def test_deprecated_N(self):
n_patterns = 10
m = np.random.randint(low=0, high=2, size=(n_patterns, 2))
with self.assertWarns(DeprecationWarning):
# check *args
h = ue.hash_from_pattern(m, n_patterns)
with self.assertWarns(DeprecationWarning):
# check **kwargs
h = ue.hash_from_pattern(m, N=n_patterns)
def test_hash_base_not_two(self):
m = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0],
[1, 0, 1], [0, 1, 1], [1, 1, 1]])
m = m.T
base = 3
expected = np.array([0, 9, 3, 1, 12, 10, 4, 13])
h = ue.hash_from_pattern(m, base=base)
self.assertTrue(np.all(expected == h))
def test_hash_base_not_two(self):
m = np.array([[0,0,0], [1,0,0], [0,1,0], [0,0,1], [1,1,0],
[1,0,1],[0,1,1],[1,1,1]])
m = m.T
base = 3
expected = np.array([0,9,3,1,12,10,4,13])
h = ue.hash_from_pattern(m, N=3, base=base)
self.assertTrue(np.all(expected == h))
def test_hash_inverse_longpattern(self):
n_patterns = 100
m = np.random.randint(low=0, high=2, size=(n_patterns, 2))
h = ue.hash_from_pattern(m)
m_inv = ue.inverse_hash_from_pattern(h, N=n_patterns)
assert_array_equal(m, m_inv)
def test_hash_default_longpattern(self):
m = np.zeros((100, 2))
m[0, 0] = 1
expected = np.array([2**99, 0])
h = ue.hash_from_pattern(m)
self.assertTrue(np.all(expected == h))
def test_hash_default(self):
m = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0],
[1, 0, 1], [0, 1, 1], [1, 1, 1]])
expected = np.array([77, 43, 23])
h = ue.hash_from_pattern(m)
self.assertTrue(np.all(expected == h))
def test_hash_invhash_consistency(self):
m = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0],
[1, 0, 1], [0, 1, 1], [1, 1, 1]])
inv_h = ue.hash_from_pattern(m)
m1 = ue.inverse_hash_from_pattern(inv_h, N=8)
self.assertTrue(np.all(m == m1))
t_stop=10000 * ms,
rate=10 * Hz,
assembly_sizes=[2],
method="CPP",
bkgr_corr=.00)
data += [st_list]
# plotting.rasterplot(data)
# plt.show()
Js_dict = ue.jointJ_window_analysis(
data=data,
binsize=5 * ms,
winsize=100 * ms,
winstep=20 * ms,
pattern_hash=[ue.hash_from_pattern([1, 1], 2)])
print Js_dict
# Why not save settings in Js_dict?
dict_args = {
'events': {
'SO': [10 * ms]
},
'save_fig': True,
'path_filename_format': 'UE1.pdf',
'showfig': True,
'suptitle': True,
'figsize': (25, 20),
'unit_ids': [10, 19],
def test_hash_invhash_consistency(self):
m = np.array([[0, 0, 0],[1, 0, 0],[0, 1, 0],[0, 0, 1],[1, 1, 0],[1, 0, 1],[0, 1, 1],[1, 1, 1]])
inv_h = ue.hash_from_pattern(m, N=8)
m1 = ue.inverse_hash_from_pattern(inv_h, N = 8)
self.assertTrue(np.all(m == m1))
def test_hash_default_longpattern(self):
m = np.zeros((100,2))
m[0,0] = 1
expected = np.array([2**99,0])
h = ue.hash_from_pattern(m, N=100)
self.assertTrue(np.all(expected == h))
def test_hash_default(self):
m = np.array([[0,0,0], [1,0,0], [0,1,0], [0,0,1], [1,1,0],
[1,0,1],[0,1,1],[1,1,1]])
expected = np.array([77,43,23])
h = ue.hash_from_pattern(m, N=8)
self.assertTrue(np.all(expected == h))
