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))