TopoDevPOC_n39_save.py

#!/usr/bin/env python3
"""
TopoDevPOC_n39.py
Topologically Unique Developing Point of Control Patterns
Pre-market K-Lines, n = 39 three-minute candlesticks

Paper : TopoDevPOC.tex  (ConQ Research Team, Continual Quasars)
Compute: Vectorized NumPy + GPU Torch (T4) + Warp branchless ops
         Architectural patterns from core_engine_v11.py (Hyper-Warp Edition)

Outputs (CLI):
  1. Total combination count for n=39
  2. Matrix state-transition validation
  3. 100 random ternary matrices  (1×38 each)
  4. 100 random symbolic sequences (length-38 strings)
  5. 100 developing_poc charts saved as PNG + ASCII CLI preview
  6. CSV export — bit-packed uint64 pattern_id (39-bit encoding, min bytes)
  7. Binary export — raw .npy uint64 array (fastest machine read)

COMPRESSION SCHEME:
  Each pattern encodes into a single uint64 (8 bytes):
    bit  0      : direction  (0=Bullish, 1=Bearish)
    bits 1..38  : transitions (1=strict, 0=equality)
  Decode: direction = id & 1;  trans[k] = (id >> (k+1)) & 1
  Full matrix + direction reconstructed from one integer in O(1).
  Entire 2^39 space = integers [0, 2^39). No file required for enumeration.

Run on Google Colab T4:
    !python TopoDevPOC_n39.py
"""

# ── stdlib ────────────────────────────────────────────────────────────────────
import os, sys, time, math, csv, struct
import numpy as np

# ── matplotlib (non-interactive for Colab CLI) ────────────────────────────────
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

# ── GPU setup (T4 Colab) ─────────────────────────────────────────────────────
try:
    import torch
    HAS_CUDA = torch.cuda.is_available()
    DEVICE   = 'cuda' if HAS_CUDA else 'cpu'
except ImportError:
    HAS_CUDA = False
    DEVICE   = 'cpu'
    torch    = None

# Optional Warp (core_engine_v11.py pattern) ─────────────────────────────────
HAS_WARP = False
try:
    import warp as wp
    wp.init()
    wp.set_module_options({"enable_backward": False, "fast_math": True, "max_unroll": 8})
    HAS_WARP = bool(wp.get_cuda_devices())
except Exception:
    pass

# ─────────────────────────────────────────────────────────────────────────────
# CONSTANTS  (tex Section II)
# ─────────────────────────────────────────────────────────────────────────────
N         = 39          # pre-market 3-min candles  (C_0 … C_{-(n-1)})
N_TRANS   = N - 1       # 38 adjacent-pair relations
N_SAMPLES = 100
SEED      = int(time.time() * 1000) & 0x7FFFFFFF

SEP = "=" * 74

# ─────────────────────────────────────────────────────────────────────────────
# 0. HEADER
# ─────────────────────────────────────────────────────────────────────────────
print(SEP)
print("  TopoDevPOC — Developing POC Pattern Enumerator")
print(f"  n = {N} candles | {N_TRANS} transitions | device = {DEVICE}")
if HAS_CUDA and torch:
    print(f"  GPU = {torch.cuda.get_device_name(0)}")
if HAS_WARP:
    print(f"  Warp = enabled (branchless kernel path)")
print(SEP)

# ─────────────────────────────────────────────────────────────────────────────
# SECTION 1 — COMBINATORIAL ENUMERATION  (tex Theorem, Sec. III)
#
#   Bullish:  each of (n-1) transitions ∈ {>, =}  → 2^(n-1) patterns
#   Bearish:  each of (n-1) transitions ∈ {<, =}  → 2^(n-1) patterns
#   Total   : 2^(n-1) + 2^(n-1) = 2^n  (disjoint families)
#   n=39    : 2^39 = 549,755,813,888
# ─────────────────────────────────────────────────────────────────────────────
TOTAL = 1 << N          # exact Python int (arbitrary precision)
HALF  = 1 << (N - 1)

print(f"\n[THEOREM]  Total unique developing POC patterns for n={N}")
print(f"  Bullish (non-increasing) : 2^{N-1} = {HALF:,}")
print(f"  Bearish (non-decreasing) : 2^{N-1} = {HALF:,}")
print(f"  Total  (2^{N})           : {TOTAL:,}")

# ─────────────────────────────────────────────────────────────────────────────
# SECTION 2 — MATRIX STATE-TRANSITION VALIDATION  (tex Sec. IV)
#
#   States  : S_0 (equality), S_± (strict move)
#   A = [[1,1],[1,1]]   (fully-connected 2-state digraph)
#   v_0 = [1,1]^T       (both states reachable initially)
#
#   B_n = 1^T · A^(n-2) · v_0
#       = 2^(n-3) · 1^T · A · 1     (using A^k = 2^(k-1)·A for k≥1)
#       = 2^(n-3) · 4  =  2^(n-1)   per direction
#
#   Implementation: exact Python-int matrix exponentiation (no float rounding)
# ─────────────────────────────────────────────────────────────────────────────

def _mm2(A, B):
    """Exact 2×2 matrix multiply with Python arbitrary-precision ints."""
    return [
        [A[0][0]*B[0][0] + A[0][1]*B[1][0],  A[0][0]*B[0][1] + A[0][1]*B[1][1]],
        [A[1][0]*B[0][0] + A[1][1]*B[1][0],  A[1][0]*B[0][1] + A[1][1]*B[1][1]],
    ]

def _mpow2(M, k):
    """Fast 2×2 matrix power, exact ints, O(log k)."""
    if k == 0: return [[1,0],[0,1]]
    if k == 1: return M
    h = _mpow2(M, k >> 1)
    s = _mm2(h, h)
    return s if (k & 1) == 0 else _mm2(s, M)

A_mat  = [[1,1],[1,1]]
v0     = [1, 1]
A_pow  = _mpow2(A_mat, N - 2)
# 1^T · A^(n-2) · v0
Av0_0  = A_pow[0][0]*v0[0] + A_pow[0][1]*v0[1]
Av0_1  = A_pow[1][0]*v0[0] + A_pow[1][1]*v0[1]
B_n    = Av0_0 + Av0_1          # = 1^T · (A^(n-2)·v0)

print(f"\n[MATRIX]  A = [[1,1],[1,1]] | v_0 = [1,1]^T")
print(f"  A^{N-2} = [[{A_pow[0][0]}, {A_pow[0][1]}],")
print(f"             [{A_pow[1][0]}, {A_pow[1][1]}]]")
print(f"  B_{N} = 1^T · A^{N-2} · v_0 = {B_n:,}")
print(f"  Expected 2^{{n-1}}           = {HALF:,}")
assert B_n == HALF,          f"Matrix B_n mismatch: {B_n} ≠ {HALF}"
assert 2 * B_n == TOTAL,     f"Total mismatch: {2*B_n} ≠ {TOTAL}"
print(f"  [OK] 2 × {B_n:,} = {TOTAL:,}  ✓")

# ─────────────────────────────────────────────────────────────────────────────
# SECTION 3 — PATTERN ENCODING SCHEME
#
#   Each of the 2^39 patterns maps bijectively to a 39-bit integer:
#     bit  0          : direction   (0 = Bullish, 1 = Bearish)
#     bits 1 … N-1   : transitions  (1 = strict move, 0 = equality)
#
#   pattern_id ∈ [0, 2^39)   uniquely identifies every valid pattern.
#
#   Ternary matrix m ∈ {+1,0}^(n-1) for bullish,
#                  m ∈ {-1,0}^(n-1) for bearish.
#   Bijection: m_k = sign(direction) × trans_bit_k
# ─────────────────────────────────────────────────────────────────────────────

