def test_no_error_if_not_da_all_positive(self):
"""
Test that function does not raise ValueError if any da is not positive
"""
# da should have only positive entries
args = copy.deepcopy(self.default_params)
args['da'][1, 1] = 0.
fssa.scaledata(**args)
args = copy.deepcopy(self.default_params)
args['da'][1, 0] = -1.
fssa.scaledata(**args)
def test_rho_c_in_range(self):
"""
Test that function issues warning when rho_c is out of range
"""
args = copy.deepcopy(self.default_params)
args['rho_c'] = args['rho'].max() + 1.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
fssa.scaledata(**args)
assert len(w) == 1
assert issubclass(w[-1].category, RuntimeWarning)
assert "out of range" in str(w[-1].message)
def test_no_error_if_not_da_all_positive(self):
"""
Test that function does not raise ValueError if any da is not positive
"""
# da should have only positive entries
args = copy.deepcopy(self.default_params)
args['da'][1, 1] = 0.
fssa.scaledata(**args)
args = copy.deepcopy(self.default_params)
args['da'][1, 0] = -1.
fssa.scaledata(**args)
def test_rho_c_in_range(self):
"""
Test that function issues warning when rho_c is out of range
"""
args = copy.deepcopy(self.default_params)
args['rho_c'] = args['rho'].max() + 1.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
fssa.scaledata(**args)
assert len(w) == 1
assert issubclass(w[-1].category, RuntimeWarning)
assert "out of range" in str(w[-1].message)
def setUp(self):
rho = np.linspace(-1, 1, num=11)
l = np.logspace(1, 3, num=3)
l_mesh, rho_mesh = np.meshgrid(l, rho, indexing='ij')
a = 1. / (1. + np.exp(-np.log10(l_mesh) * rho_mesh))
da = np.ones_like(a) * 1e-2
self.scaled_data = fssa.scaledata(l, rho, a, da, 0, 1, 0)
def setUp(self):
rho = np.linspace(-1, 1, num=11)
l = np.logspace(1, 3, num=3)
l_mesh, rho_mesh = np.meshgrid(l, rho, indexing='ij')
a = 1. / (1. + np.exp(- np.log10(l_mesh) * rho_mesh))
da = np.ones_like(a) * 1e-2
self.scaled_data = fssa.scaledata(l, rho, a, da, 0, 1, 0)
def test_zero_zeta_keeps_y_values(self):
"""
Check that $\zeta = 0$ does not affect the y values
"""
args = copy.deepcopy(self.default_params)
args['zeta'] = 0.0
result = fssa.scaledata(**self.default_params)
self.assertTrue(np.all(args['a'] == result.y))
def test_zero_zeta_keeps_y_values(self):
"""
Check that $\zeta = 0$ does not affect the y values
"""
args = copy.deepcopy(self.default_params)
args['zeta'] = 0.0
result = fssa.scaledata(**self.default_params)
self.assertTrue(np.all(args['a'] == result.y))
def test_output_namedtuple(self):
"""
Test that function returns namedtuple with correct field names
"""
result = fssa.scaledata(**self.default_params)
fields = ['x', 'y', 'dy']
for i in range(len(fields)):
try: # python 3
with self.subTest(i=i):
self.assertTrue(hasattr(result, fields[i]))
self.assertIs(getattr(result, fields[i]), result[i])
except AttributeError: # python 2
self.assertTrue(hasattr(result, fields[i]))
self.assertIs(getattr(result, fields[i]), result[i])
def test_output_namedtuple(self):
"""
Test that function returns namedtuple with correct field names
"""
result = fssa.scaledata(**self.default_params)
fields = ['x', 'y', 'dy']
for i in range(len(fields)):
try: # python 3
with self.subTest(i=i):
self.assertTrue(hasattr(result, fields[i]))
self.assertIs(getattr(result, fields[i]), result[i])
