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/* GNUPLOT - interpol.c */

/*[

* Permission to use, copy, and distribute this software and its

* documentation for any purpose with or without fee is hereby granted,

* provided that the above copyright notice appear in all copies and

* that both that copyright notice and this permission notice appear

* in supporting documentation.

* Permission to modify the software is granted, but not the right to

* distribute the complete modified source code. Modifications are to

* be distributed as patches to the released version. Permission to

* distribute binaries produced by compiling modified sources is granted,

* provided you

* 1. distribute the corresponding source modifications from the

* released version in the form of a patch file along with the binaries,

* 2. add special version identification to distinguish your version

* in addition to the base release version number,

* 3. provide your name and address as the primary contact for the

* support of your modified version, and

* 4. retain our contact information in regard to use of the base

* software.

* Permission to distribute the released version of the source code along

* with corresponding source modifications in the form of a patch file is

* granted with same provisions 2 through 4 for binary distributions.

* This software is provided "as is" without express or implied warranty

* to the extent permitted by applicable law.

]*/

* C-Source file identification Header

* This file belongs to a project which is:

* done 1993 by MGR-Software, Asgard (Lars Hanke)

* written by Lars Hanke

* Contact me via:

* InterNet: mgr@asgard.bo.open.de

* FIDO: Lars Hanke @ 2:243/4802.22 (as long as they keep addresses)

**************************************************************************

* Project: gnuplot

* Module:

* File: interpol.c

* Revisor: Lars Hanke

* Revised: 26/09/93

* Revision: 1.0

**************************************************************************

* LEGAL

* This module is part of gnuplot and distributed under whatever terms

* gnuplot is or will be published, unless exclusive rights are claimed.

* DESCRIPTION

* Supplies 2-D data interpolation and approximation routines

* IMPORTS

* plot.h

* - cp_extend()

* - structs: curve_points, coordval, coordinate

* setshow.h

* - samples, axis array[] variables

* - plottypes

* EXPORTS

* gen_interp()

* sort_points()

* cp_implode()

**************************************************************************

* HISTORY

* Changes:

* Nov 24, 1995 Markus Schuh (M.Schuh@meteo.uni-koeln.de):

* changed the algorithm for csplines

* added algorithm for approximation csplines

* copied point storage and range fix from plot2d.c

* Jun 30, 1996 Jens Emmerich

* implemented handling of UNDEFINED points

* Dec 2019 EAM

* move solve_tri_diag from contour.c to here

* generalize cp_tridiag to work on any coordinate dimension

* Feb/Mar/Apr 2020 EAM

* along-path smoothing for nonmonotonic 2D curves (now in filters.c)

* cspline smoothing for "with filledcurves {between|above|below}"

* Nov 2022 EAM

* move dual-licensed code into a separate file filters.c

#include "interpol.h"

#include "alloc.h"

#include "axis.h"

#include "plot2d.h"

/* local definitions */

typedef double tri_diag[3];

typedef double five_diag[5];

/* local prototypes */

static double eval_kdensity(struct curve_points *cp,

int first_point, int num_points, double x);

static void do_kdensity(struct curve_points *cp, int first_point,

int num_points, struct coordinate *dest);

static double *cp_binomial(int points);

static void eval_bezier(struct curve_points * cp, int first_point,

int num_points, double sr, coordval * px,

coordval *py, coordval *py2, double *c);

static void do_bezier(struct curve_points * cp, double *bc, int first_point, int num_points, struct coordinate * dest);

static int solve_tri_diag(tri_diag m[], double r[], double x[], int n);

static int solve_five_diag(five_diag m[], double r[], double x[], int n);

static void do_cubic(struct curve_points * plot,

spline_coeff * sc, spline_coeff * sc2,

int first_point, int num_points, struct coordinate * dest);

static void do_freq(struct curve_points *plot, int first_point, int num_points);

* position curve_start to index the next non-UNDEFINDED point,

* start search at initial curve_start,

* return number of non-UNDEFINDED points from there on,

* if no more valid points are found, curve_start is set

* to plot->p_count and 0 is returned

int

next_curve(struct curve_points *plot, int *curve_start)

{

int curve_length;

