The Speed Gap Nobody Talks About
NumPy's fft.fft() ran 2.3x slower than SciPy's fft.fft() on my 50kHz bearing vibration data. Same input array, same machine, same Python 3.11 environment. That's not a typo—SciPy's FFT implementation genuinely outperforms NumPy's by a significant margin on large arrays.
I stumbled onto this while processing a 10-minute continuous vibration capture from an SKF 6205 bearing running at 1800 RPM. At 50kHz sampling rate, that's 30 million samples. My original NumPy pipeline took 847ms per FFT batch. After a one-import change to SciPy, the same operation dropped to 368ms.
Why does this matter? In real-time vibration monitoring, you're often running FFT continuously on sliding windows. A 2.3x speedup means the difference between keeping up with your data stream and falling behind. On resource-constrained edge devices (which most industrial gateways are), this gap becomes critical.
The Benchmark Setup
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