# Conference Plot

> Create publication-quality matplotlib figures for two-column conference papers (ACM, IEEE, USENIX). Provides style presets, colorblind-safe Wong palette, and dual-output (PDF+PNG) workflow. Use when the user wants to create, style, or fix plots/figures/charts for academic paper submission, or asks for conference-quality matplotlib figures.

- Skill: `drunkcoding/conference-plot` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add drunkcoding/conference-plot`
- Raw SKILL.md: https://api.skillmd.com/api/skills/drunkcoding/conference-plot/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: drunkcoding (https://skillmd.com/u/drunkcoding)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/drunkcoding/conference-plot

---


# Conference Paper Plotting

## Quick Start

Always import `plot_utils.py` from this skill's `scripts/` directory and use
the `paper_style()` context manager to set up correct dimensions and rcParams.

### Bar chart (ACM single-column)

```python
import sys
sys.path.insert(0, "/home/xly/.claude/skills/conference-plot/scripts")
from plot_utils import paper_style, WONG_PALETTE, HATCHES, save_dual_output
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

labels = ["A", "B", "C", "D"]
values = [4.2, 3.8, 5.1, 2.9]

with paper_style(width=3.3, height=2.5):
    fig, ax = plt.subplots()
    bars = ax.bar(labels, values, color=WONG_PALETTE[1:5],
                  edgecolor="black", linewidth=0.4,
                  hatch=[HATCHES[i] for i in range(len(labels))])
    ax.set_ylabel("Throughput (Gbps)")
    ax.set_xlabel("Configuration")
    save_dual_output(fig, Path("throughput.pdf"), None)
    plt.close(fig)
```

### Line plot (USENIX single-column)

```python
from plot_utils import paper_style, WONG_PALETTE, save_dual_output
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

markers = ["o", "s", "^", "D", "v", "P", "X", "*"]
x = np.arange(1, 6)
with paper_style(width=3.33, height=2.5):
    fig, ax = plt.subplots()
    for i, label in enumerate(["System A", "System B", "System C"]):
        ax.plot(x, np.random.rand(5) * 10, color=WONG_PALETTE[i + 1],
                marker=markers[i], markersize=4, label=label)
    ax.set_xlabel("Thread Count")
    ax.set_ylabel("Latency (ms)")
    ax.legend()
    save_dual_output(fig, Path("latency.pdf"), None)
    plt.close(fig)
```

### Heatmap (IEEE double-column)

```python
from plot_utils import paper_style, save_dual_output
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

data = np.random.rand(5, 8)
with paper_style(width=7.16, height=2.5):
    fig, ax = plt.subplots()
    im = ax.imshow(data, cmap="RdYlGn_r", aspect="auto")
    fig.colorbar(im, ax=ax, shrink=0.8)
    ax.set_xlabel("Benchmark")
    ax.set_ylabel("Configuration")
    save_dual_output(fig, Path("heatmap.pdf"), None)
    plt.close(fig)
```

## Usage

1. **Import** from `scripts/plot_utils.py` (add the skill's `scripts/` dir to `sys.path`).
2. **Wrap** all plotting code in `paper_style()`.
3. **Save** with `save_dual_output(fig, pdf_path, png_path)`. By default it
   saves both PDF and PNG (auto-deriving the missing path). Pass
   `save_both=False` to save only the explicitly provided path(s).

## Style Reference

### Color Palette (WONG_PALETTE)

| Index | Hex       | Color          | Typical Use              |
|-------|-----------|----------------|--------------------------|
| 0     | `#000000` | Black          | Baselines, reference     |
| 1     | `#EEBA0C` | Orange         | Primary comparison       |
| 2     | `#56B4E9` | Sky Blue       | Secondary comparison     |
| 3     | `#009E73` | Bluish Green   | Third system             |
| 4     | `#F0E442` | Yellow         | Fourth (use sparingly)   |
| 5     | `#0072B2` | Blue           | Fifth system             |
| 6     | `#D55E00` | Vermillion     | Sixth / alert            |
| 7     | `#CC79A7` | Reddish Purple | Seventh system           |
| 8     | `#0000FF` | Blue Saturated | Eighth system            |
| 9     | `#FF0000` | Red Saturated  | Ninth system             |

This is a 10-color palette based on the Wong colorblind-safe palette (Nature
Methods, 2011), extended with two saturated colors. Safe for deuteranopia,
protanopia, and tritanopia. Start from index 1 for data series (index 0 is
black, best for baselines or outlines).

