def define_flux_crab_above_energy(emin=1 * u.TeV, emax=10 * u.TeV):
crab = CrabSpectrum('meyer').model
crabMAGIC = LogParabola(amplitude=3.23e-11 * u.Unit('cm-2 s-1 TeV-1'), reference=1 * u.TeV, alpha=2.47, beta=0.24)
crab_flux_above_1TeV = crabMAGIC.integral(emin=emin, emax=emax)
crab_flux_above_1TeV_model = crab.integral(emin=emin, emax=emax)
return crab_flux_above_1TeV, crab_flux_above_1TeV_model
def plot_spectral_models(self):
for component in self.get_components(tags=['pwn', 'composite']):
fig, ax = plt.subplots()
table = component['table']
tag = component['tag']
vals = []
idx = 0
energies = Energy.equal_log_spacing(emin=0.02,
emax=100,
unit='TeV',
nbins=40)
for row in table:
idx += 1
if (idx < 100):
spec = LogParabola(
amplitude=row['spec_norm'] * u.Unit('MeV-1 s-1 cm-2'),
alpha=row['spec_alpha'],
reference=1 * u.TeV,
beta=row['spec_beta'],
)
fluxes = []
e2dnde = []
for energy in energies:
dnde = spec.evaluate(
energy=energy,
amplitude=row['spec_norm'] *
u.Unit('MeV-1 s-1 cm-2'),
alpha=row['spec_alpha'],
reference=1 * u.TeV,
beta=row['spec_beta'],
)
fluxes.append(dnde.value)
e2dnde.append(
((energy**2 * dnde).to('erg cm-2 s-1')).value)
ax.plot(energies.value,
e2dnde,
color='black',
alpha=0.2,
lw=2)
else:
break
ax.set_title('{} spectra'.format(component['tag']))
ax.loglog()
ax.set_xlabel('Energy (TeV)')
ax.set_ylabel('e2dnde (erg cm-2 s-1)')
ax.set_ylim(2e-18, 5e-10)
fig.tight_layout()
filename = 'ctadc_skymodel_gps_sources_spectra_{}.png'.format(
component['tag'])
log.info('Writing {}'.format(filename))
fig.savefig(filename)
plt.close(fig)
def define_flux_crab_above_energy():
emin, emax = [1, 10] * u.TeV
# crab = CrabSpectrum('meyer').model
crabMAGIC = LogParabola(amplitude=3.23e-11 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV,
alpha=2.47,
beta=-0.24)
crab_flux_above_1TeV = crabMAGIC.integral(emin=emin, emax=emax)
print(crab_flux_above_1TeV)
crab_flux_above_1TeV = crab_flux_above_1TeV.to('cm-2 s-1')
return crab_flux_above_1TeV
def get_model_gammapy(config):
if config['model']['template'] == 'Shell2D':
spatial_model = Shell2D(
amplitude=1,
x_0=config['model']['ra'],
y_0=config['model']['dec'],
r_in=config['model']['rin'],
width=config['model']['width'],
# Note: for now we need spatial models that are normalised
# to integrate to 1 or results will be incorrect!!!
normed=True,
)
if config['model']['template'] == 'Sphere2D':
spatial_model = Sphere2D(
amplitude=1,
x_0=config['model']['ra'],
y_0=config['model']['dec'],
r_0=config['model']['rad'],
# Note: for now we need spatial models that are normalised
# to integrate to 1 or results will be incorrect!!!
normed=True,
)
if config['model']['template'] == 'Gauss2D':
spatial_model = Gauss2DPDF(
#amplitude=1,
#x_0=config['model']['ra'],
#y_0=config['model']['dec'],
sigma=config['model']['sigma'],
# Note: for now we need spatial models that are normalised
# to integrate to 1 or results will be incorrect!!!
#normed=True,
)
if config['model']['spectrum'] == 'pl':
spectral_model = PowerLaw(
amplitude=config['model']['prefactor'] * u.Unit('cm-2 s-1 TeV-1'),
index=config['model']['index'],
reference=config['model']['pivot_energy'] * u.Unit('TeV'),
)
if config['model']['spectrum'] == 'ecpl':
spectral_model = ExponentialCutoffPowerLaw(
amplitude=config['model']['prefactor'] * u.Unit('cm-2 s-1 TeV-1'),
index=config['model']['index'],
reference=config['model']['pivot_energy'] * u.Unit('TeV'),
lambda_=config['model']['cutoff'] * u.Unit('TeV-1'),
)
if config['model']['spectrum'] == 'LogParabola':
spectral_model = LogParabola(
amplitude=config['model']['prefactor'] * u.Unit('cm-2 s-1 TeV-1'),
alpha=config['model']['alphapar'],
beta=config['model']['beta'],
reference=config['model']['pivot_energy'] * u.Unit('TeV'),
)
return CombinedModel3D(
spatial_model=spatial_model,
spectral_model=spectral_model,
)
def spectral_model(self):
"""Best fit spectral model (`~gammapy.spectrum.models.SpectralModel`)."""
d = self.data
spec_type = self.data["SpectrumType"].strip()
pars, errs = {}, {}
pars["amplitude"] = d["Flux_Density"]
errs["amplitude"] = d["Unc_Flux_Density"]
pars["reference"] = d["Pivot_Energy"]
if spec_type == "PowerLaw":
pars["index"] = d["PowerLaw_Index"]
errs["index"] = d["Unc_PowerLaw_Index"]
model = PowerLaw(**pars)
elif spec_type == "LogParabola":
pars["alpha"] = d["Spectral_Index"]
pars["beta"] = d["beta"]
errs["alpha"] = d["Unc_Spectral_Index"]
errs["beta"] = d["Unc_beta"]
model = LogParabola(**pars)
else:
raise ValueError("Invalid spec_type: {!r}".format(spec_type))
model.parameters.set_parameter_errors(errs)
return model
def flux_amplitude_from_energy_flux(alpha, beta, energy_flux):
spec = LogParabola(
amplitude=1 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV,
alpha=alpha,
beta=-beta,
)
# Assumption before for `energy_flux` was energy band 1 to 10 TeV
emin_int = 1 * u.TeV
emax_int = 10 * u.TeV
pivot_energy = 1 * u.TeV
energy_flux_standard_candle = spec.energy_flux(emin=emin_int, emax=emax_int)
flux_at_1TeV = energy_flux / energy_flux_standard_candle * u.Unit('cm-2 s-1 TeV-1')
# print('std: ', energy_flux_standard_candle, 'energy_flux: ', energy_flux, flux_at_1TeV)
flux_at_1TeV = flux_at_1TeV.to('cm-2 s-1 MeV-1')
spec2 = LogParabola(amplitude=flux_at_1TeV, reference=pivot_energy, alpha=alpha, beta=beta)
energy_flux_above_1TeV = spec2.energy_flux(emin=emin_int, emax=10 * u.TeV)
# print('c',energy_flux_above_1TeV.to('TeV cm-2 s-1'),energy_flux_above_1TeV.to('TeV cm-2 s-1')/(flux_at_1TeV.to('cm-2 s-1 TeV-1')/)) )
flux_above_1TeV = spec2.integral(emin=emin_int, emax=10 * u.TeV)
flux_above_1TeV = flux_above_1TeV.to('cm-2 s-1')
# evaluating Crab flux at and above 1 TeV by using MAGIC Crab spectrum from JHEA 2015
crab_flux_above_1TeV, crab_flux_above_1TeV_model = define_flux_crab_above_energy(emin=1 * u.TeV, emax=10 * u.TeV)
crabMAGIC = LogParabola(amplitude=3.23e-11 * u.Unit('cm-2 s-1 TeV-1'), reference=1 * u.TeV, alpha=2.47, beta=0.24)
# plt.figure()
# min, max = [50*u.GeV,50*u.TeV]
# crabMAGIC.plot(min,max)
# crab = CrabSpectrum('meyer').model
crab_flux_above_1TeV = crab_flux_above_1TeV.to('cm-2 s-1')
crab_flux_above_1TeV_model = crab_flux_above_1TeV_model.to('cm-2 s-1')
crab_flux_at_1TeV = crabMAGIC(pivot_energy).to('MeV-1 cm-2 s-1')
# computing flux at and above 1 TeV in crab units
flux_at_1TeV_cu = (flux_at_1TeV / crab_flux_at_1TeV)*100
flux_above_1TeV_cu = (flux_above_1TeV / crab_flux_above_1TeV)*100
# print(crab_flux_above_1TeV, flux_above_1TeV, flux_above_1TeV / crab_flux_above_1TeV, flux_above_1TeV_cu )
flux_above_1TeV_cu_model = (flux_above_1TeV / crab_flux_above_1TeV_model).to('%')
return flux_at_1TeV, flux_at_1TeV_cu, flux_above_1TeV, flux_above_1TeV_cu
def spectral_model(self):
"""Best fit spectral model (`~gammapy.spectrum.models.SpectralModel`)."""
spec_type = self.data["SpectrumType"].strip()
pars, errs = {}, {}
pars["amplitude"] = self.data["Flux_Density"]
errs["amplitude"] = self.data["Unc_Flux_Density"]
pars["reference"] = self.data["Pivot_Energy"]
if spec_type == "PowerLaw":
pars["index"] = self.data["Spectral_Index"]
errs["index"] = self.data["Unc_Spectral_Index"]
model = PowerLaw(**pars)
elif spec_type == "PLExpCutoff":
pars["index"] = self.data["Spectral_Index"]
pars["ecut"] = self.data["Cutoff"]
errs["index"] = self.data["Unc_Spectral_Index"]
errs["ecut"] = self.data["Unc_Cutoff"]
model = ExponentialCutoffPowerLaw3FGL(**pars)
elif spec_type == "LogParabola":
pars["alpha"] = self.data["Spectral_Index"]
pars["beta"] = self.data["beta"]
errs["alpha"] = self.data["Unc_Spectral_Index"]
errs["beta"] = self.data["Unc_beta"]
model = LogParabola(**pars)
elif spec_type == "PLSuperExpCutoff":
# TODO: why convert to GeV here? Remove?
pars["reference"] = pars["reference"].to("GeV")
pars["index_1"] = self.data["Spectral_Index"]
pars["index_2"] = self.data["Exp_Index"]
pars["ecut"] = self.data["Cutoff"].to("GeV")
errs["index_1"] = self.data["Unc_Spectral_Index"]
errs["index_2"] = self.data["Unc_Exp_Index"]
errs["ecut"] = self.data["Unc_Cutoff"].to("GeV")
model = PLSuperExpCutoff3FGL(**pars)
else:
raise ValueError("Invalid spec_type: {!r}".format(spec_type))
model.parameters.set_parameter_errors(errs)
return model
def spectral_model(self):
"""Best fit spectral model (`~gammapy.spectrum.models.SpectralModel`)."""
spec_type = self.data["SpectrumType"].strip()
pars, errs = {}, {}
pars["reference"] = self.data["Pivot_Energy"]
if spec_type == "PowerLaw":
pars["amplitude"] = self.data["PL_Flux_Density"]
pars["index"] = self.data["PL_Index"]
errs["amplitude"] = self.data["Unc_PL_Flux_Density"]
errs["index"] = self.data["Unc_PL_Index"]
model = PowerLaw(**pars)
elif spec_type == "LogParabola":
pars["amplitude"] = self.data["LP_Flux_Density"]
pars["alpha"] = self.data["LP_Index"]
pars["beta"] = self.data["LP_beta"]
errs["amplitude"] = self.data["Unc_LP_Flux_Density"]
errs["alpha"] = self.data["Unc_LP_Index"]
errs["beta"] = self.data["Unc_LP_beta"]
model = LogParabola(**pars)
elif spec_type == "PLSuperExpCutoff":
pars["amplitude"] = self.data["PLEC_Flux_Density"]
pars["index_1"] = self.data["PLEC_Index"]
pars["index_2"] = self.data["PLEC_Exp_Index"]
pars["expfactor"] = self.data["PLEC_Expfactor"] / u.MeV**pars[
"index_2"]
errs["amplitude"] = self.data["Unc_PLEC_Flux_Density"]
errs["index_1"] = self.data["Unc_PLEC_Index"]
errs["index_2"] = np.nan_to_num(self.data["Unc_PLEC_Exp_Index"])
errs["expfactor"] = (self.data["Unc_PLEC_Expfactor"] /
u.MeV**pars["index_2"])
model = PLSuperExpCutoff4FGL(**pars)
else:
raise ValueError("Invalid spec_type: {!r}".format(spec_type))
model.parameters.set_parameter_errors(errs)
return model
ax = flux_points.plot(energy_power=2)
result_ecpl.model.plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
result_ecpl.model.plot_error(energy_range=[1e-4, 1e2] * u.TeV,
ax=ax,
energy_power=2)
ax.set_ylim(1e-13, 1e-11)
# ## Log-Parabola Fit
#
# Finally we try to fit a [log-parabola](https://docs.gammapy.org/0.10/api/gammapy.spectrum.models.LogParabola.html#gammapy.spectrum.models.LogParabola) model:
# In[ ]:
log_parabola = LogParabola(alpha=2,
amplitude="1e-12 cm-2 s-1 TeV-1",
reference="1 TeV",
beta=0.1)
# In[ ]:
fitter = FluxPointFit(log_parabola, flux_points, stat="chi2assym")
result_log_parabola = fitter.run()
print(result_log_parabola.model)
# In[ ]:
ax = flux_points.plot(energy_power=2)
result_log_parabola.model.plot(energy_range=[1e-4, 1e2] * u.TeV,
ax=ax,
energy_power=2)
result_log_parabola.model.plot_error(energy_range=[1e-4, 1e2] * u.TeV,
def plot_fit_results(tool):
"""plot the SEDs result of the gammapy / sherpa fit
for comparison with the literature we choose only the Mayer spectrum
Here we plot the butterfly as a result of the multivariate sampling
with the 68% containment in flux
"""
fig, ax = plt.subplots()
model_meyer_ref = CrabSpectrum("meyer").model
model_meyer_ref.plot(
[10 * u.GeV, 100 * u.TeV],
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=":",
lw=2.2,
color="#555555",
label="Meyer et al. (2010)",
)
# where to take the results, configurations for the individual butterflies
instruments = ["fermi", "magic", "veritas", "fact", "hess", "joint"]
labels = ["Fermi-LAT", "MAGIC", "VERITAS", "FACT", "H.E.S.S.", "joint fit"]
lss = ["--", "--", "--", "--", "--", "-"]
colors = COLORS
# with one loop we realize all the butterfly plots
for instrument, label, color, ls in zip(instruments, labels, colors, lss):
path = (
config.repo_path /
f"results/fit/{tool}/{instrument}/fit_results_logparabola.yaml")
if not path.exists():
log.warning(f"Missing: {path} . Skipping.")
continue
results = load_yaml(path)
parameters = results["parameters"]
model_lp = LogParabola.from_log10(
amplitude=parameters[0]["value"] * u.Unit(parameters[0]["unit"]),
reference=parameters[1]["value"] * u.Unit(parameters[1]["unit"]),
alpha=parameters[2]["value"] * u.Unit(parameters[2]["unit"]),
beta=parameters[3]["value"] * u.Unit(parameters[3]["unit"]),
)
# energy range for the plot
dataset = config.get_dataset(instrument)
energy_range = dataset.energy_range
# just in case of the joint fit put a thicker line and a less transparent butterfly
if instrument == "joint":
model_lp.plot(
energy_range,
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=ls,
lw=3,
color=color,
label=label,
)
else:
model_lp.plot(
energy_range,
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
ls=ls,
lw=2.2,
color=color,
label=label,
)
# read the butterfly from the multivariate sampling results
table_path = Path(
f"{config.repo_path}/results/figures/stat_err/{instrument}_flux_errorband.dat"
)
log.info(f"reading butterfly values from {table_path}")
t = Table.read(table_path, format="ascii.ecsv")
energies = t["energies"].data * t["energies"].unit
flux_lo = t["flux_lo"].data * t["flux_lo"].unit
flux_hi = t["flux_hi"].data * t["flux_hi"].unit
