# PyTree
A fast implementation of trees from the mathematical theory of graphs
Inspired and applied in condensed matter physics,
cellular biology and the World Wide Web.
## Mathematical Introduction
### Graphs
Mathematical objects of the theory of *graphs* have one-to-one matrix
representations, as described by the theory of linear algebra.
In general, a graph is a network structure, whose off-diagonal terms
in the matrix representation are known to Condensed Matter Physicists
as hopping or interaction terms.
### Trees
A particular graph, known as a tree, is a network with a
hierarchical structure, which we use to index
objects in their matrix representation.
## Python implementation
The Tree is a Python subclass of a List, which adds the following *attributes* and *methods*:
### Attributes
- parent : Tree or none (default)
children : Tree or none (default)
- ancestors : list of Trees lineage upwards
- root: root of the tree (parent without parent)
- descendants : lists lineage downwards (list of list of Trees)
- buds : lists descendants without descendants
### Methods
- set_ids : add an index (id) to the buds of this Tree
- get_ids : get the indices (ids) of the buds of this Tree
.attribute : attributes are inherited down the linage
## Summary
A *graph structure defines a matrix*; a *tree structure
indexes the matrix*.
The tree structure adds parents,
children, ancestors, descendants and inheritance of attributes.
## Installation
### Development environment
```sh
pip install --editable . --break-system-packages
```
## Build and upload
```sh
python3 -m build
twine upload dist/*
```
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"description": "# PyTree\nA fast implementation of trees from the mathematical theory of graphs\n\nInspired and applied in condensed matter physics, \ncellular biology and the World Wide Web.\n\n## Mathematical Introduction\n### Graphs\nMathematical objects of the theory of *graphs* have one-to-one matrix \nrepresentations, as described by the theory of linear algebra.\nIn general, a graph is a network structure, whose off-diagonal terms \nin the matrix representation are known to Condensed Matter Physicists \nas hopping or interaction terms.\n\n### Trees\nA particular graph, known as a tree, is a network with a \nhierarchical structure, which we use to index \nobjects in their matrix representation.\n\n## Python implementation\nThe Tree is a Python subclass of a List, which adds the following *attributes* and *methods*: \n\n### Attributes\n- parent : Tree or none (default)\nchildren : Tree or none (default)\n- ancestors : list of Trees lineage upwards \n- root: root of the tree (parent without parent)\n- descendants : lists lineage downwards (list of list of Trees) \n- buds : lists descendants without descendants\n\n### Methods\n- set_ids : add an index (id) to the buds of this Tree\n- get_ids : get the indices (ids) of the buds of this Tree\n.attribute : attributes are inherited down the linage\n\n## Summary\nA *graph structure defines a matrix*; a *tree structure\nindexes the matrix*. \n\nThe tree structure adds parents, \nchildren, ancestors, descendants and inheritance of attributes.\n\n## Installation\n### Development environment\n```sh\npip install --editable . --break-system-packages\n```\n\n## Build and upload\n```sh\npython3 -m build\ntwine upload dist/*\n```\n",
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"license": "# PythU Scanning tunnelling microscopy (STM), quasiparticle interference (QPI) and angle-resolved photoemission spectrocopy (ARPES) simulation of (topological) non-unitary spin-triplet unconventional superconductivity and magnetism, in quantum dots, thin films and 3D, with impurities. COPYRIGHT (C) 2018--2023, Henry Joseph Sheehy. All rights reserved. ",
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