def is_sorted(self):
""" Tests whether the data are sorted into slow/fast order
rows are slow direction
columns are fast """
# TODO: non uint16 cases
assert self.row.dtype == np.uint16 and \
cImageD11.sparse_is_sorted( self.row, self.col ) == 0
def test2(self):
np.random.seed(42)
N = self.N
a = np.random.random(N * 2)
i, j = (a * 10).astype('i').reshape(2, N)
oj = np.empty(N, 'i')
oi = np.empty(N, 'i')
tmp = np.empty(20, 'i')
d = np.ones(N, int)
coo = scipy.sparse.coo_matrix((d, (i.copy(), j.copy())))
csr = coo.tocsr()
coo = csr.tocoo()
#print("",i,"\n",j)
n = cImageD11.compress_duplicates(i, j, oi, oj, tmp)
self.assertEqual(
cImageD11.sparse_is_sorted(i[:n].astype(np.uint16),
j[:n].astype(np.uint16)), 0)
r = coo.row
c = coo.col
d = coo.data
self.assertTrue(n == len(r))
self.assertTrue((r == i[:n]).all())
self.assertTrue((c == j[:n]).all())
self.assertTrue((d == oi[:n]).all())
def testbadj(self):
self.i[5] = self.i[4]
self.j[5] = self.j[4] - 1
ret = cImageD11.sparse_is_sorted(self.i, self.j)
self.assertEqual(ret, 5)
def testrpt(self):
self.i[5] = self.i[4]
self.j[5] = self.j[4]
ret = cImageD11.sparse_is_sorted(self.i, self.j)
self.assertEqual(ret, -5)
def testOK(self):
ret = cImageD11.sparse_is_sorted(self.i, self.j)
self.assertEqual(ret, 0)
