From 5119d47d0f00c3f7203cf94460730b59a7e473ec Mon Sep 17 00:00:00 2001 From: Mario Mulansky Date: Tue, 7 Jul 2015 18:55:32 +0200 Subject: add disable_backend_warning property Users can now disable the warning messages produced when the cython backend is not available by writing spk.disable_backend_warning = True in the beginning --- examples/performance.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'examples/performance.py') diff --git a/examples/performance.py b/examples/performance.py index 1c31e8f..d0c3b91 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 -- cgit v1.2.3 From 2c42e59e5097d3b9745e6eae2bee8f1ff27f7e09 Mon Sep 17 00:00:00 2001 From: Igor Gnatenko Date: Sun, 13 Dec 2015 10:57:12 +0100 Subject: py3: xrange() -> range() Signed-off-by: Igor Gnatenko --- examples/multivariate.py | 2 +- examples/performance.py | 2 +- pyspike/DiscreteFunc.py | 4 ++-- pyspike/directionality/spike_train_order.py | 4 ++-- pyspike/generic.py | 4 ++-- pyspike/psth.py | 2 +- test/test_distance.py | 6 +++--- 7 files changed, 12 insertions(+), 12 deletions(-) (limited to 'examples/performance.py') diff --git a/examples/multivariate.py b/examples/multivariate.py index 9a44758..93f8516 100644 --- a/examples/multivariate.py +++ b/examples/multivariate.py @@ -24,7 +24,7 @@ t_loading = time.clock() print("Number of spike trains: %d" % len(spike_trains)) num_of_spikes = sum([len(spike_trains[i]) - for i in xrange(len(spike_trains))]) + 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 d0c3b91..ec6c830 100644 --- a/examples/performance.py +++ b/examples/performance.py @@ -26,7 +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/pyspike/DiscreteFunc.py b/pyspike/DiscreteFunc.py index 55c0bc8..fe97bc2 100644 --- a/pyspike/DiscreteFunc.py +++ b/pyspike/DiscreteFunc.py @@ -80,7 +80,7 @@ class DiscreteFunc(object): expected_mp = (averaging_window_size+1) * int(self.mp[0]) y_plot = np.zeros_like(self.y) # compute the values in a loop, could be done in cython if required - for i in xrange(len(y_plot)): + for i in range(len(y_plot)): if self.mp[i] >= expected_mp: # the current value contains already all the wanted @@ -244,7 +244,7 @@ def average_profile(profiles): assert len(profiles) > 1 avrg_profile = profiles[0].copy() - for i in xrange(1, len(profiles)): + for i in range(1, len(profiles)): avrg_profile.add(profiles[i]) avrg_profile.mul_scalar(1.0/len(profiles)) # normalize diff --git a/pyspike/directionality/spike_train_order.py b/pyspike/directionality/spike_train_order.py index 44d931d..e6c9830 100644 --- a/pyspike/directionality/spike_train_order.py +++ b/pyspike/directionality/spike_train_order.py @@ -260,7 +260,7 @@ def optimal_spike_train_order(spike_trains, indices=None, interval=None, def permutate_matrix(D, p): N = len(D) D_p = np.empty_like(D) - for n in xrange(N): - for m in xrange(N): + for n in range(N): + for m in range(N): D_p[n, m] = D[p[n], p[m]] return D_p diff --git a/pyspike/generic.py b/pyspike/generic.py index 904c3c2..81ae660 100644 --- a/pyspike/generic.py +++ b/pyspike/generic.py @@ -137,8 +137,8 @@ def _generic_distance_matrix(spike_trains, dist_function, assert (indices < len(spike_trains)).all() and (indices >= 0).all(), \ "Invalid index list." # generate a list of possible index pairs - pairs = [(i, j) for i in xrange(len(indices)) - for j in xrange(i+1, len(indices))] + pairs = [(i, j) for i in range(len(indices)) + for j in range(i+1, len(indices))] distance_matrix = np.zeros((len(indices), len(indices))) for i, j in pairs: diff --git a/pyspike/psth.py b/pyspike/psth.py index 4027215..7cf1140 100644 --- a/pyspike/psth.py +++ b/pyspike/psth.py @@ -24,7 +24,7 @@ def psth(spike_trains, bin_size): # N = len(spike_trains) combined_spike_train = spike_trains[0].spikes - for i in xrange(1, len(spike_trains)): + for i in range(1, len(spike_trains)): combined_spike_train = np.append(combined_spike_train, spike_trains[i].spikes) diff --git a/test/test_distance.py b/test/test_distance.py index e45ac16..d5bce30 100644 --- a/test/test_distance.py +++ b/test/test_distance.py @@ -309,10 +309,10 @@ def check_dist_matrix(dist_func, dist_matrix_func): f_matrix = dist_matrix_func(spike_trains) # check zero diagonal - for i in xrange(4): + for i in range(4): assert_equal(0.0, f_matrix[i, i]) - for i in xrange(4): - for j in xrange(i+1, 4): + for i in range(4): + for j in range(i+1, 4): assert_equal(f_matrix[i, j], f_matrix[j, i]) assert_equal(f12, f_matrix[1, 0]) assert_equal(f13, f_matrix[2, 0]) -- cgit v1.2.3