b1_py.ipynb

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  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "b1.py.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Niharikajo/plotting-the-given-dataset/blob/main/b1_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fd33f8f0"
      },
      "source": [
        "#importing numpy pandas libraries \n",
        "import numpy as np \n",
        "import pandas as pd"
      ],
      "id": "fd33f8f0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "694f70af"
      },
      "source": [
        "#read saved a csv file\n",
        "data=pd.read_csv('batterydata.csv')"
      ],
      "id": "694f70af",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7af057a",
        "outputId": "c1879ccf-d71d-4c2a-bb35-19508459c924"
      },
      "source": [
        "#print the loaded csv file\n",
        "data"
      ],
      "id": "c7af057a",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Battery_Level</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Current</th>\n",
              "      <th>Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:17</td>\n",
              "      <td>1</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:18</td>\n",
              "      <td>2</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:19</td>\n",
              "      <td>3</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:20</td>\n",
              "      <td>4</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:21</td>\n",
              "      <td>5</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:22</td>\n",
              "      <td>6</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:23</td>\n",
              "      <td>7</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:24</td>\n",
              "      <td>8</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:25</td>\n",
              "      <td>9</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:26</td>\n",
              "      <td>10</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:27</td>\n",
              "      <td>11</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:28</td>\n",
              "      <td>12</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:29</td>\n",
              "      <td>13</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:30</td>\n",
              "      <td>14</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:31</td>\n",
              "      <td>15</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:32</td>\n",
              "      <td>16</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>402982</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:33</td>\n",
              "      <td>17</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>37536</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:34</td>\n",
              "      <td>18</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>37536</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:35</td>\n",
              "      <td>19</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>37536</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>30-09-2020</td>\n",
              "      <td>14:55:36</td>\n",
              "      <td>20</td>\n",
              "      <td>27.5</td>\n",
              "      <td>3732</td>\n",
              "      <td>37536</td>\n",
              "      <td>Charging</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date      Time  Battery_Level  Temperature  Voltage  Current  \\\n",
              "0   30-09-2020  14:55:17              1         27.5     3732   402982   \n",
              "1   30-09-2020  14:55:18              2         27.5     3732   402982   \n",
              "2   30-09-2020  14:55:19              3         27.5     3732   402982   \n",
              "3   30-09-2020  14:55:20              4         27.5     3732   402982   \n",
              "4   30-09-2020  14:55:21              5         27.5     3732   402982   \n",
              "5   30-09-2020  14:55:22              6         27.5     3732   402982   \n",
              "6   30-09-2020  14:55:23              7         27.5     3732   402982   \n",
