The trick to that is numpy.asarray(aWeights) ;)
On Sat, Jun 1, 2013 at 1:15 PM, Eric Thivierge <[email protected]> wrote: > It's very dense geo probably no where near the gorgo though. It's just the > Softimage default xsi man armored. The body mesh local subdivided and > fetching from there. > > In my previous posts it's simply the data fetch that is taking 6 seconds. > The other processes aren't taking too long regardless. I was surprised > because I remember the interaction on the dinos with your tools. > On May 31, 2013 8:00 PM, "Raffaele Fragapane" <[email protected]> > wrote: > >> You should use numpy regardless, because all operations you will need to >> work on after you pull will be A TON faster. So start from there. >> As for the time it takes, what's the size of the table we're talking >> about? The data fetching stage for the HR Gorgo (the only one I tested when >> I refactored the weight handling tools) was shy of two seconds. You know >> the boxes and assets :) >> >> You working with a heavier meshes and deformer counts than that? >> That was a straight fetch and cast to numpy. >> >> >> On Fri, May 31, 2013 at 8:44 AM, Eric Thivierge <[email protected]>wrote: >> >>> >>> On Thu, May 30, 2013 at 6:35 PM, Alok Gandhi <[email protected]>wrote: >>> >>>> Maybe you can still get some optimization using numpy, I think. Throw >>>> your weights array directly into numpy. It is worth a try at least. >>> >>> >>> I'll give it a shot tomorrow. :) >>> >>> -------------------------------------------- >>> Eric Thivierge >>> http://www.ethivierge.com >>> >> >> >> >> -- >> Our users will know fear and cower before our software! Ship it! Ship it >> and let them flee like the dogs they are! >> > -- Our users will know fear and cower before our software! Ship it! Ship it and let them flee like the dogs they are!