# ─────────────────────────────────────────────────────────────────────────────
# SECTION 4 — GPU-ACCELERATED RANDOM SAMPLING
#   Generate (N_SAMPLES × N) binary matrix at once on T4 GPU.
#   Inspired by core_engine_v11.py XOR-shift PRNG kernel design.
#   bits[:,0]  = direction flags
#   bits[:,1:] = N_TRANS transition flags per pattern
# ─────────────────────────────────────────────────────────────────────────────

t_sample = time.perf_counter()

if HAS_WARP:
    # ── Warp branchless path (core_engine_v11.py style) ─────────────────────
    # Launch one thread per sample; XOR-shift fills N bits per thread.
    _seed_wp = SEED

    @wp.kernel
    def k_rng_bits(seed: int, N_cols: int,
                   out: wp.array(dtype=wp.int8)):
        tid = wp.tid()
        rng = wp.uint32(seed) ^ wp.uint32(tid)
        if rng == wp.uint32(0):
            rng = wp.uint32(123456789)
        base = tid * N_cols
        for col in range(N_cols):
            rng = rng ^ (rng << wp.uint32(13))
            rng = rng ^ (rng >> wp.uint32(17))
            rng = rng ^ (rng << wp.uint32(5))
            out[base + col] = wp.int8(int(rng) & 1)

    out_wp  = wp.zeros(N_SAMPLES * N, dtype=wp.int8, device='cuda')
    wp.launch(k_rng_bits, dim=N_SAMPLES, block_dim=128,
              inputs=[_seed_wp, N, out_wp], device='cuda')
    wp.synchronize()
    bits_np = out_wp.numpy().reshape(N_SAMPLES, N)
    print(f"\n[SAMPLE]  {N_SAMPLES} patterns sampled via Warp XOR-shift kernel")

elif HAS_CUDA and torch is not None:
    # ── Torch GPU path ───────────────────────────────────────────────────────
    gen = torch.Generator(device='cuda')
    gen.manual_seed(SEED)
    bits_t  = torch.randint(0, 2, (N_SAMPLES, N), device='cuda',
                            generator=gen, dtype=torch.int8)
    bits_np = bits_t.cpu().numpy()
    print(f"\n[SAMPLE]  {N_SAMPLES} patterns sampled on GPU (torch.randint)")

else:
    # ── CPU NumPy fallback ────────────────────────────────────────────────────
    rng_cpu = np.random.default_rng(SEED)
    bits_np = rng_cpu.integers(0, 2, size=(N_SAMPLES, N), dtype=np.int8)
    print(f"\n[SAMPLE]  {N_SAMPLES} patterns sampled on CPU (NumPy)")

sample_ms = (time.perf_counter() - t_sample) * 1e3
print(f"          Sampling time: {sample_ms:.2f} ms")

# ─────────────────────────────────────────────────────────────────────────────
# SECTION 5 — VECTORISED DECODING  (NumPy, no Python loops over patterns)
#
#   bits[:,0]   → direction array  (0=Bullish, 1=Bearish), shape (100,)
#   bits[:,1:]  → trans array,     shape (100, 38)
#   ternary_mat: +trans if Bullish, -trans if Bearish  → shape (100, 38)
#   sign: Bullish→+1, Bearish→-1, broadcast multiply
# ─────────────────────────────────────────────────────────────────────────────

dir_bits  = bits_np[:, 0].astype(np.int8)      # 0 or 1
trans     = bits_np[:, 1:].astype(np.int8)      # (100,38), values 0 or 1
signs     = 1 - 2 * dir_bits                    # 0→+1 (bull), 1→-1 (bear)
ternary   = (signs[:, None] * trans).astype(np.int8)  # (100,38): +1/0/-1

# Compact 39-bit pattern IDs
pows64    = (np.uint64(1) << np.arange(N, dtype=np.uint64))
pat_ids   = (bits_np.astype(np.uint64) * pows64[None, :]).sum(axis=1)

