def fit(self, method='simplex'):
"""Initiate a fit of the model using Sherpa.
:param method: Method to be used to fit the model (e.g. simplex, levmar, or moncar)
"""
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(self.model, self.fit_logger),
'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.fit_logger)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
ui.fit(1)
self.save_snapshot(fit_stat=calc_stat.min_fit_stat, method=method)
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 fit_pix_values(t_ccd, esec, id=1):
logger = logging.getLogger("sherpa")
logger.setLevel(logging.WARN)
data_id = id
ui.clean()
ui.set_method('simplex')
ui.load_user_model(dark_scale_model, 'model')
ui.add_user_pars('model', ['scale', 'dark_t_ref'])
ui.set_model(data_id, 'model')
ui.load_arrays(
data_id,
np.array(t_ccd),
np.array(esec),
)
ui.set_staterror(data_id, 30 * np.ones(len(t_ccd)))
model.scale.val = 0.588
model.scale.min = 0.3
model.scale.max = 1.0
model.dark_t_ref.val = 500
ui.freeze(model.scale)
# If more than 5 degrees in the temperature range,
# thaw and fit for model.scale. Else just use/return
# the fit of dark_t_ref
if np.max(t_ccd) - np.min(t_ccd) > 2:
# Fit first for dark_t_ref
ui.fit(data_id)
ui.thaw(model.scale)
ui.fit(data_id)
return ui.get_fit_results(), ui.get_model(data_id)
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 fit_sbp():
ui.set_model(sbp)
ui.thaw(sbp)
ui.freeze(sbp.x_r)
ui.freeze(sbp.gamma1)
ui.fit()
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
logging.debug('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
logging.debug('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
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_err_estimate_single_parameter(strings, idval, otherids, clean_ui):
"""Ensure we can fti a single parameter with conf/proj/covar.
Since this uses the same logic we only test the conf routine;
ideally we'd use all but that's harder to test.
We use the same model as test_err_estimate_multi_ids but
here we only want to evaluate the error for the mdl.c1 component.
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)
ui.fit(idval, *otherids)
# pick an odd ordering just to check we pick it up
ui.conf(datasets[0], mdl.c1, datasets[1], datasets[2])
res = ui.get_conf_results()
assert res.datasets == datasets
assert res.parnames == ("mdl.c1", )
assert res.parmins == pytest.approx([ERR_EST_C1_MIN])
assert res.parmaxes == pytest.approx([ERR_EST_C1_MAX])
def fit(self):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(self.method)
ui.get_method().config.update(sherpa_configs.get(self.method, {}))
ui.load_user_model(CalcModel(self.model), 'xijamod') # sets global xijamod
ui.add_user_pars('xijamod', self.model.parnames)
ui.set_model(1, 'xijamod')
calc_stat = CalcStat(self.model, self.child_pipe, self.maxiter)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
# Set frozen, min, and max attributes for each xijamod parameter
for par in self.model.pars:
xijamod_par = getattr(xijamod, par.full_name)
xijamod_par.val = par.val
xijamod_par.frozen = par.frozen
xijamod_par.min = par.min
xijamod_par.max = par.max
if any(not par.frozen for par in self.model.pars):
try:
ui.fit(1)
calc_stat.message['status'] = 'finished'
fit_logger.info('Fit finished normally')
except FitTerminated as err:
calc_stat.message['status'] = 'terminated'
fit_logger.warning('Got FitTerminated exception {}'.format(err))
self.child_pipe.send(calc_stat.message)
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_covar_as_argument(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
self.assertEqual(niter + 1, stat.size)
self.assertEqual(niter + 1, accept.size)
self.assertEqual((2, niter + 1), params.shape)
self.assertTrue(numpy.any(accept))
