lfs release

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

app/app.py

@@ -8,20 +8,16 @@ import numpy as np
 import example_data
 import core
 
-
 outside_temp = example_data.ExampleDailyOutsideTemperature
 energy_price = example_data.ExampleDailyEnergyCost
 boiler_temperature = example_data.ExampleBoilerTemperature
 
-
-
-data = pd.DataFrame(columns=['hour', 'energy_consumption', 'confort', 'policy_readable'])
-data = pd.concat([data, pd.DataFrame({'hour': np.arange(0, 24), 'energy_consumption': energy_price.value, 'confort': np.random.rand(24),
+data = pd.DataFrame(columns=['hour', 'energy_consumption', 'comfort', 'policy_readable'])
+data = pd.concat([data, pd.DataFrame({'hour': np.arange(0, 24), 'energy_consumption': energy_price.value, 'comfort': np.random.rand(24),
                                        'policy_readable': np.random.choice(['A', 'B', 'C', 'D', 'E'], 24)
                                        })])
 debug = False
 
-
 external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
 
 app = Dash(__name__, external_stylesheets=external_stylesheets)
@@ -33,8 +29,7 @@ app.layout = html.Div([
 
 server = app.server
 
-
-# SOlution options
+# Solution options
 solution_options = [
     "Discrete Optimization",
     "Continuous Optimization",
@@ -42,6 +37,47 @@ solution_options = [
 ]
 solution_options_default_value = solution_options[0]
 
+# Datasets
+dataset_options = [
+    "16NSJNnjLK4MndjZYaKYGKEV",
+    "7uLwefnSt8CgVlmIGY8emqJK",
+    "8yS04Ddkk3pPL8e9Rku4LJtc",
+    "Cwp33jA19hp9VdoNJUlj6USf",
+    "iBFIAuvh7bCNyOQDo0jkjhRV",
+    "iNVKpGfGW6rU17eOtxpZSFWR",
+    "kaTMmHVh8gXUbHMppzdmdzpv",
+    "KN9Z3gANLftDuUGvgs8O38dI",
+    "LzbMafI31IosheUI7YGhj5at",
+    "PHqaZDuMTRvCZCvA259Z1vJu",
+    "RZngVU6axOdshmfma0yNAajE",
+    "SQUOjMB6zAgYpSJEMy46tKXJ",
+    "UcufQVoJQPbfLzIBnSsUodJP",
+    "VTuh8oxtC6YlOCLeScXfLuY3",
+    "VZTnyVO3TP3ILuYsN5Xw9UR0",
+    "WyPRjcmBXJZoC9DIURMXKxn8",
+    "yPtlbyLlDlzeBZQYrPYaByoB",
+]
+dataset_options_default_value = dataset_options[0]
+dataset_dfs = {
+    "16NSJNnjLK4MndjZYaKYGKEV": pd.read_csv('data/16NSJNnjLK4MndjZYaKYGKEV.csv')[:100000],
+    "7uLwefnSt8CgVlmIGY8emqJK": pd.read_csv('data/7uLwefnSt8CgVlmIGY8emqJK.csv')[:100000],
+    "8yS04Ddkk3pPL8e9Rku4LJtc": pd.read_csv('data/8yS04Ddkk3pPL8e9Rku4LJtc.csv')[:100000],
+    "Cwp33jA19hp9VdoNJUlj6USf": pd.read_csv('data/Cwp33jA19hp9VdoNJUlj6USf.csv')[:100000],
+    "iBFIAuvh7bCNyOQDo0jkjhRV": pd.read_csv('data/iBFIAuvh7bCNyOQDo0jkjhRV.csv')[:100000],
+    "iNVKpGfGW6rU17eOtxpZSFWR": pd.read_csv('data/iNVKpGfGW6rU17eOtxpZSFWR.csv')[:100000],
+    "kaTMmHVh8gXUbHMppzdmdzpv": pd.read_csv('data/kaTMmHVh8gXUbHMppzdmdzpv.csv')[:100000],
+    "KN9Z3gANLftDuUGvgs8O38dI": pd.read_csv('data/KN9Z3gANLftDuUGvgs8O38dI.csv')[:100000],
+    "LzbMafI31IosheUI7YGhj5at": pd.read_csv('data/LzbMafI31IosheUI7YGhj5at.csv')[:100000],
+    "PHqaZDuMTRvCZCvA259Z1vJu": pd.read_csv('data/PHqaZDuMTRvCZCvA259Z1vJu.csv')[:100000],
+    "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/RZngVU6axOdshmfma0yNAajE.csv')[:100000],
+    "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/SQUOjMB6zAgYpSJEMy46tKXJ.csv')[:100000],
+    "UcufQVoJQPbfLzIBnSsUodJP": pd.read_csv('data/UcufQVoJQPbfLzIBnSsUodJP.csv')[:100000],
+    "VTuh8oxtC6YlOCLeScXfLuY3": pd.read_csv('data/VTuh8oxtC6YlOCLeScXfLuY3.csv')[:100000],
+    "VZTnyVO3TP3ILuYsN5Xw9UR0": pd.read_csv('data/VZTnyVO3TP3ILuYsN5Xw9UR0.csv')[:100000],
+    "WyPRjcmBXJZoC9DIURMXKxn8": pd.read_csv('data/WyPRjcmBXJZoC9DIURMXKxn8.csv')[:100000],
+    "yPtlbyLlDlzeBZQYrPYaByoB": pd.read_csv('data/yPtlbyLlDlzeBZQYrPYaByoB.csv')[:100000],
+}
+
 # provide a scalar value to enable the slider to select ideal temperature
 ideal_temperature = 50
 
@@ -52,6 +88,14 @@ dashboard_layout =  html.Div([
     #small subtitle that says of solution is possible or not
     html.Div(id='solution-status', children='', style={'color': 'lighrgrey'}),
     html.Div([
+        html.Div([
+                    html.H3('Dataset'),
+                    dcc.Dropdown(
+                        id='dataset-dropdown',
+                        options=dataset_options,
+                        value=dataset_options_default_value,
+                    )
+                ], className='three columns'),
         html.Div([
                     html.H3('Solution'),
                     dcc.Dropdown(
@@ -79,6 +123,15 @@ dashboard_layout =  html.Div([
                 ], className='three columns'),
 
 
+    ], className='row'),
+    html.Div(
+        [
+            html.Div(
+                [
+                    html.H3('Dataset'),
+                    dcc.Graph(id='dataset-graph')
+                ], className='twelve columns',
+            )
     ], className='row'),
     html.Div(
         [
@@ -91,27 +144,27 @@ dashboard_layout =  html.Div([
             html.Div(
                 [
                     html.H3('Energy Consumption'),
-                    dcc.Graph(id='energy-comsumption-graph')
+                    dcc.Graph(id='energy-consumption-graph')
                 ], className='six columns'
-            )
-    ], className='row'),
+            ),
+        ]
+    ),
     html.Div(
         [
             html.Div(
                 [
-                    html.H3('Confort'),
-                    dcc.Graph(id='confort-graph')
+                    html.H3('Comfort'),
+                    dcc.Graph(id='comfort-graph')
                 ], className='six columns'
             )
     ], className='row'),
 ],
 #add background image from local file and make it transparent
 #, style={'background-image':'url(/assets/background_1.png)'}
-style={'background-color': '#4B5320', 'font-family': 'Fantasy', 'color': '#8F9779', 'padding': '10px'}
+style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}
 )
 
 
-
 wiki_layout = html.Div([
     dcc.Link('Dashboard', href='/'),
 
@@ -119,11 +172,11 @@ wiki_layout = html.Div([
 
     html.Div([
         html.Div([
-                    
+
                     html.H3('What is this project about?'),
 
