FIX: Updates code to fix scalar warnings.

pyart/map/gates_to_grid.py

@@ -114,9 +114,9 @@ def map_gates_to_grid(
         skip_transform = True
 
     if grid_origin_alt is None:
-        try:
-            grid_origin_alt = float(radars[0].altitude["data"])
-        except TypeError:
+        if len(radars[0].altitude["data"]) == 1:
+            grid_origin_alt = float(radars[0].altitude["data"].item())
+        else:
             grid_origin_alt = np.mean(radars[0].altitude["data"])
 
     # convert input h_factor and dist_factor from scalar, tuple, or list to array
@@ -242,10 +242,10 @@ def _find_projparams(grid_origin, radars, grid_projection):
 
     # parse grid_origin
     if grid_origin is None:
-        try:
-            lat = float(radars[0].latitude["data"])
-            lon = float(radars[0].longitude["data"])
-        except TypeError:
+        if len(radars[0].latitude["data"]) == 1 & len(radars[0].longitude["data"]) == 1:
+            lat = float(radars[0].latitude["data"].item())
+            lon = float(radars[0].longitude["data"].item())
+        else:
             lat = np.mean(radars[0].latitude["data"])
             lon = np.mean(radars[0].longitude["data"])
         grid_origin = (lat, lon)
@@ -293,10 +293,15 @@ def _find_offsets(radars, projparams, grid_origin_alt):
         x_disp, y_disp = geographic_to_cartesian(
             radar.longitude["data"], radar.latitude["data"], projparams
         )
-        try:
-            z_disp = float(radar.altitude["data"]) - grid_origin_alt
-            offsets.append((z_disp, float(y_disp), float(x_disp)))
-        except TypeError:
+        if (
+            len(radar.latitude["data"])
+            == 1 & len(radar.longitude["data"])
+            == 1 & len(radar.altitude["data"])
+            == 1
+        ):
+            z_disp = float(radar.altitude["data"].item()) - grid_origin_alt
+            offsets.append((z_disp, float(y_disp.item()), float(x_disp.item())))
+        else:
             z_disp = np.mean(radar.altitude["data"]) - grid_origin_alt
             offsets.append((z_disp, np.mean(y_disp), np.mean(x_disp)))
     return offsets

pyart/map/grid_mapper.py

@@ -481,19 +481,19 @@ def map_to_grid(
 
     # find the grid origin if not given
     if grid_origin is None:
-        try:
-            lat = float(radars[0].latitude["data"])
-            lon = float(radars[0].longitude["data"])
-        except TypeError:
+        if len(radars[0].latitude["data"]) == 1 & len(radars[0].longitude["data"]) == 1:
+            lat = float(radars[0].latitude["data"].item())
+            lon = float(radars[0].longitude["data"].item())
+        else:
             lat = np.mean(radars[0].latitude["data"])
             lon = np.mean(radars[0].longitude["data"])
         grid_origin = (lat, lon)
     grid_origin_lat, grid_origin_lon = grid_origin
 
     if grid_origin_alt is None:
-        try:
-            grid_origin_alt = float(radars[0].altitude["data"])
-        except TypeError:
+        if len(radars[0].altitude["data"]) == 1:
+            grid_origin_alt = float(radars[0].altitude["data"].item())
+        else:
             grid_origin_alt = np.mean(radars[0].altitude["data"])
 
     # fields which should be mapped, None for fields which are in all radars
@@ -544,10 +544,15 @@ def map_to_grid(
         x_disp, y_disp = geographic_to_cartesian(
             radar.longitude["data"], radar.latitude["data"], projparams
         )
-        try:
-            z_disp = float(radar.altitude["data"]) - grid_origin_alt
-            offsets.append((z_disp, float(y_disp), float(x_disp)))
-        except TypeError:
+        if (
+            len(radar.latitude["data"])
+            == 1 & len(radar.longitude["data"])
+            == 1 & len(radar.altitude["data"])
+            == 1
+        ):
+            z_disp = float(radar.altitude["data"].item()) - grid_origin_alt
+            offsets.append((z_disp, float(y_disp.item()), float(x_disp.item())))
+        else:
             z_disp = np.mean(radar.altitude["data"]) - grid_origin_alt
             offsets.append((z_disp, np.mean(y_disp), np.mean(x_disp)))
 

pyart/retrieve/kdp_proc.py

@@ -301,7 +301,7 @@ def _kdp_estimation_backward_fixed(
         p = np.dot((identity_i - np.dot(k, h_plus)), p_pred)
 
         # Fill the output
-        kdp[ii] = s[0]
+        kdp[ii] = s[0].item()
         kdp_error[ii] = p[0, 0]
 
     # Shift
@@ -407,9 +407,9 @@ def _kdp_estimation_forward_fixed(
         p = np.dot((identity_i - np.dot(k, h_plus)), p_pred)
 
         # Fill the output
-        kdp[ii] = s[0]
+        kdp[ii] = s[0].item()
         kdp_error[ii] = p[0, 0]
-        phidp[ii] = s[2]
+        phidp[ii] = s[2].item()
 
     # Shift
     dummy = np.copy(kdp)

pyart/util/radar_utils.py

@@ -573,12 +573,12 @@ def join_radar(radar1, radar2):
         == 1 & len(radar2.altitude["data"])
         == 1
     ):
-        lat1 = float(radar1.latitude["data"])
-        lon1 = float(radar1.longitude["data"])
-        alt1 = float(radar1.altitude["data"])
-        lat2 = float(radar2.latitude["data"])
-        lon2 = float(radar2.longitude["data"])
-        alt2 = float(radar2.altitude["data"])
+        lat1 = float(radar1.latitude["data"].item())
+        lon1 = float(radar1.longitude["data"].item())
+        alt1 = float(radar1.altitude["data"].item())
+        lat2 = float(radar2.latitude["data"].item())
+        lon2 = float(radar2.longitude["data"].item())
+        alt2 = float(radar2.altitude["data"].item())
 
         if (lat1 != lat2) or (lon1 != lon2) or (alt1 != alt2):
             ones1 = np.ones(len(radar1.time["data"]), dtype="float32")

tests/core/test_grid.py

@@ -92,7 +92,7 @@ def test_grid_to_xarray():
     time = np.array([netCDF4.num2date(grid.time["data"][0], grid.time["units"])])
 
     # Check dimensions
-    assert ds.dims == {"time": 1, "z": 2, "y": 400, "x": 320, "nradar": 1}
+    assert ds.sizes == {"time": 1, "z": 2, "y": 400, "x": 320, "nradar": 1}
 
     # Check coordinate data
     assert np.array_equal(ds.x.data, grid.x["data"])