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>
50 lines
1.6 KiB
Python
50 lines
1.6 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, 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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# Remove NaN distances
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trained_clean = trained_distances.dropna(subset=['distance'])
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untrained_clean = untrained_distances.dropna(subset=['distance'])
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# Create the plot
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plt.figure(figsize=(12, 6))
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# Sample 1000 points from each group to avoid overcrowding
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if len(trained_clean) > 1000:
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trained_sample = trained_clean.sample(1000, random_state=42)
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else:
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trained_sample = trained_clean
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if len(untrained_clean) > 1000:
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untrained_sample = untrained_clean.sample(1000, random_state=42)
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else:
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untrained_sample = untrained_clean
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plt.scatter(trained_sample['t'], trained_sample['distance'],
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alpha=0.5, s=1, label='Trained', color='blue')
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plt.scatter(untrained_sample['t'], untrained_sample['distance'],
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alpha=0.5, s=1, label='Untrained', color='red')
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plt.xlabel('Time')
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plt.ylabel('Distance')
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plt.title('Distance Between Flies Over Time')
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(FIGURES / 'distance_over_time.png', dpi=300, bbox_inches='tight')
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plt.show()
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print("Trained flies:")
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print(f" Mean distance: {trained_clean['distance'].mean():.2f}")
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print(f" Std distance: {trained_clean['distance'].std():.2f}")
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print("\nUntrained flies:")
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print(f" Mean distance: {untrained_clean['distance'].mean():.2f}")
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print(f" Std distance: {untrained_clean['distance'].std():.2f}")
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