)

        if path.exists() and not overwrite:
            raise IOError(f"File exists already: {path}")

        if (
            write_covariance
            and self.covariance is not None
            and len(self.parameters) != 0
        ):
            filecovar = path.stem + "_covariance.dat"
            kwargs = dict(
                format="ascii.fixed_width", delimiter="|", overwrite=overwrite
            )
            self.write_covariance(base_path / filecovar, **kwargs)
            self._covar_file = filecovar

        path.write_text(self.to_yaml(full_output, overwrite_templates))

    def to_yaml(self, full_output=False, overwrite_templates=False):
        """Convert to YAML string."""
        data = self.to_dict(full_output, overwrite_templates)
        return yaml.dump(
            data, sort_keys=False, indent=4, width=80, default_flow_style=False
        )

    def update_link_label(self):
        """update linked parameters labels used for serialization and print"""
        params_list = []
        params_shared = []
        for param in self.parameters:
            if param not in params_list:
                params_list.append(param)
                params_list.append(param)
            elif param not in params_shared:
                params_shared.append(param)
        for param in params_shared:
            param._link_label_io = param.name + "@" + make_name()

    def to_dict(self, full_output=False, overwrite_templates=False):
        """Convert to dict."""

        self.update_link_label()

        models_data = []
        for model in self._models:
            model_data = model.to_dict(full_output)
            models_data.append(model_data)
            if (
                hasattr(model, "spatial_model")
                and model.spatial_model is not None
                and "template" in model.spatial_model.tag
            ):
                model.spatial_model.write(overwrite=overwrite_templates)

        if self._covar_file is not None:
            return {
                "components": models_data,
                "covariance": str(self._covar_file),
            }
        else:
            return {"components": models_data}

    def to_parameters_table(self):
        """Convert Models parameters to an astropy Table."""
        table = self.parameters.to_table()
        # Warning: splitting of parameters will break is source name has a "." in its name.
        model_name = [name.split(".")[0] for name in self.parameters_unique_names]
        table.add_column(model_name, name="model", index=0)
        return table

    def update_parameters_from_table(self, t):
        """Update Models from an astropy Table."""
        parameters_dict = [dict(zip(t.colnames, row)) for row in t]
        for k, data in enumerate(parameters_dict):
            self.parameters[k].update_from_dict(data)

    def read_covariance(self, path, filename="_covariance.dat", **kwargs):
        """Read covariance data from file

        Parameters
        ----------
        path : str or `Path`
            Base path
        filename : str
            Filename
        **kwargs : dict
            Keyword arguments passed to `~astropy.table.Table.read`

        """
        path = make_path(path)
        filepath = str(path / filename)
        t = Table.read(filepath, **kwargs)
        t.remove_column("Parameters")
        arr = np.array(t)
        data = arr.view(float).reshape(arr.shape + (-1,))
        self.covariance = data
        self._covar_file = filename

    def write_covariance(self, filename, **kwargs):
        """Write covariance to file

        Parameters
        ----------
        filename : str
            Filename
        **kwargs : dict
            Keyword arguments passed to `~astropy.table.Table.write`

        """
        names = self.parameters_unique_names
        table = Table()
        table["Parameters"] = names

        for idx, name in enumerate(names):
            values = self.covariance.data[idx]
            table[str(idx)] = values

        table.write(make_path(filename), **kwargs)

    def __str__(self):

        self.update_link_label()

        str_ = f"{self.__class__.__name__}\n\n"

        for idx, model in enumerate(self):
            str_ += f"Component {idx}: "
            str_ += str(model)

        return str_.expandtabs(tabsize=2)

    def __add__(self, other):
        if isinstance(other, (Models, list)):
            return Models([*self, *other])
        elif isinstance(other, ModelBase):
            if other.name in self.names:
                raise (ValueError("Model names must be unique"))
            return Models([*self, other])
        else:
            raise TypeError(f"Invalid type: {other!r}")

    def __getitem__(self, key):
        if isinstance(key, np.ndarray) and key.dtype == bool:
            return self.__class__(list(np.array(self._models)[key]))
        else:
            return self._models[self.index(key)]

    def index(self, key):
        if isinstance(key, (int, slice)):
            return key
        elif isinstance(key, str):
            return self.names.index(key)
        elif isinstance(key, ModelBase):
            return self._models.index(key)
        else:
            raise TypeError(f"Invalid type: {type(key)!r}")

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

    def _ipython_key_completions_(self):
        return self.names

    @copy_covariance
    def copy(self, copy_data=False):
        """A deep copy.

