Merge branch 'main' into bnb/masked_fwp

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.

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

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

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