merge dev to master

LICENSE.md

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022 Peter Van Katwyk
+Copyright (c) 2024 Peter Van Katwyk
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -9,4 +9,6 @@ This repository contains source code for processing climate forcings from [ISMIP
 
 Documentation can be found at here: <https://brown-sciml.github.io/ise/>.
 
+To access code for exact replication of "A Variational LSTM Emulator of Sea Level Contribution From the Antarctic Ice Sheet", see the release [https://github.com/Brown-SciML/ise/releases/tag/v1.0.0](https://github.com/Brown-SciML/ise/releases/tag/v1.0.0).
+
 *This repository is a work in progress that is actively being updated and improved. Feel free to contact Peter Van Katwyk, Ph.D. student @ Brown University at peter_van_katwyk@brown.edu with further questions.*

examples/grids/process.py

@@ -0,0 +1,49 @@
+from ise.data.process import DatasetMerger, DimensionalityReducer, ProjectionProcessor
+
+ice_sheet = "AIS"
+print(f"ice sheet: {ice_sheet}")
+
+# all filepaths...
+forcing_directory = r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/GHub-ISMIP6-Forcing/"
+projections_directory = (
+    r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/GHub-ISMIP6-Projection-AIS/"
+    if ice_sheet == "AIS"
+    else r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/GHub-ISMIP6-Projection-GrIS/"
+)
+scalefac_fp = (
+    r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/af2_el_ismip6_ant_01.nc"
+    if ice_sheet == "AIS"
+    else r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/GHub-ISMIP6-Projection-GrIS/af2_ISMIP6_GrIS_05000m.nc"
+)
+densities_path = (
+    r"/users/pvankatw/research/current/supplemental/AIS_densities.csv"
+    if ice_sheet == "AIS"
+    else r"/users/pvankatw/research/current/supplemental/GIS_densities.csv"
+)
+
+scaler = 'robust'
+output_dir = f"/oscar/scratch/pvankatw/datasets/pca_full_{scaler}/{ice_sheet}"
+converted_forcing_dir = f"{output_dir}/forcings/"
+converted_projection_dir = f"{output_dir}/projections/"
+experiment_file = r"/users/pvankatw/research/current/ise/utils/ismip6_experiments_updated.csv"
+# df = get_model_densities(r"/gpfs/data/kbergen/pvankatw/pvankatw-bfoxkemp/v7_CMIP5_pub", r"/users/pvankatw/research/current/supplemental/")
+
+
+# Take both the forcing files and the projections, train PCA models, and convert forcings and projections to PCA space
+pca = DimensionalityReducer(
+    forcing_dir=forcing_directory,
+    projection_dir=projections_directory,
+    output_dir=output_dir,
+    ice_sheet=ice_sheet,
+)
+pca.generate_pca_models(num_forcing_pcs='99%', num_projection_pcs='99.99%', scaling_method=scaler)
+pca.convert_forcings(pca_model_directory=f"{output_dir}/pca_models/")
+pca.convert_projections(pca_model_directory=f"{output_dir}/pca_models/")
+
+# Merge the converted forcings and projections into a single dataset
+merger = DatasetMerger(
+    ice_sheet, converted_forcing_dir, converted_projection_dir, experiment_file, output_dir
+)
+merger.merge_dataset()
+
+print("Done!")

examples/analyze_model.py → examples/sectors/analyze_model.py

@@ -1,25 +1,26 @@
 import sys
-sys.path.append("..")
-from ise.models.timeseries import TimeSeriesEmulator
-from ise.pipelines.testing import analyze_model
+
 import pandas as pd
 
+sys.path.append("../..")
+from ise.models.timeseries import TimeSeriesEmulator
+from ise.pipelines.testing import analyze_model
 
 DATA_DIRECTORY = r"/users/pvankatw/emulator/untracked_folder/ml_data_directory"
-PRETRAINED_MODELS = r'/users/pvankatw/emulator/ise/models/pretrained/'
-UNTRACKED = r'/users/pvankatw/emulator/untracked_folder'
+PRETRAINED_MODELS = r"/users/pvankatw/emulator/ise/models/pretrained/"
+UNTRACKED = r"/users/pvankatw/emulator/untracked_folder"
 
 
 train_features = pd.read_csv(f"{DATA_DIRECTORY}/ts_train_features.csv")
 architecture = {
-    'num_rnn_layers': 4,
-    'num_rnn_hidden': 256,
-    'input_layer_size': train_features.shape[1]
+    "num_rnn_layers": 4,
+    "num_rnn_hidden": 256,
+    "input_layer_size": train_features.shape[1],
 }
 
-model_path = f'{PRETRAINED_MODELS}/Emulator.pt'
+model_path = f"{PRETRAINED_MODELS}/Emulator.pt"
 
-print('\nAnalyzing')
+print("\nAnalyzing")
 analyze_model(
     data_directory=DATA_DIRECTORY,
     model_path=model_path,
@@ -30,5 +31,5 @@
     dropout_prob=0.3,
     mc_iterations=100,
     verbose=False,
-    save_directory=f'{UNTRACKED}/analyze_model'
-)
+    save_directory=f"{UNTRACKED}/analyze_model",
+)