extract dtypes from underlying duck arrays without coercing to numpy

xarray/core/duck_array_ops.py

@@ -214,26 +214,32 @@ def astype(data, dtype, **kwargs):
 
 
 def asarray(data, xp=np):
+    print(data)
+    print(type(data))
     return data if is_duck_array(data) else xp.asarray(data)
 
 
-def as_shared_dtype(scalars_or_arrays, xp=np):
-    """Cast a arrays to a shared dtype using xarray's type promotion rules."""
-    array_type_cupy = array_type("cupy")
-    if array_type_cupy and any(
-        isinstance(x, array_type_cupy) for x in scalars_or_arrays
-    ):
-        import cupy as cp
-
-        arrays = [asarray(x, xp=cp) for x in scalars_or_arrays]
+def as_duck_array(data, xp=np):
+    if is_duck_array(data):
+        return data
+    elif hasattr(data, "get_duck_array"):
+        # must be a lazy indexing class wrapping a duck array
+        return data.get_duck_array()
     else:
-        arrays = [asarray(x, xp=xp) for x in scalars_or_arrays]
-    # Pass arrays directly instead of dtypes to result_type so scalars
-    # get handled properly.
-    # Note that result_type() safely gets the dtype from dask arrays without
-    # evaluating them.
-    out_type = dtypes.result_type(*arrays)
-    return [astype(x, out_type, copy=False) for x in arrays]
+        array_type_cupy = array_type("cupy")
+        if array_type_cupy and any(isinstance(data, array_type_cupy)):
+            import cupy as cp
+
+            return asarray(data, xp=cp)
+        else:
+            return asarray(data, xp=xp)
+
+
+def as_shared_dtype(scalars_or_arrays, xp=np):
+    """Cast arrays to a shared dtype using xarray's type promotion rules."""
+    duckarrays = [as_duck_array(obj, xp=xp) for obj in scalars_or_arrays]
+    out_type = dtypes.result_type(*duckarrays)
+    return [astype(x, out_type, copy=False) for x in duckarrays]
 
 
 def broadcast_to(array, shape):
@@ -327,7 +333,7 @@ def sum_where(data, axis=None, dtype=None, where=None):
 def where(condition, x, y):
     """Three argument where() with better dtype promotion rules."""
     xp = get_array_namespace(condition)
-    return xp.where(condition, *as_shared_dtype([x, y], xp=xp))
+    return xp.where(condition, *as_shared_dtype([x, y]))
 
 
 def where_method(data, cond, other=dtypes.NA):
@@ -350,14 +356,14 @@ def concatenate(arrays, axis=0):
         arrays[0], np.ndarray
     ):
         xp = get_array_namespace(arrays[0])
-        return xp.concat(as_shared_dtype(arrays, xp=xp), axis=axis)
+        return xp.concat(as_shared_dtype(arrays), axis=axis)
     return _concatenate(as_shared_dtype(arrays), axis=axis)
 
 
 def stack(arrays, axis=0):
     """stack() with better dtype promotion rules."""
     xp = get_array_namespace(arrays[0])
-    return xp.stack(as_shared_dtype(arrays, xp=xp), axis=axis)
+    return xp.stack(as_shared_dtype(arrays), axis=axis)
 
 
 def reshape(array, shape):