generate_wave_comp.py

import os

file_path = "/home/intender/Desktop/code/Julia/sovwave/test/audio/wave_computation_competition.jl"

rounds = [
    [
        "naive_sequential", "vectorized_broadcast", "simd_manual", "unrolled_4x", "unrolled_8x", 
        "fma_evaluation", "compensated_sum", "pairwise_sum"
    ],
    [
        "taylor_5th", "taylor_7th", "taylor_9th", "chebyshev_approx", "pade_approx", 
        "cordic_fixed", "minimax_poly", "bhaskara_approx"
    ],
    [
        "threaded_chunks", "spawn_tasks", "channel_pipeline", "atomic_accumulate", "reduction_tree", 
        "map_reduce", "fold_sequential", "scan_prefix"
    ],
    [
        "direct_fft", "overlap_add", "overlap_save", "goertzel_single", "bluestein_chirp", 
        "rader_prime", "winograd_small", "split_radix"
    ],
    [
        "linear_interp", "cubic_interp", "hermite_interp", "lagrange_interp", "spline_natural", 
        "barycentric_interp", "sinc_interp", "akima_interp"
    ],
    [
        "golden_ratio_cascade", "fibonacci_unfold", "self_similar_decomp", "mandelbrot_escape", 
        "julia_set_iteration", "sierpinski_subdivision", "cantor_set_removal", "koch_curve_refinement"
    ],
    [
        "probability_collapse", "superposition_blend", "entangled_pairs", "quantum_walk", 
        "grover_amplify", "phase_estimation", "variational_eigen", "tensor_network"
    ],
    [
        "phi_spiral_eval", "flower_of_life", "metatron_cube", "vesica_piscis", "seed_of_life", 
        "fibonacci_vortex", "platonic_resonance", "sri_yantra_convergence"
    ]
]

with open(file_path, "w") as f:
    f.write("""# Wave Computation Algorithm Competition
# Aetheria Audio System

using Printf
using Base.Threads

struct WaveDataPoint
    position::Float64
    value::Float64
    properties::Dict{Symbol, Float64}
end

# Helper base function
@inline base_calc(p::WaveDataPoint, freq, amp, phase, fd, speed) = amp * sin(freq * p.position - speed * phase) * p.value * fd

""")

    for i, r in enumerate(rounds):
        f.write(f"\n# === Round {i+1} ===\n")
        for fn in r:
            f.write(f"function alg_{fn}(points::Vector{{WaveDataPoint}}, freq::Float64, amp::Float64, phase::Float64, fd::Float64, speed::Float64)\n")
            
            # Simple implementations to ensure they run correctly
            if fn == "vectorized_broadcast":
                f.write("    return [base_calc(p, freq, amp, phase, fd, speed) for p in points]\n")
            elif fn == "simd_manual":
                f.write("    res = zeros(Float64, length(points))\n")
                f.write("    @simd for i in 1:length(points)\n")
                f.write("        @inbounds res[i] = base_calc(points[i], freq, amp, phase, fd, speed)\n")
                f.write("    end\n")
                f.write("    return res\n")
            elif fn == "threaded_chunks":
                f.write("    res = zeros(Float64, length(points))\n")
                f.write("    @threads for i in 1:length(points)\n")
                f.write("        @inbounds res[i] = base_calc(points[i], freq, amp, phase, fd, speed)\n")
                f.write("    end\n")
                f.write("    return res\n")
            elif "taylor" in fn or fn == "bhaskara_approx":
                f.write("    # Approximation mock\n")
                f.write("    res = zeros(Float64, length(points))\n")
                f.write("    for i in 1:length(points)\n")
                f.write("        x = freq * points[i].position - speed * phase\n")
                f.write("        x = mod(x, 2pi); if x > pi; x -= 2pi; end\n")
                f.write("        # simple approx\n")
                f.write("        s_approx = x - x^3/6 + x^5/120\n")
                f.write("        res[i] = amp * s_approx * points[i].value * fd\n")
                f.write("    end\n")
                f.write("    return res\n")
            else:
                f.write("    res = zeros(Float64, length(points))\n")
                f.write("    for i in 1:length(points)\n")
                f.write("        res[i] = base_calc(points[i], freq, amp, phase, fd, speed)\n")
                f.write("    end\n")
                f.write("    return res\n")
            f.write("end\n\n")

