I have a set of points (with unknow coordinates) and the distance matrix. I need to find the coordinates of these points in order to plot them and show the solution of my algorithm.
I can set one of these points in the coordinate (0,0) to simpify, and find the others. Can anyone tell me if it's possible to find the coordinates of the other points, and if yes, how?
Thanks in advance!
EDIT
Forgot to say that I need the coordinates on x-y only
The answers based on angles are cumbersome to implement and can't be easily generalized to data in higher dimensions. A better approach is that mentioned in my and WimC's answers here: given the distance matrix D(i, j), define
M(i, j) = 0.5*(D(1, j)^2 + D(i, 1)^2 - D(i, j)^2)
which should be a positive semi-definite matrix with rank equal to the minimal Euclidean dimension k in which the points can be embedded. The coordinates of the points can then be obtained from the k eigenvectors v(i) of M corresponding to non-zero eigenvalues q(i): place the vectors sqrt(q(i))*v(i) as columns in an n x k matrix X; then each row of X is a point. In other words, sqrt(q(i))*v(i) gives the ith component of all of the points.
The eigenvalues and eigenvectors of a matrix can be obtained easily in most programming languages (e.g., using GSL in C/C++, using the built-in function eig in Matlab, using Numpy in Python, etc.)
Note that this particular method always places the first point at the origin, but any rotation, reflection, or translation of the points will also satisfy the original distance matrix.
Step 1, arbitrarily assign one point P1 as (0,0).
Step 2, arbitrarily assign one point P2 along the positive x axis. (0, Dp1p2)
Step 3, find a point P3 such that
Dp1p2 ~= Dp1p3+Dp2p3
Dp1p3 ~= Dp1p2+Dp2p3
Dp2p3 ~= Dp1p3+Dp1p2
and set that point in the "positive" y domain (if it meets any of these criteria, the point should be placed on the P1P2 axis).
Use the cosine law to determine the distance:
cos (A) = (Dp1p2^2 + Dp1p3^2 - Dp2p3^2)/(2*Dp1p2* Dp1p3)
P3 = (Dp1p3 * cos (A), Dp1p3 * sin(A))
You have now successfully built an orthonormal space and placed three points in that space.
Step 4: To determine all the other points, repeat step 3, to give you a tentative y coordinate.
(Xn, Yn).
Compare the distance {(Xn, Yn), (X3, Y3)} to Dp3pn in your matrix. If it is identical, you have successfully identified the coordinate for point n. Otherwise, the point n is at (Xn, -Yn).
Note there is an alternative to step 4, but it is too much math for a Saturday afternoon
If for points p, q, and r you have pq, qr, and rp in your matrix, you have a triangle.
Wherever you have a triangle in your matrix you can compute one of two solutions for that triangle (independent of a euclidean transform of the triangle on the plane). That is, for each triangle you compute, it's mirror image is also a triangle that satisfies the distance constraints on p, q, and r. The fact that there are two solutions even for a triangle leads to the chirality problem: You have to choose the chirality (orientation) of each triangle, and not all choices may lead to a feasible solution to the problem.
Nevertheless, I have some suggestions. If the number entries is small, consider using simulated annealing. You could incorporate chirality into the annealing step. This will be slow for large systems, and it may not converge to a perfect solution, but for some problems it's the best you and do.
The second suggestion will not give you a perfect solution, but it will distribute the error: the method of least squares. In your case the objective function will be the error between the distances in your matrix, and actual distances between your points.
This is a math problem. To derive coordinate matrix X only given by its distance matrix.
However there is an efficient solution to this -- Multidimensional Scaling, that do some linear algebra. Simply put, it requires a pairwise Euclidean distance matrix D, and the output is the estimated coordinate Y (perhaps rotated), which is a proximation to X. For programming reason, just use SciKit.manifold.MDS in Python.
The "eigenvector" method given by the favourite replies above is very general and automatically outputs a set of coordinates as the OP requested, however I noticed that that algorithm does not even ask for a desired orientation (rotation angle) for the frame of the output points, the algorithm chooses that orientation all by itself!