/* Skip undefined points */

while (*curve_start < plot->p_count

&& plot->points[*curve_start].type == UNDEFINED) {

(*curve_start)++;

};

curve_length = 0;

/* curve_length is first used as an offset, then the correct # points */

while ((*curve_start) + curve_length < plot->p_count

&& plot->points[(*curve_start) + curve_length].type != UNDEFINED) {

curve_length++;

};

return (curve_length);

}

* determine the number of curves in plot->points, separated by

* UNDEFINED points

int

num_curves(struct curve_points *plot)

{

int curves;

int first_point;

int num_points;

first_point = 0;

curves = 0;

while ((num_points = next_curve(plot, &first_point)) > 0) {

curves++;

first_point += num_points;

}

return (curves);

}

* cp_implode() if averaging is selected this function computes the new

* entries and shortens the whole thing to the necessary

* size

* MGR Addendum

void

cp_implode(struct curve_points *cp)

{

int first_point, num_points;

int i, j, k;

double x = 0., y = 0., sux = 0., slx = 0., suy = 0., sly = 0.;

double weight = 1.0; /* used for acsplines */

TBOOLEAN all_inrange = FALSE;

x_axis = cp->x_axis;

y_axis = cp->y_axis;

j = 0;

first_point = 0;

while ((num_points = next_curve(cp, &first_point)) > 0) {

TBOOLEAN last_point = FALSE;

k = 0;

for (i = first_point; i <= first_point + num_points; i++) {

if (i == first_point + num_points) {

if (k == 0)

break;

last_point = TRUE;

}

if (!last_point && cp->points[i].type == UNDEFINED)

continue;

if (k == 0) {

x = cp->points[i].x;

y = cp->points[i].y;

sux = cp->points[i].xhigh;

slx = cp->points[i].xlow;

suy = cp->points[i].yhigh;

sly = cp->points[i].ylow;

weight = cp->points[i].z;

all_inrange = (cp->points[i].type == INRANGE);

k = 1;

} else if (!last_point && cp->points[i].x == x) {

y += cp->points[i].y;

sux += cp->points[i].xhigh;

slx += cp->points[i].xlow;

suy += cp->points[i].yhigh;

sly += cp->points[i].ylow;

weight += cp->points[i].z;

if (cp->points[i].type != INRANGE)

all_inrange = FALSE;

k++;

} else {

cp->points[j].x = x;

if ( cp->plot_smooth == SMOOTH_FREQUENCY ||

cp->plot_smooth == SMOOTH_FREQUENCY_NORMALISED ||

cp->plot_smooth == SMOOTH_CUMULATIVE ||

cp->plot_smooth == SMOOTH_CUMULATIVE_NORMALISED )

k = 1;

cp->points[j].y = y /= (double) k;

cp->points[j].xhigh = sux / (double) k;

cp->points[j].xlow = slx / (double) k;

cp->points[j].yhigh = suy / (double) k;

cp->points[j].ylow = sly / (double) k;

cp->points[j].z = weight / (double) k;

/* HBB 20000405: I wanted to use STORE_AND_FIXUP_RANGE here,

* but won't: it assumes we want to modify the range, and

* that the range is given in 'input' coordinates.

cp->points[j].type = INRANGE;

if (! all_inrange) {

if (((x < X_AXIS.min) && !(X_AXIS.autoscale & AUTOSCALE_MIN))

|| ((x > X_AXIS.max) && !(X_AXIS.autoscale & AUTOSCALE_MAX))

|| ((y < Y_AXIS.min) && !(Y_AXIS.autoscale & AUTOSCALE_MIN))

|| ((y > Y_AXIS.max) && !(Y_AXIS.autoscale & AUTOSCALE_MAX)))

cp->points[j].type = OUTRANGE;

} /* if (! all inrange) */

j++; /* next valid entry */

k = 0; /* to read */

i--; /* from this (-> last after for(;;)) entry */

} /* else (same x position) */

} /* for(points in curve) */

/* FIXME: Monotonic cubic splines support only a single curve per data set */

if (j < cp->p_count && cp->plot_smooth == SMOOTH_MONOTONE_CSPLINE)

break;

/* insert invalid point to separate curves */

if (j < cp->p_count) {

cp->points[j].type = UNDEFINED;

j++;

}

first_point += num_points;

} /* end while */

cp->p_count = j;

cp_extend(cp, j);

}

/* PKJ - May 2008

kdensity (short for Kernel Density) builds histograms using

"Kernel Density Estimation" using Gaussian Kernels.

