def test_padua_fit_even_degree(self):
points = padua_points(10)
C0f, abs_error = padua_fit(points, example_functions, 6)
expected = np.zeros((11, 11))
expected[0, 0] = 1
assert_array_almost_equal(C0f, expected, 15)
assert_array_almost_equal(abs_error, 1.2168216554799264e-15)
def test_padua_fit_odd_degree(self):
points = padua_points(9)
C0f, abs_error = padua_fit(points, example_functions, 6)
expected = np.zeros((10, 10))
expected[0, 0] = 1
assert_array_almost_equal(C0f, expected, 15)
assert_array_almost_equal(abs_error, 4.509537093983535e-17)
def test_padua_cubature(self):
domain = [0,1,0,1]
points = padua_points(500, domain)
C0f, abs_error = padua_fit(points, testfunct, 0)
val = padua_cubature(C0f, domain)
expected = 4.06969589491556e-01
assert_array_almost_equal(val, expected, 15)
def test_padua_cubature(self):
domain = [0, 1, 0, 1]
points = padua_points(500, domain)
C0f, _abs_error = padua_fit(points, example_functions, 0)
val = padua_cubature(C0f, domain)
expected = 4.06969589491556e-01
assert_array_almost_equal(val, expected, 15)
def test_padua_val_unordered(self):
domain = [0, 1, 0, 1]
points = padua_points(20, domain)
C0f, _abs_error = padua_fit(points, example_functions, 0)
X = [0, 0.5, 1]
val = padua_val(X, X, C0f, domain)
expected = [7.664205912849228e-01, 3.2621734202884815e-01, 3.587865112678535e-02]
assert_array_almost_equal(val, expected, 14)
def test_padua_val_unordered(self):
domain = [0, 1, 0, 1]
points = padua_points(20, domain)
C0f, abs_error = padua_fit(points, example_functions, 0)
X = np.array([0, 0.5, 1])
# true_val = example_functions.franke(X, X)
val = padua_val(X, X, C0f, domain)
expected = [0.76642059128493, 0.32621734202885, 0.03587865112678]
assert_array_almost_equal(val, expected, 14)
assert_array_almost_equal(abs_error, 0.003897032262116954)
def test_padua_val_grid(self):
domain = [0, 1, 0, 1]
a, b, c, d = domain
points = padua_points(21, domain)
C0f, _abs_error = padua_fit(points, example_functions, 0)
X1 = np.linspace(a, b, 2)
X2 = np.linspace(c, d, 2)
val = padua_val(X1, X2, C0f, domain, use_meshgrid=True)
expected = [[7.664205912849229e-01, 1.0757071952145181e-01], [2.703371615911344e-01, 3.5734971024838565e-02]]
assert_array_almost_equal(val, expected, 14)
def test_padua_val_grid(self):
domain = [0, 1, 0, 1]
a, b, c, d = domain
points = padua_points(21, domain)
C0f, _abs_error = padua_fit(points, example_functions, 0)
X1 = np.linspace(a, b, 2)
X2 = np.linspace(c, d, 2)
val = padua_val(X1, X2, C0f, domain, use_meshgrid=True)
expected = [[7.664205912849229e-01, 1.0757071952145181e-01],
[2.703371615911344e-01, 3.5734971024838565e-02]]
assert_array_almost_equal(val, expected, 14)
def test_padua_val_unordered(self):
domain = [0, 1, 0, 1]
points = padua_points(20, domain)
C0f, _abs_error = padua_fit(points, example_functions, 0)
X = [0, 0.5, 1]
val = padua_val(X, X, C0f, domain)
expected = [
7.664205912849228e-01, 3.2621734202884815e-01,
3.587865112678535e-02
]
assert_array_almost_equal(val, expected, 14)
def test_padua_val_grid(self):
domain = [0, 1, 0, 1]
a, b, c, d = domain
points = padua_points(21, domain)
C0f, abs_error = padua_fit(points, example_functions, 0)
X1 = np.linspace(a, b, 2)
X2 = np.linspace(c, d, 2)
val = padua_val(X1, X2, C0f, domain, use_meshgrid=True)
expected = [[0.76642059128493, 0.10757071952145],
[0.27033716159114, 0.03573497102484]]
assert_array_almost_equal(val, expected, 14)
assert_array_almost_equal(abs_error, 0.0022486904061664046)
def test_padua_fit_even_degree(self):
points = padua_points(10)
C0f, abs_error = padua_fit(points, testfunct, 6)
expected = np.zeros((11, 11))
expected[0, 0] = 1
assert_array_almost_equal(C0f, expected, 15)
def test_padua_points_degree2(self):
pad = padua_points(2, domain=[0, 1, 0, 2])
expected = [(cos(np.r_[0, 0, 1, 1, 2, 2] * pi / 2) + 1) / 2,
cos(np.r_[1, 3, 0, 2, 1, 3] * pi / 3) + 1]
assert_array_almost_equal(pad, expected, 15)
def test_padua_points_degree1(self):
pad = padua_points(1)
expected = [cos(np.r_[0, 1, 1] * pi), cos(np.r_[1, 0, 2] * pi / 2)]
assert_array_almost_equal(pad, expected, 15)
def test_padua_points_degree0(self):
pad = padua_points(0)
expected = [[-1], [-1]]
assert_array_almost_equal(pad, expected, 15)
def test_padua_points_degree0(self):
pad = padua_points(0)
expected = [[-1],[-1]]
assert_array_almost_equal(pad, expected, 15)
def test_padua_points_degree1(self):
pad = padua_points(1)
expected = [cos(np.r_[0,1,1]*pi),
cos(np.r_[1,0,2]*pi/2)]
assert_array_almost_equal(pad, expected, 15)
def test_padua_points_degree2(self):
pad = padua_points(2, domain=[0,1,0,2])
expected = [(cos(np.r_[0,0,1,1,2,2]*pi/2)+1)/2,
cos(np.r_[1,3,0,2,1,3]*pi/3)+1]
assert_array_almost_equal(pad, expected, 15)
def test_padua_fit_even_degree(self):
points = padua_points(10)
C0f, abs_error = padua_fit(points, testfunct, 6)
expected = np.zeros((11, 11))
expected[0,0] = 1;
assert_array_almost_equal(C0f, expected, 15)
def test_padua_fit_odd_degree2(self):
points = padua_points(9)
C0f, abs_error = padua_fit2(points, testfunct, 6)
expected = np.zeros((10, 10))
expected[0,0] = 1;
assert_array_almost_equal(C0f, expected, 15)
def test_padua_fit_odd_degree2(self):
points = padua_points(9)
C0f, abs_error = padua_fit2(points, testfunct, 6)
expected = np.zeros((10, 10))
expected[0, 0] = 1
assert_array_almost_equal(C0f, expected, 15)
def test_padua_fit_odd_degree(self):
points = padua_points(9)
C0f, _abs_error = padua_fit(points, example_functions, 6)
expected = np.zeros((10, 10))
expected[0, 0] = 1
assert_array_almost_equal(C0f, expected, 15)
