def test_centroid_outlier(dials_regression, method, colnames, expected_nout):
flex.set_random_seed(42)
data_dir = os.path.join(dials_regression, "refinement_test_data",
"centroid_outlier")
residuals = flex.reflection_table.from_file(
os.path.join(data_dir, "residuals.refl"))
params = phil_scope.extract()
params.outlier.algorithm = method
params.outlier.sauter_poon.px_sz = (0.1, 0.1) # must be set for SauterPoon
outlier_detector = CentroidOutlierFactory.from_parameters_and_colnames(
params, colnames)
outlier_detector(residuals)
outliers = residuals.get_flags(residuals.flags.centroid_outlier)
assert outliers.count(True) == expected_nout
def from_parameters_reflections_experiments(
params, reflections, experiments, do_stills=False
):
"""Given a set of parameters and models, build a reflection manager
Params:
params The input parameters
Returns:
The reflection manager instance
"""
# While a random subset of reflections is used, continue to
# set random.seed to get consistent behaviour
if params.random_seed is not None:
random.seed(params.random_seed)
flex.set_random_seed(params.random_seed)
logger.debug("Random seed set to %d", params.random_seed)
# check whether we deal with stills or scans
if do_stills:
refman = StillsReflectionManager
# check incompatible weighting strategy
if params.weighting_strategy.override == "statistical":
raise DialsRefineConfigError(
'The "statistical" weighting strategy is not compatible '
"with stills refinement"
)
else:
refman = ReflectionManager
# check incompatible weighting strategy
if params.weighting_strategy.override in ["stills", "external_deltapsi"]:
msg = (
'The "{0}" weighting strategy is not compatible with '
"scan refinement"
).format(params.weighting_strategy.override)
raise DialsRefineConfigError(msg)
# set automatic outlier rejection options
if params.outlier.algorithm in ("auto", libtbx.Auto):
if do_stills:
params.outlier.algorithm = "sauter_poon"
else:
params.outlier.algorithm = "mcd"
if params.outlier.separate_panels is libtbx.Auto:
if do_stills:
params.outlier.separate_panels = False
else:
params.outlier.separate_panels = True
if params.outlier.algorithm == "sauter_poon":
if params.outlier.sauter_poon.px_sz is libtbx.Auto:
# get this from the first panel of the first detector
params.outlier.sauter_poon.px_sz = experiments.detectors()[0][
0
].get_pixel_size()
# do outlier rejection?
if params.outlier.algorithm in ("null", None):
outlier_detector = None
else:
if do_stills:
colnames = ["x_resid", "y_resid"]
params.outlier.block_width = None
else:
colnames = ["x_resid", "y_resid", "phi_resid"]
from dials.algorithms.refinement.outlier_detection import (
CentroidOutlierFactory,
)
outlier_detector = CentroidOutlierFactory.from_parameters_and_colnames(
params, colnames
)
# override default weighting strategy?
weighting_strategy = None
if params.weighting_strategy.override == "statistical":
from dials.algorithms.refinement.weighting_strategies import (
StatisticalWeightingStrategy,
)
weighting_strategy = StatisticalWeightingStrategy()
elif params.weighting_strategy.override == "stills":
from dials.algorithms.refinement.weighting_strategies import (
StillsWeightingStrategy,
)
weighting_strategy = StillsWeightingStrategy(
params.weighting_strategy.delpsi_constant
)
elif params.weighting_strategy.override == "external_deltapsi":
from dials.algorithms.refinement.weighting_strategies import (
ExternalDelPsiWeightingStrategy,
)
weighting_strategy = ExternalDelPsiWeightingStrategy()
elif params.weighting_strategy.override == "constant":
from dials.algorithms.refinement.weighting_strategies import (
ConstantWeightingStrategy,
)
weighting_strategy = ConstantWeightingStrategy(
*params.weighting_strategy.constants, stills=do_stills
)
# Check for deprecated parameter
if params.trim_scan_edges is not None:
warnings.warn(
"The parameter trim_scan_edges is deprecated and will be removed shortly",
FutureWarning,
)
params.scan_margin = params.trim_scan_edges
return refman(
reflections=reflections,
experiments=experiments,
nref_per_degree=params.reflections_per_degree,
max_sample_size=params.maximum_sample_size,
min_sample_size=params.minimum_sample_size,
close_to_spindle_cutoff=params.close_to_spindle_cutoff,
scan_margin=params.scan_margin,
outlier_detector=outlier_detector,
weighting_strategy_override=weighting_strategy,
)