Source code for gammapy.datasets.map

# 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.nddata.utils import NoOverlapError
from astropy.table import Table
from astropy.utils import lazyproperty
from regions import CircleSkyRegion
from gammapy.data import GTI
from gammapy.irf import EDispKernel, EffectiveAreaTable
from gammapy.irf.edisp_map import EDispMap
from gammapy.irf.psf_kernel import PSFKernel
from gammapy.irf.psf_map import PSFMap
from gammapy.maps import Map, MapAxis
from gammapy.modeling.models import BackgroundModel, Models
from gammapy.stats import cash, cash_sum_cython, wstat
from gammapy.utils.random import get_random_state
from gammapy.utils.scripts import make_name, make_path
from .core import Dataset

__all__ = ["MapDataset", "MapDatasetOnOff"]

log = logging.getLogger(__name__)

CUTOUT_MARGIN = 0.1 * u.deg
RAD_MAX = 0.66
RAD_AXIS_DEFAULT = MapAxis.from_bounds(
    0, RAD_MAX, nbin=66, node_type="edges", name="theta", unit="deg"
)
MIGRA_AXIS_DEFAULT = MapAxis.from_bounds(
    0.2, 5, nbin=48, node_type="edges", name="migra"
)

BINSZ_IRF_DEFAULT = 0.2


[docs]class MapDataset(Dataset): """Perform sky model likelihood fit on maps. Parameters ---------- models : `~gammapy.modeling.models.Models` Source sky models. counts : `~gammapy.maps.WcsNDMap` Counts cube exposure : `~gammapy.maps.WcsNDMap` Exposure cube mask_fit : `~gammapy.maps.WcsNDMap` Mask to apply to the likelihood for fitting. psf : `~gammapy.cube.PSFKernel` or `~gammapy.cube.PSFMap` PSF kernel edisp : `~gammapy.irf.EDispKernel` or `~gammapy.cube.EDispMap` Energy dispersion kernel evaluation_mode : {"local", "global"} Model evaluation mode. The "local" mode evaluates the model components on smaller grids to save computation time. This mode is recommended for local optimization algorithms. The "global" evaluation mode evaluates the model components on the full map. This mode is recommended for global optimization algorithms. 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 """ stat_type = "cash" tag = "MapDataset" def __init__( self, models=None, counts=None, exposure=None, mask_fit=None, psf=None, edisp=None, name=None, evaluation_mode="local", mask_safe=None, gti=None, ): if mask_fit is not None and mask_fit.data.dtype != np.dtype("bool"): raise ValueError("mask data must have dtype bool") if mask_safe is not None and mask_safe.data.dtype != np.dtype("bool"): raise ValueError("mask data must have dtype bool") self._name = make_name(name) self.background_model = None self.evaluation_mode = evaluation_mode self.counts = counts self.exposure = exposure self.mask_fit = mask_fit self.psf = psf self.edisp = edisp self.mask_safe = mask_safe self.models = models self.gti = gti # check whether a reference geom is defined _ = self._geom @property def name(self): return self._name def __str__(self): str_ = f"{self.__class__.__name__}\n" str_ += "-" * len(self.__class__.__name__) + "\n" str_ += "\n" str_ += "\t{:32}: {} \n\n".format("Name", self.name) counts = np.nan if self.counts is not None: counts = np.sum(self.counts.data) str_ += "\t{:32}: {:.0f} \n".format("Total counts", counts) npred = np.nan if self.models is not None: npred = np.sum(self.npred().data) str_ += "\t{:32}: {:.2f}\n".format("Total predicted counts", npred) background = np.nan if self.background_model is not None: background = np.sum(self.background_model.evaluate().data) str_ += "\t{:32}: {:.2f}\n\n".format("Total background counts", background) exposure_min, exposure_max, exposure_unit = np.nan, np.nan, "" if self.exposure is not None: if self.mask_safe is not None: mask = self.mask_safe.reduce_over_axes(np.logical_or).data if not mask.any(): mask = None else: mask = None exposure_min = np.min(self.exposure.data[..., mask]) exposure_max = np.max(self.exposure.data[..., mask]) exposure_unit = self.exposure.unit str_ += "\t{:32}: {:.2e} {}\n".format( "Exposure min", exposure_min, exposure_unit ) str_ += "\t{:32}: {:.2e} {}\n\n".format( "Exposure max", exposure_max, exposure_unit ) # data section n_bins = 0 if self.counts is not None: n_bins = self.counts.data.size str_ += "\t{:32}: {} \n".format("Number of total bins", n_bins) n_fit_bins = 0 if self.mask is not None: n_fit_bins = np.sum(self.mask.data) str_ += "\t{:32}: {} \n\n".format("Number of fit bins", n_fit_bins) # likelihood section str_ += "\t{:32}: {}\n".format("Fit statistic type", self.stat_type) stat = np.nan if self.counts is not None and self.models is not None: stat = self.stat_sum() str_ += "\t{:32}: {:.2f}\n\n".format("Fit statistic value (-2 log(L))", stat) # 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 models(self): """Models (`~gammapy.modeling.models.Models`).""" return self._models @models.setter def models(self, models): if models is None: self._models = None else: self._models = Models(models) # TODO: clean this up (probably by removing) if self.models is not None: for model in self.models: if isinstance(model, BackgroundModel): if model.datasets_names is not None: if self.name in model.datasets_names: self.background_model = model break else: log.warning(f"No background model defined for dataset {self.name}") self._evaluators = {} @property def evaluators(self): """Model evaluators""" # this call is needed to trigger the setup of the evaluators if not self._evaluators: self.npred() return self._evaluators @property def _geom(self): """Main analysis geometry""" if self.counts is not None: return self.counts.geom elif self.background_model is not None: return self.background_model.map.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_model', '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
[docs] def npred(self): """Predicted source and background counts (`~gammapy.maps.Map`).""" npred_total = Map.from_geom(self._geom, dtype=float) if self.models: for model in self.models: if model.datasets_names is not None: if self.name not in model.datasets_names: continue evaluator = self._evaluators.get(model.name) if evaluator is None: evaluator = MapEvaluator( model=model, evaluation_mode=self.evaluation_mode, gti=self.gti ) self._evaluators[model.name] = evaluator # if the model component drifts out of its support the evaluator has # has to be updated if evaluator.needs_update: evaluator.update(self.exposure, self.psf, self.edisp, self._geom) if evaluator.contributes: npred = evaluator.compute_npred() npred_total.stack(npred) return npred_total
[docs] @classmethod def from_geoms( cls, geom, geom_exposure, geom_psf, geom_edisp, 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 map reference_time : `~astropy.time.Time` the reference time to use in GTI definition name : str Name of the returned dataset. Returns ------- empty_maps : `MapDataset` A MapDataset containing zero filled maps """ name = make_name(name) kwargs = kwargs.copy() kwargs["name"] = name kwargs["counts"] = Map.from_geom(geom, unit="") background = Map.from_geom(geom, unit="") kwargs["models"] = Models( [BackgroundModel(background, name=name + "-bkg", datasets_names=[name])] ) kwargs["exposure"] = Map.from_geom(geom_exposure, unit="m2 s") kwargs["edisp"] = EDispMap.from_geom(geom_edisp) 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)
[docs] @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, **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` Migration axis for the energy dispersion map 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. Returns ------- empty_maps : `MapDataset` A MapDataset containing zero filled maps """ migra_axis = migra_axis or MIGRA_AXIS_DEFAULT rad_axis = rad_axis or RAD_AXIS_DEFAULT if energy_axis_true is not None: if energy_axis_true.name != "energy_true": raise ValueError("True enery axis name must be 'energy_true'") else: energy_axis_true = geom.get_axis_by_name("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]) geom_edisp = geom_irf.to_cube([migra_axis, energy_axis_true]) return cls.from_geoms( geom, geom_exposure, geom_psf, geom_edisp, reference_time=reference_time, name=name, **kwargs, )
