{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    },
    "colab": {
      "name": "kl_py_cycle_detect_01.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/klajosw/python/blob/master/kl_py_cycle_detect_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zW7XYGxrfCCv",
        "colab_type": "text"
      },
      "source": [
        "<p align=\"left\"> \n",
        "    <img src=\"https://raw.githubusercontent.com/klajosw/python/master/kl_mie_python_logo_250.jpg\" \n",
        "         align=\"left\" width=\"251\" height=\"251\">\n",
        "    \n",
        "</p>\n",
        "\n",
        "\n",
        "<p> </p>\n",
        "\n",
        "\n",
        "\n",
        "# Python hurok detektállás \n",
        "\n",
        "## A networkx és pyvis csomag használata\n",
        "\n",
        "<https://klajosw.blogspot.com/>\n",
        "\n",
        "\n",
        "https://networkx.github.io/documentation/stable/auto_examples/index.html\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xi__MvALfCCw",
        "colab_type": "code",
        "colab": {},
        "outputId": "1fbd416a-eb61-41f1-d4f9-e2b521b45a98"
      },
      "source": [
        "### pyvis\n",
        "## ha szükséges installáljuk a csomagokat\n",
        "## !pip install pyvis --upgrade\n",
        "!pip install jsonpickle --upgrade\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting jsonpickle\n",
            "  Downloading jsonpickle-1.4.1-py2.py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata in c:\\users\\user\\anaconda3\\lib\\site-packages (from jsonpickle) (1.6.1)\n",
            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in c:\\users\\user\\anaconda3\\lib\\site-packages (from importlib-metadata->jsonpickle) (3.1.0)\n",
            "Installing collected packages: jsonpickle\n",
            "Successfully installed jsonpickle-1.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j42KXc2VfCC1",
        "colab_type": "code",
        "colab": {},
        "outputId": "912381e9-25c7-4aae-db4c-5536c68d3e59"
      },
      "source": [
        "def draw_graph(graph):\n",
        "    # irányitott hálózat gráf megjelenítése networkx csomaggal\n",
        "    G=nx.DiGraph()\n",
        "\n",
        "    # élek hozzáadása\n",
        "    G.add_edges_from(graph)\n",
        "\n",
        "    graph_pos = nx.shell_layout(G)\n",
        "\n",
        "    # nodok hozzáadása, élek és cimkék összekapcsolása\n",
        "    nx.draw_networkx_nodes(G, graph_pos, node_size=1000, node_color='blue', alpha=0.3)\n",
        "    # stilus, színezés\n",
        "    nx.draw_networkx_edges(G, graph_pos, width=2, alpha=0.3, edge_color='green')\n",
        "    nx.draw_networkx_labels(G, graph_pos, font_size=12, font_family='sans-serif')\n",
        "\n",
        "    # megjelenítés\n",
        "    plt.show()\n",
        "\n",
        "# we can add more edges here as the direction is a factor now,\n",
        "# edges are added as (from_node, to_node) tuples\n",
        "# hence (22, 25) and (25, 22) are different. In undirected graph,\n",
        "# we couldn't have told the difference.\n",
        "graph = graph = [\n",
        "        (20, 21), (21, 22), (22, 23), (23, 24), (24, 25), (25, 20),\n",
        "        (25, 21), (23, 20), (24, 22), (21, 24), (20, 21)\n",
        "    ]\n",
        "draw_graph(graph)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBW6iLfVfCC4",
        "colab_type": "code",
        "colab": {},
        "outputId": "01ca23f1-cf8a-4c69-830d-1ce6af677aa0"
      },
      "source": [
        "## különböző megjelenítési módok \n",
        "import networkx as nx \n",
        "import matplotlib.pyplot as plt \n",
        "  \n",
        "g = nx.Graph() \n",
        "  \n",
        "g.add_edge(1, 2) \n",
        "g.add_edge(2, 3) \n",
        "g.add_edge(3, 4) \n",
        "g.add_edge(1, 4) \n",
        "g.add_edge(1, 5) \n",
        "g.add_edge(5, 6) \n",
        "g.add_edge(5, 7) \n",
        "g.add_edge(4, 8) \n",
        "g.add_edge(3, 8) \n",
        "  \n",
        "print('drawing in circular layout') \n",
        "nx.draw_circular(g, with_labels = True) \n",
        "plt.savefig(\"filename1.png\") ## mentés\n",
        "plt.show()\n",
        "  \n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() \n",
        "  \n",
        "print('drawing in planar layout') \n",
        "nx.draw_planar(g, with_labels = True) \n",
        "plt.savefig(\"filename2.png\")  ## mentés\n",
        "plt.show()\n",
        "  \n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() \n",
        "  \n",
        "print('drawing in random layout') \n",
        "nx.draw_random(g, with_labels = True) \n",
        "plt.savefig(\"filename3.png\")  ## mentés\n",
        "plt.show()\n",
        "\n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() \n",
        "  \n",
        "print('drawing in specrtal layout') \n",
        "nx.draw_spectral(g, with_labels = True) \n",
        "plt.savefig(\"filename4.png\") ## mentés\n",
        "plt.show()\n",
        "  \n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() \n",
        "  \n",
        "print('drawing in spring layout') \n",
        "nx.draw_spring(g, with_labels = True) \n",
        "plt.savefig(\"filename5.png\") ## mentés\n",
        "plt.show()\n",
        "  \n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() \n",
        "  \n",
        "print('drawing in shell layout') \n",
        "nx.draw_shell(g, with_labels = True) \n",
        "plt.savefig(\"filename6.png\") \n",
        "  \n",
        "# törlése az aktuális vászonnak \n",
        "plt.clf() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drawing in circular layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "drawing in planar layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "drawing in random layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "drawing in specrtal layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "drawing in spring layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "drawing in shell layout\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOCYwxK4fCC7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pyvis.network import Network\n",
        "import pandas as pd\n",
        "\n",
        "got_net = Network(height=\"750px\", width=\"100%\", bgcolor=\"#222222\", font_color=\"white\")\n",
        "\n",
        "# set the physics layout of the network\n",
        "got_net.barnes_hut()\n",
        "got_data = pd.read_csv(\"https://www.macalester.edu/~abeverid/data/stormofswords.csv\")\n",
        "\n",
        "sources = got_data['Source']\n",
        "targets = got_data['Target']\n",
        "weights = got_data['Weight']\n",
        "\n",
        "edge_data = zip(sources, targets, weights)\n",
        "\n",
        "for e in edge_data:\n",
        "    src = e[0]\n",
        "    dst = e[1]\n",
        "    w = e[2]\n",
        "\n",
        "    got_net.add_node(src, src, title=src)\n",
        "    got_net.add_node(dst, dst, title=dst)\n",
        "    got_net.add_edge(src, dst, value=w)\n",
        "\n",
        "neighbor_map = got_net.get_adj_list()\n",
        "\n",
        "# add neighbor data to node hover data\n",
        "for node in got_net.nodes:\n",
        "    node[\"title\"] += \" Neighbors:<br>\" + \"<br>\".join(neighbor_map[node[\"id\"]])\n",
        "    node[\"value\"] = len(neighbor_map[node[\"id\"]])\n",
        "\n",
        "got_net.show(\"gameofthrones.html\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSzkELH8fCC9",
        "colab_type": "code",
        "colab": {},
        "outputId": "78cae7c6-a9dd-48b0-8d7f-fe0f3340eebc"
      },
      "source": [
        "import networkx as nx \n",
        "import matplotlib.pyplot as plt \n",
        "g = nx.Graph()\n",
        "g.add_edge('a', 'b', weight=0.1)\n",
        "g.add_edge('b', 'c', weight=1.5)\n",
        "g.add_edge('a', 'c', weight=1.0)\n",
        "g.add_edge('c', 'd', weight=2.2)\n",
        "print (nx.shortest_path(g, 'b', 'd'))\n",
        "\n",
        "print (nx.shortest_path(g, 'b', 'd', weight='weight') )\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['b', 'c', 'd']\n",
            "['b', 'a', 'c', 'd']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NW-ZT5kZfCC_",
        "colab_type": "code",
        "colab": {},
        "outputId": "1664e57b-1d21-46a1-e448-8a3b2c4d4899"
      },
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def draw_graph(graph):\n",
        "    # create networkx graph\n",
        "    G=nx.Graph()\n",
        "\n",
        "    # add edges\n",
        "    for edge in graph:\n",
        "        G.add_edge(edge[0], edge[1])\n",
        "\n",
        "    # There are graph layouts like shell, spring, spectral and random.\n",
        "    # Shell layout usually looks better, so we're choosing it.\n",
        "    # I will show some examples later of other layouts\n",
        "    graph_pos = nx.shell_layout(G)\n",
        "\n",
        "    # draw nodes, edges and labels\n",
        "    nx.draw_networkx_nodes(G, graph_pos, node_size=1000, node_color='blue', alpha=0.3)\n",
        "    nx.draw_networkx_edges(G, graph_pos)\n",
        "    nx.draw_networkx_labels(G, graph_pos, font_size=12, font_family='sans-serif')\n",
        "\n",
        "    # show graph\n",
        "    plt.show()\n",
        "\n",
        "# draw example\n",
        "# graph is a list of tuples of nodes. Each tuple defining the\n",
        "# connection between 2 nodes\n",
        "graph = [(20, 21), (21, 22), (22, 23), (23, 24), (24, 25), (25, 20)]\n",
        "\n",
        "draw_graph(graph)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3GQDsbXfCDD",
        "colab_type": "code",
        "colab": {},
        "outputId": "9bc0b6c7-d2ae-4c2a-e990-695141b59e7c"
      },
      "source": [
        "import networkx as nx\n",
        "G = nx.DiGraph()\n",
        "G.add_node(\"A\")\n",
        "G.add_node(\"B\")\n",
        "G.add_node(\"C\")\n",
        "G.add_node(\"D\")\n",
        "G.add_node(\"E\")\n",
        "G.add_node(\"F\")\n",