                     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.'),
-                    html.P('The best policy is the one that maximizes the confort of the shower and minimizes the energy consumption of the boiler.'),
+                    html.P('The best policy is the one that maximizes the comfort of the shower and minimizes the energy consumption of the boiler.'),
 
                     html.H3('How does it work?'),
 
@@ -151,7 +204,7 @@ wiki_layout = html.Div([
 
         ], className='six columns'),], className='row'),
 ],
-style={'background-color': '#4B5320', 'font-family': 'Fantasy', 'color': '#8F9779', 'padding': '10px'}
+style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}
 
 )
 
@@ -167,20 +220,43 @@ def display_page(pathname):
     # You could also return a 404 "URL not found" page here
 
 
-
 @app.callback(
     Output('policy_readable-graph', 'figure'),
-    Output('energy-comsumption-graph', 'figure'),
-    Output('confort-graph', 'figure'),
+    Output('energy-consumption-graph', 'figure'),
+    Output('comfort-graph', 'figure'),
+    Output('dataset-graph', 'figure'),
     Output('solution-status', 'children'),
+    Input('dataset-dropdown', 'value'),
     Input('solution-dropdown', 'value'),
     Input('ideal-temperature-slider', 'value')
 )
-def update_graph(solution, ideal_temperature):
+def update_graph(dataset, solution, ideal_temperature):
     energy_consumption = data['energy_consumption'].values
-    confort_obtained = data['confort'].values
+    comfort_obtained = data['comfort'].values
+
+
+    # Dataset Graph
+    dataset_df = dataset_dfs[dataset]
+    dataset_df['ChActive'] = dataset_df['ChActive'].map({'Yes': 90, 'No': 5})
+    dataset_df['HwActive'] = dataset_df['HwActive'].map({'Yes': 100, 'No': 0})
 
+    dataset_graph = px.line()
+    #Add another line to the plot
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['ActPow'], mode='lines', name='ActPow')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwActive'], mode='lines', name='HwActive')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['ChActive'], mode='lines', name='ChActive')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTSet'], mode='lines', name='HwTSet')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['DHW_E21_T3_START_TEMP'], mode='lines', name='START_TEMP')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTStor'], mode='lines', name='HwTStor')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTAct'], mode='lines', name='HwTAct')
+    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['OutTemp'], mode='lines', name='OutTemp')
 
+    start_time = pd.Timestamp(dataset_df['ts'][0])
+
+    dataset_graph.update_xaxes(range=[start_time, start_time+pd.Timedelta(days=2)])
+
+
+    # Policy Graph
     policy_readable_graph = px.line(data, x='hour', y='policy_readable',
                                     labels={'hour': 'Hour', 'policy_readable': 'Policy'},
                                     color_discrete_sequence=['lightgreen'])
@@ -190,8 +266,10 @@ def update_graph(solution, ideal_temperature):
         legend_title="Policy"
     )
 
-    energy_consumption_graph = px.line(data, x='hour', 
-                                       y='energy_consumption', 
+
+    # Energy Consumption Graph
+    energy_consumption_graph = px.line(data, x='hour',
+                                       y='energy_consumption',
                                        labels={'hour': 'Hour', 'energy_consumption': 'Energy Consumption (kWh)'},
                                        color_discrete_sequence=['lightgreen'])
     energy_consumption_graph.update_layout(
@@ -201,28 +279,31 @@ def update_graph(solution, ideal_temperature):
     )
     #add accumulated energy consumption
     energy_consumption = np.cumsum(energy_consumption)
-    energy_consumption_graph.add_trace(px.line(data, x='hour', 
+    energy_consumption_graph.add_trace(px.line(data, x='hour',
                                                y=energy_consumption,
                                                labels={'y': 'Acc. Energy Consumption (kWh)'},
                                                 color_discrete_sequence=['green']).data[0])
 
-    confort_graph = px.line(data, x='hour', y='confort',
-                            labels={'hour': 'Hour', 'confort': 'Confort Score'},
+
+    # Comfort Graph
+    comfort_graph = px.line(data, x='hour', y='comfort',
+                            labels={'hour': 'Hour', 'comfort': 'comfort Score'},
                             color_discrete_sequence=['lightgreen'])
 
