Source code for openpnm.models.misc._simple_equations

"""
Simple Equations
================

"""
import logging
import numpy as np
logger = logging.getLogger(__name__)


__all__ = ['linear', 'polynomial', 'generic_function']



[docs]
def generic_function(target, prop, func, **kwargs):
    r"""
    Runs an arbitrary function on the given data

    This allows users to place a customized calculation into the automatated
    model regeneration pipeline.

    Parameters
    ----------
    target : Base
        The object which this model is associated with. This controls the
        length of the calculated array, and also provides access to other
        necessary properties.
    prop : str
        The dictionary key containing the array to be operated on
    func : Numpy function
        A handle to the function to apply
    kwargs : keyward arguments
        All arguments required by the specific Numpy function

    Returns
    -------
    result : ndarray
        Array containing ``func(target[prop], **kwargs)``.

    Examples
    --------
    The following example shows how to use a Numpy function, but any function
    can be used, as long as it returns an array object:

    >>> import openpnm as op
    >>> import numpy as np
    >>> pn = op.network.Cubic(shape=[5, 5, 5])
    >>> pn['pore.rand'] = np.random.rand(pn.Np)
    >>> pn.add_model(propname='pore.cos',
    ...              model=op.models.misc.generic_function,
    ...              func=np.cos,
    ...              prop='pore.rand')

    """
    values = target[prop]
    result = func(values, **kwargs)
    if not isinstance(result, np.ndarray):
        logger.warning('Given function must return a Numpy array')
    return result




[docs]
def linear(target, m, b, prop):
    r"""
    Calculates a property as a linear function of a given property

    Parameters
    ----------
    target : Base
        The object for which these values are being calculated.  This
        controls the length of the calculated array, and also provides
        access to other necessary thermofluid properties.
    m, b : floats
        Slope and intercept of the linear corelation
    prop : str
        The dictionary key containing the independent variable or phase
        property to be used in the correlation.

    Returns
    -------
    value : ndarray
        Array containing ``m * target[prop] + b``.

    """
    x = target[prop]
    value = m*x + b
    return value




[docs]
def polynomial(target, a, prop):
    r"""
    Calculates a property as a polynomial function of a given property

    Parameters
    ----------
    target : Base
        The object for which these values are being calculated.  This
        controls the length of the calculated array, and also provides
        access to other necessary thermofluid properties.
    a : array_like
        A list containing the polynomial coefficients, where element 0 in the
        list corresponds to a0 and so on.  Note that no entries can be skipped
        so 0 coefficients must be sent as 0.
    prop : str
        The dictionary key containing the independent variable or phase
        property to be used in the polynomial.

    Returns
    -------
    value : ndarray
        Array containing ``Pn(target[prop])``, where ``Pn`` is nth order
        polynomial with coefficients stored in ``a``.

    """
    x = target[prop].astype(float)
    value = 0.0
    for i in range(0, len(a)):
        value += a[i]*(x**i)
    return value