Using atan2 to find angle between two vectors - math

I understand that:
atan2(vector.y, vector.x) = the angle between the vector and the X axis.
But I wanted to know how to get the angle between two vectors using atan2. So I came across this solution:
atan2(vector1.y - vector2.y, vector1.x - vector2.x)
My question is very simple:
Will the two following formulas produce the same number?
atan2(vector1.y - vector2.y, vector1.x - vector2.x)
atan2(vector2.y - vector1.y, vector2.x - vector1.x)
If not: How do I know what vector comes first in the subtractions?

atan2(vector1.y - vector2.y, vector1.x - vector2.x)
is the angle between the difference vector (connecting vector2 and vector1) and the x-axis,
which is problably not what you meant.
The (directed) angle from vector1 to vector2 can be computed as
angle = atan2(vector2.y, vector2.x) - atan2(vector1.y, vector1.x);
and you may want to normalize it to the range [0, 2 π):
if (angle < 0) { angle += 2 * M_PI; }
or to the range (-π, π]:
if (angle > M_PI) { angle -= 2 * M_PI; }
else if (angle <= -M_PI) { angle += 2 * M_PI; }

A robust way to do it is by finding the sine of the angle using the cross product, and the cosine of the angle using the dot product and combining the two with the Atan2() function.
In C# this is:
public struct Vector2
{
public double X, Y;
/// <summary>
/// Returns the angle between two vectos
/// </summary>
public static double GetAngle(Vector2 A, Vector2 B)
{
// |A·B| = |A| |B| COS(θ)
// |A×B| = |A| |B| SIN(θ)
return Math.Atan2(Cross(A,B), Dot(A,B));
}
public double Magnitude { get { return Math.Sqrt(Dot(this,this)); } }
public static double Dot(Vector2 A, Vector2 B)
{
return A.X*B.X+A.Y*B.Y;
}
public static double Cross(Vector2 A, Vector2 B)
{
return A.X*B.Y-A.Y*B.X;
}
}
class Program
{
static void Main(string[] args)
{
Vector2 A=new Vector2() { X=5.45, Y=1.12};
Vector2 B=new Vector2() { X=-3.86, Y=4.32 };
double angle=Vector2.GetAngle(A, B) * 180/Math.PI;
// angle = 120.16850967865749
}
}
See the test case above in GeoGebra.

I think a better formula was posted here:
http://www.mathworks.com/matlabcentral/answers/16243-angle-between-two-vectors-in-3d
angle = atan2(norm(cross(a,b)), dot(a,b))
So this formula works in 2 or 3 dimensions.
For 2 dimensions this formula simplifies to the one stated above.

Nobody pointed out that if you have a single vector, and want to find the angle of the vector from the X axis, you can take advantage of the fact that the argument to atan2() is actually the slope of the line, or (delta Y / delta X). So if you know the slope, you can do the following:
given:
A = angle of the vector/line you wish to determine (from the X axis).
m = signed slope of the vector/line.
then:
A = atan2(m, 1)
Very useful!

If you care about accuracy for small angles, you want to use this:
angle = 2*atan2(|| ||b||a - ||a||b ||, || ||b||a + ||a||b ||)
Where "||" means absolute value, AKA "length of the vector". See https://math.stackexchange.com/questions/1143354/numerically-stable-method-for-angle-between-3d-vectors/1782769
However, that has the downside that in two dimensions, it loses the sign of the angle.

As a complement to the answer of #martin-r one should note that it is possible to use the sum/difference formula for arcus tangens.
angle = atan2(vec2.y, vec2.x) - atan2(vec1.y, vec1.x);
angle = -atan2(vec1.x * vec2.y - vec1.y * vec2.x, dot(vec1, vec2))
where dot = vec1.x * vec2.x + vec1.y * vec2.y
Caveat 1: make sure the angle remains within -pi ... +pi
Caveat 2: beware when the vectors are getting very similar, you might get extinction in the first argument, leading to numerical inaccuracies

You don't have to use atan2 to calculate the angle between two vectors. If you just want the quickest way, you can use dot(v1, v2)=|v1|*|v2|*cos A
to get
A = Math.acos( dot(v1, v2)/(v1.length()*v2.length()) );

angle(vector.b,vector.a)=pi/2*((1+sgn(xa))*(1-sgn(ya^2))-(1+sgn(xb))*(1-sgn(yb^2)))
+pi/4*((2+sgn(xa))*sgn(ya)-(2+sgn(xb))*sgn(yb))
+sgn(xa*ya)*atan((abs(xa)-abs(ya))/(abs(xa)+abs(ya)))
-sgn(xb*yb)*atan((abs(xb)-abs(yb))/(abs(xb)+abs(yb)))
xb,yb and xa,ya are the coordinates of the two vectors

The formula, angle(vector.b,vector.a), that I sent, give results
in the four quadrants and for any coordinates xa,ya and xb,yb.
For coordinates xa=ya=0 and or xb=yb=0 is undefined.
The angle can be bigger or smaller than pi, and can be positive
or negative.

