def main():
if len(sys.argv) != 2:
print("Usage: python extract_r125_vle.py [iteration number]")
exit(1)
else:
iternum = sys.argv[1]
R125 = R125Constants()
run_path = "/scratch365/rdefever/hfcs-fffit/hfcs-fffit/runs/"
itername = "r125-vle-iter" + str(iternum)
project_path = run_path + itername
csv_name = "csv/" + itername + "-results.csv"
property_names = [
"liq_density",
"vap_density",
"Hvap",
"Pvap",
"liq_enthalpy",
"vap_enthalpy",
]
project = signac.get_project(project_path)
save_signac_results(project, R125.param_names, property_names, csv_name)
from fffit.plot import (
plot_model_performance,
plot_slices_temperature,
plot_slices_params,
plot_model_vs_test,
)
from fffit.models import run_gpflow_scipy
sys.path.append("../")
from utils.r125 import R125Constants
from utils.id_new_samples import prepare_df_vle
R125 = R125Constants()
pdf = PdfPages('figs/gp_models_eval.pdf')
############################# QUANTITIES TO EDIT #############################
##############################################################################
iternum = 1
gp_shuffle_seed = 584745
##############################################################################
##############################################################################
csv_path = "/scratch365/rdefever/hfcs-fffit/hfcs-fffit/analysis/csv/"
in_csv_names = [
"r125-vle-iter" + str(i) + "-results.csv" for i in range(1, iternum + 1)
def init_project():
# Initialize project
project = signac.init_project("r125-vle-iter1")
# Define temps
temps = [
229.0 * u.K,
249.0 * u.K,
269.0 * u.K,
289.0 * u.K,
309.0 * u.K,
]
# Run at vapor pressure
press = {
229: (123.65 * u.kPa),
249: (290.76 * u.kPa),
269: (592.27 * u.kPa),
289: (1082.84 * u.kPa),
309: (1824.93 * u.kPa),
}
n_vap = 160
n_liq = 640
# Experimental density
R125 = R125Constants()
# Load samples from Latin hypercube
lh_samples = np.genfromtxt(
"../../analysis/csv/r125-vle-iter1-params.csv",
delimiter=",",
skip_header=1,
)[:, 1:]
# Define bounds on sigma/epsilon
bounds_sigma = np.asarray([
[3.0, 4.0], # C
[3.0, 4.0], # C
[2.5, 3.5], # F
[2.5, 3.5], # F
[1.7, 2.7], # H
])
bounds_epsilon = np.asarray([
[20.0, 60.0], # C
[20.0, 60.0], # C
[15.0, 40.0], # F
[15.0, 40.0], # F
[2.0, 10.0], # H
])
bounds = np.vstack((bounds_sigma, bounds_epsilon))
# Convert scaled latin hypercube samples to physical values
scaled_params = values_scaled_to_real(lh_samples, bounds)
for temp in temps:
for sample in scaled_params:
# Unpack the sample
(
sigma_C1,
sigma_C2,
sigma_F1,
sigma_F2,
sigma_H1,
epsilon_C1,
epsilon_C2,
epsilon_F1,
epsilon_F2,
epsilon_H1,
) = sample
# Define the state point
state_point = {
"T":
float(temp.in_units(u.K).value),
"P":
float(press[int(temp.in_units(u.K).value)].in_units(
u.bar).value),
"sigma_C1":
float((sigma_C1 * u.Angstrom).in_units(u.nm).value),
"sigma_C2":
float((sigma_C2 * u.Angstrom).in_units(u.nm).value),
"sigma_F1":
float((sigma_F1 * u.Angstrom).in_units(u.nm).value),
"sigma_F2":
float((sigma_F2 * u.Angstrom).in_units(u.nm).value),
"sigma_H1":
float((sigma_H1 * u.Angstrom).in_units(u.nm).value),
"epsilon_C1":
float((epsilon_C1 * u.K * u.kb).in_units("kJ/mol").value),
"epsilon_C2":
float((epsilon_C2 * u.K * u.kb).in_units("kJ/mol").value),
"epsilon_F1":
float((epsilon_F1 * u.K * u.kb).in_units("kJ/mol").value),
"epsilon_F2":
float((epsilon_F2 * u.K * u.kb).in_units("kJ/mol").value),
"epsilon_H1":
float((epsilon_H1 * u.K * u.kb).in_units("kJ/mol").value),
"N_vap":
n_vap,
"N_liq":
n_liq,
"expt_liq_density":
R125.expt_liq_density[int(temp.in_units(u.K).value)],
"nsteps_liqeq":
5000,
"nsteps_eq":
10000,
"nsteps_prod":
100000,
}
job = project.open_job(state_point)
job.init()