CarbonEmission_fuelmix.ipynb

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  "metadata": {
    "colab": {
      "name": "US_CarbonEmission_granular_powergen.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNPTLgP67SQXmll0vugIzeO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/renewable-energy-experiments/carbonfoorprint/blob/master/CarbonEmission_fuelmix.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qoy7C-2K_VdY"
      },
      "source": [
        "<h1><center>Electricity generation by energy source grids and Carbon Emissions <center><h1>  \n",
        "<h2> Includes Carbon Life Cyccle Emission from Renewables </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAG4GYl1_d3H"
      },
      "source": [
        "# Table of Contents\n",
        "* [1. DataSet Description](#section-one)\n",
        "* [2. Plot Generation graph](#section-two)\n",
        "* [3. Carbon Emissions](#section-three)\n",
        "* [4. Carbon graph](#section-four)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Itm_n0uPOTZD"
      },
      "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": 546,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQ89WJCF_hwp"
      },
      "source": [
        "<a id=\"section-one\"></a>\n",
        "# 1. DataSet Description\n",
        "\n",
        "Dataset is weekly reports from EDA  for CA ISO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Gx95-URU_bcn",
        "outputId": "2bc836d1-7aee-47cc-e633-db7b08b6863c"
      },
      "source": [
        "# Import generation  data \n",
        "\n",
        "Region=\"UK\"\n",
        "date = datetime.date(2020, 7, 6)\n",
        "\n",
        "# NYISO\n",
        "# df = pd.read_csv(\"/content/CA ISO_2020-09-07.csv\")\n",
        "\n",
        "# New England\n",
        "# df = pd.read_csv(\"/content/NewEngland_2020-07-11.csv\")\n",
        "\n",
        "# North West BPA \n",
        "# North Western BPA https://transmission.bpa.gov/business/operations/Wind/baltwg.aspx\n",
        "# df = pd.read_csv('/content/baltwg_16_23_jun_2021.txt', \n",
        "#                 #  skiprows=[0,1], \n",
        "#                  sep = \"\\t\",delimiter = \"\\t\")\n",
        "\n",
        "# CA ISO \n",
        "# df = pd.read_csv(\"/content/CA ISO_2020-11-07.csv\")\n",
        "\n",
        "# Texas\n",
        "# df = pd.read_csv(\"/content/CA ISO_2020-09-07.csv\")\n",
        "\n",
        "# Canada\n",
        "# df = pd.read_csv(\"/content/xml-to-csv-generation_fuel_type_multiday.csv\")\n",
        "\n",
        "# UK\n",
        "df = pd.read_csv(\"/content/GenerationbyFuelType_20210615_1905.csv\")\n",
        "\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 547,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>HDR</th>\n",
              "      <th>HALF HOURLY OUTTURN GENERATION BY FUEL TYPE DATA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">FUELHH</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">20200706</th>\n",
              "      <th>1.0</th>\n",
              "      <th>3403.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>5278.0</th>\n",
              "      <th>9815.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>311.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>111.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>66.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>208.0</th>\n",
              "      <th>923.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <th>3394.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>5274.0</th>\n",
              "      <th>9494.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>302.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>111.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>72.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>186.0</th>\n",
              "      <th>910.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <th>3422.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>5258.0</th>\n",
              "      <th>9282.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>279.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>126.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>336.0</th>\n",
              "      <th>908.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <th>3291.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>5261.0</th>\n",
              "      <th>9279.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>276.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>113.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>458.0</th>\n",
              "      <th>908.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <th>3268.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>5246.0</th>\n",
              "      <th>9385.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>245.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>120.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>366.0</th>\n",
              "      <th>909.0</th>\n",
              "      <th>0.0</th>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                       HDR  HALF HOURLY OUTTURN GENERATION BY FUEL TYPE DATA\n",
              "FUELHH 20200706 1.0 3403.0 0.0 0.0 5278.0 9815.0 0.0 311.0 0.0 111.0 0.0 66.0 0.0 208.0 923.0 0.0 0.0  0.0                                               0.0\n",
              "                2.0 3394.0 0.0 0.0 5274.0 9494.0 0.0 302.0 0.0 111.0 0.0 72.0 0.0 186.0 910.0 0.0 0.0  0.0                                               0.0\n",
              "                3.0 3422.0 0.0 0.0 5258.0 9282.0 0.0 279.0 0.0 126.0 0.0 0.0  0.0 336.0 908.0 0.0 0.0  0.0                                               0.0\n",
              "                4.0 3291.0 0.0 0.0 5261.0 9279.0 0.0 276.0 0.0 113.0 0.0 0.0  0.0 458.0 908.0 0.0 0.0  0.0                                               0.0\n",
              "                5.0 3268.0 0.0 0.0 5246.0 9385.0 0.0 245.0 0.0 120.0 0.0 0.0  0.0 366.0 909.0 0.0 0.0  0.0                                               0.0"
            ]
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            "tags": []
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          "execution_count": 547
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmG5PW3HTDvb"
      },
      "source": [
        "column_list = (\"Nuclear\",\"Wind\",\"Solar\",\"Hydro\",\"Other\",  \"Oil\", \"NaturalGas\",\"Coal\")\n",
        "\n",
        "def df_manipulation_casio(dfs):\n",
        "    dfs['Timestamp'] = dfs.index\n",
        "    date =  dfs.iloc[0].Timestamp.replace('Supply ','')\n",
        "    dfs['DATE'] = date\n",
        "    dfs = dfs[1:len(dfs)-1]\n",
        "    dfs['DATE'] =  pd.to_datetime(dfs['DATE'],format = '%m/%d/%Y').dt.date\n",
        "    dfs['TIME'] = pd.to_datetime(dfs['Timestamp'],format = '%H:%M').dt.time\n",
        "    dfs['HOURS'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.hour\n",
        "    dfs['MINUTES'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.minute\n",
        "    dfs['MINUTES_PASS'] = dfs['MINUTES'] + dfs['HOURS']*60\n",
        "    dfs['Timestamp'] = pd.to_datetime(dfs['DATE'].apply(str) +' '+ dfs['TIME'].apply(str), format = '%Y-%m-%d %H:%M:%S')\n",
        "\n",
        "      # Fill up Nan as 0\n",
        "    dfs = dfs.replace('undefined', 0)\n",
        "    dfs = dfs.fillna(0)\n",
        "    dfs = dfs.replace(np.nan, 0)\n",
        "\n",
        "    return dfs\n",
        "\n",
        "def df_manipulation_northwest(dfs):\n",
        "    dfs = dfs.rename(columns={\"Fossil/Biomass\": \"NaturalGas\",\n",
        "                            \"Large hydro\": \"Hydro\"\n",
        "                          })\n",
        "    for col in column_list:\n",
        "      if col not in dfs.columns:\n",
        "          dfs[col] = 0\n",
        "    dfs[\"total_generation\"] = dfs[np.array(column_list)].sum(axis=1)\n",
        "    dfs['Timestamp'] = pd.to_datetime(dfs['Date/Time       '], format = '%m/%d/%Y %H:%M')\n",
        "                  \n",
        "    # adding separate time and date columns\n",
        "    dfs[\"DATE\"] = pd.to_datetime(dfs[\"Timestamp\"]).dt.date # add new column with date\n",
        "    dfs['TIME'] = pd.to_datetime(dfs['Timestamp'],format = '%H:%M').dt.time\n",
        "    dfs['HOURS'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.hour\n",
        "    dfs['MINUTES'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.minute\n",
        "    dfs['MINUTES_PASS'] = dfs['MINUTES'] + dfs['HOURS']*60\n",
        "\n",
        "    # Fill up Nan as 0\n",
        "    dfs = dfs.replace('undefined', 0)\n",
        "    dfs = dfs.fillna(0)\n",
        "    dfs = dfs.replace(np.nan, 0)\n",
        "\n",
        "    # drop BeginDate\n",
        "    dfs.drop('Date/Time       ', axis=1, inplace=True)\n",
        "    day1 = dfs[dfs['DATE']== date].reset_index()\n",
        "\n",
        "    return day1\n",
        "\n",
        "def df_manipulation_isone(dfs):\n",
        "\n",
        "    return dfs\n",
        "\n",
        "def df_manipulation_caiso(dfs):\n",
        "\n",
        "    return dfs\n",
        "\n",
        "def df_manipulation_canada(dfs):\n",
        "    cols = list(dfs.columns)\n",
        "    cols = [cols[-1]] + cols[:-1]\n",
        "    dfs = dfs[cols]\n",
        "\n",
        "    dfs.reset_index(inplace=True)\n",
        "    dfs.set_index('_Series', drop=True, inplace=True)\n",
        "    dfs= dfs.transpose()\n",
        "    dfs = dfs[1:]\n",
        "\n",
        "    dfs['Timestamp']=pd.date_range(start='2020-06-10', freq='1H', end='2020-06-16')\n",
        "    dfs = dfs.rename(columns={\"Date\" : \"DATE\",\n",
        "                        \"Hour\" : \"HOUR\",\n",
        "                        \"GAS\": \"NaturalGas\",\n",
        "                        \"SOLAR\" : \"Solar\",\n",
        "                        \"NUCLEAR\" : \"Nuclear\",\n",
        "                        \"WIND\" : \"Wind\",\n",
        "                        \"HYDRO\": \"Hydro\",\n",
        "                        \"BIOFUEL\": \"Other\",\n",
        "                        \"Total Output\":\"total_generation\"\n",
        "                       })\n",
        "    dfs['DATE'] =  pd.to_datetime(dfs['Timestamp'],format = '%Y-%m-%d', errors='coerce').dt.date\n",
        "    dfs['TIME'] = pd.to_datetime(dfs['Timestamp'],format = '%H:%M', errors='coerce').dt.time \n",
        "    dfs['HOURS'] = pd.to_datetime(dfs['Timestamp'],format = '%H', errors='coerce').dt.hour\n",
        "    dfs['MINUTES'] = pd.to_datetime(dfs['Timestamp'],format='%M' , errors='coerce').dt.minute\n",
        "    dfs['MINUTES_PASS'] = dfs['MINUTES'] + dfs['HOURS']*60\n",
        "\n",
        "    for col in column_list:\n",
        "        if col not in dfs.columns:\n",
        "            dfs[col] = 0\n",
        "\n",
        "    dfs['Nuclear'] = pd.to_numeric(dfs['Nuclear'], errors='coerce').fillna(0)\n",
        "    dfs['Hydro'] = pd.to_numeric(dfs['Hydro'], errors='coerce').fillna(0)\n",
        "    dfs['NaturalGas'] = pd.to_numeric(dfs['NaturalGas'], errors='coerce').fillna(0)\n",
        "    dfs['Coal'] = pd.to_numeric(dfs['Coal'], errors='coerce').fillna(0)\n",
        "    dfs['Solar'] = pd.to_numeric(dfs['Solar'], errors='coerce').fillna(0)\n",
        "    dfs['Coal'] = pd.to_numeric(dfs['Coal'], errors='coerce').fillna(0)\n",
        "    dfs['Oil'] = pd.to_numeric(dfs['Oil'], errors='coerce').fillna(0)\n",
        "    dfs['Other'] = pd.to_numeric(dfs['Other'], errors='coerce').fillna(0)\n",
        "    dfs[\"total_generation\"] = dfs[np.array(column_list)].sum(axis=1)\n",
        "\n",
        "    dfs = dfs[dfs['DATE']==date]\n",
        "    return dfs\n",
        "\n",
        "def df_manipulation_uk(dfs):\n",
        "    col_names = ['','Timestamp','MINUTES','NaturalGas', 'Oil', 'Coal', 'Nuclear', 'Wind','','Hydro','','Other','','','','','','','']\n",
        "    dfs = pd.DataFrame(dfs.index.values.tolist(), columns=col_names)\n",
        "\n",
        "    for col in column_list:\n",
        "        if col not in dfs.columns:\n",
        "            dfs[col] = 0\n",
        "    # dfs['Timestamp'] = pd.date_range(start='2020-07-06', freq='1H', end='2020-07-12')\n",
        "    dfs['DATE'] =  pd.to_datetime(dfs['Timestamp'], errors='coerce', format = '%Y%m%d').dt.date\n",
        "    dfs[\"total_generation\"] = dfs[np.array(column_list)].sum(axis=1)\n",
        "\n",
        "    df2 = pd.DataFrame()\n",
        "\n",
        "    dhours = dfs[dfs['DATE']==date]\n",
        "    dunique_minpass = dhours['Timestamp'].unique()\n",
        "    dunique_minpass = pd.to_datetime(dunique_minpass ,format = '%Y-%m-%d %H:%M')\n",
        "    \n",
        "    index = pd.date_range(date, periods=1440, freq='T')\n",
        "    series = pd.Series(range(1440), index=index, name='Timestamp')\n",
        "    series = pd.to_datetime(series.index,format = '%Y-%m-%d %H:%M')\n",
        "\n",
        "    for m in series: \n",
        "        if m not in dunique_minpass:\n",
        "            dhours = dhours.append({ \n",
        "                'Timestamp': m,\n",
        "                'Solar': 0\n",
        "            }, ignore_index = True) \n",
        "    dhours = dhours.interpolate(method ='linear', limit_direction ='forward')\n",
        "    df2 = df2.append(dhours)\n",
        "\n",
        "    # convert minutes ( 30 min apart to hours)\n",
        "    df2['HOURS'] = pd.to_datetime(df2['MINUTES']/2,errors =\"coerce\",format='%H').dt.hour\n",
        "    df2['HOURS'] = df2['HOURS'] .replace(np.nan, 24)\n",
        "\n",
        "    df2 = df2.replace('undefined', 0)\n",
        "    df2 = df2.fillna(0)\n",
        "    df2 = df2.replace(np.nan, 0)\n",
        "    # drop unrecognized fuel types and empty column header columns\n",
        "    df2.drop([''], axis=1, inplace=True)  \n",
        "\n",
        "    df2['MINUTES_PASS'] = df2['MINUTES'] + df2['HOURS']*60\n",
        "\n",
        "    return df2"
      ],
      "execution_count": 548,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kyYfITqTEtB",
        "outputId": "a8a8bb78-604e-4055-b7c8-2a11fa3781c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# df = df_manipulation_northwest(df)\n",
        "# df = df_manipulation_isone(df)\n",
        "\n",
        "# df = df_manipulation_canada(df)\n",
        "\n",
        "# df = df_manipulation_caiso(df)\n",
        "\n",
        "df = df_manipulation_uk(df)\n",
        "df.head()"
      ],
      "execution_count": 549,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Other</th>\n",
              "      <th>Solar</th>\n",
              "      <th>DATE</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200706</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3403.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5278.0</td>\n",
              "      <td>9815.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18918.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200706</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5274.0</td>\n",
              "      <td>9494.0</td>\n",
              "      <td>302.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18575.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200706</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3422.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5258.0</td>\n",
              "      <td>9282.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18367.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>63.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200706</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3291.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>9279.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18220.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200706</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3268.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5246.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18264.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>125.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Timestamp  MINUTES  NaturalGas  ...  total_generation  HOURS  MINUTES_PASS\n",
              "0  20200706      1.0      3403.0  ...           18918.0    0.0           1.0\n",
              "1  20200706      2.0      3394.0  ...           18575.0    1.0          62.0\n",
              "2  20200706      3.0      3422.0  ...           18367.0    1.0          63.0\n",
              "3  20200706      4.0      3291.0  ...           18220.0    2.0         124.0\n",
              "4  20200706      5.0      3268.0  ...           18264.0    2.0         125.0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 549
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "a-o4PHH0NrRT",
        "outputId": "e18611bf-8997-4a82-de40-95c7f709c5e6"
      },
      "source": [
        "df_carbon = pd.read_csv(\"/content/US Utility grids - LCOE, Co2 Emissions - Carbon3.csv\")\n",
        "df_carbon"
      ],
      "execution_count": 550,
      "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>Lifecycle emissions (gCO2eq/kWh)</th>\n",
              "      <th>Lifecycle emissions (kgCO2eq/MWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuclear</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hydro</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wind</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Solar</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaturalGas</td>\n",
              "      <td>490</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Oil</td>\n",
              "      <td>490</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Coal</td>\n",
              "      <td>820</td>\n",
              "      <td>820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Other</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Fuel Type  ...  Lifecycle emissions (kgCO2eq/MWh)\n",
              "0     Nuclear  ...                                 12\n",
              "1       Hydro  ...                                 24\n",
              "2        Wind  ...                                 12\n",
              "3       Solar  ...                                 48\n",
              "4  NaturalGas  ...                                490\n",
              "5         Oil  ...                                490\n",
              "6        Coal  ...                                820\n",
              "7       Other  ...                                 24\n",
              "\n",
              "[8 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 550
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zEFpLCe5ogz"
      },
      "source": [
        "<a id=\"section-two\"></a>\n",
        "# 2. Plot Generation graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_FY2_YI5-SC"
      },
      "source": [
        "color_list = [\"green\",\"skyblue\",\"gold\",\"teal\",\"deeppink\",\"red\",\"brown\",\"darkgrey\"]"
      ],
      "execution_count": 551,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "Cak47qzp5rSo",
        "outputId": "6b4d13da-7f67-435c-fffb-d87c45b88203"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "\n",
        "df.plot(x='MINUTES_PASS', y = np.array(column_list), \n",
        "             ax = ax, \n",
        "             kind='area', stacked=True, \n",
        "             color = np.array(color_list),\n",
        "             title = \"Fuel usage for generation in \"+ Region + \" \"+  str(date), \n",
        "             grid=True,legend=False)\n",
        "\n",
        "ax.set_ylabel(\"generation(Mw)\")\n",
        "\n",
        "mean_gen = round(df[\"total_generation\"].mean())\n",
        "max_gen = df[\"total_generation\"].max()\n",
        "maxh = df.loc[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)+ \"(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=39000)\n",
        "    \n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": 552,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3bef439f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 552
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "st7b2img6gSl",
        "outputId": "7f2a5661-c533-4200-dd16-76726987a634",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "maxgen = df[\"total_generation\"].max()\n",
        "maxhour = df.loc[df[\"total_generation\"]== maxgen][\"HOURS\"]\n",
        "peakhour = df[ df['HOURS'].values == maxhour.values ]\n",
        "dfpeakfuelmix = pd.DataFrame(peakhour[np.array(column_list)].sum(numeric_only=True, axis=0))\n",
        "\n",
        "for col in dfpeakfuelmix.columns:\n",
