swizz 0.1.1

Publication-ready plotting library for ML papers

📄📊 Swizz

Swizz is a Python library for generating publication-ready visualizations, LaTeX tables, and subfigure layouts with minimal code and consistent style. Check out the live docs for examples and usage.

Built for AI/ML researchers, it's designed to make NeurIPS/ICLR/CVPR-style figures effortless — no more LaTeX hacks and style mismatches. Focus on your results, not your rendering.

If you use Swizz in your research, please consider citing it using:

@software{quaedvlieg2025swizz,
  author = {Quaedvlieg, Lars and Miele, Andrea},
  license = {MIT},
  month = apr,
  title = {{Swizz: Publication-ready plots and LaTeX tables for ML papers}},
  url = {https://github.com/lars-quaedvlieg/swizz},
  version = {0.1.0},
  year = {2025}
}

---

🚀 Features

• 🧾 Auto-generated LaTeX tables from your data
• 📊 One-liner plotting functions
• 🧩 Easy layout builders for stacked, grid, and subfigure formats
• 📚 Expanding Jupyter Book documentation with live examples

---

📦 Installation

git clone [e-mail]:lars-quaedvlieg/swizz.git swizz
cd swizz
pip install .

(Coming soon to PyPI)

---

📁 Project Structure

Module	Description
swizz.table	Table generators
swizz.plot	Plotting utilities built on Seaborn & Matplotlib
swizz.layout	Layout builders for stacked / side-by-side images

---

🧪 Examples

Multi-level table example:

from swizz.table import table

complex_df = ...

latex_string = table(
    "grouped_multicol_latex",
    df=complex_df,
    row_index="Model",
    col_index=["Split", "Budget"],
    groupby="Task",
    value_column="score",
    highlight="min",
    stderr=True,
    caption="Combinatorial optimization results",
    label="tab:combo_results"
)

Simple bar chart example:

from matplotlib import pyplot as plt
from swizz import plot

data_dict = ...

# Style map for each metric (hatch patterns for filling)
style_map = {
    "Accuracy": '',
    "Precision": '\\',
    "Recall": 'x'  # Cross hatch pattern for Recall
}

plot("general_bar_plot", data_dict, style_map=style_map, save="bar")
plt.show()

Complex nested layouts:

from swizz.layouts.blocks import Row, Col, LegendBlock, Label
from swizz.layouts import render_layout
from matplotlib import pyplot as plt

plot1, plot2, plot3 = ...

nested_layout = Col([
    Row([
        LegendBlock(labels=["Accuracy", "Precision", "Recall"], ncol=3, fixed_width=0.35),
        LegendBlock(labels=["Forward KL", "Reverse KL"], ncol=2)
    ], fixed_height=0.08, spacing=0.15),
    Row([
        Col([
            plot3,
            Label("(a) Bar chart", align="center", fixed_height=0.05),
        ]),
        Col([
            plot1,
            Label("(b) Line plot 1", align="center", fixed_height=0.05),
            plot2,
            Label("(c) Line plot 2", align="center", fixed_height=0.05)
        ], spacing=0.07)
    ], spacing=0.1),
], spacing=0.02)

fig = render_layout(nested_layout, figsize=(10, 8))
plt.show()

---

🛠️ Roadmap

• Add more plot types (confusion, UMAP, attention, histograms, etc.)
• Add Manim integrations for dynamic plot videos and function evolutions, etc.
• Add more tables
• W&B / MLflow integration

---

🤝 Contributing

Contributions are very welcome! See CONTRIBUTING.md for setup and module structure.

---