cypher_projection.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:81d29166c9ce7fb7a71955aa7cc0e0437051543c8770dc67937eec7864fb821a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cypher"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper\n",
      "def draw_simple_graph(graph):\n",
      "    pos = nx.spring_layout(graph)\n",
      "    nx.draw_networkx(graph, pos=pos), \n",
      "    nx.draw_networkx_labels(graph, pos=pos)\n",
      "    return graph"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Start with a clean db."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cypher\n",
      "\n",
      "MATCH (n)\n",
      "OPTIONAL MATCH (n)-[r]-() \n",
      "DELETE n, r;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8 relationship deleted.\n",
        "7 nodes deleted.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Our little social network."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cypher\n",
      "\n",
      "CREATE(versae:Person {name: \"Javi\"})\n",
      "CREATE(davebshow:Person {name: \"Dave\"})\n",
      "CREATE(eastwood:Person {name: \"Gabi\"})\n",
      "CREATE(macho:Person {name: \"Josema\"})\n",
      "CREATE(cplex:Lab {name: \"CulturePlex\"})\n",
      "CREATE(londs:City {name: \"London\"})\n",
      "CREATE(sevil:City {name: \"Sevilla\"})\n",
      "\n",
      "CREATE (versae)-[:WORKS_AT]->(cplex)\n",
      "CREATE (davebshow)-[:WORKS_AT]->(cplex)\n",
      "CREATE (eastwood)-[:WORKS_AT]->(cplex)\n",
      "CREATE (macho)-[:WORKS_AT]->(cplex)\n",
      "CREATE (davebshow)-[:LIVES_IN]->(londs)\n",
      "CREATE (versae)-[:LIVES_IN]->(londs)\n",
      "CREATE (eastwood)-[:LIVES_IN]->(sevil)\n",
      "CREATE (macho)-[:LIVES_IN]->(sevil)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7 labels added.\n",
        "7 nodes created.\n",
        "12 properties set.\n",
        "8 relationships created.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = %cypher MATCH (m)-->(n) RETURN m, n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8 rows affected.\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "paths = [(n['name'], m['name']) for (n, m) in results]\n",
      "g = nx.Graph(paths)\n",
      "draw_simple_graph(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<networkx.classes.graph.Graph at 0xa2d6b4c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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t27ZlzuhbtWql7DBVDiV+QhqxwMBA/PLLLzhx4gTs7e2VHQ4yMjJw8eJF2Rl9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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa2d6bec>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Project an association network."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "projection = %cypher MATCH (p1:Person)-->(wild)<--(p2:Person) RETURN p1, p2, wild.name"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "16 rows affected.\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "paths = [(p1['name'], p2['name'], {'type': tp}) for (p1, p2, tp) in projection]\n",
      "proj = nx.Graph(paths)\n",
      "draw_simple_graph(proj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<networkx.classes.graph.Graph at 0xa35546c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cLhUWFjZ8/vx5C2Nj44SEhATj8tuAuno+6vDhw6xPnz5cx6g1RCIR2759O2vZ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\n8wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa3553ac>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Projection by city."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "projection = %cypher MATCH (p1:Person)-->(c:City)<--(p2:Person) RETURN p1, p2, c.name"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4 rows affected.\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "paths = [(p1['name'], p2['name'], {'type': tp}) for (p1, p2, tp) in projection]\n",
      "proj = nx.Graph(paths)\n",
      "draw_simple_graph(proj)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<networkx.classes.graph.Graph at 0xa53362c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fqWMQOZx3liwRA9zcxGxBEC8BotjGpRoQ3xUEMcDNTXxnyRKpI3eoqTdN6m6z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\n6oxxnQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa533a2c>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "And so on."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}