Peakoscope is a python package for hierarchical analysis of peak and valley regions in numeric data.

• Peak regions can be nested, for example, when a large peak region contains smaller subpeak regions.
• A one-pass algorithm that finds all nested regions and orders them into a tree.
• Trees that can filtered, traversed, partitioned, pruned, grafted and serialized.
• Optional interfaces: matplotlib, pandas, polars, CLI and object-oriented.

Compute the tree of nested peak regions in a data set:

>>> import peakoscope
>>> data = [10, 30, 40, 30, 10, 50, 70, 70, 50, 80]
>>> peaktree = peakoscope.tree(data)
>>> print(peaktree)
0:10
├─5:10
│ ├─9:10
│ └─6:8
└─1:4
  └─2:3

Print the tree minus its root:

>>> peaktree = peakoscope.tree(data, forest=True)
>>> print(peaktree - peaktree.roots())
5:10
├─9:10
└─6:8
1:4
└─2:3

Print the leaf nodes (which are local maxima in data) and corresponding slices of data:

>>> for leaf in peaktree.leaf_nodes():
...    print(leaf, leaf.subarray(data))
...
9:10 [80]
6:8 [70, 70]
2:3 [40]

Demo, tutorials and a glossary:

• Streamlit demo
• plotting_tutorial.ipynb
• dataframes_tutorial.ipynb
• glossary.rst

• Eivind Tøstesen, [email protected]

Copyright (C) 2021-2025 Eivind Tøstesen. This software is licensed under GPL-3.0-or-later

Citation can include one or more of:

• Peakoscope v1.2.0

• Github URL: https://github.com/eivindtostesen/hierarchical_peak_finding

• PyPI URL: https://pypi.org/project/peakoscope/

• The open-access article:

  Tøstesen, E. A stitch in time: Efficient computation of genomic DNA melting bubbles. Algorithms for Molecular Biology, 3, 10 (2008). DOI: 10.1186/1748-7188-3-10