except AttributeError: # python 2
self.assertTrue(hasattr(result, fields[i]))
self.assertIs(getattr(result, fields[i]), result[i])
def test_values(self):
"""
Check some arbitrary value
"""
l = self.default_params['l'][2]
rho = self.default_params['rho'][3]
rho_c = self.default_params['rho_c']
nu = self.default_params['nu']
zeta = self.default_params['zeta']
a = self.default_params['a'][2, 3]
da = self.default_params['da'][2, 3]
result = fssa.scaledata(**self.default_params)
self.assertAlmostEqual(result.x[2, 3], l ** (1. / nu) * (rho - rho_c))
self.assertAlmostEqual(result.y[2, 3], l ** (- zeta / nu) * a)
self.assertAlmostEqual(result.dy[2, 3], l ** (- zeta / nu) * da)
def test_output_shape(self):
"""
Test that function returns three ndarrays of correct shape
"""
correct_shape = (
self.default_params['l'].size,
self.default_params['rho'].size,
)
result = fssa.scaledata(**self.default_params)
for i in range(3):
try: # python 3
with self.subTest(i=i):
self.assertTupleEqual(
np.asarray(result[i]).shape, correct_shape)
except AttributeError: # python 2
self.assertTupleEqual(
np.asarray(result[i]).shape, correct_shape)
def test_values(self):
"""
Check some arbitrary value
"""
l = self.default_params['l'][2]
rho = self.default_params['rho'][3]
rho_c = self.default_params['rho_c']
nu = self.default_params['nu']
zeta = self.default_params['zeta']
a = self.default_params['a'][2, 3]
da = self.default_params['da'][2, 3]
result = fssa.scaledata(**self.default_params)
self.assertAlmostEqual(result.x[2, 3], l**(1. / nu) * (rho - rho_c))
self.assertAlmostEqual(result.y[2, 3], l**(-zeta / nu) * a)
self.assertAlmostEqual(result.dy[2, 3], l**(-zeta / nu) * da)
def test_output_shape(self):
"""
Test that function returns three ndarrays of correct shape
"""
correct_shape = (
self.default_params['l'].size,
self.default_params['rho'].size,
)
result = fssa.scaledata(**self.default_params)
for i in range(3):
try: # python 3
with self.subTest(i=i):
self.assertTupleEqual(
np.asarray(result[i]).shape,
correct_shape
)
except AttributeError: # python 2
self.assertTupleEqual(
np.asarray(result[i]).shape, correct_shape
)
L_C[2][i] = C_20[i]
L_C[3][i] = C_25[i]
d_L_C = np.zeros((len(L), len(C_5)))
for i in range(len(C_5)):
d_L_C[0][i] = C_10_s[i]
d_L_C[1][i] = C_15_s[i]
d_L_C[2][i] = C_20_s[i]
d_L_C[3][i] = C_25_s[i]
C_reg = 9999
f_reg = 9999
C_reg_array = np.linspace(0, 3, 10000)
for C_0 in C_reg_array:
scaled_data = fssa.scaledata(L, T, L_C + C_0, d_L_C, 1 / 0.2216, 0.62997,
0.11008)
f = fssa.quality(scaled_data.x, scaled_data.y, scaled_data.dy)
if f < f_reg:
C_reg = C_0
f_reg = f
print(C_reg, f_reg)
ret = fssa.autoscale(L, T, L_C + C_reg, d_L_C, 1 / 0.2216, 0.62997, 0.11008)
print("C_reg:", C_reg)
print("rho:", ret.rho, ret.drho)
print("nu:", ret.nu, ret.dnu)
print("zeta:", ret.zeta, ret.dzeta)
print("f:", ret.fun)
data = fssa.scaledata(L, T, L_C + C_reg, d_L_C, ret.rho, ret.nu, ret.zeta)
def test_output_len(self):
"""
Test that function returns at least three items
"""
result = fssa.scaledata(**self.default_params)
self.assertGreaterEqual(len(result), 3)
def test_output_len(self):
"""
Test that function returns at least three items
"""
result = fssa.scaledata(**self.default_params)
self.assertGreaterEqual(len(result), 3)