def apply_aniso_correction(self, f_array=None):
if self.b_cart is None or self.b_cart_aniso_removed is None:
return f_array # nothing to do
from mmtbx.scaling import absolute_scaling
from cctbx import adptbx
u_star = adptbx.u_cart_as_u_star(f_array.unit_cell(),
adptbx.b_as_u(self.b_cart))
u_star_aniso_removed = adptbx.u_cart_as_u_star(
f_array.unit_cell(), adptbx.b_as_u(self.b_cart_aniso_removed))
no_aniso_array = absolute_scaling.anisotropic_correction(
f_array, 0.0, u_star, must_be_greater_than=-0.0001)
no_aniso_array = absolute_scaling.anisotropic_correction(
no_aniso_array,
0.0,
u_star_aniso_removed,
must_be_greater_than=-0.0001)
no_aniso_array = no_aniso_array.set_observation_type(f_array)
return no_aniso_array
def __init__(self,
miller_native,
miller_derivative,
use_intensities=True,
scale_weight=True,
use_weights=True):
self.native = miller_native.deep_copy().map_to_asu()
self.derivative = miller_derivative.deep_copy().map_to_asu()
lsq_object = refinery(self.native,
self.derivative,
use_intensities=use_intensities,
scale_weight=scale_weight,
use_weights=use_weights)
self.p_scale = lsq_object.p_scale
self.b_cart = lsq_object.b_cart
self.u_star = lsq_object.u_star
## very well, all done and set.
## apply the scaling on the data please and compute some r values
tmp_nat, tmp_der = self.native.common_sets(self.derivative)
self.r_val_before = flex.sum( flex.abs(tmp_nat.data()-tmp_der.data()) )
if flex.sum( flex.abs(tmp_nat.data()+tmp_der.data()) ) > 0:
self.r_val_before /=flex.sum( flex.abs(tmp_nat.data()+tmp_der.data()) )/2.0
self.derivative = absolute_scaling.anisotropic_correction(
self.derivative,self.p_scale,self.u_star )
self.scaled_original_derivative = self.derivative.deep_copy().set_observation_type(
self.derivative ).map_to_asu()
tmp_nat = self.native
tmp_der = self.derivative
tmp_nat, tmp_der = self.native.map_to_asu().common_sets(self.derivative.map_to_asu())
self.r_val_after = flex.sum( flex.abs( tmp_nat.data()-
tmp_der.data() )
)
if (flex.sum( flex.abs(tmp_nat.data()) ) +
flex.sum( flex.abs(tmp_der.data()) )) > 0:
self.r_val_after /=(flex.sum( flex.abs(tmp_nat.data()) ) +
flex.sum( flex.abs(tmp_der.data()) ))/2.0
self.native=tmp_nat
self.derivative=tmp_der
def __init__(self,
miller_native,
miller_derivative,
use_intensities=True,
scale_weight=True,
use_weights=True):
self.native = miller_native.deep_copy().map_to_asu()
self.derivative = miller_derivative.deep_copy().map_to_asu()
lsq_object = refinery(self.native,
self.derivative,
use_intensities=use_intensities,
scale_weight=scale_weight,
use_weights=use_weights)
self.p_scale = lsq_object.p_scale
self.b_cart = lsq_object.b_cart
self.u_star = lsq_object.u_star
## very well, all done and set.
## apply the scaling on the data please and compute some r values
tmp_nat, tmp_der = self.native.common_sets(self.derivative)
self.r_val_before = flex.sum( flex.abs(tmp_nat.data()-tmp_der.data()) )
if flex.sum( flex.abs(tmp_nat.data()+tmp_der.data()) ) > 0:
self.r_val_before /=flex.sum( flex.abs(tmp_nat.data()+tmp_der.data()) )/2.0
self.derivative = absolute_scaling.anisotropic_correction(
self.derivative,self.p_scale,self.u_star )
self.scaled_original_derivative = self.derivative.deep_copy().set_observation_type(
self.derivative ).map_to_asu()
tmp_nat = self.native
tmp_der = self.derivative
tmp_nat, tmp_der = self.native.map_to_asu().common_sets(self.derivative.map_to_asu())
self.r_val_after = flex.sum( flex.abs( tmp_nat.data()-
tmp_der.data() )
)
if (flex.sum( flex.abs(tmp_nat.data()) ) +
flex.sum( flex.abs(tmp_der.data()) )) > 0:
self.r_val_after /=(flex.sum( flex.abs(tmp_nat.data()) ) +
flex.sum( flex.abs(tmp_der.data()) ))/2.0
self.native=tmp_nat
self.derivative=tmp_der
def anisotropic_correction(self):
if not self.params.anisotropic_correction: return
self.aniso_scale_and_b = None
n_copies_solc = self.params.asu_contents.n_copies_per_asu
n_residues = self.params.asu_contents.n_residues
n_bases = self.params.asu_contents.n_bases
self.aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array=self.f_obs,
n_residues=n_residues*self.f_obs.space_group().order_z()*n_copies_solc,
n_bases=n_bases*self.f_obs.space_group().order_z()*n_copies_solc)
self.aniso_scale_and_b.show(out=self.log)
b_cart = self.aniso_scale_and_b.b_cart
trace = sum(b_cart[:3])/3
b_cart = [b_cart[0]-trace, b_cart[1]-trace, b_cart[2]-trace,
b_cart[3], b_cart[4], b_cart[5]]
u_star = adptbx.u_cart_as_u_star(self.f_obs.unit_cell(), adptbx.b_as_u(b_cart))
self.f_obs = absolute_scaling.anisotropic_correction(
self.f_obs, 0.0, u_star).set_observation_type(self.f_obs)
def anisotropic_correction(self):
if not self.params.anisotropic_correction: return
self.aniso_scale_and_b = None
n_copies_solc = self.params.asu_contents.n_copies_per_asu
n_residues = self.params.asu_contents.n_residues
n_bases = self.params.asu_contents.n_bases
self.aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array=self.f_obs,
n_residues=n_residues*self.f_obs.space_group().order_z()*n_copies_solc,
n_bases=n_bases*self.f_obs.space_group().order_z()*n_copies_solc)
self.aniso_scale_and_b.show(out=self.log)
b_cart = self.aniso_scale_and_b.b_cart
trace = sum(b_cart[:3])/3
b_cart = [b_cart[0]-trace, b_cart[1]-trace, b_cart[2]-trace,
b_cart[3], b_cart[4], b_cart[5]]
u_star = adptbx.u_cart_as_u_star(self.f_obs.unit_cell(), adptbx.b_as_u(b_cart))
self.f_obs = absolute_scaling.anisotropic_correction(
self.f_obs, 0.0, u_star).set_observation_type(self.f_obs)
def __init__(self,
miller_array,
parameters,
out=None,
n_residues=100,
n_bases=0):
self.params=parameters
self.miller_array=miller_array.deep_copy().set_observation_type(
miller_array).merge_equivalents().array()
self.out = out
if self.out is None:
self.out = sys.stdout
if self.out == "silent":
self.out = null_out()
self.no_aniso_array = self.miller_array
if self.params.aniso.action == "remove_aniso":
# first perfom aniso scaling
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = self.miller_array,
n_residues = n_residues,
n_bases = n_bases)
aniso_scale_and_b.p_scale = 0 # set the p_scale back to 0!
aniso_scale_and_b.show(out=out)
# now do aniso correction please
self.aniso_p_scale = aniso_scale_and_b.p_scale
self.aniso_u_star = aniso_scale_and_b.u_star
self.aniso_b_cart = aniso_scale_and_b.b_cart
if self.params.aniso.final_b == "eigen_min":
b_use=aniso_scale_and_b.eigen_values[2]
elif self.params.aniso.final_b == "eigen_mean" :
b_use=flex.mean(aniso_scale_and_b.eigen_values)
elif self.params.aniso.final_b == "user_b_iso":
assert self.params.aniso.b_iso is not None
b_use=self.params.aniso.b_iso
else:
b_use = 30
b_cart_aniso_removed = [ -b_use,
-b_use,
-b_use,
0,
0,
0]
u_star_aniso_removed = adptbx.u_cart_as_u_star(
miller_array.unit_cell(),
adptbx.b_as_u( b_cart_aniso_removed ) )
## I do things in two steps, but can easely be done in 1 step
## just for clarity, thats all.
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.miller_array,0.0,aniso_scale_and_b.u_star )
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.no_aniso_array,0.0,u_star_aniso_removed)
self.no_aniso_array = self.no_aniso_array.set_observation_type(
miller_array )
# that is done now, now we can do outlier detection if desired
outlier_manager = outlier_rejection.outlier_manager(
self.no_aniso_array,
None,
out=self.out)
self.new_miller_array = self.no_aniso_array
if self.params.outlier.action == "basic":
print >> self.out, "Non-outliers found by the basic wilson statistics"
print >> self.out, "protocol will be written out."
basic_array = outlier_manager.basic_wilson_outliers(
p_basic_wilson = self.params.outlier.parameters.basic_wilson.level,
return_data = True)
self.new_miller_array = basic_array
if self.params.outlier.action == "extreme":
print >> self.out, "Non-outliers found by the extreme value wilson statistics"
print >> self.out, "protocol will be written out."
extreme_array = outlier_manager.extreme_wilson_outliers(
p_extreme_wilson = self.params.outlier.parameters.extreme_wilson.level,
return_data = True)
self.new_miller_array = extreme_array
if self.params.outlier.action == "beamstop":
print >> self.out, "Outliers found for the beamstop shadow"
print >> self.out, "problems detection protocol will be written out."
beamstop_array = outlier_manager.beamstop_shadow_outliers(
level = self.params.outlier.parameters.beamstop.level,
d_min = self.params.outlier.parameters.beamstop.d_min,
return_data=True)
self.new_miller_array = beamstop_array
if self.params.outlier.action == "None":
self.new_miller_array = self.no_aniso_array
# now we can twin or detwin the data if needed
self.final_array = self.new_miller_array
if self.params.symmetry.action == "twin":
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None):
raise Sorry("Twin fraction not specified, not twinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out
print >> self.out, "Twinning given data"
print >> self.out, "-------------------"
print >> self.out
print >> self.out, "Artifically twinning the data with fraction %3.2f" %\
alpha
self.final_array = self.new_miller_array.twin_data(
twin_law = self.params.symmetry.twinning_parameters.twin_law,
alpha=alpha).as_intensity_array()
elif (self.params.symmetry.action == "detwin"):
twin_law = self.params.symmetry.twinning_parameters.twin_law
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None):
raise Sorry("Twin fraction not specified, not detwinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out, """
Attempting to detwin data
-------------------------
Detwinning data with:
- twin law: %s
- twin fraciton: %.2f
BE WARNED! DETWINNING OF DATA DOES NOT SOLVE YOUR TWINNING PROBLEM!
PREFERABLY, REFINEMENT SHOULD BE CARRIED OUT AGAINST ORIGINAL DATA
ONLY USING A TWIN SPECIFIC TARGET FUNCTION!
""" % (twin_law, alpha)
self.final_array = self.new_miller_array.detwin_data(
twin_law=twin_law,
alpha=alpha).as_intensity_array()
assert self.final_array is not None
class basic_analyses(object): # XXX is this ever used?
def __init__(self,
miller_array,
phil_object,
out=None,
out_plot=None,
miller_calc=None,
original_intensities=None,
completeness_as_non_anomalous=None,
verbose=0):
if out is None:
out = sys.stdout
if verbose > 0:
print >> out
print >> out
print >> out, "Matthews coefficient and Solvent content statistics"
n_copies_solc = 1.0
self.nres_known = False
if (phil_object.scaling.input.asu_contents.n_residues is not None
or phil_object.scaling.input.asu_contents.n_bases is not None):
self.nres_known = True
if (phil_object.scaling.input.asu_contents.sequence_file
is not None):
print >> out, " warning: ignoring sequence file"
elif (phil_object.scaling.input.asu_contents.sequence_file
is not None):
print >> out, " determining composition from sequence file %s" % \
phil_object.scaling.input.asu_contents.sequence_file
seq_comp = iotbx.bioinformatics.composition_from_sequence_file(
file_name=phil_object.scaling.input.asu_contents.sequence_file,
log=out)
if (seq_comp is not None):
phil_object.scaling.input.asu_contents.n_residues = seq_comp.n_residues
phil_object.scaling.input.asu_contents.n_bases = seq_comp.n_bases
self.nres_known = True
matthews_results = matthews.matthews_rupp(
crystal_symmetry=miller_array,
n_residues=phil_object.scaling.input.asu_contents.n_residues,
n_bases=phil_object.scaling.input.asu_contents.n_bases,
out=out,
verbose=1)
phil_object.scaling.input.asu_contents.n_residues = matthews_results[0]
phil_object.scaling.input.asu_contents.n_bases = matthews_results[1]
n_copies_solc = matthews_results[2]
self.matthews_results = matthews_results
if phil_object.scaling.input.asu_contents.n_copies_per_asu is not None:
n_copies_solc = phil_object.scaling.input.asu_contents.n_copies_per_asu
self.defined_copies = n_copies_solc
if verbose > 0:
print >> out, "Number of copies per asymmetric unit provided"
print >> out, " Will use user specified value of ", n_copies_solc
else:
phil_object.scaling.input.asu_contents.n_copies_per_asu = n_copies_solc
self.guessed_copies = n_copies_solc
# first report on I over sigma
miller_array_new = miller_array
self.data_strength = None
miller_array_intensities = miller_array
if (original_intensities is not None):
assert original_intensities.is_xray_intensity_array()
miller_array_intensities = original_intensities
if miller_array_intensities.sigmas() is not None:
data_strength = data_statistics.i_sigi_completeness_stats(
miller_array_intensities,
isigi_cut=phil_object.scaling.input.parameters.
misc_twin_parameters.twin_test_cuts.isigi_cut,
completeness_cut=phil_object.scaling.input.parameters.
misc_twin_parameters.twin_test_cuts.completeness_cut,
completeness_as_non_anomalous=completeness_as_non_anomalous)
data_strength.show(out)
self.data_strength = data_strength
if phil_object.scaling.input.parameters.misc_twin_parameters.twin_test_cuts.high_resolution is None:
if data_strength.resolution_cut > data_strength.resolution_at_least:
phil_object.scaling.input.parameters.misc_twin_parameters.twin_test_cuts.high_resolution = data_strength.resolution_at_least
else:
phil_object.scaling.input.parameters.misc_twin_parameters.twin_test_cuts.high_resolution = data_strength.resolution_cut
## Isotropic wilson scaling
if verbose > 0:
print >> out
print >> out
print >> out, "Maximum likelihood isotropic Wilson scaling "
n_residues = phil_object.scaling.input.asu_contents.n_residues
n_bases = phil_object.scaling.input.asu_contents.n_bases
if n_residues is None:
n_residues = 0
if n_bases is None:
n_bases = 0
if n_bases + n_residues == 0:
raise Sorry("No scatterers available")
iso_scale_and_b = absolute_scaling.ml_iso_absolute_scaling(
miller_array=miller_array_new,
n_residues=n_residues * miller_array.space_group().order_z() *
n_copies_solc,
n_bases=n_bases * miller_array.space_group().order_z() *
n_copies_solc)
iso_scale_and_b.show(out=out, verbose=verbose)
self.iso_scale_and_b = iso_scale_and_b
## Store the b and scale values from isotropic ML scaling
self.iso_p_scale = iso_scale_and_b.p_scale
self.iso_b_wilson = iso_scale_and_b.b_wilson
## Anisotropic ml wilson scaling
if verbose > 0:
print >> out
print >> out
print >> out, "Maximum likelihood anisotropic Wilson scaling "
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array=miller_array_new,
n_residues=n_residues * miller_array.space_group().order_z() *
n_copies_solc,
n_bases=n_bases * miller_array.space_group().order_z() *
n_copies_solc)
aniso_scale_and_b.show(out=out, verbose=1)
self.aniso_scale_and_b = aniso_scale_and_b
try:
b_cart = aniso_scale_and_b.b_cart
except AttributeError, e:
print >> out, "*** ERROR ***"
print >> out, str(e)
show_exception_info_if_full_testing()
return
self.aniso_p_scale = aniso_scale_and_b.p_scale
self.aniso_u_star = aniso_scale_and_b.u_star
self.aniso_b_cart = aniso_scale_and_b.b_cart
# XXX: for GUI
self.overall_b_cart = getattr(aniso_scale_and_b, "overall_b_cart",
None)
## Correcting for anisotropy
if verbose > 0:
print >> out, "Correcting for anisotropy in the data"
print >> out
b_cart_observed = aniso_scale_and_b.b_cart
b_trace_average = (b_cart_observed[0] + b_cart_observed[1] +
b_cart_observed[2]) / 3.0
b_trace_min = b_cart_observed[0]
if b_cart_observed[1] < b_trace_min: b_trace_min = b_cart_observed[1]
if b_cart_observed[2] < b_trace_min: b_trace_min = b_cart_observed[2]
if phil_object.scaling.input.optional.aniso.final_b == "eigen_min":
b_use = aniso_scale_and_b.eigen_values[2]
elif phil_object.scaling.input.optional.aniso.final_b == "eigen_mean":
b_use = flex.mean(aniso_scale_and_b.eigen_values)
elif phil_object.scaling.input.optional.aniso.final_b == "user_b_iso":
assert phil_object.scaling.input.optional.aniso.b_iso is not None
b_use = phil_object.scaling.input.optional.aniso.b_iso
else:
b_use = 30
b_cart_aniso_removed = [-b_use, -b_use, -b_use, 0, 0, 0]
u_star_aniso_removed = adptbx.u_cart_as_u_star(
miller_array.unit_cell(), adptbx.b_as_u(b_cart_aniso_removed))
## I do things in two steps, but can easely be done in 1 step
## just for clarity, thats all.
self.no_aniso_array = absolute_scaling.anisotropic_correction(
miller_array_new, 0.0, aniso_scale_and_b.u_star)
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.no_aniso_array, 0.0, u_star_aniso_removed)
self.no_aniso_array = self.no_aniso_array.set_observation_type(
miller_array)
## Make normalised structure factors please
sel_big = self.no_aniso_array.data() > 1.e+50
self.no_aniso_array = self.no_aniso_array.array(
data=self.no_aniso_array.data().set_selected(sel_big, 0))
self.no_aniso_array = self.no_aniso_array.set_observation_type(
miller_array)
normalistion = absolute_scaling.kernel_normalisation(
self.no_aniso_array, auto_kernel=True)
self.normalised_miller = normalistion.normalised_miller.deep_copy()
self.phil_object = phil_object
## Some basic statistics and sanity checks follow
if verbose > 0:
print >> out, "Some basic intensity statistics follow."
print >> out
basic_data_stats = data_statistics.basic_intensity_statistics(
miller_array,
aniso_scale_and_b.p_scale,
aniso_scale_and_b.u_star,
iso_scale_and_b.scat_info,
out=out,
out_plot=out_plot)
self.basic_data_stats = basic_data_stats
self.miller_array = basic_data_stats.new_miller
#relative wilson plot
self.rel_wilson = None
if (miller_calc is not None) and (miller_calc.d_min() < 4.0):
try:
self.rel_wilson = relative_wilson.relative_wilson(
miller_obs=miller_array, miller_calc=miller_calc)
except RuntimeError, e:
print >> out, "*** Error calculating relative Wilson plot - skipping."
print >> out, ""
def __init__(self,
miller_array,
pre_scaling_protocol,
basic_info,
out=None):
## Make deep copy of the miller array of interest
self.x1 = miller_array.deep_copy()
self.options=pre_scaling_protocol
self.basic_info= basic_info
## Determine unit_cell contents
print(file=out)
print("Matthews analyses", file=out)
print("-----------------", file=out)
print(file=out)
print("Inspired by: Kantardjieff and Rupp. Prot. Sci. 12(9): 1865-1871 (2003).", file=out)
matthews_analyses = matthews.matthews_rupp(
crystal_symmetry = self.x1,
n_residues = self.basic_info.n_residues,
n_bases = self.basic_info.n_bases,
out=out, verbose=1)
n_residues=matthews_analyses[0]
n_bases=matthews_analyses[1]
n_copies_solc=matthews_analyses[2]
if (self.basic_info.n_residues==None):
self.basic_info.n_residues = n_residues
if (self.basic_info.n_bases == None):
self.basic_info.n_bases = n_bases
## apply resolution cut
print(file=out)
print("Applying resolution cut", file=out)
print("-----------------------", file=out)
if self.options.low_resolution is None:
if self.options.high_resolution is None:
print("No resolution cut is made", file=out)
low_cut=float(1e6)
if self.options.low_resolution is not None:
low_cut = self.options.low_resolution
print("Specified low resolution limit: %3.2f"%(
float(self.options.low_resolution) ), file=out)
high_cut = 0
if self.options.high_resolution is not None:
high_cut = self.options.high_resolution
print("Specified high resolution limit: %3.2f"%(
float(self.options.high_resolution) ), file=out)
## perform outlier analyses
##
## Do a simple outlier analyses please
print(file=out)
print("Wilson statistics based outlier analyses", file=out)
print("----------------------------------------", file=out)
print(file=out)
native_outlier = data_statistics.possible_outliers(
miller_array = self.x1,
prob_cut_ex = self.options.outlier_level_extreme,
prob_cut_wil = self.options.outlier_level_wilson )
native_outlier.show(out=out)
self.x1 = native_outlier.remove_outliers(
self.x1 )
## apply anisotropic scaling (final B-value will be set to b_add)!
if self.options.aniso_correction:
b_final = self.options.b_add
if b_final is None:
b_final = 0.0
print(file=out)
print("Anisotropic absolute scaling of data", file=out)
print("--------------------------------------", file=out)
print(file=out)
aniso_correct = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = self.x1,
n_residues = n_residues*\
self.x1.space_group().order_z()*n_copies_solc,
n_bases = n_bases*\
self.x1.space_group().order_z()*n_copies_solc)
aniso_correct.show(out=out,verbose=1)
print(file=out)
print(" removing anisotropy for native ", file=out)
print(file=out)
u_star_correct_nat = aniso_correct.u_star
self.x1 = absolute_scaling.anisotropic_correction(
self.x1,
aniso_correct.p_scale,
u_star_correct_nat )
def __init__(self,
miller_array,
parameters,
out=None,
n_residues=100,
n_bases=0):
self.params=parameters
self.miller_array=miller_array.deep_copy().set_observation_type(
miller_array).merge_equivalents().array()
self.out = out
if self.out is None:
self.out = sys.stdout
if self.out == "silent":
self.out = null_out()
self.no_aniso_array = self.miller_array
if self.params.aniso.action == "remove_aniso":
# first perfom aniso scaling
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = self.miller_array,
n_residues = n_residues,
n_bases = n_bases)
aniso_scale_and_b.p_scale = 0 # set the p_scale back to 0!
aniso_scale_and_b.show(out=out)
# now do aniso correction please
self.aniso_p_scale = aniso_scale_and_b.p_scale
self.aniso_u_star = aniso_scale_and_b.u_star
self.aniso_b_cart = aniso_scale_and_b.b_cart
if self.params.aniso.final_b == "eigen_min":
b_use=aniso_scale_and_b.eigen_values[2]
elif self.params.aniso.final_b == "eigen_mean" :
b_use=flex.mean(aniso_scale_and_b.eigen_values)
elif self.params.aniso.final_b == "user_b_iso":
assert self.params.aniso.b_iso is not None
b_use=self.params.aniso.b_iso
else:
b_use = 30
b_cart_aniso_removed = [ -b_use,
-b_use,
-b_use,
0,
0,
0]
u_star_aniso_removed = adptbx.u_cart_as_u_star(
miller_array.unit_cell(),
adptbx.b_as_u( b_cart_aniso_removed ) )
## I do things in two steps, but can easely be done in 1 step
## just for clarity, thats all.
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.miller_array,0.0,aniso_scale_and_b.u_star )
self.no_aniso_array = absolute_scaling.anisotropic_correction(
self.no_aniso_array,0.0,u_star_aniso_removed)
self.no_aniso_array = self.no_aniso_array.set_observation_type(
miller_array )
# that is done now, now we can do outlier detection if desired
outlier_manager = outlier_rejection.outlier_manager(
self.no_aniso_array,
None,
out=self.out)
self.new_miller_array = self.no_aniso_array
if self.params.outlier.action == "basic":
print >> self.out, "Non-outliers found by the basic wilson statistics"
print >> self.out, "protocol will be written out."
basic_array = outlier_manager.basic_wilson_outliers(
p_basic_wilson = self.params.outlier.parameters.basic_wilson.level,
return_data = True)
self.new_miller_array = basic_array
if self.params.outlier.action == "extreme":
print >> self.out, "Non-outliers found by the extreme value wilson statistics"
print >> self.out, "protocol will be written out."
extreme_array = outlier_manager.extreme_wilson_outliers(
p_extreme_wilson = self.params.outlier.parameters.extreme_wilson.level,
return_data = True)
self.new_miller_array = extreme_array
if self.params.outlier.action == "beamstop":
print >> self.out, "Outliers found for the beamstop shadow"
print >> self.out, "problems detection protocol will be written out."
