Repository files navigation

Gdstk (GDSII Tool Kit) is a C++ library for creation and manipulation of GDSII and OASIS files.It is also available as a Python module meant to be a successor toGdspy.

Key features for the creation of complex CAD layouts are included:

• Boolean operations on polygons (AND, OR, NOT, XOR) based on clipping algorithm
• Polygon offset (inward and outward rescaling of polygons)
• Efficient point-in-polygon solutions for large array sets

Typical applications of Gdstk are in the fields of electronic chip design, planar lightwave circuit design, and mechanical engineering.

The complete documentation is availablehere.

The source files can be found in thedocs directory.

The C++ library is meant to be used by including it in your own source code.

If you prefer to install a static library, the includedCMakeLists.txt should be a good starting option (use-DCMAKE_INSTALL_PREFIX=path to control the installation path):

cmake -S. -B buildcmake --build build --target install

The library depends onzlib andqhull

The Python module can be installed via pip, Conda or compiled directly from source.It depends on:

• zlib
• qhull
• CMake
• Python
• Numpy
• Sphinx,Read the Docs Theme, andSphinx Inline Tabs (to build thedocumentation)

Simply run the following to install the package for the current user:

pip install --user gdstk

Or download and install the available wheels manually.

Installation from source requires thebuild module (plus CMake and Ninja, for faster compilation):

pip install --user build

With that, simply build the wheel package using:

python -m build -w

This will create adist directory containing the compiled.whl package that can be installed withpip.

Help support Gdstk development bydonating via PayPal orsponsoring me on GitHub.

Thebenchmarks directory contains a few tests to compare the performance gain of the Python interface versus Gdspy.They are only for reference; the real improvement is heavily dependent on the type of layout and features used.If maximal performance is important, the library should be used directly from C++, without the Python interface.

Timing results were obtained with Python 3.11 on an Intel Core i7-9750H @ 2.60 GHzThey represent the best average time to run each function out of 16 sets of 8 runs each.

Memory usage per object for 100000 objects: