def load_cubes(config):
cube_dir = Path(config['logging']['working_dir'])
npred_cube = SkyCube.read(cube_dir / 'npred_cube.fits.gz')
exposure_cube = SkyCube.read(cube_dir / 'exposure_cube.fits', format='fermi-exposure')
# print(exposure_cube)
# print('exposure sum: {}'.format(np.nansum(exposure_cube.data)))
i_nan = np.where(np.isnan(exposure_cube.data))
exposure_cube.data[i_nan] = 0
# npred_cube_convolved = SkyCube.read(cube_dir / 'npred_cube_convolved.fits.gz')
return dict(counts=npred_cube, exposure=exposure_cube)
def prepare_images():
# Read in data
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file)
exposure_cube = SkyCube.read(exposure_file)
# Add correct units
exposure_cube.data = Quantity(exposure_cube.data.value, 'cm2 s')
# Re-project background cube
repro_bg_cube = background_model.reproject_to(exposure_cube)
# Define energy band required for output
energies = EnergyBounds([10, 500], 'GeV')
# Compute the predicted counts cube
npred_cube = compute_npred_cube(repro_bg_cube, exposure_cube, energies)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
convolved_npred_cube = convolve_cube(npred_cube,
psf,
offset_max=Angle(3, 'deg'))
# Counts data
counts_data = fits.open(counts_file)[0].data
counts_wcs = WCS(fits.open(counts_file)[0].header)
counts_cube = SkyCube(data=Quantity(counts_data, ''),
wcs=counts_wcs,
energy=energies)
counts_cube = counts_cube.reproject_to(npred_cube,
projection_type='nearest-neighbor')
counts = counts_cube.data[0]
model = convolved_npred_cube.data[0]
# Load Fermi tools gtmodel background-only result
gtmodel = fits.open(
FermiVelaRegion.filenames()['background_image'])[0].data.astype(float)
# Ratio for the two background images
ratio = np.nan_to_num(model / gtmodel)
# Header is required for plotting, so returned here
wcs = npred_cube.wcs
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
def make_ref_cube(config):
WCS_SPEC = {
'nxpix': config['binning']['nxpix'],
'nypix': config['binning']['nypix'],
'binsz': config['binning']['binsz'],
'xref': config['pointing']['ra'],
'yref': config['pointing']['dec'],
'proj': config['binning']['proj'],
'coordsys': config['binning']['coordsys'],
}
# define reconstructed energy binning
ENERGY_SPEC = {
'mode': 'edges',
'enumbins': config['binning']['enumbins'],
'emin': config['selection']['emin'],
'emax': config['selection']['emax'],
'eunit': 'TeV',
}
CUBE_SPEC = {}
CUBE_SPEC.update(WCS_SPEC)
CUBE_SPEC.update(ENERGY_SPEC)
cube = SkyCube.empty(**CUBE_SPEC)
return cube
def make_empty_cube(image_size, energy, center, data_unit=""):
"""
Parameters
----------
image_size:int, Total number of pixel of the 2D map
energy: Energybounds
center: SkyCoord of the source
unit : str, Data unit.
