def run(self):
"""Performs the spectrum extraction
Returns
-------
spectrum_observation : `~gammapy.spectrum.SpectrumObservation`
Spectrum observation
"""
self.make_empty_vectors(self.bkg_estimate)
self.extract_counts(self.bkg_estimate)
aeff = extract_aeff(self.exposure, self.target_position, self.energy)
if self.containment_correction:
containment_factor = extract_psf_containment(
self.psf, self.on_radius, self.energy
)
aeff.data.data *= containment_factor
edisp = extract_edisp(self.energy)
self._aeff = aeff
self._edisp = edisp
return SpectrumObservation(
on_vector=self._on_vector,
aeff=self._aeff,
off_vector=self._off_vector,
edisp=self._edisp,
)
def check_edisp_normalisation():
import matplotlib.pyplot as plt
obs = SpectrumObservation.read(obs_path)
p = obs.edisp.data.data.sum(axis=1)
e = obs.edisp.data.axis("e_true").nodes
plt.plot(e, p)
plt.semilogx()
plt.show()
def setup(self):
self.model = PowerLaw(
index=Quantity(2, ""), amplitude=Quantity(1e-11, "m-2 s-1 TeV-1"), reference=Quantity(1, "TeV")
)
# TODO: simulate known spectrum instead of using this example:
filename = "$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits"
self.obs = SpectrumObservation.read(filename)
self.seg = SpectrumEnergyGroupMaker(obs=self.obs)
ebounds = [0.3, 1.001, 3, 10.001, 30] * u.TeV
self.seg.compute_range_safe()
self.seg.compute_groups_fixed(ebounds=ebounds)
self.groups = self.seg.groups
def setup(self):
self.model = PowerLaw(
index=Quantity(2, ''),
amplitude=Quantity(1e-11, 'm-2 s-1 TeV-1'),
reference=Quantity(1, 'TeV'),
)
# TODO: simulate known spectrum instead of using this example:
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
self.obs = SpectrumObservation.read(filename)
self.seg = SpectrumEnergyGroupMaker(obs=self.obs)
ebounds = [0.3, 1.001, 3, 10.001, 30] * u.TeV
self.seg.compute_range_safe()
self.seg.compute_groups_fixed(ebounds=ebounds)
self.groups = self.seg.groups
def fit_gammapy():
"""
Current results
Parameters:
name value error unit min max frozen
--------- --------- --------- --------------- --- --- ------
index 2.602e+00 1.555e-01 nan nan False
amplitude 2.441e-11 3.452e-12 1 / (cm2 s TeV) nan nan False
reference 1.000e+00 0.000e+00 TeV nan nan True
Covariance:
name/name index amplitude
--------- -------- ---------
index 0.0242 3.79e-13
amplitude 3.79e-13 1.19e-23
Statistic: -157.719 (cash)
Fit Range: [1.0000000e+09 2.7825594e+10] keV
"""
obs = SpectrumObservation.read(obs_path)
# obs.peek()
# plt.show()
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
fit = SpectrumFit(obs_list=obs, model=model, fit_range=energy_range, stat="cash")
fit.run()
print(fit.result[0])
pprint(fit.__dict__)
obs = fit.obs_list[0]
print(obs)
print("This is fit_gammapy")
obs.peek()
import matplotlib.pyplot as plt
plt.savefig("fit_gammapy.png")
def setup(self):
pha = gammapy_extra.filename("datasets/hess-crab4_pha/pha_obs23592.fits")
self.obs = SpectrumObservation.read(pha)
self.best_fit_model = models.PowerLaw(index=2 * u.Unit(''),
amplitude=1e-11 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV)
self.npred = self.obs.predicted_counts(self.best_fit_model).data.value
self.covar_axis = ['index', 'amplitude']
self.covar = np.diag([0.1 ** 2, 1e-12 ** 2])
self.fit_range = [0.1, 50] * u.TeV
self.fit_result = SpectrumFitResult(
model=self.best_fit_model,
covariance=self.covar,
covar_axis=self.covar_axis,
fit_range=self.fit_range,
statname='wstat',
statval=42,
npred=self.npred,
obs=self.obs,
)
def run(self):
self.make_empty_vectors(self.bkg_estimate)
self.extract_counts(self.bkg_estimate)
aeff = extract_aeff(self.exposure, self.target_position, self.energy)
containment_factor = extract_psf_containment(
self.psf, self.on_radius, self.energy
)
aeff.data.data *= containment_factor
# Not the real Fermi-LAT EDISP
# Use 5% energy resolution as approximation
edisp = EnergyDispersion.from_gauss(
e_true=self.energy, e_reco=self.energy, sigma=0.05, bias=0
)
return SpectrumObservation(
on_vector=self._on_vector,
aeff=aeff,
off_vector=self._off_vector,
edisp=edisp,
)
def check_energy_binning_effects():
"""Check how spectral fit results change with energy binnings.
Actually this is still using the default:
In [14]: print(analysis.extraction.observations[0].edisp)
EnergyDispersion
NDDataArray summary info
e_true : size = 108, min = 0.010 TeV, max = 301.416 TeV
e_reco : size = 72, min = 0.011 TeV, max = 93.804 TeV
Data : size = 7776, min = 0.000, max = 1.000
But now, the fit results from SpectrumAnalysisIACT, which is just
driving Fitspectrum, are different, almost the same as Sherpa.
Current results
Parameters:
name value error unit min max frozen
--------- --------- --------- --------------- --- --- ------
index 2.620e+00 1.540e-01 nan nan False
amplitude 3.849e-11 5.407e-12 1 / (cm2 s TeV) nan nan False
reference 1.000e+00 0.000e+00 TeV nan nan True
Covariance:
name/name index amplitude
--------- -------- ---------
index 0.0237 5.85e-13
amplitude 5.85e-13 2.92e-23
Statistic: -157.166 (cash)
Fit Range: [ 1. 27.82559402] TeV
???
"""
data_store = DataStore.from_dir("data/hess")
obs_list = data_store.obs_list([23523])
on_region = CircleSkyRegion(conf.crab_position, conf.on_radius["hess"])
fp_binning = [1, 10, 30] * u.TeV
exclusion_mask = get_exclusion_mask(conf.crab_position)
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
cfg = dict(
outdir=None,
background=dict(
on_region=on_region,
exclusion_mask=exclusion_mask,
# min_distance=0.1 * u.rad,
),
extraction=dict(containment_correction=True),
fit=dict(
model=model,
stat="cash",
# forward_folded=True,
fit_range=energy_range,
),
fp_binning=fp_binning,
)
analysis = SpectrumAnalysisIACT(obs_list, cfg)
analysis.run()
analysis.fit.est_errors()
print("BEFORE", analysis.fit.result[0])
# print(analysis.extraction.observations[0].edisp)
# pprint(analysis.fit.__dict__)
# obs = analysis.fit.obs_list[0]
# print(obs)
# # import IPython; IPython.embed()
# obs.peek()
# import matplotlib.pyplot as plt
# plt.savefig('check_energy_binning_effects.png')
# print('This is check_energy_binning_effects')
# Try to see if the I/O causes a change in results.
analysis.extraction.observations.write("temp123")
analysis.extraction.observations.write("temp123", use_sherpa=True)
obs = SpectrumObservation.read("temp123/pha_obs23523.fits")
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
fit = SpectrumFit(obs_list=obs, model=model, fit_range=energy_range, stat="cash")
fit.run()
print("AFTER", fit.result[0])
"""Compute flux points
This is an example script that show how to compute flux points in Gammapy.
