Source code for gammapy.irf.psf.map

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import astropy.units as u
from astropy.visualization import quantity_support
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from gammapy.maps import HpxGeom, Map, MapAxes, MapAxis, MapCoord, WcsGeom
from gammapy.maps.axes import UNIT_STRING_FORMAT
from gammapy.modeling.models import PowerLawSpectralModel
from gammapy.utils.deprecation import deprecated_renamed_argument
from gammapy.utils.gauss import Gauss2DPDF
from gammapy.utils.random import InverseCDFSampler, get_random_state
from ..core import IRFMap
from .core import PSF
from .kernel import PSFKernel

__all__ = ["PSFMap", "RecoPSFMap"]


PSF_MAX_OVERSAMPLING = 4  # for backward compatibility


def _psf_upsampling_factor(psf, geom, position, energy=None, precision_factor=12):
    """Minimal factor between the bin half-width of the geom and the median R68% containment radius."""
    if energy is None:
        energy = geom.axes[psf.energy_name].center
    psf_r68 = psf.containment_radius(
        0.68, geom.axes[psf.energy_name].center, position=position
    )
    psf_r68_median = np.percentile(psf_r68, 50)
    base_factor = (2 * psf_r68_median / geom.pixel_scales.max()).to_value("")
    factor = np.minimum(
        int(np.ceil(precision_factor / base_factor)), PSF_MAX_OVERSAMPLING
    )
    if isinstance(geom, HpxGeom):
        factor = int(2 ** np.ceil(np.log(factor) / np.log(2)))
    return factor


class IRFLikePSF(PSF):
    required_axes = ["energy_true", "rad", "lat_idx", "lon_idx"]
    tag = "irf_like_psf"


[docs]class PSFMap(IRFMap): """Class containing the Map of PSFs and allowing to interact with it. Parameters ---------- psf_map : `~gammapy.maps.Map` The input PSF Map. Should be a Map with 2 non spatial axes. rad and true energy axes should be given in this specific order. exposure_map : `~gammapy.maps.Map` Associated exposure map. Needs to have a consistent map geometry. Examples -------- :: from astropy.coordinates import SkyCoord from gammapy.maps import WcsGeom, MapAxis from gammapy.data import Observation, FixedPointingInfo from gammapy.irf import load_irf_dict_from_file from gammapy.makers import MapDatasetMaker # Define observation pointing_position = SkyCoord(0, 0, unit="deg", frame="galactic") pointing = FixedPointingInfo( fixed_icrs=pointing_position.icrs, ) filename = "$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits" irfs = load_irf_dict_from_file(filename) obs = Observation.create(pointing=pointing, irfs=irfs, livetime="1h") # Define energy axis. Note that the name is fixed. energy_axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3, name="energy_true") # Define rad axis. Again note the axis name rad_axis = MapAxis.from_bounds(0, 0.5, nbin=100, name="rad", unit="deg") # Create WcsGeom geom = WcsGeom.create( binsz=0.25, width="5 deg", skydir=pointing_position, axes=[rad_axis, energy_axis] ) maker = MapDatasetMaker() psf = maker.make_psf(geom=geom, observation=obs) # Get a PSF kernel at the center of the image upsample_geom = geom.upsample(factor=10).drop("rad") psf_kernel = psf.get_psf_kernel(geom=upsample_geom) """ tag = "psf_map" required_axes = ["rad", "energy_true"] def __init__(self, psf_map, exposure_map=None): super().__init__(irf_map=psf_map, exposure_map=exposure_map) @property def energy_name(self): return self.required_axes[-1] @property def psf_map(self): return self._irf_map @psf_map.setter def psf_map(self, value): self._irf_map = value
[docs] def normalize(self): """Normalize PSF map.""" self.psf_map.normalize(axis_name="rad")
[docs] @classmethod def from_geom(cls, geom): """Create PSF map from geometry. Parameters ---------- geom : `Geom` PSF map geometry. Returns ------- psf_map : `PSFMap` Point spread function map. """ geom_exposure = geom.squash(axis_name="rad") exposure_psf = Map.from_geom(geom_exposure, unit="m2 s") psf_map = Map.from_geom(geom, unit="sr-1") return cls(psf_map, exposure_psf)
