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.