get_exo_loss_input -> get_high_res_exo_input

sup3r/models/abstract.py

@@ -55,6 +55,15 @@ def load(cls, model_dir, verbose=True):
             model_dir
         """
 
+    @abstractmethod
+    def generate(self,
+                 low_res,
+                 norm_in=True,
+                 un_norm_out=True,
+                 exogenous_data=None):
+        """Use the generator model to generate high res data from low res
+        input. This is the public generate function."""
+
     @staticmethod
     def seed(s=0):
         """
@@ -141,7 +150,9 @@ def input_resolution(self):
         return input_resolution
 
     def _get_numerical_resolutions(self):
-        """Get the input and output resolutions without units"""
+        """Get the input and output resolutions without units. e.g. for
+        {"spatial": "30km", "temporal": "60min"} this returns
+        {"spatial": 30, "temporal": 60}"""
         ires_num = {k: int(re.search(r'\d+', v).group(0))
                     for k, v in self.input_resolution.items()}
         enhancements = {'spatial': self.s_enhance,
@@ -797,7 +808,7 @@ def load_saved_params(out_dir, verbose=True):
 
         return params
 
-    def get_exo_loss_input(self, high_res):
+    def get_high_res_exo_input(self, high_res):
         """Get exogenous feature data from high_res
 
         Parameters
@@ -1418,7 +1429,7 @@ def get_single_grad(self,
             with tf.GradientTape(watch_accessed_variables=False) as tape:
                 tape.watch(training_weights)
 
-                hi_res_exo = self.get_exo_loss_input(hi_res_true)
+                hi_res_exo = self.get_high_res_exo_input(hi_res_true)
                 hi_res_gen = self._tf_generate(low_res, hi_res_exo)
                 loss_out = self.calc_loss(hi_res_true, hi_res_gen,
                                           **calc_loss_kwargs)

sup3r/models/base.py

@@ -18,7 +18,7 @@
 logger = logging.getLogger(__name__)
 
 
-class Sup3rGan(AbstractInterface, AbstractSingleModel):
+class Sup3rGan(AbstractSingleModel, AbstractInterface):
     """Basic sup3r GAN model."""
 
     def __init__(self,
@@ -602,7 +602,7 @@ def calc_val_loss(self, batch_handler, weight_gen_advers, loss_details):
         logger.debug('Starting end-of-epoch validation loss calculation...')
         loss_details['n_obs'] = 0
         for val_batch in batch_handler.val_data:
-            val_exo_data = self.get_exo_loss_input(val_batch.high_res)
+            val_exo_data = self.get_high_res_exo_input(val_batch.high_res)
             high_res_gen = self._tf_generate(val_batch.low_res, val_exo_data)
             _, v_loss_details = self.calc_loss(
                 val_batch.high_res, high_res_gen,

sup3r/models/conditional_moments.py

@@ -16,7 +16,7 @@
 logger = logging.getLogger(__name__)
 
 
-class Sup3rCondMom(AbstractInterface, AbstractSingleModel):
+class Sup3rCondMom(AbstractSingleModel, AbstractInterface):
     """Basic Sup3r conditional moments model."""
 
     def __init__(self, gen_layers,
@@ -283,7 +283,7 @@ def calc_val_loss(self, batch_handler, loss_details):
         logger.debug('Starting end-of-epoch validation loss calculation...')
         loss_details['n_obs'] = 0
         for val_batch in batch_handler.val_data:
-            val_exo_data = self.get_exo_loss_input(val_batch.high_res)
+            val_exo_data = self.get_high_res_exo_input(val_batch.high_res)
             output_gen = self._tf_generate(val_batch.low_res, val_exo_data)
             _, v_loss_details = self.calc_loss(
                 val_batch.output, output_gen, val_batch.mask)

sup3r/models/data_centric.py

@@ -38,7 +38,7 @@ def calc_val_loss_gen(self, batch_handler, weight_gen_advers):
         """
         losses = []
         for obs in batch_handler.val_data:
-            exo_data = self.get_exo_loss_input(obs.high_res)
+            exo_data = self.get_high_res_exo_input(obs.high_res)
             gen = self._tf_generate(obs.low_res, exo_data)
             loss, _ = self.calc_loss(obs.high_res, gen,
                                      weight_gen_advers=weight_gen_advers,
@@ -66,7 +66,7 @@ def calc_val_loss_gen_content(self, batch_handler):
         """
         losses = []
         for obs in batch_handler.val_data:
-            exo_data = self.get_exo_loss_input(obs.high_res)
+            exo_data = self.get_high_res_exo_input(obs.high_res)
             gen = self._tf_generate(obs.low_res, exo_data)
             loss = self.calc_loss_gen_content(obs.high_res, gen)
             losses.append(float(loss))

sup3r/preprocessing/data_handling/exogenous_data_handling.py

@@ -63,18 +63,29 @@ def __init__(self,
         feature : str
             Exogenous feature to extract from source_h5
         models : list
-            List of models used with the given steps list
+            List of models used with the given steps list. This list of models
+            is used to determine the input and output resolution and
+            enhancement factors for each model step which is then used to
+            determine aggregation factors. If agg factors and enhancement
+            factors are provided in the steps list the model list is not
+            needed.
         steps : list
             List of dictionaries containing info on which models to use for a
             given step index and what type of exo data the step requires. e.g.
             [{'model': 0, 'combine_type': 'input'},
              {'model': 0, 'combine_type': 'layer'}]
             Each step entry can also contain s_enhance, t_enhance,
-            s_agg_factor, t_agg_factor. If they are not included they will be
-            computed using exo_resolution and model attributes
+            s_agg_factor, t_agg_factor. e.g.
+            [{'model': 0, 'combine_type': 'input', 's_agg_factor': 900,
+              's_enhance': 1, 't_agg_factor': 5, 't_enhance': 1},
+             {'model': 0, 'combine_type': 'layer', 's_agg_factor', 100,
+              's_enhance': 3, 't_agg_factor': 5, 't_enhance': 1}]
+            If they are not included they will be computed using exo_resolution
+            and model attributes.
         exo_resolution : dict
             Dictionary of spatiotemporal resolution for the given exo data
-            source. e.g. {'spatial': '4km', 'temporal': '60min'}
+            source. e.g. {'spatial': '4km', 'temporal': '60min'}. This is used
+            only if agg factors are not provided in the steps list.
         source_file : str
             Filepath to source wtk, nsrdb, or netcdf file to get hi-res (2km or
             4km) data from which will be mapped to the enhanced grid of the