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PDL::Slatec - PDL interface to the slatec numerical programming library |
PDL::Slatec - PDL interface to the slatec numerical programming library
use PDL::Slatec;
($ndeg, $r, $ierr, $a) = polyfit($x, $y, $w, $maxdeg, $eps);
This module serves the dual purpose of providing an interface to parts of the slatec library and showing how to interface PDL to an external library. Using this library requires a fortran compiler; the source for the routines is provided for convenience.
Currently available are routines to: manipulate matrices; calculate FFT's; fit data using polynomials; and interpolate/integrate data using piecewise cubic Hermite interpolation.
PCHIP is the slatec package of routines to perform piecewise cubic Hermite interpolation of data. It features software to produce a monotone and ``visually pleasing'' interpolant to monotone data. According to Fritsch & Carlson (``Monotone piecewise cubic interpolation'', SIAM Journal on Numerical Analysis 17, 2 (April 1980), pp. 238-246), such an interpolant may be more reasonable than a cubic spline if the data contains both ``steep'' and ``flat'' sections. Interpolation of cumulative probability distribution functions is another application. These routines are cryptically named (blame FORTRAN), beginning with 'ch', and accept either float or double piddles.
Most of the routines require an integer parameter called check;
if set to 0, then no checks on the validity of the input data are
made, otherwise these checks are made.
The value of check can be set to 0 if a routine
such as chim has already been successfully called.
f)
and derivative (d) values at a set of points (x)).
Eigenvalues and eigenvectors of a real positive definite symmetric matrix.
($eigvals,$eigvecs) = eigsys($mat)
Note: this function should be extended to calculate only eigenvalues if called in scalar context!
Inverse of a square matrix
($inv) = matinv($mat)
Convenience wrapper routine about the polfit slatec function.
Separates supplied arguments and return values.
Fit discrete data in a least squares sense by polynomials
in one variable. Handles threading correctly--one can pass in a 2D PDL (as $y)
and it will pass back a 2D PDL, the rows of which are the polynomial regression
results (in $r corresponding to the rows of $y.
($ndeg, $r, $ierr, $a) = polyfit($x, $y, $w, $maxdeg, $eps);
where on input:
C<x> and C<y> are the values to fit to a polynomial. C<w> are weighting factors C<maxdeg> is the maximum degree of polynomial to use and C<eps> is the required degree of fit.
and on output:
C<ndeg> is the degree of polynomial actually used C<r> is the values of the fitted polynomial C<ierr> is a return status code, and C<a> is some working array or other C<eps> is modified to contain the rms error of the fit.
This version of polyfit handles bad values correctly. It strips them out
of the $x variable and creates an appropriate $y variable containing indices
of the non-bad members of $x before calling the Slatec routine polfit.
Convenience wrapper routine around the pcoef slatec function.
Separates supplied arguments and return values.
Convert the polyfit/polfit coefficients to Taylor series form.
$tc = polycoef($l, $c, $a);
Convenience wrapper routine around the pvalue slatec function.
Separates supplied arguments and return values.
For multiple input x positions, a corresponding y position is calculated.
The derivatives PDL is one dimensional (of size nder) if a single x
position is supplied, two dimensional if more than one x position is
supplied.
Use the coefficients generated by polyfit (or polfit) to evaluate
the polynomial fit of degree l, along with the first nder of its
derivatives, at a specified point.
($yfit, $yp) = polyvalue($l, $nder, $x, $a);
compute the determinant of an invertible matrix
$mat = zeroes(5,5); $mat->diagonal(0,1) .= 1; # unity matrix $det = detslatec $mat;
Usage:
$determinant = detslatec $matrix;
Signature: detslatec(mat(n,m); [o] det())
detslatec computes the determinant of an invertible matrix and barfs if
the matrix argument provided is non-invertible. The matrix threads as usual.
This routine was previously known as det which clashes now with
det in the det:PDL::MatrixOps manpage which is provided by
PDL::MatrixOps. For the moment the PDL::Slatec manpage will
also load the PDL::MatrixOps manpage thereby making sure that older scripts work.
Signature: (x(n,p);[o]s(p);[o]e(p);[o]u(n,p);[o]v(p,p);[o]work(n);int job();int [o]info())
singular value decomposition of a matrix
Signature: (a(n,n);rcond();[o]z(n);int [o]info())
Factor a real symmetric positive definite matrix and estimate the condition number of the matrix.
Signature: (a(n,n);int [o]ipvt(n);[o]rcond();[o]z(n))
Factor a matrix using Gaussian elimination and estimate the condition number of the matrix.
Signature: (a(n,n);int [o]ipvt(n);int [o]info())
Factor a matrix using Gaussian elimination.
Signature: (a(n,n);[o]det(two=2);int job())
Compute the determinant and inverse of a certain real symmetric positive definite matrix using the factors computed by poco.
