import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime, timedelta

# Generate sample environmental data (24 hours)
time_range = pd.date_range(start='2024-01-01', periods=288, freq='5min')
np.random.seed(42)

# Realistic environmental patterns
temperature = 18 + 8 * np.sin(2 * np.pi * np.arange(288) / 288) + np.random.normal(0, 0.5, 288)
humidity = 65 + 20 * np.sin(2 * np.pi * np.arange(288) / 288 + np.pi) + np.random.normal(0, 2, 288)
pressure = 1013 + 3 * np.sin(2 * np.pi * np.arange(288) / 288 + np.pi/4) + np.random.normal(0, 0.8, 288)
soil_moisture = 45 + 10 * np.sin(2 * np.pi * np.arange(288) / 288 + np.pi/2) + np.random.normal(0, 1.5, 288)

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))

# Temperature
ax1.plot(time_range, temperature, 'r-', linewidth=1.5, alpha=0.8)
ax1.axhline(y=25, color='orange', linestyle='--', alpha=0.7, label='High Threshold')
ax1.axhline(y=15, color='blue', linestyle='--', alpha=0.7, label='Low Threshold')
ax1.set_ylabel('Temperature (°C)')
ax1.set_title('Temperature Monitoring')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Humidity
ax2.plot(time_range, humidity, 'b-', linewidth=1.5, alpha=0.8)
ax2.axhline(y=80, color='red', linestyle='--', alpha=0.7, label='High Alert')
ax2.axhline(y=40, color='orange', linestyle='--', alpha=0.7, label='Low Alert')
ax2.set_ylabel('Humidity (%)')
ax2.set_title('Relative Humidity')
ax2.legend()
ax2.grid(True, alpha=0.3)

# Atmospheric Pressure
ax3.plot(time_range, pressure, 'g-', linewidth=1.5, alpha=0.8)
ax3.set_ylabel('Pressure (hPa)')
ax3.set_title('Atmospheric Pressure')
ax3.grid(True, alpha=0.3)

# Soil Moisture
ax4.plot(time_range, soil_moisture, 'brown', linewidth=1.5, alpha=0.8)
ax4.axhline(y=30, color='red', linestyle='--', alpha=0.7, label='Irrigation Needed')
ax4.axhline(y=60, color='blue', linestyle='--', alpha=0.7, label='Optimal Range')
ax4.set_ylabel('Soil Moisture (%)')
ax4.set_title('Soil Moisture Content')
ax4.set_xlabel('Time')
ax4.legend()
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.xticks(rotation=45)
plt.show()

# Correlation matrix and scatter plots
data = pd.DataFrame({
    'Temperature': temperature,
    'Humidity': humidity,
    'Pressure': pressure,
    'Soil_Moisture': soil_moisture
})

# Calculate correlation matrix
correlation_matrix = data.corr()

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))

# Correlation heatmap
im = ax1.imshow(correlation_matrix, cmap='coolwarm', vmin=-1, vmax=1)
ax1.set_xticks(range(len(correlation_matrix.columns)))
ax1.set_yticks(range(len(correlation_matrix.columns)))
ax1.set_xticklabels(correlation_matrix.columns, rotation=45)
ax1.set_yticklabels(correlation_matrix.columns)
ax1.set_title('Sensor Data Correlation Matrix')

# Add correlation values to heatmap
for i in range(len(correlation_matrix.columns)):
    for j in range(len(correlation_matrix.columns)):
        ax1.text(j, i, f'{correlation_matrix.iloc[i, j]:.2f}', 
                ha='center', va='center', color='white' if abs(correlation_matrix.iloc[i, j]) > 0.5 else 'black')

plt.colorbar(im, ax=ax1)

# Temperature vs Humidity scatter
ax2.scatter(temperature, humidity, alpha=0.6, c='blue', s=20)
ax2.set_xlabel('Temperature (°C)')
ax2.set_ylabel('Humidity (%)')
ax2.set_title('Temperature vs Humidity')
ax2.grid(True, alpha=0.3)

# Pressure vs Temperature
ax3.scatter(pressure, temperature, alpha=0.6, c='green', s=20)
ax3.set_xlabel('Pressure (hPa)')
ax3.set_ylabel('Temperature (°C)')
ax3.set_title('Pressure vs Temperature')
ax3.grid(True, alpha=0.3)

# Soil Moisture Distribution
ax4.hist(soil_moisture, bins=20, alpha=0.7, color='brown', edgecolor='black')
ax4.axvline(x=np.mean(soil_moisture), color='red', linestyle='--', linewidth=2, label=f'Mean: {np.mean(soil_moisture):.1f}%')
ax4.set_xlabel('Soil Moisture (%)')
ax4.set_ylabel('Frequency')
ax4.set_title('Soil Moisture Distribution')
ax4.legend()
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Battery and solar charging analysis
hours = np.arange(0, 24, 0.5)
solar_irradiance = np.maximum(0, np.sin(np.pi * (hours - 6) / 12))  # Daylight hours
solar_power = solar_irradiance * 2.5  # Watts peak

