app/dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_34004/147804699.py:17: FutureWarning:\n",
      "\n",
      "The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/water_consumption/montly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb Cell 1\u001b[0m line \u001b[0;36m8\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m dataset_original_dfs \u001b[39m=\u001b[39m {\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=57'>58</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m16NSJNnjLK4MndjZYaKYGKEV\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/original/16NSJNnjLK4MndjZYaKYGKEV.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=58'>59</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m7uLwefnSt8CgVlmIGY8emqJK\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/original/7uLwefnSt8CgVlmIGY8emqJK.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=68'>69</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mSQUOjMB6zAgYpSJEMy46tKXJ\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/original/SQUOjMB6zAgYpSJEMy46tKXJ.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=69'>70</a>\u001b[0m }\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=70'>71</a>\u001b[0m dataset_water_consumption_dfs \u001b[39m=\u001b[39m {\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=71'>72</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m16NSJNnjLK4MndjZYaKYGKEV\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/16NSJNnjLK4MndjZYaKYGKEV_water_consumption.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=72'>73</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m7uLwefnSt8CgVlmIGY8emqJK\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/7uLwefnSt8CgVlmIGY8emqJK_water_consumption.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=82'>83</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mSQUOjMB6zAgYpSJEMy46tKXJ\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=83'>84</a>\u001b[0m }\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=84'>85</a>\u001b[0m dataset_water_consumption_monthly_dfs \u001b[39m=\u001b[39m {\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=85'>86</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m16NSJNnjLK4MndjZYaKYGKEV\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39;49mread_csv(\u001b[39m'\u001b[39;49m\u001b[39mdata/water_consumption/montly/16NSJNnjLK4MndjZYaKYGKEV_month.csv\u001b[39;49m\u001b[39m'\u001b[39;49m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=86'>87</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m7uLwefnSt8CgVlmIGY8emqJK\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/7uLwefnSt8CgVlmIGY8emqJK_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=87'>88</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m8yS04Ddkk3pPL8e9Rku4LJtc\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/8yS04Ddkk3pPL8e9Rku4LJtc_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=88'>89</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mCwp33jA19hp9VdoNJUlj6USf\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/Cwp33jA19hp9VdoNJUlj6USf_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=89'>90</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39miBFIAuvh7bCNyOQDo0jkjhRV\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/iBFIAuvh7bCNyOQDo0jkjhRV_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=90'>91</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39miNVKpGfGW6rU17eOtxpZSFWR\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/iNVKpGfGW6rU17eOtxpZSFWR_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=91'>92</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mkaTMmHVh8gXUbHMppzdmdzpv\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/kaTMmHVh8gXUbHMppzdmdzpv_monthcsv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=92'>93</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mKN9Z3gANLftDuUGvgs8O38dI\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/KN9Z3gANLftDuUGvgs8O38dI_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=93'>94</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mLzbMafI31IosheUI7YGhj5at\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/LzbMafI31IosheUI7YGhj5at_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=94'>95</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mPHqaZDuMTRvCZCvA259Z1vJu\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/PHqaZDuMTRvCZCvA259Z1vJu_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=95'>96</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mRZngVU6axOdshmfma0yNAajE\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/RZngVU6axOdshmfma0yNAajE_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=96'>97</a>\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mSQUOjMB6zAgYpSJEMy46tKXJ\u001b[39m\u001b[39m\"\u001b[39m: pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata/water_consumption/montly/SQUOjMB6zAgYpSJEMy46tKXJ_month.csv\u001b[39m\u001b[39m'\u001b[39m),\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=97'>98</a>\u001b[0m }\n\u001b[1;32m    <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=99'>100</a>\u001b[0m \u001b[39m# provide a scalar value to enable the slider to select ideal temperature\u001b[39;00m\n\u001b[1;32m    <a href='vscode-notebook-cell:/Users/ruimelo/Documents/GitHub/atc-smart-shower/frontend/app/dashboard.ipynb#W5sZmlsZQ%3D%3D?line=100'>101</a>\u001b[0m ideal_temperature \u001b[39m=\u001b[39m \u001b[39m50\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/atc/lib/python3.10/site-packages/pandas/io/parsers/readers.py:948\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    944\u001b[0m     dtype_backend\u001b[39m=\u001b[39mdtype_backend,\n\u001b[1;32m    945\u001b[0m )\n\u001b[1;32m    946\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 948\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[0;32m~/anaconda3/envs/atc/lib/python3.10/site-packages/pandas/io/parsers/readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    608\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[1;32m    610\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 611\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    613\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
      "File \u001b[0;32m~/anaconda3/envs/atc/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1448\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m   1447\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1448\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
      "File \u001b[0;32m~/anaconda3/envs/atc/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1705\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1703\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mode:\n\u001b[1;32m   1704\u001b[0m         mode \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(\n\u001b[1;32m   1706\u001b[0m     f,\n\u001b[1;32m   1707\u001b[0m     mode,\n\u001b[1;32m   1708\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1709\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1710\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[1;32m   1711\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[1;32m   1712\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1713\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[1;32m   1714\u001b[0m )\n\u001b[1;32m   1715\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m   1716\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
      "File \u001b[0;32m~/anaconda3/envs/atc/lib/python3.10/site-packages/pandas/io/common.py:863\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[1;32m    859\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    860\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[1;32m    862\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[0;32m--> 863\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[1;32m    864\u001b[0m             handle,\n\u001b[1;32m    865\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[1;32m    866\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[1;32m    867\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m    868\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    869\u001b[0m         )\n\u001b[1;32m    870\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    871\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[1;32m    872\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/water_consumption/montly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pandas as pd\n",
    "from dash import Dash, html, dcc, Input, Output, callback\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import example_data\n",
    "import core\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "outside_temp = example_data.ExampleDailyOutsideTemperature\n",
