NumPy convolve()
The numpy.convolve() function computes the discrete, linear convolution of two one-dimensional sequences.
Convolution is commonly used in signal processing to model the effect of a linear time-invariant system on a signal.
Syntax
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numpy.convolve(a, v, mode='full')Parameters
| Parameter | Type | Description |
|---|---|---|
a | (N,) array_like | First one-dimensional input array. |
v | (M,) array_like | Second one-dimensional input array. |
mode | {‘full’, ‘valid’, ‘same’}, optional | Determines the size of the output array. |
Modes in Convolution
| Mode | Description |
|---|---|
'full' | Returns the convolution at each point of overlap with an output size of (N+M-1). Includes boundary effects. |
'same' | Returns an output of length max(N, M), keeping the center of the convolution. |
'valid' | Returns only values where the input arrays fully overlap, with an output size of max(N, M) – min(N, M) + 1. |
Return Value
Returns an ndarray containing the discrete, linear convolution of the input arrays.
Examples
1. Basic Convolution Using Default Mode (‘full’)
In this example, we perform a simple convolution using two one-dimensional arrays.
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import numpy as np
# Define two input sequences
a = np.array([1, 2, 3])
v = np.array([0, 1, 0.5])
# Compute the convolution using default mode ('full')
result = np.convolve(a, v)
# Print the result
print("Convolution result (full mode):", result)Output:
Convolution result (full mode): [0. 1. 2.5 4. 1.5]