Ieee Figure

IEEE Publication Figures

Use this skill to generate or repair figures that will survive IEEE production: vector, correctly sized for the column, with embedded fonts and readable text at print size.

Core stance

• Size to the column from the start. Single-column ≈ 3.5 in (88.9 mm) wide; double-column ≈

7.16 in (181.6 mm). Design at final size so fonts end up readable — never shrink a big figure.

• Vector first. Output PDF or EPS for line/vector art; embed fonts. Use high-res raster only

for photographic content (≥ 300 dpi color/grayscale, ≥ 600 dpi for line art / combinations).

• Readable and grayscale-safe. Minimum ~8 pt text at final size; distinguish series by

marker/linestyle/hatch as well as colour so the figure survives B/W printing; use a colorblind-safe palette.

• Comms axis conventions. Error/outage curves use a log y-axis (semilogy); keep the SNR

range wide enough to show the high-SNR slope (diversity order); define the SNR axis (transmit vs receive). Plot a derived expression as a line and its Monte-Carlo check as markers.

• One panel, one question. No two panels answer the same thing. The figure must be legible

and self-explanatory with its caption.

• No fabricated data. Plot only the user's real numbers; if data is missing, ask or stub with

a clearly labelled placeholder.

When to open extra files

| File | Open when | |---|---| | [references/ieee-figure-spec.md](references/ieee-figure-spec.md) | Sizing, resolution, fonts, colour/grayscale, file format, multi-panel layout, caption rules, submission checklist | | [references/matplotlib-patterns.md](references/matplotlib-patterns.md) | Concrete matplotlib setup (rcParams for embedded fonts, sizing, palettes) and ready patterns for BER/outage-vs-SNR (semilog), rate vs SNR/antennas, analysis-vs-simulation overlay, convergence, CDF, NMSE-vs-SNR, training/validation loss, and ISAC plots (rate–CRB tradeoff, beampattern, ROC) |

Workflow

1. Target column: ask single- or double-column; set the width accordingly.
2. Pick the chart type that matches the result (see matplotlib-patterns.md): BER/outage vs SNR

(semilog y) for reliability, line vs SNR/antennas for rate/efficiency, analysis-line + simulation-marker overlay to validate a derivation, objective-vs-iteration for convergence, CDF for distributions.

1. Set the rendering rcParams first (embedded fonts, sans-serif, sizes) — before any plotting.
2. Plot the real data; encode each scheme redundantly (colour + marker/linestyle). Use

semilogy for error/outage; floor the y-axis at the lowest reliably simulated value.

1. Label fully: axis titles with units (dB, bps/Hz), defined SNR axis, legend; text ≥ 8 pt.
2. Export .pdf (or .eps) as primary and a 300–600 dpi .png preview.
3. Grayscale check: convert to gray and confirm series are still distinguishable.
4. Return the script, the output paths, and a one-line caption draft.

Output format

1. Script: a self-contained, runnable plotting script (matplotlib) using the user's data.
2. Outputs: the vector file path + a PNG preview path.
3. Caption: a one-sentence draft stating what the figure shows (the question it answers).
4. Checks: confirmation of size, font embedding, and grayscale legibility.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: TenWalk
• Source: TenWalk/ieee-skills
• License: MIT

Install and usage instructions live in the source repository linked above.