        "G.add_node(\"G\")\n",
        "G.add_edge(\"A\",\"B\")\n",
        "G.add_edge(\"B\",\"C\")\n",
        "G.add_edge(\"C\",\"E\")\n",
        "G.add_edge(\"C\",\"F\")\n",
        "G.add_edge(\"D\",\"E\")\n",
        "G.add_edge(\"F\",\"G\")\n",
        "print(G.nodes())\n",
        "print(G.edges())\n",
        "pos = nx.spring_layout(G)\n",
        "nx.draw_networkx_nodes(G, pos)\n",
        "nx.draw_networkx_labels(G, pos)\n",
        "nx.draw_networkx_edges(G, pos, edge_color='r', arrows = True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['A', 'B', 'C', 'D', 'E', 'F', 'G']\n",
            "[('A', 'B'), ('B', 'C'), ('C', 'E'), ('C', 'F'), ('D', 'E'), ('F', 'G')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SB5F7KbFfCDF",
        "colab_type": "code",
        "colab": {},
        "outputId": "454a5dea-1824-44c5-b058-ae5f95cc97de"
      },
      "source": [
        "import networkx as nx\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pylab\n",
        "\n",
        "G = nx.DiGraph()\n",
        "\n",
        "G.add_edges_from([('A', 'B'),('C','D'),('G','D')], weight=1)\n",
        "G.add_edges_from([('D','A'),('D','E'),('B','D'),('D','E')], weight=2)\n",
        "G.add_edges_from([('B','C'),('E','F')], weight=3)\n",
        "G.add_edges_from([('C','F')], weight=4)\n",
        "\n",
        "\n",
        "val_map = {'A': 1.0,\n",
        "                   'D': 0.5714285714285714,\n",
        "                              'H': 0.0}\n",
        "\n",
        "### nx.draw_networkx(G, arrows=True, **options) ## You can add options by initialising that ** variable like this:\n",
        "KL = '''\n",
        "options = {\n",
        "    'node_color': 'blue',\n",
        "    'node_size': 100,\n",
        "    'width': 3,\n",
        "    'arrowstyle': '-|>',\n",
        "    'arrowsize': 12,\n",
        "}\n",
        "'''\n",
        "\n",
        "values = [val_map.get(node, 0.45) for node in G.nodes()]\n",
        "edge_labels=dict([((u,v,),d['weight'])\n",
        "                 for u,v,d in G.edges(data=True)])\n",
        "red_edges = [('C','D'),('D','A')]\n",
        "edge_colors = ['black' if not edge in red_edges else 'red' for edge in G.edges()]\n",
        "\n",
        "pos=nx.spring_layout(G)\n",
        "nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)\n",
        "nx.draw(G,pos, node_color = values, node_size=1500,edge_color=edge_colors,edge_cmap=plt.cm.Reds)\n",
        "pylab.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvFuj8s0fCDI",
        "colab_type": "code",
        "colab": {},
        "outputId": "880ce781-e0c1-476d-d5a5-0df4418a2d2a"
      },
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "G = nx.Graph()\n",
        "G.add_edge(1,2)\n",
        "G.add_edge(1,3)\n",
        "nx.draw(G, with_labels=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9ofB5uwfCDK",
        "colab_type": "code",
        "colab": {},
        "outputId": "14d42f04-5bb3-4f4d-a667-45201352c9d5"
      },
      "source": [
        "import networkx as nx\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "G = nx.Graph()\n",
        "G.add_edges_from(\n",
        "    [('A', 'B'), ('A', 'C'), ('D', 'B'), ('E', 'C'), ('E', 'F'),\n",
        "     ('B', 'H'), ('B', 'G'), ('B', 'F'), ('C', 'G')])\n",
        "\n",
        "val_map = {'A': 1.0,\n",
        "           'D': 0.5714285714285714,\n",
        "           'H': 0.0}\n",
        "\n",
        "values = [val_map.get(node, 0.25) for node in G.nodes()]\n",
        "nx.draw(G, with_labels=True)\n",
        "\n",
        "nx.draw(G, cmap = plt.get_cmap('jet'), node_color = values)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZssNws6fCDM",
        "colab_type": "code",
        "colab": {},
        "outputId": "36a69441-04dd-4742-d6e0-2872ee443f75"
      },
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "G = nx.DiGraph()\n",
        "G.add_edges_from(\n",
        "    [('A', 'B'), ('A', 'C'), ('D', 'B'), ('E', 'C'), ('E', 'F'),\n",
        "     ('B', 'H'), ('B', 'G'), ('B', 'F'), ('C', 'G')])\n",
        "\n",
        "val_map = {'A': 1.0,\n",
        "           'D': 0.5714285714285714,\n",
        "           'H': 0.0}\n",
        "\n",
        "values = [val_map.get(node, 0.25) for node in G.nodes()]\n",
        "\n",
        "# Specify the edges you want here\n",
        "red_edges = [('A', 'C'), ('E', 'C')]\n",
        "edge_colours = ['black' if not edge in red_edges else 'red'\n",
        "                for edge in G.edges()]\n",
        "black_edges = [edge for edge in G.edges() if edge not in red_edges]\n",
        "\n",
        "# Need to create a layout when doing\n",
        "# separate calls to draw nodes and edges\n",
        "pos = nx.spring_layout(G)\n",
        "nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap('jet'), \n",
        "                       node_color = values, node_size = 500)\n",
        "nx.draw_networkx_labels(G, pos)\n",
        "nx.draw_networkx_edges(G, pos, edgelist=red_edges, edge_color='r', arrows=True)\n",
        "nx.draw_networkx_edges(G, pos, edgelist=black_edges, arrows=False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k6WHbWWi6GMK1UDmDWYHExESWLFnC7NmzqVOnDj/++COdO3e2+yqE9Jw4cQJfX1/KlClDdHS0bJ+TD8mNNJGnRVzJWeA+kmaEa4ng6Q8P05597+bNm0yZMoWaNWsSFhZGQEAA27dvp0uXLnkucE0mEz/++CPt27dn+PDhBAcHS+DmU1LpijwrWZ+7wH3EYIKEhzB2GyzuBRcvXmT27NmsXLkSLy+vPL8lzeXLl9FqtSQmJrJ//35q165t7yGJXJBKV+RZU/fBrSTLnCvZACtjTbz57mSaNWuGq6srcXFxLF68OM8GrqIorFq1iubNm9OxY0f27dsngVsASKUr8qRUA8w7pIZlhuZUh8QEcHzqRleTYfDmvHQPT9ErXKo6iLNnx1GqVClLDtfibt68yfvvv098fDzbtm2jadOm9h6SsBAJXZEnBZ0GRcn6OLy3QK2u5p3U0YnTjvVwcsvV0KwuKCiIUaNGMWjQIFauXEmRIkXsPSRhQRK6Ik/afRES07I+LrtcNXD4OrxWzfLnzq1H+5X98ccfrFmzhg4dOth7SMIKZE5X5Elhl8CcQje7Ug0Qfc0KJ86l0NBQGjdujF6vJyYmRgK3AJNKV+RJt5LNPHBdH3B86o9xtxnQ4t0MD081wl8Pczc2S0pNTWXKlCksX76cRYsW8dZbb9l7SMLKJHRF/jYw0Pw53b855ZG/38XGxuLj40PNmjVlv7JCJI/88RPiWZWLW+e87hqoYud+NUajkenTp9OlSxfGjh1LQECABG4hIpWuyJM6VIMDV8Fk4YldJ0do/qJlz5kd586dw9fXF2dnZyIjI6lWLQ/e0RNWJZWuyJO61AB3ZzMOXNMLvin25LW2b6aHmxRoZIeiUlEUfv75Z1q1akW/fv0ICQmRwC2kpNIVeVKnxKMUf1iZROdM2hWOvZCtczo7wvAm6rIxW7p+/TojR47kzz//ZPfu3TRo0MC2AxB5ilS6Im85dAj69MHx9e5MdoukqMZy8wsaRxhn4w0VNm3aRJMmTWjatCkHDhyQwBVS6Yo8Yt8++OYbOH4cPv8c1q5lVBE3dEvVdbXGXGZvUWeY9BrUKG2Z4Wbl7t27fPjhh0RERMh+ZeIZUukK+1EU2L4dOnSA4cPBywvOnIExY8DNDUcH+MULSrhCbhotFtFAi0rwWRuLjTxTISEheHh4ULJkSQ4fPiyBK54hla6wPUWBLVtg6lRITISJE2HAANA8/8exeikIGw7tl8GDVEgzZe9S7s7Q4kX43dv663OTk5OZMGECAQEBLF26lO7du1v3giJfkkpX2I7RCOvXQ5Mm8NVXMH48xMXB4MHpBu4j9crB8Q/UFQ1FzVnRADg5gJsG/u0JIb5mroTIhUOHDtGsWTNu3rxJbGysBK7IkFS6wvr0elizBr79Fl54Ab77Dnr2hGzszlC+KAR5q9vvfBsKh/4EFye1Kc6jtbwuTupUgt4IgxrC+HbwygtW+p7+ptfr+eabb1i4cCE//vgj77zzjnUvKPI9CV1hPSkpsGwZ+PlBzZqwaBF07JitsH2agwP0eFl9/fkADl5VN6xMeKhOHbxcWp27bVEJirlY9DtJ14kTJ/Dx8aFs2bIcPnyYSpUqWf+iIt+T0BWW9/AhLF4MM2dC06ZqldvGsnexKhWHPnXVl62ZTCbmzZvH119/zddff817772X5/ZUE3mXhK6wnHv3YMECmDsX2reH335TQ7cAuXz5MsOHDycpKYnw8HBefvllew9J5DNyI03k3q1bMHky1KqlrrPdtQs2bixQgasoCitXrqR58+Z06dKFvXv3SuCKHJFKV+Tc9eswaxbodNCvH0REqMFbwNy8eZPRo0dz4sQJ/vjjD5o0aWLvIYl8TCpdkX2XLqkPMNSvD6mpcOSIOodbAAP3t99+o3HjxtSoUYPIyEgJXJFrUukK8505A9OmwebNMHIkxMdDhQr2HpVVPHjwgHHjxrFjxw7Wrl3La6+9Zu8hiQJCKl2RtWPH1AcY2rSBKlXg9Gl1GVgBDdzQ0FCaNGmCoijExMRI4AqLkkpXZCwqSm1Cs38/jB0LCxdCCTtvu2BFqampTJ48mZUrV7Jo0SJ69+5t7yGJAkhCVzwvLEwN29hY+Ne/YNUqcHe396isKiYmBh8fH15++WViYmIoV66cvYckCiiZXhAqRYGQEOjUCXx8oE8fOHsWPv64QAeu0Whk2rRpdO3alc8++4xNmzZJ4Aqrkkq3sFMUCApSO37dvQv//jcMGgTOVu4QkwecPXuWoUOH4uLiIvuVCZuRSrewMhphwwb1AYaJE+HTT9UbZr6+BT5wFUVh8eLFtG7dGi8vL3bs2CGBK2xGKt3CxmCAtWvVjl8lS6oV7ptv5rgJja2YFLhwV+2pq3FUd4DISbvGa9euMXLkSBISEtizZw/169e3/GCFyISEbmGRmgrLl6vrbKtVgx9/hC5d8nTYJulh/TGYfwiO3VB75Do5qjMiKQaoWAz61oWPWkKtTPavfGTjxo2MGTOGUaNGMWnSJJwLeEUv8iYJ3YIuKQl+/lnt+NWwIaxYAZ6e9h5VphQFlh6BsdsABRL16R93+T4sjITF0fBmbVj8Dyjj9vxxd+/eZcyYMRw6dIhff/2VVq1aWXX8QmRG5nQLqvv3n/Sx3bMHAgMhODjPB+6DVOiyEj7ZqjYozyhwH9Gb1Kp3yyl4+UcIvfTs+zt27MDDw4PSpUtz+PBhCVxhd1LpFjS3b8MPP8D8+dC9O+zYoVa4+UBiGnj6w8lbkGrM3mfTjOrr9dUQNAhalk9iwoQJBAYGsnTpUrp165br8aVwg2vs5BbRPOAsRlJwwIkilKc0HlSgLWVohkOuttEUBZ2EbkGRkACzZ8OSJeoa2/37oXZte48qW3w25yxwn5akhzdXG6m4sQet679ETEwMpUvnbt/1JK5xnLnc5ggKCgrPlt967vOAs1wlGA1FeYVRVKKrhK9Il4Rufnf5MsyYoT415u0N0dHqjbJ85teT8Me53AXuI0lpJooM3sCqTyvk+j7hRQI5ySJM6IHMtiJWMJKMkWSOM5urbKUJk3ChVO4GIAocmdPNr86ehVGjoHFjcHVV19jOm5cvA1dRYMzvapVqEU7OXNRXeG5+N1tjQuEECznFT5hIJfPAfZaRFO4Qy35GkcKtnA9CFEgSuvnN8ePqY7qtWqldvk6dUivdF1+098hybNcFuJuaxUFzqsPXbvBNMfiuNKx6E+5dzvDwJD3MDM/5mC4SwCX+i5GUHH1ewUAqtznIJ5hIy/lARIEjoZtfHD4M/furvRHq1VMr3a+/hrJl7T2yXFt9VL2JliXvLTAxET67BsUqwO8fZnioAgSfAYP5BepjD7nCKX7GlMPAfTIGIync4BS6XJ1HFCwSunldeDj84x/qq00bOHdO7Y9QsqS9R2YxYRkXrOlzLgL1veDG8UwPc9VA/I3sjyeOmX/P4eaeiVQusZkkrlrkfCL/kxtpeZGiwO7d6iO6Z8/ChAnqRo9Fith7ZFZx4W42P5CWBHHroUrrTA9zAOJvQqNs9FpP4ir3iCejOdyh1cO5m6DHSeOAoxNUrV+ULr4V6DmqEo6O6d+1UzBxkQDqkXFlLgoPCd28RFHUBximTlV32P3iC3XHhgL+uKrZUwDr+oCjBtISoWh58NmW6eGKopBsyN7yhStsRcniptlXWxrStGsZHt4zcHTPXRZ9fIaTEQ8Y5183/XFg4ArB1GWMLCMTErp5gsmk7jv2zTeg18OXX4KXFzg52XtkNuHsBEaDGQcODIRaXcFkhBO/gn8H+OdxKF4x3cMTH9znow8+Y3biQSpWrPjM68UXX3zm30uWLImDgwO3OYyCOYOBoiU1tO5dltIVXRjbOpq3P61C9YbF0j1WwUQy13En/97wFJYhoWtPBgOsX692/HJ3hylToFcvcCwcU+0Gg4GYmBhKmGqQghkdax5xdIL6b8OW9+BSKDTwSvewYsVLELj4W0qlXeH69euPXxcuXODAgQPPfC01NZWKFSsy92h1ihTPXjVap2UJylZxJW7fvQxD1wEnHnBGQldI6NpFWpraeOa776ByZZgzB7p1y9Mdvyzh/v37HDhwgLCwMEJDQzl48CBVq1al9D8W8VfRdph9X1dR4OR/IeUOlKuX4WFpJgfa1yuHxjHrnSCSkpJISEjgZNGRZGdN7iNlKrnw4HZmFbIJPQ+yfV5R8Ejo2lJysvqY7vTpUL8++PtDAd5p9vLly48DNiwsjFOnTtG8eXPatWvHuHHjaNiwISEhIczZ/As0bgou6VeJj63ppVa5OECpatB3OZRvkO6hDsA/aqu9d83h7u5OjRo1OIVTlnO66bl1NY3iZTL7cXLAgcIxXSQyJ6FrCw8eqDvpzpmjPtQQEACvvvr47bTERK4fOcKdc+cwGQy4lixJxcaNKV2rFg75pPo1Go3ExcU9DtjQ0FCSk5Np164dnp6eDB48mGbNmuHi4kJoaCg6nY7BgwfTsWNHpo7U8uH5oly+n8kFxl7I1njcneHTNtn/PlwpTQp/ZeszJw/d59bVVBp4ZryMzwFH3GRqQSCha1137qjNwh81DN+2DTw8ADCmpREfEEConx83jh3D2c0NxWRCURQcnZwwGdUmBB4+PrT+5BPK1qljz+/kOQ8fPiQiIuJxwB44cICKFSvi6elJ165d+eqrr6hdu/bjXxpXr15l9uzZ+Pv7o9FoGDFiBNOmTaNCBXU9l+YUvLPJMo8COztC25egTZXsf7Yk9cwO3Yf3DcTtVVcvdB5SgRqNMq7UjaRQgvzVgEhYh4OiKBm+2aJFCyUyMtKGwykg/vpLrWoXL4bevdV1tk+F5tWDB9nQvz/Jt2+TlpiY6akcNRocnZ1pqtXSbcYMnN3S6dJtA3/++SdhYWGPX8