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           Math::Spline  - Cubic Spline Interpolation of data


           use Math::Spline;
           $spline = Math::Spline->new(\@x,\@y)

           use Math::Spline qw(spline linsearch binsearch);
           use Math::Derivative qw(Derivative2);


       This package provides cubic spline interpolation of numeric data. The
       data is passed as references to two arrays containing the x and y
       ordinates. It may be used as an exporter of the numerical functions or,
       more easily as a class module.

       The Math::Spline class constructor new takes references to the arrays
       of x and y ordinates of the data. An interpolation is performed using
       the evaluate method, which, when given an x ordinate returns the
       interpolate y ordinate at that value.

       The spline function takes as arguments references to the x and y
       ordinate array, a reference to the 2nd derivatives (calculated using
       Derivative2, the low index of the interval in which to interpolate and
       the x ordinate in that interval. Returned is the interpolated y
       ordinate. Two functions are provided to look up the appropriate index
       in the array of x data. For random calls binsearch can be used - give a
       reference to the x ordinates and the x loopup value it returns the low
       index of the interval in the data in which the value lies. Where the
       lookups are strictly in ascending sequence (e.g. if interpolating to
       produce a higher resolution data set to draw a curve) the linsearch
       function may more efficiently be used. It performs like binsearch, but
       requires a third argument being the previous index value, which is
       incremented if necessary.


       requires Math::Derivative module


           require Math::Spline;
           my @x=(1,3,8,10);
           my @y=(1,2,3,4);
           $spline = Math::Spline->new(\@x,\@y);
           print $spline->evaluate(5)."

       produces the output



       $Log:,v $ Revision 1.1  1995/12/26 17:28:17  willijar Initial


       Bug reports or constructive comments are welcome.


       John A.R. Williams <>


       "Numerical Recipies: The Art of Scientific Computing" W.H. Press, B.P.
       Flannery, S.A. Teukolsky, W.T. Vetterling.  Cambridge University Press.
       ISBN 0 521 30811 9.

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