core: Implement elementwise comparison operators

Including immediate variants of these operators.

nac3artiq/src/symbol_resolver.rs

@@ -5,7 +5,7 @@ use nac3core::{
     toplevel::{
         DefinitionId, 
         helper::PRIMITIVE_DEF_IDS, 
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelDef,
     },
     typecheck::{
@@ -654,7 +654,7 @@ impl InnerResolver {
                 }
             }
             (TypeEnum::TObj { obj_id, .. }, false) if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                let (ty, ndims) = unpack_ndarray_tvars(unifier, extracted_ty);
+                let (ty, ndims) = unpack_ndarray_var_tys(unifier, extracted_ty);
                 let len: usize = self.helper.len_fn.call1(py, (obj,))?.extract(py)?;
                 if len == 0 {
                     assert!(matches!(

nac3core/src/codegen/classes.rs

@@ -11,7 +11,7 @@ use crate::codegen::{
     stmt::gen_for_callback_incrementing,
 };
 
-/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of 
+/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of
 /// elements.
 pub trait ArrayLikeValue<'ctx> {
     /// Returns the element type of this array-like value.

nac3core/src/codegen/expr.rs

@@ -16,6 +16,7 @@ use crate::{
         get_llvm_abi_type,
         irrt::*,
         llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
+        numpy,
         stmt::{gen_raise, gen_var},
         CodeGenContext, CodeGenTask,
     },
@@ -23,7 +24,7 @@ use crate::{
     toplevel::{
         DefinitionId, 
         helper::PRIMITIVE_DEF_IDS,
-        numpy::make_ndarray_ty,
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelDef,
     },
     typecheck::{
@@ -38,6 +39,7 @@ use inkwell::{
     types::{AnyType, BasicType, BasicTypeEnum},
     values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue}
 };
+use inkwell::values::BasicValue;
 use itertools::{chain, izip, Itertools, Either};
 use nac3parser::ast::{
     self, Boolop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
@@ -1129,6 +1131,76 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
             Some("f_pow_i")
         );
         Ok(Some(res.into()))
+    } else if ty1.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray || ty2.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        let is_ndarray1 = ty1.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+        let is_ndarray2 = ty2.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+        if is_ndarray1 && is_ndarray2 {
+            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+
+            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+
+            let left_val = NDArrayValue::from_ptr_val(
+                left_val.into_pointer_value(),
+                llvm_usize,
+                None
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype1,
+                if is_aug_assign { Some(left_val) } else { None },
+                (left_val.as_ptr_value().into(), false),
+                (right_val, false),
+                |generator, ctx, elem_ty, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), lhs),
+                        op,
+                        (&Some(elem_ty), rhs),
+                        ctx.current_loc,
+                        is_aug_assign,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        } else {
+            let (ndarray_dtype, _) = unpack_ndarray_var_tys(
+                &mut ctx.unifier,
+                if is_ndarray1 { ty1 } else { ty2 },
+            );
+            let ndarray_val = NDArrayValue::from_ptr_val(
+                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype,
+                if is_aug_assign { Some(ndarray_val) } else { None },
+                (left_val, !is_ndarray1),
+                (right_val, !is_ndarray2),
+                |generator, ctx, elem_ty, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), lhs),
+                        op,
+                        (&Some(elem_ty), rhs),
+                        ctx.current_loc,
+                        is_aug_assign,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
         let TypeEnum::TObj { fields, obj_id, .. } = left_ty_enum.as_ref() else {
@@ -1220,6 +1292,294 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
     )
 }
 
+pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    op: &ast::Unaryop,
+    operand: (&Option<Type>, BasicValueEnum<'ctx>),
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    let (ty, val) = operand;
+    let ty = ctx.unifier.get_representative(ty.unwrap());
+
+    Ok(Some(if ty == ctx.primitives.bool {
+        let val = val.into_int_value();
+        match op {
+            ast::Unaryop::Invert | ast::Unaryop::Not => {
+                ctx.builder.build_not(val, "not").map(Into::into).unwrap()
+            }
+            _ => val.into(),
+        }
+    } else if [ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64].contains(&ty) {
+        let val = val.into_int_value();
+        match op {
+            ast::Unaryop::USub => ctx.builder.build_int_neg(val, "neg").map(Into::into).unwrap(),
+            ast::Unaryop::Invert => ctx.builder.build_not(val, "not").map(Into::into).unwrap(),
+            ast::Unaryop::Not => ctx.builder.build_xor(val, val.get_type().const_all_ones(), "not").map(Into::into).unwrap(),
+            ast::Unaryop::UAdd => val.into(),
+        }
+    } else if ty == ctx.primitives.float {
+        let val = val.into_float_value();
+        match op {
+            ast::Unaryop::USub => ctx.builder.build_float_neg(val, "neg").map(Into::into).unwrap(),
+            ast::Unaryop::Not => ctx
+                .builder
+                .build_float_compare(
+                    inkwell::FloatPredicate::OEQ,
+                    val,
+                    val.get_type().const_zero(),
+                    "not",
+                )
+                .map(Into::into)
+                .unwrap(),
+            _ => val.into(),
+        }
+    } else if ty.obj_id(&ctx.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+
+        let val = NDArrayValue::from_ptr_val(
+            val.into_pointer_value(),
+            llvm_usize,
+            None,
+        );
+
+        let res = numpy::ndarray_elementwise_unaryop_impl(
+            generator,
+            ctx,
+            ndarray_dtype,
+            None,
+            val,
+            |generator, ctx, elem_ty, val| {
+                gen_unaryop_expr_with_values(
+                    generator,
+                    ctx,
+                    op,
+                    (&Some(elem_ty), val)
+                )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
+            },
+        )?;
+
+        res.as_ptr_value().into()
+    } else {
+        unimplemented!()
+    })) 
+}
+
+pub fn gen_unaryop_expr<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    op: &ast::Unaryop,
+    operand: &Expr<Option<Type>>,
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    let val = if let Some(v) = generator.gen_expr(ctx, operand)? {
+        v.to_basic_value_enum(ctx, generator, operand.custom.unwrap())?
+    } else {
+        return Ok(None)
+    };
+
+    gen_unaryop_expr_with_values(generator, ctx, op, (&operand.custom, val))
+}
+
+pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    left: (Option<Type>, BasicValueEnum<'ctx>),
+    ops: &[ast::Cmpop],
+    comparators: &[(Option<Type>, BasicValueEnum<'ctx>)],
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    debug_assert_eq!(comparators.len(), ops.len());
+
+    if comparators.len() == 1 {
+        let left_ty = ctx.unifier.get_representative(left.0.unwrap());
+        let right_ty = ctx.unifier.get_representative(comparators[0].0.unwrap());
+
+        if left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) || right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray) {
+            let llvm_usize = generator.get_size_type(ctx.ctx);
+
+            let (Some(left_ty), lhs) = left else { unreachable!() };
+            let (Some(right_ty), rhs) = comparators[0] else { unreachable!() };
+            let op = ops[0].clone();
+            
+            let is_ndarray1 = left_ty.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+            let is_ndarray2 = right_ty.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+            return if is_ndarray1 && is_ndarray2 {
+                let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
+                let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
+            
+                assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+            
+                let left_val = NDArrayValue::from_ptr_val(
+                    lhs.into_pointer_value(),
+                    llvm_usize,
+                    None
+                );
+                let res = numpy::ndarray_elementwise_binop_impl(
+                    generator,
+                    ctx,
+                    ctx.primitives.bool,
+                    None,
+                    (left_val.as_ptr_value().into(), false),
+                    (rhs, false),
+                    |generator, ctx, elem_ty, (lhs, rhs)| {
+                        let val = gen_cmpop_expr_with_values(
+                            generator,
+                            ctx,
+                            (Some(ndarray_dtype1), lhs),
+                            &[op.clone()],
+                            &[(Some(ndarray_dtype2), rhs)],
+                        )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
+
+                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
+                    },
+                )?;
+            
+                Ok(Some(res.as_ptr_value().into()))
+            } else {
+                let (ndarray_dtype, _) = unpack_ndarray_var_tys(
+                    &mut ctx.unifier,
+                    if is_ndarray1 { left_ty } else { right_ty },
+                );
+                let res = numpy::ndarray_elementwise_binop_impl(
+                    generator,
+                    ctx,
+                    ctx.primitives.bool,
+                    None,
+                    (lhs, !is_ndarray1),
+                    (rhs, !is_ndarray2),
+                    |generator, ctx, elem_ty, (lhs, rhs)| {
+                        let val = gen_cmpop_expr_with_values(
+                            generator,
+                            ctx,
+                            (Some(ndarray_dtype), lhs),
+                            &[op.clone()],
+                            &[(Some(ndarray_dtype), rhs)],
+                        )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
+
+                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
+                    },
+                )?;
+            
+                Ok(Some(res.as_ptr_value().into()))
+            }
+        }
+    }
+
+    let cmp_val = izip!(chain(once(&left), comparators.iter()), comparators.iter(), ops.iter(),)
+        .fold(Ok(None), |prev: Result<Option<_>, String>, (lhs, rhs, op)| {
+            let (left_ty, lhs) = lhs;
+            let (right_ty, rhs) = rhs;
+
+            let left_ty = ctx.unifier.get_representative(left_ty.unwrap());
+            let right_ty = ctx.unifier.get_representative(right_ty.unwrap());
+
+            let current =
+                if [ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64, ctx.primitives.bool]
+                    .contains(&left_ty)
+                {
+                    assert!(ctx.unifier.unioned(left_ty, right_ty));
+
+                    let use_unsigned_ops = [
+                        ctx.primitives.uint32,
+                        ctx.primitives.uint64,
+                    ].contains(&left_ty);
+ 
+                    let lhs = lhs.into_int_value();
+                    let rhs = rhs.into_int_value();
+    
+                    let op = match op {
+                        ast::Cmpop::Eq | ast::Cmpop::Is => IntPredicate::EQ,
+                        ast::Cmpop::NotEq => IntPredicate::NE,
+                        _ if left_ty == ctx.primitives.bool => unreachable!(),
+                        ast::Cmpop::Lt => if use_unsigned_ops {
+                            IntPredicate::ULT
+                        } else {
+                            IntPredicate::SLT
+                        },
+                        ast::Cmpop::LtE => if use_unsigned_ops {
+                            IntPredicate::ULE
+                        } else {
+                            IntPredicate::SLE
+                        },
+                        ast::Cmpop::Gt => if use_unsigned_ops {
+                            IntPredicate::UGT
+                        } else {
+                            IntPredicate::SGT
+                        },
+                        ast::Cmpop::GtE => if use_unsigned_ops {
+                            IntPredicate::UGE
+                        } else {
+                            IntPredicate::SGE
+                        },
+                        _ => unreachable!(),
+                    };
+    
+                    ctx.builder.build_int_compare(op, lhs, rhs, "cmp").unwrap()
+                } else if left_ty == ctx.primitives.float {
+                    assert!(ctx.unifier.unioned(left_ty, right_ty));
+
+                    let lhs = lhs.into_float_value();
+                    let rhs = rhs.into_float_value();
+
+                    let op = match op {
+                        ast::Cmpop::Eq | ast::Cmpop::Is => inkwell::FloatPredicate::OEQ,
+                        ast::Cmpop::NotEq => inkwell::FloatPredicate::ONE,
+                        ast::Cmpop::Lt => inkwell::FloatPredicate::OLT,
+                        ast::Cmpop::LtE => inkwell::FloatPredicate::OLE,
+                        ast::Cmpop::Gt => inkwell::FloatPredicate::OGT,
+                        ast::Cmpop::GtE => inkwell::FloatPredicate::OGE,
+                        _ => unreachable!(),
+                    };
+                    ctx.builder.build_float_compare(op, lhs, rhs, "cmp").unwrap()
+                } else {
+                    unimplemented!()
+                };
+
+            Ok(prev?.map(|v| ctx.builder.build_and(v, current, "cmp").unwrap()).or(Some(current)))
+        })?;
+    
+    Ok(Some(match cmp_val {
+        Some(v) => v.into(),
+        None => return Ok(None),
+    }))
+}
+
+pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    left: &Expr<Option<Type>>,
+    ops: &[ast::Cmpop],
+    comparators: &[Expr<Option<Type>>],
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
+        v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
+    } else {
+        return Ok(None)
+    };
+    let comparators = {
+        let mut new_comparators = Vec::new();
+        new_comparators.reserve(comparators.len());
+
+        for cmptor in comparators {
+            if let Some(v) = generator.gen_expr(ctx, cmptor)? {
+                new_comparators.push((cmptor.custom, v.to_basic_value_enum(ctx, generator, cmptor.custom.unwrap())?))
+            } else {
+                return Ok(None)
+            }
+        }
+
+        new_comparators
+    };
+
+    gen_cmpop_expr_with_values(
+        generator,
+        ctx,
+        (left.custom, left_val),
+        ops,
+        comparators.as_slice(),
+    )
+}
+
 /// Generates code for a subscript expression on an `ndarray`.
 ///
 /// * `ty` - The `Type` of the `NDArray` elements.
@@ -1596,130 +1956,10 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
             return gen_binop_expr(generator, ctx, left, op, right, expr.location, false);
         }
         ExprKind::UnaryOp { op, operand } => {
-            let ty = ctx.unifier.get_representative(operand.custom.unwrap());
-            let val = if let Some(v) = generator.gen_expr(ctx, operand)? {
-                v.to_basic_value_enum(ctx, generator, operand.custom.unwrap())?
-            } else {
-                return Ok(None)
-            };
-            if ty == ctx.primitives.bool {
-                let val = val.into_int_value();
-                match op {
-                    ast::Unaryop::Invert | ast::Unaryop::Not => {
-                        ctx.builder.build_not(val, "not").map(Into::into).unwrap()
-                    }
-                    _ => val.into(),
-                }
-            } else if [ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64].contains(&ty) {
-                let val = val.into_int_value();
-                match op {
-                    ast::Unaryop::USub => ctx.builder.build_int_neg(val, "neg").map(Into::into).unwrap(),
-                    ast::Unaryop::Invert => ctx.builder.build_not(val, "not").map(Into::into).unwrap(),
-                    ast::Unaryop::Not => ctx.builder.build_xor(val, val.get_type().const_all_ones(), "not").map(Into::into).unwrap(),
-                    ast::Unaryop::UAdd => val.into(),
-                }
-            } else if ty == ctx.primitives.float {
-                let val = val.into_float_value();
-                match op {
-                    ast::Unaryop::USub => ctx.builder.build_float_neg(val, "neg").map(Into::into).unwrap(),
-                    ast::Unaryop::Not => ctx
-                        .builder
-                        .build_float_compare(
-                            inkwell::FloatPredicate::OEQ,
-                            val,
-                            val.get_type().const_zero(),
-                            "not",
-                        )
-                        .map(Into::into)
-                        .unwrap(),
-                    _ => val.into(),
-                }
-            } else {
-                unimplemented!()
-            }
+            return gen_unaryop_expr(generator, ctx, op, operand)
         }
         ExprKind::Compare { left, ops, comparators } => {
-            let cmp_val = izip!(chain(once(left.as_ref()), comparators.iter()), comparators.iter(), ops.iter(),)
-                .fold(Ok(None), |prev: Result<Option<_>, String>, (lhs, rhs, op)| {
-                    let ty = ctx.unifier.get_representative(lhs.custom.unwrap());
-                    let current =
-                        if [ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64, ctx.primitives.bool]
-                            .contains(&ty)
-                        {
-                            let use_unsigned_ops = [
-                                ctx.primitives.uint32,
-                                ctx.primitives.uint64,
-                            ].contains(&ty);
-
-                            let BasicValueEnum::IntValue(lhs) = (match generator.gen_expr(ctx, lhs)? {
-                                Some(v) => v.to_basic_value_enum(ctx, generator, lhs.custom.unwrap())?,
-                                None => return Ok(None),
-                            }) else { unreachable!() };
-
-                            let BasicValueEnum::IntValue(rhs) = (match generator.gen_expr(ctx, rhs)? {
-                                Some(v) => v.to_basic_value_enum(ctx, generator, rhs.custom.unwrap())?,
-                                None => return Ok(None),
-                            }) else { unreachable!() };
-
-                            let op = match op {
-                                ast::Cmpop::Eq | ast::Cmpop::Is => IntPredicate::EQ,
-                                ast::Cmpop::NotEq => IntPredicate::NE,
-                                _ if ty == ctx.primitives.bool => unreachable!(),
-                                ast::Cmpop::Lt => if use_unsigned_ops {
-                                    IntPredicate::ULT
-                                } else {
-                                    IntPredicate::SLT
-                                },
-                                ast::Cmpop::LtE => if use_unsigned_ops {
-                                    IntPredicate::ULE
-                                } else {
-                                    IntPredicate::SLE
-                                },
-                                ast::Cmpop::Gt => if use_unsigned_ops {
-                                    IntPredicate::UGT
-                                } else {
-                                    IntPredicate::SGT
-                                },
-                                ast::Cmpop::GtE => if use_unsigned_ops {
-                                    IntPredicate::UGE
-                                } else {
-                                    IntPredicate::SGE
-                                },
-                                _ => unreachable!(),
-                            };
-
-                            ctx.builder.build_int_compare(op, lhs, rhs, "cmp").unwrap()
-                        } else if ty == ctx.primitives.float {
-                            let BasicValueEnum::FloatValue(lhs) = (match generator.gen_expr(ctx, lhs)? {
-                                Some(v) => v.to_basic_value_enum(ctx, generator, lhs.custom.unwrap())?,
-                                None => return Ok(None),
-                            }) else { unreachable!() };
-
-                            let BasicValueEnum::FloatValue(rhs) = (match generator.gen_expr(ctx, rhs)? {
-                                Some(v) => v.to_basic_value_enum(ctx, generator, rhs.custom.unwrap())?,
-                                None => return Ok(None),
-                            }) else { unreachable!() };
-
-                            let op = match op {
-                                ast::Cmpop::Eq | ast::Cmpop::Is => inkwell::FloatPredicate::OEQ,
-                                ast::Cmpop::NotEq => inkwell::FloatPredicate::ONE,
-                                ast::Cmpop::Lt => inkwell::FloatPredicate::OLT,
-                                ast::Cmpop::LtE => inkwell::FloatPredicate::OLE,
-                                ast::Cmpop::Gt => inkwell::FloatPredicate::OGT,
-                                ast::Cmpop::GtE => inkwell::FloatPredicate::OGE,
-                                _ => unreachable!(),
-                            };
-                            ctx.builder.build_float_compare(op, lhs, rhs, "cmp").unwrap()
-                        } else {
-                            unimplemented!()
-                        };
-                    Ok(prev?.map(|v| ctx.builder.build_and(v, current, "cmp").unwrap()).or(Some(current)))
-                })?;
-
-                match cmp_val {
-                    Some(v) => v.into(),
-                    None => return Ok(None),
-                }
+            return gen_cmpop_expr(generator, ctx, left, &ops, &comparators)
         }
         ExprKind::IfExp { test, body, orelse } => {
             let test = match generator.gen_expr(ctx, test)? {

nac3core/src/codegen/irrt/irrt.c

@@ -8,6 +8,8 @@ typedef unsigned _BitInt(64) uint64_t;
 # define MAX(a, b) (a > b ? a : b)
 # define MIN(a, b) (a > b ? b : a)
 
