Implement input loaders for retrievals.

gprof_nn/data/l1c.py

@@ -492,17 +492,17 @@ def to_xarray_dataset(self, roi=None):
                 eia.append(eia_s)
             if "S3" in input.keys():
                 tbs.append(input["S3/Tc"][:][indices])
-                eia_s = input[f"s2/incidenceangle"][:][indices]
+                eia_s = input[f"S2/incidenceAngle"][:][indices]
                 eia_s = np.broadcast_to(eia_s, tbs[-1].shape)
                 eia.append(eia_s)
             if "S4" in input.keys():
                 tbs.append(input["S4/Tc"][:][indices])
-                eia_s = input[f"s2/incidenceangle"][:][indices]
+                eia_s = input[f"S2/incidenceAngle"][:][indices]
                 eia_s = np.broadcast_to(eia_s, tbs[-1].shape)
                 eia.append(eia_s)
             if "S5" in input.keys():
                 tbs_s = input["S5/Tc"][:][indices]
-                eia_s = input[f"s2/incidenceangle"][:][indices]
+                eia_s = input[f"S2/incidenceAngle"][:][indices]
                 if tbs_s.shape[-2] > tbs[-1].shape[-2]:
                     tbs_s = tbs_s[..., ::2, :]
                     eia_s = eia_s[..., ::2]
@@ -511,12 +511,12 @@ def to_xarray_dataset(self, roi=None):
                 eia.append(eia_s)
             if "S6" in input.keys():
                 tbs_s = input["S6/Tc"][:][indices]
-                eia_s = input[f"s2/incidenceangle"][:][indices]
+                eia_s = input[f"S2/incidenceAngle"][:][indices]
                 if tbs_s.shape[-2] > tbs[-1].shape[-2]:
                     tbs_s = tbs_s[..., ::2, :]
                     eia_s = eia_s[..., ::2]
                 tbs.append(tbs_s)
-                eia_s = input[f"s2/incidenceangle"][:][indices]
+                eia_s = input[f"S2/incidenceAngle"][:][indices]
                 eia_s = np.broadcast_to(eia_s, tbs[-1].shape)
                 eia.append(eia_s)
 

gprof_nn/data/preprocessor.py

@@ -227,8 +227,8 @@ def __init__(self, filename):
                 self.first_scan_time
             )
         except AttributeError as e:
-            raise e
-            #raise ValueError(f"The sensor '{sensor}' is not yet supported.")
+            raise ValueError(f"The sensor '{sensor}' is not yet supported.")
+
         # Reread full header.
         self.orbit_header = np.frombuffer(
             self.data, self.sensor.preprocessor_orbit_header, count=1

gprof_nn/data/sim.py

@@ -694,6 +694,10 @@ def process_files(
             Must be one of 'training', 'validation', 'test'.
         include_cmb_precip: Flag to trigger include of surface precip derived solely
              from cmb.
+        lonlat_bounds: Optional coordinate tuple ``(lon_ll, lat_ll, lon_ur, lat_ur)``
+            containing the longitude and latitude coordinates of the lower-left corner
+            (``lon_ll`` and ``lat_ll``) followed by the longitude and latitude coordinates
+            of the upper right corner (``lon_ur``, ``lat_ur``).
     """
     sim_files = sorted(list(path.glob("**/*.sim")))
     files = []

gprof_nn/data/training_data.py

@@ -470,8 +470,10 @@ def load_tbs_1d_xtrack_sim(
 
     biases = (
         biases_full /
-        np.cos(np.deg2rad(EIA_GMI))[None] *
-        np.cos(np.deg2rad(angles.data[..., None]))
+        (
+            np.cos(np.deg2rad(EIA_GMI)) *
+            np.cos(np.deg2rad(angles.data[..., None]))
+        )
     )
 
     return torch.tensor(tbs_full - biases)
@@ -529,7 +531,8 @@ def load_tbs_1d_xtrack_other(
     tbs_full = np.nan * np.zeros((tbs.shape[0], 15), dtype=np.float32)
     tbs_full[:, sensor.gmi_channels] = tbs
     angles = training_data["earth_incidence_angle"].data
-    angles_full = np.broadcast_to(angles[..., None], tbs_full.shape)
+    angles_full = np.nan * np.zeros_like(tbs_full)
+    angles_full[:, sensor.gprof_channels] = angles[..., None]
 
     tbs = torch.tensor(tbs_full.astype("float32"))
     angles = torch.tensor(angles_full.astype("float32"))
@@ -763,7 +766,7 @@ def load_training_data(self, dataset: xr.Dataset) -> Dict[str, torch.Tensor]:
                     size=dataset.samples.size,
                 ).astype(np.float32)
                 tbs = load_tbs_1d_xtrack_sim(dataset, angs, sensor)
-                angs = torch.tensor(angs)
+                angs = torch.tensor(np.broadcast_to(angs[..., None], tbs.shape))
             else:
                 tbs = dataset["brightness_temperatures"].data
                 y_t = dataset[ref_target].data
@@ -1193,7 +1196,7 @@ def load_training_data_3d_other(
         scn_start = rng.integers(0, scene.scans.size - height + 1)
     else:
         pix_start = (scene.pixels.size - width) // 2
-        scn_start = (scene.scns.size - height) // 2
+        scn_start = (scene.scans.size - height) // 2
     pix_end = pix_start + width
     scn_end = scn_start + height
     scene = scene[{"pixels": slice(pix_start, pix_end), "scans": slice(scn_start, scn_end)}]