to_be_model_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa836da5",
   "metadata": {},
   "source": [
    "# Visualisation of trained Smart Nanogrid models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4bf7bd7",
   "metadata": {},
   "source": [
    "## 1. Importing libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8026e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "import os\n",
    "import argparse\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots, go\n",
    "import plotly.io as pio\n",
    "\n",
    "import json\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "from stable_baselines3 import DDPG, PPO\n",
    "from smart_nanogrid_gym.utils.config import solvers_files_directory_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55d2b3e",
   "metadata": {},
   "source": [
    "## 2. Specify plotting info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c215eb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.plotting.backend = \"plotly\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d91c8d3c",
   "metadata": {},
   "source": [
    "Plotly templates to use: 'ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', 'presentation', 'xgridoff', 'ygridoff', 'gridon', 'none'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edbc66c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.templates.default = \"plotly_white\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92c4d2c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93b30c7",
   "metadata": {},
   "source": [
    "Available pattern shapes to use in plotting: \"\", \"/\", \"\\\\\", \"x\", \"|\", \"-\", \".\", \"+\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a020f05a",
   "metadata": {},
   "source": [
    "Named CSS colors to use in plotting: `aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "512d3149",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgrey', 'darkgreen', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'grey', 'green', 'greenyellow', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgrey', 'lightgreen', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'rebeccapurple', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen']"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53e877e2",
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       "  background-color: white;\n",
       "}\n",
       "#T_f3f3e_row19_col4 {\n",
       "  background-color: whitesmoke;\n",
       "}\n",
       "#T_f3f3e_row19_col5 {\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_f3f3e_row19_col6 {\n",
       "  background-color: yellowgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f3f3e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f3f3e_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_f3f3e_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_f3f3e_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_f3f3e_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
       "      <th id=\"T_f3f3e_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
       "      <th id=\"T_f3f3e_level0_col5\" class=\"col_heading level0 col5\" >5</th>\n",
       "      <th id=\"T_f3f3e_level0_col6\" class=\"col_heading level0 col6\" >6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f3f3e_row0_col0\" class=\"data row0 col0\" >aliceblue</td>\n",
       "      <td id=\"T_f3f3e_row0_col1\" class=\"data row0 col1\" >antiquewhite</td>\n",
       "      <td id=\"T_f3f3e_row0_col2\" class=\"data row0 col2\" >aqua</td>\n",
       "      <td id=\"T_f3f3e_row0_col3\" class=\"data row0 col3\" >aquamarine</td>\n",
       "      <td id=\"T_f3f3e_row0_col4\" class=\"data row0 col4\" >azure</td>\n",
       "      <td id=\"T_f3f3e_row0_col5\" class=\"data row0 col5\" >beige</td>\n",
       "      <td id=\"T_f3f3e_row0_col6\" class=\"data row0 col6\" >bisque</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f3f3e_row1_col0\" class=\"data row1 col0\" >blanchedalmond</td>\n",
       "      <td id=\"T_f3f3e_row1_col1\" class=\"data row1 col1\" >blue</td>\n",
       "      <td id=\"T_f3f3e_row1_col2\" class=\"data row1 col2\" >blueviolet</td>\n",
       "      <td id=\"T_f3f3e_row1_col3\" class=\"data row1 col3\" >brown</td>\n",
       "      <td id=\"T_f3f3e_row1_col4\" class=\"data row1 col4\" >burlywood</td>\n",
       "      <td id=\"T_f3f3e_row1_col5\" class=\"data row1 col5\" >cadetblue</td>\n",
       "      <td id=\"T_f3f3e_row1_col6\" class=\"data row1 col6\" >chartreuse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f3f3e_row2_col0\" class=\"data row2 col0\" >chocolate</td>\n",
       "      <td id=\"T_f3f3e_row2_col1\" class=\"data row2 col1\" >coral</td>\n",
       "      <td id=\"T_f3f3e_row2_col2\" class=\"data row2 col2\" >cornflowerblue</td>\n",
       "      <td id=\"T_f3f3e_row2_col3\" class=\"data row2 col3\" >cornsilk</td>\n",
       "      <td id=\"T_f3f3e_row2_col4\" class=\"data row2 col4\" >crimson</td>\n",
       "      <td id=\"T_f3f3e_row2_col5\" class=\"data row2 col5\" >cyan</td>\n",
       "      <td id=\"T_f3f3e_row2_col6\" class=\"data row2 col6\" >darkblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f3f3e_row3_col0\" class=\"data row3 col0\" >darkcyan</td>\n",
       "      <td id=\"T_f3f3e_row3_col1\" class=\"data row3 col1\" >darkgoldenrod</td>\n",
       "      <td id=\"T_f3f3e_row3_col2\" class=\"data row3 col2\" >darkgrey</td>\n",
       "      <td id=\"T_f3f3e_row3_col3\" class=\"data row3 col3\" >darkgreen</td>\n",
       "      <td id=\"T_f3f3e_row3_col4\" class=\"data row3 col4\" >darkkhaki</td>\n",
       "      <td id=\"T_f3f3e_row3_col5\" class=\"data row3 col5\" >darkmagenta</td>\n",
       "      <td id=\"T_f3f3e_row3_col6\" class=\"data row3 col6\" >darkolivegreen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f3f3e_row4_col0\" class=\"data row4 col0\" >darkorange</td>\n",
       "      <td id=\"T_f3f3e_row4_col1\" class=\"data row4 col1\" >darkorchid</td>\n",
       "      <td id=\"T_f3f3e_row4_col2\" class=\"data row4 col2\" >darkred</td>\n",
       "      <td id=\"T_f3f3e_row4_col3\" class=\"data row4 col3\" >darksalmon</td>\n",
       "      <td id=\"T_f3f3e_row4_col4\" class=\"data row4 col4\" >darkseagreen</td>\n",
       "      <td id=\"T_f3f3e_row4_col5\" class=\"data row4 col5\" >darkslateblue</td>\n",
       "      <td id=\"T_f3f3e_row4_col6\" class=\"data row4 col6\" >darkslategrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f3f3e_row5_col0\" class=\"data row5 col0\" >darkturquoise</td>\n",
       "      <td id=\"T_f3f3e_row5_col1\" class=\"data row5 col1\" >darkviolet</td>\n",
       "      <td id=\"T_f3f3e_row5_col2\" class=\"data row5 col2\" >deeppink</td>\n",
       "      <td id=\"T_f3f3e_row5_col3\" class=\"data row5 col3\" >deepskyblue</td>\n",
       "      <td id=\"T_f3f3e_row5_col4\" class=\"data row5 col4\" >dimgrey</td>\n",
       "      <td id=\"T_f3f3e_row5_col5\" class=\"data row5 col5\" >dodgerblue</td>\n",
       "      <td id=\"T_f3f3e_row5_col6\" class=\"data row5 col6\" >firebrick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f3f3e_row6_col0\" class=\"data row6 col0\" >floralwhite</td>\n",
       "      <td id=\"T_f3f3e_row6_col1\" class=\"data row6 col1\" >forestgreen</td>\n",
       "      <td id=\"T_f3f3e_row6_col2\" class=\"data row6 col2\" >fuchsia</td>\n",
       "      <td id=\"T_f3f3e_row6_col3\" class=\"data row6 col3\" >gainsboro</td>\n",
       "      <td id=\"T_f3f3e_row6_col4\" class=\"data row6 col4\" >ghostwhite</td>\n",
       "      <td id=\"T_f3f3e_row6_col5\" class=\"data row6 col5\" >gold</td>\n",
       "      <td id=\"T_f3f3e_row6_col6\" class=\"data row6 col6\" >goldenrod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f3f3e_row7_col0\" class=\"data row7 col0\" >grey</td>\n",
       "      <td id=\"T_f3f3e_row7_col1\" class=\"data row7 col1\" >green</td>\n",
       "      <td id=\"T_f3f3e_row7_col2\" class=\"data row7 col2\" >greenyellow</td>\n",
       "      <td id=\"T_f3f3e_row7_col3\" class=\"data row7 col3\" >honeydew</td>\n",
       "      <td id=\"T_f3f3e_row7_col4\" class=\"data row7 col4\" >hotpink</td>\n",
       "      <td id=\"T_f3f3e_row7_col5\" class=\"data row7 col5\" >indianred</td>\n",
       "      <td id=\"T_f3f3e_row7_col6\" class=\"data row7 col6\" >indigo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f3f3e_row8_col0\" class=\"data row8 col0\" >ivory</td>\n",
       "      <td id=\"T_f3f3e_row8_col1\" class=\"data row8 col1\" >khaki</td>\n",
       "      <td id=\"T_f3f3e_row8_col2\" class=\"data row8 col2\" >lavender</td>\n",
       "      <td id=\"T_f3f3e_row8_col3\" class=\"data row8 col3\" >lavenderblush</td>\n",
       "      <td id=\"T_f3f3e_row8_col4\" class=\"data row8 col4\" >lawngreen</td>\n",
       "      <td id=\"T_f3f3e_row8_col5\" class=\"data row8 col5\" >lemonchiffon</td>\n",
       "      <td id=\"T_f3f3e_row8_col6\" class=\"data row8 col6\" >lightblue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f3f3e_row9_col0\" class=\"data row9 col0\" >lightcoral</td>\n",
       "      <td id=\"T_f3f3e_row9_col1\" class=\"data row9 col1\" >lightcyan</td>\n",
       "      <td id=\"T_f3f3e_row9_col2\" class=\"data row9 col2\" >lightgoldenrodyellow</td>\n",
       "      <td id=\"T_f3f3e_row9_col3\" class=\"data row9 col3\" >lightgrey</td>\n",
       "      <td id=\"T_f3f3e_row9_col4\" class=\"data row9 col4\" >lightgreen</td>\n",
       "      <td id=\"T_f3f3e_row9_col5\" class=\"data row9 col5\" >lightpink</td>\n",
       "      <td id=\"T_f3f3e_row9_col6\" class=\"data row9 col6\" >lightsalmon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_f3f3e_row10_col0\" class=\"data row10 col0\" >lightseagreen</td>\n",
       "      <td id=\"T_f3f3e_row10_col1\" class=\"data row10 col1\" >lightskyblue</td>\n",
       "      <td id=\"T_f3f3e_row10_col2\" class=\"data row10 col2\" >lightslategrey</td>\n",
       "      <td id=\"T_f3f3e_row10_col3\" class=\"data row10 col3\" >lightsteelblue</td>\n",
       "      <td id=\"T_f3f3e_row10_col4\" class=\"data row10 col4\" >lightyellow</td>\n",
       "      <td id=\"T_f3f3e_row10_col5\" class=\"data row10 col5\" >lime</td>\n",