[docs] def stack(self, other): """Stack another dataset in place. Parameters ---------- other: `~gammapy.cube.MapDataset` or `~gammapy.cube.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. """ if self.counts and other.counts: self.counts *= self.mask_safe self.counts.stack(other.counts, weights=other.mask_safe) if self.exposure and other.exposure: mask_image = self.mask_safe.reduce_over_axes(func=np.logical_or) self.exposure *= mask_image.data # TODO: apply energy dependent mask to exposure. Does this require # a mask_safe in true energy? mask_image_other = other.mask_safe.reduce_over_axes(func=np.logical_or) self.exposure.stack(other.exposure, weights=mask_image_other) # TODO: unify background model handling if other.stat_type == "wstat": background_model = BackgroundModel(other.background) else: background_model = other.background_model if self.background_model and background_model: self.background_model.map *= self.mask_safe self.background_model.stack(background_model, other.mask_safe) if self.mask_safe is not None and other.mask_safe is not None: self.mask_safe.stack(other.mask_safe) if self.psf and other.psf: if isinstance(self.psf, PSFMap) and isinstance(other.psf, PSFMap): mask_irf = self._mask_safe_irf(self.psf.psf_map, mask_image) self.psf.psf_map *= mask_irf.data self.psf.exposure_map *= mask_irf.data mask_image_other = other.mask_safe.reduce_over_axes(func=np.logical_or) mask_irf_other = self._mask_safe_irf( other.psf.psf_map, mask_image_other ) self.psf.stack(other.psf, weights=mask_irf_other) else: raise ValueError("Stacking of PSF kernels not supported") if self.edisp and other.edisp: if isinstance(self.edisp, EDispMap) and isinstance(other.edisp, EDispMap): mask_irf = self._mask_safe_irf(self.edisp.edisp_map, mask_image) self.edisp.edisp_map *= mask_irf.data self.edisp.exposure_map *= mask_irf.data mask_image_other = other.mask_safe.reduce_over_axes(func=np.logical_or) mask_irf_other = self._mask_safe_irf( other.edisp.edisp_map, mask_image_other ) self.edisp.stack(other.edisp, weights=mask_irf_other) else: raise ValueError("Stacking of edisp kernels not supported") if self.gti and other.gti: self.gti = self.gti.stack(other.gti).union()
@staticmethod def _mask_safe_irf(irf_map, mask): geom = irf_map.geom.to_image() coords = geom.get_coord() data = mask.get_by_coord(coords).astype(bool) return Map.from_geom(geom=geom, data=data)
[docs] def stat_array(self): """Likelihood per bin given the current model parameters""" return cash(n_on=self.counts.data, mu_on=self.npred().data)
[docs] def residuals(self, method="diff"): """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) Returns ------- residuals : `gammapy.maps.WcsNDMap` Residual map. """ return self._compute_residuals(self.counts, self.npred(), method=method)
[docs] def plot_residuals( self, method="diff", smooth_kernel="gauss", smooth_radius="0.1 deg", region=None, figsize=(12, 4), **kwargs, ): """ Plot spatial and spectral residuals. The spectral residuals are extracted from the provided region, and the normalization used for the residuals computation can be controlled using the method parameter. If no region is passed, only the spatial residuals are shown. Parameters ---------- method : {"diff", "diff/model", "diff/sqrt(model)"} Method 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. region: `~regions.Region` Region (pixel or sky regions accepted) figsize : tuple Figure size used for the plotting. **kwargs : dict Keyword arguments passed to `~matplotlib.pyplot.imshow`. Returns ------- ax_image, ax_spec : `~matplotlib.pyplot.Axes`, Image and spectrum axes. """ import matplotlib.pyplot as plt fig = plt.figure(figsize=figsize) counts, npred = self.counts, self.npred() if self.mask is not None: counts = counts * self.mask npred = 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 ) spatial_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) spatial_residuals.data[~mask.data] = np.nan # If no region is provided, skip spectral residuals ncols = 2 if region is not None else 1 ax_image = fig.add_subplot(1, ncols, 1, projection=spatial_residuals.geom.wcs) ax_spec = None kwargs.setdefault("cmap", "coolwarm") kwargs.setdefault("stretch", "linear") kwargs.setdefault("vmin", -5) kwargs.setdefault("vmax", 5) spatial_residuals.plot(ax=ax_image, add_cbar=True, **kwargs) # Spectral residuals if region: ax_spec = fig.add_subplot(1, 2, 2) counts_spec = counts.get_spectrum(region=region) npred_spec = npred.get_spectrum(region=region) residuals = self._compute_residuals(counts_spec, npred_spec, method) ax = residuals.plot() ax.set_yscale("linear") ax.axhline(0, color="black", lw=0.5) y_max = 2 * np.nanmax(residuals.data) plt.ylim(-y_max, y_max) label = self._residuals_labels[method] plt.ylabel(f"Residuals ({label})") # Overlay spectral extraction region on the spatial residuals pix_region = region.to_pixel(wcs=spatial_residuals.geom.wcs) pix_region.plot(ax=ax_image) return ax_image, ax_spec
