Metadata-Version: 2.1
Name: visible_residues
Version: 0.1.0
Summary: Find residues with sidechains in a specific region
Author-email: Kale Kundert <kale@thekunderts.net>
Project-URL: Documentation, https://visible-residues.readthedocs.io/en/latest/
Project-URL: Version Control, https://github.com/kalekundert/visible_residues
Project-URL: Bug Tracker, https://github.com/kalekundert/visible_residues/issues
Project-URL: Continuous Integration, https://github.com/kalekundert/visible_residues/actions
Project-URL: Test Coverage, https://app.codecov.io/github/kalekundert/visible_residues
Classifier: Programming Language :: Python :: 3.10
Requires-Python: ~=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: polars
Requires-Dist: macromol-dataframe
Requires-Dist: macromol-voxelize
Provides-Extra: doc
Requires-Dist: sphinx ; extra == 'doc'
Requires-Dist: sphinx-rtd-theme ; extra == 'doc'
Requires-Dist: myst-parser ; extra == 'doc'
Requires-Dist: autoclasstoc ; extra == 'doc'
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: parametrize-from-file ; extra == 'test'

Visible Residues
================

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This is an extension module implementing one performance-critical function used 
to create a machine learning dataset of 3D images of macromolecules with 
labeled amino acid identities.  Specifically, the function accepts a dataframe 
of atoms in the image, then samples a fixed number of residues whose sidechains 
are likely to be mostly within the image.  To avoid biasing the samples based 
on the specific sidechains involved (e.g. glycine has a smaller sidechain than 
arginine, so it might be sampled more often if the actual sidechain was 
considered), this "within the image" determination is based entirely on 
backbone coordinates.
