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334  import math
import copy
numrev = 5 # Number of revolutions
numper = 36 # Number of frames per revolution
Rt = 1.0 # Toroid radius
Rcs = .3 # Toroid cross section
Nf = numrev * numper # Number of cross section frames
Ns = 8 # Number of sides for the cross section
thetat = 0.0 # Angle in the xy plane at which a cross sectional frame is located (toroid originally lies in the xy plane with z axis as the central axis)
dthetat = 2.0 * math.pi / numper # xy plane angular separation between cross sectional frames
thetacs = 0.0 # Angle for determinng cross section points
dthetacs = 2.0 * math.pi / Ns # Angular separation between the points in a cross section
disprotxy = (10.0 / 180.0) * math.pi # Rotation angle in the xy plane for display #10 is default
disprotxz = (40.0 / 180.0) * math.pi # Rotation angle in the xz plane for display # 40 is default
disprotyz = (50.0 / 180.0) * math.pi # Rotation angle in the yz plane for display # 50 is default
coord = [] # xyz coordinates of a point within the toroid / frame
frame = [] # Array containing coordinates for all points within a frame
toroid = [] # Array containing all frames within the toroid
points = [] # Master array of all points
block = [] # Blocking array
# Create the frames
# Initialize xyz coordinates
x = 0.0
y = 0.0
z = 0.0
for k in range(0,Nf): # For each frame
frame = []
for j in range(0,Ns): # Assign coordinates to every point within the frame and append to the frame array
coord = []
x = (Rt + Rcs * math.cos(thetacs))*math.cos(k*dthetat)
y = (Rt + Rcs * math.cos(thetacs))*math.sin(k*dthetat)
z = Rcs * math.sin(thetacs) + k * (3.0 * Rcs)/(numper)
coord.append(x)
coord.append(y)
coord.append(z)
frame.append(coord[:])
thetacs = thetacs + dthetacs
toroid.append(frame[:]) # Append first frame to the toroid array
# Apply display rotations to all points within the toroid
x = 0.0
y = 0.0
z = 0.0
Rxy = 0.0
thetaxy = 0.0
Rxz = 0.0
thetaxz = 0.0
Ryz = 0.0
thetayz = 0.0
for j in range(0,Nf): # For every frame
for k in range(0,Ns): # For every point
x = toroid[j][k][0]
y = toroid[j][k][1]
z = toroid[j][k][2]
Rxy = math.hypot(x,y)
thetaxy = math.atan2(y,x)
x = Rxy * math.cos(thetaxy + disprotxy)
y = Rxy * math.sin(thetaxy + disprotxy)
Rxz = math.hypot(x,z)
thetaxz = math.atan2(z,x)
x = Rxz * math.cos(thetaxz + disprotxz)
z = Rxz * math.sin(thetaxz + disprotxz)
Ryz = math.hypot(z,y)
thetayz = math.atan2(z,y)
y = Ryz * math.cos(thetayz + disprotyz)
z = Ryz * math.sin(thetayz + disprotyz)
toroid[j][k][0] = x
toroid[j][k][1] = y
toroid[j][k][2] = z
#print toroid[j][k][0],toroid[j][k][1],toroid[j][k][2]
# Form master point array
for j in range(0,Nf1): # For every frame
for k in range(0,Ns): # For every point
xp = toroid[j][k][0]
yp = toroid[j][k][1]
zp = toroid[j][k][2]
diffx = (toroid[j][k1][0]  xp) / 10.0
diffy = (toroid[j][k1][1]  yp) / 10.0
diffz = (toroid[j][k1][2]  zp) / 10.0
for m in range(0,10):
points.append([xp,yp,zp])
xp = xp + diffx
yp = yp + diffy
zp = zp + diffz
diffx = (toroid[j+1][k1][0]  xp) / 10.0
diffy = (toroid[j+1][k1][1]  yp) / 10.0
diffz = (toroid[j+1][k1][2]  zp) / 10.0
for m in range(0,10):
points.append([xp,yp,zp])
xp = xp + diffx
yp = yp + diffy
zp = zp + diffz
diffx = (toroid[j+1][k][0]  xp) / 10.0
diffy = (toroid[j+1][k][1]  yp) / 10.0
diffz = (toroid[j+1][k][2]  zp) / 10.0
for m in range(0,10):
points.append([xp,yp,zp])
xp = xp + diffx
yp = yp + diffy
zp = zp + diffz
diffx = (toroid[j][k][0]  xp) / 10.0
diffy = (toroid[j][k][1]  yp) / 10.0
diffz = (toroid[j][k][2]  zp) / 10.0
for m in range(0,10):
points.append([xp,yp,zp])
xp = xp + diffx
yp = yp + diffy
zp = zp + diffz
#for j in range(0,len(points)):
#print points[j][0],points[j][1]
# Form blocking surfaces
for j in range(0,Nf1): # For every frame
for k in range(0,Ns): # For every cross section point
block.append([[toroid[j][k][0],toroid[j][k][1],toroid[j][k][2]],[toroid[j][k1][0],toroid[j][k1][1],toroid[j][k1][2]],[toroid[j+1][k][0],toroid[j+1][k][1],toroid[j+1][k][2]]])
block.append([[toroid[j+1][k1][0],toroid[j+1][k1][1],toroid[j+1][k1][2]],[toroid[j][k1][0],toroid[j][k1][1],toroid[j][k1][2]],[toroid[j+1][k][0],toroid[j+1][k][1],toroid[j+1][k][2]]])
Robs = 10.0
thetaobs = 0.0
phiobs = 0.0
screendist = 9.0
scsize = 1.0
screen = [[screendist,0.0,0.0],[math.hypot(scsize,screendist),math.atan2(scsize,screendist),0.0],[math.hypot(scsize,screendist),0.0,math.atan2(scsize,screendist)]]
nn = 50.0
dtheta = (nn * 7.2 / 180.0) * math.pi
dphi = (0.0 / 180.0) * math.pi
thetaobs = thetaobs + dtheta
screen[0][1] = screen[0][1] + dtheta
screen[1][1] = screen[1][1] + dtheta
screen[2][1] = screen[2][1] + dtheta
phiobs = phiobs + dphi
screen[0][2] = screen[0][2] + dphi
screen[1][2] = screen[1][2] + dphi
screen[2][2] = screen[2][2] + dphi
Ox = Robs * math.sin(thetaobs) * math.cos(phiobs)
Oy = Robs * math.sin(phiobs)
Oz = Robs * math.cos(thetaobs) * math.cos(phiobs)
dummy0 = screen[0][:]
dummy1 = screen[1][:]
dummy2 = screen[2][:]
screen[0][0] = dummy0[0] * math.sin(dummy0[1]) * math.cos(dummy0[2])
screen[0][1] = dummy0[0] * math.sin(dummy0[2])
screen[0][2] = dummy0[0] * math.cos(dummy0[1]) * math.cos(dummy0[2])
screen[1][0] = dummy1[0] * math.sin(dummy1[1]) * math.cos(dummy1[2])
screen[1][1] = dummy1[0] * math.sin(dummy1[2])
screen[1][2] = dummy1[0] * math.cos(dummy1[1]) * math.cos(dummy1[2])
screen[2][0] = dummy2[0] * math.sin(dummy2[1]) * math.cos(dummy2[2])
screen[2][1] = dummy2[0] * math.sin(dummy2[2])
screen[2][2] = dummy2[0] * math.cos(dummy2[1]) * math.cos(dummy2[2])
#print Ox,Oy,Oz
#print screen
for j in range(0,len(points)): # For every point
disp = 1
for m in range(0,len(block)): # Compare every point against each surface to find intersections
B1x = (block[m][1][0]  block[m][0][0])
B1y = (block[m][1][1]  block[m][0][1])
B1z = (block[m][1][2]  block[m][0][2])
B2x = (block[m][2][0]  block[m][0][0])
B2y = (block[m][2][1]  block[m][0][1])
B2z = (block[m][2][2]  block[m][0][2])
Nx = B1y * B2z  B2y * B1z
Ny = B2x * B1z  B1x * B2z
Nz = B1x * B2y  B2x * B1y
Px = points[j][0]
Py = points[j][1]
Pz = points[j][2]
Cx = block[m][0][0]
Cy = block[m][0][1]
Cz = block[m][0][2]
alpha = (Nx * (Cx  Px) + Ny * (Cy  Py) + Nz * (Cz  Pz)) / (Nx * (Ox  Px) + Ny * (Oy  Py) + Nz * (Oz  Pz))
if (alpha > 0.0) and (alpha <=1.0): # if the ray intersects the infinte planar region between P and O
Rx = Px + alpha * (Ox  Px)
Ry = Py + alpha * (Oy  Py)
Rz = Pz + alpha * (Oz  Pz)
vdeltax = Rx  Cx
vdeltay = Ry  Cy
vdeltaz = Rz  Cz
B1x = B1x + .000000001
B2y = B2y + .000000001
sigma = (vdeltay  (B1y * vdeltax) / B1x) / (B2y  (B1y * B2x) / B1x)
gamma = (vdeltax  sigma * B2x) / B1x
if (sigma > 0.0) and (gamma > 0.0) and (sigma + gamma < 1) and (alpha > .007): # if the ray intersects a surface, don't display
disp = 0
#print j,m
if disp == 1:
B1x = (screen[1][0]  screen[0][0]) / scsize
B1y = (screen[1][1]  screen[0][1]) / scsize
B1z = (screen[1][2]  screen[0][2]) / scsize
B2x = (screen[2][0]  screen[0][0]) / scsize
B2y = (screen[2][1]  screen[0][1]) / scsize
B2z = (screen[2][2]  screen[0][2]) / scsize
#print B1x,B1y,B1z,B2x,B2y,B2z
Nx = B1y * B2z  B2y * B1z
Ny = B2x * B1z  B1x * B2z
Nz = B1x * B2y  B2x * B1y
#print Nx,Ny,Nz
Px = points[j][0]
Py = points[j][1]
Pz = points[j][2]
Cx = screen[0][0]
Cy = screen[0][1]
Cz = screen[0][2]
#print Cx,Cy,Cz
alpha = (Nx * (Cx  Px) + Ny * (Cy  Py) + Nz * (Cz  Pz)) / (Nx * (Ox  Px) + Ny * (Oy  Py) + Nz * (Oz  Pz))
#print alpha
Rx = Px + alpha * (Ox  Px)
Ry = Py + alpha * (Oy  Py)
Rz = Pz + alpha * (Oz  Pz)
vdeltax = Rx  Cx
vdeltay = Ry  Cy
vdeltaz = Rz  Cz
#print vdeltax,vdeltay,vdeltaz
B1x = B1x + .000000001
B2y = B2y + .000000001
sigma = (vdeltay  (B1y * vdeltax) / B1x) / (B2y  (B1y * B2x) / B1x)
gamma = (vdeltax  sigma * B2x) / B1x
if math.fabs(sigma) < scsize and math.fabs(gamma) < scsize:
print gamma,sigma

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Comments
Sort by:
Top Newest@Neeraj Anand Badgujar Here it is, as requested
@Karan Chatrath In case you are interested
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@Steven Chase $VERY BEAUTIFUL$ I was thinking that this profile image you have taken from somewhere but after seeing this I realized that it is made by you only.
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@Steven Chase in your profile the image is of light blue colour. Can you please give me the image in green colour.
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I have added a green version
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@Steven Chase This Images are very nice. Thanks for the green version.
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@Steven Chase yesterday in evening when I shared these images to my friends ,they were gone mad. But they don't use brilliant. They said me to say Thanks to the creator of this images. So, Thank you. Can you upload more image like this now.
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@Steven Chase Sir in phython programming we can make this types of good images which we can't get in the web. Beside this image . Is there any more crazy images. I like this types of physics crazy images.
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Yeah, I have some more. I'll post those later as well
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@Steven Chase Sir can you add $\textcolor{#FCA04A}{YellowOrange}$ and $\textcolor{#D61F06}{Red}$ ? Please
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Thank you for sharing this code. These graphics are pretty impressive considering you used just a python script to generate them. As for my profile picture, I just plotted two solutions of the Lorenz equations one over another in a dark background.
I will take a closer look at the code a little later as there seems to be a lot to unpack there. Pretty sure I'll learn something new in the process.
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Nice image! I would personally do it using WebGL or OpenGL. Most of your graphics code in python would run on the CPU, so I think it would be a rather slow render for accuracy. That way GLSL is much faster as it makes use of the GPU. Very cool! I love python so much... I should learn CUDA python sometime. Maybe you should find the points using CUDA and then paste it into Microsoft Excel.
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Yeah, this program takes a ridiculously long time to run, but I've got time, so it's OK. Does the CUDA processing improve speed for serial algorithms, or only for parallel ones?
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You can parallelise the algorithm by coding a "kernel", so the computation will be much, much faster when all are computed simultaneously. I have been using computational methods to solve your problems recently. Same thing I noticed with the question "Curly Trajectory"; my code based on Euler Integration took a whopping 2 minutes to compile. CPU based
If you use GLSL and WebGL, and you can compute all the data points and store them in the vertex buffer in a parallel manner without going through the details of how OpenGL does that on your GPU.
It's so cool because there are in built functions in WebGL which can allow you to achieve the wireframe. You can do it without CUDA, because anyway you are producing a graphical output, which OpenGL excels at. What you have done in your python code would be done by C or C++, when defining the rasterisation and painting of the pixels in high resolution by WebGL. You've put together a very clever piece of code! You've done it from scratch. Well done man!
By the way, what are the specs of your PC?
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@Steven Chase Just came here to see this beautiful images last time.
When you are going to share the files of your solutions and problems?
Do you find an alternative approach to share files ??
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