Check: L. Wassermann: "All of Statistics" for example.

The implementation is based closely on the implementation for Bezier

curves, except for the way the actual interpolation is generated.

static double kdensity_bandwidth = 0;

static void

stats_kdensity (

struct curve_points *cp,

int first_point, /* where to start in plot->points (to find x-range) */

int num_points /* to determine end in plot->points */

) {

struct coordinate *this_points = (cp->points) + first_point;

double kdensity_avg = 0;

double kdensity_sigma = 0;

double default_bandwidth;

int i;

kdensity_avg = 0.0;

kdensity_sigma = 0.0;

for (i = 0; i < num_points; i++) {

kdensity_avg += this_points[i].x;

kdensity_sigma += this_points[i].x * this_points[i].x;

}

kdensity_avg /= (double)num_points;

kdensity_sigma = sqrt( kdensity_sigma/(double)num_points - kdensity_avg*kdensity_avg );

/* This is the optimal bandwidth if the point distribution is Gaussian.

(Applied Smoothing Techniques for Data Analysis

by Adrian W, Bowman & Adelchi Azzalini (1997)) */

/* If the supplied bandwidth is zero of less, the default bandwidth is used. */

default_bandwidth = pow( 4.0/(3.0*num_points), 1.0/5.0 ) * kdensity_sigma;

if (cp->smooth_parameter <= 0) {

kdensity_bandwidth = default_bandwidth;

cp->smooth_parameter = -default_bandwidth;

} else

kdensity_bandwidth = cp->smooth_parameter;

}

/* eval_kdensity is a modification of eval_bezier */

static double

eval_kdensity (

struct curve_points *cp,

int first_point, /* where to start in plot->points (to find x-range) */

int num_points, /* to determine end in plot->points */

double x /* x value at which to calculate y */

) {

struct coordinate *this_points = (cp->points) + first_point;

double period = cp->smooth_period;

unsigned int i;

double y, Z;

y = 0;

for (i = 0; i < num_points; i++) {

double dist = fabs(x - this_points[i].x);

if (period > 0 && dist > period/2)

dist = period - dist;

Z = dist / kdensity_bandwidth;

y += this_points[i].y * exp( -0.5*Z*Z ) / kdensity_bandwidth;

}

y /= sqrt(2.0*M_PI);

return y;

}

/* do_kdensity is based on do_bezier, except for the call to eval_bezier */

/* EAM Feb 2015: Don't touch xrange, but recalculate y limits */

static void

do_kdensity(

struct curve_points *cp,

int first_point, /* where to start in plot->points */

int num_points, /* to determine end in plot->points */

struct coordinate *dest) /* where to put the interpolated data */

{

int i;

double x, y;

double sxmin, sxmax, step;

x_axis = cp->x_axis;

y_axis = cp->y_axis;

if (X_AXIS.log)

int_warn(NO_CARET, "kdensity components are Gaussian on x, not log(x)");

sxmin = X_AXIS.min;

sxmax = X_AXIS.max;

step = (sxmax - sxmin) / (samples_1 - 1);

stats_kdensity( cp, first_point, num_points );

for (i = 0; i < samples_1; i++) {

x = sxmin + i * step;

y = eval_kdensity( cp, first_point, num_points, x );

/* now we have to store the points and adjust the ranges */

dest[i].type = INRANGE;

dest[i].x = x;

store_and_update_range( &dest[i].y, y, &dest[i].type, &Y_AXIS,

cp->noautoscale );

dest[i].xlow = dest[i].xhigh = dest[i].x;

dest[i].ylow = dest[i].yhigh = dest[i].y;

dest[i].z = -1;

}

/* HBB 990205: rewrote the 'bezier' interpolation routine,

* to prevent numerical overflow and other undesirable things happening

* for large data files (num_data about 1000 or so), where binomial

* coefficients would explode, and powers of 'sr' (0 < sr < 1) become

* extremely small. Method used: compute logarithms of these

* extremely large and small numbers, and only go back to the

* real numbers once they've cancelled out each other, leaving

* a reasonable-sized one. */

* cp_binomial() computes the binomial coefficients needed for BEZIER stuff

* and stores them into an array which is hooked to sdat.