        "    dfpeakfuelmix = dfpeakfuelmix.rename(columns={col:'peak generation'})\n",
        "\n",
        "dfpeakfuelmix[\"percent\"] = (dfpeakfuelmix['peak generation'] / dfpeakfuelmix['peak generation'].sum()) * 100\n",
        "dfpeakfuelmix[\"percent\"] = dfpeakfuelmix[\"percent\"].round(3)\n",
        "\n",
        "dfpeakfuelmix"
      ],
      "execution_count": 553,
      "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>peak generation</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>10293.0</td>\n",
              "      <td>22.944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>8481.0</td>\n",
              "      <td>18.905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>1031.0</td>\n",
              "      <td>2.298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>482.0</td>\n",
              "      <td>1.074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>24575.0</td>\n",
              "      <td>54.779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            peak generation  percent\n",
              "Nuclear             10293.0   22.944\n",
              "Wind                 8481.0   18.905\n",
              "Solar                   0.0    0.000\n",
              "Hydro                1031.0    2.298\n",
              "Other                 482.0    1.074\n",
              "Oil                     0.0    0.000\n",
              "NaturalGas          24575.0   54.779\n",
              "Coal                    0.0    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 553
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn3akcXi8rYE",
        "outputId": "07e23e77-2be3-4c23-dd7b-3ff9b24434a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "pie= dfpeakfuelmix.plot.pie(y='percent',title=\"Fuel Mix in \"+ Region + \" on \"+ str(date) + \" \" + str(maxh.values) , \n",
        "                            colors= np.array(color_list),\n",
        "                            autopct='%1.1f%%',\n",
        "                            figsize=(5, 5))\n",
        "pie.legend(bbox_to_anchor=(1.5,1), loc=\"bottom right\")"
      ],
      "execution_count": 554,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3bf06b18d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 554
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRhkVcTX6imf"
      },
      "source": [
        "<a id=\"section-three\"></a>\n",
        "# 3. Carbon Emissions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "Nsb_pOcWOvJ9",
        "outputId": "43a2c91e-b60e-471c-8346-0ec0c7a2fd4c"
      },
      "source": [
        "for col in df.columns:\n",
        "    # print(df[col])\n",
        "    for j,carbon in df_carbon.iterrows():\n",
        "        if col in carbon[0]:\n",
        "            print(\"Match between two tables for \"+col+ \" and \"+ carbon[0] +\" - \"+ str(carbon[1]))\n",
        "            df['carbon_'+col] = df[col] * carbon[1]\n",
        "            \n",
        "df.head()"
      ],
      "execution_count": 555,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Match between two tables for NaturalGas and NaturalGas - 490\n",
            "Match between two tables for Oil and Oil - 490\n",
            "Match between two tables for Coal and Coal - 820\n",
            "Match between two tables for Nuclear and Nuclear - 12\n",
            "Match between two tables for Wind and Wind - 12\n",
            "Match between two tables for Hydro and Hydro - 24\n",
            "Match between two tables for Other and Other - 24\n",
            "Match between two tables for Solar and Solar - 48\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>Timestamp</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Other</th>\n",
              "      <th>Solar</th>\n",
              "      <th>DATE</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>carbon_NaturalGas</th>\n",
              "      <th>carbon_Oil</th>\n",
              "      <th>carbon_Coal</th>\n",
              "      <th>carbon_Nuclear</th>\n",
              "      <th>carbon_Wind</th>\n",
              "      <th>carbon_Hydro</th>\n",
              "      <th>carbon_Other</th>\n",
              "      <th>carbon_Solar</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200706</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3403.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5278.0</td>\n",
              "      <td>9815.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18918.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1667470.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63336.0</td>\n",
              "      <td>117780.0</td>\n",
              "      <td>7464.0</td>\n",
              "      <td>2664.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200706</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5274.0</td>\n",
              "      <td>9494.0</td>\n",
              "      <td>302.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18575.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>1663060.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63288.0</td>\n",
              "      <td>113928.0</td>\n",
              "      <td>7248.0</td>\n",
