pyodeint
========
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`pyodeint <https://github.com/bjodah/pyodeint>`_ provides a
`Python <http://www.python.org>`_ binding to `odeint <http://www.odeint.com>`_.
Currently, the following steppers are exposed:
- ``rosenbrock4``: 4th order Rosenbrock (implicit multistep) stepper
- ``dopri5``: 5th order DOPRI5 (explicit runge-kutta)
- ``bs``: Bulirsch-Stoer stepper (modified midpoint rule).
The Rosenbrock4 stepper requires that the user provides a routine for
calculating the Jacobian.
You may also want to know that you can use ``pyodeint`` from
`pyodesys <https://github.com/bjodah/pyodesys>`_
which can e.g. derive the Jacobian analytically for you (pyodesys also provides
plotting functions, C++ code-generation and more).
Documentation
-------------
Autogenerated API documentation for latest stable release is found here:
`<https://bjodah.github.io/pyodeint/latest>`_
(and the development version for the current master branch are found here:
`<http://hera.physchem.kth.se/~pyodeint/branches/master/html>`_).
Installation
------------
Simplest way to install is to use the `conda package manager <http://conda.pydata.org/docs/>`_:
::
$ conda install -c conda-forge pyodeint pytest
$ python -m pytest --pyargs pyodeint
tests should pass.
Binary distribution is available here:
`<https://anaconda.org/bjodah/pyodeint>`_
Source distribution is available here:
`<https://pypi.python.org/pypi/pyodeint>`_
here is an example of how to build from source::
$ CPATH=/opt/boost_1_65_0/include python3 setup.py build_ext -i
Examples
--------
The classic van der Pol oscillator (see `examples/van_der_pol.py <examples/van_der_pol.py>`_)
.. code:: python
>>> from pyodeint import integrate_adaptive # also: integrate_predefined
>>> mu = 1.0
>>> def f(t, y, dydt):
... dydt[0] = y[1]
... dydt[1] = -y[0] + mu*y[1]*(1 - y[0]**2)
...
>>> def j(t, y, Jmat, dfdt, fy=None):
... Jmat[0, 0] = 0
... Jmat[0, 1] = 1
... Jmat[1, 0] = -1 -mu*2*y[1]*y[0]
... Jmat[1, 1] = mu*(1 - y[0]**2)
... dfdt[0] = 0
... dfdt[1] = 0
...
>>> y0 = [1, 0]; tend=10.0; dt0=1e-8; t0=0.0; atol=1e-8; rtol=1e-8
>>> tout, yout, info = integrate_adaptive(f, j, y0, t0, tend, dt0, atol, rtol,
... method='rosenbrock4', nsteps=1000)
>>> import matplotlib.pyplot as plt
>>> series = plt.plot(tout, yout)
>>> plt.show() # doctest: +SKIP
.. image:: https://raw.githubusercontent.com/bjodah/pyodeint/master/examples/van_der_pol.png
For more examples see `examples/ <https://github.com/bjodah/pyodeint/tree/master/examples>`_, and rendered jupyter notebooks here:
`<http://hera.physchem.kth.se/~pyodeint/branches/master/examples>`_
See also
--------
`pyodesys <https://github.com/bjodah/pyodesys>`_ for how to automatically
generate the jacobian callback function (and easily swtich to other solvers).
License
-------
The source code is Open Source and is released under the very permissive
"simplified (2-clause) BSD license". See ``LICENSE`` for further details.
Contributors are welcome to suggest improvements at https://github.com/bjodah/pyodeint
Author
------
Björn I. Dahlgren, contact:
- gmail address: bjodah
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"description": "pyodeint\n========\n\n.. image:: http://hackspett.bjodah.se/api/badges/6/status.svg\n :target: http://hackspett.bjodah.se/repos/6\n :alt: Build status\n.. image:: https://img.shields.io/pypi/v/pyodeint.svg\n :target: https://pypi.python.org/pypi/pyodeint\n :alt: PyPI version\n.. image:: https://img.shields.io/pypi/l/pyodeint.svg\n :target: https://github.com/bjodah/pyodeint/blob/master/LICENSE\n :alt: License\n.. image:: http://hera.physchem.kth.se/~pyodeint/branches/master/htmlcov/coverage.svg\n :target: http://hera.physchem.kth.se/~pyodeint/branches/master/htmlcov\n :alt: coverage\n.. image:: https://zenodo.org/badge/41257136.svg\n :target: https://zenodo.org/badge/latestdoi/41257136\n\n`pyodeint <https://github.com/bjodah/pyodeint>`_ provides a\n`Python <http://www.python.org>`_ binding to `odeint <http://www.odeint.com>`_.\nCurrently, the following steppers are exposed:\n\n- ``rosenbrock4``: 4th order Rosenbrock (implicit multistep) stepper\n- ``dopri5``: 5th order DOPRI5 (explicit runge-kutta)\n- ``bs``: Bulirsch-Stoer stepper (modified midpoint rule).\n\nThe Rosenbrock4 stepper requires that the user provides a routine for\ncalculating the Jacobian.\n\nYou may also want to know that you can use ``pyodeint`` from\n`pyodesys <https://github.com/bjodah/pyodesys>`_\nwhich can e.g. derive the Jacobian analytically for you (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/pyodeint/latest>`_\n(and the development version for the current master branch are found here:\n`<http://hera.physchem.kth.se/~pyodeint/branches/master/html>`_).\n\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 pyodeint pytest\n $ python -m pytest --pyargs pyodeint\n\ntests should pass.\n\nBinary distribution is available here:\n`<https://anaconda.org/bjodah/pyodeint>`_\n\nSource distribution is available here:\n`<https://pypi.python.org/pypi/pyodeint>`_\n\nhere is an example of how to build from source::\n\n $ CPATH=/opt/boost_1_65_0/include python3 setup.py build_ext -i\n\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 >>> from pyodeint import integrate_adaptive # also: integrate_predefined\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, 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 ... dfdt[0] = 0\n ... dfdt[1] = 0\n ...\n >>> y0 = [1, 0]; tend=10.0; dt0=1e-8; t0=0.0; atol=1e-8; rtol=1e-8\n >>> tout, yout, info = integrate_adaptive(f, j, y0, t0, tend, dt0, atol, rtol,\n ... method='rosenbrock4', nsteps=1000)\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/pyodeint/master/examples/van_der_pol.png\n\nFor more examples see `examples/ <https://github.com/bjodah/pyodeint/tree/master/examples>`_, and rendered jupyter notebooks here:\n`<http://hera.physchem.kth.se/~pyodeint/branches/master/examples>`_\n\nSee also\n--------\n`pyodesys <https://github.com/bjodah/pyodesys>`_ for how to automatically\ngenerate the jacobian callback function (and easily swtich to other solvers).\n\nLicense\n-------\nThe source code is Open Source and is released under the very permissive\n\"simplified (2-clause) BSD license\". See ``LICENSE`` for further details.\nContributors are welcome to suggest improvements at https://github.com/bjodah/pyodeint\n\nAuthor\n------\nBj\u00f6rn I. Dahlgren, contact:\n\n- gmail address: bjodah\n",
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