def _make_extension_fits(self, ext, filename, **kwargs):
maps = {'EXT_TOT_MAP': ext['ext_tot_map'],
'EXT_SRC_MAP': ext['ext_src_map'],
'EXT_BKG_MAP': ext['ext_bkg_map'],
'PTSRC_TOT_MAP': ext['ptsrc_tot_map'],
'PTSRC_SRC_MAP': ext['ptsrc_src_map'],
'PTSRC_BKG_MAP': ext['ptsrc_bkg_map']}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.make_hdu(k)]
tab = fits_utils.dict_to_table(ext)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'EXT_DATA'
if ext.get('tsmap'):
hdus = [ext['tsmap'].make_hdu(hdu='PRIMARY')]
else:
hdus = [fits.PrimaryHDU()]
hdus += [hdu_data] + hdu_images
hdus[0].header['CONFIG'] = json.dumps(utils.tolist(ext['config']))
hdus[1].header['CONFIG'] = json.dumps(utils.tolist(ext['config']))
fits_utils.write_hdus(hdus, filename,
keywords={'SRCNAME': ext['name']})
def _make_extension_fits(self, ext, filename, **kwargs):
maps = {
'EXT_TOT_MAP': ext['ext_tot_map'],
'EXT_SRC_MAP': ext['ext_src_map'],
'EXT_BKG_MAP': ext['ext_bkg_map'],
'PTSRC_TOT_MAP': ext['ptsrc_tot_map'],
'PTSRC_SRC_MAP': ext['ptsrc_src_map'],
'PTSRC_BKG_MAP': ext['ptsrc_bkg_map']
}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.make_hdu(k)]
tab = fits_utils.dict_to_table(ext)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'EXT_DATA'
if ext.get('tsmap'):
hdus = [ext['tsmap'].make_hdu(hdu='PRIMARY')]
else:
hdus = [fits.PrimaryHDU()]
hdus += [hdu_data] + hdu_images
hdus[0].header['CONFIG'] = json.dumps(utils.tolist(ext['config']))
hdus[1].header['CONFIG'] = json.dumps(utils.tolist(ext['config']))
fits_utils.write_hdus(hdus,
filename,
keywords={'SRCNAME': ext['name']})
def _make_lc_fits(self, lc, filename, **kwargs):
# produce columns in fits file
cols = OrderedDict()
cols['tmin'] = Column(name='tmin', dtype='f8',
data=lc['tmin'], unit='s')
cols['tmax'] = Column(name='tmax', dtype='f8',
data=lc['tmax'], unit='s')
cols['tmin_mjd'] = Column(name='tmin_mjd', dtype='f8',
data=lc['tmin_mjd'], unit='day')
cols['tmax_mjd'] = Column(name='tmax_mjd', dtype='f8',
data=lc['tmax_mjd'], unit='day')
# add in columns for model parameters
for k, v in lc.items():
if k in cols:
continue
if isinstance(v, np.ndarray):
cols[k] = Column(name=k, data=v, dtype=v.dtype)
tab = Table(cols.values())
hdu_lc = fits.table_to_hdu(tab)
hdu_lc.name = 'LIGHTCURVE'
hdus = [fits.PrimaryHDU(), hdu_lc]
keywords = {'SRCNAME': lc['name'],
'CONFIG': json.dumps(lc['config'])}
fits_utils.write_hdus(hdus, filename, keywords=keywords)
def _make_lc_fits(self, lc, filename, **kwargs):
# produce columns in fits file
cols = OrderedDict()
cols['tmin'] = Column(name='tmin', dtype='f8',
data=lc['tmin'], unit='s')
cols['tmax'] = Column(name='tmax', dtype='f8',
data=lc['tmax'], unit='s')
cols['tmin_mjd'] = Column(name='tmin_mjd', dtype='f8',
data=lc['tmin_mjd'], unit='day')
cols['tmax_mjd'] = Column(name='tmax_mjd', dtype='f8',
data=lc['tmax_mjd'], unit='day')
# add in columns for model parameters
for k, v in lc.items():
if k in cols:
continue
if isinstance(v, np.ndarray):
cols[k] = Column(name=k, data=v, dtype=v.dtype)
tab = Table(cols.values())
hdu_lc = fits.table_to_hdu(tab)
hdu_lc.name = 'LIGHTCURVE'
hdus = [fits.PrimaryHDU(), hdu_lc]
keywords = {'SRCNAME': lc['name'],
'CONFIG': json.dumps(lc['config'])}
fits_utils.write_hdus(hdus, filename, keywords=keywords)
def _make_localize_fits(self, loc, filename, **kwargs):
tab = fits_utils.dict_to_table(loc)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'LOC_DATA'