+# define NULL ((void *) 0)
+
 // adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
 // need to make sure `exp >= 0` before calling this function
 #define DEF_INT_EXP(T) T __nac3_int_exp_##T( \
@@ -293,3 +295,87 @@ uint64_t __nac3_ndarray_flatten_index64(
     }
     return idx;
 }
+
+void __nac3_ndarray_calc_broadcast(
+    const uint32_t *lhs_dims,
+    uint32_t lhs_ndims,
+    const uint32_t *rhs_dims,
+    uint32_t rhs_ndims,
+    uint32_t *out_dims
+) {
+    uint32_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (uint32_t i = 0; i < max_ndims; ++i) {
+        uint32_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
+        uint32_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
+        uint32_t *out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == NULL) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == NULL) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == 1) {
+            *out_dim = *rhs_dim_sz;
+        } else if (*rhs_dim_sz == 1) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == *rhs_dim_sz) {
+            *out_dim = *lhs_dim_sz;
+        } else {
+            __builtin_unreachable();
+        }
+    }
+}
+
+void __nac3_ndarray_calc_broadcast64(
+    const uint64_t *lhs_dims,
+    uint64_t lhs_ndims,
+    const uint64_t *rhs_dims,
+    uint64_t rhs_ndims,
+    uint64_t *out_dims
+) {
+    uint64_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (uint64_t i = 0; i < max_ndims; ++i) {
+        uint64_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
+        uint64_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
+        uint64_t *out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == NULL) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == NULL) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == 1) {
+            *out_dim = *rhs_dim_sz;
+        } else if (*rhs_dim_sz == 1) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == *rhs_dim_sz) {
+            *out_dim = *lhs_dim_sz;
+        } else {
+            __builtin_unreachable();
+        }
+    }
+}
+
+void __nac3_ndarray_calc_broadcast_idx(
+    const uint32_t *src_dims,
+    uint32_t src_ndims,
+    const uint32_t *in_idx,
+    uint32_t *out_idx
+) {
+    for (uint32_t i = 0; i < src_ndims; ++i) {
+        uint32_t src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
+    }
+}
+
+void __nac3_ndarray_calc_broadcast_idx64(
+    const uint64_t *src_dims,
+    uint64_t src_ndims,
+    const uint32_t *in_idx,
+    uint32_t *out_idx
+) {
+    for (uint64_t i = 0; i < src_ndims; ++i) {
+        uint64_t src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : (uint32_t) in_idx[src_i];
+    }
+}

nac3core/src/codegen/irrt/mod.rs

@@ -8,9 +8,11 @@ use super::{
         ListValue,
         NDArrayValue,
         TypedArrayLikeAdapter,
+        UntypedArrayLikeAccessor,
     },
     CodeGenContext,
     CodeGenerator,
+    llvm_intrinsics,
 };
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -783,4 +785,153 @@ pub fn call_ndarray_flatten_index<'ctx, G, Index>(
         ndarray,
         indices,
     )
+}
+
+/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
+/// dimension and size of each dimension of the resultant `ndarray`.
+pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    lhs: NDArrayValue<'ctx>,
+    rhs: NDArrayValue<'ctx>,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_broadcast",
+        64 => "__nac3_ndarray_calc_broadcast64",
+        bw => unreachable!("Unsupported size type bit width: {}", bw)
+    };
+    let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
+        let fn_type = llvm_usize.fn_type(
+            &[
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pusize.into(),
+            ],
+            false,
+        );
+
+        ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
+    });
+
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_ndims = rhs.load_ndims(ctx);
+    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
+
+    // TODO: Generate assertion checks for whether each dimension is compatible
+    // gen_for_callback_incrementing(
+    //     generator,
+    //     ctx,
+    //     llvm_usize.const_zero(),
+    //     (max_ndims, false),
+    //     |generator, ctx, idx| {
+    //         let lhs_dim_sz =
+    //
+    //         let lhs_elem = lhs.get_dims().get(ctx, generator, idx, None);
+    //         let rhs_elem = rhs.get_dims().get(ctx, generator, idx, None);
+    //
+    //
+    //     },
+    //     llvm_usize.const_int(1, false),
+    // ).unwrap();
+
+    let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
+    let rhs_ndims = rhs.load_ndims(ctx);
+    let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
+    let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_broadcast_fn,
+            &[
+                lhs_dims.into(),
+                lhs_ndims.into(),
+                rhs_dims.into(),
+                rhs_ndims.into(),
+                out_dims.base_ptr(ctx, generator).into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        out_dims,
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
+}
+
+/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
+/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
+/// array `broadcast_idx`.
+pub fn call_ndarray_calc_broadcast_index<'ctx, G: CodeGenerator + ?Sized, BroadcastIdx: UntypedArrayLikeAccessor<'ctx>>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    array: NDArrayValue<'ctx>,
+    broadcast_idx: &BroadcastIdx,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_broadcast_idx",
+        64 => "__nac3_ndarray_calc_broadcast_idx64",
+        bw => unreachable!("Unsupported size type bit width: {}", bw)
+    };
+    let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
+        let fn_type = llvm_usize.fn_type(
+            &[
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pi32.into(),
+                llvm_pi32.into(),
+            ],
+            false,
+        );
+
+        ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
+    });
+
+    // TODO: Assertions
+
+    let broadcast_size = broadcast_idx.size(ctx, generator);
+    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
+
+    let array_dims = array.dim_sizes().base_ptr(ctx, generator);
+    let array_ndims = array.load_ndims(ctx);
+    let broadcast_idx_ptr = unsafe {
+        broadcast_idx.ptr_offset_unchecked(
+            ctx,
+            generator,
+            llvm_usize.const_zero(),
+            None
+        )
+    };
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_broadcast_fn,
+            &[
+                array_dims.into(),
+                array_ndims.into(),
+                broadcast_idx_ptr.into(),
+                out_idx.into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
 }

nac3core/src/codegen/mod.rs

@@ -2,7 +2,7 @@ use crate::{
     symbol_resolver::{StaticValue, SymbolResolver},
     toplevel::{
         helper::PRIMITIVE_DEF_IDS, 
-        numpy::unpack_ndarray_tvars, 
+        numpy::unpack_ndarray_var_tys, 
         TopLevelContext, 
         TopLevelDef,
     },
@@ -451,7 +451,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
 
                         TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
                             let llvm_usize = generator.get_size_type(ctx);
-                            let (dtype, _) = unpack_ndarray_tvars(unifier, ty);
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
                             let element_type = get_llvm_type(
                                 ctx, 
                                 module, 

nac3core/src/codegen/numpy.rs

@@ -18,6 +18,8 @@ use crate::{
         CodeGenContext,
         CodeGenerator,
         irrt::{
+            call_ndarray_calc_broadcast,
+            call_ndarray_calc_broadcast_index,
             call_ndarray_calc_nd_indices,
             call_ndarray_calc_size,
         },
@@ -27,7 +29,7 @@ use crate::{
     symbol_resolver::ValueEnum,
     toplevel::{
         DefinitionId,
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     },
     typecheck::typedef::{FunSignature, Type},
 };
@@ -344,6 +346,92 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
     )
 }
 
+fn ndarray_fill_mapping<'ctx, G, MapFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src: NDArrayValue<'ctx>,
+    dest: NDArrayValue<'ctx>,
+    map_fn: MapFn,
+) -> Result<(), String>
+    where
+        G: CodeGenerator + ?Sized,
+        MapFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    ndarray_fill_flattened(
+        generator,
+        ctx,
+        dest,
+        |generator, ctx, i| {
+            let elem = unsafe {
+                src.data().get_unchecked(ctx, generator, i, None)
+            };
+
+            map_fn(generator, ctx, elem)
+        },
+    )
+}
+
+/// Generates the LLVM IR for populating the entire `NDArray` from two `ndarray` or scalar value
+/// with broadcast-compatible shapes.
+fn ndarray_broadcast_fill<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: NDArrayValue<'ctx>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    ndarray_fill_indexed(
+        generator,
+        ctx,
+        res,
+        |generator, ctx, idx| {
+            let lhs_elem = if lhs_scalar {
+                lhs_val
+            } else {
+                let lhs = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+                let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, &idx);
+
+                unsafe {
+                    lhs.data().get_unchecked(ctx, generator, lhs_idx, None)
+                }
+            };
+
+            let rhs_elem = if rhs_scalar {
+                rhs_val
+            } else {
+                let rhs = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+                let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, &idx);
+                
+                unsafe {
+                    rhs.data().get_unchecked(ctx, generator, rhs_idx, None)
+                }
+            };
+
+            debug_assert_eq!(lhs_elem.get_type(), rhs_elem.get_type());
+
+            value_fn(generator, ctx, elem_ty, (lhs_elem, rhs_elem))
+        },
+    )?;
+
+    Ok(res)
+}
+
 /// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
 ///
 /// * `elem_ty` - The element type of the `NDArray`.
@@ -579,6 +667,151 @@ fn ndarray_copy_impl<'ctx, G: CodeGenerator + ?Sized>(
     Ok(ndarray)
 }
 
+pub fn ndarray_elementwise_unaryop_impl<'ctx, G, MapFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: Option<NDArrayValue<'ctx>>,
+    operand: NDArrayValue<'ctx>,
+    map_fn: MapFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator,
+        MapFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, BasicValueEnum<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let res = res.unwrap_or_else(|| {
+        create_ndarray_dyn_shape(
+            generator,
+            ctx,
+            elem_ty,
+            &operand,
+            |_, ctx, v| {
+                Ok(v.load_ndims(ctx))
+            },
+            |generator, ctx, v, idx| {
+                unsafe {
+                    Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, idx, None))
+                }
+            },
+        ).unwrap()
+    });
+
+    ndarray_fill_mapping(
+        generator,
+        ctx,
+        operand,
+        res,
+        |generator, ctx, elem| {
+            map_fn(generator, ctx, elem_ty, elem)
+        }
+    )?;
+
+    Ok(res)
+}
+
+/// LLVM-typed implementation for computing elementwise binary operations on two input operands.
+///
+/// If the operand is a `ndarray`, the broadcast index corresponding to each element in the output 
+/// is computed, the element accessed and used as an operand of the `value_fn` arguments tuple. 
+/// Otherwise, the operand is treated as a scalar value, and is used as an operand of the 
+/// `value_fn` arguments tuple for all output elements.
+///
+/// The second element of the tuple indicates whether to treat the operand value as a `ndarray`
+/// (which would be accessed by its broadcast index) or as a scalar value (which would be 
+/// broadcast to all elements).
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
+/// written to a new `ndarray`.
+/// * `value_fn` - Function mapping the two input elements into the result.
+///
+/// # Panic
+///
+/// This function will panic if neither input operands (`lhs` or `rhs`) is a `ndarray`.
+// TODO: Remove elem_ty from value_fn
+pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: Option<NDArrayValue<'ctx>>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    let ndarray = res.unwrap_or_else(|| {
+        if lhs_scalar && rhs_scalar {
+            let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+            let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+
+            let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, lhs_val, rhs_val);
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray_dims,
+                |generator, ctx, v| {
+                    Ok(v.size(ctx, generator))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        } else {
+            let ndarray = NDArrayValue::from_ptr_val(
+                if lhs_scalar { rhs_val } else { lhs_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray,
+                |_, ctx, v| {
+                    Ok(v.load_ndims(ctx))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        }
+    });
+
+    ndarray_broadcast_fill(
+        generator,
+        ctx,
+        elem_ty,
+        ndarray,
+        lhs,
+        rhs,
+        |generator, ctx, elem_ty, elems| {
+            value_fn(generator, ctx, elem_ty, elems)
+        },
+    )?;
+
+    Ok(ndarray)
+}
+
 /// Generates LLVM IR for `ndarray.empty`.
 pub fn gen_ndarray_empty<'ctx>(
     context: &mut CodeGenContext<'ctx, '_>,
@@ -765,7 +998,7 @@ pub fn gen_ndarray_copy<'ctx>(
     let llvm_usize = generator.get_size_type(context.ctx);
 
