Follow NumPy rules for scalar-array operations

Author: FrancescAlted

examples/ndarray/jit-numpy-funcs.py

@@ -18,7 +18,7 @@
 # Create some sample data
 a = blosc2.linspace(0, 1, 10 * 100, dtype="float32", shape=(10, 100))
 b = blosc2.linspace(1, 2, 10 * 100, dtype="float32", shape=(10, 100))
-c = blosc2.linspace(-10, 10, 10, dtype="float32", shape=(100,))
+c = blosc2.linspace(-10, 10, 100, dtype="float32", shape=(100,))
 
 
 # Example 1: Basic usage of the jit decorator with reduction

src/blosc2/lazyexpr.py

@@ -1903,9 +1903,18 @@ def infer_dtype(op, value1, value2):
         if op != "~" and value2.dtype != np.bool_:
             raise ValueError(f"Invalid operand type for {op}: {value2.dtype}")
         return np.dtype(np.bool_)
-    dtype1 = value1.dtype if hasattr(value1, "dtype") else np.array(value1).dtype
-    dtype2 = value2.dtype if hasattr(value2, "dtype") else np.array(value2).dtype
-    return np.result_type(dtype1, dtype2)
+
+    # Follow NumPy rules for scalar-array operations
+    # Create small arrays with the same dtypes and let NumPy's type promotion determine the result type
+    if np.isscalar(value1) and hasattr(value2, "shape"):
+        arr2 = np.array([0], dtype=value2.dtype)
+        return (value1 + arr2).dtype
+    elif np.isscalar(value2) and hasattr(value1, "shape"):
+        arr1 = np.array([0], dtype=value1.dtype)
+        return (arr1 + value2).dtype
+    else:
+        # Both are arrays or both are scalars, use NumPy's type promotion rules
+        return np.result_type(value1, value2)
 
 
 class LazyExpr(LazyArray):

tests/ndarray/test_lazyexpr.py

@@ -1402,3 +1402,24 @@ def test_chain_persistentexpressions():
     le4_.save("expr4.b2nd", mode="w")
     myle4 = blosc2.open("expr4.b2nd")
     assert (myle4[:] == le4[:]).all()
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        (np.ones(10, dtype=np.uint16), 2),
+        (np.ones(10, dtype=np.uint16), np.uint32(2)),
+        (2, np.ones(10, dtype=np.uint16)),
+        (np.uint32(2), np.ones(10, dtype=np.uint16)),
+        (np.ones(10, dtype=np.uint16), 2.0),
+        (np.ones(10, dtype=np.float32), 2.0),
+        (np.ones(10, dtype=np.float32), 2.0j),
+    ],
+)
+def test_scalar_dtypes(values):
+    value1, value2 = values
+    dtype1 = (value1 + value2).dtype
+    avalue1 = blosc2.asarray(value1) if hasattr(value1, "shape") else value1
+    avalue2 = blosc2.asarray(value2) if hasattr(value2, "shape") else value2
+    dtype2 = (avalue1 * avalue2).dtype
+    assert dtype1 == dtype2, f"Expected {dtype1} but got {dtype2}"