def test_fitting(self):
xdata = np.linspace(1,10,10)
ydata = 3*xdata**2
a = Parameter() #3.1, min=2.5, max=3.5
b = Parameter()
x = Variable()
new = a*x**b
fit = NumericalLeastSquares(new, xdata, ydata)
self.assertTrue(issubclass(fit.model.chi_squared.__class__, sympy.Expr))
self.assertTrue(issubclass(fit.model.chi.__class__, sympy.Expr))
self.assertTrue(type(fit.model.numerical_chi_squared) is types.LambdaType)
self.assertTrue(type(fit.model.numerical_chi) is types.LambdaType)
fit_result = fit.execute()
self.assertIsInstance(fit_result, FitResults)
self.assertAlmostEqual(fit_result.params.a, 3.0)
self.assertAlmostEqual(fit_result.params.b, 2.0)
self.assertIsInstance(fit_result.params.a_stdev, float)
self.assertIsInstance(fit_result.params.b_stdev, float)
self.assertIsInstance(fit_result.r_squared, float)
self.assertEqual(fit_result.r_squared, 1.0) # by definition since there's no fuzzyness
# Test several illegal ways to access the data.
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_fdska'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'c'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_stdev_stdev'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_stdev_'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a__stdev'])
def test_fitting(self):
xdata = np.linspace(1,10,10)
ydata = 3*xdata**2
a = Parameter() #3.1, min=2.5, max=3.5
b = Parameter()
x = Variable()
new = a*x**b
fit = NumericalLeastSquares(new, xdata, ydata)
fit_result = fit.execute()
self.assertIsInstance(fit_result, FitResults)
self.assertAlmostEqual(fit_result.params.a, 3.0)
self.assertAlmostEqual(fit_result.params.b, 2.0)
self.assertIsInstance(fit_result.params.a_stdev, float)
self.assertIsInstance(fit_result.params.b_stdev, float)
self.assertIsInstance(fit_result.r_squared, float)
self.assertEqual(fit_result.r_squared, 1.0) # by definition since there's no fuzzyness
# Test several false ways to access the data.
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_fdska'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'c'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_stdev_stdev'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a_stdev_'])
self.assertRaises(AttributeError, getattr, *[fit_result.params, 'a__stdev'])
def test_vector_none_fitting(self):
"""
Fit to a vector model with one var's data set to None
"""
a, b, c = parameters('a, b, c')
a_i, b_i, c_i = variables('a_i, b_i, c_i')
model = {a_i: a, b_i: b, c_i: c}
xdata = np.array([
[10.1, 9., 10.5, 11.2, 9.5, 9.6, 10.],
[102.1, 101., 100.4, 100.8, 99.2, 100., 100.8],
[71.6, 73.2, 69.5, 70.2, 70.8, 70.6, 70.1],
])
fit_none = NumericalLeastSquares(
model=model,
a_i=xdata[0],
b_i=xdata[1],
c_i=None,
)
fit = NumericalLeastSquares(
model=model,
a_i=xdata[0],
b_i=xdata[1],
c_i=xdata[2],
)
fit_none_result = fit_none.execute()
fit_result = fit.execute()
self.assertAlmostEqual(fit_none_result.params.a, fit_result.params.a, 4)
self.assertAlmostEqual(fit_none_result.params.b, fit_result.params.b, 4)
self.assertAlmostEqual(fit_none_result.params.c, 1.0)
def test_named_fitting(self):
xdata = np.linspace(1,10,10)
ydata = 3*xdata**2
a = Parameter(1.0)
b = Parameter(2.5)
x, y = variables('x, y')
model = {y: a*x**b}
fit = NumericalLeastSquares(model, x=xdata, y=ydata)
fit_result = fit.execute()
self.assertIsInstance(fit_result, FitResults)
self.assertAlmostEqual(fit_result.params.a, 3.0)
self.assertAlmostEqual(fit_result.params.b, 2.0)
def test_vector_fitting(self):
a, b, c = parameters('a, b, c')
a_i, b_i, c_i = variables('a_i, b_i, c_i')
model = {a_i: a, b_i: b, c_i: c}
xdata = np.array([
[10.1, 9., 10.5, 11.2, 9.5, 9.6, 10.],
[102.1, 101., 100.4, 100.8, 99.2, 100., 100.8],
[71.6, 73.2, 69.5, 70.2, 70.8, 70.6, 70.1],
])
fit = NumericalLeastSquares(
model=model,
a_i=xdata[0],
b_i=xdata[1],
c_i=xdata[2],
)
fit_result = fit.execute()
self.assertAlmostEqual(fit_result.params.a, 9.985691, 6)
self.assertAlmostEqual(fit_result.params.b, 1.006143e+02, 4)
self.assertAlmostEqual(fit_result.params.c, 7.085713e+01, 5)
