On Tuesday, August 27, 2019, 11:12:51 AM PDT, Chris Angelico
<ros...@gmail.com> wrote:
>On Wed, Aug 28, 2019 at 3:10 AM Andrew Barnert via Python-ideas
><python-ideas@python.org> wrote:
>> Before I get into this, let me ask you a question. What does the j suffix
>> give us? You can write complex numbers without it just fine:
>>
>> c = complex
>> c(1, 2)
>>
>> And you can even write a j function trivially:
>>
>> def j(x): return complex(0, x)
>> 1 + j(2)
>>
>> But would anyone ever write that when they can write it like this:
>>
>> 1 + 2j
>>
>> I don’t think so. What does the j suffix give us? The two extra keystrokes
>> are trivial. The visual noise of the parens is a bigger deal. The real issue
>> is that this matches the way we conceptually think of complex numbers, and
>> the way we write them in other contexts. (Well, the way electrical engineers
>> write them; most of the rest of us use i rather than j… but still, having to
>> use j instead of i is less of an impediment to reading 1+2j than having to
>> use function syntax like 1+i(2).
>>
>> And the exact same thing is true in 3D or CUDA code that uses a lot of
>> float32 values. Or code that uses a lot of Decimal values. In those cases, I
>> actually have to go through a string for implementation reasons (because
>> otherwise Python would force me to go through a float64 and distort the
>> values), but conceptually; there are no strings involved when I write this:
>>
>> array([f('0.2'), f('0.3'), f('0.1')])
>>
>> … and it would be a lot more readable if I could write it the same way I do
>> in other programming languages:
>>
>> array([0.2f, 0.3f, 0.1f])
>>
>> Again, it’s not about saving 4 keystrokes per number, and the visual noise
>> of the parens is an issue but not the main one (and quotes are barely any
>> noise by comparison); it’s the fact that these numeric values look like
>> numeric values instead of looking like strings
>>
> If your conclusion here were "and that's why Python needs a proper> syntax
> for Decimal literals", then I would be inclined to agree with> you - a
> Decimal literal would be lossless (as it can entirely encode> whatever was in
> the source file), and you could then create the> float32 values from those.
I think builtin Decimal literals are a non-starter. The type isn't even
builtin. You surely wouldn't want to incur the cost of importing it to every
Python session. And implementing some kind of lazy import mechanism in the
middle of the json module is one thing, but in the middle of the compiler? So
how _could_ you implement them? (While we're at it, what would that do to
MicroPython and… one of the browser Pythons, I forget which… that have 100%
syntax compatibility with Python but leave out much of the stdlib, including
decimal? Sure, nobody ever promised they could do that, but it's a happy
accident that they could, and do we want to break that capriciously?)
Maybe you could come up with some kind of DecimalLiteral object that doesn't
actually act like a number, but can be converted to all of the different
numeric types as needed (so, e.g., if you add or radd one to a `float` it
converts to a `float`, etc.). That works great in languages like Swift and
Haskell, but I don't think there's a feasible design for a dynamically-typed
language.
So, even if Decimal literals really were the only thing we needed, a way to
register Decimal literals may be the best way to do that.
But they're not. You didn't even attempt to answer the comparison with complex
that you quoted. The problem that `j` solves is not that there's no way to
create complex values losslessly out of floats, but that there's no way to
create them _readably_, in a way that's consistent with the way you read and
write them in every other context. Which is exactly the problem that `f`
solves. Adding a Decimal literal would not help that at all—letting me write
`f(1.23d)` instead of `f('1.23')` does not let me write `1.23f`.
Also, I think you're the one who brought up performance earlier? `%timeit
np.float32('1.23')` is 671ns, while `%timeit np.float32(d)` with a
pre-constructed `Decimal(1.23)` is 2.56us on my laptop, so adding a Decimal
literal instead of custom literals actually encourages _slower_ code, not
faster.
Also, as the OP has pointed out repeatedly and nobody has yet answered, if I
want to write `f(1.23d)` or `f('1.23')`, I have to pollute the global namespace
with a function named `f` (a very commonly-used name); if I want to write
`1.23f`, I don't, since the converter gets stored in some out-of-the-way place
like `__user_literals_registry__['f']` rather than `f`. That seems like a
serious benefit to me.
> But you haven't made the case for generic string prefixes or any sort
> of "arbitrary literal" that would let you import something that
> registers something to make your float32 literals.
Sure I did; you just cut off the rest of the email that had other cases. And
ignored most of what you quoted about the float32 case. And ignored the
previous emails by both me and the OP that had other cases. Or can you explain
to me how a builtin Decimal literal could solve the problem of Windows paths?
Here's a few more: Numeric types that can't be losslessly converted to and from
Decimal, like Fraction. Something more similar to complex (e.g., `quat = 1.0x +
0.0y + 0.1z + 1.0w`). What would Decimal literals do for me there?
I think your reluctance and the OP's excitement here both come from the same
source: Any feature that gives you a more convenient way to write and read
something is good, because it lets you write things in a way that's consistent
with your actual domain, and also bad, because it lets you write things in a
way that's not readable to people who aren't steeped in your domain. Those are
_always_ both true, so just arguing from first principles is pointless. The
question is whether, for this specific feature, there are good uses where the
benefit outweighs the cost. And I think there are.
In fact, if you're already convinced that we need Decimal literals, unless you
can come up with a more feasible way to add builtin Decimal literals to Python,
Decimal on its own seems like a sufficient use case for the feature.
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