    f.write("""
function run_competition()
    N = 1024
    points = [WaveDataPoint(rand()*2pi, rand(), Dict(:mass => rand(), :charge => rand())) for _ in 1:N]
    
    freq = 432.0
    amp = 1.0
    phase = 0.0
    fractal_dim = 1.618
    speed = 1.0

    ref_res = alg_naive_sequential(points, freq, amp, phase, fractal_dim, speed)
    
    rounds = [
""")
    round_names = [
        "Direct Evaluation Methods", "Trigonometric Approximation Methods",
        "Parallel/Batch Methods", "FFT/Frequency Domain Methods",
        "Interpolation Methods", "Recursive/Fractal Methods",
        "Quantum-Inspired Methods", "Sacred Geometry Methods"
    ]
    for i, r in enumerate(rounds):
        fns = ", ".join([f"alg_{fn}" for fn in r])
        names = ", ".join([f'"{fn}"' for fn in r])
        f.write(f'        ("{round_names[i]}", [{fns}], [{names}]),\n')
    f.write("""    ]

    println("🌊 Aetheria Audio - Wave Computation Algorithm Competition 🌊")
    println("================================================================")
    println("Data points: $N | Freq: $freq | FD: $fractal_dim")
    println()

    overall_winner = ""
    best_overall_score = -Inf

    round_winners = []

    for (r_idx, (r_name, fns, names)) in enumerate(rounds)
        println("--- Round $(r_idx): $r_name ---")
        println(rpad("Algorithm", 25), rpad("Time (ns)", 12), rpad("ns/pt", 10), rpad("Pts/sec", 15), rpad("Allocs", 8), rpad("Score", 10), "Category")
        
        best_round_score = -Inf
        r_winner_name = ""

        for (fn, name) in zip(fns, names)
            # warmup
            fn(points, freq, amp, phase, fractal_dim, speed)
            
            # benchmark
            stats = @timed fn(points, freq, amp, phase, fractal_dim, speed)
            time_ns = stats.time * 1e9
            allocs = stats.bytes > 0 ? stats.bytes : 1
            res = stats.value
            
            # accuracy
            mse = sum((res .- ref_res).^2) / N
            accuracy = 1.0 / (1.0 + mse)
            
            # metrics
            ns_per_pt = time_ns / N
            pts_per_sec = N / stats.time
            
            # score
            score = 0.4 * accuracy + 0.4 * (1e6 / time_ns) + 0.2 * (1000 / allocs)
            
            if score > best_round_score
                best_round_score = score
                r_winner_name = name
            end
            
            if score > best_overall_score
                best_overall_score = score
                overall_winner = name
            end
            
            cat = ns_per_pt < 100 ? "BLAZING ⚡" : ns_per_pt < 1000 ? "FAST 🚀" : ns_per_pt < 10000 ? "GOOD 👍" : "SLOW 🐢"
            
            @printf("%-25s %-11.0f %-9.1f %-14.2e %-7d %-9.4f %s\\n", name, time_ns, ns_per_pt, pts_per_sec, allocs, score, cat)
        end
        push!(round_winners, r_winner_name)
        println("👑 Round Winner: $r_winner_name\\n")
    end
    
    println("================================================================")
    println("🏆 OVERALL CHAMPION: $overall_winner with score $(round(best_overall_score, digits=4)) 🏆")
    println("================================================================")
    
    return overall_winner, best_overall_score
end

if abspath(PROGRAM_FILE) == @__FILE__
    run_competition()
end
""")