def __get_plot_data(real, predict, *plt_row_col):
plot_1_row, plot_1_col, plot_2_row, plot_2_col = plt_row_col
plot_1 = {
'grid_id': int(real[0, 0, plot_1_row, plot_1_col, 0]),
'predict': [],
'real': [],
'time_str': []
}
plot_2 = {
'grid_id': int(real[0, 0, plot_2_row, plot_2_col, 0]),
'predict': [],
'real': [],
'time_str': []
}
for i in range(x_ragne):
for j in range(real.shape[1]):
plot_1['real'].append(real[i, j, plot_1_row, plot_1_col, 2,
np.newaxis])
plot_1['predict'].append(predict[i, j, plot_1_row, plot_1_col])
# print('id:{},real:{},predict:{}'.format(plot_1['grid_id'],real[i,j,10,10,2],predict[i,j,10,10]))
plot_2['real'].append(real[i, j, plot_2_row, plot_2_col, 2,
np.newaxis])
plot_2['predict'].append(predict[i, j, plot_2_row, plot_2_col])
# print('id: {},real: {},predict: {}'.format(plot_2['grid_id'], real[i, j, plot_2_row, plot_2_col, 2], predict[i, j, plot_2_row, plot_2_col]))
data_time = set_time_zone(
int(real[i, j, plot_1_row, plot_1_col, 1]))
plot_1['time_str'].append(date_time_covert_to_str(data_time))
data_time = set_time_zone(
int(real[i, j, plot_2_row, plot_2_col, 1]))
plot_2['time_str'].append(date_time_covert_to_str(data_time))
__plot(plot_1, plot_2)
def time_feature(X_array_date):
for i in range(X_array_date.shape[0]):
for j in range(X_array_date.shape[1]):
for row in range(X_array_date.shape[2]):
for col in range(X_array_date.shape[3]):
date = set_time_zone(X_array_date[i, j, row, col])
# print(date_time_covert_to_str(date)[6:])
X_array_date[i, j, row, col,
0] = date_time_covert_to_str(date)[6:]
return X_array_date
def get_xlabel(timestamps):
xlabel_list = []
for timestamp in timestamps:
datetime = utility.set_time_zone(timestamp)
xlabel_list.append(utility.date_time_covert_to_str(datetime))
return xlabel_list