def test_physical_model_from_data(mock_physical_params, mock_exp, test_reflections):
"""Test that it passes the correct dict to physical model."""
physicalmodel = PhysicalScalingModel.from_data(
mock_physical_params, mock_exp, test_reflections
)
assert physicalmodel.configdict["lmax"] == (mock_physical_params.physical.lmax)
assert physicalmodel.components["absorption"].n_params == 24
assert list(physicalmodel.components["absorption"].parameters) == [0.0] * 24
def test_model_creation_from_data(default_params, mock_exp, test_reflections):
"""Test the factory creation of the three standard scaling models with the
default params."""
_ = KBScalingModel.from_data(default_params, [], [])
_ = PhysicalScalingModel.from_data(default_params, mock_exp, test_reflections)
_ = ArrayScalingModel.from_data(default_params, mock_exp, test_reflections)
def test_physical_model_from_data(mock_physical_params, mock_exp,
test_reflections):
"""Test that it passes the correct dict to physical model."""
physicalmodel = PhysicalScalingModel.from_data(mock_physical_params,
mock_exp, test_reflections)
assert physicalmodel.configdict["lmax"] == (
mock_physical_params.physical.lmax)
assert physicalmodel.components["absorption"].n_params == 24
assert list(
physicalmodel.components["absorption"].parameters) == [0.0] * 24
# test updating the absorption parameters
mock_physical_params.physical.absorption_level = "high"
physicalmodel.update(mock_physical_params)
assert len(physicalmodel.components["absorption"].parameters) == 48
assert physicalmodel.configdict["abs_surface_weight"] == 5e3
mock_physical_params.physical.absorption_level = "medium"
physicalmodel.update(mock_physical_params)
assert len(physicalmodel.components["absorption"].parameters) == 48
assert physicalmodel.configdict["abs_surface_weight"] == 5e4
mock_physical_params.physical.absorption_level = None
mock_physical_params.physical.lmax = 4
physicalmodel.update(mock_physical_params)
assert len(physicalmodel.components["absorption"].parameters) == 24
assert physicalmodel.configdict["abs_surface_weight"] == 5e4
mock_physical_params.physical.surface_weight = 1e5
physicalmodel.update(mock_physical_params)
assert len(physicalmodel.components["absorption"].parameters) == 24
assert physicalmodel.configdict["abs_surface_weight"] == 1e5
# try fixing a parameter
mock_physical_params.physical.correction.fix = ["decay"]
physicalmodel = PhysicalScalingModel.from_data(mock_physical_params,
mock_exp, test_reflections)
assert physicalmodel.configdict["lmax"] == (
mock_physical_params.physical.lmax)
assert physicalmodel.components["absorption"].n_params == 24
assert list(
physicalmodel.components["absorption"].parameters) == [0.0] * 24
assert physicalmodel.fixed_components == ["decay"]
def test_create_scaling_model():
"""Test the create scaling model function."""
# Test that one can create the correct scaling model with the phil param.
for m in ["physical", "array", "KB"]:
params = generated_param()
exp = generated_exp()
rt = generated_refl()
params.model = m
new_exp = create_scaling_model(params, exp, [rt])
assert new_exp[0].scaling_model.id_ == m
# If a scaling model already exists, then nothing else should happen.
params = generated_param()
exp = generated_exp()
rt = generated_refl()
exp[0].scaling_model = PhysicalScalingModel.from_data(params, exp[0], rt)
old_scaling_model = exp[0].scaling_model
params.model = "KB"
new_exp = create_scaling_model(params, exp, [rt])
new_scaling_model = new_exp[0].scaling_model
assert new_scaling_model is old_scaling_model # Should not modify original.
# Test multiple datasets, where one already has a scaling model.
exp = generated_exp(3)
params = generated_param()
rt = generated_refl()
rt_2 = generated_refl()
rt_3 = generated_refl()
exp[0].scaling_model = PhysicalScalingModel.from_data(params, exp[0], rt)
params.model = "KB"
new_exp = create_scaling_model(params, exp, [rt, rt_2, rt_3])
assert new_exp[0].scaling_model is exp[0].scaling_model
assert isinstance(new_exp[1].scaling_model, KBScalingModel)
assert isinstance(new_exp[2].scaling_model, KBScalingModel)
# Now test overwrite_existing_models option
params.overwrite_existing_models = True
params.model = "physical"
newer_exp = create_scaling_model(params, new_exp, [rt, rt_2, rt_3])
for exp in newer_exp:
assert isinstance(exp.scaling_model, PhysicalScalingModel)
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)