              "7   30-09-2020  14:55:24              8         27.5     3732   402982   \n",
              "8   30-09-2020  14:55:25              9         27.5     3732   402982   \n",
              "9   30-09-2020  14:55:26             10         27.5     3732   402982   \n",
              "10  30-09-2020  14:55:27             11         27.5     3732   402982   \n",
              "11  30-09-2020  14:55:28             12         27.5     3732   402982   \n",
              "12  30-09-2020  14:55:29             13         27.5     3732   402982   \n",
              "13  30-09-2020  14:55:30             14         27.5     3732   402982   \n",
              "14  30-09-2020  14:55:31             15         27.5     3732   402982   \n",
              "15  30-09-2020  14:55:32             16         27.5     3732   402982   \n",
              "16  30-09-2020  14:55:33             17         27.5     3732    37536   \n",
              "17  30-09-2020  14:55:34             18         27.5     3732    37536   \n",
              "18  30-09-2020  14:55:35             19         27.5     3732    37536   \n",
              "19  30-09-2020  14:55:36             20         27.5     3732    37536   \n",
              "\n",
              "      Status  \n",
              "0   Charging  \n",
              "1   Charging  \n",
              "2   Charging  \n",
              "3   Charging  \n",
              "4   Charging  \n",
              "5   Charging  \n",
              "6   Charging  \n",
              "7   Charging  \n",
              "8   Charging  \n",
              "9   Charging  \n",
              "10  Charging  \n",
              "11  Charging  \n",
              "12  Charging  \n",
              "13  Charging  \n",
              "14  Charging  \n",
              "15  Charging  \n",
              "16  Charging  \n",
              "17  Charging  \n",
              "18  Charging  \n",
              "19  Charging  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "318cd76f"
      },
      "source": [
        "#importing matplotlib.pyplot library to plot graphs \n",
        "import matplotlib.pyplot as plt"
      ],
      "id": "318cd76f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c53f2fbc",
        "outputId": "3f357e14-8e20-4307-8f58-84d77c3e668a"
      },
      "source": [
        "#sctter plot of time vs battery level\n",
        "plt.scatter(data['Time'],data['Battery_Level']) #time is taken in the x axis and battery level in y axis\n",
        "#labeling thr axes\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('battery level')\n",
        "#title of the plot\n",
        "plt.title('scatter plot time vs battery level')\n",
        "plt.xticks(rotation=60) # rotates x axis ticks values by 60"
      ],
      "id": "c53f2fbc",
      "execution_count": null,
      "outputs": [
        {
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          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
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        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e91331d",
        "outputId": "348a7c82-b5f1-440c-921d-5a4d36b3d6d1"
      },
      "source": [
        "#line graph of time vs battery level\n",
        "plt.plot(data['Time'],data['Battery_Level'])\n",
        "# giving labels to x and y axis\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('battery level and temperature')\n",
        "plt.title('line plot time vs battery level') #title of the plot \n",
        "plt.xticks(rotation=50)\n",
        "plt.xticks(data.Time[::2]) # skips every two ticks of x axis\n"
      ],
      "id": "0e91331d",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "([<matplotlib.axis.XTick at 0x2acba0ade50>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0ade20>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0c6160>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0d3a30>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0d3f40>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0df490>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0df9a0>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0dfeb0>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0e5400>,\n",
