Source code for gammapy.modeling.models.cube

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Cube models (axes: lon, lat, energy)."""
import copy
from pathlib import Path
import numpy as np
import astropy.units as u
from gammapy.maps import Map, MapAxis, RegionGeom, WcsGeom
from gammapy.modeling import Covariance, Parameter, Parameters
from gammapy.modeling.parameter import _get_parameters_str
from gammapy.utils.scripts import make_name, make_path
from gammapy.utils.fits import LazyFitsData, HDULocation
from .core import Model, Models
from .spatial import SpatialModel
from .spectral import SpectralModel, PowerLawNormSpectralModel
from .temporal import TemporalModel


[docs]class SkyModelBase(Model): """Sky model base class""" def __add__(self, other): if isinstance(other, (Models, list)): return Models([self, *other]) elif isinstance(other, (SkyModel, BackgroundModel)): return Models([self, other]) else: raise TypeError(f"Invalid type: {other!r}") def __radd__(self, model): return self.__add__(model)
[docs] def __call__(self, lon, lat, energy, time=None): return self.evaluate(lon, lat, energy, time)
[docs] def evaluate_geom(self, geom, gti=None): coords = geom.get_coord(frame=self.frame) return self(coords.lon, coords.lat, coords["energy_true"])
[docs]class SkyModel(SkyModelBase): """Sky model component. This model represents a factorised sky model. It has `~gammapy.modeling.Parameters` combining the spatial and spectral parameters. Parameters ---------- spectral_model : `~gammapy.modeling.models.SpectralModel` Spectral model spatial_model : `~gammapy.modeling.models.SpatialModel` Spatial model (must be normalised to integrate to 1) temporal_model : `~gammapy.modeling.models.temporalModel` Temporal model name : str Model identifier apply_irf : dict Dictionary declaring which IRFs should be applied to this model. Options are {"exposure": True, "psf": True, "edisp": True} datasets_names : list of str Which datasets this model is applied to. """ tag = "SkyModel" _apply_irf_default = {"exposure": True, "psf": True, "edisp": True} def __init__( self, spectral_model, spatial_model=None, temporal_model=None, name=None, apply_irf=None, datasets_names=None, ): self.spatial_model = spatial_model self.spectral_model = spectral_model self.temporal_model = temporal_model self._name = make_name(name) if apply_irf is None: apply_irf = self._apply_irf_default.copy() self.apply_irf = apply_irf self.datasets_names = datasets_names self._check_unit() super().__init__() @property def _models(self): models = self.spectral_model, self.spatial_model, self.temporal_model return [model for model in models if model is not None] def _check_covariance(self): if not self.parameters == self._covariance.parameters: self._covariance = Covariance.from_stack( [model.covariance for model in self._models], ) def _check_unit(self): from astropy.time import Time from gammapy.data.gti import GTI # evaluate over a test geom to check output unit # TODO simpler way to test this ? axis = MapAxis.from_edges(np.logspace(-1, 1, 3), unit=u.TeV, name="energy_true") geom = WcsGeom.create(skydir=(0, 0), npix=(2, 2), frame="galactic", axes=[axis]) t_ref = Time(55555, format="mjd") gti = GTI.create([1, 5] * u.day, [2, 6] * u.day, reference_time=t_ref) value = self.evaluate_geom(geom, gti) if self.spatial_model is not None: ref_unit = "cm-2 s-1 MeV-1 sr-1" else: ref_unit = "cm-2 s-1 MeV-1" if not value.unit.is_equivalent(ref_unit): raise ValueError( f"SkyModel unit {value.unit} is not equivalent to {ref_unit}" ) @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 name(self): return self._name @property def parameters(self): parameters = [] parameters.append(self.spectral_model.parameters) if self.spatial_model is not None: parameters.append(self.spatial_model.parameters) if self.temporal_model is not None: parameters.append(self.temporal_model.parameters) return Parameters.from_stack(parameters) @property def spatial_model(self): """`~gammapy.modeling.models.SpatialModel`""" return self._spatial_model @spatial_model.setter def spatial_model(self, model): if not (model is None or isinstance(model, SpatialModel)): raise TypeError(f"Invalid type: {model!r}") self._spatial_model = model @property def spectral_model(self): """`~gammapy.modeling.models.SpectralModel`""" return self._spectral_model @spectral_model.setter def spectral_model(self, model): if not (model is None or isinstance(model, SpectralModel)): raise TypeError(f"Invalid type: {model!r}") self._spectral_model = model @property def temporal_model(self): """`~gammapy.modeling.models.TemporalModel`""" return self._temporal_model @temporal_model.setter def temporal_model(self, model): if not (model is None or isinstance(model, TemporalModel)): raise TypeError(f"Invalid type: {model!r}") self._temporal_model = model @property def position(self): """`~astropy.coordinates.SkyCoord`""" return self.spatial_model.position @property def evaluation_radius(self): """`~astropy.coordinates.Angle`""" return self.spatial_model.evaluation_radius @property def frame(self): return self.spatial_model.frame def __repr__(self): return ( f"{self.__class__.__name__}(" f"spatial_model={self.spatial_model!r}, " f"spectral_model={self.spectral_model!r})" f"temporal_model={self.temporal_model!r})" )
[docs] def evaluate(self, lon, lat, energy, time=None): """Evaluate the model at given points. The model evaluation follows numpy broadcasting rules. Return differential surface brightness cube. At the moment in units: ``cm-2 s-1 TeV-1 deg-2`` Parameters ---------- lon, lat : `~astropy.units.Quantity` Spatial coordinates energy : `~astropy.units.Quantity` Energy coordinate time: `~astropy.time.Time` Time co-ordinate Returns ------- value : `~astropy.units.Quantity` Model value at the given point. """ value = self.spectral_model(energy) # pylint:disable=not-callable # TODO: case if self.temporal_model is not None, introduce time in arguments ? if self.spatial_model is not None: if self.spatial_model.is_energy_dependent: spatial = self.spatial_model(lon, lat, energy) else: spatial = self.spatial_model(lon, lat) value = value * spatial # pylint:disable=not-callable if (self.temporal_model is not None) and (time is not None): value = value * self.temporal_model(time) return value
[docs] def evaluate_geom(self, geom, gti=None): """Evaluate model on `~gammapy.maps.Geom`.""" energy = geom.axes["energy_true"].center[:, np.newaxis, np.newaxis] value = self.spectral_model(energy) if self.spatial_model: value = value * self.spatial_model.evaluate_geom(geom) if self.temporal_model: integral = self.temporal_model.integral(gti.time_start, gti.time_stop) value = value * np.sum(integral) return value
[docs] def integrate_geom(self, geom, gti=None): """Integrate model on `~gammapy.maps.Geom`. Parameters ---------- geom : `Geom` Map geometry gti : `GTI` GIT table Returns ------- flux : `Map` Predicted flux map """ energy = geom.axes["energy_true"].edges value = self.spectral_model.integral( energy[:-1], energy[1:], intervals=True ).reshape((-1, 1, 1)) if self.spatial_model and not isinstance(geom, RegionGeom): # TODO: integrate spatial model over region to correct for # containment value = value * self.spatial_model.integrate_geom(geom).quantity if self.temporal_model: integral = self.temporal_model.integral(gti.time_start, gti.time_stop) value = value * np.sum(integral) return Map.from_geom(geom=geom, data=value.value, unit=value.unit)
[docs] def copy(self, name=None, **kwargs): """Copy SkyModel""" if self.spatial_model is not None: spatial_model = self.spatial_model.copy() else: spatial_model = None if self.temporal_model is not None: temporal_model = self.temporal_model.copy() else: temporal_model = None kwargs.setdefault("name", make_name(name)) kwargs.setdefault("spectral_model", self.spectral_model.copy()) kwargs.setdefault("spatial_model", spatial_model) kwargs.setdefault("temporal_model", temporal_model) kwargs.setdefault("apply_irf", self.apply_irf.copy()) kwargs.setdefault("datasets_names", self.datasets_names) return self.__class__(**kwargs)