# TODO: this is a workaround for now, probably add Map.integral() or similar @property def _psf_irf(self): geom = self.psf_map.geom npix_x, npix_y = geom.npix axis_lon = MapAxis.from_edges(np.arange(npix_x[0] + 1) - 0.5, name="lon_idx") axis_lat = MapAxis.from_edges(np.arange(npix_y[0] + 1) - 0.5, name="lat_idx") axes = MapAxes( [geom.axes[self.energy_name], geom.axes["rad"], axis_lat, axis_lon] ) psf = IRFLikePSF psf.required_axes = axes.names return psf( axes=axes, data=self.psf_map.data, unit=self.psf_map.unit, ) def _get_irf_coords(self, **kwargs): coords = MapCoord.create(kwargs) geom = self.psf_map.geom.to_image() lon_pix, lat_pix = geom.coord_to_pix(coords.skycoord) coords_irf = { "lon_idx": lon_pix, "lat_idx": lat_pix, self.energy_name: coords[self.energy_name], } try: coords_irf["rad"] = coords["rad"] except KeyError: pass return coords_irf
[docs] def containment(self, rad, energy_true, position=None): """Containment at given coordinates. Parameters ---------- rad : `~astropy.units.Quantity` Rad value. energy_true : `~astropy.units.Quantity` Energy true value. position : `~astropy.coordinates.SkyCoord`, optional Sky position. If None, the center of the map is chosen. Default is None. Returns ------- containment : `~astropy.units.Quantity` Containment values. """ if position is None: position = self.psf_map.geom.center_skydir coords = {"skycoord": position, "rad": rad, self.energy_name: energy_true} return self.psf_map.integral(axis_name="rad", coords=coords).to("")
[docs] def containment_radius(self, fraction, energy_true, position=None): """Containment at given coordinates. Parameters ---------- fraction : float Containment fraction. energy_true : `~astropy.units.Quantity` Energy true value. position : `~astropy.coordinates.SkyCoord`, optional Sky position. If None, the center of the map is chosen. Default is None. Returns ------- containment : `~astropy.units.Quantity` Containment values. """ if position is None: position = self.psf_map.geom.center_skydir kwargs = {self.energy_name: energy_true, "skycoord": position} coords = self._get_irf_coords(**kwargs) return self._psf_irf.containment_radius(fraction, **coords)
[docs] def containment_radius_map(self, energy_true, fraction=0.68): """Containment radius map. Parameters ---------- energy_true : `~astropy.units.Quantity` Energy true at which to compute the containment radius. fraction : float, optional Containment fraction (range: 0 to 1). Default is 0.68. Returns ------- containment_radius_map : `~gammapy.maps.Map` Containment radius map. """ geom = self.psf_map.geom.to_image() data = self.containment_radius( fraction, energy_true, geom.get_coord().skycoord, ) return Map.from_geom(geom=geom, data=data.value, unit=data.unit)
[docs] @deprecated_renamed_argument( "factor", "precision_factor", "v1.2", arg_in_kwargs=True ) def get_psf_kernel( self, geom, position=None, max_radius=None, containment=0.999, factor=None, precision_factor=12, ): """Return a PSF kernel at the given position. The PSF is returned in the form a WcsNDMap defined by the input Geom. Parameters ---------- geom : `~gammapy.maps.Geom` Target geometry to use. position : `~astropy.coordinates.SkyCoord`, optional Target position. Should be a single coordinate. By default, the center position is used. max_radius : `~astropy.coordinates.Angle`, optional Maximum angular size of the kernel map. containment : float, optional Containment fraction to use as size of the kernel. The max. radius across all energies is used. The radius can be overwritten using the `max_radius` argument. Default is 0.999. factor : int, optional Oversampling factor to compute the PSF. Default is None and it will be computed