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 create_datastructures_for_structural_model(reflections, experiments,
cif_file):
"""Read a cif file, calculate intensities and scale them to the average
intensity of the reflections. Return an experiment and reflection table to
be used for the structural model in scaling."""
# read model, compute Fc, square to F^2
ic = intensity_array_from_cif_file(cif_file)
exp = deepcopy(experiments[0])
params = Mock()
params.decay_correction.return_value = False
exp.scaling_model = KBScalingModel.from_data(params, [], [])
exp.scaling_model.set_scaling_model_as_scaled(
) # Set as scaled to fix scale.
# Now put the calculated I's on roughly a common scale with the data.
miller_indices = flex.miller_index([])
intensities = flex.double([])
for refl in reflections:
miller_indices.extend(refl["miller_index"])
intensities.extend(refl["intensity.prf.value"])
miller_set = miller.set(
crystal_symmetry=crystal.symmetry(
space_group=experiments[0].crystal.get_space_group()),
indices=miller_indices,
anomalous_flag=True,
)
idata = miller.array(miller_set, data=intensities)
match = idata.match_indices(ic)
pairs = match.pairs()
icalc = flex.double()
iobs = flex.double()
miller_idx = flex.miller_index()
for p in pairs:
# Note : will create miller_idx duplicates in i_calc - problem?
iobs.append(idata.data()[p[0]])
icalc.append(ic.data()[p[1]])
miller_idx.append(ic.indices()[p[1]])
icalc *= flex.sum(iobs) / flex.sum(icalc)
rt = flex.reflection_table()
rt["intensity"] = icalc
rt["miller_index"] = miller_idx
used_ids = experiments.identifiers()
unique_id = get_next_unique_id(len(used_ids), used_ids)
exp.identifier = str(unique_id)
rt.experiment_identifiers()[unique_id] = str(unique_id)
rt["id"] = flex.int(rt.size(), unique_id)
return exp, rt
def test_scale_against_target(KB_test_param):
"""Integration testing of the scale_against_target library function/targeted
scaling."""
# Based on input - have Target Ih of 1.0, 10.0 and d of 1.0, 2.0. Then refl to
# target have I's of 2 and 5, (with the same ds). Therefore for a KB model the
# problem can be minimised exactly by solving the equations:
# 2 = K * exp(B/2)
# 1/2 = K * exp(B/8)
# Solving these gives the form tested for at the end of this test.
target_reflections = test_target_refl()
reflections = test_refl_to_scale()
target_experiments = test_exp()
experiments = test_exp(idval=1)
scaled_reflections = scale_against_target(
reflections, experiments, target_reflections, target_experiments, KB_test_param
)
assert list(scaled_reflections["inverse_scale_factor"]) == pytest.approx(
[2.0, 0.5, 2.0, 2.0 * (4.0 ** (-1.0 / 3.0))]
)
experiments = test_exp()
experiments.append(test_exp(idval=1)[0])
experiments[0].scaling_model = KBScalingModel.from_data(KB_test_param, [], [])
experiments[0].scaling_model.set_scaling_model_as_scaled()
experiments[1].scaling_model = KBScalingModel.from_data(KB_test_param, [], [])
target_reflections = test_target_refl()
reflections = test_refl_to_scale()
# Repeat the test but calling the TargetScaler directly, to allow inspection
# of the model components.
targetscaler = TargetScalerFactory.create(
KB_test_param, experiments, [target_reflections, reflections]
)
targetscaler.perform_scaling()
assert list(
targetscaler.unscaled_scalers[0].components["scale"].parameters
) == pytest.approx([(4.0 ** (-1.0 / 3.0)) / 2.0])
assert list(
targetscaler.unscaled_scalers[0].components["decay"].parameters
) == pytest.approx([(log(4.0) * 8.0 / 3.0)])
def get_scaling_model():
return KBScalingModel.from_data(generated_params(), [], [])
def create_datastructures_for_target_mtz(experiments, mtz_file):
"""Read a merged mtz file and extract miller indices, intensities and
variances."""
