high_granularity_power_generation_iso_newengland.ipynb

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      "source": [
        "<h1><center> US electricity generation by energy source ( price , co2 emission by grids)<center><h1> \n",
        "  High granularity Power generation ISO NewEngland  "
      ]
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      "source": [
        "# Table of Contents\n",
        "* [1. DataSet Description](#section-one)\n",
        "* [2. Import and Preprocessing](#section-two)\n",
        "* [3. Filler and Filter](#section-three)\n",
        "* [4. Daily Grpahs](#section-four)\n",
        "* [5. Carbon Emission vs Fuel](#section-five)\n",
        "* [6. Price vs Fuel](#section-six)\n",
        "* [7. LMP Locational Marginal Pricing](#section-seven)"
      ]
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      "source": [
        "<a id=\"section-one\"></a>\n",
        "# 1. DataSet Description\n",
        "\n",
        "Dataset is weekly reports from EDA for ISO-NE"
      ]
    },
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        "<a id=\"section-two\"></a>\n",
        "# 2. Import & Preprocessing\n",
        "\n",
        "Let's have a first look at the data. For the sake of simplicity we are only looking at data of plant 1 (power generation and weather). Data of power plant 2 can then be explored in a similar manner."
      ]
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      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import matplotlib.dates as mdates \n",
        "xformatter = mdates.DateFormatter('%H:%M') # for time axis plots\n",
        "\n",
        "# import plotly.offline as py\n",
        "# py.init_notebook_mode(connected=True)\n",
        "\n",
        "import sklearn\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
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      "source": [
        "# Import all available data \n",
        "# df = pd.read_csv(\"../input/us-electricity-by-energy-source-in-spring/930-data-export.csv\")\n",
        "\n",
        "df = pd.read_csv(\"../input/nyiso-april-2ndweek/fuelmixgraph_Newengland_6_7_jul_2020.csv\")\n",
        "df2 = pd.read_csv(\"../input/nyiso-april-2ndweek/fuelmixgraph_Newengland_8_9_jul_2020.csv\")\n",
        "df3 = pd.read_csv(\"../input/nyiso-april-2ndweek/fuelmixgraph_Newengland_10_11_jul_2020.csv\")"
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      "source": [
        "df= df.append(df2)\n",
        "df = df.append(df3)\n",
        "len(df) "
      ],
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              "881"
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      "source": [
        "# Region = df['Region Code'].head(1).item()\n",
        "# Region\n",
        "\n",
        "Region=\"NewEngland\""
      ],
      "execution_count": null,
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        "df.head()"
      ],
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BeginDate</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Renewables</th>\n",
              "      <th>Wood</th>\n",
              "      <th>Refuse</th>\n",
              "      <th>Wind</th>\n",
              "      <th>LandfillGas</th>\n",
              "      <th>Solar</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07/05/2020 21:07</td>\n",
              "      <td>6428</td>\n",
              "      <td>3328</td>\n",
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              "      <td>07/05/2020 21:12</td>\n",
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              "      <td>undefined</td>\n",
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              "      <td>07/05/2020 21:21</td>\n",
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              "      <td>12</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>07/05/2020 21:34</td>\n",
              "      <td>6164</td>\n",
              "      <td>3328</td>\n",
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              "      <th>4</th>\n",
              "      <td>07/05/2020 21:50</td>\n",
              "      <td>6047</td>\n",
              "      <td>3331</td>\n",
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              "      <td>32</td>\n",
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              "      <td>12</td>\n",
              "      <td>1</td>\n",
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              "          BeginDate  NaturalGas  Nuclear  Hydro       Coal        Oil  \\\n",
              "0  07/05/2020 21:07        6428     3328    407          0  undefined   \n",
              "1  07/05/2020 21:12        6312     3329    409  undefined  undefined   \n",
              "2  07/05/2020 21:21        6041     3330    409  undefined  undefined   \n",
              "3  07/05/2020 21:34        6164     3328    407          2  undefined   \n",
              "4  07/05/2020 21:50        6047     3331    407          1  undefined   \n",
              "\n",
              "   Renewables  Wood  Refuse  Wind LandfillGas Solar      Other  \n",
              "0         627   222     333    58           2    12  undefined  \n",
              "1         626   221     338    53           2    12  undefined  \n",
              "2         617   222     335    46           2    12          0  \n",
              "3         592   219     333    26           2    12          4  \n",
              "4         600   221     333    32           2    12          1  "
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      "source": [
        "<a id=\"section-three\"></a>\n",
        "## 3. Filter and filler "
      ]
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      "source": [
        "print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BeginDate      object\n",
            "NaturalGas      int64\n",
            "Nuclear         int64\n",
            "Hydro           int64\n",
            "Coal           object\n",
            "Oil            object\n",
            "Renewables      int64\n",
            "Wood            int64\n",
            "Refuse          int64\n",
            "Wind            int64\n",
            "LandfillGas    object\n",
            "Solar          object\n",
            "Other          object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "source": [
        "Change datatype"
      ]
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      "source": [
        "df['Solar'] = pd.to_numeric(df['Solar'], errors='coerce').fillna(0)\n",
        "df['Coal'] = pd.to_numeric(df['Coal'], errors='coerce').fillna(0)\n",
        "df['Oil'] = pd.to_numeric(df['Oil'], errors='coerce').fillna(0)\n",
        "print(df.dtypes)"
      ],
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            "BeginDate       object\n",
            "NaturalGas       int64\n",
            "Nuclear          int64\n",
            "Hydro            int64\n",
            "Coal           float64\n",
            "Oil            float64\n",
            "Renewables       int64\n",
            "Wood             int64\n",
            "Refuse           int64\n",
            "Wind             int64\n",
            "LandfillGas     object\n",
            "Solar          float64\n",
            "Other           object\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "# check find and remove duplicates - doesnt work \n",
        "\n",
        "# boolean = df[\"BeginDate\"].is_unique  \n",
        "# print(\"Is Unique \" , boolean, df.size)\n",
        "\n",
        "# if(not boolean):\n",
        "#     duplicate = df[df.duplicated()]\n",
        "#     df = df.drop(duplicate.index)\n",
        "\n",
        "# boolean = df[\"BeginDate\"].is_unique  \n",
        "# print(\"Now Is Unique \" , boolean, df.size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.045024,
          "end_time": "2021-05-31T04:26:18.191073",
          "exception": false,
          "start_time": "2021-05-31T04:26:18.146049",
          "status": "completed"
        },
        "tags": [],
        "id": "kL8P_LiSiD0u"
      },
      "source": [
        "## Timestamp  different utility Graphs  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:18.296480Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "ki4DKBMJiD0u"
      },
      "source": [
        "df['Timestamp'] = pd.to_datetime(df['BeginDate'], format = '%m/%d/%Y %H:%M')\n",
        "               \n",
        "# adding separate time and date columns\n",
        "df[\"DATE\"] = pd.to_datetime(df[\"Timestamp\"]).dt.date # add new column with date\n",
        "# df[\"TIME\"] = pd.to_datetime(df[\"Timestamp\"]).dt.time # add new column with time\n",
        "\n",
        "# # add hours and minutes for ml models\n",
        "# df['HOURS'] = pd.to_datetime(df['TIME'],format='%H:%M').dt.hour\n",
        "# df['MINUTES'] = pd.to_datetime(df['TIME'],format='%H:%M').dt.minute\n",
        "# df['MINUTES_PASS'] = df['MINUTES'] + df['HOURS']*60\n",
        "\n",
        "# drop BeginDate\n",
        "df.drop('BeginDate', axis=1, inplace=True)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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          "status": "completed"
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        "outputId": "9938b8cc-3c36-4c15-b3c5-6a8d81aa812a"
      },
      "source": [
        "df = df.sort_values(by=['DATE'])\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "881"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:18.501960Z",
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          "end_time": "2021-05-31T04:26:18.505265",
          "exception": false,
          "start_time": "2021-05-31T04:26:18.449680",
          "status": "completed"
        },
        "tags": [],
        "id": "THxTE2eUiD0v",
        "outputId": "6d648b34-54fe-44c5-d0d1-866da4b3586e"
      },
      "source": [
        "# unique values of DATES\n",
        "dates = df['DATE'].unique()\n",
        "# remove first day which doesnt have all hours \n",
        "dates = dates[1:]\n",
        "len(dates)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "duration": 0.045223,
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          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "ozEVexOhiD0v"
      },
      "source": [
        "### Inperpolate values for missing dates "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:18.692984Z",
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        },
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          "status": "completed"
        },
        "tags": [],
        "id": "BbltVtwRiD0v"
      },
      "source": [
        "# unique timestamps in a day\n",
        "\n",
        "# dhours = df[ df['DATE'] ==  datetime.date(2020, 7, 6)]\n",
        "# dunique_minpass = dhours['Timestamp'].unique()\n",
        "# dunique_minpass = pd.to_datetime(dunique_minpass ,format = '%m/%d/%Y %H:%M')\n",
        "# dunique_minpass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:18.819200Z",
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          "shell.execute_reply": "2021-05-31T04:26:18.820764Z",
          "shell.execute_reply.started": "2021-05-31T02:38:58.921565Z"
        },
        "papermill": {
          "duration": 0.065938,
          "end_time": "2021-05-31T04:26:18.820901",
          "exception": false,
          "start_time": "2021-05-31T04:26:18.754963",
          "status": "completed"
        },
        "tags": [],
        "id": "p2EHxZRSiD0v"
      },
      "source": [
        "# for m in series: \n",
        "#     if m not in dunique_minpass:\n",
        "# #         print(\"missing tiemstamp \" , m)\n",
        "#         dhours = dhours.append({ \n",
        "#             'Timestamp': m,\n",
        "#             'Renewables': 0\n",
        "#         }, ignore_index = True) \n",
        "# #     else :\n",
        "# #         print(\"Exists tmestamp \" , m) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:18.916519Z",
          "iopub.status.busy": "2021-05-31T04:26:18.915871Z",
          "iopub.status.idle": "2021-05-31T04:26:18.918908Z",
          "shell.execute_reply": "2021-05-31T04:26:18.919403Z",
          "shell.execute_reply.started": "2021-05-31T02:38:58.934053Z"
        },
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          "duration": 0.052247,
          "end_time": "2021-05-31T04:26:18.919534",
          "exception": false,
          "start_time": "2021-05-31T04:26:18.867287",
          "status": "completed"
        },
        "tags": [],
        "id": "-lVoN6WbiD0w"
      },
      "source": [
        "# interpolate \n",
        "\n",
        "# dhours = df[ df['DATE'] ==  datetime.date(2020, 7, 6)]\n",
        "# dhours = dhours.sort_values(by='Timestamp')\n",
        "# dhours = dhours.interpolate(method ='nearest', limit_direction ='forward')\n",
        "# dhours.size\n",
        "# dhours.head(20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:19.016377Z",
          "iopub.status.busy": "2021-05-31T04:26:19.015818Z",
          "iopub.status.idle": "2021-05-31T04:26:19.018808Z",
          "shell.execute_reply": "2021-05-31T04:26:19.019241Z",
          "shell.execute_reply.started": "2021-05-31T02:38:58.947177Z"
        },
        "papermill": {
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          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "Tn6VT3SgiD0w"
      },
      "source": [
        "## appending dataframe to an empty one \n",
        "\n",
        "# df2 = pd.DataFrame()\n",
        "# df2 = df2.append(dhours)\n",
        "# df2.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:26:19.115494Z",
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          "shell.execute_reply.started": "2021-05-31T02:38:58.965410Z"
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        "papermill": {
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          "end_time": "2021-05-31T04:27:07.126011",
          "exception": false,
          "start_time": "2021-05-31T04:26:19.065651",
          "status": "completed"
        },
        "tags": [],
        "id": "i4Y_UdOsiD0w",
        "outputId": "2ceeeff0-ab4b-4028-ac87-5acb223dc43d"
      },
      "source": [
        "df2 = pd.DataFrame()\n",
        "\n",
        "for d in dates:\n",
        "#     print(d)\n",
        "    dhours = df[df['DATE']==d]\n",
        "    dunique_minpass = dhours['Timestamp'].unique()\n",
        "    dunique_minpass = pd.to_datetime(dunique_minpass ,format = '%m/%d/%Y %H:%M')\n",
        "    \n",
        "    index = pd.date_range(d, periods=1440, freq='T')\n",
        "    series = pd.Series(range(1440), index=index, name='Timestamp')\n",
        "    series = pd.to_datetime(series.index,format = '%m/%d/%Y %H:%M')\n",
        "\n",
        "    for m in series: \n",
        "        if m not in dunique_minpass:\n",
        "    #         print(\"missing tiemstamp \" , m)\n",
        "            dhours = dhours.append({ \n",
        "                'Timestamp': m,\n",
        "                'Renewables': 0\n",
        "            }, ignore_index = True) \n",
        "    #     else :\n",
        "    #         print(\"Exists tmestamp \" , m) \n",
        "    \n",
        "    dhours = dhours.sort_values(by='Timestamp')\n",
        "    dhours = dhours.interpolate(method ='linear', limit_direction ='forward')\n",
        "    \n",
        "    # adding separate time and date columns\n",
        "    dhours[\"DATE\"] = pd.to_datetime(dhours[\"Timestamp\"]).dt.date # add new column with date\n",
        "    dhours[\"TIME\"] = pd.to_datetime(dhours[\"Timestamp\"]).dt.time # add new column with time\n",
        "\n",
        "    # add hours and minutes for ml models\n",
        "    dhours['HOURS'] = pd.to_datetime(dhours['TIME'],format='%H:%M:%S').dt.hour\n",
        "    dhours['MINUTES'] = pd.to_datetime(dhours['TIME'],format='%H:%M:%S').dt.minute\n",
        "    dhours['MINUTES_PASS'] = dhours['MINUTES'] + dhours['HOURS']*60\n",
        "    df2 = df2.append(dhours)\n",
        "\n",
        "df = df2\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8645"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
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          "status": "completed"
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        "tags": [],
        "id": "G3QdB1YhiD0y"
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      "source": [
        "Replace undefined with 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:07.317604Z",
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          "status": "completed"
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        "tags": [],
        "id": "ry8lPdMNiD0z",
        "outputId": "234ec1ab-b218-4536-d892-0059244e8f6f"
      },
      "source": [
        "df = df.replace('undefined', 0)\n",
        "df = df.fillna(0)\n",
        "df = df.replace(np.nan, 0)\n",
        "df[15:25]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Renewables</th>\n",
              "      <th>Wood</th>\n",
              "      <th>Refuse</th>\n",
              "      <th>Wind</th>\n",
              "      <th>LandfillGas</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:15:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>5182.000000</td>\n",
              "      <td>3326.000000</td>\n",
              "      <td>405.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>573</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:16:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:16:00</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
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              "      <td>3326.666667</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>42.666667</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:17:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:17:00</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>5156.000000</td>\n",
              "      <td>3327.333333</td>\n",
              "      <td>404.333333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>187.333333</td>\n",
              "      <td>335.333333</td>\n",
              "      <td>44.333333</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:18:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:18:00</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>5143.000000</td>\n",
              "      <td>3328.000000</td>\n",
              "      <td>404.000000</td>\n",
              "      <td>8.000000</td>\n",
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              "      <td>585</td>\n",
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              "      <td>3</td>\n",
              "      <td>2020-07-06 00:19:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:19:00</td>\n",
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              "      <td>19</td>\n",
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              "      <td>403.823529</td>\n",
              "      <td>7.529412</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>189.352941</td>\n",
              "      <td>335.411765</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:20:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>5138.176471</td>\n",
              "      <td>3328.235294</td>\n",
              "      <td>403.647059</td>\n",
              "      <td>7.058824</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>189.705882</td>\n",
              "      <td>334.823529</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:21:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:21:00</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>5135.764706</td>\n",
              "      <td>3328.352941</td>\n",
              "      <td>403.470588</td>\n",
              "      <td>6.588235</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>190.058824</td>\n",
              "      <td>334.235294</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:22:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:22:00</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>5133.352941</td>\n",
              "      <td>3328.470588</td>\n",
              "      <td>403.294118</td>\n",
              "      <td>6.117647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>190.411765</td>\n",
              "      <td>333.647059</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:23:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:23:00</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>5130.941176</td>\n",
              "      <td>3328.588235</td>\n",
              "      <td>403.117647</td>\n",
              "      <td>5.647059</td>\n",
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              "      <td>0</td>\n",
              "      <td>190.764706</td>\n",
              "      <td>333.058824</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06 00:24:00</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>00:24:00</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      NaturalGas      Nuclear       Hydro      Coal  Oil  Renewables  \\\n",
              "186     0.000000     0.000000    0.000000  0.000000  0.0           0   \n",
              "109  5182.000000  3326.000000  405.000000  2.000000  0.0         573   \n",
              "187  5169.000000  3326.666667  404.666667  4.000000  0.0           0   \n",
              "188  5156.000000  3327.333333  404.333333  6.000000  0.0           0   \n",
              "110  5143.000000  3328.000000  404.000000  8.000000  0.0         585   \n",
              "189  5140.588235  3328.117647  403.823529  7.529412  0.0           0   \n",
              "190  5138.176471  3328.235294  403.647059  7.058824  0.0           0   \n",
              "191  5135.764706  3328.352941  403.470588  6.588235  0.0           0   \n",
              "192  5133.352941  3328.470588  403.294118  6.117647  0.0           0   \n",
              "193  5130.941176  3328.588235  403.117647  5.647059  0.0           0   \n",
              "\n",
              "           Wood      Refuse       Wind LandfillGas  Solar Other  \\\n",
              "186    0.000000    0.000000   0.000000           0    0.0     0   \n",
              "109  184.000000  334.000000  41.000000           2   12.0     0   \n",
              "187  185.666667  334.666667  42.666667           0   12.0     0   \n",
              "188  187.333333  335.333333  44.333333           0   12.0     0   \n",
              "110  189.000000  336.000000  46.000000           2   12.0     3   \n",
              "189  189.352941  335.411765  46.000000           0   12.0     0   \n",
              "190  189.705882  334.823529  46.000000           0   12.0     0   \n",
              "191  190.058824  334.235294  46.000000           0   12.0     0   \n",
              "192  190.411765  333.647059  46.000000           0   12.0     0   \n",
              "193  190.764706  333.058824  46.000000           0   12.0     0   \n",
              "\n",
              "              Timestamp        DATE      TIME  HOURS  MINUTES  MINUTES_PASS  \n",
              "186 2020-07-06 00:15:00  2020-07-06  00:15:00      0       15            15  \n",
              "109 2020-07-06 00:16:00  2020-07-06  00:16:00      0       16            16  \n",
              "187 2020-07-06 00:17:00  2020-07-06  00:17:00      0       17            17  \n",
              "188 2020-07-06 00:18:00  2020-07-06  00:18:00      0       18            18  \n",
              "110 2020-07-06 00:19:00  2020-07-06  00:19:00      0       19            19  \n",
              "189 2020-07-06 00:20:00  2020-07-06  00:20:00      0       20            20  \n",
              "190 2020-07-06 00:21:00  2020-07-06  00:21:00      0       21            21  \n",
              "191 2020-07-06 00:22:00  2020-07-06  00:22:00      0       22            22  \n",
              "192 2020-07-06 00:23:00  2020-07-06  00:23:00      0       23            23  \n",
              "193 2020-07-06 00:24:00  2020-07-06  00:24:00      0       24            24  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "status": "completed"
        },
        "tags": [],
        "id": "dKc7TU1GiD01"
      },
      "source": [
        "Set others for un-matched catagories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:07.549741Z",
          "iopub.status.busy": "2021-05-31T04:27:07.548856Z",
          "iopub.status.idle": "2021-05-31T04:27:07.578951Z",
          "shell.execute_reply": "2021-05-31T04:27:07.577778Z",
          "shell.execute_reply.started": "2021-05-31T02:39:46.565184Z"
        },
        "papermill": {
          "duration": 0.087239,
          "end_time": "2021-05-31T04:27:07.579143",
          "exception": false,
          "start_time": "2021-05-31T04:27:07.491904",
          "status": "completed"
        },
        "tags": [],
        "id": "7ll2OrTKiD03",
        "outputId": "cb1996e9-9a1e-480d-d915-9b9a70f5ba5f"
      },
      "source": [
        "df['Other'] = df['Other'].astype(int) + df['Wood'].astype(int) + df['Refuse'].astype(int) + df['LandfillGas'].astype(int)\n",
        "df = df.drop(columns=['Wood','Refuse','Renewables','LandfillGas'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>NaturalGas</th>\n",
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              "      <td>2020-07-06 00:04:00</td>\n",
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              "      <td>4</td>\n",
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            "text/plain": [
