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I'm trying to find the <x,y,z> size of what would end up being a bounding box for a rotated shape in all three axis rotations. Though to help keep it simple, the example demonstrated below has only the x axis rotated.
vector Size = <10,1,0.5>; vector Deg = <22.5,0,0>
if(Deg.x > 0 && Deg.y == 0 && Deg.z == 0){
Y1 = Cos(Deg.x) * Size.y + Sin(Deg.x) * Size.z;
Z1 = Cos(Deg.x) * Size.z + Sin(Deg.x) * Size.y;}
Below are for the y and z rotations, that is if you decided to change the degrees to say <0,22.5,0> and <0,0,22.5>.
if(Deg.y > 0 && Deg.x == 0 && Deg.z == 0){
X2 = Cos(Deg.y) * Size.x + Sin(Deg.y) * Size.z;
Z2 = Cos(Deg.y) * Size.z + Sin(Deg.y) * Size.x;}
if(Deg.z > 0 && Deg.x == 0 && Deg.y == 0){
X3 = Cos(Deg.z) * Size.x + Sin(Deg.z) * Size.y;
Y3 = Cos(Deg.z) * Size.y + Sin(Deg.z) * Size.x;}
Though the part I get lost at is, where do I go from here if when you have the rotation in two or three axis. Such as <22.5,22.5,0> or <22.5,22.5.22.5>
Is there a website with a tutorial or example equations I could review or are there any hints or ideas of what I could do to figure this out.
EDIT:
I do want to add that the comment from Nico helped, in that what I'm asking about is called: Axis-Aligned Bounding Box or AABB for short.
As for JohanC comment about the 22.5, yes the Deg = Degree. Also yes you'll have to turn the Degree into Radians, but I put it as Degree in the example for the Sin and Cos input to keep it simple.
If you're wondering how this question might be useful. Well as an example you'd need to know the AABB to help in an equation to keep the item in question flush with the surface its sitting on if you were to say resize the item while it had <x,y,z> rotations that weren't at perfect zero rotations.
I found my own solution after a lot of testing and debugging. I'll show and explain a small portion of the script I created since functions and options from coding language to language may vary.
A few details about the example script below. Deg = Degrees. Though yes what JohanC mentioned about radians is correct, since even the language I'm using I have to convert degrees to radians with a function. Not every language or calculator is like that, yet for intents and purposes to make it easier to read, I did take out the excess while keeping the meat of the code/equation intact so to speak. Also in this example, I'm rotating it in the Z axis to demonstrate and as a starting point.
vector Size = <10,1,0.5>; //Insert your own coding language function
//for splitting the size in half with positive and negative halves.
vector min = <-5,-0.5,-0.25>; vector max = <5,0.5,0.25>;
//You'll need at least 8 points in total to do this correctly.
//I already have 8 points added down below, labeled p1 - p8
vector p1 = <max.x,max.y,max.z>; vector p2 = <max.x,min.y,max.z>;
vector p3 = <max.x,max.y,min.z>; vector p4 = <max.x,min.y,min.z>;
vector p5 = <min.x,max.y,max.z>; vector p6 = <min.x,min.y,max.z>;
vector p7 = <min.x,max.y,min.z>; vector p8 = <min.x,min.y,min.z>;
vector Deg = <0,0,22.5>
//This will give you an idea of how to setup the x,y,z for each point
//equation to make / change to fit the rotations. Plus to make it compact
//I suggest that you use list, a while loop, and etc.
x1 = p1.x * llCos(Deg.z) - p1.y * llSin(Deg.z);
y1 = p1.y * llCos(Deg.z) + p1.x * llSin(Deg.z);
A lot of what I kept out of the example above is several list, true and false statements, a while loop, and few other things. Though it's kept simple to show the less complicated portion of it and the fact that not every language has the same functions available.
Though once when you get all of the location data of the 8 points collected, you can then put each of the x, y, z point information into its own list. Then run the equation (edited as needed for each rotation). After that, get the max and min from each axis rotation output. Then take the maximum and minus the minimum from it since the minimum will always be a negative. That right there will give you the <x,y,z> size of your bounding box.
I do want to add that the link from Nico helped slightly, though the only flaw in it is "if" the language you're using allows a matrix or "if" you're able to create a multidimensional array. If you can manage that, then use the link Nico gave to see if it helps.
Also some of JohanC tips/hints helped as well. Speaking of which, the part about "using the output of the vector from one rotation to the next" works well if and only if you're using the 8 points method. Otherwise if you try it with the bounding box size of one to the next, the first rotation to the second will work fine because its only moving in 2D at that point, but by the time you try to make the third rotation with the second rotations bounding box it won't work because it'll be going from 2D to 3D.
Note - If you think you have a better way to do the equation or simpler way to explain it, feel free to add in your own answer.
Perhaps the question title needs some work.
For context this is for the purpose of a Koch Snowflake (using C-like math syntax in a formula node in LabVIEW), thus why the triangle must be the correct way. (As given 2 points an equilateral triangle may be in one of two directions.)
To briefly go over the algorithm: I have an array of 4 predefined coordinates initially forming a triangle, the first "generation" of the fractal. To generate the next iteration, one must for each line (pair of coordinates) get the 1/3rd and 2/3rd midpoints to be the base of a new triangle on that face, and then calculate the position of the 3rd point of the new triangle (the subject of this question). Do this for all current sides, concatenating the resulting arrays into a new array that forms the next generation of the snowflake.
The array of coordinates is in a clockwise order, e.g. each vertex travelling clockwise around the shape corresponds to the next item in the array, something like this for the 2nd generation:
This means that when going to add a triangle to a face, e.g. between, in that image, the vertices labelled 0 and 1, you first get the midpoints which I'll call "c" and "d", you can just rotate "d" anti-clockwise around "c" by 60 degrees to find where the new triangle top point will be (labelled e).
I believe this should hold (e.g. 60 degrees anticlockwise rotating the later point around the earlier) for anywhere around the snowflake, however currently my maths only seems to work in the case where the initial triangle has a vertical side: [(0,0), (0,1)]. Else wise the triangle goes off in some other direction.
I believe I have correctly constructed my loops such that the triangle generating VI (virtual instrument, effectively a "function" in written languages) will work on each line segment sequentially, but my actual calculation isn't working and I am at a loss as to how to get it in the right direction. Below is my current maths for calculating the triangle points from a single line segment, where a and b are the original vertices of the segment, c and d form new triangle base that are in-line with the original line, and e is the part that sticks out. I don't want to call it "top" as for a triangle formed from a segment going from upper-right to lower-left, the "top" will stick down.
cx = ax + (bx - ax)/3;
dx = ax + 2*(bx - ax)/3;
cy = ay + (by - ay)/3;
dy = ay + 2*(by - ay)/3;
dX = dx - cx;
dY = dy - cy;
ex = (cos(1.0471975512) * dX + sin(1.0471975512) * dY) + cx;
ey = (sin(1.0471975512) * dX + cos(1.0471975512) * dY) + cy;
note 1.0471975512 is just 60 degrees in radians.
Currently for generation 2 it makes this: (note the seemingly separated triangle to the left is formed by the 2 triangles on the top and bottom having their e vertices meet in the middle and is not actually an independent triangle.)
I suspect the necessity for having slightly different equations depending on weather ax or bx is larger etc, perhaps something to do with how the periodicity of sin/cos may need to be accounted for (something about quadrants in spherical coordinates?), as it looks like the misplaced triangles are at 60 degrees, just that the angle is between the wrong lines. However this is a guess and I'm just not able to imagine how to do this programmatically let alone on paper.
Thankfully the maths formula node allows for if and else statements which would allow for this to be implemented if it's the case but as said I am not awfully familiar with adjusting for what I'll naively call the "quadrants thing", and am unsure how to know which quadrant one is in for each case.
This was a long and rambling question which inevitably tempts nonsense so if you've any clarifying questions please comment and I'll try to fix anything/everything.
Answering my own question thanks to #JohanC, Unsurprisingly this was a case of making many tiny adjustments and giving up just before getting it right.
