yapocis.lib.gaussian

Module Contents

Functions

are_close(a: yapocis.utils.typing.Array, b: yapocis.utils.typing.Array) → bool

gauss_1d(sigma: float = 1.0, dt: float = 1.0, limit: float = 0.01, normalize: bool = True) → yapocis.utils.typing.Array

Get a Gaussian distribution that sums to 1 along 1 dimension, quantized by discrete steps.

gaussians(maxwidth=100, sigma=1.0, scale=1.6, limit=0.001, sigmas=[])

Generate a list of gaussian kernels with size less than maxwidth

gaussian_kernels(gs)

get_gaussian(scale)

get_scales()

get_gaussian_width(scale)

get_gaussian_kernels()

get_gaussian_kernel(scale)

gauss_image(a, scale)

zcsdog(a, scale, clearmargin=True, frame=True, res=True)

yapocis.lib.gaussian.are_close(a: yapocis.utils.typing.Array, b: yapocis.utils.typing.Array) → bool[source]
yapocis.lib.gaussian.gauss_1d(sigma: float = 1.0, dt: float = 1.0, limit: float = 0.01, normalize: bool = True) → yapocis.utils.typing.Array[source]

Get a Gaussian distribution that sums to 1 along 1 dimension, quantized by discrete steps.

Parameters

  • sigma – width

  • dt – step

  • limit – smallest change

  • normalize – normalize sum to 1.0

Returns: 1D Array

yapocis.lib.gaussian.gaussians(maxwidth=100, sigma=1.0, scale=1.6, limit=0.001, sigmas=[])[source]

Generate a list of gaussian kernels with size less than maxwidth :param maxwidth: :param sigma: :param scale: :param limit: :param sigmas:

Returns:

yapocis.lib.gaussian.program[source]
yapocis.lib.gaussian.gaussian_kernels(gs)[source]
yapocis.lib.gaussian._gaussian_basis[source]
yapocis.lib.gaussian.get_gaussian(scale)[source]
yapocis.lib.gaussian.get_scales()[source]
yapocis.lib.gaussian.get_gaussian_width(scale)[source]
yapocis.lib.gaussian._gaussian_kernels[source]
yapocis.lib.gaussian.get_gaussian_kernels()[source]
yapocis.lib.gaussian.get_gaussian_kernel(scale)[source]
yapocis.lib.gaussian.gauss_image(a, scale)[source]
yapocis.lib.gaussian.zcsdog(a, scale, clearmargin=True, frame=True, res=True)[source]