ePH6dx48aPK9m2bds+t4tuWloaW7ZsQafTER4eTv/+/dFqtbRs2TLdCn7ARvjvSUjJZdOb4i5waoy6s0R2/ckOjjEbI8npvv/0Ol0HR3WdbuchFXhjdCWcnDL+W0kxauApT6YVGg4ODlGKorRI9z0JXQu6elXdoWH5chg4ED7/HKpXf+aQ8Nmz2fnllxiS0/+hzojGzY2i5cszfO9eSlatasFBP89kMhEfH//MVMHdu3dp27Ytnp6etGvXjhYtWuCWwS+Ao0ePotPpWL16NQ0aNECr1fL2229TtGjRTK+bpIfXlkFcgolUU85WcLg7wx+DoV0O/xOZSCOEvhhJytkJ0uGEGw0YSyVy39NX5A+Zha5ML1jC+fPqLg2//ALDhkFcHFSq9Nxh4bNmsWvy5GwHLoAhOZn7V67wc8uWvBcdTfF0zp9TycnJHDp06HHAhoeHU7p0aTw9PfH09GTChAnUrVsXx0yWst29e5e1a9ei0+m4fv06w4YNIzw8nFq1apk9Dndn2DYghRpfRKFUaEka5j8U4uoExVzgt0HQOgfTCo844kJNBnKWNbnuvfCIBncq0sEi5xL5n1S6uXHihLrs67ffYPRo+OQTKJf+8qQrBw6wvHPnHAXu0xw0Giq/+irasLAc32T766+/npkqiI2NpUGDBs9MFbxoRtcyk8nErl270Ol0BAUF8frrr6PVaunatStOOXyw4/333+evGzfoO3kD/wx2wKRk3gzHxQkcHeDturDgDShpgSelTRgJQ8tDLqO2zsk5R1x5lRmUplHuBybyDal0LS0mRn2gYdcu+OgjtT9CqYybVRtSU9nwzju5DlwAxWAgITaWqJ9+osXo0VkfryicPHnyccCGhoby119/0bp1azw9Pfn2229p2bJlln/1f9rFixdZtmwZ/v7+lCpVihEjRvDDDz/wwgsv5OZbY/Xq1YSEhBAZGUmJEg70rw+b4tXdgGMS1G3YNX/vBpxqhKoloV89GPMqvGTB/j+OONGMbwlnNAYektPgdaQINfGWwBXPkEo3OyIi1Ed1IyNh3Di1ui2W9d2a2NWrCRo9OsubZtnhXrYsn16/juP/VJSpqalERUU9Dtj9+/fj7u7+eC7W09OTBg0aZLsSTU5OJjAwEJ1Ox+HDhxk0aBBarZamTZta5Ps5fvw4HTp0ICQkBI+/V3g8TVHgzwdwP1V9bPilEmoXMWtK5AIRfIKRpGx3HS82GBAAABWZSURBVHPElZp48zK+VhqdyMuk0s0NRYG9e9UmNCdPwvjx6qO72VhFEObnZ1bg+gMJwGdk/X+MITWV07//Trl27di/f//jm17R0dHUqVOHdu3a4e3tzfz586lSJWeTnIqiEB0djU6nY926dbRo0YKRI0fy1ltvUcSCHc8SExPp168f06dPTzdwQX1Yr3IJqGyxq2atGNVpzwqOMYsbRPzdjDzzqteRImhwozGTeYEmthmoyFek0s2Ioqjrar/5Bq5fVzt+DRkCLi7ZOk3KvXvMKFcOkz7zSukO8APgCvQC0n/O6lnnSpZks8lEy5YtH1eyrVu3pnjx4tka4/+6efMmq1evRqfT8eDBA4YPH87QoUOpaoVVE4qiMHjwYNzc3Fi6dKnFz28pdznOedbxF+E44gKYMGHAAQccccFEGq6UowYDqUw3nCiYbTiFeaTSzQ6TCf77X7WyTUlRG4a/8w5ocvaf6lp0NM5ubqRmEboxQBXUSu4I5oWuR+nSLD19Gk0Ox/Y0g8HAH3/8gU6nY8eOHfTq1Yu5c+fSoUOHTFct5NaiRYs4duwYBw4csNo1LKEU9WnK/2EkjQec5QHnMPAQB5xwpxIlqYNrdpr2iEJLQvcRo1Fd8vXNN2qz8IkT4a23ct3x687Zs5gMWbcKjAHaoAbvEiARyGq2OO3mzVwH7unTp/H392f58uW89NJLaLVali5dSkkb7Exx6NAhJk+ezP79+zNc85vXOOFCKepRiowb7QiRGQndtDR1+/Jp06B8efXhhtdft1jHL5PBQGZTOAAXgXuo1W1RoDRwFDWEszp3TiQmJrJx40Z0Oh0nT57Ex8eHP/74gwYNzKmvLeP27du88847LFq0iNq15fFYUXgU3tBNSQGdTn2o4ZVX4Oef1Y5fFm4w41qiBI5ZVKMxQC3UwAVohDrFkFXoOru7mz0ORVEIDw9Hp9OxadMm2rdvz7hx43jjjTdwyeY8dW6ZTCaGDh1K37596devn02vLYS9Fb7QTUyEn36CWbOgRQt1SqFVK+tcy2ikgpMTpGa8v7geOIbawXXGo48BKcB1IP09EVTlGjbMcgjXrl1j5cqV6HTqc/9arZbjx4+b9fCDtUyfPp3bt2/j5+dntzEIYS+FJ3Tv3oV58+CHH6BjR/j9d2hi4SU9JhMcO6Y+NLFzJ+zZQ9kXX3zcMSw9J1B7v/4Tnum2ugG1As4odB2dnanRqVO67+n1eoKCgtDpdOzbt49+/fqh0+lo06aN3VtF7t69m7lz5xIZGYlzAd/7TYj0FPzQvXED5s5Vq9s331TX3NZNfwPBbFMUOHNGDdidO9WwLVECOndWG94sWoRjxYrU9/Xl6Jo1KOmE7xGgKfC/z7O1BIKBrpBu62tHJyc8fHye+dqxY8fQ6XSsWrWKOnXqoNVqWbNmDcXMeIDDFq5du4a3tzcrVqzI8dphIfK7ghu6f/6pTiH4+6tLvg4ehJo1c3/eS5eeVLI7d6rB27kz9Oyp7ghRrdpzH2nz6acc37gx3ceAfZ77iqrh36/0ODg6UqllS8rUqsW9e/dYt24dOp2OK1euMGzYMEJDQ/PczSmDwcCgQYN477336N69u72HI4Td2DR0Uw1w/AbcTgYnR/XZ+RqlLHzv6sIFNfzWrQNfX4iNhdxUVQkJz4bs/fvQqZMatF9+CS+/nOU3ULFxYxoMGMCxdeswpOS+c5WTqyvltVp8fHzYsmUL3bp146uvvqJbt24WWbNrDZMmTcLFxYUvv/zS3kMRwq6s/hP6IBVWHYUfIuDsHXBzVucwAfR/b0XVoRp81gY6Vc9FAJ86pXb8+u9/YdQotQNY+fLZP8+dO7Bnz5OQvXIFOnRQQ/bDD6FBgxyt3e35ww+c3baNhwkJKKbs78H1mLMzB4oUYf2sWYwYMYI5c+ZQtmzZnJ/PBn777TdWr15NVFRUjruPCVFQWC10FQX8Y+Djrer/fvj3A1n6dG7kB5+BfReheilY7wX1s9689YmjR9WOXzt2qKF45gyULm3+5xMTYd++JyF7+jS0aaNur+PvD02b5vhptKe5Fi/O8L17WdK6NSl37uQoePWA/pVXmLJsGc2bN7f7TTFznD9/nhEjRrB58+bndpYQojCySu+FJD30XQ9hl5+ErVmDAYpo4PvX4d3mWRx86JD69FhEBIwdC++/D+b0HEhJgfDwJyEbE6MuHevcWX21bJnt/grZcffiRdb16cPt06fRP3xo1mcUwNHFhfaTJtFx4sR8Ebagdjxr164dgwcPZuzYsfYejhA2Y9PtelIM0HG52v80JWcPTOHuDNO7wj9fRb0B9uWXEBwMTk5qVTp1Khw/rm6HM3Jk5h2/9Ho1oB+F7MGD0KjRk5Bt2zZbHcMswWQ0cmDuXPZOnYpiMGTcgczRESdnZyq1aEGvn3+mXL389ejpBx98QEJCAhs3bsw3vyiEsASbhu57v8HKWEjOYeA+4qaBnb3v0rpTbfXm1aefQliYOsf6xRfqTbL0KlKjEY4cebKEKzRUvdn16OZX+/bqsq48wKjXczwggD2LFnHt0CEcHz7EycEBjZsbL3p4UL1zZ5oMG8YLeWwlgjnWrFnDlClTiIyMtEkfByHyEpuF7t6L0GN17gP3kZeSEzg1uyZF0pLUKnfxYjVsn55jVRS16n1Uye7ZAxUrPqlkO3SAXO5oYA1nz55l2bJlLFu2jIoVK6LVahk4cCClszMfnUc9aki+Y8cOGjdubO/hCGFzNmvtOHab5QIX4LZTUVY3HMiIaJ1awZ47p4bv/z6QUKyYGrD9+8OCBWDHR1wz8/DhQzZt2oS/vz/Hjh1j8ODBBAUFZdi4Oz9KTEzEy8sLPz8/CVwh0mGxSvfYX/DqEjNCN3YNhM+GmyfApThUbAKvTYRqnuke/vKds5z6vjYOiqKGqUajBvCjSrZTp+e2Oc9LFEUhIiICnU7Hxo0badOmDVqtll69etm80Yy1KYrCkCFDcHV1fdzrQYjCyCaVbuBJMGS1Cmr/bAidBv9YBC+/Dk4ucGYrnPg1w9C9UqwSV4pX5qX7V9Q2jLt3Q+3aFu8GZmkJCQmPG80YDAa0Wi1Hjx6lcmVbbjhjWz/99BNxcXGEh4fbeyhC5FkWC93dF5487JCulHuwazL08Yf6bz/5ep1e6isDLoqBqGqteelcMNy6BWXL5tnA1ev1BAcHo9Pp2LNnD3379mXx4sW0a9euwN+9j4yMZNKkSY83whRCpM9ioXvyVhYHXA4HQwrU7Zut8ya5Fufk/PVQMk69YZZHVh48LT4+Hn9/f1auXEmtWrXQarWsXLky13uV5Re3b9+mf//+LFy4MM/1fBAir7FY6KZl3L1QlXwL3MuCU/YuaVAg1eQIHh7qK4+4f/8+v/zyCzqdjvPnzzN06FB2795NnTp17D00m3rUkLxPnz54eXnZezhC5HkWC133rFqjur0ASTfBaMhW8Do7QtE8cr9JURT27duHTqcjMDCQzp078+9//5sePXrk2UYz1jZjxgxu3brFpk2b7D0UIfIFiyWFRwU4fzeTA15qA5oicCIQGphfEbk5Q0M7P7J/5coVVqxYgb+/Py4uLowYMYLp06dTPicNdQqQ3bt3M2fOHA4dOlTgVmIIYS0W21u7YzUoklkDqSIlodP/QdA/IT4Q0pLAqIfTwfDH5xl+LNUAzStZapTmS01NZcOGDfTs2RMPDw8uXbrE6tWriYuLY9y4cYU+cK9fv87gwYMf7yIshDCPxSpdr/owISSLg9qOg2IVYO9U2DQYXIvDi83VdboZaFIRytrwZnhMTAw6nY41a9bQqFEjtFotmzZtkjvyT3nUkPzdd9/l9ddft/dwhMhXLBa6VUqo1e7282DKbMdxj8HqywzFnGF8O8uMLzN37txhzZo16HQ6bty4wbBhw4iIiKCmJXaaKIAmT56MRqNh0qRJ9h6KEPmORe/+zO0BzRZb5lFgJweoVw7estJiAJPJREhICDqdjuDgYHr06MF3331Hly5dpNF2Jn777TdWrlxJdHS0/HcSIgcsGrp1y8JXHeE/e9SeurlRRAPr+oGjhZ8pOH/+/ONGM2XLlkWr1TJ//nzKlClj2QsVQBcuXGDEiBEEBARIQ3Ihcsji65z+1UbdB23D8ZwHr7szBA6AmhZquJWcnExAQAA6nY7Y2Fi8vb359ddfaWLpLdgLsNTUVPr378+ECRNo184Gcz5CFFAWD10HB9D1hopF4fsISMnqoYmnuDqpa3I3vwOvPb+pbrYoikJkZCQ6nY7169fTqlUrRo8eTe/evXF1dc3dyQuhcePGUbVqVT755BN7D0WIfM0qK/ofpoGrBpydIM2UxY011LAF6FcP5r8BpYrk/No3btxg1apV6HQ6kpKS0Gq1xMTEyLKmXFizZg3btm0jKiqqwPeQEMLaLB66IedgUAA8SDN/ux6TAv/XUV2pkJOfaYPBwNatW/H39yckJIS33nqLefPm0b59exxzsHOveCI+Pp6PP/6Y7du3yw4QQliARUN3wSH4147sz+XqTTB1Hxy/CcveMv/m2cmTJ/H392fFihVUq1YNrVaLv78/JfJgU5z8KDExkX79+jFt2jSZ/xbCQiwWuquPwr+2Q1IOl4s91MOmeHVt7oI3Mz7uwYMHbNiwAZ1Ox5kzZ/D19SUkJIR6+WzTxrxOURRGjx5Nq1at0Gq19h6OEAWGRUL30j11Q8qcBu4jSXpYHgt96kL3Wk++rigKYWFh6HQ6Nm/eTIcOHfj888/p2bMnzs5ZddoROfHTTz8RGxvLgQMHZB5XCAuySOiO+C+kZmOVQmaS9DBkM1wZCzcT/mTFihXodDqcnJwYMWIE3377LRUrVrTMxUS6oqKimDRpEmFhYfL4sxAWluvQPXMbQi+bsVXP0XUQPgf+igOXolCqBjQZCq++/9zdswcpeloNn8OFLd/Rv39/VqxYQatWraTisoE7d+48bkj+yiuv2Hs4QhQ4uQ7dhZFgzCpww2ZB2HR4c766N5pLMbh+BMJmQrMRoHl23WyKyZk79UdwZeE/KVq0aG6HKMz0qCF57969pSG5EFaS69DddtbMvdHeXgH1+z35+otNwWt1hh/70/ACLrlYryuyb+bMmdy4cYONGzfaeyhCFFi5Cl2jCU7fzuKgy+FgTIU6b2Xr3EU06uPEjWX61ib27NnD7NmzpSG5EFaWq9B9qAeyeNqMpJvP7422pC3cOA6GVPDZBtVfe+5jjg6Q8DA3oxPmun79Ot7e3ixbtkye3BPCyqy/sZd7Onujjdyv/nNWFVCymhAW1vSoIfnIkSPp0aOHvYcjRIGXq2dkizoDWS0oqNIGnFzh5K/ZOrdJgQpyD83qpkyZgkajYfLkyfYeihCFQq4qXSdHqF0Gjt3I5CC3UtBxCvz2ASgKvNwDnN0hIRbSMp4/SDFAfWnZalVBQUGsWLGCqKgoaUguhI3kenqhey04dSuLFQyen0OJyuqysc2+4FwUSteEbn7wUtt0P9KovNqlTFjHhQsX0Gq1BAQEFPpNNoWwpVyH7vvN1bW6mYYuZG9vNBf4LP0sFhaQmprKO++8w/jx46UhuRA2luu+h7VfgHYvgcaCHRSLaODtupY7n3jWp59+SpUqVRg7dqy9hyJEoWORqNT1ftKIPLfcNbC6r9oEXVje2rVr2bp1KzqdTh6rFsIOLBK6VUvCT/8At1wGpbsz+DZ+tsOYsJz4+Hg++ugjNm7cSKlSpew9HCEKJYtNCgxuBDO7qcGZE0Wdn2zXIyzv4cOHeHl5SUNyIezMonvZfPAq/HcAlHNX52XNoXFQA3dGV1iejV0jhPkUReG9997j1VdflYbkQtiZxWdOu9SEsx/CzHD48aDanyHFCGlP9dt1+jto9SbwbgST2kM1+duu1SxevFgakguRRzgoSsbNE1q0aKFERkbm+OQGE+y7CIf+hIircD9VrYCbVoRXK0HH6lBcdkO3qqioKHr27EloaKj0xxXCRhwcHKIURWmR3ntWXSOgcYRONdSXsL1HDcnnz58vgStEHiH7kxdQiqIwbNgwevXqRf/+/e09HCHE32Q1bAE1c+ZMEhIS2LBhg72HIoR4ioRuAbR3715mzZrFwYMHpSG5EHmMTC8UMNevX2fQoEEsW7aMqlWr2ns4Qoj/IaFbgBiNRry9vRkxYoQ0JBcij5LQLUCmTJmCo6MjU6ZMsfdQhBAZkDndAiIoKIjly5dLQ3Ih8jgJ3QLg4sWLaLVaNm3aJA3JhcjjZHohn3vUkPzzzz/H09PT3sMRQmRBQjef++yzz6hUqRLjxo2z91CEEGaQ6YV8bN26dQQHBxMZGSmNbITIJyR08wiDwcT27WcJC7tMWNhlbt1KQqNxpFatMnToUI2uXWtSt27Zx8efOHGCDz/8kD/++EMakguRj0jo2llysh4/vzC+/z4Co9FEUpIeo/FJ57fDh68TFHQKgAYNyvPtt51p27YiXl5efPfddzRt2tReQxdC5IBVWzuKzIWHX6Z//w3cvp1McrLBrM+4u2soX/4OrVvfZM0af5lWECIPsltrR5GxzZvjGTJkM0lJ+mx9LinJwMWL7mg0zbh1K5myZd2tNEIhhDXI6gU72Lv3IkOGBGQ7cB9RFCcuXrzHa6/5k5pqXoUshMgbJHRtLDExjf79N5CUlLuw1OtNXLx4ly+/3GWhkQkhbEFC18YmTgzh/v1Ui5wrKcnA/PkHiY+/YZHzCSGsT0LXhh4+TGPJksOkpGRU5c4Bzv7P1w4DSzM8p15vZM6cAxYaoRDC2iR0bSggIB5HC+8xbzAorFoVS9rT2y0LIfIsCV0b2rHjPImJaRY/r0bjyLFjf1n8vEIIy5PQtaEDB65Y5bxGo0J09DWrnFsIYVmyTteG7txJNuOodTz7u9AIvJjpJ1JTDdy4kZSLkQkhbEVC14bMe3hsIFDrqX8/DERneV5LzxULIaxDphdsqGLFYlY5b5EizlSqVNwq5xZCWJaErg15elprd16FFi0qWencQghLktC1oe7da1G8uIvFz6vROFK7dhmLn1cIYXkyp2tDb775ChpNZr/nxqbztaZ/v9Ln6urE+++/ipOT/P4UIj+Qn1Qb0mgcGT/ek6JFnS12TicnR8aMaWmx8wkhrEtC18Y+/bQNVauWNHMlQ+aKFnVmxoxuchNNiHxEQtfGNBpHAgIGULy4a67O4+amoX37arz/frp9koUQeZSErh3UrVuWXbuGUrKkaxZzvOlzd3fG07MqgYEDZOcIIfIZCV07adbsReLiPqBt25fMnuPVaBxwc9Pw1VcdCA4ejKur3AcVIr/JdI80BweHG8BF2w1HCCEKhGqKopRL741MQ1cIIYRlyfSCEELYkISuEELYkISuEELYkISuEELYkISuEELY0P8D4gT3ytcEOdQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4Aub5emfCDO",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "\n",
        "## Csomagok tartalma\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9h9QzZnfCDO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "####   import BProp   ## Használata\n",
        "import networkx as nx\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "class BPGraph(nx.DiGraph):           \n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.next_index = 0\n",
        "        self.binary_predecessors = []\n",
        "        self.binary_successors = []\n",
        "        self.neighbors = []\n",
        "    \n",
        "    def neighbors(self, index):\n",
        "        return list(self[index])\n",
        "    \n",
        "    def __propagate(self, index_to, visited = 0):        \n",
        "        visited = visited | 1 << index_to\n",
        "        if(self.binary_successors[index_to] > 0):\n",
        "            for successor in self.__get_indexes(self.binary_successors[index_to]): \n",
        "                self.binary_predecessors[successor] = self.binary_predecessors[successor] | self.binary_predecessors[index_to]\n",
        "                \n",
        "            for successor in self.__get_indexes(self.binary_successors[index_to]): \n",
        "                if 1 << successor & visited != 0:\n",
        "                    self.__propagate(successor, visited)\n",