        Parameters
        ----------
        copy_data : bool
            Whether to copy data attached to template models

        Returns
        -------
        models: `Models`
            Copied models.
        """
        models = []

        for model in self:
            model_copy = model.copy(name=model.name, copy_data=copy_data)
            models.append(model_copy)

        return self.__class__(models=models)

    def select(
        self,
        name_substring=None,
        datasets_names=None,
        tag=None,
        model_type=None,
        frozen=None,
    ):
        """Select models that meet all specified conditions

        Parameters
        ----------

        name_substring : str
            Substring contained in the model name
        datasets_names : str or list
            Name of the dataset
        tag : str or list
            Model tag
        model_type : {None, spatial, spectral}
           Type of model, used together with "tag", if the tag is not unique.
        frozen : bool
            Select models with all parameters frozen if True, exclude them if False.

        Returns
        -------
        models : `DatasetModels`
            Selected models
        """
        mask = self.selection_mask(
            name_substring, datasets_names, tag, model_type, frozen
        )
        return self[mask]

    def selection_mask(
        self,
        name_substring=None,
        datasets_names=None,
        tag=None,
        model_type=None,
        frozen=None,
    ):
        """Create a mask of models, that meet all specified conditions

        Parameters
        ----------
        name_substring : str
            Substring contained in the model name
        datasets_names : str or list of str
            Name of the dataset
        tag : str or list of str
            Model tag
        model_type : {None, spatial, spectral}
           Type of model, used together with "tag", if the tag is not unique.
        frozen : bool
            Select models with all parameters frozen if True, exclude them if False.

        Returns
        -------
        mask : `numpy.array`
            Boolean mask, True for selected models
        """
        selection = np.ones(len(self), dtype=bool)

        if tag and not isinstance(tag, list):
            tag = [tag]

        if datasets_names and not isinstance(datasets_names, list):
            datasets_names = [datasets_names]

        for idx, model in enumerate(self):
            if name_substring:
                selection[idx] &= name_substring in model.name

            if datasets_names:
                selection[idx] &= model.datasets_names is None or np.any(
                    [name in model.datasets_names for name in datasets_names]
                )

            if tag:
                if model_type is None:
                    sub_model = model
                else:
                    sub_model = getattr(model, f"{model_type}_model", None)

                if sub_model:
                    selection[idx] &= np.any([t in sub_model.tag for t in tag])
                else:
                    selection[idx] &= False

            if frozen is not None:
                if frozen:
                    selection[idx] &= model.frozen
                else:
                    selection[idx] &= ~model.frozen

        return np.array(selection, dtype=bool)

    def select_mask(self, mask, margin="0 deg", use_evaluation_region=True):
        """Check if sky models contribute within a mask map.

        Parameters
        ----------
        mask : `~gammapy.maps.WcsNDMap` of boolean type
            Map containing a boolean mask
        margin : `~astropy.unit.Quantity`
            Add a margin in degree to the source evaluation radius.
            Used to take into account PSF width.
        use_evaluation_region : bool
            Account for the extension of the model or not. The default is True.

