[standalone] Add tests creating ndarrays with tuple dims

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1550,36 +1550,29 @@ impl<'a> Inferencer<'a> {
         }
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
-            let ExprKind::List { elts, .. } = &args[0].node else {
-                return report_error(
-                    format!(
-                        "Expected List literal for first argument of {id}, got {}",
-                        args[0].node.name()
-                    )
-                    .as_str(),
-                    args[0].location,
-                );
-            };
+            // Parse arguments
+            let shape_expr = args.remove(0);
+            let (ndims, shape) =
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
 
-            let ndims = elts.len() as u64;
+            let fill_value = self.fold_expr(args.remove(0))?;
 
-            let arg0 = self.fold_expr(args.remove(0))?;
-            let arg1 = self.fold_expr(args.remove(0))?;
-
-            let ty = arg1.custom.unwrap();
+            // Build the return type
+            let dtype = fill_value.custom.unwrap();
             let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
-            let ret = make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims));
+            let ret = make_ndarray_ty(self.unifier, self.primitives, Some(dtype), Some(ndims));
+
             let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
                 args: vec![
                     FuncArg {
                         name: "shape".into(),
-                        ty: arg0.custom.unwrap(),
+                        ty: shape.custom.unwrap(),
                         default_value: None,
                         is_vararg: false,
                     },
                     FuncArg {
                         name: "fill_value".into(),
-                        ty: arg1.custom.unwrap(),
+                        ty: fill_value.custom.unwrap(),
                         default_value: None,
                         is_vararg: false,
                     },
@@ -1597,7 +1590,7 @@ impl<'a> Inferencer<'a> {
                         location: func.location,
                         node: ExprKind::Name { id: *id, ctx: *ctx },
                     }),
-                    args: vec![arg0, arg1],
+                    args: vec![shape, fill_value],
                     keywords: vec![],
                 },
             }));

nac3core/src/typecheck/typedef/mod.rs

@@ -670,8 +670,8 @@ impl Unifier {
         let num_args = posargs.len() + kwargs.len();
 
         // Now we check the arguments against the parameters,
-        // and depending on what `call_info` is, we might change how the behavior `unify_call()`
-        // in hopes to improve user error messages when type checking fails.
+        // and depending on what `call_info` is, we might change how `unify_call()` behaves
+        // to improve user error messages when type checking fails.
         match operator_info {
             Some(OperatorInfo::IsBinaryOp { self_type, operator }) => {
                 // The call is written in the form of (say) `a + b`.

nac3standalone/demo/src/ndarray.py

@@ -114,12 +114,22 @@ def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])
     output_ndarray_float_1(n)
 
+    dim = (1,)
+    n_tup: ndarray[float, 1] = np_ones(dim)
+    output_ndarray_float_1(n_tup)
+
 def test_ndarray_full():
     n_float: ndarray[float, 1] = np_full([1], 2.0)
     output_ndarray_float_1(n_float)
     n_i32: ndarray[int32, 1] = np_full([1], 2)
     output_ndarray_int32_1(n_i32)
 
+    dim = (1,)
+    n_float_tup: ndarray[float, 1] = np_full(dim, 2.0)
+    output_ndarray_float_1(n_float_tup)
+    n_i32_tup: ndarray[int32, 1] = np_full(dim, 2)
+    output_ndarray_int32_1(n_i32_tup)
+
 def test_ndarray_eye():
     n: ndarray[float, 2] = np_eye(2)
     output_ndarray_float_2(n)