              "     NaturalGas  Nuclear  Hydro  Coal  Oil  Wind  Solar  Other  \\\n",
              "171         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "172         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "173         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "174         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "175         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "\n",
              "              Timestamp        DATE      TIME  HOURS  MINUTES  MINUTES_PASS  \n",
              "171 2020-07-06 00:00:00  2020-07-06  00:00:00      0        0             0  \n",
              "172 2020-07-06 00:01:00  2020-07-06  00:01:00      0        1             1  \n",
              "173 2020-07-06 00:02:00  2020-07-06  00:02:00      0        2             2  \n",
              "174 2020-07-06 00:03:00  2020-07-06  00:03:00      0        3             3  \n",
              "175 2020-07-06 00:04:00  2020-07-06  00:04:00      0        4             4  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:07.697820Z",
          "iopub.status.busy": "2021-05-31T04:27:07.697077Z",
          "iopub.status.idle": "2021-05-31T04:27:07.723416Z",
          "shell.execute_reply": "2021-05-31T04:27:07.724055Z",
          "shell.execute_reply.started": "2021-05-31T02:39:46.593959Z"
        },
        "papermill": {
          "duration": 0.087973,
          "end_time": "2021-05-31T04:27:07.724204",
          "exception": false,
          "start_time": "2021-05-31T04:27:07.636231",
          "status": "completed"
        },
        "tags": [],
        "id": "Jmmqz4SgiD06",
        "outputId": "0abfe2c9-e4c7-4101-b492-f62d29a785ef"
      },
      "source": [
        "# if 'Petroleum Generation (MWh)' in df_nw:\n",
        "#     print(\"petrolum is there \")\n",
        "# else    \n",
        "#     print(\"petrolum isnt there \")\n",
        "\n",
        "column_list = (\"Wind\",\"Solar\",\"Hydro\",\"Other\", \n",
        "               \"Oil\", \"NaturalGas\",\"Coal\",\"Nuclear\")\n",
        "\n",
        "for col in column_list:\n",
        "    if col not in df.columns:\n",
        "        df[col] = 0\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>2020-07-06 00:04:00</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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            ],
            "text/plain": [
              "     NaturalGas  Nuclear  Hydro  Coal  Oil  Wind  Solar  Other  \\\n",
              "171         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "172         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "173         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "174         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "175         0.0      0.0    0.0   0.0  0.0   0.0    0.0      0   \n",
              "\n",
              "              Timestamp        DATE      TIME  HOURS  MINUTES  MINUTES_PASS  \n",
              "171 2020-07-06 00:00:00  2020-07-06  00:00:00      0        0             0  \n",
              "172 2020-07-06 00:01:00  2020-07-06  00:01:00      0        1             1  \n",
              "173 2020-07-06 00:02:00  2020-07-06  00:02:00      0        2             2  \n",
              "174 2020-07-06 00:03:00  2020-07-06  00:03:00      0        3             3  \n",
              "175 2020-07-06 00:04:00  2020-07-06  00:04:00      0        4             4  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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          "shell.execute_reply.started": "2021-05-31T02:39:46.616351Z"
        },
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          "status": "completed"
        },
        "tags": [],
        "id": "JAmoJvEniD07"
      },
      "source": [
        "# Pie chart ( Fuel Mix)\n",
        "# df.plot.pie(\n",
        "#     index = [\"Wind\",\"Solar\",\"Hydro\",\"Other\", \n",
        "#                \"Oil\", \"NaturalGas\",\"Coal\",\"Nuclear\"],\n",
        "#     subplots = True, \n",
        "#     name=\"Fuel Mix throughout the week\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.095898,
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          "status": "completed"
        },
        "tags": [],
        "id": "rrqjyOp1iD07"
      },
      "source": [
        "Calculate total generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:08.097589Z",
          "iopub.status.busy": "2021-05-31T04:27:08.096672Z",
          "iopub.status.idle": "2021-05-31T04:27:08.111795Z",
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          "shell.execute_reply.started": "2021-05-31T02:39:46.627048Z"
        },
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          "exception": false,
          "start_time": "2021-05-31T04:27:08.044054",
          "status": "completed"
        },
        "tags": [],
        "id": "mrWcDTbNiD08",
        "outputId": "efb49a1f-22e1-4cd1-b1da-912f4c2a4be5"
      },
      "source": [
        "df[\"total_generation\"] = df[\"Wind\"] + df[\"Solar\"] + df[\"Hydro\"] + df[\"Other\"] + df[\"Oil\"] + df[\"NaturalGas\"] + df[\"Coal\"] + df[\"Nuclear\"]\n",
        "df[[\"TIME\",\"total_generation\"]].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TIME</th>\n",
              "      <th>total_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>00:01:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>00:02:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>00:03:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>00:04:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         TIME  total_generation\n",
              "171  00:00:00               0.0\n",
              "172  00:01:00               0.0\n",
              "173  00:02:00               0.0\n",
              "174  00:03:00               0.0\n",
              "175  00:04:00               0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.048048,
          "end_time": "2021-05-31T04:27:08.216913",
          "exception": false,
          "start_time": "2021-05-31T04:27:08.168865",
          "status": "completed"
        },
        "tags": [],
        "id": "wGUMDd3piD08"
      },
      "source": [
        "Add unix timestamp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:08.328365Z",
          "iopub.status.busy": "2021-05-31T04:27:08.327065Z",
          "iopub.status.idle": "2021-05-31T04:27:08.336177Z",
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          "shell.execute_reply.started": "2021-05-31T02:39:46.650280Z"
        },
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          "exception": false,
          "start_time": "2021-05-31T04:27:08.265247",
          "status": "completed"
        },
        "tags": [],
        "id": "UvDNh7dAiD08",
        "outputId": "d081b915-4d34-4dd5-eaa0-efef95ef4651"
      },
      "source": [
        "df_unix = pd.to_datetime(df[\"Timestamp\"],origin='unix')\n",
        "df[\"Timestamp (Unix)\"] = (df_unix - pd.Timestamp(\"1970-01-01\")) // pd.Timedelta('1s')\n",
        "df[\"Timestamp (Unix)\"].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171    1593993600\n",
              "172    1593993660\n",
              "173    1593993720\n",
              "174    1593993780\n",
              "175    1593993840\n",
              "Name: Timestamp (Unix), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.047924,
          "end_time": "2021-05-31T04:27:08.432380",
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          "start_time": "2021-05-31T04:27:08.384456",
          "status": "completed"
        },
        "tags": [],
        "id": "0Apb5r-_iD09"
      },
      "source": [
        "### output csv \n",
        "\n",
        "CSV outPut total_generation(MW) and total_generation(W) and \n",
        "one Timestamp (Unix) and total_generation(W)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:08.533956Z",
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          "shell.execute_reply.started": "2021-05-31T02:39:46.671262Z"
        },
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          "start_time": "2021-05-31T04:27:08.480792",
          "status": "completed"
        },
        "tags": [],
        "id": "PhAdcdR7iD0-"
      },
      "source": [
        "ot = df\n",
        "ot['total_generation(W)'] = ot['total_generation']*1000000\n",
        "ot.to_csv(Region+'.csv',index=False, header=True)\n",
        "ot.to_csv(Region+'_genunix.csv', columns=['Timestamp (Unix)','total_generation(W)'], index=False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.047504,
          "end_time": "2021-05-31T04:27:09.047175",
          "exception": false,
          "start_time": "2021-05-31T04:27:08.999671",
          "status": "completed"
        },
        "tags": [],
        "id": "7juZfdlLiD0-"
      },
      "source": [
        "Output date wise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:09.147270Z",
          "iopub.status.busy": "2021-05-31T04:27:09.146383Z",
          "iopub.status.idle": "2021-05-31T04:27:09.341291Z",
          "shell.execute_reply": "2021-05-31T04:27:09.340748Z",
          "shell.execute_reply.started": "2021-05-31T02:39:47.000158Z"
        },
        "papermill": {
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          "end_time": "2021-05-31T04:27:09.341393",
          "exception": false,
          "start_time": "2021-05-31T04:27:09.095072",
          "status": "completed"
        },
        "tags": [],
        "id": "C5i4_ZNaiD0-",
        "outputId": "f0fe5f58-74c0-4e33-e582-b4bf18ed5f6a"
      },
      "source": [
        "for d in dates:\n",
        "    print(d)\n",
        "    # all the hours in day\n",
        "    ot2 = df[df['DATE']==d]\n",
        "    ot2.to_csv(Region+\"_\"+str(d)+'.csv',index=False, header=True)\n",
        "    ot2.to_csv(Region+'_genunix_'+str(d)+'.csv', columns=['Timestamp (Unix)','total_generation(W)'],index=False, header=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-07-06\n",
            "2020-07-07\n",
            "2020-07-08\n",
            "2020-07-09\n",
            "2020-07-10\n",
            "2020-07-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:09.443322Z",
          "iopub.status.busy": "2021-05-31T04:27:09.442419Z",
          "iopub.status.idle": "2021-05-31T04:27:09.445220Z",
          "shell.execute_reply": "2021-05-31T04:27:09.445694Z",
          "shell.execute_reply.started": "2021-05-31T02:39:47.193854Z"
        },
        "papermill": {
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          "exception": false,
          "start_time": "2021-05-31T04:27:09.390074",
          "status": "completed"
        },
        "tags": [],
        "id": "hPcLD0wxiD0-"
      },
      "source": [
        "# chats of all days in series \n",
        "\n",
        "# for pp in df_nw['DATE'].unique():\n",
        "# #     print(df_nw[pp])\n",
        "#     print(pp)\n",
        "#     sub_df=df_nw[df_nw['DATE']==pp]\n",
        "#     ax = sub_df.plot(x='HOURS', y=[\"total_generation\"], title = \"Total generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'))\n",
        "#     ax.axhline(y=sub_df[\"total_generation\"].mean(), xmin=-1, xmax=1, color='r', linestyle='--', lw=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.049941,
          "end_time": "2021-05-31T04:27:09.544067",
          "exception": false,
          "start_time": "2021-05-31T04:27:09.494126",
          "status": "completed"
        },
        "tags": [],
        "id": "WyMqJ7DhiD0_"
      },
      "source": [
        "<a id=\"section-four\"></a>\n",
        "# 4.  Daily graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:09.646850Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "kfi5y151iD0_",
        "outputId": "8051aaad-ac3a-4ead-a9d6-540d978098a6"
      },
      "source": [
        "column_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal', 'Nuclear')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:09.753765Z",
          "iopub.status.busy": "2021-05-31T04:27:09.753089Z",
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          "shell.execute_reply": "2021-05-31T04:27:10.117737Z",
          "shell.execute_reply.started": "2021-05-31T02:39:47.203758Z"
        },
        "papermill": {
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          "start_time": "2021-05-31T04:27:09.699023",
          "status": "completed"
        },
        "tags": [],
        "id": "L1LS4LrwiD0_",
        "outputId": "5ffbe50e-9fec-46f2-f7e7-c15e535a9487"
      },
      "source": [
        "# # Line Chart ( Trend during the day)\n",
        "weekplot = df.plot.line(x='Timestamp (Unix)',\n",
        "                y = ['Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal', 'Nuclear'],\n",
        "                    title = \"Generation in \"+ Region)\n",
        "weekplot.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fae6d0dbf10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.04958,
          "end_time": "2021-05-31T04:27:10.219180",
          "exception": false,
          "start_time": "2021-05-31T04:27:10.169600",
          "status": "completed"
        },
        "tags": [],
        "id": "_NwGP3EtiD0_"
      },
      "source": [
        "## Daily chart for the week by MINUTES_PASS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:10.330636Z",
          "iopub.status.busy": "2021-05-31T04:27:10.329881Z",
          "iopub.status.idle": "2021-05-31T04:27:11.305355Z",
          "shell.execute_reply": "2021-05-31T04:27:11.305809Z",
          "shell.execute_reply.started": "2021-05-31T02:39:47.525808Z"
        },
        "papermill": {
          "duration": 1.036812,
          "end_time": "2021-05-31T04:27:11.305944",
          "exception": false,
          "start_time": "2021-05-31T04:27:10.269132",
          "status": "completed"
        },
        "tags": [],
        "id": "IfvL-XmCiD1A",
        "outputId": "befd2d4c-056a-460f-86c9-af46de8343cb"
      },
      "source": [
        "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
        "fig.set_size_inches(20, 10)\n",
        "fig.subplots_adjust(wspace=0.2)\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "\n",
        "for pp,ax in zip(dates,axs.flatten()):\n",
        "#     print(df_nw[pp])\n",
        "    print(pp)\n",
        "    sub_df=df[df['DATE']==pp]\n",
        "    sub_df.plot(x='MINUTES_PASS', y=[\"total_generation\"], ax = ax, title = \"Total generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'))\n",
        "    \n",
        "    ax.set_ylabel(\"generation(Mw)\")\n",
        "    \n",
        "    mean_gen = round(sub_df[\"total_generation\"].mean())\n",
        "    max_gen = sub_df[\"total_generation\"].max()\n",
        "    maxh = sub_df.loc[sub_df[\"total_generation\"]== max_gen]\n",
        "    maxh = maxh[\"MINUTES_PASS\"]\n",
        "    ax.axhline(y=mean_gen, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "    \n",
        "    ax.text(5,mean_gen+1000, \"Mean \" + str(mean_gen) + \"(MWh)\" )\n",
        "    \n",
        "    ax.get_legend().remove()\n",
        "    \n",
        "    ax.annotate('Peak '+ str(max_gen)+ \"(MWh)\", xy=(maxh,max_gen), xytext=(maxh+1, max_gen+1000),\n",
        "             arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)\n",
        "    \n",
        "    ax.set_ylim(bottom = 0,top=22000)\n",
        "\n",
        "# ax.axhline(y=df_nw[\"total_generation\"].median(), xmin=-1, xmax=1, color='g', linestyle='--', lw=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-07-06\n",
            "2020-07-07\n",
            "2020-07-08\n",
            "2020-07-09\n",
            "2020-07-10\n",
            "2020-07-11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.052401,
          "end_time": "2021-05-31T04:27:11.411635",
          "exception": false,
          "start_time": "2021-05-31T04:27:11.359234",
          "status": "completed"
        },
        "tags": [],
        "id": "oGvCo0ftiD1A"
      },
      "source": [
        "## Daily chart for the week as per fuel sources by MINUTES_PASS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:11.539625Z",
          "iopub.status.busy": "2021-05-31T04:27:11.536087Z",
          "iopub.status.idle": "2021-05-31T04:27:12.993396Z",
          "shell.execute_reply": "2021-05-31T04:27:12.993883Z",
          "shell.execute_reply.started": "2021-05-31T02:39:48.479920Z"
        },
        "papermill": {
          "duration": 1.529752,
          "end_time": "2021-05-31T04:27:12.994019",
          "exception": false,
          "start_time": "2021-05-31T04:27:11.464267",
          "status": "completed"
        },
        "tags": [],
        "id": "7ONSUtsciD1B",
        "outputId": "2fb9f6e9-067a-4784-95f0-9d23e6ae4e53"
      },
      "source": [
        "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
        "fig.set_size_inches(20, 10)\n",
        "fig.subplots_adjust(wspace=0.2)\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "\n",
        "fig.legend(column_list)\n",
        "    \n",
        "for pp,ax in zip(dates,axs.flatten()):\n",
        "#     print(df_nw[pp])\n",
        "    print(pp)\n",
        "    sub_df = df[df['DATE']==pp]\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[0], ax = ax, title = \"Fuel Mix for generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'),legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[1], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[2], ax = ax,legend=False)    \n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[3], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[4], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[5], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[6], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[7], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y =['total_generation'], ax = ax,legend=False)\n",
        "    \n",
        "    ax.set_ylabel(\"generation(Mw)\")\n",
        "    \n",
        "    mean_gen = round(sub_df[\"total_generation\"].mean())\n",
        "    max_gen = sub_df[\"total_generation\"].max()\n",
        "    maxh = sub_df.loc[sub_df[\"total_generation\"]== max_gen]\n",
        "    maxh = maxh[\"MINUTES_PASS\"]\n",
        "    ax.axhline(y=mean_gen, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "    \n",
        "    ax.text(5,mean_gen+1000, \"Mean \" + str(mean_gen) + \"(MW)\" )\n",
        "\n",
        "#     ax.get_legend().remove()\n",
        "    \n",
        "    ax.annotate('Peak '+ str(max_gen)+ \"(MW)\", xy=(maxh,max_gen), xytext=(maxh+1, max_gen+1000),\n",
        "             arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)\n",
        "    \n",
        "    ax.set_ylim(bottom = 0,top=22000)\n",
        "    \n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-07-06\n",
            "2020-07-07\n",
            "2020-07-08\n",
            "2020-07-09\n",
            "2020-07-10\n",
            "2020-07-11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fae65991150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:13.114237Z",
          "iopub.status.busy": "2021-05-31T04:27:13.113624Z",
          "iopub.status.idle": "2021-05-31T04:27:13.116887Z",
          "shell.execute_reply": "2021-05-31T04:27:13.116382Z",
          "shell.execute_reply.started": "2021-05-31T02:39:49.972725Z"
        },
        "papermill": {
          "duration": 0.06469,
          "end_time": "2021-05-31T04:27:13.116994",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.052304",
          "status": "completed"
        },
        "tags": [],
        "id": "wo_Xb1S0iD1D"
      },
      "source": [
        "# usage in one day \n",
        "# df_nw.plot(x='HOURS',  kind='hist')\n",
        "\n",
        "# df_nw.plot(x='HOURS', y=[\"Wind Generation (MWh)\",\"Solar Generation (MWh)\",\"Hydro Generation (MWh)\",\"Other Generation (MWh)\", \n",
        "#                         \"Petroleum Generation (MWh)\", \"Natural gas Generation (MWh)\",\"Coal Generation (MWh)\",\"Nuclear Generation (MWh)\"], kind='hist')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057966,
          "end_time": "2021-05-31T04:27:13.233636",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.175670",
          "status": "completed"
        },
        "tags": [],
        "id": "7_crVhFdiD1D"
      },
      "source": [
        "### Peak Minute"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:13.371805Z",
          "iopub.status.busy": "2021-05-31T04:27:13.370958Z",
          "iopub.status.idle": "2021-05-31T04:27:13.375824Z",
          "shell.execute_reply": "2021-05-31T04:27:13.375242Z",
          "shell.execute_reply.started": "2021-05-31T02:39:49.977974Z"
        },
        "papermill": {
          "duration": 0.083152,
          "end_time": "2021-05-31T04:27:13.375935",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.292783",
          "status": "completed"
        },
        "tags": [],
        "id": "0Mm0kX-4iD1D",
        "outputId": "441ad899-a1dc-4b61-9562-bb07e9732728"
      },
      "source": [
        "## Peak Minute\n",
        "peakmin = df[df[\"total_generation\"] == df[\"total_generation\"].max()]\n",
        "peakmin = peakmin.reset_index(drop=True)\n",
        "peakmin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>Timestamp (Unix)</th>\n",
              "      <th>total_generation(W)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>3322.0</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>127.0</td>\n",
              "      <td>658</td>\n",
              "      <td>2020-07-09 14:01:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:01:00</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>841</td>\n",
              "      <td>20829.0</td>\n",
              "      <td>1594303260</td>\n",
              "      <td>2.082900e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NaturalGas  Nuclear   Hydro  Coal   Oil  Wind  Solar  Other  \\\n",
              "0     14636.0   3322.0  1975.0  16.0  48.0  47.0  127.0    658   \n",
              "\n",
              "            Timestamp        DATE      TIME  HOURS  MINUTES  MINUTES_PASS  \\\n",
              "0 2020-07-09 14:01:00  2020-07-09  14:01:00     14        1           841   \n",
              "\n",
              "   total_generation  Timestamp (Unix)  total_generation(W)  \n",
              "0           20829.0        1594303260         2.082900e+10  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.058106,
          "end_time": "2021-05-31T04:27:13.493507",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.435401",
          "status": "completed"
        },
        "tags": [],
        "id": "SxIpyZsaiD1E"
      },
      "source": [
        "### Peak Hour"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:13.650741Z",
          "iopub.status.busy": "2021-05-31T04:27:13.645297Z",
          "iopub.status.idle": "2021-05-31T04:27:13.659402Z",
          "shell.execute_reply": "2021-05-31T04:27:13.658921Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.016505Z"
        },
        "papermill": {
          "duration": 0.106226,
          "end_time": "2021-05-31T04:27:13.659521",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.553295",
          "status": "completed"
        },
        "tags": [],
        "id": "dGStdo24iD1E",
        "outputId": "333b2299-3e5c-4f6c-cb5b-5050f5f649bd"
      },
      "source": [
        "## Peak Hour\n",
        "# df_nw= df_nw[df_nw[\"DATE\"].astype(str) == Date]\n",
        "peakhour = df[(df['HOURS'].values == peakmin.HOURS.values) & (df[\"DATE\"].values == peakmin.DATE.values) ]\n",
        "peakhour"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Solar</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>Timestamp (Unix)</th>\n",
              "      <th>total_generation(W)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>891</th>\n",
              "      <td>14616.600000</td>\n",
              "      <td>3322.400000</td>\n",
              "      <td>1973.800000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>49.800000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>128.600000</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:00:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:00:00</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>840</td>\n",
              "      <td>20768.800000</td>\n",
              "      <td>1594303200</td>\n",
              "      <td>2.076880e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>14636.000000</td>\n",
              "      <td>3322.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>658</td>\n",
              "      <td>2020-07-09 14:01:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:01:00</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>841</td>\n",
              "      <td>20829.000000</td>\n",
              "      <td>1594303260</td>\n",
              "      <td>2.082900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>14638.285714</td>\n",
              "      <td>3322.285714</td>\n",
              "      <td>1970.142857</td>\n",
              "      <td>15.142857</td>\n",
              "      <td>46.428571</td>\n",
              "      <td>46.142857</td>\n",
              "      <td>126.571429</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:02:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:02:00</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>842</td>\n",
              "      <td>20780.000000</td>\n",
              "      <td>1594303320</td>\n",
              "      <td>2.078000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>14640.571429</td>\n",
