Skip to main content

Sasa 0.9.4 Released

I've just uploaded the final Sasa v0.9.4 release to Sourceforge and Nuget. The full API documentation for all assemblies in the Sasa framework is available here. The full changelog is available in the Sourceforge download, or directly in the repo here. Suffice it to say, changes since v0.9.3 include hundreds of bugfixes, and many, many new features.

A brief overview of what Sasa is, and what features it provides is covered on the wiki, and reproduced below.

Sasa Class Libraries for .NET

Sasa is a set of organized class libraries for the .NET framework 3.5 or higher. Here's an overview of the assemblies available and the features they provide:

AssemblyDescriptionDependencies
Sasa.dllTuples, sums, generic operators, LINQ extensions, string extensions, thread-safe and null-safe events, and more
Sasa.Arrow.dllArrow computations for .NETSasa.dll
Sasa.Binary.dllLow-level functions on bitdata, fast endian conversions, untagged unions, and more
Sasa.Collections.dllPurely functional lists, trees, stacksSasa.dll, Sasa.Binary.dll
Sasa.ConcurrencyConcurrency abstractions including faster thread-local data and simple software transactional memory in pure C#Sasa.dll
Sasa.Contracts.dllA simple API-complete reimplementation of Microsoft's code contracts (rewriter not yet provided)
Sasa.FP.dllMore obscure functional abstractions like binomial collections, lenses and function curryingSasa.dll, Sasa.Collections.dll
Sasa.IoC.dllA simple, efficient inversion of control container based on delegates
Sasa.Linq.dllExtensions on LINQ expressions, including a faster expression compiler, expression substitutions, and base classes for query providers and expression visitorsSasa.dll
Sasa.Mime.dllExtended media type directory and mappings between file extensions and media types
Sasa.Net.dllNetwork extensions, including a POP3 client, MIME message parsing, and a preliminary HTTP session state machineSasa.dll, Sasa.Collections.dll
Sasa.Numerics.dllAnalytical extensions for .NET, including statistical functions, minimal Steiner tree approximations and dense matrix math
Sasa.Parsing.dllTyped, extensible lexing and parsing librarySasa.dll
Sasa.Reactive.dllNamed and anonymous reactive values and propertiesSasa.dll
ilrewrite.exeSasa's IL rewriter performs type erasure on type constraints

Blog Series

My blog posts on Sasa cover some of the features in various assemblies:

I recently completed a blog series covering the core Sasa.dll assembly:

If anyone has any problems with the code or the documentation, please post here or in the Sasa forums. I'm happy to help!

Labels:

Comments

Post a Comment

Popular posts from this blog

async.h - asynchronous, stackless subroutines in C

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C! Features: It's 100% portable C. It requires very little state (2 bytes). It's not dependent on an OS. It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved. #include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground bre...
1 comment
Read more

Easy Automatic Differentiation in C#

I've recently been researching optimization and automatic differentiation (AD) , and decided to take a crack at distilling its essence in C#. Note that automatic differentiation (AD) is different than numerical differentiation . Math.NET already provides excellent support for numerical differentiation . C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries . Neither of these are suitable for learning how AD works. So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique. What is Automatic Differentiation? Simply put, automatic differentiation is a technique for calcu...
Post a Comment
Read more

Building a Query DSL in C#

I recently built a REST API prototype where one of the endpoints accepted a string representing a filter to apply to a set of results. For instance, for entities with named properties "Foo" and "Bar", a string like "(Foo = 'some string') or (Bar > 99)" would filter out the results where either Bar is less than or equal to 99, or Foo is not "some string". This would translate pretty straightforwardly into a SQL query, but as a masochist I was set on using Google Datastore as the backend, which unfortunately has a limited filtering API : It does not support disjunctions, ie. "OR" clauses. It does not support filtering using inequalities on more than one property. It does not support a not-equal operation. So in this post, I will describe the design which achieves the following goals: A backend-agnostic querying API supporting arbitrary clauses, conjunctions ("AND"), and disjunctions ("OR"). Implemen...
Post a Comment
Read more