       "      <td id=\"T_f3f3e_row10_col6\" class=\"data row10 col6\" >limegreen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_f3f3e_row11_col0\" class=\"data row11 col0\" >linen</td>\n",
       "      <td id=\"T_f3f3e_row11_col1\" class=\"data row11 col1\" >magenta</td>\n",
       "      <td id=\"T_f3f3e_row11_col2\" class=\"data row11 col2\" >maroon</td>\n",
       "      <td id=\"T_f3f3e_row11_col3\" class=\"data row11 col3\" >mediumaquamarine</td>\n",
       "      <td id=\"T_f3f3e_row11_col4\" class=\"data row11 col4\" >mediumblue</td>\n",
       "      <td id=\"T_f3f3e_row11_col5\" class=\"data row11 col5\" >mediumorchid</td>\n",
       "      <td id=\"T_f3f3e_row11_col6\" class=\"data row11 col6\" >mediumpurple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_f3f3e_row12_col0\" class=\"data row12 col0\" >mediumseagreen</td>\n",
       "      <td id=\"T_f3f3e_row12_col1\" class=\"data row12 col1\" >mediumslateblue</td>\n",
       "      <td id=\"T_f3f3e_row12_col2\" class=\"data row12 col2\" >mediumspringgreen</td>\n",
       "      <td id=\"T_f3f3e_row12_col3\" class=\"data row12 col3\" >mediumturquoise</td>\n",
       "      <td id=\"T_f3f3e_row12_col4\" class=\"data row12 col4\" >mediumvioletred</td>\n",
       "      <td id=\"T_f3f3e_row12_col5\" class=\"data row12 col5\" >midnightblue</td>\n",
       "      <td id=\"T_f3f3e_row12_col6\" class=\"data row12 col6\" >mintcream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_f3f3e_row13_col0\" class=\"data row13 col0\" >mistyrose</td>\n",
       "      <td id=\"T_f3f3e_row13_col1\" class=\"data row13 col1\" >moccasin</td>\n",
       "      <td id=\"T_f3f3e_row13_col2\" class=\"data row13 col2\" >navajowhite</td>\n",
       "      <td id=\"T_f3f3e_row13_col3\" class=\"data row13 col3\" >navy</td>\n",
       "      <td id=\"T_f3f3e_row13_col4\" class=\"data row13 col4\" >oldlace</td>\n",
       "      <td id=\"T_f3f3e_row13_col5\" class=\"data row13 col5\" >olive</td>\n",
       "      <td id=\"T_f3f3e_row13_col6\" class=\"data row13 col6\" >olivedrab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_f3f3e_row14_col0\" class=\"data row14 col0\" >orange</td>\n",
       "      <td id=\"T_f3f3e_row14_col1\" class=\"data row14 col1\" >orangered</td>\n",
       "      <td id=\"T_f3f3e_row14_col2\" class=\"data row14 col2\" >orchid</td>\n",
       "      <td id=\"T_f3f3e_row14_col3\" class=\"data row14 col3\" >palegoldenrod</td>\n",
       "      <td id=\"T_f3f3e_row14_col4\" class=\"data row14 col4\" >palegreen</td>\n",
       "      <td id=\"T_f3f3e_row14_col5\" class=\"data row14 col5\" >paleturquoise</td>\n",
       "      <td id=\"T_f3f3e_row14_col6\" class=\"data row14 col6\" >palevioletred</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_f3f3e_row15_col0\" class=\"data row15 col0\" >papayawhip</td>\n",
       "      <td id=\"T_f3f3e_row15_col1\" class=\"data row15 col1\" >peachpuff</td>\n",
       "      <td id=\"T_f3f3e_row15_col2\" class=\"data row15 col2\" >peru</td>\n",
       "      <td id=\"T_f3f3e_row15_col3\" class=\"data row15 col3\" >pink</td>\n",
       "      <td id=\"T_f3f3e_row15_col4\" class=\"data row15 col4\" >plum</td>\n",
       "      <td id=\"T_f3f3e_row15_col5\" class=\"data row15 col5\" >powderblue</td>\n",
       "      <td id=\"T_f3f3e_row15_col6\" class=\"data row15 col6\" >purple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_f3f3e_row16_col0\" class=\"data row16 col0\" >red</td>\n",
       "      <td id=\"T_f3f3e_row16_col1\" class=\"data row16 col1\" >rosybrown</td>\n",
       "      <td id=\"T_f3f3e_row16_col2\" class=\"data row16 col2\" >royalblue</td>\n",
       "      <td id=\"T_f3f3e_row16_col3\" class=\"data row16 col3\" >rebeccapurple</td>\n",
       "      <td id=\"T_f3f3e_row16_col4\" class=\"data row16 col4\" >saddlebrown</td>\n",
       "      <td id=\"T_f3f3e_row16_col5\" class=\"data row16 col5\" >salmon</td>\n",
       "      <td id=\"T_f3f3e_row16_col6\" class=\"data row16 col6\" >sandybrown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_f3f3e_row17_col0\" class=\"data row17 col0\" >seagreen</td>\n",
       "      <td id=\"T_f3f3e_row17_col1\" class=\"data row17 col1\" >seashell</td>\n",
       "      <td id=\"T_f3f3e_row17_col2\" class=\"data row17 col2\" >sienna</td>\n",
       "      <td id=\"T_f3f3e_row17_col3\" class=\"data row17 col3\" >silver</td>\n",
       "      <td id=\"T_f3f3e_row17_col4\" class=\"data row17 col4\" >skyblue</td>\n",
       "      <td id=\"T_f3f3e_row17_col5\" class=\"data row17 col5\" >slateblue</td>\n",
       "      <td id=\"T_f3f3e_row17_col6\" class=\"data row17 col6\" >slategrey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_f3f3e_row18_col0\" class=\"data row18 col0\" >snow</td>\n",
       "      <td id=\"T_f3f3e_row18_col1\" class=\"data row18 col1\" >springgreen</td>\n",
       "      <td id=\"T_f3f3e_row18_col2\" class=\"data row18 col2\" >steelblue</td>\n",
       "      <td id=\"T_f3f3e_row18_col3\" class=\"data row18 col3\" >tan</td>\n",
       "      <td id=\"T_f3f3e_row18_col4\" class=\"data row18 col4\" >teal</td>\n",
       "      <td id=\"T_f3f3e_row18_col5\" class=\"data row18 col5\" >thistle</td>\n",
       "      <td id=\"T_f3f3e_row18_col6\" class=\"data row18 col6\" >tomato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3f3e_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_f3f3e_row19_col0\" class=\"data row19 col0\" >turquoise</td>\n",
       "      <td id=\"T_f3f3e_row19_col1\" class=\"data row19 col1\" >violet</td>\n",
       "      <td id=\"T_f3f3e_row19_col2\" class=\"data row19 col2\" >wheat</td>\n",
       "      <td id=\"T_f3f3e_row19_col3\" class=\"data row19 col3\" >white</td>\n",
       "      <td id=\"T_f3f3e_row19_col4\" class=\"data row19 col4\" >whitesmoke</td>\n",
       "      <td id=\"T_f3f3e_row19_col5\" class=\"data row19 col5\" >yellow</td>\n",
       "      <td id=\"T_f3f3e_row19_col6\" class=\"data row19 col6\" >yellowgreen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x251dcf12370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_rows = len(colors)//7\n",
    "color_columns = 7\n",
    "\n",
    "aray = np.array(colors)\n",
    "aray = np.reshape(aray, (color_rows, color_columns))\n",
    "\n",
    "aray_df = pd.DataFrame(aray)\n",
    "\n",
    "\n",
    "def color_dataframe_on_cell_value(cell_value):\n",
    "    return f'background-color: {cell_value}'\n",
    "\n",
    "\n",
    "aray_df.style.applymap(color_dataframe_on_cell_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9664467",
   "metadata": {},
   "source": [
    "## 3. Specify model info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd295496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modes: 'training', 'evaluation', 'prediction'\n",
    "environment_mode = 'training'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "8a07bd95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modes: 'no_penalty', 'on_departure', 'sparse', 'dense'\n",
    "penalty_mode = 'on_departure'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "942aefcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "penalty_mode = 'sparse'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "6541cace",
   "metadata": {},
   "outputs": [],
   "source": [
    "penalty_mode = 'dense'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "095a676d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variants: \n",
    "#    -'basic'(only vehicle charging from the grid)\n",
    "#    -'b-pv'(vehicle charging alongside grid, solar energy from the PV system and dis/charging battery system)\n",
    "#    -'v2x'(like 'basic' but including vehicle discharging to the grid and to other vehicle)\n",
    "#    -'v2x-b-pv'(everything above included)\n",
    "model_variant = 'b-pv'\n",
    "# model_variant = 'basic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0702cd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modes: 'bounded'(currently only available mode - [-1 or 0, 1]*max_power)\n",
    "charging_mode = 'bounded'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c222ee08",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_chargers = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e193bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time intervals: '15min'(0.25), '30min'(0.5), '45min'(0.75), '1h'(1), '2h'(2)\n",
    "time_interval_string = '1h'\n",
    "time_interval = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "38c1e0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agent algorithms: 'PPO', ('DDPG')\n",
    "agent_algorithm = 'PPO'\n",
    "# agent_algorithm = 'DDPG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d21b8496",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_algorithm = 'DDPG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6e72bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_episodes = 850\n",
    "timesteps_per_episode = 24 / time_interval\n",
    "total_timesteps = timesteps_per_episode * training_episodes\n",
    "training_epochs = 50\n",
    "\n",
    "evaluation_episodes = 100\n",
    "\n",
    "prediction_episodes = 1\n",
    "prediction_steps = timesteps_per_episode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f8cf3f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if number_of_chargers >= 8 else 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49272823",
   "metadata": {},
   "source": [
    "## 4. Specify data files info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e44f51ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "if environment_mode == 'training':\n",
    "    data_directory = 'training_files'\n",
    "elif environment_mode == 'evaluation':\n",
    "    data_directory = 'evaluation_files'\n",
    "elif environment_mode == 'prediction':\n",
    "    data_directory = 'single_prediction_files'\n",
    "else:\n",
    "    raise ValueError('Error: Wrong environment mode specified!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2c585b",
   "metadata": {},
   "source": [
    "JSON file containing INITIAL model values used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "7ceff36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'solvers/RL/training_files/PPO-b-pv-bounded-sparse-4ch-1h-initial_values.json'"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = f'{agent_algorithm}-{model_variant}-{charging_mode}-{penalty_mode}-{number_of_chargers}ch-{time_interval_string}'\n",
    "initial_values_path = f'solvers/RL/{data_directory}/{file_name}-initial_values.json'\n",
    "initial_values_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6721eedd",
   "metadata": {},
   "source": [
    "JSON file containing PREDICTED model values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "6817a818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'solvers/RL/training_files/PPO-b-pv-bounded-sparse-4ch-1h-prediction_results.json'"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = f'{agent_algorithm}-{model_variant}-{charging_mode}-{penalty_mode}-{number_of_chargers}ch-{time_interval_string}'\n",
    "prediction_results_path = f'solvers/RL/{data_directory}/{file_name}-prediction_results.json'\n",
    "prediction_results_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8cf7fd",
   "metadata": {},
   "source": [
    "## *Here add training data preprocessing and model training*\n",
    "... (If needed.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c9a2a6",
   "metadata": {},
   "source": [
    "## 5. Generate energy tariff dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "ac718790",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_tariff_high = 0.028\n",
    "grid_tariff_low = 0.013333333\n",
    "energy_tariff_high = 0.148933333\n",
    "energy_tariff_low = 0.087613333\n",
    "res_incentive = 0.014\n",
    "\n",
    "high_tariff = grid_tariff_high + energy_tariff_high + res_incentive\n",
    "low_tariff = grid_tariff_low + energy_tariff_low + res_incentive\n",
    "\n",
    "del grid_tariff_high, energy_tariff_high, grid_tariff_low, energy_tariff_low, res_incentive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "96cbdce5",
   "metadata": {},
   "outputs": [
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Day Tariff</th>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.190933</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "      <td>0.114947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3         4         5   \\\n",
       "Day Tariff  0.114947  0.114947  0.114947  0.114947  0.114947  0.114947   \n",
       "\n",
       "                  6         7         8         9         10        11  \\\n",
       "Day Tariff  0.114947  0.190933  0.190933  0.190933  0.190933  0.190933   \n",
       "\n",
       "                  12        13        14        15        16        17  \\\n",
       "Day Tariff  0.190933  0.190933  0.190933  0.190933  0.190933  0.190933   \n",
       "\n",
       "                  18        19        20        21        22        23  \\\n",
       "Day Tariff  0.190933  0.190933  0.114947  0.114947  0.114947  0.114947   \n",
       "\n",
       "                  24  \n",
       "Day Tariff  0.114947  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tariffs = []\n",
    "for i in range(int(timesteps_per_episode) + 1):\n",
    "    if i < 7 / time_interval or i > 19 / time_interval:\n",
    "        tariffs.append(low_tariff)\n",
    "    else:\n",
    "        tariffs.append(high_tariff)\n",
    "\n",
    "day_tariffs = pd.DataFrame({'Day Tariff': tariffs})\n",
    "\n",
    "del tariffs, high_tariff, low_tariff\n",
    "day_tariffs.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de604828",
   "metadata": {},
   "source": [
    "## 6. Load and process data for training results visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e3152b",
   "metadata": {},
   "source": [
    "#### Initial values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "3f40d384",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_initial_values(soc, arrivals, departures, capacities, requested_soc):\n",
    "    vehicles = [i for i in arrivals.columns]\n",
    "    \n",
    "    initial_data = pd.DataFrame()\n",
    "    for charger in range(number_of_chargers):\n",
    "        for vehicle in vehicles:\n",
    "            arrival = arrivals.loc[arrivals.index == charger, [vehicle]]\n",
    "            arrival = arrival[arrival.columns[0]].item()\n",
    "\n",
    "            if not pd.isna(arrival):\n",
    "                df = pd.DataFrame()\n",
    "                df['Arrival'] = [arrival]\n",
    "\n",
    "                departure = departures.loc[departures.index == charger, [vehicle]]\n",
    "                departure = departure[departure.columns[0]].item()\n",
    "                df['Departure'] = departure\n",
    "\n",
    "                df['Vehicle ID'] = str(vehicle)\n",
    "                df['Charger ID'] = str(charger)\n",
    "                \n",
    "                df['EV Capacity'] = capacities[charger][arrival]\n",
    "                \n",
    "                df['Initial State of Charge'] = soc[charger][arrival]\n",
    "                df['Requested State of Charge'] = requested_soc[charger][arrival]\n",
    "                \n",
    "                initial_data = pd.concat([initial_data.loc[:], df]).reset_index(drop=True)\n",
    "\n",
    "    initial_data = initial_data[['Arrival', 'Departure', 'EV Capacity', 'Initial State of Charge', \n",
    "                                 'Requested State of Charge', 'Vehicle ID', 'Charger ID']]\n",
    "    return initial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "5f3234cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(initial_values_path) as json_data:\n",
    "    initial_data = json.load(json_data)\n",
    "    \n",
    "    df_charger_occupancy = pd.DataFrame(initial_data['Charger_occupancy']).T\n",
    "    df_vehicle_capacities = pd.DataFrame(initial_data['Vehicle_capacities']).T\n",
    "    \n",
    "    initial_training_data = load_initial_values(soc=pd.DataFrame(initial_data['SOC']).T,\n",
    "                                                requested_soc=pd.DataFrame(initial_data['Requested_SOC']).T,\n",
    "                                                arrivals=pd.DataFrame(initial_data['Arrivals']),\n",
    "                                                departures=pd.DataFrame(initial_data['Departures']),\n",
    "                                                capacities=df_vehicle_capacities)\n",
    "    \n",
    "    del initial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "fac23f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "charger_occupancy = pd.DataFrame()\n",
    "\n",
    "for charger in df_charger_occupancy.columns:\n",
    "    temp_df = pd.DataFrame(df_charger_occupancy[charger])\n",
    "    temp_df.columns = ['Charger Occupation State']\n",
    "    temp_df['Charger ID'] = charger\n",
    "    charger_occupancy = pd.concat([charger_occupancy, temp_df])\n",
    "\n",
    "del df_charger_occupancy, temp_df\n",
    "# charger_occupancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "e0321795",
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_capacities = pd.DataFrame()\n",
    "\n",
    "for charger in df_vehicle_capacities.columns:\n",
    "    temp_df = pd.DataFrame(df_vehicle_capacities[charger])\n",
    "    temp_df.columns = ['Vehicle Capacities']\n",
    "    temp_df['Charger ID'] = charger\n",
    "    vehicle_capacities = pd.concat([vehicle_capacities, temp_df])\n",
    "\n",
    "del df_vehicle_capacities, temp_df\n",
    "# vehicle_capacities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4033d49",
   "metadata": {},
   "source": [
    "#### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "7a2c8d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(prediction_results_path) as json_data:\n",
    "    prediction_data = json.load(json_data)\n",
    "    \n",
    "    df_soc = pd.DataFrame(prediction_data['SOC']).T\n",
    "    df_charger_actions = pd.DataFrame(prediction_data['Charger_actions'])\n",
    "    df_charger_power_values = pd.DataFrame(prediction_data['Charger_power_values'])\n",
    "    \n",
    "    chargers_predictions = pd.DataFrame()\n",
    "    for charger in df_soc.columns:\n",
    "        temp_df = pd.DataFrame(df_soc[charger])\n",
    "        temp_df.columns = ['Vehicle State of Charge']\n",
    "        temp_df['Charger Action'] = df_charger_actions[charger]\n",
    "        temp_df['Charger Power Value'] = df_charger_power_values[charger]\n",
    "        temp_df['Charger ID'] = charger\n",
    "        chargers_predictions = pd.concat([chargers_predictions, temp_df])\n",
    "        del temp_df\n",
    "    \n",
    "    del df_soc, df_charger_actions, df_charger_power_values\n",
    "    \n",
    "    if 'Initial_battery_state_of_charge' in prediction_data:\n",
    "        initial_battery_soc = float(prediction_data['Initial_battery_state_of_charge']) \n",
    "    else: \n",
    "        initial_battery_soc = 0.5\n",
    "    df_battery_soc = pd.DataFrame(prediction_data['Battery_state_of_charge'])\n",
    "    df_battery_action = pd.DataFrame(prediction_data['Battery_action'])\n",
    "    df_battery_power_value = pd.DataFrame(prediction_data['Battery_power_value'])\n",
    "    df_battery_calculated_power_value = pd.DataFrame(prediction_data['Battery_calculated_power_value'])\n",
    "    \n",
    "    battery_predictions= pd.DataFrame()\n",
    "    battery_predictions['Battery SOC'] = df_battery_soc\n",
    "    battery_predictions['Battery Action'] = df_battery_action\n",
    "    battery_predictions['Battery Power Value'] = df_battery_power_value\n",
    "    battery_predictions['Battery Calculated Power Value'] = df_battery_calculated_power_value\n",
    "    \n",
    "    del df_battery_soc, df_battery_action, df_battery_power_value, df_battery_calculated_power_value\n",
    "\n",
    "    df_total_cost = pd.DataFrame(prediction_data['Total_cost'])\n",
    "    df_total_reward = df_total_cost * (-1)\n",
    "    df_total_penalties = pd.DataFrame(prediction_data['Total_penalties'])\n",
    "    df_grid_energy_cost = pd.DataFrame(prediction_data['Grid_energy_cost'])\n",
    "    \n",
    "    costs_predictions = pd.DataFrame()\n",
