def merging_stats_from_scaled_array(
scaled_miller_array, n_bins=20, use_internal_variance=False, anomalous=True
):
"""Calculate the normal and anomalous merging statistics."""
if scaled_miller_array.is_unique_set_under_symmetry():
raise DialsMergingStatisticsError(
"Dataset contains no equivalent reflections, merging statistics "
"cannot be calculated."
)
try:
result = iotbx.merging_statistics.dataset_statistics(
i_obs=scaled_miller_array,
n_bins=n_bins,
anomalous=False,
sigma_filtering=None,
eliminate_sys_absent=False,
use_internal_variance=use_internal_variance,
cc_one_half_significance_level=0.01,
)
except (RuntimeError, Sorry) as e:
raise DialsMergingStatisticsError(
f"Error encountered during merging statistics calculation:\n{e}"
)
anom_result = None
if anomalous:
intensities_anom = scaled_miller_array.as_anomalous_array()
intensities_anom = intensities_anom.map_to_asu().customized_copy(
info=scaled_miller_array.info()
)
if intensities_anom.is_unique_set_under_symmetry():
logger.warning(
"Anomalous dataset contains no equivalent reflections, anomalous "
"merging statistics cannot be calculated."
)
else:
try:
anom_result = iotbx.merging_statistics.dataset_statistics(
i_obs=intensities_anom,
n_bins=n_bins,
anomalous=True,
sigma_filtering=None,
cc_one_half_significance_level=0.01,
eliminate_sys_absent=False,
use_internal_variance=use_internal_variance,
)
except (RuntimeError, Sorry) as e:
logger.warning(
"Error encountered during anomalous merging statistics "
"calculation:\n%s",
e,
exc_info=True,
)
return result, anom_result
def merging_stats_from_scaled_array(
scaled_miller_array, n_bins=20, use_internal_variance=False
):
"""Calculate the normal and anomalous merging statistics."""
if scaled_miller_array.is_unique_set_under_symmetry():
raise DialsMergingStatisticsError(
"Dataset contains no equivalent reflections, merging statistics cannot be calculated."
)
try:
result = iotbx.merging_statistics.dataset_statistics(
i_obs=scaled_miller_array,
n_bins=n_bins,
anomalous=False,
sigma_filtering=None,
eliminate_sys_absent=False,
use_internal_variance=use_internal_variance,
cc_one_half_significance_level=0.01,
)
intensities_anom = scaled_miller_array.as_anomalous_array()
intensities_anom = intensities_anom.map_to_asu().customized_copy(
info=scaled_miller_array.info()
)
anom_result = iotbx.merging_statistics.dataset_statistics(
i_obs=intensities_anom,
n_bins=n_bins,
anomalous=True,
sigma_filtering=None,
cc_one_half_significance_level=0.01,
eliminate_sys_absent=False,
use_internal_variance=use_internal_variance,
)
except RuntimeError:
raise DialsMergingStatisticsError(
"Failure during merging statistics calculation"
)
else:
return result, anom_result
def _test_Imid_combinations(self):
rows = []
results = {}
for Imid in self.Imids:
combined_intensities = flex.double([])
combined_sigmas = flex.double([])
combined_scales = flex.double([])
combined_indices = flex.miller_index([])
for dataset in self.datasets:
Int, Var = _get_Is_from_Imidval(dataset, Imid)
Int *= dataset["prescaling_correction"]
sigma = (Var**0.5) * dataset["prescaling_correction"]
combined_intensities.extend(Int)
combined_sigmas.extend(sigma)
combined_scales.extend(dataset["inverse_scale_factor"])
combined_indices.extend(dataset["miller_index"])
# apply scale factor before determining merging stats
miller_set = miller.set(
crystal_symmetry=self.experiment.crystal.get_crystal_symmetry(
),
indices=combined_indices,
anomalous_flag=False,
)
i_obs = miller.array(miller_set,
data=combined_intensities / combined_scales)
i_obs.set_observation_type_xray_intensity()
i_obs.set_sigmas(combined_sigmas / combined_scales)
try:
rmeas, cchalf = fast_merging_stats(array=i_obs)
logger.debug("Imid: %s, Rmeas %s, cchalf %s", Imid, rmeas,
cchalf)
except RuntimeError:
raise DialsMergingStatisticsError(
"Unable to merge for intensity combination")
# record the results
results[Imid] = rmeas
res_str = {0: "prf only", 1: "sum only"}
if not Imid in res_str:
res_str[Imid] = "Imid = " + str(round(Imid, 2))
rows.append(
[res_str[Imid],
str(round(cchalf, 5)),
str(round(rmeas, 5))])
return rows, results
def _test_Imid_combinations(self):
"""Test the different combinations, returning the rows and results dict."""
rows = []
results = {}
for Imid in self.Imids:
Int, Var = _get_Is_from_Imidval(self.dataset, Imid)
miller_set = miller.set(
crystal_symmetry=self.experiment.crystal.get_crystal_symmetry(
assert_is_compatible_unit_cell=False
),
indices=self.dataset["miller_index"],
anomalous_flag=False,
)
i_obs = miller.array(
miller_set,
data=(
Int
* self.dataset["prescaling_correction"]
/ self.dataset["inverse_scale_factor"]
),
)
i_obs.set_observation_type_xray_intensity()
i_obs.set_sigmas(
flex.sqrt(Var)
* self.dataset["prescaling_correction"]
/ self.dataset["inverse_scale_factor"]
)
try:
rmeas, cchalf = fast_merging_stats(array=i_obs)
logger.debug("Imid: %s, Rmeas %s, cchalf %s", Imid, rmeas, cchalf)
except RuntimeError:
raise DialsMergingStatisticsError(
"Unable to merge for intensity combination"
)
# record the results
results[Imid] = rmeas
res_str = {0: "prf only", 1: "sum only"}
if Imid not in res_str:
res_str[Imid] = "Imid = " + str(round(Imid, 2))
rows.append([res_str[Imid], str(round(cchalf, 5)), str(round(rmeas, 5))])
return rows, results
