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        "description": "pycvodes\n========\n\n.. image:: http://hera.physchem.kth.se:9090/api/badges/bjodah/pycvodes/status.svg\n   :target: http://hera.physchem.kth.se:9090/bjodah/pycvodes\n   :alt: Build status on private Drone server\n.. image:: https://circleci.com/gh/bjodah/pycvodes.svg?style=svg\n   :target: https://circleci.com/gh/bjodah/pycvodes\n   :alt: Build status on CircleCI\n.. image:: https://secure.travis-ci.org/bjodah/pycvodes.svg?branch=master\n   :target: http://travis-ci.org/bjodah/pycvodes\n   :alt: Build status on Travis-CI\n.. image:: https://img.shields.io/pypi/v/pycvodes.svg\n   :target: https://pypi.python.org/pypi/pycvodes\n   :alt: PyPI version\n.. image:: https://img.shields.io/pypi/l/pycvodes.svg\n   :target: https://github.com/bjodah/pycvodes/blob/master/LICENSE\n   :alt: License\n.. image:: http://hera.physchem.kth.se/~pycvodes/branches/master/htmlcov/coverage.svg\n   :target: http://hera.physchem.kth.se/~pycvodes/branches/master/htmlcov\n   :alt: coverage\n.. image:: https://zenodo.org/badge/43224425.svg\n   :target: https://zenodo.org/badge/latestdoi/43224425\n\n`pycvodes <https://github.com/bjodah/pycvodes>`_ provides a\n`Python <http://www.python.org>`_ binding to the\n`Ordinary Differential Equation <https://en.wikipedia.org/wiki/Ordinary_differential_equation>`_\nintegration routines from `cvodes <https://computation.llnl.gov/casc/sundials/description/description.html#descr_cvodes>`_ in the\n`SUNDIALS suite <https://computation.llnl.gov/casc/sundials/main.html>`_. ``pycvodes`` allows a user to numerically integrate\n(systems of) differential equations. Note that routines for sensitivity analysis is not yet exposed in this binding (which makes\nthe functionality essentially the same as cvode). \n\nThe following multistep methods are available:\n\n- ``bdf``: Backward differentiation formula (of order 1 to 5)\n- ``adams``: implicit Adams method (order 1 to 12)\n\nNote that bdf (as an implicit stepper) requires a user supplied\ncallback for calculating the jacobian.\n\nYou may also want to know that you can use ``pycvodes`` from\n`pyodesys <https://github.com/bjodah/pyodesys>`_\nwhich can e.g. derive the Jacobian analytically (using SymPy). Pyodesys also provides\nplotting functions, C++ code-generation and more.\n\nDocumentation\n-------------\nAutogenerated API documentation for latest stable release is found here:\n`<https://bjodah.github.io/pycvodes/latest>`_\n(and the development version for the current master branch are found here:\n`<http://hera.physchem.kth.se/~pycvodes/branches/master/html>`_).\n\nInstallation\n------------\nSimplest way to install is to use the `conda package manager <http://conda.pydata.org/docs/>`_:\n\n::\n\n   $ conda install -c conda-forge pycvodes pytest\n   $ python -m pytest --pyargs pycvodes\n\ntests should pass.\n\nManual installation\n~~~~~~~~~~~~~~~~~~~\nBinary distribution is available here:\n`<https://anaconda.org/bjodah/pycvodes>`_\n\nSource distribution is available here:\n`<https://pypi.python.org/pypi/pycvodes>`_\n\nWhen installing from source you can choose what lapack lib to link against by setting\nthe environment variable ``PYCVODES_LAPACK``, your choice can later be accessed from python:\n\n.. code:: python\n\n   >>> from pycvodes import _config\n   >>> _config.env['LAPACK']  # doctest: +SKIP\n   'lapack,blas'\n\nIf you use ``pip`` to install ``pycvodes`` note that you will need to install sundials\n(and its development headers, with cvodes & lapack enabled) prior to installing pycvodes.\n\nExamples\n--------\nThe classic van der Pol oscillator (see `examples/van_der_pol.py <examples/van_der_pol.py>`_)\n\n.. code:: python\n\n   >>> import numpy as np\n   >>> from pycvodes import integrate_predefined  # also: integrate_adaptive\n   >>> mu = 1.0\n   >>> def f(t, y, dydt):\n   ...     dydt[0] = y[1]\n   ...     dydt[1] = -y[0] + mu*y[1]*(1 - y[0]**2)\n   ... \n   >>> def j(t, y, Jmat, dfdt=None, fy=None):\n   ...     Jmat[0, 0] = 0\n   ...     Jmat[0, 1] = 1\n   ...     Jmat[1, 0] = -1 - mu*2*y[1]*y[0]\n   ...     Jmat[1, 1] = mu*(1 - y[0]**2)\n   ...     if dfdt is not None:\n   ...         dfdt[:] = 0\n   ...\n   >>> y0 = [1, 0]; dt0=1e-8; t0=0.0; atol=1e-8; rtol=1e-8\n   >>> tout = np.linspace(0, 10.0, 200)\n   >>> yout, info = integrate_predefined(f, j, y0, tout, atol, rtol, dt0,\n   ...                                   method='bdf')\n   >>> import matplotlib.pyplot as plt\n   >>> series = plt.plot(tout, yout)\n   >>> plt.show()  # doctest: +SKIP\n\n\n.. image:: https://raw.githubusercontent.com/bjodah/pycvodes/master/examples/van_der_pol.png\n\nFor more examples see `examples/ <https://github.com/bjodah/pycvodes/tree/master/examples>`_, and rendered jupyter notebooks here:\n`<http://hera.physchem.kth.se/~pycvodes/branches/master/examples>`_\n\n\nLicense\n-------\nThe source code is Open Source and is released under the simplified 2-clause BSD license. See `LICENSE <LICENSE>`_ for further details.\n\nContributors are welcome to suggest improvements at https://github.com/bjodah/pycvodes\n\nAuthor\n------\nBj\u00f6rn I. Dahlgren, contact:\n\n- gmail address: bjodah\n\nSee file `AUTHORS <AUTHORS>`_ in root for a list of all authors.",
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