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        "author": "Pierre Chanial",
        "author_email": "pierre.chanial@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
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            "Programming Language :: C",
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            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering"
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        "description": "===========\nPyOperators\n===========\n\nThe PyOperators package defines operators and solvers for high-performance computing. These operators are multi-dimensional functions with optimised and controlled memory management. If linear, they behave like matrices with a sparse storage footprint.\n\nMore documentation can be found here: http://pchanial.github.io/pyoperators.\n\nGetting started\n===============\n\nTo define an operator, one needs to define a direct function\nwhich will replace the usual matrix-vector operation:\n\n>>> def f(x, out):\n...     out[...] = 2 * x\n\nThen, you can instantiate an ``Operator``:\n\n>>> A = pyoperators.Operator(direct=f, flags='symmetric')\n\nAn alternative way to define an operator is to define a subclass:\n\n>>> from pyoperators import flags, Operator\n... @flags.symmetric\n... class MyOperator(Operator):\n...     def direct(x, out):\n...         out[...] = 2 * x\n...\n... A = MyOperator()\n\nThis operator does not have an explicit shape, it can handle inputs of any shape:\n\n>>> A(np.ones(5))\narray([ 2.,  2.,  2.,  2.,  2.])\n>>> A(np.ones((2,3)))\narray([[ 2.,  2.,  2.],\n       [ 2.,  2.,  2.]])\n\nBy setting the ``symmetric`` flag, we ensure that A's transpose is A:\n\n>>> A.T is A\nTrue\n\nFor non-explicit shape operators, we get the corresponding dense matrix by specifying the input shape:\n\n>>> A.todense(shapein=2)\narray([[2, 0],\n       [0, 2]])\n\nOperators do not have to be linear. Many operators are already `predefined <http://pchanial.github.io/pyoperators/2000/doc-operators/#list>`_, such as the ``IdentityOperator``, the ``DiagonalOperator`` or the nonlinear ``ClipOperator``.\n\nThe previous ``A`` matrix could be defined more easily like this:\n\n>>> from pyoperators import I\n>>> A = 2 * I\n\nwhere ``I`` is the identity operator with no explicit shape.\n\nOperators can be combined together by addition, element-wise multiplication or composition. Note that the operator ``*`` stands for matrix multiplication if the two operators are linear, or for element-wise multiplication otherwise:\n\n>>> from pyoperators import I, DiagonalOperator\n>>> B = 2 * I + DiagonalOperator(range(3))\n>>> B.todense()\narray([[2, 0, 0],\n       [0, 3, 0],\n       [0, 0, 4]])\n\nAlgebraic rules can easily be attached to operators. They are used to simplify expressions to speed up their execution. The ``B`` Operator has been reduced to:\n\n>>> B\nDiagonalOperator(array([2, ..., 4], dtype=int64), broadcast='disabled', dtype=int64, shapein=3, shapeout=3)\n\nMany simplifications are available. For instance:\n\n>>> from pyoperators import Operator\n>>> C = Operator(flags='idempotent,linear')\n>>> C * C is C\nTrue\n>>> D = Operator(flags='involutary')\n>>> D(D)\nIdentityOperator()\n\n\nRequirements\n============\n\nList of requirements:\n\n- python 2.6\n- numpy >= 1.6\n- scipy >= 0.9\n\nOptional requirements:\n\n- numexpr (>= 2.0 is better)\n- PyWavelets : wavelet transforms\n",
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