8000 Merge pull request #196 from SylvainCorlay/update-xtensor · robertodr/xtensor-python@fa15a4b · GitHub
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Merge pull request xtensor-stack#196 from SylvainCorlay/update-xtensor
Update xtensor to 0.20.4
2 parents a8b72e3 + 6c0c719 commit fa15a4b

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.appveyor.yml

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@@ -22,7 +22,7 @@ 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 pytest numpy pybind11==2.2.1 xtensor==0.20.0 -c conda-forge
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- conda install pytest numpy pybind11==2.2.1 xtensor==0.20.4 -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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else
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conda install pybind11==${PYBIND11_VERSION} -c conda-forge
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fi
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- conda install xtensor==0.20.0 -c conda-forge
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- conda install xtensor==0.20.4 -c conda-forge
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- cmake -D DOWNLOAD_GTEST=ON -D CMAKE_INSTALL_PREFIX=$HOME/miniconda -D PYTHON_EXECUTABLE=$PY_EXE .
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- make -j2 test_xtensor_python
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- make install

README.md

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| `xtensor-python` | `xtensor` | `pybind11` |
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|------------------|-----------|------------------|
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| master | ^0.20.0 | ~2.2.1 |
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| master | ^0.20.4 | ~2.2.1 |
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| 0.22.x | ^0.19.0 | ~2.2.1 |
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| 0.21.x | ^0.18.0 | ~2.2.1 |
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| 0.20.x | ^0.17.0 | ~2.2.1 |

include/xtensor-python/pyarray.hpp

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* @class pyarray
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* @brief Multidimensional container providing the xtensor container semantics to a numpy array.
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*
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* pyarray is similar to the xarray container in that it has a dynamic dimensionality. Reshapes of
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* a pyarray container are reflected in the underlying numpy array.
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* pyarray is similar to the xarray container in that it has a dynamic dimensionality.
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* Reshapes of a pyarray container are reflected in the underlying numpy array.
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*
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* @tparam T The type of the element stored in the pyarray.
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* @tparam L Static layout of the pyarray
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*
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* @sa pytensor
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*/
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template <class T, layout_type L>

include/xtensor-python/pytensor.hpp

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*
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* pytensor is similar to the xtensor container in that it has a static dimensionality.
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*
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* Unlike with the pyarray container, pytensor cannot be reshaped with a different number of dimensions
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* Unlike the pyarray container, pytensor cannot be reshaped with a different number of dimensions
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* and reshapes are not reflected on the Python side. However, pytensor has benefits compared to pyarray
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* in terms of performances. pytensor shapes are stack-allocated which makes iteration upon pytensor
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* faster than with pyarray.

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