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        "author": "Martin R. Albrecht",
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        "description": "fpylll\n======\n\nA Python (2 and 3) wrapper for `fplll <https://github.com/fplll/fplll>`__.\n\n.. image:: https://travis-ci.org/fplll/fpylll.svg?branch=master\n    :target: https://travis-ci.org/fplll/fpylll\n.. image:: https://badge.fury.io/py/fpylll.svg\n    :target: https://badge.fury.io/py/fpylll\n.. image:: https://readthedocs.org/projects/fpylll/badge/?version=latest\n    :target: http://fpylll.readthedocs.io/en/latest/?badge=latest\n\n.. code-block:: python\n\n    >>> from fpylll import *\n   \n    >>> A = IntegerMatrix(50, 50)\n    >>> A.randomize(\"ntrulike\", bits=50, q=127)\n    >>> A[0].norm()\n    3564748886669202.5\n\n    >>> M = GSO.Mat(A)\n    >>> M.update_gso()\n    >>> M.get_mu(1,0)\n    0.815748944429783\n\n    >>> L = LLL.Reduction(M)\n    >>> L()\n    >>> M.get_mu(1,0)\n    0.41812865497076024\n    >>> A[0].norm()\n    24.06241883103193\n\nThe basic BKZ algorithm can be implemented in about 60 pretty readable lines of Python code (cf. `simple_bkz.py <https://github.com/fplll/fpylll/blob/master/src/fpylll/algorithms/simple_bkz.py>`__).\nFor a quick tour of the library, you can check out the `tutorial`.\n             \nRequirements\n------------\n\n**fpylll** relies on the following C/C++ libraries:\n\n- `GMP <https://gmplib.org>`__ or `MPIR <http://mpir.org>`__ for arbitrary precision integer arithmetic.\n- `MPFR <http://www.mpfr.org>`__ for arbitrary precision floating point arithmetic.\n- `QD <http://crd-legacy.lbl.gov/~dhbailey/mpdist/>`__ for double double and quad double arithmetic (optional).\n- `fplll <https://github.com/fplll/fplll>`__ for pretty much everything.\n\n**fpylll** also relies on\n\n- `Cython <http://cython.org>`__ for linking Python and C/C++.\n- `cysignals <https://github.com/sagemath/cysignals>`__ for signal handling such as interrupting C++ code.\n- `py.test <http://pytest.org/latest/>`__ for testing Python.\n- `flake8 <https://flake8.readthedocs.org/en/latest/>`__ for linting.\n\nWe also suggest\n\n- `IPython  <https://ipython.org>`__ for interacting with Python\n- `Numpy <http://www.numpy.org>`__ for numerical computations (e.g. with Gram-Schmidt values)\n\nOnline\n------\n\n**fpylll** ships with Sage 7.4. Thus, it is available via `SageMathCell <http://sagecell.sagemath.org/?z=eJxtjk1rwzAMhu-F_gfRUzpCKGODXXxwWTfGWlrWDPZBMWrjFK-2lcketPv1U0657CJePUiP1DIFaLuL9x5c6IgzXI1HGhQ8xWyPlleY2Z0rxthQKO5mJUy-kS-TEoLqu5O6kbp3OUmYjkcdu5hBf852VSQOhaCUGcXlbBKtJ2zMQMxXoljMnz-q-8WDfl3WZlu_6Hrx-C6LPWbb_ByykyFdQg82yBiKvafDyST3a9W13B-EaojyIp6NJ-qSui2h9XhMqles9JtZrteb7fT_h_8AredZkw==&lang=sage>`__ and `SageMathCloud <https://cloud.sagemath.com>`__ (select a Jupyter notebook with a Sage 7.4 kernel, the default Sage worksheet still runs Sage 7.3 at the time of writing). You can also fire up a `dply.co virtual server <https://dply.co/b/pBZ2QbxW>`__ with the latest fpylll/fplll preinstalled (it takes perhaps 15 minutes until everything is compiled).\n\nGetting Started\n---------------\n\n**Note:** fpylll is also available via `PyPI <https://pypi.python.org/pypi/fpylll/>`__ and `Conda-Forge <https://conda-forge.github.io>`__ for `Conda <https://conda.io/docs/>`__. In what follows, we explain manual installation.