The pypmj (python project manager for JCMsuite; pronounce "py pi ɛm dʒe") package extends the python interface shipped with the excellent commercial finite element Maxwell solver JCMsuite, distributed by the JCMwave GmbH.

It simplifies the setup, execution and data storage of JCMsuite simulations. Some of the main advantages are:

• The JCMsuite installation directory, the preferred storage directories and computation resources can be set up using a configuration file.
• Projects can be collected in one place as a project library and used from there.
• Parameter scans can be efficiently executed and evaluated using the SimulationSet class. Different combinations of input parameter lists make nested loops unnecessary.
• User defined processing of post process results.
• Computational costs and user results are efficiently stored in an HDF5 data base.
• Automatic detection of known results in the database.

pypmj is tested under Python 2.7. It is tried to assure full compatibility to Python 3.x. Feedback is highly appreciated.

The following packages are required:

• NumPy (probably >= 1.6.1, but untested)
• SciPy (probably >= 0.9, but untested)
• pandas >= 0.17.0
• PyTables (i.e. tables) >= 3.2

Many example notebooks need matplotlib and largely profit from seaborn.

Yet no installation via pip or package managers is possible. Anyhow, pypmj is a Python-only solution, so you simply need to download the source code and append the pypmj subfolder to your Python path.

Use a configuration file or import pypmj and jcmwave by providing the path to your JCMsuite installation directory manually:

import numpy as np
import pypmj as jpy
jpy.import_jcmwave('/path/to/your/JCMsuite/installation/directory')

Create a JCMPProject by referring to the folder were your JCM files are:

project = jpy.JCMProject('../projects/scattering/mie/mie2D')

Set the keys that are necessary to translate the JCM template files.

mie_keys = {'constants' :{},
            'parameters': {}, 
            'geometry': {'radius':np.linspace(0.3, 0.5, 40)}}

Initialize a SimulationSet, schedule and run it:

simuset = jpy.SimulationSet(project, mie_keys)
simuset.make_simulation_schedule()
simuset.run()

See the examples section for more info.

The pypmj-documentation is available online at Read the Docs. You can also download a PDF version.

The examples directory contains a collection of ipython/jupyter notebooks. If you have ipython/jupyter, you can start by making a copy of the examples directory (in the same parent folder!), e.g. to a folder my_examples. The Getting started - for the impatient-notebook gives you a fast access to pypmj. Other notebooks may require a configuration file, which is very easily and fastly created using the Setting up a configuration file-notebook. The Using pypmj - the mie2D-project gives you a rigorous introduction to the basic usage of pypmj. Check out the other notebooks as well.

The German Federal Ministry of Education and Research is acknowledged for funding research activities within the program NanoMatFutur (No. 03X5520) which made this software project possible.