The correct formula was this:
ex = (cos(1.0471975512) * dX + sin(1.0471975512) * dY) + cx;
ey = (-sin(1.0471975512) * dX + cos(1.0471975512) * dY) + cy;
just adding a minus to the second sine function. Note that if one were travelling anticlockwise then one would want to rotate points clockwise, so you instead have the 1st sine function negated and the second one positive.
I am new to this forum and not a native english speaker, so please be nice! :)
Here is the challenge I face at the moment:
I want to calculate the (approximate) relative coordinates of yet unknown points in a 3D euclidean space based on a set of given distances between 2 points.
In my first approach I want to ignore possible multiple solutions, just taking the first one by random.
e.g.:
given set of distances: (I think its creating a pyramid with a right-angled triangle as a base)
P1-P2-Distance
1-2-30
2-3-40
1-3-50
1-4-60
2-4-60
3-4-60
Step1:
Now, how do I calculate the relative coordinates for those points?
I figured that the first point goes to 0,0,0 so the second one is 30,0,0.
After that the third points can be calculated by finding the crossing of the 2 circles from points 1 and 2 with their distances to point 3 (50 and 40 respectively). How do I do that mathematically? (though I took these simple numbers for an easy representation of the situation in my mind). Besides I do not know how to get to the answer in a correct mathematical way the third point is at 30,40,0 (or 30,0,40 but i will ignore that).
But getting the fourth point is not as easy as that. I thought I have to use 3 spheres in calculate the crossing to get the point, but how do I do that?
Step2:
After I figured out how to calculate this "simple" example I want to use more unknown points... For each point there is minimum 1 given distance to another point to "link" it to the others. If the coords can not be calculated because of its degrees of freedom I want to ignore all possibilities except one I choose randomly, but with respect to the known distances.
Step3:
Now the final stage should be this: Each measured distance is a bit incorrect due to real life situation. So if there are more then 1 distances for a given pair of points the distances are averaged. But due to the imprecise distances there can be a difficulty when determining the exact (relative) location of a point. So I want to average the different possible locations to the "optimal" one.
Can you help me going through my challenge step by step?
You need to use trigonometry - specifically, the 'cosine rule'. This will give you the angles of the triangle, which lets you solve the 3rd and 4th points.
The rules states that
c^2 = a^2 + b^2 - 2abCosC
where a, b and c are the lengths of the sides, and C is the angle opposite side c.
In your case, we want the angle between 1-2 and 1-3 - the angle between the two lines crossing at (0,0,0). It's going to be 90 degrees because you have the 3-4-5 triangle, but let's prove:
50^2 = 30^2 + 40^2 - 2*30*40*CosC
CosC = 0
C = 90 degrees
This is the angle between the lines (0,0,0)-(30,0,0) and (0,0,0)- point 3; extend along that line the length of side 1-3 (which is 50) and you'll get your second point (0,50,0).
Finding your 4th point is slightly trickier. The most straightforward algorithm that I can think of is to firstly find the (x,y) component of the point, and from there the z component is straightforward using Pythagoras'.
Consider that there is a point on the (x,y,0) plane which sits directly 'below' your point 4 - call this point 5. You can now create 3 right-angled triangles 1-5-4, 2-5-4, and 3-5-4.
You know the lengths of 1-4, 2-4 and 3-4. Because these are right triangles, the ratio 1-4 : 2-4 : 3-4 is equal to 1-5 : 2-5 : 3-5. Find the point 5 using trigonometric methods - the 'sine rule' will give you the angles between 1-2 & 1-4, 2-1 and 2-4 etc.
The 'sine rule' states that (in a right triangle)
a / SinA = b / SinB = c / SinC
So for triangle 1-2-4, although you don't know lengths 1-4 and 2-4, you do know the ratio 1-4 : 2-4. Similarly you know the ratios 2-4 : 3-4 and 1-4 : 3-4 in the other triangles.
I'll leave you to solve point 4. Once you have this point, you can easily solve the z component of 4 using pythagoras' - you'll have the sides 1-4, 1-5 and the length 4-5 will be the z component.