# Battery simulation
battery_capacity = 2000  # mAh
consumption_rate = 85    # mA average
charging_efficiency = 0.85

battery_level = []
current_charge = battery_capacity

for hour in hours:
    solar_hour = int(hour * 2) % len(solar_irradiance)
    charging_current = solar_power[solar_hour] * 200 * charging_efficiency  # mA
    net_current = charging_current - consumption_rate
    current_charge += net_current * 0.5  # 0.5 hour intervals
    current_charge = max(0, min(battery_capacity, current_charge))
    battery_level.append(current_charge)

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))

# Solar irradiance
ax1.fill_between(hours, solar_irradiance, alpha=0.6, color='gold', label='Solar Irradiance')
ax1.set_ylabel('Relative Irradiance')
ax1.set_title('Solar Energy Availability')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Power generation vs consumption
ax2.plot(hours, solar_power, 'orange', linewidth=2, label='Solar Power Generation')
ax2.axhline(y=consumption_rate/1000*3.7, color='red', linestyle='--', linewidth=2, label='Power Consumption')
ax2.set_ylabel('Power (W)')
ax2.set_title('Power Generation vs Consumption')
ax2.legend()
ax2.grid(True, alpha=0.3)

# Battery level
battery_percentage = [(level/battery_capacity)*100 for level in battery_level]
ax3.plot(hours, battery_percentage, 'green', linewidth=2, label='Battery Level')
ax3.axhline(y=20, color='red', linestyle='--', alpha=0.7, label='Low Battery Alert')
ax3.axhline(y=80, color='blue', linestyle='--', alpha=0.7, label='Optimal Range')
ax3.set_xlabel('Hour of Day')
ax3.set_ylabel('Battery Level (%)')
ax3.set_title('Battery Charge Level Over 24 Hours')
ax3.legend()
ax3.grid(True, alpha=0.3)
ax3.set_ylim(0, 100)

plt.tight_layout()
plt.show()

# System performance metrics
days = range(1, 31)  # 30 days of operation
uptime = np.random.normal(99.5, 0.8, 30)
uptime = np.clip(uptime, 95, 100)

packet_loss = np.random.exponential(0.3, 30)
packet_loss = np.clip(packet_loss, 0, 2)

sensor_drift = np.cumsum(np.random.normal(0, 0.02, 30))
sensor_drift = np.clip(sensor_drift, -0.5, 0.5)

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 10))

# System uptime
ax1.plot(days, uptime, 'g-', linewidth=2, marker='o', markersize=4)
ax1.axhline(y=99, color='orange', linestyle='--', alpha=0.7, label='Target Uptime')
ax1.set_ylabel('Uptime (%)')
ax1.set_title('System Uptime Performance')
ax1.legend()
ax1.grid(True, alpha=0.3)
ax1.set_ylim(95, 100)

# Data transmission reliability
ax2.plot(days, packet_loss, 'r-', linewidth=2, marker='s', markersize=4)
ax2.axhline(y=1, color='orange', linestyle='--', alpha=0.7, label='Acceptable Loss')
ax2.set_ylabel('Packet Loss (%)')
ax2.set_title('Data Transmission Reliability')
ax2.legend()
ax2.grid(True, alpha=0.3)

# Sensor calibration drift
ax3.plot(days, sensor_drift, 'purple', linewidth=2, marker='^', markersize=4)
ax3.axhline(y=0.3, color='red', linestyle='--', alpha=0.7, label='Recalibration Needed')
ax3.axhline(y=-0.3, color='red', linestyle='--', alpha=0.7)
ax3.set_ylabel('Calibration Drift (°C)')
ax3.set_title('Temperature Sensor Drift')
ax3.legend()
ax3.grid(True, alpha=0.3)

# Alert frequency
alert_types = ['Temperature', 'Humidity', 'Soil Moisture', 'Battery', 'Connectivity']
alert_counts = [12, 8, 15, 3, 5]
colors = ['red', 'blue', 'brown', 'orange', 'purple']

bars = ax4.bar(alert_types, alert_counts, color=colors, alpha=0.7)
ax4.set_ylabel('Alert Count (30 days)')
ax4.set_title('Alert Frequency by Type')
ax4.tick_params(axis='x', rotation=45)

for bar, count in zip(bars, alert_counts):
    ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3, 
             str(count), ha='center', va='bottom')

plt.tight_layout()
plt.show()