Sampling, Fourier Transform, and Discrete Fourier Transform

The Fourier Transform and the inverse Fourier Transform and are defined as: $$F(k) = \int_<-\infty>^\infty f(x)e^<-2\pi i k x>dx \\ f(x) = \int_<-\infty>^\infty F(k)e^<2\pi i k x>dk$$ The Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (iDFT) are defined as $$G[k] =\sum_0^ g[n] e^<-i2\pi \frac>\\ g[n] = \frac1N \sum_0^ G[k] e^>$$ Let $f$ be a function such that $f:\mathbb\rightarrow\mathbb$ . Consider the sampled signal $f_N(n)=f(nx\Delta)$ for $n\in<0,1,\ldots,N-1>$ and some positive scalar $\Delta$ . Note that $f_N$ is an N-tuple. Under what conditions is $\text(f_N)$ directly related to the Fourier Transform of $f$ ? (By directly related, I'm hoping that the values of the DFT are either equal to or proportional to values of the Fourier Transform.) What is that relationship?

$\begingroup$ You can try see what happens to the Fourier Transform expression contra DFT if you consider a point wise sampling on one hand versus sampling done as short integral snippets or maybe even windowed sampling (some weighted integration). $\endgroup$

$\begingroup$ Also I edited as you probably meant convolution with dirac impulses and not multiplication.i $\endgroup$

$\begingroup$ @Winther There are different definitions of the Fourier Transform. I have elected to use the one written above. $\endgroup$

$\begingroup$ @NicNic8 Sorry, I did not see you had $2\pi i k$ in the exponential. Then it's fine. My bad! $\endgroup$

2 Answers 2

Let me expand on Matt L.'s excellent answer to a very similar question (please read that first).

We start with a continuous signal $s(t)$, which might be finite or infinite in length, but we're interested in signals that vanish beyond some window:

Now we truncate the signal in time (if it's infinite or too long) and create a list of equidistant samples of this signal:

This defines a new signal $x(t)$ with length $T_0$ (or a "rate" $f_0 = 1/T_0$), and a sequence $x[k]$ with length $N$ and sampling period $T_s$ (sampling rate $f_s = 1/T_s$). Note that the signals $s(t)$ and $x(t)$ do not have Fourier Series, because they're not periodic. But they do have Fourier Transforms:

One of the similarities between both transforms is that they can be used to recover their respective signals:

Most importantly, if the sampling rate ($f_s$) and the length of $x(t)$ ($T_0=1/f_0$) are high enough, then:

That is, the Discrete Fourier Transform is approximately a sampling of the regular Fourier Transform starting at $f=0$ and in steps of $\Delta f = f_0 = 1/T_0$ (where $T_0$ is the length of the truncated signal) and then scaled by $f_s$ (the sampling rate).

It's not very obvious from the definitions $(1)$ and $(2)$ that this is the case, so I'll try to present a "proof" for the relationship in $(5)$.

"Proof"

We start by constructing a periodic signal (and its corresponding periodic sequence of samples) out of $x(t)$:

$$ x_p(t) \triangleq \sum_^\infty x(t-nT_0) \tag $$$$ x_p[k] \triangleq x_p(kT_s) \tag $$

Now $x_p(t)$ has a Fourier Series:

And $x(t)$ has a Fourier Transform $(1)$ which looks a lot like $c[n]$. In fact it's simply:

Substituting $(10)$ into $(8)$ and the result into $(6)$, we get one of the forms of the Poisson Summation Formula:

This allows us to write $x_p[k]$ in a new way:

where $N = T_0/T_s$ is the number of samples in the finite sequence. To simplify slightly, we assume that $N$ is even. If $|X(f)|$ drops fast enough, the terms for $|n|\ge N/2$ will vanish, giving:

Now we work on the other representation of $x_p[k]$, using $(4)$ and remembering that $x_p[k] = x[k]$ for $k=0,\dots,N-1$:

Finally, comparing $(20)$ to $(14)$, we conclude that:

Simulations

In practice, when dealing with real signals, instead of calculating the Fourier Transform of the continuous signal, we sample the data (often the data is already in discrete form) and calculate its Fast Fourier Transform (which is exactly the same as the Discrete Fourier Transform, but computed by a faster method). Then we use this to get a decent approximation of the continuous Fourier Transform. Here is an example of such signal:

Notice how this one is not perfectly symmetric. Its Fourier Transform can be computed by applying the definition $(1)$ directly, which might take a long time:

Notice how the argument goes crazy. That's because most of the energy of the signal is shifted to $t\approx 2.3$, which adds a phase of $\approx -2\pi\cdot 2.3f$ to the spectrum relative to a more "centered" one.