Signature: (a(n,n);int [o]ipvt(n);[o]det(two=2);[o]work(n);int job())
Compute the determinant and inverse of a matrix using the factors computed by geco or gefa.
Signature: (a(lda,n);int ipvt(n);b(n);int job())
Solve the real system A*X=B or TRANS(A)*X=B using the
factors computed by geco or gefa.
Signature: (a(n,n);[o]w(n);int matz();[o]z(n,n);[t]fvone(n);[t]fvtwo(n);int [o]ierr())
This subroutine calls the recommended sequence of subroutines from the eigensystem subroutine package (EISPACK) to find the eigenvalues and eigenvectors (if desired) of a REAL SYMMETRIC matrix.
Signature: (int n();[o]wsave(foo))
Subroutine ezffti initializes the work array wsave()
which is used in both ezfftf and
ezfftb.
The prime factorization
of n together with a tabulation of the trigonometric functions
are computed and stored in wsave().
Signature: (r(n);[o]azero();[o]a(n);[o]b(n);wsave(foo))
Signature: ([o]r(n);azero();a(n);b(n);wsave(foo))
Signature: (int l();c();[o]tc(bar);a(foo))
Convert the polfit coefficients to Taylor series form.
c and a() must be of the same type.
Signature: (int l();x();[o]yfit();[o]yp(nder);a(foo))
Use the coefficients generated by polfit to evaluate the
polynomial fit of degree l, along with the first nder of
its derivatives, at a specified point. x and a must be of the
same type.
Signature: (x(n);f(n);[o]d(n);int [o]ierr())
Calculate the derivatives of (x,f(x)) using cubic Hermite interpolation.
Calculate the derivatives at the given set of points ($x,$f,
where $x is strictly increasing).
The resulting set of points - $x,$f,$d, referred to
as the cubic Hermite representation - can then be used in
other functions, such as chfe, chfd,
and chia.
The boundary conditions are compatible with monotonicity, and if the data are only piecewise monotonic, the interpolant will have an extremum at the switch points; for more control over these issues use chic.
Error status returned by $ierr:
ierr switches in the direction of
monotonicity (data still valid).
nelem($x) < 2.
$x is not strictly increasing.
Signature: (int ic(two=2);vc(two=2);mflag();x(n);f(n);[o]d(n);wk(nwk);int [o]ierr())
Calculate the derivatives of (x,f(x)) using cubic Hermite interpolation.
Calculate the derivatives at the given points ($x,$f,
where $x is strictly increasing).
Control over the boundary conditions is given by the
$ic and $vc piddles, and the value of $mflag determines
the treatment of points where monotoncity switches
direction. A simpler, more restricted, interface is available
using chim.
The first and second elements of $ic determine the boundary
conditions at the start and end of the data respectively.
If the value is 0, then the default condition, as used by
chim, is adopted.
If greater than zero, no adjustment for monotonicity is made,
otherwise if less than zero the derivative will be adjusted.
The allowed magnitudes for ic(0) are:
x(0) is given in vc(0).
x(0) is given in vc(0).
d(0).
(Reverts to the default b.c. if n < 3)
d(0).
(Reverts to the default b.c. if n < 4)
d(0) so that the second derivative is
continuous at x(1).
(Reverts to the default b.c. if n < 4)
The values for ic(1) are the same as above, except that
the first-derivative value is stored in vc(1) for cases 1 and 2.
The values of $vc need only be set if options 1 or 2 are chosen
for $ic.
Set $mflag = 0 if interpolant is required to be monotonic in
each interval, regardless of the data. This causes $d to be
set to 0 at all switch points. Set $mflag to be non-zero to
use a formula based on the 3-point difference formula at switch
points. If $mflag > 0, then the interpolant at swich points
is forced to not deviate from the data by more than $mflag*dfloc,
where dfloc is the maximum of the change of $f on this interval
and its two immediate neighbours.
If $mflag < 0, no such control is to be imposed.
The piddle $wk is only needed for work space. However, I could
not get it to work as a temporary variable, so you must supply
it; it is a 1D piddle with 2*n elements.
Error status returned by $ierr:
ic(0) < 0 and d(0) had to be adjusted for
monotonicity.
ic(1) < 0 and d(n-1) had to be adjusted
for monotonicity.
n < 2.
$x is not strictly increasing.
abs(ic(0)) > 5.
abs(ic(1)) > 5.
nwk < 2*(n-1).
Signature: (int ic(two=2);vc(two=2);x(n);f(n);[o]d(n);wk(nwk);int [o]ierr())
Calculate the derivatives of (x,f(x)) using cubic spline interpolation.
Calculate the derivatives, using cubic spline interpolation,
at the given points ($x,$f), with the specified
boundary conditions.
