ptsnet 0.0.11

Parallel Transient Simulation in Water Networks

Parallel Transient Simulation in Water Networks
Explore the docs »

View Demo · Report Bug · Request Feature

Getting Started

To get a local copy up and running follow these simple steps. PTSNET can be downloaded via pip

Prerequisites

We highly encourage using a conda environment for the installation

• Install conda

  # Linux
  https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html
  

  # Windows
  https://conda.io/projects/conda/en/latest/user-guide/install/windows.html
  

• Basic dependencies

  # Open the shell and type
  conda init
  conda create -n ptsnet python=3.6 # create conda environment
  conda activate ptsnet
  conda install numpy matplotlib pandas scipy
  conda install -c conda-forge tqdm
  conda install -c conda-forge wntr
  

• Dependencies for parallelization

  conda install openmpi
  conda install openmpi-mpicc
  conda install -c conda-forge mpi4py
  
  # Install parallel HDF5
  wget https://support.hdfgroup.org/ftp/HDF5/current/src/hdf5-1.10.5.tar
  tar xvf hdf5-1.10.5.tar
  cd hdf5-1.10.5
  ./configure --enable-shared --enable-parallel CC=mpicc MPICCc=mpiccmake
  make install
  
  # Install h5py
  CC="mpicc" HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install --no-binary=h5py h5py
  

• Install ptsnet

  pip install ptsnet
  

Installation

1. Clone the repo

   git clone https://github.com/gandresr/PTSNET.git
   

2. Install NPM packages

   npm install
   

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

License

Distributed under the Unlicense License. See LICENSE for more information.

Contact

Gerardo Riano - [e-mail]

Project Link: https://github.com/gandresr/PTSNET

Acknowledgements

This publication was developed under Cooperative Agreement No. 83595001 awarded by the U.S. Environmental Protection Agency to The University of Texas at Austin. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors, and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. URL: http://www.tacc.utexas.edu