scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
fourier_ellipsoid(input, size, n=-1, axis=-1, output=None)
Multi-dimensional ellipsoid fourier filter.
The array is multiplied with the fourier transform of a ellipsoid of given sizes. If the parameter n is negative,
then the input is assumed to be the result of a complex fft. If n is larger or equal to zero, the input is assumed
to be the result of a real fft, and n gives the length of the of the array before transformation along the the real
transform direction. The axis of the real transform is given by the axis parameter. This function is implemented
for arrays of rank 1, 2, or 3.
fourier_gaussian(input, sigma, n=-1, axis=-1, output=None)
Multi-dimensional Gaussian fourier filter.
The array is multiplied with the fourier transform of a Gaussian kernel. If the parameter n is negative, then the
input is assumed to be the result of a complex fft. If n is larger or equal to zero, the input is assumed to be the
result of a real fft, and n gives the length of the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis parameter.
fourier_shift(input, shift, n=-1, axis=-1, output=None)
Multi-dimensional fourier shift filter.
The array is multiplied with the fourier transform of a shift operation If the parameter n is negative, then the
input is assumed to be the result of a complex fft. If n is larger or equal to zero, the input is assumed to be the
result of a real fft, and n gives the length of the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis parameter.
fourier_uniform(input, size, n=-1, axis=-1, output=None)
Multi-dimensional Uniform fourier filter.
The array is multiplied with the fourier transform of a box of given sizes. If the parameter n is negative, then the
input is assumed to be the result of a complex fft. If n is larger or equal to zero, the input is assumed to be the
result of a real fft, and n gives the length of the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis parameter.
3.10.3 Interpolation scipy.ndimage.interpolation
affine_transform(input, matrix[, offset, ...]) Apply an affine transformation.
geometric_transform(input, mapping[, ...]) Apply an arbritrary geometric transform.
map_coordinates(input, coordinates[, ...]) Map the input array to new coordinates by interpolation.
rotate(input, angle[, axes, reshape, ...]) Rotate an array.
shift(input, shift[, output_type, output, ...]) Shift an array.
spline_filter(input[, order, output, ...]) Multi-dimensional spline filter.
spline_filter1d(input[, order, axis, ...]) Calculates a one-dimensional spline filter along the given axis.
zoom(input, zoom[, output_type, output, ...]) Zoom an array.
affine_transform(input, matrix, offset=0.0, output_shape=None, output_type=None, output=None, order=3,
mode=’constant’, cval=0.0, prefilter=True)
Apply an affine transformation.
The given matrix and offset are used to find for each point in the output the corresponding coordinates in the input
by an affine transformation. The value of the input at those coordinates is determined by spline interpolation of
the requested order. Points outside the boundaries of the input are filled according to the given mode. The output
shape can optionally be given. If not given it is equal to the input shape. The parameter prefilter determines if
the input is pre-filtered before interpolation, if False it is assumed that the input is already filtered.
The matrix must be two-dimensional or can also be given as a one-dimensional sequence or array. In the latter
case, it is assumed that the matrix is diagonal. A more efficient algorithms is then applied that exploits the
separability of the problem.
280 Chapter 3. Reference