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