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 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"])