def E4(ma): import sys import cStringIO cache_stdout = sys.stdout sys.stdout = cStringIO.StringIO() from mmtbx.scaling.twin_analyses import twin_analyses analysis = twin_analyses(ma) sys.stdout = cache_stdout return analysis.wilson_moments.acentric_i_ratio
def E4(ma):
import sys
import cStringIO
cache_stdout = sys.stdout
sys.stdout = cStringIO.StringIO()
sg = ma.space_group()
if sg.is_centric():
asg = sg.build_derived_acentric_group()
ma = ma.customized_copy(space_group_info=asg.info())
from mmtbx.scaling.twin_analyses import twin_analyses
analysis = twin_analyses(ma)
sys.stdout = cache_stdout
return analysis.wilson_moments.acentric_i_ratio
def E4(ma):
import sys
from six.moves import cStringIO as StringIO
cache_stdout = sys.stdout
sys.stdout = StringIO()
sg = ma.space_group()
if sg.is_centric():
asg = sg.build_derived_acentric_group()
ma = ma.customized_copy(space_group_info=asg.info())
from mmtbx.scaling.twin_analyses import twin_analyses
analysis = twin_analyses(ma)
sys.stdout = cache_stdout
return analysis.wilson_moments.acentric_i_ratio
def twin_the_data_and_analyse(twin_operator, twin_fraction=0.2):
out_string = StringIO()
miller_array = random_data(35).map_to_asu()
miller_array = miller_array.f_as_f_sq()
cb_op = sgtbx.change_of_basis_op(twin_operator)
miller_array_mod, miller_array_twin = miller_array.common_sets(
miller_array.change_basis(cb_op).map_to_asu())
twinned_miller = miller_array_mod.customized_copy(
data = (1.0-twin_fraction)*miller_array_mod.data()
+ twin_fraction*miller_array_twin.data(),
sigmas = flex.sqrt(
flex.pow( ((1.0-twin_fraction)*miller_array_mod.sigmas()),2.0)+\
flex.pow( ((twin_fraction)*miller_array_twin.sigmas()),2.0))
)
twinned_miller.set_observation_type(miller_array.observation_type())
twin_anal_object = t_a.twin_analyses(twinned_miller,
out=out_string,
verbose=-100)
index = twin_anal_object.twin_summary.most_worrysome_twin_law
assert approx_equal(twin_anal_object.twin_summary.britton_alpha[index],
twin_fraction,
eps=0.1)
assert approx_equal(twin_anal_object.twin_law_dependent_analyses[index].
ml_murray_rust.estimated_alpha,
twin_fraction,
eps=0.1)
## Untwinned data standards
if twin_fraction == 0:
## L-test
assert approx_equal(twin_anal_object.l_test.mean_l, 0.50, eps=0.1)
## Wilson ratios
assert approx_equal(twin_anal_object.twin_summary.i_ratio,
2.00,
eps=0.1)
## H-test
assert approx_equal(
twin_anal_object.twin_law_dependent_analyses[index].h_test.mean_h,
0.50,
eps=0.1)
## Perfect twin standards
if twin_fraction == 0.5:
assert approx_equal(twin_anal_object.l_test.mean_l, 0.375, eps=0.1)
assert approx_equal(twin_anal_object.twin_summary.i_ratio,
1.50,
eps=0.1)
assert approx_equal(
twin_anal_object.twin_law_dependent_analyses[index].h_test.mean_h,
0.00,
eps=0.1)
## Just make sure we actually detect significant twinning
if twin_fraction > 0.10:
assert (twin_anal_object.twin_summary.maha_l > 3.0)
## The patterson origin peak should be smallish ...
assert (twin_anal_object.twin_summary.patterson_p_value > 0.01)
# and the brief test should be passed as well
answer = t_a.twin_analyses_brief(twinned_miller,
out=out_string,
verbose=-100)
if twin_fraction > 0.10:
assert answer is True
def exercise_twin_detection(verbose=False):
