class EnergyDependentTablePSF:
"""Energy-dependent radially-symmetric table PSF (``gtpsf`` format).
TODO: add references and explanations.
Parameters
----------
energy_axis_true : `MapAxis`
Energy axis
rad_axis : `MapAxis`
Offset angle wrt source position axis
exposure : `~astropy.units.Quantity`
Exposure (1-dim)
data : `~astropy.units.Quantity`
PSF (2-dim with axes: psf[energy_index, offset_index]
interp_kwargs : dict
Interpolation keyword arguments pass to `ScaledRegularGridInterpolator`.
"""
def __init__(
self,
energy_axis_true,
rad_axis,
exposure=None,
data=None,
interp_kwargs=None,
):
interp_kwargs = interp_kwargs or {}
axes = MapAxes([energy_axis_true, rad_axis])
axes.assert_names(["energy_true", "rad"])
self.data = NDDataArray(axes=axes,
data=u.Quantity(data).to("sr^-1"),
interp_kwargs=interp_kwargs)
if exposure is None:
self.exposure = u.Quantity(np.ones(self.energy_axis_true.nbin),
"cm^2 s")
else:
self.exposure = u.Quantity(exposure).to("cm^2 s")
@property
def energy_axis_true(self):
return self.data.axes["energy_true"]
@property
def rad_axis(self):
return self.data.axes["rad"]
def __str__(self):
ss = "EnergyDependentTablePSF\n"
ss += "-----------------------\n"
ss += "\nAxis info:\n"
ss += " " + array_stats_str(self.rad_axis.center.to("deg"), "rad")
ss += " " + array_stats_str(self.energy_axis_true.center, "energy")
ss += "\nContainment info:\n"
# Print some example containment radii
fractions = [0.68, 0.95]
energies = u.Quantity([10, 100], "GeV")
for fraction in fractions:
rads = self.containment_radius(energy=energies, fraction=fraction)
for energy, rad in zip(energies, rads):
ss += f" {100 * fraction}% containment radius at {energy:3.0f}: {rad:.2f}\n"
return ss
@classmethod
def from_hdulist(cls, hdu_list):
"""Create `EnergyDependentTablePSF` from ``gtpsf`` format HDU list.
Parameters
----------
hdu_list : `~astropy.io.fits.HDUList`
HDU list with ``THETA`` and ``PSF`` extensions.
"""
# TODO: move this to MapAxis.from_table()
rad = Angle(hdu_list["THETA"].data["Theta"], "deg")
rad_axis = MapAxis.from_nodes(rad, name="rad")
energy = u.Quantity(hdu_list["PSF"].data["Energy"], "MeV")
energy_axis_true = MapAxis.from_nodes(energy,
name="energy_true",
interp="log")
exposure = u.Quantity(hdu_list["PSF"].data["Exposure"], "cm^2 s")
data = u.Quantity(hdu_list["PSF"].data["PSF"], "sr^-1")
return cls(
energy_axis_true=energy_axis_true,
rad_axis=rad_axis,
exposure=exposure,
data=data,
)
def to_hdulist(self):
"""Convert to FITS HDU list format.
Returns
-------
hdu_list : `~astropy.io.fits.HDUList`
PSF in HDU list format.
"""
theta_hdu = self.rad_axis.to_table_hdu(format="gtpsf")
psf_table = self.energy_axis_true.to_table(format="gtpsf")
psf_table["Exposure"] = self.exposure.to("cm^2 s")
psf_table["PSF"] = self.data.data.to("sr^-1")
psf_hdu = fits.BinTableHDU(data=psf_table, name="PSF")
return fits.HDUList([fits.PrimaryHDU(), theta_hdu, psf_hdu])
@classmethod
def read(cls, filename):
"""Create `EnergyDependentTablePSF` from ``gtpsf``-format FITS file.
Parameters
----------
filename : str
File name
"""
with fits.open(str(make_path(filename)), memmap=False) as hdulist:
return cls.from_hdulist(hdulist)
def write(self, filename, *args, **kwargs):
"""Write to FITS file.
Calls `~astropy.io.fits.HDUList.writeto`, forwarding all arguments.
"""
self.to_hdulist().writeto(str(make_path(filename)), *args, **kwargs)
def evaluate(self, energy=None, rad=None, method="linear"):
"""Evaluate the PSF at a given energy and offset
Parameters
----------
energy : `~astropy.units.Quantity`
Energy value
rad : `~astropy.coordinates.Angle`
Offset wrt source position
method : {"linear", "nearest"}
Linear or nearest neighbour interpolation.
Returns
-------
values : `~astropy.units.Quantity`
Interpolated value
"""
if energy is None:
energy = self.energy_axis_true.center
if rad is None:
rad = self.rad_axis.center
energy = u.Quantity(energy, ndmin=1)[:, np.newaxis]
rad = u.Quantity(rad, ndmin=1)
return self.data._interpolate((energy, rad), method=method)
def table_psf_at_energy(self, energy, method="linear", **kwargs):
"""Create `~gammapy.irf.TablePSF` at one given energy.
