def test_minimum_multiplicity_selection():
# some groups of indices with increasing multiplicity
hkl = flex.miller_index([(0, 0, 1), (0, 0, 2), (0, 0, 2), (0, 0, 3),
(0, 0, 3), (0, 0, 3), (0, 0, 4), (0, 0, 4),
(0, 0, 4), (0, 0, 4), (0, 0, 5), (0, 0, 5),
(0, 0, 5), (0, 0, 5), (0, 0, 5)])
# test the various possible selections with multiplicity from 1 to 6
sel = minimum_multiplicity_selection(hkl, 1)
assert sel.count(True) == len(hkl)
sel = minimum_multiplicity_selection(hkl, 2)
assert (sel == flex.bool([
False,
] + [True] * 14)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 3)
assert (sel == flex.bool([False] * 3 + [True] * 12)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 4)
assert (sel == flex.bool([False] * 6 + [True] * 9)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 5)
assert (sel == flex.bool([False] * 10 + [True] * 5)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 6)
assert sel.count(False) == len(hkl)
print "OK"
def test_minimum_multiplicity_selection():
# some groups of indices with increasing multiplicity
hkl = flex.miller_index(
[(0,0,1),
(0,0,2), (0,0,2),
(0,0,3), (0,0,3), (0,0,3),
(0,0,4), (0,0,4), (0,0,4), (0,0,4),
(0,0,5), (0,0,5), (0,0,5), (0,0,5), (0,0,5)])
# test the various possible selections with multiplicity from 1 to 6
sel = minimum_multiplicity_selection(hkl, 1)
assert sel.count(True) == len(hkl)
sel = minimum_multiplicity_selection(hkl, 2)
assert (sel == flex.bool([False,] + [True] * 14)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 3)
assert (sel == flex.bool([False] * 3 + [True] * 12)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 4)
assert (sel == flex.bool([False] * 6 + [True] * 9)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 5)
assert (sel == flex.bool([False] * 10 + [True] * 5)).all_eq(True)
sel = minimum_multiplicity_selection(hkl, 6)
assert sel.count(False) == len(hkl)
print "OK"
def __init__(self, reflections, experiment, params=None):
if params is None:
params = om_scope.extract()
# initial filter
reflections = reflections.select(
reflections.get_flags(reflections.flags.integrated)
)
# create new column containing the reduced Miller index
xl = experiment.crystal
symm = cctbx.crystal.symmetry(
xl.get_unit_cell(), space_group=xl.get_space_group()
)
hkl_set = cctbx.miller.set(symm, reflections["miller_index"])
asu_set = hkl_set.map_to_asu()
reflections["asu_miller_index"] = asu_set.indices()
# sort table by reduced hkl
reflections.sort("asu_miller_index")
if params.observations.integration_type == "mix":
raise Sorry("integration_type=mix is not supported yet")
else:
ikey = "intensity." + params.observations.integration_type + ".value"
vkey = "intensity." + params.observations.integration_type + ".variance"
# filters
sel = reflections[vkey] > 0
reflections = reflections.select(sel)
if params.observations.i_over_sigma_cutoff > 0:
ios = reflections[ikey] / flex.sqrt(reflections[vkey])
sel = ios >= params.observations.i_over_sigma_cutoff
reflections = reflections.select(sel)
if params.observations.min_multiplicity > 0:
sel = minimum_multiplicity_selection(
reflections["asu_miller_index"], params.observations.min_multiplicity
)
reflections = reflections.select(sel)
# extract columns of interest
gp_idx = reflections["asu_miller_index"]
intensity = reflections[ikey]
weight = 1.0 / reflections[vkey]
phi = reflections["xyzcal.mm"].parts()[2]
scale = flex.double(len(reflections), 1.0)
# set up reflection grouping object
self._go = GroupedObservations(gp_idx, intensity, weight, phi, scale)
return
def __init__(self, reflections, experiment, params=None):
if params is None: params = om_scope.extract()
# initial filter
reflections = reflections.select(reflections.get_flags(
reflections.flags.integrated))
# create new column containing the reduced Miller index
xl = experiment.crystal
symm = cctbx.crystal.symmetry(xl.get_unit_cell(),
space_group=xl.get_space_group())
hkl_set = cctbx.miller.set(symm, reflections['miller_index'])
asu_set = hkl_set.map_to_asu()
reflections['asu_miller_index'] = asu_set.indices()
# sort table by reduced hkl
reflections.sort('asu_miller_index')
if params.observations.integration_type == 'mix':
raise Sorry('integration_type=mix is not supported yet')
else:
ikey = 'intensity.' + params.observations.integration_type + '.value'
vkey = 'intensity.' + params.observations.integration_type + '.variance'
# filters
sel = reflections[vkey] > 0
reflections = reflections.select(sel)
if params.observations.i_over_sigma_cutoff > 0:
ios = reflections[ikey] / flex.sqrt(reflections[vkey])
sel = ios >= params.observations.i_over_sigma_cutoff
reflections = reflections.select(sel)
if params.observations.min_multiplicity > 0:
sel = minimum_multiplicity_selection(reflections['asu_miller_index'],
params.observations.min_multiplicity)
reflections = reflections.select(sel)
# extract columns of interest
gp_idx = reflections['asu_miller_index']
intensity = reflections[ikey]
weight = 1. / reflections[vkey]
phi = reflections['xyzcal.mm'].parts()[2]
scale = flex.double(len(reflections), 1.0)
# set up reflection grouping object
self._go = GroupedObservations(gp_idx, intensity, weight, phi, scale)
return