# simple model with translational pseudosymmetry
# XXX one big disadvantage: no centric reflections!
pdb_str = """\
CRYST1 12.000 8.000 12.000 90.02 89.96 90.05 P 1 1
ATOM 39 N ASN A 6 5.514 2.664 4.856 1.00 11.99 N
ATOM 40 CA ASN A 6 6.831 2.310 4.318 1.00 12.30 C
ATOM 41 C ASN A 6 7.854 2.761 5.324 1.00 13.40 C
ATOM 42 O ASN A 6 8.219 3.943 5.374 1.00 13.92 O
ATOM 43 CB ASN A 6 7.065 3.016 2.993 1.00 12.13 C
ATOM 44 CG ASN A 6 5.961 2.735 2.003 1.00 12.77 C
ATOM 45 OD1 ASN A 6 5.798 1.604 1.551 1.00 14.27 O
ATOM 46 ND2 ASN A 6 5.195 3.747 1.679 1.00 10.07 N
ATOM 47 N TYR A 7 8.292 1.817 6.147 1.00 14.70 N
ATOM 48 CA TYR A 7 9.159 2.144 7.299 1.00 15.18 C
ATOM 49 C TYR A 7 10.603 2.331 6.885 1.00 15.91 C
ATOM 50 O TYR A 7 11.041 1.811 5.855 1.00 15.76 O
ATOM 51 CB TYR A 7 9.061 1.065 8.369 1.00 15.35 C
ATOM 52 CG TYR A 7 7.665 0.929 8.902 1.00 14.45 C
ATOM 53 CD1 TYR A 7 6.771 0.021 8.327 1.00 15.68 C
ATOM 54 CD2 TYR A 7 7.210 1.756 9.920 1.00 14.80 C
ATOM 55 CE1 TYR A 7 5.480 -0.094 8.796 1.00 13.46 C
ATOM 56 CE2 TYR A 7 5.904 1.649 10.416 1.00 14.33 C
ATOM 57 CZ TYR A 7 5.047 0.729 9.831 1.00 15.09 C
ATOM 58 OH TYR A 7 3.766 0.589 10.291 1.00 14.39 O
ATOM 59 OXT TYR A 7 11.358 2.999 7.612 1.00 17.49 O
TER
ATOM 1 N ASN B 6 1.414 5.113 6.019 1.00 12.99 N
ATOM 2 CA ASN B 6 2.720 4.776 5.445 1.00 13.30 C
ATOM 3 C ASN B 6 3.763 5.209 6.438 1.00 14.40 C
ATOM 4 O ASN B 6 4.125 6.391 6.507 1.00 14.92 O
ATOM 5 CB ASN B 6 2.922 5.513 4.131 1.00 13.13 C
ATOM 6 CG ASN B 6 1.798 5.250 3.160 1.00 13.77 C
ATOM 7 OD1 ASN B 6 1.629 4.129 2.686 1.00 15.27 O
ATOM 8 ND2 ASN B 6 1.022 6.266 2.875 1.00 11.07 N
ATOM 9 N TYR B 7 4.222 4.248 7.230 1.00 15.70 N
ATOM 10 CA TYR B 7 5.113 4.552 8.370 1.00 16.18 C
ATOM 11 C TYR B 7 6.547 4.754 7.929 1.00 16.91 C
ATOM 12 O TYR B 7 6.964 4.259 6.878 1.00 16.76 O
ATOM 13 CB TYR B 7 5.042 3.449 9.417 1.00 16.35 C
ATOM 14 CG TYR B 7 3.659 3.296 9.977 1.00 15.45 C
ATOM 15 CD1 TYR B 7 2.756 2.398 9.402 1.00 16.68 C
ATOM 16 CD2 TYR B 7 3.224 4.098 11.023 1.00 15.80 C
ATOM 17 CE1 TYR B 7 1.476 2.267 9.896 1.00 14.46 C
ATOM 18 CE2 TYR B 7 1.929 3.975 11.545 1.00 15.33 C
ATOM 19 CZ TYR B 7 1.063 3.065 10.959 1.00 16.09 C
ATOM 20 OH TYR B 7 -0.207 2.910 11.443 1.00 15.39 O
ATOM 21 OXT TYR B 7 7.316 5.408 8.654 1.00 18.49 O
END
"""
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=pdb_str)
xrs = pdb_in.input.xray_structure_simple()
fc = abs(xrs.structure_factors(d_min=2.5).f_calc())
fc = fc.set_observation_type_xray_amplitude()
sigf = flex.double(fc.size(), 0.1) + (fc.data() * 0.03)
fc = fc.customized_copy(sigmas=sigf)
# and now add twinning
fc_twin = fc.twin_data(twin_law='-l,-k,-h', alpha=0.4)
fc_twin.as_mtz_dataset(
column_root_label="F").mtz_object().write("tmp_xtriage_twinned.mtz")
# twin_laws
laws = twin_analyses.twin_laws(miller_array=fc_twin)
assert (len(laws.operators) == 7)
delta_santoro = [tl.delta_santoro for tl in laws.operators]
assert approx_equal(
delta_santoro,
[0.104655, 0.104655, 0.066599, 0.104655, 0.076113, 0.066599, 0.028542])
delta_le_page = [tl.delta_le_page for tl in laws.operators]
assert approx_equal(
delta_le_page,
[0.053839, 0.053839, 0.053839, 0.064020, 0.044706, 0.049480, 0.021221])
delta_lebedev = [tl.delta_lebedev for tl in laws.operators]
assert approx_equal(
delta_lebedev,
[0.000787, 0.000787, 0.000767, 0.000912, 0.000637, 0.000705, 0.000302])
# TNCS
tps = twin_analyses.detect_pseudo_translations(miller_array=fc_twin,
out=null_out())
assert ([tps.mod_h, tps.mod_k, tps.mod_l] == [3, 2, 3])
assert approx_equal(tps.high_peak, 47.152352)
assert approx_equal(tps.high_p_value, 0.000103)
# L test
fc_norm_acentric = twin_analyses.wilson_normalised_intensities(
miller_array=fc_twin, normalise=True, out=null_out()).acentric
ltest = twin_analyses.l_test(miller_array=fc_norm_acentric,
parity_h=3,
parity_l=3)
assert approx_equal(ltest.mean_l, 0.498974)
assert approx_equal(ltest.mean_l2, 0.371674)
# we need to go through the wrapper class for a lot of these...
out = StringIO()
tw = twin_analyses.twin_analyses(miller_array=fc_twin, out=out)
tw2 = twin_analyses.twin_analyses(miller_array=fc_twin,
out=out,
d_hkl_for_l_test=[3, 2, 3])
# Wilson moments
wm = tw.wilson_moments
assert ([
wm.centric_i_ratio, wm.centric_f_ratio, wm.centric_e_sq_minus_one
] == [None, None, None])
assert approx_equal(wm.acentric_i_ratio, 2.878383)
assert approx_equal(wm.acentric_f_ratio, 0.717738)
assert approx_equal(wm.acentric_abs_e_sq_minus_one, 0.808899)
assert (not wm.centric_present)
# Overall analyses
out = StringIO()
tw.show(out=out)
assert ("significantly different than is expected" in out.getvalue())
summary = tw.twin_summary
assert approx_equal(summary.l_mean, 0.4989738)
assert approx_equal(summary.patterson_height, 47.152352)
for k, twin_r_factor in enumerate(summary.r_obs):
if (k == 6): assert twin_r_factor < 0.11
else: assert twin_r_factor > 0.3
out2 = StringIO()
tw.l_test.show(out=out2)
assert ("""\
Mean |L| :0.499 (untwinned: 0.500; perfect twin: 0.375)
Mean L^2 :0.372 (untwinned: 0.333; perfect twin: 0.200)
""" in out2.getvalue()), out2.getvalue()
out3 = StringIO()
tw2.l_test.show(out=out3)
assert not show_diff(out2.getvalue(), out3.getvalue())
if (verbose):
print out.getvalue()
# twin_results_interpretation object via cctbx.miller.array API extension
# XXX I get slightly different numbers here versus running through the
# twin_analyses call above - this seems to be caused by the resolution
# limits passed here. Need to confirm these with PHZ.
result = fc_twin.analyze_intensity_statistics()
assert approx_equal(result.maha_l, 27.580674)
assert approx_equal(
result.r_obs,
[0.514901, 0.514901, 0.397741, 0.580353, 0.579294, 0.420914, 0.114999])
assert approx_equal(
result.h_alpha,
[0.019980, 0.019980, 0.211788, 0.073926, 0.079920, 0.150849, 0.389610])
assert result.has_twinning()
def twin_the_data_and_analyse(twin_operator,twin_fraction=0.2):
out_string = StringIO()
miller_array = random_data(35).map_to_asu()
miller_array = miller_array.f_as_f_sq()
cb_op = sgtbx.change_of_basis_op( twin_operator )
miller_array_mod, miller_array_twin = miller_array.common_sets(
miller_array.change_basis( cb_op ).map_to_asu() )
twinned_miller = miller_array_mod.customized_copy(
data = (1.0-twin_fraction)*miller_array_mod.data()
+ twin_fraction*miller_array_twin.data(),
sigmas = flex.sqrt(
flex.pow( ((1.0-twin_fraction)*miller_array_mod.sigmas()),2.0)+\
flex.pow( ((twin_fraction)*miller_array_twin.sigmas()),2.0))
)
twinned_miller.set_observation_type( miller_array.observation_type())
twin_anal_object = t_a.twin_analyses(twinned_miller,
out=out_string,
verbose=-100)
index = twin_anal_object.twin_summary.most_worrysome_twin_law
assert approx_equal(
twin_anal_object.twin_summary.britton_alpha[index],
twin_fraction,eps=0.1)
assert approx_equal(twin_anal_object.twin_law_dependent_analyses[index].ml_murray_rust.estimated_alpha,
twin_fraction, eps=0.1)
## Untwinned data standards
if twin_fraction==0:
## L-test
assert approx_equal(twin_anal_object.l_test.mean_l, 0.50,eps=0.1)
## Wilson ratios
assert approx_equal(twin_anal_object.twin_summary.i_ratio,
2.00,
eps=0.1)
## H-test
assert approx_equal(
twin_anal_object.twin_law_dependent_analyses[index].h_test.mean_h,
0.50,eps=0.1)
## Perfect twin standards
if twin_fraction==0.5:
assert approx_equal(twin_anal_object.l_test.mean_l, 0.375,eps=0.1)
assert approx_equal(twin_anal_object.twin_summary.i_ratio,
1.50,eps=0.1)
assert approx_equal(
twin_anal_object.twin_law_dependent_analyses[index].h_test.mean_h,
0.00,eps=0.1)
## Just make sure we actually detect significant twinning
if twin_fraction > 0.10:
assert (twin_anal_object.twin_summary.maha_l > 3.0)
## The patterson origin peak should be smallish ...
assert (twin_anal_object.twin_summary.patterson_p_value > 0.01)
# and the brief test should be passed as well
answer = t_a.twin_analyses_brief( twinned_miller,out=out_string,verbose=-100 )
if twin_fraction > 0.10:
assert answer is True
def exercise_twin_detection (verbose=False) :
# simple model with translational pseudosymmetry
# XXX one big disadvantage: no centric reflections!
pdb_str = """\
CRYST1 12.000 8.000 12.000 90.02 89.96 90.05 P 1 1
ATOM 39 N ASN A 6 5.514 2.664 4.856 1.00 11.99 N
ATOM 40 CA ASN A 6 6.831 2.310 4.318 1.00 12.30 C
ATOM 41 C ASN A 6 7.854 2.761 5.324 1.00 13.40 C
ATOM 42 O ASN A 6 8.219 3.943 5.374 1.00 13.92 O
ATOM 43 CB ASN A 6 7.065 3.016 2.993 1.00 12.13 C
ATOM 44 CG ASN A 6 5.961 2.735 2.003 1.00 12.77 C
ATOM 45 OD1 ASN A 6 5.798 1.604 1.551 1.00 14.27 O
ATOM 46 ND2 ASN A 6 5.195 3.747 1.679 1.00 10.07 N
ATOM 47 N TYR A 7 8.292 1.817 6.147 1.00 14.70 N
ATOM 48 CA TYR A 7 9.159 2.144 7.299 1.00 15.18 C
ATOM 49 C TYR A 7 10.603 2.331 6.885 1.00 15.91 C
ATOM 50 O TYR A 7 11.041 1.811 5.855 1.00 15.76 O
ATOM 51 CB TYR A 7 9.061 1.065 8.369 1.00 15.35 C
ATOM 52 CG TYR A 7 7.665 0.929 8.902 1.00 14.45 C
ATOM 53 CD1 TYR A 7 6.771 0.021 8.327 1.00 15.68 C
ATOM 54 CD2 TYR A 7 7.210 1.756 9.920 1.00 14.80 C
ATOM 55 CE1 TYR A 7 5.480 -0.094 8.796 1.00 13.46 C
ATOM 56 CE2 TYR A 7 5.904 1.649 10.416 1.00 14.33 C
ATOM 57 CZ TYR A 7 5.047 0.729 9.831 1.00 15.09 C
ATOM 58 OH TYR A 7 3.766 0.589 10.291 1.00 14.39 O
ATOM 59 OXT TYR A 7 11.358 2.999 7.612 1.00 17.49 O
TER
ATOM 1 N ASN B 6 1.414 5.113 6.019 1.00 12.99 N
ATOM 2 CA ASN B 6 2.720 4.776 5.445 1.00 13.30 C
ATOM 3 C ASN B 6 3.763 5.209 6.438 1.00 14.40 C
ATOM 4 O ASN B 6 4.125 6.391 6.507 1.00 14.92 O
ATOM 5 CB ASN B 6 2.922 5.513 4.131 1.00 13.13 C
ATOM 6 CG ASN B 6 1.798 5.250 3.160 1.00 13.77 C
ATOM 7 OD1 ASN B 6 1.629 4.129 2.686 1.00 15.27 O
ATOM 8 ND2 ASN B 6 1.022 6.266 2.875 1.00 11.07 N
ATOM 9 N TYR B 7 4.222 4.248 7.230 1.00 15.70 N
ATOM 10 CA TYR B 7 5.113 4.552 8.370 1.00 16.18 C
ATOM 11 C TYR B 7 6.547 4.754 7.929 1.00 16.91 C
ATOM 12 O TYR B 7 6.964 4.259 6.878 1.00 16.76 O
ATOM 13 CB TYR B 7 5.042 3.449 9.417 1.00 16.35 C
ATOM 14 CG TYR B 7 3.659 3.296 9.977 1.00 15.45 C
ATOM 15 CD1 TYR B 7 2.756 2.398 9.402 1.00 16.68 C
ATOM 16 CD2 TYR B 7 3.224 4.098 11.023 1.00 15.80 C
ATOM 17 CE1 TYR B 7 1.476 2.267 9.896 1.00 14.46 C
ATOM 18 CE2 TYR B 7 1.929 3.975 11.545 1.00 15.33 C
ATOM 19 CZ TYR B 7 1.063 3.065 10.959 1.00 16.09 C
ATOM 20 OH TYR B 7 -0.207 2.910 11.443 1.00 15.39 O
ATOM 21 OXT TYR B 7 7.316 5.408 8.654 1.00 18.49 O
END
"""
pdb_in = iotbx.pdb.hierarchy.input(pdb_string=pdb_str)
xrs = pdb_in.input.xray_structure_simple()
fc = abs(xrs.structure_factors(d_min=2.5).f_calc())
fc = fc.set_observation_type_xray_amplitude()
sigf = flex.double(fc.size(), 0.1) + (fc.data() * 0.03)
fc = fc.customized_copy(sigmas=sigf)
# and now add twinning
fc_twin = fc.twin_data(twin_law='-l,-k,-h', alpha=0.4)
fc_twin.as_mtz_dataset(column_root_label="F").mtz_object().write(
"tmp_xtriage_twinned.mtz")
# twin_laws
laws = twin_analyses.twin_laws(miller_array=fc_twin)
assert (len(laws.operators) == 7)
delta_santoro = [ tl.delta_santoro for tl in laws.operators ]