\n\nWe recommend `virtualenv <https://virtualenv.readthedocs.org/>`__ for isolating Python build environments and `virtualenvwrapper <https://virtualenvwrapper.readthedocs.org/>`__ to manage virtual environments.\n\n1. Create a new virtualenv and activate it:\n\n   .. code-block:: bash\n\n     $ virtualenv env\n     $ source ./env/bin/activate\n\n   We indicate active virtualenvs by the prefix ``(fpylll)``.\n\n2. Install the required libraries - `GMP <https://gmplib.org>`__ or `MPIR <http://mpir.org>`__ and `MPFR <http://www.mpfr.org>`__  - if not available already. You may also want to install `QD <http://crd-legacy.lbl.gov/~dhbailey/mpdist/>`__.\n\n3. Install fplll:\n\n   .. code-block:: bash\n\n     $ (fpylll) ./install-dependencies.sh $VIRTUAL_ENV\n\n   Some OSX users report that they required ``export CXXFLAGS=\"-stdlib=lic++ -mmacosx-version-min=10.7`` and ``export CXX=clang++`` (after installing a recent clang with `brew <https://brew.sh>`__) since the default GCC installed by Apple does not have full C++11 support.\n    \n4. Then, execute:\n\n   .. code-block:: bash\n\n     $ (fpylll) pip install Cython\n     $ (fpylll) pip install -r requirements.txt\n\n   to install the required Python packages (see above).\n\n5. If you are so inclined, run:\n\n   .. code-block:: bash\n\n     $ (fpylll) pip install -r suggestions.txt\n\n   to install suggested Python packages as well (optional).\n\n6. Build the Python extension:\n\n   .. code-block:: bash\n\n     $ (fpylll) export PKG_CONFIG_PATH=\"$VIRTUAL_ENV/lib/pkgconfig:$PKG_CONFIG_PATH\"\n     $ (fpylll) python setup.py build_ext\n     $ (fpylll) python setup.py install\n\n7. To run **fpylll**, you will need to:\n\n   .. code-block:: bash\n\n     $ (fpylll) export LD_LIBRARY_PATH=\"$VIRTUAL_ENV/lib\"\n\n   so that Python can find fplll and friends.\n\n8. Start Python:\n\n   .. code-block:: bash\n\n    $ (fpylll) ipython\n\nTo reactivate the virtual environment later, simply run:\n\n   .. code-block:: bash\n\n    $ source ./env/bin/activate\n\nNote that you can also patch ``activate`` to set ``LD_LIBRRY_PATH``. For this, add:\n\n.. code-block:: bash\n\n    ### LD_LIBRARY_HACK\n    _OLD_LD_LIBRARY_PATH=\"$LD_LIBRARY_PATH\"\n    LD_LIBRARY_PATH=\"$VIRTUAL_ENV/lib:$LD_LIBRARY_PATH\"\n    export LD_LIBRARY_PATH\n    ### END_LD_LIBRARY_HACK\n\n    ### PKG_CONFIG_HACK\n    _OLD_PKG_CONFIG_PATH=\"$PKG_CONFIG_PATH\"\n    PKG_CONFIG_PATH=\"$VIRTUAL_ENV/lib/pkgconfig:$PKG_CONFIG_PATH\"\n    export PKG_CONFIG_PATH\n    ### END_PKG_CONFIG_HACK\n\ntowards the end and:\n\n.. code-block:: bash\n\n    ### LD_LIBRARY_HACK\n    if ! [ -z ${_OLD_LD_LIBRARY_PATH+x} ] ; then\n        LD_LIBRARY_PATH=\"$_OLD_LD_LIBRARY_PATH\"\n        export LD_LIBRARY_PATH\n        unset _OLD_LD_LIBRARY_PATH\n    fi\n    ### END_LD_LIBRARY_HACK\n\n    ### PKG_CONFIG_HACK\n    if ! [ -z ${_OLD_PKG_CONFIG_PATH+x} ] ; then\n        PKG_CONFIG_PATH=\"$_OLD_PKG_CONFIG_PATH\"\n        export PKG_CONFIG_PATH\n        unset _OLD_PKG_CONFIG_PATH\n    fi\n    ### END_PKG_CONFIG_HACK\n\nin the ``deactivate`` function in the ``activate`` script.