"""
def_image = dict()
def_image["nxpix"] = image_size
def_image["nypix"] = image_size
def_image["binsz"] = 0.02
def_image["xref"] = center.galactic.l.deg
def_image["yref"] = center.galactic.b.deg
def_image["proj"] = 'TAN'
def_image["coordsys"] = 'GAL'
def_image["unit"] = data_unit
e_min = energy[0]
e_max = energy[-1]
nbins = len(energy) - 1
empty_cube = SkyCube.empty(emin=e_min.value,
emax=e_max.value,
enumbins=nbins,
eunit=e_min.unit,
mode='edges',
**def_image)
return empty_cube
def prepare_images():
# Read in data
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file)
exposure_cube = SkyCube.read(exposure_file)
# Add correct units
exposure_cube.data = Quantity(exposure_cube.data.value, 'cm2 s')
# Re-project background cube
repro_bg_cube = background_model.reproject_to(exposure_cube)
# Define energy band required for output
energies = EnergyBounds([10, 500], 'GeV')
# Compute the predicted counts cube
npred_cube = compute_npred_cube(repro_bg_cube, exposure_cube, energies)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
convolved_npred_cube = convolve_cube(npred_cube, psf,
offset_max=Angle(3, 'deg'))
# Counts data
counts_data = fits.open(counts_file)[0].data
counts_wcs = WCS(fits.open(counts_file)[0].header)
counts_cube = SkyCube(data=Quantity(counts_data, ''),
wcs=counts_wcs,
energy=energies)
counts_cube = counts_cube.reproject_to(npred_cube, projection_type='nearest-neighbor')
counts = counts_cube.data[0]
model = convolved_npred_cube.data[0]
# Load Fermi tools gtmodel background-only result
gtmodel = fits.open(FermiVelaRegion.filenames()['background_image'])[0].data.astype(float)
# Ratio for the two background images
ratio = np.nan_to_num(model / gtmodel)
# Header is required for plotting, so returned here
wcs = npred_cube.wcs
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
def prepare_images():
# Read in data
fermi_vela = FermiVelaRegion()
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file, format='fermi-background')
exposure_cube = SkyCube.read(exposure_file, format='fermi-exposure')
# Re-project background cube
repro_bg_cube = background_model.reproject(exposure_cube)
# Define energy band required for output
energies = EnergyBounds([10, 500], 'GeV')
# Compute the predicted counts cube
npred_cube = compute_npred_cube(repro_bg_cube,
exposure_cube,
energies,
integral_resolution=5)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
kernels = psf.kernels(npred_cube)
convolved_npred_cube = npred_cube.convolve(kernels, mode='reflect')
# Counts data
counts_cube = SkyCube.read(counts_file, format='fermi-counts')
counts_cube = counts_cube.reproject(npred_cube)
counts = counts_cube.data[0]
model = convolved_npred_cube.data[0]
# Load Fermi tools gtmodel background-only result
gtmodel = fits.open(
FermiVelaRegion.filenames()['background_image'])[0].data.astype(float)
# Ratio for the two background images
ratio = np.nan_to_num(model / gtmodel)
# Header is required for plotting, so returned here
wcs = npred_cube.wcs
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
def _get_ref_cube(self, enumbins=11):
p = self.parameters
wcs = self.reference.wcs.deepcopy()
shape = (enumbins, ) + self.reference.data.shape
data = np.zeros(shape)
energy = Energy.equal_log_spacing(p['emin'], p['emax'], enumbins,
'TeV')
energy_axis = LogEnergyAxis(energy, mode='center')
return SkyCube(data=data, wcs=wcs, energy_axis=energy_axis)
def prepare_images():
# Read in data
fermi_vela = FermiVelaRegion()
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file, format='fermi-background')
exposure_cube = SkyCube.read(exposure_file, format='fermi-exposure')
# Re-project background cube
repro_bg_cube = background_model.reproject(exposure_cube)
