// thermodynamic.sy -- Boltzmann Sampling & Ising Dynamics
// Temperature-controlled state exploration and lateral inhibition
// for the cascade hierarchy.
//
// Ported from Marjorie McCubbins' Python NCA architecture.
// Aislinn McCubbins, April 2026
//
// All gates use H-H rate functions. NO SIGMOID.
//
// Patent Pending: 63/939,190 & 63/962,385 & 63/988,485

use cascade::{Cascade, ConductanceHead}
use gate::HHGate
use energy::ATPCycling

// === THERMODYNAMIC STATE ===
// Telemetry container for thermodynamic observables.

struct ThermodynamicState {
    temperature: Float,
    energy: Float,
    entropy: Float,
    free_energy: Float,
    step_count: Int,
}

impl ThermodynamicState {
    fn new() -> ThermodynamicState {
        ThermodynamicState {
            temperature: 1.0,
            energy: 0.0,
            entropy: 0.0,
            free_energy: 0.0,
            step_count: 0,
        }
    }

    fn helmholtz(&self) -> Float {
        // Helmholtz free energy: F = E - T*S
        self.energy - self.temperature * self.entropy
    }
}

// === TEMPERATURE SCHEDULE ===
// Cooling schedule for simulated annealing / Boltzmann sampling.
// Supports linear, cosine, and exponential cooling.

temporal struct TemperatureSchedule {
    state current_step: Int = 0,

    const t_max: Float,
    const t_min: Float,
    const total_steps: Int,
    const schedule_type: Int,
}

impl Evolve for TemperatureSchedule {
    fn evolve(&mut self, dt: Duration) {
        // Step advances with each call
    }
}

impl TemperatureSchedule {
    fn linear(t_max: Float, t_min: Float, steps: Int) -> TemperatureSchedule {
        TemperatureSchedule {
            current_step: 0,
            t_max: t_max,
            t_min: t_min,
            total_steps: steps,
            schedule_type: 0,
        }
    }

    fn cosine(t_max: Float, t_min: Float, steps: Int) -> TemperatureSchedule {
        TemperatureSchedule {
            current_step: 0,
            t_max: t_max,
            t_min: t_min,
            total_steps: steps,
            schedule_type: 1,
        }
    }

    fn exponential(t_max: Float, t_min: Float, steps: Int) -> TemperatureSchedule {
        TemperatureSchedule {
            current_step: 0,
            t_max: t_max,
            t_min: t_min,
            total_steps: steps,
            schedule_type: 2,
        }
    }

    fn step(&mut self) -> Float {
        // Advance schedule and return current temperature.
        let progress = self.current_step / self.total_steps
        if progress > 1.0 { progress = 1.0 }

        let temp = self.t_max

        // Linear: T = T_max - (T_max - T_min) * progress
        if self.schedule_type == 0 {
            temp = self.t_max - (self.t_max - self.t_min) * progress
        }

        // Cosine: T = T_min + 0.5 * (T_max - T_min) * (1 + cos(pi * progress))
        if self.schedule_type == 1 {
            temp = self.t_min + 0.5 * (self.t_max - self.t_min) * (1.0 + cos(3.14159265 * progress))
        }

        // Exponential: T = T_max * exp(-decay * progress)
        if self.schedule_type == 2 {
            let decay = ln(self.t_max / (self.t_min + 0.00000001))
            temp = self.t_max * exp(0.0 - decay * progress)
        }

        self.current_step = self.current_step + 1
        temp
    }

    fn reset(&mut self) {
        self.current_step = 0
    }
}

// === BOLTZMANN SAMPLER ===
// Metropolis-Hastings MCMC state exploration.
// Proposes perturbations and accepts/rejects based on
// energy difference and temperature.
//
// Higher temperature = more exploration (accept worse states)
// Lower temperature = more exploitation (only improvements)

temporal struct BoltzmannSampler {
    state proposal_scale: Float = 0.1,

    const state_dim: Int,
    const equilibrium: Vec<Float>,
}

impl Evolve for BoltzmannSampler {
    fn evolve(&mut self, dt: Duration) {
        // Proposal scale can be learned
    }
}

impl BoltzmannSampler {
    fn new(state_dim: Int) -> BoltzmannSampler {
        // Equilibrium = 0.5 for all dimensions
        let eq = []
        for i in 0..state_dim {
            eq = eq + [0.5]
        }
        BoltzmannSampler {
            proposal_scale: 0.1,
            state_dim: state_dim,
            equilibrium: eq,
        }
    }

    fn energy(&self, state: Vec<Float>) -> Float {
        // Quadratic energy: E = sum((s - eq)^2)
        let e = 0.0
        for i in 0..self.state_dim {
            let diff = state[i] - self.equilibrium[i]
            e = e + diff * diff
        }
        e
    }

    fn sample(&self, state: Vec<Float>, n_steps: Int, temperature: Float) -> Vec<Float> {
        // Run Metropolis-Hastings MCMC from given state.
        //
        // For each step:
        //   1. Propose: current + gaussian_noise * proposal_scale
        //   2. Clamp proposal to [0, 1]
        //   3. Compute energy difference: dE = E(proposal) - E(current)
        //   4. Accept if dE < 0, or with probability exp(-dE / T)
        //   5. Update if accepted
        //
        // Returns final state after n_steps.
        let current = state
        let current_e = self.energy(current)

        for step in 0..n_steps {
            // Propose perturbation
            // proposal = current + randn() * scale, clamped [0, 1]
            let proposal_e = self.energy(current)
            let delta_e = proposal_e - current_e

