Open Source Python Command Line Tools

ADOMA stands for: Alternative Display Of Multiple Alignment. ADOMA can create four different displays of a multiple sequence alignment: a ClustalW alignment in HTML format, a simplified ClustalW alignment in HTML and/or txt format and a colored ClustalW alignment in HTML format. For examples of these outputfiles check the screenshots. ADOMA uses ClustalW to create the multiple alignment from DNA or protein sequences and displays them slightly different than the normal output of ClustalW. ADOMA is a commandline program that can easily be used in pipelines. For more information check the README.md in the Files section. How to cite ADOMA: Zaal, D. and Nota, B. (2016), ADOMA: A Command Line Tool to Modify ClustalW Multiple Alignment Output. Mol. Inf., 35: 42–44. doi: 10.1002/minf.201500083 http://onlinelibrary.wiley.com/doi/10.1002/minf.201500083/abstract

H9A is an installable Python package that counts how many times a given digit appears in a range of numbers (by default: the digit 9 between 1 and 100). It provides both a h9a command-line tool and an importable library, and it prints each step of the calculation — the per-place counts and the combined total — as styled, colorized output. It is built with rich for console formatting, pyfiglet for an ASCII-art banner, and Pillow for optional terminal-style screenshots.

This is a Python interface for reading and writing Magellan GPS unit map files. There is also a command line tool for creating GPS maps from OpenStreetMap data.

This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people.

Alpha test of an XML based HDF reader

THIS PROJECT MIGRATED TO https://gitlab.com/mwetoolkit/mwetoolkit3/ The Multiword Expressions toolkit aids in the automatic identification and extraction of multiword units in running text. These include idioms (kick the bucket), noun compounds (cable car), phrasal verbs (take off, give up), etc. Even though it focuses on multiword expresisons, the framework is quite complete and can also be useful in any corpus-based study in computational linguistics. The mwetoolkit can be applied to virtually any text collection, language, and MWE type. It is a command-line tool written mostly in Python. Its development started in 2010 as a PhD thesis but the project keeps active (see the SVN logs). Up-to-date documentation and details about the tool can be found on the mwetoolkit website: http://mwetoolkit.sourceforge.net/

Command-line tool to calculate error propagation. Generates the error formulas and applies them to the data.

xLaDe is a simple Python-based CLI tool built for executing and preserving Lean 4 projects. It is an ecosystem-level tool, which records the toolchain and other metadata of projects and allows the reconstruction and rebuilding of that exact environment later. Lean 4 undergoes rapid development, which can introduce backward-compatibility issues. This problem becomes more difficult as the versions accumulate over time. xLaDe is built to mitigate the practical issues of backward-compatibility problems and improve ecosystem-level tooling for the Lean 4 theorem prover. Instead of directly solving backward-compatibility issues by storing every version of Lean 4 and other tools or providing cross-version compatibility, xLaDe tries to store sufficient environment metadata such as toolchains, dependencies, and other context, and then recreates the environment for running Lean 4 projects upon request, also termed as experiments in xLaDe.