if instrument == "joint":
alpha = 0.38
else:
alpha = 0.28
plt.fill_between(
energies.to("TeV"),
(energies**2 * flux_lo).to("erg cm-2 s-1"),
(energies**2 * flux_hi).to("erg cm-2 s-1"),
color=color,
alpha=alpha,
label="",
)
ax.legend(fontsize=FONTSIZE)
ax.set_ylim([1e-12, 2e-10])
ax.set_xlabel(E_UNIT_LABEL, size=FONTSIZE)
ax.set_ylabel(SED_UNIT_LABEL, size=FONTSIZE)
# make axis thicker
for axis in ["top", "bottom", "left", "right"]:
ax.spines[axis].set_linewidth(1.6)
ax.tick_params("both",
length=7,
width=1.6,
which="major",
labelsize=FONTSIZE)
ax.tick_params("both",
length=4,
width=1.6,
which="minor",
labelsize=FONTSIZE)
plt.tight_layout()
filename = f"results/figures/crab_sed_{tool}_fit.png"
filename_pdf = f"results/figures/crab_sed_{tool}_fit.pdf"
log.info(f"Writing {filename}")
fig.savefig(filename)
fig.savefig(filename_pdf)
amplitude=flare_amplitude,
reference=flare_reference,
lambda_=lambda_flare)
#flare_model = PowerLaw(index=flare_index, amplitude=flare_amplitude, reference=flare_reference)
#######################################################################
# Model
if spectype == 'PowerLaw':
neb_model = PowerLaw(index=index, amplitude=amplitude,
reference=reference) ### HEGRA
elif spectype == 'LogParabola':
neb_model = LogParabola(amplitude=amplitude,
reference=reference,
alpha=alphapar,
beta=beta) ###MAGIC HESSII
elif spectype == 'ExponentialCutoffPowerLaw':
neb_model = ExponentialCutoffPowerLaw(index=index,
amplitude=amplitude,
reference=reference,
lambda_=lambda_) ####HESS
else:
raise ValueError(
'Spectra Model must be either "LogParabola", "PowerLaw", or "ExponentialCutoffPowerLaw"'
)
energy_array = np.linspace(emin.value, emax.value, 300) * u.TeV
flare_plus_neb_model = np.zeros(len(energy_array)) * u.Unit('cm-2 s-1 TeV-1')
ecpl.plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
# assign covariance for plotting
ecpl.parameters.covariance = result_ecpl.parameters.covariance
ecpl.plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
ax.set_ylim(1e-13, 1e-11)
# ## Log-Parabola Fit
#
# Finally we try to fit a [log-parabola](https://docs.gammapy.org/0.11/api/gammapy.spectrum.models.LogParabola.html#gammapy.spectrum.models.LogParabola) model:
# In[ ]:
log_parabola = LogParabola(alpha=2,
amplitude="1e-12 cm-2 s-1 TeV-1",
reference="1 TeV",
beta=0.1)
# In[ ]:
dataset_log_parabola = FluxPointsDataset(log_parabola,
flux_points,
likelihood="chi2assym")
fitter = Fit(dataset_log_parabola)
result_log_parabola = fitter.run()
print(log_parabola)
# In[ ]:
ax = flux_points.plot(energy_power=2)
log_parabola.plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
obs_ids = data_store.obs_table["OBS_ID"][mask].data
observations = data_store.obs_list(obs_ids)
print(obs_ids)
# ## Configure the analysis
#
# Now we'll define the input for the spectrum analysis. It will be done the python way, i.e. by creating a config dict containing python objects. We plan to add also the convenience to configure the analysis using a plain text config file.