Control over the boundary conditions is given by the
$ic and $vc piddles.
The resulting values - $x,$f,$d - can
be used in all the functions which expect a cubic
Hermite function.
The first and second elements of $ic determine the boundary
conditions at the start and end of the data respectively.
The allowed values for ic(0) are:
d(0) so that the third derivative is
continuous at x(1).
x(0) is given in vc(0).
x(0) is given in vc(0).
d(0).
(Reverts to the default b.c. if n < 3.)
d(0).
(Reverts to the default b.c. if n < 4.)
The values for ic(1) are the same as above, except that
the first-derivative value is stored in vc(1) for cases 1 and 2.
The values of $vc need only be set if options 1 or 2 are chosen
for $ic.
The piddle $wk is only needed for work space. However, I could
not get it to work as a temporary variable, so you must supply
it; it is a 1D piddle with 2*n elements.
Error status returned by $ierr:
nelem($x) < 2.
$x is not strictly increasing.
ic(0) < 0 or ic(0) > 4.
ic(1) < 0 or ic(1) > 4.
nwk < 2*n.
Signature: (x(n);f(n);d(n);int check();xe(ne);[o]fe(ne);[o]de(ne);int [o]ierr())
Interpolate function and derivative values.
Given a piecewise cubic Hermite function - such as from
chim - evaluate the function ($fe) and
derivative ($de) at a set of points ($xe).
If function values alone are required, use chfe.
Set check to 0 to skip checks on the input data.
Error status returned by $ierr:
ierr points
(data still valid).
nelem($x) < 2
$x is not strictly increasing.
nelem($xe) < 1.
Signature: (x(n);f(n);d(n);int check();xe(ne);[o]fe(ne);int [o]ierr())
Interpolate function values.
Given a piecewise cubic Hermite function - such as from
chim - evaluate the function ($fe) at
a set of points ($xe).
If derivative values are also required, use chfd.
Set check to 0 to skip checks on the input data.
Error status returned by $ierr:
ierr points
(data still valid).
nelem($x) < 2
$x is not strictly increasing.
nelem($xe) < 1.
Signature: (x(n);f(n);d(n);int check();a();b();[o]ans();int [o]ierr())
Integrate (x,f(x)) over arbitrary limits.
Evaluate the definite integral of a a piecewise
cubic Hermite function over an arbitrary interval,
given by [$a,$b].
See chid if the integration limits are
data points.
Set check to 0 to skip checks on the input data.
The values of $a and $b do not have
to lie within $x, although the resulting integral
value will be highly suspect if they are not.
Error status returned by $ierr:
$a lies outside $x.
$b lies outside $x.
nelem($x) < 2
$x is not strictly increasing.
Signature: (x(n);f(n);d(n);int check();int ia();int ib();[o]ans();int [o]ierr())
Integrate (x,f(x)) between data points.
Evaluate the definite integral of a a piecewise
cubic Hermite function between x($ia) and
x($ib).
See chia for integration between arbitrary limits.
Although using a fortran routine, the values of
$ia and $ib are zero offset.
Set check to 0 to skip checks on the input data.
Error status returned by $ierr:
nelem($x) < 2.
$x is not strictly increasing.
$ia or $ib is out of range.
Signature: (x(n);f(n);d(n);int check();int [o]ismon(n);int [o]ierr())
Check the given piecewise cubic Hermite function for monotonicity.
The outout piddle $ismon indicates over
which intervals the function is monotonic.
Set check to 0 to skip checks on the input data.
For the data interval [x(i),x(i+1)], the
values of ismon(i) can be:
If abs(ismon(i)) == 3, the derivative values are
near the boundary of the monotonicity region. A small
increase produces non-monotonicity, whereas a decrease
produces strict monotonicity.
The above applies to i = 0 .. nelem($x)-1. The last element of
$ismon indicates whether
the entire function is monotonic over $x.
Error status returned by $ierr:
n < 2.
$x is not strictly increasing.
Signature: (x(n);y(n);w(n);int maxdeg();int [o]ndeg();[o]eps();[o]r(n);int [o]ierr();[o]a(foo);[t]xtmp(n);[t]ytmp(n);[t]wtmp(n);[t]rtmp(n))
Fit discrete data in a least squares sense by polynomials
in one variable. x(), y() and w() must be of the same type.
This version handles bad values appropriately
Copyright (C) 1997 Tuomas J. Lukka. Copyright (C) 2000 Tim Jenness, Doug Burke. All rights reserved. There is no warranty. You are allowed to redistribute this software / documentation under certain conditions. For details, see the file COPYING in the PDL distribution. If this file is separated from the PDL distribution, the copyright notice should be included in the file.
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PDL::Slatec - PDL interface to the slatec numerical programming library |