def setUp(self):
xlo, xhi, y = sherpa.utils.dataspace1d(0.1, 10.1, 0.1)
data = sherpa.data.Data1D('dataspace1d', xlo, y)
g1 = sherpa.models.Gauss1D('g1')
g1.thawedpars = self._initial
g1.thawedparmins = self._min
g1.thawedparmaxes = self._max
fit = sherpa.fit.Fit(data, g1, stat=sherpa.stats.Cash())
thawedparmins = numpy.array(fit.model.thawedparhardmins)
thawedparmaxes = numpy.array(fit.model.thawedparhardmaxes)
def calc_stat(proposal):
prop = numpy.atleast_1d(proposal)
mins = sherpa.utils.sao_fcmp(prop, thawedparmins, _tol)
maxes = sherpa.utils.sao_fcmp(thawedparmaxes, prop, _tol)
if -1 in mins or -1 in maxes:
raise LimitError('Sherpa parameter hard limit exception')
fit.model.thawedpars = proposal
return -0.5 * fit.calc_stat()
sigma = g1.thawedpars
dof = len(xlo) - len(sigma)
mu = numpy.random.rand(3)
self.mh = MH(calc_stat, sigma, mu, dof)
current, stat = self.mh.init()
class TestParameterProposal(unittest.TestCase):
_min = [0.001, 0.1, 0.0]
_max = [10., 10., 10.]
_initial = [1., 1., 1.]
_bad = [1.e+40, 1.e+40, 1.e+40]
_good = [5., 5., 5.]
_above = [20., 20., 20.]
_below = [0.0001, 0.01, -1.0 ]
def setUp(self):
xlo, xhi, y = sherpa.utils.dataspace1d(0.1, 10.1, 0.1)
data = sherpa.data.Data1D('dataspace1d', xlo, y)
g1 = sherpa.models.Gauss1D('g1')
g1.thawedpars = self._initial
g1.thawedparmins = self._min
g1.thawedparmaxes = self._max
fit = sherpa.fit.Fit(data, g1, stat=sherpa.stats.Cash())
thawedparmins = numpy.array(fit.model.thawedparhardmins)
thawedparmaxes = numpy.array(fit.model.thawedparhardmaxes)
def calc_stat(proposal):
prop = numpy.atleast_1d(proposal)
mins = sherpa.utils.sao_fcmp(prop, thawedparmins, _tol)
maxes = sherpa.utils.sao_fcmp(thawedparmaxes, prop, _tol)
if -1 in mins or -1 in maxes:
raise LimitError('Sherpa parameter hard limit exception')
fit.model.thawedpars = proposal
return -0.5*fit.calc_stat()
sigma = g1.thawedpars
dof = len(xlo) - len(sigma)
mu = numpy.random.rand(3)
self.mh = MH(calc_stat, sigma, mu, dof)
current, stat = self.mh.init()
def test_bad_proposal(self):
self.assertRaises(LimitError, self.mh.calc_stat, self._bad)
def test_good_proposal(self):
stat = self.mh.calc_stat(self._good)
def test_good_proposal_above_softlimits(self):
stat = self.mh.calc_stat(self._above)
def test_good_proposal_below_softlimits(self):
stat = self.mh.calc_stat(self._below)
class TestParameterProposal(unittest.TestCase):
_min = [0.001, 0.1, 0.0]
_max = [10., 10., 10.]
_initial = [1., 1., 1.]
_bad = [1.e+40, 1.e+40, 1.e+40]
_good = [5., 5., 5.]
_above = [20., 20., 20.]
_below = [0.0001, 0.01, -1.0]
def setUp(self):
xlo, xhi, y = sherpa.utils.dataspace1d(0.1, 10.1, 0.1)
data = sherpa.data.Data1D('dataspace1d', xlo, y)
g1 = sherpa.models.Gauss1D('g1')
g1.thawedpars = self._initial
g1.thawedparmins = self._min
g1.thawedparmaxes = self._max
fit = sherpa.fit.Fit(data, g1, stat=sherpa.stats.Cash())
thawedparmins = numpy.array(fit.model.thawedparhardmins)
thawedparmaxes = numpy.array(fit.model.thawedparhardmaxes)
def calc_stat(proposal):
prop = numpy.atleast_1d(proposal)
mins = sherpa.utils.sao_fcmp(prop, thawedparmins, _tol)
maxes = sherpa.utils.sao_fcmp(thawedparmaxes, prop, _tol)
if -1 in mins or -1 in maxes:
raise LimitError('Sherpa parameter hard limit exception')
fit.model.thawedpars = proposal
return -0.5 * fit.calc_stat()
sigma = g1.thawedpars
dof = len(xlo) - len(sigma)
mu = numpy.random.rand(3)
self.mh = MH(calc_stat, sigma, mu, dof)
current, stat = self.mh.init()
def test_bad_proposal(self):
self.assertRaises(LimitError, self.mh.calc_stat, self._bad)
def test_good_proposal(self):
stat = self.mh.calc_stat(self._good)
def test_good_proposal_above_softlimits(self):
stat = self.mh.calc_stat(self._above)
def test_good_proposal_below_softlimits(self):
stat = self.mh.calc_stat(self._below)
def setUp(self):
xlo, xhi, y = sherpa.utils.dataspace1d(0.1, 10.1, 0.1)
data = sherpa.data.Data1D('dataspace1d', xlo, y)
g1 = sherpa.models.Gauss1D('g1')
g1.thawedpars = self._initial
g1.thawedparmins = self._min
g1.thawedparmaxes = self._max
fit = sherpa.fit.Fit(data, g1, stat=sherpa.stats.Cash())
thawedparmins = numpy.array(fit.model.thawedparhardmins)
thawedparmaxes = numpy.array(fit.model.thawedparhardmaxes)
def calc_stat(proposal):
prop = numpy.atleast_1d(proposal)
mins = sherpa.utils.sao_fcmp(prop, thawedparmins, _tol)
maxes = sherpa.utils.sao_fcmp(thawedparmaxes, prop, _tol)
if -1 in mins or -1 in maxes:
raise LimitError('Sherpa parameter hard limit exception')
fit.model.thawedpars = proposal
return -0.5*fit.calc_stat()
sigma = g1.thawedpars
dof = len(xlo) - len(sigma)
mu = numpy.random.rand(3)
self.mh = MH(calc_stat, sigma, mu, dof)
current, stat = self.mh.init()
