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()
def _nH_uplimit(fixgamma, sigma_level=0.955):
# Fix nH to 1e19 cm-2 and find the best fit
shp.set_par("abs1.nH", val=0.001, frozen=True)
properFit()
dsmod.freeze("po1.PhoIndex")
fitresult = properFit()
chi2_min = fitresult.statval
# Estimate the probability distribution of NH using chi2
NHtest = np.logspace(-3, 3, num=120)
PNHtest = np.full(len(NHtest), np.nan)
cumNHtest = np.full(len(NHtest), np.nan)
for i, nh in enumerate(NHtest):
shp.set_par("abs1.nH", val=nh)
fitresult = properFit()
Dchi2 = fitresult.statval - chi2_min
PNHtest[i] = np.exp(-Dchi2 / 2)
cumNHtest[i] = np.trapz(PNHtest[:i + 1], NHtest[:i + 1])
# Normalize
Pnorm = 1 / np.trapz(PNHtest, NHtest)
C = Pnorm * cumNHtest
nH = 10**np.interp(np.log10(sigma_level),
np.log10(C),
np.log10(NHtest),
left=0,
right=1)
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.loglog(NHtest, P, linewidth=0, marker='o')
# ax.loglog(NHtest, C, linewidth=0, marker='o')
# ax.axvline(nH)
# plt.show()
# Restore original fit (NH=0)
if not fixgamma:
dsmod.thaw("po1.PhoIndex")
shp.set_par("abs1.nH", val=0)
properFit()
return nH
def test_case_4(self):
x1 = np.arange(50) + 100
y1 = 2 * (3 * x1**2 + x1)
x2 = np.arange(50)
y2 = 2 * (x2**2 + 3 * x2)
x3 = np.arange(50) + 200
y3 = 2 * (x3**2 + 3 * x3)
ds = self.ds
datastack.load_arrays(ds, [[x1, y1], [x2, y2], [x3, y3]])
datastack.set_source(ds, 'const1d.const * polynom1d.poly__ID')
poly1 = ui._session._get_model_component('poly1')
poly2 = ui._session._get_model_component('poly2')
poly3 = ui._session._get_model_component('poly3')
const = ui._session._get_model_component('const')
datastack.freeze(ds, 'poly')
datastack.thaw(ds, 'poly.c0')
datastack.thaw(ds, 'poly.c1')
datastack.thaw(ds, 'poly.c2')
datastack.thaw(ds, 'const')
assert poly1.c0.frozen is False
assert poly1.c1.frozen is False
assert poly1.c2.frozen is False
assert poly1.c3.frozen is True
assert poly2.c0.frozen is False
assert poly2.c1.frozen is False
assert poly2.c2.frozen is False
assert poly2.c3.frozen is True
assert poly3.c0.frozen is False
assert poly3.c1.frozen is False
assert poly3.c2.frozen is False
assert poly3.c3.frozen is True
datastack.set_par(ds, 'poly.c1', 0.45)
assert poly1.c1.val == 0.45
assert poly2.c1.val == 0.45
assert poly3.c1.val == 0.45
datastack.set_par(ds[1], 'poly.c1', 0.1)
assert poly1.c1.val == 0.1
assert poly2.c1.val == 0.45
assert poly3.c1.val == 0.45
datastack.set_par(ds, 'const.c0', 2)
assert const.c0.val == 2
datastack.set_par(ds, 'const.integrate', False)
datastack.freeze(ds, 'const.c0')
vals = datastack.get_par(ds, 'poly.c1.val')
assert ([0.1, 0.45, 0.45] == vals).all()
