core/builtins: Refactored numpy builtins to accept scalar and ndarrays

nac3core/src/codegen/numpy.rs

@@ -451,8 +451,6 @@ fn ndarray_broadcast_fill<'ctx, 'a, G, ValueFn>(
                 }
             };
 
-            debug_assert_eq!(lhs_elem.get_type(), rhs_elem.get_type());
-
             value_fn(generator, ctx, (lhs_elem, rhs_elem))
         },
     )?;

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [127]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [222]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar116]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar116\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar211]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar211\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [129]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [224]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [134]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [229]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar115, typevar116]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar115\", \"typevar116\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar210, typevar211]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar210\", \"typevar211\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [135]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [230]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [143]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [238]\n}\n",
 ]

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -14,17 +14,7 @@ use crate::{
     },
 };
 use itertools::{Itertools, izip};
-use nac3parser::ast::{
+use nac3parser::ast::{self, fold::{self, Fold}, Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef};
-    self,
-    fold::{self, Fold},
-    Arguments,
-    Comprehension,
-    ExprContext,
-    ExprKind,
-    Located,
-    Location,
-    StrRef
-};
 
 #[cfg(test)]
 mod test;
@@ -860,57 +850,194 @@ impl<'a> Inferencer<'a> {
                 },
             }))
         }
-        // int64 is special because its argument can be a constant larger than int32
+
-        if id == &"int64".into() && args.len() == 1 {
+        if [
-            if let ExprKind::Constant { value: ast::Constant::Int(val), kind } =
+            "int32",
-                &args[0].node
+            "float",
-            {
+            "bool",
-                let custom = Some(self.primitives.int64);
+            "np_isnan",
-                let v: Result<i64, _> = (*val).try_into();
+            "np_isinf",
-                return if v.is_ok() {
+        ].iter().any(|fun_id| id == &(*fun_id).into()) && args.len() == 1 {
+            let target_ty = if id == &"int32".into() {
+                self.primitives.int32
+            } else if id == &"float".into() {
+                self.primitives.float
+            } else if id == &"bool".into() || id == &"np_isnan".into() || id == &"np_isinf".into() {
+                self.primitives.bool
+            } else { unreachable!() };
+
+            let arg0 = self.fold_expr(args.remove(0))?;
+            let arg0_ty = arg0.custom.unwrap();
+
+            let ret = if arg0_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+                let (_, ndarray_ndims) = unpack_ndarray_var_tys(self.unifier, arg0_ty);
+
+                make_ndarray_ty(self.unifier, self.primitives, Some(target_ty), Some(ndarray_ndims))
+            } else {
+                target_ty
+            };
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg {
+                        name: "n".into(),
+                        ty: arg0.custom.unwrap(),
+                        default_value: None,
+                    },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: ctx.clone() },
+                    }),
+                    args: vec![arg0],
+                    keywords: vec![],
+                },
+            }))
+        }
+
+        if [
+            "np_arctan2",
+            "np_copysign",
+            "np_fmax",
+            "np_fmin",
+            "np_ldexp",
+            "np_hypot",
+            "np_nextafter",
+        ].iter().any(|fun_id| id == &(*fun_id).into()) && args.len() == 2 {
+            let target_ty = self.primitives.float;
+
+            let arg0 = self.fold_expr(args.remove(0))?;
+            let arg0_ty = arg0.custom.unwrap();
+            let arg1 = self.fold_expr(args.remove(0))?;
+            let arg1_ty = arg1.custom.unwrap();
+
+            let arg0_dtype = if arg0_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+                unpack_ndarray_var_tys(self.unifier, arg0_ty).0
+            } else {
+                arg0_ty
+            };
+
+            let arg1_dtype = if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+                unpack_ndarray_var_tys(self.unifier, arg1_ty).0
+            } else {
+                arg1_ty
+            };
+            let expected_arg1_dtype = if id == &"np_ldexp".into() {
+                self.primitives.int32
+            } else {
+                arg0_dtype
+            };
+            if !self.unifier.unioned(arg1_dtype, expected_arg1_dtype) {
+                return report_error(
+                    format!(
+                        "Expected {} for second argument of {id}, got {}",
+                        self.unifier.stringify(expected_arg1_dtype),
+                        self.unifier.stringify(arg1_dtype),
+                    ).as_str(),
+                    arg0.location,
+                )
+            }
+
+            let ret = if [
+                &arg0_ty,
+                &arg1_ty,
+            ].into_iter().any(|arg_ty| arg_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray)) {
+                // typeof_ndarray_broadcast requires both dtypes to be the same, but ldexp accepts
+                // (float, int32), so convert it to align with the dtype of the first arg
