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* Add monotone cubic Hermite spline. Patch by Marcin Wiazowski. Issue #33588

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* More unified code with Lazarus ipf_fix module

git-svn-id: trunk@38786 -
This commit is contained in:
maciej-izak 2018-04-18 22:12:43 +00:00
parent bd3d35f2da
commit 4cdb2e832d
1 changed files with 171 additions and 15 deletions
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184
packages/numlib/src/ipf.pas
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@ -32,6 +32,12 @@ interface
uses typ, mdt, dsl, sle, spe;
type
THermiteSplineType = (
hstMonotone // preserves monotonicity of the interpolated function by using
// a Fritsch-Carlson algorithm
);
{ Determine natural cubic spline "s" for data set (x,y), output to (a,d2a)
term=1 success,
=2 failure calculating "s"
@ -52,7 +58,36 @@ Does NOT take source points into account.}
procedure ipfsmm(n: ArbInt; var x, y, d2s, minv, maxv: ArbFloat;
var term: ArbInt);
{Calculate n-degree polynomal b for dataset (x,y) with m elements
{Calculates tangents for each data point (d1s), for a given array of input data
points (x,y), by using a selected variant of a Hermite cubic spline interpolation.
Inputs:
hst - algorithm selection
n - highest array index
x[0..n] - array of X values (one value for each data point)
y[0..n] - array of Y values (one value for each data point)
Outputs:
d1s[0..n] - array of tangent values (one value for each data point)
term - status: 1 if function succeeded, 3 if less than two data points given
}
procedure ipfish(hst: THermiteSplineType; n: ArbInt; var x, y, d1s: ArbFloat; var term: ArbInt);
{Calculates interpolated function value for a given array of input data points
(x,y) and tangents for each data point (d1s), for input value t, by using a
Hermite cubic spline interpolation; d1s array can be obtained by calling the
ipfish procedure.
Inputs:
n - highest array index
x[0..n] - array of X values (one value for each data point)
y[0..n] - array of Y values (one value for each data point)
d1s[0..n] - array of tangent values (one value for each data point)
t - input value X
Outputs:
term - status: 1 if function succeeded, 3 if less than two data points given
result - interpolated function value Y
}
function ipfsph(n: ArbInt; var x, y, d1s: ArbFloat; t: ArbFloat; var term: ArbInt): ArbFloat;
{Calculate n-degree polynomal b for dataset (x,y) with n elements
using the least squares method.}
procedure ipfpol(m, n: ArbInt; var x, y, b: ArbFloat; var term: ArbInt);
@ -81,7 +116,7 @@ implementation
procedure ipffsn(n: ArbInt; var x, y, a, d2a: ArbFloat; var term: ArbInt);
var i, j, sr, n1s, ns1, ns2: ArbInt;
var i, sr, n1s, ns1, ns2: ArbInt;
s, lam, lam0, lam1, lambda, ey, ca, p, q, r: ArbFloat;
px, py, pd, pa, pd2a,
h, z, diagb, dinv, qty, qtdinvq, c, t, tl: ^arfloat1;
@ -89,8 +124,9 @@ var i, j, sr, n1s, ns1, ns2: ArbInt;
procedure solve; {n, py, qty, h, qtdinvq, dinv, lam, t, pa, pd2a, term}
var i: ArbInt;
p, q, r, ca: ArbFloat;
p, q, r: ArbFloat;
f, c: ^arfloat1;
ca: ArbFloat = 0.0;
begin
getmem(f, 3*ns1); getmem(c, ns1);
for i:=1 to n-1 do
@ -513,7 +549,7 @@ procedure ipfpol(m, n: ArbInt; var x, y, b: ArbFloat; var term: ArbInt);
var i, ns: ArbInt;
fsum: ArbFloat;
px, py, alfa, beta: ^arfloat1;
py, alfa, beta: ^arfloat1;
pb, a: ^arfloat0;
begin
if (n<0) or (m<1)
@ -554,18 +590,22 @@ end; {ipfpol}
procedure ipfisn(n: ArbInt; var x, y, d2s: ArbFloat; var term: ArbInt);
var
s, i : ArbInt;
p, q, ca : ArbFloat;
s, i, L : ArbInt;
p, q : ArbFloat;
px, py, h, b, t : ^arfloat0;
pd2s : ^arfloat1;
ca : ArbFloat = 0.0;
begin
px:=@x; py:=@y; pd2s:=@d2s;
term:=1;
if n < 2
if n < 1
then
begin
term:=3; exit
end; {n<2}
end; {n<1}
if n = 1 then
exit;
px:=@x; py:=@y; pd2s:=@d2s;
s:=sizeof(ArbFloat);
getmem(h, n*s);
getmem(b, (n-1)*s);
@ -583,7 +623,8 @@ begin
begin
q:=1/h^[i-1]; b^[i-2]:=py^[i]*q-py^[i-1]*(p+q)+py^[i-2]*p; p:=q
end;
slegpb(n-1, 1, {2,} t^[1], b^[0], pd2s^[1], ca, term);
if n > 2 then L := 1 else L := 0;
slegpb(n-1, L, {2,} t^[1], b^[0], pd2s^[1], ca, term);
freemem(h, n*s);
freemem(b, (n-1)*s);
freemem(t, 2*(n-1)*s);
@ -598,13 +639,21 @@ var
i, j, m : ArbInt;
d, s3, h, dy : ArbFloat;
begin
i:=1; term:=1;
if n<2
term:=1;
if n<1
then
begin
term:=3; exit
end; {n<2}
end; {n<1}
px:=@x; py:=@y; pd2s:=@d2s;
if n = 1
then
begin
h:=px^[1]-px^[0];
dy:=(py^[1]-py^[0])/h;
ipfspn:=py^[0]+(t-px^[0])*dy
end { n = 1 }
else
if t <= px^[0]
then
begin
@ -714,15 +763,122 @@ var
begin
term:=1;
if n<2 then begin
if n<1 then begin
term:=3;
exit;
end;
if n = 1 then
exit;
px:=@x; py:=@y; pd2s:=@d2s;
for i:=0 to n-1 do
MinMaxOnSegment;
end;
procedure ipfish(hst: THermiteSplineType; n: ArbInt; var x, y, d1s: ArbFloat; var term: ArbInt);
var
px, py, pd1s : ^arfloat0;
i : ArbInt;
dks : array of ArbFloat;
begin
term:=1;
if n < 1 then
begin
term:=3;
exit;
end;
px:=@x;
py:=@y;
pd1s:=@d1s;
{Monotone cubic Hermite interpolation}
{See: https://en.wikipedia.org/wiki/Monotone_cubic_interpolation
and: https://en.wikipedia.org/wiki/Cubic_Hermite_spline}
{For each two adjacent data points, calculate tangent of the segment between them}
SetLength(dks,n);
for i:=0 to n-1 do
dks[i]:=(py^[i+1]-py^[i])/(px^[i+1]-px^[i]);
{As proposed by Fritsch and Carlson: For each data point - except the first and
the last one - assign point's tangent (stored in a "d1s" array) as an average
of tangents of the two adjacent segments (this is called 3PD, three-point
difference) - but only if both tangents are either positive (segments are
raising) or negative (segments are falling); in all other cases there is a local
extremum at the data point, or a non-monotonic range begins/continues/ends there,
so spline at this point must be flat to preserve monotonicity - so assign point's
tangent as zero}
for i:=0 to n-2 do
if ((dks[i] > 0) and (dks[i+1] > 0)) or ((dks[i] < 0) and (dks[i+1] < 0)) then
pd1s^[i+1]:=0.5*(dks[i]+dks[i+1])
else
pd1s^[i+1]:=0;
{For the first and the last data point, assign point's tangent as a tangent of
the adjacent segment (this is called one-sided difference)}
pd1s^[0]:=dks[0];
pd1s^[n]:=dks[n-1];
{As proposed by Fritsch and Carlson: Reduce point's tangent if needed, to prevent
overshoot}
for i:=0 to n-1 do
if dks[i] <> 0 then
try
if pd1s^[i]/dks[i] > 3 then
pd1s^[i]:=3*dks[i];
if pd1s^[i+1]/dks[i] > 3 then
pd1s^[i+1]:=3*dks[i];
except
{There may be an exception for dks[i] values that are very close to zero}
pd1s^[i]:=0;
pd1s^[i+1]:=0;
end;
{Addition to the original algorithm: For the first and the last data point,
modify point's tangent in such a way that the cubic Hermite interpolation
polynomial has its inflection point exactly at the data point - so there
will be a smooth transition to the extrapolated part of the graph}
pd1s^[0]:=1.5*dks[0]-0.5*pd1s^[1];
pd1s^[n]:=1.5*dks[n-1]-0.5*pd1s^[n-1];
end; {ipfish}
function ipfsph(n: ArbInt; var x, y, d1s: ArbFloat; t: ArbFloat; var term: ArbInt): ArbFloat;
var
px, py, pd1s : ^arfloat0;
i, j, m : ArbInt;
h : ArbFloat;
begin
term:=1;
if n < 1 then
begin
term:=3;
exit;
end;
px:=@x;
py:=@y;
pd1s:=@d1s;
if t <= px^[0] then
ipfsph:=py^[0]+(t-px^[0])*pd1s^[0]
else
if t >= px^[n] then
ipfsph:=py^[n]+(t-px^[n])*pd1s^[n]
else
begin
i:=0;
j:=n;
while j <> i+1 do
begin
m:=(i+j) div 2;
if t>=px^[m] then
i:=m
else
j:=m;
end; {j}
h:=px^[i+1]-px^[i];
t:=(t-px^[i])/h;
ipfsph:= py^[i]*(1+2*t)*Sqr(1-t) + h*pd1s^[i]*t*Sqr(1-t) + py^[i+1]*Sqr(t)*(3-2*t) + h*pd1s^[i+1]*Sqr(t)*(t-1);
end;
end; {ipfsph}
function p(x, a, z:complex): ArbFloat;
begin
x.sub(a);