assert approx_equal(delta_santoro,
[0.104655, 0.104655, 0.066599, 0.104655, 0.076113, 0.066599, 0.028542])
delta_le_page = [ tl.delta_le_page for tl in laws.operators ]
assert approx_equal(delta_le_page,
[0.053839, 0.053839, 0.053839, 0.064020, 0.044706, 0.049480, 0.021221])
delta_lebedev = [ tl.delta_lebedev for tl in laws.operators ]
assert approx_equal(delta_lebedev,
[0.000787, 0.000787, 0.000767, 0.000912, 0.000637, 0.000705, 0.000302])
# TNCS
tps = twin_analyses.detect_pseudo_translations(
miller_array=fc_twin, out=null_out())
assert ([tps.mod_h, tps.mod_k, tps.mod_l] == [3,2,3])
assert approx_equal(tps.high_peak, 47.152352)
assert approx_equal(tps.high_p_value, 0.000103)
# L test
fc_norm_acentric = twin_analyses.wilson_normalised_intensities(
miller_array=fc_twin, normalise=True, out=null_out()).acentric
ltest = twin_analyses.l_test(miller_array=fc_norm_acentric,
parity_h=3, parity_l=3)
assert approx_equal(ltest.mean_l, 0.498974)
assert approx_equal(ltest.mean_l2, 0.371674)
# we need to go through the wrapper class for a lot of these...
out = StringIO()
tw = twin_analyses.twin_analyses(miller_array=fc_twin, out=out)
tw2 = twin_analyses.twin_analyses(miller_array=fc_twin, out=out,
d_hkl_for_l_test=[3,2,3])
# Wilson moments
wm = tw.wilson_moments
assert ([wm.centric_i_ratio, wm.centric_f_ratio, wm.centric_e_sq_minus_one]
== [None, None, None])
assert approx_equal(wm.acentric_i_ratio, 2.878383)
assert approx_equal(wm.acentric_f_ratio, 0.717738)
assert approx_equal(wm.acentric_abs_e_sq_minus_one, 0.808899)
assert (not wm.centric_present)
# Overall analyses
out = StringIO()
tw.show(out=out)
assert ("significantly different than is expected" in out.getvalue())
summary = tw.twin_summary
assert approx_equal(summary.l_mean, 0.4989738)
assert approx_equal(summary.patterson_height, 47.152352)
for k, twin_r_factor in enumerate(summary.r_obs) :
if (k == 6) : assert twin_r_factor < 0.11
else : assert twin_r_factor > 0.3
out2 = StringIO()
tw.l_test.show(out=out2)
assert ("""\
Mean |L| :0.499 (untwinned: 0.500; perfect twin: 0.375)
Mean L^2 :0.372 (untwinned: 0.333; perfect twin: 0.200)
""" in out2.getvalue()), out2.getvalue()
out3 = StringIO()
tw2.l_test.show(out=out3)
assert not show_diff(out2.getvalue(), out3.getvalue())
if (verbose) :
print out.getvalue()
# twin_results_interpretation object via cctbx.miller.array API extension
# XXX I get slightly different numbers here versus running through the
# twin_analyses call above - this seems to be caused by the resolution
# limits passed here. Need to confirm these with PHZ.
result = fc_twin.analyze_intensity_statistics()
assert approx_equal(result.maha_l, 27.580674)
assert approx_equal(result.r_obs,
[0.514901, 0.514901, 0.397741, 0.580353, 0.579294, 0.420914, 0.114999])
assert approx_equal(result.h_alpha,
[0.019980, 0.019980, 0.211788, 0.073926, 0.079920, 0.150849, 0.389610])
assert result.has_twinning()
