training works. Checking now for many timesteps

NWC-CUAHSI-Summer-Institute · Jul 12, 2023 · efb2560 · efb2560
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diff --git a/data/LittleWashita/PET.csv b/data/LittleWashita/PET.csv
diff --git a/data/LittleWashita/Q.csv b/data/LittleWashita/Q.csv
diff --git a/data/LittleWashita/ars_a134_RAIN.csv b/data/LittleWashita/ars_a134_RAIN.csv
diff --git a/data/LittleWashita/ars_a134_VW05.csv b/data/LittleWashita/ars_a134_VW05.csv
diff --git a/data/LittleWashita/ars_a134_VW25.csv b/data/LittleWashita/ars_a134_VW25.csv
diff --git a/data/LittleWashita/ars_a134_VW45.csv b/data/LittleWashita/ars_a134_VW45.csv
diff --git a/data/LittleWashita/ars_a149_RAIN.csv b/data/LittleWashita/ars_a149_RAIN.csv
diff --git a/data/LittleWashita/ars_a149_VW05.csv b/data/LittleWashita/ars_a149_VW05.csv
diff --git a/data/LittleWashita/ars_a149_VW25.csv b/data/LittleWashita/ars_a149_VW25.csv
diff --git a/data/LittleWashita/ars_a149_VW45.csv b/data/LittleWashita/ars_a149_VW45.csv
diff --git a/data/LittleWashita/basin-averaged/PET.csv b/data/LittleWashita/basin-averaged/PET.csv
diff --git a/data/LittleWashita/basin-averaged/PET.png b/data/LittleWashita/basin-averaged/PET.png
diff --git a/data/LittleWashita/basin-averaged/Q.csv b/data/LittleWashita/basin-averaged/Q.csv
diff --git a/data/LittleWashita/basin-averaged/ars_RAIN.csv b/data/LittleWashita/basin-averaged/ars_RAIN.csv
diff --git a/data/LittleWashita/basin-averaged/ars_RAIN.png b/data/LittleWashita/basin-averaged/ars_RAIN.png
diff --git a/data/LittleWashita/basin-averaged/ars_VW05.csv b/data/LittleWashita/basin-averaged/ars_VW05.csv
diff --git a/data/LittleWashita/basin-averaged/ars_VW05.png b/data/LittleWashita/basin-averaged/ars_VW05.png
diff --git a/data/LittleWashita/basin-averaged/ars_VW25.csv b/data/LittleWashita/basin-averaged/ars_VW25.csv
diff --git a/data/LittleWashita/basin-averaged/ars_VW25.png b/data/LittleWashita/basin-averaged/ars_VW25.png
diff --git a/data/LittleWashita/basin-averaged/ars_VW45.csv b/data/LittleWashita/basin-averaged/ars_VW45.csv
diff --git a/data/LittleWashita/basin-averaged/ars_VW45.png b/data/LittleWashita/basin-averaged/ars_VW45.png
diff --git a/data/LittleWashita/basin-depth-averaged/little_washita_2006_2012.csv b/data/LittleWashita/basin-depth-averaged/little_washita_2006_2012.csv
diff --git a/data/LittleWashita/basin-depth-averaged/test_sm_basinavg.csv b/data/LittleWashita/basin-depth-averaged/test_sm_basinavg.csv
diff --git a/data/observations/ET_location134_all.csv b/data/observations/ET_location134_all.csv
diff --git a/data/soil_columns/OK_points_soiltextures_index_transpose.csv b/data/soil_columns/OK_points_soiltextures_index_transpose.csv
diff --git a/dpLGAR/agents/DifferentiableLGAR.py b/dpLGAR/agents/DifferentiableLGAR.py
@@ -129,7 +129,6 @@ def train_one_epoch(self):
         self.y_t = y_t_[warmup:]
         # If there is no gradient (i.e. no runoff), then we shouldn't validate
         self.validate()
-        # self.model.update_soil_parameters()
 
     # Commenting out until we get the normal model to work
     # def train_one_epoch(self):

diff --git a/dpLGAR/agents/__pycache__/DifferentiableLGAR.cpython-310.pyc b/dpLGAR/agents/__pycache__/DifferentiableLGAR.cpython-310.pyc
diff --git a/dpLGAR/models/config/shorter_subcycle.yaml b/dpLGAR/models/config/shorter_subcycle.yaml
@@ -7,7 +7,7 @@ subcycle_length_h: ??? #[hr]
 num_subcycles: ???
 #num_time_step: 90000  # total simulation time is this number times the time_step_s
 #endtime: 372.0 #[hr]
-endtime: 744.0 #[hr]
+endtime: 3000.0 #[hr]
 endtime_s: ???
 forcing_resolution_h: ???
 time_per_step: ???

diff --git a/dpLGAR/models/physics/layers/Layer.py b/dpLGAR/models/physics/layers/Layer.py
@@ -1520,16 +1520,16 @@ def insert_water(
         else:
             # if it got to this point, no ponding is allowed, either infiltrate or runoff
             # order is important here; assign zero to ponded depth once we compute volume in and runoff
-            #TODO TEST THIS MORE
             infiltration = torch.min(ponded_depth, fp_cm)
             # if ponded_depth < fp_cm:
             #     runoff = torch.tensor(0.0, device=self.global_params.device)
             # else:
             #     runoff = ponded_depth - infiltration
             # ponded_depth = torch.tensor(0.0, device=self.global_params.device)
-            ponded_depth = ponded_depth_temp
             _runoff_ = ponded_depth - infiltration
+            ponded_depth = self.global_params.ponded_depth_max
             runoff = torch.clamp(_runoff_, min=0.0)
+            _x_ = "test"
 
         return runoff, infiltration, ponded_depth
 

diff --git a/dpLGAR/models/physics/layers/__pycache__/Layer.cpython-310.pyc b/dpLGAR/models/physics/layers/__pycache__/Layer.cpython-310.pyc