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various

Bilinear

Bilinear(**kwargs: Any)

Bases: CustomComplexKernel

Bilinear resizer.

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def __init__(self, **kwargs: Any) -> None:
    self.kwargs = kwargs

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    return max(1.0 - abs(x), 0.0)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:
    return _default_kernel_radius(__class__, self)  # type: ignore

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

BlackMan

BlackMan(taps: float = 4, **kwargs: Any)

Bases: CustomComplexTapsKernel

Blackman resizer.

Source code
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def __init__(self, taps: float = 4, **kwargs: Any) -> None:
    super().__init__(taps, **kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    return sinc(x) * self._win_coef(x)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

BlackManMinLobe

BlackManMinLobe(taps: float = 4, **kwargs: Any)

Bases: BlackMan

Blackmanminlobe resizer.

Source code
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def __init__(self, taps: float = 4, **kwargs: Any) -> None:
    super().__init__(taps, **kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
Source code
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    return sinc(x) * self._win_coef(x)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Bohman

Bohman(taps: float, **kwargs: Any)

Bases: CustomComplexTapsKernel

Bohman kernel.

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def __init__(self, taps: float, **kwargs: Any) -> None:
    self.taps = taps
    super().__init__(**kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    cosine = cos(pi * x)
    sine = sqrt(1.0 - cosine * cosine)

    return (1.0 - x) * cosine + (1.0 / pi) * sine

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Box

Box(**kwargs: Any)

Bases: CustomComplexKernel

Box resizer.

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def __init__(self, **kwargs: Any) -> None:
    self.kwargs = kwargs

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    return 1.0 if x >= -0.5 and x < 0.5 else 0.0

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:
    return _default_kernel_radius(__class__, self)  # type: ignore

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Cosine

Cosine(taps: float, **kwargs: Any)

Bases: CustomComplexTapsKernel

Cosine kernel.

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def __init__(self, taps: float, **kwargs: Any) -> None:
    self.taps = taps
    super().__init__(**kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    cosine = cos(pi * x)

    return 0.34 + cosine * (0.5 + cosine * 0.16)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Gaussian

Gaussian(sigma: float = 0.5, taps: float = 2, **kwargs: Any)

Bases: CustomComplexTapsKernel

Gaussian resizer.

Sigma is the same as imagemagick's sigma scaling.

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def __init__(self, sigma: float = 0.5, taps: float = 2, **kwargs: Any) -> None:
    """Sigma is the same as imagemagick's sigma scaling."""

    self._sigma = sigma

    super().__init__(taps, **kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    return 1 / (self._sigma * sqrt(2 * pi)) * exp(-x ** 2 / (2 * self._sigma ** 2))

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

sigma

sigma() -> gauss_sigma
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@inject_self.property
def sigma(self) -> gauss_sigma:
    return gauss_sigma(self._sigma)

Hamming

Hamming(taps: float, **kwargs: Any)

Bases: CustomComplexTapsKernel

Hamming kernel.

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def __init__(self, taps: float, **kwargs: Any) -> None:
    self.taps = taps
    super().__init__(**kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    return 0.54 + 0.46 * cos(pi * x)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Hann

Hann(taps: float, **kwargs: Any)

Bases: CustomComplexTapsKernel

Hann kernel.

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def __init__(self, taps: float, **kwargs: Any) -> None:
    self.taps = taps
    super().__init__(**kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    return 0.5 + 0.5 * cos(pi * x)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Lanczos

Lanczos(taps: float = 3, **kwargs: Any)

Bases: CustomComplexTapsKernel

Lanczos resizer.

Parameters:

  • taps

    (float, default: 3 ) –

    taps param for lanczos kernel

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def __init__(self, taps: float = 3, **kwargs: Any) -> None:
    super().__init__(taps, **kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    x, taps = abs(x), self.kernel_radius

    return sinc(x) * sinc(x / taps) if x < taps else 0.0

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Point

Point(**kwargs: Any)

Bases: CustomComplexKernel

Point resizer.

