# XXX This overlaps heavily with tst_scaling.py, which is much more # comprehensive. Maybe remove this one? from __future__ import absolute_import, division, print_function from mmtbx.scaling import twin_analyses import iotbx.pdb from scitbx.array_family import flex from libtbx.test_utils import approx_equal, show_diff from libtbx.utils import null_out from six.moves import cStringIO as StringIO import sys 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.input(source_info=None, lines=pdb_str) xrs = pdb_in.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() if (__name__ == "__main__"): exercise_twin_detection(verbose=("--verbose" in sys.argv)) print("OK")