def make_mask_energy_aeff_max(self, dataset):
"""Make safe energy mask from effective area maximum value.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset`
Dataset to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
geom = dataset._geom
if self.position is None:
position = PointSkyRegion(dataset.counts.geom.center_skydir)
else:
position = PointSkyRegion(self.position)
exposure = dataset.exposure.get_spectrum(position)
energy = exposure.geom.axes["energy_true"]
aeff = EffectiveAreaTable(
energy_axis_true=energy,
data=(exposure.quantity / dataset.gti.time_sum).squeeze(),
)
aeff_thres = (self.aeff_percent / 100) * aeff.max_area
e_min = aeff.find_energy(aeff_thres)
return geom.energy_mask(emin=e_min)
def make_mask_energy_aeff_max(self, dataset):
"""Make safe energy mask from effective area maximum value.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.SpectrumDataset`
Dataset to compute mask for.
Returns
-------
mask_safe : `~numpy.ndarray`
Safe data range mask.
"""
geom = dataset._geom
if isinstance(dataset, MapDataset):
position = self.position
if position is None:
position = dataset.counts.geom.center_skydir
exposure = dataset.exposure
energy = exposure.geom.get_axis_by_name("energy_true")
coord = MapCoord.create({"skycoord": position, "energy_true": energy.center})
exposure_1d = exposure.interp_by_coord(coord)
aeff = EffectiveAreaTable(
energy_lo=energy.edges[:-1],
energy_hi=energy.edges[1:],
data=exposure_1d,
)
else:
aeff = dataset.aeff
aeff_thres = (self.aeff_percent / 100) * aeff.max_area
e_min = aeff.find_energy(aeff_thres)
return geom.energy_mask(emin=e_min)
from gammapy.utils.random import get_random_state
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
e_true = SpectrumExtraction.DEFAULT_TRUE_ENERGY
e_reco = SpectrumExtraction.DEFAULT_RECO_ENERGY
# EDISP
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_true, sigma=0.2)
# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)
# MODEL
model = PowerLaw(index=2.3 * u.Unit(""), amplitude=2.5 * 1e-12 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV)
# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model, aeff=aeff, edisp=edisp, livetime=livetime)
bkg = 0.2 * npred.data
alpha = 0.1
counts_kwargs = dict(
energy=npred.energy,
exposure=livetime,
obs_id=31415,
from gammapy.utils.random import get_random_state
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
e_true = SpectrumExtraction.DEFAULT_TRUE_ENERGY
e_reco = SpectrumExtraction.DEFAULT_RECO_ENERGY
# EDISP
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_true, sigma=0.2)
# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)
# MODEL
model = PowerLaw(index=2.3 * u.Unit(''),
amplitude=2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV)
# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model,
aeff=aeff,
edisp=edisp,
livetime=livetime)
bkg = 0.2 * npred.data
alpha = 0.1
