def python_main():
sys.path.append('../python')
from training_data import TrainingData
from component_contribution import ComponentContribution
from kegg_model import KeggModel
reaction_strings = open(REACTION_FNAME, 'r').readlines()
model = KeggModel.from_formulas(reaction_strings)
td = TrainingData()
cc = ComponentContribution(td)
model.add_thermo(cc)
dG0_prime, dG0_std = model.get_transformed_dG0(pH=7.5, I=0.2, T=298.15)
sys.stdout.write('[' +
', '.join([str(x) for x in model.dG0.flat]) + '; ' +
', '.join([str(x) for x in dG0_prime.flat]) +
']')
params["contributions"] = [dG0_rc, dG0_gc]
params["covariances"] = [V_rc, V_gc, V_inf]
params["MSEs"] = [MSE_rc, MSE_gc, MSE_inf]
params["projections"] = [P_R_rc, P_R_gc * G.T * P_N_rc, P_N_gc * G.T * P_N_rc, P_R_gc, P_N_rc, P_N_gc]
params["inverses"] = [inv_S, inv_GS, inv_SWS, inv_GSWGS]
# Calculate the total of the contributions and covariances
cov_dG0 = V_rc * MSE_rc + V_gc * MSE_gc + V_inf * MSE_inf
return dG0_cc, cov_dG0, params
if __name__ == "__main__":
from kegg_model import KeggModel
reaction_strings = sys.stdin.readlines()
td = TrainingData()
cc = ComponentContribution(td)
model = KeggModel.from_formulas(reaction_strings)
model_dG0, model_cov_dG0 = cc.estimate_kegg_model(model)
model_dG0_prime = model_dG0 + model.get_transform_ddG0(pH=7.5, I=0.2, T=298.15)
sys.stdout.write(
"["
+ ", ".join([str(x) for x in model_dG0.flat])
+ "; "
+ ", ".join([str(x) for x in model_dG0_prime.flat])
+ "]"
)
with open('../examples/vz/StoiMat.tsv', 'r') as tsv:
tabinput = [line.strip().split('\t') for line in tsv]
reactions = tabinput[0]
reactions = reactions[1:]
tabinput = tabinput[1:]
S = np.zeros((len(tabinput), len(tabinput[0]) - 1))
cids = list()
for i in range(len(tabinput)):
cids.append(float(tabinput[i][0].replace('"', '')))
for j in range(len(tabinput[0]) - 1):
S[i, j] = tabinput[i][j + 1]
model = KeggModel(S, cids)
model = model.check_S_balance()
td = TrainingData()
cc = ComponentContribution(td)
model_dG0, model_cov_dG0 = cc.estimate_kegg_model(model)
model_dG0_prime = model_dG0 + model.get_transform_ddG0(pH=7.5, I=0.2, T=298.15)
dG0_std = np.sqrt(model_cov_dG0.diagonal())
out_file = '../examples/vz/output/cc_dG.tsv'
outf = open(out_file, "w")
outf.write("reaction\tdGr\tdGrSD\n")
for i in range(len(reactions)):
outf.write(reactions[i] + "\t" + str(float(model_dG0_prime[i])) + "\t" +
str(float(dG0_std[0, i])) + "\n")
params['contributions'] = [dG0_rc, dG0_gc]
params['covariances'] = [V_rc, V_gc, V_inf]
params['MSEs'] = [MSE_rc, MSE_gc, MSE_inf]
params['projections'] = [P_R_rc,
P_R_gc * G.T * P_N_rc,
P_N_gc * G.T * P_N_rc,
P_R_gc,
P_N_rc,
P_N_gc]
params['inverses'] = [inv_S, inv_GS, inv_SWS, inv_GSWGS]
# Calculate the total of the contributions and covariances
cov_dG0 = V_rc * MSE_rc + V_gc * MSE_gc + V_inf * MSE_inf
return dG0_cc, cov_dG0, params
if __name__ == '__main__':
from kegg_model import KeggModel
reaction_strings = sys.stdin.readlines()
td = TrainingData()
cc = ComponentContribution(td)
model = KeggModel.from_formulas(reaction_strings)
model_dG0, model_cov_dG0 = cc.estimate_kegg_model(model)
model_dG0_prime = model_dG0 + model.get_transform_ddG0(pH=7.5, I=0.2, T=298.15)
sys.stdout.write('[' +
', '.join([str(x) for x in model_dG0.flat]) + '; ' +
', '.join([str(x) for x in model_dG0_prime.flat]) +
']')
with open('../examples/vz/StoiMat.tsv','r') as tsv:
tabinput = [line.strip().split('\t') for line in tsv]
reactions = tabinput[0]
reactions = reactions[1:]
tabinput = tabinput[1:]
S = np.zeros((len(tabinput), len(tabinput[0])-1))
cids = list()
for i in range(len(tabinput)):
cids.append(float(tabinput[i][0].replace('"','')))
for j in range(len(tabinput[0])-1):
S[i,j] = tabinput[i][j+1]
model = KeggModel(S, cids)
model = model.check_S_balance()
td = TrainingData()
cc = ComponentContribution(td)
model_dG0, model_cov_dG0 = cc.estimate_kegg_model(model)
model_dG0_prime = model_dG0 + model.get_transform_ddG0(pH=7.5, I=0.2, T=298.15)
dG0_std = np.sqrt(model_cov_dG0.diagonal())
out_file = '../examples/vz/output/cc_dG.tsv'
outf = open(out_file, "w")
outf.write("reaction\tdGr\tdGrSD\n")
for i in range(len(reactions)):
outf.write(reactions[i] + "\t" + str(float(model_dG0_prime[i])) + "\t" +str(float(dG0_std[0,i]))+ "\n")
outf.close()
