Jason Rhinelander f7f5bc8e37 Numpy: better compilation errors, long double support (#619) ... * Clarify PYBIND11_NUMPY_DTYPE documentation The current documentation and example reads as though PYBIND11_NUMPY_DTYPE is a declarative macro along the same lines as PYBIND11_DECLARE_HOLDER_TYPE, but it isn't. The changes the documentation and docs example to make it clear that you need to "call" the macro. * Add satisfies_{all,any,none}_of<T, Preds> `satisfies_all_of<T, Pred1, Pred2, Pred3>` is a nice legibility-enhanced shortcut for `is_all<Pred1<T>, Pred2<T>, Pred3<T>>`. * Give better error message for non-POD dtype attempts If you try to use a non-POD data type, you get difficult-to-interpret compilation errors (about ::name() not being a member of an internal pybind11 struct, among others), for which isn't at all obvious what the problem is. This adds a static_assert for such cases. It also changes the base case from an empty struct to the is_pod_struct case by no longer using `enable_if<is_pod_struct>` but instead using a static_assert: thus specializations avoid the base class, POD types work, and non-POD types (and unimplemented POD types like std::array) get a more informative static_assert failure. * Prefix macros with PYBIND11_ numpy.h uses unprefixed macros, which seems undesirable. This prefixes them with PYBIND11_ to match all the other macros in numpy.h (and elsewhere). * Add long double support This adds long double and std::complex<long double> support for numpy arrays. This allows some simplification of the code used to generate format descriptors; the new code uses fewer macros, instead putting the code as different templated options; the template conditions end up simpler with this because we are now supporting all basic C++ arithmetic types (and so can use is_arithmetic instead of is_integral + multiple different specializations). In addition to testing that it is indeed working in the test script, it also adds various offset and size calculations there, which fixes the test failures under x86 compilations.		2017-01-31 17:00:15 +01:00
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pybind11	Numpy: better compilation errors, long double support (#619)	2017-01-31 17:00:15 +01:00