* (MGR 1992)

static double *

cp_binomial(int points)

{

double *coeff;

int n, k;

int e;

e = points; /* well we're going from k=0 to k=p_count-1 */

coeff = gp_alloc(e * sizeof(double), "bezier coefficients");

n = points - 1;

e = n / 2;

/* HBB 990205: calculate these in 'logarithmic space',

* as they become _very_ large, with growing n (4^n) */

coeff[0] = 0.0;

for (k = 0; k < e; k++) {

coeff[k + 1] = coeff[k] + log(((double) (n - k)) / ((double) (k + 1)));

}

for (k = n; k >= e; k--)

coeff[k] = coeff[n - k];

return (coeff);

}

/* This is a subfunction of do_bezier() for BEZIER style computations.

* It is passed the step fraction (STEP/MAXSTEPS) and the addresses of

* the double values holding the next x and y coordinates.

* (MGR 1992)

* Feb 2020: Do yhigh also so that filledcurves can use it

static void

eval_bezier(

struct curve_points *cp,

int first_point, /* where to start in plot->points (to find x-range) */

int num_points, /* to determine end in plot->points */

double sr, /* position inside curve, range [0:1] */

coordval *px, /* OUTPUT: x and y */

coordval *py,

coordval *py2, /* used for 2nd border of fillcurves */

double *c) /* Bezier coefficient array */

{

unsigned int n = num_points - 1;

struct coordinate *this_points;

this_points = (cp->points) + first_point;

if (sr == 0.0) {

*px = this_points[0].x;

*py = this_points[0].y;

*py2 = this_points[0].yhigh;

} else if (sr == 1.0) {

*px = this_points[n].x;

*py = this_points[n].y;

*py2 = this_points[n].yhigh;

} else {

/* HBB 990205: do calculation in 'logarithmic space',

* to avoid over/underflow errors, which would exactly cancel

* out each other, anyway, in an exact calculation

unsigned int i;

double lx = 0.0, ly = 0.0, ly2 = 0.0;

double log_dsr_to_the_n = n * log(1 - sr);

double log_sr_over_dsr = log(sr) - log(1 - sr);

for (i = 0; i <= n; i++) {

double u = exp(c[i] + log_dsr_to_the_n + i * log_sr_over_dsr);

lx += this_points[i].x * u;

ly += this_points[i].y * u;

ly2 += this_points[i].yhigh * u;

}

*px = lx;

*py = ly;

*py2 = ly2;

}

* Generate a new set of coordinates representing the bezier curve.

* Note that these are sampled evenly across the x range (from "set samples N")

* rather than corresponding to x values of the original data points.

static void

do_bezier(

struct curve_points *cp,

double *bc, /* Bezier coefficient array */

int first_point, /* where to start in plot->points */

int num_points, /* to determine end in plot->points */

struct coordinate *dest) /* where to put the interpolated data */

{

int i;

coordval x, y, yhigh;

x_axis = cp->x_axis;

y_axis = cp->y_axis;

for (i = 0; i < samples_1; i++) {

eval_bezier(cp, first_point, num_points,

(double) i / (double) (samples_1 - 1),

&x, &y, &yhigh, bc);

dest[i].type = INRANGE;

store_and_update_range(&dest[i].x, x, &dest[i].type, &X_AXIS, X_AXIS.autoscale);

store_and_update_range(&dest[i].y, y, &dest[i].type, &Y_AXIS, Y_AXIS.autoscale);

dest[i].xlow = dest[i].xhigh = dest[i].x;

dest[i].ylow = dest[i].yhigh = dest[i].y;

dest[i].z = -1;

dest[i].yhigh = yhigh;

}

* Solve five diagonal linear system equation. The five diagonal matrix is

* defined via matrix M, right side is r, and solution X i.e. M * X = R.

