support char arrays and complicated dtypes (structs) - #149
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The way it's implemented now, it falls back to the typenum check for arithmetic types, and checks dtypes for more complicated types (like arrays, or structs). We reuse Pybind11s mechanism to bind struct arrays but that's experimental for now. |
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| xt::pyarray<B> dtype_from_python(xt::pyarray<B>& b) | ||
| { | ||
| if (b(0).a != 1 || b(0).b != 'p' || b(1).a != 123 || b(1).b != 'c') | ||
| throw std::runtime_error("FAIL"); |
| template <class T> | ||
| bool check_array(const pybind11::handle& src) | ||
| { | ||
| using is_arithmetic_type = std::integral_constant<bool, !!pybind11::detail::satisfies_any_of<T, std::is_arithmetic, xtl::is_complex>::value>; |
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Could we convert to bool instead of doing !!?
| import os | ||
| import sys | ||
| import subprocess | ||
| import gc |
| ****************************************************************************/ | ||
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| #include <numeric> | ||
| #include <limits> |
| # Useful for debugging any issues with conda | ||
| - conda info -a | ||
| - conda install pytest numpy pybind11==2.2.1 -c conda-forge | ||
| - conda install pytest numpy pybind11==2.2.3 -c conda-forge |
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Do we need 2.2.3, (i.e. do we need to update the readme?)
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This adds support for char arrays:
It also adds some initial dtype support (i.e. you can declare structs, use them inside C++ and serialize them from numpy). However, pybind has a lot of code that deals with padding differences from C++ / NumPy which I haven't really looked at.
This code also removes our own typenum stuff as well as switching to use
PyArray_EquivTypesinstead of comparing the typenums. There is a good chance that comparing typenums is faster. Therefore, we could statically enable typenums if we know (from the C++ type) that a statically determinable typenum is available.@iamthebot you might be interested in this based off of your question in #142