Redes atv 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1 NetworkX Basics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G = nx.DiGraph()\n",
    "G = nx.MultiGraph()\n",
    "G = nx.MultiDiGraph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.3 Graph Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_edge(1, 2) # default edge data=1\n",
    "G.add_edge(2, 3, weight=0.9) # specify edge data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "G.add_edge('y', 'x', function=math.cos)\n",
    "G.add_node(math.cos) # any hashable can be a node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "elist = [(1, 2), (2, 3), (1, 4), (4, 2)]\n",
    "G.add_edges_from(elist)\n",
    "elist = [('a', 'b', 5.0), ('b', 'c', 3.0), ('a', 'c', 1.0), ('c', 'd', 7.3)]\n",
    "G.add_weighted_edges_from(elist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.5 Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'c', 'd']\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "e = [('a', 'b', 0.3), ('b', 'c', 0.9), ('a', 'c', 0.5), ('c', 'd', 1.2)]\n",
    "G.add_weighted_edges_from(e)\n",
    "print(nx.dijkstra_path(G, 'a', 'd'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.6 Drawing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "G = nx.cubical_graph()\n",
    "subax1 = plt.subplot(121)\n",
    "nx.draw(G) # default spring_layout\n",
    "subax2 = plt.subplot(122)\n",
    "nx.draw(G, pos=nx.circular_layout(G), node_color='r', edge_color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.7 Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'A': {'B': {}}, 'B': {'A': {}, 'C': {}}, 'C': {'B': {}}}\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "G.add_edge('A', 'B')\n",
    "G.add_edge('B', 'C')\n",
    "print(G.adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300\n",
      "red\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "G.add_edge(1, 2, color='red', weight=0.84, size=300)\n",
    "print(G[1][2]['size'])\n",
    "print(G.edges[1, 2]['color'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 Basic graph types  \n",
    "2.2.1 Graph—Undirected graphs with self loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_node(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from([2, 3])\n",
    "G.add_nodes_from(range(100, 110))\n",
    "H = nx.path_graph(10)\n",
    "G.add_nodes_from(H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_node(H)\n",
    "G.add_edge(1, 2)\n",
    "G.add_edges_from([(1, 2), (1, 3)])\n",
    "G.add_edges_from(H.edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'day': 'Friday'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.Graph(day=\"Friday\")\n",
    "G.graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': '5pm'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.add_node(1, time=\"5pm\")\n",
    "G.add_nodes_from([3], time=\"2pm\")\n",
    "G.nodes[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, {'time': '5pm'}), (3, {'time': '2pm'})]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes[1][\"room\"] = 714 # node must exist already to use G.nodes\n",
    "del G.nodes[1][\"room\"] # remove attribute\n",
    "list(G.nodes(data=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edge(1, 2, weight=4.7)\n",
    "G.add_edges_from([(3, 4), (4, 5)], color=\"red\")\n",
    "G.add_edges_from([(1, 2, {\"color\": \"blue\"}), (2, 3, {\"weight\": 8})])\n",
    "G[1][2][\"weight\"] = 4.7\n",
    "G.edges[1, 2][\"weight\"] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in G # check if node in graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[n for n in G if n < 3] # iterate through nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n, nbrsdict in G.adjacency():\n",
    "    for nbr, eattr in nbrsdict.items():\n",
    "        if \"weight\" in eattr:\n",
    "            # Do something useful with the edges\n",
    "            pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for u, v, weight in G.edges.data(\"weight\"):\n",
    "    if weight is not None:\n",
    "        # Do something useful with the edges\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weight': 1}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class ThinGraph(nx.Graph):\n",
    "    all_edge_dict = {\"weight\": 1}\n",
    "    def single_edge_dict(self):\n",
    "        return self.all_edge_dict\n",
    "    edge_attr_dict_factory = single_edge_dict\n",
    "G = ThinGraph()\n",
    "G.add_edge(2, 1)\n",
    "G[2][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.add_edge(2, 2)\n",
    "G[2][1] is G[2][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methods  \n",
    "Adding and removing nodes and edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "G = nx.Graph(name=\"my graph\")\n",
    "e = [(1, 2), (2, 3), (3, 4)] # list of edges\n",
    "G = nx.Graph(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'day': 'Friday'}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.Graph(e, day=\"Friday\")\n",
    "G.graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "G.add_node(1)\n",
    "G.add_node(\"Hello\")\n",
    "K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])\n",
    "G.add_node(K3)\n",
    "G.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_node(1, size=10)\n",
    "G.add_node(3, weight=0.4, UTM=(\"13S\", 382871, 3972649))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 'H', 'e', 'l', 'o']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "G.add_nodes_from(\"Hello\")\n",
    "K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])\n",
    "G.add_nodes_from(K3)\n",
    "sorted(G.nodes(), key=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from([1, 2], size=10)\n",
    "G.add_nodes_from([3, 4], weight=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.add_nodes_from([(1, dict(size=11)), (2, {\"color\": \"blue\"})])\n",
    "G.nodes[1][\"size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = nx.Graph()\n",
    "H.add_nodes_from(G.nodes(data=True))\n",
    "H.nodes[1][\"size\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1), (1, 2)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "list(G.edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.remove_node(1)\n",
    "list(G.edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "e = list(G.nodes)\n",
    "e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.remove_nodes_from(e)\n",
    "list(G.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "e = (1, 2)\n",
    "G.add_edge(1, 2) # explicit two-node form\n",
    "G.add_edge(*e) # single edge as tuple of two nodes\n",
    "G.add_edges_from([(1, 2)]) # add edges from iterable container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edge(1, 2, weight=3)\n",
    "G.add_edge(1, 3, weight=7, capacity=15, length=342.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edge(1, 2)\n",
    "G[1][2].update({0: 5})\n",
    "G.edges[1, 2].update({0: 5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pg 16"
   ]
  }
 ],
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