              "  <matplotlib.axis.XTick at 0x2acba0e5910>],\n",
              " [Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, '')])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bf6ba0b",
        "outputId": "19c96c41-bc18-4d91-c323-fb8c6b59129c"
      },
      "source": [
        "#line plot of time vs battery level and time vs temperature in a single plot with only 2 axes\n",
        "plt.plot(data['Time'],data['Battery_Level']) # plots time vs batterylevel\n",
        "plt.plot(data['Time'],data['Temperature']) #plots time vs temperature\n",
        "#labeling x and y axis\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('battery level and temperature')\n",
        "plt.title('plot time vs battery level and time vs temperature') # title of the plot\n",
        "plt.legend(['BL','temp']) #we have two plots in the same graph so we use legends identify different plots\n",
        "plt.xticks(rotation=50)\n",
        "plt.xticks(data.Time[::3]) #skips every three values in between in x axis"
      ],
      "id": "8bf6ba0b",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "([<matplotlib.axis.XTick at 0x2acbb437670>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb437e50>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb5daac0>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb5cf4c0>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb5c1550>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb5cf730>,\n",
              "  <matplotlib.axis.XTick at 0x2acbb5c1670>],\n",
              " [Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, ''),\n",
              "  Text(0, 0, '')])"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "893f63f8",
        "outputId": "5926af90-3b62-4d8a-a73a-b790aa057bec"
      },
      "source": [
        "# two plots in a single graph with twin axis\n",
        "fig,ax1=plt.subplots() #subplot is added\n",
        "color='tab:orange'\n",
        "ax1.set_xlabel('Time') # x axis label \n",
        "ax1.set_ylabel('Battery Level',color=color) #y axis label and color is set\n",
        "ax1.plot(data['Time'],data['Battery_Level'],color=color,label='bl') #ploting time vs battery level\n",
        "ax1.legend(loc='best') # legend for battery level with location specified as best\n",
        "plt.xticks(rotation=60)\n",
        "ax2=ax1.twinx() # twin axis created \n",
        "color='tab:cyan'\n",
        "ax2.set_ylabel('temperature',color=color) # secondary y axis label and color of the label is set \n",
        "ax2.plot(data['Time'],data['Temperature'],color=color,label='temp') # time vs temperature is plotted\n",
        "ax2.legend(loc='upper left',bbox_to_anchor=(0.0,0.9)) #legend for temperature with specified location\n",
        "plt.xticks(data.Time[::3]) \n",
        "fig.suptitle('Time vs Battery Level Time vs Temparature') #title of the plot\n",
        "plt.show() #complete plot is displayed\n",
        "\n"
      ],
      "id": "893f63f8",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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3eP1m6LQ+HPcQbHlI/fs5wJOZc87Fb9qYcG3sm09hx0Ghy32HLnFHlSiezJxzLi5L5sGLl8P4u2DtXnDKM7DJPnFHlUiezJxzLg4fjYRnz4f5M2G3s2G/P7b4Kh6ro0UkMzNLTHf4QppfzjmXwcI5MOISeO9RWHcrOPZe6LFz3FElXsEns/bt2zNnzpxETANTM59Z+/bt4w7FOdfYzGDykzD8D7CkCva5BPa6ENq0izuyglDwyaxHjx5UVFTw1VdfxR1KTmpmmnbOFZB5M+C5C+HD4dBtRzj8P7D+NnFHVVBUSKe1OnbsaAsXtuxpEJxzecQMJt4DI/8cBkLv90fY7TfQqnXcka0kaZGZJf5iXcG3zJxzLhbffArP/Bam/Q967QUDboKum8YdVcEq+GS2bNkyKioqWLJkSdyhNIua05Rt27aNOxTnWqaawsAvXwWt24YktuMgr+DRxAo+mVVUVNC5c2d69eqV9x1AVldNB5KKigo23njjuMNxruWZ9T48cw58OQE27w+H3QBrdYs7qhah4JPZkiVLWkQig1CNv2vXronp7OJcwaheCmNugNH/hPZrwdFDoM/R3hprRgWfzKBlTbnSko7VubxQMSG0xma/D9seCwdfAx27xh1Vi9MikplzzjW6pYug7G8w9lboVAwnPAqbHxR3VC2WTwHTxObOncutt94adxjOucb02Wj47+7wxn9gp1Ph7Dc9kcWsyZKZpLskzZY0KW3ZI5LKo9s0SeVZ9p0m6b1ou/FNFWNz8GTmXAFZUhW6298zANQKTn0ODvtXuE7mYtWULbO7gYPTF5jZz82sxMxKgCeAJ+vYvzTaNtFFyy655BI++eQTSkpKuOiii7juuuvo27cv2223HZdffjkA06ZNY8stt+RXv/oVffr04cQTT+TFF1+kX79+9O7dm3HjxgGQSqU4+eST2W+//ejduzd33HFHnIfmXMvy4fNwy67w9n2wx2/hzNegV+HPGi+pp6QySVMkTZZ0XrS8RNLYmkaHpF2y7N8sjZMmu2ZmZqMl9cq0TqGXwrHAfk31+pn8eWoFkxYsbtTn7NOpA1f2zl5+6pprrmHSpEmUl5czcuRIHn/8ccaNG4eZMXDgQEaPHs2GG27Ixx9/zGOPPcbgwYPp27cvDz74IGPGjOGZZ57h6quv5umnnwbg3XffZezYsSxcuJAddtiBQw89lG7dvOuvc01mwVcw4mKY9ASstw0c9yB03zHuqJpTNXChmU2U1BmYIGkUcC3wVzN7XtIh0eN9szxHqZl93ZRBxtUBZC9glplNzbLegJGSDLjdzAZneyJJpwOnA7Rrl98FO0eOHMnIkSPZYYcdAFiwYAFTp05lww03ZOONN2bbbbcFYJtttmH//fdHEttuuy3Tpk1b+RyHH344HTp0oEOHDpSWljJu3DiOOOKIGI7GuQJnBu89Bs9fDEsXQOmfoN95La4wsJnNBGZG9+dLmgJ0J3xO15xfLQJmxBNhEFcyOx54qI71/cxshqT1gFGSPjCz0Zk2jBLdYAi1Get60bpaUM3BzLj00ks544wzfrB82rRprLHGGisft2rVauXjVq1aUV1dvXJd7a733hXfuSZQVQHP/g6mvgA9+sLA/8B6W8YdVVNpU+v03+BsDYjobNsOwJvA+cALkv5JuGS1R5bnz7lxsjqavTejpDbAUcAj2bYxsxnRz9nAU0DGc7FJ0LlzZ+bPnw/AQQcdxF133cWCBQsA+PLLL5k9e/YqPd/QoUNZsmQJc+bM4ZVXXqFv376NHrNzLdaKFfDWELhlt1BT8eBr4LQXCjmRAVSb2c5pt2yJrBOhr8P5ZjYPOAu4wMx6AhcAQ7I8fz8z2xHoD5wtae8mOIZYWmYHAB+YWUWmlZI6Aq2i5mxH4KfAFc0ZYGPq2rUr/fr1o0+fPvTv358TTjiB3XffHYBOnTpx//3307p17hW0d9llFw499FC++OIL/vznP/v1Mucay5xP4Jlz4fPXYJN9Q03FtXvFHVVekNSWkMgeMLOajnuDgPOi+48Bd2baN71xIqmmcZLxTNvqaLJkJukhwsXAdSRVAJeb2RDgOGqdYpTUDbjTzA4B1geeik6ftQEeNLMRTRVnc3jwwQd/8Pi888770TaTJq0cwcDdd9+98n6vXr1+sG7zzTdn8OAmaaU71zItrw7jxV75O7ReI5xS3OEkL0UViTrsDQGmmNkNaatmAPsArxA68/2oD0RzNk6asjfj8VmWn5ph2QzgkOj+p8D2TRWXc86tVPkeDD0HZpbDlofBIf+EtTaIO6p80w84GXgvbWzwZcCvgZuiS0dLiDrixdU4KfjJOadMmcJWW20VU0TxaInH7Nwqqf4ORl8HY/4FHdaGQ66DrY9oka0xn5wzQcysxfT6K6QvJ841ienjQmvs6w9h++PhoKthzZ/EHZVbTQWfzNq3b8+cOXPo2rVrwSe0mvnM2rdvH3cozuWfpQvhpSvhzdtgre5w4uPQ+8C4o3KNpOCTWY8ePaioqGgxc3zVzDTtnEvzSRkM+y3M/QL6/hoOuBzW6Bx3VK4RFfw1M+dcC7Z4Loz8I7x9P3TdDAbeDBtlG9vbMvk1M+ecy2dTnoXnLoSFX8GeF8A+l0BbPwVfqDyZOecKy4LZMPwieP9pKN4WTngEupXEHZVrYp7MnHOFwQzeeRhGXALLFsF+fw6FgVu3jTsy1ww8mTnnkm/udHj2fPj4Rei5a6jise7mcUflmpEnM+dccq1YAeOHwIup0DLrf23ordiq2Wuou5h5MnPOJdPXU0Nh4C/egE1Ko8LAG8UdlYuJJzPnXLIsr4bX/w2vXBN6Jx5+K5Sc0CJLUbnveTJzziXHzHfhmXNg5juw1cBQGLjz+nFH5fKAJzPnXP5btgRGXwtjboQ1u8Kx98LWh8cdlcsjnsycc/nti7GhMPCcqVByIvz0Ki8M7H7Ek5lzLj99twBeugLGDYainnDSk7DZ/nFH5fKUJzPnXP75+EUYdgFUTYddzwgDoNfoFHdULo812WAMSXdJmi1pUtqylKQvJZVHt0Oy7HuwpA8lfSzpkqaK0TmXZxZ9A0+dBfcfHXoqnjYC+v/DE5mrV5NVzZe0N7AAuNfM+kTLUsACM/tnHfu1Bj4CDgQqgLeA483s/fpe06vmO5dg7w+F534Pi+aEwsB7X+SFgZuBV82vh5mNltSrAbvuAnxsZp8CSHoYOByoN5k55xJo/iwYfiFMGQbF28FJT8AG28UdlUuYOGq+nCPp3eg05NoZ1ncHpqc9roiWOecKiRm8/QDc0hc+GgkHpODXZZ7IXIM0dzL7L7ApUALMBK7PsE2mYfxZz4VKOl3SeEnjq6urGyVI51wT+/ZzuO9IGPobWG8bOOu1cGqxtfdJcw3TrO8cM5tVc1/SHcCzGTarAHqmPe4BzKjjOQcDgyFcM2ucSJ1zTWLFchh3R+hyL4UKHjv/0gsDu9XWrMlM0gZmNjN6eCQwKcNmbwG9JW0MfAkcB5zQTCE655rKVx+GwsDT34TNDoDDboQuPevdzblcNFkyk/QQsC+wjqQK4HJgX0klhNOG04Azom27AXea2SFmVi3pHOAFoDVwl5lNbqo4nXNNbPkyeO1GePVaaNcRjhwM2x3rhYFdo2qyrvlx8K75zuWZGW/D0HNh1nuwzZHQ/zrotG7cUbk03jXfOeeyWbY4TNHy+s3QcV34+QOw1WFxR+UKmCcz51zjmvZauDb2zSeww8mhMHCHLnFH5QqcdyFyzjWOJfPguQvh7kNgRTWcMhQO/48nsoST1FNSmaQpkiZLOi9aXiJpbFSacLykXbLs3yzlCf2amXNu9X00Ep69AOZ9Cbv9Bvb7Y+js4fJefdfMJG0AbGBmEyV1BiYARwA3Av8ys+ejOrt/MLN9a+3b4PKEq8pPMzrnGm7hHHjhUnj3EVh3S/jlKOjZN+6oXCOKhlPNjO7PlzSFUJXJgLWizYrIPB642coTejJzzq06M5j8FAy/CJbMhb3/AHv/HtqsEXdkbtW1kTQ+7fHgqBjFj0T1dncA3gTOB16Q9E/CJas9MuySqTzhro0Q8494MnPOrZp5M8O1sQ+fg247wMChUNwn7qhcw1Wb2c71bSSpE/AEcL6ZzZN0FXCBmT0h6VhgCHBA7d0yPFWTXNvyZOacy40ZvH0fvPAnWP4dHHhluD7m9RQLnqS2hET2gJk9GS0eBJwX3X8MuDPDrqtUnnB1+LvQOVe/bz6DYb+Fz0bDRnvCwH9D103jjso1A0kitLqmmNkNaatmAPsArwD7AVMz7N5s5Qk9mTnnsluxHN68HV6+EtQaDvsX7HiqFwZuWfoBJwPvSSqPll0G/Bq4SVIbYAlwOsRXnjB71/xU0e/q3DNVdUOd62PgXfOda0Szp8DQc+DL8dD7oJDIinxqwULTEspZdW62KJxz+aN66feFgduvBUcPgT5He2Fgl9d80LRz7ntfTgiFgWdPhj7HQP9/QMd14o7KNaGW0DILUkWbE2aIXp9UVR9SRdsBA0lVXdXUwTnnmsnSRfDK1fDGLdCpGI5/GLboH3dUroUpLitfmW8qS0v6FJeVbwcMrCwtqTff5HIV9w7gUmAZAKmqdwk9UpxzhWDaGLitX6hwv+MpcPZYT2QuLj/IN5WlJTnnm1yS2ZqkqsbVWla9SuE55/LPkioYdj7cfSjYChg0DAbcBO2L4o7MtVxrVpaWNCjf5NI1/2tSRZtSM2o7VXQMUZ0u51xCffRCSGQLKmH3c6D0j9Buzbijcu7r4rLylfmmuKw853yTSzI7GxgMbEmq6EvgM+DE+naSdBdwGDDbzPpEy64DBgBLgU+AX5jZ3Az7TgPmA8vJsdSKcy4HC7+GEZfAe4/BelvDz++HHjvFHZVzNVbmm+Ky8pzzDeTSmzFV1JpU1XJSRR2BVqSq5uf0xNLewALg3rRk9lPg5Wgg3T8AzOziDPtOA3Y2s69zea0a3pvRuSzMYNIT8Pwfwrxje/8e9vwdtGkXd2QuZvnSm7G4rLw1cE1laclFxWXlHYFWlaUlOeUbyK1l9hmpohHAI8DLuT6xmY2OKiynLxuZ9nAscEyuz+eca6CqL+G538FHI6D7TjDwP7D+1nFH5dwPVJaWLC8uK98pur/KrZJcktkWhFODZwNDSBU9CzxMqmrMqr5YLacREmQmBoyUZMDt2aYjcM7VYcUKmHgPjPoLLF8GB10Nu54JrVrHHZlz2bxdXFb+DKFw8cqEVlla8mT2XYIcxplVLQYeBR4lVbQ2cBPwKqHOVoNI+iOhh8oDWTbpZ2YzJK0HjJL0gZmNzvJcpxPVBGvXzk+ZOAfAnE9g2Hkw7X/Qa69QGPgnm8QdlXP1+Qkwh1C4uIYBjZDMAFJF+wA/B/oTqiAfu8ohRiQNInQM2d+yXLAzsxnRz9mSniLMVpoxmUWttsEQrpk1NC7nCsLyanjzv/Dy36B1Wxjw7zB2zEtRuQSoLC35RUP3zaUCyGdAOaF1dhGpqgb3sJB0MHAxsI+ZLcqyTUegVTQ9d0fgp8AVDX1N51qMWZNDYeAZE2Hz/nDYDbBWt7ijci5nxWXl/0eGyTsrS0tOq2/fXFpm25OqmreqQUl6CNgXWEdSBXA5YWT3GoRThwBjzezM9CkDgPWBp6L1bYAHzWzEqr6+cy1G9Xfwv+vDrX0XOOYu2OYob425JHo27X574EhynMwzl675ianN6F3zXYtTMT60xr6aAtseCwdfAx27xh2VS5B86ZqfSXFZeSvgxcrSkv3q29ZrMzqXREsXwojL4M4D4Lt5cMKjcPQdnshcoekNbJjLhrmcZgy1GVM/qNfmtRmdi8unr8Kw38K302DnX8IBqTDvmHMJV1xWPp8fXjOrJPSzqJfXZnQuKRbPhVF/hon3hm72pz4HvfaMOyrnGk1laUmDJ4XO5TTj2cDtfF+b8XzgzIa+oHOuAT4YDrfuBm/fD/3Og7Ne90TmCk5xWflLuSzLJJdB058CB/ygNmOq6HzgxlUL0zm3yhZ8FeopTn4S1tsGjnsQuu8Yd1TONarisvL2wJrAOsVl5WsDNV1x1wJyGl+S26BpoNb4st/hycy5pmMG7z4KIy4OnT1K/xRaZF4Y2BWmMwhn/boBE/g+mc0DbsnlCXJPZj/kA1icaypVFfDsBTB1JPToGwoDr7dl3FE512QqS0tuAm4qLis/t7K05OaGPEdDk5mXjXKusa1YARPuglEpsOVhzNgup3thYNdiVJaW3FxcVt4H2JowaLpm+b317Zs9maWKaneRrCGgw6qH6ZzLas4n8My58PlrsMm+MOAmWLtX3FE516yKy8ovJ1SO2hoYTqgHPAZYnWRW1eAuks65HC2vhrG3QNnV0HqNcEpxh5O8FJVrqY4Btgferiwt+UVxWfn6wJ257NjQ04zOudVV+V4oRTWzHLY8DA75J6y1QdxRORenxZWlJSuKy8qri8vK1wJmAznNXeTJzLnmVv0djL4OxvwLOqwNP7sbtj7CW2POwfjisvIuhDKKE4AFwLhcdqy/0HCCeKFhl/emjwutsa8/hO2PD7M/r/mTuKNyLVh9hYYl9SRcsyoGVgCDzewmSY8AW0SbdQHmmllJhv2nAfOB5UC1me2c6XWKy8oF9KgsLZkePe4FrFVZWvJuTseRQ9X8c4AHSFV9m8sTxsmTmctb3y2Al6+CN2+DtbrDgBuh94FxR+VcLslsA2ADM5soqTOhxXSEmb2fts31QJWZ/WjuySiZ7WxmX9cXS3FZ+YTK0pKdGnIcuZxmLAbeIlU0EbgLeIFUVeE054A/T61g0oLFcYfhCtXiuTDnY6jeAvZ8BNbeCBa0hrenxh2ZKxB9OnXgyt49muS5zWwmUT3eaNLkKUB34H0AhcknjwXqnaYlB2OLy8r7VpaWvLWqO9ZfmzFV9SdCGf4hwKnAVFJFV0fFh51z2Syvhq+nwqxJ4XpY8XbQdRMfN+byTRtJ49Nup2fbUFIvYAfgzbTFewGzzCzbtzMDRkqaUNdzR0oJCe2T4rLyd4vLyt8rLivP6TRjbh1AUlVGqqiSUI6/GlgbeJxU0ShSVX/I6TnyWFN9o3Et2JRh8NyFsPBr6Pdb2OcSaNu+/v2ca35Zr2Olk9QJeAI438zmpa06Hniojl37mdkMSesBoyR9YGajs2zbP+eoa8mhZVb0W1JFE4BrgdeAbUlVnQXsBBydbTdJd0maLWlS2rKfSBolaWr0c+0s+x4s6UNJH0u6ZFUPyrnYLJgNjw6CR06CTuvBr18O8415InMJJqktIZE9YGZPpi1vAxwFPJJtXzObEf2cDTwF7JJt28rSks+BnsB+0f1F5Da7S04bdQWOIlV1EKmqx0hV1cw4vQI4rI797gYOrrXsEuAlM+sNvBQ9/gFJrQmFJfsTRoEfL2nrHOJ0Lj5mUP4Q/KcvfDgc9vsz/LoMupXEHZlzqyW6JjYEmGJmN9RafQDwgZlVZNm3Y9RpBEkdgZ8CkzJtCysrgFwMXBotagvcn0ucdSezVFEr4GhSVZ9nXl81JduuUTPym1qLDwfuie7fAxyRYdddgI/N7FMzWwo8HO3nXH6a+wXcfzQ8fSasuwWc+Rrs/Xto3TbuyJxrDP2Ak4H9JJVHt0OidcdR6xSjpG6ShkcP1wfGSHqHMF7sOTMbUcdrHQkMBBYCVJaWzAByqkZV9zWzVNUKUkXvkCrakFTVF7k8YT3Wj3rGYGYzo3OotXUHpqc9rgB2bYTXdq5xrVgB44fAi6nQMut/HfT9FbTK6ayIc4lgZmPIMlOKmZ2aYdkM4JDo/qeE8lS5WlpZWmLFZeUGUFxWnnXIQG25dADZAJhMqmgcUbYEIFU1cBUCXBWZfmlZhwJEvWNOB2jXzud6cs3k66mhMPAXb8Cm+8FhN4Yu98651fFocVn57UCX4rLyXwOnEaqB1CuXZPbX1YmsllmSNohaZRsQ6m7VVkG4AFijBzAj2xOa2WBgMIRB040Yq3M/tnwZvH4zvHINtO0AR/w3VPLwUlTOrbbK0pJ/FpeVH0iYlHNz4C+VpSWjctk3l3FmrwLTgLbR/beAiQ2M9RlgUHR/EDA0wzZvAb0lbSypHeGc7DMNfD3nGs/Md+CO/eClv8IWB8PZ46DkBE9kzjWu94D/AaOj+znJpWv+r4HHgdujJd2Bp+vbTdJDwBvAFpIqJP0SuAY4UNJU4MDo8Q8uGJpZNXAO8AIwBXjUzCbnekDONbplS+DFv8LgUphfCcfeB8feC53Xjzsy5wpKcVn5rwgdRY4iTAcztris/LRc9s3lNOPZhB6GYcR3qmoqqaJMHTd+wMyOz7Jq/wzbrrxgGD0eTpiYzbl4fTE2FAaeMxVKToKDrgqV7p1zTeEiYIfK0pI5AMVl5V2B1wmlFOuUS7er70hVLV35KFXUhjo6ZDhXEL6bD8MvgrsODlO2nPQkHHGLJzLnmlYFocJ+jfn8sHd7Vrm0zF4lVXQZ0IFU0YHAb4Bhqxyic0nx8Ysw7HyoqoBdzwgDoNfoFHdUzrUEXwJvFpeVDyU0mg4HxhWXlf8OoLK0pPag7ZVySWaXAL8kXIg7AxhOqiqnrpLOJcqib+CFy+Cdh2CdzeG0F2BDH+LoXDP6JLrVqOkkWO/A6VzmMzuPVNVN9S7LAz6fmWuwyU/D8N/D4m+h3/mw90VeT9G1CPXNZ5YUubTMBgG1E9epGZY5lzzzK0MSmzIMNtg+XBvbYLu4o3KuRSouK98Z+COwEWn5qbK0pN5/yuzJLFV0PHACsDGpovRxXp2BOQ0N1rm8YAblD4TTisuWhMr2u58LrXObFck51yQeIPRofA9YsSo71vWf+zphdtF1gOvTls8Hcposzbm89O3nMOw8+LQMNtwDBt4M62wWd1TOOfiqsrSkQUUy6miZVX0OfE6qaHRU+SNtXdE/CGX6nUuOFcth3B3w0hWhaseh18NOp3lhYOfyx+XFZeV3EqYI+65mYWVpyZPZdwlyOadyID9OXP0zLHMuf331YSgMPP1N2OxAOOxf0KVn/fs555rTL4AtCfOY1ZxmNGA1klmq6CzCmLJNSRWln1bsTJhx2rn8t3wZvHYjvHottOsIRw6G7Y71eorO5aftK0tLtm3IjnW1zB4Engf+zg9nhJ5Pqqr2pJvO5Z8Zb4dSVLMmwTZHhvnGOq0bd1TOuezGFpeVb11ZWvL+qu5Y/zizGqEe4/cDbxpnss5G5ePMHADLFocpWl6/GTquG66NbXVY3FE5l5fyaZxZcVn5FGBT4DPCNTMBtnpd82ukigYANwDdCPOPbUSoZr9Nw0N2rolMey1cG/vmE9jxFDjwSujQJe6onHO5ObihO+bSjesqYDfgI1JVGxOq3vs1M5dflsyD5y6Euw+BFdVwytDQ5d4TmXOJUVla8jlhcub9ovuLyC1P5bTRMlJVc4BWpIpakaoqA0oaGqxzjW7qKLh1d3hrCOx2NvzmDdhk37ijcs6touKy8ssJPeUvjRa1Be7PZd9cuubPJVXUiTDr5wOkimYD1Q0J1LlGtegbGHEpvPswrLsl/HIU9Owbd1TOuYY7EtgBmAhQWVoyo7isvN4iw5Bby+xwQlPvAmAEoaLxgIbF6VwjMINJT8J/+sKkx2Gfi+GM0Z7InEu+pZWlJUY0Z2ZxWXnOHVNy6ABSVdM9cAWpoueAOaSqGjw5p6QtgEfSFm0C/MXMbkzbZl9C6f/PokVPmtkVDX1NV0DmzQzXxj58DrrtAAOHQnGfuKNyzjWOR4vLym8HuhSXlf8aOA3IacqxugZN7wZcA3wDXAncR6jT2IpU0SmkqkY0JFIz+5Dompuk1oTJ2J7KsOn/zMz7U7vADN6+D174Eyz/LvRS3O03XhjYucKyLvA4MA/YAvgLcEAuO9b1SfAf4DKgCHgZ6E+qaiypoi2BhwinHFfX/sAnZvZ5IzyXK1TffAbDfgufjYaN9oSB/4aum8YdlXOu8R1YWVpyMTCqZkFxWfn15FA+sa5k1oZU1UgAUkVXkKoaG+5XfUCqaLWiTXMcITFmsrukd4AZwO/NbHJjvahLiBXL4c3b4eUrQa3hsBthx0FeGNi5AlNcVl5TPnGT4rLyBpVPrCuZpc8ls7jWugZfM6shqR0wkO+7YKabCGxkZgskHQI8DfTO8jynA6cDtGvXbnXDcvli9pRQiurL8dD7oFAYuKh73FE555pG1vKJlaUlOZVPzF7OKlW0HFhIKCfSgdCjkehxe1JVbRsWc/Qk0uHA2Wb20xy2nQbsbGZf17Wdl7MqANVLYcy/YPR10H4t6H8t9DnaCwM710TqK2clqSdwL1BMaOQMNrObJD1CuK4F0AWYa2YlGfY/GLgJaA3caWbXNO4RBHXNZ9a6KV4wzfFkOcUoqRiYZWYmaRfCEAKf3brQfTkBhp4LsydDn2Og/z+g4zpxR+VcS1cNXGhmEyV1BiZIGmVmP6/ZQNL1QFXtHaNOfrcQphKrAN6S9IyZrXIh4frE0hVM0pqEgzsjbdmZAGZ2G3AMcJakasIpzuMs54rILnGWLoJXroY3boFOxXD8w7BF/7ijcs4BZjYTmBndny9pCtAdeB9AkoBjgf0y7L4L8LGZfRpt+zBh7HJhJDMzWwR0rbXstrT7/yH0pnSF7rP/hZ6K33wKO/0CDvwrtG+0DkbOufq1kTQ+7fFgMxucaUNJvQgVOt5MW7wX4Uza1Ay7dAempz2uAHZdvXAz80E6Lh5LqmDU5TDh/2DtjWHQMNh477ijcq4lqjaznevbSFIn4AngfDObl7Yq6yUjQh+L2prkLJsnM9f8PhwBz14ACyphj3Nh38ug3ZpxR+Wcy0JSW0Iie8DMnkxb3gY4Ctgpy64VhCr4NXoQhls1Ok9mrvks/BqevzjUU1xva/j5/dAj2/+Acy4fRNfEhgBTzOyGWqsPAD4ws4osu78F9Ja0MaHa03HACU0Rpycz1/TMYNIT8Pwfwrxj+14Ge14AbXxcoHMJ0A84GXhPUnm07DIzG06GwheSuhG64B9iZtWSzgFeIHTNv6upCmBkH2eWQD7OLA9VfQnP/Q4+GgHdd4KB/4H1t447KudcpL5xZknhLTPXNFasgIn3wKi/wPJlcNDVsOuZ0Kqphy8651oiT2au8c35BIadB9P+B732CoWBf7JJ3FE55wqYJzPXeFYsh7G3wst/g9ZtYcC/YcdTvBSVc67JeTJzjWPW+zD0bJgxEbY4BA69HtbqFndUzrkWwpOZWz3VS+F/14db+yI45i7Y5ihvjTnnmpUnM9dwFRNCa+yrKbDtsXDwNdCxa/37OedcI/Nk5lbd0kVQ9rdwfazzBnDCo7D5QXFH5ZxrwTyZuVXz6auhMPC302DnX8IBqTDvmHPOxciTmcvN4rkw6s8w8d7Qzf7U56DXnnFH5ZxzgCczl4sPhocqHgtmQb/zYN9LoW2HuKNyzrmVPJm57BZ8FeopTn4S1u8Dxz0I3XeMOyrnnPsRT2bux8zg3UdhxMWwdCGU/gn2PD8MhHbOuTwUSzKTNA2YDywnw8Rw0ZQDNwGHAIuAU81sYnPH2SJVVYS5xqaOhB59Q2Hg9baMOyrnnKtTnC2zUjP7Osu6/kDv6LYr8F+aaKptF1mxAibcBaNSYMvDmLFdTvfCwM65RMjX04yHA/damJ9mrKQukjYws5lxB1aQ5nwCz5wLn78Gm+wLA26CtXvFHZVzzuUsrmRmwEhJBtxuZoNrre8OTE97XBEt82TWmJZXwxv/gVf+Dm3WgMNvgZITvRSVcy5x4kpm/cxshqT1gFGSPjCz0WnrM32aZpxFVNLpwOkA7dr5zMU5q3wvlKKa+Q5seVgoDNy5OO6onHOuQWJJZmY2I/o5W9JTwC5AejKrAHqmPe4BzMjyXIOBwRBmmm6SgAtJ9Xcw+joY8y/osDb87B7Y+nBvjTnnEq1Vc7+gpI6SOtfcB34KTKq12TPAKQp2A6r8elkjmD4ObtsrJLNtfwZnj4NtjvBE5pxLvDhaZusDT4Xe97QBHjSzEZLOBDCz24DhhG75HxO65v8ihjgLx3cL4OWr4M3boKgHnPgE9D4g7qicc67RKHQYLAwdO3a0hQsXxh1GfvnkZRh2Hsz9InS13/8vsEbnuKNyzuUJSYvMrGPccayufO2a71bX4m/hhT9B+f3QtTf8YgRstHvcUTnnXJPwZFaIpgyD5y6EhV/Dnr+DfS6Gtu3jjso555qMJ7NCMn8WPH8RvD8UircNk2Z2K4k7Kueca3KezAqBGbzzMIy4BJYtDtfF9vitFwZ2zrUYnsySbu4XMOx8+OQl6LkbDLwZ1t087qicc65ZeTJLqhUr4K074cVUeNz/Ouj7K2jV7EMHnXMudp7MkujrqTD0HJg+FjbdHwbcCF02jDsq51wBktQTuBcoBlYAg83spmjducA5QDXwnJn9IcP+06hjyq/G4sksSZYvg9f/Da/8A9p2gCP+C9sf7xU8nHNNqRq40MwmRtWbJkgaRSiAcTiwnZl9F9XazaauKb8ahSezpJj5TmiNVb4bain2vw46rx93VM65AheVEpwZ3Z8vaQphFpNfA9eY2XfRutnxRRlDbUa3ipYtgRf/CoNLYX4lHHsfHHuvJzLnXGNpI2l82u30bBtK6gXsALwJbA7sJelNSa9K6ptlt5opvybU9dyry1tm+ezzN8KkmXOmQslJcNBVodK9c841npyuY0nqBDwBnG9m8yS1AdYGdgP6Ao9K2sR+XCOxvim/GoUns3z03fzQGnvrDijaEE56EjbbP+6onHMtlKS2hET2gJk9GS2uAJ6Mktc4SSuAdYCv0vfNYcqvRuGnGfPNxy/CrbuHbve7ngm/ecMTmXMuNgpTnAwBppjZDWmrngb2i7bZHGgHfF1r31ym/GoU3jLLF4u+gRcug3cegnU2h9NGwIa7xR2Vc871A04G3pNUHi27DLgLuEvSJGApMMjMTFI34E4zO4QsU341RZA+BUw+mPw0DP99qHTf73zY+yIvDOycaxY+BYxbffMrQxKbMgw22D5cG9tgu7ijcs65xPFkFgczKH8gnFZctgQOSMHu50Jr/3M451xDNPunZ12lUdK22RcYCnwWLXrSzK5oxjCbzrfTwszPn74CG+4RCgOvs1ncUTnnXKLF0RTIWBrFzN6vtd3/zOywGOJrGiuWw7g74KW/glrBodfDTqd5YWDnnGsEzZ7M6iiNUjuZFY6vPgylqCrGwWYHwmH/gi49447KOecKRqwXaWqVRqltd0nvADOA35vZ5OaMrVEsXwZjboTR10K7jnDkYNjuWC8M7JxzjSy2ZFa7NEqt1ROBjcxsgaRDCIPzemd5ntOB0wHatWvXdAGvqhlvh9bYrEmwzZGhMHCndeOOyjnnClIs48yi0ijPAi/UGlGebftpwM71TSGQF+PMli2GV/4Or98MHdcL18a2KpxLf865wuLjzBqojtIo6dsUA7Oi0eS7EMpuzWnGMBtm2muhMPA3n8COp8CBV0KHLnFH5ZxzBS+O04zZSqNsCGBmtwHHAGdJqgYWA8dlqMScP5bMgxdTMH4IdNkIThkKm+wbd1TOOddieDmr1fXRSHj2fJg3A3b7Dez3x9DZwznnEsBPM7Z0C+fAiEvgvUdh3S3hl6OgZ7a56ZxzzjUlT2arygwmPwnD/wBL5sI+F8NeF0KbNeKOzDnnWixPZqti3kx47nfw4XDotgMMHArFfeKOyjnnWjxPZrkwg4n3wsg/w/Lv4KdXwa5neWFg55zLE/5pXJ9vPg2FgT8bDRvtCQP/DV03jTsq55xzaTyZZbNiOYz9L7x8FbRqA4fdCDsO8sLAzjmXhzyZZTLrfXjmHPhyAvQ+KBQGLuoed1TOOeey8GSWrnopjLkBRv8T2q8FRw+BPkd7YWDnnMtznsxqfDkhFAae/T5s+zM4+BrouE7cUTnnnMuBJ7Nli8N1sbG3QqdiOP5h2KJ/3FE555xbBZ7MAD4aETp3HPhXaF8UdzTOOedWkddmBPhuAazRqfEDcs65PFcotRm9nzl4InPOuYTzZOaccy7xPJk555xLPE9mzjnnEs+TmXPOuawk9ZRUJmmKpMmSzktbd66kD6Pl12bZ/+Bom48lXdJUcXrXfOecc3WpBi40s4mSOgMTJI0C1gcOB7Yzs+8krVd7R0mtgVuAA4EK4C1Jz5jZ+40dpLfMnHPOZWVmM81sYnR/PjAF6A6cBVxjZt9F62Zn2H0X4GMz+9TMlgIPExJgo/Nk5pxzLVsbSePTbqdn21BSL2AH4E1gc2AvSW9KelVS3wy7dAempz2uiJY1uoI6zbho0SKTtLiBu7chNKeTyuOPl8cfL4+/4TqY2c71bSSpE/AEcL6ZzZPUBlgb2A3oCzwqaRP7YSWOTFXam6RSR0ElMzNrcEtT0vhc/qD5yuOPl8cfL4+/aUlqS0hkD5jZk9HiCuDJKHmNk7QCWAf4Km3XCqBn2uMewIymiNFPMzrnnMtKkoAhwBQzuyFt1dPAftE2mwPtgK9r7f4W0FvSxpLaAccBzzRFnJ7MnHPO1aUfcDKwn6Ty6HYIcBewiaRJhI4dg8zMJHWTNBzAzKqBc4AXCB1HHjWzyU0RZEGdZlxNg+MOYDV5/PHy+OPl8TcRMxtD5mtfACdl2H4GcEja4+HA8KaJ7nsFVTXfOedcy+SnGZ1zziWeJzPnnHOJ58msHlFPHucazN9DzjU9T2b1sAK4qJj0D9Okx+/vofglPX5XP09mWUjqJOlaSV3jjmV1SOpU82EaFf1MFEldkpoMJHWQdGw04LRmWeI+VAvgPdQpie8hSW0lbRZ3HEnhXfOz+yvwM2AScC+ED6Kk/FNI+gOwHrCRpOnApTUFQZNwHNFUEesAm0r6t5mVpa1rZWYr4osuZ9cB883s0ZoFaUkh749B0oVAJ8Kg1xnAn6JisUl5D50D9ALWkzTCzB5Mwu89zT8JY7M+rr0iYcfRLLxlloGkbYF9gIuBCyWdCck5XSRpO+AXwAjCP0R3YHr0z533xyFpe8IXieeBscDWkraPBmqShH9iSZsCB5rZpdHjn0q6XtKFSfggiv4GJwIvAv8gjBv6VNKvIBHvoRLC/8AEwoDd/SStm++/9xrR/3A/4Pbo8eaSBkg6CpLxP9DcPJlldgVwi5k9BFwEHCXpCAjfiOIMLEf9gHFm9qKZvWlmPydMu/BzSX9LwKmii4HBZvYSoTzOHwllcH4n6X5Ja8YaXW7aAh8BSBoAXAm8D/QH3pFUHGNsuRgIDDOz18zsPcL/xFPAcZIGxRtaTi4Fbo3+h58m/D0urFmZgN//JYSpU0xSKWFOsMOAcyX9n6QO8YaXf5LwwdysJK1DqEH2fwBmNhJ4HDhd0uYJ+Ub0JICkXWsWmNkbwCmECfXWjSmueklaCxgN3BEtKgV+EbVwTgSWAXl/HdPMPiAkrROAPsCVZjbEzA4AXgW2jDXA+pUBu0naKHr8M+BlwheLflHF9LwUvYfeAcYDmNlCQjLYLlpfCvw9tgDrIakj8DYwQ9JlwP8B15rZGcDxhGocP4kxxLzkyawWM/vazC6D71thZjYYeA+4R9JuccaXCzObBbwGPC3pxrTlnwE7A1vHFFq9zGyemd2W9qXhWjN7IVo3C9gC2CS2AFfNc8AZhFbOrmmdiXYAusQVVC6iEkZvE2YVvhdYZmZPmdmbwGZA71gDrEP0HrqaqGUceRdYrDAb8u8If5u8ZGYLzew64DGgMzDUzEZF6yoJ84htGmOIecnLWWWQlsRWpF/olnQD8ImZ3RJrgFlE36I3Aj41s4ro8e2ED59HCEngJ2Z2UIxhZhXFuzEwDZhtZotqrT8V+JmZHdr80eUmOoYNgS/M7PPodNYlhDmfRhD+Fu3M7NgYw8wqir8XMNXMZkTxtwMqzWyppN8AJ5nZHnHGmY2kbQjTjHwBfBj9D7c2s+VRK+cC4BUz+1msgWaRFv/nZvZB1EoriuodIukXhP+BQ+p6npbIk1kkOr34d8J59rejZa2BFRAueEvaktA77cv4Is0s+sb5KKG1vYAw79AD0brdgBJgKuEfvCKuOLOpFf884D4zeyRt/WaEXqVn1/x98k2tY5gPPJj2N+hD+JCaCcy0zFPMx6qu91C0fl3gT8AQM3s3niizi+IfClQSEvIpZvaepDXM7LvobzAe2NnMJsUYaka14t8YONXMyiW1NbNlktYH7gcuMrPyGEPNS57MIpLuB3YEFhKmBP+LmX2Ttr5NNJ1BXpI0hPBt7gpJA4HrgSOsiaZbaGxZ4j/MzD5M22ZzM/so65PELMsxHG5m79faLi+7tWeJf0B0/a9mm7XN7NvYgqyDpMHAl2b2V0l/5PtJIT8FRpnZ25L2MLPX44syu3riHxkltq3MbEp8UeYvv2ZG+AclTOV9DOFCdzugLBpnQ9Rz6DgpPwe8SupOOH1V02nlGUJ35EOj9RtJOiCB8R8erd9Q0v55nsiyHcNh0foNa/4GeZrIssU/MFrfM/ob5Gsi60Ho4HF9tOhEwozHLwMbEDo/kceJLNf4PZFlY2Z+C58tawFd0h6XAs8Co4BPgAvjjrGe+LcFOqU97ks4TQShd+MZccdYyPEXwjEUQPxdop9rEc6s1CxfmzCf1mZxx7ia8W8ad4z5fPPTjPxwNH3tAa2SbgP2NbO87kpdcxo0an21BdYkdP6YAuxlZvvHGmA9kh4/JP8Ykhx/XS1eSfsQesXumml9Pkh6/Pkgb8eKxCHLdbEtCQMw81aUgKvT4l8KLFUoQfQX4MB4I6xb0uOH5B9D0uMnjL2y2v/DktoDNxI6ruSzpMcfO79mxsou+J2BJyR1gtCTMerN+E8zeyreCOuWKf7IEOBfFipp5K2kxw/JP4ZCjD9KBJsSepXm7bgySH78+aDFnmaU1AXY2NK6eUvawqLeczVjU+KKrz45xN/OwrigfO1w0IUExw/JP4ZCjz/fJT3+fNOSW2a3EaphrFTrTXSJ8rsGXX3xXyTppHz8EIokPX5I/jEUdPyS/ijp5GaPKndJjz+vtMiWmUJttr9ZVMVA0t7AVoTpLm60UC1gXeDbDNfQYufxxy/px+Dxxyvp8eejltoy+w1hICIKhWD/Qhig2Bd4RFJHM/sqj99EHn/8kn4MHn+8kh5/3mlRLbOoy/EawEl8Xyj1ZOBgM3tXodr2zcDV+Xje2uOPX9KPweOPV9Ljz2ctqmVmwRIzuxO4B/iGMG/Zu9H6eYSK8p1jDDMrjz9+ST8Gjz9eSY8/n7W0ltnVwGgzG5G2bK3oDYSkS4DtzOyEuGKsi8cfv6Qfg8cfr6THn89aTMtMUm9gAKGIMJLWVKi52CZ63A3YgzDLcd7x+OOX9GPw+OOV9PjzXYtpmUl6CnjBzG6TtAehkOe2wP8Ik9+Nk7Se5eHUHODx54OkH4PHH6+kx5/vWkTLTGEeoJ2BPRQmG0wRZmK+nPA7OEOhnE9evok8/vgl/Rg8/nglPf4kaBHJzMxmEaa3+Ar4kNAifdDMyoDLCJMm9oovwrp5/PFL+jF4/PFKevxJ0CJOMyqaqTW6vylhmot3osfHAmeZWWmcMdbF449f0o/B449X0uNPgpaSzGRmJmkDM5uZtrwIeAsYZGZvxBdh3Tz++CX9GDz+eCU9/iQo+NOMaW+idYHBCpWokdSGMGfTZfn8JvL445f0Y/D445X0+JOiYFtm0UXWb83su7RlXc1sTnS/o5ktjC3Aenj88Uv6MXj88Up6/ElTyC2z+4BJkg5JW/ZN2v0Lld8VqT3++CX9GDz+eCU9/kQpyJaZwqSa5wFnAUuASuAXwMHAKDP7XFJ7M1sSY5hZefzxS/oxePzxSnr8SdQm7gCagoXpE/4NLAfuIhT1fAPoDmwRbZO3byKPP35JPwaPP15Jjz+JCvI0o6Q1LEydsB7h4up/ganAWGCMpONiDbAeHn/8kn4MHn+8kh5/EhXUaUaFEfQr0h6vSZgnaBEwwMz6StodmGVmn8YVZzYef/ySfgwef7ySHn+imVnB3AjzAN0OrJu2bBAwBxgYd3wef/wxFvoxePwef0u9xR5Aox0I7ALMBG4D3gH+kLauR9zxefzxx1jox+Dxe/wt+VZo18xuMrMzgd8C/SS9LukIM6uQ9DtJW8UdYD08/vgl/Rg8/nglPf7EKrRrZm0sXHRFUlvCFAunAMVAOzPbLM746uPxxy/px+Dxxyvp8SdZQSWzTCR1BmYAP7O02V2TwuOPX9KPweOPV9LjT4pCO82YyUDCIMWkvok8/vgl/Rg8/nglPf5EaAkts9ZARzObF3csDeHxxy/px+Dxxyvp8SdFwScz55xzha8lnGZ0zjlX4DyZOeecSzxPZs455xLPk5lzDSCpq6Ty6FYp6cvo/gJJt8Ydn3MtjXcAcW41SUoBC8zsn3HH4lxL5S0z5xqRpH0lPRvdT0m6R9JISdMkHSXpWknvSRoRVYhA0k6SXpU0QdILkjaI9yicSx5PZs41rU2BQ4HDgfuBMjPbFlgMHBoltJuBY8xsJ8JEjn+LK1jnkqogZ5p2Lo88b2bLJL0HtAZqqkC8B/QizDrcBxgliWibmTHE6VyieTJzrml9B2BmKyQts+8vUq8g/P8JmGxmu8cVoHOFwE8zOhevD4F1o9mHkdRW0jYxx+Rc4ngycy5GZrYUOAb4h6R3gHJgj1iDci6BvGu+c865xPOWmXPOucTzZOaccy7xPJk555xLPE9mzjnnEs+TmXPOucTzZOaccy7xPJk555xLPE9mzjnnEu//AXCq6O9q7hA+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9c7dc1cf",
        "outputId": "ada95d18-b60c-49c3-dc51-10113a0e7231"
      },
      "source": [
        "#plot of Time vs Voltage vs Current using twin axis \n",
        "fig,ax1=plt.subplots() # subplot is created\n",
        "color='tab:red'\n",
        "ax1.set_xlabel('Time') # x axis labelis set\n",
        "ax1.set_ylabel('Battery Level',color=color) # y axis label and label color is set\n",
        "ax1.plot(data['Time'],data['Battery_Level'],color=color,label='battery level') #plot time vs battery level\n",
        "ax1.legend(loc=(0.0,0.7)) # here we use two different legends (for time vs battery level and time vs current) \n",
        "#legend with specified location for time vs battery level loctaion\n",
        "plt.xticks(rotation=60)\n",
        "ax2=ax1.twinx() # twin axis is created\n",
        "color='tab:blue'\n",
        "ax2.set_ylabel('Current',color=color) # twin y axis label and label color is set\n",
        "ax2.plot(data['Time'],data['Current'],color=color,label='current') # second plot of time vs current is plotted\n",
        "ax2.legend(loc=(0.01,0.8)) #location of second legend is specified\n",
        "plt.xticks(data.Time[::3])\n",
        "fig.suptitle('Time vs Voltage vs Current') #title of the plot\n",
        "plt.show() # complete plot is displayed\n"
      ],
      "id": "9c7dc1cf",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e497f68",
        "outputId": "79f37bf1-1eb6-44b3-b2b6-0ea7877c9038"
      },
      "source": [
        "#plot of time vs battery level vs current using twin axis and single legend for both plots \n",
        "fig,ax1=plt.subplots() # subplot is created\n",
        "color='tab:red'\n",
        "ax1.set_xlabel('Time') # x axis label is set\n",
        "ax1.set_ylabel('Battery Level',color=color)# y axis label and label color is set\n",
        "ax1.tick_params(axis='y',colors='red') # y axis tick color is set as red\n",
        "l1=ax1.plot(data['Time'],data['Battery_Level'],color=color,label='battery level') # first plot of time vs battery level\n",
        "plt.xticks(rotation=60) # x ticks are rotated by 60\n",
        "ax2=ax1.twinx() # twin axis is created\n",
        "color='tab:blue'\n",
        "ax2.set_ylabel('Current',color=color) # twin y axis label and label color is set\n",
        "l2=ax2.plot(data['Time'],data['Current'],color=color,label='Current') # second plot of time vs current\n",
        "plt.xticks(data.Time[::3]) # skips 3 vales in between (x axis)\n",
        "ax2.tick_params(axis='y',colors='blue') # twin y axis label color is set as blue\n",
        "#single label for both the plots\n",
        "lines = l1 +l2\n",
        "labels = [l.get_label() for l in lines]\n",
        "ax1.legend(lines,labels,loc='lower center')\n",
        "fig.suptitle('Time vs Battery level vs Current',color='green') # title of the plot\n",
        "plt.tight_layout() # automatically adjusts subplot params\n",
        "plt.show()# displayes complete plot\n"
      ],
      "id": "9e497f68",
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58daa0df"
      },
      "source": [
        ""
      ],
      "id": "58daa0df",
      "execution_count": null,
      "outputs": []
    }
  ]
}