Merge branch 'develop' into documentation

pyerrors/fits.py

@@ -365,7 +365,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
         raise Exception('The minimization procedure did not converge.')
 
     output.chisquare = chisquare
-    output.dof = x_all.shape[-1] - n_parms + len(loc_priors)
+    output.dof = y_all.shape[-1] - n_parms + len(loc_priors)
     output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
     if output.dof > 0:
         output.chisquare_by_dof = output.chisquare / output.dof
@@ -393,7 +393,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
             hat_vector = prepare_hat_matrix()
             A = W @ hat_vector
             P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
-            expected_chisquare = np.trace((np.identity(x_all.shape[-1]) - P_phi) @ W @ cov @ W)
+            expected_chisquare = np.trace((np.identity(y_all.shape[-1]) - P_phi) @ W @ cov @ W)
             output.chisquare_by_expected_chisquare = output.chisquare / expected_chisquare
             if not silent:
                 print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare)

tests/fits_test.py

@@ -1142,6 +1142,53 @@ def test_fit_dof():
     assert cd[0] != cd[0]  # Check for nan
     assert np.all(np.array(cd[1:]) > 0)
 
+    N = 5
+
+    def fitf(a, x):
+        return a[0] + 0 * x
+
+    def fitf_multi(a, x):
+        return a[0] + 0 * x[0] + 0*x[1]
+
+    for priors in [None, [pe.cov_Obs(3, 1, 'p')]]:
+        if priors is None:
+            lp = 0
+        else:
+            lp = len(priors)
+        x = [1. for i in range(N)]
+        y = [pe.cov_Obs(i, .1, '%d' % (i)) for i in range(N)]
+        [o.gm() for o in y]
+        res = pe.fits.least_squares(x, y, fitf, expected_chisquare=True, priors=priors)
+        assert(res.dof == N - 1 + lp)
+        if priors is None:
+            assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
+
+        kl = ['a', 'b']
+        x = {k: [1. for i in range(N)] for k in kl}
+        y = {k: [pe.cov_Obs(i, .1, '%d%s' % (i, k)) for i in range(N)] for k in kl}
+        [[o.gm() for o in y[k]] for k in y]
+        res = pe.fits.least_squares(x, y, {k: fitf for k in kl}, expected_chisquare=True, priors=priors)
+        assert(res.dof == 2 * N - 1 + lp)
+        if priors is None:
+            assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
+
+        x = np.array([[1., 2.] for i in range(N)]).T
+        y = [pe.cov_Obs(i, .1, '%d' % (i)) for i in range(N)]
+        [o.gm() for o in y]
+        res = pe.fits.least_squares(x, y, fitf_multi, expected_chisquare=True, priors=priors)
+        assert(res.dof == N - 1 + lp)
+        if priors is None:
+            assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
+
+        x = {k: np.array([[1., 2.] for i in range(N)]).T for k in kl}
+        y = {k: [pe.cov_Obs(i, .1, '%d%s' % (i, k)) for i in range(N)] for k in kl}
+        [[o.gm() for o in y[k]] for k in y]
+        res = pe.fits.least_squares(x, y, {k: fitf_multi for k in kl}, expected_chisquare=True, priors=priors)
+
+        assert(res.dof == 2 * N - 1 + lp)
+        if priors is None:
+            assert(np.isclose(res.chisquare_by_expected_chisquare, res.chisquare_by_dof))
+
 
 def test_combined_fit_constant_shape():
     N1 = 16