Here a little program in Python that uses the angle between vectors to determine if a point is inside or outside a certain polygon
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from shapely.geometry import Point, Polygon
from pprint import pprint
# Plot variables
x_min, x_max = -6, 12
y_min, y_max = -3, 8
tick_interval = 1
FIG_SIZE = (10, 10)
DELTA_ERROR = 0.00001
IN_BOX_COLOR = 'yellow'
OUT_BOX_COLOR = 'black'
def angle_between(v1, v2):
""" Returns the angle in radians between vectors 'v1' and 'v2'
The sign of the angle is dependent on the order of v1 and v2
so acos(norm(dot(v1, v2))) does not work and atan2 has to be used, see:
https://stackoverflow.com/questions/21483999/using-atan2-to-find-angle-between-two-vectors
"""
arg1 = np.cross(v1, v2)
arg2 = np.dot(v1, v2)
angle = np.arctan2(arg1, arg2)
return angle
def point_inside(point, border):
""" Returns True if point is inside border polygon and False if not
Arguments:
:point: x, y in shapely.geometry.Point type
:border: [x1 y1, x2 y2, ... , xn yn] in shapely.geomettry.Polygon type
"""
assert len(border.exterior.coords) > 2,\
'number of points in the polygon must be > 2'
point = np.array(point)
side1 = np.array(border.exterior.coords[0]) - point
sum_angles = 0
for border_point in border.exterior.coords[1:]:
side2 = np.array(border_point) - point
angle = angle_between(side1, side2)
sum_angles += angle
side1 = side2
# if wn is 1 then the point is inside
wn = sum_angles / 2 / np.pi
if abs(wn - 1) < DELTA_ERROR:
return True
else:
return False
class MainMap():
#classmethod
def settings(cls, fig_size):
# set the plot outline, including axes going through the origin
cls.fig, cls.ax = plt.subplots(figsize=fig_size)
cls.ax.set_xlim(-x_min, x_max)
cls.ax.set_ylim(-y_min, y_max)
cls.ax.set_aspect(1)
tick_range_x = np.arange(round(x_min + (10*(x_max - x_min) % tick_interval)/10, 1),
x_max + 0.1, step=tick_interval)
tick_range_y = np.arange(round(y_min + (10*(y_max - y_min) % tick_interval)/10, 1),
y_max + 0.1, step=tick_interval)
cls.ax.set_xticks(tick_range_x)
cls.ax.set_yticks(tick_range_y)
cls.ax.tick_params(axis='both', which='major', labelsize=6)
cls.ax.spines['left'].set_position('zero')
cls.ax.spines['right'].set_color('none')
cls.ax.spines['bottom'].set_position('zero')
cls.ax.spines['top'].set_color('none')
#classmethod
def get_ax(cls):
return cls.ax
#staticmethod
def plot():
plt.tight_layout()
plt.show()
class PlotPointandRectangle(MainMap):
def __init__(self, start_point, rectangle_polygon, tolerance=0):
self.current_object = None
self.currently_dragging = False
self.fig.canvas.mpl_connect('key_press_event', self.on_key)
self.plot_types = ['o', 'o-']
self.plot_type = 1
self.rectangle = rectangle_polygon
# define a point that can be moved around
self.point = patches.Circle((start_point.x, start_point.y), 0.10,
alpha=1)
if point_inside(start_point, self.rectangle):
_color = IN_BOX_COLOR
else:
_color = OUT_BOX_COLOR
self.point.set_color(_color)
self.ax.add_patch(self.point)
self.point.set_picker(tolerance)
cv_point = self.point.figure.canvas
cv_point.mpl_connect('button_release_event', self.on_release)
cv_point.mpl_connect('pick_event', self.on_pick)
cv_point.mpl_connect('motion_notify_event', self.on_motion)
self.plot_rectangle()
def plot_rectangle(self):
x = [point[0] for point in self.rectangle.exterior.coords]
y = [point[1] for point in self.rectangle.exterior.coords]
# y = self.rectangle.y
self.rectangle_plot, = self.ax.plot(x, y,
self.plot_types[self.plot_type], color='r', lw=0.4, markersize=2)
def on_release(self, event):
self.current_object = None
self.currently_dragging = False
def on_pick(self, event):
self.currently_dragging = True
self.current_object = event.artist
def on_motion(self, event):
if not self.currently_dragging:
return
if self.current_object == None:
return
point = Point(event.xdata, event.ydata)
self.current_object.center = point.x, point.y
if point_inside(point, self.rectangle):
_color = IN_BOX_COLOR
else:
_color = OUT_BOX_COLOR
self.current_object.set_color(_color)
self.point.figure.canvas.draw()
def remove_rectangle_from_plot(self):
try:
self.rectangle_plot.remove()
except ValueError:
pass
def on_key(self, event):
# with 'space' toggle between just points or points connected with
# lines
if event.key == ' ':
self.plot_type = (self.plot_type + 1) % 2
self.remove_rectangle_from_plot()
self.plot_rectangle()
self.point.figure.canvas.draw()
def main(start_point, rectangle):
MainMap.settings(FIG_SIZE)
plt_me = PlotPointandRectangle(start_point, rectangle) #pylint: disable=unused-variable
MainMap.plot()
if __name__ == "__main__":
try:
start_point = Point([float(val) for val in sys.argv[1].split()])
except IndexError:
start_point= Point(0, 0)
border_points = [(-2, -2),
(1, 1),
(3, -1),
(3, 3.5),
(4, 1),
(5, 1),
(4, 3.5),
(5, 6),
(3, 4),
(3, 5),
(-0.5, 1),
(-3, 1),
(-1, -0.5),
]
border_points_polygon = Polygon(border_points)
main(start_point, border_points_polygon)