-    confort_graph.update_layout(
+    comfort_graph.update_layout(
         xaxis_title="Hour",
-        yaxis_title="Confort Score",
-        legend_title="Confort"
+        yaxis_title="comfort Score",
+        legend_title="comfort"
     )
-    #add accumulated confort
-    confort_obtained = np.cumsum(confort_obtained)
-    confort_graph.add_trace(px.line(data, x='hour', y=confort_obtained,
-                                    labels={'y': 'Acc. Confort Score'},
+    #add accumulated comfort
+    comfort_obtained = np.cumsum(comfort_obtained)
+    comfort_graph.add_trace(px.line(data, x='hour', y=comfort_obtained,
+                                    labels={'y': 'Acc. comfort Score'},
                                     color_discrete_sequence=['green']
                                     ).data[0])
     result = "No solution found"
-    return policy_readable_graph, energy_consumption_graph, confort_graph, result
-if __name__ == "__main__":
-    app.run_server(host="0.0.0.0", port="8050", debug=debug)
+    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, result
 
+
+if __name__ == "__main__":
+    app.run_server(host="0.0.0.0", port="8050", debug=debug)

app/dashboard.ipynb

@@ -2,41 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_7355/1062829466.py:19: 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"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"650\"\n",
-       "            src=\"http://0.0.0.0:8050/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x28c80a4d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import plotly.express as px\n",
@@ -48,20 +16,16 @@
     "import example_data\n",
     "import core\n",
     "\n",
-    "\n",
     "outside_temp = example_data.ExampleDailyOutsideTemperature\n",
     "energy_price = example_data.ExampleDailyEnergyCost\n",
     "boiler_temperature = example_data.ExampleBoilerTemperature\n",
     "\n",
-    "\n",
-    "\n",
-    "data = pd.DataFrame(columns=['hour', 'energy_consumption', 'confort', 'policy_readable'])\n",
-    "data = pd.concat([data, pd.DataFrame({'hour': np.arange(0, 24), 'energy_consumption': energy_price.value, 'confort': np.random.rand(24),\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",
-    "\n",
     "external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n",
     "\n",
     "app = Dash(__name__, external_stylesheets=external_stylesheets)\n",
@@ -73,8 +37,7 @@
     "\n",
     "server = app.server\n",
     "\n",
-    "\n",
-    "# SOlution options\n",
+    "# Solution options\n",
     "solution_options = [\n",
     "    \"Discrete Optimization\",\n",
     "    \"Continuous Optimization\",\n",
@@ -82,6 +45,47 @@
     "]\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",
+    "    \"UcufQVoJQPbfLzIBnSsUodJP\",\n",
+    "    \"VTuh8oxtC6YlOCLeScXfLuY3\",\n",
+    "    \"VZTnyVO3TP3ILuYsN5Xw9UR0\",\n",
+    "    \"WyPRjcmBXJZoC9DIURMXKxn8\",\n",
+    "    \"yPtlbyLlDlzeBZQYrPYaByoB\",\n",
+    "]\n",
+    "dataset_options_default_value = dataset_options[0]\n",
+    "dataset_dfs = {\n",
+    "    \"16NSJNnjLK4MndjZYaKYGKEV\": pd.read_csv('data/16NSJNnjLK4MndjZYaKYGKEV.csv')[:100000],\n",
+    "    \"7uLwefnSt8CgVlmIGY8emqJK\": pd.read_csv('data/7uLwefnSt8CgVlmIGY8emqJK.csv')[:100000],\n",
+    "    \"8yS04Ddkk3pPL8e9Rku4LJtc\": pd.read_csv('data/8yS04Ddkk3pPL8e9Rku4LJtc.csv')[:100000],\n",
+    "    \"Cwp33jA19hp9VdoNJUlj6USf\": pd.read_csv('data/Cwp33jA19hp9VdoNJUlj6USf.csv')[:100000],\n",
+    "    \"iBFIAuvh7bCNyOQDo0jkjhRV\": pd.read_csv('data/iBFIAuvh7bCNyOQDo0jkjhRV.csv')[:100000],\n",
