def ml_normalisation(self, aniso=False):
# estimate number of residues per unit cell
mr = matthews.matthews_rupp(self.intensities.crystal_symmetry())
n_residues = mr.n_residues
# estimate B-factor and scale factors for normalisation
if aniso:
normalisation = absolute_scaling.ml_aniso_absolute_scaling(
self.intensities, n_residues=n_residues)
u_star = normalisation.u_star
else:
normalisation = absolute_scaling.ml_iso_absolute_scaling(
self.intensities, n_residues=n_residues)
u_star = adptbx.b_as_u(
adptbx.u_iso_as_u_star(
self.intensities.unit_cell(), normalisation.b_wilson))
# apply scales
self.intensities = self.intensities.customized_copy(
data=scaling.ml_normalise_aniso(
self.intensities.indices(), self.intensities.data(),
normalisation.p_scale, self.intensities.unit_cell(),
u_star),
sigmas=scaling.ml_normalise_aniso(
self.intensities.indices(), self.intensities.sigmas(),
normalisation.p_scale, self.intensities.unit_cell(),
u_star)).set_info(self.intensities.info())
# record output in log file
s = StringIO()
mr.show(out=s)
normalisation.show(out=s)
logger.info(s.getvalue())
def get_b_aniso_mean(i_obs):
from mmtbx.scaling import absolute_scaling
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(miller_array=i_obs, n_residues=200, n_bases=0)
try:
b_cart = aniso_scale_and_b.b_cart
except AttributeError, e:
raise Sorry("\nCannot correct the column %s for anisotropy\n" % (best_label))
def wilson_scaling(F, n_residues, n_bases):
from mmtbx.scaling import absolute_scaling
iso_scale = absolute_scaling.ml_iso_absolute_scaling(miller_array=F,
n_residues=n_residues,
n_bases=n_bases)
aniso_scale = absolute_scaling.ml_aniso_absolute_scaling(
miller_array=F, n_residues=n_residues, n_bases=n_bases)
return iso_scale, aniso_scale
def _ml_normalisation(intensities, aniso):
# estimate number of residues per unit cell
mr = matthews.matthews_rupp(intensities.crystal_symmetry())
n_residues = mr.n_residues
# estimate B-factor and scale factors for normalisation
if aniso:
normalisation = absolute_scaling.ml_aniso_absolute_scaling(
intensities, n_residues=n_residues
)
u_star = normalisation.u_star
else:
normalisation = absolute_scaling.ml_iso_absolute_scaling(
intensities, n_residues=n_residues
)
u_star = adptbx.b_as_u(
adptbx.u_iso_as_u_star(intensities.unit_cell(), normalisation.b_wilson)
)
# record output in log file
if aniso:
b_cart = normalisation.b_cart
logger.info("ML estimate of overall B_cart value:")
logger.info(
"""\
%5.2f, %5.2f, %5.2f
%12.2f, %5.2f
%19.2f"""
% (b_cart[0], b_cart[3], b_cart[4], b_cart[1], b_cart[5], b_cart[2])
)
else:
logger.info("ML estimate of overall B value:")
logger.info(" %5.2f A**2" % normalisation.b_wilson)
logger.info("ML estimate of -log of scale factor:")
logger.info(" %5.2f" % (normalisation.p_scale))
s = StringIO()
mr.show(out=s)
normalisation.show(out=s)
logger.debug(s.getvalue())
# apply scales
return intensities.customized_copy(
data=scaling.ml_normalise_aniso(
intensities.indices(),
intensities.data(),
normalisation.p_scale,
intensities.unit_cell(),
u_star,
),
sigmas=scaling.ml_normalise_aniso(
intensities.indices(),
intensities.sigmas(),
normalisation.p_scale,
intensities.unit_cell(),
u_star,
),
)
def wilson_scaling (F, n_residues, n_bases) :
from mmtbx.scaling import absolute_scaling
iso_scale = absolute_scaling.ml_iso_absolute_scaling(
miller_array=F,
n_residues=n_residues,
n_bases=n_bases)
aniso_scale = absolute_scaling.ml_aniso_absolute_scaling(
miller_array=F,
n_residues=n_residues,
n_bases=n_bases)
return iso_scale, aniso_scale
def calc_principal_vectors(miller_array, log_out):
"""
Reference: Aimless program
Triclinic & Monoclinic: from B_cart
Orthorhombic: along a*, b*, c*
Trigonal, Hexagonal, Tetragonal: c* and a*b*-plane
Cubic: no anisotropy
return type: list of (vector, label, in-plane or not)
"""
n_residues = p_vm_calculator(miller_array, 1, 0).best_guess
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(miller_array=miller_array,
n_residues=n_residues,
n_bases=0)
if aniso_scale_and_b.eigen_values[0] == 0:
print >>log_out, "Error! Cannot determine B_cart"
return
b_cart = aniso_scale_and_b.b_cart
print >>log_out, """ML estimate of overall B_cart:
/ %5.2f %5.2f %5.2f \\
| %11.2f %5.2f |
\\ %17.2f /
""" % (b_cart[0], b_cart[3], b_cart[4],
b_cart[1], b_cart[5], b_cart[2])
ev = aniso_scale_and_b.eigen_vectors
orth_mat = numpy.array(miller_array.unit_cell().orthogonalization_matrix()).reshape(3,3)
print >>log_out, "Eigenvalues/vectors:"
for i, eg in enumerate(aniso_scale_and_b.eigen_values):
v = ev[3*i:3*(i+1)]
vs = ", ".join(map(lambda x: "% .4f"%x, v))
vl = axis_label(v, orth_mat)
print >>log_out, " %8.3f (%s) %s" % (eg, vs, vl)
cs = miller_array.space_group().crystal_system()
if cs == "Cubic":
print >>log_out, "No anisotropy in this symmetry."