        Returns
        -------
        models : `DatasetModels`
            Selected models contributing inside the region where mask==True
        """
        models = []

        if not mask.geom.is_image:
            mask = mask.reduce_over_axes(func=np.logical_or)

        for model in self.select(tag="sky-model"):
            if use_evaluation_region:
                contributes = model.contributes(mask=mask, margin=margin)
            else:
                contributes = mask.get_by_coord(model.position, fill_value=0)

            if np.any(contributes):
                models.append(model)

        return self.__class__(models=models)

    def select_region(self, regions, wcs=None):
        """Select sky models with center position contained within a given region

        Parameters
        ----------
        regions : str, `~regions.Region` or list of `~regions.Region`
            Region or list of regions (pixel or sky regions accepted).
            A region can be defined as a string ind DS9 format as well.
            See http://ds9.si.edu/doc/ref/region.html for details.
        wcs : `~astropy.wcs.WCS`
            World coordinate system transformation

        Returns
        -------
        models : `DatasetModels`
            Selected models
        """
        geom = RegionGeom.from_regions(regions, wcs=wcs)

        models = []

        for model in self.select(tag="sky-model"):
            if geom.contains(model.position):
                models.append(model)

        return self.__class__(models=models)

    def restore_status(self, restore_values=True):
        """Context manager to restore status.

        A copy of the values is made on enter,
        and those values are restored on exit.

        Parameters
        ----------
        restore_values : bool
            Restore values if True,
            otherwise restore only frozen status and covariance matrix.

        """
        return restore_models_status(self, restore_values)

    def set_parameters_bounds(
        self, tag, model_type, parameters_names=None, min=None, max=None, value=None
    ):
        """Set bounds for the selected models types and parameters names

        Parameters
        ----------
        tag : str or list
            Tag of the models
        model_type :  {"spatial", "spectral", "temporal"}
            Type of model
        parameters_names : str or list
            parameters names
        min : float
            min value
        max : float
            max value
        value : float
            init value
        """
        models = self.select(tag=tag, model_type=model_type)
        parameters = models.parameters.select(name=parameters_names, type=model_type)
        n = len(parameters)

        if min is not None:
            parameters.min = np.ones(n) * min
        if max is not None:
            parameters.max = np.ones(n) * max
        if value is not None:
            parameters.value = np.ones(n) * value

    def freeze(self, model_type=None):
        """Freeze parameters depending on model type

        Parameters
        ----------
        model_type : {None, "spatial", "spectral"}
           freeze all parameters or only spatial or only spectral
        """

        for m in self:
            m.freeze(model_type)

    def unfreeze(self, model_type=None):
        """Restore parameters frozen status to default depending on model type

        Parameters
        ----------
        model_type : {None, "spatial", "spectral"}
           restore frozen status to default for all parameters or only spatial or only spectral
        """

        for m in self:
            m.unfreeze(model_type)

    @property
    def frozen(self):
        """Boolean mask, True if all parameters of a given model are frozen"""
        return np.all([m.frozen for m in self])

    def reassign(self, dataset_name, new_dataset_name):
        """Reassign a model from one dataset to another

        Parameters
        ----------
        dataset_name : str or list
            Name of the datasets where the model is currently defined
        new_dataset_name : str or list
            Name of the datasets where the model should be defined instead.
            If multiple names are given the two list must have the save length,
            as the reassignment is element-wise.
        """
        models = [m.reassign(dataset_name, new_dataset_name) for m in self]
        return self.__class__(models)

    def to_template_sky_model(self, geom, spectral_model=None, name=None):
        """Merge a list of models into a single `~gammapy.modeling.models.SkyModel`

        Parameters
        ----------
        geom : `Geom`
            Map geometry of the result template model.
        spectral_model : `~gammapy.modeling.models.SpectralModel`
            One of the NormSpectralMdel
        name : str
            Name of the new model

        Returns
        -------
        model : `SkyModel`
            Template sky model.
        """
        from . import PowerLawNormSpectralModel, SkyModel, TemplateSpatialModel

        unit = u.Unit("1 / (cm2 s sr TeV)")
        map_ = Map.from_geom(geom, unit=unit)

        for m in self:
            map_ += m.evaluate_geom(geom).to(unit)

        spatial_model = TemplateSpatialModel(map_, normalize=False)

        if spectral_model is None:
            spectral_model = PowerLawNormSpectralModel()

        return SkyModel(
            spectral_model=spectral_model, spatial_model=spatial_model, name=name
        )

