############################################################################## ############################################################################## 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", }