def print_validation(log, results, debug, pdb_hierarchy_selected): box_1 = results.box_1 box_2 = results.box_2 box_3 = results.box_3 sites_cart_box = box_1.xray_structure_box.sites_cart() sel = maptbx.grid_indices_around_sites( unit_cell=box_1.xray_structure_box.unit_cell(), fft_n_real=box_1.map_box.focus(), fft_m_real=box_1.map_box.all(), sites_cart=sites_cart_box, site_radii=flex.double(sites_cart_box.size(), 2.0)) b1 = box_1.map_box.select(sel).as_1d() b2 = box_2.map_box.select(sel).as_1d() b3 = box_3.map_box.select(sel).as_1d() print >> log, "Map 1: calculated Fobs with ligand" print >> log, "Map 2: calculated Fobs without ligand" print >> log, "Map 3: real Fobs data" cc12 = flex.linear_correlation(x=b1, y=b2).coefficient() cc13 = flex.linear_correlation(x=b1, y=b3).coefficient() cc23 = flex.linear_correlation(x=b2, y=b3).coefficient() print >> log, "CC(1,2): %6.4f" % cc12 print >> log, "CC(1,3): %6.4f" % cc13 print >> log, "CC(2,3): %6.4f" % cc23 #### D-function b1 = maptbx.volume_scale_1d(map=b1, n_bins=10000).map_data() b2 = maptbx.volume_scale_1d(map=b2, n_bins=10000).map_data() b3 = maptbx.volume_scale_1d(map=b3, n_bins=10000).map_data() print >> log, "Peak CC:" print >> log, "CC(1,2): %6.4f" % flex.linear_correlation( x=b1, y=b2).coefficient() print >> log, "CC(1,3): %6.4f" % flex.linear_correlation( x=b1, y=b3).coefficient() print >> log, "CC(2,3): %6.4f" % flex.linear_correlation( x=b2, y=b3).coefficient() cutoffs = flex.double([i / 10. for i in range(1, 10)] + [i / 100 for i in range(91, 100)]) d12 = maptbx.discrepancy_function(map_1=b1, map_2=b2, cutoffs=cutoffs) d13 = maptbx.discrepancy_function(map_1=b1, map_2=b3, cutoffs=cutoffs) d23 = maptbx.discrepancy_function(map_1=b2, map_2=b3, cutoffs=cutoffs) print >> log, "q D(1,2) D(1,3) D(2,3)" for c, d12_, d13_, d23_ in zip(cutoffs, d12, d13, d23): print >> log, "%4.2f %6.4f %6.4f %6.4f" % (c, d12_, d13_, d23_) ### if (debug): #box_1.write_ccp4_map(file_name="box_1_polder.ccp4") #box_2.write_ccp4_map(file_name="box_2_polder.ccp4") #box_3.write_ccp4_map(file_name="box_3_polder.ccp4") write_map_box(box=box_1, filename="box_1_polder.ccp4") write_map_box(box=box_2, filename="box_2_polder.ccp4") write_map_box(box=box_3, filename="box_3_polder.ccp4") pdb_hierarchy_selected.adopt_xray_structure(box_1.xray_structure_box) pdb_hierarchy_selected.write_pdb_file( file_name="box_polder.pdb", crystal_symmetry=box_1.box_crystal_symmetry) # print >> log, '*' * 79 message = result_message(cc12=cc12, cc13=cc13, cc23=cc23) print >> log, message return message
def exercise_cc_peak(): def get_map(): av = [random.random() for i in xrange(10*20*30)] m = flex.double(av) m = m-flex.min(m) m = m/flex.max(m) m.resize(flex.grid((10,20,30))) return m m1 = get_map() m2 = get_map() for t in range(0,11): t=t/10. ccp=maptbx.cc_peak(map_1=m1, map_2=m2, cutoff=t) # sites_frac = flex.vec3_double([ (0.50,0.50,0.50)]) from cctbx import xray xray_structure = xray.structure( crystal_symmetry=crystal.symmetry( unit_cell=(5,5,5,90,90,90), space_group_symbol="P1"), scatterers=flex.xray_scatterer([ xray.scatterer(label=str(i), scattering_type="C", site=site_frac) for i,site_frac in enumerate(sites_frac)])) fc1 = xray_structure.structure_factors(d_min=1.6).f_calc() fc2 = xray_structure.structure_factors(d_min=1.7).f_calc() for t in range(0,11): t=t/10. ccp=maptbx.cc_peak(map_coeffs_1=fc1, map_coeffs_2=fc2, cutoff=t) # m1_he = maptbx.volume_scale(map = m1, n_bins = 10000).map_data() m2_he = maptbx.volume_scale(map = m2, n_bins = 10000).map_data() cutoffs = flex.double([i/20. for i in range(1,20)]) df = maptbx.discrepancy_function(map_1=m1_he, map_2=m2_he, cutoffs=cutoffs) # fc1 = xray_structure.structure_factors(d_min=2.2).f_calc() fc2 = xray_structure.structure_factors(d_min=2.2).f_calc() for t in range(0,10): t=t/10. ccp=maptbx.cc_peak(map_coeffs_1=fc1, map_coeffs_2=fc2, cutoff=t) assert approx_equal(ccp, 1) # 1D case m1_he_1d = maptbx.volume_scale_1d(map = m1.as_1d(), n_bins = 10000).map_data() m2_he_1d = maptbx.volume_scale_1d(map = m2.as_1d(), n_bins = 10000).map_data() df_1d = maptbx.discrepancy_function( map_1=m1_he_1d, map_2=m2_he_1d, cutoffs=cutoffs) assert approx_equal(df, df_1d)
def cmd_run(args, validated=False, out=sys.stdout): if (len(args) == 0): print >> out, "-" * 79 print >> out, " phenix.polder" print >> out, "-" * 79 print >> out, legend print >> out, "-" * 79 master_params.show(out=out) return log = multi_out() log.register("stdout", out) log_file_name = "polder.log" logfile = open(log_file_name, "w") log.register("logfile", logfile) print >> log, "phenix.polder is running..." print >> log, "input parameters:\n", args parsed = master_params inputs = mmtbx.utils.process_command_line_args(args=args, master_params=parsed) #inputs.params.show() #check params = inputs.params.extract() # check model file if len(inputs.pdb_file_names) == 0: if (params.model_file_name is None): raise Sorry("No model file found.") elif (len(inputs.pdb_file_names) == 1): params.model_file_name = inputs.pdb_file_names[0] else: raise Sorry("Only one model file should be given") # check reflection file reflection_files = inputs.reflection_files if (len(reflection_files) == 0): if (params.reflection_file_name is None): raise Sorry("No reflection file found.") else: hkl_in = file_reader.any_file(params.reflection_file_name, force_type="hkl") hkl_in.assert_file_type("hkl") reflection_files = [hkl_in.file_object] # crystal symmetry crystal_symmetry = None crystal_symmetry = inputs.crystal_symmetry if (crystal_symmetry is None): crystal_symmetries = [] for f in [ str(params.model_file_name), str(params.reflection_file_name) ]: cs = crystal_symmetry_from_any.extract_from(f) if (cs is not None): crystal_symmetries.append(cs) if (len(crystal_symmetries) == 1): crystal_symmetry = crystal_symmetries[0] elif (len(crystal_symmetries) == 0): raise Sorry("No crystal symmetry found.") else: if (not crystal_symmetries[0].is_similar_symmetry( crystal_symmetries[1])): raise Sorry( "Crystal symmetry mismatch between different files.") crystal_symmetry = crystal_symmetries[0] f_obs, r_free_flags = None, None rfs = reflection_file_utils.reflection_file_server( crystal_symmetry=crystal_symmetry, force_symmetry=True, reflection_files=reflection_files, err=StringIO()) parameters = mmtbx.utils.data_and_flags_master_params().extract() if (params.data_labels is not None): parameters.labels = params.data_labels if (params.r_free_flags_labels is not None): parameters.r_free_flags.label = params.r_free_flags_labels determined_data_and_flags = mmtbx.utils.determine_data_and_flags( reflection_file_server=rfs, parameters=parameters, keep_going=True, log=StringIO()) f_obs = determined_data_and_flags.f_obs if (params.data_labels is None): params.data_labels = f_obs.info().label_string() if (params.reflection_file_name is None): params.reflection_file_name = parameters.file_name r_free_flags = determined_data_and_flags.r_free_flags assert f_obs is not None print >> log, "Input data:" print >> log, " Iobs or Fobs:", f_obs.info().labels if (r_free_flags is not None): print >> log, " Free-R flags:", r_free_flags.info().labels params.r_free_flags_labels = r_free_flags.info().label_string() else: print >> log, " Free-R flags: Not present" model_basename = os.path.basename(params.model_file_name.split(".")[0]) if (len(model_basename) > 0 and params.output_file_name_prefix is None): params.output_file_name_prefix = model_basename print params.output_file_name_prefix new_params = master_params.format(python_object=params) new_params.show() if (not validated): validate_params(params) pdb_input = iotbx.pdb.input(file_name=params.model_file_name) pdb_hierarchy = pdb_input.construct_hierarchy() xray_structure = pdb_hierarchy.extract_xray_structure( crystal_symmetry=crystal_symmetry) # DON'T USE: # xray_structure = pdb_input.xray_structure_simple() # atom order might be wrong mmtbx.utils.setup_scattering_dictionaries( scattering_table=params.scattering_table, xray_structure=xray_structure, d_min=f_obs.d_min()) #if f_obs is not None: f_obs = f_obs.resolution_filter(d_min=params.high_resolution, d_max=params.low_resolution) if (r_free_flags is not None): r_free_flags = r_free_flags.resolution_filter( d_min=params.high_resolution, d_max=params.low_resolution) # Grab case that data are anomalous if (f_obs.anomalous_flag()): f_obs, r_free_flags = prepare_f_obs_and_flags( f_obs=f_obs, r_free_flags=r_free_flags) cpm_obj = compute_polder_map(f_obs=f_obs, r_free_flags=r_free_flags, xray_structure=xray_structure, pdb_hierarchy=pdb_hierarchy, params=params, log=log) # Significance check fmodel = mmtbx.f_model.manager(f_obs=f_obs, r_free_flags=r_free_flags, xray_structure=xray_structure) fmodel.update_all_scales(remove_outliers=False, fast=True) f_obs_1 = abs(fmodel.f_model()) fmodel.update_xray_structure( xray_structure=cpm_obj.xray_structure_noligand, update_f_calc=True, update_f_mask=True, force_update_f_mask=True) # PVA: do we need it? fmodel.update_all_scales(remove_outliers=False) f_obs_2 = abs(fmodel.f_model()) xrs_selected = cpm_obj.pdb_hierarchy_selected.extract_xray_structure( crystal_symmetry=f_obs.crystal_symmetry()) f_calc = f_obs.structure_factors_from_scatterers( xray_structure=cpm_obj.xray_structure_noligand).f_calc() f_mask = f_obs.structure_factors_from_map(map=cpm_obj.mask_polder, use_scale=True, anomalous_flag=False, use_sg=False) def get_poler_diff_map(f_obs): fmodel = mmtbx.f_model.manager(f_obs=f_obs, r_free_flags=r_free_flags, f_calc=f_calc, f_mask=f_mask) fmodel.update_all_scales(remove_outliers=False) mc_diff = map_tools.electron_density_map( fmodel=fmodel).map_coefficients(map_type="mFo-DFc", isotropize=True, fill_missing=False) fft_map = miller.fft_map(crystal_gridding=cpm_obj.crystal_gridding, fourier_coefficients=mc_diff) fft_map.apply_sigma_scaling() map_data = fft_map.real_map_unpadded() return mmtbx.utils.extract_box_around_model_and_map( xray_structure=xrs_selected, map_data=map_data, box_cushion=2.1) box_1 = get_poler_diff_map(f_obs=f_obs_1) box_2 = get_poler_diff_map(f_obs=f_obs_2) box_3 = get_poler_diff_map(f_obs=f_obs) sites_cart_box = box_1.xray_structure_box.sites_cart() sel = maptbx.grid_indices_around_sites( unit_cell=box_1.xray_structure_box.unit_cell(), fft_n_real=box_1.map_box.focus(), fft_m_real=box_1.map_box.all(), sites_cart=sites_cart_box, site_radii=flex.double(sites_cart_box.size(), 2.0)) b1 = box_1.map_box.select(sel).as_1d() b2 = box_2.map_box.select(sel).as_1d() b3 = box_3.map_box.select(sel).as_1d() print >> log, "Map 1: calculated Fobs with ligand" print >> log, "Map 2: calculated Fobs without ligand" print >> log, "Map 3: real Fobs data" print >> log, "CC(1,2): %6.4f" % flex.linear_correlation( x=b1, y=b2).coefficient() print >> log, "CC(1,3): %6.4f" % flex.linear_correlation( x=b1, y=b3).coefficient() print >> log, "CC(2,3): %6.4f" % flex.linear_correlation( x=b2, y=b3).coefficient() ### D-function b1 = maptbx.volume_scale_1d(map=b1, n_bins=10000).map_data() b2 = maptbx.volume_scale_1d(map=b2, n_bins=10000).map_data() b3 = maptbx.volume_scale_1d(map=b3, n_bins=10000).map_data() print >> log, "Peak CC:" print >> log, "CC(1,2): %6.4f" % flex.linear_correlation( x=b1, y=b2).coefficient() print >> log, "CC(1,3): %6.4f" % flex.linear_correlation( x=b1, y=b3).coefficient() print >> log, "CC(2,3): %6.4f" % flex.linear_correlation( x=b2, y=b3).coefficient() cutoffs = flex.double([i / 10. for i in range(1, 10)] + [i / 100 for i in range(91, 100)]) d12 = maptbx.discrepancy_function(map_1=b1, map_2=b2, cutoffs=cutoffs) d13 = maptbx.discrepancy_function(map_1=b1, map_2=b3, cutoffs=cutoffs) d23 = maptbx.discrepancy_function(map_1=b2, map_2=b3, cutoffs=cutoffs) print >> log, "q D(1,2) D(1,3) D(2,3)" for c, d12_, d13_, d23_ in zip(cutoffs, d12, d13, d23): print >> log, "%4.2f %6.4f %6.4f %6.4f" % (c, d12_, d13_, d23_) ### if (params.debug): box_1.write_ccp4_map(file_name="box_1_polder.ccp4") box_2.write_ccp4_map(file_name="box_2_polder.ccp4") box_3.write_ccp4_map(file_name="box_3_polder.ccp4") cpm_obj.pdb_hierarchy_selected.adopt_xray_structure( box_1.xray_structure_box) cpm_obj.pdb_hierarchy_selected.write_pdb_file( file_name="box_polder.pdb", crystal_symmetry=box_1.box_crystal_symmetry) # polder_file_name = "polder_map_coeffs.mtz" if (params.output_file_name_prefix is not None): polder_file_name = params.output_file_name_prefix + "_" + polder_file_name # print >> log, '*' * 79 print >> log, 'File %s was written.' % polder_file_name print >> log, "Finished." return True
def run(args, out=sys.stdout, validated=False): show_citation(out=out) if (len(args) == 0): master_phil.show(out=out) print('\nUsage: phenix.map_comparison <CCP4> <CCP4>\n',\ ' phenix.map_comparison <CCP4> <MTZ> mtz_label_1=<label>\n',\ ' phenix.map_comparison <MTZ 1> mtz_label_1=<label 1> <MTZ 2> mtz_label_2=<label 2>\n', file=out) sys.exit() # process arguments params = None input_attributes = ['map_1', 'mtz_1', 'map_2', 'mtz_2'] try: # automatic parsing params = phil.process_command_line_with_files( args=args, master_phil=master_phil).work.extract() except Exception: # map_file_def only handles one map phil from libtbx.phil.command_line import argument_interpreter arg_int = argument_interpreter(master_phil=master_phil) command_line_args = list() map_files = list() for arg in args: if (os.path.isfile(arg)): map_files.append(arg) else: command_line_args.append(arg_int.process(arg)) params = master_phil.fetch(sources=command_line_args).extract() # check if more files are necessary n_defined = 0 for attribute in input_attributes: if (getattr(params.input, attribute) is not None): n_defined += 1 # matches files to phil scope, stops once there is sufficient data for map_file in map_files: if (n_defined < 2): current_map = file_reader.any_file(map_file) if (current_map.file_type == 'ccp4_map'): n_defined += 1 if (params.input.map_1 is None): params.input.map_1 = map_file elif (params.input.map_2 is None): params.input.map_2 = map_file elif (current_map.file_type == 'hkl'): n_defined += 1 if (params.input.mtz_1 is None): params.input.mtz_1 = map_file elif (params.input.mtz_2 is None): params.input.mtz_2 = map_file else: print('WARNING: only the first two files are used', file=out) break # validate arguments (GUI sets validated to true, no need to run again) assert (params is not None) if (not validated): validate_params(params) # --------------------------------------------------------------------------- # check if maps need to be generated from mtz n_maps = 0 maps = list() map_names = list() for attribute in input_attributes: filename = getattr(params.input, attribute) if (filename is not None): map_names.append(filename) current_map = file_reader.any_file(filename) maps.append(current_map) if (current_map.file_type == 'ccp4_map'): n_maps += 1 # construct maps, if necessary crystal_gridding = None m1 = None m2 = None # 1 map, 1 mtz file if (n_maps == 1): for current_map in maps: if (current_map.file_type == 'ccp4_map'): uc = current_map.file_object.unit_cell() sg_info = space_group_info( current_map.file_object.space_group_number) n_real = current_map.file_object.unit_cell_grid crystal_gridding = maptbx.crystal_gridding( uc, space_group_info=sg_info, pre_determined_n_real=n_real) m1 = current_map.file_object.map_data() if (crystal_gridding is not None): label = None for attribute in [('mtz_1', 'mtz_label_1'), ('mtz_2', 'mtz_label_2')]: filename = getattr(params.input, attribute[0]) label = getattr(params.input, attribute[1]) if ((filename is not None) and (label is not None)): break # labels will match currently open mtz file for current_map in maps: if (current_map.file_type == 'hkl'): m2 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=current_map.file_server. get_miller_array( label)).apply_sigma_scaling().real_map_unpadded() else: raise Sorry('Gridding is not defined.') # 2 mtz files elif (n_maps == 0): crystal_symmetry = get_crystal_symmetry(maps[0]) d_min = min(get_d_min(maps[0]), get_d_min(maps[1])) crystal_gridding = maptbx.crystal_gridding( crystal_symmetry.unit_cell(), d_min=d_min, resolution_factor=params.options.resolution_factor, space_group_info=crystal_symmetry.space_group_info()) m1 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=maps[0].file_server.get_miller_array( params.input.mtz_label_1)).apply_sigma_scaling( ).real_map_unpadded() m2 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=maps[1].file_server.get_miller_array( params.input.mtz_label_2)).apply_sigma_scaling( ).real_map_unpadded() # 2 maps else: m1 = maps[0].file_object.map_data() m2 = maps[1].file_object.map_data() # --------------------------------------------------------------------------- # analyze maps assert ((m1 is not None) and (m2 is not None)) # show general statistics s1 = maptbx.more_statistics(m1) s2 = maptbx.more_statistics(m2) show_overall_statistics(out=out, s=s1, header="Map 1 (%s):" % map_names[0]) show_overall_statistics(out=out, s=s2, header="Map 2 (%s):" % map_names[1]) cc_input_maps = flex.linear_correlation(x=m1.as_1d(), y=m2.as_1d()).coefficient() print("CC, input maps: %6.4f" % cc_input_maps, file=out) # compute CCpeak cc_peaks = list() m1_he = maptbx.volume_scale(map=m1, n_bins=10000).map_data() m2_he = maptbx.volume_scale(map=m2, n_bins=10000).map_data() cc_quantile = flex.linear_correlation(x=m1_he.as_1d(), y=m2_he.as_1d()).coefficient() print("CC, quantile rank-scaled (histogram equalized) maps: %6.4f" % \ cc_quantile, file=out) print("Peak correlation:", file=out) print(" cutoff CCpeak", file=out) cutoffs = [i / 100. for i in range(1, 90)] + [i / 1000 for i in range(900, 1000)] for cutoff in cutoffs: cc_peak = maptbx.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff) print(" %3.2f %7.4f" % (cutoff, cc_peak), file=out) cc_peaks.append((cutoff, cc_peak)) # compute discrepancy function (D-function) discrepancies = list() cutoffs = flex.double(cutoffs) df = maptbx.discrepancy_function(map_1=m1_he, map_2=m2_he, cutoffs=cutoffs) print("Discrepancy function:", file=out) print(" cutoff D", file=out) for c, d in zip(cutoffs, df): print(" %3.2f %7.4f" % (c, d), file=out) discrepancies.append((c, d)) # compute and output histograms h1 = maptbx.histogram(map=m1, n_bins=10000) h2 = maptbx.histogram(map=m2, n_bins=10000) print("Map histograms:", file=out) print("Map 1 (%s) Map 2 (%s)"%\ (params.input.map_1,params.input.map_2), file=out) print("(map_value,cdf,frequency) <> (map_value,cdf,frequency)", file=out) for a1, c1, v1, a2, c2, v2 in zip(h1.arguments(), h1.c_values(), h1.values(), h2.arguments(), h2.c_values(), h2.values()): print("(%9.5f %9.5f %9.5f) <> (%9.5f %9.5f %9.5f)"%\ (a1,c1,v1, a2,c2,v2), file=out) # store results s1_dict = create_statistics_dict(s=s1) s2_dict = create_statistics_dict(s=s2) results = dict() inputs = list() for attribute in input_attributes: filename = getattr(params.input, attribute) if (filename is not None): inputs.append(filename) assert (len(inputs) == 2) results['map_files'] = inputs results['map_statistics'] = (s1_dict, s2_dict) results['cc_input_maps'] = cc_input_maps results['cc_quantile'] = cc_quantile results['cc_peaks'] = cc_peaks results['discrepancies'] = discrepancies # TODO, verify h1,h2 are not dicts, e.g. .values is py2/3 compat. I assume it is here results['map_histograms'] = ((h1.arguments(), h1.c_values(), h1.values()), (h2.arguments(), h2.c_values(), h2.values())) return results
def validate_polder_map(self, selection_bool, xray_structure_noligand, mask_data_polder, box_cushion=2.1): ''' The parameter box_cushion is hardcoded to be 2.1 The value is related to the site_radii used for CC calculation (box_cushion - 0.1) Ideally the site_radii are calculated according to resolution, atom type and B factor for each atom However, for the purpose of polder map validation, it is a reasonable approximation to use 2.0. If this value is changed, it will affect the values of the CCs and therefore also the output messages (see mmtbx/programs/polder.py --> result_message) So modify this value with caution. ''' # Significance check fmodel = mmtbx.f_model.manager(f_obs=self.f_obs, r_free_flags=self.r_free_flags, xray_structure=self.xray_structure) fmodel.update_all_scales(remove_outliers=False, fast=True) f_obs_1 = abs(fmodel.f_model()) fmodel.update_xray_structure(xray_structure=xray_structure_noligand, update_f_calc=True, update_f_mask=True, force_update_f_mask=True) ## PVA: do we need it? fmodel.update_all_scales(remove_outliers=False) f_obs_2 = abs(fmodel.f_model()) pdb_hierarchy_selected = self.pdb_hierarchy.select(selection_bool) xrs_selected = pdb_hierarchy_selected.extract_xray_structure( crystal_symmetry=self.cs) f_calc = fmodel.f_obs().structure_factors_from_scatterers( xray_structure=xray_structure_noligand).f_calc() f_mask = fmodel.f_obs().structure_factors_from_map( map=mask_data_polder, use_scale=True, anomalous_flag=False, use_sg=False) box_1 = self.get_polder_diff_map(f_obs=f_obs_1, r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected, box_cushion=box_cushion) box_2 = self.get_polder_diff_map(f_obs=f_obs_2, r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected, box_cushion=box_cushion) box_3 = self.get_polder_diff_map(f_obs=fmodel.f_obs(), r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected, box_cushion=box_cushion) sites_cart_box = box_1.xray_structure_box.sites_cart() sel = maptbx.grid_indices_around_sites( unit_cell=box_1.xray_structure_box.unit_cell(), fft_n_real=box_1.map_box.focus(), fft_m_real=box_1.map_box.all(), sites_cart=sites_cart_box, site_radii=flex.double(sites_cart_box.size(), box_cushion - 0.1)) b1 = box_1.map_box.select(sel).as_1d() b2 = box_2.map_box.select(sel).as_1d() b3 = box_3.map_box.select(sel).as_1d() # Map 1: calculated Fobs with ligand # Map 2: calculated Fobs without ligand # Map 3: real Fobs data cc12 = flex.linear_correlation(x=b1, y=b2).coefficient() cc13 = flex.linear_correlation(x=b1, y=b3).coefficient() cc23 = flex.linear_correlation(x=b2, y=b3).coefficient() #### D-function b1 = maptbx.volume_scale_1d(map=b1, n_bins=10000).map_data() b2 = maptbx.volume_scale_1d(map=b2, n_bins=10000).map_data() b3 = maptbx.volume_scale_1d(map=b3, n_bins=10000).map_data() cc12_peak = flex.linear_correlation(x=b1, y=b2).coefficient() cc13_peak = flex.linear_correlation(x=b1, y=b3).coefficient() cc23_peak = flex.linear_correlation(x=b2, y=b3).coefficient() #### Peak CC: cutoffs = flex.double([i / 10. for i in range(1, 10)] + [i / 100 for i in range(91, 100)]) d12 = maptbx.discrepancy_function(map_1=b1, map_2=b2, cutoffs=cutoffs) d13 = maptbx.discrepancy_function(map_1=b1, map_2=b3, cutoffs=cutoffs) d23 = maptbx.discrepancy_function(map_1=b2, map_2=b3, cutoffs=cutoffs) pdb_hierarchy_selected.adopt_xray_structure(box_1.xray_structure_box) return group_args(box_1=box_1, box_2=box_2, box_3=box_3, cc12=cc12, cc13=cc13, cc23=cc23, cc12_peak=cc12_peak, cc13_peak=cc13_peak, cc23_peak=cc23_peak, d12=d12, d13=d13, d23=d23, cutoffs=cutoffs, ph_selected=pdb_hierarchy_selected)
def run(args, out=sys.stdout, validated=False): show_citation(out=out) if (len(args) == 0): master_phil.show(out=out) print >> out,\ '\nUsage: phenix.map_comparison <CCP4> <CCP4>\n',\ ' phenix.map_comparison <CCP4> <MTZ> mtz_label_1=<label>\n',\ ' phenix.map_comparison <MTZ 1> mtz_label_1=<label 1> <MTZ 2> mtz_label_2=<label 2>\n' sys.exit() # process arguments params = None input_attributes = ['map_1', 'mtz_1', 'map_2', 'mtz_2'] try: # automatic parsing params = phil.process_command_line_with_files( args=args, master_phil=master_phil).work.extract() except Exception: # map_file_def only handles one map phil from libtbx.phil.command_line import argument_interpreter arg_int = argument_interpreter(master_phil=master_phil) command_line_args = list() map_files = list() for arg in args: if (os.path.isfile(arg)): map_files.append(arg) else: command_line_args.append(arg_int.process(arg)) params = master_phil.fetch(sources=command_line_args).extract() # check if more files are necessary n_defined = 0 for attribute in input_attributes: if (getattr(params.input, attribute) is not None): n_defined += 1 # matches files to phil scope, stops once there is sufficient data for map_file in map_files: if (n_defined < 2): current_map = file_reader.any_file(map_file) if (current_map.file_type == 'ccp4_map'): n_defined += 1 if (params.input.map_1 is None): params.input.map_1 = map_file elif (params.input.map_2 is None): params.input.map_2 = map_file elif (current_map.file_type == 'hkl'): n_defined += 1 if (params.input.mtz_1 is None): params.input.mtz_1 = map_file elif (params.input.mtz_2 is None): params.input.mtz_2 = map_file else: print >> out, 'WARNING: only the first two files are used' break # validate arguments (GUI sets validated to true, no need to run again) assert (params is not None) if (not validated): validate_params(params) # --------------------------------------------------------------------------- # check if maps need to be generated from mtz n_maps = 0 maps = list() map_names = list() for attribute in input_attributes: filename = getattr(params.input, attribute) if (filename is not None): map_names.append(filename) current_map = file_reader.any_file(filename) maps.append(current_map) if (current_map.file_type == 'ccp4_map'): n_maps += 1 # construct maps, if necessary crystal_gridding = None m1 = None m2 = None # 1 map, 1 mtz file if (n_maps == 1): for current_map in maps: if (current_map.file_type == 'ccp4_map'): uc = current_map.file_object.unit_cell() sg_info = space_group_info(current_map.file_object.space_group_number) n_real = current_map.file_object.unit_cell_grid crystal_gridding = maptbx.crystal_gridding( uc, space_group_info=sg_info, pre_determined_n_real=n_real) m1 = current_map.file_object.map_data() if (crystal_gridding is not None): label = None for attribute in [('mtz_1', 'mtz_label_1'), ('mtz_2', 'mtz_label_2')]: filename = getattr(params.input, attribute[0]) label = getattr(params.input, attribute[1]) if ( (filename is not None) and (label is not None) ): break # labels will match currently open mtz file for current_map in maps: if (current_map.file_type == 'hkl'): m2 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=current_map.file_server.get_miller_array( label)).apply_sigma_scaling().real_map_unpadded() else: raise Sorry('Gridding is not defined.') # 2 mtz files elif (n_maps == 0): crystal_symmetry = get_crystal_symmetry(maps[0]) d_min = min(get_d_min(maps[0]), get_d_min(maps[1])) crystal_gridding = maptbx.crystal_gridding( crystal_symmetry.unit_cell(), d_min=d_min, resolution_factor=params.options.resolution_factor, space_group_info=crystal_symmetry.space_group_info()) m1 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=maps[0].file_server.get_miller_array( params.input.mtz_label_1)).apply_sigma_scaling().real_map_unpadded() m2 = miller.fft_map( crystal_gridding=crystal_gridding, fourier_coefficients=maps[1].file_server.get_miller_array( params.input.mtz_label_2)).apply_sigma_scaling().real_map_unpadded() # 2 maps else: m1 = maps[0].file_object.map_data() m2 = maps[1].file_object.map_data() # --------------------------------------------------------------------------- # analyze maps assert ( (m1 is not None) and (m2 is not None) ) # show general statistics s1 = maptbx.more_statistics(m1) s2 = maptbx.more_statistics(m2) show_overall_statistics(out=out, s=s1, header="Map 1 (%s):"%map_names[0]) show_overall_statistics(out=out, s=s2, header="Map 2 (%s):"%map_names[1]) cc_input_maps = flex.linear_correlation(x = m1.as_1d(), y = m2.as_1d()).coefficient() print >> out, "CC, input maps: %6.4f" % cc_input_maps # compute CCpeak cc_peaks = list() m1_he = maptbx.volume_scale(map = m1, n_bins = 10000).map_data() m2_he = maptbx.volume_scale(map = m2, n_bins = 10000).map_data() cc_quantile = flex.linear_correlation(x = m1_he.as_1d(), y = m2_he.as_1d()).coefficient() print >> out, "CC, quantile rank-scaled (histogram equalized) maps: %6.4f" % \ cc_quantile print >> out, "Peak correlation:" print >> out, " cutoff CCpeak" cutoffs = [i/100. for i in range(1,90)]+ [i/1000 for i in range(900,1000)] for cutoff in cutoffs: cc_peak = maptbx.