{"id":5690,"date":"2020-11-28T20:10:11","date_gmt":"2020-11-28T14:40:11","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=5690"},"modified":"2026-07-13T12:29:02","modified_gmt":"2026-07-13T06:59:02","slug":"numpy-convolve","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/numpy-convolve\/","title":{"rendered":"NumPy convolve(): 1D Convolution Modes and Examples"},"content":{"rendered":"

Quick answer:<\/strong> NumPy’s np.convolve computes the discrete one-dimensional convolution of two sequences. Use full when every overlap matters, same when the result should align with the first input length, and valid when only complete overlaps are meaningful. Remember that same mode uses the first argument’s length, so swapping operands can change the shape even though the mathematical convolution is commutative.<\/p>\n

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NumPy convolve slides one one-dimensional array across another; choose full, same, or valid mode from the signal and kernel lengths you need.<\/figcaption><\/figure>\n

numpy.convolve()<\/code> performs one-dimensional discrete convolution. It combines two sequences by sliding one sequence across the other and summing the overlapping products.<\/p>\n

The official NumPy documentation covers numpy.convolve()<\/a>, numpy.polymul()<\/a>, and SciPy’s fftconvolve()<\/a>.<\/p>\n

convolve()<\/code> is for one-dimensional inputs. If either input has more dimensions, flatten or choose a tool designed for multidimensional signal processing.<\/p>\n

The mode<\/code> argument controls output length and boundary behavior. The default full<\/code> mode returns every overlap, including partial overlaps at the edges. same<\/code> returns an output with length equal to the longer input. valid<\/code> returns only positions where the inputs fully overlap.<\/p>\n

Convolution is not the same as correlation. Convolution reverses one sequence before the sliding operation. That distinction matters for asymmetric kernels.<\/p>\n

For very large inputs, direct convolution can be slower than FFT-based methods. SciPy’s fftconvolve()<\/code> is often a better choice when the arrays are large and the dependency is available.<\/p>\n

The order of the inputs usually does not change the full convolution result for numeric one-dimensional arrays, but it can change how you think about the operation. In signal examples, the longer array is often called the signal and the shorter array is often called the kernel or filter.<\/p>\n

Before using convolution in a data pipeline, decide how edge values should be handled. The mode controls how much edge overlap appears in the output, and explicit padding can make boundary assumptions easier to audit.<\/p>\n

Also keep dtype in mind. Integer inputs can produce integer outputs, while averaging kernels usually require floating-point values. If a kernel contains fractions, create it as a float array so the result reflects the intended math.<\/p>\n

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