+                let arg1_ty = if id == &"np_ldexp".into() {
+                    if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, arg1_ty);
+
+                        make_ndarray_ty(self.unifier, self.primitives, Some(target_ty), Some(ndims))
+                    } else {
+                        target_ty
+                    }
+                } else {
+                    arg1_ty
+                };
+
+                match typeof_ndarray_broadcast(self.unifier, self.primitives, arg0_ty, arg1_ty) {
+                    Ok(broadcasted_ty) => broadcasted_ty,
+                    Err(err) => return report_error(err.as_str(), location),
+                }
+            } else {
+                target_ty
+            };
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg {
+                        name: "x1".into(),
+                        ty: arg0.custom.unwrap(),
+                        default_value: None,
+                    },
+                    FuncArg {
+                        name: "x2".into(),
+                        ty: arg1.custom.unwrap(),
+                        default_value: None,
+                    },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: ctx.clone() },
+                    }),
+                    args: vec![arg0, arg1],
+                    keywords: vec![],
+                },
+            }))
+        }
+
+        // int64, uint32 and uint64 are special because their argument can be a constant outside the 
+        // range of int32s
+        if [
+            "int64",
+            "uint32",
+            "uint64",
+        ].iter().any(|fun_id| id == &(*fun_id).into()) && args.len() == 1 {
+            let target_ty = if id == &"int64".into() {
+                self.primitives.int64
+            } else if id == &"uint32".into() {
+                self.primitives.uint32
+            } else if id == &"uint64".into() {
+                self.primitives.uint64
+            } else { unreachable!() };
+
+            // Handle constants first to ensure that their types are not defaulted to int32, which
+            // causes an "Integer out of bound" error
+            if let ExprKind::Constant { 
+                value: ast::Constant::Int(val),
+                kind 
+            } = &args[0].node {
+                let conv_is_ok = if self.unifier.unioned(target_ty, self.primitives.int64) {
+                    i64::try_from(*val).is_ok()
+                } else if self.unifier.unioned(target_ty, self.primitives.uint32) {
+                    u32::try_from(*val).is_ok()
+                } else if self.unifier.unioned(target_ty, self.primitives.uint64) {
+                    u64::try_from(*val).is_ok()
+                } else { unreachable!() };
+        
+                return if conv_is_ok {
                     Ok(Some(Located {
                         location: args[0].location,
-                        custom,
+                        custom: Some(target_ty),
-                        node: ExprKind::Constant {
-                            value: ast::Constant::Int(*val),
-                            kind: kind.clone(),
-                        },
-                    }))
-                } else {
-                    report_error("Integer out of bound", args[0].location)
-                }
-            }
-        }
-        if id == &"uint32".into() && args.len() == 1 {
-            if let ExprKind::Constant { value: ast::Constant::Int(val), kind } =
-                &args[0].node
-            {
-                let custom = Some(self.primitives.uint32);
-                let v: Result<u32, _> = (*val).try_into();
-                return if v.is_ok() {
-                     Ok(Some(Located {
-                        location: args[0].location,
-                        custom,
-                        node: ExprKind::Constant {
-                            value: ast::Constant::Int(*val),
-                            kind: kind.clone(),
-                        },
-                    }))
-                } else {
-                    report_error("Integer out of bound", args[0].location)
-                }
-            }
-        }
-        if id == &"uint64".into() && args.len() == 1 {
-            if let ExprKind::Constant { value: ast::Constant::Int(val), kind } =
-            &args[0].node
-            {
-                let custom = Some(self.primitives.uint64);
-                let v: Result<u64, _> = (*val).try_into();
-                return if v.is_ok() {
-                    Ok(Some(Located {
-                        location: args[0].location,
-                        custom,
                         node: ExprKind::Constant {
                             value: ast::Constant::Int(*val),
                             kind: kind.clone(),
@@ -920,6 +1047,43 @@ impl<'a> Inferencer<'a> {
                     report_error("Integer out of bound", args[0].location)
                 }
             }
+
+            let arg0 = self.fold_expr(args.remove(0))?;
+            let arg0_ty = arg0.custom.unwrap();
+
+            let ret = if arg0_ty.obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+                let (_, ndarray_ndims) = unpack_ndarray_var_tys(self.unifier, arg0_ty);
+
+                make_ndarray_ty(self.unifier, self.primitives, Some(target_ty), Some(ndarray_ndims))
+            } else {
+                target_ty
+            };
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg {
+                        name: "n".into(),
+                        ty: arg0.custom.unwrap(),
+                        default_value: None,
+                    },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: ctx.clone() },
+                    }),
+                    args: vec![arg0],
+                    keywords: vec![],
+                },
+            }))
         }
 
         // 1-argument ndarray n-dimensional creation functions

nac3standalone/demo/interpret_demo.py

@@ -58,11 +58,38 @@ class _NDArrayDummy(Generic[T, N]):
 # https://stackoverflow.com/questions/67803260/how-to-create-a-type-alias-with-a-throw-away-generic
 NDArray = Union[npt.NDArray[T], _NDArrayDummy[T, N]]
 
-def round_away_zero(x):
+def _bool(x):
-    if x >= 0.0:
+    if isinstance(x, np.ndarray):
-        return math.floor(x + 0.5)
+        return np.bool_(x)
     else:
-        return math.ceil(x - 0.5)
+        return bool(x)
+
+def _float(x):
+    if isinstance(x, np.ndarray):
+        return np.float_(x)
+    else:
+        return float(x)
+
+def round_away_zero(x):
+    if isinstance(x, np.ndarray):
+        return np.vectorize(round_away_zero)(x)
+    else:
+        if x >= 0.0:
+            return math.floor(x + 0.5)
+        else:
+            return math.ceil(x - 0.5)
+
+def _floor(x):
+    if isinstance(x, np.ndarray):
+        return np.vectorize(_floor)(x)
+    else:
+        return math.floor(x)
+
+def _ceil(x):
+    if isinstance(x, np.ndarray):
+        return np.vectorize(_ceil)(x)
+    else:
+        return math.ceil(x)
 
 def patch(module):
     def dbl_nan():
@@ -112,6 +139,8 @@ def patch(module):
     module.int64 = int64
     module.uint32 = uint32
     module.uint64 = uint64
+    module.bool = _bool
+    module.float = _float
     module.TypeVar = TypeVar
     module.ConstGeneric = ConstGeneric
     module.Generic = Generic
@@ -125,11 +154,11 @@ def patch(module):
     module.round = round_away_zero
     module.round64 = round_away_zero
     module.np_round = np.round
-    module.floor = math.floor
+    module.floor = _floor
-    module.floor64 = math.floor
+    module.floor64 = _floor
     module.np_floor = np.floor
-    module.ceil = math.ceil
+    module.ceil = _ceil
-    module.ceil64 = math.ceil
+    module.ceil64 = _ceil
     module.np_ceil = np.ceil
 
     # NumPy ndarray functions

nac3standalone/demo/src/ndarray.py

@@ -1,3 +1,11 @@
+@extern
+def dbl_nan() -> float:
+    ...
+
+@extern
+def dbl_inf() -> float:
+    ...
+
 @extern
 def output_bool(x: bool):
     ...
@@ -6,6 +14,18 @@ def output_bool(x: bool):
 def output_int32(x: int32):
     ...
 
+@extern
+def output_int64(x: int64):
+    ...
+
+@extern
+def output_uint32(x: uint32):
+    ...
+
+@extern
+def output_uint64(x: uint64):
+    ...
+
 @extern
 def output_float64(x: float):
     ...
@@ -24,6 +44,21 @@ def output_ndarray_int32_2(n: ndarray[int32, Literal[2]]):
         for c in range(len(n[r])):
             output_int32(n[r][c])
 
+def output_ndarray_int64_2(n: ndarray[int64, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_int64(n[r][c])
+
+def output_ndarray_uint32_2(n: ndarray[uint32, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_uint32(n[r][c])
+
+def output_ndarray_uint64_2(n: ndarray[uint64, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_uint64(n[r][c])
+
 def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
     for i in range(len(n)):
         output_float64(n[i])
@@ -649,6 +684,586 @@ def test_ndarray_ge_broadcast_rhs_scalar():
     output_ndarray_float_2(x)
     output_ndarray_bool_2(y)
 