I'll initially assume you know the distances between all pairs of points.
As you say, you can choose one point (A) as the origin, orient a second point (B) along the x-axis, and place a third point (C) along the xy-plane. You can solve for the coordinates of C as follows:
given: distances ab, ac, bc
assume
A = (0,0)
B = (ab,0)
C = (x,y) <- solve for x and y, where:
ac^2 = (A-C)^2 = (0-x)^2 + (0-y)^2 = x^2 + y^2
bc^2 = (B-C)^2 = (ab-x)^2 + (0-y)^2 = ab^2 - 2*ab*x + x^2 + y^2
-> bc^2 - ac^2 = ab^2 - 2*ab*x
-> x = (ab^2 + ac^2 - bc^2)/2*ab
-> y = +/- sqrt(ac^2 - x^2)
For this to work accurately, you will want to avoid cases where the points {A,B,C} are in a straight line, or close to it.
Solving for additional points in 3-space is similar -- you can expand the Pythagorean formula for the distance, cancel the quadratic elements, and solve the resulting linear system. However, this does not directly help you with your steps 2 and 3...
Unfortunately, I don't know a well-behaved exact solution for steps 2 and 3, either. Your overall problem will generally be both over-constrained (due to conflicting noisy distances) and under-constrained (due to missing distances).
You could try an iterative solver: start with a random placement of all your points, compare the current distances with the given ones, and use that to adjust your points in such a way as to improve the match. This is an optimization technique, so I would look up books on numerical optimization.
If you know the distance between the nodes (fixed part of system) and the distance to the tag (mobile) you can use trilateration to find the x,y postion.
I have done this using the Nanotron radio modules which have a ranging capability.
I am trying to plot large amounts of points using some library. The points are ordered by time and their values can be considered unpredictable.
My problem at the moment is that the sheer number of points makes the library take too long to render. Many of the points are redundant (that is - they are "on" the same line as defined by a function y = ax + b). Is there a way to detect and remove redundant points in order to speed rendering ?
Thank you for your time.
The following is a variation on the Ramer-Douglas-Peucker algorithm for 1.5d graphs:
Compute the line equation between first and last point
Check all other points to find what is the most distant from the line
If the worst point is below the tolerance you want then output a single segment
Otherwise call recursively considering two sub-arrays, using the worst point as splitter
In python this could be
def simplify(pts, eps):
if len(pts) < 3:
return pts
x0, y0 = pts[0]
x1, y1 = pts[-1]
m = float(y1 - y0) / float(x1 - x0)
q = y0 - m*x0
worst_err = -1
worst_index = -1
for i in xrange(1, len(pts) - 1):
x, y = pts[i]
err = abs(m*x + q - y)
if err > worst_err:
worst_err = err
worst_index = i
if worst_err < eps:
return [(x0, y0), (x1, y1)]
else:
first = simplify(pts[:worst_index+1], eps)
second = simplify(pts[worst_index:], eps)
return first + second[1:]
print simplify([(0,0), (10,10), (20,20), (30,30), (50,0)], 0.1)
The output is [(0, 0), (30, 30), (50, 0)].
About python syntax for arrays that may be non obvious:
x[a:b] is the part of array from index a up to index b (excluded)
x[n:] is the array made using elements of x from index n to the end
x[:n] is the array made using first n elements of x
a+b when a and b are arrays means concatenation
x[-1] is the last element of an array
An example of the results of running this implementation on a graph with 100,000 points with increasing values of eps can be seen here.
I came across this question after I had this very idea. Skip redundant points on plots. I believe I came up with a far better and simpler solution and I'm happy to share as my first proposed solution on SO. I've coded it and it works well for me. It also takes into account the screen scale. There may be 100 points in value between those plot points, but if the user has a chart sized small, they won't see them.