    "    costs_predictions['Total Cost'] = df_total_cost\n",
    "    costs_predictions['Total Reward'] = df_total_reward\n",
    "    costs_predictions['Total Penalty'] = df_total_penalties\n",
    "    costs_predictions['Grid Energy Cost'] = df_grid_energy_cost\n",
    "    costs_predictions['Electricity Day Tariffs'] = day_tariffs\n",
    "    \n",
    "    del df_total_cost, df_total_reward, df_grid_energy_cost, day_tariffs\n",
    "    \n",
    "    df_total_vehicle_penalties = pd.DataFrame(prediction_data['Total_vehicle_penalties'])\n",
    "    df_insufficient_vehicle_charging_penalties = pd.DataFrame(prediction_data['Insufficiently_charged_vehicle_penalties'])\n",
    "    df_needless_vehicle_charging_penalties = pd.DataFrame(prediction_data['Needlessly_charged_vehicle_penalties'])\n",
    "    df_overcharged_vehicle_penalties = pd.DataFrame(prediction_data['Overcharged_vehicle_penalties'])\n",
    "    df_over_discharged_vehicle_penalties = pd.DataFrame(prediction_data['Over_discharged_vehicle_penalties'])\n",
    "    df_total_battery_penalties = pd.DataFrame(prediction_data['Total_battery_penalties'])\n",
    "    df_battery_soc_below_dod_penalties = pd.DataFrame(prediction_data['Battery_SOC_below_DoD_penalties'])\n",
    "    df_battery_overcharging_penalties = pd.DataFrame(prediction_data['Battery_overcharging_penalties'])\n",
    "    df_battery_over_discharging_penalties = pd.DataFrame(prediction_data['Battery_over_discharging_penalties'])\n",
    "    df_low_resource_utilisation_penalties = pd.DataFrame(prediction_data['Low_resource_utilisation_penalties'])\n",
    "    df_discharging_nonexistent_vehicles_penalties = pd.DataFrame(prediction_data['DisCharging_nonexistent_vehicles_penalties'])\n",
    "#     df_excess_battery_charging_penalties = pd.DataFrame(prediction_data['Excessively_charged_battery_penalties'])\n",
    "#     df_excess_battery_discharging_penalties = pd.DataFrame(prediction_data['Excessively_discharged_battery_penalties'])\n",
    "    \n",
    "    penalties = pd.DataFrame()\n",
    "    penalties['Total Penalty'] = df_total_penalties\n",
    "    penalties['Total Vehicle Penalty'] = df_total_vehicle_penalties\n",
    "    penalties['Insufficiently Charged Vehicle Penalty'] = df_insufficient_vehicle_charging_penalties\n",
    "    penalties['Needless Vehicle Charging Penalty'] = df_needless_vehicle_charging_penalties\n",
    "    penalties['Overcharged Vehicle Penalty'] = df_overcharged_vehicle_penalties\n",
    "    penalties['Over Discharged Vehicle Penalty'] = df_over_discharged_vehicle_penalties\n",
    "    penalties['Total Battery Penalty'] = df_total_battery_penalties\n",
    "    penalties['Battery SOC Below DoD Penalty'] = df_battery_soc_below_dod_penalties\n",
    "    penalties['Battery Overcharging Penalty'] = df_battery_overcharging_penalties\n",
    "    penalties['Battery Over Discharging Penalty'] = df_battery_over_discharging_penalties\n",
    "    penalties['Low Resource Utilisation Penalty'] = df_low_resource_utilisation_penalties\n",
    "    penalties['DisCharging Nonexistent Vehicles Penalty'] = df_discharging_nonexistent_vehicles_penalties\n",
    "    \n",
    "    del df_total_penalties, df_total_vehicle_penalties, df_total_battery_penalties, df_overcharged_vehicle_penalties\n",
    "    del df_insufficient_vehicle_charging_penalties, df_over_discharged_vehicle_penalties\n",
    "    del df_needless_vehicle_charging_penalties, df_battery_soc_below_dod_penalties, df_low_resource_utilisation_penalties\n",
    "    del df_discharging_nonexistent_vehicles_penalties, df_battery_overcharging_penalties\n",
    "    del df_battery_over_discharging_penalties\n",
    "    \n",
    "    df_grid_power = pd.DataFrame(prediction_data['Grid_power'])\n",
    "    df_grid_energy = pd.DataFrame(prediction_data['Grid_energy'])\n",
    "    df_utilised_solar_energy = pd.DataFrame(prediction_data['Utilized_solar_energy'])\n",
    "    df_available_solar_energy = pd.DataFrame(prediction_data['Available_solar_energy']).T\n",
    "    df_total_charging_power = pd.DataFrame(prediction_data['Total_charging_power'])\n",
    "    df_total_discharging_power = pd.DataFrame(prediction_data['Total_discharging_power'])\n",
    "    \n",
    "    energy_predictions = pd.DataFrame()\n",
    "    energy_predictions['Grid Power'] = df_grid_power\n",
    "    energy_predictions['Grid Energy'] = df_grid_energy\n",
    "    energy_predictions['Utilised Solar Power'] = df_utilised_solar_energy\n",
    "    \n",
    "    if len(df_available_solar_energy) > 1:\n",
    "        energy_predictions['Available Solar Energy - 0-24h'] = df_available_solar_energy.loc[0:24]\n",
    "        energy_predictions['Available Solar Energy - 24-48h'] = df_available_solar_energy.loc[24:47][0].tolist()\n",
    "    else:\n",
    "        energy_predictions['Available Solar Energy - 0-24h'] = [0]*24\n",
    "        energy_predictions['Available Solar Energy - 24-48h'] = [0]*24    \n",
    "    \n",
    "    energy_predictions['Total Charging Power'] = df_total_charging_power\n",
    "    energy_predictions['Total Discharging Power'] = df_total_discharging_power\n",
    "    \n",
    "    del df_grid_power, df_grid_energy, df_utilised_solar_energy, df_available_solar_energy\n",
    "    del df_total_charging_power, df_total_discharging_power\n",
    "    \n",
    "    del prediction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "2374cf79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# chargers_predictions.T\n",
    "# battery_predictions.T\n",
    "# costs_predictions.T\n",
    "# penalties.T\n",
    "# energy_and_power_predictions.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01658fb8",
   "metadata": {},
   "source": [
    "## 8. Training data visualisation"
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  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "327fbee1",
   "metadata": {
    "scrolled": false
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       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_fig = make_subplots(rows=4, cols=1, shared_xaxes=True, vertical_spacing=0.02)\n",
    "\n",
    "max_ev_cap = vehicle_capacities['Vehicle Capacities'].max()\n",
    "\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    ev_cap_text = str(initial_training_data.loc[v_id, 'EV Capacity'])\n",
    "    \n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        # Occupied\n",
    "        subplot_fig.add_scatter(line_shape='hv',\n",
    "                                y=charger_occupancy.loc[charger_occupancy['Charger ID']==charger,'Charger Occupation State'], \n",
    "                                row=counter, col=1, name=f'Zauzeto', \n",
    "                                fillpattern=dict(shape='/', fgcolor='rgba(71, 164, 235, 0.5)', fgopacity=0.5),\n",
    "                                fillcolor=f'rgba(71, 164, 235, 0.4)', \n",
    "                                line_width=0, fill='tozeroy', \n",
    "                                legendgroup='Charger occupancy', showlegend=(not v_id), \n",
    "                                legendgrouptitle=dict(text='Mjesto punjenja'))\n",
    "        subplot_fig.add_scatter(line_shape='hv',\n",
    "                                y=vehicle_capacities.loc[vehicle_capacities['Charger ID']==charger,'Vehicle Capacities']/max_ev_cap, \n",
    "                                row=counter, col=1, name='Kapacitet vozila [kWh]',\n",
    "                                fillcolor=f'rgba(71, 164, 235, 0.5)', \n",
    "                                line_width=0, fill='tozeroy', legendgroup='Charger occupancy',\n",
    "                                showlegend=(not v_id))\n",
    "        # Requested - on departure\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure'], \n",
    "                            row=counter, col=1,\n",
    "                            y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], \n",
    "                            name=f'Traženo - pri odlasku',\n",
    "                            width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                            legendgrouptitle=dict(text='Stanje napunjenosti'))\n",
    "        # Initial - on arrival\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], \n",
    "                            row=counter, col=1,\n",
    "                            y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], \n",
    "                            name=f'Početno - kod dolaska', width=0.5, \n",
    "                            marker=dict(color='crimson', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id))\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, \n",
    "                                   row=charger_id+1, col=1, font_size=13, showarrow=False, font_color='grey')\n",
    "    \n",
    "    if dep > 24 and arr == 23:\n",
    "        subplot_fig.add_annotation(x=arr+0.5, y=0.4, text=f'<i>{ev_cap_text}</i>', textangle=-90, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    elif dep > 24 and arr == 22:\n",
    "        subplot_fig.add_annotation(x=(arr+24)/2, y=0.4, text=f'<i>{ev_cap_text}</i>', textangle=-90, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    elif dep > 24:\n",
    "        subplot_fig.add_annotation(x=(arr+24)/2, y=0.12, text=f'<i>{ev_cap_text}</i>', textangle=0, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    else:\n",
    "        subplot_fig.add_annotation(x=(arr+dep)/2, y=0.12, text=f'<i>{ev_cap_text}</i>', textangle=0, opacity=0.9, \n",
    "                                   font_size=14, row=charger_id+1, col=1, showarrow=False, font_color='white')\n",
    "    \n",
    "    subplot_fig.add_shape(type='line', x0=arr, x1=arr, y0=0, y1=1, layer='above',\n",
    "                          line_color='crimson', line_width=1.5, row=charger_id+1, col=1)\n",
    "    subplot_fig.add_shape(type='line', x0=dep, x1=dep, y0=0, y1=1, layer='above',\n",
    "                          line_color='teal', line_width=1.5, row=charger_id+1, col=1)\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.15, y=0.5, text=\"Stanje napunjenosti\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Vremenski korak (Sat) u Danu\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "# subplot_fig.update_layout(title=\"Initial Model Info\", barmode='overlay')\n",
    "subplot_fig.update_layout(title=\"\", barmode='overlay')\n",
    "subplot_fig.update_xaxes(griddash=\"dot\", range=[-0.2, 24.2], tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 850\n",
    "plot_height = 650\n",
    "\n",
    "subplot_fig.update_yaxes(tickformat=\" ,~%\", nticks=5, range=[0, 1.1], dtick=0.25)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.43, font_size=24, y=0.9),\n",
    "                          showlegend=True, legend=dict(groupclick='togglegroup', x=1.03, y=0.97))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_ev_cap, charger_id, counter, charger, arr, dep, ev_cap_text, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "ef1e0fdb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=4,\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.02\n",
    ")\n",
    "\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Vehicle State of Charge']\n",
    "        subplot_fig.add_scatter(y=psoc, line_shape='linear', row=counter, col=1, name='Vrijednost na kraju vremenskog koraka', mode='markers+lines',\n",
    "                                marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkturquoise'), showlegend=(not v_id), \n",
    "                                legendgrouptitle=dict(text='Predviđeno stanje napunjenosti'), legendgroup='Predicted Charger SOC', )\n",
    "        subplot_fig.add_bar(y=psoc, row=counter, col=1, legendgroup='Predicted Charger SOC', showlegend=False, \n",
    "                            marker=dict(color=psoc, opacity=1, line_width=0, coloraxis='coloraxis'))\n",
    "        \n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure']-1, row=counter, col=1,\n",
    "                    y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], name=f'Traženo - pri odlasku',\n",
    "                    width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                    legendgrouptitle=dict(text='Stanje napunjenosti'))\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], row=counter, col=1,\n",
    "                    y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], name=f'Početno - kod dolaska',\n",
    "                    width=0.5, marker=dict(color='crimson', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id))\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, row=charger_id+1, col=1,\n",
    "                                   font_size=13, showarrow=False, font_color='grey')    \n",
    "\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.15, y=0.5, text=\"Stanje napunjenosti\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Vremenski korak (Sat) u Danu\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "# Predicted State of Charge per Charger\n",
    "subplot_fig.update_layout(title=\"\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 950\n",
    "plot_height = 750\n",
    "\n",
    "subplot_fig.update_yaxes(tickformat=\" ,~%\", nticks=5, range=[0, 1.1], dtick=0.25)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.42, font_size=24, y=0.9), \n",
    "                          showlegend=True, \n",
    "                          coloraxis=dict(\n",
    "                              colorbar=dict(len=0.7, thickness=14, yanchor='middle', y=0.35, x=1.025, \n",
    "                                            title='Stanje napunjenosti', titleside='right', tickvals=[0, 0.25, 0.5, 0.75, 0.95, 0.98, 1],\n",
    "                                            tickformat=',.0%', ticktext=['0%','25%','50%','75%','95%','\\t  ] Optimalni Raspon Vrijednosti','100%']),\n",
    "                              colorscale=[[0, 'rgba(220,20,60, 0.45)'], [0.9, 'rgba(0,128,128, 0.45)'], [0.95, 'rgba(0,206,209, 0.45)'], [1, 'rgba(0,206,209, 0.45)']]\n",
    "                          )\n",
    "                         )\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del psoc, charger_id, counter, charger, arr, dep, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8920efe",
   "metadata": {},
   "source": [
    "`Todo: Add different vehicle penalties to the 5th plot below`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d16f2430",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "legendgroup": "State of Charge",
         "legendgrouptitle": {
          "text": "State of Charge"
         },
         "marker": {
          "color": "teal",
          "line": {
           "width": 0
          },
          "opacity": 0.8
         },
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   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=5,\n",
    "    row_heights=[0.17, 0.17, 0.17, 0.17, 0.32],\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.04,\n",
    "    specs=[[{'secondary_y': True}], [{'secondary_y': True}], [{'secondary_y': True}], [{'secondary_y': True}],\n",
    "           [{'secondary_y': True}]]\n",
    ")\n",
    "\n",
    "charger_patterns = ['', '/', '\\\\', '+']\n",
    "charger_markers = ['circle', 'cross', 'diamond', 'square']\n",
    "for v_id in initial_training_data.index:\n",
    "    charger_id = int(initial_training_data.loc[v_id, 'Charger ID'])\n",
    "    counter = number_of_chargers - charger_id\n",
    "    charger = counter - 1\n",
    "    \n",
    "    arr = int(initial_training_data.loc[v_id, 'Arrival'])\n",
    "    dep = int(initial_training_data.loc[v_id, 'Departure'])\n",
    "    \n",
    "    if initial_training_data.loc[v_id, 'Vehicle ID'] == '0':\n",
    "        psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Vehicle State of Charge']\n",
    "        pactions = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Charger Action']\n",
    "        ppowers = chargers_predictions.loc[chargers_predictions['Charger ID']==charger,'Charger Power Value']\n",
    "        \n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Departure']-1, row=counter, col=1,\n",
    "                    y=initial_training_data.loc[(initial_training_data['Charger ID']==str(charger))&(initial_training_data.index[v_id]==v_id), 'Requested State of Charge'], name=f'Requested - on departure',\n",
    "                    width=0.5, marker=dict(color='teal', opacity=0.8, line_width=0), legendgroup='State of Charge', showlegend=(not v_id),\n",
    "                    legendgrouptitle=dict(text='State of Charge'), secondary_y=False)\n",
    "        subplot_fig.add_bar(x=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Arrival'], row=counter, col=1,\n",
    "                    y=initial_training_data.loc[initial_training_data['Charger ID']==str(charger), 'Initial State of Charge'], name=f'Initial - on arrival',\n",
    "                    width=0.5, marker=dict(color='crimson', opacity=0.8, line_width=0), \n",
    "                            legendgroup='State of Charge', showlegend=(not v_id), secondary_y=False)\n",
    "        \n",
    "        subplot_fig.add_scatter(y=psoc, line_shape='linear', row=counter, col=1, name='Value at timestep end', mode='markers+lines',\n",
    "                                marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkturquoise'), \n",
    "                                showlegend=(not v_id), secondary_y=False,\n",
    "                                legendgrouptitle=dict(text='Predicted State of Charge'), legendgroup='Predicted Charger SOC')\n",
    "        subplot_fig.add_bar(y=pactions, row=counter, col=1, legendgroup='Charger Action', showlegend=True, name=f'Charger {charger}', legendgrouptitle=dict(text='Charging Action'),\n",
    "                            marker=dict(color='darkturquoise', opacity=0.45, line_width=1, line_color='darkturquoise',\n",
    "                                        pattern_shape=charger_patterns[charger], pattern_fgopacity=0.9, pattern_fgcolor='white', pattern_size=10), secondary_y=False)\n",
    "        \n",
    "        \n",
    "        \n",
    "        subplot_fig.add_scatter(y=ppowers, line_shape='linear', row=counter, col=1, name=f'Charger {charger}', mode='markers',\n",
    "                                marker=dict(size=8, symbol=charger_markers[charger]), line=dict(color='darkorange'), \n",
    "                                showlegend=True, secondary_y=True,\n",
    "                                legendgrouptitle=dict(text='Power Values'), legendgroup='Charger Power')\n",
    "        \n",
    "        subplot_fig.add_annotation(x=23, y=1.2, text=f'<i>Charger {charger_id}</i>', textangle=0, opacity=0.9, row=charger_id+1, col=1,\n",
    "                                   font_size=13, showarrow=False, font_color='grey')    \n",
    "\n",
    "pevpenalties = penalties['Total Vehicle Penalty']\n",
    "subplot_fig.add_scatter(y=pevpenalties, line_shape='linear', row=5, col=1, name='Value at timestep end', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='crimson'), fill='tozeroy',\n",
    "                        showlegend=True, secondary_y=False,\n",
    "                        legendgrouptitle=dict(text='Total Vehicle Penalty'), legendgroup='Total Vehicle Penalty')\n",
    "subplot_fig.add_bar(x=penalties.index, y=pevpenalties, row=5, col=1, name='Value at timestep end', secondary_y=False,\n",
    "                        width=0.2, legendgrouptitle=dict(text='Total Battery Penalty'), legendgroup='Total Battery Penalty',\n",
    "                        marker=dict(line_width=0, opacity=0.8, color='crimson'), showlegend=False)\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.14, y=0.65, yanchor='middle', text=\"State of Charge - Action Value\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.04, y=0.65, text=\"Power [kW]\", textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=12/26, y=-0.08, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Predicted State of Charge with Action and Power Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", range=[-0.4, 24.2],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 880\n",
    "plot_height = 890\n",
    "\n",
    "total_ev_pen_max = pevpenalties.max()\n",
