01_CreateGraph.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "UgtcphR3IUik",
        "outputId": "8bacc4ff-c015-42ad-c0d7-cb5af5d97147"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n",
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            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.21.6)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting obspy\n",
            "  Downloading obspy-1.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 14.3 MB 4.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: sqlalchemy in /usr/local/lib/python3.7/dist-packages (from obspy) (1.4.36)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->obspy) (4.11.4)\n",
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            "Installing collected packages: obspy\n",
            "Successfully installed obspy-1.3.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "signal"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install scikit-learn\n",
        "!pip install obspy\n"
      ],
      "id": "UgtcphR3IUik"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "900633ce-f03a-4f25-bda0-452a701d1d8d",
        "outputId": "42ddd913-8e4d-4734-c7f6-05bd90eb1d7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "import numpy as np\n",
        "from datetime import datetime,timedelta\n",
        "import networkx as nx\n",
        "from obspy.geodetics.base import gps2dist_azimuth\n",
        "# to print logs in jupyter notebook\n",
        "import logging\n",
        "logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.ERROR)\n",
        "\n",
        "\n",
        "# load python-louvain for modularity computation\n",
        "# import community as community_louvain\n",
        "from community import community_louvain\n",
        "# for ARI computation\n",
        "from sklearn import preprocessing, metrics\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "id": "900633ce-f03a-4f25-bda0-452a701d1d8d"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "f4eabd2e-0f52-4392-9030-4b5d9ee2d73a"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('drive/MyDrive/Final_project/Event.txt',sep='\\s+', names=['lon','lat','DecimalYear','Month','Day','Mag','Depth','hour','minute','second'])"
      ],
      "id": "f4eabd2e-0f52-4392-9030-4b5d9ee2d73a"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Q99oJRjPIN8-"
      },
      "outputs": [],
      "source": [
        "def decimalYear2Datetime(time):\n",
        "    year = int(time)\n",
        "    rem = time - year\n",
        "    base = datetime(year, 1, 1)\n",
        "    result = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n",
        "    return result\n",
        "def draw_graph(G):\n",
        "    pos = nx.spring_layout(G)\n",
        "    # colors = nx.get_node_attributes(G,'color').values()\n",
        "    colors = [node[1]['color'] for node in G.nodes(data=True)]\n",
        "    sizes  = [node[1]['size'] for node in G.nodes(data=True)]\n",
        "\n",
        "    # nx.draw_networkx_nodes(G, pos,colors=colors)\n",
        "    # nx.draw_networkx_labels(G, pos)\n",
        "    # nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)\n",
        "    nx.draw(G, node_color=colors, node_size=sizes, alpha=0.8)#, with_labels=True\n",
        "\n",
        "\n"
      ],
      "id": "Q99oJRjPIN8-"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ad3a6cc1"
      },
      "outputs": [],
      "source": [
        "def show_results(G):\n",
        "    # Print the first five results\n",
        "    print(\"Karate Club Graph, first 5 edges: \")\n",
        "    for n1,n2 in list(G.edges())[:5]:\n",
        "        print(\"Ollivier-Ricci curvature of edge (%s,%s) is %f\" % (n1 ,n2, G[n1][n2][\"ricciCurvature\"]))\n",
        "\n",
        "    # Plot the histogram of Ricci curvatures\n",
        "    plt.subplot(2, 1, 1)\n",
        "    ricci_curvtures = nx.get_edge_attributes(G, \"ricciCurvature\").values()\n",
        "    plt.hist(ricci_curvtures,bins=20)\n",
        "    plt.xlabel('Ricci curvature')\n",
        "    plt.title(\"Histogram of Ricci Curvatures\")\n",
        "\n",
        "    # Plot the histogram of edge weights\n",
        "    plt.subplot(2, 1, 2)\n",
        "    weights = nx.get_edge_attributes(G, \"weight\").values()\n",
        "    plt.hist(weights,bins=20)\n",
        "    plt.xlabel('Edge weight')\n",
        "    plt.title(\"Histogram of Edge weights\")\n",
        "\n",
        "    plt.tight_layout()\n"
      ],
      "id": "ad3a6cc1"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "bce6d735-494f-40d0-8f06-7134179da17b"
      },
      "outputs": [],
      "source": [
        "LON = df['lon'] ##longitude 經度\n",
        "LAT = df['lat'] ##latitude 緯度\n",
        "DEP = df['Depth'] ##深度\n",
        "MAG = df['Mag'] ##規模\n",
        "EventTime = df['DecimalYear']"
      ],
      "id": "bce6d735-494f-40d0-8f06-7134179da17b"
    },
    {
      "cell_type": "code",
      "source": [
        "G = nx.Graph()\n",
        "for i in range(len(df)):\n",
        "  time = decimalYear2Datetime(df['DecimalYear'].values[i])\n",
        "  mag = 2*np.exp(MAG.values[i])\n",
        "  color = sm.to_rgba(x=time.month)\n",
        "  G.add_node(i, color=color, size=mag,month=time.month)\n",
        "for i in range(len(df)-1):\n",
        "  lon = LON.values[i]\n",
        "  lat = LAT.values[i]\n",
        "  dep = DEP.values[i]\n",
        "  for ii in range(i+1,len(df)):\n",
        "    lon_ = LON.values[ii]\n",
        "    lat_ = LAT.values[ii]\n",
        "    dep_ = DEP.values[ii]\n",
        "    dist,az,baz = gps2dist_azimuth(lat_,lon_,lat,lon)\n",
        "    distance3D = np.sqrt( (dist/1000)**2 + (dep-dep_)**2 )\n",
        "    if distance3D <= 70: ## compare \n",
        "      weight = distance3D\n",
        "      G.add_edge(i,ii,weight=weight)\n",
        "print(f'Nodes: {G.number_of_nodes()}')\n",
        "print(f'Edge: {G.number_of_edges()}')\n",
        "# nx.write_gpickle(G, \"/content/drive/MyDrive/Final_project/Gtest7.gpickle\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpQGbUiBdlDt",
        "outputId": "15dbd714-00ae-4e3a-94c7-db45cb8949e7"
      },
      "id": "gpQGbUiBdlDt",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nodes: 229\n",
            "Edge: 6021\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "draw_graph(G)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "0x51UsxUfGns",
        "outputId": "648ea733-463d-4bcc-dbc8-17b4ae53ebbc"
      },
      "id": "0x51UsxUfGns",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CZJmpjAsWz6G"
      },
      "id": "CZJmpjAsWz6G",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "01.3_CreateGraph.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.8.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}