import os, sys, platform sys.path.append(os.path.dirname(os.getcwd()) + '/soret_model/cpp/release') sys.path.append(os.path.dirname(os.getcwd()) + '/soret_model') sys.path.append(os.path.dirname(os.getcwd()) + '/soret_model/cpp') import matplotlib.pyplot as plt from matplotlib import cm import pandas as pd import numpy as np from datetime import datetime ROOT_PATH = os.path.dirname(os.path.abspath(__file__)) DATA_PATH = ROOT_PATH + '/data/benchmark' # Defining styles globaly for consistency lines_dict = {'VdW': '-', 'SRK': '--', 'PR': '-.', 'PT': ':', 'SW': (0, (1, 2, 1, 2, 4, 2)), 'PC-SAFT': '-', 'SPUNG': '-', 'CPA': '-', 'SAFT-VR-MIE' : '-'} marker_dict = {'VdW': '', 'SRK': '', 'PR': '', 'PT': '', 'SW': '', 'PC-SAFT': 'v', 'SPUNG': 'o', 'CPA': 's', 'SAFT-VR-MIE' : '2'} eos_cmap = cm.get_cmap('viridis') color_dict = {'VdW': eos_cmap(0), 'SRK': eos_cmap(1 / 6), 'PR': eos_cmap(2 / 6), 'PT': eos_cmap(3 / 6), 'SW': eos_cmap(4 / 6), 'PC-SAFT': eos_cmap(5 / 6), 'SPUNG': eos_cmap(6 / 6), 'CPA': eos_cmap(6 / 6), 'SAFT-VR-MIE' : 'black'} class BenchmarkPlotter: def __init__(self, mode='cov', version=None, pred=True, exp=True): self.pred = pred self.exp = exp self.model_name = 'Kempers01, KineticGas' # Make output dir for this benchmark run if version is None: i = 0 while os.path.isdir(ROOT_PATH + '/plots/benchmark/V' + str(i)): i += 1 self.outdir = ROOT_PATH + '/plots/benchmark/V' + str(i) self.outpath = self.outdir + '/' + mode self.indir = ROOT_PATH + '/output/benchmark/V' + str(i) self.inpath = self.indir + '/' + mode if not os.path.isdir(self.inpath): raise FileNotFoundError('No data matching this plot run! (V'+str(i)+')') os.mkdir(self.outdir) os.mkdir(self.outpath) # Writing meta file to output dir with open(self.outdir + '/meta.txt', 'w') as ofile: tags = ['Date'] vals = [datetime.now().strftime("%d/%m/%Y %H:%M:%S")] for tag, val in zip(tags, vals): ofile.write(tag + ' : ' + str(val) + '\n') with open(self.indir + '/meta.txt', 'r') as ifile: ofile.write('\nMeta file in input dir :\n') for line in ifile.readlines(): ofile.write(line) ofile.write('\nContains plots for :\n') else: self.outdir = ROOT_PATH + '/plots/benchmark/V' + str(version) self.outpath = self.outdir + '/'+mode self.indir = ROOT_PATH + '/output/benchmark/V' + str(version) self.inpath = self.indir + '/' + mode if not os.path.isdir(self.inpath): raise FileNotFoundError('No data matching this plot run! (V' + str(version) + ')') if os.path.isdir(self.outpath): print('This BenchmarkPlotter instance may overwrite files in', self.outpath) verify = input("'Y' to verify") if verify != 'Y': exit(0) else: os.mkdir(self.outpath) # Writing meta file to output dir with open(self.outdir + '/meta.txt', 'a') as ofile: ofile.write('\n#########################\n') tags = ['Date'] vals = [datetime.now().strftime("%d/%m/%Y %H:%M:%S")] for tag, val in zip(tags, vals): ofile.write(tag + ' : ' + str(val) + '\n') with open(self.indir + '/meta.txt', 'r') as ifile: ofile.write('\nMeta file in input dir :\n') for line in ifile.readlines(): ofile.write(line) ofile.write('\nContains plots for :\n') def update_meta(self, filename): with open(self.outpath + '/meta.txt', 'a') as file: file.write(filename + '\n') def data_298(self, filename, comps, compname): exp_data = pd.read_excel(DATA_PATH + '/298_files/' + filename + '_298.xlsx') x_data = exp_data['c'] ST_data = exp_data['ST'] pred_data = pd.read_csv(self.inpath + '/298_files/' + filename + '.csv') x_axis = pred_data['x_'+comps.split(',')[0]] eos_list = ['VdW', 'SRK', 'PR', 'PT', 'SW', 'PC-SAFT', 'SPUNG'] fig, ax = plt.subplots() if self.pred: for i, eos_key in enumerate(eos_list[:-2]): soret = pred_data[eos_key] plt.plot(x_axis, soret, linestyle=lines_dict[eos_key], label=eos_key, color=color_dict[eos_key]) soret = pred_data['PC-SAFT'] plt.plot(x_axis, soret * 1e3, linestyle=lines_dict['PC-SAFT'], marker=marker_dict['PC-SAFT'], label=eos_list[-2], color=color_dict['PC-SAFT'], markevery=5) soret = pred_data['SPUNG'] plt.plot(x_axis, soret * 1e3, linestyle=lines_dict['SPUNG'], marker=marker_dict['SPUNG'], label=eos_list[-1], color=color_dict['SPUNG'], markevery=5) if self.exp: plt.scatter(x_data, ST_data, marker='x', color='black', label='Experimental') plt.xlabel('$x_{' + compname + '}$[-]', fontsize=14) ax.set_ylabel('$S_T$ [mK$^{-1}$]', fontsize=14) plt.title('Soret coefficient of ' + compname + ' in ' + filename.split('_')[-1] + ' at 298K using ' + self.model_name) save_path = self.outpath + '/298_files/' + filename + '_298' fig.legend() plt.savefig(save_path, dpi=600) print('Saved', save_path + '.png') self.update_meta(filename) plt.close(fig) #fig = plt.figure() #for key in eos_list: # plt.plot(0, 0, linestyle=lines_dict[key], marker=marker_dict[key], # color=color_dict[key], label=key) #plt.scatter(0, 0, color='black', marker='x', label='Experimental') #plt.legend(ncol=3) #plt.savefig(self.outpath + '298_files/legend.png', dpi=600) #print('Saved', self.outpath + '298_files/legend.png') #plt.close(fig) def plot_298(self): os.mkdir(self.outpath+'/298_files') solute_names = ['benzene', 'toluene'] solvent_names = ['_hexane', '_heptane'] solvent_codes = [',NC6', ',NC7'] comp_codes = ['BENZENE', 'TOLU'] compnames = ['benzene', 'toluene'] for i in range(2): for j in range(2): self.data_298(solute_names[i] + solvent_names[j], comp_codes[i] + solvent_codes[j], compnames[i]) def n_alkanes(self): os.mkdir(self.outpath + '/n_alkanes') mixtures = ['NC10,NC5', 'NC12,NC6', 'NC12,NC7', 'NC12,NC8'] eos_list = ['VdW', 'SRK', 'PR', 'PT', 'SW', 'PC-SAFT', 'SPUNG'] for comps in mixtures: comp1, comp2 = comps.split(',') save_path = self.outpath + '/n_alkanes/' save_path += comp1 + '_' + comp2 exp_data = pd.read_excel(DATA_PATH + '/n-alkanes/' + comp1 + '_' + comp2 + '_298K.xlsx') x1_list = exp_data['c'] soret_list = exp_data['ST'] pred_data = pd.read_csv(self.inpath + '/n_alkanes/' + comp1 + '_' + comp2 + '.csv') x_axis = pred_data['x_'+comp2] fig, ax = plt.subplots() if self.pred: for i, eos_key in enumerate(eos_list[:-2]): soret = pred_data[eos_key] plt.plot(x_axis, soret * 1e3, linestyle=lines_dict[eos_key], label=eos_key, color=color_dict[eos_key]) soret = pred_data['PC-SAFT'] plt.plot(x_axis, soret * 1e3, linestyle=lines_dict['PC-SAFT'], marker=marker_dict['PC-SAFT'], label=eos_list[-2], color=color_dict['PC-SAFT'], markevery=5) soret = pred_data['SPUNG'] plt.plot(x_axis, soret * 1e3, linestyle=lines_dict['SPUNG'], marker=marker_dict['SPUNG'], label=eos_list[-1], color=color_dict['SPUNG'], markevery=5) if self.exp: plt.scatter(x1_list, soret_list, marker='x', color='black', label='Experimental') if self.pred and self.exp: pass elif self.pred: save_path += '_pred' elif self.exp: save_path += '_exp' plt.yticks(fontsize=15) plt.xticks(fontsize=15) plt.xlabel(r'$x_{' + comp1 + '}$ [-]', fontsize=14) plt.ylabel(r'$S_T$ [mK$^{-1}$]', fontsize=14) plt.title(comps, fontsize=16) fig.legend() plt.savefig(save_path, dpi=600) print('Saved', save_path + '.png') self.update_meta(comp1+'_'+comp2) plt.close(fig) #fig = plt.figure() #for key in eos_list: # plt.plot(0, 0, linestyle=lines_dict[key], marker=marker_dict[key], # color=color_dict[key], label=key) #plt.scatter(0, 0, color='black', marker='x', label='Experimental') #plt.legend(ncol=3) #plt.savefig(self.plots_path + 'n_alkanes/legend.png', dpi=600) #print('Saved', self.plots_path + 'n_alkanes/legend.png') #plt.close(fig) def cold_gases(self): comps = 'C1,AR' T = 88 save_path = self.outpath + '/AR_C1' exp_data = pd.read_excel(DATA_PATH + '/cold_gases/AR_C1_' + str(T) + 'K.xlsx') x_list = exp_data['c'] soret_list = exp_data['ST'] pred_data = pd.read_csv(self.inpath + '/AR_C1.csv') x_axis = pred_data['x_Ar'] eos_list = ['VdW', 'SRK', 'PR', 'PT', 'SW', 'SPUNG', 'SAFT-VR-MIE'] fig = plt.figure() if self.pred is True: for i, eos_key in enumerate(eos_list): soret = pred_data[eos_key] plt.plot(x_axis, soret * 1e3, color=color_dict[eos_key], linestyle=lines_dict[eos_key], label=eos_key, marker=marker_dict[eos_key], markevery=5) if self.exp is True: plt.scatter(x_list, soret_list, marker='x', color='black', label='Experimental') fig.legend() plt.xlabel(r'$x_{AR}$ [-]', fontsize=14) plt.ylabel(r'$S_T$ [mK$^{-1}$]', fontsize=14) plt.title('Soret coefficient of ' + comps + ' at ' + str(T) + 'K, computed with ' + self.model_name) plt.savefig(save_path, dpi=600) print('Saved', save_path + '.png') self.update_meta('AR_C1') plt.close(fig) #fig = plt.figure() #for key in eos_list: # plt.plot(0, 0, linestyle=lines_dict[key], marker=marker_dict[key], # color=color_dict[key], label=key) #plt.plot(0, 0, linestyle=lines_dict['SPUNG'], marker=marker_dict['SPUNG'], # color=color_dict['SPUNG'], label='SPUNG') #plt.scatter(0, 0, marker='x', color='black', label='Experimental') #plt.legend(ncol=3) #plt.savefig(self.plots_path + 'cold_gases/legend.png', dpi=600) #plt.close(fig) def etoh_h2o_T(self, eos_name, ylim=None): if not