Peaks in pandas

Peakoscope

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

Peak and valley regions can be nested, for example, when a large peak region contains smaller subpeak regions.
Based on a one-pass algorithm that finds all peak regions and orders them into a tree.
Classes for peak/valley objects and tree objects.
Optional interfaces to matplotlib, pandas and polars.

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

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

From the tree, select default peak regions and print their subarrays of data:

>>> for peak in peakoscope.tree(data).size_filter():
...    print(peak.subarray(data))
... 
[80]
[70, 70]
[30, 40, 30]

The github repo contains tutorials and a glossary:

Citation can include one or more of:

Peakoscope + version
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

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