def setup_method(self, tmpdir):
self.path = os.path.dirname(os.path.abspath(__file__))
self.tmpdir = tmpdir.strpath
theoretical = np.loadtxt(os.path.join(self.path, "gauss_data.txt"))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
# these best weighted values and uncertainties obtained with Igor
self.best_weighted = [-0.00246095, 19.5299, -8.28446e-2, 1.24692]
self.best_weighted_errors = [
0.0220313708486,
1.12879436221,
0.0447659158681,
0.0412022938883,
]
self.best_weighted_chisqr = 77.6040960351
self.best_unweighted = [
-0.10584111872702096,
19.240347049328989,
0.0092623066070940396,
1.501362314145845,
]
self.best_unweighted_errors = [
0.34246565477,
0.689820935208,
0.0411243173041,
0.0693429375282,
]
self.best_unweighted_chisqr = 497.102084956
self.p0 = np.array([0.1, 20.0, 0.1, 0.1])
self.names = ["bkg", "A", "x0", "width"]
self.bounds = [(-1, 1), (0, 30), (-5.0, 5.0), (0.001, 2)]
self.params = Parameters(name="gauss_params")
for p, name, bound in zip(self.p0, self.names, self.bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
self.params.append(param)
self.model = Model(self.params, fitfunc=gauss)
self.data = Data1D((xvals, yvals, evals))
self.objective = Objective(self.model, self.data)
return 0
def test_logp(self):
self.p[0].range(0, 10)
assert_almost_equal(self.objective.logp(), np.log(0.1))
# logp should set parameters
self.objective.logp([8, 2])
assert_equal(np.array(self.objective.parameters), [8, 2])
# if we supply a value outside the range it should return -inf
assert_equal(self.objective.logp([-1, 2]), -np.inf)
# are auxiliary parameters included in log?
assert_almost_equal(self.objective.logp([8, 2]), np.log(0.1))
p = Parameter(2.0, bounds=(1.0, 3.0))
self.objective.auxiliary_params = Parameters([p])
assert len(self.objective.varying_parameters()) == 2
assert_equal(self.objective.logp(), np.log(0.1))
assert p in self.objective.parameters.flattened()
p.vary = True
assert len(self.objective.varying_parameters()) == 3
assert_equal(self.objective.logp(), np.log(0.1) + np.log(0.5))
assert p in self.objective.varying_parameters().flattened()
def test_covar(self):
# checks objective.covar against optimize.least_squares covariance.
path = os.path.dirname(os.path.abspath(__file__))
theoretical = np.loadtxt(os.path.join(path, 'gauss_data.txt'))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
p0 = np.array([0.1, 20., 0.1, 0.1])
names = ['bkg', 'A', 'x0', 'width']
bounds = [(-1, 1), (0, 30), (-5., 5.), (0.001, 2)]
params = Parameters(name="gauss_params")
for p, name, bound in zip(p0, names, bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
params.append(param)
model = Model(params, fitfunc=gauss)
data = Data1D((xvals, yvals, evals))
objective = Objective(model, data)
# first calculate least_squares jac/hess/covariance matrices
res = least_squares(objective.residuals,
np.array(params),
jac='3-point')
hess_least_squares = np.matmul(res.jac.T, res.jac)
covar_least_squares = np.linalg.inv(hess_least_squares)
# now calculate corresponding matrices by hand, to see if the approach
# concurs with least_squares
objective.setp(res.x)
_pvals = np.array(res.x)
def residuals_scaler(vals):
return np.squeeze(objective.residuals(_pvals * vals))
jac = approx_derivative(residuals_scaler, np.ones_like(_pvals))
hess = np.matmul(jac.T, jac)
covar = np.linalg.inv(hess)
covar = covar * np.atleast_2d(_pvals) * np.atleast_2d(_pvals).T
assert_allclose(covar, covar_least_squares)
# check that objective.covar corresponds to the least_squares
# covariance matrix
objective.setp(res.x)
_pvals = np.array(res.x)
covar_objective = objective.covar()
assert_allclose(covar_objective, covar_least_squares)
# now see what happens with a parameter that has no effect on residuals
param = Parameter(1.234, name='dummy')
param.vary = True
params.append(param)
from pytest import raises
with raises(LinAlgError):
objective.covar()