+    "    \"iNVKpGfGW6rU17eOtxpZSFWR\": pd.read_csv('data/iNVKpGfGW6rU17eOtxpZSFWR.csv')[:100000],\n",
+    "    \"kaTMmHVh8gXUbHMppzdmdzpv\": pd.read_csv('data/kaTMmHVh8gXUbHMppzdmdzpv.csv')[:100000],\n",
+    "    \"KN9Z3gANLftDuUGvgs8O38dI\": pd.read_csv('data/KN9Z3gANLftDuUGvgs8O38dI.csv')[:100000],\n",
+    "    \"LzbMafI31IosheUI7YGhj5at\": pd.read_csv('data/LzbMafI31IosheUI7YGhj5at.csv')[:100000],\n",
+    "    \"PHqaZDuMTRvCZCvA259Z1vJu\": pd.read_csv('data/PHqaZDuMTRvCZCvA259Z1vJu.csv')[:100000],\n",
+    "    \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/RZngVU6axOdshmfma0yNAajE.csv')[:100000],\n",
+    "    \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/SQUOjMB6zAgYpSJEMy46tKXJ.csv')[:100000],\n",
+    "    \"UcufQVoJQPbfLzIBnSsUodJP\": pd.read_csv('data/UcufQVoJQPbfLzIBnSsUodJP.csv')[:100000],\n",
+    "    \"VTuh8oxtC6YlOCLeScXfLuY3\": pd.read_csv('data/VTuh8oxtC6YlOCLeScXfLuY3.csv')[:100000],\n",
+    "    \"VZTnyVO3TP3ILuYsN5Xw9UR0\": pd.read_csv('data/VZTnyVO3TP3ILuYsN5Xw9UR0.csv')[:100000],\n",
+    "    \"WyPRjcmBXJZoC9DIURMXKxn8\": pd.read_csv('data/WyPRjcmBXJZoC9DIURMXKxn8.csv')[:100000],\n",
+    "    \"yPtlbyLlDlzeBZQYrPYaByoB\": pd.read_csv('data/yPtlbyLlDlzeBZQYrPYaByoB.csv')[:100000],\n",
+    "}\n",
+    "\n",
     "# provide a scalar value to enable the slider to select ideal temperature\n",
     "ideal_temperature = 50\n",
     "\n",
@@ -93,6 +97,14 @@
     "    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",
@@ -124,6 +136,15 @@
     "        [\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('Policy'),\n",
     "                    dcc.Graph(id='policy_readable-graph')\n",
     "                ], className='six columns',\n",
@@ -131,27 +152,27 @@
     "            html.Div(\n",
     "                [\n",
     "                    html.H3('Energy Consumption'),\n",
-    "                    dcc.Graph(id='energy-comsumption-graph')\n",
+    "                    dcc.Graph(id='energy-consumption-graph')\n",
     "                ], className='six columns'\n",
-    "            )\n",
-    "    ], className='row'),\n",
+    "            ),\n",
+    "        ]\n",
+    "    ),\n",
     "    html.Div(\n",
     "        [\n",
     "            html.Div(\n",
     "                [\n",
-    "                    html.H3('Confort'),\n",
-    "                    dcc.Graph(id='confort-graph')\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': '#4B5320', 'font-family': 'Fantasy', 'color': '#8F9779', 'padding': '10px'}\n",
+    "style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}\n",
     ")\n",
     "\n",
     "\n",
-    "\n",
     "wiki_layout = html.Div([\n",
     "    dcc.Link('Dashboard', href='/'),\n",
     "\n",
@@ -163,7 +184,7 @@
     "                    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 confort of the shower and minimizes the energy consumption of the boiler.'),\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",
@@ -191,7 +212,7 @@
     "\n",
     "        ], className='six columns'),], className='row'),\n",
     "],\n",
-    "style={'background-color': '#4B5320', 'font-family': 'Fantasy', 'color': '#8F9779', 'padding': '10px'}\n",
+    "style={'background-color': '#333', 'font-family': 'Fantasy', 'color': '#999', 'padding': '10px'}\n",
     "\n",
     ")\n",
     "\n",
@@ -207,20 +228,43 @@
     "    # You could also return a 404 \"URL not found\" page here\n",
     "\n",
     "\n",
-    "\n",
     "@app.callback(\n",
     "    Output('policy_readable-graph', 'figure'),\n",
-    "    Output('energy-comsumption-graph', 'figure'),\n",
-    "    Output('confort-graph', 'figure'),\n",
+    "    Output('energy-consumption-graph', 'figure'),\n",
+    "    Output('comfort-graph', 'figure'),\n",
+    "    Output('dataset-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(solution, ideal_temperature):\n",
+    "def update_graph(dataset, solution, ideal_temperature):\n",
     "    energy_consumption = data['energy_consumption'].values\n",
-    "    confort_obtained = data['confort'].values\n",
-    "\n",
-    "\n",
+    "    comfort_obtained = data['comfort'].values\n",
+    "\n",
+    "\n",
+    "    # Dataset Graph\n",
+    "    dataset_df = dataset_dfs[dataset]\n",
+    "    dataset_df['ChActive'] = dataset_df['ChActive'].map({'Yes': 90, 'No': 5})\n",
+    "    dataset_df['HwActive'] = dataset_df['HwActive'].map({'Yes': 100, 'No': 0})\n",
+    "\n",
+    "    dataset_graph = px.line()\n",
+    "    #Add another line to the plot\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['ActPow'], mode='lines', name='ActPow')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwActive'], mode='lines', name='HwActive')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['ChActive'], mode='lines', name='ChActive')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTSet'], mode='lines', name='HwTSet')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['DHW_E21_T3_START_TEMP'], mode='lines', name='START_TEMP')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTStor'], mode='lines', name='HwTStor')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['HwTAct'], mode='lines', name='HwTAct')\n",
+    "    dataset_graph.add_scatter(x=dataset_df['ts'], y=dataset_df['OutTemp'], mode='lines', name='OutTemp')\n",
+    "    \n",
+    "    start_time = pd.Timestamp(dataset_df['ts'][0])\n",
+    "    \n",
+    "    dataset_graph.update_xaxes(range=[start_time, start_time+pd.Timedelta(days=2)])\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",
@@ -230,6 +274,8 @@
     "        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",
@@ -246,26 +292,29 @@
     "                                               labels={'y': 'Acc. Energy Consumption (kWh)'},\n",
     "                                                color_discrete_sequence=['green']).data[0])\n",
     "\n",
-    "    confort_graph = px.line(data, x='hour', y='confort',\n",
-    "                            labels={'hour': 'Hour', 'confort': 'Confort Score'},\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",
-    "    confort_graph.update_layout(\n",
+    "    comfort_graph.update_layout(\n",
     "        xaxis_title=\"Hour\",\n",
-    "        yaxis_title=\"Confort Score\",\n",
-    "        legend_title=\"Confort\"\n",
+    "        yaxis_title=\"comfort Score\",\n",
+    "        legend_title=\"comfort\"\n",
     "    )\n",
-    "    #add accumulated confort\n",
-    "    confort_obtained = np.cumsum(confort_obtained)\n",
-    "    confort_graph.add_trace(px.line(data, x='hour', y=confort_obtained,\n",
-    "                                    labels={'y': 'Acc. Confort Score'},\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, confort_graph, result\n",
+    "    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, result\n",
+    "\n",
+    "\n",
     "if __name__ == \"__main__\":\n",
-    "    app.run_server(host=\"0.0.0.0\", port=\"8050\", debug=debug)\n",
-    "\n"
+    "    app.run_server(host=\"127.0.0.1\", port=\"8050\", debug=debug)\n"
    ]
   }
  ],
@@ -285,7 +334,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.2"
   },
   "orig_nbformat": 4
  },

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