def test_error_model_target(large_reflection_table, test_sg):
"""Test the error model target."""
Ih_table = IhTable([large_reflection_table], test_sg, nblocks=1)
block = Ih_table.blocked_data_list[0]
em = BasicErrorModel
em.min_reflections_required = 1
params = generated_param()
params.weighting.error_model.basic.n_bins = 2
params.weighting.error_model.basic.min_Ih = 1.0
error_model = em(basic_params=params.weighting.error_model.basic)
error_model.configure_for_refinement(block)
error_model.parameters = [1.0, 0.05]
parameterisation = ErrorModelB_APM(error_model)
target = ErrorModelTargetB(error_model)
target.predict(parameterisation)
# Test residual calculation
residuals = target.calculate_residuals(parameterisation)
assert residuals == (
flex.double(2, 1.0) -
flex.pow2(error_model.binner.binning_info["bin_variances"]))
# Test gradient calculation against finite differences.
gradients = target.calculate_gradients(parameterisation)
gradient_fd = calculate_gradient_fd(target, parameterisation)
assert list(gradients) == pytest.approx(list(gradient_fd))
# Test the method calls
r, g = target.compute_functional_gradients(parameterisation)
assert r == residuals
assert list(gradients) == pytest.approx(list(g))
r, g = target.compute_functional_gradients(parameterisation)
assert r == residuals
assert list(gradients) == pytest.approx(list(g))
def _refine_b(self):
parameterisation = ErrorModelB_APM(self.model)
self._refine_component(self.model, ErrorModelTargetB(self.model),
parameterisation)