def test_fit_profile_cov_mat_correlated(self):
_fit_with_constraint = IndexedFit(self._data_container,
model_function=self._model)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'],
self._means,
self._cov_mat_cor)
self._test_consistency(_fit_with_constraint, self._cov_mat_cor_inv)
def setUp(self):
_data = [-2.1, 0.2, 1.9, 3.8, 6.1]
self._means = np.array([3.654, 7.789])
self._vars = np.array([2.467, 1.543])
self._cov_mat_uncor = np.array([[self._vars[0], 0.0], [0.0, self._vars[1]]])
self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor)
self._cov_mat_cor = np.array([[self._vars[0], 0.1], [0.1, self._vars[1]]])
self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor)
self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0], [0.0, 0.0]])
self._cov_mat_simple_b_inv = np.array([[0.0, 0.0], [0.0, 1.0 / self._vars[1]]])
self._data_container = IndexedContainer(data=_data)
self._data_container.add_error(err_val=1.0)
_a_test = np.linspace(start=0, stop=4, num=9, endpoint=True)
_b_test = np.linspace(start=-4, stop=0, num=9, endpoint=True)
self._test_par_values = np.zeros((4, 2, 9))
self._test_par_values[0, 0] = _a_test
self._test_par_values[1, 1] = _b_test
self._test_par_values[2, 0] = _a_test
self._test_par_values[2, 1] = _b_test
self._test_par_values[3, 0] = _a_test
self._test_par_values[3, 1] = -_b_test
self._test_par_res = self._test_par_values - self._means.reshape((1, 2, 1))
self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1))
self._fit_no_constraints = IndexedFit(self._data_container, model_function=self._model)
self._fit_no_constraints.do_fit()
_cost_function = self._fit_no_constraints._fitter._fcn_wrapper
self._profile_no_constraints = np.zeros((4, 9))
for _i in range(4):
for _j in range(9):
self._profile_no_constraints[_i, _j] = _cost_function(
self._test_par_values[_i, 0, _j],
self._test_par_values[_i, 1, _j])
def test_fit_profile_simple_b(self):
_fit_with_constraint = IndexedFit(self._data_container,
model_function=self._model)
_fit_with_constraint.add_parameter_constraint('b', self._means[1],
np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint,
self._cov_mat_simple_b_inv)
def test_bad_input_exception(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_parameter_constraint('c', 1.0, 1.0)
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'c'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
def _get_fit(self, errors=None):
'''convenience'''
errors = errors or [dict(err_val=1.0)]
_fit = IndexedFit(
data=self._ref_data,
model_function=simple_indexed_model,
cost_function=IndexedCostFunction_Chi2(errors_to_use='covariance'),
minimizer=self.MINIMIZER)
for _err in errors:
_fit.add_error(**_err)
return _fit
def _get_fit(self, model_function=None, cost_function=None, error=None):
'''convenience'''
model_function = model_function or simple_indexed_model
# TODO: fix default
cost_function = cost_function or IndexedCostFunction_Chi2(
errors_to_use='covariance')
error = error or 1.0
_fit = IndexedFit(data=self._ref_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER)
_fit.add_error(err_val=error)
return _fit
def dummy_model_no_pars_raise(self):
def dummy_model():
pass
with self.assertRaises(IndexedModelFunctionException) as _exc:
IndexedFit(data=self._ref_data,
model_function=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn('needs at least one parameter', _exc.exception.args[0])
def test_reserved_parameter_names_raise(self):
def dummy_model(data):
pass
with self.assertRaises(IndexedFitException) as _exc:
IndexedFit(data=self._ref_data,
model_function=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn('reserved', _exc.exception.args[0])
self.assertIn('data', _exc.exception.args[0])
def dummy_model_varargs_varkwargs_raise(self):
# TODO: raise even without 'par'
def dummy_model(x, par, *varargs, **varkwargs):
pass
with self.assertRaises(IndexedModelFunctionException) as _exc:
IndexedFit(data=self._ref_data,
model_function=dummy_model,
minimizer=self.MINIMIZER)
self.assertIn('variable', _exc.exception.args[0])
self.assertIn('varargs', _exc.exception.args[0])
def _get_fit(self,
model_function=None,
cost_function=None,
errors=None,
dynamic_error_algorithm=None):
'''convenience'''
model_function = model_function or simple_indexed_model
# TODO: fix default
cost_function = cost_function or IndexedCostFunction_Chi2(
errors_to_use='covariance')
errors = errors or [dict(err_val=1.0)]
dynamic_error_algorithm = dynamic_error_algorithm or "nonlinear"
_fit = IndexedFit(data=self._ref_data,
model_function=model_function,
