#include #include #include #include "bltInt.h" #include "bltOp.h" #include "bltVector.h" typedef int (SplineProc)(Point2d origPts[], int nOrigPts, Point2d intpPts[], int nIntpPts); typedef double TriDiagonalMatrix[3]; typedef struct { double b, c, d; } Cubic2D; typedef struct { double b, c, d, e, f; } Quint2D; /* * Quadratic spline parameters */ #define E1 param[0] #define E2 param[1] #define V1 param[2] #define V2 param[3] #define W1 param[4] #define W2 param[5] #define Z1 param[6] #define Z2 param[7] #define Y1 param[8] #define Y2 param[9] /* *--------------------------------------------------------------------------- * * Search -- * * Conducts a binary search for a value. This routine is called * only if key is between x(0) and x(len - 1). * * Results: * Returns the index of the largest value in xtab for which * x[i] < key. * *--------------------------------------------------------------------------- */ static int Search( Point2d points[], /* Contains the abscissas of the data * points of interpolation. */ int nPoints, /* Dimension of x. */ double key, /* Value whose relative position in * x is to be located. */ int *foundPtr) /* (out) Returns 1 if s is found in * x and 0 otherwise. */ { int high, low, mid; low = 0; high = nPoints - 1; while (high >= low) { mid = (high + low) / 2; if (key > points[mid].x) { low = mid + 1; } else if (key < points[mid].x) { high = mid - 1; } else { *foundPtr = 1; return mid; } } *foundPtr = 0; return low; } /* *--------------------------------------------------------------------------- * * QuadChoose -- * * Determines the case needed for the computation of the parame- * ters of the quadratic spline. * * Results: * Returns a case number (1-4) which controls how the parameters * of the quadratic spline are evaluated. * *--------------------------------------------------------------------------- */ static int QuadChoose( Point2d *p, /* Coordinates of one of the points of * interpolation */ Point2d *q, /* Coordinates of one of the points of * interpolation */ double m1, /* Derivative condition at point P */ double m2, /* Derivative condition at point Q */ double epsilon) /* Error tolerance used to distinguish * cases when m1 or m2 is relatively * close to the slope or twice the * slope of the line segment joining * the points P and Q. If * epsilon is not 0.0, then epsilon * should be greater than or equal to * machine epsilon. */ { double slope; /* Calculate the slope of the line joining P and Q. */ slope = (q->y - p->y) / (q->x - p->x); if (slope != 0.0) { double relerr; double mref, mref1, mref2, prod1, prod2; prod1 = slope * m1; prod2 = slope * m2; /* Find the absolute values of the slopes slope, m1, and m2. */ mref = fabs(slope); mref1 = fabs(m1); mref2 = fabs(m2); /* * If the relative deviation of m1 or m2 from slope is less than * epsilon, then choose case 2 or case 3. */ relerr = epsilon * mref; if ((fabs(slope - m1) > relerr) && (fabs(slope - m2) > relerr) && (prod1 >= 0.0) && (prod2 >= 0.0)) { double prod; prod = (mref - mref1) * (mref - mref2); if (prod < 0.0) { /* * l1, the line through (x1,y1) with slope m1, and l2, * the line through (x2,y2) with slope m2, intersect * at a point whose abscissa is between x1 and x2. * The abscissa becomes a knot of the spline. */ return 1; } if (mref1 > (mref * 2.0)) { if (mref2 <= ((2.0 - epsilon) * mref)) { return 3; } } else if (mref2 <= (mref * 2.0)) { /* * Both l1 and l2 cross the line through * (x1+x2)/2.0,y1 and (x1+x2)/2.0,y2, which is the * midline of the rectangle formed by P and Q or both * m1 and m2 have signs different than the sign of * slope, or one of m1 and m2 has opposite sign from * slope and l1 and l2 intersect to the left of x1 or * to the right of x2. The point (x1+x2)/2. is a knot * of the spline. */ return 2; } else if (mref1 <= ((2.0 - epsilon) * mref)) { /* * In cases 3 and 4, sign(m1)=sign(m2)=sign(slope). * Either l1 or l2 crosses the midline, but not both. * Choose case 4 if mref1 is greater than * (2.-epsilon)*mref; otherwise, choose case 3. */ return 3; } /* * If neither l1 nor l2 crosses the midline, the spline * requires two knots between x1 and x2. */ return 4; } else { /* * The sign of at least one of the slopes m1 or m2 does not * agree with the sign of *slope*. */ if ((prod1 < 0.0) && (prod2 < 0.0)) { return 2; } else if (prod1 < 0.0) { if (mref2 > ((epsilon + 1.0) * mref)) { return 1; } else { return 2; } } else if (mref1 > ((epsilon + 1.0) * mref)) { return 1; } else { return 2; } } } else if ((m1 * m2) >= 0.0) { return 2; } else { return 1; } } /* *--------------------------------------------------------------------------- * * QuadCases -- * * Computes the knots and other parameters of the spline on the * interval PQ. * * * On input-- * * P and Q are the coordinates of the points of interpolation. * * m1 is the slope at P. * * m2 is the slope at Q. * * ncase controls the number and location of the knots. * * * On output-- * * (v1,v2),(w1,w2),(z1,z2), and (e1,e2) are the coordinates of * the knots and other parameters of the spline on P. * (e1,e2) and Q are used only if ncase=4. * *--------------------------------------------------------------------------- */ static void QuadCases(Point2d *p, Point2d *q, double m1, double m2, double param[], int which) { if ((which == 3) || (which == 4)) { /* Parameters used in both 3 and 4 */ double mbar1, mbar2, mbar3, c1, d1, h1, j1, k1; c1 = p->x + (q->y - p->y) / m1; d1 = q->x + (p->y - q->y) / m2; h1 = c1 * 2.0 - p->x; j1 = d1 * 2.0 - q->x; mbar1 = (q->y - p->y) / (h1 - p->x); mbar2 = (p->y - q->y) / (j1 - q->x); if (which == 4) { /* Case 4. */ Y1 = (p->x + c1) / 2.0; V1 = (p->x + Y1) / 2.0; V2 = m1 * (V1 - p->x) + p->y; Z1 = (d1 + q->x) / 2.0; W1 = (q->x + Z1) / 2.0; W2 = m2 * (W1 - q->x) + q->y; mbar3 = (W2 - V2) / (W1 - V1); Y2 = mbar3 * (Y1 - V1) + V2; Z2 = mbar3 * (Z1 - V1) + V2; E1 = (Y1 + Z1) / 2.0; E2 = mbar3 * (E1 - V1) + V2; } else { /* Case 3. */ k1 = (p->y - q->y + q->x * mbar2 - p->x * mbar1) / (mbar2 - mbar1); if (fabs(m1) > fabs(m2)) { Z1 = (k1 + p->x) / 2.0; } else { Z1 = (k1 + q->x) / 2.0; } V1 = (p->x + Z1) / 2.0; V2 = p->y + m1 * (V1 - p->x); W1 = (q->x + Z1) / 2.0; W2 = q->y + m2 * (W1 - q->x); Z2 = V2 + (W2 - V2) / (W1 - V1) * (Z1 - V1); } } else if (which == 2) { /* Case 2. */ Z1 = (p->x + q->x) / 2.0; V1 = (p->x + Z1) / 2.0; V2 = p->y + m1 * (V1 - p->x); W1 = (Z1 + q->x) / 2.0; W2 = q->y + m2 * (W1 - q->x); Z2 = (V2 + W2) / 2.0; } else { /* Case 1. */ double ztwo; Z1 = (p->y - q->y + m2 * q->x - m1 * p->x) / (m2 - m1); ztwo = p->y + m1 * (Z1 - p->x); V1 = (p->x + Z1) / 2.0; V2 = (p->y + ztwo) / 2.0; W1 = (Z1 + q->x) / 2.0; W2 = (ztwo + q->y) / 2.0; Z2 = V2 + (W2 - V2) / (W1 - V1) * (Z1 - V1); } } static int QuadSelect(Point2d *p, Point2d *q, double m1, double m2, double epsilon, double param[]) { int ncase; ncase = QuadChoose(p, q, m1, m2, epsilon); QuadCases(p, q, m1, m2, param, ncase); return ncase; } /* *--------------------------------------------------------------------------- * * QuadGetImage -- * *--------------------------------------------------------------------------- */ INLINE static double QuadGetImage(double p1, double p2, double p3, double x1, double x2, double x3) { double A, B, C; double y; A = x1 - x2; B = x2 - x3; C = x1 - x3; y = (p1 * (A * A) + p2 * 2.0 * B * A + p3 * (B * B)) / (C * C); return y; } /* *--------------------------------------------------------------------------- * * QuadSpline -- * * Finds the image of a point in x. * * On input * * x Contains the value at which the spline is evaluated. * leftX, leftY * Coordinates of the left-hand data point used in the * evaluation of x values. * rightX, rightY * Coordinates of the right-hand data point used in the * evaluation of x values. * Z1, Z2, Y1, Y2, E2, W2, V2 * Parameters of the spline. * ncase Controls the evaluation of the spline by indicating * whether one or two knots were placed in the interval * (xtabs,xtabs1). * * Results: * The image of the spline at x. * *--------------------------------------------------------------------------- */ static void QuadSpline( Point2d *intp, /* Value at which spline is evaluated */ Point2d *left, /* Point to the left of the data point to * be evaluated */ Point2d *right, /* Point to the right of the data point to * be evaluated */ double param[], /* Parameters of the spline */ int ncase) /* Controls the evaluation of the * spline by indicating whether one or * two knots were placed in the * interval (leftX,rightX) */ { double y; if (ncase == 4) { /* * Case 4: More than one knot was placed in the interval. */ /* * Determine the location of data point relative to the 1st knot. */ if (Y1 > intp->x) { y = QuadGetImage(left->y, V2, Y2, Y1, intp->x, left->x); } else if (Y1 < intp->x) { /* * Determine the location of the data point relative to * the 2nd knot. */ if (Z1 > intp->x) { y = QuadGetImage(Y2, E2, Z2, Z1, intp->x, Y1); } else if (Z1 < intp->x) { y = QuadGetImage(Z2, W2, right->y, right->x, intp->x, Z1); } else { y = Z2; } } else { y = Y2; } } else { /* * Cases 1, 2, or 3: * * Determine the location of the data point relative to the * knot. */ if (Z1 < intp->x) { y = QuadGetImage(Z2, W2, right->y, right->x, intp->x, Z1); } else if (Z1 > intp->x) { y = QuadGetImage(left->y, V2, Z2, Z1, intp->x, left->x); } else { y = Z2; } } intp->y = y; } /* *--------------------------------------------------------------------------- * * QuadSlopes -- * * Calculates the derivative at each of the data points. The * slopes computed will insure that an osculatory quadratic * spline will have one additional knot between two adjacent * points of interpolation. Convexity and monotonicity are * preserved wherever these conditions are compatible with the * data. * * Results: * The output array "m" is filled with the derivates at each * data point. * *--------------------------------------------------------------------------- */ static void QuadSlopes(Point2d *points, double *m, int nPoints) { double xbar, xmid, xhat, ydif1, ydif2; double yxmid; double m1, m2; double m1s, m2s; int i, n, l; m1s = m2s = m1 = m2 = 0; for (l = 0, i = 1, n = 2; i < (nPoints - 1); l++, i++, n++) { /* * Calculate the slopes of the two lines joining three * consecutive data points. */ ydif1 = points[i].y - points[l].y; ydif2 = points[n].y - points[i].y; m1 = ydif1 / (points[i].x - points[l].x); m2 = ydif2 / (points[n].x - points[i].x); if (i == 1) { m1s = m1, m2s = m2; /* Save slopes of starting point */ } /* * If one of the preceding slopes is zero or if they have opposite * sign, assign the value zero to the derivative at the middle * point. */ if ((m1 == 0.0) || (m2 == 0.0) || ((m1 * m2) <= 0.0)) { m[i] = 0.0; } else if (fabs(m1) > fabs(m2)) { /* * Calculate the slope by extending the line with slope m1. */ xbar = ydif2 / m1 + points[i].x; xhat = (xbar + points[n].x) / 2.0; m[i] = ydif2 / (xhat - points[i].x); } else { /* * Calculate the slope by extending the line with slope m2. */ xbar = -ydif1 / m2 + points[i].x; xhat = (points[l].x + xbar) / 2.0; m[i] = ydif1 / (points[i].x - xhat); } } /* Calculate the slope at the last point, x(n). */ i = nPoints - 2; n = nPoints - 1; if ((m1 * m2) < 0.0) { m[n] = m2 * 2.0; } else { xmid = (points[i].x + points[n].x) / 2.0; yxmid = m[i] * (xmid - points[i].x) + points[i].y; m[n] = (points[n].y - yxmid) / (points[n].x - xmid); if ((m[n] * m2) < 0.0) { m[n] = 0.0; } } /* Calculate the slope at the first point, x(0). */ if ((m1s * m2s) < 0.0) { m[0] = m1s * 2.0; } else { xmid = (points[0].x + points[1].x) / 2.0; yxmid = m[1] * (xmid - points[1].x) + points[1].y; m[0] = (yxmid - points[0].y) / (xmid - points[0].x); if ((m[0] * m1s) < 0.0) { m[0] = 0.0; } } } /* *--------------------------------------------------------------------------- * * QuadEval -- * * QuadEval controls the evaluation of an osculatory quadratic * spline. The user may provide his own slopes