update readme and setup.py

README.md

 # 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

notebook/cycle_coalescence_asymmetry.ipynb

@@ -7,7 +7,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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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",

setup.py

@@ -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.",