Related

Finding a specific point on an OBB (Oriented Bounding Box)

Hello, I've got a question which I cannot solve so I need a bit help.
In the picture above you can see an Oriented Bounding Box specified by 4 points (A, B, C, D). There is also a point in space called P. If I cast a ray from P against the OBB the ray is going to intersect the OBB at some point. This point of intersection is called Q in the picture. By the way the ray is always going to be x-axis aligned which means its directional vector is either (1, 0) or (-1,0) if normalized. My goal is to find the point of intersection - Q. Is there a way (if possible computationaly inexpensive) to do so?
Thanks in advance.
One way to do this is to consider each side of the bounding box to be a linear equation of the form y = ax + b, where a is the slope and b is the y-intercept. Then consider the ray from P to be an equation of the form y = c, where c is a constant. Then compare this equation to each of the four other equations to see where it intersects each one. One of these intersections will be our Q, if a Q exists; it's possible that the ray will miss the bounding box entirely. We will need to do a few checks:
Firstly, eliminate all potential Q's that are on the wrong side of P.
Secondly, check each of the four intersections to make sure they are within the bounds of the lines that they represent, and eliminate the ones that are not.
Finally, if any potential Q's remain, the one closest to P will be our Q. If no potential Q's remain, this means that the ray from P misses the bounding box entirely.
For example...
The line drawn from D to B would have a slope equal to (B.y - D.y) / (B.x - D.x) and a y-intercept equal to B.y - B.x * slope. Then the entire equation is y = (B.y - D.y) / (B.x - D.x) * x + B.y - B.x * (B.y - D.y) / (B.x - D.x). Set this equation equal to y = P.y and solve for x:
x = (P.y - B.y + B.x*(B.y - D.y)/(B.x - D.x))*(B.x - D.x)/(B.y - D.y)
The result of this equation will give you the x-value of the intersection. The y-value is P.y. Do this for each of the other 3 lines as well: A-B, A-C, C-D. I will refer to these intersections as Q(D-B), Q(A-B), Q(A-C), and Q(C-D) respectively.
Next, eliminate candidate-Q's that are on the wrong side of P. In our example, this eliminates Q(A-B), since it is way off to the right side of the screen. Mathematically, Q(A-B).x > P.x.
Then eliminate all candidate-Q's that are not on the line they represent. We can do this check by finding the lowest and highest x-values and y-values given by the two points that represent the line. For example, to check that Q(A-C) is on the line A-C, check that C.x <= Q(A-C).x <= A.x and C.y <= Q(A-C).y <= A.y. Q(A-C) passes the test, as well as Q(D-B). However, Q(C-D) does not pass, as it is way off to the left side of the screen, far from the box. Therefore, Q(C-D) is eliminated from candidacy.
Finally, of the two points that remain, Q(A-C) and Q(D-B), we choose Q(D-B) to be our winner, because it is closest to P.
We now can say that the ray from P hits the bounding box at Q(D-B).
Of course, when you implement this in code, you will need to account for divisions by zero. If a line is perfectly vertical, there does not exist a point-slope equation of the line, so you will need to create a separate formula for this case. If a line is perfectly horizontal, it's respective candidate-Q should be automatically eliminated from candidacy, as the ray from P will never touch it.
Edit:
It would be more efficient to only do this process with lines whose two points are on vertically opposite sides of point P. If both points of a line are above P, or they are both below P, they would be eliminated from candidacy from the beginning.
Find the two sides that straddle p on Y. (Test of the form (Ya < Yp) != (Yb < Yp)).
Then compute the intersection points of the horizontal by p with these two sides, and keep the first to the left of p.
If the ray points to the left(right) then it must intersect an edge that connects to the point in the OOB with max(min) x-value. We can determine which edge by simply comparing the y-value of the ray with the y value of the max(min) point and its neighbors. We also need to consider OBBs that are actually axis-aligned, and thus have two points with equal max(min) x-value. Once we have the edge it's simple to confirm that the ray does in fact intersect the OBB and calculate its x-value.
Here's some Java code to illustrate (ideone):
static double nearestX(Point[] obb, int y, int dir)
{
// Find min(max) point
int n = 0;
for(int i=1; i<4; i++)
if((obb[n].x < obb[i].x) == (dir == -1)) n = i;
// Determine next or prev edge
int next = (n+1) % 4;
int prev = (n+3) % 4;
int nn;
if((obb[n].x == obb[next].x) || (obb[n].y < y) == (obb[n].y < obb[next].y))
nn = next;
else
nn = prev;
// Check that the ray intersects the OBB
if(Math.abs(y) > Math.abs(obb[nn].y)) return Double.NaN;
// Standard calculation of x from y for line segment
return obb[n].x + (y-obb[n].y)*(obb[nn].x-obb[n].x)/(obb[nn].y-obb[n].y);
}
Test:
public static void main(String[] args)
{
test("Diamond", new Point[]{p(0, -2), p(2, 0), p(0, 2), p(-2,0)});
test("Square", new Point[]{p(-2, -2), p(2, -2), p(2, 2), p(-2,2)});
}
static void test(String label, Point[] obb)
{
System.out.println(label + ": " + Arrays.toString(obb));
for(int dir : new int[] {-1, 1})
{
for(int y : new int[] {-3, -2, -1, 0, 1, 2, 3})
System.out.printf("(% d, % d) = %.0f\n", y , dir, nearestX(obb, y, dir));
System.out.println();
}
}
Output:
Diamond: [(0,-2), (2,0), (0,2), (-2,0)]
(-3, -1) = NaN
(-2, -1) = 0
(-1, -1) = 1
( 0, -1) = 2
( 1, -1) = 1
( 2, -1) = 0
( 3, -1) = NaN
(-3, 1) = NaN
(-2, 1) = 0
(-1, 1) = -1
( 0, 1) = -2
( 1, 1) = -1
( 2, 1) = 0
( 3, 1) = NaN
Square: [(-2,-2), (2,-2), (2,2), (-2,2)]
(-3, -1) = NaN
(-2, -1) = 2
(-1, -1) = 2
( 0, -1) = 2
( 1, -1) = 2
( 2, -1) = 2
( 3, -1) = NaN
(-3, 1) = NaN
(-2, 1) = -2
(-1, 1) = -2
( 0, 1) = -2
( 1, 1) = -2
( 2, 1) = -2
( 3, 1) = NaN