at the points of * interpolation or use the subroutine 'QuadSlopes' to calculate * slopes which are consistent with the shape of the data. * * ON INPUT-- * intpPts must be a nondecreasing vector of points at which the * spline will be evaluated. * origPts contains the abscissas of the data points to be * interpolated. xtab must be increasing. * y contains the ordinates of the data points to be * interpolated. * m contains the slope of the spline at each point of * interpolation. * nPoints number of data points (dimension of xtab and y). * numEval is the number of points of evaluation (dimension of * xval and yval). * epsilon is a relative error tolerance used in subroutine * 'QuadChoose' to distinguish the situation m(i) or * m(i+1) is relatively close to the slope or twice * the slope of the linear segment between xtab(i) and * xtab(i+1). If this situation occurs, roundoff may * cause a change in convexity or monotonicity of the * resulting spline and a change in the case number * provided by 'QuadChoose'. If epsilon is not equal to zero, * then epsilon should be greater than or equal to machine * epsilon. * ON OUTPUT-- * yval contains the images of the points in xval. * err is one of the following error codes: * 0 - QuadEval ran normally. * 1 - xval(i) is less than xtab(1) for at least one * i or xval(i) is greater than xtab(num) for at * least one i. QuadEval will extrapolate to provide * function values for these abscissas. * 2 - xval(i+1) < xval(i) for some i. * * * QuadEval calls the following subroutines or functions: * Search * QuadCases * QuadChoose * QuadSpline *--------------------------------------------------------------------------- */ static int QuadEval( Point2d origPts[], int nOrigPts, Point2d intpPts[], int nIntpPts, double *m, /* Slope of the spline at each point * of interpolation. */ double epsilon) /* Relative error tolerance (see choose) */ { int error; int i, j; double param[10]; int ncase; int start, end; int l, p; int n; int found; /* Initialize indices and set error result */ error = 0; l = nOrigPts - 1; p = l - 1; ncase = 1; /* * Determine if abscissas of new vector are non-decreasing. */ for (j = 1; j < nIntpPts; j++) { if (intpPts[j].x < intpPts[j - 1].x) { return 2; } } /* * Determine if any of the points in xval are LESS than the * abscissa of the first data point. */ for (start = 0; start < nIntpPts; start++) { if (intpPts[start].x >= origPts[0].x) { break; } } /* * Determine if any of the points in xval are GREATER than the * abscissa of the l data point. */ for (end = nIntpPts - 1; end >= 0; end--) { if (intpPts[end].x <= origPts[l].x) { break; } } if (start > 0) { error = 1; /* Set error value to indicate that * extrapolation has occurred. */ /* * Calculate the images of points of evaluation whose abscissas * are less than the abscissa of the first data point. */ ncase = QuadSelect(origPts, origPts + 1, m[0], m[1], epsilon, param); for (j = 0; j < (start - 1); j++) { QuadSpline(intpPts + j, origPts, origPts + 1, param, ncase); } if (nIntpPts == 1) { return error; } } if ((nIntpPts == 1) && (end != (nIntpPts - 1))) { goto noExtrapolation; } /* * Search locates the interval in which the first in-range * point of evaluation lies. */ i = Search(origPts, nOrigPts, intpPts[start].x, &found); n = i + 1; if (n >= nOrigPts) { n = nOrigPts - 1; i = nOrigPts - 2; } /* * If the first in-range point of evaluation is equal to one * of the data points, assign the appropriate value from y. * Continue until a point of evaluation is found which is not * equal to a data point. */ if (found) { do { intpPts[start].y = origPts[i].y; start++; if (start >= nIntpPts) { return error; } } while (intpPts[start - 1].x == intpPts[start].x); for (;;) { if (intpPts[start].x < origPts[n].x) { break; /* Break out of for-loop */ } if (intpPts[start].x == origPts[n].x) { do { intpPts[start].y = origPts[n].y; start++; if (start >= nIntpPts) { return error; } } while (intpPts[start].x == intpPts[start - 1].x); } i++; n++; } } /* * Calculate the images of all the points which lie within * range of the data. */ if ((i > 0) || (error != 1)) { ncase = QuadSelect(origPts + i, origPts + n, m[i], m[n], epsilon, param); } for (j = start; j <= end; j++) { /* * If xx(j) - x(n) is negative, do not recalculate * the parameters for this section of the spline since * they are already known. */ if (intpPts[j].x == origPts[n].x) { intpPts[j].y = origPts[n].y; continue; } else if (intpPts[j].x > origPts[n].x) { double delta; /* Determine that the routine is in the correct part of the spline. */ do { i++, n++; delta = intpPts[j].x - origPts[n].x; } while (delta > 0.0); if (delta < 0.0) { ncase = QuadSelect(origPts + i, origPts + n, m[i], m[n], epsilon, param); } else if (delta == 0.0) { intpPts[j].y = origPts[n].y; continue; } } QuadSpline(intpPts + j, origPts + i, origPts + n, param, ncase); } if (end == (nIntpPts - 1)) { return error; } if ((n == l) && (intpPts[end].x != origPts[l].x)) { goto noExtrapolation; } error = 1; /* Set error value to indicate that * extrapolation has occurred. */ ncase = QuadSelect(origPts + p, origPts + l, m[p], m[l], epsilon, param); noExtrapolation: /* * Calculate the images of the points of evaluation whose * abscissas are greater than the abscissa of the last data point. */ for (j = (end + 1); j < nIntpPts; j++) { QuadSpline(intpPts + j, origPts + p, origPts + l, param, ncase); } return error; } /* *--------------------------------------------------------------------------- * * Shape preserving quadratic splines * by D.F.Mcallister & J.A.Roulier * Coded by S.L.Dodd & M.Roulier * N.C.State University * *--------------------------------------------------------------------------- */ /* * Driver routine for quadratic spline package * On input-- * X,Y Contain n-long arrays of data (x is increasing) * XM Contains m-long array of x values (increasing) * eps Relative error tolerance * n Number of input data points * m Number of output data points * On output-- * work Contains the value of the first derivative at each data point * ym Contains the interpolated spline value at each data point */ int Blt_QuadraticSpline(Point2d *origPts, int nOrigPts, Point2d *intpPts, int nIntpPts) { double epsilon; double *work; int result; work = malloc(nOrigPts * sizeof(double)); epsilon = 0.0; /* TBA: adjust error via command-line option */ /* allocate space for vectors used in calculation */ QuadSlopes(origPts, work, nOrigPts); result = QuadEval(origPts, nOrigPts, intpPts, nIntpPts, work, epsilon); free(work); if (result > 1) { return FALSE; } return TRUE; } /* *--------------------------------------------------------------------------- * * Reference: * Numerical Analysis, R. Burden, J. Faires and A. Reynolds. * Prindle, Weber & Schmidt 1981 pp 112 * * Parameters: * origPts - vector of points, assumed to be sorted along x. * intpPts - vector of new points. * *--------------------------------------------------------------------------- */ int Blt_NaturalSpline(Point2d *origPts, int nOrigPts, Point2d *intpPts, int nIntpPts) { Cubic2D *eq; Point2d *ip, *iend; TriDiagonalMatrix *A; double *dx; /* vector of deltas in x */ double x, dy, alpha; int isKnot; int i, j, n; dx = malloc(sizeof(double) * nOrigPts); /* Calculate vector of differences */ for (i = 0, j = 1; j < nOrigPts; i++, j++) { dx[i] = origPts[j].x - origPts[i].x; if (dx[i] < 0.0) { return 0; } } n = nOrigPts - 1; /* Number of intervals. */ A = malloc(sizeof(TriDiagonalMatrix) * nOrigPts); if (A == NULL) { free(dx); return 0; } /* Vectors to solve the tridiagonal matrix */ A[0][0] = A[n][0] = 1.0; A[0][1] = A[n][1] = 0.0; A[0][2] = A[n][2] = 0.0; /* Calculate the intermediate results */ for (i = 0, j = 1; j < n; j++, i++) { alpha = 3.0 * ((origPts[j + 1].y / dx[j]) - (origPts[j].y / dx[i]) - (origPts[j].y / dx[j]) + (origPts[i].y / dx[i])); A[j][0] = 2 * (dx[j] + dx[i]) - dx[i] * A[i][1]; A[j][1] = dx[j] / A[j][0]; A[j][2] = (alpha - dx[i] * A[i][2]) / A[j][0]; } eq = malloc(sizeof(Cubic2D) * nOrigPts); if (eq == NULL) { free(A); free(dx); return FALSE; } eq[0].c = eq[n].c = 0.0; for (j = n, i = n - 1; i >= 0; i--, j--) { eq[i].c = A[i][2] - A[i][1] * eq[j].c; dy = origPts[i+1].y - origPts[i].y; eq[i].b = (dy) / dx[i] - dx[i] * (eq[j].c + 2.0 * eq[i].c) / 3.0; eq[i].d = (eq[j].c - eq[i].c) / (3.0 * dx[i]); } free(A); free(dx); /* Now calculate the new values */ for (ip = intpPts, iend = ip + nIntpPts; ip < iend; ip++) { ip->y = 0.0; x = ip->x; /* Is it outside the interval? */ if ((x < origPts[0].x) || (x > origPts[n].x)) { continue; } /* Search for the interval containing x in the point array */ i = Search(origPts, nOrigPts, x, &isKnot); if (isKnot) { ip->y = origPts[i].y; } else { i--; x -= origPts[i].x; ip->y = origPts[i].y + x * (eq[i].b + x * (eq[i].c + x * eq[i].d)); } } free(eq); return TRUE; } typedef struct { double t; /* Arc length of interval. */ double x; /* 2nd derivative of X with respect to T */ double y; /* 2nd derivative of Y with respect to T */ } CubicSpline; /* * The following two procedures solve the special linear system which arise * in cubic spline interpolation. If x is assumed cyclic ( x[i]=x[n+i] ) the * equations can be written as (i=0,1,...,n-1): * m[i][0] * x[i-1] + m[i][1] * x[i] + m[i][2] * x[i+1] = b[i] . * In matrix notation one gets A * x = b, where the matrix A is tridiagonal * with additional elements in the upper right and lower left position: * A[i][0] = A_{i,i-1} for i=1,2,...,n-1 and m[0][0] = A_{0,n-1} , * A[i][1] = A_{i, i } for i=0,1,...,n-1 * A[i][2] = A_{i,i+1} for i=0,1,...,n-2 and m[n-1][2] = A_{n-1,0}. * A should be symmetric (A[i+1][0] == A[i][2]) and positive definite. * The size of the system is given in n (n>=1). * * In the first procedure the Cholesky decomposition A = C^T * D * C * (C is upper triangle with unit diagonal, D is diagonal) is calculated. * Return TRUE if decomposition exist. */ static int SolveCubic1(TriDiagonalMatrix A[], int n) { int i; double m_ij, m_n, m_nn, d; if (n < 1) { return FALSE; /* Dimension should be at least 1 */ } d = A[0][1]; /* D_{0,0} = A_{0,0} */ if (d <= 0.0) { return FALSE; /* A (or D) should be positive definite */ } m_n = A[0][0]; /* A_{0,n-1} */ m_nn = A[n - 1][1]; /* A_{n-1,n-1} */ for (i = 0; i < n - 2; i++) { m_ij = A[i][2]; /* A_{i,1} */ A[i][2] = m_ij / d; /* C_{i,i+1} */ A[i][0] = m_n / d; /* C_{i,n-1} */ m_nn -= A[i][0] * m_n; /* to get C_{n-1,n-1} */ m_n = -A[i][2] * m_n; /* to get C_{i+1,n-1} */ d = A[i + 1][1] - A[i][2] * m_ij; /* D_{i+1,i+1} */ if (d <= 0.0) { return FALSE; /* Elements of D should be positive */ } A[i + 1][1] = d; } if (n >= 2) { /* Complete last column */ m_n += A[n - 2][2]; /* add A_{n-2,n-1} */ A[n - 2][0] = m_n / d; /* C_{n-2,n-1} */ A[n - 1][1] = d = m_nn - A[n - 2][0] * m_n; /* D_{n-1,n-1} */ if (d <= 0.0) { return FALSE; } } return TRUE; } /* * The second procedure solves the linear system, with the Cholesky * decomposition calculated above (in m[][]) and the right side b given * in x[]. The solution x overwrites the right side in x[]. */ static void SolveCubic2(TriDiagonalMatrix A[], CubicSpline spline[], int nIntervals) { int i; double x, y; int n, m; n = nIntervals - 2; m = nIntervals - 1; /* Division by transpose of C : b = C^{-T} * b */ x = spline[m].x; y = spline[m].y; for (i = 0; i < n; i++) { spline[i + 1].x -= A[i][2] * spline[i].x; /* C_{i,i+1} * x(i) */ spline[i + 1].y -= A[i][2] * spline[i].y; /* C_{i,i+1} * x(i) */ x -= A[i][0] * spline[i].x; /* C_{i,n-1} * x(i) */ y -= A[i][0] * spline[i].y; /* C_{i,n-1} * x(i) */ } if (n >= 0) { /* C_{n-2,n-1} * x_{n-1} */ spline[m].x = x - A[n][0] * spline[n].x; spline[m].y = y - A[n][0] * spline[n].y; } /* Division by D: b = D^{-1} * b */ for (i = 0; i < nIntervals; i++) { spline[i].x /= A[i][1]; spline[i].y /= A[i][1]; } /* Division by C: b = C^{-1} * b */ x = spline[m].x; y = spline[m].y; if (n >= 0) { /* C_{n-2,n-1} * x_{n-1} */ spline[n].x -= A[n][0] * x; spline[n].y -= A[n][0] * y; } for (i = (n - 1); i >= 0; i--) { /* C_{i,i+1} * x_{i+1} + C_{i,n-1} * x_{n-1} */ spline[i].x -= A[i][2] * spline[i + 1].x + A[i][0] * x; spline[i].y -= A[i][2] * spline[i + 1].y + A[i][0] * y; } } /* * Find second derivatives (x''(t_i),y''(t_i)) of cubic spline interpolation * through list of points (x_i,y_i). The parameter t is calculated as the * length of the linear stroke. The number of points must be at least 3. * Note: For CLOSED_CONTOURs the first and last point must be equal. */ static CubicSpline * CubicSlopes( Point2d points[], int nPoints, /* Number of points (nPoints>=3) */ int isClosed, /* CLOSED_CONTOUR or OPEN_CONTOUR */ double unitX, double unitY) /* Unit length in x and y (norm=1) */ { CubicSpline *spline; CubicSpline *s1, *s2; int n, i; double norm, dx, dy; TriDiagonalMatrix *A; /* The tri-diagonal matrix is saved here. */ spline = malloc(sizeof(CubicSpline) * nPoints); if (spline == NULL) { return NULL; } A = malloc(sizeof(TriDiagonalMatrix) * nPoints); if (A == NULL) { free(spline); return NULL; } /* * Calculate first differences in (dxdt2[i], y[i]) and interval lengths * in dist[i]: */ s1 = spline; for (i = 0; i < nPoints - 1; i++) { s1->x = points[i+1].x - points[i].x; s1->y = points[i+1].y - points[i].y; /* * The Norm of a linear stroke is calculated in "normal coordinates" * and used as interval length: */ dx = s1->x / unitX; dy = s1->y / unitY; s1->t = sqrt(dx * dx + dy * dy); s1->x /= s1->t; /* first difference, with unit norm: */ s1->y /= s1->t; /* || (dxdt2[i], y[i]) || = 1 */ s1++; } /* * Setup linear System: Ax = b */ n = nPoints - 2; /* Without first and last point */ if (isClosed) { /* First and last points must be equal for CLOSED_CONTOURs */ spline[nPoints - 1].t = spline[0].t; spline[nPoints - 1].x = spline[0].x; spline[nPoints - 1].y = spline[0].y; n++; /* Add last point (= first point) */ } s1 = spline, s2 = s1 + 1; for (i = 0; i < n; i++) { /* Matrix A, mainly tridiagonal with cyclic second index ("j = j+n mod n") */ A[i][0] = s1->t; /* Off-diagonal element A_{i,i-1} */ A[i][1] = 2.0 * (s1->t + s2->t); /* A_{i,i} */ A[i][2] = s2->t; /* Off-diagonal element A_{i,i+1} */ /* Right side b_x and b_y */ s1->x = (s2->x - s1->x) * 6.0; s1->y = (s2->y - s1->y) * 6.0; /* * If the linear stroke shows a cusp of more than 90 degree, * the right side is reduced to avoid oscillations in the * spline: */ /* * The Norm of a linear stroke is calculated in "normal coordinates" * and used as interval length: */ dx = s1->x / unitX; dy = s1->y / unitY; norm = sqrt(dx * dx + dy * dy) / 8.5; if (norm > 1.0) { /* The first derivative will not be continuous */ s1->x /= norm; s1->y /= norm; } s1++, s2++; } if (!isClosed) { /* Third derivative is set to zero at both ends */ A[0][1] += A[0][0]; /* A_{0,0} */ A[0][0] = 0.0; /* A_{0,n-1} */ A[n-1][1] += A[n-1][2]; /* A_{n-1,n-1} */ A[n-1][2] = 0.0; /* A_{n-1,0} */ } /* Solve linear systems for dxdt2[] and y[] */ if (SolveCubic1(A, n)) { /* Cholesky decomposition */ SolveCubic2(A, spline, n); /* A * dxdt2 = b_x */ } else { /* Should not happen, but who knows ... */ free(A); free(spline); return NULL; } /* Shift all second derivatives one place right and update the ends. */ s2 = spline + n, s1 = s2 - 1; for (/* empty */; s2 > spline; s2--, s1--) { s2->x = s1->x; s2->y = s1->y; } if (isClosed) { spline[0].x = spline[n].x; spline[0].y = spline[n].y; } else { /* Third derivative is 0.0 for the first and last interval. */ spline[0].x = spline[1].x; spline[0].y = spline[1].y; spline[n + 1].x = spline[n].x; spline[n + 1].y = spline[n].y; } free( A); return spline; } /* * Calculate interpolated values of the spline function (defined via p_cntr * and the second derivatives dxdt2[] and dydt2[]). The number of tabulated * values is n. On an equidistant grid n_intpol values are calculated. */ static int CubicEval(Point2d *origPts, int nOrigPts, Point2d *intpPts, int