def test_covar_as_none(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
self.assertEqual(niter + 1, stat.size)
self.assertEqual(niter + 1, accept.size)
self.assertEqual((2, niter + 1), params.shape)
self.assertTrue(numpy.any(accept))
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_covar_as_none(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
self.assertEqual(niter+1, stat.size)
self.assertEqual(niter+1, accept.size)
self.assertEqual((2, niter+1), params.shape)
self.assertTrue(numpy.any(accept))
def test_covar_as_argument(self):
for stat in self.right_stats - {'wstat'}:
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
self.assertEqual(niter+1, stat.size)
self.assertEqual(niter+1, accept.size)
self.assertEqual((2, niter+1), params.shape)
self.assertTrue(numpy.any(accept))
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_covar_as_none(stat, clean_ui, setup_covar):
ui.set_stat(stat)
ui.fit()
ui.covar()
niter = 10
stat, accept, params = ui.get_draws(niter=niter)
n = niter + 1
assert stat.size == n
assert accept.size == n
assert params.shape == (2, n)
assert np.any(accept)
def test_covar_as_argument(stat, clean_ui, setup_covar):
ui.set_stat(stat)
ui.fit()
matrix = [[0.00064075, 0.01122127], [0.01122127, 0.20153251]]
niter = 10
stat, accept, params = ui.get_draws(niter=niter, covar_matrix=matrix)
n = niter + 1
assert stat.size == n
assert accept.size == n
assert params.shape == (2, n)
assert np.any(accept)
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 _fit_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
# Let's screw up Y on purpose
if data_id == 0:
fitpoly.c0.val = 0
fitpoly.c1.val = 7.5e-05
fitpoly.c2.val = -1.0e-09
fitpoly.c3.val = 0
fitpoly.c4.val = 0
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
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_err_estimate_multi_ids(strings, idval, otherids, clean_ui):
"""Ensure we can use multiple ids with conf/proj/covar.
Since this uses the same logic we only test the conf routine;
ideally we'd use all but that's harder to test.
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)
ui.fit(idval, *otherids)
# The "reduced statistic" is ~0.42 for the fit.
#
res = ui.get_fit_results()
assert res.datasets == datasets
assert res.numpoints == 10 # sum of datasets 1, 2, 3
assert res.statval == pytest.approx(3.379367979541458)
# since there's a model assigned to dataset not-used the
# overall statistic is not the same as res.statval.
#
assert ui.calc_stat() == pytest.approx(4255.615602052843)
assert mdl.c0.val == pytest.approx(46.046607302070015)
assert mdl.c1.val == pytest.approx(-1.9783953989993386)
ui.conf(*datasets)
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 ccd_bias(bias):
"""
Calculate the mean and width of a gaussian fit to the bias
histogram.
`bias` is a numpy array.
"""
import sherpa.ui as ui
from numpy import histogram, arange
values, bins = histogram(bias, bins=arange(bias.min(),bias.max()+1))
ui.load_arrays(1, bins[:-1],values)
ui.set_model(ui.gauss1d.g1)
g1.pos = bias.mean()
g1.fwhm = bias.std()
ui.fit()
return g1
def fit_model(
model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
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 fit_model(model,
comm=None,
method='simplex',
config=None,
nofit=None,
freeze_pars=freeze_pars,
thaw_pars=[],
):
dummy_data = np.zeros(1)
dummy_times = np.arange(1)
ui.load_arrays(1, dummy_times, dummy_data)
ui.set_method(method)
ui.get_method().config.update(config or sherpa_configs.get(method, {}))
ui.load_user_model(CalcModel(model, comm), 'xijamod')
ui.add_user_pars('xijamod', model.parnames)
ui.set_model(1, 'xijamod')
fit_parnames = set()
for parname, parval in zip(model.parnames, model.parvals):
getattr(xijamod, parname).val = parval
