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👽️ ndimage: 1.15.0 support #346

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merged 4 commits into from
Dec 17, 2024
Merged

👽️ ndimage: 1.15.0 support #346

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6b369d4
🔥 `ndimage`: remove unnecessary private module stubs
jorenham Dec 17, 2024
4bf697b
👽️ `ndimage`: remove explicit exports of deprecated submodules
jorenham Dec 17, 2024
b52e4ac
👽️ `ndimage`: 1.15.0 support for the filter functions
jorenham Dec 17, 2024
a6a3e60
👽️ `ndimage`: 1.15.0 support for the morphological functions
jorenham Dec 17, 2024
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13 changes: 0 additions & 13 deletions .mypyignore-todo
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Original file line number Diff line number Diff line change
@@ -1,16 +1,3 @@
scipy\.ndimage\.__all__
scipy\.ndimage\.(_?filters\.)?convolve
scipy\.ndimage\.(_?filters\.)?correlate
scipy\.ndimage\.(_?filters\.)?laplace
scipy\.ndimage\.(_?filters\.)?generic_filter
scipy\.ndimage\.(_?filters\.)?(gaussian|generic)_(gradient_magnitude|laplace)
scipy\.ndimage\.(_?morphology\.)?(black|white)_tophat
scipy\.ndimage\.(_?morphology\.)?binary_(propagation|fill_holes|hit_or_miss)
scipy\.ndimage\.(_?morphology\.)?(binary|grey)_(closing|opening|dilation|erosion)
scipy\.ndimage\.(_?morphology\.)?morphological_(gradient|laplace)
scipy\.ndimage\._ni_docstrings\.docfiller
scipy\.ndimage\._ni_support\._extend_mode_to_code

scipy\.optimize\.elementwise
scipy\.optimize\._cobyqa_py\.COBYQA_LOCK
scipy\.optimize\.(_basinhopping\.)?basinhopping
Expand Down
60 changes: 52 additions & 8 deletions scipy-stubs/ndimage/__init__.pyi
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Original file line number Diff line number Diff line change
@@ -1,4 +1,13 @@
from . import filters, fourier, interpolation, measurements, morphology
# Deprecated namespaces, to be removed in v2.0.0
from . import (
filters as filters,
fourier as fourier,
interpolation as interpolation,
measurements as measurements,
morphology as morphology,
)

#
from ._filters import (
convolve,
convolve1d,
Expand Down Expand Up @@ -36,8 +45,48 @@ from ._interpolation import (
spline_filter1d,
zoom,
)
from ._measurements import *
from ._morphology import *
from ._measurements import (
center_of_mass,
extrema,
find_objects,
histogram,
label,
labeled_comprehension,
maximum,
maximum_position,
mean,
median,
minimum,
minimum_position,
standard_deviation,
sum,
sum_labels,
value_indices,
variance,
watershed_ift,
)
from ._morphology import (
binary_closing,
binary_dilation,
binary_erosion,
binary_fill_holes,
binary_hit_or_miss,
binary_opening,
binary_propagation,
black_tophat,
distance_transform_bf,
distance_transform_cdt,
distance_transform_edt,
generate_binary_structure,
grey_closing,
grey_dilation,
grey_erosion,
grey_opening,
iterate_structure,
morphological_gradient,
morphological_laplace,
white_tophat,
)

__all__ = [
"affine_transform",
Expand All @@ -58,9 +107,7 @@ __all__ = [
"distance_transform_cdt",
"distance_transform_edt",
"extrema",
"filters",
"find_objects",
"fourier",
"fourier_ellipsoid",
"fourier_gaussian",
"fourier_shift",
Expand All @@ -80,7 +127,6 @@ __all__ = [
"grey_erosion",
"grey_opening",
"histogram",
"interpolation",
"iterate_structure",
"label",
"labeled_comprehension",
Expand All @@ -91,7 +137,6 @@ __all__ = [
"maximum_filter1d",
"maximum_position",
"mean",
"measurements",
"median",
"median_filter",
"minimum",
Expand All @@ -100,7 +145,6 @@ __all__ = [
"minimum_position",
"morphological_gradient",
"morphological_laplace",
"morphology",
"percentile_filter",
"prewitt",
"rank_filter",
Expand Down
71 changes: 52 additions & 19 deletions scipy-stubs/ndimage/_filters.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,24 @@ _Mode: TypeAlias = Literal["reflect", "constant", "nearest", "mirror", "wrap", "
_Modes: TypeAlias = _Mode | Sequence[_Mode]
_Ints: TypeAlias = int | Sequence[int]

_FilterFunc1D: TypeAlias = Callable[Concatenate[onp.ArrayND[np.float64], onp.ArrayND[np.float64], ...], None]
_FilterFuncND: TypeAlias = Callable[
Concatenate[onp.ArrayND[np.float64], ...],
onp.ToComplex | _ScalarValueOut | _ScalarArrayOut,
]
_Derivative: TypeAlias = Callable[
# (input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
Concatenate[_ScalarArrayOut, int, np.dtype[_ScalarValueOut], _Mode, onp.ToComplex, ...],
_ScalarArrayOut,
]

@type_check_only
class _GaussianKwargs(TypedDict, total=False):
truncate: float
radius: _Ints

###

# TODO: allow passing dtype-likes to `output`

#
Expand All @@ -46,6 +64,8 @@ def laplace(
output: _ScalarArrayOut | None = None,
mode: _Modes = "reflect",
cval: onp.ToComplex = 0.0,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
Expand All @@ -56,6 +76,8 @@ def prewitt(
mode: _Modes = "reflect",
cval: onp.ToComplex = 0.0,
) -> _ScalarArrayOut: ...

