Just clarifying: For a two part package name that begins with an acronym
and ends in a word
the present guidance:
the acronym is to be uppercased and the second word is to be
capitalized, no separator.
so: CSSScripts, HTMLLinks
the desired guidance (from 24hrs of feedback):
the acronym is to be titlecased and the second word is to be
capitalized, no separator.
so: CssScripts, HtmlLinks
What is behind the present guidance?
On Saturday, October 8, 2016 at 8:42:05 AM UTC-4, Jeffrey Sarnoff wrote:
>
> I have created a new Organization on github: *JuliaPraxis.*
> Everyone who has added to this thread will get an invitation to join, and
> so contribute.
> I will set up the site and let you know how do include your wor(l)d views.
>
> Anyone else is welcome to post to this thread, and I will send an
> invitation.
>
>
>
> On Saturday, October 8, 2016 at 6:59:51 AM UTC-4, Chris Rackauckas wrote:
>>
>> Conventions would have to be arrived at before this is possible.
>>
>> On Saturday, October 8, 2016 at 3:39:55 AM UTC-7, Traktor Toni wrote:
>>>
>>> In my opinion the solutions to this are very clear, or would be:
>>>
>>> 1. make a mandatory linter for all julia code
>>> 2. julia IDEs should offer good intellisense
>>>
>>> Am Freitag, 7. Oktober 2016 17:35:46 UTC+2 schrieb Gabriel Gellner:
>>>>
>>>> Something that I have been noticing, as I convert more of my research
>>>> code over to Julia, is how the super easy to use package manager (which I
>>>> love), coupled with the talent base of the Julia community seems to have a
>>>> detrimental effect on the API consistency of the many “micro” packages
>>>> that
>>>> cover what I would consider the de-facto standard library.
>>>>
>>>> What I mean is that whereas a commercial package like
>>>> Matlab/Mathematica etc., being written under one large umbrella, will
>>>> largely (clearly not always) choose consistent names for similar API
>>>> keyword arguments, and have similar calling conventions for master
>>>> function
>>>> like tools (`optimize` versus `lbfgs`, etc), which I am starting to
>>>> realize
>>>> is one of the great selling points of these packages as an end user. I can
>>>> usually guess what a keyword will be in Mathematica, whereas even after a
>>>> year of using Julia almost exclusively I find I have to look at the
>>>> documentation (or the source code depending on the documentation ...) to
>>>> figure out the keyword names in many common packages.
>>>>
>>>> Similarly, in my experience with open source tools, due to the
>>>> complexity of the package management, we get large “batteries included”
>>>> distributions that cover a lot of the standard stuff for doing science,
>>>> like python’s numpy + scipy combination. Whereas in Julia the equivalent
>>>> of
>>>> scipy is split over many, separately developed packages (Base, Optim.jl,
>>>> NLopt.jl, Roots.jl, NLsolve.jl, ODE.jl/DifferentialEquations.jl). Many of
>>>> these packages are stupid awesome, but they can have dramatically
>>>> different
>>>> naming conventions and calling behavior, for essential equivalent
>>>> behavior.
>>>> Recently I noticed that tolerances, for example, are named as `atol/rtol`
>>>> versus `abstol/reltol` versus `abs_tol/rel_tol`, which means is extremely
>>>> easy to have a piece of scientific code that will need to use all three
>>>> conventions across different calls to seemingly similar libraries.
>>>>
>>>> Having brought this up I find that the community is largely sympathetic
>>>> and, in general, would support a common convention, the issue I have
>>>> slowly
>>>> realized is that it is rarely that straightforward. In the above example
>>>> the abstol/reltol versus abs_tol/rel_tol seems like an easy example of
>>>> what
>>>> can be tidied up, but the latter underscored name is consistent with
>>>> similar naming conventions from Optim.jl for other tolerances, so that
>>>> community is reluctant to change the convention. Similarly, I think there
>>>> would be little interest in changing abstol/reltol to the underscored
>>>> version in packages like Base, ODE.jl etc as this feels consistent with
>>>> each of these code bases. Hence I have started to think that the problem
>>>> is
>>>> the micro-packaging. It is much easier to look for consistency within a
>>>> package then across similar packages, and since Julia seems to distribute
>>>> so many of the essential tools in very narrow boundaries of functionality
>>>> I
>>>> am not sure that this kind of naming convention will ever be able to reach
>>>> something like a Scipy, or the even higher standard of commercial packages
>>>> like Matlab/Mathematica. (I am sure there are many more examples like
>>>> using
>>>> maxiter, versus iterations for describing stopping criteria in iterative
>>>> solvers ...)
>>>>
>>>> Even further I have noticed that even when packages try to find
>>>> consistency across packages, for example Optim.jl <-> Roots.jl <->
>>>> NLsolve.jl, when one package changes how they do things (Optim.jl moving
>>>> to
>>>> delegation on types for method choice) then again the consistency
>>>> fractures
>>>> quickly, where we now have a common divide of using either Typed dispatch
>>>> keywords versus :method symbol names across the previous packages (not to
>>>> mention the whole inplace versus not-inplace for function arguments …)
>>>>
>>>> Do people, with more experience in scientific packages ecosystems, feel
>>>> this is solvable? Or do micro distributions just lead to many, many
>>>> varying
>>>> degrees of API conventions that need to be learned by end users? Is this
>>>> common in communities that use C++ etc? I ask as I wonder how much this
>>>> kind of thing can be worried about when making small packages is so easy.
>>>>
>>>
