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()
f = h5py.File(file_path2+'nino34.h5', 'r')
X = f['X']
Y = f['Y']
line3, = plt.plot(X, Y, 'r', linewidth=1.5, linestyle='-')
print(model_name2)
print(math_tool.pearson_distance(Y[:month-directly_month*prediction_month],
nino34_from_data[directly_month*prediction_month:]))
f.close()
f = h5py.File(file_path3+'nino34.h5', 'r')
X = f['X']
Y = f['Y']
line4, = plt.plot(X, Y, 'y', linewidth=1.5, linestyle='-')
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)
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(
math_tool.pearson_distance(Y[:month - 3 * prediction_month],
nino34_from_data[3 * prediction_month:]))
f.close()
f = h5py.File(file_path2 + 'nino34.h5', 'r')
X = f['X']
Y = f['Y']
line3, = plt.plot(X, Y, 'r', linewidth=1.5, linestyle='-')
print(
math_tool.pearson_distance(Y[:month - 6 * prediction_month],
nino34_from_data[6 * prediction_month:]))
f.close()
f = h5py.File(file_path3 + 'nino34.h5', 'r')
X = f['X']
Y = f['Y']
line4, = plt.plot(X, Y, 'y', linewidth=1.5, linestyle='-')
print(
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(file_num, start_month + prediction_month, prediction_month + 1)
x = np.linspace(file_num, file_num + month, month + 1)
plt.plot(x, nino34, 'b')
nino34_from_data = index_calculation.get_nino34_from_data(file_num, month)
plt.plot(x, nino34_from_data, 'r', linewidth=1)
print(math_tool.pearson_distance(nino34, nino34_from_data))
# 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)
# predict_result = model.predict(predict_data)
prediction_month = 12
directly_month = predict_369_data.directly_month
data_preprocess_method = name_list.data_preprocess_method
# make directory
file_path = "D:\msy\projects\zc\zcdata\data_predict_" + str(prediction_month)
file_hisotircal = 'D:\msy\projects\zc\zcdata\data_historical'
rmse_all = []
nino34 = []
nino34_historical = []
for start_month in range(file_num + prediction_month,
file_num + month + prediction_month + 1, interval):
data_x = file_helper_unformatted.read_data_best(file_path,
start_month + 192)
data_from_data = file_helper_unformatted.read_data_historical(
file_hisotircal, start_month)
nino34_temp = index_calculation.get_nino34(data_x)
nino34.append(nino34_temp)
nino34_temp = index_calculation.get_nino34(data_from_data)
nino34_historical.append(nino34_temp)
rmse_all.append(
math_tool.calculate_rmse(data_x[:, :, 0], data_from_data[:, :, 0]))
print(math_tool.pearson_distance(nino34, nino34_historical))
plt.plot(nino34, 'r', linewidth=1)
plt.plot(nino34_historical, 'y', linewidth=1)
print(rmse_all)
print(sum(rmse_all) / rmse_all.__len__())
plt.show()