chapter7.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UdayLab/Hands-on-Pattern-Mining/blob/main/chapter7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chapter 7: Spatial Databases: Representation, Creation, and Statistics"
      ],
      "metadata": {
        "id": "y3s-3Q_piu8t"
      },
      "id": "y3s-3Q_piu8t"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installation of PAMI library"
      ],
      "metadata": {
        "id": "rOTWYp6tizpr"
      },
      "id": "rOTWYp6tizpr"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "initial_id",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "initial_id",
        "outputId": "29b3000b-0e43-404c-b14f-de4ff8d0f30d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "!pip install --upgrade pami"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download a sample database"
      ],
      "metadata": {
        "id": "koJb4FUvi3-A"
      },
      "id": "koJb4FUvi3-A"
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -nc https://web-ext.u-aizu.ac.jp/~udayrage/datasets/spatioTransactionalDatabases/spatioTransactional_T10I4D100K.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8bz5U2SjAai",
        "outputId": "dceb5249-4674-4667-97c3-0fc78b5d523e"
      },
      "id": "D8bz5U2SjAai",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-12-03 08:04:04--  https://web-ext.u-aizu.ac.jp/~udayrage/datasets/spatioTemporalDatabases/spatiotemporal_T10I4D100K.csv\n",
            "Resolving web-ext.u-aizu.ac.jp (web-ext.u-aizu.ac.jp)... 163.143.103.34\n",
            "Connecting to web-ext.u-aizu.ac.jp (web-ext.u-aizu.ac.jp)|163.143.103.34|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8530208 (8.1M) [text/csv]\n",
            "Saving to: ‘spatiotemporal_T10I4D100K.csv’\n",
            "\n",
            "spatiotemporal_T10I 100%[===================>]   8.13M  5.32MB/s    in 1.5s    \n",
            "\n",
            "2024-12-03 08:04:07 (5.32 MB/s) - ‘spatiotemporal_T10I4D100K.csv’ saved [8530208/8530208]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generating Synthetic Geo-referenced Transactional Database"
      ],
      "metadata": {
        "id": "UGMSyoSojUBF"
      },
      "id": "UGMSyoSojUBF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.extras.syntheticDataGenerator import geoReferentialTransactionalDatabase as db\n",
        "  \n",
        "obj = db.geoReferentialTransactionalDatabase(\n",
        "        databaseSize=numberOfTransactions,\n",
        "        avgItemsPerTransaction=averageNumberOfItemsInATransaction,\n",
        "        numItems=totalNumberOfItems,\n",
        "        x1=lowestXaxisCoordinate,\n",
        "        y1=lowestYaxisCoordinate,\n",
        "        x2=maximalXaxisCoordinate,\n",
        "        y2=maximalYaxisCoordinate,\n",
        "        sep='\\t'\n",
        "        )\n",
        "obj.create()\n",
        "obj.save('outputFileName')\n",
        "#read the generated transactions into a dataframe\n",
        "GRTDF=obj.getTransactions()\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "hW_57pxdjcCh"
      },
      "id": "hW_57pxdjcCh"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 1"
      ],
      "metadata": {
        "id": "Ir799i2cjesT"
      },
      "id": "Ir799i2cjesT"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.syntheticDataGenerator import GeoReferentialTransactionalDatabase as db\n",
        "\n",
        "obj = db.GeoReferentialTransactionalDatabase(\n",
        "        databaseSize=1000,\n",
        "        avgItemsPerTransaction=10,\n",
        "        numItems=1000,\n",
        "        x1=0,\n",
        "        y1=0,\n",
        "        x2=100,\n",
        "        y2=100,\n",
        "        sep='\\t'\n",
        "        )\n",
        "obj.create()\n",
        "obj.save('geoReferencedTransactionalDatabase.csv')\n",
        "#read the generated transactions into a dataframe\n",
        "GRTDF=obj.getTransactions()\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "w-AgIvTGjbRW",
        "outputId": "c7f216c8-837f-49f0-c5a2-132afc24946d"
      },
      "id": "w-AgIvTGjbRW",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Runtime: 14.117074012756348\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'GeoReferentialTransactionalDatabase' object has no attribute 'getMemoryRSS'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-237632323d0d>\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mGRTDF\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetTransactions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Runtime: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetRuntime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Memory (RSS): '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetMemoryRSS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Memory (USS): '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetMemoryUSS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GeoReferentialTransactionalDatabase' object has no attribute 'getMemoryRSS'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generating Synthetic Geo-referenced Temporal Database"
      ],
      "metadata": {
        "id": "6a4RgP7ykcxY"
      },
      "id": "6a4RgP7ykcxY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.extras.syntheticDataGenerator import geoReferentialTemporalDatabase as db\n",
        "  \n",
        "obj = db.geoReferentialTemporalDatabase(\n",
        "        databaseSize=numberOfTransactions,\n",
        "        avgItemsPerTransaction=averageNumberOfItemsInATransaction,\n",
        "        numItems=totalNumberOfItems,\n",
        "        occurrenceProbabilityOfSameTimestamp=probabilityWithWhichTheNextTransactionAppearsAtTheSameTimeStamp,\n",
        "        occurrenceProbabilityToSkipSubsequentTimestamp=probabilityWithWhichTheNextTransactionSkipsTheNextTimeStamp,\n",
        "        x1=lowestXaxisCoordinate,\n",
        "        y1=lowestYaxisCoordinate,\n",
        "        x2=maximumXaxisCoordinate,\n",
        "        y2=maximumYaxisCoordinate,\n",
        "        sep='\\t'\n",
        "        )\n",
        "obj.create()\n",
        "obj.save('outputFileName')\n",
        "GRTempDF=obj.getTransactions()\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "-V_IiGkfklCj"
      },
      "id": "-V_IiGkfklCj"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 2"
      ],
      "metadata": {
        "id": "5C3iQJg6koQI"
      },
      "id": "5C3iQJg6koQI"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.syntheticDataGenerator \\\n",
        "    import geoReferentialTemporalDatabase as db\n",
        "\n",
        "obj = db.geoReferentialTemporalDatabase(\n",
        "        databaseSize=1000,\n",
        "        avgItemsPerTransaction=10,\n",
        "        numItems=1000,\n",
        "        occurrenceProbabilityOfSameTimestamp=0,\n",
        "        occurrenceProbabilityToSkipSubsequentTimestamp=0,\n",
        "        x1=0,\n",
        "        y1=0,\n",
        "        x2=100,\n",
        "        y2=100,\n",
        "        sep='\\t'\n",
        "        )\n",
        "obj.create()\n",
        "obj.save('geoReferentialTemporalDatabase.csv')\n",
        "GRTempDF=obj.getTransactions()\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "4c1G8w0ekkb6",
        "outputId": "18f7877f-8e19-4d01-889e-01c01407bfa6"
      },
      "id": "4c1G8w0ekkb6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "cannot import name 'geoReferentialTemporalDatabase' from 'PAMI.extras.syntheticDataGenerator' (/usr/local/lib/python3.10/dist-packages/PAMI/extras/syntheticDataGenerator/__init__.py)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-27de3809c1d3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mPAMI\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msyntheticDataGenerator\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mgeoReferentialTemporalDatabase\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m obj = db.geoReferentialTemporalDatabase(\n\u001b[1;32m      5\u001b[0m         \u001b[0mdatabaseSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'geoReferentialTemporalDatabase' from 'PAMI.extras.syntheticDataGenerator' (/usr/local/lib/python3.10/dist-packages/PAMI/extras/syntheticDataGenerator/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting a Georeferenced Dataframe into a Georeferenced Transactional Database"
      ],
      "metadata": {
        "id": "WWxrCCXymdf8"
      },
      "id": "WWxrCCXymdf8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "\n",
        "from PAMI.extras.convert import DF2DB as alg\n",
        "\n",
        "obj = alg.DF2DB(dataFrame)\n",
        "obj.convert2TransactionalDatabase(oFile='outputFileName.txt', condition='>=|>|<=|<|==|!=', thresholdValue=thresholdValue)\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "7oxhLiH0m-qK"
      },
      "id": "7oxhLiH0m-qK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 3"
      ],
      "metadata": {
        "id": "OSqfgkWanCkc"
      },
      "id": "OSqfgkWanCkc"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.convert import DF2DB as alg\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "#creating a 4 x 4 dataframe with random values\n",
        "data = np.random.randint(1, 100, size=(4, 4))\n",
        "dataFrame = pd.DataFrame(data,\n",
        "columns=['POINT(0 0)', 'POINT(0 1)',\n",
        "'POINT(0 2)', 'POINT(0 3)']\n",
        ")\n",
        "obj = alg.DF2DB(dataFrame)\n",
        "obj.convert2TransactionalDatabase(\n",
        "oFile='georeferencedTransactionalDatabase.txt',\n",
        "condition='>=', thresholdValue=36\n",
        ")\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kt1L-C0Umj3q",
        "outputId": "01e580ff-cd99-41de-ce06-746363e3d857"
      },
      "id": "kt1L-C0Umj3q",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Runtime: 0.021925687789916992\n",
            "Memory (RSS): 166391808\n",
            "Memory (USS): 144105472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting a Georeferenced Dataframe into a Georeferenced Temporal Database"
      ],
      "metadata": {
        "id": "rAcb22r-psuQ"
      },
      "id": "rAcb22r-psuQ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.extras.convert import DF2DB as alg\n",
        "\n",
        "\n",
        "#converting the database into a transactional database by\n",
        "#considering values greater than or equal to 36\n",
        "obj = alg.DF2DB(dataFrame)\n",
        "obj.convert2TemporalDatabase(oFile='outputFileName', condition='>=|>|<=|<|==|!=', thresholdValue=36)\n",
        "\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "VMfa4ehep2sF"
      },
      "id": "VMfa4ehep2sF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 4"
      ],
      "metadata": {
        "id": "ndimpkv7p5iE"
      },
      "id": "ndimpkv7p5iE"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.convert import DF2DB as alg\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#creating a 5 x 4 dataframe with random values\n",
