ezmsg.sigproc.gaussiansmoothing#

Functions

gaussian_smoothing_filter_design(sigma=1.0, width=4, kernel_size=None)[source]#
Parameters:
Return type:

tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]] | None

Classes

class GaussianSmoothingFilter(*args, settings=None, **kwargs)[source]#

Bases: BaseFilterByDesignTransformerUnit[GaussianSmoothingSettings, GaussianSmoothingFilterTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of GaussianSmoothingSettings

class GaussianSmoothingFilterTransformer(*args, **kwargs)[source]#

Bases: FilterByDesignTransformer[GaussianSmoothingSettings, tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]]

get_design_function()[source]#

Return a function that takes sampling frequency and returns filter coefficients.

Return type:

Callable[[float], tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]]

class GaussianSmoothingSettings(axis: str | None = None, coef_type: str = 'ba', sigma: float | None = 1.0, width: int | None = 4, kernel_size: int | None = None)[source]#

Bases: FilterBaseSettings

Parameters:

  • axis (str | None)

  • coef_type (str)

  • sigma (float | None)

  • width (int | None)

  • kernel_size (int | None)

sigma: float | None = 1.0#

float Standard deviation of the Gaussian kernel.

Type:

sigma

width: int | None = 4#

int Number of standard deviations covered by the kernel window if kernel_size is not provided.

Type:

width

kernel_size: int | None = None#

int | None Length of the kernel in samples. If provided, overrides automatic calculation.

Type:

kernel_size

__init__(axis=None, coef_type='ba', sigma=1.0, width=4, kernel_size=None)#
Parameters:

  • axis (str | None)

  • coef_type (str)

  • sigma (float | None)

  • width (int | None)

  • kernel_size (int | None)

Return type:

None

class GaussianSmoothingSettings(axis: str | None = None, coef_type: str = 'ba', sigma: float | None = 1.0, width: int | None = 4, kernel_size: int | None = None)[source]#

Bases: FilterBaseSettings

Parameters:

  • axis (str | None)

  • coef_type (str)

  • sigma (float | None)

  • width (int | None)

  • kernel_size (int | None)

sigma: float | None = 1.0#

float Standard deviation of the Gaussian kernel.

Type:

sigma

width: int | None = 4#

int Number of standard deviations covered by the kernel window if kernel_size is not provided.

Type:

width

kernel_size: int | None = None#

int | None Length of the kernel in samples. If provided, overrides automatic calculation.

Type:

kernel_size

__init__(axis=None, coef_type='ba', sigma=1.0, width=4, kernel_size=None)#
Parameters:

  • axis (str | None)

  • coef_type (str)

  • sigma (float | None)

  • width (int | None)

  • kernel_size (int | None)

Return type:

None

gaussian_smoothing_filter_design(sigma=1.0, width=4, kernel_size=None)[source]#
Parameters:
Return type:

tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]] | None

class GaussianSmoothingFilterTransformer(*args, **kwargs)[source]#

Bases: FilterByDesignTransformer[GaussianSmoothingSettings, tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]]

get_design_function()[source]#

Return a function that takes sampling frequency and returns filter coefficients.

Return type:

Callable[[float], tuple[ndarray[tuple[Any, …], dtype[_ScalarT]], ndarray[tuple[Any, …], dtype[_ScalarT]]]]

class GaussianSmoothingFilter(*args, settings=None, **kwargs)[source]#

Bases: BaseFilterByDesignTransformerUnit[GaussianSmoothingSettings, GaussianSmoothingFilterTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of GaussianSmoothingSettings