Super-ellipse Point Picking

https://en.wikipedia.org/wiki/Superellipse
I have read the SO questions on how to point-pick from a circle and an ellipse.
How would one uniformly select random points from the interior of a super-ellipse?
More generally, how would one uniformly select random points from the interior of the curve described by an arbitrary super-formula?
https://en.wikipedia.org/wiki/Superformula
The discarding method is not considered a solution, as it is mathematically unenlightening.
In order to sample the superellipse, let's assume without loss of generality that a = b = 1. The general case can be then obtained by rescaling the corresponding axis.
The points in the first quadrant (positive x-coordinate and positive y-coordinate) can be then parametrized as:
x = r * ( cos(t) )^(2/n)
y = r * ( sin(t) )^(2/n)
with 0 <= r <= 1 and 0 <= t <= pi/2:
Now, we need to sample in r, t so that the sampling transformed into x, y is uniform. To this end, let's calculate the Jacobian of this transform:
dx*dy = (2/n) * r * (sin(2*t)/2)^(2/n - 1) dr*dt
= (1/n) * d(r^2) * d(f(t))
Here, we see that as for the variable r, it is sufficient to sample uniformly the value of r^2 and then transform back with a square root. The dependency on t is a bit more complicated. However, with some effort, one gets
f(t) = -(n/2) * 2F1(1/n, (n-1)/n, 1 + 1/n, cos(t)^2) * cos(t)^(2/n)
where 2F1 is the hypergeometric function.
In order to obtain uniform sampling in x,y, we need now to sample uniformly the range of f(t) for t in [0, pi/2] and then find the t which corresponds to this sampled value, i.e., to solve for t the equation u = f(t) where u is a uniform random variable sampled from [f(0), f(pi/2)]. This is essentially the same method as for r, nevertheless in that case one can calculate the inverse directly.
One small issue with this approach is that the function f is not that well-behaved near zero - the infinite slope makes it quite challenging to find a root of u = f(t). To circumvent this, we can sample only the "upper part" of the first quadrant (i.e., area between lines x=y and x=0) and then obtain all the other points by symmetry (not only in the first quadrant but also for all the other ones).
An implementation of this method in Python could look like:
import numpy as np
from numpy.random import uniform, randint, seed
from scipy.optimize import brenth, ridder, bisect, newton
from scipy.special import gamma, hyp2f1
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
seed(100)
def superellipse_area(n):
#https://en.wikipedia.org/wiki/Superellipse#Mathematical_properties
inv_n = 1. / n
return 4 * ( gamma(1 + inv_n)**2 ) / gamma(1 + 2*inv_n)
def sample_superellipse(n, num_of_points = 2000):
def f(n, x):
inv_n = 1. / n
return -(n/2)*hyp2f1(inv_n, 1 - inv_n, 1 + inv_n, x)*(x**inv_n)
lb = f(n, 0.5)
ub = f(n, 0.0)
points = [None for idx in range(num_of_points)]
for idx in range(num_of_points):
r = np.sqrt(uniform())
v = uniform(lb, ub)
w = bisect(lambda w: f(n, w**n) - v, 0.0, 0.5**(1/n))
z = w**n
x = r * z**(1/n)
y = r * (1 - z)**(1/n)
if uniform(-1, 1) < 0:
y, x = x, y
x = (2*randint(0, 2) - 1)*x
y = (2*randint(0, 2) - 1)*y
points[idx] = [x, y]
return points
def plot_superellipse(ax, n, points):
coords_x = [p[0] for p in points]
coords_y = [p[1] for p in points]
ax.set_xlim(-1.25, 1.25)
ax.set_ylim(-1.25, 1.25)
ax.text(-1.1, 1, '{n:.1f}'.format(n = n), fontsize = 12)
ax.scatter(coords_x, coords_y, s = 0.6)
params = np.array([[0.5, 1], [2, 4]])
fig = plt.figure(figsize = (6, 6))
gs = gridspec.GridSpec(*params.shape, wspace = 1/32., hspace = 1/32.)
n_rows, n_cols = params.shape
for i in range(n_rows):
for j in range(n_cols):
n = params[i, j]
ax = plt.subplot(gs[i, j])
if i == n_rows-1:
ax.set_xticks([-1, 0, 1])
else:
ax.set_xticks([])
if j == 0:
ax.set_yticks([-1, 0, 1])
else:
ax.set_yticks([])
#ensure that the ellipses have similar point density
num_of_points = int(superellipse_area(n) / superellipse_area(2) * 4000)
points = sample_superellipse(n, num_of_points)
plot_superellipse(ax, n, points)
fig.savefig('fig.png')
This produces:

