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()
#out_file = '../examples/vz/output/cc_dG_cov.tsv'
#outf = open(out_file, "w")
#outf.write("reaction\tdGr\tdGrSD\n")
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()
#out_file = '../examples/vz/output/cc_dG_cov.tsv'
#outf = open(out_file, "w")
#outf.write("reaction\tdGr\tdGrSD\n")
#for i in range(len(reactions)):
