def setup_bkg(make_data_path):
from sherpa.astro.io import read_pha, read_arf, read_rmf
from sherpa.astro import xspec
infile = make_data_path("9774_bg.pi")
bkg = read_pha(infile)
bkg.exposure = 1
arf = read_arf(make_data_path('9774.arf'))
rmf = read_rmf(make_data_path('9774.rmf'))
bkg.set_arf(arf)
bkg.set_rmf(rmf)
bkg.set_analysis('energy')
bkg.notice(0.5, 7.0)
# We stay with a linear scale for the absorption model
# here as the values don't seem to go below 0.1.
#
abs1 = xspec.XSwabs('abs1')
p1 = PowLaw1D('p1')
model = abs1 * p1
p1.ampl = 1e-4
rsp = Response1D(bkg)
return {'bkg': bkg, 'model': rsp(model)}
def test_xspec_expression():
"""Check we can enforce XSPEC expressions"""
from sherpa.astro import xspec
# pick an additive and multiplicative model
a1 = xspec.XSpowerlaw()
m1 = xspec.XSwabs()
m = 2 * (a1 + m1)
assert a1.ndim == 1
assert m1.ndim == 1
assert m.ndim == 1
def test_instrument_model(make_data_path):
"""Check the full response model"""
from sherpa.astro import xspec
s = Session()
s.load_pha(make_data_path('3c273.pi'))
a1 = xspec.XSpowerlaw()
m1 = xspec.XSwabs()
s.set_source(m1 * a1)
src = s.get_source()
mdl = s.get_model()
assert src.ndim == 1
assert mdl.ndim == 1
def setup(make_data_path):
from sherpa.astro.io import read_pha
from sherpa.astro import xspec
infile = make_data_path("9774.pi")
data = read_pha(infile)
data.notice(0.3, 7.0)
# Change the exposure time to make the fitted amplitude
# > 1
#
data.exposure = 1
# Use the wabs model because it is unlikely to change
# (as scientifically it is no-longer useful). The problem
# with using something like the phabs model is that
# changes to that model in XSPEC could change the results
# here.
#
# We fit the log of the nH since this makes the numbers
# a bit closer to O(1), and so checking should be easier.
#
abs1 = xspec.XSwabs('abs1')
p1 = PowLaw1D('p1')
factor = Const1D('factor')
factor.integrate = False
model = abs1 * p1 + 0 * factor
factor.c0 = 0
abs1.nh = 10**factor.c0
# Ensure the nh limits are honoured by factor (upper limit only).
# If you don't do this then the fit can fail because a value
# outside the abs1.nh but within factor.c0 can be picked.
#
factor.c0.max = numpy.log10(abs1.nh.max)
rsp = Response1D(data)
return {'data': data, 'model': rsp(model)}