# Symbolic sequences: vectorised char lookup
#   Bullish (sign=+1): trans=1 → '>', trans=0 → '='
#   Bearish (sign=-1): trans=1 → '<', trans=0 → '='
SYM_BULL = np.array(['=', '>'], dtype='<U1')   # index by trans bit
SYM_BEAR = np.array(['=', '<'], dtype='<U1')
sym_matrix = np.where(dir_bits[:, None] == 0,
                      SYM_BULL[trans],
                      SYM_BEAR[trans])          # (100, 38)

# POC price sequence (right-to-left: p[0]=C_0=newest, p[N-1]=C_{-(N-1)}=oldest)
#   p_raw[i, k] = POC of candle C_{-k} for sample i
#   Transition k: p[k] = p[k+1] + ternary[k]
#   Build by cumsum from oldest→newest: p_raw[:, N-1] = 50, then forward
BASE    = 50.0
step    = 1.0
p_raw   = np.zeros((N_SAMPLES, N), dtype=np.float32)
p_raw[:, N-1] = BASE
# Vectorised: cumulative sum of ternary (columns N-2 down to 0)
for k in range(N-2, -1, -1):
    p_raw[:, k] = p_raw[:, k+1] + ternary[:, k].astype(np.float32) * step

# Display order: oldest(left) → newest(right)
# poc_disp[:, j] = p_raw[:, N-1-j]
poc_disp = p_raw[:, ::-1].copy()   # (100, 39), column 0=oldest, col N-1=newest

# ─────────────────────────────────────────────────────────────────────────────
# OUTPUT 1 — TERNARY MATRICES  (100 × 38)
# ─────────────────────────────────────────────────────────────────────────────
print(f"\n{SEP}")
print(f"  OUTPUT 1 — TERNARY MATRICES  (1×{N_TRANS}, values ∈ {{-1,0,+1}})")
print(f"  Format: [#] Direction | PatternID | M = [m_0 … m_37]")
print(SEP)

for i in range(N_SAMPLES):
    d_label = "Bullish" if dir_bits[i] == 0 else "Bearish"
    pid     = int(pat_ids[i])
    row_str = np.array2string(ternary[i], separator=',',
                              max_line_width=400).replace('\n','')
    print(f"  [{i+1:3d}] {d_label:7s} | ID={pid:>15d} | M={row_str}")

# ─────────────────────────────────────────────────────────────────────────────
# OUTPUT 2 — SYMBOLIC SEQUENCES  (length 38)
# ─────────────────────────────────────────────────────────────────────────────
print(f"\n{SEP}")
print(f"  OUTPUT 2 — SYMBOLIC SEQUENCES  (Σ, length {N_TRANS})")
print(f"  Format: [#] Direction | PatternID | Σ = σ_0 σ_1 … σ_37")
print(SEP)

for i in range(N_SAMPLES):
    d_label = "Bullish" if dir_bits[i] == 0 else "Bearish"
    pid     = int(pat_ids[i])
    seq_str = ' '.join(sym_matrix[i])
    print(f"  [{i+1:3d}] {d_label:7s} | ID={pid:>15d} | Σ = {seq_str}")

# ─────────────────────────────────────────────────────────────────────────────
# OUTPUT 3 — CHARTS
#   (a) Full matplotlib figure: 10×10 grid, saved to PNG
#   (b) ASCII CLI preview for first 10 patterns
# ─────────────────────────────────────────────────────────────────────────────
print(f"\n{SEP}")
print(f"  OUTPUT 3 — DEVELOPING POC CHARTS  (100 patterns)")
print(SEP)

# ── x-axis: position 0=oldest C_{-(N-1)}, N-1=newest C_0 ──────────────────
x_pos  = np.arange(N, dtype=np.float32)
x_tick_pos = [0, 9, 19, 29, N-1]
x_tick_lbl = [f'C_{{-{N-1}}}', f'C_{{-29}}', f'C_{{-19}}', f'C_{{-9}}', 'C_0']

