def test_show_all_basic(clean_ui):
"""Set up a very basic data/model/fit"""
ui.load_arrays(1, [1, 2, 4], [3, 5, 5])
ui.set_source(ui.scale1d.mdl)
ui.fit()
ui.conf()
ui.proj()
ui.covar()
def get(value):
out = StringIO()
getattr(ui, f"show_{value}")(outfile=out)
ans = out.getvalue()
assert len(ans) > 1
# trim the trailing "\n"
return ans[:-1]
# All we are really checking is that the show_all output is the
# comppsite of the following. We are not checking that the
# actual output makes sense for any command.
#
expected = get("data") + get("model") + get("fit") + get("conf") + \
get("proj") + get("covar")
got = get("all")
assert expected == got
def test_est_errors_works_single_parameter(mdlcls, method, getter, clean_ui):
"""This is issue #1397.
Rather than require XSPEC, we create a subclass of the Parameter
class to check it works. We are not too concerned with the actual
results hence the relatively low tolerance on the numeric checks.
"""
mdl = mdlcls()
ui.load_arrays(1, [1, 2, 3, 4], [4, 2, 1, 3.5])
ui.set_source(mdl)
with SherpaVerbosity("ERROR"):
ui.fit()
# this is where #1397 fails with Const2
method(mdl.con)
atol = 1e-4
assert ui.calc_stat() == pytest.approx(0.7651548418626658, abs=atol)
results = getter()
assert results.parnames == (f"{mdl.name}.con", )
assert results.sigma == pytest.approx(1.0)
assert results.parvals == pytest.approx((2.324060647544594, ), abs=atol)
# The covar errors are -/+ 1.3704388763054511
# conf -1.3704388763054511 / +1.3704388763054514
# proj -1.3704388762971822 / +1.3704388763135826
#
err = 1.3704388763054511
assert results.parmins == pytest.approx((-err, ), abs=atol)
assert results.parmaxes == pytest.approx((err, ), abs=atol)
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_thaw_model(string, clean_ui):
"""Can we thaw a model?
We use a model with an alwaysfrozen parameter.
"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
mdl.c1.freeze()
mdl.ampl.freeze()
assert mdl.ref.frozen
assert mdl.c1.frozen
assert not mdl.c2.frozen
assert mdl.ampl.frozen
assert ui.get_num_par_thawed() == 1
assert ui.get_num_par_frozen() == 3
if string:
ui.thaw("mdl")
else:
ui.thaw(mdl)
assert mdl.ref.frozen
assert not mdl.c1.frozen
assert not mdl.c2.frozen
assert not mdl.ampl.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
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_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_show_conf_basic(clean_ui):
"""Set up a very basic data/model/fit"""
ui.load_arrays(1, [1, 2, 4], [3, 5, 5])
ui.set_source(ui.scale1d.mdl)
ui.fit()
ui.conf()
out = StringIO()
ui.show_conf(outfile=out)
got = out.getvalue().split('\n')
assert len(got) == 12
assert got[0] == "Confidence:Dataset = 1"
assert got[1] == "Confidence Method = confidence"
assert got[2] == "Iterative Fit Method = None"
assert got[3] == "Fitting Method = levmar"
assert got[4] == "Statistic = chi2gehrels"
assert got[5] == "confidence 1-sigma (68.2689%) bounds:"
assert got[6] == " Param Best-Fit Lower Bound Upper Bound"
assert got[7] == " ----- -------- ----------- -----------"
assert got[8] == " mdl.c0 4.19798 -1.85955 1.85955"
assert got[9] == ""
assert got[10] == ""
assert got[11] == ""
def test_err_estimate_model(strings, idval, otherids, clean_ui):
"""Ensure we can use model with conf/proj/covar.
This is test_err_estimate_multi_ids but
- added an extra model to each source (that evaluates to 0)
- we include the model expression in the call.
The fit and error analysis should be the same however the ordering
is done.
