The test function numpy.testing.assert_equal fails when comparing -0.0 and 0.0:
In [16]: np.testing.assert_equal(-0.0, 0.0) --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-16-4063bd6da228> in <module>() ----> 1 np.testing.assert_equal(-0.0, 0.0) /Users/warren/anaconda/lib/python2.7/site-packages/numpy/testing/utils.pyc in assert_equal(actual, desired, err_msg, verbose) 309 elif desired == 0 and actual == 0: 310 if not signbit(desired) == signbit(actual): --> 311 raise AssertionError(msg) 312 # If TypeError or ValueError raised while using isnan and co, just handle 313 # as before AssertionError: Items are not equal: ACTUAL: -0.0 DESIRED: 0.0 There is code that checks for this specific case, so this is intentional. But this is not consistent with how negative zeros in arrays are compared: In [22]: np.testing.assert_equal(np.array(-0.0), np.array(0.0)) # PASS In [23]: a = np.array([-0.0]) In [24]: b = np.array([0.0]) In [25]: np.testing.assert_array_equal(a, b) # PASS Is there a reason the values are considered equal in an array, but not when compared as scalars? Warren _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion