from __future__ import absolute_import, division, print_function import iotbx.phil import sys class phil_lego(object): """ This class facilitates the construction of phil parameter files for the FA estimation program FATSO. """ def __init__(self): self.default_expert_level_for_parameters_that_should_be_sensible_defaults='1' self.scaling_input = """ scaling.input{ __REPLACE__ expert_level=0 .type=int .expert_level=__EXPERT_LEVEL__ } """ self.basic_info = """basic{ n_residues=None .type=float n_bases=None .type=float n_copies_per_asu=None .type=float } """ self.xray_data_basic="""xray_data{ unit_cell=None .type=unit_cell space_group=None .type=space_group __REPLACE__ } """ self.data_type="""__REPLACE__{ file_name=None .type=path labels=None .type=strings } """ self.scaling_strategy="""scaling_strategy .expert_level=__EXPERT_LEVEL__ { __REPLACE__ } """ self.pre_scaler_protocol="""pre_scaler_protocol .expert_level=__EXPERT_LEVEL__ { high_resolution=None .type=float low_resolution=None .type=float aniso_correction=True .type=bool b_add = None .type = float outlier_level_wilson=1e-6 .type=float outlier_level_extreme=1e-2 .type=float }""" self.scale_protocol="""__REPLACE__ .expert_level=__EXPERT_LEVEL__ { target = ls loc *ls_and_loc None .type=choice iterations = *auto specified_by_max_iterations .type=choice max_iterations = 2 .type=int least_squares_options{ use_experimental_sigmas=True .type=bool scale_data=*intensities amplitudes .type=choice scale_target=basic *fancy .type=choice } local_scaling_options{ use_experimental_sigmas=True .type=bool scale_data=intensities *amplitudes .type=choice scale_target=local_moment local_lsq *local_nikonov .type=choice max_depth=10 .type=int target_neighbours=150 .type=int neighbourhood_sphere=1 .type=int } outlier_rejection_options{ cut_level_sigma=3 .type=float cut_level_rms_primary=4 .type=float cut_level_rms_secondary=4 .type=float protocol=solve rms *rms_and_sigma .type=choice } }""" self.fa_estimation="""fa_estimation .expert_level=__EXPERT_LEVEL__ { protocol = *algebraic cns combine_ano .type = choice number_of_terms_in_normalisation_curve=23 .type=int } """ self.output="""output { log = 'fatso.log' .type = path hklout = 'fatso.mtz' .type = path outlabel = '_ATSO' .type = str } """ self.omit="""omit .expert_level=__EXPERT_LEVEL__ { perform_omit=False .type=bool fraction=0.15 .type=float max_number=1e5 .type=int number_of_sets=10 .type=int root_name='omit_' .type=str } """ def add_wavelength_info(self): tmp= """ use_anomalous=True .type=bool .expert_level=5 use_in_dispersive=True .type=bool .expert_level=5 wavelength=None .type=float .expert_level=15 f_prime=None .type=float .expert_level=0 f_double_prime=None .type=float .expert_level=0 } """ self.data_type =self.data_type.replace( '}', tmp) def default_sad(self): outer_level = self.scaling_input outer_level = outer_level.replace( '__EXPERT_LEVEL__', self.default_expert_level_for_parameters_that_should_be_sensible_defaults) basic = self.basic_info data = self.data_type.replace( '__REPLACE__', 'reference' ) data = self.xray_data_basic.replace('__REPLACE__', data ) scaler = self.pre_scaler_protocol + \ self.scale_protocol.replace('__REPLACE__', 'ano_protocol' ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = self.scaling_strategy.replace('__REPLACE__', scaler ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = scaler.replace( 'ls loc *ls_and_loc None', '*loc None' ) output = self.output omit = self.omit omit = omit.replace('__EXPERT_LEVEL__', '1' ) result = outer_level.replace('__REPLACE__', basic+data+scaler+omit+output) return result def default_sir(self): outer_level = self.scaling_input outer_level = outer_level.replace( '__EXPERT_LEVEL__', self.default_expert_level_for_parameters_that_should_be_sensible_defaults) basic = self.basic_info data = self.data_type.replace( '__REPLACE__', 'native' ) \ + \ self.data_type.replace( '__REPLACE__', 'derivative' ) data = self.xray_data_basic.replace('__REPLACE__', data ) scaler = self.scale_protocol.replace('__REPLACE__','iso_protocol' ) scaler = self.pre_scaler_protocol + scaler scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = self.scaling_strategy.replace('__REPLACE__', scaler ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) output = self.output result = outer_level.replace('__REPLACE__', basic+data+scaler+output) return result def default_rip(self): tmp = self.default_sir() tmp = tmp.replace( 'native', 'after_burn' ) tmp = tmp.replace( 'derivative', 'before_burn' ) tmp = tmp.replace( 'least_squares_options{', """nsr_bias=1.0 .type=float .expert_level=0 least_squares_options{""") return tmp def default_siras(self): outer_level = self.scaling_input outer_level = outer_level.replace( '__EXPERT_LEVEL__', self.default_expert_level_for_parameters_that_should_be_sensible_defaults) basic = self.basic_info data = self.data_type.replace( '__REPLACE__', 'native' ) \ + \ self.data_type.replace( '__REPLACE__', 'derivative' ) data = self.xray_data_basic.replace('__REPLACE__', data ) scaler = self.scale_protocol.replace('__REPLACE__', 'ano_protocol' ) scaler = scaler.replace('ls loc *ls_and_loc None', '*loc None' ) scaler = self.pre_scaler_protocol + scaler + \ self.scale_protocol.replace('__REPLACE__','iso_protocol' ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = self.scaling_strategy.replace('__REPLACE__', scaler ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) fa = self.fa_estimation.replace('__EXPERT_LEVEL__', '10' ) output = self.output result = outer_level.replace('__REPLACE__', basic+data+scaler+fa+output) return result def default_2wmad(self): self.add_wavelength_info() outer_level = self.scaling_input outer_level = outer_level.replace( '__EXPERT_LEVEL__', self.default_expert_level_for_parameters_that_should_be_sensible_defaults) basic = self.basic_info data = self.data_type.replace( '__REPLACE__', 'wavelength1' ) \ + \ self.data_type.replace( '__REPLACE__', 'wavelength2' ) data = self.xray_data_basic.replace('__REPLACE__', data ) scaler = self.scale_protocol.replace('__REPLACE__', 'ano_protocol' ) scaler = scaler.replace('ls loc *ls_and_loc None', '*loc None' ) scaler = self.pre_scaler_protocol + scaler + \ self.scale_protocol.replace('__REPLACE__','iso_protocol' ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = self.scaling_strategy.replace('__REPLACE__', scaler ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) fa = self.fa_estimation.replace('__EXPERT_LEVEL__', '10' ) output = self.output result = outer_level.replace('__REPLACE__', basic+data+scaler+fa+output) return result def default_3wmad(self): self.add_wavelength_info() outer_level = self.scaling_input outer_level = outer_level.replace( '__EXPERT_LEVEL__', self.default_expert_level_for_parameters_that_should_be_sensible_defaults) basic = self.basic_info data = self.data_type.replace( '__REPLACE__', 'wavelength1' ) \ + \ self.data_type.replace( '__REPLACE__', 'wavelength2' ) \ + \ self.data_type.replace( '__REPLACE__', 'wavelength3' ) data = self.xray_data_basic.replace('__REPLACE__', data ) scaler = self.scale_protocol.replace('__REPLACE__', 'ano_protocol' ) scaler = scaler.replace('ls loc *ls_and_loc None', '*loc None' ) scaler = self.pre_scaler_protocol + scaler + \ self.scale_protocol.replace('__REPLACE__','iso_protocol' ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) scaler = self.scaling_strategy.replace('__REPLACE__', scaler ) scaler = scaler.replace('__EXPERT_LEVEL__', '1' ) fa = self.fa_estimation.replace('__EXPERT_LEVEL__', '10' ) output = self.output result = outer_level.replace('__REPLACE__', basic+data+scaler+fa+output) return result def run(args): okai=True if len(args)==0: print("Example parameter files lego-ed together from several phil blocks") print() print("specifiy 'expert level' on command line via ") print(" python make_param.py ") okai=False if okai: tester = phil_lego() print(" ---------- SAD ----------") master_params = iotbx.phil.parse( tester.default_sad() ) master_params.show(expert_level = int(args[0]) ) print(" ---------- SIR ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_sir() ) master_params.show(expert_level=int(args[0])) print(" ---------- SIRAS ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_siras() ) master_params.show(expert_level=int(args[0])) print(" ---------- 2WMAD ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_2wmad() ) master_params.show(expert_level=int(args[0])) print(" ---------- 3WMAD ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_3wmad() ) master_params.show(expert_level=int(args[0])) print(" ---------- 3WMAD ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_3wmad() ) master_params.show(expert_level=int(args[0])) print(" ---------- RIP ----------") del master_params del tester tester = phil_lego() master_params = iotbx.phil.parse( tester.default_rip() ) master_params.show(expert_level=int(args[0])) if (__name__ == "__main__"): run(sys.argv[1:])