def test_ui_source_methods_with_full_model(clean_ui, setup_ui_full):
ui.load_data('full', setup_ui_full.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
with pytest.raises(IdentifierErr) as exc:
ui.get_source('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use get_model instead."
assert str(exc.value) == emsg
with pytest.raises(IdentifierErr) as exc:
ui.plot_source('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use plot_model instead."
assert str(exc.value) == emsg
with pytest.raises(IdentifierErr) as exc:
ui.get_source_plot('full')
emsg = "Convolved model\n'powlaw1d.p1'\n is set for dataset full. You should use get_model_plot instead."
assert str(exc.value) == emsg
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', setup_ui_full.ascii)
with pytest.raises(IdentifierErr) as exc:
ui.get_source('not_full')
emsg = 'source not_full has not been set, consider using set_source() or set_model()'
assert emsg == str(exc.value)
def test_source_methods_with_full_model(self):
from sherpa.utils.err import IdentifierErr
ui.load_data('full', self.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
try:
ui.get_source('full')
except IdentifierErr as e:
self.assertRegex(
str(e),
"Convolved model\n.*\n is set for dataset full. You should use get_model instead.",
str(e))
try:
ui.plot_source('full')
except IdentifierErr as e:
self.assertRegex(
str(e),
"Convolved model\n.*\n is set for dataset full. You should use plot_model instead.",
str(e))
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', self.ascii)
try:
ui.get_source('not_full')
except IdentifierErr as e:
self.assertEqual(
'source not_full has not been set, consider using set_source() or set_model()',
str(e))
def test_source_methods_with_full_model(self):
from sherpa.utils.err import IdentifierErr
ui.load_data('full', self.ascii)
ui.set_full_model('full', 'powlaw1d.p1')
# Test Case 1
try:
ui.get_source('full')
except IdentifierErr as e:
self.assertRegexpMatches(str(e), "Convolved model\n.*\n is set for dataset full. You should use get_model instead.", str(e))
try:
ui.plot_source('full')
except IdentifierErr as e:
self.assertEquals("Convolved model\n'p1'\n is set for dataset full. You should use plot_model instead.", str(e))
# Test Case 2
ui.set_source('full', 'powlaw1d.p2')
ui.get_source('full')
# Test Case 3
ui.load_data('not_full', self.ascii)
try:
ui.get_source('not_full')
except IdentifierErr as e:
self.assertEquals('source not_full has not been set, consider using set_source() or set_model()', str(e))
def test_ui_set_full_model_checks_dimensions_match(clean_ui, setup_ui_2d):
ui.load_psf('psf1', 'gauss2d.g1')
with pytest.raises(ArgumentErr) as err:
ui.set_full_model('psf1(gauss1d.g2)+const2d.c1')
assert str(
err.value
) == "invalid model expression: Models do not match: 2D (gauss2d.g1) and 1D (gauss1d.g2)"
def test_ui_set_full_model_2d_mismatch_1d(clean_ui, setup_ui_2d):
ui.load_psf('psf1', 'gauss1d.g1')
ui.set_full_model('psf1(gauss1d.g2 ) +const1d.c1')
# Ideally this would fail but it currently does not
ui.get_model()
def test_ui_set_full_model_2d(clean_ui, setup_ui_2d):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2 ) +const2d.c1')
ui.get_model()
def test_set_full_model(self):
ui.load_psf("psf1", "gauss2d.g1")
ui.set_full_model("psf1(gauss2d.g2)+const2d.c1")
ui.get_model()
ui.get_source()
def test_set_full_model(self):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2)+const2d.c1')
ui.get_model()
def test_set_full_model(self):
ui.load_psf('psf1', 'gauss2d.g1')
ui.set_full_model('psf1(gauss2d.g2)+const2d.c1')
ui.get_model()
def fitne(ne_data, nemodeltype, tspec_data=None):
'''
Fits gas number density profile according to selected profile model.
The fit is performed using python sherpa with the Levenberg-Marquardt
method of minimizing chi-squared .
Args:
-----
ne_data (astropy table): observed gas density profile
in the form established by set_prof_data()
tspec_data (astropy table): observed temperature profile
in the form established by set_prof_data()
Returns:
--------
nemodel (dictionary): stores relevant information about the model gas
density profile
nemodel['type']: ne model type; one of the following:
['single_beta','cusped_beta','double_beta_tied','double_beta']
nemodel['parnames']: names of the stored ne model parameters
nemodel['parvals']: parameter values of fitted gas density model
nemodel['parmins']: lower error bound on parvals
nemodel['parmaxes']: upper error bound on parvals
nemodel['chisq']: chi-squared of fit
nemodel['dof']: degrees of freedom
nemodel['rchisq']: reduced chi-squared of fit
nemodel['nefit']: ne model values at radial values matching
tspec_data (the observed temperature profile)
References:
-----------
python sherpa: https://github.com/sherpa/
'''
# remove any existing models and data
ui.clean()
# load data
ui.load_arrays(1, np.array(ne_data['radius']), np.array(ne_data['ne']),
np.array(ne_data['ne_err']))
# set guess and boundaries on params given selected model
if nemodeltype == 'single_beta':
# param estimate
betaguess = 0.6
rcguess = 20. # units?????
ne0guess = max(ne_data['ne'])
# beta model
ui.load_user_model(betamodel, "beta1d")
ui.add_user_pars("beta1d", ["ne0", "rc", "beta"])
ui.set_source(beta1d) # creates model
ui.set_full_model(beta1d)
# set parameter values
ui.set_par(beta1d.ne0, ne0guess, min=0, max=10. * max(ne_data['ne']))
ui.set_par(beta1d.rc, rcguess, min=0.1, max=max(ne_data['radius']))
ui.set_par(beta1d.beta, betaguess, min=0.1, max=1.)
if nemodeltype == 'cusped_beta':
# param estimate
betaguess = 0.7
rcguess = 5. # [kpc]
ne0guess = max(ne_data['ne'])
alphaguess = 10. # ????
# beta model
ui.load_user_model(cuspedbetamodel, "cuspedbeta1d")
ui.add_user_pars("cuspedbeta1d", ["ne0", "rc", "beta", "alpha"])
ui.set_source(cuspedbeta1d) # creates model
ui.set_full_model(cuspedbeta1d)
# set parameter values
ui.set_par(cuspedbeta1d.ne0,
ne0guess,
min=0.001 * max(ne_data['ne']),
max=10. * max(ne_data['ne']))
ui.set_par(cuspedbeta1d.rc,
rcguess,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(cuspedbeta1d.beta, betaguess, min=0.1, max=1.)
ui.set_par(cuspedbeta1d.alpha, alphaguess, min=0., max=100.)
if nemodeltype == 'double_beta':
# param estimate
ne0guess1 = max(ne_data['ne']) # [cm^-3]
rcguess1 = 10. # [kpc]
betaguess1 = 0.6
ne0guess2 = 0.01 * max(ne_data['ne']) # [cm^-3]
rcguess2 = 100. # [kpc]
betaguess2 = 0.6
# double beta model
ui.load_user_model(doublebetamodel, "doublebeta1d")
ui.add_user_pars("doublebeta1d",
["ne01", "rc1", "beta1", "ne02", "rc2", "beta2"])
ui.set_source(doublebeta1d) # creates model
ui.set_full_model(doublebeta1d)
# set parameter values
ui.set_par(doublebeta1d.ne01,
ne0guess1,
min=0.0001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d.rc1,
rcguess1,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d.beta1, betaguess1, min=0.1, max=1.)
ui.set_par(doublebeta1d.ne02,
ne0guess2,
min=0.0001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d.rc2,
rcguess2,
min=10.,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d.beta2, betaguess2, min=0.1, max=1.)
if nemodeltype == 'double_beta_tied':
# param estimate
ne0guess1 = max(ne_data['ne'])
rcguess1 = 10.
betaguess1 = 0.6
ne0guess2 = 0.01 * max(ne_data['ne'])
rcguess2 = 100.
# double beta model
ui.load_user_model(doublebetamodel_tied, "doublebeta1d_tied")
ui.add_user_pars("doublebeta1d_tied",
["ne01", "rc1", "beta1", "ne02", "rc2"])
ui.set_source(doublebeta1d_tied) # creates model
ui.set_full_model(doublebeta1d_tied)
# set parameter values
ui.set_par(doublebeta1d_tied.ne01,
ne0guess1,
min=0.00001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d_tied.rc1,
rcguess1,
min=0.1,
max=max(ne_data['radius']))
ui.set_par(doublebeta1d_tied.beta1, betaguess1, min=0.1, max=1.)
ui.set_par(doublebeta1d_tied.ne02,
ne0guess2,
min=0.00001 * max(ne_data['ne']),
max=100. * max(ne_data['ne']))
ui.set_par(doublebeta1d_tied.rc2,
rcguess2,
min=10.,
max=max(ne_data['radius']))
# fit model
ui.fit()
# fit statistics
chisq = ui.get_fit_results().statval
dof = ui.get_fit_results().dof
rchisq = ui.get_fit_results().rstat
# error analysis
ui.set_conf_opt("max_rstat", 1e9)
ui.conf()
parvals = np.array(ui.get_conf_results().parvals)
parmins = np.array(ui.get_conf_results().parmins)
parmaxes = np.array(ui.get_conf_results().parmaxes)
parnames = [
str(x).split('.')[1] for x in list(ui.get_conf_results().parnames)
]
# where errors are stuck on a hard limit, change error to Inf
if None in list(parmins):
ind = np.where(parmins == np.array(None))[0]
parmins[ind] = float('Inf')
if None in list(parmaxes):
ind = np.where(parmaxes == np.array(None))[0]
parmaxes[ind] = float('Inf')
# set up a dictionary to contain useful results of fit
nemodel = {}
nemodel['type'] = nemodeltype
nemodel['parnames'] = parnames
nemodel['parvals'] = parvals
nemodel['parmins'] = parmins
nemodel['parmaxes'] = parmaxes
nemodel['chisq'] = chisq
nemodel['dof'] = dof
nemodel['rchisq'] = rchisq
# if tspec_data included, calculate value of ne model at the same radius
# positions as temperature profile
if tspec_data is not None:
if nemodeltype == 'double_beta':
nefit_arr = doublebetamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'single_beta':
nefit_arr = betamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'cusped_beta':
nefit_arr = cuspedbetamodel(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
if nemodeltype == 'double_beta_tied':
nefit_arr = doublebetamodel_tied(nemodel['parvals'],
np.array(tspec_data['radius']))
# [cm-3]
nemodel['nefit'] = nefit_arr
return nemodel