updated examples to use new unified interface

removed all occasions of *multi functions from examples as they are considered
deprecated now. Uses unified interface everywhere.

examples/averages.py

@@ -12,7 +12,7 @@
 import pyspike as spk
 
 spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt",
-                                              time_interval=(0, 4000))
+                                              edges=(0, 4000))
 
 f = spk.isi_profile(spike_trains[0], spike_trains[1])
 

examples/merge.py

@@ -21,9 +21,9 @@
 
 print(merged_spike_train.spikes)
 
-plt.plot(spike_trains[0].spikes, np.ones_like(spike_trains[0].spikes), 'o')
-plt.plot(spike_trains[1].spikes, np.ones_like(spike_trains[1].spikes), 'x')
+plt.plot(spike_trains[0], np.ones_like(spike_trains[0]), 'o')
+plt.plot(spike_trains[1], np.ones_like(spike_trains[1]), 'x')
 plt.plot(merged_spike_train.spikes,
-         2*np.ones_like(merged_spike_train.spikes), 'o')
+         2*np.ones_like(merged_spike_train), 'o')
 
 plt.show()

examples/multivariate.py

@@ -28,7 +28,7 @@ def time_diff_in_ms(start, end):
 print("Number of spikes: %d" % num_of_spikes)
 
 # calculate the multivariate spike distance
-f = spk.spike_profile_multi(spike_trains)
+f = spk.spike_profile(spike_trains)
 
 t_spike = time.clock()
 
@@ -39,7 +39,7 @@ def time_diff_in_ms(start, end):
 t_avrg = time.clock()
 
 # compute average distance directly, should give the same result as above
-spike_dist = spk.spike_distance_multi(spike_trains)
+spike_dist = spk.spike_distance(spike_trains)
 print("Spike distance directly:     %.8f" % spike_dist)
 
 t_dist = time.clock()

examples/performance.py

@@ -31,38 +31,41 @@
 t_end = datetime.now()
 runtime = (t_end-t_start).total_seconds()
 
+sort_by = 'tottime'
+# sort_by = 'cumtime'
+
 print("Spike generation runtime: %.3fs" % runtime)
 print()
 
 print("================ ISI COMPUTATIONS ================")
 print("    MULTIVARIATE DISTANCE")
-cProfile.run('spk.isi_distance_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.isi_distance(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)
 
 print("    MULTIVARIATE PROFILE")
-cProfile.run('spk.isi_profile_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.isi_profile(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)
 
 print("================ SPIKE COMPUTATIONS ================")
 print("    MULTIVARIATE DISTANCE")
-cProfile.run('spk.spike_distance_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.spike_distance(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)
 
 print("    MULTIVARIATE PROFILE")
-cProfile.run('spk.spike_profile_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.spike_profile(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)
 
 print("================ SPIKE-SYNC COMPUTATIONS ================")
 print("    MULTIVARIATE DISTANCE")
-cProfile.run('spk.spike_sync_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.spike_sync(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)
 
 print("    MULTIVARIATE PROFILE")
-cProfile.run('spk.spike_sync_profile_multi(spike_trains)', 'performance.stat')
+cProfile.run('spk.spike_sync_profile(spike_trains)', 'performance.stat')
 p = pstats.Stats('performance.stat')
-p.strip_dirs().sort_stats('tottime').print_stats(5)
+p.strip_dirs().sort_stats(sort_by).print_stats(5)

examples/plot.py

@@ -24,15 +24,16 @@
 for (i, spike_train) in enumerate(spike_trains):
     plt.scatter(spike_train, i*np.ones_like(spike_train), marker='|')
 
-f = spk.isi_profile(spike_trains[0], spike_trains[1])
+# profile of the first two spike trains
+f = spk.isi_profile(spike_trains, indices=[0, 1])
 x, y = f.get_plottable_data()
 
 plt.figure()
 plt.plot(x, np.abs(y), '--k', label="ISI-profile")
 
 print("ISI-distance: %.8f" % f.avrg())
 
-f = spk.spike_profile(spike_trains[0], spike_trains[1])
+f = spk.spike_profile(spike_trains, indices=[0, 1])
 x, y = f.get_plottable_data()
 
 plt.plot(x, y, '-b', label="SPIKE-profile")

examples/profiles.py

@@ -29,7 +29,7 @@
 print()
 
 # compute the multivariate ISI profile
-f = spk.isi_profile_multi(spike_trains)
+f = spk.isi_profile(spike_trains)
 
 t = 1200
 print("Multivariate ISI value at t =", t, ":", f(t))
@@ -56,7 +56,7 @@
 print()
 
 # compute the multivariate SPIKE profile
-f = spk.spike_profile_multi(spike_trains)
+f = spk.spike_profile(spike_trains)
 
 # SPIKE values at certain points
 t = 1200

examples/spike_sync.py

@@ -31,7 +31,7 @@
 
 plt.subplot(211)
 
-f = spk.spike_sync_profile_multi(spike_trains)
+f = spk.spike_sync_profile(spike_trains)
 x, y = f.get_plottable_data()
 plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile")