Skip to main content

An open source EM FEM simulator in Python

Project description

Introduction

Hello everybody. Thanks for showing interest in this repository.

Feel free to download your version of EMerge and start playing around with it! If you have suggestions/changes/questions either use the Github issue system or join the Discord using the following link:

Discord Invitation

How to install

You can now install the basic version of emerge from PyPi!

pip install emerge

On MacOS and Linux you can install it with the very fast UMFPACK through scikit-umfpack

brew install cmake swig suite-sparse #MacOS
sudo apt-get install libsuitesparse-dev #Linux

Then on MacOS do:

export PKG_CONFIG_PATH="/opt/homebrew/lib/pkgconfig:$PKG_CONFIG_PATH"
export CFLAGS="-I/opt/homebrew/include"
export LDFLAGS="-L/opt/homebrew/lib"

Finally:

pip install emerge[umfpack]

Experimental

If you have a new NVidia card you can try the first test implementation of the cuDSS solver. The dependencies can be installed through:

pip install emerge[cudss]

The scikit-umfpack solver can be installed on Windows as well from binaries with conda. This is a bit more complicated and is described in the installation guide which can be downloaded from the official website:

https://www.emerge-software.com/resources

Compatibility

As far as I know, the library should work on all systems. PARDISO is not supported on ARM but the current SuperLU and UMFPACK solvers work on ARM as well. Both SuperLU and UMFPACK can run on multi-processing implementations as long as you do entry-point protection:

import emerge as em

def main():
    # setup simulation

    model.mw.run_sweep(True, ..., multi_processing=True)

if __name__ == "__main__":
    main()

Otherwise, the parallel solver will default to SuperLU which can be slower on larger problems with a very densely connected/compact matrix.

Required libraries

To run this FEM library you need the following libraries

  • numpy
  • scipy
  • gmsh
  • loguru
  • numba
  • matplotlib (for the matplotlib base display)
  • pyvista (for the PyVista base display)
  • cloudpickle
  • mkl (x86 devices only)

Optional:

  • scikit-umfpack
  • cudss
  • ezdxf

Resources / Manual

You can find the latest versions of the manual on: https://www.emerge-software.com/resources/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

emerge-1.0.6.tar.gz (344.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

emerge-1.0.6-py3-none-any.whl (288.2 kB view details)

Uploaded Python 3

File details

Details for the file emerge-1.0.6.tar.gz.

File metadata

  • Download URL: emerge-1.0.6.tar.gz
  • Upload date:
  • Size: 344.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for emerge-1.0.6.tar.gz
Algorithm Hash digest
SHA256 8063e8072674f6a44456617e5123240c5f0ba8ccfa330f239916db6e5c75418c
MD5 0a3a90258295622707dc12858d5f7523
BLAKE2b-256 aeb034f973567761dad67fa854553f62401ed1ed7c59f66a9ec9d8fcc9be6b04

See more details on using hashes here.

File details

Details for the file emerge-1.0.6-py3-none-any.whl.

File metadata

  • Download URL: emerge-1.0.6-py3-none-any.whl
  • Upload date:
  • Size: 288.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for emerge-1.0.6-py3-none-any.whl
Algorithm Hash digest
SHA256 de8fa4861ae1eaa9f7dd8dbf02933319f8940605234f5dcf43ba20ef4446642c
MD5 d61151e8564f8c416aed6e094f16ced4
BLAKE2b-256 f6e6e3f6e09c69e9351f856f5abb43480f886b4f299b379dfcdaac25037cd7f6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page