@lazyproperty def _counts_data(self): return self.counts.data.astype(float)
[docs] def stat_sum(self): """Total likelihood given the current model parameters.""" counts, npred = self._counts_data, 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())
[docs] 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() npred.data = random_state.poisson(npred.data) self.counts = npred
[docs] 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_model is not None: hdulist += self.background_model.map.to_hdulist(hdu="background")[ exclude_primary ] if self.edisp is not None: if isinstance(self.edisp, EDispKernel): hdus = self.edisp.to_hdulist() hdus["MATRIX"].name = "edisp_matrix" hdus["EBOUNDS"].name = "edisp_matrix_ebounds" hdulist.append(hdus["EDISP_MATRIX"]) hdulist.append(hdus["EDISP_MATRIX_EBOUNDS"]) else: hdulist += self.edisp.edisp_map.to_hdulist(hdu="EDISP")[exclude_primary] hdulist += self.edisp.exposure_map.to_hdulist(hdu="edisp_exposure")[ exclude_primary ] if self.psf is not None: if isinstance(self.psf, PSFKernel): hdulist += self.psf.psf_kernel_map.to_hdulist(hdu="psf_kernel")[ exclude_primary ] else: hdulist += self.psf.psf_map.to_hdulist(hdu="psf")[exclude_primary] hdulist += self.psf.exposure_map.to_hdulist(hdu="psf_exposure")[ exclude_primary ] if self.mask_safe is not None: mask_safe_int = self.mask_safe.copy() mask_safe_int.data = mask_safe_int.data.astype(int) hdulist += mask_safe_int.to_hdulist(hdu="mask_safe")[exclude_primary] if self.mask_fit is not None: mask_fit_int = self.mask_fit.copy() mask_fit_int.data = mask_fit_int.data.astype(int) hdulist += mask_fit_int.to_hdulist(hdu="mask_fit")[exclude_primary] if self.gti is not None: hdulist.append(fits.BinTableHDU(self.gti.table, name="GTI")) return hdulist
[docs] @classmethod def from_hdulist(cls, hdulist, name=None): """Create map dataset from list of HDUs. Parameters ---------- hdulist : `~astropy.io.fits.HDUList` List of HDUs. name : str Name of the new dataset. Returns ------- dataset : `MapDataset` Map dataset. """ name = make_name(name) kwargs = {"name": name} if "COUNTS" in hdulist: kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts") if "EXPOSURE" in hdulist: exposure = Map.from_hdulist(hdulist, hdu="exposure") if exposure.geom.axes[0].name == "energy": exposure.geom.axes[0].name = "energy_true" kwargs["exposure"] = exposure if "BACKGROUND" in hdulist: background_map = Map.from_hdulist(hdulist, hdu="background") kwargs["models"] = Models( [ BackgroundModel( background_map, datasets_names=[name], name=name + "-bkg" ) ] ) if "EDISP_MATRIX" in hdulist: kwargs["edisp"] = EDispKernel.from_hdulist( hdulist, hdu1="EDISP_MATRIX", hdu2="EDISP_MATRIX_EBOUNDS" ) if "EDISP" in hdulist: edisp_map = Map.from_hdulist(hdulist, hdu="edisp") exposure_map = Map.from_hdulist(hdulist, hdu="edisp_exposure") kwargs["edisp"] = EDispMap(edisp_map, exposure_map) if "PSF_KERNEL" in hdulist: psf_map = Map.from_hdulist(hdulist, hdu="psf_kernel") kwargs["psf"] = PSFKernel(psf_map) if "PSF" in hdulist: psf_map = Map.from_hdulist(hdulist, hdu="psf") exposure_map = Map.from_hdulist(hdulist, hdu="psf_exposure") kwargs["psf"] = PSFMap(psf_map, exposure_map) if "MASK_SAFE" in hdulist: mask_safe = Map.from_hdulist(hdulist, hdu="mask_safe") 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") 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)
[docs] 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(make_path(filename), overwrite=overwrite)
[docs] @classmethod def read(cls, filename, name=None): """Read map dataset from file. Parameters ---------- filename : str Filename to read from. name : str Name of the new dataset. Returns ------- dataset : `MapDataset` Map dataset. """ with fits.open(make_path(filename), memmap=False) as hdulist: return cls.from_hdulist(hdulist, name=name)
[docs] @classmethod def from_dict(cls, data, models): """Create from dicts and models list generated from YAML serialization.""" dataset = cls.read(data["filename"], name=data["name"]) for model in models: if ( isinstance(model, BackgroundModel) and model.filename is None and dataset.name == model.datasets_names[0] ): model.map = dataset.background_model.map dataset.models = models return dataset
[docs] def to_dict(self, filename=""): """Convert to dict for YAML serialization.""" return { "name": self.name, "type": self.tag, "filename": str(filename), }
