def test_pha_case_6(ds_setup, ds_datadir):
datadir = ds_datadir
ls = '@' + '/'.join((datadir, 'pha.lis'))
rmf1 = '/'.join((datadir, "acisf04938_000N002_r0043_rmf3.fits"))
rmf2 = '/'.join((datadir, "acisf07867_000N001_r0002_rmf3.fits"))
arf1 = '/'.join((datadir, "acisf04938_000N002_r0043_arf3.fits"))
arf2 = '/'.join((datadir, "acisf07867_000N001_r0002_arf3.fits"))
datastack.load_pha(ls)
datastack.load_bkg_rmf([], rmf1)
datastack.load_bkg_rmf([], rmf2)
datastack.load_bkg_arf([], arf1)
datastack.load_bkg_arf([], arf2)
# Define background models
bkg_arfs = datastack.get_bkg_arf([])
bkg_scales = datastack.get_bkg_scale([])
bkg_models = [
ui.const1d.c1 * acis_bkg_model('acis7s'),
ui.const1d.c2 * acis_bkg_model('acis7s')
]
bkg_rsps = datastack.get_response([], bkg_id=1)
for i in range(2):
id_ = i + 1
# Make the ARF spectral response flat. This is required for using
# the acis_bkg_model.
bkg_arfs[i].specresp = bkg_arfs[i].specresp * 0 + 1.
datastack.set_bkg_full_model(id_, bkg_rsps[i](bkg_models[i]))
# Fit background
datastack.notice(0.5, 8.)
datastack.set_method("neldermead")
datastack.set_stat("cash")
datastack.thaw(c1.c0)
datastack.thaw(c2.c0)
datastack.fit_bkg()
datastack.freeze(c1.c0)
datastack.freeze(c2.c0)
# Define source models
rsps = datastack.get_response([])
src_model = ui.powlaw1d.pow1
src_models = [src_model, src_model * ui.const1d.ratio_12]
for i in range(2):
id_ = i + 1
datastack.set_full_model(id_,
(rsps[i](src_models[i]) +
bkg_scales[i] * bkg_rsps[i](bkg_models[i])))
datastack.fit()
def test_case_6(self):
datadir = '/'.join((self._this_dir, 'data'))
ls = '@'+'/'.join((datadir, 'pha.lis'))
rmf1 = '/'.join((datadir, "acisf04938_000N002_r0043_rmf3.fits"))
rmf2 = '/'.join((datadir, "acisf07867_000N001_r0002_rmf3.fits"))
arf1 = '/'.join((datadir, "acisf04938_000N002_r0043_arf3.fits"))
arf2 = '/'.join((datadir, "acisf07867_000N001_r0002_arf3.fits"))
datastack.load_pha(ls)
datastack.load_bkg_rmf([], rmf1)
datastack.load_bkg_rmf([], rmf2)
datastack.load_bkg_arf([], arf1)
datastack.load_bkg_arf([], arf2)
# Define background models
bkg_arfs = datastack.get_bkg_arf([])
bkg_scales = datastack.get_bkg_scale([])
bkg_models = [ui.const1d.c1 * acis_bkg_model('acis7s'),
ui.const1d.c2 * acis_bkg_model('acis7s')]
bkg_rsps = datastack.get_response([], bkg_id=1)
for i in range(2):
id_ = i + 1
# Make the ARF spectral response flat. This is required for using
# the acis_bkg_model.
bkg_arfs[i].specresp = bkg_arfs[i].specresp * 0 + 1.
datastack.set_bkg_full_model(id_, bkg_rsps[i](bkg_models[i]))
# Fit background
datastack.notice(0.5, 8.)
datastack.set_method("neldermead")
datastack.set_stat("cash")
datastack.thaw(c1.c0)
datastack.thaw(c2.c0)
datastack.fit_bkg()
datastack.freeze(c1.c0)
datastack.freeze(c2.c0)
# Define source models
rsps = datastack.get_response([])
src_model = ui.powlaw1d.pow1
src_models = [src_model,
src_model * ui.const1d.ratio_12]
for i in range(2):
id_ = i + 1
datastack.set_full_model(id_, (rsps[i](src_models[i]) +
bkg_scales[i] *
bkg_rsps[i](bkg_models[i])))
datastack.fit()