automatically. precision_factor : int, optional Factor between the bin half-width of the geom and the median R68% containment radius. Used only for the oversampling method. Default is 10. Returns ------- kernel : `~gammapy.irf.PSFKernel` The resulting kernel. """ if factor is None: # TODO: remove once deprecated factor = _psf_upsampling_factor(self, geom, position, precision_factor) if position is None: position = self.psf_map.geom.center_skydir position = self._get_nearest_valid_position(position) if max_radius is None: energy_axis = self.psf_map.geom.axes[self.energy_name] kwargs = { "fraction": containment, "position": position, self.energy_name: energy_axis.center, } radii = self.containment_radius(**kwargs) max_radius = np.max(radii) geom = geom.to_odd_npix(max_radius=max_radius).upsample(factor=factor) coords = geom.get_coord(sparse=True) rad = coords.skycoord.separation(geom.center_skydir) coords = { self.energy_name: coords[self.energy_name], "rad": rad, "skycoord": position, } data = self.psf_map.interp_by_coord( coords=coords, method="linear", ) kernel_map = Map.from_geom(geom=geom, data=np.clip(data, 0, np.inf)) kernel_map = kernel_map.downsample(factor=factor, preserve_counts=True) return PSFKernel(kernel_map, normalize=True)
[docs] def sample_coord(self, map_coord, random_state=0, chunk_size=10000): """Apply PSF 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`}, optional Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. Default is 0. chunk_size : int If set, this will slice the input MapCoord into smaller chunks of chunk_size elements. Default is 10000. Returns ------- corr_coord : `~gammapy.maps.MapCoord` Sequence of PSF-corrected coordinates of the input map_coord map. """ random_state = get_random_state(random_state) rad_axis = self.psf_map.geom.axes["rad"] position = map_coord.skycoord energy = map_coord[self.energy_name] size = position.size separation = np.ones(size) * u.deg chunk_size = size if chunk_size is None else chunk_size index = 0 while index < size: chunk = slice(index, index + chunk_size, 1) coord = { "skycoord": position[chunk].reshape(-1, 1), self.energy_name: energy[chunk].reshape(-1, 1), "rad": rad_axis.center, } pdf = ( self.psf_map.interp_by_coord(coord) * rad_axis.center.value * rad_axis.bin_width.value ) sample_pdf = InverseCDFSampler(pdf, axis=1, random_state=random_state) pix_coord = sample_pdf.sample_axis() separation[chunk] = rad_axis.pix_to_coord(pix_coord) index += chunk_size position_angle = random_state.uniform(360, size=len(map_coord.lon)) * u.deg event_positions = map_coord.skycoord.directional_offset_by( position_angle=position_angle, separation=separation ) return MapCoord.create({"skycoord": event_positions, self.energy_name: energy})
[docs] @classmethod def from_gauss(cls, energy_axis_true, rad_axis=None, sigma=0.1 * u.deg, geom=None): """Create all-sky PSF map from Gaussian width. This is used for testing and examples. The width can be the same for all energies or be an array with one value per energy node. It does not depend on position. Parameters ---------- energy_axis_true : `~gammapy.maps.MapAxis` True energy axis. rad_axis : `~gammapy.maps.MapAxis` Offset angle wrt source position axis. sigma : `~astropy.coordinates.Angle` Gaussian width. geom : `Geom` Image geometry. By default, an all-sky geometry is created. Returns ------- psf_map : `PSFMap` Point spread function map. """ from gammapy.datasets.map import RAD_AXIS_DEFAULT if rad_axis is None: rad_axis = RAD_AXIS_DEFAULT.copy() if geom is None: geom = WcsGeom.create( npix=(2, 1), proj="CAR", binsz=180, ) geom = geom.to_cube([rad_axis, energy_axis_true]) coords = geom.get_coord(sparse=True) sigma = u.Quantity(sigma).reshape((-1, 1, 1, 1)) gauss = Gauss2DPDF(sigma=sigma) data = gauss(coords["rad"]) * np.ones(geom.data_shape) psf_map = Map.from_geom(geom=geom, data=data.to_value("sr-1"), unit="sr-1") exposure_map = Map.from_geom( geom=geom.squash(axis_name="rad"), unit="m2 s", data=1.0 ) return cls(psf_map=psf_map, exposure_map=exposure_map)