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def __init__(self, **kwargs: Any) -> None:
    self.kwargs = kwargs

descale class-attribute instance-attribute

descale = scale

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    return 1.0

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:
    return _default_kernel_radius(__class__, self)  # type: ignore

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Sinc

Sinc(taps: float = 4, **kwargs: Any)

Bases: CustomComplexTapsKernel

Sinc resizer.

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def __init__(self, taps: float = 4, **kwargs: Any) -> None:
    super().__init__(taps, **kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if x >= self.kernel_radius:
        return 0.0

    return sinc(x)

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

Welch

Welch(taps: float, **kwargs: Any)

Bases: CustomComplexTapsKernel

Welch kernel.

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def __init__(self, taps: float, **kwargs: Any) -> None:
    self.taps = taps
    super().__init__(**kwargs)

kwargs instance-attribute

kwargs: KwargsT = kwargs

Arguments passed to the internal scale function

resample_function class-attribute instance-attribute

resample_function = scale_function

taps instance-attribute

taps = taps

descale

descale(
    clip: VideoNode,
    width: int,
    height: int,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    blur: float = 1.0,
    border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    ignore_mask: VideoNode | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def descale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, shift: tuple[TopShift, LeftShift] = (0, 0),
    *, blur: float = 1.0, border_handling: BorderHandling,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    ignore_mask: vs.VideoNode | None = None, linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False, **kwargs: Any
) -> vs.VideoNode:
    ...

descale_function

descale_function(
    clip: VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> VideoNode
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@inject_self
def descale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int, height: int, *args: Any, **kwargs: Any
) -> vs.VideoNode:
    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    try:
        return core.descale.Decustom(clip, width, height, kernel, ceil(support), *args, **clean_kwargs)
    except vs.Error as e:
        if 'Output dimension must be' in str(e):
            raise CustomValueError(
                f'Output dimension ({width}x{height}) must be less than or equal to '
                f'input dimension ({clip.width}x{clip.height}).', self.__class__
            )

        raise CustomValueError(e, self.__class__)

ensure_obj classmethod

ensure_obj(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> Kernel
ensure_obj(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler
ensure_obj(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Descaler
ensure_obj(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Resampler
ensure_obj(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> Scaler | Descaler | Resampler | Kernel
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@classmethod
def ensure_obj(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> Scaler | Descaler | Resampler | Kernel:
    from ..util import abstract_kernels
    return _base_ensure_obj(  # type: ignore
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except
    )

from_param classmethod

from_param(
    kernel: KernelT | None = None, func_except: FuncExceptT | None = None
) -> type[Kernel]
from_param(
    kernel: ScalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler]
from_param(
    kernel: DescalerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Descaler]
from_param(
    kernel: ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Resampler]
from_param(
    kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None,
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]
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@classmethod
def from_param(
    cls: type[Kernel], kernel: ScalerT | DescalerT | ResamplerT | KernelT | None = None,
    func_except: FuncExceptT | None = None
) -> type[Scaler] | type[Descaler] | type[Resampler] | type[Kernel]:
    from ..util import abstract_kernels
    return _base_from_param(
        cls, Kernel, kernel, UnknownKernelError, abstract_kernels, func_except  # type: ignore
    )

get_clean_kwargs

get_clean_kwargs(*funcs: Callable[..., Any] | None) -> KwargsT
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def get_clean_kwargs(self, *funcs: Callable[..., Any] | None) -> KwargsT:
    return _clean_self_kwargs(funcs, self)

get_descale_args

get_descale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_descale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(True, clip, width, height, **kwargs)
    )

get_implemented_funcs

get_implemented_funcs() -> tuple[Callable[..., Any], ...]
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def get_implemented_funcs(self) -> tuple[Callable[..., Any], ...]:
    return (self.shift, )  # type: ignore

get_params_args

get_params_args(
    is_descale: bool,
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    **kwargs: Any
) -> dict[str, Any]
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def get_params_args(
    self, is_descale: bool, clip: vs.VideoNode, width: int | None = None, height: int | None = None, **kwargs: Any
) -> dict[str, Any]:
    args = super().get_params_args(is_descale, clip, width, height, **kwargs)

    if not is_descale:
        for key in ('border_handling', 'ignore_mask'):
            args.pop(key, None)

