def test_run(self):
a = arange(100).reshape(10,10)[::2]
b = arange(10)
expr = NumExpr("2*a+3*b",[('a', double),('b', double)])
assert_array_equal(expr(a,b), expr.run(a,b))
def test_r0_reuse(self):
assert_equal(disassemble(NumExpr("x * x + 2", [('x', double)])),
[(b'mul_ddd', b'r0', b'r1[x]', b'r1[x]'),
(b'add_ddd', b'r0', b'r0', b'c2[2.0]')])
def test_all_scalar(self):
a = 3.
b = 4.
assert_allclose(evaluate("a+b"), a + b)
expr = NumExpr("2*a+3*b", [('a', double), ('b', double)])
assert_equal(expr(a, b), 2 * a + 3 * b)
def test_reductions(self):
# Check that they compile OK.
assert_equal(disassemble(
NumExpr("sum(x**2+2, axis=None)", [('x', double)])),
[(b'mul_ddd', b't3', b'r1[x]', b'r1[x]'),
(b'add_ddd', b't3', b't3', b'c2[2.0]'),
(b'sum_ddn', b'r0', b't3', None)])
assert_equal(disassemble(
NumExpr("sum(x**2+2, axis=1)", [('x', double)])),
[(b'mul_ddd', b't3', b'r1[x]', b'r1[x]'),
(b'add_ddd', b't3', b't3', b'c2[2.0]'),
(b'sum_ddn', b'r0', b't3', 1)])
assert_equal(disassemble(
NumExpr("prod(x**2+2, axis=2)", [('x', double)])),
[(b'mul_ddd', b't3', b'r1[x]', b'r1[x]'),
(b'add_ddd', b't3', b't3', b'c2[2.0]'),
(b'prod_ddn', b'r0', b't3', 2)])
# Check that full reductions work.
x = zeros(100000) + .01 # checks issue #41
assert_allclose(evaluate("sum(x+2,axis=None)"), sum(x + 2, axis=None))
assert_allclose(evaluate("sum(x+2,axis=0)"), sum(x + 2, axis=0))
assert_allclose(evaluate("prod(x,axis=0)"), prod(x, axis=0))
assert_allclose(evaluate("min(x)"), np.min(x))
assert_allclose(evaluate("max(x,axis=0)"), np.max(x, axis=0))
# Fix for #277, array with leading singleton dimension
x = np.arange(10).reshape(1,10)
assert_allclose(evaluate("sum(x,axis=None)"), sum(x, axis=None) )
assert_allclose(evaluate("sum(x,axis=0)"), sum(x, axis=0) )
assert_allclose(evaluate("sum(x,axis=1)"), sum(x, axis=1) )
x = arange(10.0)
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x**2+2,axis=0)"), prod(x ** 2 + 2, axis=0))
assert_allclose(evaluate("min(x**2+2,axis=0)"), np.min(x ** 2 + 2, axis=0))
assert_allclose(evaluate("max(x**2+2,axis=0)"), np.max(x ** 2 + 2, axis=0))
x = arange(100.0)
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x-1,axis=0)"), prod(x - 1, axis=0))
assert_allclose(evaluate("min(x-1,axis=0)"), np.min(x - 1, axis=0))
assert_allclose(evaluate("max(x-1,axis=0)"), np.max(x - 1, axis=0))
x = linspace(0.1, 1.0, 2000)
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x-1,axis=0)"), prod(x - 1, axis=0))
assert_allclose(evaluate("min(x-1,axis=0)"), np.min(x - 1, axis=0))
assert_allclose(evaluate("max(x-1,axis=0)"), np.max(x - 1, axis=0))
# Check that reductions along an axis work
y = arange(9.0).reshape(3, 3)
assert_allclose(evaluate("sum(y**2, axis=1)"), sum(y ** 2, axis=1))
assert_allclose(evaluate("sum(y**2, axis=0)"), sum(y ** 2, axis=0))
assert_allclose(evaluate("sum(y**2, axis=None)"), sum(y ** 2, axis=None))
assert_allclose(evaluate("prod(y**2, axis=1)"), prod(y ** 2, axis=1))
assert_allclose(evaluate("prod(y**2, axis=0)"), prod(y ** 2, axis=0))
assert_allclose(evaluate("prod(y**2, axis=None)"), prod(y ** 2, axis=None))
assert_allclose(evaluate("min(y**2, axis=1)"), np.min(y ** 2, axis=1))
assert_allclose(evaluate("min(y**2, axis=0)"), np.min(y ** 2, axis=0))
assert_allclose(evaluate("min(y**2, axis=None)"), np.min(y ** 2, axis=None))
assert_allclose(evaluate("max(y**2, axis=1)"), np.max(y ** 2, axis=1))
assert_allclose(evaluate("max(y**2, axis=0)"), np.max(y ** 2, axis=0))
assert_allclose(evaluate("max(y**2, axis=None)"), np.max(y ** 2, axis=None))
# Check integers
x = arange(10.)
x = x.astype(int)
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x**2+2,axis=0)"), prod(x ** 2 + 2, axis=0))
assert_allclose(evaluate("min(x**2+2,axis=0)"), np.min(x ** 2 + 2, axis=0))
assert_allclose(evaluate("max(x**2+2,axis=0)"), np.max(x ** 2 + 2, axis=0))
# Check longs
x = x.astype(int)
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x**2+2,axis=0)"), prod(x ** 2 + 2, axis=0))
assert_allclose(evaluate("min(x**2+2,axis=0)"), np.min(x ** 2 + 2, axis=0))
assert_allclose(evaluate("max(x**2+2,axis=0)"), np.max(x ** 2 + 2, axis=0))
# Check complex
x = x + .1j
assert_allclose(evaluate("sum(x**2+2,axis=0)"), sum(x ** 2 + 2, axis=0))
assert_allclose(evaluate("prod(x-1,axis=0)"), prod(x - 1, axis=0))
def test_simple_expr(self):
func = NumExpr(E.a)
x = arange(1e6)
y = func(x)
assert_array_equal(x, y)
def test_simple_expr_small_array(self):
func = NumExpr(E.a)
x = arange(100.0)
y = func(x)
assert_array_equal(x, y)
def test_simple(self):
ex = 2.0 * E.a + 3.0 * E.b * E.c
sig = [('a', double), ('b', double), ('c', double)]
func = NumExpr(ex, signature=sig)
x = func(array([1., 2, 3]), array([4., 5, 6]), array([7., 8, 9]))
assert_array_equal(x, array([86., 124., 168.]))
def test_run(self):
a = arange(100).reshape(10, 10)[::2]
b = arange(10)
expr = NumExpr("2*a+3*b", [('a', double), ('b', double)])
assert_array_equal(expr(a, b), expr.run(a, b))