# In[ ]:
crab_pos = SkyCoord.from_name("crab")
on_region = CircleSkyRegion(crab_pos, 0.15 * u.deg)
model = LogParabola(
alpha=2.3,
beta=0.01,
amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV,
)
flux_point_binning = EnergyBounds.equal_log_spacing(0.7, 30, 5, u.TeV)
# In[ ]:
exclusion_mask = Map.create(skydir=crab_pos, width=(10, 10), binsz=0.02)
gammacat = SourceCatalogGammaCat()
regions = []
for source in gammacat:
if not exclusion_mask.geom.contains(source.position):
continue
emin = 0.03 * u.TeV
emax = 5 * u.TeV
obs_param = ObservationParameters(alpha=alpha,
livetime=livetime,
emin=emin,
emax=emax)
# Target, PKS 2155-304 from 3FHL
name = "2155"
# model parameters
alpha = 1.88 * u.Unit('')
beta = 0.15 * u.Unit('')
reference = 18.3 * u.GeV
amplitude = 7.7e-11 * u.Unit('cm-2 s-1 GeV-1')
model = LogParabola(alpha=alpha,
beta=beta,
reference=reference,
amplitude=amplitude)
# redshift
redshift = 0.116
# EBL model
ebl_model_name = 'dominguez'
target = Target(name=name,
model=model,
redshift=redshift,
ebl_model_name=ebl_model_name)
# Performance
filename = '$GAMMAPY_EXTRA/datasets/cta/perf_prod2/point_like_non_smoothed/South_5h.fits.gz'
cta_perf = CTAPerf.read(filename)
# Simulation
beta= 0.104 * u.Unit('') ### MAGIC: 0.24/np.log(10) HESSII: 0.24 ### Needed for: 'LogParabola'
#######################################################################
######## Define Flare model
flare_index = 3.161 * u.Unit('')
flare_amplitude = 3.320e-11 * u.Unit('cm-2 s-1 TeV-1')
flare_reference = 1.25 * u.TeV
lambda_flare = 0.0 * u.Unit('TeV-1')
flare_model = ExponentialCutoffPowerLaw(index=flare_index, amplitude=flare_amplitude, reference=flare_reference,lambda_ = lambda_flare)
if spectype == 'LogParabola':
neb_model = LogParabola(amplitude=amplitude, reference=reference, alpha=alphapar, beta=beta) ###MAGIC HESSII
elif spectype == 'ExponentialCutoffPowerLaw':
neb_model = ExponentialCutoffPowerLaw(index=index, amplitude=amplitude, reference=reference, lambda_=lambda_) ## HESS
elif spectype == 'PowerLaw':
neb_model = PowerLaw(index=index, amplitude=amplitude, reference=reference) ### HEGRA
else:
raise ValueError('Spectra Model must be either "LogParabola", "PowerLaw", or "ExponentialCutoffPowerLaw"')
energy_array = np.linspace(emin.value, emax.value, 300) * u.TeV
flare_plus_neb_model = np.zeros(len(energy_array)) * u.Unit('cm-2 s-1 TeV-1')
for i in range(len(energy_array)):
flare_plus_neb_model.value[i] = (neb_model.evaluate(energy_array[i], amplitude=amplitude, reference=reference, alpha=alphapar, beta=beta).value + flare_model.evaluate(energy_array[i], index = flare_index, amplitude = flare_amplitude, reference = flare_reference,lambda_ = lambda_flare).value) * 1e11
ax = flux_points.plot(energy_power=2)
result_ecpl['best-fit-model'].plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
result_ecpl['best-fit-model'].plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
ax.set_ylim(1e-13, 1e-11)
# ## Log-Parabola Fit
#
# Finally we try to fit a [log-parabola](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.LogParabola.html#gammapy.spectrum.models.LogParabola) model:
# In[18]:
log_parabola = LogParabola(
alpha=2. * u.Unit(''),
amplitude=1e-12 * u.Unit('cm-2 s-1 TeV-1'),
reference=1. * u.TeV,
beta=0. * u.Unit('')
)
# In[19]:
result_log_parabola = fitter.run(flux_points, log_parabola)
print(result_log_parabola['best-fit-model'])
# In[20]:
print(result_log_parabola['statval/dof'])
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))]
def counts_histogram(predicted=False):
"""function to plot the excesses per dataset and compare them with the predicted counts
if predicted == True will shwo the predicted counts from the results of the fit (for debug purpose)
"""
log.info("loading the results from the joint fit to predict the counts")
results = load_yaml(