TODO: Refactor and add to FluxPointsComputation class or so
"""
from gammapy.spectrum import (SpectrumObservation, SpectrumFit, FluxPoints,
SpectrumResult)
import astropy.units as u
from astropy.table import Table
import numpy as np
import copy
import matplotlib.pyplot as plt
plt.style.use('ggplot')
obs = SpectrumObservation.read(
'$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits')
fit = SpectrumFit(obs)
fit.run()
best_fit = copy.deepcopy(fit.result[0].fit)
# Define Flux points binning
emin = np.log10(obs.lo_threshold.to('TeV').value)
emax = np.log10(40)
binning = np.logspace(emin, emax, 8) * u.TeV
# Fix index
fit.model.gamma.freeze()
# Fit norm in bands
diff_flux = list()
aeff=aeff,
edisp=edisp,
livetime=livetime)
bkg = 0.2 * npred.data
alpha = 0.1
counts_kwargs = dict(energy=npred.energy,
exposure=livetime,
obs_id=31415,
creator='Simulation',
hi_threshold=50 * u.TeV,
lo_threshold=lo_threshold)
rand = get_random_state(42)
on_counts = rand.poisson(npred.data) + rand.poisson(bkg)
on_vector = PHACountsSpectrum(data=on_counts, backscal=1, **counts_kwargs)
off_counts = rand.poisson(1. / alpha * bkg)
off_vector = PHACountsSpectrum(data=off_counts,
backscal=1. / alpha,
is_bkg=True,
**counts_kwargs)
obs = SpectrumObservation(on_vector=on_vector,
off_vector=off_vector,
aeff=aeff,
edisp=edisp)
obs.write()
"""Test spectrum energy grouping.
"""
import warnings
import astropy.units as u
from gammapy.spectrum import SpectrumObservation, SpectrumEnergyGroupMaker
warnings.filterwarnings('ignore')
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
obs = SpectrumObservation.read(filename)
table = obs.stats_table()
seg = SpectrumEnergyGroupMaker(obs=obs)
ebounds = [0.03, 1, 3, 10, 30] * u.TeV
seg.compute_range_safe()
seg.compute_groups_fixed(ebounds=ebounds)
print('\nTable of original energy bins:')
seg.table[['energy_group_idx', 'bin_idx', 'energy_min', 'energy_max']].pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_group_table().pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_total_table().pprint(max_lines=-1)
print(seg)
"""Test spectrum energy grouping.
"""
import warnings
import astropy.units as u
from gammapy.spectrum import SpectrumObservation, SpectrumEnergyGroupMaker
warnings.filterwarnings('ignore')
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
obs = SpectrumObservation.read(filename)
table = obs.stats_table()
seg = SpectrumEnergyGroupMaker(obs=obs)
ebounds = [0.03, 1, 3, 10, 30] * u.TeV
seg.compute_range_safe()
seg.compute_groups_fixed(ebounds=ebounds)
print('\nTable of original energy bins:')
seg.table[['energy_group_idx', 'bin_idx', 'energy_min',
'energy_max']].pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_group_table().pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_total_table().pprint(max_lines=-1)
print(seg)
from gammapy.spectrum import (
SpectrumObservation,
SpectrumFit,
DifferentialFluxPoints,
SpectrumFitResult,
SpectrumResult
)
import astropy.units as u
import numpy as np
import copy
import matplotlib.pyplot as plt
plt.style.use('ggplot')
obs = SpectrumObservation.read('$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits')
fit = SpectrumFit(obs)
fit.run()
best_fit = copy.deepcopy(fit.result[0].fit)
# Define Flux points binning
emin = np.log10(obs.lo_threshold.to('TeV').value)
emax = np.log10(40)
binning = np.logspace(emin, emax, 8) * u.TeV
# Fix index
fit.model.gamma.freeze()
# Fit norm in bands
diff_flux = list()
def simulate_obs(perf,
target,
obs_param,
obs_id=0,
random_state='random-seed'):
"""Simulate observation with given parameters.