    "energy_price = example_data.ExampleDailyEnergyCost\n",
    "boiler_temperature = example_data.ExampleBoilerTemperature\n",
    "\n",
    "data = pd.DataFrame(columns=['hour', 'energy_consumption', 'comfort', 'policy_readable'])\n",
    "data = pd.concat([data, pd.DataFrame({'hour': np.arange(0, 24), 'energy_consumption': energy_price.value, 'comfort': np.random.rand(24),\n",
    "                                       'policy_readable': np.random.choice(['A', 'B', 'C', 'D', 'E'], 24)\n",
    "                                       })])\n",
    "debug = False\n",
    "\n",
    "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
    "\n",
    "app = Dash(__name__, external_stylesheets=external_stylesheets)\n",
    "\n",
    "app.layout = html.Div([\n",
    "    dcc.Location(id='url', refresh=False),\n",
    "    html.Div(id='page-content')\n",
    "])\n",
    "\n",
    "server = app.server\n",
    "\n",
    "# Solution options\n",
    "solution_options = [\n",
    "    \"Discrete Optimization\",\n",
    "    \"Continuous Optimization\",\n",
    "    \"Reinforcement Learning\"\n",
    "]\n",
    "solution_options_default_value = solution_options[0]\n",
    "\n",
    "# Datasets\n",
    "dataset_options = [\n",
    "    \"16NSJNnjLK4MndjZYaKYGKEV\",\n",
    "    \"7uLwefnSt8CgVlmIGY8emqJK\",\n",
    "    \"8yS04Ddkk3pPL8e9Rku4LJtc\",\n",
    "    \"Cwp33jA19hp9VdoNJUlj6USf\",\n",
    "    \"iBFIAuvh7bCNyOQDo0jkjhRV\",\n",
    "    \"iNVKpGfGW6rU17eOtxpZSFWR\",\n",
    "    \"kaTMmHVh8gXUbHMppzdmdzpv\",\n",
    "    \"KN9Z3gANLftDuUGvgs8O38dI\",\n",
    "    \"LzbMafI31IosheUI7YGhj5at\",\n",
    "    \"PHqaZDuMTRvCZCvA259Z1vJu\",\n",
    "    \"RZngVU6axOdshmfma0yNAajE\",\n",
    "    \"SQUOjMB6zAgYpSJEMy46tKXJ\",\n",
    "]\n",
    "dataset_options_default_value = dataset_options[0]\n",
    "dataset_original_dfs = {\n",
    "    \"16NSJNnjLK4MndjZYaKYGKEV\": pd.read_csv('data/original/16NSJNnjLK4MndjZYaKYGKEV.csv'),\n",
    "    \"7uLwefnSt8CgVlmIGY8emqJK\": pd.read_csv('data/original/7uLwefnSt8CgVlmIGY8emqJK.csv'),\n",
    "    \"8yS04Ddkk3pPL8e9Rku4LJtc\": pd.read_csv('data/original/8yS04Ddkk3pPL8e9Rku4LJtc.csv'),\n",
    "    \"Cwp33jA19hp9VdoNJUlj6USf\": pd.read_csv('data/original/Cwp33jA19hp9VdoNJUlj6USf.csv'),\n",
    "    \"iBFIAuvh7bCNyOQDo0jkjhRV\": pd.read_csv('data/original/iBFIAuvh7bCNyOQDo0jkjhRV.csv'),\n",
    "    \"iNVKpGfGW6rU17eOtxpZSFWR\": pd.read_csv('data/original/iNVKpGfGW6rU17eOtxpZSFWR.csv'),\n",
    "    \"kaTMmHVh8gXUbHMppzdmdzpv\": pd.read_csv('data/original/kaTMmHVh8gXUbHMppzdmdzpv.csv'),\n",
    "    \"KN9Z3gANLftDuUGvgs8O38dI\": pd.read_csv('data/original/KN9Z3gANLftDuUGvgs8O38dI.csv'),\n",
    "    \"LzbMafI31IosheUI7YGhj5at\": pd.read_csv('data/original/LzbMafI31IosheUI7YGhj5at.csv'),\n",
    "    \"PHqaZDuMTRvCZCvA259Z1vJu\": pd.read_csv('data/original/PHqaZDuMTRvCZCvA259Z1vJu.csv'),\n",
    "    \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/original/RZngVU6axOdshmfma0yNAajE.csv'),\n",
    "    \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/original/SQUOjMB6zAgYpSJEMy46tKXJ.csv'),\n",
    "}\n",
    "dataset_water_consumption_dfs = {\n",
    "    \"16NSJNnjLK4MndjZYaKYGKEV\": pd.read_csv('data/water_consumption/16NSJNnjLK4MndjZYaKYGKEV_water_consumption.csv'),\n",
    "    \"7uLwefnSt8CgVlmIGY8emqJK\": pd.read_csv('data/water_consumption/7uLwefnSt8CgVlmIGY8emqJK_water_consumption.csv'),\n",
    "    \"8yS04Ddkk3pPL8e9Rku4LJtc\": pd.read_csv('data/water_consumption/8yS04Ddkk3pPL8e9Rku4LJtc_water_consumption.csv'),\n",
    "    \"Cwp33jA19hp9VdoNJUlj6USf\": pd.read_csv('data/water_consumption/Cwp33jA19hp9VdoNJUlj6USf_water_consumption.csv'),\n",
    "    \"iBFIAuvh7bCNyOQDo0jkjhRV\": pd.read_csv('data/water_consumption/iBFIAuvh7bCNyOQDo0jkjhRV_water_consumption.csv'),\n",
    "    \"iNVKpGfGW6rU17eOtxpZSFWR\": pd.read_csv('data/water_consumption/iNVKpGfGW6rU17eOtxpZSFWR_water_consumption.csv'),\n",
    "    \"kaTMmHVh8gXUbHMppzdmdzpv\": pd.read_csv('data/water_consumption/kaTMmHVh8gXUbHMppzdmdzpv_water_consumption.csv'),\n",
    "    \"KN9Z3gANLftDuUGvgs8O38dI\": pd.read_csv('data/water_consumption/KN9Z3gANLftDuUGvgs8O38dI_water_consumption.csv'),\n",
    "    \"LzbMafI31IosheUI7YGhj5at\": pd.read_csv('data/water_consumption/LzbMafI31IosheUI7YGhj5at_water_consumption.csv'),\n",
    "    \"PHqaZDuMTRvCZCvA259Z1vJu\": pd.read_csv('data/water_consumption/PHqaZDuMTRvCZCvA259Z1vJu_water_consumption.csv'),\n",
