This is a staging site. Uploads will not persist. Testing only.

python3-recurring-ical-events

Recurring ICal events for Python (Python 3)

ICal has some complexity to it: Events can be repeated, removed from the feed and edited later on. This tool takes care of these circumstances.

libmspack0t64

library for Microsoft compression formats (shared library)

The purpose of libmspack is to provide both compression and decompression of some loosely related file formats used by Microsoft. The intention is to support all of the following formats: COMPRESS.EXE [SZDD], Microsoft Help (.HLP), COMPRESS.EXE [KWAJ], Microsoft Cabinet (.CAB), HTML Help (.CHM), Microsoft eBook (.LIT), Windows Imaging Format (.WIM), Exchange Offline Address Book (.LZX).

libefisec1

Library to manage UEFI variables

Library to allow for the simple manipulation of UEFI variables related to security.

libaunit21

AUnit, a unit testing framework for Ada: shared library

AUnit is a set of Ada packages based on the xUnit family of unit test frameworks. It's intended as a developer's tool to facilitate confident writing and evolution of Ada software. It is purposely lightweight, as one of its main goals is to make it easy to develop and run unit tests, rather than to generate artifacts for process management. The framework supports easy composition of sets of unit tests to provide flexibility in determining what tests to run for a given purpose.

libgnatcoll-python3-1

Ada binding to the Python3 language (runtime)

The GNAT Component Collection deals with: module tracing, efficient file IO, static string searching (Boyer-Moore), e-mails and mailboxes, Ravenscar tasking profiles, storage pools, JSON, logging, shell scripting. Components relying on external dependencies are distributed in separate packages.

libmems1t64

library to support DNA string matching and comparative genomics

libMems is a freely available software development library to support DNA string matching and comparative genomics. Among other things, libMems implements an algorithm to perform approximate multi-MUM and multi-MEM identification. The algorithm uses spaced seed patterns in conjunction with a seed-and-extend style hashing method to identify matches. The method is efficient, requiring a maximum of only 16 bytes per base of the largest input sequence, and this data can be stored externally (i.e. on disk) to further reduce memory requirements.