{ "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 57\u001b[0m dataset_original_dfs \u001b[39m=\u001b[39m {\n\u001b[1;32m 58\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 59\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 69\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 70\u001b[0m }\n\u001b[1;32m 71\u001b[0m dataset_water_consumption_dfs \u001b[39m=\u001b[39m {\n\u001b[1;32m 72\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 73\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 83\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 84\u001b[0m }\n\u001b[1;32m 85\u001b[0m dataset_water_consumption_monthly_dfs \u001b[39m=\u001b[39m {\n\u001b[0;32m---> 86\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 87\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 88\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 89\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 90\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 91\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 92\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 93\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 94\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 95\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 96\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 97\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 98\u001b[0m }\n\u001b[1;32m 100\u001b[0m \u001b[39m# provide a scalar value to enable the slider to select ideal temperature\u001b[39;00m\n\u001b[1;32m 101\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": [] } ], "metadata": { "kernelspec": { "display_name": "atc-smart-shower-YhjpRjjr-py3.10", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }