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Visualization Guide

Overview

This guide covers the visualization system for generating publication-quality figures with automatic management and integration.

Visualization Engine

The VisualizationEngine class provides consistent styling and export capabilities:

from visualization import VisualizationEngine

engine = VisualizationEngine(
    style="publication",
    color_palette="default",
    output_dir="output/figures"
)

Style Presets

  • publication: High-quality figures for papers
  • presentation: Optimized for slides
  • draft: Quick previews

Color Palettes

  • default: Standard matplotlib colors
  • colorblind: Colorblind-friendly palette
  • grayscale: Grayscale for printing

Plot Types

Line Plots

from plots import plot_line

ax = plot_line(x, y, label="Data", color="#1f77b4")

Scatter Plots

from plots import plot_scatter

ax = plot_scatter(x, y, alpha=0.6, size=50)

Bar Charts

from plots import plot_bar

ax = plot_bar(categories, values, color="#2ca02c")

Convergence Plots

from plots import plot_convergence

ax = plot_convergence(iterations, values, target=0.0)

Figure Management

Registering Figures

from infrastructure.documentation import FigureManager

manager = FigureManager()
fig_meta = manager.register_figure(
    filename="convergence.png",
    caption="Convergence analysis showing exponential decay",
    section="experimental_results",
    generated_by="my_script.py"
)

Generating LaTeX Blocks

latex_block = manager.generate_latex_figure_block("fig:convergence")

Cross-References

ref = manager.generate_reference("fig:convergence")
# Returns: \ref{fig:convergence}

Best Practices

  1. Use consistent styling - Apply publication style to all figures
  2. Register all figures - Use FigureManager for tracking
  3. Generate captions - Provide descriptive captions
  4. Use appropriate formats - PNG for manuscripts, PDF for presentations
  5. Validate figures - Check file existence and paths