class IsochronTestCase(unittest.TestCase):
def setUp(self):
xs, ys, wxs, wys = pearson()
exs = wxs**-0.5
eys = wys**-0.5
self.reg = ReedYorkRegressor(xs=xs, ys=ys, xserr=exs, yserr=eys)
self.reg.calculate()
def test_slope(self):
exp = pearson('reed')
self.assertAlmostEqual(self.reg.slope, exp['slope'], 4)
def test_y_intercept(self):
expected = pearson('reed')
self.assertAlmostEqual(self.reg.intercept, expected['intercept'], 4)
def test_x_intercept(self):
pass
#self.assertEqual(True, False)
def test_age(self):
pass
def test_mswd(self):
expected = pearson('reed')
self.assertAlmostEqual(self.reg.mswd, expected['mswd'], 4)
class IsochronTestCase(unittest.TestCase):
def setUp(self):
xs, ys, wxs, wys = pearson()
exs = wxs ** -0.5
eys = wys ** -0.5
self.reg = ReedYorkRegressor(xs=xs, ys=ys,
xserr=exs,
yserr=eys)
self.reg.calculate()
def test_slope(self):
exp = pearson('reed')
self.assertAlmostEqual(self.reg.slope, exp['slope'], 4)
def test_y_intercept(self):
expected = pearson('reed')
self.assertAlmostEqual(self.reg.intercept, expected['intercept'], 4)
def test_x_intercept(self):
pass
#self.assertEqual(True, False)
def test_age(self):
pass
def test_mswd(self):
expected = pearson('reed')
self.assertAlmostEqual(self.reg.mswd, expected['mswd'], 4)
def setUp(self):
xs, ys, wxs, wys = pearson()
exs = wxs**-0.5
eys = wys**-0.5
self.reg = ReedYorkRegressor(xs=xs, ys=ys, xserr=exs, yserr=eys)
self.reg.calculate()
def setUp(self):
xs, ys, wxs, wys = pearson()
exs = wxs ** -0.5
eys = wys ** -0.5
self.reg = ReedYorkRegressor(xs=xs, ys=ys,
xserr=exs,
yserr=eys)
self.reg.calculate()
def setUpClass(cls):
# Pearson Data with Weights
xs = [0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4]
ys = [5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5]
# xs = [5, 10, 6, 8, 4, 4, 3, 10, 2, 6, 7, 9]
# ys = [5, 20, 4, 15, 11, 9, 12, 18, 7, 2, 14, 17]
wxs = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])
wys = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])
exs = 1 / wxs**0.5
eys = 1 / wys**0.5
cls.reg = ReedYorkRegressor(xs=xs, ys=ys, xserr=exs, yserr=eys)
def setUpClass(cls):
xs = [0.03692, 1.07118]
exs = [0.00061, 0.00066]
ys = [0.003121, 0.00022]
eys = [0.0003, 0.000013]
xs = [
0.89, 1.0, 0.92, 0.87, 0.9, 0.86, 1.08, 0.86, 1.25, 1.01, 0.86,
0.85, 0.88, 0.84, 0.79, 0.88, 0.70, 0.81, 0.88, 0.92, 0.92, 1.01,
0.88, 0.92, 0.96, 0.85, 1.04
]
ys = [
0.67, 0.64, 0.76, 0.61, 0.74, 0.61, 0.77, 0.61, 0.99, 0.77, 0.73,
0.64, 0.62, 0.63, 0.57, 0.66, 0.53, 0.46, 0.79, 0.77, 0.7, 0.88,
0.62, 0.80, 0.74, 0.64, 0.93
]
exs = np.ones(27) * 0.01
eys = np.ones(27) * 0.01
# xs = [ 1.333, -1.009, 9.720, -2.079, 8.920, -0.938, 10.94, 5.138, 11.37, 9.421]
# exs = [ 2.469 , 6.363, 6.045 , 4.061, 5.325, 5.865 , 3.993, 3.787, 3.693, 4.687]
# ys = [ -1.367 , 7.232, -0.593, 7.124, 0.468, 8.664 , 5.854, 13.35, 4.279, 11.63]
# eys = [0.297 , 4.672 , 2.014, 0.022, 6.868, 2.834 , 4.647, 4.728, 2.274, 4.659]
# Pearson Data with Weights
xs = [0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4]
ys = [5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5]
wxs = np.array([1000, 1000, 500, 800, 200, 80, 60, 20, 1.8, 1])
wys = np.array([1, 1.8, 4, 8, 20, 20, 70, 70, 100, 500])
exs = 1 / wxs**0.5
eys = 1 / wys**0.5
# reed 1992 solutions
cls.pred_slope = -0.4805
cls.pred_intercept = 5.4799
cls.pred_intercept_error = 0.3555
cls.pred_slope_error = 0.0702
cls.reg = ReedYorkRegressor(ys=ys, xs=xs, xserr=exs, yserr=eys)
