print 'Number of model datasets:', nmodel
for model_name in model_names:
print model_name
""" Step 4: Spatial regriding of the reference datasets """
print 'Regridding datasets: ', config['regrid']
if not config['regrid']['regrid_on_reference']:
obs_dataset = dsp.spatial_regrid(obs_dataset, new_lat, new_lon)
print 'Reference dataset has been regridded'
for i, dataset in enumerate(model_datasets):
model_datasets[i] = dsp.spatial_regrid(dataset,
new_lat,
new_lon,
boundary_check=boundary_check)
print model_names[i] + ' has been regridded'
print 'Propagating missing data information'
obs_dataset = dsp.mask_missing_data([obs_dataset] + model_datasets)[0]
model_datasets = dsp.mask_missing_data([obs_dataset] + model_datasets)[1:]
""" Step 5: Checking and converting variable units """
print 'Checking and converting variable units'
obs_dataset = dsp.variable_unit_conversion(obs_dataset)
for idata, dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.variable_unit_conversion(dataset)
print 'Generating multi-model ensemble'
if len(model_datasets) >= 2.:
model_datasets.append(dsp.ensemble(model_datasets))
model_names.append('ENS')
""" Step 6: Generate subregion average and standard deviation """
if config['use_subregions']:
# sort the subregion by region names and make a list
subregions = sorted(config['subregions'].items(),
print('Reference data: {}'.format(reference_name))
print('Number of target datasets: {}'.format(ntarget))
for target_name in target_names:
print(target_name)
""" Step 3: Spatial regriding of the datasets """
print('Regridding datasets: {}'.format(config['regrid']))
if not config['regrid']['regrid_on_reference']:
reference_dataset = dsp.spatial_regrid(reference_dataset, new_lat, new_lon)
print('Reference dataset has been regridded')
for i, dataset in enumerate(target_datasets):
target_datasets[i] = dsp.spatial_regrid(dataset, new_lat, new_lon,
boundary_check=boundary_check)
print('{} has been regridded'.format(target_names[i]))
print('Propagating missing data information')
datasets = dsp.mask_missing_data([reference_dataset]+target_datasets)
reference_dataset = datasets[0]
target_datasets = datasets[1:]
""" Step 4: Checking and converting variable units """
print('Checking and converting variable units')
reference_dataset = dsp.variable_unit_conversion(reference_dataset)
for i, dataset in enumerate(target_datasets):
target_datasets[i] = dsp.variable_unit_conversion(dataset)
print('Generating multi-model ensemble')
if len(target_datasets) >= 2.:
target_datasets.append(dsp.ensemble(target_datasets))
target_names.append('ENS')
""" Step 5: Generate subregion average and standard deviation """
print 'Dataset loading completed'
print 'Observation data:', ref_name
print 'Number of model datasets:',nmodel
for model_name in model_names:
print model_name
""" Step 4: Spatial regriding of the reference datasets """
print 'Regridding datasets: ', config['regrid']
if not config['regrid']['regrid_on_reference']:
ref_dataset = dsp.spatial_regrid(ref_dataset, new_lat, new_lon)
print 'Reference dataset has been regridded'
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.spatial_regrid(dataset, new_lat, new_lon, boundary_check = boundary_check_model)
print model_names[idata]+' has been regridded'
print 'Propagating missing data information'
ref_dataset = dsp.mask_missing_data([ref_dataset]+model_datasets)[0]
model_datasets = dsp.mask_missing_data([ref_dataset]+model_datasets)[1:]
""" Step 5: Checking and converting variable units """
print 'Checking and converting variable units'
ref_dataset = dsp.variable_unit_conversion(ref_dataset)
for idata,dataset in enumerate(model_datasets):
model_datasets[idata] = dsp.variable_unit_conversion(dataset)
print 'Generating multi-model ensemble'
if len(model_datasets) >= 2.:
model_datasets.append(dsp.ensemble(model_datasets))
model_names.append('ENS')
""" Step 6: Generate subregion average and standard deviation """
print('Number of target datasets: {}'.format(ntarget))
for target_name in target_names:
print(target_name)
""" Step 3: Spatial regriding of the datasets """
print('Regridding datasets: {}'.format(config['regrid']))
if not config['regrid']['regrid_on_reference']:
reference_dataset = dsp.spatial_regrid(reference_dataset, new_lat, new_lon)
print('Reference dataset has been regridded')
for i, dataset in enumerate(target_datasets):
target_datasets[i] = dsp.spatial_regrid(dataset,
new_lat,
new_lon,
boundary_check=boundary_check)
print('{} has been regridded'.format(target_names[i]))
print('Propagating missing data information')
datasets = dsp.mask_missing_data([reference_dataset] + target_datasets)
reference_dataset = datasets[0]
target_datasets = datasets[1:]
""" Step 4: Checking and converting variable units """
print('Checking and converting variable units')
reference_dataset = dsp.variable_unit_conversion(reference_dataset)
for i, dataset in enumerate(target_datasets):
target_datasets[i] = dsp.variable_unit_conversion(dataset)
print('Generating multi-model ensemble')
if len(target_datasets) >= 2.:
target_datasets.append(dsp.ensemble(target_datasets))
target_names.append('ENS')
""" Step 5: Generate subregion average and standard deviation """
if config['use_subregions']:
# sort the subregion by region names and make a list