return []
elif cs == "Orthorhombic":
return ([1.,0.,0.], "a*", False), ([0.,1.,0.], "b*", False), ([0.,0.,1.], "c*", False)
elif cs in ("Triclinic", "Monoclinic"):
return ((ev[:3], axis_label(ev[:3], orth_mat), False),
(ev[3:6], axis_label(ev[3:6], orth_mat), False),
(ev[6:], axis_label(ev[6:], orth_mat), False))
else: # in "Tetragonal", "Hexagonal", "Trigonal", "Cubic"
return ([0.,0.,1.], "c*", False), ([0.,0.,1.], "a*b*", True)
def test_scaling_on_random_data(B_add):
miller_array = random_data(B_add, n_residues=100.0)
scale_object_iso = absolute_scaling.ml_iso_absolute_scaling(
miller_array, n_residues=100.0)
## compare the results please
assert approx_equal(B_add, scale_object_iso.b_wilson, eps=5)
scale_object_aniso = absolute_scaling.ml_aniso_absolute_scaling(
miller_array, n_residues=100.0)
assert approx_equal(B_add, scale_object_aniso.b_cart[0], eps=5)
assert approx_equal(B_add, scale_object_aniso.b_cart[1], eps=5)
assert approx_equal(B_add, scale_object_aniso.b_cart[2], eps=5)
def get_b_aniso_mean(i_obs):
from mmtbx.scaling import absolute_scaling
aniso_scale_and_b = absolute_scaling.ml_aniso_absolute_scaling(
miller_array = i_obs,
n_residues = 200,
n_bases = 0)
try: b_cart=aniso_scale_and_b.b_cart
except AttributeError as e:
raise Sorry("\nCannot correct the column %s for anisotropy\n"%(
best_label))
b_aniso_mean=0.
for k in [0,1,2]:
b_aniso_mean+=b_cart[k]
return b_aniso_mean/3.0
def test_scaling_on_random_data(B_add):
miller_array = random_data(B_add,n_residues=100.0)
scale_object_iso = absolute_scaling.ml_iso_absolute_scaling(
miller_array,
n_residues=100.0)
## compare the results please
assert approx_equal(B_add, scale_object_iso.b_wilson, eps=5)
scale_object_aniso = absolute_scaling.ml_aniso_absolute_scaling(
miller_array,
n_residues=100.0)
assert approx_equal(B_add, scale_object_aniso.b_cart[0], eps=5)
assert approx_equal(B_add, scale_object_aniso.b_cart[1], eps=5)
assert approx_equal(B_add, scale_object_aniso.b_cart[2], eps=5)
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 run(hklin, pdbin, wdir, anisotropy_correction=False):
arrays = iotbx.file_reader.any_file(hklin).file_server.miller_arrays
i_arrays = filter(
lambda x: x.is_xray_intensity_array() and x.anomalous_flag(), arrays)
f_arrays = filter(
lambda x: x.is_xray_amplitude_array() and x.anomalous_flag(), arrays)
if not i_arrays and not f_arrays:
print "No anomalous observation data"
return
if os.path.exists(wdir):
print "%s already exists. quiting." % wdir
return
os.mkdir(wdir)
xs = crystal_symmetry_from_any.extract_from(pdbin)
sh_out = open(os.path.join(wdir, "run_anode.sh"), "w")
sh_out.write("#!/bin/sh\n\n")
sh_out.write("shelxc anode <<+ > shelxc.log 2>&1\n")
sh_out.write("cell %s\n" % format_unit_cell(xs.unit_cell()))
sh_out.write("spag %s\n" % str(xs.space_group_info()).replace(" ", ""))
if i_arrays:
obs_array = i_arrays[0]
infile = "%s.hkl" % os.path.splitext(os.path.basename(hklin))[0]
in_opt = "%s" % infile
print "Using intensity array:", obs_array.info().label_string()
else:
obs_array = f_arrays[0]
infile = "%s_f.hkl" % os.path.splitext(os.path.basename(hklin))[0]
in_opt = "-f %s" % infile
print "No intensity arrays. Using amplitude arrays instead:", obs_array.info(
).label_string()
sh_out.write("! data from %s : %s\n" %
(os.path.abspath(hklin), obs_array.info().label_string()))
obs_array.crystal_symmetry().show_summary(sh_out, prefix="! ")
check_symm(obs_array.crystal_symmetry(), xs)
n_org = obs_array.size()
obs_array = obs_array.eliminate_sys_absent()
n_sys_abs = n_org - obs_array.size()
if n_sys_abs > 0:
print " %d systematic absences removed." % n_sys_abs
if anisotropy_correction:
print "Correcting anisotropy.."
n_residues = p_vm_calculator(obs_array, 1, 0).best_guess
abss = ml_aniso_absolute_scaling(obs_array, n_residues=n_residues)
abss.show()
tmp = -2. if i_arrays else -1.
b_cart = map(lambda x: x * tmp, abss.b_cart)
obs_array = obs_array.apply_debye_waller_factors(b_cart=b_cart)
sh_out.write("sad %s\n" % in_opt)
iotbx.shelx.hklf.miller_array_export_as_shelx_hklf(
obs_array,
open(os.path.join(wdir, infile), "w"),
normalise_if_format_overflow=True)
sh_out.write("+\n\n")
sh_out.write('ln -s "%s" anode.pdb\n\n' % os.path.relpath(pdbin, wdir))
sh_out.write("anode anode\n")
sh_out.close()
call(cmd="sh", arg="./run_anode.sh", wdir=wdir)
pha_file = os.path.join(wdir, "anode.pha")
if os.path.isfile(pha_file):
pha2mtz(pha_file, xs, os.path.join(wdir, "anode.pha.mtz"))
print "Done. See %s/" % wdir
fa_file = os.path.join(wdir, "anode_fa.hkl")
if os.path.isfile(fa_file):
r = iotbx.shelx.hklf.reader(open(fa_file))
fa_array = r.as_miller_arrays(crystal_symmetry=xs)[0]
print "\nData stats:"
print " # Cmpl.o = Anomalous completeness in original data"
print " # Cmpl.c = Anomalous completeness in shelxc result (rejections)"
print " # SigAno = <d''/sigma> in shelxc result"
print " d_max d_min Cmpl.o Cmpl.c SigAno"
binner = obs_array.setup_binner(n_bins=12)
for i_bin in binner.range_used():
d_max_bin, d_min_bin = binner.bin_d_range(i_bin)
obs_sel = obs_array.resolution_filter(d_max_bin, d_min_bin)
obs_sel_ano = obs_sel.anomalous_differences()
fa_sel = fa_array.resolution_filter(d_max_bin, d_min_bin)
cmplset = obs_sel_ano.complete_set(
d_max=d_max_bin, d_min=d_min_bin).select_acentric()
n_acentric = cmplset.size()
sigano = flex.mean(
fa_sel.data() /
fa_sel.sigmas()) if fa_sel.size() else float("nan")
print " %5.2f %5.2f %6.2f %6.2f %6.2f" % (
d_max_bin, d_min_bin, 100. * obs_sel_ano.size() / n_acentric,
100. * fa_sel.size() / n_acentric, sigano)
lsa_file = os.path.join(wdir, "anode.lsa")
if os.path.isfile(lsa_file):
print ""
flag = False
for l in open(lsa_file):
if "Strongest unique anomalous peaks" in l:
flag = True
elif "Reflections written to" in l:
flag = False
if flag:
print l.rstrip()
if os.path.isfile(("anode_fa.res")):
x = iotbx.shelx.cctbx_xray_structure_from(file=open("anode_fa.res"))
open("anode_fa.pdb", "w").write(x.as_pdb_file())
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
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,
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
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,
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(file=out)
print(file=out)
print("Matthews coefficient and Solvent content statistics",
file=out)
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(" warning: ignoring sequence file", file=out)
elif (phil_object.scaling.input.asu_contents.sequence_file
is not None):
print(" determining composition from sequence file %s" % \
phil_object.scaling.input.asu_contents.sequence_file, file=out)
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("Number of copies per asymmetric unit provided",
file=out)
print(" Will use user specified value of ",
n_copies_solc,
file=out)
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(file=out)
print(file=out)
print("Maximum likelihood isotropic Wilson scaling ", file=out)
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(file=out)
print(file=out)
print("Maximum likelihood anisotropic Wilson scaling ", file=out)
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 as e:
print("*** ERROR ***", file=out)
print(str(e), file=out)
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("Correcting for anisotropy in the data", file=out)
print(file=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("Some basic intensity statistics follow.", file=out)
print(file=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 as e:
print("*** Error calculating relative Wilson plot - skipping.",
file=out)
print("", file=out)
if verbose > 0:
print("Basic analyses completed", file=out)
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 )
