114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/11/20 19:30
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@Usage :
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@Desc :
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'''
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import matplotlib.pyplot as plt
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import time
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def plot_prediction(total_data, predicted_data_easy, predicted_data_hard, save_fig_name, predict_num=50):
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体
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font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体
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timestamp = str(int(time.time()))[-4:] # 画图的时间戳取后四位
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plt.figure(1)
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'''保存的模型参数的路径'''
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from matplotlib import rcParams
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config = {
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"font.family": 'Times New Roman', # 设置字体类型
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"axes.unicode_minus": False, # 解决负号无法显示的问题
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"axes.labelsize": 13
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}
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rcParams.update(config)
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# 简单预测图
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length = len(predicted_data_easy)
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plt.scatter(list(range(length)), total_data, c='blue', s=12, label='Actual value')
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plt.plot(list(range(length - predict_num)),
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predicted_data_easy[:length - predict_num], linewidth=2, color='red',
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label='Traning value')
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plt.scatter(list(range(length - predict_num, length)), predicted_data_easy[length - predict_num:length], c='black',
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s=15, label='Predictive value')
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plt.axhline(total_data[-1], linewidth=2, c='green', label='Failure threshold')
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# plt.title()
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plt.xlabel('Serial number of the fusion feature point', font=font1)
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plt.ylabel('Virtual health indicator', font=font1)
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plt.legend(loc='upper left', prop=font2)
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plt.savefig(save_fig_name + 'easy{0}.png'.format(timestamp))
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plt.show()
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# 困难预测图
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plt.figure(2)
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'''保存的模型参数的路径'''
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config = {
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"font.family": 'Times New Roman', # 设置字体类型
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"axes.unicode_minus": False, # 解决负号无法显示的问题
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"axes.labelsize": 13
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}
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rcParams.update(config)
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# 简单预测图
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length = len(predicted_data_easy)
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plt.scatter(list(range(length)), total_data, c='blue', s=12, label='Actual value')
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plt.plot(list(range(length - predict_num)),
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predicted_data_hard[:length - predict_num], linewidth=2, color='red',
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label='Traning value')
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plt.scatter(list(range(length - predict_num, length)), predicted_data_hard[length - predict_num:length], c='black',
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s=15, label='Predictive value')
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# plt.title()
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plt.xlabel('Serial number of the fusion feature point', font=font1)
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plt.ylabel('Virtual health indicator', font=font1)
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plt.axhline(total_data[-1], linewidth=2, c='green', label='Failure threshold')
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plt.legend(loc='upper left', prop=font2)
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plt.savefig(save_fig_name + 'hard{0}.png'.format(timestamp))
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plt.show()
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def plot_forSelf(total_data, predicted_data_easy, predicted_data_hard):
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pic1 = plt.figure(figsize=(8, 6), dpi=200)
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'''保存的模型参数的路径'''
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from matplotlib import rcParams
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config = {
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"font.family": 'Times New Roman', # 设置字体类型
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"axes.unicode_minus": False, # 解决负号无法显示的问题
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"axes.labelsize": 13
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}
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rcParams.update(config)
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# 简单预测图
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plt.subplot(2, 1, 1)
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plt.plot(total_data)
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plt.plot(predicted_data_easy)
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plt.title('Easy Prediction')
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plt.xlabel('time')
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plt.ylabel('loss')
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# plt.legend(loc='upper right')
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# 困难预测图
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plt.subplot(2, 1, 2)
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plt.plot(total_data)
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plt.plot(predicted_data_hard)
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plt.title('Easy Prediction')
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plt.xlabel('time')
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plt.ylabel('loss')
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# plt.legend(loc='upper right')
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# plt.scatter()
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plt.show()
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