[docs] def to_dict(self, full_output=False): """Create dict for YAML serilisation""" data = {} data["name"] = self.name data["type"] = self.tag data["spectral"] = self.spectral_model.to_dict(full_output) if self.spatial_model is not None: data["spatial"] = self.spatial_model.to_dict(full_output) if self.temporal_model is not None: data["temporal"] = self.temporal_model.to_dict(full_output) if self.apply_irf != self._apply_irf_default: data["apply_irf"] = self.apply_irf if self.datasets_names is not None: data["datasets_names"] = self.datasets_names return data
[docs] @classmethod def from_dict(cls, data): """Create SkyModel from dict""" from gammapy.modeling.models import ( SPATIAL_MODEL_REGISTRY, SPECTRAL_MODEL_REGISTRY, TEMPORAL_MODEL_REGISTRY, ) model_class = SPECTRAL_MODEL_REGISTRY.get_cls(data["spectral"]["type"]) spectral_model = model_class.from_dict(data["spectral"]) spatial_data = data.get("spatial") if spatial_data is not None: model_class = SPATIAL_MODEL_REGISTRY.get_cls(spatial_data["type"]) spatial_model = model_class.from_dict(spatial_data) else: spatial_model = None temporal_data = data.get("temporal") if temporal_data is not None: model_class = TEMPORAL_MODEL_REGISTRY.get_cls(temporal_data["type"]) temporal_model = model_class.from_dict(temporal_data) else: temporal_model = None return cls( name=data["name"], spatial_model=spatial_model, spectral_model=spectral_model, temporal_model=temporal_model, apply_irf=data.get("apply_irf", cls._apply_irf_default), datasets_names=data.get("datasets_names"), )
def __str__(self): str_ = self.__class__.__name__ + "\n\n" str_ += "\t{:26}: {}\n".format("Name", self.name) str_ += "\t{:26}: {}\n".format("Datasets names", self.datasets_names) str_ += "\t{:26}: {}\n".format( "Spectral model type", self.spectral_model.__class__.__name__ ) if self.spatial_model is not None: spatial_type = self.spatial_model.__class__.__name__ else: spatial_type = "" str_ += "\t{:26}: {}\n".format("Spatial model type", spatial_type) if self.temporal_model is not None: temporal_type = self.temporal_model.__class__.__name__ else: temporal_type = "" str_ += "\t{:26}: {}\n".format("Temporal model type", temporal_type) str_ += "\tParameters:\n" info = _get_parameters_str(self.parameters) lines = info.split("\n") str_ += "\t" + "\n\t".join(lines[:-1]) str_ += "\n\n" return str_.expandtabs(tabsize=2)
[docs]class BackgroundModel(Model): """Background model. Create a new map by a tilt and normalization on the available map Parameters ---------- map : `~gammapy.maps.Map` Background model map spectral_model : `~gammapy.modeling.models.SpectralModel` Normalized spectral model, default is `~gammapy.modeling.models.PowerLawNormSpectralModel` """ tag = "BackgroundModel" map = LazyFitsData(cache=True) def __init__( self, map, spectral_model=None, name=None, filename=None, datasets_names=None, ): if isinstance(map, Map): axis = map.geom.axes["energy"] if axis.node_type != "edges": raise ValueError( 'Need an integrated map, energy axis node_type="edges"' ) self.map = map self._name = make_name(name) self.filename = filename if spectral_model is None: spectral_model = PowerLawNormSpectralModel() spectral_model.tilt.frozen = True self.spectral_model = spectral_model if isinstance(datasets_names, list): if len(datasets_names) != 1: raise ValueError( "Currently background models can only be assigned to one dataset." ) self.datasets_names = datasets_names super().__init__() @property def name(self): return self._name @property def energy_center(self): """True energy axis bin centers (`~astropy.units.Quantity`)""" energy_axis = self.map.geom.axes["energy"] energy = energy_axis.center return energy[:, np.newaxis, np.newaxis] @property def spectral_model(self): """`~gammapy.modeling.models.SpectralModel`""" return self._spectral_model @spectral_model.setter def spectral_model(self, model): if not (model is None or isinstance(model, SpectralModel)): raise TypeError(f"Invalid type: {model!r}") self._spectral_model = model @property def parameters(self): parameters = [] parameters.append(self.spectral_model.parameters) return Parameters.from_stack(parameters)
