harmonic_balancer.py

import numpy as np
import matplotlib.pyplot as plt
from scipy.constants import golden_ratio, pi, e
from .quantum_system import QuantumSystem
from ..utils.helpers import phi_pi_transition, generate_harmony_vector, state_to_dna, count_valid_codons, calculate_gc_content, calculate_base_balance
import qiskit
from qiskit import QuantumCircuit, execute, Aer

class HarmonicBalancer:
    def __init__(self, num_qubits, max_iterations, harmony_memory_size, objective_function=None, convergence_threshold=1e-6):
        """
        Initialize the HarmonicBalancer object.

        This constructor sets up the initial state of the HarmonicBalancer, including its parameters,
        memory, and optimization settings.

        Parameters:
        num_qubits (int): The number of qubits in the quantum system.
        max_iterations (int): The maximum number of iterations for the optimization process.
        harmony_memory_size (int): The size of the harmony memory used in the algorithm.
        objective_function (callable, optional): A custom objective function to be optimized. 
                                                 If None, a default function will be used.
        convergence_threshold (float, optional): The threshold for determining convergence of the algorithm.
                                                 Default is 1e-6.

        Returns:
        None
        """
        self.num_qubits = num_qubits
        self.max_iterations = max_iterations
        self.harmony_memory_size = harmony_memory_size
        self.harmony_memory = [generate_harmony_vector(num_qubits) for _ in range(harmony_memory_size)]
        self.best_solution = None
        self.best_score = -np.inf
        self.history = {'scores': [], 'states': []}
        self.quantum_system = QuantumSystem(num_qubits)
        self.objective_function = objective_function if objective_function else self.default_objective_function
        self.convergence_threshold = convergence_threshold
        self.HMCR_initial = 0.9
        self.HMCR_final = 0.7
        self.PAR_initial = 0.1
        self.PAR_final = 0.3

    def combine_harmony_equations(self, R, F, E):
        """
        Combines harmony equations using golden ratio, pi, and e constants.

        This function applies the golden harmony with three different mathematical constants
        (golden ratio, pi, and e) and returns their average.

        Parameters:
        R (float): The resonance parameter.
        F (float): The frequency parameter.
        E (float): The energy parameter.

        Returns:
        float: The average of the three harmony equations applied with different constants.
        """
        phi_harmony = self.apply_golden_harmony_with_constant(R, F, E, golden_ratio)
        pi_harmony = self.apply_golden_harmony_with_constant(R, F, E, pi)
        e_harmony = self.apply_golden_harmony_with_constant(R, F, E, e)
        return (phi_harmony + pi_harmony + e_harmony) / 3

    def apply_phi_pi_transition(self, state):
        """
        Apply the phi-pi transition to the given quantum state.

        This function applies the phi-pi transition operation to the input quantum state,
        which is a transformation between the golden ratio (phi) and pi.

        Parameters:
        state (numpy.ndarray): The quantum state vector to which the transition is applied.

        Returns:
        numpy.ndarray: The transformed quantum state after applying the phi-pi transition.
        """
        return phi_pi_transition(state)

    def analyze_dna_sequence(self, state):
        """
        Analyze a DNA sequence derived from a quantum state.

        This function converts a quantum state to a DNA sequence and performs
        various analyses on it, including counting valid codons, calculating
        GC content, and determining base balance.

        Parameters:
        state (numpy.ndarray): The quantum state vector to be analyzed.

        Returns:
        dict: A dictionary containing the analysis results with the following keys:
            - 'valid_codons': The number of valid codons in the DNA sequence.
            - 'gc_content': The GC content of the DNA sequence.
            - 'base_balance': A measure of the balance between different bases in the DNA sequence.
        """
        dna_sequence = state_to_dna(state)
        valid_codons = count_valid_codons(dna_sequence)
        gc_content = calculate_gc_content(dna_sequence)
        base_balance = calculate_base_balance(dna_sequence)
        return {
            'valid_codons': valid_codons,
            'gc_content': gc_content,
            'base_balance': base_balance
        }


    def optimize_dna_sequence(self, target_gc_content, target_base_balance):
        # Implement optimization logic here
        pass

    def default_objective_function(self, vector, param=1):
        """
        Calculate the default objective function value for a given vector.

        This function computes the sum of all elements in the input vector
        and multiplies it by a parameter value.

        Parameters:
        vector (array-like): The input vector to be evaluated.
        param (float, optional): A scaling parameter to multiply the sum by.
                                 Defaults to 1.

        Returns:
        float: The calculated objective function value.
        """
        return np.sum(vector) * param

    def run_experiment(self):
        """
        Run the harmonic balancing experiment.

