I believe your problem is this: in your while loop, n is divided by 2, but never cast as an integer again, so it becomes a float at some point. It is then added onto y, which is then a float too, and that gives you the warning.
SciPy does more: http://docs.scipy.org/doc/numpy/reference/routines.fft.html http://docs.scipy.org/doc/scipy/reference/fftpack.html# In addition, SciPy exports some of the NumPy features through its own interface, for example if you execute scipy.fftpack.helper.fftfreq and numpy.fft.helper.fftfreq you’re actually running the same code. However, SciPy has its own implementations of much functionality. The source has performance benchmarks that compare the original NumPy and new SciPy versions. … Read more
take a signal, a time-varying voltage v(t) units are V, values are real. throw it into an FFT — ok, you get back a sequence of complex numbers units are still V, values are complex ( not V/Hz – the FFT a DC signal becomes a point at the DC level, not an dirac delta … Read more
Try convert the image to RGB: … new_p = Image.fromarray(fft_p) if new_p.mode != ‘RGB’: new_p = new_p.convert(‘RGB’) …
I just got the FFT code working for an iPhone project: create a new project delete all the files except for main.m and xxx_info.plist going to project settings and search for pch and stop it from trying to load a .pch (seeing as we have just deleted it) copy paste the code example over whatever … Read more
The guy that did AForge did a fairly good job but it’s not commercial quality. It’s great to learn from but you can tell he was learning too so he has some pretty serious mistakes like assuming the size of an image instead of using the correct bits per pixel. I’m not knocking the guy, … Read more
The FFT is fundamentally a change of basis. The basis into which the FFT changes your original signal is a set of sine waves instead. In order for that basis to describe all the possible inputs it needs to be able to represent phase as well as amplitude; the phase is represented using complex numbers. … Read more
The array you are showing is the Fourier Transform coefficients of the audio signal. These coefficients can be used to get the frequency content of the audio. The FFT is defined for complex valued input functions, so the coefficients you get out will be imaginary numbers even though your input is all real values. In … Read more
So I run a functionally equivalent form of your code in an IPython notebook: %matplotlib inline import numpy as np import matplotlib.pyplot as plt import scipy.fftpack # Number of samplepoints N = 600 # sample spacing T = 1.0 / 800.0 x = np.linspace(0.0, N*T, N) y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x) … Read more
The first bin in the FFT is DC (0 Hz), the second bin is Fs / N, where Fs is the sample rate and N is the size of the FFT. The next bin is 2 * Fs / N. To express this in general terms, the nth bin is n * Fs / N. … Read more