Parameters
----------
energy : `~astropy.units.Quantity`
Energy
method : {"linear", "nearest"}
Linear or nearest neighbour interpolation.
Returns
-------
psf : `~gammapy.irf.TablePSF`
Table PSF
"""
psf_value = self.evaluate(energy=energy, method=method)[0, :]
return TablePSF(rad_axis=self.rad_axis, data=psf_value, **kwargs)
def table_psf_in_energy_range(self,
energy_range,
spectrum=None,
n_bins=11,
**kwargs):
"""Average PSF in a given energy band.
Expected counts in sub energy bands given the given exposure
and spectrum are used as weights.
Parameters
----------
energy_range : `~astropy.units.Quantity`
Energy band
spectrum : `~gammapy.modeling.models.SpectralModel`
Spectral model used for weighting the PSF. Default is a power law
with index=2.
n_bins : int
Number of energy points in the energy band, used to compute the
weigthed PSF.
Returns
-------
psf : `TablePSF`
Table PSF
"""
from gammapy.modeling.models import PowerLawSpectralModel, TemplateSpectralModel
if spectrum is None:
spectrum = PowerLawSpectralModel()
exposure = TemplateSpectralModel(self.energy_axis_true.center,
self.exposure)
e_min, e_max = energy_range
energy = MapAxis.from_energy_bounds(e_min, e_max, n_bins).edges
weights = spectrum(energy) * exposure(energy)
weights /= weights.sum()
psf_value = self.evaluate(energy=energy)
psf_value_weighted = weights[:, np.newaxis] * psf_value
return TablePSF(self.rad_axis, psf_value_weighted.sum(axis=0),
**kwargs)
def containment_radius(self, energy, fraction=0.68):
"""Containment radius.
Parameters
----------
energy : `~astropy.units.Quantity`
Energy
fraction : float
Containment fraction.
Returns
-------
rad : `~astropy.units.Quantity`
Containment radius in deg
"""
# upsamle for better precision
rad_max = Angle(self.rad_axis.upsample(factor=10).center)
containment = self.containment(energy=energy, rad_max=rad_max)
# find nearest containment value
fraction_idx = np.argmin(np.abs(containment - fraction), axis=1)
return rad_max[fraction_idx].to("deg")
def containment(self, energy, rad_max):
"""Compute containment of the PSF.
Parameters
----------
energy : `~astropy.units.Quantity`
Energy
rad_max : `~astropy.coordinates.Angle`
Maximum offset angle.
Returns
-------
fraction : array_like
Containment fraction (in range 0 .. 1)
"""
energy = np.atleast_1d(u.Quantity(energy))[:, np.newaxis]
rad_max = np.atleast_1d(u.Quantity(rad_max))
return self.data._integrate_rad((energy, rad_max))
def info(self):
"""Print basic info"""
print(str(self))
def plot_psf_vs_rad(self, energy=None, ax=None, **kwargs):
"""Plot PSF vs radius.
Parameters
----------
energy : `~astropy.units.Quantity`
Energies where to plot the PSF.
**kwargs : dict
Keyword arguments pass to `~matplotlib.pyplot.plot`.
"""
import matplotlib.pyplot as plt
if energy is None:
energy = [100, 1000, 10000] * u.GeV
ax = plt.gca() if ax is None else ax
for value in energy:
psf_value = np.squeeze(self.evaluate(energy=value))
label = f"{value:.0f}"
ax.plot(
self.rad_axis.center.to_value("deg"),
psf_value.to_value("sr-1"),
label=label,
**kwargs,
)
ax.set_yscale("log")
ax.set_xlabel("Offset (deg)")
ax.set_ylabel("PSF (1 / sr)")
plt.legend()
return ax
def plot_containment_vs_energy(self,
ax=None,
fractions=[0.68, 0.8, 0.95],
**kwargs):
"""Plot containment versus energy."""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
for fraction in fractions:
rad = self.containment_radius(self.energy_axis_true.center,
fraction)
label = f"{100 * fraction:.1f}% Containment"
ax.plot(
self.energy_axis_true.center.to("GeV").value,
rad.to("deg").value,
label=label,
**kwargs,
)
ax.semilogx()
ax.legend(loc="best")
ax.set_xlabel("Energy (GeV)")
ax.set_ylabel("Containment radius (deg)")
def plot_exposure_vs_energy(self):
"""Plot exposure versus energy."""
import matplotlib.pyplot as plt
plt.figure(figsize=(4, 3))
plt.plot(self.energy_axis_true.center,
self.exposure,
color="black",
lw=3)
plt.semilogx()
plt.xlabel("Energy (MeV)")
plt.ylabel("Exposure (cm^2 s)")
plt.xlim(1e4 / 1.3, 1.3 * 1e6)
plt.ylim(0, 1.5e11)
plt.tight_layout()
class TablePSF:
"""Radially-symmetric table PSF.
Parameters
----------
rad_axis : `~astropy.units.Quantity` with angle units
Offset wrt source position
data : `~astropy.units.Quantity` with sr^-1 units
PSF value array
interp_kwargs : dict
Keyword arguments passed to `ScaledRegularGridInterpolator`
"""
def __init__(self, rad_axis, data, interp_kwargs=None):
interp_kwargs = interp_kwargs or {}
rad_axis.assert_name("rad")
self.data = NDDataArray(axes=[rad_axis],
data=u.Quantity(data).to("sr^-1"),
interp_kwargs=interp_kwargs)
@property
def rad_axis(self):
return self.data.axes["rad"]
@classmethod
def from_shape(cls, shape, width, rad):
"""Make TablePSF objects with commonly used shapes.
This function is mostly useful for examples and testing.
Parameters
----------
shape : {'disk', 'gauss'}
PSF shape.
width : `~astropy.units.Quantity` with angle units
PSF width angle (radius for disk, sigma for Gauss).
rad : `~astropy.units.Quantity` with angle units
Offset angle
Returns
-------
psf : `TablePSF`
Table PSF
Examples
--------
>>> import numpy as np
>>> from astropy.coordinates import Angle
>>> from gammapy.irf import TablePSF
>>> rad = Angle(np.linspace(0, 0.7, 100), 'deg')
>>> psf = TablePSF.from_shape(shape='gauss', width='0.2 deg', rad=rad)
"""
width = Angle(width)
rad = Angle(rad)
if shape == "disk":
amplitude = 1 / (np.pi * width.radian**2)
data = np.where(rad < width, amplitude, 0)
elif shape == "gauss":
gauss2d_pdf = Gauss2DPDF(sigma=width.radian)
data = gauss2d_pdf(rad.radian)
else:
raise ValueError(f"Invalid shape: {shape}")
data = u.Quantity(data, "sr^-1")
rad_axis = MapAxis.from_nodes(rad, name="rad")
return cls(rad_axis=rad_axis, data=data)
def info(self):
"""Print basic info."""
ss = array_stats_str(self.rad_axis.center, "offset")
ss += f"integral = {self.containment(self.rad_axis.edges[-1])}\n"
for containment in [68, 80, 95]:
radius = self.containment_radius(0.01 * containment)
ss += f"containment radius {radius.deg} deg for {containment}%\n"
return ss
def evaluate(self, rad):
r"""Evaluate PSF.
The following PSF quantities are available:
* 'dp_domega': PDF per 2-dim solid angle :math:`\Omega` in sr^-1
.. math:: \frac{dP}{d\Omega}
Parameters
----------
rad : `~astropy.coordinates.Angle`
Offset wrt source position
Returns
-------
psf_value : `~astropy.units.Quantity`
PSF value
"""
return self.data.evaluate(rad=rad)
def containment(self, rad_max):
"""Compute PSF containment fraction.
Parameters
----------
rad_max : `~astropy.units.Quantity`
Offset angle range
Returns
-------
integral : float
PSF integral
"""
rad_max = np.atleast_1d(rad_max)
return self.data._integrate_rad((rad_max, ))
def containment_radius(self, fraction):
"""Containment radius.
Parameters
----------
fraction : array_like
Containment fraction (range 0 .. 1)
Returns
-------
rad : `~astropy.coordinates.Angle`
Containment radius angle
"""
# TODO: check whether starting
rad_max = Angle(
np.linspace(0 * u.deg, self.rad_axis.center[-1],
10 * self.rad_axis.nbin),
"rad",
)
containment = self.containment(rad_max=rad_max)
fraction = np.atleast_1d(fraction)
fraction_idx = np.argmin(np.abs(containment - fraction[:, np.newaxis]),
axis=1)
return rad_max[fraction_idx].to("deg")
def normalize(self):
"""Normalize PSF to unit integral.
Computes the total PSF integral via the :math:`dP / dr` spline
and then divides the :math:`dP / dr` array.
"""
integral = self.containment(self.rad_axis.edges[-1])
self.data /= integral
def plot_psf_vs_rad(self, ax=None, **kwargs):
"""Plot PSF vs radius.
Parameters
----------
ax : ``
kwargs : dict
Keyword arguments passed to `matplotlib.pyplot.plot`
"""
import matplotlib.pyplot as plt
ax = plt.gca() if ax is None else ax
ax.plot(
self.rad_axis.center.to_value("deg"),
self.data.data.to_value("sr-1"),
**kwargs,
)
ax.set_yscale("log")
ax.set_xlabel("Radius (deg)")
ax.set_ylabel("PSF (sr-1)")