def test_bug44():
"Bug 44"
# In instances where axis=None, the operation runs
# on the flattened array. Here it makes sense to return
# the op on the underlying np.ndarray.
A = [[1,2,3],[4,5,6]]
x = DataArray(A, 'xy').std()
y = np.std(A)
nt.assert_equal( x.sum(), y.sum() )
def test_bug44():
"Bug 44"
# In instances where axis=None, the operation runs
# on the flattened array. Here it makes sense to return
# the op on the underlying np.ndarray.
A = [[1, 2, 3], [4, 5, 6]]
x = DataArray(A, 'xy').std()
y = np.std(A)
nt.assert_equal(x.sum(), y.sum())
def test_bug3():
"Bug 3"
x = np.array([1,2,3])
y = DataArray(x, 'x')
nt.assert_equal( x.sum(), y.sum() )
nt.assert_equal( x.max(), y.max() )
def test_bug3():
"Bug 3"
x = np.array([1, 2, 3])
y = DataArray(x, 'x')
nt.assert_equal(x.sum(), y.sum())
nt.assert_equal(x.max(), y.max())
