def test_target_jacobian_calculation_finite_difference(physical_param,
single_exp,
large_reflection_table):
"""Test the calculated jacobian against a finite difference calculation."""
test_params, exp, test_refl = physical_param, single_exp, large_reflection_table
test_params.parameterisation.decay_term = False
test_params.model = "physical"
experiments = create_scaling_model(test_params, exp, test_refl)
assert experiments[0].scaling_model.id_ == "physical"
scaler = create_scaler(test_params, experiments, test_refl)
apm = multi_active_parameter_manager([scaler.components], [["scale"]],
scaling_active_parameter_manager)
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
fd_jacobian = calculate_jacobian_fd(target, scaler, apm)
_, jacobian, _ = target.compute_residuals_and_gradients(
scaler.Ih_table.blocked_data_list[0])
n_rows = jacobian.n_rows
n_cols = jacobian.n_cols
print(jacobian)
print(fd_jacobian)
for i in range(0, n_rows):
for j in range(0, n_cols):
assert jacobian[i, j] == pytest.approx(fd_jacobian[i, j], abs=1e-4)
def test_target_gradient_calculation_finite_difference(small_reflection_table,
single_exp,
physical_param):
"""Test the calculated gradients against a finite difference calculation."""
model = PhysicalScalingModel.from_data(physical_param, single_exp,
small_reflection_table)
# need to 'add_data'
model.configure_components(small_reflection_table, single_exp,
physical_param)
model.components["scale"].update_reflection_data()
model.components["decay"].update_reflection_data()
apm = multi_active_parameter_manager(
ScalingTarget(),
[model.components],
[["scale", "decay"]],
scaling_active_parameter_manager,
)
model.components["scale"].inverse_scales = flex.double([2.0, 1.0, 2.0])
model.components["decay"].inverse_scales = flex.double([1.0, 1.0, 0.4])
Ih_table = IhTable([small_reflection_table],
single_exp.crystal.get_space_group())
with patch.object(SingleScaler, "__init__", lambda x, y, z, k: None):
scaler = SingleScaler(None, None, None)
scaler._Ih_table = Ih_table
# Now do finite difference check.
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
grad = target.calculate_gradients(scaler.Ih_table.blocked_data_list[0])
res = target.calculate_residuals(scaler.Ih_table.blocked_data_list[0])
assert (res >
1e-8), """residual should not be zero, or the gradient test
below will not really be working!"""
# Now compare to finite difference
f_d_grad = calculate_gradient_fd(target, scaler, apm)
print(list(f_d_grad))
print(list(grad))
assert list(grad) == pytest.approx(list(f_d_grad))
sel = f_d_grad > 1e-8
assert sel, """assert sel has some elements, as finite difference grad should
def test_target_jacobian_calculation_finite_difference(physical_param,
single_exp,
large_reflection_table):
"""Test the calculated jacobian against a finite difference calculation."""
physical_param.physical.decay_correction = False
model = PhysicalScalingModel.from_data(physical_param, single_exp,
large_reflection_table)
# need to 'add_data'
model.configure_components(large_reflection_table, single_exp,
physical_param)
model.components["scale"].update_reflection_data()
apm = multi_active_parameter_manager(
ScalingTarget(),
[model.components],
[["scale"]],
scaling_active_parameter_manager,
)
Ih_table = IhTable([large_reflection_table],
single_exp.crystal.get_space_group())
with patch.object(SingleScaler, "__init__", lambda x, y, z, k: None):
scaler = SingleScaler(None, None, None)
scaler._Ih_table = Ih_table
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
fd_jacobian = calculate_jacobian_fd(target, scaler, apm)
r, jacobian, w = target.compute_residuals_and_gradients(
scaler.Ih_table.blocked_data_list[0])
assert r == pytest.approx(
[-50.0 / 3.0, 70.0 / 3.0, -20.0 / 3.0, 12.5, -2.5] +
[-25.0, 0.0, -75.0, 0.0, 200.0])
assert w == pytest.approx(
[0.1, 0.1, 0.1, 0.02, 0.1, 0.02, 0.01, 0.02, 0.01, 0.01])
n_rows = jacobian.n_rows
n_cols = jacobian.n_cols
print(jacobian)
print(fd_jacobian)
for i in range(0, n_rows):
for j in range(0, n_cols):
assert jacobian[i, j] == pytest.approx(fd_jacobian[i, j],
abs=1e-4)
def test_multi_apm():
"""Test for the general multi_active_parameter_manage class."""
components_1 = {
"scale": mock_component(),
"decay": mock_component(),
"absorption": mock_component(),
}
components_2 = {"scale": mock_component(), "decay": mock_component()}
multi_apm = multi_active_parameter_manager(
ScalingTarget(),
[components_1, components_2],
[["scale", "decay"], ["scale"]],
active_parameter_manager,
)
# Test correct setup of apm_list attribute.
for apm in multi_apm.apm_list:
assert isinstance(apm, active_parameter_manager)
assert len(multi_apm.apm_list) == 2
assert multi_apm.components_list == ["scale", "decay", "scale"]
assert multi_apm.n_active_params == 3
assert multi_apm.apm_data[0] == {"start_idx": 0, "end_idx": 2}
assert multi_apm.apm_data[1] == {"start_idx": 2, "end_idx": 3}
# Test parameter selection.
multi_apm.set_param_vals(flex.double([3.0, 2.5, 2.0]))
assert multi_apm.get_param_vals() == flex.double([3.0, 2.5, 2.0])
assert multi_apm.select_parameters(0) == flex.double([3.0, 2.5])
assert multi_apm.select_parameters(1) == flex.double([2.0])
# Test setting parameter esds.
multi_apm.set_param_esds(flex.double([0.1, 0.2, 0.3]))
assert components_1["scale"].free_parameter_esds == flex.double([0.1])
assert components_1["decay"].free_parameter_esds == flex.double([0.2])
assert components_2["scale"].free_parameter_esds == flex.double([0.3])
# Test setting var_cov matrices for each component.
var_cov = flex.double([1.0, 0.5, 0.5, 0.5, 2.0, 0.5, 0.5, 0.5, 3.0])
var_cov.reshape(flex.grid(3, 3))
multi_apm.calculate_model_state_uncertainties(var_cov)
assert components_1["scale"].var_cov_matrix[0, 0] == 1.0
assert components_1["decay"].var_cov_matrix[0, 0] == 2.0
assert components_2["scale"].var_cov_matrix[0, 0] == 3.0
def test_target_gradient_calculation_finite_difference(small_reflection_table,
single_exp,
physical_param):
"""Test the calculated gradients against a finite difference calculation."""
(test_reflections, test_experiments, params) = (
small_reflection_table,
single_exp,
physical_param,
)
assert len(test_experiments) == 1
assert len(test_reflections) == 1
experiments = create_scaling_model(params, test_experiments,
test_reflections)
scaler = create_scaler(params, experiments, test_reflections)
assert scaler.experiment.scaling_model.id_ == "physical"
# Initialise the parameters and create an apm
scaler.components["scale"].inverse_scales = flex.double([2.0, 1.0, 2.0])
scaler.components["decay"].inverse_scales = flex.double([1.0, 1.0, 0.4])
apm = multi_active_parameter_manager([scaler.components],
[["scale", "decay"]],
scaling_active_parameter_manager)
# Now do finite difference check.
target = ScalingTarget()
scaler.update_for_minimisation(apm, 0)
grad = target.calculate_gradients(scaler.Ih_table.blocked_data_list[0])
res = target.calculate_residuals(scaler.Ih_table.blocked_data_list[0])
assert (res > 1e-8), """residual should not be zero, or the gradient test
below will not really be working!"""
# Now compare to finite difference
f_d_grad = calculate_gradient_fd(target, scaler, apm)
print(list(f_d_grad))
print(list(grad))
assert approx_equal(list(grad), list(f_d_grad))
sel = f_d_grad > 1e-8
assert sel, """assert sel has some elements, as finite difference grad should
