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Commit06d86c9

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Merge pull request#267 from tdegeus/qad
Adding possibility to 'cast' or copy to `xt::xarray` etc
2 parents43b244e +af91def commit06d86c9

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9 files changed

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9 files changed

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‎.azure-pipelines/unix-build.yml

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displayName: Example - readme 1
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workingDirectory: $(Build.SourcesDirectory)/docs/source/examples/readme_example_1
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-script:|
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source activate xtensor-python
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cmake -Bbuild -DPython_EXECUTABLE=`which python`
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cd build
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cmake --build .
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cp ../example.py .
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python example.py
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cd ..
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displayName: Example - Copy 'cast'
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workingDirectory: $(Build.SourcesDirectory)/docs/source/examples/copy_cast
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-script:|
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source activate xtensor-python
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cmake -Bbuild -DPython_EXECUTABLE=`which python`

‎docs/source/examples.rst

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@@ -143,5 +143,54 @@ Then we can test the module:
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Since we did not install the module,
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we should compile and run the example from the same folder.
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To install, please consult
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`this*pybind11* /*CMake* example<https://github.com/pybind/cmake_example>`_.
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`this pybind11 / CMake example<https://github.com/pybind/cmake_example>`_.
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**Tip**: take care to modify that example with the correct *CMake* case ``Python_EXECUTABLE``.
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Fall-back cast
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==============
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The previous example showed you how to design your module to be flexible in accepting data.
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From C++ we used ``xt::xarray<double>``,
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whereas for the Python API we used ``xt::pyarray<double>`` to operate directly on the memory
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of a NumPy array from Python (without copying the data).
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Sometimes, you might not have the flexibility to design your module's methods
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with template parameters.
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This might occur when you want to ``override`` functions
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(though it is recommended to use CRTP to still use templates).
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In this case we can still bind the module in Python using *xtensor-python*,
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however, we have to copy the data from a (NumPy) array.
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This means that although the following signatures are quite different when used from C++,
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as follows:
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1. *Constant reference*: read from the data, without copying it.
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..code-block::cpp
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void foo(const xt::xarray<double>& a);
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2. *Reference*: read from and/or write to the data, without copying it.
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..code-block::cpp
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void foo(xt::xarray<double>& a);
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3. *Copy*: copy the data.
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..code-block::cpp
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void foo(xt::xarray<double> a);
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The Python will all cases result in a copy to a temporary variable
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(though the last signature will lead to a copy to a temporary variable, and another copy to ``a``).
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On the one hand, this is more costly than when using ``xt::pyarray`` and ``xt::pyxtensor``,
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on the other hand, it means that all changes you make to a reference, are made to the temporary
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copy, and are thus lost.
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Still, it might be a convenient way to create Python bindings, using a minimal effort.
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Consider this example:
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:download:`main.cpp<examples/copy_cast/main.cpp>`
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..literalinclude::examples/copy_cast/main.cpp
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:language: cpp
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cmake_minimum_required(VERSION 3.1..3.19)
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project(mymodule)
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find_package(pybind11 CONFIG REQUIRED)
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find_package(xtensor REQUIRED)
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find_package(xtensor-python REQUIRED)
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find_package(Python REQUIRED COMPONENTS NumPy)
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pybind11_add_module(mymodule main.cpp)
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target_link_libraries(mymodulePUBLIC pybind11::module xtensor-python Python::NumPy)
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target_compile_definitions(mymodulePRIVATE VERSION_INFO=0.1.0)
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importmymodule
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importnumpyasnp
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c=np.array([[1,2,3], [4,5,6]])
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assertnp.isclose(np.sum(np.sin(c)),mymodule.sum_of_sines(c))
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assertnp.isclose(np.sum(np.cos(c)),mymodule.sum_of_cosines(c))
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#include<numeric>
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#include<xtensor.hpp>
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#include<pybind11/pybind11.h>
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#defineFORCE_IMPORT_ARRAY
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#include<xtensor-python/pyarray.hpp>
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template<classT>
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doublesum_of_sines(T& m)
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{
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auto sines =xt::sin(m);// sines does not actually hold values.
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returnstd::accumulate(sines.begin(), sines.end(),0.0);
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}
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// In the Python API this a reference to a temporary variable
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doublesum_of_cosines(const xt::xarray<double>& m)
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{
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auto cosines =xt::cos(m);// cosines does not actually hold values.
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returnstd::accumulate(cosines.begin(), cosines.end(),0.0);
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}
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PYBIND11_MODULE(mymodule, m)
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{
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xt::import_numpy();
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m.doc() ="Test module for xtensor python bindings";
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m.def("sum_of_sines", sum_of_sines<xt::pyarray<double>>,"Sum the sines of the input values");
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m.def("sum_of_cosines", sum_of_cosines,"Sum the cosines of the input values");
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}

