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 __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()
