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WaCo is a library for simulating contaminations in water networks built on-top of the WNTR library.
For further details, consult the documentation.

Installation

The only requirement is the WNTR library that is used for the hydraulic simulations:

• Python >= 3.9
• wntr >= 1.1.0

The latest release of WaCo can be easily installed via pip.

pip install waco

You can also install the latest developement version directly from GitHub.

pip install --upgrade git+https://github.com/andreaponti5/waco

Getting Started

WaCo is composed by two modules:

• sim: contains the functionalities to perform hydraulic simulations for extracting the demands and the contaminant diffusion in water networks. It mainly acts as a wrapper around the WNTR library.
• analyzer: contains the functionalities to extract the detection times and the volumes of contaminated water from the simulations.

Note: Refer to the API Reference for more details about the two modules.

To extract the detection times you need to simulate the diffusion of contaminant with the sim and then use the analyzer module.

import waco
import wntr

wn = wntr.network.WaterNetworkModel("examples/networks/Anytown.inp")
trace = waco.sim.contamination(wn=wn)
det_time = waco.analyzer.detection_time(trace)

The detection times are returned in a Dataframe containing the time when the contaminant exceeds a given threshold in a node considering a specific injection point.

node	inj_node	time
1	1	0
1	2	18900
...	...	...
9	21	18900
9	22	5400

To extract the volume of contaminated water prior detection you also need to compute the demand at each node using the sim module.

demand = waco.sim.water_demand(wn)
contam_vol = waco.analyzer.contaminated_volume(trace=trace,
                                               det_time=det_time,
                                               demand=demand)

The volumes are returned in a Dataframe containing the volume of contaminated water prior detection in each node considering a specific injection point.

node	inj_node	volume
1	1	0.031545
1	2	0.116008
...	...	...
9	21	0.118453
9	22	0.102254