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This Recipe is a variant of recipe 576934: Numerical Inversion of the Laplace Transform using the Talbot method by Fernando Damian Nieuwveldt adapted to high precision mpmath

Python, 94 lines
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 # -*- coding: iso-8859-1 -*- # laplace.py with mpmath # appropriate for high precision # Talbot suggested that the Bromwich line be deformed into a contour that begins # and ends in the left half plane, i.e., z \to \infty at both ends. # Due to the exponential factor the integrand decays rapidly # on such a contour. In such situations the trapezoidal rule converge # extraordinarily rapidly. # For example here we compute the inverse transform of F(s) = 1/(s+1) at t = 1 # # >>> error = Talbot(1,24)-exp(-1) # >>> error # (3.3306690738754696e-015+0j) # # Talbot method is very powerful here we see an error of 3.3e-015 # with only 24 function evaluations # # Created by Fernando Damian Nieuwveldt # email:fdnieuwveldt@gmail.com # Date : 25 October 2009 # # Adapted to mpmath and classes by Dieter Kadelka # email: Dieter.Kadelka@kit.edu # Date : 27 October 2009 # # Reference # L.N.Trefethen, J.A.C.Weideman, and T.Schmelzer. Talbot quadratures # and rational approximations. BIT. Numerical Mathematics, # 46(3):653 670, 2006. from mpmath import mpf,mpc,pi,sin,tan,exp # testfunction: Laplace-transform of exp(-t) def F(s): return 1.0/(s+1.0) class Talbot(object): def __init__(self,F=F,shift=0.0): self.F = F # test = Talbot() or test = Talbot(F) initializes with testfunction F self.shift = shift # Shift contour to the right in case there is a pole on the # positive real axis : # Note the contour will not be optimal since it was originally devoloped # for function with singularities on the negative real axis For example # take F(s) = 1/(s-1), it has a pole at s = 1, the contour needs to be # shifted with one unit, i.e shift = 1. # But in the test example no shifting is necessary self.N = 24 # with double precision this constant N seems to best for the testfunction # given. For N = 22 or N = 26 the error is larger (for this special # testfunction). # With laplace.py: # >>> test.N = 500 # >>> print test(1) - exp(-1) # >>> -2.10032517928e+21 # Huge (rounding?) error! # with mp_laplace.py # >>> mp.dps = 100 # >>> test.N = 500 # >>> print test(1) - exp(-1) # >>> -5.098571435907316903360293189717305540117774982775731009465612344056911792735539092934425236391407436e-64 def __call__(self,t): if t == 0: print "ERROR: Inverse transform can not be calculated for t=0" return ("Error"); # Initiate the stepsize h = 2*pi/self.N ans = 0.0 # parameters from # T. Schmelzer, L.N. Trefethen, SIAM J. Numer. Anal. 45 (2007) 558-571 c1 = mpf('0.5017') c2 = mpf('0.6407') c3 = mpf('0.6122') c4 = mpc('0','0.2645') # The for loop is evaluating the Laplace inversion at each point theta i # which is based on the trapezoidal rule for k in range(self.N): theta = -pi + (k+0.5)*h z = self.shift + self.N/t*(c1*theta/tan(c2*theta) - c3 + c4*theta) dz = self.N/t * (-c1*c2*theta/sin(c2*theta)**2 + c1/tan(c2*theta)+c4) ans += exp(z*t)*self.F(z)*dz return ((h/(2j*pi))*ans).real 

Fernando Damian Nieuwveldt implemented in recipe 576934: Numerical Inversion of the Laplace Transform using the Talbot method by Fernando Damian Nieuwveldt adapted to high precision mpmath a method for numerical inversion of Laplace Transforms, which seems to work very well, at least for the testfunction. But the algorithm seems to have a drawback: the marvellous performance depends on the correct parameter N. Enlarging N from N = 24 (the best value in Nieuwveldts implementation) to f.i. N = 500 makes the answer completely useless. The problem seems to be rounding error.

So I modified his algorithm using mpmath and additionally introduced classes. The algorithm is not changed essentially.

#### 1 comment

Fernando Nieuwveldt 14 years, 6 months ago

This is very nice!!! I was unaware of mpmath and very new to python programming. Must say again this is amazing and I have something else I would like to post, but now I first have to check out mpmath.

Cheers. Fernando

 Created by Dieter Kadelka on Tue, 27 Oct 2009 (MIT)