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rufimelo commited on
Commit
2f26201
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1 Parent(s): 3d4f3ff

more graphs

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Files changed (19) hide show
  1. app/app.py +63 -1
  2. app/dashboard.ipynb +79 -22
  3. app/data/water_consumption/monthly/16NSJNnjLK4MndjZYaKYGKEV_month.csv +3 -0
  4. app/data/water_consumption/monthly/7uLwefnSt8CgVlmIGY8emqJK_month.csv +3 -0
  5. app/data/water_consumption/monthly/8yS04Ddkk3pPL8e9Rku4LJtc_month.csv +3 -0
  6. app/data/water_consumption/monthly/Cwp33jA19hp9VdoNJUlj6USf_month.csv +3 -0
  7. app/data/water_consumption/monthly/KN9Z3gANLftDuUGvgs8O38dI_month.csv +3 -0
  8. app/data/water_consumption/monthly/LzbMafI31IosheUI7YGhj5at_month.csv +3 -0
  9. app/data/water_consumption/monthly/PHqaZDuMTRvCZCvA259Z1vJu_month.csv +3 -0
  10. app/data/water_consumption/monthly/RZngVU6axOdshmfma0yNAajE_month.csv +3 -0
  11. app/data/water_consumption/monthly/SQUOjMB6zAgYpSJEMy46tKXJ_month.csv +3 -0
  12. app/data/water_consumption/monthly/UcufQVoJQPbfLzIBnSsUodJP_month.csv +3 -0
  13. app/data/water_consumption/monthly/VTuh8oxtC6YlOCLeScXfLuY3_month.csv +3 -0
  14. app/data/water_consumption/monthly/VZTnyVO3TP3ILuYsN5Xw9UR0_month.csv +3 -0
  15. app/data/water_consumption/monthly/WyPRjcmBXJZoC9DIURMXKxn8_month.csv +3 -0
  16. app/data/water_consumption/monthly/iBFIAuvh7bCNyOQDo0jkjhRV_month.csv +3 -0
  17. app/data/water_consumption/monthly/iNVKpGfGW6rU17eOtxpZSFWR_month.csv +3 -0
  18. app/data/water_consumption/monthly/kaTMmHVh8gXUbHMppzdmdzpv_month.csv +3 -0
  19. app/data/water_consumption/monthly/yPtlbyLlDlzeBZQYrPYaByoB_month.csv +3 -0
app/app.py CHANGED
@@ -82,6 +82,20 @@ dataset_water_consumption_dfs = {
82
  "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),
83
  "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),
84
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85
 
86
  # provide a scalar value to enable the slider to select ideal temperature
87
  ideal_temperature = 50
@@ -154,6 +168,21 @@ dashboard_layout = html.Div([
154
  html.H4('Hourly'),
155
  dcc.Graph(id='water_consumption_hourly_graph')
156
  ], className='six columns',
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
157
  )
158
  ], className='row'),
159
  html.Div(
@@ -245,6 +274,8 @@ def display_page(pathname):
245
  Output('dataset-graph', 'figure'),
246
  Output('water-consumption-graph', 'figure'),
247
  Output('water_consumption_hourly_graph', 'figure'),
 
 
248
  Output('solution-status', 'children'),
249
  Input('dataset-dropdown', 'value'),
250
  Input('solution-dropdown', 'value'),
@@ -299,6 +330,37 @@ def update_graph(dataset, solution, ideal_temperature):
299
  water_consumption_hourly_graph.update_xaxes(title_text="Hour of day")
300
  water_consumption_hourly_graph.update_yaxes(title_text="Number of water usages")
301
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
302
 
303
  # Policy Graph
304
  policy_readable_graph = px.line(data, x='hour', y='policy_readable',
@@ -346,7 +408,7 @@ def update_graph(dataset, solution, ideal_temperature):
346
  color_discrete_sequence=['green']
347
  ).data[0])
348
  result = "No solution found"
349
- return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, water_consumption_graph, water_consumption_hourly_graph, result
350
 
351
 
352
  if __name__ == "__main__":
 
