Bnb/cond obs fwp

README.rst

@@ -78,4 +78,4 @@ Brandon Benton, Grant Buster, Guilherme Pimenta Castelao, Malik Hassanaly, Pavlo
 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. 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.
+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.

examples/sup3rwind/README.rst

@@ -2,7 +2,7 @@
 Sup3rWind Examples
 ###################
 
-Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data (Sup3rWind) is one application of the sup3r software. In this work, we train generative models to create high-resolution (2km 5-minute) wind data based on coarse (30km hourly) ERA5 data. The generative models and high-resolution output data is publicly available via the `Open Energy Data Initiative (OEDI) <https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2F>`__ and via HSDS at the bucket ``nrel-pds-hsds`` and path ``/nrel/wtk/sup3rwind``. This data covers recent historical time periods for an expanding selection of countries.
+Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data (Sup3rWind) is one application of the sup3r software. In this work, we train generative models to create high-resolution (2km 5-minute) wind data based on coarse (30km hourly) ERA5 data. The generative models, high-resolution output data, and training data is publicly available via the `Open Energy Data Initiative (OEDI) <https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2F>`__ and via HSDS at the bucket ``nrel-pds-hsds`` and path ``/nrel/wtk/sup3rwind``. This data covers recent historical time periods for an expanding selection of countries.
 
 Sup3rWind Data Access
 ----------------------
@@ -11,8 +11,8 @@ The Sup3rWind data and models are publicly available in a public AWS S3 bucket.
 
 The Sup3rWind data is also loaded into `HSDS <https://www.hdfgroup.org/solutions/highly-scalable-data-service-hsds/>`__ so that you may stream the data via the `NREL developer API <https://developer.nrel.gov/signup/>`__ or your own HSDS server. This is the best option if you're not going to want a full annual dataset. See these `rex instructions <https://nrel.github.io/rex/misc/examples.hsds.html>`__ for more details on how to access this data with HSDS and rex.
 
-Example Sup3rWind Data Usage
------------------------------
+Sup3rWind Data Usage
+---------------------
 
 Sup3rWind data can be used in generally the same way as `Sup3rCC <https://nrel.github.io/sup3r/examples/sup3rcc.html>`__ data, with the condition that Sup3rWind includes only wind data and ancillary variables for modeling wind energy generation. Refer to the Sup3rCC `example notebook <https://github.com/NREL/sup3r/tree/main/examples/sup3rcc/using_the_data.ipynb>`__ for usage patterns.
 
@@ -32,6 +32,41 @@ The process for running the Sup3rWind models is much the same as for `Sup3rCC <h
 #. If you're running on a slurm cluster, this will kick off a number of jobs that you can see with the ``squeue`` command. If you're running locally, your terminal should now be running the Sup3rWind models. The software will create a ``./logs/`` directory in which you can monitor the progress of your jobs.
 #. The ``sup3r-pipeline`` is designed to run several modules in serial, with each module running multiple chunks in parallel. Once the first module (forward-pass) finishes, you'll want to run ``python -m sup3r.cli -c config_pipeline.json pipeline`` again. This will clean up status files and kick off the next step in the pipeline (if the current step was successful).
 
+You can also checkout the `example notebook <https://github.com/NREL/sup3r/tree/main/examples/sup3rwind/running_sup3r_models.ipynb>`__ for how to run models without config files.
+
+Training from scratch
+---------------------
+
+To train Sup3rWind models from scratch use the public training `data <https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2Ftraining_data%2F>`__. This data is for training the spatial enhancement models only. The 2024-01 `models <https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2Fmodels%2Fsup3rwind_models_202401%2F>`__ perform spatial enhancement in two steps, 3x from ERA5 to coarsened WTK and 5x from coarsened WTK to uncoarsened WTK. The currently used approach performs spatial enhancement in a single 15x step.
+
+For a given year and training domain, initialize low-resolution and high-resolution data handlers and wrap these in a dual rasterizer object. Do this for as many years and training regions as desired, and use these containers to initialize a batch handler. To train models for 3x spatial enhancement use ``hr_spatial_coarsen=5`` in the ``hr_dh``. To train models for 15x (the currently used approach) ``hr_spatial_coarsen=1``. (Refer to tests and docs for information on additional arguments, denoted by the ellipses)::
+
+  from sup3r.preprocessing import DataHandler, DualBatchHandler, DualRasterizer
+  containers = []
+  for tdir in training_dirs:
+    lr_dh = DataHandler(f"{tdir}/lr_*.h5", ...)
+    hr_dh = DataHandler(f"{tdir}/hr_*.h5", hr_spatial_coarsen=...)
+    container = DualRasterizer({'low_res': lr_dh, 'high_res': hr_dh}, ...)
+    containers.append(container)
+  bh = DualBatchHandler(train_containers=containers, ...)
+
+To train a 5x model use the ``hr_*.h5`` files for both the ``lr_dh`` and the ``hr_dh``. Use ``hr_spatial_coarsen=3`` in the ``lr_dh`` and ``hr_spatial_coarsen=1`` in the ``hr_dh``::
+
+  for tdir in training_dirs:
+    lr_dh = DataHandler(f"{tdir}/hr_*.h5", hr_spatial_coarsen=3, ...)
+    hr_dh = DataHandler(f"{tdir}/hr_*.h5", hr_spatial_coarsen=1, ...)
+    container = DualRasterizer({'low_res': lr_dh, 'high_res': hr_dh}, ...)
+    containers.append(container)
+  bh = DualBatchHandler(train_containers=containers, ...)
+
+
+Initialize a 3x, 5x, or 15x spatial enhancement model, with 14 output channels, and train for the desired number of epochs. (The 3x and 5x generator configs can be copied from the ``model_params.json`` files in each OEDI model `directory <https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2Fmodels%2Fsup3rwind_models_202401%2F>`__. The 15x generator config can be created from the OEDI model configs by changing the spatial enhancement factor or from the configs in the repo by changing the enhancement factor and the number of output channels)::
+
+  from sup3r.models import Sup3rGan
+  model = Sup3rGan(gen_layers="./gen_config.json", disc_layers="./disc_config.json", ...)
+  model.train(batch_handler, ...)
+
+
 Sup3rWind Versions
 -------------------
 

