def t_up(speed1, speed2, t_time): if speed1<0: speed1=-speed1 L_Motor.ChangeDutyCycle(speed1) GPIO.output(AIN2, True) # AIN2 GPIO.output(AIN1, False) # AIN1 else: L_Motor.ChangeDutyCycle(speed1) GPIO.output(AIN2, False) # AIN2 GPIO.output(AIN1, True) # AIN1 if speed2<0: speed2=-speed2 L_Motor.ChangeDutyCycle(speed2) GPIO.output(BIN2, True) # AIN2 GPIO.output(BIN1, False) # AIN1 else: R_Motor.ChangeDutyCycle(speed2) GPIO.output(BIN2, False) # BIN2 GPIO.output(BIN1, True) # BIN1 time.sleep(t_time)

这段代码是用来控制两个电机的转速和运行时间的函数。它接受三个参数：speed1、speed2和t_time。speed1表示左侧电机的转速，speed2表示右侧电机的转速，t_time表示运行时间。

首先，判断speed1是否小于0。如果是，则将speed1取绝对值，并将电机的占空比设置为speed1。同时，将AIN2引脚设置为高电平，AIN1引脚设置为低电平，以控制电机的转向。

如果speed1大于等于0，则将电机的占空比设置为speed1，并将AIN2引脚设置为低电平，AIN1引脚设置为高电平。

接下来，判断speed2是否小于0。如果是，则将speed2取绝对值，并将电机的占空比设置为speed2。同时，将BIN2引脚设置为高电平，BIN1引脚设置为低电平，以控制电机的转向。

如果speed2大于等于0，则将电机的占空比设置为speed2，并将BIN2引脚设置为低电平，BIN1引脚设置为高电平。

最后，程序暂停t_time秒钟，即控制电机运行的时间。

这段代码是用来控制两个电机的转速和运行时间，从而实现小车的移动。具体的引脚连接和占空比的设置需要根据具体的硬件配置进行调整。

相关问题

def update(self): global speed self.rect.centery -= speed[1] if self.rect.centery < -32: self.kill()什么意思

这段代码是一个类的方法，名为`update()`。这个方法中，有一个`speed`变量是全局变量。首先，`self.rect.centery -= speed[1]`这行代码是将当前对象的`rect`属性的垂直中心坐标减去`speed`列表的第二个元素的值。这可能是为了实现游戏中的垂直移动效果。然后，`if self.rect.centery < -32:`这行代码判断当前对象的垂直中心坐标是否小于-32。如果是，就调用`kill()`方法来移除当前对象。这段代码可能在游戏循环中被调用，用于更新和处理该对象的状态和位置。

def PrepareDataset(speed_matrix, BATCH_SIZE = 40, seq_len = 10, pred_len = 1, train_propotion = 0.7, valid_propotion = 0.2):

"""
Function to prepare dataset for training and validation

Args:
speed_matrix: numpy array of shape (num_days, num_timesteps, num_nodes)
BATCH_SIZE: batch size for training (default = 40)
seq_len: sequence length (number of timesteps) for input (default = 10)
pred_len: number of timesteps to predict (default = 1)
train_propotion: proportion of dataset to use for training (default = 0.7)
valid_propotion: proportion of dataset to use for validation (default = 0.2)

Returns:
train_data: PyTorch DataLoader object for training data
valid_data: PyTorch DataLoader object for validation data
"""

# Calculate number of days
num_days = speed_matrix.shape[0]

# Calculate number of nodes
num_nodes = speed_matrix.shape[2]

# Calculate total number of timesteps
total_timesteps = num_days * speed_matrix.shape[1]

# Create input and target sequences
input_seq = []
target_seq = []

# Loop through each day
for day in range(num_days):

# Loop through each timestep
for timestep in range(speed_matrix.shape[1] - seq_len - pred_len):

# Extract input sequence
input_seq.append(speed_matrix[day, timestep:timestep+seq_len, :])

# Extract target sequence
target_seq.append(speed_matrix[day, timestep+seq_len:timestep+seq_len+pred_len, :])

# Convert input and target sequences to numpy arrays
input_seq = np.array(input_seq)
target_seq = np.array(target_seq)

# Split dataset into training, validation, and testing sets
train_size = int(total_timesteps * train_propotion)
valid_size = int(total_timesteps * valid_propotion)
test_size = total_timesteps - train_size - valid_size

train_input = input_seq[:train_size]
train_target = target_seq[:train_size]

valid_input = input_seq[train_size:train_size+valid_size]
valid_target = target_seq[train_size:train_size+valid_size]

test_input = input_seq[train_size+valid_size:]
test_target = target_seq[train_size+valid_size:]

# Convert training and validation data to PyTorch DataLoader objects
train_data = DataLoader(TensorDataset(torch.Tensor(train_input), torch.Tensor(train_target)), batch_size=BATCH_SIZE, shuffle=True)
valid_data = DataLoader(TensorDataset(torch.Tensor(valid_input), torch.Tensor(valid_target)), batch_size=BATCH_SIZE, shuffle=True)

return train_data, valid_data