Plot smooth curve like other people do

smooth_plot.py

import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns


def smooth(y, box_pts):
    box_pts = max(box_pts, 1) if len(y) > box_pts else 1
    box = np.ones(box_pts)/box_pts
    y_smooth = np.convolve(y, box, mode='valid')
    return y_smooth

repeat = 5
length = 1000

tag_list = [
        'Plot A',
        'Plot B',
        'Plot C'
]

value_list = [
        ('Model 1', np.random.rand(len(tag_list), repeat, length)),  
        ('Model 2', np.random.rand(len(tag_list), repeat, length)),  
        ('Model 3', np.random.rand(len(tag_list), repeat, length)),  
        ('Model 4', np.random.rand(len(tag_list), repeat, length)),  
]

fontsize = 13
title = 'Demo'

def main():
    fig = plt.figure()
    fig.set_size_inches(15, 5)
    fig.clf()
    num_plots = len(tag_list) 
    clrs = sns.color_palette("husl", len(value_list))
    base = fig.add_subplot(111)
    base.spines['top'].set_color('none')
    base.spines['bottom'].set_color('none')
    base.spines['left'].set_color('none')
    base.spines['right'].set_color('none')
    base.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
    with sns.axes_style('darkgrid'):
        axes = fig.subplots(1, num_plots)
        for ind_model, item in enumerate(value_list):
            k, v = item
            values_list = v
            for ind, (tag, values) in enumerate(zip(np.array(tag_list), np.array(values_list))):
                a_min = np.iinfo(np.int64).min
                a_max = np.iinfo(np.int64).max
                
                # when extra smoothness is needed for each repeated runnings
                tmp = []
                win_size = 10  # critic
                for i in values:
                    tmp.append(smooth(i, win_size))
                tmp = np.array(tmp)
                mean = np.mean(tmp, axis=0)
                std = np.std(tmp, axis=0)
                
                # when there is no need for extra smoothness
                # mean = np.mean(values, axis=0)
                # std = np.std(values, axis=0)

                axes[ind].plot(np.arange(len(mean)), mean, '-', c=clrs[ind_model], label=k.upper())
                axes[ind].fill_between(np.arange(len(mean)), np.clip(mean - std, a_min=a_min, a_max=a_max), 
                                       np.clip(mean + std, a_min=a_min, a_max=a_max), alpha=0.3, facecolor=clrs[ind_model])
                axes[ind].set_ylabel(tag, fontsize=fontsize)

        axes[-1].legend(fontsize=fontsize, loc=4) # inplot
        # axes[-1].legend(fontsize=fontsize, loc='center left', bbox_to_anchor=(1, 0.5))  # right side
        # axes[-1].legend(fontsize=fontsize, loc='center left', bbox_to_anchor=(0.5, -0.05))  # down side(not working yet)
    base.set_title(title, fontsize=fontsize)
    base.set_xlabel('Trials', fontsize=fontsize)
    fig.tight_layout(pad=0)
    fig.savefig('{}.pdf'.format(title))
    

if __name__ == '__main__':
    main()