fit_parnames.add(parname)
if any([re.match(x + '$', parname) for x in freeze_pars]):
fit_logger.info('Freezing ' + parname)
ui.freeze(getattr(xijamod, parname))
fit_parnames.remove(parname)
if any([re.match(x + '$', parname) for x in thaw_pars]):
fit_logger.info('Thawing ' + parname)
ui.thaw(getattr(xijamod, parname))
fit_parnames.add(parname)
if 'tau' in parname:
getattr(xijamod, parname).min = 0.1
calc_stat = CalcStat(model, comm)
ui.load_user_stat('xijastat', calc_stat, lambda x: np.ones_like(x))
ui.set_stat(xijastat)
if fit_parnames and not nofit:
ui.fit(1)
else:
model.calc()
def test_electron_models():
"""
test import
"""
from ..sherpa_models import InverseCompton, Synchrotron, Bremsstrahlung
for modelclass in [InverseCompton, Synchrotron, Bremsstrahlung]:
model = modelclass()
model.ampl = 1e-8
model.index = 2.1
print(model)
# point calc
output = model.calc([p.val for p in model.pars], energies)
# test as well ECPL
model.cutoff = 100
# integrated
output = model.calc([p.val for p in model.pars], elo, xhi=ehi)
if modelclass is InverseCompton:
# Perform a fit to fake data
ui.load_arrays(1, energies, test_spec_points, test_err_points)
ui.set_model(model)
ui.guess()
ui.fit()
# add FIR and NIR components and test verbose
model.uNIR.set(1.0)
model.uFIR.set(1.0)
model.verbose.set(1)
# test with integrated data
ui.load_arrays(1, elo, ehi, test_spec_int, test_err_int,
ui.Data1DInt)
ui.set_model(model)
ui.guess()
ui.fit()
def fit_pix_values(t_ccd, esec, id=1):
logger = logging.getLogger("sherpa")
logger.setLevel(logging.WARN)
data_id = id
ui.clean()
ui.set_method("simplex")
ui.load_user_model(dark_scale_model, "model")
ui.add_user_pars("model", ["scale", "dark_t_ref"])
ui.set_model(data_id, "model")
ui.load_arrays(data_id, np.array(t_ccd), np.array(esec), 0.1 * np.ones(len(t_ccd)))
model.scale.val = 0.70
model.dark_t_ref.val = 500
ui.freeze(model.scale)
# If more than 5 degrees in the temperature range,
# thaw and fit for model.scale. Else just use/return
# the fit of dark_t_ref
ui.fit(data_id)
ui.thaw(model.scale)
ui.fit(data_id)
return ui.get_fit_results(), ui.get_model(data_id)
def _fit_poly(fit_data, evt_times, degree, data_id=0):
"""
Given event data transformed into Y or Z angle positions, and a degree of the desired
fit polynomial, fit a polynomial to the data.
:param fit_data: event y or z angle position data
:param evt_times: times of event/fit_data
:param degree: degree of polynomial to use for the fit model
:param data_id: sherpa dataset id to use for the fit
:returns: (sherpa model plot, sherpa model)
"""
# Set initial value for fit data position error
init_error = 1
ui.clean()
ui.load_arrays(data_id, evt_times - evt_times[0], fit_data,
np.zeros_like(fit_data) + init_error)
v2("Fitting a line to the data to get reduced stat errors")
# First just fit a line to get reduced errors on this set
ui.polynom1d.line
ui.set_model(data_id, 'line')
ui.thaw('line.c1')
ui.fit(data_id)
fit = ui.get_fit_results()
calc_error = init_error * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_error)
# Then fit the specified model
v2("Fitting a polynomial of degree {} to the data".format(degree))
ui.polynom1d.fitpoly
ui.freeze('fitpoly')
# Thaw the coefficients requested by the degree of the desired polynomial
ui.thaw('fitpoly.c0')
fitpoly.c0.val = 0
for deg in range(1, 1 + degree):
ui.thaw("fitpoly.c{}".format(deg))
ui.set_model(data_id, 'fitpoly')
ui.fit(data_id)
mp = ui.get_model_plot(data_id)
model = ui.get_model(data_id)
return mp, model
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 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 fit_gauss_sbp():
g1 = ui.gauss1d.g1
ui.set_model(sbp + g1)
ui.set_method('simplex')
g1.fwhm = 5.0
g1.pos = 7.0
g1.ampl = 30000.