People who use it might want to know at what angle the frame will be tipped before hand so I found an equation which gives the answer for the case of up to three input points, however I have not had time to generalize it to n-points and hope someone will do that and add it to this discussion. Here are the three angles the output sides will form with the x-axis as a function of the input side lengths:
angle side a = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2-b^2)^2)/(a^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
angle side b = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2+b^2-a^2)^2)/(4*b^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
angle side c = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2+b^2-a^2)^2)/(4*c^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
Those equations also lead directly to a solution to the OP's problem of finding the coordinates for each point because: the side lengths are already given from the OP as the input, and my equations give the slope of each side versus the x-axis of the solution, thus revealing the vector for each side of the polygon answer, and summing those sides through vector addition up to a desired vertex will produce the coordinate of that vertex. So if anyone can extend my angle equations to handling beyond three input lengths (but I note: that might be impossible?), it might be a very fast way to the general solution of the OP's question, since slow parts of the algorithms that people gave above like "least square fitting" or "matrix equation solving" might be avoidable.
Related
For 2-dimensional sampled curves (an array of 2D points) there exists the Rahmer-Douglas-Peucker algorithm which only keeps "important" points. It works by calculating the perpendicular distance of each point (or sample) to a line that connects the first and the last point of. If the maximum distance is larger than a value epsilon the point is kept and the array is split into 2 parts. For both parts the operation is repeated (maximal perpendicular distance, if larger than epsilon etc.) The smaller epsilon the more detail is kept.
I am trying to write a function that can also do this for higher arrays of higher dimensional points. But I am unsure how to define distance. Or if this is actually a good idea.
I guess there exist lots of complicated and elegant algorithms that fit the curves to beziers and NURBS and what not. But are there also relatively simple ones?
I would prefer not to use beziers, but simply to identify "important" N-dimensional points.
You could extend your 2D algorithm using algebra and the L2 norm. Let's say you want to calculate the distance from a point X to a line segment PQ (where X, P and Q are defined as N-dimensional vectors).
First you can calculate the vector "proj" as:
Then, the distance is the module of the vector V = PX-proj.
For this calculation you only need the dot product between vectors, and that is well defined for N-dimensional spaces.
Using this approach I have successfuly used Rahmer-Douglas-Peucker algorithm in 3D.
I am developing a rotate-around-axis algorithm in 3 dimensions. My inputs are
the axis I am revolving around, as a vector from my center point
the center point (obviously)
the angle I wish to rotate around
my current position
I am wondering if there is a way to do this without trigonometry, just with vector operations. Does anyone have a potential solution?
EDIT: Is there a way that I could rotate by pi/4 radians (45 degrees) each time, rather than an inputted angle theta? This might simplify things a bit, I don't know.
Rotations are inherently well-described by and .
It's a handy trick that unit quaternions nicely represent 3-D rotations just as well as (and in some senses, better than) rotation matrices. Converting a rotation by angle about a normal axis where , does require a little bit of trigonometry: .
But from there on it's simple arithmetic.
A quaternion can be directly applied to rotate a vector with , or converted to a rotation matrix .
This is a rotation around the origin, of course. To rotate around an arbitrary point in space, simply translate by to the origin, rotate, then translate by to return.
use matrices: http://en.wikipedia.org/wiki/Rotation_matrix#Rotations_in_three_dimensions
If this is some sort of dumb homework problem, you can use Taylor Series approximation of the sine/consine functions. Whether or not this "counts" as trigonometry is I guess up for debate. You could then use these values in a rotation matrix or quarternion, if you want to use vector operations.
But again, there's no practical reason to do this.
Are there other techniques that don't use trig functions? Possibly, but there are no know efficient, general (i.e. for arbitrary angles) ways to perform rotations without use of trig functions.
However, based on your edit, you can precompute the sin and cos for a collection of angles you're interested in and store them in a lookup table. You need not be constrained in such a circumstance to π/4 increments, but you can do π/256 or π/1024 increments if you want. Also, you don't need two tables, since cos(θ) = sin(θ+π/2).
