gdstk 0.3.0

Python module for creation and manipulation of GDSII files.

# GDSTK README

[](https://www.boost.org/LICENSE_1_0.txt) [](https://github.com/heitzmann/gdstk/actions) [](http://heitzmann.github.io/gdstk) [](https://github.com/heitzmann/gdstk/releases)

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 to [Gdspy](https://github.com/heitzmann/gdspy).

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.

## Documentation

The complete documentation is available [here](http://heitzmann.github.io/gdstk).

The source files can be found in the _docs_ directory.

## Instalation

### C++ library only

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 included _CMakeLists.txt_ should be a good starting option (use -DCMAKE_INSTALL_PREFIX=path to control the installation path):

`sh cmake -S . -B build cmake --build build --target install `

The library depends on LAPACK and [zlib](https://zlib.net/).

### Python wrapper

The Python module can be installed via Conda (recommended) or compiled directly from source. It depends on:

• LAPACK

• [zlib](https://zlib.net/)

• [CMake](https://cmake.org/)

• [Python](https://www.python.org/)

• [Numpy](https://numpy.org/)

• [Sphinx](https://www.sphinx-doc.org/) and [rtd theme](https://sphinx-rtd-theme.readthedocs.io/) (to build the [documentation](http://heitzmann.github.io/gdstk))

#### Conda

Windows users are suggested to install via [Conda](https://www.anaconda.com/) using the available [conda-forge recipe](https://github.com/conda-forge/gdstk-feedstock). The recipe works on MacOS and Linux as well.

#### From source

The module must be linked aginst LAPACK and zlib. The included CMakeLists.txt file can be used as a guide.

Installation from source should follow the usual method (there is no need to compile the static library beforehand):

`sh python setup.py install `

## Support

Help support Gdstk development by [donating via PayPal](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=JD2EUE2WPPBQQ)

## Benchmarks

The _benchmarks_ 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 3.9 on an Intel Core i7-3930K with 6 cores and 16 GB of RAM at 3.2 GHz. They represent the best average time to run each function out of 16 sets of 8 runs each.

Benchmark | Gdspy 1.6.3 | Gdstk 0.3.0 | Gain |

:————— | :————–: | :————–: | :——: |

10k_rectangles | 264 ms | 7.53 ms | 35.1 |

1k_circles | 641 ms | 376 ms | 1.71 |

boolean-offset | 444 μs | 78.1 μs | 5.69 |

bounding_box | 752 μs | 14.8 μs | 50.8 |

curves | 3.49 ms | 65.1 μs | 53.6 |

flatten | 1.19 ms | 15.8 μs | 75.2 |

flexpath | 6.44 ms | 30.6 μs | 211 |

flexpath-param | 7.48 ms | 1.45 ms | 5.14 |

fracture | 1.81 ms | 1.07 ms | 1.69 |

inside | 42.7 μs | 13.9 μs | 3.07 |

read_gds | 7.71 ms | 143 μs | 54 |

read_rawcells | 742 μs | 70.9 μs | 10.5 |

robustpath | 456 μs | 19.3 μs | 23.7 |

Memory usage per object for 100.000 objects using Python 3.9:

Object | Gdspy 1.6.1 | Gdstk 0.2.0 | Reduction |

:——————- | :————–: | :————–: | :——-: |

Rectangle | 594 B | 233 B | 61% |

Circle (r = 10) | 1.67 kB | 1.27 kB | 24% |

FlexPath segment | 1.42 kB | 441 B | 70% |

FlexPath arc | 2.23 kB | 1.49 kB | 33% |

RobustPath segment | 2.78 kB | 922 B | 68% |

RobustPath arc | 2.58 kB | 919 B | 65% |

Label | 398 B | 218 B | 45% |

Reference | 159 B | 180 B | -13% |

Reference (array) | 191 B | 182 B | 5% |

Cell | 435 B | 211 B | 52% |