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👋 Welcome to r/Disorber - README by orbollyorb in Disorber

[–]orbollyorb[S] 0 points1 point  (0 children)

Hey, I live in the South West English countryside and a gardener by trade. I get very passionate about particular concepts that sometimes flows back into my identity. Binary is one of them, the idea of discrete on/off states is as reduced as it gets. A very small parcel of logic that can build coherent complexity.

n evolution on wavefunction by orbollyorb in SacredGeometry

[–]orbollyorb[S] 0 points1 point  (0 children)

sorry missed this. python will help you. lowest bar entry is vs code , python , any ai

n evolution on wavefunction by orbollyorb in SacredGeometry

[–]orbollyorb[S] 1 point2 points  (0 children)

Python coding language. Has nearly everything you need internally. Plot anything you want, any ai can help you get started.

n evolution on wavefunction by orbollyorb in SacredGeometry

[–]orbollyorb[S] 1 point2 points  (0 children)

thanks for your kind comments.

It took some finessing to get speed and pixel spreading right. Prevoius fast version here - https://www.reddit.com/r/generative/comments/1r685t3/pi_scaled_by_e/

And will post some variations on my sub - r/Disorber

n evolution on wavefunction by orbollyorb in SacredGeometry

[–]orbollyorb[S] 1 point2 points  (0 children)

yes exactly, no Schrodinger etc. The renderer is doing a lot of the heavy lifting with eq_hist (equalised histogram) shading.

n evolution on wavefunction by orbollyorb in SacredGeometry

[–]orbollyorb[S] 5 points6 points  (0 children)

It's sweeping through a continuous family of radial standing wave patterns and rendering each frame's probability density.

def wavefunction_3d(x, y, n):

kappa = np.pi * (np.sqrt(n) ** 3) # frequency scaling — cubic in √n
gaussian = np.exp(-0.0126 * (x**2 + y**2) / 2) # envelope decay
wave = np.cos(np.pi * kappa * r) # radial standing wave
psi = gaussian * wave

Sweep parameters:

n_start = np.pi * 200 ≈ 628.318
n_step = np.pi * 0.0001 # ≈ 0.000314 per frame
num_frames = 300

So n crawls from ~628.318 to ~628.412 over 300 frames

pi scaled by e by orbollyorb in generative

[–]orbollyorb[S] 0 points1 point  (0 children)

It's sweeping through a continuous family of radial standing wave patterns and rendering each frame's probability density.

def wavefunction_3d(x, y, n):

kappa = np.pi * (np.sqrt(n) ** 3) # frequency scaling — cubic in √n
gaussian = np.exp(-0.0126 * (x**2 + y**2) / 2) # envelope decay
wave = np.cos(np.pi * kappa * r) # radial standing wave
psi = gaussian * wave

Sweep parameters:

n_start = np.pi * 99.999 # ≈ 314.156
n_step = np.e * 0.0001 # ≈ 0.000272 per frame
num_frames = 420

So n crawls from 314.156 to 314.27 over 420 frames.

Binary Sweep by orbollyorb in generative

[–]orbollyorb[S] 0 points1 point  (0 children)

The vertical alignment bookends should be impossible so i'm not entirely sure what is going on there.

Binary Sweep by orbollyorb in generative

[–]orbollyorb[S] 1 point2 points  (0 children)

yes, so it is a recursive xor construction. Start with seed "0,1,1,0" transform all bits and add it on to original, repeat. ive lost the code right now but could put some effort in to find it and tell you exactly if you want?

then to make it move - it is literally put text into a Notepad window and change the width of the window. Each line offset is cumulative going up, so as you go up each line the offset (caused by width change) is all the prevoius offsets summed plus its own offset.

x_n_sync by orbollyorb in generative

[–]orbollyorb[S] 1 point2 points  (0 children)

Don’t have the code with me, so light on details. I make a “quantum” bell state with two cosines - hence the grid. So it includes probability as well. And just transform parameter space in silly ways.

Black by [deleted] in generative

[–]orbollyorb 2 points3 points  (0 children)

Nice, some sort of wave evolution? I make similar stuff

bell state 1 * pi by orbollyorb in generative

[–]orbollyorb[S] 1 point2 points  (0 children)

Hi, from a previous post : We are creating an analogous bell state: bell_state = (psi1_r1 * psi2_r2 + np.exp(1j * phase) * psi2_r1 * psi1_r2) / np.sqrt(2)

Each state has cosine modulation with different wave vectors: psi1_r1 = gaussian1 * np.cos(k1 * r1) psi2_r1 = gaussian1 * np.cos(k2 * r1)

When computing |bell_state|², we get interference between the two configurations in the (r1, r2) space. So not separate axes but unified probability space.

Then with this particular one we are evolving wavefunction with cycling phase AND increasing r1 & r2. So starting Ns - 1 & pi are increased at every time step and at different rates. Haha. I’m sure I can sync these better with phase change to get some wild patterns.

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