nIntpPts, CubicSpline *spline) { double t, tSkip, tMax; Point2d q; int i, j, count; /* Sum the lengths of all the segments (intervals). */ tMax = 0.0; for (i = 0; i < nOrigPts - 1; i++) { tMax += spline[i].t; } /* Need a better way of doing this... */ /* The distance between interpolated points */ tSkip = (1. - 1e-7) * tMax / (nIntpPts - 1); t = 0.0; /* Spline parameter value. */ q = origPts[0]; count = 0; intpPts[count++] = q; /* First point. */ t += tSkip; for (i = 0, j = 1; j < nOrigPts; i++, j++) { Point2d p; double d, hx, dx0, dx01, hy, dy0, dy01; d = spline[i].t; /* Interval length */ p = q; q = origPts[i+1]; hx = (q.x - p.x) / d; hy = (q.y - p.y) / d; dx0 = (spline[j].x + 2 * spline[i].x) / 6.0; dy0 = (spline[j].y + 2 * spline[i].y) / 6.0; dx01 = (spline[j].x - spline[i].x) / (6.0 * d); dy01 = (spline[j].y - spline[i].y) / (6.0 * d); while (t <= spline[i].t) { /* t in current interval ? */ p.x += t * (hx + (t - d) * (dx0 + t * dx01)); p.y += t * (hy + (t - d) * (dy0 + t * dy01)); intpPts[count++] = p; t += tSkip; } /* Parameter t relative to start of next interval */ t -= spline[i].t; } return count; } /* * Generate a cubic spline curve through the points (x_i,y_i) which are * stored in the linked list p_cntr. * The spline is defined as a 2d-function s(t) = (x(t),y(t)), where the * parameter t is the length of the linear stroke. */ int Blt_NaturalParametricSpline(Point2d *origPts, int nOrigPts, Region2d *extsPtr, int isClosed, Point2d *intpPts, int nIntpPts) { double unitX, unitY; /* To define norm (x,y)-plane */ CubicSpline *spline; int result; if (nOrigPts < 3) { return 0; } if (isClosed) { origPts[nOrigPts].x = origPts[0].x; origPts[nOrigPts].y = origPts[0].y; nOrigPts++; } /* Width and height of the grid is used at unit length (2d-norm) */ unitX = extsPtr->right - extsPtr->left; unitY = extsPtr->bottom - extsPtr->top; if (unitX < FLT_EPSILON) { unitX = FLT_EPSILON; } if (unitY < FLT_EPSILON) { unitY = FLT_EPSILON; } /* Calculate parameters for cubic spline: * t = arc length of interval. * dxdt2 = second derivatives of x with respect to t, * dydt2 = second derivatives of y with respect to t, */ spline = CubicSlopes(origPts, nOrigPts, isClosed, unitX, unitY); if (spline == NULL) { return 0; } result= CubicEval(origPts, nOrigPts, intpPts, nIntpPts, spline); free(spline); return result; } static INLINE void CatromCoeffs(Point2d *p, Point2d *a, Point2d *b, Point2d *c, Point2d *d) { a->x = -p[0].x + 3.0 * p[1].x - 3.0 * p[2].x + p[3].x; b->x = 2.0 * p[0].x - 5.0 * p[1].x + 4.0 * p[2].x - p[3].x; c->x = -p[0].x + p[2].x; d->x = 2.0 * p[1].x; a->y = -p[0].y + 3.0 * p[1].y - 3.0 * p[2].y + p[3].y; b->y = 2.0 * p[0].y - 5.0 * p[1].y + 4.0 * p[2].y - p[3].y; c->y = -p[0].y + p[2].y; d->y = 2.0 * p[1].y; } /* *--------------------------------------------------------------------------- * * Blt_ParametricCatromSpline -- * * Computes a spline based upon the data points, returning a new (larger) * coordinate array of points. * * Results: * None. * *--------------------------------------------------------------------------- */ int Blt_CatromParametricSpline(Point2d *points, int nPoints, Point2d *intpPts, int nIntpPts) { int i; Point2d *origPts; double t; int interval; Point2d a, b, c, d; assert(nPoints > 0); /* * The spline is computed in screen coordinates instead of data points so * that we can select the abscissas of the interpolated points from each * pixel horizontally across the plotting area. */ origPts = malloc((nPoints + 4) * sizeof(Point2d)); memcpy(origPts + 1, points, sizeof(Point2d) * nPoints); origPts[0] = origPts[1]; origPts[nPoints + 2] = origPts[nPoints + 1] = origPts[nPoints]; for (i = 0; i < nIntpPts; i++) { interval = (int)intpPts[i].x; t = intpPts[i].y; assert(interval < nPoints); CatromCoeffs(origPts + interval, &a, &b, &c, &d); intpPts[i].x = (d.x + t * (c.x + t * (b.x + t * a.x))) / 2.0; intpPts[i].y = (d.y + t * (c.y + t * (b.y + t * a.y))) / 2.0; } free(origPts); return 1; }