Python lambdify.implemented_function函数代码示例

def test_lambdify():
    # Test lambdify with implemented functions
    # first test basic (sympy) lambdify
    f = sympy.cos
    assert_equal(lambdify(x, f(x))(0), 1)
    assert_equal(lambdify(x, 1 + f(x))(0), 2)
    assert_equal(lambdify((x, y), y + f(x))(0, 1), 2)
    # make an implemented function and test
    f = implemented_function("f", lambda x : x+100)
    assert_equal(lambdify(x, f(x))(0), 100)
    assert_equal(lambdify(x, 1 + f(x))(0), 101)
    assert_equal(lambdify((x, y), y + f(x))(0, 1), 101)
    # Error for functions with same name and different implementation
    f2 = implemented_function("f", lambda x : x+101)
    assert_raises(ValueError, lambdify, x, f(f2(x)))
    # our lambdify, like sympy's lambdify, can also handle tuples,
    # lists, dicts as expressions
    lam = lambdify(x, (f(x), x))
    assert_equal(lam(3), (103, 3))
    lam = lambdify(x, [f(x), x])
    assert_equal(lam(3), [103, 3])
    lam = lambdify(x, [f(x), (f(x), x)])
    assert_equal(lam(3), [103, (103, 3)])
    lam = lambdify(x, {f(x): x})
    assert_equal(lam(3), {103: 3})
    lam = lambdify(x, {f(x): x})
    assert_equal(lam(3), {103: 3})
    lam = lambdify(x, {x: f(x)})
    assert_equal(lam(3), {3: 103})

def test_alias():
    x = F.Term('x')
    f = implemented_function('f', lambda x: 2*x)
    g = implemented_function('g', lambda x: np.sqrt(x))
    ff = F.Formula([f(x), g(x)**2])
    n = F.make_recarray([2,4,5], 'x')
    assert_almost_equal(ff.design(n)['f(x)'], n['x']*2)
    assert_almost_equal(ff.design(n)['g(x)**2'], n['x'])

def test_2d():
    B1, B2 = [gen_BrownianMotion() for _ in range(2)]
    B1s = implemented_function("B1", B1)
    B2s = implemented_function("B2", B2)
    s, t = sympy.symbols(('s', 't'))
    e = B1s(s)+B2s(t)
    ee = lambdify((s,t), e)
    assert_almost_equal(ee(B1.x, B2.x), B1.y + B2.y)

def natural_spline(t, knots=None, order=3, intercept=False):
    """ Return a Formula containing a natural spline

    Spline for a Term with specified `knots` and `order`.

    Parameters
    ----------
    t : ``Term``
    knots : None or sequence, optional
       Sequence of float.  Default None (same as empty list)
    order : int, optional
       Order of the spline. Defaults to a cubic (==3)
    intercept : bool, optional
       If True, include a constant function in the natural
       spline. Default is False

    Returns
    -------
    formula : Formula
         A Formula with (len(knots) + order) Terms (if intercept=False,
         otherwise includes one more Term), made up of the natural spline
         functions.

    Examples
    --------
    >>> x = Term('x')
    >>> n = natural_spline(x, knots=[1,3,4], order=3)
    >>> xval = np.array([3,5,7.]).view(np.dtype([('x', np.float)]))
    >>> n.design(xval, return_float=True)
    array([[   3.,    9.,   27.,    8.,    0.,   -0.],
           [   5.,   25.,  125.,   64.,    8.,    1.],
           [   7.,   49.,  343.,  216.,   64.,   27.]])
    >>> d = n.design(xval)
    >>> print(d.dtype.descr)
    [('ns_1(x)', '<f8'), ('ns_2(x)', '<f8'), ('ns_3(x)', '<f8'), ('ns_4(x)', '<f8'), ('ns_5(x)', '<f8'), ('ns_6(x)', '<f8')]
    """
    if knots is None:
        knots = {}
    fns = []
    for i in range(order+1):
        n = 'ns_%d' % i
        def f(x, i=i):
            return x**i
        s = implemented_function(n, f)
        fns.append(s(t))

    for j, k in enumerate(knots):
        n = 'ns_%d' % (j+i+1,)
        def f(x, k=k, order=order):
            return (x-k)**order * np.greater(x, k)
        s = implemented_function(n, f)
        fns.append(s(t))

    if not intercept:
        fns.pop(0)

