8000 ENH: change list-of-array to tuple-of-array returns (Numba compat) by rgommers · Pull Request #25570 · numpy/numpy · GitHub
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ENH: change list-of-array to tuple-of-array returns (Numba compat) #25570

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Merged
merged 5 commits into from
Jan 21, 2024
Merged

ENH: change list-of-array to tuple-of-array returns (Numba compat) #25570

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f379ba9
WIP: MAINT: change list-of-array to tuple-of-array returns (Numba com…
rgommers Jan 11, 2024
ac37530
ENH: finish list[ndarray] -> tuple[ndarray] changes
rgommers Jan 19, 2024
779d74d
ENH: tuple returns for ogrid/meshgrid/histogramdd
rgommers Jan 19, 2024
02f6b7f
DOC: add release note for the `list[ndarray]` -> `tuple[ndarray]` change
rgommers Jan 19, 2024
760836f
BUG: fix type annotations for list/tuple
rgommers Jan 21, 2024
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ENH: tuple returns for ogrid/meshgrid/histogramdd 
Also some small docstring improvements to make clearer what
is returned.
  • Loading branch information
@rgommers
rgommers committed Jan 19, 2024
commit 779d74d6847724aa0e71d69bc03b63ffea501083
6 changes: 3 additions & 3 deletions numpy/lib/_function_base_impl.py
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Expand Up @@ -4986,7 +4986,7 @@ def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
@array_function_dispatch(_meshgrid_dispatcher)
def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
"""
Return a list of coordinate matrices from coordinate vectors.
Return a tuple of coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
Expand Down Expand Up @@ -5027,7 +5027,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
Returns
-------
X1, X2,..., XN : list of ndarrays
X1, X2,..., XN : tuple of ndarrays
For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
Expand Down Expand Up @@ -5136,7 +5136,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
output = np.broadcast_arrays(*output, subok=True)

8000
if copy:
output = [x.copy() for x in output]
output = tuple(x.copy() for x in output)

return output

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2 changes: 1 addition & 1 deletion numpy/lib/_function_base_impl.pyi
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Expand Up @@ -626,7 +626,7 @@ def meshgrid(
copy: bool = ...,
sparse: bool = ...,
indexing: L["xy", "ij"] = ...,
) -> list[NDArray[Any]]: ...
) -> tuple[NDArray[Any]]: ...

@overload
def delete(
Expand Down
4 changes: 2 additions & 2 deletions numpy/lib/_histograms_impl.py
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Expand Up @@ -957,8 +957,8 @@ def histogramdd(sample, bins=10, range=None, density=None, weights=None):
H : ndarray
The multidimensional histogram of sample x. See density and weights
for the different possible semantics.
edges : list
A list of D arrays describing the bin edges for each dimension.
edges : tuple of ndarrays
A tuple of D arrays describing the bin edges for each dimension.
See Also
--------
Expand Down
2 changes: 1 addition & 1 deletion numpy/lib/_histograms_impl.pyi
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Expand Up @@ -44,4 +44,4 @@ def histogramdd(
range: Sequence[tuple[float, float]] = ...,
density: None | bool = ...,
weights: None | ArrayLike = ...,
) -> tuple[NDArray[Any], list[NDArray[Any]]]: ...
) -> tuple[NDArray[Any], tuple[NDArray[Any]]]: ...
23 changes: 17 additions & 6 deletions numpy/lib/_index_tricks_impl.py
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -189,7 +189,8 @@ def __getitem__(self, key):
slobj[k] = slice(None, None)
nn[k] = nn[k][tuple(slobj)]
slobj[k] = _nx.newaxis
return nn
return tuple(nn) # ogrid -> tuple of arrays
return nn # mgrid -> ndarray
except (IndexError, TypeError):
step = key.step
stop = key.stop
Expand Down Expand Up @@ -225,8 +226,9 @@ class MGridClass(nd_grid):

Returns
-------
mesh-grid
`ndarray`\\ s all of the same dimensions
mesh-grid : ndarray
A single array, containing a set of `ndarray`\\ s all of the same
dimensions. stacked along the first axis.