\n\nMulticore Support\n-----------------\n\n**fpylll** supports parallelisation on multiple cores. For all C++ support to drop the `GIL <https://wiki.python.org/moin/GlobalInterpreterLock>`_ is enabled, allowing the use of threads to parallelise. Fplll is thread safe as long as each thread works on a separate object such as ``IntegerMatrix`` or ``MatGSO``. Also, **fpylll** does not actually drop the GIL in all calls to C++ functions yet. In many scenarios using `multiprocessing <https://docs.python.org/2/library/multiprocessing.html>`_, which sidesteps the GIL and thread safety issues by using processes instead of threads, will be the better choice.\n\nThe example below calls ``LLL.reduction`` on 128 matrices of dimension 30 on four worker processes.\n\n.. code-block:: python\n\n    from fpylll import IntegerMatrix, LLL\n    from multiprocessing import Pool\n\n    d, workers, tasks = 30, 4, 128\n    \n    def run_it(p, f, A, prefix=\"\"):\n        \"\"\"Print status during parallel execution.\"\"\"         \n        import sys\n        r = []\n        for i, retval in enumerate(p.imap_unordered(f, A, 1)):\n            r.append(retval)\n            sys.stderr.write('\\r{0} done: {1:.2%}'.format(prefix, float(i)/len(A)))\n            sys.stderr.flush()\n        sys.stderr.write('\\r{0} done {1:.2%}\\n'.format(prefix, float(i+1)/len(A)))\n        return r\n        \n    A = [IntegerMatrix.random(d, \"uniform\", bits=30) for _ in range(tasks)]    \n    A = run_it(Pool(workers), LLL.reduction, A)\n\nTo test threading simply replace the line ``from multiprocessing import Pool`` with ``from multiprocessing.pool import ThreadPool as Pool``. For calling ``BKZ.reduction`` this way, which expects a second parameter with options, using `functools.partial <https://docs.python.org/2/library/functools.html#functools.partial>`_ is a good choice. \n    \nContributing\n------------\n\n**fpylll** welcomes contributions, cf. the list of `open issues <https://github.com/fplll/fpylll/issues>`_. To contribute, clone this repository, commit your code on a separate branch and send a pull request. Please write tests for your code. You can run them by calling::\n\n    $ (fpylll) py.test\n\nfrom the top-level directory which runs all tests in ``tests/test_*.py``. We run `flake8 <https://flake8.readthedocs.org/en/latest/>`_ on every commit automatically, In particular, we run::\n\n    $ (fpylll) flake8 --max-line-length=120 --max-complexity=16 --ignore=E22,E241 src\n\nNote that **fpylll** supports Python 2 and 3. In particular, tests are run using Python 2.7 and 3.5. See `.travis.yml <https://github.com/fplll/fpylll/blob/master/.travis.yml>`_ for details on automated testing.\n\nAttribution & License\n---------------------\n\n**fpylll** is maintained by Martin Albrecht.\n\nThe following people have contributed to **fpylll**\n\n+ E M Bray\n+ Fernando Virdia\n+ Guillaume Bonnoron\n+ Jeroen Demeyer\n+ J\u00e9r\u00f4me Benoit\n+ Konstantinos Draziotis\n+ Leo Ducas\n+ Martin Albrecht\n+ Michael Walter\n+ Omer Katz\n+ Fernando Virdia\n\nWe copied a decent bit of code over from Sage, mostly from it's fpLLL interface.\n\n**fpylll** is licensed under the GPLv2+.  \n",
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