Parameters
----------
perf : `~gammapy.scripts.CTAPerf`
CTA performance
target : `~gammapy.scripts.Target`
Source
obs_param : `~gammapy.scripts.ObservationParameters`
Observation parameters
obs_id : `int`, optional
Observation Id
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
"""
livetime = obs_param.livetime
alpha = obs_param.alpha.value
emin = obs_param.emin
emax = obs_param.emax
model = target.model
# Compute expected counts
reco_energy = perf.bkg.energy
bkg_rate_values = perf.bkg.data.data * livetime.to('s')
predicted_counts = CountsPredictor(model=model,
aeff=perf.aeff,
livetime=livetime,
edisp=perf.rmf)
predicted_counts.run()
npred = predicted_counts.npred
# set negative values to zero (interpolation issue)
idx = np.where(npred.data.data < 0.)
npred.data.data[idx] = 0
# Randomise counts
random_state = get_random_state(random_state)
on_counts = random_state.poisson(npred.data.data.value) # excess
bkg_counts = random_state.poisson(
bkg_rate_values.value) # bkg in ON region
off_counts = random_state.poisson(bkg_rate_values.value /
alpha) # bkg in OFF region
on_counts += bkg_counts # evts in ON region
on_vector = PHACountsSpectrum(
data=on_counts,
backscal=1,
energy_lo=reco_energy.lo,
energy_hi=reco_energy.hi,
)
on_vector.livetime = livetime
off_vector = PHACountsSpectrum(
energy_lo=reco_energy.lo,
energy_hi=reco_energy.hi,
data=off_counts,
backscal=1. / alpha,
is_bkg=True,
)
off_vector.livetime = livetime
obs = SpectrumObservation(on_vector=on_vector,
off_vector=off_vector,
aeff=perf.aeff,
edisp=perf.rmf)
obs.obs_id = obs_id
# Set threshold according to the closest energy reco from bkg bins
idx_min = np.abs(reco_energy.lo - emin).argmin()
idx_max = np.abs(reco_energy.lo - emax).argmin()
obs.lo_threshold = reco_energy.lo[idx_min]
obs.hi_threshold = reco_energy.lo[idx_max]
return obs
# 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,
creator="Simulation",
hi_threshold=50 * u.TeV,
lo_threshold=lo_threshold,
)
rand = get_random_state(42)
on_counts = rand.poisson(npred.data) + rand.poisson(bkg)
on_vector = PHACountsSpectrum(data=on_counts, backscal=1, **counts_kwargs)
off_counts = rand.poisson(1.0 / alpha * bkg)
off_vector = PHACountsSpectrum(data=off_counts, backscal=1.0 / alpha, is_bkg=True, **counts_kwargs)
obs = SpectrumObservation(on_vector=on_vector, off_vector=off_vector, aeff=aeff, edisp=edisp)
obs.write()
def simulate_obs(perf, target, obs_param, obs_id=0, random_state='random-seed'):
"""Simulate observation with given parameters.
Parameters
----------
perf : `~gammapy.scripts.CTAPerf`
CTA performance
target : `~gammapy.scripts.Target`
Source
obs_param : `~gammapy.scripts.ObservationParameters`
Observation parameters
obs_id : `int`, optional
Observation Id
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
"""
livetime = obs_param.livetime
alpha = obs_param.alpha.value
emin = obs_param.emin
emax = obs_param.emax
model = target.model
# Compute expected counts
reco_energy = perf.bkg.energy
bkg_rate_values = perf.bkg.data.data * livetime.to('s')
predicted_counts = CountsPredictor(model=model,
aeff=perf.aeff,
livetime=livetime,
edisp=perf.rmf)
predicted_counts.run()
npred = predicted_counts.npred
# set negative values to zero (interpolation issue)
idx = np.where(npred.data.data < 0.)
