def test_plot_complex(self):
c = np.exp(1j * Chebfun.identity(domain=[-np.pi, np.pi]))
xs, ys, xi, yi, d = plot_data(c, plot_res)
self.assertEqual(d, 2, "dimension is two for complex chebfun")
for X, Y in [(xs, ys), (xi, yi)]:
dist = np.square(X) + np.square(Y)
npt.assert_allclose(dist, 1, err_msg="The plot should be a circle")
plot(c)
def test_plot_complex(self):
c = np.exp(1j*Chebfun.identity(domain=[-np.pi,np.pi]))
xs,ys,xi,yi,d = plot_data(c, plot_res)
self.assertEqual(d, 2, "dimension is two for complex chebfun")
for X,Y in [(xs,ys), (xi,yi)]:
dist = np.square(X) + np.square(Y)
npt.assert_allclose(dist, 1, err_msg="The plot should be a circle")
plot(c)
def test_plot_circle(self):
T = .5
def cirper(x):
return tools.circle(x, period=T)
c = Chebfun.from_function(cirper, domain=[0,T])
xs,ys,xi,yi,d = plot_data(c, plot_res)
self.assertEqual(d, 2,)
for X,Y in [(xs,ys), (xi,yi)]:
dist = np.square(X) + np.square(Y)
npt.assert_allclose(dist, 1, err_msg="The plot should be a circle")
plot(c)
def test_plot_circle(self):
T = .5
def cirper(x):
return tools.circle(x, period=T)
c = Chebfun.from_function(cirper, domain=[0, T])
xs, ys, xi, yi, d = plot_data(c, plot_res)
self.assertEqual(
d,
2,
)
for X, Y in [(xs, ys), (xi, yi)]:
dist = np.square(X) + np.square(Y)
npt.assert_allclose(dist, 1, err_msg="The plot should be a circle")
plot(c)
def test_too_many_dimensions(self):
c = Chebfun.from_data(np.random.random_sample([3,4]))
with self.assertRaises(ValueError):
plot(c)
def setUp(self):
# Constuct the O(dx^-16) "spectrally accurate" chebfun p
self.p = Chebfun.from_function(tools.f)
def test_too_many_dimensions(self):
c = Chebfun.from_data(np.random.random_sample([3, 4]))
with self.assertRaises(ValueError):
plot(c)
def setUp(self):
# Constuct the O(dx^-16) "spectrally accurate" chebfun p
self.p = Chebfun.from_function(tools.f)
