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testing networks
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import requests\n", | |
| "import json\n", | |
| "import datetime\n", | |
| "\n", | |
| "# project imports\n", | |
| "import sys\n", | |
| "sys.path.append('../src')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%load_ext autoreload\n", | |
| "%autoreload 2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "base_url = 'https://www.sensible.dtu.dk/sensible-dtu/connectors/connector_raw/v1/'\n", | |
| "token = '45c01778fecc3ed3bc9b399397d1f5'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "start = pd.to_datetime('9/1/14 05:00').strftime('%s')\n", | |
| "end = pd.to_datetime('9/1/14 6:00').strftime('%s')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "('1409540400', '1409544000')" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "start, end" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 32, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import sensible_utils as su\n", | |
| "res = su.get_bt_data(int(start), int(end), token)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 84, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "scans = res['data']\n", | |
| "users = [s['user'] for s in scans]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 46, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(41427, 69983)" | |
| ] | |
| }, | |
| "execution_count": 46, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "non_empty_scans = [s for s in scans if s['scanned_user']]\n", | |
| "len(non_empty_scans), len(scans)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 47, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "(37800, 69983)" | |
| ] | |
| }, | |
| "execution_count": 47, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "user_scans = [s for s in non_empty_scans if s['scanned_user'] in users]\n", | |
| "len(user_scans), len(scans)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 48, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{u'bt_mac': u'9e1a886daf2e029f844ce9c6f2badace05cff4a19e35648873a0397d415adbe3',\n", | |
| " u'class': 5898764,\n", | |
| " u'id': 133543900,\n", | |
| " u'name': u'd565ccd0b683ad49f3d1cdd5d02ae79e',\n", | |
| " u'rssi': -93,\n", | |
| " u'scanned_user': u'cfff503ae13e87f1073fff40234c68',\n", | |
| " u'timestamp': 1409540404,\n", | |
| " u'user': u'03eb449d4c70f8e3ac56103ac73c3b'}" | |
| ] | |
| }, | |
| "execution_count": 48, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "user_scans[0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import collections\n", | |
| "import networkx as nx\n", | |
| "\n", | |
| "graph = collections.defaultdict(list)\n", | |
| "\n", | |
| "for scan in user_scans:\n", | |
| " graph[scan['user']].append(scan['scanned_user'])\n", | |
| " \n", | |
| "\n", | |
| "G = nx.Graph()\n", | |
| "for user, scans in graph.items():\n", | |
| " scan_counter = collections.Counter(scans)\n", | |
| " for scanned_user, count in scan_counter.items():\n", | |
| " G.add_edge(user, scanned_user, weight=count)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 81, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| }, | |
| { | |
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| "rkJggg==\n" | |
| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x8c44190>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "%pylab inline\n", | |
| "nx.draw(G)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wow, that's really hairbally..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Weight distribution:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 128, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x18a98b90>" | |
| ] | |
| }, | |
| "execution_count": 128, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": [ | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x12931210>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "weights = [attrs['weight'] for source, target, attrs in G.edges_iter(data=True)]\n", | |
| "weights = pd.Series(weights)\n", | |
| "weights.hist(bins=50)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wow, this is just from one hour. Let's use serrano 2008 to get just the backbone:\n", | |
| "## Backbone\n", | |
| "\n", | |
| "I'm using an algorithm from [this paper](http://www.pnas.org/content/106/16/6483.abstract)\n", | |
| "\n", | |
| "setting alpha to 0.05 works well" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 154, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# from: https://gist.github.com/brianckeegan/8846206\n", | |
| "def extract_backbone(g, alpha):\n", | |
| " backbone_graph = nx.Graph()\n", | |
| " for node in g:\n", | |
| " k_n = len(g[node])\n", | |
| " if k_n > 1:\n", | |
| " sum_w = sum( g[node][neighbor]['weight'] for neighbor in g[node] )\n", | |
| " for neighbor in g[node]:\n", | |
| " edgeWeight = g[node][neighbor]['weight']\n", | |
| " pij = float(edgeWeight)/sum_w\n", | |
| " if (1-pij)**(k_n-1) < alpha: # equation 2\n", | |
| " backbone_graph.add_edge( node,neighbor, weight = edgeWeight)\n", | |
| " return backbone_graph" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 180, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x17b6f510>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "backbone = extract_backbone(G, 0.05)\n", | |
| "nx.draw(backbone)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Let's remove isolated nodes and 1-degree nodes" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 181, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| ], | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x12022b10>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import networkx.algorithms.isolate as isolate\n", | |
| "\n", | |
| "# remove one degree nodes first\n", | |
| "one_degree = [n for n, d in backbone.degree_iter() if d == 1]\n", | |
| "backbone.remove_nodes_from(one_degree)\n", | |
| "\n", | |
| "# now remove isolates\n", | |
| "backbone.remove_nodes_from(isolate.isolates(backbone))\n", | |
| "nx.draw(backbone)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 182, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "nx.write_gexf(G, '../data/9-1-14-5:00_9-1-14-6:00_backbone_trimmed.gexf')" | |
| ] | |
| } | |
| ], | |
| "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": "2.7.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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