npred.data.data[idx] = 0
# Randomise counts
random_state = get_random_state(random_state)
on_counts = random_state.poisson(npred.data.data.value) # excess
bkg_counts = random_state.poisson(bkg_rate_values.value) # bkg in ON region
off_counts = random_state.poisson(bkg_rate_values.value / alpha) # bkg in OFF region
on_counts += bkg_counts # evts in ON region
on_vector = PHACountsSpectrum(
data=on_counts,
backscal=1,
energy_lo=reco_energy.lo,
energy_hi=reco_energy.hi,
)
on_vector.livetime = livetime
off_vector = PHACountsSpectrum(energy_lo=reco_energy.lo,
energy_hi=reco_energy.hi,
data=off_counts,
backscal=1. / alpha,
is_bkg=True,
)
off_vector.livetime = livetime
obs = SpectrumObservation(on_vector=on_vector,
off_vector=off_vector,
aeff=perf.aeff,
edisp=perf.rmf)
obs.obs_id = obs_id
# Set threshold according to the closest energy reco from bkg bins
idx_min = np.abs(reco_energy.lo - emin).argmin()
idx_max = np.abs(reco_energy.lo - emax).argmin()
obs.lo_threshold = reco_energy.lo[idx_min]
obs.hi_threshold = reco_energy.lo[idx_max]
return obs
lw=2,
label=name)
ax.step(x[start:stop],
bkg_counts[start:stop],
where='post',
lw=1,
color=line.get_color(),
alpha=0.5)
return ax
fig, ax = plt.subplots(1, 1)
# filenames = glob.glob('./plots/data/joint_crab/spectra/magic/pha_*.fits')
filenames = glob.glob('./build/pymc_results/spectra/magic/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'MAGIC')
filenames = glob.glob('./build/pymc_results/spectra/fact/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'FACT')
filenames = glob.glob('./build/pymc_results/spectra/veritas/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'VERITAS')
filenames = glob.glob('./build/pymc_results/spectra/hess/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'H.E.S.S')
ax.legend()
from gammapy.spectrum import SpectrumFit, SpectrumObservation, SpectrumFitResult
import matplotlib.pyplot as plt
import astropy.units as u
obs = SpectrumObservation.read('pha_obs31415.fits')
fit = SpectrumFit(obs)
obs.peek()
plt.savefig('observation.png')
plt.cla()
fit.run()
fit.result[0].plot_fit()
plt.savefig('debug_fit.png')
# TODO: implement properly
plt.cla()
fig = plt.figure()
ax = fig.add_subplot(111)
fit.result[0].fit.plot_butterfly(ax=ax, label='Fit result')
input_parameters = dict(index = 2.3 * u.Unit(''),
norm = 2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
reference = 1 * u.TeV)
input_parameter_errors = dict(index = 0 * u.TeV,
norm = 0 * u.Unit('cm-2 s-1 TeV-1'),
reference = 0 * u.TeV)
input_model = SpectrumFitResult(spectral_model = 'PowerLaw',
parameters = input_parameters,
parameter_errors = input_parameter_errors)
def main():
#Low energy of spectral fitting range.
lo_fit_energ = 0.1 * u.Unit('TeV')
hi_fit_energ = 10 * u.Unit('TeV')
#If you want an internal estimation of a high energy limit for the fitting range: est_hi_lim = 'yes'. If 'no' the hi_fit_energ will be used.
est_hi_lim = 'yes'
# Read ON and OFF cubes
filename_on = 'non_cube.fits.gz' # non_cube_convolved.fits
cube_on = SkyCube.read(filename_on)
ann_filename_off = 'noff_withpuls_cube.fits.gz'
ann_cube_off = SkyCube.read(ann_filename_off)
circ_filename_off = 'noff_withneb_cube.fits.gz'
circ_cube_off = SkyCube.read(circ_filename_off)
# Read config and IRFs
config = read_config('config.yaml')
irfs = get_irfs(config)
offset = Angle(config['selection']['offset_fov'] * u.deg)
livetime = u.Quantity(config['pointing']['livetime']).to('second')
alpha_obs = 1.
binsz = config['binning']['binsz']
aeff = irfs['aeff'].to_effective_area_table(offset = offset, energy = cube_on.energies('edges'))
edisp = irfs['edisp'].to_energy_dispersion(offset = offset, e_true = aeff.energy.bins, e_reco = cube_on.energies('edges') )
# Define circular on/off Regions parameters
on_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
on_sizes = np.ones(20) * binsz * u.deg
off_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
off_sizes = on_sizes * np.sqrt(1./alpha_obs)
# Make Annular region
on_rad_sizes = np.ones(len(on_sizes)) * 0.1 * binsz * u.deg
off_rad_sizes = on_rad_sizes * np.sqrt(1./alpha_obs)
widths = np.ones(len(on_sizes)) * 22 * binsz * u.deg
out_rad_sizes = on_rad_sizes + widths
ann_on_data, ann_off_data, ann_stats = make_annular_spectrum(on_pos, off_pos, on_rad_sizes, off_rad_sizes, out_rad_sizes, cube_on, ann_cube_off, alpha_obs)
# Make circular region
circ_on_data, circ_off_data, circ_stats = make_circular_spectrum(on_pos, off_pos, on_sizes, off_sizes, cube_on, circ_cube_off, alpha_obs)
# Undo "holes" in circ/ann_stats
if np.max(np.where(circ_stats == 1)) + 1 != circ_stats.sum():
circ_stats[0:np.max(np.where(circ_stats == 1)) + 1][circ_stats[0:np.max(np.where(circ_stats == 1)) + 1] == 0] = 1.
