##############################################################################
##############################################################################
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)
]
out_csv_name = "r125-vle-iter" + str(iternum + 1) + "-params.csv"
# Read files
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_all = prepare_df_vle(df_csv, R125)
### Fit GP Model to liquid density
param_names = list(R125.param_names) + ["temperature"]
property_name = "sim_liq_density"
x_train, y_train, x_test, y_test = shuffle_and_split(
df_all,
param_names,
property_name,
shuffle_seed=gp_shuffle_seed,
fraction_train=0.8)
# Fit model
models = {}
models["RBF"] = run_gpflow_scipy(
x_train,
iternum = 5
##############################################################################
##############################################################################
csv_path = "../csv/"
in_csv_names = [
"r125-vle-iter" + str(i) + "-results.csv" for i in range(1, iternum + 1)
]
# Read files
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
dfs = [prepare_df_vle(df_csv, R125) for df_csv in df_csvs]
def main():
# Create a dataframe with one row per parameter set
dfs_paramsets = [prepare_df_vle_errors(df, R125) for df in dfs]
names = {
"mape_liq_density": "Liquid density",
"mape_vap_density": "Vapor density",
"mape_Pvap": "Vapor pressure",
"mape_Hvap": "Enthalpy of vaporization",
"mape_Tc": "Critical temperature",
"mape_rhoc": "Critical density",
}
md_gp_shuffle_seed = 1
distance_seed = 10
liquid_density_threshold = 500 # kg/m^3
# Read VLE files
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)
]
out_csv_name = "r125-vle-iter" + str(iternum + 1) + "-params.csv"
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_vle = prepare_df_vle(df_csv, R125)
# Read liquid density files
max_density_iter = 4
in_csv_names = [
"r125-density-iter" + str(i) + "-results.csv"
for i in range(1, max_density_iter + 1)
]
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_all, df_liquid, df_vapor = prepare_df_density(df_csv, R125,
liquid_density_threshold)
##############################################################################
##############################################################################
csv_path = "/scratch365/rdefever/hfcs-fffit/hfcs-fffit/analysis/csv/"
in_csv_names = [
"r32-vle-iter" + str(i) + "-results.csv" for i in range(1, iternum + 1)
]
out_csv_name = "r32-pareto.csv"
# Read files
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_all = prepare_df_vle(df_csv, R32)
def main():
# Create a dataframe with one row per parameter set
df_paramsets = prepare_df_vle_errors(df_all, R32)
# ID pareto points
# ID pareto points
result, pareto_points, dominated_points = find_pareto_set(
df_paramsets.filter(["mse_liq_density", "mse_vap_density", "mse_Pvap", "mse_Hvap", "mse_Tc", "mse_rhoc"]).values,
is_pareto_efficient
)
df_paramsets = df_paramsets.join(pd.DataFrame(result, columns=["is_pareto"]))
df_paramsets[df_paramsets["is_pareto"]==True].to_csv(csv_path + "/" + out_csv_name)
md_gp_shuffle_seed = 1
distance_seed = 10
liquid_density_threshold = 500 # kg/m^3
# Read VLE files
csv_path = "/scratch365/rdefever/hfcs-fffit/hfcs-fffit/analysis/csv/"
in_csv_names = [
"r32-vle-iter" + str(i) + "-results.csv" for i in range(1, iternum + 1)
]
out_csv_name = "r32-vle-iter" + str(iternum + 1) + "-params.csv"
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_vle = prepare_df_vle(df_csv, R32)
# Read liquid density files
max_density_iter = 4
in_csv_names = [
"r32-density-iter" + str(i) + "-results.csv"
for i in range(1, max_density_iter + 1)
]
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
df_csv = pd.concat(df_csvs)
df_all, df_liquid, df_vapor = prepare_df_density(df_csv, R32,
liquid_density_threshold)
iternum = 3
##############################################################################
##############################################################################
csv_path = "../csv/"
in_csv_names = [
"r32-vle-iter" + str(i) + "-results.csv" for i in range(1, iternum + 1)
]
# Read files
df_csvs = [
pd.read_csv(csv_path + in_csv_name, index_col=0)
for in_csv_name in in_csv_names
]
dfs = [prepare_df_vle(df_csv, R32) for df_csv in df_csvs]
def main():
# Create a dataframe with one row per parameter set
dfs_paramsets = [prepare_df_vle_errors(df, R32) for df in dfs]
names = {
"mape_liq_density": "Liquid density",
"mape_vap_density": "Vapor density",
"mape_Pvap": "Vapor pressure",
"mape_Hvap": "Enthalpy of vaporization",
"mape_Tc": "Critical temperature",
"mape_rhoc": "Critical density",
}
