Hi Sylvain, > On 15. Aug 2018, at 19:38, Sylvain Corlay <sylvain.cor...@gmail.com> wrote: > > If `pybind11` is included, it could be interesting to also include `xtensor` > and `xtensor-python`. > > - Xtensor is a C++ dynamic N-d array library that offers numpy-like features > including broadcasting and universal functions. It is also lazy evaluated and > continuously benchmarked against numpy, eigen, pythran and numba. You can > check out the numpy to xtensor cheat sheet: > https://xtensor.readthedocs.io/en/latest/numpy.html > <https://xtensor.readthedocs.io/en/latest/numpy.html>. > > - Xtensor-python makes it possible to operate on numpy arrays inplace using > the xtensor API. So that e.g. an xtensor reshape will result in a reshape on > the python side (using the numpy C API under the hood). > > Xtensor-python is built upon pybind11, but brings it much closer to feature > parity with NumPy. There is a vibrant community of users and developers, > actively working to make xtensor faster and cover more of numpy APIs. > > I would argue that xtensor-python is one of the easiest ways to make use of > numpy arrays from a C++ program, given the similar high level API, and tools > to make ufuncs and bindings with one-liners. > > Resources: > > - xtensor: https://github.com/QuantStack/xtensor > <https://github.com/QuantStack/xtensor> (documentation: > https://xtensor.readthedocs.io/ <https://xtensor.readthedocs.io/>) > - xtensor-python: https://github.com/QuantStack/xtensor-python > <https://github.com/QuantStack/xtensor-python> (documentation: > https://xtensor-python.readthedocs.io/ > <https://xtensor-python.readthedocs.io/>) > - xtensor-blas: https://github.com/QuantStack/xtensor-blas > <https://github.com/QuantStack/xtensor-blas> (documentation: > https://xtensor-blas.readthedocs.io <https://xtensor-blas.readthedocs.io/>) > - xtensor-io: https://github.com/QuantStack/xtensor-io > <https://github.com/QuantStack/xtensor-io> (documentation: > https://xtensor-io.readthedocs.io <https://xtensor-io.readthedocs.io/>) for > reading and writing various file formats > > Other language bindings: > > - xtensor-julia: https://github.com/QuantStack/xtensor-julia > <https://github.com/QuantStack/xtensor-julia> (documentation: > https://xtensor-julia.readthedocs.io/en/latest/ > <https://xtensor-julia.readthedocs.io/en/latest/>) > - xtensor-r: https://github.com/QuantStack/xtensor-r > <https://github.com/QuantStack/xtensor-r> (documentation: > https://xtensor-r.readthedocs.io/en/latest/ > <https://xtensor-r.readthedocs.io/en/latest/>)
sounds good, I think it should be mentioned in the pybind11 part. I just stumbled over xtensor yesterday. Based on your post I read a bit more about it. I like the expression engine and lazy evaluation, the concept is similar to Eigen. xtensor itself has nothing to do with binding, but makes working with numpy arrays on the C++ side easier - especially when you are familiar with the numpy API. The docs say: "Xtensor operations are continuously benchmarked, and are significantly improved at each new version. Current performances on statically dimensioned tensors match those of the Eigen library. Dynamically dimension tensors for which the shape is heap allocated come at a small additional cost." I couldn't find these benchmark results online, though, could you point me to the right page? Google only produced an outdated SO post where numpy performed better than xtensor. Best regards, Hans PS: A bit of nitpicking: you use the term "tensor" for an n-dimensional block of numbers - a generalisation of "matrix", but the term "tensor" in mathematics and physics is more specific. A tensor has well-defined transformation properties when you change the basis of your vector space, just like a "vector" (a vector is a one-dimensional tensor), while a general block of numbers does not. https://en.wikipedia.org/wiki/Tensor
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