              "      <td>3322.571429</td>\n",
              "      <td>1965.285714</td>\n",
              "      <td>14.285714</td>\n",
              "      <td>44.857143</td>\n",
              "      <td>45.285714</td>\n",
              "      <td>126.142857</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:03:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:03:00</td>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>843</td>\n",
              "      <td>20774.000000</td>\n",
              "      <td>1594303380</td>\n",
              "      <td>2.077400e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>14642.857143</td>\n",
              "      <td>3322.857143</td>\n",
              "      <td>1960.428571</td>\n",
              "      <td>13.428571</td>\n",
              "      <td>43.285714</td>\n",
              "      <td>44.428571</td>\n",
              "      <td>125.714286</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:04:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:04:00</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>844</td>\n",
              "      <td>20768.000000</td>\n",
              "      <td>1594303440</td>\n",
              "      <td>2.076800e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>14645.142857</td>\n",
              "      <td>3323.142857</td>\n",
              "      <td>1955.571429</td>\n",
              "      <td>12.571429</td>\n",
              "      <td>41.714286</td>\n",
              "      <td>43.571429</td>\n",
              "      <td>125.285714</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:05:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:05:00</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>845</td>\n",
              "      <td>20761.000000</td>\n",
              "      <td>1594303500</td>\n",
              "      <td>2.076100e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>14647.428571</td>\n",
              "      <td>3323.428571</td>\n",
              "      <td>1950.714286</td>\n",
              "      <td>11.714286</td>\n",
              "      <td>40.142857</td>\n",
              "      <td>42.714286</td>\n",
              "      <td>124.857143</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:06:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:06:00</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>846</td>\n",
              "      <td>20755.000000</td>\n",
              "      <td>1594303560</td>\n",
              "      <td>2.075500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>897</th>\n",
              "      <td>14649.714286</td>\n",
              "      <td>3323.714286</td>\n",
              "      <td>1945.857143</td>\n",
              "      <td>10.857143</td>\n",
              "      <td>38.571429</td>\n",
              "      <td>41.857143</td>\n",
              "      <td>124.428571</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:07:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:07:00</td>\n",
              "      <td>14</td>\n",
              "      <td>7</td>\n",
              "      <td>847</td>\n",
              "      <td>20749.000000</td>\n",
              "      <td>1594303620</td>\n",
              "      <td>2.074900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>14652.000000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>657</td>\n",
              "      <td>2020-07-09 14:08:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:08:00</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>848</td>\n",
              "      <td>20786.000000</td>\n",
              "      <td>1594303680</td>\n",
              "      <td>2.078600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>898</th>\n",
              "      <td>14657.857143</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.142857</td>\n",
              "      <td>10.142857</td>\n",
              "      <td>34.857143</td>\n",
              "      <td>40.857143</td>\n",
              "      <td>122.285714</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:09:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:09:00</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>849</td>\n",
              "      <td>20746.142857</td>\n",
              "      <td>1594303740</td>\n",
              "      <td>2.074614e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>899</th>\n",
              "      <td>14663.714286</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.285714</td>\n",
              "      <td>10.285714</td>\n",
              "      <td>32.714286</td>\n",
              "      <td>40.714286</td>\n",
              "      <td>120.571429</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:10:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:10:00</td>\n",
              "      <td>14</td>\n",
              "      <td>10</td>\n",
              "      <td>850</td>\n",
              "      <td>20748.285714</td>\n",
              "      <td>1594303800</td>\n",
              "      <td>2.074829e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>900</th>\n",
              "      <td>14669.571429</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.428571</td>\n",
              "      <td>10.428571</td>\n",
              "      <td>30.571429</td>\n",
              "      <td>40.571429</td>\n",
              "      <td>118.857143</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:11:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:11:00</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>851</td>\n",
              "      <td>20750.428571</td>\n",
              "      <td>1594303860</td>\n",
              "      <td>2.075043e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>901</th>\n",
              "      <td>14675.428571</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.571429</td>\n",
              "      <td>10.571429</td>\n",
              "      <td>28.428571</td>\n",
              "      <td>40.428571</td>\n",
              "      <td>117.142857</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:12:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:12:00</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "      <td>852</td>\n",
              "      <td>20752.571429</td>\n",
              "      <td>1594303920</td>\n",
              "      <td>2.075257e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>902</th>\n",
              "      <td>14681.285714</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.714286</td>\n",
              "      <td>10.714286</td>\n",
              "      <td>26.285714</td>\n",
              "      <td>40.285714</td>\n",
              "      <td>115.428571</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:13:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:13:00</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>853</td>\n",
              "      <td>20754.714286</td>\n",
              "      <td>1594303980</td>\n",
              "      <td>2.075471e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>903</th>\n",
              "      <td>14687.142857</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1941.857143</td>\n",
              "      <td>10.857143</td>\n",
              "      <td>24.142857</td>\n",
              "      <td>40.142857</td>\n",
              "      <td>113.714286</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:14:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:14:00</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>854</td>\n",
              "      <td>20756.857143</td>\n",
              "      <td>1594304040</td>\n",
              "      <td>2.075686e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>14693.000000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1942.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>658</td>\n",
              "      <td>2020-07-09 14:15:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:15:00</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>855</td>\n",
              "      <td>20802.000000</td>\n",
              "      <td>1594304100</td>\n",
              "      <td>2.080200e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>904</th>\n",
              "      <td>14692.777778</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1929.777778</td>\n",
              "      <td>10.555556</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>112.222222</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:16:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:16:00</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>856</td>\n",
              "      <td>20746.333333</td>\n",
              "      <td>1594304160</td>\n",
              "      <td>2.074633e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>905</th>\n",
              "      <td>14692.555556</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1917.555556</td>\n",
              "      <td>10.111111</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>112.444444</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:17:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:17:00</td>\n",
              "      <td>14</td>\n",
              "      <td>17</td>\n",
              "      <td>857</td>\n",
              "      <td>20733.666667</td>\n",
              "      <td>1594304220</td>\n",
              "      <td>2.073367e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>906</th>\n",
              "      <td>14692.333333</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1905.333333</td>\n",
              "      <td>9.666667</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>112.666667</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:18:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:18:00</td>\n",
              "      <td>14</td>\n",
              "      <td>18</td>\n",
              "      <td>858</td>\n",
              "      <td>20721.000000</td>\n",
              "      <td>1594304280</td>\n",
              "      <td>2.072100e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>907</th>\n",
              "      <td>14692.111111</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1893.111111</td>\n",
              "      <td>9.222222</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>112.888889</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:19:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:19:00</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>859</td>\n",
              "      <td>20707.333333</td>\n",
              "      <td>1594304340</td>\n",
              "      <td>2.070733e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>908</th>\n",
              "      <td>14691.888889</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1880.888889</td>\n",
              "      <td>8.777778</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>113.111111</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:20:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:20:00</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>860</td>\n",
              "      <td>20695.666667</td>\n",
              "      <td>1594304400</td>\n",
              "      <td>2.069567e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>909</th>\n",
              "      <td>14691.666667</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1868.666667</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>113.333333</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:21:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:21:00</td>\n",
              "      <td>14</td>\n",
              "      <td>21</td>\n",
              "      <td>861</td>\n",
              "      <td>20683.000000</td>\n",
              "      <td>1594304460</td>\n",
              "      <td>2.068300e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>910</th>\n",
              "      <td>14691.444444</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1856.444444</td>\n",
              "      <td>7.888889</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>113.555556</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:22:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:22:00</td>\n",
              "      <td>14</td>\n",
              "      <td>22</td>\n",
              "      <td>862</td>\n",
              "      <td>20669.333333</td>\n",
              "      <td>1594304520</td>\n",
              "      <td>2.066933e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>911</th>\n",
              "      <td>14691.222222</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1844.222222</td>\n",
              "      <td>7.444444</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>113.777778</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:23:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:23:00</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>863</td>\n",
              "      <td>20656.666667</td>\n",
              "      <td>1594304580</td>\n",
              "      <td>2.065667e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>14691.000000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1832.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>663</td>\n",
              "      <td>2020-07-09 14:24:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:24:00</td>\n",
              "      <td>14</td>\n",
              "      <td>24</td>\n",
              "      <td>864</td>\n",
              "      <td>20693.000000</td>\n",
              "      <td>1594304640</td>\n",
              "      <td>2.069300e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912</th>\n",
              "      <td>14698.500000</td>\n",
              "      <td>3324.062500</td>\n",
              "      <td>1831.625000</td>\n",
              "      <td>7.312500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.125000</td>\n",
              "      <td>112.937500</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:25:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:25:00</td>\n",
              "      <td>14</td>\n",
              "      <td>25</td>\n",
              "      <td>865</td>\n",
              "      <td>20650.562500</td>\n",
              "      <td>1594304700</td>\n",
              "      <td>2.065056e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>913</th>\n",
              "      <td>14706.000000</td>\n",
              "      <td>3324.125000</td>\n",
              "      <td>1831.250000</td>\n",
              "      <td>7.625000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.250000</td>\n",
              "      <td>111.875000</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:26:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:26:00</td>\n",
              "      <td>14</td>\n",
              "      <td>26</td>\n",
              "      <td>866</td>\n",
              "      <td>20657.125000</td>\n",
              "      <td>1594304760</td>\n",
              "      <td>2.065712e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>914</th>\n",
              "      <td>14713.500000</td>\n",
              "      <td>3324.187500</td>\n",
              "      <td>1830.875000</td>\n",
              "      <td>7.937500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.375000</td>\n",
              "      <td>110.812500</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:27:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:27:00</td>\n",
              "      <td>14</td>\n",
              "      <td>27</td>\n",
              "      <td>867</td>\n",
              "      <td>20663.687500</td>\n",
              "      <td>1594304820</td>\n",
              "      <td>2.066369e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>14721.000000</td>\n",
              "      <td>3324.250000</td>\n",
              "      <td>1830.500000</td>\n",
              "      <td>8.250000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.500000</td>\n",
              "      <td>109.750000</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:28:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:28:00</td>\n",
              "      <td>14</td>\n",
              "      <td>28</td>\n",
              "      <td>868</td>\n",
              "      <td>20671.250000</td>\n",
              "      <td>1594304880</td>\n",
              "      <td>2.067125e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>14728.500000</td>\n",
              "      <td>3324.312500</td>\n",
              "      <td>1830.125000</td>\n",
              "      <td>8.562500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.625000</td>\n",
              "      <td>108.687500</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:29:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:29:00</td>\n",
              "      <td>14</td>\n",
              "      <td>29</td>\n",
              "      <td>869</td>\n",
              "      <td>20677.812500</td>\n",
              "      <td>1594304940</td>\n",
              "      <td>2.067781e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>14736.000000</td>\n",
              "      <td>3324.375000</td>\n",
              "      <td>1829.750000</td>\n",
              "      <td>8.875000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.750000</td>\n",
              "      <td>107.625000</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:30:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:30:00</td>\n",
              "      <td>14</td>\n",
              "      <td>30</td>\n",
              "      <td>870</td>\n",
              "      <td>20684.375000</td>\n",
              "      <td>1594305000</td>\n",
              "      <td>2.068438e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>918</th>\n",
              "      <td>14743.500000</td>\n",
              "      <td>3324.437500</td>\n",
              "      <td>1829.375000</td>\n",
              "      <td>9.187500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.875000</td>\n",
              "      <td>106.562500</td>\n",
              "      <td>616</td>\n",
              "      <td>2020-07-09 14:31:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:31:00</td>\n",
              "      <td>14</td>\n",
              "      <td>31</td>\n",
              "      <td>871</td>\n",
              "      <td>20691.937500</td>\n",
              "      <td>1594305060</td>\n",
              "      <td>2.069194e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>14751.000000</td>\n",
              "      <td>3324.500000</td>\n",
              "      <td>1829.000000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>105.500000</td>\n",
              "      <td>616</td>\n",
              "      <td>2020-07-09 14:32:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:32:00</td>\n",
              "      <td>14</td>\n",
              "      <td>32</td>\n",
              "      <td>872</td>\n",
              "      <td>20698.500000</td>\n",
              "      <td>1594305120</td>\n",
              "      <td>2.069850e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>920</th>\n",
              "      <td>14758.500000</td>\n",
              "      <td>3324.562500</td>\n",
              "      <td>1828.625000</td>\n",
              "      <td>9.812500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.125000</td>\n",
              "      <td>104.437500</td>\n",
              "      <td>616</td>\n",
              "      <td>2020-07-09 14:33:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:33:00</td>\n",
              "      <td>14</td>\n",
              "      <td>33</td>\n",
              "      <td>873</td>\n",
              "      <td>20705.062500</td>\n",
              "      <td>1594305180</td>\n",
              "      <td>2.070506e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>921</th>\n",
              "      <td>14766.000000</td>\n",
              "      <td>3324.625000</td>\n",
              "      <td>1828.250000</td>\n",
              "      <td>10.125000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.250000</td>\n",
              "      <td>103.375000</td>\n",
              "      <td>617</td>\n",
              "      <td>2020-07-09 14:34:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:34:00</td>\n",
              "      <td>14</td>\n",
              "      <td>34</td>\n",
              "      <td>874</td>\n",
              "      <td>20712.625000</td>\n",
              "      <td>1594305240</td>\n",
              "      <td>2.071262e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>14773.500000</td>\n",
              "      <td>3324.687500</td>\n",
              "      <td>1827.875000</td>\n",
              "      <td>10.437500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.375000</td>\n",
              "      <td>102.312500</td>\n",
              "      <td>617</td>\n",
              "      <td>2020-07-09 14:35:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:35:00</td>\n",
              "      <td>14</td>\n",
              "      <td>35</td>\n",
              "      <td>875</td>\n",
              "      <td>20719.187500</td>\n",
              "      <td>1594305300</td>\n",
              "      <td>2.071919e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>14781.000000</td>\n",
              "      <td>3324.750000</td>\n",
              "      <td>1827.500000</td>\n",
              "      <td>10.750000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>101.250000</td>\n",
              "      <td>617</td>\n",
              "      <td>2020-07-09 14:36:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:36:00</td>\n",
              "      <td>14</td>\n",
              "      <td>36</td>\n",
              "      <td>876</td>\n",
              "      <td>20725.750000</td>\n",
              "      <td>1594305360</td>\n",
              "      <td>2.072575e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>924</th>\n",
              "      <td>14788.500000</td>\n",
              "      <td>3324.812500</td>\n",
              "      <td>1827.125000</td>\n",
              "      <td>11.062500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.625000</td>\n",
              "      <td>100.187500</td>\n",
              "      <td>618</td>\n",
              "      <td>2020-07-09 14:37:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:37:00</td>\n",
              "      <td>14</td>\n",
              "      <td>37</td>\n",
              "      <td>877</td>\n",
              "      <td>20733.312500</td>\n",
              "      <td>1594305420</td>\n",
              "      <td>2.073331e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>14796.000000</td>\n",
              "      <td>3324.875000</td>\n",
              "      <td>1826.750000</td>\n",
              "      <td>11.375000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.750000</td>\n",
              "      <td>99.125000</td>\n",
              "      <td>618</td>\n",
              "      <td>2020-07-09 14:38:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:38:00</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "      <td>878</td>\n",
              "      <td>20739.875000</td>\n",
              "      <td>1594305480</td>\n",
              "      <td>2.073988e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>926</th>\n",
              "      <td>14803.500000</td>\n",
              "      <td>3324.937500</td>\n",
              "      <td>1826.375000</td>\n",
              "      <td>11.687500</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.875000</td>\n",
              "      <td>98.062500</td>\n",
              "      <td>618</td>\n",
              "      <td>2020-07-09 14:39:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:39:00</td>\n",
              "      <td>14</td>\n",
              "      <td>39</td>\n",
              "      <td>879</td>\n",
              "      <td>20746.437500</td>\n",
              "      <td>1594305540</td>\n",
              "      <td>2.074644e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>14811.000000</td>\n",
              "      <td>3325.000000</td>\n",
              "      <td>1826.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>669</td>\n",
              "      <td>2020-07-09 14:40:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:40:00</td>\n",
              "      <td>14</td>\n",
              "      <td>40</td>\n",
              "      <td>880</td>\n",
              "      <td>20804.000000</td>\n",
              "      <td>1594305600</td>\n",
              "      <td>2.080400e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>927</th>\n",
              "      <td>14813.642857</td>\n",
              "      <td>3324.928571</td>\n",
              "      <td>1826.214286</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.857143</td>\n",
              "      <td>96.357143</td>\n",
              "      <td>617</td>\n",
              "      <td>2020-07-09 14:41:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:41:00</td>\n",
              "      <td>14</td>\n",
              "      <td>41</td>\n",
              "      <td>881</td>\n",
              "      <td>20754.000000</td>\n",
              "      <td>1594305660</td>\n",
              "      <td>2.075400e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928</th>\n",
              "      <td>14816.285714</td>\n",
              "      <td>3324.857143</td>\n",
              "      <td>1826.428571</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.714286</td>\n",
