# Adding this constant background components the fit works with cash statistics as well
#spatial_model_bkg = Const2D('spatial-model-bkg')
#spectral_model_bkg = PowLaw1D('spectral-model-bkg')
#bkg_model = CombinedModel3D(spatial_model=spatial_model_bkg, spectral_model=spectral_model_bkg)
bkg = TableModel('bkg')
bkg.load(None, bkg_3D.data.value.ravel())
# Freeze bkg amplitude
bkg.ampl=1
bkg.ampl.freeze()
model = bkg+1E-11 * (source_model)
# Fit
# For now only Chi2 statistics seems to work, using Cash, the optimizer doesn't run at all,
# maybe because of missing background model?
fit = Fit(data=cube, model=model, stat=Cash(), method=NelderMead(), estmethod=Covariance())
result = fit.fit()
err=fit.est_errors()
print(err)
def PWL(E,phi_0,gamma):
return phi_0*E**(-gamma)
def EXP(E,phi_0,gamma,beta):
return phi_0*E**(-gamma)*np.exp(-beta*E)
coord=exposure_3D.sky_image_ref.coordinates(mode="edges")
d = coord.separation(center)
pix_size=exposure_3D.wcs.to_header()["CDELT2"]
i=np.where(d<pix_size*u.deg)
#i permet de faire la moyenne exposure autour de pixel autour de la source
mean_exposure=list()
def testCombinedModel3DIntConvolveEdisp():
from sherpa.models import PowLaw1D, TableModel
from sherpa.estmethods import Covariance
from sherpa.optmethods import NelderMead
from sherpa.stats import Cash
from sherpa.fit import Fit
from ..sherpa_ import CombinedModel3DIntConvolveEdisp, NormGauss2DInt
# Set the counts
filename = gammapy_extra.filename('test_datasets/cube/counts_cube.fits')
counts_3d = SkyCube.read(filename)
cube = counts_3d.to_sherpa_data3d(dstype='Data3DInt')
# Set the bkg
filename = gammapy_extra.filename('test_datasets/cube/bkg_cube.fits')
bkg_3d = SkyCube.read(filename)
bkg = TableModel('bkg')
bkg.load(None, bkg_3d.data.value.ravel())
bkg.ampl = 1
bkg.ampl.freeze()
# Set the exposure
filename = gammapy_extra.filename(
'test_datasets/cube/exposure_cube_etrue.fits')
exposure_3d = SkyCube.read(filename)
i_nan = np.where(np.isnan(exposure_3d.data))
exposure_3d.data[i_nan] = 0
# In order to have the exposure in cm2 s
exposure_3d.data = exposure_3d.data * 1e4
# Set the mean psf model
filename = gammapy_extra.filename('test_datasets/cube/psf_cube_etrue.fits')
psf_3d = SkyCube.read(filename)
# Set the mean rmf
filename = gammapy_extra.filename('test_datasets/cube/rmf.fits')
rmf = EnergyDispersion.read(filename)
# Setup combined spatial and spectral model
spatial_model = NormGauss2DInt('spatial-model')
spectral_model = PowLaw1D('spectral-model')
coord = counts_3d.sky_image_ref.coordinates(mode="edges")
energies = counts_3d.energies(mode='edges').to("TeV")
source_model = CombinedModel3DIntConvolveEdisp(
coord=coord,
energies=energies,
use_psf=True,
exposure=exposure_3d,
psf=psf_3d,
spatial_model=spatial_model,
spectral_model=spectral_model,
edisp=rmf.data.data,
)
# Set starting values
center = SkyCoord(83.633083, 22.0145, unit="deg").galactic
source_model.gamma = 2.2
source_model.xpos = center.l.value
source_model.ypos = center.b.value
source_model.fwhm = 0.12
source_model.ampl = 1.0
# Fit
model = bkg + 1e-11 * (source_model)
fit = Fit(data=cube,
model=model,
stat=Cash(),
method=NelderMead(),
estmethod=Covariance())
result = fit.fit()
# TODO: The fact that it doesn't converge to the right Crab postion, flux and source size is due to the dummy psf
reference = [
1.841920e+02, -6.175650e+00, 6.227756e+00, 7.129763e-02, 2.269026e+00
]
assert_allclose(result.parvals, reference, rtol=1e-3)
# Add a region to exclude in the fit: Here we will exclude some events from the Crab since there is no region to
# exclude in the FOV for this example. It's just an example to show how it works and how to proceed in the fit.
# Read the mask for the exclude region
filename_mask = gammapy_extra.filename('test_datasets/cube/mask.fits')
cube_mask = SkyCube.read(filename_mask)
index_region_selected_3d = np.where(cube_mask.data.value == 1)
# Set the counts and create a gammapy Data3DInt object
# on which we apply a mask for the region we don't want to use in the fit
cube = counts_3d.to_sherpa_data3d(dstype='Data3DInt')
cube.mask = cube_mask.data.value.ravel()
# Set the bkg and select only the data points of the selected region
bkg = TableModel('bkg')
bkg.load(None, bkg_3d.data.value[index_region_selected_3d].ravel())
bkg.ampl = 1
bkg.ampl.freeze()
# The model is evaluated on all the points then it is compared with the data only on the selected_region
source_model = CombinedModel3DIntConvolveEdisp(
coord=coord,
energies=energies,
use_psf=True,
exposure=exposure_3d,
psf=psf_3d,
spatial_model=spatial_model,
spectral_model=spectral_model,
edisp=rmf.data.data,
select_region=True,
index_selected_region=index_region_selected_3d,
)
# Set starting values
source_model.gamma = 2.2
source_model.xpos = center.l.value
source_model.ypos = center.b.value
source_model.fwhm = 0.12
source_model.ampl = 1.0
# Fit
model = bkg + 1e-11 * (source_model)
fit = Fit(data=cube,
model=model,
stat=Cash(),
method=NelderMead(),
estmethod=Covariance())
result2 = fit.fit()
# TODO: The fact that it doesn't converge to the right Crab postion is due to the dummy psf
reference2 = [
1.841921e+02, -6.169094e+00, 5.497368e+00, 7.513465e-02, 2.250965e+00
]
assert_allclose(result2.parvals, reference2, rtol=1e-3)
def test_cash_stat(self):
fit = Fit(self.data, self.model, Cash(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycash_results_bench, results)