Type inference that is closer to NumPy

examples/ndarray/reduce_and_enlarge.py

@@ -14,12 +14,14 @@
 # In particular, note how re-opening a stored expression can adapt to
 # changes in the operands.
 
+import numpy as np
+
 import blosc2
 
 # Create arrays with specific dimensions
 a = blosc2.full((2, 3, 4), 1, urlpath="a.b2nd", mode="w")  # 3D array with dimensions (2, 3, 4)
 b = blosc2.full((2, 4), 2, urlpath="b.b2nd", mode="w")  # 2D array with dimensions (2, 4)
-c = blosc2.full(4, 3, urlpath="c.b2nd", mode="w")  # 1D array with dimensions (4,)
+c = blosc2.full(4, 3, dtype=np.int8, urlpath="c.b2nd", mode="w")  # 1D array with dimensions (4,)
 
 print("Array a:", a[:])
 print("Array b:", b[:])
@@ -28,11 +30,18 @@
 # Define an expression using the arrays above
 # expression = "a.sum() + b * c"
 # expression = "a.sum(axis=1) + b * c"
-expression = "sum(a, axis=1) + b * c"
+# expression = "sum(a, axis=1) + b * c + 0"
+expression = "c + np.int8(0)"
+# expression = "sum(a, axis=1) + b * sin(c)"
 # Define the operands for the expression
-operands = {"a": a, "b": b, "c": c}
+# operands = {"a": a, "b": b, "c": c}
+operands = {"c": c}
 # Create a lazy expression
+print("expression:", expression, type(expression), operands["c"].dtype)
 lazy_expression = blosc2.lazyexpr(expression, operands)
+print(lazy_expression.shape, lazy_expression.dtype)  # Print the shape of the lazy expression
+# lazy_expression = blosc2.lazyexpr(expression)
+print(lazy_expression[:])  # Evaluate and print the result of the lazy expression (should be a 2x4 arr)
 
 # Store and reload the expressions
 url_path = "my_expr.b2nd"  # Define the path for the file
@@ -41,15 +50,5 @@
 url_path = "my_expr.b2nd"  # Define the path for the file
 lazy_expression = blosc2.open(urlpath=url_path)  # Open the saved file
 print(lazy_expression)  # Print the lazy expression
-print(lazy_expression.shape)  # Print the shape of the lazy expression
+print(lazy_expression.shape, lazy_expression.dtype)  # Print the shape of the lazy expression
 print(lazy_expression[:])  # Evaluate and print the result of the lazy expression (should be a 2x4 arr)
-
-# Enlarge the arrays and re-evaluate the expression
-a.resize((3, 3, 4))
-a[2] = 3
-b.resize((3, 4))
-b[2] = 5
-lazy_expression = blosc2.open(urlpath=url_path)  # Open the saved file
-print(lazy_expression.shape)
-result = lazy_expression.compute()
-print(result[:])