# Define energy band required for output
energies = EnergyBounds([10, 500], 'GeV')
# Compute the predicted counts cube
npred_cube = compute_npred_cube(repro_bg_cube, exposure_cube, energies,
integral_resolution=5)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
kernels = psf.kernels(npred_cube)
convolved_npred_cube = npred_cube.convolve(kernels, mode='reflect')
# Counts data
counts_cube = SkyCube.read(counts_file, format='fermi-counts')
counts_cube = counts_cube.reproject(npred_cube)
counts = counts_cube.data[0]
model = convolved_npred_cube.data[0]
# Load Fermi tools gtmodel background-only result
gtmodel = fits.open(FermiVelaRegion.filenames()['background_image'])[0].data.astype(float)
# Ratio for the two background images
ratio = np.nan_to_num(model / gtmodel)
# Header is required for plotting, so returned here
wcs = npred_cube.wcs
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
def main():
# Read cubes
cube_on = SkyCube.read('non_cube.fits.gz')
cube_off = SkyCube.read('noff_cube.fits.gz')
#Read config
config = yaml.load(open('config.yaml'))
binsz = config['binning']['binsz']
offset_fov = config['selection']['offset_fov']
#countson_vals = []
#countsoff_vals = []
diff_vals = np.ones(int(config['binning']['enumbins']))
sigmaslimas = np.ones(int(config['binning']['enumbins']))
# Define PSF region
irffile = 'irf_file.fits'
psf_table = psf_fromfits(irffile)
psfs = psf_table[3]
on_sizes = np.ones(int(config['binning']['enumbins'])) * u.deg
energarr = cube_on.energies('edges')
for idx in range(len(cube_on.energies('center'))):
i = np.argmin(np.abs(energarr[idx].value - psf_table[0].value))
j = np.argmin(np.abs(offset_fov - psf_table[2].value))
on_sizes.value[idx] = psfs[0][j][i] * 2.12
alpha_obs = 0.2
on_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
##Debug
#print(on_sizes/binsz)
off_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
off_sizes = on_sizes / np.sqrt(alpha_obs)
on_data = Table()
off_data = Table()
on_data['value'] = np.zeros(len(on_sizes))
off_data['value'] = np.zeros(len(on_sizes))
for i in range(cube_on.data.shape[0]):
# Make PSF region
on_region = CircleSkyRegion(on_pos, on_sizes[i])
off_region = CircleSkyRegion(off_pos, off_sizes[i])
# Take spectrum
on_data['value'][i] = cube_on.spectrum(on_region)['value'][i]
off_data['value'][i] = cube_off.spectrum(off_region)['value'][
i] #* alpha_obs
non_val = on_data['value'][i]
noff_val = off_data['value'][i]
diff_vals[i] = non_val - noff_val
if non_val != 0 and noff_val != 0:
siglima = np.sqrt(2) * np.sqrt(non_val * np.log(
(1.0 + (1.0 / alpha_obs)) * non_val /
(non_val + noff_val)) + noff_val * np.log(
(alpha_obs + 1.0) * noff_val / (non_val + noff_val)))
elif non_val != 0 and noff_val == 0:
siglima = np.sqrt(2) * np.sqrt(non_val * np.log(
(1.0 + (1.0 / alpha_obs))))
else:
siglima = 0
sigmaslimas[i] = siglima
##Debug
#non_val = cube_on.data.sum().value
#noff_val = cube_off.data.sum().value
lo_lim_idx = np.where(
abs(cube_on.energies('edges').value -
0.4) == np.min(abs(cube_on.energies('edges').value - 0.4)))[0][0]
max_energ_idx = np.where(
abs(cube_on.energies('edges').value -
3.0) == np.min(abs(cube_on.energies('edges').value - 3.0)))[0][0]
non_val = on_data['value'][lo_lim_idx:max_energ_idx].sum()
noff_val = off_data['value'][lo_lim_idx:max_energ_idx].sum()
siglima = np.sqrt(2) * np.sqrt(non_val * np.log(
(1.0 + (1.0 / alpha_obs)) * non_val /
(non_val + noff_val)) + noff_val * np.log(
(alpha_obs + 1.0) * noff_val / (non_val + noff_val)))
#print('On events: ', on_data)
#print('Off events: ', off_data)
diff_vals[np.isnan(diff_vals)] = 0
sigmaslimas[np.isnan(sigmaslimas)] = 0
print('Excess: ', diff_vals)