        "data = np.random.randint(1, 100, size=(5, 4))\n",
        "dataFrame = pd.DataFrame(data,\n",
        "             columns=['POINT(0 0)', 'POINT(0 1)',\n",
        "             'POINT(0 2)', 'POINT(0 3)']\n",
        "            )\n",
        "# Adding a timestamp column with specific values\n",
        "timestamps = [1, 3, 3, 5, 8]\n",
        "dataFrame.insert(0, 'timestamp', timestamps)\n",
        "\n",
        "#converting the database into a transactional database by\n",
        "#considering values greater than or equal to 36\n",
        "obj = alg.DF2DB(dataFrame)\n",
        "obj.convert2TemporalDatabase(\n",
        "        oFile='georeferencedTemporalDatabase.txt',\n",
        "       condition='>=', thresholdValue=36\n",
        "     )\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDASdnHEpxtV",
        "outputId": "9265e09e-c98e-4b43-b4bc-662749e949ca"
      },
      "id": "xDASdnHEpxtV",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Runtime: 0.005326509475708008\n",
            "Memory (RSS): 166391808\n",
            "Memory (USS): 144150528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting Statistical Details for a Geo-referenced Transactional Database"
      ],
      "metadata": {
        "id": "0zEqbScpq5hZ"
      },
      "id": "0zEqbScpq5hZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "```\n",
        "from PAMI.extras.dbStats import GeoreferencedTransactionalDatabase as stat\n",
        "\n",
        "obj = stat.GeoreferencedTransactionalDatabase(iFile=\"transactionalDatabase.csv\")\n",
        "obj.run()\n",
        "obj.printStats()\n",
        "obj.plotGraphs()\n",
        "obj.plotLocations()\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "IjJIYsOcq7vP"
      },
      "id": "IjJIYsOcq7vP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 5"
      ],
      "metadata": {
        "id": "2xklh1MKq9px"
      },
      "id": "2xklh1MKq9px"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.dbStats import GeoreferencedTransactionalDatabase as stat\n",
        "\n",
        "obj = stat.GeoreferencedTransactionalDatabase(iFile=\"georeferencedTransactionalDatabase.txt\")\n",
        "obj.run()\n",
        "obj.printStats()\n",
        "obj.plotGraphs()\n",
        "obj.plotLocations()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tfe5hD25q7VG",
        "outputId": "c67a8f61-1aed-4dac-aa5f-2fa15a7575dd"
      },
      "id": "tfe5hD25q7VG",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Database size (total no of transactions) : 4\n",
            "Number of items : 4\n",
            "Minimum Transaction Size : 3\n",
            "Average Transaction Size : 3.0\n",
            "Maximum Transaction Size : 3\n",
            "Standard Deviation Transaction Size : 0.0\n",
            "Variance in Transaction Sizes : 0\n",
            "Sparsity : 0.25\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'GeoreferencedTransactionalDatabase' object has no attribute 'plotLocations'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-1f45a52e40d5>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprintStats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotGraphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotLocations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'GeoreferencedTransactionalDatabase' object has no attribute 'plotLocations'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting Statistical Details for a Geo-referenced Timeseries Database"
      ],
      "metadata": {
        "id": "sc6bbeRrvdH6"
      },
      "id": "sc6bbeRrvdH6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "```python\n",
        "from PAMI.extras.dbStats import  GeoreferencedTemporalDatabase as stat\n",
        "\n",
        "obj = stat.GeoreferencedTemporalDatabase(iFile=\"georeferencedTemporalDatabase.txt\")\n",
        "obj.run()\n",
        "obj.printStats()\n",
        "obj.plotGraphs()\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "tcU6Pp-5FGyR"
      },
      "id": "tcU6Pp-5FGyR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 6"
      ],
      "metadata": {
        "id": "w5bz6FfFFRKr"
      },
      "id": "w5bz6FfFFRKr"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.dbStats import  GeoreferencedTemporalDatabase as stat\n",
        "\n",
        "obj = stat.GeoreferencedTemporalDatabase(iFile=\"georeferencedTemporalDatabase.txt\")\n",
        "obj.run()\n",
        "obj.printStats()\n",
        "obj.plotGraphs()"
      ],
      "metadata": {
        "id": "AjR7CDylrb2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "617a067e-2425-4a0c-f7d6-68ee285080bd"
      },
      "id": "AjR7CDylrb2q",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Database size : 5\n",
            "Number of items : 4\n",
            "Minimum Transaction Size : 1\n",
            "Average Transaction Size : 2.0\n",
            "Maximum Transaction Size : 3\n",
            "Minimum Inter Arrival Period : 1\n",
            "Average Inter Arrival Period : 1.0\n",
            "Maximum Inter Arrival Period : 1\n",
            "Minimum periodicity : 2\n",
            "Average periodicity : 2.5\n",
            "Maximum periodicicty : 4\n",
            "Standard Deviation Transaction Size : 0.6324555320336759\n",
            "Variance : 0.5\n",
            "Sparsity : 0.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oIepW8fbFPeh"
      },
      "id": "oIepW8fbFPeh",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}