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Python wrapper around cvodes (from the sundials library)
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pycvodes provides a Python binding to the Ordinary Differential Equation integration routines from cvodes in the SUNDIALS suite. pycvodes allows a user to numerically integrate (systems of) differential equations. Note that routines for sensitivity analysis is not yet exposed in this binding (which makes the functionality essentially the same as cvode).

The following multistep methods are available:

  • bdf: Backward differentiation formula (of order 1 to 5)
  • adams: implicit Adams method (order 1 to 12)

Note that bdf (as an implicit stepper) requires a user supplied callback for calculating the jacobian.

You may also want to know that you can use pycvodes from pyodesys which can e.g. derive the Jacobian analytically (using SymPy). Pyodesys also provides plotting functions, C++ code-generation and more.


Autogenerated API documentation for latest stable release is found here: (and the development version for the current master branch are found here:


Simplest way to install is to use the conda package manager:

$ conda install -c conda-forge pycvodes pytest
$ python -m pytest --pyargs pycvodes

tests should pass.

Manual installation

Binary distribution is available here:

Source distribution is available here:

When installing from source you can choose what lapack lib to link against by setting the environment variable PYCVODES_LAPACK, your choice can later be accessed from python:

>>> from pycvodes import _config
>>> _config.env['LAPACK']  # doctest: +SKIP

If you use pip to install pycvodes note that you will need to install sundials (and its development headers, with cvodes & lapack enabled) prior to installing pycvodes.


The classic van der Pol oscillator (see examples/

>>> import numpy as np
>>> from pycvodes import integrate_predefined  # also: integrate_adaptive
>>> 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=None, 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)
...     if dfdt is not None:
...         dfdt[:] = 0
>>> y0 = [1, 0]; dt0=1e-8; t0=0.0; atol=1e-8; rtol=1e-8
>>> tout = np.linspace(0, 10.0, 200)
>>> yout, info = integrate_predefined(f, j, y0, tout, atol, rtol, dt0,
...                                   method='bdf')
>>> import matplotlib.pyplot as plt
>>> series = plt.plot(tout, yout)
>>>  # doctest: +SKIP

For more examples see examples/, and rendered jupyter notebooks here:


The source code is Open Source and is released under the simplified 2-clause BSD license. See LICENSE for further details.

Contributors are welcome to suggest improvements at


Björn I. Dahlgren, contact:

  • gmail address: bjodah

See file AUTHORS in root for a list of all authors.

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