Scruff AI
56c71c87b2
- cuda/ — main LBM kernel (khra_gixx_1024_v5.cu) - navigator/ — lattice_observer, golden_weave, bridges, mock daemon - scripts/ — compile, start, launch, setup (paths updated) - docs/ — system manual - archive/ — everything else (old kernels, inquiries, experiments) - README.md — full setup guide: requirements, quick start, use your own LLM - removed training/ entirely (broken LoRA scripts + datasets) - .gitignore: exclude build/ logs/ training/ *.jsonl
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
#!/usr/bin/env python3
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"""
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Khra'gixx Encrypted Field - Mathematical Visualization
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Raw encoding of lattice data, phi ratios, fractal structure
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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import math
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# Constants
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PHI = (1 + math.sqrt(5)) / 2 # 1.618...
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SIZE = 1024 # Native lattice resolution
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# Create custom colormap: obsidian (void) to amber to white (peak)
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colors = [
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(0.0, 0.0, 0.0), # Black - void
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(0.2, 0.1, 0.0), # Dark brown
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(0.6, 0.3, 0.0), # Amber
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(0.9, 0.6, 0.2), # Golden
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(1.0, 0.9, 0.7), # White-hot
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]
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cmap = LinearSegmentedColormap.from_list('khragixx', colors)
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# Generate the lattice pattern
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def generate_lattice(size):
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"""Generate diagonal checkerboard lattice - discrete nodes, not waves"""
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# Create grid
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x = np.arange(size)
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y = np.arange(size)
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X, Y = np.meshgrid(x, y)
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# Diagonal checkerboard: (x + y) mod period
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# Khra period = 128, Gixx period = 8
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khra_period = int(128 * PHI / 2) # Scaled by phi
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gixx_period = 8
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# Diagonal pattern
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diagonal = (X + Y)
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# Checkerboard: alternating peaks and valleys
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# Use modulo to create discrete cells
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checker = (diagonal // khra_period) % 2
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# Fine grain modulation (Gixx wave within cells)
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fine = np.sin(2 * np.pi * diagonal / gixx_period) * 0.2
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# Combine: discrete checkerboard + fine modulation
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pattern = checker.astype(float) + fine
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pattern = (pattern - pattern.min()) / (pattern.max() - pattern.min())
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return pattern
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# Generate the encoded field
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field = generate_lattice(SIZE)
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# Create figure
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fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
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im = ax.imshow(field, cmap=cmap, interpolation='nearest')
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ax.set_axis_off()
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# Add mathematical annotations
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# Encode key ratios as positions
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mercury_pos = int(SIZE * 0.387 / 30) # Scaled position
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earth_pos = int(SIZE * 1.0 / 30)
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jupiter_pos = int(SIZE * 5.2 / 30)
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# Mark phi-harmonic nodes
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for n in range(1, 6):
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pos = int(SIZE * (PHI ** n) / 30)
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if pos < SIZE:
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ax.axhline(y=pos, color='gold', alpha=0.3, linewidth=0.5)
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ax.axvline(x=pos, color='gold', alpha=0.3, linewidth=0.5)
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# Title with encoded data
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ax.set_title(f'Ψ = ∇²ψ + ψ□ψ - ∂ₙψ + ε = φ²\nCoherence: 0.725 | Asymmetry: 14.85 | Correlation: -0.987',
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color='white', fontsize=10, pad=10)
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plt.tight_layout()
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plt.savefig('D:/fractal-brain/beast-build/images/2026-03-23-khragixx-mathematical-encoded.png',
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dpi=150, bbox_inches='tight', pad_inches=0, facecolor='black')
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plt.close()
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print("Mathematically encoded image generated.")
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print(f"Contains: PHI={PHI:.6f}, lattice structure, phi-harmonic frequencies")
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print("Saved to: images/2026-03-23-khragixx-mathematical-encoded.png")
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