From 9335f9a4f78b561b8c875704a2e52ba27a45ad9b Mon Sep 17 00:00:00 2001
From: Mario Mulansky <mario.mulansky@gmx.net>
Date: Wed, 1 Oct 2014 10:32:31 +0200
Subject: removed performance tests

---
 examples/perf_isi.py   | 46 ----------------------------------------------
 examples/perf_spike.py | 42 ------------------------------------------
 2 files changed, 88 deletions(-)
 delete mode 100644 examples/perf_isi.py
 delete mode 100644 examples/perf_spike.py

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))
diff --git a/examples/perf_spike.py b/examples/perf_spike.py
deleted file mode 100644
index 5b1c1cc..0000000
--- a/examples/perf_spike.py
+++ /dev/null
@@ -1,42 +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
-
-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]")
-
-# 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.spike_distance, t1, t2))
-
-    t_python = measure_perf(partial(spk.distances.spike_distance_python, 
-                                    t1, t2))
-    
-    print("%d\t%.3f\t%.1f" % (N, t_cython*1000, t_python*1000))
-- 
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