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Use xbuffer_adaptor · NKI-AI/xtensor-python@9eb21db · GitHub

  1. NKI-AI
  2. xtensor-python

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Commit 9eb21db

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SylvainCorlay
committed
Use xbuffer_adaptor
1 parent 09eaf4a commit 9eb21db

12 files changed

Lines changed: 255 additions & 493 deletions

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‎.appveyor.yml‎

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@@ -23,7 +23,7 @@ install:
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- conda update -q conda
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- conda info -a
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- conda install gtest cmake -c conda-forge
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- conda install xtensor==0.10.0 pytest numpy pybind11==2.1.1 -c conda-forge
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- conda install xtensor==0.10.2 pytest numpy pybind11==2.1.1 -c conda-forge
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- "set PYTHONHOME=%MINICONDA%"
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- cmake -G "NMake Makefiles" -D CMAKE_INSTALL_PREFIX=%MINICONDA%\\Library -D BUILD_TESTS=ON -D PYTHON_EXECUTABLE=%MINICONDA%\\python.exe .
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- nmake test_xtensor_python

‎.travis.yml‎

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@@ -94,7 +94,7 @@ install:
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- conda update -q conda
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# Useful for debugging any issues with conda
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- conda info -a
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- conda install xtensor==0.10.0 pytest numpy pybind11==2.1.1 -c conda-forge
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- conda install xtensor==0.10.2 pytest numpy pybind11==2.1.1 -c conda-forge
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- cd test
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- conda env create -f ./test-environment.yml
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- source activate test-xtensor-python

‎CMakeLists.txt‎

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@@ -33,8 +33,8 @@ message(STATUS "xtensor-python v${${PROJECT_NAME}_VERSION}")
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set(XTENSOR_PYTHON_HEADERS
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pyarray.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pybuffer_adaptor.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pycontainer.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pystrides_adaptor.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pytensor.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/pyvectorize.hpp
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${XTENSOR_PYTHON_INCLUDE_DIR}/xtensor-python/xtensor_python_config.hpp

‎README.md‎

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@@ -49,15 +49,15 @@ Both containers enable the numpy-style APIs of xtensor (see [the numpy to xtenso
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double sum_of_sines(xt::pyarray<double>& m)
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{
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auto sines = xt::sin(m); // sines does not actually hold any value, which are only computed upon access
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auto sines = xt::sin(m); // sines does not actually hold values.
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return std::accumulate(sines.begin(), sines.end(), 0.0);
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}
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PYBIND11_PLUGIN(xtensor_python_test)
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{
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pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");
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m.def("sum_of_sines", sum_of_sines, "Computes the sum of the sines of the values of the input array");
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m.def("sum_of_sines", sum_of_sines, "Sum the sines of the input values");
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return m.ptr();
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}
@@ -189,7 +189,7 @@ from the `docs` subdirectory.
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| `xtensor-python` | `xtensor` | `pybind11` |
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|-------------------|------------|-------------|
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| master | ^0.10.0 | ^2.1.0 |
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| master | ^0.10.2 | ^2.1.0 |
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| 0.11.x | ^0.10.0 | ^2.1.0 |
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| 0.10.x | ^0.9.0 | ^2.1.0 |
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| 0.9.x | ^0.8.1 | ^2.1.0 |

‎docs/source/basic_usage.rst‎

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@@ -20,20 +20,18 @@ Example 1: Use an algorithm of the C++ library on a numpy array inplace
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#define FORCE_IMPORT_ARRAY // numpy C api loading
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#include "xtensor-python/pyarray.hpp" // Numpy bindings
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double sum_of_sines(xt::pyarray<double> &m)
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double sum_of_sines(xt::pyarray<double>& m)
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{
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auto sines = xt::sin(m);
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// sines does not actually hold any value, which are only computed upon access
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return std::accumulate(sines.begin(), sines.end(), 0.0);
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auto sines = xt::sin(m); // sines does not actually hold values.
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return std::accumulate(sines.cbegin(), sines.cend(), 0.0);
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}
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PYBIND11_PLUGIN(xtensor_python_test)
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{
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xt::import_numpy();
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pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");
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m.def("sum_of_sines", sum_of_sines,
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"Computes the sum of the sines of the values of the input array");
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m.def("sum_of_sines", sum_of_sines, "Sum the sines of the input values");
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return m.ptr();
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}

‎include/xtensor-python/pyarray.hpp‎

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@@ -12,11 +12,13 @@
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#include <cstddef>
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#include <vector>
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#include <algorithm>
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#include "xtensor/xsemantic.hpp"
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#include "xtensor/xiterator.hpp"
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#include "xtensor/xbuffer_adaptor.hpp"
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#include "pycontainer.hpp"
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#include "pybuffer_adaptor.hpp"
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#include "pystrides_adaptor.hpp"
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namespace xt
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{
@@ -103,11 +105,11 @@ namespace xt
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template <class T>
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struct xcontainer_inner_types<pyarray<T>>
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{
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using container_type = pybuffer_adaptor<T>;
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using container_type = xbuffer_adaptor<T>;
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using shape_type = std::vector<typename container_type::size_type>;
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using strides_type = shape_type;
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using backstrides_type = pyarray_backstrides<pyarray<T>>;
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using inner_shape_type = pybuffer_adaptor<std::size_t>;
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using inner_shape_type = xbuffer_adaptor<std::size_t>;
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using inner_strides_type = pystrides_adaptor<sizeof(T)>;
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using inner_backstrides_type = backstrides_type;
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using temporary_type = pyarray<T>;

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