        "            \n",
        "    def __get_indexes(self, bin_sum):\n",
        "        num = bin_sum\n",
        "        powers = []\n",
        "        counter = 0\n",
        "        while num > 0:\n",
        "            if(num % 2):\n",
        "                powers.append(counter)\n",
        "            num = num >> 1\n",
        "            counter = counter + 1\n",
        "        return powers\n",
        "    \n",
        "    def __get_number(self, index):\n",
        "        number = 1\n",
        "        for i in range(0, index):\n",
        "            number = number << 2\n",
        "        return number\n",
        "    \n",
        "    def add_node(self):\n",
        "        super().add_node(self.next_index)\n",
        "        self.next_index = self.next_index + 1\n",
        "        self.binary_predecessors.append(0)\n",
        "        self.binary_successors.append(0)\n",
        "        self.neighbors.append(0)\n",
        "        return self.next_index\n",
        "    \n",
        "    def add_n_nodes(self, n):\n",
        "        nodes = []\n",
        "        for i in range(0, n):\n",
        "            nodes.append(self.add_node())\n",
        "        return nodes\n",
        "    \n",
        "    def add_incremental_edges(self, edges):\n",
        "        cycles = []\n",
        "        pbar = tqdm(total = len(edges))\n",
        "        for edge in edges:\n",
        "            if(not self.add_edge(edge[0], edge[1])):\n",
        "                cycles.append(edge)\n",
        "            pbar.update(1)\n",
        "        pbar.close()\n",
        "        return cycles\n",
        "        \n",
        "    def add_edge(self, node_from, node_to, decrement = False):        \n",
        "        if(node_from == node_to):     # if it's a reflexive edge, then it's a cycle\n",
        "            return False\n",
        "        \n",
        "        if decrement:\n",
        "            index_from = node_from -1   # if the nodes start from 1 instead of 0, we must decrement them\n",
        "            index_to = node_to - 1\n",
        "        else:\n",
        "            index_from = node_from\n",
        "            index_to = node_to\n",
        "\n",
        "        \n",
        "        if(self.binary_predecessors[index_from] & (1 << index_to) or # if the target edge is an predecessorof the origin\n",
        "          self.binary_predecessors[index_from] & self.binary_successors[index_to]):  # if the target edge has a successor that is an predecessorof the starting edge\n",
        "            return False\n",
        "        else:        \n",
        "            self.binary_predecessors[index_to] = self.binary_predecessors[index_to] | self.binary_predecessors[index_from] | (1 << index_from)\n",
        "            self.neighbors[index_from] = self.neighbors[index_from] | (1 << index_to)\n",
        "            self.__propagate(index_to)\n",
        "            super().add_edge(index_from, index_to)            \n",
        "            return True\n",
        "        \n",
        "######################### DFS #############################################\n",
        "\n",
        "    def classic_dfs(self, start, target, visited):\n",
        "        cycle = False\n",
        "        for next_edge in list(self[start]):\n",
        "            if next_edge == target:\n",
        "                return True\n",
        "            elif next_edge not in visited:\n",
        "                visited.append(next_edge)\n",
        "                cycle = self.classic_dfs(next_edge, target, visited)\n",
        "                if cycle:\n",
        "                    return True\n",
        "                else:\n",
        "                    visited.pop()  \n",
        "                    if visited == []:\n",
        "                        return False\n",
        "        return False\n",
        "    \n",
        "    def incremental_dfs(self, edges):\n",
        "        cycles = []\n",
        "        pbar = tqdm(total = len(edges))\n",
        "        \n",
        "        for edge in edges:\n",
        "            if not self.add_dfs_edge(edge[0], edge[1]):\n",
        "                cycles.append(edge)\n",
        "            pbar.update(1)\n",
        "        pbar.close()\n",
        "        return cycles\n",
        "            \n",
        "    def add_dfs_edge(self, node_from, node_to):\n",
        "        if node_from == node_to:\n",
        "            return False\n",
        "        elif self.classic_dfs(node_to, node_from, [node_to]):\n",
        "            return False\n",
        "        else:\n",
        "            super().add_edge(node_from, node_to)\n",
        "            return True    \n",
        "        \n",
        "######################### BFS #############################################\n",
        "\n",
        "    def classic_bfs(self, start, target, visited):\n",
        "        cycle = False\n",
        "        for next_edge in list(self[start]):\n",
        "            if next_edge == target:\n",
        "                return True\n",
        "            \n",
        "        for next_edge in list(self[start]):\n",
        "            if next_edge not in visited:\n",
        "                visited.append(next_edge)\n",
        "                cycle = self.classic_bfs(next_edge, target, visited)\n",
        "                if cycle:\n",
        "                    return True\n",
        "                else:\n",
        "                    visited.pop()\n",
        "                    if visited == []:\n",
        "                        return False\n",
        "            \n",
        "        return False\n",
        "    \n",
        "    def incremental_bfs(self, edges):\n",
        "        cycles = []\n",
        "        pbar = tqdm(total = len(edges))\n",
        "        \n",
        "        for edge in edges:\n",
        "            if not self.add_bfs_edge(edge[0], edge[1]):\n",
        "                cycles.append(edge)\n",
        "            pbar.update(1)\n",
        "        pbar.close()\n",
        "        return cycles\n",
        "            \n",
        "    def add_bfs_edge(self, node_from, node_to):\n",
        "        if node_from == node_to:\n",
        "            return False\n",
        "        elif self.classic_bfs(node_to, node_from, [node_to]):\n",
        "            return False\n",
        "        else:\n",
        "            super().add_edge(node_from, node_to)\n",
        "            return True    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhXv7Ch0fCDQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "######## import Bernstein\n",
        "import random\n",
        "import math\n",
        "import sys\n",
        "from tqdm import tqdm as tqdm_no_notebook\n",
        "from tqdm import tqdm_notebook\n",
        "import networkx as nx\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class Bernstein(nx.DiGraph):           \n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.A = []\n",
        "        self.D = []\n",
        "        self.As = []\n",
        "        self.Ds = []\n",
        "        self.AA = []\n",
        "        self.logs = []\n",
        "        self.S = set()\n",
        "        \n",
        "    def backable_add(self, obj, element):\n",
        "        if element not in obj:\n",
        "            obj.add(element)\n",
        "            self.logs.append((obj, element))\n",
        "\n",
        "            \n",
        "    def update(self, a,b):\n",
        "        a_ancestors = [x for x in nx.ancestors(self, a)] + [a]\n",
        "        b_descendants = [x for x in nx.descendants(self, b)] + [b]\n",
        "        \n",
        "        for s in b_descendants:\n",
        "            if s in self.S:\n",
        "                for anc in a_ancestors:\n",
        "                    self.backable_add(self.A[s], anc)\n",
        "                    self.backable_add(self.Ds[anc], s)\n",
        "        for s in a_ancestors:\n",
        "            if s in self.S:\n",
        "                for des in b_descendants:\n",
        "                    self.backable_add(self.D[s], des)\n",
        "                    self.backable_add(self.As[des], s)\n",
        "\n",
        "            \n",
        "    def is_s_equivalent(self, u,v):\n",
        "        return len(self.As[u]) == len(self.As[v]) and len(self.Ds[u]) == len(self.Ds[v])\n",
        "            \n",
        "        \n",
        "    def check(self, a,b):\n",
        "        to_explore = set([b])\n",
        "        while len(to_explore) > 0:\n",
        "            w = to_explore.pop()\n",
        "            if w == a:\n",
        "                return False\n",
        "            if w in self.AA[a]:\n",
        "                return False\n",
        "            elif a in self.AA[w]:\n",
        "                pass\n",
        "            elif not self.is_s_equivalent(a, w):\n",
        "                pass\n",
        "            else:\n",
        "                self.backable_add(self.AA[w], a)\n",
        "                for w,z in self.out_edges(w):\n",
        "                    to_explore.add(z)\n",
        "        return True\n",
        "    \n",
        "    def add_n_nodes(self, N):\n",
        "        self.add_nodes_from(list(range(N)))\n",
        "        sample_prob_threshold = 11 * math.log(N) / math.sqrt(N)\n",
        "        self.S = set([i for i in range(N) if random.random() <= sample_prob_threshold])\n",
        "\n",
        "        self.A = [set([i]) for i in range(N)]\n",
        "        self.D = [set([i]) for i in range(N)]\n",
        "        self.As = [set([i]).intersection(self.S) for i in range(N)]\n",
        "        self.Ds = [set([i]).intersection(self.S) for i in range(N)]\n",
        "\n",
        "        self.AA = [set([i]) for i in range(N)]\n",
        "        \n",
        "    def add_edge(self, s, t):\n",
