def movingrank(x, window, axis=-1):
"""Moving rank (normalized to -1 and 1) of a given window along axis.
Normalized for missing (NaN) data.
A data point with NaN data is returned as NaN
If a window is all NaNs except last, this is returned as NaN
"""
if window > x.shape[axis]:
raise ValueError, 'Window is too big.'
if window < 2:
raise ValueError, 'Window is too small.'
nt = x.shape[axis]
mr = np.nan * np.zeros(x.shape)
for i in xrange(window-1, nt):
index1 = [slice(None)] * x.ndim
index1[axis] = i
index2 = [slice(None)] * x.ndim
index2[axis] = slice(i-window+1, i+1, None)
mr[index1] = np.squeeze(lastrank(x[index2], axis=axis))
return mr
def test_lastrank_3(self):
"farray.lastrank_3"
s = lastrank(np.array([]))
aae(s, np.nan, err_msg="size 0 lastrank fail")
def test_lastrank_2(self):
"farray.lastrank_2"
s = lastrank(np.array([[1, 3, 2], [1, 2, 3]]))
aae(s, np.array([0.0, 1.0]), err_msg="2d lastrank fail")
def test_lastrank_1(self):
"farray.lastrank_1"
s = lastrank(np.array([1, 3, 2]))
aae(s, 0.0, err_msg="1d lastrank fail")
def test_lastrank_3(self):
"farray.lastrank_3"
s = lastrank(np.array([]))
aae(s, np.nan, err_msg="size 0 lastrank fail")
def test_lastrank_2(self):
"farray.lastrank_2"
s = lastrank(np.array([[1, 3, 2], [1, 2, 3]]))
aae(s, np.array([0.0, 1.0]), err_msg="2d lastrank fail")
def test_lastrank_1(self):
"farray.lastrank_1"
s = lastrank(np.array([1, 3, 2]))
aae(s, 0.0, err_msg="1d lastrank fail")