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SymCXX is an experimental Python package (with its implemented in C++) for symbolic manipulation with limited scope (as in minimal). It tries to stay compatible with the API of SymPy. Its main goal is to explore designs related to automatic differentiation and numerical evaluation similar to SymEngine’s Lambdify functionality.

The capabilities include:

  • Differentiation (including taking the jacobian of a matrix)

  • Numerical evaluation

The above capabilities are exactly what is needed by pyodesys and pyneqsys.

Note that integers in symcxx may overflow, i.e. if correctness is important please use SymPy/SymEngine.

Documentation

Currently users may refer to SymPy’s / SymEngine’s documentation.

Installation

Simplest way to install SymCXX and its (optional) dependencies is to use the conda package manager:

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

or pip (requires a C++11 compliant compiler):

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

There are no requirements outside the Python standard library.

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

Example

Differentiation

>>> from symcxx import NameSpace
>>> ns = NameSpace()
>>> x, y = map(ns.Symbol, 'x y'.split())
>>> expr = x*y**2 - ns.tan(2*x)
>>> print(expr.diff(x))
Sub(Pow(Symbol(y), Integer(2)), Mul2(Integer(2), Add2(Integer(1), Pow(Tan(Mul2(Symbol(x), Integer(2))), Integer(2)))))

Lambdify

>>> lmb = ns.Lambdify([x, y], expr)
>>> lmb([1.3, 0.2])
array([[ 0.65359661]])

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/symcxx

Author

Björn I. Dahlgren, contact:

  • gmail address: bjodah

  • kth.se address: bda

Project details


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