from sympy import Piecewise, lambdify, Equality, Unequality, Sum, Mod
from sympy.abc import x, i, j, a, b, c, d
from sympy.printing.lambdarepr import NumPyPrinter

from sympy.utilities.pytest import skip
from sympy.external import import_module

np = import_module('numpy')

def test_numpy_piecewise_regression():
    """
    NumPyPrinter needs to print Piecewise()'s choicelist as a list to avoid
    breaking compatibility with numpy 1.8. This is not necessary in numpy 1.9+.
    See gh-9747 and gh-9749 for details.
    """
    p = Piecewise((1, x < 0), (0, True))
    assert NumPyPrinter().doprint(p) == 'select([less(x, 0),True], [1,0], default=nan)'


def test_sum():
    if not np:
        skip("NumPy not installed")

    s = Sum(x ** i, (i, a, b))
    f = lambdify((a, b, x), s, 'numpy')

    a_, b_ = 0, 10
    x_ = np.linspace(-1, +1, 10)
    assert np.allclose(f(a_, b_, x_), sum(x_ ** i_ for i_ in range(a_, b_ + 1)))

    s = Sum(i * x, (i, a, b))
    f = lambdify((a, b, x), s, 'numpy')

    a_, b_ = 0, 10
    x_ = np.linspace(-1, +1, 10)
    assert np.allclose(f(a_, b_, x_), sum(i_ * x_ for i_ in range(a_, b_ + 1)))


def test_multiple_sums():
    if not np:
        skip("NumPy not installed")

    s = Sum((x + j) * i, (i, a, b), (j, c, d))
    f = lambdify((a, b, c, d, x), s, 'numpy')

    a_, b_ = 0, 10
    c_, d_ = 11, 21
    x_ = np.linspace(-1, +1, 10)
    assert np.allclose(f(a_, b_, c_, d_, x_),
                       sum((x_ + j_) * i_ for i_ in range(a_, b_ + 1) for j_ in range(c_, d_ + 1)))


def test_relational():
    if not np:
        skip("NumPy not installed")

    e = Equality(x, 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [False, True, False])

    e = Unequality(x, 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [True, False, True])

    e = (x < 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [True, False, False])

    e = (x <= 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [True, True, False])

    e = (x > 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [False, False, True])

    e = (x >= 1)

    f = lambdify((x,), e)
    x_ = np.array([0, 1, 2])
    assert np.array_equal(f(x_), [False, True, True])


def test_mod():
    if not np:
        skip("NumPy not installed")

    e = Mod(a, b)
    f = lambdify((a, b), e)

    a_ = np.array([0, 1, 2, 3])
    b_ = 2
    assert np.array_equal(f(a_, b_), [0, 1, 0, 1])

    a_ = np.array([0, 1, 2, 3])
    b_ = np.array([2, 2, 2, 2])
    assert np.array_equal(f(a_, b_), [0, 1, 0, 1])

    a_ = np.array([2, 3, 4, 5])
    b_ = np.array([2, 3, 4, 5])
    assert np.array_equal(f(a_, b_), [0, 0, 0, 0])
