from __future__ import absolute_import, division, print_function from mmtbx.scaling import data_statistics as ds from mmtbx.scaling import xtriage from mmtbx.command_line import fmodel from iotbx import file_reader from cctbx import crystal from cctbx import miller from cctbx import sgtbx from cctbx.array_family import flex from libtbx.test_utils import approx_equal, Exception_expected, show_diff from libtbx.development import show_pickle_sizes from libtbx.easy_pickle import dumps, loads from libtbx.utils import null_out, Sorry import libtbx.load_env from six.moves import cStringIO as StringIO import warnings import os.path import sys from six.moves import range # synthetic data def exercise_1(): pdb_raw = """\ CRYST1 23.000 6.666 25.000 90.00 107.08 90.00 P 1 21 1 2 ATOM 1 N GLY A 1 -9.009 4.612 6.102 1.00 16.77 N ATOM 2 CA GLY A 1 -9.052 4.207 4.651 1.00 16.57 C ATOM 3 C GLY A 1 -8.015 3.140 4.419 1.00 16.16 C ATOM 4 O GLY A 1 -7.523 2.521 5.381 1.00 16.78 O ATOM 5 N ASN A 2 -7.656 2.923 3.155 1.00 15.02 N ATOM 6 CA ASN A 2 -6.522 2.038 2.831 1.00 14.10 C ATOM 7 C ASN A 2 -5.241 2.537 3.427 1.00 13.13 C ATOM 8 O ASN A 2 -4.978 3.742 3.426 1.00 11.91 O ATOM 9 CB ASN A 2 -6.346 1.881 1.341 1.00 15.38 C ATOM 10 CG ASN A 2 -7.584 1.342 0.692 1.00 14.08 C ATOM 11 OD1 ASN A 2 -8.025 0.227 1.016 1.00 17.46 O ATOM 12 ND2 ASN A 2 -8.204 2.155 -0.169 1.00 11.72 N ATOM 13 N ASN A 3 -4.438 1.590 3.905 1.00 12.26 N ATOM 14 CA ASN A 3 -3.193 1.904 4.589 1.00 11.74 C ATOM 15 C ASN A 3 -1.955 1.332 3.895 1.00 11.10 C ATOM 16 O ASN A 3 -1.872 0.119 3.648 1.00 10.42 O ATOM 17 CB ASN A 3 -3.259 1.378 6.042 1.00 12.15 C ATOM 18 CG ASN A 3 -2.006 1.739 6.861 1.00 12.82 C ATOM 19 OD1 ASN A 3 -1.702 2.925 7.072 1.00 15.05 O ATOM 20 ND2 ASN A 3 -1.271 0.715 7.306 1.00 13.48 N ATOM 21 N MET A 4 -1.005 2.228 3.598 1.00 10.29 N ATOM 22 CA MET A 4 0.384 1.888 3.199 1.00 10.53 C ATOM 23 C MET A 4 1.435 2.606 4.088 1.00 10.24 C ATOM 24 O MET A 4 1.547 3.843 4.115 1.00 8.86 O ATOM 25 CB MET A 4 0.616 2.241 1.729 1.00 20.00 C ATOM 26 CG MET A 4 -0.207 1.416 0.754 1.00 20.00 C ATOM 27 SD MET A 4 0.132 -0.349 0.876 1.00 20.00 S ATOM 28 CE MET A 4 1.822 -0.411 0.285 1.00 20.00 C ATOM 29 N GLN A 5 2.154 1.821 4.871 1.00 10.38 N ATOM 30 CA GLN A 5 3.270 2.361 5.640 1.00 11.39 C ATOM 31 C GLN A 5 4.594 1.768 5.172 1.00 11.52 C ATOM 32 O GLN A 5 4.768 0.546 5.054 1.00 12.05 O ATOM 33 CB GLN A 5 3.056 2.183 7.147 1.00 11.96 C ATOM 34 CG GLN A 5 1.829 2.950 7.647 1.00 10.81 C ATOM 35 CD GLN A 5 1.344 2.414 8.954 1.00 13.10 C ATOM 36 OE1 GLN A 5 0.774 1.325 9.002 1.00 10.65 O ATOM 37 NE2 GLN A 5 1.549 3.187 10.039 1.00 12.30 N ATOM 38 N ASN A 6 5.514 2.664 4.856 1.00 11.99 N ATOM 39 CA ASN A 6 6.831 2.310 4.318 1.00 12.30 C ATOM 40 C ASN A 6 7.854 2.761 5.324 1.00 13.40 C ATOM 41 O ASN A 6 8.219 3.943 5.374 1.00 13.92 O ATOM 42 CB ASN A 6 7.065 3.016 2.993 1.00 12.13 C ATOM 43 CG ASN A 6 5.961 2.735 2.003 1.00 12.77 C ATOM 44 OD1 ASN A 6 5.798 1.604 1.551 1.00 14.27 O ATOM 45 ND2 ASN A 6 5.195 3.747 1.679 1.00 10.07 N ATOM 46 N TYR A 7 8.292 1.817 6.147 1.00 14.70 N ATOM 47 CA TYR A 7 9.159 2.144 7.299 1.00 15.18 C ATOM 48 C TYR A 7 10.603 2.331 6.885 1.00 15.91 C ATOM 49 O TYR A 7 11.041 1.811 5.855 1.00 15.76 O ATOM 50 CB TYR A 7 9.061 1.065 8.369 1.00 15.35 C ATOM 51 CG TYR A 7 7.665 0.929 8.902 1.00 14.45 C ATOM 52 CD1 TYR A 7 6.771 0.021 8.327 1.00 15.68 C ATOM 53 CD2 TYR A 7 7.210 1.756 9.920 1.00 14.80 C ATOM 54 CE1 TYR A 7 5.480 -0.094 8.796 1.00 13.46 C ATOM 55 CE2 TYR A 7 5.904 1.649 10.416 1.00 14.33 C ATOM 56 CZ TYR A 7 5.047 0.729 9.831 1.00 15.09 C ATOM 57 OH TYR A 7 3.766 0.589 10.291 1.00 14.39 O ATOM 58 OXT TYR A 7 11.358 2.999 7.612 1.00 17.49 O TER 59 TYR A 7 HETATM 1 CA CA A 8 10.431 1.858 3.216 1.00 30.00 CA HETATM 60 O HOH A 9 -6.471 5.227 7.124 1.00 22.62 O HETATM 62 O HOH A 10 -11.286 1.756 -1.468 1.00 17.08 O HETATM 63 O HOH A 11 11.808 4.179 9.970 1.00 23.99 O HETATM 64 O HOH A 12 13.605 1.327 9.198 1.00 26.17 O HETATM 65 O HOH A 13 -2.749 3.429 10.024 1.00 39.15 O HETATM 66 O HOH A 14 -1.500 0.682 10.967 1.00 43.49 O END """ pdb_file = "tst_xtriage_in.pdb" with open(pdb_file, "w") as f: f.write(pdb_raw) fmodel_args = [ pdb_file, "high_resolution=1.5", "k_sol=0.35", "b_sol=20", "wavelength=1.54", "add_random_error_to_amplitudes_percent=3", "random_seed=12345", "output.type=real", "output.label=F", "output.file_name=tst_xtriage_fmodel.mtz", ] # read it instead so python3 will be the same # fmodel.run(args=fmodel_args, log=null_out()) hkl_file = libtbx.env.find_in_repositories( relative_path="mmtbx/regression/mtz/tst_xtriage_fmodel.mtz", test=os.path.isfile) mtz_in = file_reader.any_file( hkl_file).assert_file_type("hkl") f_obs = mtz_in.file_server.miller_arrays[0].remove_cone(0.1) data = f_obs.data() # add some outliers #data[17] = 20 #data[334] = 26 #data[1908] = 13 # and sigmas sigf = flex.double(f_obs.size(), 0.1) + (f_obs.data() * 0.03) f_obs = f_obs.customized_copy(sigmas=sigf) mtz_file = "tst_xtriage_in.mtz" f_obs.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file) seq_file = "tst_xtriage_in.fa" with open(seq_file, "w") as f: f.write("> tst_xtriage\nGNNMQNY") # check with completeness_as_non_anomalous=True xtriage_args = [ mtz_file, pdb_file, seq_file, "log=tst_xtriage_1.log", "l_test_dhkl=2,2,2", "completeness_as_non_anomalous=True", ] result = xtriage.run(args=xtriage_args, out=null_out()) test_pickle_consistency_and_size(result) assert (result.matthews.n_copies == 1) assert (str(result.matthews.table) == """\ Solvent content analysis Copies Solvent content Matthews coeff. P(solvent content) 1 0.472 2.33 1.000 """) data_strength = result.data_strength_and_completeness assert approx_equal(data_strength.data_strength.resolution_cut, 1.5351, eps=0.001) out1 = data_strength.low_resolution_completeness.format() assert (out1 == """\ --------------------------------------------------------- | Resolution range | N(obs)/N(possible) | Completeness | --------------------------------------------------------- | 21.9858 - 10.4368 | [6/7] | 0.857 | | 10.4368 - 8.4369 | [3/3] | 1.000 | | 8.4369 - 7.4172 | [3/4] | 0.750 | | 7.4172 - 6.7606 | [4/4] | 1.000 | | 6.7606 - 6.2882 | [5/5] | 1.000 | | 6.2882 - 5.9252 | [3/4] | 0.750 | | 5.9252 - 5.6337 | [7/7] | 1.000 | | 5.6337 - 5.3922 | [5/5] | 1.000 | | 5.3922 - 5.1874 | [4/4] | 1.000 | | 5.1874 - 5.0106 | [4/4] | 1.000 | ---------------------------------------------------------"""), out1 # ANOMALOUS SIGNAL a_meas = result.anomalous_info.measurability #assert approx_equal(a_meas.high_d_cut, 4.7636, eps=0.0001) # Why it's None? assert approx_equal(a_meas.low_d_cut, 2.3566, eps=0.0001) # ABSOLUTE SCALING ws = result.wilson_scaling assert ("%.2f" % ws.iso_p_scale) == "0.65",ws.iso_p_scale assert ("%.2f" % ws.iso_b_wilson) == "14.42", ws.iso_b_wilson # FIXME these may need to be adjusted for different hardware/OS assert approx_equal(ws.aniso_p_scale, 0.64723, eps=0.001) assert approx_equal(ws.aniso_u_star, [0.00034229, 0.00475982, 0.000285989, -0.0, 8.95386085999e-05, 0.0]) assert approx_equal(ws.aniso_b_cart, (13.218423, 16.840142, 12.948426, 1.0354e-15, -0.0685311, -7.92862e-16), 0.3) # convenience methods for GUI assert approx_equal(result.aniso_b_min, 12.895580) assert approx_equal(result.aniso_range_of_b, 3.804215) # assert approx_equal(ws.outlier_shell_table.data[0], # d_spacing [9.865131, 8.369653, 4.648634]) assert approx_equal(ws.outlier_shell_table.data[1], # z_score [5.306713, 18.068284, 5.319230]) assert (len(ws.outliers.acentric_outliers_table.data[0]) == 2) assert (ws.outliers.acentric_outliers_table.data[1] == [(0,-1,-1), (0,1,1)]) assert approx_equal(ws.outliers.acentric_outliers_table.data[2], [3.507247, 3.315550]) assert (ws.outliers.centric_outliers_table.data is None) assert (len(ws.ice_rings.table._rows) == 10) assert (ws.ice_rings.table._rows[0] == [' 3.897', ' 1.000', ' 0.76', ' 1.00']), \ ws.ice_rings.table._rows[0] tw = result.twin_results wm = tw.wilson_moments out = StringIO() wm.show(out) assert not show_diff(out.getvalue(), """ ----------Wilson ratio and moments---------- Acentric reflections: /^2 :2.063 (untwinned: 2.000; perfect twin 1.500) ^2/ :0.778 (untwinned: 0.785; perfect twin 0.885) <|E^2 - 1|> :0.745 (untwinned: 0.736; perfect twin 0.541) Centric reflections: /^2 :3.076 (untwinned: 3.000; perfect twin 2.000) ^2/ :0.628 (untwinned: 0.637; perfect twin 0.785) <|E^2 - 1|> :0.999 (untwinned: 0.968; perfect twin 0.736) """) # XXX PDB validation server assert approx_equal(result.iso_b_wilson, 14.51, eps=0.1) assert approx_equal(result.aniso_b_ratio, 0.271, eps=0.1) assert (result.number_of_wilson_outliers == 2) assert approx_equal(result.l_test_mean_l, 0.481, eps=0.1) assert approx_equal(result.l_test_mean_l_squared, 0.322, eps=0.1) assert approx_equal(result.i_over_sigma_outer_shell, 10.71, eps=0.01) assert ("indicating pseudo-translationa" in result.patterson_verdict) # check relative Wilson # FIXME #result.relative_wilson.show() #assert (result.relative_wilson.n_outliers() == 0) #show_pickled_object_sizes(result) # # check with completeness_as_non_anomalous=False xtriage_args = [ mtz_file, pdb_file, seq_file, "log=tst_xtriage_1.log", "l_test_dhkl=2,2,2", "completeness_as_non_anomalous=False", ] result = xtriage.run(args=xtriage_args, out=null_out()) test_pickle_consistency_and_size(result) assert (result.matthews.n_copies == 1) assert (str(result.matthews.table) == """\ Solvent content analysis Copies Solvent content Matthews coeff. P(solvent content) 1 0.472 2.33 1.000 """) data_strength = result.data_strength_and_completeness assert approx_equal(data_strength.data_strength.resolution_cut, 1.5351, eps=0.001) out1 = data_strength.low_resolution_completeness.format() assert (out1 == """\ --------------------------------------------------------- | Resolution range | N(obs)/N(possible) | Completeness | --------------------------------------------------------- | 21.9858 - 10.4368 | [ 6/7 ] | 0.857 | | 10.4368 - 8.4369 | [ 3/3 ] | 1.000 | | 8.4369 - 7.4172 | [ 3/4 ] | 0.750 | | 7.4172 - 6.7606 | [ 4/4 ] | 1.000 | | 6.7606 - 6.2882 | [ 8/8 ] | 1.000 | | 6.2882 - 5.9252 | [ 4/5 ] | 0.800 | | 5.9252 - 5.6337 | [11/11] | 1.000 | | 5.6337 - 5.3922 | [ 7/7 ] | 1.000 | | 5.3922 - 5.1874 | [ 6/6 ] | 1.000 | | 5.1874 - 5.0106 | [ 7/7 ] | 1.000 | ---------------------------------------------------------"""), out1 # ANOMALOUS SIGNAL a_meas = result.anomalous_info.measurability #assert approx_equal(a_meas.high_d_cut, 4.7636, eps=0.0001) # Why? assert approx_equal(a_meas.low_d_cut, 2.3565, eps=0.0001) # ABSOLUTE SCALING ws = result.wilson_scaling assert ("%.2f" % ws.iso_p_scale) == "0.65", ws.iso_p_scale assert ("%.2f" % ws.iso_b_wilson) == "14.42", ws.iso_b_wilson # FIXME these may need to be adjusted for different hardware/OS assert approx_equal(ws.aniso_p_scale, 0.64723, eps=0.001) assert approx_equal(ws.aniso_u_star, [0.00034473, 0.00479983, 0.000287162, -0.0, 9.00962e-05, 0.0], 6.e-5) assert approx_equal(ws.aniso_b_cart, [13.12, 16.69, 12.89, 0, -0.08, 0], 0.01) # convenience methods for GUI assert approx_equal(result.aniso_b_min, 12.9, 0.1) assert approx_equal(result.aniso_range_of_b, 3.8, 0.1) # assert approx_equal(ws.outlier_shell_table.data[0], # d_spacing [9.86, 8.36, 4.64], 0.02) assert approx_equal(ws.outlier_shell_table.data[1], # z_score [5.30, 18.06, 5.31], 0.01) assert (len(ws.outliers.acentric_outliers_table.data[0]) == 2) assert (ws.outliers.acentric_outliers_table.data[1] == [(0,-1,-1), (0,1,1)]) assert approx_equal(ws.outliers.acentric_outliers_table.data[2], [3.5, 3.3], 0.1) assert (ws.outliers.centric_outliers_table.data is None) assert (len(ws.ice_rings.table._rows) == 10) assert (ws.ice_rings.table._rows[0] == [' 3.897', ' 1.000', ' 0.76', ' 1.00']), \ ws.ice_rings.table._rows[0] tw = result.twin_results wm = tw.wilson_moments out = StringIO() wm.show(out) assert not show_diff(out.getvalue(), """ ----------Wilson ratio and moments---------- Acentric reflections: /^2 :2.063 (untwinned: 2.000; perfect twin 1.500) ^2/ :0.778 (untwinned: 0.785; perfect twin 0.885) <|E^2 - 1|> :0.745 (untwinned: 0.736; perfect twin 0.541) Centric reflections: /^2 :3.076 (untwinned: 3.000; perfect twin 2.000) ^2/ :0.628 (untwinned: 0.637; perfect twin 0.785) <|E^2 - 1|> :0.999 (untwinned: 0.968; perfect twin 0.736) """) # XXX PDB validation server assert approx_equal(result.iso_b_wilson, 14.51, eps=0.1) assert approx_equal(result.aniso_b_ratio, 0.271, eps=0.1) assert (result.number_of_wilson_outliers == 2) assert approx_equal(result.l_test_mean_l, 0.481, eps=0.1) assert approx_equal(result.l_test_mean_l_squared, 0.322, eps=0.1) assert approx_equal(result.i_over_sigma_outer_shell, 10.71, eps=0.01) assert ("indicating pseudo-translationa" in result.patterson_verdict) # check relative Wilson # FIXME #result.relative_wilson.show() #assert (result.relative_wilson.n_outliers() == 0) #show_pickled_object_sizes(result) # # test without sigmas f_obs_2 = f_obs.customized_copy(sigmas=None) mtz_file = "tst_xtriage_in_2.mtz" f_obs_2.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file) xtriage_args = [ mtz_file, pdb_file, seq_file, "log=tst_xtriage_1.log", ] result = xtriage.run(args=xtriage_args, out=null_out()) result.summarize_issues() # test in lower symmetry f_obs_3 = f_obs.expand_to_p1() mtz_file = "tst_xtriage_in_3.mtz" f_obs_3.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file) xtriage_args = [ mtz_file, seq_file, "log=tst_xtriage_2.log", ] result = xtriage.run(args=xtriage_args, out=null_out()) assert ((1, 'One or more symmetry operators suggest that the data has a higher crystallographic symmetry (P 2 1 1).', 'Point group and R-factor analysis') in result.summarize_issues()._issues) # test with elliptical truncation f_obs_3 = f_obs.customized_copy( crystal_symmetry=crystal.symmetry((23,5,20,90,107.8,90), "P 21")) f_obs_3 = f_obs_3.resolution_filter(d_min=1.5) f_obs_3 = f_obs_3.customized_copy(crystal_symmetry=f_obs.crystal_symmetry()) reso = ds.analyze_resolution_limits(f_obs_3) out = StringIO() reso.show(out=out) assert ("max. difference between axes = 0.652" in out.getvalue()), \ out.getvalue() assert ("elliptically truncated" in out.getvalue()) # make sure the elliptical truncation detection still works in higher space # groups - we only need a miller.set for this miller_set = miller.build_set( crystal_symmetry=crystal.symmetry((20,20,20,90,90,90), "P422"), d_min=1.5, anomalous_flag=False) reso = ds.analyze_resolution_limits(miller_set) out = StringIO() reso.show(out=out) assert ("Resolution limits are within expected tolerances" in out.getvalue()) # log binning out = StringIO() log_binned = ds.log_binned_completeness(f_obs_3) log_binned.show(out=out) assert ("""| 1.9724 - 1.5094 | 368/1230 | 29.9% |""" in out.getvalue()), out.getvalue() # test with no acentrics cf = f_obs.centric_flags().data() centrics = f_obs.select(cf) acentrics = f_obs.select(~cf) mtz_file = "tst_xtriage_in_3.mtz" centrics.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file) args = [ mtz_file, pdb_file, seq_file, "log=tst_xtriage_3.log", ] try : xtriage.run(args=args, out=null_out()) except Sorry : pass else : raise Exception_expected # with only a handful of acentrics sel = flex.bool(acentrics.size(), False) for i in range(10): sel[i] = True f_obs_4 = centrics.concatenate(acentrics.select(sel)) f_obs_4.as_mtz_dataset(column_root_label="F").mtz_object().write(mtz_file) try : xtriage.run(args=args, out=null_out()) except Sorry : pass else : raise Exception_expected # XXX code for debugging pickle size issues def show_pickled_object_sizes(result): result_pkl = dumps(result) print("result", len(result_pkl)) show_pickle_sizes(result, " ") # test consistency of output after pickling and unpickling def test_pickle_consistency_and_size(result): all_out = StringIO() result.show(out=all_out) result_pkl_str = dumps(result) pkl_size = len(result_pkl_str) if (pkl_size >= 100000): print("Oversized pickle:", pkl_size) show_pickled_object_sizes(result) raise OverflowError() assert (pkl_size < 100000), pkl_size # limit pickle size result_pkl = loads(result_pkl_str) all_out_pkl = StringIO() result_pkl.show(out=all_out_pkl) assert not show_diff(all_out.getvalue(), all_out_pkl.getvalue()) def exercise_analyze_resolution_limits(): for x in range(1, 231): sg = sgtbx.space_group_info(number=x) #sg = sgtbx.space_group_info("P222") uc = sg.any_compatible_unit_cell(80000) ms = miller.build_set( crystal_symmetry=crystal.symmetry( space_group_info=sg, unit_cell=uc), anomalous_flag=True, d_min=1.5) arl = ds.analyze_resolution_limits(ms) if (x > 2): assert (arl.max_d_min_delta() < 0.1) # real data def exercise_2(): hkl_file = libtbx.env.find_in_repositories( relative_path="phenix_regression/wizards/data/p9_se_w2.sca", test=os.path.isfile) if (hkl_file is None): warnings.warn("phenix_regression not available, skipping test") return hkl_in = file_reader.any_file(hkl_file).assert_file_type("hkl") i_obs_raw = hkl_in.file_object.as_miller_arrays( merge_equivalents=False, crystal_symmetry=crystal.symmetry( space_group_symbol="I4", unit_cell=(113.949,113.949,32.474,90,90,90)))[0] i_obs = i_obs_raw.merge_equivalents().array() # completeness