def main():
if len(sys.argv) != 2:
print("Usage: python extract_r32_vle.py [iteration number]")
exit(1)
else:
iternum = sys.argv[1]
R32 = R32Constants()
run_path = "/scratch365/rdefever/hfcs-fffit/hfcs-fffit/runs/"
itername = "r32-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, R32.param_names, property_names, csv_name)
import sys
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn
sys.path.append("../../")
from scipy.stats import linregress
from utils.r32 import R32Constants
from matplotlib.ticker import MultipleLocator, AutoMinorLocator
R32 = R32Constants()
matplotlib.rc("font", family="sans-serif")
matplotlib.rc("font", serif="Arial")
df = pd.read_csv("../../csv/r32-pareto.csv", index_col=0).loc[23]
df_gaff = pd.read_csv("../../csv/r32-gaff.csv", index_col=0)
df_raabe = pd.read_csv("../../csv/r32-raabe.csv", index_col=0)
def main():
# Plot VLE envelopes
clrs = seaborn.color_palette('bright', n_colors=1)
fig, ax = plt.subplots(figsize=(8, 6))
temps = list(R32.expt_liq_density.keys())
final_temp = temps[-1]
def init_project():
# Initialize project
project = signac.init_project("r32-vle-iter3")
# Define temps
temps = [241.0 * u.K, 261.0 * u.K, 281.0 * u.K, 301.0 * u.K, 321.0 * u.K]
# Run at vapor pressure
press = {
241: (2.5159 * u.bar),
261: (5.4327 * u.bar),
281: (10.426 * u.bar),
301: (18.295 * u.bar),
321: (29.989 * u.bar),
}
n_vap = 200
n_liq = 800
# Experimental density
R32 = R32Constants()
# Load samples
lh_samples = np.genfromtxt(
"../../analysis/csv/r32-vle-iter3-params.csv",
delimiter=",",
skip_header=1,
)[:, 1:]
# Define bounds on sigma/epsilon
bounds_sigma = np.asarray(
[[3.0, 4.0], [2.5, 3.5], [1.7, 2.7],]
) # C # F # H
bounds_epsilon = np.asarray(
[[20.0, 60.0], [15.0, 40.0], [2.0, 10.0],] # C # F # 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_C,
sigma_F,
sigma_H,
epsilon_C,
epsilon_F,
epsilon_H,
) = 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_C": float((sigma_C * u.Angstrom).in_units(u.nm).value),
"sigma_F": float((sigma_F * u.Angstrom).in_units(u.nm).value),
"sigma_H": float((sigma_H * u.Angstrom).in_units(u.nm).value),
"epsilon_C": float(
(epsilon_C * u.K * u.kb).in_units("kJ/mol").value
),
"epsilon_F": float(
(epsilon_F * u.K * u.kb).in_units("kJ/mol").value
),
"epsilon_H": float(
(epsilon_H * u.K * u.kb).in_units("kJ/mol").value
),
"N_vap": n_vap,
"N_liq": n_liq,
"expt_liq_density": R32.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()
