diff options
| -rw-r--r-- | pyspike/cython/cython_distances.pyx | 10 | ||||
| -rw-r--r-- | pyspike/cython/python_backend.py | 21 | ||||
| -rw-r--r-- | test/test_distance.py | 1 | ||||
| -rw-r--r-- | test/test_regression/regression_random_results_cSPIKY.mat | bin | 0 -> 149130 bytes | |||
| -rw-r--r-- | test/test_regression/regression_random_spikes.mat | bin | 0 -> 16241579 bytes | |||
| -rw-r--r-- | test/test_regression/test_regression_random_spikes.py | 113 |
6 files changed, 140 insertions, 5 deletions
diff --git a/pyspike/cython/cython_distances.pyx b/pyspike/cython/cython_distances.pyx index 41baa60..6c6e7e5 100644 --- a/pyspike/cython/cython_distances.pyx +++ b/pyspike/cython/cython_distances.pyx @@ -184,7 +184,8 @@ def spike_distance_cython(double[:] t1, double[:] t2, N1 = len(t1) N2 = len(t2) - with nogil: # release the interpreter to allow multithreading + # with nogil: # release the interpreter to allow multithreading + if True: t_last = t_start # t_p1 = t_start # t_p2 = t_start @@ -193,6 +194,7 @@ def spike_distance_cython(double[:] t1, double[:] t2, t_aux1[1] = fmax(t_end, t1[N1-1]+(t1[N1-1]-t1[N1-2])) t_aux2[0] = fmin(t_start, t2[0]-(t2[1]-t2[0])) t_aux2[1] = fmax(t_end, t2[N2-1]+(t2[N2-1]-t2[N2-2])) + # print "aux spikes %.15f, %.15f ; %.15f, %.15f" % (t_aux1[0], t_aux1[1], t_aux2[0], t_aux2[1]) t_p1 = t_start if (t1[0] == t_start) else t_aux1[0] t_p2 = t_start if (t2[0] == t_start) else t_aux2[0] if t1[0] > t_start: @@ -207,7 +209,8 @@ def spike_distance_cython(double[:] t1, double[:] t2, else: t_f1 = t1[1] dt_f1 = get_min_dist_cython(t_f1, t2, N2, 0, t_aux2[0], t_aux2[1]) - dt_p1 = 0.0 + # dt_p1 = t_start-t_p2 # 0.0 + dt_p1 = get_min_dist_cython(t_p1, t2, N2, 0, t_aux2[0], t_aux2[1]) isi1 = t1[1]-t1[0] s1 = dt_p1 index1 = 0 @@ -223,7 +226,8 @@ def spike_distance_cython(double[:] t1, double[:] t2, else: t_f2 = t2[1] dt_f2 = get_min_dist_cython(t_f2, t1, N1, 0, t_aux1[0], t_aux1[1]) - dt_p2 = 0.0 + # dt_p2 = t_start-t_p1 # 0.0 + dt_p2 = get_min_dist_cython(t_p2, t1, N1, 0, t_aux1[0], t_aux1[1]) isi2 = t2[1]-t2[0] s2 = dt_p2 index2 = 0 diff --git a/pyspike/cython/python_backend.py b/pyspike/cython/python_backend.py index a5f7ae4..a007a7f 100644 --- a/pyspike/cython/python_backend.py +++ b/pyspike/cython/python_backend.py @@ -153,6 +153,8 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end): t_p1 = t_start if (t1[0] == t_start) else t_aux1[0] t_p2 = t_start if (t2[0] == t_start) else t_aux2[0] + print "t_aux1", t_aux1, ", t_aux2:", t_aux2 + spike_events[0] = t_start if t1[0] > t_start: t_f1 = t1[0] @@ -163,7 +165,8 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end): s1 = dt_p1 index1 = -1 else: - dt_p1 = 0.0 + # dt_p1 = t_start-t_p2 + dt_p1 = get_min_dist(t_p1, t2, 0, t_aux2[0], t_aux2[1]) t_f1 = t1[1] dt_f1 = get_min_dist(t_f1, t2, 0, t_aux2[0], t_aux2[1]) isi1 = t1[1]-t1[0] @@ -179,13 +182,18 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end): s2 = dt_p2 index2 = -1 else: - dt_p2 = 0.0 + dt_p2 = t_start-t_p1 + dt_p2 = get_min_dist(t_p2, t1, 0, t_aux1[0], t_aux1[1]) t_f2 = t2[1] dt_f2 = get_min_dist(t_f2, t1, 0, t_aux1[0], t_aux1[1]) isi2 = t2[1]-t2[0] s2 = dt_p2 index2 = 0 + print "t_p1:", repr(t_p1), ", t_f1:", repr(t_f1), ", dt_p1:", repr(dt_p1), ", dt_f1:", repr(dt_f1) + print "t_p2:", repr(t_p2), ", t_f2:", repr(t_f2), ", dt_p2:", repr(dt_p2), ", dt_f2:", repr(dt_f2) + print "s1: ", repr(s1), ", s2:", repr(s2) + y_starts[0] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) index = 1 @@ -276,6 +284,11 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end): dt_f2 = dt_p2 isi2 = max(t_end-t2[N2-1], t2[N2-1]-t2[N2-2]) index += 1 + + print "t_p1:", repr(t_p1), ", t_f1:", repr(t_f1), ", dt_p1:", repr(dt_p1), ", dt_f1:", repr(dt_f1) + print "t_p2:", repr(t_p2), ", t_f2:", repr(t_f2), ", dt_p2:", repr(dt_p2), ", dt_f2:", repr(dt_f2) + print "s1: ", repr(s1), ", s2:", repr(s2) + # the last event is the interval end if spike_events[index-1] == t_end: index -= 1 @@ -288,6 +301,10 @@ def spike_distance_python(spikes1, spikes2, t_start, t_end): s2 = dt_f2 # *(t_end-t2[N2-1])/isi2 y_ends[index-1] = (s1*isi2 + s2*isi1) / (0.5*(isi1+isi2)**2) + print "t_p1:", repr(t_p1), ", t_f1:", repr(t_f1), ", dt_p1:", repr(dt_p1), ", dt_f1:", repr(dt_f1) + print "t_p2:", repr(t_p2), ", t_f2:", repr(t_f2), ", dt_p2:", repr(dt_p2), ", dt_f2:", repr(dt_f2) + print "s1: ", repr(s1), ", s2:", repr(s2) + # use only the data added above # could be less than original length due to equal spike times return spike_events[:index+1], y_starts[:index], y_ends[:index] diff --git a/test/test_distance.py b/test/test_distance.py index 8cf81e2..083d8a3 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -20,6 +20,7 @@ from pyspike import SpikeTrain import os TEST_PATH = os.path.dirname(os.path.realpath(__file__)) + def test_isi(): # generate two spike trains: t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0) diff --git a/test/test_regression/regression_random_results_cSPIKY.mat b/test/test_regression/regression_random_results_cSPIKY.mat Binary files differnew file mode 100644 index 0000000..47c9b50 --- /dev/null +++ b/test/test_regression/regression_random_results_cSPIKY.mat diff --git a/test/test_regression/regression_random_spikes.mat b/test/test_regression/regression_random_spikes.mat Binary files differnew file mode 100644 index 0000000..e5ebeb1 --- /dev/null +++ b/test/test_regression/regression_random_spikes.mat diff --git a/test/test_regression/test_regression_random_spikes.py b/test/test_regression/test_regression_random_spikes.py new file mode 100644 index 0000000..757bab2 --- /dev/null +++ b/test/test_regression/test_regression_random_spikes.py @@ -0,0 +1,113 @@ +""" regression benchmark + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" +import numpy as np +from scipy.io import loadmat +import pyspike as spk + +from numpy.testing import assert_almost_equal + +spk.disable_backend_warning = True + + +def test_regression_random(): + + spike_file = "test/test_regression/regression_random_spikes.mat" + spikes_name = "spikes" + result_name = "Distances" + result_file = "test/test_regression/regression_random_results_cSPIKY.mat" + + spike_train_sets = loadmat(spike_file)[spikes_name][0] + results_cSPIKY = loadmat(result_file)[result_name] + + for i, spike_train_data in enumerate(spike_train_sets): + spike_trains = [] + for spikes in spike_train_data[0]: + spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0)) + + isi = spk.isi_distance_multi(spike_trains) + spike = spk.spike_distance_multi(spike_trains) + # spike_sync = spk.spike_sync_multi(spike_trains) + + assert_almost_equal(isi, results_cSPIKY[i][0], decimal=14, + err_msg="Index: %d, ISI" % i) + assert_almost_equal(spike, results_cSPIKY[i][1], decimal=14, + err_msg="Index: %d, SPIKE" % i) + + +def check_regression_dataset(spike_file="benchmark.mat", + spikes_name="spikes", + result_file="results_cSPIKY.mat", + result_name="Distances"): + """ Debuging function """ + np.set_printoptions(precision=15) + + spike_train_sets = loadmat(spike_file)[spikes_name][0] + + results_cSPIKY = loadmat(result_file)[result_name] + + err_max = 0.0 + err_max_ind = -1 + err_count = 0 + + for i, spike_train_data in enumerate(spike_train_sets): + spike_trains = [] + for spikes in spike_train_data[0]: + spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0)) + + isi = spk.isi_distance_multi(spike_trains) + spike = spk.spike_distance_multi(spike_trains) + # spike_sync = spk.spike_sync_multi(spike_trains) + + if abs(isi - results_cSPIKY[i][0]) > 1E-14: + print "Error in ISI:", i, isi, results_cSPIKY[i][0] + print "Spike trains:" + for st in spike_trains: + print st.spikes + + err = abs(spike - results_cSPIKY[i][1]) + if err > 1E-14: + err_count += 1 + if err > err_max: + err_max = err + err_max_ind = i + + print "Total Errors:", err_count + + if err_max_ind > -1: + print "Max SPIKE distance error:", err_max, "at index:", err_max_ind + spike_train_data = spike_train_sets[err_max_ind] + for spikes in spike_train_data[0]: + print spikes.flatten() + + +def check_single_spike_train_set(index): + """ Debuging function """ + np.set_printoptions(precision=15) + + spike_train_sets = loadmat("benchmark.mat")['spikes'][0] + + results_cSPIKY = loadmat("results_cSPIKY.mat")['Distances'] + + spike_train_data = spike_train_sets[index] + + spike_trains = [] + for spikes in spike_train_data[0]: + print "Spikes:", spikes.flatten() + spike_trains.append(spk.SpikeTrain(spikes.flatten(), 100.0)) + + print spk.spike_distance_multi(spike_trains) + + print results_cSPIKY[index][1] + + print spike_trains[1].spikes + + +if __name__ == "__main__": + + test_regression_random() + # check_regression_dataset() + # check_single_spike_train_set(7633) |