print('Total positive Excess: ', diff_vals[diff_vals > 0].sum())
print('LiMa by energy bins: ', sigmaslimas)
print('Total LiMa: ', siglima, 'Energy range: ',
cube_on.energies('edges')[lo_lim_idx], ' - ',
cube_on.energies('edges')[max_energ_idx])
lo_lim_idx = np.where(
abs(cube_on.energies('edges').value -
1.0) == np.min(abs(cube_on.energies('edges').value - 1.0)))[0][0]
non_val = on_data['value'][lo_lim_idx:max_energ_idx].sum()
noff_val = off_data['value'][lo_lim_idx:max_energ_idx].sum()
siglima_tves = np.sqrt(
2) * np.sqrt(non_val * np.log(2 * non_val / (non_val + noff_val)) +
noff_val * np.log(2 * noff_val / (non_val + noff_val)))
print('Total LiMa: ', siglima_tves, 'Energy range: ',
cube_on.energies('edges')[lo_lim_idx], ' - ',
cube_on.energies('edges')[max_energ_idx])
return [siglima, siglima_tves, on_data, off_data, diff_vals, sigmaslimas]
"_TeV.fits")["exposure"].data * 1e4
#exposure_data[i_E,:,:] = SkyImageList.read(outdir_data + "/fov_bg_maps" + str(E1) + "_" + str(E2) + "_TeV.fits")["exposure"].data
bkg_data[i_E, :, :] = SkyImageList.read(outdir_data + "/fov_bg_maps" +
str(E1) + "_" + str(E2) +
"_TeV.fits")["bkg"].data
psf_file = Table.read(outdir_data + "/psf_table_" + source_name + "_" +
str(E1) + "_" + str(E2) + ".fits")
header = on.to_image_hdu().header
psf_data[i_E, :, :] = fill_acceptance_image(
header, on.center, psf_file["theta"].to("deg"),
psf_file["psf_value"].data, psf_file["theta"].to("deg")[-1]).data
#logenergy_axis=LogEnergyAxis(energy_bins[0:imax+1],mode='edges')
logenergy_axis = LogEnergyAxis(energy_bins, mode='edges')
counts_3D = SkyCube(name="counts3D",
data=Quantity(counts, " "),
wcs=on.wcs,
energy_axis=logenergy_axis)
cube = counts_3D.to_sherpa_data3d(dstype='Data3DInt')
logenergy_axis = LogEnergyAxis(energy_bins[0:imax + 1], mode='edges')
psf_image_3D = SkyCube(name="counts3D",
data=Quantity(psf_data, " "),
wcs=on.wcs,
energy_axis=logenergy_axis)
exposure_image_3D = SkyCube(name="counts3D",
data=Quantity(exposure_data, "m2 s "),
wcs=on.wcs,
energy_axis=logenergy_axis)
spatial_model = NormGauss2DInt('spatial-model')
spectral_model = PowLaw1D('spectral-model')
#spectral_model = MyPLExpCutoff('spectral-model')
use_cube,
use_etrue=False)
outdir_result = make_outdir_filesresult(source_name,
name_method_fond,
len(energy_bins),
config_name,
image_size,
for_integral_flux,
use_cube,
use_etrue=False)
"""
Source model paramaters initial
"""
#Dans HGPS, c est une gaussienne de 0.05deg en sigma donc *2.35 pour fwhm
#avec HESS meme une source pontuelle ne fera jamais en dessous de 0.03-0.05 degre,
counts_3D = SkyCube.read(outdir_data + "/counts_cube.fits")
cube = counts_3D.to_sherpa_data3d(dstype='Data3DInt')
bkg_3D = SkyCube.read(outdir_data + "/bkg_cube.fits")
exposure_3D = SkyCube.read(outdir_data + "/exposure_cube.fits")
i_nan = np.where(np.isnan(exposure_3D.data))
exposure_3D.data[i_nan] = 0
exposure_3D.data = exposure_3D.data * 1e4
psf_3D = SkyCube.read(outdir_data + "/mean_psf_cube_" + source_name + ".fits",
format="fermi-counts")
# Setup combined spatial and spectral model
spatial_model = NormGauss2DInt('spatial-model')
spectral_model = PowLaw1D('spectral-model')
#spectral_model = MyPLExpCutoff('spectral-model')
source_model = CombinedModel3DInt(use_psf=True,
exposure=exposure_3D,
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))]
'xref': 83.63,
'yref': 22.01,
'proj': 'TAN',
'coordsys': 'CEL'
}
# define reconstructed energy binning
ENERGY_SPEC = {
'mode': 'edges',
'enumbins': 5,
'emin': 0.5,
'emax': 40,
'eunit': 'TeV'
}
REF_CUBE = SkyCube.empty(emin=0.5, emax=40.0, enumbins=5, **WCS_SPEC)
# setting up the data store
data_store = DataStore.from_dir("$GAMMAPY_EXTRA/test_datasets/cube/data")