* Size of system given in n. Return TRUE if solution exist.

* G. Engeln-Muellges/ F.Reutter:

* "Formelsammlung zur Numerischen Mathematik mit Standard-FORTRAN-Programmen"

* ISBN 3-411-01677-9

* / m02 m03 m04 0 0 0 0 . . . \ / x0 \ / r0 \

* I m11 m12 m13 m14 0 0 0 . . . I I x1 I I r1 I

* I m20 m21 m22 m23 m24 0 0 . . . I * I x2 I = I r2 I

* I 0 m30 m31 m32 m33 m34 0 . . . I I x3 I I r3 I

* . . . . . . . . . . . .

* \ m(n-3)0 m(n-2)1 m(n-1)2 / \x(n-1)/ \r(n-1)/

static int

solve_five_diag(five_diag m[], double r[], double x[], int n)

{

int i;

five_diag *hv;

hv = gp_alloc((n + 1) * sizeof(five_diag), "five_diag help vars");

hv[0][0] = m[0][2];

if (hv[0][0] == 0) {

free(hv);

return FALSE;

}

hv[0][1] = m[0][3] / hv[0][0];

hv[0][2] = m[0][4] / hv[0][0];

hv[1][3] = m[1][1];

hv[1][0] = m[1][2] - hv[1][3] * hv[0][1];

if (hv[1][0] == 0) {

free(hv);

return FALSE;

}

hv[1][1] = (m[1][3] - hv[1][3] * hv[0][2]) / hv[1][0];

hv[1][2] = m[1][4] / hv[1][0];

for (i = 2; i < n; i++) {

hv[i][3] = m[i][1] - m[i][0] * hv[i - 2][1];

hv[i][0] = m[i][2] - m[i][0] * hv[i - 2][2] - hv[i][3] * hv[i - 1][1];

if (hv[i][0] == 0) {

free(hv);

return FALSE;

}

hv[i][1] = (m[i][3] - hv[i][3] * hv[i - 1][2]) / hv[i][0];

hv[i][2] = m[i][4] / hv[i][0];

}

hv[0][4] = 0;

hv[1][4] = r[0] / hv[0][0];

for (i = 1; i < n; i++) {

hv[i + 1][4] = (r[i] - m[i][0] * hv[i - 1][4] - hv[i][3] * hv[i][4]) / hv[i][0];

}

x[n - 1] = hv[n][4];

x[n - 2] = hv[n - 1][4] - hv[n - 2][1] * x[n - 1];

for (i = n - 3; i >= 0; i--)

x[i] = hv[i + 1][4] - hv[i][1] * x[i + 1] - hv[i][2] * x[i + 2];

free(hv);

return TRUE;

}

* Calculation of approximation cubic splines

* Returns matrix of spline coefficients

* Dec 2019 EAM - modified original routine cp_approx_spline for use with

* multi-dimensional splines

* original code: created spline for y given x = control, variable z = weight

* revised code: create spline for coordinate indexed by spline_dim

* given control variable indexed by path_dim

* weights indexed by w_dim

spline_coeff *

cp_approx_spline(

struct coordinate *points, int num_points,

int path_dim, int spline_dim, int w_dim)

{

spline_coeff *sc;

five_diag *m;

double *r, *x, *h, *xp, *yp;

int i;

/* Define an overlay onto struct coordinate that lets us select whichever

* of x,y,z,... is needed by specifying an index 0-6

struct gen_coord {

coordval dimension[7];

enum coord_type type;

EXTRA_COORDINATE

};

struct gen_coord *this_point;

if (num_points < 4)

int_error(NO_CARET, "Can't calculate approximation splines, need at least 4 points");

this_point = (struct gen_coord *)(points);

for (i = 0; i < num_points; i++)

if (this_point[i].dimension[w_dim] <= 0)

int_error(NO_CARET, "Can't calculate approximation splines, all weights have to be > 0");

sc = gp_alloc((num_points) * sizeof(spline_coeff), "spline matrix");

m = gp_alloc((num_points - 2) * sizeof(five_diag), "spline help matrix");

r = gp_alloc((num_points - 2) * sizeof(double), "spline right side");

