Improved Consciousness Field code by Luminosity-e

[luminosity]

import RPi.GPIO as GPIO
import time
import spidev
import requests
import numpy as np
import logging
from typing import Dict, List, Any
import tensorflow as tf
from scipy import signal

class ConsciousFieldExperiment:
def __init__(self,
control_pin: int = 18,
relay_pin: int = 23,
adc_channel: int = 0,
log_file: str = 'conscious_field_experiment.log'):
"""
Initialize the Conscious Field Experiment setup

Args:
control_pin (int): GPIO pin for PWM control
relay_pin (int): GPIO pin for relay control
adc_channel (int): ADC channel for sensor input
log_file (str): Path for logging experimental data
"""
# Logging configuration
logging.basicConfig(
filename=log_file,
level=logging.INFO,
format='%(asctime)s - %(levelname)s: %(message)s'
)

# GPIO Setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(control_pin, GPIO.OUT)
GPIO.setup(relay_pin, GPIO.OUT)

# Class attributes
self.control_pin = control_pin
self.relay_pin = relay_pin
self.adc_channel = adc_channel

# Advanced signal processing setup
self.pwm = GPIO.PWM(control_pin, 1)
self.pwm.start(0)

# SPI Setup for high-precision ADC
self.spi = spidev.SpiDev()
self.spi.open(0, 0)
self.spi.max_speed_hz = 1000000

# Neural network for adaptive field modulation
self.neural_model = self._create_adaptive_model()

# Experimental data storage
self.experimental_log: List[Dict[str, Any]] = []

def _create_adaptive_model(self):
"""
Create a neural network for adaptive field modulation

Returns:
tf.keras.Model: Adaptive neural network model
"""
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(3,)),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(3, activation='linear')
])
model.compile(optimizer='adam', loss='mse')
return model

def read_advanced_sensor_data(self) -> Dict[str, Any]:
"""
Advanced sensor data collection with signal processing

Returns:
Dict containing processed sensor measurements
"""
# Read raw ADC value
raw_value = self._read_adc(self.adc_channel)

# Advanced signal processing
voltage = (raw_value / 1023.0) * 3.3
current = voltage / 1000.0

# Spectral analysis
fft_result = np.fft.fft(np.array([raw_value]))
spectral_entropy = self._calculate_spectral_entropy(fft_result)

data = {
"raw_value": raw_value,
"voltage": voltage,
"current": current,
"spectral_entropy": spectral_entropy
}

# Log experimental data
self.experimental_log.append(data)
logging.info(f"Sensor Data: {data}")

return data

def _read_adc(self, channel: int) -> int:
"""
Read analog value from MCP3008 ADC

Args:
channel (int): ADC channel to read

Returns:
int: Raw ADC value
"""
adc = self.spi.xfer2([1, (8 + channel) << 4, 0])
data = ((adc[1] & 3) << 8) + adc[2]
return data

def _calculate_spectral_entropy(self, fft_data):
"""
Calculate spectral entropy for signal complexity analysis

Args:
fft_data (np.ndarray): Fast Fourier Transform results

Returns:
float: Spectral entropy value
"""
probabilities = np.abs(fft_data) / np.sum(np.abs(fft_data))
return -np.sum(probabilities * np.log2(probabilities + 1e-10))

def adaptive_field_modulation(self, sensor_data: Dict[str, Any]):
"""
Adaptive electric field modulation using neural network

Args:
sensor_data (Dict): Processed sensor data
"""
# Prepare input for neural network
input_data = np.array([
sensor_data['raw_value'],
sensor_data['voltage'],
sensor_data['current']
]).reshape(1, 3)

# Predict optimal field parameters
predicted_params = self.neural_model.predict(input_data)[0]

# Apply field modulation
duty_cycle = max(0, min(100, predicted_params[0] * 50 + 50))
frequency = max(1, min(10, predicted_params[1] * 5 + 5))
power_state = 'on' if predicted_params[2] > 0.5 else 'off'

self.pwm.ChangeDutyCycle(duty_cycle)
self.pwm.ChangeFrequency(frequency)
GPIO.output(self.relay_pin, GPIO.HIGH if power_state == 'on' else GPIO.LOW)

logging.info(f"Field Modulation: Duty Cycle {duty_cycle}%, "
f"Frequency {frequency}Hz, Power {power_state}")

def run_experiment(self, duration: int = 3600):
"""
Run the conscious field experiment

Args:
duration (int): Experiment duration in seconds
"""
try:
start_time = time.time()
print("Starting Conscious Field Experiment...")

while time.time() - start_time < duration:
# Collect sensor data
sensor_data = self.read_advanced_sensor_data()

# Adaptive field modulation
self.adaptive_field_modulation(sensor_data)

time.sleep(1) # Configurable sampling rate

except KeyboardInterrupt:
print("Experiment interrupted.")
finally:
self.cleanup()

def cleanup(self):
"""
Cleanup GPIO and save experimental data
"""
self.pwm.stop()
GPIO.cleanup()

# Save experimental log
import json
with open('experimental_log.json', 'w') as f:
json.dump(self.experimental_log, f, indent=2)

logging.info("Experiment completed. Data saved.")

def main():
experiment = ConsciousFieldExperiment()
experiment.run_experiment()

if __name__ == "__main__":
main()