# temporary fix for load psftable for one of the run that is not implemented yet...
data_store.hdu_table.remove_row(14)
# read in TeVCat exclusion mask
exclusion_mask = SkyImage.read(
'$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits')
# reproject exclusion mask to reference cube
exclusion_mask = exclusion_mask.reproject(reference=REF_CUBE.sky_image_ref,
order='nearest-neighbor')
exclusion_mask.show()
#Select the offset band on which you want to select the events in the FOV of each observation
# define reconstructed energy binning
ENERGY_SPEC = {
'mode': 'edges',
'enumbins': 5,
'emin': 0.5,
'emax': 40,
'eunit': 'TeV'
}
# From this we create an empty `SkyCube` object, that we will use as reference for all other sky cube data:
# In[5]:
# instanciate reference cube
REF_CUBE = SkyCube.empty(**WCS_SPEC, **ENERGY_SPEC)
# Next we create a `DataStore` object, which allows us to access and load DL3 level data. In this case we will use four Crab runs of simulated HESS data, which is bundled in gammapy-extra:
# In[6]:
# setting up the data store
data_store = DataStore.from_dir("$GAMMAPY_EXTRA/test_datasets/cube/data")
# temporary fix for load psftable for one of the run that is not implemented yet...
data_store.hdu_table.remove_row(14)
# For the later background estimation we define an exclusion mask. The background image will normalized on the counts map outside the exclusion region (the so called FOV background method). For convenience we use and all-sky exclusin mask, which was created from the TeVCat catalog and reproject it to our analysis region:
# In[7]:
dirname = '$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2'
log.info('Reading data from {}'.format(dirname))
data_store = DataStore.from_dir(dirname)
obs = data_store.obs(23523)
events = obs.events
log.info('Number of events in event list: {}'.format(len(events)))
log.info('Max. event energy: {}'.format(events['ENERGY'].max()))
log.info('Min. event energy: {}'.format(events['ENERGY'].min()))
aeff = obs.aeff
counts = SkyCube.empty(emin=0.5,
emax=80,
enumbins=8,
eunit='TeV',
nxpix=200,
nypix=200,
xref=events.meta['RA_OBJ'],
yref=events.meta['DEC_OBJ'],
dtype='int',
coordsys='CEL')
log.info('Bin events into cube.')
counts.fill_events(events)
log.info('Counts cube shape: {}'.format(counts.data.shape))
log.info('Number of events in cube: {}'.format(counts.data.sum()))
counts.write('counts.fits.gz', format='fermi-counts', clobber=True)
# Exposure cube
pointing = SkyCoord(events.meta['RA_PNT'],
events.meta['DEC_PNT'],
"icrs",
def main():
# Read file to fit
#filename = 'nexcess_cube.fits.gz'
filename = 'non_cube_convolved.fits.gz'
cube = SkyCube.read(filename)
# Read configuration
config = read_config('config.yaml')
binsz = config['binning']['binsz']
offset_fov = config['selection']['offset_fov']
# Take PSF data
irffile = 'irf_file.fits'
psf_table = psf_fromfits(irffile)
energarr = cube.energies('edges')
sigmas = psf_table[3]
norms = psf_table[4]
hdu = pyfits.open(filename)
im_sizex = hdu[0].header['NAXIS1']
im_sizey = hdu[0].header['NAXIS2']
cx = 0.5 * im_sizex
cy = 0.5 * im_sizey
# Check the significance
filename_on = 'non_cube.fits.gz'
cube_on = SkyCube.read(filename_on)
filename_off = 'noff_cube.fits.gz'
cube_off = SkyCube.read(filename_off)
alpha_obs = 1.
