优化函数，使用GPU加速 def get_index(df, sendtime, idx_initial, temp_upper, temp_low, soc_low, soc_upper, sample_interval, time_interval, curr_interval, min_curr, soc_interval, data_point): index = [0] for i in idx_initial: n = 0 k = i while (n <= data_point) & (i <= len(df) - 100): idx_list = [] idx_list.append(i) for j in np.arange(i + 1, len(df)): if ((sendtime.iloc[j] - sendtime.iloc[k]).total_seconds() >= time_interval): break elif (df['max_temp'].iloc[j] <= temp_upper) & (df['min_temp'].iloc[j] >= temp_low) & \ (df['bat_module_soc_00'].iloc[j] >= soc_low) & (df['bat_module_soc_00'].iloc[j] <= soc_upper) & \ ((sendtime[j] - sendtime[i]).total_seconds() >= sample_interval) & \ ((sendtime.iloc[j] - sendtime.iloc[k]).total_seconds() <= time_interval) & \ (np.abs(total_current[j] - total_current[i]) >= curr_interval) & ( np.abs(soc[j] - soc[i]) <= soc_interval) & (np.abs(total_current[j]) >= min_curr): n += 1 idx_list.append(j) i = j if ((sendtime.iloc[j] - sendtime.iloc[k]).total_seconds() >= time_interval): break if len(idx_list) >= data_point: print(idx_list) index.append(idx_list) return index

为了使用GPU加速，在Python中，您可以使用Numba库来将函数编译为CUDA内核。以下是优化后的代码：

from numba import jit, cuda
import numpy as np

@jit(target ="cuda")
def get_index(df, sendtime, idx_initial, temp_upper, temp_low, soc_low, soc_upper, sample_interval, time_interval, curr_interval, min_curr, soc_interval, data_point):
    index = [0]
    total_current = df['total_current'].values
    soc = df['bat_module_soc_00'].values
    for i in idx_initial:
        n = 0
        k = i
        while (n <= data_point) & (i <= len(df) - 100):
            idx_list = []
            idx_list.append(i)
            for j in np.arange(i + 1, len(df)):
                if ((sendtime[j] - sendtime[k]).total_seconds() >= time_interval):
                    break
                elif (df['max_temp'].iloc[j] <= temp_upper) & (df['min_temp'].iloc[j] >= temp_low) & \
                        (soc[j] >= soc_low) & (soc[j] <= soc_upper) & ((sendtime[j] - sendtime[i]).total_seconds() >= sample_interval) & \
                        ((sendtime[j] - sendtime[k]).total_seconds() <= time_interval) & (np.abs(total_current[j] - total_current[i]) >= curr_interval) & \
                        (np.abs(soc[j] - soc[i]) <= soc_interval) & (np.abs(total_current[j]) >= min_curr):
                    n += 1
                    idx_list.append(j)
                    i = j
            if ((sendtime[j] - sendtime[k]).total_seconds() >= time_interval):
                break
        if len(idx_list) >= data_point:
            print(idx_list)
            index.append(idx_list)
    return index

请注意，Numba的CUDA目标需要将函数中的所有数据转换为Numpy数组或CUDA设备数组。因此，我们将`total_current`和`soc`列转换为NumPy数组。此外，我们还使用`target ="cuda"`将函数编译为CUDA内核。这将使Numba利用GPU加速函数。