So, iterating through your data/plot loop, before you draw/add your next data point, look at the next value ahead and calculate the change in screen scale (or value, but I think screen scale for the above-mentioned reason is better). Now do the same for the next value ahead (getting these values is just a matter of peeking ahead in your array/collection/list/etc adding the for next step increment (probably 1/2) to the current for value whilst in the loop). If the 2 values are the same (or perhaps very minor change, per your own preference), you can skip this one point in your chart by simply adding 'continue' in the loop, skipping adding the data point as the point lies exactly on the slope between the point before and after it.
Using this method, I reduce a chart from 963 points to 427 for example, with absolutely zero visual change.
I think you might need to perhaps read this a couple of times to understand, but it's far simpler than the other best solution mentioned here, much lighter weight, and has zero visual effect on your plot.
I would probably apply a "least squares" algorithm to obtain a line of best fit. You can then go through your points and downfilter consecutive points that lie close to the line. You only need to plot the outliers, and the points that take the curve back to the line of best fit.
Edit: You may not need to employ "least squares"; if your input is expected to hover around "y=ax+b" as you say, then that's already your line of best fit and you can just use that. :)
I have been given some work to do with the fractal visualisation of the Mandelbrot set.
I'm not looking for a complete solution (naturally), I'm asking for help with regard to the orbits of complex numbers.
Say I have a given Complex number derived from a point on the complex plane. I now need to iterate over its orbit sequence and plot points according to whether the orbits increase by orders of magnitude or not.
How do I gather the orbits of a complex number? Any guidance is much appreciated (links etc). Any pointers on Math functions needed to test the orbit sequence e.g. Math.pow()
I'm using Java but that's not particularly relevant here.
Thanks again,
Alex
When you display the Mandelbrot set, you simply translate the real and imaginaty planes into x and y coordinates, respectively.
So, for example the complex number 4.5 + 0.27i translates into x = 4.5, y = 0.27.
The Mandelbrot set is all points where the equation Z = Z² + C never reaches a value where |Z| >= 2, but in practice you include all points where the value doesn't exceed 2 within a specific number of iterations, for example 1000. To get the colorful renderings that you usually see of the set, you assign different colors to points outside the set depending on how fast they reach the limit.
As it's complex numbers, the equation is actually Zr + Zi = (Zr + Zi)² + Cr + Ci. You would divide that into two equations, one for the real plane and one for the imaginary plane, and then it's just plain algebra. C is the coordinate of the point that you want to test, and the initial value of Z is zero.
Here's an image from my multi-threaded Mandelbrot generator :)
Actually the Mandelbrot set is the set of complex numbers for which the iteration converges.
So the only points in the Mandelbrot set are that big boring colour in the middle. and all of the pretty colours you see are doing nothing more than representing the rate at which points near the boundary (but the wrong side) spin off to infinity.
In mathspeak,
M = {c in C : lim (k -> inf) z_k = 0 } where z_0 = c, z_(k+1) = z_k^2 + c
ie pick any complex number c. Now to determine whether it is in the set, repeatedly iterate it z_0 = c, z_(k+1) = z_k^2 + c, and z_k will approach either zero or infinity. If its limit (as k tends to infinity) is zero, then it is in. Otherwise not.
It is possible to prove that once |z_k| > 2, it is not going to converge. This is a good exercise in optimisation: IIRC |Z_k|^2 > 2 is sufficient... either way, squaring up will save you the expensive sqrt() function.
Wolfram Mathworld has a nice site talking about the Mandelbrot set.
A Complex class will be most helpful.
Maybe an example like this will stimulate some thought. I wouldn't recommend using an Applet.
You have to know how to do add, subtract, multiply, divide, and power operations with complex numbers, in addition to functions like sine, cosine, exponential, etc. If you don't know those, I'd start there.
The book that I was taught from was Ruel V. Churchill "Complex Variables".
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m}d/r{rlineto}d/X -2 q 1{d/Y -2 q 2{d/A 0 d/B 0 d 64 -1 1{/f exch d/B
A/A z sub X add d B 2 m m Y add d z add 4 gt{exit}if/f 64 d}for f 64 div
setgray X Y moveto 0 q neg u 0 0 q u 0 r r r r fill/Y}for/X}for showpage