# SPDX-License-Identifier: AGPL-3.0-or-later | Commercial license available
# © Concepts 1996–2026 Miroslav Šotek. All rights reserved.
# © Code 2020–2026 Miroslav Šotek. All rights reserved.
# ORCID: 0009-0009-3560-0851
# Contact: www.anulum.li | protoscience@anulum.li
# SC-NeuroCore — SCPN L3 Genomic-Epigenomic Adapter (JAX Implementation)

"""
SCPN L3: Genomic-Epigenomic Adapter (JAX Implementation)
========================================================

This module implements the JAX-accelerated uplift of Layer 3, focused on
the CBC (CISS-Bioelectric-Chromatin) Bridge and the Read-Write Genome
dynamics described in Paper 3 and Monograph 28.

Key Equations:
- Spin Polarization: P_spin ~ 0.6 (CISS induced)
- Field Coupling: B_eff = alpha_B * d(Phi_T)/d(B)
- Channel Gating: V_half_mod = V_half + alpha_B * B_eff
- Bioelectric पोटेंशियल: Delta V_mem = G * Delta P_open
"""

from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, Optional

from ._jax_compat import jnp, make_rng, maybe_jit, split_rng, uniform

from ..base import BaseStochasticAdapter


@dataclass
class L3_HolonomicParameters:
    """Parameters derived from Paper 3 and the CBC Bridge specification."""

    n_genes: int = 100
    bitstream_length: int = 1024

    # CBC Bridge Coefficients
    p_spin_baseline: float = 0.6  # CISS spin selectivity factor
    alpha_b: float = 0.05  # Bioelectric sensitivity to effective field
    g_operator: float = 1.2  # Bioelectric Green's Operator gain

    # Epigenetic Dynamics (Ising-like)
    j_chromatin: float = 0.1  # Chromatin coupling strength
    h_accessibility: float = 0.05  # Baseline accessibility drive


class L3_GenomicAdapter(BaseStochasticAdapter):
    """
    JAX-traceable adapter for the SCPN Genomic/Epigenomic layer.
    """

    def __init__(self, params: Optional[L3_HolonomicParameters] = None, seed: int = 43) -> None:
        self.params = params or L3_HolonomicParameters()
        self.rng_key = make_rng(seed)

        # State: Chromatin Accessibility (0.0 to 1.0)
        self.accessibility = jnp.full((self.params.n_genes,), 0.1)
        # State: Local Bioelectric Potential (mV normalized)
        self.v_bio = jnp.zeros((self.params.n_genes,))
        # State: Spin Polarization level
        self.p_spin = jnp.full((self.params.n_genes,), self.params.p_spin_baseline)

    def encode(self, domain_state: Any) -> jnp.ndarray:
        """
        Maps accessibility states to stochastic bitstreams.
        """
        self.rng_key, subkey = split_rng(self.rng_key)
        rands = uniform(subkey, (self.params.n_genes, self.params.bitstream_length))
        bitstreams = (rands < self.accessibility[:, None]).astype(jnp.uint8)
        return bitstreams

    @staticmethod
    @maybe_jit
    def _cbc_kernel(
        v_bio: jnp.ndarray, p_spin: jnp.ndarray, alpha_b: float, g_op: float, dt: float
    ) -> jnp.ndarray:
        """
        Solves the CBC Bridge transduction:
        Delta V = G * (alpha_B * P_spin)
        """
        dv = g_op * (alpha_b * p_spin) - 0.05 * v_bio
        return v_bio + dv * dt

    def step_jax(self, dt: float, inputs: Optional[jnp.ndarray] = None) -> jnp.ndarray:
        """
        Advances the L3 CBC bridge dynamics using JAX.

        inputs: (n_genes, bitstream_length) representing L1/L2 feedback (e.g. Ca2+ levels).
        Returns: (n_genes, bitstream_length) output bitstreams.
        """
        # 1. Update Spin Polarization based on L1/L2 input (Stochastic Shielding)
        if inputs is not None:
            raw_drive = jnp.mean(inputs.astype(jnp.float32), axis=1)
            # Map input dimensions to gene count if necessary
            if raw_drive.shape[0] != self.params.n_genes:
                drive = jnp.full((self.params.n_genes,), jnp.mean(raw_drive))
            else:
                drive = raw_drive
            self.p_spin = jnp.clip(self.p_spin + 0.1 * drive * dt, 0.0, 1.0)

        # 2. Execute CBC Bridge Transduction (Field -> Bioelectric)
        self.v_bio = self._cbc_kernel(
            self.v_bio, self.p_spin, self.params.alpha_b, self.params.g_operator, dt
        )

        # 3. Update Chromatin Accessibility (Bioelectric -> Structural)
        # dA/dt = V_bio * Gain - k * A
        da = self.v_bio * 0.2 - 0.01 * self.accessibility
        self.accessibility = jnp.clip(self.accessibility + da * dt, 0.0, 1.0)

        # 4. Return encoded bitstreams
        return self.encode(None)

    def decode(self, bitstreams: jnp.ndarray) -> Dict[str, float]:
        """
        Maps bitstreams back to average genomic accessibility.
        """
        return {
            "avg_accessibility": float(jnp.mean(bitstreams.astype(jnp.float32))),
            "max_expression": float(jnp.max(jnp.mean(bitstreams.astype(jnp.float32), axis=1))),
        }

    def get_metrics(self) -> Dict[str, float]:
        """
        Returns L3-specific metrics like Spin Polarization and Bio-Potential.
        """
        return {
            "avg_p_spin": float(jnp.mean(self.p_spin)),
            "avg_v_bio": float(jnp.mean(self.v_bio)),
            "chromatin_coherence_r3": float(jnp.mean(self.accessibility)),
        }
