On Thu, Jun 23, 2011 at 5:14 PM, Mark Wiebe <[email protected]> wrote:
> On Thu, Jun 23, 2011 at 6:02 PM, Charles R Harris < > [email protected]> wrote: > >> On Thu, Jun 23, 2011 at 4:43 PM, Mark Wiebe <[email protected]> wrote: >> >>> On Thu, Jun 23, 2011 at 5:24 PM, Pierre GM <[email protected]> wrote: >>> >>>> >>>> On Jun 23, 2011, at 11:55 PM, Mark Wiebe wrote: >>>> >>>> > On Thu, Jun 23, 2011 at 4:46 PM, Charles R Harris < >>>> [email protected]> wrote: >>>> > On Thu, Jun 23, 2011 at 2:53 PM, Mark Wiebe <[email protected]> >>>> wrote: >>>> > Enthought has asked me to look into the "missing data" problem and how >>>> NumPy could treat it better. I've considered the different ideas of adding >>>> dtype variants with a special signal value and masked arrays, and concluded >>>> that adding masks to the core ndarray appears is the best way to deal with >>>> the problem in general. >>>> > >>>> > I've written a NEP that proposes a particular design, viewable here: >>>> > >>>> > >>>> https://github.com/m-paradox/numpy/blob/cmaskedarray/doc/neps/c-masked-array.rst >>>> >>>> Mmh, after timeseries, now masked arrays... Mark, I start to see a >>>> pattern here ;) >>> >>> >>> I think it speaks to what's on Enthought's mind, in any case. :) >>> >> >> What is the thinking at Enthought about this? I sense a meeting in the >> background and it would be nice to know what the motivations were. >> > > A lot of these things were discussed at the DataArray summit they held here > a while ago, but the general push is towards being more approachable and > friendly to people who use R or do statistical data analysis on possibly > messy data. I don't use R, but I think I'm fairly good at producing things > which are both powerful and intuitive in the end, which is what I'm trying > to do here. That's my understanding of the thinking, others at Enthought > will have to correct me if I'm wrong... > > One thing that wasn't clear to me in the NEP was how one might experiment with different masks on a fixed set of data. Chuck
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