              "      <td>95.714286</td>\n",
              "      <td>617</td>\n",
              "      <td>2020-07-09 14:42:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:42:00</td>\n",
              "      <td>14</td>\n",
              "      <td>42</td>\n",
              "      <td>882</td>\n",
              "      <td>20756.000000</td>\n",
              "      <td>1594305720</td>\n",
              "      <td>2.075600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929</th>\n",
              "      <td>14818.928571</td>\n",
              "      <td>3324.785714</td>\n",
              "      <td>1826.642857</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.571429</td>\n",
              "      <td>95.071429</td>\n",
              "      <td>616</td>\n",
              "      <td>2020-07-09 14:43:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:43:00</td>\n",
              "      <td>14</td>\n",
              "      <td>43</td>\n",
              "      <td>883</td>\n",
              "      <td>20757.000000</td>\n",
              "      <td>1594305780</td>\n",
              "      <td>2.075700e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>930</th>\n",
              "      <td>14821.571429</td>\n",
              "      <td>3324.714286</td>\n",
              "      <td>1826.857143</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.428571</td>\n",
              "      <td>94.428571</td>\n",
              "      <td>616</td>\n",
              "      <td>2020-07-09 14:44:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:44:00</td>\n",
              "      <td>14</td>\n",
              "      <td>44</td>\n",
              "      <td>884</td>\n",
              "      <td>20759.000000</td>\n",
              "      <td>1594305840</td>\n",
              "      <td>2.075900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>931</th>\n",
              "      <td>14824.214286</td>\n",
              "      <td>3324.642857</td>\n",
              "      <td>1827.071429</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.285714</td>\n",
              "      <td>93.785714</td>\n",
              "      <td>615</td>\n",
              "      <td>2020-07-09 14:45:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:45:00</td>\n",
              "      <td>14</td>\n",
              "      <td>45</td>\n",
              "      <td>885</td>\n",
              "      <td>20760.000000</td>\n",
              "      <td>1594305900</td>\n",
              "      <td>2.076000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>14826.857143</td>\n",
              "      <td>3324.571429</td>\n",
              "      <td>1827.285714</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.142857</td>\n",
              "      <td>93.142857</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:46:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:46:00</td>\n",
              "      <td>14</td>\n",
              "      <td>46</td>\n",
              "      <td>886</td>\n",
              "      <td>20761.000000</td>\n",
              "      <td>1594305960</td>\n",
              "      <td>2.076100e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>933</th>\n",
              "      <td>14829.500000</td>\n",
              "      <td>3324.500000</td>\n",
              "      <td>1827.500000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>92.500000</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:47:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:47:00</td>\n",
              "      <td>14</td>\n",
              "      <td>47</td>\n",
              "      <td>887</td>\n",
              "      <td>20763.000000</td>\n",
              "      <td>1594306020</td>\n",
              "      <td>2.076300e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>934</th>\n",
              "      <td>14832.142857</td>\n",
              "      <td>3324.428571</td>\n",
              "      <td>1827.714286</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.857143</td>\n",
              "      <td>91.857143</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:48:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:48:00</td>\n",
              "      <td>14</td>\n",
              "      <td>48</td>\n",
              "      <td>888</td>\n",
              "      <td>20765.000000</td>\n",
              "      <td>1594306080</td>\n",
              "      <td>2.076500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>935</th>\n",
              "      <td>14834.785714</td>\n",
              "      <td>3324.357143</td>\n",
              "      <td>1827.928571</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.714286</td>\n",
              "      <td>91.214286</td>\n",
              "      <td>613</td>\n",
              "      <td>2020-07-09 14:49:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:49:00</td>\n",
              "      <td>14</td>\n",
              "      <td>49</td>\n",
              "      <td>889</td>\n",
              "      <td>20766.000000</td>\n",
              "      <td>1594306140</td>\n",
              "      <td>2.076600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>14837.428571</td>\n",
              "      <td>3324.285714</td>\n",
              "      <td>1828.142857</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.571429</td>\n",
              "      <td>90.571429</td>\n",
              "      <td>612</td>\n",
              "      <td>2020-07-09 14:50:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:50:00</td>\n",
              "      <td>14</td>\n",
              "      <td>50</td>\n",
              "      <td>890</td>\n",
              "      <td>20767.000000</td>\n",
              "      <td>1594306200</td>\n",
              "      <td>2.076700e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>937</th>\n",
              "      <td>14840.071429</td>\n",
              "      <td>3324.214286</td>\n",
              "      <td>1828.357143</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.428571</td>\n",
              "      <td>89.928571</td>\n",
              "      <td>612</td>\n",
              "      <td>2020-07-09 14:51:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:51:00</td>\n",
              "      <td>14</td>\n",
              "      <td>51</td>\n",
              "      <td>891</td>\n",
              "      <td>20769.000000</td>\n",
              "      <td>1594306260</td>\n",
              "      <td>2.076900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>938</th>\n",
              "      <td>14842.714286</td>\n",
              "      <td>3324.142857</td>\n",
              "      <td>1828.571429</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.285714</td>\n",
              "      <td>89.285714</td>\n",
              "      <td>611</td>\n",
              "      <td>2020-07-09 14:52:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:52:00</td>\n",
              "      <td>14</td>\n",
              "      <td>52</td>\n",
              "      <td>892</td>\n",
              "      <td>20770.000000</td>\n",
              "      <td>1594306320</td>\n",
              "      <td>2.077000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>939</th>\n",
              "      <td>14845.357143</td>\n",
              "      <td>3324.071429</td>\n",
              "      <td>1828.785714</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.142857</td>\n",
              "      <td>88.642857</td>\n",
              "      <td>611</td>\n",
              "      <td>2020-07-09 14:53:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:53:00</td>\n",
              "      <td>14</td>\n",
              "      <td>53</td>\n",
              "      <td>893</td>\n",
              "      <td>20772.000000</td>\n",
              "      <td>1594306380</td>\n",
              "      <td>2.077200e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>14848.000000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1829.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>655</td>\n",
              "      <td>2020-07-09 14:54:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:54:00</td>\n",
              "      <td>14</td>\n",
              "      <td>54</td>\n",
              "      <td>894</td>\n",
              "      <td>20818.000000</td>\n",
              "      <td>1594306440</td>\n",
              "      <td>2.081800e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>940</th>\n",
              "      <td>14856.800000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1822.400000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>40.200000</td>\n",
              "      <td>87.200000</td>\n",
              "      <td>611</td>\n",
              "      <td>2020-07-09 14:55:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:55:00</td>\n",
              "      <td>14</td>\n",
              "      <td>55</td>\n",
              "      <td>895</td>\n",
              "      <td>20773.800000</td>\n",
              "      <td>1594306500</td>\n",
              "      <td>2.077380e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>14865.600000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1815.800000</td>\n",
              "      <td>12.400000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>40.400000</td>\n",
              "      <td>86.400000</td>\n",
              "      <td>612</td>\n",
              "      <td>2020-07-09 14:56:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:56:00</td>\n",
              "      <td>14</td>\n",
              "      <td>56</td>\n",
              "      <td>896</td>\n",
              "      <td>20774.600000</td>\n",
              "      <td>1594306560</td>\n",
              "      <td>2.077460e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>942</th>\n",
              "      <td>14874.400000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1809.200000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>40.600000</td>\n",
              "      <td>85.600000</td>\n",
              "      <td>613</td>\n",
              "      <td>2020-07-09 14:57:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:57:00</td>\n",
              "      <td>14</td>\n",
              "      <td>57</td>\n",
              "      <td>897</td>\n",
              "      <td>20775.400000</td>\n",
              "      <td>1594306620</td>\n",
              "      <td>2.077540e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>943</th>\n",
              "      <td>14883.200000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1802.600000</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>40.800000</td>\n",
              "      <td>84.800000</td>\n",
              "      <td>614</td>\n",
              "      <td>2020-07-09 14:58:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:58:00</td>\n",
              "      <td>14</td>\n",
              "      <td>58</td>\n",
              "      <td>898</td>\n",
              "      <td>20776.200000</td>\n",
              "      <td>1594306680</td>\n",
              "      <td>2.077620e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>14892.000000</td>\n",
              "      <td>3324.000000</td>\n",
              "      <td>1796.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>660</td>\n",
              "      <td>2020-07-09 14:59:00</td>\n",
              "      <td>2020-07-09</td>\n",
              "      <td>14:59:00</td>\n",
              "      <td>14</td>\n",
              "      <td>59</td>\n",
              "      <td>899</td>\n",
              "      <td>20822.000000</td>\n",
              "      <td>1594306740</td>\n",
              "      <td>2.082200e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       NaturalGas      Nuclear        Hydro       Coal        Oil       Wind  \\\n",
              "891  14616.600000  3322.400000  1973.800000  16.000000  49.800000  46.600000   \n",
              "30   14636.000000  3322.000000  1975.000000  16.000000  48.000000  47.000000   \n",
              "892  14638.285714  3322.285714  1970.142857  15.142857  46.428571  46.142857   \n",
              "893  14640.571429  3322.571429  1965.285714  14.285714  44.857143  45.285714   \n",
              "894  14642.857143  3322.857143  1960.428571  13.428571  43.285714  44.428571   \n",
              "895  14645.142857  3323.142857  1955.571429  12.571429  41.714286  43.571429   \n",
              "896  14647.428571  3323.428571  1950.714286  11.714286  40.142857  42.714286   \n",
              "897  14649.714286  3323.714286  1945.857143  10.857143  38.571429  41.857143   \n",
              "47   14652.000000  3324.000000  1941.000000  10.000000  37.000000  41.000000   \n",
              "898  14657.857143  3324.000000  1941.142857  10.142857  34.857143  40.857143   \n",
              "899  14663.714286  3324.000000  1941.285714  10.285714  32.714286  40.714286   \n",
              "900  14669.571429  3324.000000  1941.428571  10.428571  30.571429  40.571429   \n",
              "901  14675.428571  3324.000000  1941.571429  10.571429  28.428571  40.428571   \n",
              "902  14681.285714  3324.000000  1941.714286  10.714286  26.285714  40.285714   \n",
              "903  14687.142857  3324.000000  1941.857143  10.857143  24.142857  40.142857   \n",
              "32   14693.000000  3324.000000  1942.000000  11.000000  22.000000  40.000000   \n",
              "904  14692.777778  3324.000000  1929.777778  10.555556  22.000000  40.000000   \n",
              "905  14692.555556  3324.000000  1917.555556  10.111111  22.000000  40.000000   \n",
              "906  14692.333333  3324.000000  1905.333333   9.666667  22.000000  40.000000   \n",
              "907  14692.111111  3324.000000  1893.111111   9.222222  22.000000  40.000000   \n",
              "908  14691.888889  3324.000000  1880.888889   8.777778  22.000000  40.000000   \n",
              "909  14691.666667  3324.000000  1868.666667   8.333333  22.000000  40.000000   \n",
              "910  14691.444444  3324.000000  1856.444444   7.888889  22.000000  40.000000   \n",
              "911  14691.222222  3324.000000  1844.222222   7.444444  22.000000  40.000000   \n",
              "62   14691.000000  3324.000000  1832.000000   7.000000  22.000000  40.000000   \n",
              "912  14698.500000  3324.062500  1831.625000   7.312500  22.000000  40.125000   \n",
              "913  14706.000000  3324.125000  1831.250000   7.625000  22.000000  40.250000   \n",
              "914  14713.500000  3324.187500  1830.875000   7.937500  22.000000  40.375000   \n",
              "915  14721.000000  3324.250000  1830.500000   8.250000  22.000000  40.500000   \n",
              "916  14728.500000  3324.312500  1830.125000   8.562500  22.000000  40.625000   \n",
              "917  14736.000000  3324.375000  1829.750000   8.875000  22.000000  40.750000   \n",
              "918  14743.500000  3324.437500  1829.375000   9.187500  22.000000  40.875000   \n",
              "919  14751.000000  3324.500000  1829.000000   9.500000  22.000000  41.000000   \n",
              "920  14758.500000  3324.562500  1828.625000   9.812500  22.000000  41.125000   \n",
              "921  14766.000000  3324.625000  1828.250000  10.125000  22.000000  41.250000   \n",
              "922  14773.500000  3324.687500  1827.875000  10.437500  22.000000  41.375000   \n",
              "923  14781.000000  3324.750000  1827.500000  10.750000  22.000000  41.500000   \n",
              "924  14788.500000  3324.812500  1827.125000  11.062500  22.000000  41.625000   \n",
              "925  14796.000000  3324.875000  1826.750000  11.375000  22.000000  41.750000   \n",
              "926  14803.500000  3324.937500  1826.375000  11.687500  22.000000  41.875000   \n",
              "61   14811.000000  3325.000000  1826.000000  12.000000  22.000000  42.000000   \n",
              "927  14813.642857  3324.928571  1826.214286  12.000000  22.000000  41.857143   \n",
              "928  14816.285714  3324.857143  1826.428571  12.000000  22.000000  41.714286   \n",
              "929  14818.928571  3324.785714  1826.642857  12.000000  22.000000  41.571429   \n",
              "930  14821.571429  3324.714286  1826.857143  12.000000  22.000000  41.428571   \n",
              "931  14824.214286  3324.642857  1827.071429  12.000000  22.000000  41.285714   \n",
              "932  14826.857143  3324.571429  1827.285714  12.000000  22.000000  41.142857   \n",
              "933  14829.500000  3324.500000  1827.500000  12.000000  22.000000  41.000000   \n",
              "934  14832.142857  3324.428571  1827.714286  12.000000  22.000000  40.857143   \n",
              "935  14834.785714  3324.357143  1827.928571  12.000000  22.000000  40.714286   \n",
              "936  14837.428571  3324.285714  1828.142857  12.000000  22.000000  40.571429   \n",
              "937  14840.071429  3324.214286  1828.357143  12.000000  22.000000  40.428571   \n",
              "938  14842.714286  3324.142857  1828.571429  12.000000  22.000000  40.285714   \n",
              "939  14845.357143  3324.071429  1828.785714  12.000000  22.000000  40.142857   \n",
              "31   14848.000000  3324.000000  1829.000000  12.000000  22.000000  40.000000   \n",
              "940  14856.800000  3324.000000  1822.400000  12.200000  20.000000  40.200000   \n",
              "941  14865.600000  3324.000000  1815.800000  12.400000  18.000000  40.400000   \n",
              "942  14874.400000  3324.000000  1809.200000  12.600000  16.000000  40.600000   \n",
              "943  14883.200000  3324.000000  1802.600000  12.800000  14.000000  40.800000   \n",
              "59   14892.000000  3324.000000  1796.000000  13.000000  12.000000  41.000000   \n",
              "\n",
              "          Solar  Other           Timestamp        DATE      TIME  HOURS  \\\n",
              "891  128.600000    615 2020-07-09 14:00:00  2020-07-09  14:00:00     14   \n",
              "30   127.000000    658 2020-07-09 14:01:00  2020-07-09  14:01:00     14   \n",
              "892  126.571429    615 2020-07-09 14:02:00  2020-07-09  14:02:00     14   \n",
              "893  126.142857    615 2020-07-09 14:03:00  2020-07-09  14:03:00     14   \n",
              "894  125.714286    615 2020-07-09 14:04:00  2020-07-09  14:04:00     14   \n",
              "895  125.285714    614 2020-07-09 14:05:00  2020-07-09  14:05:00     14   \n",
              "896  124.857143    614 2020-07-09 14:06:00  2020-07-09  14:06:00     14   \n",
              "897  124.428571    614 2020-07-09 14:07:00  2020-07-09  14:07:00     14   \n",
              "47   124.000000    657 2020-07-09 14:08:00  2020-07-09  14:08:00     14   \n",
              "898  122.285714    615 2020-07-09 14:09:00  2020-07-09  14:09:00     14   \n",
              "899  120.571429    615 2020-07-09 14:10:00  2020-07-09  14:10:00     14   \n",
              "900  118.857143    615 2020-07-09 14:11:00  2020-07-09  14:11:00     14   \n",
              "901  117.142857    615 2020-07-09 14:12:00  2020-07-09  14:12:00     14   \n",
              "902  115.428571    615 2020-07-09 14:13:00  2020-07-09  14:13:00     14   \n",
              "903  113.714286    615 2020-07-09 14:14:00  2020-07-09  14:14:00     14   \n",
              "32   112.000000    658 2020-07-09 14:15:00  2020-07-09  14:15:00     14   \n",
              "904  112.222222    615 2020-07-09 14:16:00  2020-07-09  14:16:00     14   \n",
              "905  112.444444    615 2020-07-09 14:17:00  2020-07-09  14:17:00     14   \n",
              "906  112.666667    615 2020-07-09 14:18:00  2020-07-09  14:18:00     14   \n",
              "907  112.888889    614 2020-07-09 14:19:00  2020-07-09  14:19:00     14   \n",
              "908  113.111111    615 2020-07-09 14:20:00  2020-07-09  14:20:00     14   \n",
              "909  113.333333    615 2020-07-09 14:21:00  2020-07-09  14:21:00     14   \n",
              "910  113.555556    614 2020-07-09 14:22:00  2020-07-09  14:22:00     14   \n",
              "911  113.777778    614 2020-07-09 14:23:00  2020-07-09  14:23:00     14   \n",
              "62   114.000000    663 2020-07-09 14:24:00  2020-07-09  14:24:00     14   \n",
              "912  112.937500    614 2020-07-09 14:25:00  2020-07-09  14:25:00     14   \n",
              "913  111.875000    614 2020-07-09 14:26:00  2020-07-09  14:26:00     14   \n",
              "914  110.812500    614 2020-07-09 14:27:00  2020-07-09  14:27:00     14   \n",
              "915  109.750000    615 2020-07-09 14:28:00  2020-07-09  14:28:00     14   \n",
              "916  108.687500    615 2020-07-09 14:29:00  2020-07-09  14:29:00     14   \n",
              "917  107.625000    615 2020-07-09 14:30:00  2020-07-09  14:30:00     14   \n",
              "918  106.562500    616 2020-07-09 14:31:00  2020-07-09  14:31:00     14   \n",
              "919  105.500000    616 2020-07-09 14:32:00  2020-07-09  14:32:00     14   \n",
              "920  104.437500    616 2020-07-09 14:33:00  2020-07-09  14:33:00     14   \n",
              "921  103.375000    617 2020-07-09 14:34:00  2020-07-09  14:34:00     14   \n",
              "922  102.312500    617 2020-07-09 14:35:00  2020-07-09  14:35:00     14   \n",
              "923  101.250000    617 2020-07-09 14:36:00  2020-07-09  14:36:00     14   \n",
              "924  100.187500    618 2020-07-09 14:37:00  2020-07-09  14:37:00     14   \n",
              "925   99.125000    618 2020-07-09 14:38:00  2020-07-09  14:38:00     14   \n",
              "926   98.062500    618 2020-07-09 14:39:00  2020-07-09  14:39:00     14   \n",
              "61    97.000000    669 2020-07-09 14:40:00  2020-07-09  14:40:00     14   \n",
              "927   96.357143    617 2020-07-09 14:41:00  2020-07-09  14:41:00     14   \n",
              "928   95.714286    617 2020-07-09 14:42:00  2020-07-09  14:42:00     14   \n",
              "929   95.071429    616 2020-07-09 14:43:00  2020-07-09  14:43:00     14   \n",
              "930   94.428571    616 2020-07-09 14:44:00  2020-07-09  14:44:00     14   \n",
              "931   93.785714    615 2020-07-09 14:45:00  2020-07-09  14:45:00     14   \n",
              "932   93.142857    614 2020-07-09 14:46:00  2020-07-09  14:46:00     14   \n",
              "933   92.500000    614 2020-07-09 14:47:00  2020-07-09  14:47:00     14   \n",
              "934   91.857143    614 2020-07-09 14:48:00  2020-07-09  14:48:00     14   \n",
              "935   91.214286    613 2020-07-09 14:49:00  2020-07-09  14:49:00     14   \n",
              "936   90.571429    612 2020-07-09 14:50:00  2020-07-09  14:50:00     14   \n",
              "937   89.928571    612 2020-07-09 14:51:00  2020-07-09  14:51:00     14   \n",
              "938   89.285714    611 2020-07-09 14:52:00  2020-07-09  14:52:00     14   \n",
              "939   88.642857    611 2020-07-09 14:53:00  2020-07-09  14:53:00     14   \n",
              "31    88.000000    655 2020-07-09 14:54:00  2020-07-09  14:54:00     14   \n",
              "940   87.200000    611 2020-07-09 14:55:00  2020-07-09  14:55:00     14   \n",
              "941   86.400000    612 2020-07-09 14:56:00  2020-07-09  14:56:00     14   \n",
              "942   85.600000    613 2020-07-09 14:57:00  2020-07-09  14:57:00     14   \n",
              "943   84.800000    614 2020-07-09 14:58:00  2020-07-09  14:58:00     14   \n",
              "59    84.000000    660 2020-07-09 14:59:00  2020-07-09  14:59:00     14   \n",
              "\n",
              "     MINUTES  MINUTES_PASS  total_generation  Timestamp (Unix)  \\\n",
              "891        0           840      20768.800000        1594303200   \n",
              "30         1           841      20829.000000        1594303260   \n",
              "892        2           842      20780.000000        1594303320   \n",
              "893        3           843      20774.000000        1594303380   \n",
              "894        4           844      20768.000000        1594303440   \n",
              "895        5           845      20761.000000        1594303500   \n",
              "896        6           846      20755.000000        1594303560   \n",
              "897        7           847      20749.000000        1594303620   \n",
              "47         8           848      20786.000000        1594303680   \n",
              "898        9           849      20746.142857        1594303740   \n",
              "899       10           850      20748.285714        1594303800   \n",
              "900       11           851      20750.428571        1594303860   \n",
              "901       12           852      20752.571429        1594303920   \n",
              "902       13           853      20754.714286        1594303980   \n",
              "903       14           854      20756.857143        1594304040   \n",
              "32        15           855      20802.000000        1594304100   \n",
              "904       16           856      20746.333333        1594304160   \n",
              "905       17           857      20733.666667        1594304220   \n",
              "906       18           858      20721.000000        1594304280   \n",
              "907       19           859      20707.333333        1594304340   \n",
              "908       20           860      20695.666667        1594304400   \n",
              "909       21           861      20683.000000        1594304460   \n",
              "910       22           862      20669.333333        1594304520   \n",
              "911       23           863      20656.666667        1594304580   \n",
              "62        24           864      20693.000000        1594304640   \n",
              "912       25           865      20650.562500        1594304700   \n",
              "913       26           866      20657.125000        1594304760   \n",
              "914       27           867      20663.687500        1594304820   \n",
              "915       28           868      20671.250000        1594304880   \n",
              "916       29           869      20677.812500        1594304940   \n",
              "917       30           870      20684.375000        1594305000   \n",
              "918       31           871      20691.937500        1594305060   \n",
              "919       32           872      20698.500000        1594305120   \n",
              "920       33           873      20705.062500        1594305180   \n",
              "921       34           874      20712.625000        1594305240   \n",
              "922       35           875      20719.187500        1594305300   \n",
              "923       36           876      20725.750000        1594305360   \n",
              "924       37           877      20733.312500        1594305420   \n",
              "925       38           878      20739.875000        1594305480   \n",
              "926       39           879      20746.437500        1594305540   \n",
              "61        40           880      20804.000000        1594305600   \n",
              "927       41           881      20754.000000        1594305660   \n",
              "928       42           882      20756.000000        1594305720   \n",
              "929       43           883      20757.000000        1594305780   \n",
              "930       44           884      20759.000000        1594305840   \n",
              "931       45           885      20760.000000        1594305900   \n",
              "932       46           886      20761.000000        1594305960   \n",
              "933       47           887      20763.000000        1594306020   \n",
              "934       48           888      20765.000000        1594306080   \n",
              "935       49           889      20766.000000        1594306140   \n",
              "936       50           890      20767.000000        1594306200   \n",
              "937       51           891      20769.000000        1594306260   \n",
              "938       52           892      20770.000000        1594306320   \n",
              "939       53           893      20772.000000        1594306380   \n",
              "31        54           894      20818.000000        1594306440   \n",
              "940       55           895      20773.800000        1594306500   \n",
              "941       56           896      20774.600000        1594306560   \n",
              "942       57           897      20775.400000        1594306620   \n",
              "943       58           898      20776.200000        1594306680   \n",
              "59        59           899      20822.000000        1594306740   \n",
              "\n",
              "     total_generation(W)  \n",
              "891         2.076880e+10  \n",
              "30          2.082900e+10  \n",
              "892         2.078000e+10  \n",
              "893         2.077400e+10  \n",
              "894         2.076800e+10  \n",
              "895         2.076100e+10  \n",
              "896         2.075500e+10  \n",
              "897         2.074900e+10  \n",
              "47          2.078600e+10  \n",
              "898         2.074614e+10  \n",
              "899         2.074829e+10  \n",
              "900         2.075043e+10  \n",
              "901         2.075257e+10  \n",
              "902         2.075471e+10  \n",
              "903         2.075686e+10  \n",
              "32          2.080200e+10  \n",
              "904         2.074633e+10  \n",
              "905         2.073367e+10  \n",
              "906         2.072100e+10  \n",
              "907         2.070733e+10  \n",
              "908         2.069567e+10  \n",
              "909         2.068300e+10  \n",
              "910         2.066933e+10  \n",
              "911         2.065667e+10  \n",
              "62          2.069300e+10  \n",
              "912         2.065056e+10  \n",
              "913         2.065712e+10  \n",
              "914         2.066369e+10  \n",
              "915         2.067125e+10  \n",
              "916         2.067781e+10  \n",
              "917         2.068438e+10  \n",
              "918         2.069194e+10  \n",
              "919         2.069850e+10  \n",
              "920         2.070506e+10  \n",
              "921         2.071262e+10  \n",
              "922         2.071919e+10  \n",
              "923         2.072575e+10  \n",
              "924         2.073331e+10  \n",
              "925         2.073988e+10  \n",
              "926         2.074644e+10  \n",
              "61          2.080400e+10  \n",
              "927         2.075400e+10  \n",
              "928         2.075600e+10  \n",
              "929         2.075700e+10  \n",
              "930         2.075900e+10  \n",
              "931         2.076000e+10  \n",
              "932         2.076100e+10  \n",
              "933         2.076300e+10  \n",
              "934         2.076500e+10  \n",
              "935         2.076600e+10  \n",
              "936         2.076700e+10  \n",
              "937         2.076900e+10  \n",
              "938         2.077000e+10  \n",
              "939         2.077200e+10  \n",
              "31          2.081800e+10  \n",
              "940         2.077380e+10  \n",
              "941         2.077460e+10  \n",
              "942         2.077540e+10  \n",
              "943         2.077620e+10  \n",
              "59          2.082200e+10  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:13.817828Z",
          "iopub.status.busy": "2021-05-31T04:27:13.816806Z",
          "iopub.status.idle": "2021-05-31T04:27:13.820806Z",
          "shell.execute_reply": "2021-05-31T04:27:13.820213Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.064185Z"
        },
        "papermill": {
          "duration": 0.092662,
          "end_time": "2021-05-31T04:27:13.820915",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.728253",
          "status": "completed"
        },
        "tags": [],
        "id": "f13IkEGQiD1E",
        "outputId": "c3b1e4e0-ac1b-48a6-84d9-c39273ac3b0f"
      },
      "source": [
        "## Average generation \n",
        "average = peakhour[\"total_generation\"].mean()\n",
        "average"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20742.138333333332"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:13.955010Z",
          "iopub.status.busy": "2021-05-31T04:27:13.950854Z",
          "iopub.status.idle": "2021-05-31T04:27:14.124178Z",
          "shell.execute_reply": "2021-05-31T04:27:14.123651Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.071549Z"
        },
        "papermill": {
          "duration": 0.243666,
          "end_time": "2021-05-31T04:27:14.124288",
          "exception": false,
          "start_time": "2021-05-31T04:27:13.880622",
          "status": "completed"
        },
        "tags": [],
        "id": "i-6Oa1KciD1G",
        "outputId": "63293593-6010-4858-cc87-1f462a11185a"
      },
      "source": [
        "peakhour.plot(y = ['Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal'])\n",
        "ax.hlines(23, -.5,.5, linestyles='dashed')\n",
        "ax.annotate('average',(-0.4, average))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(-0.4, 20742.138333333332, 'average')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.06068,
          "end_time": "2021-05-31T04:27:14.246298",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.185618",
          "status": "completed"
        },
        "tags": [],
        "id": "Z9YU6GaoiD1I"
      },
      "source": [
        "pick value in order off release fromm peakhour"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.060462,
          "end_time": "2021-05-31T04:27:14.368133",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.307671",
          "status": "completed"
        },
        "tags": [],
        "id": "wtDGesI_iD1J"
      },
      "source": [
        "## Transpose for working with one generation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:14.501908Z",
          "iopub.status.busy": "2021-05-31T04:27:14.501097Z",
          "iopub.status.idle": "2021-05-31T04:27:14.505301Z",
          "shell.execute_reply": "2021-05-31T04:27:14.504760Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.262726Z"
        },
        "papermill": {
          "duration": 0.076725,
          "end_time": "2021-05-31T04:27:14.505405",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.428680",
          "status": "completed"
        },
        "tags": [],
        "id": "DvRr11IDiD1K",
        "outputId": "2da7765d-5f94-4e6a-beb4-75ccc17a211b"
      },
      "source": [
        "# peakhour =  peakhour.iloc[: , column_list].copy()  \n",
        "peakt =  peakmin[['Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal', 'Nuclear']].transpose()\n",
        "\n",
        "# # peakhourt=peakhour2.reindex([\"Nuclear Generation (MW)\", \"Wind Generation (MW)\", \"Solar Generation (MW)\",\"Hydro Generation (MW)\" ,  \n",
        "# #                          \"Coal Generation (MW)\", \"Natural gas Generation (MW)\",\"Petroleum Generation (MW)\",\"Other Generation (MW)\"])\n",
        "\n",
        "for col in peakt.columns:\n",
        "    peakt = peakt.rename(columns={col:'generation'})\n",
        "    \n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation\n",
              "Wind              47.0\n",
              "Solar            127.0\n",
              "Hydro           1975.0\n",
              "Other            658.0\n",
              "Oil               48.0\n",
              "NaturalGas     14636.0\n",
              "Coal              16.0\n",
              "Nuclear         3322.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:14.640064Z",
          "iopub.status.busy": "2021-05-31T04:27:14.639388Z",
          "iopub.status.idle": "2021-05-31T04:27:14.643402Z",
          "shell.execute_reply": "2021-05-31T04:27:14.642852Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.277373Z"
        },
        "papermill": {
          "duration": 0.076271,
          "end_time": "2021-05-31T04:27:14.643513",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.567242",
          "status": "completed"
        },
        "tags": [],
        "id": "If8a2I66iD1L",
        "outputId": "9dd59067-e292-4272-f9e3-f85a3585d596"
      },
      "source": [
        "# pie chart Percentage for Contribution by fuel during peak hour\n",
        "peakt[\"percent\"] = (peakt['generation'] / peakt['generation'].sum()) * 100\n",
        "peakt[\"percent\"] = peakt[\"percent\"].round(1)\n",
        "\n",
        "# peakt = peakt.mask(peakhour2< 0, 0)\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>70.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>15.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation  percent\n",
              "Wind              47.0      0.2\n",
              "Solar            127.0      0.6\n",
              "Hydro           1975.0      9.5\n",
              "Other            658.0      3.2\n",
              "Oil               48.0      0.2\n",
              "NaturalGas     14636.0     70.3\n",
              "Coal              16.0      0.1\n",
              "Nuclear         3322.0     15.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:14.774638Z",
          "iopub.status.busy": "2021-05-31T04:27:14.774012Z",
          "iopub.status.idle": "2021-05-31T04:27:14.903121Z",
          "shell.execute_reply": "2021-05-31T04:27:14.902611Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.303507Z"
        },
        "papermill": {
          "duration": 0.196478,
          "end_time": "2021-05-31T04:27:14.903241",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.706763",
          "status": "completed"
        },
        "tags": [],
        "id": "rHpPSrvgiD1M",
        "outputId": "ebeabaa0-fe1d-4488-96c7-0804e502c13b"
      },
      "source": [
        "peakt[\"generation\"].plot.bar(stacked=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae657317d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.062114,
          "end_time": "2021-05-31T04:27:15.029084",
          "exception": false,
          "start_time": "2021-05-31T04:27:14.966970",
          "status": "completed"
        },
        "tags": [],
        "id": "gAtuuaNfiD1M"
      },
      "source": [
        "### PeaK Pie char Fuel Mix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:15.174540Z",
          "iopub.status.busy": "2021-05-31T04:27:15.173961Z",
          "iopub.status.idle": "2021-05-31T04:27:15.268251Z",
          "shell.execute_reply": "2021-05-31T04:27:15.268698Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.442302Z"
        },
        "papermill": {
          "duration": 0.176418,
          "end_time": "2021-05-31T04:27:15.268843",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.092425",
          "status": "completed"
        },
        "tags": [],
        "id": "jSQDQNS6iD1M",
        "outputId": "f7a6a6bf-02cd-4fe0-d68c-49ad7c31dcf6"
      },
      "source": [
        "pie_peak= peakt.plot.pie(y='percent',title=\"Fuel Mix in \"+ Region + \" on \"+ str(peakmin.Timestamp.values[0]) , figsize=(5, 5))\n",
        "pie_peak.legend(bbox_to_anchor=(1,1), loc=\"centre right\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fae65744d50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:15.398668Z",
          "iopub.status.busy": "2021-05-31T04:27:15.398059Z",
          "iopub.status.idle": "2021-05-31T04:27:15.403769Z",
          "shell.execute_reply": "2021-05-31T04:27:15.403245Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.563740Z"
        },
        "papermill": {
          "duration": 0.071766,
          "end_time": "2021-05-31T04:27:15.403883",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.332117",
          "status": "completed"
        },
        "tags": [],
        "id": "6fUwHO6TiD1M"
      },
      "source": [
        "peakt = peakt.drop(columns='percent')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:15.535332Z",
          "iopub.status.busy": "2021-05-31T04:27:15.534766Z",
          "iopub.status.idle": "2021-05-31T04:27:15.537830Z",
          "shell.execute_reply": "2021-05-31T04:27:15.538311Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.572303Z"
        },
        "papermill": {
          "duration": 0.07093,
          "end_time": "2021-05-31T04:27:15.538471",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.467541",
          "status": "completed"
        },
        "tags": [],
        "id": "Di3M2ddDiD1N"
      },
      "source": [
        "# Cummulative_generation\n",
        "# peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:15.674057Z",
          "iopub.status.busy": "2021-05-31T04:27:15.673170Z",
          "iopub.status.idle": "2021-05-31T04:27:15.676501Z",
          "shell.execute_reply": "2021-05-31T04:27:15.675933Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.585529Z"
        },
        "papermill": {
          "duration": 0.07159,
          "end_time": "2021-05-31T04:27:15.676628",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.605038",
          "status": "completed"
        },
        "tags": [],
        "id": "BmvWvyusiD1N"
      },
      "source": [
        "# peakt.plot(x=\"Cummulative_generation\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:15.807498Z",
          "iopub.status.busy": "2021-05-31T04:27:15.806860Z",
          "iopub.status.idle": "2021-05-31T04:27:15.810193Z",
          "shell.execute_reply": "2021-05-31T04:27:15.810715Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.601726Z"
        },
        "papermill": {
          "duration": 0.07096,
          "end_time": "2021-05-31T04:27:15.810858",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.739898",
          "status": "completed"
        },
        "tags": [],
        "id": "McWE3Fn-iD1N"
      },
      "source": [
        "# draw graph for cummultaive consumption \n",
        "# peakhour2.plot(\"Cummulative_consumption\")\n",
        "\n",
        "# peakhour2.plot.bar(peakhour2[\"Cummulative_consumption\"], drawstyle='steps')\n",
        "# peakhour2.reset_index().plot.line(peakhour2[\"Cummulative_consumption\"], peakhour2.index, drawstyle='steps')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.064373,
          "end_time": "2021-05-31T04:27:15.939426",
          "exception": false,
          "start_time": "2021-05-31T04:27:15.875053",
          "status": "completed"
        },
        "tags": [],
        "id": "XVmjggqxiD1N"
      },
      "source": [
        "<a id=\"section-five\"></a>\n",
        "# 5. Carbon Emission vs Fuel\n",
        "\n",
        "Now merge dataset and look at data distributions and correlations for Co2 and fuel sources "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:16.072594Z",
          "iopub.status.busy": "2021-05-31T04:27:16.070656Z",
          "iopub.status.idle": "2021-05-31T04:27:16.092037Z",
          "shell.execute_reply": "2021-05-31T04:27:16.092521Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.615493Z"
        },
        "papermill": {
          "duration": 0.088264,
          "end_time": "2021-05-31T04:27:16.092697",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.004433",
          "status": "completed"
        },
        "tags": [],
        "id": "3oHPAYZFiD1N",
        "outputId": "c1228b0e-bb0e-48d7-e165-b3991163518e"
      },
      "source": [
        "df_carbon = pd.read_csv(\"../input/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Carbon2.csv\")\n",
        "df_carbon"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Life cycle CO2 equivalent  ( kgCO2eq/MWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuclear</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Solar</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Other</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Petroleum</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Oil</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Natural gas</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaturalGas</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Coal</td>\n",
              "      <td>820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hydro</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Fuel Type  Life cycle CO2 equivalent  ( kgCO2eq/MWh)\n",
              "0      Nuclear                                         12\n",
              "1        Solar                                         48\n",
              "2        Other                                         24\n",
              "3    Petroleum                                        490\n",
              "4          Oil                                        490\n",
              "5  Natural gas                                        490\n",
              "6   NaturalGas                                        490\n",
              "7         Coal                                        820\n",
              "8        Hydro                                          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:16.224689Z",
          "iopub.status.busy": "2021-05-31T04:27:16.223760Z",
          "iopub.status.idle": "2021-05-31T04:27:16.241276Z",
          "shell.execute_reply": "2021-05-31T04:27:16.241725Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.639494Z"
        },
        "papermill": {
          "duration": 0.084712,
          "end_time": "2021-05-31T04:27:16.241875",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.157163",
          "status": "completed"
        },
        "tags": [],
        "id": "DlkJ6JNviD1O",
        "outputId": "073341b3-5cc2-4c5e-f555-84c6d86a7be5"
      },
      "source": [
        "df_carbon.info()\n",
        "df_carbon['Life cycle CO2 equivalent  ( kgCO2eq/MWh)']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9 entries, 0 to 8\n",
            "Data columns (total 2 columns):\n",
            " #   Column                                     Non-Null Count  Dtype \n",
            "---  ------                                     --------------  ----- \n",
            " 0   Fuel Type                                  9 non-null      object\n",
            " 1   Life cycle CO2 equivalent  ( kgCO2eq/MWh)  9 non-null      int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 272.0+ bytes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     12\n",
              "1     48\n",
              "2     24\n",
              "3    490\n",
              "4    490\n",
              "5    490\n",
              "6    490\n",
              "7    820\n",
              "8      0\n",
              "Name: Life cycle CO2 equivalent  ( kgCO2eq/MWh), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:16.377083Z",
          "iopub.status.busy": "2021-05-31T04:27:16.376138Z",
          "iopub.status.idle": "2021-05-31T04:27:16.533492Z",
          "shell.execute_reply": "2021-05-31T04:27:16.534039Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.662096Z"
        },
        "papermill": {
          "duration": 0.227037,
          "end_time": "2021-05-31T04:27:16.534180",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.307143",
          "status": "completed"
        },
        "tags": [],
        "id": "nDvmhWABiD1O",
        "outputId": "0ad16f9a-3838-4de2-bcd7-2ccd747b5f79"
      },
      "source": [
        "df_carbon.sort_values('Life cycle CO2 equivalent  ( kgCO2eq/MWh)',ascending=True).plot( x='Fuel Type',kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae656a4c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.067794,
          "end_time": "2021-05-31T04:27:16.669693",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.601899",
          "status": "completed"
        },
        "tags": [],
        "id": "3sX-d3RJiD1O"
      },
      "source": [
        "### Time vs consumption"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:16.821362Z",
          "iopub.status.busy": "2021-05-31T04:27:16.819315Z",
          "iopub.status.idle": "2021-05-31T04:27:16.833274Z",
          "shell.execute_reply": "2021-05-31T04:27:16.832817Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.829442Z"
        },
        "papermill": {
          "duration": 0.093159,
          "end_time": "2021-05-31T04:27:16.833382",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.740223",
          "status": "completed"
        },
        "tags": [],
        "id": "1dkSSO1BiD1O",
        "outputId": "ec650daf-c3f2-4dd2-dfc2-211ec1d104b4"
      },
      "source": [
        "# for i, row in peakhour2.iterrows():\n",
        "#     print(i)\n",
        "            \n",
        "# for j,lcoe in df_lcoe.iterrows():\n",
        "#         print(lcoe.values[0])\n",
        "\n",
        "peakt['carbon']=0\n",
        "for i, row in peakt.iterrows():\n",
        "    for j,carbon in df_carbon.iterrows():\n",
        "        if i in carbon[0]:\n",
        "            print(\"Match between two tables for \"+i+ \" and \"+ carbon[0])\n",
        "            generation=row[0]\n",
        "            print(str(generation) + ' * '+ str(carbon[1]))\n",
        "            peakt.at[i,'carbon'] = generation * carbon[1]\n",
        "            \n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Match between two tables for Solar and Solar\n",
            "127.0 * 48\n",
            "Match between two tables for Hydro and Hydro\n",
            "1975.0 * 0\n",
            "Match between two tables for Other and Other\n",
            "658.0 * 24\n",
            "Match between two tables for Oil and Oil\n",
            "48.0 * 490\n",
            "Match between two tables for NaturalGas and NaturalGas\n",
            "14636.0 * 490\n",
            "Match between two tables for Coal and Coal\n",
            "16.0 * 820\n",
            "Match between two tables for Nuclear and Nuclear\n",
            "3322.0 * 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>carbon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>6096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>15792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>23520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>7171640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>13120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>39864</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon\n",
              "Wind              47.0        0\n",
              "Solar            127.0     6096\n",
              "Hydro           1975.0        0\n",
              "Other            658.0    15792\n",
              "Oil               48.0    23520\n",
              "NaturalGas     14636.0  7171640\n",
              "Coal              16.0    13120\n",
              "Nuclear         3322.0    39864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:16.979146Z",
          "iopub.status.busy": "2021-05-31T04:27:16.978186Z",
          "iopub.status.idle": "2021-05-31T04:27:16.982302Z",
          "shell.execute_reply": "2021-05-31T04:27:16.981893Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.857495Z"
        },
        "papermill": {
          "duration": 0.081573,
          "end_time": "2021-05-31T04:27:16.982405",
