How does one create a cube with Julia and Makie? - julia
The following Julia program uses Makie to create the image shown below, a "cube with holes." However, I would like to eliminate the holes, and also make the cube nearer the shape of an actual cube, with only slightly rounded corners and edges. I've tried changing the exponent to values other than ^2 and also passing the range directly to the volume! function, to no avail. Examples of the types of cubes desired can be found in three-rounded-box images at CodeSandbox.
using Makie, GLMakie
fig = Figure()
range = LinRange(-1, 1, 100) # 100-element LinRange{Float64, Int64}
cube = [ (x.^2 + y.^2 + z.^2) for x = range, y = range, z = range ] # 100×100×100 Array{Float64, 3}
ax = Axis3( fig[1,1], aspect = :data, azimuth = 1.17 * pi, viewmode = :fit, title = "Cube" )
volume!( cube, algorithm = :iso, isorange = 0.05, isovalue = 1.7 )
fig
Like this?
using Makie, GLMakie
fig = Figure()
range = LinRange(-1, 1, 100) # 100-element LinRange{Float64, Int64}
cube = [ (abs.(x).^10 + abs.(y).^10 + abs.(z).^10) for x = range, y = range, z = range ] # 100×100×100 Array{Float64, 3}
ax = Axis3( fig[1,1], aspect = :data, azimuth = 1.17 * pi, viewmode = :fit, title = "Cube" )
volume!( cube, algorithm = :iso, isorange = 0.05, isovalue = 1 )
fig
Or you can use a parametric surface, a supershape:
using Makie, GLMakie
function r(phi; a, b, m, n1, n2, n3)
return 1 / (abs(cos(m*phi/4)/a)^n2 + (abs(sin(m*phi/4)/b)^n3))^(1/n1)
end
phi = (-pi/2):0.01:(pi/2)
theta = (-pi):0.01:(pi)
x = [
r(theta; a=1, b=1, m=4, n1=10, n2=10, n3=10) * cos(theta) *
r(phi; a=1, b=1, m=4, n1=10, n2=10, n3=10) * cos(phi)
for phi in phi, theta in theta
]
y = [
r(theta; a=1, b=1, m=4, n1=10, n2=10, n3=10) * sin(theta) *
r(phi; a=1, b=1, m=4, n1=10, n2=10, n3=10) * cos(phi)
for phi in phi, theta in theta
]
z = [
r(phi; a=1, b=1, m=4, n1=10, n2=10, n3=10) * sin(phi)
for phi in phi, theta in theta
]
fig, _ = surface(x, y, z)
fig
Better to use a function:
function r(phi; a, b, m, n1, n2, n3)
return 1 / (abs(cos(m*phi/4)/a)^n2 + (abs(sin(m*phi/4)/b)^n3))^(1/n1)
end
function supershape(p1, p2)
phi = (-pi/2):0.01:(pi/2)
theta = (-pi):0.01:(pi)
x = [
r(theta; a=p1.a, b=p1.b, m=p1.m, n1=p1.n1, n2=p1.n2, n3=p1.n3) * cos(theta) *
r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * cos(phi)
for phi in phi, theta in theta
]
y = [
r(theta; a=p1.a, b=p1.b, m=p1.m, n1=p1.n1, n2=p1.n2, n3=p1.n3) * sin(theta) *
r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * cos(phi)
for phi in phi, theta in theta
]
z = [
r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * sin(phi)
for phi in phi, theta in theta
]
return (x = x, y = y, z = z)
end
params1 = (a=1, b=1, m=4, n1=10, n2=10, n3=10)
params2 = params1
x, y, z = supershape(params1, params2)
fig, _ = surface(x, y, z)
EDIT
The rendering is not nice with surface. It's better to do a mesh:
function parametricMesh(f, umin, umax, vmin, vmax, nu, nv)
u_ = LinRange(umin, umax, nu)
v_ = LinRange(vmin, vmax, nv)
vertices = Array{Float64}(undef, nu*nv, 3)
triangles = Array{Int64}(undef, 2*(nu-1)*(nv-1), 3)
k = 1
for i in 1:nv
v = v_[i]
for j in 1:nu
vertices[k,:] = f(u_[j], v)
k = k+1
end
end
k = 1
for i in 1:(nv-1)
for j in 1:(nu-1)
a = (i-1) * nu + j
b = (i-1) * nu + j + 1
c = i*nu + j + 1
d = i*nu + j
triangles[2*(k-1)+1,:] = [b, a, d]
triangles[2*k,:] = [c, b, d]
k = k+1
end
end
return (vertices = vertices, triangles = triangles)
end
function r(phi; a, b, m, n1, n2, n3)
return 1 / (abs(cos(m*phi/4)/a)^n2 + (abs(sin(m*phi/4)/b)^n3))^(1/n1)
end
p1 = (a=1, b=1, m=4, n1=10, n2=10, n3=10)
p2 = p1
function f(phi, theta)
x = r(theta; a=p1.a, b=p1.b, m=p1.m, n1=p1.n1, n2=p1.n2, n3=p1.n3) * cos(theta) *
r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * cos(phi)
y = r(theta; a=p1.a, b=p1.b, m=p1.m, n1=p1.n1, n2=p1.n2, n3=p1.n3) * sin(theta) *
r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * cos(phi)
z = r(phi; a=p2.a, b=p2.b, m=p2.m, n1=p2.n1, n2=p2.n2, n3=p2.n3) * sin(phi)
return [x, y, z]
end
vertices, triangles = parametricMesh(f, -pi/2, pi/2, -pi, pi, 50, 50)
mesh(vertices, triangles, color = "yellow")
Related
How to create a Matlab pumpkin in R?
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Coding likelihood and log-likelihood function in r to perform optimization
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Your concern appears to be about the warning message In log(A+B+C+D): NaNs produced Such warnings are usually harmless — it just means that the optimization algorithm tried a set of parameters somewhere along the way that violated the condition A+B+C+D ≥ 0. Since these are reasonably complex expressions it would take a little bit of effort to figure out how one might constrain the parameters (or reparameterize the function, e.g. fitting some of the parameters on the log scale) to avoid the warning, but taking a guess that keeping the parameters non-negative will help, we can try using the L-BFGS-B algorithm (which is the only algorithm available in optim() that allows multidimensional bounded optimization). r1 <- optim(par = c(1,2,1), fn = log.likelihood.x, dat = data) r2 <- optim(par = c(1,2,1), fn = log.likelihood.x, lower = rep(0,3), method = "L-BFGS-B", dat = data) The second does not generate warnings, and the results are close (if not identical): all.equal(r1$par, r2$par) ## "Mean relative difference: 0.0001451953" You might want to use bbmle, which has some additional features for likelihood modeling: library(bbmle) fwrap <- function(x) log.likelihood.x(x, dat = data) parnames(fwrap) <- c("lambda1", "lambda2", "theta") m1 <- mle2(fwrap, start = c(lambda1 = 1, lambda2 = 2, theta = 1), vecpar = TRUE, method = "L-BFGS-B", lower = c(0, 0, -0.5)) pp <- profile(m1) plot(pp) confint(pp) confint(m1, method = "quad")
Can't plot the complete Cobweb diagram in Scilab
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Mapping image on spherical surface
In simple words i need to map a image to be use in a spherical surface. I being trying to do this for several hours. searching in google I din't find any proper solution (explained for dumb people). I thinks the code from this link: https://www.codeproject.com/articles/19712/mapping-images-on-spherical-surfaces-using-c is what i need. but (i think everything is alright) can make it work in Julia. This is my code so far: image = brightNoise(height,width,seed,rand=true) arr = Array{Float64}(height,width) function MapCoordinate(i1, i2,w1,w2,p) return ((p - i1) / (i2 - i1)) * (w2 - w1) + w1 end function Rotate(angle, axisA, axisB) return axisA * cos(angle) - axisB * sin(angle),axisA * sin(angle) + axisB * cos(angle) end phi0 = 0.0 phi1 = pi theta0 = 0.0 theta1 = 2.0*pi radius = 50 arr = Array{Float64}(height,width) for i= 1:size(image)[1] for j= 1:size(image)[2] #map the angles from image coordinates theta = MapCoordinate(0.0,width - 1,theta1, theta0, i) phi = MapCoordinate(0.0,height - 1,phi0,phi1, j) #find the cartesian coordinates x = radius * sin(phi) * cos(theta); y = radius * sin(phi) * sin(theta); z = radius * cos(phi); #apply rotation around X and Y axis to reposition the sphere y,z=Rotate(1.5, y, z); x,z=Rotate(pi/2, x, z); #plot only positive points if (z > 0) color = image[i,j] ix = floor(Int64,x) iy = floor(Int64,y) arr[ix,iy] = color println(ix,iy) end end end The image is just a black and white noise generated in Julia, i need to wrap a sphere with it.
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How to randomize points on a sphere surface evenly?
Im trying to make stars on the sky, but the stars distribution isnt even. This is what i tried: rx = rand(0.0f, PI*2.0f); ry = rand(0.0f, PI); x = sin(ry)*sin(rx)*range; y = sin(ry)*cos(rx)*range; z = cos(ry)*range; Which results to: img http://img716.imageshack.us/img716/3320/sphererandom.jpg And: rx = rand(-1.0f, 1.0f); ry = rand(-1.0f, 1.0f); rz = rand(-1.0f, 1.0f); x = rx*range; y = ry*range; z = rz*range; Which results to: img2 http://img710.imageshack.us/img710/5152/squarerandom.jpg (doesnt make a sphere, but opengl will not tell a difference, though). As you can see, there is always some "corner" where are more points in average. How can i create random points on a sphere where the points will be distributed evenly?
you can do z = rand(-1, 1) rxy = sqrt(1 - z*z) phi = rand(0, 2*PI) x = rxy * cos(phi) y = rxy * sin(phi) Here rand(u,v) draws a uniform random from interal [u,v]
You don't need trigonometry if you can generate random gaussian variables, you can do (pseudocode) x <- gauss() y <- gauss() z <- gauss() norm <- sqrt(x^2 + y^2 + z^2) result = (x / norm, y / norm, z / norm) Or draw points inside the unit cube until one of them is inside the unit ball, then normalize: double x, y, z; do { x = rand(-1, 1); y = rand(-1, 1); z = rand(-1, 1); } while (x * x + y * y + z * z > 1); double norm = sqrt(x * x + y * y + z * z); x / norm; y /= norm; z /= norm;
It looks like you can see that it's the cartesian coordinates that are creating the concentrations. Here is an explanation of one right (and wrong) way to get a proper distribution.