Merge pull request #23 from fjosw/fix/r_values_bug

Fix/r values bug

pyerrors/fits.py

@@ -301,8 +301,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda x, **kwargs: x[0], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
-        result[-1]._value = out.beta[i]
+        result.append(derived_observable(lambda my_var, **kwargs: my_var[0] / x.ravel()[0].value * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
 
     output.fit_parameters = result + const_par
 
@@ -419,8 +418,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda x, **kwargs: x[0], list(y) + list(loc_priors), man_grad=list(deriv[i])))
-        result[-1]._value = params[i]
+        result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
 
     output.fit_parameters = result
     output.chisquare = chisqfunc(np.asarray(params))
@@ -614,8 +612,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
 
     result = []
     for i in range(n_parms):
-        result.append(derived_observable(lambda x, **kwargs: x[0], list(y), man_grad=list(deriv[i])))
-        result[-1]._value = fit_result.x[i]
+        result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * fit_result.x[i], list(y), man_grad=list(deriv[i])))
 
     output.fit_parameters = result + const_par
 

pyerrors/roots.py

@@ -33,6 +33,5 @@ def find_root(d, func, guess=1.0, **kwargs):
     da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
     deriv = - da / dx
 
-    res = derived_observable(lambda x, **kwargs: x[0], [d], man_grad=[deriv])
-    res._value = root[0]
+    res = derived_observable(lambda x, **kwargs: x[0] / d.value * root[0], [d], man_grad=[deriv])
     return res

tests/fits_test.py

@@ -52,7 +52,7 @@ def test_least_squares():
 
     outc = pe.least_squares(x, oyc, func)
     betac = outc.fit_parameters
-    
+
     for i in range(2):
         betac[i].gamma_method(S=1.0)
         assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
@@ -97,7 +97,7 @@ def test_least_squares():
                 return p[1] * np.exp(-p[0] * x)
 
         fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
-        
+
         fitpc = pe.least_squares(x, data, fitf, correlated_fit=True)
         for i in range(2):
             diff = fitp[i] - fitpc[i]
@@ -170,7 +170,7 @@ def test_total_least_squares():
     diffc = outc.fit_parameters[0] - betac[0]
     assert(diffc / betac[0] < 1e-3 * betac[0].dvalue)
     assert((outc.fit_parameters[1] - betac[1]).is_zero())
-    
+
     outc = pe.total_least_squares(oxc, oy, func)
     betac = outc.fit_parameters
 
@@ -208,3 +208,41 @@ def test_odr_derivatives():
         tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
     assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
                   - np.array(list(tfit[1].deltas.values())))) < 10e-8
+
+
+def test_r_value_persistence():
+    def f(a, x):
+        return a[0] + a[1] * x
+
+    a = pe.pseudo_Obs(1.1, .1, 'a')
+    assert np.isclose(a.value, a.r_values['a'])
+
+    a_2 = a ** 2
+    assert np.isclose(a_2.value, a_2.r_values['a'])
+
+    b = pe.pseudo_Obs(2.1, .2, 'b')
+
+    y = [a, b]
+    [o.gamma_method() for o in y]
+
+    fitp = pe.fits.least_squares([1, 2], y, f)
+
+    assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
+    assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
+
+    fitp = pe.fits.total_least_squares(y, y, f)
+
+    assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
+    assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
+
+    fitp = pe.fits.least_squares([1, 2], y, f, priors=y)
+
+    assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
+    assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
+    assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
+

tests/obs_test.py

@@ -381,6 +381,16 @@ def test_merge_obs():
     assert diff == -(my_obs1.value + my_obs2.value) / 2
 
 
+def test_merge_obs_r_values():
+    a1 = pe.pseudo_Obs(1.1, .1, 'a|1')
+    a2 = pe.pseudo_Obs(1.2, .1, 'a|2')
+    a = pe.merge_obs([a1, a2])
+
+    assert np.isclose(a.r_values['a|1'], a1.value)
+    assert np.isclose(a.r_values['a|2'], a2.value)
+    assert np.isclose(a.value, np.mean([a1.value, a2.value]))
+
+
 def test_correlate():
     my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
     my_obs2 = pe.Obs([np.random.rand(100)], ['t'])

tests/roots_test.py

@@ -15,6 +15,7 @@ def test_root_linear():
     my_root = pe.roots.find_root(my_obs, root_function)
 
     assert np.isclose(my_root.value, value)
+    assert np.isclose(my_root.value, my_root.r_values['t'])
     difference = my_obs - my_root
     assert difference.is_zero()