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        "author": "Francois Cuvelier",
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        "description": ".. _fc-bench: http://www.math.univ-paris13.fr/~cuvelier/software/Python/fc-bench.html \n   \n.. _www.python.org: http://www.python.org/\n\n.. _Miniconda: https://conda.io/miniconda.html\n\n.. _Anaconda: https://www.anaconda.com\n\n.. _Canopy: https://www.enthought.com/product/canopy/\n\n.. _Mayavi: http://docs.enthought.com/mayavi/mayavi/\n\n.. _Matplotlib: https://matplotlib.org/\n    \n.. image:: http://www.math.univ-paris13.fr/~cuvelier/software/codes/Python/fc-bench/pyfc-bench_400.png\n  :width: 400px\n  :align: center\n\nThe **fc\\_bench** Python package allows to benchmark functions and much more\n   \n\n   \nIntroduction:\n------------- \n\n.. _fc\\_bench Python package: http://www.math.univ-paris13.fr/~cuvelier/software/Python/fc-bench.html \n\n.. _User's Guide: http://www.math.univ-paris13.fr/~cuvelier/software/codes/Python/fc-bench/0.0.3/fc_bench-0.0.3.pdf\n   \nMore documentation is available on `fc\\_bench Python package`_ dedicated web page with an `User's Guide`_ (pdf).\n\n\nThis package was tested under:\n\n    - **CentOS 7**, **Fedora 27**\n       - with Python 2.7.14 compiled from source (www.python.org_)\n       - with Python 3.5.5 compiled from source (www.python.org_)\n       - with Python 3.6.4 compiled from source (www.python.org_)\n    - **MacOS High Sierra**\n       - with Miniconda_ Python 2.7 distribution (release 2.7.14)\n       - with Miniconda_ Python 3.6 distribution (release 3.6.4)\n       - with Python 2.7.14 (www.python.org_)\n       - with Python 3.5.4 (www.python.org_)\n       - with Python 3.6.5 (www.python.org_)\n    - **openSUSE Leap 42.3 7**\n       - with Python 2.7.14 compiled from source (www.python.org_)\n       - with Python 3.4.8 compiled from source (www.python.org_)\n       - with Python 3.5.5 compiled from source (www.python.org_)\n       - with Python 3.6.5 compiled from source (www.python.org_)\n    - **Ubuntu 18.04 LTS, 17.10, 16.04 LTS** \n       - with Python 2.7.x (x=13,14,15) compiled from source (www.python.org_)\n       - with Python 3.6.x (x=3,4,5) compiled from source (www.python.org_)\n       - with Python 3.5.5 compiled from source (www.python.org_)\n    - **Windows 10** \n       - with Miniconda_ Python 2.7 distribution (release 2.7.14)\n       - with Miniconda_ Python 3.6 distribution (release 3.6.4)\n       - with Python 2.7.15 (www.python.org_)\n       - with Python 3.5.4 (www.python.org_)\n       - with Python 3.6.5 (www.python.org_)\n\n\nInstallation:\n-------------\n\nThe **fc\\_bench** Python package is available from the Python Package Index, so to install/upgrade simply type\n\n.. code:: \n\n    pip install fc_bench -U\n      \nTesting :\n---------      \n  There are demos functions in the ``fc_bench.demos`` module named ``bench_*``.\n  For example, run the following code under Python:\n  \n  .. code:: python\n  \n      from fc_bench import demos\n      demos.bench_Lagrange()\n      \n  The output of this code is::\n  \n      #---------------------------------------------------------------------------\n      # Benchmarking functions:\n      #  fun[0],            Lag: fc_bench.demos.Lagrange\n      #  fun[1],         lagint: fc_bench.demos.lagint\n      # cmpErr[i], error between fun[0] and fun[i] outputs computed with function\n      #    lambda o1,o2: np.linalg.norm(o1-o2,np.inf)\n      # where\n      #    - 1st input parameter is the output of fun[0]\n      #    - 2nd input parameter is the output of fun[i]\n      #---------------------------------------------------------------------------\n      # Setting inputs of Lagrange polynomial functions: y=LAGRANGE(X,Y,x)\n      # where X is numpy.linspace(a,b,n+1), Y=fun(X) and x is random values on [a,b]\n      #   n is the order of the Lagrange polynomial\n      #   fun function is: lambda x: np.sin(x)\n      #   [a,b]=[-1,1]\n      #   X: (n+1,) numpy array\n      #   Y: (n+1,) numpy array\n      #   x: (m,)   numpy array\n      #   Error[i] computed with fun[i] output:\n      #     lambda y: np.linalg.norm(y-fun(x),np.inf)\n      #---------------------------------------------------------------------------\n      #date:2018/05/03 10:16:55\n      #nbruns:5\n      #numpy:      i4     i4        f4          f4           f4          f4           f4\n      #format:  {:>5}  {:>5}   {:8.3f}    {:10.3e}     {:11.3f}    {:10.3e}     {:11.3e}\n      #labels:      m      n    Lag(s)    Error[0]    lagint(s)    Error[1]    cmpErr[1]\n                  100      5     0.012   1.163e-05        0.014   1.163e-05    3.331e-16\n                  100      9     0.020   2.859e-10        0.023   2.859e-10    8.882e-16\n                  100     15     0.036   2.143e-14        0.038   2.143e-14    2.565e-14\n                  500      5     0.056   1.162e-05        0.071   1.162e-05    5.551e-16\n                  500      9     0.102   2.901e-10        0.118   2.901e-10    1.443e-15\n                  500     15     0.178   2.232e-14        0.188   2.232e-14    2.287e-14\n                 1000      5     0.111   1.163e-05        0.146   1.163e-05    5.551e-16\n                 1000      9     0.202   2.902e-10        0.235   2.902e-10    1.554e-15\n                 1000     15     0.361   2.576e-14        0.377   2.576e-14    2.620e-14",
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