more graphs

app/app.py

@@ -7,6 +7,7 @@ import plotly.express as px
 import numpy as np
 import example_data
 import core
+from plotly.subplots import make_subplots
 
 outside_temp = example_data.ExampleDailyOutsideTemperature
 energy_price = example_data.ExampleDailyEnergyCost
@@ -51,31 +52,35 @@ dataset_options = [
     "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],
+dataset_original_dfs = {
+    "16NSJNnjLK4MndjZYaKYGKEV": pd.read_csv('data/original/16NSJNnjLK4MndjZYaKYGKEV.csv'),
+    "7uLwefnSt8CgVlmIGY8emqJK": pd.read_csv('data/original/7uLwefnSt8CgVlmIGY8emqJK.csv'),
+    "8yS04Ddkk3pPL8e9Rku4LJtc": pd.read_csv('data/original/8yS04Ddkk3pPL8e9Rku4LJtc.csv'),
+    "Cwp33jA19hp9VdoNJUlj6USf": pd.read_csv('data/original/Cwp33jA19hp9VdoNJUlj6USf.csv'),
+    "iBFIAuvh7bCNyOQDo0jkjhRV": pd.read_csv('data/original/iBFIAuvh7bCNyOQDo0jkjhRV.csv'),
+    "iNVKpGfGW6rU17eOtxpZSFWR": pd.read_csv('data/original/iNVKpGfGW6rU17eOtxpZSFWR.csv'),
+    "kaTMmHVh8gXUbHMppzdmdzpv": pd.read_csv('data/original/kaTMmHVh8gXUbHMppzdmdzpv.csv'),
+    "KN9Z3gANLftDuUGvgs8O38dI": pd.read_csv('data/original/KN9Z3gANLftDuUGvgs8O38dI.csv'),
+    "LzbMafI31IosheUI7YGhj5at": pd.read_csv('data/original/LzbMafI31IosheUI7YGhj5at.csv'),
+    "PHqaZDuMTRvCZCvA259Z1vJu": pd.read_csv('data/original/PHqaZDuMTRvCZCvA259Z1vJu.csv'),
+    "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/original/RZngVU6axOdshmfma0yNAajE.csv'),
+    "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/original/SQUOjMB6zAgYpSJEMy46tKXJ.csv'),
+}
+dataset_water_consumption_dfs = {
+    "16NSJNnjLK4MndjZYaKYGKEV": pd.read_csv('data/water_consumption/16NSJNnjLK4MndjZYaKYGKEV_water_consumption.csv'),
+    "7uLwefnSt8CgVlmIGY8emqJK": pd.read_csv('data/water_consumption/7uLwefnSt8CgVlmIGY8emqJK_water_consumption.csv'),
+    "8yS04Ddkk3pPL8e9Rku4LJtc": pd.read_csv('data/water_consumption/8yS04Ddkk3pPL8e9Rku4LJtc_water_consumption.csv'),
+    "Cwp33jA19hp9VdoNJUlj6USf": pd.read_csv('data/water_consumption/Cwp33jA19hp9VdoNJUlj6USf_water_consumption.csv'),
+    "iBFIAuvh7bCNyOQDo0jkjhRV": pd.read_csv('data/water_consumption/iBFIAuvh7bCNyOQDo0jkjhRV_water_consumption.csv'),
+    "iNVKpGfGW6rU17eOtxpZSFWR": pd.read_csv('data/water_consumption/iNVKpGfGW6rU17eOtxpZSFWR_water_consumption.csv'),
+    "kaTMmHVh8gXUbHMppzdmdzpv": pd.read_csv('data/water_consumption/kaTMmHVh8gXUbHMppzdmdzpv_water_consumption.csv'),
+    "KN9Z3gANLftDuUGvgs8O38dI": pd.read_csv('data/water_consumption/KN9Z3gANLftDuUGvgs8O38dI_water_consumption.csv'),
+    "LzbMafI31IosheUI7YGhj5at": pd.read_csv('data/water_consumption/LzbMafI31IosheUI7YGhj5at_water_consumption.csv'),
+    "PHqaZDuMTRvCZCvA259Z1vJu": pd.read_csv('data/water_consumption/PHqaZDuMTRvCZCvA259Z1vJu_water_consumption.csv'),
+    "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),
+    "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),
 }
 
 # provide a scalar value to enable the slider to select ideal temperature
@@ -133,6 +138,24 @@ dashboard_layout =  html.Div([
                 ], className='twelve columns',
             )
     ], className='row'),
+    html.Div(
+        [
+            html.Div(
+                [
+                    html.H3('Water Comsumption Patterns'),
+                    dcc.Graph(id='water-consumption-graph')
+                ], className='twelve columns',
+            )
+    ], className='row'),
+    html.Div(
+        [
+            html.Div(
+                [
+                    html.H4('Hourly'),
+                    dcc.Graph(id='water_consumption_hourly_graph')
+                ], className='six columns',
+            )
+    ], className='row'),
     html.Div(
         [
             html.Div(
@@ -187,15 +210,10 @@ wiki_layout = html.Div([
                     html.P('This project was developed by a team of 3, in the context of the Aveiro Tech City 2023 hackathon.'),
                     html.P('The team members are:'),
                     html.H4('Rui Melo'),
-                    html.P('Rui Melo is a ....'),
                     html.H4('André Catarino'),
-                    html.P('André Catarino is a ....'),
                     html.H4('Francisco Petronilho'),
-                    html.P('Francisco Petronilho is a ....'),
                     html.H4('André Tomás'),
-                    html.P('André Tomás is a ....'),
                     html.H4('Zé Miguel'),
-                    html.P('Zé Miguel is a ....'),
 
 
                     html.H3('References'),
@@ -225,6 +243,8 @@ def display_page(pathname):
     Output('energy-consumption-graph', 'figure'),
     Output('comfort-graph', 'figure'),
     Output('dataset-graph', 'figure'),
+    Output('water-consumption-graph', 'figure'),
+    Output('water_consumption_hourly_graph', 'figure'),
     Output('solution-status', 'children'),
     Input('dataset-dropdown', 'value'),
     Input('solution-dropdown', 'value'),
@@ -233,29 +253,53 @@ def display_page(pathname):
 def update_graph(dataset, solution, ideal_temperature):
     energy_consumption = data['energy_consumption'].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})
+    
+    # Original Dataset Graph
+    original_df = dataset_original_dfs[dataset]
 
     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.add_scatter(x=original_df['ts'], y=original_df['ActPow'], mode='lines', name='ActPow')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwActive'], mode='lines', name='HwActive')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['ChActive'], mode='lines', name='ChActive')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTSet'], mode='lines', name='HwTSet')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['DHW_E21_T3_START_TEMP'], mode='lines', name='START_TEMP')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTStor'], mode='lines', name='HwTStor')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['HwTAct'], mode='lines', name='HwTAct')
+    dataset_graph.add_scatter(x=original_df['ts'], y=original_df['OutTemp'], mode='lines', name='OutTemp')
+    start_time = pd.Timestamp(original_df['ts'][0])
     dataset_graph.update_xaxes(range=[start_time, start_time+pd.Timedelta(days=2)])
 
 
+    # Water Consumption Graph
+    water_consumption_df = dataset_water_consumption_dfs[dataset]
+    if len(water_consumption_df) / 24 > 365:
+        water_consumption_df = water_consumption_df.head(365* 24)
+    water_consumption_df.index = pd.to_datetime(water_consumption_df["ts"], errors='coerce')
+    water_consumption_graph = go.Figure()
+    water_consumption_graph = make_subplots(specs=[[{"secondary_y": True}]])
+    water_consumption_graph.add_trace(go.Scatter(x=water_consumption_df[:]["ts"], y=water_consumption_df[:]['water_consumption_bool'], mode='lines', name='Water consumption'),
+    secondary_y=False,)
+    water_consumption_graph.add_trace(go.Scatter(x=water_consumption_df[:]["ts"], y=water_consumption_df[:]['HwTStor'], mode='lines', name='Water temperature')
+    ,secondary_y=True,)
+    water_consumption_graph.update_layout(
+        title_text="Water consumption"
+    )
+    water_consumption_graph.update_xaxes(title_text="time")
+    water_consumption_graph.update_yaxes(title_text="water consumption", secondary_y=False)
+    water_consumption_graph.update_yaxes(title_text="water temperature", secondary_y=True)
+
+    # Water Consumption Hourly Graph
+    water_consumption_df["ts_hour"] = water_consumption_df["ts"].apply(lambda x: x.split(" ")[1].split(":")[0])
+    hour_series = water_consumption_df.groupby("ts_hour")["water_consumption_bool"].sum()
+    water_consumption_hourly_graph = go.Figure()
+    water_consumption_hourly_graph.add_trace(go.Bar(x=hour_series.index, y=hour_series.values, name='Water consumption per hour'))
+    water_consumption_hourly_graph.update_layout(
+        title_text="Water consumption per hour"
+    )
+    water_consumption_hourly_graph.update_xaxes(title_text="Hour of day")
+    water_consumption_hourly_graph.update_yaxes(title_text="Number of water usages")
+
+
     # Policy Graph
     policy_readable_graph = px.line(data, x='hour', y='policy_readable',
                                     labels={'hour': 'Hour', 'policy_readable': 'Policy'},
@@ -302,7 +346,7 @@ def update_graph(dataset, solution, ideal_temperature):
                                     color_discrete_sequence=['green']
                                     ).data[0])
     result = "No solution found"
-    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, result
+    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, water_consumption_graph, water_consumption_hourly_graph, result
 
 
 if __name__ == "__main__":

app/dashboard.ipynb

@@ -2,9 +2,41 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_34004/98337313.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"
+     ]
+    },
+    {
+     "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 0x15e45a110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "import plotly.express as px\n",
@@ -15,6 +47,7 @@
     "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",
@@ -59,31 +92,35 @@
     "    \"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",
+    "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",
     "\n",
     "# provide a scalar value to enable the slider to select ideal temperature\n",
@@ -145,6 +182,24 @@
     "        [\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",
+    "    ], 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",
@@ -180,7 +235,7 @@
     "\n",
     "    html.Div([\n",
     "        html.Div([\n",
-    "                    \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",
@@ -195,15 +250,10 @@
     "                    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.P('Rui Melo is a ....'),\n",
     "                    html.H4('André Catarino'),\n",
-    "                    html.P('André Catarino is a ....'),\n",
     "                    html.H4('Francisco Petronilho'),\n",
-    "                    html.P('Francisco Petronilho is a ....'),\n",
     "                    html.H4('André Tomás'),\n",
-    "                    html.P('André Tomás is a ....'),\n",
     "                    html.H4('Zé Miguel'),\n",
-    "                    html.P('Zé Miguel is a ....'),\n",
     "\n",
     "\n",
     "                    html.H3('References'),\n",
@@ -233,6 +283,8 @@
     "    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('solution-status', 'children'),\n",
     "    Input('dataset-dropdown', 'value'),\n",
     "    Input('solution-dropdown', 'value'),\n",
@@ -241,29 +293,53 @@
     "def update_graph(dataset, solution, ideal_temperature):\n",
     "    energy_consumption = data['energy_consumption'].values\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",
+    "    # Original Dataset Graph\n",
+    "    original_df = dataset_original_dfs[dataset]\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.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",
+    "\n",
     "    # Policy Graph\n",
     "    policy_readable_graph = px.line(data, x='hour', y='policy_readable',\n",
     "                                    labels={'hour': 'Hour', 'policy_readable': 'Policy'},\n",
@@ -276,8 +352,8 @@
     "\n",
     "\n",
     "    # Energy Consumption Graph\n",
-    "    energy_consumption_graph = px.line(data, x='hour', \n",
-    "                                       y='energy_consumption', \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",
@@ -287,7 +363,7 @@
     "    )\n",
     "    #add accumulated energy consumption\n",
     "    energy_consumption = np.cumsum(energy_consumption)\n",
-    "    energy_consumption_graph.add_trace(px.line(data, x='hour', \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",
@@ -310,12 +386,19 @@
     "                                    color_discrete_sequence=['green']\n",
     "                                    ).data[0])\n",
     "    result = \"No solution found\"\n",
-    "    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, result\n",
+    "    return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, water_consumption_graph, water_consumption_hourly_graph, result\n",
     "\n",
     "\n",
     "if __name__ == \"__main__\":\n",
-    "    app.run_server(host=\"127.0.0.1\", port=\"8050\", debug=debug)\n"
+    "    app.run_server(host=\"0.0.0.0\", port=\"8050\", debug=debug)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

app/data/{UcufQVoJQPbfLzIBnSsUodJP.csv → water_consumption/16NSJNnjLK4MndjZYaKYGKEV_water_consumption.csv}

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