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python3-parsley-1.3-22.fc36.noarch
A parser generator library based on OMeta, and other useful parsing tools. Parsley is a parsing library for people who find parsers scary or annoying. I wrote it because I wanted to parse a programming language, and tools like PLY or ANTLR or Bison were very hard to understand and integrate into my Python code. Most parser generators are based on LL or LR parsing algorithms that compile to big state machine tables. It was like I had to wake up a different section of my brain to understand or work on grammar rules. Parsley, like pyparsing and ZestyParser, uses the PEG algorithm, so each expression in the grammar rules works like a Python expression. In particular, alternatives are evaluated in order, unlike table-driven parsers such as yacc, bison or PLY. Parsley is an implementation of OMeta, an object-oriented pattern-matching language developed by Alessandro Warth at thesis, which provides a detailed description of OMeta: http://www.vpri.org/pdf/tr2008003_experimenting.pdf
Located in LBN / … / Core Linux / BastionLinux 36
python3-pygtrie-2.5.0-1.lbn36.noarch
pygtrie is a Python library implementing a trie data structure.Trie data structure < also known
Located in LBN / … / Core Linux / BastionLinux 36
python3-solv-0.7.31-1.lbn36.x86_64
Python bindings for the libsolv library. Python 3 version.
Located in LBN / … / Core Linux / BastionLinux 36
python3-strict-rfc3339-0.7-5.fc36.noarch
Goals: - Convert UNIX timestamps to and from RFC3339. - Either produce RFC3339 strings with a UTC offset (Z) or with the offset that the C time module reports is the local timezone offset. - Simple with minimal dependencies/libraries. - Avoid timezones as much as possible. - Be very strict and follow RFC3339.
Located in LBN / … / Core Linux / BastionLinux 36
python3-subprocess-tee-0.4.1-5.lbn36.noarch
This package provides an drop-in alternative to subprocess.run that captures the output while still printing it in real time, just the way tee does.
Located in LBN / … / Core Linux / BastionLinux 36
python3-url-normalize-2.2.1-1.lbn36.noarch
url-normalize [![Build Status]( [![Coverage Status]( Normalization function:* Take care of IDN domains. * Always provide the URI scheme in lowercase characters. * Always provide the host, if any, in lowercase characters. * Only perform percent-encoding where it is essential. * Always use uppercase A-through-F characters when percent-encoding. * Prevent dot-segments appearing in non-relative URI paths.
Located in LBN / … / Core Linux / BastionLinux 36
python3-wcmatch-8.4.1-1.lbn36.noarch
Wildcard Match provides an enhanced fnmatch, glob, and pathlib library in order to provide file matching and globbing that more closely follows the features found in Bash. In some ways these libraries are similar to Python's builtin libraries as they provide a similar interface to match, filter, and glob the file system. But they also include a number of features found in Bash's globbing such as backslash escaping, brace expansion, extended glob pattern groups, etc. They also add a number of new useful functions as well, such as globmatch which functions like fnmatch, but for paths.
Located in LBN / … / Core Linux / BastionLinux 36
python3-webcolors-24.11.1-2.lbn36.noarch
webcolors is a module for working with and converting between the various HTML/CSS color formats.
Located in LBN / … / Core Linux / BastionLinux 36
python3-whitenoise-6.4.0-8.lbn36.noarch
Radically simplified static file serving for python web apps. with a couple of lines of config whitenoise allows your web app to serve its own static files, making it a self-contained unit that can be deployed anywhere without relying on nginx, amazon s3 or any other external service. (especially useful on heroku, openshift and other paas providers.)
Located in LBN / … / Core Linux / BastionLinux 36
jvector-3.0.6-1.lbn36.noarch
Exact nearest neighbor search (k-nearest-neighbor or KNN) is prohibitively expensive at higher dimensions, because approaches to segment the search space that work in 2D or 3D like quadtree or k-d tree devolve to linear scans at higher dimensions. This is one aspect of what is called “the curse of dimensionality.” With larger datasets, it is almost always more useful to get an approximate answer in logarithmic time, than the exact answer in linear time. This is abbreviated as ANN (approximate nearest neighbor) search. There are two broad categories of ANN index: Partition-based indexes, like LSH or IVF or SCANN Graph indexes, like HNSW or DiskANN Graph-based indexes tend to be simpler to implement and faster, but more importantly they can be constructed and updated incrementally. This makes them a much better fit for a general-purpose index than partitioning approaches that only work on static datasets that are completely specified up front. That is why all the major commercial vector indexes use graph approaches. JVector is a graph index in the DiskANN family tree.
Located in LBN / … / Data Science / BastionLinux 36

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