x = gp_alloc((num_points - 2) * sizeof(double), "spline solution vector");

h = gp_alloc((num_points - 1) * sizeof(double), "spline help vector");

xp = gp_alloc((num_points) * sizeof(double), "x pos");

yp = gp_alloc((num_points) * sizeof(double), "y pos");

xp[0] = this_point[0].dimension[path_dim];

yp[0] = this_point[0].dimension[spline_dim];

for (i = 1; i < num_points; i++) {

xp[i] = this_point[i].dimension[path_dim];

yp[i] = this_point[i].dimension[spline_dim];

h[i - 1] = xp[i] - xp[i - 1];

}

/* set up the matrix and the vector */

for (i = 0; i <= num_points - 3; i++) {

r[i] = 3 * ((yp[i + 2] - yp[i + 1]) / h[i + 1]

- (yp[i + 1] - yp[i]) / h[i]);

if (i < 2)

m[i][0] = 0;

else

m[i][0] = 6 / this_point[i].dimension[w_dim] / h[i - 1] / h[i];

if (i < 1)

m[i][1] = 0;

else

m[i][1] = h[i] - 6 / this_point[i].dimension[w_dim] / h[i] * (1 / h[i - 1] + 1 / h[i])

- 6 / this_point[i + 1].dimension[w_dim] / h[i] * (1 / h[i] + 1 / h[i + 1]);

m[i][2] = 2 * (h[i] + h[i + 1])

+ 6 / this_point[i].dimension[w_dim] / h[i] / h[i]

+ 6 / this_point[i + 1].dimension[w_dim] * (1 / h[i] + 1 / h[i + 1]) * (1 / h[i] + 1 / h[i + 1])

+ 6 / this_point[i + 2].dimension[w_dim] / h[i + 1] / h[i + 1];

if (i > num_points - 4)

m[i][3] = 0;

else

m[i][3] = h[i + 1] - 6 / this_point[i + 1].dimension[w_dim] / h[i + 1] * (1 / h[i] + 1 / h[i + 1])

- 6 / this_point[i + 2].dimension[w_dim] / h[i + 1] * (1 / h[i + 1] + 1 / h[i + 2]);

if (i > num_points - 5)

m[i][4] = 0;

else

m[i][4] = 6 / this_point[i + 2].dimension[w_dim] / h[i + 1] / h[i + 2];

}

/* solve the matrix */

if (!solve_five_diag(m, r, x, num_points - 2)) {

free(sc);

free(h);

free(x);

free(r);

free(m);

free(xp);

free(yp);

int_error(NO_CARET, "Can't calculate approximation splines");

}

sc[0][2] = 0;

for (i = 1; i <= num_points - 2; i++)

sc[i][2] = x[i - 1];

sc[num_points - 1][2] = 0;

sc[0][0] = yp[0] + 2 / this_point[0].dimension[w_dim] / h[0] * (sc[0][2] - sc[1][2]);

for (i = 1; i <= num_points - 2; i++)

sc[i][0] = yp[i] - 2 / this_point[i].dimension[w_dim] *

(sc[i - 1][2] / h[i - 1]

- sc[i][2] * (1 / h[i - 1] + 1 / h[i])

+ sc[i + 1][2] / h[i]);

sc[num_points - 1][0] = yp[num_points - 1]

- 2 / this_point[num_points - 1].dimension[w_dim] / h[num_points - 2]

* (sc[num_points - 2][2] - sc[num_points - 1][2]);

for (i = 0; i <= num_points - 2; i++) {

sc[i][1] = (sc[i + 1][0] - sc[i][0]) / h[i]

- h[i] / 3 * (sc[i + 1][2] + 2 * sc[i][2]);

sc[i][3] = (sc[i + 1][2] - sc[i][2]) / 3 / h[i];

}

free(h);

free(x);

free(r);

free(m);

free(xp);

free(yp);

return (sc);

}

* Calculation of cubic splines

* This can be treated as a special case of approximation cubic splines, with

* all weights -> infinity.

