On Mon, Sep 12, 2016 at 8:03 AM, Patrick Kofod Mogensen <
patrick.mogen...@gmail.com> wrote:
> This surprised me as well, where did you find this syntax?
>
Call overload.
>
>
> On Monday, September 12, 2016 at 1:59:33 PM UTC+2, DNF wrote:
>>
>> I haven't looked very closely at your code, but a brief look reveals that
>> you are defining your functions in a very unusual way. Two examples:
>>
>> function (f::FIRFilter)(x)
>> return filt(f, x)
>> end
>>
>> function(p::pnseq)(n,T=Int64)
>> out = Array{T}(n)
>> for i in eachindex(out)
>> if p.count < p.width
>> p.cache = rand(Int64)
>> p.count = 64
>> end
>> out[i] = p.cache & p.mask
>> p.cache >>= p.width
>> p.count -= p.width
>> end
>> return out
>> end
>>
>> I have never seen this way of defining them before, and I am pretty
>> surprised that it's not a syntax error. Long-form function signatures
>> should be of the form
>> function myfunc{T<:SomeType}(myarg1::T, myarg2)
>> where the type parameter section (in curly bracket) is optional.
>>
>> As I said, I'm surprised it's not a syntax error, but maybe it gets
>> parsed as an anonymous function (just guessing here). If so, and if you are
>> using version 0.4, you can get slow performance.
>>
>> You can read here about the right way to define functions:
>> http://docs.julialang.org/en/stable/manual/functions/
>>
>> On Monday, September 12, 2016 at 1:32:48 PM UTC+2, Neal Becker wrote:
>>>
>>> As a first (nontrivial) try at julia, I put together some simple DSP
>>> code,
>>> which represents a
>>> pn generator (random fixed-width integer generator)
>>> constellation mapping
>>> interpolating FIR filter (from DSP.jl)
>>> decimating FIR filter (from DSP.jl)
>>> mean-square error measure
>>>
>>> Source code is here:
>>> https://github.com/nbecker/julia-test
>>>
>>> Profile result is here:
>>> https://gist.github.com/anonymous/af2459fc831ddbeb6e3be25e5c8d5197
>>>
>>> If I understand how to read this profile (not sure I do) it looks like
>>> 1/2
>>> the time is spent in PnSeq.jl, which seems surprising.
>>>
>>> PnSeq.jl calls rand() to get a Int64, caching the result and then
>>> providing
>>> N bits at a time to fill an Array. It's supposed to be a fast way to
>>> get an
>>> Array of small-width random integers.
>>>
>>> Most of the number crunching should be in the FIR filter functions,
>>> which I
>>> would have expected to use the most time.
>>>
>>> Anyone care to make suggestions on this code, how to make it faster, or
>>> more
>>> idiomatic Julia? I'm not proficient with Julia or with Matlab (I've
>>> been
>>> using python/numpy/c++ for all my work for years).
>>>
>>>
>>>