    "    \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),\n",
    "    \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),\n",
    "}\n",
    "dataset_water_consumption_monthly_dfs = {\n",
    "    \"16NSJNnjLK4MndjZYaKYGKEV\": pd.read_csv('data/water_consumption/monthly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'),\n",
    "    \"7uLwefnSt8CgVlmIGY8emqJK\": pd.read_csv('data/water_consumption/monthly/7uLwefnSt8CgVlmIGY8emqJK_month.csv'),\n",
    "    \"8yS04Ddkk3pPL8e9Rku4LJtc\": pd.read_csv('data/water_consumption/monthly/8yS04Ddkk3pPL8e9Rku4LJtc_month.csv'),\n",
    "    \"Cwp33jA19hp9VdoNJUlj6USf\": pd.read_csv('data/water_consumption/monthly/Cwp33jA19hp9VdoNJUlj6USf_month.csv'),\n",
    "    \"iBFIAuvh7bCNyOQDo0jkjhRV\": pd.read_csv('data/water_consumption/monthly/iBFIAuvh7bCNyOQDo0jkjhRV_month.csv'),\n",
    "    \"iNVKpGfGW6rU17eOtxpZSFWR\": pd.read_csv('data/water_consumption/monthly/iNVKpGfGW6rU17eOtxpZSFWR_month.csv'),\n",
    "    \"kaTMmHVh8gXUbHMppzdmdzpv\": pd.read_csv('data/water_consumption/monthly/kaTMmHVh8gXUbHMppzdmdzpv_monthcsv'),\n",
    "    \"KN9Z3gANLftDuUGvgs8O38dI\": pd.read_csv('data/water_consumption/monthly/KN9Z3gANLftDuUGvgs8O38dI_month.csv'),\n",
    "    \"LzbMafI31IosheUI7YGhj5at\": pd.read_csv('data/water_consumption/monthly/LzbMafI31IosheUI7YGhj5at_month.csv'),\n",
    "    \"PHqaZDuMTRvCZCvA259Z1vJu\": pd.read_csv('data/water_consumption/monthly/PHqaZDuMTRvCZCvA259Z1vJu_month.csv'),\n",
    "    \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/water_consumption/monthly/RZngVU6axOdshmfma0yNAajE_month.csv'),\n",
    "    \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/water_consumption/monthly/SQUOjMB6zAgYpSJEMy46tKXJ_month.csv'),\n",
    "}\n",
    "\n",
    "# provide a scalar value to enable the slider to select ideal temperature\n",
    "ideal_temperature = 50\n",
    "\n",
    "dashboard_layout =  html.Div([\n",
    "    dcc.Link('About this project', href='/wiki'),\n",
    "\n",
    "    html.H1('System Evaluation'),\n",
    "    #small subtitle that says of solution is possible or not\n",
    "    html.Div(id='solution-status', children='', style={'color': 'lighrgrey'}),\n",
    "    html.Div([\n",
    "        html.Div([\n",
    "                    html.H3('Dataset'),\n",
    "                    dcc.Dropdown(\n",
    "                        id='dataset-dropdown',\n",
    "                        options=dataset_options,\n",
    "                        value=dataset_options_default_value,\n",
    "                    )\n",
    "                ], className='three columns'),\n",
    "        html.Div([\n",
    "                    html.H3('Solution'),\n",
    "                    dcc.Dropdown(\n",
    "                        id='solution-dropdown',\n",
    "                        options=solution_options,\n",
    "                        value=solution_options_default_value,\n",
    "                    )\n",
    "                ], className='three columns'),\n",
    "        html.Div([\n",
    "                    html.H3('Ideal Shower Temperature'),\n",
    "                    dcc.Slider(\n",
    "                        id='ideal-temperature-slider',\n",
    "                        min=0,\n",
    "                        max=100,\n",
    "                        step=1,\n",
    "                        value=ideal_temperature,\n",
    "                        marks={\n",
    "                            0: '0°C',\n",
    "                            25: '25°C',\n",
    "                            50: '50°C',\n",
    "                            75: '75°C',\n",
    "                            100: '100°C'\n",
    "                        },\n",
    "                    )\n",
    "                ], className='three columns'),\n",
    "\n",
    "\n",
    "    ], className='row'),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H3('Dataset'),\n",
    "                    dcc.Graph(id='dataset-graph')\n",
    "                ], className='twelve columns',\n",
    "            )\n",
    "    ], className='row'),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H3('Water Comsumption Patterns'),\n",
    "                    dcc.Graph(id='water-consumption-graph')\n",
    "                ], className='twelve columns',\n",
    "            )\n",
    "    ], className='row'),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H4('Hourly'),\n",
