From 0fde99b30dca61faf5a366e842b4af24eda22601 Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Fri, 7 Jul 2023 10:14:40 -0600
Subject: [PATCH] pr review changes

---
 sup3r/preprocessing/data_handling.py |  2 +-
 sup3r/utilities/loss_metrics.py      | 38 +++++++++++++++++++++++++++-
 2 files changed, 38 insertions(+), 2 deletions(-)

diff --git a/sup3r/preprocessing/data_handling.py b/sup3r/preprocessing/data_handling.py
index b8ea75ea44..9245dc8cc5 100644
--- a/sup3r/preprocessing/data_handling.py
+++ b/sup3r/preprocessing/data_handling.py
@@ -271,7 +271,7 @@ def file_paths(self, file_paths):
 
         msg = ('No valid files provided to DataHandler. '
                f'Received file_paths={file_paths}. Aborting.')
-        assert len(self._file_paths) > 0 and file_paths is not None, msg
+        assert file_paths is not None and len(self._file_paths) > 0, msg
 
         self._file_paths = sorted(self._file_paths)
 
diff --git a/sup3r/utilities/loss_metrics.py b/sup3r/utilities/loss_metrics.py
index 312397f8c5..64a0680ed7 100644
--- a/sup3r/utilities/loss_metrics.py
+++ b/sup3r/utilities/loss_metrics.py
@@ -1,7 +1,7 @@
 """Loss metrics for Sup3r"""
 
-from tensorflow.keras.losses import MeanSquaredError, MeanAbsoluteError
 import tensorflow as tf
+from tensorflow.keras.losses import MeanAbsoluteError, MeanSquaredError
 
 
 def gaussian_kernel(x1, x2, sigma=1.0):
@@ -173,6 +173,42 @@ def __call__(self, x1, x2):
         return self.MSE_LOSS(x1_coarse, x2_coarse)
 
 
+class SpatialExtremesLoss(tf.keras.losses.Loss):
+    """Loss class that encourages accuracy of the min/max values in the
+    spatial domain"""
+
+    MAE_LOSS = MeanAbsoluteError()
+
+    def __call__(self, x1, x2):
+        """Custom content loss that encourages temporal min/max accuracy
+
+        Parameters
+        ----------
+        x1 : tf.tensor
+            synthetic generator output
+            (n_observations, spatial_1, spatial_2, features)
+        x2 : tf.tensor
+            high resolution data
+            (n_observations, spatial_1, spatial_2, features)
+
+        Returns
+        -------
+        tf.tensor
+            0D tensor with loss value
+        """
+        x1_min = tf.reduce_min(x1, axis=(1, 2))
+        x2_min = tf.reduce_min(x2, axis=(1, 2))
+
+        x1_max = tf.reduce_max(x1, axis=(1, 2))
+        x2_max = tf.reduce_max(x2, axis=(1, 2))
+
+        mae = self.MAE_LOSS(x1, x2)
+        mae_min = self.MAE_LOSS(x1_min, x2_min)
+        mae_max = self.MAE_LOSS(x1_max, x2_max)
+
+        return mae + mae_min + mae_max
+
+
 class TemporalExtremesLoss(tf.keras.losses.Loss):
     """Loss class that encourages accuracy of the min/max values in the
     timeseries"""