Source code for gammapy.irf.psf_king

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy.table import Table
from astropy.units import Quantity
from astropy.coordinates import Angle
from astropy.io import fits
from ..utils.scripts import make_path
from ..utils.array import array_stats_str
from . import EnergyDependentTablePSF

__all__ = ["PSFKing"]

log = logging.getLogger(__name__)


[docs]class PSFKing: """King profile analytical PSF depending on energy and offset. This PSF parametrisation and FITS data format is described here: :ref:`gadf:psf_king`. Parameters ---------- energy_lo : `~astropy.units.Quantity` Lower energy boundary of the energy bin. energy_hi : `~astropy.units.Quantity` Upper energy boundary of the energy bin. offset : `~astropy.coordinates.Angle` Offset nodes (1D) gamma : `~numpy.ndarray` PSF parameter (2D) sigma : `~astropy.coordinates.Angle` PSF parameter (2D) """ def __init__( self, energy_lo, energy_hi, offset, gamma, sigma, energy_thresh_lo=Quantity(0.1, "TeV"), energy_thresh_hi=Quantity(100, "TeV"), ): self.energy_lo = energy_lo.to("TeV") self.energy_hi = energy_hi.to("TeV") self.offset = Angle(offset) self.energy = np.sqrt(self.energy_lo * self.energy_hi) self.gamma = np.asanyarray(gamma) self.sigma = Angle(sigma) self.energy_thresh_lo = Quantity(energy_thresh_lo).to("TeV") self.energy_thresh_hi = Quantity(energy_thresh_hi).to("TeV")
[docs] def info(self): """Print some basic info. """ ss = "\nSummary PSFKing info\n" ss += "---------------------\n" ss += array_stats_str(self.offset, "offset") ss += array_stats_str(self.energy, "energy") ss += array_stats_str(self.gamma, "gamma") ss += array_stats_str(self.sigma, "sigma") # TODO: should quote containment values also return ss
[docs] @classmethod def read(cls, filename, hdu=1): """Create `PSFKing` from FITS file. Parameters ---------- filename : str File name """ filename = str(make_path(filename)) # TODO: implement it so that HDUCLASS is used # http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/hdu_index/index.html table = Table.read(filename, hdu=hdu) return cls.from_table(table)
# hdu_list = fits.open(filename) # hdu = hdu_list[hdu] # return cls.from_fits(hdu)
[docs] @classmethod def from_table(cls, table): """Create `PSFKing` from `~astropy.table.Table`. Parameters ---------- table : `~astropy.table.Table` Table King PSF info. """ offset_lo = table["THETA_LO"].quantity[0] offset_hi = table["THETA_HI"].quantity[0] offset = (offset_hi + offset_lo) / 2 offset = Angle(offset, unit=table["THETA_LO"].unit) energy_lo = table["ENERG_LO"].quantity[0] energy_hi = table["ENERG_HI"].quantity[0] gamma = table["GAMMA"].quantity[0] sigma = table["SIGMA"].quantity[0] opts = {} try: opts["energy_thresh_lo"] = Quantity(table.meta["LO_THRES"], "TeV") opts["energy_thresh_hi"] = Quantity(table.meta["HI_THRES"], "TeV") except KeyError: pass return cls(energy_lo, energy_hi, offset, gamma, sigma, **opts)
[docs] def to_fits(self): """ Convert PSF table data to FITS HDU list. Returns ------- hdu_list : `~astropy.io.fits.HDUList` PSF in HDU list format. """ # Set up data names = ["ENERG_LO", "ENERG_HI", "THETA_LO", "THETA_HI", "SIGMA", "GAMMA"] units = ["TeV", "TeV", "deg", "deg", "deg", ""] data = [ self.energy_lo, self.energy_hi, self.offset, self.offset, self.sigma, self.gamma, ] table = Table() for name_, data_, unit_ in zip(names, data, units): table[name_] = [data_] table[name_].unit = unit_ hdu = fits.BinTableHDU(table) hdu.header["LO_THRES"] = self.energy_thresh_lo.value hdu.header["HI_THRES"] = self.energy_thresh_hi.value return fits.HDUList([fits.PrimaryHDU(), hdu])
[docs] def write(self, filename, *args, **kwargs): """Write PSF to FITS file. Calls `~astropy.io.fits.HDUList.writeto`, forwarding all arguments. """ self.to_fits().writeto(filename, *args, **kwargs)
[docs] @staticmethod def evaluate_direct(r, gamma, sigma): """Evaluate the PSF model. Formula is given here: :ref:`gadf:psf_king`. Parameters ---------- r : `~astropy.coordinates.Angle` Offset from PSF center used for evaluating the PSF on a grid gamma : `~astropy.units.Quantity` model parameter, no unit sigma : `~astropy.coordinates.Angle` model parameter Returns ------- psf_value : `~astropy.units.Quantity` PSF value """ r2 = r * r sigma2 = sigma * sigma with np.errstate(divide="ignore"): term1 = 1 / (2 * np.pi * sigma2) term2 = 1 - 1 / gamma term3 = (1 + r2 / (2 * gamma * sigma2)) ** (-gamma) return term1 * term2 * term3
[docs] def evaluate(self, energy=None, offset=None): """Evaluate analytic PSF parameters at a given energy and offset. Uses nearest-neighbor interpolation. Parameters ---------- energy : `~astropy.units.Quantity` energy value offset : `~astropy.coordinates.Angle` Offset in the field of view Returns ------- values : `~astropy.units.Quantity` Interpolated value """ param = dict() energy = Quantity(energy) offset = Angle(offset) # Find nearest energy value i = np.argmin(np.abs(self.energy - energy)) j = np.argmin(np.abs(self.offset - offset)) # TODO: Use some kind of interpolation to get PSF # parameters for every energy and theta # Select correct gauss parameters for given energy and theta sigma = self.sigma[j][i] gamma = self.gamma[j][i] param["sigma"] = sigma param["gamma"] = gamma return param
[docs] def to_energy_dependent_table_psf(self, theta=None, rad=None, exposure=None): """Convert to energy-dependent table PSF. Parameters ---------- theta : `~astropy.coordinates.Angle` Offset in the field of view. Default theta = 0 deg rad : `~astropy.coordinates.Angle` Offset from PSF center used for evaluating the PSF on a grid. Default offset = [0, 0.005, ..., 1.495, 1.5] deg. exposure : `~astropy.units.Quantity` Energy dependent exposure. Should be in units equivalent to 'cm^2 s'. Default exposure = 1. Returns ------- table_psf : `~gammapy.irf.EnergyDependentTablePSF` Energy-dependent PSF """ # self.energy is already the logcenter energies = self.energy # Defaults theta = theta if theta is not None else Angle(0, "deg") rad = rad if rad is not None else Angle(np.arange(0, 1.5, 0.005), "deg") psf_value = Quantity(np.empty((len(energies), len(rad))), "deg^-2") for i, energy in enumerate(energies): param_king = self.evaluate(energy, theta) val = self.evaluate_direct(rad, param_king["gamma"], param_king["sigma"]) psf_value[i] = Quantity(val, "deg^-2") return EnergyDependentTablePSF( energy=energies, rad=rad, exposure=exposure, psf_value=psf_value )