        "        super().add_edge(s,t)\n",
        "        self.update(s,t)\n",
        "        if not self.check(s,t):\n",
        "            #yield G, (s,t), id_node\n",
        "            # rollback\n",
        "            while len(self.logs) > 0:\n",
        "                obj, element = self.logs.pop()\n",
        "                obj.remove(element)\n",
        "            self.remove_edge(s,t)\n",
        "            return False\n",
        "        else:\n",
        "            return True\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDlr3k_rfCDS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#######  import TSVParse as tsv\n",
        "import csv\n",
        "\n",
        "def decode_konect_tsv(file_name):\n",
        "    tsv_list = []\n",
        "    with open(file_name) as tsvfile:\n",
        "        tsvreader = csv.reader(tsvfile, delimiter=\"\\t\")\n",
        "        skip = True\n",
        "        meta = False\n",
        "        line_counter = 1\n",
        "        for line in tsvreader:\n",
        "            if not skip:\n",
        "                if not meta:\n",
        "                    tup = decode_tsv_line(line[0])\n",
        "                    if not tup:\n",
        "                        print(\"Parse error on line \" + str(line_counter) + \". The program will continue.\")\n",
        "                    else:\n",
        "                        tsv_list.append(tup)\n",
        "                else:\n",
        "                    aux = line[0].replace('% ', '')\n",
        "                    tup = decode_tsv_line(aux)\n",
        "                    if not tup:\n",
        "                        print(\"Parse error on line \" + str(line_counter) + \". The program will continue.\")\n",
        "                    else:\n",
        "                        tsv_list.append(tup)\n",
        "                    meta = False\n",
        "            else:\n",
        "                skip = False\n",
        "                meta = True\n",
        "        line_counter = line_counter + 1\n",
        "    return tsv_list\n",
        "                \n",
        "def decode_tsv_line(string):    \n",
        "    nodes = ''\n",
        "    edges = ''\n",
        "    node = False\n",
        "    edge = True\n",
        "    for char in string:\n",
        "        if node and char == ' ':\n",
        "            break\n",
        "        if node:\n",
        "            nodes = nodes + char\n",
        "        if edge and char == ' ':\n",
        "            edge = False\n",
        "            node = True\n",
        "        if edge:\n",
        "            edges = edges + char\n",
        "    try:\n",
        "        return ((int(nodes, 10), int(edges, 10)))\n",
        "    except:\n",
        "        return False\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0nWqfGAfCDU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import networkx as nx\n",
        "import BProp\n",
        "import Bernstein\n",
        "import TSVParse as tsv\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.animation import FuncAnimation\n",
        "%matplotlib notebook\n",
        "matplotlib.use(\"nbagg\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-kLMXStfCDX",
        "colab_type": "code",
        "colab": {},
        "outputId": "9d26720e-93f2-4159-9b51-a284c131eb17"
      },
      "source": [
        "ut=set('')\n",
        "utirany=set('')\n",
        "ut2=[]\n",
        "edges = [(5, 4), (2, 4), (5, 2), (2, 3), (2, 5), (2, 1), (2, 3), (3, 5), (1, 3), (5, 2), (2,4)]\n",
        "for x, y in edges :\n",
        "    utirany.add((x, y))\n",
        "    \n",
        "    if x > y :\n",
        "        ut.add((y,x))\n",
        "        ut2.append((y,x))\n",
        "    else :\n",
        "        ut.add((x,y))\n",
        "        ut2.append((x, y))\n",
        "print(ut)\n",
        "print('------------')\n",
        "print(ut2)\n",
        "print('------------')\n",
        "print(utirany)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{(1, 2), (1, 3), (4, 5), (2, 3), (2, 5), (2, 4), (3, 5)}\n",
            "------------\n",
            "[(4, 5), (2, 4), (2, 5), (2, 3), (2, 5), (1, 2), (2, 3), (3, 5), (1, 3), (2, 5), (2, 4)]\n",
            "------------\n",
            "{(5, 4), (1, 3), (2, 1), (2, 3), (2, 5), (5, 2), (2, 4), (3, 5)}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kERqUsZfCDY",
        "colab_type": "code",
        "colab": {},
        "outputId": "74f22425-c6cb-4b31-cafa-b26dfc570aa0"
      },
      "source": [
        "M = 100\n",
        "N = 5\n",
        "edges = [(5, 4), (2, 4), (5, 2), (2, 3), (2, 5), (2, 1), (2, 3), (3, 5), (1, 3), (5, 2),(2,4)]\n",
        "print(edges)    \n",
        "G = BProp.BPGraph()\n",
        "num = G.add_n_nodes(N)\n",
        "cycles_G = 0\n",
        "pbar = tqdm(total = len(edges))\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if not G.add_edge(u, v, decrement = True): # use decrement = False if using nodes starting from 0 instead of 1\n",
        "        cycles_G = cycles_G + 1\n",
        "        print('Cyle', u, v)\n",
        "    else:\n",
        "        print('no', u, v)\n",
        "    pbar.update(1)\n",
        "    \n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"Time:\", float(end - start), \"s \\nCycles:\", cycles_G, \"\\n\")\n",
        "#nx.draw(G)\n",
        "nx.draw(G, with_labels=True, font_weight='bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|████████████████████████████████████████████████████████████████████████████████| 11/11 [00:00<00:00, 5523.45it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[(5, 4), (2, 4), (5, 2), (2, 3), (2, 5), (2, 1), (2, 3), (3, 5), (1, 3), (5, 2), (2, 4)]\n",
            "no 5 4\n",
            "no 2 4\n",
            "no 5 2\n",
            "no 2 3\n",
            "Cyle 2 5\n",
            "no 2 1\n",
            "no 2 3\n",
            "Cyle 3 5\n",
            "no 1 3\n",
            "no 5 2\n",
            "no 2 4\n",
            "Time: 0.0009903907775878906 s \n",
            "Cycles: 2 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAwhT2ezfCDb",
        "colab_type": "text"
      },
      "source": [
        "## Generating a random graph\n",
        "### N is the number of nodes and M is the number of edges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4DkKqvOfCDc",
        "colab_type": "code",
        "colab": {},
        "outputId": "64b13fbc-dfe0-4d66-d9e7-ad96244aabcc"
      },
      "source": [
        "M = 10\n",
        "N = 5\n",
        "edges = []\n",
        "for i in range(M):\n",
        "    s = random.randrange(N)\n",
        "    t = random.randrange(N)\n",
        "    edges.append((s + 1,t + 1))\n",
        "  \n",
        "print(edges)    \n",
        "G = BProp.BPGraph()\n",
        "num = G.add_n_nodes(N)\n",
        "cycles_G = 0\n",
        "pbar = tqdm(total = len(edges))\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if not G.add_edge(u, v, decrement = True): # use decrement = False if using nodes starting from 0 instead of 1\n",
        "        cycles_G = cycles_G + 1\n",
        "        print('Cyle', u, v)\n",
        "    else:\n",
        "        print('no', u, v)\n",
        "    pbar.update(1)\n",
        "    \n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"Time:\", float(end - start), \"s \\nCycles:\", cycles_G, \"\\n\")\n",
        "#nx.draw(G)\n",
        "nx.draw(G, with_labels=True, font_weight='bold')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 5039.41it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[(1, 2), (3, 4), (1, 5), (2, 2), (4, 1), (5, 4), (1, 1), (5, 3), (2, 2), (2, 3)]\n",
            "no 1 2\n",
            "no 3 4\n",
            "no 1 5\n",
            "Cyle 2 2\n",
            "no 4 1\n",
            "no 5 4\n",
            "Cyle 1 1\n",
            "no 5 3\n",
            "Cyle 2 2\n",
            "no 2 3\n",
            "Time: 0.0009851455688476562 s \n",
            "Cycles: 3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7XkWHY0fCDe",
        "colab_type": "text"
      },
      "source": [
        "## Importing a graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z20PIluAfCDe",
        "colab_type": "code",
        "colab": {},
        "outputId": "0404c290-c0eb-4b25-a571-8ea21def1152"
      },
      "source": [
        "edges = tsv.decode_konect_tsv(\"graphs/sheep.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/cattle.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/rhesus.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/us airports.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/little rock lake.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/macaques.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/dutch college.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/hens.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/sampson.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/seventh graders.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/florida dry.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/florida wet.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/bison.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/blogs.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/high school.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/manufacturing emails.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/physicians.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/residence hall.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/adolescent health.tsv\")\n",
        "#edges = tsv.decode_konect_tsv(\"graphs/open flights.tsv\")\n",
        "print(edges[0])\n",
        "N = edges[0][0]\n",
        "M = edges[0][1]\n",
        "del(edges[0])\n",
        "G = BProp.BPGraph()\n",
        "num = G.add_n_nodes(N)\n",
        "cycles_G = 0\n",
        "pbar = tqdm(total = len(edges))\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if not G.add_edge(u, v, decrement = True): # use decrement = False if using nodes starting from 0 instead of 1\n",