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff) print >> out, " %3.2f %7.4f" % (cutoff, cc_peak) cc_peaks.append((cutoff, cc_peak)) # compute discrepancy function (D-function) discrepancies = list() cutoffs = flex.double(cutoffs) df = maptbx.discrepancy_function(map_1=m1_he, map_2=m2_he, cutoffs=cutoffs) print >> out, "Discrepancy function:" print >> out, " cutoff D" for c, d in zip(cutoffs, df): print >> out, " %3.2f %7.4f" % (c,d) discrepancies.append((c, d)) # compute and output histograms h1 = maptbx.histogram(map=m1, n_bins=10000) h2 = maptbx.histogram(map=m2, n_bins=10000) print >> out, "Map histograms:" print >> out, "Map 1 (%s) Map 2 (%s)"%\ (params.input.map_1,params.input.map_2) print >> out, "(map_value,cdf,frequency) <> (map_value,cdf,frequency)" for a1,c1,v1, a2,c2,v2 in zip(h1.arguments(), h1.c_values(), h1.values(), h2.arguments(), h2.c_values(), h2.values()): print >> out, "(%9.5f %9.5f %9.5f) <> (%9.5f %9.5f %9.5f)"%\ (a1,c1,v1, a2,c2,v2) # store results s1_dict = create_statistics_dict(s=s1) s2_dict = create_statistics_dict(s=s2) results = dict() inputs = list() for attribute in input_attributes: filename = getattr(params.input,attribute) if (filename is not None): inputs.append(filename) assert (len(inputs) == 2) results['map_files'] = inputs results['map_statistics'] = (s1_dict, s2_dict) results['cc_input_maps'] = cc_input_maps results['cc_quantile'] = cc_quantile results['cc_peaks'] = cc_peaks results['discrepancies'] = discrepancies results['map_histograms'] = ( (h1.arguments(), h1.c_values(), h1.values()), (h2.arguments(), h2.c_values(), h2.values()) ) return results
def cmd_run(args, validated=False, out=sys.stdout): if (len(args) == 0): print >> out, "-"*79 print >> out, " phenix.polder" print >> out, "-"*79 print >> out, legend print >> out, "-"*79 master_params.show(out=out) return log = multi_out() log.register("stdout", out) log_file_name = "polder.log" logfile = open(log_file_name, "w") log.register("logfile", logfile) print >> log, "phenix.polder is running..." print >> log, "input parameters:\n", args parsed = master_params inputs = mmtbx.utils.process_command_line_args(args = args, master_params = parsed) #inputs.params.show() #check params = inputs.params.extract() # check model file if len(inputs.pdb_file_names) == 0: if (params.model_file_name is None): raise Sorry("No model file found.") elif (len(inputs.pdb_file_names) == 1): params.model_file_name = inputs.pdb_file_names[0] else: raise Sorry("Only one model file should be given") # check reflection file reflection_files = inputs.reflection_files if (len(reflection_files) == 0): if (params.reflection_file_name is None): raise Sorry("No reflection file found.") else: hkl_in = file_reader.any_file(params.reflection_file_name, force_type="hkl") hkl_in.assert_file_type("hkl") reflection_files = [ hkl_in.file_object ] # crystal symmetry crystal_symmetry = None crystal_symmetry = inputs.crystal_symmetry if (crystal_symmetry is None): crystal_symmetries = [] for f in [str(params.model_file_name), str(params.reflection_file_name)]: cs = crystal_symmetry_from_any.extract_from(f) if(cs is not None): crystal_symmetries.append(cs) if(len(crystal_symmetries) == 1): crystal_symmetry = crystal_symmetries[0] elif(len(crystal_symmetries) == 0): raise Sorry("No crystal symmetry found.") else: if(not crystal_symmetries[0].is_similar_symmetry(crystal_symmetries[1])): raise Sorry("Crystal symmetry mismatch between different files.") crystal_symmetry = crystal_symmetries[0] f_obs, r_free_flags = None, None rfs = reflection_file_utils.reflection_file_server( crystal_symmetry = crystal_symmetry, force_symmetry = True, reflection_files = reflection_files, err = StringIO()) parameters = mmtbx.utils.data_and_flags_master_params().extract() if (params.data_labels is not None): parameters.labels = params.data_labels if (params.r_free_flags_labels is not None): parameters.r_free_flags.label = params.r_free_flags_labels determined_data_and_flags = mmtbx.utils.determine_data_and_flags( reflection_file_server = rfs, parameters = parameters, keep_going = True, log = StringIO()) f_obs = determined_data_and_flags.f_obs if (params.data_labels is None): params.data_labels = f_obs.info().label_string() if (params.reflection_file_name is None): params.reflection_file_name = parameters.file_name r_free_flags = determined_data_and_flags.r_free_flags assert f_obs is not None print >> log, "Input data:" print >> log, " Iobs or Fobs:", f_obs.info().labels if (r_free_flags is not None): print >> log, " Free-R flags:", r_free_flags.info().labels params.r_free_flags_labels = r_free_flags.info().label_string() else: print >> log, " Free-R flags: Not present" model_basename = os.path.basename(params.model_file_name.split(".")