"""
# This is a bit ugly
if strings:
idval = str(idval)
if type(otherids) == tuple:
otherids = (str(otherids[0]), str(otherids[1]))
else:
otherids = [str(otherids[0]), str(otherids[1])]
datasets = tuple([idval] + list(otherids))
setup_err_estimate_multi_ids(strings=strings)
zero = ui.create_model_component("scale1d", "zero")
zero.c0 = 0
zero.c0.freeze()
for id in datasets:
# In this case we have
# orig == mdl
# but let's be explicit in case the code changes
#
orig = ui.get_source(id)
ui.set_source(id, orig + zero)
ui.fit(idval, *otherids)
res = ui.get_fit_results()
assert res.datasets == datasets
assert res.numpoints == 10
assert res.statval == pytest.approx(3.379367979541458)
assert ui.calc_stat() == pytest.approx(4255.615602052843)
assert mdl.c0.val == pytest.approx(46.046607302070015)
assert mdl.c1.val == pytest.approx(-1.9783953989993386)
# I wanted to have zero.co thawed at this stage, but then we can not
# use the ERR_EST_C0/1_xxx values as the fit has changed (and mdl.c0
# and zero.c0 are degenerate to boot).
#
ui.conf(*datasets, mdl)
res = ui.get_conf_results()
assert res.datasets == datasets
assert res.parnames == ("mdl.c0", "mdl.c1")
assert res.parmins == pytest.approx([ERR_EST_C0_MIN, ERR_EST_C1_MIN])
assert res.parmaxes == pytest.approx([ERR_EST_C0_MAX, ERR_EST_C1_MAX])
def test_err_estimate_errors_on_frozen(method, clean_ui):
"""Check we error out with frozen par with conf/proj/covar.
"""
ui.load_arrays(1, [1, 2, 3], [1, 2, 3])
ui.set_source(ui.polynom1d.mdl)
with pytest.raises(ParameterErr) as exc:
method(mdl.c0, mdl.c1)
assert str(exc.value) == "parameter 'mdl.c1' is frozen"
def test_freeze_no_arguments(clean_ui):
"""This is a no-op"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
ui.freeze()
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
def setUp(clean_ui, hide_logging):
x = [-13, -5, -3, 2, 7, 12]
y = [102.3, 16.7, -0.6, -6.7, -9.9, 33.2]
dy = np.ones(6) * 5
ui.load_arrays(1, x, y, dy)
ui.set_source(ui.polynom1d.poly)
poly.c1.thaw()
poly.c2.thaw()
ui.int_proj(poly.c0)
ui.fit()
def test_err_estimate_errors_model_all_frozen(method, clean_ui):
"""Check we error out with frozen model with conf/proj/covar.
"""
ui.load_arrays(1, [1, 2, 3], [1, 2, 3])
ui.set_source(ui.polynom1d.mdl)
for par in mdl.pars:
par.freeze()
with pytest.raises(ParameterErr) as exc:
method(mdl)
assert str(exc.value) == "Model 'polynom1d.mdl' has no thawed parameters"
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def test_user_model1d_fit():
"""Check can use in a fit."""
mname = "test_model"
ui.load_user_model(um_line, mname)
ui.add_user_pars(mname, ["slope", "intercept"],
parvals = [1.0, 1.0])
mdl = ui.get_model_component(mname)
x = numpy.asarray([-2.4, 2.3, 5.4, 8.7, 12.3])
# Set up the data to be scattered around y = -0.2 x + 2.8
# Pick the deltas so that they sum to 0 (except for central
# point)
#
slope = -0.2
intercept = 2.8
dy = numpy.asarray([0.1, -0.2, 0.14, -0.1, 0.2])
ydata = x * slope + intercept + dy
ui.load_arrays(1, x, ydata)
ui.set_source(mname)
ui.ignore(5.0, 6.0) # drop the central bin
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.fit()
fres = ui.get_fit_results()
assert fres.succeeded
assert fres.parnames == ('test_model.slope', 'test_model.intercept')
