I have a data cube with 2 dimensions of coordinates and a third dimension for wavelength. My goal is to write a mask for coordinates outside a circle of given radius to the central coordinates (x0 and y0 in my code). For this, I'm trying to use a dictionary, but I'm having throuble because it seems that I'll have to make a double loop inside the dictionary to iterate over the two dimensions, and as a beginner with dictionaries, I don't know yet how to do that.
I wrote the following code
x0 = 38
y0 = 45
radius = 9
xcoords = np.arange(1,flux.shape[1]+1,1)
ycoords = np.arange(1,flux.shape[2]+1,1)
mask = {'xmask': [xcoords[np.sqrt((xcoords[:]-x0)**2 + (y-y0)**2) < radius] for y in ycoords], 'ymask': [ycoords[np.sqrt((x-x0)**2 + (ycoords[:]-y0)**2) < radius] for x in xcoords]}
And it returned several arrays, one for each value of y (for xmasks), and one for each value of x (for ymasks), although I want just one array for each one. Could anyone say what I made wrong and how to achieve my goal?
Note: I also made it without using a dictionary, as
xmask = []
for x in xcoords:
for y in ycoords:
if np.sqrt((x-x0)**2 + (y-y0)**2) < radius:
xmask.append(x)
break
ymask = []
for y in xcoords:
for x in ycoords:
if np.sqrt((x-x0)**2 + (y-y0)**2) < radius:
ymask.append(y)
break
but I hope it's possible to make it more efficiently.
Thanks for any help!
Edit: I realized that no loop was needed. If I select y = y0 and x = x0, I get the values of x and y that are inside the circle, respectively. So I stayed with
mask = {'xmask': [xcoords[abs(xcoords[:]-x0) < radius]], 'ymask': [ycoords[abs(ycoords[:]-y0) < radius]]}
The OP explains that assigning
mask = {'xmask': [xcoords[abs(xcoords[:] - x0) < radius]],
'ymask': [ycoords[abs(ycoords[:] - y0) < radius]]}
solves the problem.
I've been trying to plot the trajectories of charged particles in the field of a magnetic dipole in an attempt to give a rough pictorial representation of the northern lights. While the spiraling appears to be what I would have expected, it looks as though the spirals start out tight and get wider, as if the particles are somehow gaining energy. I'm not sure what the issue is in the code, and I would appreciate any pointers!
Shown below are the contents of the main loop (initial conditions and all were set outside).
# polar to cartesian
r = np.sqrt(X*X+Y*Y+Z*Z)
theta = np.arccos(Z/r)
phi = np.arctan(Y/X)
# magnetic field/mass
Bx = K*(1/(r**(3)))*(2*np.cos(theta)*np.sin(theta)*np.cos(phi)+np.sin(theta)*np.cos(theta)*np.cos(phi))
By = K*(1/(r**(3)))*(2*np.cos(theta)*np.sin(theta)*np.sin(phi)+np.sin(theta)*np.cos(theta)*np.sin(phi))
Bz = K*(1/(r**(3)))*(2*np.cos(theta)*np.cos(theta)-np.sin(theta)*np.sin(theta))
# acceleration components
ax = (1.6*10**(-19))*(vy*Bz - vz*By)
ay = (1.6*10**(-19))*(vz*Bx - vx*Bz)
az = (1.6*10**(-19))*(vx*By - vy*Bx)
# velocity components
vx = vx + ax*dt
vy = vy + ay*dt
vz = vz + az*dt
# position components
X = X + vx*dt + 0.5*ax*dt*dt
Y = Y + vy*dt + 0.5*ay*dt*dt
Z = Z + vz*dt + 0.5*az*dt*dt
# add position values to position vectors
x1.append(X)
y1.append(Y)
z1.append(Z)
And a figure of what the current trajectories look like (the particles are starting out at the top in tighter spirals before gradually increasing their radii):
figure
(I'm using Python 3.6 for this project)
Turns out it was just a matter of the timestep being too large. After decreasing the time step size, the trajectories are much more reasonable.
I have triangle: a, b, c. Each vertex has a value: va, vb, vc. In my software the user drags point p around inside and outside of this triangle. I use barycentric coordinates to determine the value vp at p based on va, vb, and vc. So far, so good.
Now I want to limit p so that vp is within range min and max. If a user chooses p where vp is < min or > max, how can I find the point closest to p where vp is equal to min or max, respectively?
Edit: Here is an example where I test each point. Light gray is within min/max. How can I find the equations of the lines that make up the min/max boundary?
a = 200, 180
b = 300, 220
c = 300, 300
va = 1
vb = 1.4
vc = 3.2
min = 0.5
max = 3.5
Edit: FWIW, so far first I get the barycentric coordinates v,w for p using the triangle vertices a, b, c (standard stuff I think, but looks like this). Then to get vp:
u = 1 - w - v
vp = va * u + vb * w + vc * v
That is all fine. My trouble is that I need the line equations for min/max so I can choose a new position for p when vp is out of range. The new position for p is the point closest to p on the min or max line.
Note that p is an XY coordinate and vp is a value for that coordinate determined by the triangle and the values at each vertex. min and max are also values. The two line equations I need will give me XY coordinates for which the values determined by the triangle are min or max.
It doesn't matter if barycentric coordinates are used in the solution.
The trick is to use the ratio of value to cartesian distance to extend each triangle edge until it hits min or max. Easier to see with a pic:
The cyan lines show how the triangle edges are extended, the green Xs are points on the min or max lines. With just 2 of these points we know the slope if the line. The yellow lines show connecting the Xs aligns with the light gray.
The math works like this, first get the value distance between vb and vc:
valueDistBtoC = vc - vb
Then get the cartesian distance from b to c:
cartesianDistBtoC = b.distance(c)
Then get the value distance from b to max:
valueDistBtoMax = max - vb
Now we can cross multiply to get the cartesian distance from b to max:
cartesianDistBtoMax = (valueDistBtoMax * cartesianDistBtoC) / valueDistBtoC
Do the same for min and also for a,b and c,a. The 6 points are enough to restrict the position of p.
Consider your triangle to actually be a 3D triangle, with points (ax,ay,va), (bx,by,vb), and (cx,cy,vc). These three points define a plane, containing all the possible p,vp triplets obtainable through barycentric interpolation.
Now think of your constraints as two other planes, at z>=max and z<=min. Each of these planes intersects your triangle's plane along an infinite line; the infinite beam between them, projected back down onto the xy plane, represents the area of points which satisfy the constraints. Once you have the lines (projected down), you can just find which (if either) is violated by a particular point, and move it onto that constraint (along a vector which is perpendicular to the constraint).
Now I'm not sure about your hexagon, though. That's not the shape I would expect.
Mathematically speaking the problem is simply a change of coordinates. The more difficult part is finding a good notation for the quantities involved.
You have two systems of coordinates: (x,y) are the cartesian coordinates of your display and (v,w) are the baricentric coordinates with respect to the vectors (c-a),(b-a) which determine another (non orthogonal) system.
What you need is to find the equation of the two lines in the (x,y) system, then it will be easy to project the point p on these lines.
To achieve this you could explicitly find the matrix to pass from (x,y) coordinates to (v,w) coordinates and back. The function you are using toBaryCoords makes this computation to find the coordinates (v,w) from (x,y) and we can reuse that function.
We want to find the coefficients of the transformation from world coordinates (x,y) to barycentric coordinates (v,w). It must be in the form
v = O_v + x_v * x + y_v * y
w = O_w + x_w * x + y_w * y
i.e.
(v,w) = (O_v,O_w) + (x_v,y_y) * (x,y)
and you can determine (O_v,O_w) by computing toBaryCoord(0,0), then find (x_v,x_w) by computing the coordinates of (1,0) and find (y_v,y_w)=toBaryCoord(1,0) - (O_v,O_w) and then find (y_v,y_w) by computing (y_v,y_w) = toBaryCoord(0,1)-(O_v,O_w).
This computation requires calling toBaryCoord three times, but actually the coefficients are computed inside that routine every time, so you could modify it to compute at once all six values.
The value of your function vp can be computed as follows. I will use f instead of v because we are using v for a baricenter coordinate. Hence in the following I mean f(x,y) = vp, fa = va, fb = vb, fc = vc.
You have:
f(v,w) = fa + (fb-fa)*v + (fc-fa)*w
i.e.
f(x,y) = fa + (fb-fa) (O_v + x_v * x + y_v * y) + (fc-fa) (O_w + x_w * x + y_w * y)
where (x,y) are the coordinates of your point p. You can check the validity of this equation by inserting the coordinates of the three vertices a, b, c and verify that you obtain the three values fa, fb and fc. Remember that the barycenter coordinates of a are (0,0) hence O_v + x_v * a_x + y_v * a_y = 0 and so on... (a_x and a_y are the x,y coordinates of the point a).
If you let
q = fa + (fb_fa)*O_v + (fc-fa)*O_w
fx = (fb-fa)*x_v + (fc-fa) * x_w
fy = (fb-fa)*y_v + (fc-fa) * y_w
you get
f(x,y) = q + fx*x + fy * y
Notice that q, fx and fy can be computed once from a,b,c,fa,fb,fc and you can reuse them if you only change the coordinates (x,y) of the point p.
Now if f(x,y)>max, you can easily project (x,y) on the line where max is achieved. The coordinates of the projection are:
(x',y') = (x,y) - [(x,y) * (fx,fy) - max + q]/[(fx,fy) * (fx,fy)] (fx,fy)
Now. You would like to have the code. Well here is some pseudo-code:
toBarycoord(Vector2(0,0),a,b,c,O);
toBarycoord(Vector2(1,0),a,b,c,X);
toBarycoord(Vector2(0,1),a,b,c,Y);
X.sub(O); // X = X - O
Y.sub(O); // Y = Y - O
V = Vector2(fb-fa,fc-fa);
q = fa + V.dot(O); // q = fa + V*O
N = Vector2(V.dot(X),V.dot(Y)); // N = (V*X,V*Y)
// p is the point to be considered
f = q + N.dot(p); // f = q + N*p
if (f > max) {
Vector2 tmp;
tmp.set(N);
tmp.multiply((N.dot(p) - max + q)/(N.dot(N))); // scalar multiplication
p.sub(tmp);
}
if (f < min) {
Vector2 tmp;
tmp.set(N);
tmp.multiply((N.dot(p) - min + q)/(N.dot(N))); // scalar multiplication
p.sum(tmp);
}
We think of the problem as follows: The three points are interpreted as a triangle floating in 3D space with the value being the Z-axis and the cartesian coordinates mapped to the X- and Y- axes respectively.
Then the question is to find the gradient of the plane that is defined by the three points. The lines where the plane intersects with the z = min and z = max planes are the lines you want to restrict your points to.
If you have found a point p where v(p) > max or v(p) < min we need to go in the direction of the steepest slope (the gradient) until v(p + k * g) = max or min respectively. g is the direction of the gradient and k is the factor we need to find. The coordinates you are looking for (in the cartesian coordinates) are the corresponding components of p + k * g.
In order to determine g we calculate the orthonormal vector that is perpendicular to the plane that is determined by the three points using the cross product:
// input: px, py, pz,
// output: p2x, p2y
// local variables
var v1x, v1y, v1z, v2x, v2y, v2z, nx, ny, nz, tp, k,
// two vectors pointing from b to a and c respectively
v1x = ax - bx;
v1y = ay - by;
v1z = az - bz;
v2x = cx - bx;
v2y = cy - by;
v2z = cz - bz;
// the cross poduct
nx = v2y * v1z - v2z * v1y;
ny = v2z * v1x - v2x * v1z;
nz = v2x * v1y - v2y * v1x;
// using the right triangle altitude theorem
// we can calculate the vector that is perpendicular to n
// in our triangle we are looking for q where p is nz, and h is sqrt(nx*nx+ny*ny)
// the theorem says p*q = h^2 so p = h^2 / q - we use tp to disambiguate with the point p - we need to negate the value as it points into the opposite Z direction
tp = -(nx*nx + ny*ny) / nz;
// now our vector g = (nx, ny, tp) points into the direction of the steepest slope
// and thus is perpendicular to the bounding lines
// given a point p (px, py, pz) we can now calculate the nearest point p2 (p2x, p2y, p2z) where min <= v(p2z) <= max
if (pz > max){
// find k
k = (max - pz) / tp;
p2x = px + k * nx;
p2y = py + k * ny;
// proof: p2z = v = pz + k * tp = pz + ((max - pz) / tp) * tp = pz + max - pz = max
} else if (pz < min){
// find k
k = (min - pz) / tp;
p2x = px + k * nx;
p2y = py + k * ny;
} else {
// already fits
p2x = px;
p2y = py;
}
Note that obviously if the triangle is vertically oriented (in 2D it's not a triangle anymore actually), nz becomes zero and tp cannot be calculated. That's because there are no more two lines where the value is min or max respectively. For this case you will have to choose another value on the remaining line or point.
I am building a tilt-based Arduino device that needs to detect the "fall-line" vector of the device once it is tilted in a particular orientation. By "fall-line" I'll use the following example:
Imagine a frictionless plane with a point mass in the the middle of it and a 3-axis accelerometer mounted in the plane so that the x and y axes of the accelerometer are parallel to the plane. At rest, the plane is flat and the point mass does not move. Once the plane is tilted, the point mass will move in a particular direction at a given acceleration due to gravity. I need to calculate the angle in the x-y plane that the mass will move toward and a magnitude measure corresponding to the acceleration in that direction.
I realise this is probably simple Newtonian mechanics, but I have no idea how to work this out.
The direction of the "fall-line" and the magnitude of the acceleration are both determined by the projection of the gravitational pull vector onto the plane. If the plane has a normal vector n, then the projector operator is P( n ) = 1 - nn, where 1 is the identity operator and nn is the outer (tensor) product of the normal vector with itself. The projection of the gravitational pull vector g is simply g' = P( n ).g = (1 - nn) g = g - (n . g) n, where the dot denotes inner (dot) product. Now you only have to choose a suitable orthonormal reference frame (ex, ey, ez), where ei is a unit vector along direction i. In this reference frame:
n = nx ex + ny ey + nz ez
g = gx ex + gy ey + gz ez
The dot product n . g is then:
n . g = nx * gx + ny * gy + nz * gz
A very suitable choice of a reference frame is one where ez is collinear with n. Then nx = 0 and ny = 0 and nz = ||n|| = 1, because normal vectors are of unit length. In this frame n . g is simply gz. The components of the projection of g are then:
g'x = gx
g'y = gy
g'z = 0
The direction of g' in the XY plane can be determined by the fact that for the dot product in orthonormal reference frames a . b = ||a|| ||b|| cos(a, b), where ||a|| denotes the norm (length) of a and cos(a, b) is the cosine of the angle between a and b. If you measure the angle from the X direction, then:
g' . ex = (gx ex + gy ey) . ex = gx = ||g'|| ||ex|| cos(g', ex) = g' cos(g', ex)
where g' = ||g'|| = sqrt(gx^2 + gy^2). The angle is simply arccos(gx/g'), i.e. arc-cosine of the ratio between the X component of the gravity pull vector and the magnitude of its projection onto the XY plane:
angle = arccos[gx / sqrt(gx^2 + gy^2)]
The magnitude of the acceleration is proportional to the magnitude of g', which is (once again):
g' = ||g'|| = sqrt(gx^2 + gy^2)
Now the nice thing is that all accelerometers measure the components of the gravity field in a reference frame that usually have ex aligned with the height (or the width) of the device, the ex aligned with the width (or the height) of the device and ez is perpendicular to the surface of the device, which matches exactly the reference frame, where ez is collinear with the plane normal. If this is not the case with your Arduino device, simply rotate the accelerometer and align it as needed.
I need a function that returns points on a circle in three dimensions.
The circle should "cap" a line segment defined by points A and B and it's radius. each cap is perpendicular to the line segment. and centered at one of the endpoints.
Here is a shitty diagram
Let N be the unit vector in the direction from A to B, i.e., N = (B-A) / length(A-B). The first step is to find two more vectors X and Y such that {N, X, Y} form a basis. That means you want two more vectors so that all pairs of {N, X, Y} are perpendicular to each other and also so that they are all unit vectors. Another way to think about this is that you want to create a new coordinate system whose x-axis lines up with the line segment. You need to find vectors pointing in the direction of the y-axis and z-axis.
Note that there are infinitely many choices for X and Y. You just need to somehow find two that work.
One way to do this is to first find vectors {N, W, V} where N is from above and W and V are two of (1,0,0), (0,1,0), and (0,0,1). Pick the two vectors for W and V that correspond to the smallest coordinates of N. So if N = (.31, .95, 0) then you pick (1,0,0) and (0,0,1) for W and V. (Math geek note: This way of picking W and V ensures that {N,W,V} spans R^3). Then you apply the Gram-Schmidt process to {N, W, V} to get vectors {N, X, Y} as above. Note that you need the vector N to be the first vector so that it doesn't get changed by the process.
So now you have two vectors that are perpendicular to the line segment and perpendicular to each other. This means the points on the circle around A are X * cos t + Y * sin t + A where 0 <= t < 2 * pi. This is exactly like the usual description of a circle in two dimensions; it is just written in the new coordinate system described above.
As David Norman noted the crux is to find two orthogonal unit vectors X,Y that are orthogonal to N. However I think a simpler way to compute these is by finding the householder reflection Q that maps N to a multiple of (1,0,0) and then to take as X the image of (0,1,0) under Q and Y as the image of (0,0,1) under Q. While this might sound complicated it comes down to:
s = (N[0] > 0.0) ? 1.0 : -1.0
t = N[0] + s; f = -1.0/(s*t);
X[0] = f*N[1]*t; X[1] = 1 + f*N[1]*N[1]; X[2] = f*N[1]*N[2];
Y[0] = f*N[2]*t; Y[1] = f*N[1]*N[2]; Y[2] = 1 + f*N[2]*N[2];