beamstop_array = outlier_manager.beamstop_shadow_outliers(
level = self.params.outlier.parameters.beamstop.level,
d_min = self.params.outlier.parameters.beamstop.d_min,
return_data=True)
self.new_miller_array = beamstop_array
if self.params.outlier.action == "None":
self.new_miller_array = self.no_aniso_array
# now we can twin or detwin the data if needed
self.final_array = self.new_miller_array
if self.params.symmetry.action == "twin":
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None) :
raise Sorry("Twin fraction not specified, not twinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out
print >> self.out, "Twinning given data"
print >> self.out, "-------------------"
print >> self.out
print >> self.out, "Artifically twinning the data with fraction %3.2f" %\
alpha
self.final_array = self.new_miller_array.twin_data(
twin_law = self.params.symmetry.twinning_parameters.twin_law,
alpha=alpha).as_intensity_array()
elif (self.params.symmetry.action == "detwin") :
twin_law = self.params.symmetry.twinning_parameters.twin_law
alpha = self.params.symmetry.twinning_parameters.fraction
if (alpha is None) :
raise Sorry("Twin fraction not specified, not detwinning data")
elif not (0 <= alpha <= 0.5):
raise Sorry("Twin fraction must be between 0 and 0.5.")
print >> self.out, """
Attempting to detwin data
-------------------------
Detwinning data with:
- twin law: %s
- twin fraciton: %.2f
BE WARNED! DETWINNING OF DATA DOES NOT SOLVE YOUR TWINNING PROBLEM!
PREFERABLY, REFINEMENT SHOULD BE CARRIED OUT AGAINST ORIGINAL DATA
ONLY USING A TWIN SPECIFIC TARGET FUNCTION!
""" % (twin_law, alpha)
self.final_array = self.new_miller_array.detwin_data(
twin_law=twin_law,
alpha=alpha).as_intensity_array()
assert self.final_array is not None
def __init__(self,
miller_array,
pre_scaling_protocol,
basic_info,
out=None):
## Make deep copy of the miller array of interest
self.x1 = miller_array.deep_copy()
self.options=pre_scaling_protocol
self.basic_info= basic_info
## Determine unit_cell contents
print >> out
print >> out, "Matthews analyses"
print >> out, "-----------------"
print >> out
print >> out, "Inspired by: Kantardjieff and Rupp. Prot. Sci. 12(9): 1865-1871 (2003)."
matthews_analyses = matthews.matthews_rupp(
crystal_symmetry = self.x1,
n_residues = self.basic_info.n_residues,
n_bases = self.basic_info.n_bases,
out=out, verbose=1)
n_residues=matthews_analyses[0]
n_bases=matthews_analyses[1]
n_copies_solc=matthews_analyses[2]
if (self.basic_info.n_residues==None):
self.basic_info.n_residues = n_residues
if (self.basic_info.n_bases == None):
self.basic_info.n_bases = n_bases
## apply resolution cut
print >> out
print >> out, "Applying resolution cut"
print >> out, "-----------------------"
if self.options.low_resolution is None:
if self.options.high_resolution is None:
print >> out, "No resolution cut is made"
low_cut=float(1e6)
if self.options.low_resolution is not None:
low_cut = self.options.low_resolution
print >> out, "Specified low resolution limit: %3.2f"%(
float(self.options.low_resolution) )
high_cut = 0
if self.options.high_resolution is not None:
high_cut = self.options.high_resolution
print >> out, "Specified high resolution limit: %3.2f"%(
float(self.options.high_resolution) )
## perform outlier analyses
##
## Do a simple outlier analyses please
print >> out
print >> out, "Wilson statistics based outlier analyses"
print >> out, "----------------------------------------"
print >> out
native_outlier = data_statistics.possible_outliers(
miller_array = self.x1,
prob_cut_ex = self.options.outlier_level_extreme,
prob_cut_wil = self.options.outlier_level_wilson )
native_outlier.show(out=out)
self.x1 = native_outlier.remove_outliers(
self.x1 )
## apply anisotropic scaling (final B-value will be set to b_add)!
if self.options.aniso_correction:
b_final = self.options.b_add
if b_final is None:
b_final = 0.0
print >> out
print >> out, "Anisotropic absolute scaling of data"
print >> out, "--------------------------------------"
print >> out
aniso_correct = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = self.x1,
n_residues = n_residues*\
self.x1.space_group().order_z()*n_copies_solc,
n_bases = n_bases*\
self.x1.space_group().order_z()*n_copies_solc)
aniso_correct.show(out=out,verbose=1)
print >> out
print >> out, " removing anisotropy for native "
print >> out
u_star_correct_nat = aniso_correct.u_star
self.x1 = absolute_scaling.anisotropic_correction(
self.x1,
aniso_correct.p_scale,
u_star_correct_nat )
