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PlotNado

PlotNado creates genome browser-style figures from Python or YAML templates. It is designed for reproducible genomic plots in scripts, notebooks, Quarto docs, and CLI workflows. Use it when you want compact tracks for BigWig-like signals, BED/narrowPeak intervals, links, genes, and optional matrix/QuantNado data.

PlotNado logo

Tests

Start

Install for day-to-day use:

uv tool install plotnado
plotnado --help

Work from source:

git clone https://github.com/alsmith151/plotnado
cd plotnado
uv sync --extra dev --extra docs
uv run pytest tests/

Python API

from plotnado import GenomicFigure
import numpy as np
import pandas as pd

bins = np.arange(1_000_000, 1_100_000, 1_000)
signal = pd.DataFrame({
    "chrom": "chr1",
    "start": bins,
    "end": bins + 1_000,
    "value": 5 + 2 * np.sin(np.linspace(0, 6, len(bins))),
})

fig = (
    GenomicFigure()
    .scalebar()
    .axis()
    .bigwig(signal, title="Synthetic signal", style="fill", color="#1f77b4")
)
fig.save("quickstart.png", region="chr1:1,010,000-1,080,000")

CLI + YAML

uv run plotnado init sample1.bw sample2.bw peaks.narrowpeak --auto --output template.yaml
uv run plotnado validate template.yaml
uv run plotnado plot template.yaml --region chr1:1,000,000-1,100,000 --output browser_view.png

Templates are editable YAML:

genome: hg38
guides:
  genes: true
tracks:
  - path: sample1.bw
    type: bigwig
    title: sample1
  - path: peaks.narrowpeak
    type: narrowpeak
    title: peaks

The same template can be loaded from Python:

from plotnado import GenomicFigure

fig = GenomicFigure.from_template("template.yaml")
fig.save("browser_view.png", region="chr1:1,000,000-1,100,000")

What It Covers

  • Chainable GenomicFigure API for programmatic plotting.
  • plotnado init, plotnado validate, and plotnado plot for YAML workflows.
  • In-memory tabular examples for reproducible docs and notebooks.
  • Runtime alias and option lookup through GenomicFigure.available_track_aliases() and GenomicFigure.track_options(...).
  • Optional cooler, CapCruncher, and QuantNado tracks when their dependencies and input datasets are installed.

Documentation

Troubleshooting

If a plot is empty, first check that the region overlaps your data and that chromosome names match (chr1 versus 1). If Quarto uses the wrong Python, render with QUARTO_PYTHON=.venv/bin/python quarto render. For option names, use GenomicFigure.track_options("bigwig").

Development

uv sync --extra dev --extra docs
uv run pytest tests/
uv run python examples/run_examples.py
uv run plotnado --help
QUARTO_PYTHON=.venv/bin/python quarto render

Docs are Quarto pages with executable Python cells. Prefer deterministic in-memory data in docs examples and keep generated option tables secondary to plotted examples.

Contributions should start with a source install, the test suite, the example runner, and a Quarto render. The main branch is protected, so open a pull request for review.

License

PlotNado is licensed under GPL-3.0-or-later. See LICENSE.