(functions, var, method='bareis')
return W.det(method)
Compute Wronskian for [] of functions

    ::

                         | f1       f2        ...   fn      |
                         | f1'      f2'       ...   fn'     |
                         |  .        .        .      .      |
        W(f1, ..., fn) = |  .        .         .     .      |
                         |  .        .          .    .      |
                         |  (n)      (n)            (n)     |
                         | D   (f1) D   (f2)  ...  D   (fn) |

    see: http://en.wikipedia.org/wiki/Wronskian

    See Also
    ========

    sympy.matrices.mutable.Matrix.jacobian
    hessian
Compute Wronskian for [] of functions
Compute Wronskian for [] of functions
def wronskian(functions, var, method='bareis'):
    """
    Compute Wronskian for [] of functions

    ::

                         | f1       f2        ...   fn      |
                         | f1'      f2'       ...   fn'     |
                         |  .        .        .      .      |
        W(f1, ..., fn) = |  .        .         .     .      |
                         |  .        .          .    .      |
                         |  (n)      (n)            (n)     |
                         | D   (f1) D   (f2)  ...  D   (fn) |

    see: http://en.wikipedia.org/wiki/Wronskian

    See Also
    ========

    sympy.matrices.mutable.Matrix.jacobian
    hessian
    """
    from .dense import Matrix

    for index in range(0, len(functions)):
        functions[index] = sympify(functions[index])
    n = len(functions)
    if n == 0:
        return 1
    W = Matrix(n, n, lambda i, j: functions[i].diff(var, j))
    return W.det(method)
def wronskian ( functions , var , method = 'bareis' ) : from . dense import Matrix for index in range ( 0 , len ( functions ) ) : functions [ index ] = sympify ( functions [ index ] ) n = len ( functions ) if n == 0 : return 1 W = Matrix ( n , n , lambda i , j : functions [ i ] . diff ( var , j ) ) return W . det ( method )