Giorgio
e7e4db264d
Reorganized flat 41-file directory into structured layout with: - scripts/ for Python analysis code with shared config.py - notebooks/ for Jupyter analysis notebooks - data/ split into raw/, metadata/, processed/ - docs/ with analysis summary, experimental design, and bimodal hypothesis tutorial - tasks/ with todo checklist and lessons learned - Comprehensive README, PLANNING.md, and .gitignore Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from config import DATA_PROCESSED, DATA_METADATA, FIGURES
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# Load data
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trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
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untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
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barrier_data = pd.read_csv(DATA_METADATA / '2025_07_15_barrier_opening.csv')
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# Convert opening_time to milliseconds and create a mapping
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barrier_data['opening_time_ms'] = barrier_data['opening_time'] * 1000
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opening_times = dict(zip(barrier_data['machine'], barrier_data['opening_time_ms']))
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def align_to_opening_time(df, opening_times, max_time=300000):
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"""Align distance data to barrier opening time.
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Args:
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df (pd.DataFrame): Distance data.
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opening_times (dict): Machine to opening time mapping.
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max_time (int): Maximum time in ms to include.
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Returns:
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pd.DataFrame: Aligned data filtered to +/-150s around opening.
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"""
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df_aligned = df.copy()
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df_aligned['aligned_time'] = np.nan
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for machine in df['machine_name'].unique():
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if machine in opening_times:
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opening_time = opening_times[machine]
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mask = (df['machine_name'] == machine) & (df['t'] <= max_time)
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df_aligned.loc[mask, 'aligned_time'] = df.loc[mask, 't'] - opening_time
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df_aligned = df_aligned.dropna(subset=['aligned_time'])
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df_aligned = df_aligned[(df_aligned['aligned_time'] >= -150000) &
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(df_aligned['aligned_time'] <= 150000)]
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return df_aligned
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# Align the data
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trained_aligned = align_to_opening_time(trained_distances, opening_times)
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untrained_aligned = align_to_opening_time(untrained_distances, opening_times)
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# Calculate average distance over aligned time
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trained_avg = trained_aligned.groupby('aligned_time')['distance'].mean()
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untrained_avg = untrained_aligned.groupby('aligned_time')['distance'].mean()
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# Apply smoothing
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window_size = 50
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trained_smooth = trained_avg.rolling(window=window_size, center=True).mean()
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untrained_smooth = untrained_avg.rolling(window=window_size, center=True).mean()
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# Create the plot
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plt.figure(figsize=(12, 6))
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plt.plot(trained_smooth.index/1000, trained_smooth.values,
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label='Trained (smoothed)', color='blue', linewidth=2)
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plt.plot(untrained_smooth.index/1000, untrained_smooth.values,
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label='Untrained (smoothed)', color='red', linewidth=2)
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plt.axvline(x=0, color='black', linestyle='--', alpha=0.7, label='Barrier Opening')
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plt.xlabel('Time (seconds relative to barrier opening)')
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plt.ylabel('Average Distance')
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plt.title('Average Distance Between Flies Aligned to Barrier Opening Time')
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.xlim(-150, 150)
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plt.tight_layout()
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plt.savefig(FIGURES / 'avg_distance_aligned_to_opening.png', dpi=300, bbox_inches='tight')
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plt.show()
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# Print statistics
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print("Trained flies (aligned to barrier opening):")
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print(f" Data points: {len(trained_aligned)}")
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print(f" Mean distance: {trained_aligned['distance'].mean():.2f}")
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print(f" Std distance: {trained_aligned['distance'].std():.2f}")
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print("\nUntrained flies (aligned to barrier opening):")
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print(f" Data points: {len(untrained_aligned)}")
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print(f" Mean distance: {untrained_aligned['distance'].mean():.2f}")
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print(f" Std distance: {untrained_aligned['distance'].std():.2f}")
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# Pre/post analysis
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trained_pre = trained_aligned[trained_aligned['aligned_time'] < 0]
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trained_post = trained_aligned[trained_aligned['aligned_time'] > 0]
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untrained_pre = untrained_aligned[untrained_aligned['aligned_time'] < 0]
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untrained_post = untrained_aligned[untrained_aligned['aligned_time'] > 0]
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print("\nPre-opening period (t < 0):")
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print(f" Trained mean distance: {trained_pre['distance'].mean():.2f}")
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print(f" Untrained mean distance: {untrained_pre['distance'].mean():.2f}")
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print("\nPost-opening period (t > 0):")
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print(f" Trained mean distance: {trained_post['distance'].mean():.2f}")
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print(f" Untrained mean distance: {untrained_post['distance'].mean():.2f}")
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