Digital-Twin/gwx/笔记/焊接数据处理/draw_change_yita.py

import matplotlib.pyplot as plt
import numpy as np
import time
from models import MLP
import torch
from torch import nn
import mpldatacursor

time_point = 1
low=0.82
high=0.83
model = MLP.MLP()
model.load_state_dict(torch.load('lstm_model.pt'))
# 创建初始的折线图数据
learning_rate = 0.01
# learning_rate=1
criterion = nn.MSELoss()
# criterion=nn.L1Loss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)


x = np.array([1])
y = np.array([model.fc0.weight.tolist()[0][0]])

# 绘制初始的折线图
fig, ax = plt.subplots()
line, = ax.plot(x, y, '-o')



# 显示初始的折线图
plt.show(block=False)

# scatter = ax.scatter(x, y)
# # 定义标注文本
# def formatter(**kwargs):
#     x, y = kwargs['x'], kwargs['y']
#     return f"x={x:.2f}\ny={y:.2f}"
# # 配置悬浮窗
# cursor = mpldatacursor.datacursor(
#     scatter,
#     formatter=formatter,
#     display='multiple',
#     draggable=True,
#     bbox=dict(fc='white', alpha=0.9),
#     arrowprops=dict(arrowstyle='->', connectionstyle='arc3'),
# )

# 定义自定义事件类型
class AddPointEvent:
    def __init__(self, x, y):
        self.x = x
        self.y = y

# 定义事件处理函数，用于处理自定义事件
def on_add_point(event):
    # 添加新的点
    print(f"x:{event.x},y:{event.y}")
    new_x = np.array([event.x])
    new_y = np.array([event.y])

    # 使用set_xdata()和set_ydata()方法更新折线图的数据
    line.set_xdata(np.append(line.get_xdata(), new_x))
    line.set_ydata(np.append(line.get_ydata(), new_y))

    # 更新折线图的坐标轴范围
    ax.relim()
    ax.autoscale_view()
    ax.annotate("hi", xy=(event.x, event.y), xytext=(event.x, event.y+0.5), ha='center', va='bottom')

    # 更新折线图
    fig.canvas.draw()

# 连接自定义事件到事件处理函数上
cid = fig.canvas.mpl_connect(AddPointEvent, on_add_point)

# 定义计时器回调函数，用于触发自定义事件
def timer_callback():
    # 生成一个随机的点，并触发自定义事件
    global time_point
    time_point=time_point+1
    y=np.random.uniform(low=50, high=101)
    y_hat = y*np.random.uniform(low=low, high=high)
    batch_sequences = np.array([[y,y_hat]])
    # print(batch_sequences.shape)
    inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)
    
    targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)

    
    # 前向传播
    outputs = model(inputs)
    # print(f"targets {targets}")
    
    # print(f"input:{inputs}")
    
    # print(f"outputs {outputs}")
    
    outputs=outputs.reshape(targets.shape)
    # print(outputs.shape)
    # print(f"output:{outputs.shape}")
    # print(f"target:{targets.shape}")
    loss = criterion(outputs, targets)
    # print(f"loss:{loss}")
    # loss=loss*1000
    # 反向传播和优化
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
        
    
    new_y = model.fc0.weight.tolist()[0][0]
    
    
    new_event = AddPointEvent(time_point, new_y)
    fig.canvas.callbacks.process(AddPointEvent, new_event)

    # 重新启动计时器
    timer = fig.canvas.new_timer(interval=1000)
    timer.add_callback(timer_callback)
    timer.start()

# 启动计时器
timer = fig.canvas.new_timer(interval=1000)
timer.add_callback(timer_callback)
timer.start()


def on_hover(event):
    # 检查是否有Artist在鼠标位置
    if event.inaxes == ax:
        # 获取鼠标位置
        x, y = event.xdata, event.ydata
        # 显示自定义信息
        ax.annotate(f'({x:.2f}, {y:.2f})', (x, y))

# 将on_hover函数绑定到Figure的鼠标移动事件上
# fig.canvas.mpl_connect('motion_notify_event', on_hover)

# 显示动态更新的折线图，并保持程序运行
plt.show()