    "y9type = 'log' if total_ev_pen_max > 100000 else 'linear'\n",
    "y9max_margin = 6 if y9type == 'log' else round(total_ev_pen_max + 10)\n",
    "y9yaxistext = '- log scale' if y9type == 'log' else ''\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.92), \n",
    "                          showlegend=True, legend=dict(x=26/24), margin=dict(autoexpand=True),\n",
    "                          xaxis4=dict(showticklabels=True),\n",
    "                          yaxis=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.2),\n",
    "                          yaxis2=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis3=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis4=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis5=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis6=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis7=dict(tickformat=\" ,~\", nticks=5, range=[0, 1.1], dtick=0.25),\n",
    "                          yaxis8=dict(tickformat=\" ,~\", nticks=5, range=[0, 22], dtick=4),\n",
    "                          yaxis9=dict(tickformat=\" ,~g\", nticks=9, type=y9type, \n",
    "                                      title=dict(text=f'Penalty Value {y9yaxistext}')))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del psoc, charger_id, counter, charger, arr, dep, max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "335ac56e",
   "metadata": {},
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   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Insufficiently Charged Vehicle Penalty']\n",
    "pen2 = penalties['Overcharged Vehicle Penalty']\n",
    "pen3 = penalties['Needless Vehicle Charging Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen1, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.8, line_width=0), name='Insufficiently Charged Vehicle')\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.8, line_width=0), name='Overcharged Vehicle')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.8, line_width=0), name='Needless Vehicle Charging')\n",
    "\n",
    "pevpenalties = penalties['Total Vehicle Penalty']\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pevpenalties, line_shape='linear', name='Total Vehicle penalty', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='diamond', line=dict(width=1.5), color=pevpenalties, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)', width=2),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Vehicle Penalty')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Vehicle Prediction Penalties\",\n",
    "                          yaxis=dict(title=''))\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 950\n",
    "plot_height = 650\n",
    "\n",
    "subplot_fig.update_yaxes(nticks=6)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'orange'], [1, 'sienna']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Vehicle Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, type='linear'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
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   ],
   "source": [
    "subplot_fig = make_subplots(\n",
    "    rows=2,\n",
    "    row_heights=[0.6, 0.4],\n",
    "    cols=1,\n",
    "    shared_xaxes=True,\n",
    "    vertical_spacing=0.04,\n",
    "    specs=[[{'secondary_y': True}], [{'secondary_y': True}]]\n",
    ")\n",
    "\n",
    "# Initial State of Charge % Current State of Charge %\n",
    "subplot_fig.add_scatter(y=[initial_battery_soc, battery_predictions['Battery SOC'][0]], yaxis='y', row=1, col=1, secondary_y=False,\n",
    "                        x=[0, 1], line_shape='linear', name='Početno stanje napunjenosti %', mode='markers+lines',\n",
    "                        marker=dict(size=8, symbol=['square-dot', 'circle']), line=dict(color='darkorange'), showlegend=True)\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery SOC'], yaxis='y', row=1, col=1, secondary_y=False,\n",
    "                        x=battery_predictions.index+1, line_shape='linear', name='Trenutno stanje napunjenosti %', mode='markers+lines',\n",
    "                        marker=dict(size=8, symbol='circle-dot'), line=dict(color='darkorange'), showlegend=True)\n",
    "\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Action'], showlegend=True, yaxis='y2', width=0.9, row=1, col=1, secondary_y=False,\n",
    "                    marker=dict(color='darkturquoise', coloraxis='coloraxis', opacity=0.3, line_width=0), name='Radnja baterije')\n",
    "\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Calculated Power Value'], showlegend=True, yaxis='y3', width=0.9, row=1, col=1, secondary_y=True,\n",
    "                    marker=dict(color='dimgrey', pattern=dict(shape='\\\\', fillmode='replace', size=5), opacity=0.8, line_width=1.2, line_color='dimgrey'), name='Snaga punjenja/pražnjenja - izračunata [kW]')\n",
    "subplot_fig.add_bar(y=battery_predictions['Battery Power Value'], showlegend=True, yaxis='y3', width=0.9, row=1, col=1, secondary_y=True,\n",
    "                    marker=dict(color='dimgrey', pattern=dict(shape='/', fillmode='replace', size=15), opacity=0.8, line_width=1.2, line_color='dimgrey'), name='Snaga punjenja/pražnjenja - stvarna [kW]')\n",
    "# Dis/Charging Calculated Power\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery Calculated Power Value'], line_shape='linear', secondary_y=True, \n",
    "                        name='Snaga punjenja/pražnjenja - izračunata [kW]', mode='markers', yaxis='y3', row=1, col=1,\n",
    "                        marker=dict(size=8, symbol='cross'), line=dict(color='black'), showlegend=True)\n",
    "subplot_fig.add_scatter(y=battery_predictions['Battery Power Value'], line_shape='linear', secondary_y=True,\n",
    "                        name='Snaga punjenja/pražnjenja - stvarna [kW]', mode='markers', yaxis='y3', row=1, col=1,\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='black'), showlegend=True)\n",
    "\n",
    "# subplot_fig.add_scatter(y=[0.5*80/1], yaxis='y2', row=1, col=1, secondary_y=True,\n",
    "#                         x=[0], line_shape='linear', name='Initial capacity', mode='markers',\n",
    "#                         marker=dict(size=7, symbol='x'), line=dict(color='darkgreen'), showlegend=True)\n",
    "# subplot_fig.add_scatter(y=battery_predictions['Battery SOC']*80/1, yaxis='y2', row=1, col=1, secondary_y=True,\n",
    "#                         x=battery_predictions.index+1, line_shape='linear', name='Current capacity', mode='markers+lines',\n",
    "#                         marker=dict(size=7, symbol='x'), line=dict(color='darkgreen', dash='dot'), showlegend=True)\n",
    "\n",
    "pbpenalties = penalties['Total Battery Penalty']\n",
    "subplot_fig.add_scatter(y=pbpenalties, line_shape='linear', row=2, col=1, name='Value at timestep end', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle'), line=dict(color='crimson'), fill='tozeroy',\n",
    "                        showlegend=False, secondary_y=False,\n",
    "                        )\n",
    "# Total Battery Penalty\n",
    "subplot_fig.add_bar(x=penalties.index, y=pbpenalties, row=2, col=1, name='Vrijednost na kraju vremenskog koraka', secondary_y=False,\n",
    "                        width=0.2, legendgrouptitle=dict(text='Ukupna penalizacija'), legendgroup='Total Battery Penalty',\n",
    "                        marker=dict(line_width=0, opacity=0.8, color='crimson', coloraxis='coloraxis'), showlegend=True)\n",
    "\n",
    "subplot_fig.add_annotation(x=12/26, y=-0.1, text=\"Vremenski korak (Sat) u Danu\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "# Predicted Battery Values\n",
    "subplot_fig.update_layout(title=\"\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 950\n",
    "plot_height = 790\n",
    "\n",
    "total_pen_max = pbpenalties.max()\n",
    "y3type = 'log' if total_pen_max > 1000000 else 'linear'\n",
    "y3max_margin = 6 if y3type == 'log' else round(total_pen_max + total_pen_max+5)\n",
    "y3yaxistext = '- log scale' if y3type == 'log' else ''\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=12/32, font_size=24, y=0.92), \n",
    "                          showlegend=True, legend=dict(x=26/24),\n",
    "                          yaxis=dict(tickformat=\" ,~\", nticks=5, range=[-1.1, 1.1], dtick=0.25, \n",
    "                                     title='Stanje napunjenosti - Vrijednost akcije'),\n",
    "                          yaxis2=dict(tickformat=\" ,~\", nticks=5, range=[-50.2, 50.2], dtick=10, title='Snaga [kW]'),\n",
    "                          yaxis3=dict(tickformat=\" ,~g\", nticks=6, type=y3type, \n",
    "                                      title=dict(text=f'Vrijednost penalizacije {y3yaxistext}')))\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "84473d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "name": "Battery SOC Below DoD",
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   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Battery SOC Below DoD Penalty']\n",
    "pen2 = penalties['Battery Overcharging Penalty']\n",
    "pen3 = penalties['Battery Over Discharging Penalty']\n",
    "# pen4 = penalties['DisCharging Nonexistent Vehicles Penalty']\n",
    "# pen5 = penalties['Low Resource Utilisation Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen1, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.9, line_width=0), name='Battery SOC Below DoD')\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.9, line_width=0), name='Battery Overcharging')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.9, line_width=0), name='Battery Over Discharging')\n",
    "# subplot_fig.add_bar(y=pen4, showlegend=True, yaxis='y', width=0.9,\n",
    "#                     marker=dict(color='tan', opacity=0.9, line_width=0), name='Needless Battery Charging')\n",
    "# subplot_fig.add_bar(y=pen5, showlegend=True, yaxis='y', width=0.9,\n",
    "#                     marker=dict(color='darkgoldenrod', opacity=0.8, line_width=0), name='Needless Battery Discharging')\n",
    "\n",
