class ObservationManager(object):
"""A class to maintain information about the reflections during
optimisation of their scale factors."""
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
@property
def intensity(self):
return self._go.get_intensity()
@intensity.setter
def intensity(self, intensity):
self._go.set_intensity(intensity)
@property
def weight(self):
return self._go.get_weight()
@weight.setter
def weight(self, weight):
self._go.set_weight(weight)
@property
def scale(self):
return self._go.get_scale()
@scale.setter
def scale(self, scale):
self._go.set_scale(scale)
@property
def group_index(self):
return self._go.get_group_index()
@property
def phi(self):
return self._go.get_phi()
@property
def group_size(self):
return self._go.get_group_size()
def get_average_intensity(self):
'''Calculate the weighted average intensity in reflection groups at the
current scales using the formula of Hamilton, Rollett and Sparks (1965).
Return as a vector for each observation'''
# delegate to the GroupedObservations object
return self._go.get_average_intensity()
class ObservationManager(object):
"""A class to maintain information about the reflections during
optimisation of their scale factors."""
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
@property
def intensity(self):
return self._go.get_intensity()
@intensity.setter
def intensity(self, intensity):
self._go.set_intensity(intensity)
@property
def weight(self):
return self._go.get_weight()
@weight.setter
def weight(self, weight):
self._go.set_weight(weight)
@property
def scale(self):
return self._go.get_scale()
@scale.setter
def scale(self, scale):
self._go.set_scale(scale)
@property
def group_index(self):
return self._go.get_group_index()
@property
def phi(self):
return self._go.get_phi()
@property
def group_size(self):
return self._go.get_group_size()
def get_average_intensity(self):
'''Calculate the weighted average intensity in reflection groups at the
current scales using the formula of Hamilton, Rollett and Sparks (1965).
Return as a vector for each observation'''
# delegate to the GroupedObservations object
return self._go.get_average_intensity()
