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#Numpy and scipy code
The NumPy source code can be downloaded from: Step 2 - Downloading NumPy and SciPy Source Code These have been verified with Intel® MKL 2018, Intel® Compilers 18.0 from Intel® Parallel Studio XE 2018, numpy 1.13.3 and scipy 1.0.0rc2. The procedures described in this article have been tested for both Python 2.7 and Python 3.6. This application note was created to help NumPy/SciPy users to make use of the latest versions of Intel MKL on Linux platforms. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization for python users. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. SciPy include modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
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#Numpy and scipy download
For a prebuilt ready solution, download the Intel® Distribution for Python*. This guide is intended to help current NumPy/SciPy users to take advantage of Intel® Math Kernel Library (Intel® MKL). Installing Intel ® Distribution for Python* and Intel® Performance Libraries with Anaconda* by : /content/www/us/en/develop/articles/using-intel-distribution-for-python-with-anaconda.html Please refer to Intel ® Distribution for Python * mainpage : /content/www/us/en/develop/tools/distribution-for-python.html Instead of build Numpy/Scipy with Intel ® MKL manually as below, we strongly recommend developer to use Intel ® Distribution for Python *, which has prebuild Numpy/Scipy based on Intel® Math Kernel Library (Intel ® MKL) and more. Please note: The application notes is outdated, but keep here for reference.