    "pbpenalties = penalties['Total Battery Penalty']\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pbpenalties, line_shape='linear', name='Total Battery penalty', mode='markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=pbpenalties, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Battery Penalty')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Battery Prediction Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_pen_max = pbpenalties.max()\n",
    "ytype = 'log' if total_pen_max > 100000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_pen_max + total_pen_max)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6, dtick=0.2)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'gold'], [1, 'darkgoldenrod']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Battery Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, dtick=ydtick, type=f'{ytype}'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "eea9ee92",
   "metadata": {},
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   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "pen1 = penalties['Total Penalty']\n",
    "pen2 = penalties['Total Vehicle Penalty']\n",
    "pen3 = penalties['Total Battery Penalty']\n",
    "pen4 = penalties['DisCharging Nonexistent Vehicles Penalty']\n",
    "pen5 = penalties['Low Resource Utilisation Penalty']\n",
    "\n",
    "subplot_fig.add_bar(y=pen2, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='sienna', opacity=0.9, line_width=0), name='Total Vehicle Penalty')\n",
    "subplot_fig.add_bar(y=pen3, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkorange', opacity=0.9, line_width=0), name='Total Battery Penalty')\n",
    "subplot_fig.add_bar(y=pen4, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='tan', opacity=0.9, line_width=0), name='DisCharging Nonexistent Vehicles Penalty')\n",
    "subplot_fig.add_bar(y=pen5, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='darkgoldenrod', opacity=0.8, line_width=0), name='Low Resource Utilisation Penalty')\n",
    "\n",
    "subplot_fig.add_scatter(x=penalties.index, y=pen1, line_shape='linear', name='Total penalty', mode='lines+markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=pen1, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Penalty')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_pen1_max = pen1.max()\n",
    "ytype = 'log' if total_pen1_max > 100000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_pen1_max + total_pen1_max/10)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'gold'], [1, 'darkgoldenrod']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Penalty Value', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, type=f'{ytype}'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "146d5fb9",
   "metadata": {},
   "outputs": [
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   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "total_cost = costs_predictions['Total Cost']\n",
    "total_penalty = costs_predictions['Total Penalty']\n",
    "grid_cost = costs_predictions['Grid Energy Cost']\n",
    "\n",
    "subplot_fig.add_bar(y=total_penalty, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='crimson', opacity=0.9, line_width=0), name='Total Penalty')\n",
    "subplot_fig.add_bar(y=grid_cost, showlegend=True, yaxis='y', width=0.9,\n",
    "                    marker=dict(color='silver', opacity=0.9, line_width=0), name='Grid Energy Cost')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=total_cost, line_shape='linear', name='Total Cost', mode='markers',\n",
    "                        marker=dict(size=10, symbol='diamond', line=dict(width=1.5), color=total_cost, coloraxis='coloraxis'), line=dict(color='rgba(0,0,0,0.7)'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Cost')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Cost & Penalty Value\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Costs & Penalties\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)])\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "\n",
    "total_cost_max = total_cost.max()\n",
    "ytype = 'log' if total_cost_max > 10000 else 'linear'\n",
    "ymax_margin = 6 if ytype == 'log' else round(total_cost_max + total_cost_max/10)\n",
    "yyaxistext = '- log scale' if ytype == 'log' else ''\n",
    "ydtick = 1 if ytype == 'log' else 5\n",
    "subplot_fig.update_yaxes(nticks=6)\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'rgb(22, 22, 22)'], [0.25, 'dimgrey'], [0.5, 'lightgrey'], [0.7, '#E93556'], [0.9, 'crimson'], [1, '#B81432']], \n",
    "                                                          colorbar=dict(title=dict(text='Total Cost', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", nticks=5, type=f'{ytype}'))\n",
    "\n",
    "\n",
    "subplot_fig.show()\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "6043d77c",
   "metadata": {
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   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "grid_p = energy_predictions['Grid Power']\n",
    "pv_p = energy_predictions['Utilised Solar Power']\n",
    "total_ev_p = energy_predictions['Total Charging Power'] + energy_predictions['Total Discharging Power']\n",
    "battery_p = battery_predictions['Battery Power Value']\n",
    "day_tariff = costs_predictions['Electricity Day Tariffs']\n",
    "\n",
    "# Low Demand Peak Demand\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Niska potražnja\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Visoka potražnja\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Niska potražnja\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "# Grid Power Utilised Solar Power Total Vehicle Power Total Battery Power\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=grid_p, line_shape='linear', name='Mrežna snaga', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='turquoise', coloraxis='coloraxis'), line=dict(color='turquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Grid Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=pv_p, line_shape='linear', name='Snaga iz PV sustava', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='gold', coloraxis='coloraxis'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Solar Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=total_ev_p, line_shape='linear', name='Ukupna potražnja EV', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Power')\n",
    "subplot_fig.add_scatter(x=battery_predictions.index, y=battery_p, line_shape='linear', name='Snaga Baterijske pohrane', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='crimson', coloraxis='coloraxis'), line=dict(color='crimson'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Total Power')\n",
    "# Electricity Day Tariff\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Dnevna tarifa električne energije', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Day Tariff')\n",
    "\n",
    "# Grid Tariff\n",
    "subplot_fig.add_annotation(x=-0.08, y=0.5, text=\"Snaga [kW]\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.07, y=0.75, text=\"Mrežna tarifa\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Vremenski korak (Sat) u Danu\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "# Prediction Power Values\n",
    "subplot_fig.update_layout(title=\"\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24],\n",
    "                         tickvals=[i for i in range(max_timestep)], ticktext=[str(i%24) for i in range(max_timestep)], \n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 1080\n",
    "plot_height = 750\n",
    "\n",
    "max_p = max(grid_p.max(), pv_p.max(), total_ev_p.max(), battery_p.max())\n",
    "power_margin = int(round(max_p/10)*10)+5\n",
    "power_ticks = int(power_margin/5)\n",
    "max_tariff = day_tariff.max()\n",
    "tariff_margin = int(max_tariff)+0.25\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'lightgrey'], [1, 'grey']], \n",
    "                                                          colorbar=dict(title=dict(text='', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", range=[-power_margin, power_margin], dtick=power_ticks, showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', nticks=11, dtick=0.05, tickmode='array', tickvals=[str(i/20) for i in range(12)],\n",
    "                                      autoshift=True, shift=2, side='right', range=[-tariff_margin, tariff_margin], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(overlaying='y',\n",
    "                                      range=[0, tariff_margin], visible=False))\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "77e1d39d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "data": {
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          "format": {
           "currency": [
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        "plotlyServerURL": "https://plot.ly"
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "grid_p = energy_predictions['Grid Power']\n",
    "pv_p = energy_predictions['Utilised Solar Power']\n",
    "total_ev_p = energy_predictions['Total Charging Power'] + energy_predictions['Total Discharging Power']\n",
    "battery_soc = battery_predictions['Battery SOC']\n",
    "day_tariff = costs_predictions['Electricity Day Tariffs']\n",
    "\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Peak Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=grid_p, line_shape='linear', name='Grid Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='turquoise', coloraxis='coloraxis'), line=dict(color='turquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Grid Power')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=pv_p, line_shape='linear', name='Utilised Solar Power', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='gold', coloraxis='coloraxis'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='Solar Power')\n",
    "\n",
    "subplot_fig.add_scatter(x=[0, 1], y=[initial_battery_soc, battery_soc[0]], line_shape='linear', name='Initial Battery State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=False, yaxis='y3', legendgroup='Battery SOC')\n",
    "subplot_fig.add_scatter(x=battery_predictions.index+1, y=battery_soc, line_shape='linear', name='Battery State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='limegreen', coloraxis='coloraxis'), line=dict(color='limegreen'),\n",