m = mtz.object(mtz_file)
ind = m.extract_miller_indices()
cols = m.columns()
col_dict = {c.label(): c for c in cols}
r_t = flex.reflection_table()
if "I" in col_dict: # nice and simple
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I"].extract_values().as_double()
r_t["variance"] = flex.pow2(col_dict["SIGI"].extract_values().as_double())
elif "IMEAN" in col_dict: # nice and simple
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["IMEAN"].extract_values().as_double()
r_t["variance"] = flex.pow2(col_dict["SIGIMEAN"].extract_values().as_double())
elif "I(+)" in col_dict: # need to combine I+ and I- together into target Ih
if col_dict["I(+)"].n_valid_values() == 0: # use I(-)
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I(-)"].extract_values().as_double()
r_t["variance"] = flex.pow2(
col_dict["SIGI(-)"].extract_values().as_double()
)
elif col_dict["I(-)"].n_valid_values() == 0: # use I(+)
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I(+)"].extract_values().as_double()
r_t["variance"] = flex.pow2(
col_dict["SIGI(+)"].extract_values().as_double()
)
else: # Combine both - add together then use Ih table to calculate I and sigma
r_tplus = flex.reflection_table()
r_tminus = flex.reflection_table()
r_tplus["miller_index"] = ind
r_tplus["intensity"] = col_dict["I(+)"].extract_values().as_double()
r_tplus["variance"] = flex.pow2(
col_dict["SIGI(+)"].extract_values().as_double()
)
r_tminus["miller_index"] = ind
r_tminus["intensity"] = col_dict["I(-)"].extract_values().as_double()
r_tminus["variance"] = flex.pow2(
col_dict["SIGI(-)"].extract_values().as_double()
)
r_tplus.extend(r_tminus)
r_tplus.set_flags(
flex.bool(r_tplus.size(), False), r_tplus.flags.bad_for_scaling
)
r_tplus = r_tplus.select(r_tplus["variance"] != 0.0)
Ih_table = create_Ih_table(
[experiments[0]], [r_tplus], anomalous=True
).blocked_data_list[0]
r_t["intensity"] = Ih_table.Ih_values
inv_var = (
Ih_table.weights * Ih_table.h_index_matrix
) * Ih_table.h_expand_matrix
r_t["variance"] = 1.0 / inv_var
r_t["miller_index"] = Ih_table.miller_index
else:
assert 0, """Unrecognised intensities in mtz file."""
r_t = r_t.select(r_t["variance"] > 0.0)
r_t["d"] = (
miller.set(
crystal_symmetry=crystal.symmetry(
space_group=m.space_group(), unit_cell=m.crystals()[0].unit_cell()
),
indices=r_t["miller_index"],
)
.d_spacings()
.data()
)
r_t.set_flags(flex.bool(r_t.size(), True), r_t.flags.integrated)
exp = Experiment()
exp.crystal = deepcopy(experiments[0].crystal)
exp.identifier = str(uuid.uuid4())
r_t.experiment_identifiers()[len(experiments)] = exp.identifier
r_t["id"] = flex.int(r_t.size(), len(experiments))
# create a new KB scaling model for the target and set as scaled to fix scale
# for targeted scaling.
params = Mock()
params.KB.decay_correction.return_value = False
exp.scaling_model = KBScalingModel.from_data(params, [], [])
exp.scaling_model.set_scaling_model_as_scaled() # Set as scaled to fix scale.
return exp, r_t
def get_scaling_model():
"""Make a KB Scaling model instance"""
return KBScalingModel.from_data(generated_params(), [], [])
def test_KBScalingModel():
"""Test for the KB Scaling Model."""
# Test standard initialisation method.
configdict = {"corrections": ["scale", "decay"]}
parameters_dict = {
"scale": {
"parameters": flex.double([1.2]),
"parameter_esds": flex.double([0.1]),
},
"decay": {
"parameters": flex.double([0.01]),
"parameter_esds": flex.double([0.02]),
},
}
KBmodel = KBScalingModel(parameters_dict, configdict)
assert KBmodel.id_ == "KB"
assert "scale" in KBmodel.components
assert "decay" in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [1.2]
assert list(KBmodel.components["decay"].parameters) == [0.01]
assert list(KBmodel.components["scale"].parameter_esds) == [0.1]
assert list(KBmodel.components["decay"].parameter_esds) == [0.02]
# Test from_dict initialisation method.
KB_dict = {
"__id__": "KB",
"is_scaled": True,
"scale": {
"n_parameters": 1,
"parameters": [0.5],
"est_standard_devs": [0.05],
"null_parameter_value": 1,
},
"configuration_parameters": {
"corrections": ["scale"]
},
}
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.is_scaled is True
assert "scale" in KBmodel.components
assert "decay" not in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [0.5]
assert list(KBmodel.components["scale"].parameter_esds) == [0.05]
new_dict = KBmodel.to_dict()
assert new_dict == KB_dict
# Test again with all parameters
KB_dict = {
"__id__": "KB",
"is_scaled": True,
"scale": {
"n_parameters": 1,
"parameters": [0.5],
"est_standard_devs": [0.05],
"null_parameter_value": 1,
},
"decay": {
"n_parameters": 1,
"parameters": [0.2],
"est_standard_devs": [0.02],
"null_parameter_value": 0,
},
"configuration_parameters": {
"corrections": ["scale", "decay"]
},
}
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.is_scaled is True
assert "scale" in KBmodel.components
assert "decay" in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [0.5]
assert list(KBmodel.components["scale"].parameter_esds) == [0.05]
assert list(KBmodel.components["decay"].parameters) == [0.2]
assert list(KBmodel.components["decay"].parameter_esds) == [0.02]
new_dict = KBmodel.to_dict()
assert new_dict == KB_dict
with pytest.raises(RuntimeError):
KB_dict["__id__"] = "physical"
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.consecutive_refinement_order == [["scale", "decay"]]
assert "Decay component" in str(KBmodel)
def test_ScalingModelObserver():
"""Test that the observer correctly logs data when passed a scaler."""
KB_dict = {
"__id__": "KB",
"is_scaled": True,
"scale": {
"n_parameters": 1,
"parameters": [0.5],
"est_standard_devs": [0.05],
"null_parameter_value": 1,
},
"configuration_parameters": {
"corrections": ["scale"]
},
}
KBmodel = KBScalingModel.from_dict(KB_dict)
experiment = Experiment()
experiment.scaling_model = KBmodel
experiment.identifier = "0"
scaler1 = mock.Mock()
scaler1.experiment = experiment
scaler1.active_scalers = None
observer = ScalingModelObserver()
observer.update(scaler1)
assert observer.data["0"] == KB_dict
msg = observer.return_model_error_summary()
assert msg != ""
mock_func = mock.Mock()
mock_func.return_value = {"plot": {"layout": {"title": ""}}}
with mock.patch("dials.algorithms.scaling.observers.plot_scaling_models",
new=mock_func):
observer.make_plots()
assert mock_func.call_count == 1
assert mock_func.call_args_list == [mock.call(KB_dict)]
experiment2 = Experiment()
experiment2.scaling_model = KBmodel
experiment2.identifier = "1"
scaler2 = mock.Mock()
scaler2.experiment = experiment2
scaler2.active_scalers = None
multiscaler = mock.Mock()
multiscaler.active_scalers = [scaler1, scaler2]
observer.data = {}
observer.update(multiscaler)
assert observer.data["0"] == KB_dict
assert observer.data["1"] == KB_dict
mock_func = mock.Mock()
mock_func.return_value = {"plot": {"layout": {"title": ""}}}
with mock.patch("dials.algorithms.scaling.observers.plot_scaling_models",
new=mock_func):
r = observer.make_plots()
assert mock_func.call_count == 2
assert mock_func.call_args_list == [
mock.call(KB_dict), mock.call(KB_dict)
]
assert all(i in r["scaling_model"] for i in ["plot_0", "plot_1"])
def from_dict(d):
"""creates a scaling model from a dict"""
from dials.algorithms.scaling.model.model import KBScalingModel
return KBScalingModel.from_dict(d)
