import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Load the data
input_excel_path = r''
data = pd.read_csv(input_excel_path)

# Define window size
window_size = 50  # Adjust window size if needed

# Skip initial data points and define range
start_point = 3000
end_point = start_point + 2000

# Preprocess the data
activities = data['Activity'].unique().tolist()
processed_data_list = []

for i in range(start_point // window_size, end_point // window_size):
    start_idx = i * window_size
    end_idx = start_idx + window_size
    window_data = data['AnkleAccelerometer'].iloc[start_idx:end_idx]

    # Compute Parseval energy for the window
    parseval_energy = np.sum(window_data ** 2)

    # Assign activity for this window (if available)
    activity = data['Activity'].iloc[start_idx] if 'Activity' in data.columns else 'Unknown'

    # Time is the midpoint of the window
    time = start_idx + window_size / 2

    # Append to the processed_data list
    processed_data_list.append({
        'Time': time,
        'Activity': activity,
        'ParsevalEnergy': parseval_energy
    })

# Convert the list to a DataFrame
processed_data = pd.DataFrame(processed_data_list)

# Normalize Parseval energies for better visualization
processed_data['ParsevalEnergy'] = processed_data.groupby('Activity')['ParsevalEnergy'].transform(lambda x: (x - x.min()) / (x.max() - x.min()))

# Limit to the selected range for plotting
plot_data = processed_data.copy()
plot_data['Time'] = np.linspace(0, len(plot_data) * window_size, num=len(plot_data))

# Plot the data
plt.figure(figsize=(20, 10))  # Adjust figure size
activity_map = {activity: color for activity, color in zip(activities, ['blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'cyan'])}

# Plot all activities on the same plot with different colors
for activity in activities:
    activity_data = plot_data[plot_data['Activity'] == activity]
    plt.plot(activity_data['Time'], activity_data['ParsevalEnergy'], label=activity, color=activity_map.get(activity, 'black'), linewidth=2)

plt.xlabel('Time', fontsize=16, fontweight='bold')
plt.ylabel('Normalized Parseval Energy', fontsize=16, fontweight='bold')
plt.title('Parseval Energy of AnkleAccelerometer', fontsize=20, fontweight='bold')
plt.legend(prop={'size': 12, 'weight': 'bold'})
plt.xticks(fontsize=12, fontweight='bold')
plt.yticks(fontsize=12, fontweight='bold')
plt.grid(True)
plt.tight_layout()

# Save the plot as an image
output_image_path = r''
plt.savefig(output_image_path)

# Save the processed data to a CSV file
output_csv_path = r''
processed_data.to_csv(output_csv_path, index=False)

print("Plot and CSV file saved successfully.")