    "                        showlegend=True, yaxis='y3', legendgroup='Battery SOC')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Electricity Day Tariff', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Day Tariff')\n",
    "\n",
    "color = ['yellow', 'orange', 'skyblue', 'chocolate']\n",
    "for ca in range(number_of_chargers):\n",
    "    psoc = chargers_predictions.loc[chargers_predictions['Charger ID']==ca,'Vehicle State of Charge']\n",
    "    subplot_fig.add_scatter(x=battery_predictions.index, y=psoc, line_shape='linear', name=f'Charger {ca} State of Charge', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color=ca, coloraxis='coloraxis'), line=dict(color=f'{color[ca]}'),\n",
    "                        showlegend=True, yaxis='y3')\n",
    "\n",
    "subplot_fig.add_annotation(x=-0.1, y=0.5, text=\"Power [kW]\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.065, y=0.75, text=\"Grid Tariff\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=1.14, y=0.75, text=\"State of Charge\", xanchor='left', yanchor='middle', textangle=-90, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "subplot_fig.add_annotation(x=0.5, y=-0.1, text=\"Timestep (Hour) in a Day\", textangle=0, xref=\"paper\", yref=\"paper\", font_size=14, showarrow=False)\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Prediction Power & State of Charge Values\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title='', range=[-0.4, 24],\n",
    "                         tickvals=[i for i in range(max_timestep)], ticktext=[str(i%24) for i in range(max_timestep)], \n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 1000\n",
    "plot_height = 650\n",
    "\n",
    "max_p = max(grid_p.max(), pv_p.max(), total_ev_p.max(), battery_p.max())\n",
    "power_margin = int(round(max_p/10)*10)+5\n",
    "power_ticks = int(power_margin/5)\n",
    "max_tariff = day_tariff.max()\n",
    "tariff_margin = int(max_tariff)+0.25\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), margin=dict(r=180, t=150),\n",
    "                          showlegend=True, coloraxis=dict(colorscale=[[0, 'crimson'], [0.5, 'lightgrey'], [1, 'grey']],\n",
    "                                                          colorbar=dict(title=dict(text='', side='right'))), barmode='stack',\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.05, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", range=[-power_margin*1.2, power_margin*1.2], dtick=power_ticks, showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', nticks=11, dtick=0.05, tickmode='array', tickvals=[str(i/20) for i in range(12)],\n",
    "                                      autoshift=True, shift=2, side='right', range=[-tariff_margin*1.2, tariff_margin*1.2], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(overlaying='y', tickformat='~%', autoshift=True, shift=25, side='right', anchor='free',\n",
    "                                      range=[-1*1.2, 1*1.2], tickmode='array', tickvals=[str(i/100) for i in range(0, 120, 20)]))\n",
    "\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "58d28d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "locales": {
         "en": {
          "format": {
           "currency": [
            " € ",
            ""
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         }
        },
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "legendgroup": "PV",
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          "color": "darkturquoise",
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          "line": {
           "width": 0.5
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          "size": 8,
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         "name": "Utilised Solar Power",
         "showlegend": true,
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         "yaxis": "y"
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        {
         "legendgroup": "Solar",
         "line": {
          "color": "gold",
          "shape": "linear"
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         "marker": {
          "color": "gold",
          "line": {
           "width": 0.5
          },
          "size": 8,
          "symbol": "diamond"
         },
         "mode": "lines+markers",
         "name": "Available Solar Energy",
         "showlegend": true,
         "type": "scatter",
         "x": [
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         "yaxis": "y2"
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        {
         "legendgroup": "Day Tariff",
         "line": {
          "color": "teal",
          "shape": "hv"
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         "marker": {
          "color": "teal",
          "coloraxis": "coloraxis",
          "line": {
           "width": 0
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          "size": 0,
          "symbol": "circle"
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         "mode": "lines+markers",
         "name": "Electricity Day Tariff",
         "showlegend": true,
         "type": "scatter",
         "x": [
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         "yaxis": "y3"
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       "layout": {
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          "showarrow": false,
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        "margin": {
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     },
     "metadata": {},
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   ],
   "source": [
    "subplot_fig = go.Figure()\n",
    "\n",
    "upv = energy_predictions['Utilised Solar Power']\n",
    "apv = energy_predictions['Available Solar Energy - 0-24h']\n",
    "\n",
    "subplot_fig.add_vrect(x0=0, x1=7, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=3.5/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=7, x1=20, fillcolor='gold', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=13.5/24, y=0.05, text=\"Peak Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='goldenrod'), showarrow=False)\n",
    "subplot_fig.add_vrect(x0=20, x1=24, fillcolor='grey', opacity=0.1, layer='above', line=dict(width=0))\n",
    "subplot_fig.add_annotation(x=22/24, y=0.05, text=\"Low Demand\", xanchor='center', yanchor='middle', xref=\"paper\", yref=\"paper\", font=dict(size=14, color='dimgrey'), showarrow=False)\n",
    "\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=upv, line_shape='linear', name='Utilised Solar Power', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='circle', line=dict(width=0.5), color='darkturquoise'), line=dict(color='darkturquoise'),\n",
    "                        showlegend=True, yaxis='y', legendgroup='PV')\n",
    "subplot_fig.add_scatter(x=energy_predictions.index, y=apv, line_shape='linear', name='Available Solar Energy', mode='lines+markers',\n",
    "                        marker=dict(size=8, symbol='diamond', line=dict(width=0.5), color='gold'), line=dict(color='gold'),\n",
    "                        showlegend=True, yaxis='y2', legendgroup='Solar')\n",
    "\n",
    "subplot_fig.add_scatter(x=costs_predictions.index, y=day_tariff, line_shape='hv', name='Electricity Day Tariff', mode='lines+markers',\n",
    "                        marker=dict(size=0, symbol='circle', line=dict(width=0), color='teal', coloraxis='coloraxis'), line=dict(color='teal'),\n",
    "                        showlegend=True, yaxis='y3', legendgroup='Day Tariff')\n",
    "\n",
    "max_timestep = int(initial_training_data.Departure.max() + 1)\n",
    "subplot_fig.update_layout(title=\"Available Solar Energy and Utilised Solar Power\",\n",
    "                          yaxis=dict(title=''), barmode='overlay')\n",
    "subplot_fig.update_xaxes(tickformat='', griddash=\"dot\", title=\"Timestep (Hour) in a Day\", range=[-0.4, 24.4],\n",
    "                         tickvals=[i for i in range(max_timestep)],\n",
    "                         ticktext=[str(i%24) for i in range(max_timestep)],\n",
    "                         showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2)\n",
    "\n",
    "# For plot export: w(1280)xh(720), w(1920)xh(1080)\n",
    "# For better overview in jupyter: w(850)xh(650)\n",
    "plot_width = 980\n",
    "plot_height = 650\n",
    "\n",
    "max_solar_power = energy_predictions['Utilised Solar Power'].max()\n",
    "solar_margin = int(round(max_solar_power/2)*2)+2\n",
    "\n",
    "max_solar_energy = energy_predictions['Available Solar Energy - 0-24h'].max()\n",
    "solar_e_margin = int(round(max_solar_energy/2)*2)+2\n",
    "\n",
    "subplot_fig.update_layout(height=plot_height, width=plot_width, title=dict(xanchor=\"center\", x=0.5, font_size=24, y=0.99), \n",
    "                          showlegend=True, barmode='stack', margin=dict(r=180),\n",
    "                          legend=dict(x=0.5, orientation='h', yanchor='bottom', entrywidth=0, y=1.01, xanchor='center'), scattermode='overlay',\n",
    "                          yaxis=dict(tickformat=\" ,.2~g\", dtick=2, range=[0, solar_margin+5], title='Power [kW]', showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis2=dict(tickformat=\" ,.2~g\", overlaying='y', dtick=2, range=[0, solar_e_margin+3], \n",
    "                                      side='right', title='Energy [kWh]', showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2),\n",
    "                          yaxis3=dict(tickformat=\"$,.2f\", overlaying='y', anchor='free', dtick=0.02, title='Grid Tariff',\n",
    "                                      autoshift=True, shift=10, side='right', range=[0, tariff_margin], showspikes=True, spikecolor='black', spikedash='dot', spikethickness=-2))\n",
    "\n",
    "config = {'locales': {'en': {'format': {'currency': [\" € \", \"\"]}}}}\n",
    "subplot_fig.show(config=config)\n",
    "\n",
    "del max_timestep, plot_width, plot_height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cb4c688",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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