          "exception": false,
          "start_time": "2021-05-31T04:27:16.900832",
          "status": "completed"
        },
        "tags": [],
        "id": "fP_EIqQYiD1P",
        "outputId": "97afe1af-c94a-417b-ed58-1c88d246073a"
      },
      "source": [
        "# sort by ascending order of carbon emission\n",
        "peakt = peakt.sort_values(by=['carbon'])\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>carbon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>6096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>13120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>15792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>23520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>39864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>7171640</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon\n",
              "Wind              47.0        0\n",
              "Hydro           1975.0        0\n",
              "Solar            127.0     6096\n",
              "Coal              16.0    13120\n",
              "Other            658.0    15792\n",
              "Oil               48.0    23520\n",
              "Nuclear         3322.0    39864\n",
              "NaturalGas     14636.0  7171640"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.123538Z",
          "iopub.status.busy": "2021-05-31T04:27:17.122839Z",
          "iopub.status.idle": "2021-05-31T04:27:17.125260Z",
          "shell.execute_reply": "2021-05-31T04:27:17.125737Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.872715Z"
        },
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          "status": "completed"
        },
        "tags": [],
        "id": "8MEcGEsXiD1P"
      },
      "source": [
        "# peakt[\"total_carbon(metric tons)\"] = (peakt[\"generation\"] * peakt[\"carbon\"]) / 1000\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.270925Z",
          "iopub.status.busy": "2021-05-31T04:27:17.269937Z",
          "iopub.status.idle": "2021-05-31T04:27:17.274347Z",
          "shell.execute_reply": "2021-05-31T04:27:17.273762Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.885030Z"
        },
        "papermill": {
          "duration": 0.081934,
          "end_time": "2021-05-31T04:27:17.274458",
          "exception": false,
          "start_time": "2021-05-31T04:27:17.192524",
          "status": "completed"
        },
        "tags": [],
        "id": "anvWkseKiD1P",
        "outputId": "26ad1ec0-348d-4a06-bf62-e85e23c00223"
      },
      "source": [
        "# Cummulative_generation with respect to nw ordering of carbon \n",
        "peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>carbon</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>6096</td>\n",
              "      <td>2149.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>13120</td>\n",
              "      <td>2165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>15792</td>\n",
              "      <td>2823.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>23520</td>\n",
              "      <td>2871.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>39864</td>\n",
              "      <td>6193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>7171640</td>\n",
              "      <td>20829.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon  Cummulative_generation\n",
              "Wind              47.0        0                    47.0\n",
              "Hydro           1975.0        0                  2022.0\n",
              "Solar            127.0     6096                  2149.0\n",
              "Coal              16.0    13120                  2165.0\n",
              "Other            658.0    15792                  2823.0\n",
              "Oil               48.0    23520                  2871.0\n",
              "Nuclear         3322.0    39864                  6193.0\n",
              "NaturalGas     14636.0  7171640                 20829.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.421994Z",
          "iopub.status.busy": "2021-05-31T04:27:17.421161Z",
          "iopub.status.idle": "2021-05-31T04:27:17.425473Z",
          "shell.execute_reply": "2021-05-31T04:27:17.424942Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.907371Z"
        },
        "papermill": {
          "duration": 0.083379,
          "end_time": "2021-05-31T04:27:17.425598",
          "exception": false,
          "start_time": "2021-05-31T04:27:17.342219",
          "status": "completed"
        },
        "tags": [],
        "id": "cjA52FBniD1P",
        "outputId": "6a9791ab-dd52-4d9a-e05e-e822df1c54c0"
      },
      "source": [
        "# Cummulative_carbon emission \n",
        "peakt['Cummulative_carbon(kg)']  = peakt[\"carbon\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>carbon</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "      <th>Cummulative_carbon(kg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>6096</td>\n",
              "      <td>2149.0</td>\n",
              "      <td>6096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>13120</td>\n",
              "      <td>2165.0</td>\n",
              "      <td>19216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>15792</td>\n",
              "      <td>2823.0</td>\n",
              "      <td>35008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>23520</td>\n",
              "      <td>2871.0</td>\n",
              "      <td>58528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>39864</td>\n",
              "      <td>6193.0</td>\n",
              "      <td>98392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>7171640</td>\n",
              "      <td>20829.0</td>\n",
              "      <td>7270032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon  Cummulative_generation  \\\n",
              "Wind              47.0        0                    47.0   \n",
              "Hydro           1975.0        0                  2022.0   \n",
              "Solar            127.0     6096                  2149.0   \n",
              "Coal              16.0    13120                  2165.0   \n",
              "Other            658.0    15792                  2823.0   \n",
              "Oil               48.0    23520                  2871.0   \n",
              "Nuclear         3322.0    39864                  6193.0   \n",
              "NaturalGas     14636.0  7171640                 20829.0   \n",
              "\n",
              "            Cummulative_carbon(kg)  \n",
              "Wind                             0  \n",
              "Hydro                            0  \n",
              "Solar                         6096  \n",
              "Coal                         19216  \n",
              "Other                        35008  \n",
              "Oil                          58528  \n",
              "Nuclear                      98392  \n",
              "NaturalGas                 7270032  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.568762Z",
          "iopub.status.busy": "2021-05-31T04:27:17.567900Z",
          "iopub.status.idle": "2021-05-31T04:27:17.570633Z",
          "shell.execute_reply": "2021-05-31T04:27:17.570138Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.927592Z"
        },
        "papermill": {
          "duration": 0.077247,
          "end_time": "2021-05-31T04:27:17.570749",
          "exception": false,
          "start_time": "2021-05-31T04:27:17.493502",
          "status": "completed"
        },
        "tags": [],
        "id": "vdMtOF93iD1Q"
      },
      "source": [
        "def def_plot_limity(ymax,ypos):\n",
        "    if (ypos < ymax ):\n",
        "        return ypos\n",
        "    elif (ypos < 0 ):\n",
        "        return 0\n",
        "    return (ymax - (0.2*ymax))\n",
        "\n",
        "def def_plot_limitx(xmax,xpos):\n",
        "    if (xpos < xmax ):\n",
        "        return xpos\n",
        "    return xmax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.709919Z",
          "iopub.status.busy": "2021-05-31T04:27:17.709250Z",
          "iopub.status.idle": "2021-05-31T04:27:17.712269Z",
          "shell.execute_reply": "2021-05-31T04:27:17.711832Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.944830Z"
        },
        "papermill": {
          "duration": 0.07431,
          "end_time": "2021-05-31T04:27:17.712369",
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          "start_time": "2021-05-31T04:27:17.638059",
          "status": "completed"
        },
        "tags": [],
        "id": "ZUcWH8i2iD1Q"
      },
      "source": [
        "## standard plot consumption vs total carbon \n",
        "# carbonplot = peakt.plot(x=\"generation\",y=\"carbon\",grid=True, style='.-', \n",
        "#                             title='carbon vs consumption in '+ Region + \" on \" + str(peakmin.DATE.values) ,legend=True)\n",
        "# carbonplot.set_xlabel(\"generation(MW)\")\n",
        "# carbonplot.set_ylabel(\"carbon(kg)\")\n",
        "\n",
        "# index=0\n",
        "# colcount=len(peakt.columns)\n",
        "# for i, row in peakt.iterrows():\n",
        "#     index=index+1\n",
        "#     generation = row.values[0]\n",
        "#     carbon = row.values[1]\n",
        "#     print(generation, carbon)\n",
        "#     carbonplot.annotate(i, xy=(generation, carbon), \n",
        "#                         xytext=(generation+(colcount-index)*1000, carbon+(colcount-index)*220000),\n",
        "#                         arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=45)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:17.969178Z",
          "iopub.status.busy": "2021-05-31T04:27:17.968400Z",
          "iopub.status.idle": "2021-05-31T04:27:18.318107Z",
          "shell.execute_reply": "2021-05-31T04:27:18.317363Z",
          "shell.execute_reply.started": "2021-05-31T02:39:50.954190Z"
        },
        "papermill": {
          "duration": 0.538735,
          "end_time": "2021-05-31T04:27:18.318229",
          "exception": false,
          "start_time": "2021-05-31T04:27:17.779494",
          "status": "completed"
        },
        "tags": [],
        "id": "UdiC8nY7iD1Q",
        "outputId": "43e97073-541b-4228-b532-3762dd2572a1"
      },
      "source": [
        "## Cumulative plot \n",
        "carbonplot = peakt.plot(x=\"Cummulative_generation\",y=\"Cummulative_carbon(kg)\", \n",
        "                        title=\"Cummulative_carbon vs Cummulative_generation \\n\"+\n",
        "                        \"in \"+ Region + \" on \" + str(peakmin.Timestamp.values[0]) ,\n",
        "                        grid=True, style='.-', legend=True)\n",
        "carbonplot.set_xlabel(\"Cumulative_generation(MW)\")\n",
        "carbonplot.set_ylabel(\"Cummulative_carbon(kg)\")\n",
        "\n",
        "xmax = peakt['Cummulative_generation'].max()\n",
        "ymax = peakt['Cummulative_carbon(kg)'].max()\n",
        "index=0\n",
        "colcount=len(peakt.columns)\n",
        "\n",
        "for i, row in peakt.iterrows():\n",
        "    index = index+1\n",
        "    x = row.values[2]\n",
        "    y = row.values[3]\n",
        "    xpos = def_plot_limitx(xmax, x+(index)*1000)\n",
        "    ypos = def_plot_limity(ymax, y+(index)*350000)\n",
        "    carbonplot.annotate(i, xy=(x, y), \n",
        "                         xytext=(xpos, ypos), ha='center', va='center',\n",
        "                         arrowprops=dict(arrowstyle='-|>'), rotation=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MEnA4gnErUR5nhbKINHMJvxLjMO0qTGUeLyKVnLDxmFWw1YH+zpEhInId5otrOMYf0CLgG0nrKqAHpiVQGzMvPSwLvV1phzE4OLrVwBjkFNZjvroykz2kqsed8+AMZANFpEom8t7E7wtMV+e/NwOuA5JUdUcmugtpfUml/HZ1wXA5vI/Z6+J8+gAR+VZERjmnwcAuvejvKL2eWSIio0TkW+e3u+cVDGxIV2YbMsrLB2ll9SzdPZs0smqcLf7pJm3XsKzKM33af+IYqWzU9xwnXxoGp6/tiIhs8lAkGOMH5i3MF8AMoLEbGcUsY39BLu1/fghYpKqL1Ljh/QnjAuEeNZ5CIzEvo1BMpVwO3IRpZfzh8oICWCguXh5FZKBLWIYupMXM8W4OvKCq8aq6nCxcTah719cJwH/UuDdehOkKuV6Mw7duTj5nVXWTo0dm9MRsWvOTGpfRb2OazW1c4mTmCjlTROQOzAv3BedSilO/0y7RTpPBjmliHOSNx7hCSaFcBrJkJO9NfOe53ELWZZQ+rfS6L8K4GG/kfEi8gKmLZbhMRKQLphW5IKNwVe2kqm94qGeWqOobqtrJJa0U+YzS8iavy00rq2fprawnaZdzPuYuJ22P63tukS8NA6YZ3dGTiGL2G7gZ8yIPxnzNnsM4Nsty1o7zktyLaUK6Ugu4X9K67b2Zi+6Xl2G6aNo5v8MxL4xbnHNXOmtaL4+uXjwzcyFdAzjhcg3SuQl2Rdy7vj6uaf0lpbj9vQLTVeCatqvL6/Skdy+c7Mhm272wiLTCtI66u3zJxTp/XZ28lcd0lbnKXoExghM0refM2AxkAWJE5EG5dPFepvHTqdsH0832l4sOrgPGD2aQVhrd1fjVGYPxq7MH0/0Ww2X6VxLjEPBNjK8pT8hSTy9x97y8yety08rqWXor60nasU7r5XLS9qi+5yb50jCo6q8Y52apiEhdMTM4okTkN8ygIxhnWRGYZhmqesTL7EZjButcv9j2ATPSvdDLunxtpTcMy8jcMGSHg0BlEXHVqWZGEcUz19eZcRTT0nJN+5os4qd3LyyObLbcC4tIU0xLqL/zwgRAVU9iysC11dcYp6vJka2EMQpfq+qr6ZLenIHsYTWb1MxU1XLOcbe7+OnS7UO61oKq3u2S3kyMk7rizgdLhrqr6nhVraeq1TDPrjjGzfPlUA8z9vSbiBzCdJVWd2a4BGUQfzNQR9J6HE2jp6d48Lw2A41cZ/BguhkvycsHaWX1LN09mzSyjrGt6yZt17CsyjN92nUwg+c7PKnvuU5eDW64OzCVfJPL+c8421Vi+ubPY7xmLsR4QjyC2ZazI25m7Thh17qc/4SZpTTVOa+J+fK9C7M1oD/GEFytFwelLjh5psyGOID5Qq7mku5u3Aw+Z6aXcy9vYmZhtMY0LS8ZfMY0N5MwRkkwg47nuLjtYHuy3nryc8zYTBmgPubLNcPBZ4wLg7OYAb0SmLGXXS5lkOZ+yWC7SJewBpjZIz0zCX8DY2QrYXagO8jFAcbymPGXDzKR7eg8v/qO/C9kPSvJbXxMd9lZXAYXs0hvDmYspiymi9F15ou/c++CMcLhuAyWuqQxlQwGnzEvE3/nOd3p/BanLlzpcnTFGPIrAb9M9FyF+aDwx+xXkTqLxkXXFJfU15PFRAI3z8t1G89SGI/HWc1KynZa7p6lm2dzhXPezbn3/5J2VtJgzMD+VZjW82bSzkrKtDwx3d1nMF28ZTGu5Od4cs95ceRJph4p5mIYMN0R5zF7GqQc8RjD8K3zsGZiBjz3Y74gvDEMLZ1rU9NdW4ZpuRzFDEZf4xK+Eljqcj4P2Joun92O3rEuxwInLIysDUNdzCB2DMYoTgImu5RN6v1h9t897FTEGU7l99QwXOGUoaezkrpgpumluN8Ozihd5/xFMjcMn2K213Qtm80u4aUw7rbPOPc2wiWsr3P/Z9PJuz6fEY7cGSevTPcf9iQ+8BGmFelJ3a2M+WA5i+mqdJ0WXREzLnUW8wJ7nQxe3GRuGHY79+56BGUQL6Pn/j3wXLr/sXBMHd1Ouo+YDPJRl7DngO89eV5OeFOMu/nzOPuou4Q96Omzd5eWu2eZ1bNxwm/HbNx03imbIJcwwXysnXCON0k7O8pdefZ28jyL2X63sqf3nNtHvvWV5DR/v1XVBiJSHtiuqpdssSgiEzFWfapz/jMwSlUjclHdHEdEPsfM1R+T17pYLJbCTb4cY0iPqp4B/hKR+yF1BWJKf9xCzNoCRKQqZkrarjxR1IeISHNnXKWYiHQE7uPihkMWi8WSY+RLwyAiszFdNdeLyH4ReQTT3HxERNZj+vbuc6L/iNl1aguwFHhaLx00LIhciWmWxgLjgMdUdV2eamSxWIoE+bYryWKxWCx5Q75sMVgsFosl7/C52+bLoWrVqhoUFJQt2bNnz1K2bFnfKlTIsGXkHltGnmHLyT25WUZRUVHHVPUKX6WXrwxDUFAQkZGR2ZINDw+nffv2vlWokGHLyD22jDzDlpN7crOMRCQrjwVeY7uSLBaLxZIGaxgsFovFkgZrGCwWi8WShnw1xpARCQkJ7N+/n7i4uCzjVahQga1bt+aSVgUTW0buyaiM/P39ufrqqylRokQmUhZL4SLfG4b9+/cTEBBAUFAQaR0qpiUmJoaAgDxzX14gsGXknvRlpKocP36c/fv3U7t27TzUzGLJPfJ9V1JcXBxVqlTJ0ihYLDmFiFClShW3LVaLpTCR7w0DYI2CJU+x9c+S00TtOcn4pTuJ2nMyr1UBCkBXksVisRRmovacpNfHq0hITKZUiWLMHNCKkFqV3AvmIAWixWCxWCyFldlr9hCfmIwCCYnJrNqV9z5ArWHwkEOHDvHAAw9Qt25d6tevzz333MOOHTvcC+Ygu3fvpkGDBm7jzJo1K/U8MjKSYcOG5bRqHtO+fftsr3ZPz/nz57nllltISkoiPDycTp06uRdyiI+Pp127diQmJrqPbLH4iIXrDrBg7QEE8BMoUbwYrepUyWu1Cqdh8HV/narSpUsX2rdvz59//smWLVt47bXXOHz4sE/Sz0nSG4bQ0FDGjRuXhxpdJCkpyafpTZkyha5du+Ln5+e1bMmSJbntttuYP3++T3WyWDLjk992MfzzaFrUrsK0/s156o7r8kU3EhSwMYaXvtnMlr/PZBiWlJSEn58fMXEJbDsUQ7JCMYEbrgwgwD/z+ef1a5RnzD+Ds8x36dKllChRgsGDB6dea9KkSepX6bfffgvAkCFDCA0NJSwsjKCgIHr37s3SpUtJSEhg0qRJPPvss+zcuZOnn36awYMHEx4ezpgxYwgMDCQ6OpquXbvSsGFD3nvvPc6fP8/ChQupW7cuYWFhdOrUie7duwNQrlw5YmNj0+i4e/duHn74Yc6ePQvABx98QJs2bRg1ahRbt26lSZMm9OzZk9atW/P222/z9ddfU6dOHaKjo6lYsSIA1157Lb///jvFihVj8ODB7N27F4B3332Xm266KcOyiY2NZejQoURGRiIijBkzhm7duvHYY48RERHB+fPn6d69Oy+99BJg/GH179+fxYsXM2TIEAA+++wzhg0bxpkzZ5gyZQotWrTgxIkT9O/fn127dlGmTBkmTZpEo0aNePHFF9m7dy+7du1i7969DB8+PLUFNHPmzDRGMIWIiAgGDRrE/PnzCQgIoHfv3hw/fpzmzZvzww8/EBUVRdWqVencuTNPP/00AwYMyLI+WCyXg6ry3x+2M3HZn9zd4ErG9mxCjx7389fuPQxZF5XX6gE53GIQketFJNrlOCMiw3MyzzNxiSQ7W0wkqzm/XDZt2kRISIjXcjVr1mTlypW0bduWsLAw5s2bx6pVq3jhhRdS46xfv5733nuPjRs3MmPGDHbs2MGaNWsYMGAA77//vsd5VatWjZ9++om1a9fy+eefp74s33jjDdq2bUt0dHTqixigWLFi3HfffSxYsACA1atXExQURGBgIE8++SRPPfUUERERzJ8/P8sX5csvv0yFChXYuHEjGzZs4NZbbwXg1VdfJTIykg0bNrBs2TI2bNiQKuPv78/y5ct54IEHAOOFcsWKFUyYMIH+/fsDMGbMGJo2bcqGDRt47bXX6NOnT6r8tm3b+PHHH1mzZg0vvfQSCQkJxMfHs2vXLtJ7512xYgWDBw/mq6++ok6dOrz00kvceuutrF27li5duqQaP4AGDRqwdu1aj8vcYvGWxKRk/jVvAxOX/cmDLa/hg97NOHvmFIsX/8S2bdvZtm1bXqsI5HCLQVW3A00ARMQPOAAsyG56WX3ZpyxMitpzkgc/MSP8JYoX470HmuZZ0+zee+8FoGHDhsTGxhIQEEBAQAD+/v6cOnUKgObNm1O9utnKum7dutx5552pMkuXLvU4r4SEBIYMGUJ0dDR+fn4ejX/07NmT//znP/Tr1485c+bQs2dPAJYsWcKWLVtS4505cybTxXFLlixhzpw5qeeVKpmynjt3LpMmTSIxMZGDBw+yZcsWGjVqlJqvK7169QKgXbt2nDlzhlOnTrF8+fLUbp1bb72V48ePc/r0aQD+8Y9/UKpUKUqVKkW1atU4fPgwxYoVS235pLB161YGDRrE4sWLqVGjBgDLly9PNYYdO3ZM1RfAz8+PkiVL2oWAlhzhfHwSQ2evZcnWIwy/vR5P3lYPEeG/b71D6evaIAHVeO75F/nyiznuE8thcrMr6TbgT1X1qXvY9ITUqsTMAa1Ytes4repU8YlRCA4OZt68eZdcL168OMnJyann6RdBlSpVCjBf5ym/U85TBjnTX3eVSYnjmo+qEh8ff4kuY8eOJTAwkPXr15OcnIy/v7/b+2rdujU7d+7k6NGjLFy4kNGjRwOQnJzMypUrKV26tNs0VPWSef5//fUXb7/9NhEREVSqVImwsLA0ZZPeR316eREho50FU+K5lpmfnx+JiYlUqFDhkvKvXr06cXFxrFu3LtUwuNux8MKFCx6VncXiDafPJfDItAii9p7k5c4NeLhVLQCOHz/O+AkTqNDjdYr5l+OHaUPZtm0bN9xwQ57qm5uDzw8As3Mjo5BalXiiw7U+aynceuutXLhwgY8//jj1WkREBElJSWzZsoULFy5w+vRpfv75Z5/kl56goCCiokzf41dffUVCQsIlcU6fPk316tUpVqwYM2bMSB3YDQgIICYmJsN0RYQuXbowYsQIbrzxRqpUMbMh7rzzTj744IPUeNHR0Znqlj7uyZMnOXPmDGXLlqVChQocPnyY77//Psv7+/zzzwHzNV+hQgUqVKhAu3btmDlzJmD82letWpXy5ctnmkalSpVISkpKYxwqVqzId999x3PPPUd4eDgAN998M3PnzgVg8eLFnDx5cYLC8ePHqVq1qvWJZPEph07Hcf9HK9iw/zTjezdLNQoAK1euJCkhniPTh3PoowEkJcbn2HvEG3KlxSAiJYF7gWczCBsEDAIIDAxM/QdOoUKFCpm+2FxJSkryKF52mTFjBqNGjeK1117D39+fa665hjfeeIPOnTvToEED6tatS8OGDYmLiyMmJgZVJTY2llKlShEXF0d8fHyqfilh586dIzExMfV6UlISZ8+eJSYmJk1Yr169eOCBBwgJCaF9+/aULVuWmJgYYmNjSU5OJiYmhj59+vDwww8zZ84c2rVrlxqndu3aiAgNGzakV69eNGnSJE2enTp1on379nz44Yep11577TVGjhxJgwYNSExM5KabbuLdd9/NsFyefPJJRo4cSf369fHz82PUqFHce++9NGjQgBtvvJGgoCBatmyZYbmk3HPZsmVp2bIlMTExjB8/npiYGEaOHMnjjz9OgwYNKF26NBMmTCAmJoYLFy5QokSJVF2Tk5OJjY0lJiaGDh06sHjxYjp06JBafmXKlGH27Nl069aN8ePHM3LkSPr378/s2bO56aabuPLKKwHTFblo0SLuuOOODOtRXFzcJXWzKBMbG2vLww2xsbHM+vYX3omM42yCMryZP2WObyc8fHtqnHLlyvHD94uYN28ehw4dSh0HzPOyVdUcP4D7gMXu4oWEhGh6tmzZcsm1jDhz5oxH8Yoyhb2M1q5dqw899FCWceLi4jQhIUFVVVesWKGNGzdODevSpTV+7q4AACAASURBVItGRUVlKOdpPSwqLF26NK9VyPdMXrBEm7z0o4a8vFg37j+VZdyxY8fqk08+me28gEj14Ts7t8YYepFL3UiWokvTpk3p0KFD6tTljNi7dy89evQgOTmZkiVLpnYPxsfH07lzZ+rVq5ebKlsKKb/uOMp/I+IIrFCG6f1bEFS1YO2PneOGQUTKAHcAj+Z0Xpac49NPP+W9995Lc+2mm25i/PjxeaRRxqRMd82MevXqsW7dukuulyxZkj59+uRod6SlaPBV9AFGzl1P9bLFmPdYa6oFFLzJDDluGFT1HHBZa7w1g5kvltylX79+9OvXL6/VyBPUzUwmiyWFKcv/4j/fbqFVncr0qR1XII0CFACXGP7+/hw/ftz+c1ryBHU26rFTWC1Zoaq8+cM2/vPtFjoGX8nUfi0oU6Lgfszme5cYV199Nfv37+fo0aNZxouLi7P/vG6wZeSejMooZWtPiyUjEpOSeW7BRuZG7qdXi2t4pXMD/IoVXKMABcAwlChRwqMtFcPDw2natGkuaFRwsWXkHltGFm+IS0hiyKx1LNl6mGG31eOp2+sVim7vfG8YLBaLJT9y+lwCA6ZHELnnJP+5L5g+rYPyWiWfYQ2DxWKxeMnhM3H0mbyGXcdieb9XUzo1qpHXKvkUaxgsFovFC3YdjeXhyWs4dS6eqf1acNO1VfNaJZ9jDYPFYrF4yPp9p+g3NQIB5gxqTcOrK+S1SjmCNQwWi8XiAb/9cZRHZ0RRuWxJZjzSktoFbDWzN1jDYLFYLG74ev3fjJwbTd0ryjG9fwuqlS/c076tYbBYLJYs+PT3v3jpmy20qF2ZT/qGUj6LrYILC9YwWCwWSwaoKu8s3sEHS3dyZ/1AxvVqin+JjJ0zFjasYbBYLJZ0JCYlM3rhJuZE7KNXi5q8fF8Divvlew9CPsMaBovFYnEhLiGJYbPXsXjLYYbeei0j7riuUKxm9gZrGCwWi8Xh9PkEBk6LJGLPCV78Z33CbnLvjqcwYg2DxWKxAEfOxNFnyhr+PBrLuAea8s/GhWs1szdYw2CxWIo8fx07y8OTV3PibDxTwprTtt4Vea1SnmINg8ViKdJs3H+asE/XoMCcQa1odHXFvFYpz7GGwWKxFFmW/3GMR2dEUrFMSWY80oI6V5TLEz1EJF8NcFvDYLFYiiTfrP+bEc5q5mn9WxCYh6uZe/Xqxblz5/Is//TkuGEQkYrAJ0ADQIH+qroyp/O1WCyWzJi2YjcvfrOZ5rUq83HfUCqUztvVzNWqVcvT/NOTGy2G94AfVLW7iJQEyuRCnhaLxXIJqsr/ftrB+7/s5I76gbxfhFYze0OOGgYRKQ+0A8IAVDUeiM/JPC0WiyUjkpKV0Qs3MXvNXnqG1uTVLkVrNbM3iKrmXOIiTYBJwBagMRAFPKmqZ13iDAIGAQQGBobMmTMnW3nFxsZSrlzeDBwVFGwZuceWkWcUtHKKT1I+2nCBqMNJdKpTgm71SuT4YG9ullGHDh2iVDXUZwmqao4dQCiQCLR0zt8DXs4sfkhIiGaXpUuXZlu2qGDLyD22jDyjIJXT6fPx2mPiCq31zLc6Zfkur2QBHTFiROr5W2+9pWPGjMlSZunSpfr777/7tIxq1aqlR48eVVXVQ4cOaa9evbR27drarFkzbdWqlQI71Yfv7pxuR+0H9qvqaud8HtAsh/O0WCwWwKxm7vnRKtbuPcl7DzShn5cuLkqVKsWXX37JsWPHPJYJDw9nxYoVXuWTlJTkUTxVpXPnzrRr145du3YRFRWF08tS0qsM3ZCjhkFVDwH7ROR659JtmG4li8ViyVF2HztLt4kr2HP8LJP7Nue+Jld5nUbx4sUZNGgQY8eOvSTsm2++oWXLljRt2pTbb7+dw4cPs3v3biZOnMjYsWMZMGAAv/32G2FhYcybNy9VLqV7KTw8nA4dOtC7d28aNmwIQOfOnQkJCSE4OJhJkyZdkucvv/xCyZIlGTx4cOq1WrVqARwBEJEgEflNRNY6RxvnenUR+VVEokVkk4i0zfK+vS0oESkLxKmqZyYOhgIznRlJu4B+3uZpsVgs3rDpwGn6TjGrmWcPbEXjmtlfzfzEE0/QqFEj/vWvf6W5fvPNN7Nq1SpEhE8++YQ333yTd955h8GDB1OuXDlCQ0Np27YtkydPzjTtNWvWsGnTJmrXNi2ZKVOmULlyZc6fP0/z5s3p1q0bVapUSY2/efNmmjXLstPlCHCHqsaJSD1gNqZLvzfwo6q+KiJ+uJkd6tYwiEgx4AHgQaA5cAEoJSJHgUXAJFX9IzN5VY12FLNYLJYc5/edxxg03axmnv5IC+pe5mrm8uXL06dPH8aNG0fp0qVTr+/fv5+ePXty8OBB4uPjU1/u3tCiRYs0cuPGjWPBggUA7Nu3jz/++CONYUjPE088wfLlywFudC6VAD5wJv4kAdc51yOAKSJSAljovJczxZOupKVAXeBZ4EpVramq1YC2wCrgDRF5yIN0LBaLJUf5bsNB+n0awdWVyjD/sTaXbRRSGD58OJMnT+bs2dQJlQwdOpQhQ4awceNGPvroI+Li4jKULV68OMnJyYAZI4iPvzhjv2zZsqm/w8PDWbJkCStXrmT9+vU0bdr0kjSDg4NZu3Zt6vn48eP5+eef4eJH/lPAYcws0FCcsQdV/RWzdOAAMENE+mR1v54YhttV9WVV3aCqySkXVfWEqs5X1W7A5x6kY7FYLDnGjJW7GTJ7LY1rVmDuo625soLvXFxUrlyZHj16pOkWOn36NFddZcYtpk2blno9ICCAmJiY1POgoCCioqIA+Oqrr0hISMgwj9OnT1OpUiXKlCnDtm3bWLVq1SVxbr31VuLi4vjwww9Tr6VzpVEBOOi8qx8G/ABEpBZwRFU/BibjZhKQW8OgqglOwpUzOEq4xrFYLJbcRp3VzM9/tZnbbghkxiMtqVDG9y4uRo4cmWZ20osvvsj9999P27ZtqVq1aur1f/7znyxYsCB18HngwIEsW7aMFi1asHr16jStBFc6duxIYmIijRo14vnnn6dVq1aXxBERFi5cyLJly6hduzYtWrSgb9++YGaAAkwA+orIKkw3UkoTpz0QLSLrgG6YpQOZ4vECNxHZDdQETgICVAQOYgY7BqpqlEcJZUFoaKhGRkZmSzY8PJz27dtfrgqFGltG7rFl5Bn5pZySkpXnv9rErNV76RF6Na91aZhvVjPnZhmJiE8XuHkzK+kHYIGq/ugocifQEZiLsVItfaWUxWKxuCMuIYnhc6L5YfMhHmtfl3/ddX2+cl1dkPHGtIamGAUAVV0MtFPVVUApn2tmsVgsmXAmLoGwT9fww+ZDPN+pPs90vMEaBR/iTYvhhIg8A6Q4M+oJnHLmxCZnLmaxWCy+40hMHGFTIthxOIb3HmiSrYVrlqzxxjD0BsYAC53z5UAvzKh3Dx/rZbFYLJew5/hZHp68hmOxF5gc1pxbrivaezPnFN4YhnKqOtT1gog0V9UIYKdv1bJYLJa0bDpg9mZOSlZmDWxFk8tYzWzJGm/GGL4UkdQ2m4i0A6b4XiWLxWJJy4o/j/HApFWUKu7HF4PbWKOQw3hjGB4FForIlSJyDzAOuCdn1LJYLBbDoo0HCZsSQY2K/sx7rDXXVis4+0AUVDzuSlLVCBEZBiwG4jCOmo7mmGYWi6XI89mqPTz/1SaaXVOJyX1DqVjGp96lLZngiRO9bwDXVXBlgNPAZBFBVe/NKeUsFkvRRFV5d8kfvPfzH9x2QzU+6N2M0iXt3sy5hScthrdzXAuLxWJxSEpWxny9ic9W7aV7yNW80TX/rGYuKnhiGH5VN34zRETcxbFYLBZ3XEhM4qnPo1m08RCP3lKHUXbhWp7gkdttERkqIte4XhSRkiJyq4hMA/rmjHoWi6WoEBOXQNiUCBZtPMTof9zIs3ffaI1CHuFJi6Ej0B+YLSK1gVNAaYxRWQyMdbfpg8VisWTF0ZgLhH26hu2HYhjbszFdml6d1yoVadwaBlWNwzjJm+C42a4KnFfVUzmtnMViKfzsPX6Oh6es5siZC3zcN5QO11fLa5WKPN7u+ZyMcbldXkTKA6jq3qwEHHfdMZht5hJ96RrWYrEUbDb/fZq+UyJITE5m1sCWNL2mUl6rZMELwyAiQzG+kg5z0WmeAo08EO+gqsfcR7NYLEWFlX8eZ9D0SAL8izNnUGuurRaQ1ypZHLxpMTwJXK+qx3NKGYvFUjT4YdNBhs2OplaVMkzr34IaFUvntUoWF7zZwW0pZrVzolcZiPyF2fVNgY9UdVK68EHAIIDAwMCQOXPmXJqIB8TGxlKunF0qnxW2jNxjy8gzLqeclu5NYPqWeOpWLMbwZv6UK1k4Zx7lZl3q0KGDT3dw88YwTAauB74DLqRcV9X/uZGroap/i0g14CdgqKr+mlFcu7VnzmLLyD22jDwjO+Wkqoz7eSdjl+zg1huqMb6Qr2YuKlt77nWOks7hEar6t/P3iIgsAFoAGRoGi8VSOElKVl76ZjPTV+6hW7OreaNbQ0rY1cz5Fm+c6L0EICIB5lRj3cmISFmgmKrGOL/vBP6TXWUtFkvB40JiEiM+X893Gw/yaLs6jLrbrmbO73gzK6kBMAOo7JwfA/qo6uYsxAKBBU4lKA7MUtUfsq+uxWIpSMReSOTRGZH8vvM4z91zA4Pa1c1rlSwe4E1X0iRghKouBRCR9sDHQJvMBFR1F9D4chS0WCwFk2OxZjXz1oMxvHN/Y7qF2NXMBQVvDEPZFKMAoKrhTveQxWKxpGHfiXM8PHk1h87E8UmfUDrcYFczFyS8MQy7ROR5THcSwEPAX75XyWKxFGS2/H2Gvp+uIT4xmZkDWhFSy65mLmh4My2gP3AF8CWwwPndLyeUslgsBZNVu47T86OVFC8mzBvc2hqFAoo3s5JOAsNEpAKQrKoxOaeWxWIpaPyw6RDD5qzjmsplmG5XMxdoPG4xiEhzEdkIrAc2ish6EQnJOdUsFktBYfaavTw+M4rgGuX54tHW1igUcLwZY5gMPK6qvwGIyM3Ap3jmRM9isRRCVJUPftnJOz/toP31VzDhwWaUKemt02ZLfsObJxiTYhQAVHW5iNjuJIuliJLsrGaetnIPXZtexX+7N7KrmQsJbg2DiDRzfq4RkY+A2RiHeD2B8JxTzWKx5FcSkpVhc9bx7YaDDGxbm2fvvpFixexq5sKCJy2Gd9Kdj3H57ZkHPovFUmiIvZDIu1FxbD5+jmfvvoFHb7GrmQsbnmzt2UFEigHdVXVuLuhksVjyKcdiL9B/agRbTyTz9v2N6W5XMxdKPOoQVNVkYEgO62KxWPIx+06c4/6JK9lxOIZhTUtZo1CI8Wak6CcR+T8RqSkilVOOHNPMYrHkG7YePEO3D1dw4mw8Mwe0pEk1O/OoMOPN0+3v/H3C5ZoCdXynjsViyW+s3nWcAdMjKVuyOF8Mbs11gQGEW2c4hRpvVj7XzklFLBZL/uPHzYcYOnsdNSuVZvojLbnKLlwrEnjVHnT2ZKgP+KdcU9XpvlbKYrHkPXPW7OW5BRtpdHVFpoQ1p3JZjzdutBRwvNmoZwzQHmMYFgF3A8sBaxgslkKEqjIh/E/e+nE7t1x3BR8+ZFczFzW8GXzuDtwGHFLVfpgNeErliFYWiyVPMKuZt/DWj9vp3KQGn/QNtUahCOLNEz+vqskikigi5YEj2IFni6XQEJ+YzMgv1vPN+r955Oba/Pseu5q5qOKNYYgUkYqY7TyjgFhgjSeCIuIHRAIHVLWT11paLJYcJfZCIo99FsVvfxxj1N038Gi7Ojh7tVuKIN7MSnrc+TlRRH4AyqvqBg/FnwS2AuW91M9iseQwx2Mv0G9qBJv/PsOb3RvRI7RmXqtkyWO82Y+hi7NJD6q6G9grIp09kLsa+AfwSXaVtFgsOUPKaubth2L46KEQaxQsAIiqZ37wRCRaVZuku7ZOVZu6kZsHvA4EAP+XvitJRAYBgwACAwND5syZ44X6F4mNjaVcuXLZki0q2DJyT1Eqo30xybwTGUd8kvJUiD/1Kvl5LFuUyim75GYZdejQIUpVQ32VnjdjDBm1LrKUF5FOwBFVjRKR9hnFUdVJwCSA0NBQbd8+w2huCQ8PJ7uyRQVbRu4pKmUUsfsEb02NoHSpknzevyXXXxnglXxRKafLoSCXkTfTVSNF5H8iUldE6ojIWMwgdFbcBNwrIruBOcCtIvJZNnW1WCw+4Kcth3nok9VUDSjF/MfaeG0ULIUfbwzDUCAe+ByYC5wnrd+kS1DVZ1X1alUNAh4AflHVh7Kpq8ViuUzmRuzj0RmR3HBlAPMGt+HqSmXyWiVLPsSbWUlngVGZhYvI+6o61CdaWSwWn6KqfLjsT978YTtt61Vl4kMhlC1lF65ZMsaXNeOmrAJVNRy7FajFkuskJyuvfLeVKb//xb2Na/D2/Y0pWdzuzWzJHPvJYLEUYuITk3l63nq+iv6bfjcF8fw/6tvVzBa3WMNgsRRSzl5I5LGZa/l1x1H+1fF6Hrulrl3NbPEIXxoGW+MslnzCibPx9Jsawcb9p/hvt4b0bH5NXqtkKUB4bRhEpKwzEJ2e93ygj8ViuUz2nzxHnylrOHDyPB89HMod9QPzWiVLAcMblxhtRGQLxucRItJYRCakhKvqVN+rZ7FYvGH7oRi6fbiCozEXmPFIS2sULNnCm6kJY4G7gOMAqroeaJcTSlksFu+J3H2C+yeuQBW+GNyaFrUr57VKlgKKV11Jqrov3eBVkm/VsVgs2WHJlsM8MWstV1UszbT+LahZ2S5cs2QfbwzDPhFpA6iIlASG4XQrWSyWvGNu5D6e/XIjwTXK82lYc6qUsxsrWi4PbwzDYMwA81XAfmAxblxiWCyWnENVmbhsF//9YZtdzWzxKd7UIlHVB3NME4vF4jHJycqri7Yyeflf/LNxDd6xq5ktPsQbw7BCRP7CONGbr6qnckgni8WSBQlJyfxr3gYWrDtAWJsgXuhkVzNbfIvHnxiqWg8YDQQDa0XkWxGxnlItllzkXHwiA6ZFsmDdAZ6+63rG/NMaBYvv8artqaprVHUE0AI4AUzLEa0sFsslnDwbT++PV/PbH0d5o2tDnuhwrXVxYckRPO5KEpHyQBfMvgp1gQUYA2GxWHKYA6fO02fyavadPM+HD4VwV/CVea2SpRDjzRjDemAh8B9VXZlD+lgslnTsOBxDn8lrOBufyIz+LWhZp0peq2Qp5HhjGOqoquaYJhaL5RKi9pyg/9RIShYvxtxHW3Nj9fJ5rZKlCODWMIjIu6o6HPhaRC4xDKp6b45oZrEUcX7ZdpjHZ66leoXSTLermS25iCcthhnO37dzUhGLxXKReVH7eWb+BupXL8+n/ZpT1a5mtuQibg2DqkY5P5uoahrX2iLyJLAsM1kR8Qd+BUo5ec1T1THZV9diKfx8tOxPXv9+GzdfW5WJD4dQzq5mtuQy3kxX7ZvBtTA3MheAW1W1MdAE6CgirbzI02IpMiQnK69+t4XXv99Gp0bVmRwWao2CJU/wZIyhF9AbqC0iX7sEBeC44M4MZ7A61jkt4Rx2ANtiSUdCUjLPzNvAl+sO0Ld1Lcb8M9guXLPkGeJuopGI1AJqA68Do1yCYoANqproRt4PiAKuBcar6jPpwgcBgwACAwND5syZ4+09ABAbG0u5cuWyJVtUsGXknrwoowuJyvj1F9hwNImu9Urwzzol8v3CNVuX3JObZdShQ4coVQ31VXpuDYPPMhKpiFkUN1RVN2UUJzQ0VCMjI7OVfnh4OO3bt8++gkUAW0buye0yOnk2nv7TIli/7xSvdmlIrxYFY29mW5fck5tlJCI+NQzebO3ZSkQiRCRWROJFJElEzngq7zjdCwc6ZkNPi6XQ8fep89z/0Uo2/32GCQ+GFBijYCn8eDP4/AHQC/gDKA0MAN7PSkBErnBaCohIaeB2YFv2VLVYCg9/HDZ7Mx8+Hcf0/i3o2MC6uLDkH7zd2nOniPipahLwqYiscCNSHZjmjDMUA+aq6rfZ1NViKRRE7TnJI9MiKOFXjM8fbU39GnY1syV/4Y1hOOds6RktIm8CB4GyWQmo6gag6WXoZ7EUKpZuO8JjM6O4srw/0/u35JoqdjWzJf/hTVfSw4AfMAQ4C9QEuuWEUhZLYeTLtfsZMD2Sa6uV44vBbaxRsORbPG4xqOoe5+d54KWcUcdiKZx8/OsuXl20lTZ1q/DRwyEE+JfIa5Uslkxx22IQkY0isiGzIzeUtFgKKqrK64u28uqirfyjYXU+7df8EqMgIowcOTL1/O233+bFF1/MVn5hYWHMmzfvclS2WDxqMXTKcS0slkJIQlIyo+ZvZP7a/TzcqhYv3huMXwarmUuVKsWXX37Js88+S9WqVfNA00tJTEykeHHrjqOo4rbFoKp7sjpyQ0mLpaBxPj6JR2dEMX/tfp66/Tr+c1/GRgGgePHiDBo0iLFjx14Slr4F4LqS9s0336Rhw4Y0btyYUaNGXSIbFRXFLbfcQkhICHfddRcHDx4E4OOPP6Z58+Y0btyYbt26ce7cudS8RowYQYcOHXjmmWcuSc9SdPBmgVuMiJxxjjhvF7hZLEWFU+fiefCTVYRvP8KrXRrw5O313Lq4eOKJJ5g5cyanT5/2KI/vv/+ehQsXsnr1atavX8+//vWvNOEJCQkMHTqUefPmERUVRf/+/fn3v/8NQNeuXYmIiGD9+vXceOONTJ48OVVux44dLFmyhHfeecfLu7YUJrwZfA5wPReRztg9ny2WNBw8fZ4+k9ew5/g5JjzYjI4NqnskV758efr06cO4ceMoXbq02/hLliyhX79+lCljZjZVrlw5Tfj27dvZtGkTd9xxBwBJSUlUr2502bRpE6NHj+bUqVPExsZy1113pcrdf//9+Pn5eaSzpfCS7U5EVV0oIpe2Xy2WIsrOI2Zv5jNxiUzt35w2db0bLxg+fDjNmjWjX79+qdeKFy9OcnIyYAay4+PjU39n1QpRVYKDg1m58tLt2cPCwli4cCGNGzdm6tSphIeHp4aVLZvl0iRLEcGbrqSuLkd3EXkD60LbYgFg3d6TdJ+4kvgkZc6gVl4bBTBf/T169EjTtRMUFERUlNkr66uvviIhIQGAO++8kylTpqSOD5w4cSJNWtdffz1Hjx5NNQwJCQls3rwZgJiYGKpXr05CQgIzZ870/mYthR5vFrj90+W4C+N2+76cUMpiKUgs3X6E3h+vpkLpEsx/rDUNrqqQ7bRGjhzJsWPHUs8HDhzIsmXLaNGiBatXr079ou/YsSP33nsvoaGhNGnShLffTrvzbsmSJZk3bx7PPPMMjRs3pkmTJqxYYTzYvPzyy7Rs2ZI77riDG264Idu6WgovueZ22xOs2+2cxZaRe7wto4XrDvB/X6zn+isDmNqvBVcEFI29mW1dck9Bdrvt8RiDiNQGhgJBrnKqeq+vlLFYChKf/LaLV77bSus6VZjUx65mthQevBl8XghMBr4BknNGHYsl/6OqvPHDNj5atot7Gl7J/3o0wb+EncljKTx4M8YQp6rjVHWpqi5LOXJMM4vFDfv37+e+++6jXr161K1blyeffJL4+HgiIyMZNmwYAFOnTmXIkCE+yzMxKZmn523go2W7eKjVNbzfq5k1CpZChzeG4T0RGSMirUWkWcqRY5pZLFmgqnTt2pXOnTvzxx9/sGPHDmJjY/n3v/9NaGgo48aN83meKauZ50XtZ/jt9Xj5vgaZrma+HE6dOsUzzzzDvn37fJ62xeIJ3hiGhsBA4A3gHed4O0sJiyWH+OWXX/D390+d8+/n58fYsWOZMmUKixYtolMn37r4OnUunocnr+aX7Ud4uXMDht9+ndvVzNnhq6++okGDBsTExFCtWjWfp2+xeII3YwxdgDqqGp9TylgsnrJ582ZCQkLSXCtfvjzXXHMNO3fu9GleB0+fp++UNew+do7xvZtxT0PPVjN7w+HDhxk2bBjr1q1j1qxZtGvXzud5WCye4k2LYT1QMacUsVi8IbOVv+5WBHvLziOxdP9wJX+fimNqv+Y+NwqqymeffUajRo0ICgpi/fr11ihY8hxvWgyBwDYRiQAupFzMarqqiNQEpgNXYmYyTVLV97Kpq8WSSnBwMPPnz09z7cyZM+zbt4+6dev6JI/ofafo9+ka/IoJcwa1uqyFaxmxd+9eBg8ezIEDB/juu+8IDfXZNHSL5bLwpsUwBtOd9BoXxxjcuWBMBEaq6o1AK+AJEamfHUUtFlduu+02zp07x/Tp0wHjJG7kyJGEhYWlOpa7HJbtOEqvSasI8C/BvMFtfGoUkpOT+fDDDwkJCaFNmzZERkZao2DJV3jjXXUZgIiU91ROVQ8CB53fMSKyFbgK2OK9qhbLRUSEBQsW8Pjjj/Pyyy+TnJzMPffcw2uvvZah4zhvSFnNXC8wgGn9m1MtwN9HWhu31gMGDCAhIYFly5ZRv779TrLkP7xZ+TwIeBmz53MyIBgnenU8lA8CmgKrvVXSYsmImjVr8s0331xyvX379qmuCMLCwggLC/M4zR93JzB7WzSt6lRmUp9QyvtoNXNiYiL/+9//ePPNN3n++ecZMmSIdW9tybd47CtJRP4AWqvqMbeRL5UtBywDXlXVL9OFDQIGAQQGBobMmTPH2+QBiI2NTbO7leVSbBlljqoyb0cC3/2VQEigH482KkVJP98MYu/cuZM333yT8uXLM3LkyNR9EQoyti65JzfLqEOHDj71lYSqenQAPwBlPI3vIlcC+BEY4S5uSEiIZpelS5dmW7aokBdltG/fPr333nv12muv1Tp16uiwYcP0woULum7dOv3uu+9S440ZM0bfeuutS+Tj4uJ08eLFOapjQmKSPv1FtNZ65lvt+8EPmpiU7JN04+LidPTo0XrFFVfo5MmTNTnZN+nmB+z/m3tys4yASPXy3ZzV4c3g87PAChH5WLGdDAAAIABJREFUSETGpRxZCYiZNzgZ2Kqq/8uO4bIUXDSL1cnR0dEsWrQoS/nVq1cTEhLCxx9/nPKRkSlJSUnZ0jEuIYnBn61lbuR+ht1Wj771S/pkNfPKlStp2rQpmzZtIjo6mv79++fIgjiLJSfwZrrqR8AvwEY8d6J3E/AwsFFEop1rz6lq1m8ES6Egs9XJtWrVokSJEqgqy5cv59lnnwVgy5YttG/fnt27d1O7dm22bt3Ku+++S0JCAi1btiQ+Pp6WLVsyYcIE/Pz8KFeuHCNGjODHH3/knXfe4eabb/ZKv9PnEhgwPYLIPSf5z33B9GkdRHj435d1z7GxsYwePZrPP/+ccePG0b17d2sQLAUOb1oMiao6QlU/VdVpKUdWAqq6XFVFVRupahPnsEahiJDZ6uSgoCBGjx5Nz549iY6OpmfPngBs27aNUaNGISKsWLGCdevW0bhxY+bOncvvv/9OdHQ0fn5+qbuOnT17lgYNGrB69WqvjcKh03H0+Ggl0ftO8X6vpvRpHXTZ9/vTTz/RsGFDTp48yaZNm7j//vutUbAUSLxpMSx1Boq/Ie0CtxOZi1iKMurF6uS4uDji4+MZOHAgH374IU8//TRJSUn8/PPPREVF0bx5cwDOnz+f6kPIz8+Pbt26ea3Xn0dj6TN5DafOxTO1Xwtuutb7bThdOXnyJP/3f//HkiVLmDhxInffffdlpWex5DXeGIbezt9nXa55PF3VUvTIanWy61TNr7/+mgkTJhAcHMymTZuoUKECo0aNIjExEVWlb9++vP7665ek7+/v7/WUz/X7TtFvagQCzBnUmoZXX97CtQULFjBkyBC6dOnCpk2bCAgIuKz0LJb8gMddSapaO4PDGgVLpmS1OjkwMJAjR47wwAMPMGLECLp27Uq3bt2oUKHCJWnMmzePI0eOAGbT+z179mRLn193HKXXx6soU9KPeY+1uSyjcPjwYXr06MEzzzzDnDlz+OCDD6xRsBQaPDYMItInoyMnlbMUbFJWJ3/xxRfUq1eP6667Dn9/f1599VWOHTvGggUL+OWXX3jhhRcICgrKMI369evzyiuvcOedd9KoUSPuuOMODh486LUuX0Uf4JFpEVxTuQxfPtaG2lXLZuueVJXp06fTqFEj6taty/r162nbtm220rJY8ivedCU1d/ntD9wGrMU4ybNYMiT96uR9+/bRvXt39u7dy4oVK1LHDtKzadOm1N89e/ZMHaB2JTY21iMdPv39L176Zgstalfmk77ZX828d+9eHn30UQ4ePMiiRYsuGVi3WAoL3nQlDXU5BmLcW5TMOdUshYnk5GQmTpxIs2bNaNGiBZGRkZkaBV+hqrz14zZe+mYLdwUHMr1/i2wZheTkZMaPH0+zZs1o27YtERER1ihYCjXetBjScw6o5ytFLHnPoUOHGD58OBEREZQqVYqgoCDeffddrrvuOq/SKVeuXJqv+T/++IOBAwcSFxdHeHg4wcHBvlb9EhKTkvn3gk18HrmPXi1q8krnhtlauLZ9+/b/b+/M46qq1sb/fRhEyRHHUFPMISdSwFLpKpqpjYaaw70lOKTWVVNv3V9571vmW2bpbdAmrRx6tTQ1h8ypK86ggIoCmomKCpGII6hM56zfH3tDBxRBPHAA1/fz2Z+z9xqf9bBZz15r7/UsRo0ahcViYefOnbRu3boEpNVoyha3847hJxFZax7rgKPAmpITTVOaKKUIDAwkICCA48ePc/jwYaZPn87Zs2eLXWZ2djYzZ86kS5cu9OvXj927d5eKUUjPsvDSkv0sizzD+J7NmR54+0YhOzubGTNm4O/vz3PPPaeNguauotARg4g0x9ikx3Z/52zAGUgsIbk0pczWrVtxdXVl7NixuWEdOnRAKcVrr73Ghg0bEJHchWlpaWn069ePixcvkpWVxTvvvEO/fv1y8+7cuZP+/ftTs2ZNwsPDadasdD5gu3w9ixcXRRJx6gJTn25DsL/XbZcRFRXFyJEj8fDwICIiAi+v2y9DoynPFGUq6WMMNxaHbANFxM+Me7okBNOULjExMTedN//xxx+Jiori4MGDpKSk0KlTJ7p160bdunVZtWoV1atXJyUlhc6dO/PMM8+QmZlJeno6AQEBdO/endWrV1O9evVSacPZK+kEzQ/n+Lk0Zg/pyNMPet5W/vT0dL7++ms2b97M+++/T3BwsF65rLkrKcpUUtP8RgFAKRUJNLW7RJoyxa5duxg6dCjOzs7Ur1+f7t27ExERgVKKKVOm4O3tTa9evUhMTGTJkiXUrVsXi8XCihUrCAkJKTWjcOJcGgO+COXMhWssCH7oto1CaGgoHTt25PTp0xw8eJDhw4dro6C5aynKiOFW21dVsZcgGsfStm1bVqxYcUN4QV5NlyxZwrlz59i3bx8ZGRnUrVuXYcOG8cwzz7B582YCAwNLWuRcDiVcIniBsZr5+9Gd8W5Us8h509LSmDJlCsuXL2fOnDnUrl27QuyXoNHcCUUZMUSIyIv5A0VkJLDP/iJpHEHPnj3JyMjgq6++yg2LiIigVq1aLFu2DIvFwrlz59ixYwcPPfQQly9fpl69evzwww/Url2b9PR0Fi9ezOrVq3Fyuh3fjHfGrmMpDJ23hyquziwf2+W2jMLmzZtp3749ly9fJiYmRntC1WhMijJimAisEpG/8ach8MNYw1B6j4WaEiVnlfLEiROZMWMGlStXzv1cNS0tjQcffBAR4YMPPqBBgwY88cQT+Pj48Omnn9K8eXOcnZ3p2rVrqcr808HfmfxDFPfXrcqiEQ9Rv3rR9ma+ePEikydPJiQkhLlz59K3b98SllSjKV8UahiUUmeBriLSA2hnBv+slAopUck0pY6npyc//PDDDeEzZ85k5syZudefffYZkydPxsPDg61bt+Lnl3dHwaKuSL4TFu4+ydvrDtOpiQdfBflRo0rRFq79+OOPjBs3jv79+2undxpNARR5gZtSaiuwtQRl0ZRxEhMTefzxx4mNjWXcuHF89NFHpTptBMY7jw9/+Y05IXE81qY+c4Z2pLJr4R5W//jjD8aNG0d0dDTLli3T/o00mltQuv/VmhLj3XffpW3btnh7e9OhQwf27t1bYNqAgAAiIyOLXLZSinfeeYemTZuSmprK4cOH+eSTT0rdKGRbrExZFc2ckDgG+zXmi7/5FGoUlFIsWrQIb29vmjdvTlRUlDYKGk0h3IlLDE0ZISwsjHXr1rF//37c3NxISUkhMzPTLmUfO3aMvn37curUKf7973/z1ltvOeQFbXqWhQnfH2Dz4bOM69Gcf/RuWagcp06dYsyYMZw9e5aNGzfi4+NTStJqNOUbPWKoACQlJVGnTh3c3NwAqFOnDp6enmzZsoWOHTvSvn17RowYkcdYHDp0CB8fHwYOHIifnx9t27blrbfeyo1v0qQJXbt2pWXLlmRmZnLixAmmTp3qEKNw+XoWw+aHs/nwWaY+3YZX+7S6pRxWq5VPP/0UX19funXrRnh4uDYKGs1tUKIjBhGZDzwFJCul2hWWXlM8evfuzbRp02jZsiW9evVi8ODBPPzwwwQHB7NlyxZatmzJsGHDWLt2Lf7+/sTFxfHPf/6TL774gp49e1K7dm0sFguPPvoohw4dwmKxkJiYSEJCAh9//DGvvPKKw9qWfCWdYeZq5k+GdKBfh4a3TH/06FFGjhyJUopdu3bxwAMPlJKkGk3FoaRHDAsB/S1gCVO1alX27dvHvHnzqFu3LoMHD2bu3Ll4eXnlekYNCgpi+/bttG/fnuzsbJYuXcpzzz3H8uXL8fHxoWPHjsTExPDKK6/g5+eHs7MzkZGRDjUKJ1OuMuDLUE5fuMY3QZ1uaRSysrJ477338Pf3Z/DgwezcuVMbBY2mmJToiEEptUNEmpZkHRoDZ2dnAgICCAgIoH379ixatCg37tKlS8yaNYtjx46xatUq3n//fWrWrMnJkyeZNWsWERERREdH07t3b3bv3s38+fN56623aNy4scPaE51wmeAF4Sjg+xc782DjgheuHThwgJEjR1KnTh0iIyML3A1Oo9EUDYe/fBaR0cBogPr167Nt27ZilZOWllbsvOWd06dP4+TkRKNGjQBYs2YNVquVo0ePMmnSJBYvXkyVKlUYMmQIVapU4dKlS+zbtw9nZ2esViv9+vVj165dODs7M27cOJo0aUJ6ejq7d+++YQ/m0uDweQuz96dzj6vwml9lLh6PYtvxG9NlZmayaNEi1q9fz5gxY+jTpw/x8fHEx8cXu+67+T66HbSeCqdc60gpVaIHhqO9mKKk9fX1VcVl69atxc5b3omMjFRdunRRrVu3Vu3bt1eBgYEqNjZWdevWTbm5uSkvLy81fPhwtWnTJqWUUt27d1cRERFq7dq1ysXFRYmI8vHxUYGBgWrBggVKKaWaNGmizp07V+pt+elgomoxZb3q/eF29cfl6wWm27Vrl2rVqpXq37+/SkpKslv9d/N9dDtoPRVOaeoIiFR27LcdPmLQ3Dm+vr6EhoYChqFfvHgxPXr0YMSIEWzcuJEqVQxfhzlPL+vWrSMwMJCQkBCeffbZ3BGFLXfy1F1cvg2L5621sfg1qcXXwzpRw/3G1cypqalMmTKFlStXMmfOHAYMGFDqcmo0FR1tGCoQp0+fZuzYsfz+++8Fbla/ePFiRo8eTeXKldmyZQsBAQGlL2g+lFJ89MtvzA6Jo1fr+nz615uvZt60aRNjxowhICCAmJgYPDw8HCCtRlPxKdGvkkTkeyAMaCUiCaZHVo1J1apV81wvXLiQcePGFTl/fHw87dq1w2q18vnnn+Pr64u/v/9NN6s/f/48L7/8MkFBQQwZMoTk5OQyYRQsVsWUVTHMDoljkF8jvnz+xtXMFy5cIDg4mDFjxjB37lwWLlyojYJGU4KU9FdJQ0uyfI2x61j37t2xWCzs2LGD1q1bk52dnSfNnDlzePXVV6latSrh4eE3HUk4gvQsCxOXRrEx9g9eDrif126ycG3lypWMHz+eAQMGEB0drZ3eaTSlgJ5KKoOkpqbi7e3Nb7/9hqurK1euXMHb25tjx45x6NAhRowYQZUqVbh27RrHjx+nR48edOnShTfffJP09HSuXr3KihUrGDp0KNu3bycjI4Pnn3+e4cOHlxmjcCXd2Jt578kLvPlUG0Y8kndf5RyndzExMfzwww888sgjDpJUo7n70C4xHMj169fp0KFD7vHmm28CUK1aNQICAvj5558BWLp0KQMGDMDV1ZXhw4czatQo4uLiOHz4MJ6ennz11Vc4OTkRFhbGokWL2LJlC48//jj//e9/8fT0ZOHChURHR5e607uCSE5NZ/DcPew7dZFPhnTIYxSUUixcuBBvb29atmxJVFSUNgoaTSlTNnqKu5QqVaoQFRWVe0ybNi03btSoUSxYsACABQsWMHz4cM6ePUtcXBwTJ07E29ub7du3U6tWrdw8jz32GCkpKTRv3pzw8HAmTJjA8ePHCQoK4vz586WyT0JhxKdcZeAXYZw6f5VvgvOuZo6Pj6dv37588sknbNq0ienTp1O5ctE239FoNPZDG4Yyir+/P/Hx8Wzfvh2LxUJ8fDzNmzcnPT2dFStWEBISkme+3Wq1cuDAAdq0aYO7uzutW7dm/PjxeebsHb1tZUziZQZ+GUpqehbfvdiZ7i3rAobsc+bMwc/Pj4CAAMLDw+nYsaNDZdVo7ma0YSjDDBs2jEGDBpGWlka/fv149NFHad26NXXrGh3qkiVLANi/fz+TJ08mOjqajz76iOjoaHr16pUbv23bNurUqcM999zjsLaExqUwZN4e3FycWT62Kx1MFxe//vor3bp1Y+nSpezatYs33ngDV9ei7cam0WhKBv3yuQzj5uZGcnIyGRkZbNq0iV69erFv3z5GjBiBu7s7jz32GImJiXTq1ImmTZvSv39/xo8fD8DUqVMZPnw43t7euLu7s2jRIi5cuOCQdqyPTmLi0iia1nHn2xEP06BGZbKyspg5cyYffvghU6dO5eWXXy4z70A0mrsdbRgcSP45/+DgYIKDgzl//jxPPfUUe/fuxcvLi6NHj+Y+Rfv6+nLw4EG2b99O//79SU9PZ+HChbzwwgt5yvLw8GDNmjV5whzht+X/9pzizTUx+NxXi2+C/KjpXokDBw4wYsQI6tWrp53eaTRlkAr5iDZp0iQ+/vjj3Os+ffowatSo3Ot//OMfTJs2jRkzZtxWucHBwaxYscJuct6Mzz77DE9PTw4ePIinpycbN27MM7Vy/fp1BgwYQM+ePfHx8SE5OfkGo1AWyFnN/D+rY+jZqh6LRz5MZScrb7zxBn369GHixIls3LhRGwWNpgxSIQ1D165dc30HWa1WUlJSiI2NzY0PDQ2lT58+vP76644S8QYSEhLw9vZmwoQJjBkzhtTUVBISEnL3UwBYu3Yt9erV45dffmH16tX88ssvZXLBl8Wq+PfqGD7ZcoyBvo2Y+4Iv+8LDePDBB3PXYgQFBTn8ZbhGo7k5FdIw+Pv75xqG2NhY2rVrR7Vq1bh48SIZGRkcOXKEgwcP5rqfCA4OZsKECXTt2pVmzZrljgqUUowbN442bdrw5JNPkpycbFc5r169yvr163nnnXfw8vIiNTWVmJgYZs+ejbPzn24hUlNT6dWrF4GBgfTu3Zvk5GSefvppu8piL9KzLIz7bj9L9p5mbPf7ebOPFxNfmcCgQYOYPn06K1asoEGDBo4WU6PR3IIK+Y7B09MTFxcXTp8+TWhoKF26dCExMZGwsDBq1KiBt7c3lSpVypMnKSmJXbt28euvv/LMM88wcOBAVq1axdGjR4mOjubs2bO0adOGESNG2EXGkJAQgoKCuHLlClevXmXKlCm8/fbbNzxFf/vtt4wZMwZ3d3dCQkLo3r27XeovCVLTs3jx20j2nLjAv59sTeNrx/D27kuPHj200zuNphxRIQ0D/DlqCA0NZfLkySQmJhIaGkqNGjXo2rXrDemfffZZnJycaNOmDWfPngVgx44dDB06FGdnZzw9PenZs+cdy3Xp0iVeffVVli1bxrVr12jVqhUbNmygSZMmedKlpKTw5JNPEhkZSVBQEPPmzcPFpez+uc6lZhC8IJyjf6Qyre99bPn6bbZv3868efPo3bu3o8XTaDS3QYWcSoI/3zNER0fTrl07OnfuTFhYGKGhofj7+9+Q3s3NLffc2PfCwJ7z4GvWrKFly5Z8//33ZGRkMGvWLGJjY28wCrNnz8bT05MzZ84QERHB/Pnzy6xRyM7OZtiLL/HMrJ85ce4qz9dP4vW/9qFGjRrExMRoo6DRlEMqrGHw9/dn3bp1eHh44OzsjIeHB5cuXSIsLIwuXboUqYychVcWi4WkpCS2bt1aLFmSk5MZNGgQL7zwAikpKbRt25aEhAQmTZrE6tVr2LlzJwBnzpyhXbt2TJo0ifHjx5OQkICPj0+x6iwt3nz/YxYvWkDssll4Rn7G0i9msnz5cmbPnn2DW3GNRlM+qLCGoX379qSkpNC5c+c8YTVq1KBOnTpFKiMwMJAWLVrQvn17XnrppSLN7yulsFqtfPDBB6SlpbF48WJatWrFTz/9RGZmJvPnzyc8PJx69erxzTff8NyQoQQOHMwbb7xBs2bNuHbtGkeOHOE///lPmV/wtSHyNz6Y/g71h0znWuJv1LqnMlFRUTcdkWk0mnKEPfcJvdPDnns+JyUlqQb3NlR79uwpdpm3S1xcnKrX4F4VFBSkAHVvo8aqevXqSkRUz5491eXLl3PTpqWlKbeq1VVVnyfVPe16KidXN/X2228rq9VaYvLZcw/a9Yd+V1Xb9VRVvR9TDV+ar2p2D1a16tRTly5dslsdjkDvZVw0tJ4KR+/5XAaZ9Nr/47JUZegLwRyJjsrzDqGkGPP3CVxS7nz73TLq9HudPzbOxk2srFy5ksDAwDxpx770d7KyrdR95HksVy+ScSyMbt26lflv+/edusiX24+zISyGtJgQEME1KRrPhg1p5v8Xrl+/To0aNRwtpkajuQMqpGHYu3cvP/28gTpBn3F500dMe+dd3v3faYVnvAM2bNjA3v2HoFI13O5rj3srf6zpqXDgR5544gnA6FT3nDiPR3oSi79bAkqR8OnziLMrVe6pSmLi7yUmn8WqyLQorqRnkZVtJduqyMy2kmW58Twr20qmxUqWRZFt+fP8eHIq83aexGJVVKpZl5Xb9vN013ba6Z1GU8EoccMgIn2BTwBn4Gul1O35obhNsrOzCR41BtWgNWmHNmNxcuP9GTMYO/pFGjduXCJ1hh5NYsBfh2GtdR+Z8QeQSu6c/k8gIk44ObuQlJRE6Fkn/mdNDFar4tLWr3GrUpV7vVrQqGkL6t7bkEYt25Fcz48PNv5KltkRG79Wsi3K7Jzzhv/ZcSsznRGWeZN8uR9a/bLZLm0WIMlaTRsFjaYCUqKGQUScgc+Ax4AEIEJE1iqlDtu7rriLFmK3xvHmIH+upafzyF+6cb9XZe7v+wRNm75M/fr17V1l7rTKz79sJTNbIWmXcHOvTiOv+2nQtAUPdH6Mag2aMG1bMlt/PUdO31yjxyhq9BiFAs6Yx/5zwJZjuDgJrs5OuDgLlZydbnru6uxEJWcnKrs64eLmYly7CC5OTrnnrs5OxrWLkdfFyYkzp0/yQIvmRh0uTrnl2JaZc36zuCN/XGHC9wfItlhxdXGic7PadtepRqNxPCU9YngIiFNKnQAQkaVAP8CuhiE0LoV396ajOEq14C/p0siD6u5uXAEOAAcuwapF++1ZJanpWcQkXkEBlRq2odHfF+XGZWNYwaSrgvsZC0pdRNnkdXYSPv+bDx3vq5m3w3dywsmp5N4xbNuWSMBfmhU7/3213fnuxc7sOXGezs1q49ukVuGZNBpNuaOkDUNDjAfiHBKAh20TiMhoYDRA/fr1i+UaenVcJgoFCDhX4o+LV8lMv1ZsoYvChesqT2cP4CTw9wcr0crDBTcXcBFjgVzcRQsfRGSTZTXSvPCAK27nfuXwuRIV8QbS0tLs4nq7rUDqyQS2nbxzmcoa9tJRRUfrqXDKs45K2jDc7PE3T3+qlJoHzAPw8/NTAQEBt11JNa+LrD8RikWBq4sTc4d3LvGn2X2nLjJ0XhiZFqM5zk7C//Zrx18fvu+GtAFAR5+LDn/S3rZtG8XR792E1lHR0HoqnPKso5I2DAmA7RvfRoDdP73xbVKLf3aqTEbNJqXW8fo2qcX3o7uwcn8CAvT3aXTLen2b1NJTLxqNplxQ0oYhAmghIl5AIjAE+GtJVNS8ljMBAc1LougC0Z29RqOpiJSoYVBKZYvIOGATxueq85VSsYVk02g0Go0DKfF1DEqp9cD6kq5Ho9FoNPahbHtp02g0Gk2pow2DRqPRaPKgDYNGo9Fo8qANg0aj0WjyIErlX7/rOETkHHCqmNnrACl2FKcionVUOFpHRUPrqXBKU0dNlFJ17VVYmTIMd4KIRCql/BwtR1lG66hwtI6KhtZT4ZRnHempJI1Go9HkQRsGjUaj0eShIhmGeY4WoBygdVQ4WkdFQ+upcMqtjirMOwaNRqPR2IeKNGLQaDQajR3QhkGj0Wg0eSj3hkFE+orIURGJE5HXHS1PaSMi8SISLSJRIhJphnmIyC8icsz8rWWT/g1TV0dFpI9NuK9ZTpyIzBaRkttjtBQQkfkikiwiMTZhdtOLiLiJyDIzfK+INC3N9tmDAnQ0VUQSzfspSkSesIm7G3XUWES2isgREYkVkVfM8Ip9Lymlyu2B4cr7ONAMqAQcBNo4Wq5S1kE8UCdf2AfA6+b568D75nkbU0dugJepO2czLhzogrHr3gbgcUe37Q710g3wAWJKQi/Ay8CX5vkQYJmj22wnHU0FXr1J2rtVR/cCPuZ5NeA3UxcV+l4q7yOGh4A4pdQJpVQmsBTo52CZygL9gEXm+SLgWZvwpUqpDKXUSSAOeEhE7gWqK6XClHF3fmuTp1yilNoBXMgXbE+92Ja1Ani0vI2yCtBRQdytOkpSSu03z1OBIxh72Vfoe6m8G4aGwBmb6wQz7G5CAZtFZJ+IjDbD6iulksC4sYF6ZnhB+mponucPr2jYUy+5eZRS2cBloHaJSV66jBORQ+ZUU84UyV2vI3OKpyOwlwp+L5V3w3Azq3q3fX/rr5TyAR4H/i4i3W6RtiB93e16LI5eKqrOvgDuBzoAScB/zPC7WkciUhVYCUxUSl25VdKbhJU7PZV3w5AANLa5bgT87iBZHIJS6nfzNxlYhTG9dtYcumL+JpvJC9JXgnmeP7yiYU+95OYRERegBkWflimzKKXOKqUsSikr8BXG/QR3sY5ExBXDKCxRSv1oBlfoe6m8G4YIoIWIeIlIJYwXN2sdLFOpISL3iEi1nHOgNxCDoYMgM1kQsMY8XwsMMb+C8AJaAOHmUDhVRDqbc5vDbPJUJOypF9uyBgIh5txxuSanszMJxLif4C7Vkdmmb4AjSqkPbaIq9r3k6Lffd3oAT2B8KXAc+Jej5SnltjfD+ALiIBCb036M+cktwDHz18Mmz79MXR3F5ssjwA+jEzgOfIq5Kr68HsD3GFMhWRhPZCPtqRegMrAc4+ViONDM0W22k47+D4gGDmF0WPfe5Tp6BGNa5xAQZR5PVPR7SbvE0Gg0Gk0eyvtUkkaj0WjsjDYMGo1Go8mDNgwajUajyYM2DBqNRqPJgzYMGo1Go8mDNgwajUajyYM2DJobEJEGIrJURI6LyGERWS8iLUuwvm0i4ldImoki4m5zvV5EapaUTI7E3m0VkY9zXKWYuj5t66RNRFaLSJp5vkpEnrWJOyoi/7a5Xiki/UWkvYgsLK5MmrKNNgyaPJgdxipgm1LqfqVUG2AKUN+xkjERyO0slVJPKKUuOVCeYiMGt/rfs1tbRcQD6KwMT6o5XAL8zfiaGK6lcwgFuppxtYE0DFfROXQBQpVS0UAjEbmvOHJPyKsgAAAEtklEQVRpyjbaMGjy0wPIUkp9mROglIoCnEVkXU6YiHwqIsHmebyITBeRMBGJFBEfEdlkjjjGmmkCCspvi4h8YZYRKyJvm2ETAE9gq4hstamzjoi8LyIv2+SfKiL/MM9fE5EI01Po27dqtIj8j4j8KsamK9+LyKtm+P0istH0XrtTRB4wwxeKsdlKqIicEJGBNmXdUK+INBVjs5fPgf1A49ttq3k+WURizGNivrK/MsvaLCJVTHEGAhvzNXcphvsYgP7AjzZxuzENg/m7DqhrGjMv4LpS6g8z/iebcjQVCG0YNPlpB+wrRr4zSqkuwE5gIUaH1BmYdpvl/Esp5Qd4A91FxFspNRvD4VgPpVSPfOmXAoNtrgcBy0WkN4afmocwPIX6SgGeZ81prAEYLpX7Y7guyGEeMF4p5Qu8CnxuE3cvhsuEp4AZZlm3qrcV8K1SqqNS6tTttlVEfIHhwMMYun1RRDqa0S2Az5RSbTFGBAPMcH9u/HtuAbqJiDPmxjA2cfuAdmL4HusKhGG4dmhtXu+2SRsJ/AVNhcPF0QJoKgw5zgujgarK2NQkVUTSb3N+fJAY+0q4YHS8bTD81NwUpdQBEaknIp5AXeCiUuq0+eTdGzhgJq2K0XnuuEkxjwBrlFLXAUTkJ/O3KkZnuNxmSt7NJt9qZXghPSwiOVNtvQuo9zRwSim1p7htNeVcpZS6asr3I0bHvBY4aY7swOjcm5rn9wLn8pVjAXZhGNQqSqn4nPYppTJEJBZjZ7fOGDuVNTP10BFjqimHZIzRjaaCoQ2DJj+xGE/7+ckm7wizcr74DPPXanOec+1ShPyYUxWvAp2UUhfNl5s3pLsJK0yZG2CMIMDwcf+eUmpuEfIXtFuWE3BJKdWhgHjbdorN7w31irHJy1Wb6+K09Va7etnKYgFyppKuF1DuUox3SVNvEheKse1nNVO2PcA4DMPwpU26ymb5mgqGnkrS5CcEcBORF3MCRKQTxv7abcRwJ1wDePQ2yz1VhPzVMTrPy+YT+OM2cakYe+7ejJw584EYRgJgEzDCfOpHRBqKSL0C8u8CnhaRymb6JwGUsSHLSRF5zixDROTBQtpZ1HqL09YdwLMi4i6Gm/VAjKm7W3EEaH6T8J3AexgeVvOzGxiD4bUXjFFMZ+A+jAeHHFryp1tuTQVCjxg0eVBKKREJBD4WkdeBdCAe40uZHzA6iWP8OVVS1HLPiMgt8yulDorIAYzO5wR557PnARtEJCn/3LtSKlaMfSkS1Z/bLW4WkdZAmDlNkgY8z58bqtjmjxCRtRgd4SmMufPLZvTfgC/E+GTTFcMIHcxfhk1ZBdVrudO2KqX2myOLcDPoa3MqrWlB8gA/Y3TyX+erXwGzCsgTijF99J6ZNltEkjHeI1lt0vUwy9dUMLTbbY0G432CUipNjPUDO4DRytwEvrwjIruAp+z5ea+IuAHbgUeUsU+xpgKhDYNGA4jIdxgvfysDi5RS7zlYJLshIg9jfGZ6qxfbt1tmC6ChUmqbvcrUlB20YdDcNYixYGvLTaIeVUqdL215NJqyijYMGo1Go8mD/ipJo9FoNHnQhkGj0Wg0edCGQaPRaDR50IZBo9FoNHn4/xg50R0pPvriAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:18.525252Z",
          "iopub.status.busy": "2021-05-31T04:27:18.524656Z",
          "iopub.status.idle": "2021-05-31T04:27:18.528148Z",
          "shell.execute_reply": "2021-05-31T04:27:18.527519Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.453040Z"
        },
        "papermill": {
          "duration": 0.141376,
          "end_time": "2021-05-31T04:27:18.528259",
          "exception": false,
          "start_time": "2021-05-31T04:27:18.386883",
          "status": "completed"
        },
        "tags": [],
        "id": "ybKrX3I1iD1Q"
      },
      "source": [
        "\n",
        "# cplot = peakhour3.plot(y=\"commulative_CO2 emissions\",x=\"commulative_demand\",grid=True, style='.-', \n",
        "#                             title='commulative_CO2 emissions vs commulative_demand',legend=True)\n",
        "# cplot.set_xlabel(\"commulative_demand (kW) \")\n",
        "# cplot.set_ylabel(\"commulative_CO2 emissions (kg/kWh)\")\n",
        "\n",
        "# index=0\n",
        "# colcount=len(df.columns)\n",
        "# for i, row in peakhour3.iterrows():\n",
        "#     index=index+1\n",
        "#     print(row.values[3])\n",
        "#     tag= row.values[0], row.values[3], row.values[5]\n",
        "#     cplot.annotate(tag, \n",
        "#                     xy=(row.values[3], row.values[5]), \n",
        "#                     xytext=(row.values[3]+(colcount-index), row.values[5]+(colcount-index)),\n",
        "#                     arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.067694,
          "end_time": "2021-05-31T04:27:18.665811",
          "exception": false,
          "start_time": "2021-05-31T04:27:18.598117",
          "status": "completed"
        },
        "tags": [],
        "id": "4JRg9LeKiD1R"
      },
      "source": [
        "<a id=\"section-six\"></a>\n",
        "# 6. Price vs Fuel Type\n",
        "LCOE (Levelized Cost Of generating Electricity) - price per MWh\n",
        "Now that we have a merged dataset we can take a closer look at data distributions and correlations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:18.809811Z",
          "iopub.status.busy": "2021-05-31T04:27:18.809192Z",
          "iopub.status.idle": "2021-05-31T04:27:18.825221Z",
          "shell.execute_reply": "2021-05-31T04:27:18.824483Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.459240Z"
        },
        "papermill": {
          "duration": 0.091818,
          "end_time": "2021-05-31T04:27:18.825343",
          "exception": false,
          "start_time": "2021-05-31T04:27:18.733525",
          "status": "completed"
        },
        "tags": [],
        "id": "rQvIU8JbiD1R",
        "outputId": "1b826270-9081-4cb6-b19c-680daeaea247"
      },
      "source": [
        "df_cost = pd.read_csv(\"../input/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Price2.csv\")\n",
        "df_cost\n",
        "## Data source : IPCC ANEX III Technology-specific Cost and Performance Parameters -2014\n",
        "# Schlömer S., T. Bruckner, L. Fulton, E. Hertwich, A. McKinnon, D. Perczyk, J. Roy, R. Schaeffer, R. Sims, P. Smith, and R. Wiser,\n",
        "# 2014: Annex III: Technology-specific cost and performance parameters. In: Climate Change 2014: Mitigation of Climate\n",
        "# Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate\n",
        "# Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P.\n",
        "# Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University\n",
        "# Press, Cambridge, United Kingdom and New York, NY, USA."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>LCOE / Marginal Cost ( with subsidy)  ( $/Mwh)</th>\n",
              "      <th>LCOE from IEA 2020 ($/MWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuclear</td>\n",
              "      <td>25.0</td>\n",
              "      <td>46.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Wind</td>\n",
              "      <td>NaN</td>\n",
              "      <td>69.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Solar</td>\n",
              "      <td>24.0</td>\n",
              "      <td>98.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>9.0</td>\n",
              "      <td>83.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petroleum</td>\n",
              "      <td>34.0</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Oil</td>\n",
              "      <td>34.0</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Natural gas</td>\n",
              "      <td>23.0</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaturalGas</td>\n",
              "      <td>23.0</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Coal</td>\n",
              "      <td>34.0</td>\n",
              "      <td>114.260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hydro</td>\n",
              "      <td>NaN</td>\n",
              "      <td>97.950</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Fuel Type  LCOE / Marginal Cost ( with subsidy)  ( $/Mwh)  \\\n",
              "0      Nuclear                                            25.0   \n",
              "1         Wind                                             NaN   \n",
              "2       Solar                                             24.0   \n",
              "3        Other                                             9.0   \n",
              "4    Petroleum                                            34.0   \n",
              "5          Oil                                            34.0   \n",
              "6  Natural gas                                            23.0   \n",
              "7   NaturalGas                                            23.0   \n",
              "8         Coal                                            34.0   \n",
              "9        Hydro                                             NaN   \n",
              "\n",
              "   LCOE from IEA 2020 ($/MWh)   \n",
              "0                       46.850  \n",
              "1                       69.920  \n",
              "2                       98.270  \n",
              "3                       83.935  \n",
              "4                       57.660  \n",
              "5                       57.660  \n",
              "6                       57.660  \n",
              "7                       57.660  \n",
              "8                      114.260  \n",
              "9                       97.950  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:18.973086Z",
          "iopub.status.busy": "2021-05-31T04:27:18.972087Z",
          "iopub.status.idle": "2021-05-31T04:27:18.976440Z",
          "shell.execute_reply": "2021-05-31T04:27:18.975910Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.485439Z"
        },
        "papermill": {
          "duration": 0.082514,
          "end_time": "2021-05-31T04:27:18.976563",
          "exception": false,
          "start_time": "2021-05-31T04:27:18.894049",
          "status": "completed"
        },
        "tags": [],
        "id": "4IbEyclqiD1R",
        "outputId": "893416d7-6129-4786-ba93-5c2d2fde7848"
      },
      "source": [
        "# df_cost['LCOE / Marginal Cost ( with subsidy)  ( $/Mwh)']\n",
        "df_cost[['Fuel Type','LCOE from IEA 2020 ($/MWh) ']]\n",
        "\n",
        "# df_cost.info()\n",
        "## Data source : https://www.iea.org/articles/levelised-cost-of-electricity-calculator LCOE estimates from Projected Costs of Generating Electricity 2020"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>LCOE from IEA 2020 ($/MWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuclear</td>\n",
              "      <td>46.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Wind</td>\n",
              "      <td>69.920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Solar</td>\n",
              "      <td>98.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>83.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petroleum</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Oil</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Natural gas</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaturalGas</td>\n",
              "      <td>57.660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Coal</td>\n",
              "      <td>114.260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Hydro</td>\n",
              "      <td>97.950</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Fuel Type  LCOE from IEA 2020 ($/MWh) \n",
              "0      Nuclear                       46.850\n",
              "1         Wind                       69.920\n",
              "2       Solar                        98.270\n",
              "3        Other                       83.935\n",
              "4    Petroleum                       57.660\n",
              "5          Oil                       57.660\n",
              "6  Natural gas                       57.660\n",
              "7   NaturalGas                       57.660\n",
              "8         Coal                      114.260\n",
              "9        Hydro                       97.950"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:19.123205Z",
          "iopub.status.busy": "2021-05-31T04:27:19.122184Z",
          "iopub.status.idle": "2021-05-31T04:27:19.270250Z",
          "shell.execute_reply": "2021-05-31T04:27:19.270683Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.502667Z"
        },
        "papermill": {
          "duration": 0.223801,
          "end_time": "2021-05-31T04:27:19.270833",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.047032",
          "status": "completed"
        },
        "tags": [],
        "id": "YIOH2waviD1R",
        "outputId": "625a570e-f08e-4b4a-c93e-89ecba783cd3"
      },
      "source": [
        "df_cost.sort_values('LCOE from IEA 2020 ($/MWh) ',ascending=True).plot(x=\"Fuel Type\",y='LCOE from IEA 2020 ($/MWh) ', kind='bar')\n",
        "# df_cost.xticks(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae6575a690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.069036,
