Metadata-Version: 1.1
Name: pyneqsys
Version: 0.4.3
Summary: Package for numerically solving symbolically defined systems of non-linear equations.
Home-page: https://github.com/bjodah/pyneqsys
Author: Bjoern I. Dahlgren 
Author-email: bjodah@gmail.com
License: BSD
Description: pyneqsys
        ========
        
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        `pyneqsys <https://github.com/bjodah/pyneqsys>`_ provides a convenience class for 
        representing and solving non-linear equation systems from symbolic expressions
        (provided e.g. with the help of `SymPy <http://www.sympy.org>`_).
        
        The numerical root finding is perfomed using either:
        
        - scipy: `scipy.optimize.root <http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html>`_
        - nleq2: `pynleq2.solve <http://bjodah.github.io/pynleq2/pynleq2.html#pynleq2.solve>`_ (unsettled API)
        - kinsol: `pykinsol.solve <http://bjodah.github.io/pykinsol/pykinsol.html#pykinsol.solve>`_
        
        Documentation
        -------------
        Autogenerated API documentation is found `here <http://hera.physchem.kth.se/~pyneqsys/branches/master/html>`_.
        
        Installation
        ------------
        Simplest way to install pyneqsys and its dependencies is through the `conda package manager <http://conda.pydata.org/docs/>`_:
        
        ::
        
           $ conda install -c bjodah pyneqsys pytest
           $ python -m pytest --pyargs pyneqsys
        
        
        Source distribution is available here:
        `<https://pypi.python.org/pypi/pyneqsys>`_
        
        Example
        -------
        Example reformulated from `SciPy documentation <http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html>`_:
        
        .. code:: python
        
           >>> from pyneqsys.symbolic import SymbolicSys
           >>> neqsys = SymbolicSys.from_callback(
           ...     lambda x: [(x[0] - x[1])**3/2 + x[0] - 1,
           ...                (x[1] - x[0])**3/2 + x[1]], 2)
           >>> x, info = neqsys.solve([1, 0])
           >>> assert info['success']
           >>> print(x)
           [ 0.8411639  0.1588361]
        
        here we did not need to enter the jacobian manually, SymPy did that for us.
        For expressions containing transcendental functions we need to provide a
        "backend" keyword arguemnt to enable symbolic derivation of the jacobian:
        
        .. code:: python
        
           >>> import math
           >>> def powell(x, params, backend=math):
           ...     A, exp = params[0], backend.exp
           ...     return A*x[0]*x[1] - 1, exp(-x[0]) + exp(-x[1]) - (1 + A**-1)
           >>> powell_sys = SymbolicSys.from_callback(powell, 2, 1)
           >>> x, info = powell_sys.solve([1, 1], [1000.0])
           >>> assert info['success']
           >>> print(', '.join(['%.6e' % _ for _ in sorted(x)]))
           1.477106e-04, 6.769996e+00
        
        For more examples look see
        `examples/ <https://github.com/bjodah/pyneqsys/tree/master/examples>`_, and rendered jupyter notebooks here:
        `<http://hera.physchem.kth.se/~pyneqsys/branches/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/pyneqsys
        
        Author
        ------
        Björn I. Dahlgren, contact:
        
        - gmail address: bjodah
        - kth.se address: bda
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
