q2_list = []
rmse_list = []
for experiment in experiments:
print(experiment)
if experiment in experiments_with_unif_prior:
print("Use uniform prior for concentrations")
uniform_P0 = True
uniform_Ls = True
else:
uniform_P0 = False
uniform_Ls = False
print("Use lognormal prior for concentrations")
actual_q_micro_cal = load_heat_micro_cal(
os.path.join(args.heat_dir, experiment + ".DAT"))
actual_q_cal = actual_q_micro_cal * 10**(-6)
exper_info_2cbm = ITCExperiment(
os.path.join(args.two_component_mcmc_dir, experiment,
args.exper_info_file))
exper_info_rmbm = ITCExperiment(
os.path.join(args.racemic_mixture_mcmc_dir, experiment,
args.exper_info_file))
exper_info_embm = ITCExperiment(
os.path.join(args.enantiomer_mcmc_dir, experiment,
args.exper_info_file))
trace_2cbm = pickle.load(
open(
os.path.join(args.two_component_mcmc_dir, experiment,
exper_info_file = "/home/tnguye46/bayesian_itc_racemic/05.exper_info/Baum_59/experimental_information.pickle"
heat_file = "/home/tnguye46/bayesian_itc_racemic/04.heat_in_origin_format/Baum_59.DAT"
# "2cbm", "rmbm", "embm"
model_name = "2cbm"
dcell = 0.1
dsyringe = 0.1
uniform_P0 = True
uniform_Ls = True
concentration_range_factor = 10.
auto_transform = False
exper_info = ITCExperiment(exper_info_file)
q_actual_micro_cal = load_heat_micro_cal(heat_file)
q_actual_cal = q_actual_micro_cal * 10. ** (-6)
if model_name == "2cbm":
pm_model = make_TwoComponentBindingModel(q_actual_cal, exper_info,
dcell=dcell, dsyringe=dsyringe,
uniform_P0=uniform_P0, uniform_Ls=uniform_Ls,
concentration_range_factor=concentration_range_factor,
auto_transform=auto_transform)
elif model_name == "rmbm":
pm_model = make_RacemicMixtureBindingModel(q_actual_cal, exper_info,
dcell=dcell, dsyringe=dsyringe,
uniform_P0=uniform_P0, uniform_Ls=uniform_Ls,
concentration_range_factor=concentration_range_factor,
is_rho_free_param=False,
dP0 = args.dP0
dLs = args.dLs
concentration_range_factor = args.concentration_range_factor
models = ["2cbm", "rmbm", "embm"]
list_data_dirs = [two_component_dirs, racemic_mixture_dirs, enantiomer_dirs]
info_criteria = []
for exper in experiments:
print("")
print(exper)
heat_file = os.path.join(args.heat_data_dir, exper + ".DAT")
print("heat_file:", heat_file)
n_samples = load_heat_micro_cal(heat_file).shape[0]
print("n_samples:", n_samples)
if exper in experiments_flat_prior_P0:
uniform_P0 = True
else:
uniform_P0 = False
if exper in experiments_flat_prior_Ls:
uniform_Ls = True
else:
uniform_Ls = False
for model, data_dirs in zip(models, list_data_dirs):
print("")
print("model", model)
