On Thu, May 21, 2015 at 9:22 PM, Andrew Barnert via Python-ideas < python-ideas@python.org> wrote:
On May 21, 2015, at 19:08, Greg <greg.ewing@canterbury.ac.nz> wrote:
On 22/05/2015 1:51 p.m., Andrew Barnert via Python-ideas wrote: Or just use MacroPy, which wraps up all the hard stuff (especially 2.x compatibility) and provides a huge framework of useful tools. What do you want to do that can't be done that way?
You might not want to drag in a huge framework just to do one thing.
But "all kinds of LINQ-style things, like ORMs" isn't just one thing. If you're going to build a huge framework, why not build it on top of another framework that does the hard part of the work for you?
* MacroPy looks interesting * PonyORM -> SQLAlchemy http://dask.pydata.org/en/latest/array-blaze.html#why-use-blaze """These different projects (Blaze -> dask.array -> NumPy -> Numba) act as
different stages in a compiler. They start at abstract syntax trees, move to task DAGs, then to in-core computations, finally to LLVM and beyond. For simple problems you may only need to think about the middle of this chain (NumPy, dask.array) but as you require more performance optimizations you extend your interest to the outer edges (Blaze, Numba)."""
... http://continuum.io/blog/blaze """Once a graph is evaluated, Blaze attempts to gather all available type
and metadata available from the user input to inform better computation selection and scheduling. The compiler converts expressions graph objects into an intermediate form called ATerm, drawn from the StrategoXT project. This intermediate form is roughly a subset of Python expressions but allows the explicit annotation of type and metadata information directly on the AST. The ATerm IR forms the meeting point where both Numba and Blaze can come together to code generation and graph rewriting to produce more efficient kernels."""
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