from sherpa.plot import DataPlot, ModelPlot
dplot = DataPlot()
dplot.prepare(f.data)
mplot = ModelPlot()
mplot.prepare(f.data, f.model)
dplot.plot()
mplot.overplot()
savefig("data_model_c0_c2.png")
dump("f.method.name")
original_method = f.method
from sherpa.optmethods import NelderMead
f.method = NelderMead()
resn = f.fit()
print("Change in statistic: {}".format(resn.dstatval))
fit2 = Fit(d, mdl, method=NelderMead())
fit2.fit()
mdl.c1.thaw()
f.method = original_method
res2 = f.fit()
report("res2.format()")
from sherpa.plot import DelchiPlot, FitPlot, SplitPlot
fplot = FitPlot()
rplot = DelchiPlot()
# Requires sherpa 4.9
import numpy as np
from astropy import units as u
from sherpa.models import ArithmeticModel, Parameter
from sherpa.data import DataND, BaseData, Data
from sherpa.stats import Likelihood
from sherpa.optmethods import LevMar, NelderMead, MonCar, GridSearch
__all__ = ['SherpaDataWrapper', 'SherpaModelWrapper', 'SherpaStatWrapper']
SHERPA_OPTMETHODS = {
'simplex': NelderMead(),
'moncar': MonCar(),
'gridsearch': GridSearch()
}
class SherpaDataWrapper(Data):
def __init__(self, gp_data, name='GPData'):
# sherpa does some magic here: it sets class attributes from constructor
# arguments so `gp-data` will be available later on the instance.
self._data_dummy = np.empty_like(gp_data.e_ref)
BaseData.__init__(self)
def to_fit(self, staterr):
return self._data_dummy, None, None
def eval_model_to_fit(self, model):
return self._data_dummy
def run(fit,
null_comp,
alt_comp,
conv_mdl=None,
stat=None,
method=None,
niter=500,
numcores=None):
if stat is None: stat = CStat()
if method is None: method = NelderMead()
if not isinstance(stat, (Cash, CStat)):
raise TypeError("Sherpa fit statistic must be Cash or CStat" +
" for likelihood ratio test")
niter = int(niter)
alt = alt_comp
null = null_comp
oldaltvals = numpy.array(alt.thawedpars)
oldnullvals = numpy.array(null.thawedpars)
data = fit.data
if conv_mdl is not None:
# Copy the PSF
null_conv_mdl = deepcopy(conv_mdl)
alt = conv_mdl(alt_comp)
if hasattr(conv_mdl, 'fold'):
conv_mdl.fold(data)
# Convolve the null model
null = null_conv_mdl(null_comp)
if hasattr(null_conv_mdl, 'fold'):
null_conv_mdl.fold(data)
nullfit = Fit(data, null, stat, method, Covariance())
# Fit with null model
nullfit_results = nullfit.fit()
debug(nullfit_results.format())
null_stat = nullfit_results.statval
null_vals = nullfit_results.parvals
# Calculate niter samples using null best-fit and covariance
sampler = NormalParameterSampleFromScaleMatrix()
samples = sampler.get_sample(nullfit, None, niter)
# Fit with alt model, null component starts at null's best fit params.
altfit = Fit(data, alt, stat, method, Covariance())
altfit_results = altfit.fit()
debug(altfit_results.format())
alt_stat = altfit_results.statval
alt_vals = altfit_results.parvals
LR = -(alt_stat - null_stat)
olddep = data.get_dep(filter=False)
try:
statistics = parallel_map(
LikelihoodRatioTestWorker(nullfit, altfit, null_vals,
alt_vals), samples, numcores)
finally:
data.set_dep(olddep)
alt.thawedpars = list(oldaltvals)
null.thawedpars = list(oldnullvals)
debug("statistic null = " + repr(null_stat))
debug("statistic alt = " + repr(alt_stat))
debug("LR = " + repr(LR))
statistics = numpy.asarray(statistics)
pppvalue = numpy.sum(statistics[:, 2] > LR) / (1.0 * niter)
debug('ppp value = ' + str(pppvalue))
return LikelihoodRatioResults(statistics[:, 2], statistics[:, 0:2],
samples, LR, pppvalue, null_stat,
alt_stat)
def test_wstat_error(self):
data = self.bkg
data.notice(0.5, 7.0)
fit = Fit(data, self.model, WStat(), NelderMead())
self.assertRaises(StatErr, fit.fit)
report("f")
print("Starting statistic: {}".format(f.calc_stat()))
fitres = f.fit()
report("fitres.format()")
print("Reduced chi square = {:.2f}".format(fitres.rstat))
mplot.prepare(d, mdl)
dplot.plot()
mplot.overplot()
savefig("model_data_fit1.png")
from sherpa.optmethods import NelderMead
f.method = NelderMead()
fitres2 = f.fit()
report("mdl")
dump("fitres2.dstatval")
mdl.reset()
report("mdl")
plateau.c0 = np.max(d.y)
mplot.prepare(d, mdl)
dplot.plot()
mplot.overplot()
savefig("model_data_reset.png")
fitres3 = f.fit()
def test_mycash_datahasbkg_modelhasnobkg(self):
fit = Fit(self.data, self.model, MyCashNoBkg(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycash_results_bench, results)
def test_mycash_nobkgdata_modelhasbkg(self):
data = self.bkg
fit = Fit(data, self.model, MyCashWithBkg(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycashnobkg_results_bench, results,
tol=1.0e-3)
def test_mycash_data_and_model_donothave_bkg(self):
data = self.bkg
fit = Fit(data, self.model, MyCashNoBkg(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycashnobkg_results_bench, results,
tol=1.0e-3)
def test_mycash_data_and_model_have_bkg(self):
fit = Fit(self.data, self.model, MyCashWithBkg(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycash_results_bench, results)
def test_cash_stat(self):
fit = Fit(self.data, self.model, Cash(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_mycash_results_bench, results)
def test_chi2gehrels_stat(self):
fit = Fit(self.data, self.model, Chi2Gehrels(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_chi2gehrels_results_bench, results)
def test_chi2modvar_stat(self):
fit = Fit(self.data, self.model, Chi2ModVar(), NelderMead())
results = fit.fit()
self.compare_results(self._fit_chi2modvar_results_bench, results)