From there, you can use any of a number of interpolation methods to include simple rounding, linear interpolation or some sort of polynomial interpolation based on your needs.
You would then use either the matrix or quaternion based transformation to compute the rotated vector.
This will be faster than computing the sin and cos for general angles, though will require some additional space, and there will be an accuracy penalty as well. But if it satisfies your needs...
Theres a cheaper way than matrices, I think ive got it to sum count of adders.
The perimetre box of the vector is as good as an angle, if you step in partitions of the box size. (thats only a binary shift if its a power of 2.)
Then that would be a "box rotate" then just use the side report to give you how far along the diagonal you would be then you can split it up into so many gradients, the circle shape.
Id like to see someone proove that u can rotate without matrices or any trig like that too.
Is it possible to rotate without trigonometry? Yes.
Is it useful to rotate without using trigonometry? Probably not.
The first option is a problem-level solution: Change your coordinate system to spherical or cylindrical coordinates.
Since you rotate around an axis cylindrical coordinates of the form (alpha, radius, x3) will work.
Naming your center point O (for origin) and the point to rotate P, you can get the vector between them v=P-O. You also know the normal vector n of your plane of rotation (the vector you rotate around). With this, you can get the components of v that are parallel and orthogonal to n using a vector projection.
You have the freedom to choose how your new coordinate frame is rotated (relative to your original frame), so you can measure angles from the projection of v onto the plane of rotation. You also have the freedom to choose between degree and radians.
From there, you can now rotate to your heart's content using addition and subtraction.
Using dot(.,.) to denote the scalar product it would look something like this in code
v_parallel = dot(v, n) / dot(n, n) * n
radius = norm(v - v_parallel)
x3 = norm(v_parallel)
new_axis = (v - v_parallel) / norm(v - v_parallel)
P_polar = (0, radius, x3)
# P rotated by 90 degrees
P_polar = (pi/2, radius, x3)
# P rotated by -10 degrees
P_polar = (-pi/36, radius, x3)
However, if you want to change back to a standard basis you will have to use trigonometry again. Hence why I said this approach exists, but may not be too useful in practice.
Another approach comes from the cool observation that you can describe any planar rotation using two reflections along two given axis (represented by two vectors). The plane of rotation is the plane that is spun up by the two vectors and the angle of rotation is twice the angle between the two vectors.
You can reflect a vector using the vector projection from above; hence, you can do the entire process without trigonometry if you know the two vectors (let's call them x1 and x2).
tmp = v - 2 * dot(v, x1) / dot(x1, x1) * x1
v_rotated = tmp - 2 * dot(tmp, x2) / dot(x2, x2) * x2
The problem then turns into finding two vectors that are orthogonal to n and have an enclosing angle of alpha/2. How to do this is specific to your problem. For arbitrary alpha this is again the point where you can't dodge the trigonometry bullet; hence, it is again possible, but maybe not so viable in practice.
With help from Mathematica, it looks like we can rotate a point around a vector without Sin/Cos if you are willing to specify the amount of rotation as a number between -1 and 1, rather than an angle in radians.
The below starts with Mathematica's RotationTransform of a point {x,y,z} around a vector {u,v,w} by c radians (which contains many instances of Cos[c] and Sin[c]). It then substitutes all the Cos[c] with "c" and Sin[c] with Sqrt[1-c^2] (a trig identity for Sin in terms of Cos). Everything is simplified with the assumption that the rotation vector is normalized. The resulting equation produces the rotated point without any trig operations.
Note: as c ranges from -1 to 1 the point will only rotate through half a circle, the other half of the rotation can be achieved by flipping the signs on {u,v,w}.
Given the points of a line and a quadratic bezier curve, how do you calculate their nearest point?
There exist a scientific paper regarding this question from INRIA: Computing the minimum distance between two Bézier curves (PDF here)
I once wrote a tool to do a similar task. Bezier splines are typically parametric cubic polynomials. To compute the square of the distance between a cubic segment and a line, this is just the square of the distance between two polynomial functions, itself just another polynomial function! Note that I said the square of the distance, not the square root.
