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

from config import DATA_PROCESSED, DATA_METADATA, FIGURES

# Load data
trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
barrier_data = pd.read_csv(DATA_METADATA / '2025_07_15_barrier_opening.csv')

# Convert opening_time to milliseconds and create a mapping
barrier_data['opening_time_ms'] = barrier_data['opening_time'] * 1000
opening_times = dict(zip(barrier_data['machine'], barrier_data['opening_time_ms']))


def align_to_opening_time(df, opening_times, max_time=300000):
    """Align distance data to barrier opening time.

    Args:
        df (pd.DataFrame): Distance data.
        opening_times (dict): Machine to opening time mapping.
        max_time (int): Maximum time in ms to include.

    Returns:
        pd.DataFrame: Aligned data filtered to +/-150s around opening.
    """
    df_aligned = df.copy()
    df_aligned['aligned_time'] = np.nan

    for machine in df['machine_name'].unique():
        if machine in opening_times:
            opening_time = opening_times[machine]
            mask = (df['machine_name'] == machine) & (df['t'] <= max_time)
            df_aligned.loc[mask, 'aligned_time'] = df.loc[mask, 't'] - opening_time

    df_aligned = df_aligned.dropna(subset=['aligned_time'])
    df_aligned = df_aligned[(df_aligned['aligned_time'] >= -150000) &
                           (df_aligned['aligned_time'] <= 150000)]

    return df_aligned


# Align the data
trained_aligned = align_to_opening_time(trained_distances, opening_times)
untrained_aligned = align_to_opening_time(untrained_distances, opening_times)

# Calculate average distance over aligned time
trained_avg = trained_aligned.groupby('aligned_time')['distance'].mean()
untrained_avg = untrained_aligned.groupby('aligned_time')['distance'].mean()

# Apply smoothing
window_size = 50
trained_smooth = trained_avg.rolling(window=window_size, center=True).mean()
untrained_smooth = untrained_avg.rolling(window=window_size, center=True).mean()

# Create the plot
plt.figure(figsize=(12, 6))

plt.plot(trained_smooth.index/1000, trained_smooth.values,
         label='Trained (smoothed)', color='blue', linewidth=2)
plt.plot(untrained_smooth.index/1000, untrained_smooth.values,
         label='Untrained (smoothed)', color='red', linewidth=2)

plt.axvline(x=0, color='black', linestyle='--', alpha=0.7, label='Barrier Opening')

plt.xlabel('Time (seconds relative to barrier opening)')
plt.ylabel('Average Distance')
plt.title('Average Distance Between Flies Aligned to Barrier Opening Time')
plt.legend()
plt.grid(True, alpha=0.3)
plt.xlim(-150, 150)

plt.tight_layout()
plt.savefig(FIGURES / 'avg_distance_aligned_to_opening.png', dpi=300, bbox_inches='tight')
plt.show()

# Print statistics
print("Trained flies (aligned to barrier opening):")
print(f"  Data points: {len(trained_aligned)}")
print(f"  Mean distance: {trained_aligned['distance'].mean():.2f}")
print(f"  Std distance: {trained_aligned['distance'].std():.2f}")

print("\nUntrained flies (aligned to barrier opening):")
print(f"  Data points: {len(untrained_aligned)}")
print(f"  Mean distance: {untrained_aligned['distance'].mean():.2f}")
print(f"  Std distance: {untrained_aligned['distance'].std():.2f}")

# Pre/post analysis
trained_pre = trained_aligned[trained_aligned['aligned_time'] < 0]
trained_post = trained_aligned[trained_aligned['aligned_time'] > 0]
untrained_pre = untrained_aligned[untrained_aligned['aligned_time'] < 0]
untrained_post = untrained_aligned[untrained_aligned['aligned_time'] > 0]

print("\nPre-opening period (t < 0):")
print(f"  Trained mean distance: {trained_pre['distance'].mean():.2f}")
print(f"  Untrained mean distance: {untrained_pre['distance'].mean():.2f}")

print("\nPost-opening period (t > 0):")
print(f"  Trained mean distance: {trained_post['distance'].mean():.2f}")
print(f"  Untrained mean distance: {untrained_post['distance'].mean():.2f}")