production-line-workflow/plot_result.py at master · ebrahimpichka/production-line-workflow · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def plot_queues(queue_status, time_log):

    plt.style.use('seaborn')
    plt.subplot(5,1,1)
    plt.step(time_log, queue_status['m1'])
    plt.yticks(list(range(max(queue_status['m1']))))
    plt.title('Q status of Machine A')
    plt.grid(True)
    plt.subplot(5,2,3)
    plt.step(time_log , queue_status['m2']['p1'])
    plt.yticks(list(range(max(queue_status['m2']['p1']))))
    plt.title('Q status of Machine B for part 1')
    plt.grid(True)
    plt.subplot(5,2,4)
    plt.step(time_log , queue_status['m2']['p2'])
    plt.yticks(list(range(0,max(queue_status['m2']['p2'])+1,2)))
    plt.title('Q status of Machine B for part 2')
    plt.grid(True)
    plt.subplot(5,1,3)
    plt.step(time_log , [queue_status['m2']['p1'][i]+queue_status['m2']['p2'][i] for i in range(len(queue_status['m2']['p2']))])
    plt.yticks(list(range(0,max([queue_status['m2']['p1'][i]+queue_status['m2']['p2'][i] for i in range(len(queue_status['m2']['p2']))])+1,2)))
    plt.title('Q status of Machine B for both parts')
    plt.grid(True)
    plt.subplot(5,2,7)
    plt.step(time_log , queue_status['m3']['p1'])
    plt.yticks(list(range(max(queue_status['m3']['p1']))))
    plt.title('Q status of Machine C for part 1')
    plt.grid(True)
    plt.subplot(5,2,8)
    plt.step(time_log , queue_status['m3']['p2'])
    plt.yticks(list(range(0,max(queue_status['m3']['p2'])+1,2)))
    plt.title('Q status of Machine C for part 2')
    plt.grid(True)
    plt.subplot(5,1,5)
    plt.step(time_log , [queue_status['m3']['p1'][i]+queue_status['m3']['p2'][i] for i in range(len(queue_status['m3']['p2']))])
    plt.yticks(list(range(0,max([queue_status['m3']['p1'][i]+queue_status['m3']['p2'][i] for i in range(len(queue_status['m3']['p2']))])+1,2)))
    plt.title('Q status of Machine C for both parts')
    plt.grid(True)
    plt.tight_layout(pad=0.5)
    plt.show()