        "        cycles_G = cycles_G + 1\n",
        "        #break # uncomment if you want to stop when you detect an edge that forms a cycle\n",
        "    pbar.update(1)\n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"Time:\", float(end - start), \"s \\nCycles:\", cycles_G, \"\\n\")\n",
        "#nx.draw(G)\n",
        "nx.draw(G, with_labels=True, font_weight='bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 250)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  0%|                                                                                          | 0/250 [00:00<?, ?it/s]\n",
            "100%|█████████████████████████████████████████████████████████████████████████████| 250/250 [00:00<00:00, 50058.53it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time: 0.003001689910888672 s \n",
            "Cycles: 22 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JLj3X9P-fCDg",
        "colab_type": "text"
      },
      "source": [
        "## BProp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUjnou-RfCDh",
        "colab_type": "code",
        "colab": {},
        "outputId": "9950ddc2-df6d-4c22-bd0d-3cf85a78ab2c"
      },
      "source": [
        "G = BProp.BPGraph()\n",
        "num = G.add_n_nodes(N)\n",
        "cycles_G = 0\n",
        "pbar = tqdm(total = len(edges))\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if not G.add_edge(u, v, decrement = True): # use decrement = False if using nodes starting from 0 instead of 1\n",
        "        cycles_G = cycles_G + 1\n",
        "        #break # uncomment if you want to stop when you detect an edge that forms a cycle\n",
        "    pbar.update(1)\n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"Time:\", float(end - start), \"s \\nCycles:\", cycles_G, \"\\n\")\n",
        "nx.draw(G)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time: 0.0 s \n",
            "Cycles: 3 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wj6hv2X7fCDj",
        "colab_type": "text"
      },
      "source": [
        "## Bernstein and Chechik's algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqE4jfWwfCDj",
        "colab_type": "code",
        "colab": {},
        "outputId": "df557dac-10a6-431c-cc7c-8e2036f890bb"
      },
      "source": [
        "B = Bernstein.Bernstein()\n",
        "B.add_n_nodes(N)\n",
        "cycles = 0\n",
        "pbar = tqdm(total = len(edges))\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if v - 1 not in B.neighbors(u - 1):\n",
        "        if not B.add_edge(u - 1, v - 1):\n",
        "            cycles = cycles + 1\n",
        "            #break # uncomment if you want to stop when you detect an edge that forms a cycle\n",
        "    pbar.update(1)\n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"Time:\", float(end - start), \"s \\nCycles:\", cycles, \"\\n\")\n",
        "nx.draw(G)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10000.72it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Time: 0.0009999275207519531 s \n",
            "Cycles: 6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqNNmdWFfCDl",
        "colab_type": "text"
      },
      "source": [
        "## DFS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUz2uSJBfCDm",
        "colab_type": "code",
        "colab": {},
        "outputId": "24e32560-0bb3-49aa-fb08-5b84aeee9f59"
      },
      "source": [
        "D = BProp.BPGraph()\n",
        "num = D.add_n_nodes(N)\n",
        "pbar = tqdm(total = len(edges))\n",
        "cycles_dfs = 0\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if v - 1 not in list(D[u - 1]):\n",
        "        if not D.add_dfs_edge(u - 1, v - 1):\n",
        "            cycles_dfs = cycles_dfs + 1\n",
        "            #break # uncomment if you want to stop when you detect an edge that forms a cycle\n",
        "    pbar.update(1)\n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"\\nTime:\", float(end - start), \"s \\nCycles:\", cycles_dfs, \"\\n\")\n",
        "#nx.draw(G)\n",
        "nx.draw(G, with_labels=True, font_weight='bold')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 10003.11it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time: 0.0009996891021728516 s \n",
            "Cycles: 6 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDmb4RmofCDn",
        "colab_type": "text"
      },
      "source": [
        "## BFS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfBpathUfCDo",
        "colab_type": "code",
        "colab": {},
        "outputId": "ab392b38-b6a9-49d8-d717-eeb72d3e604e"
      },
      "source": [
        "M = 10\n",
        "N = 5\n",
        "edges = []\n",
        "for i in range(M):\n",
        "    s = random.randrange(N)\n",
        "    t = random.randrange(N)\n",
        "    edges.append((s + 1,t + 1))\n",
        "b = BProp.BPGraph()\n",
        "num = b.add_n_nodes(N)\n",
        "pbar = tqdm(total = len(edges))\n",
        "cycles_bfs = 0\n",
        "start = time.time()\n",
        "\n",
        "for u, v in edges:\n",
        "    if v - 1 not in list(b[u - 1]):\n",
        "        if not b.add_dfs_edge(u - 1, v - 1):\n",
        "            cycles_bfs = cycles_bfs + 1\n",
        "            #break # uncomment if you want to stop when you detect an edge that forms a cycle\n",
        "    pbar.update(1)\n",
        "end = time.time()\n",
        "pbar.close()\n",
        "print(\"\\nTime:\", float(end - start), \"s \\nCycles:\", cycles_bfs, \"\\n\")\n",
        "#nx.draw(G)\n",
        "nx.draw(G, with_labels=True, font_weight='bold')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Time: 0.0 s \n",
            "Cycles: 5 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYgjWqfLfCDq",
        "colab_type": "text"
      },
      "source": [
        "## Printing a graph while inserting edges"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IN2MYlsJfCDq",
        "colab_type": "code",
        "colab": {},
        "outputId": "20b1f370-2c10-44ee-8613-2059fdfff27c"
      },
      "source": [
        "G = BProp.BPGraph()\n",
        "num = G.add_n_nodes(N)\n",
        "start = time.time()\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.ion()\n",
        "\n",
        "def init():\n",
        "    nx.draw(G, with_labels=True, font_weight='bold')\n",
        "\n",
        "def update(edge):\n",
        "    if G.add_edge(edge[0], edge[1], decrement = True):\n",
        "        plt.cla()\n",
        "        nx.draw(G, with_labels=True, font_weight='bold')\n",
        "\n",
        "ani = FuncAnimation(fig, update, frames = edges, interval = 100., init_func = init, repeat = False)       \n",
        "#nx.draw(G, with_labels=True, font_weight='bold')\n",
        "\n",
        "end = time.time()\n",
        "print(\"Time:\", float(end - start), \"s\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "/* Put everything inside the global mpl namespace */\n",
              "window.mpl = {};\n",
              "\n",
              "\n",
              "mpl.get_websocket_type = function() {\n",
              "    if (typeof(WebSocket) !== 'undefined') {\n",
              "        return WebSocket;\n",
              "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
              "        return MozWebSocket;\n",
              "    } else {\n",
              "        alert('Your browser does not have WebSocket support. ' +\n",
              "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
              "              'Firefox 4 and 5 are also supported but you ' +\n",
              "              'have to enable WebSockets in about:config.');\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
              "    this.id = figure_id;\n",
              "\n",
              "    this.ws = websocket;\n",
              "\n",
              "    this.supports_binary = (this.ws.binaryType != undefined);\n",
              "\n",
              "    if (!this.supports_binary) {\n",
              "        var warnings = document.getElementById(\"mpl-warnings\");\n",
              "        if (warnings) {\n",
              "            warnings.style.display = 'block';\n",
              "            warnings.textContent = (\n",
              "                \"This browser does not support binary websocket messages. \" +\n",
              "                    \"Performance may be slow.\");\n",
              "        }\n",
              "    }\n",
              "\n",
              "    this.imageObj = new Image();\n",
              "\n",
              "    this.context = undefined;\n",
              "    this.message = undefined;\n",
              "    this.canvas = undefined;\n",
              "    this.rubberband_canvas = undefined;\n",
              "    this.rubberband_context = undefined;\n",
              "    this.format_dropdown = undefined;\n",
              "\n",
              "    this.image_mode = 'full';\n",
              "\n",
              "    this.root = $('<div/>');\n",
              "    this._root_extra_style(this.root)\n",
              "    this.root.attr('style', 'display: inline-block');\n",
              "\n",
              "    $(parent_element).append(this.root);\n",
              "\n",
              "    this._init_header(this);\n",
              "    this._init_canvas(this);\n",
              "    this._init_toolbar(this);\n",
              "\n",
              "    var fig = this;\n",
              "\n",
              "    this.waiting = false;\n",
              "\n",
              "    this.ws.onopen =  function () {\n",
              "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
              "            fig.send_message(\"send_image_mode\", {});\n",
              "            if (mpl.ratio != 1) {\n",
              "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
              "            }\n",
              "            fig.send_message(\"refresh\", {});\n",
              "        }\n",
              "\n",
