Python binding for cvodes from the sundials library.

## Project description

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.

## Documentation

Autogenerated API documentation for latest stable release is found here: https://bjodah.github.io/pycvodes/latest (and the development version for the current master branch are found here: http://hera.physchem.kth.se/~pycvodes/branches/master/html).

## Installation

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: https://anaconda.org/bjodah/pycvodes

Source distribution is available here: https://pypi.python.org/pypi/pycvodes

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 'lapack,blas'

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.

## Examples

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

>>> 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) >>> plt.show() # doctest: +SKIP

For more examples see examples/, and rendered jupyter notebooks here: http://hera.physchem.kth.se/~pycvodes/branches/master/examples

## License

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 https://github.com/bjodah/pycvodes

## Project details

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Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
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pycvodes-0.11.9.tar.gz (298.7 kB) Copy SHA256 hash SHA256 | Source | None | Oct 11, 2018 |