Metadata-Version: 2.1
Name: mistake
Version: 0.0.2
Summary: A tensor-oriented programming language with semantic validation
Home-page: https://github.com/kjosib/mistake
Author: Ian Kjos
Author-email: kjosib@gmail.com
License: MIT
Description: # Mistake: A tensor-oriented programming language with semantic validation
        
        Some 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.html
        
            APL 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 structure *holographically* throughout 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 ideas *about using* the 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
        is *no verified* part of the program that says how you *should* use data. If two
        things are numbers, you *can* add, subtract, multiply, or divide them --
        but *does 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 write *Python* where *Python*  makes
        the most sense, and write *Mistake* code where *Mistake* makes the most sense.
        
        ## Where do we stand?
        
        Very little. It's still mostly conceptual:
        
        * There is a `sandbox` folder with some embryonic bits that
          are slowly growing into a usable system.
        * There is a grammar file at `src/mistake/mistake_grammar.md`.
        * The `docs` folder contains design considerations.
        * Some modicum of functionality is in the `src/mistake` folder.
        
        The main entry point right now is at `sandbox/cradle.py`. It's currently
        focused on developing a simple validation layer. Don't be surprised when
        it vomits all over `STDERR`: that is literally its job.
        
        If you want to play with this, it's currently better to
        work from a copy of the github version.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Interpreters
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.7
Description-Content-Type: text/markdown
