[cctbxbb] repr for flex arrays

[Nicholas Sauter]

Yes, there is a philosophical objection to using .as_numpy_array().  Numpy
only deals with a fixed number of data types, such as int, double, etc.
Flex arrays can be extended to many other types such as Bragg spots,
2-vectors, 3-vectors, and 3x3 matrices.  Therefore, if we are going to take
the performance hit and do a copy, I would simply recommend using a Python
list:

__repr__(a) :== list(a)

Nick

Nicholas K. Sauter, Ph. D.
Senior Scientist, Molecular Biophysics & Integrated Bioimaging Division
Lawrence Berkeley National Laboratory
1 Cyclotron Rd., Bldg. 33R0345
Berkeley, CA 94720
(510) 486-5713

On Fri, Sep 14, 2018 at 9:56 AM, Nicholas Devenish <ndevenish at gmail.com>
wrote:

> I think as_numpy_array was just a suggestion of the simplest possible
> starting implementation.
>
> But is there a philosophical objection to them being added?
>
> Nick
>
> On Fri, Sep 14, 2018 at 5:27 PM Graeme.Winter at Diamond.ac.uk
> <Graeme.Winter at diamond.ac.uk> wrote:
> >
> > Does it? Dang! Thought it would be fly weight and just make the data
> structures needed.
> >
> > If that’s the case then completely agree.
> >
> > Cheers Graeme
> > ________________________________
> > From: cctbxbb-bounces at phenix-online.org <cctbxbb-bounces at phenix-
> online.org> on behalf of Aaron Brewster <asbrewster at lbl.gov>
> > Sent: 14 September 2018 17:23:41
> > To: cctbx mailing list
> > Subject: Re: [cctbxbb] repr for flex arrays
> >
> > Hi Graeme, I'm not sure what the right answer here is except I don't
> think it is as_numpy_array().__repr__ as that does a copy.  No good for big
> arrays.
> >
> >
> >
> >
> > On Fri, Sep 14, 2018 at 7:30 AM Graeme.Winter at Diamond.ac.uk<mailto:
> Graeme.Winter at Diamond.ac.uk> <Graeme.Winter at diamond.ac.uk<mailto:
> Graeme.Winter at diamond.ac.uk>> wrote:
> > Hi Folks,
> >
> > Is there a reason for not having a helpful __repr__ for flex arrays i.e.
> >
> > Grey-Area:002-decide-when graeme$ dials.python
> > Python 2.7.14 (default, Apr 17 2018, 15:02:10)
> > [GCC 4.2.1 Compatible Apple LLVM 9.1.0 (clang-902.0.39.1)] on darwin
> > Type "help", "copyright", "credits" or "license" for more information.
> > >>> from dials.array_family import flex
> > >>> a = flex.double([1, 2, 3])
> > >>> print a
> > <scitbx_array_family_flex_ext.double object at 0x1095673c0>
> >
> > (I have a feeling I asked this before but cannot find reference)
> >
> > For example
> >
> > >>> print a.as_numpy_array()
> > [ 1.  2.  3.]
> >
> > is more useful… but annoying to type
> >
> > I appreciate a could be “big” but numpy seems to handle this OK
> >
> > even - could __repr__ be punned to as_numpy_array(),__repr__()?
> >
> > Currently it prints the type of the array and its location in ram, which
> are adequately served by
> >
> > >>> type(a)
> > <class 'scitbx_array_family_flex_ext.double'>
> > >>> print id(a)
> > 4451627968
> >
> > If I print an array I more often want to see the content and less often
> the storage class and location (I humbly assert)
> >
> > Thoughts? I know this tends to confuse and annoy people who are new to
> flex arrays (and veterans who are busy debugging stuff…)
> >
> > Many thanks Graeme
> >
> >
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