              "    this.imageObj.onload = function() {\n",
              "            if (fig.image_mode == 'full') {\n",
              "                // Full images could contain transparency (where diff images\n",
              "                // almost always do), so we need to clear the canvas so that\n",
              "                // there is no ghosting.\n",
              "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
              "            }\n",
              "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
              "        };\n",
              "\n",
              "    this.imageObj.onunload = function() {\n",
              "        fig.ws.close();\n",
              "    }\n",
              "\n",
              "    this.ws.onmessage = this._make_on_message_function(this);\n",
              "\n",
              "    this.ondownload = ondownload;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_header = function() {\n",
              "    var titlebar = $(\n",
              "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
              "        'ui-helper-clearfix\"/>');\n",
              "    var titletext = $(\n",
              "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
              "        'text-align: center; padding: 3px;\"/>');\n",
              "    titlebar.append(titletext)\n",
              "    this.root.append(titlebar);\n",
              "    this.header = titletext[0];\n",
              "}\n",
              "\n",
              "\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_canvas = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var canvas_div = $('<div/>');\n",
              "\n",
              "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
              "\n",
              "    function canvas_keyboard_event(event) {\n",
              "        return fig.key_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
              "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
              "    this.canvas_div = canvas_div\n",
              "    this._canvas_extra_style(canvas_div)\n",
              "    this.root.append(canvas_div);\n",
              "\n",
              "    var canvas = $('<canvas/>');\n",
              "    canvas.addClass('mpl-canvas');\n",
              "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
              "\n",
              "    this.canvas = canvas[0];\n",
              "    this.context = canvas[0].getContext(\"2d\");\n",
              "\n",
              "    var backingStore = this.context.backingStorePixelRatio ||\n",
              "\tthis.context.webkitBackingStorePixelRatio ||\n",
              "\tthis.context.mozBackingStorePixelRatio ||\n",
              "\tthis.context.msBackingStorePixelRatio ||\n",
              "\tthis.context.oBackingStorePixelRatio ||\n",
              "\tthis.context.backingStorePixelRatio || 1;\n",
              "\n",
              "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
              "\n",
              "    var rubberband = $('<canvas/>');\n",
              "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
              "\n",
              "    var pass_mouse_events = true;\n",
              "\n",
              "    canvas_div.resizable({\n",
              "        start: function(event, ui) {\n",
              "            pass_mouse_events = false;\n",
              "        },\n",
              "        resize: function(event, ui) {\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "        stop: function(event, ui) {\n",
              "            pass_mouse_events = true;\n",
              "            fig.request_resize(ui.size.width, ui.size.height);\n",
              "        },\n",
              "    });\n",
              "\n",
              "    function mouse_event_fn(event) {\n",
              "        if (pass_mouse_events)\n",
              "            return fig.mouse_event(event, event['data']);\n",
              "    }\n",
              "\n",
              "    rubberband.mousedown('button_press', mouse_event_fn);\n",
              "    rubberband.mouseup('button_release', mouse_event_fn);\n",
              "    // Throttle sequential mouse events to 1 every 20ms.\n",
              "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
              "\n",
              "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
              "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
              "\n",
              "    canvas_div.on(\"wheel\", function (event) {\n",
              "        event = event.originalEvent;\n",
              "        event['data'] = 'scroll'\n",
              "        if (event.deltaY < 0) {\n",
              "            event.step = 1;\n",
              "        } else {\n",
              "            event.step = -1;\n",
              "        }\n",
              "        mouse_event_fn(event);\n",
              "    });\n",
              "\n",
              "    canvas_div.append(canvas);\n",
              "    canvas_div.append(rubberband);\n",
              "\n",
              "    this.rubberband = rubberband;\n",
              "    this.rubberband_canvas = rubberband[0];\n",
              "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
              "    this.rubberband_context.strokeStyle = \"#000000\";\n",
              "\n",
              "    this._resize_canvas = function(width, height) {\n",
              "        // Keep the size of the canvas, canvas container, and rubber band\n",
              "        // canvas in synch.\n",
              "        canvas_div.css('width', width)\n",
              "        canvas_div.css('height', height)\n",
              "\n",
              "        canvas.attr('width', width * mpl.ratio);\n",
              "        canvas.attr('height', height * mpl.ratio);\n",
              "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
              "\n",
              "        rubberband.attr('width', width);\n",
              "        rubberband.attr('height', height);\n",
              "    }\n",
              "\n",
              "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
              "    // upon first draw.\n",
              "    this._resize_canvas(600, 600);\n",
              "\n",
              "    // Disable right mouse context menu.\n",
              "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
              "        return false;\n",
              "    });\n",
              "\n",
              "    function set_focus () {\n",
              "        canvas.focus();\n",
              "        canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    window.setTimeout(set_focus, 100);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            // put a spacer in here.\n",
              "            continue;\n",
              "        }\n",
              "        var button = $('<button/>');\n",
              "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
              "                        'ui-button-icon-only');\n",
              "        button.attr('role', 'button');\n",
              "        button.attr('aria-disabled', 'false');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "\n",
              "        var icon_img = $('<span/>');\n",
              "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
              "        icon_img.addClass(image);\n",
              "        icon_img.addClass('ui-corner-all');\n",
              "\n",
              "        var tooltip_span = $('<span/>');\n",
              "        tooltip_span.addClass('ui-button-text');\n",
              "        tooltip_span.html(tooltip);\n",
              "\n",
              "        button.append(icon_img);\n",
              "        button.append(tooltip_span);\n",
              "\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    var fmt_picker_span = $('<span/>');\n",
              "\n",
              "    var fmt_picker = $('<select/>');\n",
              "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
              "    fmt_picker_span.append(fmt_picker);\n",
              "    nav_element.append(fmt_picker_span);\n",
              "    this.format_dropdown = fmt_picker[0];\n",
              "\n",
              "    for (var ind in mpl.extensions) {\n",
              "        var fmt = mpl.extensions[ind];\n",
              "        var option = $(\n",
              "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
              "        fmt_picker.append(option);\n",
              "    }\n",
              "\n",
              "    // Add hover states to the ui-buttons\n",
              "    $( \".ui-button\" ).hover(\n",
              "        function() { $(this).addClass(\"ui-state-hover\");},\n",
              "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
              "    );\n",
              "\n",
              "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_message = function(type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function() {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
              "    }\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1]);\n",
              "        fig.send_message(\"refresh\", {});\n",
              "    };\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
              "    var x0 = msg['x0'] / mpl.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
              "    var x1 = msg['x1'] / mpl.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
              "    var cursor = msg['cursor'];\n",
              "    switch(cursor)\n",
              "    {\n",
              "    case 0:\n",
              "        cursor = 'pointer';\n",
              "        break;\n",
              "    case 1:\n",
              "        cursor = 'default';\n",
              "        break;\n",
              "    case 2:\n",
              "        cursor = 'crosshair';\n",
              "        break;\n",
              "    case 3:\n",
              "        cursor = 'move';\n",
              "        break;\n",
