def test_linear_quality_x_bounds(self):
x = np.array([
[0.0, 1.0, 2.0],
[0.5, 1.5, 2.5],
[-0.25, 0.25, 0.75],
])
y = x.copy()
dy = 0.05 * np.ones_like(y)
ret = fssa.quality(x, y, dy, x_bounds=(0.3, 2.3))
self.assertEqual(ret, 0.0)
def test_linear_quality_x_bounds(self):
x = np.array([
[0.0, 1.0, 2.0],
[0.5, 1.5, 2.5],
[-0.25, 0.25, 0.75],
])
y = x.copy()
dy = 0.05 * np.ones_like(y)
ret = fssa.quality(x, y, dy, x_bounds=(0.3, 2.3))
self.assertEqual(ret, 0.0)
def test_linear_quality_fail(self):
x = np.array([
[0.0, 1.0, 2.0],
[0.5, 1.5, 2.5],
[-0.25, 0.25, 0.75],
])
y = x.copy()
y[1, 1] = 1.0
dy = 0.05 * np.ones_like(y)
ret = fssa.quality(x, y, dy)
self.assertGreater(ret, 0.0)
def test_linear_quality_fail(self):
x = np.array([
[0.0, 1.0, 2.0],
[0.5, 1.5, 2.5],
[-0.25, 0.25, 0.75],
])
y = x.copy()
y[1, 1] = 1.0
dy = 0.05 * np.ones_like(y)
ret = fssa.quality(x, y, dy)
self.assertGreater(ret, 0.0)
def test_zero_quality(self):
"""
Test that function returns close to zero value if fed with the same x
and y values
"""
def master_curve(x):
return 1. / (1. + np.exp(5 * (1. - x)))
x = np.linspace(0, 2)
x_array = np.row_stack([x for i in range(10)])
y_array = master_curve(x_array)
dy = 0.05
dy_array = dy * np.ones_like(y_array)
ret = fssa.quality(x_array, y_array, dy_array)
self.assertGreaterEqual(ret, 0.)
self.assertLess(ret, 0.1)
def test_zero_quality(self):
"""
Test that function returns close to zero value if fed with the same x
and y values
"""
def master_curve(x):
return 1. / (1. + np.exp(5 * (1. - x)))
x = np.linspace(0, 2)
x_array = np.row_stack([x for i in range(10)])
y_array = master_curve(x_array)
dy = 0.05
dy_array = dy * np.ones_like(y_array)
ret = fssa.quality(x_array, y_array, dy_array)
self.assertGreaterEqual(ret, 0.)
self.assertLess(ret, 0.1)
def test_standard_quality(self):
"""
Test that function returns close to one value if fed with the same x
and y values with some standard error applied
"""
def master_curve(x):
return 1. / (1. + np.exp(5 * (1. - x)))
x = np.linspace(0, 2)
x_array = np.row_stack([x for i in range(10)])
y_array = master_curve(x_array)
dy = 0.05
dy_array = dy * np.ones_like(y_array)
y_array += dy * np.random.randn(*y_array.shape)
ret = fssa.quality(x_array, y_array, dy_array)
self.assertGreater(ret, np.power(10, -0.5))
self.assertLess(ret, np.power(10, 0.5))
def test_standard_quality(self):
"""
Test that function returns close to one value if fed with the same x
and y values with some standard error applied
"""
def master_curve(x):
return 1. / (1. + np.exp(5 * (1. - x)))
x = np.linspace(0, 2)
x_array = np.row_stack([x for i in range(10)])
y_array = master_curve(x_array)
dy = 0.05
dy_array = dy * np.ones_like(y_array)
y_array += dy * np.random.randn(*y_array.shape)
ret = fssa.quality(x_array, y_array, dy_array)
self.assertGreater(ret, np.power(10, -0.5))
self.assertLess(ret, np.power(10, 0.5))
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)
#data = fssa.scaledata(L, T, L_C + C_reg, d_L_C, 4.51, 0.63, 0.11)