hdus = [loc['tsmap_peak'].make_hdu(hdu='PRIMARY'),
loc['tsmap'].make_hdu(hdu='TSMAP'),
hdu_data]
hdus[0].header['CONFIG'] = json.dumps(loc['config'])
hdus[2].header['CONFIG'] = json.dumps(loc['config'])
fits_utils.write_hdus(hdus, filename)
def _make_localize_fits(self, loc, filename, **kwargs):
tab = fits_utils.dict_to_table(loc)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'LOC_DATA'
hdus = [loc['tsmap_peak'].create_primary_hdu(),
loc['tsmap'].create_image_hdu('TSMAP'),
hdu_data]
hdus[0].header['CONFIG'] = json.dumps(loc['config'])
hdus[2].header['CONFIG'] = json.dumps(loc['config'])
fits_utils.write_hdus(hdus, filename)
def _make_residmap_fits(self, data, filename, **kwargs):
maps = {'DATA_MAP': data['data'],
'MODEL_MAP': data['model'],
'EXCESS_MAP': data['excess']}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.create_image_hdu(k)]
hdus = [data['sigma'].create_primary_hdu()] + hdu_images
hdus[0].header['CONFIG'] = json.dumps(data['config'])
hdus[1].header['CONFIG'] = json.dumps(data['config'])
fits_utils.write_hdus(hdus, filename)
def _make_residmap_fits(self, data, filename, **kwargs):
maps = {'DATA_MAP': data['data'],
'MODEL_MAP': data['model'],
'EXCESS_MAP': data['excess']}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.make_hdu(k)]
if data['projtype'] == 'WCS':
hdus = [data['sigma'].make_hdu(hdu='PRIMARY')] + hdu_images
hdus[0].header['CONFIG'] = json.dumps(data['config'])
hdus[1].header['CONFIG'] = json.dumps(data['config'])
elif data['projtype'] == 'HPX':
hdus = [fits.PrimaryHDU(),
data['sigma'].make_hdu(hdu="SIGMA")] + hdu_images
hdus[1].header['CONFIG'] = json.dumps(data['config'])
hdus[2].header['CONFIG'] = json.dumps(data['config'])
fits_utils.write_hdus(hdus, filename)
def _make_tsmap_fits(self, data, filename, **kwargs):
maps = {'SQRT_TS_MAP': data['sqrt_ts'],
'NPRED_MAP': data['npred'],
'N_MAP': data['amplitude']}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.create_image_hdu(k)]
tab = fits_utils.dict_to_table(data)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'TSMAP_DATA'
hdus = [data['ts'].create_primary_hdu(),
hdu_data] + hdu_images
hdus[0].header['CONFIG'] = json.dumps(data['config'])
hdus[1].header['CONFIG'] = json.dumps(data['config'])
fits_utils.write_hdus(hdus, filename)
def _make_tsmap_fits(self, data, filename, **kwargs):
maps = {'SQRT_TS_MAP': data['sqrt_ts'],
'NPRED_MAP': data['npred'],
'N_MAP': data['amplitude']}
hdu_images = []
for k, v in sorted(maps.items()):
if v is None:
continue
hdu_images += [v.to_hdu(hdu=k)]
tab = fits_utils.dict_to_table(data)
hdu_data = fits.table_to_hdu(tab)
hdu_data.name = 'TSMAP_DATA'
hdus = [data['ts'].to_hdu(hdu='PRIMARY'),
hdu_data] + hdu_images
data['config'].pop('map_skydir', None)
hdus[0].header['CONFIG'] = json.dumps(data['config'])
hdus[1].header['CONFIG'] = json.dumps(data['config'])
fits_utils.write_hdus(hdus, filename)
def _make_sed_fits(self, sed, filename, **kwargs):
# Write a FITS file
cols = [Column(name='e_min', dtype='f8', data=sed['e_min'], unit='MeV'),
Column(name='e_ref', dtype='f8',
data=sed['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8',
data=sed['e_max'], unit='MeV'),
Column(name='ref_dnde_e_min', dtype='f8',
data=sed['ref_dnde_e_min'], unit='ph / (MeV cm2 s)'),
Column(name='ref_dnde_e_max', dtype='f8',
data=sed['ref_dnde_e_max'], unit='ph / (MeV cm2 s)'),
Column(name='ref_dnde', dtype='f8',
