Python Developer Information#

The meep Python package consists of a low-level interface and a high-level interface. The low-level interface is the direct result of running SWIG on the C++ headers.

Next, we compile meep-python.cxx, rename to and put them in a folder called meep. Putting all the code in allows us to access the symbols directly from the meep namespace rather than going through an additional module like meep.meep.vec. Now we have a complete Python package. contains "proxy" classes for all public meep objects. They hold a this pointer that dispatches to the appropriate C++ functions in the extension module. The interface this package exposes is basically the same as the C++ interface. That is, a simulation written in this low-level Python interface would not look much different from the same simulation written in C++. By implementing a high-level interface on top of the basic SWIG wrappers, we can abstract away many of the low details of setting up a simulation, take advantage of Python language features like keyword arguments, and gain productivity from libraries like NumPy.

Package Organization#

After adding the high-level interface files, the meep package looks like this.


The Python MPB interface is also included in the meep package. It's constructed in the same manner as the meep package. The low-level interface is in meep/mpb/ and meep/mpb/, and the high-level interface is in Here is a view of the complete package.

├── mpb
│   ├──
│   ├──
│   ├──
│   └──

Description of Files#


SWIG interface file for the meep Python module. Includes typemaps, helper functions, and module initialization code. The typemaps mostly call other helper functions defined either in meep.i, or in typemap_utils.cpp (if the function is used in mpb.i too). There are also various py_*_wrap functions that handle calling user defined Python functions from C++. Everything in the %pythoncode block at the end of the file is run once when the meep module is first imported. See the SWIG documenation for more details.


SWIG interface file for vec.hpp. Included into meep.i. SWIG warnings are disabled (if found benign) in this file.


Typemaps for numpy arrays (taken from the NumPy Github repository). See the documentation for instructions on using these typemaps.


Utility functions for writing SWIG typemaps. Since this file is included into both meep.i and mpb.i, only code that is useful to both interface files should be put here (otherwise the compiler complains about unused functions). Code used only by one interface should be put in the respective .i file. The majority of the code in this file is for converting the Python geometric objects defined in to C objects. The get_attr_* functions are helpers for getting C versions of attributes on Python objects. The convention in the file is for functions to return 1 on success and 0 on failure so that the top level typemaps in meep.i can be written as:

%typemap(in) type {
    if(!py_type_to_type($input, &$1)) {

Keeping the code within %typemap blocks small is valuable because it gets copied everywhere the typemap is used, which can lead to code bloat.

Pure Python implementations of the geometric objects defined in libctl. The user-defined list of objects (Simulation.geometry) gets converted to C objects when meep.set_materials_from_geometry is called in Simulation._init_structure. The function responsible for this conversion is typemap_utils.cpp:py_list_to_gobj_list. This file also contains classes that represent materials (Medium) and susceptibilities, and cartesian/reciprocal/lattice conversion functions. Note that when adding a class or function to this file, it must also be imported in the %pythoncode block at the end of python/meep.i if you want it to be directly accessible from the meep namespace (i.e., to get meep.MyClass instead of meep.geom.MyClass).

Holds the Simulation class, which is the primary abstraction of the high-level interface. Minimally, a simulation script amounts to passing the desired keyword arguments to the Simulation constructor and calling the run method on the resulting instance. The various step functions are also included in this file. When adding extra functions or class to this file, an import statement should also be added to the %pythoncode block at the bottom of python/meep.i.

Holds classes representing sources, including GaussianSource, ContinuousSource, CustomSource, and EigenModeSource. When adding extra functions or class to this file, an import statement should also be added to the %pythoncode block at the bottom of python/meep.i.


SWIG interface file for the meep.mpb Python module.

Classes and functions related to the high-level Python interface to MPB. Additional classes or functions in this file should be accompanied by an import statement in the %pythoncode block at the bottom of mpb.i.

Definition of MPBData, a Python class useful for MPB data analysis (documented here). This is is a Python port of the functionality available in the mpb-data command line program originally written in C.


By default, the SWIG Python bindings are built with threads disabled (GIL is held for all SWIG wrapped python calls by default). You can optionally build the Python bindings with threads enabled (releasing the GIL for all SWIG wrapped Python calls) by passing the --enable-swig-python-threads option to the configure script.

Since the bindings could be built with threads enabled, one needs to be careful to protect (acquire the GIL) code that calls back into Python or custom python wrapper code that uses PyAPI. Look for SWIG_PYTHON_THREAD_SCOPED_BLOCK in the SWIG interface files or the custom wrapper code for how this is done.


The tests for the Python interface are located in python/tests. To run the whole test suite, run make check in the python build tree. During development it is more convenient to run individual tests. This can be accomplished by running python <path_to_test>/ MyTestCase.test_method. See the Python unittest framework documentation for more info.

The Makefile will set up a copy of the meep package in the build directory. Pointing PYTHONPATH at this package allows you to modify the files in meep/python, run make, and test the newly assembled package without installing it. If you're using an out-of-tree build (recommended), the development process will look something like this:

cd meep/build/python
export PYTHONPATH=`pwd`

# After making changes, rebuild the Python package
# Run test
python ../../python/tests/ MyNewTest.test_my_feature

Creating a Release Tarball#

Since the pymeep package version is based off of the Git version tag, it is important to tag the release in Github before running make dist to create the release tarball. You may also have to run git fetch --tags.

Conda Packages#

Binary conda packages for official releases of libctl, MPB, and pymeep are hosted on conda-forge. Whenever a new release tarball is created and posted to GitHub, a conda-forge bot will automatically detect that an update is needed and open a pull request in the corresponding repository. The developer need only review and merge the pull request and a conda package of the newest release will automatically be posted to the conda-forge channel on within an hour or so.

The conda-forge "feedstocks" (or package repositories) all have a similar structure. The recipe for building the package itself is in a folder called recipe, and everything else in the repo is support code and configuration for the conda-forge infrastructure. When the bot creates an automated pull request, the only files the developer needs to review are those in the recipe directory. The rest of the changes are conda-forge automatically keeping its configuration up to date. The recipe consists mainly of the meta.yaml and files. A tool called conda-build uses these files to create conda packages. contains the shell commands to build the project (docs) and meta.yaml defines the package version, specifies how to obtain the source code, and lists build and run dependencies (docs). Push access to the feedstocks is granted by adding a GitHub username to the recipe-maintainers tag in meta.yaml.