About My Profile Image

I've been using an opaque wire frame helix as my profile image during my time on Brilliant. About ten years ago, I became interested in how to render and display images, particularly ones in which certain parts of an object block other parts of the image from view. Around that same time, I wrote some very primitive Python code to generate the opaque helix. At a high level, it works like this;

1) There are two basic elements to the image: points and surfaces
2) The points form the wire frame object (you can see the individual points if you look closely)
3) The surfaces are similar to rectangles, and each "rectangle" is divided into two triangles
4) To display the image, a line (or "ray") is traced from every point to an observer's location
5) The ray's path is checked for intersections with every single triangle
6) If the ray does not intersect any triangle, the corresponding point is displayed; otherwise, it is hidden

I have attached the Python code, as well as some images of the helix in different orientations. The exact functioning of the code is beyond the scope of this note, but it is possible to generate images of the helix in different orientations by running with different values of the angular parameters "disprotxy", "disprotxz", and "disprotyz" (near the top of the code).

The Python code spits out (x,y)(x,y) pairs, which can be visualized as a scatter plot in Microsoft Excel. Click the images below to enlarge them.

I'm somewhat proud to say that the code does not call any fancy libraries of image processing routines. It just uses elementary math functions, loops, and arrays.

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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,Nf-1):                               # 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][k-1][0] - xp) / 10.0
        diffy = (toroid[j][k-1][1] - yp) / 10.0
        diffz = (toroid[j][k-1][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-1][0] - xp) / 10.0
        diffy = (toroid[j+1][k-1][1] - yp) / 10.0
        diffz = (toroid[j+1][k-1][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,Nf-1):                               # 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][k-1][0],toroid[j][k-1][1],toroid[j][k-1][2]],[toroid[j+1][k][0],toroid[j+1][k][1],toroid[j+1][k][2]]])
        block.append([[toroid[j+1][k-1][0],toroid[j+1][k-1][1],toroid[j+1][k-1][2]],[toroid[j][k-1][0],toroid[j][k-1][1],toroid[j][k-1][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

Note by Steven Chase
5 months, 1 week ago

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1 vote

  Easy Math Editor

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Comments

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Top Newest

@Neeraj Anand Badgujar Here it is, as requested

@Karan Chatrath In case you are interested

Steven Chase - 5 months, 1 week ago

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@Steven Chase VERYBEAUTIFULVERY 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.

A Former Brilliant Member - 5 months, 1 week ago

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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.

A Former Brilliant Member - 5 months, 1 week ago

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I have added a green version

Steven Chase - 5 months, 1 week ago

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@Steven Chase @Steven Chase This Images are very nice. Thanks for the green version.

A Former Brilliant Member - 5 months, 1 week ago

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@A Former Brilliant Member You're welcome. Glad you like them

Steven Chase - 5 months, 1 week ago

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@Steven Chase @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.

A Former Brilliant Member - 5 months, 1 week ago

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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.

A Former Brilliant Member - 5 months, 1 week ago

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Yeah, I have some more. I'll post those later as well

Steven Chase - 5 months, 1 week ago

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@Steven Chase Sir can you add YellowOrange\textcolor{#FCA04A}{YellowOrange} and Red\textcolor{#D61F06}{Red} ? Please

A Former Brilliant Member - 4 months, 4 weeks ago

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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.

Karan Chatrath - 5 months, 1 week ago

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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.

Krishna Karthik - 5 months, 1 week ago

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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?

Steven Chase - 5 months, 1 week ago

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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?

Krishna Karthik - 5 months, 1 week ago

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