Bug fixes in run_RCMES.py
Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/7187cf30 Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/7187cf30 Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/7187cf30 Branch: refs/heads/master Commit: 7187cf306877ea471335d8d22704bee7653342e9 Parents: b04d86c Author: Alex Goodman <ago...@users.noreply.github.com> Authored: Tue Nov 15 17:58:57 2016 -0800 Committer: Alex Goodman <ago...@users.noreply.github.com> Committed: Tue Nov 15 17:58:57 2016 -0800 ---------------------------------------------------------------------- RCMES/run_RCMES.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/7187cf30/RCMES/run_RCMES.py ---------------------------------------------------------------------- diff --git a/RCMES/run_RCMES.py b/RCMES/run_RCMES.py index e65a452..027d2e2 100644 --- a/RCMES/run_RCMES.py +++ b/RCMES/run_RCMES.py @@ -90,20 +90,23 @@ extra_opts = {'min_lat': min_lat, 'max_lat': max_lat, 'min_lon': min_lon, data_info = config['datasets'] # Extract info we don't want to put into the loader config -# Multiplying Factor to scale obs by -multiplying_factor = np.ones(len(data_info)) -for i, info in enumerate(reference_data_info): - multiplying_factor[i] = info.pop('multiplying_factor', 1) +# Multiplying Factor to scale obs by. Currently only supported for reference +# (first) dataset. We should instead make this a parameter for each +# loader and Dataset objects. +fact = data_info[0].pop('multiplying_factor', 1) """ Step 1: Load the datasets """ print('Loading datasets:\n{}'.format(data_info)) datasets = load_datasets_from_config(extra_opts, *data_info) +multiplying_factor = np.ones(len(datasets)) +multiplying_factor[0] = fact names = [dataset.name for dataset in datasets] for i, dataset in enumerate(datasets): if temporal_resolution == 'daily' or temporal_resolution == 'monthly': datasets[i] = dsp.normalize_dataset_datetimes(dataset, temporal_resolution) - datasets[i].values *= multiplying_factor[i] + if multiplying_factor[i] != 1: + datasets[i].values *= multiplying_factor[i] """ Step 2: Subset the data for temporal and spatial domain """ # Create a Bounds object to use for subsetting @@ -215,7 +218,7 @@ if config['use_subregions']: nsubregion = len(subregions) print('Calculating spatial averages and standard deviations of {} subregions' - .format(nsubregions)) + .format(nsubregion)) reference_subregion_mean, reference_subregion_std, subregion_array = ( utils.calc_subregion_area_mean_and_std([reference_dataset], subregions)) @@ -237,8 +240,8 @@ if config['use_subregions']: subregions=subregions, subregion_array=subregion_array, ref_subregion_mean=reference_subregion_mean, ref_subregion_std=reference_subregion_std, - target_subregion_mean=target_subregion_mean, - target_subregion_std=target_subregion_std) + model_subregion_mean=target_subregion_mean, + model_subregion_std=target_subregion_std) else: dsp.write_netcdf_multiple_datasets_with_subregions( reference_dataset, reference_name, target_datasets,