f"{config.repo_path}/results/fit/gammapy/joint/fit_results_logparabola.yaml"
)
parameters = results["parameters"]
model_lp = LogParabola.from_log10(
amplitude=parameters[0]["value"] * u.Unit(parameters[0]["unit"]),
reference=parameters[1]["value"] * u.Unit(parameters[1]["unit"]),
alpha=parameters[2]["value"] * u.Unit(parameters[2]["unit"]),
beta=parameters[3]["value"] * u.Unit(parameters[3]["unit"]),
)
# defining the figure
dict_color = {
"fermi": COLORS[0],
"magic": COLORS[1],
"veritas": COLORS[2],
"fact": COLORS[3],
"hess": COLORS[4],
}
fig, ax = plt.subplots()
for which in config.all_datasets:
log.info(f"predicting counts for {which} dataset")
dataset = config.get_dataset(which)
obs = dataset.get_SpectrumObservationList().stack()
cts_pred = CountsPredictor(model=model_lp,
aeff=obs.aeff,
edisp=obs.edisp,
livetime=obs.livetime)
cts_pred.run()
e_max = dataset.energy_range[1].to("TeV").value
e_min = dataset.energy_range[0].to("TeV").value
kwargs_mdl = dict(ls=":",
range=(e_min, e_max),
lw=2.2,
color=dict_color[which])
kwargs_data = dict(ls="-",
range=(e_min, e_max),
lw=2.2,
color=dict_color[which])
# CountsSpectrum with observed and predicted excesses
ex_pred = cts_pred.npred
ex_obs = obs.excess_vector
# if it is an IACT rebin the counts before plotting
if which != "fermi":
ex_pred = ex_pred.rebin(2)
ex_obs = ex_obs.rebin(2)
if predicted: # if you want to display the predicted counts
ex_pred.plot_hist(ax, **kwargs_mdl)
ex_obs.plot_hist(ax, **kwargs_data)
# custom legend
legend_observed = mlines.Line2D([], [],
color="gray",
marker="",
ls="-",
lw=2,
label="observed")
legend_expected = mlines.Line2D([], [],
color="gray",
marker="",
ls=":",
lw=2,
label="expected")
legend_fermi = mlines.Line2D([], [],
color=COLORS[0],
marker="",
ls="-",
lw=2,
label="Fermi-LAT")
legend_magic = mlines.Line2D([], [],
color=COLORS[1],
marker="",
ls="-",
lw=2,
label="MAGIC")
legend_veritas = mlines.Line2D([], [],
color=COLORS[2],
marker="",
ls="-",
lw=2,
label="VERITAS")
legend_fact = mlines.Line2D([], [],
color=COLORS[3],
marker="",
ls="-",
lw=2,
label="FACT")
legend_hess = mlines.Line2D([], [],
color=COLORS[4],
marker="",
ls="-",
lw=2,
label="H.E.S.S.")
legend_handles = [
legend_fermi,
legend_magic,
legend_veritas,
legend_fact,
legend_hess,
]
if predicted: # if you want to display the predicted counts
legend_handles = [legend_observed, legend_expected] + legend_handles
ax.legend(handles=legend_handles, fontsize=FONTSIZE)
ax.set_xscale("log")
ax.set_ylabel("Excess counts", size=FONTSIZE)
ax.set_xlabel(E_UNIT_LABEL, size=FONTSIZE)
# make axis thicker
for axis in ["top", "bottom", "left", "right"]:
ax.spines[axis].set_linewidth(1.6)
ax.tick_params("both",
length=7,
width=1.6,
which="major",
labelsize=FONTSIZE)
ax.tick_params("both",
length=4,
width=1.6,
which="minor",
labelsize=FONTSIZE)
plt.tight_layout()
filename = f"{config.repo_path}/results/figures/counts_spectra.png"
filename_pdf = f"{config.repo_path}/results/figures/counts_spectra.pdf"
log.info(f"saving figure in {filename}")
fig.savefig(filename)
fig.savefig(filename_pdf)
model=PLSuperExpCutoff4FGL(
index_1=1.5,
index_2=2,
amplitude=1 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
expfactor=1e-2 / u.TeV ** 2,
),
val_at_2TeV=u.Quantity(0.3431043087721737, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(1.2125247, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.38072082, "TeV cm-2 s-1"),
),
dict(
name="logpar",
model=LogParabola(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=0.5 * u.Unit(""),
),
val_at_2TeV=u.Quantity(0.6387956571420305, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.255689748270628, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.9586515834989267, "TeV cm-2 s-1"),
e_peak=0.74082 * u.TeV,
),
dict(
name="logpar10",
model=LogParabola.from_log10(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=1.151292546497023 * u.Unit(""),
),
