def LoadAllEstimators():
db_public = SqliteDatabase('../data/public_data.sqlite')
db_gibbs = SqliteDatabase('../res/gibbs.sqlite')
if not db_gibbs.DoesTableExist('prc_pseudoisomers'):
nist_regression = NistRegression(db_gibbs)
nist_regression.Train()
tables = {
'alberty': (db_public, 'alberty_pseudoisomers', 'Alberty'),
'PRC': (db_gibbs, 'prc_pseudoisomers', 'our method (PRC)')
}
estimators = {}
for key, (db, table_name, thermo_name) in tables.iteritems():
if db.DoesTableExist(table_name):
estimators[key] = PsuedoisomerTableThermodynamics.FromDatabase(
db, table_name, name=thermo_name)
else:
logging.warning('The table %s does not exist in %s' %
(table_name, str(db)))
estimators['hatzi_gc'] = Hatzi(use_pKa=False)
#estimators['hatzi_gc_pka'] = Hatzi(use_pKa=True)
if db.DoesTableExist('bgc_pseudoisomers'):
estimators['BGC'] = GroupContribution(db=db_gibbs, transformed=True)
estimators['BGC'].init()
estimators['BGC'].name = 'our method (BGC)'
if db.DoesTableExist('pgc_pseudoisomers'):
estimators['PGC'] = GroupContribution(db=db_gibbs, transformed=False)
estimators['PGC'].init()
estimators['PGC'].name = 'our method (PGC)'
estimators['UGC'] = UnifiedGroupContribution(db=db_gibbs)
estimators['UGC'].init()
estimators['UGC'].name = 'our method (UGC)'
estimators['C1'] = ReactionThermodynamics.FromCsv(
'../data/thermodynamics/c1_reaction_thermodynamics.csv',
estimators['alberty'])
if 'PGC' in estimators:
estimators['merged'] = BinaryThermodynamics(estimators['alberty'],
estimators['PGC'])
estimators['merged_C1'] = BinaryThermodynamics(estimators['C1'],
estimators['PGC'])
for thermo in estimators.values():
thermo.load_bounds('../data/thermodynamics/concentration_bounds.csv')
return estimators
options, _ = MakeOpts().parse_args(sys.argv)
if options.sbml_model_filename == None:
raise ValueError("Must provide a SBML model")
print 'SBML model filename:', options.sbml_model_filename
print 'CSV output filename:', options.csv_output_filename
print 'KEGG Database filename:', options.kegg_db_filename
print 'Observed Thermodynamics filename:', options.thermo_filename
print 'Thermodynamic Database filename:', options.db_filename
print 'Group Contribution Table Name:', options.gc_table_name
db = SqliteDatabase(options.db_filename)
observed_thermo = PsuedoisomerTableThermodynamics.FromCsvFile(
options.thermo_filename)
if not db.DoesTableExist(options.gc_table_name):
raise ValueError('The table %s does not exist in the database. '
'Please run the groups.py script and try again.'
% options.gc_table_name)
thermo = PsuedoisomerTableThermodynamics.FromDatabase(
db, options.gc_table_name)
thermo.override_data(observed_thermo)
kegg = Kegg.getInstance()
document = libsbml.readSBML(options.sbml_model_filename)
if document.getNumErrors():
raise Exception('cannot read SBML model from file %s due to error: %s' %
(options.sbml_model_filename, document.getError(0).getMessage()))
model = document.getModel()
logging.info('Done parsing the model: ' + model.getName())
model_annotation = semanticSBML.annotate.ModelElementsAnnotations(model, suppress_errors=True)