[docs] def to_image(self, spectrum=None, keepdims=True): """Reduce to a 2D map after weighing with the associated exposure and a spectrum. Parameters ---------- spectrum : `~gammapy.modeling.models.SpectralModel`, optional Spectral model to compute the weights. Default is power-law with spectral index of 2. keepdims : bool, optional If True, the energy axis is kept with one bin. If False, the axis is removed. Returns ------- psf_out : `PSFMap` `PSFMap` with the energy axis summed over. """ from gammapy.makers.utils import _map_spectrum_weight if spectrum is None: spectrum = PowerLawSpectralModel(index=2.0) exp_weighed = _map_spectrum_weight(self.exposure_map, spectrum) exposure = exp_weighed.sum_over_axes( axes_names=[self.energy_name], keepdims=keepdims ) psf_data = exp_weighed.data * self.psf_map.data / exposure.data psf_map = Map.from_geom(geom=self.psf_map.geom, data=psf_data, unit="sr-1") psf = psf_map.sum_over_axes(axes_names=[self.energy_name], keepdims=keepdims) return self.__class__(psf_map=psf, exposure_map=exposure)
[docs] def plot_containment_radius_vs_energy( self, ax=None, fraction=(0.68, 0.95), **kwargs ): """Plot containment fraction as a function of energy. The method plots the containment radius at the center of the map. Parameters ---------- ax : `~matplotlib.pyplot.Axes`, optional Matplotlib axes. Default is None. fraction : list of float or `~numpy.ndarray` Containment fraction between 0 and 1. **kwargs : dict Keyword arguments passed to `~matplotlib.pyplot.plot` Returns ------- ax : `~matplotlib.pyplot.Axes` Matplotlib axes. """ ax = plt.gca() if ax is None else ax position = self.psf_map.geom.center_skydir energy_axis = self.psf_map.geom.axes[self.energy_name] energy_true = energy_axis.center for frac in fraction: radius = self.containment_radius(frac, energy_true, position) label = f"Containment: {100 * frac:.1f}%" with quantity_support(): ax.plot(energy_true, radius, label=label, **kwargs) ax.semilogx() ax.legend(loc="best") ax.yaxis.set_major_formatter(FormatStrFormatter("%.2f")) energy_axis.format_plot_xaxis(ax=ax) ax.set_ylabel( f"Containment radius [{ax.yaxis.units.to_string(UNIT_STRING_FORMAT)}]" ) return ax
[docs] def plot_psf_vs_rad(self, ax=None, energy_true=[0.1, 1, 10] * u.TeV, **kwargs): """Plot PSF vs radius. The method plots the profile at the center of the map. Parameters ---------- ax : `~matplotlib.pyplot.Axes`, optional Matplotlib axes. Default is None. energy : `~astropy.units.Quantity` Energies where to plot the PSF. **kwargs : dict Keyword arguments pass to `~matplotlib.pyplot.plot`. Returns ------- ax : `~matplotlib.pyplot.Axes` Matplotlib axes. """ ax = plt.gca() if ax is None else ax rad = self.psf_map.geom.axes["rad"].center for value in energy_true: psf_value = self.psf_map.interp_by_coord( { "skycoord": self.psf_map.geom.center_skydir, self.energy_name: value, "rad": rad, } ) label = f"{value:.0f}" psf_value *= self.psf_map.unit with quantity_support(): ax.plot(rad, psf_value, label=label, **kwargs) ax.set_yscale("log") ax.set_xlabel(f"Rad [{ax.xaxis.units.to_string(UNIT_STRING_FORMAT)}]") ax.set_ylabel(f"PSF [{ax.yaxis.units.to_string(UNIT_STRING_FORMAT)}]") ax.xaxis.set_major_formatter(FormatStrFormatter("%.2f")) plt.legend() return ax
def __str__(self): return str(self.psf_map)