os.path.isdir(self.outpath + '/ethanol_water'): os.mkdir(self.outpath + '/ethanol_water') save_path = self.outpath + '/ethanol_water/temp_' + eos_name # Get data exp_data = pd.read_csv(DATA_PATH + '/ethanol_water.csv', na_values='NaN') temp_list = [int(x) for x in exp_data.columns[1:]] cons_list = exp_data['c'] pred_data = pd.read_csv(self.inpath + '/ethanol_water/temp_'+eos_name+'.csv') temp_ax = pred_data['Temp'] # set up plot fig = plt.figure(figsize=(10, 5)) ax = plt.subplot(111) ax.set_position([0.08, 0.11, 0.75, 0.77]) cmap = cm.get_cmap('viridis') # Matrix containing all experimental data points ST_matr = np.array([[x for x in exp_data[str(T)]] for T in temp_list]).transpose() # Plotting max_c = max(cons_list) for ST, c in zip(ST_matr, cons_list): soret = pred_data[str(c)] plt.plot(temp_ax - 273, soret * 1e3, color=cmap(c / max_c)) plt.scatter(temp_list, ST, color=cmap(c / max_c), label=str(c), marker='x') plt.xlabel(r'T [$^{\circ}$C]', fontsize=14) plt.ylabel(r'$s_T$ [mK$^{-1}$]', fontsize=14) if ylim is not None: plt.ylim(ylim[0], ylim[1]) save_path += '_ylim' legend = plt.legend(title=r'$\omega_{EtOH}$ [–]', bbox_to_anchor=(1.01, 1.015), loc='upper left') plt.setp(legend.get_title(), fontsize=14) plt.sca(ax) plt.title(eos_name, fontsize=14) plt.savefig(save_path, dpi=600) print('Saved', save_path + '.png') self.update_meta('EtOH_H2O_T_'+eos_name) plt.close(fig) def etoh_h2o_c(self, eos_name, ylim=None): if not os.path.isdir(self.outpath + '/ethanol_water'): os.mkdir(self.outpath + '/ethanol_water') save_path = self.outpath + '/ethanol_water/cons_' + eos_name # Get data exp_data = pd.read_csv(DATA_PATH + '/ethanol_water.csv', na_values='NaN') temp_list = [int(x) for x in exp_data.columns[1:]] cons_list = exp_data['c'] pred_data = pd.read_csv(self.inpath + '/ethanol_water/cons_'+eos_name+'.csv') w_etoh_ax = pred_data['w_EtOH'] # Set up plot cmap = cm.get_cmap('cool') max_T = max(temp_list) fig = plt.figure(figsize=(10, 5)) ax = plt.subplot(111) ax.set_position([0.1, 0.11, 0.75, 0.77]) for T in temp_list: soret = pred_data[str(T)] if self.pred is True: plt.plot(w_etoh_ax, soret*1e3, color=cmap((T) / (max_T))) if self.exp is True: plt.scatter(cons_list, exp_data[str(T)], color=cmap((T) / (max_T)), label=str(T), marker='x') plt.xlabel(r'$\omega_{{EtOH}}$ [–]', fontsize=14) plt.ylabel(r'$s_T$ [mK$^{-1}$]', fontsize=14) if ylim is not None: plt.ylim(ylim[0], ylim[1]) save_path += '_ylim' legend = plt.legend(title='T [$^{\circ}$C]', bbox_to_anchor=(1.015, 1.015), loc='upper left', fontsize=14) plt.setp(legend.get_title(), fontsize=14) plt.title(eos_name) plt.savefig(save_path, dpi=600) print('Saved', save_path + '.png') self.update_meta('EtOH_H2O_T_'+eos_name) plt.close(fig) if __name__ == "__main__": plotter = BenchmarkPlotter(mode='com', version=3) plotter.plot_298() plotter.n_alkanes() plotter.cold_gases() plotter.etoh_h2o_T('CPA') plotter.etoh_h2o_c('CPA') plotter.etoh_h2o_T('PC-SAFT') plotter.etoh_h2o_c('PC-SAFT')