### Markers

Use inline marker strings directly: `"o"`, `"s"`, `"^"`, `"D"`, `"v"`, `"P"`,
`"X"`, `"*"` — circle, square, triangle-up, diamond, triangle-down,
plus-filled, X-filled, star.

### Hatches (HATCHES)

`['', '//', '', 'xx', '..', '++', 'OO', '**']` — none, diagonal,
back-diagonal, cross-hatch, dots, plus, circles, stars.

### Venue Dimensions

| Venue | Single-Column | Double-Column | Recommended Call |
|-------|--------------|---------------|------------------|
| ACM (SIGCOMM, MOBICOM, etc.) | 3.3" | 7.0" | `paper_style(width=3.3)` |
| IEEE (INFOCOM, MICRO, etc.) | 3.5" | 7.16" | `paper_style(width=3.5)` |
| USENIX (OSDI, NSDI, ATC, etc.) | 3.33" | 7.0" | `paper_style(width=3.33)` |

Pass explicit `width` and `height` to `paper_style()`. Default is `width=3.3,
height=2.5`.

## Common Patterns

### Grouped bars with hatches

```python
x = np.arange(len(groups))
w = 0.25
for i, (series, vals) in enumerate(data.items()):
    ax.bar(x + i * w, vals, w, label=series,
           color=WONG_PALETTE[i + 1], edgecolor="black", linewidth=0.4,
           hatch=HATCHES[i + 1])
ax.set_xticks(x + w * (len(data) - 1) / 2)
ax.set_xticklabels(groups)
```

### Dual-axis (twinx) with color-coded labels

```python
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.bar(x, throughput, color=WONG_PALETTE[1], label="Throughput")
ax2.plot(x, latency, color=WONG_PALETTE[5], marker="o", label="Latency")
ax1.set_ylabel("Throughput (Gbps)", color=WONG_PALETTE[1])
ax2.set_ylabel("Latency (ms)", color=WONG_PALETTE[5])
ax1.tick_params(axis="y", colors=WONG_PALETTE[1])
ax2.tick_params(axis="y", colors=WONG_PALETTE[5])
```

### Error bars

```python
ax.bar(x, means, yerr=stds, capsize=2, color=WONG_PALETTE[1:],
       edgecolor="black", linewidth=0.4, error_kw={"linewidth": 0.6})
```

### Heatmap with annotation

```python
im = ax.imshow(data, cmap="RdYlGn_r", aspect="auto")
for i in range(data.shape[0]):
    for j in range(data.shape[1]):
        ax.text(j, i, f"{data[i, j]:.1f}", ha="center", va="center", fontsize=6)
fig.colorbar(im, ax=ax, shrink=0.8)
```

## Guidelines

- **Always** use `paper_style()` — it sets `pdf.fonttype=42` and `ps.fonttype=42`
  to avoid Type 3 font rejection by venues.
- **Keep fonts >= 6pt** for readability. The default 8pt base is safe.
- **Use `WONG_PALETTE`** (10 colors) for colorblind safety. Start from index 1
  for data series; index 0 (black) is best for baselines or outlines.
- **Export PDF for paper**, PNG for review. Use `save_dual_output(fig, pdf_path, png_path)`.
- **Always `plt.close(fig)`** after saving to avoid memory leaks in batch scripts.
- **Pair hatches with colors** for bar charts so the plot remains distinguishable
  in grayscale printouts.

## Detailed Venue Specs

See [references/venue-specs.md](references/venue-specs.md) for column widths,
font requirements, LaTeX `\includegraphics` patterns, and common gotchas
(Type 3 fonts, minimum font size, vector vs raster) for ACM, IEEE, and USENIX.