cost_function=cost_function,
minimizer=self.MINIMIZER,
dynamic_error_algorithm=dynamic_error_algorithm)
for _err in errors:
_fit.add_error(**_err)
return _fit
def _get_fit(self, errors=None):
'''convenience'''
_fit = IndexedFit(
data=self._ref_data,
model_function=simple_indexed_model,
cost_function=IndexedCostFunction_Chi2(errors_to_use='covariance'),
minimizer=self.MINIMIZER)
if errors is None:
_fit.add_matrix_error(err_matrix=np.eye(self._n_points),
matrix_type='cov')
else:
for _err in errors:
if 'err_matrix' in _err:
_fit.add_matrix_error(**_err)
else:
_fit.add_error(**_err)
return _fit
class TestParameterConstraintInIndexedFit(unittest.TestCase):
def _expected_profile_diff(self, res, cov_mat_inv):
return res.dot(cov_mat_inv).dot(res)
def _test_consistency(self, constrained_fit, par_cov_mat_inv):
constrained_fit.do_fit()
_cost_function = constrained_fit._fitter._fcn_wrapper
for _i in range(4):
for _j in range(9):
_profile_constrained = _cost_function(self._test_par_values[_i, 0, _j], self._test_par_values[_i, 1, _j])
_diff = _profile_constrained - self._profile_no_constraints[_i, _j]
_expected_profile_diff = self._expected_profile_diff(self._test_par_res[_i, _j], par_cov_mat_inv)
self.assertTrue(np.abs(_diff - _expected_profile_diff) < 1e-12)
@staticmethod
def _model(a, b):
return a * np.arange(5) + b
def setUp(self):
_data = [-2.1, 0.2, 1.9, 3.8, 6.1]
self._means = np.array([3.654, 7.789])
self._vars = np.array([2.467, 1.543])
self._cov_mat_uncor = np.array([[self._vars[0], 0.0], [0.0, self._vars[1]]])
self._cov_mat_uncor_inv = np.linalg.inv(self._cov_mat_uncor)
self._cov_mat_cor = np.array([[self._vars[0], 0.1], [0.1, self._vars[1]]])
self._cov_mat_cor_inv = np.linalg.inv(self._cov_mat_cor)
self._cov_mat_simple_a_inv = np.array([[1.0 / self._vars[0], 0.0], [0.0, 0.0]])
self._cov_mat_simple_b_inv = np.array([[0.0, 0.0], [0.0, 1.0 / self._vars[1]]])
self._data_container = IndexedContainer(data=_data)
self._data_container.add_error(err_val=1.0)
_a_test = np.linspace(start=0, stop=4, num=9, endpoint=True)
_b_test = np.linspace(start=-4, stop=0, num=9, endpoint=True)
self._test_par_values = np.zeros((4, 2, 9))
self._test_par_values[0, 0] = _a_test
self._test_par_values[1, 1] = _b_test
self._test_par_values[2, 0] = _a_test
self._test_par_values[2, 1] = _b_test
self._test_par_values[3, 0] = _a_test
self._test_par_values[3, 1] = -_b_test
self._test_par_res = self._test_par_values - self._means.reshape((1, 2, 1))
self._test_par_res = np.transpose(self._test_par_res, axes=(0, 2, 1))
self._fit_no_constraints = IndexedFit(self._data_container, model_function=self._model)
self._fit_no_constraints.do_fit()
_cost_function = self._fit_no_constraints._fitter._fcn_wrapper
self._profile_no_constraints = np.zeros((4, 9))
for _i in range(4):
for _j in range(9):
self._profile_no_constraints[_i, _j] = _cost_function(
self._test_par_values[_i, 0, _j],
self._test_par_values[_i, 1, _j])
def test_bad_input_exception(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_parameter_constraint('c', 1.0, 1.0)
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'c'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
with self.assertRaises(IndexedFitException):
_fit_with_constraint.add_matrix_parameter_constraint(['a'], [1.0, 2.0], [[0.2, 0.0], [0.0, 0.1]])
def test_fit_profile_cov_mat_uncorrelated(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_uncor)
self._test_consistency(_fit_with_constraint, self._cov_mat_uncor_inv)
_fit_with_constraint_alt = IndexedFit(self._data_container, model_function=self._model)
_fit_with_constraint_alt.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0]))
_fit_with_constraint_alt.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint_alt, self._cov_mat_uncor_inv)
def test_fit_profile_cov_mat_correlated(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
_fit_with_constraint.add_matrix_parameter_constraint(['a', 'b'], self._means, self._cov_mat_cor)
self._test_consistency(_fit_with_constraint, self._cov_mat_cor_inv)
def test_fit_profile_simple_a(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
_fit_with_constraint.add_parameter_constraint('a', self._means[0], np.sqrt(self._vars[0]))
self._test_consistency(_fit_with_constraint, self._cov_mat_simple_a_inv)
def test_fit_profile_simple_b(self):
_fit_with_constraint = IndexedFit(self._data_container, model_function=self._model)
_fit_with_constraint.add_parameter_constraint('b', self._means[1], np.sqrt(self._vars[1]))
self._test_consistency(_fit_with_constraint, self._cov_mat_simple_b_inv)