See Also
--------
Expand All @@ -251,6 +253,13 @@ class MGridClass(nd_grid):
>>> np.mgrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])

>>> np.mgrid[0:4].shape
(4,)
>>> np.mgrid[0:4, 0:5].shape
(2, 4, 5)
>>> np.mgrid[0:4, 0:5, 0:6].shape
(3, 4, 5, 6)

"""

def __init__(self):
Expand All @@ -277,8 +286,10 @@ class OGridClass(nd_grid):

Returns
-------
mesh-grid
`ndarray`\\ s with only one dimension not equal to 1
mesh-grid : ndarray or tuple of ndarrays
If the input is a single slice, returns an array.
If the input is multiple slices, returns a tuple of arrays, with
only one dimension not equal to 1.

See Also
--------
Expand All @@ -292,7 +303,7 @@ class OGridClass(nd_grid):
>>> from numpy import ogrid
>>> ogrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
>>> ogrid[0:5,0:5]
>>> ogrid[0:5, 0:5]
[array([[0],
[1],
[2],
Expand Down
2 changes: 1 addition & 1 deletion numpy/lib/_index_tricks_impl.pyi
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Expand Up @@ -74,7 +74,7 @@ class nd_grid(Generic[_BoolType]):
def __getitem__(
self: nd_grid[Literal[True]],
key: slice | Sequence[slice],
) -> list[NDArray[Any]]: ...
) -> tuple[NDArray[Any]]: ...

class MGridClass(nd_grid[Literal[False]]):
def __init__(self) -> None: ...
Expand Down
8 changes: 4 additions & 4 deletions numpy/typing/tests/data/reveal/histograms.pyi
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Expand Up @@ -21,7 +21,7 @@ assert_type(np.histogram(AR_i8, bins="rice", range=(0, 3)), tuple[npt.NDArray[An
assert_type(np.histogram(AR_i8, bins="scott", weights=AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]])
assert_type(np.histogram(AR_f8, bins=1, density=True), tuple[npt.NDArray[Any], npt.NDArray[Any]])

assert_type(np.histogramdd(AR_i8, bins=[1]), tuple[npt.NDArray[Any], list[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_i8, range=[(0, 3)]), tuple[npt.NDArray[Any], list[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_i8, weights=AR_f8), tuple[npt.NDArray[Any], list[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_f8, density=True), tuple[npt.NDArray[Any], list[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_i8, bins=[1]), tuple[npt.NDArray[Any], tuple[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_i8, range=[(0, 3)]), tuple[npt.NDArray[Any], tuple[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_i8, weights=AR_f8), tuple[npt.NDArray[Any], tuple[npt.NDArray[Any]]])
assert_type(np.histogramdd(AR_f8, density=True), tuple[npt.NDArray[Any], tuple[npt.NDArray[Any]]])
4 changes: 2 additions & 2 deletions numpy/typing/tests/data/reveal/index_tricks.pyi
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Expand Up @@ -51,8 +51,8 @@ assert_type(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), np.intp)
assert_type(np.mgrid[1:1:2], npt.NDArray[Any])
assert_type(np.mgrid[1:1:2, None:10], npt.NDArray[Any])

assert_type(np.ogrid[1:1:2], list[npt.NDArray[Any]])
assert_type(np.ogrid[1:1:2, None:10], list[npt.NDArray[Any]])
assert_type(np.ogrid[1:1:2], tuple[npt.NDArray[Any]])
assert_type(np.ogrid[1:1:2, None:10], tuple[npt.NDArray[Any]])

assert_type(np.index_exp[0:1], tuple[slice])
assert_type(np.index_exp[0:1, None:3], tuple[slice, slice])
Expand Down
4 changes: 2 additions & 2 deletions numpy/typing/tests/data/reveal/lib_function_base.pyi
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Expand Up @@ -159,8 +159,8 @@ assert_type(np.quantile(AR_f8, [0.5], keepdims=True), Any)
assert_type(np.quantile(AR_f8, [0.5], axis=[1]), Any)
assert_type(np.quantile(AR_f8, [0.5], out=AR_c16), npt.NDArray[np.complex128])

assert_type(np.meshgrid(AR_f8, AR_i8, copy=False), list[npt.NDArray[Any]])
assert_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij"), list[npt.NDArray[Any]])
assert_type(np.meshgrid(AR_f8, AR_i8, copy=False), tuple[npt.NDArray[Any]])
assert_type(np.meshgrid(AR_f8, AR_i8, AR_c16, indexing="ij"), tuple[npt.NDArray[Any]])

assert_type(np.delete(AR_f8, np.s_[:5]), npt.NDArray[np.float64])
assert_type(np.delete(AR_LIKE_f8, [0, 4, 9], axis=0), npt.NDArray[Any])
Expand Down
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