Package: mistake
Mistake: A tensor-oriented programming language with semantic validationSome sizable amount of programming problems boil down to gnashing tensors
together in various ways. If you are running simple algorithms over vast
amounts of data, go look at numpy or pandas or tensorflow or...On the other hand, if you do a lot of interrelated numeric-processing projects
with fiddly requirements for the careful selection and massaging of tensor-like
data on the scale of a few dozen megabytes, then perhaps this package is for you.
(Maybe one day someone will contribute bindings for numpy so we can all grow fat,
dumb, and happy.)I began work on this language because I think something like it is missing from
the ecosystem. I'd like it to plug into Python because these days everything does,
and I'd like the notation to be sufficiently expressive.Why the funny name?Casting about madly for a project name, I noticed that Edsger Dijkstra called APL
on the carpet as follows:https://www.cs.utexas.edu/users/EWD/transcriptions/EWD04xx/EWD498.htmlAPL is a mistake, carried through to perfection.
It is the language of the future for the programming
techniques of the past: it creates a new generation
of coding bums.Well, shoot. My whole idea is inspired by APL's array-oriented nature, although
I do take measures to keep things intellectually manageable in ways APL does not.Alright then. What's the big idea?Small to medium-sized data sets, generally numerical, with a great deal of
structure: conventionally the programs to process such data wind up reflecting
that structureholographicallythroughout the code base.What's more: requirements (or, our understanding of them) change with time.
When your assumptions fall, the ripple effects in a conventionally-written
program are operational in nature: to make the correct changes, you need to
understand how things work at a fairly detailed level.A language is more than syntax: it's a coherent set of semantics. These can be
so arranged that the structure and constraints on data are expressed once (together)
and the essential ideasabout usingthe data are not cluttered up with
holographic echoes of the structure.Let me amplify that point: In ordinary 3GL programming (and Python is a 3GL) there
isno verifiedpart of the program that says how youshoulduse data. If two
things are numbers, youcanadd, subtract, multiply, or divide them --
butdoes that make any sense?If we can compute the semantic nature of
expressions in advance of the main bulk of runtime, then we can detect and
prevent semantic errors before they endanger the validity of our computations.We get some additional benefits from treating the language as a semantic description
of relationships between data and computations. For example:Iteration/looping is banished from the syntax. Statements are inherently
parallel (so you can implement that parallelism any way you like).The system can (and should) be "lazy" by only performing that fraction of
specified computation which is strictly necessary to answer a user's query.By tying the DSL back to Python in key places, it's easy (I hope) to bolt this
into an existing pipeline. Ideally you'll writePythonwherePythonmakes
the most sense, and writeMistakecode whereMistakemakes the most sense.Where do we stand?Some bits probably work, but it's still mostly conceptual.There is asandboxfolder with some embryonic bits that
are slowly growing into a usable system.Some of those bits are automated tests.There's an entry point atsandbox/cradle.py. It was
focused on the basics of semantic validation, but remains
quite capable of scribbling all overSTDERR.The grammar file is atsrc/mistake/mistake_grammar.md.Thedocsfolder contains design considerations.At the moment, the most important of these is KISS: "Keep it simple, Sally!"Some modicum of functionality is in thesrc/mistakefolder.If you want to play with this, it's currently better to
work from a copy of the github version.