[docs] def to_spectrum_dataset(self, on_region, containment_correction=False, name=None): """Return a ~gammapy.spectrum.SpectrumDataset from on_region. Counts and background are summed 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.spectrum.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.spectrum.SpectrumDataset` the resulting reduced dataset """ from .spectrum import SpectrumDataset kwargs = {"gti": self.gti, "name": name} if self.gti is not None: kwargs["livetime"] = self.gti.time_sum else: raise ValueError("No GTI in `MapDataset`, cannot compute livetime") if self.counts is not None: kwargs["counts"] = self.counts.get_spectrum(on_region, np.sum) if self.background_model is not None: kwargs["background"] = self.background_model.evaluate().get_spectrum( on_region, np.sum ) if self.exposure is not None: exposure = self.exposure.get_spectrum(on_region, np.mean) energy = exposure.geom.axes[0].edges kwargs["aeff"] = EffectiveAreaTable( energy_lo=energy[:-1], energy_hi=energy[1:], data=exposure.quantity[:, 0, 0] / kwargs["livetime"], ) if containment_correction: if not isinstance(on_region, CircleSkyRegion): raise TypeError( "Containement correction is only supported for" " `CircleSkyRegion`." ) elif self.psf is None or isinstance(self.psf, PSFKernel): raise ValueError("No PSFMap set. Containement correction impossible") else: psf = self.psf.get_energy_dependent_table_psf(on_region.center) containment = psf.containment( kwargs["aeff"].energy.center, on_region.radius ) kwargs["aeff"].data.data *= containment.squeeze() if self.edisp is not None: if isinstance(self.edisp, EDispKernel): edisp = self.edisp else: axis = self._geom.get_axis_by_name("energy") edisp = self.edisp.get_edisp_kernel(on_region.center, e_reco=axis.edges) kwargs["edisp"] = edisp return SpectrumDataset(**kwargs)
[docs] def to_image(self, spectrum=None, name=None): """Create images by summing over the energy axis. Exposure is weighted with an assumed spectrum, resulting in a weighted mean exposure image. Currently the PSFMap and EdispMap are dropped from the resulting image dataset. Parameters ---------- spectrum : `~gammapy.modeling.models.SpectralModel` Spectral model to compute the weights. Default is power-law with spectral index of 2. name : str Name of the new dataset. Returns ------- dataset : `MapDataset` Map dataset containing images. """ from gammapy.makers.utils import _map_spectrum_weight name = make_name(name) kwargs = {} kwargs["name"] = name kwargs["gti"] = self.gti if self.mask_safe is not None: mask_safe = self.mask_safe kwargs["mask_safe"] = mask_safe.reduce_over_axes( func=np.logical_or, keepdims=True ) else: mask_safe = 1 if self.counts is not None: counts = self.counts * mask_safe kwargs["counts"] = counts.sum_over_axes(keepdims=True) if self.exposure is not None: exposure = _map_spectrum_weight(self.exposure, spectrum) kwargs["exposure"] = exposure.sum_over_axes(keepdims=True) if self.background_model is not None: background = self.background_model.evaluate() * mask_safe background = background.sum_over_axes(keepdims=True) kwargs["models"] = Models( [BackgroundModel(background, datasets_names=[name])] ) if self.psf is not None: # TODO: implement PSFKernel.to_image() if not isinstance(self.psf, PSFKernel): kwargs["psf"] = self.psf.to_image(spectrum=spectrum, keepdims=True) else: # assume exposure at center position kwargs["psf"] = None return self.__class__(**kwargs)
[docs] 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} 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_model is not None: model = self.background_model.cutout(**cutout_kwargs, name=name + "-bkg") model.datasets_names = [name] kwargs["models"] = model 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)
