data[j] = np.pad(case_all_data[:-1], ((0, 0), (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)), (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)), (-min(0, bbox_z_lb), max(bbox_z_ub - shape[2], 0))), self.pad_mode, **self.pad_kwargs_data)

这是一段 Python 代码，它使用了 numpy 库中的 pad 函数对 case_all_data 数组进行填充，填充的方式是在数组的四个维度上分别填充 bbox_x_lb、bbox_x_ub、bbox_y_lb、bbox_y_ub、bbox_z_lb、bbox_z_ub 所指定的边界值。具体填充的方式由 self.pad_mode 和 self.pad_kwargs_data 决定。

相关问题

data00=data m,n = np.shape(data00) a = np.array(data00) Data00 = a[1:m,2:n] Data00 = Data00.astype(np.float64) Power = Data00[:,13] Power_train = Power[0:96] P_min = np.min(Power_train) P_gap = np.max(Power_train)-np.min(Power_train) Power_uni = (Power-P_min)/P_gap # 提取imfs和剩余信号res emd = EMD() emd.emd(Power_uni) imfs, res = emd.get_imfs_and_residue() N = len(imfs) P_H = np.sum(imfs[0:6,:],axis=0) P_M = np.sum(imfs[6:12,:],axis=0) P_L = res P_H =np.expand_dims(P_H,axis=1) P_M =np.expand_dims(P_M,axis=1) P_L =np.expand_dims(P_L,axis=1) Nwp = Data00[:,0:7] Nwp_train = Nwp[0:96] N_min = np.min(Nwp_train,axis=0) N_gap = np.max(Nwp_train,axis=0)-np.min(Nwp_train,axis=0) Nwp_uni = (Nwp-N_min)/N_gap#(N,7) Weather = Data00[:,7:13] Weather_train = Weather[0:96] W_min = np.min(Weather_train,axis=0) W_gap = np.max(Weather_train,axis=0)-np.min(Weather_train,axis=0) Weather_uni = (Weather-W_min)/W_gap#(N,6) 优化代码

以下是部分代码的优化建议：

1. 对于以下代码段：

   a = np.array(data00)
   Data00 = a[1:m,2:n]

可以合并为一行：

   Data00 = np.array(data00)[1:m,2:n]

2. 对于以下代码段：

   P_H = np.sum(imfs[0:6,:],axis=0)
   P_M = np.sum(imfs[6:12,:],axis=0)
   P_L = res
   P_H =np.expand_dims(P_H,axis=1)
   P_M =np.expand_dims(P_M,axis=1)
   P_L =np.expand_dims(P_L,axis=1)

可以使用 `np.newaxis` 替代 `np.expand_dims` 来实现：

   P_H = np.sum(imfs[0:6,:],axis=0)[:, np.newaxis]
   P_M = np.sum(imfs[6:12,:],axis=0)[:, np.newaxis]
   P_L = res[:, np.newaxis]

3. 对于以下代码段：

   N_min = np.min(Nwp_train,axis=0)
   N_gap = np.max(Nwp_train,axis=0)-np.min(Nwp_train,axis=0)
   Nwp_uni = (Nwp-N_min)/N_gap#(N,7)

可以使用 `np.ptp` 函数（peak-to-peak）来计算最大值和最小值的差：

   N_min = np.min(Nwp_train,axis=0)
   N_gap = np.ptp(Nwp_train,axis=0)
   Nwp_uni = (Nwp-N_min)/N_gap#(N,7)

4. 对于以下代码段：

   Weather = Data00[:,7:13]
   Weather_train = Weather[0:96]
   W_min = np.min(Weather_train,axis=0)
   W_gap = np.max(Weather_train,axis=0)-np.min(Weather_train,axis=0)
   Weather_uni = (Weather-W_min)/W_gap#(N,6)

可以使用与第三个优化建议类似的方法：

   Weather = Data00[:,7:13]
   Weather_train = Weather[0:96]
   W_min = np.min(Weather_train,axis=0)
   W_gap = np.ptp(Weather_train,axis=0)
   Weather_uni = (Weather-W_min)/W_gap#(N,6)

当然，以上只是一些简单的优化建议，具体的优化效果还需要根据实际情况进行评估。

def map_data(data_map, axis_num): data_map = np.max(data_map, axis=axis_num) data_map -= data_map.min() data_map = data_map / data_map.max() data_map = np.array(data_map * 255, dtype=np.uint8) return data_map 转为c++ Eigen::Tensor

这里提供一种使用 Eigen::Tensor 库实现的 C++ 版本代码：

#include <Eigen/Dense>
#include <Eigen/Tensor>

Eigen::Tensor<uint8_t, 2> map_data(const Eigen::Tensor<float, 3>& data_map, int axis_num) {
    int rows = data_map.dimension(0);
    int cols = data_map.dimension(1);

    Eigen::Tensor<float, 2> max_vals;
    if (axis_num == 0) {
        max_vals = data_map.maximum(Eigen::array<int, 1>{2}).reshape(Eigen::array<int, 2>{rows, cols});
    }
    else {
        max_vals = data_map.maximum(Eigen::array<int, 1>{1}).reshape(Eigen::array<int, 2>{rows, cols});
    }
    Eigen::Tensor<uint8_t, 2> mapped_data = (data_map / max_vals).maximum(0.0f).minimum(1.0f) * 255.0f;
    return mapped_data;
}

这里使用了 Eigen::Tensor 库来处理多维数组。输入数据类型为 `Eigen::Tensor<float, 3>`，输出数据类型为 `Eigen::Tensor<uint8_t, 2>`。函数中使用 `maximum()` 和 `minimum()` 函数来进行数值的归一化，并使用 `reshape()` 函数来将三维数组转换为二维数组。