"""
Brian2 Driver Module.

Implements the digital twin driver using the Brian2 simulator.
"""

import brian2 as b2
import numpy as np
from typing import List, Any
from .base import ElectrophysiologyDriver
from ..biocompiler.isa import OpCode, Instruction
from ..opu.device import OPU

class Brian2Driver(ElectrophysiologyDriver):
    """
    Driver for the Brian2-based Digital Twin.
    """
    
    def __init__(self, opu: OPU):
        self.opu = opu
        self.network = None
        self.neurons = None
        self.J = None
        self.h = None
        self.sigma = 0.0
        
    def connect(self):
        """Initialize the Brian2 environment."""
        # Reset Brian2 scope
        b2.start_scope()
        
    def disconnect(self):
        """Clean up resources."""
        self.network = None
        self.neurons = None
        
    def execute(self, instructions: List[Instruction]) -> Any:
        """
        Execute BioASM instructions using Brian2.
        """
        results = {}
        
        for instr in instructions:
            if instr.opcode == OpCode.ALC:
                self._allocate(instr.operands[0])
            elif instr.opcode == OpCode.LDJ:
                self.J = np.array(instr.operands[0])
            elif instr.opcode == OpCode.LDH:
                self.h = np.array(instr.operands[0])
            elif instr.opcode == OpCode.SIG:
                self.sigma = float(instr.operands[0])
                # Update noise in the neuron model if possible, or re-init
                # For simplicity in this driver, we might need to re-create or update a variable
                if self.neurons:
                    self.neurons.sigma_noise = self.sigma * b2.volt
            elif instr.opcode == OpCode.RUN:
                duration = float(instr.operands[0])
                self._run_simulation(duration)
            elif instr.opcode == OpCode.RD:
                # Read state (spikes or membrane potential)
                # For PUBO, we might look at firing rates or final potential
                # Here we return the final membrane potentials as a proxy for state
                results['state'] = self.neurons.v[:]
                
        return results
        
    def _allocate(self, num_neurons: int):
        """Create the neuron group."""
        specs = self.opu.specs
        
        # LIF Model with noise
        # dv/dt = (-(v - El) + R * I) / tau : volt
        # I = I_syn + I_offset + I_noise : amp
        # I_noise = sigma * xi * tau**-0.5 : amp (White noise approximation)
        # Actually, standard Langevin: dv/dt = (-(v-El) + R*I)/tau + sigma*sqrt(2/tau)*xi
        
        # Using the user's specs:
        # R=50*Mohm, tau=20*ms, El=-70*mV, Vt=-50*mV, Vr=-70*mV
        # I_offset=0.36*nA, sigma=2.0*mV
        
        # Brian2 equations
        eqs = '''
        dv/dt = (-(v - El) + R * (I_offset + I_input)) / tau + sigma_noise * sqrt(2/tau) * xi : volt
        I_input : amp
        R : ohm
        tau : second
        El : volt
        Vt : volt
        Vr : volt
        I_offset : amp
        sigma_noise : volt
        '''
        
        self.neurons = b2.NeuronGroup(
            num_neurons,
            eqs,
            threshold='v > Vt',
            reset='v = Vr',
            method='euler'
        )
        
        # Set parameters
        self.neurons.R = specs.R * b2.ohm
        self.neurons.tau = specs.tau * b2.second
        self.neurons.El = specs.El * b2.volt
        self.neurons.Vt = specs.Vt * b2.volt
        self.neurons.Vr = specs.Vr * b2.volt
        self.neurons.I_offset = specs.I_offset * b2.amp
        self.neurons.sigma_noise = specs.sigma * b2.volt # Initial sigma
        
        # Initialize v
        self.neurons.v = specs.El * b2.volt
        
        # Create Network
        self.network = b2.Network(self.neurons)
        
        # Add feedback loop
        # We need to update I_input based on J and h and spikes
        # For a continuous approximation or rate-based, we might use v directly
        # But the prompt asks for "implement the feedback loop using @network_operation"
        # "to apply matrix J and vector h in real time"
        
        @b2.network_operation(dt=1*b2.ms)
        def feedback_loop():
            # Simple recurrent interaction model
            # I_input = J @ s + h
            # What is 's'? Spikes? Firing rate? Or just v?
            # In Hopfield/Ising implementations on neuromorphic, usually:
            # s_i = 1 if v_i > threshold (spike), then filtered.
            # Or continuous: s_i = sigmoid(v_i)
            
            # Let's assume a simplified model where we use the current normalized potential as 'state'
            # or we just integrate spikes.
            # Given the prompt is about "organoid", let's assume a direct coupling.
            
            if self.J is not None and self.h is not None:
                # Normalize v to [0, 1] or [-1, 1] for the coupling
                # v_norm = (self.neurons.v - self.neurons.El) / (self.neurons.Vt - self.neurons.El)
                # For now, let's just use a proxy:
                # I_input = sum(J * spikes) ...
                
                # Since we don't have a full spike monitor inside the loop easily without lag,
                # let's use a continuous approximation for the "field" effect
                
                # s = (self.neurons.v > -60*b2.mV).astype(float) # Soft threshold
                
                # Let's use the raw potential for coupling (Gap Junction style approximation)
                # I_rec = J @ v
                
                # BUT, strictly speaking for PUBO/Ising:
                # We want to minimize E. The network dynamics should follow the gradient.
                # The user asked for "apply matrix J and vector h".
                
                # Let's implement a simple linear feedback:
                # I_input = J @ (v - El) + h
                
                # We need to handle units carefully.
                # J elements should be conductances or currents per volt?
                # The user said "normalize weights to dynamic range (approx +/- 1.5 nA)".
                # So J @ state should result in Current.
                
                # Let's assume 'state' is binary 0/1 based on recent spiking,
                # OR 'state' is the instantaneous probability.
                
                # For this implementation, let's assume 'state' is derived from v
                # s = clip((v - El) / (Vt - El), 0, 1)
                
                v_raw = self.neurons.v / b2.volt
                el_raw = self.neurons.El / b2.volt
                vt_raw = self.neurons.Vt / b2.volt
                
                s = np.clip((v_raw - el_raw) / (vt_raw - el_raw), 0, 1)
                
                # I_input = J @ s + h
                # We assume J and h are already scaled to produce Amperes when multiplied by s
                
                currents = (self.J @ s + self.h) * b2.amp
                self.neurons.I_input = currents
                
        self.network.add(feedback_loop)
        
    def _run_simulation(self, duration: float):
        """Run the simulation."""
        if self.network:
            self.network.run(duration * b2.second)
