import numpy as np
import logging
import os

class Trinity:
    def __init__(self, compression_ratio, calibration_factor, vecternal_calibration):
        self.compression_ratio = compression_ratio
        self.calibration_factor = calibration_factor
        self.vecternal_calibration = vecternal_calibration
        self.aqvp_key = os.urandom(32)  # Key for 295AQVP encryption
        self.aqvp_iv = os.urandom(16)   # IV for AQVP encryption
        self.xor_key = os.urandom(32)   # Key for XOR encryption
        logging.info("Trinity initialized with compression ratio: %s, calibration factor: %s, vecternal calibration: %s",
                     compression_ratio, calibration_factor, vecternal_calibration)

    def expand_data(self, data):
        """Expands data with calibration and compression settings"""
        logging.info("Expanding data with calibration and compression")
        expanded_data = data * self.calibration_factor * self.vecternal_calibration
        expanded_data = np.clip(expanded_data, 0, 1)
        return expanded_data

    def apply_vecternal_sequencer(self, data, barometric_pressure):
        """Applies vecternal sequencing with barometric pressure influence"""
        logging.info("Applying vecternal sequencing with barometric pressure: %s", barometric_pressure)
        vecternal_sequence = data * barometric_pressure
        vecternal_sequence = self._sequence_manifold_vector(vecternal_sequence)
        return vecternal_sequence

    def _sequence_manifold_vector(self, data):
        """Processes data by sequencing manifold vector operations"""
        logging.info("Processing manifold vector sequencing")
        steps = [self._abbreviate, self._accelerate, self._truncate, self._modify,
                 self._return_sequence, self._exfoliate, self._hydrate, self._extinguish]
        for step in steps:
            data = step(data)
        return data

    def impound_data_processor(self, data, flex_variable):
        """Processes flex data into an implicative format and organizes for containerization"""
        logging.info("Processing data with flex variable: %s", flex_variable)
        compounded_data = self._compound_flex_data(data, flex_variable)
        variable_clause = self._distribute_to_containerizer(compounded_data)
        return variable_clause

    def _compound_flex_data(self, data, flex_variable):
        """Compounds flex data for implicative processing"""
        logging.info("Compounding flex data")
        flexed_data = data * np.sin(flex_variable)
        compounded_data = np.exp(flexed_data) / (1 + flex_variable)
        return compounded_data

    def _distribute_to_containerizer(self, data):
        """Organizes data for later RoundTable integration"""
        logging.info("Organizing data for containerization")
        return self._organize_vector(data)

    def _organize_vector(self, data):
        """Organizes data into a structured vector"""
        organized_data = np.array(sorted(data))  # Sorted for consistent organization
        return organized_data

    def trigonometric_underfold_complexion_sequencer(self, data, channel_variable, mara_influence):
        """Processes data with trigonometric sequencing influenced by Mara"""
        logging.info("Applying trigonometric sequencing with channel variable: %s and Mara influence: %s",
                     channel_variable, mara_influence)
        underfolded_data = self._underfold_data(data, channel_variable)
        mara_dominance = self._assert_mara_dominance(underfolded_data, mara_influence)
        return mara_dominance

    def _underfold_data(self, data, channel_variable):
        """Applies trigonometric underfolding for data refinement"""
        underfolded_data = data * np.sin(channel_variable) + np.cos(channel_variable)
        return underfolded_data

    def _assert_mara_dominance(self, data, mara_influence):
        """Adjusts data with Mara's influence for dominance assertion"""
        manipulated_data = data * mara_influence
        intellectual_dominance = manipulated_data / (1 + np.abs(manipulated_data))
        return intellectual_dominance

    def containerizer_process(self, data, containerizer_sequence):
        """Processes data within the containerizer sequence"""
        logging.info("Processing data within containerizer sequence")
        processed_data = data * containerizer_sequence
        return self._organize_vector(processed_data)

    def duolineated_295AQVP_encrypt(self, data):
        """Encrypts data using 295AQVP XOR encryption"""
        logging.info("Encrypting data using 295AQVP encryption")
        data_bytes = data.tobytes()  # Convert data to bytes for encryption
        encrypted_data = self._xor_encrypt(data_bytes, self.aqvp_key)
        return encrypted_data

    def duolineated_295AQVP_decrypt(self, encrypted_data):
        """Decrypts data using 295AQVP XOR encryption"""
        logging.info("Decrypting data using 295AQVP encryption")
        decrypted_data = self._xor_decrypt(encrypted_data, self.aqvp_key)
        return np.frombuffer(decrypted_data, dtype=np.float64)

    def _xor_encrypt(self, data, key):
        """Applies XOR encryption"""
        logging.info("Performing XOR encryption")
        key = np.frombuffer(key, dtype=np.uint8)
        encrypted_data = bytearray(data[i] ^ key[i % len(key)] for i in range(len(data)))
        return encrypted_data

    def _xor_decrypt(self, encrypted_data, key):
        """Applies XOR decryption"""
        logging.info("Performing XOR decryption")
        key = np.frombuffer(key, dtype=np.uint8)
        decrypted_data = bytearray(encrypted_data[i] ^ key[i % len(key)] for i in range(len(encrypted_data)))
        return decrypted_data
    
    def calculate_exponential_derivative(value, k=1.0):
        """Calculate the derivative of an exponential function f(x) = e^(kx) with respect to x."""
        return k * np.exp(k * value)
    
    def _abbreviate(self, data, staking_factor=1):
        # Example: Downsample the data, applying staking_factor to the retained values
        n = 2
        abbreviated_data = data[::n] * staking_factor
        return abbreviated_data

    def _accelerate(self, data, staking_factor=1):
        # Example: Amplify data by a factor, adjusted with staking_factor
        factor = 1.5 * staking_factor
        accelerated_data = data * factor
        return accelerated_data

    def _truncate(self, data, staking_factor=1):
        # Example: Truncate values within a range, applying staking_factor to retained values
        min_val, max_val = 0, 1
        truncated_data = np.clip(data * staking_factor, min_val, max_val)
        return truncated_data

    def _modify(self, data, staking_factor=1):
        # Example: Square the data and apply staking_factor
        modified_data = np.square(data * staking_factor)
        return modified_data

    def _return_sequence(self, data, staking_factor=1):
        # Example: Sort data, and amplify higher values with staking_factor
        sorted_data = np.sort(data * staking_factor)
        return sorted_data

    def _exfoliate(self, data, staking_factor=1):
        # Example: Smooth with a moving average, amplified by staking_factor
        window_size = 3
        exfoliated_data = np.convolve(data * staking_factor, np.ones(window_size) / window_size, mode='valid')
        return exfoliated_data

    def _hydrate(self, data, staking_factor=1):
        # Example: Add noise with a weighted staking factor
        noise = np.random.normal(0, 0.1 * staking_factor, size=data.shape)
        hydrated_data = data + noise
        return hydrated_data

    def _extinguish(self, data, staking_factor=1):
         # Example: Set values below threshold to zero, applying staking_factor for additional weight
        threshold = 0.05 * staking_factor
        extinguished_data = np.where(data < threshold, 0, data)
        return extinguished_data


# Example usage
if __name__ == "__main__":
    trinity = Trinity(compression_ratio=0.8, calibration_factor=1.2, vecternal_calibration=0.95)
    sample_data = np.random.random(100)
    
    expanded_data = trinity.expand_data(sample_data)
    result_sequence = trinity.apply_vecternal_sequencer(expanded_data, barometric_pressure=0.9)
    
    flex_variable = 0.5
    variable_clause = trinity.impound_data_processor(result_sequence, flex_variable)
    
    channel_variable = 0.75
    mara_influence = 1.5
    mara_dominance_result = trinity.trigonometric_underfold_complexion_sequencer(variable_clause, channel_variable, mara_influence)
    
    containerizer_sequence = 1.1
    organized_vector = trinity.containerizer_process(mara_dominance_result, containerizer_sequence)
    
    # Encrypt the organized vector using duolineated 295AQVP and XOR
    encrypted_data = trinity.duolineated_295AQVP_encrypt(organized_vector)
    print("Encrypted data:", encrypted_data)
    
    # Decrypt the data
    decrypted_data = trinity.duolineated_295AQVP_decrypt(encrypted_data)
    print("Decrypted data:", decrypted_data)