improve test coverage and fix no uncertainty in y

uravu/optimize.py

@@ -99,14 +99,12 @@ def ln_likelihood(
         var = variables
         log_f = -np.inf
     model = function(abscissa.m, *var)
-    if isinstance(ordinate.m.any(), uncertainties.core.AffineScalarFunc):
-        y_data = unp.nominal_values(ordinate.m)
-        dy_data = unp.std_devs(ordinate.m)
-
-        sigma2 = dy_data ** 2 + model ** 2 * np.exp(2 * log_f)
-        return -0.5 * np.sum((model - y_data) ** 2 / sigma2 + np.log(sigma2))
-    else:
-        y_data = ordinate.m
+    y_data = unp.nominal_values(ordinate.m)
+    dy_data = unp.std_devs(ordinate.m)
 
+    if np.isclose(dy_data.all(), 0.0):
         sigma2 = model ** 2 * np.exp(2 * log_f)
-        return -0.5 * np.sum((model - y_data) ** 2 / sigma2 + np.log(sigma2))
+    else:
+        sigma2 = dy_data ** 2 + model ** 2 * np.exp(2 * log_f)
+    return -0.5 * np.sum((model - y_data) ** 2 / sigma2 + np.log(sigma2))
+

uravu/relationship.py

@@ -61,7 +61,9 @@ class Relationship:
             shape `(N,)`.
         ordinate_uncertainty (array_like, optional): The uncertainty in each
             of the ordinate data points. This should have a shape `(N,)`.
-            Default to no uncertainties on the ordiante
+            Default to no uncertainties on the ordiante. If there is no
+            ordinate uncertainty, an unaccounted uncertainty is automatically
+            added. 
         abscissa_uncertainty (array_like, optional): The uncertainty in each
             of the absiccsa data points. This should have a shape `(N, d)`.
             Default is no uncertainties on absicca.
@@ -109,32 +111,34 @@ def __init__(
         if abscissa_uncertainty is not None:
             abscissa_uncertainty = np.array(abscissa_uncertainty)
         if abscissa.shape[0] == ordinate.shape[0]:
-            if ordinate.shape[0] == ordinate_uncertainty.shape[0]:
-                if (abscissa_uncertainty is not None) and (
-                    abscissa.shape[0] == abscissa_uncertainty.shape[0]
-                ):
-                    self.abscissa = unp.uarray(abscissa, abscissa_uncertainty)
-                else:
-                    self.abscissa = abscissa
-                self.abscissa *= abscissa_unit
-                if ordinate_uncertainty is not None:
+            if (abscissa_uncertainty is not None) and (
+                abscissa.shape[0] == abscissa_uncertainty.shape[0]
+            ):
+                self.abscissa = unp.uarray(abscissa, abscissa_uncertainty)
+            else:
+                self.abscissa = abscissa
+            self.abscissa *= abscissa_unit
+            if ordinate_uncertainty is not None:
+                if ordinate.shape[0] == ordinate_uncertainty.shape[0]:
                     self.ordinate = (
                         unp.uarray(ordinate, ordinate_uncertainty)
                         * ordinate_unit
                     )
                 else:
-                    self.ordinate = ordinate * ordinate_unit
+                    raise ValueError(
+                        "The number of data points in the ordinate does not "
+                        "match that in the ordinate uncertainty."
+                    )
             else:
-                raise ValueError(
-                    "The number of data points in the ordinate does not "
-                    "match that in the ordinate uncertainty."
-                )
+                self.ordinate = ordinate * ordinate_unit
         else:
             raise ValueError(
                 "The number of data points in the abscissa does "
                 "not match that for the ordinate."
             )
-        if unaccounted_uncertainty:
+        if (unp.std_devs(self.ordinate.m) == 0.0).any():
+            self.unaccounted_uncertainty = True
+        if self.unaccounted_uncertainty:
             self.variables = np.ones((self.len_parameters() + 1))
         else:
             self.variables = np.ones((self.len_parameters()))

uravu/tests/test_optimize.py

@@ -17,7 +17,7 @@ class TestOptimize(unittest.TestCase):
     Unit tests for optimize module.
     """
 
-    def test_ln_likelihood(self):
+    def test_ln_likelihood_a(self):
         """
         Test ln_likelihood function.
         """
@@ -33,6 +33,21 @@ def test_ln_likelihood(self):
         )
         assert_almost_equal(actual_lnl, expected_lnl)
 
+    def test_ln_likelihood_b(self):
+        """
+        Test ln_likelihood function with no uncertainty.
+        """
+        test_x = np.linspace(0, 99, 10)
+        test_y = np.ones(10)
+        test_rel = relationship.Relationship(
+            utils.straight_line, test_x, test_y
+        )
+        expected_lnl = -45.21054955719477
+        actual_lnl = optimize.ln_likelihood(
+            test_rel.variables, test_rel.function, test_rel.x, test_rel.y, unaccounted_uncertainty=test_rel.unaccounted_uncertainty
+        )
+        assert_almost_equal(actual_lnl, expected_lnl)
+
     def test_negative_lnl_a(self):
         """
         Test negative_lnl function.