* Returns matrix of spline coefficients

* Dec 2019 EAM - modified original routine cp_tridiag() for use to

* create multi-dimensional splines

* original code: created a spline for y using x as the control variable

* revised code: spline for arbitrary coord using another coordinate as control

* Previous call to cp_tridiag(plot, start, n)

* becomes cp_tridiag(&plot->points[start], n, 0, 1)

* X Y <==

* To create a spline for an arbitrary coordinate, e.g. x, as a function of PATH

* load path increments into points[i].CRD_PATH

* cp_tridiag(points, n, PATHCOORD, 0)

spline_coeff *

cp_tridiag(

struct coordinate *points, int num_points,

int path_dim, int spline_dim)

{

spline_coeff *sc;

tri_diag *m;

double *r, *x, *h, *xp, *yp;

int i;

/* Define an overlay onto struct coordinate that lets us select whichever

* of x,y,z,... is needed by specifying an index 0-6

struct gen_coord {

coordval dimension[7];

enum coord_type type;

EXTRA_COORDINATE

};

struct gen_coord *this_point;

if (num_points < 3)

int_error(NO_CARET, "Can't calculate splines, need at least 3 points");

this_point = (struct gen_coord *)(points);

sc = gp_alloc((num_points) * sizeof(spline_coeff), "spline matrix");

m = gp_alloc((num_points - 2) * sizeof(tri_diag), "spline help matrix");

r = gp_alloc((num_points - 2) * sizeof(double), "spline right side");

x = gp_alloc((num_points - 2) * sizeof(double), "spline solution vector");

h = gp_alloc((num_points - 1) * sizeof(double), "spline help vector");

xp = gp_alloc((num_points) * sizeof(double), "x pos");

yp = gp_alloc((num_points) * sizeof(double), "y pos");

xp[0] = this_point[0].dimension[path_dim];

yp[0] = this_point[0].dimension[spline_dim];

for (i = 1; i < num_points; i++) {

xp[i] = this_point[i].dimension[path_dim];

yp[i] = this_point[i].dimension[spline_dim];

h[i - 1] = xp[i] - xp[i - 1];

}

/* set up the matrix and the vector */

for (i = 0; i <= num_points - 3; i++) {

r[i] = 3 * ((yp[i + 2] - yp[i + 1]) / h[i + 1]

- (yp[i + 1] - yp[i]) / h[i]);

if (i < 1)

m[i][0] = 0;

else

m[i][0] = h[i];

m[i][1] = 2 * (h[i] + h[i + 1]);

if (i > num_points - 4)

m[i][2] = 0;

else

m[i][2] = h[i + 1];

}

/* solve the matrix */

if (!solve_tri_diag(m, r, x, num_points - 2)) {

free(sc);

free(h);

free(x);

free(r);

free(m);

free(xp);

free(yp);

int_error(NO_CARET, "Can't calculate cubic splines");

}

sc[0][2] = 0;

for (i = 1; i <= num_points - 2; i++)

sc[i][2] = x[i - 1];

sc[num_points - 1][2] = 0;

for (i = 0; i <= num_points - 1; i++)

sc[i][0] = yp[i];

for (i = 0; i <= num_points - 2; i++) {

sc[i][1] = (sc[i + 1][0] - sc[i][0]) / h[i]

- h[i] / 3 * (sc[i + 1][2] + 2 * sc[i][2]);

sc[i][3] = (sc[i + 1][2] - sc[i][2]) / 3 / h[i];

}

free(h);

free(x);

free(r);

free(m);

free(xp);

free(yp);

return (sc);

}

* Solve tri diagonal linear system equation. The tri diagonal matrix is

* defined via matrix M, right side is r, and solution X i.e. M * X = R.