pyproject.toml

@@ -28,7 +28,7 @@ classifiers=[
 ]
 dependencies = [
   "NREL-rex>=0.2.91",
-  "NREL-phygnn>=0.0.23",
+  "NREL-phygnn>=0.0.31",
   "NREL-gaps>=0.6.13",
   "NREL-farms>=1.0.4",
   "dask>=2022.0",
@@ -42,10 +42,21 @@ dependencies = [
   "pytest>=5.2",
   "scipy>=1.0.0",
   "sphinx>=7.0",
-  "tensorflow>2.4,<2.16",
   "xarray>=2023.0"
 ]
 
+# If used, cause glibc conflict
+# [tool.pixi.target.linux-64.dependencies]
+# cuda = ">=11.8"
+# cudnn = {version = ">=8.6.0", channel = "conda-forge"}
+# # 8.9.7
+
+[tool.pixi.target.linux-64.pypi-dependencies]
+tensorflow = {version = "~=2.15.1", extras = ["and-cuda"] }
+
+[tool.pixi.target.osx-arm64.dependencies]
+tensorflow = {version = "~=2.15.0", channel = "conda-forge"}
+
 [project.optional-dependencies]
 dev = [
   "build>=0.5",
@@ -64,6 +75,16 @@ test = [
   "pytest-env"
 ]
 
+[project.scripts]
+sup3r = "sup3r.cli:main"
+sup3r-pipeline = "sup3r.pipeline.pipeline_cli:main"
+sup3r-batch = "sup3r.batch.batch_cli:main"
+sup3r-qa = "sup3r.qa.qa_cli:main"
+sup3r-bias-calc = "sup3r.bias.bias_calc_cli:main"
+sup3r-solar = "sup3r.solar.solar_cli:main"
+sup3r-forward-pass = "sup3r.pipeline.forward_pass_cli:main"
+sup3r-collect = "sup3r.postprocessing.data_collect_cli:main"
+
 [project.urls]
 homepage = "https://github.com/NREL/sup3r"
 documentation = "https://nrel.github.io/sup3r/"
@@ -272,7 +293,6 @@ matplotlib = ">=3.1"
 numpy = "~=1.7"
 pandas = ">=2.0"
 scipy = ">=1.0.0"
-tensorflow = ">2.4,<2.16"
 xarray = ">=2023.0"
 
 [tool.pixi.pypi-dependencies]
@@ -284,6 +304,7 @@ NREL-farms = { version = ">=1.0.4" }
 
 [tool.pixi.environments]
 default = { solve-group = "default" }
+kestrel = { features = ["kestrel"], solve-group = "default" }
 dev = { features = ["dev", "doc", "test"], solve-group = "default" }
 doc = { features = ["doc"], solve-group = "default" }
 test = { features = ["test"], solve-group = "default" }

sup3r/models/__init__.py

@@ -6,6 +6,7 @@
 from .multi_step import MultiStepGan, MultiStepSurfaceMetGan, SolarMultiStepGan
 from .solar_cc import SolarCC
 from .surface import SurfaceSpatialMetModel
+from .with_obs import Sup3rGanWithObs
 
 SPATIAL_FIRST_MODELS = (MultiStepSurfaceMetGan,
                         SolarMultiStepGan)