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Source code for dials.algorithms.spot_prediction.reflection_predictor
#!/usr/bin/env python
#
# reflection_predictor.py
#
# Copyright (C) 2014 Diamond Light Source
#
# Author: James Parkhurst
#
# This code is distributed under the BSD license, a copy of which is
# included in the root directory of this package.
from __future__ import absolute_import, division
from __future__ import print_function
import logging
logger = logging.getLogger(__name__)
from libtbx.phil import parse
from dials.util import Sorry
# The phil parameters
phil_scope = parse(
"""
prediction {
d_min = None
.type = float
.help = "The maximum resolution limit"
d_max = None
.type = float
.help = "The minimum resolution limit"
margin = 1
.type = int
.help = "The margin to use to scan varying prediction"
force_static = False
.type = bool
.help = "For scan-varying prediction force scan-static prediction"
padding = 1.0
.type = float(value_min=0)
.help = "The padding in degrees"
}
"""
)
[docs]class ReflectionPredictor(object):
"""
A reflection predictor that takes a number of experiments and does the proper
prediction for each type of experiment.
"""
def __init__(
self, experiment, dmin=None, dmax=None, margin=1, force_static=False, padding=0
):
"""
Initialise a predictor for each experiment.
:param experiment: The experiment to predict for
:param dmin: The maximum resolution
:param dmax: The minimum resolution
:param margin: The margin of hkl to predict
:param force_static: force scan varying prediction to be static
"""
from dials.algorithms.spot_prediction import ScanStaticReflectionPredictor
from dials.algorithms.spot_prediction import ScanVaryingReflectionPredictor
from dials.algorithms.spot_prediction import StillsReflectionPredictor
from dxtbx.imageset import ImageSweep
from dials.array_family import flex
class Predictor(object):
def __init__(self, name, func):
self.name = name
self.func = func
def __call__(self):
result = self.func()
if dmax is not None:
assert dmax > 0
result.compute_d_single(experiment)
mask = result["d"] > dmax
result.del_selected(mask)
return result
# Check prediction to maximum resolution is possible
wl = experiment.beam.get_wavelength()
if dmin is not None and dmin < 0.5 * wl:
raise Sorry(
"Prediction at d_min of {0} is not possible "
"with wavelength {1}".format(dmin, wl)
)
# Select the predictor class
if isinstance(experiment.imageset, ImageSweep):
xl_nsp = experiment.crystal.num_scan_points
bm_nsp = experiment.beam.num_scan_points
gn_nsp = experiment.goniometer.num_scan_points
nim = experiment.scan.get_num_images()
sv_compatible = (xl_nsp == nim + 1) or (bm_nsp == nim + 1)
if not force_static and sv_compatible:
predictor = ScanVaryingReflectionPredictor(
experiment, dmin=dmin, margin=margin, padding=padding
)
if bm_nsp == 0 and gn_nsp == 0:
# Only varying crystal
A = [
experiment.crystal.get_A_at_scan_point(i)
for i in range(experiment.crystal.num_scan_points)
]
predict = Predictor(
"scan varying crystal prediction",
lambda: predictor.for_ub(flex.mat3_double(A)),
)
else:
# Any allowed model may vary
if xl_nsp == nim + 1:
A = [
experiment.crystal.get_A_at_scan_point(i)
for i in range(experiment.crystal.num_scan_points)
]
else:
A = [experiment.crystal.get_A() for i in range(nim + 1)]
if bm_nsp == nim + 1:
s0 = [
experiment.beam.get_s0_at_scan_point(i)
for i in range(experiment.beam.num_scan_points)
]
else:
s0 = [experiment.beam.get_s0() for i in range(nim + 1)]
if gn_nsp == nim + 1:
S = [
experiment.goniometer.get_setting_rotation_at_scan_point(i)
for i in range(experiment.goniometer.num_scan_points)
]
else:
S = [
experiment.goniometer.get_setting_rotation()
for i in range(nim + 1)
]
predict = Predictor(
"scan varying model prediction",
lambda: predictor.for_varying_models(
flex.mat3_double(A),
flex.vec3_double(s0),
flex.mat3_double(S),
),
)
else:
predictor = ScanStaticReflectionPredictor(
experiment, dmin=dmin, padding=padding
)
# Choose index generation method based on number of images
# https://github.com/dials/dials/issues/585
if experiment.scan.get_num_images() > 50:
predict_method = predictor.for_ub_old_index_generator
else:
predict_method = predictor.for_ub
predict = Predictor(
"scan static prediction",
lambda: predict_method(experiment.crystal.get_A()),
)
else:
predictor = StillsReflectionPredictor(experiment, dmin=dmin)
predict = Predictor(
"stills prediction",
lambda: predictor.for_ub(experiment.crystal.get_A()),
)
# Create and add the predictor class
self._predict = predict
def __call__(self):
"""
Predict all the observable reflections.
:return: A reflection table
"""
logger.info("Prediction type: %s" % self._predict.name)
table = self._predict()
logger.info("Predicted %d reflections" % len(table))
return table
[docs] def predictor(self, index):
"""
Get the predictor for the given experiment index.
:param index: The experiment index
:return: The predictor
"""
return self._predict[index]