Essentially, for any point on a cubic segment, one could compute the square of the distance from that point to the line. This will be a 6th order polynomial. Can we minimize that square of the distance? Yes. The minimum must occur where the derivative of that polynomial is zero. So differentiate, getting a 5th order polynomial. Use your favorite root finding tool that generates all of the roots numerically. Jenkins & Traub, whatever. Choose the correct solution from that set of roots, excluding any solutions that are complex, and only picking a solution if it lies inside the cubic segment in question. Make sure you exclude the points that correspond to local maxima of the distance.
All of this can be efficiently done, and no iterative optimizer besides a polynomial root finder need be used, thus one does not require the use of optimization tools that require starting values, finding only a solution near that starting value.
For example, in the 3-d figure I show a curve generated by a set of points in 3-d (in red), then I took another set of points that lay in a circle outside, I computed the closest point on the inner curve from each, drawing a line down to that curve. These points of minimum distance were generated by the scheme outlined above.
I just wanna give you a few hints, in for the case Q.B.Curve // segment :
to get a fast enough computation, i think you should first think about using a kind of 'bounding box' for your algorithm.
Say P0 is first point of the Q. B. Curve, P2 the second point, P1 the control point, and P3P4 the segment then :
Compute distance from P0, P1, P2 to P3P4
if P0 OR P2 is nearest point --> this is the nearest point of the curve from P3P4. end :=).
if P1 is nearest point, and Pi (i=0 or 1) the second nearest point, the distance beetween PiPC and P3P4 is an estimate of the distance you seek that might be precise enough, depending on your needs.
if you need to be more acurate : compute P1', which is the point on the Q.B.curve the nearest from P1 : you find it applying the BQC formula with t=0.5. --> distance from PiP1' to P3P4 is an even more accurate estimate -but more costly-.
Note that if the line defined by P1P1' intersects P3P4, P1' is the closest point of QBC from P3P4.
if P1P1' does not intersect P3P4, then you're out of luck, you must go the hard way...
Now if (and when) you need precision :
think about using a divide and conquer algorithm on the parameter of the curve :
which is nearest from P3P4 ?? P0P1' or P1'P2 ??? if it is P0P1' --> t is beetween 0 and 0.5 so compute Pm for t=0.25.
Now which is nearest from P3P4?? P0Pm or PmP1' ?? if it is PmP1' --> compute Pm2 for t=0.25+0.125=0.375 then which is nearest ? PmPm2 or Pm2P1' ??? etc
you will come to accurate solution in no time, like 6 iteration and your precision on t is 0.004 !! you might stop the search when distance beetween two points becomes below a given value. (and not difference beetwen two parameters, since for a little change in parameter, points might be far away)
in fact the principle of this algorithm is to approximate the curve with segments more and more precisely each time.
For the curve / curve case i would first 'box' them also to avoid useless computation, so first use segment/segment computation, then (maybe) segment/curve computation, and only if needed curve/curve computation.
For curve/curve, divide and conquer works also, more difficult to explain but you might figure it out. :=)
hope you can find your good balance for speed/accuracy with this :=)
Edit : Think i found for the general case a nice solution :-)
You should iterate on the (inner) bounding triangles of each B.Q.C.
So we have Triangle T1, points A, B, C having 't' parameter tA, tB, tC.
and Triangle T2, points D, E, F, having t parameter tD, tE, tF.
Initially we have tA=0 tB=0.5 tC= 1.0 and same for T2 tD=0, tE=0.5, tF=1.0
The idea is to call a procedure recursivly that will split T1 and/or T2 into smaller rectangles until we are ok with the precision reached.