assert fres.numpoints == 4
assert fres.dof == 2
# Tolerance has been adjusted to get the tests to pass on my
# machine. It's really just to check that the values have chanegd
# from their default values.
#
assert fres.parvals[0] == pytest.approx(slope, abs=0.01)
assert fres.parvals[1] == pytest.approx(intercept, abs=0.05)
# Thse should be the same values, so no need to use pytest.approx
# (unless there's some internal translation between types done
# somewhere?).
#
assert mdl.slope.val == fres.parvals[0]
assert mdl.intercept.val == fres.parvals[1]
def tst_ui(self, thaw_c1):
ui.load_arrays(1, self._x, self._y, self._e)
ui.set_source(1, ui.polynom1d.mdl)
if thaw_c1:
ui.thaw(mdl.c1)
ui.thaw(mdl.c2)
mdl.c2 = 1
ui.fit()
if not thaw_c1:
ui.thaw(mdl.c1)
ui.fit()
ui.conf()
result = ui.get_conf_results()
self.cmp_results(result)
def test_guess_warns_no_guess_no_argument(caplog, clean_ui):
"""Do we warn when the (implied) model has no guess"""
ui.load_arrays(1, [1, 2, 3], [-3, 4, 5])
cpt = DummyModel('dummy')
ui.set_source(cpt + cpt)
assert len(caplog.records) == 0
ui.guess()
assert len(caplog.records) == 1
lname, lvl, msg = caplog.record_tuples[0]
assert lname == "sherpa.ui.utils"
assert lvl == logging.INFO
assert msg == "WARNING: No guess found for (dummy + dummy)"
def test_thaw_invalid_arguments(string, clean_ui):
"""We error out with an invalid argument"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
with pytest.raises(ArgumentTypeErr) as ae:
if string:
ui.thaw("1")
else:
ui.thaw(1)
assert str(
ae.value
) == "'par' must be a parameter or model object or expression string"
def setUp(self):
# defensive programming (one of the tests has been seen to fail
# when the whole test suite is run without this)
ui.clean()
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
x = [-13, -5, -3, 2, 7, 12]
y = [102.3, 16.7, -0.6, -6.7, -9.9, 33.2]
dy = np.ones(6) * 5
ui.load_arrays(1, x, y, dy)
ui.set_source(ui.polynom1d.poly)
poly.c1.thaw()
poly.c2.thaw()
ui.int_proj(poly.c0)
ui.fit()
def test_thaw_thawed_parameter(string, clean_ui):
"""Can we thaw a thawed parameter? String argument"""
mdl = ui.create_model_component("polynom1d", "mdl")
ui.set_source(mdl)
assert not mdl.c0.frozen
assert ui.get_num_par_thawed() == 1
assert ui.get_num_par_frozen() == 9
if string:
ui.thaw("mdl.c0")
else:
ui.thaw(mdl.c0)
assert not mdl.c0.frozen
assert ui.get_num_par_thawed() == 1
assert ui.get_num_par_frozen() == 9
def test_freeze_frozen_parameter(string, clean_ui):
"""Can we freeze a frozen_parameter?"""
mdl = ui.create_model_component("polynom1d", "mdl")
ui.set_source(mdl)
assert mdl.c1.frozen
assert ui.get_num_par_thawed() == 1
assert ui.get_num_par_frozen() == 9
if string:
ui.freeze("mdl.c1")
else:
ui.freeze(mdl.c1)
assert mdl.c1.frozen
assert ui.get_num_par_thawed() == 1
assert ui.get_num_par_frozen() == 9
def test_freeze_parameter(string, clean_ui):
"""Can we freeze a parameter?"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
assert not mdl.c1.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
if string:
ui.freeze("mdl.c1")
else:
ui.freeze(mdl.c1)
assert mdl.c1.frozen
assert ui.get_num_par_thawed() == 2
assert ui.get_num_par_frozen() == 2
def test_freeze_alwaysfrozen_parameter(string, clean_ui):
"""Can we freeze an always-frozen parameter?"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
assert mdl.ref.alwaysfrozen
assert mdl.ref.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
if string:
ui.freeze("mdl.ref")
else:
ui.freeze(mdl.ref)
assert mdl.ref.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
def setup_example(idval):
"""Set up a simple dataset for use in the tests.
Parameters
----------
idval : None, int, str
The dataset identifier.
"""
d = example_data()
m = example_model()
if idval is None:
ui.set_data(d)
ui.set_source(m)
else:
ui.set_data(idval, d)
ui.set_source(idval, m)
def tst_ui(thaw_c1, setUp, clean_ui):
data, mdl = setUp
ui.load_arrays(1, data.x, data.y, data.staterror)
ui.set_source(1, ui.polynom1d.mdl)
if thaw_c1:
ui.thaw(mdl.c1)
ui.thaw(mdl.c2)
mdl.c2 = 1
ui.fit()
if not thaw_c1:
ui.thaw(mdl.c1)
ui.fit()
ui.conf()
result = ui.get_conf_results()
cmp_results(result)
def test_freeze_multi_arguments(string, clean_ui):
"""Check we can combine model and parameters"""
mdl1 = ui.create_model_component("logparabola", "mdl1")
mdl2 = ui.create_model_component("polynom1d", "mdl2")
ui.set_source(mdl1 + mdl2)
assert not mdl1.c2.frozen
assert not mdl2.c0.frozen
assert ui.get_num_par_thawed() == 4
assert ui.get_num_par_frozen() == 10
if string:
ui.freeze("mdl2", "mdl1.c2")
else:
ui.freeze(mdl2, mdl1.c2)
assert mdl1.c2.frozen
assert mdl2.c0.frozen
assert ui.get_num_par_thawed() == 2
assert ui.get_num_par_frozen() == 12
def mwl_fit_high_level():
"""Use high-level Sherpa API.
High-level = session and convenience functions
Example: http://cxc.harvard.edu/sherpa/threads/simultaneous/
Example: http://python4astronomers.github.io/fitting/spectrum.html
"""
import sherpa.ui as ui
fermi_data = FermiData()
ui.load_arrays(fermi_data.name, fermi_data.x, fermi_data.y,
fermi_data.staterror)
ui.load_user_stat('fermi_stat', FermiStat.calc_stat,
FermiStat.calc_staterror)
# TODO: is there a good way to get the stat??
# ui.get_stat('fermi_stat')
# fermi_stat = ui._session._get_stat_by_name('fermi_stat')
ui.set_stat(fermi_stat)
# IPython.embed()
iact_data = IACTData()
ui.load_arrays(iact_data.name, iact_data.x, iact_data.y,
iact_data.staterror)
spec_model = ui.logparabola.spec_model
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
ui.set_source(fermi_data.name, spec_model)
ui.set_source(iact_data.name, spec_model)
ui.notice(lo=1e-3, hi=None)
# IPython.embed()
ui.fit()
return dict(results=ui.get_fit_results(), model=spec_model)
def test_thaw_alwaysfrozen_parameter(string, clean_ui):
"""Can we thaw an always-frozen parameter?"""
mdl = ui.create_model_component("logparabola", "mdl")
ui.set_source(mdl)
assert mdl.ref.alwaysfrozen
assert mdl.ref.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
with pytest.raises(ParameterErr) as pe:
if string:
ui.thaw("mdl.ref")
else:
ui.thaw(mdl.ref)
assert mdl.ref.frozen
assert ui.get_num_par_thawed() == 3
assert ui.get_num_par_frozen() == 1
assert str(
pe.value) == "parameter mdl.ref is always frozen and cannot be thawed"
def mwl_fit_high_level():
"""Use high-level Sherpa API.
