7 files changed, 244 insertions, 6 deletions

diff --git a/test/numeric/regression_random_results_cSPIKY.mat b/test/numeric/regression_random_results_cSPIKY.mat
new file mode 100644
index 0000000..26f29ff
--- /dev/null
+++ b/test/numeric/regression_random_results_cSPIKY.mat
diff --git a/test/numeric/regression_random_spikes.mat b/test/numeric/regression_random_spikes.mat
new file mode 100644
index 0000000..e5ebeb1
--- /dev/null
+++ b/test/numeric/regression_random_spikes.mat
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)
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_empty.py b/test/test_empty.py
index 5a0042f..4d0a5cf 100644
--- a/test/test_empty.py
+++ b/test/test_empty.py
@@ -24,7 +24,9 @@ def test_get_non_empty():
 
     st = SpikeTrain([0.5, ], edges=(0.0, 1.0))
     spikes = st.get_spikes_non_empty()
-    assert_array_equal(spikes, [0.0, 0.5, 1.0])
+    # assert_array_equal(spikes, [0.0, 0.5, 1.0])
+    # spike trains with one spike don't get edge spikes anymore
+    assert_array_equal(spikes, [0.5, ])
 
 
 def test_isi_empty():
@@ -70,21 +72,23 @@ def test_spike_empty():
     st1 = SpikeTrain([], edges=(0.0, 1.0))
     st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
     d = spk.spike_distance(st1, st2)
-    d_expect = 0.4*0.4*1.0/(0.4+1.0)**2 + 0.6*0.4*1.0/(0.6+1.0)**2
+    d_expect = 2*0.4*0.4*1.0/(0.4+1.0)**2 + 2*0.6*0.4*1.0/(0.6+1.0)**2
     assert_almost_equal(d, d_expect, decimal=15)
     prof = spk.spike_profile(st1, st2)
     assert_equal(d, prof.avrg())
     assert_array_equal(prof.x, [0.0, 0.4, 1.0])
-    assert_array_almost_equal(prof.y1, [0.0, 2*0.4*1.0/(0.6+1.0)**2],
+    assert_array_almost_equal(prof.y1, [2*0.4*1.0/(0.4+1.0)**2,
+                                        2*0.4*1.0/(0.6+1.0)**2],
                               decimal=15)
-    assert_array_almost_equal(prof.y2, [2*0.4*1.0/(0.4+1.0)**2, 0.0],
+    assert_array_almost_equal(prof.y2, [2*0.4*1.0/(0.4+1.0)**2,
+                                        2*0.4*1.0/(0.6+1.0)**2],
                               decimal=15)
 
     st1 = SpikeTrain([0.6, ], edges=(0.0, 1.0))
     st2 = SpikeTrain([0.4, ], edges=(0.0, 1.0))
     d = spk.spike_distance(st1, st2)
-    s1 = np.array([0.0, 0.4*0.2/0.6, 0.2, 0.0])
-    s2 = np.array([0.0, 0.2, 0.2*0.4/0.6, 0.0])
+    s1 = np.array([0.2, 0.2, 0.2, 0.2])
+    s2 = np.array([0.2, 0.2, 0.2, 0.2])
     isi1 = np.array([0.6, 0.6, 0.4])
     isi2 = np.array([0.4, 0.6, 0.6])
     expected_y1 = (s1[:-1]*isi2+s2[:-1]*isi1) / (0.5*(isi1+isi2)**2)
diff --git a/test/test_generic_interfaces.py b/test/test_generic_interfaces.py
new file mode 100644
index 0000000..7f08067
--- /dev/null
+++ b/test/test_generic_interfaces.py
@@ -0,0 +1,105 @@
+""" test_generic_interface.py
+
+Tests the generic interfaces of the profile and distance functions
+
+Copyright 2016, Mario Mulansky <mario.mulansky@gmx.net>
+
+Distributed under the BSD License
+
+"""
+
+from __future__ import print_function
+from numpy.testing import assert_equal
+
+import pyspike as spk
+from pyspike import SpikeTrain
+
+
+class dist_from_prof:
+    """ Simple functor that turns profile function into distance function by
+    calling profile.avrg().
+    """
+    def __init__(self, prof_func):
+        self.prof_func = prof_func
+
+    def __call__(self, *args, **kwargs):
+        if "interval" in kwargs:
+            # forward interval arg into avrg function
+            interval = kwargs.pop("interval")
+            return self.prof_func(*args, **kwargs).avrg(interval=interval)
+        else:
+            return self.prof_func(*args, **kwargs).avrg()
+
+
+def check_func(dist_func):
+    """ generic checker that tests the given distance function.
+    """
+    # generate spike trains:
+    t1 = SpikeTrain([0.2, 0.4, 0.6, 0.7], 1.0)
+    t2 = SpikeTrain([0.3, 0.45, 0.8, 0.9, 0.95], 1.0)
+    t3 = SpikeTrain([0.2, 0.4, 0.6], 1.0)
+    t4 = SpikeTrain([0.1, 0.4, 0.5, 0.6], 1.0)
+    spike_trains = [t1, t2, t3, t4]
+
+    isi12 = dist_func(t1, t2)
+    isi12_ = dist_func([t1, t2])
+    assert_equal(isi12, isi12_)
+
+    isi12_ = dist_func(spike_trains, indices=[0, 1])
+    assert_equal(isi12, isi12_)
+
+    isi123 = dist_func(t1, t2, t3)
+    isi123_ = dist_func([t1, t2, t3])
+    assert_equal(isi123, isi123_)
+
+    isi123_ = dist_func(spike_trains, indices=[0, 1, 2])
+    assert_equal(isi123, isi123_)
+
+    # run the same test with an additional interval parameter
+
+    isi12 = dist_func(t1, t2, interval=[0.0, 0.5])
+    isi12_ = dist_func([t1, t2], interval=[0.0, 0.5])
+    assert_equal(isi12, isi12_)
+
+    isi12_ = dist_func(spike_trains, indices=[0, 1], interval=[0.0, 0.5])
+    assert_equal(isi12, isi12_)
+
+    isi123 = dist_func(t1, t2, t3, interval=[0.0, 0.5])
+    isi123_ = dist_func([t1, t2, t3], interval=[0.0, 0.5])
+    assert_equal(isi123, isi123_)
+
+    isi123_ = dist_func(spike_trains, indices=[0, 1, 2], interval=[0.0, 0.5])
+    assert_equal(isi123, isi123_)
+
+
+def test_isi_profile():
+    check_func(dist_from_prof(spk.isi_profile))
+
+
+def test_isi_distance():
+    check_func(spk.isi_distance)
+
+
+def test_spike_profile():
+    check_func(dist_from_prof(spk.spike_profile))
+
+
+def test_spike_distance():
+    check_func(spk.spike_distance)
+
+
+def test_spike_sync_profile():
+    check_func(dist_from_prof(spk.spike_sync_profile))
+
+
+def test_spike_sync():
+    check_func(spk.spike_sync)
+
+
+if __name__ == "__main__":
+    test_isi_profile()
+    test_isi_distance()
+    test_spike_profile()
+    test_spike_distance()
+    test_spike_sync_profile()
+    test_spike_sync()
diff --git a/test/test_regression/test_regression_15.py b/test/test_regression/test_regression_15.py
index dcacae2..54adf23 100644
--- a/test/test_regression/test_regression_15.py
+++ b/test/test_regression/test_regression_15.py
@@ -20,6 +20,7 @@ import os
 TEST_PATH = os.path.dirname(os.path.realpath(__file__))
 TEST_DATA = os.path.join(TEST_PATH, "..", "SPIKE_Sync_Test.txt")
 
+
 def test_regression_15_isi():
     # load spike trains
     spike_trains = spk.load_spike_trains_from_txt(TEST_DATA, edges=[0, 4000])