compute_bias(mdl20_clim, obs1_clim),
compute_bias(mdl21_clim, obs1_clim),
compute_bias(mdl22_clim, obs1_clim),
compute_bias(mdl23_clim, obs1_clim),
compute_bias(mdl24_clim, obs1_clim),
compute_bias(mdl25_clim, obs1_clim),
compute_bias(mdl26_clim, obs1_clim),
compute_bias(mdl27_clim, obs1_clim),
compute_bias(mdl28_clim, obs1_clim),
compute_bias(mdl29_clim, obs1_clim),
compute_bias(mdl30_clim, obs1_clim),
compute_bias(mdl31_clim, obs1_clim),
compute_bias(mdl32_clim, obs1_clim)
],
pbias=[
compute_pbias(mdl1_clim, obs1_clim),
compute_pbias(mdl2_clim, obs1_clim),
compute_pbias(mdl3_clim, obs1_clim),
compute_pbias(mdl4_clim, obs1_clim),
compute_pbias(mdl5_clim, obs1_clim),
compute_pbias(mdl6_clim, obs1_clim),
compute_pbias(mdl7_clim, obs1_clim),
compute_pbias(mdl8_clim, obs1_clim),
compute_pbias(mdl9_clim, obs1_clim),
compute_pbias(mdl10_clim, obs1_clim),
compute_pbias(mdl11_clim, obs1_clim),
compute_pbias(mdl12_clim, obs1_clim),
compute_pbias(mdl13_clim, obs1_clim),
compute_pbias(mdl14_clim, obs1_clim),
compute_pbias(mdl15_clim, obs1_clim),
compute_pbias(mdl16_clim, obs1_clim),
'ensmean_cmip5'
]
for mdl in mdl_list:
print 'CMIP5 Model:', mdl
# Import cmip5 model end obs database monthly
mdl_clim = import_cmip5_clim(mdl)
obs = u'cru_ts4.02'
obs_clim = import_obs_clim(obs)
# Compute statiscts index from CMIP5 models
x.append(metrics.mean_absolute_error(obs_clim, mdl_clim))
y.append(metrics.mean_squared_error(obs_clim, mdl_clim))
z.append(compute_pbias(obs_clim, mdl_clim))
fig = pyplot.figure()
ax = Axes3D(fig)
types = [
'BCC-CSM1.1', 'BCC-CSM1.1M', 'BNU-ESM', 'CanESM2', 'CNRM-CM5',
'CSIRO-ACCESS-1', 'CSIRO-ACCESS-3', 'CSIRO-MK36', 'FIO-ESM',
'GISS-E2-H-CC', 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',
'HadGEM2-ES', 'INMCM4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR',
'LASG-FGOALS-G2', 'LASG-FGOALS-S2', 'MIROC5', 'MIROC-ESM-CHEM',
'MIROC-ESM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NCAR-CCSM4',
'NCAR-CESM1-BGC', 'NCAR-CESM1-CAM5', 'NorESM1-ME', 'NorESM1-M',
'ensmean_cmip5'
]
print 'CMIP5 Model:', mdl # Import cmip5 model end obs database monthly mdl_clim = import_cmip5(mdl) obs = u'cru_ts4.02' obs_clim = import_obs(obs) # Compute statiscts index from CMIP5 models r = round((np.corrcoef(np.array(mdl_clim), np.array(obs_clim)))[0][1], 3) r2 = metrics.r2_score(obs_clim, mdl_clim) mae = metrics.mean_absolute_error(obs_clim, mdl_clim) mse = metrics.mean_squared_error(obs_clim, mdl_clim) rmse = compute_rmse(obs_clim, mdl_clim) bias = compute_bias(mdl_clim, obs_clim) pbias = compute_pbias(mdl_clim, obs_clim) apb = compute_apb(mdl_clim, obs_clim) effic = compute_effic_coeffic(mdl_clim, obs_clim) mae = metrics.median_absolute_error(obs_clim, mdl_clim) print r, r2, mae, mse, rmse, bias, pbias, apb, effic, Mae tab_inform.append([mdl, r, r2, mae, mse, rmse, bias, pbias, apb, effic, mae]) tab.add_rows(tab_inform) tab.set_cols_align(['c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c', 'c']) tab.header([u'CMIP5 Models', u'R', u'R2_score', u'MAE', u'MSE', U'RMSE', U'BIAS', U'PBIAS', U'APB', U'NASH', u'MAE']) table = str(tab.draw()) file_name = 'table_monthly_pre_neb_statist_indices_cmip5_models_1975-2005.asc' file_save = open(file_name, 'w')