datamatrix.py 文件源码

def _make_actual_bg_array(bg_df, start_index, end_index, prediction_start_time):
    total_len = start_index - end_index + 1
    time_bg_array = np.zeros(total_len)
    actual_bg_array = np.zeros(total_len)
    array_index = 0
    miss = 0

    for df_index in range(start_index, end_index - 1, -1):
        #Keep track of the time starting at 0 at the start_index
        time = (bg_df.iloc[df_index]['created_at'] - bg_df.iloc[start_index]['created_at']) / np.timedelta64(1, 'm')

        if time > prediction_start_time:
            time_bg_array[array_index] = time
            try:
                actual_bg_array[array_index] = bg_df.iloc[df_index]['openaps']['enacted']['bg']
                array_index += 1
                last_time = time
            except:
                try:
                    actual_bg_array[array_index] = bg_df.iloc[df_index]['openaps']['suggested']['bg']
                    array_index += 1
                    last_time = time
                except:
                    #If a miss, don't move to the next index and instead add one to the number missed
                    miss += 1
        else:
            miss += 1


    #Remove the number of missed data
    time_bg_array = np.resize(time_bg_array, total_len - miss)
    actual_bg_array = np.resize(actual_bg_array, total_len - miss)

    return time_bg_array, actual_bg_array


#Returns true if the data lies in a data gap, so it will not be used