    @property
    def positions(self):
        """Positions of the models (`~astropy.coordinates.SkyCoord`)"""
        positions = []

        for model in self.select(tag="sky-model"):
            if model.position:
                positions.append(model.position.icrs)
            else:
                log.warning(
                    f"Skipping model {model.name} - no spatial component present"
                )

        return SkyCoord(positions)

    def to_regions(self):
        """Returns a list of the regions for the spatial models

        Returns
        -------
        regions: list of `~regions.SkyRegion`
            Regions
        """
        regions = []

        for model in self.select(tag="sky-model"):
            try:
                region = model.spatial_model.to_region()
                regions.append(region)
            except AttributeError:
                log.warning(
                    f"Skipping model {model.name} - no spatial component present"
                )
        return regions

    @property
    def wcs_geom(self):
        """Minimum WCS geom in which all the models are contained"""
        regions = self.to_regions()
        try:
            return RegionGeom.from_regions(regions).to_wcs_geom()
        except IndexError:
            log.error("No spatial component in any model. Geom not defined")

    def plot_regions(self, ax=None, kwargs_point=None, path_effect=None, **kwargs):
        """Plot extent of the spatial models on a given wcs axis

        Parameters
        ----------
        ax : `~astropy.visualization.WCSAxes`
            Axes to plot on. If no axes are given, an all-sky wcs
            is chosen using a CAR projection
        kwargs_point : dict
            Keyword arguments passed to `~matplotlib.lines.Line2D` for plotting
            of point sources
        path_effect : `~matplotlib.patheffects.PathEffect`
            Path effect applied to artists and lines.
        **kwargs : dict
            Keyword arguments passed to `~matplotlib.artists.Artist`


        Returns
        -------
        ax : `~astropy.visualization.WcsAxes`
            WCS axes
        """
        regions = self.to_regions()

        geom = RegionGeom.from_regions(regions=regions)
        return geom.plot_region(
            ax=ax, kwargs_point=kwargs_point, path_effect=path_effect, **kwargs
        )

    def plot_positions(self, ax=None, **kwargs):
        """ "Plot the centers of the spatial models on a given wcs axis

        Parameters
        ----------
        ax : `~astropy.visualization.WCSAxes`
            Axes to plot on. If no axes are given, an all-sky wcs
            is chosen using a CAR projection
        **kwargs : dict
            Keyword arguments passed to `~matplotlib.pyplot.scatter`


        Returns
        -------
        ax : `~astropy.visualization.WcsAxes`
            Wcs axes
        """
        from astropy.visualization.wcsaxes import WCSAxes

        if ax is None or not isinstance(ax, WCSAxes):
            ax = Map.from_geom(self.wcs_geom).plot()

        kwargs.setdefault("marker", "*")
        kwargs.setdefault("color", "tab:blue")
        path_effects = kwargs.get("path_effects", None)

        xp, yp = self.positions.to_pixel(ax.wcs)
        p = ax.scatter(xp, yp, **kwargs)

        if path_effects:
            plt.setp(p, path_effects=path_effects)

        return ax


class Models(DatasetModels, collections.abc.MutableSequence):
    """Sky model collection.

    Parameters
    ----------
    models : `SkyModel`, list of `SkyModel` or `Models`
        Sky models
    """

    def __delitem__(self, key):
        del self._models[self.index(key)]

    def __setitem__(self, key, model):
        from gammapy.modeling.models import FoVBackgroundModel, SkyModel

        if isinstance(model, (SkyModel, FoVBackgroundModel)):
            self._models[self.index(key)] = model
        else:
            raise TypeError(f"Invalid type: {model!r}")

    def insert(self, idx, model):
        if model.name in self.names:
            raise (ValueError("Model names must be unique"))

        self._models.insert(idx, model)


class restore_models_status:
    def __init__(self, models, restore_values=True):
        self.restore_values = restore_values
        self.models = models
        self.values = [_.value for _ in models.parameters]
        self.frozen = [_.frozen for _ in models.parameters]
        self.covariance_data = models.covariance.data

    def __enter__(self):
        pass

    def __exit__(self, type, value, traceback):
        for value, par, frozen in zip(self.values, self.models.parameters, self.frozen):
            if self.restore_values:
                par.value = value
            par.frozen = frozen
        self.models.covariance = self.covariance_data