    return args

get_resample_args

get_resample_args(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None,
    matrix_in: MatrixT | None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_resample_args(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None, matrix_in: MatrixT | None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(
            format=get_video_format(format).id,
            matrix=Matrix.from_param(matrix),
            matrix_in=Matrix.from_param(matrix_in)
        )
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, **kwargs)
    )

get_scale_args

get_scale_args(
    clip: VideoNode,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None,
    height: int | None = None,
    *funcs: Callable[..., Any],
    **kwargs: Any
) -> KwargsT
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@inject_kwargs_params
def get_scale_args(
    self, clip: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0),
    width: int | None = None, height: int | None = None,
    *funcs: Callable[..., Any], **kwargs: Any
) -> KwargsT:
    return (
        dict(src_top=shift[0], src_left=shift[1])
        | self.get_clean_kwargs(*funcs)
        | self.get_params_args(False, clip, width, height, **kwargs)
    )

kernel

kernel(*, x: float) -> float
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@inject_self.cached
def kernel(self, *, x: float) -> float:
    if abs(x) >= 1.0:
        return 0.0

    return 1.0 - x * x

kernel_radius

kernel_radius() -> int
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@inject_self.cached.property
def kernel_radius(self) -> int:  # type: ignore
    return ceil(self.taps)

multi

multi(
    clip: VideoNode,
    multi: float = 2,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
def multi(
    self, clip: vs.VideoNode, multi: float = 2, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> vs.VideoNode:
    assert check_variable_resolution(clip, self.multi)

    dst_width, dst_height = ceil(clip.width * multi), ceil(clip.height * multi)

    if max(dst_width, dst_height) <= 0.0:
        raise CustomValueError(
            'Multiplying the resolution by "multi" must result in a positive resolution!', self.multi, multi
        )

    return self.scale(clip, dst_width, dst_height, shift, **kwargs)

pretty_string

pretty_string() -> str
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@inject_self.cached.property
def pretty_string(self) -> str:
    attrs = {}

    if hasattr(self, 'b'):
        attrs.update(b=self.b, c=self.c)
    elif hasattr(self, 'taps'):
        attrs['taps'] = self.taps

    if hasattr(self, 'kwargs'):
        attrs.update(self.kwargs)

    return f"{self.__class__.__name__}{' (' + ', '.join(f'{k}={v}' for k, v in attrs.items()) + ')' if attrs else ''}"

resample

resample(
    clip: VideoNode,
    format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None,
    matrix_in: MatrixT | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def resample(
    self, clip: vs.VideoNode, format: int | VideoFormatT | HoldsVideoFormatT,
    matrix: MatrixT | None = None, matrix_in: MatrixT | None = None, **kwargs: Any
) -> vs.VideoNode:
    return self.resample_function(
        clip, **_norm_props_enums(self.get_resample_args(clip, format, matrix, matrix_in, **kwargs))
    )

scale

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = MIRROR,
    sample_grid_model: SampleGridModel = MATCH_EDGES,
    sar: Sar | bool | float | None = None,
    dar: Dar | bool | float | None = None,
    keep_ar: bool | None = None,
    linear: bool = False,
    sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached
@inject_kwargs_params
def scale(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    *,
    border_handling: BorderHandling = BorderHandling.MIRROR,
    sample_grid_model: SampleGridModel = SampleGridModel.MATCH_EDGES,
    sar: Sar | bool | float | None = None, dar: Dar | bool | float | None = None, keep_ar: bool | None = None,
    linear: bool = False, sigmoid: bool | tuple[Slope, Center] = False,
    **kwargs: Any
) -> vs.VideoNode:
    width, height = Scaler._wh_norm(clip, width, height)
    return super().scale(
        clip, width, height, shift, sar=sar, dar=dar, keep_ar=keep_ar,
        linear=linear, sigmoid=sigmoid, border_handling=border_handling,
        sample_grid_model=sample_grid_model, **kwargs
    )

