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Analysis & Plotting Tools
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Shared Configuration
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Comparison Visualization
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CLI Arguments
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Convergence Analysis
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Usage
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Configuration
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Outputs
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Poster Integration (Figma)
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SVG & Scaling
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Brittle star environment
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<span class="md-ellipsis">
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Checkpoint & Model Evaluation
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<h1 id="analysis-plotting-tools">Analysis & Plotting Tools</h1>
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<p>This guide outlines the tools available for analyzing experimental data and generating poster-quality visualizations for the Brittle Star project.</p>
|
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<h2 id="shared-configuration">Shared Configuration</h2>
|
|
<p>All plotting scripts share a central configuration in <code>scripts/plots/plot_config.py</code>. This file defines:</p>
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<ul>
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<li><strong>Color Palette:</strong> A color-blind friendly, high-contrast palette for different architectures.</li>
|
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<li><strong>Typography:</strong> Consistent font sizes and styles tailored for A0 posters.</li>
|
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<li><strong>Markers:</strong> Shared visual indicators, such as the ★ used for best performers.</li>
|
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</ul>
|
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<h2 id="comparison-visualization">Comparison Visualization</h2>
|
|
<p>The <code>scripts/plots/analyze_comparisons.py</code> script generates grouped bar charts comparing the performance of different architectures across various morphologies.</p>
|
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<h3 id="usage">Usage</h3>
|
|
<p>Run the script from the root of the project, providing the path to your evaluation CSV:</p>
|
|
<div class="highlight"><pre><span></span><code><span class="c1"># Basic usage (saves PNG and SVG to runs/evaluation/plots/)</span>
|
|
uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/plots/analyze_comparisons.py<span class="w"> </span>path/to/results.csv
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<span class="c1"># Advanced usage for Figma/Poster integration</span>
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uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/plots/analyze_comparisons.py<span class="w"> </span>path/to/results.csv<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--output_dir<span class="w"> </span>docs/assets/plots/<span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--font_size<span class="w"> </span><span class="m">30</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--fig_width<span class="w"> </span><span class="m">14</span><span class="w"> </span><span class="se">\</span>
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<span class="w"> </span>--fig_height<span class="w"> </span><span class="m">10</span>
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</code></pre></div>
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<h3 id="cli-arguments">CLI Arguments</h3>
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<ul>
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<li><code>input_csv</code>: (Required) Path to the CSV file containing evaluation results.</li>
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<li><code>--output_dir</code>, <code>-o</code>: Directory where plots will be saved (default: <code>runs/evaluation/plots</code>).</li>
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<li><code>--show_titles</code>: Include titles in the plots. Default is <strong>False</strong>, as titles are typically added natively in design tools like Figma.</li>
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<li><code>--font_size</code>: Base font size in points (default: 28).</li>
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<li><code>--fig_width</code> / <code>--fig_height</code>: Physical dimensions of the plot in inches. Match these to your Figma layout to maintain exact font sizes.</li>
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</ul>
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<h3 id="outputs">Outputs</h3>
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<p>The script generates four key plots, each saved as both <code>.png</code> and <code>.svg</code>:</p>
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<ol>
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<li><strong>Forward Velocity:</strong> Grouped bar chart (cm/s).</li>
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<li><strong>Accumulated Reward:</strong> Mean cumulative reward.</li>
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<li><strong>Success Rate:</strong> Target acquisition percentage.</li>
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<li><strong>Distance Remaining:</strong> Navigational accuracy.</li>
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</ol>
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<hr />
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<h2 id="convergence-analysis">Convergence Analysis</h2>
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<p>The <code>scripts/plots/analyze_convergence.py</code> script determines the convergence point of training runs.</p>
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<h3 id="usage_1">Usage</h3>
|
|
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>run<span class="w"> </span>python<span class="w"> </span>scripts/plots/analyze_convergence.py<span class="w"> </span>--output_dir<span class="w"> </span>runs/convergence/
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</code></pre></div>
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<h3 id="configuration">Configuration</h3>
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<ul>
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<li><strong>File Mapping:</strong> The script uses hardcoded paths in the <code>FILE_MAPPING</code> dictionary. Update these paths to point to your specific run evaluation files.</li>
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<li><strong>CLI Arguments:</strong> Supports the same <code>--show_titles</code>, <code>--font_size</code>, and <code>--fig_width/height</code> flags as the comparison script.</li>
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</ul>
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<h3 id="outputs_1">Outputs</h3>
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<p>Generates three plots (PNG & SVG):</p>
|
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<ol>
|
|
<li><code>convergence_comparison</code>: Grouped horizontal bar chart.</li>
|
|
<li><code>progress_reward_curves</code>: Line plots of reward over time.</li>
|
|
<li><code>progress_velocity_curves</code>: Line plots of velocity over time.</li>
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|
</ol>
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<hr />
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<h2 id="poster-integration-figma">Poster Integration (Figma)</h2>
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<h3 id="svg-scaling">SVG & Scaling</h3>
|
|
<p>We recommend using the <strong>SVG</strong> outputs for poster design in Figma:</p>
|
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<ol>
|
|
<li><strong>No Resolution Loss:</strong> SVGs are vector-based and will remain sharp at any size.</li>
|
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<li><strong>Native Text:</strong> Text in the SVG imports as native text layers in Figma.</li>
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<li><strong>Exact Font Matching:</strong> To ensure a <code>28pt</code> font in the plot matches a <code>28pt</code> font in your poster, set the <code>--fig_width</code> and <code>--fig_height</code> to match the physical dimensions of the plot box in your Figma layout.</li>
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<li><strong>Editable:</strong> You can "Ungroup" the SVG in Figma to manually move labels, adjust colors, or tweak individual bars.</li>
|
|
</ol>
|
|
<h3 id="image-placeholders">Image Placeholders</h3>
|
|
<p>The comparison charts include light-gray square placeholders below the X-axis. These are designed as guides; in Figma, you can drop your morphology renders or illustrations directly on top of these squares.</p>
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