def test_jit_scalar(self):
a = blaze.array(range(10), dshape=dshape('10, int32'))
b = 10
expr = add(a, mul(a, b))
result = blaze.eval(expr)
np_a = np.arange(10)
expected = np_a + np_a * b
self.assertTrue(np.all(result == expected))
def test_graph(self):
a = array(np.arange(10), dshape=dshape('10, int32'))
b = array(np.arange(10), dshape=dshape('10, float32'))
expr = add(a, mul(a, b))
graph, ctx = expr.expr
self.assertEqual(len(ctx.params), 2)
self.assertFalse(ctx.constraints)
self.assertEqual(graph.dshape, dshape('10, float64'))
def test_interp(self):
a = array(range(10), dshape=dshape('10, int32'))
b = array(range(10), dshape=dshape('10, float32'))
expr = add(a, mul(a, b))
result = blaze.eval(expr, strategy='py')
expected = blaze.array([ 0, 2, 6, 12, 20, 30, 42, 56, 72, 90])
self.assertEqual(type(result), blaze.Array)
self.assertTrue(np.all(result == expected))
def make_graph():
a = blaze.array(range(10), dshape('10, int32'))
b = blaze.array(range(10), dshape('10, float64'))
c = blaze.array([i+0j for i in range(10)],
dshape('10, complex128'))
result = mul(add(a, b), c)
graph, expr_ctx = result.expr
ctx = ExecutionContext()
f, values = from_expr(graph, expr_ctx, ctx)
return f, values, graph
def make_expr(ds1, ds2):
a = array(range(10), dshape=ds1)
b = array(range(10), dshape=ds2)
expr = add(a, mul(a, b))
return expr