82
  "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),
83
  "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),
84
  }
85
+ dataset_water_consumption_monthly_dfs = {
86
+ "16NSJNnjLK4MndjZYaKYGKEV": pd.read_csv('data/water_consumption/monthly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'),
87
+ "7uLwefnSt8CgVlmIGY8emqJK": pd.read_csv('data/water_consumption/monthly/7uLwefnSt8CgVlmIGY8emqJK_month.csv'),
88
+ "8yS04Ddkk3pPL8e9Rku4LJtc": pd.read_csv('data/water_consumption/monthly/8yS04Ddkk3pPL8e9Rku4LJtc_month.csv'),
89
+ "Cwp33jA19hp9VdoNJUlj6USf": pd.read_csv('data/water_consumption/monthly/Cwp33jA19hp9VdoNJUlj6USf_month.csv'),
90
+ "iBFIAuvh7bCNyOQDo0jkjhRV": pd.read_csv('data/water_consumption/monthly/iBFIAuvh7bCNyOQDo0jkjhRV_month.csv'),
91
+ "iNVKpGfGW6rU17eOtxpZSFWR": pd.read_csv('data/water_consumption/monthly/iNVKpGfGW6rU17eOtxpZSFWR_month.csv'),
92
+ "kaTMmHVh8gXUbHMppzdmdzpv": pd.read_csv('data/water_consumption/monthly/kaTMmHVh8gXUbHMppzdmdzpv_month.csv'),
93
+ "KN9Z3gANLftDuUGvgs8O38dI": pd.read_csv('data/water_consumption/monthly/KN9Z3gANLftDuUGvgs8O38dI_month.csv'),
94
+ "LzbMafI31IosheUI7YGhj5at": pd.read_csv('data/water_consumption/monthly/LzbMafI31IosheUI7YGhj5at_month.csv'),
95
+ "PHqaZDuMTRvCZCvA259Z1vJu": pd.read_csv('data/water_consumption/monthly/PHqaZDuMTRvCZCvA259Z1vJu_month.csv'),
96
+ "RZngVU6axOdshmfma0yNAajE": pd.read_csv('data/water_consumption/monthly/RZngVU6axOdshmfma0yNAajE_month.csv'),
97
+ "SQUOjMB6zAgYpSJEMy46tKXJ": pd.read_csv('data/water_consumption/monthly/SQUOjMB6zAgYpSJEMy46tKXJ_month.csv'),
98
+ }
99
 
100
  # provide a scalar value to enable the slider to select ideal temperature
101
  ideal_temperature = 50
 
168
  html.H4('Hourly'),
169
  dcc.Graph(id='water_consumption_hourly_graph')
170
  ], className='six columns',
171
+ ),
172
+ html.Div(
173
+ [
174
+ html.H4('Day of the Week'),
175
+ dcc.Graph(id='water_consumption_week_day_graph')
176
+ ], className='six columns',
177
+ )
178
+ ], className='row'),
179
+ html.Div(
180
+ [
181
+ html.Div(
182
+ [
183
+ html.H4('Monthly'),
184
+ dcc.Graph(id='water_consumption_monthly_graph')
185
+ ], className='six columns',
186
  )
187
  ], className='row'),
188
  html.Div(
 
274
  Output('dataset-graph', 'figure'),
275
  Output('water-consumption-graph', 'figure'),
276
  Output('water_consumption_hourly_graph', 'figure'),
277
+ Output('water_consumption_week_day_graph', 'figure'),
278
+ Output('water_consumption_monthly_graph', 'figure'),
279
  Output('solution-status', 'children'),
280
  Input('dataset-dropdown', 'value'),
281
  Input('solution-dropdown', 'value'),
 
330
  water_consumption_hourly_graph.update_xaxes(title_text="Hour of day")
331
  water_consumption_hourly_graph.update_yaxes(title_text="Number of water usages")
332
 
333
+ # Water Consumption Week Day Graph
334
+ order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
335
+ water_consumption_df["datetime"] = pd.to_datetime(water_consumption_df["ts"], errors='coerce')
336
+ #create columns with day of week
337
+ water_consumption_df['day_of_week'] = water_consumption_df["datetime"].apply(lambda x: x.day_name())
338
+ #turn day of week number into day of week name
339
+ #df['day_of_week'] = df['day_of_week'].apply(lambda x: calendar.day_name[x])
340
+ week_series = water_consumption_df.groupby("day_of_week")["water_consumption_bool"].sum().loc[order]
341
+
342
+ # Water Consumption Week Day Graph
343
+ water_consumption_week_day_graph = go.Figure()
344
+ water_consumption_week_day_graph.add_trace(go.Bar(x=week_series.index, y=week_series.values, name='Water consumption per day of week'))
345
+ water_consumption_week_day_graph.update_layout(
346
+ title_text="Water consumption per day of week"
347
+ )
348
+ water_consumption_week_day_graph.update_xaxes(title_text="Day of week")
349
+ water_consumption_week_day_graph.update_yaxes(title_text="Number of water usages")
350
+
351
+ #dataset_water_consumption_monthly_dfs
352
+ #Water Consumption Monthly Graph
353
+ water_consumption_monthly_df = dataset_water_consumption_monthly_dfs[dataset]
354
+ water_consumption_monthly_graph = go.Figure()
355
+ 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'))
356
+ water_consumption_monthly_graph.update_layout(
357
+ title_text="Water consumption per month"
358
+ )
359
+ water_consumption_monthly_graph.update_xaxes(title_text="Month")
360
+ water_consumption_monthly_graph.update_yaxes(title_text="Number of water usages")
361
+
362
+
363
+
364
 
365
  # Policy Graph
366
  policy_readable_graph = px.line(data, x='hour', y='policy_readable',
 
408
  color_discrete_sequence=['green']
409
  ).data[0])
410
  result = "No solution found"
411
+ 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
412
 
413
 
414
  if __name__ == "__main__":
app/dashboard.ipynb CHANGED
@@ -2,39 +2,34 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 30,
6
  "metadata": {},
7
  "outputs": [
8
  {
9
  "name": "stderr",
10
  "output_type": "stream",
11
  "text": [
12
- "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_34004/98337313.py:17: FutureWarning:\n",
13
  "\n",
14
  "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",
15
  "\n"
16
  ]
17
  },
18
  {
19
- "data": {
20
- "text/html": [
21
- "\n",
22
- " <iframe\n",
23
- " width=\"100%\"\n",
24
- " height=\"650\"\n",
25
- " src=\"http://0.0.0.0:8050/\"\n",
26
- " frameborder=\"0\"\n",
27
- " allowfullscreen\n",
28
- " \n",
29
- " ></iframe>\n",
30
- " "
31
- ],
32
- "text/plain": [
33
- "<IPython.lib.display.IFrame at 0x15e45a110>"
34
- ]
35
- },
36
- "metadata": {},
37
- "output_type": "display_data"
38
  }
39
  ],
40
  "source": [
@@ -122,6 +117,20 @@
122
  " \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),\n",
123
  " \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),\n",
124
  "}\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
125
  "\n",
126
  "# provide a scalar value to enable the slider to select ideal temperature\n",
127
  "ideal_temperature = 50\n",
@@ -194,6 +203,21 @@
194
  " html.H4('Hourly'),\n",
195
  " dcc.Graph(id='water_consumption_hourly_graph')\n",
196
  " ], className='six columns',\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
197
  " )\n",
198
  " ], className='row'),\n",
199
  " html.Div(\n",
@@ -285,6 +309,8 @@
285
  " Output('dataset-graph', 'figure'),\n",
286
  " Output('water-consumption-graph', 'figure'),\n",
287
  " Output('water_consumption_hourly_graph', 'figure'),\n",
 
 
288
  " Output('solution-status', 'children'),\n",
289
  " Input('dataset-dropdown', 'value'),\n",
290
  " Input('solution-dropdown', 'value'),\n",
@@ -339,6 +365,37 @@
339
  " water_consumption_hourly_graph.update_xaxes(title_text=\"Hour of day\")\n",
340
  " water_consumption_hourly_graph.update_yaxes(title_text=\"Number of water usages\")\n",
341
  "\n",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
342
  "\n",
343
  " # Policy Graph\n",
344
  " policy_readable_graph = px.line(data, x='hour', y='policy_readable',\n",
@@ -386,7 +443,7 @@
386
  " color_discrete_sequence=['green']\n",
387
  " ).data[0])\n",
388
  " result = \"No solution found\"\n",
389
- " return policy_readable_graph, energy_consumption_graph, comfort_graph, dataset_graph, water_consumption_graph, water_consumption_hourly_graph, result\n",
390
  "\n",
391
  "\n",
392
  "if __name__ == \"__main__\":\n",
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 32,
6
  "metadata": {},
7
  "outputs": [
8
  {
9
  "name": "stderr",
10
  "output_type": "stream",
11
  "text": [
12
+ "/var/folders/b4/lwfgccm95kqd2skcwvrt2fr00000gn/T/ipykernel_34004/147804699.py:17: FutureWarning:\n",
13
  "\n",
14
  "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",
15
  "\n"
16
  ]
17
  },
18
  {
19
+ "ename": "FileNotFoundError",
20
+ "evalue": "[Errno 2] No such file or directory: 'data/water_consumption/montly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'",
21
+ "output_type": "error",
22
+ "traceback": [
23
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
24
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
25
+ "\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",
26
+ "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",
27
+ "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",
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+ "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",
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+ "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",
30
+ "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",
31
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/water_consumption/montly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'"
32
+ ]
 
 
 
 
 
33
  }
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  ],
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  "source": [
 
117
  " \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/water_consumption/RZngVU6axOdshmfma0yNAajE_water_consumption.csv'),\n",
118
  " \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/water_consumption/SQUOjMB6zAgYpSJEMy46tKXJ_water_consumption.csv'),\n",
119
  "}\n",
120
+ "dataset_water_consumption_monthly_dfs = {\n",
121
+ " \"16NSJNnjLK4MndjZYaKYGKEV\": pd.read_csv('data/water_consumption/monthly/16NSJNnjLK4MndjZYaKYGKEV_month.csv'),\n",
122
+ " \"7uLwefnSt8CgVlmIGY8emqJK\": pd.read_csv('data/water_consumption/monthly/7uLwefnSt8CgVlmIGY8emqJK_month.csv'),\n",
123
+ " \"8yS04Ddkk3pPL8e9Rku4LJtc\": pd.read_csv('data/water_consumption/monthly/8yS04Ddkk3pPL8e9Rku4LJtc_month.csv'),\n",
124
+ " \"Cwp33jA19hp9VdoNJUlj6USf\": pd.read_csv('data/water_consumption/monthly/Cwp33jA19hp9VdoNJUlj6USf_month.csv'),\n",
125
+ " \"iBFIAuvh7bCNyOQDo0jkjhRV\": pd.read_csv('data/water_consumption/monthly/iBFIAuvh7bCNyOQDo0jkjhRV_month.csv'),\n",
126
+ " \"iNVKpGfGW6rU17eOtxpZSFWR\": pd.read_csv('data/water_consumption/monthly/iNVKpGfGW6rU17eOtxpZSFWR_month.csv'),\n",
127
+ " \"kaTMmHVh8gXUbHMppzdmdzpv\": pd.read_csv('data/water_consumption/monthly/kaTMmHVh8gXUbHMppzdmdzpv_monthcsv'),\n",
128
+ " \"KN9Z3gANLftDuUGvgs8O38dI\": pd.read_csv('data/water_consumption/monthly/KN9Z3gANLftDuUGvgs8O38dI_month.csv'),\n",
129
+ " \"LzbMafI31IosheUI7YGhj5at\": pd.read_csv('data/water_consumption/monthly/LzbMafI31IosheUI7YGhj5at_month.csv'),\n",
130
+ " \"PHqaZDuMTRvCZCvA259Z1vJu\": pd.read_csv('data/water_consumption/monthly/PHqaZDuMTRvCZCvA259Z1vJu_month.csv'),\n",
131
+ " \"RZngVU6axOdshmfma0yNAajE\": pd.read_csv('data/water_consumption/monthly/RZngVU6axOdshmfma0yNAajE_month.csv'),\n",
132
+ " \"SQUOjMB6zAgYpSJEMy46tKXJ\": pd.read_csv('data/water_consumption/monthly/SQUOjMB6zAgYpSJEMy46tKXJ_month.csv'),\n",
133
+ "}\n",
134
  "\n",
135
  "# provide a scalar value to enable the slider to select ideal temperature\n",
136
  "ideal_temperature = 50\n",
 
