Fix CI

gpm/gv/routines.py

@@ -416,7 +416,7 @@ def plot_quicklook(
     mask_sr = da_sr > z_min_threshold_sr
 
     # Retrieve SR extent
-    sr_extent = ds_sr.gpm.extent()
+    sr_extent = ds_sr.gpm.extent()  # noqa
     gr_extent = ds_gr.xradar_dev.extent()
 
     # Create figure

gpm/utils/pyresample.py

@@ -35,12 +35,8 @@
 def remap(src_ds, dst_ds, radius_of_influence=20000, fill_value=np.nan):
     """Remap dataset to another one using nearest-neighbour."""
     from gpm.checks import get_spatial_dimensions
-    from gpm.dataset.crs import (
-        _get_crs_coordinates,
-        _get_swath_dim_coords,
-        _get_proj_dim_coords,
-        set_dataset_crs
-    )
+    from gpm.dataset.crs import _get_crs_coordinates, _get_proj_dim_coords, _get_swath_dim_coords, set_dataset_crs
+
     try:
         from pyresample.future.resamplers.nearest import KDTreeNearestXarrayResampler
     except ImportError:
@@ -59,45 +55,28 @@ def remap(src_ds, dst_ds, radius_of_influence=20000, fill_value=np.nan):
     dst_crs_coords = _get_crs_coordinates(dst_ds)[0]
 
     # Rename dimensions to x, y for pyresample compatibility
-<<<<<<< HEAD
     x_dim, y_dim = get_spatial_dimensions(src_ds)
     src_ds = src_ds.swap_dims({y_dim: "y", x_dim: "x"})
 
     # Define spatial coordinates of new object
-    if dst_ds.gpm.is_orbit: # SwathDefinition
-        x_coord, y_coord = _get_swath_dim_coords(dst_ds) # dst_ds.gpm.x, # dst_ds.gpm.y
+    if dst_ds.gpm.is_orbit:  # SwathDefinition
+        x_coord, y_coord = _get_swath_dim_coords(dst_ds)  # dst_ds.gpm.x, # dst_ds.gpm.y
         dst_spatial_coords = {
-            x_coord: xr.DataArray(dst_ds[x_coord].data,
-                                 dims=list(dst_ds[x_coord].dims),
-                                 attrs=dst_ds[x_coord].attrs),
-            y_coord: xr.DataArray(dst_ds[y_coord].data,
-                                 dims=list(dst_ds[y_coord].dims),
-                                 attrs=dst_ds[y_coord].attrs),
+            x_coord: xr.DataArray(dst_ds[x_coord].data, dims=list(dst_ds[x_coord].dims), attrs=dst_ds[x_coord].attrs),
+            y_coord: xr.DataArray(dst_ds[y_coord].data, dims=list(dst_ds[y_coord].dims), attrs=dst_ds[y_coord].attrs),
         }
-    else: # AreaDefinition
+    else:  # AreaDefinition
         x_arr, y_arr = dst_area.get_proj_coords()
-        x_coord, y_coord = _get_proj_dim_coords(dst_ds) # dst_ds.gpm.x, # dst_ds.gpm.y
+        x_coord, y_coord = _get_proj_dim_coords(dst_ds)  # dst_ds.gpm.x, # dst_ds.gpm.y
         dst_spatial_coords = {
-            x_coord: xr.DataArray(x_arr,
-                                  dims=list(dst_ds[x_coord].dims),
-                                  attrs=dst_ds[x_coord].attrs),
-            y_coord: xr.DataArray(y_arr,
-                                  dims=list(dst_ds[y_coord].dims),
-                                  attrs=dst_ds[y_coord].attrs),
+            x_coord: xr.DataArray(x_arr, dims=list(dst_ds[x_coord].dims), attrs=dst_ds[x_coord].attrs),
+            y_coord: xr.DataArray(y_arr, dims=list(dst_ds[y_coord].dims), attrs=dst_ds[y_coord].attrs),
         }
         # Update units attribute if was rad or radians for geostationary data !
         if dst_spatial_coords[x_coord].attrs.get("units", "") in ["rad", "radians"]:
             dst_spatial_coords[x_coord].attrs["units"] = "deg"
             dst_spatial_coords[y_coord].attrs["units"] = "deg"
 
-=======
-    if src_ds.gpm.is_orbit:
-        src_ds = src_ds.swap_dims({"cross_track": "y", "along_track": "x"})
-    elif not np.all(np.isin(["x", "y"], list(src_ds.dims))):
-        # TODO: GENERALIZE to allow also latitude, longitude !
-        src_ds = src_ds.swap_dims({"lat": "y", "lon": "x"})
-
->>>>>>> e740a7b5f4b1f3d796f1207a41d1706653876c2a
     # Define resampler
     resampler = KDTreeNearestXarrayResampler(src_area, dst_area)
     resampler.precompute(radius_of_influence=radius_of_influence)
@@ -121,16 +100,17 @@ def remap(src_ds, dst_ds, radius_of_influence=20000, fill_value=np.nan):
 
     # Revert to original spatial dimensions (of destination dataset)
     x_dim, y_dim = get_spatial_dimensions(dst_ds)
-    ds = ds.swap_dims({"y": y_dim, "x": x_dim })
+    ds = ds.swap_dims({"y": y_dim, "x": x_dim})
 
     # Add spatial coordinates
     ds = ds.assign_coords(dst_spatial_coords)
 
     # Add destination crs
-    ds = set_dataset_crs(ds,
-                         crs=dst_area.crs,
-                         grid_mapping_name=dst_crs_coords,
-                         )
+    ds = set_dataset_crs(
+        ds,
+        crs=dst_area.crs,
+        grid_mapping_name=dst_crs_coords,
+    )
     # Coordinates specifics to gpm-api
     gpm_api_coords = ["gpm_id", "gpm_time", "gpm_granule_id", "gpm_along_track_id", "gpm_cross_track_id"]
     gpm_api_coords_dict = {c: dst_ds.reset_coords()[c] for c in gpm_api_coords if c in dst_ds.coords}