        This method performs the main optimization loop of the Harmonic Balancer algorithm.
        It iterates through a specified number of iterations, generating new harmony vectors,
        evaluating their performance, analyzing DNA sequences, and updating the harmony memory.
        The experiment continues until the maximum number of iterations is reached or convergence is achieved.

        Parameters:
        None

        Returns:
        None

        Side effects:
        - Updates the HMCR (Harmony Memory Considering Rate) and PAR (Pitch Adjustment Rate) values.
        - Generates new harmony vectors and evaluates their performance.
        - Analyzes DNA sequences derived from the harmony vectors.
        - Updates the harmony memory with new solutions.
        - Appends scores and DNA analysis results to the history.
        - Checks for convergence and terminates the loop if achieved.
        """
        for iteration in range(self.max_iterations):
            self.HMCR = self.HMCR_initial + (self.HMCR_final - self.HMCR_initial) * (iteration / self.max_iterations)
            self.PAR = self.PAR_initial + (self.PAR_final - self.PAR_initial) * (iteration / self.max_iterations)
            new_harmony_vector = self.generate_new_harmony(transition_constant=0.1)
            evolved_state = new_harmony_vector
            score = self.objective_function(evolved_state)
            # Analyze DNA sequence
            dna_analysis = self.analyze_dna_sequence(evolved_state)
            self.update_harmony_memory(new_harmony_vector, evolved_state, score)
            self.history['scores'].append(score)
            self.history['dna_analysis'] = self.history.get('dna_analysis', []) + [dna_analysis]
            if self.check_convergence():
                break

    def generate_new_harmony(self, transition_constant):
        """
        Generate a new harmony vector based on the transition constant.

        This function creates a new harmony vector for the Harmonic Balancer algorithm,
        taking into account the provided transition constant.

        Parameters:
        transition_constant (float): A value that influences the generation of the new harmony vector.
                                     It determines the degree of transition or change in the vector.

        Returns:
        numpy.ndarray: A new harmony vector with length equal to the number of qubits in the system.
        """
        # Generate a new harmony vector based on the transition constant
        return generate_harmony_vector(self.num_qubits)

    def update_harmony_memory(self, new_vector, evolved_state, score):
        """
        Update the harmony memory with a new vector and its corresponding score.

        This function updates the harmony memory by potentially replacing the current best solution
        if the new score is higher, and adds the new vector to the harmony memory. If the harmony
        memory exceeds its maximum size, the oldest vector is removed.

        Parameters:
        new_vector (numpy.ndarray): The new harmony vector to be added to the memory.
        evolved_state (numpy.ndarray): The evolved quantum state corresponding to the new vector.
        score (float): The objective function score of the new vector.

        Returns:
        None
        """
        # Update the harmony memory with the new vector and score
        if score > self.best_score:
            self.best_score = score
            self.best_solution = new_vector
        self.harmony_memory.append(new_vector)
        if len(self.harmony_memory) > self.harmony_memory_size:
            self.harmony_memory.pop(0)

    def check_convergence(self):
        """
        Check if the algorithm has converged based on the difference between the last two scores.

        This method determines convergence by comparing the absolute difference between
        the last two scores in the history to a predefined convergence threshold.

        Returns:
            bool: True if the algorithm has converged, False otherwise.
                  Returns False if there are fewer than two scores in the history.
        """
        # Check if the algorithm has converged
        if len(self.history['scores']) < 2:
            return False
        return abs(self.history['scores'][-1] - self.history['scores'][-2]) < self.convergence_threshold

    def apply_golden_harmony(self, R, F, E):
        """
        Apply the Golden Harmony Theory Integration to calculate a harmonic value.

        This function implements the Golden Harmony Theory by combining resonance,
        frequency, and energy parameters in a specific mathematical formula.

        Parameters:
        R (float): The resonance parameter, representing the system's responsiveness.
        F (float): The frequency parameter, typically the oscillation frequency of the system.
        E (float): The energy parameter, representing the system's energy level.

        Returns:
        float: The calculated harmonic value based on the Golden Harmony Theory Integration.
        """
        # Apply the Golden Harmony Theory Integration
        return np.sqrt((R * F**2) + E**2)

    def apply_resonance_condition(self, F0, k, m, omega, b):
        """
        Apply the resonance condition to calculate the amplitude of oscillation.

        This function calculates the amplitude of oscillation for a forced harmonic oscillator
        under resonance conditions using the given parameters.

        Parameters:
        F0 (float): The amplitude of the driving force.
        k (float): The spring constant or stiffness of the system.
        m (float): The mass of the oscillating object.
        omega (float): The angular frequency of the driving force.
        b (float): The damping coefficient of the system.