#
def sobel(
input: onp.ToComplex | onp.ToComplexND,
axis: int = -1,
Expand All @@ -74,6 +96,8 @@ def correlate1d(
cval: onp.ToComplex = 0.0,
origin: int = 0,
) -> _ScalarArrayOut: ...

#
def convolve1d(
input: onp.ToComplex | onp.ToComplexND,
weights: onp.ToFloat | onp.ToFloat1D,
Expand All @@ -92,38 +116,43 @@ def correlate(
mode: _Mode = "reflect",
cval: onp.ToComplex = 0.0,
origin: _Ints = 0,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
def convolve(
input: onp.ToComplex | onp.ToComplexND,
weights: onp.ToFloat | onp.ToFloatND,
output: _ScalarArrayOut | None = None,
mode: _Mode = "reflect",
cval: onp.ToComplex = 0.0,
origin: _Ints = 0,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#

@type_check_only
class _GaussianKwargs(TypedDict, total=False):
truncate: float
radius: _Ints
axes: tuple[int, ...]

def gaussian_laplace(
input: onp.ToComplex | onp.ToComplexND,
sigma: onp.ToFloat | onp.ToFloatND,
output: _ScalarArrayOut | None = None,
mode: _Modes = "reflect",
cval: onp.ToComplex = 0.0,
*,
axes: tuple[int, ...] | None = None,
**kwargs: Unpack[_GaussianKwargs],
) -> _ScalarArrayOut: ...

#
def gaussian_gradient_magnitude(
input: onp.ToComplex | onp.ToComplexND,
sigma: onp.ToFloat | onp.ToFloatND,
output: _ScalarArrayOut | None = None,
mode: _Modes = "reflect",
cval: onp.ToComplex = 0.0,
*,
axes: tuple[int, ...] | None = None,
**kwargs: Unpack[_GaussianKwargs],
) -> _ScalarArrayOut: ...

Expand Down Expand Up @@ -156,12 +185,6 @@ def gaussian_filter(
) -> _ScalarArrayOut: ...

#
_Derivative: TypeAlias = Callable[
# (input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
Concatenate[_ScalarArrayOut, int, np.dtype[_ScalarValueOut], _Mode, onp.ToComplex, ...],
_ScalarArrayOut,
]

def generic_laplace(
input: onp.ToComplex | onp.ToComplexND,
derivative2: _Derivative,
Expand All @@ -170,7 +193,11 @@ def generic_laplace(
cval: onp.ToComplex = 0.0,
extra_arguments: tuple[object, ...] = (),
extra_keywords: dict[str, object] | None = None,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
def generic_gradient_magnitude(
input: onp.ToComplex | onp.ToComplexND,
derivative: _Derivative,
Expand All @@ -179,10 +206,11 @@ def generic_gradient_magnitude(
cval: onp.ToComplex = 0.0,
extra_arguments: tuple[object, ...] = (),
extra_keywords: dict[str, object] | None = None,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
_FilterFunc1D: TypeAlias = Callable[Concatenate[onp.ArrayND[np.float64], onp.ArrayND[np.float64], ...], None]

def generic_filter1d(
input: onp.ToFloat | onp.ToFloatND,
Expand All @@ -198,11 +226,6 @@ def generic_filter1d(
) -> _FloatArrayOut: ...

#
_FilterFuncND: TypeAlias = Callable[
Concatenate[onp.ArrayND[np.float64], ...],
onp.ToComplex | _ScalarValueOut | _ScalarArrayOut,
]

def generic_filter(
input: onp.ToFloat | onp.ToFloatND,
function: _FilterFuncND | LowLevelCallable,
Expand All @@ -214,6 +237,8 @@ def generic_filter(
origin: _Ints = 0,
extra_arguments: tuple[object, ...] = (),
extra_keywords: dict[str, object] | None = None,
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
Expand All @@ -226,6 +251,8 @@ def uniform_filter1d(
cval: onp.ToComplex = 0.0,
origin: int = 0,
) -> _ScalarArrayOut: ...

#
def minimum_filter1d(
input: onp.ToComplex | onp.ToComplexND,
size: int,
Expand All @@ -235,6 +262,8 @@ def minimum_filter1d(
cval: onp.ToComplex = 0.0,
origin: int = 0,
) -> _ScalarArrayOut: ...

#
def maximum_filter1d(
input: onp.ToComplex | onp.ToComplexND,
size: int,
Expand Down Expand Up @@ -269,6 +298,8 @@ def minimum_filter(
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
def maximum_filter(
input: onp.ToComplex | onp.ToComplexND,
size: int | tuple[int, ...] | None = None,
Expand Down Expand Up @@ -307,6 +338,8 @@ def rank_filter(
*,
axes: tuple[int, ...] | None = None,
) -> _ScalarArrayOut: ...

#
def percentile_filter(
input: onp.ToComplex | onp.ToComplexND,
percentile: onp.ToFloat,
Expand Down
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