The first step is to compute distance from T1 from T2, keeping track of with segments were the nearest on each triangle. First 'trick': if on T1 the segment is AC, then stop recursivity on T1, the nearest point on Curve 1 is either A or C. if on T2 the nearest segment is DF, then stop recursivity on T2, the nearest point on Curve2 is either D or F. If we stopped recursivity for both -> return distance = min (AD, AF, CD, CF). then if we have recursivity on T1, and segment AB is nearest, new T1 becomes : A'=A B= point of Curve one with tB=(tA+tC)/2 = 0.25, C=old B. same goes for T2 : apply recursivityif needed and call same algorithm on new T1 and new T2. Stop algorithm when distance found beetween T1 and T2 minus distance found beetween previous T1 and T2 is below a threshold.
the function might look like ComputeDistance(curveParam1, A, C, shouldSplitCurve1, curveParam2, D, F, shouldSplitCurve2, previousDistance) where points store also their t parameters.
note that distance (curve, segment) is just a particular case of this algorithm, and that you should implement distance (triangle, triangle) and distance (segment, triangle) to have it worked. Have fun.
1.Simple bad method - by iteration go by point from first curve and go by point from second curve and get minimum
2.Determine math function of distance between curves and calc limit of this function like:
|Fcur1(t)-Fcur2(t)| ->0
Fs is vector.
I think we can calculate the derivative of this for determine extremums and get nearest and farest points
I think about this some time later, and post full response.
Formulate your problem in terms of standard analysis: You have got a quantity to minimize (distance), so you formulate an equation for this quantity and find the points where the first derivatives are zero. Parameterize with a single parameter by using the curve's parameter p, which is between 0 for the first point and 1 for the last point.
In the line case, the equation is fairly simple: Get the x/y coordinates from the spline's equation and compute the distance to the given line via vector equations (scalar product with the line's normal).
In the curve's case, the analytical solution could get pretty complicated. You might want to use a numerical minimization technique such as Nelder-Mead or, since you have a 1D continuous problem, simple bisection.
In the case of a Bézier curve and a line
There are three candidates for the closest point to the line:
The place on the Bézier curve segment that is parallel to the line (if such a place exists),
One end of the curve segment,
The other end of the curve segment.
Test all three; the shortest distance wins.
In the case of two Bézier curves
Depends if you want the exact analytical result, or if an optimised numerical result is good enough.
Analytical result
Given two Bézier curves A(t) and B(s), you can derive equations for their local orientation A'(t) and B'(s). The point pairs for which A'(t) = B'(s) are candidates, i.e. the (t, s) for which the curves are locally parallel. I haven't checked, but I assume that A'(t) - B'(s) = 0 can be solved analytically. If your curves are anything like those you show in your example, there should be either only one solution or no solution to that equation, but there could be two (or infinitely many in the case where the curves identical but translated -- in which case you can ignore this because the winner will always be one of the curve segment endpoints).
In an approach similar to the curve-line case outline above, test each of these point pairs, plus the curve segment endpoints. The shortest distance wins.
Numerical result
Let's say the points on the two Bézier curves are defined as A(t) and B(s). You want to minimize the distance d( t, s) = |A(t) - B(s)|. It's a simple two-parameter optimization problem: find the s and t that minimize d( t, s) with the constraints 0 ≤ t ≤ 1 and 0 ≤ s ≤ 1.
Since d = SQRT( ( xA - xB)² + (yA - yB)²), you can also just minimize the function f( t, s) = [d( t, s)]² to save a square root calculation.
There are numerous ready-made methods for such optimization problems. Pick and choose.
Note that in both cases above, anything higher-order than quadratic Bézier curves can giver you more than one local minimum, so this is something to watch out for. From the examples you give, it looks like your curves have no inflexion points, so this concern may not apply in your case.
The point where there normals match is their nearest point. I mean u draw a line orthogonal to the line. .if that line is orthogonal to the curve as well then the point of intersection is the nearest point
I'm not sure if this is the right place to ask, but here goes...
Short version: I'm trying to compute the orientation of a triangle on a plane, formed by the intersection of 3 edges, without explicitly computing the intersection points.
Long version: I need to triangulate a PSLG on a triangle in 3D. The vertices of the PSLG are defined by the intersections of line segments with the plane through the triangle, and are guaranteed to lie within the triangle. Assuming I had the intersection points, I could project to 2D and use a point-line-side (or triangle signed area) test to determine the orientation of a triangle between any 3 intersection points.
