def test_stats_calc_chisqr_1dint_multi_cvar(usestat, usesys, expected):
"""chi-quare statistics calculates expected values: constvar
Here multiple datasets are used. To save time, the first
dataset is re-used, excluding the third point. This means that
the constvar results are a little-bit different to the other
statistics.
"""
data, model = setup_multiple_1dint(usestat, usesys)
statobj = Chi2ConstVar()
answer = statobj.calc_chisqr(data, model)
assert_almost_equal(answer, expected)
def test_sherpa_crab_fit():
from sherpa.models import NormGauss2D, PowLaw1D, TableModel, Const2D
from sherpa.stats import Chi2ConstVar
from sherpa.optmethods import LevMar
from sherpa.fit import Fit
from ..sherpa_ import Data3D, CombinedModel3D
filename = gammapy_extra.filename(
'experiments/sherpa_cube_analysis/counts.fits.gz')
counts = SkyCube.read(filename)
cube = counts.to_sherpa_data3d()
# Set up exposure table model
filename = gammapy_extra.filename(
'experiments/sherpa_cube_analysis/exposure.fits.gz')
exposure_data = fits.getdata(filename)
exposure = TableModel('exposure')
exposure.load(None, exposure_data.ravel())
# Freeze exposure amplitude
exposure.ampl.freeze()
# Setup combined spatial and spectral model
spatial_model = NormGauss2D('spatial-model')
spectral_model = PowLaw1D('spectral-model')
source_model = CombinedModel3D(spatial_model=spatial_model,
spectral_model=spectral_model)
# Set starting values
source_model.gamma = 2.2
source_model.xpos = 83.6
source_model.ypos = 22.01
source_model.fwhm = 0.12
source_model.ampl = 0.05
model = 1E-9 * exposure * (source_model) # 1E-9 flux factor
# Fit
fit = Fit(data=cube, model=model, stat=Chi2ConstVar(), method=LevMar())
result = fit.fit()
reference = (0.11925401159500593, 83.640630749333056, 22.020525848447541,
0.036353759774770608, 1.1900312815970555)
assert_allclose(result.parvals, reference, rtol=1E-8)
def test_sherpa_crab_fit():
from sherpa.models import NormGauss2D, PowLaw1D, TableModel, Const2D
from sherpa.stats import Chi2ConstVar
from sherpa.optmethods import LevMar
from sherpa.fit import Fit
from ..sherpa_ import CombinedModel3D
filename = gammapy_extra.filename(
'experiments/sherpa_cube_analysis/counts.fits.gz')
# Note: The cube is stored in incorrect format
counts = SkyCube.read(filename, format='fermi-counts')
cube = counts.to_sherpa_data3d()
# Set up exposure table model
filename = gammapy_extra.filename(
'experiments/sherpa_cube_analysis/exposure.fits.gz')
exposure_data = fits.getdata(filename)
exposure = TableModel('exposure')
exposure.load(None, exposure_data.ravel())
# Freeze exposure amplitude
exposure.ampl.freeze()
# Setup combined spatial and spectral model
spatial_model = NormGauss2D('spatial-model')
spectral_model = PowLaw1D('spectral-model')
source_model = CombinedModel3D(spatial_model=spatial_model,
spectral_model=spectral_model)
# Set starting values
source_model.gamma = 2.2
source_model.xpos = 83.6
source_model.ypos = 22.01
source_model.fwhm = 0.12
source_model.ampl = 0.05
model = 1E-9 * exposure * source_model # 1E-9 flux factor
# Fit
fit = Fit(data=cube, model=model, stat=Chi2ConstVar(), method=LevMar())
result = fit.fit()
reference = [0.121556, 83.625627, 22.015564, 0.096903, 2.240989]
assert_allclose(result.parvals, reference, rtol=1E-5)
def test_chi2constvar_stat(hide_logging, reset_xspec, setup_group):
fit = Fit(setup_group['data'], setup_group['model'], Chi2ConstVar(),
LevMar())
results = fit.fit()
_fit_chi2constvar_results_bench = {
'succeeded':
1,
'numpoints':
143,
'dof':
140,
'istatval':
3903.1647954751857,
'statval':
140.1384389790626,
'parvals':
numpy.array(
[1.8081424949164122, 5.461611041944607, -1.9077365276482876])
}
compare_results(_fit_chi2constvar_results_bench, results, tol=2e-4)
def test_chi2constvar_stat(self):
fit = Fit(self.data, self.model, Chi2ConstVar(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_chi2constvar_results_bench, results)
def main():
config = read_config('config.yaml')
# ref_cube = make_ref_cube(config)
# target_position = SkyCoord(config['model']['ra1'], config['model']['dec1'], unit="deg").galactic
cubes = load_cubes(config)
print('which available cubes:', cubes)
# converting data SkyCube to sherpa-format cube
counts = cubes['counts'].to_sherpa_data3d() #dstype='Data3DInt')
print('counts: ', counts)
# Define a 2D gaussian for the spatial model
# spatial_model = NormGauss2DInt('spatial-model')
# Define a power law for the spectral model
# spectral_model = PowLaw1D('spectral-model')
coord = cubes['counts'].sky_image_ref.coordinates(mode="edges")
energies = cubes['counts'].energies(mode='edges').to("TeV")
print('my energy bins: ', energies)
# import IPython; IPython.embed();
# Set up exposure table model
exposure = TableModel('exposure')
exposure.load(None, cubes['exposure'].data.ravel())
exposure.ampl.freeze()
use_psf = config['model']['use_psf']
model_gammapy = get_model_gammapy(config)
spectral_model_sherpa = model_gammapy.spectral_model.to_sherpa()
spectral_model_sherpa.ampl.thaw()
spatial_model_sherpa = model_gammapy.spatial_model.to_sherpa()
spatial_model_sherpa.xpos.freeze()
spatial_model_sherpa.ypos.freeze()
spatial_model_sherpa.r0.freeze()
spatial_model_sherpa.width.freeze()
source_model = CombinedModel3D(
spatial_model=spatial_model_sherpa,
spectral_model=spectral_model_sherpa,
)
# source_model = CombinedModel3DInt(
# spatial_model=spatial_model_sherpa,
# spectral_model=spectral_model_sherpa,
# exposure=exposure,
# coord=coord,
# energies=energies,
# use_psf=use_psf,
# psf=None,
# )
print(source_model)
# source_model_cube = source_model.evaluate_cube(ref_cube)
# model = source_model # + background_model
# source_model2 = CombinedModel3DInt(
# coord=coord,
# energies=energies,
# use_psf=False,
# exposure=cubes['exposure'],
# psf=None,
# spatial_model=spatial_model,
# spectral_model=spectral_model,
# )
model = 1e-9 * exposure * source_model # 1e-9 flux factor
fit = Fit(
data=counts,
model=model,
stat=Chi2ConstVar(),
method=LevMar(),
# estmethod=Covariance(),
)
fit_results = fit.fit()
print(fit_results.format())
print('------------------------------------ end fitting')
