sym

sym

.. image:: http://hera.physchem.kth.se:9090/api/badges/bjodah/sym/status.svg :target: http://hera.physchem.kth.se:9090/bjodah/sym :alt: Build status .. image:: https://circleci.com/gh/bjodah/sym.svg?style=svg :target: https://circleci.com/gh/bjodah/sym :alt: Build status on CircleCI .. image:: https://secure.travis-ci.org/bjodah/sym.svg?branch=master :target: http://travis-ci.org/bjodah/sym :alt: Build status on Travis-CI .. image:: https://img.shields.io/pypi/v/sym.svg :target: https://pypi.python.org/pypi/sym :alt: PyPI version .. image:: https://img.shields.io/badge/python-2.7,3.5,3.6-blue.svg :target: https://www.python.org/ :alt: Python version .. image:: https://img.shields.io/pypi/l/sym.svg :target: https://github.com/bjodah/sym/blob/master/LICENSE :alt: License .. image:: http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat :target: http://hera.physchem.kth.se/~sym/benchmarks :alt: airspeedvelocity .. image:: http://hera.physchem.kth.se/~sym/branches/master/htmlcov/coverage.svg :target: http://hera.physchem.kth.se/~sym/branches/master/htmlcov :alt: coverage

sym provides a unified wrapper to some symbolic manipulation libraries in Python. It makes it easy for library authors to test their packages against several symbolic manipulation libraries.

Currently the following Python pacakges are available as "backends":

- `SymPy <https://github.com/sympy/sympy>`_
- `SymEngine <https://github.com/symengine/symengine.py>`_
- `PySym <https://github.com/bjodah/pysym>`_
- `SymCXX <https://github.com/bjodah/symcxx>`_
- `Diofant <https://github.com/diofant/diofant>`_

The capabilities exposed here are those needed by

- `pyodesys <https://pypi.python.org/pypi/pyodesys>`_
- `pyneqsys <https://pypi.python.org/pypi/pyneqsys>`_

and include:

- Differentiation
- Numerical evaluation (including "lambdify" support)

see tests <https://github.com/bjodah/sym/tree/master/sym/tests/>_ for examples. Note that pyodesys and pyneqsys also act as test suits for this package.

Documentation

Auto-generated API documentation for the latest stable release is found here: <https://bjodah.github.io/sym/latest>_ (and the development version for the current master branch is found here: <http://hera.physchem.kth.se/~sym/branches/master/html>_).

Installation

Simplest way to install sym and its (optional) dependencies is to use pip:

::

$ pip install --user sym pytest $ python -m pytest --pyargs sym

or the conda package manager <http://conda.pydata.org/docs/>_:

::

$ conda install -c bjodah sym pytest $ python -m pytest --pyargs sym

Source distribution is available here: <https://pypi.python.org/pypi/sym>_

Example

Differentiation

.. code:: python

from sym import Backend be = Backend('pysym') # just an example, use SymPy rather than pysym x, y = map(be.Symbol, 'x y'.split()) expr = xy**2 - be.tan(2x) print(expr.diff(x)) ((y**2) - ((1 + (tan((2*x))**2))*2))

for more examples, see examples/ <https://github.com/bjodah/sym/tree/master/examples>, and rendered jupyter notebooks here: <http://hera.physchem.kth.se/~sym/master/examples>

License

The source code is Open Source and is released under the simplified 2-clause BSD license. See LICENSE <LICENSE>_ for further details. Contributors are welcome to suggest improvements at https://github.com/bjodah/sym

Author

Björn I. Dahlgren, contact:

• gmail address: bjodah