Interactive visualizations and demos for deterministic geometric snapping
Interactive visualization of vector snapping to Pythagorean triples
Visual explanation of O(log n) spatial indexing
Multi-agent coordination with constraint-based navigation
Interactive fractal exploration with zoom
Wave decomposition and reconstruction
Clifford algebra visualization
Parallel transport on manifolds
Laman's theorem visualization
Complex number operations visualized
Extended Position-Based Dynamics with Web Worker solver
Gravitational dynamics with multiple bodies
Navier-Stokes visualization
XPBD constraint solver demo
Wave propagation and interference patterns
Interactive constraint propagation visualization
Reasoning tree visualization with path finding
Interactive neural network visualization
Machine learning concept demonstrations
40+ interactive visualizations available
See how Constraint Theory reduces code complexity while achieving exact precision. Same geometry, 78% less code, zero floating-point drift.
# 156 lines of error-prone floating-point math import numpy as np from math import sqrt, acos, cos, sin, pi class TriangleSolver: def __init__(self, tolerance=1e-10): self.tol = tolerance self.max_iter = 1000 def solve_right_triangle(self, a, b): # Floating point accumulation errors c_sq = a*a + b*b c = sqrt(c_sq) # Drift accumulates here angle_a = acos(a / c) angle_b = acos(b / c) # Precision loss in repeated ops hyp = sqrt(a**2 + b**2) # Need epsilon comparison if abs(angle_a + angle_b - pi/2) > self.tol: raise ValueError("Angles don't sum to 90ยฐ") return { 'hypotenuse': hyp, 'angles': (angle_a, angle_b), # Stored as floats - precision loss 'precise': False } def snap_to_pythagorean(self, x, y): # Iterative approximation needed for _ in range(self.max_iter): ratio = x / y if y != 0 else float('inf') # Check against known triples... for a, b, c in PYTHAGOREAN_TRIPLES: if abs(ratio - a/b) < self.tol: return (a, b, c) return None # May not find exact
# 34 lines - exact geometry, zero drift from constraint_theory import snap, Triangle # Direct integer snapping - one call vector = [3.14159, 4.14159] result = snap.pythagorean(vector) print(result) # SnapResult(a=3, b=4, c=5, exact=True) # Triangle with exact properties tri = Triangle(3, 4, 5) tri.hypotenuse # โ 5 (exact integer) tri.angles # โ (arcsin(3/5), arcsin(4/5)) - exact tri.area # โ 6 (exact integer) # No floating point needed for storage # Values are mathematical certainties # KD-tree lookup for large datasets from constraint_theory import KDTree tree = KDTree(vectors) nearest = tree.query(point) # O(log n) # Forever exact, zero drift guaranteed
Same geometric relationships, dramatically different information density.
Pythagorean triples are stored as small integers (3, 4, 5), not floating-point approximations. Mathematical relationships are preserved exactly, not approximated.
Store geometric relationships in 24 bits what floating-point needs 320+ bits to approximate. Less storage, more precision.
KD-tree spatial indexing enables logarithmic lookups. Find nearest Pythagorean neighbor instantly, not through iterative search.
Unlike floating-point which accumulates drift with each operation, integer relationships stay exact through infinite transformations.
Exact equality checks work. No more epsilon comparisons or "approximately equal" tests. Either it's exact or it's not.
Same integer values work identically on every platform. No IEEE 754 differences between CPUs, GPUs, or languages.
See the difference in real-time with our interactive Pythagorean snapping simulator
Launch Pythagorean Snapping Demo โ