     let this_ty = obj.as_ref().unwrap().0;
-    let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
+    let (this_elem_ty, _) = unpack_ndarray_var_tys(&mut context.unifier, this_ty);
     let this_arg = obj
         .as_ref()
         .unwrap()

nac3core/src/codegen/stmt.rs

@@ -13,7 +13,7 @@ use crate::{
     toplevel::{
         DefinitionId,
         helper::PRIMITIVE_DEF_IDS,
-        numpy::unpack_ndarray_tvars,
+        numpy::unpack_ndarray_var_tys,
         TopLevelDef,
     },
     typecheck::typedef::{FunSignature, Type, TypeEnum},
@@ -251,7 +251,7 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
             let ty = match &*ctx.unifier.get_ty_immutable(target.custom.unwrap()) {
                 TypeEnum::TList { ty } => *ty,
                 TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                    unpack_ndarray_tvars(&mut ctx.unifier, target.custom.unwrap()).0
+                    unpack_ndarray_var_tys(&mut ctx.unifier, target.custom.unwrap()).0
                 }
                 _ => unreachable!(),
             };
@@ -546,7 +546,7 @@ pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
 ///     body(x);
 /// }
 /// ```
-/// 
+///
 /// * `init_val` - The initial value of the loop variable. The type of this value will also be used 
 /// as the type of the loop variable.
 /// * `max_val` - A tuple containing the maximum value of the loop variable, and whether the maximum

nac3core/src/symbol_resolver.rs

@@ -170,13 +170,13 @@ impl SymbolValue {
     /// Returns the [`TypeAnnotation`] representing the data type of this value.
     pub fn get_type_annotation(&self, primitives: &PrimitiveStore, unifier: &mut Unifier) -> TypeAnnotation {
         match self {
-            SymbolValue::Bool(..) => TypeAnnotation::Primitive(primitives.bool),
-            SymbolValue::Double(..) => TypeAnnotation::Primitive(primitives.float),
-            SymbolValue::I32(..) => TypeAnnotation::Primitive(primitives.int32),
-            SymbolValue::I64(..) => TypeAnnotation::Primitive(primitives.int64),
-            SymbolValue::U32(..) => TypeAnnotation::Primitive(primitives.uint32),
-            SymbolValue::U64(..) => TypeAnnotation::Primitive(primitives.uint64),
-            SymbolValue::Str(..) => TypeAnnotation::Primitive(primitives.str),
+            SymbolValue::Bool(..)
+            | SymbolValue::Double(..)
+            | SymbolValue::I32(..)
+            | SymbolValue::I64(..)
+            | SymbolValue::U32(..)
+            | SymbolValue::U64(..)
+            | SymbolValue::Str(..) => TypeAnnotation::Primitive(self.get_type(primitives, unifier)),
             SymbolValue::Tuple(vs) => {
                 let vs_tys = vs
                     .iter()
@@ -230,6 +230,36 @@ impl Display for SymbolValue {
     }
 }
 
+impl TryFrom<SymbolValue> for u64 {
+    type Error = ();
+
+    /// TODO
+    fn try_from(value: SymbolValue) -> Result<Self, Self::Error> {
+        match value {
+            SymbolValue::I32(v) => Ok(v as u64),
+            SymbolValue::I64(v) => u64::try_from(v).map_err(|_| ()),
+            SymbolValue::U32(v) => Ok(v as u64),
+            SymbolValue::U64(v) => Ok(v),
+            _ => Err(()),
+        }
+    }
+}
+
+impl TryFrom<SymbolValue> for i128 {
+    type Error = ();
+
+    /// TODO
+    fn try_from(value: SymbolValue) -> Result<Self, Self::Error> {
+        match value {
+            SymbolValue::I32(v) => Ok(v as i128),
+            SymbolValue::I64(v) => Ok(v as i128),
+            SymbolValue::U32(v) => Ok(v as i128),
+            SymbolValue::U64(v) => Ok(v as i128),
+            _ => Err(()),
+        }
+    }
+}
+
 pub trait StaticValue {
     /// Returns a unique identifier for this value.
     fn get_unique_identifier(&self) -> u64;

nac3core/src/toplevel/builtins.rs

@@ -299,6 +299,8 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         Some("N".into()),
         None,
     );
+    let size_t = primitives.0.usize();
+
     let var_map: VarMap = vec![(num_ty.1, num_ty.0)].into_iter().collect();
     let exception_fields = vec![
         ("__name__".into(), int32, true),
@@ -345,8 +347,27 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         .nth(1)
         .map(|(var_id, ty)| (*ty, *var_id))
         .unwrap();
+    let ndarray_usized_ndims_tvar = primitives.1.get_fresh_const_generic_var(
+        size_t, 
+        Some("ndarray_ndims".into()), 
+        None,
+    );
     let ndarray_copy_ty = *ndarray_fields.get(&"copy".into()).unwrap();
     let ndarray_fill_ty = *ndarray_fields.get(&"fill".into()).unwrap();
+    let ndarray_add_ty = *ndarray_fields.get(&"__add__".into()).unwrap();
+    let ndarray_sub_ty = *ndarray_fields.get(&"__sub__".into()).unwrap();
+    let ndarray_mul_ty = *ndarray_fields.get(&"__mul__".into()).unwrap();
+    let ndarray_truediv_ty = *ndarray_fields.get(&"__truediv__".into()).unwrap();
+    let ndarray_floordiv_ty = *ndarray_fields.get(&"__floordiv__".into()).unwrap();
+    let ndarray_mod_ty = *ndarray_fields.get(&"__mod__".into()).unwrap();
+    let ndarray_pow_ty = *ndarray_fields.get(&"__pow__".into()).unwrap();
+    let ndarray_iadd_ty = *ndarray_fields.get(&"__iadd__".into()).unwrap();
+    let ndarray_isub_ty = *ndarray_fields.get(&"__isub__".into()).unwrap();
+    let ndarray_imul_ty = *ndarray_fields.get(&"__imul__".into()).unwrap();
+    let ndarray_itruediv_ty = *ndarray_fields.get(&"__itruediv__".into()).unwrap();
+    let ndarray_ifloordiv_ty = *ndarray_fields.get(&"__ifloordiv__".into()).unwrap();
+    let ndarray_imod_ty = *ndarray_fields.get(&"__imod__".into()).unwrap();
+    let ndarray_ipow_ty = *ndarray_fields.get(&"__ipow__".into()).unwrap();
 
     let top_level_def_list = vec![
         Arc::new(RwLock::new(TopLevelComposer::make_top_level_class_def(
@@ -524,6 +545,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             methods: vec![
                 ("copy".into(), ndarray_copy_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 1)),
                 ("fill".into(), ndarray_fill_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 2)),
+                ("__add__".into(), ndarray_add_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 3)),
+                ("__sub__".into(), ndarray_sub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 4)),
+                ("__mul__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 5)),
+                ("__truediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 6)),
+                ("__floordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 7)),
+                ("__mod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 8)),
+                ("__pow__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 9)),
+                ("__iadd__".into(), ndarray_iadd_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 10)),
+                ("__isub__".into(), ndarray_isub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 11)),
+                ("__imul__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 12)),
+                ("__itruediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 13)),
+                ("__ifloordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 14)),
+                ("__imod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 15)),
+                ("__ipow__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 16)),
             ],
             ancestors: Vec::default(),
             constructor: None,
@@ -562,6 +597,216 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             )))),
             loc: None,
         })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__add__".into(),
+            simple_name: "__add__".into(),
+            signature: ndarray_add_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__sub__".into(),
+            simple_name: "__sub__".into(),
+            signature: ndarray_sub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mul__".into(),
+            simple_name: "__mul__".into(),
+            signature: ndarray_mul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__truediv__".into(),
+            simple_name: "__truediv__".into(),
+            signature: ndarray_truediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__floordiv__".into(),
+            simple_name: "__floordiv__".into(),
+            signature: ndarray_floordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mod__".into(),
+            simple_name: "__mod__".into(),
+            signature: ndarray_mod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__pow__".into(),
+            simple_name: "__pow__".into(),
+            signature: ndarray_pow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__iadd__".into(),
+            simple_name: "__iadd__".into(),
+            signature: ndarray_iadd_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id, ndarray_usized_ndims_tvar.1],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__isub__".into(),
+            simple_name: "__isub__".into(),
+            signature: ndarray_isub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imul__".into(),
+            simple_name: "__imul__".into(),
+            signature: ndarray_imul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__itruediv__".into(),
+            simple_name: "__itruediv__".into(),
+            signature: ndarray_itruediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ifloordiv__".into(),
+            simple_name: "__ifloordiv__".into(),
+            signature: ndarray_ifloordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imod__".into(),
+            simple_name: "__imod__".into(),
+            signature: ndarray_imod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ipow__".into(),
+            simple_name: "__ipow__".into(),
+            signature: ndarray_ipow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
         Arc::new(RwLock::new(TopLevelDef::Function {
             name: "int32".into(),
             simple_name: "int32".into(),

nac3core/src/toplevel/helper.rs

@@ -1,6 +1,7 @@
 use std::convert::TryInto;
 
 use crate::symbol_resolver::SymbolValue;
+use crate::toplevel::numpy::subst_ndarray_tvars;
 use crate::typecheck::typedef::{Mapping, VarMap};
 use nac3parser::ast::{Constant, Location};
 
