Update with NumPy 1.25 (#382)

* Fix behaviour with new find_common_dtype

* Replace amax and amin aliases by max and min

* Remove exceptions namespace for backward compatibility

* Broaden promote_types exception to TypeError for backward compatibility

* Use both amax and max aliases in handled functions

* Only use TypeError for DtypePromotionError

dev-environment.yml

@@ -7,7 +7,7 @@ dependencies:
   - matplotlib
   - pyproj
   - rasterio>=1.3
-  - numpy=1.24
+  - numpy
   - scipy
   - tqdm
   - xarray

environment.yml

@@ -7,7 +7,7 @@ dependencies:
   - matplotlib
   - pyproj
   - rasterio>=1.3
-  - numpy=1.24
+  - numpy
   - scipy
   - tqdm
   - xarray

geoutils/raster/raster.py

@@ -79,6 +79,8 @@
         "sum",
         "amax",
         "amin",
+        "max",
+        "min",
         "argmax",
         "argmin",
         "mean",
@@ -893,7 +895,7 @@ def _overloading_check(
             dtype2 = rio.dtypes.get_minimum_dtype(other_data)
 
         # Figure out output dtype
-        out_dtype = np.find_common_type([dtype1, dtype2], [])
+        out_dtype = np.promote_types(dtype1, dtype2)
 
         # Figure output nodata
         out_nodata = None
@@ -3091,7 +3093,7 @@ def polygonize(
         gpd_dtypes = ["uint8", "uint16", "int16", "int32", "float32"]
         list_common_dtype_index = []
         for gpd_type in gpd_dtypes:
-            polygonize_dtype = np.find_common_type([gpd_type, self.dtypes[0]], [])
+            polygonize_dtype = np.promote_types(gpd_type, self.dtypes[0])
             if str(polygonize_dtype) in gpd_dtypes:
                 list_common_dtype_index.append(gpd_dtypes.index(gpd_type))
         if len(list_common_dtype_index) == 0:

tests/test_raster.py

@@ -3054,7 +3054,7 @@ def test_ops_2args_expl(self, op: str) -> None:
         r1 = self.r1
         r2 = self.r2
         r3 = getattr(r1, op)(r2)
-        ctype = np.find_common_type([r1.data.dtype, r2.data.dtype], [])
+        ctype = np.promote_types(r1.data.dtype, r2.data.dtype)
         numpy_output = getattr(r1.data.astype(ctype), op)(r2.data.astype(ctype))
         assert isinstance(r3, gu.Raster)
         assert np.all(r3.data == numpy_output)
@@ -3559,7 +3559,12 @@ def test_array_ufunc_1nin_1nout(self, ufunc_str: str, nodata_init: None | str, d
         ufunc = getattr(np, ufunc_str)
 
         # Find the common dtype between the Raster and the most constrained input type (first character is the input)
-        com_dtype = np.find_common_type([dtype] + [ufunc.types[0][0]], [])
+        try:
+            com_dtype = np.promote_types(dtype, ufunc.types[0][0])
+        # The promote_types function raises an error for object dtypes (previously returned by find_common_dtypes)
+        # (TypeError needed for backwards compatibility; also exceptions.DTypePromotionError for NumPy 1.25 and above)
+        except TypeError:
+            com_dtype = np.dtype("O")
 
         # Catch warnings
         with warnings.catch_warnings():
@@ -3618,13 +3623,27 @@ def test_array_ufunc_2nin_1nout(
         ufunc = getattr(np, ufunc_str)
 
         # Find the common dtype between the Raster and the most constrained input type (first character is the input)
-        com_dtype1 = np.find_common_type([dtype1] + [ufunc.types[0][0]], [])
-        com_dtype2 = np.find_common_type([dtype2] + [ufunc.types[0][1]], [])
+        try:
+            com_dtype1 = np.promote_types(dtype1, ufunc.types[0][0])
+        # The promote_types function raises an error for object dtypes (previously returned by find_common_dtypes)
+        # (TypeError needed for backwards compatibility; also exceptions.DTypePromotionError for NumPy 1.25 and above)
+        except TypeError:
+            com_dtype1 = np.dtype("O")
+
+        try:
+            com_dtype2 = np.promote_types(dtype2, ufunc.types[0][1])
+        # The promote_types function raises an error for object dtypes (previously returned by find_common_dtypes)
+        # (TypeError needed for backwards compatibility; also exceptions.DTypePromotionError for NumPy 1.25 and above)
+        except TypeError:
+            com_dtype2 = np.dtype("O")
 
         # If the two input types can be the same type, pass a tuple with the common type of both
-        # Below we ignore datetime and timedelta types "m" and "M"
-        if all(t[0] == t[1] for t in ufunc.types if not ("m" or "M") in t[0:2]):
-            com_dtype_both = np.find_common_type([com_dtype1, com_dtype2], [])
+        # Below we ignore datetime and timedelta types "m" and "M", and int64 types "q" and "Q"
+        if all(t[0] == t[1] for t in ufunc.types if not any(x in t[0:2] for x in ["m", "M", "q", "Q"])):
+            try:
+                com_dtype_both = np.promote_types(com_dtype1, com_dtype2)
+            except TypeError:
+                com_dtype_both = np.dtype("O")
             com_dtype_tuple = (com_dtype_both, com_dtype_both)
 
         # Otherwise, pass the tuple with each common type