name	matplotlib-publication-figures
description	Academic figure standards using matplotlib, color accessibility, DPI settings, LaTeX integration. Use when creating charts, plots, and figures for publication.

Matplotlib Publication Figures

Create publication-ready, high-quality figures using matplotlib. Follow these guidelines.

Sizing and Layout

Determine the target column width of the academic template (e.g., ACM or IEEE two-column is usually ~3.3 to 3.5 inches per column).

Set the figure size explicitly in inches to avoid scaling artifacts when inserted into the paper.

fig, ax = plt.subplots(figsize=(3.5, 2.5)) # Single column
fig, ax = plt.subplots(figsize=(7.0, 3.0)) # Double column (span)

Use fig.tight_layout() or constrained_layout=True to minimize whitespace and ensure labels aren't clipped.

Typography and LaTeX Integration

Fonts in the figure should ideally match the paper's font style and size (e.g., size 9 or 10).
Avoid fonts that are too small to read when printed. Minimum font size is usually 8pt.

Enable LaTeX text rendering if equations or specific fonts are needed:

plt.rcParams.update({
    "text.usetex": True,
    "font.family": "serif",
    "font.serif": ["Times", "Palatino", "Computer Modern Roman"]
})

Colors and Accessibility

Never rely on color alone to distinguish data. Use varying line styles (-, --, -., :) or markers (o, s, ^, x).
Use colorblind-friendly palettes. Consider palettes like viridis, plasma, cividis, or specific categorical palettes designed for accessibility (e.g., seaborn's colorblind palette).
Ensure high contrast between text/lines and the background.

Styling Elements

Include a clear, descriptive legend. For complex charts, place the legend outside the plot box to avoid overlapping data:
```
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=3)
```
Label all axes with both the metric and its unit (e.g., Throughput (Tx/sec)).
Turn on appropriate gridlines (ax.grid(True, linestyle='--', alpha=0.6)) to help the reader estimate values, but ensure they don't overpower the data series.

Remove top and right spines if they are not necessary to reduce chart junk:

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Exporting for Publication

Export vector graphics for plots (PDF or EPS) so they scale infinitely without pixelation.

If raster images must be used (e.g., for massive scatter scale or heatmaps), use a high DPI:

plt.savefig("figure.pdf", format="pdf", bbox_inches="tight")
plt.savefig("retina_figure.png", dpi=300, bbox_inches="tight")

Matplotlib Publication Figures

Create publication-ready, high-quality figures using matplotlib. Follow these guidelines.

Sizing and Layout

Determine the target column width of the academic template (e.g., ACM or IEEE two-column is usually ~3.3 to 3.5 inches per column).

Set the figure size explicitly in inches to avoid scaling artifacts when inserted into the paper.

fig, ax = plt.subplots(figsize=(3.5, 2.5)) # Single column
fig, ax = plt.subplots(figsize=(7.0, 3.0)) # Double column (span)

Use fig.tight_layout() or constrained_layout=True to minimize whitespace and ensure labels aren't clipped.

Typography and LaTeX Integration

Fonts in the figure should ideally match the paper's font style and size (e.g., size 9 or 10).

Avoid fonts that are too small to read when printed. Minimum font size is usually 8pt.

Enable LaTeX text rendering if equations or specific fonts are needed:

plt.rcParams.update({
    "text.usetex": True,
    "font.family": "serif",
    "font.serif": ["Times", "Palatino", "Computer Modern Roman"]
})

Colors and Accessibility

Never rely on color alone to distinguish data. Use varying line styles (-, --, -., :) or markers (o, s, ^, x).

Use colorblind-friendly palettes. Consider palettes like viridis, plasma, cividis, or specific categorical palettes designed for accessibility (e.g., seaborn's colorblind palette).

Ensure high contrast between text/lines and the background.

Styling Elements

Include a clear, descriptive legend. For complex charts, place the legend outside the plot box to avoid overlapping data:

ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=3)

Label all axes with both the metric and its unit (e.g., Throughput (Tx/sec)).

Turn on appropriate gridlines (ax.grid(True, linestyle='--', alpha=0.6)) to help the reader estimate values, but ensure they don't overpower the data series.

Remove top and right spines if they are not necessary to reduce chart junk:

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

Exporting for Publication

Export vector graphics for plots (PDF or EPS) so they scale infinitely without pixelation.

If raster images must be used (e.g., for massive scatter scale or heatmaps), use a high DPI:

plt.savefig("figure.pdf", format="pdf", bbox_inches="tight")
plt.savefig("retina_figure.png", dpi=300, bbox_inches="tight")

matplotlib-publication-figures