@@ -45,10 +46,15 @@ impl PrimitiveDefinitionIds {
         ]
     }
 
+    /// Returns an iterator over all [`DefinitionId`]s of this instance.
+    pub fn iter(&self) -> impl Iterator<Item=DefinitionId> {
+        self.as_vec().into_iter()
+    }
+
     /// Returns the primitive with the largest [`DefinitionId`].
     #[must_use]
     pub fn max_id(&self) -> DefinitionId {
-        self.as_vec().into_iter().max().unwrap()
+        self.iter().max().unwrap()
     }
 }
 
@@ -226,11 +232,57 @@ impl TopLevelComposer {
                 (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
             ]),
         }));
+        let ndarray_binop_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_binop_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_binop_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
+        let ndarray_truediv_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_truediv_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_truediv_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
         let ndarray = unifier.add_ty(TypeEnum::TObj {
             obj_id: PRIMITIVE_DEF_IDS.ndarray,
             fields: Mapping::from([
                 ("copy".into(), (ndarray_copy_fun_ty, true)),
                 ("fill".into(), (ndarray_fill_fun_ty, true)),
+                ("__add__".into(), (ndarray_binop_fun_ty, true)),
+                ("__sub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__truediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__floordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__pow__".into(), (ndarray_binop_fun_ty, true)),
+                ("__iadd__".into(), (ndarray_binop_fun_ty, true)),
+                ("__isub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__itruediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__ifloordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__ipow__".into(), (ndarray_binop_fun_ty, true)),
             ]),
             params: VarMap::from([
                 (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
@@ -238,7 +290,16 @@ impl TopLevelComposer {
             ]),
         });
 
+        let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t_ty, Some("ndarray_ndims".into()), None);
+        let ndarray_unsized = subst_ndarray_tvars(&mut unifier, ndarray, Some(ndarray_usized_ndims_tvar.0), None);
+
         unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_binop_fun_other_ty.0, ndarray_unsized).unwrap();
+        unifier.unify(ndarray_binop_fun_ret_ty.0, ndarray).unwrap();
+
+        let ndarray_float = subst_ndarray_tvars(&mut unifier, ndarray, Some(float), None);
+        unifier.unify(ndarray_truediv_fun_other_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_truediv_fun_ret_ty.0, ndarray_float).unwrap();
 
         let primitives = PrimitiveStore {
             int32,

nac3core/src/toplevel/numpy.rs

@@ -19,13 +19,30 @@ pub fn make_ndarray_ty(
     dtype: Option<Type>,
     ndims: Option<Type>,
 ) -> Type {
-    let ndarray = primitives.ndarray;
+    subst_ndarray_tvars(unifier, primitives.ndarray, dtype, ndims)
+}
 
+/// Substitutes type variables in `ndarray`.
+///
+/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
+/// specialized.
+/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
+/// specialized.
+pub fn subst_ndarray_tvars(
+    unifier: &mut Unifier,
+    ndarray: Type,
+    dtype: Option<Type>,
+    ndims: Option<Type>,
+) -> Type {
     let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
         panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
     };
     debug_assert_eq!(*obj_id, PRIMITIVE_DEF_IDS.ndarray);
 
+    if dtype.is_none() && ndims.is_none() {
+        return ndarray
+    }
+
     let tvar_ids = params.iter()
         .map(|(obj_id, _)| *obj_id)
         .collect_vec();
@@ -42,12 +59,10 @@ pub fn make_ndarray_ty(
     unifier.subst(ndarray, &tvar_subst).unwrap_or(ndarray)
 }
 
-/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
-/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
-pub fn unpack_ndarray_tvars(
+fn unpack_ndarray_tvars(
     unifier: &mut Unifier,
     ndarray: Type,
-) -> (Type, Type) {
+) -> Vec<(u32, Type)> {
     let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
         panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
     };
@@ -56,7 +71,33 @@ pub fn unpack_ndarray_tvars(
 
     params.iter()
         .sorted_by_key(|(obj_id, _)| *obj_id)
-        .map(|(_, ty)| *ty)
+        .map(|(var_id, ty)| (*var_id, *ty))
+        .collect_vec()
+}
+
+/// Unpacks the type variable IDs of `ndarray` into a tuple. The elements of the tuple corresponds 
+/// to `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` 
+/// respectively.
+pub fn unpack_ndarray_var_ids(
+    unifier: &mut Unifier,
+    ndarray: Type,
+) -> (u32, u32) {
+    unpack_ndarray_tvars(unifier, ndarray)
+        .into_iter()
+        .map(|v| v.0)
+        .collect_tuple()
+        .unwrap()
+}
+
+/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
+/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
+pub fn unpack_ndarray_var_tys(
+    unifier: &mut Unifier,
+    ndarray: Type,
+) -> (Type, Type) {
+    unpack_ndarray_tvars(unifier, ndarray)
+        .into_iter()
+        .map(|v| v.1)
         .collect_tuple()
         .unwrap()
 }

nac3core/src/typecheck/magic_methods.rs

@@ -1,3 +1,7 @@
+use std::cmp::max;
+use crate::symbol_resolver::SymbolValue;
+use crate::toplevel::helper::PRIMITIVE_DEF_IDS;
+use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
 use crate::typecheck::{
     type_inferencer::*,
     typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
@@ -6,6 +10,7 @@ use nac3parser::ast::StrRef;
 use nac3parser::ast::{Cmpop, Operator, Unaryop};
 use std::collections::HashMap;
 use std::rc::Rc;
+use itertools::Itertools;
 
 #[must_use]
 pub fn binop_name(op: &Operator) -> &'static str {
@@ -90,7 +95,7 @@ pub fn impl_binop(
     _store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
     ops: &[Operator],
 ) {
     with_fields(unifier, ty, |unifier, fields| {
@@ -107,6 +112,8 @@ pub fn impl_binop(
             VarMap::new()
         };
 
+        let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).0);
+
         for op in ops {
             fields.insert(binop_name(op).into(), {
                 (
@@ -141,8 +148,10 @@ pub fn impl_binop(
     });
 }
 
-pub fn impl_unaryop(unifier: &mut Unifier, ty: Type, ret_ty: Type, ops: &[Unaryop]) {
+pub fn impl_unaryop(unifier: &mut Unifier, ty: Type, ret_ty: Option<Type>, ops: &[Unaryop]) {
     with_fields(unifier, ty, |unifier, fields| {
+        let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).0);
+
         for op in ops {
             fields.insert(
                 unaryop_name(op).into(),
@@ -161,19 +170,35 @@ pub fn impl_unaryop(unifier: &mut Unifier, ty: Type, ret_ty: Type, ops: &[Unaryo
 
 pub fn impl_cmpop(
     unifier: &mut Unifier,
-    store: &PrimitiveStore,
+    _store: &PrimitiveStore,
     ty: Type,
-    other_ty: Type,
+    other_ty: &[Type],
     ops: &[Cmpop],
+    ret_ty: Option<Type>,
 ) {
     with_fields(unifier, ty, |unifier, fields| {
+        let (other_ty, other_var_id) = if other_ty.len() == 1 {
+            (other_ty[0], None)
+        } else {
+            let (ty, var_id) = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
+            (ty, Some(var_id))
+        };
+
+        let function_vars = if let Some(var_id) = other_var_id {
+            vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
+        } else {
+            VarMap::new()
+        };
+
+        let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).0);
+
         for op in ops {
             fields.insert(
                 comparison_name(op).unwrap().into(),
                 (
                     unifier.add_ty(TypeEnum::TFunc(FunSignature {
-                        ret: store.bool,
-                        vars: VarMap::new(),
+                        ret: ret_ty,
+                        vars: function_vars.clone(),
                         args: vec![FuncArg {
                             ty: other_ty,
                             default_value: None,
@@ -193,7 +218,7 @@ pub fn impl_basic_arithmetic(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(
         unifier,
@@ -211,7 +236,7 @@ pub fn impl_pow(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Pow]);
 }
@@ -223,19 +248,25 @@ pub fn impl_bitwise_arithmetic(unifier: &mut Unifier, store: &PrimitiveStore, ty
         store,
         ty,
         &[ty],
-        ty,
+        Some(ty),
         &[Operator::BitAnd, Operator::BitOr, Operator::BitXor],
     );
 }
 
 /// `LShift`, `RShift`
 pub fn impl_bitwise_shift(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type) {
-    impl_binop(unifier, store, ty, &[store.int32, store.uint32], ty, &[Operator::LShift, Operator::RShift]);
+    impl_binop(unifier, store, ty, &[store.int32, store.uint32], Some(ty), &[Operator::LShift, Operator::RShift]);
 }
 
 /// `Div`
-pub fn impl_div(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type]) {
-    impl_binop(unifier, store, ty, other_ty, store.float, &[Operator::Div]);
+pub fn impl_div(
+    unifier: &mut Unifier,
+    store: &PrimitiveStore,
+    ty: Type,
+    other_ty: &[Type],
+    ret_ty: Option<Type>,
+) {
+    impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Div]);
 }
 
 /// `FloorDiv`
@@ -244,7 +275,7 @@ pub fn impl_floordiv(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::FloorDiv]);
 }
@@ -255,40 +286,220 @@ pub fn impl_mod(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Mod]);
 }
 
 /// `UAdd`, `USub`
-pub fn impl_sign(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type) {
-    impl_unaryop(unifier, ty, ty, &[Unaryop::UAdd, Unaryop::USub]);
+pub fn impl_sign(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type, ret_ty: Option<Type>) {
+    impl_unaryop(unifier, ty, ret_ty, &[Unaryop::UAdd, Unaryop::USub]);
 }
 
 /// `Invert`
-pub fn impl_invert(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type) {
-    impl_unaryop(unifier, ty, ty, &[Unaryop::Invert]);
+pub fn impl_invert(unifier: &mut Unifier, _store: &PrimitiveStore, ty: Type, ret_ty: Option<Type>) {
+    impl_unaryop(unifier, ty, ret_ty, &[Unaryop::Invert]);
 }
 
