Yes, this is how I've been doing things so far. -- John
On Dec 8, 2014, at 9:12 AM, Tim Holy <[email protected]> wrote: > My suspicion is you should read into a 1d vector (and use `append!`), then at > the end do a reshape and finally a transpose. I bet that will be many times > faster than any other alternative, because we have a really fast transpose > now. > > The only disadvantage I see is taking twice as much memory as would be > minimally needed. (This can be fixed once we have row-major arrays.) > > --Tim > > On Monday, December 08, 2014 08:38:06 AM John Myles White wrote: >> I believe/hope the proposed solution will work for most cases, although >> there's still a bunch of performance work left to be done. I think the >> decoupling problem isn't as hard as it might seem since there are very >> clearly distinct stages in parsing a CSV file. But we'll find out if the >> indirection I've introduced causes performance problems when things can't >> be inlined. >> >> While writing this package, I found the two most challenging problems to be: >> >> (A) The disconnect between CSV files providing one row at a time and Julia's >> usage of column major arrays, which encourage reading one column at a time. >> (B) The inability to easily resize! a matrix. >> >> -- John >> >> On Dec 8, 2014, at 5:16 AM, Stefan Karpinski <[email protected]> wrote: >>> Doh. Obfuscate the code quick, before anyone uses it! This is very nice >>> and something I've always felt like we need for data formats like CSV – a >>> way of decoupling the parsing of the format from the populating of a data >>> structure with that data. It's a tough problem. >>> >>> On Mon, Dec 8, 2014 at 8:08 AM, Tom Short <[email protected]> wrote: >>> Exciting, John! Although your documentation may be "very sparse", the code >>> is nicely documented. >>> >>> On Mon, Dec 8, 2014 at 12:35 AM, John Myles White >>> <[email protected]> wrote: Over the last month or so, I've been >>> slowly working on a new library that defines an abstract toolkit for >>> writing CSV parsers. The goal is to provide an abstract interface that >>> users can implement in order to provide functions for reading data into >>> their preferred data structures from CSV files. In principle, this >>> approach should allow us to unify the code behind Base's readcsv and >>> DataFrames's readtable functions. >>> >>> The library is still very much a work-in-progress, but I wanted to let >>> others see what I've done so that I can start getting feedback on the >>> design. >>> >>> Because the library makes heavy use of Nullables, you can only try out the >>> library on Julia 0.4. If you're interested, it's available at >>> https://github.com/johnmyleswhite/CSVReaders.jl >>> >>> For now, I've intentionally given very sparse documentation to discourage >>> people from seriously using the library before it's officially released. >>> But there are some examples in the README that should make clear how the >>> library is intended to be used.> >>> -- John >