            // Metropolis criterion
            // accept_prob = exp(-delta_e / max(temperature, 1e-8))
            // if random() < accept_prob: accept
        }

        current
    }
}

// === ENERGY LANDSCAPE ===
// Tier-aware energy function for the cascade hierarchy.
// Computes energy respecting the 4-tier biological structure.

temporal struct EnergyLandscape {
    state tier_weights: Vec<Float>,

    const state_dim: Int,
    const tier_sizes: Vec<Int>,
    const equilibrium: Vec<Float>,
}

impl Evolve for EnergyLandscape {
    fn evolve(&mut self, dt: Duration) {
        // Tier weights are learned
    }
}

impl EnergyLandscape {
    fn new() -> EnergyLandscape {
        EnergyLandscape {
            tier_weights: [1.0, 1.0, 1.0, 1.0],
            state_dim: 34,
            tier_sizes: [8, 6, 8, 12],
            equilibrium: [],
        }
    }

    fn energy(&self, state: Vec<Float>) -> Float {
        // Per-tier weighted quadratic energy:
        //   E = sum_tier( weight_tier * sum((s_i - eq_i)^2) )
        //
        // Each tier contributes independently, weighted by learned coefficient.
        // Tier 0 (amino acids): indices 0-7
        // Tier 1 (precursors):  indices 8-13
        // Tier 2 (NTs):         indices 14-21
        // Tier 3 (brain regions): indices 22-33
        let total = 0.0
        let offset = 0

        for tier in 0..4 {
            let tier_e = 0.0
            let size = self.tier_sizes[tier]
            for i in 0..size {
                let diff = state[offset + i] - 0.5
                tier_e = tier_e + diff * diff
            }
            total = total + self.tier_weights[tier] * tier_e
            offset = offset + size
        }

        total
    }
}

// === ISING FIELD ===
// Lateral inhibition between heads within a tier.
// Neighboring heads suppress each other proportional to activation.
// Computational analogue of biological lateral inhibition.
//
// Coupling matrix is learned, symmetric, zero diagonal.
// Inhibition gated by H-H rate function (NOT sigmoid).

temporal struct IsingField {
    state coupling: Vec<Vec<Float>>,

    const num_heads: Int,
}

impl Evolve for IsingField {
    fn evolve(&mut self, dt: Duration) {
        // Coupling is learned, not temporal
    }
}

impl IsingField {
    fn new(num_heads: Int) -> IsingField {
        // Initialize symmetric coupling matrix with zero diagonal
        IsingField {
            coupling: [],
            num_heads: num_heads,
        }
    }

    fn inhibit(&self, head_states: Vec<Float>) -> Vec<Float> {
        // Apply lateral inhibition.
        //
        // 1. Compute inhibition signal from neighbors:
        //    inhibition_i = sum_j( coupling_ij * state_j )
        //
        // 2. H-H rate gate on inhibition (NOT sigmoid):
        //    alpha = exp(clamp(inhibition, -3, 3))
        //    beta  = exp(clamp(-inhibition, -3, 3))
        //    gate = alpha / (alpha + beta + 1e-8)
        //
        // 3. Modulate: state_i * gate_i
        //    High inhibition -> low gate -> suppressed activation
        let modulated = []
        for i in 0..self.num_heads {
            let inhibition = 0.0
            for j in 0..self.num_heads {
                // inhibition += coupling[i][j] * head_states[j]
            }

            // H-H gate (NOT sigmoid)
            let clamped = inhibition
            if clamped > 3.0 { clamped = 3.0 }
            if clamped < -3.0 { clamped = -3.0 }
            let alpha = exp(clamped)
            let beta = exp(0.0 - clamped)
            let gate = alpha / (alpha + beta + 0.00000001)

            // Modulate activation
            modulated = modulated + [head_states[i] * gate]
        }
        modulated
    }
}

// === CONVENIENCE FUNCTION ===

fn thermodynamic_step(schedule: TemperatureSchedule, sampler: BoltzmannSampler, landscape: EnergyLandscape, state: Vec<Float>) -> ThermodynamicState {
    // Combined thermodynamic step:
    //   1. Get temperature from schedule
    //   2. Run Boltzmann sampling
    //   3. Compute energy on landscape
    //   4. Return telemetry
    let temp = schedule.step()
    let new_state = sampler.sample(state, 10, temp)
    let energy = landscape.energy(new_state)

    ThermodynamicState {
        temperature: temp,
        energy: energy,
        entropy: 0.0,
        free_energy: energy,
        step_count: schedule.current_step,
    }
}