              "      <td>2664.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200706</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3422.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5258.0</td>\n",
              "      <td>9282.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18367.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1676780.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63096.0</td>\n",
              "      <td>111384.0</td>\n",
              "      <td>6696.0</td>\n",
              "      <td>3024.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200706</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3291.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>9279.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18220.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>1612590.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63132.0</td>\n",
              "      <td>111348.0</td>\n",
              "      <td>6624.0</td>\n",
              "      <td>2712.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200706</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3268.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5246.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18264.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1601320.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62952.0</td>\n",
              "      <td>112620.0</td>\n",
              "      <td>5880.0</td>\n",
              "      <td>2880.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Timestamp  MINUTES  NaturalGas  ...  carbon_Hydro  carbon_Other  carbon_Solar\n",
              "0  20200706      1.0      3403.0  ...        7464.0        2664.0             0\n",
              "1  20200706      2.0      3394.0  ...        7248.0        2664.0             0\n",
              "2  20200706      3.0      3422.0  ...        6696.0        3024.0             0\n",
              "3  20200706      4.0      3291.0  ...        6624.0        2712.0             0\n",
              "4  20200706      5.0      3268.0  ...        5880.0        2880.0             0\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 555
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InD-yyGCSoe6"
      },
      "source": [
        "column_list_carbon = (\"carbon_Nuclear\",\"carbon_Wind\",\"carbon_Solar\",\"carbon_Hydro\", \"carbon_Other\", \n",
        "                \"carbon_NaturalGas\",\"carbon_Oil\",\"carbon_Coal\")"
      ],
      "execution_count": 556,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "MjEsuMdZ0zw0",
        "outputId": "f370e68a-4d39-4e24-d8bd-a3d8e6d49777"
      },
      "source": [
        "df['carbon_total_emission']= df[np.array(column_list_carbon)].sum(axis=1)\n",
        "df.head()"
      ],
      "execution_count": 557,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        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>Timestamp</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Other</th>\n",
              "      <th>Solar</th>\n",
              "      <th>DATE</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>carbon_NaturalGas</th>\n",
              "      <th>carbon_Oil</th>\n",
              "      <th>carbon_Coal</th>\n",
              "      <th>carbon_Nuclear</th>\n",
              "      <th>carbon_Wind</th>\n",
              "      <th>carbon_Hydro</th>\n",
              "      <th>carbon_Other</th>\n",
              "      <th>carbon_Solar</th>\n",
              "      <th>carbon_total_emission</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20200706</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3403.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5278.0</td>\n",
              "      <td>9815.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18918.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1667470.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63336.0</td>\n",
              "      <td>117780.0</td>\n",
              "      <td>7464.0</td>\n",
              "      <td>2664.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1858714.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20200706</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5274.0</td>\n",
              "      <td>9494.0</td>\n",
              "      <td>302.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18575.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>1663060.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63288.0</td>\n",
              "      <td>113928.0</td>\n",
              "      <td>7248.0</td>\n",
              "      <td>2664.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1850188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20200706</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3422.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5258.0</td>\n",
              "      <td>9282.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18367.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1676780.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63096.0</td>\n",
              "      <td>111384.0</td>\n",
