def test_mh(setup):
setup.fit.method = NelderMead()
setup.fit.stat = Cash()
setup.fit.fit()
results = setup.fit.est_errors()
cov = results.extra_output
mcmc = sim.MCMC()
for par in setup.fit.model.pars:
mcmc.set_prior(par, sim.flat)
prior = mcmc.get_prior(par)
assert prior.__name__ == 'flat'
mcmc.set_sampler('MH')
opt = mcmc.get_sampler_opt('defaultprior')
mcmc.set_sampler_opt('defaultprior', opt)
# mcmc.set_sampler_opt('verbose', True)
log = logging.getLogger("sherpa")
level = log.level
log.setLevel(logging.ERROR)
try:
stats, accept, params = mcmc.get_draws(setup.fit, cov, niter=1e2)
finally:
log.setLevel(level)
def test_mh(self):
self.fit.method = NelderMead()
self.fit.stat = Cash()
results = self.fit.fit()
results = self.fit.est_errors()
cov = results.extra_output
mcmc = MCMC()
samplers = mcmc.list_samplers()
priors = mcmc.list_priors()
for par in self.fit.model.pars:
mcmc.set_prior(par, flat)
prior = mcmc.get_prior(par)
sampler = mcmc.get_sampler()
name = mcmc.get_sampler_name()
mcmc.set_sampler('MH')
opt = mcmc.get_sampler_opt('defaultprior')
mcmc.set_sampler_opt('defaultprior', opt)
#mcmc.set_sampler_opt('verbose', True)
log = logging.getLogger("sherpa")
level = log.level
log.setLevel(logging.ERROR)
stats, accept, params = mcmc.get_draws(self.fit, cov, niter=1e2)
log.setLevel(level)
def test_same_cache(self):
poly = Polynom1D()
poly.pars[1].thaw()
sdata = DataSimulFit('d1d2d3', (self.d1, self.d2, self.d3))
smodel = SimulFitModel('same', (poly, poly, poly))
sfit = Fit(sdata, smodel, method=NelderMead(), stat=Cash())
result = sfit.fit()
self.compare_results(self._fit_same_poly_bench, result)
def test_diff_cache(self):
poly1 = Polynom1D()
poly2 = Polynom1D()
poly3 = Polynom1D()
poly1.pars[1].thaw()
poly2.pars[1].thaw()
poly3.pars[1].thaw()
sdata = DataSimulFit('d123', (self.d1, self.d2, self.d3))
smodel = SimulFitModel('diff', (poly1, poly2, poly3))
sfit = Fit(sdata, smodel, method=NelderMead(), stat=Cash())
result = sfit.fit()
self.compare_results(self._fit_diff_poly_bench, result)
spatial_model=spatial_model,
spectral_model=spectral_model,
)
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
# Define the model
flux_factor = 1e-11
model = bkg + flux_factor * source_model
fit = Fit(
data=cube,
model=model,
stat=Cash(),
method=NelderMead(),
estmethod=Covariance(),
)
fit_results = fit.fit()
print(fit_results.format())
fit = Fit(
data=cube,
model=model,
stat=Cash(),
method=NelderMead(),
estmethod=Covariance(),
)
fit_results = fit.fit()
fit_results
fit_results.format()
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)
def testCombinedModel3DInt():
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 CombinedModel3DInt, 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.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.fits')
psf_3d = SkyCube.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 = CombinedModel3DInt(coord=coord,
energies=energies,
use_psf=True,
exposure=exposure_3d,
psf=psf_3d,
spatial_model=spatial_model,
spectral_model=spectral_model)
# 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 is due to the dummy psf
reference = [
184.19524957441664, -6.1693008203971562, 6.1666646581766011,
0.076340497278248376, 2.305912037549
]
assert_allclose(result.parvals, reference, rtol=1E-5)
# 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 = CombinedModel3DInt(
coord=coord,
energies=energies,
use_psf=True,
exposure=exposure_3d,
psf=psf_3d,
spatial_model=spatial_model,
spectral_model=spectral_model,
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 = [
184.20146538191321, -6.1600047997645975, 5.4193056837212374,
0.08635929788659219, 2.2979723660330
]
assert_allclose(result2.parvals, reference2, rtol=1E-5)
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')
# dimensions = [exposure_3d.data.shape[1], exposure_3d.data.shape[2], rmf.data.data.shape[1],
# exposure_3d.data.shape[0]]
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 = [
184.19189525423425, -6.1758238877562386, 6.2283155506945755,
0.071013932890499717, 2.2685809241308674
]
assert_allclose(result.parvals, reference, rtol=1E-5)
# 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 = [
184.1919580251583, -6.1692775561065769, 5.4976586957354581,
0.074821281329729109, 2.2504892463464699
]
assert_allclose(result2.parvals, reference2, rtol=1E-5)
report("dplot.plot_prefs")
dplot.plot_prefs['ylog'] = True
dplot.plot(marker='s', linestyle='dashed')
savefig('settings_dataplot_combined.png')
dplot.plot()
savefig('settings_dataplot_ylog.png')
dplot.plot_prefs['ylog'] = False
from sherpa.optmethods import NelderMead
from sherpa.stats import Cash
from sherpa.fit import Fit
f = Fit(d, mdl, stat=Cash(), method=NelderMead())
out = f.fit()
# How bad a fit is this?
print(out)
# f.est_errors()
mplot.prepare(d, mdl)
fplot.plot()
savefig('fitplot_histogram_after.png')
from sherpa.plot import IntervalProjection
iproj = IntervalProjection()
iproj.calc(f, mdl.pars[2])
# 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()
