9.31556123e+00, 3.22439204e-08, 5.71925258e-05,
3.70698682e-01, 3.85329914e+04, 2.41529096e+04,
2.43090986e+04, 2.86685025e+04, 1.11990152e+03,
1.46303304e+03, 9.01419378e+05, 9.16429780e+04,
7.99801158e+04, 1.69026159e+04, 5.09448820e-01,
7.84471874e-05, 7.47324740e-07, 9.20472237e-03,
7.66006690e-01, 1.17392697e-08, 1.92114387e-02,
1.51333432e+00, 5.89673352e-09, 5.41091972e-04,
1.81968382e+00, 1.80402454e-06, 2.24990216e-03,
1.43036340e+00, 1.00064585e-07, 7.26550483e-03,
1.00000000e-02, 3.64962469e-06, 2.09630718e-01,
3.55723180e-07, 2.61192682e-02, 2.62023580e-05,
1.54476111e-03, 1.01089158e-06, 8.08297025e-03,
1.10796394e-07, 6.87439549e-04, 1.28003247e-05,
2.11267099e-02, 4.86731639e-06, 2.37896838e-02,
7.98431070e-08, 2.03144337e-01, 4.92250704e-06,
2.97646552e-02, 4.87999878e-07, 8.02754496e-03,
2.80093210e-06, 1.33217623e-04, 1.17453801e-04,
3.07145084e-03, 6.03418156e-04, 1.50334426e-02,
6.65686789e-05, 1.64949367e-02, 7.59172307e-04,
5.24390424e-03, 1.89608777e-04, 1.80147595e-03,
1.30296705e-04, 1.47706376e-03, 1.14915557e+00,
6.26994247e-06, 6.42049097e-05, 9.89376804e-01,
1.58266178e-05, 4.84818743e-03, 6.05651201e+00,
7.66416641e-06, 2.52347502e-04, 9.60186500e+00])
tspan = np.linspace(0, 20160, 1000)
run_flux_visualization(model, tspan, parameters=parames, verbose=False)
print('finished')
from corm import model
from visualization.species_visualization_bidirectional_equilibration import run_flux_visualization
import numpy as np
# tipe 1 cluster: 8509
# type 2 cluster: 7848
# type 3 cluster: 5113
all_dream_log_parames = np.load("/home/oscar/PycharmProjects/CORM/results/2015_02_02_COX2_all_traces.npy")[1784]
pysb_sampled_parameter_names = ['kr_AA_cat2', 'kcat_AA2', 'kr_AA_cat3', 'kcat_AA3', 'kr_AG_cat2', 'kr_AG_cat3',
'kcat_AG3', 'kr_AA_allo1', 'kr_AA_allo2', 'kr_AA_allo3', 'kr_AG_allo1', 'kr_AG_allo2']
generic_kf = np.log10(1.5e4)
param_dict = {pname: pvalue for pname, pvalue in zip(pysb_sampled_parameter_names, all_dream_log_parames)}
for pname, pvalue in param_dict.items():
# Sub in parameter values at current location in parameter space
if 'kr' in pname:
model.parameters[pname].value = 10 ** (pvalue + generic_kf)
elif 'kcat' in pname:
model.parameters[pname].value = 10 ** pvalue
tspan = np.linspace(0, 10, num=100)
run_flux_visualization(model, tspan)
print('finished')
