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