Ensure that noise is added in training even if real obs are used.

gprof_nn/bin/train.py

@@ -326,7 +326,7 @@ def add_parser(subparsers):
         )
     )
     parser.add_argument(
-        "--delta_tbs",
+        "--nedts",
         type=float,
         nargs="*",
         default=None,
@@ -360,7 +360,7 @@ def run(args):
         return 1
     if args.simulated_tbs:
         sensor.use_simulated_tbs = True
-        sensor.delta_tbs = args.delta_tbs
+        sensor.nedts = args.nedts
 
     variant = args.variant
     if variant.upper() not in ["1D", "3D", "SIM", "HR"]:
@@ -466,7 +466,7 @@ def run_training_1d(
         lr = lr * len(n_epochs)
 
     if args.normalizer is None:
-        nedts = args.delta_tbs
+        nedts = args.nedts
         normalizer = get_normalizer(sensor, nedts=nedts)
     else:
         normalizer = Normalizer.load(args.normalizer)

gprof_nn/sensors.py

@@ -223,7 +223,7 @@ def __init__(self, kind, name, channels, angles, platform, viewing_geometry):
         self.missing_channels = None
         self.l1c_version = ""
         self.use_simulated_tbs = False
-        self.delta_tbs = None
+        self.nedts = None
 
         # Bin file types
         self._bin_file_header = types.get_bin_file_header(n_chans, n_angles, kind)
@@ -637,8 +637,8 @@ def _load_scene_1d(self, scene, targets, augment, rng):
         valid = sp >= 0
 
         tbs = self.load_brightness_temperatures(scene, mask=valid)
-        if self.use_simulated_tbs and self.delta_tbs is not None:
-            noise = np.array(self.delta_tbs) * rng.normal(size=tbs.shape)
+        if self.nedts is not None:
+            noise = np.array(self.nedts) * rng.normal(size=tbs.shape)
             tbs += noise
 
         if augment: