gdstk 0.1.1

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 stream 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.

## Installation

The C++ library is meant to be used straight from source. The only requirement is that it must be linked against LAPACK. The included CMakeLists.txt file can be used as a guide.

### Dependencies for the Python wrapper

• LAPACK

• [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))

Installation from source should follow the usual method:

`sh python setup.py install `

Windows users have the option to use a pre-compiled binary from the [releases](https://github.com/heitzmann/gdstk/releases) page. It depends on openBLAS, which is currently not included in the package, so the recommendation is to [install it](https://anaconda.org/anaconda/openblas) via [conda](https://www.anaconda.com/).

## 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 3.8 on an Intel Core i7-3820 with 8 cores and 64 GB of RAM. They represent the best average time to run each function out of 32 sets of 8 runs each.

Benchmark | Gdspy 1.6 | Gdstk 0.1.1 | Gain |

:————— | :————–: | :————–: | :——: |

10k_rectangles | 222 ms | 6.22 ms | 35.6 |

1k_circles | 565 ms | 358 ms | 1.58 |

boolean-offset | 414 μs | 48.9 μs | 8.45 |

bounding_box | 625 μs | 8.57 μs | 72.9 |

curves | 3.01 ms | 65.3 μs | 46.1 |

flatten | 993 μs | 9.52 μs | 104 |

flexpath-param | 7.25 ms | 1.5 ms | 4.85 |

flexpath | 5.6 ms | 24.5 μs | 228 |

fracture | 1.7 ms | 851 μs | 2 |

inside | 38.4 μs | 8.36 μs | 4.6 |

read_gds | 7 ms | 107 μs | 65.5 |

read_rawcells | 623 μs | 74.2 μs | 8.39 |

robustpath | 376 μs | 13.3 μs | 28.3 |

Memory usage per object for 100.000 objects using Python 3.8:

Object | Gdspy 1.6 | Gdstk 0.1.1 | Reduction |

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

Rectangle | 608 B | 184 B | 70% |

Circle (r = 10) | 1.67 kB | 1.23 kB | 27% |

FlexPath segment | 1.42 kB | 392 B | 73% |

FlexPath arc | 2.23 kB | 1.44 kB | 35% |

RobustPath segment | 2.79 kB | 872 B | 69% |

RobustPath arc | 2.58 kB | 872 B | 67% |

Label | 397 B | 170 B | 57% |

Reference | 160 B | 131 B | 18% |

Reference (array) | 187 B | 138 B | 26% |

Cell | 431 B | 214 B | 50% |