core/expr: Implement negative indices for ndarray

nac3core/src/codegen/expr.rs

@@ -9,6 +9,7 @@ use crate::{
             ListValue,
             NDArrayValue,
             RangeValue,
+            TypedArrayLikeAccessor,
             UntypedArrayLikeAccessor,
         },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
@@ -18,7 +19,7 @@ use crate::{
         irrt::*,
         llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
         numpy,
-        stmt::{gen_raise, gen_var},
+        stmt::{gen_if_else_expr_callback, gen_raise, gen_var},
         CodeGenContext, CodeGenTask,
     },
     symbol_resolver::{SymbolValue, ValueEnum},
@@ -1692,21 +1693,55 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         slice.location,
     );
 
-    if ndims.len() == 1 && ndims[0] == 1 {
-        // Accessing an element from a 1-dimensional `ndarray`
-
     if let ExprKind::Slice { .. } = &slice.node {
         return Err(String::from("subscript operator for ndarray not implemented"))
     }
 
-        let index = if let Some(v) = generator.gen_expr(ctx, slice)? {
+    let index = if let Some(index) = generator.gen_expr(ctx, slice)? {
-            v.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value()
+        let index = index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
+
+        gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_, ctx| {
+                Ok(ctx.builder.build_int_compare(
+                    IntPredicate::SGE,
+                    index,
+                    index.get_type().const_zero(),
+                    "",
+                ).unwrap())
+            },
+            |_, _| Ok(Some(index)),
+            |generator, ctx| {
+                let llvm_i32 = ctx.ctx.i32_type();
+
+                let len = unsafe {
+                    v.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        llvm_usize.const_zero(),
+                        None,
+                    )
+                };
+
+                let index = ctx.builder.build_int_add(
+                    len,
+                    ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
+                    "",
+                ).unwrap();
+
+                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
+            },
+        )?.map(BasicValueEnum::into_int_value).unwrap()
     } else {
         return Ok(None)
     };
     let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
     ctx.builder.build_store(index_addr, index).unwrap();
 
+    if ndims.len() == 1 && ndims[0] == 1 {
+        // Accessing an element from a 1-dimensional `ndarray`
+
         Ok(Some(v.data()
             .get(
                 ctx,
@@ -1718,18 +1753,6 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
     } else {
         // Accessing an element from a multi-dimensional `ndarray`
 
-        if let ExprKind::Slice { .. } = &slice.node {
-            return Err(String::from("subscript operator for ndarray not implemented"))
-        }
-
-        let index = if let Some(v) = generator.gen_expr(ctx, slice)? {
-            v.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value()
-        } else {
-            return Ok(None)
-        };
-        let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
-        ctx.builder.build_store(index_addr, index).unwrap();
-
         // Create a new array, remove the top dimension from the dimension-size-list, and copy the
         // elements over
         let subscripted_ndarray = generator.gen_var_alloc(

nac3standalone/demo/src/ndarray.py

@@ -79,6 +79,13 @@ def test_ndarray_copy():
     output_ndarray_float_2(x)
     output_ndarray_float_2(y)
 
+def test_ndarray_neg_idx():
+    x = np_identity(2)
+
+    for i in range(-1, -3, -1):
+        for j in range(-1, -3, -1):
+            output_float64(x[i][j])
+
 def test_ndarray_add():
     x = np_identity(2)
     y = x + np_ones([2, 2])
@@ -639,6 +646,7 @@ def run() -> int32:
     test_ndarray_identity()
     test_ndarray_fill()
     test_ndarray_copy()
+    test_ndarray_neg_idx()
     test_ndarray_add()
     test_ndarray_add_broadcast()
     test_ndarray_add_broadcast_lhs_scalar()