This is a scikit offering some extra ode/dae solvers, so they can mature outside of scipy. The main solvers to use are the Sundials solvers.
- You need scipy,
- Tested with python 2.7 and 3.3-3.5
- Available solvers:
- BDF linear multistep method for stiff problems. This is done or via cvode, which is an improvement on the ode (vode/dvode) solver in scipy.integrate, or for DAE systems via ida. Both are part of the sundials package. Use it to have modern features
- Adams-Moulton linear multistep method for nonstiff problems. This is done also via cvode or ida (option lmm_type='ADAMS')
- Explicit Runge-Kutta method of order (4)5 with stepsize control. This is done via dopri5 from scipy.integrate.
- Explicit Runge-Kutta method of order 8(5,3) with stepsize control. This is done via dop853 from scipy.integrate.
- Historical solvers: lsodi and ddaspk are available for comparison reasons. Use ida instead!
Since 2.2.0, a new API is available, which will become the default. Typical usage is:
import pylab
import numpy as np
from scikits.odes import ode
t0, y0 = 1, np.array([0.5, 0.5]) # initial condition
def van_der_pol(t, y, ydot):
""" we create rhs equations for the problem"""
ydot[0] = y[1]
ydot[1] = 1000*(1.0-y[0]**2)*y[1]-y[0]
solution = ode('cvode', van_der_pol, old_api=False).solve(np.linspace(t0,500,200), y0)
pylab.plot(solution.values.t, solution.values.y[:,0], label='Van der Pol oscillator')
pylab.show()
For simplicity there is also a convenience function odeint wrapping the ode solver class. See example use in simple.py.
Basic use:
- Simple oscillator solved with cvode
- DAE example: planar pendulum solved with ida
Advanced use:
- Double pendulum Example of using classes to pass residual and jacobian functions to IDA, and of how to implement roots functionality.
For examples, see the docs/src/examples directory and scikits/odes/tests directory.
You can learn by example from following code that uses odes
- Centrifuge simulation, a wrapper around the ida solver: see centrifuge-1d
You have a project using odes? Do a pull request to add your project.
- You need numpy and scipy installed, as the aim is to extend scipy.integrate
- You need to have cython installed and executable
- You need python development files available (python-dev package)
- You need a fortran compiler to install from source.
- If you use python < 3.4, you need the enum34 package (eg via command: pip install enum34)
- You need to have the sundials package version 2.7.0 installed, see (https://computation.llnl.gov/casc/sundials/download/download.html)
It is required that the Blas/Lapack interface in included in sundials, so check the Fortran Settings section. A typical install if sundials download package is extracted into directory sundials-2.7.0 is on a *nix system:
mkdir build-sundials-2.7.0
cd build-sundials-2.7.0/
cmake -DLAPACK_ENABLE=ON ../sundials-2.7.0/
make
as root:
make install
This should install sundials in /usr/local/lib Make sure you use the fortran compiler as used for your lapack/blas install!
You can copy the git repository locally in directory odes with:
git clone git://github.com/bmcage/odes.git odes
In the top directory (the same as the file you are reading now), just do as root:
python setup.py build
This builds the packages in the build directory.
Libraries are searched in /usr/lib and /usr/local/lib by default.
You can also set $SUNDIALS_INST in your environment to the directory you have installed SUNDIALS into.
It should contain lib and include directories.
For a working scikit compile, LAPACK, ATLAS and BLAS must be found. A typical output of the build is: lapack_info: FOUND: libraries = ['lapack'] library_dirs = ['/usr/lib'] language = f77
blas_info:
FOUND:
libraries = ['blas']
library_dirs = ['/usr/lib']
language = f77
FOUND:
libraries = ['lapack', 'blas']
library_dirs = ['/usr/lib']
define_macros = [('NO_ATLAS_INFO', 1)]
language = f77
You can try it without installation by using PYTHONPATH and if needed adding local sundials install to LD_LIBRARY_PATH. For example:
On my box, the build libs are in odes/build/lib.linux-x86_64-2.7/, hence I can
use them with:
PYTHONPATH=/path-to-odes/odes/build/lib.linux-x86_64-2.7/ python -c'import scikits.odes.sundials'
To install, as root:
python setup.py install
This installs the scikit, to use it in your python scripts use eg:
from scikits.odes import daeNote: you need to add the local sundials install to LD_LIBRARY_PATH if not installed in the standard locations.
See the examples for more info.
You need nose to run the tests. Eg, to install it, run
pip install nose
To run the tests do in the python shell:
>>> import scikits.odes as od; od.test()or shorter, in a terminal:
PYTHONPATH=/path-to-build python -c 'import scikits.odes as od; od.test()'
Please submit extra ipython notebook examples of usage of odes scikit. To install and use ipython, typical install instructions on Ubuntu 14.04 would be:
pip install "ipython[notebook]"
ipython notebook
Which should open a browser window from the current directory to work on a python notebook. Do this in the directory odes/docs/ipython. You might obtain errors due to missing dependencies. For example, common is simplegeneric missing. Again, in Ubuntu 14.04 you would install it with
sudo apt-get install python-simplegeneric
Release:
- set in common.py version string and DEV=False, commit this.
- tag like:
git tag -a v2.2.0 -m "version 2.2.0" - push tag:
git push --tags - update to pypi repo:
python setup.py sdist --formats=gztar,zip register upload - update version string to a higher number, and DEV=True
For the documentation, you need following packages
After local install, create the new documentation via
- go to the sphinx directory:
cd sphinxdoc - create the documentation:
make html - upload the new html doc.