Check logic for altitude of a triangle using d3

I have this jsbin that shows my working.
In the jsbin, I am trying to draw a line through the altitude through point A (1, 1) that is perpendicular to Line BC which has points B (6, 18) and C (14, 6).
The way I have worked this out is to try and get 2 equations into the form y = mx + c and then rearrange them to y -mx = c and then solve them through simultaneous equations using matrices.
I have this altitude function that does the work:
function altitude(vertex, a, b) {
var slope = gradient(a, b),
x1 = - slope,
y1 = 1,
c1 = getYIntercept(a, slope),
perpendicularSlope = perpendicularGradient(a, b),
x2 = - perpendicularSlope,
y2 = 1,
c2 = getYIntercept(vertex, perpendicularSlope);
var matrix = [
[x1, y1],
[x2, y2]
];
var result = solve(matrix, [c1, c2]);
var g = svg.append('g');
g.append('line')
.style('stroke', 'red')
.attr('class', 'line')
.attr('x1', xScale(vertex.x))
.attr('y1', yScale(vertex.y))
.attr('x2', xScale(result.x))
.attr('y2', yScale(result.y));
}
I first of all get the gradient of BC using this function
var gradient = function(a, b) {
return (b.y - a.y) / (b.x - a.x);
};
Which is -1.5 and from that I can get the perpendicular gradient using this function:
var perpendicularGradient = function (a, b) {
return -1 / gradient(a, b);
};
I make that to be 0.66666 or (2/3).
I get the 2 equations to look like this:
y + 1.5 = 27
y -0.6666666666666666 = 0.33333333333333337
I have some functions in the jsbin that will solve these simultaneously using matrices and cramer's rule, the main one being solve:
function solve(matrix, r) {
var determinant = det(matrix);
var x = det([
[r[0], matrix[0][1]],
[r[1], matrix[1][1]]
]) / determinant;
var y = det([
[matrix[0][0], r[0]],
[matrix[1][0], r[1]]
]) / determinant;
return {x: Math.approx(x), y: Math.approx(y)};
}
function det(matrix) {
return (matrix[0][0]*matrix[1][1])-(matrix[0][1]*matrix[1][0]);
}
I get the coordinates of the intercept to be roughly (12.31, 8.54).
The problem is, it does not look right on the diagram.
Have I taken a wrong step somewhere? I think my calculations are right but I would not rule out them being wrong. It might be down to scale perhaps.
You want to find projection of point A onto line BC.
Let's make vectors
Q = C - B
P = A - B
normalized (unit length):
uQ = Q/ |Q|
Needed projection point D is
D = B + uQ * DotProduct(P, uQ)
For your example A(1,1), B(6,18), C(14,6)
Q = (8, -12)
|Q| = Sqrt(8*8+12*12)~14.4
uQ= (0.55, -0.83)
P=(-5,-17)
DotProduct(P, uQ)=0.55*(-5) -(0.83*-17)=11.39
D = (6+0.55*11.39, 18-0.83*11.39) = (12.26, 8,54)
So your calculation gives right result (though approach is not very efficient), but picture is not exact - different scales of X and Y axes deform angles.
P.S: Second line width = 660 - margin.left - margin.right, makes the picture more reliable

