Read EPANET result files generated by MIKE+¶
In this notebook we give examples how to read EPANET result files res generated by MIKE+. Most of the functionality is the same as reading res1d files, which is described in res1d.ipynb.
In [1]:
from mikeio1d import Res1D
from mikeio1d.query import QueryDataNode, QueryDataReach
EPANET res file¶
Read entire res file into a data frame. Here we use results from a standard Net1.inp (EPANET Example Network 1) distributed together with EPANET 2.2 and imported into MIKE+.
In [2]:
file_path = "../tests/testdata/epanet.res"
res = Res1D(file_path)
df = res.read()
Let's peek at the full data frame coming from EPANET result.
In [3]:
df.head()
Out[3]:
| Demand:10 | Head:10 | Pressure:10 | WaterQuality:10 | Demand:11 | Head:11 | Pressure:11 | WaterQuality:11 | Demand:12 | Head:12 | ... | ReactorRate:31 | FrictionFactor:31 | Flow:9 | Velocity:9 | HeadlossPer1000Unit:9 | AvgWaterQuality:9 | StatusCode:9 | Setting:9 | ReactorRate:9 | FrictionFactor:9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-10-13 00:00:00 | 0.0 | 306.125000 | 89.716995 | 0.5 | 9.463531 | 300.298187 | 83.890175 | 0.5 | 9.463531 | 295.677277 | ... | 0.0 | 0.048410 | 117.737534 | 0.0 | -62.285000 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 |
| 2022-10-13 01:00:00 | 0.0 | 306.862823 | 90.454834 | 0.5 | 9.463531 | 301.137360 | 84.729355 | 0.5 | 9.463531 | 296.611542 | ... | 0.0 | 0.048470 | 116.627487 | 0.0 | -63.022831 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 |
| 2022-10-13 02:00:00 | 0.0 | 307.325562 | 90.917557 | 0.5 | 11.356236 | 301.663696 | 85.255707 | 0.5 | 11.356236 | 297.515137 | ... | 0.0 | 0.048168 | 115.925919 | 0.0 | -63.485558 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 |
| 2022-10-13 03:00:00 | 0.0 | 307.824982 | 91.416985 | 0.5 | 11.356236 | 302.231873 | 85.823860 | 0.5 | 11.356236 | 298.146301 | ... | 0.0 | 0.048195 | 115.163910 | 0.0 | -63.984989 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 |
| 2022-10-13 04:00:00 | 0.0 | 308.052460 | 91.644470 | 0.5 | 13.248942 | 302.490662 | 86.082672 | 0.5 | 13.248942 | 298.756622 | ... | 0.0 | 0.047632 | 114.815155 | 0.0 | -64.212471 | 0.0 | 3.0 | 1.0 | 0.0 | 0.0 |
5 rows × 148 columns
The available quantities are:
In [4]:
res.quantities
Out[4]:
['Demand', 'Head', 'Pressure', 'WaterQuality', 'Flow', 'Velocity', 'HeadlossPer1000Unit', 'AvgWaterQuality', 'StatusCode', 'Setting', 'ReactorRate', 'FrictionFactor']
From the above list the following quantities are defined on pipes (reaches):
- Average Water Quality
- Flow
- Friction Factor
- Headloss per 1000Unit
- Reactor Rate
- Setting
- Status Code
- Velocity
Quantities defined on junctions (nodes):
- Head
- Pressure
- Water Demand
- Water Quality
Here we retrieve flow in a pipe with ID 10, which has connecting junctions 10 and 11
In [5]:
pipe_id = '10'
pipe_data_item = res.reaches[pipe_id]
node_start = res.data.Nodes[pipe_data_item.StartNodeIndex]
node_end = res.data.Nodes[pipe_data_item.EndNodeIndex]
print(f'Start node ID: {node_start.ID}')
print(f'End node ID: {node_end.ID}')
Start node ID: 10 End node ID: 11
In [6]:
df[['Flow:10', 'Pressure:10', 'Pressure:11']]
Out[6]:
| Flow:10 | Pressure:10 | Pressure:11 | |
|---|---|---|---|
| 2022-10-13 00:00:00 | 117.737534 | 89.716995 | 83.890175 |
| 2022-10-13 01:00:00 | 116.627487 | 90.454834 | 84.729355 |
| 2022-10-13 02:00:00 | 115.925919 | 90.917557 | 85.255707 |
| 2022-10-13 03:00:00 | 115.163910 | 91.416985 | 85.823860 |
| 2022-10-13 04:00:00 | 114.815155 | 91.644470 | 86.082672 |
| 2022-10-13 05:00:00 | 114.398529 | 91.915298 | 86.390823 |
| 2022-10-13 06:00:00 | 114.390770 | 91.920341 | 86.396553 |
| 2022-10-13 07:00:00 | 114.311264 | 91.971931 | 86.455261 |
| 2022-10-13 08:00:00 | 113.833015 | 92.281364 | 86.807350 |
| 2022-10-13 09:00:00 | 113.436226 | 92.537125 | 87.098404 |
| 2022-10-13 10:00:00 | 112.646294 | 93.043625 | 87.674835 |
| 2022-10-13 11:00:00 | 111.941292 | 93.492691 | 88.185966 |
| 2022-10-13 12:00:00 | 110.851845 | 94.181076 | 88.969604 |
| 2022-10-13 13:00:00 | -0.000053 | 84.220718 | 84.220718 |
| 2022-10-13 14:00:00 | -0.000052 | 83.050537 | 83.050537 |
| 2022-10-13 15:00:00 | -0.000051 | 81.976448 | 81.976448 |
| 2022-10-13 16:00:00 | -0.000050 | 81.041527 | 81.041527 |
| 2022-10-13 17:00:00 | -0.000049 | 80.235962 | 80.235962 |
| 2022-10-13 18:00:00 | -0.000048 | 79.534828 | 79.534828 |
| 2022-10-13 19:00:00 | -0.000048 | 78.997780 | 78.997780 |
| 2022-10-13 20:00:00 | -0.000047 | 78.356293 | 78.356293 |
| 2022-10-13 21:00:00 | -0.000046 | 77.550705 | 77.550705 |
| 2022-10-13 22:00:00 | -0.000046 | 76.605965 | 76.605965 |
| 2022-10-13 23:00:00 | 120.466057 | 87.873650 | 81.794281 |
| 2022-10-14 00:00:00 | 119.382088 | 88.611023 | 82.632576 |
Let's plot the above results, but this time we will retrieve the data using QueryDataReach and QueryDataNode.
In [7]:
pipe_query = QueryDataReach('Flow', pipe_id)
df_pipe = res.read(queries=[pipe_query])
df_pipe.plot();
node_start_query = QueryDataNode('Pressure', node_start.ID)
node_end_query = QueryDataNode('Pressure', node_end.ID)
df_nodes = res.read(queries=[node_start_query, node_end_query])
df_nodes.plot();
In [ ]: