diff --git a/README.rst b/README.rst index 8c49c9d90..b1c62ffac 100644 --- a/README.rst +++ b/README.rst @@ -41,7 +41,7 @@ as your package/environment manager. Option 1: Install from PIP (recommended for analysts): ------------------------------------------------------ -1. Create a new environment: ``conda create --name sup3r python=3.9`` +1. Create a new environment: ``conda create --name sup3r python=3.11`` 2. Activate environment: ``conda activate sup3r`` @@ -73,9 +73,9 @@ Recommended Citation Update with current version and DOI: -Brandon Benton, Grant Buster, Andrew Glaws, Ryan King. Super Resolution for Renewable Resource Data (sup3r). https://github.com/NREL/sup3r (version v0.0.3), 2022. DOI: 10.5281/zenodo.6808547 +Brandon Benton, Grant Buster, Guilherme Pimenta Castelao, Malik Hassanaly, Pavlo Pinchuk, Slater Podgorny, Andrew Glaws, and Ryan King. Super Resolution for Renewable Resource Data (sup3r). https://github.com/NREL/sup3r (version v0.2.0), 2024. DOI: 10.5281/zenodo.14042894 Acknowledgments =============== -This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the DOE Wind Energy Technologies Office (WETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. +This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This research was supported by the Grid Modernization Initiative of the U.S. Department of Energy (DOE) as part of its Grid Modernization Laboratory Consortium, a strategic partnership between DOE and the national laboratories to bring together leading experts, technologies, and resources to collaborate on the goal of modernizing the nation’s grid. Funding provided by the the DOE Office of Energy Efficiency and Renewable Energy (EERE), the DOE Office of Electricity (OE), DOE Grid Deployment Office (GDO), the DOE Office of Fossil Energy and Carbon Management (FECM), and the DOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the DOE Wind Energy Technologies Office (WETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. diff --git a/sup3r/preprocessing/data_handlers/factory.py b/sup3r/preprocessing/data_handlers/factory.py index 4adc81ec5..99ef6d184 100644 --- a/sup3r/preprocessing/data_handlers/factory.py +++ b/sup3r/preprocessing/data_handlers/factory.py @@ -88,10 +88,11 @@ def __init__( or raster_file. shape : tuple (rows, cols) grid size. Either need target+shape or raster_file. - time_slice : slice + time_slice : slice | list Slice specifying extent and step of temporal extraction. e.g. - slice(start, stop, step). If equal to slice(None, None, 1) the - full time dimension is selected. + slice(start, stop, step). If equal to slice(None, None, 1) the full + time dimension is selected. Can be also be a list ``[start, stop, + step]`` threshold : float Nearest neighbor euclidean distance threshold. If the coordinates are more than this value away from the target lat/lon, an error is diff --git a/sup3r/preprocessing/rasterizers/base.py b/sup3r/preprocessing/rasterizers/base.py index 5f8af8018..897b9c033 100644 --- a/sup3r/preprocessing/rasterizers/base.py +++ b/sup3r/preprocessing/rasterizers/base.py @@ -51,10 +51,11 @@ def __init__( raster_file. shape : tuple (rows, cols) grid size. Either need target+shape or raster_file. - time_slice : slice + time_slice : slice | list Slice specifying extent and step of temporal extraction. e.g. slice(start, stop, step). If equal to slice(None, None, 1) the full - time dimension is selected. + time dimension is selected. Can be also be a list ``[start, stop, + step]`` threshold : float Nearest neighbor euclidean distance threshold. If the coordinates are more than this value away from the target lat/lon, an error is diff --git a/sup3r/preprocessing/rasterizers/extended.py b/sup3r/preprocessing/rasterizers/extended.py index 29293bd4a..d62874c0f 100644 --- a/sup3r/preprocessing/rasterizers/extended.py +++ b/sup3r/preprocessing/rasterizers/extended.py @@ -58,10 +58,11 @@ def __init__( shape : tuple (rows, cols) grid size. Either need target+shape or raster_file. - time_slice : slice + time_slice : slice | list Slice specifying extent and step of temporal extraction. e.g. - slice(start, stop, step). If equal to slice(None, None, 1) the - full time dimension is selected. + slice(start, stop, step). If equal to slice(None, None, 1) the full + time dimension is selected. Can be also be a list ``[start, stop, + step]`` threshold : float Nearest neighbor euclidean distance threshold. If the coordinates are more than this value away from the target