        Returns:
        float: The calculated amplitude of oscillation under the given resonance condition.
        """
        # Apply the Resonance Condition
        return F0 / np.sqrt((k - m * omega**2)**2 + (b * omega)**2)

    def apply_wave_interference(self, y1, y2):
        """
        Apply wave interference by summing two wave amplitudes.

        This function simulates the interference of two waves by adding their amplitudes.
        It represents the superposition principle in wave theory.

        Parameters:
        y1 (float or array-like): The amplitude of the first wave.
        y2 (float or array-like): The amplitude of the second wave.

        Returns:
        float or array-like: The resulting amplitude after interference,
                             which is the sum of the input amplitudes.
        """
        # Apply the Wave Interference
        return y1 + y2

    def harmonic_series(self, n, f1):
        """
        Calculate the frequency of the nth harmonic in a harmonic series.

        This function computes the frequency of the nth harmonic based on the fundamental frequency.
        In a harmonic series, each subsequent harmonic is an integer multiple of the fundamental frequency.

        Parameters:
        n (int): The harmonic number (1 for fundamental, 2 for second harmonic, etc.).
        f1 (float): The fundamental frequency of the harmonic series.

        Returns:
        float: The frequency of the nth harmonic in the series.
        """
        return n * f1

    def harmonic_mean(self, a, b):
        """
        Calculate the harmonic mean of two numbers.

        The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals
        of the given numbers. It is often used for averaging rates.

        Parameters:
        a (float): The first number.
        b (float): The second number.

        Returns:
        float: The harmonic mean of the two input numbers.
        """
        return 2 * a * b / (a + b)

    def resonant_harmony_equation(self, R, F, E):
        """
        Calculate the resonant harmony value based on given parameters.

        This function computes a resonant harmony value using a combination of
        resonance, frequency, and energy parameters in a specific mathematical formula.

        Parameters:
        R (float): The resonance parameter, representing the system's responsiveness.
        F (float): The frequency parameter, typically the oscillation frequency of the system.
        E (float): The energy parameter, representing the system's energy level.

        Returns:
        float: The calculated resonant harmony value, which represents the
               combined effect of resonance, frequency, and energy in the system.
        """
        return np.sqrt((R * F)**2 + E**2)

    def objective_function_phi(self, R, F, E):
        """
        Calculate the objective function value based on the golden ratio.

        This function computes the absolute difference between the resonant harmony equation
        result and the golden ratio, serving as an optimization target.

        Parameters:
        R (float): The resonance parameter, representing the system's responsiveness.
        F (float): The frequency parameter, typically the oscillation frequency of the system.
        E (float): The energy parameter, representing the system's energy level.

        Returns:
        float: The absolute difference between the resonant harmony equation result and the golden ratio.
        """
        return abs(self.resonant_harmony_equation(R, F, E) - golden_ratio)

    def apply_golden_harmony_with_constant(self, R, F, E, constant):
        """
        Apply the golden harmony with a specified constant multiplier.

        This function calculates the golden harmony using the base parameters
        and then multiplies the result by a given constant.

        Parameters:
        R (float): The resonance parameter, representing the system's responsiveness.
        F (float): The frequency parameter, typically the oscillation frequency of the system.
        E (float): The energy parameter, representing the system's energy level.
        constant (float): A multiplier to be applied to the golden harmony result.

        Returns:
        float: The result of the golden harmony calculation multiplied by the constant.
        """
        return self.apply_golden_harmony(R, F, E) * constant

    def quantum_entanglement_possibilities(self, n, f, P):
        """
        Calculate the total quantum entanglement possibilities for a system.

        This function computes the sum of entanglement possibilities for all pairs
        of particles in a quantum system, based on a given interaction function and
        probability distribution.

        Parameters:
        n (int): The number of particles in the quantum system.
        f (callable): A function that calculates the interaction strength between two particles.
                      It should take two integer arguments (particle indices) and return a float.
        P (callable): A function that calculates the probability of entanglement between two particles.
                      It should take two integer arguments (particle indices) and return a float.

        Returns:
        float: The total sum of entanglement possibilities for all particle pairs in the system.
        """
        T = 0
        for i in range(n):
            for j in range(i+1, n):
                T += f(i, j) * P(i, j)
        return T

    def simulate_microwave_effects(self, system, frequency, duration):
        # Placeholder for microwave simulation
        # This method should be implemented based on the specific system and parameters
        pass

    def plot_convergence(self):
        """
        Plot the convergence of the Harmonic Balancer Algorithm.

        This function creates a line plot showing the progression of the best scores
        over the iterations of the algorithm. It visualizes how the algorithm's
        performance improves over time.