 /// `Not`
-pub fn impl_not(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type) {
-    impl_unaryop(unifier, ty, store.bool, &[Unaryop::Not]);
+pub fn impl_not(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, ret_ty: Option<Type>) {
+    impl_unaryop(unifier, ty, ret_ty, &[Unaryop::Not]);
 }
 
 /// `Lt`, `LtE`, `Gt`, `GtE`
-pub fn impl_comparison(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: Type) {
+pub fn impl_comparison(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type], ret_ty: Option<Type>) {
     impl_cmpop(
         unifier,
         store,
         ty,
         other_ty,
         &[Cmpop::Lt, Cmpop::Gt, Cmpop::LtE, Cmpop::GtE],
+        ret_ty,
     );
 }
 
 /// `Eq`, `NotEq`
-pub fn impl_eq(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type) {
-    impl_cmpop(unifier, store, ty, ty, &[Cmpop::Eq, Cmpop::NotEq]);
+pub fn impl_eq(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type], ret_ty: Option<Type>) {
+    impl_cmpop(unifier, store, ty, other_ty, &[Cmpop::Eq, Cmpop::NotEq], ret_ty);
+}
+
+/// Returns the expected return type of binary operations with at least one `ndarray` operand.
+pub fn typeof_ndarray_broadcast(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    left: Type,
+    right: Type,
+) -> Result<Type, String> {
+    let is_left_ndarray = left.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+    let is_right_ndarray = right.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+    assert!(is_left_ndarray || is_right_ndarray);
+
+    if is_left_ndarray && is_right_ndarray {
+        // Perform broadcasting on two ndarray operands.
+
+        let (left_ty_dtype, left_ty_ndims) = unpack_ndarray_var_tys(unifier, left);
+        let (right_ty_dtype, right_ty_ndims) = unpack_ndarray_var_tys(unifier, right);
+
+        assert!(unifier.unioned(left_ty_dtype, right_ty_dtype));
+
+        let left_ty_ndims = match &*unifier.get_ty_immutable(left_ty_ndims) {
+            TypeEnum::TLiteral { values, .. } => values.clone(),
+            _ => unreachable!(),
+        };
+        let right_ty_ndims = match &*unifier.get_ty_immutable(right_ty_ndims) {
+            TypeEnum::TLiteral { values, .. } => values.clone(),
+            _ => unreachable!(),
+        };
+
+        let res_ndims = left_ty_ndims.into_iter()
+            .cartesian_product(right_ty_ndims)
+            .map(|(left, right)| {
+                let left_val = u64::try_from(left).unwrap();
+                let right_val = u64::try_from(right).unwrap();
+
+                max(left_val, right_val)
+            })
+            .unique()
+            .map(SymbolValue::U64)
+            .collect_vec();
+        let res_ndims = unifier.get_fresh_literal(res_ndims, None);
+
+        Ok(make_ndarray_ty(unifier, primitives, Some(left_ty_dtype), Some(res_ndims)))
+    } else {
+        let (ndarray_ty, scalar_ty) = if is_left_ndarray {
+            (left, right)
+        } else {
+            (right, left)
+        };
+
+        let (ndarray_ty_dtype, _) = unpack_ndarray_var_tys(unifier, ndarray_ty);
+
+        if unifier.unioned(ndarray_ty_dtype, scalar_ty) {
+            Ok(ndarray_ty)
+        } else {
+            let (expected_ty, actual_ty) = if is_left_ndarray {
+                (ndarray_ty_dtype, scalar_ty)
+            } else {
+                (scalar_ty, ndarray_ty_dtype)
+            };
+
+            Err(format!(
+                "Expected right-hand side operand to be {}, got {}",
+                unifier.stringify(expected_ty),
+                unifier.stringify(actual_ty),
+            ))
+        }
+    }
+}
+
+/// Returns the return type given a binary operator and its primitive operands.
+pub fn typeof_binop(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    op: &Operator,
+    lhs: Type,
+    rhs: Type,
+) -> Result<Option<Type>, String> {
+    let is_left_ndarray = lhs
+        .obj_id(unifier)
+        .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+    let is_right_ndarray = rhs
+        .obj_id(unifier)
+        .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+
+    Ok(Some(match op {
+        Operator::Add
+        | Operator::Sub
+        | Operator::Mult
+        | Operator::Mod
+        | Operator::FloorDiv => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if unifier.unioned(lhs, rhs) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::MatMult => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
+        Operator::Div => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if unifier.unioned(lhs, rhs) {
+                primitives.float
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::Pow => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if [primitives.int32, primitives.int64, primitives.uint32, primitives.uint64, primitives.float].into_iter().any(|ty| unifier.unioned(lhs, ty)) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::LShift
+        | Operator::RShift => lhs,
+        Operator::BitOr
+        | Operator::BitXor
+        | Operator::BitAnd => {
+            if unifier.unioned(lhs, rhs) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+    }))
+}
+
+pub fn typeof_unaryop(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    op: &Unaryop,
+    operand: Type,
+) -> Result<Option<Type>, String> {
+    if *op == Unaryop::Not && operand.obj_id(unifier).is_some_and(|id| id == primitives.ndarray.obj_id(unifier).unwrap()) {
+        return Err("The truth value of an array with more than one element is ambiguous".to_string()) 
+    }
+
+    Ok(if operand.obj_id(unifier).is_some_and(|id| PRIMITIVE_DEF_IDS.iter().any(|prim_id| id == prim_id)) {
+        Some(operand)
+    } else {
+        None
+    })
+}
+
+/// Returns the return type given a comparison operator and its primitive operands.
+pub fn typeof_cmpop(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    _op: &Cmpop,
+    lhs: Type,
+    rhs: Type,
+) -> Result<Option<Type>, String> {
+    let is_left_ndarray = lhs
+        .obj_id(unifier)
+        .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+    let is_right_ndarray = rhs
+        .obj_id(unifier)
+        .is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray);
+
+    Ok(Some(if is_left_ndarray || is_right_ndarray {
+        let brd = typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?;
+        let (_, ndims) = unpack_ndarray_var_tys(unifier, brd);
+
+        make_ndarray_ty(unifier, primitives, Some(primitives.bool), Some(ndims))
+    } else if unifier.unioned(lhs, rhs) {
+        primitives.bool
+    } else {
+        return Ok(None)
+    }))
 }
 
 pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifier) {
@@ -299,39 +510,60 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
         bool: bool_t,
         uint32: uint32_t,
         uint64: uint64_t,
+        ndarray: ndarray_t,
         ..
     } = *store;
+    let size_t = store.usize();
 
     /* int ======== */
     for t in [int32_t, int64_t, uint32_t, uint64_t] {
-        impl_basic_arithmetic(unifier, store, t, &[t], t);
-        impl_pow(unifier, store, t, &[t], t);
+        let ndarray_int_t = make_ndarray_ty(unifier, store, Some(t), None);
+        impl_basic_arithmetic(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_pow(unifier, store, t, &[t, ndarray_int_t], None);
         impl_bitwise_arithmetic(unifier, store, t);
         impl_bitwise_shift(unifier, store, t);
-        impl_div(unifier, store, t, &[t]);
-        impl_floordiv(unifier, store, t, &[t], t);
-        impl_mod(unifier, store, t, &[t], t);
-        impl_invert(unifier, store, t);
-        impl_not(unifier, store, t);
-        impl_comparison(unifier, store, t, t);
-        impl_eq(unifier, store, t);
+        impl_div(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_floordiv(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_mod(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_invert(unifier, store, t, Some(t));
+        impl_not(unifier, store, t, Some(bool_t));
+        impl_comparison(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_eq(unifier, store, t, &[t, ndarray_int_t], None);
     }
     for t in [int32_t, int64_t] {
-        impl_sign(unifier, store, t);
+        impl_sign(unifier, store, t, Some(t));
     }
 
     /* float ======== */
-    impl_basic_arithmetic(unifier, store, float_t, &[float_t], float_t);
-    impl_pow(unifier, store, float_t, &[int32_t, float_t], float_t);
-    impl_div(unifier, store, float_t, &[float_t]);
-    impl_floordiv(unifier, store, float_t, &[float_t], float_t);
-    impl_mod(unifier, store, float_t, &[float_t], float_t);
-    impl_sign(unifier, store, float_t);
-    impl_not(unifier, store, float_t);
-    impl_comparison(unifier, store, float_t, float_t);
-    impl_eq(unifier, store, float_t);
+    let ndarray_float_t = make_ndarray_ty(unifier, store, Some(float_t), None);
+    let ndarray_int32_t = make_ndarray_ty(unifier, store, Some(int32_t), None);
+    impl_basic_arithmetic(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_pow(unifier, store, float_t, &[int32_t, float_t, ndarray_int32_t, ndarray_float_t], None);
+    impl_div(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_floordiv(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_mod(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_sign(unifier, store, float_t, Some(float_t));
+    impl_not(unifier, store, float_t, Some(bool_t));
+    impl_comparison(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_eq(unifier, store, float_t, &[float_t, ndarray_float_t], None);
 
     /* bool ======== */
-    impl_not(unifier, store, bool_t);
-    impl_eq(unifier, store, bool_t);
+    let ndarray_bool_t = make_ndarray_ty(unifier, store, Some(bool_t), None);
+    impl_not(unifier, store, bool_t, Some(bool_t));
+    impl_eq(unifier, store, bool_t, &[bool_t, ndarray_bool_t], None);
+
+    /* ndarray ===== */
+    let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t, Some("ndarray_ndims".into()), None);
+    let ndarray_unsized_t = make_ndarray_ty(unifier, store, None, Some(ndarray_usized_ndims_tvar.0));
+    let (ndarray_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_t);
+    let (ndarray_unsized_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_unsized_t);
+    impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_pow(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
+    impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
+    impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
+    impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_comparison(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
 }

