Source code for gammapy.irf.edisp.map

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from gammapy.maps import Map, MapAxis, MapCoord, RegionGeom, WcsGeom
from gammapy.utils.random import InverseCDFSampler, get_random_state
from ..core import IRFMap
from .kernel import EDispKernel

__all__ = ["EDispMap", "EDispKernelMap"]


def get_overlap_fraction(energy_axis, energy_axis_true):
    a_min = energy_axis.edges[:-1]
    a_max = energy_axis.edges[1:]

    b_min = energy_axis_true.edges[:-1][:, np.newaxis]
    b_max = energy_axis_true.edges[1:][:, np.newaxis]

    xmin = np.fmin(a_max, b_max)
    xmax = np.fmax(a_min, b_min)
    return np.clip(xmin - xmax, 0, np.inf) / (b_max - b_min)


[docs]class EDispMap(IRFMap): """Energy dispersion map. Parameters ---------- edisp_map : `~gammapy.maps.Map` the input Energy Dispersion Map. Should be a Map with 2 non spatial axes. migra and true energy axes should be given in this specific order. exposure_map : `~gammapy.maps.Map`, optional Associated exposure map. Needs to have a consistent map geometry. Examples -------- :: import numpy as np from astropy import units as u from astropy.coordinates import SkyCoord from gammapy.maps import WcsGeom, MapAxis from gammapy.irf import EnergyDispersion2D, EffectiveAreaTable2D from gammapy.makers.utils import make_edisp_map, make_map_exposure_true_energy # Define energy dispersion map geometry energy_axis = MapAxis.from_edges(np.logspace(-1, 1, 4), unit="TeV", name="energy") migra_axis = MapAxis.from_edges(np.linspace(0, 3, 100), name="migra") pointing = SkyCoord(0, 0, unit="deg") max_offset = 4 * u.deg geom = WcsGeom.create( binsz=0.25 * u.deg, width=10 * u.deg, skydir=pointing, axes=[migra_axis, energy_axis], ) # Extract EnergyDispersion2D from CTA 1DC IRF filename = "$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits" edisp2D = EnergyDispersion2D.read(filename, hdu="ENERGY DISPERSION") aeff2d = EffectiveAreaTable2D.read(filename, hdu="EFFECTIVE AREA") # Create the exposure map exposure_geom = geom.to_image().to_cube([energy_axis]) exposure_map = make_map_exposure_true_energy(pointing, "1 h", aeff2d, exposure_geom) # create the EDispMap for the specified pointing edisp_map = make_edisp_map(edisp2D, pointing, geom, max_offset, exposure_map) # Get an Energy Dispersion (1D) at any position in the image pos = SkyCoord(2.0, 2.5, unit="deg") energy = np.logspace(-1.0, 1.0, 10) * u.TeV edisp = edisp_map.get_edisp_kernel(pos=pos, energy=energy) # Write map to disk edisp_map.write("edisp_map.fits") """ tag = "edisp_map" required_axes = ["migra", "energy_true"] def __init__(self, edisp_map, exposure_map=None): super().__init__(irf_map=edisp_map, exposure_map=exposure_map) @property def edisp_map(self): return self._irf_map @edisp_map.setter def edisp_map(self, value): self._irf_map = value
[docs] def normalize(self): """Normalize PSF map""" self.edisp_map.normalize(axis_name="migra")
[docs] def get_edisp_kernel(self, energy_axis, position=None): """Get energy dispersion at a given position. Parameters ---------- energy_axis : `MapAxis` Reconstructed energy axis position : `~astropy.coordinates.SkyCoord` the target position. Should be a single coordinates Returns ------- edisp : `~gammapy.irf.EnergyDispersion` the energy dispersion (i.e. rmf object) """ edisp_map = self.to_region_nd_map(region=position) edisp_kernel_map = edisp_map.to_edisp_kernel_map(energy_axis=energy_axis) return edisp_kernel_map.get_edisp_kernel()
[docs] def to_edisp_kernel_map(self, energy_axis): """Convert to map with edisp kernels Parameters ---------- energy_axis : `~gammapy.maps.MapAxis` Reconstructed energy axis. Returns ------- edisp : `~gammapy.maps.EDispKernelMap` Energy dispersion kernel map. """ energy_axis_true = self.edisp_map.geom.axes["energy_true"] geom_image = self.edisp_map.geom.to_image() geom = geom_image.to_cube([energy_axis, energy_axis_true]) coords = geom.get_coord(sparse=True, mode="edges", axis_name="energy") migra = coords["energy"] / coords["energy_true"] coords = { "skycoord": coords.skycoord, "energy_true": coords["energy_true"], "migra": migra, } values = self.edisp_map.integral(axis_name="migra", coords=coords) axis = self.edisp_map.geom.axes.index_data("migra") data = np.clip(np.diff(values, axis=axis), 0, np.inf) edisp_kernel_map = Map.from_geom(geom=geom, data=data.to_value(""), unit="") if self.exposure_map: geom = geom.squash(axis_name=energy_axis.name) exposure_map = self.exposure_map.copy(geom=geom) else: exposure_map = None return EDispKernelMap( edisp_kernel_map=edisp_kernel_map, exposure_map=exposure_map )
[docs] @classmethod def from_geom(cls, geom): """Create edisp map from geom. By default a diagonal edisp matrix is created. Parameters ---------- geom : `~gammapy.maps.Geom` Edisp map geometry. Returns ------- edisp_map : `~gammapy.maps.EDispMap` Energy dispersion map. """ if "energy_true" not in [ax.name for ax in geom.axes]: raise ValueError("EDispMap requires true energy axis") exposure_map = Map.from_geom(geom=geom.squash(axis_name="migra"), unit="m2 s") edisp_map = Map.from_geom(geom, unit="") migra_axis = geom.axes["migra"] migra_0 = migra_axis.coord_to_pix(1) # distribute over two pixels migra = geom.get_idx()[2] data = np.abs(migra - migra_0) data = np.where(data < 1, 1 - data, 0) edisp_map.quantity = data / migra_axis.bin_width.reshape((1, -1, 1, 1)) return cls(edisp_map, exposure_map)
[docs] def sample_coord(self, map_coord, random_state=0): """Apply the energy dispersion corrections on the coordinates of a set of simulated events. Parameters ---------- map_coord : `~gammapy.maps.MapCoord` object. Sequence of coordinates and energies of sampled events. random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`} Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. Returns ------- `~gammapy.maps.MapCoord`. Sequence of Edisp-corrected coordinates of the input map_coord map. """ random_state = get_random_state(random_state) migra_axis = self.edisp_map.geom.axes["migra"] coord = { "skycoord": map_coord.skycoord.reshape(-1, 1), "energy_true": map_coord["energy_true"].reshape(-1, 1), "migra": migra_axis.center, } pdf_edisp = self.edisp_map.interp_by_coord(coord) sample_edisp = InverseCDFSampler(pdf_edisp, axis=1, random_state=random_state) pix_edisp = sample_edisp.sample_axis() migra = migra_axis.pix_to_coord(pix_edisp) energy_reco = map_coord["energy_true"] * migra return MapCoord.create({"skycoord": map_coord.skycoord, "energy": energy_reco})
[docs] @classmethod def from_diagonal_response(cls, energy_axis_true, migra_axis=None): """Create an allsky EDisp map with diagonal response. Parameters ---------- energy_axis_true : `~gammapy.maps.MapAxis` True energy axis migra_axis : `~gammapy.maps.MapAxis` Migra axis Returns ------- edisp_map : `~gammapy.maps.EDispMap` Energy dispersion map. """ migra_res = 1e-5 migra_axis_default = MapAxis.from_bounds( 1 - migra_res, 1 + migra_res, nbin=3, name="migra", node_type="edges" ) migra_axis = migra_axis or migra_axis_default geom = WcsGeom.create( npix=(2, 1), proj="CAR", binsz=180, axes=[migra_axis, energy_axis_true] ) return cls.from_geom(geom)
[docs] def peek(self, figsize=(15, 5)): """Quick-look summary plots. Plots corresponding to the center of the map. Parameters ---------- figsize : tuple Size of figure. """ e_true = self.edisp_map.geom.axes[1] e_reco = MapAxis.from_energy_bounds( e_true.edges.min(), e_true.edges.max(), nbin=len(e_true.center), name="energy", ) self.get_edisp_kernel(energy_axis=e_reco).peek(figsize)
[docs]class EDispKernelMap(IRFMap): """Energy dispersion kernel map. Parameters ---------- edisp_kernel_map : `~gammapy.maps.Map` The input energy dispersion kernel map. Should be a Map with 2 non spatial axes. Reconstructed and and true energy axes should be given in this specific order. exposure_map : `~gammapy.maps.Map`, optional Associated exposure map. Needs to have a consistent map geometry. """ tag = "edisp_kernel_map" required_axes = ["energy", "energy_true"] def __init__(self, edisp_kernel_map, exposure_map=None): super().__init__(irf_map=edisp_kernel_map, exposure_map=exposure_map) @property def edisp_map(self): return self._irf_map @edisp_map.setter def edisp_map(self, value): self._irf_map = value
[docs] @classmethod def from_geom(cls, geom): """Create edisp map from geom. By default a diagonal edisp matrix is created. Parameters ---------- geom : `~gammapy.maps.Geom` Edisp map geometry. Returns ------- edisp_map : `EDispKernelMap` Energy dispersion kernel map. """ geom.axes.assert_names(cls.required_axes) geom_exposure = geom.squash(axis_name="energy") exposure = Map.from_geom(geom_exposure, unit="m2 s") energy_axis = geom.axes["energy"] energy_axis_true = geom.axes["energy_true"] data = get_overlap_fraction(energy_axis, energy_axis_true) edisp_kernel_map = Map.from_geom(geom, unit="") edisp_kernel_map.quantity += data.reshape(geom.data_shape_axes) return cls(edisp_kernel_map=edisp_kernel_map, exposure_map=exposure)