+def test_ndarray_int32():
+    x = np_identity(2)
+    y = int32(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int32_2(y)
+
+def test_ndarray_int64():
+    x = np_identity(2)
+    y = int64(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int64_2(y)
+
+def test_ndarray_uint32():
+    x = np_identity(2)
+    y = uint32(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_uint32_2(y)
+
+def test_ndarray_uint64():
+    x = np_identity(2)
+    y = uint64(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_uint64_2(y)
+
+def test_ndarray_float():
+    x = np_full([2, 2], 1)
+    y = float(x)
+
+    output_ndarray_int32_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_bool():
+    x = np_identity(2)
+    y = bool(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_round():
+    x = np_identity(2)
+    xf32 = round(x)
+    xf64 = round64(x)
+    xff = np_round(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int32_2(xf32)
+    output_ndarray_int64_2(xf64)
+    output_ndarray_float_2(xff)
+
+def test_ndarray_floor():
+    x = np_identity(2)
+    xf32 = floor(x)
+    xf64 = floor64(x)
+    xff = np_floor(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int32_2(xf32)
+    output_ndarray_int64_2(xf64)
+    output_ndarray_float_2(xff)
+
+def test_ndarray_ceil():
+    x = np_identity(2)
+    xf32 = ceil(x)
+    xf64 = ceil64(x)
+    xff = np_ceil(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int32_2(xf32)
+    output_ndarray_int64_2(xf64)
+    output_ndarray_float_2(xff)
+
+def test_ndarray_abs():
+    x = np_identity(2)
+    y = abs(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_isnan():
+    x = np_identity(2)
+    x_isnan = np_isnan(x)
+    y = np_full([2, 2], dbl_nan())
+    y_isnan = np_isnan(y)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(x_isnan)
+    output_ndarray_float_2(y)
+    output_ndarray_bool_2(y_isnan)
+
+def test_ndarray_isinf():
+    x = np_identity(2)
+    x_isinf = np_isinf(x)
+    y = np_full([2, 2], dbl_inf())
+    y_isinf = np_isinf(y)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(x_isinf)
+    output_ndarray_float_2(y)
+    output_ndarray_bool_2(y_isinf)
+
+def test_ndarray_sin():
+    x = np_identity(2)
+    y = np_sin(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_cos():
+    x = np_identity(2)
+    y = np_cos(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_exp():
+    x = np_identity(2)
+    y = np_exp(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_exp2():
+    x = np_identity(2)
+    y = np_exp2(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_log():
+    x = np_identity(2)
+    y = np_log(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_log10():
+    x = np_identity(2)
+    y = np_log10(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_log2():
+    x = np_identity(2)
+    y = np_log2(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_fabs():
+    x = -np_identity(2)
+    y = np_fabs(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sqrt():
+    x = np_identity(2)
+    y = np_sqrt(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_rint():
+    x = np_identity(2)
+    y = np_rint(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_tan():
+    x = np_identity(2)
+    y = np_tan(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arcsin():
+    x = np_identity(2)
+    y = np_arcsin(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arccos():
+    x = np_identity(2)
+    y = np_arccos(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arctan():
+    x = np_identity(2)
+    y = np_arctan(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sinh():
+    x = np_identity(2)
+    y = np_sinh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_cosh():
+    x = np_identity(2)
+    y = np_cosh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_tanh():
+    x = np_identity(2)
+    y = np_tanh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arcsinh():
+    x = np_identity(2)
+    y = np_arcsinh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arccosh():
+    x = np_identity(2)
+    y = np_arccosh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arctanh():
+    x = np_identity(2)
+    y = np_arctanh(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_expm1():
+    x = np_identity(2)
+    y = np_expm1(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_cbrt():
+    x = np_identity(2)
+    y = np_cbrt(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_erf():