[docs] def peek(self, figsize=(12, 10)): """Quick-look summary plots. Parameters ---------- figsize : tuple Size of figure. """ fig, axes = plt.subplots( ncols=2, nrows=2, subplot_kw={"projection": self.psf_map.geom.wcs}, figsize=figsize, gridspec_kw={"hspace": 0.3, "wspace": 0.3}, ) axes = axes.flat axes[0].remove() ax0 = fig.add_subplot(2, 2, 1) ax0.set_title("Containment radius at center of map") self.plot_containment_radius_vs_energy(ax=ax0) axes[1].remove() ax1 = fig.add_subplot(2, 2, 2) ax1.set_ylim(1e-4, 1e4) ax1.set_title("PSF at center of map") self.plot_psf_vs_rad(ax=ax1) axes[2].set_title("Exposure") if self.exposure_map is not None: self.exposure_map.reduce_over_axes().plot(ax=axes[2], add_cbar=True) axes[3].set_title("Containment radius at 1 TeV") kwargs = {self.energy_name: 1 * u.TeV} self.containment_radius_map(**kwargs).plot(ax=axes[3], add_cbar=True)
[docs]class RecoPSFMap(PSFMap): """Class containing the Map of PSFs in reconstructed energy and allowing to interact with it. Parameters ---------- psf_map : `~gammapy.maps.Map` the input PSF Map. Should be a Map with 2 non spatial axes. rad and energy axes should be given in this specific order. exposure_map : `~gammapy.maps.Map` Associated exposure map. Needs to have a consistent map geometry. """ tag = "psf_map_reco" required_axes = ["rad", "energy"] @property def energy_name(self): return self.required_axes[-1]
[docs] @classmethod def from_gauss(cls, energy_axis, rad_axis=None, sigma=0.1 * u.deg, geom=None): """Create all -sky PSF map from Gaussian width. This is used for testing and examples. The width can be the same for all energies or be an array with one value per energy node. It does not depend on position. Parameters ---------- energy_axis : `~gammapy.maps.MapAxis` Energy axis. rad_axis : `~gammapy.maps.MapAxis` Offset angle wrt source position axis. sigma : `~astropy.coordinates.Angle` Gaussian width. geom : `Geom` Image geometry. By default, an all-sky geometry is created. Returns ------- psf_map : `PSFMap` Point spread function map. """ return super().from_gauss(energy_axis, rad_axis, sigma, geom)
[docs] def containment(self, rad, energy, position=None): """Containment at given coordinates. Parameters ---------- rad : `~astropy.units.Quantity` Rad value. energy : `~astropy.units.Quantity` Energy value. position : `~astropy.coordinates.SkyCoord`, optional Sky position. By default, the center of the map is chosen. Returns ------- containment : `~astropy.units.Quantity` Containment values. """ return super().containment(rad, energy, position)
[docs] def containment_radius(self, fraction, energy, position=None): """Containment at given coordinates. Parameters ---------- fraction : float Containment fraction. energy : `~astropy.units.Quantity` Energy value. position : `~astropy.coordinates.SkyCoord`, optional Sky position. By default, the center of the map is chosen. Returns ------- containment : `~astropy.units.Quantity` Containment values. """ return super().containment_radius(fraction, energy, position)
[docs] def containment_radius_map(self, energy, fraction=0.68): """Containment radius map. Parameters ---------- energy : `~astropy.units.Quantity` Energy at which to compute the containment radius fraction : float, optional Containment fraction (range: 0 to 1). Default is 0.68. Returns ------- containment_radius_map : `~gammapy.maps.Map` Containment radius map. """ return super().containment_radius_map(energy, fraction=0.68)
[docs] def plot_psf_vs_rad(self, ax=None, energy=[0.1, 1, 10] * u.TeV, **kwargs): """Plot PSF vs radius. The method plots the profile at the center of the map. Parameters ---------- ax : `~matplotlib.pyplot.Axes`, optional Matplotlib axes. Default is None. energy : `~astropy.units.Quantity` Energies where to plot the PSF. **kwargs : dict Keyword arguments pass to `~matplotlib.pyplot.plot`. Returns ------- ax : `~matplotlib.pyplot.Axes` Matplotlib axes. """ return super().plot_psf_vs_rad(ax, energy_true=energy, **kwargs)
[docs] def stack(self, other, weights=None, nan_to_num=True): """Stack IRF map with another one in place.""" raise NotImplementedError( "Stacking is not supported for PSF in reconstructed energy." )