quipu 0.5.0

Codec criptográfico post-cuántico híbrido con endurecimiento online verificable (VOPRF) y canal visual de glifos.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159

#!/usr/bin/env python3
"""Exploración generativa de glifos (vía CPU, sin GPU).

Implementa un DECODIFICADOR LATENTE -> GLIFO: un vector latente continuo z se
mapea a curvas Bézier suaves. Es un stand-in de un VAE/difusión (que APRENDERÍA
ese decodificador de un dataset). Aquí el decodificador está hecho a mano, pero
el resto del pipeline (muestrear latentes -> rasterizar -> glyphopt) es idéntico
al que usaría un modelo entrenado. Produce glifos más ORGÁNICOS (curvos) que la
versión geométrica de líneas rectas.

    python scripts/glyph_generative.py
"""

import math
import random
import struct
import zlib

import quipu  # glyphopt de Rust (select_separable, glyph_min_distance)

SIZE = 16
LATENT_DIM = 12
N_CANDIDATES = 1500
K_SELECT = 94


# ------------------------- decodificador latente -> glifo -------------------------
def cubic_bezier(p, t):
    mt = 1 - t
    x = (mt**3 * p[0][0] + 3 * mt**2 * t * p[1][0]
         + 3 * mt * t**2 * p[2][0] + t**3 * p[3][0])
    y = (mt**3 * p[0][1] + 3 * mt**2 * t * p[1][1]
         + 3 * mt * t**2 * p[2][1] + t**3 * p[3][1])
    return x, y


def draw_bezier(g, pts, steps=48):
    for i in range(steps + 1):
        x, y = cubic_bezier(pts, i / steps)
        xi, yi = int(round(x)), int(round(y))
        if 0 <= xi < SIZE and 0 <= yi < SIZE:
            g[yi][xi] = 1


def decode(z):
    """Decodifica un vector latente z en un bitmap 16x16 (curvas suaves)."""
    g = [[0] * SIZE for _ in range(SIZE)]
    nstrokes = 2 + (1 if z[0] > 0 else 0)
    for k in range(nstrokes):
        pts = []
        for j in range(4):
            a = z[(k * 3 + j) % LATENT_DIM]
            b = z[(k * 3 + j + 5) % LATENT_DIM]
            x = 8 + 5.5 * math.sin(math.pi * a + 0.8 * j + k)
            y = 8 + 5.5 * math.cos(math.pi * b + 0.6 * j + 1.3 * k)
            pts.append((x, y))
        draw_bezier(g, pts)
    return g


def fingerprint(g):
    bits = [g[y][x] for y in range(SIZE) for x in range(SIZE)]
    out = bytearray()
    for i in range(0, SIZE * SIZE, 8):
        byte = 0
        for j in range(8):
            byte = (byte << 1) | bits[i + j]
        out.append(byte)
    return bytes(out)


def sample_candidates():
    random.seed(7)
    cands = {}
    attempts = 0
    while len(cands) < N_CANDIDATES and attempts < N_CANDIDATES * 20:
        attempts += 1
        z = [random.uniform(-1, 1) for _ in range(LATENT_DIM)]
        g = decode(z)
        filled = sum(sum(r) for r in g)
        if filled < 8 or filled > 110:
            continue
        fp = fingerprint(g)
        if fp not in cands:
            cands[fp] = g
    return list(cands.keys()), list(cands.values())


# ------------------------- salida -------------------------
def write_png(path, width, height, pixels):
    def chunk(typ, data):
        body = typ + data
        return struct.pack(">I", len(data)) + body + struct.pack(">I", zlib.crc32(body) & 0xFFFFFFFF)

    ihdr = struct.pack(">IIBBBBB", width, height, 8, 0, 0, 0, 0)
    raw = bytearray()
    for y in range(height):
        raw.append(0)
        raw.extend(pixels[y * width:(y + 1) * width])
    idat = zlib.compress(bytes(raw), 9)
    with open(path, "wb") as f:
        f.write(b"\x89PNG\r\n\x1a\n")
        f.write(chunk(b"IHDR", ihdr))
        f.write(chunk(b"IDAT", idat))
        f.write(chunk(b"IEND", b""))


def sheet_png(path, grids, cols=10):
    pad = 1
    cell = SIZE + 2 * pad
    rows = (len(grids) + cols - 1) // cols
    W, H = cols * cell, rows * cell
    px = bytearray([255]) * (W * H)
    for idx, g in enumerate(grids):
        gx = (idx % cols) * cell + pad
        gy = (idx // cols) * cell + pad
        for y in range(SIZE):
            for x in range(SIZE):
                if g[y][x]:
                    px[(gy + y) * W + (gx + x)] = 0
    write_png(path, W, H, px)
    return W, H


def ascii_row(grids):
    lines = []
    for y in range(SIZE):
        parts = ["".join("#" if g[y][x] else " " for x in range(SIZE)) for g in grids]
        lines.append("  ".join(parts))
    return "\n".join(lines)


def main():
    fps, grids = sample_candidates()
    print(f"Glifos muestreados del espacio latente (únicos): {len(fps)}")

    idx = quipu.select_separable(fps, K_SELECT)
    sel_fps = [fps[i] for i in idx]
    sel_grids = [grids[i] for i in idx]

    baseline = quipu.glyph_min_distance(fps[:K_SELECT])
    optimized = quipu.glyph_min_distance(sel_fps)
    print(f"Alfabeto generativo: {len(sel_fps)} glifos curvos (orgánicos)")
    print(f"Distancia mínima  -  sin optimizar: {baseline}  |  optimizada: {optimized}")
    print(f"Mejora de separabilidad: x{optimized / max(baseline, 1):.1f}\n")

    print("Muestra (8 glifos generativos, lado a lado):\n")
    print(ascii_row(sel_grids[:8]))

    out = "/mnt/data/decod/glyph_generative.png"
    w, h = sheet_png(out, sel_grids)
    print(f"\nAlfabeto generativo ({len(sel_grids)} glifos): {out} ({w}x{h})")
    print("\nNOTA: el decodificador latente está hecho a mano. Un VAE/difusión")
    print("ENTRENADO reemplazaría `decode(z)` aprendiéndolo de datos; el resto")
    print("del pipeline (muestrear -> glyphopt -> font) no cambia.")


if __name__ == "__main__":
    main()