[docs]class MapDatasetOnOff(MapDataset): """Map dataset for on-off likelihood fitting. 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 : `~numpy.ndarray` Mask to apply to the likelihood for fitting. psf : `~gammapy.cube.PSFKernel` PSF kernel edisp : `~gammapy.irf.EDispKernel` Energy dispersion evaluation_mode : {"local", "global"} Model evaluation mode. The "local" mode evaluates the model components on smaller grids to save computation time. This mode is recommended for local optimization algorithms. The "global" evaluation mode evaluates the model components on the full map. This mode is recommended for global optimization algorithms. mask_safe : `~numpy.ndarray` Mask defining the safe data range. gti : `~gammapy.data.GTI` GTI of the observation or union of GTI if it is a stacked observation name : str Name of the dataset. """ 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, evaluation_mode="local", mask_safe=None, gti=None, ): if mask_fit is not None and mask_fit.dtype != np.dtype("bool"): raise ValueError("mask data must have dtype bool") self.evaluation_mode = evaluation_mode self.counts = counts self.counts_off = counts_off if np.isscalar(acceptance): acceptance = np.ones(self.data_shape) * acceptance if np.isscalar(acceptance_off): acceptance_off = np.ones(self.data_shape) * acceptance_off self.acceptance = acceptance self.acceptance_off = acceptance_off self.exposure = exposure self.background_model = None self.mask_fit = mask_fit self.psf = psf self.edisp = edisp self.models = models self._name = make_name(name) self.mask_safe = mask_safe self.gti = gti 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=4) @property def alpha(self): """Exposure ratio between signal and background regions""" alpha = self.acceptance / self.acceptance_off alpha.data = np.nan_to_num(alpha.data) return alpha @property def background(self): """Predicted background in the on region. Notice that this definition is valid under the assumption of cash statistic. """ return self.alpha * self.counts_off @property def excess(self): """Excess (counts - alpha * counts_off)""" return self.counts.data - self.background.data
[docs] def stat_array(self): """Likelihood per bin given the current model parameters""" mu_sig = self.npred().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_)
[docs] @classmethod def from_geoms( cls, geom, geom_exposure, geom_psf, geom_edisp, reference_time="2000-01-01", name=None, **kwargs, ): """ Create a MapDatasetOnOff object with 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 map 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 """ kwargs = kwargs.copy() kwargs["name"] = name for key in ["counts", "counts_off", "acceptance", "acceptance_off"]: kwargs[key] = Map.from_geom(geom, unit="") kwargs["exposure"] = Map.from_geom(geom_exposure, unit="m2 s") kwargs["edisp"] = EDispMap.from_geom(geom_edisp) kwargs["psf"] = PSFMap.from_geom(geom_psf) kwargs["gti"] = GTI.create([] * u.s, [] * u.s, reference_time=reference_time) kwargs["mask_safe"] = Map.from_geom(geom, dtype=bool) return cls(**kwargs)
[docs] @classmethod def from_map_dataset( cls, dataset, acceptance, acceptance_off, counts_off=None, name=None ): """Create spectrum dataseton off from another 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. """ kwargs = {"name": name} if counts_off is None and dataset.background_model is not None: alpha = acceptance / acceptance_off kwargs["counts_off"] = dataset.background_model.evaluate() / alpha return cls( counts=dataset.counts, exposure=dataset.exposure, counts_off=counts_off, edisp=dataset.edisp, gti=dataset.gti, mask_safe=dataset.mask_safe, mask_fit=dataset.mask_fit, acceptance=acceptance, acceptance_off=acceptance_off, name=dataset.name, evaluation_mode=dataset.evaluation_mode, )
@property def _is_stackable(self): """Check if the Dataset contains enough information to be stacked""" if ( self.acceptance_off is None or self.acceptance is None or self.counts_off is None ): return False else: return True
[docs] def stack(self, other): 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 """ 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.") # Factor containing: self.alpha * self.counts_off + other.alpha * other.counts_off tmp_factor = (self.alpha * self.counts_off).copy() tmp_factor.data[~self.mask_safe.data] = 0 tmp_factor.stack(other.alpha * other.counts_off, weights=other.mask_safe) # Stack the off counts (in place) self.counts_off.data[~self.mask_safe.data] = 0 self.counts_off.stack(other.counts_off, weights=other.mask_safe) self.acceptance_off = self.counts_off / tmp_factor self.acceptance.data = np.ones(self.data_shape) super().stack(other)
[docs] def stat_sum(self): """Total likelihood given the current model parameters.""" return Dataset.stat_sum(self)