    "                    dcc.Graph(id='water_consumption_hourly_graph')\n",
    "                ], className='six columns',\n",
    "            ),\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H4('Day of the Week'),\n",
    "                    dcc.Graph(id='water_consumption_week_day_graph')\n",
    "                ], className='six columns',\n",
    "            )\n",
    "    ], className='row'),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H4('Monthly'),\n",
    "                    dcc.Graph(id='water_consumption_monthly_graph')\n",
    "                ], className='six columns',\n",
    "            )\n",
    "    ], className='row'),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H3('Policy'),\n",
    "                    dcc.Graph(id='policy_readable-graph')\n",
    "                ], className='six columns',\n",
    "            ),\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H3('Energy Consumption'),\n",
    "                    dcc.Graph(id='energy-consumption-graph')\n",
    "                ], className='six columns'\n",
    "            ),\n",
    "        ]\n",
    "    ),\n",
    "    html.Div(\n",
    "        [\n",
    "            html.Div(\n",
    "                [\n",
    "                    html.H3('Comfort'),\n",
    "                    dcc.Graph(id='comfort-graph')\n",
    "                ], className='six columns'\n",
    "            )\n",
    "    ], className='row'),\n",
    "],\n",
    "#add background image from local file and make it transparent\n",
    "#, style={'background-image':'url(/assets/background_1.png)'}\n",
    "style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}\n",
    ")\n",
    "\n",
    "\n",
    "wiki_layout = html.Div([\n",
    "    dcc.Link('Dashboard', href='/'),\n",
    "\n",
    "    html.H1('About this project'),\n",
    "\n",
    "    html.Div([\n",
    "        html.Div([\n",
    "\n",
    "                    html.H3('What is this project about?'),\n",
    "\n",
    "                    html.P('This project is a simulation of a shower system. The goal is to find the best policy for the boiler to heat the water for the shower. The policy is a function that takes the current hour of the day and the current temperature of the water in the boiler and returns the temperature that the boiler should heat the water to.'),\n",
    "                    html.P('The best policy is the one that maximizes the comfort of the shower and minimizes the energy consumption of the boiler.'),\n",
    "\n",
    "                    html.H3('How does it work?'),\n",
    "\n",
    "                    #Insert image of the system\n",
    "\n",
    "                    html.H3('\\'Bout us'),\n",
    "                    html.Img(src='/assets/hackatos.png', style={'width': '40%', 'height': 'auto', 'display': 'block', 'margin-left': 'auto', 'margin-right': 'auto'}),\n",
    "                    html.P('This project was developed by a team of 3, in the context of the Aveiro Tech City 2023 hackathon.'),\n",
    "                    html.P('The team members are:'),\n",
    "                    html.H4('Rui Melo'),\n",
    "                    html.H4('André Catarino'),\n",
    "                    html.H4('Francisco Petronilho'),\n",
    "                    html.H4('André Tomás'),\n",
    "                    html.H4('Zé Miguel'),\n",
    "\n",
    "\n",
    "                    html.H3('References'),\n",
    "                    html.P('The boiler model was based on the following paper:'),\n",
    "\n",
    "\n",
    "        ], className='six columns'),], className='row'),\n",
    "],\n",
    "style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}\n",
    "\n",
    ")\n",
    "\n",
    "# Update the index\n",
    "@callback(Output('page-content', 'children'), Input('url', 'pathname'))\n",
    "def display_page(pathname):\n",
    "    if pathname == '/':\n",
    "        return dashboard_layout\n",
    "    elif pathname == '/wiki':\n",
    "        return wiki_layout\n",
    "    else:\n",
    "        return '404'\n",
    "    # You could also return a 404 \"URL not found\" page here\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "    Output('policy_readable-graph', 'figure'),\n",
    "    Output('energy-consumption-graph', 'figure'),\n",
    "    Output('comfort-graph', 'figure'),\n",
    "    Output('dataset-graph', 'figure'),\n",
