def find_energy(self, aeff, emin=None, emax=None):
"""Find energy for a given effective area.
In case the solution is not unique, provide the `emin` or `emax` arguments
to limit the solution to the given range. By default the peak energy of the
effective area is chosen as `emax`.
Parameters
----------
aeff : `~astropy.units.Quantity`
Effective area value
emin : `~astropy.units.Quantity`
Lower bracket value in case solution is not unique.
emax : `~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 = self.energy.center
if emin is None:
emin = energy[0]
if emax is None:
# use the peak effective area as a default for the energy maximum
emax = energy[np.argmax(self.data.data)]
aeff_spectrum = TemplateSpectralModel(energy, self.data.data)
return aeff_spectrum.inverse(aeff, emin=emin, emax=emax)
def get_bias_energy(self, bias, energy_min=None, energy_max=None):
"""Find energy corresponding to a given bias.
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
bias is chosen as ``energy_min``.
Parameters
----------
bias : float
Bias 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
-------
bias_energy : `~astropy.units.Quantity`
Reconstructed energy corresponding to the given bias.
"""
from gammapy.modeling.models import TemplateSpectralModel
energy_true = self.axes["energy_true"].center
values = self.get_bias(energy_true)
if energy_min is None:
# use the peak bias energy as default minimum
energy_min = energy_true[np.nanargmax(values)]
if energy_max is None:
energy_max = energy_true[-1]
bias_spectrum = TemplateSpectralModel(energy=energy_true,
values=values)
energy_true_bias = bias_spectrum.inverse(Quantity(bias),
energy_min=energy_min,
energy_max=energy_max)
if np.isnan(energy_true_bias[0]):
energy_true_bias[0] = energy_min
# return reconstructed energy
return energy_true_bias * (1 + bias)