[docs] def evaluate(self): """Evaluate background model. Returns ------- background_map : `~gammapy.maps.Map` Background evaluated on the Map """ value = self.spectral_model(self.energy_center).value back_values = self.map.data * value return self.map.copy(data=back_values)
[docs] def to_dict(self, full_output=False): data = {} data["name"] = self.name data["type"] = self.tag data["spectral"] = self.spectral_model.to_dict(full_output) if self.filename is not None: data["filename"] = self.filename if self.datasets_names is not None: data["datasets_names"] = self.datasets_names return data
[docs] @classmethod def from_dict(cls, data): from gammapy.modeling.models import SPECTRAL_MODEL_REGISTRY spectral_data = data.get("spectral") if spectral_data is not None: model_class = SPECTRAL_MODEL_REGISTRY.get_cls(spectral_data["type"]) spectral_model = model_class.from_dict(spectral_data) else: spectral_model = None if "filename" in data: bkg_map = Map.read(data["filename"]) elif "map" in data: bkg_map = data["map"] else: # TODO: for now create a fake map for serialization, # uptdated in MapDataset.from_dict() axis = MapAxis.from_edges(np.logspace(-1, 1, 2), unit=u.TeV, name="energy") geom = WcsGeom.create( skydir=(0, 0), npix=(1, 1), frame="galactic", axes=[axis] ) bkg_map = Map.from_geom(geom) return cls( map=bkg_map, spectral_model=spectral_model, name=data["name"], datasets_names=data.get("datasets_names"), filename=data.get("filename"), )
[docs] def copy(self, name=None): """A deep copy.""" new = copy.deepcopy(self) new._name = make_name(name) return new
[docs] def cutout(self, position, width, mode="trim", name=None): """Cutout background model. 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 returned background model. Returns ------- cutout : `BackgroundModel` Cutout background model. """ cutout_kwargs = {"position": position, "width": width, "mode": mode} bkg_map = self.map.cutout(**cutout_kwargs) spectral_model = self.spectral_model.copy() return self.__class__(bkg_map, spectral_model=spectral_model, name=name)
[docs] def stack(self, other, weights=None): """Stack background model in place. Stacking the background model resets the current parameters values. Parameters ---------- other : `BackgroundModel` Other background model. """ bkg = self.evaluate() other_bkg = other.evaluate() bkg.stack(other_bkg, weights=weights) self.map = bkg # reset parameter values self.spectral_model.norm.value = 1 self.spectral_model.tilt.value = 0
def __str__(self): str_ = self.__class__.__name__ + "\n\n" str_ += "\t{:26}: {}\n".format("Name", self.name) str_ += "\t{:26}: {}\n".format("Datasets names", self.datasets_names) str_ += "\tParameters:\n" info = _get_parameters_str(self.parameters) lines = info.split("\n") str_ += "\t" + "\n\t".join(lines[:-1]) str_ += "\n\n" return str_.expandtabs(tabsize=2) @property def position(self): """`~astropy.coordinates.SkyCoord`""" return self.map.geom.center_skydir @property def evaluation_radius(self): """`~astropy.coordinates.Angle`""" return np.max(self.map.geom.width) / 2.0
def create_fermi_isotropic_diffuse_model(filename, **kwargs): """Read Fermi isotropic diffuse model. See `LAT Background models <https://fermi.gsfc.nasa.gov/ssc/data/access/lat/BackgroundModels.html>`_ Parameters ---------- filename : str filename kwargs : dict Keyword arguments forwarded to `TemplateSpectralModel` Returns ------- diffuse_model : `SkyModel` Fermi isotropic diffuse sky model. """ from .spectral import TemplateSpectralModel from .spatial import ConstantSpatialModel vals = np.loadtxt(make_path(filename)) energy = u.Quantity(vals[:, 0], "MeV", copy=False) values = u.Quantity(vals[:, 1], "MeV-1 s-1 cm-2", copy=False) kwargs.setdefault("interp_kwargs", {"fill_value": None}) spatial_model = ConstantSpatialModel() spectral_model = ( TemplateSpectralModel(energy=energy, values=values, **kwargs) * PowerLawNormSpectralModel() ) return SkyModel( spatial_model=spatial_model, spectral_model=spectral_model, name="fermi-diffuse-iso", )