file_num = 417
month = 47
interval = 1
prediction_month = 1
directly_month = 12
# make directory
file_path1 = "D:\msy\projects\zc\zcdata\data_networks\\" + model_name1 + '\\' + str(directly_month) + '\\'
file_path2 = "D:\msy\projects\zc\zcdata\data_networks\\" + model_name2 + '\\' + str(directly_month) + '\\'
file_path3 = "D:\msy\projects\zc\zcdata\data_networks\\" + model_name3 + '\\' + str(directly_month) + '\\'
data_x = np.empty([1, 20, 27, 2])
fig, ax = plt.subplots()
x = np.linspace(file_num, file_num+month, month+1)
nino34_from_data = index_calculation.get_nino34_from_data(file_num, month)
line0 = plt.plot(x, nino34_from_data, 'b', linewidth=0, linestyle='-')
line1, = plt.plot(x, nino34_from_data, 'black', linewidth=1.5)
plt.tick_params(labelsize=15)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
f = h5py.File(file_path1+'nino34.h5', 'r')
X = f['X']
Y = f['Y']
# plt.legend(['prediction', 'ZCdata'], loc='upper right')
line2, = plt.plot(X, Y, 'b', linewidth=1.5, linestyle='-')
print(model_name1)
print(math_tool.pearson_distance(Y[:month-directly_month*prediction_month],
nino34_from_data[directly_month*prediction_month:]))
f.close()
data_y = data_preprocess.dimensionless(data_y, 1)
# data preprocess 0-1
if data_preprocess_method == 'preprocess_01':
data_y = data_preprocess.preprocess_01(data_y, 1)
# data preprocess no month mean
if data_preprocess_method == 'nomonthmean':
data_y = data_preprocess.no_month_mean(data_y, 1)
# calculate nino 3.4 index
nino34_temp1 = index_calculation.get_nino34(data_y[0])
nino34.append(nino34_temp1)
# file_helper_unformatted.write_data(file_num+month, data_temp[1])
x = np.linspace(start_month, start_month + prediction_month, prediction_month + 1)
plt.plot(x, nino34, 'b')
x = np.linspace(file_num, file_num + month, month + 1)
plt.plot(x, index_calculation.get_nino34_from_data(file_num, month), 'r', linewidth=1)
# plt.legend(['prediction', 'ZCdata'], loc='upper right')
plt.show()
# file_helper_unformatted.write_data(file_num, model.predict(data_x)[0])
# for convolutional model only ssta
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# predict_result = model.predict(np.reshape(predict_data[0], (1, 20, 27, 1)))
# file_helper.write_data_conv2d(file_num, predict_result[0])
# for convolutional model ssta and ha
# predict_data = np.empty([1, 20, 27, 2])
# predict_data[0] = file_helper.read_data_sstaha(file_num)
# month = 124
interval = 1
prediction_month = name_list.prediction_month
directly_months = [3, 6, 9, 12]
# directly_months = [1, 2, 3, 4]
data_preprocess_method = name_list.data_preprocess_method
is_sequence = name_list.is_sequence
time_step = name_list.time_step
is_nino_output = name_list.is_nino_output
# for dense_model
# predict_data = np.empty([1, 540])
# predict_data[0] = file_helper.read_data(file_num)
# file_helper.write_data(file_num, model.predict(predict_data)[0])
# for dense_model ssta and ha
nino34_from_data = index_calculation.get_nino34_from_data(
file_num + time_step, month - time_step)
# print(nino34_from_data)
for di, directly_month in enumerate(directly_months):
if not is_sequence:
predict_data = np.empty([1, 20, 27, 2])
if model_type == 'conv':
data_x = np.empty([1, 20, 27, 2])
else:
data_x = np.empty([1, 1080])
else:
predict_data = np.empty([1, time_step, 20, 27, 2])
if is_seasonal_circle:
pass
else:
pass
data_x = np.empty([1, time_step, 20, 27, 2])