We can also sample the signal and calculate the FFT for different values of $N$ and compare the result with samples of the exact Fourier Transform. The approximation gets better and better for higher sampling rates ($T_0$ is fixed here):

The simulations above were done on Python 3, with the SciPy and NumPy libraries. The code was written for personal use only, so it's a mess, but it's given below just for reference:

import matplotlib.pyplot as plt from cmath import * from scipy.integrate import quad from numpy import linspace, angle, inf, nan import matplotlib.lines as mlines from numpy.fft import fft from numpy import array as vec hide_discontinuity = True fourier_transform_plot_samples = 100 #recommended >1000, but it's very slow signal_plot_samples = 1000 #signal(t+DT/2) = exp(t/5)*sin(2*pi*F*t)*bump(t) F = 5.0 DT = 4.0 def bump(t): if abs(t)>=1: return 0 return exp(-1/(1-t*t)) def centralized_signal(t): return (exp(t/3)*sin(2*pi*F*t)*bump(t)).real def signal(t): return centralized_signal(t-DT/2) t = linspace(0,DT,num=signal_plot_samples) signal_samples = [signal(k) for k in t] plt.plot(t,signal_samples) plt.title('Signal') plt.xlabel('Time (s)') plt.ylabel('Amplitude') plt.grid(True, linestyle='dotted') fig = plt.gcf() fig.canvas.set_window_title('Signal') plt.show() ############################### def integrate(func, a, b): def real_func(k): return func(k).real def imag_func(k): return func(k).imag real_integral = quad(real_func, a, b) imag_integral = quad(imag_func, a, b) return real_integral[0] + 1j*imag_integral[0] def fourier(func, f, a, b): return integrate(lambda t: func(t)*exp(-2j*pi*f*t), a, b) def show_fourier_transform(): f = linspace(-2*F,2*F,num=fourier_transform_plot_samples) Xm = [] Xa = [] Xa_prev = nan for k in f: Xf = fourier(signal, k, -inf,inf) Xm.append(abs(Xf)) Xa_new = angle(Xf) if hide_discontinuity and Xa_prev != nan and abs(Xa_prev-Xa_new) > 0.999*pi/2: Xa_new = nan Xa_prev = Xa_new Xa.append(Xa_new) ay = plt.gca() ay2 = ay.twinx() ay.plot(f,Xm, color='C0') ay2.plot(f,Xa, color='C1', linewidth=0.5) C0_line = mlines.Line2D([], [], color='C0', label='Magnitude') C1_line = mlines.Line2D([], [], color='C1', linewidth=0.5, label='Argument') plt.legend(handles=[C0_line, C1_line], loc=1) plt.title('Fourier Transform') plt.xlabel('Frequency (Hz)') ay.set_ylabel('Magnitude') ay2.set_ylabel('Argument') ay.grid(True, linestyle='dotted') fig = plt.gcf() fig.canvas.set_window_title('Fourier Transform') plt.show() show_fourier_transform() ############################### def show_relation(N): t = linspace(0,DT,num=N) Fs = N/DT Ts = 1/Fs f_samples = round(2*F*DT) f_max = (f_samples-1)/DT indices_range = range(0,f_samples) frequency_range = linspace(0,f_max,num=f_samples) dense_frequency_range = linspace(0,f_max,num=fourier_transform_plot_samples) ########## DFT ########## DFT = [] for k in t: DFT.append(signal(k)) DFT = fft(DFT)[indices_range] ########## FT ########### FT = [] for k in frequency_range: FT.append(fourier(signal, k, -inf,inf)) FT = vec(FT) dense_FT = [] for k in dense_frequency_range: dense_FT.append(fourier(signal, k, -inf,inf)) dense_FT = vec(dense_FT) ######## Error ########## dF = Ts*DFT - FT ######### Plot ########## ax = plt.gca() ax2 = ax.twiny() ax.plot(dense_frequency_range, abs(dense_FT), color='C1') ax.scatter(frequency_range, abs(FT), marker='o', color='C1') #ax.plot(frequency_range, angle(FT), marker='o', color='C1', linewidth=0.5) ax2.plot(indices_range, abs(Ts*DFT), marker='d', color='C0') #ax2.plot(indices_range, angle(Ts*DFT), marker='d', color='C0', linewidth=0.5) ax2.plot(indices_range, abs(dF), color='C2') C0_line = mlines.Line2D([], [], color='C1', marker='o', label='Fourier Transform') C1_line = mlines.Line2D([], [], color='C0', marker='d', label='DFT × Tₛ') C2_line = mlines.Line2D([], [], color='C2', label='Error') plt.legend(handles=[C0_line, C1_line, C2_line], loc=1) ax.set_xlim((-f_max/f_samples, f_max+f_max/f_samples)) ax2.set_xlim((-1, f_samples-1+1)) ax.set_xlabel('Frequency (Hz)') #ax2.set_xlabel('Sample') ax.set_ylabel('Magnitude') plt.title('N = '+str(N)+', Tₛ = '+str(1000*Ts)+' ms', y=1.08) ax.yaxis.grid(True, linestyle='dotted') ax.xaxis.grid(True, linestyle='dotted') fig = plt.gcf() fig.canvas.set_window_title('Relation between Fourier Transform and DFT') plt.show() show_relation(100) show_relation(1000) show_relation(100000)