    ff = Formula(fns)
    return ff

def test_implemented_function_evalf():
    from sympy.utilities.lambdify import implemented_function
    f = Function('f')
    f = implemented_function(f, lambda x: x + 1)
    assert str(f(x)) == "f(x)"
    assert str(f(2)) == "f(2)"
    assert f(2).evalf() == 3
    assert f(x).evalf() == f(x)
    f = implemented_function(Function('sin'), lambda x: x + 1)
    assert f(2).evalf() != sin(2)
    del f._imp_     # XXX: due to caching _imp_ would influence all other tests

def test_jscode_inline_function():
    x = symbols("x")
    g = implemented_function("g", Lambda(x, 2 * x))
    assert jscode(g(x)) == "2*x"
    g = implemented_function("g", Lambda(x, 2 * x / Catalan))
    assert jscode(g(x)) == "var Catalan = %s;\n2*x/Catalan" % Catalan.n()
    A = IndexedBase("A")
    i = Idx("i", symbols("n", integer=True))
    g = implemented_function("g", Lambda(x, x * (1 + x) * (2 + x)))
    assert jscode(g(A[i]), assign_to=A[i]) == (
        "for (var i=0; i<n; i++){\n" "   A[i] = A[i]*(1 + A[i])*(2 + A[i]);\n" "}"
    )

def test_glsl_code_inline_function():
    x = symbols('x')
    g = implemented_function('g', Lambda(x, 2*x))
    assert glsl_code(g(x)) == "2*x"
    g = implemented_function('g', Lambda(x, 2*x/Catalan))
    assert glsl_code(g(x)) == "float Catalan = 0.915965594;\n2*x/Catalan"
    A = IndexedBase('A')
    i = Idx('i', symbols('n', integer=True))
    g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
    assert glsl_code(g(A[i]), assign_to=A[i]) == (
        "for (int i=0; i<n; i++){\n"
        "   A[i] = (A[i] + 1)*(A[i] + 2)*A[i];\n"
        "}"
    )

def test_jscode_inline_function():
    x = symbols('x')
    g = implemented_function('g', Lambda(x, 2*x))
    assert jscode(g(x)) == "2*x"
    g = implemented_function('g', Lambda(x, 2*x/Catalan))
    assert jscode(g(x)) == "var Catalan = %s;\n2*x/Catalan" % Catalan.n()
    A = IndexedBase('A')
    i = Idx('i', symbols('n', integer=True))
    g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
    assert jscode(g(A[i]), assign_to=A[i]) == (
        "for (var i=0; i<n; i++){\n"
        "   A[i] = (A[i] + 1)*(A[i] + 2)*A[i];\n"
        "}"
    )

def test_ccode_inline_function():
    x = symbols('x')
    g = implemented_function('g', Lambda(x, 2*x))
    assert ccode(g(x)) == "2*x"
    g = implemented_function('g', Lambda(x, 2*x/Catalan))
    assert ccode(g(x)) == "double const Catalan = %s;\n2*x/Catalan" %Catalan.n()
    A = IndexedBase('A')
    i = Idx('i', symbols('n', integer=True))
    g = implemented_function('g', Lambda(x, x*(1 + x)*(2 + x)))
    assert ccode(g(A[i]), assign_to=A[i]) == (
            "for (int i=0; i<n; i++){\n"
            "   A[i] = A[i]*(1 + A[i])*(2 + A[i]);\n"
            "}"
            )

def test_implemented_function():
    # Here we check if the default returned functions are anonymous - in
    # the sense that we can have more than one function with the same name
    f = implemented_function('f', lambda x: 2*x)
    g = implemented_function('f', lambda x: np.sqrt(x))
    l1 = lambdify(x, f(x))
    l2 = lambdify(x, g(x))
    assert_equal(str(f(x)), str(g(x)))
    assert_equal(l1(3), 6)
    assert_equal(l2(3), np.sqrt(3))
    # check that we can pass in a sympy function as input
    func = sympy.Function('myfunc')
    assert_false(hasattr(func, '_imp_'))
    f = implemented_function(func, lambda x: 2*x)
    assert_true(hasattr(func, '_imp_'))

def test_glsl_code_Pow():
    g = implemented_function('g', Lambda(x, 2*x))
    assert glsl_code(x**3) == "pow(x, 3.0)"
    assert glsl_code(x**(y**3)) == "pow(x, pow(y, 3.0))"
    assert glsl_code(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
        "pow(3.5*2*x, -x + pow(y, x))/(pow(x, 2.0) + y)"
    assert glsl_code(x**-1.0) == '1.0/x'

def ufuncify(args, expr, **kwargs):
    """
    Generates a binary ufunc-like lambda function for numpy arrays

    ``args``
        Either a Symbol or a tuple of symbols. Specifies the argument sequence
        for the ufunc-like function.