+    x = np_identity(2)
+    y = sp_spec_erf(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_erfc():
+    x = np_identity(2)
+    y = sp_spec_erfc(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_gamma():
+    x = np_identity(2)
+    y = sp_spec_gamma(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_gammaln():
+    x = np_identity(2)
+    y = sp_spec_gammaln(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_j0():
+    x = np_identity(2)
+    y = sp_spec_j0(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_j1():
+    x = np_identity(2)
+    y = sp_spec_j1(x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_arctan2():
+    x = np_identity(2)
+    zeros = np_zeros([2, 2])
+    ones = np_ones([2, 2])
+    atan2_x_zeros = np_arctan2(x, zeros)
+    atan2_x_ones = np_arctan2(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(zeros)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(atan2_x_zeros)
+    output_ndarray_float_2(atan2_x_ones)
+
+def test_ndarray_arctan2_broadcast():
+    x = np_identity(2)
+    atan2_x_zeros = np_arctan2(x, np_zeros([2]))
+    atan2_x_ones = np_arctan2(x, np_ones([2]))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(atan2_x_zeros)
+    output_ndarray_float_2(atan2_x_ones)
+
+def test_ndarray_arctan2_broadcast_lhs_scalar():
+    x = np_identity(2)
+    atan2_x_zeros = np_arctan2(0.0, x)
+    atan2_x_ones = np_arctan2(1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(atan2_x_zeros)
+    output_ndarray_float_2(atan2_x_ones)
+
+def test_ndarray_arctan2_broadcast_rhs_scalar():
+    x = np_identity(2)
+    atan2_x_zeros = np_arctan2(x, 0.0)
+    atan2_x_ones = np_arctan2(x, 1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(atan2_x_zeros)
+    output_ndarray_float_2(atan2_x_ones)
+
+def test_ndarray_copysign():
+    x = np_identity(2)
+    ones = np_ones([2, 2])
+    negones = np_full([2, 2], -1.0)
+    copysign_x_ones = np_copysign(x, ones)
+    copysign_x_negones = np_copysign(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(negones)
+    output_ndarray_float_2(copysign_x_ones)
+    output_ndarray_float_2(copysign_x_negones)
+
+def test_ndarray_copysign_broadcast():
+    x = np_identity(2)
+    copysign_x_ones = np_copysign(x, np_ones([2]))
+    copysign_x_negones = np_copysign(x, np_full([2], -1.0))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(copysign_x_ones)
+    output_ndarray_float_2(copysign_x_negones)
+
+def test_ndarray_copysign_broadcast_lhs_scalar():
+    x = np_identity(2)
+    copysign_x_ones = np_copysign(1.0, x)
+    copysign_x_negones = np_copysign(-1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(copysign_x_ones)
+    output_ndarray_float_2(copysign_x_negones)
+
+def test_ndarray_copysign_broadcast_rhs_scalar():
+    x = np_identity(2)
+    copysign_x_ones = np_copysign(x, 1.0)
+    copysign_x_negones = np_copysign(x, -1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(copysign_x_ones)
+    output_ndarray_float_2(copysign_x_negones)
+
+def test_ndarray_fmax():
+    x = np_identity(2)
+    ones = np_ones([2, 2])
+    negones = np_full([2, 2], -1.0)
+    fmax_x_ones = np_fmax(x, ones)
+    fmax_x_negones = np_fmax(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(negones)
+    output_ndarray_float_2(fmax_x_ones)
+    output_ndarray_float_2(fmax_x_negones)
+
+def test_ndarray_fmax_broadcast():
+    x = np_identity(2)
+    fmax_x_ones = np_fmax(x, np_ones([2]))
+    fmax_x_negones = np_fmax(x, np_full([2], -1.0))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmax_x_ones)
+    output_ndarray_float_2(fmax_x_negones)
+
+def test_ndarray_fmax_broadcast_lhs_scalar():
+    x = np_identity(2)
+    fmax_x_ones = np_fmax(1.0, x)
+    fmax_x_negones = np_fmax(-1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmax_x_ones)
+    output_ndarray_float_2(fmax_x_negones)
+
+def test_ndarray_fmax_broadcast_rhs_scalar():
+    x = np_identity(2)
+    fmax_x_ones = np_fmax(x, 1.0)
+    fmax_x_negones = np_fmax(x, -1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmax_x_ones)
+    output_ndarray_float_2(fmax_x_negones)
+
+def test_ndarray_fmin():
+    x = np_identity(2)
+    ones = np_ones([2, 2])
+    negones = np_full([2, 2], -1.0)
+    fmin_x_ones = np_fmin(x, ones)
+    fmin_x_negones = np_fmin(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(negones)
+    output_ndarray_float_2(fmin_x_ones)
+    output_ndarray_float_2(fmin_x_negones)
+
+def test_ndarray_fmin_broadcast():
+    x = np_identity(2)
+    fmin_x_ones = np_fmin(x, np_ones([2]))
+    fmin_x_negones = np_fmin(x, np_full([2], -1.0))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmin_x_ones)
+    output_ndarray_float_2(fmin_x_negones)
+
+def test_ndarray_fmin_broadcast_lhs_scalar():
+    x = np_identity(2)
+    fmin_x_ones = np_fmin(1.0, x)
+    fmin_x_negones = np_fmin(-1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmin_x_ones)
+    output_ndarray_float_2(fmin_x_negones)
+
+def test_ndarray_fmin_broadcast_rhs_scalar():
+    x = np_identity(2)
+    fmin_x_ones = np_fmin(x, 1.0)
+    fmin_x_negones = np_fmin(x, -1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(fmin_x_ones)
+    output_ndarray_float_2(fmin_x_negones)
+
+def test_ndarray_ldexp():
+    x = np_identity(2)
+    zeros = np_full([2, 2], 0)
+    ones = np_full([2, 2], 1)
+    ldexp_x_zeros = np_ldexp(x, zeros)
+    ldexp_x_ones = np_ldexp(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_int32_2(zeros)
+    output_ndarray_int32_2(ones)
+    output_ndarray_float_2(ldexp_x_zeros)
+    output_ndarray_float_2(ldexp_x_ones)
+
+def test_ndarray_ldexp_broadcast():
+    x = np_identity(2)
+    ldexp_x_zeros = np_ldexp(x, np_full([2], 0))
+    ldexp_x_ones = np_ldexp(x, np_full([2], 1))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(ldexp_x_zeros)
+    output_ndarray_float_2(ldexp_x_ones)
+
+def test_ndarray_ldexp_broadcast_lhs_scalar():
+    x = int32(np_identity(2))
+    ldexp_x_zeros = np_ldexp(0.0, x)
+    ldexp_x_ones = np_ldexp(1.0, x)
+
+    output_ndarray_int32_2(x)
+    output_ndarray_float_2(ldexp_x_zeros)
+    output_ndarray_float_2(ldexp_x_ones)
+
+def test_ndarray_ldexp_broadcast_rhs_scalar():
+    x = np_identity(2)
+    ldexp_x_zeros = np_ldexp(x, 0)
+    ldexp_x_ones = np_ldexp(x, 1)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(ldexp_x_zeros)
+    output_ndarray_float_2(ldexp_x_ones)
+
+def test_ndarray_hypot():
+    x = np_identity(2)
+    zeros = np_zeros([2, 2])
+    ones = np_ones([2, 2])
+    hypot_x_zeros = np_hypot(x, zeros)
+    hypot_x_ones = np_hypot(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(zeros)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(hypot_x_zeros)
+    output_ndarray_float_2(hypot_x_ones)
+
+def test_ndarray_hypot_broadcast():
+    x = np_identity(2)
+    hypot_x_zeros = np_hypot(x, np_zeros([2]))
+    hypot_x_ones = np_hypot(x, np_ones([2]))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(hypot_x_zeros)
+    output_ndarray_float_2(hypot_x_ones)
+
+def test_ndarray_hypot_broadcast_lhs_scalar():
+    x = np_identity(2)
+    hypot_x_zeros = np_hypot(0.0, x)
+    hypot_x_ones = np_hypot(1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(hypot_x_zeros)
+    output_ndarray_float_2(hypot_x_ones)
+
+def test_ndarray_hypot_broadcast_rhs_scalar():
+    x = np_identity(2)
+    hypot_x_zeros = np_hypot(x, 0.0)
+    hypot_x_ones = np_hypot(x, 1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(hypot_x_zeros)
+    output_ndarray_float_2(hypot_x_ones)
+
+def test_ndarray_nextafter():
+    x = np_identity(2)
+    zeros = np_zeros([2, 2])
+    ones = np_ones([2, 2])
+    nextafter_x_zeros = np_nextafter(x, zeros)
+    nextafter_x_ones = np_nextafter(x, ones)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(zeros)
+    output_ndarray_float_2(ones)
+    output_ndarray_float_2(nextafter_x_zeros)
+    output_ndarray_float_2(nextafter_x_ones)
+
+def test_ndarray_nextafter_broadcast():
+    x = np_identity(2)
+    nextafter_x_zeros = np_nextafter(x, np_zeros([2]))
+    nextafter_x_ones = np_nextafter(x, np_ones([2]))
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(nextafter_x_zeros)
+    output_ndarray_float_2(nextafter_x_ones)
+
+def test_ndarray_nextafter_broadcast_lhs_scalar():
+    x = np_identity(2)
+    nextafter_x_zeros = np_nextafter(0.0, x)
+    nextafter_x_ones = np_nextafter(1.0, x)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(nextafter_x_zeros)
+    output_ndarray_float_2(nextafter_x_ones)
+
+def test_ndarray_nextafter_broadcast_rhs_scalar():
+    x = np_identity(2)
+    nextafter_x_zeros = np_nextafter(x, 0.0)
+    nextafter_x_ones = np_nextafter(x, 1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(nextafter_x_zeros)
+    output_ndarray_float_2(nextafter_x_ones)
+
 def run() -> int32:
     test_ndarray_ctor()
     test_ndarray_empty()
@@ -739,4 +1354,76 @@ def run() -> int32:
     test_ndarray_ge_broadcast_lhs_scalar()
     test_ndarray_ge_broadcast_rhs_scalar()
 
