Skip to content
/ CLU Public

Common Lightweight Utilities, or Command-Line Utilities (your pick)

License

Unknown, Unknown licenses found

Licenses found

Unknown
LICENSE.txt
Unknown
COPYING.md
Notifications You must be signed in to change notification settings

fish2000/CLU

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CLU – Common Lightweight Utilities (née Command-Line Utilities)

codecov

This is a packaged-up and sanely version-controlled version of the Python tools I wrote for my REPLs, divided up into a bunch of subordinate packages:

  • compilation: Abstractions for command-line macro definitions and JSON compilation databases.

  • constants: Definitions of useful constants, as well as polyfills to allow certain dependencies from the Python standard library to work even when some of their moving parts are missing (i.e. Python 2, various PyPy implementations, etc).

  • fs: Filesystem-related things. Submodules include:

    • fs.filesystem: classes representing filesystem primitives like Directory, TemporaryDirectory, TemporaryName; a version of tempfile.NamedTemporaryFile that works with or without the leading dot in the provided suffix (a peeve of mine); functions for common filesystem operations like which(…), back_ticks(…), rm_rf(…) (be careful with that one), and so forth.
    • fs.appdirectories: pretty much a wholesale duplication of the popular appdirs package.
    • fs.misc: a bunch of miscellany – noteworthy standouts include a memoized current_umask(…) and a corresponding masked_permissions(…)
    • fs.pypath: functions for safe manipulation of sys.pathappend_paths(…) and remove_paths(…), respectively
  • repl: Tools useful in Python REPL environments – currently ANSI printing functions, for the most part.

  • typespace: Contains a submodule typespace.namespace defining SimpleNamespace (á la Python 3’s types.SimpleNamespace) and a slightly more useful Namespace ancestor class; these are used to furnish a typespace.types namespace that contains everything in the standard-library types module, but with all the typenames shortened so they no longer gratuitously end in “Type”. Like e.g. types.ModuleType is to be found in typespace.types.Module, types.FunctionType is typespace.types.Function, et cetera, ad nauseum, so you can just do from clu.typespace import types to lose the redundant “Type” naming suffix – which I don’t know about you but that annoys me, I mean we all know that these things are types because they are in the fucking types module and don’t need those overly verbose extra four characters there at the end.

  • dicts: Functions for wrangling dicts (and actually Mapping and MutableMapping descendants in general) – tools for merging and whatnot.

  • exporting: Classes and enums for automatically exporting module members, naming and assigning docstrings to things that aren’t classes, functions, or modules (like e.g. lambda functions or Namespace instances), categorizing such things (using a kind of enum we call a Clade that formulates classification predicates from typelists), and APIs for all of these things. Currently a WIP – check out the first (working!) implementation in my dotfiles repo.

  • keyvalue: A module that offers a super-simple key/value store for use in REPLs, based on the zict package – so you can stash data in one REPL instance and get it back in another separate REPL process, even those of different implementations running on disparate Python versions.

  • math: the future home for math-related stuff. All there is right now is a clamp(…) function that works with numpy dtypes.

  • naming: functions for determining the names of things (even module constants and other random things that generally lack things like __name__ attributes) and for the importing and exporting of things by “qualified names” – for instance, you can use naming.qualified_import(…) to import a class CurveSet from its package instakit.processors.curves by doing qualified_import('instakit.processors.curves.CurveSet'), which that may be handier for you than composing and hard-coding an import statement.

  • predicates: tons and tons and tons of useful lambda functions, which I call “predicates” even though that may not be totally accurate in many cases. A lot of them take the form isXXX(thing) e.g. isiterable(thing) or isclass(thing), in each case returning a boolean value. There are also:

    • Convenience accessor functions: attr(thing, 'attributeone', 'attributetwo') will return thing.attributeone if it exists, thing.attributetwo if the first one does not exist but the second one does, and finally None if neither can be found. attr(…) and other similar accessors work with any amount of attribute names (as long as there is at least one). Shortcuts like pyattr(…) do the same thing, only they automatically expand their attributes to __dunder__ names.
    • AND MORE!!! There really are a ton of useful things in here and one day I will list them (but not today). Have fun with it!!
  • sanitizer: functions for cleaning up unicode. Right now there is just a list-based sanitize(…) function that tones down anything with high-value code points to the point where it can be safely ascii-ified.

  • typology: This is like predicates but with more predicates, most of which are based on typelists. The module is full of typelists and it uses them extensively in its predicates, via isinstance(…), issubclass(…) and a custom version of same called graceful_issubclass(…) which tries very hard to work with what you give it (instead of just crapping out with a False return value).

  • version: This is basically my take on the semver package. I originally developed it for internal use in some of my Python projects, but it stands on its own decently enough.

Yes. Like I said, there is more to come. Do enjoy!