    ``expr``
        A SymPy expression that defines the element wise operation

    ``kwargs``
        Optional keyword arguments are forwarded to autowrap().

    The returned function can only act on one array at a time, as only the
    first argument accept arrays as input.

    .. Note:: a *proper* numpy ufunc is required to support broadcasting, type
       casting and more.  The function returned here, may not qualify for
       numpy's definition of a ufunc.  That why we use the term ufunc-like.

    References
    ==========
    [1] http://docs.scipy.org/doc/numpy/reference/ufuncs.html

    Examples
    ========

    >>> from sympy.utilities.autowrap import ufuncify
    >>> from sympy.abc import x, y
    >>> import numpy as np
    >>> f = ufuncify([x, y], y + x**2)
    >>> f([1, 2, 3], 2)
    [ 3.  6.  11.]
    >>> a = f(np.arange(5), 3)
    >>> isinstance(a, np.ndarray)
    True
    >>> print a
    [ 3. 4. 7. 12. 19.]

    """
    y = C.IndexedBase(C.Dummy('y'))
    x = C.IndexedBase(C.Dummy('x'))
    m = C.Dummy('m', integer=True)
    i = C.Dummy('i', integer=True)
    i = C.Idx(i, m)
    l = C.Lambda(args, expr)
    f = implemented_function('f', l)

    if isinstance(args, C.Symbol):
        args = [args]
    else:
        args = list(args)

    # ensure correct order of arguments
    kwargs['args'] = [y, x] + args[1:] + [m]

    # first argument accepts an array
    args[0] = x[i]
    return autowrap(C.Equality(y[i], f(*args)), **kwargs)

def test_jscode_Pow():
    g = implemented_function('g', Lambda(x, 2*x))
    assert jscode(x**3) == "Math.pow(x, 3)"
    assert jscode(x**(y**3)) == "Math.pow(x, Math.pow(y, 3))"
    assert jscode(1/(g(x)*3.5)**(x - y**x)/(x**2 + y)) == \
        "Math.pow(3.5*2*x, -x + Math.pow(y, x))/(Math.pow(x, 2) + y)"
    assert jscode(x**-1.0) == '1/x'

def test_inline_function():
    from sympy.tensor import IndexedBase, Idx
    from sympy import symbols
    n, m = symbols('n m', integer=True)
    A, x, y = map(IndexedBase, 'Axy')
    i = Idx('i', m)
    p = FCodeGen()
    func = implemented_function('func', Lambda(n, n*(n + 1)))
    routine = make_routine('test_inline', Eq(y[i], func(x[i])))
    code = get_string(p.dump_f95, [routine])
    expected = (
        'subroutine test_inline(m, x, y)\n'
        'implicit none\n'
        'INTEGER*4, intent(in) :: m\n'
        'REAL*8, intent(in), dimension(1:m) :: x\n'
        'REAL*8, intent(out), dimension(1:m) :: y\n'
        'INTEGER*4 :: i\n'
        'do i = 1, m\n'
        '   y(i) = %s*%s\n'
        'end do\n'
        'end subroutine\n'
    )
    args = ('x(i)', '(x(i) + 1)')
    assert code == expected % args or\
        code == expected % args[::-1]

def test_lambdify_imps():
    # Test lambdify with implemented functions
    # first test basic (sympy) lambdify
    f = sympy.cos
    assert lambdify(x, f(x))(0) == 1
    assert lambdify(x, 1 + f(x))(0) == 2
    assert lambdify((x, y), y + f(x))(0, 1) == 2
    # make an implemented function and test
    f = implemented_function("f", lambda x: x + 100)
    assert lambdify(x, f(x))(0) == 100
    assert lambdify(x, 1 + f(x))(0) == 101
    assert lambdify((x, y), y + f(x))(0, 1) == 101
    # Can also handle tuples, lists, dicts as expressions
    lam = lambdify(x, (f(x), x))
    assert lam(3) == (103, 3)
    lam = lambdify(x, [f(x), x])
    assert lam(3) == [103, 3]
    lam = lambdify(x, [f(x), (f(x), x)])
    assert lam(3) == [103, (103, 3)]
    lam = lambdify(x, {f(x): x})
    assert lam(3) == {103: 3}
    lam = lambdify(x, {f(x): x})
    assert lam(3) == {103: 3}
    lam = lambdify(x, {x: f(x)})
    assert lam(3) == {3: 103}
    # Check that imp preferred to other namespaces by default
    d = {'f': lambda x: x + 99}
    lam = lambdify(x, f(x), d)
    assert lam(3) == 103
    # Unless flag passed
    lam = lambdify(x, f(x), d, use_imps=False)
    assert lam(3) == 102