[SciPy-User] frequency components of a signal buried in a noisy time domain signal
Fri Feb 26 15:24:34 CST 2010
On Fri, 26 Feb 2010 15:56:29 -0500
Ivo Maljevic <email@example.com> wrote:
> Yes, FFT can be used to visualize the frequency content
>of a signal buried
> in the noise, especially if it is is narrowband,
> even though more advanced spectral analysis is required
Thank you very much for your reply.
Are you aware of free software for advanced applications ?
What could be done in case of broadband noise ?
> I cannot help you with non-uniformly spaced samples
>(some sort of
> interpolation comes to mind), but next power of 2 should
>be trivial. Without
> checking for the argument type, you can implement the
>function like this:
> def nextpow2(n):
> m_f = np.log2(n)
> m_i = np.ceil(m_f)
> return 2**m_i
> Hope it helps,
> On 26 February 2010 14:05, Nils Wagner
>> Hi all,
>> A common use of Fourier transforms is to find the
>> frequency components of a signal buried in a noisy time
>> domain signal.
>> I found a Matlab template at
>> Matlab has a function
>> Is there a similar build-in function in numpy/scipy ?
>> I tried to convert the m-file into a pythonic form.
>> What is needed to obtain a similar figure of the
>> single-sided amplitude spectrum using
>> numpy/scipy/matplotlib ?
>> from numpy import sin, linspace, pi
>> from numpy.random import randn
>> from pylab import plot, show, title, xlabel, ylabel
>> from scipy.fft import fft
>> Fs = 1000. # Sampling frequency
>> T = 1./Fs # Sample time
>> L = 1000 # length of signal
>> t = arange(0,L)*T
>> x = 0.7*sin(2*pi*50*t)+sin(2*pi*120*t)
>> y = x + 2*randn(len(t))
>> title('Signal corrupted with zero-mean random noise')
>> xlabel('Time (milliseconds)')
>> #NFFT = 2^nextpow2(L); # Next power of 2 from length of
>> Y = fft(y,NFFT)/L
>> f = Fs/2*linspace(0,1,NFFT/2+1)
>> title('Single-sided amplitude spectrum of y(t)')
>> xlabel('Frequency (Hz)')
>> What can be done in case of nonequispaced data ?
>> Thanks in advance
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