        Parameters:
        None

        Returns:
        None

        Side effects:
        - Displays a matplotlib plot showing the convergence of the algorithm.
        - The plot includes a title, labeled axes, and a grid for better readability.
        """
        plt.plot(self.history['scores'])
        plt.title('Convergence of Harmonic Balancer Algorithm')
        plt.xlabel('Iteration')
        plt.ylabel('Best Score')
        plt.grid(True)
        plt.show()

    def plot_dna_analysis(self):
        """
        Plot the DNA analysis results over iterations.

        This function creates two subplots:
        1. GC Content over Iterations
        2. Base Balance over Iterations

        The function uses the DNA analysis data stored in the object's history
        to visualize how these properties change throughout the algorithm's execution.

        Parameters:
        None

        Returns:
        None

        Side effects:
        - Displays a matplotlib figure with two subplots showing the progression
          of GC Content and Base Balance over iterations.
        - The plot includes titles, labeled axes, and uses tight layout for better visibility.
        """
        gc_content = [analysis['gc_content'] for analysis in self.history['dna_analysis']]
        base_balance = [analysis['base_balance'] for analysis in self.history['dna_analysis']]

        plt.figure(figsize=(12, 6))
        plt.subplot(2, 1, 1)
        plt.plot(gc_content)
        plt.title('GC Content over Iterations')
        plt.xlabel('Iteration')
        plt.ylabel('GC Content')

        plt.subplot(2, 1, 2)
        plt.plot(base_balance)
        plt.title('Base Balance over Iterations')
        plt.xlabel('Iteration')
        plt.ylabel('Base Balance')

        plt.tight_layout()
        plt.show()

    def quantum_circuit_simulation(self):
        """
        Simulates a quantum circuit with 4 qubits and applies specific quantum gates.

        This function creates a quantum circuit with 4 qubits, applies Hadamard and Pauli gates
        to different qubits, measures all qubits, and then executes the circuit on a quantum
        simulator. The simulation is run 1000 times to gather statistics on the measurement outcomes.

        Parameters:
        self: The instance of the class containing this method.

        Returns:
        dict: A dictionary where keys are binary strings representing the measured states,
              and values are the number of times each state was observed in the 1000 shots.
              For example, {'0000': 500, '0001': 250, ...}
        """
        # Create a quantum circuit with 4 qubits
        qc = QuantumCircuit(4, 4)

        # Apply gates as per the configuration
        qc.h(0)  # Hadamard gate on Q0 (Graphene)
        qc.x(1)  # Pauli-X gate on Q1 (Silicon)
        qc.y(2)  # Pauli-Y gate on Q2 (Silicon)
        qc.z(3)  # Pauli-Z gate on Q3 (Diamond)

        # Measure all qubits
        qc.measure_all()

        # Execute the circuit on a simulator
        backend = Aer.get_backend('qasm_simulator')
        job = execute(qc, backend, shots=1000)
        result = job.result()

        # Get the count of measurement outcomes
        counts = result.get_counts(qc)

        return counts

    def simulate_fusion_reactor(self, parameters):
        # Placeholder for fusion reactor simulation
        # This method should be implemented based on specific fusion reactor parameters
        pass

    def simulate_mechanical_vibration(self, mass, stiffness, damping, force):
        """
        Simulate a simple mechanical vibration system.

        This function calculates the displacement of a mass-spring-damper system
        over time when subjected to a constant force.

        Parameters:
        mass (float): The mass of the object in kg.
        stiffness (float): The spring constant in N/m.
        damping (float): The damping coefficient in Ns/m.
        force (float): The applied constant force in N.

        Returns:
        tuple: A tuple containing two numpy arrays:
            - t (numpy.ndarray): Time values from 0 to 10 seconds.
            - x (numpy.ndarray): Displacement values corresponding to each time point.
        """
        # Simulate a simple mechanical vibration system
        omega_n = np.sqrt(stiffness / mass)  # Natural frequency
        zeta = damping / (2 * np.sqrt(mass * stiffness))  # Damping ratio

        t = np.linspace(0, 10, 1000)
        x = force / stiffness * (1 - np.exp(-zeta * omega_n * t) * 
             (np.cos(omega_n * np.sqrt(1 - zeta**2) * t) + 
              zeta / np.sqrt(1 - zeta**2) * np.sin(omega_n * np.sqrt(1 - zeta**2) * t)))

        return t, x

    def optimize_energy_distribution(self, grid_parameters):
        # Placeholder for energy grid optimization
        # This method should be implemented based on specific grid parameters
        pass

    def simulate_mri(self, parameters):
        # Placeholder for MRI simulation
        # This method should be implemented based on specific MRI parameters
        pass