{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "matplotlib.use('PS') # generate postscript output by default\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/chengjun/anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n", "\n", " UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "/Users/chengjun/anaconda/lib/python2.7/site-packages/scholarNetwork/scholarNetwork.pyc\n", "$version = 1.2.2.1$\n" ] } ], "source": [ "import networkx as nx\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scholarNetwork import scholarNetwork as sn\n", "print(sn.__file__)\n", "print(sn.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = nx.DiGraph()\n", "G.add_weighted_edges_from([(1,2,50),(1,3,30), (3, 2, 10), (2, 4, 20), (2, 5, 30), (5, 3, 5), (4, 5, 10)])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1: 1.0,\n", " 2: 2.223639455782313,\n", " 3: 2.352324263038549,\n", " 4: 3.2236394557823136,\n", " 5: 3.4736394557823136,\n", " 'sink': 3.935728458049887}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Gb=sn.flowBalancing(G)\n", "sn.flowDistanceFromSource(Gb)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAE4CAYAAADW9AHMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYyyG/w+w8RSbNoGdnwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5, 5),facecolor='white')\n", "edge_labels = nx.get_edge_attributes(G, 'weight')\n", "sn.draw_graph(edge_labels.keys(), graph_layout='spring', labels = edge_labels.values())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }