elo, ehi = float(elo), float(ehi)
load_pha(id, filename)
ignore_id(id, None, None)
notice_id(id, elo, ehi)
ids.append(id)
set_analysis(id, 'ener', 'counts')
set_xlog()
set_ylog()
set_stat('cstat')
set_xsabund('wilm')
set_xsxsect('vern')
id = ids[0]
galabso = bxa.auto_galactic_absorption(id)
galabso.nH.freeze()
# Models available at https://doi.org/10.5281/zenodo.602282
load_table_model("torus",
'/home/user/Downloads/specmodels/uxclumpy-cutoff.fits')
load_table_model("scat",
'/home/user/Downloads/specmodels/uxclumpy-cutoff-omni.fits')
# the limits correspond to fluxes between Sco X-1 and CDFS7Ms faintest fluxes
srclevel = Parameter('src', 'level', 0, -8, 3, -20, 20)
print('combining components')
model = torus + scat
print('linking parameters')
torus.norm = 10**srclevel
srcnh = Parameter('src', 'nH', 22, 20, 26, 20, 26)
torus.nh = 10**(srcnh - 22)
print('loading background fitting module...')
from bxa.sherpa.background.pca import auto_background
# BXA fully supports fitting multiple ids with the usual id=2, otherids=(3,4,5)
# parameters
id = 2
load_pha(id, 'interval0pc.pi')
if get_rmf(id).energ_lo[0] == 0: get_rmf(id).energ_lo[0] = 0.001
if get_arf(id).energ_lo[0] == 0: get_arf(id).energ_lo[0] = 0.001
set_stat('cstat')
set_xsabund('wilm')
set_xsxsect('vern')
# next we set up a source model.
# with automatic milky way absorption
model = xszpowerlw.src * xszwabs.abso * bxa.auto_galactic_absorption(id)
src.norm.val = 1e-5
# with automatic background from PCA
set_model(id, model)
convmodel = get_model(id)
bkg_model = auto_background(id)
#set_bkg_full_model(id, bkg_model)
set_full_model(id, convmodel + bkg_model*get_bkg_scale(id))
id2 = None
if os.path.exists('interval0wt.pi'):
id2 = 3
load_pha(id2, 'interval0wt.pi')
if get_rmf(id2).energ_lo[0] == 0: get_rmf(id2).energ_lo[0] = 0.001
if get_arf(id2).energ_lo[0] == 0: get_arf(id2).energ_lo[0] = 0.001