+    test_ndarray_int32()
+    test_ndarray_int64()
+    test_ndarray_uint32()
+    test_ndarray_uint64()
+    test_ndarray_float()
+    test_ndarray_bool()
+
+    test_ndarray_round()
+    test_ndarray_floor()
+    test_ndarray_abs()
+    test_ndarray_isnan()
+    test_ndarray_isinf()
+
+    test_ndarray_sin()
+    test_ndarray_cos()
+    test_ndarray_exp()
+    test_ndarray_exp2()
+    test_ndarray_log()
+    test_ndarray_log10()
+    test_ndarray_log2()
+    test_ndarray_fabs()
+    test_ndarray_sqrt()
+    test_ndarray_rint()
+    test_ndarray_tan()
+    test_ndarray_arcsin()
+    test_ndarray_arccos()
+    test_ndarray_arctan()
+    test_ndarray_sinh()
+    test_ndarray_cosh()
+    test_ndarray_tanh()
+    test_ndarray_arcsinh()
+    test_ndarray_arccosh()
+    test_ndarray_arctanh()
+    test_ndarray_expm1()
+    test_ndarray_cbrt()
+
+    test_ndarray_erf()
+    test_ndarray_erfc()
+    test_ndarray_gamma()
+    test_ndarray_gammaln()
+    test_ndarray_j0()
+    test_ndarray_j1()
+
+    test_ndarray_arctan2()
+    test_ndarray_arctan2_broadcast()
+    test_ndarray_arctan2_broadcast_lhs_scalar()
+    test_ndarray_arctan2_broadcast_rhs_scalar()
+    test_ndarray_copysign()
+    test_ndarray_copysign_broadcast()
+    test_ndarray_copysign_broadcast_lhs_scalar()
+    test_ndarray_copysign_broadcast_rhs_scalar()
+    test_ndarray_fmax()
+    test_ndarray_fmax_broadcast()
+    test_ndarray_fmax_broadcast_lhs_scalar()
+    test_ndarray_fmax_broadcast_rhs_scalar()
+    test_ndarray_fmin()
+    test_ndarray_fmin_broadcast()
+    test_ndarray_fmin_broadcast_lhs_scalar()
+    test_ndarray_fmin_broadcast_rhs_scalar()
+    test_ndarray_ldexp()
+    test_ndarray_ldexp_broadcast()
+    test_ndarray_ldexp_broadcast_lhs_scalar()
+    test_ndarray_ldexp_broadcast_rhs_scalar()
+    test_ndarray_hypot()
+    test_ndarray_hypot_broadcast()
+    test_ndarray_hypot_broadcast_lhs_scalar()
+    test_ndarray_hypot_broadcast_rhs_scalar()
+    test_ndarray_nextafter()
+    test_ndarray_nextafter_broadcast()
+    test_ndarray_nextafter_broadcast_lhs_scalar()
+    test_ndarray_nextafter_broadcast_rhs_scalar()
+
     return 0