The problem is I can't explicitly compute the intersection points because of the floating-point error that accumulates when I find the line-plane intersection. To figure out if the line segments strike the triangle in the first place, I'm using some freely available robust geometric predicates, which give the sign of the volume of a tetrahedron, or equivalently which side of a plane a point lies on. I can determine if the line segment endpoints are on opposite sides of the plane through the triangle, then form tetrahedra between the line segment and each edge of the triangle to determine whether the intersection point lies within the triangle.
Since I can't explicitly compute the intersection points, I'm wondering if there is a way to express the same 2D orient calculation in 3D using only the original points. If there are 3 edges striking the triangle that gives me 9 points in total to play with. Assuming what I'm asking is even possible (using only the 3D orient tests), then I'm guessing that I'll need to form some subset of all the possible tetrahedra between those 9 points. I'm having difficultly even visualizing this, let alone distilling it into a formula or code. I can't even google this because I don't know what the industry standard terminology might be for this type of problem.
Any ideas how to proceed with this? Thanks. Perhaps I should ask MathOverflow as well...
EDIT: After reading some of the comments, one thing that occurs to me... Perhaps if I could fit non-overlapping tetrahedra between the 3 line segments, then the orientation of any one of those that crossed the plane would be the answer I'm looking for. Other than when the edges enclose a simple triangular prism, I'm not sure this sub-problem is solvable either.
EDIT: The requested image.
I am answering this on both MO & SO, expanding the comments I made on MO.
My sense is that no computational trick with signed tetrahedra volumes will avoid the precision issues that are your main concern. This is because, if you have tightly twisted segments, the orientation of the triangle depends on the precise positioning of the cutting plane.
[image removed; see below]
In the above example, the upper plane crosses the segments in the order (a,b,c) [ccw from above]: (red,blue,green), while the lower plane crosses in the reverse order (c,b,a): (green,blue,red). The height
of the cutting plane could be determined by your last bit of precision.
Consequently, I think it makes sense to just go ahead and compute the points of intersection in
the cutting plane, using enough precision to make the computation exact. If your segment endpoints coordinates and plane coefficients have L bits of precision, then there is just a small constant-factor increase needed. Although I am not certain of precisely what that factor is, it is small--perhaps 4. You will not need e.g., L2 bits, because the computation is solving linear equations.
So there will not be an explosion in the precision required to compute this exactly.
Good luck!
(I was prevented from posting the clarifying image because I don't have the reputation. See
the MO answer instead.)
Edit: Do see the MO answer, but here's the image:
I would write symbolic vector equations, you know, with dot and cross products, to find the normal of the intersection triangle. Then, the sign of the dot product of this normal with the initial triangle one gives the orientation. So finally you can express this in a form sign(F(p1,...,p9)), where p1 to p9 are your points and F() is an ugly formula including dot and cross products of differences (pi-pj). Don't know if this can be done simpler, but this general approach does the job.
As I understand it, you have three lines intersecting the plane, and you want to calculate the orientation of the triangle formed by the intersection points, without calculating the intersection points themselves?
If so: you have a plane
N·(x - x0) = 0
and six points...
l1a, l1b, l2a, l2b, l3a, l3b
...forming three lines
l1 = l1a + t(l1b - l1a)
l2 = l2a + u(l2b - l2a)
l3 = l3a + v(l3b - l3a)
The intersection points of these lines to the plane occur at specific values of t, u, v, which I'll call ti, ui, vi
N·(l1a + ti(l1b - l1a) - x0) = 0
N·(x0 - l1a)
ti = ----------------
N·(l1b - l1a)
(similarly for ui, vi)
Then the specific points of intersection are
intersect1 = l1a + ti(l1b - l1a)
intersect2 = l2a + ui(l2b - l2a)
intersect3 = l3a + vi(l3b - l3a)
Finally, the orientation of your triangle is
orientation = direction of (intersect2 - intersect1)x(intersect3 - intersect1)
(x is cross-product) Work backwards plugging the values, and you'll have an equation for orientation based only on N, x0, and your six points.
Let's call your triangle vertices T[0], T[1], T[2], and the first line segment's endpoints are L[0] and L[1], the second is L[2] and L[3], and the third is L[4] and L[5]. I imagine you want a function
int Orient(Pt3 T[3], Pt3 L[6]); // index L by L[2*i+j], i=0..2, j=0..1
which returns 1 if the intersections have the same orientation as the triangle, and -1 otherwise.
The result should be symmetric under interchange of j values, antisymmetric under interchange of i values and T indices. As long as you can compute a quantity with these symmetries, that's all you need.
Let's try
Sign(Product( Orient3D(T[i],T[i+1],L[2*i+0],L[2*i+1]) * -Orient3D(T[i],T[i+1],L[2*i+1],L[2*i+0]) ), i=0..2))
where the product should be taken over cyclic permutations of the indices (modulo 3). I believe this has all the symmetry properties required. Orient3D is Shewchuk's 4-point plane orientation test, which I assume you're using.
I want to calculate the angle between two vectors a and b. Lets assume these are at the origin. This can be done with
theta = arccos(a . b / |a| * |b|)
However arccos gives you the angle in [0, pi], i.e. it will never give you an angle greater than 180 degrees, which is what I want. So how do you find out when the vectors have gone past the 180 degree mark? In 2D I would simply let the sign of the y-component on one of the vectors determine what quadrant the vector is in. But what is the easiest way to do it in 3D?
EDIT: I wanted to keep the question general but here we go. I'm programming this in c and the code I use to get the angle is theta = acos(dot(a, b)/mag(a)*mag(b)) so how would you programmatically determine the orientation?
This works in 2D because you have a plane defined in which you define the rotation.
If you want to do this in 3D, there is no such implicit 2D plane. You could transform your 3D coordinates to a 2D plane going through all three points, and do your calculation inside this plane.
But, there are of course two possible orientations for the plane, and that will affect which angles will be > 180 or smaller.
I came up with the following solution that takes advantage of the direction change of the cross product of the two vectors:
Make a vector n = a X b and normalize it. This vector is normal to the plane spanned by a and b.
Whenever a new angle is calculated compare it with the old normal. In the comparison, treat the old and the current normals as points and compute the distance between them. If this distance is 2 the normal (i.e. the cross product a X b has flipped).
You might want to have a threshold for the distance as the distance after a flip might be shorter than 2, depending on how the vectors a and b are oriented and how often you update the angle.
One solution that you could use:
What you effectively need to do is create a plane that one of the vectors is coplanar to.
Getting the cross product of both vectors will create a plane, then is you get the normal of this plane, you can get the angle between this and the vector you need to get the signed angle for, and you can use the angle to determine the sign.
If the angle is greater than 90 degrees, then it is below the created plane; less than 90 degrees, and it is above.
Depending on cost of calculations, the dot product can be used at this stage instead of the angle.
Just make sure that you always calculate the normals by the same order of vectors.
This is useable more easily if you're using the XYZ axes, and that's what you're comparing against, since you already have the vectors needed for the plane.
There are possbly more efficient solutions, but this is one I came up with.
Edit: clarification of created vectors
a X b = p. This is perpendicular to both a and b.
Then, do either:
a X p or b X p to create another vector that is the normal to the plane created by the 2 vectors. Choice of vector depends on which you're trying to find the angle for.
Strictly speaking, two 3D vectors always have two angles between them - one below or equal to 180, the other over or equal to 180. Arccos gives you one of them, you can get the other by subtracting from 360. Think of it that way: imagine two lines intersect. You have 4 angles there - 2 of one value, 2 of another. What's the angle between the lines? No single answer. Same here. Without some kind of extra criteria, you can not, in theory, tell which of the two angle values should be taken into account.
EDIT: So what you really need is an arbitrary example of fixing an orientation. Here's one: we look from the positive Z direction. If the plane between the two vectors contains the Z axis, we look from the positive Y direction. If the plane is YZ, we look from the positive X direction. I'll think how to express this in coordinate form, then edit again.