Source code for gammapy.irf.effective_area

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import astropy.units as u
from import fits
from astropy.table import Table
from gammapy.maps import MapAxis, RegionGeom, RegionNDMap
from gammapy.utils.nddata import NDDataArray
from gammapy.utils.scripts import make_path

__all__ = ["EffectiveAreaTable", "EffectiveAreaTable2D"]

[docs]class EffectiveAreaTable: """Effective area table. TODO: Document Parameters ---------- energy_axis_true : `MapAxis` Energy axis data : `~astropy.units.Quantity` Effective area Examples -------- Plot parametrized effective area for HESS, HESS2 and CTA. .. plot:: :include-source: import numpy as np import matplotlib.pyplot as plt import astropy.units as u from gammapy.irf import EffectiveAreaTable energy = np.logspace(-3, 3, 100) * u.TeV for instrument in ['HESS', 'HESS2', 'CTA']: aeff = EffectiveAreaTable.from_parametrization(energy, instrument) ax = aeff.plot(label=instrument) ax.set_yscale('log') ax.set_xlim([1e-3, 1e3]) ax.set_ylim([1e3, 1e12]) plt.legend(loc='best') Find energy where the effective area is at 10% of its maximum value >>> import numpy as np >>> import astropy.units as u >>> from gammapy.irf import EffectiveAreaTable >>> energy = np.logspace(-1, 2) * u.TeV >>> aeff_max = aeff.max_area >>> print(aeff_max).to('m2') 156909.413371 m2 >>> energy_threshold = aeff.find_energy(0.1 * aeff_max) >>> print(energy_threshold) 0.185368478744 TeV """ def __init__(self, energy_axis_true, data, meta=None): interp_kwargs = {"extrapolate": False, "bounds_error": False} assert == "energy_true" = NDDataArray( axes=[energy_axis_true], data=data, interp_kwargs=interp_kwargs ) self.meta = meta or {} @property def energy(self): return["energy_true"]
[docs] def plot(self, ax=None, energy=None, show_energy=None, **kwargs): """Plot effective area. Parameters ---------- ax : `~matplotlib.axes.Axes`, optional Axis energy : `~astropy.units.Quantity` Energy nodes show_energy : `~astropy.units.Quantity`, optional Show energy, e.g. threshold, as vertical line Returns ------- ax : `~matplotlib.axes.Axes` Axis """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax kwargs.setdefault("lw", 2) if energy is None: energy = eff_area = xerr = ( (energy -[:-1]).value, ([1:] - energy).value, ) ax.errorbar(energy.value, eff_area.value, xerr=xerr, **kwargs) if show_energy is not None: ener_val = u.Quantity(show_energy).to_value( ax.vlines(ener_val, 0, 1.1 * self.max_area.value, linestyles="dashed") ax.set_xscale("log") ax.set_xlabel(f"Energy [{}]") ax.set_ylabel(f"Effective Area [{}]") return ax
[docs] @classmethod def from_parametrization(cls, energy, instrument="HESS"): r"""Create parametrized effective area. Parametrizations of the effective areas of different Cherenkov telescopes taken from Appendix B of Abramowski et al. (2010), see . .. math:: A_{eff}(E) = g_1 \left(\frac{E}{\mathrm{MeV}}\right)^{-g_2}\exp{\left(-\frac{g_3}{E}\right)} Parameters ---------- energy : `~astropy.units.Quantity` Energy binning, analytic function is evaluated at log centers instrument : {'HESS', 'HESS2', 'CTA'} Instrument name """ energy = u.Quantity(energy) # Put the parameters g in a dictionary. # Units: g1 (cm^2), g2 (), g3 (MeV) # Note that whereas in the paper the parameter index is 1-based, # here it is 0-based pars = { "HESS": [6.85e9, 0.0891, 5e5], "HESS2": [2.05e9, 0.0891, 1e5], "CTA": [1.71e11, 0.0891, 1e5], } if instrument not in pars.keys(): ss = f"Unknown instrument: {instrument}\n" ss += "Valid instruments: HESS, HESS2, CTA" raise ValueError(ss) energy_axis_true = MapAxis.from_edges(energy, interp="log", name="energy_true") g1 = pars[instrument][0] g2 = pars[instrument][1] g3 = -pars[instrument][2] energy ="MeV") value = g1 * energy ** (-g2) * np.exp(g3 / energy) data = u.Quantity(value, "cm2", copy=False) return cls(energy_axis_true=energy_axis_true, data=data)