    "    Output('water-consumption-graph', 'figure'),\n",
    "    Output('water_consumption_hourly_graph', 'figure'),\n",
    "    Output('water_consumption_week_day_graph', 'figure'),\n",
    "    Output('water_consumption_monthly_graph', 'figure'),\n",
    "    Output('solution-status', 'children'),\n",
    "    Input('dataset-dropdown', 'value'),\n",
    "    Input('solution-dropdown', 'value'),\n",
    "    Input('ideal-temperature-slider', 'value')\n",
    ")\n",
    "def update_graph(dataset, solution, ideal_temperature):\n",
    "    energy_consumption = data['energy_consumption'].values\n",
    "    comfort_obtained = data['comfort'].values\n",
    "    \n",
    "    # Original Dataset Graph\n",
    "    original_df = dataset_original_dfs[dataset]\n",
    "\n",
    "    dataset_graph = px.line()\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['ActPow'], mode='lines', name='ActPow')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwActive'], mode='lines', name='HwActive')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['ChActive'], mode='lines', name='ChActive')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTSet'], mode='lines', name='HwTSet')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['DHW_E21_T3_START_TEMP'], mode='lines', name='START_TEMP')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTStor'], mode='lines', name='HwTStor')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTAct'], mode='lines', name='HwTAct')\n",
    "    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['OutTemp'], mode='lines', name='OutTemp')\n",
    "    start_time = pd.Timestamp(original_df['ts'][0])\n",
    "    dataset_graph.update_xaxes(range=[start_time, start_time+pd.Timedelta(days=2)])\n",
    "\n",
    "\n",
    "    # Water Consumption Graph\n",
    "    water_consumption_df = dataset_water_consumption_dfs[dataset]\n",
    "    if len(water_consumption_df) / 24 > 365:\n",
    "        water_consumption_df = water_consumption_df.head(365* 24)\n",
    "    water_consumption_df.index = pd.to_datetime(water_consumption_df[\"ts\"], errors='coerce')\n",
    "    water_consumption_graph = go.Figure()\n",
    "    water_consumption_graph = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "    water_consumption_graph.add_trace(go.Scatter(x=water_consumption_df[:][\"ts\"], y=water_consumption_df[:]['water_consumption_bool'], mode='lines', name='Water consumption'),\n",
    "    secondary_y=False,)\n",
    "    water_consumption_graph.add_trace(go.Scatter(x=water_consumption_df[:][\"ts\"], y=water_consumption_df[:]['HwTStor'], mode='lines', name='Water temperature')\n",
    "    ,secondary_y=True,)\n",
    "    water_consumption_graph.update_layout(\n",
    "        title_text=\"Water consumption\"\n",
    "    )\n",
    "    water_consumption_graph.update_xaxes(title_text=\"time\")\n",
    "    water_consumption_graph.update_yaxes(title_text=\"water consumption\", secondary_y=False)\n",
    "    water_consumption_graph.update_yaxes(title_text=\"water temperature\", secondary_y=True)\n",
    "\n",
    "    # Water Consumption Hourly Graph\n",
    "    water_consumption_df[\"ts_hour\"] = water_consumption_df[\"ts\"].apply(lambda x: x.split(\" \")[1].split(\":\")[0])\n",
    "    hour_series = water_consumption_df.groupby(\"ts_hour\")[\"water_consumption_bool\"].sum()\n",
    "    water_consumption_hourly_graph = go.Figure()\n",
    "    water_consumption_hourly_graph.add_trace(go.Bar(x=hour_series.index, y=hour_series.values, name='Water consumption per hour'))\n",
    "    water_consumption_hourly_graph.update_layout(\n",
    "        title_text=\"Water consumption per hour\"\n",
    "    )\n",
    "    water_consumption_hourly_graph.update_xaxes(title_text=\"Hour of day\")\n",
    "    water_consumption_hourly_graph.update_yaxes(title_text=\"Number of water usages\")\n",
    "\n",
    "    # Water Consumption Week Day Graph\n",
    "    order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "    water_consumption_df[\"datetime\"] = pd.to_datetime(water_consumption_df[\"ts\"], errors='coerce')\n",
    "    #create columns with day of week\n",
    "    water_consumption_df['day_of_week'] = water_consumption_df[\"datetime\"].apply(lambda x: x.day_name())\n",
    "    #turn day of week number into day of week name\n",
    "    #df['day_of_week'] = df['day_of_week'].apply(lambda x: calendar.day_name[x])\n",