[0]) if (len(model_basename) > 0 and params.output_file_name_prefix is None): params.output_file_name_prefix = model_basename print params.output_file_name_prefix new_params = master_params.format(python_object=params) new_params.show() if (not validated): validate_params(params) pdb_input = iotbx.pdb.input(file_name = params.model_file_name) pdb_hierarchy = pdb_input.construct_hierarchy() xray_structure = pdb_hierarchy.extract_xray_structure( crystal_symmetry = crystal_symmetry) # DON'T USE: # xray_structure = pdb_input.xray_structure_simple() # atom order might be wrong mmtbx.utils.setup_scattering_dictionaries( scattering_table = params.scattering_table, xray_structure = xray_structure, d_min = f_obs.d_min()) #if f_obs is not None: f_obs = f_obs.resolution_filter( d_min = params.high_resolution, d_max = params.low_resolution) if (r_free_flags is not None): r_free_flags = r_free_flags.resolution_filter( d_min = params.high_resolution, d_max = params.low_resolution) # Grab case that data are anomalous if (f_obs.anomalous_flag()): f_obs, r_free_flags = prepare_f_obs_and_flags( f_obs = f_obs, r_free_flags = r_free_flags) cpm_obj = compute_polder_map( f_obs = f_obs, r_free_flags = r_free_flags, xray_structure = xray_structure, pdb_hierarchy = pdb_hierarchy, params = params, log = log) # Significance check fmodel = mmtbx.f_model.manager( f_obs = f_obs, r_free_flags = r_free_flags, xray_structure = xray_structure) fmodel.update_all_scales(remove_outliers=False, fast=True) f_obs_1 = abs(fmodel.f_model()) fmodel.update_xray_structure(xray_structure=cpm_obj.xray_structure_noligand, update_f_calc=True, update_f_mask=True, force_update_f_mask=True) # PVA: do we need it? fmodel.update_all_scales(remove_outliers=False) f_obs_2 = abs(fmodel.f_model()) xrs_selected = cpm_obj.pdb_hierarchy_selected.extract_xray_structure( crystal_symmetry = f_obs.crystal_symmetry()) f_calc = f_obs.structure_factors_from_scatterers( xray_structure = cpm_obj.xray_structure_noligand).f_calc() f_mask = f_obs.structure_factors_from_map( map = cpm_obj.mask_polder, use_scale = True, anomalous_flag = False, use_sg = False) def get_poler_diff_map(f_obs): fmodel = mmtbx.f_model.manager( f_obs = f_obs, r_free_flags = r_free_flags, f_calc = f_calc, f_mask = f_mask) fmodel.update_all_scales(remove_outliers=False) mc_diff = map_tools.electron_density_map( fmodel = fmodel).map_coefficients( map_type = "mFo-DFc", isotropize = True, fill_missing = False) fft_map = miller.fft_map( crystal_gridding = cpm_obj.crystal_gridding, fourier_coefficients = mc_diff) fft_map.apply_sigma_scaling() map_data = fft_map.real_map_unpadded() return mmtbx.utils.extract_box_around_model_and_map( xray_structure = xrs_selected, map_data = map_data, box_cushion = 2.1) box_1=get_poler_diff_map(f_obs = f_obs_1) box_2=get_poler_diff_map(f_obs = f_obs_2) box_3=get_poler_diff_map(f_obs = f_obs) sites_cart_box = box_1.xray_structure_box.sites_cart() sel = maptbx.grid_indices_around_sites( unit_cell = box_1.xray_structure_box.unit_cell(), fft_n_real = box_1.map_box.focus(), fft_m_real = box_1.map_box.all(), sites_cart = sites_cart_box, site_radii = flex.double(sites_cart_box.size(), 2.0)) b1 = box_1.map_box.select(sel).as_1d() b2 = box_2.map_box.select(sel).as_1d() b3 = box_3.map_box.select(sel).as_1d() print >> log, "Map 1: calculated Fobs with ligand" print >> log, "Map 2: calculated Fobs without ligand" print >> log, "Map 3: real Fobs data" print >>log, "CC(1,2): %6.4f"%flex.linear_correlation(x=b1,y=b2).coefficient() print >>log, "CC(1,3): %6.4f"%flex.linear_correlation(x=b1,y=b3).coefficient() print >>log, "CC(2,3): %6.4f"%flex.linear_correlation(x=b2,y=b3).coefficient() ### D-function b1 = maptbx.volume_scale_1d(map=b1, n_bins=10000).map_data() b2 = maptbx.volume_scale_1d(map=b2, n_bins=10000).map_data() b3 = maptbx.volume_scale_1d(map=b3, n_bins=10000).map_data() print >> log, "Peak CC:" print >>log, "CC(1,2): %6.4f"%flex.linear_correlation(x=b1,y=b2).coefficient() print >>log, "CC(1,3): %6.4f"%flex.linear_correlation(x=b1,y=b3).coefficient() print >>log, "CC(2,3): %6.4f"%flex.linear_correlation(x=b2,y=b3).coefficient() cutoffs = flex.double( [i/10. for i in range(1,10)]+[i/100 for i in range(91,100)]) d12 = maptbx.discrepancy_function(map_1=b1, map_2=b2, cutoffs=cutoffs) d13 = maptbx.discrepancy_function(map_1=b1, map_2=b3, cutoffs=cutoffs) d23 = maptbx.discrepancy_function(map_1=b2, map_2=b3, cutoffs=cutoffs) print >> log, "q D(1,2) D(1,3) D(2,3)" for c,d12_,d13_,d23_ in zip(cutoffs,d12,d13,d23): print >> log, "%4.2f %6.4f %6.4f %6.4f"%(c,d12_,d13_,d23_) ### if(params.debug): box_1.write_ccp4_map(file_name="box_1_polder.ccp4") box_2.write_ccp4_map(file_name="box_2_polder.ccp4") box_3.write_ccp4_map(file_name="box_3_polder.ccp4") cpm_obj.pdb_hierarchy_selected.adopt_xray_structure( box_1.xray_structure_box) cpm_obj.pdb_hierarchy_selected.write_pdb_file(file_name="box_polder.pdb", crystal_symmetry=box_1.box_crystal_symmetry) # print >> log, "Finished." return True
def validate_polder_map(self): # Significance check fmodel = mmtbx.f_model.manager(f_obs=self.f_obs, r_free_flags=self.r_free_flags, xray_structure=self.xray_structure) fmodel.update_all_scales(remove_outliers=False, fast=True) f_obs_1 = abs(fmodel.f_model()) fmodel.update_xray_structure( xray_structure=self.xray_structure_noligand, update_f_calc=True, update_f_mask=True, force_update_f_mask=True) ## PVA: do we need it? fmodel.update_all_scales(remove_outliers=False) f_obs_2 = abs(fmodel.f_model()) pdb_hierarchy_selected = self.pdb_hierarchy.select(self.selection_bool) xrs_selected = pdb_hierarchy_selected.extract_xray_structure( crystal_symmetry=self.f_obs.crystal_symmetry()) f_calc = fmodel.f_obs().structure_factors_from_scatterers( xray_structure=self.xray_structure_noligand).f_calc() f_mask = fmodel.f_obs().structure_factors_from_map( map=self.mask_data_polder, use_scale=True, anomalous_flag=False, use_sg=False) box_1 = self.get_polder_diff_map(f_obs=f_obs_1, r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected) box_2 = self.get_polder_diff_map(f_obs=f_obs_2, r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected) box_3 = self.get_polder_diff_map(f_obs=fmodel.f_obs(), r_free_flags=fmodel.r_free_flags(), f_calc=f_calc, f_mask=f_mask, xrs_selected=xrs_selected) sites_cart_box = box_1.xray_structure_box.sites_cart() sel = maptbx.grid_indices_around_sites( unit_cell=box_1.xray_structure_box.unit_cell(), fft_n_real=box_1.map_box.focus(), fft_m_real=box_1.map_box.all(), sites_cart=sites_cart_box, site_radii=flex.double(sites_cart_box.size(), 2.0)) b1 = box_1.map_box.select(sel).as_1d() b2 = box_2.map_box.select(sel).as_1d() b3 = box_3.map_box.select(sel).as_1d() # Map 1: calculated Fobs with ligand # Map 2: calculated Fobs without ligand # Map 3: real Fobs data cc12 = flex.linear_correlation(x=b1, y=b2).coefficient() cc13 = flex.linear_correlation(x=b1, y=b3).coefficient() cc23 = flex.linear_correlation(x=b2, y=b3).coefficient() #### D-function b1 = maptbx.volume_scale_1d(map=b1, n_bins=10000).map_data() b2 = maptbx.volume_scale_1d(map=b2, n_bins=10000).map_data() b3 = maptbx.volume_scale_1d(map=b3, n_bins=10000).map_data() cc12_peak = flex.linear_correlation(x=b1, y=b2).coefficient() cc13_peak = flex.linear_correlation(x=b1, y=b3).coefficient() cc23_peak = flex.linear_correlation(x=b2, y=b3).coefficient() #### Peak CC: cutoffs = flex.double([i / 10. for i in range(1, 10)] + [i / 100 for i in range(91, 100)]) d12 = maptbx.discrepancy_function(map_1=b1, map_2=b2, cutoffs=cutoffs) d13 = maptbx.discrepancy_function(map_1=b1, map_2=b3, cutoffs=cutoffs) d23 = maptbx.discrepancy_function(map_1=b2, map_2=b3, cutoffs=cutoffs) pdb_hierarchy_selected.adopt_xray_structure(box_1.xray_structure_box) self.validation_results = group_args( box_1=box_1, box_2=box_2, box_3=box_3, cc12=cc12, cc13=cc13, cc23=cc23, cc12_peak=cc12_peak, cc13_peak=cc13_peak, cc23_peak=cc23_peak, d12=d12, d13=d13, d23=d23, cutoffs=cutoffs, ph_selected=pdb_hierarchy_selected)
def run(args, validated=False): show_citation() if ( (len(args) == 0) or (len(args) > 2) ): print '\nUsage: phenix.map_comparison map_1=<first map> map_2=<second map>\n' sys.exit() # process arguments try: # automatic parsing params = phil.process_command_line_with_files( args=args, master_phil=master_phil).work.extract() except Exception: # map_file_def only handles one map phil from libtbx.phil.command_line import argument_interpreter arg_int = argument_interpreter(master_phil=master_phil) command_line_args = list() map_files = list() for arg in args: if (os.path.isfile(arg)): map_files.append(arg) else: command_line_args.append(arg_int.process(arg)) params = master_phil.fetch(sources=command_line_args).extract() for map_file in map_files: if (params.input.map_1 is None): params.input.map_1 = map_file else: params.input.map_2 = map_file # validate arguments (GUI sets validated to true, no need to run again) if (not validated): validate_params(params) # --------------------------------------------------------------------------- # map 1 ccp4_map_1 = iotbx.ccp4_map.map_reader(file_name=params.input.map_1) cs_1 = crystal.symmetry(ccp4_map_1.unit_cell().parameters(), ccp4_map_1.space_group_number) m1 = ccp4_map_1.map_data() # map 2 ccp4_map_2 = iotbx.ccp4_map.map_reader(file_name=params.input.map_2) cs_2 = crystal.symmetry(ccp4_map_2.unit_cell().parameters(), ccp4_map_2.space_group_number) m2 = ccp4_map_2.map_data() # show general statistics s1 = maptbx.more_statistics(m1) s2 = maptbx.more_statistics(m2) show_overall_statistics(s=s1, header="Map 1 (%s):"%params.input.map_1) show_overall_statistics(s=s2, header="Map 2 (%s):"%params.input.map_2) cc_input_maps = flex.linear_correlation(x = m1.as_1d(), y = m2.as_1d()).coefficient() print "CC, input maps: %6.4f" % cc_input_maps # compute CCpeak cc_peaks = list() m1_he = maptbx.volume_scale(map = m1, n_bins = 10000).map_data() m2_he = maptbx.volume_scale(map = m2, n_bins = 10000).map_data() cc_quantile = flex.linear_correlation(x = m1_he.as_1d(), y = m2_he.as_1d()).coefficient() print "CC, quantile rank-scaled (histogram equalized) maps: %6.4f" % \ cc_quantile print "Peak correlation:" print " cutoff CCpeak" for cutoff in [i/100. for i in range(0,100,5)]+[0.99, 1.0]: cc_peak = maptbx.cc_peak(map_1=m1_he, map_2=m2_he, cutoff=cutoff) print " %3.2f %7.4f" % (cutoff, cc_peak) cc_peaks.append((cutoff, cc_peak)) # compute discrepancy function (D-function) discrepancies = list() cutoffs = flex.double([i/20. for i in range(1,20)]) df = maptbx.discrepancy_function(map_1=m1_he, map_2=m2_he, cutoffs=cutoffs) print "Discrepancy function:" print " cutoff D" for c, d in zip(cutoffs, df): print " %3.2f %7.4f" % (c,d) discrepancies.append((c, d)) # compute and output histograms h1 = maptbx.histogram(map=m1, n_bins=10000) h2 = maptbx.histogram(map=m2, n_bins=10000) print "Map histograms:" print "Map 1 (%s) Map 2 (%s)"%(params.input.map_1,params.input.map_2) print "(map_value,cdf,frequency) <> (map_value,cdf,frequency)" for a1,c1,v1, a2,c2,v2 in zip(h1.arguments(), h1.c_values(), h1.values(), h2.arguments(), h2.c_values(), h2.values()): print "(%9.5f %9.5f %9.5f) <> (%9.5f %9.5f %9.5f)"%(a1,c1,v1, a2,c2,v2) # store results s1_dict = create_statistics_dict(s1) s2_dict = create_statistics_dict(s2) results = dict() results['map_files'] = (params.input.map_1, params.input.map_2) results['map_statistics'] = (s1_dict, s2_dict) results['cc_input_maps'] = cc_input_maps results['cc_quantile'] = cc_quantile results['cc_peaks'] = cc_peaks results['discrepancies'] = discrepancies results['map_histograms'] = ( (h1.arguments(), h1.c_values(), h1.values()), (h2.arguments(), h2.c_values(), h2.values()) ) return results