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convolution_matrix(a, n, mode='full')

Constructs the Toeplitz matrix representing one-dimensional convolution . See the notes below for details.

Notes

The code:

A = convolution_matrix(a, n, mode)

creates a Toeplitz matrix A such that A @ v is equivalent to using convolve(a, v, mode) . The returned array always has n columns. The number of rows depends on the specified :None:None:`mode`, as explained above.

In the default 'full' mode, the entries of A are given by:

A[i, j] == (a[i-j] if (0 <= (i-j) < m) else 0)

where m = len(a) . Suppose, for example, the input array is [x, y, z] . The convolution matrix has the form:

[x, 0, 0, ..., 0, 0]
[y, x, 0, ..., 0, 0]
[z, y, x, ..., 0, 0]
...
[0, 0, 0, ..., x, 0]
[0, 0, 0, ..., y, x]
[0, 0, 0, ..., z, y]
[0, 0, 0, ..., 0, z]

In 'valid' mode, the entries of A are given by:

A[i, j] == (a[i-j+m-1] if (0 <= (i-j+m-1) < m) else 0)

This corresponds to a matrix whose rows are the subset of those from the 'full' case where all the coefficients in a are contained in the row. For input [x, y, z] , this array looks like:

[z, y, x, 0, 0, ..., 0, 0, 0]
[0, z, y, x, 0, ..., 0, 0, 0]
[0, 0, z, y, x, ..., 0, 0, 0]
...
[0, 0, 0, 0, 0, ..., x, 0, 0]
[0, 0, 0, 0, 0, ..., y, x, 0]
[0, 0, 0, 0, 0, ..., z, y, x]

In the 'same' mode, the entries of A are given by:

d = (m - 1) // 2
A[i, j] == (a[i-j+d] if (0 <= (i-j+d) < m) else 0)

The typical application of the 'same' mode is when one has a signal of length n (with n greater than len(a) ), and the desired output is a filtered signal that is still of length n.

For input [x, y, z] , this array looks like:

[y, x, 0, 0, ..., 0, 0, 0]
[z, y, x, 0, ..., 0, 0, 0]
[0, z, y, x, ..., 0, 0, 0]
[0, 0, z, y, ..., 0, 0, 0]
...
[0, 0, 0, 0, ..., y, x, 0]
[0, 0, 0, 0, ..., z, y, x]
[0, 0, 0, 0, ..., 0, z, y]
versionadded

Parameters

a : (m,) array_like

The 1-D array to convolve.

n : int

The number of columns in the resulting matrix. It gives the length of the input to be convolved with a. This is analogous to the length of :None:None:`v` in numpy.convolve(a, v) .

mode : str

This is analogous to :None:None:`mode` in numpy.convolve(v, a, mode) . It must be one of ('full', 'valid', 'same'). See below for how :None:None:`mode` determines the shape of the result.

Returns

A : (k, n) ndarray

The convolution matrix whose row count :None:None:`k` depends on :None:None:`mode`:

=======  =========================
 mode    k
=======  =========================
'full'   m + n -1
'same'   max(m, n)
'valid'  max(m, n) - min(m, n) + 1
=======  =========================

Construct a convolution matrix.

See Also

toeplitz

Toeplitz matrix

Examples

>>> from scipy.linalg import convolution_matrix
... A = convolution_matrix([-1, 4, -2], 5, mode='same')
... A array([[ 4, -1, 0, 0, 0], [-2, 4, -1, 0, 0], [ 0, -2, 4, -1, 0], [ 0, 0, -2, 4, -1], [ 0, 0, 0, -2, 4]])

Compare multiplication by A with the use of numpy.convolve .

>>> x = np.array([1, 2, 0, -3, 0.5])
... A @ x array([ 2. , 6. , -1. , -12.5, 8. ])

Verify that A @ x produced the same result as applying the convolution function.

>>> np.convolve([-1, 4, -2], x, mode='same')
array([  2. ,   6. ,  -1. , -12.5,   8. ])

For comparison to the case mode='same' shown above, here are the matrices produced by mode='full' and mode='valid' for the same coefficients and size.

>>> convolution_matrix([-1, 4, -2], 5, mode='full')
array([[-1,  0,  0,  0,  0],
       [ 4, -1,  0,  0,  0],
       [-2,  4, -1,  0,  0],
       [ 0, -2,  4, -1,  0],
       [ 0,  0, -2,  4, -1],
       [ 0,  0,  0, -2,  4],
       [ 0,  0,  0,  0, -2]])
>>> convolution_matrix([-1, 4, -2], 5, mode='valid')
array([[-2,  4, -1,  0,  0],
       [ 0, -2,  4, -1,  0],
       [ 0,  0, -2,  4, -1]])
See :

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scipy.linalg._special_matrices.convolution_matrix

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GitHub : /scipy/linalg/_special_matrices.py#1195
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