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Diffstat (limited to 'examples/perf_isi.py')
-rw-r--r-- | examples/perf_isi.py | 46 |
1 files changed, 0 insertions, 46 deletions
diff --git a/examples/perf_isi.py b/examples/perf_isi.py deleted file mode 100644 index 8b44946..0000000 --- a/examples/perf_isi.py +++ /dev/null @@ -1,46 +0,0 @@ -# performance measure of the isi calculation - -from __future__ import print_function - -import numpy as np -import matplotlib.pyplot as plt -import time -from functools import partial - -import pyspike as spk -#import pyspike.distances # for the python functions - -def measure_perf(func, loops=10): - times = np.empty(loops) - for i in xrange(loops): - start = time.clock() - func() - times[i] = time.clock() - start - return np.min(times) - -print("# approximate number of spikes\tcython time [ms]\tpython time [ms]") - -# fix seed to get reproducible results -np.random.seed(1) - -# max times -Ns = np.arange(10000, 50001, 10000) -for N in Ns: - - # first generate some data - times = 2.0*np.random.random(1.1*N) - t1 = np.cumsum(times) - # only up to T - t1 = spk.add_auxiliary_spikes(t1[t1<N], N) - - times = 2.0*np.random.random(N) - t2 = np.cumsum(times) - # only up to T - t2 = spk.add_auxiliary_spikes(t2[t2<N], N) - - t_cython = measure_perf(partial(spk.isi_distance, t1, t2)) - - t_python = measure_perf(partial(spk.distances.isi_distance_python, - t1, t2)) - - print("%d\t%.3f\t%.1f" % (N, t_cython*1000, t_python*1000)) |