Publication Figure Design Theory (Derived from Repository Scripts)

Publication Figure Design Theory (Derived from Repository Scripts)

This theory is inferred from 22 Python plotting scripts in this repository.

1) Core Matplotlib Style System

The dominant house style is minimalist, high-contrast, and publication-oriented:

• Typography:
  • Primary: font.family = 'helvetica' (used in 16 scripts).
  • Secondary exception: font.family = 'sans-serif' (2 scripts, mostly geometric illustrations).
  • Practical portability recommendation: prefer fallback stack ['Arial', 'Helvetica', 'DejaVu Sans', 'sans-serif'].
• Size hierarchy:
  • Large-panel bar/comparison figures: font.size = 24, axes.linewidth = 3.
  • Paper subfigures/compact analytic plots: font.size = 15-16, axes.linewidth = 2.
• Axes cleanup:
  • axes.spines.right = False
  • axes.spines.top = False
• LaTeX usage:
  • text.usetex = True appears in TeX-heavy scripts (6 files), especially when math-rich labels are needed.
• Vector text:
  • svg.fonttype = 'none' appears when preserving editable text in vector exports.

2) Export and Output Policy

• DPI defaults:
  • Standard: dpi=300 (dominant: 23 save calls).
  • Very dense bar panels: dpi=600 (used in ImmunoStruct bars).
• Layout finalization:
  • fig.tight_layout(pad=2) is the default finishing pass (21 occurrences).
  • pad=1 is used for compact multi-panel plots.
• Mostly opaque white-background output; occasional bbox_inches='tight' with explicit pad_inches for edge-to-edge composites.

3) Color Theory and Palette Structure

The repository uses a consistent semantic palette family:

• Anchor/brand blue:
  • #0F4D92 and #3775BA (method-of-interest / reference baseline).
• Green performance bands (often incremental gains):
  • #DDF3DE, #AADCA9, #8BCF8B.
• Warm red/pink comparator bands:
  • #F6CFCB, #E9A6A1, #B64342.
• Neutral support grays:
  • #CFCECE, #767676, #4D4D4D, #272727.
• Occasional accent/highlight colors:
  • Gold #FFD700, magenta-like #EA84DD, teal #42949E, violet #9A4D8E.

Design intent:

• Use blue for "proposed" or key method.
• Use green shades for related positives/improvements.
• Use pink/red shades for alternatives or contrasts.
• Keep neutrals for baselines and background categories.

4) Layout and Composition Logic

Common geometric/layout decisions:

• Ultra-Wide Aspect Ratios: For multi-metric comparisons (e.g., 3-4 metrics), the repository uses extremely wide canvases (e.g., figsize=(45, 12) or (28, 6)). This prevents vertical crowding and allows metrics to be read as a narrative from left to right.
• Dedicated Legend Panels: In complex multi-axis figures, a sub-plot is often dedicated solely to the legend (ax.set_axis_off()). This keeps the data panels clean and prevents legend boxes from overlapping critical data regions.
• Categorical Abstracting: Category bars usually hide x-tick labels (ax.set_xticks([])) and rely on legends/titles instead. This is particularly effective when comparing many methods across multiple metrics.
• Dynamic Y-Axis Scaling: Y-limits are manually tightened to relevant ranges (often derived from data.min() - data.std()), emphasizing comparative differences rather than absolute values.
• Consistency over Embellishment: Multi-panel consistency is favored over per-axis embellishment. All subplots in a row share the same font sizes, linewidths, and color semantic mapping.

5) Bar Encoding Strategy

For publication-grade grouped/ablation bars:

• In-Place Annotation: Scientific values are often printed directly above bars (ax.text) with large fonts (36pt) to make exact numbers readable without a grid.
• Manual Tick Positioning: Uses FixedLocator for precise control over Y-axis granularity.
• Strong edge treatment: Bars use black edges (edgecolor='black') with linewidth=1.5-3 for sharp separation.
• Alpha-Based Ablation: Ablation studies often use the same primary color (e.g., blue_secondary) with varying alpha levels (0.2 to 1.0) to represent the "completeness" of a method.
• Hatch Encoding: Optional hatch channels (slashes/backslashes/dots) are used for subtype overlays to remain readable in grayscale print.

6) Trend/Line Encoding Strategy

• Limited line count per axis (usually 2-4 primary curves).
• Use consistent line width around 2-3 with controlled alpha.
• Use fill_between for uncertainty when needed.
• Keep grid minimal or absent; rely on axis ticks and direct legend reading.

7) Scatter/Illustration Encoding Strategy

• Dense geometric scenes use lowered alpha and muted fills.
• Important trajectories/relations use saturated warm accents with arrows.
• Axis ticks often removed for conceptual diagrams.

8) Recommended Reusable rcParams Preset

PUBLICATION_RCPARAMS = {
    "font.family": ["Arial", "Helvetica", "DejaVu Sans", "sans-serif"],
    "font.size": 16,            # use 24 for large comparison bars
    "axes.spines.right": False,
    "axes.spines.top": False,
    "axes.linewidth": 2.5,      # 3 for big bars, 2 for compact figures
    "legend.frameon": False,
    "svg.fonttype": "none",
}

9) Recommended Default Palette

PALETTE = {
    "blue_main": "#0F4D92",
    "blue_secondary": "#3775BA",
    "green_1": "#DDF3DE",
    "green_2": "#AADCA9",
    "green_3": "#8BCF8B",
    "red_1": "#F6CFCB",
    "red_2": "#E9A6A1",
    "red_strong": "#B64342",
    "neutral": "#CFCECE",
    "highlight": "#FFD700",
}

10) Reproduction Rules

To match the repository's visual identity for new figures:

1. Apply minimalist spines (top/right off), frameless legends, and explicit y-limits.
2. Use Helvetica/Arial-like sans fonts with larger sizing for bars.
3. Choose colors from the blue-green-red-neutral family above; avoid unrelated palettes.
4. Finalize with tight_layout(pad=2) and export at dpi=300 (or 600 for dense bar panels).
5. Use hatch/edge encodings for print-safe category separation when bars overlap in hue.