‎include/xtensor-python/pynative_casters.hpp

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#include"xtensor_type_caster_base.hpp"
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namespacepybind11
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{
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namespacedetail

‎include/xtensor-python/xtensor_type_caster_base.hpp

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@@ -23,6 +23,97 @@ namespace pybind11
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{
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namespacedetail
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{
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template<typename T, xt::layout_type L>
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structpybind_array_getter_impl
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{
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staticautorun(handle src)
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{
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returnarray_t<T, array::c_style | array::forcecast>::ensure(src);
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}
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};
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template<typename T>
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structpybind_array_getter_impl<T, xt::layout_type::column_major>
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{
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staticautorun(handle src)
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{
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returnarray_t<T, array::f_style | array::forcecast>::ensure(src);
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}
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};
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template<classT>
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structpybind_array_getter
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{
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};
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template<classT, xt::layout_type L>
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structpybind_array_getter<xt::xarray<T, L>>
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{
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staticautorun(handle src)
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{
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return pybind_array_getter_impl<T, L>::run(src);
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}
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};
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template<classT, std::size_t N, xt::layout_type L>
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structpybind_array_getter<xt::xtensor<T, N, L>>
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{
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staticautorun(handle src)
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{
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return pybind_array_getter_impl<T, L>::run(src);
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}
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};
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template<classCT,classS, xt::layout_type L,classFST>
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structpybind_array_getter<xt::xstrided_view<CT, S, L, FST>>
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{
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staticautorun(handle/*src*/)
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{
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returnfalse;
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}
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};
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template<classEC, xt::layout_type L,classSC,classTag>
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structpybind_array_getter<xt::xarray_adaptor<EC, L, SC, Tag>>
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{
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staticautorun(handle src)
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{
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auto buf = pybind_array_getter_impl<EC, L>::run(src);
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return buf;
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}
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};
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template<classEC, std::size_t N, xt::layout_type L,classTag>
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structpybind_array_getter<xt::xtensor_adaptor<EC, N, L, Tag>>
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{
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staticautorun(handle/*src*/)
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{
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returnfalse;
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}
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};
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template<classT>
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structpybind_array_dim_checker
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{
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template<classB>
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staticboolrun(const B& buf)
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{
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returntrue;
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}
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};
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template<classT, std::size_t N, xt::layout_type L>
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structpybind_array_dim_checker<xt::xtensor<T, N, L>>
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{
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template<classB>
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staticboolrun(const B& buf)
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{
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return buf.ndim() == N;
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}
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};
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// Casts a strided expression type to numpy array.If given a base,
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// the numpy array references the src data, otherwise it'll make a copy.
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// The writeable attributes lets you specify writeable flag for the array.
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template<classType>
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structxtensor_type_caster_base
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{
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boolload(handle/*src*/,bool)
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{
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returnfalse;
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}
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private:
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public:
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PYBIND11_TYPE_CASTER(Type, _("numpy.ndarray[") + npy_format_descriptor<typename Type::value_type>::name + _("]"));
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boolload(handle src,bool convert)
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{
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using T =typename Type::value_type;
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if (!convert && !array_t<T>::check_(src))
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{
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returnfalse;
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}
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auto buf = pybind_array_getter<Type>::run(src);
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if (!buf)
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{
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returnfalse;
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}
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if (!pybind_array_dim_checker<Type>::run(buf))
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{
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returnfalse;
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}
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std::vector<size_t>shape(buf.ndim());
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std::copy(buf.shape(), buf.shape() + buf.ndim(), shape.begin());
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value =Type::from_shape(shape);
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std::copy(buf.data(), buf.data() + buf.size(), value.data());
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returntrue;
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}
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// Normal returned non-reference, non-const value:
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static handlecast(Type&& src, return_value_policy/* policy*/, handle parent)
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{
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{
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returncast_impl(src, policy, parent);
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}
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#ifdef PYBIND11_DESCR// The macro is removed from pybind11 since 2.3
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static PYBIND11_DESCRname()
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{
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return_("xt::xtensor");
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}
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#else
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staticconstexprauto name = _("xt::xtensor");
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#endif
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template<typename T>
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using cast_op_type = cast_op_type<T>;
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};
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}
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}