on_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
on_sizes = np.ones(len(
cube.energies('center'))) * 120 * binsz * u.deg #0.167
off_pos = SkyCoord(83.6333 * u.deg, 22.0144 * u.deg, frame='icrs')
off_sizes = on_sizes * alpha_obs
on_data = Table()
off_data = Table()
on_data['value'] = np.zeros(len(on_sizes))
off_data['value'] = np.zeros(len(on_sizes))
for idx in range(len(cube.energies('center'))):
on_region = CircleSkyRegion(on_pos, on_sizes[idx])
off_region = CircleSkyRegion(off_pos, off_sizes[idx])
on_data['value'][idx] = cube_on.spectrum(on_region)['value'][idx]
off_data['value'][idx] = cube_off.spectrum(off_region)['value'][idx]
limasig = np.sqrt(
2) * np.sqrt(on_data['value'][idx] * np.log(
((1 + alpha_obs) / alpha_obs) * on_data['value'][idx] /
(on_data['value'][idx] + off_data['value'][idx])) +
off_data['value'][idx] * np.log(
(1 + alpha_obs) * off_data['value'][idx] /
(on_data['value'][idx] + off_data['value'][idx])))
print(limasig, 'Energy range: ',
cube_on.energies('edges')[idx], ' - ',
cube_on.energies('edges')[idx + 1])
#Fit only if data is enough
#and on_data['value'][i] - off_data['value'][i] >= 0.01 * off_data['value'][i]
if limasig >= 3 and on_data['value'][idx] - off_data['value'][idx] >= 7:
# Make image cube from slice excess convolved cube
cube_sum = np.zeros(
(cube.data.shape[1], cube.data.shape[2])) * u.ct
cube_sum = np.add(cube_sum, cube.data[idx])
cube_sum.value[np.isnan(cube_sum.value)] = 0
cube_sum.value[cube_sum.value < 0] = abs(
cube_sum.value[cube_sum.value < 0])
image_sum = SkyCube.empty_like(cube)
image_sum.data = cube_sum
image_sum.write('sum_image.fits.gz', overwrite=True)
# Find nearest energy and theta value
i = np.argmin(np.abs(energarr[idx].value -
psf_table[0].value)) ######
j = np.argmin(np.abs(offset_fov - psf_table[2].value))
# Make PSF
#psfname="mypsf"
#load_user_model(PSFGauss,psfname)
s1 = sigmas[0][j][i] / binsz
s2 = sigmas[1][j][i] / binsz
s3 = sigmas[2][j][i] / binsz
print(sigmas[0][j][i], sigmas[1][j][i], sigmas[2][j][i])
ampl = norms[0][j][i]
ampl2 = norms[1][j][i]
ampl3 = norms[2][j][i]
t0 = time()
#Morphological fitting
load_image("sum_image.fits.gz")
#image_data()
#set_coord("physical")
set_method("simplex")
set_stat("cash")
# Position and radius
x0 = 125
y0 = 125
rad0 = 80.0
image_getregion(coord="physical")
'circle(x0,y0,rad0);'
notice2d("circle(" + str(x0) + "," + str(y0) + "," + str(rad0) +
")")
load_user_model(GaussianSource, "sph2d")
add_user_pars("sph2d", [
"sigma1", "sigma2", "sigma3", "alpha", "beta", "ampl", "size",
"xpos", "ypos"
])
set_model(sph2d + const2d.bgnd)
# Constant PSF
#gpsf.fwhm = 4.2
#gpsf.xpos = x0
#gpsf.ypos = y0
#gpsf.ellip = 0.2
#gpsf.theta = 30 * np.pi / 180
#### Set PSF
set_par(sph2d.sigma1, val=s1, frozen=True)
set_par(sph2d.sigma2, val=0, frozen=True)
set_par(sph2d.sigma3, val=0, frozen=True)
set_par(sph2d.alpha, val=0, frozen=True)
set_par(sph2d.beta, val=0, frozen=True)
# HESS PSF
#set_par(sph2d.sigma1, val = 0.025369, frozen = True)
#set_par(sph2d.alpha, val = 0.691225, frozen = True)
#set_par(sph2d.sigma2, val = 0.0535014, frozen = True)
#set_par(sph2d.beta, val = 0.13577, frozen = True)
#set_par(sph2d.sigma3, val = 0.11505, frozen = True)
set_par(sph2d.xpos, val=x0, frozen=True)
set_par(sph2d.ypos, val=y0, frozen=True)
set_par(sph2d.ampl, val=10000, min=1e-11, max=100000000)
set_par(sph2d.size, val=10, min=1e-11, max=100)
set_par(bgnd.c0, val=1, min=0, max=100)
show_model()
fit()
#do_fit()
conf()
#do_conf()
#image_fit()
#save_model("model_" + str(idx) + ".fits")
#save_resid("resid_" + str(idx) + ".fits")
t1 = time()
print('Simul time', t1 - t0)
