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fix: DH-18153: Handle numpy's new shape typing in UDF parsing #6605

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Feb 1, 2025
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fix: DH-18153: Handle numpy's new shape typing in UDF parsing #6605

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a5a1534
Handle numpy's new shape typing
jmao-denver Jan 30, 2025
191edff
Add back imports used in dynamic code
jmao-denver Jan 30, 2025
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6 changes: 5 additions & 1 deletion py/server/deephaven/_udf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -330,7 +330,11 @@ def _np_ndarray_component_type(t: type) -> Optional[type]:
elif nargs == 2: # for npt.NDArray[np.int64], etc.
a0 = t.__args__[0]
a1 = t.__args__[1]
if a0 == typing.Any and isinstance(a1, types.GenericAlias): # novermin
# a0 is typing.Any before numpy 2.2.0 or a generic alias of tuple[int, ...] in numpy 2.2.0+. The latter
# is to support shape typing for numpy arrays. e.g. np.ndarray[tuple[Literal[2], Literal[3]], np.int32]
# is a 2x3 array of int32.
if ((a0 == typing.Any or (isinstance(a0, types.GenericAlias) and a0.__origin__ is tuple))
and isinstance(a1, types.GenericAlias)): # novermin
component_type = a1.__args__[0]
return component_type

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11 changes: 10 additions & 1 deletion py/server/tests/test_udf_return.py
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Expand Up @@ -6,7 +6,6 @@
import unittest
from typing import List, Union, Tuple, Sequence, Optional

import numba as nb
import numpy as np
import numpy.typing as npt
import pandas as pd
Expand Down Expand Up @@ -206,6 +205,7 @@ def f4557_1(x, y) -> np.ndarray[np.int64]:
return np.array(x) + y

# Testing https://github.com/deephaven/deephaven-core/issues/4562
import numba as nb
@nb.guvectorize([(nb.int32[:], nb.int32, nb.int32[:])], "(m),()->(m)", nopython=True)
def f4562_1(x, y, res):
res[:] = x + y
Expand Down Expand Up @@ -321,6 +321,15 @@ def udf() -> List[dtypes.Instant]:
t = empty_table(10).update(["X1 = udf()"])
self.assertEqual(t.columns[0].data_type, dtypes.instant_array)

def test_alternative_np_typehint(self):
import numpy.typing as npt

def f() -> npt.NDArray[np.int64]:
return np.array([1, 2], dtype=np.int64)

t = empty_table(10).update(["X1 = f()"])
self.assertEqual(t.columns[0].data_type, dtypes.long_array)


if __name__ == '__main__':
unittest.main()