DOLPHIN/nautilus_dolphin/dvae/convnext_5s_sensor.py

"""
convnext_5s_sensor.py — Inference wrapper for the 5s ConvNeXt-1D β-TCVAE.

Usage
-----
    sensor = ConvNext5sSensor(model_path)
    z_mu, z_post_std = sensor.encode_raw(arr)
    # arr: (C_IN, T_WIN) float64
    # z_mu: (z_dim,) float64 — latent mean
    # z_post_std: float   — mean posterior std (>1 = wide/uncertain)

    z_mu, z_post_std = sensor.encode_scan_window(arr2d)
    # arr2d: (T_WIN, C_IN) or (T_WIN, 7) — from scan parquet rows
    # If 7 columns, proxy_B is appended as ch7.

Key differences from the 1m sensor (convnext_sensor.py):
    - Model path: convnext_model_5s.json
    - C_IN = 8  (7 FEATURE + proxy_B — NO ExF channels)
    - No dvol_btc, fng, funding_btc channels
    - FEATURE_COLS are the same 7 features as the 1m sensor
    - proxy_B = instability_50 - v750_lambda_max_velocity  (ch7, same formula as 1m)

Architecture: ConvNeXtVAE  C_in=8  T_in=32  z_dim=32  base_ch=32  n_blocks=3
Input channels:
    ch0  v50_lambda_max_velocity
    ch1  v150_lambda_max_velocity
    ch2  v300_lambda_max_velocity
    ch3  v750_lambda_max_velocity
    ch4  vel_div
    ch5  instability_50
    ch6  instability_150
    ch7  proxy_B  (= instability_50 - v750_lambda_max_velocity)
"""

import os
import sys
import json
import numpy as np

_DVAE_DIR = os.path.dirname(os.path.abspath(__file__))
if _DVAE_DIR not in sys.path:
    sys.path.insert(0, _DVAE_DIR)

from convnext_dvae import ConvNeXtVAE

FEATURE_COLS = [
    'v50_lambda_max_velocity',
    'v150_lambda_max_velocity',
    'v300_lambda_max_velocity',
    'v750_lambda_max_velocity',
    'vel_div',
    'instability_50',
    'instability_150',
]
T_WIN = 32
C_IN  = 8   # 7 FEATURE + proxy_B (no ExF)


class ConvNext5sSensor:
    """
    Stateless inference wrapper for the 5s ConvNeXt model.
    No ExF channels — 8-channel input only.
    Thread-safe (model weights are read-only numpy).
    """

    def __init__(self, model_path: str):
        with open(model_path) as f:
            meta = json.load(f)

        arch = meta.get('architecture', {})
        self.model = ConvNeXtVAE(
            C_in     = arch.get('C_in',     C_IN),
            T_in     = arch.get('T_in',     T_WIN),
            z_dim    = arch.get('z_dim',    32),
            base_ch  = arch.get('base_ch',  32),
            n_blocks = arch.get('n_blocks', 3),
            seed     = 42,
        )
        self.model.load(model_path)

        self.norm_mean = np.array(meta['norm_mean'], dtype=np.float64) if 'norm_mean' in meta else None
        self.norm_std  = np.array(meta['norm_std'],  dtype=np.float64) if 'norm_std'  in meta else None
        self.epoch     = meta.get('epoch', '?')
        self.val_loss  = meta.get('val_loss', float('nan'))
        self.z_dim     = arch.get('z_dim', 32)

    # ── low-level: encode a (C_IN, T_WIN) array ──────────────────────────────
    def encode_raw(self, arr: np.ndarray):
        """
        arr: (C_IN, T_WIN) float64, already in raw (un-normalised) units.
        Returns z_mu (z_dim,), z_post_std float.
        """
        x = arr[np.newaxis].astype(np.float64)           # (1, C, T)
        if self.norm_mean is not None:
            x = (x - self.norm_mean[None, :, None]) / self.norm_std[None, :, None]
            np.clip(x, -6.0, 6.0, out=x)
        z_mu, z_logvar = self.model.encode(x)             # (1, D)
        z_post_std = float(np.exp(0.5 * z_logvar).mean())
        return z_mu[0], z_post_std

    # ── high-level: encode from a 2D scan array ───────────────────────────────
    def encode_scan_window(self, arr2d: np.ndarray):
        """
        arr2d: (T_WIN, C_IN) or (T_WIN, 7) — rows from scan parquet.
        If arr2d has 7 columns, proxy_B (instability_50 - v750_lambda_max_velocity)
        is appended as ch7 before encoding.

        Returns
        -------
        z_mu       : (z_dim,) float64
        z_post_std : float  (>1 suggests OOD regime)
        """
        arr2d = np.asarray(arr2d, dtype=np.float64)
        T_actual, n_cols = arr2d.shape

        if n_cols == 7:
            # Append proxy_B = instability_50 (col5) - v750_lambda_max_velocity (col3)
            proxy_b = arr2d[:, 5] - arr2d[:, 3]
            arr2d   = np.concatenate([arr2d, proxy_b[:, np.newaxis]], axis=1)  # (T, 8)

        # Pad / trim to T_WIN rows (zero-pad at the start if shorter)
        if T_actual < T_WIN:
            pad  = np.zeros((T_WIN - T_actual, C_IN), dtype=np.float64)
            arr2d = np.concatenate([pad, arr2d], axis=0)
        else:
            arr2d = arr2d[-T_WIN:]   # keep the most recent T_WIN rows

        return self.encode_raw(arr2d.T)   # (C_IN, T_WIN)

    # ── find proxy_B dimension via correlation probe ──────────────────────────
    def find_proxy_b_dim(self, probe_windows: np.ndarray):
        """
        Given probe_windows of shape (N, C_IN, T_WIN), find the z-dim most
        correlated with the mean proxy_B value (ch7 mean) across windows.

        Parameters
        ----------
        probe_windows : (N, C_IN, T_WIN) float64

        Returns
        -------
        dim_idx : int    — z-dim index with highest |r|
        corr    : float  — Pearson r with proxy_B mean
        """
        N = len(probe_windows)
        if N == 0:
            return 0, 0.0

        proxy_b_means = probe_windows[:, 7, :].mean(axis=1)   # (N,) — mean of ch7 per window

        z_mus = []
        for i in range(N):
            z_mu, _ = self.encode_raw(probe_windows[i])
            z_mus.append(z_mu)
        z_mus = np.stack(z_mus, axis=0)   # (N, z_dim)

        # Pearson r between each z-dim and proxy_B mean
        pb_centered = proxy_b_means - proxy_b_means.mean()
        pb_std      = pb_centered.std() + 1e-12

        best_dim  = 0
        best_corr = 0.0
        for d in range(z_mus.shape[1]):
            zd = z_mus[:, d]
            zd_c = zd - zd.mean()
            zd_std = zd_c.std() + 1e-12
            r = float((pb_centered * zd_c).mean() / (pb_std * zd_std))
            if abs(r) > abs(best_corr):
                best_corr = r
                best_dim  = d

        return best_dim, best_corr