"""Test npred model image computation. """ from astropy.coordinates import Angle from gammapy.datasets import FermiGalacticCenter from gammapy.utils.energy import EnergyBounds from gammapy.irf import EnergyDependentTablePSF from gammapy.cube import SkyCube, compute_npred_cube, convolve_cube filenames = FermiGalacticCenter.filenames() flux_cube = SkyCube.read(filenames['diffuse_model']) exposure_cube = SkyCube.read(filenames['exposure_cube']) psf = EnergyDependentTablePSF.read(filenames['psf']) flux_cube = flux_cube.reproject_to(exposure_cube) energy_bounds = EnergyBounds([10, 30, 100, 500], 'GeV') npred_cube = compute_npred_cube(flux_cube, exposure_cube, energy_bounds) offset_max = Angle(1, 'deg') npred_cube_convolved = convolve_cube(npred_cube, psf, offset_max)
else:
center = SkyCoord.from_name(
input_param["general"]["sourde_name_skycoord"]).galactic
extraction_size = input_param["param_fit_3D"]["extraction_region"]
empty_cube_reco = make_empty_cube(extraction_size,
energy_bins,
center,
data_unit="")
empty_cube_true = make_empty_cube(extraction_size,
energy_bins_true,
center,
data_unit="")
"""
Define SkyCube
"""
cube_mask = SkyCube.read("skycube_mask_CG_binE_" +
str(input_param["energy binning"]["nbin"]) + ".fits")
index_region_selected_3d = np.where(cube_mask.data.value == 1)
counts_3D = SkyCube.read(outdir_data + "/counts_cube.fits").cutout(
center, extraction_size)
coord = counts_3D.sky_image_ref.coordinates(mode="edges")
energies = counts_3D.energies(mode='edges').to("TeV")
cube = counts_3D.to_sherpa_data3d(dstype='Data3DInt')
#apply the cube_mask
cube.mask = cube_mask.data.value.ravel()
bkg_3D = SkyCube.read(outdir_data + "/bkg_cube.fits").cutout(
center, extraction_size)
exposure_3D = SkyCube.read(outdir_data + "/exposure_cube.fits").cutout(
center, extraction_size)
i_nan = np.where(np.isnan(exposure_3D.data))
from gammapy.cube import SkyCube
from gammapy.cube.sherpa_ import (
CombinedModel3DInt,
CombinedModel3DIntConvolveEdisp,
NormGauss2DInt,
)
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
import sherpa
import os
cube_dir = Path(os.getcwd())
counts_3d = SkyCube.read(cube_dir / 'counts_cube.fits')
cube
cube = counts.to_sherpa_data3d(dstype='Data3DInt')
background
bkg_3d = SkyCube.read(cube_dir / 'bkg_cube.fits')
cube_dir = Path('$GAMMAPY_EXTRA/test_datasets/cube')
bkg_3d = SkyCube.read(cube_dir / 'bkg_cube.fits')
background
bkg_3d
bkg = TableModel('bkg')
bkg.load(None, background.data.value.ravel())
bkg.ampl = 1
bkg.ampl.freeze()
i_nan = np.where(np.isnan(exposure.data))
exposure.data[i_nan] = 0
# In order to have the exposure in cm2 s
from gammapy.cube import exposure_cube, SkyCube
from gammapy.utils.energy import EnergyBounds
dirname = '$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2'
log.info('Reading data from {}'.format(dirname))
data_store = DataStore.from_dir(dirname)
obs = data_store.obs(23523)
events = obs.events
log.info('Number of events in event list: {}'.format(len(events)))
log.info('Max. event energy: {}'.format(events['ENERGY'].max()))
log.info('Min. event energy: {}'.format(events['ENERGY'].min()))
aeff = obs.aeff
counts = SkyCube.empty(emin=0.5, emax=80, enumbins=8, eunit='TeV',
nxpix=200, nypix=200, xref=events.meta['RA_OBJ'],
yref=events.meta['DEC_OBJ'], dtype='int',
coordsys='CEL')
log.info('Bin events into cube.')
counts.fill_events(events)
log.info('Counts cube shape: {}'.format(counts.data.shape))
log.info('Number of events in cube: {}'.format(counts.data.sum()))
counts.write('counts.fits.gz', format='fermi-counts', clobber=True)
# Exposure cube
pointing = SkyCoord(events.meta['RA_PNT'], events.meta['DEC_PNT'], "icrs", unit="deg")
livetime = Quantity(events.meta['LIVETIME'], 's')
exposure = exposure_cube(pointing, livetime, aeff2d=aeff, ref_cube=counts,
offset_max=Quantity(2.5, 'deg'))
log.info('Exposure cube shape: {}'.format(exposure.data.shape))
log.info('Exposure unit: {}'.format(exposure.data.unit))