def _component_contribution_wrapper(self, reaction_list_I, pH_I,
temperature_I, ionic_strength_I):
"""wrapper for component contribution"""
# Orginal implementation involves an input of a reaction list
# the dG_prime and dG_std for each reaction are then calculated.
# Because we are interested in accounting for transportation
# and metabolite concentration,
# we need to extraction out the dG_prime and dG_std
# of formation and calculated the dG_prime and
# dG_std for each reaction after accounting for
# transportation and metabolite concentration.
# TODO: change input from reaction list to metabolite list
# however this may be problematic due to the implementation
# of component_contribution (i.e., the group contribution
# and reactant contribution are calculated along orthoganol planes
# in order to gain greater coverage and accuracy of predicted
# dG_f values)
# Debugging:
#wolf = ['C00049 + C00002 <=> C03082 + C00008',
# 'C00026 + C00049 <=> C00025 + C00036',
# 'C00158 <=> C00311',
# 'C00631 <=> C00001 + C00074',
# 'C00354 <=> C00111 + C00118',
# 'C00020 + C00002 <=> 2 C00008',
# 'C00002 + C00001 <=> C00008 + C00009',
# 'C00015 + C00002 <=> C00075 + C00008',
# 'C00033 + C00002 + C00010 <=> C00024 + C00020 + C00013']
#model = KeggModel.from_formulas(wolf)
model = KeggModel.from_formulas(reaction_list_I)
td = TrainingData()
cc = ComponentContribution(td)
model.add_thermo(cc)
dG0_prime_f, dG0_var_f = model.get_transformed_dG0(pH=pH_I,
I=ionic_strength_I,
T=temperature_I)
return dG0_prime_f, dG0_var_f
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 3 15:25:49 2015
@author: noore
"""
from component_contribution.component_contribution_trainer import ComponentContribution
from scipy.io import savemat
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=
'Prepare all thermodynamic training data in a .mat file for running '
'component contribution.')
parser.add_argument('out_file',
type=argparse.FileType('wb'),
help='path to the .mat file that should be written '
'containing the training data')
args = parser.parse_args()
cc = ComponentContribution()
mdict = {
'w': cc.train_w,
'b': cc.train_b,
'G': cc.create_group_incidence_matrix(),
'cids': cc.train_cids,
'S': cc.train_S
}
savemat(args.out_file, mdict, do_compression=True)
