graphs_path = root_path + '/results_analysis/graphs/' print(root_path) import sys sys.path.append(root_path) from results_reader import read_two_stage, read_pure_esvr from fit_line import compute_linear_fit, compute_list_linear_fit h_records, h_predictions, h_r2, h_nrmse, h_mae, h_mape, h_ppts, h_timecost = read_pure_esvr( "Huaxian") x_records, x_predictions, x_r2, x_nrmse, x_mae, x_mape, x_ppts, x_timecost = read_pure_esvr( "Xianyang") z_records, z_predictions, z_r2, z_nrmse, z_mae, z_mape, z_ppts, z_timecost = read_pure_esvr( "Zhangjiashan") h_vmd_records, h_vmd_predictions, h_vmd_r2, h_vmd_nrmse, h_vmd_mae, h_vmd_mape, h_vmd_ppts, h_vmd_timecost = read_two_stage( station="Huaxian", decomposer="vmd", predict_pattern="one_step_1_month_forecast") x_vmd_records, x_vmd_predictions, x_vmd_r2, x_vmd_nrmse, x_vmd_mae, x_vmd_mape, x_vmd_ppts, x_vmd_timecost = read_two_stage( station="Xianyang", decomposer="vmd", predict_pattern="one_step_1_month_forecast") z_vmd_records, z_vmd_predictions, z_vmd_r2, z_vmd_nrmse, z_vmd_mae, z_vmd_mape, z_vmd_ppts, z_vmd_timecost = read_two_stage( station="Zhangjiashan", decomposer="vmd", predict_pattern="one_step_1_month_forecast") h_eemd_records, h_eemd_predictions, h_eemd_r2, h_eemd_nrmse, h_eemd_mae, h_eemd_mape, h_eemd_ppts, h_eemd_timecost = read_two_stage( station="Huaxian", decomposer="eemd", predict_pattern="one_step_1_month_forecast") x_eemd_records, x_eemd_predictions, x_eemd_r2, x_eemd_nrmse, x_eemd_mae, x_eemd_mape, x_eemd_ppts, x_eemd_timecost = read_two_stage(
import matplotlib.pyplot as plt plt.rcParams['font.size'] = 10 import pandas as pd import numpy as np import os root_path = os.path.dirname(os.path.abspath('__file__')) # root_path = os.path.abspath(os.path.join(root_path,os.path.pardir)) graphs_path = root_path + '/results_analysis/graphs/' import sys sys.path.append(root_path) from results_reader import read_two_stage h_records, h_predictions, h_r2, h_rmse, h_mae, h_mape, h_ppts, h_timecost = read_two_stage( station="Huaxian", decomposer="vmd", predict_pattern="one_step_1_month_forecast") x_records, x_predictions, x_r2, x_rmse, x_mae, x_mape, x_ppts, x_timecost = read_two_stage( station="Xianyang", decomposer="vmd", predict_pattern="one_step_1_month_forecast") z_records, z_predictions, z_r2, z_rmse, z_mae, z_mape, z_ppts, z_timecost = read_two_stage( station="Zhangjiashan", decomposer="vmd", predict_pattern="one_step_1_month_forecast") plt.figure(figsize=(7.48, 7.48)) # plot predictions for huaxian station records = h_records predictions = h_predictions plt.subplot(3, 2, 1) plt.text(138, -12, '(a)')