and data strength cstats = ds.i_sigi_completeness_stats(i_obs) d_min_cut = cstats.resolution_cut assert approx_equal(d_min_cut, 2.150815) ws = ds.wilson_scaling( miller_array=i_obs, n_residues=120) # outliers - this shouldn't actually work, since it requires additional # processing steps on the input data try : outliers = ds.possible_outliers(i_obs) except AssertionError : pass else : raise Exception_expected ###################################################################### # OVERALL ANALYSIS pdb_file = libtbx.env.find_in_repositories( relative_path="phenix_examples/p9-build/p9.pdb", test=os.path.isfile) f_calc = None if (pdb_file is not None): pdb_in = file_reader.any_file(pdb_file).assert_file_type("pdb") hierarchy = pdb_in.file_object.hierarchy xrs = pdb_in.file_object.xray_structure_simple( crystal_symmetry=i_obs) f_calc = xrs.structure_factors(d_min=i_obs.d_min()).f_calc() f_calc = abs(f_calc).generate_bijvoet_mates() f_calc = f_calc.set_observation_type_xray_amplitude() i_obs, f_calc = i_obs.common_sets(other=f_calc) with open("tmp_xtriage.pdb", "w") as f: f.write(hierarchy.as_pdb_string(crystal_symmetry=i_obs)) pdb_file = "tmp_xtriage.pdb" params = xtriage.master_params.extract() params.scaling.input.asu_contents.n_residues = 141 text_out = open("logfile3.log", "w") result = xtriage.xtriage_analyses( miller_obs=i_obs, miller_calc=f_calc, params=params, unmerged_obs=i_obs_raw, text_out=text_out)#sys.stdout) text_out.close() # XXX there appears to be some system-dependence here, hence sloppy limits assert (15.5 < result.aniso_b_min < 15.9) assert (10 < result.aniso_range_of_b < 11) # check relative Wilson if (pdb_file is not None): assert (result.relative_wilson is not None) # FIXME #assert (result.relative_wilson.n_outliers() == 34) #show_pickled_object_sizes(result) test_pickle_consistency_and_size(result) # XXX PDB validation server assert approx_equal(result.iso_b_wilson, 18.33, eps=0.1) assert approx_equal(result.aniso_b_ratio, 0.546, eps=0.1) assert (result.number_of_wilson_outliers == 0) assert approx_equal(result.l_test_mean_l, 0.493, eps=0.1) assert approx_equal(result.l_test_mean_l_squared, 0.326, eps=0.1) assert approx_equal(result.i_over_sigma_outer_shell, 3.25, eps=0.1) assert approx_equal(result.overall_i_sig_i,10.34,eps=0.1) assert approx_equal(result.anomalous_info.plan_sad_experiment_stats.get_overall( item="i_over_sigma_dict"),10.61,eps=0.1) assert approx_equal(result.anomalous_info.plan_sad_experiment_stats.get_overall( item="anom_signal_dict"),15.35,eps=0.1) assert ("No significant pseudotranslation is detected" in result.patterson_verdict) # test consistency of output after pickling and unpickling try : from phenix_dev.phenix_cloud import xtriage_json except ImportError : pass else : json_out = xtriage_json.json_output("p9.sca") result.show(out=json_out) with open("xtriage.json", "w") as f: f.write(json_out.export()) # unmerged data assert result.merging_stats is not None out = StringIO() result.merging_stats.show(out=out) assert ("R-merge: 0.073" in out.getvalue()) assert approx_equal(result.estimate_d_min(min_i_over_sigma=10), 1.9645, eps=0.001) # FIXME PDB doesn't actually have unit cell! # test detection of symmetry in reference file if (pdb_file is not None): args = [hkl_file, pdb_file] result = xtriage.run(args=args, out=null_out()) if (__name__ == "__main__"): #exercise_2() exercise_1() exercise_analyze_resolution_limits() print("OK")