ROWS, COLS = 10, 10
fig = plt.figure(figsize=(COLS * 3.2, ROWS * 2.0))
fig.suptitle(
    f"TopoDevPOC — 100 Random Developing POC Patterns   "
    f"n={N} pre-market 3-min K-lines\n"
    f"Total pattern space: 2^{N} = {TOTAL:,}   "
    f"(Bullish: {HALF:,}  |  Bearish: {HALF:,})",
    fontsize=10, y=1.005
)

for i in range(N_SAMPLES):
    ax  = fig.add_subplot(ROWS, COLS, i + 1)
    poc = poc_disp[i]
    is_bull = (dir_bits[i] == 0)
    color   = '#1a6eb5' if is_bull else '#c0392b'
    label   = 'B↑' if is_bull else 'B↓'

    # Background shade
    ax.set_facecolor('#f7f9fc' if is_bull else '#fdf4f4')

    # POC line
    ax.plot(x_pos, poc, color=color, linewidth=1.2, zorder=3)

    # Mark strict-move positions (non-zero ternary in display coords)
    # Transition k corresponds to display segment [N-2-k, N-1-k]
    # Highlight the newer-side node at display index N-1-k = N-1-k
    strict_k   = np.where(ternary[i] != 0)[0]          # transition indices
    strict_disp = (N - 1 - strict_k).astype(int)        # display x-positions
    if strict_disp.size > 0:
        ax.scatter(strict_disp, poc[strict_disp],
                   color=color, s=5, zorder=5, linewidths=0)

    # Flat segments (equality)
    flat_k     = np.where(ternary[i] == 0)[0]
    flat_disp  = (N - 1 - flat_k).astype(int)
    if flat_disp.size > 0:
        ax.scatter(flat_disp, poc[flat_disp],
                   color='gray', s=3, zorder=4, linewidths=0, alpha=0.5)

    n_strict = int(np.abs(ternary[i]).sum())
    pid_short = int(pat_ids[i]) % 10**9      # last 9 digits for readability
    ax.set_title(f"#{i+1} {label}  mv={n_strict}  …{pid_short:09d}",
                 fontsize=5.5, pad=2, color=color)

    ax.set_xlim(-0.5, N - 0.5)
    ax.set_xticks(x_tick_pos)
    ax.set_xticklabels(['←old', '', '', '', 'new→'], fontsize=3.5)
    ax.tick_params(axis='y', labelsize=3.5)
    ax.yaxis.set_major_locator(mticker.MaxNLocator(4))
    for sp in ('top', 'right'):
        ax.spines[sp].set_visible(False)
    ax.spines['left'].set_color(color)
    ax.spines['left'].set_linewidth(1.5)
    ax.spines['bottom'].set_color('#cccccc')

plt.tight_layout(rect=[0, 0, 1, 1])
chart_path = "TopoDevPOC_n39_100samples.png"
plt.savefig(chart_path, dpi=110, bbox_inches='tight')
plt.close(fig)
print(f"  [SAVED] {chart_path}")

# ── ASCII CLI chart for first 10 patterns ────────────────────────────────────
H  = 7          # chart height in rows
W  = 39         # chart width  = N

print(f"\n  ASCII CLI Charts — first 10 samples (right side = C_0 = newest)\n")
for i in range(10):
    poc    = poc_disp[i]
    d_lbl  = "Bullish" if dir_bits[i] == 0 else "Bearish"
    pid    = int(pat_ids[i])
    n_mv   = int(np.abs(ternary[i]).sum())
    pmin, pmax = poc.min(), poc.max()
    span   = pmax - pmin if pmax != pmin else 1.0