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -9,11 +9,11 @@ use crate::{
     symbol_resolver::{SymbolResolver, SymbolValue}, 
     toplevel::{
         helper::PRIMITIVE_DEF_IDS,
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelContext,
     },
 };
-use itertools::izip;
+use itertools::{Itertools, izip};
 use nac3parser::ast::{
     self,
     fold::{self, Fold},
@@ -59,6 +59,16 @@ pub struct PrimitiveStore {
 }
 
 impl PrimitiveStore {
+    /// Returns a [`Type`] representing a signed representation of `size_t`.
+    #[must_use]
+    pub fn isize(&self) -> Type {
+        match self.size_t {
+            32 => self.int32,
+            64 => self.int64,
+            _ => unreachable!(),
+        }
+    }
+
     /// Returns a [Type] representing `size_t`.
     #[must_use]
     pub fn usize(&self) -> Type {
@@ -539,7 +549,9 @@ impl<'a> Fold<()> for Inferencer<'a> {
             ExprKind::BinOp { left, op, right } => {
                 Some(self.infer_bin_ops(expr.location, left, op, right, false)?)
             }
-            ExprKind::UnaryOp { op, operand } => Some(self.infer_unary_ops(op, operand)?),
+            ExprKind::UnaryOp { op, operand } => {
+                Some(self.infer_unary_ops(expr.location, op, operand)?)
+            }
             ExprKind::Compare { left, ops, comparators } => {
                 Some(self.infer_compare(left, ops, comparators)?)
             }
@@ -1193,8 +1205,11 @@ impl<'a> Inferencer<'a> {
         right: &ast::Expr<Option<Type>>,
         is_aug_assign: bool,
     ) -> InferenceResult {
+        let left_ty = left.custom.unwrap();
+        let right_ty = right.custom.unwrap();
+
         let method = if let TypeEnum::TObj { fields, .. } =
-            self.unifier.get_ty_immutable(left.custom.unwrap()).as_ref()
+            self.unifier.get_ty_immutable(left_ty).as_ref()
         {
             let (binop_name, binop_assign_name) = (
                 binop_name(op).into(),
@@ -1209,22 +1224,45 @@ impl<'a> Inferencer<'a> {
         } else {
             binop_name(op).into()
         };
+
+        let ret = if is_aug_assign {
+            // The type of augmented assignment operator should never change
+            Some(left_ty)
+        } else {
+            typeof_binop(
+                self.unifier,
+                self.primitives,
+                op,
+                left_ty,
+                right_ty,
+            ).map_err(|e| HashSet::from([format!("{e} (at {location})")]))?
+        };
+
         self.build_method_call(
             location,
             method,
-            left.custom.unwrap(),
-            vec![right.custom.unwrap()],
-            None,
+            left_ty,
+            vec![right_ty],
+            ret,
         )
     }
 
     fn infer_unary_ops(
         &mut self,
+        location: Location,
         op: &ast::Unaryop,
         operand: &ast::Expr<Option<Type>>,
     ) -> InferenceResult {
         let method = unaryop_name(op).into();
-        self.build_method_call(operand.location, method, operand.custom.unwrap(), vec![], None)
+
+        let ret = typeof_unaryop(
+            self.unifier,
+            self.primitives,
+            op,
+            operand.custom.unwrap(),
+        ).map_err(|e| HashSet::from([format!("{e} (at {location})")]))?;
+
+        self.build_method_call(operand.location, method, operand.custom.unwrap(), vec![], ret)
     }
 
     fn infer_compare(
@@ -1233,22 +1271,45 @@ impl<'a> Inferencer<'a> {
         ops: &[ast::Cmpop],
         comparators: &[ast::Expr<Option<Type>>],
     ) -> InferenceResult {
-        let boolean = self.primitives.bool;
+        if ops.len() > 1 && once(left).chain(comparators).any(|expr| expr.custom.unwrap().obj_id(self.unifier).is_some_and(|id| id == PRIMITIVE_DEF_IDS.ndarray)) {
+            return Err(HashSet::from([String::from("Comparator chaining with ndarray types not supported")]))
+        }
+
         for (a, b, c) in izip!(once(left).chain(comparators), comparators, ops) {
             let method = comparison_name(c)
                 .ok_or_else(|| HashSet::from([
                     "unsupported comparator".to_string()
                 ]))?
                 .into();
+
+            let ret = typeof_cmpop(
+                self.unifier,
+                self.primitives,
+                c,
+                a.custom.unwrap(),
+                b.custom.unwrap(),
+            ).map_err(|e| HashSet::from([format!("{e} (at {})", b.location)]))?;
+            
             self.build_method_call(
                 a.location,
                 method,
                 a.custom.unwrap(),
                 vec![b.custom.unwrap()],
-                Some(boolean),
+                ret,
             )?;
         }
-        Ok(boolean)
+
+        let res_lhs = comparators.iter().rev().nth(1).unwrap_or(left);
+        let res_rhs = comparators.iter().rev().nth(0).unwrap();
+        let res_op = ops.iter().rev().nth(0).unwrap();
+
+        Ok(typeof_cmpop(
+            self.unifier,
+            self.primitives,
+            res_op,
+            res_lhs.custom.unwrap(),
+            res_rhs.custom.unwrap(),
+        ).unwrap().unwrap())
     }
 
     /// Infers the type of a subscript expression on an `ndarray`.
@@ -1334,7 +1395,7 @@ impl<'a> Inferencer<'a> {
                 let list_like_ty = match &*self.unifier.get_ty(value.custom.unwrap()) {
                     TypeEnum::TList { .. } => self.unifier.add_ty(TypeEnum::TList { ty }),
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
 
                         make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims))
                     }
@@ -1347,7 +1408,7 @@ impl<'a> Inferencer<'a> {
             ExprKind::Constant { value: ast::Constant::Int(val), .. } => {
                 match &*self.unifier.get_ty(value.custom.unwrap()) {
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
                         self.infer_subscript_ndarray(value, ty, ndims)
                     }
                     _ => {
@@ -1379,9 +1440,18 @@ impl<'a> Inferencer<'a> {
                         Ok(ty)
                     }
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
 
-                        self.constrain(slice.custom.unwrap(), self.primitives.usize(), &slice.location)?;
+                        let valid_index_tys = [
+                            self.primitives.int32,
+                            self.primitives.isize(),
+                        ].into_iter().unique().collect_vec();
+                        let valid_index_ty = self.unifier.get_fresh_var_with_range(
+                            valid_index_tys.as_slice(),
+                            None,
+                            None,
+                        ).0;
+                        self.constrain(slice.custom.unwrap(), valid_index_ty, &slice.location)?;
                         self.infer_subscript_ndarray(value, ty, ndims)
                     }
                     _ => unreachable!(),

nac3core/src/typecheck/type_inferencer/test.rs

@@ -135,10 +135,15 @@ impl TestEnvironment {
             fields: HashMap::new(),
             params: VarMap::new(),
         });
+        let ndarray_dtype_tvar = unifier.get_fresh_var(Some("ndarray_dtype".into()), None);
+        let ndarray_ndims_tvar = unifier.get_fresh_const_generic_var(uint64, Some("ndarray_ndims".into()), None);
         let ndarray = unifier.add_ty(TypeEnum::TObj {
             obj_id: PRIMITIVE_DEF_IDS.ndarray,
             fields: HashMap::new(),
-            params: VarMap::new(),
+            params: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
         });
         let primitives = PrimitiveStore {
             int32,

nac3core/src/typecheck/typedef/mod.rs

@@ -61,15 +61,21 @@ pub enum RecordKey {
 }
 
 impl Type {
-    // a wrapper function for cleaner code so that we don't need to
-    // write this long pattern matching just to get the field `obj_id`
-    pub fn get_obj_id(self, unifier: &Unifier) -> DefinitionId {
-        if let TypeEnum::TObj { obj_id, .. } = unifier.get_ty_immutable(self).as_ref() {
-            *obj_id
+    /// Wrapper function for cleaner code so that we don't need to write this long pattern matching 
+    /// just to get the field `obj_id`.
+    #[must_use]
+    pub fn obj_id(self, unifier: &Unifier) -> Option<DefinitionId> {
+        if let TypeEnum::TObj { obj_id, .. } = &*unifier.get_ty_immutable(self) {
+            Some(*obj_id)
         } else {
-            unreachable!("expect a object type")
+            None
         }
     }
+
+    #[deprecated = "Prefer using `Type::obj_id` instead to handle non-TObj cases."]
+    pub fn get_obj_id(self, unifier: &Unifier) -> DefinitionId {
+        self.obj_id(unifier).expect("expect a object type")
+    }
 }
 
 impl From<&RecordKey> for StrRef {
@@ -765,12 +771,8 @@ impl Unifier {
 
                 // If the types don't match, try to implicitly promote integers
                 if !self.unioned(ty, value_ty) {
-                    let num_val = match *value {
-                        SymbolValue::I32(v) => v as i128,
-                        SymbolValue::I64(v) => v as i128,
-                        SymbolValue::U32(v) => v as i128,
-                        SymbolValue::U64(v) => v as i128,
-                        _ => return Self::incompatible_types(a, b),
+                    let Ok(num_val) = i128::try_from(value.clone()) else {
+                        return Self::incompatible_types(a, b)
                     };
 
                     let can_convert = if self.unioned(ty, primitives.int32) {

nac3standalone/demo/src/ndarray.py

@@ -1,3 +1,7 @@
+@extern
+def output_bool(x: bool):
+    ...
+
 @extern
 def output_int32(x: int32):
     ...
@@ -6,6 +10,29 @@ def output_int32(x: int32):
 def output_float64(x: float):
     ...
 
+def output_ndarray_bool_2(n: ndarray[bool, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_bool(n[r][c])
+
+def output_ndarray_int32_1(n: ndarray[int32, Literal[1]]):
+    for i in range(len(n)):
+        output_int32(n[i])
+
+def output_ndarray_int32_2(n: ndarray[int32, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_int32(n[r][c])
+
+def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
+    for i in range(len(n)):
+        output_float64(n[i])
+
+def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_float64(n[r][c])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -19,53 +46,582 @@ def test_ndarray_empty():
 
 def test_ndarray_zeros():
     n: ndarray[float, 1] = np_zeros([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_full():
     n_float: ndarray[float, 1] = np_full([1], 2.0)
-    output_float64(n_float[0])
+    output_ndarray_float_1(n_float)
     n_i32: ndarray[int32, 1] = np_full([1], 2)
-    output_int32(n_i32[0])
+    output_ndarray_int32_1(n_i32)
 
 def test_ndarray_eye():
     n: ndarray[float, 2] = np_eye(2)
-    n0: ndarray[float, 1] = n[0]
-    v: float = n0[0]
-    output_float64(v)
+    output_ndarray_float_2(n)
 
 def test_ndarray_identity():
     n: ndarray[float, 2] = np_identity(2)
-    output_float64(n[0][0])
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
+    output_ndarray_float_2(n)
 
 def test_ndarray_fill():
     n: ndarray[float, 2] = np_empty([2, 2])
     n.fill(1.0)
-    output_float64(n[0][0])
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
+    output_ndarray_float_2(n)
 
 def test_ndarray_copy():
     x: ndarray[float, 2] = np_identity(2)
     y = x.copy()
     x.fill(0.0)
 
-    output_float64(x[0][0])
-    output_float64(x[0][1])
-    output_float64(x[1][0])
-    output_float64(x[1][1])
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
 
-    output_float64(y[0][0])
-    output_float64(y[0][1])
-    output_float64(y[1][0])
-    output_float64(y[1][1])
+def test_ndarray_add():
+    x = np_identity(2)
+    y = x + np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + np_ones([2])
+    y = x + np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 + x
+    y = 1.0 + x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + 1.0
+    y = x + 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_iadd():
+    x = np_identity(2)
+    x += np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast():
+    x = np_identity(2)
+    x += np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast_scalar():
+    x = np_identity(2)
+    x += 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_sub():
+    x = np_ones([2, 2])
+    y = x - np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - np_ones([2])
+    y = x - np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 - x
+    y = 1.0 - x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - 1
+    y = x - 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_isub():
+    x = np_ones([2, 2])
+    x -= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast():
+    x = np_identity(2)
+    x -= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast_scalar():
+    x = np_identity(2)
+    x -= 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mul():
+    x = np_ones([2, 2])
+    y = x * np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * np_ones([2])
+    y = x * np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 * x
+    y = 2.0 * x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * 2.0
+    y = x * 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imul():
+    x = np_ones([2, 2])
+    x *= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast():
+    x = np_identity(2)
+    x *= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast_scalar():
+    x = np_identity(2)
+    x *= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_truediv():
+    x = np_identity(2)
+    y = x / np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / np_ones([2])
+    y = x / np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 / x
+    y = 2.0 / x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / 2.0
+    y = x / 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_itruediv():
+    x = np_identity(2)
+    x /= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast():
+    x = np_identity(2)
+    x /= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast_scalar():
+    x = np_identity(2)
+    x /= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_floordiv():
+    x = np_identity(2)
+    y = x // np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // np_ones([2])
+    y = x // np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 // x
+    y = 2.0 // x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // 2.0
+    y = x // 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ifloordiv():
+    x = np_identity(2)
+    x //= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast():
+    x = np_identity(2)
+    x //= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast_scalar():
+    x = np_identity(2)
+    x //= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mod():
+    x = np_identity(2)
+    y = x % np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % np_ones([2])
+    y = x % np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 % x
+    y = 2.0 % x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x % 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imod():
+    x = np_identity(2)
+    x %= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast():
+    x = np_identity(2)
+    x %= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast_scalar():
+    x = np_identity(2)
+    x %= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_pow():
+    x = np_identity(2)
+    y = x ** np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x ** np_full([2], 2.0)
+    y = x ** np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 ** x
+    y = 2.0 ** x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x ** 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ipow():
+    x = np_identity(2)
+    x **= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast():
+    x = np_identity(2)
+    x **= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast_scalar():
+    x = np_identity(2)
+    x **= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_pos():
+    x_int32 = np_full([2, 2], -2)
+    y_int32 = +x_int32
+
+    output_ndarray_int32_2(x_int32)
+    output_ndarray_int32_2(y_int32)
+
+    x_float = np_full([2, 2], -2.0)
+    y_float = +x_float
+
+    output_ndarray_float_2(x_float)
+    output_ndarray_float_2(y_float)
+
+def test_ndarray_neg():
+    x_int32 = np_full([2, 2], -2)
+    y_int32 = -x_int32
+
+    output_ndarray_int32_2(x_int32)
+    output_ndarray_int32_2(y_int32)
+
+    x_float = np_full([2, 2], 2.0)
+    y_float = -x_float
+
+    output_ndarray_float_2(x_float)
+    output_ndarray_float_2(y_float)
+
+def test_ndarray_inv():
+    x_int32 = np_full([2, 2], -2)
+    y_int32 = ~x_int32
+
+    output_ndarray_int32_2(x_int32)
+    output_ndarray_int32_2(y_int32)
+
+def test_ndarray_eq():
+    x = np_identity(2)
+    y = x == np_full([2, 2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_eq_broadcast():
+    x = np_identity(2)
+    y = x == np_full([2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_eq_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 0.0 == x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_eq_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x == 0.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ne():
+    x = np_identity(2)
+    y = x != np_full([2, 2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ne_broadcast():
+    x = np_identity(2)
+    y = x != np_full([2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ne_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 0.0 != x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ne_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x != 0.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_lt():
+    x = np_identity(2)
+    y = x < np_full([2, 2], 1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_lt_broadcast():
+    x = np_identity(2)
+    y = x < np_full([2], 1.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_lt_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 1.0 < x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_lt_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x < 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_le():
+    x = np_identity(2)
+    y = x <= np_full([2, 2], 0.5)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_le_broadcast():
+    x = np_identity(2)
+    y = x <= np_full([2], 0.5)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_le_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 0.5 <= x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_le_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x <= 0.5
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_gt():
+    x = np_identity(2)
+    y = x > np_full([2, 2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_gt_broadcast():
+    x = np_identity(2)
+    y = x > np_full([2], 0.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_gt_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 0.0 > x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_gt_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x > 0.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ge():
+    x = np_identity(2)
+    y = x >= np_full([2, 2], 0.5)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ge_broadcast():
+    x = np_identity(2)
+    y = x >= np_full([2], 0.5)
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ge_broadcast_lhs_scalar():
+    x = np_identity(2)
+    y = 0.5 >= x
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
+
+def test_ndarray_ge_broadcast_rhs_scalar():
+    x = np_identity(2)
+    y = x >= 0.5
+
+    output_ndarray_float_2(x)
+    output_ndarray_bool_2(y)
 
 def run() -> int32:
     test_ndarray_ctor()
@@ -77,5 +633,81 @@ def run() -> int32:
     test_ndarray_identity()
     test_ndarray_fill()
     test_ndarray_copy()
+    test_ndarray_add()
+    test_ndarray_add_broadcast()
+    test_ndarray_add_broadcast_lhs_scalar()
+    test_ndarray_add_broadcast_rhs_scalar()
+    test_ndarray_iadd()
+    test_ndarray_iadd_broadcast()
+    test_ndarray_iadd_broadcast_scalar()
+    test_ndarray_sub()
+    test_ndarray_sub_broadcast()
+    test_ndarray_sub_broadcast_lhs_scalar()
+    test_ndarray_sub_broadcast_rhs_scalar()
+    test_ndarray_isub()
+    test_ndarray_isub_broadcast()
+    test_ndarray_isub_broadcast_scalar()
+    test_ndarray_mul()
+    test_ndarray_mul_broadcast()
+    test_ndarray_mul_broadcast_lhs_scalar()
+    test_ndarray_mul_broadcast_rhs_scalar()
+    test_ndarray_imul()
+    test_ndarray_imul_broadcast()
+    test_ndarray_imul_broadcast_scalar()
+    test_ndarray_truediv()
+    test_ndarray_truediv_broadcast()
+    test_ndarray_truediv_broadcast_lhs_scalar()
+    test_ndarray_truediv_broadcast_rhs_scalar()
+    test_ndarray_itruediv()
+    test_ndarray_itruediv_broadcast()
+    test_ndarray_itruediv_broadcast_scalar()
+    test_ndarray_floordiv()
+    test_ndarray_floordiv_broadcast()
+    test_ndarray_floordiv_broadcast_lhs_scalar()
+    test_ndarray_floordiv_broadcast_rhs_scalar()
+    test_ndarray_ifloordiv()
+    test_ndarray_ifloordiv_broadcast()
+    test_ndarray_ifloordiv_broadcast_scalar()
+    test_ndarray_mod()
+    test_ndarray_mod_broadcast()
+    test_ndarray_mod_broadcast_lhs_scalar()
+    test_ndarray_mod_broadcast_rhs_scalar()
+    test_ndarray_imod()
+    test_ndarray_imod_broadcast()
+    test_ndarray_imod_broadcast_scalar()
+    test_ndarray_pow()
+    test_ndarray_pow_broadcast()
+    test_ndarray_pow_broadcast_lhs_scalar()
+    test_ndarray_pow_broadcast_rhs_scalar()
+    test_ndarray_ipow()
+    test_ndarray_ipow_broadcast()
+    test_ndarray_ipow_broadcast_scalar()
+    test_ndarray_pos()
+    test_ndarray_neg()
+    test_ndarray_inv()
+    test_ndarray_eq()
+    test_ndarray_eq_broadcast()
+    test_ndarray_eq_broadcast_lhs_scalar()
+    test_ndarray_eq_broadcast_rhs_scalar()
+    test_ndarray_ne()
+    test_ndarray_ne_broadcast()
+    test_ndarray_ne_broadcast_lhs_scalar()
+    test_ndarray_ne_broadcast_rhs_scalar()
+    test_ndarray_lt()
+    test_ndarray_lt_broadcast()
+    test_ndarray_lt_broadcast_lhs_scalar()
+    test_ndarray_lt_broadcast_rhs_scalar()
+    test_ndarray_lt()
+    test_ndarray_le_broadcast()
+    test_ndarray_le_broadcast_lhs_scalar()
+    test_ndarray_le_broadcast_rhs_scalar()
+    test_ndarray_gt()
+    test_ndarray_gt_broadcast()
+    test_ndarray_gt_broadcast_lhs_scalar()
+    test_ndarray_gt_broadcast_rhs_scalar()
+    test_ndarray_gt()
+    test_ndarray_ge_broadcast()
+    test_ndarray_ge_broadcast_lhs_scalar()
+    test_ndarray_ge_broadcast_rhs_scalar()
 
     return 0