203
  " html.H4('Hourly'),\n",
204
  " dcc.Graph(id='water_consumption_hourly_graph')\n",
205
  " ], className='six columns',\n",
206
+ " ),\n",
207
+ " html.Div(\n",
208
+ " [\n",
209
+ " html.H4('Day of the Week'),\n",
210
+ " dcc.Graph(id='water_consumption_week_day_graph')\n",
211
+ " ], className='six columns',\n",
212
+ " )\n",
213
+ " ], className='row'),\n",
214
+ " html.Div(\n",
215
+ " [\n",
216
+ " html.Div(\n",
217
+ " [\n",
218
+ " html.H4('Monthly'),\n",
219
+ " dcc.Graph(id='water_consumption_monthly_graph')\n",
220
+ " ], className='six columns',\n",
221
  " )\n",
222
  " ], className='row'),\n",
223
  " html.Div(\n",
 
309
  " Output('dataset-graph', 'figure'),\n",
310
  " Output('water-consumption-graph', 'figure'),\n",
311
  " Output('water_consumption_hourly_graph', 'figure'),\n",
312
+ " Output('water_consumption_week_day_graph', 'figure'),\n",
313
+ " Output('water_consumption_monthly_graph', 'figure'),\n",
314
  " Output('solution-status', 'children'),\n",
315
  " Input('dataset-dropdown', 'value'),\n",
316
  " Input('solution-dropdown', 'value'),\n",
 
365
  " water_consumption_hourly_graph.update_xaxes(title_text=\"Hour of day\")\n",
366
  " water_consumption_hourly_graph.update_yaxes(title_text=\"Number of water usages\")\n",
367
  "\n",
368
+ " # Water Consumption Week Day Graph\n",
369
+ " order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
370
+ " water_consumption_df[\"datetime\"] = pd.to_datetime(water_consumption_df[\"ts\"], errors='coerce')\n",
371
+ " #create columns with day of week\n",
372
+ " water_consumption_df['day_of_week'] = water_consumption_df[\"datetime\"].apply(lambda x: x.day_name())\n",
373
+ " #turn day of week number into day of week name\n",
374
+ " #df['day_of_week'] = df['day_of_week'].apply(lambda x: calendar.day_name[x])\n",
375
+ " week_series = water_consumption_df.groupby(\"day_of_week\")[\"water_consumption_bool\"].sum().loc[order]\n",
376
+ "\n",
377
+ " # Water Consumption Week Day Graph\n",
378
+ " water_consumption_week_day_graph = go.Figure()\n",
379
+ " 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",
380
+ " water_consumption_week_day_graph.update_layout(\n",
381
+ " title_text=\"Water consumption per day of week\"\n",
382
+ " )\n",
383
+ " water_consumption_week_day_graph.update_xaxes(title_text=\"Day of week\")\n",
384
+ " water_consumption_week_day_graph.update_yaxes(title_text=\"Number of water usages\")\n",
385
+ "\n",
386
+ " #dataset_water_consumption_monthly_dfs\n",
387
+ " #Water Consumption Monthly Graph\n",
388
+ " water_consumption_monthly_df = dataset_water_consumption_monthly_dfs[dataset]\n",
389
+ " water_consumption_monthly_graph = go.Figure()\n",
390
+ " 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",
391
+ " water_consumption_monthly_graph.update_layout(\n",
392
+ " title_text=\"Water consumption per month\"\n",
393
+ " )\n",
394
+ " water_consumption_monthly_graph.update_xaxes(title_text=\"Month\")\n",
395
+ " water_consumption_monthly_graph.update_yaxes(title_text=\"Number of water usages\")\n",
396
+ "\n",
397
+ "\n",
398
+ "\n",
399
  "\n",
400
  " # Policy Graph\n",
401
  " policy_readable_graph = px.line(data, x='hour', y='policy_readable',\n",
 
443
  " color_discrete_sequence=['green']\n",
444
  " ).data[0])\n",
445
  " result = \"No solution found\"\n",
446
+ " 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",
447
  "\n",
448
  "\n",
449
  "if __name__ == \"__main__\":\n",
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