Drawing a Bezier cubic spline through 4 data points

Drawing a Bezier cubic spline through 4 data points

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Contents:
Drawing a Bezier Cubic Spline Through Four Data Points
Don Lancaster Synergetics, Box 809, Thatcher, AZ 85552 copyright c2005 as GuruGram #59. http://www.tinaja.com don@tinaja.com (928) 428-4073

ezier Cubic Splines are an excellent and preferred method to draw the smooth continuous curves often found in typography, CAD/CAM, and graphics in general. Among their many advantages is a very sparse data set allowing a mere eight values (or four x,y points) to completely define a full and carefully controlled and device independent curve. Many tutorials and examples are now present in our Cubic Spline Library. A brief and useful intro appears here. The fundamental math behind Cubic Splines appears here. A normal and typical Bezier cubic spline accepts four data points of x0,y0, x1,y1, x2,y2, and x3,y3. It turns out that x0,y0 and x3,y3 define the end points of the curve, while x1,y1 and x2,y2 establish the initial and final slope and the "enthusiasm" or "tension" with which the curve enters or leaves the final points. These interior control points are normally distant from the actual final curve. We might instead like to fit a Bezier Cubic Spline to four data points, all of which are on the curve. New point variables of x4,y4, and x5,y5 might be introduced as these on-curve points. The underlying math problem would then be to relate or transform the on-curve points x4,y4, and x5,y5 to the standard control points of x1,y1, and x2,y2. It turns out you can draw an infinite number of cubic spline curves through four data points, depending upon your choice of t1 and t2 for your intermediate point locations...

B

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While the black curve looks "best", the red one and the blue one are both "too loopey" in some areas and "too straight" in others. The dilemma is to pick the "best" or "most aesthetic" solution. While such terms are hard to quantify, they most likely would take place with the shortest possible cubic spline curve. Such a curve would be the "most efficient" as well. Optimizing the length of a cubic spline might involve some horrendous math and repeated converging approximations. Instead, we will use a apportioned chords approximation that is far simpler and seems to give acceptable results...
APPORTIONED CHORDS -- An approximation to the "best" cubic spline four point curve fit. Straight lines C1, C2, and C3 are drawn between the points and their lenthts are calculated. The t values for the inside points are then calculated as t1 = C1/(C1 + C2 + C3) and as t2 = (C1 + C2)/(C1 + C2 + C3).

Some Utility Code
A greatly improved replacement for our earlier four-point code can be found as IMBZ4P01.PSL. As with most of our utilities, this is written in raw PostScript, makes optional use of my Gonzo Utilities, and is used to create standard ASCII text files sent to Acrobat Distiller. With the latter acting as a General Purpose Host Based PostScript Interpreter. What follows can be best understood by having IMBZ4P01.PSL viewable as an open window. Basis Functions can enormously simplify use and understanding of cubic splines. Any point on the curve can be expressed as...
x(t) = x0B0(t) + x1B1(t) + x2B2(t) + x3B3(t)

Separating our knowns and our unknowns...
x4 - x0B0(t1) - x3B3(t1) = x1B1(t1) + x2B2(t1) x5 - x0B0(t2) - x3B3(t2) = x1B1(t2) + x2B2(t2)

Or, in English, "The x4 value we need at t1 is made from known contributions of x0 and x3 and as yet unknown contributions of x1 and x2". Similarly "The x5
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value we need at t2 is made from known contributions of x0 and x3 and as yet unknown contributions of x1 and x2". We thus have two plain old algebraic equations in two unknowns of x0 and x1. A similar set of two equations can be written and solved for the y values. Our first order of business should be creating an equation solver sub-utility...
% Linear equation solver utility for ai + bj = c and di + ej = f /solvexy {/ff exch store /ee exch store /dd exch store /cc exch store /bb exch store /aa exch store % grab data values

cc aa dd div ff mul sub % find j bb aa ee mul dd div sub div /jj exch store /ii cc bb jj mul sub aa div store % find i ii jj } store % return to stack

This works by scaling the second equation by a/d and subtracting it from the first one to produce variable j. Variable i is then found by back substitution. We can now start our actual 4-point plotting code...
/bez4pts1 {/y3 exch store /x3 exch store /y5 exch store /x5 exch store /y4 exch store /x4 exch store /y0 exch store /x0 exch store /c1 sub /c2 sub /c3 sub x4 x0 sub dup mul y4 y0 dup mul add sqrt store x5 x4 sub dup mul y5 y4 dup mul add sqrt store x3 x5 sub dup mul y3 y5 dup mul add sqrt store % grab data % strange numbering

% find chord lengths

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The chords are simply the vector sum of the x axis and y axis differences between the data points. Continuing...
/t1 c1 dup c2 add c3 add div store % guess "best" t /t2 c1 c2 add dup c3 add div store /b0 /b1 /b2 /b3 {1 exch sub dup dup mul mul} store % basis functions {dup 1 exch sub dup mul mul 3 mul} store {dup 1 exch sub exch dup mul mul 3 mul} store {dup dup mul mul} store

t1 b1 t1 b2 x4 x0 t1 b0 mul sub % transform x1 and x2 x3 t1 b3 mul sub t2 b1 t2 b2 x5 x0 t2 b0 mul sub x3 t2 b3 mul sub solvexy /x2 exch store /x1 exch store t1 b1 t1 b2 y4 y0 t1 b0 mul sub % transform y1 and y2 y3 t1 b3 mul sub t2 b1 t2 b2 y5 y0 t2 b0 mul sub y3 t2 b3 mul sub solvexy /y2 exch store /y1 exch store x0 y0 moveto x1 y1 x2 y2 x3 y3 curveto } def % and draw the curve

t1 and t2 are found by chord apportioning. The basis functions are standard definitions per this tutorial. The transforms to get from the on-curve points to

the off curve control points may look a little obtuse, but they are nothing but the equations in the last aqua box above. Once the two linear equations in two unknowns are found, they are sent to the equation solver sub-utility. First to find x1 and x2, and second to find y1 and y2. Finally, a plain old PostScript curveto is used to generate the actual Bezier Cubic Spline through four points. While chord apportionment seems "good enough" for most uses, you can further optimize for shortest spline by subdividing the chords or going to multi-pass schemes. IMBZ4P01.PSL has been updated with a new example and demo.

For More Help
Additional info on cubic splines can be found on our Cubic Spline library page. As are many dozens of examples of Bezier cubic spline techniques. Additional consulting services are available per our Infopack services and on a contract or an hourly basis. Additional GuruGrams are found here. Further GuruGrams await your ongoing support as a Synergetics Partner.
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