Uniformly distribute x points inside a circle

I would like to uniformly distribute a predetermined set of points within a circle. By uniform distribution, I mean they should all be equally distanced from each other (hence a random approach won't work). I tried a hexagonal approach, but I had problems consistently reaching the outermost radius.
My current approach is a nested for loop where each outer iteration reduces the radius & number of points, and each inner loop evenly drops points on the new radius. Essentially, it's a bunch of nested circles. Unfortunately, it's far from even. Any tips on how to do this correctly?
The goals of having a uniform distribution within the area and a uniform distribution on the boundary conflict; any solution will be a compromise between the two. I augmented the sunflower seed arrangement with an additional parameter alpha that indicates how much one cares about the evenness of boundary.
alpha=0 gives the typical sunflower arrangement, with jagged boundary:
With alpha=2 the boundary is smoother:
(Increasing alpha further is problematic: Too many points end up on the boundary).
The algorithm places n points, of which the kth point is put at distance sqrt(k-1/2) from the boundary (index begins with k=1), and with polar angle 2*pi*k/phi^2 where phi is the golden ratio. Exception: the last alpha*sqrt(n) points are placed on the outer boundary of the circle, and the polar radius of other points is scaled to account for that. This computation of the polar radius is done in the function radius.
It is coded in MATLAB.
function sunflower(n, alpha) % example: n=500, alpha=2
clf
hold on
b = round(alpha*sqrt(n)); % number of boundary points
phi = (sqrt(5)+1)/2; % golden ratio
for k=1:n
r = radius(k,n,b);
theta = 2*pi*k/phi^2;
plot(r*cos(theta), r*sin(theta), 'r*');
end
end
function r = radius(k,n,b)
if k>n-b
r = 1; % put on the boundary
else
r = sqrt(k-1/2)/sqrt(n-(b+1)/2); % apply square root
end
end
Might as well tag on my Python translation.
from math import sqrt, sin, cos, pi
phi = (1 + sqrt(5)) / 2 # golden ratio
def sunflower(n, alpha=0, geodesic=False):
points = []
angle_stride = 360 * phi if geodesic else 2 * pi / phi ** 2
b = round(alpha * sqrt(n)) # number of boundary points
for k in range(1, n + 1):
r = radius(k, n, b)
theta = k * angle_stride
points.append((r * cos(theta), r * sin(theta)))
return points
def radius(k, n, b):
if k > n - b:
return 1.0
else:
return sqrt(k - 0.5) / sqrt(n - (b + 1) / 2)
# example
if __name__ == '__main__':
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
points = sunflower(500, alpha=2, geodesic=False)
xs = [point[0] for point in points]
ys = [point[1] for point in points]
ax.scatter(xs, ys)
ax.set_aspect('equal') # display as square plot with equal axes
plt.show()
Stumbled across this question and the answer above (so all cred to user3717023 & Matt).
Just adding my translation into R here, in case someone else needed that :)
library(tibble)
library(dplyr)
library(ggplot2)
sunflower <- function(n, alpha = 2, geometry = c('planar','geodesic')) {
b <- round(alpha*sqrt(n)) # number of boundary points
phi <- (sqrt(5)+1)/2 # golden ratio
r <- radius(1:n,n,b)
theta <- 1:n * ifelse(geometry[1] == 'geodesic', 360*phi, 2*pi/phi^2)
tibble(
x = r*cos(theta),
y = r*sin(theta)
)
}
radius <- function(k,n,b) {
ifelse(
k > n-b,
1,
sqrt(k-1/2)/sqrt(n-(b+1)/2)
)
}
# example:
sunflower(500, 2, 'planar') %>%
ggplot(aes(x,y)) +
geom_point()
Building on top of #OlivelsAWord , here is a Python implementation using numpy:
import numpy as np
import matplotlib.pyplot as plt
def sunflower(n: int, alpha: float) -> np.ndarray:
