# [ANN] A decimals lib like Java BigDecimal

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isaactsang, Are you and gfZeng, one and the same person?

(I am assuming this is a package announcement, and changed the category accordingly)

Yeah, it’s me, both

thanks

Could you explain the benefit of this package over using BigFloat or BigInt?

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### The real infinite precision support

``````big"1.0" / big"3.0"
=> 0.3333333333333333333333333333333333333333333333333333333333333333333333333333348

using Decimals
Decimals.setprecision!(precision=79)
d"1.0" / d"3.0"
=> Decimal(3333333333333333333333333333333333333333333333333333333333333333333333333333333, -79)
``````

You will need to add an open-source license, such as MIT, for people to be able to use the package.

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Well, I don’t know much about GitHub - JuliaMath/FixedPointNumbers.jl: fixed point types for julia

Well, the only comparison with `Decimals.jl` that you give at the referenced link is that your implementation is simpler and the use is more friendly. Now that you made such statement, could you be a bit more specific? In which aspect is your implementation simpler? And in which situations is the use more friendly? Perhaps examples might help.

You may also want to take an inspiration from a comparision displayed on the `Decimals.jl` page: https://github.com/JuliaMath/Decimals.jl#comparison-with-other-packages

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The code is simple enough, best docs is code. yeah,it is, the idea of a string macro is very good. with that said, as i saw a lot of times with number packages, a speed comparison would be very good. the main difference i saw is that the JuliaMath Decimals.jl uses the following struct:

``````struct Decimal <: AbstractFloat
s::Integer  # sign can be 0 (+) or 1 (-)
c::BigInt   # coefficient (significand), must be non-negative
q::Integer  # exponent
end
``````

``````struct Decimal <: AbstractFloat
c::BigInt  # coefficient
q::Integer # exponent
end
``````

a glaring fault is the absence of compatibility with other number types. (for example,` 1+d"3.0"`)

1+d"3.0" should be working, you can try again

Isaactsang,

I take great offense that your implementation is simpler and the use is more friendly.

Let me give you an example of my PDFPs.jl which is more simpler and more friendly

[ENTER SARCASM MODE]

``````julia> using PDFPs

julia> s = PDFP_toCommonString
PDFP_toCommonString (generic function with 1 method)

"Now using 4 digit precision via format of PDFP(number,4)";

julia> A=PDFP("7.0",4); B=PDFP(pi,4); C=A+B; s(C)
"10.14"

"Now using the simpler and more friendlier 0 digit precision";

julia> A=PDFP("7.0",0); B=PDFP(pi,0); C=A+B; s(C)
""

julia> show(C)
PDFP(0, 0, [])
``````

Any number can be represented by my 0 digit precision PDFP and the result is also a 0 digit precision PDFP. NOTHING could be simpler or more friendly to use. In fact my 0 digit precision PDFP is the fastest decimal floating point number system in the world because it can give you the result before you even started the calculation!!! It is also 100% foolproof because it is absolutely impossible to get the wrong result.

[LEAVE SARCASM MODE]

Well, we should be considering what features we use most.

You can write a simple function to achieve your case, or just use `setexponent(d, 0, RoundDown)`