On 10-Apr-2014 5:15 PM, njsmith wrote:
    
      In doc/neps/return-of-revenge-of-matmul-pep.rst:
      > +much larger than both projects put together (stdlib: 411575 SLOC,
> +scikit-learn: 50924 SLOC, nipy: 37078 SLOC). [#sloc-details]
> +
> +The **dot** row (marked ``******``) counts how common matrix multiply
> +operations are in each codebase.
> +
> +::
> +
> +    ====  ======  ============  ====  ========
> +      op  stdlib  scikit-learn  nipy  combined
> +    ====  ======  ============  ====  ========
> +       =    2969          5536  4932      3376 / 10,000 SLOC
> +       -     218           444   496       261
> +       +     224           201   348       231
> +      ==     177           248   334       196
> +       *     156           284   465       192

      I know it can be frustrating to feel
        like you're being ignored, and I'm
        trying to be constructive, but if you're going to reply to my
        post saying
        "please engage with the specific points instead of just
        repeating
        assertions over and over" by repeating vague assertions and
        ignoring
        specific points then I've kind of run out of responses.
        A PEP which has, in fact, convinced the BDFL and (apparently)
        the rest of
        the Python core team is, by definition, convincing. Maybe we
        were all
        *wrong* to be convinced, it's always possible. But asserting
        that it isn't
        convincing and saying nothing else just makes it sound like you
        think we're
        all idiots.
        (To be clear, there's nothing wrong with thinking we're all
        idiots. That
        could be true to. But as a practical matter it's not going to
        produce any
        of the changes in behaviour that you're hoping for.)
      …
      
        On Thu, Apr 10, 2014 at 10:05
          PM, Robert Bradshaw <notifications@github.com>wrote: In
          doc/neps/return-of-revenge-of-matmul-pep.rst: > +much
          larger than both projects put together (stdlib: 411575 SLOC,
          > +scikit-learn: 50924 SLOC, nipy: 37078 SLOC).
          [#sloc-details] > + > +The **dot** row (marked
          ``******``) counts how common matrix multiply > +operations
          are in each codebase. > + > +:: > + > + ====
          ====== ============ ==== ======== > + op stdlib
          scikit-learn nipy combined > + ==== ====== ============
          ==== ======== > + = 2969 5536 4932 3376 / 10,000 SLOC >
          + - 218 444 496 261 > + + 224 201 348 231 > + == 177 248
          334 196 > + * 156 284 465 192 On Thu, Apr 10, 2014 at 12:58
          PM, njsmith <notifications@github.com> wrote: Hi Colin,
          I honestly have no idea what you're trying to insinuate about
          the connection between the Space Telescope, SciPy, and
          scikit-learn (??), but I don't think it's helping anything.
          Nor is simply posting over and over in different forums that
          you like Matrix and Vector classes. We know that now :-). But
          just saying that is not going to convince anyone to like them
          with you. Realistically, my best guess is that nothing you say
          is likely to convince anyone of anything, given that np.matrix
          has come to its present state of poor reputation through years
          of experience, and it isn't clear you even understand why most
          numpy developers and users are convinced that the approach is
          fundamentally flawed and unfixable. But if you really want to
          try, then the place to do that is the numpy-discussion list,
          and the way to do that is to understand and engage with
          specific the problems people have raised. This keeps coming up
          (and not by just one or two isolated people) because the PEP
          doesn't convincingly show that a better Matrix class than
          np.matrix isn't a viable alternative. — Reply to this email
          directly or view it on GitHub<https://github.com/numpy/numpy/pull/4351/files#r11506487>

          .
        
        -- Nathaniel J. Smith
          Postdoctoral researcher - Informatics - University of
          Edinburgh
          http://vorpus.org
        
      
      —
        Reply to this email directly or view
          it on GitHub.
    
    Nathaniel,
    I have explained to you that I am preparing a critique of the PEP
    465, which, in my view is flawed.
    It provides no balanced view of the status quo.  You
    provide an illustration of some calculations using @, but have not
    shown how objects of the Matrix Class fail to handle the similar
    calculations.
    I suggest that balance is important as you need to convince those
    outside the Numpy community that the addition of two new operators
    is justified for all the Python users, most of whom are not users of
    linear algebra.  It adds to the Python learning curve with no
    benefit to most and very limited benefit to the users of linear
    algebra.
    Best wishes,
    Colin W.