stacked_plots.py

import os
import numpy as np
import matplotlib.pyplot as plt
from args import init_arguments

def plot_together(loadPATH, COLORS, i, label, fn):
    arr_globalv = np.loadtxt(loadPATH)
    m = arr_globalv.mean(axis=0)
    err = arr_globalv.std(axis=0)
    x = np.arange(len(m))
    
    plt.plot(x, m, c=COLORS['deep'][i], label=label)
    plt.legend()
    plt.fill_between(x, m-err, m+err, facecolor=COLORS['light'][i], alpha=.2)
    plt.title('The Training Curve of The Global Best Value')
    plt.xlabel('Epoch')
    plt.ylabel('Global Best Value')
    plt.grid(linestyle='--')
    plt.savefig(fn)
    print('The plot is save as')
    print('-->', fn)

if __name__ == '__main__':
    args = init_arguments().parse_args()
    if not os.path.exists(args.savePATH):
        os.makedirs(args.savePATH)
    COLORS = {
        'deep': ('salmon', 'seagreen', 'royalblue', 'mediumorchid'),
        'light': ('sandybrown', 'limegreen', '#089FFF', 'violet')
    }

    for d in ('10', '20'):
        # Plot different algorithms together
        for OBJ in (1, 2):
            plt.figure()
            fn = os.path.join(args.savePATH, 'plot_globalv_algorithms_f' + str(OBJ) + '_d' + d + '.png')
            for i, A in enumerate(('CS', 'BA', 'FPA')):
                loadPATH = os.path.join('./output_r20/f' + str(OBJ) + 'd' + d, A, 'arr_globalv.txt')
                plot_together(loadPATH, COLORS, i, A, fn)
                
        
        # Plot different objective functions together
        for A in ('CS', 'BA', 'FPA'):
            plt.figure()
            fn = os.path.join(args.savePATH, 'plot_globalv_functions_' + A + '_d' + d + '.png')
            for OBJ in (1, 2):
                loadPATH = os.path.join('./output_r20/f' + str(OBJ) + 'd' + d, A, 'arr_globalv.txt')
                plot_together(loadPATH, COLORS, OBJ-1, 'function '+ str(OBJ), fn)