def load_cache(tables, names, cachefile='cache.fits'):
if os.path.isfile(cachefile):
return Table.read(cachefile)
tab0 = Table.read(tab_ext_filename)
tab1 = Table.read(tab_lnl_filename)
tab = join(tab0,tab1)
tab = load_source_rows(tab, names)
tab.write(cachefile)
return tab
prefix = os.path.splitext(f)[0]
igmf = tab['igmf'][0]*np.ones(tab['dloglike'][0].shape)
lcoh = tab['lcoh'][0]*np.ones(tab['dloglike'][0].shape)
dloglike = tab['dloglike'][0]
tab_ebounds = Table.read('tables/table_std_all_tev_sources_lnl.fits','EBOUNDS')
tab_sed_tev = Table.read('../haloanalysis/data/CompiledTeVSources.fits')
tab_casc = join(Table.read('tables/table_std_all_tev_sources_lnl.fits'), Table.read('tables/table_std_all_tev_sources.fits'))
tab_casc_sed = Table.read('tables/table_std_all_tev_sources_sed.fits')
source_full = tab['SOURCE_FULL'][0]
rows_sed_tev = load_source_rows(tab_sed_tev, [tab['SOURCE_FULL'][0]], key='SOURCE_FULL')
rows_sed_gev = load_source_rows(tab_casc_sed, [tab['name'][0]], key='NAME')
sed_prim0 = SED.create_from_row(rows_sed_tev)
sed_prim1 = SED.create_from_row2(rows_sed_gev, tab_ebounds)
plt.figure()
sed_prim0.plot(color='k',marker='o')
sed_prim1.plot(color='k',marker='s')
#plt.errorbar(sed_prim.ectr/1E6,
# sed_prim.ectr*sed_prim.flux,
# sed_prim.ectr*sed_prim.flux_err,
# marker='o',linestyle='None')
def main():
usage = "usage: %(prog)s"
description = "Fit SED for cascade component."
parser = argparse.ArgumentParser(usage=usage,description=description)
parser.add_argument('--output', default = 'igmf_casc_lnl.fits')
parser.add_argument('--make_plots', default = False, action='store_true')
parser.add_argument('--cache', default = False, action='store_true')
parser.add_argument('--modelfile', default = False, required=True,
help='FITS file containing the IGMF models')
parser.add_argument('--sedfile', default = False, required=True,
help='FITS file containing the TeV SEDs.')
parser.add_argument('--nstep', default = 5, type=int)
parser.add_argument('--name', default = [], action='append')
parser.add_argument('tables', nargs='+', default = None,
help='Extension and likelihood tables.')
args = parser.parse_args()
# list of sources
src_names = args.name
casc_model = CascModel.create_from_fits(args.modelfile)
tab_pars = Table.read(args.tables[0],'SCAN_PARS')
tab_ebounds = Table.read(args.tables[0],'EBOUNDS')
# Use cached fits file
if args.cache:
tab_casc = load_cache(args.tables, src_names)
else:
tables = [Table.read(t) for t in args.tables]
for i, t in enumerate(tables):
if 'NAME' in t.columns:
t['name'] = t['NAME']
tab_casc = join(tables[0],tables[1])
tab_casc = join(tab_casc,tables[2])
tab_casc = load_source_rows(tab_casc, src_names)
tab_sed_tev = Table.read(args.sedfile)
tab_igmf = []
for name in src_names:
rows_sed_tev = load_source_rows(tab_sed_tev, [name], key='SOURCE')
cat_names = [ '3FGL %s'%row['3FGL_NAME'] for row in rows_sed_tev ]
cat_names = np.unique(np.array(cat_names))
rows_sed_gev = load_source_rows(tab_casc, cat_names, key='NAME')
rows_casc = load_source_rows(tab_casc, cat_names, key='name')
tab = scan_igmf_likelihood(casc_model, rows_sed_tev, rows_sed_gev,
rows_casc, tab_pars, tab_ebounds, args.nstep)
tab_igmf += [tab]
tab = vstack(tab_igmf)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab))
hdulist[1].name = 'SCAN_DATA'
hdulist.writeto(args.output, clobber=True)