[docs] def fake(self, background_model, 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() npred.data = random_state.poisson(npred.data) npred_bkg = background_model.copy() npred_bkg.data = random_state.poisson(npred_bkg.data) self.counts = npred + npred_bkg npred_off = background_model / self.alpha npred_off.data = random_state.poisson(npred_off.data) self.counts_off = npred_off
[docs] 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) 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
[docs] @classmethod def from_hdulist(cls, hdulist, name=None): """Create map dataset from list of HDUs. Parameters ---------- hdulist : `~astropy.io.fits.HDUList` List of HDUs. name : str Name of the new dataset. Returns ------- dataset : `MapDataset` Map dataset. """ kwargs = {} kwargs["name"] = name if "COUNTS" in hdulist: kwargs["counts"] = Map.from_hdulist(hdulist, hdu="counts") if "COUNTS_OFF" in hdulist: kwargs["counts_off"] = Map.from_hdulist(hdulist, hdu="counts_off") if "ACCEPTANCE" in hdulist: kwargs["acceptance"] = Map.from_hdulist(hdulist, hdu="acceptance") if "ACCEPTANCE_OFF" in hdulist: kwargs["acceptance_off"] = Map.from_hdulist(hdulist, hdu="acceptance_off") if "EXPOSURE" in hdulist: kwargs["exposure"] = Map.from_hdulist(hdulist, hdu="exposure") if "EDISP_MATRIX" in hdulist: kwargs["edisp"] = EDispKernel.from_hdulist( hdulist, hdu1="EDISP_MATRIX", hdu2="EDISP_MATRIX_EBOUNDS" ) if "PSF_KERNEL" in hdulist: psf_map = Map.from_hdulist(hdulist, hdu="psf_kernel") kwargs["psf"] = PSFKernel(psf_map) if "MASK_SAFE" in hdulist: mask_safe_map = Map.from_hdulist(hdulist, hdu="mask_safe") kwargs["mask_safe"] = mask_safe_map.data.astype(bool) if "MASK_FIT" in hdulist: mask_fit_map = Map.from_hdulist(hdulist, hdu="mask_fit") kwargs["mask_fit"] = mask_fit_map.data.astype(bool) if "GTI" in hdulist: gti = GTI(Table.read(hdulist, hdu="GTI")) kwargs["gti"] = gti return cls(**kwargs)
[docs] def to_spectrum_dataset(self, on_region, containment_correction=False, name=None): """Return a ~gammapy.spectrum.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.spectrum.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.spectrum.SpectrumDatasetOnOff` the resulting reduced dataset """ from .spectrum import SpectrumDatasetOnOff dataset = super().to_spectrum_dataset(on_region, containment_correction, name) kwargs = {} if self.counts_off is not None: kwargs["counts_off"] = self.counts_off.get_spectrum(on_region, np.sum) if self.acceptance is not None: kwargs["acceptance"] = self.acceptance.get_spectrum(on_region, np.mean) background = self.background.get_spectrum(on_region, np.sum) kwargs["acceptance_off"] = ( kwargs["acceptance"] * kwargs["counts_off"] / background ) return SpectrumDatasetOnOff.from_spectrum_dataset(dataset=dataset, **kwargs)
[docs] 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
[docs] def to_image(self, spectrum=None, name=None): """Create images by summing over the energy axis. Exposure is weighted with an assumed spectrum, resulting in a weighted mean exposure image. Currently the PSFMap and EdispMap are dropped from the resulting image dataset. Parameters ---------- spectrum : `~gammapy.modeling.models.SpectralModel` Spectral model to compute the weights. Default is power-law with spectral index of 2. name : str Name of the new dataset. Returns ------- dataset : `MapDatasetOnOff` Map dataset containing images. """ kwargs = {"name": name} dataset = super().to_image(spectrum, name) if self.mask_safe is not None: mask_safe = self.mask_safe else: mask_safe = 1 if self.counts_off is not None: counts_off = self.counts_off * mask_safe kwargs["counts_off"] = counts_off.sum_over_axes(keepdims=True) if self.acceptance is not None: acceptance = self.acceptance * mask_safe kwargs["acceptance"] = acceptance.sum_over_axes(keepdims=True) background = self.background * mask_safe background = background.sum_over_axes(keepdims=True) kwargs["acceptance_off"] = ( kwargs["acceptance"] * kwargs["counts_off"] / background ) return self.from_map_dataset(dataset, **kwargs)
