# Kernel¶

class astropy.convolution.Kernel(array)[source]

Bases: object

Convolution kernel base class.

Parameters: array : ndarray Kernel array.

Attributes Summary

 array Filter kernel array. center Index of the kernel center. dimension Kernel dimension. is_bool Indicates if kernel is bool. model Kernel response model. separable Indicates if the filter kernel is separable. shape Shape of the kernel array. truncation Deviation from the normalization to one.

Methods Summary

 normalize([mode]) Normalize the filter kernel.

Attributes Documentation

array

Filter kernel array.

center

Index of the kernel center.

dimension

Kernel dimension.

is_bool

Indicates if kernel is bool.

If the kernel is bool the multiplication in the convolution could be omitted, to increase the performance.

model

Kernel response model.

separable

Indicates if the filter kernel is separable.

A 2D filter is separable, when its filter array can be written as the outer product of two 1D arrays.

If a filter kernel is separable, higher dimension convolutions will be performed by applying the 1D filter array consecutively on every dimension. This is significantly faster, than using a filter array with the same dimension.

shape

Shape of the kernel array.

truncation

Deviation from the normalization to one.

Methods Documentation

normalize(mode='integral')[source]

Normalize the filter kernel.

Parameters: mode : {‘integral’, ‘peak’} One of the following modes: ‘integral’ (default) Kernel is normalized such that its integral = 1. ‘peak’ Kernel is normalized such that its peak = 1.