add test, remove unused code

polars/polars-arrow/src/kernels/rolling/no_nulls.rs

@@ -24,41 +24,6 @@ impl Default for QuantileInterpolOptions {
     }
 }
 
-fn rolling_apply_convolve<Fo, Fa>(
-    values: &[f64],
-    window_size: usize,
-    min_periods: usize,
-    det_offsets_fn: Fo,
-    aggregator: Fa,
-    weights: &[f64],
-) -> ArrayRef
-where
-    Fo: Fn(Idx, WindowSize, Len) -> (Start, End),
-    Fa: Fn(&[f64]) -> f64,
-{
-    assert_eq!(weights.len(), window_size);
-    let mut buf = vec![0.0; window_size];
-    let len = values.len();
-    let out = (0..len)
-        .map(|idx| {
-            let (start, end) = det_offsets_fn(idx, window_size, len);
-            let vals = unsafe { values.get_unchecked(start..end) };
-            buf.iter_mut()
-                .zip(vals.iter().zip(weights))
-                .for_each(|(b, (v, w))| *b = *v * *w);
-
-            aggregator(&buf)
-        })
-        .collect_trusted::<Vec<f64>>();
-
-    let validity = create_validity(min_periods, len as usize, window_size, det_offsets_fn);
-    Arc::new(PrimitiveArray::from_data(
-        DataType::Float64,
-        out.into(),
-        validity.map(|b| b.into()),
-    ))
-}
-
 fn rolling_apply_weights<Fo, Fa>(
     values: &[f64],
     window_size: usize,

py-polars/tests/test_lazy.py

@@ -730,19 +730,19 @@ def test_rolling_apply() -> None:
     assert (roll_app_sum - roll_sum).abs().sum() < 0.0001
 
     s = pl.Series("A", list(range(6)), dtype=pl.Float64)
-    roll_app_sum = s.rolling_apply(
-        function=lambda s: s.sum(),
+    roll_app_std = s.rolling_apply(
+        function=lambda s: s.std(),
         window_size=4,
         weights=[1.0, 2.0, 3.0, 0.1],
         min_periods=3,
         center=False,
     )
 
-    roll_sum = s.rolling_sum(
+    roll_std = s.rolling_std(
         window_size=4, weights=[1.0, 2.0, 3.0, 0.1], min_periods=3, center=False
     )
 
-    assert (roll_app_sum - roll_sum).abs().sum() < 0.0001
+    assert (roll_app_std - roll_std).abs().sum() < 0.0001
 
 
 def test_arr_namespace(fruits_cars: pl.DataFrame) -> None: