scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
3.10.1 Filters scipy.ndimage.filters
convolve(input, weights[, output, Multi-dimensional convolution.
mode, ...])
convolve1d(input, weights[, axis, Calculate a one-dimensional convolution along the given axis.
output, ...])
correlate(input, weights[, output, Multi-dimensional correlation.
mode, ...])
correlate1d(input, weights[, axis, Calculate a one-dimensional correlation along the given axis.
output, ...])
gaussian_filter(input, sigma[, Multi-dimensional Gaussian filter.
order, ...])
gaussian_filter1d(input, One-dimensional Gaussian filter.
sigma[, axis, ...])
gaussian_gradient_magnitude(input, Calculate a multidimensional gradient magnitude using gaussian
sigma[, ...])
derivatives.
gaussian_laplace(input, sigma[, Calculate a multidimensional laplace filter using gaussian second
output, ...])
derivatives.
generic_filter(input, function[, Calculates a multi-dimensional filter using the given function.
size, ...])
generic_filter1d(input, Calculate a one-dimensional filter along the given axis.
function, filter_size)
generic_gradient_magnitude(input, Calculate a gradient magnitude using the provided function for the
derivative)
gradient.
generic_laplace(input,
Calculate a multidimensional laplace filter using the provided second
derivative2[, ...])
derivative function.
laplace(input[, output, mode, cval]) Calculate a multidimensional laplace filter using an estimation for the
second derivative based on differences.
maximum_filter(input[, size, Calculates a multi-dimensional maximum filter.
footprint, ...])
maximum_filter1d(input, size[, Calculate a one-dimensional maximum filter along the given axis.
axis, ...])
median_filter(input[, size, Calculates a multi-dimensional median filter.
footprint, ...])
minimum_filter(input[, size, Calculates a multi-dimensional minimum filter.
footprint, ...])
minimum_filter1d(input, size[, Calculate a one-dimensional minimum filter along the given axis.
axis, ...])
percentile_filter(input, Calculates a multi-dimensional percentile filter.
percentile[, size, ...])
prewitt(input[, axis, output, mode, Calculate a Prewitt filter.
cval])
rank_filter(input, rank[, size, Calculates a multi-dimensional rank filter.
footprint, ...])
sobel(input[, axis, output, mode, Calculate a Sobel filter.
cval])
uniform_filter(input[, size, Multi-dimensional uniform filter.
output, mode, ...])
uniform_filter1d(input, size[, Calculate a one-dimensional uniform filter along the given axis.
axis, ...])
convolve(input, weights, output=None, mode=’reflect’, cval=0.0, origin=0)
Multi-dimensional convolution.
The array is convolved with the given kernel.
266 Chapter 3. Reference