              "    }\n",
              "    fig.rubberband_canvas.style.cursor = cursor;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message(\"ack\", {});\n",
              "}\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            /* FIXME: We get \"Resource interpreted as Image but\n",
              "             * transferred with MIME type text/plain:\" errors on\n",
              "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "             * to be part of the websocket stream */\n",
              "            evt.data.type = \"image/png\";\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src);\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                evt.data);\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig[\"handle_\" + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "}\n",
              "\n",
              "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
              "mpl.findpos = function(e) {\n",
              "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
              "    var targ;\n",
              "    if (!e)\n",
              "        e = window.event;\n",
              "    if (e.target)\n",
              "        targ = e.target;\n",
              "    else if (e.srcElement)\n",
              "        targ = e.srcElement;\n",
              "    if (targ.nodeType == 3) // defeat Safari bug\n",
              "        targ = targ.parentNode;\n",
              "\n",
              "    // jQuery normalizes the pageX and pageY\n",
              "    // pageX,Y are the mouse positions relative to the document\n",
              "    // offset() returns the position of the element relative to the document\n",
              "    var x = e.pageX - $(targ).offset().left;\n",
              "    var y = e.pageY - $(targ).offset().top;\n",
              "\n",
              "    return {\"x\": x, \"y\": y};\n",
              "};\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * http://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys (original) {\n",
              "  return Object.keys(original).reduce(function (obj, key) {\n",
              "    if (typeof original[key] !== 'object')\n",
              "        obj[key] = original[key]\n",
              "    return obj;\n",
              "  }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function(event, name) {\n",
              "    var canvas_pos = mpl.findpos(event)\n",
              "\n",
              "    if (name === 'button_press')\n",
              "    {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    var x = canvas_pos.x * mpl.ratio;\n",
              "    var y = canvas_pos.y * mpl.ratio;\n",
              "\n",
              "    this.send_message(name, {x: x, y: y, button: event.button,\n",
              "                             step: event.step,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "\n",
              "    /* This prevents the web browser from automatically changing to\n",
              "     * the text insertion cursor when the button is pressed.  We want\n",
              "     * to control all of the cursor setting manually through the\n",
              "     * 'cursor' event from matplotlib */\n",
              "    event.preventDefault();\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.key_event = function(event, name) {\n",
              "\n",
              "    // Prevent repeat events\n",
              "    if (name == 'key_press')\n",
              "    {\n",
              "        if (event.which === this._key)\n",
              "            return;\n",
              "        else\n",
              "            this._key = event.which;\n",
              "    }\n",
              "    if (name == 'key_release')\n",
              "        this._key = null;\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.which != 17)\n",
              "        value += \"ctrl+\";\n",
              "    if (event.altKey && event.which != 18)\n",
              "        value += \"alt+\";\n",
              "    if (event.shiftKey && event.which != 16)\n",
              "        value += \"shift+\";\n",
              "\n",
              "    value += 'k';\n",
              "    value += event.which.toString();\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, {key: value,\n",
              "                             guiEvent: simpleKeys(event)});\n",
              "    return false;\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
              "    if (name == 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message(\"toolbar_button\", {name: name});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
              "\n",
              "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.close = function() {\n",
              "        comm.close()\n",
              "    };\n",
              "    ws.send = function(m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function(msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(msg['content']['data'])\n",
              "    });\n",
              "    return ws;\n",
              "}\n",
              "\n",
              "mpl.mpl_figure_comm = function(comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = $(\"#\" + id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm)\n",
              "\n",
              "    function ondownload(figure, format) {\n",
              "        window.open(figure.imageObj.src);\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy,\n",
              "                           ondownload,\n",
              "                           element.get(0));\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element.get(0);\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error(\"Failed to find cell for figure\", id, fig);\n",
              "        return;\n",
              "    }\n",
              "\n",
              "    var output_index = fig.cell_info[2]\n",
              "    var cell = fig.cell_info[0];\n",
              "\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
              "    var width = fig.canvas.width/mpl.ratio\n",
              "    fig.root.unbind('remove')\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable()\n",
              "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
              "    fig.close_ws(fig, msg);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.close_ws = function(fig, msg){\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width/mpl.ratio\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function() {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message(\"ack\", {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function() {\n",
              "    var fig = this;\n",
              "\n",
              "    var nav_element = $('<div/>');\n",
              "    nav_element.attr('style', 'width: 100%');\n",
              "    this.root.append(nav_element);\n",
              "\n",
              "    // Define a callback function for later on.\n",
              "    function toolbar_event(event) {\n",
              "        return fig.toolbar_button_onclick(event['data']);\n",
              "    }\n",
              "    function toolbar_mouse_event(event) {\n",
              "        return fig.toolbar_button_onmouseover(event['data']);\n",
              "    }\n",
              "\n",
              "    for(var toolbar_ind in mpl.toolbar_items){\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) { continue; };\n",
              "\n",
              "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
              "        button.click(method_name, toolbar_event);\n",
              "        button.mouseover(tooltip, toolbar_mouse_event);\n",
              "        nav_element.append(button);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
              "    nav_element.append(status_bar);\n",
              "    this.message = status_bar[0];\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
              "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
              "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
              "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
              "    buttongrp.append(button);\n",
              "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
              "    titlebar.prepend(buttongrp);\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function(el){\n",
              "    var fig = this\n",
              "    el.on(\"remove\", function(){\n",
              "\tfig.close_ws(fig, {});\n",
              "    });\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function(el){\n",
              "    // this is important to make the div 'focusable\n",
              "    el.attr('tabindex', 0)\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    }\n",
              "    else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "\n",
              "}\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
              "    var manager = IPython.notebook.keyboard_manager;\n",
              "    if (!manager)\n",
              "        manager = IPython.keyboard_manager;\n",
              "\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which == 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "}\n",
              "\n",
              "\n",
              "mpl.find_output_cell = function(html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i=0; i<ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code'){\n",
              "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] == html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel != null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
              "}\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "<img 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\" width=\"640\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Time: 0.0060155391693115234 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQbYFz8efCDt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}