diff options
Diffstat (limited to 'examples')
-rw-r--r-- | examples/multivariate.py | 4 | ||||
-rw-r--r-- | examples/performance.py | 5 | ||||
-rw-r--r-- | examples/plot.py | 5 |
3 files changed, 9 insertions, 5 deletions
diff --git a/examples/multivariate.py b/examples/multivariate.py index 53dbf0f..93f8516 100644 --- a/examples/multivariate.py +++ b/examples/multivariate.py @@ -23,8 +23,8 @@ spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", t_loading = time.clock() print("Number of spike trains: %d" % len(spike_trains)) -num_of_spikes = sum([len(spike_trains[i].spikes) - for i in xrange(len(spike_trains))]) +num_of_spikes = sum([len(spike_trains[i]) + for i in range(len(spike_trains))]) print("Number of spikes: %d" % num_of_spikes) # calculate the multivariate spike distance diff --git a/examples/performance.py b/examples/performance.py index 1c31e8f..ec6c830 100644 --- a/examples/performance.py +++ b/examples/performance.py @@ -14,6 +14,9 @@ from datetime import datetime import cProfile import pstats +# in case you dont have the cython backends, disable the warnings as follows: +# spk.disable_backend_warning = True + M = 100 # number of spike trains r = 1.0 # rate of Poisson spike times T = 1E3 # length of spike trains @@ -23,7 +26,7 @@ print("%d spike trains with %d spikes" % (M, int(r*T))) spike_trains = [] t_start = datetime.now() -for i in xrange(M): +for i in range(M): spike_trains.append(spk.generate_poisson_spikes(r, T)) t_end = datetime.now() runtime = (t_end-t_start).total_seconds() diff --git a/examples/plot.py b/examples/plot.py index 9670286..1922939 100644 --- a/examples/plot.py +++ b/examples/plot.py @@ -16,12 +16,13 @@ import matplotlib.pyplot as plt import pyspike as spk + spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", edges=(0, 4000)) -# plot the spike time +# plot the spike times for (i, spike_train) in enumerate(spike_trains): - plt.plot(spike_train.spikes, i*np.ones_like(spike_train.spikes), 'o') + plt.scatter(spike_train, i*np.ones_like(spike_train), marker='|') f = spk.isi_profile(spike_trains[0], spike_trains[1]) x, y = f.get_plottable_data() |