#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Aug 7 14:20:34 2023 @author: mohamedazizbhouri """ import numpy as onp import time from matplotlib import pyplot as plt plt.close('all') plt.rcParams.update(plt.rcParamsDefault) plt.rc('font', family='serif') plt.rcParams.update({'font.size': 32, 'lines.linewidth': 2, 'axes.labelsize': 32, 'axes.titlesize': 32, 'xtick.labelsize': 32, 'ytick.labelsize': 32, 'legend.fontsize': 32, 'axes.linewidth': 2, "pgf.texsystem": "pdflatex" }) dim_y = 48 dim_heat = 26 dim_moist = 22 n_remove = 4 ind_input = onp.concatenate( (onp.arange(26),n_remove+26+onp.arange(26-n_remove),onp.array([52,53,54,55])) ) dim_xH = ind_input.shape[0] dim_xL = ind_input.shape[0] ind_output_heat = onp.arange(26) ind_output_moist = n_remove+onp.arange(26-n_remove) mu_error_out = onp.concatenate((onp.zeros((1,dim_heat),dtype=onp.float32), onp.zeros((1,dim_moist),dtype=onp.float32)),axis=1) sigma_error_out = onp.concatenate((1/1004.6*onp.ones((1,dim_heat),dtype=onp.float32), 1/2.26e6*onp.ones((1,dim_moist),dtype=onp.float32)),axis=1) is_reshape_single_pred = 1 is_MF = 0 is_LF = 0 is_SF = 1 if is_reshape_single_pred == 1: test_SPCAM =['2003_02_06','2003_02_12','2003_02_18','2003_02_24','2003_02_28', '2003_03_06','2003_03_12','2003_03_18','2003_03_24','2003_03_30','2003_03_31', '2003_04_06','2003_04_12','2003_04_18','2003_04_24','2003_04_30', '2003_05_06','2003_05_12','2003_05_18','2003_05_24','2003_05_30','2003_05_31', '2003_06_06','2003_06_12','2003_06_18','2003_06_24','2003_06_30', '2003_07_06','2003_07_12','2003_07_18','2003_07_24','2003_07_30','2003_07_31', '2003_08_06','2003_08_12','2003_08_18','2003_08_24','2003_08_30','2003_08_31', '2003_09_06','2003_09_12','2003_09_18','2003_09_24','2003_09_30', '2003_10_06','2003_10_12','2003_10_18','2003_10_24','2003_10_30','2003_10_31', '2003_11_06','2003_11_12','2003_11_18','2003_11_24','2003_11_30', '2003_12_06','2003_12_12','2003_12_18','2003_12_24','2003_12_30','2003_12_31', '2004_01_06','2004_01_12','2004_01_18','2004_01_24','2004_01_30','2004_01_31'] Npts_per_file = onp.load('data_SPCAM5_4K/Npts_per_file_test.npy') def reshape_loc_onp(pred, dim_y): pred_loc = pred[:Npts_per_file[0],:] pred = pred[Npts_per_file[0]:,:] nt_total = pred_loc.shape[0]//(lat*lon) pred_array = onp.reshape(pred_loc.T, (dim_y,nt_total,lat,lon)) for i in range(len(test_SPCAM)-1): print(i,len(test_SPCAM)-1) pred_loc = pred[:Npts_per_file[i+1],:] pred = pred[Npts_per_file[i+1]:,:] nt_total = pred_loc.shape[0]//(lat*lon) pred_array = onp.concatenate( (pred_array, onp.reshape(pred_loc.T, (dim_y,nt_total,lat,lon))),axis=1) return pred_array case_var = 'all' lat = 96 lon = 144 N_dt_day = 24 # we have a dt=1hour def daily_avg(test): test_daily = [] N_time_steps = test.shape[1] for i in range(test.shape[0]): test_daily.append( onp.mean( test[i,:,:,:].reshape( (N_time_steps//N_dt_day, N_dt_day, lat, lon) ), axis=1 ) ) return onp.array(test_daily) # dim_y x N_day x lat x lon if is_MF == 1 or is_LF == 1: mu_SF_out = onp.concatenate((onp.load('norm/mu_y_heat_CAM5.npy')[None,ind_output_heat], onp.load('norm/mu_y_moist_CAM5.npy')[None,ind_output_moist]),axis=1) sigma_SF_out = onp.concatenate((onp.load('norm/sigma_y_heat_CAM5.npy')[None,ind_output_heat], onp.load('norm/sigma_y_moist_CAM5.npy')[None,ind_output_moist]),axis=1) if is_SF == 1: mu_SF_out = onp.concatenate((onp.load('norm/mu_y_heat_SPCAM5.npy')[None,ind_output_heat], onp.load('norm/mu_y_moist_SPCAM5.npy')[None,ind_output_moist]),axis=1) sigma_SF_out = onp.concatenate((onp.load('norm/sigma_y_heat_SPCAM5.npy')[None,ind_output_heat], onp.load('norm/sigma_y_moist_SPCAM5.npy')[None,ind_output_moist]),axis=1) tt = time.time() for i in range(32): ieff = i + 0 print(ieff,time.time()-tt) tt = time.time() if is_MF == 1: samples_test_H = onp.concatenate( (onp.load('MF_param/MF_param_'+str(ieff)+'/test_pred_1.npy')[0,:,:], onp.load('MF_param/MF_param_'+str(ieff)+'/test_pred_2.npy')[0,:,:]),axis=0) if is_SF == 1: samples_test_H = onp.concatenate( (onp.load('SF_param/SF_param_'+str(ieff)+'/test_pred_1.npy')[0,:,:], onp.load('SF_param/SF_param_'+str(ieff)+'/test_pred_1.npy')[0,:,:]),axis=0) if is_LF == 1: samples_test_H = onp.concatenate( (onp.load('MF_param/MF_param_'+str(ieff)+'/LF_test_pred_1.npy')[0,:,:], onp.load('MF_param/MF_param_'+str(ieff)+'/LF_test_pred_2.npy')[0,:,:]),axis=0) samples_test_H = mu_SF_out + sigma_SF_out * samples_test_H samples_test_H = (samples_test_H - mu_error_out) / sigma_error_out samples_test_H = reshape_loc_onp(samples_test_H, dim_y) samples_test_H = daily_avg(samples_test_H) samples_test_H = samples_test_H.reshape((dim_y, samples_test_H.shape[1]*lat*lon)) samples_test_H = samples_test_H.T print(samples_test_H.shape) # Npts x dim_y if is_MF == 1: onp.save('MF_param/MF_param_'+str(ieff)+'/test_pred_reshaped.npy', samples_test_H) if is_SF == 1: onp.save('SF_param/SF_param_'+str(ieff)+'/test_pred_reshaped.npy', samples_test_H) if is_LF == 1: onp.save('MF_param/MF_param_'+str(ieff)+'/LF_test_pred_reshaped.npy', samples_test_H) test = onp.load('data_SPCAM5_4K/all_outputs_reshaped.npy') test = daily_avg(test) test = test.reshape((dim_y, test.shape[1]*lat*lon)) # dim_y x N_samples test = test.T test = (test - mu_error_out) / sigma_error_out onp.save('data_SPCAM5_4K/all_outputs_reshaped_temp_avg.npy', test) else: test = onp.load('data_SPCAM5_4K/all_outputs_reshaped_temp_avg.npy') test = onp.array(test,dtype=onp.float64) def crps(outputs, target, weights=None): """ Computes the Continuous Ranked Probability Score (CRPS) between the target and the ecdf for each output variable and then takes a weighted average over them. Input ----- outputs - float[B, F, S] samples from the model target - float[B, F] ground truth target """ tt = time.time() n = outputs.shape[2] y_hats = onp.sort(outputs, axis=-1) print('sort',time.time()-tt) tt = time.time() # E[Y - y] mae = onp.abs(target[..., None] - y_hats).mean(axis=(0, -1)) print('abs',time.time()-tt) tt = time.time() # E[Y - Y'] ~= sum_i sum_j |Y_i - Y_j| / (2 * n * (n-1)) diff = y_hats[..., 1:] - y_hats[..., :-1] print('abs2',time.time()-tt) tt = time.time() count = onp.arange(1, n) * onp.arange(n - 1, 0, -1) print('arange',time.time()-tt) tt = time.time() crps = mae - (diff * count).sum(axis=-1).mean(axis=0) / (2 * n * (n-1)) print('crps final',time.time()-tt) return crps if is_MF == 1: ieff = 0 pred_daily = onp.load('MF_param/MF_param_'+str(ieff)+'/test_pred_reshaped.npy')[:,:,None] for i in range(31): print(i) ieff = i+1 pred_daily = onp.concatenate( (pred_daily,onp.load('MF_param/MF_param_'+str(ieff)+'/test_pred_reshaped.npy')[:,:,None]),axis=2) pred_daily = onp.array(pred_daily,dtype=onp.float64) crps_f = crps(pred_daily, test) onp.save('glob_errors/crps_rpn_MF.npy',crps_f) if is_SF == 1: ieff = 0 pred_daily = onp.load('SF_param/SF_param_'+str(ieff)+'/test_pred_reshaped.npy')[:,:,None] for i in range(31): print(i) ieff = i+1 pred_daily = onp.concatenate( (pred_daily,onp.load('SF_param/SF_param_'+str(ieff)+'/test_pred_reshaped.npy')[:,:,None]),axis=2) pred_daily = onp.array(pred_daily,dtype=onp.float64) crps_f = crps(pred_daily, test) print(crps_f.shape) print(onp.array(crps_f).shape) onp.save('glob_errors/crps_rpn_SF.npy',crps_f) if is_LF == 1: ieff = 0 pred_daily = onp.load('MF_param/MF_param_'+str(ieff)+'/LF_test_pred_reshaped.npy')[:,:,None] for i in range(31): print(i) ieff = i+1 pred_daily = onp.concatenate( (pred_daily,onp.load('MF_param/MF_param_'+str(ieff)+'/LF_test_pred_reshaped.npy')[:,:,None]),axis=2) pred_daily = onp.array(pred_daily,dtype=onp.float64) crps_f = crps(pred_daily, test) print(crps_f.shape) print(onp.array(crps_f).shape) onp.save('glob_errors/crps_rpn_LF.npy',crps_f)