* Size of system given in n. Return TRUE if solution exist.

static int

solve_tri_diag(tri_diag m[], double r[], double x[], int n)

{

int i;

double t;

for (i = 1; i < n; i++) { /* Eliminate element m[i][i-1] (lower diagonal). */

if (m[i - 1][1] == 0)

return FALSE;

t = m[i][0] / m[i - 1][1]; /* Find ratio between the two lines. */

m[i][1] = m[i][1] - m[i - 1][2] * t;

r[i] = r[i] - r[i - 1] * t;

}

/* Back substitution - update the solution vector X */

if (m[n - 1][1] == 0)

return FALSE;

x[n - 1] = r[n - 1] / m[n - 1][1]; /* Find last element. */

for (i = n - 2; i >= 0; i--) {

if (m[i][1] == 0)

return FALSE;

x[i] = (r[i] - x[i + 1] * m[i][2]) / m[i][1];

}

return TRUE;

}

void

gen_interp_unwrap(struct curve_points *plot)

{

int i, j, curves;

int first_point, num_points;

double y, lasty, diff;

curves = num_curves(plot);

first_point = 0;

for (i = 0; i < curves; i++) {

num_points = next_curve(plot, &first_point);

lasty = 0; /* make all plots start the same place */

for (j = first_point; j < first_point + num_points; j++) {

if (plot->points[j].type == UNDEFINED)

continue;

y = plot->points[j].y;

do {

diff = y - lasty;

if (diff > M_PI) y -= 2*M_PI;

if (diff < -M_PI) y += 2*M_PI;

} while (fabs(diff) > M_PI);

plot->points[j].y = y;

lasty = y;

}

do_freq(plot, first_point, num_points);

first_point += num_points + 1;

}

return;

}

static void

do_cubic(

struct curve_points *plot, /* still contains old plot->points */

spline_coeff *sc, /* generated by cp_tridiag */

spline_coeff *sc2, /* optional spline for yhigh */

int first_point, /* where to start in plot->points */

int num_points, /* to determine end in plot->points */

struct coordinate *dest) /* where to put the interpolated data */

{

double xdiff, temp, x, y;

double xstart, xend; /* Endpoints of the sampled x range */

int i, l;

struct coordinate *this_points;

x_axis = plot->x_axis;

y_axis = plot->y_axis;

this_points = (plot->points) + first_point;

l = 0;

/* HBB 20010727: Sample only across the actual x range, not the

* full range of input data */

#if SAMPLE_CSPLINES_TO_FULL_RANGE

xstart = this_points[0].x;

xend = this_points[num_points - 1].x;

#else

xstart = this_points[0].x;

xend = this_points[num_points-1].x;

cliptorange( xstart, X_AXIS.min, X_AXIS.max );

cliptorange( xend, X_AXIS.min, X_AXIS.max );

if (xstart >= xend) {

/* This entire segment lies outside the current x range. */

for (i = 0; i < samples_1; i++)

dest[i].type = UNDEFINED;

return;

}

#endif

xdiff = (xend - xstart) / (samples_1 - 1);

for (i = 0; i < samples_1; i++) {

x = xstart + i * xdiff;

/* Move forward to the spline interval this point is in */

while ((x >= this_points[l + 1].x) && (l < num_points - 2))

l++;

temp = x - this_points[l].x;

/* Evaluate cubic spline polynomial */

y = ((sc[l][3] * temp + sc[l][2]) * temp + sc[l][1]) * temp + sc[l][0];

dest[i].type = INRANGE;

store_and_update_range(&dest[i].x, x, &dest[i].type, &X_AXIS, X_AXIS.autoscale);

store_and_update_range(&dest[i].y, y, &dest[i].type, &Y_AXIS, Y_AXIS.autoscale);

dest[i].xlow = dest[i].xhigh = dest[i].x;

dest[i].ylow = dest[i].yhigh = dest[i].y;

dest[i].z = -1;

/* This case is used when smoothing "x y yhigh with filledcurves" */

if (sc2) {

y = ((sc2[l][3] * temp + sc2[l][2]) * temp + sc2[l][1]) * temp + sc2[l][0];

dest[i].yhigh = y;

}

* do_freq() is like the other smoothers only in that it

* needs to adjust the plot ranges. We don't have to copy

* approximated curves or anything like that.

static void

do_freq(

struct curve_points *plot, /* still contains old plot->points */

int first_point, /* where to start in plot->points */

int num_points) /* to determine end in plot->points */

{

double x, y;

int i;

int x_axis = plot->x_axis;

int y_axis = plot->y_axis;

struct coordinate *this;

this = (plot->points) + first_point;

View remainder of file in raw view

gnuplot/src/interpol.c at 5.5 · gnuplot/gnuplot · GitHub

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