Add support for pytest.approx comparisons between array and scalar

_pytest/python_api.py

@@ -31,6 +31,10 @@ class ApproxBase(object):
     or sequences of numbers.
     """
 
+    # Tell numpy to use our `__eq__` operator instead of its when left side in a numpy array but right side is
+    # an instance of ApproxBase
+    __array_ufunc__ = None
+
     def __init__(self, expected, rel=None, abs=None, nan_ok=False):
         self.expected = expected
         self.abs = abs
@@ -89,7 +93,7 @@ class ApproxNumpy(ApproxBase):
         except:  # noqa
             raise TypeError("cannot compare '{0}' to numpy.ndarray".format(actual))
 
-        if actual.shape != self.expected.shape:
+        if not np.isscalar(self.expected) and actual.shape != self.expected.shape:
             return False
 
         return ApproxBase.__eq__(self, actual)
@@ -100,8 +104,13 @@ class ApproxNumpy(ApproxBase):
         # We can be sure that `actual` is a numpy array, because it's
         # casted in `__eq__` before being passed to `ApproxBase.__eq__`,
         # which is the only method that calls this one.
-        for i in np.ndindex(self.expected.shape):
-            yield actual[i], self.expected[i]
+
+        if np.isscalar(self.expected):
+            for i in np.ndindex(actual.shape):
+                yield actual[i], self.expected
+        else:
+            for i in np.ndindex(self.expected.shape):
+                yield actual[i], self.expected[i]
 
 
 class ApproxMapping(ApproxBase):
@@ -189,6 +198,8 @@ class ApproxScalar(ApproxBase):
         Return true if the given value is equal to the expected value within
         the pre-specified tolerance.
         """
+        if _is_numpy_array(actual):
+            return actual == ApproxNumpy(self.expected, self.abs, self.rel, self.nan_ok)
 
         # Short-circuit exact equality.
         if actual == self.expected:
@@ -308,12 +319,18 @@ def approx(expected, rel=None, abs=None, nan_ok=False):
         >>> {'a': 0.1 + 0.2, 'b': 0.2 + 0.4} == approx({'a': 0.3, 'b': 0.6})
         True
 
-    And ``numpy`` arrays::
+    ``numpy`` arrays::
 
         >>> import numpy as np                                                          # doctest: +SKIP
         >>> np.array([0.1, 0.2]) + np.array([0.2, 0.4]) == approx(np.array([0.3, 0.6])) # doctest: +SKIP
         True
 
+    And for a ``numpy`` array against a scalar::
+
+        >>> import numpy as np                                         # doctest: +SKIP
+        >>> np.array([0.1, 0.2]) + np.array([0.2, 0.1]) == approx(0.3) # doctest: +SKIP
+        True
+
     By default, ``approx`` considers numbers within a relative tolerance of
     ``1e-6`` (i.e. one part in a million) of its expected value to be equal.
     This treatment would lead to surprising results if the expected value was