Back to library index.

Package splinef (in splinef.i) -

Index of documented functions or symbols:

splined

DOCUMENT yp = splined(dydx, y, x, xp)
      or yp = splined(x_y_dydx, xp)
  returns the derivative of the piecewise cubic function specified
  by DYDX, Y, X at the points XP.  Extrapolation beyond the extreme
  endpoints of X is linear, so splined gives the final value of DYDX.
  The return value dimensions are the same as the dimensions of XP.

  In the second form, X_Y_DYDX is a 3-by-nknots array of [x,y,dydx]
  values.  The values of X in either case must either increase or
  decrease monotonically.

SEE ALSO: splined, splinei

splinef

DOCUMENT yp = splinef(dydx, y, x, xp)
      or yp = splinef(x_y_dydx, xp)
  returns piecewise cubic function specified by DYDX, Y, X at
  the points XP.  Extrapolation beyond the extreme endpoints of X
  is linear, with slope equal to the final value of DYDX.  The
  return value dimensions are the same as the dimensions of XP.

  In the second form, X_Y_DYDX is a 3-by-nknots array of [x,y,dydx]
  values.  The values of X in either case must either increase or
  decrease monotonically.

SEE ALSO: interp, splined, splinei, splinelsq

splinei

DOCUMENT yp = splinei(dydx, y, x, xp)
      or yp = splinei(x_y_dydx, xp)
  returns the integral of the piecewise cubic function specified
  by DYDX, Y, X at the points XP.  The integral is quadratic beyond
  the extreme endpoints of X, and zero at X(1).  The dimensions of
  the return value are the same as the dimensions of XP.
  This is the cubic analog of the integ function.

  In the second form, X_Y_DYDX is a 3-by-nknots array of [x,y,dydx]
  values.  The values of X in either case must either increase or
  decrease monotonically.

SEE ALSO: integ, splinef, splined

splinelsq

DOCUMENT x_y_dydx = splinelsq(y, x, xfit)
         ...
         yp = splinef(x_y_dydx, xp)
  performs a least squares fit to the data points (X, Y).  The input
  XFIT are the abcissas of the piecewise cubic function with knot
  points XFIT which is the least squares best fit to the data (X,Y).
  The XFIT must be strictly increase or decrease.

  Any points in XFIT with no data points in the intervals on
  either side will be removed.

  A weight= keyword of the same length as X and Y may be supplied in
  order to weight the various data points differently; a typical
  WEIGHT function is 1/sigma^2 where sigma are the standard deviations
  associated with the Y values.

  You can specify y0=, dydx0=, y1=, and dydx1= keywords to fix the
  value of the function or its derivative at the first (0) or last (1)
  endpoint.  Be sure there is at least one point in the final
  interval so that the XFIT at the endpoint is not removed.

  More generally, you can specify a constrain= keyword.  The value
  of constrain is a hook function which will be called just before
  the matrix solve.  Your constrain subroutine will be passed no
  arguments, but it can access and modify the mat and rhs variables.

SEE ALSO: splinef

spline_coef

DOCUMENT spline_coef
  is the worker for the splinef, splined, and splinei functions.
  If you need to compute both function and derivative or integral,
  you will improve performance using spline_coef.  See the source
  code for those functions for usage.

SEE ALSO: splinef, splined, splinei