              "      <td>6696.0</td>\n",
              "      <td>3024.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1860980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20200706</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3291.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5261.0</td>\n",
              "      <td>9279.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18220.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>1612590.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>63132.0</td>\n",
              "      <td>111348.0</td>\n",
              "      <td>6624.0</td>\n",
              "      <td>2712.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1796406.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20200706</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3268.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5246.0</td>\n",
              "      <td>9385.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-06</td>\n",
              "      <td>18264.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>1601320.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>62952.0</td>\n",
              "      <td>112620.0</td>\n",
              "      <td>5880.0</td>\n",
              "      <td>2880.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1785652.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Timestamp  MINUTES  ...  carbon_Solar  carbon_total_emission\n",
              "0  20200706      1.0  ...             0              1858714.0\n",
              "1  20200706      2.0  ...             0              1850188.0\n",
              "2  20200706      3.0  ...             0              1860980.0\n",
              "3  20200706      4.0  ...             0              1796406.0\n",
              "4  20200706      5.0  ...             0              1785652.0\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmuYArKivgpK"
      },
      "source": [
        "<a id=\"section-four\"></a>\n",
        "# 4. Carbon Graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHJG96xAv0US"
      },
      "source": [
        ""
      ],
      "execution_count": 557,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aq7hZ_1XAEFo",
        "outputId": "896eb0e8-06f7-4758-8dbb-ae5718ff7d52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "subdfcarbon = pd.DataFrame(df[np.array(column_list_carbon)].sum(numeric_only=True, axis=0))\n",
        "\n",
        "for col in subdfcarbon.columns:\n",
        "    subdfcarbon = subdfcarbon.rename(columns={col:'carbon emission'})\n",
        "\n",
        "subdfcarbon[\"percent\"] = (subdfcarbon['carbon emission'] / subdfcarbon['carbon emission'].sum()) * 100\n",
        "subdfcarbon[\"percent\"] = subdfcarbon[\"percent\"].round(3)\n",
        "\n",
        "subdfcarbon"
      ],
      "execution_count": 558,
      "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>carbon emission</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>carbon_Nuclear</th>\n",
              "      <td>9.267764e+07</td>\n",
              "      <td>1.612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Wind</th>\n",
              "      <td>7.822189e+07</td>\n",
              "      <td>1.361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Solar</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Hydro</th>\n",
              "      <td>1.409791e+07</td>\n",
              "      <td>0.245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Other</th>\n",
              "      <td>4.198848e+06</td>\n",
              "      <td>0.073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_NaturalGas</th>\n",
              "      <td>5.560263e+09</td>\n",
              "      <td>96.709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Oil</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>carbon_Coal</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   carbon emission  percent\n",
              "carbon_Nuclear        9.267764e+07    1.612\n",
              "carbon_Wind           7.822189e+07    1.361\n",
              "carbon_Solar          0.000000e+00    0.000\n",
              "carbon_Hydro          1.409791e+07    0.245\n",
              "carbon_Other          4.198848e+06    0.073\n",
              "carbon_NaturalGas     5.560263e+09   96.709\n",
              "carbon_Oil            0.000000e+00    0.000\n",
              "carbon_Coal           0.000000e+00    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 558
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S11_RwBuAlxL",
        "outputId": "6220258f-24ed-4a49-e70b-e2c42b8e1bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "cmap = sns.color_palette(\"Purples_r\", n_colors=8)\n",
        "carbonpie = subdfcarbon[0:8].plot.pie(y='percent',title=\"Carbon emission contribution in \"+ Region + \" on \"+ str(date) , \n",
        "                            colors= cmap,\n",
        "                            autopct='%1.1f%%',\n",
        "                            figsize=(5, 5))\n",
        "carbonpie.legend(bbox_to_anchor=(0,1), loc=\"bottom right\")"
      ],
      "execution_count": 559,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3bf0a0f250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 559
