Add dtype conversion to SimpleProxy

Author: lshaw8317

src/blosc2/lazyexpr.py

@@ -2530,7 +2530,7 @@ def where(self, value1=None, value2=None):
         # This just acts as a 'decorator' for the existing expression
         if value1 is not None and value2 is not None:
             # Guess the outcome dtype for value1 and value2
-            dtype = np.result_type(value1, value2)
+            dtype = blosc2.result_type(value1, value2)
             args = {"_where_x": value1, "_where_y": value2}
         elif value1 is not None:
             if hasattr(value1, "dtype"):

src/blosc2/linalg.py

@@ -100,7 +100,7 @@ def matmul(x1: blosc2.Array, x2: blosc2.NDArray, **kwargs: Any) -> blosc2.NDArra
     n, k = x1.shape[-2:]
     m = x2.shape[-1]
     result_shape = np.broadcast_shapes(x1.shape[:-2], x2.shape[:-2]) + (n, m)
-    result = blosc2.zeros(result_shape, dtype=np.result_type(x1, x2), **kwargs)
+    result = blosc2.zeros(result_shape, dtype=blosc2.result_type(x1, x2), **kwargs)
 
     if 0 not in result.shape + x1.shape + x2.shape:  # if any array is empty, return array of 0s
         p, q = result.chunks[-2:]
@@ -227,7 +227,7 @@ def tensordot(
         raise ValueError("x1 and x2 must have same shapes along reduction dimensions")
 
     result_shape = tuple(x1shape[a_keep]) + tuple(x2shape[b_keep])
-    result = blosc2.zeros(result_shape, dtype=np.result_type(x1, x2), **kwargs)
+    result = blosc2.zeros(result_shape, dtype=blosc2.result_type(x1, x2), **kwargs)
 
     op_chunks = [
         slice_to_chunktuple(slice(0, s, 1), c) for s, c in zip(x1shape[a_axes], a_chunks_red, strict=True)
@@ -363,7 +363,7 @@ def vecdot(x1: blosc2.NDArray, x2: blosc2.NDArray, axis: int = -1, **kwargs) ->
         raise ValueError("x1 and x2 must have same shapes along reduction dimensions")
 
     result_shape = np.broadcast_shapes(x1shape[a_keep], x2shape[b_keep])
-    result = blosc2.zeros(result_shape, dtype=np.result_type(x1, x2), **kwargs)
+    result = blosc2.zeros(result_shape, dtype=blosc2.result_type(x1, x2), **kwargs)
 
     res_chunks = [
         slice_to_chunktuple(s, c)

src/blosc2/proxy.py

@@ -569,6 +569,18 @@ def __getitem__(self, item: slice | list[slice]) -> np.ndarray:
         return nparr[self.field]
 
 
+def _convert_dtype(dt: str | np.typing.DTypeLike):
+    """
+    Attempts to convert to blosc2.dtype (i.e. numpy dtype)
+    """
+    try:
+        return np.dtype(dt)
+    except TypeError:  # likely passed e.g. a torch.float64
+        return np.dtype(str(dt).split(".")[1])
+    except Exception as e:
+        raise TypeError("Could not parse dtype arg {dt}.") from e
+
+
 class SimpleProxy(blosc2.Operand):
     """
     Simple proxy for any data container to be used with the compute engine.
@@ -597,7 +609,7 @@ def __init__(self, src, chunks: tuple | None = None, blocks: tuple | None = None
         if not hasattr(src, "__getitem__"):
             raise TypeError("The source must have a __getitem__ method")
         self._src = src
-        self._dtype = src.dtype
+        self._dtype = _convert_dtype(src.dtype)
         self._shape = src.shape
         # Compute reasonable values for chunks and blocks
         cparams = blosc2.CParams(clevel=0)