# Number of points respectively on the boundary and inside the cirlce.
n_exterior = np.round(alpha * np.sqrt(n)).astype(int)
n_interior = n - n_exterior
# Ensure there are still some points in the inside...
if n_interior < 1:
raise RuntimeError(f"Parameter 'alpha' is too large ({alpha}), all "
f"points would end-up on the boundary.")
# Generate the angles. The factor k_theta corresponds to 2*pi/phi^2.
k_theta = np.pi * (3 - np.sqrt(5))
angles = np.linspace(k_theta, k_theta * n, n)
# Generate the radii.
r_interior = np.sqrt(np.linspace(0, 1, n_interior))
r_exterior = np.ones((n_exterior,))
r = np.concatenate((r_interior, r_exterior))
# Return Cartesian coordinates from polar ones.
return r * np.stack((np.cos(angles), np.sin(angles)))
# NOTE: say the returned array is called s. The layout is such that s[0,:]
# contains X values and s[1,:] contains Y values. Change the above to
# return r.reshape(n, 1) * np.stack((np.cos(angles), np.sin(angles)), axis=1)
# if you want s[:,0] and s[:,1] to contain X and Y values instead.
if __name__ == '__main__':
fig, ax = plt.subplots()
# Let's plot three sunflowers with different values of alpha!
for alpha in (0, 1, 2):
s = sunflower(500, alpha)
# NOTE: the 'alpha=0.5' parameter is to control transparency, it does
# not have anything to do with the alpha used in 'sunflower' ;)
ax.scatter(s[0], s[1], alpha=0.5, label=f"alpha={alpha}")
# Display as square plot with equal axes and add a legend. Then show the result :)
ax.set_aspect('equal')
ax.legend()
plt.show()
Adding my Java implementation of previous answers with an example (Processing).
int n = 2000; // count of nodes
Float alpha = 2.; // constant that can be adjusted to vary the geometry of points at the boundary
ArrayList<PVector> vertices = new ArrayList<PVector>();
Float scaleFactor = 200.; // scale points beyond their 0.0-1.0 range for visualisation;
void setup() {
size(500, 500);
// Test
vertices = sunflower(n, alpha);
displayTest(vertices, scaleFactor);
}
ArrayList<PVector> sunflower(int n, Float alpha) {
Double phi = (1 + Math.sqrt(5)) / 2; // golden ratio
Double angle = 2 * PI / Math.pow(phi, 2); // value used to calculate theta for each point
ArrayList<PVector> points = new ArrayList<PVector>();
Long b = Math.round(alpha*Math.sqrt(n)); // number of boundary points
Float theta, r, x, y;
for (int i = 1; i < n + 1; i++) {
r = radius(i, n, b.floatValue());
theta = i * angle.floatValue();
x = r * cos(theta);
y = r * sin(theta);
PVector p = new PVector(x, y);
points.add(p);
}
return points;
}
Float radius(int k, int n, Float b) {
if (k > n - b) {
return 1.0;
} else {
Double r = Math.sqrt(k - 0.5) / Math.sqrt(n - (b+1) / 2);
return r.floatValue();
}
}
void displayTest(ArrayList<PVector> points, Float size) {
for (int i = 0; i < points.size(); i++) {
Float x = size * points.get(i).x;
Float y = size * points.get(i).y;
pushMatrix();
translate(width / 2, height / 2);
ellipse(x, y, 5, 5);
popMatrix();
}
}
Here's my Unity implementation.
Vector2[] Sunflower(int n, float alpha = 0, bool geodesic = false){
float phi = (1 + Mathf.Sqrt(5)) / 2;//golden ratio
float angle_stride = 360 * phi;
float radius(float k, float n, float b)
{
return k > n - b ? 1 : Mathf.Sqrt(k - 0.5f) / Mathf.Sqrt(n - (b + 1) / 2);
}
int b = (int)(alpha * Mathf.Sqrt(n)); //# number of boundary points
List<Vector2>points = new List<Vector2>();
for (int k = 0; k < n; k++)
{
float r = radius(k, n, b);
float theta = geodesic ? k * 360 * phi : k * angle_stride;
float x = !float.IsNaN(r * Mathf.Cos(theta)) ? r * Mathf.Cos(theta) : 0;
float y = !float.IsNaN(r * Mathf.Sin(theta)) ? r * Mathf.Sin(theta) : 0;
points.Add(new Vector2(x, y));
}
return points.ToArray();
}

Draw Lines Over a Circle

There's a line A-B and C at the center between A and B. It forms a circle as in the figure. If we assume A-B line as a diameter of the circle and then C is it's center. My problem is I have no idea how to draw another three lines (in blue) each 45 degree away from AC or AB. No, this is not a homework, it's part of my complex geometry in a rendering.
alt text http://www.freeimagehosting.net/uploads/befcd84d8c.png
There are a few ways to solve this problem, one of which is to find the angle of the line and then add 45 degrees to this a few times. Here's an example, it's in Python, but translating the math should be easy (and I've tried to write the Python in a simplistic way).
Here's the output for a few lines:
The main function is calc_points, the rest is just to give it A and B that intersect the circle, and make the plots.
from math import atan2, sin, cos, sqrt, pi
from matplotlib import pyplot
def calc_points(A, B, C):
dx = C[0]-A[0]
dy = C[1]-A[1]
line_angle = atan2(dy, dx)
radius = sqrt(dy*dy + dx*dx)
new_points = []
# now go around the circle and find the points
for i in range(3):
angle = line_angle + (i+1)*45*(pi/180) # new angle in radians
x = radius*cos(angle) + C[0]
y = radius*sin(angle) + C[1]
new_points.append([x, y])
return new_points
# test this with some reasonable values
pyplot.figure()
for i, a in enumerate((-20, 20, 190)):
radius = 5
C = [2, 2]
# find an A and B on the circle and plot them
angle = a*(pi/180)
A = [radius*cos(pi+angle)+C[0], radius*sin(pi+angle)+C[1]]
B = [radius*cos(angle)+C[0], radius*sin(angle)+C[1]]
pyplot.subplot(1,3,i+1)
pyplot.plot([A[0], C[0]], [A[1], C[1]], 'r')
pyplot.plot([B[0], C[0]], [B[1], C[1]], 'r')
# now run these through the calc_points function and the new lines
new_points = calc_points(A, B, C)
for np in new_points:
pyplot.plot([np[0], C[0]], [np[1], C[1]], 'b')
pyplot.xlim(-8, 8)
pyplot.ylim(-8, 8)
for x, X in (("A", A), ("B", B), ("C", C)):
pyplot.text(X[0], X[1], x)
pyplot.show()
If you want to find coordinates of blue lines, may be you will find helpful some information about tranformations (rotations):
http://en.wikipedia.org/wiki/Rotation_matrix
You need to rotate for example vector AC and then you can find coordinate of end point of blue line.
start with this and add a button with code:
private void btnCircleLined_Click(object sender, System.EventArgs e)
{
Graphics graph = Graphics.FromImage(DrawArea);
int x = 100, y = 100, diameter = 50;
myPen.Color = Color.Green;
myPen.Width = 10;
graph.DrawEllipse(myPen, x, y, diameter, diameter);
myPen.Color = Color.Red;
double radian = 45 * Math.PI / 180;
int xOffSet = (int)(Math.Cos(radian) * diameter / 2);
int yOffSet = (int)(Math.Sin(radian) * diameter / 2);
graph.DrawLine(myPen, x, y + yOffSet + myPen.Width + diameter / 2, x + xOffSet + myPen.Width + diameter / 2, y);
graph.DrawLine(myPen, x, y, x + xOffSet + myPen.Width + diameter / 2, y + yOffSet + myPen.Width + diameter / 2);
graph.Dispose();
this.Invalidate();
}
edit: could not see your picture so I misinterpeted your question, but this should get you started.
Translate A with C at the origin (i.e. A-C), rotate CW 45°, then translate back. Repeat three more times.
If I were doing this I'd use polar co-ordinates (apologies for including the link if you are already well aware what they are) as an easy way of figuring out the co-ordinates of the points on the circumference that you need. Then draw lines to there from the centre of the circle.

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