# QUS: pars is not checked, so is this just
# checking that get_par doesn't fail?
pars = datastack.get_par(ds, 'const.c0')
datastack.fit(ds)
assert round(poly1.c1.val) == 1
assert round(poly1.c2.val) == 3
assert round(poly2.c1.val) == 3
assert round(poly2.c2.val) == 1
assert round(poly3.c1.val) == 3
assert round(poly3.c2.val) == 1
ds.clear_stack()
x1 = np.arange(50) + 100
y1 = 7 * (3 * x1**2 + x1)
x2 = np.arange(50)
y2 = 2 * (x2**2 + 3 * x2)
x3 = np.arange(50) + 200
y3 = 2 * (x3**2 + 3 * x3)
datastack.load_arrays(ds, [[x1, y1], [x2, y2], [x3, y3]])
datastack.set_template_id("foo")
datastack.set_source(ds, 'const1d.constfoo * polynom1d.polyfoo')
const1 = ui._session._get_model_component('const1')
const2 = ui._session._get_model_component('const2')
const3 = ui._session._get_model_component('const3')
datastack.link(ds[2, 3], 'const.c0')
datastack.set_par(ds[2], 'const.c0', 3)
datastack.set_par(ds[1], 'const.c0', 7)
datastack.freeze(ds[1], 'const.c0')
assert const2.c0.frozen is False
datastack.fit(ds)
assert const2.c0.val == const3.c0.val
assert const3.c0._link is const2.c0
datastack.unlink(ds, "const.c0")
assert const3.c0._link is not const2.c0
def test_case_4(self):
x1 = np.arange(50)+100
y1 = 2*(3*x1**2 + x1)
x2 = np.arange(50)
y2 = 2*(x2**2 + 3*x2)
x3 = np.arange(50)+200
y3 = 2*(x3**2+3*x3)
ds = self.ds
datastack.load_arrays(ds, [[x1, y1], [x2, y2], [x3, y3]])
datastack.set_source(ds, 'const1d.const * polynom1d.poly__ID')
poly1 = ui._session._get_model_component('poly1')
poly2 = ui._session._get_model_component('poly2')
poly3 = ui._session._get_model_component('poly3')
const = ui._session._get_model_component('const')
datastack.freeze(ds, 'poly')
datastack.thaw(ds, 'poly.c0')
datastack.thaw(ds, 'poly.c1')
datastack.thaw(ds, 'poly.c2')
datastack.thaw(ds, 'const')
assert poly1.c0.frozen is False
assert poly1.c1.frozen is False
assert poly1.c2.frozen is False
assert poly1.c3.frozen is True
assert poly2.c0.frozen is False
assert poly2.c1.frozen is False
assert poly2.c2.frozen is False
assert poly2.c3.frozen is True
assert poly3.c0.frozen is False
assert poly3.c1.frozen is False
assert poly3.c2.frozen is False
assert poly3.c3.frozen is True
datastack.set_par(ds, 'poly.c1', 0.45)
assert poly1.c1.val == 0.45
assert poly2.c1.val == 0.45
assert poly3.c1.val == 0.45
datastack.set_par(ds[1], 'poly.c1', 0.1)
assert poly1.c1.val == 0.1
assert poly2.c1.val == 0.45
assert poly3.c1.val == 0.45
datastack.set_par(ds, 'const.c0', 2)
assert const.c0.val == 2
datastack.set_par(ds, 'const.integrate', False)
datastack.freeze(ds, 'const.c0')
vals = datastack.get_par(ds, 'poly.c1.val')
assert ([0.1, 0.45, 0.45] == vals).all()
# QUS: pars is not checked, so is this just
# checking that get_par doesn't fail?
pars = datastack.get_par(ds, 'const.c0')
datastack.fit(ds)
assert round(poly1.c1.val) == 1
assert round(poly1.c2.val) == 3
assert round(poly2.c1.val) == 3
assert round(poly2.c2.val) == 1
assert round(poly3.c1.val) == 3
assert round(poly3.c2.val) == 1
ds.clear_stack()
x1 = np.arange(50)+100
y1 = 7*(3*x1**2 + x1)
x2 = np.arange(50)
y2 = 2*(x2**2 + 3*x2)
x3 = np.arange(50)+200
y3 = 2*(x3**2+3*x3)
datastack.load_arrays(ds, [[x1, y1], [x2, y2], [x3, y3]])
datastack.set_template_id("foo")
datastack.set_source(ds, 'const1d.constfoo * polynom1d.polyfoo')
const1 = ui._session._get_model_component('const1')
const2 = ui._session._get_model_component('const2')
const3 = ui._session._get_model_component('const3')
datastack.link(ds[2,3], 'const.c0')
datastack.set_par(ds[2], 'const.c0', 3)
datastack.set_par(ds[1], 'const.c0', 7)
datastack.freeze(ds[1], 'const.c0')
assert const2.c0.frozen is False
datastack.fit(ds)
assert const2.c0.val == const3.c0.val
assert const3.c0._link is const2.c0
datastack.unlink(ds, "const.c0")
assert const3.c0._link is not const2.c0
