diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/PySpike_testdata.txt | 43 | ||||
| -rw-r--r-- | examples/averages.py | 59 | ||||
| -rw-r--r-- | examples/distance_matrix.py | 38 | ||||
| -rw-r--r-- | examples/merge.py | 29 | ||||
| -rw-r--r-- | examples/multivariate.py | 51 | ||||
| -rw-r--r-- | examples/performance.py | 71 | ||||
| -rw-r--r-- | examples/plot.py | 45 | ||||
| -rw-r--r-- | examples/profiles.py | 66 | ||||
| -rw-r--r-- | examples/spike_sync.py | 50 | ||||
| -rw-r--r-- | examples/spike_train_order.py | 52 |
10 files changed, 504 insertions, 0 deletions
diff --git a/examples/PySpike_testdata.txt b/examples/PySpike_testdata.txt new file mode 100644 index 0000000..41b2362 --- /dev/null +++ b/examples/PySpike_testdata.txt @@ -0,0 +1,43 @@ +# PySpike exemplary spike trains +# lines starting with # are ignored, just like empty lines + +64.886 305.81 696 937.77 1059.7 1322.2 1576.1 1808.1 2121.5 2381.1 2728.6 2966.9 3223.7 3473.7 3644.3 3936.3 +65.553 307.49 696.63 948.66 1070.4 1312.2 1712.7 1934.3 2117.6 2356.9 2727.3 2980.6 3226.9 3475.7 3726.4 3944 +69.064 319.1 688.32 947.85 1071.8 1300.8 1697.2 1930.6 2139.4 2354.2 2723.7 2963.6 3221.3 3470.1 +59.955 313.83 692.23 955.95 1070.4 1319.6 1681.9 1963.5 2151.4 2373.8 2729.4 2971.2 3220.2 3475.5 3632.3 3788.9 +59.977 306.84 686.09 935.08 1059.9 1325.9 1543.4 1821.9 2150.2 2390.4 2724.5 2969.6 3222.5 3471.5 3576 3913.9 +66.415 313.41 688.83 931.43 1051.8 1304.6 1555.6 1820.2 2150.5 2383.1 2723.4 2947.7 3196.6 3443.5 3575 3804.9 +66.449 311.02 689.26 947.12 1058.9 1286.6 1708.2 1957.3 2124.8 2375.7 2709.4 2977.6 3191.1 3449.6 3590.4 3831.2 +63.764 318.45 697.48 936.97 1059.3 1325 1687.9 1944.7 2132.5 2377.1 2713.1 2976.6 3196.8 3442.6 3741.6 3998.3 +63.906 314.79 693.26 937.12 1065.9 1315.8 1584.3 1821.5 2126.3 2396.8 2709.1 2967 3197.4 3444 3732.8 3849.5 +69.493 316.62 689.81 943.62 1071.9 1296.3 1654.8 1931.9 2127.5 2390.6 2708.9 2950.4 3194.8 3445.2 3670.1 3903.3 +61.789 317.53 555.82 813.15 1198.7 1448.7 1686.7 1943.5 2060.7 2311.4 2658.2 2900.2 3167.4 3418.2 3617.3 3771 +64.098 309.86 567.27 813.91 1182 1464.3 1576.8 1822.5 2063.1 2311.7 2655.8 2911.7 3168.3 3418.2 3586.4 3999.7 +68.59 315.5 559.52 806.23 1182.5 1441.1 1567.2 1804.8 2074.9 2315.8 2655.1 2913.2 3165.9 3419.5 3648.1 3884.4 +66.507 314.42 556.42 814.83 1182.5 1440.3 1701.3 1911.1 2069.7 2319.3 2662.3 2903.2 3167.4 3418.5 3545 3893.9 +72.744 318.45 554.4 819.64 1186.9 1449.7 1676 1957.4 2051.4 2302.8 2657.8 2916.2 3169.4 3416.7 3570.4 3884.8 +64.779 324.42 560.56 828.99 1174.8 1439.9 1563.7 1790.6 2067.7 2287.6 2657.4 2905.2 3139.2 3389.1 3507.8 3807.5 +64.852 316.63 568.89 815.61 1198.3 1454.1 1710.6 1933.9 2091.5 2309.6 2660.9 2907.5 3137.2 3389.3 3617.2 +63.089 314.52 553.8 827.06 1183.9 1457.6 1558.9 1808.3 2064.5 2337.1 2653.6 2897 3143.7 3385.7 3668.7 3803.8 +62.23 315.16 564.35 812.15 1199.6 1448.9 1562.7 1839.1 2069.7 2308.9 2649.6 2919.7 3141 3389.9 3723.6 3882.2 +69.662 311.93 564.91 805.25 1209.7 1451.4 1691.9 1932.1 2044.2 2329.4 2657.1 2908.5 3142.8 3390.5 3597.3 3991.1 +183.42 431.34 562.41 809.57 1086.3 1308.9 1555.9 1831.3 2057 2326.9 2591.3 2831.4 3113.9 3367.9 3555.3 3956 +188.49 442.39 572.4 810.76 1065 1326.7 1564.3 1803.4 2060.4 2322.4 2607.2 2824.1 3110.2 3363.9 3644.1 3819.6 +177 437.76 569.82 819.66 1064.1 1309.2 1685.7 1957.5 2066.9 2313.8 2593.2 2847 3116.8 3364.5 3727.3 3881.6 +193.9 441.93 586.9 804.98 1062.5 1312.4 1542.4 1793.1 2073.9 2314.7 2587.8 2845.9 3112.4 3359.8 +193.01 440.26 555.64 814.08 1056.3 1315 1689.9 1961.4 2049.1 2305 2593.9 2847.5 3110.6 3361.1 3711.6 3914.7 +194.71 437.57 566.18 806.73 