Merge branch 'master' into new_directionality

Conflicts:
	pyspike/__init__.py
	pyspike/cython/cython_directionality.pyx
	pyspike/cython/directionality_python_backend.py
	pyspike/spike_directionality.py
	setup.py

1 files changed, 127 insertions, 0 deletions

diff --git a/test/numeric/test_regression_random_spikes.py b/test/numeric/test_regression_random_spikes.py
new file mode 100644
index 0000000..6156bb4
--- /dev/null
+++ b/test/numeric/test_regression_random_spikes.py
@@ -0,0 +1,127 @@
+""" regression benchmark
+
+Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net>
+
+Distributed under the BSD License
+"""
+from __future__ import print_function
+
+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/numeric/regression_random_spikes.mat"
+    spikes_name = "spikes"
+    result_name = "Distances"
+    result_file = "test/numeric/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)
+        isi_prof = spk.isi_profile_multi(spike_trains).avrg()
+
+        spike = spk.spike_distance_multi(spike_trains)
+        spike_prof = spk.spike_profile_multi(spike_trains).avrg()
+        # 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(isi_prof, 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)
+        assert_almost_equal(spike_prof, 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_file = "regression_random_spikes.mat"
+    spikes_name = "spikes"
+    result_name = "Distances"
+    result_file = "regression_random_results_cSPIKY.mat"
+
+    spike_train_sets = loadmat(spike_file)[spikes_name][0]
+
+    results_cSPIKY = loadmat(result_file)[result_name]
+
+    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)