def test_lda_metrics():
all_pcs1, all_labels1 = create_ground_truth_pc_distributions([1,-1],[1000, 1000])
all_pcs2, all_labels2 = create_ground_truth_pc_distributions([1,-2],[1000, 1000]) # increase distance between clusters
d_prime1 = lda_metrics(all_pcs1, all_labels1, 0)
d_prime2 = lda_metrics(all_pcs2, all_labels2, 0)
assert d_prime1 < d_prime2
def test_nearest_neighbors_metrics():
all_pcs1, all_labels1 = create_ground_truth_pc_distributions([1,-1],[1000, 1000])
all_pcs2, all_labels2 = create_ground_truth_pc_distributions([1,-2],[1000, 1000]) # increase distance between clusters
hit_rate1, miss_rate1 = nearest_neighbors_metrics(all_pcs1, all_labels1, 0, 1000, 3)
hit_rate2, miss_rate2 = nearest_neighbors_metrics(all_pcs2, all_labels2, 0, 1000, 3)
assert hit_rate1 < hit_rate2
assert miss_rate1 > miss_rate2
def test_mahalanobis_metrics():
all_pcs1, all_labels1 = create_ground_truth_pc_distributions([1,-1],[1000, 1000])
all_pcs2, all_labels2 = create_ground_truth_pc_distributions([1,-2],[1000, 1000]) # increase distance between clusters
isolation_distance1, l_ratio1 = mahalanobis_metrics(all_pcs1, all_labels1, 0)
isolation_distance2, l_ratio2 = mahalanobis_metrics(all_pcs2, all_labels2, 0)
assert isolation_distance1 < isolation_distance2
assert l_ratio1 > l_ratio2
def test_calculate_pc_metrics():
num_channels_to_compare = 6
total_units = 4
center_locs = np.array(
[[1, -1, 10, 20], [10, 20, 30, 10], [2, 9, 1, 4], [4, -2, 4, -1], [-1, 2, 4, 3], [10, 80, 3, 17]])
n_spikes = [1000, 1000, 500, 20]
pc_features, spike_clusters = create_ground_truth_pc_distributions(center_locs, n_spikes)
pc_feature_ind = np.tile(np.arange(0, num_channels_to_compare), (total_units, 1))
max_spikes_for_cluster = 500
spikes_for_nn = 1000
n_neighbors = 3
channel_locations = np.zeros((num_channels_to_compare, 2))
channel_locations[:, 1] = np.arange(0, num_channels_to_compare)
isolation_distances, l_ratios, d_primes, nn_hit_rates, nn_miss_rates = \
calculate_pc_metrics(spike_clusters,
total_units,
pc_features,
pc_feature_ind,
num_channels_to_compare,
max_spikes_for_cluster,
spikes_for_nn,
n_neighbors,
channel_locations,
verbose=False)
assert np.sum(np.isnan(isolation_distances)) == 0
assert np.sum(np.isnan(l_ratios)) == 0
assert np.sum(np.isnan(d_primes)) == 0
assert np.sum(np.isnan(nn_hit_rates)) == 0
assert np.sum(np.isnan(nn_miss_rates)) == 0
def test_calculate_silhouette_score():
pc_features, spike_clusters = create_ground_truth_pc_distributions([1, -1, 10, 20], [1000, 1000, 500, 20])
pc_feature_ind = np.zeros((4, 1), dtype='int')
total_units = 4
pc_features = np.expand_dims(pc_features, axis=2)
ss = calculate_silhouette_score(spike_clusters,
total_units,
pc_features,
pc_feature_ind,
1000,
verbose=False)
assert np.sum(np.isnan(ss)) == 0
def test_calculate_pc_metrics():
pc_features, spike_clusters = create_ground_truth_pc_distributions([1,-1, 10, 20],[1000, 1000, 500, 20])
pc_feature_ind = np.zeros((4,1),dtype='int')
total_units = 4
pc_features = np.expand_dims(pc_features, axis=2)
isolation_distances, l_ratios, d_primes, nn_hit_rates, nn_miss_rates = \
calculate_pc_metrics(spike_clusters,
total_units,
pc_features,
pc_feature_ind,
1,
500,
1000,
3,
verbose=False)
assert np.sum(np.isnan(isolation_distances)) == 0
assert np.sum(np.isnan(l_ratios)) == 0
assert np.sum(np.isnan(d_primes)) == 0
assert np.sum(np.isnan(nn_hit_rates)) == 0
assert np.sum(np.isnan(nn_miss_rates)) == 0