ui.freeze(sbp.gamma1)
ui.freeze(sbp.gamma2)
ui.freeze(sbp.x_b)
ui.freeze(sbp.x_r)
ui.freeze(g1.fwhm)
ui.freeze(g1.pos)
ui.thaw(g1.ampl)
ui.fit()
ui.thaw(g1.fwhm)
ui.thaw(g1.pos)
ui.fit()
ui.thaw(sbp)
ui.freeze(sbp.x_r)
ui.fit()
# coding: utf-8
import sherpa.ui as ui
from sherpa.models.template import KNNInterpolator
ui.load_data("custom_interp", "load_template_interpolator-bb_data.dat")
ui.load_template_interpolator('knn', KNNInterpolator, k=2, order=1)
ui.load_template_model('bb1', "bb_index.dat", template_interpolator_name='knn')
ui.set_model("custom_interp", "bb1")
ui.freeze("bb1.dummy")
ui.fit("custom_interp")
class AstropyToSherpa(object):
def __init__(self, model):
self.model = model
def __call__(self, pars, x):
self.model.parameters[:] = pars
return self.model(x)
ap_model = (models.Gaussian1D(amplitude=1.2, mean=0.9, stddev=0.5) +
models.Gaussian1D(amplitude=2.0, mean=-0.9, stddev=0.75))
err = 0.02
x = np.arange(-3, 3, .1)
y = ap_model(x) + err * np.random.uniform(size=len(x))
sh_model = AstropyToSherpa(ap_model)
ui.load_arrays(1, x, y, err * np.ones_like(x))
ui.load_user_model(sh_model, 'sherpa_model')
ui.add_user_pars('sherpa_model', ap_model.param_names, ap_model.parameters)
ui.set_model(1, 'sherpa_model')
ui.fit(1)
ui.plot_fit(1)
print()
print('Params from astropy model: {}'.format(ap_model.parameters))
plt.show()
def run_fits(obsids,
ax,
user_pars=None,
fixed_pars=None,
guess_pars=None,
label='model',
per_obs_dir='per_obs_nfits',
outdir=None,
redo=False):
if len(obsids) == 0:
print "No obsids, nothing to fit"
return None
if user_pars is None:
user_pars = USER_PARS
if not os.path.exists(per_obs_dir):
os.makedirs(per_obs_dir)
obsfits = []
for obsid in obsids:
outdir = os.path.join(per_obs_dir, 'obs{:05d}'.format(obsid))
if not os.path.exists(outdir):
os.makedirs(outdir)
model_file = os.path.join(outdir, '{}.pkl'.format(label))
if os.path.exists(model_file) and not redo:
#logger.warn('Using previous fit found in %s' % model_file)
print model_file
mod_pick = open(model_file, 'r')
modelfit = cPickle.load(mod_pick)
mod_pick.close()
obsfits.append(modelfit)
continue
modelfit = {'label': obsid}
ui.clean()
data_id = 0
obsdir = "%s/obs%05d" % (DATADIR, obsid)
tf = open(os.path.join(obsdir, 'tilt.pkl'), 'r')
tilt = cPickle.load(tf)
tf.close()
pf = open(os.path.join(obsdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
point_error = 5
pos_data_mean = np.mean(pos_data)
ui.set_method('simplex')
# Fit a line to get more reasonable errors
init_staterror = np.zeros(len(pos_data)) + point_error
ui.load_arrays(data_id, pos['time'] - pos['time'][0],
pos_data - np.mean(pos_data), init_staterror)
ui.polynom1d.ypoly
ui.set_model(data_id, 'ypoly')
ui.thaw(ypoly.c0, ypoly.c1)
ui.fit(data_id)
fit = ui.get_fit_results()
calc_staterror = init_staterror * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_staterror)
# Confirm those errors
ui.fit(data_id)
fit = ui.get_fit_results()
if (abs(fit.rstat - 1) > .2):
raise ValueError('Reduced statistic not close to 1 for error calc')
# Load up data to do the real model fit
fit_times = pos['time']
tm_func = tilt_model(tilt, fit_times, user_pars=user_pars)
ui.get_data(data_id).name = str(obsid)
ui.load_user_model(tm_func, 'tiltm%d' % data_id)
ui.add_user_pars('tiltm%d' % data_id, user_pars)
ui.set_method('simplex')
ui.set_model(data_id, 'tiltm%d' % (data_id))
ui.set_par('tiltm%d.diam' % data_id, 0)
if fixed_pars is not None and ax in fixed_pars:
for par in fixed_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), fixed_pars[ax][par])
ui.freeze('tiltm{}.{}'.format(0, par))
if guess_pars is not None and ax in guess_pars:
for par in guess_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), guess_pars[ax][par])
ui.show_all()
# Fit the tilt model
ui.fit(data_id)
fitres = ui.get_fit_results()
ui.confidence(data_id)
myconf = ui.get_confidence_results()
# save_fits(ax=ax, fit=fitres, conf=myconf, outdir=outdir)
# plot_fits(ids,outdir=os.path.join(outdir,'fit_plots'))
axmod = dict(fit=fitres, conf=myconf)
for idx, modpar in enumerate(myconf.parnames):
par = modpar.lstrip('tiltm0.')
axmod[par] = ui.get_par('tiltm0.%s' % par).val
axmod["{}_parmax".format(par)] = myconf.parmaxes[idx]
axmod["{}_parmin".format(par)] = myconf.parmins[idx]
modelfit[ax] = axmod
mod_pick = open(model_file, 'w')
cPickle.dump(modelfit, mod_pick)
mod_pick.close()
obsfits.append(modelfit)
plot_fits([dict(obsid=obsid, data_id=data_id, ax=ax)],
posdir=obsdir,
outdir=outdir)
return obsfits
def run_fits(obsids, ax, user_pars=None,
fixed_pars=None, guess_pars=None, label='model',
per_obs_dir='per_obs_nfits',
outdir=None, redo=False):
if len(obsids) == 0:
print "No obsids, nothing to fit"
return None
if user_pars is None:
user_pars = USER_PARS
if not os.path.exists(per_obs_dir):
os.makedirs(per_obs_dir)
obsfits = []
for obsid in obsids:
outdir = os.path.join(per_obs_dir, 'obs{:05d}'.format(obsid))
if not os.path.exists(outdir):
os.makedirs(outdir)
model_file = os.path.join(outdir, '{}.pkl'.format(label))
if os.path.exists(model_file) and not redo:
#logger.warn('Using previous fit found in %s' % model_file)
print model_file
mod_pick = open(model_file, 'r')
modelfit = cPickle.load( mod_pick )
mod_pick.close()
obsfits.append(modelfit)
continue
modelfit = {'label': obsid}
ui.clean()
data_id = 0
obsdir = "%s/obs%05d" % (DATADIR, obsid)
tf = open(os.path.join(obsdir,'tilt.pkl'), 'r')
tilt = cPickle.load(tf)
tf.close()
pf = open(os.path.join(obsdir, 'pos.pkl'), 'r')
pos = cPickle.load(pf)
pf.close()
pos_data = pos[ax]
point_error = 5
pos_data_mean = np.mean(pos_data)
ui.set_method('simplex')
# Fit a line to get more reasonable errors
init_staterror = np.zeros(len(pos_data))+point_error
ui.load_arrays(data_id,
pos['time']-pos['time'][0],
pos_data-np.mean(pos_data),
init_staterror)
ui.polynom1d.ypoly
ui.set_model(data_id, 'ypoly')
ui.thaw(ypoly.c0, ypoly.c1)
ui.fit(data_id)
fit = ui.get_fit_results()
calc_staterror = init_staterror * np.sqrt(fit.rstat)
ui.set_staterror(data_id, calc_staterror)
# Confirm those errors
ui.fit(data_id)
fit = ui.get_fit_results()
if ( abs(fit.rstat-1) > .2):
raise ValueError('Reduced statistic not close to 1 for error calc')
# Load up data to do the real model fit
fit_times = pos['time']
tm_func = tilt_model(tilt,
fit_times,
user_pars=user_pars)
ui.get_data(data_id).name = str(obsid)
ui.load_user_model(tm_func, 'tiltm%d' % data_id)
ui.add_user_pars('tiltm%d' % data_id, user_pars)
ui.set_method('simplex')
ui.set_model(data_id, 'tiltm%d' % (data_id))
ui.set_par('tiltm%d.diam' % data_id, 0)
if fixed_pars is not None and ax in fixed_pars:
for par in fixed_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), fixed_pars[ax][par])
ui.freeze('tiltm{}.{}'.format(0, par))
if guess_pars is not None and ax in guess_pars:
for par in guess_pars[ax]:
ui.set_par('tiltm{}.{}'.format(0, par), guess_pars[ax][par])
ui.show_all()
# Fit the tilt model
ui.fit(data_id)
fitres = ui.get_fit_results()
ui.confidence(data_id)
myconf = ui.get_confidence_results()
# save_fits(ax=ax, fit=fitres, conf=myconf, outdir=outdir)
# plot_fits(ids,outdir=os.path.join(outdir,'fit_plots'))
axmod = dict(fit=fitres, conf=myconf)
for idx, modpar in enumerate(myconf.parnames):
par = modpar.lstrip('tiltm0.')
axmod[par] = ui.get_par('tiltm0.%s' % par).val
axmod["{}_parmax".format(par)] = myconf.parmaxes[idx]
axmod["{}_parmin".format(par)] = myconf.parmins[idx]
modelfit[ax] = axmod
mod_pick = open(model_file, 'w')
cPickle.dump( modelfit, mod_pick)
mod_pick.close()
obsfits.append(modelfit)
plot_fits([dict(obsid=obsid, data_id=data_id, ax=ax)],
posdir=obsdir,
outdir=outdir)
return obsfits
data_id = fail_types[ftype]
ui.set_method('simplex')
ui.load_arrays(data_id,
rates['time'],
rates['rate'])
ui.set_staterror(data_id,
rates['err'])
ftype_poly = ui.polynom1d(ftype)
ui.set_model(data_id, ftype_poly)
ui.thaw(ftype_poly.c0)
ui.thaw(ftype_poly.c1)
ui.notice(DateTime(trend_date_start).frac_year)
ui.fit(data_id)
ui.notice()
myfit = ui.get_fit_results()
axplot = ui.get_model_plot(data_id)
if myfit.succeeded:
b = ftype_poly.c1.val * DateTime(trend_date_start).frac_year + ftype_poly.c0.val
m = ftype_poly.c1.val
rep_file = open('%s_fitfile.json' % ftype, 'w')
rep_file.write(json.dumps(dict(time0=DateTime(trend_date_start).frac_year,
datestart=trend_date_start,
datestop=data_stop,
bin=trend_type,
m=m,
b=b,
comment="mx+b with b at time0 and m = (delta rate)/year"),
sort_keys=True,
# coding: utf-8
import sherpa.ui as ui
ui.load_data("default_interp", "bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat")
ui.load_template_model('bb2', "bb_index.dat")
ui.set_model("default_interp", bb1+bb2)
ui.freeze("bb1.dummy")
ui.freeze("bb2.dummy")
ui.fit("default_interp")
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
#ui.set_stat('leastsq')
#ui.load_user_stat("chi2custom", my_chi2, my_err)
#ui.set_stat(chi2custom)
ui.load_user_model(scaled_warm_frac, 'model')
ui.add_user_pars('model', ['scale', 'offset'])
ui.set_model(data_id, 'model')
ui.load_arrays(data_id,
np.array(times),
np.array(bad_frac))
fmod = ui.get_model_component('model')
fmod.scale.min = 1e-9
fmod.offset.val = 0
ui.freeze(fmod.offset)
max_err = np.max([err_high, err_low], axis=0)
ui.set_staterror(data_id, max_err)
ui.fit(data_id)
f = ui.get_fit_results()
scale = f.rstat ** .5
ui.set_staterror(data_id, max_err * scale)
ui.fit()
f = ui.get_fit_results()
if f.rstat > 3:
raise ValueError
ui.confidence()
conf = ui.get_confidence_results()
fit_info[range_type][mag][ftype][limit] = dict(fit=str(f),
conf=str(conf),
fmod=fmod,
fit_orig=f,
conf_orig=conf,
mag_mean=np.mean(data[range_type][mag][ok]['mag_mean']))
# coding: utf-8
import sherpa.ui as ui
ui.load_data("default_interp", "load_template_with_interpolation-bb_data.dat")
ui.load_template_model('bb1', "bb_index.dat")
ui.set_model("default_interp", bb1)
ui.set_method('gridsearch')
ui.set_method_opt('sequence', ui.get_model_component('bb1').parvals)
ui.fit("default_interp")
# 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()