def test_2D_fitting(self):
xdata = np.random.randint(-10, 11, size=(2, 400))
zdata = 2.5*xdata[0]**2 + 7.0*xdata[1]**2
a = Parameter('a')
b = Parameter('b')
x = Variable('x')
y = Variable('y')
new = a*x**2 + b*y**2
fit = Fit(new, xdata, zdata)
result = fit.scipy_func(fit.xdata, [2, 3])
import inspect
args, varargs, keywords, defaults = inspect.getargspec(fit.scipy_func)
self.assertEqual(args, ['x', 'p'])
fit_result = fit.execute()
self.assertIsInstance(fit_result, FitResults)
def test_fitting(self):
xdata = np.linspace(1,10,10)
ydata = 3*xdata**2
a = Parameter('a')
b = Parameter('b')
x = Variable('x')
new = a*x**b
fit = Fit(new, xdata, ydata)
func = sympy_to_py(new, [x], [a, b])
result = func(xdata, 3, 2)
self.assertTrue(np.array_equal(result, ydata))
result = fit.scipy_func(fit.xdata, [3, 2])
self.assertTrue(np.array_equal(result, ydata))
import inspect
args, varargs, keywords, defaults = inspect.getargspec(fit.scipy_func)
# self.assertEqual(args, ['x', 'a', 'b'])
fit_result = fit.execute()
self.assertIsInstance(fit_result, FitResults)
print(fit_result)
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)
# 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'])