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "0kvbA_qu3CUT",
        "outputId": "3c596c43-9fa1-4d3e-f352-1b736528a246"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "df.plot(x='MINUTES_PASS', y = np.array(column_list_carbon), \n",
        "            ax = ax, \n",
        "             color=cmap, \n",
        "              kind='area', stacked=False,\n",
        "              title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ str(date),legend=False)\n",
        "ax.set_ylabel(\"Carbon Emission (kgCO2eq/MWh)\")   \n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": 560,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3bf0641bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 560
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "kQIhDn1u4Day",
        "outputId": "cdd81786-6bb3-4ea6-cc31-1cc95554b56f"
      },
      "source": [
        "import seaborn as sns\n",
        "cmap = sns.color_palette(\"gray_r\", n_colors=9)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "df[200:600].plot(x='MINUTES_PASS', y = np.array(column_list), ax = ax, \n",
        "             color=cmap, \n",
        "              # kind='area', stacked=False,\n",
        "              title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ str(date),legend=False)\n",
        "ax.set_ylabel(\"Carbon Emission (kgCO2eq/MWh)\")   \n",
        "# ax.set_ylim(bottom = 0,top=50000)\n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": 561,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3bf06f0f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 561
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "LhPZ6OYQ8EO2",
        "outputId": "3192f21f-f3c2-45e7-b881-561778ca0a53"
      },
      "source": [
        "import seaborn as sns\n",
        "cmap = sns.color_palette(\"Blues_r\", n_colors=4)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "df.plot(x='MINUTES_PASS', y = np.array(column_list)[0:4], ax = ax, \n",
        "             color=cmap, \n",
        "              # kind='area', stacked=False,\n",
        "              title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ pp,legend=False)\n",
        "ax.set_ylabel(\"Carbon Emission (kgCO2eq/MWh)\")   \n",
        "# ax.set_ylim(bottom = 0,top=50000)\n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": 562,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-562-b2e176c0c757>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m              \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m               \u001b[0;31m# kind='area', stacked=False,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m               title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ pp,legend=False)\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Carbon Emission (kgCO2eq/MWh)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# ax.set_ylim(bottom = 0,top=50000)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pp' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qs7FH7WZ-_VS"
      },
      "source": [
        "## Without total carbon emission \n",
        "import seaborn as sns\n",
        "cmap = sns.color_palette(\"gray_r\", n_colors=8)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "df.plot(x='MINUTES_PASS', y = np.array(column_list)[0:8], ax = ax, \n",
        "             color=cmap, \n",
        "              kind='area', stacked=True,\n",
        "              title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ pp,legend=False)\n",
        "ax.set_ylabel(\"Carbon Emission (kgCO2eq/MWh)\")\n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBxpCNcaxCRT"
      },
      "source": [
        "## With total carbon emission \n",
        "import seaborn as sns\n",
        "cmap = sns.color_palette(\"gray_r\", n_colors=9)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(12, 5)\n",
        "df.plot(x='MINUTES_PASS', y = np.array(column_list), ax = ax, \n",
        "             color=cmap, \n",
        "          kind='area', stacked=True,\n",
        "              title = \"Carbon Emission by Fuel for generation in \"+ Region + \" on \"+ pp,legend=False)\n",
        "ax.set_ylabel(\"Carbon Emission (kgCO2eq/MWh)\")\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[\"HOURS\"]\n",
        "\n",
        "# Mark Mean Line \n",
        "# ax.axhline(y=mean_gen, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "# ax.text(5,mean_gen+1000, \"Mean \" + str(mean_gen) + \"(MW)\" )\n",
        "\n",
        "# ax.get_legend().remove()\n",
        "\n",
        "# Mark Peak consumption \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.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "# plot.show(block=True);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HDzdPQWwQly"
      },
      "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(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,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)+ \"(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=39000)\n",
        "    \n",
        "# ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}