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#!/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)
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