          "end_time": "2021-05-31T04:27:19.410246",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.341210",
          "status": "completed"
        },
        "tags": [],
        "id": "PCyoV4IhiD1S"
      },
      "source": [
        "IEA LCOE estimates from Projected Costs of Generating Electricity 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:19.554189Z",
          "iopub.status.busy": "2021-05-31T04:27:19.553620Z",
          "iopub.status.idle": "2021-05-31T04:27:19.556516Z",
          "shell.execute_reply": "2021-05-31T04:27:19.556976Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.657246Z"
        },
        "papermill": {
          "duration": 0.076899,
          "end_time": "2021-05-31T04:27:19.557131",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.480232",
          "status": "completed"
        },
        "tags": [],
        "id": "LTdH61zFiD1S"
      },
      "source": [
        "# Assumptions \n",
        "# Discount rate 7% Central case\n",
        "# Carbon price 30.00 USD/ton Default assumption\n",
        "# Heat price 37.06 USD/MWh Default assumption\n",
        "# Coal price 100% Default assumption\n",
        "# Gas price 100% Default assumption"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:19.699930Z",
          "iopub.status.busy": "2021-05-31T04:27:19.699332Z",
          "iopub.status.idle": "2021-05-31T04:27:19.703873Z",
          "shell.execute_reply": "2021-05-31T04:27:19.704618Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.663208Z"
        },
        "papermill": {
          "duration": 0.076703,
          "end_time": "2021-05-31T04:27:19.704775",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.628072",
          "status": "completed"
        },
        "tags": [],
        "id": "Sw5Q9xVBiD1S"
      },
      "source": [
        "peakt = peakt.drop(columns=['Cummulative_generation','carbon','Cummulative_carbon(kg)'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:19.851490Z",
          "iopub.status.busy": "2021-05-31T04:27:19.850784Z",
          "iopub.status.idle": "2021-05-31T04:27:19.876635Z",
          "shell.execute_reply": "2021-05-31T04:27:19.876116Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.675666Z"
        },
        "papermill": {
          "duration": 0.099585,
          "end_time": "2021-05-31T04:27:19.876749",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.777164",
          "status": "completed"
        },
        "tags": [],
        "id": "npmjcvIOiD1S",
        "outputId": "3df84228-04b0-4b55-da45-11e8e0137c02"
      },
      "source": [
        "peakt['price']=0\n",
        "for i, row in peakt.iterrows():\n",
        "#     print(i)\n",
        "    for j,cost in df_cost.iterrows():\n",
        "#         print(lcoe.values[0])\n",
        "        if i in cost[0]:\n",
        "            print(\"Match between two tables for \"+i+ \" and \"+ cost[0])\n",
        "            print(cost.values[2])\n",
        "            print(row.values[0])\n",
        "            peakt.at[i,'price']= cost[2] * row[0]\n",
        "\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Match between two tables for Wind and Wind\n",
            "69.92\n",
            "47.0\n",
            "Match between two tables for Hydro and Hydro\n",
            "97.95\n",
            "1975.0\n",
            "Match between two tables for Solar and Solar \n",
            "98.27\n",
            "127.0\n",
            "Match between two tables for Coal and Coal\n",
            "114.26\n",
            "16.0\n",
            "Match between two tables for Other and Other\n",
            "83.935\n",
            "658.0\n",
            "Match between two tables for Oil and Oil\n",
            "57.66\n",
            "48.0\n",
            "Match between two tables for Nuclear and Nuclear\n",
            "46.85\n",
            "3322.0\n",
            "Match between two tables for NaturalGas and NaturalGas\n",
            "57.66\n",
            "14636.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>3286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>193451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>12480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>55229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>2767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>155635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>843911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   price\n",
              "Wind              47.0    3286\n",
              "Hydro           1975.0  193451\n",
              "Solar            127.0   12480\n",
              "Coal              16.0    1828\n",
              "Other            658.0   55229\n",
              "Oil               48.0    2767\n",
              "Nuclear         3322.0  155635\n",
              "NaturalGas     14636.0  843911"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.030824Z",
          "iopub.status.busy": "2021-05-31T04:27:20.029829Z",
          "iopub.status.idle": "2021-05-31T04:27:20.034800Z",
          "shell.execute_reply": "2021-05-31T04:27:20.034345Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.707073Z"
        },
        "papermill": {
          "duration": 0.085943,
          "end_time": "2021-05-31T04:27:20.034912",
          "exception": false,
          "start_time": "2021-05-31T04:27:19.948969",
          "status": "completed"
        },
        "tags": [],
        "id": "YFbYbHNAiD1T",
        "outputId": "15b3cad3-3a8a-4f31-910d-9ec16faee5bc"
      },
      "source": [
        "# sort by ascending order of price\n",
        "peakt = peakt.sort_values(by=['price'])\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>2767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>3286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>12480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>55229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>155635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>193451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>843911</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   price\n",
              "Coal              16.0    1828\n",
              "Oil               48.0    2767\n",
              "Wind              47.0    3286\n",
              "Solar            127.0   12480\n",
              "Other            658.0   55229\n",
              "Nuclear         3322.0  155635\n",
              "Hydro           1975.0  193451\n",
              "NaturalGas     14636.0  843911"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.187047Z",
          "iopub.status.busy": "2021-05-31T04:27:20.186451Z",
          "iopub.status.idle": "2021-05-31T04:27:20.189570Z",
          "shell.execute_reply": "2021-05-31T04:27:20.190049Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.716798Z"
        },
        "papermill": {
          "duration": 0.084423,
          "end_time": "2021-05-31T04:27:20.190193",
          "exception": false,
          "start_time": "2021-05-31T04:27:20.105770",
          "status": "completed"
        },
        "tags": [],
        "id": "TjS6MwMkiD1T",
        "outputId": "ba187854-cf8c-44b9-95c8-79bdd5c1ac7a"
      },
      "source": [
        "# Cummulative_generation with respect to nw ordering of cost \n",
        "peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>price</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1828</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>2767</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>3286</td>\n",
              "      <td>111.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>12480</td>\n",
              "      <td>238.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>55229</td>\n",
              "      <td>896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>155635</td>\n",
              "      <td>4218.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>193451</td>\n",
              "      <td>6193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>843911</td>\n",
              "      <td>20829.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   price  Cummulative_generation\n",
              "Coal              16.0    1828                    16.0\n",
              "Oil               48.0    2767                    64.0\n",
              "Wind              47.0    3286                   111.0\n",
              "Solar            127.0   12480                   238.0\n",
              "Other            658.0   55229                   896.0\n",
              "Nuclear         3322.0  155635                  4218.0\n",
              "Hydro           1975.0  193451                  6193.0\n",
              "NaturalGas     14636.0  843911                 20829.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.335546Z",
          "iopub.status.busy": "2021-05-31T04:27:20.335006Z",
          "iopub.status.idle": "2021-05-31T04:27:20.338254Z",
          "shell.execute_reply": "2021-05-31T04:27:20.338723Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.736775Z"
        },
        "papermill": {
          "duration": 0.076864,
          "end_time": "2021-05-31T04:27:20.338859",
          "exception": false,
          "start_time": "2021-05-31T04:27:20.261995",
          "status": "completed"
        },
        "tags": [],
        "id": "VTH-YR6hiD1T"
      },
      "source": [
        "# peakt[\"total_price($thousand)\"] = (peakt[\"generation\"] * peakt[\"price\"]) / 1000\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.485243Z",
          "iopub.status.busy": "2021-05-31T04:27:20.484713Z",
          "iopub.status.idle": "2021-05-31T04:27:20.496220Z",
          "shell.execute_reply": "2021-05-31T04:27:20.495641Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.748443Z"
        },
        "papermill": {
          "duration": 0.085422,
          "end_time": "2021-05-31T04:27:20.496323",
          "exception": false,
          "start_time": "2021-05-31T04:27:20.410901",
          "status": "completed"
        },
        "tags": [],
        "id": "Y5k33w3ViD1T",
        "outputId": "1dfd7780-820b-4779-954a-4b7fdf43ee91"
      },
      "source": [
        "# Cummulative price\n",
        "peakt['Cummulative_price($)']  = peakt[\"price\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>generation</th>\n",
              "      <th>price</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "      <th>Cummulative_price($)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>16.0</td>\n",
              "      <td>1828</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>48.0</td>\n",
              "      <td>2767</td>\n",
              "      <td>64.0</td>\n",
              "      <td>4595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>47.0</td>\n",
              "      <td>3286</td>\n",
              "      <td>111.0</td>\n",
              "      <td>7881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>127.0</td>\n",
              "      <td>12480</td>\n",
              "      <td>238.0</td>\n",
              "      <td>20361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>658.0</td>\n",
              "      <td>55229</td>\n",
              "      <td>896.0</td>\n",
              "      <td>75590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>3322.0</td>\n",
              "      <td>155635</td>\n",
              "      <td>4218.0</td>\n",
              "      <td>231225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1975.0</td>\n",
              "      <td>193451</td>\n",
              "      <td>6193.0</td>\n",
              "      <td>424676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>14636.0</td>\n",
              "      <td>843911</td>\n",
              "      <td>20829.0</td>\n",
              "      <td>1268587</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   price  Cummulative_generation  Cummulative_price($)\n",
              "Coal              16.0    1828                    16.0                  1828\n",
              "Oil               48.0    2767                    64.0                  4595\n",
              "Wind              47.0    3286                   111.0                  7881\n",
              "Solar            127.0   12480                   238.0                 20361\n",
              "Other            658.0   55229                   896.0                 75590\n",
              "Nuclear         3322.0  155635                  4218.0                231225\n",
              "Hydro           1975.0  193451                  6193.0                424676\n",
              "NaturalGas     14636.0  843911                 20829.0               1268587"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.646457Z",
          "iopub.status.busy": "2021-05-31T04:27:20.645779Z",
          "iopub.status.idle": "2021-05-31T04:27:20.649034Z",
          "shell.execute_reply": "2021-05-31T04:27:20.648544Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.772330Z"
        },
        "papermill": {
          "duration": 0.08138,
          "end_time": "2021-05-31T04:27:20.649147",
          "exception": false,
          "start_time": "2021-05-31T04:27:20.567767",
          "status": "completed"
        },
        "tags": [],
        "id": "-v3DVvweiD1U"
      },
      "source": [
        "# Plot price vs generation\n",
        "\n",
        "# costplot = peakt.plot(y=\"price\",x=\"generation\",grid=True, style='.-', \n",
        "#                             title='price vs generation in ' + Region ,legend=True)\n",
        "# costplot.set_xlabel(\"generation (MW) \")\n",
        "# costplot.set_ylabel(\"Price LCOE ($/MWh)\")\n",
        "\n",
        "# index =0\n",
        "# colcount=len(peakt.columns)\n",
        "# for i, row in peakt.iterrows():\n",
        "#     index=index+1\n",
        "# #     print(row.values[6])\n",
        "# #     costplot.text(row.values[2]+index*10, row.values[6]+index*30, str(i))\n",
        "#     costplot.annotate(i, xy=(row.values[0], row.values[1]), \n",
        "#                         xytext=(row.values[0]+(colcount-index)*1000, row.values[1]+(colcount-index)*3000),\n",
        "#                         arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:20.802653Z",
          "iopub.status.busy": "2021-05-31T04:27:20.801992Z",
          "iopub.status.idle": "2021-05-31T04:27:21.143449Z",
          "shell.execute_reply": "2021-05-31T04:27:21.142950Z",
          "shell.execute_reply.started": "2021-05-31T02:39:51.784862Z"
        },
        "papermill": {
          "duration": 0.422006,
          "end_time": "2021-05-31T04:27:21.143580",
          "exception": false,
          "start_time": "2021-05-31T04:27:20.721574",
          "status": "completed"
        },
        "tags": [],
        "id": "3wXsQCshiD1U",
        "outputId": "82207a7e-7a0c-4ec7-f323-04c4e442f5d5"
      },
      "source": [
        "# Plot Cummulative_price vs Cummulative_generation\n",
        "\n",
        "costplot = peakt.plot(x=\"Cummulative_generation\",y='Cummulative_price($)',\n",
        "                            title=\"Cummulative_price vs Cummulative_generation in \\n\"+\n",
        "                            \"in \"+ Region + \" on \" + str(peakmin.Timestamp.values[0]),\n",
        "                            grid=True, style='.-', legend=True)\n",
        "costplot.set_xlabel(\"Cumulative generation(MW)\")\n",
        "costplot.set_ylabel(\"Cummulative_price($)\")\n",
        "\n",
        "xmax = peakt['Cummulative_generation'].max()\n",
        "ymax = peakt['Cummulative_price($)'].max()\n",
        "index = 0\n",
        "colcount=len(peakt.columns)\n",
        "for i, row in peakt.iterrows():\n",
        "    index=index+1\n",
        "    x = row.values[2]\n",
        "    y = row.values[3]\n",
        "    xpos = def_plot_limitx(xmax, x+(colcount-index)*2000)\n",
        "    ypos = def_plot_limity(ymax, y+(index)*30000)\n",
        "    costplot.annotate(i, xy=(x, y), \n",
        "                        xytext=(xpos, ypos), ha='center', va='center',\n",
        "                        arrowprops=dict(arrowstyle='-|>') , rotation=0)"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073994,
          "end_time": "2021-05-31T04:27:21.290789",
          "exception": false,
          "start_time": "2021-05-31T04:27:21.216795",
          "status": "completed"
        },
        "tags": [],
        "id": "_j63V2rbiD1U"
      },
      "source": [
        "<a id=\"section-seven\"></a>\n",
        "# 7. LMP Locational Marginal Pricing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:21.439194Z",
          "iopub.status.busy": "2021-05-31T04:27:21.438278Z",
          "iopub.status.idle": "2021-05-31T04:27:21.476363Z",
          "shell.execute_reply": "2021-05-31T04:27:21.475829Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.140778Z"
        },
        "papermill": {
          "duration": 0.113294,
          "end_time": "2021-05-31T04:27:21.476474",
          "exception": false,
          "start_time": "2021-05-31T04:27:21.363180",
          "status": "completed"
        },
        "tags": [],
        "id": "wL_fk-7JiD1U"
      },
      "source": [
        "# Import all available data \n",
        "# df = pd.read_csv(\"../input/us-electricity-by-energy-source-in-spring/930-data-export.csv\")\n",
        "\n",
        "dfl = pd.read_csv(\"../input/lcoe-co2/lmp_Newengland_6_7_jul_2020.csv\")\n",
        "dfl2 = pd.read_csv(\"../input/lcoe-co2/lmp_Newengland_8_9_jul_2020.csv\")\n",
        "dfl3 = pd.read_csv(\"../input/lcoe-co2/lmp_Newengland_10_11_jul_2020.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:21.629743Z",
          "iopub.status.busy": "2021-05-31T04:27:21.629082Z",
          "iopub.status.idle": "2021-05-31T04:27:21.633861Z",
          "shell.execute_reply": "2021-05-31T04:27:21.634331Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.159337Z"
        },
        "papermill": {
          "duration": 0.083823,
          "end_time": "2021-05-31T04:27:21.634462",
          "exception": false,
          "start_time": "2021-05-31T04:27:21.550639",
          "status": "completed"
        },
        "tags": [],
        "id": "M7cPbjkFiD1U",
        "outputId": "833e7774-b642-426e-edce-b2458a56a153"
      },
      "source": [
        "dfl = dfl.append(dfl2)\n",
        "dfl = dfl.append(dfl3)\n",
        "len(dfl) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:21.786044Z",
          "iopub.status.busy": "2021-05-31T04:27:21.785410Z",
          "iopub.status.idle": "2021-05-31T04:27:21.796951Z",
          "shell.execute_reply": "2021-05-31T04:27:21.797495Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.170528Z"
        },
        "papermill": {
          "duration": 0.088226,
          "end_time": "2021-05-31T04:27:21.797652",
          "exception": false,
          "start_time": "2021-05-31T04:27:21.709426",
          "status": "completed"
        },
        "tags": [],
        "id": "X8838D0diD1V",
        "outputId": "b6e3ad8f-45ac-4f80-826d-1a860213bcfe"
      },
      "source": [
        "dfl.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BeginDate</th>\n",
              "      <th>ID</th>\n",
              "      <th>Energy</th>\n",
              "      <th>LMP</th>\n",
              "      <th>Loss</th>\n",
              "      <th>Congestion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07/05/2020 21:00</td>\n",
              "      <td>4000</td>\n",
              "      <td>16.47</td>\n",
              "      <td>16.49</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>07/05/2020 21:05</td>\n",
              "      <td>4000</td>\n",
              "      <td>16.47</td>\n",
              "      <td>16.49</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>07/05/2020 21:10</td>\n",
              "      <td>4000</td>\n",
              "      <td>15.81</td>\n",
              "      <td>15.83</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>07/05/2020 21:15</td>\n",
              "      <td>4000</td>\n",
              "      <td>15.81</td>\n",
              "      <td>15.83</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07/05/2020 21:20</td>\n",
              "      <td>4000</td>\n",
              "      <td>17.30</td>\n",
              "      <td>17.33</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          BeginDate    ID  Energy    LMP  Loss  Congestion\n",
              "0  07/05/2020 21:00  4000   16.47  16.49  0.02         0.0\n",
              "1  07/05/2020 21:05  4000   16.47  16.49  0.02         0.0\n",
              "2  07/05/2020 21:10  4000   15.81  15.83  0.02         0.0\n",
              "3  07/05/2020 21:15  4000   15.81  15.83  0.02         0.0\n",
              "4  07/05/2020 21:20  4000   17.30  17.33  0.03         0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:21.948155Z",
          "iopub.status.busy": "2021-05-31T04:27:21.947435Z",
          "iopub.status.idle": "2021-05-31T04:27:22.110460Z",
          "shell.execute_reply": "2021-05-31T04:27:22.110943Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.191975Z"
        },
        "papermill": {
          "duration": 0.240156,
          "end_time": "2021-05-31T04:27:22.111083",
          "exception": false,
          "start_time": "2021-05-31T04:27:21.870927",
          "status": "completed"
        },
        "tags": [],
        "id": "K38XBl3piD1V",
        "outputId": "1be19d21-7a57-4312-e0dc-addd7c5fc6d5"
      },
      "source": [
        "dfl.plot(y=['Energy','LMP','Loss','Congestion'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae67457450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:22.267006Z",
          "iopub.status.busy": "2021-05-31T04:27:22.266340Z",
          "iopub.status.idle": "2021-05-31T04:27:22.572502Z",
          "shell.execute_reply": "2021-05-31T04:27:22.573052Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.374077Z"
        },
        "papermill": {
          "duration": 0.386879,
          "end_time": "2021-05-31T04:27:22.573189",
          "exception": false,
          "start_time": "2021-05-31T04:27:22.186310",
          "status": "completed"
        },
        "tags": [],
        "id": "31WsIjNViD1V"
      },
      "source": [
        "dfl['Timestamp'] = pd.to_datetime(dfl['BeginDate'],format = '%d-%m-%Y %H:%M', errors='ignore')\n",
        "\n",
        "# adding separate time and date columns\n",
        "dfl[\"DATE\"] = pd.to_datetime(dfl[\"Timestamp\"]).dt.date # add new column with date\n",
        "dfl[\"TIME\"] = pd.to_datetime(dfl[\"Timestamp\"]).dt.time # add new column with time\n",
        "\n",
        "# add hours and minutes for ml models\n",
        "dfl['HOURS'] = pd.to_datetime(dfl['TIME'],format='%H:%M:%S').dt.hour\n",
        "dfl['MINUTES'] = pd.to_datetime(dfl['TIME'],format='%H:%M:%S').dt.minute\n",
        "dfl['MINUTES_PASS'] = dfl['MINUTES'] + dfl['HOURS']*60\n",
        "\n",
        "\n",
        "dates= dfl['DATE'].unique()\n",
        "\n",
        "# remove first day which doesnt have all hours \n",
        "dates= dates[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-05-31T04:27:22.726506Z",
          "iopub.status.busy": "2021-05-31T04:27:22.725867Z",
          "iopub.status.idle": "2021-05-31T04:27:23.533276Z",
          "shell.execute_reply": "2021-05-31T04:27:23.533803Z",
          "shell.execute_reply.started": "2021-05-31T02:39:52.694309Z"
        },
        "papermill": {
          "duration": 0.885155,
          "end_time": "2021-05-31T04:27:23.533964",
          "exception": false,
          "start_time": "2021-05-31T04:27:22.648809",
          "status": "completed"
        },
        "tags": [],
        "id": "cGoEsswaiD1W",
        "outputId": "c139c0b5-f5ba-4088-f96b-3296606ad125"
      },
      "source": [
        "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
        "fig.set_size_inches(20, 10)\n",
        "fig.subplots_adjust(wspace=0.2)\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "\n",
        "fig.legend('LMP')\n",
        "\n",
        "    \n",
        "for pp,ax in zip(dates,axs.flatten()):\n",
        "#     print(df_nw[pp])\n",
        "    print(pp)\n",
        "    sub_df = dfl[dfl['DATE']==pp]\n",
        "    sub_df.plot(x='MINUTES_PASS', y = 'LMP', ax = ax, \n",
        "                title = \"LMP for generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'),\n",
        "                legend=False)\n",
        "    \n",
        "    ax.set_ylabel(\"LMP($/Mw)\")\n",
        "    \n",
        "    mean_lmp = round(sub_df[\"LMP\"].mean())\n",
        "#     max_gen = sub_df[\"LMP\"].max()\n",
        "#     maxh = sub_df.loc[sub_df[\"LMP\"]== max_gen]\n",
        "#     maxh = maxh[\"HOURS\"]\n",
        "\n",
        "    ax.axhline(y=mean_lmp, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "    ax.text(5,mean_lmp, \"Mean \" + str(mean_lmp) + \"(MW)\" )\n",
        "\n",
        "# #     ax.get_legend().remove()\n",
        "    \n",
        "#     ax.annotate('Peak '+ str(max_gen)+ \"(MWh)\", xy=(maxh,max_gen), xytext=(maxh+1, max_gen+1000),\n",
        "#              arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)\n",
        "    \n",
        "    ax.set_ylim(bottom = 0,top=100)\n",
        "    \n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-07-06\n",
            "2020-07-07\n",
            "2020-07-08\n",
            "2020-07-09\n",
            "2020-07-10\n",
            "2020-07-11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fae654f0610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}