gdstk 0.2.0

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 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.

## Instalation

### 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 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 `

## 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 16 sets of 8 runs each.

Benchmark | Gdspy 1.6.1 | Gdstk 0.2.0 | Gain |

:————— | :————–: | :————–: | :——: |

10k_rectangles | 222 ms | 6.67 ms | 33.3 |

1k_circles | 572 ms | 363 ms | 1.57 |

boolean-offset | 419 μs | 60.7 μs | 6.91 |

bounding_box | 634 μs | 11.2 μs | 56.8 |

curves | 3.02 ms | 65.4 μs | 46.1 |

flatten | 1.01 ms | 12.6 μs | 79.8 |

flexpath | 5.59 ms | 25.2 μs | 222 |

flexpath-param | 7.34 ms | 1.45 ms | 5.08 |

fracture | 1.71 ms | 911 μs | 1.88 |

inside | 41.2 μs | 11.6 μs | 3.55 |

read_gds | 7.03 ms | 119 μs | 59 |

read_rawcells | 609 μs | 58.5 μs | 10.4 |

robustpath | 386 μs | 15.9 μs | 24.3 |

Memory usage per object for 100.000 objects using Python 3.8:

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% |