scale_function

scale_function(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    *args: Any,
    **kwargs: Any
) -> VideoNode
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@inject_self
def scale_function(  # type: ignore[override]
    self, clip: vs.VideoNode, width: int | None = None, height: int | None = None, *args: Any, **kwargs: Any
) -> vs.VideoNode:

    if not hasattr(core, 'resize2'):
        raise DependencyNotFoundError(
            self.__class__, 'resize2', 'Missing dependency \'resize2\'! '
            'You can find it here: https://github.com/Jaded-Encoding-Thaumaturgy/vapoursynth-resize2'
        )

    kernel, support = self._modify_kernel_func(kwargs)

    clean_kwargs = {
        k: v for k, v in kwargs.items()
        if k not in Signature.from_callable(self._modify_kernel_func).parameters.keys()
    }

    return core.resize2.Custom(clip, kernel, ceil(support), width, height, *args, **clean_kwargs)

shift

shift(
    clip: VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0), **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shift_top: float | list[float] = 0.0,
    shift_left: float | list[float] = 0.0,
    **kwargs: Any
) -> VideoNode
shift(
    clip: VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self.cached  # type: ignore
@inject_kwargs_params
def shift(
    self, clip: vs.VideoNode,
    shifts_or_top: float | tuple[float, float] | list[float] | None = None,
    shift_left: float | list[float] | None = None, **kwargs: Any
) -> vs.VideoNode:
    assert clip.format

    n_planes = clip.format.num_planes

    def _shift(src: vs.VideoNode, shift: tuple[TopShift, LeftShift] = (0, 0)) -> vs.VideoNode:
        return Scaler.scale(self, src, src.width, src.height, shift, **kwargs)

    if not shifts_or_top and not shift_left:
        return _shift(clip)
    elif isinstance(shifts_or_top, tuple):
        return _shift(clip, shifts_or_top)
    elif isinstance(shifts_or_top, float) and isinstance(shift_left, float):
        return _shift(clip, (shifts_or_top, shift_left))

    if shifts_or_top is None:
        shifts_or_top = 0.0
    if shift_left is None:
        shift_left = 0.0

    shifts_top = shifts_or_top if isinstance(shifts_or_top, list) else [shifts_or_top]
    shifts_left = shift_left if isinstance(shift_left, list) else [shift_left]

    if not shifts_top:
        shifts_top = [0.0] * n_planes
    elif (ltop := len(shifts_top)) > n_planes:
        shifts_top = shifts_top[:n_planes]
    else:
        shifts_top += shifts_top[-1:] * (n_planes - ltop)

    if not shifts_left:
        shifts_left = [0.0] * n_planes
    elif (lleft := len(shifts_left)) > n_planes:
        shifts_left = shifts_left[:n_planes]
    else:
        shifts_left += shifts_left[-1:] * (n_planes - lleft)

    if len(set(shifts_top)) == len(set(shifts_left)) == 1 or n_planes == 1:
        return _shift(clip, (shifts_top[0], shifts_left[0]))

    planes = cast(list[vs.VideoNode], clip.std.SplitPlanes())

    shifted_planes = [
        plane if top == left == 0 else _shift(plane, (top, left))
        for plane, top, left in zip(planes, shifts_top, shifts_left)
    ]

    return core.std.ShufflePlanes(shifted_planes, [0, 0, 0], clip.format.color_family)

gauss_sigma

Bases: float

from_fmtc

from_fmtc(curve: float) -> float
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def from_fmtc(self, curve: float) -> float:
    if not curve:
        return 0.0
    return sqrt(1.0 / (2.0 * (curve / 10.0) * log(2)))

from_libplacebo

from_libplacebo(sigma: float) -> float
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def from_libplacebo(self, sigma: float) -> float:
    if not sigma:
        return 0.0
    return sqrt(sigma / 4)

to_fmtc

to_fmtc(sigma: float) -> float
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def to_fmtc(self, sigma: float) -> float:
    if not sigma:
        return 0.0
    return 10 / (2 * log(2) * (sigma ** 2))

to_libplacebo

to_libplacebo(sigma: float) -> float
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def to_libplacebo(self, sigma: float) -> float:
    if not sigma:
        return 0.0
    return 4 * (sigma ** 2)