def __init__(self, intensities, dose, n_bins=8,
range_min=None, range_max=None, range_width=1):
if isinstance(dose, flex.double):
sorted_dose = flex.sorted(dose)
dd = sorted_dose[1:] - sorted_dose[:-1]
dd = dd.select(dd > 0)
if len(dd):
step_size = flex.min(dd)
dose /= step_size
dose = dose.iround()
if flex.min(dose) == 0:
dose += 1
# fix for completeness > 1 if screw axes present
intensities = intensities.customized_copy(
space_group_info=intensities.space_group().build_derived_reflection_intensity_group(
anomalous_flag=intensities.anomalous_flag()).info(),
info=intensities.info())
self.intensities = intensities
self.dose = dose
self.n_bins = n_bins
self.range_min = range_min
self.range_max = range_max
self.range_width = range_width
assert self.range_width > 0
if self.range_min is None:
self.range_min = flex.min(self.dose) - self.range_width
if self.range_max is None:
self.range_max = flex.max(self.dose)
self.n_steps = 2 + int((self.range_max - self.range_min) - self.range_width)
sel = (self.dose.as_double() <= self.range_max) & (self.dose.as_double() >= self.range_min)
self.dose = self.dose.select(sel)
self.intensities = self.intensities.select(sel)
self.d_star_sq = self.intensities.d_star_sq().data()
self.binner = self.intensities.setup_binner_d_star_sq_step(
d_star_sq_step=(flex.max(self.d_star_sq)-flex.min(self.d_star_sq)+1e-8)/self.n_bins)
#self.dose /= range_width
self.dose -= int(self.range_min)
self.dose = flex.size_t(list(self.dose))
binner_non_anom = intensities.as_non_anomalous_array().use_binning(
self.binner)
n_complete = flex.size_t(binner_non_anom.counts_complete()[1:-1])
from xia2.Modules.PyChef2 import ChefStatistics
chef_stats = ChefStatistics(
intensities.indices(), intensities.data(), intensities.sigmas(),
intensities.d_star_sq().data(), self.dose, n_complete, self.binner,
intensities.space_group(), intensities.anomalous_flag(), self.n_steps)
self.iplus_comp_bins = chef_stats.iplus_completeness_bins()
self.iminus_comp_bins = chef_stats.iminus_completeness_bins()
self.ieither_comp_bins = chef_stats.ieither_completeness_bins()
self.iboth_comp_bins = chef_stats.iboth_completeness_bins()
self.iplus_comp_overall = chef_stats.iplus_completeness()
self.iminus_comp_overall = chef_stats.iminus_completeness()
self.ieither_comp_overall = chef_stats.ieither_completeness()
self.iboth_comp_overall = chef_stats.iboth_completeness()
self.rcp_bins = chef_stats.rcp_bins()
self.rcp = chef_stats.rcp()
self.scp_bins = chef_stats.scp_bins()
self.scp = chef_stats.scp()
self.rd = chef_stats.rd()
def test_exercise_accumulators(xia2_regression):
from xia2.Modules.PyChef2 import PyChef
from xia2.Modules.PyChef2 import ChefStatistics
from iotbx.reflection_file_reader import any_reflection_file
from cctbx.array_family import flex
from libtbx.test_utils import approx_equal
import os
f = os.path.join(xia2_regression, "test/insulin_dials_scaled_unmerged.mtz")
reader = any_reflection_file(f)
assert reader.file_type() == 'ccp4_mtz'
arrays = reader.as_miller_arrays(merge_equivalents=False)
for ma in arrays:
if ma.info().labels == ['BATCH']:
batches = ma
elif ma.info().labels == ['I', 'SIGI']:
intensities = ma
elif ma.info().labels == ['I(+)', 'SIGI(+)', 'I(-)', 'SIGI(-)']:
intensities = ma
assert intensities is not None
assert batches is not None
anomalous_flag = True
if anomalous_flag:
intensities = intensities.as_anomalous_array()
pystats = PyChef.PyStatistics(intensities, batches.data())
miller_indices = batches.indices()
sg = batches.space_group()
n_steps = pystats.n_steps
dose = batches.data()
range_width = 1
range_max = flex.max(dose)
range_min = flex.min(dose) - range_width
dose /= range_width
dose -= range_min
binner_non_anom = intensities.as_non_anomalous_array().use_binning(
pystats.binner)
n_complete = flex.size_t(binner_non_anom.counts_complete()[1:-1])
dose = flex.size_t(list(dose))
chef_stats = ChefStatistics(miller_indices, intensities.data(),
intensities.sigmas(),
intensities.d_star_sq().data(), dose,
n_complete, pystats.binner, sg, anomalous_flag,
n_steps)
# test completeness
assert approx_equal(chef_stats.iplus_completeness(),
pystats.iplus_comp_overall)
assert approx_equal(chef_stats.iminus_completeness(),
pystats.iminus_comp_overall)
assert approx_equal(chef_stats.ieither_completeness(),
pystats.ieither_comp_overall)
assert approx_equal(chef_stats.iboth_completeness(),
pystats.iboth_comp_overall)
# test rcp,scp
assert approx_equal(chef_stats.rcp(), pystats.rcp)
assert approx_equal(chef_stats.scp(), pystats.scp)
# test Rd
assert approx_equal(chef_stats.rd(), pystats.rd)
def __init__(self, intensities, dose, n_bins=8,