High-level = session and convenience functions
Example: http://cxc.harvard.edu/sherpa/threads/simultaneous/
Example: http://python4astronomers.github.io/fitting/spectrum.html
"""
import sherpa.ui as ui
fermi_data = FermiData()
ui.load_arrays(fermi_data.name, fermi_data.x, fermi_data.y, fermi_data.staterror)
ui.load_user_stat('fermi_stat', FermiStat.calc_stat, FermiStat.calc_staterror)
# TODO: is there a good way to get the stat??
# ui.get_stat('fermi_stat')
# fermi_stat = ui._session._get_stat_by_name('fermi_stat')
ui.set_stat(fermi_stat)
# IPython.embed()
iact_data = IACTData()
ui.load_arrays(iact_data.name, iact_data.x, iact_data.y, iact_data.staterror)
spec_model = ui.logparabola.spec_model
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
ui.set_source(fermi_data.name, spec_model)
ui.set_source(iact_data.name, spec_model)
ui.notice(lo=1e-3, hi=None)
# IPython.embed()
ui.fit()
return Bunch(results=ui.get_fit_results(), model=spec_model)
def setup_err_estimate_multi_ids(strings=False):
"""Create the environment used in test_err_estimate_xxx tests.
The model being fit is polynom1d with c0=50 c1=-2
and was evaluated and passed through sherpa.utils.poisson_noise
to create the datasets.
Since we can have string or integer ids we allow either,
but do not try to mix them.
"""
if strings:
id1 = "1"
id2 = "2"
id3 = "3"
else:
id1 = 1
id2 = 2
id3 = 3
ui.load_arrays(id1, [1, 3, 7, 12], [50, 40, 27, 20])
ui.load_arrays(id2, [-3, 4, 5], [55, 34, 37])
ui.load_arrays(id3, [10, 12, 20], [24, 26, 7])
# NOTE: dataset "not-used" is not used in the fit and is not
# drawn from the distributino used to create the other datasets.
#
ui.load_arrays("not-used", [2000, 2010, 2020], [10, 12, 14])
mdl = ui.create_model_component("polynom1d", "mdl")
mdl.c1.thaw()
ui.set_source(id1, mdl)
ui.set_source(id2, mdl)
ui.set_source(id3, mdl)
# apply the model to dataset not-used just so we can check we
# don't end up using it
mdl_not_used = ui.create_model_component("scale1d", "mdl_not_used")
ui.set_source("not-used", mdl + mdl_not_used)
# use cstat so we have an approximate goodness-of-fit just to
# check we are getting sensible results.
#
ui.set_stat("cstat")
ui.set_method("simplex")
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
# coding: utf-8
import sherpa.ui as ui
ui.load_data("load_template_without_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name=None)
ui.set_source('bb1')
assert ui.get_source().is_discrete
ui.set_source('bb1*const1d.c1+gauss1d.g2**const1d.c2')
assert ui.get_source().is_discrete
from matplotlib import pyplot as plt
import numpy as np
from sherpa import ui
x1 = np.arange(-5.0, 30, 0.5)
x2 = np.arange(1.0, 29.0, 0.2)
ui.load_arrays(1, x1, x1 * 0, ui.Data1D)
ui.load_arrays(2, x2, x2 * 0, ui.Data1D)
ui.set_source(1, ui.box1d.box)
box = ui.get_model_component('box')
ui.set_source(2, box)
box.xlow = 10.0
box.xhi = 20.0
# Copy all the objects just to make sure
g1 = ui.gauss1d('g1')
g1.fwhm = 3.0
g2 = ui.gauss1d('g2')
g2.fwhm = 3.0
ui.load_psf('psf1', g1)
ui.load_psf('psf2', g2)
ui.set_psf(1, 'psf1')
ui.set_psf(2, 'psf2')
# coding: utf-8
import sherpa.ui as ui
ui.load_data("load_template_without_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name=None)
ui.set_source('bb1')
ui.fit()
