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|rss_image|  **blog page - 1/1** :ref:`Blog <ap-main-0>` :ref:`C++ (3) <ap-cat-C++-0>`

.. |rss_image| image:: feed-icon-16x16.png
    :target: ../_downloads/rss.xml
    :alt: RSS

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.. index:: blog


.. _ap-main-0:

blog page - 1/1
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.. blogpostagg::
    :title: Issues with installation from source on Windows
    :date: 2021-01-04
    :keywords: Windows,win32,pip
    :categories: installation
    :rawfile: 2021/2021-01-04_win.rst

    The installation may fail on Windows when the model
    is compiled from the source. I could not find a particular
    reason. It works when :epkg:`numpy` is uninstalled
    before running `pip install cpyquickhelper`.
    Otherwise, this is the kind of error which appear:
    

    ...




.. blogpostagg::
    :title: cppyy: a new module for to C++ in python
    :date: 2019-09-25
    :keywords: C++,cppyy,clang,cling
    :categories: C++
    :rawfile: 2019/2019-09-25_cppyy.rst

    I did not try but the approach seems interesting
    and less verbose than any others.
    `cppyy <https://cppyy.readthedocs.io/en/latest/index.html>`_
    uses `cling <https://root.cern.ch/cling>`_ to extend
    Python code with C++ code during the execution.
    The `tutorial
    <https://bitbucket.org/wlav/cppyy/src/master/doc/tutorial/CppyyTutorial.ipynb
    ?viewer=nbviewer&fileviewer=notebook-viewer%3Anbviewer>`_
    contains the following example:
    

    ...




.. blogpostagg::
    :title: Mixing pybind11 modules
    :date: 2019-03-19
    :keywords: pybind11,C++
    :categories: pybind11
    :rawfile: 2019/2019-03-19_splitcppmodule.rst

    My scenario was the following: expose a C++ class in
    one C++ module exported with :epkg:`pybind11`, then
    create a function in a another C++ module returning
    a result of this class. The solution for that is
    described in the following issue
    `Return type that is implemented in another library with another set of python bindings
    <https://github.com/pybind/pybind11/issues/616>`_, which
    links to this part of the documentation
    `Custom type casters <https://pybind11.readthedocs.io/en/stable/advanced/cast/custom.html>`_.
    and implemented in this module somewhere in
    `cbenchmark_dot.cpp <https://github.com/sdpython/cpyquickhelper/blob/master/cpyquickhelper/numbers/cbenchmark_dot.cpp#L608>`_.




.. blogpostagg::
    :title: Branching
    :date: 2019-02-04
    :keywords: branching,optimization
    :categories: processor
    :rawfile: 2019/2019-02-04_benchmark.rst

    I wanted to test some facts described in the following blog post:
    `Why is it faster to process a sorted array than an unsorted array?
    <https://stackoverflow.com/questions/11227809/why-is-it-faster-to-process-a-sorted-array-than-an-unsorted-array>`_.
    I ended it writing this notebook :ref:`cbenchmarkbranchingrst`
    which compares a couple of implementations of the same computation,
    a dot product.




.. blogpostagg::
    :title: Cython, Pythran, nuitka, numba
    :date: 2018-08-05
    :keywords: Cython,Pythran,nuitka,numba
    :categories: C++
    :rawfile: 2018/2018-08-05_pythran.rst

    I discovered than :epkg:`Pythran` was now a possible
    backend for :epkg:`Cython`:
    `Pythran as a Numpy backend
    <https://cython.readthedocs.io/en/latest/src/userguide/numpy_pythran.html>`_.
    On one benchmark, it increases the speed by 2. I recommend the reading of
    the following article:
    `Optimizing your code with NumPy, Cython, pythran and numba
    <https://flothesof.github.io/optimizing-python-code-numpy-cython-pythran-numba.html>`_
    which instigates the performance brought by the four
    following tool and gives some hints on how to write
    efficient code with these tools:
    

    ...




.. blogpostagg::
    :title: Exploration with pybind11 and ExtensionArray
    :date: 2018-08-03
    :keywords: pybind11,C++,pandas
    :categories: C++
    :rawfile: 2018/2018-08-03_pybind11.rst

    I tried the version of :epkg:`pybind11`
    to expose a dummy C++ object
    :class:`WeightedDouble <cpyquickhelper.numbers.weighted.number.WeightedDouble>`,
    to implement a couple of operators and to see
    how it behaves into a dataframe.
    

    ...




.. blogpostagg::
    :title: Call C# from Python
    :date: 2017-09-17
    :keywords: reference,blog,post
    :categories: C#,DLL
    :rawfile: 2017/2017-09-17_csharp_python.rst

    A couple of questions must be answers to do that.
    

    ...





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|rss_image|  **blog page - 1/1** :ref:`2018-08 (2) <ap-month-2018-08-0>` :ref:`2019-02 (1) <ap-month-2019-02-0>` :ref:`2019-03 (1) <ap-month-2019-03-0>` :ref:`2019-09 (1) <ap-month-2019-09-0>` :ref:`2021-01 (1) <ap-month-2021-01-0>`