converted sweml notebooks into python script.

Author: karnesh

Model/Neural_Network/sweml_hindcast.py

@@ -0,0 +1,81 @@
+import os
+from tqdm import tqdm
+import MLP_Model
+import sys
+sys.path.insert(0, '..')
+from shared_scripts import DataProcess, Hindcast_Initialization, NSM_SCA
+import warnings
+
+warnings.filterwarnings('ignore')
+
+# Set working directories
+cwd = os.getcwd()
+datapath = f"{os.path.expanduser('~')}/SWEML"
+
+
+def sweml_hindcast(new_year, threshold, Region_list, fSCA, frequency, NewSim):
+    model = 'Neural_Network'
+
+    datelist = Hindcast_Initialization.Hindcast_Initialization(cwd, datapath, new_year, threshold, Region_list,
+                                                               frequency, fSCA=fSCA)
+
+    # Run data processing script to partition key regional dataframes
+    # note, need to load RegionTrain_SCA.h5,
+    if datelist[0][-5:] == '10-01':
+        RegionTrain, RegionTest, RegionObs_Train, RegionObs_Test, RegionTest_notScaled = DataProcess.DataProcess(
+            new_year, frequency, model, Region_list, fSCA=fSCA)
+
+        """
+        # model training, each participants model will be different but should follow the prescribed input feature 
+        template epochs= 30 
+        batchsize = 60 
+        MLP_Model.Model_train(epochs, batchsize, RegionTrain, RegionTest, RegionObs_Train, RegionObs_Test, Region_list, 
+                              fSCA = fSCA)
+        """
+
+        # Need to create Predictions folder if running for the first time
+        Predictions = MLP_Model.Model_predict(RegionTest, RegionObs_Test, RegionTest_notScaled, Region_list, fSCA=fSCA)
+
+    for day in tqdm(datelist):
+        # connect interactive script to Wasatch Snow module
+        snow = NSM_SCA.NSM_SCA(day, threshold=threshold, Regions=Region_list, modelname=model, frequency=frequency,
+                               fSCA=fSCA, NewSim=NewSim)
+
+        # Go get SNOTEL observations - all data currently loaded, set to True to download
+        snow.Get_Monitoring_Data_Threaded(getdata=True)
+
+        # Initialize/Download the granules, all data preprocessed for the SSM activRegion_listhange to True to use
+        # the functions.
+        snow.initializeGranules(getdata=True)
+
+        # Process observations into Model prediction ready format,
+        snow.Data_Processing(SCA=True)
+
+        # Agument with SCA
+        snow.augmentPredictionDFs()
+
+        # Make predictions, set NewSim to False Look to multiprocess, each region can do a prediction to speed things
+        # up. set NewSim to true for New simulation, turn to false once all data has been proces and saved.
+        snow.SWE_Predict(NewSim=NewSim, Corrections=False, fSCA=fSCA)
+
+    modelname = 'Neural_Network'
+    folderpath = 'Predictions/Hold_Out_Year/Daily/fSCA_True/'
+    AWSpath = f"Hold_Out_Year/Daily/"
+    file_type = '.h5'
+    Hindcast_Initialization.Hindcast_to_AWS(modelname, folderpath, AWSpath, file_type)
+
+
+if __name__ == "__main__":
+    new_year = 2023
+    threshold = 10
+    Region_list = ['N_Sierras', 'S_Sierras_High', 'S_Sierras_Low', 'Greater_Yellowstone',
+                   'N_Co_Rockies', 'SW_Mont', 'SW_Co_Rockies', 'GBasin', 'N_Wasatch', 'N_Cascade',
+                   'S_Wasatch', 'SW_Mtns', 'E_WA_N_Id_W_Mont', 'S_Wyoming', 'SE_Co_Rockies',
+                   'Sawtooth', 'Ca_Coast', 'E_Or', 'N_Yellowstone', 'S_Cascade', 'Wa_Coast',
+                   'Greater_Glacier', 'Or_Coast'
+                   ]
+    fSCA = True
+    frequency = 'Daily'
+    NewSim = True
+
+    sweml_hindcast(new_year, threshold, Region_list, fSCA, frequency, NewSim)