[docs] @classmethod def from_constant(cls, energy, value): """Create constant value effective area. Parameters ---------- energy : `~astropy.units.Quantity` Energy binning, analytic function is evaluated at log centers value : `~astropy.units.Quantity` Effective area """ data = np.ones((len(energy) - 1)) * u.Quantity(value) energy_axis_true = MapAxis.from_energy_edges(energy, name="energy_true") return cls(energy_axis_true=energy_axis_true, data=data)
[docs] @classmethod def from_table(cls, table): """Create from `~astropy.table.Table` in ARF format. Data format specification: :ref:`gadf:ogip-arf` """ energy_axis_true = MapAxis.from_table(table, format="ogip-arf") data = table["SPECRESP"].quantity return cls(energy_axis_true=energy_axis_true, data=data)
[docs] @classmethod def from_hdulist(cls, hdulist, hdu="SPECRESP"): """Create from ``.""" return cls.from_table([hdu]))
[docs] @classmethod def read(cls, filename, hdu="SPECRESP"): """Read from file.""" filename = str(make_path(filename)) with, memmap=False) as hdulist: try: return cls.from_hdulist(hdulist, hdu=hdu) except KeyError: raise ValueError( f"File {filename} contains no HDU {hdu!r}\n" f"Available: {[ for _ in hdulist]}" )
[docs] def to_table(self): """Convert to `~astropy.table.Table` in ARF format. Data format specification: :ref:`gadf:ogip-arf` """ table = Table() table.meta = { "EXTNAME": "SPECRESP", "hduclass": "OGIP", "hduclas1": "RESPONSE", "hduclas2": "SPECRESP", } energy = table["ENERG_LO"] = energy[:-1] table["ENERG_HI"] = energy[1:] table["SPECRESP"] = self.evaluate_fill_nan() return table
[docs] def to_region_map(self, region=None): """""" axis =["energy_true"] geom = RegionGeom(region=region, axes=[axis]) return RegionNDMap.from_geom( geom=geom,, )
[docs] def to_hdulist(self, name=None, use_sherpa=False): """Convert to ``.""" table = self.to_table() if use_sherpa: table["ENERG_HI"] = table["ENERG_HI"]"keV") table["ENERG_LO"] = table["ENERG_LO"]"keV") table["SPECRESP"] = table["SPECRESP"]"cm2") return fits.HDUList([fits.PrimaryHDU(), fits.BinTableHDU(table, name=name)])
[docs] def write(self, filename, use_sherpa=False, **kwargs): """Write to file.""" filename = str(make_path(filename)) self.to_hdulist(use_sherpa=use_sherpa).writeto(filename, **kwargs)
[docs] def evaluate_fill_nan(self, **kwargs): """Modified evaluate function. Calls :func:`gammapy.utils.nddata.NDDataArray.evaluate` and replaces possible nan values. Below the finite range the effective area is set to zero and above to value of the last valid note. This is needed since other codes, e.g. sherpa, don't like nan values in FITS files. Make sure that the replacement happens outside of the energy range, where the `~gammapy.irf.EffectiveAreaTable` is used. """ retval =**kwargs) idx = np.where(np.isfinite(retval))[0] retval[np.arange(idx[0])] = 0 retval[np.arange(idx[-1], len(retval))] = retval[idx[-1]] return retval
@property def max_area(self): """Maximum effective area.""" cleaned_data =[np.where(~np.isnan(] return cleaned_data.max()
