scipy.signal.upfirdn

scipy.signal.upfirdn(h, x, up=1, down=1, axis=-1)

Upsample, FIR filter, and downsample

Parameters:

h : array_like

1-dimensional FIR (finite-impulse response) filter coefficients.

x : array_like

Input signal array.

up : int, optional

Upsampling rate. Default is 1.

down : int, optional

Downsampling rate. Default is 1.

axis : int, optional

The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis. Default is -1.

Returns:

y : ndarray

The output signal array. Dimensions will be the same as x except for along axis, which will change size according to the h, up, and down parameters.

Notes

The algorithm is an implementation of the block diagram shown on page 129 of the Vaidyanathan text [R305] (Figure 4.3-8d).

[R305]

P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall, 1993.

The direct approach of upsampling by factor of P with zero insertion, FIR filtering of length N, and downsampling by factor of Q is O(N*Q) per output sample. The polyphase implementation used here is O(N/P).

New in version 0.18.

Examples

Simple operations:

>>> from scipy.signal import upfirdn
>>> upfirdn([1, 1, 1], [1, 1, 1])   # FIR filter
array([ 1.,  2.,  3.,  2.,  1.])
>>> upfirdn([1], [1, 2, 3], 3)  # upsampling with zeros insertion
array([ 1.,  0.,  0.,  2.,  0.,  0.,  3.,  0.,  0.])
>>> upfirdn([1, 1, 1], [1, 2, 3], 3)  # upsampling with sample-and-hold
array([ 1.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.])
>>> upfirdn([.5, 1, .5], [1, 1, 1], 2)  # linear interpolation
array([ 0.5,  1. ,  1. ,  1. ,  1. ,  1. ,  0.5,  0. ])
>>> upfirdn([1], np.arange(10), 1, 3)  # decimation by 3
array([ 0.,  3.,  6.,  9.])
>>> upfirdn([.5, 1, .5], np.arange(10), 2, 3)  # linear interp, rate 2/3
array([ 0. ,  1. ,  2.5,  4. ,  5.5,  7. ,  8.5,  0. ])

Apply a single filter to multiple signals:

>>> x = np.reshape(np.arange(8), (4, 2))
>>> x
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7]])

Apply along the last dimension of x:

>>> h = [1, 1]
>>> upfirdn(h, x, 2)
array([[ 0.,  0.,  1.,  1.],
       [ 2.,  2.,  3.,  3.],
       [ 4.,  4.,  5.,  5.],
       [ 6.,  6.,  7.,  7.]])

Apply along the 0th dimension of x:

>>> upfirdn(h, x, 2, axis=0)
array([[ 0.,  1.],
       [ 0.,  1.],
       [ 2.,  3.],
       [ 2.,  3.],
       [ 4.,  5.],
       [ 4.,  5.],
       [ 6.,  7.],
       [ 6.,  7.]])