range_min=None, range_max=None, range_width=1):
if isinstance(dose, flex.double):
sorted_dose = flex.sorted(dose)
dd = sorted_dose[1:] - sorted_dose[:-1]
step_size = flex.min(dd.select(dd > 0))
dose /= step_size
dose = dose.iround()
if flex.min(dose) == 0:
dose += 1
# fix for completeness > 1 if screw axes present
intensities = intensities.customized_copy(
space_group_info=intensities.space_group().build_derived_reflection_intensity_group(
anomalous_flag=intensities.anomalous_flag()).info(),
info=intensities.info())
self.intensities = intensities
self.dose = dose
self.n_bins = n_bins
self.range_min = range_min
self.range_max = range_max
self.range_width = range_width
assert self.range_width > 0
if self.range_min is None:
self.range_min = flex.min(self.dose) - self.range_width
if self.range_max is None:
self.range_max = flex.max(self.dose)
self.n_steps = 2 + int((self.range_max - self.range_min) - self.range_width)
sel = (self.dose.as_double() <= self.range_max) & (self.dose.as_double() >= self.range_min)
self.dose = self.dose.select(sel)
self.intensities = self.intensities.select(sel)
self.d_star_sq = self.intensities.d_star_sq().data()
self.binner = self.intensities.setup_binner_d_star_sq_step(
d_star_sq_step=(flex.max(self.d_star_sq)-flex.min(self.d_star_sq)+1e-8)/self.n_bins)
#self.dose /= range_width
self.dose -= int(self.range_min)
self.dose = flex.size_t(list(self.dose))
binner_non_anom = intensities.as_non_anomalous_array().use_binning(
self.binner)
n_complete = flex.size_t(binner_non_anom.counts_complete()[1:-1])
from xia2.Modules.PyChef2 import ChefStatistics
chef_stats = ChefStatistics(
intensities.indices(), intensities.data(), intensities.sigmas(),
intensities.d_star_sq().data(), self.dose, n_complete, self.binner,
intensities.space_group(), intensities.anomalous_flag(), self.n_steps)
self.iplus_comp_bins = chef_stats.iplus_completeness_bins()
self.iminus_comp_bins = chef_stats.iminus_completeness_bins()
self.ieither_comp_bins = chef_stats.ieither_completeness_bins()
self.iboth_comp_bins = chef_stats.iboth_completeness_bins()
self.iplus_comp_overall = chef_stats.iplus_completeness()
self.iminus_comp_overall = chef_stats.iminus_completeness()
self.ieither_comp_overall = chef_stats.ieither_completeness()
self.iboth_comp_overall = chef_stats.iboth_completeness()
self.rcp_bins = chef_stats.rcp_bins()
self.rcp = chef_stats.rcp()
self.scp_bins = chef_stats.scp_bins()
self.scp = chef_stats.scp()
self.rd = chef_stats.rd()
def exercise_accumulators():
from xia2.Modules.PyChef2 import PyChef
from xia2.Modules.PyChef2 import ChefStatistics
from iotbx.reflection_file_reader import any_reflection_file
from cctbx.array_family import flex
import libtbx.load_env
import os
xia2_regression = libtbx.env.find_in_repositories("xia2_regression")
if xia2_regression is None:
print "Skipping exercise_accumulators(): xia2_regression not available"
return
f = os.path.join(xia2_regression, "test/insulin_dials_scaled_unmerged.mtz")
reader = any_reflection_file(f)
assert reader.file_type() == 'ccp4_mtz'
arrays = reader.as_miller_arrays(merge_equivalents=False)
for ma in arrays:
if ma.info().labels == ['BATCH']:
batches = ma
elif ma.info().labels == ['I', 'SIGI']:
intensities = ma
elif ma.info().labels == ['I(+)', 'SIGI(+)', 'I(-)', 'SIGI(-)']:
intensities = ma
assert intensities is not None
assert batches is not None
anomalous_flag = True
if anomalous_flag:
intensities = intensities.as_anomalous_array()
pystats = PyChef.PyStatistics(intensities, batches.data())
miller_indices = batches.indices()
sg = batches.space_group()
n_steps = pystats.n_steps
dose = batches.data()
range_width = 1
range_max = flex.max(dose)
range_min = flex.min(dose) - range_width
dose /= range_width
dose -= range_min
binner_non_anom = intensities.as_non_anomalous_array().use_binning(
pystats.binner)
n_complete = flex.size_t(binner_non_anom.counts_complete()[1:-1])
dose = flex.size_t(list(dose))
chef_stats = ChefStatistics(
miller_indices, intensities.data(), intensities.sigmas(),
intensities.d_star_sq().data(), dose, n_complete, pystats.binner,
sg, anomalous_flag, n_steps)
# test completeness
assert approx_equal(chef_stats.iplus_completeness(), pystats.iplus_comp_overall)
assert approx_equal(chef_stats.iminus_completeness(), pystats.iminus_comp_overall)
assert approx_equal(chef_stats.ieither_completeness(), pystats.ieither_comp_overall)
assert approx_equal(chef_stats.iboth_completeness(), pystats.iboth_comp_overall)
# test rcp,scp
assert approx_equal(chef_stats.rcp(), pystats.rcp)
assert approx_equal(chef_stats.scp(), pystats.scp)
# test Rd
assert approx_equal(chef_stats.rd(), pystats.rd)
print "OK"