+cython version for isi and performance tests

2 files changed, 85 insertions, 0 deletions

diff --git a/examples/perf_isi.py b/examples/perf_isi.py
new file mode 100644
index 0000000..8b44946
--- /dev/null
+++ b/examples/perf_isi.py
@@ -0,0 +1,46 @@
+# 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))
diff --git a/examples/perf_spike.py b/examples/perf_spike.py
new file mode 100644
index 0000000..37154ae
--- /dev/null
+++ b/examples/perf_spike.py
@@ -0,0 +1,39 @@
+# 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
+
+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\texecution time [ms]")
+
+# 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 = measure_perf(partial(spk.spike_distance, t1, t2))
+    
+    print("%d\t%.1f" % (N, t*1000))