EPyT: https://github.com/KIOS-Research/EPyT

Streamlit: https://streamlit.io/

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#
Category EPANET

# EPANET Viewer using EPyT and #streamlit

# Battle of the Leakage Detection and Isolation Methods (BattLeDIM 2020)

# Merge networks using the EPANET-MATLAB-Toolkit

# Create standalone app “Network Viewer” using EPANET-Matlab-Toolkit

# Load basemaps from #QGIS to #EPANET

# Rotate water network via EPANET-MATLAB-Toolkit

# Building the EPANET library and executable files #EPANET

# SWMM-EPANET User Interface

# Demands in distribution networks #QGIS #EPANET

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**Calculates demand on nodes:**

### Where to find the algorithm:

EPyT: https://github.com/KIOS-Research/EPyT

Streamlit: https://streamlit.io/

The Battle of the Leakage Detection and Isolation Methods (**BattLeDIM**), which will take place online on September 3rd, 2020, 08:30-10:00 CET (https://time.is/0830_03_September_2020_in_Brussels).

Participants and audience can register to enter the online Zoom meeting: https://ucy.zoom.us/meeting/register/tJUkceisrTooGdYW31lWq3x0DKQBdwoLioxD.

Also, the event will be broadcasted online on YouTube, accessible through the competition website: https://battledim.ucy.ac.cy

**BattLeDIM** aims at objectively comparing the performance of methods for the detection and localization of leakage events, relying on SCADA measurements of flow and pressure sensors installed within a new benchmark water distribution network.

This is a joint collaboration of academics from @KIOSCoE @UCYOfficial, TU Delft @tudelft, Technion @TechnionLive joint, and Tsinghua University @Tsinghua_Uni.

This event has been partially supported by the SmartWater2020 project @SWater2020 @Interreg_GRCY, the H2020 KIOS CoE @KIOSCoE Teaming project @EU_H2020 and Deutsche Forschungsgemeinschaft (DFG).

Collaborations:

KIOS Center of Excellence, University of Cyprus, Cyprus

Technical University Delft, the Netherlands

Technion – Israel Institute of Technology, Israel

Technical University Delft, the Netherlands

Tsinghua University, China

Source: @eldemet https://twitter.com/SWater2020/status/1300003714237952000

**Using plugin “Lat Lon Tools” to copy canvas bounding box!**

%% Example: Rotate EPANET Inp File clc; close all; clear all; % Load network and paths start_toolkit; d = epanet('Net1.inp'); %Plot nework initial h = d.plot; % Rotate degrees theta theta = pi/2; %90 degrees for theta = 0:pi/20:pi*2 % define the x- and y-data for the original line we would like to rotate x = d.getNodeCoordinates{1}'; y = d.getNodeCoordinates{2}'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % **** % create a matrix of these points, which will be useful in future calculations v = [x;y]; % choose a point which will be the center of rotation x_center = x(1); y_center = y(1); % create a matrix which will be used later in calculations center = repmat([x_center; y_center], 1, length(x)); % define a 60 degree counter-clockwise rotation matrix % theta = pi/3; % pi/3 radians = 60 degrees R = [cos(theta) -sin(theta); sin(theta) cos(theta)]; % do the rotation... s = v - center; % shift points in the plane so that the center of rotation is at the origin so = R*s; % apply the rotation about the origin vo = so + center; % shift again so the origin goes back to the desired center of rotation % this can be done in one line as: % vo = R*(v - center) + center % pick out the vectors of rotated x- and y-data x_new = vo(1,:); y_new = vo(2,:); % **** %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i=1:d.NodeCount d.setNodeCoordinates(i, [x_new(i) y_new(i)]); end % Plot rotated network cla; d.plot('axes', h) pause(.05) end % d.saveInputFile('rotated.inp'); %% Source: % https://www.mathworks.com/matlabcentral/answers/93554-how-can-i-rotate-a-set-of-points-in-a-plane-by-a-certain-angle-about-an-arbitrary-point

Load basemaps in EPANETUI (Google satellite & Openstreetmap).

Estimate the demand at the nodes (hubs), based on household consumption. The calculation is performed by adding the demand in the households and designating the total demand in the nodes of the distribution network. This processing plugin is based on component “**Distance to nearest hub**“.**Operation: **Given a layer of origin (households) and another layer representing destination points (nodes), the algorithm calculates the distance between each point of origin (households) and the nearest point of detention (Nodes) households, and totaling the demand value that will be assigned to the nearest node (hub).

In addition, the algorithm displays a “line” layer, created by the “Distance to nearest hub” component, which identifies households (origin) and Nodes (destination), allowing easy verification of geoprocessing connectivity.

QGIS 2.8 or higher processing provider plugin that calculates demand on distribution network nodes.null

Once installed and active, this plugin adds the new provider (Algorithms to designate demands on nodes in the distribution network) in the **Processing Toolbox**. You find the algorithm in the Toolbox under Algorithms to designate demands on the nodes in the distribution network -> Algorithms -> Calculate the Demand Flow in the Nodes.