Commit 4e68699d authored by Felix's avatar Felix
Browse files

update readme and setup.py

parent 4f975a62
# cycle-coalescence-algorithm
## Introduction
Hello everyone,
I wrote this package during my PhD, when working on the characterization of transport networks.
Have you ever wondered how cycles in graphs form a vector space and encapsulate nesting information? If so, were you never really sure how to deal with this? Here is a tool ready to use, enabling you to calculate the cycle bases, mapping them onto a merging tree, and analyze this tree's asymmetry.
......@@ -12,7 +9,7 @@ This project is based on the algorithm published in 'Extracting Hidden Hierarchi
./notebook contains examples to play with in the form of jupyter notebooks
## Installation
python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps cycle_analysis
pip install cycle_analysis
## Usage
```python
......
......@@ -7,7 +7,7 @@
"outputs": [
{
"data": {
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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -19,20 +19,17 @@
}
],
"source": [
"import sys\n",
"sys.path.insert(0,'../cycle_analysis')\n",
"import cycle_tools_coalescence as ctc\n",
"import test as cat\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"# import cycle_analysis.cycle_tools_coalescence as ctc\n",
"# import cycle_analysis.test as cat\n",
"import cycle_analysis.cycle_tools_coalescence as ctc\n",
"import cycle_analysis.test as cat\n",
"\n",
"# generate a dummy graph for testing\n",
"# put an edge weight distribution on the system, available are random/gradient/bigradient/nested_square\n",
"n=7\n",
"G=nx.grid_graph(( n,n,1))\n",
"G=cat.generate_pattern(G,'nested_square')\n",
"G=cat.generate_pattern(G,'random')\n",
"\n",
"weights=[G.edges[e]['weight'] for e in G.edges()]\n",
"pos=nx.get_node_attributes(G,'pos')\n",
......
......@@ -5,7 +5,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
setuptools.setup(
name="cycle_analysis", # Replace with your own username
version="0.0.1",
version="0.0.3",
author="felixk1990",
author_email="felixuwekramer@protonmail.com",
description="cycle_analysis module, performing minimal cycle basis calculation and cycle coalescecne.",
......
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