Merge remote-tracking branch 'origin/feature/remove_implicit_use_crs' into feature/relative_matching_distance

Author: Chahan Kropf

CHANGELOG.md

@@ -26,6 +26,10 @@ Removed:
 ### Changed
 - Changed the default mask_distance in `util.plot.geo_im_from_array` to 0.03 to avoid white gaps in gridded hazard data with comparably low resolution (>80 centroids per axis) [#1073](https://github.com/CLIMADA-project/climada_python/pull/1073)
 - Increased speed of `util.plot.add_shapes` by avoiding for loops, substantially speeding up `Hazard.plot_intensity` and other functions. [#1073](https://github.com/CLIMADA-project/climada_python/pull/1073)
+- Update `util.coordinates.match_centroids` and `util.coordinates.match_coordinates` so that they also
+accept coordinates that are not defined in degree.
+- Implement cheap test to check that input coordinates at least seem geographic for functions that require
+geographic coordinates as input (e.g. `util.coordinates.dist_to_coast`, `util.coordinates.coord_on_land`).
 - `Hazard.local_exceedance_intensity`, `Hazard.local_return_period` and `Impact.local_exceedance_impact`, `Impact.local_return_period`, using the `climada.util.interpolation` module: New default (no binning), binning on decimals, and faster implementation [#1012](https://github.com/CLIMADA-project/climada_python/pull/1012)
 - World Bank indicator data is now downloaded directly from their API via the function `download_world_bank_indicator`, instead of relying on the `pandas-datareader` package [#1033](https://github.com/CLIMADA-project/climada_python/pull/1033)
 - `Exposures.write_hdf5` pickles geometry data in WKB format, which is faster and more sustainable. [#1051](https://github.com/CLIMADA-project/climada_python/pull/1051)

climada/hazard/test/test_tc_tracks.py

@@ -1350,7 +1350,7 @@ def test_track_land_params(self):
         lon_test = np.array([170, 179.18, 180.05])
         lat_test = np.array([-60, -16.56, -16.85])
         on_land = np.array([False, True, True])
-        lon_shift = np.array([-360, 0, 360])
+        lon_shift = np.array([360, 360, 360])
         # ensure both points are considered on land as is
         np.testing.assert_array_equal(
             u_coord.coord_on_land(lat=lat_test, lon=lon_test), on_land

climada/hazard/trop_cyclone/trop_cyclone_windfields.py

@@ -902,6 +902,8 @@ def _coriolis_parameter(lat: np.ndarray) -> np.ndarray:
     cp : np.ndarray of same shape as input
         Coriolis parameter.
     """
+    u_coord.check_if_geo_coords(lat, 0)
+
     return 2 * V_ANG_EARTH * np.sin(np.radians(np.abs(lat)))