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Merged
pull merged 4 commits into from
Jun 28, 2026
Merged

[pull] master from polakowo:master #59

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d80409b
fix(records): handle zero-N masks and optimize top/bottom selection
emiliolugo Jun 27, 2026
ca81c00
Fixed line length w black and merge conf
emiliolugo Jun 28, 2026
63c2e53
reformat
emiliolugo Jun 28, 2026
2987de5
Merge pull request #857 from emiliolugo/perf/top-bottom-n-argpartition
polakowo Jun 28, 2026
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8 changes: 8 additions & 0 deletions tests/test_records.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,17 +174,25 @@ def test_top_n_mask(self):
np.testing.assert_array_equal(
mapped_array.top_n_mask(1), np.array([False, False, True, False, True, False, True, False, False])
)
np.testing.assert_array_equal(
mapped_array.top_n_mask(0), np.array([False, False, False, False, False, False, False, False, False])
)

def test_bottom_n_mask(self):
np.testing.assert_array_equal(
mapped_array.bottom_n_mask(1), np.array([True, False, False, True, False, False, False, False, True])
)
np.testing.assert_array_equal(
mapped_array.bottom_n_mask(0), np.array([False, False, False, False, False, False, False, False, False])
)

def test_top_n(self):
np.testing.assert_array_equal(mapped_array.top_n(1).id_arr, np.array([2, 4, 6]))
np.testing.assert_array_equal(mapped_array.top_n(0).id_arr, np.array([], dtype=np.int64))

def test_bottom_n(self):
np.testing.assert_array_equal(mapped_array.bottom_n(1).id_arr, np.array([0, 3, 8]))
np.testing.assert_array_equal(mapped_array.bottom_n(0).id_arr, np.array([], dtype=np.int64))

def test_to_pd(self):
target = pd.DataFrame(
Expand Down
46 changes: 43 additions & 3 deletions vectorbt/records/nb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -221,14 +221,54 @@ def mapped_to_mask_nb(
@njit(cache=True)
def top_n_inout_map_nb(inout: tp.Array1d, idxs: tp.Array1d, col: int, mapped_arr: tp.Array1d, n: int) -> None:
"""`inout_map_func_nb` that returns indices of top N elements."""
# TODO: np.argpartition
inout[idxs[np.argsort(mapped_arr)[-n:]]] = True
if n <= 0:
return
col_len = mapped_arr.shape[0]
if n >= col_len:
inout[idxs] = True
return
n_partition = col_len - n
pivot = np.partition(mapped_arr, n_partition)[n_partition]
n_greater = 0
for i in range(col_len):
if mapped_arr[i] > pivot:
n_greater += 1
inout[idxs[i]] = True
n_remaining = n - n_greater
if n_remaining <= 0:
return
for i in range(col_len - 1, -1, -1):
if mapped_arr[i] == pivot:
inout[idxs[i]] = True
n_remaining -= 1
if n_remaining == 0:
break


@njit(cache=True)
def bottom_n_inout_map_nb(inout: tp.Array1d, idxs: tp.Array1d, col: int, mapped_arr: tp.Array1d, n: int) -> None:
"""`inout_map_func_nb` that returns indices of bottom N elements."""
inout[idxs[np.argsort(mapped_arr)[:n]]] = True
if n <= 0:
return
col_len = mapped_arr.shape[0]
if n >= col_len:
inout[idxs] = True
return
pivot = np.partition(mapped_arr, n - 1)[n - 1]
n_less = 0
for i in range(col_len):
if mapped_arr[i] < pivot:
n_less += 1
inout[idxs[i]] = True
n_remaining = n - n_less
if n_remaining <= 0:
return
for i in range(col_len):
if mapped_arr[i] == pivot:
inout[idxs[i]] = True
n_remaining -= 1
if n_remaining == 0:
break


@njit
Expand Down
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