1069.2 1314.6 1682.7 1942.2 2061.8 2304.6 2607.6 2841.7 3082.9 3330.3 3679.7 3848.2 +184.88 441.22 570.92 794.35 1063.7 1309.9 1678.7 1930 2058 2321.3 2606.7 2845 3084.8 3337.3 3640 3952.1 +189.66 443.59 560.67 816.89 1070.4 1303.4 1550.1 1815.5 2057.6 2323.7 2587.1 2843.5 3086.6 3333.6 3618.2 3815.4 +190.41 440.77 568.96 808.56 1073.8 1322.1 1686.5 1952.8 2068.7 2335.7 2595.7 2845.4 3086 3333.5 3635.6 3939.3 +181.16 440.67 577.54 823.52 1052.5 1322.3 1578.4 1822.2 2079.4 2309.1 2596.9 2851.9 3083.5 3335.1 3531.2 3770.6 +181.09 434.97 687.15 943.33 1192.9 1444 1699.4 1942 2194.6 2445.9 2549.4 2785.1 3056.5 3308.2 3620.5 3932.7 +186.7 446.53 688.18 942.86 1186.1 1441.9 1688.1 1922.2 2196.6 2455.3 2534.8 2776.5 3060.3 3309.4 3514.1 3808.6 +196.76 446 681.26 948.27 1195.8 1433.1 1699 1933 2201.2 2461.4 2547.4 2777.8 3055.7 3307.1 3590.6 3952.8 +200.68 427.11 695.67 946.42 1178.6 1440.1 1538.4 1809 2199.8 2432.5 2531.6 2793.2 3056.6 3308.6 3510.6 3928.1 +190.83 429.57 698.73 931.16 1190.6 1428.9 1698.3 1935 2176.8 2424.7 2530.5 2766.9 3062 3309.7 3689.8 +181.47 441.93 682.32 943.01 1190.1 1459.1 1570.6 1819.6 2189.8 2437.9 2543.3 2782.8 3025.9 3280.2 3581 3855.9 +191.38 435.69 702.76 935.62 1188.3 1438.3 1564.2 1823.9 2191.3 2444.9 2531.9 2782.4 3030.7 3275.7 3677.7 3829.2 +191.97 433.85 686.29 932.65 1183.1 1432.7 1563.9 1826.5 2214.1 2436.8 2529.8 2778.3 3028.3 3281.8 3582 3863.4 +189.51 453.21 691.3 940.86 1180.1 1430.1 1567.1 1835 2199 2448.2 2526.7 2773.8 3030.5 3280.1 3576.2 3893.6 +190.88 435.48 692.66 940.51 1189.5 1448.9 1575.1 1824.2 2190.8 2425.9 2530.6 2783.3 3033.3 3279.5 3733 3838.9 diff --git a/examples/averages.py b/examples/averages.py new file mode 100644 index 0000000..8b405d0 --- /dev/null +++ b/examples/averages.py @@ -0,0 +1,59 @@ +""" averages.py + +Simple example showing how to compute averages of distance profiles + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + +from __future__ import print_function + +import pyspike as spk + +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", + edges=(0, 4000)) + +f = spk.isi_profile(spike_trains[0], spike_trains[1]) + +print("ISI-distance: %.8f" % f.avrg()) + +isi1 = f.avrg(interval=(0, 1000)) +isi2 = f.avrg(interval=(1000, 2000)) +isi3 = f.avrg(interval=[(0, 1000), (2000, 3000)]) +isi4 = f.avrg(interval=[(1000, 2000), (3000, 4000)]) + +print("ISI-distance (0-1000): %.8f" % isi1) +print("ISI-distance (1000-2000): %.8f" % isi2) +print("ISI-distance (0-1000) and (2000-3000): %.8f" % isi3) +print("ISI-distance (1000-2000) and (3000-4000): %.8f" % isi4) +print() + +f = spk.spike_profile(spike_trains[0], spike_trains[1]) + +print("SPIKE-distance: %.8f" % f.avrg()) + +spike1 = f.avrg(interval=(0, 1000)) +spike2 = f.avrg(interval=(1000, 2000)) +spike3 = f.avrg(interval=[(0, 1000), (2000, 3000)]) +spike4 = f.avrg(interval=[(1000, 2000), (3000, 4000)]) + +print("SPIKE-distance (0-1000): %.8f" % spike1) +print("SPIKE-distance (1000-2000): %.8f" % spike2) +print("SPIKE-distance (0-1000) and (2000-3000): %.8f" % spike3) +print("SPIKE-distance (1000-2000) and (3000-4000): %.8f" % spike4) +print() + +f = spk.spike_sync_profile(spike_trains[0], spike_trains[1]) + +print("SPIKE-Synchronization: %.8f" % f.avrg()) + +spike_sync1 = f.avrg(interval=(0, 1000)) +spike_sync2 = f.avrg(interval=(1000, 2000)) +spike_sync3 = f.avrg(interval=[(0, 1000), (2000, 3000)]) +spike_sync4 = f.avrg(interval=[(1000, 2000), (3000, 4000)]) + +print("SPIKE-Sync (0-1000): %.8f" % spike_sync1) +print("SPIKE-Sync (1000-2000): %.8f" % spike_sync2) +print("SPIKE-Sync (0-1000) and (2000-3000): %.8f" % spike_sync3) +print("SPIKE-Sync (1000-2000) and (3000-4000): %.8f" % spike_sync4) diff --git a/examples/distance_matrix.py b/examples/distance_matrix.py new file mode 100644 index 0000000..0e4d825 --- /dev/null +++ b/examples/distance_matrix.py @@ -0,0 +1,38 @@ +""" distance_matrix.py + +Simple example showing how to compute the isi distance matrix of a set of spike +trains. + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + + +from __future__ import print_function + +import matplotlib.pyplot as plt + +import pyspike as spk + +# first load the data, interval ending time = 4000, start=0 (default) +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", 4000) + +print(len(spike_trains)) + +plt.figure() +isi_distance = spk.isi_distance_matrix(spike_trains) +plt.imshow(isi_distance, interpolation='none') +plt.title("ISI-distance") + +plt.figure() +spike_distance = spk.spike_distance_matrix(spike_trains, interval=(0, 1000)) +plt.imshow(spike_distance, interpolation='none') +plt.title("SPIKE-distance, T=0-1000") + +plt.figure() +spike_sync = spk.spike_sync_matrix(spike_trains, interval=(2000, 4000)) +plt.imshow(spike_sync, interpolation='none') +plt.title("SPIKE-Sync, T=2000-4000") + +plt.show() diff --git a/examples/merge.py b/examples/merge.py new file mode 100644 index 0000000..b4437a3 --- /dev/null +++ b/examples/merge.py @@ -0,0 +1,29 @@ +""" merge.py + +Simple example showing the merging of two spike trains. + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + +from __future__ import print_function + +import numpy as np +import matplotlib.pyplot as plt + +import pyspike as spk + +# first load the data, ending time = 4000 +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", 4000) + +merged_spike_train = spk.merge_spike_trains([spike_trains[0], spike_trains[1]]) + +print(merged_spike_train.spikes) + +plt.plot(spike_trains[0], np.ones_like(spike_trains[0]), 'o') +plt.plot(spike_trains[1], np.ones_like(spike_trains[1]), 'x') +plt.plot(merged_spike_train.spikes, + 2*np.ones_like(merged_spike_train), 'o') + +plt.show() diff --git a/examples/multivariate.py b/examples/multivariate.py new file mode 100644 index 0000000..e9579a5 --- /dev/null +++ b/examples/multivariate.py @@ -0,0 +1,51 @@ +""" Example for the multivariate spike distance + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +""" +from __future__ import print_function +import time +import pyspike as spk + + +def time_diff_in_ms(start, end): + """ Returns the time difference end-start in ms. + """ + return (end-start)*1000 + + +t_start = time.clock() + +# load the data +time_loading = time.clock() +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", + edges=(0, 4000)) +t_loading = time.clock() + +print("Number of spike trains: %d" % 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 +f = spk.spike_profile(spike_trains) + +t_spike = time.clock() + +# print the average +avrg = f.avrg() +print("Spike distance from average: %.8f" % avrg) + +t_avrg = time.clock() + +# compute average distance directly, should give the same result as above +spike_dist = spk.spike_distance(spike_trains) +print("Spike distance directly: %.8f" % spike_dist) + +t_dist = time.clock() + +print("Loading: %9.1f ms" % time_diff_in_ms(t_start, t_loading)) +print("Computing profile: %9.1f ms" % time_diff_in_ms(t_loading, t_spike)) +print("Averaging: %9.1f ms" % time_diff_in_ms(t_spike, t_avrg)) +print("Computing distance: %9.1f ms" % time_diff_in_ms(t_avrg, t_dist)) +print("Total: %9.1f ms" % time_diff_in_ms(t_start, t_dist)) diff --git a/examples/performance.py b/examples/performance.py new file mode 100644 index 0000000..30691f8 --- /dev/null +++ b/examples/performance.py @@ -0,0 +1,71 @@ +""" +Compute distances of large sets of spike trains for performance tests + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License + +""" + +from __future__ import print_function + +import pyspike as spk +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 + +print("%d spike trains with %d spikes" % (M, int(r*T))) + +spike_trains = [] + +t_start = datetime.now() +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() + +sort_by = 'tottime' +# sort_by = 'cumtime' + +print("Spike generation runtime: %.3fs" % runtime) +print() + +print("================ ISI COMPUTATIONS ================") +print(" MULTIVARIATE DISTANCE") +cProfile.run('spk.isi_distance(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) + +print(" MULTIVARIATE PROFILE") +cProfile.run('spk.isi_profile(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) + +print("================ SPIKE COMPUTATIONS ================") +print(" MULTIVARIATE DISTANCE") +cProfile.run('spk.spike_distance(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) + +print(" MULTIVARIATE PROFILE") +cProfile.run('spk.spike_profile(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) + +print("================ SPIKE-SYNC COMPUTATIONS ================") +print(" MULTIVARIATE DISTANCE") +cProfile.run('spk.spike_sync(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) + +print(" MULTIVARIATE PROFILE") +cProfile.run('spk.spike_sync_profile(spike_trains)', 'performance.stat') +p = pstats.Stats('performance.stat') +p.strip_dirs().sort_stats(sort_by).print_stats(5) diff --git a/examples/plot.py b/examples/plot.py new file mode 100644 index 0000000..a0e04da --- /dev/null +++ b/examples/plot.py @@ -0,0 +1,45 @@ +""" plot.py + +Simple example showing how to load and plot spike trains and their distance +profiles. + +Copyright 2014, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + + +from __future__ import print_function + +import numpy as np +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 times +for (i, spike_train) in enumerate(spike_trains): + plt.scatter(spike_train, i*np.ones_like(spike_train), marker='|') + +# profile of the first two spike trains +f = spk.isi_profile(spike_trains, indices=[0, 1]) +x, y = f.get_plottable_data() + +plt.figure() +plt.plot(x, np.abs(y), '--k', label="ISI-profile") + +print("ISI-distance: %.8f" % f.avrg()) + +f = spk.spike_profile(spike_trains, indices=[0, 1]) +x, y = f.get_plottable_data() + +plt.plot(x, y, '-b', label="SPIKE-profile") + +print("SPIKE-distance: %.8f" % f.avrg()) + +plt.legend(loc="upper left") + +plt.show() diff --git a/examples/profiles.py b/examples/profiles.py new file mode 100644 index 0000000..8412ffb --- /dev/null +++ b/examples/profiles.py @@ -0,0 +1,66 @@ +""" profiles.py + +Simple example showing some functionality of distance profiles. + +Copyright 2015, Mario Mulansky <mario.mulansky@gmx.net> + +Distributed under the BSD License +""" + + +from __future__ import print_function + +import pyspike as spk + +spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt", + edges=(0, 4000)) + +##### ISI PROFILES ####### + +# compute the ISI profile of the first two spike trains +f = spk.isi_profile(spike_trains[0], spike_trains[1]) + +# ISI values at certain points +t = 1200 +print("ISI value at t =", t, ":", f(t)) +t = [900, 1100, 2000, 3100] +print("ISI value at t =", t, ":", f(t)) +print("Average ISI distance:", f.avrg()) +print() + +# compute the multivariate ISI profile +f = spk.isi_profile(spike_trains) + +t = 1200 +print("Multivariate ISI value at t =", t, ":", f(t)) +t = [900, 1100, 2000, 3100] +print("Multivariate ISI value at t =", t, ":", f(t)) +print("Average multivariate ISI distance:", f.avrg()) +print() +print() + +# for plotting, use the get_plottable_data() member function, see plot.py + + +##### SPIKE PROFILES ####### + +# compute the SPIKE profile of the first two spike trains +f = spk.spike_profile(spike_trains[0], spike_trains[1]) + +# SPIKE distance values at certain points +t = 1200 +print("SPIKE value at t =", t, ":", f(t)) +t = [900, 1100, 2000, 3100] +print("SPIKE value at t =", t, ":", f(t)) +print("Average SPIKE distance:", f.avrg()) +print() + +# compute the multivariate SPIKE profile +f = spk.spike_profile(spike_trains) + +# SPIKE values at certain points +t = 1200 +print("Multivariate SPIKE value at t =", t, ":", f(t)) +t = [900, 1100, 2000, 3100] +print("Multivariate SPIKE value at t =", t, ":", f(t)) +print("Average multivariate SPIKE distance:", f.avrg()) diff --git a/examples/spike_sync.py b/examples/spike_sync.py new file mode 100644 index 0000000..13ca0ce --- /dev/null +++ b/examples/spike_sync.py @@ -0,0 +1,50 @@ +from __future__ import print_function + +import matplotlib.pyplot as plt + +import pyspike as spk + +spike_trains = spk.load_spike_trains_from_txt("../test/SPIKE_Sync_Test.txt", + edges=(0, 4000)) + +plt.figure() + +f = spk.spike_sync_profile(spike_trains[0], spike_trains[1]) +x, y = f.get_plottable_data() +plt.plot(x, y, '--ok', label="SPIKE-SYNC profile") +print(f.x) +print(f.y) +print(f.mp) + +print("Average:", f.avrg()) + + +f = spk.spike_profile(spike_trains[0], spike_trains[1]) +x, y = f.get_plottable_data() + +plt.plot(x, y, '-b', label="SPIKE-profile") + +plt.axis([0, 4000, -0.1, 1.1]) +plt.legend(loc="center right") + +plt.figure() + +plt.subplot(211) + +f = spk.spike_sync_profile(spike_trains) +x, y = f.get_plottable_data() +plt.plot(x, y, '-b', alpha=0.7, label="SPIKE-Sync profile") + +x1, y1 = f.get_plottable_data(averaging_window_size=50) +plt.plot(x1, y1, '-k', lw=2.5, label="averaged SPIKE-Sync profile") + +plt.subplot(212) + +f_psth = spk.psth(spike_trains, bin_size=50.0) +x, y = f_psth.get_plottable_data() +plt.plot(x, y, '-k', alpha=1.0, label="PSTH") + + +print("Average:", f.avrg()) + +plt.show() diff --git a/examples/spike_train_order.py b/examples/spike_train_order.py new file mode 100644 index 0000000..3a42472 --- /dev/null +++ b/examples/spike_train_order.py @@ -0,0 +1,52 @@ +import numpy as np +from matplotlib import pyplot as plt +import pyspike as spk + + +st1 = spk.generate_poisson_spikes(1.0, [0, 20]) +st2 = spk.generate_poisson_spikes(1.0, [0, 20]) + +d = spk.spike_directionality(st1, st2) + +print "Spike Directionality of two Poissonian spike trains:", d + +E = spk.spike_train_order_profile(st1, st2) + +plt.figure() +x, y = E.get_plottable_data() +plt.plot(x, y, '-ob') +plt.ylim(-1.1, 1.1) +plt.xlabel("t") +plt.ylabel("E") +plt.title("Spike Train Order Profile") + + +###### Optimize spike train order of 20 Random spike trains ####### + +M = 20 + +spike_trains = [spk.generate_poisson_spikes(1.0, [0, 100]) for m in xrange(M)] + +F_init = spk.spike_train_order(spike_trains) + +print "Initial Synfire Indicator for 20 Poissonian spike trains:", F_init + +D_init = spk.spike_directionality_matrix(spike_trains) + +phi, _ = spk.optimal_spike_train_sorting(spike_trains) + +F_opt = spk.spike_train_order(spike_trains, indices=phi) + +print "Synfire Indicator of optimized spike train sorting:", F_opt + +D_opt = spk.permutate_matrix(D_init, phi) + +plt.figure() +plt.imshow(D_init) +plt.title("Initial Directionality Matrix") + +plt.figure() +plt.imshow(D_opt) +plt.title("Optimized Directionality Matrix") + +plt.show() |