def setUp(self):
f = lambda x: sin(30 * x)
subinterval = Interval(-2, 3)
self.f = f
self.ff = Bndfun.initfun_adaptive(f, subinterval)
self.xx = subinterval(np.linspace(-1, 1, 100))
self.emptyfun = Bndfun(Chebtech2.initempty(), subinterval)
self.constfun = Bndfun(Chebtech2.initconst(1.), subinterval)
def test_size(self):
cfs = np.random.rand(10)
subinterval = Interval()
b0 = Bndfun(Chebtech2(np.array([])), subinterval)
b1 = Bndfun(Chebtech2(np.array([1.])), subinterval)
b2 = Bndfun(Chebtech2(cfs), subinterval)
self.assertEquals(b0.size, 0)
self.assertEquals(b1.size, 1)
self.assertEquals(b2.size, cfs.size)
def test_onefun_construction(self):
coeffs = np.random.rand(10)
subinterval = Interval()
onefun = Chebtech2(coeffs)
f = Bndfun(onefun, subinterval)
self.assertIsInstance(f, Bndfun)
self.assertLess(infnorm(f.coeffs - coeffs), eps)
def setUp(self):
self.emptyfun = Bndfun(Chebtech2.initempty(), Interval())
self.yy = np.linspace(-1, 1, 2000)
def setUp(self):
self.emptyfun = Bndfun(Chebtech2.initempty(), Interval())
self.yy = -1 + 2*rand(2000)
