pyodeint 0.5.0

Python binding for odeint from boost.

pyodeint provides a Python binding to odeint. 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.

Documentation

Autogenerated API documentation is found here: https://bjodah.github.com/pyodeint

Installation

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

$ conda install -c bjodah 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

Example

The classic van der Pol oscillator (see examples/van_der_pol.py)

>>> 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):
...     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')
>>> import matplotlib.pyplot as plt
>>> series = plt.plot(tout, yout)
>>> plt.show()  # doctest: +SKIP

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

• kth.se address: bda