    # Map each x-position to a row
    rows   = (H - 1 - ((poc - pmin) / span * (H - 1))).round().astype(int)
    rows   = np.clip(rows, 0, H - 1)

    grid   = [[' '] * W for _ in range(H)]
    for j in range(W):
        r = rows[j]
        # Strict-move node in the pair ending at display j:
        # transition index k = N-1-j  (if j < N-1)
        is_strict = (j < N - 1) and (ternary[i, N - 2 - j] != 0)
        grid[r][j] = '●' if is_strict else '·'
    # Connect with horizontal dash where poc is flat
    for row_idx in range(H):
        line = grid[row_idx]
        for j in range(1, W):
            if line[j] == ' ' and rows[j] == row_idx:
                line[j] = '-'

    print(f"  [{i+1:2d}] {d_lbl:7s} | ID={pid} | strict_moves={n_mv}/{N_TRANS}")
    poc_hi = poc[N-1]; poc_lo = poc[0]
    print(f"       POC range: oldest={poc_lo:.0f}  →  newest={poc_hi:.0f}")
    print(f"       ┌{'─'*W}┐")
    for row_idx in range(H):
        print(f"       │{''.join(grid[row_idx])}│")
    print(f"       └{'─'*W}┘")
    sym_preview = ' '.join(sym_matrix[i, :12]) + ' …'
    print(f"       Σ (first 12): {sym_preview}\n")

# ─────────────────────────────────────────────────────────────────────────────
# OUTPUT 4 — COMPRESSED CSV EXPORT  (bit-packed uint64 pattern_id)
#
#  COMPRESSION LOGIC:
#   Naive CSV text per row (direction label + 38 integers): ~110 bytes/row
#   Compressed: single uint64 hex string per row             ~  18 bytes/row
#   Reduction: ~6× on text CSV; binary .npy = 8 bytes/row (machine-optimal)
#
#  ENCODING (39-bit integer into uint64):
#   pattern_id = dir_bit | (trans[0]<<1) | (trans[1]<<2) | … | (trans[37]<<38)
#   bit 0      = direction (0=Bullish, 1=Bearish)
#   bits 1..38 = transition flags
#
#  DECODE (one-liner, O(1)):
#   direction  = "Bullish" if (pid & 1) == 0 else "Bearish"
#   trans[k]   = (pid >> (k+1)) & 1         for k in 0..37
#   ternary[k] = trans[k] if Bullish else -trans[k]
#
#  NOTE ON "ALL PATTERNS":
#   2^39 = 549,755,813,888 patterns.  At 8 bytes/pattern → 4.4 TB.
#   At minimum 39 bits/pattern (bit-packed) → 2.68 TB.
#   The pattern space IS [0, 2^39). Pattern k = integer k. No file needed.
#   Use the streaming generator below for on-demand enumeration.
# ─────────────────────────────────────────────────────────────────────────────

print(f"\n{SEP}")
print(f"  OUTPUT 4 — COMPRESSED EXPORT")
print(SEP)

# ── Verify encoding round-trip before writing ─────────────────────────────
def encode_pattern_id(dir_bit, trans_bits):
    """Pack direction + 38 transition bits into one uint64."""
    pid = np.uint64(dir_bit)
    for k, t in enumerate(trans_bits):
        pid |= (np.uint64(int(t)) << np.uint64(k + 1))
    return int(pid)

def decode_pattern_id(pid, n_trans=38):
    """Unpack uint64 → direction + ternary matrix. O(1) per pattern."""
    pid = int(pid)
    dir_bit = pid & 1
    direction = "Bullish" if dir_bit == 0 else "Bearish"
    sign = 1 if dir_bit == 0 else -1
    trans = np.array([(pid >> (k + 1)) & 1 for k in range(n_trans)], dtype=np.int8)
    ternary = (sign * trans).astype(np.int8)
    return direction, trans, ternary

# Round-trip verification on all 100 samples
for i in range(N_SAMPLES):
    pid_ref  = int(pat_ids[i])
    pid_enc  = encode_pattern_id(int(dir_bits[i]), trans[i])
    assert pid_ref == pid_enc, f"Encode mismatch at sample {i}"
    d2, t2, m2 = decode_pattern_id(pid_enc)
    assert (d2 == ("Bullish" if dir_bits[i] == 0 else "Bearish")), "Direction mismatch"
    assert np.array_equal(m2, ternary[i]), f"Ternary mismatch at sample {i}"
print("  [OK] 100-sample encode/decode round-trip verified")

# ── CSV: bit-packed uint64 — one integer per row ──────────────────────────
# Columns: seq_id (1-based), pattern_id_uint64 (hex), direction, n_strict
# Total row bytes: ~50 chars vs ~110 for expanded matrix text
CSV_PATH = "TopoDevPOC_n39_samples.csv"
with open(CSV_PATH, 'w', newline='') as f:
    writer = csv.writer(f)
    # Header — human-readable but compact
    writer.writerow([
        "seq_id",           # 1-based sample index
        "pattern_id_uint64",# hex string — encodes everything, 18 chars
        "pattern_id_dec",   # decimal for inspection
        "direction",        # B+ or B- (1 char each saves space vs full label)
        "n_strict_moves",   # popcount of transitions
    ])
    for i in range(N_SAMPLES):
        pid     = int(pat_ids[i])
        d_char  = "B+" if dir_bits[i] == 0 else "B-"
        n_mv    = int(np.abs(ternary[i]).sum())
        writer.writerow([
            i + 1,
            f"0x{pid:016X}",    # 18-char hex — decode with int(x, 16)
            pid,
            d_char,
            n_mv,
        ])

csv_bytes = os.path.getsize(CSV_PATH)
print(f"  [CSV]    {CSV_PATH}  →  {csv_bytes:,} bytes  ({csv_bytes/N_SAMPLES:.1f} bytes/row)")

# ── Binary .npy — uint64 array, 8 bytes/row, machine-optimal ─────────────
NPY_PATH = "TopoDevPOC_n39_samples.npy"
np.save(NPY_PATH, pat_ids.astype(np.uint64))
npy_bytes = os.path.getsize(NPY_PATH)
print(f"  [NPY]    {NPY_PATH}  →  {npy_bytes:,} bytes  ({(npy_bytes-128)/N_SAMPLES:.1f} bytes/row payload)")

# ── Raw bit-packed .bin — 39 bits/pattern → ceil(39/8)=5 bytes each ──────
# Pack 8 patterns × 39 bits = 312 bits = 39 bytes per block (tight packing)
# Simple implementation: pack pattern_ids as 5-byte little-endian chunks
BIN_PATH = "TopoDevPOC_n39_samples.bin"
BYTES_PER = 5   # ceil(39 / 8) = 5 bytes holds one 39-bit pattern_id
with open(BIN_PATH, 'wb') as f:
    # 8-byte magic + 4-byte n_patterns + 1-byte bits_per_pattern
    f.write(b'TPOC3900')
    f.write(struct.pack('<IB', N_SAMPLES, 39))
    for i in range(N_SAMPLES):
        pid = int(pat_ids[i])
        f.write(pid.to_bytes(BYTES_PER, byteorder='little'))
bin_bytes = os.path.getsize(BIN_PATH)
payload   = N_SAMPLES * BYTES_PER
print(f"  [BIN]    {BIN_PATH}  →  {bin_bytes:,} bytes  (13 hdr + {payload} payload = {BYTES_PER} bytes/pattern)")

# ── Print size comparison ─────────────────────────────────────────────────
print()
print(f"  SIZE COMPARISON (100 samples, n=39, {N_TRANS} transitions):")
print(f"    Naive text (direction + 38 ints):  ~{100 * 110:,} bytes")
print(f"    CSV hex uint64  (this file):        {csv_bytes:,} bytes  ({100*110//csv_bytes:.1f}× smaller)")
print(f"    NumPy .npy uint64:                  {npy_bytes:,} bytes")
print(f"    Raw bit-packed .bin (39 bits each): {bin_bytes:,} bytes  ({100*110//bin_bytes:.1f}× smaller)")
print()
print(f"  SCALING TO ALL 2^{N} = {TOTAL:,} PATTERNS:")
full_npy_tb  = (TOTAL * 8) / 1e12
full_bin_tb  = (TOTAL * BYTES_PER) / 1e12
full_text_tb = (TOTAL * 110) / 1e12
print(f"    Naive text:           ~{full_text_tb:.1f} TB  (infeasible)")
print(f"    uint64 binary (.npy): ~{full_npy_tb:.2f} TB  (infeasible to store)")
print(f"    5-byte bit-packed:    ~{full_bin_tb:.2f} TB  (infeasible to store)")
print(f"    Optimal:               0 bytes  — pattern k = integer k, range [0, 2^{N})")
print(f"    Use streaming generator below for on-demand enumeration.")

# ── Streaming generator (no memory, yields all 2^39 on-demand) ───────────
def stream_all_patterns(n=N):
    """
    Yields (pattern_id, direction, ternary_matrix) for all 2^n patterns.
    Zero memory beyond one pattern at a time. Works for any n.
    Pattern_id k: bit0=direction, bits1..n-1=transitions.
    """
    sign_map = {0: 1, 1: -1}
    for k in range(1 << n):
        dir_bit   = k & 1
        sign      = sign_map[dir_bit]
        direction = "Bullish" if dir_bit == 0 else "Bearish"
        ternary_k = np.array(
            [sign * ((k >> (j + 1)) & 1) for j in range(n - 1)],
            dtype=np.int8
        )
        yield k, direction, ternary_k

# Show the first 4 and last 4 pattern_ids to confirm ordering
print()
print(f"  STREAMING GENERATOR — first 4 / last 4 of all {TOTAL:,} patterns:")
gen = stream_all_patterns(N)
for _ in range(4):
    pid_s, d_s, _ = next(gen)
    print(f"    pattern_id={pid_s:>3d}  direction={d_s}")
print(f"    ... ({TOTAL - 8:,} patterns omitted) ...")
# Last 4: decode directly from known IDs
for pid_s in range(TOTAL - 4, TOTAL):
    d_s = "Bullish" if (pid_s & 1) == 0 else "Bearish"
    print(f"    pattern_id={pid_s}  direction={d_s}")

# ─────────────────────────────────────────────────────────────────────────────
# FINAL SUMMARY
# ─────────────────────────────────────────────────────────────────────────────
total_ms = (time.perf_counter() - t_sample) * 1e3
print(SEP)
print("  SUMMARY")
print(SEP)
print(f"  n (candles)                = {N}")
print(f"  Transitions per pattern    = {N_TRANS}")
print(f"  Total patterns  [2^{N}]    = {TOTAL:,}")
print(f"  Bullish         [2^{N-1}]  = {HALF:,}")
print(f"  Bearish         [2^{N-1}]  = {HALF:,}")
print(f"  Matrix B_n validated       = {B_n:,}  ✓")
print(f"  Samples generated          = {N_SAMPLES}")
print(f"  Chart file                 = {chart_path}")
print(f"  CSV export (hex uint64)    = {CSV_PATH}  ({csv_bytes:,} bytes)")
print(f"  Binary export (.npy)       = {NPY_PATH}   ({npy_bytes:,} bytes)")
print(f"  Bit-packed export (.bin)   = {BIN_PATH}  ({bin_bytes:,} bytes)")
print(f"  Compute device             = {DEVICE}")
print(f"  Wall-clock (sample+decode) = {total_ms:.2f} ms")
print(SEP)
print()
print("  CONVERSION FORMULAS  (tex Sec. V)")
print("  Symbolic → Ternary:")
print("    Bullish: '>' → +1,  '=' →  0")
print("    Bearish: '<' → -1,  '=' →  0")
print("  Ternary → Symbolic:")
print("    +1 → '>',  0 → '=',  -1 → '<'")
print()
print("  TEMPORAL CONVENTION:")
print(f"    Chart x-axis: left = C_{{-{N-1}}} (oldest) → right = C_0 (newest)")
print("    Bullish pattern: POC non-increasing toward right (higher on left)")
print("    Bearish pattern: POC non-decreasing toward right (lower on left)")
print(SEP)