EPANET - basic

Extract EPANET results for a water distribution system to a pandas DataFrame.

Overview

Tip

MIKE IO 1D treats EPANET (.res/.resx) and Res1D results in the same way, so you may also refer to Res1D examples.

from mikeio1d import Res1D
Caution

Reading ‘.res’ files requires the accompanying ‘.inp’ file having the same name.

res = Res1D("../data/epanet.res")
res.info()
Start time: 2022-10-13 00:00:00
End time: 2022-10-14 00:00:00
# Timesteps: 25
# Catchments: 0
# Nodes: 11
# Reaches: 13
# Globals: 0
0 - Water Demand (l/s)
1 - Head (m)
2 - Pressure (m)
3 - Water Quality (-)
4 - Flow (l/s)
5 - Velocity (m/s)
6 - Headloss Per 1000Unit (m)
7 - Average Water Quality (-)
8 - Status Code (-)
9 - Setting (-)
10 - Reactor Rate (-)
11 - Friction Factor (-)

Plot network

res.network.to_geopandas().plot()

Explore network

res.reaches
<ResultReaches> (13)
Quantities (8)
  • Flow (l/s)
  • Velocity (m/s)
  • Headloss Per 1000Unit (m)
  • Average Water Quality (-)
  • Status Code (-)
  • Setting (-)
  • Reactor Rate (-)
  • Friction Factor (-)
Derived Quantities (0)
    res.nodes
    <ResultNodes> (11)
    Quantities (4)
    • Water Demand (l/s)
    • Head (m)
    • Pressure (m)
    • Water Quality (-)
    Derived Quantities (0)

      Extract results to a DataFrame

      df = res.read()
df.head()
      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

      df = res.reaches.Flow.read()
df.head()
      Flow:10 Flow:11 Flow:110 Flow:111 Flow:112 Flow:113 Flow:12 Flow:121 Flow:122 Flow:21 Flow:22 Flow:31 Flow:9
      2022-10-13 00:00:00 117.737534 77.866478 -48.338314 30.407526 11.904859 1.850754 8.159774 8.883767 3.734274 12.060229 7.612776 2.574746 117.737534
      2022-10-13 01:00:00 116.627487 76.997108 -47.228264 30.166845 12.096551 1.899742 8.208762 8.861935 3.756105 11.841382 7.563788 2.552915 116.627487
      2022-10-13 02:00:00 115.925919 73.462334 -32.646851 31.107346 18.547157 3.341266 10.912090 10.234302 4.907347 9.516808 8.014970 2.663477 115.925919
      2022-10-13 03:00:00 115.163910 72.857773 -31.884855 30.949911 18.671141 3.374713 10.945538 10.223918 4.917730 9.369758 7.981522 2.653094 115.163910
      2022-10-13 04:00:00 114.815155 69.403328 -17.656246 32.162888 24.881876 4.783631 13.616260 11.704790 5.960466 7.209155 8.465311 2.872162 114.815155

      Plot results

      res.reaches['110'].Flow.plot()

      import matplotlib.pyplot as plt

fig, ax = plt.subplots(2)
res.reaches['110'].Flow.plot(ax=ax[0])
res.reaches['110'].Velocity.plot(ax=ax[1])

plt.tight_layout()