def run(args):
assert args in [[], ["silent"]]
if (len(args) == 0):
libtbx.env.full_testing = True
else:
libtbx.env.full_testing = False
try:
raise RuntimeError("Just for testing.")
except RuntimeError:
show_exception_info_if_full_testing()
def run(args):
assert args in [[], ["silent"]]
if (len(args) == 0):
libtbx.env.full_testing = True
else:
libtbx.env.full_testing = False
try:
raise RuntimeError("Just for testing.")
except RuntimeError:
show_exception_info_if_full_testing()
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,
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,
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
