def Portrait_diagram_subregion(obs_subregion_mean,
obs_name,
model_subregion_mean,
model_names,
seasonal_cycle,
file_name,
normalize=True):
nmodel, nt, nregion = model_subregion_mean.shape
if seasonal_cycle:
obs_data = ma.mean(
obs_subregion_mean.reshape([1, nt / 12, 12, nregion]), axis=1)
model_data = ma.mean(
model_subregion_mean.reshape([nmodel, nt / 12, 12, nregion]),
axis=1)
nt = 12
else:
obs_data = obs_subregion_mean
model_data = model_subregion_mean
subregion_metrics = ma.zeros([4, nregion, nmodel])
for imodel in np.arange(nmodel):
for iregion in np.arange(nregion):
# First metric: bias
subregion_metrics[0, iregion, imodel] = metrics.calc_bias(
model_data[imodel, :, iregion],
obs_data[0, :, iregion],
average_over_time=True)
# Second metric: standard deviation
subregion_metrics[1, iregion, imodel] = metrics.calc_stddev_ratio(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Third metric: RMSE
subregion_metrics[2, iregion, imodel] = metrics.calc_rmse(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Fourth metric: correlation
subregion_metrics[3, iregion, imodel] = metrics.calc_correlation(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
if normalize:
for iregion in np.arange(nregion):
subregion_metrics[0, iregion, :] = subregion_metrics[
0, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
subregion_metrics[
1, iregion, :] = subregion_metrics[1, iregion, :] * 100.
subregion_metrics[2, iregion, :] = subregion_metrics[
2, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
region_names = ['R%02d' % i for i in np.arange(nregion) + 1]
for imetric, metric in enumerate(['bias', 'std', 'RMSE', 'corr']):
plotter.draw_portrait_diagram(
subregion_metrics[imetric, :, :],
region_names,
model_names,
file_name + '_' + metric,
xlabel='model',
ylabel='region')
def Portrait_diagram_subregion(obs_subregion_mean,
obs_name,
model_subregion_mean,
model_names,
seasonal_cycle,
file_name,
normalize=True):
nmodel, nt, nregion = model_subregion_mean.shape
if seasonal_cycle:
obs_data = ma.mean(obs_subregion_mean.reshape(
[1, nt / 12, 12, nregion]),
axis=1)
model_data = ma.mean(model_subregion_mean.reshape(
[nmodel, nt / 12, 12, nregion]),
axis=1)
nt = 12
else:
obs_data = obs_subregion_mean
model_data = model_subregion_mean
subregion_metrics = ma.zeros([4, nregion, nmodel])
for imodel in np.arange(nmodel):
for iregion in np.arange(nregion):
# First metric: bias
subregion_metrics[0, iregion, imodel] = metrics.calc_bias(
model_data[imodel, :, iregion],
obs_data[0, :, iregion],
average_over_time=True)
# Second metric: standard deviation
subregion_metrics[1, iregion, imodel] = metrics.calc_stddev_ratio(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Third metric: RMSE
subregion_metrics[2, iregion, imodel] = metrics.calc_rmse(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
# Fourth metric: correlation
subregion_metrics[3, iregion, imodel] = metrics.calc_correlation(
model_data[imodel, :, iregion], obs_data[0, :, iregion])
if normalize:
for iregion in np.arange(nregion):
subregion_metrics[0, iregion, :] = subregion_metrics[
0, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
subregion_metrics[
1, iregion, :] = subregion_metrics[1, iregion, :] * 100.
subregion_metrics[2, iregion, :] = subregion_metrics[
2, iregion, :] / ma.std(obs_data[0, :, iregion]) * 100.
region_names = ['R%02d' % i for i in np.arange(nregion) + 1]
for imetric, metric in enumerate(['bias', 'std', 'RMSE', 'corr']):
plotter.draw_portrait_diagram(subregion_metrics[imetric, :, :],
region_names,
model_names,
file_name + '_' + metric,
xlabel='model',
ylabel='region')