class MapEvaluator: """Sky model evaluation on maps. This evaluates a sky model on a 3D map and convolves with the IRFs, and returns a map of the predicted counts. Note that background counts are not added. For now, we only make it work for 3D WCS maps with an energy axis. No HPX, no other axes, those can be added later here or via new separate model evaluator classes. Parameters ---------- model : `~gammapy.modeling.models.SkyModel` Sky model exposure : `~gammapy.maps.Map` Exposure map psf : `~gammapy.cube.PSFKernel` PSF kernel edisp : `~gammapy.irf.EDispKernel` Energy dispersion gti : `~gammapy.data.GTI` GTI of the observation or union of GTI if it is a stacked observation evaluation_mode : {"local", "global"} Model evaluation mode. """ def __init__( self, model=None, exposure=None, psf=None, edisp=None, gti=None, evaluation_mode="local", ): self.model = model self.exposure = exposure self.psf = psf self.edisp = edisp self.gti = gti self.contributes = True if evaluation_mode not in {"local", "global"}: raise ValueError(f"Invalid evaluation_mode: {evaluation_mode!r}") self.evaluation_mode = evaluation_mode # TODO: this is preliminary solution until we have further unified the model handling if isinstance(model, BackgroundModel): self.compute_npred = model.evaluate self.evaluation_mode = "global" @property def geom(self): """True energy map geometry (`~gammapy.maps.Geom`)""" return self.exposure.geom @property def needs_update(self): """Check whether the model component has drifted away from its support.""" # TODO: simplify and clean up if isinstance(self.model, BackgroundModel): return False elif self.exposure is None: return True elif self.evaluation_mode == "global" or self.model.evaluation_radius is None: return False else: position = self.model.position separation = self._init_position.separation(position) update = separation > (self.model.evaluation_radius + CUTOUT_MARGIN) return update def update(self, exposure, psf, edisp, geom): """Update MapEvaluator, based on the current position of the model component. Parameters ---------- exposure : `~gammapy.maps.Map` Exposure map. psf : `gammapy.cube.PSFMap` PSF map. edisp : `gammapy.cube.EDispMap` Edisp map. geom : `WcsGeom` Counts geom """ # TODO: simplify and clean up log.debug("Updating model evaluator") # cache current position of the model component if isinstance(edisp, EDispMap): e_reco = geom.get_axis_by_name("energy").edges self.edisp = edisp.get_edisp_kernel(self.model.position, e_reco=e_reco) else: self.edisp = edisp if isinstance(psf, PSFMap): self.psf = psf.get_psf_kernel(self.model.position, geom=exposure.geom) else: self.psf = psf if self.evaluation_mode == "local" and self.model.evaluation_radius is not None: self._init_position = self.model.position if self.psf is not None: psf_width = np.max(self.psf.psf_kernel_map.geom.width) else: psf_width = 0 * u.deg width = psf_width + 2 * (self.model.evaluation_radius + CUTOUT_MARGIN) try: self.exposure = exposure.cutout( position=self.model.position, width=width ) self.contributes = True except (NoOverlapError, ValueError): self.contributes = False else: self.exposure = exposure def compute_dnde(self): """Compute model differential flux at map pixel centers. Returns ------- model_map : `~gammapy.maps.Map` Sky cube with data filled with evaluated model values. Units: ``cm-2 s-1 TeV-1 deg-2`` """ return self.model.evaluate_geom(self.geom, self.gti) def compute_flux(self): """Compute model integral flux over map pixel volumes. For now, we simply multiply dnde with bin volume. """ return self.model.integrate_geom(self.geom, self.gti) def apply_exposure(self, flux): """Compute npred cube For now just divide flux cube by exposure """ npred = (flux.quantity * self.exposure.quantity).to_value("") return Map.from_geom(self.geom, data=npred, unit="") def apply_psf(self, npred): """Convolve npred cube with PSF""" tmp = npred.convolve(self.psf) tmp.data[tmp.data < 0.0] = 0 return tmp def apply_edisp(self, npred): """Convolve map data with energy dispersion. Parameters ---------- npred : `~gammapy.maps.Map` Predicted counts in true energy bins Returns ------- npred_reco : `~gammapy.maps.Map` Predicted counts in reco energy bins """ return npred.apply_edisp(self.edisp) def compute_npred(self): """ Evaluate model predicted counts. Returns ------- npred : `~gammapy.maps.Map` Predicted counts on the map (in reco energy bins) """ flux = self.compute_flux() if self.model.apply_irf["exposure"]: npred = self.apply_exposure(flux) if self.psf and self.model.apply_irf["psf"]: npred = self.apply_psf(npred) if self.model.apply_irf["edisp"]: npred = self.apply_edisp(npred) return npred