👀 Views: 359 💬 Answers: 1 📅 Created: 2025-06-09

numpy fft signal-processing Python

I'm working through a tutorial and I tried several approaches but none seem to work. I'm encountering unexpected behavior while using `np.fft.fft` on an array that isn't a power of two in size. It seems like the output is not matching my expectations based on the input frequency components. I have an input array of size 30, and when I apply the FFT, the resulting transformed array doesn't seem to represent the expected frequency information accurately. Here's the code I am using: ```python import numpy as np t = np.linspace(0, 1, 30) input_signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.sin(2 * np.pi * 10 * t) # Applying FFT fft_output = np.fft.fft(input_signal) print("FFT Output:", fft_output) ``` When I print the `fft_output`, I notice that the magnitude of the frequencies doesn’t align with the two sine waves I should be seeing (5 Hz and 10 Hz). Instead, the result appears distorted. I did some research and found that FFT has optimizations for power-of-two sizes, but I wasn't sure how this would affect my results. I tried padding the input array to make it a power of two like so: ```python padded_signal = np.pad(input_signal, (0, 34), 'constant') # Pad to size 64 fft_padded_output = np.fft.fft(padded_signal) print("Padded FFT Output:", fft_padded_output) ``` However, I’m still unsure how to interpret the results. Is there a best practice for handling FFT on arrays that aren't power of two? What should I check to ensure I'm getting accurate frequency representations in my FFT results? Any insights would be greatly appreciated! What am I doing wrong? The project is a service built with Python. Any advice would be much appreciated.