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-1.1.0.tar.gz (361.2 kB view details)

Uploaded Source

Built Distribution

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

emerge-1.1.0-py3-none-any.whl (304.0 kB view details)

Uploaded Python 3

File details

Details for the file emerge-1.1.0.tar.gz.

File metadata

  • Download URL: emerge-1.1.0.tar.gz
  • Upload date:
  • Size: 361.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for emerge-1.1.0.tar.gz
Algorithm Hash digest
SHA256 7957c34b39221979cb5d082cb220cc3c280e29b16eb6c09bb6aae74f6fc98acc
MD5 b6081d459daa228efeadecea001a2da6
BLAKE2b-256 2fea2840da28539ec656de6e8bb0e71b5d29cce9fbabfd19d96139e4573d7278

See more details on using hashes here.

File details

Details for the file emerge-1.1.0-py3-none-any.whl.

File metadata

  • Download URL: emerge-1.1.0-py3-none-any.whl
  • Upload date:
  • Size: 304.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.5

File hashes

Hashes for emerge-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ae5ebb05cf6da782e5d3c05e06ca1a355d2d5f4001a0a60da48a0487536069cf
MD5 b488cab69607b2ddd25ed1db726db18f
BLAKE2b-256 2acae161327e5dd6ffd7496e9022cf16efc520c8c4544280877f55c0bdf9a68d

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