[docs] def get_edisp_kernel(self, position=None, energy_axis=None): """Get energy dispersion at a given position. Parameters ---------- position : `~astropy.coordinates.SkyCoord` or `~regions.SkyRegion` The target position. Should be a single coordinates energy_axis : `MapAxis` Reconstructed energy axis, only used for checking. Returns ------- edisp : `~gammapy.irf.EnergyDispersion` the energy dispersion (i.e. rmf object) """ if energy_axis: assert energy_axis == self.edisp_map.geom.axes["energy"] if isinstance(self.edisp_map.geom, RegionGeom): kernel_map = self.edisp_map else: if position is None: position = self.edisp_map.geom.center_skydir position = self._get_nearest_valid_position(position) kernel_map = self.edisp_map.to_region_nd_map(region=position) return EDispKernel( axes=kernel_map.geom.axes[["energy_true", "energy"]], data=kernel_map.data[..., 0, 0], )
[docs] @classmethod def from_diagonal_response(cls, energy_axis, energy_axis_true, geom=None): """Create an energy dispersion map with diagonal response. Parameters ---------- energy_axis : `~gammapy.maps.MapAxis` Energy axis. energy_axis_true : `~gammapy.maps.MapAxis` True energy axis geom : `~gammapy.maps.Geom` The (2D) geom object to use. Default creates an all sky geometry with 2 bins. Returns ------- edisp_map : `EDispKernelMap` Energy dispersion kernel map. """ if geom is None: geom = WcsGeom.create( npix=(2, 1), proj="CAR", binsz=180, axes=[energy_axis, energy_axis_true] ) else: geom = geom.to_image().to_cube([energy_axis, energy_axis_true]) return cls.from_geom(geom)
[docs] @classmethod def from_edisp_kernel(cls, edisp, geom=None): """Create an energy dispersion map from the input 1D kernel. The kernel will be duplicated over all spatial bins. Parameters ---------- edisp : `~gammapy.irfs.EDispKernel` the input 1D kernel. geom : `~gammapy.maps.Geom` The (2D) geom object to use. Default creates an all sky geometry with 2 bins. Returns ------- edisp_map : `EDispKernelMap` Energy dispersion kernel map. """ edisp_map = cls.from_diagonal_response( edisp.axes["energy"], edisp.axes["energy_true"], geom=geom ) edisp_map.edisp_map.data *= 0 edisp_map.edisp_map.data[:, :, ...] = edisp.pdf_matrix[ :, :, np.newaxis, np.newaxis ] return edisp_map
[docs] @classmethod def from_gauss( cls, energy_axis, energy_axis_true, sigma, bias, pdf_threshold=1e-6, geom=None ): """Create an energy dispersion map from the input 1D kernel. The kernel will be duplicated over all spatial bins. Parameters ---------- energy_axis_true : `~astropy.units.Quantity` Bin edges of true energy axis energy_axis : `~astropy.units.Quantity` Bin edges of reconstructed energy axis bias : float or `~numpy.ndarray` Center of Gaussian energy dispersion, bias sigma : float or `~numpy.ndarray` RMS width of Gaussian energy dispersion, resolution pdf_threshold : float, optional Zero suppression threshold geom : `~gammapy.maps.Geom` The (2D) geom object to use. Default creates an all sky geometry with 2 bins. Returns ------- edisp_map : `EDispKernelMap` Energy dispersion kernel map. """ kernel = EDispKernel.from_gauss( energy_axis=energy_axis, energy_axis_true=energy_axis_true, sigma=sigma, bias=bias, pdf_threshold=pdf_threshold, ) return cls.from_edisp_kernel(kernel, geom=geom)
[docs] def to_image(self, weights=None): """ "Return a 2D EdispKernelMap by summing over the reconstructed energy axis. Parameters ---------- weights: `~gammapy.maps.Map`, optional Weights to be applied Returns ------- edisp : `EDispKernelMap` Edisp kernel map """ edisp = self.edisp_map.data if weights: edisp = edisp * weights.data data = np.sum(edisp, axis=1, keepdims=True) geom = self.edisp_map.geom.squash(axis_name="energy") edisp_map = Map.from_geom(geom=geom, data=data) return self.__class__( edisp_kernel_map=edisp_map, exposure_map=self.exposure_map )
[docs] def resample_energy_axis(self, energy_axis, weights=None): """Returns a resampled EdispKernelMap Bins are grouped according to the edges of the reconstructed energy axis provided. The true energy is left unchanged. Parameters ---------- energy_axis : `~gammapy.maps.MapAxis` The reco energy axis to use for the reco energy grouping weights: `~gammapy.maps.Map`, optional Weights to be applied Returns ------- edisp : `EDispKernelMap` Edisp kernel map """ new_edisp_map = self.edisp_map.resample_axis(axis=energy_axis, weights=weights) return self.__class__( edisp_kernel_map=new_edisp_map, exposure_map=self.exposure_map )
[docs] def peek(self, figsize=(15, 5)): """Quick-look summary plots. Plots corresponding to the center of the map. Parameters ---------- figsize : tuple Size of figure. """ self.get_edisp_kernel().peek(figsize)