‎test_python/main.cpp

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return m +2;
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}
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xt::xarray<int>example3_xarray(const xt::xarray<int>& m)
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{
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returnxt::transpose(m) +2;
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}
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xt::xarray<int, xt::layout_type::column_major>example3_xarray_colmajor(
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const xt::xarray<int, xt::layout_type::column_major>& m)
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{
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returnxt::transpose(m) +2;
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}
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xt::xtensor<int,3>example3_xtensor3(const xt::xtensor<int,3>& m)
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{
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returnxt::transpose(m) +2;
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}
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xt::xtensor<int,2>example3_xtensor2(const xt::xtensor<int,2>& m)
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{
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returnxt::transpose(m) +2;
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}
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xt::xtensor<int,2, xt::layout_type::column_major>example3_xtensor2_colmajor(
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const xt::xtensor<int,2, xt::layout_type::column_major>& m)
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{
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returnxt::transpose(m) +2;
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}
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// Readme Examples
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doublereadme_example1(xt::pyarray<double>& m)
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m.def("example1", example1);
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m.def("example2", example2);
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m.def("example3_xarray", example3_xarray);
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m.def("example3_xarray_colmajor", example3_xarray_colmajor);
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m.def("example3_xtensor3", example3_xtensor3);
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m.def("example3_xtensor2", example3_xtensor2);
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m.def("example3_xtensor2_colmajor", example3_xtensor2_colmajor);
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m.def("complex_overload", no_complex_overload);
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m.def("complex_overload", complex_overload);

‎test_python/test_pyarray.py

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y=xt.example2(x)
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np.testing.assert_allclose(y,res,1e-12)
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deftest_example3(self):
40+
x=np.arange(2*3).reshape(2,3)
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xc=np.asfortranarray(x)
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y=np.arange(2*3*4).reshape(2,3,4)
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v=y[1:,1:,0]
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z=np.arange(2*3*4*5).reshape(2,3,4,5)
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np.testing.assert_array_equal(xt.example3_xarray(x),x.T+2)
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np.testing.assert_array_equal(xt.example3_xarray_colmajor(xc),xc.T+2)
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np.testing.assert_array_equal(xt.example3_xtensor3(y),y.T+2)
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np.testing.assert_array_equal(xt.example3_xtensor2(x),x.T+2)
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np.testing.assert_array_equal(xt.example3_xtensor2(y[1:,1:,0]),v.T+2)
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np.testing.assert_array_equal(xt.example3_xtensor2_colmajor(xc),xc.T+2)
51+
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withself.assertRaises(TypeError):
53+
xt.example3_xtensor3(x)
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deftest_vectorize(self):
4056
x1=np.array([[0,1], [2,3]])
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x2=np.array([0,1])

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