[docs] def find_energy(self, aeff, energy_min=None, energy_max=None): """Find energy for a given effective area. In case the solution is not unique, provide the `energy_min` or `energy_max` arguments to limit the solution to the given range. By default the peak energy of the effective area is chosen as `energy_max`. Parameters ---------- aeff : `~astropy.units.Quantity` Effective area value energy_min : `~astropy.units.Quantity` Lower bracket value in case solution is not unique. energy_max : `~astropy.units.Quantity` Upper bracket value in case solution is not unique. Returns ------- energy : `~astropy.units.Quantity` Energy corresponding to the given aeff. """ from gammapy.modeling.models import TemplateSpectralModel energy = if energy_min is None: energy_min = energy[0] if energy_max is None: # use the peak effective area as a default for the energy maximum energy_max = energy[np.argmax(] aeff_spectrum = TemplateSpectralModel(energy, return aeff_spectrum.inverse(aeff, energy_min=energy_min, energy_max=energy_max)
[docs]class EffectiveAreaTable2D: """2D effective area table. Data format specification: :ref:`gadf:aeff_2d` Parameters ---------- energy_axis_true : `MapAxis` True energy axis offset_axis : `MapAxis` Field of view offset axis. data : `~astropy.units.Quantity` Effective area Examples -------- Here's an example you can use to learn about this class: >>> from gammapy.irf import EffectiveAreaTable2D >>> filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits' >>> aeff =, hdu='EFFECTIVE AREA') >>> print(aeff) EffectiveAreaTable2D NDDataArray summary info energy : size = 42, min = 0.014 TeV, max = 177.828 TeV offset : size = 6, min = 0.500 deg, max = 5.500 deg Data : size = 252, min = 0.000 m2, max = 5371581.000 m2 Here's another one, created from scratch, without reading a file: >>> from gammapy.irf import EffectiveAreaTable2D >>> import astropy.units as u >>> import numpy as np >>> energy = np.logspace(0,1,11) * u.TeV >>> offset = np.linspace(0,1,4) * u.deg >>> data = np.ones(shape=(10,3)) * * >>> aeff = EffectiveAreaTable2D(energy_lo=energy[:-1], energy_hi=energy[1:], offset_lo=offset[:-1], >>> offset_hi=offset[1:], data= data) >>> print(aeff) Data array summary info energy : size = 11, min = 1.000 TeV, max = 10.000 TeV offset : size = 4, min = 0.000 deg, max = 1.000 deg Data : size = 30, min = 1.000 cm2, max = 1.000 cm2 """ tag = "aeff_2d" default_interp_kwargs = dict(bounds_error=False, fill_value=None) """Default Interpolation kwargs for `~NDDataArray`. Extrapolate.""" def __init__( self, energy_axis_true, offset_axis, data, meta=None, interp_kwargs=None, ): assert == "energy_true" assert == "offset" if interp_kwargs is None: interp_kwargs = self.default_interp_kwargs = NDDataArray( axes=[energy_axis_true, offset_axis], data=data, interp_kwargs=interp_kwargs ) self.meta = meta or {} def __str__(self): ss = self.__class__.__name__ ss += f"\n{}" return ss @property def low_threshold(self): """Low energy threshold""" return self.meta["LO_THRES"] * u.TeV @property def high_threshold(self): """High energy threshold""" return self.meta["HI_THRES"] * u.TeV
[docs] @classmethod def from_table(cls, table): """Read from `~astropy.table.Table`.""" energy_axis_true = MapAxis.from_table( table, column_prefix="ENERG", format="gadf-dl3" ) offset_axis = MapAxis.from_table( table, column_prefix="THETA", format="gadf-dl3" ) return cls( energy_axis_true=energy_axis_true, offset_axis=offset_axis, data=table["EFFAREA"].quantity[0].transpose(), meta=table.meta, )
[docs] @classmethod def from_hdulist(cls, hdulist, hdu="EFFECTIVE AREA"): """Create from ``.""" return cls.from_table([hdu]))
[docs] @classmethod def read(cls, filename, hdu="EFFECTIVE AREA"): """Read from file.""" with, memmap=False) as hdulist: return cls.from_hdulist(hdulist, hdu=hdu)
[docs] def to_effective_area_table(self, offset, energy=None): """Evaluate at a given offset and return `~gammapy.irf.EffectiveAreaTable`. Parameters ---------- offset : `~astropy.coordinates.Angle` Offset energy : `~astropy.units.Quantity` Energy axis bin edges """ if energy is None: energy_axis_true =["energy_true"] else: energy_axis_true = MapAxis.from_energy_edges(energy, name="energy_true") area =, return EffectiveAreaTable(energy_axis_true=energy_axis_true, data=area)
[docs] def plot_energy_dependence(self, ax=None, offset=None, energy=None, **kwargs): """Plot effective area versus energy for a given offset. Parameters ---------- ax : `~matplotlib.axes.Axes`, optional Axis offset : `~astropy.coordinates.Angle` Offset energy : `~astropy.units.Quantity` Energy axis kwargs : dict Forwarded tp plt.plot() Returns ------- ax : `~matplotlib.axes.Axes` Axis """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax if offset is None: off_min, off_max =["offset"].center[[0, -1]] offset = np.linspace(off_min.value, off_max.value, 4) * off_min.unit if energy is None: energy =["energy_true"].center for off in offset: area =, energy_true=energy) kwargs.setdefault("label", f"offset = {off:.1f}") ax.plot(energy, area.value, **kwargs) ax.set_xscale("log") ax.set_xlabel(f"Energy [{energy.unit}]") ax.set_ylabel(f"Effective Area [{}]") ax.set_xlim(min(energy.value), max(energy.value)) return ax
[docs] def plot_offset_dependence(self, ax=None, offset=None, energy=None, **kwargs): """Plot effective area versus offset for a given energy. Parameters ---------- ax : `~matplotlib.axes.Axes`, optional Axis offset : `~astropy.coordinates.Angle` Offset axis energy : `~astropy.units.Quantity` Energy Returns ------- ax : `~matplotlib.axes.Axes` Axis """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax if energy is None: energy_axis =["energy_true"] e_min, e_max = np.log10([[0, -1]]) energy = np.logspace(e_min, e_max, 4) * energy_axis.unit if offset is None: offset =["offset"].center for ee in energy: area =, energy_true=ee) area /= np.nanmax(area) if np.isnan(area).all(): continue label = f"energy = {ee:.1f}" ax.plot(offset, area, label=label, **kwargs) ax.set_ylim(0, 1.1) ax.set_xlabel(f"Offset ({['offset'].unit})") ax.set_ylabel("Relative Effective Area") ax.legend(loc="best") return ax
[docs] def plot(self, ax=None, add_cbar=True, **kwargs): """Plot effective area image.""" import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax energy =["energy_true"].edges offset =["offset"].edges aeff =, energy_true=energy[:, np.newaxis]) vmin, vmax = np.nanmin(aeff.value), np.nanmax(aeff.value) kwargs.setdefault("cmap", "GnBu") kwargs.setdefault("edgecolors", "face") kwargs.setdefault("vmin", vmin) kwargs.setdefault("vmax", vmax) caxes = ax.pcolormesh(energy.value, offset.value, aeff.value.T, **kwargs) ax.set_xscale("log") ax.set_ylabel(f"Offset ({offset.unit})") ax.set_xlabel(f"Energy ({energy.unit})") xmin, xmax = energy.value.min(), energy.value.max() ax.set_xlim(xmin, xmax) if add_cbar: label = f"Effective Area ({aeff.unit})" ax.figure.colorbar(caxes, ax=ax, label=label) return ax
[docs] def peek(self, figsize=(15, 5)): """Quick-look summary plots.""" import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=1, ncols=3, figsize=figsize) self.plot(ax=axes[2]) self.plot_energy_dependence(ax=axes[0]) self.plot_offset_dependence(ax=axes[1]) plt.tight_layout()
[docs] def to_table(self): """Convert to `~astropy.table.Table`.""" table ="gadf-dl3") table.meta = self.meta.copy() table["EFFAREA"] =[np.newaxis] return table
[docs] def to_table_hdu(self, name="EFFECTIVE AREA"): """Convert to ``.""" return fits.BinTableHDU(self.to_table(), name=name)