def reflection_tables_to_batch_dependent_properties(reflection_tables,
experiments,
scaled_array=None):
"""Extract batch dependent properties from a reflection table list."""
offsets = calculate_batch_offsets(experiments)
reflection_tables = assign_batches_to_reflections(reflection_tables,
offsets)
# filter bad refls and negative scales
batches = flex.int()
scales = flex.double()
for r in reflection_tables:
sel = ~r.get_flags(r.flags.bad_for_scaling, all=False)
sel &= r["inverse_scale_factor"] > 0
batches.extend(r["batch"].select(sel))
scales.extend(r["inverse_scale_factor"].select(sel))
if not scaled_array:
scaled_array = scaled_data_as_miller_array(reflection_tables,
experiments)
ms = scaled_array.customized_copy()
batch_array = miller.array(ms, data=batches)
batch_ranges = get_batch_ranges(experiments, offsets)
batch_data = [{"id": i, "range": r} for i, r in enumerate(batch_ranges)]
properties = batch_dependent_properties(batch_array, scaled_array,
miller.array(ms, data=scales))
return properties + (batch_data, )
def test_get_batch_ranges():
"""Test for namesake function"""
scan = Scan(image_range=[1, 200], oscillation=[0.0, 1.0])
exp1 = Experiment(scan=scan)
exp2 = Experiment(scan=scan)
batch_offsets = [0, 300]
experiments = [exp1, exp2]
batch_ranges = get_batch_ranges(experiments, batch_offsets)
assert batch_ranges == [(1, 200), (301, 500)]