    "    week_series = water_consumption_df.groupby(\"day_of_week\")[\"water_consumption_bool\"].sum().loc[order]\n",
    "\n",
    "    # Water Consumption Week Day Graph\n",
    "    water_consumption_week_day_graph = go.Figure()\n",
    "    water_consumption_week_day_graph.add_trace(go.Bar(x=week_series.index, y=week_series.values, name='Water consumption per day of week'))\n",
    "    water_consumption_week_day_graph.update_layout(\n",
    "        title_text=\"Water consumption per day of week\"\n",
    "    )\n",
    "    water_consumption_week_day_graph.update_xaxes(title_text=\"Day of week\")\n",
    "    water_consumption_week_day_graph.update_yaxes(title_text=\"Number of water usages\")\n",
    "\n",
    "    #dataset_water_consumption_monthly_dfs\n",
    "    #Water Consumption Monthly Graph\n",
    "    water_consumption_monthly_df = dataset_water_consumption_monthly_dfs[dataset]\n",
    "    water_consumption_monthly_graph = go.Figure()\n",
    "    water_consumption_monthly_graph.add_trace(go.Bar(x=water_consumption_monthly_df[\"month\"], y=water_consumption_monthly_df[\"water_consumption_bool\"], name='Water consumption per month'))\n",
    "    water_consumption_monthly_graph.update_layout(\n",
    "        title_text=\"Water consumption per month\"\n",
    "    )\n",
    "    water_consumption_monthly_graph.update_xaxes(title_text=\"Month\")\n",
    "    water_consumption_monthly_graph.update_yaxes(title_text=\"Number of water usages\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Policy Graph\n",
    "    policy_readable_graph = px.line(data, x='hour', y='policy_readable',\n",
    "                                    labels={'hour': 'Hour', 'policy_readable': 'Policy'},\n",
    "                                    color_discrete_sequence=['lightgreen'])\n",
    "    policy_readable_graph.update_layout(\n",
    "        xaxis_title=\"Hour\",\n",
    "        yaxis_title=\"Temperature (°C)\",\n",
    "        legend_title=\"Policy\"\n",
    "    )\n",
    "\n",
    "\n",
    "    # Energy Consumption Graph\n",
    "    energy_consumption_graph = px.line(data, x='hour',\n",
    "                                       y='energy_consumption',\n",
    "                                       labels={'hour': 'Hour', 'energy_consumption': 'Energy Consumption (kWh)'},\n",
    "                                       color_discrete_sequence=['lightgreen'])\n",
    "    energy_consumption_graph.update_layout(\n",
    "        xaxis_title=\"Hour\",\n",
    "        yaxis_title=\"Energy Consumption (kWh)\",\n",
    "        legend_title=\"Energy Consumption\",\n",
    "    )\n",
    "    #add accumulated energy consumption\n",
    "    energy_consumption = np.cumsum(energy_consumption)\n",
    "    energy_consumption_graph.add_trace(px.line(data, x='hour',\n",
    "                                               y=energy_consumption,\n",
    "                                               labels={'y': 'Acc. Energy Consumption (kWh)'},\n",
    "                                                color_discrete_sequence=['green']).data[0])\n",
    "\n",
    "\n",
    "    # Comfort Graph\n",
    "    comfort_graph = px.line(data, x='hour', y='comfort',\n",
    "                            labels={'hour': 'Hour', 'comfort': 'comfort Score'},\n",
    "                            color_discrete_sequence=['lightgreen'])\n",
    "\n",
    "    comfort_graph.update_layout(\n",
    "        xaxis_title=\"Hour\",\n",
    "        yaxis_title=\"comfort Score\",\n",
    "        legend_title=\"comfort\"\n",
    "    )\n",
    "    #add accumulated comfort\n",
    "    comfort_obtained = np.cumsum(comfort_obtained)\n",
    "    comfort_graph.add_trace(px.line(data, x='hour', y=comfort_obtained,\n",
    "                                    labels={'y': 'Acc. comfort Score'},\n",
    "                                    color_discrete_sequence=['green']\n",
    "                                    ).data[0])\n",
    "    result = \"No solution found\"\n",
    "    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, water_consumption_graph, water_consumption_hourly_graph, water_consumption_week_day_graph,water_consumption_monthly_graph, result\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    app.run_server(host=\"0.0.0.0\", port=\"8050\", debug=debug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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