if np.max(np.where(ann_stats == 1)) + 1 != ann_stats.sum():
ann_stats[0:np.max(np.where(ann_stats == 1)) + 1][ann_stats[0:np.max(np.where(ann_stats == 1)) + 1] == 0] = 1.
# Make on/off vector
ann_on_vector = PHACountsSpectrum(energy_lo = cube_on.energies('edges')[:-1], energy_hi= cube_on.energies('edges')[1:], data= ann_on_data['value'].data * ann_stats * u.ct, backscal = on_sizes[0].value, meta={'EXPOSURE' : livetime.value})
circ_on_vector = PHACountsSpectrum(energy_lo = cube_on.energies('edges')[:-1], energy_hi= cube_on.energies('edges')[1:], data= circ_on_data['value'].data * circ_stats * u.ct, backscal = on_sizes[0].value, meta={'EXPOSURE' : livetime.value})
ann_off_vector = PHACountsSpectrum(energy_lo = ann_cube_off.energies('edges')[:-1], energy_hi= ann_cube_off.energies('edges')[1:], data= ann_off_data['value'].data * ann_stats * u.ct, backscal = off_sizes[0].value, meta={'EXPOSURE' : livetime.value, 'OFFSET' : 0.3 * u.deg})
circ_off_vector = PHACountsSpectrum(energy_lo = circ_cube_off.energies('edges')[:-1], energy_hi= circ_cube_off.energies('edges')[1:], data= circ_off_data['value'].data * circ_stats * u.ct, backscal = off_sizes[0].value, meta={'EXPOSURE' : livetime.value, 'OFFSET' : 0.3 * u.deg})
# Make SpectrumObservation
ann_sed_table = SpectrumObservation(on_vector = ann_on_vector, off_vector = ann_off_vector, aeff = aeff, edisp = edisp)
circ_sed_table = SpectrumObservation(on_vector = circ_on_vector, off_vector = circ_off_vector, aeff = aeff, edisp = edisp)
##Debug
#print(ann_stats)
#print(circ_stats)
# Define Spectral Model
model2fit1 = LogParabola(amplitude=1e-11 * u.Unit('cm-2 s-1 TeV-1'), reference=1 * u.TeV, alpha=2.5 * u.Unit(''), beta=0.1 * u.Unit(''))
model2fit2 = ExponentialCutoffPowerLaw(index = 1. * u.Unit(''), amplitude = 1e-11 * u.Unit('cm-2 s-1 TeV-1'), reference= 1 * u.TeV, lambda_= 0. * u.Unit('TeV-1'))
model2fit3 = PowerLaw(index= 2.5 * u.Unit(''), amplitude= 5e-11 * u.Unit('cm-2 s-1 TeV-1'), reference= 0.15 * u.TeV)
model2fit3.parameters['amplitude'].parmin = 1e-12
model2fit3.parameters['amplitude'].parmax = 1e-10
model2fit3.parameters['index'].parmin = 2.0
model2fit3.parameters['index'].parmax = 4.0
#Models to fit the circular and annular observations
models_ann_fit = [model2fit1, model2fit2, model2fit3]
models_circ_fit = [model2fit1, model2fit2, model2fit3]
# Fit
if est_hi_lim = 'yes':
hi_fit_energ = cube_on.energies('edges')[int(np.sum(ann_stats))]
