Fourier Approximations Worksheet by Michael K. May, S.J., revised by Russell Blyth. restart:with(plots): Warning, the name changecoords has been redefined
<Text-field style="Heading 1" layout="Heading 1">Objectives</Text-field> Explore Fourier approximations both symbolically and graphically
<Text-field style="Heading 1" layout="Heading 1">More on the Example, and then more Examples</Text-field> We first explore further the example given in class, and then tackle new examples. First, instruct Maple to assume that i and j are integers. assume(i,integer): assume(j,integer): interface(showassumed=0): Compute the Fourier coefficients for the function f(x) = x F := x; Int(F*sin(j*Pi*x),x=-1..1) = int(F*sin(j*Pi*x),x=-1..1); Int(F*cos(j*Pi*x),x=-1..1) = int(F*cos(j*Pi*x),x=-1..1); Int(F,x=-1..1) = int(x,x=-1..1); 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We have obtained the following n term Fourier approximation to the function f(x) = x. Fapprox := (x,n) -> sum(-2*(-1)^j/(j*Pi)*sin(j*Pi*x),j=1..n); 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Let's plot a 6 term approximation against the function. eval(Fapprox(x,6)); plot([Fapprox(x,6),x],x=-2..2,y = -2..2, title = " 6th Order Fourier approximation to y =x", titlefont = [HELVETICA,12], color = [green,blue]); 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It is also useful to plot the error, that is, the difference between the function and the approximation. plot(Fapprox(x,6)-x,x=-1..1,y=-1..1, title = "error in 6th Order Fourier approximation to y = x", titlefont = [HELVETICA,10], color = red); 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<Text-field style="Heading 2" layout="Heading 2">Exercises</Text-field> 1) Plot the function f(x) = x against the Fourier approximation with 100 terms 2) Plot the error function for f(x) = x and its Fourier approximation with 100 terms.
It is instructive to see an animation of the Fourier approximations as they converge to the function that is being approximated. The following block of code sets up frames that plot a Fourier approximation Fapprox against the function func. func := x: Fapprox := (x,n) -> sum(-2*(-1)^j/(j*Pi)*sin(j*Pi*x), j=1..n): framer := proc(n) local A, B, C, D: A := plot([Fapprox(x,n),func], x = -2..2, y = -2..2, color = [blue,green]): B := textplot({[0,1.8, `The `||n||` term Fourier approximation (blue)`], [0,1.6, `graphed against the function (green)`]}, font = [HELVETICA, BOLD, 12] ): C := textplot([-0.05,1.4, `f(x) = `], align=LEFT, font = [HELVETICA, BOLD, 12] ): D := textplot([0.05,1.4, func], align = RIGHT, font = [HELVETICA, BOLD, 12] ): display({A, B,C, D},view=[-2..2,-2..2]); end: With framer defined, it is a simple matter to animate a sequence of 30 frames. display([seq(framer(count), count=1..30)], insequence = true); 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To run the animation click once on the graph, then use the playback controls on the control bar. A second example: We would now like to look at approximations to the function that is -1 on [-1,0) and 1 on [0,1). This is a piecewise defined function. Note that we are approximating a very discontinuous function with continuous trigonometric polynomials. g:=x->piecewise(x<0,-1,1): plot(g(x),x=-2..2); 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 We determine the Fourier coefficients Int(g(x)*sin(j*Pi*x),x=-1..1) = int(g(x)*sin(j*Pi*x),x=-1..1); Int(g(x)*cos(j*Pi*x),x=-1..1) = int(g(x)*cos(j*Pi*x),x=-1..1); Int(g(x),x=-1..1) = int(g(x),x=-1..1); 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 Again, the only Fourier coefficients that are nonzero are the sine ones, and even some of them are zero. Note that the sine coefficient is 0 if j is even and 4/(j\317\200) if j is odd. Fapprox := (x,n) -> sum((-2*((-1)^j-1))/(j*Pi)*sin(j*Pi*x), j=1..n); 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eval(Fapprox(x,10)); plot([Fapprox(x,10),g(x)],x=-2..2,y=-2..2, title=" 10th Order Fourier approximation to g(x)", titlefont=[HELVETICA,12], color=[green,blue]); 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<Text-field style="Heading 2" layout="Heading 2">Exercises:</Text-field> 3) Write down the 11th and 15th order Fourier approximations to g(x). Then animate the Fourier approximations up to order 30 4) Find the coefficients for the Fourier approximations of h(x) = x3. 5) Write down the 10th order Fourier approximation to h(x) = x3. Then animate the Fourier approximations up to order 30.
<Text-field style="Heading 1" layout="Heading 1">An example with nonzero cosines terms</Text-field> In this third example we find Fourier approximations for the absolute value function. k :=x->abs(x); 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Find the Fourier coefficients: Int(k(x)*sin(j*Pi*x),x=-1..1) = int(k(x)*sin(j*Pi*x),x=-1..1); Int(k(x)*cos(j*Pi*x),x=-1..1) = int(k(x)*cos(j*Pi*x),x=-1..1); Int(k(x),x=-1..1) = int(k(x),x=-1..1); 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 Now the sine coefficients are all zero, along with half of the cosines terms - not surprising since k is an even function. Recall that the constant term is half the integral of k on the interval. Let's check: 1/2*Int(k(x),x=-1..1) = 1/2*int(k(x),x=-1..1); 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Fapprox := (x,n) -> 1/2 + sum(2*((-1)^j-1)*cos(j*Pi*x)/(j*Pi)^2,j=1..n); 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Plot the 6th Fourier approximation eval(Fapprox(x,6)); plot({k(x), Fapprox(x,6)},x=-2..2,y=-2..2); 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
<Text-field style="Heading 2" layout="Heading 2">Exercises:</Text-field> 6) Write down the 11th and 15th order Fourier approximations to k(x) = abs(x). Animate up to the 30th order approximation. 7) Find the coefficients for the Fourier approximations of f(x) = x3- x2. (The series has coefficients for both sine and cosine functions.) 8) Write down the 6th order Fourier approximation to f(x) = x3-x2. Plot the function against the approximation. Animate up to the 30th order approximation.