data=sed['ref_dnde'], unit='ph / (MeV cm2 s)'),
Column(name='ref_flux', dtype='f8',
data=sed['ref_flux'], unit='ph / (cm2 s)'),
Column(name='ref_eflux', dtype='f8',
data=sed['ref_eflux'], unit='MeV / (cm2 s)'),
Column(name='ref_npred', dtype='f8', data=sed['ref_npred']),
Column(name='dnde', dtype='f8',
data=sed['dnde'], unit='ph / (MeV cm2 s)'),
Column(name='dnde_err', dtype='f8',
data=sed['dnde_err'], unit='ph / (MeV cm2 s)'),
Column(name='dnde_errp', dtype='f8',
data=sed['dnde_err_hi'], unit='ph / (MeV cm2 s)'),
Column(name='dnde_errn', dtype='f8',
data=sed['dnde_err_lo'], unit='ph / (MeV cm2 s)'),
Column(name='dnde_ul', dtype='f8',
data=sed['dnde_ul'], unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8',
data=sed['e2dnde'], unit='MeV / (cm2 s)'),
Column(name='e2dnde_err', dtype='f8',
data=sed['e2dnde_err'], unit='MeV / (cm2 s)'),
Column(name='e2dnde_errp', dtype='f8',
data=sed['e2dnde_err_hi'], unit='MeV / (cm2 s)'),
Column(name='e2dnde_errn', dtype='f8',
data=sed['e2dnde_err_lo'], unit='MeV / (cm2 s)'),
Column(name='e2dnde_ul', dtype='f8',
data=sed['e2dnde_ul'], unit='MeV / (cm2 s)'),
Column(name='norm', dtype='f8', data=sed['norm']),
Column(name='norm_err', dtype='f8', data=sed['norm_err']),
Column(name='norm_errp', dtype='f8', data=sed['norm_err_hi']),
Column(name='norm_errn', dtype='f8', data=sed['norm_err_lo']),
Column(name='norm_ul', dtype='f8', data=sed['norm_ul95']),
Column(name='ts', dtype='f8', data=sed['ts']),
Column(name='loglike', dtype='f8', data=sed['loglike']),
Column(name='norm_scan', dtype='f8', data=sed['norm_scan']),
Column(name='dloglike_scan', dtype='f8',
data=sed['dloglike_scan']),
]
tab = Table(cols)
tab.meta['UL_CONF'] = 0.95
hdu_sed = fits.table_to_hdu(tab)
hdu_sed.name = 'SED'
columns = fits.ColDefs([])
columns.add_col(fits.Column(name=str('energy'), format='E',
array=sed['model_flux']['energies'],
unit='MeV'))
columns.add_col(fits.Column(name=str('dnde'), format='E',
array=sed['model_flux']['dnde'],
unit='ph / (MeV cm2 s)'))
columns.add_col(fits.Column(name=str('dnde_lo'), format='E',
array=sed['model_flux']['dnde_lo'],
unit='ph / (MeV cm2 s)'))
columns.add_col(fits.Column(name=str('dnde_hi'), format='E',
array=sed['model_flux']['dnde_hi'],
unit='ph / (MeV cm2 s)'))
columns.add_col(fits.Column(name=str('dnde_err'), format='E',
array=sed['model_flux']['dnde_err'],
unit='ph / (MeV cm2 s)'))
columns.add_col(fits.Column(name=str('dnde_ferr'), format='E',
array=sed['model_flux']['dnde_ferr']))
hdu_f = fits.BinTableHDU.from_columns(columns, name='MODEL_FLUX')
columns = fits.ColDefs([])
npar = len(sed['param_names'])
columns.add_col(fits.Column(name=str('name'),
format='A32',
array=sed['param_names']))
columns.add_col(fits.Column(name=str('value'), format='E',
array=sed['param_values']))
columns.add_col(fits.Column(name=str('error'), format='E',
array=sed['param_errors']))
columns.add_col(fits.Column(name=str('covariance'),
format='%iE' % npar,
dim=str('(%i)' % npar),
array=sed['param_covariance']))
columns.add_col(fits.Column(name=str('correlation'),
format='%iE' % npar,
dim=str('(%i)' % npar),
array=sed['param_correlation']))
hdu_p = fits.BinTableHDU.from_columns(columns, name='PARAMS')
hdus = [fits.PrimaryHDU(), hdu_sed, hdu_f, hdu_p]
hdus[0].header['CONFIG'] = json.dumps(sed['config'])
hdus[1].header['CONFIG'] = json.dumps(sed['config'])
fits_utils.write_hdus(hdus, filename,
keywords={'SRCNAME': sed['name']})
